20 mmHg) or low threshold (≤ 20 mmHg) on the basis of their response threshold and profile during CRD. Single fiber activity data were analyzed by analysis of variance with repeated measures; CRD intensity was the repeated factor and the experimental group was the between-group factor. If significant main effects were present, the individual means were compared using the Fisher post-hoc test. All authors had access to the study data and reviewed and approved the final manuscript.\nSingle fiber responses (impulses/second) to CRD were calculated by subtracting SA from the mean 30 s maximal activity during distension. Fibers were considered responsive if CRD increased their activity to 30% greater than the baseline value. Mechanosensitive single units were classified as high threshold (> 20 mmHg) or low threshold (≤ 20 mmHg) on the basis of their response threshold and profile during CRD. Single fiber activity data were analyzed by analysis of variance with repeated measures; CRD intensity was the repeated factor and the experimental group was the between-group factor. If significant main effects were present, the individual means were compared using the Fisher post-hoc test. All authors had access to the study data and reviewed and approved the final manuscript."},"results_title":{"kind":"null"},"results_text":{"kind":"null"},"conclusions_title":{"kind":"string","value":"CONCLUSION"},"conclusions_text":{"kind":"string","value":"The authors would like to acknowledge the essential intellectual contributions of our recently deceased mentor, Dr. Sushil K Sarna. His guidance was essential to the successful completion of these studies. Thanks to Dr. Usman Ali for assistance in preparing and modifying the manuscript."},"other_sections_titles":{"kind":"list like","value":["INTRODUCTION","Animals","CPS and CAS models","Rat treatment","In vivo single fiber recording of L6-S2 DRG rootlets","In vitro patch clamp recordings in colonic DRG neurons","Real time reverse transcription-polymerase chain reaction (RT-PCR)","Western blot","Immunofluorescence","Serum estradiol and norepinephrine levels","Data analysis","RESULTS","Effects of CPS plus CAS on primary afferent responses to CRD in male and female rats","Increase in excitability of colon-projecting lumbosacral DRG neurons in female CPS + CAS rats","Effects of CPS and/or CAS on plasma estrogen concentration","Effects of Letrozole treatment on colon DRG neuron excitability","Spinal cord BDNF levels regulated by estrogen","Peripheral NGF level increased in CPS + CAS female rats","DISCUSSION","CONCLUSION"],"string":"[\n \"INTRODUCTION\",\n \"Animals\",\n \"CPS and CAS models\",\n \"Rat treatment\",\n \"In vivo single fiber recording of L6-S2 DRG rootlets\",\n \"In vitro patch clamp recordings in colonic DRG neurons\",\n \"Real time reverse transcription-polymerase chain reaction (RT-PCR)\",\n \"Western blot\",\n \"Immunofluorescence\",\n \"Serum estradiol and norepinephrine levels\",\n \"Data analysis\",\n \"RESULTS\",\n \"Effects of CPS plus CAS on primary afferent responses to CRD in male and female rats\",\n \"Increase in excitability of colon-projecting lumbosacral DRG neurons in female CPS + CAS rats\",\n \"Effects of CPS and/or CAS on plasma estrogen concentration\",\n \"Effects of Letrozole treatment on colon DRG neuron excitability\",\n \"Spinal cord BDNF levels regulated by estrogen\",\n \"Peripheral NGF level increased in CPS + CAS female rats\",\n \"DISCUSSION\",\n \"CONCLUSION\"\n]"},"other_sections_texts":{"kind":"list like","value":["Visceral pain of colonic origin is the most prominent symptom in irritable bowel syndrome (IBS) patients[1]. Female IBS patients report more severe pain that occurs more frequently and with longer episodes than in male patients[1,2]. The ratio of female to male IBS is about 2:1 among patients seen in medical clinics[3]. Moreover, females have a higher prevalence of IBS co-morbidities such as anxiety and depression[4,5] and are more vulnerable to stress-induced exacerbation of IBS symptoms compared with males[3,6,7].\nClinical studies show that early life adverse experiences are risk factors for the development of IBS symptoms, including visceral pain and ongoing chronic stress, especially abdominal pain[8-10]. These factors contribute to the development of visceral hypersensitivity, a key component of the IBS symptom complex and one that may be responsible for symptoms of pain[11,12]. Our previous research found that the female offspring of mothers subjected to chronic prenatal stress (CPS) had a markedly greater visceral sensitivity than their male littermates following challenge by another chronic adult stress (CAS) protocol. A critical molecular event in the development of this female-enhanced visceral hypersensitivity is upregulation of brain-derived neurotrophic factor (BDNF) expression in the lumbar-sacral spinal cord of female CPS + CAS rats[13]. However, the neurophysiological changes underlying the enhanced female-specific visceral hypersensitivity and the role of hormone in the development of stress-induced visceral hypersensitivity are not well understood.\nVisceral hypersensitivity in IBS involves abnormal changes in neurophysiology throughout the brain-gut axis. In IBS, there is evidence for sensitization of primary afferents to jejunal distention and electrical stimulation[14], and there is evidence for increased sensitivity of lumbar splanchnic afferents[15,16]. In animal models of either early life adverse events or adult stress-induced visceral hypersensitivity[17], there is evidence of colon primary afferent sensitization. However, the studies were performed in male rodents. Therefore, in this study, we established a CPS and CAS rodent model to analyze the impact on female colon afferent neuron function and the role of estrogen. Our hypothesis was that female CPS offspring subjected to chronic stress as adults would exhibit greater colonic dorsal root ganglion (DRG) neuron sensitization compared with their male littermates, and that the enhanced visceral sensitization and primary afferent sensitization in females was estrogen dependent.","The Institutional Animal Care and Use Committee of the University of Texas Medical Branch at Galveston, TX approved all animal procedures. Experiments were performed on pregnant Sprague Dawley rats and their 8-wk-old to 16-wk-old male and female offspring. Rats were housed individual cages with access to food and water in a room with controlled conditions (22 ± 2 °C, relative humidity of 50% ± 5%), and a 12 h light/12 h dark cycle.","Pregnant dams were subjected to a CPS protocol that consisted of a random sequence of twice-daily applications of one of three stress sessions, a 1-h water-avoidance, 45-min cold-restraint, or a 20-min forced swim starting on day 6 and continuing until delivery on day 21. Male and female offspring of the stressed dams were designated CPS rats. Control dams received sham stress and their offspring were designated control rats. As adults at 8-16 wk of age, control and prenatally stressed offspring were challenged by the same CAS protocol for 9 d. Ovariectomy (OVX) or sham surgery was performed on female prenatal-stress offspring on day 56. Daily letrozole treatment was initiated on day 49, 2 wk prior to initiation of adult stress. Treatment was continued through the stress protocol. A schematic diagram of the study procedures is shown in Figure 1A.\nPrimary afferent responses to colorectal distention. A: Chronic prenatal stress (CPS) plus chronic adult stress (CAS) model. Pregnant dams were subjected to prenatal stress from on day 11 of gestation. Ovariectomy (OVX) or sham surgery was performed on female prenatal-stress offspring on day 56. Daily Letrozole was initiated on day 49, 2 wk prior to initiation of adult stress. Treatment was continued through the stress protocol; B: Spontaneous activity (SA) of single afferent units in male and female control rats (n = 70 fibers in 6 rats in each group, t-test, aP < 0.05); C: Average response to graded colorectal distention (CRD) of 56 afferent fibers in 6 male and 70 afferent fibers in 6 female control rats; two-way analysis of variance (ANOVA; aP < 0.05 vs the same pressure male group); D: Responses of low-threshold (LT) fibers to CRD in 42 fibers in 6 male rats and 40 fibers in 6 female control rats (ANOVA, aP < 0.05 vs the same pressure male group); E: Responses of high-threshold (HT) afferent fibers to CRD in 14 fibers in 6 male and 29 fibers in 6 female control rats; F: Effects of CAS on afferent fiber responses to CRD from 59 fibers in 6 control and 99 fibers in 6 CPS female rats; (two-way ANOVA, aP < 0.05 vs the same pressure control group, bP < 0.05 vs the same pressure CPS group); G: Effects of CAS on afferent fiber responses to CRD in control and CPS male rats (n = 6 rats, 57 fibers for control and 95 fibers for CPS female group; two-way ANOVA, aP < 0.05 vs the same pressure-control group). ","Before OVX or letrozole treatment, vaginal smears were used to identify the estrus cycle phase. OVX or sham surgery was performed on female prenatal-stress offspring on day 56. The aromatase inhibitor letrozole [4,4’-(1H-1,2,4-triazol-l-yl-methylene)-bis-benzonitrile], (Novartis) 1.0 mg/kg was orally administered in the experimental group and vehicle (hydroxypropyl cellulose 0.3% in water) was given in the control group once daily for 14 d. Direct transcutaneous intrathecal injections of estrogen and letrozole were performed as described by Mestre et al[18].","Multiunit afferent discharges were recorded from the distal ends of L6-S2 dorsal rootlets decentralized close to their entry into the spinal cord. A bundle of multiunit fibers was distinguished into 2-6 single units off-line using wave mark template matching in Spike 2 software that differentiates spikes by shape and amplitude. Colonic afferent fibers were identified by their response to graded colorectal distention (CRD). A balloon was used to distend the colorectum. Isoflurane, 2.5%, followed by 50 mg/kg intraperitoneal sodium pentobarbital induced general anesthesia that was maintained by infusing a mixture of pentobarbital sodium + pancuronium bromide + saline by intravenous infusion through the tail vein. The adequacy of anesthesia was confirmed by the absence of corneal and pupillary reflexes and stability of the end-tidal CO2 level. A tracheotomy tube connected to a ventilator system provided a mixture of room air and oxygen. Expired CO2 was monitored and maintained at 3.5%. Body temperature was monitored and maintained at 37 °C by a servo-controlled heating blanket. A laminectomy from T12 to S2 exposed the spinal cord. The head was stabilized in a stereotaxic frame.","Retrograde fluorescence label injections: Labeling of colon-projecting DRG neurons was performed as previously described[13]. Under general 2% isoflurane anesthesia, the lipid soluble fluorescent dye, 1,1’-dioleyl-3,3,3’,3’-tetramethylindocarbocyanine methane-sulfonate (9-DiI, Invitrogen, Carlsbad, CA) was injected (50 mg/mL) into the muscularis externa on the exposed distal colon in 8 to 10 sites (2 μL each site). To prevent leakage, the needle was kept in place for 1 min following each injection.\nDissociation and culture of DRG neurons: Rats were deeply anesthetized with isoflurane followed by decapitation. Lumbosacral (L6–S2) DRGs were collected in ice cold and oxygenated dissecting solution, containing (in mM) 130 NaCl, 5 KCl, 2 KH2PO4, 1.5 CaCl2, 6 MgSO4, 10 glucose, and 10 HEPES, pH 7.2 (305 mOsm). After removal of the connective tissue, the ganglia were transferred to a 5 mL dissecting solution containing collagenase D (1.8 mg/mL; Roche) and trypsin (1.0 mg/mL; Sigma, St Louis, MO), and incubated for 1.5 h at 34.5 °C. DRGs were then taken from the enzyme solution, washed, and put in 0.5-2 mL of the dissecting solution containing DNase (0.5 mg/mL; Sigma). Cells were subsequently dissociated by gentle trituration 10 to 15 times with fire-polished glass pipettes and placed on acid-cleaned glass coverslips. The dissociated DRG neurons were kept in 1 mL DMEM (with 10% FBS) in an incubator (95% O2/5% CO2) at 37 °C overnight.\nWhole-cell patch clamp recordings from dissociated DRG neurons: Before each experiment, a glass coverslip with DRG neurons was transferred to a recording chamber perfused (1.5 mL/min) with external solution containing (10 mM): 130 NaCl, 5 KCl, 2 KH2PO4, 2.5 CaCl2, 1 MgCl2, 10 HEPES, and 10 glucose, pH adjusted to pH 7.4 with NaOH (300 mOsm) at room temperature. Recording pipettes, pulled from borosilicate glass tubing, with resistance of 1-5 MΩ, were filled with solution containing (in mM): 100 KMeSO3, 40 KCl, and 10 HEPES, pH 7.25 adjusted with KOH (290 mOsm). DiI-labeled neurons were identified by fluorescence microscopy. Whole-cell currents and voltage were recorded from DiI-labeled neurons using a Dagan 3911 patch clamp amplifier. Data were acquired and analyzed by pCLAMP 9.2 (Molecular Devices, Sunnyvale, CA). The currents were filtered at 2–5 kHz and sampled at 50 or 100 s per point. While still under voltage clamp, the Clampex Membrane Test program (Molecular Devices) was used to determine membrane capacitance (Cm) and membrane resistance (Rm), during a 10 ms, 5 mV depolarizing pulse form a holding potential of −60 mV. The configuration was then switched to current clamp (0 pA) to determine other electrophysiological properties. After stabilizing for 2–3 min, the resting membrane potential was measured. The minimum acceptable resting membrane potential was −40 mV. Spontaneous activity (SA) was then recorded over two 30 s periods separated by 60 s without recording, as described by Bedi et al[19].\nTransient A-type K+ current (IA) recording method in patch studies: To record voltage-gated K+ current (Kv), Na+ in control external solution was replaced with equimolar choline and the Ca2+ concentration was reduced to 0.03 mM to suppress Ca2+ currents and to prevent Ca2+ channels becoming Na+ conducting. The reduced external Ca2+ would also be expected to suppress Ca2+-activated K+ currents. The current traces of Kv in DRG neurons were measured at different holding potentials. The membrane potential was held at −100 mV and voltage steps were from −40 to +30 mV to record the total Kv. The membrane potential was held at −50 mV to record the sustained Kv. The IA currents were calculated by subtracting the sustained current from the total current. The current density (in pA/pF) was calculated by dividing the current amplitude by cell membrane capacitance.","Total RNA was extracted using RNeasy Mini Kits (QIAGEN, Valencia, CA). One microgram of total RNA was reverse-transcribed using the SuperScriptTM III First-Strand Synthesis System. PCR assays were performed on a StepOnePlus thermal cycler with 18 s as the normalizer using Applied Biosystems primer/probe set Rn02531967_s1 directed against the translated exon IX. Fold-change relative to control was calculated using the ΔΔCt method (Applied Biosystems).","Samples were lysed in RIPA buffer containing protease inhibitor cocktail and phenylmethanesulfonyl fluoride. Lysates were incubated for 30 min on ice and then centrifuged at 10 000 × g for 10 min at 4 °C. The protein concentration in the supernatant was determined using bicinchoninic acid (BCA) assay kits with bovine serum albumin as a standard. Equal amounts of protein (30 μg per lane) were separated by 10% sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) and then transferred to nitrocellulose membranes (Bio-Rad, United States). The membranes were blocked in Li-Cor blocking buffer for 1 h at room temperature and then incubated with primary antibodies. BDNF antibody (Santa Cruz Biotechnologies, Santa Cruz, CA) was used at 1:200 dilution; nerve growth factor (NGF) antibody (Abcam, MA) was used at 1:1000 dilution; β-actin antibody (Sigma Aldrich, St Louis, MO) was used at 1:5000 dilution. The secondary antibodies were donkey anti-rabbit Alexa Fluor 680 (Invitrogen) and goat anti-mouse IRDye 800 (Rockland). Images were acquired and band intensities measured using a Li-Cor Odyssey system (Li-Cor, Lincoln, NE).","Frozen sections of colon tissue from control, CAS, CPS and CPS + CAS female rats were mounted on glass slides, and rehydrated in phosphate buffered saline at room temperature. The slides were treated for antigen retrieval and blocked with 10% normal goat serum diluted in 0.3% phosphate buffered saline-Triton for 1 h, and then incubated with NGF primary antibody in antibody diluent (Renoir Red, Biocare Medical, Concord, CA) at 4 °C overnight. The slides were exposed to fluorescent dye-conjugated secondary antibody for 2 h at room temperature, counterstained with 4',6-diamidino-2-phenylindole and coverslipped. Images were taken in fluorescence mode on an Olympus laser scanning confocal microscope and the average signal intensity was calculated by the bundled software.","Serum estradiol, adrenocorticotropic hormone (ACTH), and norepinephrine levels were measured using specific enzyme-linked immunosorbent assay kits for each analyte (CSB-E05110r, CSB-E06875r, CSB-E07022, Cusabio Bioteck CO., United States) following the manufacturer’s instructions.","Single fiber responses (impulses/second) to CRD were calculated by subtracting SA from the mean 30 s maximal activity during distension. Fibers were considered responsive if CRD increased their activity to 30% greater than the baseline value. Mechanosensitive single units were classified as high threshold (> 20 mmHg) or low threshold (≤ 20 mmHg) on the basis of their response threshold and profile during CRD. Single fiber activity data were analyzed by analysis of variance with repeated measures; CRD intensity was the repeated factor and the experimental group was the between-group factor. If significant main effects were present, the individual means were compared using the Fisher post-hoc test. All authors had access to the study data and reviewed and approved the final manuscript."," Effects of CPS plus CAS on primary afferent responses to CRD in male and female rats The basal activity of a spinal afferent fiber was defined as the average number of action potentials per second (impulses/sec) in the 60 s period before the onset of a distention stimulus. In male controls, 66% of the afferent fibers under study displayed SA and SA was significantly higher in female controls than in male controls (0.71 ± 0.21 vs 1.24 ± 0.20 imp/sec; Figure 1B). The average single fiber activity in response to CRD was significantly higher in female control rats compared with male controls (Figure 1C). We found that the enhanced sensitization in female rats mainly came from the low-threshold fibers (Figure 1D and E).\nTo assess the effects of CPS + CAS on colon afferent fiber activities, we compared average single colon afferent fiber activities projecting from dorsal roots S1-L6 in response to CRD in male and female control, CPS, control + CAS and CPS + CAS rats recorded approximately 24 h after the last stressor. In females, CPS significantly increased single-unit afferent activity in response to CRD vs control female rats (Figure 1F). CAS alone enhanced single-unit activity compared with control. The increase in average afferent responses after CAS in prenatally stressed female rats (44.0%) was significantly greater than the increase in female control rats (39.3%). In males, CPS had no significant effect on primary afferent responses (Figure 1G). When we compared males to females within each experimental group, we found that the average single fiber activity was significantly higher in female compared with male CPS + CAS rats (Figure 1F, G). The increased activity may contribute to the enhanced female visceral hypersensitivity previously reported in this model. Average single-fiber activities were significantly greater in control and CPS and CAS females than in their corresponding male experimental groups (Figure 1F and G). Both CAS and CPS + CAS rats had significantly increased primary afferent responses compared with control and CPS rats. Thus, our CPS and CAS protocols sensitized colon-projecting primary afferent fibers, with the greatest effects produced by the combination of CPS + CAS in both males and females.\nThe basal activity of a spinal afferent fiber was defined as the average number of action potentials per second (impulses/sec) in the 60 s period before the onset of a distention stimulus. In male controls, 66% of the afferent fibers under study displayed SA and SA was significantly higher in female controls than in male controls (0.71 ± 0.21 vs 1.24 ± 0.20 imp/sec; Figure 1B). The average single fiber activity in response to CRD was significantly higher in female control rats compared with male controls (Figure 1C). We found that the enhanced sensitization in female rats mainly came from the low-threshold fibers (Figure 1D and E).\nTo assess the effects of CPS + CAS on colon afferent fiber activities, we compared average single colon afferent fiber activities projecting from dorsal roots S1-L6 in response to CRD in male and female control, CPS, control + CAS and CPS + CAS rats recorded approximately 24 h after the last stressor. In females, CPS significantly increased single-unit afferent activity in response to CRD vs control female rats (Figure 1F). CAS alone enhanced single-unit activity compared with control. The increase in average afferent responses after CAS in prenatally stressed female rats (44.0%) was significantly greater than the increase in female control rats (39.3%). In males, CPS had no significant effect on primary afferent responses (Figure 1G). When we compared males to females within each experimental group, we found that the average single fiber activity was significantly higher in female compared with male CPS + CAS rats (Figure 1F, G). The increased activity may contribute to the enhanced female visceral hypersensitivity previously reported in this model. Average single-fiber activities were significantly greater in control and CPS and CAS females than in their corresponding male experimental groups (Figure 1F and G). Both CAS and CPS + CAS rats had significantly increased primary afferent responses compared with control and CPS rats. Thus, our CPS and CAS protocols sensitized colon-projecting primary afferent fibers, with the greatest effects produced by the combination of CPS + CAS in both males and females.\n Increase in excitability of colon-projecting lumbosacral DRG neurons in female CPS + CAS rats To elucidate the electrophysiological basis of enhanced stress-induced primary afferent activity in female rats, we performed patch clamp studies on acutely dissociated retrograde-labeled colon-projecting neurons from the L6-S2 DRGs in control, prenatal stress, adult stress only, and CPS + CAS female rats isolated 24 h after the last adult stressor (Figure 2A). Input resistance (Figure 2B) and rheobase (Figure 2C) were significantly decreased in neurons from CPS + CAS rats compared with the other three groups. The number of action potentials elicited at either 2 × or 3 × the rheobase were significantly greater in adult stress and CPS + CAS neurons compared with control and to CPS neurons (Figure 2D, E). CAS significantly increased action potential overshoot with or without CPS (Figure 2F), but it did not significantly alter other electrophysiological characteristics, such as number of spontaneous spikes, membrane capacitance (pF), resting membrane potential, cell diameter, time constant, and DRG neuron action potential amplitude and duration (Table 1).\nPatch clamp recording in colonic dorsal root ganglion neurons from female rats. A: Patch clamp process of cell labeling. Under isoflurane anesthesia, the lipid soluble fluorescent dye 9-DiI was injected into muscularis externa of the exposed distal colon (left figure). Lumbosacral (L6–S2) dorsal root ganglions (upper photograph) were isolated and DiI-labeled neurons were identified by fluorescence microscopy (lower photograph). Electrophysiological properties of each neuron were measured using whole-cell current and voltage clamp protocols (right figure); B: Rheobase from all four experimental groups (n = 5 rats, 45 cells in each group, one-way ANOVA, aP < 0.05 vs control or bP < 0.05 vs CAS); C: Representative action potentials (APs) elicited by current injection at 2 × the rheobase in neurons from control, chronic adult stress (CAS), chronic prenatal stress (CPS) and CPS + CAS female rats; D: Membrane input resistance from all four groups (n = 5 rats, 45 cells in each group, one-way ANOVA, aP < 0.05 vs control; bP < 0.05 vs CPS); E: Number of APs elicited by current injection at either 2 × and 3 × the rheobase in all four experimental groups (two-way ANOVA, aP < 0.05 vs control; bP < 0.05 vs CPS); F: AP overshoot recorded from all four experimental groups (aP < 0.05 vs control); G: The proportion of neurons from each experimental group exhibiting spontaneous APs. Red numbers represent spontaneous AP firing cells; black numbers represent total cells; H: Representative total, IK and IA current tracings and average values of potassium currents: Itotal, IK and IA are shown in female CPS + CAS, CAS, CPS (n = 15 neurons, from 5 rats in each group), and control groups (n = 12 neurons from 5 rats); two-way ANOVA, aP < 0.05 vs each control group. \nElectrophysiological characteristics of colon related DRG neuron\nValues means ± standard error. \nP < 0.05;\nP < 0.001 vs control group. CAS: Chronic adult stress; CPS: Chronic prenatal stress.\nThe percentage of neurons with SA in was significantly greater in CPS + CAS rats than in control or CPS only rats (Figure 2G). Under voltage clamp conditions (Figure 2H), neurons from female CPS + CAS, CAS, CPS and control groups had IA and sustained outward rectifier K+ currents (IK). Compared with the other three groups, DRG neurons from CPS + CAS rats had significantly reduced average IA (P < 0.05). The average IK density was decreased but the change was not significant.\nTo elucidate the electrophysiological basis of enhanced stress-induced primary afferent activity in female rats, we performed patch clamp studies on acutely dissociated retrograde-labeled colon-projecting neurons from the L6-S2 DRGs in control, prenatal stress, adult stress only, and CPS + CAS female rats isolated 24 h after the last adult stressor (Figure 2A). Input resistance (Figure 2B) and rheobase (Figure 2C) were significantly decreased in neurons from CPS + CAS rats compared with the other three groups. The number of action potentials elicited at either 2 × or 3 × the rheobase were significantly greater in adult stress and CPS + CAS neurons compared with control and to CPS neurons (Figure 2D, E). CAS significantly increased action potential overshoot with or without CPS (Figure 2F), but it did not significantly alter other electrophysiological characteristics, such as number of spontaneous spikes, membrane capacitance (pF), resting membrane potential, cell diameter, time constant, and DRG neuron action potential amplitude and duration (Table 1).\nPatch clamp recording in colonic dorsal root ganglion neurons from female rats. A: Patch clamp process of cell labeling. Under isoflurane anesthesia, the lipid soluble fluorescent dye 9-DiI was injected into muscularis externa of the exposed distal colon (left figure). Lumbosacral (L6–S2) dorsal root ganglions (upper photograph) were isolated and DiI-labeled neurons were identified by fluorescence microscopy (lower photograph). Electrophysiological properties of each neuron were measured using whole-cell current and voltage clamp protocols (right figure); B: Rheobase from all four experimental groups (n = 5 rats, 45 cells in each group, one-way ANOVA, aP < 0.05 vs control or bP < 0.05 vs CAS); C: Representative action potentials (APs) elicited by current injection at 2 × the rheobase in neurons from control, chronic adult stress (CAS), chronic prenatal stress (CPS) and CPS + CAS female rats; D: Membrane input resistance from all four groups (n = 5 rats, 45 cells in each group, one-way ANOVA, aP < 0.05 vs control; bP < 0.05 vs CPS); E: Number of APs elicited by current injection at either 2 × and 3 × the rheobase in all four experimental groups (two-way ANOVA, aP < 0.05 vs control; bP < 0.05 vs CPS); F: AP overshoot recorded from all four experimental groups (aP < 0.05 vs control); G: The proportion of neurons from each experimental group exhibiting spontaneous APs. Red numbers represent spontaneous AP firing cells; black numbers represent total cells; H: Representative total, IK and IA current tracings and average values of potassium currents: Itotal, IK and IA are shown in female CPS + CAS, CAS, CPS (n = 15 neurons, from 5 rats in each group), and control groups (n = 12 neurons from 5 rats); two-way ANOVA, aP < 0.05 vs each control group. \nElectrophysiological characteristics of colon related DRG neuron\nValues means ± standard error. \nP < 0.05;\nP < 0.001 vs control group. CAS: Chronic adult stress; CPS: Chronic prenatal stress.\nThe percentage of neurons with SA in was significantly greater in CPS + CAS rats than in control or CPS only rats (Figure 2G). Under voltage clamp conditions (Figure 2H), neurons from female CPS + CAS, CAS, CPS and control groups had IA and sustained outward rectifier K+ currents (IK). Compared with the other three groups, DRG neurons from CPS + CAS rats had significantly reduced average IA (P < 0.05). The average IK density was decreased but the change was not significant.\n Effects of CPS and/or CAS on plasma estrogen concentration We did a vaginal smear test to identify the estrus cycle phases by identifying the vaginal cytological cell types. Estrogen concentration was significantly higher in the CPS proestrus/estrus phase compared with control diestrus, control proestrus/estrus, and CPS diestrus proestrus (P < 0.05; Figure 3A). Comparison of the plasma estrogen concentrations in control, CAS, CPS, CPS + CAS showed that CPS significantly increased plasma estrogen levels compared with the control rats and that CAS increased plasma estrogen level compared with the control and CPS rats (Figure 3B).\nEffects of chronic prenatal stress, chronic adult stress, ovariectomy, and letrozole treatment on plasma estrogen levels in female rats. A: Plasma estrogen level in control and chronic prenatal stress (CPS) rats by estrus cycle phase (n = 8 rats, one-way ANOVA, aP < 0.05 vs control proestrus/estrus (P-E) phase; bP < 0.05 vs CPS diestrus (D) phase); B: Plasma estrogen levels increased in CPS rats and following chronic adult stress (CAS) 24 h after the last adult stressor (n = 8 rats, one-way ANOVA, aP < 0.05 vs control; bP < 0.05 vs CPS); C: Ovariectomy (OVX) significantly reduced CPS female rat plasma estrogen levels before and after CAS (n = 5 rats, one-way ANOVA, aP < 0.05 vs sham group); D: Letrozole treatment significantly reduced CPS female rat plasma estrogen levels before or after CAS (n = 5 rats, one-way ANOVA, aP < 0.05 vs vehicle group; cP < 0.0001); E: Plasma norepinephrine levels from control, CAS, CPS and CPS + CAS group female rats (n = 5 rats, one-way ANOVA, aP < 0.05 vs control; bP < 0.05 vs CPS); F: Plasma adrenocorticotropic hormone (ACTH) levels from control and CPS + CAS group female rats (n = 5 rats, t-test, aP < 0.05 vs control). \nTo determine whether estrogen contributed to stress-induced visceral hypersensitivity in prenatal stressed females, we reduced plasma estrogen levels by either OVX or letrozole treatment. OVX significantly lowered serum estradiol levels before and after CAS (Figure 3C). Treatment was continued throughout CAS. After treatment with letrozole, serum estradiol levels were significantly reduced (Figure 3D). To study the effects of gender and stress on norepinephrine and ACTH levels, we measured plasma norepinephrine levels in female rats from all four experimental groups. CAS alone significantly increased plasma norepinephrine levels compared with both the controls and with CPS alone (Figure 3E) Plasma norepinephrine levels were significantly increased in CPS + CAS rats compared with CAS alone as well as with controls and CPS. Plasma ACTH levels were significantly increased in CPS + CAS rats compared with controls. (Figure 3F).\nWe did a vaginal smear test to identify the estrus cycle phases by identifying the vaginal cytological cell types. Estrogen concentration was significantly higher in the CPS proestrus/estrus phase compared with control diestrus, control proestrus/estrus, and CPS diestrus proestrus (P < 0.05; Figure 3A). Comparison of the plasma estrogen concentrations in control, CAS, CPS, CPS + CAS showed that CPS significantly increased plasma estrogen levels compared with the control rats and that CAS increased plasma estrogen level compared with the control and CPS rats (Figure 3B).\nEffects of chronic prenatal stress, chronic adult stress, ovariectomy, and letrozole treatment on plasma estrogen levels in female rats. A: Plasma estrogen level in control and chronic prenatal stress (CPS) rats by estrus cycle phase (n = 8 rats, one-way ANOVA, aP < 0.05 vs control proestrus/estrus (P-E) phase; bP < 0.05 vs CPS diestrus (D) phase); B: Plasma estrogen levels increased in CPS rats and following chronic adult stress (CAS) 24 h after the last adult stressor (n = 8 rats, one-way ANOVA, aP < 0.05 vs control; bP < 0.05 vs CPS); C: Ovariectomy (OVX) significantly reduced CPS female rat plasma estrogen levels before and after CAS (n = 5 rats, one-way ANOVA, aP < 0.05 vs sham group); D: Letrozole treatment significantly reduced CPS female rat plasma estrogen levels before or after CAS (n = 5 rats, one-way ANOVA, aP < 0.05 vs vehicle group; cP < 0.0001); E: Plasma norepinephrine levels from control, CAS, CPS and CPS + CAS group female rats (n = 5 rats, one-way ANOVA, aP < 0.05 vs control; bP < 0.05 vs CPS); F: Plasma adrenocorticotropic hormone (ACTH) levels from control and CPS + CAS group female rats (n = 5 rats, t-test, aP < 0.05 vs control). \nTo determine whether estrogen contributed to stress-induced visceral hypersensitivity in prenatal stressed females, we reduced plasma estrogen levels by either OVX or letrozole treatment. OVX significantly lowered serum estradiol levels before and after CAS (Figure 3C). Treatment was continued throughout CAS. After treatment with letrozole, serum estradiol levels were significantly reduced (Figure 3D). To study the effects of gender and stress on norepinephrine and ACTH levels, we measured plasma norepinephrine levels in female rats from all four experimental groups. CAS alone significantly increased plasma norepinephrine levels compared with both the controls and with CPS alone (Figure 3E) Plasma norepinephrine levels were significantly increased in CPS + CAS rats compared with CAS alone as well as with controls and CPS. Plasma ACTH levels were significantly increased in CPS + CAS rats compared with controls. (Figure 3F).\n Effects of Letrozole treatment on colon DRG neuron excitability We performed patch clamp experiments on acutely isolated retrograde-labeled DRG neurons from CPS + CAS females with or without letrozole treatment 24 h after the last adult stressor. Letrozole treatment significantly increased rheobase (Figure 4A), and significantly reduced input resistance (Figure 4B). Action potential overshoot (Figure 4C) and the number of action potentials elicited by a current injection at either 2 × or 3 × rheobase were significantly reduced by letrozole treatment (Figure 4D). Other electrophysiological properties were not significantly altered (Table 2). We also recorded electromyographic activity to determine whether the reduction in visceral sensitivity in female CPS + CAS rats caused by OVX or systemic letrozole treatment reduced visceromotor responses. The findings demonstrated a significant decrease in excitability of colon-projecting L6-S2 neurons.\nEffects of Letrozole treatment on colon dorsal root ganglion neuron excitability. A: Rheobase (n = 45 cells in 6 rats in each group, t-test, cP < 0.001 vs Veh. + chronic adult stress [CAS] + chronic prenatal stress [CPS]); B: Membrane input resistance (RIn) (t-test, aP < 0.05); C: Action potential (AP) overshoot (t-test, aP < 0.05); D: Number of APs elicited by current injection at 2 × and 3 × rheobase (two-way ANOVA, aP < 0.05; bP < 0.01). \nElectrophysiological characteristics of colon related DRG neuron after Letrozole treatment\nValues are means ± standard error. \nP < 0.05;\nP < 0.001 vs control group. CAS: Chronic adult stress; CPS: Chronic prenatal stress.\nWe performed patch clamp experiments on acutely isolated retrograde-labeled DRG neurons from CPS + CAS females with or without letrozole treatment 24 h after the last adult stressor. Letrozole treatment significantly increased rheobase (Figure 4A), and significantly reduced input resistance (Figure 4B). Action potential overshoot (Figure 4C) and the number of action potentials elicited by a current injection at either 2 × or 3 × rheobase were significantly reduced by letrozole treatment (Figure 4D). Other electrophysiological properties were not significantly altered (Table 2). We also recorded electromyographic activity to determine whether the reduction in visceral sensitivity in female CPS + CAS rats caused by OVX or systemic letrozole treatment reduced visceromotor responses. The findings demonstrated a significant decrease in excitability of colon-projecting L6-S2 neurons.\nEffects of Letrozole treatment on colon dorsal root ganglion neuron excitability. A: Rheobase (n = 45 cells in 6 rats in each group, t-test, cP < 0.001 vs Veh. + chronic adult stress [CAS] + chronic prenatal stress [CPS]); B: Membrane input resistance (RIn) (t-test, aP < 0.05); C: Action potential (AP) overshoot (t-test, aP < 0.05); D: Number of APs elicited by current injection at 2 × and 3 × rheobase (two-way ANOVA, aP < 0.05; bP < 0.01). \nElectrophysiological characteristics of colon related DRG neuron after Letrozole treatment\nValues are means ± standard error. \nP < 0.05;\nP < 0.001 vs control group. CAS: Chronic adult stress; CPS: Chronic prenatal stress.\n Spinal cord BDNF levels regulated by estrogen To investigate the effect of estrogen on BDNF expression, we measured BDNF mRNA and protein levels in the lumbar-sacral spinal cords of OVX and Sham CPS + CAS female rats. Systemic estradiol administration to naïve cycling females produced significant increases in plasma estrogen (Figure 5A), lumbar-sacral spinal cord BDNF mRNA (Figure 5B), and protein (Figure 5C). We also measured BDNF mRNA and protein levels in the lumbar-sacral spinal cords of OVX and Sham CPS + CAS female rats. BDNF mRNA and protein expression were significantly suppressed by OVX compared with sham rats. Another experiment showed that intrathecal infusion of estrogen into naïve female rats significantly increased BDNF protein levels, which proved that estrogen reversed the experimental results and contributed to the response to visceral pain[13].\nBrain-derived neurotrophic factor expression in lumbar-sacral spinal cord is regulated by estrogen. A: Plasma estrogen levels in cycling females that received a bolus estradiol (E2) infusion on day 1; B: Lumbar-sacral spinal cord brain-derived neurotrophic factor (BDNF) mRNA following bolus estrogen infusion; C: Lumbar-sacral spinal cord BDNF protein following bolus estrogen infusion. (n = 8 rats in each group, two-way ANOVA, aP < 0.05 vs vehicle group). \nTo investigate the effect of estrogen on BDNF expression, we measured BDNF mRNA and protein levels in the lumbar-sacral spinal cords of OVX and Sham CPS + CAS female rats. Systemic estradiol administration to naïve cycling females produced significant increases in plasma estrogen (Figure 5A), lumbar-sacral spinal cord BDNF mRNA (Figure 5B), and protein (Figure 5C). We also measured BDNF mRNA and protein levels in the lumbar-sacral spinal cords of OVX and Sham CPS + CAS female rats. BDNF mRNA and protein expression were significantly suppressed by OVX compared with sham rats. Another experiment showed that intrathecal infusion of estrogen into naïve female rats significantly increased BDNF protein levels, which proved that estrogen reversed the experimental results and contributed to the response to visceral pain[13].\nBrain-derived neurotrophic factor expression in lumbar-sacral spinal cord is regulated by estrogen. A: Plasma estrogen levels in cycling females that received a bolus estradiol (E2) infusion on day 1; B: Lumbar-sacral spinal cord brain-derived neurotrophic factor (BDNF) mRNA following bolus estrogen infusion; C: Lumbar-sacral spinal cord BDNF protein following bolus estrogen infusion. (n = 8 rats in each group, two-way ANOVA, aP < 0.05 vs vehicle group). \n Peripheral NGF level increased in CPS + CAS female rats We examined NGF expression in the colons of females from all four experimental groups by immunohistochemistry (Figure 6A). Morphometric analysis showed that CAS and CPS + CAS significantly increased NGF levels in the colon wall, with the increase in CPS + CAS significantly greater that of CAS alone (Figure 6B). Western blotting showed that NGF protein was significantly upregulated in CPS + CAS rats compared with controls (Figure 6C).\nNerve growth factor expression level in the colon wall. A: Immunohistochemical staining of nerve growth factor (NGF; green) was detected with nuclear counterstaining staining (blue) in controls, chronic adult stress (CAS), chronic prenatal stress (CPS) and CPS + CAS group female rat colon walls. × 400 magnification representative pictures were shown; B: Quantification of NGF levels from colon wall in immunohistochemistry (IHC) (n = 4 rats in each group, one-way ANOVA, aP < 0.05 vs control group; bP < 0.05 vs CPS group); C: Western blots of NGF protein from control and CPS + CAS female rats colon wall tissue (n = 6 rats in each group, t-test, aP < 0.05 vs control group). \nWe examined NGF expression in the colons of females from all four experimental groups by immunohistochemistry (Figure 6A). Morphometric analysis showed that CAS and CPS + CAS significantly increased NGF levels in the colon wall, with the increase in CPS + CAS significantly greater that of CAS alone (Figure 6B). Western blotting showed that NGF protein was significantly upregulated in CPS + CAS rats compared with controls (Figure 6C).\nNerve growth factor expression level in the colon wall. A: Immunohistochemical staining of nerve growth factor (NGF; green) was detected with nuclear counterstaining staining (blue) in controls, chronic adult stress (CAS), chronic prenatal stress (CPS) and CPS + CAS group female rat colon walls. × 400 magnification representative pictures were shown; B: Quantification of NGF levels from colon wall in immunohistochemistry (IHC) (n = 4 rats in each group, one-way ANOVA, aP < 0.05 vs control group; bP < 0.05 vs CPS group); C: Western blots of NGF protein from control and CPS + CAS female rats colon wall tissue (n = 6 rats in each group, t-test, aP < 0.05 vs control group). ","The basal activity of a spinal afferent fiber was defined as the average number of action potentials per second (impulses/sec) in the 60 s period before the onset of a distention stimulus. In male controls, 66% of the afferent fibers under study displayed SA and SA was significantly higher in female controls than in male controls (0.71 ± 0.21 vs 1.24 ± 0.20 imp/sec; Figure 1B). The average single fiber activity in response to CRD was significantly higher in female control rats compared with male controls (Figure 1C). We found that the enhanced sensitization in female rats mainly came from the low-threshold fibers (Figure 1D and E).\nTo assess the effects of CPS + CAS on colon afferent fiber activities, we compared average single colon afferent fiber activities projecting from dorsal roots S1-L6 in response to CRD in male and female control, CPS, control + CAS and CPS + CAS rats recorded approximately 24 h after the last stressor. In females, CPS significantly increased single-unit afferent activity in response to CRD vs control female rats (Figure 1F). CAS alone enhanced single-unit activity compared with control. The increase in average afferent responses after CAS in prenatally stressed female rats (44.0%) was significantly greater than the increase in female control rats (39.3%). In males, CPS had no significant effect on primary afferent responses (Figure 1G). When we compared males to females within each experimental group, we found that the average single fiber activity was significantly higher in female compared with male CPS + CAS rats (Figure 1F, G). The increased activity may contribute to the enhanced female visceral hypersensitivity previously reported in this model. Average single-fiber activities were significantly greater in control and CPS and CAS females than in their corresponding male experimental groups (Figure 1F and G). Both CAS and CPS + CAS rats had significantly increased primary afferent responses compared with control and CPS rats. Thus, our CPS and CAS protocols sensitized colon-projecting primary afferent fibers, with the greatest effects produced by the combination of CPS + CAS in both males and females.","To elucidate the electrophysiological basis of enhanced stress-induced primary afferent activity in female rats, we performed patch clamp studies on acutely dissociated retrograde-labeled colon-projecting neurons from the L6-S2 DRGs in control, prenatal stress, adult stress only, and CPS + CAS female rats isolated 24 h after the last adult stressor (Figure 2A). Input resistance (Figure 2B) and rheobase (Figure 2C) were significantly decreased in neurons from CPS + CAS rats compared with the other three groups. The number of action potentials elicited at either 2 × or 3 × the rheobase were significantly greater in adult stress and CPS + CAS neurons compared with control and to CPS neurons (Figure 2D, E). CAS significantly increased action potential overshoot with or without CPS (Figure 2F), but it did not significantly alter other electrophysiological characteristics, such as number of spontaneous spikes, membrane capacitance (pF), resting membrane potential, cell diameter, time constant, and DRG neuron action potential amplitude and duration (Table 1).\nPatch clamp recording in colonic dorsal root ganglion neurons from female rats. A: Patch clamp process of cell labeling. Under isoflurane anesthesia, the lipid soluble fluorescent dye 9-DiI was injected into muscularis externa of the exposed distal colon (left figure). Lumbosacral (L6–S2) dorsal root ganglions (upper photograph) were isolated and DiI-labeled neurons were identified by fluorescence microscopy (lower photograph). Electrophysiological properties of each neuron were measured using whole-cell current and voltage clamp protocols (right figure); B: Rheobase from all four experimental groups (n = 5 rats, 45 cells in each group, one-way ANOVA, aP < 0.05 vs control or bP < 0.05 vs CAS); C: Representative action potentials (APs) elicited by current injection at 2 × the rheobase in neurons from control, chronic adult stress (CAS), chronic prenatal stress (CPS) and CPS + CAS female rats; D: Membrane input resistance from all four groups (n = 5 rats, 45 cells in each group, one-way ANOVA, aP < 0.05 vs control; bP < 0.05 vs CPS); E: Number of APs elicited by current injection at either 2 × and 3 × the rheobase in all four experimental groups (two-way ANOVA, aP < 0.05 vs control; bP < 0.05 vs CPS); F: AP overshoot recorded from all four experimental groups (aP < 0.05 vs control); G: The proportion of neurons from each experimental group exhibiting spontaneous APs. Red numbers represent spontaneous AP firing cells; black numbers represent total cells; H: Representative total, IK and IA current tracings and average values of potassium currents: Itotal, IK and IA are shown in female CPS + CAS, CAS, CPS (n = 15 neurons, from 5 rats in each group), and control groups (n = 12 neurons from 5 rats); two-way ANOVA, aP < 0.05 vs each control group. \nElectrophysiological characteristics of colon related DRG neuron\nValues means ± standard error. \nP < 0.05;\nP < 0.001 vs control group. CAS: Chronic adult stress; CPS: Chronic prenatal stress.\nThe percentage of neurons with SA in was significantly greater in CPS + CAS rats than in control or CPS only rats (Figure 2G). Under voltage clamp conditions (Figure 2H), neurons from female CPS + CAS, CAS, CPS and control groups had IA and sustained outward rectifier K+ currents (IK). Compared with the other three groups, DRG neurons from CPS + CAS rats had significantly reduced average IA (P < 0.05). The average IK density was decreased but the change was not significant.","We did a vaginal smear test to identify the estrus cycle phases by identifying the vaginal cytological cell types. Estrogen concentration was significantly higher in the CPS proestrus/estrus phase compared with control diestrus, control proestrus/estrus, and CPS diestrus proestrus (P < 0.05; Figure 3A). Comparison of the plasma estrogen concentrations in control, CAS, CPS, CPS + CAS showed that CPS significantly increased plasma estrogen levels compared with the control rats and that CAS increased plasma estrogen level compared with the control and CPS rats (Figure 3B).\nEffects of chronic prenatal stress, chronic adult stress, ovariectomy, and letrozole treatment on plasma estrogen levels in female rats. A: Plasma estrogen level in control and chronic prenatal stress (CPS) rats by estrus cycle phase (n = 8 rats, one-way ANOVA, aP < 0.05 vs control proestrus/estrus (P-E) phase; bP < 0.05 vs CPS diestrus (D) phase); B: Plasma estrogen levels increased in CPS rats and following chronic adult stress (CAS) 24 h after the last adult stressor (n = 8 rats, one-way ANOVA, aP < 0.05 vs control; bP < 0.05 vs CPS); C: Ovariectomy (OVX) significantly reduced CPS female rat plasma estrogen levels before and after CAS (n = 5 rats, one-way ANOVA, aP < 0.05 vs sham group); D: Letrozole treatment significantly reduced CPS female rat plasma estrogen levels before or after CAS (n = 5 rats, one-way ANOVA, aP < 0.05 vs vehicle group; cP < 0.0001); E: Plasma norepinephrine levels from control, CAS, CPS and CPS + CAS group female rats (n = 5 rats, one-way ANOVA, aP < 0.05 vs control; bP < 0.05 vs CPS); F: Plasma adrenocorticotropic hormone (ACTH) levels from control and CPS + CAS group female rats (n = 5 rats, t-test, aP < 0.05 vs control). \nTo determine whether estrogen contributed to stress-induced visceral hypersensitivity in prenatal stressed females, we reduced plasma estrogen levels by either OVX or letrozole treatment. OVX significantly lowered serum estradiol levels before and after CAS (Figure 3C). Treatment was continued throughout CAS. After treatment with letrozole, serum estradiol levels were significantly reduced (Figure 3D). To study the effects of gender and stress on norepinephrine and ACTH levels, we measured plasma norepinephrine levels in female rats from all four experimental groups. CAS alone significantly increased plasma norepinephrine levels compared with both the controls and with CPS alone (Figure 3E) Plasma norepinephrine levels were significantly increased in CPS + CAS rats compared with CAS alone as well as with controls and CPS. Plasma ACTH levels were significantly increased in CPS + CAS rats compared with controls. (Figure 3F).","We performed patch clamp experiments on acutely isolated retrograde-labeled DRG neurons from CPS + CAS females with or without letrozole treatment 24 h after the last adult stressor. Letrozole treatment significantly increased rheobase (Figure 4A), and significantly reduced input resistance (Figure 4B). Action potential overshoot (Figure 4C) and the number of action potentials elicited by a current injection at either 2 × or 3 × rheobase were significantly reduced by letrozole treatment (Figure 4D). Other electrophysiological properties were not significantly altered (Table 2). We also recorded electromyographic activity to determine whether the reduction in visceral sensitivity in female CPS + CAS rats caused by OVX or systemic letrozole treatment reduced visceromotor responses. The findings demonstrated a significant decrease in excitability of colon-projecting L6-S2 neurons.\nEffects of Letrozole treatment on colon dorsal root ganglion neuron excitability. A: Rheobase (n = 45 cells in 6 rats in each group, t-test, cP < 0.001 vs Veh. + chronic adult stress [CAS] + chronic prenatal stress [CPS]); B: Membrane input resistance (RIn) (t-test, aP < 0.05); C: Action potential (AP) overshoot (t-test, aP < 0.05); D: Number of APs elicited by current injection at 2 × and 3 × rheobase (two-way ANOVA, aP < 0.05; bP < 0.01). \nElectrophysiological characteristics of colon related DRG neuron after Letrozole treatment\nValues are means ± standard error. \nP < 0.05;\nP < 0.001 vs control group. CAS: Chronic adult stress; CPS: Chronic prenatal stress.","To investigate the effect of estrogen on BDNF expression, we measured BDNF mRNA and protein levels in the lumbar-sacral spinal cords of OVX and Sham CPS + CAS female rats. Systemic estradiol administration to naïve cycling females produced significant increases in plasma estrogen (Figure 5A), lumbar-sacral spinal cord BDNF mRNA (Figure 5B), and protein (Figure 5C). We also measured BDNF mRNA and protein levels in the lumbar-sacral spinal cords of OVX and Sham CPS + CAS female rats. BDNF mRNA and protein expression were significantly suppressed by OVX compared with sham rats. Another experiment showed that intrathecal infusion of estrogen into naïve female rats significantly increased BDNF protein levels, which proved that estrogen reversed the experimental results and contributed to the response to visceral pain[13].\nBrain-derived neurotrophic factor expression in lumbar-sacral spinal cord is regulated by estrogen. A: Plasma estrogen levels in cycling females that received a bolus estradiol (E2) infusion on day 1; B: Lumbar-sacral spinal cord brain-derived neurotrophic factor (BDNF) mRNA following bolus estrogen infusion; C: Lumbar-sacral spinal cord BDNF protein following bolus estrogen infusion. (n = 8 rats in each group, two-way ANOVA, aP < 0.05 vs vehicle group). ","We examined NGF expression in the colons of females from all four experimental groups by immunohistochemistry (Figure 6A). Morphometric analysis showed that CAS and CPS + CAS significantly increased NGF levels in the colon wall, with the increase in CPS + CAS significantly greater that of CAS alone (Figure 6B). Western blotting showed that NGF protein was significantly upregulated in CPS + CAS rats compared with controls (Figure 6C).\nNerve growth factor expression level in the colon wall. A: Immunohistochemical staining of nerve growth factor (NGF; green) was detected with nuclear counterstaining staining (blue) in controls, chronic adult stress (CAS), chronic prenatal stress (CPS) and CPS + CAS group female rat colon walls. × 400 magnification representative pictures were shown; B: Quantification of NGF levels from colon wall in immunohistochemistry (IHC) (n = 4 rats in each group, one-way ANOVA, aP < 0.05 vs control group; bP < 0.05 vs CPS group); C: Western blots of NGF protein from control and CPS + CAS female rats colon wall tissue (n = 6 rats in each group, t-test, aP < 0.05 vs control group). ","Enhanced CPS-induced visceral hypersensitivity in female rats was associated with an increase in the responses of lumbosacral nerve fibers to CRD in both male and female offspring. These findings are further supported by data showing increased excitability of colon-projecting DRG neurons from females in patch clamp studies. The magnitude of the sensitization was the greatest in female CPS + CAS rats, suggesting that it made a major contribution to the observed enhanced female visceral hypersensitivity in our model.\nChronic stress is known to increase the excitability of colon-projecting DRG neurons in rats and mice. In adult male Sprague Dawley rats, colon DRG neuron sensitization was shown to be driven by increases in NGF expression in the colon muscularis externa[13]. In our model, we also observed a significant increase in colon NGF, but its potential role in primary afferent sensitization and visceral hypersensitivity was not investigated.. Other studies in male mice showed that stress, in the form of water avoidance, significantly increased the excitability of colon-projecting DRG neurons and that the combined activity of the stress mediators corticosterone and norepinephrine increased DRG neuron excitability in vitro[20,21]. We previously found significant increases in the serum levels of norepinephrine in CPS + CAS females. However, daily systemic treatment with adrenergic antagonists during the adult stress protocol failed to reduce visceral hypersensitivity in female neonatal + adult stress rats[13] suggesting that norepinephrine did not play a major role in the acquisition of enhanced female visceral hypersensitivity or primary afferent sensitization in our model.\nWhen we tested on lumbar-sacral afferent fibers and dissociated neurons in patch clamp studies, we found significant decreases in transient potassium IA currents in neurons isolated from CPS + CAS females compared with the other three experimental groups. Declines in A-type Kv currents in DRG neurons have been associated with persistent pain in multiple chronic pain models[22]. Whether the decline was caused by changes in channel properties or expression was not investigated in this study. However, another study demonstrated that estrogen significantly shifted the activation curve of IA currents in the hyperpolarizing direction and that estrogen inhibited Kv (+) channels in mouse DRG neurons through a membrane ER-activated nongenomic pathway[23].\nOur results showed that the excitability of colon-projecting neurons in CPS + CAS females was significantly reduced by systemic letrozole treatment, suggesting that estrogen contributed to the sensitization process. Previous studies show that estrogen receptors expressed on primary afferent neurons contributed to enhanced sensitivity in various pain models[24-26]. One study found no decline in the responses of colon-projecting nerve fibers to CRD following OVX and found no detectable estrogen receptor alpha immunoreactivity in colon-projecting DRG neurons[27]. The reasons for the differing results are not clear, but local production of estrogen in DRG neurons could be sufficient to sustain sensitization.\nNGF and its receptors play important roles in the mechanism of visceral pain and hyperalgesia in women. For example, endometriosis is estrogen dependent and is commonly diagnosed. The main symptoms are various types of pelvic pain that have a serious effect on physical and mental health, but the mechanisms of abdominal pain are still unclear. Studies have shown NGF to be an inflammatory mediator and modulator of pain in adulthood[28].","In this study, we examined the sex differences and effects of estrogen on the acquisition of enhanced visceral hypersensitivity in the offspring of rats in a model of prenatal and adult stress as shown in Figure 7. Our study shows that estrogen acted in the spinal cord and the primary afferent neurons to enhance visceral nociception. Acute blockade of the endogenous synthesis of estrogens in rat spinal cord significantly reduced visceral hypersensitivity, suggesting that locally produced estrogen in the central nervous system can regulate nociceptive neurons to modulate visceral hypersensitivity. The chronic stress-estrogen-BDNF axis sensitized visceral hypersensitivity in the offspring of females subjected to CPS. The development of chronic stress-induced visceral hypersensitivity in female rats was estrogen dependent. A key component of this hypersensitivity was estrogen-dependent sensitization of primary afferent colon neurons. Our findings provide key scientific evidence in a preclinical model in support of developing gender-based treatment for abdominal pain in IBS.\nSummary diagram of estrogen re-enhanced visceral hyperalgesia investigated in chronic prenatal stress plus chronic adult stress models. BDNF: Brain-derived neurotrophic factor; NGF: Nerve growth factor."],"string":"[\n \"Visceral pain of colonic origin is the most prominent symptom in irritable bowel syndrome (IBS) patients[1]. Female IBS patients report more severe pain that occurs more frequently and with longer episodes than in male patients[1,2]. The ratio of female to male IBS is about 2:1 among patients seen in medical clinics[3]. Moreover, females have a higher prevalence of IBS co-morbidities such as anxiety and depression[4,5] and are more vulnerable to stress-induced exacerbation of IBS symptoms compared with males[3,6,7].\\nClinical studies show that early life adverse experiences are risk factors for the development of IBS symptoms, including visceral pain and ongoing chronic stress, especially abdominal pain[8-10]. These factors contribute to the development of visceral hypersensitivity, a key component of the IBS symptom complex and one that may be responsible for symptoms of pain[11,12]. Our previous research found that the female offspring of mothers subjected to chronic prenatal stress (CPS) had a markedly greater visceral sensitivity than their male littermates following challenge by another chronic adult stress (CAS) protocol. A critical molecular event in the development of this female-enhanced visceral hypersensitivity is upregulation of brain-derived neurotrophic factor (BDNF) expression in the lumbar-sacral spinal cord of female CPS + CAS rats[13]. However, the neurophysiological changes underlying the enhanced female-specific visceral hypersensitivity and the role of hormone in the development of stress-induced visceral hypersensitivity are not well understood.\\nVisceral hypersensitivity in IBS involves abnormal changes in neurophysiology throughout the brain-gut axis. In IBS, there is evidence for sensitization of primary afferents to jejunal distention and electrical stimulation[14], and there is evidence for increased sensitivity of lumbar splanchnic afferents[15,16]. In animal models of either early life adverse events or adult stress-induced visceral hypersensitivity[17], there is evidence of colon primary afferent sensitization. However, the studies were performed in male rodents. Therefore, in this study, we established a CPS and CAS rodent model to analyze the impact on female colon afferent neuron function and the role of estrogen. Our hypothesis was that female CPS offspring subjected to chronic stress as adults would exhibit greater colonic dorsal root ganglion (DRG) neuron sensitization compared with their male littermates, and that the enhanced visceral sensitization and primary afferent sensitization in females was estrogen dependent.\",\n \"The Institutional Animal Care and Use Committee of the University of Texas Medical Branch at Galveston, TX approved all animal procedures. Experiments were performed on pregnant Sprague Dawley rats and their 8-wk-old to 16-wk-old male and female offspring. Rats were housed individual cages with access to food and water in a room with controlled conditions (22 ± 2 °C, relative humidity of 50% ± 5%), and a 12 h light/12 h dark cycle.\",\n \"Pregnant dams were subjected to a CPS protocol that consisted of a random sequence of twice-daily applications of one of three stress sessions, a 1-h water-avoidance, 45-min cold-restraint, or a 20-min forced swim starting on day 6 and continuing until delivery on day 21. Male and female offspring of the stressed dams were designated CPS rats. Control dams received sham stress and their offspring were designated control rats. As adults at 8-16 wk of age, control and prenatally stressed offspring were challenged by the same CAS protocol for 9 d. Ovariectomy (OVX) or sham surgery was performed on female prenatal-stress offspring on day 56. Daily letrozole treatment was initiated on day 49, 2 wk prior to initiation of adult stress. Treatment was continued through the stress protocol. A schematic diagram of the study procedures is shown in Figure 1A.\\nPrimary afferent responses to colorectal distention. A: Chronic prenatal stress (CPS) plus chronic adult stress (CAS) model. Pregnant dams were subjected to prenatal stress from on day 11 of gestation. Ovariectomy (OVX) or sham surgery was performed on female prenatal-stress offspring on day 56. Daily Letrozole was initiated on day 49, 2 wk prior to initiation of adult stress. Treatment was continued through the stress protocol; B: Spontaneous activity (SA) of single afferent units in male and female control rats (n = 70 fibers in 6 rats in each group, t-test, aP < 0.05); C: Average response to graded colorectal distention (CRD) of 56 afferent fibers in 6 male and 70 afferent fibers in 6 female control rats; two-way analysis of variance (ANOVA; aP < 0.05 vs the same pressure male group); D: Responses of low-threshold (LT) fibers to CRD in 42 fibers in 6 male rats and 40 fibers in 6 female control rats (ANOVA, aP < 0.05 vs the same pressure male group); E: Responses of high-threshold (HT) afferent fibers to CRD in 14 fibers in 6 male and 29 fibers in 6 female control rats; F: Effects of CAS on afferent fiber responses to CRD from 59 fibers in 6 control and 99 fibers in 6 CPS female rats; (two-way ANOVA, aP < 0.05 vs the same pressure control group, bP < 0.05 vs the same pressure CPS group); G: Effects of CAS on afferent fiber responses to CRD in control and CPS male rats (n = 6 rats, 57 fibers for control and 95 fibers for CPS female group; two-way ANOVA, aP < 0.05 vs the same pressure-control group). \",\n \"Before OVX or letrozole treatment, vaginal smears were used to identify the estrus cycle phase. OVX or sham surgery was performed on female prenatal-stress offspring on day 56. The aromatase inhibitor letrozole [4,4’-(1H-1,2,4-triazol-l-yl-methylene)-bis-benzonitrile], (Novartis) 1.0 mg/kg was orally administered in the experimental group and vehicle (hydroxypropyl cellulose 0.3% in water) was given in the control group once daily for 14 d. Direct transcutaneous intrathecal injections of estrogen and letrozole were performed as described by Mestre et al[18].\",\n \"Multiunit afferent discharges were recorded from the distal ends of L6-S2 dorsal rootlets decentralized close to their entry into the spinal cord. A bundle of multiunit fibers was distinguished into 2-6 single units off-line using wave mark template matching in Spike 2 software that differentiates spikes by shape and amplitude. Colonic afferent fibers were identified by their response to graded colorectal distention (CRD). A balloon was used to distend the colorectum. Isoflurane, 2.5%, followed by 50 mg/kg intraperitoneal sodium pentobarbital induced general anesthesia that was maintained by infusing a mixture of pentobarbital sodium + pancuronium bromide + saline by intravenous infusion through the tail vein. The adequacy of anesthesia was confirmed by the absence of corneal and pupillary reflexes and stability of the end-tidal CO2 level. A tracheotomy tube connected to a ventilator system provided a mixture of room air and oxygen. Expired CO2 was monitored and maintained at 3.5%. Body temperature was monitored and maintained at 37 °C by a servo-controlled heating blanket. A laminectomy from T12 to S2 exposed the spinal cord. The head was stabilized in a stereotaxic frame.\",\n \"Retrograde fluorescence label injections: Labeling of colon-projecting DRG neurons was performed as previously described[13]. Under general 2% isoflurane anesthesia, the lipid soluble fluorescent dye, 1,1’-dioleyl-3,3,3’,3’-tetramethylindocarbocyanine methane-sulfonate (9-DiI, Invitrogen, Carlsbad, CA) was injected (50 mg/mL) into the muscularis externa on the exposed distal colon in 8 to 10 sites (2 μL each site). To prevent leakage, the needle was kept in place for 1 min following each injection.\\nDissociation and culture of DRG neurons: Rats were deeply anesthetized with isoflurane followed by decapitation. Lumbosacral (L6–S2) DRGs were collected in ice cold and oxygenated dissecting solution, containing (in mM) 130 NaCl, 5 KCl, 2 KH2PO4, 1.5 CaCl2, 6 MgSO4, 10 glucose, and 10 HEPES, pH 7.2 (305 mOsm). After removal of the connective tissue, the ganglia were transferred to a 5 mL dissecting solution containing collagenase D (1.8 mg/mL; Roche) and trypsin (1.0 mg/mL; Sigma, St Louis, MO), and incubated for 1.5 h at 34.5 °C. DRGs were then taken from the enzyme solution, washed, and put in 0.5-2 mL of the dissecting solution containing DNase (0.5 mg/mL; Sigma). Cells were subsequently dissociated by gentle trituration 10 to 15 times with fire-polished glass pipettes and placed on acid-cleaned glass coverslips. The dissociated DRG neurons were kept in 1 mL DMEM (with 10% FBS) in an incubator (95% O2/5% CO2) at 37 °C overnight.\\nWhole-cell patch clamp recordings from dissociated DRG neurons: Before each experiment, a glass coverslip with DRG neurons was transferred to a recording chamber perfused (1.5 mL/min) with external solution containing (10 mM): 130 NaCl, 5 KCl, 2 KH2PO4, 2.5 CaCl2, 1 MgCl2, 10 HEPES, and 10 glucose, pH adjusted to pH 7.4 with NaOH (300 mOsm) at room temperature. Recording pipettes, pulled from borosilicate glass tubing, with resistance of 1-5 MΩ, were filled with solution containing (in mM): 100 KMeSO3, 40 KCl, and 10 HEPES, pH 7.25 adjusted with KOH (290 mOsm). DiI-labeled neurons were identified by fluorescence microscopy. Whole-cell currents and voltage were recorded from DiI-labeled neurons using a Dagan 3911 patch clamp amplifier. Data were acquired and analyzed by pCLAMP 9.2 (Molecular Devices, Sunnyvale, CA). The currents were filtered at 2–5 kHz and sampled at 50 or 100 s per point. While still under voltage clamp, the Clampex Membrane Test program (Molecular Devices) was used to determine membrane capacitance (Cm) and membrane resistance (Rm), during a 10 ms, 5 mV depolarizing pulse form a holding potential of −60 mV. The configuration was then switched to current clamp (0 pA) to determine other electrophysiological properties. After stabilizing for 2–3 min, the resting membrane potential was measured. The minimum acceptable resting membrane potential was −40 mV. Spontaneous activity (SA) was then recorded over two 30 s periods separated by 60 s without recording, as described by Bedi et al[19].\\nTransient A-type K+ current (IA) recording method in patch studies: To record voltage-gated K+ current (Kv), Na+ in control external solution was replaced with equimolar choline and the Ca2+ concentration was reduced to 0.03 mM to suppress Ca2+ currents and to prevent Ca2+ channels becoming Na+ conducting. The reduced external Ca2+ would also be expected to suppress Ca2+-activated K+ currents. The current traces of Kv in DRG neurons were measured at different holding potentials. The membrane potential was held at −100 mV and voltage steps were from −40 to +30 mV to record the total Kv. The membrane potential was held at −50 mV to record the sustained Kv. The IA currents were calculated by subtracting the sustained current from the total current. The current density (in pA/pF) was calculated by dividing the current amplitude by cell membrane capacitance.\",\n \"Total RNA was extracted using RNeasy Mini Kits (QIAGEN, Valencia, CA). One microgram of total RNA was reverse-transcribed using the SuperScriptTM III First-Strand Synthesis System. PCR assays were performed on a StepOnePlus thermal cycler with 18 s as the normalizer using Applied Biosystems primer/probe set Rn02531967_s1 directed against the translated exon IX. Fold-change relative to control was calculated using the ΔΔCt method (Applied Biosystems).\",\n \"Samples were lysed in RIPA buffer containing protease inhibitor cocktail and phenylmethanesulfonyl fluoride. Lysates were incubated for 30 min on ice and then centrifuged at 10 000 × g for 10 min at 4 °C. The protein concentration in the supernatant was determined using bicinchoninic acid (BCA) assay kits with bovine serum albumin as a standard. Equal amounts of protein (30 μg per lane) were separated by 10% sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) and then transferred to nitrocellulose membranes (Bio-Rad, United States). The membranes were blocked in Li-Cor blocking buffer for 1 h at room temperature and then incubated with primary antibodies. BDNF antibody (Santa Cruz Biotechnologies, Santa Cruz, CA) was used at 1:200 dilution; nerve growth factor (NGF) antibody (Abcam, MA) was used at 1:1000 dilution; β-actin antibody (Sigma Aldrich, St Louis, MO) was used at 1:5000 dilution. The secondary antibodies were donkey anti-rabbit Alexa Fluor 680 (Invitrogen) and goat anti-mouse IRDye 800 (Rockland). Images were acquired and band intensities measured using a Li-Cor Odyssey system (Li-Cor, Lincoln, NE).\",\n \"Frozen sections of colon tissue from control, CAS, CPS and CPS + CAS female rats were mounted on glass slides, and rehydrated in phosphate buffered saline at room temperature. The slides were treated for antigen retrieval and blocked with 10% normal goat serum diluted in 0.3% phosphate buffered saline-Triton for 1 h, and then incubated with NGF primary antibody in antibody diluent (Renoir Red, Biocare Medical, Concord, CA) at 4 °C overnight. The slides were exposed to fluorescent dye-conjugated secondary antibody for 2 h at room temperature, counterstained with 4',6-diamidino-2-phenylindole and coverslipped. Images were taken in fluorescence mode on an Olympus laser scanning confocal microscope and the average signal intensity was calculated by the bundled software.\",\n \"Serum estradiol, adrenocorticotropic hormone (ACTH), and norepinephrine levels were measured using specific enzyme-linked immunosorbent assay kits for each analyte (CSB-E05110r, CSB-E06875r, CSB-E07022, Cusabio Bioteck CO., United States) following the manufacturer’s instructions.\",\n \"Single fiber responses (impulses/second) to CRD were calculated by subtracting SA from the mean 30 s maximal activity during distension. Fibers were considered responsive if CRD increased their activity to 30% greater than the baseline value. Mechanosensitive single units were classified as high threshold (> 20 mmHg) or low threshold (≤ 20 mmHg) on the basis of their response threshold and profile during CRD. Single fiber activity data were analyzed by analysis of variance with repeated measures; CRD intensity was the repeated factor and the experimental group was the between-group factor. If significant main effects were present, the individual means were compared using the Fisher post-hoc test. All authors had access to the study data and reviewed and approved the final manuscript.\",\n \" Effects of CPS plus CAS on primary afferent responses to CRD in male and female rats The basal activity of a spinal afferent fiber was defined as the average number of action potentials per second (impulses/sec) in the 60 s period before the onset of a distention stimulus. In male controls, 66% of the afferent fibers under study displayed SA and SA was significantly higher in female controls than in male controls (0.71 ± 0.21 vs 1.24 ± 0.20 imp/sec; Figure 1B). The average single fiber activity in response to CRD was significantly higher in female control rats compared with male controls (Figure 1C). We found that the enhanced sensitization in female rats mainly came from the low-threshold fibers (Figure 1D and E).\\nTo assess the effects of CPS + CAS on colon afferent fiber activities, we compared average single colon afferent fiber activities projecting from dorsal roots S1-L6 in response to CRD in male and female control, CPS, control + CAS and CPS + CAS rats recorded approximately 24 h after the last stressor. In females, CPS significantly increased single-unit afferent activity in response to CRD vs control female rats (Figure 1F). CAS alone enhanced single-unit activity compared with control. The increase in average afferent responses after CAS in prenatally stressed female rats (44.0%) was significantly greater than the increase in female control rats (39.3%). In males, CPS had no significant effect on primary afferent responses (Figure 1G). When we compared males to females within each experimental group, we found that the average single fiber activity was significantly higher in female compared with male CPS + CAS rats (Figure 1F, G). The increased activity may contribute to the enhanced female visceral hypersensitivity previously reported in this model. Average single-fiber activities were significantly greater in control and CPS and CAS females than in their corresponding male experimental groups (Figure 1F and G). Both CAS and CPS + CAS rats had significantly increased primary afferent responses compared with control and CPS rats. Thus, our CPS and CAS protocols sensitized colon-projecting primary afferent fibers, with the greatest effects produced by the combination of CPS + CAS in both males and females.\\nThe basal activity of a spinal afferent fiber was defined as the average number of action potentials per second (impulses/sec) in the 60 s period before the onset of a distention stimulus. In male controls, 66% of the afferent fibers under study displayed SA and SA was significantly higher in female controls than in male controls (0.71 ± 0.21 vs 1.24 ± 0.20 imp/sec; Figure 1B). The average single fiber activity in response to CRD was significantly higher in female control rats compared with male controls (Figure 1C). We found that the enhanced sensitization in female rats mainly came from the low-threshold fibers (Figure 1D and E).\\nTo assess the effects of CPS + CAS on colon afferent fiber activities, we compared average single colon afferent fiber activities projecting from dorsal roots S1-L6 in response to CRD in male and female control, CPS, control + CAS and CPS + CAS rats recorded approximately 24 h after the last stressor. In females, CPS significantly increased single-unit afferent activity in response to CRD vs control female rats (Figure 1F). CAS alone enhanced single-unit activity compared with control. The increase in average afferent responses after CAS in prenatally stressed female rats (44.0%) was significantly greater than the increase in female control rats (39.3%). In males, CPS had no significant effect on primary afferent responses (Figure 1G). When we compared males to females within each experimental group, we found that the average single fiber activity was significantly higher in female compared with male CPS + CAS rats (Figure 1F, G). The increased activity may contribute to the enhanced female visceral hypersensitivity previously reported in this model. Average single-fiber activities were significantly greater in control and CPS and CAS females than in their corresponding male experimental groups (Figure 1F and G). Both CAS and CPS + CAS rats had significantly increased primary afferent responses compared with control and CPS rats. Thus, our CPS and CAS protocols sensitized colon-projecting primary afferent fibers, with the greatest effects produced by the combination of CPS + CAS in both males and females.\\n Increase in excitability of colon-projecting lumbosacral DRG neurons in female CPS + CAS rats To elucidate the electrophysiological basis of enhanced stress-induced primary afferent activity in female rats, we performed patch clamp studies on acutely dissociated retrograde-labeled colon-projecting neurons from the L6-S2 DRGs in control, prenatal stress, adult stress only, and CPS + CAS female rats isolated 24 h after the last adult stressor (Figure 2A). Input resistance (Figure 2B) and rheobase (Figure 2C) were significantly decreased in neurons from CPS + CAS rats compared with the other three groups. The number of action potentials elicited at either 2 × or 3 × the rheobase were significantly greater in adult stress and CPS + CAS neurons compared with control and to CPS neurons (Figure 2D, E). CAS significantly increased action potential overshoot with or without CPS (Figure 2F), but it did not significantly alter other electrophysiological characteristics, such as number of spontaneous spikes, membrane capacitance (pF), resting membrane potential, cell diameter, time constant, and DRG neuron action potential amplitude and duration (Table 1).\\nPatch clamp recording in colonic dorsal root ganglion neurons from female rats. A: Patch clamp process of cell labeling. Under isoflurane anesthesia, the lipid soluble fluorescent dye 9-DiI was injected into muscularis externa of the exposed distal colon (left figure). Lumbosacral (L6–S2) dorsal root ganglions (upper photograph) were isolated and DiI-labeled neurons were identified by fluorescence microscopy (lower photograph). Electrophysiological properties of each neuron were measured using whole-cell current and voltage clamp protocols (right figure); B: Rheobase from all four experimental groups (n = 5 rats, 45 cells in each group, one-way ANOVA, aP < 0.05 vs control or bP < 0.05 vs CAS); C: Representative action potentials (APs) elicited by current injection at 2 × the rheobase in neurons from control, chronic adult stress (CAS), chronic prenatal stress (CPS) and CPS + CAS female rats; D: Membrane input resistance from all four groups (n = 5 rats, 45 cells in each group, one-way ANOVA, aP < 0.05 vs control; bP < 0.05 vs CPS); E: Number of APs elicited by current injection at either 2 × and 3 × the rheobase in all four experimental groups (two-way ANOVA, aP < 0.05 vs control; bP < 0.05 vs CPS); F: AP overshoot recorded from all four experimental groups (aP < 0.05 vs control); G: The proportion of neurons from each experimental group exhibiting spontaneous APs. Red numbers represent spontaneous AP firing cells; black numbers represent total cells; H: Representative total, IK and IA current tracings and average values of potassium currents: Itotal, IK and IA are shown in female CPS + CAS, CAS, CPS (n = 15 neurons, from 5 rats in each group), and control groups (n = 12 neurons from 5 rats); two-way ANOVA, aP < 0.05 vs each control group. \\nElectrophysiological characteristics of colon related DRG neuron\\nValues means ± standard error. \\nP < 0.05;\\nP < 0.001 vs control group. CAS: Chronic adult stress; CPS: Chronic prenatal stress.\\nThe percentage of neurons with SA in was significantly greater in CPS + CAS rats than in control or CPS only rats (Figure 2G). Under voltage clamp conditions (Figure 2H), neurons from female CPS + CAS, CAS, CPS and control groups had IA and sustained outward rectifier K+ currents (IK). Compared with the other three groups, DRG neurons from CPS + CAS rats had significantly reduced average IA (P < 0.05). The average IK density was decreased but the change was not significant.\\nTo elucidate the electrophysiological basis of enhanced stress-induced primary afferent activity in female rats, we performed patch clamp studies on acutely dissociated retrograde-labeled colon-projecting neurons from the L6-S2 DRGs in control, prenatal stress, adult stress only, and CPS + CAS female rats isolated 24 h after the last adult stressor (Figure 2A). Input resistance (Figure 2B) and rheobase (Figure 2C) were significantly decreased in neurons from CPS + CAS rats compared with the other three groups. The number of action potentials elicited at either 2 × or 3 × the rheobase were significantly greater in adult stress and CPS + CAS neurons compared with control and to CPS neurons (Figure 2D, E). CAS significantly increased action potential overshoot with or without CPS (Figure 2F), but it did not significantly alter other electrophysiological characteristics, such as number of spontaneous spikes, membrane capacitance (pF), resting membrane potential, cell diameter, time constant, and DRG neuron action potential amplitude and duration (Table 1).\\nPatch clamp recording in colonic dorsal root ganglion neurons from female rats. A: Patch clamp process of cell labeling. Under isoflurane anesthesia, the lipid soluble fluorescent dye 9-DiI was injected into muscularis externa of the exposed distal colon (left figure). Lumbosacral (L6–S2) dorsal root ganglions (upper photograph) were isolated and DiI-labeled neurons were identified by fluorescence microscopy (lower photograph). Electrophysiological properties of each neuron were measured using whole-cell current and voltage clamp protocols (right figure); B: Rheobase from all four experimental groups (n = 5 rats, 45 cells in each group, one-way ANOVA, aP < 0.05 vs control or bP < 0.05 vs CAS); C: Representative action potentials (APs) elicited by current injection at 2 × the rheobase in neurons from control, chronic adult stress (CAS), chronic prenatal stress (CPS) and CPS + CAS female rats; D: Membrane input resistance from all four groups (n = 5 rats, 45 cells in each group, one-way ANOVA, aP < 0.05 vs control; bP < 0.05 vs CPS); E: Number of APs elicited by current injection at either 2 × and 3 × the rheobase in all four experimental groups (two-way ANOVA, aP < 0.05 vs control; bP < 0.05 vs CPS); F: AP overshoot recorded from all four experimental groups (aP < 0.05 vs control); G: The proportion of neurons from each experimental group exhibiting spontaneous APs. Red numbers represent spontaneous AP firing cells; black numbers represent total cells; H: Representative total, IK and IA current tracings and average values of potassium currents: Itotal, IK and IA are shown in female CPS + CAS, CAS, CPS (n = 15 neurons, from 5 rats in each group), and control groups (n = 12 neurons from 5 rats); two-way ANOVA, aP < 0.05 vs each control group. \\nElectrophysiological characteristics of colon related DRG neuron\\nValues means ± standard error. \\nP < 0.05;\\nP < 0.001 vs control group. CAS: Chronic adult stress; CPS: Chronic prenatal stress.\\nThe percentage of neurons with SA in was significantly greater in CPS + CAS rats than in control or CPS only rats (Figure 2G). Under voltage clamp conditions (Figure 2H), neurons from female CPS + CAS, CAS, CPS and control groups had IA and sustained outward rectifier K+ currents (IK). Compared with the other three groups, DRG neurons from CPS + CAS rats had significantly reduced average IA (P < 0.05). The average IK density was decreased but the change was not significant.\\n Effects of CPS and/or CAS on plasma estrogen concentration We did a vaginal smear test to identify the estrus cycle phases by identifying the vaginal cytological cell types. Estrogen concentration was significantly higher in the CPS proestrus/estrus phase compared with control diestrus, control proestrus/estrus, and CPS diestrus proestrus (P < 0.05; Figure 3A). Comparison of the plasma estrogen concentrations in control, CAS, CPS, CPS + CAS showed that CPS significantly increased plasma estrogen levels compared with the control rats and that CAS increased plasma estrogen level compared with the control and CPS rats (Figure 3B).\\nEffects of chronic prenatal stress, chronic adult stress, ovariectomy, and letrozole treatment on plasma estrogen levels in female rats. A: Plasma estrogen level in control and chronic prenatal stress (CPS) rats by estrus cycle phase (n = 8 rats, one-way ANOVA, aP < 0.05 vs control proestrus/estrus (P-E) phase; bP < 0.05 vs CPS diestrus (D) phase); B: Plasma estrogen levels increased in CPS rats and following chronic adult stress (CAS) 24 h after the last adult stressor (n = 8 rats, one-way ANOVA, aP < 0.05 vs control; bP < 0.05 vs CPS); C: Ovariectomy (OVX) significantly reduced CPS female rat plasma estrogen levels before and after CAS (n = 5 rats, one-way ANOVA, aP < 0.05 vs sham group); D: Letrozole treatment significantly reduced CPS female rat plasma estrogen levels before or after CAS (n = 5 rats, one-way ANOVA, aP < 0.05 vs vehicle group; cP < 0.0001); E: Plasma norepinephrine levels from control, CAS, CPS and CPS + CAS group female rats (n = 5 rats, one-way ANOVA, aP < 0.05 vs control; bP < 0.05 vs CPS); F: Plasma adrenocorticotropic hormone (ACTH) levels from control and CPS + CAS group female rats (n = 5 rats, t-test, aP < 0.05 vs control). \\nTo determine whether estrogen contributed to stress-induced visceral hypersensitivity in prenatal stressed females, we reduced plasma estrogen levels by either OVX or letrozole treatment. OVX significantly lowered serum estradiol levels before and after CAS (Figure 3C). Treatment was continued throughout CAS. After treatment with letrozole, serum estradiol levels were significantly reduced (Figure 3D). To study the effects of gender and stress on norepinephrine and ACTH levels, we measured plasma norepinephrine levels in female rats from all four experimental groups. CAS alone significantly increased plasma norepinephrine levels compared with both the controls and with CPS alone (Figure 3E) Plasma norepinephrine levels were significantly increased in CPS + CAS rats compared with CAS alone as well as with controls and CPS. Plasma ACTH levels were significantly increased in CPS + CAS rats compared with controls. (Figure 3F).\\nWe did a vaginal smear test to identify the estrus cycle phases by identifying the vaginal cytological cell types. Estrogen concentration was significantly higher in the CPS proestrus/estrus phase compared with control diestrus, control proestrus/estrus, and CPS diestrus proestrus (P < 0.05; Figure 3A). Comparison of the plasma estrogen concentrations in control, CAS, CPS, CPS + CAS showed that CPS significantly increased plasma estrogen levels compared with the control rats and that CAS increased plasma estrogen level compared with the control and CPS rats (Figure 3B).\\nEffects of chronic prenatal stress, chronic adult stress, ovariectomy, and letrozole treatment on plasma estrogen levels in female rats. A: Plasma estrogen level in control and chronic prenatal stress (CPS) rats by estrus cycle phase (n = 8 rats, one-way ANOVA, aP < 0.05 vs control proestrus/estrus (P-E) phase; bP < 0.05 vs CPS diestrus (D) phase); B: Plasma estrogen levels increased in CPS rats and following chronic adult stress (CAS) 24 h after the last adult stressor (n = 8 rats, one-way ANOVA, aP < 0.05 vs control; bP < 0.05 vs CPS); C: Ovariectomy (OVX) significantly reduced CPS female rat plasma estrogen levels before and after CAS (n = 5 rats, one-way ANOVA, aP < 0.05 vs sham group); D: Letrozole treatment significantly reduced CPS female rat plasma estrogen levels before or after CAS (n = 5 rats, one-way ANOVA, aP < 0.05 vs vehicle group; cP < 0.0001); E: Plasma norepinephrine levels from control, CAS, CPS and CPS + CAS group female rats (n = 5 rats, one-way ANOVA, aP < 0.05 vs control; bP < 0.05 vs CPS); F: Plasma adrenocorticotropic hormone (ACTH) levels from control and CPS + CAS group female rats (n = 5 rats, t-test, aP < 0.05 vs control). \\nTo determine whether estrogen contributed to stress-induced visceral hypersensitivity in prenatal stressed females, we reduced plasma estrogen levels by either OVX or letrozole treatment. OVX significantly lowered serum estradiol levels before and after CAS (Figure 3C). Treatment was continued throughout CAS. After treatment with letrozole, serum estradiol levels were significantly reduced (Figure 3D). To study the effects of gender and stress on norepinephrine and ACTH levels, we measured plasma norepinephrine levels in female rats from all four experimental groups. CAS alone significantly increased plasma norepinephrine levels compared with both the controls and with CPS alone (Figure 3E) Plasma norepinephrine levels were significantly increased in CPS + CAS rats compared with CAS alone as well as with controls and CPS. Plasma ACTH levels were significantly increased in CPS + CAS rats compared with controls. (Figure 3F).\\n Effects of Letrozole treatment on colon DRG neuron excitability We performed patch clamp experiments on acutely isolated retrograde-labeled DRG neurons from CPS + CAS females with or without letrozole treatment 24 h after the last adult stressor. Letrozole treatment significantly increased rheobase (Figure 4A), and significantly reduced input resistance (Figure 4B). Action potential overshoot (Figure 4C) and the number of action potentials elicited by a current injection at either 2 × or 3 × rheobase were significantly reduced by letrozole treatment (Figure 4D). Other electrophysiological properties were not significantly altered (Table 2). We also recorded electromyographic activity to determine whether the reduction in visceral sensitivity in female CPS + CAS rats caused by OVX or systemic letrozole treatment reduced visceromotor responses. The findings demonstrated a significant decrease in excitability of colon-projecting L6-S2 neurons.\\nEffects of Letrozole treatment on colon dorsal root ganglion neuron excitability. A: Rheobase (n = 45 cells in 6 rats in each group, t-test, cP < 0.001 vs Veh. + chronic adult stress [CAS] + chronic prenatal stress [CPS]); B: Membrane input resistance (RIn) (t-test, aP < 0.05); C: Action potential (AP) overshoot (t-test, aP < 0.05); D: Number of APs elicited by current injection at 2 × and 3 × rheobase (two-way ANOVA, aP < 0.05; bP < 0.01). \\nElectrophysiological characteristics of colon related DRG neuron after Letrozole treatment\\nValues are means ± standard error. \\nP < 0.05;\\nP < 0.001 vs control group. CAS: Chronic adult stress; CPS: Chronic prenatal stress.\\nWe performed patch clamp experiments on acutely isolated retrograde-labeled DRG neurons from CPS + CAS females with or without letrozole treatment 24 h after the last adult stressor. Letrozole treatment significantly increased rheobase (Figure 4A), and significantly reduced input resistance (Figure 4B). Action potential overshoot (Figure 4C) and the number of action potentials elicited by a current injection at either 2 × or 3 × rheobase were significantly reduced by letrozole treatment (Figure 4D). Other electrophysiological properties were not significantly altered (Table 2). We also recorded electromyographic activity to determine whether the reduction in visceral sensitivity in female CPS + CAS rats caused by OVX or systemic letrozole treatment reduced visceromotor responses. The findings demonstrated a significant decrease in excitability of colon-projecting L6-S2 neurons.\\nEffects of Letrozole treatment on colon dorsal root ganglion neuron excitability. A: Rheobase (n = 45 cells in 6 rats in each group, t-test, cP < 0.001 vs Veh. + chronic adult stress [CAS] + chronic prenatal stress [CPS]); B: Membrane input resistance (RIn) (t-test, aP < 0.05); C: Action potential (AP) overshoot (t-test, aP < 0.05); D: Number of APs elicited by current injection at 2 × and 3 × rheobase (two-way ANOVA, aP < 0.05; bP < 0.01). \\nElectrophysiological characteristics of colon related DRG neuron after Letrozole treatment\\nValues are means ± standard error. \\nP < 0.05;\\nP < 0.001 vs control group. CAS: Chronic adult stress; CPS: Chronic prenatal stress.\\n Spinal cord BDNF levels regulated by estrogen To investigate the effect of estrogen on BDNF expression, we measured BDNF mRNA and protein levels in the lumbar-sacral spinal cords of OVX and Sham CPS + CAS female rats. Systemic estradiol administration to naïve cycling females produced significant increases in plasma estrogen (Figure 5A), lumbar-sacral spinal cord BDNF mRNA (Figure 5B), and protein (Figure 5C). We also measured BDNF mRNA and protein levels in the lumbar-sacral spinal cords of OVX and Sham CPS + CAS female rats. BDNF mRNA and protein expression were significantly suppressed by OVX compared with sham rats. Another experiment showed that intrathecal infusion of estrogen into naïve female rats significantly increased BDNF protein levels, which proved that estrogen reversed the experimental results and contributed to the response to visceral pain[13].\\nBrain-derived neurotrophic factor expression in lumbar-sacral spinal cord is regulated by estrogen. A: Plasma estrogen levels in cycling females that received a bolus estradiol (E2) infusion on day 1; B: Lumbar-sacral spinal cord brain-derived neurotrophic factor (BDNF) mRNA following bolus estrogen infusion; C: Lumbar-sacral spinal cord BDNF protein following bolus estrogen infusion. (n = 8 rats in each group, two-way ANOVA, aP < 0.05 vs vehicle group). \\nTo investigate the effect of estrogen on BDNF expression, we measured BDNF mRNA and protein levels in the lumbar-sacral spinal cords of OVX and Sham CPS + CAS female rats. Systemic estradiol administration to naïve cycling females produced significant increases in plasma estrogen (Figure 5A), lumbar-sacral spinal cord BDNF mRNA (Figure 5B), and protein (Figure 5C). We also measured BDNF mRNA and protein levels in the lumbar-sacral spinal cords of OVX and Sham CPS + CAS female rats. BDNF mRNA and protein expression were significantly suppressed by OVX compared with sham rats. Another experiment showed that intrathecal infusion of estrogen into naïve female rats significantly increased BDNF protein levels, which proved that estrogen reversed the experimental results and contributed to the response to visceral pain[13].\\nBrain-derived neurotrophic factor expression in lumbar-sacral spinal cord is regulated by estrogen. A: Plasma estrogen levels in cycling females that received a bolus estradiol (E2) infusion on day 1; B: Lumbar-sacral spinal cord brain-derived neurotrophic factor (BDNF) mRNA following bolus estrogen infusion; C: Lumbar-sacral spinal cord BDNF protein following bolus estrogen infusion. (n = 8 rats in each group, two-way ANOVA, aP < 0.05 vs vehicle group). \\n Peripheral NGF level increased in CPS + CAS female rats We examined NGF expression in the colons of females from all four experimental groups by immunohistochemistry (Figure 6A). Morphometric analysis showed that CAS and CPS + CAS significantly increased NGF levels in the colon wall, with the increase in CPS + CAS significantly greater that of CAS alone (Figure 6B). Western blotting showed that NGF protein was significantly upregulated in CPS + CAS rats compared with controls (Figure 6C).\\nNerve growth factor expression level in the colon wall. A: Immunohistochemical staining of nerve growth factor (NGF; green) was detected with nuclear counterstaining staining (blue) in controls, chronic adult stress (CAS), chronic prenatal stress (CPS) and CPS + CAS group female rat colon walls. × 400 magnification representative pictures were shown; B: Quantification of NGF levels from colon wall in immunohistochemistry (IHC) (n = 4 rats in each group, one-way ANOVA, aP < 0.05 vs control group; bP < 0.05 vs CPS group); C: Western blots of NGF protein from control and CPS + CAS female rats colon wall tissue (n = 6 rats in each group, t-test, aP < 0.05 vs control group). \\nWe examined NGF expression in the colons of females from all four experimental groups by immunohistochemistry (Figure 6A). Morphometric analysis showed that CAS and CPS + CAS significantly increased NGF levels in the colon wall, with the increase in CPS + CAS significantly greater that of CAS alone (Figure 6B). Western blotting showed that NGF protein was significantly upregulated in CPS + CAS rats compared with controls (Figure 6C).\\nNerve growth factor expression level in the colon wall. A: Immunohistochemical staining of nerve growth factor (NGF; green) was detected with nuclear counterstaining staining (blue) in controls, chronic adult stress (CAS), chronic prenatal stress (CPS) and CPS + CAS group female rat colon walls. × 400 magnification representative pictures were shown; B: Quantification of NGF levels from colon wall in immunohistochemistry (IHC) (n = 4 rats in each group, one-way ANOVA, aP < 0.05 vs control group; bP < 0.05 vs CPS group); C: Western blots of NGF protein from control and CPS + CAS female rats colon wall tissue (n = 6 rats in each group, t-test, aP < 0.05 vs control group). \",\n \"The basal activity of a spinal afferent fiber was defined as the average number of action potentials per second (impulses/sec) in the 60 s period before the onset of a distention stimulus. In male controls, 66% of the afferent fibers under study displayed SA and SA was significantly higher in female controls than in male controls (0.71 ± 0.21 vs 1.24 ± 0.20 imp/sec; Figure 1B). The average single fiber activity in response to CRD was significantly higher in female control rats compared with male controls (Figure 1C). We found that the enhanced sensitization in female rats mainly came from the low-threshold fibers (Figure 1D and E).\\nTo assess the effects of CPS + CAS on colon afferent fiber activities, we compared average single colon afferent fiber activities projecting from dorsal roots S1-L6 in response to CRD in male and female control, CPS, control + CAS and CPS + CAS rats recorded approximately 24 h after the last stressor. In females, CPS significantly increased single-unit afferent activity in response to CRD vs control female rats (Figure 1F). CAS alone enhanced single-unit activity compared with control. The increase in average afferent responses after CAS in prenatally stressed female rats (44.0%) was significantly greater than the increase in female control rats (39.3%). In males, CPS had no significant effect on primary afferent responses (Figure 1G). When we compared males to females within each experimental group, we found that the average single fiber activity was significantly higher in female compared with male CPS + CAS rats (Figure 1F, G). The increased activity may contribute to the enhanced female visceral hypersensitivity previously reported in this model. Average single-fiber activities were significantly greater in control and CPS and CAS females than in their corresponding male experimental groups (Figure 1F and G). Both CAS and CPS + CAS rats had significantly increased primary afferent responses compared with control and CPS rats. Thus, our CPS and CAS protocols sensitized colon-projecting primary afferent fibers, with the greatest effects produced by the combination of CPS + CAS in both males and females.\",\n \"To elucidate the electrophysiological basis of enhanced stress-induced primary afferent activity in female rats, we performed patch clamp studies on acutely dissociated retrograde-labeled colon-projecting neurons from the L6-S2 DRGs in control, prenatal stress, adult stress only, and CPS + CAS female rats isolated 24 h after the last adult stressor (Figure 2A). Input resistance (Figure 2B) and rheobase (Figure 2C) were significantly decreased in neurons from CPS + CAS rats compared with the other three groups. The number of action potentials elicited at either 2 × or 3 × the rheobase were significantly greater in adult stress and CPS + CAS neurons compared with control and to CPS neurons (Figure 2D, E). CAS significantly increased action potential overshoot with or without CPS (Figure 2F), but it did not significantly alter other electrophysiological characteristics, such as number of spontaneous spikes, membrane capacitance (pF), resting membrane potential, cell diameter, time constant, and DRG neuron action potential amplitude and duration (Table 1).\\nPatch clamp recording in colonic dorsal root ganglion neurons from female rats. A: Patch clamp process of cell labeling. Under isoflurane anesthesia, the lipid soluble fluorescent dye 9-DiI was injected into muscularis externa of the exposed distal colon (left figure). Lumbosacral (L6–S2) dorsal root ganglions (upper photograph) were isolated and DiI-labeled neurons were identified by fluorescence microscopy (lower photograph). Electrophysiological properties of each neuron were measured using whole-cell current and voltage clamp protocols (right figure); B: Rheobase from all four experimental groups (n = 5 rats, 45 cells in each group, one-way ANOVA, aP < 0.05 vs control or bP < 0.05 vs CAS); C: Representative action potentials (APs) elicited by current injection at 2 × the rheobase in neurons from control, chronic adult stress (CAS), chronic prenatal stress (CPS) and CPS + CAS female rats; D: Membrane input resistance from all four groups (n = 5 rats, 45 cells in each group, one-way ANOVA, aP < 0.05 vs control; bP < 0.05 vs CPS); E: Number of APs elicited by current injection at either 2 × and 3 × the rheobase in all four experimental groups (two-way ANOVA, aP < 0.05 vs control; bP < 0.05 vs CPS); F: AP overshoot recorded from all four experimental groups (aP < 0.05 vs control); G: The proportion of neurons from each experimental group exhibiting spontaneous APs. Red numbers represent spontaneous AP firing cells; black numbers represent total cells; H: Representative total, IK and IA current tracings and average values of potassium currents: Itotal, IK and IA are shown in female CPS + CAS, CAS, CPS (n = 15 neurons, from 5 rats in each group), and control groups (n = 12 neurons from 5 rats); two-way ANOVA, aP < 0.05 vs each control group. \\nElectrophysiological characteristics of colon related DRG neuron\\nValues means ± standard error. \\nP < 0.05;\\nP < 0.001 vs control group. CAS: Chronic adult stress; CPS: Chronic prenatal stress.\\nThe percentage of neurons with SA in was significantly greater in CPS + CAS rats than in control or CPS only rats (Figure 2G). Under voltage clamp conditions (Figure 2H), neurons from female CPS + CAS, CAS, CPS and control groups had IA and sustained outward rectifier K+ currents (IK). Compared with the other three groups, DRG neurons from CPS + CAS rats had significantly reduced average IA (P < 0.05). The average IK density was decreased but the change was not significant.\",\n \"We did a vaginal smear test to identify the estrus cycle phases by identifying the vaginal cytological cell types. Estrogen concentration was significantly higher in the CPS proestrus/estrus phase compared with control diestrus, control proestrus/estrus, and CPS diestrus proestrus (P < 0.05; Figure 3A). Comparison of the plasma estrogen concentrations in control, CAS, CPS, CPS + CAS showed that CPS significantly increased plasma estrogen levels compared with the control rats and that CAS increased plasma estrogen level compared with the control and CPS rats (Figure 3B).\\nEffects of chronic prenatal stress, chronic adult stress, ovariectomy, and letrozole treatment on plasma estrogen levels in female rats. A: Plasma estrogen level in control and chronic prenatal stress (CPS) rats by estrus cycle phase (n = 8 rats, one-way ANOVA, aP < 0.05 vs control proestrus/estrus (P-E) phase; bP < 0.05 vs CPS diestrus (D) phase); B: Plasma estrogen levels increased in CPS rats and following chronic adult stress (CAS) 24 h after the last adult stressor (n = 8 rats, one-way ANOVA, aP < 0.05 vs control; bP < 0.05 vs CPS); C: Ovariectomy (OVX) significantly reduced CPS female rat plasma estrogen levels before and after CAS (n = 5 rats, one-way ANOVA, aP < 0.05 vs sham group); D: Letrozole treatment significantly reduced CPS female rat plasma estrogen levels before or after CAS (n = 5 rats, one-way ANOVA, aP < 0.05 vs vehicle group; cP < 0.0001); E: Plasma norepinephrine levels from control, CAS, CPS and CPS + CAS group female rats (n = 5 rats, one-way ANOVA, aP < 0.05 vs control; bP < 0.05 vs CPS); F: Plasma adrenocorticotropic hormone (ACTH) levels from control and CPS + CAS group female rats (n = 5 rats, t-test, aP < 0.05 vs control). \\nTo determine whether estrogen contributed to stress-induced visceral hypersensitivity in prenatal stressed females, we reduced plasma estrogen levels by either OVX or letrozole treatment. OVX significantly lowered serum estradiol levels before and after CAS (Figure 3C). Treatment was continued throughout CAS. After treatment with letrozole, serum estradiol levels were significantly reduced (Figure 3D). To study the effects of gender and stress on norepinephrine and ACTH levels, we measured plasma norepinephrine levels in female rats from all four experimental groups. CAS alone significantly increased plasma norepinephrine levels compared with both the controls and with CPS alone (Figure 3E) Plasma norepinephrine levels were significantly increased in CPS + CAS rats compared with CAS alone as well as with controls and CPS. Plasma ACTH levels were significantly increased in CPS + CAS rats compared with controls. (Figure 3F).\",\n \"We performed patch clamp experiments on acutely isolated retrograde-labeled DRG neurons from CPS + CAS females with or without letrozole treatment 24 h after the last adult stressor. Letrozole treatment significantly increased rheobase (Figure 4A), and significantly reduced input resistance (Figure 4B). Action potential overshoot (Figure 4C) and the number of action potentials elicited by a current injection at either 2 × or 3 × rheobase were significantly reduced by letrozole treatment (Figure 4D). Other electrophysiological properties were not significantly altered (Table 2). We also recorded electromyographic activity to determine whether the reduction in visceral sensitivity in female CPS + CAS rats caused by OVX or systemic letrozole treatment reduced visceromotor responses. The findings demonstrated a significant decrease in excitability of colon-projecting L6-S2 neurons.\\nEffects of Letrozole treatment on colon dorsal root ganglion neuron excitability. A: Rheobase (n = 45 cells in 6 rats in each group, t-test, cP < 0.001 vs Veh. + chronic adult stress [CAS] + chronic prenatal stress [CPS]); B: Membrane input resistance (RIn) (t-test, aP < 0.05); C: Action potential (AP) overshoot (t-test, aP < 0.05); D: Number of APs elicited by current injection at 2 × and 3 × rheobase (two-way ANOVA, aP < 0.05; bP < 0.01). \\nElectrophysiological characteristics of colon related DRG neuron after Letrozole treatment\\nValues are means ± standard error. \\nP < 0.05;\\nP < 0.001 vs control group. CAS: Chronic adult stress; CPS: Chronic prenatal stress.\",\n \"To investigate the effect of estrogen on BDNF expression, we measured BDNF mRNA and protein levels in the lumbar-sacral spinal cords of OVX and Sham CPS + CAS female rats. Systemic estradiol administration to naïve cycling females produced significant increases in plasma estrogen (Figure 5A), lumbar-sacral spinal cord BDNF mRNA (Figure 5B), and protein (Figure 5C). We also measured BDNF mRNA and protein levels in the lumbar-sacral spinal cords of OVX and Sham CPS + CAS female rats. BDNF mRNA and protein expression were significantly suppressed by OVX compared with sham rats. Another experiment showed that intrathecal infusion of estrogen into naïve female rats significantly increased BDNF protein levels, which proved that estrogen reversed the experimental results and contributed to the response to visceral pain[13].\\nBrain-derived neurotrophic factor expression in lumbar-sacral spinal cord is regulated by estrogen. A: Plasma estrogen levels in cycling females that received a bolus estradiol (E2) infusion on day 1; B: Lumbar-sacral spinal cord brain-derived neurotrophic factor (BDNF) mRNA following bolus estrogen infusion; C: Lumbar-sacral spinal cord BDNF protein following bolus estrogen infusion. (n = 8 rats in each group, two-way ANOVA, aP < 0.05 vs vehicle group). \",\n \"We examined NGF expression in the colons of females from all four experimental groups by immunohistochemistry (Figure 6A). Morphometric analysis showed that CAS and CPS + CAS significantly increased NGF levels in the colon wall, with the increase in CPS + CAS significantly greater that of CAS alone (Figure 6B). Western blotting showed that NGF protein was significantly upregulated in CPS + CAS rats compared with controls (Figure 6C).\\nNerve growth factor expression level in the colon wall. A: Immunohistochemical staining of nerve growth factor (NGF; green) was detected with nuclear counterstaining staining (blue) in controls, chronic adult stress (CAS), chronic prenatal stress (CPS) and CPS + CAS group female rat colon walls. × 400 magnification representative pictures were shown; B: Quantification of NGF levels from colon wall in immunohistochemistry (IHC) (n = 4 rats in each group, one-way ANOVA, aP < 0.05 vs control group; bP < 0.05 vs CPS group); C: Western blots of NGF protein from control and CPS + CAS female rats colon wall tissue (n = 6 rats in each group, t-test, aP < 0.05 vs control group). \",\n \"Enhanced CPS-induced visceral hypersensitivity in female rats was associated with an increase in the responses of lumbosacral nerve fibers to CRD in both male and female offspring. These findings are further supported by data showing increased excitability of colon-projecting DRG neurons from females in patch clamp studies. The magnitude of the sensitization was the greatest in female CPS + CAS rats, suggesting that it made a major contribution to the observed enhanced female visceral hypersensitivity in our model.\\nChronic stress is known to increase the excitability of colon-projecting DRG neurons in rats and mice. In adult male Sprague Dawley rats, colon DRG neuron sensitization was shown to be driven by increases in NGF expression in the colon muscularis externa[13]. In our model, we also observed a significant increase in colon NGF, but its potential role in primary afferent sensitization and visceral hypersensitivity was not investigated.. Other studies in male mice showed that stress, in the form of water avoidance, significantly increased the excitability of colon-projecting DRG neurons and that the combined activity of the stress mediators corticosterone and norepinephrine increased DRG neuron excitability in vitro[20,21]. We previously found significant increases in the serum levels of norepinephrine in CPS + CAS females. However, daily systemic treatment with adrenergic antagonists during the adult stress protocol failed to reduce visceral hypersensitivity in female neonatal + adult stress rats[13] suggesting that norepinephrine did not play a major role in the acquisition of enhanced female visceral hypersensitivity or primary afferent sensitization in our model.\\nWhen we tested on lumbar-sacral afferent fibers and dissociated neurons in patch clamp studies, we found significant decreases in transient potassium IA currents in neurons isolated from CPS + CAS females compared with the other three experimental groups. Declines in A-type Kv currents in DRG neurons have been associated with persistent pain in multiple chronic pain models[22]. Whether the decline was caused by changes in channel properties or expression was not investigated in this study. However, another study demonstrated that estrogen significantly shifted the activation curve of IA currents in the hyperpolarizing direction and that estrogen inhibited Kv (+) channels in mouse DRG neurons through a membrane ER-activated nongenomic pathway[23].\\nOur results showed that the excitability of colon-projecting neurons in CPS + CAS females was significantly reduced by systemic letrozole treatment, suggesting that estrogen contributed to the sensitization process. Previous studies show that estrogen receptors expressed on primary afferent neurons contributed to enhanced sensitivity in various pain models[24-26]. One study found no decline in the responses of colon-projecting nerve fibers to CRD following OVX and found no detectable estrogen receptor alpha immunoreactivity in colon-projecting DRG neurons[27]. The reasons for the differing results are not clear, but local production of estrogen in DRG neurons could be sufficient to sustain sensitization.\\nNGF and its receptors play important roles in the mechanism of visceral pain and hyperalgesia in women. For example, endometriosis is estrogen dependent and is commonly diagnosed. The main symptoms are various types of pelvic pain that have a serious effect on physical and mental health, but the mechanisms of abdominal pain are still unclear. Studies have shown NGF to be an inflammatory mediator and modulator of pain in adulthood[28].\",\n \"In this study, we examined the sex differences and effects of estrogen on the acquisition of enhanced visceral hypersensitivity in the offspring of rats in a model of prenatal and adult stress as shown in Figure 7. Our study shows that estrogen acted in the spinal cord and the primary afferent neurons to enhance visceral nociception. Acute blockade of the endogenous synthesis of estrogens in rat spinal cord significantly reduced visceral hypersensitivity, suggesting that locally produced estrogen in the central nervous system can regulate nociceptive neurons to modulate visceral hypersensitivity. The chronic stress-estrogen-BDNF axis sensitized visceral hypersensitivity in the offspring of females subjected to CPS. The development of chronic stress-induced visceral hypersensitivity in female rats was estrogen dependent. A key component of this hypersensitivity was estrogen-dependent sensitization of primary afferent colon neurons. Our findings provide key scientific evidence in a preclinical model in support of developing gender-based treatment for abdominal pain in IBS.\\nSummary diagram of estrogen re-enhanced visceral hyperalgesia investigated in chronic prenatal stress plus chronic adult stress models. BDNF: Brain-derived neurotrophic factor; NGF: Nerve growth factor.\"\n]"},"other_sections_sec_types":{"kind":"list like","value":[null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null],"string":"[\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null\n]"},"all_sections_titles":{"kind":"list like","value":["INTRODUCTION","MATERIALS AND METHODS","Animals","CPS and CAS models","Rat treatment","In vivo single fiber recording of L6-S2 DRG rootlets","In vitro patch clamp recordings in colonic DRG neurons","Real time reverse transcription-polymerase chain reaction (RT-PCR)","Western blot","Immunofluorescence","Serum estradiol and norepinephrine levels","Data analysis","RESULTS","Effects of CPS plus CAS on primary afferent responses to CRD in male and female rats","Increase in excitability of colon-projecting lumbosacral DRG neurons in female CPS + CAS rats","Effects of CPS and/or CAS on plasma estrogen concentration","Effects of Letrozole treatment on colon DRG neuron excitability","Spinal cord BDNF levels regulated by estrogen","Peripheral NGF level increased in CPS + CAS female rats","DISCUSSION","CONCLUSION"],"string":"[\n \"INTRODUCTION\",\n \"MATERIALS AND METHODS\",\n \"Animals\",\n \"CPS and CAS models\",\n \"Rat treatment\",\n \"In vivo single fiber recording of L6-S2 DRG rootlets\",\n \"In vitro patch clamp recordings in colonic DRG neurons\",\n \"Real time reverse transcription-polymerase chain reaction (RT-PCR)\",\n \"Western blot\",\n \"Immunofluorescence\",\n \"Serum estradiol and norepinephrine levels\",\n \"Data analysis\",\n \"RESULTS\",\n \"Effects of CPS plus CAS on primary afferent responses to CRD in male and female rats\",\n \"Increase in excitability of colon-projecting lumbosacral DRG neurons in female CPS + CAS rats\",\n \"Effects of CPS and/or CAS on plasma estrogen concentration\",\n \"Effects of Letrozole treatment on colon DRG neuron excitability\",\n \"Spinal cord BDNF levels regulated by estrogen\",\n \"Peripheral NGF level increased in CPS + CAS female rats\",\n \"DISCUSSION\",\n \"CONCLUSION\"\n]"},"all_sections_texts":{"kind":"list like","value":["Visceral pain of colonic origin is the most prominent symptom in irritable bowel syndrome (IBS) patients[1]. Female IBS patients report more severe pain that occurs more frequently and with longer episodes than in male patients[1,2]. The ratio of female to male IBS is about 2:1 among patients seen in medical clinics[3]. Moreover, females have a higher prevalence of IBS co-morbidities such as anxiety and depression[4,5] and are more vulnerable to stress-induced exacerbation of IBS symptoms compared with males[3,6,7].\nClinical studies show that early life adverse experiences are risk factors for the development of IBS symptoms, including visceral pain and ongoing chronic stress, especially abdominal pain[8-10]. These factors contribute to the development of visceral hypersensitivity, a key component of the IBS symptom complex and one that may be responsible for symptoms of pain[11,12]. Our previous research found that the female offspring of mothers subjected to chronic prenatal stress (CPS) had a markedly greater visceral sensitivity than their male littermates following challenge by another chronic adult stress (CAS) protocol. A critical molecular event in the development of this female-enhanced visceral hypersensitivity is upregulation of brain-derived neurotrophic factor (BDNF) expression in the lumbar-sacral spinal cord of female CPS + CAS rats[13]. However, the neurophysiological changes underlying the enhanced female-specific visceral hypersensitivity and the role of hormone in the development of stress-induced visceral hypersensitivity are not well understood.\nVisceral hypersensitivity in IBS involves abnormal changes in neurophysiology throughout the brain-gut axis. In IBS, there is evidence for sensitization of primary afferents to jejunal distention and electrical stimulation[14], and there is evidence for increased sensitivity of lumbar splanchnic afferents[15,16]. In animal models of either early life adverse events or adult stress-induced visceral hypersensitivity[17], there is evidence of colon primary afferent sensitization. However, the studies were performed in male rodents. Therefore, in this study, we established a CPS and CAS rodent model to analyze the impact on female colon afferent neuron function and the role of estrogen. Our hypothesis was that female CPS offspring subjected to chronic stress as adults would exhibit greater colonic dorsal root ganglion (DRG) neuron sensitization compared with their male littermates, and that the enhanced visceral sensitization and primary afferent sensitization in females was estrogen dependent."," Animals The Institutional Animal Care and Use Committee of the University of Texas Medical Branch at Galveston, TX approved all animal procedures. Experiments were performed on pregnant Sprague Dawley rats and their 8-wk-old to 16-wk-old male and female offspring. Rats were housed individual cages with access to food and water in a room with controlled conditions (22 ± 2 °C, relative humidity of 50% ± 5%), and a 12 h light/12 h dark cycle.\nThe Institutional Animal Care and Use Committee of the University of Texas Medical Branch at Galveston, TX approved all animal procedures. Experiments were performed on pregnant Sprague Dawley rats and their 8-wk-old to 16-wk-old male and female offspring. Rats were housed individual cages with access to food and water in a room with controlled conditions (22 ± 2 °C, relative humidity of 50% ± 5%), and a 12 h light/12 h dark cycle.\n CPS and CAS models Pregnant dams were subjected to a CPS protocol that consisted of a random sequence of twice-daily applications of one of three stress sessions, a 1-h water-avoidance, 45-min cold-restraint, or a 20-min forced swim starting on day 6 and continuing until delivery on day 21. Male and female offspring of the stressed dams were designated CPS rats. Control dams received sham stress and their offspring were designated control rats. As adults at 8-16 wk of age, control and prenatally stressed offspring were challenged by the same CAS protocol for 9 d. Ovariectomy (OVX) or sham surgery was performed on female prenatal-stress offspring on day 56. Daily letrozole treatment was initiated on day 49, 2 wk prior to initiation of adult stress. Treatment was continued through the stress protocol. A schematic diagram of the study procedures is shown in Figure 1A.\nPrimary afferent responses to colorectal distention. A: Chronic prenatal stress (CPS) plus chronic adult stress (CAS) model. Pregnant dams were subjected to prenatal stress from on day 11 of gestation. Ovariectomy (OVX) or sham surgery was performed on female prenatal-stress offspring on day 56. Daily Letrozole was initiated on day 49, 2 wk prior to initiation of adult stress. Treatment was continued through the stress protocol; B: Spontaneous activity (SA) of single afferent units in male and female control rats (n = 70 fibers in 6 rats in each group, t-test, aP < 0.05); C: Average response to graded colorectal distention (CRD) of 56 afferent fibers in 6 male and 70 afferent fibers in 6 female control rats; two-way analysis of variance (ANOVA; aP < 0.05 vs the same pressure male group); D: Responses of low-threshold (LT) fibers to CRD in 42 fibers in 6 male rats and 40 fibers in 6 female control rats (ANOVA, aP < 0.05 vs the same pressure male group); E: Responses of high-threshold (HT) afferent fibers to CRD in 14 fibers in 6 male and 29 fibers in 6 female control rats; F: Effects of CAS on afferent fiber responses to CRD from 59 fibers in 6 control and 99 fibers in 6 CPS female rats; (two-way ANOVA, aP < 0.05 vs the same pressure control group, bP < 0.05 vs the same pressure CPS group); G: Effects of CAS on afferent fiber responses to CRD in control and CPS male rats (n = 6 rats, 57 fibers for control and 95 fibers for CPS female group; two-way ANOVA, aP < 0.05 vs the same pressure-control group). \nPregnant dams were subjected to a CPS protocol that consisted of a random sequence of twice-daily applications of one of three stress sessions, a 1-h water-avoidance, 45-min cold-restraint, or a 20-min forced swim starting on day 6 and continuing until delivery on day 21. Male and female offspring of the stressed dams were designated CPS rats. Control dams received sham stress and their offspring were designated control rats. As adults at 8-16 wk of age, control and prenatally stressed offspring were challenged by the same CAS protocol for 9 d. Ovariectomy (OVX) or sham surgery was performed on female prenatal-stress offspring on day 56. Daily letrozole treatment was initiated on day 49, 2 wk prior to initiation of adult stress. Treatment was continued through the stress protocol. A schematic diagram of the study procedures is shown in Figure 1A.\nPrimary afferent responses to colorectal distention. A: Chronic prenatal stress (CPS) plus chronic adult stress (CAS) model. Pregnant dams were subjected to prenatal stress from on day 11 of gestation. Ovariectomy (OVX) or sham surgery was performed on female prenatal-stress offspring on day 56. Daily Letrozole was initiated on day 49, 2 wk prior to initiation of adult stress. Treatment was continued through the stress protocol; B: Spontaneous activity (SA) of single afferent units in male and female control rats (n = 70 fibers in 6 rats in each group, t-test, aP < 0.05); C: Average response to graded colorectal distention (CRD) of 56 afferent fibers in 6 male and 70 afferent fibers in 6 female control rats; two-way analysis of variance (ANOVA; aP < 0.05 vs the same pressure male group); D: Responses of low-threshold (LT) fibers to CRD in 42 fibers in 6 male rats and 40 fibers in 6 female control rats (ANOVA, aP < 0.05 vs the same pressure male group); E: Responses of high-threshold (HT) afferent fibers to CRD in 14 fibers in 6 male and 29 fibers in 6 female control rats; F: Effects of CAS on afferent fiber responses to CRD from 59 fibers in 6 control and 99 fibers in 6 CPS female rats; (two-way ANOVA, aP < 0.05 vs the same pressure control group, bP < 0.05 vs the same pressure CPS group); G: Effects of CAS on afferent fiber responses to CRD in control and CPS male rats (n = 6 rats, 57 fibers for control and 95 fibers for CPS female group; two-way ANOVA, aP < 0.05 vs the same pressure-control group). \n Rat treatment Before OVX or letrozole treatment, vaginal smears were used to identify the estrus cycle phase. OVX or sham surgery was performed on female prenatal-stress offspring on day 56. The aromatase inhibitor letrozole [4,4’-(1H-1,2,4-triazol-l-yl-methylene)-bis-benzonitrile], (Novartis) 1.0 mg/kg was orally administered in the experimental group and vehicle (hydroxypropyl cellulose 0.3% in water) was given in the control group once daily for 14 d. Direct transcutaneous intrathecal injections of estrogen and letrozole were performed as described by Mestre et al[18].\nBefore OVX or letrozole treatment, vaginal smears were used to identify the estrus cycle phase. OVX or sham surgery was performed on female prenatal-stress offspring on day 56. The aromatase inhibitor letrozole [4,4’-(1H-1,2,4-triazol-l-yl-methylene)-bis-benzonitrile], (Novartis) 1.0 mg/kg was orally administered in the experimental group and vehicle (hydroxypropyl cellulose 0.3% in water) was given in the control group once daily for 14 d. Direct transcutaneous intrathecal injections of estrogen and letrozole were performed as described by Mestre et al[18].\n In vivo single fiber recording of L6-S2 DRG rootlets Multiunit afferent discharges were recorded from the distal ends of L6-S2 dorsal rootlets decentralized close to their entry into the spinal cord. A bundle of multiunit fibers was distinguished into 2-6 single units off-line using wave mark template matching in Spike 2 software that differentiates spikes by shape and amplitude. Colonic afferent fibers were identified by their response to graded colorectal distention (CRD). A balloon was used to distend the colorectum. Isoflurane, 2.5%, followed by 50 mg/kg intraperitoneal sodium pentobarbital induced general anesthesia that was maintained by infusing a mixture of pentobarbital sodium + pancuronium bromide + saline by intravenous infusion through the tail vein. The adequacy of anesthesia was confirmed by the absence of corneal and pupillary reflexes and stability of the end-tidal CO2 level. A tracheotomy tube connected to a ventilator system provided a mixture of room air and oxygen. Expired CO2 was monitored and maintained at 3.5%. Body temperature was monitored and maintained at 37 °C by a servo-controlled heating blanket. A laminectomy from T12 to S2 exposed the spinal cord. The head was stabilized in a stereotaxic frame.\nMultiunit afferent discharges were recorded from the distal ends of L6-S2 dorsal rootlets decentralized close to their entry into the spinal cord. A bundle of multiunit fibers was distinguished into 2-6 single units off-line using wave mark template matching in Spike 2 software that differentiates spikes by shape and amplitude. Colonic afferent fibers were identified by their response to graded colorectal distention (CRD). A balloon was used to distend the colorectum. Isoflurane, 2.5%, followed by 50 mg/kg intraperitoneal sodium pentobarbital induced general anesthesia that was maintained by infusing a mixture of pentobarbital sodium + pancuronium bromide + saline by intravenous infusion through the tail vein. The adequacy of anesthesia was confirmed by the absence of corneal and pupillary reflexes and stability of the end-tidal CO2 level. A tracheotomy tube connected to a ventilator system provided a mixture of room air and oxygen. Expired CO2 was monitored and maintained at 3.5%. Body temperature was monitored and maintained at 37 °C by a servo-controlled heating blanket. A laminectomy from T12 to S2 exposed the spinal cord. The head was stabilized in a stereotaxic frame.\n In vitro patch clamp recordings in colonic DRG neurons Retrograde fluorescence label injections: Labeling of colon-projecting DRG neurons was performed as previously described[13]. Under general 2% isoflurane anesthesia, the lipid soluble fluorescent dye, 1,1’-dioleyl-3,3,3’,3’-tetramethylindocarbocyanine methane-sulfonate (9-DiI, Invitrogen, Carlsbad, CA) was injected (50 mg/mL) into the muscularis externa on the exposed distal colon in 8 to 10 sites (2 μL each site). To prevent leakage, the needle was kept in place for 1 min following each injection.\nDissociation and culture of DRG neurons: Rats were deeply anesthetized with isoflurane followed by decapitation. Lumbosacral (L6–S2) DRGs were collected in ice cold and oxygenated dissecting solution, containing (in mM) 130 NaCl, 5 KCl, 2 KH2PO4, 1.5 CaCl2, 6 MgSO4, 10 glucose, and 10 HEPES, pH 7.2 (305 mOsm). After removal of the connective tissue, the ganglia were transferred to a 5 mL dissecting solution containing collagenase D (1.8 mg/mL; Roche) and trypsin (1.0 mg/mL; Sigma, St Louis, MO), and incubated for 1.5 h at 34.5 °C. DRGs were then taken from the enzyme solution, washed, and put in 0.5-2 mL of the dissecting solution containing DNase (0.5 mg/mL; Sigma). Cells were subsequently dissociated by gentle trituration 10 to 15 times with fire-polished glass pipettes and placed on acid-cleaned glass coverslips. The dissociated DRG neurons were kept in 1 mL DMEM (with 10% FBS) in an incubator (95% O2/5% CO2) at 37 °C overnight.\nWhole-cell patch clamp recordings from dissociated DRG neurons: Before each experiment, a glass coverslip with DRG neurons was transferred to a recording chamber perfused (1.5 mL/min) with external solution containing (10 mM): 130 NaCl, 5 KCl, 2 KH2PO4, 2.5 CaCl2, 1 MgCl2, 10 HEPES, and 10 glucose, pH adjusted to pH 7.4 with NaOH (300 mOsm) at room temperature. Recording pipettes, pulled from borosilicate glass tubing, with resistance of 1-5 MΩ, were filled with solution containing (in mM): 100 KMeSO3, 40 KCl, and 10 HEPES, pH 7.25 adjusted with KOH (290 mOsm). DiI-labeled neurons were identified by fluorescence microscopy. Whole-cell currents and voltage were recorded from DiI-labeled neurons using a Dagan 3911 patch clamp amplifier. Data were acquired and analyzed by pCLAMP 9.2 (Molecular Devices, Sunnyvale, CA). The currents were filtered at 2–5 kHz and sampled at 50 or 100 s per point. While still under voltage clamp, the Clampex Membrane Test program (Molecular Devices) was used to determine membrane capacitance (Cm) and membrane resistance (Rm), during a 10 ms, 5 mV depolarizing pulse form a holding potential of −60 mV. The configuration was then switched to current clamp (0 pA) to determine other electrophysiological properties. After stabilizing for 2–3 min, the resting membrane potential was measured. The minimum acceptable resting membrane potential was −40 mV. Spontaneous activity (SA) was then recorded over two 30 s periods separated by 60 s without recording, as described by Bedi et al[19].\nTransient A-type K+ current (IA) recording method in patch studies: To record voltage-gated K+ current (Kv), Na+ in control external solution was replaced with equimolar choline and the Ca2+ concentration was reduced to 0.03 mM to suppress Ca2+ currents and to prevent Ca2+ channels becoming Na+ conducting. The reduced external Ca2+ would also be expected to suppress Ca2+-activated K+ currents. The current traces of Kv in DRG neurons were measured at different holding potentials. The membrane potential was held at −100 mV and voltage steps were from −40 to +30 mV to record the total Kv. The membrane potential was held at −50 mV to record the sustained Kv. The IA currents were calculated by subtracting the sustained current from the total current. The current density (in pA/pF) was calculated by dividing the current amplitude by cell membrane capacitance.\nRetrograde fluorescence label injections: Labeling of colon-projecting DRG neurons was performed as previously described[13]. Under general 2% isoflurane anesthesia, the lipid soluble fluorescent dye, 1,1’-dioleyl-3,3,3’,3’-tetramethylindocarbocyanine methane-sulfonate (9-DiI, Invitrogen, Carlsbad, CA) was injected (50 mg/mL) into the muscularis externa on the exposed distal colon in 8 to 10 sites (2 μL each site). To prevent leakage, the needle was kept in place for 1 min following each injection.\nDissociation and culture of DRG neurons: Rats were deeply anesthetized with isoflurane followed by decapitation. Lumbosacral (L6–S2) DRGs were collected in ice cold and oxygenated dissecting solution, containing (in mM) 130 NaCl, 5 KCl, 2 KH2PO4, 1.5 CaCl2, 6 MgSO4, 10 glucose, and 10 HEPES, pH 7.2 (305 mOsm). After removal of the connective tissue, the ganglia were transferred to a 5 mL dissecting solution containing collagenase D (1.8 mg/mL; Roche) and trypsin (1.0 mg/mL; Sigma, St Louis, MO), and incubated for 1.5 h at 34.5 °C. DRGs were then taken from the enzyme solution, washed, and put in 0.5-2 mL of the dissecting solution containing DNase (0.5 mg/mL; Sigma). Cells were subsequently dissociated by gentle trituration 10 to 15 times with fire-polished glass pipettes and placed on acid-cleaned glass coverslips. The dissociated DRG neurons were kept in 1 mL DMEM (with 10% FBS) in an incubator (95% O2/5% CO2) at 37 °C overnight.\nWhole-cell patch clamp recordings from dissociated DRG neurons: Before each experiment, a glass coverslip with DRG neurons was transferred to a recording chamber perfused (1.5 mL/min) with external solution containing (10 mM): 130 NaCl, 5 KCl, 2 KH2PO4, 2.5 CaCl2, 1 MgCl2, 10 HEPES, and 10 glucose, pH adjusted to pH 7.4 with NaOH (300 mOsm) at room temperature. Recording pipettes, pulled from borosilicate glass tubing, with resistance of 1-5 MΩ, were filled with solution containing (in mM): 100 KMeSO3, 40 KCl, and 10 HEPES, pH 7.25 adjusted with KOH (290 mOsm). DiI-labeled neurons were identified by fluorescence microscopy. Whole-cell currents and voltage were recorded from DiI-labeled neurons using a Dagan 3911 patch clamp amplifier. Data were acquired and analyzed by pCLAMP 9.2 (Molecular Devices, Sunnyvale, CA). The currents were filtered at 2–5 kHz and sampled at 50 or 100 s per point. While still under voltage clamp, the Clampex Membrane Test program (Molecular Devices) was used to determine membrane capacitance (Cm) and membrane resistance (Rm), during a 10 ms, 5 mV depolarizing pulse form a holding potential of −60 mV. The configuration was then switched to current clamp (0 pA) to determine other electrophysiological properties. After stabilizing for 2–3 min, the resting membrane potential was measured. The minimum acceptable resting membrane potential was −40 mV. Spontaneous activity (SA) was then recorded over two 30 s periods separated by 60 s without recording, as described by Bedi et al[19].\nTransient A-type K+ current (IA) recording method in patch studies: To record voltage-gated K+ current (Kv), Na+ in control external solution was replaced with equimolar choline and the Ca2+ concentration was reduced to 0.03 mM to suppress Ca2+ currents and to prevent Ca2+ channels becoming Na+ conducting. The reduced external Ca2+ would also be expected to suppress Ca2+-activated K+ currents. The current traces of Kv in DRG neurons were measured at different holding potentials. The membrane potential was held at −100 mV and voltage steps were from −40 to +30 mV to record the total Kv. The membrane potential was held at −50 mV to record the sustained Kv. The IA currents were calculated by subtracting the sustained current from the total current. The current density (in pA/pF) was calculated by dividing the current amplitude by cell membrane capacitance.\n Real time reverse transcription-polymerase chain reaction (RT-PCR) Total RNA was extracted using RNeasy Mini Kits (QIAGEN, Valencia, CA). One microgram of total RNA was reverse-transcribed using the SuperScriptTM III First-Strand Synthesis System. PCR assays were performed on a StepOnePlus thermal cycler with 18 s as the normalizer using Applied Biosystems primer/probe set Rn02531967_s1 directed against the translated exon IX. Fold-change relative to control was calculated using the ΔΔCt method (Applied Biosystems).\nTotal RNA was extracted using RNeasy Mini Kits (QIAGEN, Valencia, CA). One microgram of total RNA was reverse-transcribed using the SuperScriptTM III First-Strand Synthesis System. PCR assays were performed on a StepOnePlus thermal cycler with 18 s as the normalizer using Applied Biosystems primer/probe set Rn02531967_s1 directed against the translated exon IX. Fold-change relative to control was calculated using the ΔΔCt method (Applied Biosystems).\n Western blot Samples were lysed in RIPA buffer containing protease inhibitor cocktail and phenylmethanesulfonyl fluoride. Lysates were incubated for 30 min on ice and then centrifuged at 10 000 × g for 10 min at 4 °C. The protein concentration in the supernatant was determined using bicinchoninic acid (BCA) assay kits with bovine serum albumin as a standard. Equal amounts of protein (30 μg per lane) were separated by 10% sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) and then transferred to nitrocellulose membranes (Bio-Rad, United States). The membranes were blocked in Li-Cor blocking buffer for 1 h at room temperature and then incubated with primary antibodies. BDNF antibody (Santa Cruz Biotechnologies, Santa Cruz, CA) was used at 1:200 dilution; nerve growth factor (NGF) antibody (Abcam, MA) was used at 1:1000 dilution; β-actin antibody (Sigma Aldrich, St Louis, MO) was used at 1:5000 dilution. The secondary antibodies were donkey anti-rabbit Alexa Fluor 680 (Invitrogen) and goat anti-mouse IRDye 800 (Rockland). Images were acquired and band intensities measured using a Li-Cor Odyssey system (Li-Cor, Lincoln, NE).\nSamples were lysed in RIPA buffer containing protease inhibitor cocktail and phenylmethanesulfonyl fluoride. Lysates were incubated for 30 min on ice and then centrifuged at 10 000 × g for 10 min at 4 °C. The protein concentration in the supernatant was determined using bicinchoninic acid (BCA) assay kits with bovine serum albumin as a standard. Equal amounts of protein (30 μg per lane) were separated by 10% sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) and then transferred to nitrocellulose membranes (Bio-Rad, United States). The membranes were blocked in Li-Cor blocking buffer for 1 h at room temperature and then incubated with primary antibodies. BDNF antibody (Santa Cruz Biotechnologies, Santa Cruz, CA) was used at 1:200 dilution; nerve growth factor (NGF) antibody (Abcam, MA) was used at 1:1000 dilution; β-actin antibody (Sigma Aldrich, St Louis, MO) was used at 1:5000 dilution. The secondary antibodies were donkey anti-rabbit Alexa Fluor 680 (Invitrogen) and goat anti-mouse IRDye 800 (Rockland). Images were acquired and band intensities measured using a Li-Cor Odyssey system (Li-Cor, Lincoln, NE).\n Immunofluorescence Frozen sections of colon tissue from control, CAS, CPS and CPS + CAS female rats were mounted on glass slides, and rehydrated in phosphate buffered saline at room temperature. The slides were treated for antigen retrieval and blocked with 10% normal goat serum diluted in 0.3% phosphate buffered saline-Triton for 1 h, and then incubated with NGF primary antibody in antibody diluent (Renoir Red, Biocare Medical, Concord, CA) at 4 °C overnight. The slides were exposed to fluorescent dye-conjugated secondary antibody for 2 h at room temperature, counterstained with 4',6-diamidino-2-phenylindole and coverslipped. Images were taken in fluorescence mode on an Olympus laser scanning confocal microscope and the average signal intensity was calculated by the bundled software.\nFrozen sections of colon tissue from control, CAS, CPS and CPS + CAS female rats were mounted on glass slides, and rehydrated in phosphate buffered saline at room temperature. The slides were treated for antigen retrieval and blocked with 10% normal goat serum diluted in 0.3% phosphate buffered saline-Triton for 1 h, and then incubated with NGF primary antibody in antibody diluent (Renoir Red, Biocare Medical, Concord, CA) at 4 °C overnight. The slides were exposed to fluorescent dye-conjugated secondary antibody for 2 h at room temperature, counterstained with 4',6-diamidino-2-phenylindole and coverslipped. Images were taken in fluorescence mode on an Olympus laser scanning confocal microscope and the average signal intensity was calculated by the bundled software.\n Serum estradiol and norepinephrine levels Serum estradiol, adrenocorticotropic hormone (ACTH), and norepinephrine levels were measured using specific enzyme-linked immunosorbent assay kits for each analyte (CSB-E05110r, CSB-E06875r, CSB-E07022, Cusabio Bioteck CO., United States) following the manufacturer’s instructions.\nSerum estradiol, adrenocorticotropic hormone (ACTH), and norepinephrine levels were measured using specific enzyme-linked immunosorbent assay kits for each analyte (CSB-E05110r, CSB-E06875r, CSB-E07022, Cusabio Bioteck CO., United States) following the manufacturer’s instructions.\n Data analysis Single fiber responses (impulses/second) to CRD were calculated by subtracting SA from the mean 30 s maximal activity during distension. Fibers were considered responsive if CRD increased their activity to 30% greater than the baseline value. Mechanosensitive single units were classified as high threshold (> 20 mmHg) or low threshold (≤ 20 mmHg) on the basis of their response threshold and profile during CRD. Single fiber activity data were analyzed by analysis of variance with repeated measures; CRD intensity was the repeated factor and the experimental group was the between-group factor. If significant main effects were present, the individual means were compared using the Fisher post-hoc test. All authors had access to the study data and reviewed and approved the final manuscript.\nSingle fiber responses (impulses/second) to CRD were calculated by subtracting SA from the mean 30 s maximal activity during distension. Fibers were considered responsive if CRD increased their activity to 30% greater than the baseline value. Mechanosensitive single units were classified as high threshold (> 20 mmHg) or low threshold (≤ 20 mmHg) on the basis of their response threshold and profile during CRD. Single fiber activity data were analyzed by analysis of variance with repeated measures; CRD intensity was the repeated factor and the experimental group was the between-group factor. If significant main effects were present, the individual means were compared using the Fisher post-hoc test. All authors had access to the study data and reviewed and approved the final manuscript.","The Institutional Animal Care and Use Committee of the University of Texas Medical Branch at Galveston, TX approved all animal procedures. Experiments were performed on pregnant Sprague Dawley rats and their 8-wk-old to 16-wk-old male and female offspring. Rats were housed individual cages with access to food and water in a room with controlled conditions (22 ± 2 °C, relative humidity of 50% ± 5%), and a 12 h light/12 h dark cycle.","Pregnant dams were subjected to a CPS protocol that consisted of a random sequence of twice-daily applications of one of three stress sessions, a 1-h water-avoidance, 45-min cold-restraint, or a 20-min forced swim starting on day 6 and continuing until delivery on day 21. Male and female offspring of the stressed dams were designated CPS rats. Control dams received sham stress and their offspring were designated control rats. As adults at 8-16 wk of age, control and prenatally stressed offspring were challenged by the same CAS protocol for 9 d. Ovariectomy (OVX) or sham surgery was performed on female prenatal-stress offspring on day 56. Daily letrozole treatment was initiated on day 49, 2 wk prior to initiation of adult stress. Treatment was continued through the stress protocol. A schematic diagram of the study procedures is shown in Figure 1A.\nPrimary afferent responses to colorectal distention. A: Chronic prenatal stress (CPS) plus chronic adult stress (CAS) model. Pregnant dams were subjected to prenatal stress from on day 11 of gestation. Ovariectomy (OVX) or sham surgery was performed on female prenatal-stress offspring on day 56. Daily Letrozole was initiated on day 49, 2 wk prior to initiation of adult stress. Treatment was continued through the stress protocol; B: Spontaneous activity (SA) of single afferent units in male and female control rats (n = 70 fibers in 6 rats in each group, t-test, aP < 0.05); C: Average response to graded colorectal distention (CRD) of 56 afferent fibers in 6 male and 70 afferent fibers in 6 female control rats; two-way analysis of variance (ANOVA; aP < 0.05 vs the same pressure male group); D: Responses of low-threshold (LT) fibers to CRD in 42 fibers in 6 male rats and 40 fibers in 6 female control rats (ANOVA, aP < 0.05 vs the same pressure male group); E: Responses of high-threshold (HT) afferent fibers to CRD in 14 fibers in 6 male and 29 fibers in 6 female control rats; F: Effects of CAS on afferent fiber responses to CRD from 59 fibers in 6 control and 99 fibers in 6 CPS female rats; (two-way ANOVA, aP < 0.05 vs the same pressure control group, bP < 0.05 vs the same pressure CPS group); G: Effects of CAS on afferent fiber responses to CRD in control and CPS male rats (n = 6 rats, 57 fibers for control and 95 fibers for CPS female group; two-way ANOVA, aP < 0.05 vs the same pressure-control group). ","Before OVX or letrozole treatment, vaginal smears were used to identify the estrus cycle phase. OVX or sham surgery was performed on female prenatal-stress offspring on day 56. The aromatase inhibitor letrozole [4,4’-(1H-1,2,4-triazol-l-yl-methylene)-bis-benzonitrile], (Novartis) 1.0 mg/kg was orally administered in the experimental group and vehicle (hydroxypropyl cellulose 0.3% in water) was given in the control group once daily for 14 d. Direct transcutaneous intrathecal injections of estrogen and letrozole were performed as described by Mestre et al[18].","Multiunit afferent discharges were recorded from the distal ends of L6-S2 dorsal rootlets decentralized close to their entry into the spinal cord. A bundle of multiunit fibers was distinguished into 2-6 single units off-line using wave mark template matching in Spike 2 software that differentiates spikes by shape and amplitude. Colonic afferent fibers were identified by their response to graded colorectal distention (CRD). A balloon was used to distend the colorectum. Isoflurane, 2.5%, followed by 50 mg/kg intraperitoneal sodium pentobarbital induced general anesthesia that was maintained by infusing a mixture of pentobarbital sodium + pancuronium bromide + saline by intravenous infusion through the tail vein. The adequacy of anesthesia was confirmed by the absence of corneal and pupillary reflexes and stability of the end-tidal CO2 level. A tracheotomy tube connected to a ventilator system provided a mixture of room air and oxygen. Expired CO2 was monitored and maintained at 3.5%. Body temperature was monitored and maintained at 37 °C by a servo-controlled heating blanket. A laminectomy from T12 to S2 exposed the spinal cord. The head was stabilized in a stereotaxic frame.","Retrograde fluorescence label injections: Labeling of colon-projecting DRG neurons was performed as previously described[13]. Under general 2% isoflurane anesthesia, the lipid soluble fluorescent dye, 1,1’-dioleyl-3,3,3’,3’-tetramethylindocarbocyanine methane-sulfonate (9-DiI, Invitrogen, Carlsbad, CA) was injected (50 mg/mL) into the muscularis externa on the exposed distal colon in 8 to 10 sites (2 μL each site). To prevent leakage, the needle was kept in place for 1 min following each injection.\nDissociation and culture of DRG neurons: Rats were deeply anesthetized with isoflurane followed by decapitation. Lumbosacral (L6–S2) DRGs were collected in ice cold and oxygenated dissecting solution, containing (in mM) 130 NaCl, 5 KCl, 2 KH2PO4, 1.5 CaCl2, 6 MgSO4, 10 glucose, and 10 HEPES, pH 7.2 (305 mOsm). After removal of the connective tissue, the ganglia were transferred to a 5 mL dissecting solution containing collagenase D (1.8 mg/mL; Roche) and trypsin (1.0 mg/mL; Sigma, St Louis, MO), and incubated for 1.5 h at 34.5 °C. DRGs were then taken from the enzyme solution, washed, and put in 0.5-2 mL of the dissecting solution containing DNase (0.5 mg/mL; Sigma). Cells were subsequently dissociated by gentle trituration 10 to 15 times with fire-polished glass pipettes and placed on acid-cleaned glass coverslips. The dissociated DRG neurons were kept in 1 mL DMEM (with 10% FBS) in an incubator (95% O2/5% CO2) at 37 °C overnight.\nWhole-cell patch clamp recordings from dissociated DRG neurons: Before each experiment, a glass coverslip with DRG neurons was transferred to a recording chamber perfused (1.5 mL/min) with external solution containing (10 mM): 130 NaCl, 5 KCl, 2 KH2PO4, 2.5 CaCl2, 1 MgCl2, 10 HEPES, and 10 glucose, pH adjusted to pH 7.4 with NaOH (300 mOsm) at room temperature. Recording pipettes, pulled from borosilicate glass tubing, with resistance of 1-5 MΩ, were filled with solution containing (in mM): 100 KMeSO3, 40 KCl, and 10 HEPES, pH 7.25 adjusted with KOH (290 mOsm). DiI-labeled neurons were identified by fluorescence microscopy. Whole-cell currents and voltage were recorded from DiI-labeled neurons using a Dagan 3911 patch clamp amplifier. Data were acquired and analyzed by pCLAMP 9.2 (Molecular Devices, Sunnyvale, CA). The currents were filtered at 2–5 kHz and sampled at 50 or 100 s per point. While still under voltage clamp, the Clampex Membrane Test program (Molecular Devices) was used to determine membrane capacitance (Cm) and membrane resistance (Rm), during a 10 ms, 5 mV depolarizing pulse form a holding potential of −60 mV. The configuration was then switched to current clamp (0 pA) to determine other electrophysiological properties. After stabilizing for 2–3 min, the resting membrane potential was measured. The minimum acceptable resting membrane potential was −40 mV. Spontaneous activity (SA) was then recorded over two 30 s periods separated by 60 s without recording, as described by Bedi et al[19].\nTransient A-type K+ current (IA) recording method in patch studies: To record voltage-gated K+ current (Kv), Na+ in control external solution was replaced with equimolar choline and the Ca2+ concentration was reduced to 0.03 mM to suppress Ca2+ currents and to prevent Ca2+ channels becoming Na+ conducting. The reduced external Ca2+ would also be expected to suppress Ca2+-activated K+ currents. The current traces of Kv in DRG neurons were measured at different holding potentials. The membrane potential was held at −100 mV and voltage steps were from −40 to +30 mV to record the total Kv. The membrane potential was held at −50 mV to record the sustained Kv. The IA currents were calculated by subtracting the sustained current from the total current. The current density (in pA/pF) was calculated by dividing the current amplitude by cell membrane capacitance.","Total RNA was extracted using RNeasy Mini Kits (QIAGEN, Valencia, CA). One microgram of total RNA was reverse-transcribed using the SuperScriptTM III First-Strand Synthesis System. PCR assays were performed on a StepOnePlus thermal cycler with 18 s as the normalizer using Applied Biosystems primer/probe set Rn02531967_s1 directed against the translated exon IX. Fold-change relative to control was calculated using the ΔΔCt method (Applied Biosystems).","Samples were lysed in RIPA buffer containing protease inhibitor cocktail and phenylmethanesulfonyl fluoride. Lysates were incubated for 30 min on ice and then centrifuged at 10 000 × g for 10 min at 4 °C. The protein concentration in the supernatant was determined using bicinchoninic acid (BCA) assay kits with bovine serum albumin as a standard. Equal amounts of protein (30 μg per lane) were separated by 10% sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) and then transferred to nitrocellulose membranes (Bio-Rad, United States). The membranes were blocked in Li-Cor blocking buffer for 1 h at room temperature and then incubated with primary antibodies. BDNF antibody (Santa Cruz Biotechnologies, Santa Cruz, CA) was used at 1:200 dilution; nerve growth factor (NGF) antibody (Abcam, MA) was used at 1:1000 dilution; β-actin antibody (Sigma Aldrich, St Louis, MO) was used at 1:5000 dilution. The secondary antibodies were donkey anti-rabbit Alexa Fluor 680 (Invitrogen) and goat anti-mouse IRDye 800 (Rockland). Images were acquired and band intensities measured using a Li-Cor Odyssey system (Li-Cor, Lincoln, NE).","Frozen sections of colon tissue from control, CAS, CPS and CPS + CAS female rats were mounted on glass slides, and rehydrated in phosphate buffered saline at room temperature. The slides were treated for antigen retrieval and blocked with 10% normal goat serum diluted in 0.3% phosphate buffered saline-Triton for 1 h, and then incubated with NGF primary antibody in antibody diluent (Renoir Red, Biocare Medical, Concord, CA) at 4 °C overnight. The slides were exposed to fluorescent dye-conjugated secondary antibody for 2 h at room temperature, counterstained with 4',6-diamidino-2-phenylindole and coverslipped. Images were taken in fluorescence mode on an Olympus laser scanning confocal microscope and the average signal intensity was calculated by the bundled software.","Serum estradiol, adrenocorticotropic hormone (ACTH), and norepinephrine levels were measured using specific enzyme-linked immunosorbent assay kits for each analyte (CSB-E05110r, CSB-E06875r, CSB-E07022, Cusabio Bioteck CO., United States) following the manufacturer’s instructions.","Single fiber responses (impulses/second) to CRD were calculated by subtracting SA from the mean 30 s maximal activity during distension. Fibers were considered responsive if CRD increased their activity to 30% greater than the baseline value. Mechanosensitive single units were classified as high threshold (> 20 mmHg) or low threshold (≤ 20 mmHg) on the basis of their response threshold and profile during CRD. Single fiber activity data were analyzed by analysis of variance with repeated measures; CRD intensity was the repeated factor and the experimental group was the between-group factor. If significant main effects were present, the individual means were compared using the Fisher post-hoc test. All authors had access to the study data and reviewed and approved the final manuscript."," Effects of CPS plus CAS on primary afferent responses to CRD in male and female rats The basal activity of a spinal afferent fiber was defined as the average number of action potentials per second (impulses/sec) in the 60 s period before the onset of a distention stimulus. In male controls, 66% of the afferent fibers under study displayed SA and SA was significantly higher in female controls than in male controls (0.71 ± 0.21 vs 1.24 ± 0.20 imp/sec; Figure 1B). The average single fiber activity in response to CRD was significantly higher in female control rats compared with male controls (Figure 1C). We found that the enhanced sensitization in female rats mainly came from the low-threshold fibers (Figure 1D and E).\nTo assess the effects of CPS + CAS on colon afferent fiber activities, we compared average single colon afferent fiber activities projecting from dorsal roots S1-L6 in response to CRD in male and female control, CPS, control + CAS and CPS + CAS rats recorded approximately 24 h after the last stressor. In females, CPS significantly increased single-unit afferent activity in response to CRD vs control female rats (Figure 1F). CAS alone enhanced single-unit activity compared with control. The increase in average afferent responses after CAS in prenatally stressed female rats (44.0%) was significantly greater than the increase in female control rats (39.3%). In males, CPS had no significant effect on primary afferent responses (Figure 1G). When we compared males to females within each experimental group, we found that the average single fiber activity was significantly higher in female compared with male CPS + CAS rats (Figure 1F, G). The increased activity may contribute to the enhanced female visceral hypersensitivity previously reported in this model. Average single-fiber activities were significantly greater in control and CPS and CAS females than in their corresponding male experimental groups (Figure 1F and G). Both CAS and CPS + CAS rats had significantly increased primary afferent responses compared with control and CPS rats. Thus, our CPS and CAS protocols sensitized colon-projecting primary afferent fibers, with the greatest effects produced by the combination of CPS + CAS in both males and females.\nThe basal activity of a spinal afferent fiber was defined as the average number of action potentials per second (impulses/sec) in the 60 s period before the onset of a distention stimulus. In male controls, 66% of the afferent fibers under study displayed SA and SA was significantly higher in female controls than in male controls (0.71 ± 0.21 vs 1.24 ± 0.20 imp/sec; Figure 1B). The average single fiber activity in response to CRD was significantly higher in female control rats compared with male controls (Figure 1C). We found that the enhanced sensitization in female rats mainly came from the low-threshold fibers (Figure 1D and E).\nTo assess the effects of CPS + CAS on colon afferent fiber activities, we compared average single colon afferent fiber activities projecting from dorsal roots S1-L6 in response to CRD in male and female control, CPS, control + CAS and CPS + CAS rats recorded approximately 24 h after the last stressor. In females, CPS significantly increased single-unit afferent activity in response to CRD vs control female rats (Figure 1F). CAS alone enhanced single-unit activity compared with control. The increase in average afferent responses after CAS in prenatally stressed female rats (44.0%) was significantly greater than the increase in female control rats (39.3%). In males, CPS had no significant effect on primary afferent responses (Figure 1G). When we compared males to females within each experimental group, we found that the average single fiber activity was significantly higher in female compared with male CPS + CAS rats (Figure 1F, G). The increased activity may contribute to the enhanced female visceral hypersensitivity previously reported in this model. Average single-fiber activities were significantly greater in control and CPS and CAS females than in their corresponding male experimental groups (Figure 1F and G). Both CAS and CPS + CAS rats had significantly increased primary afferent responses compared with control and CPS rats. Thus, our CPS and CAS protocols sensitized colon-projecting primary afferent fibers, with the greatest effects produced by the combination of CPS + CAS in both males and females.\n Increase in excitability of colon-projecting lumbosacral DRG neurons in female CPS + CAS rats To elucidate the electrophysiological basis of enhanced stress-induced primary afferent activity in female rats, we performed patch clamp studies on acutely dissociated retrograde-labeled colon-projecting neurons from the L6-S2 DRGs in control, prenatal stress, adult stress only, and CPS + CAS female rats isolated 24 h after the last adult stressor (Figure 2A). Input resistance (Figure 2B) and rheobase (Figure 2C) were significantly decreased in neurons from CPS + CAS rats compared with the other three groups. The number of action potentials elicited at either 2 × or 3 × the rheobase were significantly greater in adult stress and CPS + CAS neurons compared with control and to CPS neurons (Figure 2D, E). CAS significantly increased action potential overshoot with or without CPS (Figure 2F), but it did not significantly alter other electrophysiological characteristics, such as number of spontaneous spikes, membrane capacitance (pF), resting membrane potential, cell diameter, time constant, and DRG neuron action potential amplitude and duration (Table 1).\nPatch clamp recording in colonic dorsal root ganglion neurons from female rats. A: Patch clamp process of cell labeling. Under isoflurane anesthesia, the lipid soluble fluorescent dye 9-DiI was injected into muscularis externa of the exposed distal colon (left figure). Lumbosacral (L6–S2) dorsal root ganglions (upper photograph) were isolated and DiI-labeled neurons were identified by fluorescence microscopy (lower photograph). Electrophysiological properties of each neuron were measured using whole-cell current and voltage clamp protocols (right figure); B: Rheobase from all four experimental groups (n = 5 rats, 45 cells in each group, one-way ANOVA, aP < 0.05 vs control or bP < 0.05 vs CAS); C: Representative action potentials (APs) elicited by current injection at 2 × the rheobase in neurons from control, chronic adult stress (CAS), chronic prenatal stress (CPS) and CPS + CAS female rats; D: Membrane input resistance from all four groups (n = 5 rats, 45 cells in each group, one-way ANOVA, aP < 0.05 vs control; bP < 0.05 vs CPS); E: Number of APs elicited by current injection at either 2 × and 3 × the rheobase in all four experimental groups (two-way ANOVA, aP < 0.05 vs control; bP < 0.05 vs CPS); F: AP overshoot recorded from all four experimental groups (aP < 0.05 vs control); G: The proportion of neurons from each experimental group exhibiting spontaneous APs. Red numbers represent spontaneous AP firing cells; black numbers represent total cells; H: Representative total, IK and IA current tracings and average values of potassium currents: Itotal, IK and IA are shown in female CPS + CAS, CAS, CPS (n = 15 neurons, from 5 rats in each group), and control groups (n = 12 neurons from 5 rats); two-way ANOVA, aP < 0.05 vs each control group. \nElectrophysiological characteristics of colon related DRG neuron\nValues means ± standard error. \nP < 0.05;\nP < 0.001 vs control group. CAS: Chronic adult stress; CPS: Chronic prenatal stress.\nThe percentage of neurons with SA in was significantly greater in CPS + CAS rats than in control or CPS only rats (Figure 2G). Under voltage clamp conditions (Figure 2H), neurons from female CPS + CAS, CAS, CPS and control groups had IA and sustained outward rectifier K+ currents (IK). Compared with the other three groups, DRG neurons from CPS + CAS rats had significantly reduced average IA (P < 0.05). The average IK density was decreased but the change was not significant.\nTo elucidate the electrophysiological basis of enhanced stress-induced primary afferent activity in female rats, we performed patch clamp studies on acutely dissociated retrograde-labeled colon-projecting neurons from the L6-S2 DRGs in control, prenatal stress, adult stress only, and CPS + CAS female rats isolated 24 h after the last adult stressor (Figure 2A). Input resistance (Figure 2B) and rheobase (Figure 2C) were significantly decreased in neurons from CPS + CAS rats compared with the other three groups. The number of action potentials elicited at either 2 × or 3 × the rheobase were significantly greater in adult stress and CPS + CAS neurons compared with control and to CPS neurons (Figure 2D, E). CAS significantly increased action potential overshoot with or without CPS (Figure 2F), but it did not significantly alter other electrophysiological characteristics, such as number of spontaneous spikes, membrane capacitance (pF), resting membrane potential, cell diameter, time constant, and DRG neuron action potential amplitude and duration (Table 1).\nPatch clamp recording in colonic dorsal root ganglion neurons from female rats. A: Patch clamp process of cell labeling. Under isoflurane anesthesia, the lipid soluble fluorescent dye 9-DiI was injected into muscularis externa of the exposed distal colon (left figure). Lumbosacral (L6–S2) dorsal root ganglions (upper photograph) were isolated and DiI-labeled neurons were identified by fluorescence microscopy (lower photograph). Electrophysiological properties of each neuron were measured using whole-cell current and voltage clamp protocols (right figure); B: Rheobase from all four experimental groups (n = 5 rats, 45 cells in each group, one-way ANOVA, aP < 0.05 vs control or bP < 0.05 vs CAS); C: Representative action potentials (APs) elicited by current injection at 2 × the rheobase in neurons from control, chronic adult stress (CAS), chronic prenatal stress (CPS) and CPS + CAS female rats; D: Membrane input resistance from all four groups (n = 5 rats, 45 cells in each group, one-way ANOVA, aP < 0.05 vs control; bP < 0.05 vs CPS); E: Number of APs elicited by current injection at either 2 × and 3 × the rheobase in all four experimental groups (two-way ANOVA, aP < 0.05 vs control; bP < 0.05 vs CPS); F: AP overshoot recorded from all four experimental groups (aP < 0.05 vs control); G: The proportion of neurons from each experimental group exhibiting spontaneous APs. Red numbers represent spontaneous AP firing cells; black numbers represent total cells; H: Representative total, IK and IA current tracings and average values of potassium currents: Itotal, IK and IA are shown in female CPS + CAS, CAS, CPS (n = 15 neurons, from 5 rats in each group), and control groups (n = 12 neurons from 5 rats); two-way ANOVA, aP < 0.05 vs each control group. \nElectrophysiological characteristics of colon related DRG neuron\nValues means ± standard error. \nP < 0.05;\nP < 0.001 vs control group. CAS: Chronic adult stress; CPS: Chronic prenatal stress.\nThe percentage of neurons with SA in was significantly greater in CPS + CAS rats than in control or CPS only rats (Figure 2G). Under voltage clamp conditions (Figure 2H), neurons from female CPS + CAS, CAS, CPS and control groups had IA and sustained outward rectifier K+ currents (IK). Compared with the other three groups, DRG neurons from CPS + CAS rats had significantly reduced average IA (P < 0.05). The average IK density was decreased but the change was not significant.\n Effects of CPS and/or CAS on plasma estrogen concentration We did a vaginal smear test to identify the estrus cycle phases by identifying the vaginal cytological cell types. Estrogen concentration was significantly higher in the CPS proestrus/estrus phase compared with control diestrus, control proestrus/estrus, and CPS diestrus proestrus (P < 0.05; Figure 3A). Comparison of the plasma estrogen concentrations in control, CAS, CPS, CPS + CAS showed that CPS significantly increased plasma estrogen levels compared with the control rats and that CAS increased plasma estrogen level compared with the control and CPS rats (Figure 3B).\nEffects of chronic prenatal stress, chronic adult stress, ovariectomy, and letrozole treatment on plasma estrogen levels in female rats. A: Plasma estrogen level in control and chronic prenatal stress (CPS) rats by estrus cycle phase (n = 8 rats, one-way ANOVA, aP < 0.05 vs control proestrus/estrus (P-E) phase; bP < 0.05 vs CPS diestrus (D) phase); B: Plasma estrogen levels increased in CPS rats and following chronic adult stress (CAS) 24 h after the last adult stressor (n = 8 rats, one-way ANOVA, aP < 0.05 vs control; bP < 0.05 vs CPS); C: Ovariectomy (OVX) significantly reduced CPS female rat plasma estrogen levels before and after CAS (n = 5 rats, one-way ANOVA, aP < 0.05 vs sham group); D: Letrozole treatment significantly reduced CPS female rat plasma estrogen levels before or after CAS (n = 5 rats, one-way ANOVA, aP < 0.05 vs vehicle group; cP < 0.0001); E: Plasma norepinephrine levels from control, CAS, CPS and CPS + CAS group female rats (n = 5 rats, one-way ANOVA, aP < 0.05 vs control; bP < 0.05 vs CPS); F: Plasma adrenocorticotropic hormone (ACTH) levels from control and CPS + CAS group female rats (n = 5 rats, t-test, aP < 0.05 vs control). \nTo determine whether estrogen contributed to stress-induced visceral hypersensitivity in prenatal stressed females, we reduced plasma estrogen levels by either OVX or letrozole treatment. OVX significantly lowered serum estradiol levels before and after CAS (Figure 3C). Treatment was continued throughout CAS. After treatment with letrozole, serum estradiol levels were significantly reduced (Figure 3D). To study the effects of gender and stress on norepinephrine and ACTH levels, we measured plasma norepinephrine levels in female rats from all four experimental groups. CAS alone significantly increased plasma norepinephrine levels compared with both the controls and with CPS alone (Figure 3E) Plasma norepinephrine levels were significantly increased in CPS + CAS rats compared with CAS alone as well as with controls and CPS. Plasma ACTH levels were significantly increased in CPS + CAS rats compared with controls. (Figure 3F).\nWe did a vaginal smear test to identify the estrus cycle phases by identifying the vaginal cytological cell types. Estrogen concentration was significantly higher in the CPS proestrus/estrus phase compared with control diestrus, control proestrus/estrus, and CPS diestrus proestrus (P < 0.05; Figure 3A). Comparison of the plasma estrogen concentrations in control, CAS, CPS, CPS + CAS showed that CPS significantly increased plasma estrogen levels compared with the control rats and that CAS increased plasma estrogen level compared with the control and CPS rats (Figure 3B).\nEffects of chronic prenatal stress, chronic adult stress, ovariectomy, and letrozole treatment on plasma estrogen levels in female rats. A: Plasma estrogen level in control and chronic prenatal stress (CPS) rats by estrus cycle phase (n = 8 rats, one-way ANOVA, aP < 0.05 vs control proestrus/estrus (P-E) phase; bP < 0.05 vs CPS diestrus (D) phase); B: Plasma estrogen levels increased in CPS rats and following chronic adult stress (CAS) 24 h after the last adult stressor (n = 8 rats, one-way ANOVA, aP < 0.05 vs control; bP < 0.05 vs CPS); C: Ovariectomy (OVX) significantly reduced CPS female rat plasma estrogen levels before and after CAS (n = 5 rats, one-way ANOVA, aP < 0.05 vs sham group); D: Letrozole treatment significantly reduced CPS female rat plasma estrogen levels before or after CAS (n = 5 rats, one-way ANOVA, aP < 0.05 vs vehicle group; cP < 0.0001); E: Plasma norepinephrine levels from control, CAS, CPS and CPS + CAS group female rats (n = 5 rats, one-way ANOVA, aP < 0.05 vs control; bP < 0.05 vs CPS); F: Plasma adrenocorticotropic hormone (ACTH) levels from control and CPS + CAS group female rats (n = 5 rats, t-test, aP < 0.05 vs control). \nTo determine whether estrogen contributed to stress-induced visceral hypersensitivity in prenatal stressed females, we reduced plasma estrogen levels by either OVX or letrozole treatment. OVX significantly lowered serum estradiol levels before and after CAS (Figure 3C). Treatment was continued throughout CAS. After treatment with letrozole, serum estradiol levels were significantly reduced (Figure 3D). To study the effects of gender and stress on norepinephrine and ACTH levels, we measured plasma norepinephrine levels in female rats from all four experimental groups. CAS alone significantly increased plasma norepinephrine levels compared with both the controls and with CPS alone (Figure 3E) Plasma norepinephrine levels were significantly increased in CPS + CAS rats compared with CAS alone as well as with controls and CPS. Plasma ACTH levels were significantly increased in CPS + CAS rats compared with controls. (Figure 3F).\n Effects of Letrozole treatment on colon DRG neuron excitability We performed patch clamp experiments on acutely isolated retrograde-labeled DRG neurons from CPS + CAS females with or without letrozole treatment 24 h after the last adult stressor. Letrozole treatment significantly increased rheobase (Figure 4A), and significantly reduced input resistance (Figure 4B). Action potential overshoot (Figure 4C) and the number of action potentials elicited by a current injection at either 2 × or 3 × rheobase were significantly reduced by letrozole treatment (Figure 4D). Other electrophysiological properties were not significantly altered (Table 2). We also recorded electromyographic activity to determine whether the reduction in visceral sensitivity in female CPS + CAS rats caused by OVX or systemic letrozole treatment reduced visceromotor responses. The findings demonstrated a significant decrease in excitability of colon-projecting L6-S2 neurons.\nEffects of Letrozole treatment on colon dorsal root ganglion neuron excitability. A: Rheobase (n = 45 cells in 6 rats in each group, t-test, cP < 0.001 vs Veh. + chronic adult stress [CAS] + chronic prenatal stress [CPS]); B: Membrane input resistance (RIn) (t-test, aP < 0.05); C: Action potential (AP) overshoot (t-test, aP < 0.05); D: Number of APs elicited by current injection at 2 × and 3 × rheobase (two-way ANOVA, aP < 0.05; bP < 0.01). \nElectrophysiological characteristics of colon related DRG neuron after Letrozole treatment\nValues are means ± standard error. \nP < 0.05;\nP < 0.001 vs control group. CAS: Chronic adult stress; CPS: Chronic prenatal stress.\nWe performed patch clamp experiments on acutely isolated retrograde-labeled DRG neurons from CPS + CAS females with or without letrozole treatment 24 h after the last adult stressor. Letrozole treatment significantly increased rheobase (Figure 4A), and significantly reduced input resistance (Figure 4B). Action potential overshoot (Figure 4C) and the number of action potentials elicited by a current injection at either 2 × or 3 × rheobase were significantly reduced by letrozole treatment (Figure 4D). Other electrophysiological properties were not significantly altered (Table 2). We also recorded electromyographic activity to determine whether the reduction in visceral sensitivity in female CPS + CAS rats caused by OVX or systemic letrozole treatment reduced visceromotor responses. The findings demonstrated a significant decrease in excitability of colon-projecting L6-S2 neurons.\nEffects of Letrozole treatment on colon dorsal root ganglion neuron excitability. A: Rheobase (n = 45 cells in 6 rats in each group, t-test, cP < 0.001 vs Veh. + chronic adult stress [CAS] + chronic prenatal stress [CPS]); B: Membrane input resistance (RIn) (t-test, aP < 0.05); C: Action potential (AP) overshoot (t-test, aP < 0.05); D: Number of APs elicited by current injection at 2 × and 3 × rheobase (two-way ANOVA, aP < 0.05; bP < 0.01). \nElectrophysiological characteristics of colon related DRG neuron after Letrozole treatment\nValues are means ± standard error. \nP < 0.05;\nP < 0.001 vs control group. CAS: Chronic adult stress; CPS: Chronic prenatal stress.\n Spinal cord BDNF levels regulated by estrogen To investigate the effect of estrogen on BDNF expression, we measured BDNF mRNA and protein levels in the lumbar-sacral spinal cords of OVX and Sham CPS + CAS female rats. Systemic estradiol administration to naïve cycling females produced significant increases in plasma estrogen (Figure 5A), lumbar-sacral spinal cord BDNF mRNA (Figure 5B), and protein (Figure 5C). We also measured BDNF mRNA and protein levels in the lumbar-sacral spinal cords of OVX and Sham CPS + CAS female rats. BDNF mRNA and protein expression were significantly suppressed by OVX compared with sham rats. Another experiment showed that intrathecal infusion of estrogen into naïve female rats significantly increased BDNF protein levels, which proved that estrogen reversed the experimental results and contributed to the response to visceral pain[13].\nBrain-derived neurotrophic factor expression in lumbar-sacral spinal cord is regulated by estrogen. A: Plasma estrogen levels in cycling females that received a bolus estradiol (E2) infusion on day 1; B: Lumbar-sacral spinal cord brain-derived neurotrophic factor (BDNF) mRNA following bolus estrogen infusion; C: Lumbar-sacral spinal cord BDNF protein following bolus estrogen infusion. (n = 8 rats in each group, two-way ANOVA, aP < 0.05 vs vehicle group). \nTo investigate the effect of estrogen on BDNF expression, we measured BDNF mRNA and protein levels in the lumbar-sacral spinal cords of OVX and Sham CPS + CAS female rats. Systemic estradiol administration to naïve cycling females produced significant increases in plasma estrogen (Figure 5A), lumbar-sacral spinal cord BDNF mRNA (Figure 5B), and protein (Figure 5C). We also measured BDNF mRNA and protein levels in the lumbar-sacral spinal cords of OVX and Sham CPS + CAS female rats. BDNF mRNA and protein expression were significantly suppressed by OVX compared with sham rats. Another experiment showed that intrathecal infusion of estrogen into naïve female rats significantly increased BDNF protein levels, which proved that estrogen reversed the experimental results and contributed to the response to visceral pain[13].\nBrain-derived neurotrophic factor expression in lumbar-sacral spinal cord is regulated by estrogen. A: Plasma estrogen levels in cycling females that received a bolus estradiol (E2) infusion on day 1; B: Lumbar-sacral spinal cord brain-derived neurotrophic factor (BDNF) mRNA following bolus estrogen infusion; C: Lumbar-sacral spinal cord BDNF protein following bolus estrogen infusion. (n = 8 rats in each group, two-way ANOVA, aP < 0.05 vs vehicle group). \n Peripheral NGF level increased in CPS + CAS female rats We examined NGF expression in the colons of females from all four experimental groups by immunohistochemistry (Figure 6A). Morphometric analysis showed that CAS and CPS + CAS significantly increased NGF levels in the colon wall, with the increase in CPS + CAS significantly greater that of CAS alone (Figure 6B). Western blotting showed that NGF protein was significantly upregulated in CPS + CAS rats compared with controls (Figure 6C).\nNerve growth factor expression level in the colon wall. A: Immunohistochemical staining of nerve growth factor (NGF; green) was detected with nuclear counterstaining staining (blue) in controls, chronic adult stress (CAS), chronic prenatal stress (CPS) and CPS + CAS group female rat colon walls. × 400 magnification representative pictures were shown; B: Quantification of NGF levels from colon wall in immunohistochemistry (IHC) (n = 4 rats in each group, one-way ANOVA, aP < 0.05 vs control group; bP < 0.05 vs CPS group); C: Western blots of NGF protein from control and CPS + CAS female rats colon wall tissue (n = 6 rats in each group, t-test, aP < 0.05 vs control group). \nWe examined NGF expression in the colons of females from all four experimental groups by immunohistochemistry (Figure 6A). Morphometric analysis showed that CAS and CPS + CAS significantly increased NGF levels in the colon wall, with the increase in CPS + CAS significantly greater that of CAS alone (Figure 6B). Western blotting showed that NGF protein was significantly upregulated in CPS + CAS rats compared with controls (Figure 6C).\nNerve growth factor expression level in the colon wall. A: Immunohistochemical staining of nerve growth factor (NGF; green) was detected with nuclear counterstaining staining (blue) in controls, chronic adult stress (CAS), chronic prenatal stress (CPS) and CPS + CAS group female rat colon walls. × 400 magnification representative pictures were shown; B: Quantification of NGF levels from colon wall in immunohistochemistry (IHC) (n = 4 rats in each group, one-way ANOVA, aP < 0.05 vs control group; bP < 0.05 vs CPS group); C: Western blots of NGF protein from control and CPS + CAS female rats colon wall tissue (n = 6 rats in each group, t-test, aP < 0.05 vs control group). ","The basal activity of a spinal afferent fiber was defined as the average number of action potentials per second (impulses/sec) in the 60 s period before the onset of a distention stimulus. In male controls, 66% of the afferent fibers under study displayed SA and SA was significantly higher in female controls than in male controls (0.71 ± 0.21 vs 1.24 ± 0.20 imp/sec; Figure 1B). The average single fiber activity in response to CRD was significantly higher in female control rats compared with male controls (Figure 1C). We found that the enhanced sensitization in female rats mainly came from the low-threshold fibers (Figure 1D and E).\nTo assess the effects of CPS + CAS on colon afferent fiber activities, we compared average single colon afferent fiber activities projecting from dorsal roots S1-L6 in response to CRD in male and female control, CPS, control + CAS and CPS + CAS rats recorded approximately 24 h after the last stressor. In females, CPS significantly increased single-unit afferent activity in response to CRD vs control female rats (Figure 1F). CAS alone enhanced single-unit activity compared with control. The increase in average afferent responses after CAS in prenatally stressed female rats (44.0%) was significantly greater than the increase in female control rats (39.3%). In males, CPS had no significant effect on primary afferent responses (Figure 1G). When we compared males to females within each experimental group, we found that the average single fiber activity was significantly higher in female compared with male CPS + CAS rats (Figure 1F, G). The increased activity may contribute to the enhanced female visceral hypersensitivity previously reported in this model. Average single-fiber activities were significantly greater in control and CPS and CAS females than in their corresponding male experimental groups (Figure 1F and G). Both CAS and CPS + CAS rats had significantly increased primary afferent responses compared with control and CPS rats. Thus, our CPS and CAS protocols sensitized colon-projecting primary afferent fibers, with the greatest effects produced by the combination of CPS + CAS in both males and females.","To elucidate the electrophysiological basis of enhanced stress-induced primary afferent activity in female rats, we performed patch clamp studies on acutely dissociated retrograde-labeled colon-projecting neurons from the L6-S2 DRGs in control, prenatal stress, adult stress only, and CPS + CAS female rats isolated 24 h after the last adult stressor (Figure 2A). Input resistance (Figure 2B) and rheobase (Figure 2C) were significantly decreased in neurons from CPS + CAS rats compared with the other three groups. The number of action potentials elicited at either 2 × or 3 × the rheobase were significantly greater in adult stress and CPS + CAS neurons compared with control and to CPS neurons (Figure 2D, E). CAS significantly increased action potential overshoot with or without CPS (Figure 2F), but it did not significantly alter other electrophysiological characteristics, such as number of spontaneous spikes, membrane capacitance (pF), resting membrane potential, cell diameter, time constant, and DRG neuron action potential amplitude and duration (Table 1).\nPatch clamp recording in colonic dorsal root ganglion neurons from female rats. A: Patch clamp process of cell labeling. Under isoflurane anesthesia, the lipid soluble fluorescent dye 9-DiI was injected into muscularis externa of the exposed distal colon (left figure). Lumbosacral (L6–S2) dorsal root ganglions (upper photograph) were isolated and DiI-labeled neurons were identified by fluorescence microscopy (lower photograph). Electrophysiological properties of each neuron were measured using whole-cell current and voltage clamp protocols (right figure); B: Rheobase from all four experimental groups (n = 5 rats, 45 cells in each group, one-way ANOVA, aP < 0.05 vs control or bP < 0.05 vs CAS); C: Representative action potentials (APs) elicited by current injection at 2 × the rheobase in neurons from control, chronic adult stress (CAS), chronic prenatal stress (CPS) and CPS + CAS female rats; D: Membrane input resistance from all four groups (n = 5 rats, 45 cells in each group, one-way ANOVA, aP < 0.05 vs control; bP < 0.05 vs CPS); E: Number of APs elicited by current injection at either 2 × and 3 × the rheobase in all four experimental groups (two-way ANOVA, aP < 0.05 vs control; bP < 0.05 vs CPS); F: AP overshoot recorded from all four experimental groups (aP < 0.05 vs control); G: The proportion of neurons from each experimental group exhibiting spontaneous APs. Red numbers represent spontaneous AP firing cells; black numbers represent total cells; H: Representative total, IK and IA current tracings and average values of potassium currents: Itotal, IK and IA are shown in female CPS + CAS, CAS, CPS (n = 15 neurons, from 5 rats in each group), and control groups (n = 12 neurons from 5 rats); two-way ANOVA, aP < 0.05 vs each control group. \nElectrophysiological characteristics of colon related DRG neuron\nValues means ± standard error. \nP < 0.05;\nP < 0.001 vs control group. CAS: Chronic adult stress; CPS: Chronic prenatal stress.\nThe percentage of neurons with SA in was significantly greater in CPS + CAS rats than in control or CPS only rats (Figure 2G). Under voltage clamp conditions (Figure 2H), neurons from female CPS + CAS, CAS, CPS and control groups had IA and sustained outward rectifier K+ currents (IK). Compared with the other three groups, DRG neurons from CPS + CAS rats had significantly reduced average IA (P < 0.05). The average IK density was decreased but the change was not significant.","We did a vaginal smear test to identify the estrus cycle phases by identifying the vaginal cytological cell types. Estrogen concentration was significantly higher in the CPS proestrus/estrus phase compared with control diestrus, control proestrus/estrus, and CPS diestrus proestrus (P < 0.05; Figure 3A). Comparison of the plasma estrogen concentrations in control, CAS, CPS, CPS + CAS showed that CPS significantly increased plasma estrogen levels compared with the control rats and that CAS increased plasma estrogen level compared with the control and CPS rats (Figure 3B).\nEffects of chronic prenatal stress, chronic adult stress, ovariectomy, and letrozole treatment on plasma estrogen levels in female rats. A: Plasma estrogen level in control and chronic prenatal stress (CPS) rats by estrus cycle phase (n = 8 rats, one-way ANOVA, aP < 0.05 vs control proestrus/estrus (P-E) phase; bP < 0.05 vs CPS diestrus (D) phase); B: Plasma estrogen levels increased in CPS rats and following chronic adult stress (CAS) 24 h after the last adult stressor (n = 8 rats, one-way ANOVA, aP < 0.05 vs control; bP < 0.05 vs CPS); C: Ovariectomy (OVX) significantly reduced CPS female rat plasma estrogen levels before and after CAS (n = 5 rats, one-way ANOVA, aP < 0.05 vs sham group); D: Letrozole treatment significantly reduced CPS female rat plasma estrogen levels before or after CAS (n = 5 rats, one-way ANOVA, aP < 0.05 vs vehicle group; cP < 0.0001); E: Plasma norepinephrine levels from control, CAS, CPS and CPS + CAS group female rats (n = 5 rats, one-way ANOVA, aP < 0.05 vs control; bP < 0.05 vs CPS); F: Plasma adrenocorticotropic hormone (ACTH) levels from control and CPS + CAS group female rats (n = 5 rats, t-test, aP < 0.05 vs control). \nTo determine whether estrogen contributed to stress-induced visceral hypersensitivity in prenatal stressed females, we reduced plasma estrogen levels by either OVX or letrozole treatment. OVX significantly lowered serum estradiol levels before and after CAS (Figure 3C). Treatment was continued throughout CAS. After treatment with letrozole, serum estradiol levels were significantly reduced (Figure 3D). To study the effects of gender and stress on norepinephrine and ACTH levels, we measured plasma norepinephrine levels in female rats from all four experimental groups. CAS alone significantly increased plasma norepinephrine levels compared with both the controls and with CPS alone (Figure 3E) Plasma norepinephrine levels were significantly increased in CPS + CAS rats compared with CAS alone as well as with controls and CPS. Plasma ACTH levels were significantly increased in CPS + CAS rats compared with controls. (Figure 3F).","We performed patch clamp experiments on acutely isolated retrograde-labeled DRG neurons from CPS + CAS females with or without letrozole treatment 24 h after the last adult stressor. Letrozole treatment significantly increased rheobase (Figure 4A), and significantly reduced input resistance (Figure 4B). Action potential overshoot (Figure 4C) and the number of action potentials elicited by a current injection at either 2 × or 3 × rheobase were significantly reduced by letrozole treatment (Figure 4D). Other electrophysiological properties were not significantly altered (Table 2). We also recorded electromyographic activity to determine whether the reduction in visceral sensitivity in female CPS + CAS rats caused by OVX or systemic letrozole treatment reduced visceromotor responses. The findings demonstrated a significant decrease in excitability of colon-projecting L6-S2 neurons.\nEffects of Letrozole treatment on colon dorsal root ganglion neuron excitability. A: Rheobase (n = 45 cells in 6 rats in each group, t-test, cP < 0.001 vs Veh. + chronic adult stress [CAS] + chronic prenatal stress [CPS]); B: Membrane input resistance (RIn) (t-test, aP < 0.05); C: Action potential (AP) overshoot (t-test, aP < 0.05); D: Number of APs elicited by current injection at 2 × and 3 × rheobase (two-way ANOVA, aP < 0.05; bP < 0.01). \nElectrophysiological characteristics of colon related DRG neuron after Letrozole treatment\nValues are means ± standard error. \nP < 0.05;\nP < 0.001 vs control group. CAS: Chronic adult stress; CPS: Chronic prenatal stress.","To investigate the effect of estrogen on BDNF expression, we measured BDNF mRNA and protein levels in the lumbar-sacral spinal cords of OVX and Sham CPS + CAS female rats. Systemic estradiol administration to naïve cycling females produced significant increases in plasma estrogen (Figure 5A), lumbar-sacral spinal cord BDNF mRNA (Figure 5B), and protein (Figure 5C). We also measured BDNF mRNA and protein levels in the lumbar-sacral spinal cords of OVX and Sham CPS + CAS female rats. BDNF mRNA and protein expression were significantly suppressed by OVX compared with sham rats. Another experiment showed that intrathecal infusion of estrogen into naïve female rats significantly increased BDNF protein levels, which proved that estrogen reversed the experimental results and contributed to the response to visceral pain[13].\nBrain-derived neurotrophic factor expression in lumbar-sacral spinal cord is regulated by estrogen. A: Plasma estrogen levels in cycling females that received a bolus estradiol (E2) infusion on day 1; B: Lumbar-sacral spinal cord brain-derived neurotrophic factor (BDNF) mRNA following bolus estrogen infusion; C: Lumbar-sacral spinal cord BDNF protein following bolus estrogen infusion. (n = 8 rats in each group, two-way ANOVA, aP < 0.05 vs vehicle group). ","We examined NGF expression in the colons of females from all four experimental groups by immunohistochemistry (Figure 6A). Morphometric analysis showed that CAS and CPS + CAS significantly increased NGF levels in the colon wall, with the increase in CPS + CAS significantly greater that of CAS alone (Figure 6B). Western blotting showed that NGF protein was significantly upregulated in CPS + CAS rats compared with controls (Figure 6C).\nNerve growth factor expression level in the colon wall. A: Immunohistochemical staining of nerve growth factor (NGF; green) was detected with nuclear counterstaining staining (blue) in controls, chronic adult stress (CAS), chronic prenatal stress (CPS) and CPS + CAS group female rat colon walls. × 400 magnification representative pictures were shown; B: Quantification of NGF levels from colon wall in immunohistochemistry (IHC) (n = 4 rats in each group, one-way ANOVA, aP < 0.05 vs control group; bP < 0.05 vs CPS group); C: Western blots of NGF protein from control and CPS + CAS female rats colon wall tissue (n = 6 rats in each group, t-test, aP < 0.05 vs control group). ","Enhanced CPS-induced visceral hypersensitivity in female rats was associated with an increase in the responses of lumbosacral nerve fibers to CRD in both male and female offspring. These findings are further supported by data showing increased excitability of colon-projecting DRG neurons from females in patch clamp studies. The magnitude of the sensitization was the greatest in female CPS + CAS rats, suggesting that it made a major contribution to the observed enhanced female visceral hypersensitivity in our model.\nChronic stress is known to increase the excitability of colon-projecting DRG neurons in rats and mice. In adult male Sprague Dawley rats, colon DRG neuron sensitization was shown to be driven by increases in NGF expression in the colon muscularis externa[13]. In our model, we also observed a significant increase in colon NGF, but its potential role in primary afferent sensitization and visceral hypersensitivity was not investigated.. Other studies in male mice showed that stress, in the form of water avoidance, significantly increased the excitability of colon-projecting DRG neurons and that the combined activity of the stress mediators corticosterone and norepinephrine increased DRG neuron excitability in vitro[20,21]. We previously found significant increases in the serum levels of norepinephrine in CPS + CAS females. However, daily systemic treatment with adrenergic antagonists during the adult stress protocol failed to reduce visceral hypersensitivity in female neonatal + adult stress rats[13] suggesting that norepinephrine did not play a major role in the acquisition of enhanced female visceral hypersensitivity or primary afferent sensitization in our model.\nWhen we tested on lumbar-sacral afferent fibers and dissociated neurons in patch clamp studies, we found significant decreases in transient potassium IA currents in neurons isolated from CPS + CAS females compared with the other three experimental groups. Declines in A-type Kv currents in DRG neurons have been associated with persistent pain in multiple chronic pain models[22]. Whether the decline was caused by changes in channel properties or expression was not investigated in this study. However, another study demonstrated that estrogen significantly shifted the activation curve of IA currents in the hyperpolarizing direction and that estrogen inhibited Kv (+) channels in mouse DRG neurons through a membrane ER-activated nongenomic pathway[23].\nOur results showed that the excitability of colon-projecting neurons in CPS + CAS females was significantly reduced by systemic letrozole treatment, suggesting that estrogen contributed to the sensitization process. Previous studies show that estrogen receptors expressed on primary afferent neurons contributed to enhanced sensitivity in various pain models[24-26]. One study found no decline in the responses of colon-projecting nerve fibers to CRD following OVX and found no detectable estrogen receptor alpha immunoreactivity in colon-projecting DRG neurons[27]. The reasons for the differing results are not clear, but local production of estrogen in DRG neurons could be sufficient to sustain sensitization.\nNGF and its receptors play important roles in the mechanism of visceral pain and hyperalgesia in women. For example, endometriosis is estrogen dependent and is commonly diagnosed. The main symptoms are various types of pelvic pain that have a serious effect on physical and mental health, but the mechanisms of abdominal pain are still unclear. Studies have shown NGF to be an inflammatory mediator and modulator of pain in adulthood[28].","In this study, we examined the sex differences and effects of estrogen on the acquisition of enhanced visceral hypersensitivity in the offspring of rats in a model of prenatal and adult stress as shown in Figure 7. Our study shows that estrogen acted in the spinal cord and the primary afferent neurons to enhance visceral nociception. Acute blockade of the endogenous synthesis of estrogens in rat spinal cord significantly reduced visceral hypersensitivity, suggesting that locally produced estrogen in the central nervous system can regulate nociceptive neurons to modulate visceral hypersensitivity. The chronic stress-estrogen-BDNF axis sensitized visceral hypersensitivity in the offspring of females subjected to CPS. The development of chronic stress-induced visceral hypersensitivity in female rats was estrogen dependent. A key component of this hypersensitivity was estrogen-dependent sensitization of primary afferent colon neurons. Our findings provide key scientific evidence in a preclinical model in support of developing gender-based treatment for abdominal pain in IBS.\nSummary diagram of estrogen re-enhanced visceral hyperalgesia investigated in chronic prenatal stress plus chronic adult stress models. BDNF: Brain-derived neurotrophic factor; NGF: Nerve growth factor."],"string":"[\n \"Visceral pain of colonic origin is the most prominent symptom in irritable bowel syndrome (IBS) patients[1]. Female IBS patients report more severe pain that occurs more frequently and with longer episodes than in male patients[1,2]. The ratio of female to male IBS is about 2:1 among patients seen in medical clinics[3]. Moreover, females have a higher prevalence of IBS co-morbidities such as anxiety and depression[4,5] and are more vulnerable to stress-induced exacerbation of IBS symptoms compared with males[3,6,7].\\nClinical studies show that early life adverse experiences are risk factors for the development of IBS symptoms, including visceral pain and ongoing chronic stress, especially abdominal pain[8-10]. These factors contribute to the development of visceral hypersensitivity, a key component of the IBS symptom complex and one that may be responsible for symptoms of pain[11,12]. Our previous research found that the female offspring of mothers subjected to chronic prenatal stress (CPS) had a markedly greater visceral sensitivity than their male littermates following challenge by another chronic adult stress (CAS) protocol. A critical molecular event in the development of this female-enhanced visceral hypersensitivity is upregulation of brain-derived neurotrophic factor (BDNF) expression in the lumbar-sacral spinal cord of female CPS + CAS rats[13]. However, the neurophysiological changes underlying the enhanced female-specific visceral hypersensitivity and the role of hormone in the development of stress-induced visceral hypersensitivity are not well understood.\\nVisceral hypersensitivity in IBS involves abnormal changes in neurophysiology throughout the brain-gut axis. In IBS, there is evidence for sensitization of primary afferents to jejunal distention and electrical stimulation[14], and there is evidence for increased sensitivity of lumbar splanchnic afferents[15,16]. In animal models of either early life adverse events or adult stress-induced visceral hypersensitivity[17], there is evidence of colon primary afferent sensitization. However, the studies were performed in male rodents. Therefore, in this study, we established a CPS and CAS rodent model to analyze the impact on female colon afferent neuron function and the role of estrogen. Our hypothesis was that female CPS offspring subjected to chronic stress as adults would exhibit greater colonic dorsal root ganglion (DRG) neuron sensitization compared with their male littermates, and that the enhanced visceral sensitization and primary afferent sensitization in females was estrogen dependent.\",\n \" Animals The Institutional Animal Care and Use Committee of the University of Texas Medical Branch at Galveston, TX approved all animal procedures. Experiments were performed on pregnant Sprague Dawley rats and their 8-wk-old to 16-wk-old male and female offspring. Rats were housed individual cages with access to food and water in a room with controlled conditions (22 ± 2 °C, relative humidity of 50% ± 5%), and a 12 h light/12 h dark cycle.\\nThe Institutional Animal Care and Use Committee of the University of Texas Medical Branch at Galveston, TX approved all animal procedures. Experiments were performed on pregnant Sprague Dawley rats and their 8-wk-old to 16-wk-old male and female offspring. Rats were housed individual cages with access to food and water in a room with controlled conditions (22 ± 2 °C, relative humidity of 50% ± 5%), and a 12 h light/12 h dark cycle.\\n CPS and CAS models Pregnant dams were subjected to a CPS protocol that consisted of a random sequence of twice-daily applications of one of three stress sessions, a 1-h water-avoidance, 45-min cold-restraint, or a 20-min forced swim starting on day 6 and continuing until delivery on day 21. Male and female offspring of the stressed dams were designated CPS rats. Control dams received sham stress and their offspring were designated control rats. As adults at 8-16 wk of age, control and prenatally stressed offspring were challenged by the same CAS protocol for 9 d. Ovariectomy (OVX) or sham surgery was performed on female prenatal-stress offspring on day 56. Daily letrozole treatment was initiated on day 49, 2 wk prior to initiation of adult stress. Treatment was continued through the stress protocol. A schematic diagram of the study procedures is shown in Figure 1A.\\nPrimary afferent responses to colorectal distention. A: Chronic prenatal stress (CPS) plus chronic adult stress (CAS) model. Pregnant dams were subjected to prenatal stress from on day 11 of gestation. Ovariectomy (OVX) or sham surgery was performed on female prenatal-stress offspring on day 56. Daily Letrozole was initiated on day 49, 2 wk prior to initiation of adult stress. Treatment was continued through the stress protocol; B: Spontaneous activity (SA) of single afferent units in male and female control rats (n = 70 fibers in 6 rats in each group, t-test, aP < 0.05); C: Average response to graded colorectal distention (CRD) of 56 afferent fibers in 6 male and 70 afferent fibers in 6 female control rats; two-way analysis of variance (ANOVA; aP < 0.05 vs the same pressure male group); D: Responses of low-threshold (LT) fibers to CRD in 42 fibers in 6 male rats and 40 fibers in 6 female control rats (ANOVA, aP < 0.05 vs the same pressure male group); E: Responses of high-threshold (HT) afferent fibers to CRD in 14 fibers in 6 male and 29 fibers in 6 female control rats; F: Effects of CAS on afferent fiber responses to CRD from 59 fibers in 6 control and 99 fibers in 6 CPS female rats; (two-way ANOVA, aP < 0.05 vs the same pressure control group, bP < 0.05 vs the same pressure CPS group); G: Effects of CAS on afferent fiber responses to CRD in control and CPS male rats (n = 6 rats, 57 fibers for control and 95 fibers for CPS female group; two-way ANOVA, aP < 0.05 vs the same pressure-control group). \\nPregnant dams were subjected to a CPS protocol that consisted of a random sequence of twice-daily applications of one of three stress sessions, a 1-h water-avoidance, 45-min cold-restraint, or a 20-min forced swim starting on day 6 and continuing until delivery on day 21. Male and female offspring of the stressed dams were designated CPS rats. Control dams received sham stress and their offspring were designated control rats. As adults at 8-16 wk of age, control and prenatally stressed offspring were challenged by the same CAS protocol for 9 d. Ovariectomy (OVX) or sham surgery was performed on female prenatal-stress offspring on day 56. Daily letrozole treatment was initiated on day 49, 2 wk prior to initiation of adult stress. Treatment was continued through the stress protocol. A schematic diagram of the study procedures is shown in Figure 1A.\\nPrimary afferent responses to colorectal distention. A: Chronic prenatal stress (CPS) plus chronic adult stress (CAS) model. Pregnant dams were subjected to prenatal stress from on day 11 of gestation. Ovariectomy (OVX) or sham surgery was performed on female prenatal-stress offspring on day 56. Daily Letrozole was initiated on day 49, 2 wk prior to initiation of adult stress. Treatment was continued through the stress protocol; B: Spontaneous activity (SA) of single afferent units in male and female control rats (n = 70 fibers in 6 rats in each group, t-test, aP < 0.05); C: Average response to graded colorectal distention (CRD) of 56 afferent fibers in 6 male and 70 afferent fibers in 6 female control rats; two-way analysis of variance (ANOVA; aP < 0.05 vs the same pressure male group); D: Responses of low-threshold (LT) fibers to CRD in 42 fibers in 6 male rats and 40 fibers in 6 female control rats (ANOVA, aP < 0.05 vs the same pressure male group); E: Responses of high-threshold (HT) afferent fibers to CRD in 14 fibers in 6 male and 29 fibers in 6 female control rats; F: Effects of CAS on afferent fiber responses to CRD from 59 fibers in 6 control and 99 fibers in 6 CPS female rats; (two-way ANOVA, aP < 0.05 vs the same pressure control group, bP < 0.05 vs the same pressure CPS group); G: Effects of CAS on afferent fiber responses to CRD in control and CPS male rats (n = 6 rats, 57 fibers for control and 95 fibers for CPS female group; two-way ANOVA, aP < 0.05 vs the same pressure-control group). \\n Rat treatment Before OVX or letrozole treatment, vaginal smears were used to identify the estrus cycle phase. OVX or sham surgery was performed on female prenatal-stress offspring on day 56. The aromatase inhibitor letrozole [4,4’-(1H-1,2,4-triazol-l-yl-methylene)-bis-benzonitrile], (Novartis) 1.0 mg/kg was orally administered in the experimental group and vehicle (hydroxypropyl cellulose 0.3% in water) was given in the control group once daily for 14 d. Direct transcutaneous intrathecal injections of estrogen and letrozole were performed as described by Mestre et al[18].\\nBefore OVX or letrozole treatment, vaginal smears were used to identify the estrus cycle phase. OVX or sham surgery was performed on female prenatal-stress offspring on day 56. The aromatase inhibitor letrozole [4,4’-(1H-1,2,4-triazol-l-yl-methylene)-bis-benzonitrile], (Novartis) 1.0 mg/kg was orally administered in the experimental group and vehicle (hydroxypropyl cellulose 0.3% in water) was given in the control group once daily for 14 d. Direct transcutaneous intrathecal injections of estrogen and letrozole were performed as described by Mestre et al[18].\\n In vivo single fiber recording of L6-S2 DRG rootlets Multiunit afferent discharges were recorded from the distal ends of L6-S2 dorsal rootlets decentralized close to their entry into the spinal cord. A bundle of multiunit fibers was distinguished into 2-6 single units off-line using wave mark template matching in Spike 2 software that differentiates spikes by shape and amplitude. Colonic afferent fibers were identified by their response to graded colorectal distention (CRD). A balloon was used to distend the colorectum. Isoflurane, 2.5%, followed by 50 mg/kg intraperitoneal sodium pentobarbital induced general anesthesia that was maintained by infusing a mixture of pentobarbital sodium + pancuronium bromide + saline by intravenous infusion through the tail vein. The adequacy of anesthesia was confirmed by the absence of corneal and pupillary reflexes and stability of the end-tidal CO2 level. A tracheotomy tube connected to a ventilator system provided a mixture of room air and oxygen. Expired CO2 was monitored and maintained at 3.5%. Body temperature was monitored and maintained at 37 °C by a servo-controlled heating blanket. A laminectomy from T12 to S2 exposed the spinal cord. The head was stabilized in a stereotaxic frame.\\nMultiunit afferent discharges were recorded from the distal ends of L6-S2 dorsal rootlets decentralized close to their entry into the spinal cord. A bundle of multiunit fibers was distinguished into 2-6 single units off-line using wave mark template matching in Spike 2 software that differentiates spikes by shape and amplitude. Colonic afferent fibers were identified by their response to graded colorectal distention (CRD). A balloon was used to distend the colorectum. Isoflurane, 2.5%, followed by 50 mg/kg intraperitoneal sodium pentobarbital induced general anesthesia that was maintained by infusing a mixture of pentobarbital sodium + pancuronium bromide + saline by intravenous infusion through the tail vein. The adequacy of anesthesia was confirmed by the absence of corneal and pupillary reflexes and stability of the end-tidal CO2 level. A tracheotomy tube connected to a ventilator system provided a mixture of room air and oxygen. Expired CO2 was monitored and maintained at 3.5%. Body temperature was monitored and maintained at 37 °C by a servo-controlled heating blanket. A laminectomy from T12 to S2 exposed the spinal cord. The head was stabilized in a stereotaxic frame.\\n In vitro patch clamp recordings in colonic DRG neurons Retrograde fluorescence label injections: Labeling of colon-projecting DRG neurons was performed as previously described[13]. Under general 2% isoflurane anesthesia, the lipid soluble fluorescent dye, 1,1’-dioleyl-3,3,3’,3’-tetramethylindocarbocyanine methane-sulfonate (9-DiI, Invitrogen, Carlsbad, CA) was injected (50 mg/mL) into the muscularis externa on the exposed distal colon in 8 to 10 sites (2 μL each site). To prevent leakage, the needle was kept in place for 1 min following each injection.\\nDissociation and culture of DRG neurons: Rats were deeply anesthetized with isoflurane followed by decapitation. Lumbosacral (L6–S2) DRGs were collected in ice cold and oxygenated dissecting solution, containing (in mM) 130 NaCl, 5 KCl, 2 KH2PO4, 1.5 CaCl2, 6 MgSO4, 10 glucose, and 10 HEPES, pH 7.2 (305 mOsm). After removal of the connective tissue, the ganglia were transferred to a 5 mL dissecting solution containing collagenase D (1.8 mg/mL; Roche) and trypsin (1.0 mg/mL; Sigma, St Louis, MO), and incubated for 1.5 h at 34.5 °C. DRGs were then taken from the enzyme solution, washed, and put in 0.5-2 mL of the dissecting solution containing DNase (0.5 mg/mL; Sigma). Cells were subsequently dissociated by gentle trituration 10 to 15 times with fire-polished glass pipettes and placed on acid-cleaned glass coverslips. The dissociated DRG neurons were kept in 1 mL DMEM (with 10% FBS) in an incubator (95% O2/5% CO2) at 37 °C overnight.\\nWhole-cell patch clamp recordings from dissociated DRG neurons: Before each experiment, a glass coverslip with DRG neurons was transferred to a recording chamber perfused (1.5 mL/min) with external solution containing (10 mM): 130 NaCl, 5 KCl, 2 KH2PO4, 2.5 CaCl2, 1 MgCl2, 10 HEPES, and 10 glucose, pH adjusted to pH 7.4 with NaOH (300 mOsm) at room temperature. Recording pipettes, pulled from borosilicate glass tubing, with resistance of 1-5 MΩ, were filled with solution containing (in mM): 100 KMeSO3, 40 KCl, and 10 HEPES, pH 7.25 adjusted with KOH (290 mOsm). DiI-labeled neurons were identified by fluorescence microscopy. Whole-cell currents and voltage were recorded from DiI-labeled neurons using a Dagan 3911 patch clamp amplifier. Data were acquired and analyzed by pCLAMP 9.2 (Molecular Devices, Sunnyvale, CA). The currents were filtered at 2–5 kHz and sampled at 50 or 100 s per point. While still under voltage clamp, the Clampex Membrane Test program (Molecular Devices) was used to determine membrane capacitance (Cm) and membrane resistance (Rm), during a 10 ms, 5 mV depolarizing pulse form a holding potential of −60 mV. The configuration was then switched to current clamp (0 pA) to determine other electrophysiological properties. After stabilizing for 2–3 min, the resting membrane potential was measured. The minimum acceptable resting membrane potential was −40 mV. Spontaneous activity (SA) was then recorded over two 30 s periods separated by 60 s without recording, as described by Bedi et al[19].\\nTransient A-type K+ current (IA) recording method in patch studies: To record voltage-gated K+ current (Kv), Na+ in control external solution was replaced with equimolar choline and the Ca2+ concentration was reduced to 0.03 mM to suppress Ca2+ currents and to prevent Ca2+ channels becoming Na+ conducting. The reduced external Ca2+ would also be expected to suppress Ca2+-activated K+ currents. The current traces of Kv in DRG neurons were measured at different holding potentials. The membrane potential was held at −100 mV and voltage steps were from −40 to +30 mV to record the total Kv. The membrane potential was held at −50 mV to record the sustained Kv. The IA currents were calculated by subtracting the sustained current from the total current. The current density (in pA/pF) was calculated by dividing the current amplitude by cell membrane capacitance.\\nRetrograde fluorescence label injections: Labeling of colon-projecting DRG neurons was performed as previously described[13]. Under general 2% isoflurane anesthesia, the lipid soluble fluorescent dye, 1,1’-dioleyl-3,3,3’,3’-tetramethylindocarbocyanine methane-sulfonate (9-DiI, Invitrogen, Carlsbad, CA) was injected (50 mg/mL) into the muscularis externa on the exposed distal colon in 8 to 10 sites (2 μL each site). To prevent leakage, the needle was kept in place for 1 min following each injection.\\nDissociation and culture of DRG neurons: Rats were deeply anesthetized with isoflurane followed by decapitation. Lumbosacral (L6–S2) DRGs were collected in ice cold and oxygenated dissecting solution, containing (in mM) 130 NaCl, 5 KCl, 2 KH2PO4, 1.5 CaCl2, 6 MgSO4, 10 glucose, and 10 HEPES, pH 7.2 (305 mOsm). After removal of the connective tissue, the ganglia were transferred to a 5 mL dissecting solution containing collagenase D (1.8 mg/mL; Roche) and trypsin (1.0 mg/mL; Sigma, St Louis, MO), and incubated for 1.5 h at 34.5 °C. DRGs were then taken from the enzyme solution, washed, and put in 0.5-2 mL of the dissecting solution containing DNase (0.5 mg/mL; Sigma). Cells were subsequently dissociated by gentle trituration 10 to 15 times with fire-polished glass pipettes and placed on acid-cleaned glass coverslips. The dissociated DRG neurons were kept in 1 mL DMEM (with 10% FBS) in an incubator (95% O2/5% CO2) at 37 °C overnight.\\nWhole-cell patch clamp recordings from dissociated DRG neurons: Before each experiment, a glass coverslip with DRG neurons was transferred to a recording chamber perfused (1.5 mL/min) with external solution containing (10 mM): 130 NaCl, 5 KCl, 2 KH2PO4, 2.5 CaCl2, 1 MgCl2, 10 HEPES, and 10 glucose, pH adjusted to pH 7.4 with NaOH (300 mOsm) at room temperature. Recording pipettes, pulled from borosilicate glass tubing, with resistance of 1-5 MΩ, were filled with solution containing (in mM): 100 KMeSO3, 40 KCl, and 10 HEPES, pH 7.25 adjusted with KOH (290 mOsm). DiI-labeled neurons were identified by fluorescence microscopy. Whole-cell currents and voltage were recorded from DiI-labeled neurons using a Dagan 3911 patch clamp amplifier. Data were acquired and analyzed by pCLAMP 9.2 (Molecular Devices, Sunnyvale, CA). The currents were filtered at 2–5 kHz and sampled at 50 or 100 s per point. While still under voltage clamp, the Clampex Membrane Test program (Molecular Devices) was used to determine membrane capacitance (Cm) and membrane resistance (Rm), during a 10 ms, 5 mV depolarizing pulse form a holding potential of −60 mV. The configuration was then switched to current clamp (0 pA) to determine other electrophysiological properties. After stabilizing for 2–3 min, the resting membrane potential was measured. The minimum acceptable resting membrane potential was −40 mV. Spontaneous activity (SA) was then recorded over two 30 s periods separated by 60 s without recording, as described by Bedi et al[19].\\nTransient A-type K+ current (IA) recording method in patch studies: To record voltage-gated K+ current (Kv), Na+ in control external solution was replaced with equimolar choline and the Ca2+ concentration was reduced to 0.03 mM to suppress Ca2+ currents and to prevent Ca2+ channels becoming Na+ conducting. The reduced external Ca2+ would also be expected to suppress Ca2+-activated K+ currents. The current traces of Kv in DRG neurons were measured at different holding potentials. The membrane potential was held at −100 mV and voltage steps were from −40 to +30 mV to record the total Kv. The membrane potential was held at −50 mV to record the sustained Kv. The IA currents were calculated by subtracting the sustained current from the total current. The current density (in pA/pF) was calculated by dividing the current amplitude by cell membrane capacitance.\\n Real time reverse transcription-polymerase chain reaction (RT-PCR) Total RNA was extracted using RNeasy Mini Kits (QIAGEN, Valencia, CA). One microgram of total RNA was reverse-transcribed using the SuperScriptTM III First-Strand Synthesis System. PCR assays were performed on a StepOnePlus thermal cycler with 18 s as the normalizer using Applied Biosystems primer/probe set Rn02531967_s1 directed against the translated exon IX. Fold-change relative to control was calculated using the ΔΔCt method (Applied Biosystems).\\nTotal RNA was extracted using RNeasy Mini Kits (QIAGEN, Valencia, CA). One microgram of total RNA was reverse-transcribed using the SuperScriptTM III First-Strand Synthesis System. PCR assays were performed on a StepOnePlus thermal cycler with 18 s as the normalizer using Applied Biosystems primer/probe set Rn02531967_s1 directed against the translated exon IX. Fold-change relative to control was calculated using the ΔΔCt method (Applied Biosystems).\\n Western blot Samples were lysed in RIPA buffer containing protease inhibitor cocktail and phenylmethanesulfonyl fluoride. Lysates were incubated for 30 min on ice and then centrifuged at 10 000 × g for 10 min at 4 °C. The protein concentration in the supernatant was determined using bicinchoninic acid (BCA) assay kits with bovine serum albumin as a standard. Equal amounts of protein (30 μg per lane) were separated by 10% sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) and then transferred to nitrocellulose membranes (Bio-Rad, United States). The membranes were blocked in Li-Cor blocking buffer for 1 h at room temperature and then incubated with primary antibodies. BDNF antibody (Santa Cruz Biotechnologies, Santa Cruz, CA) was used at 1:200 dilution; nerve growth factor (NGF) antibody (Abcam, MA) was used at 1:1000 dilution; β-actin antibody (Sigma Aldrich, St Louis, MO) was used at 1:5000 dilution. The secondary antibodies were donkey anti-rabbit Alexa Fluor 680 (Invitrogen) and goat anti-mouse IRDye 800 (Rockland). Images were acquired and band intensities measured using a Li-Cor Odyssey system (Li-Cor, Lincoln, NE).\\nSamples were lysed in RIPA buffer containing protease inhibitor cocktail and phenylmethanesulfonyl fluoride. Lysates were incubated for 30 min on ice and then centrifuged at 10 000 × g for 10 min at 4 °C. The protein concentration in the supernatant was determined using bicinchoninic acid (BCA) assay kits with bovine serum albumin as a standard. Equal amounts of protein (30 μg per lane) were separated by 10% sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) and then transferred to nitrocellulose membranes (Bio-Rad, United States). The membranes were blocked in Li-Cor blocking buffer for 1 h at room temperature and then incubated with primary antibodies. BDNF antibody (Santa Cruz Biotechnologies, Santa Cruz, CA) was used at 1:200 dilution; nerve growth factor (NGF) antibody (Abcam, MA) was used at 1:1000 dilution; β-actin antibody (Sigma Aldrich, St Louis, MO) was used at 1:5000 dilution. The secondary antibodies were donkey anti-rabbit Alexa Fluor 680 (Invitrogen) and goat anti-mouse IRDye 800 (Rockland). Images were acquired and band intensities measured using a Li-Cor Odyssey system (Li-Cor, Lincoln, NE).\\n Immunofluorescence Frozen sections of colon tissue from control, CAS, CPS and CPS + CAS female rats were mounted on glass slides, and rehydrated in phosphate buffered saline at room temperature. The slides were treated for antigen retrieval and blocked with 10% normal goat serum diluted in 0.3% phosphate buffered saline-Triton for 1 h, and then incubated with NGF primary antibody in antibody diluent (Renoir Red, Biocare Medical, Concord, CA) at 4 °C overnight. The slides were exposed to fluorescent dye-conjugated secondary antibody for 2 h at room temperature, counterstained with 4',6-diamidino-2-phenylindole and coverslipped. Images were taken in fluorescence mode on an Olympus laser scanning confocal microscope and the average signal intensity was calculated by the bundled software.\\nFrozen sections of colon tissue from control, CAS, CPS and CPS + CAS female rats were mounted on glass slides, and rehydrated in phosphate buffered saline at room temperature. The slides were treated for antigen retrieval and blocked with 10% normal goat serum diluted in 0.3% phosphate buffered saline-Triton for 1 h, and then incubated with NGF primary antibody in antibody diluent (Renoir Red, Biocare Medical, Concord, CA) at 4 °C overnight. The slides were exposed to fluorescent dye-conjugated secondary antibody for 2 h at room temperature, counterstained with 4',6-diamidino-2-phenylindole and coverslipped. Images were taken in fluorescence mode on an Olympus laser scanning confocal microscope and the average signal intensity was calculated by the bundled software.\\n Serum estradiol and norepinephrine levels Serum estradiol, adrenocorticotropic hormone (ACTH), and norepinephrine levels were measured using specific enzyme-linked immunosorbent assay kits for each analyte (CSB-E05110r, CSB-E06875r, CSB-E07022, Cusabio Bioteck CO., United States) following the manufacturer’s instructions.\\nSerum estradiol, adrenocorticotropic hormone (ACTH), and norepinephrine levels were measured using specific enzyme-linked immunosorbent assay kits for each analyte (CSB-E05110r, CSB-E06875r, CSB-E07022, Cusabio Bioteck CO., United States) following the manufacturer’s instructions.\\n Data analysis Single fiber responses (impulses/second) to CRD were calculated by subtracting SA from the mean 30 s maximal activity during distension. Fibers were considered responsive if CRD increased their activity to 30% greater than the baseline value. Mechanosensitive single units were classified as high threshold (> 20 mmHg) or low threshold (≤ 20 mmHg) on the basis of their response threshold and profile during CRD. Single fiber activity data were analyzed by analysis of variance with repeated measures; CRD intensity was the repeated factor and the experimental group was the between-group factor. If significant main effects were present, the individual means were compared using the Fisher post-hoc test. All authors had access to the study data and reviewed and approved the final manuscript.\\nSingle fiber responses (impulses/second) to CRD were calculated by subtracting SA from the mean 30 s maximal activity during distension. Fibers were considered responsive if CRD increased their activity to 30% greater than the baseline value. Mechanosensitive single units were classified as high threshold (> 20 mmHg) or low threshold (≤ 20 mmHg) on the basis of their response threshold and profile during CRD. Single fiber activity data were analyzed by analysis of variance with repeated measures; CRD intensity was the repeated factor and the experimental group was the between-group factor. If significant main effects were present, the individual means were compared using the Fisher post-hoc test. All authors had access to the study data and reviewed and approved the final manuscript.\",\n \"The Institutional Animal Care and Use Committee of the University of Texas Medical Branch at Galveston, TX approved all animal procedures. Experiments were performed on pregnant Sprague Dawley rats and their 8-wk-old to 16-wk-old male and female offspring. Rats were housed individual cages with access to food and water in a room with controlled conditions (22 ± 2 °C, relative humidity of 50% ± 5%), and a 12 h light/12 h dark cycle.\",\n \"Pregnant dams were subjected to a CPS protocol that consisted of a random sequence of twice-daily applications of one of three stress sessions, a 1-h water-avoidance, 45-min cold-restraint, or a 20-min forced swim starting on day 6 and continuing until delivery on day 21. Male and female offspring of the stressed dams were designated CPS rats. Control dams received sham stress and their offspring were designated control rats. As adults at 8-16 wk of age, control and prenatally stressed offspring were challenged by the same CAS protocol for 9 d. Ovariectomy (OVX) or sham surgery was performed on female prenatal-stress offspring on day 56. Daily letrozole treatment was initiated on day 49, 2 wk prior to initiation of adult stress. Treatment was continued through the stress protocol. A schematic diagram of the study procedures is shown in Figure 1A.\\nPrimary afferent responses to colorectal distention. A: Chronic prenatal stress (CPS) plus chronic adult stress (CAS) model. Pregnant dams were subjected to prenatal stress from on day 11 of gestation. Ovariectomy (OVX) or sham surgery was performed on female prenatal-stress offspring on day 56. Daily Letrozole was initiated on day 49, 2 wk prior to initiation of adult stress. Treatment was continued through the stress protocol; B: Spontaneous activity (SA) of single afferent units in male and female control rats (n = 70 fibers in 6 rats in each group, t-test, aP < 0.05); C: Average response to graded colorectal distention (CRD) of 56 afferent fibers in 6 male and 70 afferent fibers in 6 female control rats; two-way analysis of variance (ANOVA; aP < 0.05 vs the same pressure male group); D: Responses of low-threshold (LT) fibers to CRD in 42 fibers in 6 male rats and 40 fibers in 6 female control rats (ANOVA, aP < 0.05 vs the same pressure male group); E: Responses of high-threshold (HT) afferent fibers to CRD in 14 fibers in 6 male and 29 fibers in 6 female control rats; F: Effects of CAS on afferent fiber responses to CRD from 59 fibers in 6 control and 99 fibers in 6 CPS female rats; (two-way ANOVA, aP < 0.05 vs the same pressure control group, bP < 0.05 vs the same pressure CPS group); G: Effects of CAS on afferent fiber responses to CRD in control and CPS male rats (n = 6 rats, 57 fibers for control and 95 fibers for CPS female group; two-way ANOVA, aP < 0.05 vs the same pressure-control group). \",\n \"Before OVX or letrozole treatment, vaginal smears were used to identify the estrus cycle phase. OVX or sham surgery was performed on female prenatal-stress offspring on day 56. The aromatase inhibitor letrozole [4,4’-(1H-1,2,4-triazol-l-yl-methylene)-bis-benzonitrile], (Novartis) 1.0 mg/kg was orally administered in the experimental group and vehicle (hydroxypropyl cellulose 0.3% in water) was given in the control group once daily for 14 d. Direct transcutaneous intrathecal injections of estrogen and letrozole were performed as described by Mestre et al[18].\",\n \"Multiunit afferent discharges were recorded from the distal ends of L6-S2 dorsal rootlets decentralized close to their entry into the spinal cord. A bundle of multiunit fibers was distinguished into 2-6 single units off-line using wave mark template matching in Spike 2 software that differentiates spikes by shape and amplitude. Colonic afferent fibers were identified by their response to graded colorectal distention (CRD). A balloon was used to distend the colorectum. Isoflurane, 2.5%, followed by 50 mg/kg intraperitoneal sodium pentobarbital induced general anesthesia that was maintained by infusing a mixture of pentobarbital sodium + pancuronium bromide + saline by intravenous infusion through the tail vein. The adequacy of anesthesia was confirmed by the absence of corneal and pupillary reflexes and stability of the end-tidal CO2 level. A tracheotomy tube connected to a ventilator system provided a mixture of room air and oxygen. Expired CO2 was monitored and maintained at 3.5%. Body temperature was monitored and maintained at 37 °C by a servo-controlled heating blanket. A laminectomy from T12 to S2 exposed the spinal cord. The head was stabilized in a stereotaxic frame.\",\n \"Retrograde fluorescence label injections: Labeling of colon-projecting DRG neurons was performed as previously described[13]. Under general 2% isoflurane anesthesia, the lipid soluble fluorescent dye, 1,1’-dioleyl-3,3,3’,3’-tetramethylindocarbocyanine methane-sulfonate (9-DiI, Invitrogen, Carlsbad, CA) was injected (50 mg/mL) into the muscularis externa on the exposed distal colon in 8 to 10 sites (2 μL each site). To prevent leakage, the needle was kept in place for 1 min following each injection.\\nDissociation and culture of DRG neurons: Rats were deeply anesthetized with isoflurane followed by decapitation. Lumbosacral (L6–S2) DRGs were collected in ice cold and oxygenated dissecting solution, containing (in mM) 130 NaCl, 5 KCl, 2 KH2PO4, 1.5 CaCl2, 6 MgSO4, 10 glucose, and 10 HEPES, pH 7.2 (305 mOsm). After removal of the connective tissue, the ganglia were transferred to a 5 mL dissecting solution containing collagenase D (1.8 mg/mL; Roche) and trypsin (1.0 mg/mL; Sigma, St Louis, MO), and incubated for 1.5 h at 34.5 °C. DRGs were then taken from the enzyme solution, washed, and put in 0.5-2 mL of the dissecting solution containing DNase (0.5 mg/mL; Sigma). Cells were subsequently dissociated by gentle trituration 10 to 15 times with fire-polished glass pipettes and placed on acid-cleaned glass coverslips. The dissociated DRG neurons were kept in 1 mL DMEM (with 10% FBS) in an incubator (95% O2/5% CO2) at 37 °C overnight.\\nWhole-cell patch clamp recordings from dissociated DRG neurons: Before each experiment, a glass coverslip with DRG neurons was transferred to a recording chamber perfused (1.5 mL/min) with external solution containing (10 mM): 130 NaCl, 5 KCl, 2 KH2PO4, 2.5 CaCl2, 1 MgCl2, 10 HEPES, and 10 glucose, pH adjusted to pH 7.4 with NaOH (300 mOsm) at room temperature. Recording pipettes, pulled from borosilicate glass tubing, with resistance of 1-5 MΩ, were filled with solution containing (in mM): 100 KMeSO3, 40 KCl, and 10 HEPES, pH 7.25 adjusted with KOH (290 mOsm). DiI-labeled neurons were identified by fluorescence microscopy. Whole-cell currents and voltage were recorded from DiI-labeled neurons using a Dagan 3911 patch clamp amplifier. Data were acquired and analyzed by pCLAMP 9.2 (Molecular Devices, Sunnyvale, CA). The currents were filtered at 2–5 kHz and sampled at 50 or 100 s per point. While still under voltage clamp, the Clampex Membrane Test program (Molecular Devices) was used to determine membrane capacitance (Cm) and membrane resistance (Rm), during a 10 ms, 5 mV depolarizing pulse form a holding potential of −60 mV. The configuration was then switched to current clamp (0 pA) to determine other electrophysiological properties. After stabilizing for 2–3 min, the resting membrane potential was measured. The minimum acceptable resting membrane potential was −40 mV. Spontaneous activity (SA) was then recorded over two 30 s periods separated by 60 s without recording, as described by Bedi et al[19].\\nTransient A-type K+ current (IA) recording method in patch studies: To record voltage-gated K+ current (Kv), Na+ in control external solution was replaced with equimolar choline and the Ca2+ concentration was reduced to 0.03 mM to suppress Ca2+ currents and to prevent Ca2+ channels becoming Na+ conducting. The reduced external Ca2+ would also be expected to suppress Ca2+-activated K+ currents. The current traces of Kv in DRG neurons were measured at different holding potentials. The membrane potential was held at −100 mV and voltage steps were from −40 to +30 mV to record the total Kv. The membrane potential was held at −50 mV to record the sustained Kv. The IA currents were calculated by subtracting the sustained current from the total current. The current density (in pA/pF) was calculated by dividing the current amplitude by cell membrane capacitance.\",\n \"Total RNA was extracted using RNeasy Mini Kits (QIAGEN, Valencia, CA). One microgram of total RNA was reverse-transcribed using the SuperScriptTM III First-Strand Synthesis System. PCR assays were performed on a StepOnePlus thermal cycler with 18 s as the normalizer using Applied Biosystems primer/probe set Rn02531967_s1 directed against the translated exon IX. Fold-change relative to control was calculated using the ΔΔCt method (Applied Biosystems).\",\n \"Samples were lysed in RIPA buffer containing protease inhibitor cocktail and phenylmethanesulfonyl fluoride. Lysates were incubated for 30 min on ice and then centrifuged at 10 000 × g for 10 min at 4 °C. The protein concentration in the supernatant was determined using bicinchoninic acid (BCA) assay kits with bovine serum albumin as a standard. Equal amounts of protein (30 μg per lane) were separated by 10% sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) and then transferred to nitrocellulose membranes (Bio-Rad, United States). The membranes were blocked in Li-Cor blocking buffer for 1 h at room temperature and then incubated with primary antibodies. BDNF antibody (Santa Cruz Biotechnologies, Santa Cruz, CA) was used at 1:200 dilution; nerve growth factor (NGF) antibody (Abcam, MA) was used at 1:1000 dilution; β-actin antibody (Sigma Aldrich, St Louis, MO) was used at 1:5000 dilution. The secondary antibodies were donkey anti-rabbit Alexa Fluor 680 (Invitrogen) and goat anti-mouse IRDye 800 (Rockland). Images were acquired and band intensities measured using a Li-Cor Odyssey system (Li-Cor, Lincoln, NE).\",\n \"Frozen sections of colon tissue from control, CAS, CPS and CPS + CAS female rats were mounted on glass slides, and rehydrated in phosphate buffered saline at room temperature. The slides were treated for antigen retrieval and blocked with 10% normal goat serum diluted in 0.3% phosphate buffered saline-Triton for 1 h, and then incubated with NGF primary antibody in antibody diluent (Renoir Red, Biocare Medical, Concord, CA) at 4 °C overnight. The slides were exposed to fluorescent dye-conjugated secondary antibody for 2 h at room temperature, counterstained with 4',6-diamidino-2-phenylindole and coverslipped. Images were taken in fluorescence mode on an Olympus laser scanning confocal microscope and the average signal intensity was calculated by the bundled software.\",\n \"Serum estradiol, adrenocorticotropic hormone (ACTH), and norepinephrine levels were measured using specific enzyme-linked immunosorbent assay kits for each analyte (CSB-E05110r, CSB-E06875r, CSB-E07022, Cusabio Bioteck CO., United States) following the manufacturer’s instructions.\",\n \"Single fiber responses (impulses/second) to CRD were calculated by subtracting SA from the mean 30 s maximal activity during distension. Fibers were considered responsive if CRD increased their activity to 30% greater than the baseline value. Mechanosensitive single units were classified as high threshold (> 20 mmHg) or low threshold (≤ 20 mmHg) on the basis of their response threshold and profile during CRD. Single fiber activity data were analyzed by analysis of variance with repeated measures; CRD intensity was the repeated factor and the experimental group was the between-group factor. If significant main effects were present, the individual means were compared using the Fisher post-hoc test. All authors had access to the study data and reviewed and approved the final manuscript.\",\n \" Effects of CPS plus CAS on primary afferent responses to CRD in male and female rats The basal activity of a spinal afferent fiber was defined as the average number of action potentials per second (impulses/sec) in the 60 s period before the onset of a distention stimulus. In male controls, 66% of the afferent fibers under study displayed SA and SA was significantly higher in female controls than in male controls (0.71 ± 0.21 vs 1.24 ± 0.20 imp/sec; Figure 1B). The average single fiber activity in response to CRD was significantly higher in female control rats compared with male controls (Figure 1C). We found that the enhanced sensitization in female rats mainly came from the low-threshold fibers (Figure 1D and E).\\nTo assess the effects of CPS + CAS on colon afferent fiber activities, we compared average single colon afferent fiber activities projecting from dorsal roots S1-L6 in response to CRD in male and female control, CPS, control + CAS and CPS + CAS rats recorded approximately 24 h after the last stressor. In females, CPS significantly increased single-unit afferent activity in response to CRD vs control female rats (Figure 1F). CAS alone enhanced single-unit activity compared with control. The increase in average afferent responses after CAS in prenatally stressed female rats (44.0%) was significantly greater than the increase in female control rats (39.3%). In males, CPS had no significant effect on primary afferent responses (Figure 1G). When we compared males to females within each experimental group, we found that the average single fiber activity was significantly higher in female compared with male CPS + CAS rats (Figure 1F, G). The increased activity may contribute to the enhanced female visceral hypersensitivity previously reported in this model. Average single-fiber activities were significantly greater in control and CPS and CAS females than in their corresponding male experimental groups (Figure 1F and G). Both CAS and CPS + CAS rats had significantly increased primary afferent responses compared with control and CPS rats. Thus, our CPS and CAS protocols sensitized colon-projecting primary afferent fibers, with the greatest effects produced by the combination of CPS + CAS in both males and females.\\nThe basal activity of a spinal afferent fiber was defined as the average number of action potentials per second (impulses/sec) in the 60 s period before the onset of a distention stimulus. In male controls, 66% of the afferent fibers under study displayed SA and SA was significantly higher in female controls than in male controls (0.71 ± 0.21 vs 1.24 ± 0.20 imp/sec; Figure 1B). The average single fiber activity in response to CRD was significantly higher in female control rats compared with male controls (Figure 1C). We found that the enhanced sensitization in female rats mainly came from the low-threshold fibers (Figure 1D and E).\\nTo assess the effects of CPS + CAS on colon afferent fiber activities, we compared average single colon afferent fiber activities projecting from dorsal roots S1-L6 in response to CRD in male and female control, CPS, control + CAS and CPS + CAS rats recorded approximately 24 h after the last stressor. In females, CPS significantly increased single-unit afferent activity in response to CRD vs control female rats (Figure 1F). CAS alone enhanced single-unit activity compared with control. The increase in average afferent responses after CAS in prenatally stressed female rats (44.0%) was significantly greater than the increase in female control rats (39.3%). In males, CPS had no significant effect on primary afferent responses (Figure 1G). When we compared males to females within each experimental group, we found that the average single fiber activity was significantly higher in female compared with male CPS + CAS rats (Figure 1F, G). The increased activity may contribute to the enhanced female visceral hypersensitivity previously reported in this model. Average single-fiber activities were significantly greater in control and CPS and CAS females than in their corresponding male experimental groups (Figure 1F and G). Both CAS and CPS + CAS rats had significantly increased primary afferent responses compared with control and CPS rats. Thus, our CPS and CAS protocols sensitized colon-projecting primary afferent fibers, with the greatest effects produced by the combination of CPS + CAS in both males and females.\\n Increase in excitability of colon-projecting lumbosacral DRG neurons in female CPS + CAS rats To elucidate the electrophysiological basis of enhanced stress-induced primary afferent activity in female rats, we performed patch clamp studies on acutely dissociated retrograde-labeled colon-projecting neurons from the L6-S2 DRGs in control, prenatal stress, adult stress only, and CPS + CAS female rats isolated 24 h after the last adult stressor (Figure 2A). Input resistance (Figure 2B) and rheobase (Figure 2C) were significantly decreased in neurons from CPS + CAS rats compared with the other three groups. The number of action potentials elicited at either 2 × or 3 × the rheobase were significantly greater in adult stress and CPS + CAS neurons compared with control and to CPS neurons (Figure 2D, E). CAS significantly increased action potential overshoot with or without CPS (Figure 2F), but it did not significantly alter other electrophysiological characteristics, such as number of spontaneous spikes, membrane capacitance (pF), resting membrane potential, cell diameter, time constant, and DRG neuron action potential amplitude and duration (Table 1).\\nPatch clamp recording in colonic dorsal root ganglion neurons from female rats. A: Patch clamp process of cell labeling. Under isoflurane anesthesia, the lipid soluble fluorescent dye 9-DiI was injected into muscularis externa of the exposed distal colon (left figure). Lumbosacral (L6–S2) dorsal root ganglions (upper photograph) were isolated and DiI-labeled neurons were identified by fluorescence microscopy (lower photograph). Electrophysiological properties of each neuron were measured using whole-cell current and voltage clamp protocols (right figure); B: Rheobase from all four experimental groups (n = 5 rats, 45 cells in each group, one-way ANOVA, aP < 0.05 vs control or bP < 0.05 vs CAS); C: Representative action potentials (APs) elicited by current injection at 2 × the rheobase in neurons from control, chronic adult stress (CAS), chronic prenatal stress (CPS) and CPS + CAS female rats; D: Membrane input resistance from all four groups (n = 5 rats, 45 cells in each group, one-way ANOVA, aP < 0.05 vs control; bP < 0.05 vs CPS); E: Number of APs elicited by current injection at either 2 × and 3 × the rheobase in all four experimental groups (two-way ANOVA, aP < 0.05 vs control; bP < 0.05 vs CPS); F: AP overshoot recorded from all four experimental groups (aP < 0.05 vs control); G: The proportion of neurons from each experimental group exhibiting spontaneous APs. Red numbers represent spontaneous AP firing cells; black numbers represent total cells; H: Representative total, IK and IA current tracings and average values of potassium currents: Itotal, IK and IA are shown in female CPS + CAS, CAS, CPS (n = 15 neurons, from 5 rats in each group), and control groups (n = 12 neurons from 5 rats); two-way ANOVA, aP < 0.05 vs each control group. \\nElectrophysiological characteristics of colon related DRG neuron\\nValues means ± standard error. \\nP < 0.05;\\nP < 0.001 vs control group. CAS: Chronic adult stress; CPS: Chronic prenatal stress.\\nThe percentage of neurons with SA in was significantly greater in CPS + CAS rats than in control or CPS only rats (Figure 2G). Under voltage clamp conditions (Figure 2H), neurons from female CPS + CAS, CAS, CPS and control groups had IA and sustained outward rectifier K+ currents (IK). Compared with the other three groups, DRG neurons from CPS + CAS rats had significantly reduced average IA (P < 0.05). The average IK density was decreased but the change was not significant.\\nTo elucidate the electrophysiological basis of enhanced stress-induced primary afferent activity in female rats, we performed patch clamp studies on acutely dissociated retrograde-labeled colon-projecting neurons from the L6-S2 DRGs in control, prenatal stress, adult stress only, and CPS + CAS female rats isolated 24 h after the last adult stressor (Figure 2A). Input resistance (Figure 2B) and rheobase (Figure 2C) were significantly decreased in neurons from CPS + CAS rats compared with the other three groups. The number of action potentials elicited at either 2 × or 3 × the rheobase were significantly greater in adult stress and CPS + CAS neurons compared with control and to CPS neurons (Figure 2D, E). CAS significantly increased action potential overshoot with or without CPS (Figure 2F), but it did not significantly alter other electrophysiological characteristics, such as number of spontaneous spikes, membrane capacitance (pF), resting membrane potential, cell diameter, time constant, and DRG neuron action potential amplitude and duration (Table 1).\\nPatch clamp recording in colonic dorsal root ganglion neurons from female rats. A: Patch clamp process of cell labeling. Under isoflurane anesthesia, the lipid soluble fluorescent dye 9-DiI was injected into muscularis externa of the exposed distal colon (left figure). Lumbosacral (L6–S2) dorsal root ganglions (upper photograph) were isolated and DiI-labeled neurons were identified by fluorescence microscopy (lower photograph). Electrophysiological properties of each neuron were measured using whole-cell current and voltage clamp protocols (right figure); B: Rheobase from all four experimental groups (n = 5 rats, 45 cells in each group, one-way ANOVA, aP < 0.05 vs control or bP < 0.05 vs CAS); C: Representative action potentials (APs) elicited by current injection at 2 × the rheobase in neurons from control, chronic adult stress (CAS), chronic prenatal stress (CPS) and CPS + CAS female rats; D: Membrane input resistance from all four groups (n = 5 rats, 45 cells in each group, one-way ANOVA, aP < 0.05 vs control; bP < 0.05 vs CPS); E: Number of APs elicited by current injection at either 2 × and 3 × the rheobase in all four experimental groups (two-way ANOVA, aP < 0.05 vs control; bP < 0.05 vs CPS); F: AP overshoot recorded from all four experimental groups (aP < 0.05 vs control); G: The proportion of neurons from each experimental group exhibiting spontaneous APs. Red numbers represent spontaneous AP firing cells; black numbers represent total cells; H: Representative total, IK and IA current tracings and average values of potassium currents: Itotal, IK and IA are shown in female CPS + CAS, CAS, CPS (n = 15 neurons, from 5 rats in each group), and control groups (n = 12 neurons from 5 rats); two-way ANOVA, aP < 0.05 vs each control group. \\nElectrophysiological characteristics of colon related DRG neuron\\nValues means ± standard error. \\nP < 0.05;\\nP < 0.001 vs control group. CAS: Chronic adult stress; CPS: Chronic prenatal stress.\\nThe percentage of neurons with SA in was significantly greater in CPS + CAS rats than in control or CPS only rats (Figure 2G). Under voltage clamp conditions (Figure 2H), neurons from female CPS + CAS, CAS, CPS and control groups had IA and sustained outward rectifier K+ currents (IK). Compared with the other three groups, DRG neurons from CPS + CAS rats had significantly reduced average IA (P < 0.05). The average IK density was decreased but the change was not significant.\\n Effects of CPS and/or CAS on plasma estrogen concentration We did a vaginal smear test to identify the estrus cycle phases by identifying the vaginal cytological cell types. Estrogen concentration was significantly higher in the CPS proestrus/estrus phase compared with control diestrus, control proestrus/estrus, and CPS diestrus proestrus (P < 0.05; Figure 3A). Comparison of the plasma estrogen concentrations in control, CAS, CPS, CPS + CAS showed that CPS significantly increased plasma estrogen levels compared with the control rats and that CAS increased plasma estrogen level compared with the control and CPS rats (Figure 3B).\\nEffects of chronic prenatal stress, chronic adult stress, ovariectomy, and letrozole treatment on plasma estrogen levels in female rats. A: Plasma estrogen level in control and chronic prenatal stress (CPS) rats by estrus cycle phase (n = 8 rats, one-way ANOVA, aP < 0.05 vs control proestrus/estrus (P-E) phase; bP < 0.05 vs CPS diestrus (D) phase); B: Plasma estrogen levels increased in CPS rats and following chronic adult stress (CAS) 24 h after the last adult stressor (n = 8 rats, one-way ANOVA, aP < 0.05 vs control; bP < 0.05 vs CPS); C: Ovariectomy (OVX) significantly reduced CPS female rat plasma estrogen levels before and after CAS (n = 5 rats, one-way ANOVA, aP < 0.05 vs sham group); D: Letrozole treatment significantly reduced CPS female rat plasma estrogen levels before or after CAS (n = 5 rats, one-way ANOVA, aP < 0.05 vs vehicle group; cP < 0.0001); E: Plasma norepinephrine levels from control, CAS, CPS and CPS + CAS group female rats (n = 5 rats, one-way ANOVA, aP < 0.05 vs control; bP < 0.05 vs CPS); F: Plasma adrenocorticotropic hormone (ACTH) levels from control and CPS + CAS group female rats (n = 5 rats, t-test, aP < 0.05 vs control). \\nTo determine whether estrogen contributed to stress-induced visceral hypersensitivity in prenatal stressed females, we reduced plasma estrogen levels by either OVX or letrozole treatment. OVX significantly lowered serum estradiol levels before and after CAS (Figure 3C). Treatment was continued throughout CAS. After treatment with letrozole, serum estradiol levels were significantly reduced (Figure 3D). To study the effects of gender and stress on norepinephrine and ACTH levels, we measured plasma norepinephrine levels in female rats from all four experimental groups. CAS alone significantly increased plasma norepinephrine levels compared with both the controls and with CPS alone (Figure 3E) Plasma norepinephrine levels were significantly increased in CPS + CAS rats compared with CAS alone as well as with controls and CPS. Plasma ACTH levels were significantly increased in CPS + CAS rats compared with controls. (Figure 3F).\\nWe did a vaginal smear test to identify the estrus cycle phases by identifying the vaginal cytological cell types. Estrogen concentration was significantly higher in the CPS proestrus/estrus phase compared with control diestrus, control proestrus/estrus, and CPS diestrus proestrus (P < 0.05; Figure 3A). Comparison of the plasma estrogen concentrations in control, CAS, CPS, CPS + CAS showed that CPS significantly increased plasma estrogen levels compared with the control rats and that CAS increased plasma estrogen level compared with the control and CPS rats (Figure 3B).\\nEffects of chronic prenatal stress, chronic adult stress, ovariectomy, and letrozole treatment on plasma estrogen levels in female rats. A: Plasma estrogen level in control and chronic prenatal stress (CPS) rats by estrus cycle phase (n = 8 rats, one-way ANOVA, aP < 0.05 vs control proestrus/estrus (P-E) phase; bP < 0.05 vs CPS diestrus (D) phase); B: Plasma estrogen levels increased in CPS rats and following chronic adult stress (CAS) 24 h after the last adult stressor (n = 8 rats, one-way ANOVA, aP < 0.05 vs control; bP < 0.05 vs CPS); C: Ovariectomy (OVX) significantly reduced CPS female rat plasma estrogen levels before and after CAS (n = 5 rats, one-way ANOVA, aP < 0.05 vs sham group); D: Letrozole treatment significantly reduced CPS female rat plasma estrogen levels before or after CAS (n = 5 rats, one-way ANOVA, aP < 0.05 vs vehicle group; cP < 0.0001); E: Plasma norepinephrine levels from control, CAS, CPS and CPS + CAS group female rats (n = 5 rats, one-way ANOVA, aP < 0.05 vs control; bP < 0.05 vs CPS); F: Plasma adrenocorticotropic hormone (ACTH) levels from control and CPS + CAS group female rats (n = 5 rats, t-test, aP < 0.05 vs control). \\nTo determine whether estrogen contributed to stress-induced visceral hypersensitivity in prenatal stressed females, we reduced plasma estrogen levels by either OVX or letrozole treatment. OVX significantly lowered serum estradiol levels before and after CAS (Figure 3C). Treatment was continued throughout CAS. After treatment with letrozole, serum estradiol levels were significantly reduced (Figure 3D). To study the effects of gender and stress on norepinephrine and ACTH levels, we measured plasma norepinephrine levels in female rats from all four experimental groups. CAS alone significantly increased plasma norepinephrine levels compared with both the controls and with CPS alone (Figure 3E) Plasma norepinephrine levels were significantly increased in CPS + CAS rats compared with CAS alone as well as with controls and CPS. Plasma ACTH levels were significantly increased in CPS + CAS rats compared with controls. (Figure 3F).\\n Effects of Letrozole treatment on colon DRG neuron excitability We performed patch clamp experiments on acutely isolated retrograde-labeled DRG neurons from CPS + CAS females with or without letrozole treatment 24 h after the last adult stressor. Letrozole treatment significantly increased rheobase (Figure 4A), and significantly reduced input resistance (Figure 4B). Action potential overshoot (Figure 4C) and the number of action potentials elicited by a current injection at either 2 × or 3 × rheobase were significantly reduced by letrozole treatment (Figure 4D). Other electrophysiological properties were not significantly altered (Table 2). We also recorded electromyographic activity to determine whether the reduction in visceral sensitivity in female CPS + CAS rats caused by OVX or systemic letrozole treatment reduced visceromotor responses. The findings demonstrated a significant decrease in excitability of colon-projecting L6-S2 neurons.\\nEffects of Letrozole treatment on colon dorsal root ganglion neuron excitability. A: Rheobase (n = 45 cells in 6 rats in each group, t-test, cP < 0.001 vs Veh. + chronic adult stress [CAS] + chronic prenatal stress [CPS]); B: Membrane input resistance (RIn) (t-test, aP < 0.05); C: Action potential (AP) overshoot (t-test, aP < 0.05); D: Number of APs elicited by current injection at 2 × and 3 × rheobase (two-way ANOVA, aP < 0.05; bP < 0.01). \\nElectrophysiological characteristics of colon related DRG neuron after Letrozole treatment\\nValues are means ± standard error. \\nP < 0.05;\\nP < 0.001 vs control group. CAS: Chronic adult stress; CPS: Chronic prenatal stress.\\nWe performed patch clamp experiments on acutely isolated retrograde-labeled DRG neurons from CPS + CAS females with or without letrozole treatment 24 h after the last adult stressor. Letrozole treatment significantly increased rheobase (Figure 4A), and significantly reduced input resistance (Figure 4B). Action potential overshoot (Figure 4C) and the number of action potentials elicited by a current injection at either 2 × or 3 × rheobase were significantly reduced by letrozole treatment (Figure 4D). Other electrophysiological properties were not significantly altered (Table 2). We also recorded electromyographic activity to determine whether the reduction in visceral sensitivity in female CPS + CAS rats caused by OVX or systemic letrozole treatment reduced visceromotor responses. The findings demonstrated a significant decrease in excitability of colon-projecting L6-S2 neurons.\\nEffects of Letrozole treatment on colon dorsal root ganglion neuron excitability. A: Rheobase (n = 45 cells in 6 rats in each group, t-test, cP < 0.001 vs Veh. + chronic adult stress [CAS] + chronic prenatal stress [CPS]); B: Membrane input resistance (RIn) (t-test, aP < 0.05); C: Action potential (AP) overshoot (t-test, aP < 0.05); D: Number of APs elicited by current injection at 2 × and 3 × rheobase (two-way ANOVA, aP < 0.05; bP < 0.01). \\nElectrophysiological characteristics of colon related DRG neuron after Letrozole treatment\\nValues are means ± standard error. \\nP < 0.05;\\nP < 0.001 vs control group. CAS: Chronic adult stress; CPS: Chronic prenatal stress.\\n Spinal cord BDNF levels regulated by estrogen To investigate the effect of estrogen on BDNF expression, we measured BDNF mRNA and protein levels in the lumbar-sacral spinal cords of OVX and Sham CPS + CAS female rats. Systemic estradiol administration to naïve cycling females produced significant increases in plasma estrogen (Figure 5A), lumbar-sacral spinal cord BDNF mRNA (Figure 5B), and protein (Figure 5C). We also measured BDNF mRNA and protein levels in the lumbar-sacral spinal cords of OVX and Sham CPS + CAS female rats. BDNF mRNA and protein expression were significantly suppressed by OVX compared with sham rats. Another experiment showed that intrathecal infusion of estrogen into naïve female rats significantly increased BDNF protein levels, which proved that estrogen reversed the experimental results and contributed to the response to visceral pain[13].\\nBrain-derived neurotrophic factor expression in lumbar-sacral spinal cord is regulated by estrogen. A: Plasma estrogen levels in cycling females that received a bolus estradiol (E2) infusion on day 1; B: Lumbar-sacral spinal cord brain-derived neurotrophic factor (BDNF) mRNA following bolus estrogen infusion; C: Lumbar-sacral spinal cord BDNF protein following bolus estrogen infusion. (n = 8 rats in each group, two-way ANOVA, aP < 0.05 vs vehicle group). \\nTo investigate the effect of estrogen on BDNF expression, we measured BDNF mRNA and protein levels in the lumbar-sacral spinal cords of OVX and Sham CPS + CAS female rats. Systemic estradiol administration to naïve cycling females produced significant increases in plasma estrogen (Figure 5A), lumbar-sacral spinal cord BDNF mRNA (Figure 5B), and protein (Figure 5C). We also measured BDNF mRNA and protein levels in the lumbar-sacral spinal cords of OVX and Sham CPS + CAS female rats. BDNF mRNA and protein expression were significantly suppressed by OVX compared with sham rats. Another experiment showed that intrathecal infusion of estrogen into naïve female rats significantly increased BDNF protein levels, which proved that estrogen reversed the experimental results and contributed to the response to visceral pain[13].\\nBrain-derived neurotrophic factor expression in lumbar-sacral spinal cord is regulated by estrogen. A: Plasma estrogen levels in cycling females that received a bolus estradiol (E2) infusion on day 1; B: Lumbar-sacral spinal cord brain-derived neurotrophic factor (BDNF) mRNA following bolus estrogen infusion; C: Lumbar-sacral spinal cord BDNF protein following bolus estrogen infusion. (n = 8 rats in each group, two-way ANOVA, aP < 0.05 vs vehicle group). \\n Peripheral NGF level increased in CPS + CAS female rats We examined NGF expression in the colons of females from all four experimental groups by immunohistochemistry (Figure 6A). Morphometric analysis showed that CAS and CPS + CAS significantly increased NGF levels in the colon wall, with the increase in CPS + CAS significantly greater that of CAS alone (Figure 6B). Western blotting showed that NGF protein was significantly upregulated in CPS + CAS rats compared with controls (Figure 6C).\\nNerve growth factor expression level in the colon wall. A: Immunohistochemical staining of nerve growth factor (NGF; green) was detected with nuclear counterstaining staining (blue) in controls, chronic adult stress (CAS), chronic prenatal stress (CPS) and CPS + CAS group female rat colon walls. × 400 magnification representative pictures were shown; B: Quantification of NGF levels from colon wall in immunohistochemistry (IHC) (n = 4 rats in each group, one-way ANOVA, aP < 0.05 vs control group; bP < 0.05 vs CPS group); C: Western blots of NGF protein from control and CPS + CAS female rats colon wall tissue (n = 6 rats in each group, t-test, aP < 0.05 vs control group). \\nWe examined NGF expression in the colons of females from all four experimental groups by immunohistochemistry (Figure 6A). Morphometric analysis showed that CAS and CPS + CAS significantly increased NGF levels in the colon wall, with the increase in CPS + CAS significantly greater that of CAS alone (Figure 6B). Western blotting showed that NGF protein was significantly upregulated in CPS + CAS rats compared with controls (Figure 6C).\\nNerve growth factor expression level in the colon wall. A: Immunohistochemical staining of nerve growth factor (NGF; green) was detected with nuclear counterstaining staining (blue) in controls, chronic adult stress (CAS), chronic prenatal stress (CPS) and CPS + CAS group female rat colon walls. × 400 magnification representative pictures were shown; B: Quantification of NGF levels from colon wall in immunohistochemistry (IHC) (n = 4 rats in each group, one-way ANOVA, aP < 0.05 vs control group; bP < 0.05 vs CPS group); C: Western blots of NGF protein from control and CPS + CAS female rats colon wall tissue (n = 6 rats in each group, t-test, aP < 0.05 vs control group). \",\n \"The basal activity of a spinal afferent fiber was defined as the average number of action potentials per second (impulses/sec) in the 60 s period before the onset of a distention stimulus. In male controls, 66% of the afferent fibers under study displayed SA and SA was significantly higher in female controls than in male controls (0.71 ± 0.21 vs 1.24 ± 0.20 imp/sec; Figure 1B). The average single fiber activity in response to CRD was significantly higher in female control rats compared with male controls (Figure 1C). We found that the enhanced sensitization in female rats mainly came from the low-threshold fibers (Figure 1D and E).\\nTo assess the effects of CPS + CAS on colon afferent fiber activities, we compared average single colon afferent fiber activities projecting from dorsal roots S1-L6 in response to CRD in male and female control, CPS, control + CAS and CPS + CAS rats recorded approximately 24 h after the last stressor. In females, CPS significantly increased single-unit afferent activity in response to CRD vs control female rats (Figure 1F). CAS alone enhanced single-unit activity compared with control. The increase in average afferent responses after CAS in prenatally stressed female rats (44.0%) was significantly greater than the increase in female control rats (39.3%). In males, CPS had no significant effect on primary afferent responses (Figure 1G). When we compared males to females within each experimental group, we found that the average single fiber activity was significantly higher in female compared with male CPS + CAS rats (Figure 1F, G). The increased activity may contribute to the enhanced female visceral hypersensitivity previously reported in this model. Average single-fiber activities were significantly greater in control and CPS and CAS females than in their corresponding male experimental groups (Figure 1F and G). Both CAS and CPS + CAS rats had significantly increased primary afferent responses compared with control and CPS rats. Thus, our CPS and CAS protocols sensitized colon-projecting primary afferent fibers, with the greatest effects produced by the combination of CPS + CAS in both males and females.\",\n \"To elucidate the electrophysiological basis of enhanced stress-induced primary afferent activity in female rats, we performed patch clamp studies on acutely dissociated retrograde-labeled colon-projecting neurons from the L6-S2 DRGs in control, prenatal stress, adult stress only, and CPS + CAS female rats isolated 24 h after the last adult stressor (Figure 2A). Input resistance (Figure 2B) and rheobase (Figure 2C) were significantly decreased in neurons from CPS + CAS rats compared with the other three groups. The number of action potentials elicited at either 2 × or 3 × the rheobase were significantly greater in adult stress and CPS + CAS neurons compared with control and to CPS neurons (Figure 2D, E). CAS significantly increased action potential overshoot with or without CPS (Figure 2F), but it did not significantly alter other electrophysiological characteristics, such as number of spontaneous spikes, membrane capacitance (pF), resting membrane potential, cell diameter, time constant, and DRG neuron action potential amplitude and duration (Table 1).\\nPatch clamp recording in colonic dorsal root ganglion neurons from female rats. A: Patch clamp process of cell labeling. Under isoflurane anesthesia, the lipid soluble fluorescent dye 9-DiI was injected into muscularis externa of the exposed distal colon (left figure). Lumbosacral (L6–S2) dorsal root ganglions (upper photograph) were isolated and DiI-labeled neurons were identified by fluorescence microscopy (lower photograph). Electrophysiological properties of each neuron were measured using whole-cell current and voltage clamp protocols (right figure); B: Rheobase from all four experimental groups (n = 5 rats, 45 cells in each group, one-way ANOVA, aP < 0.05 vs control or bP < 0.05 vs CAS); C: Representative action potentials (APs) elicited by current injection at 2 × the rheobase in neurons from control, chronic adult stress (CAS), chronic prenatal stress (CPS) and CPS + CAS female rats; D: Membrane input resistance from all four groups (n = 5 rats, 45 cells in each group, one-way ANOVA, aP < 0.05 vs control; bP < 0.05 vs CPS); E: Number of APs elicited by current injection at either 2 × and 3 × the rheobase in all four experimental groups (two-way ANOVA, aP < 0.05 vs control; bP < 0.05 vs CPS); F: AP overshoot recorded from all four experimental groups (aP < 0.05 vs control); G: The proportion of neurons from each experimental group exhibiting spontaneous APs. Red numbers represent spontaneous AP firing cells; black numbers represent total cells; H: Representative total, IK and IA current tracings and average values of potassium currents: Itotal, IK and IA are shown in female CPS + CAS, CAS, CPS (n = 15 neurons, from 5 rats in each group), and control groups (n = 12 neurons from 5 rats); two-way ANOVA, aP < 0.05 vs each control group. \\nElectrophysiological characteristics of colon related DRG neuron\\nValues means ± standard error. \\nP < 0.05;\\nP < 0.001 vs control group. CAS: Chronic adult stress; CPS: Chronic prenatal stress.\\nThe percentage of neurons with SA in was significantly greater in CPS + CAS rats than in control or CPS only rats (Figure 2G). Under voltage clamp conditions (Figure 2H), neurons from female CPS + CAS, CAS, CPS and control groups had IA and sustained outward rectifier K+ currents (IK). Compared with the other three groups, DRG neurons from CPS + CAS rats had significantly reduced average IA (P < 0.05). The average IK density was decreased but the change was not significant.\",\n \"We did a vaginal smear test to identify the estrus cycle phases by identifying the vaginal cytological cell types. Estrogen concentration was significantly higher in the CPS proestrus/estrus phase compared with control diestrus, control proestrus/estrus, and CPS diestrus proestrus (P < 0.05; Figure 3A). Comparison of the plasma estrogen concentrations in control, CAS, CPS, CPS + CAS showed that CPS significantly increased plasma estrogen levels compared with the control rats and that CAS increased plasma estrogen level compared with the control and CPS rats (Figure 3B).\\nEffects of chronic prenatal stress, chronic adult stress, ovariectomy, and letrozole treatment on plasma estrogen levels in female rats. A: Plasma estrogen level in control and chronic prenatal stress (CPS) rats by estrus cycle phase (n = 8 rats, one-way ANOVA, aP < 0.05 vs control proestrus/estrus (P-E) phase; bP < 0.05 vs CPS diestrus (D) phase); B: Plasma estrogen levels increased in CPS rats and following chronic adult stress (CAS) 24 h after the last adult stressor (n = 8 rats, one-way ANOVA, aP < 0.05 vs control; bP < 0.05 vs CPS); C: Ovariectomy (OVX) significantly reduced CPS female rat plasma estrogen levels before and after CAS (n = 5 rats, one-way ANOVA, aP < 0.05 vs sham group); D: Letrozole treatment significantly reduced CPS female rat plasma estrogen levels before or after CAS (n = 5 rats, one-way ANOVA, aP < 0.05 vs vehicle group; cP < 0.0001); E: Plasma norepinephrine levels from control, CAS, CPS and CPS + CAS group female rats (n = 5 rats, one-way ANOVA, aP < 0.05 vs control; bP < 0.05 vs CPS); F: Plasma adrenocorticotropic hormone (ACTH) levels from control and CPS + CAS group female rats (n = 5 rats, t-test, aP < 0.05 vs control). \\nTo determine whether estrogen contributed to stress-induced visceral hypersensitivity in prenatal stressed females, we reduced plasma estrogen levels by either OVX or letrozole treatment. OVX significantly lowered serum estradiol levels before and after CAS (Figure 3C). Treatment was continued throughout CAS. After treatment with letrozole, serum estradiol levels were significantly reduced (Figure 3D). To study the effects of gender and stress on norepinephrine and ACTH levels, we measured plasma norepinephrine levels in female rats from all four experimental groups. CAS alone significantly increased plasma norepinephrine levels compared with both the controls and with CPS alone (Figure 3E) Plasma norepinephrine levels were significantly increased in CPS + CAS rats compared with CAS alone as well as with controls and CPS. Plasma ACTH levels were significantly increased in CPS + CAS rats compared with controls. (Figure 3F).\",\n \"We performed patch clamp experiments on acutely isolated retrograde-labeled DRG neurons from CPS + CAS females with or without letrozole treatment 24 h after the last adult stressor. Letrozole treatment significantly increased rheobase (Figure 4A), and significantly reduced input resistance (Figure 4B). Action potential overshoot (Figure 4C) and the number of action potentials elicited by a current injection at either 2 × or 3 × rheobase were significantly reduced by letrozole treatment (Figure 4D). Other electrophysiological properties were not significantly altered (Table 2). We also recorded electromyographic activity to determine whether the reduction in visceral sensitivity in female CPS + CAS rats caused by OVX or systemic letrozole treatment reduced visceromotor responses. The findings demonstrated a significant decrease in excitability of colon-projecting L6-S2 neurons.\\nEffects of Letrozole treatment on colon dorsal root ganglion neuron excitability. A: Rheobase (n = 45 cells in 6 rats in each group, t-test, cP < 0.001 vs Veh. + chronic adult stress [CAS] + chronic prenatal stress [CPS]); B: Membrane input resistance (RIn) (t-test, aP < 0.05); C: Action potential (AP) overshoot (t-test, aP < 0.05); D: Number of APs elicited by current injection at 2 × and 3 × rheobase (two-way ANOVA, aP < 0.05; bP < 0.01). \\nElectrophysiological characteristics of colon related DRG neuron after Letrozole treatment\\nValues are means ± standard error. \\nP < 0.05;\\nP < 0.001 vs control group. CAS: Chronic adult stress; CPS: Chronic prenatal stress.\",\n \"To investigate the effect of estrogen on BDNF expression, we measured BDNF mRNA and protein levels in the lumbar-sacral spinal cords of OVX and Sham CPS + CAS female rats. Systemic estradiol administration to naïve cycling females produced significant increases in plasma estrogen (Figure 5A), lumbar-sacral spinal cord BDNF mRNA (Figure 5B), and protein (Figure 5C). We also measured BDNF mRNA and protein levels in the lumbar-sacral spinal cords of OVX and Sham CPS + CAS female rats. BDNF mRNA and protein expression were significantly suppressed by OVX compared with sham rats. Another experiment showed that intrathecal infusion of estrogen into naïve female rats significantly increased BDNF protein levels, which proved that estrogen reversed the experimental results and contributed to the response to visceral pain[13].\\nBrain-derived neurotrophic factor expression in lumbar-sacral spinal cord is regulated by estrogen. A: Plasma estrogen levels in cycling females that received a bolus estradiol (E2) infusion on day 1; B: Lumbar-sacral spinal cord brain-derived neurotrophic factor (BDNF) mRNA following bolus estrogen infusion; C: Lumbar-sacral spinal cord BDNF protein following bolus estrogen infusion. (n = 8 rats in each group, two-way ANOVA, aP < 0.05 vs vehicle group). \",\n \"We examined NGF expression in the colons of females from all four experimental groups by immunohistochemistry (Figure 6A). Morphometric analysis showed that CAS and CPS + CAS significantly increased NGF levels in the colon wall, with the increase in CPS + CAS significantly greater that of CAS alone (Figure 6B). Western blotting showed that NGF protein was significantly upregulated in CPS + CAS rats compared with controls (Figure 6C).\\nNerve growth factor expression level in the colon wall. A: Immunohistochemical staining of nerve growth factor (NGF; green) was detected with nuclear counterstaining staining (blue) in controls, chronic adult stress (CAS), chronic prenatal stress (CPS) and CPS + CAS group female rat colon walls. × 400 magnification representative pictures were shown; B: Quantification of NGF levels from colon wall in immunohistochemistry (IHC) (n = 4 rats in each group, one-way ANOVA, aP < 0.05 vs control group; bP < 0.05 vs CPS group); C: Western blots of NGF protein from control and CPS + CAS female rats colon wall tissue (n = 6 rats in each group, t-test, aP < 0.05 vs control group). \",\n \"Enhanced CPS-induced visceral hypersensitivity in female rats was associated with an increase in the responses of lumbosacral nerve fibers to CRD in both male and female offspring. These findings are further supported by data showing increased excitability of colon-projecting DRG neurons from females in patch clamp studies. The magnitude of the sensitization was the greatest in female CPS + CAS rats, suggesting that it made a major contribution to the observed enhanced female visceral hypersensitivity in our model.\\nChronic stress is known to increase the excitability of colon-projecting DRG neurons in rats and mice. In adult male Sprague Dawley rats, colon DRG neuron sensitization was shown to be driven by increases in NGF expression in the colon muscularis externa[13]. In our model, we also observed a significant increase in colon NGF, but its potential role in primary afferent sensitization and visceral hypersensitivity was not investigated.. Other studies in male mice showed that stress, in the form of water avoidance, significantly increased the excitability of colon-projecting DRG neurons and that the combined activity of the stress mediators corticosterone and norepinephrine increased DRG neuron excitability in vitro[20,21]. We previously found significant increases in the serum levels of norepinephrine in CPS + CAS females. However, daily systemic treatment with adrenergic antagonists during the adult stress protocol failed to reduce visceral hypersensitivity in female neonatal + adult stress rats[13] suggesting that norepinephrine did not play a major role in the acquisition of enhanced female visceral hypersensitivity or primary afferent sensitization in our model.\\nWhen we tested on lumbar-sacral afferent fibers and dissociated neurons in patch clamp studies, we found significant decreases in transient potassium IA currents in neurons isolated from CPS + CAS females compared with the other three experimental groups. Declines in A-type Kv currents in DRG neurons have been associated with persistent pain in multiple chronic pain models[22]. Whether the decline was caused by changes in channel properties or expression was not investigated in this study. However, another study demonstrated that estrogen significantly shifted the activation curve of IA currents in the hyperpolarizing direction and that estrogen inhibited Kv (+) channels in mouse DRG neurons through a membrane ER-activated nongenomic pathway[23].\\nOur results showed that the excitability of colon-projecting neurons in CPS + CAS females was significantly reduced by systemic letrozole treatment, suggesting that estrogen contributed to the sensitization process. Previous studies show that estrogen receptors expressed on primary afferent neurons contributed to enhanced sensitivity in various pain models[24-26]. One study found no decline in the responses of colon-projecting nerve fibers to CRD following OVX and found no detectable estrogen receptor alpha immunoreactivity in colon-projecting DRG neurons[27]. The reasons for the differing results are not clear, but local production of estrogen in DRG neurons could be sufficient to sustain sensitization.\\nNGF and its receptors play important roles in the mechanism of visceral pain and hyperalgesia in women. For example, endometriosis is estrogen dependent and is commonly diagnosed. The main symptoms are various types of pelvic pain that have a serious effect on physical and mental health, but the mechanisms of abdominal pain are still unclear. Studies have shown NGF to be an inflammatory mediator and modulator of pain in adulthood[28].\",\n \"In this study, we examined the sex differences and effects of estrogen on the acquisition of enhanced visceral hypersensitivity in the offspring of rats in a model of prenatal and adult stress as shown in Figure 7. Our study shows that estrogen acted in the spinal cord and the primary afferent neurons to enhance visceral nociception. Acute blockade of the endogenous synthesis of estrogens in rat spinal cord significantly reduced visceral hypersensitivity, suggesting that locally produced estrogen in the central nervous system can regulate nociceptive neurons to modulate visceral hypersensitivity. The chronic stress-estrogen-BDNF axis sensitized visceral hypersensitivity in the offspring of females subjected to CPS. The development of chronic stress-induced visceral hypersensitivity in female rats was estrogen dependent. A key component of this hypersensitivity was estrogen-dependent sensitization of primary afferent colon neurons. Our findings provide key scientific evidence in a preclinical model in support of developing gender-based treatment for abdominal pain in IBS.\\nSummary diagram of estrogen re-enhanced visceral hyperalgesia investigated in chronic prenatal stress plus chronic adult stress models. BDNF: Brain-derived neurotrophic factor; NGF: Nerve growth factor.\"\n]"},"all_sections_sec_types":{"kind":"list like","value":[null,"methods",null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null],"string":"[\n null,\n \"methods\",\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null\n]"},"keywords":{"kind":"list like","value":["Chronic prenatal stress","Estrogen","Visceral pain","Neuronal sensitization","Excitability","Letrozole"],"string":"[\n \"Chronic prenatal stress\",\n \"Estrogen\",\n \"Visceral pain\",\n \"Neuronal sensitization\",\n \"Excitability\",\n \"Letrozole\"\n]"},"whole_article_text":{"kind":"string","value":"INTRODUCTION:\nVisceral pain of colonic origin is the most prominent symptom in irritable bowel syndrome (IBS) patients[1]. Female IBS patients report more severe pain that occurs more frequently and with longer episodes than in male patients[1,2]. The ratio of female to male IBS is about 2:1 among patients seen in medical clinics[3]. Moreover, females have a higher prevalence of IBS co-morbidities such as anxiety and depression[4,5] and are more vulnerable to stress-induced exacerbation of IBS symptoms compared with males[3,6,7].\nClinical studies show that early life adverse experiences are risk factors for the development of IBS symptoms, including visceral pain and ongoing chronic stress, especially abdominal pain[8-10]. These factors contribute to the development of visceral hypersensitivity, a key component of the IBS symptom complex and one that may be responsible for symptoms of pain[11,12]. Our previous research found that the female offspring of mothers subjected to chronic prenatal stress (CPS) had a markedly greater visceral sensitivity than their male littermates following challenge by another chronic adult stress (CAS) protocol. A critical molecular event in the development of this female-enhanced visceral hypersensitivity is upregulation of brain-derived neurotrophic factor (BDNF) expression in the lumbar-sacral spinal cord of female CPS + CAS rats[13]. However, the neurophysiological changes underlying the enhanced female-specific visceral hypersensitivity and the role of hormone in the development of stress-induced visceral hypersensitivity are not well understood.\nVisceral hypersensitivity in IBS involves abnormal changes in neurophysiology throughout the brain-gut axis. In IBS, there is evidence for sensitization of primary afferents to jejunal distention and electrical stimulation[14], and there is evidence for increased sensitivity of lumbar splanchnic afferents[15,16]. In animal models of either early life adverse events or adult stress-induced visceral hypersensitivity[17], there is evidence of colon primary afferent sensitization. However, the studies were performed in male rodents. Therefore, in this study, we established a CPS and CAS rodent model to analyze the impact on female colon afferent neuron function and the role of estrogen. Our hypothesis was that female CPS offspring subjected to chronic stress as adults would exhibit greater colonic dorsal root ganglion (DRG) neuron sensitization compared with their male littermates, and that the enhanced visceral sensitization and primary afferent sensitization in females was estrogen dependent.\n\nMATERIALS AND METHODS:\n Animals The Institutional Animal Care and Use Committee of the University of Texas Medical Branch at Galveston, TX approved all animal procedures. Experiments were performed on pregnant Sprague Dawley rats and their 8-wk-old to 16-wk-old male and female offspring. Rats were housed individual cages with access to food and water in a room with controlled conditions (22 ± 2 °C, relative humidity of 50% ± 5%), and a 12 h light/12 h dark cycle.\nThe Institutional Animal Care and Use Committee of the University of Texas Medical Branch at Galveston, TX approved all animal procedures. Experiments were performed on pregnant Sprague Dawley rats and their 8-wk-old to 16-wk-old male and female offspring. Rats were housed individual cages with access to food and water in a room with controlled conditions (22 ± 2 °C, relative humidity of 50% ± 5%), and a 12 h light/12 h dark cycle.\n CPS and CAS models Pregnant dams were subjected to a CPS protocol that consisted of a random sequence of twice-daily applications of one of three stress sessions, a 1-h water-avoidance, 45-min cold-restraint, or a 20-min forced swim starting on day 6 and continuing until delivery on day 21. Male and female offspring of the stressed dams were designated CPS rats. Control dams received sham stress and their offspring were designated control rats. As adults at 8-16 wk of age, control and prenatally stressed offspring were challenged by the same CAS protocol for 9 d. Ovariectomy (OVX) or sham surgery was performed on female prenatal-stress offspring on day 56. Daily letrozole treatment was initiated on day 49, 2 wk prior to initiation of adult stress. Treatment was continued through the stress protocol. A schematic diagram of the study procedures is shown in Figure 1A.\nPrimary afferent responses to colorectal distention. A: Chronic prenatal stress (CPS) plus chronic adult stress (CAS) model. Pregnant dams were subjected to prenatal stress from on day 11 of gestation. Ovariectomy (OVX) or sham surgery was performed on female prenatal-stress offspring on day 56. Daily Letrozole was initiated on day 49, 2 wk prior to initiation of adult stress. Treatment was continued through the stress protocol; B: Spontaneous activity (SA) of single afferent units in male and female control rats (n = 70 fibers in 6 rats in each group, t-test, aP < 0.05); C: Average response to graded colorectal distention (CRD) of 56 afferent fibers in 6 male and 70 afferent fibers in 6 female control rats; two-way analysis of variance (ANOVA; aP < 0.05 vs the same pressure male group); D: Responses of low-threshold (LT) fibers to CRD in 42 fibers in 6 male rats and 40 fibers in 6 female control rats (ANOVA, aP < 0.05 vs the same pressure male group); E: Responses of high-threshold (HT) afferent fibers to CRD in 14 fibers in 6 male and 29 fibers in 6 female control rats; F: Effects of CAS on afferent fiber responses to CRD from 59 fibers in 6 control and 99 fibers in 6 CPS female rats; (two-way ANOVA, aP < 0.05 vs the same pressure control group, bP < 0.05 vs the same pressure CPS group); G: Effects of CAS on afferent fiber responses to CRD in control and CPS male rats (n = 6 rats, 57 fibers for control and 95 fibers for CPS female group; two-way ANOVA, aP < 0.05 vs the same pressure-control group). \nPregnant dams were subjected to a CPS protocol that consisted of a random sequence of twice-daily applications of one of three stress sessions, a 1-h water-avoidance, 45-min cold-restraint, or a 20-min forced swim starting on day 6 and continuing until delivery on day 21. Male and female offspring of the stressed dams were designated CPS rats. Control dams received sham stress and their offspring were designated control rats. As adults at 8-16 wk of age, control and prenatally stressed offspring were challenged by the same CAS protocol for 9 d. Ovariectomy (OVX) or sham surgery was performed on female prenatal-stress offspring on day 56. Daily letrozole treatment was initiated on day 49, 2 wk prior to initiation of adult stress. Treatment was continued through the stress protocol. A schematic diagram of the study procedures is shown in Figure 1A.\nPrimary afferent responses to colorectal distention. A: Chronic prenatal stress (CPS) plus chronic adult stress (CAS) model. Pregnant dams were subjected to prenatal stress from on day 11 of gestation. Ovariectomy (OVX) or sham surgery was performed on female prenatal-stress offspring on day 56. Daily Letrozole was initiated on day 49, 2 wk prior to initiation of adult stress. Treatment was continued through the stress protocol; B: Spontaneous activity (SA) of single afferent units in male and female control rats (n = 70 fibers in 6 rats in each group, t-test, aP < 0.05); C: Average response to graded colorectal distention (CRD) of 56 afferent fibers in 6 male and 70 afferent fibers in 6 female control rats; two-way analysis of variance (ANOVA; aP < 0.05 vs the same pressure male group); D: Responses of low-threshold (LT) fibers to CRD in 42 fibers in 6 male rats and 40 fibers in 6 female control rats (ANOVA, aP < 0.05 vs the same pressure male group); E: Responses of high-threshold (HT) afferent fibers to CRD in 14 fibers in 6 male and 29 fibers in 6 female control rats; F: Effects of CAS on afferent fiber responses to CRD from 59 fibers in 6 control and 99 fibers in 6 CPS female rats; (two-way ANOVA, aP < 0.05 vs the same pressure control group, bP < 0.05 vs the same pressure CPS group); G: Effects of CAS on afferent fiber responses to CRD in control and CPS male rats (n = 6 rats, 57 fibers for control and 95 fibers for CPS female group; two-way ANOVA, aP < 0.05 vs the same pressure-control group). \n Rat treatment Before OVX or letrozole treatment, vaginal smears were used to identify the estrus cycle phase. OVX or sham surgery was performed on female prenatal-stress offspring on day 56. The aromatase inhibitor letrozole [4,4’-(1H-1,2,4-triazol-l-yl-methylene)-bis-benzonitrile], (Novartis) 1.0 mg/kg was orally administered in the experimental group and vehicle (hydroxypropyl cellulose 0.3% in water) was given in the control group once daily for 14 d. Direct transcutaneous intrathecal injections of estrogen and letrozole were performed as described by Mestre et al[18].\nBefore OVX or letrozole treatment, vaginal smears were used to identify the estrus cycle phase. OVX or sham surgery was performed on female prenatal-stress offspring on day 56. The aromatase inhibitor letrozole [4,4’-(1H-1,2,4-triazol-l-yl-methylene)-bis-benzonitrile], (Novartis) 1.0 mg/kg was orally administered in the experimental group and vehicle (hydroxypropyl cellulose 0.3% in water) was given in the control group once daily for 14 d. Direct transcutaneous intrathecal injections of estrogen and letrozole were performed as described by Mestre et al[18].\n In vivo single fiber recording of L6-S2 DRG rootlets Multiunit afferent discharges were recorded from the distal ends of L6-S2 dorsal rootlets decentralized close to their entry into the spinal cord. A bundle of multiunit fibers was distinguished into 2-6 single units off-line using wave mark template matching in Spike 2 software that differentiates spikes by shape and amplitude. Colonic afferent fibers were identified by their response to graded colorectal distention (CRD). A balloon was used to distend the colorectum. Isoflurane, 2.5%, followed by 50 mg/kg intraperitoneal sodium pentobarbital induced general anesthesia that was maintained by infusing a mixture of pentobarbital sodium + pancuronium bromide + saline by intravenous infusion through the tail vein. The adequacy of anesthesia was confirmed by the absence of corneal and pupillary reflexes and stability of the end-tidal CO2 level. A tracheotomy tube connected to a ventilator system provided a mixture of room air and oxygen. Expired CO2 was monitored and maintained at 3.5%. Body temperature was monitored and maintained at 37 °C by a servo-controlled heating blanket. A laminectomy from T12 to S2 exposed the spinal cord. The head was stabilized in a stereotaxic frame.\nMultiunit afferent discharges were recorded from the distal ends of L6-S2 dorsal rootlets decentralized close to their entry into the spinal cord. A bundle of multiunit fibers was distinguished into 2-6 single units off-line using wave mark template matching in Spike 2 software that differentiates spikes by shape and amplitude. Colonic afferent fibers were identified by their response to graded colorectal distention (CRD). A balloon was used to distend the colorectum. Isoflurane, 2.5%, followed by 50 mg/kg intraperitoneal sodium pentobarbital induced general anesthesia that was maintained by infusing a mixture of pentobarbital sodium + pancuronium bromide + saline by intravenous infusion through the tail vein. The adequacy of anesthesia was confirmed by the absence of corneal and pupillary reflexes and stability of the end-tidal CO2 level. A tracheotomy tube connected to a ventilator system provided a mixture of room air and oxygen. Expired CO2 was monitored and maintained at 3.5%. Body temperature was monitored and maintained at 37 °C by a servo-controlled heating blanket. A laminectomy from T12 to S2 exposed the spinal cord. The head was stabilized in a stereotaxic frame.\n In vitro patch clamp recordings in colonic DRG neurons Retrograde fluorescence label injections: Labeling of colon-projecting DRG neurons was performed as previously described[13]. Under general 2% isoflurane anesthesia, the lipid soluble fluorescent dye, 1,1’-dioleyl-3,3,3’,3’-tetramethylindocarbocyanine methane-sulfonate (9-DiI, Invitrogen, Carlsbad, CA) was injected (50 mg/mL) into the muscularis externa on the exposed distal colon in 8 to 10 sites (2 μL each site). To prevent leakage, the needle was kept in place for 1 min following each injection.\nDissociation and culture of DRG neurons: Rats were deeply anesthetized with isoflurane followed by decapitation. Lumbosacral (L6–S2) DRGs were collected in ice cold and oxygenated dissecting solution, containing (in mM) 130 NaCl, 5 KCl, 2 KH2PO4, 1.5 CaCl2, 6 MgSO4, 10 glucose, and 10 HEPES, pH 7.2 (305 mOsm). After removal of the connective tissue, the ganglia were transferred to a 5 mL dissecting solution containing collagenase D (1.8 mg/mL; Roche) and trypsin (1.0 mg/mL; Sigma, St Louis, MO), and incubated for 1.5 h at 34.5 °C. DRGs were then taken from the enzyme solution, washed, and put in 0.5-2 mL of the dissecting solution containing DNase (0.5 mg/mL; Sigma). Cells were subsequently dissociated by gentle trituration 10 to 15 times with fire-polished glass pipettes and placed on acid-cleaned glass coverslips. The dissociated DRG neurons were kept in 1 mL DMEM (with 10% FBS) in an incubator (95% O2/5% CO2) at 37 °C overnight.\nWhole-cell patch clamp recordings from dissociated DRG neurons: Before each experiment, a glass coverslip with DRG neurons was transferred to a recording chamber perfused (1.5 mL/min) with external solution containing (10 mM): 130 NaCl, 5 KCl, 2 KH2PO4, 2.5 CaCl2, 1 MgCl2, 10 HEPES, and 10 glucose, pH adjusted to pH 7.4 with NaOH (300 mOsm) at room temperature. Recording pipettes, pulled from borosilicate glass tubing, with resistance of 1-5 MΩ, were filled with solution containing (in mM): 100 KMeSO3, 40 KCl, and 10 HEPES, pH 7.25 adjusted with KOH (290 mOsm). DiI-labeled neurons were identified by fluorescence microscopy. Whole-cell currents and voltage were recorded from DiI-labeled neurons using a Dagan 3911 patch clamp amplifier. Data were acquired and analyzed by pCLAMP 9.2 (Molecular Devices, Sunnyvale, CA). The currents were filtered at 2–5 kHz and sampled at 50 or 100 s per point. While still under voltage clamp, the Clampex Membrane Test program (Molecular Devices) was used to determine membrane capacitance (Cm) and membrane resistance (Rm), during a 10 ms, 5 mV depolarizing pulse form a holding potential of −60 mV. The configuration was then switched to current clamp (0 pA) to determine other electrophysiological properties. After stabilizing for 2–3 min, the resting membrane potential was measured. The minimum acceptable resting membrane potential was −40 mV. Spontaneous activity (SA) was then recorded over two 30 s periods separated by 60 s without recording, as described by Bedi et al[19].\nTransient A-type K+ current (IA) recording method in patch studies: To record voltage-gated K+ current (Kv), Na+ in control external solution was replaced with equimolar choline and the Ca2+ concentration was reduced to 0.03 mM to suppress Ca2+ currents and to prevent Ca2+ channels becoming Na+ conducting. The reduced external Ca2+ would also be expected to suppress Ca2+-activated K+ currents. The current traces of Kv in DRG neurons were measured at different holding potentials. The membrane potential was held at −100 mV and voltage steps were from −40 to +30 mV to record the total Kv. The membrane potential was held at −50 mV to record the sustained Kv. The IA currents were calculated by subtracting the sustained current from the total current. The current density (in pA/pF) was calculated by dividing the current amplitude by cell membrane capacitance.\nRetrograde fluorescence label injections: Labeling of colon-projecting DRG neurons was performed as previously described[13]. Under general 2% isoflurane anesthesia, the lipid soluble fluorescent dye, 1,1’-dioleyl-3,3,3’,3’-tetramethylindocarbocyanine methane-sulfonate (9-DiI, Invitrogen, Carlsbad, CA) was injected (50 mg/mL) into the muscularis externa on the exposed distal colon in 8 to 10 sites (2 μL each site). To prevent leakage, the needle was kept in place for 1 min following each injection.\nDissociation and culture of DRG neurons: Rats were deeply anesthetized with isoflurane followed by decapitation. Lumbosacral (L6–S2) DRGs were collected in ice cold and oxygenated dissecting solution, containing (in mM) 130 NaCl, 5 KCl, 2 KH2PO4, 1.5 CaCl2, 6 MgSO4, 10 glucose, and 10 HEPES, pH 7.2 (305 mOsm). After removal of the connective tissue, the ganglia were transferred to a 5 mL dissecting solution containing collagenase D (1.8 mg/mL; Roche) and trypsin (1.0 mg/mL; Sigma, St Louis, MO), and incubated for 1.5 h at 34.5 °C. DRGs were then taken from the enzyme solution, washed, and put in 0.5-2 mL of the dissecting solution containing DNase (0.5 mg/mL; Sigma). Cells were subsequently dissociated by gentle trituration 10 to 15 times with fire-polished glass pipettes and placed on acid-cleaned glass coverslips. The dissociated DRG neurons were kept in 1 mL DMEM (with 10% FBS) in an incubator (95% O2/5% CO2) at 37 °C overnight.\nWhole-cell patch clamp recordings from dissociated DRG neurons: Before each experiment, a glass coverslip with DRG neurons was transferred to a recording chamber perfused (1.5 mL/min) with external solution containing (10 mM): 130 NaCl, 5 KCl, 2 KH2PO4, 2.5 CaCl2, 1 MgCl2, 10 HEPES, and 10 glucose, pH adjusted to pH 7.4 with NaOH (300 mOsm) at room temperature. Recording pipettes, pulled from borosilicate glass tubing, with resistance of 1-5 MΩ, were filled with solution containing (in mM): 100 KMeSO3, 40 KCl, and 10 HEPES, pH 7.25 adjusted with KOH (290 mOsm). DiI-labeled neurons were identified by fluorescence microscopy. Whole-cell currents and voltage were recorded from DiI-labeled neurons using a Dagan 3911 patch clamp amplifier. Data were acquired and analyzed by pCLAMP 9.2 (Molecular Devices, Sunnyvale, CA). The currents were filtered at 2–5 kHz and sampled at 50 or 100 s per point. While still under voltage clamp, the Clampex Membrane Test program (Molecular Devices) was used to determine membrane capacitance (Cm) and membrane resistance (Rm), during a 10 ms, 5 mV depolarizing pulse form a holding potential of −60 mV. The configuration was then switched to current clamp (0 pA) to determine other electrophysiological properties. After stabilizing for 2–3 min, the resting membrane potential was measured. The minimum acceptable resting membrane potential was −40 mV. Spontaneous activity (SA) was then recorded over two 30 s periods separated by 60 s without recording, as described by Bedi et al[19].\nTransient A-type K+ current (IA) recording method in patch studies: To record voltage-gated K+ current (Kv), Na+ in control external solution was replaced with equimolar choline and the Ca2+ concentration was reduced to 0.03 mM to suppress Ca2+ currents and to prevent Ca2+ channels becoming Na+ conducting. The reduced external Ca2+ would also be expected to suppress Ca2+-activated K+ currents. The current traces of Kv in DRG neurons were measured at different holding potentials. The membrane potential was held at −100 mV and voltage steps were from −40 to +30 mV to record the total Kv. The membrane potential was held at −50 mV to record the sustained Kv. The IA currents were calculated by subtracting the sustained current from the total current. The current density (in pA/pF) was calculated by dividing the current amplitude by cell membrane capacitance.\n Real time reverse transcription-polymerase chain reaction (RT-PCR) Total RNA was extracted using RNeasy Mini Kits (QIAGEN, Valencia, CA). One microgram of total RNA was reverse-transcribed using the SuperScriptTM III First-Strand Synthesis System. PCR assays were performed on a StepOnePlus thermal cycler with 18 s as the normalizer using Applied Biosystems primer/probe set Rn02531967_s1 directed against the translated exon IX. Fold-change relative to control was calculated using the ΔΔCt method (Applied Biosystems).\nTotal RNA was extracted using RNeasy Mini Kits (QIAGEN, Valencia, CA). One microgram of total RNA was reverse-transcribed using the SuperScriptTM III First-Strand Synthesis System. PCR assays were performed on a StepOnePlus thermal cycler with 18 s as the normalizer using Applied Biosystems primer/probe set Rn02531967_s1 directed against the translated exon IX. Fold-change relative to control was calculated using the ΔΔCt method (Applied Biosystems).\n Western blot Samples were lysed in RIPA buffer containing protease inhibitor cocktail and phenylmethanesulfonyl fluoride. Lysates were incubated for 30 min on ice and then centrifuged at 10 000 × g for 10 min at 4 °C. The protein concentration in the supernatant was determined using bicinchoninic acid (BCA) assay kits with bovine serum albumin as a standard. Equal amounts of protein (30 μg per lane) were separated by 10% sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) and then transferred to nitrocellulose membranes (Bio-Rad, United States). The membranes were blocked in Li-Cor blocking buffer for 1 h at room temperature and then incubated with primary antibodies. BDNF antibody (Santa Cruz Biotechnologies, Santa Cruz, CA) was used at 1:200 dilution; nerve growth factor (NGF) antibody (Abcam, MA) was used at 1:1000 dilution; β-actin antibody (Sigma Aldrich, St Louis, MO) was used at 1:5000 dilution. The secondary antibodies were donkey anti-rabbit Alexa Fluor 680 (Invitrogen) and goat anti-mouse IRDye 800 (Rockland). Images were acquired and band intensities measured using a Li-Cor Odyssey system (Li-Cor, Lincoln, NE).\nSamples were lysed in RIPA buffer containing protease inhibitor cocktail and phenylmethanesulfonyl fluoride. Lysates were incubated for 30 min on ice and then centrifuged at 10 000 × g for 10 min at 4 °C. The protein concentration in the supernatant was determined using bicinchoninic acid (BCA) assay kits with bovine serum albumin as a standard. Equal amounts of protein (30 μg per lane) were separated by 10% sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) and then transferred to nitrocellulose membranes (Bio-Rad, United States). The membranes were blocked in Li-Cor blocking buffer for 1 h at room temperature and then incubated with primary antibodies. BDNF antibody (Santa Cruz Biotechnologies, Santa Cruz, CA) was used at 1:200 dilution; nerve growth factor (NGF) antibody (Abcam, MA) was used at 1:1000 dilution; β-actin antibody (Sigma Aldrich, St Louis, MO) was used at 1:5000 dilution. The secondary antibodies were donkey anti-rabbit Alexa Fluor 680 (Invitrogen) and goat anti-mouse IRDye 800 (Rockland). Images were acquired and band intensities measured using a Li-Cor Odyssey system (Li-Cor, Lincoln, NE).\n Immunofluorescence Frozen sections of colon tissue from control, CAS, CPS and CPS + CAS female rats were mounted on glass slides, and rehydrated in phosphate buffered saline at room temperature. The slides were treated for antigen retrieval and blocked with 10% normal goat serum diluted in 0.3% phosphate buffered saline-Triton for 1 h, and then incubated with NGF primary antibody in antibody diluent (Renoir Red, Biocare Medical, Concord, CA) at 4 °C overnight. The slides were exposed to fluorescent dye-conjugated secondary antibody for 2 h at room temperature, counterstained with 4',6-diamidino-2-phenylindole and coverslipped. Images were taken in fluorescence mode on an Olympus laser scanning confocal microscope and the average signal intensity was calculated by the bundled software.\nFrozen sections of colon tissue from control, CAS, CPS and CPS + CAS female rats were mounted on glass slides, and rehydrated in phosphate buffered saline at room temperature. The slides were treated for antigen retrieval and blocked with 10% normal goat serum diluted in 0.3% phosphate buffered saline-Triton for 1 h, and then incubated with NGF primary antibody in antibody diluent (Renoir Red, Biocare Medical, Concord, CA) at 4 °C overnight. The slides were exposed to fluorescent dye-conjugated secondary antibody for 2 h at room temperature, counterstained with 4',6-diamidino-2-phenylindole and coverslipped. Images were taken in fluorescence mode on an Olympus laser scanning confocal microscope and the average signal intensity was calculated by the bundled software.\n Serum estradiol and norepinephrine levels Serum estradiol, adrenocorticotropic hormone (ACTH), and norepinephrine levels were measured using specific enzyme-linked immunosorbent assay kits for each analyte (CSB-E05110r, CSB-E06875r, CSB-E07022, Cusabio Bioteck CO., United States) following the manufacturer’s instructions.\nSerum estradiol, adrenocorticotropic hormone (ACTH), and norepinephrine levels were measured using specific enzyme-linked immunosorbent assay kits for each analyte (CSB-E05110r, CSB-E06875r, CSB-E07022, Cusabio Bioteck CO., United States) following the manufacturer’s instructions.\n Data analysis Single fiber responses (impulses/second) to CRD were calculated by subtracting SA from the mean 30 s maximal activity during distension. Fibers were considered responsive if CRD increased their activity to 30% greater than the baseline value. Mechanosensitive single units were classified as high threshold (> 20 mmHg) or low threshold (≤ 20 mmHg) on the basis of their response threshold and profile during CRD. Single fiber activity data were analyzed by analysis of variance with repeated measures; CRD intensity was the repeated factor and the experimental group was the between-group factor. If significant main effects were present, the individual means were compared using the Fisher post-hoc test. All authors had access to the study data and reviewed and approved the final manuscript.\nSingle fiber responses (impulses/second) to CRD were calculated by subtracting SA from the mean 30 s maximal activity during distension. Fibers were considered responsive if CRD increased their activity to 30% greater than the baseline value. Mechanosensitive single units were classified as high threshold (> 20 mmHg) or low threshold (≤ 20 mmHg) on the basis of their response threshold and profile during CRD. Single fiber activity data were analyzed by analysis of variance with repeated measures; CRD intensity was the repeated factor and the experimental group was the between-group factor. If significant main effects were present, the individual means were compared using the Fisher post-hoc test. All authors had access to the study data and reviewed and approved the final manuscript.\n\nAnimals:\nThe Institutional Animal Care and Use Committee of the University of Texas Medical Branch at Galveston, TX approved all animal procedures. Experiments were performed on pregnant Sprague Dawley rats and their 8-wk-old to 16-wk-old male and female offspring. Rats were housed individual cages with access to food and water in a room with controlled conditions (22 ± 2 °C, relative humidity of 50% ± 5%), and a 12 h light/12 h dark cycle.\n\nCPS and CAS models:\nPregnant dams were subjected to a CPS protocol that consisted of a random sequence of twice-daily applications of one of three stress sessions, a 1-h water-avoidance, 45-min cold-restraint, or a 20-min forced swim starting on day 6 and continuing until delivery on day 21. Male and female offspring of the stressed dams were designated CPS rats. Control dams received sham stress and their offspring were designated control rats. As adults at 8-16 wk of age, control and prenatally stressed offspring were challenged by the same CAS protocol for 9 d. Ovariectomy (OVX) or sham surgery was performed on female prenatal-stress offspring on day 56. Daily letrozole treatment was initiated on day 49, 2 wk prior to initiation of adult stress. Treatment was continued through the stress protocol. A schematic diagram of the study procedures is shown in Figure 1A.\nPrimary afferent responses to colorectal distention. A: Chronic prenatal stress (CPS) plus chronic adult stress (CAS) model. Pregnant dams were subjected to prenatal stress from on day 11 of gestation. Ovariectomy (OVX) or sham surgery was performed on female prenatal-stress offspring on day 56. Daily Letrozole was initiated on day 49, 2 wk prior to initiation of adult stress. Treatment was continued through the stress protocol; B: Spontaneous activity (SA) of single afferent units in male and female control rats (n = 70 fibers in 6 rats in each group, t-test, aP < 0.05); C: Average response to graded colorectal distention (CRD) of 56 afferent fibers in 6 male and 70 afferent fibers in 6 female control rats; two-way analysis of variance (ANOVA; aP < 0.05 vs the same pressure male group); D: Responses of low-threshold (LT) fibers to CRD in 42 fibers in 6 male rats and 40 fibers in 6 female control rats (ANOVA, aP < 0.05 vs the same pressure male group); E: Responses of high-threshold (HT) afferent fibers to CRD in 14 fibers in 6 male and 29 fibers in 6 female control rats; F: Effects of CAS on afferent fiber responses to CRD from 59 fibers in 6 control and 99 fibers in 6 CPS female rats; (two-way ANOVA, aP < 0.05 vs the same pressure control group, bP < 0.05 vs the same pressure CPS group); G: Effects of CAS on afferent fiber responses to CRD in control and CPS male rats (n = 6 rats, 57 fibers for control and 95 fibers for CPS female group; two-way ANOVA, aP < 0.05 vs the same pressure-control group). \n\nRat treatment:\nBefore OVX or letrozole treatment, vaginal smears were used to identify the estrus cycle phase. OVX or sham surgery was performed on female prenatal-stress offspring on day 56. The aromatase inhibitor letrozole [4,4’-(1H-1,2,4-triazol-l-yl-methylene)-bis-benzonitrile], (Novartis) 1.0 mg/kg was orally administered in the experimental group and vehicle (hydroxypropyl cellulose 0.3% in water) was given in the control group once daily for 14 d. Direct transcutaneous intrathecal injections of estrogen and letrozole were performed as described by Mestre et al[18].\n\nIn vivo single fiber recording of L6-S2 DRG rootlets:\nMultiunit afferent discharges were recorded from the distal ends of L6-S2 dorsal rootlets decentralized close to their entry into the spinal cord. A bundle of multiunit fibers was distinguished into 2-6 single units off-line using wave mark template matching in Spike 2 software that differentiates spikes by shape and amplitude. Colonic afferent fibers were identified by their response to graded colorectal distention (CRD). A balloon was used to distend the colorectum. Isoflurane, 2.5%, followed by 50 mg/kg intraperitoneal sodium pentobarbital induced general anesthesia that was maintained by infusing a mixture of pentobarbital sodium + pancuronium bromide + saline by intravenous infusion through the tail vein. The adequacy of anesthesia was confirmed by the absence of corneal and pupillary reflexes and stability of the end-tidal CO2 level. A tracheotomy tube connected to a ventilator system provided a mixture of room air and oxygen. Expired CO2 was monitored and maintained at 3.5%. Body temperature was monitored and maintained at 37 °C by a servo-controlled heating blanket. A laminectomy from T12 to S2 exposed the spinal cord. The head was stabilized in a stereotaxic frame.\n\nIn vitro patch clamp recordings in colonic DRG neurons:\nRetrograde fluorescence label injections: Labeling of colon-projecting DRG neurons was performed as previously described[13]. Under general 2% isoflurane anesthesia, the lipid soluble fluorescent dye, 1,1’-dioleyl-3,3,3’,3’-tetramethylindocarbocyanine methane-sulfonate (9-DiI, Invitrogen, Carlsbad, CA) was injected (50 mg/mL) into the muscularis externa on the exposed distal colon in 8 to 10 sites (2 μL each site). To prevent leakage, the needle was kept in place for 1 min following each injection.\nDissociation and culture of DRG neurons: Rats were deeply anesthetized with isoflurane followed by decapitation. Lumbosacral (L6–S2) DRGs were collected in ice cold and oxygenated dissecting solution, containing (in mM) 130 NaCl, 5 KCl, 2 KH2PO4, 1.5 CaCl2, 6 MgSO4, 10 glucose, and 10 HEPES, pH 7.2 (305 mOsm). After removal of the connective tissue, the ganglia were transferred to a 5 mL dissecting solution containing collagenase D (1.8 mg/mL; Roche) and trypsin (1.0 mg/mL; Sigma, St Louis, MO), and incubated for 1.5 h at 34.5 °C. DRGs were then taken from the enzyme solution, washed, and put in 0.5-2 mL of the dissecting solution containing DNase (0.5 mg/mL; Sigma). Cells were subsequently dissociated by gentle trituration 10 to 15 times with fire-polished glass pipettes and placed on acid-cleaned glass coverslips. The dissociated DRG neurons were kept in 1 mL DMEM (with 10% FBS) in an incubator (95% O2/5% CO2) at 37 °C overnight.\nWhole-cell patch clamp recordings from dissociated DRG neurons: Before each experiment, a glass coverslip with DRG neurons was transferred to a recording chamber perfused (1.5 mL/min) with external solution containing (10 mM): 130 NaCl, 5 KCl, 2 KH2PO4, 2.5 CaCl2, 1 MgCl2, 10 HEPES, and 10 glucose, pH adjusted to pH 7.4 with NaOH (300 mOsm) at room temperature. Recording pipettes, pulled from borosilicate glass tubing, with resistance of 1-5 MΩ, were filled with solution containing (in mM): 100 KMeSO3, 40 KCl, and 10 HEPES, pH 7.25 adjusted with KOH (290 mOsm). DiI-labeled neurons were identified by fluorescence microscopy. Whole-cell currents and voltage were recorded from DiI-labeled neurons using a Dagan 3911 patch clamp amplifier. Data were acquired and analyzed by pCLAMP 9.2 (Molecular Devices, Sunnyvale, CA). The currents were filtered at 2–5 kHz and sampled at 50 or 100 s per point. While still under voltage clamp, the Clampex Membrane Test program (Molecular Devices) was used to determine membrane capacitance (Cm) and membrane resistance (Rm), during a 10 ms, 5 mV depolarizing pulse form a holding potential of −60 mV. The configuration was then switched to current clamp (0 pA) to determine other electrophysiological properties. After stabilizing for 2–3 min, the resting membrane potential was measured. The minimum acceptable resting membrane potential was −40 mV. Spontaneous activity (SA) was then recorded over two 30 s periods separated by 60 s without recording, as described by Bedi et al[19].\nTransient A-type K+ current (IA) recording method in patch studies: To record voltage-gated K+ current (Kv), Na+ in control external solution was replaced with equimolar choline and the Ca2+ concentration was reduced to 0.03 mM to suppress Ca2+ currents and to prevent Ca2+ channels becoming Na+ conducting. The reduced external Ca2+ would also be expected to suppress Ca2+-activated K+ currents. The current traces of Kv in DRG neurons were measured at different holding potentials. The membrane potential was held at −100 mV and voltage steps were from −40 to +30 mV to record the total Kv. The membrane potential was held at −50 mV to record the sustained Kv. The IA currents were calculated by subtracting the sustained current from the total current. The current density (in pA/pF) was calculated by dividing the current amplitude by cell membrane capacitance.\n\nReal time reverse transcription-polymerase chain reaction (RT-PCR):\nTotal RNA was extracted using RNeasy Mini Kits (QIAGEN, Valencia, CA). One microgram of total RNA was reverse-transcribed using the SuperScriptTM III First-Strand Synthesis System. PCR assays were performed on a StepOnePlus thermal cycler with 18 s as the normalizer using Applied Biosystems primer/probe set Rn02531967_s1 directed against the translated exon IX. Fold-change relative to control was calculated using the ΔΔCt method (Applied Biosystems).\n\nWestern blot:\nSamples were lysed in RIPA buffer containing protease inhibitor cocktail and phenylmethanesulfonyl fluoride. Lysates were incubated for 30 min on ice and then centrifuged at 10 000 × g for 10 min at 4 °C. The protein concentration in the supernatant was determined using bicinchoninic acid (BCA) assay kits with bovine serum albumin as a standard. Equal amounts of protein (30 μg per lane) were separated by 10% sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) and then transferred to nitrocellulose membranes (Bio-Rad, United States). The membranes were blocked in Li-Cor blocking buffer for 1 h at room temperature and then incubated with primary antibodies. BDNF antibody (Santa Cruz Biotechnologies, Santa Cruz, CA) was used at 1:200 dilution; nerve growth factor (NGF) antibody (Abcam, MA) was used at 1:1000 dilution; β-actin antibody (Sigma Aldrich, St Louis, MO) was used at 1:5000 dilution. The secondary antibodies were donkey anti-rabbit Alexa Fluor 680 (Invitrogen) and goat anti-mouse IRDye 800 (Rockland). Images were acquired and band intensities measured using a Li-Cor Odyssey system (Li-Cor, Lincoln, NE).\n\nImmunofluorescence:\nFrozen sections of colon tissue from control, CAS, CPS and CPS + CAS female rats were mounted on glass slides, and rehydrated in phosphate buffered saline at room temperature. The slides were treated for antigen retrieval and blocked with 10% normal goat serum diluted in 0.3% phosphate buffered saline-Triton for 1 h, and then incubated with NGF primary antibody in antibody diluent (Renoir Red, Biocare Medical, Concord, CA) at 4 °C overnight. The slides were exposed to fluorescent dye-conjugated secondary antibody for 2 h at room temperature, counterstained with 4',6-diamidino-2-phenylindole and coverslipped. Images were taken in fluorescence mode on an Olympus laser scanning confocal microscope and the average signal intensity was calculated by the bundled software.\n\nSerum estradiol and norepinephrine levels:\nSerum estradiol, adrenocorticotropic hormone (ACTH), and norepinephrine levels were measured using specific enzyme-linked immunosorbent assay kits for each analyte (CSB-E05110r, CSB-E06875r, CSB-E07022, Cusabio Bioteck CO., United States) following the manufacturer’s instructions.\n\nData analysis:\nSingle fiber responses (impulses/second) to CRD were calculated by subtracting SA from the mean 30 s maximal activity during distension. Fibers were considered responsive if CRD increased their activity to 30% greater than the baseline value. Mechanosensitive single units were classified as high threshold (> 20 mmHg) or low threshold (≤ 20 mmHg) on the basis of their response threshold and profile during CRD. Single fiber activity data were analyzed by analysis of variance with repeated measures; CRD intensity was the repeated factor and the experimental group was the between-group factor. If significant main effects were present, the individual means were compared using the Fisher post-hoc test. All authors had access to the study data and reviewed and approved the final manuscript.\n\nRESULTS:\n Effects of CPS plus CAS on primary afferent responses to CRD in male and female rats The basal activity of a spinal afferent fiber was defined as the average number of action potentials per second (impulses/sec) in the 60 s period before the onset of a distention stimulus. In male controls, 66% of the afferent fibers under study displayed SA and SA was significantly higher in female controls than in male controls (0.71 ± 0.21 vs 1.24 ± 0.20 imp/sec; Figure 1B). The average single fiber activity in response to CRD was significantly higher in female control rats compared with male controls (Figure 1C). We found that the enhanced sensitization in female rats mainly came from the low-threshold fibers (Figure 1D and E).\nTo assess the effects of CPS + CAS on colon afferent fiber activities, we compared average single colon afferent fiber activities projecting from dorsal roots S1-L6 in response to CRD in male and female control, CPS, control + CAS and CPS + CAS rats recorded approximately 24 h after the last stressor. In females, CPS significantly increased single-unit afferent activity in response to CRD vs control female rats (Figure 1F). CAS alone enhanced single-unit activity compared with control. The increase in average afferent responses after CAS in prenatally stressed female rats (44.0%) was significantly greater than the increase in female control rats (39.3%). In males, CPS had no significant effect on primary afferent responses (Figure 1G). When we compared males to females within each experimental group, we found that the average single fiber activity was significantly higher in female compared with male CPS + CAS rats (Figure 1F, G). The increased activity may contribute to the enhanced female visceral hypersensitivity previously reported in this model. Average single-fiber activities were significantly greater in control and CPS and CAS females than in their corresponding male experimental groups (Figure 1F and G). Both CAS and CPS + CAS rats had significantly increased primary afferent responses compared with control and CPS rats. Thus, our CPS and CAS protocols sensitized colon-projecting primary afferent fibers, with the greatest effects produced by the combination of CPS + CAS in both males and females.\nThe basal activity of a spinal afferent fiber was defined as the average number of action potentials per second (impulses/sec) in the 60 s period before the onset of a distention stimulus. In male controls, 66% of the afferent fibers under study displayed SA and SA was significantly higher in female controls than in male controls (0.71 ± 0.21 vs 1.24 ± 0.20 imp/sec; Figure 1B). The average single fiber activity in response to CRD was significantly higher in female control rats compared with male controls (Figure 1C). We found that the enhanced sensitization in female rats mainly came from the low-threshold fibers (Figure 1D and E).\nTo assess the effects of CPS + CAS on colon afferent fiber activities, we compared average single colon afferent fiber activities projecting from dorsal roots S1-L6 in response to CRD in male and female control, CPS, control + CAS and CPS + CAS rats recorded approximately 24 h after the last stressor. In females, CPS significantly increased single-unit afferent activity in response to CRD vs control female rats (Figure 1F). CAS alone enhanced single-unit activity compared with control. The increase in average afferent responses after CAS in prenatally stressed female rats (44.0%) was significantly greater than the increase in female control rats (39.3%). In males, CPS had no significant effect on primary afferent responses (Figure 1G). When we compared males to females within each experimental group, we found that the average single fiber activity was significantly higher in female compared with male CPS + CAS rats (Figure 1F, G). The increased activity may contribute to the enhanced female visceral hypersensitivity previously reported in this model. Average single-fiber activities were significantly greater in control and CPS and CAS females than in their corresponding male experimental groups (Figure 1F and G). Both CAS and CPS + CAS rats had significantly increased primary afferent responses compared with control and CPS rats. Thus, our CPS and CAS protocols sensitized colon-projecting primary afferent fibers, with the greatest effects produced by the combination of CPS + CAS in both males and females.\n Increase in excitability of colon-projecting lumbosacral DRG neurons in female CPS + CAS rats To elucidate the electrophysiological basis of enhanced stress-induced primary afferent activity in female rats, we performed patch clamp studies on acutely dissociated retrograde-labeled colon-projecting neurons from the L6-S2 DRGs in control, prenatal stress, adult stress only, and CPS + CAS female rats isolated 24 h after the last adult stressor (Figure 2A). Input resistance (Figure 2B) and rheobase (Figure 2C) were significantly decreased in neurons from CPS + CAS rats compared with the other three groups. The number of action potentials elicited at either 2 × or 3 × the rheobase were significantly greater in adult stress and CPS + CAS neurons compared with control and to CPS neurons (Figure 2D, E). CAS significantly increased action potential overshoot with or without CPS (Figure 2F), but it did not significantly alter other electrophysiological characteristics, such as number of spontaneous spikes, membrane capacitance (pF), resting membrane potential, cell diameter, time constant, and DRG neuron action potential amplitude and duration (Table 1).\nPatch clamp recording in colonic dorsal root ganglion neurons from female rats. A: Patch clamp process of cell labeling. Under isoflurane anesthesia, the lipid soluble fluorescent dye 9-DiI was injected into muscularis externa of the exposed distal colon (left figure). Lumbosacral (L6–S2) dorsal root ganglions (upper photograph) were isolated and DiI-labeled neurons were identified by fluorescence microscopy (lower photograph). Electrophysiological properties of each neuron were measured using whole-cell current and voltage clamp protocols (right figure); B: Rheobase from all four experimental groups (n = 5 rats, 45 cells in each group, one-way ANOVA, aP < 0.05 vs control or bP < 0.05 vs CAS); C: Representative action potentials (APs) elicited by current injection at 2 × the rheobase in neurons from control, chronic adult stress (CAS), chronic prenatal stress (CPS) and CPS + CAS female rats; D: Membrane input resistance from all four groups (n = 5 rats, 45 cells in each group, one-way ANOVA, aP < 0.05 vs control; bP < 0.05 vs CPS); E: Number of APs elicited by current injection at either 2 × and 3 × the rheobase in all four experimental groups (two-way ANOVA, aP < 0.05 vs control; bP < 0.05 vs CPS); F: AP overshoot recorded from all four experimental groups (aP < 0.05 vs control); G: The proportion of neurons from each experimental group exhibiting spontaneous APs. Red numbers represent spontaneous AP firing cells; black numbers represent total cells; H: Representative total, IK and IA current tracings and average values of potassium currents: Itotal, IK and IA are shown in female CPS + CAS, CAS, CPS (n = 15 neurons, from 5 rats in each group), and control groups (n = 12 neurons from 5 rats); two-way ANOVA, aP < 0.05 vs each control group. \nElectrophysiological characteristics of colon related DRG neuron\nValues means ± standard error. \nP < 0.05;\nP < 0.001 vs control group. CAS: Chronic adult stress; CPS: Chronic prenatal stress.\nThe percentage of neurons with SA in was significantly greater in CPS + CAS rats than in control or CPS only rats (Figure 2G). Under voltage clamp conditions (Figure 2H), neurons from female CPS + CAS, CAS, CPS and control groups had IA and sustained outward rectifier K+ currents (IK). Compared with the other three groups, DRG neurons from CPS + CAS rats had significantly reduced average IA (P < 0.05). The average IK density was decreased but the change was not significant.\nTo elucidate the electrophysiological basis of enhanced stress-induced primary afferent activity in female rats, we performed patch clamp studies on acutely dissociated retrograde-labeled colon-projecting neurons from the L6-S2 DRGs in control, prenatal stress, adult stress only, and CPS + CAS female rats isolated 24 h after the last adult stressor (Figure 2A). Input resistance (Figure 2B) and rheobase (Figure 2C) were significantly decreased in neurons from CPS + CAS rats compared with the other three groups. The number of action potentials elicited at either 2 × or 3 × the rheobase were significantly greater in adult stress and CPS + CAS neurons compared with control and to CPS neurons (Figure 2D, E). CAS significantly increased action potential overshoot with or without CPS (Figure 2F), but it did not significantly alter other electrophysiological characteristics, such as number of spontaneous spikes, membrane capacitance (pF), resting membrane potential, cell diameter, time constant, and DRG neuron action potential amplitude and duration (Table 1).\nPatch clamp recording in colonic dorsal root ganglion neurons from female rats. A: Patch clamp process of cell labeling. Under isoflurane anesthesia, the lipid soluble fluorescent dye 9-DiI was injected into muscularis externa of the exposed distal colon (left figure). Lumbosacral (L6–S2) dorsal root ganglions (upper photograph) were isolated and DiI-labeled neurons were identified by fluorescence microscopy (lower photograph). Electrophysiological properties of each neuron were measured using whole-cell current and voltage clamp protocols (right figure); B: Rheobase from all four experimental groups (n = 5 rats, 45 cells in each group, one-way ANOVA, aP < 0.05 vs control or bP < 0.05 vs CAS); C: Representative action potentials (APs) elicited by current injection at 2 × the rheobase in neurons from control, chronic adult stress (CAS), chronic prenatal stress (CPS) and CPS + CAS female rats; D: Membrane input resistance from all four groups (n = 5 rats, 45 cells in each group, one-way ANOVA, aP < 0.05 vs control; bP < 0.05 vs CPS); E: Number of APs elicited by current injection at either 2 × and 3 × the rheobase in all four experimental groups (two-way ANOVA, aP < 0.05 vs control; bP < 0.05 vs CPS); F: AP overshoot recorded from all four experimental groups (aP < 0.05 vs control); G: The proportion of neurons from each experimental group exhibiting spontaneous APs. Red numbers represent spontaneous AP firing cells; black numbers represent total cells; H: Representative total, IK and IA current tracings and average values of potassium currents: Itotal, IK and IA are shown in female CPS + CAS, CAS, CPS (n = 15 neurons, from 5 rats in each group), and control groups (n = 12 neurons from 5 rats); two-way ANOVA, aP < 0.05 vs each control group. \nElectrophysiological characteristics of colon related DRG neuron\nValues means ± standard error. \nP < 0.05;\nP < 0.001 vs control group. CAS: Chronic adult stress; CPS: Chronic prenatal stress.\nThe percentage of neurons with SA in was significantly greater in CPS + CAS rats than in control or CPS only rats (Figure 2G). Under voltage clamp conditions (Figure 2H), neurons from female CPS + CAS, CAS, CPS and control groups had IA and sustained outward rectifier K+ currents (IK). Compared with the other three groups, DRG neurons from CPS + CAS rats had significantly reduced average IA (P < 0.05). The average IK density was decreased but the change was not significant.\n Effects of CPS and/or CAS on plasma estrogen concentration We did a vaginal smear test to identify the estrus cycle phases by identifying the vaginal cytological cell types. Estrogen concentration was significantly higher in the CPS proestrus/estrus phase compared with control diestrus, control proestrus/estrus, and CPS diestrus proestrus (P < 0.05; Figure 3A). Comparison of the plasma estrogen concentrations in control, CAS, CPS, CPS + CAS showed that CPS significantly increased plasma estrogen levels compared with the control rats and that CAS increased plasma estrogen level compared with the control and CPS rats (Figure 3B).\nEffects of chronic prenatal stress, chronic adult stress, ovariectomy, and letrozole treatment on plasma estrogen levels in female rats. A: Plasma estrogen level in control and chronic prenatal stress (CPS) rats by estrus cycle phase (n = 8 rats, one-way ANOVA, aP < 0.05 vs control proestrus/estrus (P-E) phase; bP < 0.05 vs CPS diestrus (D) phase); B: Plasma estrogen levels increased in CPS rats and following chronic adult stress (CAS) 24 h after the last adult stressor (n = 8 rats, one-way ANOVA, aP < 0.05 vs control; bP < 0.05 vs CPS); C: Ovariectomy (OVX) significantly reduced CPS female rat plasma estrogen levels before and after CAS (n = 5 rats, one-way ANOVA, aP < 0.05 vs sham group); D: Letrozole treatment significantly reduced CPS female rat plasma estrogen levels before or after CAS (n = 5 rats, one-way ANOVA, aP < 0.05 vs vehicle group; cP < 0.0001); E: Plasma norepinephrine levels from control, CAS, CPS and CPS + CAS group female rats (n = 5 rats, one-way ANOVA, aP < 0.05 vs control; bP < 0.05 vs CPS); F: Plasma adrenocorticotropic hormone (ACTH) levels from control and CPS + CAS group female rats (n = 5 rats, t-test, aP < 0.05 vs control). \nTo determine whether estrogen contributed to stress-induced visceral hypersensitivity in prenatal stressed females, we reduced plasma estrogen levels by either OVX or letrozole treatment. OVX significantly lowered serum estradiol levels before and after CAS (Figure 3C). Treatment was continued throughout CAS. After treatment with letrozole, serum estradiol levels were significantly reduced (Figure 3D). To study the effects of gender and stress on norepinephrine and ACTH levels, we measured plasma norepinephrine levels in female rats from all four experimental groups. CAS alone significantly increased plasma norepinephrine levels compared with both the controls and with CPS alone (Figure 3E) Plasma norepinephrine levels were significantly increased in CPS + CAS rats compared with CAS alone as well as with controls and CPS. Plasma ACTH levels were significantly increased in CPS + CAS rats compared with controls. (Figure 3F).\nWe did a vaginal smear test to identify the estrus cycle phases by identifying the vaginal cytological cell types. Estrogen concentration was significantly higher in the CPS proestrus/estrus phase compared with control diestrus, control proestrus/estrus, and CPS diestrus proestrus (P < 0.05; Figure 3A). Comparison of the plasma estrogen concentrations in control, CAS, CPS, CPS + CAS showed that CPS significantly increased plasma estrogen levels compared with the control rats and that CAS increased plasma estrogen level compared with the control and CPS rats (Figure 3B).\nEffects of chronic prenatal stress, chronic adult stress, ovariectomy, and letrozole treatment on plasma estrogen levels in female rats. A: Plasma estrogen level in control and chronic prenatal stress (CPS) rats by estrus cycle phase (n = 8 rats, one-way ANOVA, aP < 0.05 vs control proestrus/estrus (P-E) phase; bP < 0.05 vs CPS diestrus (D) phase); B: Plasma estrogen levels increased in CPS rats and following chronic adult stress (CAS) 24 h after the last adult stressor (n = 8 rats, one-way ANOVA, aP < 0.05 vs control; bP < 0.05 vs CPS); C: Ovariectomy (OVX) significantly reduced CPS female rat plasma estrogen levels before and after CAS (n = 5 rats, one-way ANOVA, aP < 0.05 vs sham group); D: Letrozole treatment significantly reduced CPS female rat plasma estrogen levels before or after CAS (n = 5 rats, one-way ANOVA, aP < 0.05 vs vehicle group; cP < 0.0001); E: Plasma norepinephrine levels from control, CAS, CPS and CPS + CAS group female rats (n = 5 rats, one-way ANOVA, aP < 0.05 vs control; bP < 0.05 vs CPS); F: Plasma adrenocorticotropic hormone (ACTH) levels from control and CPS + CAS group female rats (n = 5 rats, t-test, aP < 0.05 vs control). \nTo determine whether estrogen contributed to stress-induced visceral hypersensitivity in prenatal stressed females, we reduced plasma estrogen levels by either OVX or letrozole treatment. OVX significantly lowered serum estradiol levels before and after CAS (Figure 3C). Treatment was continued throughout CAS. After treatment with letrozole, serum estradiol levels were significantly reduced (Figure 3D). To study the effects of gender and stress on norepinephrine and ACTH levels, we measured plasma norepinephrine levels in female rats from all four experimental groups. CAS alone significantly increased plasma norepinephrine levels compared with both the controls and with CPS alone (Figure 3E) Plasma norepinephrine levels were significantly increased in CPS + CAS rats compared with CAS alone as well as with controls and CPS. Plasma ACTH levels were significantly increased in CPS + CAS rats compared with controls. (Figure 3F).\n Effects of Letrozole treatment on colon DRG neuron excitability We performed patch clamp experiments on acutely isolated retrograde-labeled DRG neurons from CPS + CAS females with or without letrozole treatment 24 h after the last adult stressor. Letrozole treatment significantly increased rheobase (Figure 4A), and significantly reduced input resistance (Figure 4B). Action potential overshoot (Figure 4C) and the number of action potentials elicited by a current injection at either 2 × or 3 × rheobase were significantly reduced by letrozole treatment (Figure 4D). Other electrophysiological properties were not significantly altered (Table 2). We also recorded electromyographic activity to determine whether the reduction in visceral sensitivity in female CPS + CAS rats caused by OVX or systemic letrozole treatment reduced visceromotor responses. The findings demonstrated a significant decrease in excitability of colon-projecting L6-S2 neurons.\nEffects of Letrozole treatment on colon dorsal root ganglion neuron excitability. A: Rheobase (n = 45 cells in 6 rats in each group, t-test, cP < 0.001 vs Veh. + chronic adult stress [CAS] + chronic prenatal stress [CPS]); B: Membrane input resistance (RIn) (t-test, aP < 0.05); C: Action potential (AP) overshoot (t-test, aP < 0.05); D: Number of APs elicited by current injection at 2 × and 3 × rheobase (two-way ANOVA, aP < 0.05; bP < 0.01). \nElectrophysiological characteristics of colon related DRG neuron after Letrozole treatment\nValues are means ± standard error. \nP < 0.05;\nP < 0.001 vs control group. CAS: Chronic adult stress; CPS: Chronic prenatal stress.\nWe performed patch clamp experiments on acutely isolated retrograde-labeled DRG neurons from CPS + CAS females with or without letrozole treatment 24 h after the last adult stressor. Letrozole treatment significantly increased rheobase (Figure 4A), and significantly reduced input resistance (Figure 4B). Action potential overshoot (Figure 4C) and the number of action potentials elicited by a current injection at either 2 × or 3 × rheobase were significantly reduced by letrozole treatment (Figure 4D). Other electrophysiological properties were not significantly altered (Table 2). We also recorded electromyographic activity to determine whether the reduction in visceral sensitivity in female CPS + CAS rats caused by OVX or systemic letrozole treatment reduced visceromotor responses. The findings demonstrated a significant decrease in excitability of colon-projecting L6-S2 neurons.\nEffects of Letrozole treatment on colon dorsal root ganglion neuron excitability. A: Rheobase (n = 45 cells in 6 rats in each group, t-test, cP < 0.001 vs Veh. + chronic adult stress [CAS] + chronic prenatal stress [CPS]); B: Membrane input resistance (RIn) (t-test, aP < 0.05); C: Action potential (AP) overshoot (t-test, aP < 0.05); D: Number of APs elicited by current injection at 2 × and 3 × rheobase (two-way ANOVA, aP < 0.05; bP < 0.01). \nElectrophysiological characteristics of colon related DRG neuron after Letrozole treatment\nValues are means ± standard error. \nP < 0.05;\nP < 0.001 vs control group. CAS: Chronic adult stress; CPS: Chronic prenatal stress.\n Spinal cord BDNF levels regulated by estrogen To investigate the effect of estrogen on BDNF expression, we measured BDNF mRNA and protein levels in the lumbar-sacral spinal cords of OVX and Sham CPS + CAS female rats. Systemic estradiol administration to naïve cycling females produced significant increases in plasma estrogen (Figure 5A), lumbar-sacral spinal cord BDNF mRNA (Figure 5B), and protein (Figure 5C). We also measured BDNF mRNA and protein levels in the lumbar-sacral spinal cords of OVX and Sham CPS + CAS female rats. BDNF mRNA and protein expression were significantly suppressed by OVX compared with sham rats. Another experiment showed that intrathecal infusion of estrogen into naïve female rats significantly increased BDNF protein levels, which proved that estrogen reversed the experimental results and contributed to the response to visceral pain[13].\nBrain-derived neurotrophic factor expression in lumbar-sacral spinal cord is regulated by estrogen. A: Plasma estrogen levels in cycling females that received a bolus estradiol (E2) infusion on day 1; B: Lumbar-sacral spinal cord brain-derived neurotrophic factor (BDNF) mRNA following bolus estrogen infusion; C: Lumbar-sacral spinal cord BDNF protein following bolus estrogen infusion. (n = 8 rats in each group, two-way ANOVA, aP < 0.05 vs vehicle group). \nTo investigate the effect of estrogen on BDNF expression, we measured BDNF mRNA and protein levels in the lumbar-sacral spinal cords of OVX and Sham CPS + CAS female rats. Systemic estradiol administration to naïve cycling females produced significant increases in plasma estrogen (Figure 5A), lumbar-sacral spinal cord BDNF mRNA (Figure 5B), and protein (Figure 5C). We also measured BDNF mRNA and protein levels in the lumbar-sacral spinal cords of OVX and Sham CPS + CAS female rats. BDNF mRNA and protein expression were significantly suppressed by OVX compared with sham rats. Another experiment showed that intrathecal infusion of estrogen into naïve female rats significantly increased BDNF protein levels, which proved that estrogen reversed the experimental results and contributed to the response to visceral pain[13].\nBrain-derived neurotrophic factor expression in lumbar-sacral spinal cord is regulated by estrogen. A: Plasma estrogen levels in cycling females that received a bolus estradiol (E2) infusion on day 1; B: Lumbar-sacral spinal cord brain-derived neurotrophic factor (BDNF) mRNA following bolus estrogen infusion; C: Lumbar-sacral spinal cord BDNF protein following bolus estrogen infusion. (n = 8 rats in each group, two-way ANOVA, aP < 0.05 vs vehicle group). \n Peripheral NGF level increased in CPS + CAS female rats We examined NGF expression in the colons of females from all four experimental groups by immunohistochemistry (Figure 6A). Morphometric analysis showed that CAS and CPS + CAS significantly increased NGF levels in the colon wall, with the increase in CPS + CAS significantly greater that of CAS alone (Figure 6B). Western blotting showed that NGF protein was significantly upregulated in CPS + CAS rats compared with controls (Figure 6C).\nNerve growth factor expression level in the colon wall. A: Immunohistochemical staining of nerve growth factor (NGF; green) was detected with nuclear counterstaining staining (blue) in controls, chronic adult stress (CAS), chronic prenatal stress (CPS) and CPS + CAS group female rat colon walls. × 400 magnification representative pictures were shown; B: Quantification of NGF levels from colon wall in immunohistochemistry (IHC) (n = 4 rats in each group, one-way ANOVA, aP < 0.05 vs control group; bP < 0.05 vs CPS group); C: Western blots of NGF protein from control and CPS + CAS female rats colon wall tissue (n = 6 rats in each group, t-test, aP < 0.05 vs control group). \nWe examined NGF expression in the colons of females from all four experimental groups by immunohistochemistry (Figure 6A). Morphometric analysis showed that CAS and CPS + CAS significantly increased NGF levels in the colon wall, with the increase in CPS + CAS significantly greater that of CAS alone (Figure 6B). Western blotting showed that NGF protein was significantly upregulated in CPS + CAS rats compared with controls (Figure 6C).\nNerve growth factor expression level in the colon wall. A: Immunohistochemical staining of nerve growth factor (NGF; green) was detected with nuclear counterstaining staining (blue) in controls, chronic adult stress (CAS), chronic prenatal stress (CPS) and CPS + CAS group female rat colon walls. × 400 magnification representative pictures were shown; B: Quantification of NGF levels from colon wall in immunohistochemistry (IHC) (n = 4 rats in each group, one-way ANOVA, aP < 0.05 vs control group; bP < 0.05 vs CPS group); C: Western blots of NGF protein from control and CPS + CAS female rats colon wall tissue (n = 6 rats in each group, t-test, aP < 0.05 vs control group). \n\nEffects of CPS plus CAS on primary afferent responses to CRD in male and female rats:\nThe basal activity of a spinal afferent fiber was defined as the average number of action potentials per second (impulses/sec) in the 60 s period before the onset of a distention stimulus. In male controls, 66% of the afferent fibers under study displayed SA and SA was significantly higher in female controls than in male controls (0.71 ± 0.21 vs 1.24 ± 0.20 imp/sec; Figure 1B). The average single fiber activity in response to CRD was significantly higher in female control rats compared with male controls (Figure 1C). We found that the enhanced sensitization in female rats mainly came from the low-threshold fibers (Figure 1D and E).\nTo assess the effects of CPS + CAS on colon afferent fiber activities, we compared average single colon afferent fiber activities projecting from dorsal roots S1-L6 in response to CRD in male and female control, CPS, control + CAS and CPS + CAS rats recorded approximately 24 h after the last stressor. In females, CPS significantly increased single-unit afferent activity in response to CRD vs control female rats (Figure 1F). CAS alone enhanced single-unit activity compared with control. The increase in average afferent responses after CAS in prenatally stressed female rats (44.0%) was significantly greater than the increase in female control rats (39.3%). In males, CPS had no significant effect on primary afferent responses (Figure 1G). When we compared males to females within each experimental group, we found that the average single fiber activity was significantly higher in female compared with male CPS + CAS rats (Figure 1F, G). The increased activity may contribute to the enhanced female visceral hypersensitivity previously reported in this model. Average single-fiber activities were significantly greater in control and CPS and CAS females than in their corresponding male experimental groups (Figure 1F and G). Both CAS and CPS + CAS rats had significantly increased primary afferent responses compared with control and CPS rats. Thus, our CPS and CAS protocols sensitized colon-projecting primary afferent fibers, with the greatest effects produced by the combination of CPS + CAS in both males and females.\n\nIncrease in excitability of colon-projecting lumbosacral DRG neurons in female CPS + CAS rats:\nTo elucidate the electrophysiological basis of enhanced stress-induced primary afferent activity in female rats, we performed patch clamp studies on acutely dissociated retrograde-labeled colon-projecting neurons from the L6-S2 DRGs in control, prenatal stress, adult stress only, and CPS + CAS female rats isolated 24 h after the last adult stressor (Figure 2A). Input resistance (Figure 2B) and rheobase (Figure 2C) were significantly decreased in neurons from CPS + CAS rats compared with the other three groups. The number of action potentials elicited at either 2 × or 3 × the rheobase were significantly greater in adult stress and CPS + CAS neurons compared with control and to CPS neurons (Figure 2D, E). CAS significantly increased action potential overshoot with or without CPS (Figure 2F), but it did not significantly alter other electrophysiological characteristics, such as number of spontaneous spikes, membrane capacitance (pF), resting membrane potential, cell diameter, time constant, and DRG neuron action potential amplitude and duration (Table 1).\nPatch clamp recording in colonic dorsal root ganglion neurons from female rats. A: Patch clamp process of cell labeling. Under isoflurane anesthesia, the lipid soluble fluorescent dye 9-DiI was injected into muscularis externa of the exposed distal colon (left figure). Lumbosacral (L6–S2) dorsal root ganglions (upper photograph) were isolated and DiI-labeled neurons were identified by fluorescence microscopy (lower photograph). Electrophysiological properties of each neuron were measured using whole-cell current and voltage clamp protocols (right figure); B: Rheobase from all four experimental groups (n = 5 rats, 45 cells in each group, one-way ANOVA, aP < 0.05 vs control or bP < 0.05 vs CAS); C: Representative action potentials (APs) elicited by current injection at 2 × the rheobase in neurons from control, chronic adult stress (CAS), chronic prenatal stress (CPS) and CPS + CAS female rats; D: Membrane input resistance from all four groups (n = 5 rats, 45 cells in each group, one-way ANOVA, aP < 0.05 vs control; bP < 0.05 vs CPS); E: Number of APs elicited by current injection at either 2 × and 3 × the rheobase in all four experimental groups (two-way ANOVA, aP < 0.05 vs control; bP < 0.05 vs CPS); F: AP overshoot recorded from all four experimental groups (aP < 0.05 vs control); G: The proportion of neurons from each experimental group exhibiting spontaneous APs. Red numbers represent spontaneous AP firing cells; black numbers represent total cells; H: Representative total, IK and IA current tracings and average values of potassium currents: Itotal, IK and IA are shown in female CPS + CAS, CAS, CPS (n = 15 neurons, from 5 rats in each group), and control groups (n = 12 neurons from 5 rats); two-way ANOVA, aP < 0.05 vs each control group. \nElectrophysiological characteristics of colon related DRG neuron\nValues means ± standard error. \nP < 0.05;\nP < 0.001 vs control group. CAS: Chronic adult stress; CPS: Chronic prenatal stress.\nThe percentage of neurons with SA in was significantly greater in CPS + CAS rats than in control or CPS only rats (Figure 2G). Under voltage clamp conditions (Figure 2H), neurons from female CPS + CAS, CAS, CPS and control groups had IA and sustained outward rectifier K+ currents (IK). Compared with the other three groups, DRG neurons from CPS + CAS rats had significantly reduced average IA (P < 0.05). The average IK density was decreased but the change was not significant.\n\nEffects of CPS and/or CAS on plasma estrogen concentration:\nWe did a vaginal smear test to identify the estrus cycle phases by identifying the vaginal cytological cell types. Estrogen concentration was significantly higher in the CPS proestrus/estrus phase compared with control diestrus, control proestrus/estrus, and CPS diestrus proestrus (P < 0.05; Figure 3A). Comparison of the plasma estrogen concentrations in control, CAS, CPS, CPS + CAS showed that CPS significantly increased plasma estrogen levels compared with the control rats and that CAS increased plasma estrogen level compared with the control and CPS rats (Figure 3B).\nEffects of chronic prenatal stress, chronic adult stress, ovariectomy, and letrozole treatment on plasma estrogen levels in female rats. A: Plasma estrogen level in control and chronic prenatal stress (CPS) rats by estrus cycle phase (n = 8 rats, one-way ANOVA, aP < 0.05 vs control proestrus/estrus (P-E) phase; bP < 0.05 vs CPS diestrus (D) phase); B: Plasma estrogen levels increased in CPS rats and following chronic adult stress (CAS) 24 h after the last adult stressor (n = 8 rats, one-way ANOVA, aP < 0.05 vs control; bP < 0.05 vs CPS); C: Ovariectomy (OVX) significantly reduced CPS female rat plasma estrogen levels before and after CAS (n = 5 rats, one-way ANOVA, aP < 0.05 vs sham group); D: Letrozole treatment significantly reduced CPS female rat plasma estrogen levels before or after CAS (n = 5 rats, one-way ANOVA, aP < 0.05 vs vehicle group; cP < 0.0001); E: Plasma norepinephrine levels from control, CAS, CPS and CPS + CAS group female rats (n = 5 rats, one-way ANOVA, aP < 0.05 vs control; bP < 0.05 vs CPS); F: Plasma adrenocorticotropic hormone (ACTH) levels from control and CPS + CAS group female rats (n = 5 rats, t-test, aP < 0.05 vs control). \nTo determine whether estrogen contributed to stress-induced visceral hypersensitivity in prenatal stressed females, we reduced plasma estrogen levels by either OVX or letrozole treatment. OVX significantly lowered serum estradiol levels before and after CAS (Figure 3C). Treatment was continued throughout CAS. After treatment with letrozole, serum estradiol levels were significantly reduced (Figure 3D). To study the effects of gender and stress on norepinephrine and ACTH levels, we measured plasma norepinephrine levels in female rats from all four experimental groups. CAS alone significantly increased plasma norepinephrine levels compared with both the controls and with CPS alone (Figure 3E) Plasma norepinephrine levels were significantly increased in CPS + CAS rats compared with CAS alone as well as with controls and CPS. Plasma ACTH levels were significantly increased in CPS + CAS rats compared with controls. (Figure 3F).\n\nEffects of Letrozole treatment on colon DRG neuron excitability:\nWe performed patch clamp experiments on acutely isolated retrograde-labeled DRG neurons from CPS + CAS females with or without letrozole treatment 24 h after the last adult stressor. Letrozole treatment significantly increased rheobase (Figure 4A), and significantly reduced input resistance (Figure 4B). Action potential overshoot (Figure 4C) and the number of action potentials elicited by a current injection at either 2 × or 3 × rheobase were significantly reduced by letrozole treatment (Figure 4D). Other electrophysiological properties were not significantly altered (Table 2). We also recorded electromyographic activity to determine whether the reduction in visceral sensitivity in female CPS + CAS rats caused by OVX or systemic letrozole treatment reduced visceromotor responses. The findings demonstrated a significant decrease in excitability of colon-projecting L6-S2 neurons.\nEffects of Letrozole treatment on colon dorsal root ganglion neuron excitability. A: Rheobase (n = 45 cells in 6 rats in each group, t-test, cP < 0.001 vs Veh. + chronic adult stress [CAS] + chronic prenatal stress [CPS]); B: Membrane input resistance (RIn) (t-test, aP < 0.05); C: Action potential (AP) overshoot (t-test, aP < 0.05); D: Number of APs elicited by current injection at 2 × and 3 × rheobase (two-way ANOVA, aP < 0.05; bP < 0.01). \nElectrophysiological characteristics of colon related DRG neuron after Letrozole treatment\nValues are means ± standard error. \nP < 0.05;\nP < 0.001 vs control group. CAS: Chronic adult stress; CPS: Chronic prenatal stress.\n\nSpinal cord BDNF levels regulated by estrogen:\nTo investigate the effect of estrogen on BDNF expression, we measured BDNF mRNA and protein levels in the lumbar-sacral spinal cords of OVX and Sham CPS + CAS female rats. Systemic estradiol administration to naïve cycling females produced significant increases in plasma estrogen (Figure 5A), lumbar-sacral spinal cord BDNF mRNA (Figure 5B), and protein (Figure 5C). We also measured BDNF mRNA and protein levels in the lumbar-sacral spinal cords of OVX and Sham CPS + CAS female rats. BDNF mRNA and protein expression were significantly suppressed by OVX compared with sham rats. Another experiment showed that intrathecal infusion of estrogen into naïve female rats significantly increased BDNF protein levels, which proved that estrogen reversed the experimental results and contributed to the response to visceral pain[13].\nBrain-derived neurotrophic factor expression in lumbar-sacral spinal cord is regulated by estrogen. A: Plasma estrogen levels in cycling females that received a bolus estradiol (E2) infusion on day 1; B: Lumbar-sacral spinal cord brain-derived neurotrophic factor (BDNF) mRNA following bolus estrogen infusion; C: Lumbar-sacral spinal cord BDNF protein following bolus estrogen infusion. (n = 8 rats in each group, two-way ANOVA, aP < 0.05 vs vehicle group). \n\nPeripheral NGF level increased in CPS + CAS female rats:\nWe examined NGF expression in the colons of females from all four experimental groups by immunohistochemistry (Figure 6A). Morphometric analysis showed that CAS and CPS + CAS significantly increased NGF levels in the colon wall, with the increase in CPS + CAS significantly greater that of CAS alone (Figure 6B). Western blotting showed that NGF protein was significantly upregulated in CPS + CAS rats compared with controls (Figure 6C).\nNerve growth factor expression level in the colon wall. A: Immunohistochemical staining of nerve growth factor (NGF; green) was detected with nuclear counterstaining staining (blue) in controls, chronic adult stress (CAS), chronic prenatal stress (CPS) and CPS + CAS group female rat colon walls. × 400 magnification representative pictures were shown; B: Quantification of NGF levels from colon wall in immunohistochemistry (IHC) (n = 4 rats in each group, one-way ANOVA, aP < 0.05 vs control group; bP < 0.05 vs CPS group); C: Western blots of NGF protein from control and CPS + CAS female rats colon wall tissue (n = 6 rats in each group, t-test, aP < 0.05 vs control group). \n\nDISCUSSION:\nEnhanced CPS-induced visceral hypersensitivity in female rats was associated with an increase in the responses of lumbosacral nerve fibers to CRD in both male and female offspring. These findings are further supported by data showing increased excitability of colon-projecting DRG neurons from females in patch clamp studies. The magnitude of the sensitization was the greatest in female CPS + CAS rats, suggesting that it made a major contribution to the observed enhanced female visceral hypersensitivity in our model.\nChronic stress is known to increase the excitability of colon-projecting DRG neurons in rats and mice. In adult male Sprague Dawley rats, colon DRG neuron sensitization was shown to be driven by increases in NGF expression in the colon muscularis externa[13]. In our model, we also observed a significant increase in colon NGF, but its potential role in primary afferent sensitization and visceral hypersensitivity was not investigated.. Other studies in male mice showed that stress, in the form of water avoidance, significantly increased the excitability of colon-projecting DRG neurons and that the combined activity of the stress mediators corticosterone and norepinephrine increased DRG neuron excitability in vitro[20,21]. We previously found significant increases in the serum levels of norepinephrine in CPS + CAS females. However, daily systemic treatment with adrenergic antagonists during the adult stress protocol failed to reduce visceral hypersensitivity in female neonatal + adult stress rats[13] suggesting that norepinephrine did not play a major role in the acquisition of enhanced female visceral hypersensitivity or primary afferent sensitization in our model.\nWhen we tested on lumbar-sacral afferent fibers and dissociated neurons in patch clamp studies, we found significant decreases in transient potassium IA currents in neurons isolated from CPS + CAS females compared with the other three experimental groups. Declines in A-type Kv currents in DRG neurons have been associated with persistent pain in multiple chronic pain models[22]. Whether the decline was caused by changes in channel properties or expression was not investigated in this study. However, another study demonstrated that estrogen significantly shifted the activation curve of IA currents in the hyperpolarizing direction and that estrogen inhibited Kv (+) channels in mouse DRG neurons through a membrane ER-activated nongenomic pathway[23].\nOur results showed that the excitability of colon-projecting neurons in CPS + CAS females was significantly reduced by systemic letrozole treatment, suggesting that estrogen contributed to the sensitization process. Previous studies show that estrogen receptors expressed on primary afferent neurons contributed to enhanced sensitivity in various pain models[24-26]. One study found no decline in the responses of colon-projecting nerve fibers to CRD following OVX and found no detectable estrogen receptor alpha immunoreactivity in colon-projecting DRG neurons[27]. The reasons for the differing results are not clear, but local production of estrogen in DRG neurons could be sufficient to sustain sensitization.\nNGF and its receptors play important roles in the mechanism of visceral pain and hyperalgesia in women. For example, endometriosis is estrogen dependent and is commonly diagnosed. The main symptoms are various types of pelvic pain that have a serious effect on physical and mental health, but the mechanisms of abdominal pain are still unclear. Studies have shown NGF to be an inflammatory mediator and modulator of pain in adulthood[28].\n\nCONCLUSION:\nIn this study, we examined the sex differences and effects of estrogen on the acquisition of enhanced visceral hypersensitivity in the offspring of rats in a model of prenatal and adult stress as shown in Figure 7. Our study shows that estrogen acted in the spinal cord and the primary afferent neurons to enhance visceral nociception. Acute blockade of the endogenous synthesis of estrogens in rat spinal cord significantly reduced visceral hypersensitivity, suggesting that locally produced estrogen in the central nervous system can regulate nociceptive neurons to modulate visceral hypersensitivity. The chronic stress-estrogen-BDNF axis sensitized visceral hypersensitivity in the offspring of females subjected to CPS. The development of chronic stress-induced visceral hypersensitivity in female rats was estrogen dependent. A key component of this hypersensitivity was estrogen-dependent sensitization of primary afferent colon neurons. Our findings provide key scientific evidence in a preclinical model in support of developing gender-based treatment for abdominal pain in IBS.\nSummary diagram of estrogen re-enhanced visceral hyperalgesia investigated in chronic prenatal stress plus chronic adult stress models. BDNF: Brain-derived neurotrophic factor; NGF: Nerve growth factor.\n\n"},"whole_article_abstract":{"kind":"string","value":"Background:\nChronic stress during pregnancy may increase visceral hyperalgesia of offspring in a sex-dependent way. Combining adult stress in offspring will increase this sensitivity. Based on the evidence implicating estrogen in exacerbating visceral hypersensitivity in female rodents in preclinical models, we predicted that chronic prenatal stress (CPS) + chronic adult stress (CAS) will maximize visceral hyperalgesia; and that estrogen plays an important role in colonic hyperalgesia.\n\nMethods:\nWe established a CPS plus CAS rodent model in which the balloon was used to distend the colorectum. The single-fiber recording in vivo and patch clamp experiments in vitro were used to monitor the colonic neuron's activity. The reverse transcription-polymerase chain reaction, western blot, and immunofluorescence were used to study the effects of CPS and CAS on colon primary afferent sensitivity. We used ovariectomy and letrozole to reduce estrogen levels of female rats respectively in order to assess the role of estrogen in female-specific enhanced primary afferent sensitization.\n\nResults:\nSpontaneous activity and single fiber activity were significantly greater in females than in males. The enhanced sensitization in female rats mainly came from low-threshold neurons. CPS significantly increased single-unit afferent fiber activity in L6-S2 dorsal roots in response. Activity was further enhanced by CAS. In addition, the excitability of colon-projecting dorsal root ganglion (DRG) neurons increased in CPS + CAS rats and was associated with a decrease in transient A-type K+ currents. Compared with ovariectomy, treatment with the aromatase inhibitor letrozole significantly reduced estrogen levels in female rats, confirming the gender difference. Moreover, mice treated with letrozole had decreased colonic DRG neuron excitability. The intrathecal infusion of estrogen increased brain-derived neurotrophic factor (BDNF) protein levels and contributed to the response to visceral pain. Western blotting showed that nerve growth factor protein was upregulated in CPS + CAS mice.\n\nConclusions:\nThis study adds to the evidence that estrogen-dependent sensitization of primary afferent colon neurons is involved in the development of chronic stress-induced visceral hypersensitivity in female rats."},"background_conclusion_text":{"kind":"string","value":"INTRODUCTION:\nVisceral pain of colonic origin is the most prominent symptom in irritable bowel syndrome (IBS) patients[1]. Female IBS patients report more severe pain that occurs more frequently and with longer episodes than in male patients[1,2]. The ratio of female to male IBS is about 2:1 among patients seen in medical clinics[3]. Moreover, females have a higher prevalence of IBS co-morbidities such as anxiety and depression[4,5] and are more vulnerable to stress-induced exacerbation of IBS symptoms compared with males[3,6,7].\nClinical studies show that early life adverse experiences are risk factors for the development of IBS symptoms, including visceral pain and ongoing chronic stress, especially abdominal pain[8-10]. These factors contribute to the development of visceral hypersensitivity, a key component of the IBS symptom complex and one that may be responsible for symptoms of pain[11,12]. Our previous research found that the female offspring of mothers subjected to chronic prenatal stress (CPS) had a markedly greater visceral sensitivity than their male littermates following challenge by another chronic adult stress (CAS) protocol. A critical molecular event in the development of this female-enhanced visceral hypersensitivity is upregulation of brain-derived neurotrophic factor (BDNF) expression in the lumbar-sacral spinal cord of female CPS + CAS rats[13]. However, the neurophysiological changes underlying the enhanced female-specific visceral hypersensitivity and the role of hormone in the development of stress-induced visceral hypersensitivity are not well understood.\nVisceral hypersensitivity in IBS involves abnormal changes in neurophysiology throughout the brain-gut axis. In IBS, there is evidence for sensitization of primary afferents to jejunal distention and electrical stimulation[14], and there is evidence for increased sensitivity of lumbar splanchnic afferents[15,16]. In animal models of either early life adverse events or adult stress-induced visceral hypersensitivity[17], there is evidence of colon primary afferent sensitization. However, the studies were performed in male rodents. Therefore, in this study, we established a CPS and CAS rodent model to analyze the impact on female colon afferent neuron function and the role of estrogen. Our hypothesis was that female CPS offspring subjected to chronic stress as adults would exhibit greater colonic dorsal root ganglion (DRG) neuron sensitization compared with their male littermates, and that the enhanced visceral sensitization and primary afferent sensitization in females was estrogen dependent.\n\nCONCLUSION:\nThe authors would like to acknowledge the essential intellectual contributions of our recently deceased mentor, Dr. Sushil K Sarna. His guidance was essential to the successful completion of these studies. Thanks to Dr. Usman Ali for assistance in preparing and modifying the manuscript."},"background_conclusion_abstract":{"kind":"string","value":"Background:\nChronic stress during pregnancy may increase visceral hyperalgesia of offspring in a sex-dependent way. Combining adult stress in offspring will increase this sensitivity. Based on the evidence implicating estrogen in exacerbating visceral hypersensitivity in female rodents in preclinical models, we predicted that chronic prenatal stress (CPS) + chronic adult stress (CAS) will maximize visceral hyperalgesia; and that estrogen plays an important role in colonic hyperalgesia.\n\nMethods:\nWe established a CPS plus CAS rodent model in which the balloon was used to distend the colorectum. The single-fiber recording in vivo and patch clamp experiments in vitro were used to monitor the colonic neuron's activity. The reverse transcription-polymerase chain reaction, western blot, and immunofluorescence were used to study the effects of CPS and CAS on colon primary afferent sensitivity. We used ovariectomy and letrozole to reduce estrogen levels of female rats respectively in order to assess the role of estrogen in female-specific enhanced primary afferent sensitization.\n\nResults:\nSpontaneous activity and single fiber activity were significantly greater in females than in males. The enhanced sensitization in female rats mainly came from low-threshold neurons. CPS significantly increased single-unit afferent fiber activity in L6-S2 dorsal roots in response. Activity was further enhanced by CAS. In addition, the excitability of colon-projecting dorsal root ganglion (DRG) neurons increased in CPS + CAS rats and was associated with a decrease in transient A-type K+ currents. Compared with ovariectomy, treatment with the aromatase inhibitor letrozole significantly reduced estrogen levels in female rats, confirming the gender difference. Moreover, mice treated with letrozole had decreased colonic DRG neuron excitability. The intrathecal infusion of estrogen increased brain-derived neurotrophic factor (BDNF) protein levels and contributed to the response to visceral pain. Western blotting showed that nerve growth factor protein was upregulated in CPS + CAS mice.\n\nConclusions:\nThis study adds to the evidence that estrogen-dependent sensitization of primary afferent colon neurons is involved in the development of chronic stress-induced visceral hypersensitivity in female rats."},"whole_article_text_length":{"kind":"number","value":15994,"string":"15,994"},"whole_article_abstract_length":{"kind":"number","value":391,"string":"391"},"other_sections_lengths":{"kind":"list like","value":[434,92,513,104,212,780,82,230,141,52,143,4991,409,719,542,312,243,228,598,207],"string":"[\n 434,\n 92,\n 513,\n 104,\n 212,\n 780,\n 82,\n 230,\n 141,\n 52,\n 143,\n 4991,\n 409,\n 719,\n 542,\n 312,\n 243,\n 228,\n 598,\n 207\n]"},"num_sections":{"kind":"number","value":21,"string":"21"},"most_frequent_words":{"kind":"list like","value":["cps","rats","cas","control","female","stress","cps cas","05","figure","significantly"],"string":"[\n \"cps\",\n \"rats\",\n \"cas\",\n \"control\",\n \"female\",\n \"stress\",\n \"cps cas\",\n \"05\",\n \"figure\",\n \"significantly\"\n]"},"keybert_topics":{"kind":"list like","value":["pain ibs summary","abdominal pain ibs","visceral hypersensitivity ibs","irritable bowel syndrome","development ibs symptoms"],"string":"[\n \"pain ibs summary\",\n \"abdominal pain ibs\",\n \"visceral hypersensitivity ibs\",\n \"irritable bowel syndrome\",\n \"development ibs symptoms\"\n]"},"annotated_base_background_abstract_prompt":{"kind":"string","value":""},"annotated_base_methods_abstract_prompt":{"kind":"string","value":""},"annotated_base_results_abstract_prompt":{"kind":"null"},"annotated_base_conclusions_abstract_prompt":{"kind":"string","value":""},"annotated_base_whole_article_abstract_prompt":{"kind":"string","value":""},"annotated_base_background_conclusion_abstract_prompt":{"kind":"string","value":""},"annotated_keywords_background_abstract_prompt":{"kind":"string","value":"[CONTENT] Chronic prenatal stress | Estrogen | Visceral pain | Neuronal sensitization | Excitability | Letrozole [SUMMARY]"},"annotated_keywords_methods_abstract_prompt":{"kind":"string","value":"[CONTENT] Chronic prenatal stress | Estrogen | Visceral pain | Neuronal sensitization | Excitability | Letrozole [SUMMARY]"},"annotated_keywords_results_abstract_prompt":{"kind":"null"},"annotated_keywords_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] Chronic prenatal stress | Estrogen | Visceral pain | Neuronal sensitization | Excitability | Letrozole [SUMMARY]"},"annotated_keywords_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] Chronic prenatal stress | Estrogen | Visceral pain | Neuronal sensitization | Excitability | Letrozole [SUMMARY]"},"annotated_keywords_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] Chronic prenatal stress | Estrogen | Visceral pain | Neuronal sensitization | Excitability | Letrozole [SUMMARY]"},"annotated_mesh_background_abstract_prompt":{"kind":"string","value":"[CONTENT] Animals | Colon | Estrogens | Female | Ganglia, Spinal | Hyperalgesia | Male | Mice | Neurons | Pregnancy | Rats | Rats, Sprague-Dawley | Visceral Pain [SUMMARY]"},"annotated_mesh_methods_abstract_prompt":{"kind":"string","value":"[CONTENT] Animals | Colon | Estrogens | Female | Ganglia, Spinal | Hyperalgesia | Male | Mice | Neurons | Pregnancy | Rats | Rats, Sprague-Dawley | Visceral Pain [SUMMARY]"},"annotated_mesh_results_abstract_prompt":{"kind":"null"},"annotated_mesh_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] Animals | Colon | Estrogens | Female | Ganglia, Spinal | Hyperalgesia | Male | Mice | Neurons | Pregnancy | Rats | Rats, Sprague-Dawley | Visceral Pain [SUMMARY]"},"annotated_mesh_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] Animals | Colon | Estrogens | Female | Ganglia, Spinal | Hyperalgesia | Male | Mice | Neurons | Pregnancy | Rats | Rats, Sprague-Dawley | Visceral Pain [SUMMARY]"},"annotated_mesh_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] Animals | Colon | Estrogens | Female | Ganglia, Spinal | Hyperalgesia | Male | Mice | Neurons | Pregnancy | Rats | Rats, Sprague-Dawley | Visceral Pain [SUMMARY]"},"annotated_keybert_background_abstract_prompt":{"kind":"string","value":"[CONTENT] pain ibs summary | abdominal pain ibs | visceral hypersensitivity ibs | irritable bowel syndrome | development ibs symptoms [SUMMARY]"},"annotated_keybert_methods_abstract_prompt":{"kind":"string","value":"[CONTENT] pain ibs summary | abdominal pain ibs | visceral hypersensitivity ibs | irritable bowel syndrome | development ibs symptoms [SUMMARY]"},"annotated_keybert_results_abstract_prompt":{"kind":"null"},"annotated_keybert_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] pain ibs summary | abdominal pain ibs | visceral hypersensitivity ibs | irritable bowel syndrome | development ibs symptoms [SUMMARY]"},"annotated_keybert_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] pain ibs summary | abdominal pain ibs | visceral hypersensitivity ibs | irritable bowel syndrome | development ibs symptoms [SUMMARY]"},"annotated_keybert_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] pain ibs summary | abdominal pain ibs | visceral hypersensitivity ibs | irritable bowel syndrome | development ibs symptoms [SUMMARY]"},"annotated_most_frequent_background_abstract_prompt":{"kind":"string","value":"[CONTENT] cps | rats | cas | control | female | stress | cps cas | 05 | figure | significantly [SUMMARY]"},"annotated_most_frequent_methods_abstract_prompt":{"kind":"string","value":"[CONTENT] cps | rats | cas | control | female | stress | cps cas | 05 | figure | significantly [SUMMARY]"},"annotated_most_frequent_results_abstract_prompt":{"kind":"null"},"annotated_most_frequent_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] cps | rats | cas | control | female | stress | cps cas | 05 | figure | significantly [SUMMARY]"},"annotated_most_frequent_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] cps | rats | cas | control | female | stress | cps cas | 05 | figure | significantly [SUMMARY]"},"annotated_most_frequent_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] cps | rats | cas | control | female | stress | cps cas | 05 | figure | significantly [SUMMARY]"},"annotated_tf_idf_background_abstract_prompt":{"kind":"string","value":"[CONTENT] ibs | visceral | patients | visceral hypersensitivity | hypersensitivity | development | sensitization | pain | stress | female [SUMMARY]"},"annotated_tf_idf_methods_abstract_prompt":{"kind":"string","value":"[CONTENT] 10 | fibers | control | ml | solution | crd | rats | stress | current | day [SUMMARY]"},"annotated_tf_idf_results_abstract_prompt":{"kind":"null"},"annotated_tf_idf_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] visceral | estrogen | hypersensitivity | visceral hypersensitivity | stress | hypersensitivity offspring | visceral hypersensitivity offspring | chronic | neurons | enhanced visceral [SUMMARY]"},"annotated_tf_idf_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] cps | cas | rats | control | stress | female | estrogen | neurons | 05 | group [SUMMARY]"},"annotated_tf_idf_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] cps | cas | rats | control | stress | female | estrogen | neurons | 05 | group [SUMMARY]"},"annotated_entity_plan_background_abstract_prompt":{"kind":"string","value":"[CONTENT] ||| ||| CPS | CAS [SUMMARY]"},"annotated_entity_plan_methods_abstract_prompt":{"kind":"string","value":"[CONTENT] CPS | CAS ||| ||| CPS | CAS ||| [SUMMARY]"},"annotated_entity_plan_results_abstract_prompt":{"kind":"null"},"annotated_entity_plan_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] [SUMMARY]"},"annotated_entity_plan_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] ||| ||| CPS | CAS ||| CPS | CAS ||| ||| CPS | CAS ||| ||| ||| ||| CPS | L6-S2 ||| CAS ||| CPS | K+ ||| ||| DRG neuron ||| ||| CPS ||| [SUMMARY]"},"annotated_entity_plan_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] ||| ||| CPS | CAS ||| CPS | CAS ||| ||| CPS | CAS ||| ||| ||| ||| CPS | L6-S2 ||| CAS ||| CPS | K+ ||| ||| DRG neuron ||| ||| CPS ||| [SUMMARY]"}}},{"rowIdx":256,"cells":{"title":{"kind":"string","value":"Reproducibility and validity of dietary patterns assessed by a food frequency questionnaire used in the 5-year follow-up survey of the Japan Public Health Center-Based Prospective Study."},"pmid":{"kind":"string","value":"22343330"},"background_abstract":{"kind":"string","value":"Analysis of dietary pattern is increasingly popular in nutritional epidemiology. However, few studies have examined the validity and reproducibility of dietary patterns. We assessed the reproducibility and validity of dietary patterns identified by a food frequency questionnaire (FFQ) used in the 5-year follow-up survey of the Japan Public Health Center-Based Prospective Study (JPHC Study)."},"background_abstract_label":{"kind":"string","value":"BACKGROUND"},"methods_abstract":{"kind":"string","value":"The participants were a subsample (244 men and 254 women) from the JPHC Study. Principal component analysis was used to identify dietary patterns from 28- or 14-day dietary records and 2 FFQs. To assess reproducibility and validity, we calculated Spearman correlation coefficients between dietary pattern scores derived from FFQs separated by a 1-year interval, and between dietary pattern scores derived from dietary records and those derived from a FFQ completed after the dietary records, respectively."},"methods_abstract_label":{"kind":"string","value":"METHODS"},"results_abstract":{"kind":"string","value":"We identified 3 Japanese dietary patterns from the dietary records and 2 FFQs: prudent, westernized, and traditional. Regarding reproducibility, Spearman correlation coefficients between the 2 FFQs ranged from 0.55 for the westernized Japanese pattern in men and the prudent Japanese pattern in women to 0.77 for the traditional Japanese pattern in men. Regarding validity, the corresponding values between dietary records and the FFQ ranged from 0.32 for the westernized Japanese pattern in men to 0.63 for the traditional Japanese pattern in women."},"results_abstract_label":{"kind":"string","value":"RESULTS"},"conclusions_abstract":{"kind":"string","value":"Acceptable reproducibility and validity was shown by the 3 dietary patterns identified by principal component analysis based on the FFQ used in the 5-year follow-up survey of the JPHC Study."},"conclusions_abstract_label":{"kind":"string","value":"CONCLUSIONS"},"mesh_descriptor_names":{"kind":"list like","value":["Adult","Aged","Cohort Studies","Data Collection","Diet","Female","Follow-Up Studies","Humans","Japan","Male","Middle Aged","Nutrition Surveys","Nutritional Status","Surveys and Questionnaires"],"string":"[\n \"Adult\",\n \"Aged\",\n \"Cohort Studies\",\n \"Data Collection\",\n \"Diet\",\n \"Female\",\n \"Follow-Up Studies\",\n \"Humans\",\n \"Japan\",\n \"Male\",\n \"Middle Aged\",\n \"Nutrition Surveys\",\n \"Nutritional Status\",\n \"Surveys and Questionnaires\"\n]"},"pmcid":{"kind":"string","value":"3798621"},"background_title":{"kind":"string","value":"INTRODUCTION"},"background_text":{"kind":"string","value":"In nutritional epidemiology, there is increased interest in the analysis of dietary pattern, a comprehensive variable that integrates consumption of several foods or food groups. The effect of a single nutrient, food, or food group on disease risk and morbid conditions is difficult to assess in observational studies because foods and nutrients are consumed in combination and their complex effects are likely to be interactive or synergistic.1 However, dietary pattern can overcome problems relating to the close intercorrelation among foods or nutrients and is expected to have a greater impact on disease risk than any single nutrient.1–3 Therefore, analysis using dietary pattern could be used as a complementary approach in nutritional epidemiology. Indeed, many previous studies have reported associations of dietary patterns with mortality, several diseases (such as cancer, cardiovascular disease, and type 2 diabetes), and biomarkers.4,5\nExtraction of dietary pattern by principal component analysis—which is often used to identify dietary patterns—requires some arbitrary decisions to group food items, determine the number of factors to retain, select the method of rotation of the initial factors, and label the dietary patterns.6 Moreover, dietary patterns may differ with regard not only to age and sex but also resident area, ethnic group, and culture. Therefore, it might be necessary to examine the validity and reproducibility of the identified dietary patterns among the study population of each study. To date, several studies have examined the reproducibility and validity of dietary patterns.7–13 However, among studies of the association between dietary patterns and disease risk in Japan, only 1 examined the validity of dietary patterns in Japan12 and none examined reproducibility. Therefore, we assessed the reproducibility and validity of dietary patterns identified by principal component analysis of a food frequency questionnaire (FFQ) used in the 5-year follow-up survey of the Japan Public Health Center-Based Prospective Study (JPHC Study)."},"methods_title":{"kind":"string","value":"METHODS"},"methods_text":{"kind":"string","value":" JPHC Study The JPHC Study is a population-based prospective study of cancer and cardiovascular disease and was launched in 1990 for cohort I and in 1993 for cohort II.14 The participants were 140 420 residents of 11 public health center areas and were aged 40 to 59 years for cohort I and aged 40 to 69 years for cohort II at each baseline survey. At baseline and at the 5- and 10-year follow-ups, a questionnaire survey was conducted to obtain information on medical histories and health-related lifestyle, including diet.\nThe JPHC Study is a population-based prospective study of cancer and cardiovascular disease and was launched in 1990 for cohort I and in 1993 for cohort II.14 The participants were 140 420 residents of 11 public health center areas and were aged 40 to 59 years for cohort I and aged 40 to 69 years for cohort II at each baseline survey. At baseline and at the 5- and 10-year follow-ups, a questionnaire survey was conducted to obtain information on medical histories and health-related lifestyle, including diet.\n Study population The subjects of the reproducibility and validity study of the FFQ used in the 5-year follow-up survey were a subsample of the participants in JPHC Study cohorts I and II. Married couples were recruited. The present study used a data set for 498 subjects (244 men and 254 women; 209 in cohort I and 289 in cohort II) who completed 2 FFQs with a 1-year interval and dietary records for a total of 28 or 14 days. Details of the validation study have been described elsewhere.15,16 There was no significant difference in mean age between participants of the validation study and cohort members among men and women in cohort I or among men in cohort II; however, there was a 3-year age difference among cohort II women (mean age, 56 years for the validation subsample vs 59 years for the cohort participants). In the present study, we identified dietary patterns using FFQ data from the 5-year follow-up survey. Oral or written informed consent from the participants was received before the study. The study did not undergo ethical approval since it was conducted before the advent of ethical guidelines for epidemiology research in Japan, which mandate such approval.\nThe subjects of the reproducibility and validity study of the FFQ used in the 5-year follow-up survey were a subsample of the participants in JPHC Study cohorts I and II. Married couples were recruited. The present study used a data set for 498 subjects (244 men and 254 women; 209 in cohort I and 289 in cohort II) who completed 2 FFQs with a 1-year interval and dietary records for a total of 28 or 14 days. Details of the validation study have been described elsewhere.15,16 There was no significant difference in mean age between participants of the validation study and cohort members among men and women in cohort I or among men in cohort II; however, there was a 3-year age difference among cohort II women (mean age, 56 years for the validation subsample vs 59 years for the cohort participants). In the present study, we identified dietary patterns using FFQ data from the 5-year follow-up survey. Oral or written informed consent from the participants was received before the study. The study did not undergo ethical approval since it was conducted before the advent of ethical guidelines for epidemiology research in Japan, which mandate such approval.\n Dietary assessment The participants completed the FFQ twice, with an approximately 1-year interval. The FFQ for the evaluation of validity (FFQ_V) was completed after completion of the last dietary record and was compared with the dietary records. The FFQ for the evaluation of reproducibility (FFQ_R) was administered 1 year before or after the FFQ_V and was compared with the FFQ_V. The FFQ included questions on 138 food items (with standard portions/units and eating frequency) and 14 supplementary questions during the previous year, from which a composition table was developed for 147 foods items.17 For most food items, 9 response options were available for eating frequency: rarely, 1 to 3 times/month, 1 to 2 times/week, 3 to 4 times/week, 5 to 6 times/week, once a day, 2 to 3 times/day, 4 to 6 times/day, and 7 or more times/day. Slightly different options were used for beverage intake: rarely, 1 to 2 times/week, 3 to 4 times/week, 5 to 6 times/week, 1 cup/day, 2 to 3 cups/day, 4 to 6 cups/day, 7 to 9 cups/day, and 10 or more cups/day. A standard portion size was specified for each food item, and the respondents were asked to choose their usual portion size from 3 options (less than half the standard portion size, standard portion size, or more than 1.5 times the standard portion size). We calculated daily intake of most foods by multiplying daily frequency of consumption by usual portion size. In the present analysis, we used 134 food and beverage items of the FFQ (excluding 11 items that correlated strongly with others and 2 items with no energy or nutrition).\nThe participants completed a 28- or 14-day (Okinawa) dietary record in 1 year, ie, 7-day dietary records were collected 4 (or 2) times at 3-month (or 6-month) intervals during the course of a year. The survey method using dietary records has been described elsewhere.15,16,18 We matched the 1101 unique food codes in the dietary records to food items on the FFQ to ensure that daily food intakes derived from the dietary records were comparable with those derived from the FFQ. A total of 558 food codes in the dietary records were matched to the 134 food items on the FFQ.\nThe participants completed the FFQ twice, with an approximately 1-year interval. The FFQ for the evaluation of validity (FFQ_V) was completed after completion of the last dietary record and was compared with the dietary records. The FFQ for the evaluation of reproducibility (FFQ_R) was administered 1 year before or after the FFQ_V and was compared with the FFQ_V. The FFQ included questions on 138 food items (with standard portions/units and eating frequency) and 14 supplementary questions during the previous year, from which a composition table was developed for 147 foods items.17 For most food items, 9 response options were available for eating frequency: rarely, 1 to 3 times/month, 1 to 2 times/week, 3 to 4 times/week, 5 to 6 times/week, once a day, 2 to 3 times/day, 4 to 6 times/day, and 7 or more times/day. Slightly different options were used for beverage intake: rarely, 1 to 2 times/week, 3 to 4 times/week, 5 to 6 times/week, 1 cup/day, 2 to 3 cups/day, 4 to 6 cups/day, 7 to 9 cups/day, and 10 or more cups/day. A standard portion size was specified for each food item, and the respondents were asked to choose their usual portion size from 3 options (less than half the standard portion size, standard portion size, or more than 1.5 times the standard portion size). We calculated daily intake of most foods by multiplying daily frequency of consumption by usual portion size. In the present analysis, we used 134 food and beverage items of the FFQ (excluding 11 items that correlated strongly with others and 2 items with no energy or nutrition).\nThe participants completed a 28- or 14-day (Okinawa) dietary record in 1 year, ie, 7-day dietary records were collected 4 (or 2) times at 3-month (or 6-month) intervals during the course of a year. The survey method using dietary records has been described elsewhere.15,16,18 We matched the 1101 unique food codes in the dietary records to food items on the FFQ to ensure that daily food intakes derived from the dietary records were comparable with those derived from the FFQ. A total of 558 food codes in the dietary records were matched to the 134 food items on the FFQ.\n Statistical analysis Men and women were analyzed separately. Some foods or food groups that were similar in nutritional content or culinary use were combined, leaving 48 food groups for the present analysis (Table 1). When information from dietary records was used to combine 2 or more foods into 1 food group, the amount of food before cooking was used for all food items except noodles and rice. Consumption of noodles and rice was expressed in weight of boiled noodles and steamed rice, respectively. Consumption of miso soup and beverages such as coffee and green tea was expressed as the weight of the consumed infusion rather than the weight of miso, coffee beans, instant coffee, or tea leaves. We calculated the means and standard deviations of each food group intake estimated from dietary records and the 2 FFQs. In addition, the reproducibility and validity of 48 food group intakes were evaluated by using Spearman correlation coefficients between intakes from the 2 FFQs and between intakes derived from the dietary records and those derived from the FFQ_V.\nTo identify dietary patterns, we performed principal component analysis based on log-transformed intakes of 48 food groups for each of the 2 FFQs and the dietary records. The factors were rotated by orthogonal transformation (varimax rotation) to maintain uncorrelated factors and greater interpretability. We considered eigenvalues, the scree plot, and the interpretability of the factors to determine the number of factors to retain and identified 3 dietary patterns (factors) in both men and women. The dietary patterns were named according to the food items showing high loading (absolute value) in each dietary pattern. The factor scores for each dietary pattern were calculated by summing intakes of food items weighted by their factor loadings. The scores were energy-adjusted using the residual method. When we examined reproducibility and validity by using dietary pattern scores without energy adjustment, or based on energy-adjusted intake of food groups, the derived dietary patterns and their reproducibility and validity were similar to those based on energy-adjusted scores.\nWe examined the correspondence of each dietary pattern identified from the dietary records with dietary patterns extracted from the FFQ_V by comparing the pattern of food items showing high loading. We repeated this procedure to link dietary patterns identified from the FFQ_R and FFQ_V. For each dietary pattern, Spearman correlation coefficients between the factor score for the dietary patterns derived from the dietary records and those derived from the FFQ_V were calculated to evaluate the validity of the FFQ. In addition, to determine the reproducibility of the FFQ, Spearman correlation coefficients between the factor scores for the dietary patterns derived from the 2 FFQs administered 1 year apart were calculated. We estimated Spearman correlation coefficients between factor loadings from the FFQ in each population (subsample of the validity study, overall cohort, men, women, and men and women combined) to compare the dietary patterns derived from the FFQ between the subsample of the validity study and overall cohort and between men or women and men and women combined in the overall cohort. All analyses were performed using Statistical Analysis System (SAS) version 9.1 (SAS Institute, Cary, NC, USA).\nMen and women were analyzed separately. Some foods or food groups that were similar in nutritional content or culinary use were combined, leaving 48 food groups for the present analysis (Table 1). When information from dietary records was used to combine 2 or more foods into 1 food group, the amount of food before cooking was used for all food items except noodles and rice. Consumption of noodles and rice was expressed in weight of boiled noodles and steamed rice, respectively. Consumption of miso soup and beverages such as coffee and green tea was expressed as the weight of the consumed infusion rather than the weight of miso, coffee beans, instant coffee, or tea leaves. We calculated the means and standard deviations of each food group intake estimated from dietary records and the 2 FFQs. In addition, the reproducibility and validity of 48 food group intakes were evaluated by using Spearman correlation coefficients between intakes from the 2 FFQs and between intakes derived from the dietary records and those derived from the FFQ_V.\nTo identify dietary patterns, we performed principal component analysis based on log-transformed intakes of 48 food groups for each of the 2 FFQs and the dietary records. The factors were rotated by orthogonal transformation (varimax rotation) to maintain uncorrelated factors and greater interpretability. We considered eigenvalues, the scree plot, and the interpretability of the factors to determine the number of factors to retain and identified 3 dietary patterns (factors) in both men and women. The dietary patterns were named according to the food items showing high loading (absolute value) in each dietary pattern. The factor scores for each dietary pattern were calculated by summing intakes of food items weighted by their factor loadings. The scores were energy-adjusted using the residual method. When we examined reproducibility and validity by using dietary pattern scores without energy adjustment, or based on energy-adjusted intake of food groups, the derived dietary patterns and their reproducibility and validity were similar to those based on energy-adjusted scores.\nWe examined the correspondence of each dietary pattern identified from the dietary records with dietary patterns extracted from the FFQ_V by comparing the pattern of food items showing high loading. We repeated this procedure to link dietary patterns identified from the FFQ_R and FFQ_V. For each dietary pattern, Spearman correlation coefficients between the factor score for the dietary patterns derived from the dietary records and those derived from the FFQ_V were calculated to evaluate the validity of the FFQ. In addition, to determine the reproducibility of the FFQ, Spearman correlation coefficients between the factor scores for the dietary patterns derived from the 2 FFQs administered 1 year apart were calculated. We estimated Spearman correlation coefficients between factor loadings from the FFQ in each population (subsample of the validity study, overall cohort, men, women, and men and women combined) to compare the dietary patterns derived from the FFQ between the subsample of the validity study and overall cohort and between men or women and men and women combined in the overall cohort. All analyses were performed using Statistical Analysis System (SAS) version 9.1 (SAS Institute, Cary, NC, USA)."},"results_title":{"kind":"string","value":"RESULTS"},"results_text":{"kind":"string","value":"The 48 food group intakes calculated by 28- or 14-day dietary records and the 2 FFQs, and their correlations, are shown in Table 2 for men and Table 3 for women. Regarding reproducibility, Spearman correlation coefficients between the 2 FFQs ranged from 0.21 for wine to 0.80 for coffee in men (Table 2) and from 0.37 for sake to 0.81 for miso soup in women (Table 3). In particular, intakes of rice, bread, miso soup, pickles, other fruit, salt fish, processed meat, milk, dairy products, green tea, and coffee showed high correlation coefficients (r >0.60) between the 2 FFQs in both men and women. Regarding validity, Spearman correlation coefficients between the dietary records and the FFQ_V ranged from 0.06 for oily fish to 0.75 for coffee in men (Table 2) and from 0.12 for shochu to 0.80 for coffee in women (Table 3). In both men and women, intakes of coffee, bread, pickles, and milk estimated by the FFQ_V showed a high correlation (r >0.60) with those estimated by the dietary records, whereas the validity of oily fish between the dietary records and the FFQ_V was very low.\nAbbreviations: DR, dietary record; FFQ, food frequency questionnaire; SD, standard deviation.\naFFQ_R was administered 1 year after or before FFQ_V.\nbFFQ_V was administered after completion of the DRs.\nc28- and 14-day DRs were collected over a 1-year period.\nAbbreviations: DR, dietary record; FFQ, food frequency questionnaire; SD, standard deviation.\naFFQ_R was administered 1 year after or before FFQ_V.\nbFFQ_V was administered after completion of the DRs.\nc28- and 14-day DRs were collected over a 1-year period.\nPrincipal component analysis identified 3 dietary patterns from the dietary records and 2 FFQs (Table 4 for men and Table 5 for women), and they were similar in terms of factor loading pattern across the data sources and between sexes. Of the 3 dietary patterns, a pattern highly loaded by intakes of vegetables, fruit, potatoes, soy products, mushrooms, seaweed, oily fish, and green tea was named the prudent Japanese pattern. A dietary pattern associated with high intakes of bread, meat, processed meat, fruit juice, coffee, black tea, soft drinks, sauces, mayonnaise, and dressing was named the westernized Japanese pattern. Another dietary pattern was characterized by high intakes of rice, miso soup, pickles, salmon, salty fish, seafood other than fish, fruit, and sake (men only), and was named the traditional Japanese pattern. The 3 dietary patterns from the dietary records, the FFQ_R, and the FFQ_V explained 23.9%, 29.4%, and 26.5%, respectively, of variance in men and 23.0%, 24.9%, and 32.9%, respectively, of variance in women.\nAbbreviations: FFQ, food frequency questionnaire; DR, dietary record.\naFor simplicity, factor loadings less than ±0.10 are not listed.\nbFFQ_R was administered 1 year after or before FFQ_V.\ncFFQ_V was administered after completion of the DRs.\nd28- and 14-day DRs were collected over a 1-year period.\nAbbreviations: FFQ, food frequency questionnaire; DR, dietary record.\naFor simplicity, factor loadings less than ±0.10 are not listed.\nbFFQ_R was administered 1 year after or before FFQ_V.\ncFFQ_V was administered after completion of the DRs.\nd28- and 14-day DRs were collected over a 1-year period.\nThree similar dietary patterns were identified in the overall cohort for both men and women (Appendix). The Spearman correlation coefficients between factor loadings from FFQ_R in the subsample of the validity study and those from the FFQ in the overall cohort for 48 food groups were 0.89 in men and 0.82 in women for the prudent Japanese pattern, 0.82 in men and 0.75 in women for the westernized Japanese pattern, and 0.75 in men and 0.47 in women for the traditional Japanese pattern. In the overall cohort, the corresponding values between factor loadings for men or women and those for men and women combined were 0.98 in both men and women for the prudent Japanese pattern, 0.96 in men and 0.95 in women for the westernized Japanese pattern, and 0.76 in men and 0.74 in women for the traditional Japanese pattern.\nThe Spearman correlation coefficients between the dietary records and the 2 FFQs are shown in Table 6. Regarding reproducibility between the 2 FFQs, all 3 dietary patterns were acceptable in both men and women. In particular, the traditional Japanese pattern in men and the westernized Japanese pattern in women were highly reproduced (correlation coefficients: 0.77 in men and 0.71 in women). Regarding validity, the traditional Japanese pattern had higher correlation coefficients between the dietary records and the FFQ_V than did other patterns among both men and women (correlation coefficient: 0.49 in men and 0.63 in women), whereas the westernized Japanese pattern in men (0.32) had the lowest correlation coefficient among the 3 dietary patterns.\nAbbreviations: DR, dietary record; FFQ, food frequency questionnaire.\naDietary pattern scores were adjusted for energy intake by using the residual method.\nbFFQ_R was administered 1 year after or before FFQ_V.\ncFFQ_V was administered after completion of the DRs.\nd28- or 14-day DRs were collected over a 1-year period.\nAll correlations: P < 0.001."},"conclusions_title":{"kind":"null"},"conclusions_text":{"kind":"null"},"other_sections_titles":{"kind":"list like","value":["INTRODUCTION","JPHC Study","Study population","Dietary assessment","Statistical analysis"],"string":"[\n \"INTRODUCTION\",\n \"JPHC Study\",\n \"Study population\",\n \"Dietary assessment\",\n \"Statistical analysis\"\n]"},"other_sections_texts":{"kind":"list like","value":["In nutritional epidemiology, there is increased interest in the analysis of dietary pattern, a comprehensive variable that integrates consumption of several foods or food groups. The effect of a single nutrient, food, or food group on disease risk and morbid conditions is difficult to assess in observational studies because foods and nutrients are consumed in combination and their complex effects are likely to be interactive or synergistic.1 However, dietary pattern can overcome problems relating to the close intercorrelation among foods or nutrients and is expected to have a greater impact on disease risk than any single nutrient.1–3 Therefore, analysis using dietary pattern could be used as a complementary approach in nutritional epidemiology. Indeed, many previous studies have reported associations of dietary patterns with mortality, several diseases (such as cancer, cardiovascular disease, and type 2 diabetes), and biomarkers.4,5\nExtraction of dietary pattern by principal component analysis—which is often used to identify dietary patterns—requires some arbitrary decisions to group food items, determine the number of factors to retain, select the method of rotation of the initial factors, and label the dietary patterns.6 Moreover, dietary patterns may differ with regard not only to age and sex but also resident area, ethnic group, and culture. Therefore, it might be necessary to examine the validity and reproducibility of the identified dietary patterns among the study population of each study. To date, several studies have examined the reproducibility and validity of dietary patterns.7–13 However, among studies of the association between dietary patterns and disease risk in Japan, only 1 examined the validity of dietary patterns in Japan12 and none examined reproducibility. Therefore, we assessed the reproducibility and validity of dietary patterns identified by principal component analysis of a food frequency questionnaire (FFQ) used in the 5-year follow-up survey of the Japan Public Health Center-Based Prospective Study (JPHC Study).","The JPHC Study is a population-based prospective study of cancer and cardiovascular disease and was launched in 1990 for cohort I and in 1993 for cohort II.14 The participants were 140 420 residents of 11 public health center areas and were aged 40 to 59 years for cohort I and aged 40 to 69 years for cohort II at each baseline survey. At baseline and at the 5- and 10-year follow-ups, a questionnaire survey was conducted to obtain information on medical histories and health-related lifestyle, including diet.","The subjects of the reproducibility and validity study of the FFQ used in the 5-year follow-up survey were a subsample of the participants in JPHC Study cohorts I and II. Married couples were recruited. The present study used a data set for 498 subjects (244 men and 254 women; 209 in cohort I and 289 in cohort II) who completed 2 FFQs with a 1-year interval and dietary records for a total of 28 or 14 days. Details of the validation study have been described elsewhere.15,16 There was no significant difference in mean age between participants of the validation study and cohort members among men and women in cohort I or among men in cohort II; however, there was a 3-year age difference among cohort II women (mean age, 56 years for the validation subsample vs 59 years for the cohort participants). In the present study, we identified dietary patterns using FFQ data from the 5-year follow-up survey. Oral or written informed consent from the participants was received before the study. The study did not undergo ethical approval since it was conducted before the advent of ethical guidelines for epidemiology research in Japan, which mandate such approval.","The participants completed the FFQ twice, with an approximately 1-year interval. The FFQ for the evaluation of validity (FFQ_V) was completed after completion of the last dietary record and was compared with the dietary records. The FFQ for the evaluation of reproducibility (FFQ_R) was administered 1 year before or after the FFQ_V and was compared with the FFQ_V. The FFQ included questions on 138 food items (with standard portions/units and eating frequency) and 14 supplementary questions during the previous year, from which a composition table was developed for 147 foods items.17 For most food items, 9 response options were available for eating frequency: rarely, 1 to 3 times/month, 1 to 2 times/week, 3 to 4 times/week, 5 to 6 times/week, once a day, 2 to 3 times/day, 4 to 6 times/day, and 7 or more times/day. Slightly different options were used for beverage intake: rarely, 1 to 2 times/week, 3 to 4 times/week, 5 to 6 times/week, 1 cup/day, 2 to 3 cups/day, 4 to 6 cups/day, 7 to 9 cups/day, and 10 or more cups/day. A standard portion size was specified for each food item, and the respondents were asked to choose their usual portion size from 3 options (less than half the standard portion size, standard portion size, or more than 1.5 times the standard portion size). We calculated daily intake of most foods by multiplying daily frequency of consumption by usual portion size. In the present analysis, we used 134 food and beverage items of the FFQ (excluding 11 items that correlated strongly with others and 2 items with no energy or nutrition).\nThe participants completed a 28- or 14-day (Okinawa) dietary record in 1 year, ie, 7-day dietary records were collected 4 (or 2) times at 3-month (or 6-month) intervals during the course of a year. The survey method using dietary records has been described elsewhere.15,16,18 We matched the 1101 unique food codes in the dietary records to food items on the FFQ to ensure that daily food intakes derived from the dietary records were comparable with those derived from the FFQ. A total of 558 food codes in the dietary records were matched to the 134 food items on the FFQ.","Men and women were analyzed separately. Some foods or food groups that were similar in nutritional content or culinary use were combined, leaving 48 food groups for the present analysis (Table 1). When information from dietary records was used to combine 2 or more foods into 1 food group, the amount of food before cooking was used for all food items except noodles and rice. Consumption of noodles and rice was expressed in weight of boiled noodles and steamed rice, respectively. Consumption of miso soup and beverages such as coffee and green tea was expressed as the weight of the consumed infusion rather than the weight of miso, coffee beans, instant coffee, or tea leaves. We calculated the means and standard deviations of each food group intake estimated from dietary records and the 2 FFQs. In addition, the reproducibility and validity of 48 food group intakes were evaluated by using Spearman correlation coefficients between intakes from the 2 FFQs and between intakes derived from the dietary records and those derived from the FFQ_V.\nTo identify dietary patterns, we performed principal component analysis based on log-transformed intakes of 48 food groups for each of the 2 FFQs and the dietary records. The factors were rotated by orthogonal transformation (varimax rotation) to maintain uncorrelated factors and greater interpretability. We considered eigenvalues, the scree plot, and the interpretability of the factors to determine the number of factors to retain and identified 3 dietary patterns (factors) in both men and women. The dietary patterns were named according to the food items showing high loading (absolute value) in each dietary pattern. The factor scores for each dietary pattern were calculated by summing intakes of food items weighted by their factor loadings. The scores were energy-adjusted using the residual method. When we examined reproducibility and validity by using dietary pattern scores without energy adjustment, or based on energy-adjusted intake of food groups, the derived dietary patterns and their reproducibility and validity were similar to those based on energy-adjusted scores.\nWe examined the correspondence of each dietary pattern identified from the dietary records with dietary patterns extracted from the FFQ_V by comparing the pattern of food items showing high loading. We repeated this procedure to link dietary patterns identified from the FFQ_R and FFQ_V. For each dietary pattern, Spearman correlation coefficients between the factor score for the dietary patterns derived from the dietary records and those derived from the FFQ_V were calculated to evaluate the validity of the FFQ. In addition, to determine the reproducibility of the FFQ, Spearman correlation coefficients between the factor scores for the dietary patterns derived from the 2 FFQs administered 1 year apart were calculated. We estimated Spearman correlation coefficients between factor loadings from the FFQ in each population (subsample of the validity study, overall cohort, men, women, and men and women combined) to compare the dietary patterns derived from the FFQ between the subsample of the validity study and overall cohort and between men or women and men and women combined in the overall cohort. All analyses were performed using Statistical Analysis System (SAS) version 9.1 (SAS Institute, Cary, NC, USA)."],"string":"[\n \"In nutritional epidemiology, there is increased interest in the analysis of dietary pattern, a comprehensive variable that integrates consumption of several foods or food groups. The effect of a single nutrient, food, or food group on disease risk and morbid conditions is difficult to assess in observational studies because foods and nutrients are consumed in combination and their complex effects are likely to be interactive or synergistic.1 However, dietary pattern can overcome problems relating to the close intercorrelation among foods or nutrients and is expected to have a greater impact on disease risk than any single nutrient.1–3 Therefore, analysis using dietary pattern could be used as a complementary approach in nutritional epidemiology. Indeed, many previous studies have reported associations of dietary patterns with mortality, several diseases (such as cancer, cardiovascular disease, and type 2 diabetes), and biomarkers.4,5\\nExtraction of dietary pattern by principal component analysis—which is often used to identify dietary patterns—requires some arbitrary decisions to group food items, determine the number of factors to retain, select the method of rotation of the initial factors, and label the dietary patterns.6 Moreover, dietary patterns may differ with regard not only to age and sex but also resident area, ethnic group, and culture. Therefore, it might be necessary to examine the validity and reproducibility of the identified dietary patterns among the study population of each study. To date, several studies have examined the reproducibility and validity of dietary patterns.7–13 However, among studies of the association between dietary patterns and disease risk in Japan, only 1 examined the validity of dietary patterns in Japan12 and none examined reproducibility. Therefore, we assessed the reproducibility and validity of dietary patterns identified by principal component analysis of a food frequency questionnaire (FFQ) used in the 5-year follow-up survey of the Japan Public Health Center-Based Prospective Study (JPHC Study).\",\n \"The JPHC Study is a population-based prospective study of cancer and cardiovascular disease and was launched in 1990 for cohort I and in 1993 for cohort II.14 The participants were 140 420 residents of 11 public health center areas and were aged 40 to 59 years for cohort I and aged 40 to 69 years for cohort II at each baseline survey. At baseline and at the 5- and 10-year follow-ups, a questionnaire survey was conducted to obtain information on medical histories and health-related lifestyle, including diet.\",\n \"The subjects of the reproducibility and validity study of the FFQ used in the 5-year follow-up survey were a subsample of the participants in JPHC Study cohorts I and II. Married couples were recruited. The present study used a data set for 498 subjects (244 men and 254 women; 209 in cohort I and 289 in cohort II) who completed 2 FFQs with a 1-year interval and dietary records for a total of 28 or 14 days. Details of the validation study have been described elsewhere.15,16 There was no significant difference in mean age between participants of the validation study and cohort members among men and women in cohort I or among men in cohort II; however, there was a 3-year age difference among cohort II women (mean age, 56 years for the validation subsample vs 59 years for the cohort participants). In the present study, we identified dietary patterns using FFQ data from the 5-year follow-up survey. Oral or written informed consent from the participants was received before the study. The study did not undergo ethical approval since it was conducted before the advent of ethical guidelines for epidemiology research in Japan, which mandate such approval.\",\n \"The participants completed the FFQ twice, with an approximately 1-year interval. The FFQ for the evaluation of validity (FFQ_V) was completed after completion of the last dietary record and was compared with the dietary records. The FFQ for the evaluation of reproducibility (FFQ_R) was administered 1 year before or after the FFQ_V and was compared with the FFQ_V. The FFQ included questions on 138 food items (with standard portions/units and eating frequency) and 14 supplementary questions during the previous year, from which a composition table was developed for 147 foods items.17 For most food items, 9 response options were available for eating frequency: rarely, 1 to 3 times/month, 1 to 2 times/week, 3 to 4 times/week, 5 to 6 times/week, once a day, 2 to 3 times/day, 4 to 6 times/day, and 7 or more times/day. Slightly different options were used for beverage intake: rarely, 1 to 2 times/week, 3 to 4 times/week, 5 to 6 times/week, 1 cup/day, 2 to 3 cups/day, 4 to 6 cups/day, 7 to 9 cups/day, and 10 or more cups/day. A standard portion size was specified for each food item, and the respondents were asked to choose their usual portion size from 3 options (less than half the standard portion size, standard portion size, or more than 1.5 times the standard portion size). We calculated daily intake of most foods by multiplying daily frequency of consumption by usual portion size. In the present analysis, we used 134 food and beverage items of the FFQ (excluding 11 items that correlated strongly with others and 2 items with no energy or nutrition).\\nThe participants completed a 28- or 14-day (Okinawa) dietary record in 1 year, ie, 7-day dietary records were collected 4 (or 2) times at 3-month (or 6-month) intervals during the course of a year. The survey method using dietary records has been described elsewhere.15,16,18 We matched the 1101 unique food codes in the dietary records to food items on the FFQ to ensure that daily food intakes derived from the dietary records were comparable with those derived from the FFQ. A total of 558 food codes in the dietary records were matched to the 134 food items on the FFQ.\",\n \"Men and women were analyzed separately. Some foods or food groups that were similar in nutritional content or culinary use were combined, leaving 48 food groups for the present analysis (Table 1). When information from dietary records was used to combine 2 or more foods into 1 food group, the amount of food before cooking was used for all food items except noodles and rice. Consumption of noodles and rice was expressed in weight of boiled noodles and steamed rice, respectively. Consumption of miso soup and beverages such as coffee and green tea was expressed as the weight of the consumed infusion rather than the weight of miso, coffee beans, instant coffee, or tea leaves. We calculated the means and standard deviations of each food group intake estimated from dietary records and the 2 FFQs. In addition, the reproducibility and validity of 48 food group intakes were evaluated by using Spearman correlation coefficients between intakes from the 2 FFQs and between intakes derived from the dietary records and those derived from the FFQ_V.\\nTo identify dietary patterns, we performed principal component analysis based on log-transformed intakes of 48 food groups for each of the 2 FFQs and the dietary records. The factors were rotated by orthogonal transformation (varimax rotation) to maintain uncorrelated factors and greater interpretability. We considered eigenvalues, the scree plot, and the interpretability of the factors to determine the number of factors to retain and identified 3 dietary patterns (factors) in both men and women. The dietary patterns were named according to the food items showing high loading (absolute value) in each dietary pattern. The factor scores for each dietary pattern were calculated by summing intakes of food items weighted by their factor loadings. The scores were energy-adjusted using the residual method. When we examined reproducibility and validity by using dietary pattern scores without energy adjustment, or based on energy-adjusted intake of food groups, the derived dietary patterns and their reproducibility and validity were similar to those based on energy-adjusted scores.\\nWe examined the correspondence of each dietary pattern identified from the dietary records with dietary patterns extracted from the FFQ_V by comparing the pattern of food items showing high loading. We repeated this procedure to link dietary patterns identified from the FFQ_R and FFQ_V. For each dietary pattern, Spearman correlation coefficients between the factor score for the dietary patterns derived from the dietary records and those derived from the FFQ_V were calculated to evaluate the validity of the FFQ. In addition, to determine the reproducibility of the FFQ, Spearman correlation coefficients between the factor scores for the dietary patterns derived from the 2 FFQs administered 1 year apart were calculated. We estimated Spearman correlation coefficients between factor loadings from the FFQ in each population (subsample of the validity study, overall cohort, men, women, and men and women combined) to compare the dietary patterns derived from the FFQ between the subsample of the validity study and overall cohort and between men or women and men and women combined in the overall cohort. All analyses were performed using Statistical Analysis System (SAS) version 9.1 (SAS Institute, Cary, NC, USA).\"\n]"},"other_sections_sec_types":{"kind":"list like","value":[null,null,null,null,null],"string":"[\n null,\n null,\n null,\n null,\n null\n]"},"all_sections_titles":{"kind":"list like","value":["INTRODUCTION","METHODS","JPHC Study","Study population","Dietary assessment","Statistical analysis","RESULTS","DISCUSSION"],"string":"[\n \"INTRODUCTION\",\n \"METHODS\",\n \"JPHC Study\",\n \"Study population\",\n \"Dietary assessment\",\n \"Statistical analysis\",\n \"RESULTS\",\n \"DISCUSSION\"\n]"},"all_sections_texts":{"kind":"list like","value":["In nutritional epidemiology, there is increased interest in the analysis of dietary pattern, a comprehensive variable that integrates consumption of several foods or food groups. The effect of a single nutrient, food, or food group on disease risk and morbid conditions is difficult to assess in observational studies because foods and nutrients are consumed in combination and their complex effects are likely to be interactive or synergistic.1 However, dietary pattern can overcome problems relating to the close intercorrelation among foods or nutrients and is expected to have a greater impact on disease risk than any single nutrient.1–3 Therefore, analysis using dietary pattern could be used as a complementary approach in nutritional epidemiology. Indeed, many previous studies have reported associations of dietary patterns with mortality, several diseases (such as cancer, cardiovascular disease, and type 2 diabetes), and biomarkers.4,5\nExtraction of dietary pattern by principal component analysis—which is often used to identify dietary patterns—requires some arbitrary decisions to group food items, determine the number of factors to retain, select the method of rotation of the initial factors, and label the dietary patterns.6 Moreover, dietary patterns may differ with regard not only to age and sex but also resident area, ethnic group, and culture. Therefore, it might be necessary to examine the validity and reproducibility of the identified dietary patterns among the study population of each study. To date, several studies have examined the reproducibility and validity of dietary patterns.7–13 However, among studies of the association between dietary patterns and disease risk in Japan, only 1 examined the validity of dietary patterns in Japan12 and none examined reproducibility. Therefore, we assessed the reproducibility and validity of dietary patterns identified by principal component analysis of a food frequency questionnaire (FFQ) used in the 5-year follow-up survey of the Japan Public Health Center-Based Prospective Study (JPHC Study)."," JPHC Study The JPHC Study is a population-based prospective study of cancer and cardiovascular disease and was launched in 1990 for cohort I and in 1993 for cohort II.14 The participants were 140 420 residents of 11 public health center areas and were aged 40 to 59 years for cohort I and aged 40 to 69 years for cohort II at each baseline survey. At baseline and at the 5- and 10-year follow-ups, a questionnaire survey was conducted to obtain information on medical histories and health-related lifestyle, including diet.\nThe JPHC Study is a population-based prospective study of cancer and cardiovascular disease and was launched in 1990 for cohort I and in 1993 for cohort II.14 The participants were 140 420 residents of 11 public health center areas and were aged 40 to 59 years for cohort I and aged 40 to 69 years for cohort II at each baseline survey. At baseline and at the 5- and 10-year follow-ups, a questionnaire survey was conducted to obtain information on medical histories and health-related lifestyle, including diet.\n Study population The subjects of the reproducibility and validity study of the FFQ used in the 5-year follow-up survey were a subsample of the participants in JPHC Study cohorts I and II. Married couples were recruited. The present study used a data set for 498 subjects (244 men and 254 women; 209 in cohort I and 289 in cohort II) who completed 2 FFQs with a 1-year interval and dietary records for a total of 28 or 14 days. Details of the validation study have been described elsewhere.15,16 There was no significant difference in mean age between participants of the validation study and cohort members among men and women in cohort I or among men in cohort II; however, there was a 3-year age difference among cohort II women (mean age, 56 years for the validation subsample vs 59 years for the cohort participants). In the present study, we identified dietary patterns using FFQ data from the 5-year follow-up survey. Oral or written informed consent from the participants was received before the study. The study did not undergo ethical approval since it was conducted before the advent of ethical guidelines for epidemiology research in Japan, which mandate such approval.\nThe subjects of the reproducibility and validity study of the FFQ used in the 5-year follow-up survey were a subsample of the participants in JPHC Study cohorts I and II. Married couples were recruited. The present study used a data set for 498 subjects (244 men and 254 women; 209 in cohort I and 289 in cohort II) who completed 2 FFQs with a 1-year interval and dietary records for a total of 28 or 14 days. Details of the validation study have been described elsewhere.15,16 There was no significant difference in mean age between participants of the validation study and cohort members among men and women in cohort I or among men in cohort II; however, there was a 3-year age difference among cohort II women (mean age, 56 years for the validation subsample vs 59 years for the cohort participants). In the present study, we identified dietary patterns using FFQ data from the 5-year follow-up survey. Oral or written informed consent from the participants was received before the study. The study did not undergo ethical approval since it was conducted before the advent of ethical guidelines for epidemiology research in Japan, which mandate such approval.\n Dietary assessment The participants completed the FFQ twice, with an approximately 1-year interval. The FFQ for the evaluation of validity (FFQ_V) was completed after completion of the last dietary record and was compared with the dietary records. The FFQ for the evaluation of reproducibility (FFQ_R) was administered 1 year before or after the FFQ_V and was compared with the FFQ_V. The FFQ included questions on 138 food items (with standard portions/units and eating frequency) and 14 supplementary questions during the previous year, from which a composition table was developed for 147 foods items.17 For most food items, 9 response options were available for eating frequency: rarely, 1 to 3 times/month, 1 to 2 times/week, 3 to 4 times/week, 5 to 6 times/week, once a day, 2 to 3 times/day, 4 to 6 times/day, and 7 or more times/day. Slightly different options were used for beverage intake: rarely, 1 to 2 times/week, 3 to 4 times/week, 5 to 6 times/week, 1 cup/day, 2 to 3 cups/day, 4 to 6 cups/day, 7 to 9 cups/day, and 10 or more cups/day. A standard portion size was specified for each food item, and the respondents were asked to choose their usual portion size from 3 options (less than half the standard portion size, standard portion size, or more than 1.5 times the standard portion size). We calculated daily intake of most foods by multiplying daily frequency of consumption by usual portion size. In the present analysis, we used 134 food and beverage items of the FFQ (excluding 11 items that correlated strongly with others and 2 items with no energy or nutrition).\nThe participants completed a 28- or 14-day (Okinawa) dietary record in 1 year, ie, 7-day dietary records were collected 4 (or 2) times at 3-month (or 6-month) intervals during the course of a year. The survey method using dietary records has been described elsewhere.15,16,18 We matched the 1101 unique food codes in the dietary records to food items on the FFQ to ensure that daily food intakes derived from the dietary records were comparable with those derived from the FFQ. A total of 558 food codes in the dietary records were matched to the 134 food items on the FFQ.\nThe participants completed the FFQ twice, with an approximately 1-year interval. The FFQ for the evaluation of validity (FFQ_V) was completed after completion of the last dietary record and was compared with the dietary records. The FFQ for the evaluation of reproducibility (FFQ_R) was administered 1 year before or after the FFQ_V and was compared with the FFQ_V. The FFQ included questions on 138 food items (with standard portions/units and eating frequency) and 14 supplementary questions during the previous year, from which a composition table was developed for 147 foods items.17 For most food items, 9 response options were available for eating frequency: rarely, 1 to 3 times/month, 1 to 2 times/week, 3 to 4 times/week, 5 to 6 times/week, once a day, 2 to 3 times/day, 4 to 6 times/day, and 7 or more times/day. Slightly different options were used for beverage intake: rarely, 1 to 2 times/week, 3 to 4 times/week, 5 to 6 times/week, 1 cup/day, 2 to 3 cups/day, 4 to 6 cups/day, 7 to 9 cups/day, and 10 or more cups/day. A standard portion size was specified for each food item, and the respondents were asked to choose their usual portion size from 3 options (less than half the standard portion size, standard portion size, or more than 1.5 times the standard portion size). We calculated daily intake of most foods by multiplying daily frequency of consumption by usual portion size. In the present analysis, we used 134 food and beverage items of the FFQ (excluding 11 items that correlated strongly with others and 2 items with no energy or nutrition).\nThe participants completed a 28- or 14-day (Okinawa) dietary record in 1 year, ie, 7-day dietary records were collected 4 (or 2) times at 3-month (or 6-month) intervals during the course of a year. The survey method using dietary records has been described elsewhere.15,16,18 We matched the 1101 unique food codes in the dietary records to food items on the FFQ to ensure that daily food intakes derived from the dietary records were comparable with those derived from the FFQ. A total of 558 food codes in the dietary records were matched to the 134 food items on the FFQ.\n Statistical analysis Men and women were analyzed separately. Some foods or food groups that were similar in nutritional content or culinary use were combined, leaving 48 food groups for the present analysis (Table 1). When information from dietary records was used to combine 2 or more foods into 1 food group, the amount of food before cooking was used for all food items except noodles and rice. Consumption of noodles and rice was expressed in weight of boiled noodles and steamed rice, respectively. Consumption of miso soup and beverages such as coffee and green tea was expressed as the weight of the consumed infusion rather than the weight of miso, coffee beans, instant coffee, or tea leaves. We calculated the means and standard deviations of each food group intake estimated from dietary records and the 2 FFQs. In addition, the reproducibility and validity of 48 food group intakes were evaluated by using Spearman correlation coefficients between intakes from the 2 FFQs and between intakes derived from the dietary records and those derived from the FFQ_V.\nTo identify dietary patterns, we performed principal component analysis based on log-transformed intakes of 48 food groups for each of the 2 FFQs and the dietary records. The factors were rotated by orthogonal transformation (varimax rotation) to maintain uncorrelated factors and greater interpretability. We considered eigenvalues, the scree plot, and the interpretability of the factors to determine the number of factors to retain and identified 3 dietary patterns (factors) in both men and women. The dietary patterns were named according to the food items showing high loading (absolute value) in each dietary pattern. The factor scores for each dietary pattern were calculated by summing intakes of food items weighted by their factor loadings. The scores were energy-adjusted using the residual method. When we examined reproducibility and validity by using dietary pattern scores without energy adjustment, or based on energy-adjusted intake of food groups, the derived dietary patterns and their reproducibility and validity were similar to those based on energy-adjusted scores.\nWe examined the correspondence of each dietary pattern identified from the dietary records with dietary patterns extracted from the FFQ_V by comparing the pattern of food items showing high loading. We repeated this procedure to link dietary patterns identified from the FFQ_R and FFQ_V. For each dietary pattern, Spearman correlation coefficients between the factor score for the dietary patterns derived from the dietary records and those derived from the FFQ_V were calculated to evaluate the validity of the FFQ. In addition, to determine the reproducibility of the FFQ, Spearman correlation coefficients between the factor scores for the dietary patterns derived from the 2 FFQs administered 1 year apart were calculated. We estimated Spearman correlation coefficients between factor loadings from the FFQ in each population (subsample of the validity study, overall cohort, men, women, and men and women combined) to compare the dietary patterns derived from the FFQ between the subsample of the validity study and overall cohort and between men or women and men and women combined in the overall cohort. All analyses were performed using Statistical Analysis System (SAS) version 9.1 (SAS Institute, Cary, NC, USA).\nMen and women were analyzed separately. Some foods or food groups that were similar in nutritional content or culinary use were combined, leaving 48 food groups for the present analysis (Table 1). When information from dietary records was used to combine 2 or more foods into 1 food group, the amount of food before cooking was used for all food items except noodles and rice. Consumption of noodles and rice was expressed in weight of boiled noodles and steamed rice, respectively. Consumption of miso soup and beverages such as coffee and green tea was expressed as the weight of the consumed infusion rather than the weight of miso, coffee beans, instant coffee, or tea leaves. We calculated the means and standard deviations of each food group intake estimated from dietary records and the 2 FFQs. In addition, the reproducibility and validity of 48 food group intakes were evaluated by using Spearman correlation coefficients between intakes from the 2 FFQs and between intakes derived from the dietary records and those derived from the FFQ_V.\nTo identify dietary patterns, we performed principal component analysis based on log-transformed intakes of 48 food groups for each of the 2 FFQs and the dietary records. The factors were rotated by orthogonal transformation (varimax rotation) to maintain uncorrelated factors and greater interpretability. We considered eigenvalues, the scree plot, and the interpretability of the factors to determine the number of factors to retain and identified 3 dietary patterns (factors) in both men and women. The dietary patterns were named according to the food items showing high loading (absolute value) in each dietary pattern. The factor scores for each dietary pattern were calculated by summing intakes of food items weighted by their factor loadings. The scores were energy-adjusted using the residual method. When we examined reproducibility and validity by using dietary pattern scores without energy adjustment, or based on energy-adjusted intake of food groups, the derived dietary patterns and their reproducibility and validity were similar to those based on energy-adjusted scores.\nWe examined the correspondence of each dietary pattern identified from the dietary records with dietary patterns extracted from the FFQ_V by comparing the pattern of food items showing high loading. We repeated this procedure to link dietary patterns identified from the FFQ_R and FFQ_V. For each dietary pattern, Spearman correlation coefficients between the factor score for the dietary patterns derived from the dietary records and those derived from the FFQ_V were calculated to evaluate the validity of the FFQ. In addition, to determine the reproducibility of the FFQ, Spearman correlation coefficients between the factor scores for the dietary patterns derived from the 2 FFQs administered 1 year apart were calculated. We estimated Spearman correlation coefficients between factor loadings from the FFQ in each population (subsample of the validity study, overall cohort, men, women, and men and women combined) to compare the dietary patterns derived from the FFQ between the subsample of the validity study and overall cohort and between men or women and men and women combined in the overall cohort. All analyses were performed using Statistical Analysis System (SAS) version 9.1 (SAS Institute, Cary, NC, USA).","The JPHC Study is a population-based prospective study of cancer and cardiovascular disease and was launched in 1990 for cohort I and in 1993 for cohort II.14 The participants were 140 420 residents of 11 public health center areas and were aged 40 to 59 years for cohort I and aged 40 to 69 years for cohort II at each baseline survey. At baseline and at the 5- and 10-year follow-ups, a questionnaire survey was conducted to obtain information on medical histories and health-related lifestyle, including diet.","The subjects of the reproducibility and validity study of the FFQ used in the 5-year follow-up survey were a subsample of the participants in JPHC Study cohorts I and II. Married couples were recruited. The present study used a data set for 498 subjects (244 men and 254 women; 209 in cohort I and 289 in cohort II) who completed 2 FFQs with a 1-year interval and dietary records for a total of 28 or 14 days. Details of the validation study have been described elsewhere.15,16 There was no significant difference in mean age between participants of the validation study and cohort members among men and women in cohort I or among men in cohort II; however, there was a 3-year age difference among cohort II women (mean age, 56 years for the validation subsample vs 59 years for the cohort participants). In the present study, we identified dietary patterns using FFQ data from the 5-year follow-up survey. Oral or written informed consent from the participants was received before the study. The study did not undergo ethical approval since it was conducted before the advent of ethical guidelines for epidemiology research in Japan, which mandate such approval.","The participants completed the FFQ twice, with an approximately 1-year interval. The FFQ for the evaluation of validity (FFQ_V) was completed after completion of the last dietary record and was compared with the dietary records. The FFQ for the evaluation of reproducibility (FFQ_R) was administered 1 year before or after the FFQ_V and was compared with the FFQ_V. The FFQ included questions on 138 food items (with standard portions/units and eating frequency) and 14 supplementary questions during the previous year, from which a composition table was developed for 147 foods items.17 For most food items, 9 response options were available for eating frequency: rarely, 1 to 3 times/month, 1 to 2 times/week, 3 to 4 times/week, 5 to 6 times/week, once a day, 2 to 3 times/day, 4 to 6 times/day, and 7 or more times/day. Slightly different options were used for beverage intake: rarely, 1 to 2 times/week, 3 to 4 times/week, 5 to 6 times/week, 1 cup/day, 2 to 3 cups/day, 4 to 6 cups/day, 7 to 9 cups/day, and 10 or more cups/day. A standard portion size was specified for each food item, and the respondents were asked to choose their usual portion size from 3 options (less than half the standard portion size, standard portion size, or more than 1.5 times the standard portion size). We calculated daily intake of most foods by multiplying daily frequency of consumption by usual portion size. In the present analysis, we used 134 food and beverage items of the FFQ (excluding 11 items that correlated strongly with others and 2 items with no energy or nutrition).\nThe participants completed a 28- or 14-day (Okinawa) dietary record in 1 year, ie, 7-day dietary records were collected 4 (or 2) times at 3-month (or 6-month) intervals during the course of a year. The survey method using dietary records has been described elsewhere.15,16,18 We matched the 1101 unique food codes in the dietary records to food items on the FFQ to ensure that daily food intakes derived from the dietary records were comparable with those derived from the FFQ. A total of 558 food codes in the dietary records were matched to the 134 food items on the FFQ.","Men and women were analyzed separately. Some foods or food groups that were similar in nutritional content or culinary use were combined, leaving 48 food groups for the present analysis (Table 1). When information from dietary records was used to combine 2 or more foods into 1 food group, the amount of food before cooking was used for all food items except noodles and rice. Consumption of noodles and rice was expressed in weight of boiled noodles and steamed rice, respectively. Consumption of miso soup and beverages such as coffee and green tea was expressed as the weight of the consumed infusion rather than the weight of miso, coffee beans, instant coffee, or tea leaves. We calculated the means and standard deviations of each food group intake estimated from dietary records and the 2 FFQs. In addition, the reproducibility and validity of 48 food group intakes were evaluated by using Spearman correlation coefficients between intakes from the 2 FFQs and between intakes derived from the dietary records and those derived from the FFQ_V.\nTo identify dietary patterns, we performed principal component analysis based on log-transformed intakes of 48 food groups for each of the 2 FFQs and the dietary records. The factors were rotated by orthogonal transformation (varimax rotation) to maintain uncorrelated factors and greater interpretability. We considered eigenvalues, the scree plot, and the interpretability of the factors to determine the number of factors to retain and identified 3 dietary patterns (factors) in both men and women. The dietary patterns were named according to the food items showing high loading (absolute value) in each dietary pattern. The factor scores for each dietary pattern were calculated by summing intakes of food items weighted by their factor loadings. The scores were energy-adjusted using the residual method. When we examined reproducibility and validity by using dietary pattern scores without energy adjustment, or based on energy-adjusted intake of food groups, the derived dietary patterns and their reproducibility and validity were similar to those based on energy-adjusted scores.\nWe examined the correspondence of each dietary pattern identified from the dietary records with dietary patterns extracted from the FFQ_V by comparing the pattern of food items showing high loading. We repeated this procedure to link dietary patterns identified from the FFQ_R and FFQ_V. For each dietary pattern, Spearman correlation coefficients between the factor score for the dietary patterns derived from the dietary records and those derived from the FFQ_V were calculated to evaluate the validity of the FFQ. In addition, to determine the reproducibility of the FFQ, Spearman correlation coefficients between the factor scores for the dietary patterns derived from the 2 FFQs administered 1 year apart were calculated. We estimated Spearman correlation coefficients between factor loadings from the FFQ in each population (subsample of the validity study, overall cohort, men, women, and men and women combined) to compare the dietary patterns derived from the FFQ between the subsample of the validity study and overall cohort and between men or women and men and women combined in the overall cohort. All analyses were performed using Statistical Analysis System (SAS) version 9.1 (SAS Institute, Cary, NC, USA).","The 48 food group intakes calculated by 28- or 14-day dietary records and the 2 FFQs, and their correlations, are shown in Table 2 for men and Table 3 for women. Regarding reproducibility, Spearman correlation coefficients between the 2 FFQs ranged from 0.21 for wine to 0.80 for coffee in men (Table 2) and from 0.37 for sake to 0.81 for miso soup in women (Table 3). In particular, intakes of rice, bread, miso soup, pickles, other fruit, salt fish, processed meat, milk, dairy products, green tea, and coffee showed high correlation coefficients (r >0.60) between the 2 FFQs in both men and women. Regarding validity, Spearman correlation coefficients between the dietary records and the FFQ_V ranged from 0.06 for oily fish to 0.75 for coffee in men (Table 2) and from 0.12 for shochu to 0.80 for coffee in women (Table 3). In both men and women, intakes of coffee, bread, pickles, and milk estimated by the FFQ_V showed a high correlation (r >0.60) with those estimated by the dietary records, whereas the validity of oily fish between the dietary records and the FFQ_V was very low.\nAbbreviations: DR, dietary record; FFQ, food frequency questionnaire; SD, standard deviation.\naFFQ_R was administered 1 year after or before FFQ_V.\nbFFQ_V was administered after completion of the DRs.\nc28- and 14-day DRs were collected over a 1-year period.\nAbbreviations: DR, dietary record; FFQ, food frequency questionnaire; SD, standard deviation.\naFFQ_R was administered 1 year after or before FFQ_V.\nbFFQ_V was administered after completion of the DRs.\nc28- and 14-day DRs were collected over a 1-year period.\nPrincipal component analysis identified 3 dietary patterns from the dietary records and 2 FFQs (Table 4 for men and Table 5 for women), and they were similar in terms of factor loading pattern across the data sources and between sexes. Of the 3 dietary patterns, a pattern highly loaded by intakes of vegetables, fruit, potatoes, soy products, mushrooms, seaweed, oily fish, and green tea was named the prudent Japanese pattern. A dietary pattern associated with high intakes of bread, meat, processed meat, fruit juice, coffee, black tea, soft drinks, sauces, mayonnaise, and dressing was named the westernized Japanese pattern. Another dietary pattern was characterized by high intakes of rice, miso soup, pickles, salmon, salty fish, seafood other than fish, fruit, and sake (men only), and was named the traditional Japanese pattern. The 3 dietary patterns from the dietary records, the FFQ_R, and the FFQ_V explained 23.9%, 29.4%, and 26.5%, respectively, of variance in men and 23.0%, 24.9%, and 32.9%, respectively, of variance in women.\nAbbreviations: FFQ, food frequency questionnaire; DR, dietary record.\naFor simplicity, factor loadings less than ±0.10 are not listed.\nbFFQ_R was administered 1 year after or before FFQ_V.\ncFFQ_V was administered after completion of the DRs.\nd28- and 14-day DRs were collected over a 1-year period.\nAbbreviations: FFQ, food frequency questionnaire; DR, dietary record.\naFor simplicity, factor loadings less than ±0.10 are not listed.\nbFFQ_R was administered 1 year after or before FFQ_V.\ncFFQ_V was administered after completion of the DRs.\nd28- and 14-day DRs were collected over a 1-year period.\nThree similar dietary patterns were identified in the overall cohort for both men and women (Appendix). The Spearman correlation coefficients between factor loadings from FFQ_R in the subsample of the validity study and those from the FFQ in the overall cohort for 48 food groups were 0.89 in men and 0.82 in women for the prudent Japanese pattern, 0.82 in men and 0.75 in women for the westernized Japanese pattern, and 0.75 in men and 0.47 in women for the traditional Japanese pattern. In the overall cohort, the corresponding values between factor loadings for men or women and those for men and women combined were 0.98 in both men and women for the prudent Japanese pattern, 0.96 in men and 0.95 in women for the westernized Japanese pattern, and 0.76 in men and 0.74 in women for the traditional Japanese pattern.\nThe Spearman correlation coefficients between the dietary records and the 2 FFQs are shown in Table 6. Regarding reproducibility between the 2 FFQs, all 3 dietary patterns were acceptable in both men and women. In particular, the traditional Japanese pattern in men and the westernized Japanese pattern in women were highly reproduced (correlation coefficients: 0.77 in men and 0.71 in women). Regarding validity, the traditional Japanese pattern had higher correlation coefficients between the dietary records and the FFQ_V than did other patterns among both men and women (correlation coefficient: 0.49 in men and 0.63 in women), whereas the westernized Japanese pattern in men (0.32) had the lowest correlation coefficient among the 3 dietary patterns.\nAbbreviations: DR, dietary record; FFQ, food frequency questionnaire.\naDietary pattern scores were adjusted for energy intake by using the residual method.\nbFFQ_R was administered 1 year after or before FFQ_V.\ncFFQ_V was administered after completion of the DRs.\nd28- or 14-day DRs were collected over a 1-year period.\nAll correlations: P < 0.001.","In a subsample of the validation study of diet in the JPHC Study, we identified 3 major Japanese dietary patterns (prudent, westernized, and traditional) that were similar across data sources and between sexes. The reproducibility and validity of dietary patterns derived from the FFQ were acceptable. Spearman correlation coefficients between scores of the dietary patterns derived from the 2 FFQs ranged from 0.55 to 0.77. The corresponding values between scores of the dietary patterns derived from the dietary records and those derived from the FFQ ranged from 0.32 to 0.63. These dietary patterns were also identified in the entire population of the JPHC Study.\nThe 3 major dietary patterns identified in the present study have also been reported in previous Japanese studies. Most studies in Japan have identified a dietary pattern similar to the prudent Japanese pattern in the present study.19–35 This dietary pattern is characterized by high intakes of not only vegetables and fruit but also typical Japanese foods, including soy products, seaweed, mushrooms, fish, and green tea. Dietary patterns similar to the westernized Japanese pattern in the present study, ie, high intakes of meat, processed meat, bread, coffee, black tea, mayonnaise, and dressing, have also been observed in many Japanese studies.19–35 Moreover, the traditional Japanese pattern, which is characterized by high intakes of rice, miso soup, pickles, salmon, salty fish, and fruit, has been observed in several studies.19–21,26,30,31 Because the westernized breakfast pattern22,24,28,29,33,34 and similar patterns, which were identified in some other studies, is associated with low intakes of rice and miso soup, the westernized breakfast pattern could be viewed as the mirror image of the traditional Japanese pattern.\nWe observed acceptable validity and reproducibility of the 3 dietary patterns; the Spearman correlation coefficients between the dietary records and 2 FFQs ranged from 0.55 to 0.77 for reproducibility and from 0.32 to 0.63 for validity. These values were comparable to those reported in previous studies. Regarding reproducibility, the correlation coefficients ranged from 0.67 to 0.70 (Pearson) in the Health Professional Follow-up Study,9 from 0.63 to 0.73 (over a 1-year period; Spearman),10 and from 0.30 to 0.52 (over a 10-year period; Pearson)11 in the Swedish Mammography Cohort, and from 0.64 to 0.81 (Spearman) in the Southampton Women’s Survey.7 Regarding validity, the correlation coefficients ranged from 0.34 to 0.64 (Pearson) in the Health Professionals Follow-up Study,9 from 0.34 to 0.61 (Pearson) in the Monitoring of Trends and Determinants in Cardiovascular Diseases,13 from 0.41 to 0.73 (Spearman) in the Swedish Mammography Cohort,10 from 0.35 to 0.67 (Pearson) in a UK study,8 and from 0.36 to 0.62 (Pearson) in a Japanese study.12\nIn the present study, the traditional Japanese pattern in men and the westernized Japanese pattern in women were highly reproducible. In the 2 FFQs, salty fish, salmon, pickles, sake, and rice, which are characteristic of the traditional Japanese pattern in men, showed high loadings to the pattern. In addition, these food intakes were highly correlated between the 2 FFQs. Similarly, intakes of beef, processed meat, coffee, bread, pork, chicken, and dressing showed high loadings to the westernized Japanese pattern in women in the 2 FFQs and were also highly correlated between the 2 FFQs. Of the 3 dietary patterns identified, the traditional Japanese pattern showed relatively good validity in both men and women. This might be because pickles, rice, salty fish, salmon, and sake (men only) showed high factor loadings to both dietary record and FFQ, and these intakes were moderately to highly correlated between the dietary record and FFQ. In contrast, the westernized Japanese pattern in men was less valid, perhaps because among foods that were highly and positively loaded to the westernized Japanese pattern derived using dietary record data, some fish items were inversely (not positively) associated with the dietary pattern based on FFQ data. In addition, intakes of meat and processed meat were only moderately correlated between dietary record and FFQ.\nRegarding the traditional Japanese pattern in women, the factor loadings from the FFQ in the subsample and those from the FFQ in the overall cohort for food groups were not highly correlated (correlation coefficient = 0.47). This might be because factor loadings of pickles, citrus fruit, and noodles in the traditional Japanese pattern were lower in the overall cohort than in the subsample, whereas those for pork, beef, processed meat, egg, oily fish, and fish products were higher in the overall cohort than in the subsample.\nOur study had some limitations. First, although we assessed the validity and reproducibility of dietary patterns in a subsample of the JPHC Study population, participants in the validation study might be more health conscious than nonparticipants. Therefore, the present findings might not be applicable to the entire population of the JPHC Study. However, we also identified 3 similar dietary patterns in the entire population. Second, we observed similar dietary patterns in men and women. This might be partly because married couples were recruited for the validity study. Third, we assessed validity based on a single measurement of dietary intake, which might not reflect past dietary patterns. In addition, dietary pattern alone could change during the follow-up period, due to changes in food preference and food availability. Finally, we used the FFQ completed at the end of the dietary records to evaluate validity. Participant recall of dietary intake could be influenced by the administration of dietary records.\nIn conclusion, we identified 3 Japanese dietary patterns (prudent, westernized, and traditional) using FFQ data among participants of the validation study and confirmed that validity and reproducibility of the FFQ are acceptable. These validation data provide a basis for future analysis of the association between dietary patterns and disease risk in the JPHC Study population.\n\nAbbreviation: JPHC Study, Japan Public Health Center-Based Prospective Study.\naFor simplicity, factor loadings less than ±0.10 are not listed."],"string":"[\n \"In nutritional epidemiology, there is increased interest in the analysis of dietary pattern, a comprehensive variable that integrates consumption of several foods or food groups. The effect of a single nutrient, food, or food group on disease risk and morbid conditions is difficult to assess in observational studies because foods and nutrients are consumed in combination and their complex effects are likely to be interactive or synergistic.1 However, dietary pattern can overcome problems relating to the close intercorrelation among foods or nutrients and is expected to have a greater impact on disease risk than any single nutrient.1–3 Therefore, analysis using dietary pattern could be used as a complementary approach in nutritional epidemiology. Indeed, many previous studies have reported associations of dietary patterns with mortality, several diseases (such as cancer, cardiovascular disease, and type 2 diabetes), and biomarkers.4,5\\nExtraction of dietary pattern by principal component analysis—which is often used to identify dietary patterns—requires some arbitrary decisions to group food items, determine the number of factors to retain, select the method of rotation of the initial factors, and label the dietary patterns.6 Moreover, dietary patterns may differ with regard not only to age and sex but also resident area, ethnic group, and culture. Therefore, it might be necessary to examine the validity and reproducibility of the identified dietary patterns among the study population of each study. To date, several studies have examined the reproducibility and validity of dietary patterns.7–13 However, among studies of the association between dietary patterns and disease risk in Japan, only 1 examined the validity of dietary patterns in Japan12 and none examined reproducibility. Therefore, we assessed the reproducibility and validity of dietary patterns identified by principal component analysis of a food frequency questionnaire (FFQ) used in the 5-year follow-up survey of the Japan Public Health Center-Based Prospective Study (JPHC Study).\",\n \" JPHC Study The JPHC Study is a population-based prospective study of cancer and cardiovascular disease and was launched in 1990 for cohort I and in 1993 for cohort II.14 The participants were 140 420 residents of 11 public health center areas and were aged 40 to 59 years for cohort I and aged 40 to 69 years for cohort II at each baseline survey. At baseline and at the 5- and 10-year follow-ups, a questionnaire survey was conducted to obtain information on medical histories and health-related lifestyle, including diet.\\nThe JPHC Study is a population-based prospective study of cancer and cardiovascular disease and was launched in 1990 for cohort I and in 1993 for cohort II.14 The participants were 140 420 residents of 11 public health center areas and were aged 40 to 59 years for cohort I and aged 40 to 69 years for cohort II at each baseline survey. At baseline and at the 5- and 10-year follow-ups, a questionnaire survey was conducted to obtain information on medical histories and health-related lifestyle, including diet.\\n Study population The subjects of the reproducibility and validity study of the FFQ used in the 5-year follow-up survey were a subsample of the participants in JPHC Study cohorts I and II. Married couples were recruited. The present study used a data set for 498 subjects (244 men and 254 women; 209 in cohort I and 289 in cohort II) who completed 2 FFQs with a 1-year interval and dietary records for a total of 28 or 14 days. Details of the validation study have been described elsewhere.15,16 There was no significant difference in mean age between participants of the validation study and cohort members among men and women in cohort I or among men in cohort II; however, there was a 3-year age difference among cohort II women (mean age, 56 years for the validation subsample vs 59 years for the cohort participants). In the present study, we identified dietary patterns using FFQ data from the 5-year follow-up survey. Oral or written informed consent from the participants was received before the study. The study did not undergo ethical approval since it was conducted before the advent of ethical guidelines for epidemiology research in Japan, which mandate such approval.\\nThe subjects of the reproducibility and validity study of the FFQ used in the 5-year follow-up survey were a subsample of the participants in JPHC Study cohorts I and II. Married couples were recruited. The present study used a data set for 498 subjects (244 men and 254 women; 209 in cohort I and 289 in cohort II) who completed 2 FFQs with a 1-year interval and dietary records for a total of 28 or 14 days. Details of the validation study have been described elsewhere.15,16 There was no significant difference in mean age between participants of the validation study and cohort members among men and women in cohort I or among men in cohort II; however, there was a 3-year age difference among cohort II women (mean age, 56 years for the validation subsample vs 59 years for the cohort participants). In the present study, we identified dietary patterns using FFQ data from the 5-year follow-up survey. Oral or written informed consent from the participants was received before the study. The study did not undergo ethical approval since it was conducted before the advent of ethical guidelines for epidemiology research in Japan, which mandate such approval.\\n Dietary assessment The participants completed the FFQ twice, with an approximately 1-year interval. The FFQ for the evaluation of validity (FFQ_V) was completed after completion of the last dietary record and was compared with the dietary records. The FFQ for the evaluation of reproducibility (FFQ_R) was administered 1 year before or after the FFQ_V and was compared with the FFQ_V. The FFQ included questions on 138 food items (with standard portions/units and eating frequency) and 14 supplementary questions during the previous year, from which a composition table was developed for 147 foods items.17 For most food items, 9 response options were available for eating frequency: rarely, 1 to 3 times/month, 1 to 2 times/week, 3 to 4 times/week, 5 to 6 times/week, once a day, 2 to 3 times/day, 4 to 6 times/day, and 7 or more times/day. Slightly different options were used for beverage intake: rarely, 1 to 2 times/week, 3 to 4 times/week, 5 to 6 times/week, 1 cup/day, 2 to 3 cups/day, 4 to 6 cups/day, 7 to 9 cups/day, and 10 or more cups/day. A standard portion size was specified for each food item, and the respondents were asked to choose their usual portion size from 3 options (less than half the standard portion size, standard portion size, or more than 1.5 times the standard portion size). We calculated daily intake of most foods by multiplying daily frequency of consumption by usual portion size. In the present analysis, we used 134 food and beverage items of the FFQ (excluding 11 items that correlated strongly with others and 2 items with no energy or nutrition).\\nThe participants completed a 28- or 14-day (Okinawa) dietary record in 1 year, ie, 7-day dietary records were collected 4 (or 2) times at 3-month (or 6-month) intervals during the course of a year. The survey method using dietary records has been described elsewhere.15,16,18 We matched the 1101 unique food codes in the dietary records to food items on the FFQ to ensure that daily food intakes derived from the dietary records were comparable with those derived from the FFQ. A total of 558 food codes in the dietary records were matched to the 134 food items on the FFQ.\\nThe participants completed the FFQ twice, with an approximately 1-year interval. The FFQ for the evaluation of validity (FFQ_V) was completed after completion of the last dietary record and was compared with the dietary records. The FFQ for the evaluation of reproducibility (FFQ_R) was administered 1 year before or after the FFQ_V and was compared with the FFQ_V. The FFQ included questions on 138 food items (with standard portions/units and eating frequency) and 14 supplementary questions during the previous year, from which a composition table was developed for 147 foods items.17 For most food items, 9 response options were available for eating frequency: rarely, 1 to 3 times/month, 1 to 2 times/week, 3 to 4 times/week, 5 to 6 times/week, once a day, 2 to 3 times/day, 4 to 6 times/day, and 7 or more times/day. Slightly different options were used for beverage intake: rarely, 1 to 2 times/week, 3 to 4 times/week, 5 to 6 times/week, 1 cup/day, 2 to 3 cups/day, 4 to 6 cups/day, 7 to 9 cups/day, and 10 or more cups/day. A standard portion size was specified for each food item, and the respondents were asked to choose their usual portion size from 3 options (less than half the standard portion size, standard portion size, or more than 1.5 times the standard portion size). We calculated daily intake of most foods by multiplying daily frequency of consumption by usual portion size. In the present analysis, we used 134 food and beverage items of the FFQ (excluding 11 items that correlated strongly with others and 2 items with no energy or nutrition).\\nThe participants completed a 28- or 14-day (Okinawa) dietary record in 1 year, ie, 7-day dietary records were collected 4 (or 2) times at 3-month (or 6-month) intervals during the course of a year. The survey method using dietary records has been described elsewhere.15,16,18 We matched the 1101 unique food codes in the dietary records to food items on the FFQ to ensure that daily food intakes derived from the dietary records were comparable with those derived from the FFQ. A total of 558 food codes in the dietary records were matched to the 134 food items on the FFQ.\\n Statistical analysis Men and women were analyzed separately. Some foods or food groups that were similar in nutritional content or culinary use were combined, leaving 48 food groups for the present analysis (Table 1). When information from dietary records was used to combine 2 or more foods into 1 food group, the amount of food before cooking was used for all food items except noodles and rice. Consumption of noodles and rice was expressed in weight of boiled noodles and steamed rice, respectively. Consumption of miso soup and beverages such as coffee and green tea was expressed as the weight of the consumed infusion rather than the weight of miso, coffee beans, instant coffee, or tea leaves. We calculated the means and standard deviations of each food group intake estimated from dietary records and the 2 FFQs. In addition, the reproducibility and validity of 48 food group intakes were evaluated by using Spearman correlation coefficients between intakes from the 2 FFQs and between intakes derived from the dietary records and those derived from the FFQ_V.\\nTo identify dietary patterns, we performed principal component analysis based on log-transformed intakes of 48 food groups for each of the 2 FFQs and the dietary records. The factors were rotated by orthogonal transformation (varimax rotation) to maintain uncorrelated factors and greater interpretability. We considered eigenvalues, the scree plot, and the interpretability of the factors to determine the number of factors to retain and identified 3 dietary patterns (factors) in both men and women. The dietary patterns were named according to the food items showing high loading (absolute value) in each dietary pattern. The factor scores for each dietary pattern were calculated by summing intakes of food items weighted by their factor loadings. The scores were energy-adjusted using the residual method. When we examined reproducibility and validity by using dietary pattern scores without energy adjustment, or based on energy-adjusted intake of food groups, the derived dietary patterns and their reproducibility and validity were similar to those based on energy-adjusted scores.\\nWe examined the correspondence of each dietary pattern identified from the dietary records with dietary patterns extracted from the FFQ_V by comparing the pattern of food items showing high loading. We repeated this procedure to link dietary patterns identified from the FFQ_R and FFQ_V. For each dietary pattern, Spearman correlation coefficients between the factor score for the dietary patterns derived from the dietary records and those derived from the FFQ_V were calculated to evaluate the validity of the FFQ. In addition, to determine the reproducibility of the FFQ, Spearman correlation coefficients between the factor scores for the dietary patterns derived from the 2 FFQs administered 1 year apart were calculated. We estimated Spearman correlation coefficients between factor loadings from the FFQ in each population (subsample of the validity study, overall cohort, men, women, and men and women combined) to compare the dietary patterns derived from the FFQ between the subsample of the validity study and overall cohort and between men or women and men and women combined in the overall cohort. All analyses were performed using Statistical Analysis System (SAS) version 9.1 (SAS Institute, Cary, NC, USA).\\nMen and women were analyzed separately. Some foods or food groups that were similar in nutritional content or culinary use were combined, leaving 48 food groups for the present analysis (Table 1). When information from dietary records was used to combine 2 or more foods into 1 food group, the amount of food before cooking was used for all food items except noodles and rice. Consumption of noodles and rice was expressed in weight of boiled noodles and steamed rice, respectively. Consumption of miso soup and beverages such as coffee and green tea was expressed as the weight of the consumed infusion rather than the weight of miso, coffee beans, instant coffee, or tea leaves. We calculated the means and standard deviations of each food group intake estimated from dietary records and the 2 FFQs. In addition, the reproducibility and validity of 48 food group intakes were evaluated by using Spearman correlation coefficients between intakes from the 2 FFQs and between intakes derived from the dietary records and those derived from the FFQ_V.\\nTo identify dietary patterns, we performed principal component analysis based on log-transformed intakes of 48 food groups for each of the 2 FFQs and the dietary records. The factors were rotated by orthogonal transformation (varimax rotation) to maintain uncorrelated factors and greater interpretability. We considered eigenvalues, the scree plot, and the interpretability of the factors to determine the number of factors to retain and identified 3 dietary patterns (factors) in both men and women. The dietary patterns were named according to the food items showing high loading (absolute value) in each dietary pattern. The factor scores for each dietary pattern were calculated by summing intakes of food items weighted by their factor loadings. The scores were energy-adjusted using the residual method. When we examined reproducibility and validity by using dietary pattern scores without energy adjustment, or based on energy-adjusted intake of food groups, the derived dietary patterns and their reproducibility and validity were similar to those based on energy-adjusted scores.\\nWe examined the correspondence of each dietary pattern identified from the dietary records with dietary patterns extracted from the FFQ_V by comparing the pattern of food items showing high loading. We repeated this procedure to link dietary patterns identified from the FFQ_R and FFQ_V. For each dietary pattern, Spearman correlation coefficients between the factor score for the dietary patterns derived from the dietary records and those derived from the FFQ_V were calculated to evaluate the validity of the FFQ. In addition, to determine the reproducibility of the FFQ, Spearman correlation coefficients between the factor scores for the dietary patterns derived from the 2 FFQs administered 1 year apart were calculated. We estimated Spearman correlation coefficients between factor loadings from the FFQ in each population (subsample of the validity study, overall cohort, men, women, and men and women combined) to compare the dietary patterns derived from the FFQ between the subsample of the validity study and overall cohort and between men or women and men and women combined in the overall cohort. All analyses were performed using Statistical Analysis System (SAS) version 9.1 (SAS Institute, Cary, NC, USA).\",\n \"The JPHC Study is a population-based prospective study of cancer and cardiovascular disease and was launched in 1990 for cohort I and in 1993 for cohort II.14 The participants were 140 420 residents of 11 public health center areas and were aged 40 to 59 years for cohort I and aged 40 to 69 years for cohort II at each baseline survey. At baseline and at the 5- and 10-year follow-ups, a questionnaire survey was conducted to obtain information on medical histories and health-related lifestyle, including diet.\",\n \"The subjects of the reproducibility and validity study of the FFQ used in the 5-year follow-up survey were a subsample of the participants in JPHC Study cohorts I and II. Married couples were recruited. The present study used a data set for 498 subjects (244 men and 254 women; 209 in cohort I and 289 in cohort II) who completed 2 FFQs with a 1-year interval and dietary records for a total of 28 or 14 days. Details of the validation study have been described elsewhere.15,16 There was no significant difference in mean age between participants of the validation study and cohort members among men and women in cohort I or among men in cohort II; however, there was a 3-year age difference among cohort II women (mean age, 56 years for the validation subsample vs 59 years for the cohort participants). In the present study, we identified dietary patterns using FFQ data from the 5-year follow-up survey. Oral or written informed consent from the participants was received before the study. The study did not undergo ethical approval since it was conducted before the advent of ethical guidelines for epidemiology research in Japan, which mandate such approval.\",\n \"The participants completed the FFQ twice, with an approximately 1-year interval. The FFQ for the evaluation of validity (FFQ_V) was completed after completion of the last dietary record and was compared with the dietary records. The FFQ for the evaluation of reproducibility (FFQ_R) was administered 1 year before or after the FFQ_V and was compared with the FFQ_V. The FFQ included questions on 138 food items (with standard portions/units and eating frequency) and 14 supplementary questions during the previous year, from which a composition table was developed for 147 foods items.17 For most food items, 9 response options were available for eating frequency: rarely, 1 to 3 times/month, 1 to 2 times/week, 3 to 4 times/week, 5 to 6 times/week, once a day, 2 to 3 times/day, 4 to 6 times/day, and 7 or more times/day. Slightly different options were used for beverage intake: rarely, 1 to 2 times/week, 3 to 4 times/week, 5 to 6 times/week, 1 cup/day, 2 to 3 cups/day, 4 to 6 cups/day, 7 to 9 cups/day, and 10 or more cups/day. A standard portion size was specified for each food item, and the respondents were asked to choose their usual portion size from 3 options (less than half the standard portion size, standard portion size, or more than 1.5 times the standard portion size). We calculated daily intake of most foods by multiplying daily frequency of consumption by usual portion size. In the present analysis, we used 134 food and beverage items of the FFQ (excluding 11 items that correlated strongly with others and 2 items with no energy or nutrition).\\nThe participants completed a 28- or 14-day (Okinawa) dietary record in 1 year, ie, 7-day dietary records were collected 4 (or 2) times at 3-month (or 6-month) intervals during the course of a year. The survey method using dietary records has been described elsewhere.15,16,18 We matched the 1101 unique food codes in the dietary records to food items on the FFQ to ensure that daily food intakes derived from the dietary records were comparable with those derived from the FFQ. A total of 558 food codes in the dietary records were matched to the 134 food items on the FFQ.\",\n \"Men and women were analyzed separately. Some foods or food groups that were similar in nutritional content or culinary use were combined, leaving 48 food groups for the present analysis (Table 1). When information from dietary records was used to combine 2 or more foods into 1 food group, the amount of food before cooking was used for all food items except noodles and rice. Consumption of noodles and rice was expressed in weight of boiled noodles and steamed rice, respectively. Consumption of miso soup and beverages such as coffee and green tea was expressed as the weight of the consumed infusion rather than the weight of miso, coffee beans, instant coffee, or tea leaves. We calculated the means and standard deviations of each food group intake estimated from dietary records and the 2 FFQs. In addition, the reproducibility and validity of 48 food group intakes were evaluated by using Spearman correlation coefficients between intakes from the 2 FFQs and between intakes derived from the dietary records and those derived from the FFQ_V.\\nTo identify dietary patterns, we performed principal component analysis based on log-transformed intakes of 48 food groups for each of the 2 FFQs and the dietary records. The factors were rotated by orthogonal transformation (varimax rotation) to maintain uncorrelated factors and greater interpretability. We considered eigenvalues, the scree plot, and the interpretability of the factors to determine the number of factors to retain and identified 3 dietary patterns (factors) in both men and women. The dietary patterns were named according to the food items showing high loading (absolute value) in each dietary pattern. The factor scores for each dietary pattern were calculated by summing intakes of food items weighted by their factor loadings. The scores were energy-adjusted using the residual method. When we examined reproducibility and validity by using dietary pattern scores without energy adjustment, or based on energy-adjusted intake of food groups, the derived dietary patterns and their reproducibility and validity were similar to those based on energy-adjusted scores.\\nWe examined the correspondence of each dietary pattern identified from the dietary records with dietary patterns extracted from the FFQ_V by comparing the pattern of food items showing high loading. We repeated this procedure to link dietary patterns identified from the FFQ_R and FFQ_V. For each dietary pattern, Spearman correlation coefficients between the factor score for the dietary patterns derived from the dietary records and those derived from the FFQ_V were calculated to evaluate the validity of the FFQ. In addition, to determine the reproducibility of the FFQ, Spearman correlation coefficients between the factor scores for the dietary patterns derived from the 2 FFQs administered 1 year apart were calculated. We estimated Spearman correlation coefficients between factor loadings from the FFQ in each population (subsample of the validity study, overall cohort, men, women, and men and women combined) to compare the dietary patterns derived from the FFQ between the subsample of the validity study and overall cohort and between men or women and men and women combined in the overall cohort. All analyses were performed using Statistical Analysis System (SAS) version 9.1 (SAS Institute, Cary, NC, USA).\",\n \"The 48 food group intakes calculated by 28- or 14-day dietary records and the 2 FFQs, and their correlations, are shown in Table 2 for men and Table 3 for women. Regarding reproducibility, Spearman correlation coefficients between the 2 FFQs ranged from 0.21 for wine to 0.80 for coffee in men (Table 2) and from 0.37 for sake to 0.81 for miso soup in women (Table 3). In particular, intakes of rice, bread, miso soup, pickles, other fruit, salt fish, processed meat, milk, dairy products, green tea, and coffee showed high correlation coefficients (r >0.60) between the 2 FFQs in both men and women. Regarding validity, Spearman correlation coefficients between the dietary records and the FFQ_V ranged from 0.06 for oily fish to 0.75 for coffee in men (Table 2) and from 0.12 for shochu to 0.80 for coffee in women (Table 3). In both men and women, intakes of coffee, bread, pickles, and milk estimated by the FFQ_V showed a high correlation (r >0.60) with those estimated by the dietary records, whereas the validity of oily fish between the dietary records and the FFQ_V was very low.\\nAbbreviations: DR, dietary record; FFQ, food frequency questionnaire; SD, standard deviation.\\naFFQ_R was administered 1 year after or before FFQ_V.\\nbFFQ_V was administered after completion of the DRs.\\nc28- and 14-day DRs were collected over a 1-year period.\\nAbbreviations: DR, dietary record; FFQ, food frequency questionnaire; SD, standard deviation.\\naFFQ_R was administered 1 year after or before FFQ_V.\\nbFFQ_V was administered after completion of the DRs.\\nc28- and 14-day DRs were collected over a 1-year period.\\nPrincipal component analysis identified 3 dietary patterns from the dietary records and 2 FFQs (Table 4 for men and Table 5 for women), and they were similar in terms of factor loading pattern across the data sources and between sexes. Of the 3 dietary patterns, a pattern highly loaded by intakes of vegetables, fruit, potatoes, soy products, mushrooms, seaweed, oily fish, and green tea was named the prudent Japanese pattern. A dietary pattern associated with high intakes of bread, meat, processed meat, fruit juice, coffee, black tea, soft drinks, sauces, mayonnaise, and dressing was named the westernized Japanese pattern. Another dietary pattern was characterized by high intakes of rice, miso soup, pickles, salmon, salty fish, seafood other than fish, fruit, and sake (men only), and was named the traditional Japanese pattern. The 3 dietary patterns from the dietary records, the FFQ_R, and the FFQ_V explained 23.9%, 29.4%, and 26.5%, respectively, of variance in men and 23.0%, 24.9%, and 32.9%, respectively, of variance in women.\\nAbbreviations: FFQ, food frequency questionnaire; DR, dietary record.\\naFor simplicity, factor loadings less than ±0.10 are not listed.\\nbFFQ_R was administered 1 year after or before FFQ_V.\\ncFFQ_V was administered after completion of the DRs.\\nd28- and 14-day DRs were collected over a 1-year period.\\nAbbreviations: FFQ, food frequency questionnaire; DR, dietary record.\\naFor simplicity, factor loadings less than ±0.10 are not listed.\\nbFFQ_R was administered 1 year after or before FFQ_V.\\ncFFQ_V was administered after completion of the DRs.\\nd28- and 14-day DRs were collected over a 1-year period.\\nThree similar dietary patterns were identified in the overall cohort for both men and women (Appendix). The Spearman correlation coefficients between factor loadings from FFQ_R in the subsample of the validity study and those from the FFQ in the overall cohort for 48 food groups were 0.89 in men and 0.82 in women for the prudent Japanese pattern, 0.82 in men and 0.75 in women for the westernized Japanese pattern, and 0.75 in men and 0.47 in women for the traditional Japanese pattern. In the overall cohort, the corresponding values between factor loadings for men or women and those for men and women combined were 0.98 in both men and women for the prudent Japanese pattern, 0.96 in men and 0.95 in women for the westernized Japanese pattern, and 0.76 in men and 0.74 in women for the traditional Japanese pattern.\\nThe Spearman correlation coefficients between the dietary records and the 2 FFQs are shown in Table 6. Regarding reproducibility between the 2 FFQs, all 3 dietary patterns were acceptable in both men and women. In particular, the traditional Japanese pattern in men and the westernized Japanese pattern in women were highly reproduced (correlation coefficients: 0.77 in men and 0.71 in women). Regarding validity, the traditional Japanese pattern had higher correlation coefficients between the dietary records and the FFQ_V than did other patterns among both men and women (correlation coefficient: 0.49 in men and 0.63 in women), whereas the westernized Japanese pattern in men (0.32) had the lowest correlation coefficient among the 3 dietary patterns.\\nAbbreviations: DR, dietary record; FFQ, food frequency questionnaire.\\naDietary pattern scores were adjusted for energy intake by using the residual method.\\nbFFQ_R was administered 1 year after or before FFQ_V.\\ncFFQ_V was administered after completion of the DRs.\\nd28- or 14-day DRs were collected over a 1-year period.\\nAll correlations: P < 0.001.\",\n \"In a subsample of the validation study of diet in the JPHC Study, we identified 3 major Japanese dietary patterns (prudent, westernized, and traditional) that were similar across data sources and between sexes. The reproducibility and validity of dietary patterns derived from the FFQ were acceptable. Spearman correlation coefficients between scores of the dietary patterns derived from the 2 FFQs ranged from 0.55 to 0.77. The corresponding values between scores of the dietary patterns derived from the dietary records and those derived from the FFQ ranged from 0.32 to 0.63. These dietary patterns were also identified in the entire population of the JPHC Study.\\nThe 3 major dietary patterns identified in the present study have also been reported in previous Japanese studies. Most studies in Japan have identified a dietary pattern similar to the prudent Japanese pattern in the present study.19–35 This dietary pattern is characterized by high intakes of not only vegetables and fruit but also typical Japanese foods, including soy products, seaweed, mushrooms, fish, and green tea. Dietary patterns similar to the westernized Japanese pattern in the present study, ie, high intakes of meat, processed meat, bread, coffee, black tea, mayonnaise, and dressing, have also been observed in many Japanese studies.19–35 Moreover, the traditional Japanese pattern, which is characterized by high intakes of rice, miso soup, pickles, salmon, salty fish, and fruit, has been observed in several studies.19–21,26,30,31 Because the westernized breakfast pattern22,24,28,29,33,34 and similar patterns, which were identified in some other studies, is associated with low intakes of rice and miso soup, the westernized breakfast pattern could be viewed as the mirror image of the traditional Japanese pattern.\\nWe observed acceptable validity and reproducibility of the 3 dietary patterns; the Spearman correlation coefficients between the dietary records and 2 FFQs ranged from 0.55 to 0.77 for reproducibility and from 0.32 to 0.63 for validity. These values were comparable to those reported in previous studies. Regarding reproducibility, the correlation coefficients ranged from 0.67 to 0.70 (Pearson) in the Health Professional Follow-up Study,9 from 0.63 to 0.73 (over a 1-year period; Spearman),10 and from 0.30 to 0.52 (over a 10-year period; Pearson)11 in the Swedish Mammography Cohort, and from 0.64 to 0.81 (Spearman) in the Southampton Women’s Survey.7 Regarding validity, the correlation coefficients ranged from 0.34 to 0.64 (Pearson) in the Health Professionals Follow-up Study,9 from 0.34 to 0.61 (Pearson) in the Monitoring of Trends and Determinants in Cardiovascular Diseases,13 from 0.41 to 0.73 (Spearman) in the Swedish Mammography Cohort,10 from 0.35 to 0.67 (Pearson) in a UK study,8 and from 0.36 to 0.62 (Pearson) in a Japanese study.12\\nIn the present study, the traditional Japanese pattern in men and the westernized Japanese pattern in women were highly reproducible. In the 2 FFQs, salty fish, salmon, pickles, sake, and rice, which are characteristic of the traditional Japanese pattern in men, showed high loadings to the pattern. In addition, these food intakes were highly correlated between the 2 FFQs. Similarly, intakes of beef, processed meat, coffee, bread, pork, chicken, and dressing showed high loadings to the westernized Japanese pattern in women in the 2 FFQs and were also highly correlated between the 2 FFQs. Of the 3 dietary patterns identified, the traditional Japanese pattern showed relatively good validity in both men and women. This might be because pickles, rice, salty fish, salmon, and sake (men only) showed high factor loadings to both dietary record and FFQ, and these intakes were moderately to highly correlated between the dietary record and FFQ. In contrast, the westernized Japanese pattern in men was less valid, perhaps because among foods that were highly and positively loaded to the westernized Japanese pattern derived using dietary record data, some fish items were inversely (not positively) associated with the dietary pattern based on FFQ data. In addition, intakes of meat and processed meat were only moderately correlated between dietary record and FFQ.\\nRegarding the traditional Japanese pattern in women, the factor loadings from the FFQ in the subsample and those from the FFQ in the overall cohort for food groups were not highly correlated (correlation coefficient = 0.47). This might be because factor loadings of pickles, citrus fruit, and noodles in the traditional Japanese pattern were lower in the overall cohort than in the subsample, whereas those for pork, beef, processed meat, egg, oily fish, and fish products were higher in the overall cohort than in the subsample.\\nOur study had some limitations. First, although we assessed the validity and reproducibility of dietary patterns in a subsample of the JPHC Study population, participants in the validation study might be more health conscious than nonparticipants. Therefore, the present findings might not be applicable to the entire population of the JPHC Study. However, we also identified 3 similar dietary patterns in the entire population. Second, we observed similar dietary patterns in men and women. This might be partly because married couples were recruited for the validity study. Third, we assessed validity based on a single measurement of dietary intake, which might not reflect past dietary patterns. In addition, dietary pattern alone could change during the follow-up period, due to changes in food preference and food availability. Finally, we used the FFQ completed at the end of the dietary records to evaluate validity. Participant recall of dietary intake could be influenced by the administration of dietary records.\\nIn conclusion, we identified 3 Japanese dietary patterns (prudent, westernized, and traditional) using FFQ data among participants of the validation study and confirmed that validity and reproducibility of the FFQ are acceptable. These validation data provide a basis for future analysis of the association between dietary patterns and disease risk in the JPHC Study population.\\n\\nAbbreviation: JPHC Study, Japan Public Health Center-Based Prospective Study.\\naFor simplicity, factor loadings less than ±0.10 are not listed.\"\n]"},"all_sections_sec_types":{"kind":"list like","value":[null,"methods",null,null,null,null,"results","discussion"],"string":"[\n null,\n \"methods\",\n null,\n null,\n null,\n null,\n \"results\",\n \"discussion\"\n]"},"keywords":{"kind":"list like","value":["dietary patterns","Japanese","reproducibility","validity"],"string":"[\n \"dietary patterns\",\n \"Japanese\",\n \"reproducibility\",\n \"validity\"\n]"},"whole_article_text":{"kind":"string","value":"INTRODUCTION:\nIn nutritional epidemiology, there is increased interest in the analysis of dietary pattern, a comprehensive variable that integrates consumption of several foods or food groups. The effect of a single nutrient, food, or food group on disease risk and morbid conditions is difficult to assess in observational studies because foods and nutrients are consumed in combination and their complex effects are likely to be interactive or synergistic.1 However, dietary pattern can overcome problems relating to the close intercorrelation among foods or nutrients and is expected to have a greater impact on disease risk than any single nutrient.1–3 Therefore, analysis using dietary pattern could be used as a complementary approach in nutritional epidemiology. Indeed, many previous studies have reported associations of dietary patterns with mortality, several diseases (such as cancer, cardiovascular disease, and type 2 diabetes), and biomarkers.4,5\nExtraction of dietary pattern by principal component analysis—which is often used to identify dietary patterns—requires some arbitrary decisions to group food items, determine the number of factors to retain, select the method of rotation of the initial factors, and label the dietary patterns.6 Moreover, dietary patterns may differ with regard not only to age and sex but also resident area, ethnic group, and culture. Therefore, it might be necessary to examine the validity and reproducibility of the identified dietary patterns among the study population of each study. To date, several studies have examined the reproducibility and validity of dietary patterns.7–13 However, among studies of the association between dietary patterns and disease risk in Japan, only 1 examined the validity of dietary patterns in Japan12 and none examined reproducibility. Therefore, we assessed the reproducibility and validity of dietary patterns identified by principal component analysis of a food frequency questionnaire (FFQ) used in the 5-year follow-up survey of the Japan Public Health Center-Based Prospective Study (JPHC Study).\n\nMETHODS:\n JPHC Study The JPHC Study is a population-based prospective study of cancer and cardiovascular disease and was launched in 1990 for cohort I and in 1993 for cohort II.14 The participants were 140 420 residents of 11 public health center areas and were aged 40 to 59 years for cohort I and aged 40 to 69 years for cohort II at each baseline survey. At baseline and at the 5- and 10-year follow-ups, a questionnaire survey was conducted to obtain information on medical histories and health-related lifestyle, including diet.\nThe JPHC Study is a population-based prospective study of cancer and cardiovascular disease and was launched in 1990 for cohort I and in 1993 for cohort II.14 The participants were 140 420 residents of 11 public health center areas and were aged 40 to 59 years for cohort I and aged 40 to 69 years for cohort II at each baseline survey. At baseline and at the 5- and 10-year follow-ups, a questionnaire survey was conducted to obtain information on medical histories and health-related lifestyle, including diet.\n Study population The subjects of the reproducibility and validity study of the FFQ used in the 5-year follow-up survey were a subsample of the participants in JPHC Study cohorts I and II. Married couples were recruited. The present study used a data set for 498 subjects (244 men and 254 women; 209 in cohort I and 289 in cohort II) who completed 2 FFQs with a 1-year interval and dietary records for a total of 28 or 14 days. Details of the validation study have been described elsewhere.15,16 There was no significant difference in mean age between participants of the validation study and cohort members among men and women in cohort I or among men in cohort II; however, there was a 3-year age difference among cohort II women (mean age, 56 years for the validation subsample vs 59 years for the cohort participants). In the present study, we identified dietary patterns using FFQ data from the 5-year follow-up survey. Oral or written informed consent from the participants was received before the study. The study did not undergo ethical approval since it was conducted before the advent of ethical guidelines for epidemiology research in Japan, which mandate such approval.\nThe subjects of the reproducibility and validity study of the FFQ used in the 5-year follow-up survey were a subsample of the participants in JPHC Study cohorts I and II. Married couples were recruited. The present study used a data set for 498 subjects (244 men and 254 women; 209 in cohort I and 289 in cohort II) who completed 2 FFQs with a 1-year interval and dietary records for a total of 28 or 14 days. Details of the validation study have been described elsewhere.15,16 There was no significant difference in mean age between participants of the validation study and cohort members among men and women in cohort I or among men in cohort II; however, there was a 3-year age difference among cohort II women (mean age, 56 years for the validation subsample vs 59 years for the cohort participants). In the present study, we identified dietary patterns using FFQ data from the 5-year follow-up survey. Oral or written informed consent from the participants was received before the study. The study did not undergo ethical approval since it was conducted before the advent of ethical guidelines for epidemiology research in Japan, which mandate such approval.\n Dietary assessment The participants completed the FFQ twice, with an approximately 1-year interval. The FFQ for the evaluation of validity (FFQ_V) was completed after completion of the last dietary record and was compared with the dietary records. The FFQ for the evaluation of reproducibility (FFQ_R) was administered 1 year before or after the FFQ_V and was compared with the FFQ_V. The FFQ included questions on 138 food items (with standard portions/units and eating frequency) and 14 supplementary questions during the previous year, from which a composition table was developed for 147 foods items.17 For most food items, 9 response options were available for eating frequency: rarely, 1 to 3 times/month, 1 to 2 times/week, 3 to 4 times/week, 5 to 6 times/week, once a day, 2 to 3 times/day, 4 to 6 times/day, and 7 or more times/day. Slightly different options were used for beverage intake: rarely, 1 to 2 times/week, 3 to 4 times/week, 5 to 6 times/week, 1 cup/day, 2 to 3 cups/day, 4 to 6 cups/day, 7 to 9 cups/day, and 10 or more cups/day. A standard portion size was specified for each food item, and the respondents were asked to choose their usual portion size from 3 options (less than half the standard portion size, standard portion size, or more than 1.5 times the standard portion size). We calculated daily intake of most foods by multiplying daily frequency of consumption by usual portion size. In the present analysis, we used 134 food and beverage items of the FFQ (excluding 11 items that correlated strongly with others and 2 items with no energy or nutrition).\nThe participants completed a 28- or 14-day (Okinawa) dietary record in 1 year, ie, 7-day dietary records were collected 4 (or 2) times at 3-month (or 6-month) intervals during the course of a year. The survey method using dietary records has been described elsewhere.15,16,18 We matched the 1101 unique food codes in the dietary records to food items on the FFQ to ensure that daily food intakes derived from the dietary records were comparable with those derived from the FFQ. A total of 558 food codes in the dietary records were matched to the 134 food items on the FFQ.\nThe participants completed the FFQ twice, with an approximately 1-year interval. The FFQ for the evaluation of validity (FFQ_V) was completed after completion of the last dietary record and was compared with the dietary records. The FFQ for the evaluation of reproducibility (FFQ_R) was administered 1 year before or after the FFQ_V and was compared with the FFQ_V. The FFQ included questions on 138 food items (with standard portions/units and eating frequency) and 14 supplementary questions during the previous year, from which a composition table was developed for 147 foods items.17 For most food items, 9 response options were available for eating frequency: rarely, 1 to 3 times/month, 1 to 2 times/week, 3 to 4 times/week, 5 to 6 times/week, once a day, 2 to 3 times/day, 4 to 6 times/day, and 7 or more times/day. Slightly different options were used for beverage intake: rarely, 1 to 2 times/week, 3 to 4 times/week, 5 to 6 times/week, 1 cup/day, 2 to 3 cups/day, 4 to 6 cups/day, 7 to 9 cups/day, and 10 or more cups/day. A standard portion size was specified for each food item, and the respondents were asked to choose their usual portion size from 3 options (less than half the standard portion size, standard portion size, or more than 1.5 times the standard portion size). We calculated daily intake of most foods by multiplying daily frequency of consumption by usual portion size. In the present analysis, we used 134 food and beverage items of the FFQ (excluding 11 items that correlated strongly with others and 2 items with no energy or nutrition).\nThe participants completed a 28- or 14-day (Okinawa) dietary record in 1 year, ie, 7-day dietary records were collected 4 (or 2) times at 3-month (or 6-month) intervals during the course of a year. The survey method using dietary records has been described elsewhere.15,16,18 We matched the 1101 unique food codes in the dietary records to food items on the FFQ to ensure that daily food intakes derived from the dietary records were comparable with those derived from the FFQ. A total of 558 food codes in the dietary records were matched to the 134 food items on the FFQ.\n Statistical analysis Men and women were analyzed separately. Some foods or food groups that were similar in nutritional content or culinary use were combined, leaving 48 food groups for the present analysis (Table 1). When information from dietary records was used to combine 2 or more foods into 1 food group, the amount of food before cooking was used for all food items except noodles and rice. Consumption of noodles and rice was expressed in weight of boiled noodles and steamed rice, respectively. Consumption of miso soup and beverages such as coffee and green tea was expressed as the weight of the consumed infusion rather than the weight of miso, coffee beans, instant coffee, or tea leaves. We calculated the means and standard deviations of each food group intake estimated from dietary records and the 2 FFQs. In addition, the reproducibility and validity of 48 food group intakes were evaluated by using Spearman correlation coefficients between intakes from the 2 FFQs and between intakes derived from the dietary records and those derived from the FFQ_V.\nTo identify dietary patterns, we performed principal component analysis based on log-transformed intakes of 48 food groups for each of the 2 FFQs and the dietary records. The factors were rotated by orthogonal transformation (varimax rotation) to maintain uncorrelated factors and greater interpretability. We considered eigenvalues, the scree plot, and the interpretability of the factors to determine the number of factors to retain and identified 3 dietary patterns (factors) in both men and women. The dietary patterns were named according to the food items showing high loading (absolute value) in each dietary pattern. The factor scores for each dietary pattern were calculated by summing intakes of food items weighted by their factor loadings. The scores were energy-adjusted using the residual method. When we examined reproducibility and validity by using dietary pattern scores without energy adjustment, or based on energy-adjusted intake of food groups, the derived dietary patterns and their reproducibility and validity were similar to those based on energy-adjusted scores.\nWe examined the correspondence of each dietary pattern identified from the dietary records with dietary patterns extracted from the FFQ_V by comparing the pattern of food items showing high loading. We repeated this procedure to link dietary patterns identified from the FFQ_R and FFQ_V. For each dietary pattern, Spearman correlation coefficients between the factor score for the dietary patterns derived from the dietary records and those derived from the FFQ_V were calculated to evaluate the validity of the FFQ. In addition, to determine the reproducibility of the FFQ, Spearman correlation coefficients between the factor scores for the dietary patterns derived from the 2 FFQs administered 1 year apart were calculated. We estimated Spearman correlation coefficients between factor loadings from the FFQ in each population (subsample of the validity study, overall cohort, men, women, and men and women combined) to compare the dietary patterns derived from the FFQ between the subsample of the validity study and overall cohort and between men or women and men and women combined in the overall cohort. All analyses were performed using Statistical Analysis System (SAS) version 9.1 (SAS Institute, Cary, NC, USA).\nMen and women were analyzed separately. Some foods or food groups that were similar in nutritional content or culinary use were combined, leaving 48 food groups for the present analysis (Table 1). When information from dietary records was used to combine 2 or more foods into 1 food group, the amount of food before cooking was used for all food items except noodles and rice. Consumption of noodles and rice was expressed in weight of boiled noodles and steamed rice, respectively. Consumption of miso soup and beverages such as coffee and green tea was expressed as the weight of the consumed infusion rather than the weight of miso, coffee beans, instant coffee, or tea leaves. We calculated the means and standard deviations of each food group intake estimated from dietary records and the 2 FFQs. In addition, the reproducibility and validity of 48 food group intakes were evaluated by using Spearman correlation coefficients between intakes from the 2 FFQs and between intakes derived from the dietary records and those derived from the FFQ_V.\nTo identify dietary patterns, we performed principal component analysis based on log-transformed intakes of 48 food groups for each of the 2 FFQs and the dietary records. The factors were rotated by orthogonal transformation (varimax rotation) to maintain uncorrelated factors and greater interpretability. We considered eigenvalues, the scree plot, and the interpretability of the factors to determine the number of factors to retain and identified 3 dietary patterns (factors) in both men and women. The dietary patterns were named according to the food items showing high loading (absolute value) in each dietary pattern. The factor scores for each dietary pattern were calculated by summing intakes of food items weighted by their factor loadings. The scores were energy-adjusted using the residual method. When we examined reproducibility and validity by using dietary pattern scores without energy adjustment, or based on energy-adjusted intake of food groups, the derived dietary patterns and their reproducibility and validity were similar to those based on energy-adjusted scores.\nWe examined the correspondence of each dietary pattern identified from the dietary records with dietary patterns extracted from the FFQ_V by comparing the pattern of food items showing high loading. We repeated this procedure to link dietary patterns identified from the FFQ_R and FFQ_V. For each dietary pattern, Spearman correlation coefficients between the factor score for the dietary patterns derived from the dietary records and those derived from the FFQ_V were calculated to evaluate the validity of the FFQ. In addition, to determine the reproducibility of the FFQ, Spearman correlation coefficients between the factor scores for the dietary patterns derived from the 2 FFQs administered 1 year apart were calculated. We estimated Spearman correlation coefficients between factor loadings from the FFQ in each population (subsample of the validity study, overall cohort, men, women, and men and women combined) to compare the dietary patterns derived from the FFQ between the subsample of the validity study and overall cohort and between men or women and men and women combined in the overall cohort. All analyses were performed using Statistical Analysis System (SAS) version 9.1 (SAS Institute, Cary, NC, USA).\n\nJPHC Study:\nThe JPHC Study is a population-based prospective study of cancer and cardiovascular disease and was launched in 1990 for cohort I and in 1993 for cohort II.14 The participants were 140 420 residents of 11 public health center areas and were aged 40 to 59 years for cohort I and aged 40 to 69 years for cohort II at each baseline survey. At baseline and at the 5- and 10-year follow-ups, a questionnaire survey was conducted to obtain information on medical histories and health-related lifestyle, including diet.\n\nStudy population:\nThe subjects of the reproducibility and validity study of the FFQ used in the 5-year follow-up survey were a subsample of the participants in JPHC Study cohorts I and II. Married couples were recruited. The present study used a data set for 498 subjects (244 men and 254 women; 209 in cohort I and 289 in cohort II) who completed 2 FFQs with a 1-year interval and dietary records for a total of 28 or 14 days. Details of the validation study have been described elsewhere.15,16 There was no significant difference in mean age between participants of the validation study and cohort members among men and women in cohort I or among men in cohort II; however, there was a 3-year age difference among cohort II women (mean age, 56 years for the validation subsample vs 59 years for the cohort participants). In the present study, we identified dietary patterns using FFQ data from the 5-year follow-up survey. Oral or written informed consent from the participants was received before the study. The study did not undergo ethical approval since it was conducted before the advent of ethical guidelines for epidemiology research in Japan, which mandate such approval.\n\nDietary assessment:\nThe participants completed the FFQ twice, with an approximately 1-year interval. The FFQ for the evaluation of validity (FFQ_V) was completed after completion of the last dietary record and was compared with the dietary records. The FFQ for the evaluation of reproducibility (FFQ_R) was administered 1 year before or after the FFQ_V and was compared with the FFQ_V. The FFQ included questions on 138 food items (with standard portions/units and eating frequency) and 14 supplementary questions during the previous year, from which a composition table was developed for 147 foods items.17 For most food items, 9 response options were available for eating frequency: rarely, 1 to 3 times/month, 1 to 2 times/week, 3 to 4 times/week, 5 to 6 times/week, once a day, 2 to 3 times/day, 4 to 6 times/day, and 7 or more times/day. Slightly different options were used for beverage intake: rarely, 1 to 2 times/week, 3 to 4 times/week, 5 to 6 times/week, 1 cup/day, 2 to 3 cups/day, 4 to 6 cups/day, 7 to 9 cups/day, and 10 or more cups/day. A standard portion size was specified for each food item, and the respondents were asked to choose their usual portion size from 3 options (less than half the standard portion size, standard portion size, or more than 1.5 times the standard portion size). We calculated daily intake of most foods by multiplying daily frequency of consumption by usual portion size. In the present analysis, we used 134 food and beverage items of the FFQ (excluding 11 items that correlated strongly with others and 2 items with no energy or nutrition).\nThe participants completed a 28- or 14-day (Okinawa) dietary record in 1 year, ie, 7-day dietary records were collected 4 (or 2) times at 3-month (or 6-month) intervals during the course of a year. The survey method using dietary records has been described elsewhere.15,16,18 We matched the 1101 unique food codes in the dietary records to food items on the FFQ to ensure that daily food intakes derived from the dietary records were comparable with those derived from the FFQ. A total of 558 food codes in the dietary records were matched to the 134 food items on the FFQ.\n\nStatistical analysis:\nMen and women were analyzed separately. Some foods or food groups that were similar in nutritional content or culinary use were combined, leaving 48 food groups for the present analysis (Table 1). When information from dietary records was used to combine 2 or more foods into 1 food group, the amount of food before cooking was used for all food items except noodles and rice. Consumption of noodles and rice was expressed in weight of boiled noodles and steamed rice, respectively. Consumption of miso soup and beverages such as coffee and green tea was expressed as the weight of the consumed infusion rather than the weight of miso, coffee beans, instant coffee, or tea leaves. We calculated the means and standard deviations of each food group intake estimated from dietary records and the 2 FFQs. In addition, the reproducibility and validity of 48 food group intakes were evaluated by using Spearman correlation coefficients between intakes from the 2 FFQs and between intakes derived from the dietary records and those derived from the FFQ_V.\nTo identify dietary patterns, we performed principal component analysis based on log-transformed intakes of 48 food groups for each of the 2 FFQs and the dietary records. The factors were rotated by orthogonal transformation (varimax rotation) to maintain uncorrelated factors and greater interpretability. We considered eigenvalues, the scree plot, and the interpretability of the factors to determine the number of factors to retain and identified 3 dietary patterns (factors) in both men and women. The dietary patterns were named according to the food items showing high loading (absolute value) in each dietary pattern. The factor scores for each dietary pattern were calculated by summing intakes of food items weighted by their factor loadings. The scores were energy-adjusted using the residual method. When we examined reproducibility and validity by using dietary pattern scores without energy adjustment, or based on energy-adjusted intake of food groups, the derived dietary patterns and their reproducibility and validity were similar to those based on energy-adjusted scores.\nWe examined the correspondence of each dietary pattern identified from the dietary records with dietary patterns extracted from the FFQ_V by comparing the pattern of food items showing high loading. We repeated this procedure to link dietary patterns identified from the FFQ_R and FFQ_V. For each dietary pattern, Spearman correlation coefficients between the factor score for the dietary patterns derived from the dietary records and those derived from the FFQ_V were calculated to evaluate the validity of the FFQ. In addition, to determine the reproducibility of the FFQ, Spearman correlation coefficients between the factor scores for the dietary patterns derived from the 2 FFQs administered 1 year apart were calculated. We estimated Spearman correlation coefficients between factor loadings from the FFQ in each population (subsample of the validity study, overall cohort, men, women, and men and women combined) to compare the dietary patterns derived from the FFQ between the subsample of the validity study and overall cohort and between men or women and men and women combined in the overall cohort. All analyses were performed using Statistical Analysis System (SAS) version 9.1 (SAS Institute, Cary, NC, USA).\n\nRESULTS:\nThe 48 food group intakes calculated by 28- or 14-day dietary records and the 2 FFQs, and their correlations, are shown in Table 2 for men and Table 3 for women. Regarding reproducibility, Spearman correlation coefficients between the 2 FFQs ranged from 0.21 for wine to 0.80 for coffee in men (Table 2) and from 0.37 for sake to 0.81 for miso soup in women (Table 3). In particular, intakes of rice, bread, miso soup, pickles, other fruit, salt fish, processed meat, milk, dairy products, green tea, and coffee showed high correlation coefficients (r >0.60) between the 2 FFQs in both men and women. Regarding validity, Spearman correlation coefficients between the dietary records and the FFQ_V ranged from 0.06 for oily fish to 0.75 for coffee in men (Table 2) and from 0.12 for shochu to 0.80 for coffee in women (Table 3). In both men and women, intakes of coffee, bread, pickles, and milk estimated by the FFQ_V showed a high correlation (r >0.60) with those estimated by the dietary records, whereas the validity of oily fish between the dietary records and the FFQ_V was very low.\nAbbreviations: DR, dietary record; FFQ, food frequency questionnaire; SD, standard deviation.\naFFQ_R was administered 1 year after or before FFQ_V.\nbFFQ_V was administered after completion of the DRs.\nc28- and 14-day DRs were collected over a 1-year period.\nAbbreviations: DR, dietary record; FFQ, food frequency questionnaire; SD, standard deviation.\naFFQ_R was administered 1 year after or before FFQ_V.\nbFFQ_V was administered after completion of the DRs.\nc28- and 14-day DRs were collected over a 1-year period.\nPrincipal component analysis identified 3 dietary patterns from the dietary records and 2 FFQs (Table 4 for men and Table 5 for women), and they were similar in terms of factor loading pattern across the data sources and between sexes. Of the 3 dietary patterns, a pattern highly loaded by intakes of vegetables, fruit, potatoes, soy products, mushrooms, seaweed, oily fish, and green tea was named the prudent Japanese pattern. A dietary pattern associated with high intakes of bread, meat, processed meat, fruit juice, coffee, black tea, soft drinks, sauces, mayonnaise, and dressing was named the westernized Japanese pattern. Another dietary pattern was characterized by high intakes of rice, miso soup, pickles, salmon, salty fish, seafood other than fish, fruit, and sake (men only), and was named the traditional Japanese pattern. The 3 dietary patterns from the dietary records, the FFQ_R, and the FFQ_V explained 23.9%, 29.4%, and 26.5%, respectively, of variance in men and 23.0%, 24.9%, and 32.9%, respectively, of variance in women.\nAbbreviations: FFQ, food frequency questionnaire; DR, dietary record.\naFor simplicity, factor loadings less than ±0.10 are not listed.\nbFFQ_R was administered 1 year after or before FFQ_V.\ncFFQ_V was administered after completion of the DRs.\nd28- and 14-day DRs were collected over a 1-year period.\nAbbreviations: FFQ, food frequency questionnaire; DR, dietary record.\naFor simplicity, factor loadings less than ±0.10 are not listed.\nbFFQ_R was administered 1 year after or before FFQ_V.\ncFFQ_V was administered after completion of the DRs.\nd28- and 14-day DRs were collected over a 1-year period.\nThree similar dietary patterns were identified in the overall cohort for both men and women (Appendix). The Spearman correlation coefficients between factor loadings from FFQ_R in the subsample of the validity study and those from the FFQ in the overall cohort for 48 food groups were 0.89 in men and 0.82 in women for the prudent Japanese pattern, 0.82 in men and 0.75 in women for the westernized Japanese pattern, and 0.75 in men and 0.47 in women for the traditional Japanese pattern. In the overall cohort, the corresponding values between factor loadings for men or women and those for men and women combined were 0.98 in both men and women for the prudent Japanese pattern, 0.96 in men and 0.95 in women for the westernized Japanese pattern, and 0.76 in men and 0.74 in women for the traditional Japanese pattern.\nThe Spearman correlation coefficients between the dietary records and the 2 FFQs are shown in Table 6. Regarding reproducibility between the 2 FFQs, all 3 dietary patterns were acceptable in both men and women. In particular, the traditional Japanese pattern in men and the westernized Japanese pattern in women were highly reproduced (correlation coefficients: 0.77 in men and 0.71 in women). Regarding validity, the traditional Japanese pattern had higher correlation coefficients between the dietary records and the FFQ_V than did other patterns among both men and women (correlation coefficient: 0.49 in men and 0.63 in women), whereas the westernized Japanese pattern in men (0.32) had the lowest correlation coefficient among the 3 dietary patterns.\nAbbreviations: DR, dietary record; FFQ, food frequency questionnaire.\naDietary pattern scores were adjusted for energy intake by using the residual method.\nbFFQ_R was administered 1 year after or before FFQ_V.\ncFFQ_V was administered after completion of the DRs.\nd28- or 14-day DRs were collected over a 1-year period.\nAll correlations: P < 0.001.\n\nDISCUSSION:\nIn a subsample of the validation study of diet in the JPHC Study, we identified 3 major Japanese dietary patterns (prudent, westernized, and traditional) that were similar across data sources and between sexes. The reproducibility and validity of dietary patterns derived from the FFQ were acceptable. Spearman correlation coefficients between scores of the dietary patterns derived from the 2 FFQs ranged from 0.55 to 0.77. The corresponding values between scores of the dietary patterns derived from the dietary records and those derived from the FFQ ranged from 0.32 to 0.63. These dietary patterns were also identified in the entire population of the JPHC Study.\nThe 3 major dietary patterns identified in the present study have also been reported in previous Japanese studies. Most studies in Japan have identified a dietary pattern similar to the prudent Japanese pattern in the present study.19–35 This dietary pattern is characterized by high intakes of not only vegetables and fruit but also typical Japanese foods, including soy products, seaweed, mushrooms, fish, and green tea. Dietary patterns similar to the westernized Japanese pattern in the present study, ie, high intakes of meat, processed meat, bread, coffee, black tea, mayonnaise, and dressing, have also been observed in many Japanese studies.19–35 Moreover, the traditional Japanese pattern, which is characterized by high intakes of rice, miso soup, pickles, salmon, salty fish, and fruit, has been observed in several studies.19–21,26,30,31 Because the westernized breakfast pattern22,24,28,29,33,34 and similar patterns, which were identified in some other studies, is associated with low intakes of rice and miso soup, the westernized breakfast pattern could be viewed as the mirror image of the traditional Japanese pattern.\nWe observed acceptable validity and reproducibility of the 3 dietary patterns; the Spearman correlation coefficients between the dietary records and 2 FFQs ranged from 0.55 to 0.77 for reproducibility and from 0.32 to 0.63 for validity. These values were comparable to those reported in previous studies. Regarding reproducibility, the correlation coefficients ranged from 0.67 to 0.70 (Pearson) in the Health Professional Follow-up Study,9 from 0.63 to 0.73 (over a 1-year period; Spearman),10 and from 0.30 to 0.52 (over a 10-year period; Pearson)11 in the Swedish Mammography Cohort, and from 0.64 to 0.81 (Spearman) in the Southampton Women’s Survey.7 Regarding validity, the correlation coefficients ranged from 0.34 to 0.64 (Pearson) in the Health Professionals Follow-up Study,9 from 0.34 to 0.61 (Pearson) in the Monitoring of Trends and Determinants in Cardiovascular Diseases,13 from 0.41 to 0.73 (Spearman) in the Swedish Mammography Cohort,10 from 0.35 to 0.67 (Pearson) in a UK study,8 and from 0.36 to 0.62 (Pearson) in a Japanese study.12\nIn the present study, the traditional Japanese pattern in men and the westernized Japanese pattern in women were highly reproducible. In the 2 FFQs, salty fish, salmon, pickles, sake, and rice, which are characteristic of the traditional Japanese pattern in men, showed high loadings to the pattern. In addition, these food intakes were highly correlated between the 2 FFQs. Similarly, intakes of beef, processed meat, coffee, bread, pork, chicken, and dressing showed high loadings to the westernized Japanese pattern in women in the 2 FFQs and were also highly correlated between the 2 FFQs. Of the 3 dietary patterns identified, the traditional Japanese pattern showed relatively good validity in both men and women. This might be because pickles, rice, salty fish, salmon, and sake (men only) showed high factor loadings to both dietary record and FFQ, and these intakes were moderately to highly correlated between the dietary record and FFQ. In contrast, the westernized Japanese pattern in men was less valid, perhaps because among foods that were highly and positively loaded to the westernized Japanese pattern derived using dietary record data, some fish items were inversely (not positively) associated with the dietary pattern based on FFQ data. In addition, intakes of meat and processed meat were only moderately correlated between dietary record and FFQ.\nRegarding the traditional Japanese pattern in women, the factor loadings from the FFQ in the subsample and those from the FFQ in the overall cohort for food groups were not highly correlated (correlation coefficient = 0.47). This might be because factor loadings of pickles, citrus fruit, and noodles in the traditional Japanese pattern were lower in the overall cohort than in the subsample, whereas those for pork, beef, processed meat, egg, oily fish, and fish products were higher in the overall cohort than in the subsample.\nOur study had some limitations. First, although we assessed the validity and reproducibility of dietary patterns in a subsample of the JPHC Study population, participants in the validation study might be more health conscious than nonparticipants. Therefore, the present findings might not be applicable to the entire population of the JPHC Study. However, we also identified 3 similar dietary patterns in the entire population. Second, we observed similar dietary patterns in men and women. This might be partly because married couples were recruited for the validity study. Third, we assessed validity based on a single measurement of dietary intake, which might not reflect past dietary patterns. In addition, dietary pattern alone could change during the follow-up period, due to changes in food preference and food availability. Finally, we used the FFQ completed at the end of the dietary records to evaluate validity. Participant recall of dietary intake could be influenced by the administration of dietary records.\nIn conclusion, we identified 3 Japanese dietary patterns (prudent, westernized, and traditional) using FFQ data among participants of the validation study and confirmed that validity and reproducibility of the FFQ are acceptable. These validation data provide a basis for future analysis of the association between dietary patterns and disease risk in the JPHC Study population.\n\nAbbreviation: JPHC Study, Japan Public Health Center-Based Prospective Study.\naFor simplicity, factor loadings less than ±0.10 are not listed.\n\n"},"whole_article_abstract":{"kind":"string","value":"Background:\nAnalysis of dietary pattern is increasingly popular in nutritional epidemiology. However, few studies have examined the validity and reproducibility of dietary patterns. We assessed the reproducibility and validity of dietary patterns identified by a food frequency questionnaire (FFQ) used in the 5-year follow-up survey of the Japan Public Health Center-Based Prospective Study (JPHC Study).\n\nMethods:\nThe participants were a subsample (244 men and 254 women) from the JPHC Study. Principal component analysis was used to identify dietary patterns from 28- or 14-day dietary records and 2 FFQs. To assess reproducibility and validity, we calculated Spearman correlation coefficients between dietary pattern scores derived from FFQs separated by a 1-year interval, and between dietary pattern scores derived from dietary records and those derived from a FFQ completed after the dietary records, respectively.\n\nResults:\nWe identified 3 Japanese dietary patterns from the dietary records and 2 FFQs: prudent, westernized, and traditional. Regarding reproducibility, Spearman correlation coefficients between the 2 FFQs ranged from 0.55 for the westernized Japanese pattern in men and the prudent Japanese pattern in women to 0.77 for the traditional Japanese pattern in men. Regarding validity, the corresponding values between dietary records and the FFQ ranged from 0.32 for the westernized Japanese pattern in men to 0.63 for the traditional Japanese pattern in women.\n\nConclusions:\nAcceptable reproducibility and validity was shown by the 3 dietary patterns identified by principal component analysis based on the FFQ used in the 5-year follow-up survey of the JPHC Study."},"background_conclusion_text":{"kind":"null"},"background_conclusion_abstract":{"kind":"null"},"whole_article_text_length":{"kind":"number","value":6678,"string":"6,678"},"whole_article_abstract_length":{"kind":"number","value":295,"string":"295"},"other_sections_lengths":{"kind":"list like","value":[347,99,226,462,584],"string":"[\n 347,\n 99,\n 226,\n 462,\n 584\n]"},"num_sections":{"kind":"number","value":8,"string":"8"},"most_frequent_words":{"kind":"list like","value":["dietary","food","study","patterns","ffq","dietary patterns","pattern","men","women","dietary 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Japanese | Japanese | 0.77 | Japanese ||| FFQ | 0.32 | Japanese | 0.63 | Japanese [SUMMARY]"},"annotated_entity_plan_conclusions_abstract_prompt":{"kind":"null"},"annotated_entity_plan_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] ||| ||| 5-year | the Japan Public Health Center-Based Prospective Study ||| 244 | 254 ||| 28- | 14-day | 2 ||| Spearman | 1-year ||| ||| 3 | Japanese | 2 ||| Spearman | 2 | 0.55 | Japanese | Japanese | 0.77 | Japanese ||| FFQ | 0.32 | Japanese | 0.63 | Japanese ||| 3 | FFQ | 5-year [SUMMARY]"},"annotated_entity_plan_background_conclusion_abstract_prompt":{"kind":"null"}}},{"rowIdx":257,"cells":{"title":{"kind":"string","value":"Longitudinal study of leptin levels in chronic hemodialysis patients."},"pmid":{"kind":"string","value":"21676262"},"background_abstract":{"kind":"string","value":"The influence of serum leptin levels on nutritional status and survival in chronic hemodialysis patients remained to be elucidated. We conducted a prospective longitudinal study of leptin levels and nutritional parameters to determine whether changes of serum leptin levels modify nutritional status and survival in a cohort of prevalent hemodialysis patients."},"background_abstract_label":{"kind":"string","value":"BACKGROUND"},"methods_abstract":{"kind":"string","value":"Leptin, dietary energy and protein intake, biochemical markers of nutrition and body composition (anthropometry and bioimpedance analysis) were measured at baseline and at 6, 12, 18 and 24 months following enrollment, in 101 prevalent hemodialysis patients (37% women) with a mean age of 64.6 ± 11.5 years. Observation of this cohort was continued over 2 additional years. Changes in repeated measures were evaluated, with adjustment for baseline differences in demographic and clinical parameters."},"methods_abstract_label":{"kind":"string","value":"METHODS"},"results_abstract":{"kind":"string","value":"Significant reduction of leptin levels with time were observed (linear estimate: -2.5010 ± 0.57 ng/ml/2 y; p < 0.001) with a more rapid decline in leptin levels in the highest leptin tertile in both unadjusted (p = 0.007) and fully adjusted (p = 0.047) models. A significant reduction in body composition parameters over time was observed, but was not influenced by leptin (leptin-by-time interactions were not significant). No significant associations were noted between leptin levels and changes in dietary protein or energy intake, or laboratory nutritional markers. Finally, cumulative incidences of survival were unaffected by the baseline serum leptin levels."},"results_abstract_label":{"kind":"string","value":"RESULTS"},"conclusions_abstract":{"kind":"string","value":"Thus leptin levels reflect fat mass depots, rather than independently contributing to uremic anorexia or modifying nutritional status and/or survival in chronic hemodialysis patients. The importance of such information is high if leptin is contemplated as a potential therapeutic target in hemodialysis patients."},"conclusions_abstract_label":{"kind":"string","value":"CONCLUSIONS"},"mesh_descriptor_names":{"kind":"list like","value":["Aged","Biomarkers","Body Composition","Body Mass Index","Cross-Sectional Studies","Dietary Proteins","Female","Follow-Up Studies","Humans","Kidney Failure, Chronic","Leptin","Linear Models","Longitudinal Studies","Male","Middle Aged","Multivariate Analysis","Nutritional Status","Prospective Studies","Renal Dialysis","Survival Analysis"],"string":"[\n \"Aged\",\n \"Biomarkers\",\n \"Body Composition\",\n \"Body Mass Index\",\n \"Cross-Sectional Studies\",\n \"Dietary Proteins\",\n \"Female\",\n \"Follow-Up Studies\",\n \"Humans\",\n \"Kidney Failure, Chronic\",\n \"Leptin\",\n \"Linear Models\",\n \"Longitudinal Studies\",\n \"Male\",\n \"Middle Aged\",\n \"Multivariate Analysis\",\n \"Nutritional Status\",\n \"Prospective Studies\",\n \"Renal Dialysis\",\n \"Survival Analysis\"\n]"},"pmcid":{"kind":"string","value":"3132708"},"background_title":{"kind":"string","value":"Background"},"background_text":{"kind":"string","value":"In recent years, the number of patients with end-stage renal disease (ESRD) has been increasing worldwide [1]. Depending in part upon the method used to evaluate nutritional status and the population studied, from 40 to 70 percent of patients with ESRD are malnourished [2,3] resulting in poor clinical outcomes [4]. Among the mechanisms responsible for malnutrition, leptin was believed to influence nutritional markers in patients with ESRD [5]. Leptin is a 16-kDa protein identified as the product of the obese gene; it is exclusively produced in adipocytes, and regulates food intake and energy expenditure in animal models [6]. Leptin decreases food intake by decreasing NPY (neuropeptide Y - one of the most potent stimulators of food intake) mRNA [7] and increasing alpha-MSH (alpha-melanocyte-stimulating hormone - an inhibitor of food intake) [8]. Besides linking adiposity and central nervous circuits to reduced appetite and enhanced energy expenditure in the general population [9], leptin has been shown to increase overall sympathetic nerve activity [10], facilitate glucose utilization and improve insulin sensitivity [11]. Furthermore, the prospective West of Scotland Coronary Prevention Study (WOSCOPS) reported that elevated leptin increases the relative risk of cardio-vascular disease in the general population independently of fat mass [12].\nIn general, serum leptin levels are significantly elevated in patients with renal failure, particularly when compared to age, gender and body mass index (BMI)-matched controls [13,14]. However, the role of hyperleptinemia in ESRD patients is somewhat unconventional. In contrast with its anorexogenic effects recognized in the general population [9] and even in experimental models of uremia (in subtotal nephrectomized and leptin receptor-deficient [db/db] mice) [15], leptin has not been reported to affect perceived appetite and nutrient intake in dialysis patients [16,17]. Although in some observational studies, increased serum leptin concentrations were observed in ESRD patients in parallel with loss of lean body mass [18,19] or with hypoalbuminemia and low protein intake [20], some others failed to find any correlation between hyperleptinemia and weight change [9] or lean mass [21] in this population. Moreover, several clinical studies suggested that leptin is a negative acute phase protein [22] and can serve as a marker of adequate nutritional status, rather than an appetite-reducing uremic toxin in hemodialysis patients [23-25]. Finally, the relationship between elevated serum leptin levels and clinical outcomes in ESRD has not been fully defined. In one small prospective cohort of hemodialysis patients, lower baseline serum leptin levels predicted mortality [26], but neither changes in leptin over time were measured, nor were leptin levels normalized to body fat mass in this study.\nThus, the influence of serum leptin levels on nutritional status and survival in chronic hemodialysis patients remained to be elucidated. In view of leptin's physiological role, information on effects of prolonged hyperleptinemia (independent of fat mass) on nutritional status of chronic hemodialysis patients, which may also impact on their survival, would be of interest. The aim of the present prospective longitudinal study was therefore to study longitudinal changes in serum leptin levels and to relate them to the changes in nutritional markers and survival in chronic hemodialysis patients."},"methods_title":{"kind":"string","value":"Methods"},"methods_text":{"kind":"string","value":" Patients This prospective observational study was approved by the Ethics Committee of Assaf Harofeh Medical Center (Zerifin, Affiliated to the Sackler Faculty of Medicine Tel Aviv University, Israel). Informed consent was obtained before any trial-related activities. Patients were eligible for entry when they had been on HD therapy for at least 3 months and were 18 years or older, with no clinically active cardio-vascular or infectious diseases on entry. We excluded patients with edema, pleural effusion or ascites at their initial assessment, as well as patients with malignant disease, liver cirrhosis, neuro-muscular diseases, amputations or any deformities of the body. Exclusion criteria at the entry of the study also included co-morbidity (auto-immune disease and/or acute infections) and/or medication (prednisone) that might interfere with plasma leptin concentrations. A flow chart of the study is presented in Figure 1. In total, 101 patients (64 men and 37 women) with a mean age of 64.6 ± 11.5 years, receiving maintenance hemodialysis treatment at our outpatient HD clinic, were included in the study. Of the patients studied, 52 were diabetic (all diabetic patients had type 2 diabetes). Study measurements were performed at baseline and at 6, 12, 18 and 24 months from enrollment. After the longitudinal measurements ended, we continued clinical observation on our cohort during 2 additional years. Thus, in total, the study period extended 35 ± 17 months. During this period, 33 patients (32.7%) died (the main causes of death were cardio-vascular [12 of 33 patients; 36.4%] and sepsis [12 of 33 patients; 36.4%]), 13 patients (12.9%) underwent kidney transplantation, 3 patients (3.0%) changed dialysis modality, and 10 patients (10.0%) transferred to other hemodialysis units. All patients underwent regular dialysis via their vascular access (81.2% of patients had arterio-venous fistula) 4-5 h three times per week at a blood flow rate of 250-300 ml/min. Bicarbonate dialysate (30 mEq/L) at a dialysis solution flow rate of 500 ml/min was used in all cases. All dialysis was performed with biocompatible dialyzer membrane with a surface area of 1.0-1.8 m2. The efficiency of the dialysis was assessed based on the delivered dose of dialysis (Kt/V urea) using a single-pool urea kinetic model (mean Kt/V was1.31 ± 0.23 in our population).\nFlow diagram of the study.\nInformation on vascular disease (cerebral vascular, peripheral vascular and heart disease) was obtained from a detailed medical history.\nMost patients were required antihypertensive medications as well as other drugs commonly used in ESRD, such as phosphate and potassium binders, diuretics, and supplements of vitamins B, C, and D.\nThis prospective observational study was approved by the Ethics Committee of Assaf Harofeh Medical Center (Zerifin, Affiliated to the Sackler Faculty of Medicine Tel Aviv University, Israel). Informed consent was obtained before any trial-related activities. Patients were eligible for entry when they had been on HD therapy for at least 3 months and were 18 years or older, with no clinically active cardio-vascular or infectious diseases on entry. We excluded patients with edema, pleural effusion or ascites at their initial assessment, as well as patients with malignant disease, liver cirrhosis, neuro-muscular diseases, amputations or any deformities of the body. Exclusion criteria at the entry of the study also included co-morbidity (auto-immune disease and/or acute infections) and/or medication (prednisone) that might interfere with plasma leptin concentrations. A flow chart of the study is presented in Figure 1. In total, 101 patients (64 men and 37 women) with a mean age of 64.6 ± 11.5 years, receiving maintenance hemodialysis treatment at our outpatient HD clinic, were included in the study. Of the patients studied, 52 were diabetic (all diabetic patients had type 2 diabetes). Study measurements were performed at baseline and at 6, 12, 18 and 24 months from enrollment. After the longitudinal measurements ended, we continued clinical observation on our cohort during 2 additional years. Thus, in total, the study period extended 35 ± 17 months. During this period, 33 patients (32.7%) died (the main causes of death were cardio-vascular [12 of 33 patients; 36.4%] and sepsis [12 of 33 patients; 36.4%]), 13 patients (12.9%) underwent kidney transplantation, 3 patients (3.0%) changed dialysis modality, and 10 patients (10.0%) transferred to other hemodialysis units. All patients underwent regular dialysis via their vascular access (81.2% of patients had arterio-venous fistula) 4-5 h three times per week at a blood flow rate of 250-300 ml/min. Bicarbonate dialysate (30 mEq/L) at a dialysis solution flow rate of 500 ml/min was used in all cases. All dialysis was performed with biocompatible dialyzer membrane with a surface area of 1.0-1.8 m2. The efficiency of the dialysis was assessed based on the delivered dose of dialysis (Kt/V urea) using a single-pool urea kinetic model (mean Kt/V was1.31 ± 0.23 in our population).\nFlow diagram of the study.\nInformation on vascular disease (cerebral vascular, peripheral vascular and heart disease) was obtained from a detailed medical history.\nMost patients were required antihypertensive medications as well as other drugs commonly used in ESRD, such as phosphate and potassium binders, diuretics, and supplements of vitamins B, C, and D.\n Dietary intake A continuous 3-day dietary history (which included a dialysis day, a weekend day and a non-dialysis day) was recorded on a self-completed food diary. Then, dietary energy and protein intake were calculated and normalized for adjusted body weight (ABW) by the following formula [27]:\nSBW - standard body weight was determined by the National Healthand Nutrition Examination Survey II (NHANES II) population medians for age, sex, frame size, and stature [27].\nDietary protein intake was also estimated by the protein catabolic rate (PCR) calculation from the patient's urea generation rate by urea kinetics modeling [28]. Single-pool model urea kinetics was used to estimate the nPCR.\nA continuous 3-day dietary history (which included a dialysis day, a weekend day and a non-dialysis day) was recorded on a self-completed food diary. Then, dietary energy and protein intake were calculated and normalized for adjusted body weight (ABW) by the following formula [27]:\nSBW - standard body weight was determined by the National Healthand Nutrition Examination Survey II (NHANES II) population medians for age, sex, frame size, and stature [27].\nDietary protein intake was also estimated by the protein catabolic rate (PCR) calculation from the patient's urea generation rate by urea kinetics modeling [28]. Single-pool model urea kinetics was used to estimate the nPCR.\n Anthropometric measurements BMI, triceps skinfold thickness (TSF), mid arm circumference (MAC) and calculated mid arm muscle circumference (MAMC) were measured as anthropometric variables. The BMI was calculated as dry weight in kilograms divided by the square of height in meters. TSF was measured with a conventional skinfold caliper using standard techniques. Mid arm circumference was measured with a plastic measuring tape. MAMC was estimated as follows:\nBMI, triceps skinfold thickness (TSF), mid arm circumference (MAC) and calculated mid arm muscle circumference (MAMC) were measured as anthropometric variables. The BMI was calculated as dry weight in kilograms divided by the square of height in meters. TSF was measured with a conventional skinfold caliper using standard techniques. Mid arm circumference was measured with a plastic measuring tape. MAMC was estimated as follows:\n Body composition analysis Body composition was determined by body impedance analysis (B.I.A. Nutriguard- M, Data-Input, Frankfurt, Germany). We used gel-based electrodes specifically developed for BIA measurements - Bianostic AT (Data-Input GmbH). On the day of blood collection, patients underwent BIA measurement at approximately 30 minutes postdialysis. BIA electrodes were placed on the same body side used for anthropometric measurements. The multi-frequency technique was used. FFM was calculated by using the approach of Kyle et al. validated by dual-energy x-ray absorptiometry on 343 healthy adult subjects [29]:\nFat mass and fat free mass were standardized by squared height (m2), and expressed in kg/m2 as fat mass index (FMI) and fat free mass index (FFMI), respectively.\nPhase angle (PA) describes the relationship between the two vector components of impedance [reactance and resistance] of the human body to an alternating electric current. PA has been shown to provide a BIA prognostic index of morbidity and mortality [30].\nBody composition was determined by body impedance analysis (B.I.A. Nutriguard- M, Data-Input, Frankfurt, Germany). We used gel-based electrodes specifically developed for BIA measurements - Bianostic AT (Data-Input GmbH). On the day of blood collection, patients underwent BIA measurement at approximately 30 minutes postdialysis. BIA electrodes were placed on the same body side used for anthropometric measurements. The multi-frequency technique was used. FFM was calculated by using the approach of Kyle et al. validated by dual-energy x-ray absorptiometry on 343 healthy adult subjects [29]:\nFat mass and fat free mass were standardized by squared height (m2), and expressed in kg/m2 as fat mass index (FMI) and fat free mass index (FFMI), respectively.\nPhase angle (PA) describes the relationship between the two vector components of impedance [reactance and resistance] of the human body to an alternating electric current. PA has been shown to provide a BIA prognostic index of morbidity and mortality [30].\n Laboratory evaluation Blood samples were taken in a non-fasting state before a midweek hemodialysis session. CBC, creatinine, urea, albumin, transferrin and total cholesterol were measured by routine laboratory methods. IL-6 and leptin were measured in plasma samples using commercially available enzyme-linked immunosorbent assay (ELISA) kits (R&D System, Minneapolis, MN, USA) according to the manufacturer's protocol. The mean minimal detectable level for IL-6 was 0.7 pg/ml, and 7.8 pg/ml for leptin. Intra-assay and inter-assay coefficients of variation for IL-6 were 4.2% and 6.4% respectively, and for leptin - 3.3% and 5.4% respectively.\nBlood samples were taken in a non-fasting state before a midweek hemodialysis session. CBC, creatinine, urea, albumin, transferrin and total cholesterol were measured by routine laboratory methods. IL-6 and leptin were measured in plasma samples using commercially available enzyme-linked immunosorbent assay (ELISA) kits (R&D System, Minneapolis, MN, USA) according to the manufacturer's protocol. The mean minimal detectable level for IL-6 was 0.7 pg/ml, and 7.8 pg/ml for leptin. Intra-assay and inter-assay coefficients of variation for IL-6 were 4.2% and 6.4% respectively, and for leptin - 3.3% and 5.4% respectively.\n Statistical analysis Data are expressed as mean ± standard deviation (SD), median and interquartile range (Q1 to Q3) for variables that did not follow a normal distribution, or frequencies, as noted.\nTo compare the means of continuous variables measured between sex-specific tertiles of leptin, one way ANOVA and analysis of covariance with adjustments (ANCOVA) were used. Categorical data are presented as percentages and were compared among groups by χ2 tests. Correlations between leptin and clinical and laboratory parameters were assessed using Spearman rank order correlation coefficients (because of the skewed distribution of leptin levels). Multivariate regression analysis was performed to obtain partial (adjusted) correlations (R2). Case-mix-adjusted models were controlled for age, gender, history of CV disease, presence or absence of diabetes mellitus, dialysis vintage and fat mass.\nRepeated-measures analysis of variance was performed by using the MIXED model. Only patients with ≥ 2 study visits were included in the analyses. Base models were adjusted for age, sex, diabetes status, dialysis vintage, history of cardio-vascular diseases and fat mass. F Tests were used to assess the significance of the fixed effects, and P less than 0.05 was considered significant. To evaluate whether leptin influenced the trends in the various dependent variables, we included in each base model terms for individual \"leptin-by-time\" interactions.\nSurvival analyses were performed using the Kaplan-Meier survival curve and the Cox proportional hazard model. The univariate and multivariate Cox regression analyses are presented as (HR; CI).\nAll statistical analyses were performed using SPSS software, version 16.0 (SPSS Inc, Chicago, IL).\nData are expressed as mean ± standard deviation (SD), median and interquartile range (Q1 to Q3) for variables that did not follow a normal distribution, or frequencies, as noted.\nTo compare the means of continuous variables measured between sex-specific tertiles of leptin, one way ANOVA and analysis of covariance with adjustments (ANCOVA) were used. Categorical data are presented as percentages and were compared among groups by χ2 tests. Correlations between leptin and clinical and laboratory parameters were assessed using Spearman rank order correlation coefficients (because of the skewed distribution of leptin levels). Multivariate regression analysis was performed to obtain partial (adjusted) correlations (R2). Case-mix-adjusted models were controlled for age, gender, history of CV disease, presence or absence of diabetes mellitus, dialysis vintage and fat mass.\nRepeated-measures analysis of variance was performed by using the MIXED model. Only patients with ≥ 2 study visits were included in the analyses. Base models were adjusted for age, sex, diabetes status, dialysis vintage, history of cardio-vascular diseases and fat mass. F Tests were used to assess the significance of the fixed effects, and P less than 0.05 was considered significant. To evaluate whether leptin influenced the trends in the various dependent variables, we included in each base model terms for individual \"leptin-by-time\" interactions.\nSurvival analyses were performed using the Kaplan-Meier survival curve and the Cox proportional hazard model. The univariate and multivariate Cox regression analyses are presented as (HR; CI).\nAll statistical analyses were performed using SPSS software, version 16.0 (SPSS Inc, Chicago, IL)."},"results_title":{"kind":"null"},"results_text":{"kind":"null"},"conclusions_title":{"kind":"string","value":"Conclusions"},"conclusions_text":{"kind":"string","value":"IB designed, organized and coordinated the study, managed data entry, contributed to data analysis and interpretation of data and wrote the manuscript. IS carried out the immunoassays, contributed to analyzing and interpretation of data and writing the manuscript. AA and HY carried out nutrition assessment (food intake analysis, body composition assessment), contributed to analyzing and interpretation of data and writing the manuscript. LF contributed to analyzing and interpretation of data. ZA and JW contributed to analyzing and interpretation of data and writing the manuscript. All authors read and approved the final manuscript."},"other_sections_titles":{"kind":"list like","value":["Background","Patients","Dietary intake","Anthropometric measurements","Body composition analysis","Laboratory evaluation","Statistical analysis","Results","Cross-sectional associations","Longitudinal associationas","Survival analysis","Discussion","Conclusions"],"string":"[\n \"Background\",\n \"Patients\",\n \"Dietary intake\",\n \"Anthropometric measurements\",\n \"Body composition analysis\",\n \"Laboratory evaluation\",\n \"Statistical analysis\",\n \"Results\",\n \"Cross-sectional associations\",\n \"Longitudinal associationas\",\n \"Survival analysis\",\n \"Discussion\",\n \"Conclusions\"\n]"},"other_sections_texts":{"kind":"list like","value":["In recent years, the number of patients with end-stage renal disease (ESRD) has been increasing worldwide [1]. Depending in part upon the method used to evaluate nutritional status and the population studied, from 40 to 70 percent of patients with ESRD are malnourished [2,3] resulting in poor clinical outcomes [4]. Among the mechanisms responsible for malnutrition, leptin was believed to influence nutritional markers in patients with ESRD [5]. Leptin is a 16-kDa protein identified as the product of the obese gene; it is exclusively produced in adipocytes, and regulates food intake and energy expenditure in animal models [6]. Leptin decreases food intake by decreasing NPY (neuropeptide Y - one of the most potent stimulators of food intake) mRNA [7] and increasing alpha-MSH (alpha-melanocyte-stimulating hormone - an inhibitor of food intake) [8]. Besides linking adiposity and central nervous circuits to reduced appetite and enhanced energy expenditure in the general population [9], leptin has been shown to increase overall sympathetic nerve activity [10], facilitate glucose utilization and improve insulin sensitivity [11]. Furthermore, the prospective West of Scotland Coronary Prevention Study (WOSCOPS) reported that elevated leptin increases the relative risk of cardio-vascular disease in the general population independently of fat mass [12].\nIn general, serum leptin levels are significantly elevated in patients with renal failure, particularly when compared to age, gender and body mass index (BMI)-matched controls [13,14]. However, the role of hyperleptinemia in ESRD patients is somewhat unconventional. In contrast with its anorexogenic effects recognized in the general population [9] and even in experimental models of uremia (in subtotal nephrectomized and leptin receptor-deficient [db/db] mice) [15], leptin has not been reported to affect perceived appetite and nutrient intake in dialysis patients [16,17]. Although in some observational studies, increased serum leptin concentrations were observed in ESRD patients in parallel with loss of lean body mass [18,19] or with hypoalbuminemia and low protein intake [20], some others failed to find any correlation between hyperleptinemia and weight change [9] or lean mass [21] in this population. Moreover, several clinical studies suggested that leptin is a negative acute phase protein [22] and can serve as a marker of adequate nutritional status, rather than an appetite-reducing uremic toxin in hemodialysis patients [23-25]. Finally, the relationship between elevated serum leptin levels and clinical outcomes in ESRD has not been fully defined. In one small prospective cohort of hemodialysis patients, lower baseline serum leptin levels predicted mortality [26], but neither changes in leptin over time were measured, nor were leptin levels normalized to body fat mass in this study.\nThus, the influence of serum leptin levels on nutritional status and survival in chronic hemodialysis patients remained to be elucidated. In view of leptin's physiological role, information on effects of prolonged hyperleptinemia (independent of fat mass) on nutritional status of chronic hemodialysis patients, which may also impact on their survival, would be of interest. The aim of the present prospective longitudinal study was therefore to study longitudinal changes in serum leptin levels and to relate them to the changes in nutritional markers and survival in chronic hemodialysis patients.","This prospective observational study was approved by the Ethics Committee of Assaf Harofeh Medical Center (Zerifin, Affiliated to the Sackler Faculty of Medicine Tel Aviv University, Israel). Informed consent was obtained before any trial-related activities. Patients were eligible for entry when they had been on HD therapy for at least 3 months and were 18 years or older, with no clinically active cardio-vascular or infectious diseases on entry. We excluded patients with edema, pleural effusion or ascites at their initial assessment, as well as patients with malignant disease, liver cirrhosis, neuro-muscular diseases, amputations or any deformities of the body. Exclusion criteria at the entry of the study also included co-morbidity (auto-immune disease and/or acute infections) and/or medication (prednisone) that might interfere with plasma leptin concentrations. A flow chart of the study is presented in Figure 1. In total, 101 patients (64 men and 37 women) with a mean age of 64.6 ± 11.5 years, receiving maintenance hemodialysis treatment at our outpatient HD clinic, were included in the study. Of the patients studied, 52 were diabetic (all diabetic patients had type 2 diabetes). Study measurements were performed at baseline and at 6, 12, 18 and 24 months from enrollment. After the longitudinal measurements ended, we continued clinical observation on our cohort during 2 additional years. Thus, in total, the study period extended 35 ± 17 months. During this period, 33 patients (32.7%) died (the main causes of death were cardio-vascular [12 of 33 patients; 36.4%] and sepsis [12 of 33 patients; 36.4%]), 13 patients (12.9%) underwent kidney transplantation, 3 patients (3.0%) changed dialysis modality, and 10 patients (10.0%) transferred to other hemodialysis units. All patients underwent regular dialysis via their vascular access (81.2% of patients had arterio-venous fistula) 4-5 h three times per week at a blood flow rate of 250-300 ml/min. Bicarbonate dialysate (30 mEq/L) at a dialysis solution flow rate of 500 ml/min was used in all cases. All dialysis was performed with biocompatible dialyzer membrane with a surface area of 1.0-1.8 m2. The efficiency of the dialysis was assessed based on the delivered dose of dialysis (Kt/V urea) using a single-pool urea kinetic model (mean Kt/V was1.31 ± 0.23 in our population).\nFlow diagram of the study.\nInformation on vascular disease (cerebral vascular, peripheral vascular and heart disease) was obtained from a detailed medical history.\nMost patients were required antihypertensive medications as well as other drugs commonly used in ESRD, such as phosphate and potassium binders, diuretics, and supplements of vitamins B, C, and D.","A continuous 3-day dietary history (which included a dialysis day, a weekend day and a non-dialysis day) was recorded on a self-completed food diary. Then, dietary energy and protein intake were calculated and normalized for adjusted body weight (ABW) by the following formula [27]:\nSBW - standard body weight was determined by the National Healthand Nutrition Examination Survey II (NHANES II) population medians for age, sex, frame size, and stature [27].\nDietary protein intake was also estimated by the protein catabolic rate (PCR) calculation from the patient's urea generation rate by urea kinetics modeling [28]. Single-pool model urea kinetics was used to estimate the nPCR.","BMI, triceps skinfold thickness (TSF), mid arm circumference (MAC) and calculated mid arm muscle circumference (MAMC) were measured as anthropometric variables. The BMI was calculated as dry weight in kilograms divided by the square of height in meters. TSF was measured with a conventional skinfold caliper using standard techniques. Mid arm circumference was measured with a plastic measuring tape. MAMC was estimated as follows:","Body composition was determined by body impedance analysis (B.I.A. Nutriguard- M, Data-Input, Frankfurt, Germany). We used gel-based electrodes specifically developed for BIA measurements - Bianostic AT (Data-Input GmbH). On the day of blood collection, patients underwent BIA measurement at approximately 30 minutes postdialysis. BIA electrodes were placed on the same body side used for anthropometric measurements. The multi-frequency technique was used. FFM was calculated by using the approach of Kyle et al. validated by dual-energy x-ray absorptiometry on 343 healthy adult subjects [29]:\nFat mass and fat free mass were standardized by squared height (m2), and expressed in kg/m2 as fat mass index (FMI) and fat free mass index (FFMI), respectively.\nPhase angle (PA) describes the relationship between the two vector components of impedance [reactance and resistance] of the human body to an alternating electric current. PA has been shown to provide a BIA prognostic index of morbidity and mortality [30].","Blood samples were taken in a non-fasting state before a midweek hemodialysis session. CBC, creatinine, urea, albumin, transferrin and total cholesterol were measured by routine laboratory methods. IL-6 and leptin were measured in plasma samples using commercially available enzyme-linked immunosorbent assay (ELISA) kits (R&D System, Minneapolis, MN, USA) according to the manufacturer's protocol. The mean minimal detectable level for IL-6 was 0.7 pg/ml, and 7.8 pg/ml for leptin. Intra-assay and inter-assay coefficients of variation for IL-6 were 4.2% and 6.4% respectively, and for leptin - 3.3% and 5.4% respectively.","Data are expressed as mean ± standard deviation (SD), median and interquartile range (Q1 to Q3) for variables that did not follow a normal distribution, or frequencies, as noted.\nTo compare the means of continuous variables measured between sex-specific tertiles of leptin, one way ANOVA and analysis of covariance with adjustments (ANCOVA) were used. Categorical data are presented as percentages and were compared among groups by χ2 tests. Correlations between leptin and clinical and laboratory parameters were assessed using Spearman rank order correlation coefficients (because of the skewed distribution of leptin levels). Multivariate regression analysis was performed to obtain partial (adjusted) correlations (R2). Case-mix-adjusted models were controlled for age, gender, history of CV disease, presence or absence of diabetes mellitus, dialysis vintage and fat mass.\nRepeated-measures analysis of variance was performed by using the MIXED model. Only patients with ≥ 2 study visits were included in the analyses. Base models were adjusted for age, sex, diabetes status, dialysis vintage, history of cardio-vascular diseases and fat mass. F Tests were used to assess the significance of the fixed effects, and P less than 0.05 was considered significant. To evaluate whether leptin influenced the trends in the various dependent variables, we included in each base model terms for individual \"leptin-by-time\" interactions.\nSurvival analyses were performed using the Kaplan-Meier survival curve and the Cox proportional hazard model. The univariate and multivariate Cox regression analyses are presented as (HR; CI).\nAll statistical analyses were performed using SPSS software, version 16.0 (SPSS Inc, Chicago, IL)."," Cross-sectional associations The 101 prevalent HD patients participating in this study included 36.6% women; over half of the participants (51.5%) had diabetes mellitus (DM) and nearly the same proportion had a history of cardiovascular disease (46.9%), including myocardial infarction, coronary artery procedures such as angioplasty or surgery, previous cerebral-vascular accident, or peripheral vascular disease. Average age was 64.6 ± 11.5 years and median maintenance HD vintage was 31 months (Q1 to Q3, 15.5-51.5 months) at the study initiation. For HD patients at the start of the cohort, serum leptin averaged (mean ± SD) 37.3 ± 39.4 ng/ml (median, 16.5 ng/ml; Q1 to Q3, 5.60-71.4 ng/ml).\nTo examine the relationship between different levels of serum leptin and relevant demographic, clinical and laboratory measures, serum leptin levels were divided into three equal gender specific tertiles (data not shown). Diabetes proportion and age distribution were similar across the three groups. No statistically significant differences were evident between groups in the use of medications that may affect inflammatory markers such as statins, aspirin, or angiotensin-converting enzyme inhibitors (data not shown). As expected females, tended to have higher leptin levels than males (p = 0.0001). Body composition parameters measured by anthropometry and bioelectric impedance analysis (BIA) including phase angle (PA) were incrementally greater across increasing leptin tertiles even after adjustments for baseline demographic and clinical parameters (age, DM status, dialysis vintage and cardio-vascular disease in the past). However, these associations were attenuated after including of fat mass in multivariate analyses. No significant differences in any of the biochemical markers of nutrition, normalized protein nitrogen appearance (nPNA), or actual energy or protein intake normalized to adjusted body weight were found between the HD patient groups according to leptin tertiles.\nAt baseline, anthropometric measurements (body mass index [BMI], triceps skinfold thickness [TSF], mid arm circumference [MAC] and midarm circumference calculated [MAMC]) and BIA-derived parameters of body composition (fat mass index [FMI] and fat free mass index [FFMI]) correlated positively with serum leptin levels, in both unadjusted and adjusted (for age, DM status, dialysis vintage, and previous cardio-vascular disease) models. Thus, an increased level of leptin was associated with better nutritional status (Table 1). Associations between levels of serum leptin and the two laboratory nutritional markers (albumin and cholesterol) and PA derived by BIA were statistically significant, but these associations were attenuated after adjustments for demographic and clinical parameters including fat mass. No significant correlations were observed between serum leptin and nPNA or dietary energy (DEI) and protein intake (DPI) normalized to adjusted body weight in both sexes after adjustments for demographic and clinical parameters including fat mass.\nUnadjusted and multivariate adjusted Spearman's correlation coefficients of baseline serum leptin and nutritional clinical and laboratory parameters in the study population at baseline\nCorrelation coefficient values ≥ 0.25 appear in bold.\na All case-mix-adjusted models include age, gender, DM status, dialysis vintage, CV disease in the past and fat mass.\nAbbreviations: nPNA, normalized protein nitrogen appearance; IL-6, interleukin-6; BMI, body mass index; TSF, triceps skinfold thickness; MAC, mid-arm circumference; MAMC, mid-arm muscle circumference calculated; FMI, fat mass index; FFMI, fat-free mass index, DM, diabetes mellitus; CV, cardio-vascular.\nThe 101 prevalent HD patients participating in this study included 36.6% women; over half of the participants (51.5%) had diabetes mellitus (DM) and nearly the same proportion had a history of cardiovascular disease (46.9%), including myocardial infarction, coronary artery procedures such as angioplasty or surgery, previous cerebral-vascular accident, or peripheral vascular disease. Average age was 64.6 ± 11.5 years and median maintenance HD vintage was 31 months (Q1 to Q3, 15.5-51.5 months) at the study initiation. For HD patients at the start of the cohort, serum leptin averaged (mean ± SD) 37.3 ± 39.4 ng/ml (median, 16.5 ng/ml; Q1 to Q3, 5.60-71.4 ng/ml).\nTo examine the relationship between different levels of serum leptin and relevant demographic, clinical and laboratory measures, serum leptin levels were divided into three equal gender specific tertiles (data not shown). Diabetes proportion and age distribution were similar across the three groups. No statistically significant differences were evident between groups in the use of medications that may affect inflammatory markers such as statins, aspirin, or angiotensin-converting enzyme inhibitors (data not shown). As expected females, tended to have higher leptin levels than males (p = 0.0001). Body composition parameters measured by anthropometry and bioelectric impedance analysis (BIA) including phase angle (PA) were incrementally greater across increasing leptin tertiles even after adjustments for baseline demographic and clinical parameters (age, DM status, dialysis vintage and cardio-vascular disease in the past). However, these associations were attenuated after including of fat mass in multivariate analyses. No significant differences in any of the biochemical markers of nutrition, normalized protein nitrogen appearance (nPNA), or actual energy or protein intake normalized to adjusted body weight were found between the HD patient groups according to leptin tertiles.\nAt baseline, anthropometric measurements (body mass index [BMI], triceps skinfold thickness [TSF], mid arm circumference [MAC] and midarm circumference calculated [MAMC]) and BIA-derived parameters of body composition (fat mass index [FMI] and fat free mass index [FFMI]) correlated positively with serum leptin levels, in both unadjusted and adjusted (for age, DM status, dialysis vintage, and previous cardio-vascular disease) models. Thus, an increased level of leptin was associated with better nutritional status (Table 1). Associations between levels of serum leptin and the two laboratory nutritional markers (albumin and cholesterol) and PA derived by BIA were statistically significant, but these associations were attenuated after adjustments for demographic and clinical parameters including fat mass. No significant correlations were observed between serum leptin and nPNA or dietary energy (DEI) and protein intake (DPI) normalized to adjusted body weight in both sexes after adjustments for demographic and clinical parameters including fat mass.\nUnadjusted and multivariate adjusted Spearman's correlation coefficients of baseline serum leptin and nutritional clinical and laboratory parameters in the study population at baseline\nCorrelation coefficient values ≥ 0.25 appear in bold.\na All case-mix-adjusted models include age, gender, DM status, dialysis vintage, CV disease in the past and fat mass.\nAbbreviations: nPNA, normalized protein nitrogen appearance; IL-6, interleukin-6; BMI, body mass index; TSF, triceps skinfold thickness; MAC, mid-arm circumference; MAMC, mid-arm muscle circumference calculated; FMI, fat mass index; FFMI, fat-free mass index, DM, diabetes mellitus; CV, cardio-vascular.\n Longitudinal associationas Linear mixed models were used to study the effects of longitudinal leptin changes on changes in nutritional parameters (slopes) over 24 months including fixed parameters such as age, gender, diabetes status, dialysis vintage, previous cardio-vascular events, and fat mass (Table 2). No significant changes in dietary intake or in any of the biochemical nutritional markers over time were found. In contrast, a significant reduction in body composition parameters (both those measured by anthropometry [BMI, TSF and MAMC] and by BIA [FMI, FFMI, PA]) over time was observed. However, leptin did not modulate the changes in outcome variables over time in our cohort (leptin-by-time interactions were insignificant).\nRegression coefficients with 95% Confidence Intervals for the effect of longitudinal leptin changes on nutritional parameters changes (slopes) during 24 months based on mixed-effects model with linear trends for variable and fixed parameters\nNOTE: All nutritional variables presented in Table were modeled separately as dependent variables, whereas independent variables included fixed factors (such as age, gender, diabetes status, dialysis vintage, past cardio-vascular disease and fat mass) and leptin as continuous variable. Terms for individual \"leptin-by-time\" interactions were included in each model. The model takes into account every measurements of leptin and presented nutritional variables in each time point for each patient separately. Presented regression coefficients demonstrate changes in outcome variables with time (24 months).\na P for trend for leptin-by-time interactions.\nb FMI and BMI were adjusted only by age, gender, DM status, history of cardio-vascular disease and dialysis vintage.\nAbbreviations: DEI, daily energy intake; DPI, daily protein intake; nPNA, normalized protein nitrogen appearance; IL-6, interleukin-6; EPO, erythropoietin; BMI, body mass index; TSF, triceps skinfold thickness; MAC, mid-arm circumference; MAMC, mid-arm muscle circumference calculated; FMI, fat mass index; FFMI, fat-free mass index; DM, diabetes mellitus; CV, cardio-vascular.\nA mixed model including the fixed factors sex, age, DM status, dialysis vintage, history of CV disease and some of nutritional parameters predicted variability in leptin levels during the study presented in Table 3. We observed significant changes of leptin levels with time (linear estimate ± SE: -2.5010 ± 0.57 ng/ml/2y; p < 0.001). The effect of longitudinal changes of FMI on serum leptin variability over time was evident according to this model.\nEffects of longitudinal changes of nutritional parameters on changes (slopes) of leptin during 24 months based on mixed-effects model with linear trends for variable and fixed parameters\nNOTE: The Table represents a simple Mixed-Model with leptin as dependent variable, and presented nutritional parameters and fixed factors (age, gender, DM status, dialysis vintage and history of CV disease) as independent variables. The model takes into account every measurements of leptin and presented nutritional variables in each time point for each patient separately.\nAbbreviations: DEI, daily energy intake; IL-6, interleukin-6; EPO, erythropoietin; FMI, fat mass index; DM, diabetes mellitus; CV, cardio-vascular.\nOf interest, a significant reduction over time was associated with a more rapid decline in leptin levels in the highest leptin tertile in both unadjusted (p = 0.007 for leptin-by-time interaction) and fully adjusted (p = 0.047 for leptin-by-time interaction) models (Figure 2).\nLeptin levels decline over time in the study population. Changes in estimated marginal means of serum leptin levels at baseline (month 0), month 6, month 12, month 18 and month 24 in the study population groups according to sex-specific tertiles* of baseline serum leptin: 1. Unadjusted model: P = 0.007 for leptin-by-time interaction; 2. Fully adjusted model (for age, gender, DM status, dialysis vintage, previous cardio-vascular morbidity and fat mass): P = 0.047 for leptin-by-time interaction. *Median (interquartile range) leptin (ng/ml) tertiles for men (n = 64) were 1.70 (1.2-2.4), 10.2 (7.6-13.8) and 38.3 (26.2-81.3) and for women (n = 37) were 15.8 (9.3-18.3), 68.0 (41.4-85.9) and 112.6 (109.2-116.0).\nLinear mixed models were used to study the effects of longitudinal leptin changes on changes in nutritional parameters (slopes) over 24 months including fixed parameters such as age, gender, diabetes status, dialysis vintage, previous cardio-vascular events, and fat mass (Table 2). No significant changes in dietary intake or in any of the biochemical nutritional markers over time were found. In contrast, a significant reduction in body composition parameters (both those measured by anthropometry [BMI, TSF and MAMC] and by BIA [FMI, FFMI, PA]) over time was observed. However, leptin did not modulate the changes in outcome variables over time in our cohort (leptin-by-time interactions were insignificant).\nRegression coefficients with 95% Confidence Intervals for the effect of longitudinal leptin changes on nutritional parameters changes (slopes) during 24 months based on mixed-effects model with linear trends for variable and fixed parameters\nNOTE: All nutritional variables presented in Table were modeled separately as dependent variables, whereas independent variables included fixed factors (such as age, gender, diabetes status, dialysis vintage, past cardio-vascular disease and fat mass) and leptin as continuous variable. Terms for individual \"leptin-by-time\" interactions were included in each model. The model takes into account every measurements of leptin and presented nutritional variables in each time point for each patient separately. Presented regression coefficients demonstrate changes in outcome variables with time (24 months).\na P for trend for leptin-by-time interactions.\nb FMI and BMI were adjusted only by age, gender, DM status, history of cardio-vascular disease and dialysis vintage.\nAbbreviations: DEI, daily energy intake; DPI, daily protein intake; nPNA, normalized protein nitrogen appearance; IL-6, interleukin-6; EPO, erythropoietin; BMI, body mass index; TSF, triceps skinfold thickness; MAC, mid-arm circumference; MAMC, mid-arm muscle circumference calculated; FMI, fat mass index; FFMI, fat-free mass index; DM, diabetes mellitus; CV, cardio-vascular.\nA mixed model including the fixed factors sex, age, DM status, dialysis vintage, history of CV disease and some of nutritional parameters predicted variability in leptin levels during the study presented in Table 3. We observed significant changes of leptin levels with time (linear estimate ± SE: -2.5010 ± 0.57 ng/ml/2y; p < 0.001). The effect of longitudinal changes of FMI on serum leptin variability over time was evident according to this model.\nEffects of longitudinal changes of nutritional parameters on changes (slopes) of leptin during 24 months based on mixed-effects model with linear trends for variable and fixed parameters\nNOTE: The Table represents a simple Mixed-Model with leptin as dependent variable, and presented nutritional parameters and fixed factors (age, gender, DM status, dialysis vintage and history of CV disease) as independent variables. The model takes into account every measurements of leptin and presented nutritional variables in each time point for each patient separately.\nAbbreviations: DEI, daily energy intake; IL-6, interleukin-6; EPO, erythropoietin; FMI, fat mass index; DM, diabetes mellitus; CV, cardio-vascular.\nOf interest, a significant reduction over time was associated with a more rapid decline in leptin levels in the highest leptin tertile in both unadjusted (p = 0.007 for leptin-by-time interaction) and fully adjusted (p = 0.047 for leptin-by-time interaction) models (Figure 2).\nLeptin levels decline over time in the study population. Changes in estimated marginal means of serum leptin levels at baseline (month 0), month 6, month 12, month 18 and month 24 in the study population groups according to sex-specific tertiles* of baseline serum leptin: 1. Unadjusted model: P = 0.007 for leptin-by-time interaction; 2. Fully adjusted model (for age, gender, DM status, dialysis vintage, previous cardio-vascular morbidity and fat mass): P = 0.047 for leptin-by-time interaction. *Median (interquartile range) leptin (ng/ml) tertiles for men (n = 64) were 1.70 (1.2-2.4), 10.2 (7.6-13.8) and 38.3 (26.2-81.3) and for women (n = 37) were 15.8 (9.3-18.3), 68.0 (41.4-85.9) and 112.6 (109.2-116.0).\n Survival analysis During an average follow-up of 35 months (median, 40 months; Q1 to Q3, 17-52), 33 patients died: 11 (32%) in the low tertile group, 12 (35%) in the median tertile group and 10 (30%) in the high tertile group, with a median time to death of 25 months (Q1 to Q3, 14-40 months). Cumulative incidences of survival were unaffected by the baseline serum leptin levels (Figure 3). To further assess the possible association of baseline serum leptin level with cardio-vascular morbidity and mortality, cumulative hazards of all-cause death and first composite cardio-vascular event from Cox regression (after adjustment for baseline demographic parameters and fat mass) were calculated. First cardio-vascular event was defined as myocardial infarction (MI), requiring coronary artery procedures such as angioplasty or surgery, cerebral-vascular accident (CVA), or peripheral vascular disease (PVD), requiring angioplasty, bypass or amputation and diagnosed after the participant entered the study. No statistically significant differences in hazards between the different groups according leptin tertiles were observed. Additionally, no differences were found by Cox regression analysis in terms of survival probabilities from the analysis of all-cause mortality for each 10 ng/ml increase in baseline serum leptin (data not shown).\nKaplan-Meier curves of surviving patients comparing subgroups of patients stratified by baseline serum leptin tertiles.\nThus, although chronic HD patients with higher baseline leptin had better nutritional status as evidenced by their significantly more favorable body composition parameters at the start of the study, this disparity did not appear to widen over the 24 months of follow-up; thus, leptin levels did not predict body composition parameter changes over time. Moreover, no survival advantage of the high leptin group was found in our observational cohort.\nDuring an average follow-up of 35 months (median, 40 months; Q1 to Q3, 17-52), 33 patients died: 11 (32%) in the low tertile group, 12 (35%) in the median tertile group and 10 (30%) in the high tertile group, with a median time to death of 25 months (Q1 to Q3, 14-40 months). Cumulative incidences of survival were unaffected by the baseline serum leptin levels (Figure 3). To further assess the possible association of baseline serum leptin level with cardio-vascular morbidity and mortality, cumulative hazards of all-cause death and first composite cardio-vascular event from Cox regression (after adjustment for baseline demographic parameters and fat mass) were calculated. First cardio-vascular event was defined as myocardial infarction (MI), requiring coronary artery procedures such as angioplasty or surgery, cerebral-vascular accident (CVA), or peripheral vascular disease (PVD), requiring angioplasty, bypass or amputation and diagnosed after the participant entered the study. No statistically significant differences in hazards between the different groups according leptin tertiles were observed. Additionally, no differences were found by Cox regression analysis in terms of survival probabilities from the analysis of all-cause mortality for each 10 ng/ml increase in baseline serum leptin (data not shown).\nKaplan-Meier curves of surviving patients comparing subgroups of patients stratified by baseline serum leptin tertiles.\nThus, although chronic HD patients with higher baseline leptin had better nutritional status as evidenced by their significantly more favorable body composition parameters at the start of the study, this disparity did not appear to widen over the 24 months of follow-up; thus, leptin levels did not predict body composition parameter changes over time. Moreover, no survival advantage of the high leptin group was found in our observational cohort.","The 101 prevalent HD patients participating in this study included 36.6% women; over half of the participants (51.5%) had diabetes mellitus (DM) and nearly the same proportion had a history of cardiovascular disease (46.9%), including myocardial infarction, coronary artery procedures such as angioplasty or surgery, previous cerebral-vascular accident, or peripheral vascular disease. Average age was 64.6 ± 11.5 years and median maintenance HD vintage was 31 months (Q1 to Q3, 15.5-51.5 months) at the study initiation. For HD patients at the start of the cohort, serum leptin averaged (mean ± SD) 37.3 ± 39.4 ng/ml (median, 16.5 ng/ml; Q1 to Q3, 5.60-71.4 ng/ml).\nTo examine the relationship between different levels of serum leptin and relevant demographic, clinical and laboratory measures, serum leptin levels were divided into three equal gender specific tertiles (data not shown). Diabetes proportion and age distribution were similar across the three groups. No statistically significant differences were evident between groups in the use of medications that may affect inflammatory markers such as statins, aspirin, or angiotensin-converting enzyme inhibitors (data not shown). As expected females, tended to have higher leptin levels than males (p = 0.0001). Body composition parameters measured by anthropometry and bioelectric impedance analysis (BIA) including phase angle (PA) were incrementally greater across increasing leptin tertiles even after adjustments for baseline demographic and clinical parameters (age, DM status, dialysis vintage and cardio-vascular disease in the past). However, these associations were attenuated after including of fat mass in multivariate analyses. No significant differences in any of the biochemical markers of nutrition, normalized protein nitrogen appearance (nPNA), or actual energy or protein intake normalized to adjusted body weight were found between the HD patient groups according to leptin tertiles.\nAt baseline, anthropometric measurements (body mass index [BMI], triceps skinfold thickness [TSF], mid arm circumference [MAC] and midarm circumference calculated [MAMC]) and BIA-derived parameters of body composition (fat mass index [FMI] and fat free mass index [FFMI]) correlated positively with serum leptin levels, in both unadjusted and adjusted (for age, DM status, dialysis vintage, and previous cardio-vascular disease) models. Thus, an increased level of leptin was associated with better nutritional status (Table 1). Associations between levels of serum leptin and the two laboratory nutritional markers (albumin and cholesterol) and PA derived by BIA were statistically significant, but these associations were attenuated after adjustments for demographic and clinical parameters including fat mass. No significant correlations were observed between serum leptin and nPNA or dietary energy (DEI) and protein intake (DPI) normalized to adjusted body weight in both sexes after adjustments for demographic and clinical parameters including fat mass.\nUnadjusted and multivariate adjusted Spearman's correlation coefficients of baseline serum leptin and nutritional clinical and laboratory parameters in the study population at baseline\nCorrelation coefficient values ≥ 0.25 appear in bold.\na All case-mix-adjusted models include age, gender, DM status, dialysis vintage, CV disease in the past and fat mass.\nAbbreviations: nPNA, normalized protein nitrogen appearance; IL-6, interleukin-6; BMI, body mass index; TSF, triceps skinfold thickness; MAC, mid-arm circumference; MAMC, mid-arm muscle circumference calculated; FMI, fat mass index; FFMI, fat-free mass index, DM, diabetes mellitus; CV, cardio-vascular.","Linear mixed models were used to study the effects of longitudinal leptin changes on changes in nutritional parameters (slopes) over 24 months including fixed parameters such as age, gender, diabetes status, dialysis vintage, previous cardio-vascular events, and fat mass (Table 2). No significant changes in dietary intake or in any of the biochemical nutritional markers over time were found. In contrast, a significant reduction in body composition parameters (both those measured by anthropometry [BMI, TSF and MAMC] and by BIA [FMI, FFMI, PA]) over time was observed. However, leptin did not modulate the changes in outcome variables over time in our cohort (leptin-by-time interactions were insignificant).\nRegression coefficients with 95% Confidence Intervals for the effect of longitudinal leptin changes on nutritional parameters changes (slopes) during 24 months based on mixed-effects model with linear trends for variable and fixed parameters\nNOTE: All nutritional variables presented in Table were modeled separately as dependent variables, whereas independent variables included fixed factors (such as age, gender, diabetes status, dialysis vintage, past cardio-vascular disease and fat mass) and leptin as continuous variable. Terms for individual \"leptin-by-time\" interactions were included in each model. The model takes into account every measurements of leptin and presented nutritional variables in each time point for each patient separately. Presented regression coefficients demonstrate changes in outcome variables with time (24 months).\na P for trend for leptin-by-time interactions.\nb FMI and BMI were adjusted only by age, gender, DM status, history of cardio-vascular disease and dialysis vintage.\nAbbreviations: DEI, daily energy intake; DPI, daily protein intake; nPNA, normalized protein nitrogen appearance; IL-6, interleukin-6; EPO, erythropoietin; BMI, body mass index; TSF, triceps skinfold thickness; MAC, mid-arm circumference; MAMC, mid-arm muscle circumference calculated; FMI, fat mass index; FFMI, fat-free mass index; DM, diabetes mellitus; CV, cardio-vascular.\nA mixed model including the fixed factors sex, age, DM status, dialysis vintage, history of CV disease and some of nutritional parameters predicted variability in leptin levels during the study presented in Table 3. We observed significant changes of leptin levels with time (linear estimate ± SE: -2.5010 ± 0.57 ng/ml/2y; p < 0.001). The effect of longitudinal changes of FMI on serum leptin variability over time was evident according to this model.\nEffects of longitudinal changes of nutritional parameters on changes (slopes) of leptin during 24 months based on mixed-effects model with linear trends for variable and fixed parameters\nNOTE: The Table represents a simple Mixed-Model with leptin as dependent variable, and presented nutritional parameters and fixed factors (age, gender, DM status, dialysis vintage and history of CV disease) as independent variables. The model takes into account every measurements of leptin and presented nutritional variables in each time point for each patient separately.\nAbbreviations: DEI, daily energy intake; IL-6, interleukin-6; EPO, erythropoietin; FMI, fat mass index; DM, diabetes mellitus; CV, cardio-vascular.\nOf interest, a significant reduction over time was associated with a more rapid decline in leptin levels in the highest leptin tertile in both unadjusted (p = 0.007 for leptin-by-time interaction) and fully adjusted (p = 0.047 for leptin-by-time interaction) models (Figure 2).\nLeptin levels decline over time in the study population. Changes in estimated marginal means of serum leptin levels at baseline (month 0), month 6, month 12, month 18 and month 24 in the study population groups according to sex-specific tertiles* of baseline serum leptin: 1. Unadjusted model: P = 0.007 for leptin-by-time interaction; 2. Fully adjusted model (for age, gender, DM status, dialysis vintage, previous cardio-vascular morbidity and fat mass): P = 0.047 for leptin-by-time interaction. *Median (interquartile range) leptin (ng/ml) tertiles for men (n = 64) were 1.70 (1.2-2.4), 10.2 (7.6-13.8) and 38.3 (26.2-81.3) and for women (n = 37) were 15.8 (9.3-18.3), 68.0 (41.4-85.9) and 112.6 (109.2-116.0).","During an average follow-up of 35 months (median, 40 months; Q1 to Q3, 17-52), 33 patients died: 11 (32%) in the low tertile group, 12 (35%) in the median tertile group and 10 (30%) in the high tertile group, with a median time to death of 25 months (Q1 to Q3, 14-40 months). Cumulative incidences of survival were unaffected by the baseline serum leptin levels (Figure 3). To further assess the possible association of baseline serum leptin level with cardio-vascular morbidity and mortality, cumulative hazards of all-cause death and first composite cardio-vascular event from Cox regression (after adjustment for baseline demographic parameters and fat mass) were calculated. First cardio-vascular event was defined as myocardial infarction (MI), requiring coronary artery procedures such as angioplasty or surgery, cerebral-vascular accident (CVA), or peripheral vascular disease (PVD), requiring angioplasty, bypass or amputation and diagnosed after the participant entered the study. No statistically significant differences in hazards between the different groups according leptin tertiles were observed. Additionally, no differences were found by Cox regression analysis in terms of survival probabilities from the analysis of all-cause mortality for each 10 ng/ml increase in baseline serum leptin (data not shown).\nKaplan-Meier curves of surviving patients comparing subgroups of patients stratified by baseline serum leptin tertiles.\nThus, although chronic HD patients with higher baseline leptin had better nutritional status as evidenced by their significantly more favorable body composition parameters at the start of the study, this disparity did not appear to widen over the 24 months of follow-up; thus, leptin levels did not predict body composition parameter changes over time. Moreover, no survival advantage of the high leptin group was found in our observational cohort.","In the present study, we wished to determine whether changes of serum leptin levels are correlated with nutritional status over time in a cohort of prevalent hemodialysis patients. We observed that despite the strong and significant cross-sectional associations between serum leptin and body composition parameters at baseline, leptin levels did not reflect changes in nutritional status in our population.\nOur study confirms several previous cross-sectional studies in which elevated serum leptin levels were demonstrated to be positively associated with several nutritional markers (laboratory or body composition parameters, mainly BMI and fat mass) in chronic HD patients [14,20,21,23-25]. HD patients treated with megestrole acetate (24), growth hormone [31] or high-calorie supplementation [32] demonstrated progressively increased serum leptin parallel to an improved nutritional state.\nHowever, not all studies are consistent with positive associations between elevated levels of leptin and several nutritional parameters in ESRD patients. An inverse correlation was observed between leptin/fat mass and dietary intake, as well as with significantly lower lean tissue mass in chronic renal failure (23 patients) and ESRD patients receiving PD (24 patients) and HD (22 patients) therapy in study by Young et al [5]. In a cross-sectional study of 28 dialysis patients and 41 healthy control subjects, Johansen et al [20] showed that leptin levels were negatively correlated with albumin and PCR, suggesting a possible negative role of leptin in nutrition. Based on longitudinal observation, Stenvinkel et al [18] demonstrated that increases in serum leptin levels of 36 peritoneal dialysis patients were associated with a decrease in lean body mass. The discrepancies between these studies may be related to the population recruited, to their small sample size, and to their design; specifically, serum leptin levels were measured among patients with varying degrees of CRF, some of whom were undergoing maintenance hemodialysis and peritoneal dialysis, and in healthy controls [5,18,20]; many of these studies had a low number of study participants [5,18,20,23-25] and a majority of these studies were cross-sectional [5,20,23,25].\nLeptin, a regulator of eating behavior, has been shown to be a major determinant of anorexia in uremic animals via signaling through the hypothalamic melanocortin system [15]. Subsequently, a recent experimental study by Cheung et al [33] showed that intraperitoneal administration of the melanocortin-4 receptor (MC4-R) antagonist NBI-12i stimulates food intake and weight gain in uremic mice. However, the effects of leptin in uremic patients are not unequivocal. Relevant to our findings, Bossola at al [17] demonstrated that serum leptin levels were not different in anorexic and in non-anorexic hemodialysis patients. Several other studies failed as well to demonstrate any role of hyperleptinemia on reduced dietary intake in ESRD patients receiving maintenance HD treatment [5,16,34]. Since hyperleptinemia is common in HD patients mainly due to impaired renal clearance [20], it seemed reasonable to assume that selective leptin resistance (such as occurs in obese humans [35]) is the responsible mechanism for attenuation of the negative effect of leptin on appetite, and accordingly, on dietary intake under conditions of continuous stimulation. The molecular mechanisms of leptin resistance, with a focus on the contribution of the intracellular tyrosine residues in the hypothalamic receptor of leptin (LEPRb) and their interaction with the negative feedback regulators, suppressor of cytokine signaling 1 and 3 (SOCS1 and SOCS3), are described in a recent study by Knobelspies et al [36].\nThus, although the cross-group comparisons confirm that hyperleptinemia is associated with better body composition parameters, no unequivocal associations in terms of biochemical data or dietary intake with serum leptin levels are evident in HD patients according to the available literature. Our study confirms these data.\nAnother finding of the present study was that 24 months of HD was associated with a significant reduction of plasma leptin levels, with a more rapid decline observed in patients with higher baseline leptin levels. Only limited information is available on the relationship between longitudinal serum leptin measurements and nutritional characteristics in chronic HD patients [18,32]. To the best of our knowledge, the present study is the first long-term longitudinal study to show the relationship between serum leptin level and nutritional status in ESRD patients receiving maintenance HD therapy. Several mechanisms may be involved in decrease of serum leptin levels over time in hemodialysis patients. These include, the increasing use of high flux and super-flux hemodialyzers [37], and/or use of alternative dialysis strategies such as hemodiafiltration [38]; recombinant human erythropoietin treatment (generally followed by a significant decline of leptinaemia in hemodialysed patients [39]); chronic inflammation (under the assumption that leptin, like albumin or transferrin, is a negative acute phase protein in chronic HD patients [22], although other studies [40] do not support this mechanism); metabolic acidosis, which can reduce the release of leptin from adipose tissue [41]; and finally, a decrease in fat mass leading to a decrease in the rate of leptin biosynthesis [14,18] (based on an insulin or a nutrient-sensing pathway regulating leptin gene expression in fat tissue [42]). Although patients in our facility were all treated using low flux hemodialysis, reduction of BMI and accordingly of fat mass over 24 months of the study may explain this longitudinal decrease of serum leptin levels in our cohort.\nOf interest is the observation that adverse changes in body composition parameters occur over time, supporting the hypothesis that end-stage renal disease is associated with wasting as proposed in some [43,44] but not all [45] of the previous studies. The results of our study were consistent with those reported by Johansen et al [43] who did not find significant changes in any of the biochemical markers with time, but a significant reduction in phase angle over 1 year follow up was evident in prevalent HD patients. In parallel, together with reduced body composition parameters over time, we observed a longitudinal decline in serum leptin levels in our cohort. However, the lack of an association between serum leptin levels and future changes in body composition parameters (as exhibited by non-significant leptin-by-time interactions), suggests that the levels of leptin follow body composition (mainly fat mass) trajectory or its accompanying metabolic changes, rather than governing it.\nFinally, serum leptin level did not appear as a useful predictor of all cause mortality or cardio-vascular morbidity in our study population during up to 4 years of observation. In this aspect, our findings support the results of earlier study by Tsai et al [46]. Although Scholze et al [26] showed a paradoxical reverse association between leptin level and clinical outcome in 71 chronic HD patients, our patients did not show such an association. The difference between our results and theirs might have been caused by differences in the cohorts. First, the population presented by Scholze et al [26] had a lower BMI at baseline than our patients, and no body composition parameters were measured. Further, the prevalence of diabetes mellitus was much greater in patients in the lower leptin group than in patients of the higher leptin group in the cohort of Scholze et al [26]. Indeed, HD patients with diabetes have a poor prognosis [4] that might be the cause of the lower survival rate in the lower leptin group in the above study [26]. While low levels of leptin may reflect a state of malnutrition in HD patients, proatherogenic effects of hyperleptinemia were linked to an adverse cardiovascular profile in general population [10,12]. We speculate that the lack of long-term benefits of hyperleptinemia in terms of survival of chronic HD patients could reflect the modulating influence of proatherogenic effects of high serum leptin levels. Finally, no relationships between serum leptin levels and previous cardio-vascular events were found by Diez et al [47] in a retrospective study on 82 dialysis patients. Zoccali et al [40] also did not show any difference in cardiovascular event-free survival in cohort of 192 hemodialysis patients after their stratification on the basis of the median value of plasma leptin.\nSome limitations of the present study should be considered. First, this study is based on a relatively small sample size limiting detection of more subtle changes over time. Second, this study used only an observational approach, without manipulation of exposure factors, and therefore, no definitive cause-and-effect relationship can be derived for any of the risk factors analyzed. Dietary intake assessed by 3 day food records is another limitation of the study, as results can be subjective and incomplete, and can vary considerably from day to day as a result of dialysis treatment sessions and associated disturbances in food intake. Finally, significant circadian fluctuations of plasma leptin (regardless of the underlying degree of glucose tolerance, fasting, or diet) can certainly obscure some small but significant changes in plasma leptin [48] and may be a source of potential bias for longitudinal observation. Nevertheless, the present study has the advantage of providing long-term longitudinal data on the relationship between serum concentrations of leptin and nutritional parameters in prevalent HD patients.","In conclusion, we found positive linear associations between serum leptin levels and body composition, whereas no significant relations in terms of biochemical data or dietary intake were evident in our cohort at baseline. Analyzing longitudinal data we concluded that any influence of serum leptin levels on nutritional status is limited to mirroring attained BMI and fat mass trajectory. Further, nutritional advantage at baseline of the study population with hyperleptinemia did not translate into long-term benefits in terms of survival. Taken together, our results suggest that in our cohort, leptin levels reflect fat mass depots, rather than independently contributing to uremic anorexia or modifying nutritional status and/or survival in chronic HD patients. These results are of particular importance if the use of subcutaneous injections of recombinant methionyl leptin [49] or leptin removal by super-flux polysulfone dialysers [37] is contemplated as a therapeutic intervention."],"string":"[\n \"In recent years, the number of patients with end-stage renal disease (ESRD) has been increasing worldwide [1]. Depending in part upon the method used to evaluate nutritional status and the population studied, from 40 to 70 percent of patients with ESRD are malnourished [2,3] resulting in poor clinical outcomes [4]. Among the mechanisms responsible for malnutrition, leptin was believed to influence nutritional markers in patients with ESRD [5]. Leptin is a 16-kDa protein identified as the product of the obese gene; it is exclusively produced in adipocytes, and regulates food intake and energy expenditure in animal models [6]. Leptin decreases food intake by decreasing NPY (neuropeptide Y - one of the most potent stimulators of food intake) mRNA [7] and increasing alpha-MSH (alpha-melanocyte-stimulating hormone - an inhibitor of food intake) [8]. Besides linking adiposity and central nervous circuits to reduced appetite and enhanced energy expenditure in the general population [9], leptin has been shown to increase overall sympathetic nerve activity [10], facilitate glucose utilization and improve insulin sensitivity [11]. Furthermore, the prospective West of Scotland Coronary Prevention Study (WOSCOPS) reported that elevated leptin increases the relative risk of cardio-vascular disease in the general population independently of fat mass [12].\\nIn general, serum leptin levels are significantly elevated in patients with renal failure, particularly when compared to age, gender and body mass index (BMI)-matched controls [13,14]. However, the role of hyperleptinemia in ESRD patients is somewhat unconventional. In contrast with its anorexogenic effects recognized in the general population [9] and even in experimental models of uremia (in subtotal nephrectomized and leptin receptor-deficient [db/db] mice) [15], leptin has not been reported to affect perceived appetite and nutrient intake in dialysis patients [16,17]. Although in some observational studies, increased serum leptin concentrations were observed in ESRD patients in parallel with loss of lean body mass [18,19] or with hypoalbuminemia and low protein intake [20], some others failed to find any correlation between hyperleptinemia and weight change [9] or lean mass [21] in this population. Moreover, several clinical studies suggested that leptin is a negative acute phase protein [22] and can serve as a marker of adequate nutritional status, rather than an appetite-reducing uremic toxin in hemodialysis patients [23-25]. Finally, the relationship between elevated serum leptin levels and clinical outcomes in ESRD has not been fully defined. In one small prospective cohort of hemodialysis patients, lower baseline serum leptin levels predicted mortality [26], but neither changes in leptin over time were measured, nor were leptin levels normalized to body fat mass in this study.\\nThus, the influence of serum leptin levels on nutritional status and survival in chronic hemodialysis patients remained to be elucidated. In view of leptin's physiological role, information on effects of prolonged hyperleptinemia (independent of fat mass) on nutritional status of chronic hemodialysis patients, which may also impact on their survival, would be of interest. The aim of the present prospective longitudinal study was therefore to study longitudinal changes in serum leptin levels and to relate them to the changes in nutritional markers and survival in chronic hemodialysis patients.\",\n \"This prospective observational study was approved by the Ethics Committee of Assaf Harofeh Medical Center (Zerifin, Affiliated to the Sackler Faculty of Medicine Tel Aviv University, Israel). Informed consent was obtained before any trial-related activities. Patients were eligible for entry when they had been on HD therapy for at least 3 months and were 18 years or older, with no clinically active cardio-vascular or infectious diseases on entry. We excluded patients with edema, pleural effusion or ascites at their initial assessment, as well as patients with malignant disease, liver cirrhosis, neuro-muscular diseases, amputations or any deformities of the body. Exclusion criteria at the entry of the study also included co-morbidity (auto-immune disease and/or acute infections) and/or medication (prednisone) that might interfere with plasma leptin concentrations. A flow chart of the study is presented in Figure 1. In total, 101 patients (64 men and 37 women) with a mean age of 64.6 ± 11.5 years, receiving maintenance hemodialysis treatment at our outpatient HD clinic, were included in the study. Of the patients studied, 52 were diabetic (all diabetic patients had type 2 diabetes). Study measurements were performed at baseline and at 6, 12, 18 and 24 months from enrollment. After the longitudinal measurements ended, we continued clinical observation on our cohort during 2 additional years. Thus, in total, the study period extended 35 ± 17 months. During this period, 33 patients (32.7%) died (the main causes of death were cardio-vascular [12 of 33 patients; 36.4%] and sepsis [12 of 33 patients; 36.4%]), 13 patients (12.9%) underwent kidney transplantation, 3 patients (3.0%) changed dialysis modality, and 10 patients (10.0%) transferred to other hemodialysis units. All patients underwent regular dialysis via their vascular access (81.2% of patients had arterio-venous fistula) 4-5 h three times per week at a blood flow rate of 250-300 ml/min. Bicarbonate dialysate (30 mEq/L) at a dialysis solution flow rate of 500 ml/min was used in all cases. All dialysis was performed with biocompatible dialyzer membrane with a surface area of 1.0-1.8 m2. The efficiency of the dialysis was assessed based on the delivered dose of dialysis (Kt/V urea) using a single-pool urea kinetic model (mean Kt/V was1.31 ± 0.23 in our population).\\nFlow diagram of the study.\\nInformation on vascular disease (cerebral vascular, peripheral vascular and heart disease) was obtained from a detailed medical history.\\nMost patients were required antihypertensive medications as well as other drugs commonly used in ESRD, such as phosphate and potassium binders, diuretics, and supplements of vitamins B, C, and D.\",\n \"A continuous 3-day dietary history (which included a dialysis day, a weekend day and a non-dialysis day) was recorded on a self-completed food diary. Then, dietary energy and protein intake were calculated and normalized for adjusted body weight (ABW) by the following formula [27]:\\nSBW - standard body weight was determined by the National Healthand Nutrition Examination Survey II (NHANES II) population medians for age, sex, frame size, and stature [27].\\nDietary protein intake was also estimated by the protein catabolic rate (PCR) calculation from the patient's urea generation rate by urea kinetics modeling [28]. Single-pool model urea kinetics was used to estimate the nPCR.\",\n \"BMI, triceps skinfold thickness (TSF), mid arm circumference (MAC) and calculated mid arm muscle circumference (MAMC) were measured as anthropometric variables. The BMI was calculated as dry weight in kilograms divided by the square of height in meters. TSF was measured with a conventional skinfold caliper using standard techniques. Mid arm circumference was measured with a plastic measuring tape. MAMC was estimated as follows:\",\n \"Body composition was determined by body impedance analysis (B.I.A. Nutriguard- M, Data-Input, Frankfurt, Germany). We used gel-based electrodes specifically developed for BIA measurements - Bianostic AT (Data-Input GmbH). On the day of blood collection, patients underwent BIA measurement at approximately 30 minutes postdialysis. BIA electrodes were placed on the same body side used for anthropometric measurements. The multi-frequency technique was used. FFM was calculated by using the approach of Kyle et al. validated by dual-energy x-ray absorptiometry on 343 healthy adult subjects [29]:\\nFat mass and fat free mass were standardized by squared height (m2), and expressed in kg/m2 as fat mass index (FMI) and fat free mass index (FFMI), respectively.\\nPhase angle (PA) describes the relationship between the two vector components of impedance [reactance and resistance] of the human body to an alternating electric current. PA has been shown to provide a BIA prognostic index of morbidity and mortality [30].\",\n \"Blood samples were taken in a non-fasting state before a midweek hemodialysis session. CBC, creatinine, urea, albumin, transferrin and total cholesterol were measured by routine laboratory methods. IL-6 and leptin were measured in plasma samples using commercially available enzyme-linked immunosorbent assay (ELISA) kits (R&D System, Minneapolis, MN, USA) according to the manufacturer's protocol. The mean minimal detectable level for IL-6 was 0.7 pg/ml, and 7.8 pg/ml for leptin. Intra-assay and inter-assay coefficients of variation for IL-6 were 4.2% and 6.4% respectively, and for leptin - 3.3% and 5.4% respectively.\",\n \"Data are expressed as mean ± standard deviation (SD), median and interquartile range (Q1 to Q3) for variables that did not follow a normal distribution, or frequencies, as noted.\\nTo compare the means of continuous variables measured between sex-specific tertiles of leptin, one way ANOVA and analysis of covariance with adjustments (ANCOVA) were used. Categorical data are presented as percentages and were compared among groups by χ2 tests. Correlations between leptin and clinical and laboratory parameters were assessed using Spearman rank order correlation coefficients (because of the skewed distribution of leptin levels). Multivariate regression analysis was performed to obtain partial (adjusted) correlations (R2). Case-mix-adjusted models were controlled for age, gender, history of CV disease, presence or absence of diabetes mellitus, dialysis vintage and fat mass.\\nRepeated-measures analysis of variance was performed by using the MIXED model. Only patients with ≥ 2 study visits were included in the analyses. Base models were adjusted for age, sex, diabetes status, dialysis vintage, history of cardio-vascular diseases and fat mass. F Tests were used to assess the significance of the fixed effects, and P less than 0.05 was considered significant. To evaluate whether leptin influenced the trends in the various dependent variables, we included in each base model terms for individual \\\"leptin-by-time\\\" interactions.\\nSurvival analyses were performed using the Kaplan-Meier survival curve and the Cox proportional hazard model. The univariate and multivariate Cox regression analyses are presented as (HR; CI).\\nAll statistical analyses were performed using SPSS software, version 16.0 (SPSS Inc, Chicago, IL).\",\n \" Cross-sectional associations The 101 prevalent HD patients participating in this study included 36.6% women; over half of the participants (51.5%) had diabetes mellitus (DM) and nearly the same proportion had a history of cardiovascular disease (46.9%), including myocardial infarction, coronary artery procedures such as angioplasty or surgery, previous cerebral-vascular accident, or peripheral vascular disease. Average age was 64.6 ± 11.5 years and median maintenance HD vintage was 31 months (Q1 to Q3, 15.5-51.5 months) at the study initiation. For HD patients at the start of the cohort, serum leptin averaged (mean ± SD) 37.3 ± 39.4 ng/ml (median, 16.5 ng/ml; Q1 to Q3, 5.60-71.4 ng/ml).\\nTo examine the relationship between different levels of serum leptin and relevant demographic, clinical and laboratory measures, serum leptin levels were divided into three equal gender specific tertiles (data not shown). Diabetes proportion and age distribution were similar across the three groups. No statistically significant differences were evident between groups in the use of medications that may affect inflammatory markers such as statins, aspirin, or angiotensin-converting enzyme inhibitors (data not shown). As expected females, tended to have higher leptin levels than males (p = 0.0001). Body composition parameters measured by anthropometry and bioelectric impedance analysis (BIA) including phase angle (PA) were incrementally greater across increasing leptin tertiles even after adjustments for baseline demographic and clinical parameters (age, DM status, dialysis vintage and cardio-vascular disease in the past). However, these associations were attenuated after including of fat mass in multivariate analyses. No significant differences in any of the biochemical markers of nutrition, normalized protein nitrogen appearance (nPNA), or actual energy or protein intake normalized to adjusted body weight were found between the HD patient groups according to leptin tertiles.\\nAt baseline, anthropometric measurements (body mass index [BMI], triceps skinfold thickness [TSF], mid arm circumference [MAC] and midarm circumference calculated [MAMC]) and BIA-derived parameters of body composition (fat mass index [FMI] and fat free mass index [FFMI]) correlated positively with serum leptin levels, in both unadjusted and adjusted (for age, DM status, dialysis vintage, and previous cardio-vascular disease) models. Thus, an increased level of leptin was associated with better nutritional status (Table 1). Associations between levels of serum leptin and the two laboratory nutritional markers (albumin and cholesterol) and PA derived by BIA were statistically significant, but these associations were attenuated after adjustments for demographic and clinical parameters including fat mass. No significant correlations were observed between serum leptin and nPNA or dietary energy (DEI) and protein intake (DPI) normalized to adjusted body weight in both sexes after adjustments for demographic and clinical parameters including fat mass.\\nUnadjusted and multivariate adjusted Spearman's correlation coefficients of baseline serum leptin and nutritional clinical and laboratory parameters in the study population at baseline\\nCorrelation coefficient values ≥ 0.25 appear in bold.\\na All case-mix-adjusted models include age, gender, DM status, dialysis vintage, CV disease in the past and fat mass.\\nAbbreviations: nPNA, normalized protein nitrogen appearance; IL-6, interleukin-6; BMI, body mass index; TSF, triceps skinfold thickness; MAC, mid-arm circumference; MAMC, mid-arm muscle circumference calculated; FMI, fat mass index; FFMI, fat-free mass index, DM, diabetes mellitus; CV, cardio-vascular.\\nThe 101 prevalent HD patients participating in this study included 36.6% women; over half of the participants (51.5%) had diabetes mellitus (DM) and nearly the same proportion had a history of cardiovascular disease (46.9%), including myocardial infarction, coronary artery procedures such as angioplasty or surgery, previous cerebral-vascular accident, or peripheral vascular disease. Average age was 64.6 ± 11.5 years and median maintenance HD vintage was 31 months (Q1 to Q3, 15.5-51.5 months) at the study initiation. For HD patients at the start of the cohort, serum leptin averaged (mean ± SD) 37.3 ± 39.4 ng/ml (median, 16.5 ng/ml; Q1 to Q3, 5.60-71.4 ng/ml).\\nTo examine the relationship between different levels of serum leptin and relevant demographic, clinical and laboratory measures, serum leptin levels were divided into three equal gender specific tertiles (data not shown). Diabetes proportion and age distribution were similar across the three groups. No statistically significant differences were evident between groups in the use of medications that may affect inflammatory markers such as statins, aspirin, or angiotensin-converting enzyme inhibitors (data not shown). As expected females, tended to have higher leptin levels than males (p = 0.0001). Body composition parameters measured by anthropometry and bioelectric impedance analysis (BIA) including phase angle (PA) were incrementally greater across increasing leptin tertiles even after adjustments for baseline demographic and clinical parameters (age, DM status, dialysis vintage and cardio-vascular disease in the past). However, these associations were attenuated after including of fat mass in multivariate analyses. No significant differences in any of the biochemical markers of nutrition, normalized protein nitrogen appearance (nPNA), or actual energy or protein intake normalized to adjusted body weight were found between the HD patient groups according to leptin tertiles.\\nAt baseline, anthropometric measurements (body mass index [BMI], triceps skinfold thickness [TSF], mid arm circumference [MAC] and midarm circumference calculated [MAMC]) and BIA-derived parameters of body composition (fat mass index [FMI] and fat free mass index [FFMI]) correlated positively with serum leptin levels, in both unadjusted and adjusted (for age, DM status, dialysis vintage, and previous cardio-vascular disease) models. Thus, an increased level of leptin was associated with better nutritional status (Table 1). Associations between levels of serum leptin and the two laboratory nutritional markers (albumin and cholesterol) and PA derived by BIA were statistically significant, but these associations were attenuated after adjustments for demographic and clinical parameters including fat mass. No significant correlations were observed between serum leptin and nPNA or dietary energy (DEI) and protein intake (DPI) normalized to adjusted body weight in both sexes after adjustments for demographic and clinical parameters including fat mass.\\nUnadjusted and multivariate adjusted Spearman's correlation coefficients of baseline serum leptin and nutritional clinical and laboratory parameters in the study population at baseline\\nCorrelation coefficient values ≥ 0.25 appear in bold.\\na All case-mix-adjusted models include age, gender, DM status, dialysis vintage, CV disease in the past and fat mass.\\nAbbreviations: nPNA, normalized protein nitrogen appearance; IL-6, interleukin-6; BMI, body mass index; TSF, triceps skinfold thickness; MAC, mid-arm circumference; MAMC, mid-arm muscle circumference calculated; FMI, fat mass index; FFMI, fat-free mass index, DM, diabetes mellitus; CV, cardio-vascular.\\n Longitudinal associationas Linear mixed models were used to study the effects of longitudinal leptin changes on changes in nutritional parameters (slopes) over 24 months including fixed parameters such as age, gender, diabetes status, dialysis vintage, previous cardio-vascular events, and fat mass (Table 2). No significant changes in dietary intake or in any of the biochemical nutritional markers over time were found. In contrast, a significant reduction in body composition parameters (both those measured by anthropometry [BMI, TSF and MAMC] and by BIA [FMI, FFMI, PA]) over time was observed. However, leptin did not modulate the changes in outcome variables over time in our cohort (leptin-by-time interactions were insignificant).\\nRegression coefficients with 95% Confidence Intervals for the effect of longitudinal leptin changes on nutritional parameters changes (slopes) during 24 months based on mixed-effects model with linear trends for variable and fixed parameters\\nNOTE: All nutritional variables presented in Table were modeled separately as dependent variables, whereas independent variables included fixed factors (such as age, gender, diabetes status, dialysis vintage, past cardio-vascular disease and fat mass) and leptin as continuous variable. Terms for individual \\\"leptin-by-time\\\" interactions were included in each model. The model takes into account every measurements of leptin and presented nutritional variables in each time point for each patient separately. Presented regression coefficients demonstrate changes in outcome variables with time (24 months).\\na P for trend for leptin-by-time interactions.\\nb FMI and BMI were adjusted only by age, gender, DM status, history of cardio-vascular disease and dialysis vintage.\\nAbbreviations: DEI, daily energy intake; DPI, daily protein intake; nPNA, normalized protein nitrogen appearance; IL-6, interleukin-6; EPO, erythropoietin; BMI, body mass index; TSF, triceps skinfold thickness; MAC, mid-arm circumference; MAMC, mid-arm muscle circumference calculated; FMI, fat mass index; FFMI, fat-free mass index; DM, diabetes mellitus; CV, cardio-vascular.\\nA mixed model including the fixed factors sex, age, DM status, dialysis vintage, history of CV disease and some of nutritional parameters predicted variability in leptin levels during the study presented in Table 3. We observed significant changes of leptin levels with time (linear estimate ± SE: -2.5010 ± 0.57 ng/ml/2y; p < 0.001). The effect of longitudinal changes of FMI on serum leptin variability over time was evident according to this model.\\nEffects of longitudinal changes of nutritional parameters on changes (slopes) of leptin during 24 months based on mixed-effects model with linear trends for variable and fixed parameters\\nNOTE: The Table represents a simple Mixed-Model with leptin as dependent variable, and presented nutritional parameters and fixed factors (age, gender, DM status, dialysis vintage and history of CV disease) as independent variables. The model takes into account every measurements of leptin and presented nutritional variables in each time point for each patient separately.\\nAbbreviations: DEI, daily energy intake; IL-6, interleukin-6; EPO, erythropoietin; FMI, fat mass index; DM, diabetes mellitus; CV, cardio-vascular.\\nOf interest, a significant reduction over time was associated with a more rapid decline in leptin levels in the highest leptin tertile in both unadjusted (p = 0.007 for leptin-by-time interaction) and fully adjusted (p = 0.047 for leptin-by-time interaction) models (Figure 2).\\nLeptin levels decline over time in the study population. Changes in estimated marginal means of serum leptin levels at baseline (month 0), month 6, month 12, month 18 and month 24 in the study population groups according to sex-specific tertiles* of baseline serum leptin: 1. Unadjusted model: P = 0.007 for leptin-by-time interaction; 2. Fully adjusted model (for age, gender, DM status, dialysis vintage, previous cardio-vascular morbidity and fat mass): P = 0.047 for leptin-by-time interaction. *Median (interquartile range) leptin (ng/ml) tertiles for men (n = 64) were 1.70 (1.2-2.4), 10.2 (7.6-13.8) and 38.3 (26.2-81.3) and for women (n = 37) were 15.8 (9.3-18.3), 68.0 (41.4-85.9) and 112.6 (109.2-116.0).\\nLinear mixed models were used to study the effects of longitudinal leptin changes on changes in nutritional parameters (slopes) over 24 months including fixed parameters such as age, gender, diabetes status, dialysis vintage, previous cardio-vascular events, and fat mass (Table 2). No significant changes in dietary intake or in any of the biochemical nutritional markers over time were found. In contrast, a significant reduction in body composition parameters (both those measured by anthropometry [BMI, TSF and MAMC] and by BIA [FMI, FFMI, PA]) over time was observed. However, leptin did not modulate the changes in outcome variables over time in our cohort (leptin-by-time interactions were insignificant).\\nRegression coefficients with 95% Confidence Intervals for the effect of longitudinal leptin changes on nutritional parameters changes (slopes) during 24 months based on mixed-effects model with linear trends for variable and fixed parameters\\nNOTE: All nutritional variables presented in Table were modeled separately as dependent variables, whereas independent variables included fixed factors (such as age, gender, diabetes status, dialysis vintage, past cardio-vascular disease and fat mass) and leptin as continuous variable. Terms for individual \\\"leptin-by-time\\\" interactions were included in each model. The model takes into account every measurements of leptin and presented nutritional variables in each time point for each patient separately. Presented regression coefficients demonstrate changes in outcome variables with time (24 months).\\na P for trend for leptin-by-time interactions.\\nb FMI and BMI were adjusted only by age, gender, DM status, history of cardio-vascular disease and dialysis vintage.\\nAbbreviations: DEI, daily energy intake; DPI, daily protein intake; nPNA, normalized protein nitrogen appearance; IL-6, interleukin-6; EPO, erythropoietin; BMI, body mass index; TSF, triceps skinfold thickness; MAC, mid-arm circumference; MAMC, mid-arm muscle circumference calculated; FMI, fat mass index; FFMI, fat-free mass index; DM, diabetes mellitus; CV, cardio-vascular.\\nA mixed model including the fixed factors sex, age, DM status, dialysis vintage, history of CV disease and some of nutritional parameters predicted variability in leptin levels during the study presented in Table 3. We observed significant changes of leptin levels with time (linear estimate ± SE: -2.5010 ± 0.57 ng/ml/2y; p < 0.001). The effect of longitudinal changes of FMI on serum leptin variability over time was evident according to this model.\\nEffects of longitudinal changes of nutritional parameters on changes (slopes) of leptin during 24 months based on mixed-effects model with linear trends for variable and fixed parameters\\nNOTE: The Table represents a simple Mixed-Model with leptin as dependent variable, and presented nutritional parameters and fixed factors (age, gender, DM status, dialysis vintage and history of CV disease) as independent variables. The model takes into account every measurements of leptin and presented nutritional variables in each time point for each patient separately.\\nAbbreviations: DEI, daily energy intake; IL-6, interleukin-6; EPO, erythropoietin; FMI, fat mass index; DM, diabetes mellitus; CV, cardio-vascular.\\nOf interest, a significant reduction over time was associated with a more rapid decline in leptin levels in the highest leptin tertile in both unadjusted (p = 0.007 for leptin-by-time interaction) and fully adjusted (p = 0.047 for leptin-by-time interaction) models (Figure 2).\\nLeptin levels decline over time in the study population. Changes in estimated marginal means of serum leptin levels at baseline (month 0), month 6, month 12, month 18 and month 24 in the study population groups according to sex-specific tertiles* of baseline serum leptin: 1. Unadjusted model: P = 0.007 for leptin-by-time interaction; 2. Fully adjusted model (for age, gender, DM status, dialysis vintage, previous cardio-vascular morbidity and fat mass): P = 0.047 for leptin-by-time interaction. *Median (interquartile range) leptin (ng/ml) tertiles for men (n = 64) were 1.70 (1.2-2.4), 10.2 (7.6-13.8) and 38.3 (26.2-81.3) and for women (n = 37) were 15.8 (9.3-18.3), 68.0 (41.4-85.9) and 112.6 (109.2-116.0).\\n Survival analysis During an average follow-up of 35 months (median, 40 months; Q1 to Q3, 17-52), 33 patients died: 11 (32%) in the low tertile group, 12 (35%) in the median tertile group and 10 (30%) in the high tertile group, with a median time to death of 25 months (Q1 to Q3, 14-40 months). Cumulative incidences of survival were unaffected by the baseline serum leptin levels (Figure 3). To further assess the possible association of baseline serum leptin level with cardio-vascular morbidity and mortality, cumulative hazards of all-cause death and first composite cardio-vascular event from Cox regression (after adjustment for baseline demographic parameters and fat mass) were calculated. First cardio-vascular event was defined as myocardial infarction (MI), requiring coronary artery procedures such as angioplasty or surgery, cerebral-vascular accident (CVA), or peripheral vascular disease (PVD), requiring angioplasty, bypass or amputation and diagnosed after the participant entered the study. No statistically significant differences in hazards between the different groups according leptin tertiles were observed. Additionally, no differences were found by Cox regression analysis in terms of survival probabilities from the analysis of all-cause mortality for each 10 ng/ml increase in baseline serum leptin (data not shown).\\nKaplan-Meier curves of surviving patients comparing subgroups of patients stratified by baseline serum leptin tertiles.\\nThus, although chronic HD patients with higher baseline leptin had better nutritional status as evidenced by their significantly more favorable body composition parameters at the start of the study, this disparity did not appear to widen over the 24 months of follow-up; thus, leptin levels did not predict body composition parameter changes over time. Moreover, no survival advantage of the high leptin group was found in our observational cohort.\\nDuring an average follow-up of 35 months (median, 40 months; Q1 to Q3, 17-52), 33 patients died: 11 (32%) in the low tertile group, 12 (35%) in the median tertile group and 10 (30%) in the high tertile group, with a median time to death of 25 months (Q1 to Q3, 14-40 months). Cumulative incidences of survival were unaffected by the baseline serum leptin levels (Figure 3). To further assess the possible association of baseline serum leptin level with cardio-vascular morbidity and mortality, cumulative hazards of all-cause death and first composite cardio-vascular event from Cox regression (after adjustment for baseline demographic parameters and fat mass) were calculated. First cardio-vascular event was defined as myocardial infarction (MI), requiring coronary artery procedures such as angioplasty or surgery, cerebral-vascular accident (CVA), or peripheral vascular disease (PVD), requiring angioplasty, bypass or amputation and diagnosed after the participant entered the study. No statistically significant differences in hazards between the different groups according leptin tertiles were observed. Additionally, no differences were found by Cox regression analysis in terms of survival probabilities from the analysis of all-cause mortality for each 10 ng/ml increase in baseline serum leptin (data not shown).\\nKaplan-Meier curves of surviving patients comparing subgroups of patients stratified by baseline serum leptin tertiles.\\nThus, although chronic HD patients with higher baseline leptin had better nutritional status as evidenced by their significantly more favorable body composition parameters at the start of the study, this disparity did not appear to widen over the 24 months of follow-up; thus, leptin levels did not predict body composition parameter changes over time. Moreover, no survival advantage of the high leptin group was found in our observational cohort.\",\n \"The 101 prevalent HD patients participating in this study included 36.6% women; over half of the participants (51.5%) had diabetes mellitus (DM) and nearly the same proportion had a history of cardiovascular disease (46.9%), including myocardial infarction, coronary artery procedures such as angioplasty or surgery, previous cerebral-vascular accident, or peripheral vascular disease. Average age was 64.6 ± 11.5 years and median maintenance HD vintage was 31 months (Q1 to Q3, 15.5-51.5 months) at the study initiation. For HD patients at the start of the cohort, serum leptin averaged (mean ± SD) 37.3 ± 39.4 ng/ml (median, 16.5 ng/ml; Q1 to Q3, 5.60-71.4 ng/ml).\\nTo examine the relationship between different levels of serum leptin and relevant demographic, clinical and laboratory measures, serum leptin levels were divided into three equal gender specific tertiles (data not shown). Diabetes proportion and age distribution were similar across the three groups. No statistically significant differences were evident between groups in the use of medications that may affect inflammatory markers such as statins, aspirin, or angiotensin-converting enzyme inhibitors (data not shown). As expected females, tended to have higher leptin levels than males (p = 0.0001). Body composition parameters measured by anthropometry and bioelectric impedance analysis (BIA) including phase angle (PA) were incrementally greater across increasing leptin tertiles even after adjustments for baseline demographic and clinical parameters (age, DM status, dialysis vintage and cardio-vascular disease in the past). However, these associations were attenuated after including of fat mass in multivariate analyses. No significant differences in any of the biochemical markers of nutrition, normalized protein nitrogen appearance (nPNA), or actual energy or protein intake normalized to adjusted body weight were found between the HD patient groups according to leptin tertiles.\\nAt baseline, anthropometric measurements (body mass index [BMI], triceps skinfold thickness [TSF], mid arm circumference [MAC] and midarm circumference calculated [MAMC]) and BIA-derived parameters of body composition (fat mass index [FMI] and fat free mass index [FFMI]) correlated positively with serum leptin levels, in both unadjusted and adjusted (for age, DM status, dialysis vintage, and previous cardio-vascular disease) models. Thus, an increased level of leptin was associated with better nutritional status (Table 1). Associations between levels of serum leptin and the two laboratory nutritional markers (albumin and cholesterol) and PA derived by BIA were statistically significant, but these associations were attenuated after adjustments for demographic and clinical parameters including fat mass. No significant correlations were observed between serum leptin and nPNA or dietary energy (DEI) and protein intake (DPI) normalized to adjusted body weight in both sexes after adjustments for demographic and clinical parameters including fat mass.\\nUnadjusted and multivariate adjusted Spearman's correlation coefficients of baseline serum leptin and nutritional clinical and laboratory parameters in the study population at baseline\\nCorrelation coefficient values ≥ 0.25 appear in bold.\\na All case-mix-adjusted models include age, gender, DM status, dialysis vintage, CV disease in the past and fat mass.\\nAbbreviations: nPNA, normalized protein nitrogen appearance; IL-6, interleukin-6; BMI, body mass index; TSF, triceps skinfold thickness; MAC, mid-arm circumference; MAMC, mid-arm muscle circumference calculated; FMI, fat mass index; FFMI, fat-free mass index, DM, diabetes mellitus; CV, cardio-vascular.\",\n \"Linear mixed models were used to study the effects of longitudinal leptin changes on changes in nutritional parameters (slopes) over 24 months including fixed parameters such as age, gender, diabetes status, dialysis vintage, previous cardio-vascular events, and fat mass (Table 2). No significant changes in dietary intake or in any of the biochemical nutritional markers over time were found. In contrast, a significant reduction in body composition parameters (both those measured by anthropometry [BMI, TSF and MAMC] and by BIA [FMI, FFMI, PA]) over time was observed. However, leptin did not modulate the changes in outcome variables over time in our cohort (leptin-by-time interactions were insignificant).\\nRegression coefficients with 95% Confidence Intervals for the effect of longitudinal leptin changes on nutritional parameters changes (slopes) during 24 months based on mixed-effects model with linear trends for variable and fixed parameters\\nNOTE: All nutritional variables presented in Table were modeled separately as dependent variables, whereas independent variables included fixed factors (such as age, gender, diabetes status, dialysis vintage, past cardio-vascular disease and fat mass) and leptin as continuous variable. Terms for individual \\\"leptin-by-time\\\" interactions were included in each model. The model takes into account every measurements of leptin and presented nutritional variables in each time point for each patient separately. Presented regression coefficients demonstrate changes in outcome variables with time (24 months).\\na P for trend for leptin-by-time interactions.\\nb FMI and BMI were adjusted only by age, gender, DM status, history of cardio-vascular disease and dialysis vintage.\\nAbbreviations: DEI, daily energy intake; DPI, daily protein intake; nPNA, normalized protein nitrogen appearance; IL-6, interleukin-6; EPO, erythropoietin; BMI, body mass index; TSF, triceps skinfold thickness; MAC, mid-arm circumference; MAMC, mid-arm muscle circumference calculated; FMI, fat mass index; FFMI, fat-free mass index; DM, diabetes mellitus; CV, cardio-vascular.\\nA mixed model including the fixed factors sex, age, DM status, dialysis vintage, history of CV disease and some of nutritional parameters predicted variability in leptin levels during the study presented in Table 3. We observed significant changes of leptin levels with time (linear estimate ± SE: -2.5010 ± 0.57 ng/ml/2y; p < 0.001). The effect of longitudinal changes of FMI on serum leptin variability over time was evident according to this model.\\nEffects of longitudinal changes of nutritional parameters on changes (slopes) of leptin during 24 months based on mixed-effects model with linear trends for variable and fixed parameters\\nNOTE: The Table represents a simple Mixed-Model with leptin as dependent variable, and presented nutritional parameters and fixed factors (age, gender, DM status, dialysis vintage and history of CV disease) as independent variables. The model takes into account every measurements of leptin and presented nutritional variables in each time point for each patient separately.\\nAbbreviations: DEI, daily energy intake; IL-6, interleukin-6; EPO, erythropoietin; FMI, fat mass index; DM, diabetes mellitus; CV, cardio-vascular.\\nOf interest, a significant reduction over time was associated with a more rapid decline in leptin levels in the highest leptin tertile in both unadjusted (p = 0.007 for leptin-by-time interaction) and fully adjusted (p = 0.047 for leptin-by-time interaction) models (Figure 2).\\nLeptin levels decline over time in the study population. Changes in estimated marginal means of serum leptin levels at baseline (month 0), month 6, month 12, month 18 and month 24 in the study population groups according to sex-specific tertiles* of baseline serum leptin: 1. Unadjusted model: P = 0.007 for leptin-by-time interaction; 2. Fully adjusted model (for age, gender, DM status, dialysis vintage, previous cardio-vascular morbidity and fat mass): P = 0.047 for leptin-by-time interaction. *Median (interquartile range) leptin (ng/ml) tertiles for men (n = 64) were 1.70 (1.2-2.4), 10.2 (7.6-13.8) and 38.3 (26.2-81.3) and for women (n = 37) were 15.8 (9.3-18.3), 68.0 (41.4-85.9) and 112.6 (109.2-116.0).\",\n \"During an average follow-up of 35 months (median, 40 months; Q1 to Q3, 17-52), 33 patients died: 11 (32%) in the low tertile group, 12 (35%) in the median tertile group and 10 (30%) in the high tertile group, with a median time to death of 25 months (Q1 to Q3, 14-40 months). Cumulative incidences of survival were unaffected by the baseline serum leptin levels (Figure 3). To further assess the possible association of baseline serum leptin level with cardio-vascular morbidity and mortality, cumulative hazards of all-cause death and first composite cardio-vascular event from Cox regression (after adjustment for baseline demographic parameters and fat mass) were calculated. First cardio-vascular event was defined as myocardial infarction (MI), requiring coronary artery procedures such as angioplasty or surgery, cerebral-vascular accident (CVA), or peripheral vascular disease (PVD), requiring angioplasty, bypass or amputation and diagnosed after the participant entered the study. No statistically significant differences in hazards between the different groups according leptin tertiles were observed. Additionally, no differences were found by Cox regression analysis in terms of survival probabilities from the analysis of all-cause mortality for each 10 ng/ml increase in baseline serum leptin (data not shown).\\nKaplan-Meier curves of surviving patients comparing subgroups of patients stratified by baseline serum leptin tertiles.\\nThus, although chronic HD patients with higher baseline leptin had better nutritional status as evidenced by their significantly more favorable body composition parameters at the start of the study, this disparity did not appear to widen over the 24 months of follow-up; thus, leptin levels did not predict body composition parameter changes over time. Moreover, no survival advantage of the high leptin group was found in our observational cohort.\",\n \"In the present study, we wished to determine whether changes of serum leptin levels are correlated with nutritional status over time in a cohort of prevalent hemodialysis patients. We observed that despite the strong and significant cross-sectional associations between serum leptin and body composition parameters at baseline, leptin levels did not reflect changes in nutritional status in our population.\\nOur study confirms several previous cross-sectional studies in which elevated serum leptin levels were demonstrated to be positively associated with several nutritional markers (laboratory or body composition parameters, mainly BMI and fat mass) in chronic HD patients [14,20,21,23-25]. HD patients treated with megestrole acetate (24), growth hormone [31] or high-calorie supplementation [32] demonstrated progressively increased serum leptin parallel to an improved nutritional state.\\nHowever, not all studies are consistent with positive associations between elevated levels of leptin and several nutritional parameters in ESRD patients. An inverse correlation was observed between leptin/fat mass and dietary intake, as well as with significantly lower lean tissue mass in chronic renal failure (23 patients) and ESRD patients receiving PD (24 patients) and HD (22 patients) therapy in study by Young et al [5]. In a cross-sectional study of 28 dialysis patients and 41 healthy control subjects, Johansen et al [20] showed that leptin levels were negatively correlated with albumin and PCR, suggesting a possible negative role of leptin in nutrition. Based on longitudinal observation, Stenvinkel et al [18] demonstrated that increases in serum leptin levels of 36 peritoneal dialysis patients were associated with a decrease in lean body mass. The discrepancies between these studies may be related to the population recruited, to their small sample size, and to their design; specifically, serum leptin levels were measured among patients with varying degrees of CRF, some of whom were undergoing maintenance hemodialysis and peritoneal dialysis, and in healthy controls [5,18,20]; many of these studies had a low number of study participants [5,18,20,23-25] and a majority of these studies were cross-sectional [5,20,23,25].\\nLeptin, a regulator of eating behavior, has been shown to be a major determinant of anorexia in uremic animals via signaling through the hypothalamic melanocortin system [15]. Subsequently, a recent experimental study by Cheung et al [33] showed that intraperitoneal administration of the melanocortin-4 receptor (MC4-R) antagonist NBI-12i stimulates food intake and weight gain in uremic mice. However, the effects of leptin in uremic patients are not unequivocal. Relevant to our findings, Bossola at al [17] demonstrated that serum leptin levels were not different in anorexic and in non-anorexic hemodialysis patients. Several other studies failed as well to demonstrate any role of hyperleptinemia on reduced dietary intake in ESRD patients receiving maintenance HD treatment [5,16,34]. Since hyperleptinemia is common in HD patients mainly due to impaired renal clearance [20], it seemed reasonable to assume that selective leptin resistance (such as occurs in obese humans [35]) is the responsible mechanism for attenuation of the negative effect of leptin on appetite, and accordingly, on dietary intake under conditions of continuous stimulation. The molecular mechanisms of leptin resistance, with a focus on the contribution of the intracellular tyrosine residues in the hypothalamic receptor of leptin (LEPRb) and their interaction with the negative feedback regulators, suppressor of cytokine signaling 1 and 3 (SOCS1 and SOCS3), are described in a recent study by Knobelspies et al [36].\\nThus, although the cross-group comparisons confirm that hyperleptinemia is associated with better body composition parameters, no unequivocal associations in terms of biochemical data or dietary intake with serum leptin levels are evident in HD patients according to the available literature. Our study confirms these data.\\nAnother finding of the present study was that 24 months of HD was associated with a significant reduction of plasma leptin levels, with a more rapid decline observed in patients with higher baseline leptin levels. Only limited information is available on the relationship between longitudinal serum leptin measurements and nutritional characteristics in chronic HD patients [18,32]. To the best of our knowledge, the present study is the first long-term longitudinal study to show the relationship between serum leptin level and nutritional status in ESRD patients receiving maintenance HD therapy. Several mechanisms may be involved in decrease of serum leptin levels over time in hemodialysis patients. These include, the increasing use of high flux and super-flux hemodialyzers [37], and/or use of alternative dialysis strategies such as hemodiafiltration [38]; recombinant human erythropoietin treatment (generally followed by a significant decline of leptinaemia in hemodialysed patients [39]); chronic inflammation (under the assumption that leptin, like albumin or transferrin, is a negative acute phase protein in chronic HD patients [22], although other studies [40] do not support this mechanism); metabolic acidosis, which can reduce the release of leptin from adipose tissue [41]; and finally, a decrease in fat mass leading to a decrease in the rate of leptin biosynthesis [14,18] (based on an insulin or a nutrient-sensing pathway regulating leptin gene expression in fat tissue [42]). Although patients in our facility were all treated using low flux hemodialysis, reduction of BMI and accordingly of fat mass over 24 months of the study may explain this longitudinal decrease of serum leptin levels in our cohort.\\nOf interest is the observation that adverse changes in body composition parameters occur over time, supporting the hypothesis that end-stage renal disease is associated with wasting as proposed in some [43,44] but not all [45] of the previous studies. The results of our study were consistent with those reported by Johansen et al [43] who did not find significant changes in any of the biochemical markers with time, but a significant reduction in phase angle over 1 year follow up was evident in prevalent HD patients. In parallel, together with reduced body composition parameters over time, we observed a longitudinal decline in serum leptin levels in our cohort. However, the lack of an association between serum leptin levels and future changes in body composition parameters (as exhibited by non-significant leptin-by-time interactions), suggests that the levels of leptin follow body composition (mainly fat mass) trajectory or its accompanying metabolic changes, rather than governing it.\\nFinally, serum leptin level did not appear as a useful predictor of all cause mortality or cardio-vascular morbidity in our study population during up to 4 years of observation. In this aspect, our findings support the results of earlier study by Tsai et al [46]. Although Scholze et al [26] showed a paradoxical reverse association between leptin level and clinical outcome in 71 chronic HD patients, our patients did not show such an association. The difference between our results and theirs might have been caused by differences in the cohorts. First, the population presented by Scholze et al [26] had a lower BMI at baseline than our patients, and no body composition parameters were measured. Further, the prevalence of diabetes mellitus was much greater in patients in the lower leptin group than in patients of the higher leptin group in the cohort of Scholze et al [26]. Indeed, HD patients with diabetes have a poor prognosis [4] that might be the cause of the lower survival rate in the lower leptin group in the above study [26]. While low levels of leptin may reflect a state of malnutrition in HD patients, proatherogenic effects of hyperleptinemia were linked to an adverse cardiovascular profile in general population [10,12]. We speculate that the lack of long-term benefits of hyperleptinemia in terms of survival of chronic HD patients could reflect the modulating influence of proatherogenic effects of high serum leptin levels. Finally, no relationships between serum leptin levels and previous cardio-vascular events were found by Diez et al [47] in a retrospective study on 82 dialysis patients. Zoccali et al [40] also did not show any difference in cardiovascular event-free survival in cohort of 192 hemodialysis patients after their stratification on the basis of the median value of plasma leptin.\\nSome limitations of the present study should be considered. First, this study is based on a relatively small sample size limiting detection of more subtle changes over time. Second, this study used only an observational approach, without manipulation of exposure factors, and therefore, no definitive cause-and-effect relationship can be derived for any of the risk factors analyzed. Dietary intake assessed by 3 day food records is another limitation of the study, as results can be subjective and incomplete, and can vary considerably from day to day as a result of dialysis treatment sessions and associated disturbances in food intake. Finally, significant circadian fluctuations of plasma leptin (regardless of the underlying degree of glucose tolerance, fasting, or diet) can certainly obscure some small but significant changes in plasma leptin [48] and may be a source of potential bias for longitudinal observation. Nevertheless, the present study has the advantage of providing long-term longitudinal data on the relationship between serum concentrations of leptin and nutritional parameters in prevalent HD patients.\",\n \"In conclusion, we found positive linear associations between serum leptin levels and body composition, whereas no significant relations in terms of biochemical data or dietary intake were evident in our cohort at baseline. Analyzing longitudinal data we concluded that any influence of serum leptin levels on nutritional status is limited to mirroring attained BMI and fat mass trajectory. Further, nutritional advantage at baseline of the study population with hyperleptinemia did not translate into long-term benefits in terms of survival. Taken together, our results suggest that in our cohort, leptin levels reflect fat mass depots, rather than independently contributing to uremic anorexia or modifying nutritional status and/or survival in chronic HD patients. These results are of particular importance if the use of subcutaneous injections of recombinant methionyl leptin [49] or leptin removal by super-flux polysulfone dialysers [37] is contemplated as a therapeutic intervention.\"\n]"},"other_sections_sec_types":{"kind":"list like","value":[null,null,null,null,null,null,null,null,null,null,null,null,null],"string":"[\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null\n]"},"all_sections_titles":{"kind":"list like","value":["Background","Methods","Patients","Dietary intake","Anthropometric measurements","Body composition analysis","Laboratory evaluation","Statistical analysis","Results","Cross-sectional associations","Longitudinal associationas","Survival analysis","Discussion","Conclusions"],"string":"[\n \"Background\",\n \"Methods\",\n \"Patients\",\n \"Dietary intake\",\n \"Anthropometric measurements\",\n \"Body composition analysis\",\n \"Laboratory evaluation\",\n \"Statistical analysis\",\n \"Results\",\n \"Cross-sectional associations\",\n \"Longitudinal associationas\",\n \"Survival analysis\",\n \"Discussion\",\n \"Conclusions\"\n]"},"all_sections_texts":{"kind":"list like","value":["In recent years, the number of patients with end-stage renal disease (ESRD) has been increasing worldwide [1]. Depending in part upon the method used to evaluate nutritional status and the population studied, from 40 to 70 percent of patients with ESRD are malnourished [2,3] resulting in poor clinical outcomes [4]. Among the mechanisms responsible for malnutrition, leptin was believed to influence nutritional markers in patients with ESRD [5]. Leptin is a 16-kDa protein identified as the product of the obese gene; it is exclusively produced in adipocytes, and regulates food intake and energy expenditure in animal models [6]. Leptin decreases food intake by decreasing NPY (neuropeptide Y - one of the most potent stimulators of food intake) mRNA [7] and increasing alpha-MSH (alpha-melanocyte-stimulating hormone - an inhibitor of food intake) [8]. Besides linking adiposity and central nervous circuits to reduced appetite and enhanced energy expenditure in the general population [9], leptin has been shown to increase overall sympathetic nerve activity [10], facilitate glucose utilization and improve insulin sensitivity [11]. Furthermore, the prospective West of Scotland Coronary Prevention Study (WOSCOPS) reported that elevated leptin increases the relative risk of cardio-vascular disease in the general population independently of fat mass [12].\nIn general, serum leptin levels are significantly elevated in patients with renal failure, particularly when compared to age, gender and body mass index (BMI)-matched controls [13,14]. However, the role of hyperleptinemia in ESRD patients is somewhat unconventional. In contrast with its anorexogenic effects recognized in the general population [9] and even in experimental models of uremia (in subtotal nephrectomized and leptin receptor-deficient [db/db] mice) [15], leptin has not been reported to affect perceived appetite and nutrient intake in dialysis patients [16,17]. Although in some observational studies, increased serum leptin concentrations were observed in ESRD patients in parallel with loss of lean body mass [18,19] or with hypoalbuminemia and low protein intake [20], some others failed to find any correlation between hyperleptinemia and weight change [9] or lean mass [21] in this population. Moreover, several clinical studies suggested that leptin is a negative acute phase protein [22] and can serve as a marker of adequate nutritional status, rather than an appetite-reducing uremic toxin in hemodialysis patients [23-25]. Finally, the relationship between elevated serum leptin levels and clinical outcomes in ESRD has not been fully defined. In one small prospective cohort of hemodialysis patients, lower baseline serum leptin levels predicted mortality [26], but neither changes in leptin over time were measured, nor were leptin levels normalized to body fat mass in this study.\nThus, the influence of serum leptin levels on nutritional status and survival in chronic hemodialysis patients remained to be elucidated. In view of leptin's physiological role, information on effects of prolonged hyperleptinemia (independent of fat mass) on nutritional status of chronic hemodialysis patients, which may also impact on their survival, would be of interest. The aim of the present prospective longitudinal study was therefore to study longitudinal changes in serum leptin levels and to relate them to the changes in nutritional markers and survival in chronic hemodialysis patients."," Patients This prospective observational study was approved by the Ethics Committee of Assaf Harofeh Medical Center (Zerifin, Affiliated to the Sackler Faculty of Medicine Tel Aviv University, Israel). Informed consent was obtained before any trial-related activities. Patients were eligible for entry when they had been on HD therapy for at least 3 months and were 18 years or older, with no clinically active cardio-vascular or infectious diseases on entry. We excluded patients with edema, pleural effusion or ascites at their initial assessment, as well as patients with malignant disease, liver cirrhosis, neuro-muscular diseases, amputations or any deformities of the body. Exclusion criteria at the entry of the study also included co-morbidity (auto-immune disease and/or acute infections) and/or medication (prednisone) that might interfere with plasma leptin concentrations. A flow chart of the study is presented in Figure 1. In total, 101 patients (64 men and 37 women) with a mean age of 64.6 ± 11.5 years, receiving maintenance hemodialysis treatment at our outpatient HD clinic, were included in the study. Of the patients studied, 52 were diabetic (all diabetic patients had type 2 diabetes). Study measurements were performed at baseline and at 6, 12, 18 and 24 months from enrollment. After the longitudinal measurements ended, we continued clinical observation on our cohort during 2 additional years. Thus, in total, the study period extended 35 ± 17 months. During this period, 33 patients (32.7%) died (the main causes of death were cardio-vascular [12 of 33 patients; 36.4%] and sepsis [12 of 33 patients; 36.4%]), 13 patients (12.9%) underwent kidney transplantation, 3 patients (3.0%) changed dialysis modality, and 10 patients (10.0%) transferred to other hemodialysis units. All patients underwent regular dialysis via their vascular access (81.2% of patients had arterio-venous fistula) 4-5 h three times per week at a blood flow rate of 250-300 ml/min. Bicarbonate dialysate (30 mEq/L) at a dialysis solution flow rate of 500 ml/min was used in all cases. All dialysis was performed with biocompatible dialyzer membrane with a surface area of 1.0-1.8 m2. The efficiency of the dialysis was assessed based on the delivered dose of dialysis (Kt/V urea) using a single-pool urea kinetic model (mean Kt/V was1.31 ± 0.23 in our population).\nFlow diagram of the study.\nInformation on vascular disease (cerebral vascular, peripheral vascular and heart disease) was obtained from a detailed medical history.\nMost patients were required antihypertensive medications as well as other drugs commonly used in ESRD, such as phosphate and potassium binders, diuretics, and supplements of vitamins B, C, and D.\nThis prospective observational study was approved by the Ethics Committee of Assaf Harofeh Medical Center (Zerifin, Affiliated to the Sackler Faculty of Medicine Tel Aviv University, Israel). Informed consent was obtained before any trial-related activities. Patients were eligible for entry when they had been on HD therapy for at least 3 months and were 18 years or older, with no clinically active cardio-vascular or infectious diseases on entry. We excluded patients with edema, pleural effusion or ascites at their initial assessment, as well as patients with malignant disease, liver cirrhosis, neuro-muscular diseases, amputations or any deformities of the body. Exclusion criteria at the entry of the study also included co-morbidity (auto-immune disease and/or acute infections) and/or medication (prednisone) that might interfere with plasma leptin concentrations. A flow chart of the study is presented in Figure 1. In total, 101 patients (64 men and 37 women) with a mean age of 64.6 ± 11.5 years, receiving maintenance hemodialysis treatment at our outpatient HD clinic, were included in the study. Of the patients studied, 52 were diabetic (all diabetic patients had type 2 diabetes). Study measurements were performed at baseline and at 6, 12, 18 and 24 months from enrollment. After the longitudinal measurements ended, we continued clinical observation on our cohort during 2 additional years. Thus, in total, the study period extended 35 ± 17 months. During this period, 33 patients (32.7%) died (the main causes of death were cardio-vascular [12 of 33 patients; 36.4%] and sepsis [12 of 33 patients; 36.4%]), 13 patients (12.9%) underwent kidney transplantation, 3 patients (3.0%) changed dialysis modality, and 10 patients (10.0%) transferred to other hemodialysis units. All patients underwent regular dialysis via their vascular access (81.2% of patients had arterio-venous fistula) 4-5 h three times per week at a blood flow rate of 250-300 ml/min. Bicarbonate dialysate (30 mEq/L) at a dialysis solution flow rate of 500 ml/min was used in all cases. All dialysis was performed with biocompatible dialyzer membrane with a surface area of 1.0-1.8 m2. The efficiency of the dialysis was assessed based on the delivered dose of dialysis (Kt/V urea) using a single-pool urea kinetic model (mean Kt/V was1.31 ± 0.23 in our population).\nFlow diagram of the study.\nInformation on vascular disease (cerebral vascular, peripheral vascular and heart disease) was obtained from a detailed medical history.\nMost patients were required antihypertensive medications as well as other drugs commonly used in ESRD, such as phosphate and potassium binders, diuretics, and supplements of vitamins B, C, and D.\n Dietary intake A continuous 3-day dietary history (which included a dialysis day, a weekend day and a non-dialysis day) was recorded on a self-completed food diary. Then, dietary energy and protein intake were calculated and normalized for adjusted body weight (ABW) by the following formula [27]:\nSBW - standard body weight was determined by the National Healthand Nutrition Examination Survey II (NHANES II) population medians for age, sex, frame size, and stature [27].\nDietary protein intake was also estimated by the protein catabolic rate (PCR) calculation from the patient's urea generation rate by urea kinetics modeling [28]. Single-pool model urea kinetics was used to estimate the nPCR.\nA continuous 3-day dietary history (which included a dialysis day, a weekend day and a non-dialysis day) was recorded on a self-completed food diary. Then, dietary energy and protein intake were calculated and normalized for adjusted body weight (ABW) by the following formula [27]:\nSBW - standard body weight was determined by the National Healthand Nutrition Examination Survey II (NHANES II) population medians for age, sex, frame size, and stature [27].\nDietary protein intake was also estimated by the protein catabolic rate (PCR) calculation from the patient's urea generation rate by urea kinetics modeling [28]. Single-pool model urea kinetics was used to estimate the nPCR.\n Anthropometric measurements BMI, triceps skinfold thickness (TSF), mid arm circumference (MAC) and calculated mid arm muscle circumference (MAMC) were measured as anthropometric variables. The BMI was calculated as dry weight in kilograms divided by the square of height in meters. TSF was measured with a conventional skinfold caliper using standard techniques. Mid arm circumference was measured with a plastic measuring tape. MAMC was estimated as follows:\nBMI, triceps skinfold thickness (TSF), mid arm circumference (MAC) and calculated mid arm muscle circumference (MAMC) were measured as anthropometric variables. The BMI was calculated as dry weight in kilograms divided by the square of height in meters. TSF was measured with a conventional skinfold caliper using standard techniques. Mid arm circumference was measured with a plastic measuring tape. MAMC was estimated as follows:\n Body composition analysis Body composition was determined by body impedance analysis (B.I.A. Nutriguard- M, Data-Input, Frankfurt, Germany). We used gel-based electrodes specifically developed for BIA measurements - Bianostic AT (Data-Input GmbH). On the day of blood collection, patients underwent BIA measurement at approximately 30 minutes postdialysis. BIA electrodes were placed on the same body side used for anthropometric measurements. The multi-frequency technique was used. FFM was calculated by using the approach of Kyle et al. validated by dual-energy x-ray absorptiometry on 343 healthy adult subjects [29]:\nFat mass and fat free mass were standardized by squared height (m2), and expressed in kg/m2 as fat mass index (FMI) and fat free mass index (FFMI), respectively.\nPhase angle (PA) describes the relationship between the two vector components of impedance [reactance and resistance] of the human body to an alternating electric current. PA has been shown to provide a BIA prognostic index of morbidity and mortality [30].\nBody composition was determined by body impedance analysis (B.I.A. Nutriguard- M, Data-Input, Frankfurt, Germany). We used gel-based electrodes specifically developed for BIA measurements - Bianostic AT (Data-Input GmbH). On the day of blood collection, patients underwent BIA measurement at approximately 30 minutes postdialysis. BIA electrodes were placed on the same body side used for anthropometric measurements. The multi-frequency technique was used. FFM was calculated by using the approach of Kyle et al. validated by dual-energy x-ray absorptiometry on 343 healthy adult subjects [29]:\nFat mass and fat free mass were standardized by squared height (m2), and expressed in kg/m2 as fat mass index (FMI) and fat free mass index (FFMI), respectively.\nPhase angle (PA) describes the relationship between the two vector components of impedance [reactance and resistance] of the human body to an alternating electric current. PA has been shown to provide a BIA prognostic index of morbidity and mortality [30].\n Laboratory evaluation Blood samples were taken in a non-fasting state before a midweek hemodialysis session. CBC, creatinine, urea, albumin, transferrin and total cholesterol were measured by routine laboratory methods. IL-6 and leptin were measured in plasma samples using commercially available enzyme-linked immunosorbent assay (ELISA) kits (R&D System, Minneapolis, MN, USA) according to the manufacturer's protocol. The mean minimal detectable level for IL-6 was 0.7 pg/ml, and 7.8 pg/ml for leptin. Intra-assay and inter-assay coefficients of variation for IL-6 were 4.2% and 6.4% respectively, and for leptin - 3.3% and 5.4% respectively.\nBlood samples were taken in a non-fasting state before a midweek hemodialysis session. CBC, creatinine, urea, albumin, transferrin and total cholesterol were measured by routine laboratory methods. IL-6 and leptin were measured in plasma samples using commercially available enzyme-linked immunosorbent assay (ELISA) kits (R&D System, Minneapolis, MN, USA) according to the manufacturer's protocol. The mean minimal detectable level for IL-6 was 0.7 pg/ml, and 7.8 pg/ml for leptin. Intra-assay and inter-assay coefficients of variation for IL-6 were 4.2% and 6.4% respectively, and for leptin - 3.3% and 5.4% respectively.\n Statistical analysis Data are expressed as mean ± standard deviation (SD), median and interquartile range (Q1 to Q3) for variables that did not follow a normal distribution, or frequencies, as noted.\nTo compare the means of continuous variables measured between sex-specific tertiles of leptin, one way ANOVA and analysis of covariance with adjustments (ANCOVA) were used. Categorical data are presented as percentages and were compared among groups by χ2 tests. Correlations between leptin and clinical and laboratory parameters were assessed using Spearman rank order correlation coefficients (because of the skewed distribution of leptin levels). Multivariate regression analysis was performed to obtain partial (adjusted) correlations (R2). Case-mix-adjusted models were controlled for age, gender, history of CV disease, presence or absence of diabetes mellitus, dialysis vintage and fat mass.\nRepeated-measures analysis of variance was performed by using the MIXED model. Only patients with ≥ 2 study visits were included in the analyses. Base models were adjusted for age, sex, diabetes status, dialysis vintage, history of cardio-vascular diseases and fat mass. F Tests were used to assess the significance of the fixed effects, and P less than 0.05 was considered significant. To evaluate whether leptin influenced the trends in the various dependent variables, we included in each base model terms for individual \"leptin-by-time\" interactions.\nSurvival analyses were performed using the Kaplan-Meier survival curve and the Cox proportional hazard model. The univariate and multivariate Cox regression analyses are presented as (HR; CI).\nAll statistical analyses were performed using SPSS software, version 16.0 (SPSS Inc, Chicago, IL).\nData are expressed as mean ± standard deviation (SD), median and interquartile range (Q1 to Q3) for variables that did not follow a normal distribution, or frequencies, as noted.\nTo compare the means of continuous variables measured between sex-specific tertiles of leptin, one way ANOVA and analysis of covariance with adjustments (ANCOVA) were used. Categorical data are presented as percentages and were compared among groups by χ2 tests. Correlations between leptin and clinical and laboratory parameters were assessed using Spearman rank order correlation coefficients (because of the skewed distribution of leptin levels). Multivariate regression analysis was performed to obtain partial (adjusted) correlations (R2). Case-mix-adjusted models were controlled for age, gender, history of CV disease, presence or absence of diabetes mellitus, dialysis vintage and fat mass.\nRepeated-measures analysis of variance was performed by using the MIXED model. Only patients with ≥ 2 study visits were included in the analyses. Base models were adjusted for age, sex, diabetes status, dialysis vintage, history of cardio-vascular diseases and fat mass. F Tests were used to assess the significance of the fixed effects, and P less than 0.05 was considered significant. To evaluate whether leptin influenced the trends in the various dependent variables, we included in each base model terms for individual \"leptin-by-time\" interactions.\nSurvival analyses were performed using the Kaplan-Meier survival curve and the Cox proportional hazard model. The univariate and multivariate Cox regression analyses are presented as (HR; CI).\nAll statistical analyses were performed using SPSS software, version 16.0 (SPSS Inc, Chicago, IL).","This prospective observational study was approved by the Ethics Committee of Assaf Harofeh Medical Center (Zerifin, Affiliated to the Sackler Faculty of Medicine Tel Aviv University, Israel). Informed consent was obtained before any trial-related activities. Patients were eligible for entry when they had been on HD therapy for at least 3 months and were 18 years or older, with no clinically active cardio-vascular or infectious diseases on entry. We excluded patients with edema, pleural effusion or ascites at their initial assessment, as well as patients with malignant disease, liver cirrhosis, neuro-muscular diseases, amputations or any deformities of the body. Exclusion criteria at the entry of the study also included co-morbidity (auto-immune disease and/or acute infections) and/or medication (prednisone) that might interfere with plasma leptin concentrations. A flow chart of the study is presented in Figure 1. In total, 101 patients (64 men and 37 women) with a mean age of 64.6 ± 11.5 years, receiving maintenance hemodialysis treatment at our outpatient HD clinic, were included in the study. Of the patients studied, 52 were diabetic (all diabetic patients had type 2 diabetes). Study measurements were performed at baseline and at 6, 12, 18 and 24 months from enrollment. After the longitudinal measurements ended, we continued clinical observation on our cohort during 2 additional years. Thus, in total, the study period extended 35 ± 17 months. During this period, 33 patients (32.7%) died (the main causes of death were cardio-vascular [12 of 33 patients; 36.4%] and sepsis [12 of 33 patients; 36.4%]), 13 patients (12.9%) underwent kidney transplantation, 3 patients (3.0%) changed dialysis modality, and 10 patients (10.0%) transferred to other hemodialysis units. All patients underwent regular dialysis via their vascular access (81.2% of patients had arterio-venous fistula) 4-5 h three times per week at a blood flow rate of 250-300 ml/min. Bicarbonate dialysate (30 mEq/L) at a dialysis solution flow rate of 500 ml/min was used in all cases. All dialysis was performed with biocompatible dialyzer membrane with a surface area of 1.0-1.8 m2. The efficiency of the dialysis was assessed based on the delivered dose of dialysis (Kt/V urea) using a single-pool urea kinetic model (mean Kt/V was1.31 ± 0.23 in our population).\nFlow diagram of the study.\nInformation on vascular disease (cerebral vascular, peripheral vascular and heart disease) was obtained from a detailed medical history.\nMost patients were required antihypertensive medications as well as other drugs commonly used in ESRD, such as phosphate and potassium binders, diuretics, and supplements of vitamins B, C, and D.","A continuous 3-day dietary history (which included a dialysis day, a weekend day and a non-dialysis day) was recorded on a self-completed food diary. Then, dietary energy and protein intake were calculated and normalized for adjusted body weight (ABW) by the following formula [27]:\nSBW - standard body weight was determined by the National Healthand Nutrition Examination Survey II (NHANES II) population medians for age, sex, frame size, and stature [27].\nDietary protein intake was also estimated by the protein catabolic rate (PCR) calculation from the patient's urea generation rate by urea kinetics modeling [28]. Single-pool model urea kinetics was used to estimate the nPCR.","BMI, triceps skinfold thickness (TSF), mid arm circumference (MAC) and calculated mid arm muscle circumference (MAMC) were measured as anthropometric variables. The BMI was calculated as dry weight in kilograms divided by the square of height in meters. TSF was measured with a conventional skinfold caliper using standard techniques. Mid arm circumference was measured with a plastic measuring tape. MAMC was estimated as follows:","Body composition was determined by body impedance analysis (B.I.A. Nutriguard- M, Data-Input, Frankfurt, Germany). We used gel-based electrodes specifically developed for BIA measurements - Bianostic AT (Data-Input GmbH). On the day of blood collection, patients underwent BIA measurement at approximately 30 minutes postdialysis. BIA electrodes were placed on the same body side used for anthropometric measurements. The multi-frequency technique was used. FFM was calculated by using the approach of Kyle et al. validated by dual-energy x-ray absorptiometry on 343 healthy adult subjects [29]:\nFat mass and fat free mass were standardized by squared height (m2), and expressed in kg/m2 as fat mass index (FMI) and fat free mass index (FFMI), respectively.\nPhase angle (PA) describes the relationship between the two vector components of impedance [reactance and resistance] of the human body to an alternating electric current. PA has been shown to provide a BIA prognostic index of morbidity and mortality [30].","Blood samples were taken in a non-fasting state before a midweek hemodialysis session. CBC, creatinine, urea, albumin, transferrin and total cholesterol were measured by routine laboratory methods. IL-6 and leptin were measured in plasma samples using commercially available enzyme-linked immunosorbent assay (ELISA) kits (R&D System, Minneapolis, MN, USA) according to the manufacturer's protocol. The mean minimal detectable level for IL-6 was 0.7 pg/ml, and 7.8 pg/ml for leptin. Intra-assay and inter-assay coefficients of variation for IL-6 were 4.2% and 6.4% respectively, and for leptin - 3.3% and 5.4% respectively.","Data are expressed as mean ± standard deviation (SD), median and interquartile range (Q1 to Q3) for variables that did not follow a normal distribution, or frequencies, as noted.\nTo compare the means of continuous variables measured between sex-specific tertiles of leptin, one way ANOVA and analysis of covariance with adjustments (ANCOVA) were used. Categorical data are presented as percentages and were compared among groups by χ2 tests. Correlations between leptin and clinical and laboratory parameters were assessed using Spearman rank order correlation coefficients (because of the skewed distribution of leptin levels). Multivariate regression analysis was performed to obtain partial (adjusted) correlations (R2). Case-mix-adjusted models were controlled for age, gender, history of CV disease, presence or absence of diabetes mellitus, dialysis vintage and fat mass.\nRepeated-measures analysis of variance was performed by using the MIXED model. Only patients with ≥ 2 study visits were included in the analyses. Base models were adjusted for age, sex, diabetes status, dialysis vintage, history of cardio-vascular diseases and fat mass. F Tests were used to assess the significance of the fixed effects, and P less than 0.05 was considered significant. To evaluate whether leptin influenced the trends in the various dependent variables, we included in each base model terms for individual \"leptin-by-time\" interactions.\nSurvival analyses were performed using the Kaplan-Meier survival curve and the Cox proportional hazard model. The univariate and multivariate Cox regression analyses are presented as (HR; CI).\nAll statistical analyses were performed using SPSS software, version 16.0 (SPSS Inc, Chicago, IL)."," Cross-sectional associations The 101 prevalent HD patients participating in this study included 36.6% women; over half of the participants (51.5%) had diabetes mellitus (DM) and nearly the same proportion had a history of cardiovascular disease (46.9%), including myocardial infarction, coronary artery procedures such as angioplasty or surgery, previous cerebral-vascular accident, or peripheral vascular disease. Average age was 64.6 ± 11.5 years and median maintenance HD vintage was 31 months (Q1 to Q3, 15.5-51.5 months) at the study initiation. For HD patients at the start of the cohort, serum leptin averaged (mean ± SD) 37.3 ± 39.4 ng/ml (median, 16.5 ng/ml; Q1 to Q3, 5.60-71.4 ng/ml).\nTo examine the relationship between different levels of serum leptin and relevant demographic, clinical and laboratory measures, serum leptin levels were divided into three equal gender specific tertiles (data not shown). Diabetes proportion and age distribution were similar across the three groups. No statistically significant differences were evident between groups in the use of medications that may affect inflammatory markers such as statins, aspirin, or angiotensin-converting enzyme inhibitors (data not shown). As expected females, tended to have higher leptin levels than males (p = 0.0001). Body composition parameters measured by anthropometry and bioelectric impedance analysis (BIA) including phase angle (PA) were incrementally greater across increasing leptin tertiles even after adjustments for baseline demographic and clinical parameters (age, DM status, dialysis vintage and cardio-vascular disease in the past). However, these associations were attenuated after including of fat mass in multivariate analyses. No significant differences in any of the biochemical markers of nutrition, normalized protein nitrogen appearance (nPNA), or actual energy or protein intake normalized to adjusted body weight were found between the HD patient groups according to leptin tertiles.\nAt baseline, anthropometric measurements (body mass index [BMI], triceps skinfold thickness [TSF], mid arm circumference [MAC] and midarm circumference calculated [MAMC]) and BIA-derived parameters of body composition (fat mass index [FMI] and fat free mass index [FFMI]) correlated positively with serum leptin levels, in both unadjusted and adjusted (for age, DM status, dialysis vintage, and previous cardio-vascular disease) models. Thus, an increased level of leptin was associated with better nutritional status (Table 1). Associations between levels of serum leptin and the two laboratory nutritional markers (albumin and cholesterol) and PA derived by BIA were statistically significant, but these associations were attenuated after adjustments for demographic and clinical parameters including fat mass. No significant correlations were observed between serum leptin and nPNA or dietary energy (DEI) and protein intake (DPI) normalized to adjusted body weight in both sexes after adjustments for demographic and clinical parameters including fat mass.\nUnadjusted and multivariate adjusted Spearman's correlation coefficients of baseline serum leptin and nutritional clinical and laboratory parameters in the study population at baseline\nCorrelation coefficient values ≥ 0.25 appear in bold.\na All case-mix-adjusted models include age, gender, DM status, dialysis vintage, CV disease in the past and fat mass.\nAbbreviations: nPNA, normalized protein nitrogen appearance; IL-6, interleukin-6; BMI, body mass index; TSF, triceps skinfold thickness; MAC, mid-arm circumference; MAMC, mid-arm muscle circumference calculated; FMI, fat mass index; FFMI, fat-free mass index, DM, diabetes mellitus; CV, cardio-vascular.\nThe 101 prevalent HD patients participating in this study included 36.6% women; over half of the participants (51.5%) had diabetes mellitus (DM) and nearly the same proportion had a history of cardiovascular disease (46.9%), including myocardial infarction, coronary artery procedures such as angioplasty or surgery, previous cerebral-vascular accident, or peripheral vascular disease. Average age was 64.6 ± 11.5 years and median maintenance HD vintage was 31 months (Q1 to Q3, 15.5-51.5 months) at the study initiation. For HD patients at the start of the cohort, serum leptin averaged (mean ± SD) 37.3 ± 39.4 ng/ml (median, 16.5 ng/ml; Q1 to Q3, 5.60-71.4 ng/ml).\nTo examine the relationship between different levels of serum leptin and relevant demographic, clinical and laboratory measures, serum leptin levels were divided into three equal gender specific tertiles (data not shown). Diabetes proportion and age distribution were similar across the three groups. No statistically significant differences were evident between groups in the use of medications that may affect inflammatory markers such as statins, aspirin, or angiotensin-converting enzyme inhibitors (data not shown). As expected females, tended to have higher leptin levels than males (p = 0.0001). Body composition parameters measured by anthropometry and bioelectric impedance analysis (BIA) including phase angle (PA) were incrementally greater across increasing leptin tertiles even after adjustments for baseline demographic and clinical parameters (age, DM status, dialysis vintage and cardio-vascular disease in the past). However, these associations were attenuated after including of fat mass in multivariate analyses. No significant differences in any of the biochemical markers of nutrition, normalized protein nitrogen appearance (nPNA), or actual energy or protein intake normalized to adjusted body weight were found between the HD patient groups according to leptin tertiles.\nAt baseline, anthropometric measurements (body mass index [BMI], triceps skinfold thickness [TSF], mid arm circumference [MAC] and midarm circumference calculated [MAMC]) and BIA-derived parameters of body composition (fat mass index [FMI] and fat free mass index [FFMI]) correlated positively with serum leptin levels, in both unadjusted and adjusted (for age, DM status, dialysis vintage, and previous cardio-vascular disease) models. Thus, an increased level of leptin was associated with better nutritional status (Table 1). Associations between levels of serum leptin and the two laboratory nutritional markers (albumin and cholesterol) and PA derived by BIA were statistically significant, but these associations were attenuated after adjustments for demographic and clinical parameters including fat mass. No significant correlations were observed between serum leptin and nPNA or dietary energy (DEI) and protein intake (DPI) normalized to adjusted body weight in both sexes after adjustments for demographic and clinical parameters including fat mass.\nUnadjusted and multivariate adjusted Spearman's correlation coefficients of baseline serum leptin and nutritional clinical and laboratory parameters in the study population at baseline\nCorrelation coefficient values ≥ 0.25 appear in bold.\na All case-mix-adjusted models include age, gender, DM status, dialysis vintage, CV disease in the past and fat mass.\nAbbreviations: nPNA, normalized protein nitrogen appearance; IL-6, interleukin-6; BMI, body mass index; TSF, triceps skinfold thickness; MAC, mid-arm circumference; MAMC, mid-arm muscle circumference calculated; FMI, fat mass index; FFMI, fat-free mass index, DM, diabetes mellitus; CV, cardio-vascular.\n Longitudinal associationas Linear mixed models were used to study the effects of longitudinal leptin changes on changes in nutritional parameters (slopes) over 24 months including fixed parameters such as age, gender, diabetes status, dialysis vintage, previous cardio-vascular events, and fat mass (Table 2). No significant changes in dietary intake or in any of the biochemical nutritional markers over time were found. In contrast, a significant reduction in body composition parameters (both those measured by anthropometry [BMI, TSF and MAMC] and by BIA [FMI, FFMI, PA]) over time was observed. However, leptin did not modulate the changes in outcome variables over time in our cohort (leptin-by-time interactions were insignificant).\nRegression coefficients with 95% Confidence Intervals for the effect of longitudinal leptin changes on nutritional parameters changes (slopes) during 24 months based on mixed-effects model with linear trends for variable and fixed parameters\nNOTE: All nutritional variables presented in Table were modeled separately as dependent variables, whereas independent variables included fixed factors (such as age, gender, diabetes status, dialysis vintage, past cardio-vascular disease and fat mass) and leptin as continuous variable. Terms for individual \"leptin-by-time\" interactions were included in each model. The model takes into account every measurements of leptin and presented nutritional variables in each time point for each patient separately. Presented regression coefficients demonstrate changes in outcome variables with time (24 months).\na P for trend for leptin-by-time interactions.\nb FMI and BMI were adjusted only by age, gender, DM status, history of cardio-vascular disease and dialysis vintage.\nAbbreviations: DEI, daily energy intake; DPI, daily protein intake; nPNA, normalized protein nitrogen appearance; IL-6, interleukin-6; EPO, erythropoietin; BMI, body mass index; TSF, triceps skinfold thickness; MAC, mid-arm circumference; MAMC, mid-arm muscle circumference calculated; FMI, fat mass index; FFMI, fat-free mass index; DM, diabetes mellitus; CV, cardio-vascular.\nA mixed model including the fixed factors sex, age, DM status, dialysis vintage, history of CV disease and some of nutritional parameters predicted variability in leptin levels during the study presented in Table 3. We observed significant changes of leptin levels with time (linear estimate ± SE: -2.5010 ± 0.57 ng/ml/2y; p < 0.001). The effect of longitudinal changes of FMI on serum leptin variability over time was evident according to this model.\nEffects of longitudinal changes of nutritional parameters on changes (slopes) of leptin during 24 months based on mixed-effects model with linear trends for variable and fixed parameters\nNOTE: The Table represents a simple Mixed-Model with leptin as dependent variable, and presented nutritional parameters and fixed factors (age, gender, DM status, dialysis vintage and history of CV disease) as independent variables. The model takes into account every measurements of leptin and presented nutritional variables in each time point for each patient separately.\nAbbreviations: DEI, daily energy intake; IL-6, interleukin-6; EPO, erythropoietin; FMI, fat mass index; DM, diabetes mellitus; CV, cardio-vascular.\nOf interest, a significant reduction over time was associated with a more rapid decline in leptin levels in the highest leptin tertile in both unadjusted (p = 0.007 for leptin-by-time interaction) and fully adjusted (p = 0.047 for leptin-by-time interaction) models (Figure 2).\nLeptin levels decline over time in the study population. Changes in estimated marginal means of serum leptin levels at baseline (month 0), month 6, month 12, month 18 and month 24 in the study population groups according to sex-specific tertiles* of baseline serum leptin: 1. Unadjusted model: P = 0.007 for leptin-by-time interaction; 2. Fully adjusted model (for age, gender, DM status, dialysis vintage, previous cardio-vascular morbidity and fat mass): P = 0.047 for leptin-by-time interaction. *Median (interquartile range) leptin (ng/ml) tertiles for men (n = 64) were 1.70 (1.2-2.4), 10.2 (7.6-13.8) and 38.3 (26.2-81.3) and for women (n = 37) were 15.8 (9.3-18.3), 68.0 (41.4-85.9) and 112.6 (109.2-116.0).\nLinear mixed models were used to study the effects of longitudinal leptin changes on changes in nutritional parameters (slopes) over 24 months including fixed parameters such as age, gender, diabetes status, dialysis vintage, previous cardio-vascular events, and fat mass (Table 2). No significant changes in dietary intake or in any of the biochemical nutritional markers over time were found. In contrast, a significant reduction in body composition parameters (both those measured by anthropometry [BMI, TSF and MAMC] and by BIA [FMI, FFMI, PA]) over time was observed. However, leptin did not modulate the changes in outcome variables over time in our cohort (leptin-by-time interactions were insignificant).\nRegression coefficients with 95% Confidence Intervals for the effect of longitudinal leptin changes on nutritional parameters changes (slopes) during 24 months based on mixed-effects model with linear trends for variable and fixed parameters\nNOTE: All nutritional variables presented in Table were modeled separately as dependent variables, whereas independent variables included fixed factors (such as age, gender, diabetes status, dialysis vintage, past cardio-vascular disease and fat mass) and leptin as continuous variable. Terms for individual \"leptin-by-time\" interactions were included in each model. The model takes into account every measurements of leptin and presented nutritional variables in each time point for each patient separately. Presented regression coefficients demonstrate changes in outcome variables with time (24 months).\na P for trend for leptin-by-time interactions.\nb FMI and BMI were adjusted only by age, gender, DM status, history of cardio-vascular disease and dialysis vintage.\nAbbreviations: DEI, daily energy intake; DPI, daily protein intake; nPNA, normalized protein nitrogen appearance; IL-6, interleukin-6; EPO, erythropoietin; BMI, body mass index; TSF, triceps skinfold thickness; MAC, mid-arm circumference; MAMC, mid-arm muscle circumference calculated; FMI, fat mass index; FFMI, fat-free mass index; DM, diabetes mellitus; CV, cardio-vascular.\nA mixed model including the fixed factors sex, age, DM status, dialysis vintage, history of CV disease and some of nutritional parameters predicted variability in leptin levels during the study presented in Table 3. We observed significant changes of leptin levels with time (linear estimate ± SE: -2.5010 ± 0.57 ng/ml/2y; p < 0.001). The effect of longitudinal changes of FMI on serum leptin variability over time was evident according to this model.\nEffects of longitudinal changes of nutritional parameters on changes (slopes) of leptin during 24 months based on mixed-effects model with linear trends for variable and fixed parameters\nNOTE: The Table represents a simple Mixed-Model with leptin as dependent variable, and presented nutritional parameters and fixed factors (age, gender, DM status, dialysis vintage and history of CV disease) as independent variables. The model takes into account every measurements of leptin and presented nutritional variables in each time point for each patient separately.\nAbbreviations: DEI, daily energy intake; IL-6, interleukin-6; EPO, erythropoietin; FMI, fat mass index; DM, diabetes mellitus; CV, cardio-vascular.\nOf interest, a significant reduction over time was associated with a more rapid decline in leptin levels in the highest leptin tertile in both unadjusted (p = 0.007 for leptin-by-time interaction) and fully adjusted (p = 0.047 for leptin-by-time interaction) models (Figure 2).\nLeptin levels decline over time in the study population. Changes in estimated marginal means of serum leptin levels at baseline (month 0), month 6, month 12, month 18 and month 24 in the study population groups according to sex-specific tertiles* of baseline serum leptin: 1. Unadjusted model: P = 0.007 for leptin-by-time interaction; 2. Fully adjusted model (for age, gender, DM status, dialysis vintage, previous cardio-vascular morbidity and fat mass): P = 0.047 for leptin-by-time interaction. *Median (interquartile range) leptin (ng/ml) tertiles for men (n = 64) were 1.70 (1.2-2.4), 10.2 (7.6-13.8) and 38.3 (26.2-81.3) and for women (n = 37) were 15.8 (9.3-18.3), 68.0 (41.4-85.9) and 112.6 (109.2-116.0).\n Survival analysis During an average follow-up of 35 months (median, 40 months; Q1 to Q3, 17-52), 33 patients died: 11 (32%) in the low tertile group, 12 (35%) in the median tertile group and 10 (30%) in the high tertile group, with a median time to death of 25 months (Q1 to Q3, 14-40 months). Cumulative incidences of survival were unaffected by the baseline serum leptin levels (Figure 3). To further assess the possible association of baseline serum leptin level with cardio-vascular morbidity and mortality, cumulative hazards of all-cause death and first composite cardio-vascular event from Cox regression (after adjustment for baseline demographic parameters and fat mass) were calculated. First cardio-vascular event was defined as myocardial infarction (MI), requiring coronary artery procedures such as angioplasty or surgery, cerebral-vascular accident (CVA), or peripheral vascular disease (PVD), requiring angioplasty, bypass or amputation and diagnosed after the participant entered the study. No statistically significant differences in hazards between the different groups according leptin tertiles were observed. Additionally, no differences were found by Cox regression analysis in terms of survival probabilities from the analysis of all-cause mortality for each 10 ng/ml increase in baseline serum leptin (data not shown).\nKaplan-Meier curves of surviving patients comparing subgroups of patients stratified by baseline serum leptin tertiles.\nThus, although chronic HD patients with higher baseline leptin had better nutritional status as evidenced by their significantly more favorable body composition parameters at the start of the study, this disparity did not appear to widen over the 24 months of follow-up; thus, leptin levels did not predict body composition parameter changes over time. Moreover, no survival advantage of the high leptin group was found in our observational cohort.\nDuring an average follow-up of 35 months (median, 40 months; Q1 to Q3, 17-52), 33 patients died: 11 (32%) in the low tertile group, 12 (35%) in the median tertile group and 10 (30%) in the high tertile group, with a median time to death of 25 months (Q1 to Q3, 14-40 months). Cumulative incidences of survival were unaffected by the baseline serum leptin levels (Figure 3). To further assess the possible association of baseline serum leptin level with cardio-vascular morbidity and mortality, cumulative hazards of all-cause death and first composite cardio-vascular event from Cox regression (after adjustment for baseline demographic parameters and fat mass) were calculated. First cardio-vascular event was defined as myocardial infarction (MI), requiring coronary artery procedures such as angioplasty or surgery, cerebral-vascular accident (CVA), or peripheral vascular disease (PVD), requiring angioplasty, bypass or amputation and diagnosed after the participant entered the study. No statistically significant differences in hazards between the different groups according leptin tertiles were observed. Additionally, no differences were found by Cox regression analysis in terms of survival probabilities from the analysis of all-cause mortality for each 10 ng/ml increase in baseline serum leptin (data not shown).\nKaplan-Meier curves of surviving patients comparing subgroups of patients stratified by baseline serum leptin tertiles.\nThus, although chronic HD patients with higher baseline leptin had better nutritional status as evidenced by their significantly more favorable body composition parameters at the start of the study, this disparity did not appear to widen over the 24 months of follow-up; thus, leptin levels did not predict body composition parameter changes over time. Moreover, no survival advantage of the high leptin group was found in our observational cohort.","The 101 prevalent HD patients participating in this study included 36.6% women; over half of the participants (51.5%) had diabetes mellitus (DM) and nearly the same proportion had a history of cardiovascular disease (46.9%), including myocardial infarction, coronary artery procedures such as angioplasty or surgery, previous cerebral-vascular accident, or peripheral vascular disease. Average age was 64.6 ± 11.5 years and median maintenance HD vintage was 31 months (Q1 to Q3, 15.5-51.5 months) at the study initiation. For HD patients at the start of the cohort, serum leptin averaged (mean ± SD) 37.3 ± 39.4 ng/ml (median, 16.5 ng/ml; Q1 to Q3, 5.60-71.4 ng/ml).\nTo examine the relationship between different levels of serum leptin and relevant demographic, clinical and laboratory measures, serum leptin levels were divided into three equal gender specific tertiles (data not shown). Diabetes proportion and age distribution were similar across the three groups. No statistically significant differences were evident between groups in the use of medications that may affect inflammatory markers such as statins, aspirin, or angiotensin-converting enzyme inhibitors (data not shown). As expected females, tended to have higher leptin levels than males (p = 0.0001). Body composition parameters measured by anthropometry and bioelectric impedance analysis (BIA) including phase angle (PA) were incrementally greater across increasing leptin tertiles even after adjustments for baseline demographic and clinical parameters (age, DM status, dialysis vintage and cardio-vascular disease in the past). However, these associations were attenuated after including of fat mass in multivariate analyses. No significant differences in any of the biochemical markers of nutrition, normalized protein nitrogen appearance (nPNA), or actual energy or protein intake normalized to adjusted body weight were found between the HD patient groups according to leptin tertiles.\nAt baseline, anthropometric measurements (body mass index [BMI], triceps skinfold thickness [TSF], mid arm circumference [MAC] and midarm circumference calculated [MAMC]) and BIA-derived parameters of body composition (fat mass index [FMI] and fat free mass index [FFMI]) correlated positively with serum leptin levels, in both unadjusted and adjusted (for age, DM status, dialysis vintage, and previous cardio-vascular disease) models. Thus, an increased level of leptin was associated with better nutritional status (Table 1). Associations between levels of serum leptin and the two laboratory nutritional markers (albumin and cholesterol) and PA derived by BIA were statistically significant, but these associations were attenuated after adjustments for demographic and clinical parameters including fat mass. No significant correlations were observed between serum leptin and nPNA or dietary energy (DEI) and protein intake (DPI) normalized to adjusted body weight in both sexes after adjustments for demographic and clinical parameters including fat mass.\nUnadjusted and multivariate adjusted Spearman's correlation coefficients of baseline serum leptin and nutritional clinical and laboratory parameters in the study population at baseline\nCorrelation coefficient values ≥ 0.25 appear in bold.\na All case-mix-adjusted models include age, gender, DM status, dialysis vintage, CV disease in the past and fat mass.\nAbbreviations: nPNA, normalized protein nitrogen appearance; IL-6, interleukin-6; BMI, body mass index; TSF, triceps skinfold thickness; MAC, mid-arm circumference; MAMC, mid-arm muscle circumference calculated; FMI, fat mass index; FFMI, fat-free mass index, DM, diabetes mellitus; CV, cardio-vascular.","Linear mixed models were used to study the effects of longitudinal leptin changes on changes in nutritional parameters (slopes) over 24 months including fixed parameters such as age, gender, diabetes status, dialysis vintage, previous cardio-vascular events, and fat mass (Table 2). No significant changes in dietary intake or in any of the biochemical nutritional markers over time were found. In contrast, a significant reduction in body composition parameters (both those measured by anthropometry [BMI, TSF and MAMC] and by BIA [FMI, FFMI, PA]) over time was observed. However, leptin did not modulate the changes in outcome variables over time in our cohort (leptin-by-time interactions were insignificant).\nRegression coefficients with 95% Confidence Intervals for the effect of longitudinal leptin changes on nutritional parameters changes (slopes) during 24 months based on mixed-effects model with linear trends for variable and fixed parameters\nNOTE: All nutritional variables presented in Table were modeled separately as dependent variables, whereas independent variables included fixed factors (such as age, gender, diabetes status, dialysis vintage, past cardio-vascular disease and fat mass) and leptin as continuous variable. Terms for individual \"leptin-by-time\" interactions were included in each model. The model takes into account every measurements of leptin and presented nutritional variables in each time point for each patient separately. Presented regression coefficients demonstrate changes in outcome variables with time (24 months).\na P for trend for leptin-by-time interactions.\nb FMI and BMI were adjusted only by age, gender, DM status, history of cardio-vascular disease and dialysis vintage.\nAbbreviations: DEI, daily energy intake; DPI, daily protein intake; nPNA, normalized protein nitrogen appearance; IL-6, interleukin-6; EPO, erythropoietin; BMI, body mass index; TSF, triceps skinfold thickness; MAC, mid-arm circumference; MAMC, mid-arm muscle circumference calculated; FMI, fat mass index; FFMI, fat-free mass index; DM, diabetes mellitus; CV, cardio-vascular.\nA mixed model including the fixed factors sex, age, DM status, dialysis vintage, history of CV disease and some of nutritional parameters predicted variability in leptin levels during the study presented in Table 3. We observed significant changes of leptin levels with time (linear estimate ± SE: -2.5010 ± 0.57 ng/ml/2y; p < 0.001). The effect of longitudinal changes of FMI on serum leptin variability over time was evident according to this model.\nEffects of longitudinal changes of nutritional parameters on changes (slopes) of leptin during 24 months based on mixed-effects model with linear trends for variable and fixed parameters\nNOTE: The Table represents a simple Mixed-Model with leptin as dependent variable, and presented nutritional parameters and fixed factors (age, gender, DM status, dialysis vintage and history of CV disease) as independent variables. The model takes into account every measurements of leptin and presented nutritional variables in each time point for each patient separately.\nAbbreviations: DEI, daily energy intake; IL-6, interleukin-6; EPO, erythropoietin; FMI, fat mass index; DM, diabetes mellitus; CV, cardio-vascular.\nOf interest, a significant reduction over time was associated with a more rapid decline in leptin levels in the highest leptin tertile in both unadjusted (p = 0.007 for leptin-by-time interaction) and fully adjusted (p = 0.047 for leptin-by-time interaction) models (Figure 2).\nLeptin levels decline over time in the study population. Changes in estimated marginal means of serum leptin levels at baseline (month 0), month 6, month 12, month 18 and month 24 in the study population groups according to sex-specific tertiles* of baseline serum leptin: 1. Unadjusted model: P = 0.007 for leptin-by-time interaction; 2. Fully adjusted model (for age, gender, DM status, dialysis vintage, previous cardio-vascular morbidity and fat mass): P = 0.047 for leptin-by-time interaction. *Median (interquartile range) leptin (ng/ml) tertiles for men (n = 64) were 1.70 (1.2-2.4), 10.2 (7.6-13.8) and 38.3 (26.2-81.3) and for women (n = 37) were 15.8 (9.3-18.3), 68.0 (41.4-85.9) and 112.6 (109.2-116.0).","During an average follow-up of 35 months (median, 40 months; Q1 to Q3, 17-52), 33 patients died: 11 (32%) in the low tertile group, 12 (35%) in the median tertile group and 10 (30%) in the high tertile group, with a median time to death of 25 months (Q1 to Q3, 14-40 months). Cumulative incidences of survival were unaffected by the baseline serum leptin levels (Figure 3). To further assess the possible association of baseline serum leptin level with cardio-vascular morbidity and mortality, cumulative hazards of all-cause death and first composite cardio-vascular event from Cox regression (after adjustment for baseline demographic parameters and fat mass) were calculated. First cardio-vascular event was defined as myocardial infarction (MI), requiring coronary artery procedures such as angioplasty or surgery, cerebral-vascular accident (CVA), or peripheral vascular disease (PVD), requiring angioplasty, bypass or amputation and diagnosed after the participant entered the study. No statistically significant differences in hazards between the different groups according leptin tertiles were observed. Additionally, no differences were found by Cox regression analysis in terms of survival probabilities from the analysis of all-cause mortality for each 10 ng/ml increase in baseline serum leptin (data not shown).\nKaplan-Meier curves of surviving patients comparing subgroups of patients stratified by baseline serum leptin tertiles.\nThus, although chronic HD patients with higher baseline leptin had better nutritional status as evidenced by their significantly more favorable body composition parameters at the start of the study, this disparity did not appear to widen over the 24 months of follow-up; thus, leptin levels did not predict body composition parameter changes over time. Moreover, no survival advantage of the high leptin group was found in our observational cohort.","In the present study, we wished to determine whether changes of serum leptin levels are correlated with nutritional status over time in a cohort of prevalent hemodialysis patients. We observed that despite the strong and significant cross-sectional associations between serum leptin and body composition parameters at baseline, leptin levels did not reflect changes in nutritional status in our population.\nOur study confirms several previous cross-sectional studies in which elevated serum leptin levels were demonstrated to be positively associated with several nutritional markers (laboratory or body composition parameters, mainly BMI and fat mass) in chronic HD patients [14,20,21,23-25]. HD patients treated with megestrole acetate (24), growth hormone [31] or high-calorie supplementation [32] demonstrated progressively increased serum leptin parallel to an improved nutritional state.\nHowever, not all studies are consistent with positive associations between elevated levels of leptin and several nutritional parameters in ESRD patients. An inverse correlation was observed between leptin/fat mass and dietary intake, as well as with significantly lower lean tissue mass in chronic renal failure (23 patients) and ESRD patients receiving PD (24 patients) and HD (22 patients) therapy in study by Young et al [5]. In a cross-sectional study of 28 dialysis patients and 41 healthy control subjects, Johansen et al [20] showed that leptin levels were negatively correlated with albumin and PCR, suggesting a possible negative role of leptin in nutrition. Based on longitudinal observation, Stenvinkel et al [18] demonstrated that increases in serum leptin levels of 36 peritoneal dialysis patients were associated with a decrease in lean body mass. The discrepancies between these studies may be related to the population recruited, to their small sample size, and to their design; specifically, serum leptin levels were measured among patients with varying degrees of CRF, some of whom were undergoing maintenance hemodialysis and peritoneal dialysis, and in healthy controls [5,18,20]; many of these studies had a low number of study participants [5,18,20,23-25] and a majority of these studies were cross-sectional [5,20,23,25].\nLeptin, a regulator of eating behavior, has been shown to be a major determinant of anorexia in uremic animals via signaling through the hypothalamic melanocortin system [15]. Subsequently, a recent experimental study by Cheung et al [33] showed that intraperitoneal administration of the melanocortin-4 receptor (MC4-R) antagonist NBI-12i stimulates food intake and weight gain in uremic mice. However, the effects of leptin in uremic patients are not unequivocal. Relevant to our findings, Bossola at al [17] demonstrated that serum leptin levels were not different in anorexic and in non-anorexic hemodialysis patients. Several other studies failed as well to demonstrate any role of hyperleptinemia on reduced dietary intake in ESRD patients receiving maintenance HD treatment [5,16,34]. Since hyperleptinemia is common in HD patients mainly due to impaired renal clearance [20], it seemed reasonable to assume that selective leptin resistance (such as occurs in obese humans [35]) is the responsible mechanism for attenuation of the negative effect of leptin on appetite, and accordingly, on dietary intake under conditions of continuous stimulation. The molecular mechanisms of leptin resistance, with a focus on the contribution of the intracellular tyrosine residues in the hypothalamic receptor of leptin (LEPRb) and their interaction with the negative feedback regulators, suppressor of cytokine signaling 1 and 3 (SOCS1 and SOCS3), are described in a recent study by Knobelspies et al [36].\nThus, although the cross-group comparisons confirm that hyperleptinemia is associated with better body composition parameters, no unequivocal associations in terms of biochemical data or dietary intake with serum leptin levels are evident in HD patients according to the available literature. Our study confirms these data.\nAnother finding of the present study was that 24 months of HD was associated with a significant reduction of plasma leptin levels, with a more rapid decline observed in patients with higher baseline leptin levels. Only limited information is available on the relationship between longitudinal serum leptin measurements and nutritional characteristics in chronic HD patients [18,32]. To the best of our knowledge, the present study is the first long-term longitudinal study to show the relationship between serum leptin level and nutritional status in ESRD patients receiving maintenance HD therapy. Several mechanisms may be involved in decrease of serum leptin levels over time in hemodialysis patients. These include, the increasing use of high flux and super-flux hemodialyzers [37], and/or use of alternative dialysis strategies such as hemodiafiltration [38]; recombinant human erythropoietin treatment (generally followed by a significant decline of leptinaemia in hemodialysed patients [39]); chronic inflammation (under the assumption that leptin, like albumin or transferrin, is a negative acute phase protein in chronic HD patients [22], although other studies [40] do not support this mechanism); metabolic acidosis, which can reduce the release of leptin from adipose tissue [41]; and finally, a decrease in fat mass leading to a decrease in the rate of leptin biosynthesis [14,18] (based on an insulin or a nutrient-sensing pathway regulating leptin gene expression in fat tissue [42]). Although patients in our facility were all treated using low flux hemodialysis, reduction of BMI and accordingly of fat mass over 24 months of the study may explain this longitudinal decrease of serum leptin levels in our cohort.\nOf interest is the observation that adverse changes in body composition parameters occur over time, supporting the hypothesis that end-stage renal disease is associated with wasting as proposed in some [43,44] but not all [45] of the previous studies. The results of our study were consistent with those reported by Johansen et al [43] who did not find significant changes in any of the biochemical markers with time, but a significant reduction in phase angle over 1 year follow up was evident in prevalent HD patients. In parallel, together with reduced body composition parameters over time, we observed a longitudinal decline in serum leptin levels in our cohort. However, the lack of an association between serum leptin levels and future changes in body composition parameters (as exhibited by non-significant leptin-by-time interactions), suggests that the levels of leptin follow body composition (mainly fat mass) trajectory or its accompanying metabolic changes, rather than governing it.\nFinally, serum leptin level did not appear as a useful predictor of all cause mortality or cardio-vascular morbidity in our study population during up to 4 years of observation. In this aspect, our findings support the results of earlier study by Tsai et al [46]. Although Scholze et al [26] showed a paradoxical reverse association between leptin level and clinical outcome in 71 chronic HD patients, our patients did not show such an association. The difference between our results and theirs might have been caused by differences in the cohorts. First, the population presented by Scholze et al [26] had a lower BMI at baseline than our patients, and no body composition parameters were measured. Further, the prevalence of diabetes mellitus was much greater in patients in the lower leptin group than in patients of the higher leptin group in the cohort of Scholze et al [26]. Indeed, HD patients with diabetes have a poor prognosis [4] that might be the cause of the lower survival rate in the lower leptin group in the above study [26]. While low levels of leptin may reflect a state of malnutrition in HD patients, proatherogenic effects of hyperleptinemia were linked to an adverse cardiovascular profile in general population [10,12]. We speculate that the lack of long-term benefits of hyperleptinemia in terms of survival of chronic HD patients could reflect the modulating influence of proatherogenic effects of high serum leptin levels. Finally, no relationships between serum leptin levels and previous cardio-vascular events were found by Diez et al [47] in a retrospective study on 82 dialysis patients. Zoccali et al [40] also did not show any difference in cardiovascular event-free survival in cohort of 192 hemodialysis patients after their stratification on the basis of the median value of plasma leptin.\nSome limitations of the present study should be considered. First, this study is based on a relatively small sample size limiting detection of more subtle changes over time. Second, this study used only an observational approach, without manipulation of exposure factors, and therefore, no definitive cause-and-effect relationship can be derived for any of the risk factors analyzed. Dietary intake assessed by 3 day food records is another limitation of the study, as results can be subjective and incomplete, and can vary considerably from day to day as a result of dialysis treatment sessions and associated disturbances in food intake. Finally, significant circadian fluctuations of plasma leptin (regardless of the underlying degree of glucose tolerance, fasting, or diet) can certainly obscure some small but significant changes in plasma leptin [48] and may be a source of potential bias for longitudinal observation. Nevertheless, the present study has the advantage of providing long-term longitudinal data on the relationship between serum concentrations of leptin and nutritional parameters in prevalent HD patients.","In conclusion, we found positive linear associations between serum leptin levels and body composition, whereas no significant relations in terms of biochemical data or dietary intake were evident in our cohort at baseline. Analyzing longitudinal data we concluded that any influence of serum leptin levels on nutritional status is limited to mirroring attained BMI and fat mass trajectory. Further, nutritional advantage at baseline of the study population with hyperleptinemia did not translate into long-term benefits in terms of survival. Taken together, our results suggest that in our cohort, leptin levels reflect fat mass depots, rather than independently contributing to uremic anorexia or modifying nutritional status and/or survival in chronic HD patients. These results are of particular importance if the use of subcutaneous injections of recombinant methionyl leptin [49] or leptin removal by super-flux polysulfone dialysers [37] is contemplated as a therapeutic intervention."],"string":"[\n \"In recent years, the number of patients with end-stage renal disease (ESRD) has been increasing worldwide [1]. Depending in part upon the method used to evaluate nutritional status and the population studied, from 40 to 70 percent of patients with ESRD are malnourished [2,3] resulting in poor clinical outcomes [4]. Among the mechanisms responsible for malnutrition, leptin was believed to influence nutritional markers in patients with ESRD [5]. Leptin is a 16-kDa protein identified as the product of the obese gene; it is exclusively produced in adipocytes, and regulates food intake and energy expenditure in animal models [6]. Leptin decreases food intake by decreasing NPY (neuropeptide Y - one of the most potent stimulators of food intake) mRNA [7] and increasing alpha-MSH (alpha-melanocyte-stimulating hormone - an inhibitor of food intake) [8]. Besides linking adiposity and central nervous circuits to reduced appetite and enhanced energy expenditure in the general population [9], leptin has been shown to increase overall sympathetic nerve activity [10], facilitate glucose utilization and improve insulin sensitivity [11]. Furthermore, the prospective West of Scotland Coronary Prevention Study (WOSCOPS) reported that elevated leptin increases the relative risk of cardio-vascular disease in the general population independently of fat mass [12].\\nIn general, serum leptin levels are significantly elevated in patients with renal failure, particularly when compared to age, gender and body mass index (BMI)-matched controls [13,14]. However, the role of hyperleptinemia in ESRD patients is somewhat unconventional. In contrast with its anorexogenic effects recognized in the general population [9] and even in experimental models of uremia (in subtotal nephrectomized and leptin receptor-deficient [db/db] mice) [15], leptin has not been reported to affect perceived appetite and nutrient intake in dialysis patients [16,17]. Although in some observational studies, increased serum leptin concentrations were observed in ESRD patients in parallel with loss of lean body mass [18,19] or with hypoalbuminemia and low protein intake [20], some others failed to find any correlation between hyperleptinemia and weight change [9] or lean mass [21] in this population. Moreover, several clinical studies suggested that leptin is a negative acute phase protein [22] and can serve as a marker of adequate nutritional status, rather than an appetite-reducing uremic toxin in hemodialysis patients [23-25]. Finally, the relationship between elevated serum leptin levels and clinical outcomes in ESRD has not been fully defined. In one small prospective cohort of hemodialysis patients, lower baseline serum leptin levels predicted mortality [26], but neither changes in leptin over time were measured, nor were leptin levels normalized to body fat mass in this study.\\nThus, the influence of serum leptin levels on nutritional status and survival in chronic hemodialysis patients remained to be elucidated. In view of leptin's physiological role, information on effects of prolonged hyperleptinemia (independent of fat mass) on nutritional status of chronic hemodialysis patients, which may also impact on their survival, would be of interest. The aim of the present prospective longitudinal study was therefore to study longitudinal changes in serum leptin levels and to relate them to the changes in nutritional markers and survival in chronic hemodialysis patients.\",\n \" Patients This prospective observational study was approved by the Ethics Committee of Assaf Harofeh Medical Center (Zerifin, Affiliated to the Sackler Faculty of Medicine Tel Aviv University, Israel). Informed consent was obtained before any trial-related activities. Patients were eligible for entry when they had been on HD therapy for at least 3 months and were 18 years or older, with no clinically active cardio-vascular or infectious diseases on entry. We excluded patients with edema, pleural effusion or ascites at their initial assessment, as well as patients with malignant disease, liver cirrhosis, neuro-muscular diseases, amputations or any deformities of the body. Exclusion criteria at the entry of the study also included co-morbidity (auto-immune disease and/or acute infections) and/or medication (prednisone) that might interfere with plasma leptin concentrations. A flow chart of the study is presented in Figure 1. In total, 101 patients (64 men and 37 women) with a mean age of 64.6 ± 11.5 years, receiving maintenance hemodialysis treatment at our outpatient HD clinic, were included in the study. Of the patients studied, 52 were diabetic (all diabetic patients had type 2 diabetes). Study measurements were performed at baseline and at 6, 12, 18 and 24 months from enrollment. After the longitudinal measurements ended, we continued clinical observation on our cohort during 2 additional years. Thus, in total, the study period extended 35 ± 17 months. During this period, 33 patients (32.7%) died (the main causes of death were cardio-vascular [12 of 33 patients; 36.4%] and sepsis [12 of 33 patients; 36.4%]), 13 patients (12.9%) underwent kidney transplantation, 3 patients (3.0%) changed dialysis modality, and 10 patients (10.0%) transferred to other hemodialysis units. All patients underwent regular dialysis via their vascular access (81.2% of patients had arterio-venous fistula) 4-5 h three times per week at a blood flow rate of 250-300 ml/min. Bicarbonate dialysate (30 mEq/L) at a dialysis solution flow rate of 500 ml/min was used in all cases. All dialysis was performed with biocompatible dialyzer membrane with a surface area of 1.0-1.8 m2. The efficiency of the dialysis was assessed based on the delivered dose of dialysis (Kt/V urea) using a single-pool urea kinetic model (mean Kt/V was1.31 ± 0.23 in our population).\\nFlow diagram of the study.\\nInformation on vascular disease (cerebral vascular, peripheral vascular and heart disease) was obtained from a detailed medical history.\\nMost patients were required antihypertensive medications as well as other drugs commonly used in ESRD, such as phosphate and potassium binders, diuretics, and supplements of vitamins B, C, and D.\\nThis prospective observational study was approved by the Ethics Committee of Assaf Harofeh Medical Center (Zerifin, Affiliated to the Sackler Faculty of Medicine Tel Aviv University, Israel). Informed consent was obtained before any trial-related activities. Patients were eligible for entry when they had been on HD therapy for at least 3 months and were 18 years or older, with no clinically active cardio-vascular or infectious diseases on entry. We excluded patients with edema, pleural effusion or ascites at their initial assessment, as well as patients with malignant disease, liver cirrhosis, neuro-muscular diseases, amputations or any deformities of the body. Exclusion criteria at the entry of the study also included co-morbidity (auto-immune disease and/or acute infections) and/or medication (prednisone) that might interfere with plasma leptin concentrations. A flow chart of the study is presented in Figure 1. In total, 101 patients (64 men and 37 women) with a mean age of 64.6 ± 11.5 years, receiving maintenance hemodialysis treatment at our outpatient HD clinic, were included in the study. Of the patients studied, 52 were diabetic (all diabetic patients had type 2 diabetes). Study measurements were performed at baseline and at 6, 12, 18 and 24 months from enrollment. After the longitudinal measurements ended, we continued clinical observation on our cohort during 2 additional years. Thus, in total, the study period extended 35 ± 17 months. During this period, 33 patients (32.7%) died (the main causes of death were cardio-vascular [12 of 33 patients; 36.4%] and sepsis [12 of 33 patients; 36.4%]), 13 patients (12.9%) underwent kidney transplantation, 3 patients (3.0%) changed dialysis modality, and 10 patients (10.0%) transferred to other hemodialysis units. All patients underwent regular dialysis via their vascular access (81.2% of patients had arterio-venous fistula) 4-5 h three times per week at a blood flow rate of 250-300 ml/min. Bicarbonate dialysate (30 mEq/L) at a dialysis solution flow rate of 500 ml/min was used in all cases. All dialysis was performed with biocompatible dialyzer membrane with a surface area of 1.0-1.8 m2. The efficiency of the dialysis was assessed based on the delivered dose of dialysis (Kt/V urea) using a single-pool urea kinetic model (mean Kt/V was1.31 ± 0.23 in our population).\\nFlow diagram of the study.\\nInformation on vascular disease (cerebral vascular, peripheral vascular and heart disease) was obtained from a detailed medical history.\\nMost patients were required antihypertensive medications as well as other drugs commonly used in ESRD, such as phosphate and potassium binders, diuretics, and supplements of vitamins B, C, and D.\\n Dietary intake A continuous 3-day dietary history (which included a dialysis day, a weekend day and a non-dialysis day) was recorded on a self-completed food diary. Then, dietary energy and protein intake were calculated and normalized for adjusted body weight (ABW) by the following formula [27]:\\nSBW - standard body weight was determined by the National Healthand Nutrition Examination Survey II (NHANES II) population medians for age, sex, frame size, and stature [27].\\nDietary protein intake was also estimated by the protein catabolic rate (PCR) calculation from the patient's urea generation rate by urea kinetics modeling [28]. Single-pool model urea kinetics was used to estimate the nPCR.\\nA continuous 3-day dietary history (which included a dialysis day, a weekend day and a non-dialysis day) was recorded on a self-completed food diary. Then, dietary energy and protein intake were calculated and normalized for adjusted body weight (ABW) by the following formula [27]:\\nSBW - standard body weight was determined by the National Healthand Nutrition Examination Survey II (NHANES II) population medians for age, sex, frame size, and stature [27].\\nDietary protein intake was also estimated by the protein catabolic rate (PCR) calculation from the patient's urea generation rate by urea kinetics modeling [28]. Single-pool model urea kinetics was used to estimate the nPCR.\\n Anthropometric measurements BMI, triceps skinfold thickness (TSF), mid arm circumference (MAC) and calculated mid arm muscle circumference (MAMC) were measured as anthropometric variables. The BMI was calculated as dry weight in kilograms divided by the square of height in meters. TSF was measured with a conventional skinfold caliper using standard techniques. Mid arm circumference was measured with a plastic measuring tape. MAMC was estimated as follows:\\nBMI, triceps skinfold thickness (TSF), mid arm circumference (MAC) and calculated mid arm muscle circumference (MAMC) were measured as anthropometric variables. The BMI was calculated as dry weight in kilograms divided by the square of height in meters. TSF was measured with a conventional skinfold caliper using standard techniques. Mid arm circumference was measured with a plastic measuring tape. MAMC was estimated as follows:\\n Body composition analysis Body composition was determined by body impedance analysis (B.I.A. Nutriguard- M, Data-Input, Frankfurt, Germany). We used gel-based electrodes specifically developed for BIA measurements - Bianostic AT (Data-Input GmbH). On the day of blood collection, patients underwent BIA measurement at approximately 30 minutes postdialysis. BIA electrodes were placed on the same body side used for anthropometric measurements. The multi-frequency technique was used. FFM was calculated by using the approach of Kyle et al. validated by dual-energy x-ray absorptiometry on 343 healthy adult subjects [29]:\\nFat mass and fat free mass were standardized by squared height (m2), and expressed in kg/m2 as fat mass index (FMI) and fat free mass index (FFMI), respectively.\\nPhase angle (PA) describes the relationship between the two vector components of impedance [reactance and resistance] of the human body to an alternating electric current. PA has been shown to provide a BIA prognostic index of morbidity and mortality [30].\\nBody composition was determined by body impedance analysis (B.I.A. Nutriguard- M, Data-Input, Frankfurt, Germany). We used gel-based electrodes specifically developed for BIA measurements - Bianostic AT (Data-Input GmbH). On the day of blood collection, patients underwent BIA measurement at approximately 30 minutes postdialysis. BIA electrodes were placed on the same body side used for anthropometric measurements. The multi-frequency technique was used. FFM was calculated by using the approach of Kyle et al. validated by dual-energy x-ray absorptiometry on 343 healthy adult subjects [29]:\\nFat mass and fat free mass were standardized by squared height (m2), and expressed in kg/m2 as fat mass index (FMI) and fat free mass index (FFMI), respectively.\\nPhase angle (PA) describes the relationship between the two vector components of impedance [reactance and resistance] of the human body to an alternating electric current. PA has been shown to provide a BIA prognostic index of morbidity and mortality [30].\\n Laboratory evaluation Blood samples were taken in a non-fasting state before a midweek hemodialysis session. CBC, creatinine, urea, albumin, transferrin and total cholesterol were measured by routine laboratory methods. IL-6 and leptin were measured in plasma samples using commercially available enzyme-linked immunosorbent assay (ELISA) kits (R&D System, Minneapolis, MN, USA) according to the manufacturer's protocol. The mean minimal detectable level for IL-6 was 0.7 pg/ml, and 7.8 pg/ml for leptin. Intra-assay and inter-assay coefficients of variation for IL-6 were 4.2% and 6.4% respectively, and for leptin - 3.3% and 5.4% respectively.\\nBlood samples were taken in a non-fasting state before a midweek hemodialysis session. CBC, creatinine, urea, albumin, transferrin and total cholesterol were measured by routine laboratory methods. IL-6 and leptin were measured in plasma samples using commercially available enzyme-linked immunosorbent assay (ELISA) kits (R&D System, Minneapolis, MN, USA) according to the manufacturer's protocol. The mean minimal detectable level for IL-6 was 0.7 pg/ml, and 7.8 pg/ml for leptin. Intra-assay and inter-assay coefficients of variation for IL-6 were 4.2% and 6.4% respectively, and for leptin - 3.3% and 5.4% respectively.\\n Statistical analysis Data are expressed as mean ± standard deviation (SD), median and interquartile range (Q1 to Q3) for variables that did not follow a normal distribution, or frequencies, as noted.\\nTo compare the means of continuous variables measured between sex-specific tertiles of leptin, one way ANOVA and analysis of covariance with adjustments (ANCOVA) were used. Categorical data are presented as percentages and were compared among groups by χ2 tests. Correlations between leptin and clinical and laboratory parameters were assessed using Spearman rank order correlation coefficients (because of the skewed distribution of leptin levels). Multivariate regression analysis was performed to obtain partial (adjusted) correlations (R2). Case-mix-adjusted models were controlled for age, gender, history of CV disease, presence or absence of diabetes mellitus, dialysis vintage and fat mass.\\nRepeated-measures analysis of variance was performed by using the MIXED model. Only patients with ≥ 2 study visits were included in the analyses. Base models were adjusted for age, sex, diabetes status, dialysis vintage, history of cardio-vascular diseases and fat mass. F Tests were used to assess the significance of the fixed effects, and P less than 0.05 was considered significant. To evaluate whether leptin influenced the trends in the various dependent variables, we included in each base model terms for individual \\\"leptin-by-time\\\" interactions.\\nSurvival analyses were performed using the Kaplan-Meier survival curve and the Cox proportional hazard model. The univariate and multivariate Cox regression analyses are presented as (HR; CI).\\nAll statistical analyses were performed using SPSS software, version 16.0 (SPSS Inc, Chicago, IL).\\nData are expressed as mean ± standard deviation (SD), median and interquartile range (Q1 to Q3) for variables that did not follow a normal distribution, or frequencies, as noted.\\nTo compare the means of continuous variables measured between sex-specific tertiles of leptin, one way ANOVA and analysis of covariance with adjustments (ANCOVA) were used. Categorical data are presented as percentages and were compared among groups by χ2 tests. Correlations between leptin and clinical and laboratory parameters were assessed using Spearman rank order correlation coefficients (because of the skewed distribution of leptin levels). Multivariate regression analysis was performed to obtain partial (adjusted) correlations (R2). Case-mix-adjusted models were controlled for age, gender, history of CV disease, presence or absence of diabetes mellitus, dialysis vintage and fat mass.\\nRepeated-measures analysis of variance was performed by using the MIXED model. Only patients with ≥ 2 study visits were included in the analyses. Base models were adjusted for age, sex, diabetes status, dialysis vintage, history of cardio-vascular diseases and fat mass. F Tests were used to assess the significance of the fixed effects, and P less than 0.05 was considered significant. To evaluate whether leptin influenced the trends in the various dependent variables, we included in each base model terms for individual \\\"leptin-by-time\\\" interactions.\\nSurvival analyses were performed using the Kaplan-Meier survival curve and the Cox proportional hazard model. The univariate and multivariate Cox regression analyses are presented as (HR; CI).\\nAll statistical analyses were performed using SPSS software, version 16.0 (SPSS Inc, Chicago, IL).\",\n \"This prospective observational study was approved by the Ethics Committee of Assaf Harofeh Medical Center (Zerifin, Affiliated to the Sackler Faculty of Medicine Tel Aviv University, Israel). Informed consent was obtained before any trial-related activities. Patients were eligible for entry when they had been on HD therapy for at least 3 months and were 18 years or older, with no clinically active cardio-vascular or infectious diseases on entry. We excluded patients with edema, pleural effusion or ascites at their initial assessment, as well as patients with malignant disease, liver cirrhosis, neuro-muscular diseases, amputations or any deformities of the body. Exclusion criteria at the entry of the study also included co-morbidity (auto-immune disease and/or acute infections) and/or medication (prednisone) that might interfere with plasma leptin concentrations. A flow chart of the study is presented in Figure 1. In total, 101 patients (64 men and 37 women) with a mean age of 64.6 ± 11.5 years, receiving maintenance hemodialysis treatment at our outpatient HD clinic, were included in the study. Of the patients studied, 52 were diabetic (all diabetic patients had type 2 diabetes). Study measurements were performed at baseline and at 6, 12, 18 and 24 months from enrollment. After the longitudinal measurements ended, we continued clinical observation on our cohort during 2 additional years. Thus, in total, the study period extended 35 ± 17 months. During this period, 33 patients (32.7%) died (the main causes of death were cardio-vascular [12 of 33 patients; 36.4%] and sepsis [12 of 33 patients; 36.4%]), 13 patients (12.9%) underwent kidney transplantation, 3 patients (3.0%) changed dialysis modality, and 10 patients (10.0%) transferred to other hemodialysis units. All patients underwent regular dialysis via their vascular access (81.2% of patients had arterio-venous fistula) 4-5 h three times per week at a blood flow rate of 250-300 ml/min. Bicarbonate dialysate (30 mEq/L) at a dialysis solution flow rate of 500 ml/min was used in all cases. All dialysis was performed with biocompatible dialyzer membrane with a surface area of 1.0-1.8 m2. The efficiency of the dialysis was assessed based on the delivered dose of dialysis (Kt/V urea) using a single-pool urea kinetic model (mean Kt/V was1.31 ± 0.23 in our population).\\nFlow diagram of the study.\\nInformation on vascular disease (cerebral vascular, peripheral vascular and heart disease) was obtained from a detailed medical history.\\nMost patients were required antihypertensive medications as well as other drugs commonly used in ESRD, such as phosphate and potassium binders, diuretics, and supplements of vitamins B, C, and D.\",\n \"A continuous 3-day dietary history (which included a dialysis day, a weekend day and a non-dialysis day) was recorded on a self-completed food diary. Then, dietary energy and protein intake were calculated and normalized for adjusted body weight (ABW) by the following formula [27]:\\nSBW - standard body weight was determined by the National Healthand Nutrition Examination Survey II (NHANES II) population medians for age, sex, frame size, and stature [27].\\nDietary protein intake was also estimated by the protein catabolic rate (PCR) calculation from the patient's urea generation rate by urea kinetics modeling [28]. Single-pool model urea kinetics was used to estimate the nPCR.\",\n \"BMI, triceps skinfold thickness (TSF), mid arm circumference (MAC) and calculated mid arm muscle circumference (MAMC) were measured as anthropometric variables. The BMI was calculated as dry weight in kilograms divided by the square of height in meters. TSF was measured with a conventional skinfold caliper using standard techniques. Mid arm circumference was measured with a plastic measuring tape. MAMC was estimated as follows:\",\n \"Body composition was determined by body impedance analysis (B.I.A. Nutriguard- M, Data-Input, Frankfurt, Germany). We used gel-based electrodes specifically developed for BIA measurements - Bianostic AT (Data-Input GmbH). On the day of blood collection, patients underwent BIA measurement at approximately 30 minutes postdialysis. BIA electrodes were placed on the same body side used for anthropometric measurements. The multi-frequency technique was used. FFM was calculated by using the approach of Kyle et al. validated by dual-energy x-ray absorptiometry on 343 healthy adult subjects [29]:\\nFat mass and fat free mass were standardized by squared height (m2), and expressed in kg/m2 as fat mass index (FMI) and fat free mass index (FFMI), respectively.\\nPhase angle (PA) describes the relationship between the two vector components of impedance [reactance and resistance] of the human body to an alternating electric current. PA has been shown to provide a BIA prognostic index of morbidity and mortality [30].\",\n \"Blood samples were taken in a non-fasting state before a midweek hemodialysis session. CBC, creatinine, urea, albumin, transferrin and total cholesterol were measured by routine laboratory methods. IL-6 and leptin were measured in plasma samples using commercially available enzyme-linked immunosorbent assay (ELISA) kits (R&D System, Minneapolis, MN, USA) according to the manufacturer's protocol. The mean minimal detectable level for IL-6 was 0.7 pg/ml, and 7.8 pg/ml for leptin. Intra-assay and inter-assay coefficients of variation for IL-6 were 4.2% and 6.4% respectively, and for leptin - 3.3% and 5.4% respectively.\",\n \"Data are expressed as mean ± standard deviation (SD), median and interquartile range (Q1 to Q3) for variables that did not follow a normal distribution, or frequencies, as noted.\\nTo compare the means of continuous variables measured between sex-specific tertiles of leptin, one way ANOVA and analysis of covariance with adjustments (ANCOVA) were used. Categorical data are presented as percentages and were compared among groups by χ2 tests. Correlations between leptin and clinical and laboratory parameters were assessed using Spearman rank order correlation coefficients (because of the skewed distribution of leptin levels). Multivariate regression analysis was performed to obtain partial (adjusted) correlations (R2). Case-mix-adjusted models were controlled for age, gender, history of CV disease, presence or absence of diabetes mellitus, dialysis vintage and fat mass.\\nRepeated-measures analysis of variance was performed by using the MIXED model. Only patients with ≥ 2 study visits were included in the analyses. Base models were adjusted for age, sex, diabetes status, dialysis vintage, history of cardio-vascular diseases and fat mass. F Tests were used to assess the significance of the fixed effects, and P less than 0.05 was considered significant. To evaluate whether leptin influenced the trends in the various dependent variables, we included in each base model terms for individual \\\"leptin-by-time\\\" interactions.\\nSurvival analyses were performed using the Kaplan-Meier survival curve and the Cox proportional hazard model. The univariate and multivariate Cox regression analyses are presented as (HR; CI).\\nAll statistical analyses were performed using SPSS software, version 16.0 (SPSS Inc, Chicago, IL).\",\n \" Cross-sectional associations The 101 prevalent HD patients participating in this study included 36.6% women; over half of the participants (51.5%) had diabetes mellitus (DM) and nearly the same proportion had a history of cardiovascular disease (46.9%), including myocardial infarction, coronary artery procedures such as angioplasty or surgery, previous cerebral-vascular accident, or peripheral vascular disease. Average age was 64.6 ± 11.5 years and median maintenance HD vintage was 31 months (Q1 to Q3, 15.5-51.5 months) at the study initiation. For HD patients at the start of the cohort, serum leptin averaged (mean ± SD) 37.3 ± 39.4 ng/ml (median, 16.5 ng/ml; Q1 to Q3, 5.60-71.4 ng/ml).\\nTo examine the relationship between different levels of serum leptin and relevant demographic, clinical and laboratory measures, serum leptin levels were divided into three equal gender specific tertiles (data not shown). Diabetes proportion and age distribution were similar across the three groups. No statistically significant differences were evident between groups in the use of medications that may affect inflammatory markers such as statins, aspirin, or angiotensin-converting enzyme inhibitors (data not shown). As expected females, tended to have higher leptin levels than males (p = 0.0001). Body composition parameters measured by anthropometry and bioelectric impedance analysis (BIA) including phase angle (PA) were incrementally greater across increasing leptin tertiles even after adjustments for baseline demographic and clinical parameters (age, DM status, dialysis vintage and cardio-vascular disease in the past). However, these associations were attenuated after including of fat mass in multivariate analyses. No significant differences in any of the biochemical markers of nutrition, normalized protein nitrogen appearance (nPNA), or actual energy or protein intake normalized to adjusted body weight were found between the HD patient groups according to leptin tertiles.\\nAt baseline, anthropometric measurements (body mass index [BMI], triceps skinfold thickness [TSF], mid arm circumference [MAC] and midarm circumference calculated [MAMC]) and BIA-derived parameters of body composition (fat mass index [FMI] and fat free mass index [FFMI]) correlated positively with serum leptin levels, in both unadjusted and adjusted (for age, DM status, dialysis vintage, and previous cardio-vascular disease) models. Thus, an increased level of leptin was associated with better nutritional status (Table 1). Associations between levels of serum leptin and the two laboratory nutritional markers (albumin and cholesterol) and PA derived by BIA were statistically significant, but these associations were attenuated after adjustments for demographic and clinical parameters including fat mass. No significant correlations were observed between serum leptin and nPNA or dietary energy (DEI) and protein intake (DPI) normalized to adjusted body weight in both sexes after adjustments for demographic and clinical parameters including fat mass.\\nUnadjusted and multivariate adjusted Spearman's correlation coefficients of baseline serum leptin and nutritional clinical and laboratory parameters in the study population at baseline\\nCorrelation coefficient values ≥ 0.25 appear in bold.\\na All case-mix-adjusted models include age, gender, DM status, dialysis vintage, CV disease in the past and fat mass.\\nAbbreviations: nPNA, normalized protein nitrogen appearance; IL-6, interleukin-6; BMI, body mass index; TSF, triceps skinfold thickness; MAC, mid-arm circumference; MAMC, mid-arm muscle circumference calculated; FMI, fat mass index; FFMI, fat-free mass index, DM, diabetes mellitus; CV, cardio-vascular.\\nThe 101 prevalent HD patients participating in this study included 36.6% women; over half of the participants (51.5%) had diabetes mellitus (DM) and nearly the same proportion had a history of cardiovascular disease (46.9%), including myocardial infarction, coronary artery procedures such as angioplasty or surgery, previous cerebral-vascular accident, or peripheral vascular disease. Average age was 64.6 ± 11.5 years and median maintenance HD vintage was 31 months (Q1 to Q3, 15.5-51.5 months) at the study initiation. For HD patients at the start of the cohort, serum leptin averaged (mean ± SD) 37.3 ± 39.4 ng/ml (median, 16.5 ng/ml; Q1 to Q3, 5.60-71.4 ng/ml).\\nTo examine the relationship between different levels of serum leptin and relevant demographic, clinical and laboratory measures, serum leptin levels were divided into three equal gender specific tertiles (data not shown). Diabetes proportion and age distribution were similar across the three groups. No statistically significant differences were evident between groups in the use of medications that may affect inflammatory markers such as statins, aspirin, or angiotensin-converting enzyme inhibitors (data not shown). As expected females, tended to have higher leptin levels than males (p = 0.0001). Body composition parameters measured by anthropometry and bioelectric impedance analysis (BIA) including phase angle (PA) were incrementally greater across increasing leptin tertiles even after adjustments for baseline demographic and clinical parameters (age, DM status, dialysis vintage and cardio-vascular disease in the past). However, these associations were attenuated after including of fat mass in multivariate analyses. No significant differences in any of the biochemical markers of nutrition, normalized protein nitrogen appearance (nPNA), or actual energy or protein intake normalized to adjusted body weight were found between the HD patient groups according to leptin tertiles.\\nAt baseline, anthropometric measurements (body mass index [BMI], triceps skinfold thickness [TSF], mid arm circumference [MAC] and midarm circumference calculated [MAMC]) and BIA-derived parameters of body composition (fat mass index [FMI] and fat free mass index [FFMI]) correlated positively with serum leptin levels, in both unadjusted and adjusted (for age, DM status, dialysis vintage, and previous cardio-vascular disease) models. Thus, an increased level of leptin was associated with better nutritional status (Table 1). Associations between levels of serum leptin and the two laboratory nutritional markers (albumin and cholesterol) and PA derived by BIA were statistically significant, but these associations were attenuated after adjustments for demographic and clinical parameters including fat mass. No significant correlations were observed between serum leptin and nPNA or dietary energy (DEI) and protein intake (DPI) normalized to adjusted body weight in both sexes after adjustments for demographic and clinical parameters including fat mass.\\nUnadjusted and multivariate adjusted Spearman's correlation coefficients of baseline serum leptin and nutritional clinical and laboratory parameters in the study population at baseline\\nCorrelation coefficient values ≥ 0.25 appear in bold.\\na All case-mix-adjusted models include age, gender, DM status, dialysis vintage, CV disease in the past and fat mass.\\nAbbreviations: nPNA, normalized protein nitrogen appearance; IL-6, interleukin-6; BMI, body mass index; TSF, triceps skinfold thickness; MAC, mid-arm circumference; MAMC, mid-arm muscle circumference calculated; FMI, fat mass index; FFMI, fat-free mass index, DM, diabetes mellitus; CV, cardio-vascular.\\n Longitudinal associationas Linear mixed models were used to study the effects of longitudinal leptin changes on changes in nutritional parameters (slopes) over 24 months including fixed parameters such as age, gender, diabetes status, dialysis vintage, previous cardio-vascular events, and fat mass (Table 2). No significant changes in dietary intake or in any of the biochemical nutritional markers over time were found. In contrast, a significant reduction in body composition parameters (both those measured by anthropometry [BMI, TSF and MAMC] and by BIA [FMI, FFMI, PA]) over time was observed. However, leptin did not modulate the changes in outcome variables over time in our cohort (leptin-by-time interactions were insignificant).\\nRegression coefficients with 95% Confidence Intervals for the effect of longitudinal leptin changes on nutritional parameters changes (slopes) during 24 months based on mixed-effects model with linear trends for variable and fixed parameters\\nNOTE: All nutritional variables presented in Table were modeled separately as dependent variables, whereas independent variables included fixed factors (such as age, gender, diabetes status, dialysis vintage, past cardio-vascular disease and fat mass) and leptin as continuous variable. Terms for individual \\\"leptin-by-time\\\" interactions were included in each model. The model takes into account every measurements of leptin and presented nutritional variables in each time point for each patient separately. Presented regression coefficients demonstrate changes in outcome variables with time (24 months).\\na P for trend for leptin-by-time interactions.\\nb FMI and BMI were adjusted only by age, gender, DM status, history of cardio-vascular disease and dialysis vintage.\\nAbbreviations: DEI, daily energy intake; DPI, daily protein intake; nPNA, normalized protein nitrogen appearance; IL-6, interleukin-6; EPO, erythropoietin; BMI, body mass index; TSF, triceps skinfold thickness; MAC, mid-arm circumference; MAMC, mid-arm muscle circumference calculated; FMI, fat mass index; FFMI, fat-free mass index; DM, diabetes mellitus; CV, cardio-vascular.\\nA mixed model including the fixed factors sex, age, DM status, dialysis vintage, history of CV disease and some of nutritional parameters predicted variability in leptin levels during the study presented in Table 3. We observed significant changes of leptin levels with time (linear estimate ± SE: -2.5010 ± 0.57 ng/ml/2y; p < 0.001). The effect of longitudinal changes of FMI on serum leptin variability over time was evident according to this model.\\nEffects of longitudinal changes of nutritional parameters on changes (slopes) of leptin during 24 months based on mixed-effects model with linear trends for variable and fixed parameters\\nNOTE: The Table represents a simple Mixed-Model with leptin as dependent variable, and presented nutritional parameters and fixed factors (age, gender, DM status, dialysis vintage and history of CV disease) as independent variables. The model takes into account every measurements of leptin and presented nutritional variables in each time point for each patient separately.\\nAbbreviations: DEI, daily energy intake; IL-6, interleukin-6; EPO, erythropoietin; FMI, fat mass index; DM, diabetes mellitus; CV, cardio-vascular.\\nOf interest, a significant reduction over time was associated with a more rapid decline in leptin levels in the highest leptin tertile in both unadjusted (p = 0.007 for leptin-by-time interaction) and fully adjusted (p = 0.047 for leptin-by-time interaction) models (Figure 2).\\nLeptin levels decline over time in the study population. Changes in estimated marginal means of serum leptin levels at baseline (month 0), month 6, month 12, month 18 and month 24 in the study population groups according to sex-specific tertiles* of baseline serum leptin: 1. Unadjusted model: P = 0.007 for leptin-by-time interaction; 2. Fully adjusted model (for age, gender, DM status, dialysis vintage, previous cardio-vascular morbidity and fat mass): P = 0.047 for leptin-by-time interaction. *Median (interquartile range) leptin (ng/ml) tertiles for men (n = 64) were 1.70 (1.2-2.4), 10.2 (7.6-13.8) and 38.3 (26.2-81.3) and for women (n = 37) were 15.8 (9.3-18.3), 68.0 (41.4-85.9) and 112.6 (109.2-116.0).\\nLinear mixed models were used to study the effects of longitudinal leptin changes on changes in nutritional parameters (slopes) over 24 months including fixed parameters such as age, gender, diabetes status, dialysis vintage, previous cardio-vascular events, and fat mass (Table 2). No significant changes in dietary intake or in any of the biochemical nutritional markers over time were found. In contrast, a significant reduction in body composition parameters (both those measured by anthropometry [BMI, TSF and MAMC] and by BIA [FMI, FFMI, PA]) over time was observed. However, leptin did not modulate the changes in outcome variables over time in our cohort (leptin-by-time interactions were insignificant).\\nRegression coefficients with 95% Confidence Intervals for the effect of longitudinal leptin changes on nutritional parameters changes (slopes) during 24 months based on mixed-effects model with linear trends for variable and fixed parameters\\nNOTE: All nutritional variables presented in Table were modeled separately as dependent variables, whereas independent variables included fixed factors (such as age, gender, diabetes status, dialysis vintage, past cardio-vascular disease and fat mass) and leptin as continuous variable. Terms for individual \\\"leptin-by-time\\\" interactions were included in each model. The model takes into account every measurements of leptin and presented nutritional variables in each time point for each patient separately. Presented regression coefficients demonstrate changes in outcome variables with time (24 months).\\na P for trend for leptin-by-time interactions.\\nb FMI and BMI were adjusted only by age, gender, DM status, history of cardio-vascular disease and dialysis vintage.\\nAbbreviations: DEI, daily energy intake; DPI, daily protein intake; nPNA, normalized protein nitrogen appearance; IL-6, interleukin-6; EPO, erythropoietin; BMI, body mass index; TSF, triceps skinfold thickness; MAC, mid-arm circumference; MAMC, mid-arm muscle circumference calculated; FMI, fat mass index; FFMI, fat-free mass index; DM, diabetes mellitus; CV, cardio-vascular.\\nA mixed model including the fixed factors sex, age, DM status, dialysis vintage, history of CV disease and some of nutritional parameters predicted variability in leptin levels during the study presented in Table 3. We observed significant changes of leptin levels with time (linear estimate ± SE: -2.5010 ± 0.57 ng/ml/2y; p < 0.001). The effect of longitudinal changes of FMI on serum leptin variability over time was evident according to this model.\\nEffects of longitudinal changes of nutritional parameters on changes (slopes) of leptin during 24 months based on mixed-effects model with linear trends for variable and fixed parameters\\nNOTE: The Table represents a simple Mixed-Model with leptin as dependent variable, and presented nutritional parameters and fixed factors (age, gender, DM status, dialysis vintage and history of CV disease) as independent variables. The model takes into account every measurements of leptin and presented nutritional variables in each time point for each patient separately.\\nAbbreviations: DEI, daily energy intake; IL-6, interleukin-6; EPO, erythropoietin; FMI, fat mass index; DM, diabetes mellitus; CV, cardio-vascular.\\nOf interest, a significant reduction over time was associated with a more rapid decline in leptin levels in the highest leptin tertile in both unadjusted (p = 0.007 for leptin-by-time interaction) and fully adjusted (p = 0.047 for leptin-by-time interaction) models (Figure 2).\\nLeptin levels decline over time in the study population. Changes in estimated marginal means of serum leptin levels at baseline (month 0), month 6, month 12, month 18 and month 24 in the study population groups according to sex-specific tertiles* of baseline serum leptin: 1. Unadjusted model: P = 0.007 for leptin-by-time interaction; 2. Fully adjusted model (for age, gender, DM status, dialysis vintage, previous cardio-vascular morbidity and fat mass): P = 0.047 for leptin-by-time interaction. *Median (interquartile range) leptin (ng/ml) tertiles for men (n = 64) were 1.70 (1.2-2.4), 10.2 (7.6-13.8) and 38.3 (26.2-81.3) and for women (n = 37) were 15.8 (9.3-18.3), 68.0 (41.4-85.9) and 112.6 (109.2-116.0).\\n Survival analysis During an average follow-up of 35 months (median, 40 months; Q1 to Q3, 17-52), 33 patients died: 11 (32%) in the low tertile group, 12 (35%) in the median tertile group and 10 (30%) in the high tertile group, with a median time to death of 25 months (Q1 to Q3, 14-40 months). Cumulative incidences of survival were unaffected by the baseline serum leptin levels (Figure 3). To further assess the possible association of baseline serum leptin level with cardio-vascular morbidity and mortality, cumulative hazards of all-cause death and first composite cardio-vascular event from Cox regression (after adjustment for baseline demographic parameters and fat mass) were calculated. First cardio-vascular event was defined as myocardial infarction (MI), requiring coronary artery procedures such as angioplasty or surgery, cerebral-vascular accident (CVA), or peripheral vascular disease (PVD), requiring angioplasty, bypass or amputation and diagnosed after the participant entered the study. No statistically significant differences in hazards between the different groups according leptin tertiles were observed. Additionally, no differences were found by Cox regression analysis in terms of survival probabilities from the analysis of all-cause mortality for each 10 ng/ml increase in baseline serum leptin (data not shown).\\nKaplan-Meier curves of surviving patients comparing subgroups of patients stratified by baseline serum leptin tertiles.\\nThus, although chronic HD patients with higher baseline leptin had better nutritional status as evidenced by their significantly more favorable body composition parameters at the start of the study, this disparity did not appear to widen over the 24 months of follow-up; thus, leptin levels did not predict body composition parameter changes over time. Moreover, no survival advantage of the high leptin group was found in our observational cohort.\\nDuring an average follow-up of 35 months (median, 40 months; Q1 to Q3, 17-52), 33 patients died: 11 (32%) in the low tertile group, 12 (35%) in the median tertile group and 10 (30%) in the high tertile group, with a median time to death of 25 months (Q1 to Q3, 14-40 months). Cumulative incidences of survival were unaffected by the baseline serum leptin levels (Figure 3). To further assess the possible association of baseline serum leptin level with cardio-vascular morbidity and mortality, cumulative hazards of all-cause death and first composite cardio-vascular event from Cox regression (after adjustment for baseline demographic parameters and fat mass) were calculated. First cardio-vascular event was defined as myocardial infarction (MI), requiring coronary artery procedures such as angioplasty or surgery, cerebral-vascular accident (CVA), or peripheral vascular disease (PVD), requiring angioplasty, bypass or amputation and diagnosed after the participant entered the study. No statistically significant differences in hazards between the different groups according leptin tertiles were observed. Additionally, no differences were found by Cox regression analysis in terms of survival probabilities from the analysis of all-cause mortality for each 10 ng/ml increase in baseline serum leptin (data not shown).\\nKaplan-Meier curves of surviving patients comparing subgroups of patients stratified by baseline serum leptin tertiles.\\nThus, although chronic HD patients with higher baseline leptin had better nutritional status as evidenced by their significantly more favorable body composition parameters at the start of the study, this disparity did not appear to widen over the 24 months of follow-up; thus, leptin levels did not predict body composition parameter changes over time. Moreover, no survival advantage of the high leptin group was found in our observational cohort.\",\n \"The 101 prevalent HD patients participating in this study included 36.6% women; over half of the participants (51.5%) had diabetes mellitus (DM) and nearly the same proportion had a history of cardiovascular disease (46.9%), including myocardial infarction, coronary artery procedures such as angioplasty or surgery, previous cerebral-vascular accident, or peripheral vascular disease. Average age was 64.6 ± 11.5 years and median maintenance HD vintage was 31 months (Q1 to Q3, 15.5-51.5 months) at the study initiation. For HD patients at the start of the cohort, serum leptin averaged (mean ± SD) 37.3 ± 39.4 ng/ml (median, 16.5 ng/ml; Q1 to Q3, 5.60-71.4 ng/ml).\\nTo examine the relationship between different levels of serum leptin and relevant demographic, clinical and laboratory measures, serum leptin levels were divided into three equal gender specific tertiles (data not shown). Diabetes proportion and age distribution were similar across the three groups. No statistically significant differences were evident between groups in the use of medications that may affect inflammatory markers such as statins, aspirin, or angiotensin-converting enzyme inhibitors (data not shown). As expected females, tended to have higher leptin levels than males (p = 0.0001). Body composition parameters measured by anthropometry and bioelectric impedance analysis (BIA) including phase angle (PA) were incrementally greater across increasing leptin tertiles even after adjustments for baseline demographic and clinical parameters (age, DM status, dialysis vintage and cardio-vascular disease in the past). However, these associations were attenuated after including of fat mass in multivariate analyses. No significant differences in any of the biochemical markers of nutrition, normalized protein nitrogen appearance (nPNA), or actual energy or protein intake normalized to adjusted body weight were found between the HD patient groups according to leptin tertiles.\\nAt baseline, anthropometric measurements (body mass index [BMI], triceps skinfold thickness [TSF], mid arm circumference [MAC] and midarm circumference calculated [MAMC]) and BIA-derived parameters of body composition (fat mass index [FMI] and fat free mass index [FFMI]) correlated positively with serum leptin levels, in both unadjusted and adjusted (for age, DM status, dialysis vintage, and previous cardio-vascular disease) models. Thus, an increased level of leptin was associated with better nutritional status (Table 1). Associations between levels of serum leptin and the two laboratory nutritional markers (albumin and cholesterol) and PA derived by BIA were statistically significant, but these associations were attenuated after adjustments for demographic and clinical parameters including fat mass. No significant correlations were observed between serum leptin and nPNA or dietary energy (DEI) and protein intake (DPI) normalized to adjusted body weight in both sexes after adjustments for demographic and clinical parameters including fat mass.\\nUnadjusted and multivariate adjusted Spearman's correlation coefficients of baseline serum leptin and nutritional clinical and laboratory parameters in the study population at baseline\\nCorrelation coefficient values ≥ 0.25 appear in bold.\\na All case-mix-adjusted models include age, gender, DM status, dialysis vintage, CV disease in the past and fat mass.\\nAbbreviations: nPNA, normalized protein nitrogen appearance; IL-6, interleukin-6; BMI, body mass index; TSF, triceps skinfold thickness; MAC, mid-arm circumference; MAMC, mid-arm muscle circumference calculated; FMI, fat mass index; FFMI, fat-free mass index, DM, diabetes mellitus; CV, cardio-vascular.\",\n \"Linear mixed models were used to study the effects of longitudinal leptin changes on changes in nutritional parameters (slopes) over 24 months including fixed parameters such as age, gender, diabetes status, dialysis vintage, previous cardio-vascular events, and fat mass (Table 2). No significant changes in dietary intake or in any of the biochemical nutritional markers over time were found. In contrast, a significant reduction in body composition parameters (both those measured by anthropometry [BMI, TSF and MAMC] and by BIA [FMI, FFMI, PA]) over time was observed. However, leptin did not modulate the changes in outcome variables over time in our cohort (leptin-by-time interactions were insignificant).\\nRegression coefficients with 95% Confidence Intervals for the effect of longitudinal leptin changes on nutritional parameters changes (slopes) during 24 months based on mixed-effects model with linear trends for variable and fixed parameters\\nNOTE: All nutritional variables presented in Table were modeled separately as dependent variables, whereas independent variables included fixed factors (such as age, gender, diabetes status, dialysis vintage, past cardio-vascular disease and fat mass) and leptin as continuous variable. Terms for individual \\\"leptin-by-time\\\" interactions were included in each model. The model takes into account every measurements of leptin and presented nutritional variables in each time point for each patient separately. Presented regression coefficients demonstrate changes in outcome variables with time (24 months).\\na P for trend for leptin-by-time interactions.\\nb FMI and BMI were adjusted only by age, gender, DM status, history of cardio-vascular disease and dialysis vintage.\\nAbbreviations: DEI, daily energy intake; DPI, daily protein intake; nPNA, normalized protein nitrogen appearance; IL-6, interleukin-6; EPO, erythropoietin; BMI, body mass index; TSF, triceps skinfold thickness; MAC, mid-arm circumference; MAMC, mid-arm muscle circumference calculated; FMI, fat mass index; FFMI, fat-free mass index; DM, diabetes mellitus; CV, cardio-vascular.\\nA mixed model including the fixed factors sex, age, DM status, dialysis vintage, history of CV disease and some of nutritional parameters predicted variability in leptin levels during the study presented in Table 3. We observed significant changes of leptin levels with time (linear estimate ± SE: -2.5010 ± 0.57 ng/ml/2y; p < 0.001). The effect of longitudinal changes of FMI on serum leptin variability over time was evident according to this model.\\nEffects of longitudinal changes of nutritional parameters on changes (slopes) of leptin during 24 months based on mixed-effects model with linear trends for variable and fixed parameters\\nNOTE: The Table represents a simple Mixed-Model with leptin as dependent variable, and presented nutritional parameters and fixed factors (age, gender, DM status, dialysis vintage and history of CV disease) as independent variables. The model takes into account every measurements of leptin and presented nutritional variables in each time point for each patient separately.\\nAbbreviations: DEI, daily energy intake; IL-6, interleukin-6; EPO, erythropoietin; FMI, fat mass index; DM, diabetes mellitus; CV, cardio-vascular.\\nOf interest, a significant reduction over time was associated with a more rapid decline in leptin levels in the highest leptin tertile in both unadjusted (p = 0.007 for leptin-by-time interaction) and fully adjusted (p = 0.047 for leptin-by-time interaction) models (Figure 2).\\nLeptin levels decline over time in the study population. Changes in estimated marginal means of serum leptin levels at baseline (month 0), month 6, month 12, month 18 and month 24 in the study population groups according to sex-specific tertiles* of baseline serum leptin: 1. Unadjusted model: P = 0.007 for leptin-by-time interaction; 2. Fully adjusted model (for age, gender, DM status, dialysis vintage, previous cardio-vascular morbidity and fat mass): P = 0.047 for leptin-by-time interaction. *Median (interquartile range) leptin (ng/ml) tertiles for men (n = 64) were 1.70 (1.2-2.4), 10.2 (7.6-13.8) and 38.3 (26.2-81.3) and for women (n = 37) were 15.8 (9.3-18.3), 68.0 (41.4-85.9) and 112.6 (109.2-116.0).\",\n \"During an average follow-up of 35 months (median, 40 months; Q1 to Q3, 17-52), 33 patients died: 11 (32%) in the low tertile group, 12 (35%) in the median tertile group and 10 (30%) in the high tertile group, with a median time to death of 25 months (Q1 to Q3, 14-40 months). Cumulative incidences of survival were unaffected by the baseline serum leptin levels (Figure 3). To further assess the possible association of baseline serum leptin level with cardio-vascular morbidity and mortality, cumulative hazards of all-cause death and first composite cardio-vascular event from Cox regression (after adjustment for baseline demographic parameters and fat mass) were calculated. First cardio-vascular event was defined as myocardial infarction (MI), requiring coronary artery procedures such as angioplasty or surgery, cerebral-vascular accident (CVA), or peripheral vascular disease (PVD), requiring angioplasty, bypass or amputation and diagnosed after the participant entered the study. No statistically significant differences in hazards between the different groups according leptin tertiles were observed. Additionally, no differences were found by Cox regression analysis in terms of survival probabilities from the analysis of all-cause mortality for each 10 ng/ml increase in baseline serum leptin (data not shown).\\nKaplan-Meier curves of surviving patients comparing subgroups of patients stratified by baseline serum leptin tertiles.\\nThus, although chronic HD patients with higher baseline leptin had better nutritional status as evidenced by their significantly more favorable body composition parameters at the start of the study, this disparity did not appear to widen over the 24 months of follow-up; thus, leptin levels did not predict body composition parameter changes over time. Moreover, no survival advantage of the high leptin group was found in our observational cohort.\",\n \"In the present study, we wished to determine whether changes of serum leptin levels are correlated with nutritional status over time in a cohort of prevalent hemodialysis patients. We observed that despite the strong and significant cross-sectional associations between serum leptin and body composition parameters at baseline, leptin levels did not reflect changes in nutritional status in our population.\\nOur study confirms several previous cross-sectional studies in which elevated serum leptin levels were demonstrated to be positively associated with several nutritional markers (laboratory or body composition parameters, mainly BMI and fat mass) in chronic HD patients [14,20,21,23-25]. HD patients treated with megestrole acetate (24), growth hormone [31] or high-calorie supplementation [32] demonstrated progressively increased serum leptin parallel to an improved nutritional state.\\nHowever, not all studies are consistent with positive associations between elevated levels of leptin and several nutritional parameters in ESRD patients. An inverse correlation was observed between leptin/fat mass and dietary intake, as well as with significantly lower lean tissue mass in chronic renal failure (23 patients) and ESRD patients receiving PD (24 patients) and HD (22 patients) therapy in study by Young et al [5]. In a cross-sectional study of 28 dialysis patients and 41 healthy control subjects, Johansen et al [20] showed that leptin levels were negatively correlated with albumin and PCR, suggesting a possible negative role of leptin in nutrition. Based on longitudinal observation, Stenvinkel et al [18] demonstrated that increases in serum leptin levels of 36 peritoneal dialysis patients were associated with a decrease in lean body mass. The discrepancies between these studies may be related to the population recruited, to their small sample size, and to their design; specifically, serum leptin levels were measured among patients with varying degrees of CRF, some of whom were undergoing maintenance hemodialysis and peritoneal dialysis, and in healthy controls [5,18,20]; many of these studies had a low number of study participants [5,18,20,23-25] and a majority of these studies were cross-sectional [5,20,23,25].\\nLeptin, a regulator of eating behavior, has been shown to be a major determinant of anorexia in uremic animals via signaling through the hypothalamic melanocortin system [15]. Subsequently, a recent experimental study by Cheung et al [33] showed that intraperitoneal administration of the melanocortin-4 receptor (MC4-R) antagonist NBI-12i stimulates food intake and weight gain in uremic mice. However, the effects of leptin in uremic patients are not unequivocal. Relevant to our findings, Bossola at al [17] demonstrated that serum leptin levels were not different in anorexic and in non-anorexic hemodialysis patients. Several other studies failed as well to demonstrate any role of hyperleptinemia on reduced dietary intake in ESRD patients receiving maintenance HD treatment [5,16,34]. Since hyperleptinemia is common in HD patients mainly due to impaired renal clearance [20], it seemed reasonable to assume that selective leptin resistance (such as occurs in obese humans [35]) is the responsible mechanism for attenuation of the negative effect of leptin on appetite, and accordingly, on dietary intake under conditions of continuous stimulation. The molecular mechanisms of leptin resistance, with a focus on the contribution of the intracellular tyrosine residues in the hypothalamic receptor of leptin (LEPRb) and their interaction with the negative feedback regulators, suppressor of cytokine signaling 1 and 3 (SOCS1 and SOCS3), are described in a recent study by Knobelspies et al [36].\\nThus, although the cross-group comparisons confirm that hyperleptinemia is associated with better body composition parameters, no unequivocal associations in terms of biochemical data or dietary intake with serum leptin levels are evident in HD patients according to the available literature. Our study confirms these data.\\nAnother finding of the present study was that 24 months of HD was associated with a significant reduction of plasma leptin levels, with a more rapid decline observed in patients with higher baseline leptin levels. Only limited information is available on the relationship between longitudinal serum leptin measurements and nutritional characteristics in chronic HD patients [18,32]. To the best of our knowledge, the present study is the first long-term longitudinal study to show the relationship between serum leptin level and nutritional status in ESRD patients receiving maintenance HD therapy. Several mechanisms may be involved in decrease of serum leptin levels over time in hemodialysis patients. These include, the increasing use of high flux and super-flux hemodialyzers [37], and/or use of alternative dialysis strategies such as hemodiafiltration [38]; recombinant human erythropoietin treatment (generally followed by a significant decline of leptinaemia in hemodialysed patients [39]); chronic inflammation (under the assumption that leptin, like albumin or transferrin, is a negative acute phase protein in chronic HD patients [22], although other studies [40] do not support this mechanism); metabolic acidosis, which can reduce the release of leptin from adipose tissue [41]; and finally, a decrease in fat mass leading to a decrease in the rate of leptin biosynthesis [14,18] (based on an insulin or a nutrient-sensing pathway regulating leptin gene expression in fat tissue [42]). Although patients in our facility were all treated using low flux hemodialysis, reduction of BMI and accordingly of fat mass over 24 months of the study may explain this longitudinal decrease of serum leptin levels in our cohort.\\nOf interest is the observation that adverse changes in body composition parameters occur over time, supporting the hypothesis that end-stage renal disease is associated with wasting as proposed in some [43,44] but not all [45] of the previous studies. The results of our study were consistent with those reported by Johansen et al [43] who did not find significant changes in any of the biochemical markers with time, but a significant reduction in phase angle over 1 year follow up was evident in prevalent HD patients. In parallel, together with reduced body composition parameters over time, we observed a longitudinal decline in serum leptin levels in our cohort. However, the lack of an association between serum leptin levels and future changes in body composition parameters (as exhibited by non-significant leptin-by-time interactions), suggests that the levels of leptin follow body composition (mainly fat mass) trajectory or its accompanying metabolic changes, rather than governing it.\\nFinally, serum leptin level did not appear as a useful predictor of all cause mortality or cardio-vascular morbidity in our study population during up to 4 years of observation. In this aspect, our findings support the results of earlier study by Tsai et al [46]. Although Scholze et al [26] showed a paradoxical reverse association between leptin level and clinical outcome in 71 chronic HD patients, our patients did not show such an association. The difference between our results and theirs might have been caused by differences in the cohorts. First, the population presented by Scholze et al [26] had a lower BMI at baseline than our patients, and no body composition parameters were measured. Further, the prevalence of diabetes mellitus was much greater in patients in the lower leptin group than in patients of the higher leptin group in the cohort of Scholze et al [26]. Indeed, HD patients with diabetes have a poor prognosis [4] that might be the cause of the lower survival rate in the lower leptin group in the above study [26]. While low levels of leptin may reflect a state of malnutrition in HD patients, proatherogenic effects of hyperleptinemia were linked to an adverse cardiovascular profile in general population [10,12]. We speculate that the lack of long-term benefits of hyperleptinemia in terms of survival of chronic HD patients could reflect the modulating influence of proatherogenic effects of high serum leptin levels. Finally, no relationships between serum leptin levels and previous cardio-vascular events were found by Diez et al [47] in a retrospective study on 82 dialysis patients. Zoccali et al [40] also did not show any difference in cardiovascular event-free survival in cohort of 192 hemodialysis patients after their stratification on the basis of the median value of plasma leptin.\\nSome limitations of the present study should be considered. First, this study is based on a relatively small sample size limiting detection of more subtle changes over time. Second, this study used only an observational approach, without manipulation of exposure factors, and therefore, no definitive cause-and-effect relationship can be derived for any of the risk factors analyzed. Dietary intake assessed by 3 day food records is another limitation of the study, as results can be subjective and incomplete, and can vary considerably from day to day as a result of dialysis treatment sessions and associated disturbances in food intake. Finally, significant circadian fluctuations of plasma leptin (regardless of the underlying degree of glucose tolerance, fasting, or diet) can certainly obscure some small but significant changes in plasma leptin [48] and may be a source of potential bias for longitudinal observation. Nevertheless, the present study has the advantage of providing long-term longitudinal data on the relationship between serum concentrations of leptin and nutritional parameters in prevalent HD patients.\",\n \"In conclusion, we found positive linear associations between serum leptin levels and body composition, whereas no significant relations in terms of biochemical data or dietary intake were evident in our cohort at baseline. Analyzing longitudinal data we concluded that any influence of serum leptin levels on nutritional status is limited to mirroring attained BMI and fat mass trajectory. Further, nutritional advantage at baseline of the study population with hyperleptinemia did not translate into long-term benefits in terms of survival. Taken together, our results suggest that in our cohort, leptin levels reflect fat mass depots, rather than independently contributing to uremic anorexia or modifying nutritional status and/or survival in chronic HD patients. These results are of particular importance if the use of subcutaneous injections of recombinant methionyl leptin [49] or leptin removal by super-flux polysulfone dialysers [37] is contemplated as a therapeutic intervention.\"\n]"},"all_sections_sec_types":{"kind":"list like","value":[null,"methods",null,null,null,null,null,null,null,null,null,null,null,null],"string":"[\n null,\n \"methods\",\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null\n]"},"keywords":{"kind":"list like","value":["Leptin","Nutrition","Bioimpedance","Inflammation","Hemodialysis"],"string":"[\n \"Leptin\",\n \"Nutrition\",\n \"Bioimpedance\",\n \"Inflammation\",\n \"Hemodialysis\"\n]"},"whole_article_text":{"kind":"string","value":"Background:\nIn recent years, the number of patients with end-stage renal disease (ESRD) has been increasing worldwide [1]. Depending in part upon the method used to evaluate nutritional status and the population studied, from 40 to 70 percent of patients with ESRD are malnourished [2,3] resulting in poor clinical outcomes [4]. Among the mechanisms responsible for malnutrition, leptin was believed to influence nutritional markers in patients with ESRD [5]. Leptin is a 16-kDa protein identified as the product of the obese gene; it is exclusively produced in adipocytes, and regulates food intake and energy expenditure in animal models [6]. Leptin decreases food intake by decreasing NPY (neuropeptide Y - one of the most potent stimulators of food intake) mRNA [7] and increasing alpha-MSH (alpha-melanocyte-stimulating hormone - an inhibitor of food intake) [8]. Besides linking adiposity and central nervous circuits to reduced appetite and enhanced energy expenditure in the general population [9], leptin has been shown to increase overall sympathetic nerve activity [10], facilitate glucose utilization and improve insulin sensitivity [11]. Furthermore, the prospective West of Scotland Coronary Prevention Study (WOSCOPS) reported that elevated leptin increases the relative risk of cardio-vascular disease in the general population independently of fat mass [12].\nIn general, serum leptin levels are significantly elevated in patients with renal failure, particularly when compared to age, gender and body mass index (BMI)-matched controls [13,14]. However, the role of hyperleptinemia in ESRD patients is somewhat unconventional. In contrast with its anorexogenic effects recognized in the general population [9] and even in experimental models of uremia (in subtotal nephrectomized and leptin receptor-deficient [db/db] mice) [15], leptin has not been reported to affect perceived appetite and nutrient intake in dialysis patients [16,17]. Although in some observational studies, increased serum leptin concentrations were observed in ESRD patients in parallel with loss of lean body mass [18,19] or with hypoalbuminemia and low protein intake [20], some others failed to find any correlation between hyperleptinemia and weight change [9] or lean mass [21] in this population. Moreover, several clinical studies suggested that leptin is a negative acute phase protein [22] and can serve as a marker of adequate nutritional status, rather than an appetite-reducing uremic toxin in hemodialysis patients [23-25]. Finally, the relationship between elevated serum leptin levels and clinical outcomes in ESRD has not been fully defined. In one small prospective cohort of hemodialysis patients, lower baseline serum leptin levels predicted mortality [26], but neither changes in leptin over time were measured, nor were leptin levels normalized to body fat mass in this study.\nThus, the influence of serum leptin levels on nutritional status and survival in chronic hemodialysis patients remained to be elucidated. In view of leptin's physiological role, information on effects of prolonged hyperleptinemia (independent of fat mass) on nutritional status of chronic hemodialysis patients, which may also impact on their survival, would be of interest. The aim of the present prospective longitudinal study was therefore to study longitudinal changes in serum leptin levels and to relate them to the changes in nutritional markers and survival in chronic hemodialysis patients.\n\nMethods:\n Patients This prospective observational study was approved by the Ethics Committee of Assaf Harofeh Medical Center (Zerifin, Affiliated to the Sackler Faculty of Medicine Tel Aviv University, Israel). Informed consent was obtained before any trial-related activities. Patients were eligible for entry when they had been on HD therapy for at least 3 months and were 18 years or older, with no clinically active cardio-vascular or infectious diseases on entry. We excluded patients with edema, pleural effusion or ascites at their initial assessment, as well as patients with malignant disease, liver cirrhosis, neuro-muscular diseases, amputations or any deformities of the body. Exclusion criteria at the entry of the study also included co-morbidity (auto-immune disease and/or acute infections) and/or medication (prednisone) that might interfere with plasma leptin concentrations. A flow chart of the study is presented in Figure 1. In total, 101 patients (64 men and 37 women) with a mean age of 64.6 ± 11.5 years, receiving maintenance hemodialysis treatment at our outpatient HD clinic, were included in the study. Of the patients studied, 52 were diabetic (all diabetic patients had type 2 diabetes). Study measurements were performed at baseline and at 6, 12, 18 and 24 months from enrollment. After the longitudinal measurements ended, we continued clinical observation on our cohort during 2 additional years. Thus, in total, the study period extended 35 ± 17 months. During this period, 33 patients (32.7%) died (the main causes of death were cardio-vascular [12 of 33 patients; 36.4%] and sepsis [12 of 33 patients; 36.4%]), 13 patients (12.9%) underwent kidney transplantation, 3 patients (3.0%) changed dialysis modality, and 10 patients (10.0%) transferred to other hemodialysis units. All patients underwent regular dialysis via their vascular access (81.2% of patients had arterio-venous fistula) 4-5 h three times per week at a blood flow rate of 250-300 ml/min. Bicarbonate dialysate (30 mEq/L) at a dialysis solution flow rate of 500 ml/min was used in all cases. All dialysis was performed with biocompatible dialyzer membrane with a surface area of 1.0-1.8 m2. The efficiency of the dialysis was assessed based on the delivered dose of dialysis (Kt/V urea) using a single-pool urea kinetic model (mean Kt/V was1.31 ± 0.23 in our population).\nFlow diagram of the study.\nInformation on vascular disease (cerebral vascular, peripheral vascular and heart disease) was obtained from a detailed medical history.\nMost patients were required antihypertensive medications as well as other drugs commonly used in ESRD, such as phosphate and potassium binders, diuretics, and supplements of vitamins B, C, and D.\nThis prospective observational study was approved by the Ethics Committee of Assaf Harofeh Medical Center (Zerifin, Affiliated to the Sackler Faculty of Medicine Tel Aviv University, Israel). Informed consent was obtained before any trial-related activities. Patients were eligible for entry when they had been on HD therapy for at least 3 months and were 18 years or older, with no clinically active cardio-vascular or infectious diseases on entry. We excluded patients with edema, pleural effusion or ascites at their initial assessment, as well as patients with malignant disease, liver cirrhosis, neuro-muscular diseases, amputations or any deformities of the body. Exclusion criteria at the entry of the study also included co-morbidity (auto-immune disease and/or acute infections) and/or medication (prednisone) that might interfere with plasma leptin concentrations. A flow chart of the study is presented in Figure 1. In total, 101 patients (64 men and 37 women) with a mean age of 64.6 ± 11.5 years, receiving maintenance hemodialysis treatment at our outpatient HD clinic, were included in the study. Of the patients studied, 52 were diabetic (all diabetic patients had type 2 diabetes). Study measurements were performed at baseline and at 6, 12, 18 and 24 months from enrollment. After the longitudinal measurements ended, we continued clinical observation on our cohort during 2 additional years. Thus, in total, the study period extended 35 ± 17 months. During this period, 33 patients (32.7%) died (the main causes of death were cardio-vascular [12 of 33 patients; 36.4%] and sepsis [12 of 33 patients; 36.4%]), 13 patients (12.9%) underwent kidney transplantation, 3 patients (3.0%) changed dialysis modality, and 10 patients (10.0%) transferred to other hemodialysis units. All patients underwent regular dialysis via their vascular access (81.2% of patients had arterio-venous fistula) 4-5 h three times per week at a blood flow rate of 250-300 ml/min. Bicarbonate dialysate (30 mEq/L) at a dialysis solution flow rate of 500 ml/min was used in all cases. All dialysis was performed with biocompatible dialyzer membrane with a surface area of 1.0-1.8 m2. The efficiency of the dialysis was assessed based on the delivered dose of dialysis (Kt/V urea) using a single-pool urea kinetic model (mean Kt/V was1.31 ± 0.23 in our population).\nFlow diagram of the study.\nInformation on vascular disease (cerebral vascular, peripheral vascular and heart disease) was obtained from a detailed medical history.\nMost patients were required antihypertensive medications as well as other drugs commonly used in ESRD, such as phosphate and potassium binders, diuretics, and supplements of vitamins B, C, and D.\n Dietary intake A continuous 3-day dietary history (which included a dialysis day, a weekend day and a non-dialysis day) was recorded on a self-completed food diary. Then, dietary energy and protein intake were calculated and normalized for adjusted body weight (ABW) by the following formula [27]:\nSBW - standard body weight was determined by the National Healthand Nutrition Examination Survey II (NHANES II) population medians for age, sex, frame size, and stature [27].\nDietary protein intake was also estimated by the protein catabolic rate (PCR) calculation from the patient's urea generation rate by urea kinetics modeling [28]. Single-pool model urea kinetics was used to estimate the nPCR.\nA continuous 3-day dietary history (which included a dialysis day, a weekend day and a non-dialysis day) was recorded on a self-completed food diary. Then, dietary energy and protein intake were calculated and normalized for adjusted body weight (ABW) by the following formula [27]:\nSBW - standard body weight was determined by the National Healthand Nutrition Examination Survey II (NHANES II) population medians for age, sex, frame size, and stature [27].\nDietary protein intake was also estimated by the protein catabolic rate (PCR) calculation from the patient's urea generation rate by urea kinetics modeling [28]. Single-pool model urea kinetics was used to estimate the nPCR.\n Anthropometric measurements BMI, triceps skinfold thickness (TSF), mid arm circumference (MAC) and calculated mid arm muscle circumference (MAMC) were measured as anthropometric variables. The BMI was calculated as dry weight in kilograms divided by the square of height in meters. TSF was measured with a conventional skinfold caliper using standard techniques. Mid arm circumference was measured with a plastic measuring tape. MAMC was estimated as follows:\nBMI, triceps skinfold thickness (TSF), mid arm circumference (MAC) and calculated mid arm muscle circumference (MAMC) were measured as anthropometric variables. The BMI was calculated as dry weight in kilograms divided by the square of height in meters. TSF was measured with a conventional skinfold caliper using standard techniques. Mid arm circumference was measured with a plastic measuring tape. MAMC was estimated as follows:\n Body composition analysis Body composition was determined by body impedance analysis (B.I.A. Nutriguard- M, Data-Input, Frankfurt, Germany). We used gel-based electrodes specifically developed for BIA measurements - Bianostic AT (Data-Input GmbH). On the day of blood collection, patients underwent BIA measurement at approximately 30 minutes postdialysis. BIA electrodes were placed on the same body side used for anthropometric measurements. The multi-frequency technique was used. FFM was calculated by using the approach of Kyle et al. validated by dual-energy x-ray absorptiometry on 343 healthy adult subjects [29]:\nFat mass and fat free mass were standardized by squared height (m2), and expressed in kg/m2 as fat mass index (FMI) and fat free mass index (FFMI), respectively.\nPhase angle (PA) describes the relationship between the two vector components of impedance [reactance and resistance] of the human body to an alternating electric current. PA has been shown to provide a BIA prognostic index of morbidity and mortality [30].\nBody composition was determined by body impedance analysis (B.I.A. Nutriguard- M, Data-Input, Frankfurt, Germany). We used gel-based electrodes specifically developed for BIA measurements - Bianostic AT (Data-Input GmbH). On the day of blood collection, patients underwent BIA measurement at approximately 30 minutes postdialysis. BIA electrodes were placed on the same body side used for anthropometric measurements. The multi-frequency technique was used. FFM was calculated by using the approach of Kyle et al. validated by dual-energy x-ray absorptiometry on 343 healthy adult subjects [29]:\nFat mass and fat free mass were standardized by squared height (m2), and expressed in kg/m2 as fat mass index (FMI) and fat free mass index (FFMI), respectively.\nPhase angle (PA) describes the relationship between the two vector components of impedance [reactance and resistance] of the human body to an alternating electric current. PA has been shown to provide a BIA prognostic index of morbidity and mortality [30].\n Laboratory evaluation Blood samples were taken in a non-fasting state before a midweek hemodialysis session. CBC, creatinine, urea, albumin, transferrin and total cholesterol were measured by routine laboratory methods. IL-6 and leptin were measured in plasma samples using commercially available enzyme-linked immunosorbent assay (ELISA) kits (R&D System, Minneapolis, MN, USA) according to the manufacturer's protocol. The mean minimal detectable level for IL-6 was 0.7 pg/ml, and 7.8 pg/ml for leptin. Intra-assay and inter-assay coefficients of variation for IL-6 were 4.2% and 6.4% respectively, and for leptin - 3.3% and 5.4% respectively.\nBlood samples were taken in a non-fasting state before a midweek hemodialysis session. CBC, creatinine, urea, albumin, transferrin and total cholesterol were measured by routine laboratory methods. IL-6 and leptin were measured in plasma samples using commercially available enzyme-linked immunosorbent assay (ELISA) kits (R&D System, Minneapolis, MN, USA) according to the manufacturer's protocol. The mean minimal detectable level for IL-6 was 0.7 pg/ml, and 7.8 pg/ml for leptin. Intra-assay and inter-assay coefficients of variation for IL-6 were 4.2% and 6.4% respectively, and for leptin - 3.3% and 5.4% respectively.\n Statistical analysis Data are expressed as mean ± standard deviation (SD), median and interquartile range (Q1 to Q3) for variables that did not follow a normal distribution, or frequencies, as noted.\nTo compare the means of continuous variables measured between sex-specific tertiles of leptin, one way ANOVA and analysis of covariance with adjustments (ANCOVA) were used. Categorical data are presented as percentages and were compared among groups by χ2 tests. Correlations between leptin and clinical and laboratory parameters were assessed using Spearman rank order correlation coefficients (because of the skewed distribution of leptin levels). Multivariate regression analysis was performed to obtain partial (adjusted) correlations (R2). Case-mix-adjusted models were controlled for age, gender, history of CV disease, presence or absence of diabetes mellitus, dialysis vintage and fat mass.\nRepeated-measures analysis of variance was performed by using the MIXED model. Only patients with ≥ 2 study visits were included in the analyses. Base models were adjusted for age, sex, diabetes status, dialysis vintage, history of cardio-vascular diseases and fat mass. F Tests were used to assess the significance of the fixed effects, and P less than 0.05 was considered significant. To evaluate whether leptin influenced the trends in the various dependent variables, we included in each base model terms for individual \"leptin-by-time\" interactions.\nSurvival analyses were performed using the Kaplan-Meier survival curve and the Cox proportional hazard model. The univariate and multivariate Cox regression analyses are presented as (HR; CI).\nAll statistical analyses were performed using SPSS software, version 16.0 (SPSS Inc, Chicago, IL).\nData are expressed as mean ± standard deviation (SD), median and interquartile range (Q1 to Q3) for variables that did not follow a normal distribution, or frequencies, as noted.\nTo compare the means of continuous variables measured between sex-specific tertiles of leptin, one way ANOVA and analysis of covariance with adjustments (ANCOVA) were used. Categorical data are presented as percentages and were compared among groups by χ2 tests. Correlations between leptin and clinical and laboratory parameters were assessed using Spearman rank order correlation coefficients (because of the skewed distribution of leptin levels). Multivariate regression analysis was performed to obtain partial (adjusted) correlations (R2). Case-mix-adjusted models were controlled for age, gender, history of CV disease, presence or absence of diabetes mellitus, dialysis vintage and fat mass.\nRepeated-measures analysis of variance was performed by using the MIXED model. Only patients with ≥ 2 study visits were included in the analyses. Base models were adjusted for age, sex, diabetes status, dialysis vintage, history of cardio-vascular diseases and fat mass. F Tests were used to assess the significance of the fixed effects, and P less than 0.05 was considered significant. To evaluate whether leptin influenced the trends in the various dependent variables, we included in each base model terms for individual \"leptin-by-time\" interactions.\nSurvival analyses were performed using the Kaplan-Meier survival curve and the Cox proportional hazard model. The univariate and multivariate Cox regression analyses are presented as (HR; CI).\nAll statistical analyses were performed using SPSS software, version 16.0 (SPSS Inc, Chicago, IL).\n\nPatients:\nThis prospective observational study was approved by the Ethics Committee of Assaf Harofeh Medical Center (Zerifin, Affiliated to the Sackler Faculty of Medicine Tel Aviv University, Israel). Informed consent was obtained before any trial-related activities. Patients were eligible for entry when they had been on HD therapy for at least 3 months and were 18 years or older, with no clinically active cardio-vascular or infectious diseases on entry. We excluded patients with edema, pleural effusion or ascites at their initial assessment, as well as patients with malignant disease, liver cirrhosis, neuro-muscular diseases, amputations or any deformities of the body. Exclusion criteria at the entry of the study also included co-morbidity (auto-immune disease and/or acute infections) and/or medication (prednisone) that might interfere with plasma leptin concentrations. A flow chart of the study is presented in Figure 1. In total, 101 patients (64 men and 37 women) with a mean age of 64.6 ± 11.5 years, receiving maintenance hemodialysis treatment at our outpatient HD clinic, were included in the study. Of the patients studied, 52 were diabetic (all diabetic patients had type 2 diabetes). Study measurements were performed at baseline and at 6, 12, 18 and 24 months from enrollment. After the longitudinal measurements ended, we continued clinical observation on our cohort during 2 additional years. Thus, in total, the study period extended 35 ± 17 months. During this period, 33 patients (32.7%) died (the main causes of death were cardio-vascular [12 of 33 patients; 36.4%] and sepsis [12 of 33 patients; 36.4%]), 13 patients (12.9%) underwent kidney transplantation, 3 patients (3.0%) changed dialysis modality, and 10 patients (10.0%) transferred to other hemodialysis units. All patients underwent regular dialysis via their vascular access (81.2% of patients had arterio-venous fistula) 4-5 h three times per week at a blood flow rate of 250-300 ml/min. Bicarbonate dialysate (30 mEq/L) at a dialysis solution flow rate of 500 ml/min was used in all cases. All dialysis was performed with biocompatible dialyzer membrane with a surface area of 1.0-1.8 m2. The efficiency of the dialysis was assessed based on the delivered dose of dialysis (Kt/V urea) using a single-pool urea kinetic model (mean Kt/V was1.31 ± 0.23 in our population).\nFlow diagram of the study.\nInformation on vascular disease (cerebral vascular, peripheral vascular and heart disease) was obtained from a detailed medical history.\nMost patients were required antihypertensive medications as well as other drugs commonly used in ESRD, such as phosphate and potassium binders, diuretics, and supplements of vitamins B, C, and D.\n\nDietary intake:\nA continuous 3-day dietary history (which included a dialysis day, a weekend day and a non-dialysis day) was recorded on a self-completed food diary. Then, dietary energy and protein intake were calculated and normalized for adjusted body weight (ABW) by the following formula [27]:\nSBW - standard body weight was determined by the National Healthand Nutrition Examination Survey II (NHANES II) population medians for age, sex, frame size, and stature [27].\nDietary protein intake was also estimated by the protein catabolic rate (PCR) calculation from the patient's urea generation rate by urea kinetics modeling [28]. Single-pool model urea kinetics was used to estimate the nPCR.\n\nAnthropometric measurements:\nBMI, triceps skinfold thickness (TSF), mid arm circumference (MAC) and calculated mid arm muscle circumference (MAMC) were measured as anthropometric variables. The BMI was calculated as dry weight in kilograms divided by the square of height in meters. TSF was measured with a conventional skinfold caliper using standard techniques. Mid arm circumference was measured with a plastic measuring tape. MAMC was estimated as follows:\n\nBody composition analysis:\nBody composition was determined by body impedance analysis (B.I.A. Nutriguard- M, Data-Input, Frankfurt, Germany). We used gel-based electrodes specifically developed for BIA measurements - Bianostic AT (Data-Input GmbH). On the day of blood collection, patients underwent BIA measurement at approximately 30 minutes postdialysis. BIA electrodes were placed on the same body side used for anthropometric measurements. The multi-frequency technique was used. FFM was calculated by using the approach of Kyle et al. validated by dual-energy x-ray absorptiometry on 343 healthy adult subjects [29]:\nFat mass and fat free mass were standardized by squared height (m2), and expressed in kg/m2 as fat mass index (FMI) and fat free mass index (FFMI), respectively.\nPhase angle (PA) describes the relationship between the two vector components of impedance [reactance and resistance] of the human body to an alternating electric current. PA has been shown to provide a BIA prognostic index of morbidity and mortality [30].\n\nLaboratory evaluation:\nBlood samples were taken in a non-fasting state before a midweek hemodialysis session. CBC, creatinine, urea, albumin, transferrin and total cholesterol were measured by routine laboratory methods. IL-6 and leptin were measured in plasma samples using commercially available enzyme-linked immunosorbent assay (ELISA) kits (R&D System, Minneapolis, MN, USA) according to the manufacturer's protocol. The mean minimal detectable level for IL-6 was 0.7 pg/ml, and 7.8 pg/ml for leptin. Intra-assay and inter-assay coefficients of variation for IL-6 were 4.2% and 6.4% respectively, and for leptin - 3.3% and 5.4% respectively.\n\nStatistical analysis:\nData are expressed as mean ± standard deviation (SD), median and interquartile range (Q1 to Q3) for variables that did not follow a normal distribution, or frequencies, as noted.\nTo compare the means of continuous variables measured between sex-specific tertiles of leptin, one way ANOVA and analysis of covariance with adjustments (ANCOVA) were used. Categorical data are presented as percentages and were compared among groups by χ2 tests. Correlations between leptin and clinical and laboratory parameters were assessed using Spearman rank order correlation coefficients (because of the skewed distribution of leptin levels). Multivariate regression analysis was performed to obtain partial (adjusted) correlations (R2). Case-mix-adjusted models were controlled for age, gender, history of CV disease, presence or absence of diabetes mellitus, dialysis vintage and fat mass.\nRepeated-measures analysis of variance was performed by using the MIXED model. Only patients with ≥ 2 study visits were included in the analyses. Base models were adjusted for age, sex, diabetes status, dialysis vintage, history of cardio-vascular diseases and fat mass. F Tests were used to assess the significance of the fixed effects, and P less than 0.05 was considered significant. To evaluate whether leptin influenced the trends in the various dependent variables, we included in each base model terms for individual \"leptin-by-time\" interactions.\nSurvival analyses were performed using the Kaplan-Meier survival curve and the Cox proportional hazard model. The univariate and multivariate Cox regression analyses are presented as (HR; CI).\nAll statistical analyses were performed using SPSS software, version 16.0 (SPSS Inc, Chicago, IL).\n\nResults:\n Cross-sectional associations The 101 prevalent HD patients participating in this study included 36.6% women; over half of the participants (51.5%) had diabetes mellitus (DM) and nearly the same proportion had a history of cardiovascular disease (46.9%), including myocardial infarction, coronary artery procedures such as angioplasty or surgery, previous cerebral-vascular accident, or peripheral vascular disease. Average age was 64.6 ± 11.5 years and median maintenance HD vintage was 31 months (Q1 to Q3, 15.5-51.5 months) at the study initiation. For HD patients at the start of the cohort, serum leptin averaged (mean ± SD) 37.3 ± 39.4 ng/ml (median, 16.5 ng/ml; Q1 to Q3, 5.60-71.4 ng/ml).\nTo examine the relationship between different levels of serum leptin and relevant demographic, clinical and laboratory measures, serum leptin levels were divided into three equal gender specific tertiles (data not shown). Diabetes proportion and age distribution were similar across the three groups. No statistically significant differences were evident between groups in the use of medications that may affect inflammatory markers such as statins, aspirin, or angiotensin-converting enzyme inhibitors (data not shown). As expected females, tended to have higher leptin levels than males (p = 0.0001). Body composition parameters measured by anthropometry and bioelectric impedance analysis (BIA) including phase angle (PA) were incrementally greater across increasing leptin tertiles even after adjustments for baseline demographic and clinical parameters (age, DM status, dialysis vintage and cardio-vascular disease in the past). However, these associations were attenuated after including of fat mass in multivariate analyses. No significant differences in any of the biochemical markers of nutrition, normalized protein nitrogen appearance (nPNA), or actual energy or protein intake normalized to adjusted body weight were found between the HD patient groups according to leptin tertiles.\nAt baseline, anthropometric measurements (body mass index [BMI], triceps skinfold thickness [TSF], mid arm circumference [MAC] and midarm circumference calculated [MAMC]) and BIA-derived parameters of body composition (fat mass index [FMI] and fat free mass index [FFMI]) correlated positively with serum leptin levels, in both unadjusted and adjusted (for age, DM status, dialysis vintage, and previous cardio-vascular disease) models. Thus, an increased level of leptin was associated with better nutritional status (Table 1). Associations between levels of serum leptin and the two laboratory nutritional markers (albumin and cholesterol) and PA derived by BIA were statistically significant, but these associations were attenuated after adjustments for demographic and clinical parameters including fat mass. No significant correlations were observed between serum leptin and nPNA or dietary energy (DEI) and protein intake (DPI) normalized to adjusted body weight in both sexes after adjustments for demographic and clinical parameters including fat mass.\nUnadjusted and multivariate adjusted Spearman's correlation coefficients of baseline serum leptin and nutritional clinical and laboratory parameters in the study population at baseline\nCorrelation coefficient values ≥ 0.25 appear in bold.\na All case-mix-adjusted models include age, gender, DM status, dialysis vintage, CV disease in the past and fat mass.\nAbbreviations: nPNA, normalized protein nitrogen appearance; IL-6, interleukin-6; BMI, body mass index; TSF, triceps skinfold thickness; MAC, mid-arm circumference; MAMC, mid-arm muscle circumference calculated; FMI, fat mass index; FFMI, fat-free mass index, DM, diabetes mellitus; CV, cardio-vascular.\nThe 101 prevalent HD patients participating in this study included 36.6% women; over half of the participants (51.5%) had diabetes mellitus (DM) and nearly the same proportion had a history of cardiovascular disease (46.9%), including myocardial infarction, coronary artery procedures such as angioplasty or surgery, previous cerebral-vascular accident, or peripheral vascular disease. Average age was 64.6 ± 11.5 years and median maintenance HD vintage was 31 months (Q1 to Q3, 15.5-51.5 months) at the study initiation. For HD patients at the start of the cohort, serum leptin averaged (mean ± SD) 37.3 ± 39.4 ng/ml (median, 16.5 ng/ml; Q1 to Q3, 5.60-71.4 ng/ml).\nTo examine the relationship between different levels of serum leptin and relevant demographic, clinical and laboratory measures, serum leptin levels were divided into three equal gender specific tertiles (data not shown). Diabetes proportion and age distribution were similar across the three groups. No statistically significant differences were evident between groups in the use of medications that may affect inflammatory markers such as statins, aspirin, or angiotensin-converting enzyme inhibitors (data not shown). As expected females, tended to have higher leptin levels than males (p = 0.0001). Body composition parameters measured by anthropometry and bioelectric impedance analysis (BIA) including phase angle (PA) were incrementally greater across increasing leptin tertiles even after adjustments for baseline demographic and clinical parameters (age, DM status, dialysis vintage and cardio-vascular disease in the past). However, these associations were attenuated after including of fat mass in multivariate analyses. No significant differences in any of the biochemical markers of nutrition, normalized protein nitrogen appearance (nPNA), or actual energy or protein intake normalized to adjusted body weight were found between the HD patient groups according to leptin tertiles.\nAt baseline, anthropometric measurements (body mass index [BMI], triceps skinfold thickness [TSF], mid arm circumference [MAC] and midarm circumference calculated [MAMC]) and BIA-derived parameters of body composition (fat mass index [FMI] and fat free mass index [FFMI]) correlated positively with serum leptin levels, in both unadjusted and adjusted (for age, DM status, dialysis vintage, and previous cardio-vascular disease) models. Thus, an increased level of leptin was associated with better nutritional status (Table 1). Associations between levels of serum leptin and the two laboratory nutritional markers (albumin and cholesterol) and PA derived by BIA were statistically significant, but these associations were attenuated after adjustments for demographic and clinical parameters including fat mass. No significant correlations were observed between serum leptin and nPNA or dietary energy (DEI) and protein intake (DPI) normalized to adjusted body weight in both sexes after adjustments for demographic and clinical parameters including fat mass.\nUnadjusted and multivariate adjusted Spearman's correlation coefficients of baseline serum leptin and nutritional clinical and laboratory parameters in the study population at baseline\nCorrelation coefficient values ≥ 0.25 appear in bold.\na All case-mix-adjusted models include age, gender, DM status, dialysis vintage, CV disease in the past and fat mass.\nAbbreviations: nPNA, normalized protein nitrogen appearance; IL-6, interleukin-6; BMI, body mass index; TSF, triceps skinfold thickness; MAC, mid-arm circumference; MAMC, mid-arm muscle circumference calculated; FMI, fat mass index; FFMI, fat-free mass index, DM, diabetes mellitus; CV, cardio-vascular.\n Longitudinal associationas Linear mixed models were used to study the effects of longitudinal leptin changes on changes in nutritional parameters (slopes) over 24 months including fixed parameters such as age, gender, diabetes status, dialysis vintage, previous cardio-vascular events, and fat mass (Table 2). No significant changes in dietary intake or in any of the biochemical nutritional markers over time were found. In contrast, a significant reduction in body composition parameters (both those measured by anthropometry [BMI, TSF and MAMC] and by BIA [FMI, FFMI, PA]) over time was observed. However, leptin did not modulate the changes in outcome variables over time in our cohort (leptin-by-time interactions were insignificant).\nRegression coefficients with 95% Confidence Intervals for the effect of longitudinal leptin changes on nutritional parameters changes (slopes) during 24 months based on mixed-effects model with linear trends for variable and fixed parameters\nNOTE: All nutritional variables presented in Table were modeled separately as dependent variables, whereas independent variables included fixed factors (such as age, gender, diabetes status, dialysis vintage, past cardio-vascular disease and fat mass) and leptin as continuous variable. Terms for individual \"leptin-by-time\" interactions were included in each model. The model takes into account every measurements of leptin and presented nutritional variables in each time point for each patient separately. Presented regression coefficients demonstrate changes in outcome variables with time (24 months).\na P for trend for leptin-by-time interactions.\nb FMI and BMI were adjusted only by age, gender, DM status, history of cardio-vascular disease and dialysis vintage.\nAbbreviations: DEI, daily energy intake; DPI, daily protein intake; nPNA, normalized protein nitrogen appearance; IL-6, interleukin-6; EPO, erythropoietin; BMI, body mass index; TSF, triceps skinfold thickness; MAC, mid-arm circumference; MAMC, mid-arm muscle circumference calculated; FMI, fat mass index; FFMI, fat-free mass index; DM, diabetes mellitus; CV, cardio-vascular.\nA mixed model including the fixed factors sex, age, DM status, dialysis vintage, history of CV disease and some of nutritional parameters predicted variability in leptin levels during the study presented in Table 3. We observed significant changes of leptin levels with time (linear estimate ± SE: -2.5010 ± 0.57 ng/ml/2y; p < 0.001). The effect of longitudinal changes of FMI on serum leptin variability over time was evident according to this model.\nEffects of longitudinal changes of nutritional parameters on changes (slopes) of leptin during 24 months based on mixed-effects model with linear trends for variable and fixed parameters\nNOTE: The Table represents a simple Mixed-Model with leptin as dependent variable, and presented nutritional parameters and fixed factors (age, gender, DM status, dialysis vintage and history of CV disease) as independent variables. The model takes into account every measurements of leptin and presented nutritional variables in each time point for each patient separately.\nAbbreviations: DEI, daily energy intake; IL-6, interleukin-6; EPO, erythropoietin; FMI, fat mass index; DM, diabetes mellitus; CV, cardio-vascular.\nOf interest, a significant reduction over time was associated with a more rapid decline in leptin levels in the highest leptin tertile in both unadjusted (p = 0.007 for leptin-by-time interaction) and fully adjusted (p = 0.047 for leptin-by-time interaction) models (Figure 2).\nLeptin levels decline over time in the study population. Changes in estimated marginal means of serum leptin levels at baseline (month 0), month 6, month 12, month 18 and month 24 in the study population groups according to sex-specific tertiles* of baseline serum leptin: 1. Unadjusted model: P = 0.007 for leptin-by-time interaction; 2. Fully adjusted model (for age, gender, DM status, dialysis vintage, previous cardio-vascular morbidity and fat mass): P = 0.047 for leptin-by-time interaction. *Median (interquartile range) leptin (ng/ml) tertiles for men (n = 64) were 1.70 (1.2-2.4), 10.2 (7.6-13.8) and 38.3 (26.2-81.3) and for women (n = 37) were 15.8 (9.3-18.3), 68.0 (41.4-85.9) and 112.6 (109.2-116.0).\nLinear mixed models were used to study the effects of longitudinal leptin changes on changes in nutritional parameters (slopes) over 24 months including fixed parameters such as age, gender, diabetes status, dialysis vintage, previous cardio-vascular events, and fat mass (Table 2). No significant changes in dietary intake or in any of the biochemical nutritional markers over time were found. In contrast, a significant reduction in body composition parameters (both those measured by anthropometry [BMI, TSF and MAMC] and by BIA [FMI, FFMI, PA]) over time was observed. However, leptin did not modulate the changes in outcome variables over time in our cohort (leptin-by-time interactions were insignificant).\nRegression coefficients with 95% Confidence Intervals for the effect of longitudinal leptin changes on nutritional parameters changes (slopes) during 24 months based on mixed-effects model with linear trends for variable and fixed parameters\nNOTE: All nutritional variables presented in Table were modeled separately as dependent variables, whereas independent variables included fixed factors (such as age, gender, diabetes status, dialysis vintage, past cardio-vascular disease and fat mass) and leptin as continuous variable. Terms for individual \"leptin-by-time\" interactions were included in each model. The model takes into account every measurements of leptin and presented nutritional variables in each time point for each patient separately. Presented regression coefficients demonstrate changes in outcome variables with time (24 months).\na P for trend for leptin-by-time interactions.\nb FMI and BMI were adjusted only by age, gender, DM status, history of cardio-vascular disease and dialysis vintage.\nAbbreviations: DEI, daily energy intake; DPI, daily protein intake; nPNA, normalized protein nitrogen appearance; IL-6, interleukin-6; EPO, erythropoietin; BMI, body mass index; TSF, triceps skinfold thickness; MAC, mid-arm circumference; MAMC, mid-arm muscle circumference calculated; FMI, fat mass index; FFMI, fat-free mass index; DM, diabetes mellitus; CV, cardio-vascular.\nA mixed model including the fixed factors sex, age, DM status, dialysis vintage, history of CV disease and some of nutritional parameters predicted variability in leptin levels during the study presented in Table 3. We observed significant changes of leptin levels with time (linear estimate ± SE: -2.5010 ± 0.57 ng/ml/2y; p < 0.001). The effect of longitudinal changes of FMI on serum leptin variability over time was evident according to this model.\nEffects of longitudinal changes of nutritional parameters on changes (slopes) of leptin during 24 months based on mixed-effects model with linear trends for variable and fixed parameters\nNOTE: The Table represents a simple Mixed-Model with leptin as dependent variable, and presented nutritional parameters and fixed factors (age, gender, DM status, dialysis vintage and history of CV disease) as independent variables. The model takes into account every measurements of leptin and presented nutritional variables in each time point for each patient separately.\nAbbreviations: DEI, daily energy intake; IL-6, interleukin-6; EPO, erythropoietin; FMI, fat mass index; DM, diabetes mellitus; CV, cardio-vascular.\nOf interest, a significant reduction over time was associated with a more rapid decline in leptin levels in the highest leptin tertile in both unadjusted (p = 0.007 for leptin-by-time interaction) and fully adjusted (p = 0.047 for leptin-by-time interaction) models (Figure 2).\nLeptin levels decline over time in the study population. Changes in estimated marginal means of serum leptin levels at baseline (month 0), month 6, month 12, month 18 and month 24 in the study population groups according to sex-specific tertiles* of baseline serum leptin: 1. Unadjusted model: P = 0.007 for leptin-by-time interaction; 2. Fully adjusted model (for age, gender, DM status, dialysis vintage, previous cardio-vascular morbidity and fat mass): P = 0.047 for leptin-by-time interaction. *Median (interquartile range) leptin (ng/ml) tertiles for men (n = 64) were 1.70 (1.2-2.4), 10.2 (7.6-13.8) and 38.3 (26.2-81.3) and for women (n = 37) were 15.8 (9.3-18.3), 68.0 (41.4-85.9) and 112.6 (109.2-116.0).\n Survival analysis During an average follow-up of 35 months (median, 40 months; Q1 to Q3, 17-52), 33 patients died: 11 (32%) in the low tertile group, 12 (35%) in the median tertile group and 10 (30%) in the high tertile group, with a median time to death of 25 months (Q1 to Q3, 14-40 months). Cumulative incidences of survival were unaffected by the baseline serum leptin levels (Figure 3). To further assess the possible association of baseline serum leptin level with cardio-vascular morbidity and mortality, cumulative hazards of all-cause death and first composite cardio-vascular event from Cox regression (after adjustment for baseline demographic parameters and fat mass) were calculated. First cardio-vascular event was defined as myocardial infarction (MI), requiring coronary artery procedures such as angioplasty or surgery, cerebral-vascular accident (CVA), or peripheral vascular disease (PVD), requiring angioplasty, bypass or amputation and diagnosed after the participant entered the study. No statistically significant differences in hazards between the different groups according leptin tertiles were observed. Additionally, no differences were found by Cox regression analysis in terms of survival probabilities from the analysis of all-cause mortality for each 10 ng/ml increase in baseline serum leptin (data not shown).\nKaplan-Meier curves of surviving patients comparing subgroups of patients stratified by baseline serum leptin tertiles.\nThus, although chronic HD patients with higher baseline leptin had better nutritional status as evidenced by their significantly more favorable body composition parameters at the start of the study, this disparity did not appear to widen over the 24 months of follow-up; thus, leptin levels did not predict body composition parameter changes over time. Moreover, no survival advantage of the high leptin group was found in our observational cohort.\nDuring an average follow-up of 35 months (median, 40 months; Q1 to Q3, 17-52), 33 patients died: 11 (32%) in the low tertile group, 12 (35%) in the median tertile group and 10 (30%) in the high tertile group, with a median time to death of 25 months (Q1 to Q3, 14-40 months). Cumulative incidences of survival were unaffected by the baseline serum leptin levels (Figure 3). To further assess the possible association of baseline serum leptin level with cardio-vascular morbidity and mortality, cumulative hazards of all-cause death and first composite cardio-vascular event from Cox regression (after adjustment for baseline demographic parameters and fat mass) were calculated. First cardio-vascular event was defined as myocardial infarction (MI), requiring coronary artery procedures such as angioplasty or surgery, cerebral-vascular accident (CVA), or peripheral vascular disease (PVD), requiring angioplasty, bypass or amputation and diagnosed after the participant entered the study. No statistically significant differences in hazards between the different groups according leptin tertiles were observed. Additionally, no differences were found by Cox regression analysis in terms of survival probabilities from the analysis of all-cause mortality for each 10 ng/ml increase in baseline serum leptin (data not shown).\nKaplan-Meier curves of surviving patients comparing subgroups of patients stratified by baseline serum leptin tertiles.\nThus, although chronic HD patients with higher baseline leptin had better nutritional status as evidenced by their significantly more favorable body composition parameters at the start of the study, this disparity did not appear to widen over the 24 months of follow-up; thus, leptin levels did not predict body composition parameter changes over time. Moreover, no survival advantage of the high leptin group was found in our observational cohort.\n\nCross-sectional associations:\nThe 101 prevalent HD patients participating in this study included 36.6% women; over half of the participants (51.5%) had diabetes mellitus (DM) and nearly the same proportion had a history of cardiovascular disease (46.9%), including myocardial infarction, coronary artery procedures such as angioplasty or surgery, previous cerebral-vascular accident, or peripheral vascular disease. Average age was 64.6 ± 11.5 years and median maintenance HD vintage was 31 months (Q1 to Q3, 15.5-51.5 months) at the study initiation. For HD patients at the start of the cohort, serum leptin averaged (mean ± SD) 37.3 ± 39.4 ng/ml (median, 16.5 ng/ml; Q1 to Q3, 5.60-71.4 ng/ml).\nTo examine the relationship between different levels of serum leptin and relevant demographic, clinical and laboratory measures, serum leptin levels were divided into three equal gender specific tertiles (data not shown). Diabetes proportion and age distribution were similar across the three groups. No statistically significant differences were evident between groups in the use of medications that may affect inflammatory markers such as statins, aspirin, or angiotensin-converting enzyme inhibitors (data not shown). As expected females, tended to have higher leptin levels than males (p = 0.0001). Body composition parameters measured by anthropometry and bioelectric impedance analysis (BIA) including phase angle (PA) were incrementally greater across increasing leptin tertiles even after adjustments for baseline demographic and clinical parameters (age, DM status, dialysis vintage and cardio-vascular disease in the past). However, these associations were attenuated after including of fat mass in multivariate analyses. No significant differences in any of the biochemical markers of nutrition, normalized protein nitrogen appearance (nPNA), or actual energy or protein intake normalized to adjusted body weight were found between the HD patient groups according to leptin tertiles.\nAt baseline, anthropometric measurements (body mass index [BMI], triceps skinfold thickness [TSF], mid arm circumference [MAC] and midarm circumference calculated [MAMC]) and BIA-derived parameters of body composition (fat mass index [FMI] and fat free mass index [FFMI]) correlated positively with serum leptin levels, in both unadjusted and adjusted (for age, DM status, dialysis vintage, and previous cardio-vascular disease) models. Thus, an increased level of leptin was associated with better nutritional status (Table 1). Associations between levels of serum leptin and the two laboratory nutritional markers (albumin and cholesterol) and PA derived by BIA were statistically significant, but these associations were attenuated after adjustments for demographic and clinical parameters including fat mass. No significant correlations were observed between serum leptin and nPNA or dietary energy (DEI) and protein intake (DPI) normalized to adjusted body weight in both sexes after adjustments for demographic and clinical parameters including fat mass.\nUnadjusted and multivariate adjusted Spearman's correlation coefficients of baseline serum leptin and nutritional clinical and laboratory parameters in the study population at baseline\nCorrelation coefficient values ≥ 0.25 appear in bold.\na All case-mix-adjusted models include age, gender, DM status, dialysis vintage, CV disease in the past and fat mass.\nAbbreviations: nPNA, normalized protein nitrogen appearance; IL-6, interleukin-6; BMI, body mass index; TSF, triceps skinfold thickness; MAC, mid-arm circumference; MAMC, mid-arm muscle circumference calculated; FMI, fat mass index; FFMI, fat-free mass index, DM, diabetes mellitus; CV, cardio-vascular.\n\nLongitudinal associationas:\nLinear mixed models were used to study the effects of longitudinal leptin changes on changes in nutritional parameters (slopes) over 24 months including fixed parameters such as age, gender, diabetes status, dialysis vintage, previous cardio-vascular events, and fat mass (Table 2). No significant changes in dietary intake or in any of the biochemical nutritional markers over time were found. In contrast, a significant reduction in body composition parameters (both those measured by anthropometry [BMI, TSF and MAMC] and by BIA [FMI, FFMI, PA]) over time was observed. However, leptin did not modulate the changes in outcome variables over time in our cohort (leptin-by-time interactions were insignificant).\nRegression coefficients with 95% Confidence Intervals for the effect of longitudinal leptin changes on nutritional parameters changes (slopes) during 24 months based on mixed-effects model with linear trends for variable and fixed parameters\nNOTE: All nutritional variables presented in Table were modeled separately as dependent variables, whereas independent variables included fixed factors (such as age, gender, diabetes status, dialysis vintage, past cardio-vascular disease and fat mass) and leptin as continuous variable. Terms for individual \"leptin-by-time\" interactions were included in each model. The model takes into account every measurements of leptin and presented nutritional variables in each time point for each patient separately. Presented regression coefficients demonstrate changes in outcome variables with time (24 months).\na P for trend for leptin-by-time interactions.\nb FMI and BMI were adjusted only by age, gender, DM status, history of cardio-vascular disease and dialysis vintage.\nAbbreviations: DEI, daily energy intake; DPI, daily protein intake; nPNA, normalized protein nitrogen appearance; IL-6, interleukin-6; EPO, erythropoietin; BMI, body mass index; TSF, triceps skinfold thickness; MAC, mid-arm circumference; MAMC, mid-arm muscle circumference calculated; FMI, fat mass index; FFMI, fat-free mass index; DM, diabetes mellitus; CV, cardio-vascular.\nA mixed model including the fixed factors sex, age, DM status, dialysis vintage, history of CV disease and some of nutritional parameters predicted variability in leptin levels during the study presented in Table 3. We observed significant changes of leptin levels with time (linear estimate ± SE: -2.5010 ± 0.57 ng/ml/2y; p < 0.001). The effect of longitudinal changes of FMI on serum leptin variability over time was evident according to this model.\nEffects of longitudinal changes of nutritional parameters on changes (slopes) of leptin during 24 months based on mixed-effects model with linear trends for variable and fixed parameters\nNOTE: The Table represents a simple Mixed-Model with leptin as dependent variable, and presented nutritional parameters and fixed factors (age, gender, DM status, dialysis vintage and history of CV disease) as independent variables. The model takes into account every measurements of leptin and presented nutritional variables in each time point for each patient separately.\nAbbreviations: DEI, daily energy intake; IL-6, interleukin-6; EPO, erythropoietin; FMI, fat mass index; DM, diabetes mellitus; CV, cardio-vascular.\nOf interest, a significant reduction over time was associated with a more rapid decline in leptin levels in the highest leptin tertile in both unadjusted (p = 0.007 for leptin-by-time interaction) and fully adjusted (p = 0.047 for leptin-by-time interaction) models (Figure 2).\nLeptin levels decline over time in the study population. Changes in estimated marginal means of serum leptin levels at baseline (month 0), month 6, month 12, month 18 and month 24 in the study population groups according to sex-specific tertiles* of baseline serum leptin: 1. Unadjusted model: P = 0.007 for leptin-by-time interaction; 2. Fully adjusted model (for age, gender, DM status, dialysis vintage, previous cardio-vascular morbidity and fat mass): P = 0.047 for leptin-by-time interaction. *Median (interquartile range) leptin (ng/ml) tertiles for men (n = 64) were 1.70 (1.2-2.4), 10.2 (7.6-13.8) and 38.3 (26.2-81.3) and for women (n = 37) were 15.8 (9.3-18.3), 68.0 (41.4-85.9) and 112.6 (109.2-116.0).\n\nSurvival analysis:\nDuring an average follow-up of 35 months (median, 40 months; Q1 to Q3, 17-52), 33 patients died: 11 (32%) in the low tertile group, 12 (35%) in the median tertile group and 10 (30%) in the high tertile group, with a median time to death of 25 months (Q1 to Q3, 14-40 months). Cumulative incidences of survival were unaffected by the baseline serum leptin levels (Figure 3). To further assess the possible association of baseline serum leptin level with cardio-vascular morbidity and mortality, cumulative hazards of all-cause death and first composite cardio-vascular event from Cox regression (after adjustment for baseline demographic parameters and fat mass) were calculated. First cardio-vascular event was defined as myocardial infarction (MI), requiring coronary artery procedures such as angioplasty or surgery, cerebral-vascular accident (CVA), or peripheral vascular disease (PVD), requiring angioplasty, bypass or amputation and diagnosed after the participant entered the study. No statistically significant differences in hazards between the different groups according leptin tertiles were observed. Additionally, no differences were found by Cox regression analysis in terms of survival probabilities from the analysis of all-cause mortality for each 10 ng/ml increase in baseline serum leptin (data not shown).\nKaplan-Meier curves of surviving patients comparing subgroups of patients stratified by baseline serum leptin tertiles.\nThus, although chronic HD patients with higher baseline leptin had better nutritional status as evidenced by their significantly more favorable body composition parameters at the start of the study, this disparity did not appear to widen over the 24 months of follow-up; thus, leptin levels did not predict body composition parameter changes over time. Moreover, no survival advantage of the high leptin group was found in our observational cohort.\n\nDiscussion:\nIn the present study, we wished to determine whether changes of serum leptin levels are correlated with nutritional status over time in a cohort of prevalent hemodialysis patients. We observed that despite the strong and significant cross-sectional associations between serum leptin and body composition parameters at baseline, leptin levels did not reflect changes in nutritional status in our population.\nOur study confirms several previous cross-sectional studies in which elevated serum leptin levels were demonstrated to be positively associated with several nutritional markers (laboratory or body composition parameters, mainly BMI and fat mass) in chronic HD patients [14,20,21,23-25]. HD patients treated with megestrole acetate (24), growth hormone [31] or high-calorie supplementation [32] demonstrated progressively increased serum leptin parallel to an improved nutritional state.\nHowever, not all studies are consistent with positive associations between elevated levels of leptin and several nutritional parameters in ESRD patients. An inverse correlation was observed between leptin/fat mass and dietary intake, as well as with significantly lower lean tissue mass in chronic renal failure (23 patients) and ESRD patients receiving PD (24 patients) and HD (22 patients) therapy in study by Young et al [5]. In a cross-sectional study of 28 dialysis patients and 41 healthy control subjects, Johansen et al [20] showed that leptin levels were negatively correlated with albumin and PCR, suggesting a possible negative role of leptin in nutrition. Based on longitudinal observation, Stenvinkel et al [18] demonstrated that increases in serum leptin levels of 36 peritoneal dialysis patients were associated with a decrease in lean body mass. The discrepancies between these studies may be related to the population recruited, to their small sample size, and to their design; specifically, serum leptin levels were measured among patients with varying degrees of CRF, some of whom were undergoing maintenance hemodialysis and peritoneal dialysis, and in healthy controls [5,18,20]; many of these studies had a low number of study participants [5,18,20,23-25] and a majority of these studies were cross-sectional [5,20,23,25].\nLeptin, a regulator of eating behavior, has been shown to be a major determinant of anorexia in uremic animals via signaling through the hypothalamic melanocortin system [15]. Subsequently, a recent experimental study by Cheung et al [33] showed that intraperitoneal administration of the melanocortin-4 receptor (MC4-R) antagonist NBI-12i stimulates food intake and weight gain in uremic mice. However, the effects of leptin in uremic patients are not unequivocal. Relevant to our findings, Bossola at al [17] demonstrated that serum leptin levels were not different in anorexic and in non-anorexic hemodialysis patients. Several other studies failed as well to demonstrate any role of hyperleptinemia on reduced dietary intake in ESRD patients receiving maintenance HD treatment [5,16,34]. Since hyperleptinemia is common in HD patients mainly due to impaired renal clearance [20], it seemed reasonable to assume that selective leptin resistance (such as occurs in obese humans [35]) is the responsible mechanism for attenuation of the negative effect of leptin on appetite, and accordingly, on dietary intake under conditions of continuous stimulation. The molecular mechanisms of leptin resistance, with a focus on the contribution of the intracellular tyrosine residues in the hypothalamic receptor of leptin (LEPRb) and their interaction with the negative feedback regulators, suppressor of cytokine signaling 1 and 3 (SOCS1 and SOCS3), are described in a recent study by Knobelspies et al [36].\nThus, although the cross-group comparisons confirm that hyperleptinemia is associated with better body composition parameters, no unequivocal associations in terms of biochemical data or dietary intake with serum leptin levels are evident in HD patients according to the available literature. Our study confirms these data.\nAnother finding of the present study was that 24 months of HD was associated with a significant reduction of plasma leptin levels, with a more rapid decline observed in patients with higher baseline leptin levels. Only limited information is available on the relationship between longitudinal serum leptin measurements and nutritional characteristics in chronic HD patients [18,32]. To the best of our knowledge, the present study is the first long-term longitudinal study to show the relationship between serum leptin level and nutritional status in ESRD patients receiving maintenance HD therapy. Several mechanisms may be involved in decrease of serum leptin levels over time in hemodialysis patients. These include, the increasing use of high flux and super-flux hemodialyzers [37], and/or use of alternative dialysis strategies such as hemodiafiltration [38]; recombinant human erythropoietin treatment (generally followed by a significant decline of leptinaemia in hemodialysed patients [39]); chronic inflammation (under the assumption that leptin, like albumin or transferrin, is a negative acute phase protein in chronic HD patients [22], although other studies [40] do not support this mechanism); metabolic acidosis, which can reduce the release of leptin from adipose tissue [41]; and finally, a decrease in fat mass leading to a decrease in the rate of leptin biosynthesis [14,18] (based on an insulin or a nutrient-sensing pathway regulating leptin gene expression in fat tissue [42]). Although patients in our facility were all treated using low flux hemodialysis, reduction of BMI and accordingly of fat mass over 24 months of the study may explain this longitudinal decrease of serum leptin levels in our cohort.\nOf interest is the observation that adverse changes in body composition parameters occur over time, supporting the hypothesis that end-stage renal disease is associated with wasting as proposed in some [43,44] but not all [45] of the previous studies. The results of our study were consistent with those reported by Johansen et al [43] who did not find significant changes in any of the biochemical markers with time, but a significant reduction in phase angle over 1 year follow up was evident in prevalent HD patients. In parallel, together with reduced body composition parameters over time, we observed a longitudinal decline in serum leptin levels in our cohort. However, the lack of an association between serum leptin levels and future changes in body composition parameters (as exhibited by non-significant leptin-by-time interactions), suggests that the levels of leptin follow body composition (mainly fat mass) trajectory or its accompanying metabolic changes, rather than governing it.\nFinally, serum leptin level did not appear as a useful predictor of all cause mortality or cardio-vascular morbidity in our study population during up to 4 years of observation. In this aspect, our findings support the results of earlier study by Tsai et al [46]. Although Scholze et al [26] showed a paradoxical reverse association between leptin level and clinical outcome in 71 chronic HD patients, our patients did not show such an association. The difference between our results and theirs might have been caused by differences in the cohorts. First, the population presented by Scholze et al [26] had a lower BMI at baseline than our patients, and no body composition parameters were measured. Further, the prevalence of diabetes mellitus was much greater in patients in the lower leptin group than in patients of the higher leptin group in the cohort of Scholze et al [26]. Indeed, HD patients with diabetes have a poor prognosis [4] that might be the cause of the lower survival rate in the lower leptin group in the above study [26]. While low levels of leptin may reflect a state of malnutrition in HD patients, proatherogenic effects of hyperleptinemia were linked to an adverse cardiovascular profile in general population [10,12]. We speculate that the lack of long-term benefits of hyperleptinemia in terms of survival of chronic HD patients could reflect the modulating influence of proatherogenic effects of high serum leptin levels. Finally, no relationships between serum leptin levels and previous cardio-vascular events were found by Diez et al [47] in a retrospective study on 82 dialysis patients. Zoccali et al [40] also did not show any difference in cardiovascular event-free survival in cohort of 192 hemodialysis patients after their stratification on the basis of the median value of plasma leptin.\nSome limitations of the present study should be considered. First, this study is based on a relatively small sample size limiting detection of more subtle changes over time. Second, this study used only an observational approach, without manipulation of exposure factors, and therefore, no definitive cause-and-effect relationship can be derived for any of the risk factors analyzed. Dietary intake assessed by 3 day food records is another limitation of the study, as results can be subjective and incomplete, and can vary considerably from day to day as a result of dialysis treatment sessions and associated disturbances in food intake. Finally, significant circadian fluctuations of plasma leptin (regardless of the underlying degree of glucose tolerance, fasting, or diet) can certainly obscure some small but significant changes in plasma leptin [48] and may be a source of potential bias for longitudinal observation. Nevertheless, the present study has the advantage of providing long-term longitudinal data on the relationship between serum concentrations of leptin and nutritional parameters in prevalent HD patients.\n\nConclusions:\nIn conclusion, we found positive linear associations between serum leptin levels and body composition, whereas no significant relations in terms of biochemical data or dietary intake were evident in our cohort at baseline. Analyzing longitudinal data we concluded that any influence of serum leptin levels on nutritional status is limited to mirroring attained BMI and fat mass trajectory. Further, nutritional advantage at baseline of the study population with hyperleptinemia did not translate into long-term benefits in terms of survival. Taken together, our results suggest that in our cohort, leptin levels reflect fat mass depots, rather than independently contributing to uremic anorexia or modifying nutritional status and/or survival in chronic HD patients. These results are of particular importance if the use of subcutaneous injections of recombinant methionyl leptin [49] or leptin removal by super-flux polysulfone dialysers [37] is contemplated as a therapeutic intervention.\n\n"},"whole_article_abstract":{"kind":"string","value":"Background:\nThe influence of serum leptin levels on nutritional status and survival in chronic hemodialysis patients remained to be elucidated. We conducted a prospective longitudinal study of leptin levels and nutritional parameters to determine whether changes of serum leptin levels modify nutritional status and survival in a cohort of prevalent hemodialysis patients.\n\nMethods:\nLeptin, dietary energy and protein intake, biochemical markers of nutrition and body composition (anthropometry and bioimpedance analysis) were measured at baseline and at 6, 12, 18 and 24 months following enrollment, in 101 prevalent hemodialysis patients (37% women) with a mean age of 64.6 ± 11.5 years. Observation of this cohort was continued over 2 additional years. Changes in repeated measures were evaluated, with adjustment for baseline differences in demographic and clinical parameters.\n\nResults:\nSignificant reduction of leptin levels with time were observed (linear estimate: -2.5010 ± 0.57 ng/ml/2 y; p < 0.001) with a more rapid decline in leptin levels in the highest leptin tertile in both unadjusted (p = 0.007) and fully adjusted (p = 0.047) models. A significant reduction in body composition parameters over time was observed, but was not influenced by leptin (leptin-by-time interactions were not significant). No significant associations were noted between leptin levels and changes in dietary protein or energy intake, or laboratory nutritional markers. Finally, cumulative incidences of survival were unaffected by the baseline serum leptin levels.\n\nConclusions:\nThus leptin levels reflect fat mass depots, rather than independently contributing to uremic anorexia or modifying nutritional status and/or survival in chronic hemodialysis patients. The importance of such information is high if leptin is contemplated as a potential therapeutic target in hemodialysis patients."},"background_conclusion_text":{"kind":"string","value":"Background:\nIn recent years, the number of patients with end-stage renal disease (ESRD) has been increasing worldwide [1]. Depending in part upon the method used to evaluate nutritional status and the population studied, from 40 to 70 percent of patients with ESRD are malnourished [2,3] resulting in poor clinical outcomes [4]. Among the mechanisms responsible for malnutrition, leptin was believed to influence nutritional markers in patients with ESRD [5]. Leptin is a 16-kDa protein identified as the product of the obese gene; it is exclusively produced in adipocytes, and regulates food intake and energy expenditure in animal models [6]. Leptin decreases food intake by decreasing NPY (neuropeptide Y - one of the most potent stimulators of food intake) mRNA [7] and increasing alpha-MSH (alpha-melanocyte-stimulating hormone - an inhibitor of food intake) [8]. Besides linking adiposity and central nervous circuits to reduced appetite and enhanced energy expenditure in the general population [9], leptin has been shown to increase overall sympathetic nerve activity [10], facilitate glucose utilization and improve insulin sensitivity [11]. Furthermore, the prospective West of Scotland Coronary Prevention Study (WOSCOPS) reported that elevated leptin increases the relative risk of cardio-vascular disease in the general population independently of fat mass [12].\nIn general, serum leptin levels are significantly elevated in patients with renal failure, particularly when compared to age, gender and body mass index (BMI)-matched controls [13,14]. However, the role of hyperleptinemia in ESRD patients is somewhat unconventional. In contrast with its anorexogenic effects recognized in the general population [9] and even in experimental models of uremia (in subtotal nephrectomized and leptin receptor-deficient [db/db] mice) [15], leptin has not been reported to affect perceived appetite and nutrient intake in dialysis patients [16,17]. Although in some observational studies, increased serum leptin concentrations were observed in ESRD patients in parallel with loss of lean body mass [18,19] or with hypoalbuminemia and low protein intake [20], some others failed to find any correlation between hyperleptinemia and weight change [9] or lean mass [21] in this population. Moreover, several clinical studies suggested that leptin is a negative acute phase protein [22] and can serve as a marker of adequate nutritional status, rather than an appetite-reducing uremic toxin in hemodialysis patients [23-25]. Finally, the relationship between elevated serum leptin levels and clinical outcomes in ESRD has not been fully defined. In one small prospective cohort of hemodialysis patients, lower baseline serum leptin levels predicted mortality [26], but neither changes in leptin over time were measured, nor were leptin levels normalized to body fat mass in this study.\nThus, the influence of serum leptin levels on nutritional status and survival in chronic hemodialysis patients remained to be elucidated. In view of leptin's physiological role, information on effects of prolonged hyperleptinemia (independent of fat mass) on nutritional status of chronic hemodialysis patients, which may also impact on their survival, would be of interest. The aim of the present prospective longitudinal study was therefore to study longitudinal changes in serum leptin levels and to relate them to the changes in nutritional markers and survival in chronic hemodialysis patients.\n\nConclusions:\nIB designed, organized and coordinated the study, managed data entry, contributed to data analysis and interpretation of data and wrote the manuscript. IS carried out the immunoassays, contributed to analyzing and interpretation of data and writing the manuscript. AA and HY carried out nutrition assessment (food intake analysis, body composition assessment), contributed to analyzing and interpretation of data and writing the manuscript. LF contributed to analyzing and interpretation of data. ZA and JW contributed to analyzing and interpretation of data and writing the manuscript. All authors read and approved the final manuscript."},"background_conclusion_abstract":{"kind":"string","value":"Background:\nThe influence of serum leptin levels on nutritional status and survival in chronic hemodialysis patients remained to be elucidated. We conducted a prospective longitudinal study of leptin levels and nutritional parameters to determine whether changes of serum leptin levels modify nutritional status and survival in a cohort of prevalent hemodialysis patients.\n\nMethods:\nLeptin, dietary energy and protein intake, biochemical markers of nutrition and body composition (anthropometry and bioimpedance analysis) were measured at baseline and at 6, 12, 18 and 24 months following enrollment, in 101 prevalent hemodialysis patients (37% women) with a mean age of 64.6 ± 11.5 years. Observation of this cohort was continued over 2 additional years. Changes in repeated measures were evaluated, with adjustment for baseline differences in demographic and clinical parameters.\n\nResults:\nSignificant reduction of leptin levels with time were observed (linear estimate: -2.5010 ± 0.57 ng/ml/2 y; p < 0.001) with a more rapid decline in leptin levels in the highest leptin tertile in both unadjusted (p = 0.007) and fully adjusted (p = 0.047) models. A significant reduction in body composition parameters over time was observed, but was not influenced by leptin (leptin-by-time interactions were not significant). No significant associations were noted between leptin levels and changes in dietary protein or energy intake, or laboratory nutritional markers. Finally, cumulative incidences of survival were unaffected by the baseline serum leptin levels.\n\nConclusions:\nThus leptin levels reflect fat mass depots, rather than independently contributing to uremic anorexia or modifying nutritional status and/or survival in chronic hemodialysis patients. The importance of such information is high if leptin is contemplated as a potential therapeutic target in hemodialysis patients."},"whole_article_text_length":{"kind":"number","value":12547,"string":"12,547"},"whole_article_abstract_length":{"kind":"number","value":324,"string":"324"},"other_sections_lengths":{"kind":"list like","value":[632,540,140,77,201,124,320,3811,679,861,357,1745,161],"string":"[\n 632,\n 540,\n 140,\n 77,\n 201,\n 124,\n 320,\n 3811,\n 679,\n 861,\n 357,\n 1745,\n 161\n]"},"num_sections":{"kind":"number","value":14,"string":"14"},"most_frequent_words":{"kind":"list like","value":["leptin","patients","mass","study","fat","vascular","time","serum","levels","serum leptin"],"string":"[\n \"leptin\",\n \"patients\",\n \"mass\",\n \"study\",\n \"fat\",\n \"vascular\",\n \"time\",\n \"serum\",\n \"levels\",\n \"serum leptin\"\n]"},"keybert_topics":{"kind":"list like","value":["leptin better nutritional","levels leptin nutritional","leptin nutritional 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levels leptin nutritional | leptin nutritional parameters | leptin nutritional clinical | leptin changes nutritional [SUMMARY]"},"annotated_keybert_methods_abstract_prompt":{"kind":"string","value":"[CONTENT] leptin better nutritional | levels leptin nutritional | leptin nutritional parameters | leptin nutritional clinical | leptin changes nutritional [SUMMARY]"},"annotated_keybert_results_abstract_prompt":{"kind":"null"},"annotated_keybert_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] leptin better nutritional | levels leptin nutritional | leptin nutritional parameters | leptin nutritional clinical | leptin changes nutritional [SUMMARY]"},"annotated_keybert_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] leptin better nutritional | levels leptin nutritional | leptin nutritional parameters | leptin nutritional clinical | leptin changes nutritional [SUMMARY]"},"annotated_keybert_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] leptin better nutritional | levels leptin nutritional | leptin nutritional parameters | leptin nutritional clinical | leptin changes nutritional [SUMMARY]"},"annotated_most_frequent_background_abstract_prompt":{"kind":"string","value":"[CONTENT] leptin | patients | mass | study | fat | vascular | time | serum | levels | serum leptin [SUMMARY]"},"annotated_most_frequent_methods_abstract_prompt":{"kind":"string","value":"[CONTENT] leptin | patients | mass | study | fat | vascular | time | serum | levels | serum leptin [SUMMARY]"},"annotated_most_frequent_results_abstract_prompt":{"kind":"null"},"annotated_most_frequent_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] leptin | patients | mass | study | fat | vascular | time | serum | levels | serum leptin [SUMMARY]"},"annotated_most_frequent_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] leptin | patients | mass | study | fat | vascular | time | serum | levels | serum leptin [SUMMARY]"},"annotated_most_frequent_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] leptin | patients | mass | study | fat | vascular | time | serum | levels | serum leptin [SUMMARY]"},"annotated_tf_idf_background_abstract_prompt":{"kind":"string","value":"[CONTENT] leptin | patients | hemodialysis patients | esrd | food intake | general | nutritional | serum | serum leptin | hemodialysis [SUMMARY]"},"annotated_tf_idf_methods_abstract_prompt":{"kind":"string","value":"[CONTENT] patients | dialysis | performed | urea | leptin | day | study | flow | vascular | body [SUMMARY]"},"annotated_tf_idf_results_abstract_prompt":{"kind":"null"},"annotated_tf_idf_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] leptin | results | nutritional | levels | leptin levels | nutritional status | survival | serum leptin | serum leptin levels | serum [SUMMARY]"},"annotated_tf_idf_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] leptin | patients | mass | serum | serum leptin | fat | study | levels | vascular | nutritional [SUMMARY]"},"annotated_tf_idf_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] leptin | patients | mass | serum | serum leptin | fat | study | levels | vascular | nutritional [SUMMARY]"},"annotated_entity_plan_background_abstract_prompt":{"kind":"string","value":"[CONTENT] serum leptin ||| leptin [SUMMARY]"},"annotated_entity_plan_methods_abstract_prompt":{"kind":"string","value":"[CONTENT] Leptin | 6 | 12 | 18 | 24 months | 101 | 37% | 64.6 | 11.5 years ||| 2 additional years ||| [SUMMARY]"},"annotated_entity_plan_results_abstract_prompt":{"kind":"null"},"annotated_entity_plan_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] ||| leptin [SUMMARY]"},"annotated_entity_plan_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] serum leptin ||| leptin ||| Leptin | 6 | 12 | 18 | 24 months | 101 | 37% | 64.6 | 11.5 years ||| 2 additional years ||| ||| ||| linear | 0.57 ||| p < 0.001 | 0.007 | 0.047 ||| leptin | leptin ||| ||| serum leptin ||| ||| ||| leptin [SUMMARY]"},"annotated_entity_plan_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] serum leptin ||| leptin ||| Leptin | 6 | 12 | 18 | 24 months | 101 | 37% | 64.6 | 11.5 years ||| 2 additional years ||| ||| ||| linear | 0.57 ||| p < 0.001 | 0.007 | 0.047 ||| leptin | leptin ||| ||| serum leptin ||| ||| ||| leptin [SUMMARY]"}}},{"rowIdx":258,"cells":{"title":{"kind":"string","value":"Non-communicable diseases in disasters: a protocol for a systematic review."},"pmid":{"kind":"string","value":"33459280"},"background_abstract":{"kind":"string","value":"NCDs require an ongoing management for optimal outcomes, which is challenging in emergency settings, because natural disasters increase the risk of acute NCD exacerbations and lead to health systems' inability to respond. This study aims to develop a protocol for a systematic review on non-communicable diseases in natural disaster settings."},"background_abstract_label":{"kind":"string","value":"BACKGROUND"},"methods_abstract":{"kind":"string","value":"This systematic review protocol is submitted to the International Prospective Register of Systematic Reviews (Registration No. CRD42020164032). The electronic databases to be used in this study include: Medline, Scopus, Web of Science, Clinical Key, CINAHL, EBSCO, Ovid, EMBASE, ProQuest, Google Scholar, Cochrane Library (Cochrane database of systematic reviews; Cochrane central Register of controlled Trials). Records from 1997 to 2019 are subject to this investigation. Three independent researchers will review the titles, abstracts, and full texts of articles eligible for inclusion, and if not matched, they will be reviewed by a final fourth reviewer. The proposed systematic review will be reported in accordance with the reporting guideline provided in the Preferred Reporting Items for Systematic review and Meta-Analysis (PRISMA) statement. We select studies based on: PICOs (Participants, Interventions, Comparators, and Outcomes)."},"methods_abstract_label":{"kind":"string","value":"METHODS"},"results_abstract":{"kind":"string","value":"This systematic review identifies any impacts of natural disasters on patients with NCDs in three stages i.e. before, during and in the aftermath of natural disasters."},"results_abstract_label":{"kind":"string","value":"RESULTS"},"conclusions_abstract":{"kind":"string","value":"A comprehensive response to NCD management in natural disasters is an important but neglected aspect of non-communicable disease control and humanitarian response, which can significantly reduce the potential risk of morbidity and mortality associated with natural disasters."},"conclusions_abstract_label":{"kind":"string","value":"CONCLUSIONS"},"mesh_descriptor_names":{"kind":"list like","value":["Disasters","Humans","Noncommunicable Diseases","Systematic Reviews as Topic"],"string":"[\n \"Disasters\",\n \"Humans\",\n \"Noncommunicable Diseases\",\n \"Systematic Reviews as Topic\"\n]"},"pmcid":{"kind":"string","value":"8142338"},"background_title":{"kind":"string","value":"Introduction"},"background_text":{"kind":"string","value":"Disasters ar e serious events disturbing communities.1 In terms of medical aspects, these events cause numerous casualties and the high demand of medical care may require the enhancement of responders’ capacity for delivering timely and effective services.2,3 Individuals with chronic conditions require special attention in planning, response, and recovery phases after natural disasters, given their unique needs for medication, medical equipment and continuing healthcare, and potential exacerbation of their condition that require resource-intensive management.4 Natural disasters can impact the public health infrastructure and the social protection systems essential for vulnerable populations. Patients with non-communicable diseases e.g. respiratory and cardiovascular diseases, cancer and diabetes, are among vulnerable groups in critical conditions, who are highly affected by natural disasters.5\n\nNon-Communicable diseases (NCDs) require an ongoing management for optimal outcomes, which is challenging in emergency settings, since natural disasters increase the risk of acute exacerbation in the health of people with NCDs and decrease the health systems responsiveness.6 NCD Management in emergencies requires the inclusion of non-communicable disease care into standard operating procedures, which would facilitate horizontal and vertical integration to other aspects of relief efforts.7,8\n\nPatients with chronic illnesses including those with cardiovascular diseases, diabetes, cancers, and respiratory conditions are of the most vulnerable populations in disaster settings, who face various problems after natural disasters.9,10 By the collapse in some medical care systems and overloaded operating hospitals and other medical centers, provision of services to chronic patients seems to be a critical concern.11\n\nInadequately managed chronic illnesses can present a threat to life and well-being of the community in the immediate wake of these disasters, but their treatment traditionally has not been recognized as a public health or medical priority.12 Many patients did not have their medications or medical supplies, and too many did not know the names of their illnesses or medications or how to access the information.13\n\nA critical problem in the resulting health crisis is the inability of the displaced population to manage their chronic diseases.14 The Center for Disease Control and Prevention (CDC) reported that NCDs accounted for five of the six most commonly reported conditions after Hurricane Katrina.15 This leads to indirect causes of mortality and more complications up to 70%-90%, primarily due to the deterioration of life-threatening conditions and exacerbation of chronic diseases.16 After natural disasters, inadequate care and resources, and lack of continuity of care for chronic diseases such as cardiovascular diseases, asthma, diabetes, renal diseases have led to exacerbation of symptoms associated with increased morbidity and mortality among this population.17 However, non-communicable diseases have received little attention from human-rights organizations during the acute phase of crises and emergencies, and there is a need to refocus on emergency disaster systems in the 21st Century.18 More than 45% of evacuees did not carry their daily medicines with them, meaning that over two third of total medicines provided during the disaster response were used to treat chronic diseases.19 Patients with chronic diseases face many challenges and have different needs during and after natural disasters and medical care must be continued during and after natural disasters. Statistics on different diseases reveal that at the time of natural disasters, there are an increased number of hospital admissions of patients with at least one chronic disease. As an example, in Sichuan earthquake, patients with hypertension and those with diabetes, constituted 47% and 24%, respectively, of city hospital admissions.20 Despite the significance and the critical impact of natural disasters on patients with non-communicable diseases and the exacerbation of their symptoms, there are not enough studies on the issue.21 Disaster and crisis manuals and guidelines mainly focus on communicable diseases like Aleppo boil, measles, cholera and diarrhea; and among available research literature, there is a limited number of studies on the management of non-communicable diseases in emergencies.22 Several studies have been conducted on the effects of natural disasters on non-communicable diseases reporting the exacerbation of clinical effects and insufficient medical facilities and equipment to care for patients. Therefore, this study aims to obtain a systematic review protocol for non-communicable diseases in the natural disasters."},"methods_title":{"kind":"null"},"methods_text":{"kind":"null"},"results_title":{"kind":"string","value":"Results"},"results_text":{"kind":"string","value":"The primary outcome of this study will be the identified needs of patients with chronic diseases such as diabetes, cardiovascular, hypertension, respiratory disease, and cancers that are critical before, during, and after natural disasters. Secondary outcome of the study will be the reported challenges in the process of health service provision to these patients. The following data will be extracted from the selected articles: general information (title, name of authors, year, research type, subject group (by disease type), classification (before, during and after natural disaster), and factors essential to follow a process of health service provision to patients with chronic illnesses in natural disasters.\n\nAcknowledgments\n\nThis article is extracted from a PhD thesis on Health in Emergency and Disaster, with COI: IR.UMSU.REC.1398.228, the Research Center for Social Factors Effective on Health, Urmia University of Medical Sciences. We extend our special thanks to supervisors and advisors, who collaborated in this research.\n\nAbbreviations \n\nNCDs: Non-Communicable diseases\nCDC: Center for Disease Control \nPICO: Patient/ Population/ Participants/ Problem, Intervention, Comparison, Outcome\nCOPD: Chronic Obstructive Pulmonary Disease\nPROSPERO: Prospective Register of Systematic Reviews"},"conclusions_title":{"kind":"null"},"conclusions_text":{"kind":"null"},"other_sections_titles":{"kind":"list like","value":[],"string":"[]"},"other_sections_texts":{"kind":"list like","value":[],"string":"[]"},"other_sections_sec_types":{"kind":"list like","value":[],"string":"[]"},"all_sections_titles":{"kind":"list like","value":["Introduction","Methods and Methods","Results"],"string":"[\n \"Introduction\",\n \"Methods and Methods\",\n \"Results\"\n]"},"all_sections_texts":{"kind":"list like","value":["Disasters ar e serious events disturbing communities.1 In terms of medical aspects, these events cause numerous casualties and the high demand of medical care may require the enhancement of responders’ capacity for delivering timely and effective services.2,3 Individuals with chronic conditions require special attention in planning, response, and recovery phases after natural disasters, given their unique needs for medication, medical equipment and continuing healthcare, and potential exacerbation of their condition that require resource-intensive management.4 Natural disasters can impact the public health infrastructure and the social protection systems essential for vulnerable populations. Patients with non-communicable diseases e.g. respiratory and cardiovascular diseases, cancer and diabetes, are among vulnerable groups in critical conditions, who are highly affected by natural disasters.5\n\nNon-Communicable diseases (NCDs) require an ongoing management for optimal outcomes, which is challenging in emergency settings, since natural disasters increase the risk of acute exacerbation in the health of people with NCDs and decrease the health systems responsiveness.6 NCD Management in emergencies requires the inclusion of non-communicable disease care into standard operating procedures, which would facilitate horizontal and vertical integration to other aspects of relief efforts.7,8\n\nPatients with chronic illnesses including those with cardiovascular diseases, diabetes, cancers, and respiratory conditions are of the most vulnerable populations in disaster settings, who face various problems after natural disasters.9,10 By the collapse in some medical care systems and overloaded operating hospitals and other medical centers, provision of services to chronic patients seems to be a critical concern.11\n\nInadequately managed chronic illnesses can present a threat to life and well-being of the community in the immediate wake of these disasters, but their treatment traditionally has not been recognized as a public health or medical priority.12 Many patients did not have their medications or medical supplies, and too many did not know the names of their illnesses or medications or how to access the information.13\n\nA critical problem in the resulting health crisis is the inability of the displaced population to manage their chronic diseases.14 The Center for Disease Control and Prevention (CDC) reported that NCDs accounted for five of the six most commonly reported conditions after Hurricane Katrina.15 This leads to indirect causes of mortality and more complications up to 70%-90%, primarily due to the deterioration of life-threatening conditions and exacerbation of chronic diseases.16 After natural disasters, inadequate care and resources, and lack of continuity of care for chronic diseases such as cardiovascular diseases, asthma, diabetes, renal diseases have led to exacerbation of symptoms associated with increased morbidity and mortality among this population.17 However, non-communicable diseases have received little attention from human-rights organizations during the acute phase of crises and emergencies, and there is a need to refocus on emergency disaster systems in the 21st Century.18 More than 45% of evacuees did not carry their daily medicines with them, meaning that over two third of total medicines provided during the disaster response were used to treat chronic diseases.19 Patients with chronic diseases face many challenges and have different needs during and after natural disasters and medical care must be continued during and after natural disasters. Statistics on different diseases reveal that at the time of natural disasters, there are an increased number of hospital admissions of patients with at least one chronic disease. As an example, in Sichuan earthquake, patients with hypertension and those with diabetes, constituted 47% and 24%, respectively, of city hospital admissions.20 Despite the significance and the critical impact of natural disasters on patients with non-communicable diseases and the exacerbation of their symptoms, there are not enough studies on the issue.21 Disaster and crisis manuals and guidelines mainly focus on communicable diseases like Aleppo boil, measles, cholera and diarrhea; and among available research literature, there is a limited number of studies on the management of non-communicable diseases in emergencies.22 Several studies have been conducted on the effects of natural disasters on non-communicable diseases reporting the exacerbation of clinical effects and insufficient medical facilities and equipment to care for patients. Therefore, this study aims to obtain a systematic review protocol for non-communicable diseases in the natural disasters.","This systematic review protocol is submitted to the International Prospective Register of Systematic Reviews. (http://www.crd.york.ac.uk/PROSPERO) (The registration number was: CRD42020164032). Preferred Reporting Items for Systematic review and Meta-Analysis Protocols (PRISMAP) will be applied to develop this review protocol.23\n\n\nEligibility Criteria\n\nApplying a systematic review method, authors will investigate studies focused on non-communicable diseases in disasters from different aspects including epidemiological factors, risks, effects, interventions, patient needs, preparedness, as well as academic literatures from around the world. This study and its findings are intended to serve as a roadmap for future research in this area, by giving information on intervention development and policy change.\nThe target population is the group of patients with chronic diseases. The top four leading causes of death in patients with NCDs, which constitute the tenets of the WHO 2013-2020 NCD action plan, are: cardiovascular diseases, cancer, chronic respiratory disease and diabetes. The formal search strategy will be applied for relevant controlled vocabularies and free text synonymous words and phrases in concept mapping for health conditions including heart attack, myocardia, ischemia, acute coronary syndrome, stroke, hypertension, diabetes, cancer, Chronic Obstructive Pulmonary Disease(COPD) and asthma through advanced search syntax. Authors will search for possible relevant titles in the reference list of eligible studies. There will be no natural disaster location or natural disaster type limitation in our search. Also there is no restriction on the research study design. Research papers in English will be qualified. PICO (Patient/Population/Participant, Intervention, Comparison, Outcome) framework will be applied in formulating questions and facilitate the search strategy articulation.\n\nParticipants\n\nThe subject patients in the study are those with chronic diseases including cardiovascular and chronic respiratory diseases, diabetes and cancer who are affected by natural disasters. Subject to our unlimited survey in terms of the natural disaster location, the population (participants) may belong to developing or developed countries.\n\nInterventions\n\nThe study will investigate the management of health service delivery to people with non-communicable diseases who are affected by natural disasters. There is no restriction on the type of natural disaster.\n\nComparison\n\nThe only comparison to be made in this study will be that of the impact of natural disasters on the provision of medical care services to patients with non-communicable diseases before, during and after natural disasters.\n\nOutcomes\n\nThe study will classify findings based on the type of non-communicable diseases, the type of natural disasters, and the natural disaster occurrence-NCD condition relationship (NCDs before, during and after natural disasters). Finally, results concerning the clinical impacts and symptom exacerbations in patients with non-communicable diseases and models of service delivery to these patients during natural disasters as well as challenges and deficiencies in the study will be discussed.\n\nInformation Sources and Search Strategy\n\nThe electronic database search strategy will be adopted to gather relevant information from 1997 thru 2019 (reviews published before this period are likely to be out of date) using the following databases: Medline, Scopus, Web of Science, Clinical Key, CINAHL, EBSCO, Ovid, EMBASE, ProQuest, Google Scholar, Cochrane Library (Cochrane database of systematic reviews; Cochrane central Register of controlled Trials. Three independent reviewers will evaluate titles, abstracts and full texts of eligible articles for inclusion, and the final vetting is to be by the fourth reviewer, in case of discrepancies. The search process may be re-run and more studies may be retrieved for inclusion before the final analysis. The search strategy will be developed based on the MeSH terms and Key words related to natural disasters and non-communicable disease. The focus will be on the top four leading causes of NCD-related death, constituting the tenets of the WHO 2013-2020 Global NCD Action Plan i.e. cardiovascular and chronic respiratory diseases, cancer, and diabetes. The formal search strategy will be applied for relevant controlled vocabularies and free text synonymous words and phrases in concept mapping for health conditions including heart attack, myocardia, ischemia, acute coronary syndrome, stroke, hypertension, diabetes, cancer, COPD, asthma. The search strategy applied in electronic databases like the PubMed database is provided in Appendix 1.\n\nData Collection and Extraction\n\nReference lists of searched out articles will be examined to identify further studies. Also, bibliographies in systematic and non-systematic review articles will be investigated to identify relevant studies.\nFor any query pertaining to methodology, study outcomes and data collection, authors will contact reviewers. Abstracts and full texts of searched out manuscripts will be reviewed. Reference lists of systematic reviews and included studies will be screened and citation tracking will be conducted, wherever feasible. Multiple publications and overlaps will be identified, grouped and represented as a single reference. Database search results will be imported into the citation management program to aggregate relevant review articles and exclude duplicates. \nTitles and abstracts of all reviews searched out from electronic databases will be imported into EndNote (EndNote X6) and duplicates will be excluded. Authors will search and review titles, abstracts and keywords. Duplicate references will be omitted, and afterwards two of the reviewers (EGh, FN) will screen the titles. Three independent researchers will evaluate titles, abstracts and full texts of eligible articles for inclusion (DKZ, EGH, FN), and there will be a final vetting by the fourth reviewer (IM), in case of discrepancies, who will check the results. The search process may be re-run and more studies may be retrieved for inclusion just before the final analysis. A predefined inclusion and exclusion criterion is to be used for assessment of the full texts of the remaining titles. The excluded studies will be listed in a table associated with the reason for exclusion. Data extraction will be processed electronically using a developed data abstraction form adapted from the Cochrane Public Health Group. By data extraction and assessment form, any information on aspects deemed necessary as per Methodological Expectations of Cochrane Intervention Reviews (MECIR) standards will be collected.24 The data abstraction form will be piloted on a random sample of five included articles, and modifications will be made as required, based on the team’s feedback. Full data abstraction will be started only when there is a sufficient agreement (i.e. The percentage of agreement >90 %). Data extraction for the literature review will be based on study goals, characteristics (e.g. the first author, the publication date), search strategy and terminology, to describe the review and its settings and timeframe policy. The process of selecting studies will be documented in a PRISMA flow chart. (Figure 1)\n\nQuality Assessment\n\nThe researchers will evaluate the quality of selected articles based on valid checklists for study types. The quality assessment of observational studies such as cohort and cross-sectional articles will be carried out by strengthening the reporting of observational studies in epidemiology check list (STROBE).25 Based on this checklist, ranking scores are from 0 to 34 and studies will be classified into 3 groups based on their ranking score as follows: weak quality ranking score from 0 to 11; moderate quality ranking score from 12 to 22; and high quality ranking score from 23 to 34. The quality assessment of experimental studies will be carried out by transparent reporting of evaluations with nonrandomized designs (TREND).26 Based on this checklist, ranking scores are from 0 to 59 and studies will be classified into 3 groups based on their ranking score as follows: weak quality ranking score from 0 to 21; moderate quality ranking score from 22 to 41; and high quality ranking score from 42 to 59. The quality assessment of qualitative studies will be carried out using the Critical Appraisal Skills Programme (CASP). 27 Based on this checklist, ranking scores are from 0 to 10 and studies will be classified into 2 groups based on their ranking score as follows: weak quality ranking score from 0 to 5; and high quality ranking score from 6 to 10. The quality assessment of the systematic reviews and meta-analyses will be carried out by the checklist for preferred reporting items for systematic reviews and meta-analyses (PRISMA).23 The checklist consists of 27 items and papers are reviewed for each item and marked either as implemented or not-implemented. In case of the absence of an item in a paper, it will be rated ZERO, and if the subject item exists in the paper, it will be rated ONE. When items are not as distinct, the unclear sections will be assessed several times until a precise interpretation is reached and a valid evaluation of the study is made. The risk of bias will be assessed using ROBIS Risk of Bias assessment tool.23,28\n","The primary outcome of this study will be the identified needs of patients with chronic diseases such as diabetes, cardiovascular, hypertension, respiratory disease, and cancers that are critical before, during, and after natural disasters. Secondary outcome of the study will be the reported challenges in the process of health service provision to these patients. The following data will be extracted from the selected articles: general information (title, name of authors, year, research type, subject group (by disease type), classification (before, during and after natural disaster), and factors essential to follow a process of health service provision to patients with chronic illnesses in natural disasters.\n\nAcknowledgments\n\nThis article is extracted from a PhD thesis on Health in Emergency and Disaster, with COI: IR.UMSU.REC.1398.228, the Research Center for Social Factors Effective on Health, Urmia University of Medical Sciences. We extend our special thanks to supervisors and advisors, who collaborated in this research.\n\nAbbreviations \n\nNCDs: Non-Communicable diseases\nCDC: Center for Disease Control \nPICO: Patient/ Population/ Participants/ Problem, Intervention, Comparison, Outcome\nCOPD: Chronic Obstructive Pulmonary Disease\nPROSPERO: Prospective Register of Systematic Reviews"],"string":"[\n \"Disasters ar e serious events disturbing communities.1 In terms of medical aspects, these events cause numerous casualties and the high demand of medical care may require the enhancement of responders’ capacity for delivering timely and effective services.2,3 Individuals with chronic conditions require special attention in planning, response, and recovery phases after natural disasters, given their unique needs for medication, medical equipment and continuing healthcare, and potential exacerbation of their condition that require resource-intensive management.4 Natural disasters can impact the public health infrastructure and the social protection systems essential for vulnerable populations. Patients with non-communicable diseases e.g. respiratory and cardiovascular diseases, cancer and diabetes, are among vulnerable groups in critical conditions, who are highly affected by natural disasters.5\\n\\nNon-Communicable diseases (NCDs) require an ongoing management for optimal outcomes, which is challenging in emergency settings, since natural disasters increase the risk of acute exacerbation in the health of people with NCDs and decrease the health systems responsiveness.6 NCD Management in emergencies requires the inclusion of non-communicable disease care into standard operating procedures, which would facilitate horizontal and vertical integration to other aspects of relief efforts.7,8\\n\\nPatients with chronic illnesses including those with cardiovascular diseases, diabetes, cancers, and respiratory conditions are of the most vulnerable populations in disaster settings, who face various problems after natural disasters.9,10 By the collapse in some medical care systems and overloaded operating hospitals and other medical centers, provision of services to chronic patients seems to be a critical concern.11\\n\\nInadequately managed chronic illnesses can present a threat to life and well-being of the community in the immediate wake of these disasters, but their treatment traditionally has not been recognized as a public health or medical priority.12 Many patients did not have their medications or medical supplies, and too many did not know the names of their illnesses or medications or how to access the information.13\\n\\nA critical problem in the resulting health crisis is the inability of the displaced population to manage their chronic diseases.14 The Center for Disease Control and Prevention (CDC) reported that NCDs accounted for five of the six most commonly reported conditions after Hurricane Katrina.15 This leads to indirect causes of mortality and more complications up to 70%-90%, primarily due to the deterioration of life-threatening conditions and exacerbation of chronic diseases.16 After natural disasters, inadequate care and resources, and lack of continuity of care for chronic diseases such as cardiovascular diseases, asthma, diabetes, renal diseases have led to exacerbation of symptoms associated with increased morbidity and mortality among this population.17 However, non-communicable diseases have received little attention from human-rights organizations during the acute phase of crises and emergencies, and there is a need to refocus on emergency disaster systems in the 21st Century.18 More than 45% of evacuees did not carry their daily medicines with them, meaning that over two third of total medicines provided during the disaster response were used to treat chronic diseases.19 Patients with chronic diseases face many challenges and have different needs during and after natural disasters and medical care must be continued during and after natural disasters. Statistics on different diseases reveal that at the time of natural disasters, there are an increased number of hospital admissions of patients with at least one chronic disease. As an example, in Sichuan earthquake, patients with hypertension and those with diabetes, constituted 47% and 24%, respectively, of city hospital admissions.20 Despite the significance and the critical impact of natural disasters on patients with non-communicable diseases and the exacerbation of their symptoms, there are not enough studies on the issue.21 Disaster and crisis manuals and guidelines mainly focus on communicable diseases like Aleppo boil, measles, cholera and diarrhea; and among available research literature, there is a limited number of studies on the management of non-communicable diseases in emergencies.22 Several studies have been conducted on the effects of natural disasters on non-communicable diseases reporting the exacerbation of clinical effects and insufficient medical facilities and equipment to care for patients. Therefore, this study aims to obtain a systematic review protocol for non-communicable diseases in the natural disasters.\",\n \"This systematic review protocol is submitted to the International Prospective Register of Systematic Reviews. (http://www.crd.york.ac.uk/PROSPERO) (The registration number was: CRD42020164032). Preferred Reporting Items for Systematic review and Meta-Analysis Protocols (PRISMAP) will be applied to develop this review protocol.23\\n\\n\\nEligibility Criteria\\n\\nApplying a systematic review method, authors will investigate studies focused on non-communicable diseases in disasters from different aspects including epidemiological factors, risks, effects, interventions, patient needs, preparedness, as well as academic literatures from around the world. This study and its findings are intended to serve as a roadmap for future research in this area, by giving information on intervention development and policy change.\\nThe target population is the group of patients with chronic diseases. The top four leading causes of death in patients with NCDs, which constitute the tenets of the WHO 2013-2020 NCD action plan, are: cardiovascular diseases, cancer, chronic respiratory disease and diabetes. The formal search strategy will be applied for relevant controlled vocabularies and free text synonymous words and phrases in concept mapping for health conditions including heart attack, myocardia, ischemia, acute coronary syndrome, stroke, hypertension, diabetes, cancer, Chronic Obstructive Pulmonary Disease(COPD) and asthma through advanced search syntax. Authors will search for possible relevant titles in the reference list of eligible studies. There will be no natural disaster location or natural disaster type limitation in our search. Also there is no restriction on the research study design. Research papers in English will be qualified. PICO (Patient/Population/Participant, Intervention, Comparison, Outcome) framework will be applied in formulating questions and facilitate the search strategy articulation.\\n\\nParticipants\\n\\nThe subject patients in the study are those with chronic diseases including cardiovascular and chronic respiratory diseases, diabetes and cancer who are affected by natural disasters. Subject to our unlimited survey in terms of the natural disaster location, the population (participants) may belong to developing or developed countries.\\n\\nInterventions\\n\\nThe study will investigate the management of health service delivery to people with non-communicable diseases who are affected by natural disasters. There is no restriction on the type of natural disaster.\\n\\nComparison\\n\\nThe only comparison to be made in this study will be that of the impact of natural disasters on the provision of medical care services to patients with non-communicable diseases before, during and after natural disasters.\\n\\nOutcomes\\n\\nThe study will classify findings based on the type of non-communicable diseases, the type of natural disasters, and the natural disaster occurrence-NCD condition relationship (NCDs before, during and after natural disasters). Finally, results concerning the clinical impacts and symptom exacerbations in patients with non-communicable diseases and models of service delivery to these patients during natural disasters as well as challenges and deficiencies in the study will be discussed.\\n\\nInformation Sources and Search Strategy\\n\\nThe electronic database search strategy will be adopted to gather relevant information from 1997 thru 2019 (reviews published before this period are likely to be out of date) using the following databases: Medline, Scopus, Web of Science, Clinical Key, CINAHL, EBSCO, Ovid, EMBASE, ProQuest, Google Scholar, Cochrane Library (Cochrane database of systematic reviews; Cochrane central Register of controlled Trials. Three independent reviewers will evaluate titles, abstracts and full texts of eligible articles for inclusion, and the final vetting is to be by the fourth reviewer, in case of discrepancies. The search process may be re-run and more studies may be retrieved for inclusion before the final analysis. The search strategy will be developed based on the MeSH terms and Key words related to natural disasters and non-communicable disease. The focus will be on the top four leading causes of NCD-related death, constituting the tenets of the WHO 2013-2020 Global NCD Action Plan i.e. cardiovascular and chronic respiratory diseases, cancer, and diabetes. The formal search strategy will be applied for relevant controlled vocabularies and free text synonymous words and phrases in concept mapping for health conditions including heart attack, myocardia, ischemia, acute coronary syndrome, stroke, hypertension, diabetes, cancer, COPD, asthma. The search strategy applied in electronic databases like the PubMed database is provided in Appendix 1.\\n\\nData Collection and Extraction\\n\\nReference lists of searched out articles will be examined to identify further studies. Also, bibliographies in systematic and non-systematic review articles will be investigated to identify relevant studies.\\nFor any query pertaining to methodology, study outcomes and data collection, authors will contact reviewers. Abstracts and full texts of searched out manuscripts will be reviewed. Reference lists of systematic reviews and included studies will be screened and citation tracking will be conducted, wherever feasible. Multiple publications and overlaps will be identified, grouped and represented as a single reference. Database search results will be imported into the citation management program to aggregate relevant review articles and exclude duplicates. \\nTitles and abstracts of all reviews searched out from electronic databases will be imported into EndNote (EndNote X6) and duplicates will be excluded. Authors will search and review titles, abstracts and keywords. Duplicate references will be omitted, and afterwards two of the reviewers (EGh, FN) will screen the titles. Three independent researchers will evaluate titles, abstracts and full texts of eligible articles for inclusion (DKZ, EGH, FN), and there will be a final vetting by the fourth reviewer (IM), in case of discrepancies, who will check the results. The search process may be re-run and more studies may be retrieved for inclusion just before the final analysis. A predefined inclusion and exclusion criterion is to be used for assessment of the full texts of the remaining titles. The excluded studies will be listed in a table associated with the reason for exclusion. Data extraction will be processed electronically using a developed data abstraction form adapted from the Cochrane Public Health Group. By data extraction and assessment form, any information on aspects deemed necessary as per Methodological Expectations of Cochrane Intervention Reviews (MECIR) standards will be collected.24 The data abstraction form will be piloted on a random sample of five included articles, and modifications will be made as required, based on the team’s feedback. Full data abstraction will be started only when there is a sufficient agreement (i.e. The percentage of agreement >90 %). Data extraction for the literature review will be based on study goals, characteristics (e.g. the first author, the publication date), search strategy and terminology, to describe the review and its settings and timeframe policy. The process of selecting studies will be documented in a PRISMA flow chart. (Figure 1)\\n\\nQuality Assessment\\n\\nThe researchers will evaluate the quality of selected articles based on valid checklists for study types. The quality assessment of observational studies such as cohort and cross-sectional articles will be carried out by strengthening the reporting of observational studies in epidemiology check list (STROBE).25 Based on this checklist, ranking scores are from 0 to 34 and studies will be classified into 3 groups based on their ranking score as follows: weak quality ranking score from 0 to 11; moderate quality ranking score from 12 to 22; and high quality ranking score from 23 to 34. The quality assessment of experimental studies will be carried out by transparent reporting of evaluations with nonrandomized designs (TREND).26 Based on this checklist, ranking scores are from 0 to 59 and studies will be classified into 3 groups based on their ranking score as follows: weak quality ranking score from 0 to 21; moderate quality ranking score from 22 to 41; and high quality ranking score from 42 to 59. The quality assessment of qualitative studies will be carried out using the Critical Appraisal Skills Programme (CASP). 27 Based on this checklist, ranking scores are from 0 to 10 and studies will be classified into 2 groups based on their ranking score as follows: weak quality ranking score from 0 to 5; and high quality ranking score from 6 to 10. The quality assessment of the systematic reviews and meta-analyses will be carried out by the checklist for preferred reporting items for systematic reviews and meta-analyses (PRISMA).23 The checklist consists of 27 items and papers are reviewed for each item and marked either as implemented or not-implemented. In case of the absence of an item in a paper, it will be rated ZERO, and if the subject item exists in the paper, it will be rated ONE. When items are not as distinct, the unclear sections will be assessed several times until a precise interpretation is reached and a valid evaluation of the study is made. The risk of bias will be assessed using ROBIS Risk of Bias assessment tool.23,28\\n\",\n \"The primary outcome of this study will be the identified needs of patients with chronic diseases such as diabetes, cardiovascular, hypertension, respiratory disease, and cancers that are critical before, during, and after natural disasters. Secondary outcome of the study will be the reported challenges in the process of health service provision to these patients. The following data will be extracted from the selected articles: general information (title, name of authors, year, research type, subject group (by disease type), classification (before, during and after natural disaster), and factors essential to follow a process of health service provision to patients with chronic illnesses in natural disasters.\\n\\nAcknowledgments\\n\\nThis article is extracted from a PhD thesis on Health in Emergency and Disaster, with COI: IR.UMSU.REC.1398.228, the Research Center for Social Factors Effective on Health, Urmia University of Medical Sciences. We extend our special thanks to supervisors and advisors, who collaborated in this research.\\n\\nAbbreviations \\n\\nNCDs: Non-Communicable diseases\\nCDC: Center for Disease Control \\nPICO: Patient/ Population/ Participants/ Problem, Intervention, Comparison, Outcome\\nCOPD: Chronic Obstructive Pulmonary Disease\\nPROSPERO: Prospective Register of Systematic Reviews\"\n]"},"all_sections_sec_types":{"kind":"list like","value":["introduction","methods and methods","results"],"string":"[\n \"introduction\",\n \"methods and methods\",\n \"results\"\n]"},"keywords":{"kind":"list like","value":["Crisis","Natural Disaster","Management","Non Communicable Diseases","Chronic illness"],"string":"[\n \"Crisis\",\n \"Natural Disaster\",\n \"Management\",\n \"Non Communicable Diseases\",\n \"Chronic illness\"\n]"},"whole_article_text":{"kind":"string","value":"Introduction:\nDisasters ar e serious events disturbing communities.1 In terms of medical aspects, these events cause numerous casualties and the high demand of medical care may require the enhancement of responders’ capacity for delivering timely and effective services.2,3 Individuals with chronic conditions require special attention in planning, response, and recovery phases after natural disasters, given their unique needs for medication, medical equipment and continuing healthcare, and potential exacerbation of their condition that require resource-intensive management.4 Natural disasters can impact the public health infrastructure and the social protection systems essential for vulnerable populations. Patients with non-communicable diseases e.g. respiratory and cardiovascular diseases, cancer and diabetes, are among vulnerable groups in critical conditions, who are highly affected by natural disasters.5\n\nNon-Communicable diseases (NCDs) require an ongoing management for optimal outcomes, which is challenging in emergency settings, since natural disasters increase the risk of acute exacerbation in the health of people with NCDs and decrease the health systems responsiveness.6 NCD Management in emergencies requires the inclusion of non-communicable disease care into standard operating procedures, which would facilitate horizontal and vertical integration to other aspects of relief efforts.7,8\n\nPatients with chronic illnesses including those with cardiovascular diseases, diabetes, cancers, and respiratory conditions are of the most vulnerable populations in disaster settings, who face various problems after natural disasters.9,10 By the collapse in some medical care systems and overloaded operating hospitals and other medical centers, provision of services to chronic patients seems to be a critical concern.11\n\nInadequately managed chronic illnesses can present a threat to life and well-being of the community in the immediate wake of these disasters, but their treatment traditionally has not been recognized as a public health or medical priority.12 Many patients did not have their medications or medical supplies, and too many did not know the names of their illnesses or medications or how to access the information.13\n\nA critical problem in the resulting health crisis is the inability of the displaced population to manage their chronic diseases.14 The Center for Disease Control and Prevention (CDC) reported that NCDs accounted for five of the six most commonly reported conditions after Hurricane Katrina.15 This leads to indirect causes of mortality and more complications up to 70%-90%, primarily due to the deterioration of life-threatening conditions and exacerbation of chronic diseases.16 After natural disasters, inadequate care and resources, and lack of continuity of care for chronic diseases such as cardiovascular diseases, asthma, diabetes, renal diseases have led to exacerbation of symptoms associated with increased morbidity and mortality among this population.17 However, non-communicable diseases have received little attention from human-rights organizations during the acute phase of crises and emergencies, and there is a need to refocus on emergency disaster systems in the 21st Century.18 More than 45% of evacuees did not carry their daily medicines with them, meaning that over two third of total medicines provided during the disaster response were used to treat chronic diseases.19 Patients with chronic diseases face many challenges and have different needs during and after natural disasters and medical care must be continued during and after natural disasters. Statistics on different diseases reveal that at the time of natural disasters, there are an increased number of hospital admissions of patients with at least one chronic disease. As an example, in Sichuan earthquake, patients with hypertension and those with diabetes, constituted 47% and 24%, respectively, of city hospital admissions.20 Despite the significance and the critical impact of natural disasters on patients with non-communicable diseases and the exacerbation of their symptoms, there are not enough studies on the issue.21 Disaster and crisis manuals and guidelines mainly focus on communicable diseases like Aleppo boil, measles, cholera and diarrhea; and among available research literature, there is a limited number of studies on the management of non-communicable diseases in emergencies.22 Several studies have been conducted on the effects of natural disasters on non-communicable diseases reporting the exacerbation of clinical effects and insufficient medical facilities and equipment to care for patients. Therefore, this study aims to obtain a systematic review protocol for non-communicable diseases in the natural disasters.\n\nMethods and Methods:\nThis systematic review protocol is submitted to the International Prospective Register of Systematic Reviews. (http://www.crd.york.ac.uk/PROSPERO) (The registration number was: CRD42020164032). Preferred Reporting Items for Systematic review and Meta-Analysis Protocols (PRISMAP) will be applied to develop this review protocol.23\n\n\nEligibility Criteria\n\nApplying a systematic review method, authors will investigate studies focused on non-communicable diseases in disasters from different aspects including epidemiological factors, risks, effects, interventions, patient needs, preparedness, as well as academic literatures from around the world. This study and its findings are intended to serve as a roadmap for future research in this area, by giving information on intervention development and policy change.\nThe target population is the group of patients with chronic diseases. The top four leading causes of death in patients with NCDs, which constitute the tenets of the WHO 2013-2020 NCD action plan, are: cardiovascular diseases, cancer, chronic respiratory disease and diabetes. The formal search strategy will be applied for relevant controlled vocabularies and free text synonymous words and phrases in concept mapping for health conditions including heart attack, myocardia, ischemia, acute coronary syndrome, stroke, hypertension, diabetes, cancer, Chronic Obstructive Pulmonary Disease(COPD) and asthma through advanced search syntax. Authors will search for possible relevant titles in the reference list of eligible studies. There will be no natural disaster location or natural disaster type limitation in our search. Also there is no restriction on the research study design. Research papers in English will be qualified. PICO (Patient/Population/Participant, Intervention, Comparison, Outcome) framework will be applied in formulating questions and facilitate the search strategy articulation.\n\nParticipants\n\nThe subject patients in the study are those with chronic diseases including cardiovascular and chronic respiratory diseases, diabetes and cancer who are affected by natural disasters. Subject to our unlimited survey in terms of the natural disaster location, the population (participants) may belong to developing or developed countries.\n\nInterventions\n\nThe study will investigate the management of health service delivery to people with non-communicable diseases who are affected by natural disasters. There is no restriction on the type of natural disaster.\n\nComparison\n\nThe only comparison to be made in this study will be that of the impact of natural disasters on the provision of medical care services to patients with non-communicable diseases before, during and after natural disasters.\n\nOutcomes\n\nThe study will classify findings based on the type of non-communicable diseases, the type of natural disasters, and the natural disaster occurrence-NCD condition relationship (NCDs before, during and after natural disasters). Finally, results concerning the clinical impacts and symptom exacerbations in patients with non-communicable diseases and models of service delivery to these patients during natural disasters as well as challenges and deficiencies in the study will be discussed.\n\nInformation Sources and Search Strategy\n\nThe electronic database search strategy will be adopted to gather relevant information from 1997 thru 2019 (reviews published before this period are likely to be out of date) using the following databases: Medline, Scopus, Web of Science, Clinical Key, CINAHL, EBSCO, Ovid, EMBASE, ProQuest, Google Scholar, Cochrane Library (Cochrane database of systematic reviews; Cochrane central Register of controlled Trials. Three independent reviewers will evaluate titles, abstracts and full texts of eligible articles for inclusion, and the final vetting is to be by the fourth reviewer, in case of discrepancies. The search process may be re-run and more studies may be retrieved for inclusion before the final analysis. The search strategy will be developed based on the MeSH terms and Key words related to natural disasters and non-communicable disease. The focus will be on the top four leading causes of NCD-related death, constituting the tenets of the WHO 2013-2020 Global NCD Action Plan i.e. cardiovascular and chronic respiratory diseases, cancer, and diabetes. The formal search strategy will be applied for relevant controlled vocabularies and free text synonymous words and phrases in concept mapping for health conditions including heart attack, myocardia, ischemia, acute coronary syndrome, stroke, hypertension, diabetes, cancer, COPD, asthma. The search strategy applied in electronic databases like the PubMed database is provided in Appendix 1.\n\nData Collection and Extraction\n\nReference lists of searched out articles will be examined to identify further studies. Also, bibliographies in systematic and non-systematic review articles will be investigated to identify relevant studies.\nFor any query pertaining to methodology, study outcomes and data collection, authors will contact reviewers. Abstracts and full texts of searched out manuscripts will be reviewed. Reference lists of systematic reviews and included studies will be screened and citation tracking will be conducted, wherever feasible. Multiple publications and overlaps will be identified, grouped and represented as a single reference. Database search results will be imported into the citation management program to aggregate relevant review articles and exclude duplicates. \nTitles and abstracts of all reviews searched out from electronic databases will be imported into EndNote (EndNote X6) and duplicates will be excluded. Authors will search and review titles, abstracts and keywords. Duplicate references will be omitted, and afterwards two of the reviewers (EGh, FN) will screen the titles. Three independent researchers will evaluate titles, abstracts and full texts of eligible articles for inclusion (DKZ, EGH, FN), and there will be a final vetting by the fourth reviewer (IM), in case of discrepancies, who will check the results. The search process may be re-run and more studies may be retrieved for inclusion just before the final analysis. A predefined inclusion and exclusion criterion is to be used for assessment of the full texts of the remaining titles. The excluded studies will be listed in a table associated with the reason for exclusion. Data extraction will be processed electronically using a developed data abstraction form adapted from the Cochrane Public Health Group. By data extraction and assessment form, any information on aspects deemed necessary as per Methodological Expectations of Cochrane Intervention Reviews (MECIR) standards will be collected.24 The data abstraction form will be piloted on a random sample of five included articles, and modifications will be made as required, based on the team’s feedback. Full data abstraction will be started only when there is a sufficient agreement (i.e. The percentage of agreement >90 %). Data extraction for the literature review will be based on study goals, characteristics (e.g. the first author, the publication date), search strategy and terminology, to describe the review and its settings and timeframe policy. The process of selecting studies will be documented in a PRISMA flow chart. (Figure 1)\n\nQuality Assessment\n\nThe researchers will evaluate the quality of selected articles based on valid checklists for study types. The quality assessment of observational studies such as cohort and cross-sectional articles will be carried out by strengthening the reporting of observational studies in epidemiology check list (STROBE).25 Based on this checklist, ranking scores are from 0 to 34 and studies will be classified into 3 groups based on their ranking score as follows: weak quality ranking score from 0 to 11; moderate quality ranking score from 12 to 22; and high quality ranking score from 23 to 34. The quality assessment of experimental studies will be carried out by transparent reporting of evaluations with nonrandomized designs (TREND).26 Based on this checklist, ranking scores are from 0 to 59 and studies will be classified into 3 groups based on their ranking score as follows: weak quality ranking score from 0 to 21; moderate quality ranking score from 22 to 41; and high quality ranking score from 42 to 59. The quality assessment of qualitative studies will be carried out using the Critical Appraisal Skills Programme (CASP). 27 Based on this checklist, ranking scores are from 0 to 10 and studies will be classified into 2 groups based on their ranking score as follows: weak quality ranking score from 0 to 5; and high quality ranking score from 6 to 10. The quality assessment of the systematic reviews and meta-analyses will be carried out by the checklist for preferred reporting items for systematic reviews and meta-analyses (PRISMA).23 The checklist consists of 27 items and papers are reviewed for each item and marked either as implemented or not-implemented. In case of the absence of an item in a paper, it will be rated ZERO, and if the subject item exists in the paper, it will be rated ONE. When items are not as distinct, the unclear sections will be assessed several times until a precise interpretation is reached and a valid evaluation of the study is made. The risk of bias will be assessed using ROBIS Risk of Bias assessment tool.23,28\n\n\nResults:\nThe primary outcome of this study will be the identified needs of patients with chronic diseases such as diabetes, cardiovascular, hypertension, respiratory disease, and cancers that are critical before, during, and after natural disasters. Secondary outcome of the study will be the reported challenges in the process of health service provision to these patients. The following data will be extracted from the selected articles: general information (title, name of authors, year, research type, subject group (by disease type), classification (before, during and after natural disaster), and factors essential to follow a process of health service provision to patients with chronic illnesses in natural disasters.\n\nAcknowledgments\n\nThis article is extracted from a PhD thesis on Health in Emergency and Disaster, with COI: IR.UMSU.REC.1398.228, the Research Center for Social Factors Effective on Health, Urmia University of Medical Sciences. We extend our special thanks to supervisors and advisors, who collaborated in this research.\n\nAbbreviations \n\nNCDs: Non-Communicable diseases\nCDC: Center for Disease Control \nPICO: Patient/ Population/ Participants/ Problem, Intervention, Comparison, Outcome\nCOPD: Chronic Obstructive Pulmonary Disease\nPROSPERO: Prospective Register of Systematic Reviews\n\n"},"whole_article_abstract":{"kind":"string","value":"Background:\nNCDs require an ongoing management for optimal outcomes, which is challenging in emergency settings, because natural disasters increase the risk of acute NCD exacerbations and lead to health systems' inability to respond. This study aims to develop a protocol for a systematic review on non-communicable diseases in natural disaster settings.\n\nMethods:\nThis systematic review protocol is submitted to the International Prospective Register of Systematic Reviews (Registration No. CRD42020164032). The electronic databases to be used in this study include: Medline, Scopus, Web of Science, Clinical Key, CINAHL, EBSCO, Ovid, EMBASE, ProQuest, Google Scholar, Cochrane Library (Cochrane database of systematic reviews; Cochrane central Register of controlled Trials). Records from 1997 to 2019 are subject to this investigation. Three independent researchers will review the titles, abstracts, and full texts of articles eligible for inclusion, and if not matched, they will be reviewed by a final fourth reviewer. The proposed systematic review will be reported in accordance with the reporting guideline provided in the Preferred Reporting Items for Systematic review and Meta-Analysis (PRISMA) statement. We select studies based on: PICOs (Participants, Interventions, Comparators, and Outcomes).\n\nResults:\nThis systematic review identifies any impacts of natural disasters on patients with NCDs in three stages i.e. before, during and in the aftermath of natural disasters.\n\nConclusions:\nA comprehensive response to NCD management in natural disasters is an important but neglected aspect of non-communicable disease control and humanitarian response, which can significantly reduce the potential risk of morbidity and mortality associated with natural disasters."},"background_conclusion_text":{"kind":"null"},"background_conclusion_abstract":{"kind":"null"},"whole_article_text_length":{"kind":"number","value":2640,"string":"2,640"},"whole_article_abstract_length":{"kind":"number","value":308,"string":"308"},"other_sections_lengths":{"kind":"list like","value":[],"string":"[]"},"num_sections":{"kind":"number","value":3,"string":"3"},"most_frequent_words":{"kind":"list like","value":["diseases","natural","disasters","natural disasters","chronic","studies","patients","non","communicable","search"],"string":"[\n \"diseases\",\n \"natural\",\n \"disasters\",\n \"natural disasters\",\n \"chronic\",\n \"studies\",\n \"patients\",\n \"non\",\n \"communicable\",\n \"search\"\n]"},"keybert_topics":{"kind":"list like","value":["illnesses natural disasters","disasters inadequate care","disasters patients non","communicable diseases emergencies","health emergency disaster"],"string":"[\n \"illnesses natural disasters\",\n \"disasters inadequate care\",\n \"disasters patients non\",\n \"communicable diseases emergencies\",\n \"health emergency disaster\"\n]"},"annotated_base_background_abstract_prompt":{"kind":"string","value":""},"annotated_base_methods_abstract_prompt":{"kind":"null"},"annotated_base_results_abstract_prompt":{"kind":"string","value":""},"annotated_base_conclusions_abstract_prompt":{"kind":"null"},"annotated_base_whole_article_abstract_prompt":{"kind":"string","value":""},"annotated_base_background_conclusion_abstract_prompt":{"kind":"null"},"annotated_keywords_background_abstract_prompt":{"kind":"string","value":"[CONTENT] Crisis | Natural Disaster | Management | Non Communicable Diseases | Chronic illness [SUMMARY]"},"annotated_keywords_methods_abstract_prompt":{"kind":"null"},"annotated_keywords_results_abstract_prompt":{"kind":"string","value":"[CONTENT] Crisis | Natural Disaster | Management | Non Communicable Diseases | Chronic illness [SUMMARY]"},"annotated_keywords_conclusions_abstract_prompt":{"kind":"null"},"annotated_keywords_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] Crisis | Natural Disaster | Management | Non Communicable Diseases | Chronic illness [SUMMARY]"},"annotated_keywords_background_conclusion_abstract_prompt":{"kind":"null"},"annotated_mesh_background_abstract_prompt":{"kind":"string","value":"[CONTENT] Disasters | Humans | Noncommunicable Diseases | Systematic Reviews as Topic [SUMMARY]"},"annotated_mesh_methods_abstract_prompt":{"kind":"null"},"annotated_mesh_results_abstract_prompt":{"kind":"string","value":"[CONTENT] Disasters | Humans | Noncommunicable Diseases | Systematic Reviews as Topic [SUMMARY]"},"annotated_mesh_conclusions_abstract_prompt":{"kind":"null"},"annotated_mesh_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] Disasters | Humans | Noncommunicable Diseases | Systematic Reviews as Topic [SUMMARY]"},"annotated_mesh_background_conclusion_abstract_prompt":{"kind":"null"},"annotated_keybert_background_abstract_prompt":{"kind":"string","value":"[CONTENT] illnesses natural disasters | disasters inadequate care | disasters patients non | communicable diseases emergencies | health emergency disaster [SUMMARY]"},"annotated_keybert_methods_abstract_prompt":{"kind":"null"},"annotated_keybert_results_abstract_prompt":{"kind":"string","value":"[CONTENT] illnesses natural disasters | disasters inadequate care | disasters patients non | communicable diseases emergencies | health emergency disaster [SUMMARY]"},"annotated_keybert_conclusions_abstract_prompt":{"kind":"null"},"annotated_keybert_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] illnesses natural disasters | disasters inadequate care | disasters patients non | communicable diseases emergencies | health emergency disaster [SUMMARY]"},"annotated_keybert_background_conclusion_abstract_prompt":{"kind":"null"},"annotated_most_frequent_background_abstract_prompt":{"kind":"string","value":"[CONTENT] diseases | natural | disasters | natural disasters | chronic | studies | patients | non | communicable | search [SUMMARY]"},"annotated_most_frequent_methods_abstract_prompt":{"kind":"null"},"annotated_most_frequent_results_abstract_prompt":{"kind":"string","value":"[CONTENT] diseases | natural | disasters | natural disasters | chronic | studies | patients | non | communicable | search [SUMMARY]"},"annotated_most_frequent_conclusions_abstract_prompt":{"kind":"null"},"annotated_most_frequent_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] diseases | natural | disasters | natural disasters | chronic | studies | patients | non | communicable | search [SUMMARY]"},"annotated_most_frequent_background_conclusion_abstract_prompt":{"kind":"null"},"annotated_tf_idf_background_abstract_prompt":{"kind":"string","value":"[CONTENT] diseases | disasters | natural disasters | natural | exacerbation | chronic | care | patients | communicable | medical [SUMMARY]"},"annotated_tf_idf_methods_abstract_prompt":{"kind":"null"},"annotated_tf_idf_results_abstract_prompt":{"kind":"string","value":"[CONTENT] health | disease | outcome | service provision | service provision patients | extracted | health service provision | provision patients | health service provision patients | outcome study [SUMMARY]"},"annotated_tf_idf_conclusions_abstract_prompt":{"kind":"null"},"annotated_tf_idf_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] diseases | natural | disasters | natural disasters | chronic | patients | health | studies | search | communicable [SUMMARY]"},"annotated_tf_idf_background_conclusion_abstract_prompt":{"kind":"null"},"annotated_entity_plan_background_abstract_prompt":{"kind":"string","value":"[CONTENT] ||| [SUMMARY]"},"annotated_entity_plan_methods_abstract_prompt":{"kind":"null"},"annotated_entity_plan_results_abstract_prompt":{"kind":"string","value":"[CONTENT] three [SUMMARY]"},"annotated_entity_plan_conclusions_abstract_prompt":{"kind":"null"},"annotated_entity_plan_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] ||| ||| the International Prospective Register of Systematic Reviews ||| Medline, Scopus, Web of Science, Clinical Key | EBSCO | Ovid | EMBASE | ProQuest | Google Scholar | Cochrane Library | Cochrane | Cochrane | Trials ||| 1997 | 2019 ||| Three | fourth ||| the Preferred Reporting Items for Systematic | Meta-Analysis ||| Comparators | Outcomes ||| ||| three ||| NCD [SUMMARY]"},"annotated_entity_plan_background_conclusion_abstract_prompt":{"kind":"null"}}},{"rowIdx":259,"cells":{"title":{"kind":"string","value":"The expression of interleukin-32 is activated by human cytomegalovirus infection and down regulated by hcmv-miR-UL112-1."},"pmid":{"kind":"string","value":"23402302"},"background_abstract":{"kind":"string","value":"Interleukin-32 (IL-32) is an important factor in innate and adaptive immune responses, which activates the p38MAPK, NF-kappa B and AP-1 signaling pathways. Recent reports have highlighted that IL-32 is regulated during viral infection in humans."},"background_abstract_label":{"kind":"string","value":"BACKGROUND"},"methods_abstract":{"kind":"string","value":"Enzyme-linked immunosorbent assays (ELISA) were carried out to detect IL-32 levels in serum samples. Detailed kinetics of the transcription of IL-32 mRNA and expression of IL-32 protein during human cytomegalovirus (HCMV) infection were determined by semi-quantitative RT-PCR and western blot, respectively. The expression levels of hcmv-miR-UL112-1 were detected using TaqMan® miRNA assays during a time course of 96 hours. The effects of hcmv-miR-UL112-1 on IL-32 expression were demonstrated by luciferase assay and western blot, respectively."},"methods_abstract_label":{"kind":"string","value":"METHODS"},"results_abstract":{"kind":"string","value":"Serum levels of IL-32 in HCMV-IgM positive patients (indicating an active HCMV infection) were significantly higher than those in HCMV-IgM negative controls. HCMV infection activated cellular IL-32 transcription mainly in the immediately early (IE) phase and elevated IL-32 protein levels between 6 and 72 hours post infection (hpi) in the human embryonic lung fibroblast cell line, MRC-5. The expression of hcmv-miR-UL112-1 was detected at 24 hpi and increased gradually as the HCMV-infection process was prolonged. In addition, it was demonstrated that hcmv-miR-UL112-1 targets a sequence in the IL-32 3'-UTR. The protein level of IL-32 in HEK293 cells could be functionally down-regulated by transfected hcmv-miR-UL112-1."},"results_abstract_label":{"kind":"string","value":"RESULTS"},"conclusions_abstract":{"kind":"string","value":"IL-32 expression was induced by active HCMV infection and could be functionally down-regulated by ectopically expressed hcmv-miR-UL112-1. Our data may indicate a new strategy of immune evasion by HCMV through post-transcriptional regulation."},"conclusions_abstract_label":{"kind":"string","value":"CONCLUSIONS"},"mesh_descriptor_names":{"kind":"list like","value":["Cell Line","Child, Preschool","Cytomegalovirus","Cytomegalovirus Infections","Female","Gene Expression Profiling","Gene Expression Regulation","Humans","Infant","Interleukins","Male","MicroRNAs","RNA, Viral","Serum"],"string":"[\n \"Cell Line\",\n \"Child, Preschool\",\n \"Cytomegalovirus\",\n \"Cytomegalovirus Infections\",\n \"Female\",\n \"Gene Expression Profiling\",\n \"Gene Expression Regulation\",\n \"Humans\",\n \"Infant\",\n \"Interleukins\",\n \"Male\",\n \"MicroRNAs\",\n \"RNA, Viral\",\n \"Serum\"\n]"},"pmcid":{"kind":"string","value":"3598236"},"background_title":{"kind":"string","value":"Background"},"background_text":{"kind":"string","value":"Interleukin-32 (IL-32) is a newly-discovered pro-inflammatory cytokine, which plays a role in innate and adaptive immune responses [1,2]. It lacks sequence homology to any presently known cytokine families. IL-32 is associated with the induction of inflammatory responses by activating the p38MAPK, NF-kappa B and AP-1 signaling pathways. It has been implicated in inflammatory disorders, mycobacterium tuberculosis infections and inflammatory bowel disease, as well as in some autoimmune diseases, such as rheumatoid arthritis, ulcerative colitis and Crohn’s disease [3-10]. Moreover, it has been reported that IL-32 has pro-inflammatory effects on myeloid cells and promotes the differentiation of osteoclast precursors into multinucleated cells expressing specific osteoclast markers [11,12]. In recent studies, IL-32 has also been found to be regulated during viral infections. Elevated levels of IL-32 were found in sera from patients infected with influenza A virus [13-15], hepatitis B virus (HBV) [16], hepatitis C virus (HCV) [17], human papillomavirus (HPV) [18] and human immunodeficiency virus (HIV) [19-21], suggesting that IL-32 might play an important role in host defense against viral infections\nHuman cytomegalovirus (HCMV) is an ubiquitous β-herpesvirus that infects a broad range of cell types in human hosts, contributing to its complex and varied pathogenesis. HCMV Infection leads to life-long persistence in 50%–90% of the population, which is generally subclinical in healthy individuals [22]. However, it can lead to serious complications in immunocompromised patients, such as transplant recipients or AIDS patients [23,24].\nMicroRNAs (miRNAs) are an abundant class of small non-coding RNA molecules that target mRNAs generally within their 3′ untranslated regions (3′UTRs). MiRNAs suppress gene expression mainly through inhibition of translation or, rarely, through degradation of mRNA [25,26]. Clinical isolates of HCMV encode at least 17 miRNAs [27,28]. However, only a few functional targets have been validated experimentally for some HCMV-encoded miRNAs [29-34]. It has been demonstrated that hcmv-miR-UL112-1 targets and reduces the expression of HCMV UL123 (IE1 or IE72), UL114 and the major histocompatibility complex class 1-related chain B (MICB). Moreover, BclAF1 protein, a human cytomegalovirus restriction factor, was reported to be a new target of hcmv-miR-UL112-1 [35]. In addition, multiple cellular targets of hcmv-miR-US25-1, which are associated with cell cycle control, were identified by RNA induced silencing complex immunoprecipitation (RISC-IP), including cyclin E2, BRCC3, EID1, MAPRE2 and CD147. IL-32, which is not present in the confirmed targets, was screened out as a candidate target of hcmv-miR-UL112-1 in our previous study [36]. However, no further experiments to validate these findings have been performed.\nIn the present study, the expression levels of IL-32 were compared among serum samples from patients with active HCMV infection and samples from HCMV-IgM negative individuals. The expression levels of IL-32 and hcmv-miR-UL112-1 in HCMV infected MRC-5 cells were detected at different stages of infection and time points. In addition, functional down-regulation of IL-32 by hcmv-miR-UL112-1 was detected in transfected human embryonic kidney (HEK293) cells, and the effect of hcmv-miR-UL112-1 on IL-32 during HCMV infection was primarily discussed."},"methods_title":{"kind":"null"},"methods_text":{"kind":"null"},"results_title":{"kind":"string","value":"Results"},"results_text":{"kind":"string","value":" Relatively high IL-32 levels among individuals with active HCMV infection IL-32 protein levels were measured in the sera of 40 patients with active HCMV infections (HCMV IgM positive) and 32 HCMV IgM negative control individuals by enzyme-linked immunosorbent assays (ELISA). According to the results of HCMV IgG detection, HCMV IgM negative controls were divided into two groups: Previously HCMV infected group (IgG positive, n=17) and healthy group (IgG negative, n=15). Mean IL-32 levels in the sera of patients with active HCMV infection were 4.1778±1.1663 ng/ml (shown in Figure 1). Mean IL-32 levels in sera of the previously HCMV infected group and healthy group were 2.4653±0.8287 ng/ml and 2.4480±0.9162 ng/ml, respectively. The data were analyzed by F test. IL-32 levels in the sera of patients with active HCMV infection were significantly higher than those in the HCMV IgM negative control group (F=23.957, P<0.001). No significant difference was observed between the previously HCMV infected group and the healthy group (P=0.963).\n\nAn illustration showing IL-32 levels in serum samples detected by ELISA. Serum samples were from actively HCMV-infected patients (n=40), HCMV previously infected individuals (n=17) and healthy individuals (n=15), respectively.\nIL-32 protein levels were measured in the sera of 40 patients with active HCMV infections (HCMV IgM positive) and 32 HCMV IgM negative control individuals by enzyme-linked immunosorbent assays (ELISA). According to the results of HCMV IgG detection, HCMV IgM negative controls were divided into two groups: Previously HCMV infected group (IgG positive, n=17) and healthy group (IgG negative, n=15). Mean IL-32 levels in the sera of patients with active HCMV infection were 4.1778±1.1663 ng/ml (shown in Figure 1). Mean IL-32 levels in sera of the previously HCMV infected group and healthy group were 2.4653±0.8287 ng/ml and 2.4480±0.9162 ng/ml, respectively. The data were analyzed by F test. IL-32 levels in the sera of patients with active HCMV infection were significantly higher than those in the HCMV IgM negative control group (F=23.957, P<0.001). No significant difference was observed between the previously HCMV infected group and the healthy group (P=0.963).\n\nAn illustration showing IL-32 levels in serum samples detected by ELISA. Serum samples were from actively HCMV-infected patients (n=40), HCMV previously infected individuals (n=17) and healthy individuals (n=15), respectively.\n Kinetics of the expression of IL-32 mRNA, IL-32 protein and hcmv-miR-UL112-1 in HCMV infected MRC-5 cells Cells were harvested at different infection stages and time points after HCMV infection as described in the Materials and Methods section. We used specific drugs to determine the expression profile of IL-32 at different stages of viral replication. CHX was used to determine the immediate early (IE) stage and PAA was used to determine early (E) stage. As shown in Figure 2A and B, semi-quantitative RT-PCR analysis revealed that IL-32 mRNA expression was significantly activated at IE stage but not at E or late (L) stages post-infection. IL-32 protein expression levels at different time points following HCMV infection were measured by western blot analysis. As shown in Figure 2C and D, IL-32 protein expression reached a peak value at 6 hours post infection (hpi). The relative IL-32 concentration in HCMV infected cells collected at 6 hpi was 10 fold higher than in uninfected cells. All measured values were balanced by using β-actin as an internal control. No accumulation of either IL-32 mRNA or protein was found following the prolongation of HCMV-infection process. IL-32 mRNA levels in HCMV infected cells decreased in E and L stages to a similar level as in uninfected cells. IL-32 protein levels decreased with subsequent hours post infection.\n\nExpression of IL-32 induced by HCMV infection in MRC-5 cells. (A) IL-32 mRNA levels in HCMV infected cells were compared to that in uninfected MRC-5 cells. β-actin was used as an internal control. The universal primers for all IL-32 transcripts were used. U is for uninfected MRC-5; IE is for immediate early stage; E is for early stage; L is for late stage. (B) IL-32 mRNA levels were represented relative to that in IE stage. (C) IL-32 protein levels in HCMV infected MRC-5 cells were detected at different time points. IL-32 densitometer values were normalized by β-actin values. (D) The IL-32 protein levels were represented relative to that in infected cells collected at 6 hpi. (E) The kinetics of hcmv-miR-UL112-1 expression were measured using TaqMan® miRNA assays. The expression of hcmv-miR-UL112-1 could be detected at 24 hpi and increased gradually as the HCMV-infection process was prolonged.\nIn addition, the kinetics of hcmv-miR-UL112-1 expression were measured using TaqMan® miRNA assays. Measured values were normalized by using U6 as an internal control. The expression of hcmv-miR-UL112-1 could be detected at 24 hpi. As shown in Figure 2E, a steep increase was observed at 48 hpi. The relative quantity of hcmv-miR-UL112-1 at 48 hpi was 6.5 fold higher than at 24 hpi. The expression of hcmv-miR-UL112-1 increased gradually as the HCMV-infection process was prolonged. The relative quantity of hcmv-miR-UL112-1 in HCMV infected cells at 96 hpi was 10 fold higher than at 24 hpi.\nCells were harvested at different infection stages and time points after HCMV infection as described in the Materials and Methods section. We used specific drugs to determine the expression profile of IL-32 at different stages of viral replication. CHX was used to determine the immediate early (IE) stage and PAA was used to determine early (E) stage. As shown in Figure 2A and B, semi-quantitative RT-PCR analysis revealed that IL-32 mRNA expression was significantly activated at IE stage but not at E or late (L) stages post-infection. IL-32 protein expression levels at different time points following HCMV infection were measured by western blot analysis. As shown in Figure 2C and D, IL-32 protein expression reached a peak value at 6 hours post infection (hpi). The relative IL-32 concentration in HCMV infected cells collected at 6 hpi was 10 fold higher than in uninfected cells. All measured values were balanced by using β-actin as an internal control. No accumulation of either IL-32 mRNA or protein was found following the prolongation of HCMV-infection process. IL-32 mRNA levels in HCMV infected cells decreased in E and L stages to a similar level as in uninfected cells. IL-32 protein levels decreased with subsequent hours post infection.\n\nExpression of IL-32 induced by HCMV infection in MRC-5 cells. (A) IL-32 mRNA levels in HCMV infected cells were compared to that in uninfected MRC-5 cells. β-actin was used as an internal control. The universal primers for all IL-32 transcripts were used. U is for uninfected MRC-5; IE is for immediate early stage; E is for early stage; L is for late stage. (B) IL-32 mRNA levels were represented relative to that in IE stage. (C) IL-32 protein levels in HCMV infected MRC-5 cells were detected at different time points. IL-32 densitometer values were normalized by β-actin values. (D) The IL-32 protein levels were represented relative to that in infected cells collected at 6 hpi. (E) The kinetics of hcmv-miR-UL112-1 expression were measured using TaqMan® miRNA assays. The expression of hcmv-miR-UL112-1 could be detected at 24 hpi and increased gradually as the HCMV-infection process was prolonged.\nIn addition, the kinetics of hcmv-miR-UL112-1 expression were measured using TaqMan® miRNA assays. Measured values were normalized by using U6 as an internal control. The expression of hcmv-miR-UL112-1 could be detected at 24 hpi. As shown in Figure 2E, a steep increase was observed at 48 hpi. The relative quantity of hcmv-miR-UL112-1 at 48 hpi was 6.5 fold higher than at 24 hpi. The expression of hcmv-miR-UL112-1 increased gradually as the HCMV-infection process was prolonged. The relative quantity of hcmv-miR-UL112-1 in HCMV infected cells at 96 hpi was 10 fold higher than at 24 hpi.\n Functional down-regulation of endogenous IL-32 expression by ectopically expressed hcmv-miR-UL112-1 In our previous study, a sequence in the 3′UTR of IL-32 mRNA was identified by hybrid-PCR to be a candidate target site of hcmv-miR-UL112-1. The predicted binding site for hcmv-miR-UL112-1 was 195 nt upstream of the polyA structure of IL-32 mRNA. A schematic representation of hcmv-miR-UL112-1 binding to the IL-32 3′UTR sequence is shown in Figure 3A.\n\nDown-regulation of IL-32 expression by hcmv-miR-UL112-1. (A) The diagram shows the predicted sequences of hcmv-miR-UL112-1 binding to IL-32 mRNA. (B) As a candidate target, IL-32 was validated for its ability to inhibit expression of a luciferase reporter construct in the presence of hcmv-miR-UL112-1 (pS-miR-UL112-1). Results are shown as percentage expression of negative control sample (pS-neg) following correction for transfection levels according to the control of renilla luciferase expression. Values are shown as means ± standard deviations for triplicate samples. (C) HEK 293 cells were transfected with pS-miR-UL112-1 or control vector respectively. Cells were collected 48 hpi and were subjected to western blot analysis using the indicated antibodies. IL-32 densitometer values were normalized to that of the β-actin values. (D) The amounts of proteins presented in panel C were quantified by densitometry. Results are shown as percentage expression of the negative control sample (Empty).\nTo further determine whether the IL-32 3′UTR sequence represents a functional target site for hcmv-miR-UL112-1, the IL-32 3′UTR sequence was validated by luciferase reporter assays. As shown in Figure 3B, transfected pMIR-IL-32UTR led to a significant inhibition of luciferase activity by 39.27% in the presence of pS-miR-UL112-1. pS-miR-UL112-1 had no significant inhibitory effects on the luciferase activities of pMIR and pMIR-IL-32MUT. These results demonstrate that the IL-32 3′UTR sequence provided a specific and functional binding site for hcmv-miR-UL112-1.\nThe regulatory effect of hcmv-miR-UL112-1 on IL-32 protein expression was then examined in HEK293 cells by western blot. IL-32 protein level was significantly reduced in cells transfected with hcmv-miR-UL112-1 in comparison to cells transfected with pSilencer negative control (Figure 3C and D). IL-32 densitometer values were normalized to that of β-actin values. The level of reduction of IL-32 protein was approximately 67% as determined by densitometry (Figure 3D). Our observations confirmed that the expression of endogenous IL-32 protein could be specifically inhibited by ectopically expressed hcmv-miR-UL112-1.\nIn our previous study, a sequence in the 3′UTR of IL-32 mRNA was identified by hybrid-PCR to be a candidate target site of hcmv-miR-UL112-1. The predicted binding site for hcmv-miR-UL112-1 was 195 nt upstream of the polyA structure of IL-32 mRNA. A schematic representation of hcmv-miR-UL112-1 binding to the IL-32 3′UTR sequence is shown in Figure 3A.\n\nDown-regulation of IL-32 expression by hcmv-miR-UL112-1. (A) The diagram shows the predicted sequences of hcmv-miR-UL112-1 binding to IL-32 mRNA. (B) As a candidate target, IL-32 was validated for its ability to inhibit expression of a luciferase reporter construct in the presence of hcmv-miR-UL112-1 (pS-miR-UL112-1). Results are shown as percentage expression of negative control sample (pS-neg) following correction for transfection levels according to the control of renilla luciferase expression. Values are shown as means ± standard deviations for triplicate samples. (C) HEK 293 cells were transfected with pS-miR-UL112-1 or control vector respectively. Cells were collected 48 hpi and were subjected to western blot analysis using the indicated antibodies. IL-32 densitometer values were normalized to that of the β-actin values. (D) The amounts of proteins presented in panel C were quantified by densitometry. Results are shown as percentage expression of the negative control sample (Empty).\nTo further determine whether the IL-32 3′UTR sequence represents a functional target site for hcmv-miR-UL112-1, the IL-32 3′UTR sequence was validated by luciferase reporter assays. As shown in Figure 3B, transfected pMIR-IL-32UTR led to a significant inhibition of luciferase activity by 39.27% in the presence of pS-miR-UL112-1. pS-miR-UL112-1 had no significant inhibitory effects on the luciferase activities of pMIR and pMIR-IL-32MUT. These results demonstrate that the IL-32 3′UTR sequence provided a specific and functional binding site for hcmv-miR-UL112-1.\nThe regulatory effect of hcmv-miR-UL112-1 on IL-32 protein expression was then examined in HEK293 cells by western blot. IL-32 protein level was significantly reduced in cells transfected with hcmv-miR-UL112-1 in comparison to cells transfected with pSilencer negative control (Figure 3C and D). IL-32 densitometer values were normalized to that of β-actin values. The level of reduction of IL-32 protein was approximately 67% as determined by densitometry (Figure 3D). Our observations confirmed that the expression of endogenous IL-32 protein could be specifically inhibited by ectopically expressed hcmv-miR-UL112-1."},"conclusions_title":{"kind":"string","value":"Conclusions"},"conclusions_text":{"kind":"string","value":"In summary, IL-32 expression in active HCMV infection was firstly investigated in our study. Detailed kinetics of the transcription of hcmv-miR-UL112-1, IL-32 mRNA and its protein expression were determined in HCMV infected MRC-5 cells. Furthermore, IL-32 expression was demonstrated to be functionally down-regulated by ectopically expressed hcmv-miR-UL112-1 in HEK293 cells. Follow up studies will investigate the mechanism of immune evasion by HCMV mediated by hcmv-miR-UL112-1, which has been confirmed to modulate NK cell activation during HCMV infection through post-transcriptional regulation."},"other_sections_titles":{"kind":"list like","value":["Background","Relatively high IL-32 levels among individuals with active HCMV infection","Kinetics of the expression of IL-32 mRNA, IL-32 protein and hcmv-miR-UL112-1 in HCMV infected MRC-5 cells","Functional down-regulation of endogenous IL-32 expression by ectopically expressed hcmv-miR-UL112-1","Serum samples","Cell lines","Virus","Plasmid constructs","Enzyme-linked immunosorbent assay","Semi-quantitative RT-PCR analysis","miRNA extraction and TaqMan assays","Luciferase assay","Western blot analysis","Statistical analysis","Competing interests","Authors’ contributions"],"string":"[\n \"Background\",\n \"Relatively high IL-32 levels among individuals with active HCMV infection\",\n \"Kinetics of the expression of IL-32 mRNA, IL-32 protein and hcmv-miR-UL112-1 in HCMV infected MRC-5 cells\",\n \"Functional down-regulation of endogenous IL-32 expression by ectopically expressed hcmv-miR-UL112-1\",\n \"Serum samples\",\n \"Cell lines\",\n \"Virus\",\n \"Plasmid constructs\",\n \"Enzyme-linked immunosorbent assay\",\n \"Semi-quantitative RT-PCR analysis\",\n \"miRNA extraction and TaqMan assays\",\n \"Luciferase assay\",\n \"Western blot analysis\",\n \"Statistical analysis\",\n \"Competing interests\",\n \"Authors’ contributions\"\n]"},"other_sections_texts":{"kind":"list like","value":["Interleukin-32 (IL-32) is a newly-discovered pro-inflammatory cytokine, which plays a role in innate and adaptive immune responses [1,2]. It lacks sequence homology to any presently known cytokine families. IL-32 is associated with the induction of inflammatory responses by activating the p38MAPK, NF-kappa B and AP-1 signaling pathways. It has been implicated in inflammatory disorders, mycobacterium tuberculosis infections and inflammatory bowel disease, as well as in some autoimmune diseases, such as rheumatoid arthritis, ulcerative colitis and Crohn’s disease [3-10]. Moreover, it has been reported that IL-32 has pro-inflammatory effects on myeloid cells and promotes the differentiation of osteoclast precursors into multinucleated cells expressing specific osteoclast markers [11,12]. In recent studies, IL-32 has also been found to be regulated during viral infections. Elevated levels of IL-32 were found in sera from patients infected with influenza A virus [13-15], hepatitis B virus (HBV) [16], hepatitis C virus (HCV) [17], human papillomavirus (HPV) [18] and human immunodeficiency virus (HIV) [19-21], suggesting that IL-32 might play an important role in host defense against viral infections\nHuman cytomegalovirus (HCMV) is an ubiquitous β-herpesvirus that infects a broad range of cell types in human hosts, contributing to its complex and varied pathogenesis. HCMV Infection leads to life-long persistence in 50%–90% of the population, which is generally subclinical in healthy individuals [22]. However, it can lead to serious complications in immunocompromised patients, such as transplant recipients or AIDS patients [23,24].\nMicroRNAs (miRNAs) are an abundant class of small non-coding RNA molecules that target mRNAs generally within their 3′ untranslated regions (3′UTRs). MiRNAs suppress gene expression mainly through inhibition of translation or, rarely, through degradation of mRNA [25,26]. Clinical isolates of HCMV encode at least 17 miRNAs [27,28]. However, only a few functional targets have been validated experimentally for some HCMV-encoded miRNAs [29-34]. It has been demonstrated that hcmv-miR-UL112-1 targets and reduces the expression of HCMV UL123 (IE1 or IE72), UL114 and the major histocompatibility complex class 1-related chain B (MICB). Moreover, BclAF1 protein, a human cytomegalovirus restriction factor, was reported to be a new target of hcmv-miR-UL112-1 [35]. In addition, multiple cellular targets of hcmv-miR-US25-1, which are associated with cell cycle control, were identified by RNA induced silencing complex immunoprecipitation (RISC-IP), including cyclin E2, BRCC3, EID1, MAPRE2 and CD147. IL-32, which is not present in the confirmed targets, was screened out as a candidate target of hcmv-miR-UL112-1 in our previous study [36]. However, no further experiments to validate these findings have been performed.\nIn the present study, the expression levels of IL-32 were compared among serum samples from patients with active HCMV infection and samples from HCMV-IgM negative individuals. The expression levels of IL-32 and hcmv-miR-UL112-1 in HCMV infected MRC-5 cells were detected at different stages of infection and time points. In addition, functional down-regulation of IL-32 by hcmv-miR-UL112-1 was detected in transfected human embryonic kidney (HEK293) cells, and the effect of hcmv-miR-UL112-1 on IL-32 during HCMV infection was primarily discussed.","IL-32 protein levels were measured in the sera of 40 patients with active HCMV infections (HCMV IgM positive) and 32 HCMV IgM negative control individuals by enzyme-linked immunosorbent assays (ELISA). According to the results of HCMV IgG detection, HCMV IgM negative controls were divided into two groups: Previously HCMV infected group (IgG positive, n=17) and healthy group (IgG negative, n=15). Mean IL-32 levels in the sera of patients with active HCMV infection were 4.1778±1.1663 ng/ml (shown in Figure 1). Mean IL-32 levels in sera of the previously HCMV infected group and healthy group were 2.4653±0.8287 ng/ml and 2.4480±0.9162 ng/ml, respectively. The data were analyzed by F test. IL-32 levels in the sera of patients with active HCMV infection were significantly higher than those in the HCMV IgM negative control group (F=23.957, P<0.001). No significant difference was observed between the previously HCMV infected group and the healthy group (P=0.963).\n\nAn illustration showing IL-32 levels in serum samples detected by ELISA. Serum samples were from actively HCMV-infected patients (n=40), HCMV previously infected individuals (n=17) and healthy individuals (n=15), respectively.","Cells were harvested at different infection stages and time points after HCMV infection as described in the Materials and Methods section. We used specific drugs to determine the expression profile of IL-32 at different stages of viral replication. CHX was used to determine the immediate early (IE) stage and PAA was used to determine early (E) stage. As shown in Figure 2A and B, semi-quantitative RT-PCR analysis revealed that IL-32 mRNA expression was significantly activated at IE stage but not at E or late (L) stages post-infection. IL-32 protein expression levels at different time points following HCMV infection were measured by western blot analysis. As shown in Figure 2C and D, IL-32 protein expression reached a peak value at 6 hours post infection (hpi). The relative IL-32 concentration in HCMV infected cells collected at 6 hpi was 10 fold higher than in uninfected cells. All measured values were balanced by using β-actin as an internal control. No accumulation of either IL-32 mRNA or protein was found following the prolongation of HCMV-infection process. IL-32 mRNA levels in HCMV infected cells decreased in E and L stages to a similar level as in uninfected cells. IL-32 protein levels decreased with subsequent hours post infection.\n\nExpression of IL-32 induced by HCMV infection in MRC-5 cells. (A) IL-32 mRNA levels in HCMV infected cells were compared to that in uninfected MRC-5 cells. β-actin was used as an internal control. The universal primers for all IL-32 transcripts were used. U is for uninfected MRC-5; IE is for immediate early stage; E is for early stage; L is for late stage. (B) IL-32 mRNA levels were represented relative to that in IE stage. (C) IL-32 protein levels in HCMV infected MRC-5 cells were detected at different time points. IL-32 densitometer values were normalized by β-actin values. (D) The IL-32 protein levels were represented relative to that in infected cells collected at 6 hpi. (E) The kinetics of hcmv-miR-UL112-1 expression were measured using TaqMan® miRNA assays. The expression of hcmv-miR-UL112-1 could be detected at 24 hpi and increased gradually as the HCMV-infection process was prolonged.\nIn addition, the kinetics of hcmv-miR-UL112-1 expression were measured using TaqMan® miRNA assays. Measured values were normalized by using U6 as an internal control. The expression of hcmv-miR-UL112-1 could be detected at 24 hpi. As shown in Figure 2E, a steep increase was observed at 48 hpi. The relative quantity of hcmv-miR-UL112-1 at 48 hpi was 6.5 fold higher than at 24 hpi. The expression of hcmv-miR-UL112-1 increased gradually as the HCMV-infection process was prolonged. The relative quantity of hcmv-miR-UL112-1 in HCMV infected cells at 96 hpi was 10 fold higher than at 24 hpi.","In our previous study, a sequence in the 3′UTR of IL-32 mRNA was identified by hybrid-PCR to be a candidate target site of hcmv-miR-UL112-1. The predicted binding site for hcmv-miR-UL112-1 was 195 nt upstream of the polyA structure of IL-32 mRNA. A schematic representation of hcmv-miR-UL112-1 binding to the IL-32 3′UTR sequence is shown in Figure 3A.\n\nDown-regulation of IL-32 expression by hcmv-miR-UL112-1. (A) The diagram shows the predicted sequences of hcmv-miR-UL112-1 binding to IL-32 mRNA. (B) As a candidate target, IL-32 was validated for its ability to inhibit expression of a luciferase reporter construct in the presence of hcmv-miR-UL112-1 (pS-miR-UL112-1). Results are shown as percentage expression of negative control sample (pS-neg) following correction for transfection levels according to the control of renilla luciferase expression. Values are shown as means ± standard deviations for triplicate samples. (C) HEK 293 cells were transfected with pS-miR-UL112-1 or control vector respectively. Cells were collected 48 hpi and were subjected to western blot analysis using the indicated antibodies. IL-32 densitometer values were normalized to that of the β-actin values. (D) The amounts of proteins presented in panel C were quantified by densitometry. Results are shown as percentage expression of the negative control sample (Empty).\nTo further determine whether the IL-32 3′UTR sequence represents a functional target site for hcmv-miR-UL112-1, the IL-32 3′UTR sequence was validated by luciferase reporter assays. As shown in Figure 3B, transfected pMIR-IL-32UTR led to a significant inhibition of luciferase activity by 39.27% in the presence of pS-miR-UL112-1. pS-miR-UL112-1 had no significant inhibitory effects on the luciferase activities of pMIR and pMIR-IL-32MUT. These results demonstrate that the IL-32 3′UTR sequence provided a specific and functional binding site for hcmv-miR-UL112-1.\nThe regulatory effect of hcmv-miR-UL112-1 on IL-32 protein expression was then examined in HEK293 cells by western blot. IL-32 protein level was significantly reduced in cells transfected with hcmv-miR-UL112-1 in comparison to cells transfected with pSilencer negative control (Figure 3C and D). IL-32 densitometer values were normalized to that of β-actin values. The level of reduction of IL-32 protein was approximately 67% as determined by densitometry (Figure 3D). Our observations confirmed that the expression of endogenous IL-32 protein could be specifically inhibited by ectopically expressed hcmv-miR-UL112-1.","Serum samples were obtained from 40 patients with HCMV infection (22 male, 18 female, aged 2.2 ± 0.42 yr) from Shengjing Hospital of China Medical University. All patients were HCMV-IgM positive and HCMV-IgG negative. The viral loads in urine samples from these patients were between 1.793×103 and 4.115×107 HCMV DNA copies per milliliter, as detected by routine QF-PCR (DaAn Gene). Blood samples collected from 32 HCMV-IgM negative individuals (17 male, 15 female, aged 2.7±0.35 yr) at the Medical Examination Centre of Shengjing Hospital were used as controls. According to the HCMV IgG detection results, HCMV IgM negative controls were divided into two groups: Previously HCMV infected group (IgG positive, n=17) and healthy group (IgG negative, n=15). All individuals involved in our study were seronegative for influenza A virus, HBV, HCV and HIV. This study was specifically approved by the Ethical Committee of Shengjing hospital.","HEK293 cell line was a gift of Dr. Fangjie Chen from Department of Medical Genetics, China Medical University. HEK293 cells were cultured in Dulbecco’s Modified Eagle’s Medium (DMEM) supplemented with 10% fetal bovine serum (FBS), 100 μg/ml penicillin, 100 μg/ml streptomycin sulfate and 2 mM L-glutamine. Human embryonic lung fibroblasts cells, MRC-5, were acquired from Shanghai Institute for Biological Sciences, Chinese Academy of Sciences (CAS). MRC-5 cells were maintained in Modified Eagle’s Medium (MEM) supplemented with 10% FBS, 100 μg/ml penicillin and 100 μg/ml streptomycin sulfate. All cell cultures were maintained at 37°C in a 5% CO2 incubator.","HCMV clinical strain H was isolated from a urine sample of a 5-month-old infant hospitalized in Shengjing Hospital of China Medical University. Stock virus was propagated in MRC-5 maintained in MEM supplemented with 2% FBS, 100 μg/ml penicillin and 100 μg/ml streptomycin. The supernatant was then harvested, and the aliquots were stored at −80°C before use.","Luciferase reporter construct pMIR was acquired from Ambion Company. IL-32 3′UTR sequence was obtained by RT-PCR from RNA harvested from HCMV infected cells, and inserted into SpeI and HindIII site of the multiple cloning regions (pMIR-IL-32UTR). A point mutation at corresponding seed region binding site was maintained by using Site-directed Gene Mutagenesis Kit (Beyotime), resulting in GUC to GAA. Sequence predicted to express hcmv-miR-UL112-1 was amplified by PCR directly from HCMV genome. The PCR product was inserted into BamHI and HindIII sites of pSilencer4.1 (Ambion) to generate a miRNA expression vector pS-miR-UL112-1. All primer sequences used in plasmid construction are listed in Table 1. All constructs were confirmed by DNA sequencing.\n\nPrimers used in plasmid construction and semi-quantitative RT-PCR\nNote: sequences recognized by restriction en donuclases are down lined.","The concentration of IL-32 in serum samples were detected using human IL-32 ELISA Kit (Cusabio) according to the manufacture’s protocols. The absorbance value at wavelength 450 nm was measured. The IL-32 concentrations were calculated from the standard curve.","MRC-5 cells were inoculated with H strain at 3–5 multiplicity of infection (m.o.i.). Infections were carried out under IE, E and L conditions respectively. For preparation of IE RNA, CHX (Sigma) (100 μl/ml) was added to the culture medium 1 hour before infection and the cells were harvested at 24 hpi. For E RNA, DNA synthesis inhibitor phosphonoacetic acid (PAA) (Sigma) (100 μl/ml) was added to the medium immediately after infection, and the cells were harvested at 48 hpi. L RNA and uninfected cellular RNA was derived from infected (at 96 hpi) and uninfected cells, respectively. Total RNA was isolated using Trizol reagent (TaKaRa), treated with DNase Iand then reverse-transcribed with MLV reverse transcriptase and random primers (TaKaRa). PCR was performed for 24 cycles in 50 μl reactions with the IL-32 specific primer pairs listed in Table 1. mRNA of β-actin was amplified for normalization in all reactions. The primers for IL-32 amplification were designed for detecting all known isoforms of human IL-32 based on the sequences from Gnebank database (NM_001012631.1-001012636.1, NM_004221.4 and NM_001012718.1). The PCR products were analyzed by electrophoresis on 2% agarose gel containing ethidium bromide.","Total RNA was extracted from HCMV infected cells at different time points using the mivVana miRNA isolation kit (Ambion). We measured hcmv-miR-UL112-1 in all samples using TaqMan® miRNA assays (MIMAT0001577 for hcmv-miR-UL112-1, ABI). U6 expression was used to normalize the expression of hcmv-miR-UL112-1. HCMV uninfected MRC-5 cells were used as negative control. Fold difference for hcmv-miR-UL112-1 expression was calculated using the equation 2ΔΔct, where ΔCt = Ct (hcmv-miR-UL112-1) – Ct (U6 control) and ΔΔCt = ΔCt (HCMV infected MRC-5) – ΔCt (MRC-5). The measurements were done in triplicates and the results are presented at the means ± S.D.","MRC-5 cells were plated in 24-well plate at a density of 4.0×105 cells per well and grown to reach about 80% confluence at the time for transfection. 200 ng pMIR-IL-32UTR/pMIR-IL-32MUT plasmid or an empty vector control was co-transfected with 400 ng pS-miR-UL112-1 or pS-neg control into the cells by using lipofectamine 2000 reagent (Invitrogen) according to the manufacture’s protocol. 200 ng renilla luciferase plasmid (Promega) was introduced into each co-transfected cells as an internal control plasmid at the same time. Cells were collected 48 hours post transfection and luciferase activity levels were measured using the Dual luciferase reporter assay system (Promega) according to the manufacture’s guidelines. All measurements were done in triplicates and signals were normalized for transfection efficiency to the internal renilla control. The results are presented at the means ±S.D.","Western blot analyses were performed to detect the IL-32 levels in HCMV infected MRC-5 cells and hcmv-miR-UL112-1 over expressed HEK293 cells respectively. MRC-5 cells were inoculated with H strain at 3–5 m.o.i. Cells were harvested at different time points post infection (6 h, 24 h, 48 h, 72 h and 96 h). HEK293 cells were transfected with 8 μg pS-miR-UL112-1 or pS-neg control using lipofectamine 2000 (Invitrogen) and were incubated at 37°C for 48 hours.\nProteins of the cells described above were prepared by suspending cells in lysis buffer, followed by centrifugation. Concentrations of proteins in supernatant were quantified using protein assay kit (Beyotime). Protein was separated by 10% acrylamide gel electrophoresis and transferred onto a nitrocellulose membrane. Western blot analysis was performed using IL-32 antibody (abcam). Immunoblots were visualized with ECL detection system. Sample loading was normalized by quantities of β-actin detected parallel.","All experiments were reproducible. The results are presented at the means ± S.D. Student’s t test and F test was used to determine statistical significance. Differences were considered statistically significant at value of P≤0.05.","The authors declare that they have no competing interests.","YJH carried out the semi-quantitative RT-PCR analysis, western blot analysis and Statistical analysis. YQ and RH carried out the plasmids construction and luciferase assay. QR as the corresponding author designed the study and corrected the manuscript. YPM and YHJ carried out the preparation of virus and cell lines. ZRS carried out the serum samples collection and ELISA detection. All authors have read and approved the final manuscript."],"string":"[\n \"Interleukin-32 (IL-32) is a newly-discovered pro-inflammatory cytokine, which plays a role in innate and adaptive immune responses [1,2]. It lacks sequence homology to any presently known cytokine families. IL-32 is associated with the induction of inflammatory responses by activating the p38MAPK, NF-kappa B and AP-1 signaling pathways. It has been implicated in inflammatory disorders, mycobacterium tuberculosis infections and inflammatory bowel disease, as well as in some autoimmune diseases, such as rheumatoid arthritis, ulcerative colitis and Crohn’s disease [3-10]. Moreover, it has been reported that IL-32 has pro-inflammatory effects on myeloid cells and promotes the differentiation of osteoclast precursors into multinucleated cells expressing specific osteoclast markers [11,12]. In recent studies, IL-32 has also been found to be regulated during viral infections. Elevated levels of IL-32 were found in sera from patients infected with influenza A virus [13-15], hepatitis B virus (HBV) [16], hepatitis C virus (HCV) [17], human papillomavirus (HPV) [18] and human immunodeficiency virus (HIV) [19-21], suggesting that IL-32 might play an important role in host defense against viral infections\\nHuman cytomegalovirus (HCMV) is an ubiquitous β-herpesvirus that infects a broad range of cell types in human hosts, contributing to its complex and varied pathogenesis. HCMV Infection leads to life-long persistence in 50%–90% of the population, which is generally subclinical in healthy individuals [22]. However, it can lead to serious complications in immunocompromised patients, such as transplant recipients or AIDS patients [23,24].\\nMicroRNAs (miRNAs) are an abundant class of small non-coding RNA molecules that target mRNAs generally within their 3′ untranslated regions (3′UTRs). MiRNAs suppress gene expression mainly through inhibition of translation or, rarely, through degradation of mRNA [25,26]. Clinical isolates of HCMV encode at least 17 miRNAs [27,28]. However, only a few functional targets have been validated experimentally for some HCMV-encoded miRNAs [29-34]. It has been demonstrated that hcmv-miR-UL112-1 targets and reduces the expression of HCMV UL123 (IE1 or IE72), UL114 and the major histocompatibility complex class 1-related chain B (MICB). Moreover, BclAF1 protein, a human cytomegalovirus restriction factor, was reported to be a new target of hcmv-miR-UL112-1 [35]. In addition, multiple cellular targets of hcmv-miR-US25-1, which are associated with cell cycle control, were identified by RNA induced silencing complex immunoprecipitation (RISC-IP), including cyclin E2, BRCC3, EID1, MAPRE2 and CD147. IL-32, which is not present in the confirmed targets, was screened out as a candidate target of hcmv-miR-UL112-1 in our previous study [36]. However, no further experiments to validate these findings have been performed.\\nIn the present study, the expression levels of IL-32 were compared among serum samples from patients with active HCMV infection and samples from HCMV-IgM negative individuals. The expression levels of IL-32 and hcmv-miR-UL112-1 in HCMV infected MRC-5 cells were detected at different stages of infection and time points. In addition, functional down-regulation of IL-32 by hcmv-miR-UL112-1 was detected in transfected human embryonic kidney (HEK293) cells, and the effect of hcmv-miR-UL112-1 on IL-32 during HCMV infection was primarily discussed.\",\n \"IL-32 protein levels were measured in the sera of 40 patients with active HCMV infections (HCMV IgM positive) and 32 HCMV IgM negative control individuals by enzyme-linked immunosorbent assays (ELISA). According to the results of HCMV IgG detection, HCMV IgM negative controls were divided into two groups: Previously HCMV infected group (IgG positive, n=17) and healthy group (IgG negative, n=15). Mean IL-32 levels in the sera of patients with active HCMV infection were 4.1778±1.1663 ng/ml (shown in Figure 1). Mean IL-32 levels in sera of the previously HCMV infected group and healthy group were 2.4653±0.8287 ng/ml and 2.4480±0.9162 ng/ml, respectively. The data were analyzed by F test. IL-32 levels in the sera of patients with active HCMV infection were significantly higher than those in the HCMV IgM negative control group (F=23.957, P<0.001). No significant difference was observed between the previously HCMV infected group and the healthy group (P=0.963).\\n\\nAn illustration showing IL-32 levels in serum samples detected by ELISA. Serum samples were from actively HCMV-infected patients (n=40), HCMV previously infected individuals (n=17) and healthy individuals (n=15), respectively.\",\n \"Cells were harvested at different infection stages and time points after HCMV infection as described in the Materials and Methods section. We used specific drugs to determine the expression profile of IL-32 at different stages of viral replication. CHX was used to determine the immediate early (IE) stage and PAA was used to determine early (E) stage. As shown in Figure 2A and B, semi-quantitative RT-PCR analysis revealed that IL-32 mRNA expression was significantly activated at IE stage but not at E or late (L) stages post-infection. IL-32 protein expression levels at different time points following HCMV infection were measured by western blot analysis. As shown in Figure 2C and D, IL-32 protein expression reached a peak value at 6 hours post infection (hpi). The relative IL-32 concentration in HCMV infected cells collected at 6 hpi was 10 fold higher than in uninfected cells. All measured values were balanced by using β-actin as an internal control. No accumulation of either IL-32 mRNA or protein was found following the prolongation of HCMV-infection process. IL-32 mRNA levels in HCMV infected cells decreased in E and L stages to a similar level as in uninfected cells. IL-32 protein levels decreased with subsequent hours post infection.\\n\\nExpression of IL-32 induced by HCMV infection in MRC-5 cells. (A) IL-32 mRNA levels in HCMV infected cells were compared to that in uninfected MRC-5 cells. β-actin was used as an internal control. The universal primers for all IL-32 transcripts were used. U is for uninfected MRC-5; IE is for immediate early stage; E is for early stage; L is for late stage. (B) IL-32 mRNA levels were represented relative to that in IE stage. (C) IL-32 protein levels in HCMV infected MRC-5 cells were detected at different time points. IL-32 densitometer values were normalized by β-actin values. (D) The IL-32 protein levels were represented relative to that in infected cells collected at 6 hpi. (E) The kinetics of hcmv-miR-UL112-1 expression were measured using TaqMan® miRNA assays. The expression of hcmv-miR-UL112-1 could be detected at 24 hpi and increased gradually as the HCMV-infection process was prolonged.\\nIn addition, the kinetics of hcmv-miR-UL112-1 expression were measured using TaqMan® miRNA assays. Measured values were normalized by using U6 as an internal control. The expression of hcmv-miR-UL112-1 could be detected at 24 hpi. As shown in Figure 2E, a steep increase was observed at 48 hpi. The relative quantity of hcmv-miR-UL112-1 at 48 hpi was 6.5 fold higher than at 24 hpi. The expression of hcmv-miR-UL112-1 increased gradually as the HCMV-infection process was prolonged. The relative quantity of hcmv-miR-UL112-1 in HCMV infected cells at 96 hpi was 10 fold higher than at 24 hpi.\",\n \"In our previous study, a sequence in the 3′UTR of IL-32 mRNA was identified by hybrid-PCR to be a candidate target site of hcmv-miR-UL112-1. The predicted binding site for hcmv-miR-UL112-1 was 195 nt upstream of the polyA structure of IL-32 mRNA. A schematic representation of hcmv-miR-UL112-1 binding to the IL-32 3′UTR sequence is shown in Figure 3A.\\n\\nDown-regulation of IL-32 expression by hcmv-miR-UL112-1. (A) The diagram shows the predicted sequences of hcmv-miR-UL112-1 binding to IL-32 mRNA. (B) As a candidate target, IL-32 was validated for its ability to inhibit expression of a luciferase reporter construct in the presence of hcmv-miR-UL112-1 (pS-miR-UL112-1). Results are shown as percentage expression of negative control sample (pS-neg) following correction for transfection levels according to the control of renilla luciferase expression. Values are shown as means ± standard deviations for triplicate samples. (C) HEK 293 cells were transfected with pS-miR-UL112-1 or control vector respectively. Cells were collected 48 hpi and were subjected to western blot analysis using the indicated antibodies. IL-32 densitometer values were normalized to that of the β-actin values. (D) The amounts of proteins presented in panel C were quantified by densitometry. Results are shown as percentage expression of the negative control sample (Empty).\\nTo further determine whether the IL-32 3′UTR sequence represents a functional target site for hcmv-miR-UL112-1, the IL-32 3′UTR sequence was validated by luciferase reporter assays. As shown in Figure 3B, transfected pMIR-IL-32UTR led to a significant inhibition of luciferase activity by 39.27% in the presence of pS-miR-UL112-1. pS-miR-UL112-1 had no significant inhibitory effects on the luciferase activities of pMIR and pMIR-IL-32MUT. These results demonstrate that the IL-32 3′UTR sequence provided a specific and functional binding site for hcmv-miR-UL112-1.\\nThe regulatory effect of hcmv-miR-UL112-1 on IL-32 protein expression was then examined in HEK293 cells by western blot. IL-32 protein level was significantly reduced in cells transfected with hcmv-miR-UL112-1 in comparison to cells transfected with pSilencer negative control (Figure 3C and D). IL-32 densitometer values were normalized to that of β-actin values. The level of reduction of IL-32 protein was approximately 67% as determined by densitometry (Figure 3D). Our observations confirmed that the expression of endogenous IL-32 protein could be specifically inhibited by ectopically expressed hcmv-miR-UL112-1.\",\n \"Serum samples were obtained from 40 patients with HCMV infection (22 male, 18 female, aged 2.2 ± 0.42 yr) from Shengjing Hospital of China Medical University. All patients were HCMV-IgM positive and HCMV-IgG negative. The viral loads in urine samples from these patients were between 1.793×103 and 4.115×107 HCMV DNA copies per milliliter, as detected by routine QF-PCR (DaAn Gene). Blood samples collected from 32 HCMV-IgM negative individuals (17 male, 15 female, aged 2.7±0.35 yr) at the Medical Examination Centre of Shengjing Hospital were used as controls. According to the HCMV IgG detection results, HCMV IgM negative controls were divided into two groups: Previously HCMV infected group (IgG positive, n=17) and healthy group (IgG negative, n=15). All individuals involved in our study were seronegative for influenza A virus, HBV, HCV and HIV. This study was specifically approved by the Ethical Committee of Shengjing hospital.\",\n \"HEK293 cell line was a gift of Dr. Fangjie Chen from Department of Medical Genetics, China Medical University. HEK293 cells were cultured in Dulbecco’s Modified Eagle’s Medium (DMEM) supplemented with 10% fetal bovine serum (FBS), 100 μg/ml penicillin, 100 μg/ml streptomycin sulfate and 2 mM L-glutamine. Human embryonic lung fibroblasts cells, MRC-5, were acquired from Shanghai Institute for Biological Sciences, Chinese Academy of Sciences (CAS). MRC-5 cells were maintained in Modified Eagle’s Medium (MEM) supplemented with 10% FBS, 100 μg/ml penicillin and 100 μg/ml streptomycin sulfate. All cell cultures were maintained at 37°C in a 5% CO2 incubator.\",\n \"HCMV clinical strain H was isolated from a urine sample of a 5-month-old infant hospitalized in Shengjing Hospital of China Medical University. Stock virus was propagated in MRC-5 maintained in MEM supplemented with 2% FBS, 100 μg/ml penicillin and 100 μg/ml streptomycin. The supernatant was then harvested, and the aliquots were stored at −80°C before use.\",\n \"Luciferase reporter construct pMIR was acquired from Ambion Company. IL-32 3′UTR sequence was obtained by RT-PCR from RNA harvested from HCMV infected cells, and inserted into SpeI and HindIII site of the multiple cloning regions (pMIR-IL-32UTR). A point mutation at corresponding seed region binding site was maintained by using Site-directed Gene Mutagenesis Kit (Beyotime), resulting in GUC to GAA. Sequence predicted to express hcmv-miR-UL112-1 was amplified by PCR directly from HCMV genome. The PCR product was inserted into BamHI and HindIII sites of pSilencer4.1 (Ambion) to generate a miRNA expression vector pS-miR-UL112-1. All primer sequences used in plasmid construction are listed in Table 1. All constructs were confirmed by DNA sequencing.\\n\\nPrimers used in plasmid construction and semi-quantitative RT-PCR\\nNote: sequences recognized by restriction en donuclases are down lined.\",\n \"The concentration of IL-32 in serum samples were detected using human IL-32 ELISA Kit (Cusabio) according to the manufacture’s protocols. The absorbance value at wavelength 450 nm was measured. The IL-32 concentrations were calculated from the standard curve.\",\n \"MRC-5 cells were inoculated with H strain at 3–5 multiplicity of infection (m.o.i.). Infections were carried out under IE, E and L conditions respectively. For preparation of IE RNA, CHX (Sigma) (100 μl/ml) was added to the culture medium 1 hour before infection and the cells were harvested at 24 hpi. For E RNA, DNA synthesis inhibitor phosphonoacetic acid (PAA) (Sigma) (100 μl/ml) was added to the medium immediately after infection, and the cells were harvested at 48 hpi. L RNA and uninfected cellular RNA was derived from infected (at 96 hpi) and uninfected cells, respectively. Total RNA was isolated using Trizol reagent (TaKaRa), treated with DNase Iand then reverse-transcribed with MLV reverse transcriptase and random primers (TaKaRa). PCR was performed for 24 cycles in 50 μl reactions with the IL-32 specific primer pairs listed in Table 1. mRNA of β-actin was amplified for normalization in all reactions. The primers for IL-32 amplification were designed for detecting all known isoforms of human IL-32 based on the sequences from Gnebank database (NM_001012631.1-001012636.1, NM_004221.4 and NM_001012718.1). The PCR products were analyzed by electrophoresis on 2% agarose gel containing ethidium bromide.\",\n \"Total RNA was extracted from HCMV infected cells at different time points using the mivVana miRNA isolation kit (Ambion). We measured hcmv-miR-UL112-1 in all samples using TaqMan® miRNA assays (MIMAT0001577 for hcmv-miR-UL112-1, ABI). U6 expression was used to normalize the expression of hcmv-miR-UL112-1. HCMV uninfected MRC-5 cells were used as negative control. Fold difference for hcmv-miR-UL112-1 expression was calculated using the equation 2ΔΔct, where ΔCt = Ct (hcmv-miR-UL112-1) – Ct (U6 control) and ΔΔCt = ΔCt (HCMV infected MRC-5) – ΔCt (MRC-5). The measurements were done in triplicates and the results are presented at the means ± S.D.\",\n \"MRC-5 cells were plated in 24-well plate at a density of 4.0×105 cells per well and grown to reach about 80% confluence at the time for transfection. 200 ng pMIR-IL-32UTR/pMIR-IL-32MUT plasmid or an empty vector control was co-transfected with 400 ng pS-miR-UL112-1 or pS-neg control into the cells by using lipofectamine 2000 reagent (Invitrogen) according to the manufacture’s protocol. 200 ng renilla luciferase plasmid (Promega) was introduced into each co-transfected cells as an internal control plasmid at the same time. Cells were collected 48 hours post transfection and luciferase activity levels were measured using the Dual luciferase reporter assay system (Promega) according to the manufacture’s guidelines. All measurements were done in triplicates and signals were normalized for transfection efficiency to the internal renilla control. The results are presented at the means ±S.D.\",\n \"Western blot analyses were performed to detect the IL-32 levels in HCMV infected MRC-5 cells and hcmv-miR-UL112-1 over expressed HEK293 cells respectively. MRC-5 cells were inoculated with H strain at 3–5 m.o.i. Cells were harvested at different time points post infection (6 h, 24 h, 48 h, 72 h and 96 h). HEK293 cells were transfected with 8 μg pS-miR-UL112-1 or pS-neg control using lipofectamine 2000 (Invitrogen) and were incubated at 37°C for 48 hours.\\nProteins of the cells described above were prepared by suspending cells in lysis buffer, followed by centrifugation. Concentrations of proteins in supernatant were quantified using protein assay kit (Beyotime). Protein was separated by 10% acrylamide gel electrophoresis and transferred onto a nitrocellulose membrane. Western blot analysis was performed using IL-32 antibody (abcam). Immunoblots were visualized with ECL detection system. Sample loading was normalized by quantities of β-actin detected parallel.\",\n \"All experiments were reproducible. The results are presented at the means ± S.D. Student’s t test and F test was used to determine statistical significance. Differences were considered statistically significant at value of P≤0.05.\",\n \"The authors declare that they have no competing interests.\",\n \"YJH carried out the semi-quantitative RT-PCR analysis, western blot analysis and Statistical analysis. YQ and RH carried out the plasmids construction and luciferase assay. QR as the corresponding author designed the study and corrected the manuscript. YPM and YHJ carried out the preparation of virus and cell lines. ZRS carried out the serum samples collection and ELISA detection. All authors have read and approved the final manuscript.\"\n]"},"other_sections_sec_types":{"kind":"list like","value":[null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null],"string":"[\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null\n]"},"all_sections_titles":{"kind":"list like","value":["Background","Results","Relatively high IL-32 levels among individuals with active HCMV infection","Kinetics of the expression of IL-32 mRNA, IL-32 protein and hcmv-miR-UL112-1 in HCMV infected MRC-5 cells","Functional down-regulation of endogenous IL-32 expression by ectopically expressed hcmv-miR-UL112-1","Discussion","Conclusions","Materials and methods","Serum samples","Cell lines","Virus","Plasmid constructs","Enzyme-linked immunosorbent assay","Semi-quantitative RT-PCR analysis","miRNA extraction and TaqMan assays","Luciferase assay","Western blot analysis","Statistical analysis","Competing interests","Authors’ contributions"],"string":"[\n \"Background\",\n \"Results\",\n \"Relatively high IL-32 levels among individuals with active HCMV infection\",\n \"Kinetics of the expression of IL-32 mRNA, IL-32 protein and hcmv-miR-UL112-1 in HCMV infected MRC-5 cells\",\n \"Functional down-regulation of endogenous IL-32 expression by ectopically expressed hcmv-miR-UL112-1\",\n \"Discussion\",\n \"Conclusions\",\n \"Materials and methods\",\n \"Serum samples\",\n \"Cell lines\",\n \"Virus\",\n \"Plasmid constructs\",\n \"Enzyme-linked immunosorbent assay\",\n \"Semi-quantitative RT-PCR analysis\",\n \"miRNA extraction and TaqMan assays\",\n \"Luciferase assay\",\n \"Western blot analysis\",\n \"Statistical analysis\",\n \"Competing interests\",\n \"Authors’ contributions\"\n]"},"all_sections_texts":{"kind":"list like","value":["Interleukin-32 (IL-32) is a newly-discovered pro-inflammatory cytokine, which plays a role in innate and adaptive immune responses [1,2]. It lacks sequence homology to any presently known cytokine families. IL-32 is associated with the induction of inflammatory responses by activating the p38MAPK, NF-kappa B and AP-1 signaling pathways. It has been implicated in inflammatory disorders, mycobacterium tuberculosis infections and inflammatory bowel disease, as well as in some autoimmune diseases, such as rheumatoid arthritis, ulcerative colitis and Crohn’s disease [3-10]. Moreover, it has been reported that IL-32 has pro-inflammatory effects on myeloid cells and promotes the differentiation of osteoclast precursors into multinucleated cells expressing specific osteoclast markers [11,12]. In recent studies, IL-32 has also been found to be regulated during viral infections. Elevated levels of IL-32 were found in sera from patients infected with influenza A virus [13-15], hepatitis B virus (HBV) [16], hepatitis C virus (HCV) [17], human papillomavirus (HPV) [18] and human immunodeficiency virus (HIV) [19-21], suggesting that IL-32 might play an important role in host defense against viral infections\nHuman cytomegalovirus (HCMV) is an ubiquitous β-herpesvirus that infects a broad range of cell types in human hosts, contributing to its complex and varied pathogenesis. HCMV Infection leads to life-long persistence in 50%–90% of the population, which is generally subclinical in healthy individuals [22]. However, it can lead to serious complications in immunocompromised patients, such as transplant recipients or AIDS patients [23,24].\nMicroRNAs (miRNAs) are an abundant class of small non-coding RNA molecules that target mRNAs generally within their 3′ untranslated regions (3′UTRs). MiRNAs suppress gene expression mainly through inhibition of translation or, rarely, through degradation of mRNA [25,26]. Clinical isolates of HCMV encode at least 17 miRNAs [27,28]. However, only a few functional targets have been validated experimentally for some HCMV-encoded miRNAs [29-34]. It has been demonstrated that hcmv-miR-UL112-1 targets and reduces the expression of HCMV UL123 (IE1 or IE72), UL114 and the major histocompatibility complex class 1-related chain B (MICB). Moreover, BclAF1 protein, a human cytomegalovirus restriction factor, was reported to be a new target of hcmv-miR-UL112-1 [35]. In addition, multiple cellular targets of hcmv-miR-US25-1, which are associated with cell cycle control, were identified by RNA induced silencing complex immunoprecipitation (RISC-IP), including cyclin E2, BRCC3, EID1, MAPRE2 and CD147. IL-32, which is not present in the confirmed targets, was screened out as a candidate target of hcmv-miR-UL112-1 in our previous study [36]. However, no further experiments to validate these findings have been performed.\nIn the present study, the expression levels of IL-32 were compared among serum samples from patients with active HCMV infection and samples from HCMV-IgM negative individuals. The expression levels of IL-32 and hcmv-miR-UL112-1 in HCMV infected MRC-5 cells were detected at different stages of infection and time points. In addition, functional down-regulation of IL-32 by hcmv-miR-UL112-1 was detected in transfected human embryonic kidney (HEK293) cells, and the effect of hcmv-miR-UL112-1 on IL-32 during HCMV infection was primarily discussed."," Relatively high IL-32 levels among individuals with active HCMV infection IL-32 protein levels were measured in the sera of 40 patients with active HCMV infections (HCMV IgM positive) and 32 HCMV IgM negative control individuals by enzyme-linked immunosorbent assays (ELISA). According to the results of HCMV IgG detection, HCMV IgM negative controls were divided into two groups: Previously HCMV infected group (IgG positive, n=17) and healthy group (IgG negative, n=15). Mean IL-32 levels in the sera of patients with active HCMV infection were 4.1778±1.1663 ng/ml (shown in Figure 1). Mean IL-32 levels in sera of the previously HCMV infected group and healthy group were 2.4653±0.8287 ng/ml and 2.4480±0.9162 ng/ml, respectively. The data were analyzed by F test. IL-32 levels in the sera of patients with active HCMV infection were significantly higher than those in the HCMV IgM negative control group (F=23.957, P<0.001). No significant difference was observed between the previously HCMV infected group and the healthy group (P=0.963).\n\nAn illustration showing IL-32 levels in serum samples detected by ELISA. Serum samples were from actively HCMV-infected patients (n=40), HCMV previously infected individuals (n=17) and healthy individuals (n=15), respectively.\nIL-32 protein levels were measured in the sera of 40 patients with active HCMV infections (HCMV IgM positive) and 32 HCMV IgM negative control individuals by enzyme-linked immunosorbent assays (ELISA). According to the results of HCMV IgG detection, HCMV IgM negative controls were divided into two groups: Previously HCMV infected group (IgG positive, n=17) and healthy group (IgG negative, n=15). Mean IL-32 levels in the sera of patients with active HCMV infection were 4.1778±1.1663 ng/ml (shown in Figure 1). Mean IL-32 levels in sera of the previously HCMV infected group and healthy group were 2.4653±0.8287 ng/ml and 2.4480±0.9162 ng/ml, respectively. The data were analyzed by F test. IL-32 levels in the sera of patients with active HCMV infection were significantly higher than those in the HCMV IgM negative control group (F=23.957, P<0.001). No significant difference was observed between the previously HCMV infected group and the healthy group (P=0.963).\n\nAn illustration showing IL-32 levels in serum samples detected by ELISA. Serum samples were from actively HCMV-infected patients (n=40), HCMV previously infected individuals (n=17) and healthy individuals (n=15), respectively.\n Kinetics of the expression of IL-32 mRNA, IL-32 protein and hcmv-miR-UL112-1 in HCMV infected MRC-5 cells Cells were harvested at different infection stages and time points after HCMV infection as described in the Materials and Methods section. We used specific drugs to determine the expression profile of IL-32 at different stages of viral replication. CHX was used to determine the immediate early (IE) stage and PAA was used to determine early (E) stage. As shown in Figure 2A and B, semi-quantitative RT-PCR analysis revealed that IL-32 mRNA expression was significantly activated at IE stage but not at E or late (L) stages post-infection. IL-32 protein expression levels at different time points following HCMV infection were measured by western blot analysis. As shown in Figure 2C and D, IL-32 protein expression reached a peak value at 6 hours post infection (hpi). The relative IL-32 concentration in HCMV infected cells collected at 6 hpi was 10 fold higher than in uninfected cells. All measured values were balanced by using β-actin as an internal control. No accumulation of either IL-32 mRNA or protein was found following the prolongation of HCMV-infection process. IL-32 mRNA levels in HCMV infected cells decreased in E and L stages to a similar level as in uninfected cells. IL-32 protein levels decreased with subsequent hours post infection.\n\nExpression of IL-32 induced by HCMV infection in MRC-5 cells. (A) IL-32 mRNA levels in HCMV infected cells were compared to that in uninfected MRC-5 cells. β-actin was used as an internal control. The universal primers for all IL-32 transcripts were used. U is for uninfected MRC-5; IE is for immediate early stage; E is for early stage; L is for late stage. (B) IL-32 mRNA levels were represented relative to that in IE stage. (C) IL-32 protein levels in HCMV infected MRC-5 cells were detected at different time points. IL-32 densitometer values were normalized by β-actin values. (D) The IL-32 protein levels were represented relative to that in infected cells collected at 6 hpi. (E) The kinetics of hcmv-miR-UL112-1 expression were measured using TaqMan® miRNA assays. The expression of hcmv-miR-UL112-1 could be detected at 24 hpi and increased gradually as the HCMV-infection process was prolonged.\nIn addition, the kinetics of hcmv-miR-UL112-1 expression were measured using TaqMan® miRNA assays. Measured values were normalized by using U6 as an internal control. The expression of hcmv-miR-UL112-1 could be detected at 24 hpi. As shown in Figure 2E, a steep increase was observed at 48 hpi. The relative quantity of hcmv-miR-UL112-1 at 48 hpi was 6.5 fold higher than at 24 hpi. The expression of hcmv-miR-UL112-1 increased gradually as the HCMV-infection process was prolonged. The relative quantity of hcmv-miR-UL112-1 in HCMV infected cells at 96 hpi was 10 fold higher than at 24 hpi.\nCells were harvested at different infection stages and time points after HCMV infection as described in the Materials and Methods section. We used specific drugs to determine the expression profile of IL-32 at different stages of viral replication. CHX was used to determine the immediate early (IE) stage and PAA was used to determine early (E) stage. As shown in Figure 2A and B, semi-quantitative RT-PCR analysis revealed that IL-32 mRNA expression was significantly activated at IE stage but not at E or late (L) stages post-infection. IL-32 protein expression levels at different time points following HCMV infection were measured by western blot analysis. As shown in Figure 2C and D, IL-32 protein expression reached a peak value at 6 hours post infection (hpi). The relative IL-32 concentration in HCMV infected cells collected at 6 hpi was 10 fold higher than in uninfected cells. All measured values were balanced by using β-actin as an internal control. No accumulation of either IL-32 mRNA or protein was found following the prolongation of HCMV-infection process. IL-32 mRNA levels in HCMV infected cells decreased in E and L stages to a similar level as in uninfected cells. IL-32 protein levels decreased with subsequent hours post infection.\n\nExpression of IL-32 induced by HCMV infection in MRC-5 cells. (A) IL-32 mRNA levels in HCMV infected cells were compared to that in uninfected MRC-5 cells. β-actin was used as an internal control. The universal primers for all IL-32 transcripts were used. U is for uninfected MRC-5; IE is for immediate early stage; E is for early stage; L is for late stage. (B) IL-32 mRNA levels were represented relative to that in IE stage. (C) IL-32 protein levels in HCMV infected MRC-5 cells were detected at different time points. IL-32 densitometer values were normalized by β-actin values. (D) The IL-32 protein levels were represented relative to that in infected cells collected at 6 hpi. (E) The kinetics of hcmv-miR-UL112-1 expression were measured using TaqMan® miRNA assays. The expression of hcmv-miR-UL112-1 could be detected at 24 hpi and increased gradually as the HCMV-infection process was prolonged.\nIn addition, the kinetics of hcmv-miR-UL112-1 expression were measured using TaqMan® miRNA assays. Measured values were normalized by using U6 as an internal control. The expression of hcmv-miR-UL112-1 could be detected at 24 hpi. As shown in Figure 2E, a steep increase was observed at 48 hpi. The relative quantity of hcmv-miR-UL112-1 at 48 hpi was 6.5 fold higher than at 24 hpi. The expression of hcmv-miR-UL112-1 increased gradually as the HCMV-infection process was prolonged. The relative quantity of hcmv-miR-UL112-1 in HCMV infected cells at 96 hpi was 10 fold higher than at 24 hpi.\n Functional down-regulation of endogenous IL-32 expression by ectopically expressed hcmv-miR-UL112-1 In our previous study, a sequence in the 3′UTR of IL-32 mRNA was identified by hybrid-PCR to be a candidate target site of hcmv-miR-UL112-1. The predicted binding site for hcmv-miR-UL112-1 was 195 nt upstream of the polyA structure of IL-32 mRNA. A schematic representation of hcmv-miR-UL112-1 binding to the IL-32 3′UTR sequence is shown in Figure 3A.\n\nDown-regulation of IL-32 expression by hcmv-miR-UL112-1. (A) The diagram shows the predicted sequences of hcmv-miR-UL112-1 binding to IL-32 mRNA. (B) As a candidate target, IL-32 was validated for its ability to inhibit expression of a luciferase reporter construct in the presence of hcmv-miR-UL112-1 (pS-miR-UL112-1). Results are shown as percentage expression of negative control sample (pS-neg) following correction for transfection levels according to the control of renilla luciferase expression. Values are shown as means ± standard deviations for triplicate samples. (C) HEK 293 cells were transfected with pS-miR-UL112-1 or control vector respectively. Cells were collected 48 hpi and were subjected to western blot analysis using the indicated antibodies. IL-32 densitometer values were normalized to that of the β-actin values. (D) The amounts of proteins presented in panel C were quantified by densitometry. Results are shown as percentage expression of the negative control sample (Empty).\nTo further determine whether the IL-32 3′UTR sequence represents a functional target site for hcmv-miR-UL112-1, the IL-32 3′UTR sequence was validated by luciferase reporter assays. As shown in Figure 3B, transfected pMIR-IL-32UTR led to a significant inhibition of luciferase activity by 39.27% in the presence of pS-miR-UL112-1. pS-miR-UL112-1 had no significant inhibitory effects on the luciferase activities of pMIR and pMIR-IL-32MUT. These results demonstrate that the IL-32 3′UTR sequence provided a specific and functional binding site for hcmv-miR-UL112-1.\nThe regulatory effect of hcmv-miR-UL112-1 on IL-32 protein expression was then examined in HEK293 cells by western blot. IL-32 protein level was significantly reduced in cells transfected with hcmv-miR-UL112-1 in comparison to cells transfected with pSilencer negative control (Figure 3C and D). IL-32 densitometer values were normalized to that of β-actin values. The level of reduction of IL-32 protein was approximately 67% as determined by densitometry (Figure 3D). Our observations confirmed that the expression of endogenous IL-32 protein could be specifically inhibited by ectopically expressed hcmv-miR-UL112-1.\nIn our previous study, a sequence in the 3′UTR of IL-32 mRNA was identified by hybrid-PCR to be a candidate target site of hcmv-miR-UL112-1. The predicted binding site for hcmv-miR-UL112-1 was 195 nt upstream of the polyA structure of IL-32 mRNA. A schematic representation of hcmv-miR-UL112-1 binding to the IL-32 3′UTR sequence is shown in Figure 3A.\n\nDown-regulation of IL-32 expression by hcmv-miR-UL112-1. (A) The diagram shows the predicted sequences of hcmv-miR-UL112-1 binding to IL-32 mRNA. (B) As a candidate target, IL-32 was validated for its ability to inhibit expression of a luciferase reporter construct in the presence of hcmv-miR-UL112-1 (pS-miR-UL112-1). Results are shown as percentage expression of negative control sample (pS-neg) following correction for transfection levels according to the control of renilla luciferase expression. Values are shown as means ± standard deviations for triplicate samples. (C) HEK 293 cells were transfected with pS-miR-UL112-1 or control vector respectively. Cells were collected 48 hpi and were subjected to western blot analysis using the indicated antibodies. IL-32 densitometer values were normalized to that of the β-actin values. (D) The amounts of proteins presented in panel C were quantified by densitometry. Results are shown as percentage expression of the negative control sample (Empty).\nTo further determine whether the IL-32 3′UTR sequence represents a functional target site for hcmv-miR-UL112-1, the IL-32 3′UTR sequence was validated by luciferase reporter assays. As shown in Figure 3B, transfected pMIR-IL-32UTR led to a significant inhibition of luciferase activity by 39.27% in the presence of pS-miR-UL112-1. pS-miR-UL112-1 had no significant inhibitory effects on the luciferase activities of pMIR and pMIR-IL-32MUT. These results demonstrate that the IL-32 3′UTR sequence provided a specific and functional binding site for hcmv-miR-UL112-1.\nThe regulatory effect of hcmv-miR-UL112-1 on IL-32 protein expression was then examined in HEK293 cells by western blot. IL-32 protein level was significantly reduced in cells transfected with hcmv-miR-UL112-1 in comparison to cells transfected with pSilencer negative control (Figure 3C and D). IL-32 densitometer values were normalized to that of β-actin values. The level of reduction of IL-32 protein was approximately 67% as determined by densitometry (Figure 3D). Our observations confirmed that the expression of endogenous IL-32 protein could be specifically inhibited by ectopically expressed hcmv-miR-UL112-1.","IL-32 protein levels were measured in the sera of 40 patients with active HCMV infections (HCMV IgM positive) and 32 HCMV IgM negative control individuals by enzyme-linked immunosorbent assays (ELISA). According to the results of HCMV IgG detection, HCMV IgM negative controls were divided into two groups: Previously HCMV infected group (IgG positive, n=17) and healthy group (IgG negative, n=15). Mean IL-32 levels in the sera of patients with active HCMV infection were 4.1778±1.1663 ng/ml (shown in Figure 1). Mean IL-32 levels in sera of the previously HCMV infected group and healthy group were 2.4653±0.8287 ng/ml and 2.4480±0.9162 ng/ml, respectively. The data were analyzed by F test. IL-32 levels in the sera of patients with active HCMV infection were significantly higher than those in the HCMV IgM negative control group (F=23.957, P<0.001). No significant difference was observed between the previously HCMV infected group and the healthy group (P=0.963).\n\nAn illustration showing IL-32 levels in serum samples detected by ELISA. Serum samples were from actively HCMV-infected patients (n=40), HCMV previously infected individuals (n=17) and healthy individuals (n=15), respectively.","Cells were harvested at different infection stages and time points after HCMV infection as described in the Materials and Methods section. We used specific drugs to determine the expression profile of IL-32 at different stages of viral replication. CHX was used to determine the immediate early (IE) stage and PAA was used to determine early (E) stage. As shown in Figure 2A and B, semi-quantitative RT-PCR analysis revealed that IL-32 mRNA expression was significantly activated at IE stage but not at E or late (L) stages post-infection. IL-32 protein expression levels at different time points following HCMV infection were measured by western blot analysis. As shown in Figure 2C and D, IL-32 protein expression reached a peak value at 6 hours post infection (hpi). The relative IL-32 concentration in HCMV infected cells collected at 6 hpi was 10 fold higher than in uninfected cells. All measured values were balanced by using β-actin as an internal control. No accumulation of either IL-32 mRNA or protein was found following the prolongation of HCMV-infection process. IL-32 mRNA levels in HCMV infected cells decreased in E and L stages to a similar level as in uninfected cells. IL-32 protein levels decreased with subsequent hours post infection.\n\nExpression of IL-32 induced by HCMV infection in MRC-5 cells. (A) IL-32 mRNA levels in HCMV infected cells were compared to that in uninfected MRC-5 cells. β-actin was used as an internal control. The universal primers for all IL-32 transcripts were used. U is for uninfected MRC-5; IE is for immediate early stage; E is for early stage; L is for late stage. (B) IL-32 mRNA levels were represented relative to that in IE stage. (C) IL-32 protein levels in HCMV infected MRC-5 cells were detected at different time points. IL-32 densitometer values were normalized by β-actin values. (D) The IL-32 protein levels were represented relative to that in infected cells collected at 6 hpi. (E) The kinetics of hcmv-miR-UL112-1 expression were measured using TaqMan® miRNA assays. The expression of hcmv-miR-UL112-1 could be detected at 24 hpi and increased gradually as the HCMV-infection process was prolonged.\nIn addition, the kinetics of hcmv-miR-UL112-1 expression were measured using TaqMan® miRNA assays. Measured values were normalized by using U6 as an internal control. The expression of hcmv-miR-UL112-1 could be detected at 24 hpi. As shown in Figure 2E, a steep increase was observed at 48 hpi. The relative quantity of hcmv-miR-UL112-1 at 48 hpi was 6.5 fold higher than at 24 hpi. The expression of hcmv-miR-UL112-1 increased gradually as the HCMV-infection process was prolonged. The relative quantity of hcmv-miR-UL112-1 in HCMV infected cells at 96 hpi was 10 fold higher than at 24 hpi.","In our previous study, a sequence in the 3′UTR of IL-32 mRNA was identified by hybrid-PCR to be a candidate target site of hcmv-miR-UL112-1. The predicted binding site for hcmv-miR-UL112-1 was 195 nt upstream of the polyA structure of IL-32 mRNA. A schematic representation of hcmv-miR-UL112-1 binding to the IL-32 3′UTR sequence is shown in Figure 3A.\n\nDown-regulation of IL-32 expression by hcmv-miR-UL112-1. (A) The diagram shows the predicted sequences of hcmv-miR-UL112-1 binding to IL-32 mRNA. (B) As a candidate target, IL-32 was validated for its ability to inhibit expression of a luciferase reporter construct in the presence of hcmv-miR-UL112-1 (pS-miR-UL112-1). Results are shown as percentage expression of negative control sample (pS-neg) following correction for transfection levels according to the control of renilla luciferase expression. Values are shown as means ± standard deviations for triplicate samples. (C) HEK 293 cells were transfected with pS-miR-UL112-1 or control vector respectively. Cells were collected 48 hpi and were subjected to western blot analysis using the indicated antibodies. IL-32 densitometer values were normalized to that of the β-actin values. (D) The amounts of proteins presented in panel C were quantified by densitometry. Results are shown as percentage expression of the negative control sample (Empty).\nTo further determine whether the IL-32 3′UTR sequence represents a functional target site for hcmv-miR-UL112-1, the IL-32 3′UTR sequence was validated by luciferase reporter assays. As shown in Figure 3B, transfected pMIR-IL-32UTR led to a significant inhibition of luciferase activity by 39.27% in the presence of pS-miR-UL112-1. pS-miR-UL112-1 had no significant inhibitory effects on the luciferase activities of pMIR and pMIR-IL-32MUT. These results demonstrate that the IL-32 3′UTR sequence provided a specific and functional binding site for hcmv-miR-UL112-1.\nThe regulatory effect of hcmv-miR-UL112-1 on IL-32 protein expression was then examined in HEK293 cells by western blot. IL-32 protein level was significantly reduced in cells transfected with hcmv-miR-UL112-1 in comparison to cells transfected with pSilencer negative control (Figure 3C and D). IL-32 densitometer values were normalized to that of β-actin values. The level of reduction of IL-32 protein was approximately 67% as determined by densitometry (Figure 3D). Our observations confirmed that the expression of endogenous IL-32 protein could be specifically inhibited by ectopically expressed hcmv-miR-UL112-1.","IL-32 is a proinflammatory cytokine and plays a critical role in inflammatory responses. It induces the expression of IL-1β, IL-6, IL-8 and TNF through the p38MAPK, NF-kappa B and AP-1 signaling pathways. Recent reports have highlighted that IL-32 is regulated during viral infection in humans. It has been reported that the IL-32 expression could be induced during infections of influenza A virus, HBV, HCV, HPV and HIV [13-21].\nIL-32 levels, detected by ELISA, were 4.1778±1.1663 ng/ml in sera from actively HCMV-infected patients. IL-32 levels in sera from the previously infected group and healthy group were 2.4653±0.8287 ng/ml and 2.4480±0.9162 ng/ml, respectively. IL-32 levels in sera from actively HCMV-infected patients were significantly higher than those in control groups (F=23.957, P<0.001). However, no significant differences in IL-32 levels were observed between the HCMV previously infected group and healthy group (P=0.963). These results demonstrate that IL-32 may be a newly described reactive protein corresponding to active HCMV infection.\nHCMV infection can induce the expression of IL-32 at both the mRNA and protein levels in HCMV-infected MRC-5 cells. The peak value of IL-32 mRNA was detected as early as IE stage. The IL-32 protein expression level reached peak value at 6 hpi. The IL-32 protein concentration in HCMV-infected cells at 6 hpi was 10 fold higher than that in uninfected cells. This result demonstrated that the production of IL-32 can be specifically induced by active HCMV infection, while the mechanism that induced the expression of IL-32 during HCMV infection is still unknown. Is the induction of IL-32 mediated only by viral entry or by the expression of IE proteins? Additional experiments would be necessary to provide these answers, and might include the use of UV-irradiated virus. Accumulation of either IL-32 mRNA or protein was not observed following prolonged HCMV-infection process. IL-32 mRNA levels in HCMV-infected cells decreased rapidly in E and L stage to a similar level to that of uninfected cells. IL-32 protein levels decreased in subsequent hours post infection and could only be detected in a small quantity by western blot at 96 hpi. It is hypothesized that certain pathways might be activated to down-regulate or block the expression of IL-32 during HCMV infection.\nMiRNAs are the most studied non-coding RNAs in recent years. They regulate gene expression at the post-transcriptional level and act as key regulators in diverse regulatory pathways, including early development, cell differentiation, cell proliferation, metabolism and apoptosis [37-40]. We carried out additional experiments to validate IL-32 mRNA as a target of hcmv-miR-UL112-1. In the presence of pS-miR-UL112-1, 39.27% luciferase activity of pMIR-IL-32UTR was observed to be significantly decreased, and a 67% reduction of endogenous IL-32 protein expression level was measured in HEK293 cells. It was confirmed that hcmv-miR-UL112-1 could specifically repress the expression of IL-32 through the predicted binding site.\nTo examine the role of hcmv-miR-UL112-1 in regulating IL-32 expression during an active HCMV infection, the kinetics of hcmv-miR-UL112-1 expression in HCMV-infected MRC-5 cells were measured using TaqMan® miRNA assays. The expression of hcmv-miR-UL112-1 could be detected at 24 hpi and then increased gradually with prolonged HCMV infection. This result suggests that hcmv-miR-UL112-1 might function and lead to the decrease of IL-32 levels in E stage during active HCMV infection. The decrease of IL-32 mRNA, but not IL-32 protein level, together with the dramatic increase of hcmv-miR-UL112-1 during the E stage, suggests the possibility that hcmv-miR-UL112-1 may function primarily in the degradation of mRNA. Further studies are needed to address this point.\nThrough co-evolution with its host, HCMV has evolved effective immune evasion strategies by encoding many immunomodulatory proteins that modulate the host immune response. It is conceivable that HCMV-encoded miRNAs might also be exploited during immune evasion. Hcmv-miR-UL112-1 is expressed with early kinetics. Some functional targets of hcmv-miR-UL112-1 have been shown experimentally to be involved in the modulation of NK cell activation. Stern-Ginossar et al. have identified MICB as a functional target of hcmv-miR-UL112-1 [30]. MICB is a stress-induced ligand of the NK cell activating receptor, NKG2D, which is critical for killing of virus-infected cells by NK cells. Hcmv-miR-UL112-1-mediated down-regulation of MICB, perturbed MICB binding with NKG2D and reduced killing of HCMV infected cells by NK cells. In addition, HCMV IE1, which would increase the TNF-α level by activation of the TNF-α promoter, was then identified as a target of hcmv-miR-UL112-1 by Murphy et al. in 2008 [29]. It has been confirmed that IL-32 induces significant amounts of TNF-α in a dose-dependent manner, and that silencing endogenous IL-32 by short hairpin RNA impairs the induction of TNF-α [2]. In this study, we validated that expression of IL-32 was activated by active HCMV infection and functionally down-regulated by ectopically expressed hcmv-miR-UL112-1 in HEK293 cells. It may be deduced from these results that, besides IE1 and MICB, hcmv-miR-UL112-1 might also synergistically achieve the modulation of NK cell activation through the TNF-α pathway by the down-regulation of IL-32, and immune evasion by HCMV.","In summary, IL-32 expression in active HCMV infection was firstly investigated in our study. Detailed kinetics of the transcription of hcmv-miR-UL112-1, IL-32 mRNA and its protein expression were determined in HCMV infected MRC-5 cells. Furthermore, IL-32 expression was demonstrated to be functionally down-regulated by ectopically expressed hcmv-miR-UL112-1 in HEK293 cells. Follow up studies will investigate the mechanism of immune evasion by HCMV mediated by hcmv-miR-UL112-1, which has been confirmed to modulate NK cell activation during HCMV infection through post-transcriptional regulation."," Serum samples Serum samples were obtained from 40 patients with HCMV infection (22 male, 18 female, aged 2.2 ± 0.42 yr) from Shengjing Hospital of China Medical University. All patients were HCMV-IgM positive and HCMV-IgG negative. The viral loads in urine samples from these patients were between 1.793×103 and 4.115×107 HCMV DNA copies per milliliter, as detected by routine QF-PCR (DaAn Gene). Blood samples collected from 32 HCMV-IgM negative individuals (17 male, 15 female, aged 2.7±0.35 yr) at the Medical Examination Centre of Shengjing Hospital were used as controls. According to the HCMV IgG detection results, HCMV IgM negative controls were divided into two groups: Previously HCMV infected group (IgG positive, n=17) and healthy group (IgG negative, n=15). All individuals involved in our study were seronegative for influenza A virus, HBV, HCV and HIV. This study was specifically approved by the Ethical Committee of Shengjing hospital.\nSerum samples were obtained from 40 patients with HCMV infection (22 male, 18 female, aged 2.2 ± 0.42 yr) from Shengjing Hospital of China Medical University. All patients were HCMV-IgM positive and HCMV-IgG negative. The viral loads in urine samples from these patients were between 1.793×103 and 4.115×107 HCMV DNA copies per milliliter, as detected by routine QF-PCR (DaAn Gene). Blood samples collected from 32 HCMV-IgM negative individuals (17 male, 15 female, aged 2.7±0.35 yr) at the Medical Examination Centre of Shengjing Hospital were used as controls. According to the HCMV IgG detection results, HCMV IgM negative controls were divided into two groups: Previously HCMV infected group (IgG positive, n=17) and healthy group (IgG negative, n=15). All individuals involved in our study were seronegative for influenza A virus, HBV, HCV and HIV. This study was specifically approved by the Ethical Committee of Shengjing hospital.\n Cell lines HEK293 cell line was a gift of Dr. Fangjie Chen from Department of Medical Genetics, China Medical University. HEK293 cells were cultured in Dulbecco’s Modified Eagle’s Medium (DMEM) supplemented with 10% fetal bovine serum (FBS), 100 μg/ml penicillin, 100 μg/ml streptomycin sulfate and 2 mM L-glutamine. Human embryonic lung fibroblasts cells, MRC-5, were acquired from Shanghai Institute for Biological Sciences, Chinese Academy of Sciences (CAS). MRC-5 cells were maintained in Modified Eagle’s Medium (MEM) supplemented with 10% FBS, 100 μg/ml penicillin and 100 μg/ml streptomycin sulfate. All cell cultures were maintained at 37°C in a 5% CO2 incubator.\nHEK293 cell line was a gift of Dr. Fangjie Chen from Department of Medical Genetics, China Medical University. HEK293 cells were cultured in Dulbecco’s Modified Eagle’s Medium (DMEM) supplemented with 10% fetal bovine serum (FBS), 100 μg/ml penicillin, 100 μg/ml streptomycin sulfate and 2 mM L-glutamine. Human embryonic lung fibroblasts cells, MRC-5, were acquired from Shanghai Institute for Biological Sciences, Chinese Academy of Sciences (CAS). MRC-5 cells were maintained in Modified Eagle’s Medium (MEM) supplemented with 10% FBS, 100 μg/ml penicillin and 100 μg/ml streptomycin sulfate. All cell cultures were maintained at 37°C in a 5% CO2 incubator.\n Virus HCMV clinical strain H was isolated from a urine sample of a 5-month-old infant hospitalized in Shengjing Hospital of China Medical University. Stock virus was propagated in MRC-5 maintained in MEM supplemented with 2% FBS, 100 μg/ml penicillin and 100 μg/ml streptomycin. The supernatant was then harvested, and the aliquots were stored at −80°C before use.\nHCMV clinical strain H was isolated from a urine sample of a 5-month-old infant hospitalized in Shengjing Hospital of China Medical University. Stock virus was propagated in MRC-5 maintained in MEM supplemented with 2% FBS, 100 μg/ml penicillin and 100 μg/ml streptomycin. The supernatant was then harvested, and the aliquots were stored at −80°C before use.\n Plasmid constructs Luciferase reporter construct pMIR was acquired from Ambion Company. IL-32 3′UTR sequence was obtained by RT-PCR from RNA harvested from HCMV infected cells, and inserted into SpeI and HindIII site of the multiple cloning regions (pMIR-IL-32UTR). A point mutation at corresponding seed region binding site was maintained by using Site-directed Gene Mutagenesis Kit (Beyotime), resulting in GUC to GAA. Sequence predicted to express hcmv-miR-UL112-1 was amplified by PCR directly from HCMV genome. The PCR product was inserted into BamHI and HindIII sites of pSilencer4.1 (Ambion) to generate a miRNA expression vector pS-miR-UL112-1. All primer sequences used in plasmid construction are listed in Table 1. All constructs were confirmed by DNA sequencing.\n\nPrimers used in plasmid construction and semi-quantitative RT-PCR\nNote: sequences recognized by restriction en donuclases are down lined.\nLuciferase reporter construct pMIR was acquired from Ambion Company. IL-32 3′UTR sequence was obtained by RT-PCR from RNA harvested from HCMV infected cells, and inserted into SpeI and HindIII site of the multiple cloning regions (pMIR-IL-32UTR). A point mutation at corresponding seed region binding site was maintained by using Site-directed Gene Mutagenesis Kit (Beyotime), resulting in GUC to GAA. Sequence predicted to express hcmv-miR-UL112-1 was amplified by PCR directly from HCMV genome. The PCR product was inserted into BamHI and HindIII sites of pSilencer4.1 (Ambion) to generate a miRNA expression vector pS-miR-UL112-1. All primer sequences used in plasmid construction are listed in Table 1. All constructs were confirmed by DNA sequencing.\n\nPrimers used in plasmid construction and semi-quantitative RT-PCR\nNote: sequences recognized by restriction en donuclases are down lined.\n Enzyme-linked immunosorbent assay The concentration of IL-32 in serum samples were detected using human IL-32 ELISA Kit (Cusabio) according to the manufacture’s protocols. The absorbance value at wavelength 450 nm was measured. The IL-32 concentrations were calculated from the standard curve.\nThe concentration of IL-32 in serum samples were detected using human IL-32 ELISA Kit (Cusabio) according to the manufacture’s protocols. The absorbance value at wavelength 450 nm was measured. The IL-32 concentrations were calculated from the standard curve.\n Semi-quantitative RT-PCR analysis MRC-5 cells were inoculated with H strain at 3–5 multiplicity of infection (m.o.i.). Infections were carried out under IE, E and L conditions respectively. For preparation of IE RNA, CHX (Sigma) (100 μl/ml) was added to the culture medium 1 hour before infection and the cells were harvested at 24 hpi. For E RNA, DNA synthesis inhibitor phosphonoacetic acid (PAA) (Sigma) (100 μl/ml) was added to the medium immediately after infection, and the cells were harvested at 48 hpi. L RNA and uninfected cellular RNA was derived from infected (at 96 hpi) and uninfected cells, respectively. Total RNA was isolated using Trizol reagent (TaKaRa), treated with DNase Iand then reverse-transcribed with MLV reverse transcriptase and random primers (TaKaRa). PCR was performed for 24 cycles in 50 μl reactions with the IL-32 specific primer pairs listed in Table 1. mRNA of β-actin was amplified for normalization in all reactions. The primers for IL-32 amplification were designed for detecting all known isoforms of human IL-32 based on the sequences from Gnebank database (NM_001012631.1-001012636.1, NM_004221.4 and NM_001012718.1). The PCR products were analyzed by electrophoresis on 2% agarose gel containing ethidium bromide.\nMRC-5 cells were inoculated with H strain at 3–5 multiplicity of infection (m.o.i.). Infections were carried out under IE, E and L conditions respectively. For preparation of IE RNA, CHX (Sigma) (100 μl/ml) was added to the culture medium 1 hour before infection and the cells were harvested at 24 hpi. For E RNA, DNA synthesis inhibitor phosphonoacetic acid (PAA) (Sigma) (100 μl/ml) was added to the medium immediately after infection, and the cells were harvested at 48 hpi. L RNA and uninfected cellular RNA was derived from infected (at 96 hpi) and uninfected cells, respectively. Total RNA was isolated using Trizol reagent (TaKaRa), treated with DNase Iand then reverse-transcribed with MLV reverse transcriptase and random primers (TaKaRa). PCR was performed for 24 cycles in 50 μl reactions with the IL-32 specific primer pairs listed in Table 1. mRNA of β-actin was amplified for normalization in all reactions. The primers for IL-32 amplification were designed for detecting all known isoforms of human IL-32 based on the sequences from Gnebank database (NM_001012631.1-001012636.1, NM_004221.4 and NM_001012718.1). The PCR products were analyzed by electrophoresis on 2% agarose gel containing ethidium bromide.\n miRNA extraction and TaqMan assays Total RNA was extracted from HCMV infected cells at different time points using the mivVana miRNA isolation kit (Ambion). We measured hcmv-miR-UL112-1 in all samples using TaqMan® miRNA assays (MIMAT0001577 for hcmv-miR-UL112-1, ABI). U6 expression was used to normalize the expression of hcmv-miR-UL112-1. HCMV uninfected MRC-5 cells were used as negative control. Fold difference for hcmv-miR-UL112-1 expression was calculated using the equation 2ΔΔct, where ΔCt = Ct (hcmv-miR-UL112-1) – Ct (U6 control) and ΔΔCt = ΔCt (HCMV infected MRC-5) – ΔCt (MRC-5). The measurements were done in triplicates and the results are presented at the means ± S.D.\nTotal RNA was extracted from HCMV infected cells at different time points using the mivVana miRNA isolation kit (Ambion). We measured hcmv-miR-UL112-1 in all samples using TaqMan® miRNA assays (MIMAT0001577 for hcmv-miR-UL112-1, ABI). U6 expression was used to normalize the expression of hcmv-miR-UL112-1. HCMV uninfected MRC-5 cells were used as negative control. Fold difference for hcmv-miR-UL112-1 expression was calculated using the equation 2ΔΔct, where ΔCt = Ct (hcmv-miR-UL112-1) – Ct (U6 control) and ΔΔCt = ΔCt (HCMV infected MRC-5) – ΔCt (MRC-5). The measurements were done in triplicates and the results are presented at the means ± S.D.\n Luciferase assay MRC-5 cells were plated in 24-well plate at a density of 4.0×105 cells per well and grown to reach about 80% confluence at the time for transfection. 200 ng pMIR-IL-32UTR/pMIR-IL-32MUT plasmid or an empty vector control was co-transfected with 400 ng pS-miR-UL112-1 or pS-neg control into the cells by using lipofectamine 2000 reagent (Invitrogen) according to the manufacture’s protocol. 200 ng renilla luciferase plasmid (Promega) was introduced into each co-transfected cells as an internal control plasmid at the same time. Cells were collected 48 hours post transfection and luciferase activity levels were measured using the Dual luciferase reporter assay system (Promega) according to the manufacture’s guidelines. All measurements were done in triplicates and signals were normalized for transfection efficiency to the internal renilla control. The results are presented at the means ±S.D.\nMRC-5 cells were plated in 24-well plate at a density of 4.0×105 cells per well and grown to reach about 80% confluence at the time for transfection. 200 ng pMIR-IL-32UTR/pMIR-IL-32MUT plasmid or an empty vector control was co-transfected with 400 ng pS-miR-UL112-1 or pS-neg control into the cells by using lipofectamine 2000 reagent (Invitrogen) according to the manufacture’s protocol. 200 ng renilla luciferase plasmid (Promega) was introduced into each co-transfected cells as an internal control plasmid at the same time. Cells were collected 48 hours post transfection and luciferase activity levels were measured using the Dual luciferase reporter assay system (Promega) according to the manufacture’s guidelines. All measurements were done in triplicates and signals were normalized for transfection efficiency to the internal renilla control. The results are presented at the means ±S.D.\n Western blot analysis Western blot analyses were performed to detect the IL-32 levels in HCMV infected MRC-5 cells and hcmv-miR-UL112-1 over expressed HEK293 cells respectively. MRC-5 cells were inoculated with H strain at 3–5 m.o.i. Cells were harvested at different time points post infection (6 h, 24 h, 48 h, 72 h and 96 h). HEK293 cells were transfected with 8 μg pS-miR-UL112-1 or pS-neg control using lipofectamine 2000 (Invitrogen) and were incubated at 37°C for 48 hours.\nProteins of the cells described above were prepared by suspending cells in lysis buffer, followed by centrifugation. Concentrations of proteins in supernatant were quantified using protein assay kit (Beyotime). Protein was separated by 10% acrylamide gel electrophoresis and transferred onto a nitrocellulose membrane. Western blot analysis was performed using IL-32 antibody (abcam). Immunoblots were visualized with ECL detection system. Sample loading was normalized by quantities of β-actin detected parallel.\nWestern blot analyses were performed to detect the IL-32 levels in HCMV infected MRC-5 cells and hcmv-miR-UL112-1 over expressed HEK293 cells respectively. MRC-5 cells were inoculated with H strain at 3–5 m.o.i. Cells were harvested at different time points post infection (6 h, 24 h, 48 h, 72 h and 96 h). HEK293 cells were transfected with 8 μg pS-miR-UL112-1 or pS-neg control using lipofectamine 2000 (Invitrogen) and were incubated at 37°C for 48 hours.\nProteins of the cells described above were prepared by suspending cells in lysis buffer, followed by centrifugation. Concentrations of proteins in supernatant were quantified using protein assay kit (Beyotime). Protein was separated by 10% acrylamide gel electrophoresis and transferred onto a nitrocellulose membrane. Western blot analysis was performed using IL-32 antibody (abcam). Immunoblots were visualized with ECL detection system. Sample loading was normalized by quantities of β-actin detected parallel.\n Statistical analysis All experiments were reproducible. The results are presented at the means ± S.D. Student’s t test and F test was used to determine statistical significance. Differences were considered statistically significant at value of P≤0.05.\nAll experiments were reproducible. The results are presented at the means ± S.D. Student’s t test and F test was used to determine statistical significance. Differences were considered statistically significant at value of P≤0.05.","Serum samples were obtained from 40 patients with HCMV infection (22 male, 18 female, aged 2.2 ± 0.42 yr) from Shengjing Hospital of China Medical University. All patients were HCMV-IgM positive and HCMV-IgG negative. The viral loads in urine samples from these patients were between 1.793×103 and 4.115×107 HCMV DNA copies per milliliter, as detected by routine QF-PCR (DaAn Gene). Blood samples collected from 32 HCMV-IgM negative individuals (17 male, 15 female, aged 2.7±0.35 yr) at the Medical Examination Centre of Shengjing Hospital were used as controls. According to the HCMV IgG detection results, HCMV IgM negative controls were divided into two groups: Previously HCMV infected group (IgG positive, n=17) and healthy group (IgG negative, n=15). All individuals involved in our study were seronegative for influenza A virus, HBV, HCV and HIV. This study was specifically approved by the Ethical Committee of Shengjing hospital.","HEK293 cell line was a gift of Dr. Fangjie Chen from Department of Medical Genetics, China Medical University. HEK293 cells were cultured in Dulbecco’s Modified Eagle’s Medium (DMEM) supplemented with 10% fetal bovine serum (FBS), 100 μg/ml penicillin, 100 μg/ml streptomycin sulfate and 2 mM L-glutamine. Human embryonic lung fibroblasts cells, MRC-5, were acquired from Shanghai Institute for Biological Sciences, Chinese Academy of Sciences (CAS). MRC-5 cells were maintained in Modified Eagle’s Medium (MEM) supplemented with 10% FBS, 100 μg/ml penicillin and 100 μg/ml streptomycin sulfate. All cell cultures were maintained at 37°C in a 5% CO2 incubator.","HCMV clinical strain H was isolated from a urine sample of a 5-month-old infant hospitalized in Shengjing Hospital of China Medical University. Stock virus was propagated in MRC-5 maintained in MEM supplemented with 2% FBS, 100 μg/ml penicillin and 100 μg/ml streptomycin. The supernatant was then harvested, and the aliquots were stored at −80°C before use.","Luciferase reporter construct pMIR was acquired from Ambion Company. IL-32 3′UTR sequence was obtained by RT-PCR from RNA harvested from HCMV infected cells, and inserted into SpeI and HindIII site of the multiple cloning regions (pMIR-IL-32UTR). A point mutation at corresponding seed region binding site was maintained by using Site-directed Gene Mutagenesis Kit (Beyotime), resulting in GUC to GAA. Sequence predicted to express hcmv-miR-UL112-1 was amplified by PCR directly from HCMV genome. The PCR product was inserted into BamHI and HindIII sites of pSilencer4.1 (Ambion) to generate a miRNA expression vector pS-miR-UL112-1. All primer sequences used in plasmid construction are listed in Table 1. All constructs were confirmed by DNA sequencing.\n\nPrimers used in plasmid construction and semi-quantitative RT-PCR\nNote: sequences recognized by restriction en donuclases are down lined.","The concentration of IL-32 in serum samples were detected using human IL-32 ELISA Kit (Cusabio) according to the manufacture’s protocols. The absorbance value at wavelength 450 nm was measured. The IL-32 concentrations were calculated from the standard curve.","MRC-5 cells were inoculated with H strain at 3–5 multiplicity of infection (m.o.i.). Infections were carried out under IE, E and L conditions respectively. For preparation of IE RNA, CHX (Sigma) (100 μl/ml) was added to the culture medium 1 hour before infection and the cells were harvested at 24 hpi. For E RNA, DNA synthesis inhibitor phosphonoacetic acid (PAA) (Sigma) (100 μl/ml) was added to the medium immediately after infection, and the cells were harvested at 48 hpi. L RNA and uninfected cellular RNA was derived from infected (at 96 hpi) and uninfected cells, respectively. Total RNA was isolated using Trizol reagent (TaKaRa), treated with DNase Iand then reverse-transcribed with MLV reverse transcriptase and random primers (TaKaRa). PCR was performed for 24 cycles in 50 μl reactions with the IL-32 specific primer pairs listed in Table 1. mRNA of β-actin was amplified for normalization in all reactions. The primers for IL-32 amplification were designed for detecting all known isoforms of human IL-32 based on the sequences from Gnebank database (NM_001012631.1-001012636.1, NM_004221.4 and NM_001012718.1). The PCR products were analyzed by electrophoresis on 2% agarose gel containing ethidium bromide.","Total RNA was extracted from HCMV infected cells at different time points using the mivVana miRNA isolation kit (Ambion). We measured hcmv-miR-UL112-1 in all samples using TaqMan® miRNA assays (MIMAT0001577 for hcmv-miR-UL112-1, ABI). U6 expression was used to normalize the expression of hcmv-miR-UL112-1. HCMV uninfected MRC-5 cells were used as negative control. Fold difference for hcmv-miR-UL112-1 expression was calculated using the equation 2ΔΔct, where ΔCt = Ct (hcmv-miR-UL112-1) – Ct (U6 control) and ΔΔCt = ΔCt (HCMV infected MRC-5) – ΔCt (MRC-5). The measurements were done in triplicates and the results are presented at the means ± S.D.","MRC-5 cells were plated in 24-well plate at a density of 4.0×105 cells per well and grown to reach about 80% confluence at the time for transfection. 200 ng pMIR-IL-32UTR/pMIR-IL-32MUT plasmid or an empty vector control was co-transfected with 400 ng pS-miR-UL112-1 or pS-neg control into the cells by using lipofectamine 2000 reagent (Invitrogen) according to the manufacture’s protocol. 200 ng renilla luciferase plasmid (Promega) was introduced into each co-transfected cells as an internal control plasmid at the same time. Cells were collected 48 hours post transfection and luciferase activity levels were measured using the Dual luciferase reporter assay system (Promega) according to the manufacture’s guidelines. All measurements were done in triplicates and signals were normalized for transfection efficiency to the internal renilla control. The results are presented at the means ±S.D.","Western blot analyses were performed to detect the IL-32 levels in HCMV infected MRC-5 cells and hcmv-miR-UL112-1 over expressed HEK293 cells respectively. MRC-5 cells were inoculated with H strain at 3–5 m.o.i. Cells were harvested at different time points post infection (6 h, 24 h, 48 h, 72 h and 96 h). HEK293 cells were transfected with 8 μg pS-miR-UL112-1 or pS-neg control using lipofectamine 2000 (Invitrogen) and were incubated at 37°C for 48 hours.\nProteins of the cells described above were prepared by suspending cells in lysis buffer, followed by centrifugation. Concentrations of proteins in supernatant were quantified using protein assay kit (Beyotime). Protein was separated by 10% acrylamide gel electrophoresis and transferred onto a nitrocellulose membrane. Western blot analysis was performed using IL-32 antibody (abcam). Immunoblots were visualized with ECL detection system. Sample loading was normalized by quantities of β-actin detected parallel.","All experiments were reproducible. The results are presented at the means ± S.D. Student’s t test and F test was used to determine statistical significance. Differences were considered statistically significant at value of P≤0.05.","The authors declare that they have no competing interests.","YJH carried out the semi-quantitative RT-PCR analysis, western blot analysis and Statistical analysis. YQ and RH carried out the plasmids construction and luciferase assay. QR as the corresponding author designed the study and corrected the manuscript. YPM and YHJ carried out the preparation of virus and cell lines. ZRS carried out the serum samples collection and ELISA detection. All authors have read and approved the final manuscript."],"string":"[\n \"Interleukin-32 (IL-32) is a newly-discovered pro-inflammatory cytokine, which plays a role in innate and adaptive immune responses [1,2]. It lacks sequence homology to any presently known cytokine families. IL-32 is associated with the induction of inflammatory responses by activating the p38MAPK, NF-kappa B and AP-1 signaling pathways. It has been implicated in inflammatory disorders, mycobacterium tuberculosis infections and inflammatory bowel disease, as well as in some autoimmune diseases, such as rheumatoid arthritis, ulcerative colitis and Crohn’s disease [3-10]. Moreover, it has been reported that IL-32 has pro-inflammatory effects on myeloid cells and promotes the differentiation of osteoclast precursors into multinucleated cells expressing specific osteoclast markers [11,12]. In recent studies, IL-32 has also been found to be regulated during viral infections. Elevated levels of IL-32 were found in sera from patients infected with influenza A virus [13-15], hepatitis B virus (HBV) [16], hepatitis C virus (HCV) [17], human papillomavirus (HPV) [18] and human immunodeficiency virus (HIV) [19-21], suggesting that IL-32 might play an important role in host defense against viral infections\\nHuman cytomegalovirus (HCMV) is an ubiquitous β-herpesvirus that infects a broad range of cell types in human hosts, contributing to its complex and varied pathogenesis. HCMV Infection leads to life-long persistence in 50%–90% of the population, which is generally subclinical in healthy individuals [22]. However, it can lead to serious complications in immunocompromised patients, such as transplant recipients or AIDS patients [23,24].\\nMicroRNAs (miRNAs) are an abundant class of small non-coding RNA molecules that target mRNAs generally within their 3′ untranslated regions (3′UTRs). MiRNAs suppress gene expression mainly through inhibition of translation or, rarely, through degradation of mRNA [25,26]. Clinical isolates of HCMV encode at least 17 miRNAs [27,28]. However, only a few functional targets have been validated experimentally for some HCMV-encoded miRNAs [29-34]. It has been demonstrated that hcmv-miR-UL112-1 targets and reduces the expression of HCMV UL123 (IE1 or IE72), UL114 and the major histocompatibility complex class 1-related chain B (MICB). Moreover, BclAF1 protein, a human cytomegalovirus restriction factor, was reported to be a new target of hcmv-miR-UL112-1 [35]. In addition, multiple cellular targets of hcmv-miR-US25-1, which are associated with cell cycle control, were identified by RNA induced silencing complex immunoprecipitation (RISC-IP), including cyclin E2, BRCC3, EID1, MAPRE2 and CD147. IL-32, which is not present in the confirmed targets, was screened out as a candidate target of hcmv-miR-UL112-1 in our previous study [36]. However, no further experiments to validate these findings have been performed.\\nIn the present study, the expression levels of IL-32 were compared among serum samples from patients with active HCMV infection and samples from HCMV-IgM negative individuals. The expression levels of IL-32 and hcmv-miR-UL112-1 in HCMV infected MRC-5 cells were detected at different stages of infection and time points. In addition, functional down-regulation of IL-32 by hcmv-miR-UL112-1 was detected in transfected human embryonic kidney (HEK293) cells, and the effect of hcmv-miR-UL112-1 on IL-32 during HCMV infection was primarily discussed.\",\n \" Relatively high IL-32 levels among individuals with active HCMV infection IL-32 protein levels were measured in the sera of 40 patients with active HCMV infections (HCMV IgM positive) and 32 HCMV IgM negative control individuals by enzyme-linked immunosorbent assays (ELISA). According to the results of HCMV IgG detection, HCMV IgM negative controls were divided into two groups: Previously HCMV infected group (IgG positive, n=17) and healthy group (IgG negative, n=15). Mean IL-32 levels in the sera of patients with active HCMV infection were 4.1778±1.1663 ng/ml (shown in Figure 1). Mean IL-32 levels in sera of the previously HCMV infected group and healthy group were 2.4653±0.8287 ng/ml and 2.4480±0.9162 ng/ml, respectively. The data were analyzed by F test. IL-32 levels in the sera of patients with active HCMV infection were significantly higher than those in the HCMV IgM negative control group (F=23.957, P<0.001). No significant difference was observed between the previously HCMV infected group and the healthy group (P=0.963).\\n\\nAn illustration showing IL-32 levels in serum samples detected by ELISA. Serum samples were from actively HCMV-infected patients (n=40), HCMV previously infected individuals (n=17) and healthy individuals (n=15), respectively.\\nIL-32 protein levels were measured in the sera of 40 patients with active HCMV infections (HCMV IgM positive) and 32 HCMV IgM negative control individuals by enzyme-linked immunosorbent assays (ELISA). According to the results of HCMV IgG detection, HCMV IgM negative controls were divided into two groups: Previously HCMV infected group (IgG positive, n=17) and healthy group (IgG negative, n=15). Mean IL-32 levels in the sera of patients with active HCMV infection were 4.1778±1.1663 ng/ml (shown in Figure 1). Mean IL-32 levels in sera of the previously HCMV infected group and healthy group were 2.4653±0.8287 ng/ml and 2.4480±0.9162 ng/ml, respectively. The data were analyzed by F test. IL-32 levels in the sera of patients with active HCMV infection were significantly higher than those in the HCMV IgM negative control group (F=23.957, P<0.001). No significant difference was observed between the previously HCMV infected group and the healthy group (P=0.963).\\n\\nAn illustration showing IL-32 levels in serum samples detected by ELISA. Serum samples were from actively HCMV-infected patients (n=40), HCMV previously infected individuals (n=17) and healthy individuals (n=15), respectively.\\n Kinetics of the expression of IL-32 mRNA, IL-32 protein and hcmv-miR-UL112-1 in HCMV infected MRC-5 cells Cells were harvested at different infection stages and time points after HCMV infection as described in the Materials and Methods section. We used specific drugs to determine the expression profile of IL-32 at different stages of viral replication. CHX was used to determine the immediate early (IE) stage and PAA was used to determine early (E) stage. As shown in Figure 2A and B, semi-quantitative RT-PCR analysis revealed that IL-32 mRNA expression was significantly activated at IE stage but not at E or late (L) stages post-infection. IL-32 protein expression levels at different time points following HCMV infection were measured by western blot analysis. As shown in Figure 2C and D, IL-32 protein expression reached a peak value at 6 hours post infection (hpi). The relative IL-32 concentration in HCMV infected cells collected at 6 hpi was 10 fold higher than in uninfected cells. All measured values were balanced by using β-actin as an internal control. No accumulation of either IL-32 mRNA or protein was found following the prolongation of HCMV-infection process. IL-32 mRNA levels in HCMV infected cells decreased in E and L stages to a similar level as in uninfected cells. IL-32 protein levels decreased with subsequent hours post infection.\\n\\nExpression of IL-32 induced by HCMV infection in MRC-5 cells. (A) IL-32 mRNA levels in HCMV infected cells were compared to that in uninfected MRC-5 cells. β-actin was used as an internal control. The universal primers for all IL-32 transcripts were used. U is for uninfected MRC-5; IE is for immediate early stage; E is for early stage; L is for late stage. (B) IL-32 mRNA levels were represented relative to that in IE stage. (C) IL-32 protein levels in HCMV infected MRC-5 cells were detected at different time points. IL-32 densitometer values were normalized by β-actin values. (D) The IL-32 protein levels were represented relative to that in infected cells collected at 6 hpi. (E) The kinetics of hcmv-miR-UL112-1 expression were measured using TaqMan® miRNA assays. The expression of hcmv-miR-UL112-1 could be detected at 24 hpi and increased gradually as the HCMV-infection process was prolonged.\\nIn addition, the kinetics of hcmv-miR-UL112-1 expression were measured using TaqMan® miRNA assays. Measured values were normalized by using U6 as an internal control. The expression of hcmv-miR-UL112-1 could be detected at 24 hpi. As shown in Figure 2E, a steep increase was observed at 48 hpi. The relative quantity of hcmv-miR-UL112-1 at 48 hpi was 6.5 fold higher than at 24 hpi. The expression of hcmv-miR-UL112-1 increased gradually as the HCMV-infection process was prolonged. The relative quantity of hcmv-miR-UL112-1 in HCMV infected cells at 96 hpi was 10 fold higher than at 24 hpi.\\nCells were harvested at different infection stages and time points after HCMV infection as described in the Materials and Methods section. We used specific drugs to determine the expression profile of IL-32 at different stages of viral replication. CHX was used to determine the immediate early (IE) stage and PAA was used to determine early (E) stage. As shown in Figure 2A and B, semi-quantitative RT-PCR analysis revealed that IL-32 mRNA expression was significantly activated at IE stage but not at E or late (L) stages post-infection. IL-32 protein expression levels at different time points following HCMV infection were measured by western blot analysis. As shown in Figure 2C and D, IL-32 protein expression reached a peak value at 6 hours post infection (hpi). The relative IL-32 concentration in HCMV infected cells collected at 6 hpi was 10 fold higher than in uninfected cells. All measured values were balanced by using β-actin as an internal control. No accumulation of either IL-32 mRNA or protein was found following the prolongation of HCMV-infection process. IL-32 mRNA levels in HCMV infected cells decreased in E and L stages to a similar level as in uninfected cells. IL-32 protein levels decreased with subsequent hours post infection.\\n\\nExpression of IL-32 induced by HCMV infection in MRC-5 cells. (A) IL-32 mRNA levels in HCMV infected cells were compared to that in uninfected MRC-5 cells. β-actin was used as an internal control. The universal primers for all IL-32 transcripts were used. U is for uninfected MRC-5; IE is for immediate early stage; E is for early stage; L is for late stage. (B) IL-32 mRNA levels were represented relative to that in IE stage. (C) IL-32 protein levels in HCMV infected MRC-5 cells were detected at different time points. IL-32 densitometer values were normalized by β-actin values. (D) The IL-32 protein levels were represented relative to that in infected cells collected at 6 hpi. (E) The kinetics of hcmv-miR-UL112-1 expression were measured using TaqMan® miRNA assays. The expression of hcmv-miR-UL112-1 could be detected at 24 hpi and increased gradually as the HCMV-infection process was prolonged.\\nIn addition, the kinetics of hcmv-miR-UL112-1 expression were measured using TaqMan® miRNA assays. Measured values were normalized by using U6 as an internal control. The expression of hcmv-miR-UL112-1 could be detected at 24 hpi. As shown in Figure 2E, a steep increase was observed at 48 hpi. The relative quantity of hcmv-miR-UL112-1 at 48 hpi was 6.5 fold higher than at 24 hpi. The expression of hcmv-miR-UL112-1 increased gradually as the HCMV-infection process was prolonged. The relative quantity of hcmv-miR-UL112-1 in HCMV infected cells at 96 hpi was 10 fold higher than at 24 hpi.\\n Functional down-regulation of endogenous IL-32 expression by ectopically expressed hcmv-miR-UL112-1 In our previous study, a sequence in the 3′UTR of IL-32 mRNA was identified by hybrid-PCR to be a candidate target site of hcmv-miR-UL112-1. The predicted binding site for hcmv-miR-UL112-1 was 195 nt upstream of the polyA structure of IL-32 mRNA. A schematic representation of hcmv-miR-UL112-1 binding to the IL-32 3′UTR sequence is shown in Figure 3A.\\n\\nDown-regulation of IL-32 expression by hcmv-miR-UL112-1. (A) The diagram shows the predicted sequences of hcmv-miR-UL112-1 binding to IL-32 mRNA. (B) As a candidate target, IL-32 was validated for its ability to inhibit expression of a luciferase reporter construct in the presence of hcmv-miR-UL112-1 (pS-miR-UL112-1). Results are shown as percentage expression of negative control sample (pS-neg) following correction for transfection levels according to the control of renilla luciferase expression. Values are shown as means ± standard deviations for triplicate samples. (C) HEK 293 cells were transfected with pS-miR-UL112-1 or control vector respectively. Cells were collected 48 hpi and were subjected to western blot analysis using the indicated antibodies. IL-32 densitometer values were normalized to that of the β-actin values. (D) The amounts of proteins presented in panel C were quantified by densitometry. Results are shown as percentage expression of the negative control sample (Empty).\\nTo further determine whether the IL-32 3′UTR sequence represents a functional target site for hcmv-miR-UL112-1, the IL-32 3′UTR sequence was validated by luciferase reporter assays. As shown in Figure 3B, transfected pMIR-IL-32UTR led to a significant inhibition of luciferase activity by 39.27% in the presence of pS-miR-UL112-1. pS-miR-UL112-1 had no significant inhibitory effects on the luciferase activities of pMIR and pMIR-IL-32MUT. These results demonstrate that the IL-32 3′UTR sequence provided a specific and functional binding site for hcmv-miR-UL112-1.\\nThe regulatory effect of hcmv-miR-UL112-1 on IL-32 protein expression was then examined in HEK293 cells by western blot. IL-32 protein level was significantly reduced in cells transfected with hcmv-miR-UL112-1 in comparison to cells transfected with pSilencer negative control (Figure 3C and D). IL-32 densitometer values were normalized to that of β-actin values. The level of reduction of IL-32 protein was approximately 67% as determined by densitometry (Figure 3D). Our observations confirmed that the expression of endogenous IL-32 protein could be specifically inhibited by ectopically expressed hcmv-miR-UL112-1.\\nIn our previous study, a sequence in the 3′UTR of IL-32 mRNA was identified by hybrid-PCR to be a candidate target site of hcmv-miR-UL112-1. The predicted binding site for hcmv-miR-UL112-1 was 195 nt upstream of the polyA structure of IL-32 mRNA. A schematic representation of hcmv-miR-UL112-1 binding to the IL-32 3′UTR sequence is shown in Figure 3A.\\n\\nDown-regulation of IL-32 expression by hcmv-miR-UL112-1. (A) The diagram shows the predicted sequences of hcmv-miR-UL112-1 binding to IL-32 mRNA. (B) As a candidate target, IL-32 was validated for its ability to inhibit expression of a luciferase reporter construct in the presence of hcmv-miR-UL112-1 (pS-miR-UL112-1). Results are shown as percentage expression of negative control sample (pS-neg) following correction for transfection levels according to the control of renilla luciferase expression. Values are shown as means ± standard deviations for triplicate samples. (C) HEK 293 cells were transfected with pS-miR-UL112-1 or control vector respectively. Cells were collected 48 hpi and were subjected to western blot analysis using the indicated antibodies. IL-32 densitometer values were normalized to that of the β-actin values. (D) The amounts of proteins presented in panel C were quantified by densitometry. Results are shown as percentage expression of the negative control sample (Empty).\\nTo further determine whether the IL-32 3′UTR sequence represents a functional target site for hcmv-miR-UL112-1, the IL-32 3′UTR sequence was validated by luciferase reporter assays. As shown in Figure 3B, transfected pMIR-IL-32UTR led to a significant inhibition of luciferase activity by 39.27% in the presence of pS-miR-UL112-1. pS-miR-UL112-1 had no significant inhibitory effects on the luciferase activities of pMIR and pMIR-IL-32MUT. These results demonstrate that the IL-32 3′UTR sequence provided a specific and functional binding site for hcmv-miR-UL112-1.\\nThe regulatory effect of hcmv-miR-UL112-1 on IL-32 protein expression was then examined in HEK293 cells by western blot. IL-32 protein level was significantly reduced in cells transfected with hcmv-miR-UL112-1 in comparison to cells transfected with pSilencer negative control (Figure 3C and D). IL-32 densitometer values were normalized to that of β-actin values. The level of reduction of IL-32 protein was approximately 67% as determined by densitometry (Figure 3D). Our observations confirmed that the expression of endogenous IL-32 protein could be specifically inhibited by ectopically expressed hcmv-miR-UL112-1.\",\n \"IL-32 protein levels were measured in the sera of 40 patients with active HCMV infections (HCMV IgM positive) and 32 HCMV IgM negative control individuals by enzyme-linked immunosorbent assays (ELISA). According to the results of HCMV IgG detection, HCMV IgM negative controls were divided into two groups: Previously HCMV infected group (IgG positive, n=17) and healthy group (IgG negative, n=15). Mean IL-32 levels in the sera of patients with active HCMV infection were 4.1778±1.1663 ng/ml (shown in Figure 1). Mean IL-32 levels in sera of the previously HCMV infected group and healthy group were 2.4653±0.8287 ng/ml and 2.4480±0.9162 ng/ml, respectively. The data were analyzed by F test. IL-32 levels in the sera of patients with active HCMV infection were significantly higher than those in the HCMV IgM negative control group (F=23.957, P<0.001). No significant difference was observed between the previously HCMV infected group and the healthy group (P=0.963).\\n\\nAn illustration showing IL-32 levels in serum samples detected by ELISA. Serum samples were from actively HCMV-infected patients (n=40), HCMV previously infected individuals (n=17) and healthy individuals (n=15), respectively.\",\n \"Cells were harvested at different infection stages and time points after HCMV infection as described in the Materials and Methods section. We used specific drugs to determine the expression profile of IL-32 at different stages of viral replication. CHX was used to determine the immediate early (IE) stage and PAA was used to determine early (E) stage. As shown in Figure 2A and B, semi-quantitative RT-PCR analysis revealed that IL-32 mRNA expression was significantly activated at IE stage but not at E or late (L) stages post-infection. IL-32 protein expression levels at different time points following HCMV infection were measured by western blot analysis. As shown in Figure 2C and D, IL-32 protein expression reached a peak value at 6 hours post infection (hpi). The relative IL-32 concentration in HCMV infected cells collected at 6 hpi was 10 fold higher than in uninfected cells. All measured values were balanced by using β-actin as an internal control. No accumulation of either IL-32 mRNA or protein was found following the prolongation of HCMV-infection process. IL-32 mRNA levels in HCMV infected cells decreased in E and L stages to a similar level as in uninfected cells. IL-32 protein levels decreased with subsequent hours post infection.\\n\\nExpression of IL-32 induced by HCMV infection in MRC-5 cells. (A) IL-32 mRNA levels in HCMV infected cells were compared to that in uninfected MRC-5 cells. β-actin was used as an internal control. The universal primers for all IL-32 transcripts were used. U is for uninfected MRC-5; IE is for immediate early stage; E is for early stage; L is for late stage. (B) IL-32 mRNA levels were represented relative to that in IE stage. (C) IL-32 protein levels in HCMV infected MRC-5 cells were detected at different time points. IL-32 densitometer values were normalized by β-actin values. (D) The IL-32 protein levels were represented relative to that in infected cells collected at 6 hpi. (E) The kinetics of hcmv-miR-UL112-1 expression were measured using TaqMan® miRNA assays. The expression of hcmv-miR-UL112-1 could be detected at 24 hpi and increased gradually as the HCMV-infection process was prolonged.\\nIn addition, the kinetics of hcmv-miR-UL112-1 expression were measured using TaqMan® miRNA assays. Measured values were normalized by using U6 as an internal control. The expression of hcmv-miR-UL112-1 could be detected at 24 hpi. As shown in Figure 2E, a steep increase was observed at 48 hpi. The relative quantity of hcmv-miR-UL112-1 at 48 hpi was 6.5 fold higher than at 24 hpi. The expression of hcmv-miR-UL112-1 increased gradually as the HCMV-infection process was prolonged. The relative quantity of hcmv-miR-UL112-1 in HCMV infected cells at 96 hpi was 10 fold higher than at 24 hpi.\",\n \"In our previous study, a sequence in the 3′UTR of IL-32 mRNA was identified by hybrid-PCR to be a candidate target site of hcmv-miR-UL112-1. The predicted binding site for hcmv-miR-UL112-1 was 195 nt upstream of the polyA structure of IL-32 mRNA. A schematic representation of hcmv-miR-UL112-1 binding to the IL-32 3′UTR sequence is shown in Figure 3A.\\n\\nDown-regulation of IL-32 expression by hcmv-miR-UL112-1. (A) The diagram shows the predicted sequences of hcmv-miR-UL112-1 binding to IL-32 mRNA. (B) As a candidate target, IL-32 was validated for its ability to inhibit expression of a luciferase reporter construct in the presence of hcmv-miR-UL112-1 (pS-miR-UL112-1). Results are shown as percentage expression of negative control sample (pS-neg) following correction for transfection levels according to the control of renilla luciferase expression. Values are shown as means ± standard deviations for triplicate samples. (C) HEK 293 cells were transfected with pS-miR-UL112-1 or control vector respectively. Cells were collected 48 hpi and were subjected to western blot analysis using the indicated antibodies. IL-32 densitometer values were normalized to that of the β-actin values. (D) The amounts of proteins presented in panel C were quantified by densitometry. Results are shown as percentage expression of the negative control sample (Empty).\\nTo further determine whether the IL-32 3′UTR sequence represents a functional target site for hcmv-miR-UL112-1, the IL-32 3′UTR sequence was validated by luciferase reporter assays. As shown in Figure 3B, transfected pMIR-IL-32UTR led to a significant inhibition of luciferase activity by 39.27% in the presence of pS-miR-UL112-1. pS-miR-UL112-1 had no significant inhibitory effects on the luciferase activities of pMIR and pMIR-IL-32MUT. These results demonstrate that the IL-32 3′UTR sequence provided a specific and functional binding site for hcmv-miR-UL112-1.\\nThe regulatory effect of hcmv-miR-UL112-1 on IL-32 protein expression was then examined in HEK293 cells by western blot. IL-32 protein level was significantly reduced in cells transfected with hcmv-miR-UL112-1 in comparison to cells transfected with pSilencer negative control (Figure 3C and D). IL-32 densitometer values were normalized to that of β-actin values. The level of reduction of IL-32 protein was approximately 67% as determined by densitometry (Figure 3D). Our observations confirmed that the expression of endogenous IL-32 protein could be specifically inhibited by ectopically expressed hcmv-miR-UL112-1.\",\n \"IL-32 is a proinflammatory cytokine and plays a critical role in inflammatory responses. It induces the expression of IL-1β, IL-6, IL-8 and TNF through the p38MAPK, NF-kappa B and AP-1 signaling pathways. Recent reports have highlighted that IL-32 is regulated during viral infection in humans. It has been reported that the IL-32 expression could be induced during infections of influenza A virus, HBV, HCV, HPV and HIV [13-21].\\nIL-32 levels, detected by ELISA, were 4.1778±1.1663 ng/ml in sera from actively HCMV-infected patients. IL-32 levels in sera from the previously infected group and healthy group were 2.4653±0.8287 ng/ml and 2.4480±0.9162 ng/ml, respectively. IL-32 levels in sera from actively HCMV-infected patients were significantly higher than those in control groups (F=23.957, P<0.001). However, no significant differences in IL-32 levels were observed between the HCMV previously infected group and healthy group (P=0.963). These results demonstrate that IL-32 may be a newly described reactive protein corresponding to active HCMV infection.\\nHCMV infection can induce the expression of IL-32 at both the mRNA and protein levels in HCMV-infected MRC-5 cells. The peak value of IL-32 mRNA was detected as early as IE stage. The IL-32 protein expression level reached peak value at 6 hpi. The IL-32 protein concentration in HCMV-infected cells at 6 hpi was 10 fold higher than that in uninfected cells. This result demonstrated that the production of IL-32 can be specifically induced by active HCMV infection, while the mechanism that induced the expression of IL-32 during HCMV infection is still unknown. Is the induction of IL-32 mediated only by viral entry or by the expression of IE proteins? Additional experiments would be necessary to provide these answers, and might include the use of UV-irradiated virus. Accumulation of either IL-32 mRNA or protein was not observed following prolonged HCMV-infection process. IL-32 mRNA levels in HCMV-infected cells decreased rapidly in E and L stage to a similar level to that of uninfected cells. IL-32 protein levels decreased in subsequent hours post infection and could only be detected in a small quantity by western blot at 96 hpi. It is hypothesized that certain pathways might be activated to down-regulate or block the expression of IL-32 during HCMV infection.\\nMiRNAs are the most studied non-coding RNAs in recent years. They regulate gene expression at the post-transcriptional level and act as key regulators in diverse regulatory pathways, including early development, cell differentiation, cell proliferation, metabolism and apoptosis [37-40]. We carried out additional experiments to validate IL-32 mRNA as a target of hcmv-miR-UL112-1. In the presence of pS-miR-UL112-1, 39.27% luciferase activity of pMIR-IL-32UTR was observed to be significantly decreased, and a 67% reduction of endogenous IL-32 protein expression level was measured in HEK293 cells. It was confirmed that hcmv-miR-UL112-1 could specifically repress the expression of IL-32 through the predicted binding site.\\nTo examine the role of hcmv-miR-UL112-1 in regulating IL-32 expression during an active HCMV infection, the kinetics of hcmv-miR-UL112-1 expression in HCMV-infected MRC-5 cells were measured using TaqMan® miRNA assays. The expression of hcmv-miR-UL112-1 could be detected at 24 hpi and then increased gradually with prolonged HCMV infection. This result suggests that hcmv-miR-UL112-1 might function and lead to the decrease of IL-32 levels in E stage during active HCMV infection. The decrease of IL-32 mRNA, but not IL-32 protein level, together with the dramatic increase of hcmv-miR-UL112-1 during the E stage, suggests the possibility that hcmv-miR-UL112-1 may function primarily in the degradation of mRNA. Further studies are needed to address this point.\\nThrough co-evolution with its host, HCMV has evolved effective immune evasion strategies by encoding many immunomodulatory proteins that modulate the host immune response. It is conceivable that HCMV-encoded miRNAs might also be exploited during immune evasion. Hcmv-miR-UL112-1 is expressed with early kinetics. Some functional targets of hcmv-miR-UL112-1 have been shown experimentally to be involved in the modulation of NK cell activation. Stern-Ginossar et al. have identified MICB as a functional target of hcmv-miR-UL112-1 [30]. MICB is a stress-induced ligand of the NK cell activating receptor, NKG2D, which is critical for killing of virus-infected cells by NK cells. Hcmv-miR-UL112-1-mediated down-regulation of MICB, perturbed MICB binding with NKG2D and reduced killing of HCMV infected cells by NK cells. In addition, HCMV IE1, which would increase the TNF-α level by activation of the TNF-α promoter, was then identified as a target of hcmv-miR-UL112-1 by Murphy et al. in 2008 [29]. It has been confirmed that IL-32 induces significant amounts of TNF-α in a dose-dependent manner, and that silencing endogenous IL-32 by short hairpin RNA impairs the induction of TNF-α [2]. In this study, we validated that expression of IL-32 was activated by active HCMV infection and functionally down-regulated by ectopically expressed hcmv-miR-UL112-1 in HEK293 cells. It may be deduced from these results that, besides IE1 and MICB, hcmv-miR-UL112-1 might also synergistically achieve the modulation of NK cell activation through the TNF-α pathway by the down-regulation of IL-32, and immune evasion by HCMV.\",\n \"In summary, IL-32 expression in active HCMV infection was firstly investigated in our study. Detailed kinetics of the transcription of hcmv-miR-UL112-1, IL-32 mRNA and its protein expression were determined in HCMV infected MRC-5 cells. Furthermore, IL-32 expression was demonstrated to be functionally down-regulated by ectopically expressed hcmv-miR-UL112-1 in HEK293 cells. Follow up studies will investigate the mechanism of immune evasion by HCMV mediated by hcmv-miR-UL112-1, which has been confirmed to modulate NK cell activation during HCMV infection through post-transcriptional regulation.\",\n \" Serum samples Serum samples were obtained from 40 patients with HCMV infection (22 male, 18 female, aged 2.2 ± 0.42 yr) from Shengjing Hospital of China Medical University. All patients were HCMV-IgM positive and HCMV-IgG negative. The viral loads in urine samples from these patients were between 1.793×103 and 4.115×107 HCMV DNA copies per milliliter, as detected by routine QF-PCR (DaAn Gene). Blood samples collected from 32 HCMV-IgM negative individuals (17 male, 15 female, aged 2.7±0.35 yr) at the Medical Examination Centre of Shengjing Hospital were used as controls. According to the HCMV IgG detection results, HCMV IgM negative controls were divided into two groups: Previously HCMV infected group (IgG positive, n=17) and healthy group (IgG negative, n=15). All individuals involved in our study were seronegative for influenza A virus, HBV, HCV and HIV. This study was specifically approved by the Ethical Committee of Shengjing hospital.\\nSerum samples were obtained from 40 patients with HCMV infection (22 male, 18 female, aged 2.2 ± 0.42 yr) from Shengjing Hospital of China Medical University. All patients were HCMV-IgM positive and HCMV-IgG negative. The viral loads in urine samples from these patients were between 1.793×103 and 4.115×107 HCMV DNA copies per milliliter, as detected by routine QF-PCR (DaAn Gene). Blood samples collected from 32 HCMV-IgM negative individuals (17 male, 15 female, aged 2.7±0.35 yr) at the Medical Examination Centre of Shengjing Hospital were used as controls. According to the HCMV IgG detection results, HCMV IgM negative controls were divided into two groups: Previously HCMV infected group (IgG positive, n=17) and healthy group (IgG negative, n=15). All individuals involved in our study were seronegative for influenza A virus, HBV, HCV and HIV. This study was specifically approved by the Ethical Committee of Shengjing hospital.\\n Cell lines HEK293 cell line was a gift of Dr. Fangjie Chen from Department of Medical Genetics, China Medical University. HEK293 cells were cultured in Dulbecco’s Modified Eagle’s Medium (DMEM) supplemented with 10% fetal bovine serum (FBS), 100 μg/ml penicillin, 100 μg/ml streptomycin sulfate and 2 mM L-glutamine. Human embryonic lung fibroblasts cells, MRC-5, were acquired from Shanghai Institute for Biological Sciences, Chinese Academy of Sciences (CAS). MRC-5 cells were maintained in Modified Eagle’s Medium (MEM) supplemented with 10% FBS, 100 μg/ml penicillin and 100 μg/ml streptomycin sulfate. All cell cultures were maintained at 37°C in a 5% CO2 incubator.\\nHEK293 cell line was a gift of Dr. Fangjie Chen from Department of Medical Genetics, China Medical University. HEK293 cells were cultured in Dulbecco’s Modified Eagle’s Medium (DMEM) supplemented with 10% fetal bovine serum (FBS), 100 μg/ml penicillin, 100 μg/ml streptomycin sulfate and 2 mM L-glutamine. Human embryonic lung fibroblasts cells, MRC-5, were acquired from Shanghai Institute for Biological Sciences, Chinese Academy of Sciences (CAS). MRC-5 cells were maintained in Modified Eagle’s Medium (MEM) supplemented with 10% FBS, 100 μg/ml penicillin and 100 μg/ml streptomycin sulfate. All cell cultures were maintained at 37°C in a 5% CO2 incubator.\\n Virus HCMV clinical strain H was isolated from a urine sample of a 5-month-old infant hospitalized in Shengjing Hospital of China Medical University. Stock virus was propagated in MRC-5 maintained in MEM supplemented with 2% FBS, 100 μg/ml penicillin and 100 μg/ml streptomycin. The supernatant was then harvested, and the aliquots were stored at −80°C before use.\\nHCMV clinical strain H was isolated from a urine sample of a 5-month-old infant hospitalized in Shengjing Hospital of China Medical University. Stock virus was propagated in MRC-5 maintained in MEM supplemented with 2% FBS, 100 μg/ml penicillin and 100 μg/ml streptomycin. The supernatant was then harvested, and the aliquots were stored at −80°C before use.\\n Plasmid constructs Luciferase reporter construct pMIR was acquired from Ambion Company. IL-32 3′UTR sequence was obtained by RT-PCR from RNA harvested from HCMV infected cells, and inserted into SpeI and HindIII site of the multiple cloning regions (pMIR-IL-32UTR). A point mutation at corresponding seed region binding site was maintained by using Site-directed Gene Mutagenesis Kit (Beyotime), resulting in GUC to GAA. Sequence predicted to express hcmv-miR-UL112-1 was amplified by PCR directly from HCMV genome. The PCR product was inserted into BamHI and HindIII sites of pSilencer4.1 (Ambion) to generate a miRNA expression vector pS-miR-UL112-1. All primer sequences used in plasmid construction are listed in Table 1. All constructs were confirmed by DNA sequencing.\\n\\nPrimers used in plasmid construction and semi-quantitative RT-PCR\\nNote: sequences recognized by restriction en donuclases are down lined.\\nLuciferase reporter construct pMIR was acquired from Ambion Company. IL-32 3′UTR sequence was obtained by RT-PCR from RNA harvested from HCMV infected cells, and inserted into SpeI and HindIII site of the multiple cloning regions (pMIR-IL-32UTR). A point mutation at corresponding seed region binding site was maintained by using Site-directed Gene Mutagenesis Kit (Beyotime), resulting in GUC to GAA. Sequence predicted to express hcmv-miR-UL112-1 was amplified by PCR directly from HCMV genome. The PCR product was inserted into BamHI and HindIII sites of pSilencer4.1 (Ambion) to generate a miRNA expression vector pS-miR-UL112-1. All primer sequences used in plasmid construction are listed in Table 1. All constructs were confirmed by DNA sequencing.\\n\\nPrimers used in plasmid construction and semi-quantitative RT-PCR\\nNote: sequences recognized by restriction en donuclases are down lined.\\n Enzyme-linked immunosorbent assay The concentration of IL-32 in serum samples were detected using human IL-32 ELISA Kit (Cusabio) according to the manufacture’s protocols. The absorbance value at wavelength 450 nm was measured. The IL-32 concentrations were calculated from the standard curve.\\nThe concentration of IL-32 in serum samples were detected using human IL-32 ELISA Kit (Cusabio) according to the manufacture’s protocols. The absorbance value at wavelength 450 nm was measured. The IL-32 concentrations were calculated from the standard curve.\\n Semi-quantitative RT-PCR analysis MRC-5 cells were inoculated with H strain at 3–5 multiplicity of infection (m.o.i.). Infections were carried out under IE, E and L conditions respectively. For preparation of IE RNA, CHX (Sigma) (100 μl/ml) was added to the culture medium 1 hour before infection and the cells were harvested at 24 hpi. For E RNA, DNA synthesis inhibitor phosphonoacetic acid (PAA) (Sigma) (100 μl/ml) was added to the medium immediately after infection, and the cells were harvested at 48 hpi. L RNA and uninfected cellular RNA was derived from infected (at 96 hpi) and uninfected cells, respectively. Total RNA was isolated using Trizol reagent (TaKaRa), treated with DNase Iand then reverse-transcribed with MLV reverse transcriptase and random primers (TaKaRa). PCR was performed for 24 cycles in 50 μl reactions with the IL-32 specific primer pairs listed in Table 1. mRNA of β-actin was amplified for normalization in all reactions. The primers for IL-32 amplification were designed for detecting all known isoforms of human IL-32 based on the sequences from Gnebank database (NM_001012631.1-001012636.1, NM_004221.4 and NM_001012718.1). The PCR products were analyzed by electrophoresis on 2% agarose gel containing ethidium bromide.\\nMRC-5 cells were inoculated with H strain at 3–5 multiplicity of infection (m.o.i.). Infections were carried out under IE, E and L conditions respectively. For preparation of IE RNA, CHX (Sigma) (100 μl/ml) was added to the culture medium 1 hour before infection and the cells were harvested at 24 hpi. For E RNA, DNA synthesis inhibitor phosphonoacetic acid (PAA) (Sigma) (100 μl/ml) was added to the medium immediately after infection, and the cells were harvested at 48 hpi. L RNA and uninfected cellular RNA was derived from infected (at 96 hpi) and uninfected cells, respectively. Total RNA was isolated using Trizol reagent (TaKaRa), treated with DNase Iand then reverse-transcribed with MLV reverse transcriptase and random primers (TaKaRa). PCR was performed for 24 cycles in 50 μl reactions with the IL-32 specific primer pairs listed in Table 1. mRNA of β-actin was amplified for normalization in all reactions. The primers for IL-32 amplification were designed for detecting all known isoforms of human IL-32 based on the sequences from Gnebank database (NM_001012631.1-001012636.1, NM_004221.4 and NM_001012718.1). The PCR products were analyzed by electrophoresis on 2% agarose gel containing ethidium bromide.\\n miRNA extraction and TaqMan assays Total RNA was extracted from HCMV infected cells at different time points using the mivVana miRNA isolation kit (Ambion). We measured hcmv-miR-UL112-1 in all samples using TaqMan® miRNA assays (MIMAT0001577 for hcmv-miR-UL112-1, ABI). U6 expression was used to normalize the expression of hcmv-miR-UL112-1. HCMV uninfected MRC-5 cells were used as negative control. Fold difference for hcmv-miR-UL112-1 expression was calculated using the equation 2ΔΔct, where ΔCt = Ct (hcmv-miR-UL112-1) – Ct (U6 control) and ΔΔCt = ΔCt (HCMV infected MRC-5) – ΔCt (MRC-5). The measurements were done in triplicates and the results are presented at the means ± S.D.\\nTotal RNA was extracted from HCMV infected cells at different time points using the mivVana miRNA isolation kit (Ambion). We measured hcmv-miR-UL112-1 in all samples using TaqMan® miRNA assays (MIMAT0001577 for hcmv-miR-UL112-1, ABI). U6 expression was used to normalize the expression of hcmv-miR-UL112-1. HCMV uninfected MRC-5 cells were used as negative control. Fold difference for hcmv-miR-UL112-1 expression was calculated using the equation 2ΔΔct, where ΔCt = Ct (hcmv-miR-UL112-1) – Ct (U6 control) and ΔΔCt = ΔCt (HCMV infected MRC-5) – ΔCt (MRC-5). The measurements were done in triplicates and the results are presented at the means ± S.D.\\n Luciferase assay MRC-5 cells were plated in 24-well plate at a density of 4.0×105 cells per well and grown to reach about 80% confluence at the time for transfection. 200 ng pMIR-IL-32UTR/pMIR-IL-32MUT plasmid or an empty vector control was co-transfected with 400 ng pS-miR-UL112-1 or pS-neg control into the cells by using lipofectamine 2000 reagent (Invitrogen) according to the manufacture’s protocol. 200 ng renilla luciferase plasmid (Promega) was introduced into each co-transfected cells as an internal control plasmid at the same time. Cells were collected 48 hours post transfection and luciferase activity levels were measured using the Dual luciferase reporter assay system (Promega) according to the manufacture’s guidelines. All measurements were done in triplicates and signals were normalized for transfection efficiency to the internal renilla control. The results are presented at the means ±S.D.\\nMRC-5 cells were plated in 24-well plate at a density of 4.0×105 cells per well and grown to reach about 80% confluence at the time for transfection. 200 ng pMIR-IL-32UTR/pMIR-IL-32MUT plasmid or an empty vector control was co-transfected with 400 ng pS-miR-UL112-1 or pS-neg control into the cells by using lipofectamine 2000 reagent (Invitrogen) according to the manufacture’s protocol. 200 ng renilla luciferase plasmid (Promega) was introduced into each co-transfected cells as an internal control plasmid at the same time. Cells were collected 48 hours post transfection and luciferase activity levels were measured using the Dual luciferase reporter assay system (Promega) according to the manufacture’s guidelines. All measurements were done in triplicates and signals were normalized for transfection efficiency to the internal renilla control. The results are presented at the means ±S.D.\\n Western blot analysis Western blot analyses were performed to detect the IL-32 levels in HCMV infected MRC-5 cells and hcmv-miR-UL112-1 over expressed HEK293 cells respectively. MRC-5 cells were inoculated with H strain at 3–5 m.o.i. Cells were harvested at different time points post infection (6 h, 24 h, 48 h, 72 h and 96 h). HEK293 cells were transfected with 8 μg pS-miR-UL112-1 or pS-neg control using lipofectamine 2000 (Invitrogen) and were incubated at 37°C for 48 hours.\\nProteins of the cells described above were prepared by suspending cells in lysis buffer, followed by centrifugation. Concentrations of proteins in supernatant were quantified using protein assay kit (Beyotime). Protein was separated by 10% acrylamide gel electrophoresis and transferred onto a nitrocellulose membrane. Western blot analysis was performed using IL-32 antibody (abcam). Immunoblots were visualized with ECL detection system. Sample loading was normalized by quantities of β-actin detected parallel.\\nWestern blot analyses were performed to detect the IL-32 levels in HCMV infected MRC-5 cells and hcmv-miR-UL112-1 over expressed HEK293 cells respectively. MRC-5 cells were inoculated with H strain at 3–5 m.o.i. Cells were harvested at different time points post infection (6 h, 24 h, 48 h, 72 h and 96 h). HEK293 cells were transfected with 8 μg pS-miR-UL112-1 or pS-neg control using lipofectamine 2000 (Invitrogen) and were incubated at 37°C for 48 hours.\\nProteins of the cells described above were prepared by suspending cells in lysis buffer, followed by centrifugation. Concentrations of proteins in supernatant were quantified using protein assay kit (Beyotime). Protein was separated by 10% acrylamide gel electrophoresis and transferred onto a nitrocellulose membrane. Western blot analysis was performed using IL-32 antibody (abcam). Immunoblots were visualized with ECL detection system. Sample loading was normalized by quantities of β-actin detected parallel.\\n Statistical analysis All experiments were reproducible. The results are presented at the means ± S.D. Student’s t test and F test was used to determine statistical significance. Differences were considered statistically significant at value of P≤0.05.\\nAll experiments were reproducible. The results are presented at the means ± S.D. Student’s t test and F test was used to determine statistical significance. Differences were considered statistically significant at value of P≤0.05.\",\n \"Serum samples were obtained from 40 patients with HCMV infection (22 male, 18 female, aged 2.2 ± 0.42 yr) from Shengjing Hospital of China Medical University. All patients were HCMV-IgM positive and HCMV-IgG negative. The viral loads in urine samples from these patients were between 1.793×103 and 4.115×107 HCMV DNA copies per milliliter, as detected by routine QF-PCR (DaAn Gene). Blood samples collected from 32 HCMV-IgM negative individuals (17 male, 15 female, aged 2.7±0.35 yr) at the Medical Examination Centre of Shengjing Hospital were used as controls. According to the HCMV IgG detection results, HCMV IgM negative controls were divided into two groups: Previously HCMV infected group (IgG positive, n=17) and healthy group (IgG negative, n=15). All individuals involved in our study were seronegative for influenza A virus, HBV, HCV and HIV. This study was specifically approved by the Ethical Committee of Shengjing hospital.\",\n \"HEK293 cell line was a gift of Dr. Fangjie Chen from Department of Medical Genetics, China Medical University. HEK293 cells were cultured in Dulbecco’s Modified Eagle’s Medium (DMEM) supplemented with 10% fetal bovine serum (FBS), 100 μg/ml penicillin, 100 μg/ml streptomycin sulfate and 2 mM L-glutamine. Human embryonic lung fibroblasts cells, MRC-5, were acquired from Shanghai Institute for Biological Sciences, Chinese Academy of Sciences (CAS). MRC-5 cells were maintained in Modified Eagle’s Medium (MEM) supplemented with 10% FBS, 100 μg/ml penicillin and 100 μg/ml streptomycin sulfate. All cell cultures were maintained at 37°C in a 5% CO2 incubator.\",\n \"HCMV clinical strain H was isolated from a urine sample of a 5-month-old infant hospitalized in Shengjing Hospital of China Medical University. Stock virus was propagated in MRC-5 maintained in MEM supplemented with 2% FBS, 100 μg/ml penicillin and 100 μg/ml streptomycin. The supernatant was then harvested, and the aliquots were stored at −80°C before use.\",\n \"Luciferase reporter construct pMIR was acquired from Ambion Company. IL-32 3′UTR sequence was obtained by RT-PCR from RNA harvested from HCMV infected cells, and inserted into SpeI and HindIII site of the multiple cloning regions (pMIR-IL-32UTR). A point mutation at corresponding seed region binding site was maintained by using Site-directed Gene Mutagenesis Kit (Beyotime), resulting in GUC to GAA. Sequence predicted to express hcmv-miR-UL112-1 was amplified by PCR directly from HCMV genome. The PCR product was inserted into BamHI and HindIII sites of pSilencer4.1 (Ambion) to generate a miRNA expression vector pS-miR-UL112-1. All primer sequences used in plasmid construction are listed in Table 1. All constructs were confirmed by DNA sequencing.\\n\\nPrimers used in plasmid construction and semi-quantitative RT-PCR\\nNote: sequences recognized by restriction en donuclases are down lined.\",\n \"The concentration of IL-32 in serum samples were detected using human IL-32 ELISA Kit (Cusabio) according to the manufacture’s protocols. The absorbance value at wavelength 450 nm was measured. The IL-32 concentrations were calculated from the standard curve.\",\n \"MRC-5 cells were inoculated with H strain at 3–5 multiplicity of infection (m.o.i.). Infections were carried out under IE, E and L conditions respectively. For preparation of IE RNA, CHX (Sigma) (100 μl/ml) was added to the culture medium 1 hour before infection and the cells were harvested at 24 hpi. For E RNA, DNA synthesis inhibitor phosphonoacetic acid (PAA) (Sigma) (100 μl/ml) was added to the medium immediately after infection, and the cells were harvested at 48 hpi. L RNA and uninfected cellular RNA was derived from infected (at 96 hpi) and uninfected cells, respectively. Total RNA was isolated using Trizol reagent (TaKaRa), treated with DNase Iand then reverse-transcribed with MLV reverse transcriptase and random primers (TaKaRa). PCR was performed for 24 cycles in 50 μl reactions with the IL-32 specific primer pairs listed in Table 1. mRNA of β-actin was amplified for normalization in all reactions. The primers for IL-32 amplification were designed for detecting all known isoforms of human IL-32 based on the sequences from Gnebank database (NM_001012631.1-001012636.1, NM_004221.4 and NM_001012718.1). The PCR products were analyzed by electrophoresis on 2% agarose gel containing ethidium bromide.\",\n \"Total RNA was extracted from HCMV infected cells at different time points using the mivVana miRNA isolation kit (Ambion). We measured hcmv-miR-UL112-1 in all samples using TaqMan® miRNA assays (MIMAT0001577 for hcmv-miR-UL112-1, ABI). U6 expression was used to normalize the expression of hcmv-miR-UL112-1. HCMV uninfected MRC-5 cells were used as negative control. Fold difference for hcmv-miR-UL112-1 expression was calculated using the equation 2ΔΔct, where ΔCt = Ct (hcmv-miR-UL112-1) – Ct (U6 control) and ΔΔCt = ΔCt (HCMV infected MRC-5) – ΔCt (MRC-5). The measurements were done in triplicates and the results are presented at the means ± S.D.\",\n \"MRC-5 cells were plated in 24-well plate at a density of 4.0×105 cells per well and grown to reach about 80% confluence at the time for transfection. 200 ng pMIR-IL-32UTR/pMIR-IL-32MUT plasmid or an empty vector control was co-transfected with 400 ng pS-miR-UL112-1 or pS-neg control into the cells by using lipofectamine 2000 reagent (Invitrogen) according to the manufacture’s protocol. 200 ng renilla luciferase plasmid (Promega) was introduced into each co-transfected cells as an internal control plasmid at the same time. Cells were collected 48 hours post transfection and luciferase activity levels were measured using the Dual luciferase reporter assay system (Promega) according to the manufacture’s guidelines. All measurements were done in triplicates and signals were normalized for transfection efficiency to the internal renilla control. The results are presented at the means ±S.D.\",\n \"Western blot analyses were performed to detect the IL-32 levels in HCMV infected MRC-5 cells and hcmv-miR-UL112-1 over expressed HEK293 cells respectively. MRC-5 cells were inoculated with H strain at 3–5 m.o.i. Cells were harvested at different time points post infection (6 h, 24 h, 48 h, 72 h and 96 h). HEK293 cells were transfected with 8 μg pS-miR-UL112-1 or pS-neg control using lipofectamine 2000 (Invitrogen) and were incubated at 37°C for 48 hours.\\nProteins of the cells described above were prepared by suspending cells in lysis buffer, followed by centrifugation. Concentrations of proteins in supernatant were quantified using protein assay kit (Beyotime). Protein was separated by 10% acrylamide gel electrophoresis and transferred onto a nitrocellulose membrane. Western blot analysis was performed using IL-32 antibody (abcam). Immunoblots were visualized with ECL detection system. Sample loading was normalized by quantities of β-actin detected parallel.\",\n \"All experiments were reproducible. The results are presented at the means ± S.D. Student’s t test and F test was used to determine statistical significance. Differences were considered statistically significant at value of P≤0.05.\",\n \"The authors declare that they have no competing interests.\",\n \"YJH carried out the semi-quantitative RT-PCR analysis, western blot analysis and Statistical analysis. YQ and RH carried out the plasmids construction and luciferase assay. QR as the corresponding author designed the study and corrected the manuscript. YPM and YHJ carried out the preparation of virus and cell lines. ZRS carried out the serum samples collection and ELISA detection. All authors have read and approved the final manuscript.\"\n]"},"all_sections_sec_types":{"kind":"list like","value":[null,"results",null,null,null,"discussion","conclusions","materials|methods",null,null,null,null,null,null,null,null,null,null,null,null],"string":"[\n null,\n \"results\",\n null,\n null,\n null,\n \"discussion\",\n \"conclusions\",\n \"materials|methods\",\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null\n]"},"keywords":{"kind":"list like","value":["IL-32","HCMV infection","hcmv-miR-UL112-1"],"string":"[\n \"IL-32\",\n \"HCMV infection\",\n \"hcmv-miR-UL112-1\"\n]"},"whole_article_text":{"kind":"string","value":"Background:\nInterleukin-32 (IL-32) is a newly-discovered pro-inflammatory cytokine, which plays a role in innate and adaptive immune responses [1,2]. It lacks sequence homology to any presently known cytokine families. IL-32 is associated with the induction of inflammatory responses by activating the p38MAPK, NF-kappa B and AP-1 signaling pathways. It has been implicated in inflammatory disorders, mycobacterium tuberculosis infections and inflammatory bowel disease, as well as in some autoimmune diseases, such as rheumatoid arthritis, ulcerative colitis and Crohn’s disease [3-10]. Moreover, it has been reported that IL-32 has pro-inflammatory effects on myeloid cells and promotes the differentiation of osteoclast precursors into multinucleated cells expressing specific osteoclast markers [11,12]. In recent studies, IL-32 has also been found to be regulated during viral infections. Elevated levels of IL-32 were found in sera from patients infected with influenza A virus [13-15], hepatitis B virus (HBV) [16], hepatitis C virus (HCV) [17], human papillomavirus (HPV) [18] and human immunodeficiency virus (HIV) [19-21], suggesting that IL-32 might play an important role in host defense against viral infections\nHuman cytomegalovirus (HCMV) is an ubiquitous β-herpesvirus that infects a broad range of cell types in human hosts, contributing to its complex and varied pathogenesis. HCMV Infection leads to life-long persistence in 50%–90% of the population, which is generally subclinical in healthy individuals [22]. However, it can lead to serious complications in immunocompromised patients, such as transplant recipients or AIDS patients [23,24].\nMicroRNAs (miRNAs) are an abundant class of small non-coding RNA molecules that target mRNAs generally within their 3′ untranslated regions (3′UTRs). MiRNAs suppress gene expression mainly through inhibition of translation or, rarely, through degradation of mRNA [25,26]. Clinical isolates of HCMV encode at least 17 miRNAs [27,28]. However, only a few functional targets have been validated experimentally for some HCMV-encoded miRNAs [29-34]. It has been demonstrated that hcmv-miR-UL112-1 targets and reduces the expression of HCMV UL123 (IE1 or IE72), UL114 and the major histocompatibility complex class 1-related chain B (MICB). Moreover, BclAF1 protein, a human cytomegalovirus restriction factor, was reported to be a new target of hcmv-miR-UL112-1 [35]. In addition, multiple cellular targets of hcmv-miR-US25-1, which are associated with cell cycle control, were identified by RNA induced silencing complex immunoprecipitation (RISC-IP), including cyclin E2, BRCC3, EID1, MAPRE2 and CD147. IL-32, which is not present in the confirmed targets, was screened out as a candidate target of hcmv-miR-UL112-1 in our previous study [36]. However, no further experiments to validate these findings have been performed.\nIn the present study, the expression levels of IL-32 were compared among serum samples from patients with active HCMV infection and samples from HCMV-IgM negative individuals. The expression levels of IL-32 and hcmv-miR-UL112-1 in HCMV infected MRC-5 cells were detected at different stages of infection and time points. In addition, functional down-regulation of IL-32 by hcmv-miR-UL112-1 was detected in transfected human embryonic kidney (HEK293) cells, and the effect of hcmv-miR-UL112-1 on IL-32 during HCMV infection was primarily discussed.\n\nResults:\n Relatively high IL-32 levels among individuals with active HCMV infection IL-32 protein levels were measured in the sera of 40 patients with active HCMV infections (HCMV IgM positive) and 32 HCMV IgM negative control individuals by enzyme-linked immunosorbent assays (ELISA). According to the results of HCMV IgG detection, HCMV IgM negative controls were divided into two groups: Previously HCMV infected group (IgG positive, n=17) and healthy group (IgG negative, n=15). Mean IL-32 levels in the sera of patients with active HCMV infection were 4.1778±1.1663 ng/ml (shown in Figure 1). Mean IL-32 levels in sera of the previously HCMV infected group and healthy group were 2.4653±0.8287 ng/ml and 2.4480±0.9162 ng/ml, respectively. The data were analyzed by F test. IL-32 levels in the sera of patients with active HCMV infection were significantly higher than those in the HCMV IgM negative control group (F=23.957, P<0.001). No significant difference was observed between the previously HCMV infected group and the healthy group (P=0.963).\n\nAn illustration showing IL-32 levels in serum samples detected by ELISA. Serum samples were from actively HCMV-infected patients (n=40), HCMV previously infected individuals (n=17) and healthy individuals (n=15), respectively.\nIL-32 protein levels were measured in the sera of 40 patients with active HCMV infections (HCMV IgM positive) and 32 HCMV IgM negative control individuals by enzyme-linked immunosorbent assays (ELISA). According to the results of HCMV IgG detection, HCMV IgM negative controls were divided into two groups: Previously HCMV infected group (IgG positive, n=17) and healthy group (IgG negative, n=15). Mean IL-32 levels in the sera of patients with active HCMV infection were 4.1778±1.1663 ng/ml (shown in Figure 1). Mean IL-32 levels in sera of the previously HCMV infected group and healthy group were 2.4653±0.8287 ng/ml and 2.4480±0.9162 ng/ml, respectively. The data were analyzed by F test. IL-32 levels in the sera of patients with active HCMV infection were significantly higher than those in the HCMV IgM negative control group (F=23.957, P<0.001). No significant difference was observed between the previously HCMV infected group and the healthy group (P=0.963).\n\nAn illustration showing IL-32 levels in serum samples detected by ELISA. Serum samples were from actively HCMV-infected patients (n=40), HCMV previously infected individuals (n=17) and healthy individuals (n=15), respectively.\n Kinetics of the expression of IL-32 mRNA, IL-32 protein and hcmv-miR-UL112-1 in HCMV infected MRC-5 cells Cells were harvested at different infection stages and time points after HCMV infection as described in the Materials and Methods section. We used specific drugs to determine the expression profile of IL-32 at different stages of viral replication. CHX was used to determine the immediate early (IE) stage and PAA was used to determine early (E) stage. As shown in Figure 2A and B, semi-quantitative RT-PCR analysis revealed that IL-32 mRNA expression was significantly activated at IE stage but not at E or late (L) stages post-infection. IL-32 protein expression levels at different time points following HCMV infection were measured by western blot analysis. As shown in Figure 2C and D, IL-32 protein expression reached a peak value at 6 hours post infection (hpi). The relative IL-32 concentration in HCMV infected cells collected at 6 hpi was 10 fold higher than in uninfected cells. All measured values were balanced by using β-actin as an internal control. No accumulation of either IL-32 mRNA or protein was found following the prolongation of HCMV-infection process. IL-32 mRNA levels in HCMV infected cells decreased in E and L stages to a similar level as in uninfected cells. IL-32 protein levels decreased with subsequent hours post infection.\n\nExpression of IL-32 induced by HCMV infection in MRC-5 cells. (A) IL-32 mRNA levels in HCMV infected cells were compared to that in uninfected MRC-5 cells. β-actin was used as an internal control. The universal primers for all IL-32 transcripts were used. U is for uninfected MRC-5; IE is for immediate early stage; E is for early stage; L is for late stage. (B) IL-32 mRNA levels were represented relative to that in IE stage. (C) IL-32 protein levels in HCMV infected MRC-5 cells were detected at different time points. IL-32 densitometer values were normalized by β-actin values. (D) The IL-32 protein levels were represented relative to that in infected cells collected at 6 hpi. (E) The kinetics of hcmv-miR-UL112-1 expression were measured using TaqMan® miRNA assays. The expression of hcmv-miR-UL112-1 could be detected at 24 hpi and increased gradually as the HCMV-infection process was prolonged.\nIn addition, the kinetics of hcmv-miR-UL112-1 expression were measured using TaqMan® miRNA assays. Measured values were normalized by using U6 as an internal control. The expression of hcmv-miR-UL112-1 could be detected at 24 hpi. As shown in Figure 2E, a steep increase was observed at 48 hpi. The relative quantity of hcmv-miR-UL112-1 at 48 hpi was 6.5 fold higher than at 24 hpi. The expression of hcmv-miR-UL112-1 increased gradually as the HCMV-infection process was prolonged. The relative quantity of hcmv-miR-UL112-1 in HCMV infected cells at 96 hpi was 10 fold higher than at 24 hpi.\nCells were harvested at different infection stages and time points after HCMV infection as described in the Materials and Methods section. We used specific drugs to determine the expression profile of IL-32 at different stages of viral replication. CHX was used to determine the immediate early (IE) stage and PAA was used to determine early (E) stage. As shown in Figure 2A and B, semi-quantitative RT-PCR analysis revealed that IL-32 mRNA expression was significantly activated at IE stage but not at E or late (L) stages post-infection. IL-32 protein expression levels at different time points following HCMV infection were measured by western blot analysis. As shown in Figure 2C and D, IL-32 protein expression reached a peak value at 6 hours post infection (hpi). The relative IL-32 concentration in HCMV infected cells collected at 6 hpi was 10 fold higher than in uninfected cells. All measured values were balanced by using β-actin as an internal control. No accumulation of either IL-32 mRNA or protein was found following the prolongation of HCMV-infection process. IL-32 mRNA levels in HCMV infected cells decreased in E and L stages to a similar level as in uninfected cells. IL-32 protein levels decreased with subsequent hours post infection.\n\nExpression of IL-32 induced by HCMV infection in MRC-5 cells. (A) IL-32 mRNA levels in HCMV infected cells were compared to that in uninfected MRC-5 cells. β-actin was used as an internal control. The universal primers for all IL-32 transcripts were used. U is for uninfected MRC-5; IE is for immediate early stage; E is for early stage; L is for late stage. (B) IL-32 mRNA levels were represented relative to that in IE stage. (C) IL-32 protein levels in HCMV infected MRC-5 cells were detected at different time points. IL-32 densitometer values were normalized by β-actin values. (D) The IL-32 protein levels were represented relative to that in infected cells collected at 6 hpi. (E) The kinetics of hcmv-miR-UL112-1 expression were measured using TaqMan® miRNA assays. The expression of hcmv-miR-UL112-1 could be detected at 24 hpi and increased gradually as the HCMV-infection process was prolonged.\nIn addition, the kinetics of hcmv-miR-UL112-1 expression were measured using TaqMan® miRNA assays. Measured values were normalized by using U6 as an internal control. The expression of hcmv-miR-UL112-1 could be detected at 24 hpi. As shown in Figure 2E, a steep increase was observed at 48 hpi. The relative quantity of hcmv-miR-UL112-1 at 48 hpi was 6.5 fold higher than at 24 hpi. The expression of hcmv-miR-UL112-1 increased gradually as the HCMV-infection process was prolonged. The relative quantity of hcmv-miR-UL112-1 in HCMV infected cells at 96 hpi was 10 fold higher than at 24 hpi.\n Functional down-regulation of endogenous IL-32 expression by ectopically expressed hcmv-miR-UL112-1 In our previous study, a sequence in the 3′UTR of IL-32 mRNA was identified by hybrid-PCR to be a candidate target site of hcmv-miR-UL112-1. The predicted binding site for hcmv-miR-UL112-1 was 195 nt upstream of the polyA structure of IL-32 mRNA. A schematic representation of hcmv-miR-UL112-1 binding to the IL-32 3′UTR sequence is shown in Figure 3A.\n\nDown-regulation of IL-32 expression by hcmv-miR-UL112-1. (A) The diagram shows the predicted sequences of hcmv-miR-UL112-1 binding to IL-32 mRNA. (B) As a candidate target, IL-32 was validated for its ability to inhibit expression of a luciferase reporter construct in the presence of hcmv-miR-UL112-1 (pS-miR-UL112-1). Results are shown as percentage expression of negative control sample (pS-neg) following correction for transfection levels according to the control of renilla luciferase expression. Values are shown as means ± standard deviations for triplicate samples. (C) HEK 293 cells were transfected with pS-miR-UL112-1 or control vector respectively. Cells were collected 48 hpi and were subjected to western blot analysis using the indicated antibodies. IL-32 densitometer values were normalized to that of the β-actin values. (D) The amounts of proteins presented in panel C were quantified by densitometry. Results are shown as percentage expression of the negative control sample (Empty).\nTo further determine whether the IL-32 3′UTR sequence represents a functional target site for hcmv-miR-UL112-1, the IL-32 3′UTR sequence was validated by luciferase reporter assays. As shown in Figure 3B, transfected pMIR-IL-32UTR led to a significant inhibition of luciferase activity by 39.27% in the presence of pS-miR-UL112-1. pS-miR-UL112-1 had no significant inhibitory effects on the luciferase activities of pMIR and pMIR-IL-32MUT. These results demonstrate that the IL-32 3′UTR sequence provided a specific and functional binding site for hcmv-miR-UL112-1.\nThe regulatory effect of hcmv-miR-UL112-1 on IL-32 protein expression was then examined in HEK293 cells by western blot. IL-32 protein level was significantly reduced in cells transfected with hcmv-miR-UL112-1 in comparison to cells transfected with pSilencer negative control (Figure 3C and D). IL-32 densitometer values were normalized to that of β-actin values. The level of reduction of IL-32 protein was approximately 67% as determined by densitometry (Figure 3D). Our observations confirmed that the expression of endogenous IL-32 protein could be specifically inhibited by ectopically expressed hcmv-miR-UL112-1.\nIn our previous study, a sequence in the 3′UTR of IL-32 mRNA was identified by hybrid-PCR to be a candidate target site of hcmv-miR-UL112-1. The predicted binding site for hcmv-miR-UL112-1 was 195 nt upstream of the polyA structure of IL-32 mRNA. A schematic representation of hcmv-miR-UL112-1 binding to the IL-32 3′UTR sequence is shown in Figure 3A.\n\nDown-regulation of IL-32 expression by hcmv-miR-UL112-1. (A) The diagram shows the predicted sequences of hcmv-miR-UL112-1 binding to IL-32 mRNA. (B) As a candidate target, IL-32 was validated for its ability to inhibit expression of a luciferase reporter construct in the presence of hcmv-miR-UL112-1 (pS-miR-UL112-1). Results are shown as percentage expression of negative control sample (pS-neg) following correction for transfection levels according to the control of renilla luciferase expression. Values are shown as means ± standard deviations for triplicate samples. (C) HEK 293 cells were transfected with pS-miR-UL112-1 or control vector respectively. Cells were collected 48 hpi and were subjected to western blot analysis using the indicated antibodies. IL-32 densitometer values were normalized to that of the β-actin values. (D) The amounts of proteins presented in panel C were quantified by densitometry. Results are shown as percentage expression of the negative control sample (Empty).\nTo further determine whether the IL-32 3′UTR sequence represents a functional target site for hcmv-miR-UL112-1, the IL-32 3′UTR sequence was validated by luciferase reporter assays. As shown in Figure 3B, transfected pMIR-IL-32UTR led to a significant inhibition of luciferase activity by 39.27% in the presence of pS-miR-UL112-1. pS-miR-UL112-1 had no significant inhibitory effects on the luciferase activities of pMIR and pMIR-IL-32MUT. These results demonstrate that the IL-32 3′UTR sequence provided a specific and functional binding site for hcmv-miR-UL112-1.\nThe regulatory effect of hcmv-miR-UL112-1 on IL-32 protein expression was then examined in HEK293 cells by western blot. IL-32 protein level was significantly reduced in cells transfected with hcmv-miR-UL112-1 in comparison to cells transfected with pSilencer negative control (Figure 3C and D). IL-32 densitometer values were normalized to that of β-actin values. The level of reduction of IL-32 protein was approximately 67% as determined by densitometry (Figure 3D). Our observations confirmed that the expression of endogenous IL-32 protein could be specifically inhibited by ectopically expressed hcmv-miR-UL112-1.\n\nRelatively high IL-32 levels among individuals with active HCMV infection:\nIL-32 protein levels were measured in the sera of 40 patients with active HCMV infections (HCMV IgM positive) and 32 HCMV IgM negative control individuals by enzyme-linked immunosorbent assays (ELISA). According to the results of HCMV IgG detection, HCMV IgM negative controls were divided into two groups: Previously HCMV infected group (IgG positive, n=17) and healthy group (IgG negative, n=15). Mean IL-32 levels in the sera of patients with active HCMV infection were 4.1778±1.1663 ng/ml (shown in Figure 1). Mean IL-32 levels in sera of the previously HCMV infected group and healthy group were 2.4653±0.8287 ng/ml and 2.4480±0.9162 ng/ml, respectively. The data were analyzed by F test. IL-32 levels in the sera of patients with active HCMV infection were significantly higher than those in the HCMV IgM negative control group (F=23.957, P<0.001). No significant difference was observed between the previously HCMV infected group and the healthy group (P=0.963).\n\nAn illustration showing IL-32 levels in serum samples detected by ELISA. Serum samples were from actively HCMV-infected patients (n=40), HCMV previously infected individuals (n=17) and healthy individuals (n=15), respectively.\n\nKinetics of the expression of IL-32 mRNA, IL-32 protein and hcmv-miR-UL112-1 in HCMV infected MRC-5 cells:\nCells were harvested at different infection stages and time points after HCMV infection as described in the Materials and Methods section. We used specific drugs to determine the expression profile of IL-32 at different stages of viral replication. CHX was used to determine the immediate early (IE) stage and PAA was used to determine early (E) stage. As shown in Figure 2A and B, semi-quantitative RT-PCR analysis revealed that IL-32 mRNA expression was significantly activated at IE stage but not at E or late (L) stages post-infection. IL-32 protein expression levels at different time points following HCMV infection were measured by western blot analysis. As shown in Figure 2C and D, IL-32 protein expression reached a peak value at 6 hours post infection (hpi). The relative IL-32 concentration in HCMV infected cells collected at 6 hpi was 10 fold higher than in uninfected cells. All measured values were balanced by using β-actin as an internal control. No accumulation of either IL-32 mRNA or protein was found following the prolongation of HCMV-infection process. IL-32 mRNA levels in HCMV infected cells decreased in E and L stages to a similar level as in uninfected cells. IL-32 protein levels decreased with subsequent hours post infection.\n\nExpression of IL-32 induced by HCMV infection in MRC-5 cells. (A) IL-32 mRNA levels in HCMV infected cells were compared to that in uninfected MRC-5 cells. β-actin was used as an internal control. The universal primers for all IL-32 transcripts were used. U is for uninfected MRC-5; IE is for immediate early stage; E is for early stage; L is for late stage. (B) IL-32 mRNA levels were represented relative to that in IE stage. (C) IL-32 protein levels in HCMV infected MRC-5 cells were detected at different time points. IL-32 densitometer values were normalized by β-actin values. (D) The IL-32 protein levels were represented relative to that in infected cells collected at 6 hpi. (E) The kinetics of hcmv-miR-UL112-1 expression were measured using TaqMan® miRNA assays. The expression of hcmv-miR-UL112-1 could be detected at 24 hpi and increased gradually as the HCMV-infection process was prolonged.\nIn addition, the kinetics of hcmv-miR-UL112-1 expression were measured using TaqMan® miRNA assays. Measured values were normalized by using U6 as an internal control. The expression of hcmv-miR-UL112-1 could be detected at 24 hpi. As shown in Figure 2E, a steep increase was observed at 48 hpi. The relative quantity of hcmv-miR-UL112-1 at 48 hpi was 6.5 fold higher than at 24 hpi. The expression of hcmv-miR-UL112-1 increased gradually as the HCMV-infection process was prolonged. The relative quantity of hcmv-miR-UL112-1 in HCMV infected cells at 96 hpi was 10 fold higher than at 24 hpi.\n\nFunctional down-regulation of endogenous IL-32 expression by ectopically expressed hcmv-miR-UL112-1:\nIn our previous study, a sequence in the 3′UTR of IL-32 mRNA was identified by hybrid-PCR to be a candidate target site of hcmv-miR-UL112-1. The predicted binding site for hcmv-miR-UL112-1 was 195 nt upstream of the polyA structure of IL-32 mRNA. A schematic representation of hcmv-miR-UL112-1 binding to the IL-32 3′UTR sequence is shown in Figure 3A.\n\nDown-regulation of IL-32 expression by hcmv-miR-UL112-1. (A) The diagram shows the predicted sequences of hcmv-miR-UL112-1 binding to IL-32 mRNA. (B) As a candidate target, IL-32 was validated for its ability to inhibit expression of a luciferase reporter construct in the presence of hcmv-miR-UL112-1 (pS-miR-UL112-1). Results are shown as percentage expression of negative control sample (pS-neg) following correction for transfection levels according to the control of renilla luciferase expression. Values are shown as means ± standard deviations for triplicate samples. (C) HEK 293 cells were transfected with pS-miR-UL112-1 or control vector respectively. Cells were collected 48 hpi and were subjected to western blot analysis using the indicated antibodies. IL-32 densitometer values were normalized to that of the β-actin values. (D) The amounts of proteins presented in panel C were quantified by densitometry. Results are shown as percentage expression of the negative control sample (Empty).\nTo further determine whether the IL-32 3′UTR sequence represents a functional target site for hcmv-miR-UL112-1, the IL-32 3′UTR sequence was validated by luciferase reporter assays. As shown in Figure 3B, transfected pMIR-IL-32UTR led to a significant inhibition of luciferase activity by 39.27% in the presence of pS-miR-UL112-1. pS-miR-UL112-1 had no significant inhibitory effects on the luciferase activities of pMIR and pMIR-IL-32MUT. These results demonstrate that the IL-32 3′UTR sequence provided a specific and functional binding site for hcmv-miR-UL112-1.\nThe regulatory effect of hcmv-miR-UL112-1 on IL-32 protein expression was then examined in HEK293 cells by western blot. IL-32 protein level was significantly reduced in cells transfected with hcmv-miR-UL112-1 in comparison to cells transfected with pSilencer negative control (Figure 3C and D). IL-32 densitometer values were normalized to that of β-actin values. The level of reduction of IL-32 protein was approximately 67% as determined by densitometry (Figure 3D). Our observations confirmed that the expression of endogenous IL-32 protein could be specifically inhibited by ectopically expressed hcmv-miR-UL112-1.\n\nDiscussion:\nIL-32 is a proinflammatory cytokine and plays a critical role in inflammatory responses. It induces the expression of IL-1β, IL-6, IL-8 and TNF through the p38MAPK, NF-kappa B and AP-1 signaling pathways. Recent reports have highlighted that IL-32 is regulated during viral infection in humans. It has been reported that the IL-32 expression could be induced during infections of influenza A virus, HBV, HCV, HPV and HIV [13-21].\nIL-32 levels, detected by ELISA, were 4.1778±1.1663 ng/ml in sera from actively HCMV-infected patients. IL-32 levels in sera from the previously infected group and healthy group were 2.4653±0.8287 ng/ml and 2.4480±0.9162 ng/ml, respectively. IL-32 levels in sera from actively HCMV-infected patients were significantly higher than those in control groups (F=23.957, P<0.001). However, no significant differences in IL-32 levels were observed between the HCMV previously infected group and healthy group (P=0.963). These results demonstrate that IL-32 may be a newly described reactive protein corresponding to active HCMV infection.\nHCMV infection can induce the expression of IL-32 at both the mRNA and protein levels in HCMV-infected MRC-5 cells. The peak value of IL-32 mRNA was detected as early as IE stage. The IL-32 protein expression level reached peak value at 6 hpi. The IL-32 protein concentration in HCMV-infected cells at 6 hpi was 10 fold higher than that in uninfected cells. This result demonstrated that the production of IL-32 can be specifically induced by active HCMV infection, while the mechanism that induced the expression of IL-32 during HCMV infection is still unknown. Is the induction of IL-32 mediated only by viral entry or by the expression of IE proteins? Additional experiments would be necessary to provide these answers, and might include the use of UV-irradiated virus. Accumulation of either IL-32 mRNA or protein was not observed following prolonged HCMV-infection process. IL-32 mRNA levels in HCMV-infected cells decreased rapidly in E and L stage to a similar level to that of uninfected cells. IL-32 protein levels decreased in subsequent hours post infection and could only be detected in a small quantity by western blot at 96 hpi. It is hypothesized that certain pathways might be activated to down-regulate or block the expression of IL-32 during HCMV infection.\nMiRNAs are the most studied non-coding RNAs in recent years. They regulate gene expression at the post-transcriptional level and act as key regulators in diverse regulatory pathways, including early development, cell differentiation, cell proliferation, metabolism and apoptosis [37-40]. We carried out additional experiments to validate IL-32 mRNA as a target of hcmv-miR-UL112-1. In the presence of pS-miR-UL112-1, 39.27% luciferase activity of pMIR-IL-32UTR was observed to be significantly decreased, and a 67% reduction of endogenous IL-32 protein expression level was measured in HEK293 cells. It was confirmed that hcmv-miR-UL112-1 could specifically repress the expression of IL-32 through the predicted binding site.\nTo examine the role of hcmv-miR-UL112-1 in regulating IL-32 expression during an active HCMV infection, the kinetics of hcmv-miR-UL112-1 expression in HCMV-infected MRC-5 cells were measured using TaqMan® miRNA assays. The expression of hcmv-miR-UL112-1 could be detected at 24 hpi and then increased gradually with prolonged HCMV infection. This result suggests that hcmv-miR-UL112-1 might function and lead to the decrease of IL-32 levels in E stage during active HCMV infection. The decrease of IL-32 mRNA, but not IL-32 protein level, together with the dramatic increase of hcmv-miR-UL112-1 during the E stage, suggests the possibility that hcmv-miR-UL112-1 may function primarily in the degradation of mRNA. Further studies are needed to address this point.\nThrough co-evolution with its host, HCMV has evolved effective immune evasion strategies by encoding many immunomodulatory proteins that modulate the host immune response. It is conceivable that HCMV-encoded miRNAs might also be exploited during immune evasion. Hcmv-miR-UL112-1 is expressed with early kinetics. Some functional targets of hcmv-miR-UL112-1 have been shown experimentally to be involved in the modulation of NK cell activation. Stern-Ginossar et al. have identified MICB as a functional target of hcmv-miR-UL112-1 [30]. MICB is a stress-induced ligand of the NK cell activating receptor, NKG2D, which is critical for killing of virus-infected cells by NK cells. Hcmv-miR-UL112-1-mediated down-regulation of MICB, perturbed MICB binding with NKG2D and reduced killing of HCMV infected cells by NK cells. In addition, HCMV IE1, which would increase the TNF-α level by activation of the TNF-α promoter, was then identified as a target of hcmv-miR-UL112-1 by Murphy et al. in 2008 [29]. It has been confirmed that IL-32 induces significant amounts of TNF-α in a dose-dependent manner, and that silencing endogenous IL-32 by short hairpin RNA impairs the induction of TNF-α [2]. In this study, we validated that expression of IL-32 was activated by active HCMV infection and functionally down-regulated by ectopically expressed hcmv-miR-UL112-1 in HEK293 cells. It may be deduced from these results that, besides IE1 and MICB, hcmv-miR-UL112-1 might also synergistically achieve the modulation of NK cell activation through the TNF-α pathway by the down-regulation of IL-32, and immune evasion by HCMV.\n\nConclusions:\nIn summary, IL-32 expression in active HCMV infection was firstly investigated in our study. Detailed kinetics of the transcription of hcmv-miR-UL112-1, IL-32 mRNA and its protein expression were determined in HCMV infected MRC-5 cells. Furthermore, IL-32 expression was demonstrated to be functionally down-regulated by ectopically expressed hcmv-miR-UL112-1 in HEK293 cells. Follow up studies will investigate the mechanism of immune evasion by HCMV mediated by hcmv-miR-UL112-1, which has been confirmed to modulate NK cell activation during HCMV infection through post-transcriptional regulation.\n\nMaterials and methods:\n Serum samples Serum samples were obtained from 40 patients with HCMV infection (22 male, 18 female, aged 2.2 ± 0.42 yr) from Shengjing Hospital of China Medical University. All patients were HCMV-IgM positive and HCMV-IgG negative. The viral loads in urine samples from these patients were between 1.793×103 and 4.115×107 HCMV DNA copies per milliliter, as detected by routine QF-PCR (DaAn Gene). Blood samples collected from 32 HCMV-IgM negative individuals (17 male, 15 female, aged 2.7±0.35 yr) at the Medical Examination Centre of Shengjing Hospital were used as controls. According to the HCMV IgG detection results, HCMV IgM negative controls were divided into two groups: Previously HCMV infected group (IgG positive, n=17) and healthy group (IgG negative, n=15). All individuals involved in our study were seronegative for influenza A virus, HBV, HCV and HIV. This study was specifically approved by the Ethical Committee of Shengjing hospital.\nSerum samples were obtained from 40 patients with HCMV infection (22 male, 18 female, aged 2.2 ± 0.42 yr) from Shengjing Hospital of China Medical University. All patients were HCMV-IgM positive and HCMV-IgG negative. The viral loads in urine samples from these patients were between 1.793×103 and 4.115×107 HCMV DNA copies per milliliter, as detected by routine QF-PCR (DaAn Gene). Blood samples collected from 32 HCMV-IgM negative individuals (17 male, 15 female, aged 2.7±0.35 yr) at the Medical Examination Centre of Shengjing Hospital were used as controls. According to the HCMV IgG detection results, HCMV IgM negative controls were divided into two groups: Previously HCMV infected group (IgG positive, n=17) and healthy group (IgG negative, n=15). All individuals involved in our study were seronegative for influenza A virus, HBV, HCV and HIV. This study was specifically approved by the Ethical Committee of Shengjing hospital.\n Cell lines HEK293 cell line was a gift of Dr. Fangjie Chen from Department of Medical Genetics, China Medical University. HEK293 cells were cultured in Dulbecco’s Modified Eagle’s Medium (DMEM) supplemented with 10% fetal bovine serum (FBS), 100 μg/ml penicillin, 100 μg/ml streptomycin sulfate and 2 mM L-glutamine. Human embryonic lung fibroblasts cells, MRC-5, were acquired from Shanghai Institute for Biological Sciences, Chinese Academy of Sciences (CAS). MRC-5 cells were maintained in Modified Eagle’s Medium (MEM) supplemented with 10% FBS, 100 μg/ml penicillin and 100 μg/ml streptomycin sulfate. All cell cultures were maintained at 37°C in a 5% CO2 incubator.\nHEK293 cell line was a gift of Dr. Fangjie Chen from Department of Medical Genetics, China Medical University. HEK293 cells were cultured in Dulbecco’s Modified Eagle’s Medium (DMEM) supplemented with 10% fetal bovine serum (FBS), 100 μg/ml penicillin, 100 μg/ml streptomycin sulfate and 2 mM L-glutamine. Human embryonic lung fibroblasts cells, MRC-5, were acquired from Shanghai Institute for Biological Sciences, Chinese Academy of Sciences (CAS). MRC-5 cells were maintained in Modified Eagle’s Medium (MEM) supplemented with 10% FBS, 100 μg/ml penicillin and 100 μg/ml streptomycin sulfate. All cell cultures were maintained at 37°C in a 5% CO2 incubator.\n Virus HCMV clinical strain H was isolated from a urine sample of a 5-month-old infant hospitalized in Shengjing Hospital of China Medical University. Stock virus was propagated in MRC-5 maintained in MEM supplemented with 2% FBS, 100 μg/ml penicillin and 100 μg/ml streptomycin. The supernatant was then harvested, and the aliquots were stored at −80°C before use.\nHCMV clinical strain H was isolated from a urine sample of a 5-month-old infant hospitalized in Shengjing Hospital of China Medical University. Stock virus was propagated in MRC-5 maintained in MEM supplemented with 2% FBS, 100 μg/ml penicillin and 100 μg/ml streptomycin. The supernatant was then harvested, and the aliquots were stored at −80°C before use.\n Plasmid constructs Luciferase reporter construct pMIR was acquired from Ambion Company. IL-32 3′UTR sequence was obtained by RT-PCR from RNA harvested from HCMV infected cells, and inserted into SpeI and HindIII site of the multiple cloning regions (pMIR-IL-32UTR). A point mutation at corresponding seed region binding site was maintained by using Site-directed Gene Mutagenesis Kit (Beyotime), resulting in GUC to GAA. Sequence predicted to express hcmv-miR-UL112-1 was amplified by PCR directly from HCMV genome. The PCR product was inserted into BamHI and HindIII sites of pSilencer4.1 (Ambion) to generate a miRNA expression vector pS-miR-UL112-1. All primer sequences used in plasmid construction are listed in Table 1. All constructs were confirmed by DNA sequencing.\n\nPrimers used in plasmid construction and semi-quantitative RT-PCR\nNote: sequences recognized by restriction en donuclases are down lined.\nLuciferase reporter construct pMIR was acquired from Ambion Company. IL-32 3′UTR sequence was obtained by RT-PCR from RNA harvested from HCMV infected cells, and inserted into SpeI and HindIII site of the multiple cloning regions (pMIR-IL-32UTR). A point mutation at corresponding seed region binding site was maintained by using Site-directed Gene Mutagenesis Kit (Beyotime), resulting in GUC to GAA. Sequence predicted to express hcmv-miR-UL112-1 was amplified by PCR directly from HCMV genome. The PCR product was inserted into BamHI and HindIII sites of pSilencer4.1 (Ambion) to generate a miRNA expression vector pS-miR-UL112-1. All primer sequences used in plasmid construction are listed in Table 1. All constructs were confirmed by DNA sequencing.\n\nPrimers used in plasmid construction and semi-quantitative RT-PCR\nNote: sequences recognized by restriction en donuclases are down lined.\n Enzyme-linked immunosorbent assay The concentration of IL-32 in serum samples were detected using human IL-32 ELISA Kit (Cusabio) according to the manufacture’s protocols. The absorbance value at wavelength 450 nm was measured. The IL-32 concentrations were calculated from the standard curve.\nThe concentration of IL-32 in serum samples were detected using human IL-32 ELISA Kit (Cusabio) according to the manufacture’s protocols. The absorbance value at wavelength 450 nm was measured. The IL-32 concentrations were calculated from the standard curve.\n Semi-quantitative RT-PCR analysis MRC-5 cells were inoculated with H strain at 3–5 multiplicity of infection (m.o.i.). Infections were carried out under IE, E and L conditions respectively. For preparation of IE RNA, CHX (Sigma) (100 μl/ml) was added to the culture medium 1 hour before infection and the cells were harvested at 24 hpi. For E RNA, DNA synthesis inhibitor phosphonoacetic acid (PAA) (Sigma) (100 μl/ml) was added to the medium immediately after infection, and the cells were harvested at 48 hpi. L RNA and uninfected cellular RNA was derived from infected (at 96 hpi) and uninfected cells, respectively. Total RNA was isolated using Trizol reagent (TaKaRa), treated with DNase Iand then reverse-transcribed with MLV reverse transcriptase and random primers (TaKaRa). PCR was performed for 24 cycles in 50 μl reactions with the IL-32 specific primer pairs listed in Table 1. mRNA of β-actin was amplified for normalization in all reactions. The primers for IL-32 amplification were designed for detecting all known isoforms of human IL-32 based on the sequences from Gnebank database (NM_001012631.1-001012636.1, NM_004221.4 and NM_001012718.1). The PCR products were analyzed by electrophoresis on 2% agarose gel containing ethidium bromide.\nMRC-5 cells were inoculated with H strain at 3–5 multiplicity of infection (m.o.i.). Infections were carried out under IE, E and L conditions respectively. For preparation of IE RNA, CHX (Sigma) (100 μl/ml) was added to the culture medium 1 hour before infection and the cells were harvested at 24 hpi. For E RNA, DNA synthesis inhibitor phosphonoacetic acid (PAA) (Sigma) (100 μl/ml) was added to the medium immediately after infection, and the cells were harvested at 48 hpi. L RNA and uninfected cellular RNA was derived from infected (at 96 hpi) and uninfected cells, respectively. Total RNA was isolated using Trizol reagent (TaKaRa), treated with DNase Iand then reverse-transcribed with MLV reverse transcriptase and random primers (TaKaRa). PCR was performed for 24 cycles in 50 μl reactions with the IL-32 specific primer pairs listed in Table 1. mRNA of β-actin was amplified for normalization in all reactions. The primers for IL-32 amplification were designed for detecting all known isoforms of human IL-32 based on the sequences from Gnebank database (NM_001012631.1-001012636.1, NM_004221.4 and NM_001012718.1). The PCR products were analyzed by electrophoresis on 2% agarose gel containing ethidium bromide.\n miRNA extraction and TaqMan assays Total RNA was extracted from HCMV infected cells at different time points using the mivVana miRNA isolation kit (Ambion). We measured hcmv-miR-UL112-1 in all samples using TaqMan® miRNA assays (MIMAT0001577 for hcmv-miR-UL112-1, ABI). U6 expression was used to normalize the expression of hcmv-miR-UL112-1. HCMV uninfected MRC-5 cells were used as negative control. Fold difference for hcmv-miR-UL112-1 expression was calculated using the equation 2ΔΔct, where ΔCt = Ct (hcmv-miR-UL112-1) – Ct (U6 control) and ΔΔCt = ΔCt (HCMV infected MRC-5) – ΔCt (MRC-5). The measurements were done in triplicates and the results are presented at the means ± S.D.\nTotal RNA was extracted from HCMV infected cells at different time points using the mivVana miRNA isolation kit (Ambion). We measured hcmv-miR-UL112-1 in all samples using TaqMan® miRNA assays (MIMAT0001577 for hcmv-miR-UL112-1, ABI). U6 expression was used to normalize the expression of hcmv-miR-UL112-1. HCMV uninfected MRC-5 cells were used as negative control. Fold difference for hcmv-miR-UL112-1 expression was calculated using the equation 2ΔΔct, where ΔCt = Ct (hcmv-miR-UL112-1) – Ct (U6 control) and ΔΔCt = ΔCt (HCMV infected MRC-5) – ΔCt (MRC-5). The measurements were done in triplicates and the results are presented at the means ± S.D.\n Luciferase assay MRC-5 cells were plated in 24-well plate at a density of 4.0×105 cells per well and grown to reach about 80% confluence at the time for transfection. 200 ng pMIR-IL-32UTR/pMIR-IL-32MUT plasmid or an empty vector control was co-transfected with 400 ng pS-miR-UL112-1 or pS-neg control into the cells by using lipofectamine 2000 reagent (Invitrogen) according to the manufacture’s protocol. 200 ng renilla luciferase plasmid (Promega) was introduced into each co-transfected cells as an internal control plasmid at the same time. Cells were collected 48 hours post transfection and luciferase activity levels were measured using the Dual luciferase reporter assay system (Promega) according to the manufacture’s guidelines. All measurements were done in triplicates and signals were normalized for transfection efficiency to the internal renilla control. The results are presented at the means ±S.D.\nMRC-5 cells were plated in 24-well plate at a density of 4.0×105 cells per well and grown to reach about 80% confluence at the time for transfection. 200 ng pMIR-IL-32UTR/pMIR-IL-32MUT plasmid or an empty vector control was co-transfected with 400 ng pS-miR-UL112-1 or pS-neg control into the cells by using lipofectamine 2000 reagent (Invitrogen) according to the manufacture’s protocol. 200 ng renilla luciferase plasmid (Promega) was introduced into each co-transfected cells as an internal control plasmid at the same time. Cells were collected 48 hours post transfection and luciferase activity levels were measured using the Dual luciferase reporter assay system (Promega) according to the manufacture’s guidelines. All measurements were done in triplicates and signals were normalized for transfection efficiency to the internal renilla control. The results are presented at the means ±S.D.\n Western blot analysis Western blot analyses were performed to detect the IL-32 levels in HCMV infected MRC-5 cells and hcmv-miR-UL112-1 over expressed HEK293 cells respectively. MRC-5 cells were inoculated with H strain at 3–5 m.o.i. Cells were harvested at different time points post infection (6 h, 24 h, 48 h, 72 h and 96 h). HEK293 cells were transfected with 8 μg pS-miR-UL112-1 or pS-neg control using lipofectamine 2000 (Invitrogen) and were incubated at 37°C for 48 hours.\nProteins of the cells described above were prepared by suspending cells in lysis buffer, followed by centrifugation. Concentrations of proteins in supernatant were quantified using protein assay kit (Beyotime). Protein was separated by 10% acrylamide gel electrophoresis and transferred onto a nitrocellulose membrane. Western blot analysis was performed using IL-32 antibody (abcam). Immunoblots were visualized with ECL detection system. Sample loading was normalized by quantities of β-actin detected parallel.\nWestern blot analyses were performed to detect the IL-32 levels in HCMV infected MRC-5 cells and hcmv-miR-UL112-1 over expressed HEK293 cells respectively. MRC-5 cells were inoculated with H strain at 3–5 m.o.i. Cells were harvested at different time points post infection (6 h, 24 h, 48 h, 72 h and 96 h). HEK293 cells were transfected with 8 μg pS-miR-UL112-1 or pS-neg control using lipofectamine 2000 (Invitrogen) and were incubated at 37°C for 48 hours.\nProteins of the cells described above were prepared by suspending cells in lysis buffer, followed by centrifugation. Concentrations of proteins in supernatant were quantified using protein assay kit (Beyotime). Protein was separated by 10% acrylamide gel electrophoresis and transferred onto a nitrocellulose membrane. Western blot analysis was performed using IL-32 antibody (abcam). Immunoblots were visualized with ECL detection system. Sample loading was normalized by quantities of β-actin detected parallel.\n Statistical analysis All experiments were reproducible. The results are presented at the means ± S.D. Student’s t test and F test was used to determine statistical significance. Differences were considered statistically significant at value of P≤0.05.\nAll experiments were reproducible. The results are presented at the means ± S.D. Student’s t test and F test was used to determine statistical significance. Differences were considered statistically significant at value of P≤0.05.\n\nSerum samples:\nSerum samples were obtained from 40 patients with HCMV infection (22 male, 18 female, aged 2.2 ± 0.42 yr) from Shengjing Hospital of China Medical University. All patients were HCMV-IgM positive and HCMV-IgG negative. The viral loads in urine samples from these patients were between 1.793×103 and 4.115×107 HCMV DNA copies per milliliter, as detected by routine QF-PCR (DaAn Gene). Blood samples collected from 32 HCMV-IgM negative individuals (17 male, 15 female, aged 2.7±0.35 yr) at the Medical Examination Centre of Shengjing Hospital were used as controls. According to the HCMV IgG detection results, HCMV IgM negative controls were divided into two groups: Previously HCMV infected group (IgG positive, n=17) and healthy group (IgG negative, n=15). All individuals involved in our study were seronegative for influenza A virus, HBV, HCV and HIV. This study was specifically approved by the Ethical Committee of Shengjing hospital.\n\nCell lines:\nHEK293 cell line was a gift of Dr. Fangjie Chen from Department of Medical Genetics, China Medical University. HEK293 cells were cultured in Dulbecco’s Modified Eagle’s Medium (DMEM) supplemented with 10% fetal bovine serum (FBS), 100 μg/ml penicillin, 100 μg/ml streptomycin sulfate and 2 mM L-glutamine. Human embryonic lung fibroblasts cells, MRC-5, were acquired from Shanghai Institute for Biological Sciences, Chinese Academy of Sciences (CAS). MRC-5 cells were maintained in Modified Eagle’s Medium (MEM) supplemented with 10% FBS, 100 μg/ml penicillin and 100 μg/ml streptomycin sulfate. All cell cultures were maintained at 37°C in a 5% CO2 incubator.\n\nVirus:\nHCMV clinical strain H was isolated from a urine sample of a 5-month-old infant hospitalized in Shengjing Hospital of China Medical University. Stock virus was propagated in MRC-5 maintained in MEM supplemented with 2% FBS, 100 μg/ml penicillin and 100 μg/ml streptomycin. The supernatant was then harvested, and the aliquots were stored at −80°C before use.\n\nPlasmid constructs:\nLuciferase reporter construct pMIR was acquired from Ambion Company. IL-32 3′UTR sequence was obtained by RT-PCR from RNA harvested from HCMV infected cells, and inserted into SpeI and HindIII site of the multiple cloning regions (pMIR-IL-32UTR). A point mutation at corresponding seed region binding site was maintained by using Site-directed Gene Mutagenesis Kit (Beyotime), resulting in GUC to GAA. Sequence predicted to express hcmv-miR-UL112-1 was amplified by PCR directly from HCMV genome. The PCR product was inserted into BamHI and HindIII sites of pSilencer4.1 (Ambion) to generate a miRNA expression vector pS-miR-UL112-1. All primer sequences used in plasmid construction are listed in Table 1. All constructs were confirmed by DNA sequencing.\n\nPrimers used in plasmid construction and semi-quantitative RT-PCR\nNote: sequences recognized by restriction en donuclases are down lined.\n\nEnzyme-linked immunosorbent assay:\nThe concentration of IL-32 in serum samples were detected using human IL-32 ELISA Kit (Cusabio) according to the manufacture’s protocols. The absorbance value at wavelength 450 nm was measured. The IL-32 concentrations were calculated from the standard curve.\n\nSemi-quantitative RT-PCR analysis:\nMRC-5 cells were inoculated with H strain at 3–5 multiplicity of infection (m.o.i.). Infections were carried out under IE, E and L conditions respectively. For preparation of IE RNA, CHX (Sigma) (100 μl/ml) was added to the culture medium 1 hour before infection and the cells were harvested at 24 hpi. For E RNA, DNA synthesis inhibitor phosphonoacetic acid (PAA) (Sigma) (100 μl/ml) was added to the medium immediately after infection, and the cells were harvested at 48 hpi. L RNA and uninfected cellular RNA was derived from infected (at 96 hpi) and uninfected cells, respectively. Total RNA was isolated using Trizol reagent (TaKaRa), treated with DNase Iand then reverse-transcribed with MLV reverse transcriptase and random primers (TaKaRa). PCR was performed for 24 cycles in 50 μl reactions with the IL-32 specific primer pairs listed in Table 1. mRNA of β-actin was amplified for normalization in all reactions. The primers for IL-32 amplification were designed for detecting all known isoforms of human IL-32 based on the sequences from Gnebank database (NM_001012631.1-001012636.1, NM_004221.4 and NM_001012718.1). The PCR products were analyzed by electrophoresis on 2% agarose gel containing ethidium bromide.\n\nmiRNA extraction and TaqMan assays:\nTotal RNA was extracted from HCMV infected cells at different time points using the mivVana miRNA isolation kit (Ambion). We measured hcmv-miR-UL112-1 in all samples using TaqMan® miRNA assays (MIMAT0001577 for hcmv-miR-UL112-1, ABI). U6 expression was used to normalize the expression of hcmv-miR-UL112-1. HCMV uninfected MRC-5 cells were used as negative control. Fold difference for hcmv-miR-UL112-1 expression was calculated using the equation 2ΔΔct, where ΔCt = Ct (hcmv-miR-UL112-1) – Ct (U6 control) and ΔΔCt = ΔCt (HCMV infected MRC-5) – ΔCt (MRC-5). The measurements were done in triplicates and the results are presented at the means ± S.D.\n\nLuciferase assay:\nMRC-5 cells were plated in 24-well plate at a density of 4.0×105 cells per well and grown to reach about 80% confluence at the time for transfection. 200 ng pMIR-IL-32UTR/pMIR-IL-32MUT plasmid or an empty vector control was co-transfected with 400 ng pS-miR-UL112-1 or pS-neg control into the cells by using lipofectamine 2000 reagent (Invitrogen) according to the manufacture’s protocol. 200 ng renilla luciferase plasmid (Promega) was introduced into each co-transfected cells as an internal control plasmid at the same time. Cells were collected 48 hours post transfection and luciferase activity levels were measured using the Dual luciferase reporter assay system (Promega) according to the manufacture’s guidelines. All measurements were done in triplicates and signals were normalized for transfection efficiency to the internal renilla control. The results are presented at the means ±S.D.\n\nWestern blot analysis:\nWestern blot analyses were performed to detect the IL-32 levels in HCMV infected MRC-5 cells and hcmv-miR-UL112-1 over expressed HEK293 cells respectively. MRC-5 cells were inoculated with H strain at 3–5 m.o.i. Cells were harvested at different time points post infection (6 h, 24 h, 48 h, 72 h and 96 h). HEK293 cells were transfected with 8 μg pS-miR-UL112-1 or pS-neg control using lipofectamine 2000 (Invitrogen) and were incubated at 37°C for 48 hours.\nProteins of the cells described above were prepared by suspending cells in lysis buffer, followed by centrifugation. Concentrations of proteins in supernatant were quantified using protein assay kit (Beyotime). Protein was separated by 10% acrylamide gel electrophoresis and transferred onto a nitrocellulose membrane. Western blot analysis was performed using IL-32 antibody (abcam). Immunoblots were visualized with ECL detection system. Sample loading was normalized by quantities of β-actin detected parallel.\n\nStatistical analysis:\nAll experiments were reproducible. The results are presented at the means ± S.D. Student’s t test and F test was used to determine statistical significance. Differences were considered statistically significant at value of P≤0.05.\n\nCompeting interests:\nThe authors declare that they have no competing interests.\n\nAuthors’ contributions:\nYJH carried out the semi-quantitative RT-PCR analysis, western blot analysis and Statistical analysis. YQ and RH carried out the plasmids construction and luciferase assay. QR as the corresponding author designed the study and corrected the manuscript. YPM and YHJ carried out the preparation of virus and cell lines. ZRS carried out the serum samples collection and ELISA detection. All authors have read and approved the final manuscript.\n\n"},"whole_article_abstract":{"kind":"string","value":"Background:\nInterleukin-32 (IL-32) is an important factor in innate and adaptive immune responses, which activates the p38MAPK, NF-kappa B and AP-1 signaling pathways. Recent reports have highlighted that IL-32 is regulated during viral infection in humans.\n\nMethods:\nEnzyme-linked immunosorbent assays (ELISA) were carried out to detect IL-32 levels in serum samples. Detailed kinetics of the transcription of IL-32 mRNA and expression of IL-32 protein during human cytomegalovirus (HCMV) infection were determined by semi-quantitative RT-PCR and western blot, respectively. The expression levels of hcmv-miR-UL112-1 were detected using TaqMan® miRNA assays during a time course of 96 hours. The effects of hcmv-miR-UL112-1 on IL-32 expression were demonstrated by luciferase assay and western blot, respectively.\n\nResults:\nSerum levels of IL-32 in HCMV-IgM positive patients (indicating an active HCMV infection) were significantly higher than those in HCMV-IgM negative controls. HCMV infection activated cellular IL-32 transcription mainly in the immediately early (IE) phase and elevated IL-32 protein levels between 6 and 72 hours post infection (hpi) in the human embryonic lung fibroblast cell line, MRC-5. The expression of hcmv-miR-UL112-1 was detected at 24 hpi and increased gradually as the HCMV-infection process was prolonged. In addition, it was demonstrated that hcmv-miR-UL112-1 targets a sequence in the IL-32 3'-UTR. The protein level of IL-32 in HEK293 cells could be functionally down-regulated by transfected hcmv-miR-UL112-1.\n\nConclusions:\nIL-32 expression was induced by active HCMV infection and could be functionally down-regulated by ectopically expressed hcmv-miR-UL112-1. Our data may indicate a new strategy of immune evasion by HCMV through post-transcriptional regulation."},"background_conclusion_text":{"kind":"string","value":"Background:\nInterleukin-32 (IL-32) is a newly-discovered pro-inflammatory cytokine, which plays a role in innate and adaptive immune responses [1,2]. It lacks sequence homology to any presently known cytokine families. IL-32 is associated with the induction of inflammatory responses by activating the p38MAPK, NF-kappa B and AP-1 signaling pathways. It has been implicated in inflammatory disorders, mycobacterium tuberculosis infections and inflammatory bowel disease, as well as in some autoimmune diseases, such as rheumatoid arthritis, ulcerative colitis and Crohn’s disease [3-10]. Moreover, it has been reported that IL-32 has pro-inflammatory effects on myeloid cells and promotes the differentiation of osteoclast precursors into multinucleated cells expressing specific osteoclast markers [11,12]. In recent studies, IL-32 has also been found to be regulated during viral infections. Elevated levels of IL-32 were found in sera from patients infected with influenza A virus [13-15], hepatitis B virus (HBV) [16], hepatitis C virus (HCV) [17], human papillomavirus (HPV) [18] and human immunodeficiency virus (HIV) [19-21], suggesting that IL-32 might play an important role in host defense against viral infections\nHuman cytomegalovirus (HCMV) is an ubiquitous β-herpesvirus that infects a broad range of cell types in human hosts, contributing to its complex and varied pathogenesis. HCMV Infection leads to life-long persistence in 50%–90% of the population, which is generally subclinical in healthy individuals [22]. However, it can lead to serious complications in immunocompromised patients, such as transplant recipients or AIDS patients [23,24].\nMicroRNAs (miRNAs) are an abundant class of small non-coding RNA molecules that target mRNAs generally within their 3′ untranslated regions (3′UTRs). MiRNAs suppress gene expression mainly through inhibition of translation or, rarely, through degradation of mRNA [25,26]. Clinical isolates of HCMV encode at least 17 miRNAs [27,28]. However, only a few functional targets have been validated experimentally for some HCMV-encoded miRNAs [29-34]. It has been demonstrated that hcmv-miR-UL112-1 targets and reduces the expression of HCMV UL123 (IE1 or IE72), UL114 and the major histocompatibility complex class 1-related chain B (MICB). Moreover, BclAF1 protein, a human cytomegalovirus restriction factor, was reported to be a new target of hcmv-miR-UL112-1 [35]. In addition, multiple cellular targets of hcmv-miR-US25-1, which are associated with cell cycle control, were identified by RNA induced silencing complex immunoprecipitation (RISC-IP), including cyclin E2, BRCC3, EID1, MAPRE2 and CD147. IL-32, which is not present in the confirmed targets, was screened out as a candidate target of hcmv-miR-UL112-1 in our previous study [36]. However, no further experiments to validate these findings have been performed.\nIn the present study, the expression levels of IL-32 were compared among serum samples from patients with active HCMV infection and samples from HCMV-IgM negative individuals. The expression levels of IL-32 and hcmv-miR-UL112-1 in HCMV infected MRC-5 cells were detected at different stages of infection and time points. In addition, functional down-regulation of IL-32 by hcmv-miR-UL112-1 was detected in transfected human embryonic kidney (HEK293) cells, and the effect of hcmv-miR-UL112-1 on IL-32 during HCMV infection was primarily discussed.\n\nConclusions:\nIn summary, IL-32 expression in active HCMV infection was firstly investigated in our study. Detailed kinetics of the transcription of hcmv-miR-UL112-1, IL-32 mRNA and its protein expression were determined in HCMV infected MRC-5 cells. Furthermore, IL-32 expression was demonstrated to be functionally down-regulated by ectopically expressed hcmv-miR-UL112-1 in HEK293 cells. Follow up studies will investigate the mechanism of immune evasion by HCMV mediated by hcmv-miR-UL112-1, which has been confirmed to modulate NK cell activation during HCMV infection through post-transcriptional regulation."},"background_conclusion_abstract":{"kind":"string","value":"Background:\nInterleukin-32 (IL-32) is an important factor in innate and adaptive immune responses, which activates the p38MAPK, NF-kappa B and AP-1 signaling pathways. Recent reports have highlighted that IL-32 is regulated during viral infection in humans.\n\nMethods:\nEnzyme-linked immunosorbent assays (ELISA) were carried out to detect IL-32 levels in serum samples. Detailed kinetics of the transcription of IL-32 mRNA and expression of IL-32 protein during human cytomegalovirus (HCMV) infection were determined by semi-quantitative RT-PCR and western blot, respectively. The expression levels of hcmv-miR-UL112-1 were detected using TaqMan® miRNA assays during a time course of 96 hours. The effects of hcmv-miR-UL112-1 on IL-32 expression were demonstrated by luciferase assay and western blot, respectively.\n\nResults:\nSerum levels of IL-32 in HCMV-IgM positive patients (indicating an active HCMV infection) were significantly higher than those in HCMV-IgM negative controls. HCMV infection activated cellular IL-32 transcription mainly in the immediately early (IE) phase and elevated IL-32 protein levels between 6 and 72 hours post infection (hpi) in the human embryonic lung fibroblast cell line, MRC-5. The expression of hcmv-miR-UL112-1 was detected at 24 hpi and increased gradually as the HCMV-infection process was prolonged. In addition, it was demonstrated that hcmv-miR-UL112-1 targets a sequence in the IL-32 3'-UTR. The protein level of IL-32 in HEK293 cells could be functionally down-regulated by transfected hcmv-miR-UL112-1.\n\nConclusions:\nIL-32 expression was induced by active HCMV infection and could be functionally down-regulated by ectopically expressed hcmv-miR-UL112-1. Our data may indicate a new strategy of immune evasion by HCMV through post-transcriptional regulation."},"whole_article_text_length":{"kind":"number","value":10259,"string":"10,259"},"whole_article_abstract_length":{"kind":"number","value":349,"string":"349"},"other_sections_lengths":{"kind":"list like","value":[673,225,561,516,180,137,72,170,44,237,148,166,186,38,10,78],"string":"[\n 673,\n 225,\n 561,\n 516,\n 180,\n 137,\n 72,\n 170,\n 44,\n 237,\n 148,\n 166,\n 186,\n 38,\n 10,\n 78\n]"},"num_sections":{"kind":"number","value":20,"string":"20"},"most_frequent_words":{"kind":"list like","value":["hcmv","il","32","il 32","cells","mir","ul112","mir ul112","hcmv mir","hcmv mir ul112"],"string":"[\n \"hcmv\",\n \"il\",\n \"32\",\n \"il 32\",\n \"cells\",\n \"mir\",\n \"ul112\",\n \"mir ul112\",\n \"hcmv mir\",\n \"hcmv mir ul112\"\n]"},"keybert_topics":{"kind":"list like","value":["antibodies il 32","background interleukin 32","mrna il 32","il 32 immune","interleukin 32"],"string":"[\n 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mir ul112 [SUMMARY]"},"annotated_tf_idf_background_abstract_prompt":{"kind":"string","value":"[CONTENT] hcmv | 32 | il 32 | il | inflammatory | human | mirnas | targets | hcmv mir | mir [SUMMARY]"},"annotated_tf_idf_methods_abstract_prompt":{"kind":"null"},"annotated_tf_idf_results_abstract_prompt":{"kind":"string","value":"[CONTENT] hcmv | il | il 32 | 32 | mir | mir ul112 | ul112 | expression | hcmv mir | hcmv mir ul112 [SUMMARY]"},"annotated_tf_idf_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] hcmv | expression | hcmv mir | hcmv mir ul112 | 32 expression | il 32 expression | ul112 | mir ul112 | mir | il 32 [SUMMARY]"},"annotated_tf_idf_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] hcmv | il | il 32 | 32 | cells | mir | mir ul112 | ul112 | hcmv mir | hcmv mir ul112 [SUMMARY]"},"annotated_tf_idf_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] hcmv | il | il 32 | 32 | cells | mir | mir ul112 | ul112 | hcmv mir | hcmv mir ul112 [SUMMARY]"},"annotated_entity_plan_background_abstract_prompt":{"kind":"string","value":"[CONTENT] AP-1 ||| IL-32 [SUMMARY]"},"annotated_entity_plan_methods_abstract_prompt":{"kind":"null"},"annotated_entity_plan_results_abstract_prompt":{"kind":"string","value":"[CONTENT] IL-32 | HCMV | HCMV | HCMV ||| between 6 and 72 hours | MRC-5 ||| 24 | HCMV ||| ||| IL-32 | HEK293 [SUMMARY]"},"annotated_entity_plan_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] HCMV ||| HCMV [SUMMARY]"},"annotated_entity_plan_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] AP-1 ||| IL-32 ||| ELISA | IL-32 ||| transcription | IL-32 | HCMV | RT-PCR ||| 96 hours ||| IL-32 ||| IL-32 | HCMV | HCMV | HCMV ||| between 6 and 72 hours | MRC-5 ||| 24 | HCMV ||| ||| IL-32 | HEK293 ||| HCMV ||| HCMV [SUMMARY]"},"annotated_entity_plan_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] AP-1 ||| IL-32 ||| ELISA | IL-32 ||| transcription | IL-32 | HCMV | RT-PCR ||| 96 hours ||| IL-32 ||| IL-32 | HCMV | HCMV | HCMV ||| between 6 and 72 hours | MRC-5 ||| 24 | HCMV ||| ||| IL-32 | HEK293 ||| HCMV ||| HCMV [SUMMARY]"}}},{"rowIdx":260,"cells":{"title":{"kind":"string","value":"Tools for primary care patient safety: a narrative review."},"pmid":{"kind":"string","value":"25346425"},"background_abstract":{"kind":"string","value":"Patient safety in primary care is a developing field with an embryonic but evolving evidence base. This narrative review aims to identify tools that can be used by family practitioners as part of a patient safety toolkit to improve the safety of the care and services provided by their practices."},"background_abstract_label":{"kind":"string","value":"BACKGROUND"},"methods_abstract":{"kind":"string","value":"Searches were performed in 6 healthcare databases in 2011 using 3 search stems; location (primary care), patient safety synonyms and outcome measure synonyms. Two reviewers analysed the results using a numerical and thematic analyses. Extensive grey literature exploration was also conducted."},"methods_abstract_label":{"kind":"string","value":"METHODS"},"results_abstract":{"kind":"string","value":"Overall, 114 Tools were identified with 26 accrued from grey literature. Most published literature originated from the USA (41%) and the UK (23%) within the last 10 years. Most of the literature addresses the themes of medication error (55%) followed by safety climate (8%) and adverse event reporting (8%). Minor themes included; informatics (4.5%) patient role (3%) and general measures to correct error (5%). The primary/secondary care interface is well described (5%) but few specific tools for primary care exist. Diagnostic error and results handling appear infrequently (<1% of total literature) despite their relative importance. The remainder of literature (11%) related to referrals, Out-Of-Hours (OOH) care, telephone care, organisational issues, mortality and clerical error."},"results_abstract_label":{"kind":"string","value":"RESULTS"},"conclusions_abstract":{"kind":"string","value":"This review identified tools and indicators that are available for use in family practice to measure patient safety, which is crucial to improve safety and design a patient safety toolkit. However, many of the tools have yet to be used in quality improvement strategies and cycles such as plan-do-study-act (PDSA) so there is a dearth of evidence of their utility in improving as opposed to measuring and highlighting safety issues. The lack of focus on diagnostics, systems safety and results handling provide direction and priorities for future research."},"conclusions_abstract_label":{"kind":"string","value":"CONCLUSIONS"},"mesh_descriptor_names":{"kind":"list like","value":["Family Practice","Humans","Medication Errors","Organizational Culture","Patient Safety","Primary Health Care","Risk Management","Safety Management"],"string":"[\n \"Family Practice\",\n \"Humans\",\n \"Medication Errors\",\n \"Organizational Culture\",\n \"Patient Safety\",\n \"Primary Health Care\",\n \"Risk Management\",\n \"Safety Management\"\n]"},"pmcid":{"kind":"string","value":"4288623"},"background_title":{"kind":"string","value":"Background"},"background_text":{"kind":"string","value":"Patient safety has been on the agenda of hospital physicians since the publication of the Institute of Medicine’s 2000 report, ‘To Err is Human’, revealed that more people were dying in the US as a result of medical error than from road traffic accidents\n[1]. However, most healthcare interactions in the developed world occur in family medicine: 90% of contacts in the England with the National Health Service take place in primary care\n[2]. In England there are approximately 1 billion community prescriptions dispensed each year\n[3]. The potential for adverse events is therefore huge but the knowledge base about primary care patient safety is still sparse. A literature review of the nature and frequency of error in primary care suggested that there are between 5–80 safety incidents per 100,000 consultations, which in the UK would translate to between 37–600 incidents per day\n[4]. Another review estimates that there may be a patient safety incident in approximately 2% of family practice consultations\n[5].\nA 2011 report by the American Medical Association on ambulatory patient safety concluded that the introduction of, and research into, patient safety in the primary care environment have lagged behind that of secondary care\n[6]. Understanding the epidemiology of hospital errors was crucial in developing hospital based safety interventions and the media’s reporting of this data ensured public support for efforts to improve safety\n[6]. Some of its authors concluded that there needed to be a similar focus on primary care, because there were ‘virtually no credible studies on how to improve safety’\n[7]. Moreover, a report by the Health Foundation in 2013 emphasised the importance of knowing what methods, tools and indicators are currently being used in primary care to measure patient safety\n[8]. In this paper, patient safety refers to the ‘avoidance, prevention and amelioration of adverse outcomes or injuries stemming from the process of healthcare’\n[8].\nStaff and systems in primary care environments have the potential to contribute to serious error that can cause both morbidity and mortality; which has been demonstrated in the field of prescribing\n[9]. Evidence on primary care error comes mainly from the statistics of the medical defence organisations and from small pilot studies\n[10]. And yet, experts we have consulted in the field were anecdotally aware of a multiplicity of interventions or ‘tools’ from their own and others’ work world-wide, which helped identify grey literature for this study.\nThis paper reports a narrative review of ‘tools’ to improve, measure, and monitor patient safety in the ambulatory settings with a focus on family practice. A narrative review broadly covers a specific topic but does not adhere to strict systematic methods to locate and synthesize articles and enables description and synthesis of qualitative research and categorises studies into more homogenous groups\n[11]. To the authors’ knowledge no such broad-ranging review has been attempted. The context of this study is worldwide including both the US and the UK and throughout the term primary care is used to address the terms general practice and family practice."},"methods_title":{"kind":"string","value":"Methods"},"methods_text":{"kind":"string","value":" Data sources and searches Our structured narrative review was planned and conducted according to guidance in the Preferred Reporting Items for Systematic Reviews and Meta- Analyses (PRISMA) guidelines\n[12] but following a more narrative approach (especially with regard to grey literature). The starting point for determining the search terms used in the review was a 3 point definition of our search question and exploration of Medical Subject Heading (MeSH) terms\n[13]. We used a multi-centre team (including leading UK experts on patient safety) at a strategic planning meeting to comment on and finalise the search terms for the review. References were managed in Endnote. Broad ranging search terms were used for developing a search strategy in 3 stems; setting (primary care [i.e. general/family practice, ambulatory care, community care, generalist care], safety synonyms [i.e. error, adverse event, fault, malpractice] and types of tool [i.e. indicator, survey, guideline, scale] (see web Additional file\n1: Appendix 1). The aim was to be as inclusive as possible and address administrative, clinical and patient experience issues. The search was performed on the following databases; Embase, CINAHL, Pubmed, Medline (Ovid 1996 onward), Health Management Information Consortium and Web of Science on the 1/11/2011. We did not limit our search by year of publication or to the English language, in order to capture a world-wide perspective on patient safety. However, only abstracts in English were included due to resource restraints for translation. Grey literature was identified from known internet patient safety sources from the US and UK to expand the scope of the review (see web Additional file:\n1 Appendix 6). In order to fully explore a single tool many resources often had to be read – for example the IHI trigger tool is described in a number of web publications and supporting documents. Care was taken not to count published tools that also appeared in grey literature twice by discussion between the two reviewers and wider team.\nOur structured narrative review was planned and conducted according to guidance in the Preferred Reporting Items for Systematic Reviews and Meta- Analyses (PRISMA) guidelines\n[12] but following a more narrative approach (especially with regard to grey literature). The starting point for determining the search terms used in the review was a 3 point definition of our search question and exploration of Medical Subject Heading (MeSH) terms\n[13]. We used a multi-centre team (including leading UK experts on patient safety) at a strategic planning meeting to comment on and finalise the search terms for the review. References were managed in Endnote. Broad ranging search terms were used for developing a search strategy in 3 stems; setting (primary care [i.e. general/family practice, ambulatory care, community care, generalist care], safety synonyms [i.e. error, adverse event, fault, malpractice] and types of tool [i.e. indicator, survey, guideline, scale] (see web Additional file\n1: Appendix 1). The aim was to be as inclusive as possible and address administrative, clinical and patient experience issues. The search was performed on the following databases; Embase, CINAHL, Pubmed, Medline (Ovid 1996 onward), Health Management Information Consortium and Web of Science on the 1/11/2011. We did not limit our search by year of publication or to the English language, in order to capture a world-wide perspective on patient safety. However, only abstracts in English were included due to resource restraints for translation. Grey literature was identified from known internet patient safety sources from the US and UK to expand the scope of the review (see web Additional file:\n1 Appendix 6). In order to fully explore a single tool many resources often had to be read – for example the IHI trigger tool is described in a number of web publications and supporting documents. Care was taken not to count published tools that also appeared in grey literature twice by discussion between the two reviewers and wider team.\n Study selection Reviewer one was a GP Academic Clinical Fellow with an interest in pharmacology (RS) and reviewer two was a health services researcher with an interest in family practice (SC). Similarly to the strategy used in the AMA’s report\n[6], we were interested in highly generalisable tools so research addressing single drugs or conditions in very specific settings was excluded. Inclusion and exclusion criteria are listed in below. An inclusive strategy was employed such that neither reviewer could exclude studies the other felt were potentially relevant. Disagreements were resolved by regular discussions between the 2 reviewers. The inclusion and exclusion criteria were as follows:\nExclusion criteria:hospital care/setting - unless transferableopinion pieces/editorialssingle drugs/conditions where the focus was felt to be on the specific drug or condition rather than on transferable toolsonly about quality of care without explicit patient safety componentexclude on basis of journal (for example “Health Care Food & Nutrition focus”)economic impact of errors (relevant papers were taken out at this stage for other purposes within the project)Both the abstract and main text were not in EnglishInclusion criteria:if unsure always include - for example, ‘good advice’ which might later inform other toolstools or strategies to improve or analyse safety which are of relevance to Primary Care.\nExclusion criteria:hospital care/setting - unless transferableopinion pieces/editorialssingle drugs/conditions where the focus was felt to be on the specific drug or condition rather than on transferable toolsonly about quality of care without explicit patient safety componentexclude on basis of journal (for example “Health Care Food & Nutrition focus”)economic impact of errors (relevant papers were taken out at this stage for other purposes within the project)Both the abstract and main text were not in English\nhospital care/setting - unless transferable\nopinion pieces/editorials\nsingle drugs/conditions where the focus was felt to be on the specific drug or condition rather than on transferable tools\nonly about quality of care without explicit patient safety component\nexclude on basis of journal (for example “Health Care Food & Nutrition focus”)\neconomic impact of errors (relevant papers were taken out at this stage for other purposes within the project)\nBoth the abstract and main text were not in English\nInclusion criteria:if unsure always include - for example, ‘good advice’ which might later inform other toolstools or strategies to improve or analyse safety which are of relevance to Primary Care.\nif unsure always include - for example, ‘good advice’ which might later inform other tools\ntools or strategies to improve or analyse safety which are of relevance to Primary Care.\nReviewer one was a GP Academic Clinical Fellow with an interest in pharmacology (RS) and reviewer two was a health services researcher with an interest in family practice (SC). Similarly to the strategy used in the AMA’s report\n[6], we were interested in highly generalisable tools so research addressing single drugs or conditions in very specific settings was excluded. Inclusion and exclusion criteria are listed in below. An inclusive strategy was employed such that neither reviewer could exclude studies the other felt were potentially relevant. Disagreements were resolved by regular discussions between the 2 reviewers. The inclusion and exclusion criteria were as follows:\nExclusion criteria:hospital care/setting - unless transferableopinion pieces/editorialssingle drugs/conditions where the focus was felt to be on the specific drug or condition rather than on transferable toolsonly about quality of care without explicit patient safety componentexclude on basis of journal (for example “Health Care Food & Nutrition focus”)economic impact of errors (relevant papers were taken out at this stage for other purposes within the project)Both the abstract and main text were not in EnglishInclusion criteria:if unsure always include - for example, ‘good advice’ which might later inform other toolstools or strategies to improve or analyse safety which are of relevance to Primary Care.\nExclusion criteria:hospital care/setting - unless transferableopinion pieces/editorialssingle drugs/conditions where the focus was felt to be on the specific drug or condition rather than on transferable toolsonly about quality of care without explicit patient safety componentexclude on basis of journal (for example “Health Care Food & Nutrition focus”)economic impact of errors (relevant papers were taken out at this stage for other purposes within the project)Both the abstract and main text were not in English\nhospital care/setting - unless transferable\nopinion pieces/editorials\nsingle drugs/conditions where the focus was felt to be on the specific drug or condition rather than on transferable tools\nonly about quality of care without explicit patient safety component\nexclude on basis of journal (for example “Health Care Food & Nutrition focus”)\neconomic impact of errors (relevant papers were taken out at this stage for other purposes within the project)\nBoth the abstract and main text were not in English\nInclusion criteria:if unsure always include - for example, ‘good advice’ which might later inform other toolstools or strategies to improve or analyse safety which are of relevance to Primary Care.\nif unsure always include - for example, ‘good advice’ which might later inform other tools\ntools or strategies to improve or analyse safety which are of relevance to Primary Care.\n Data quality and extraction Data were extracted independently by the two reviewers (RS and SC). A dual approach was taken to data extraction from published material using both a Word document (Web Additional file:\n1 Appendix 5) and an Excel template. For reporting data from selected papers we used a modified PRISMA\n[12] checklist, which combined aspects of different methodologies (not just systematic reviews and meta-analyses) into a form for all study types (available on application to the authors). For example the PRISMA checklist requires a discussion of limitations which is a highly transferable requirement to all methodologies, but it also requires specific methods based items such as ‘confidence intervals on meta-analyses’. Using information collected on the modified PRISMA form, a numerical data extraction system was agreed by both reviewers in order to present results from the selected papers data for analysis were extracted from that Excel document. A pilot of 10 key papers with differing methodologies was undertaken prior to commencing full data extraction – high levels of agreement were found between the two reviewers. At the end of data extraction differences in rating on the Excel sheet were discussed and analysed across a series of face-to-face and telephone meetings, attempts to reach consensus were almost always possible.\nData were extracted independently by the two reviewers (RS and SC). A dual approach was taken to data extraction from published material using both a Word document (Web Additional file:\n1 Appendix 5) and an Excel template. For reporting data from selected papers we used a modified PRISMA\n[12] checklist, which combined aspects of different methodologies (not just systematic reviews and meta-analyses) into a form for all study types (available on application to the authors). For example the PRISMA checklist requires a discussion of limitations which is a highly transferable requirement to all methodologies, but it also requires specific methods based items such as ‘confidence intervals on meta-analyses’. Using information collected on the modified PRISMA form, a numerical data extraction system was agreed by both reviewers in order to present results from the selected papers data for analysis were extracted from that Excel document. A pilot of 10 key papers with differing methodologies was undertaken prior to commencing full data extraction – high levels of agreement were found between the two reviewers. At the end of data extraction differences in rating on the Excel sheet were discussed and analysed across a series of face-to-face and telephone meetings, attempts to reach consensus were almost always possible.\n Funding This review is part of a National Institute for Health Research (NIHR), School for Primary Care Research (SPCR: http://www.spcr.nihr.ac.uk/) (UK) project, undertaken with the aim of constructing a Patient Safety Toolkit for English family practice.\nThis review is part of a National Institute for Health Research (NIHR), School for Primary Care Research (SPCR: http://www.spcr.nihr.ac.uk/) (UK) project, undertaken with the aim of constructing a Patient Safety Toolkit for English family practice."},"results_title":{"kind":"null"},"results_text":{"kind":"null"},"conclusions_title":{"kind":"string","value":"Conclusion"},"conclusions_text":{"kind":"string","value":"We have identified 114 published and unpublished tools and indicators, which can be used currently in primary care to measure patient safety. However, the AMA concluded that there are virtually no credible studies on how to improve safety in primary care\n[6] andthe challenge is still to turn measurement into improvement as few tools have been used in quality improvement cycles or as part of performance targets for safety in ambulatory care. Having a comprehensive set of tools for tracking and preventing safety events is the first step in fixing that, and this paper clearly shows where our current toolkit is wanting. The results of this review will enable a better understanding of the epidemiology of ambulatory care safety and help underpin the future development of primary care based safety interventions."},"other_sections_titles":{"kind":"list like","value":["Background","Data sources and searches","Study selection","Data quality and extraction","Funding","Results and discussion","Summary of main findings","Comparison to existing literature","Strengths/limitations","Implications for practice, policy, or future research","Authors’ information",""],"string":"[\n \"Background\",\n \"Data sources and searches\",\n \"Study selection\",\n \"Data quality and extraction\",\n \"Funding\",\n \"Results and discussion\",\n \"Summary of main findings\",\n \"Comparison to existing literature\",\n \"Strengths/limitations\",\n \"Implications for practice, policy, or future research\",\n \"Authors’ information\",\n \"\"\n]"},"other_sections_texts":{"kind":"list like","value":["Patient safety has been on the agenda of hospital physicians since the publication of the Institute of Medicine’s 2000 report, ‘To Err is Human’, revealed that more people were dying in the US as a result of medical error than from road traffic accidents\n[1]. However, most healthcare interactions in the developed world occur in family medicine: 90% of contacts in the England with the National Health Service take place in primary care\n[2]. In England there are approximately 1 billion community prescriptions dispensed each year\n[3]. The potential for adverse events is therefore huge but the knowledge base about primary care patient safety is still sparse. A literature review of the nature and frequency of error in primary care suggested that there are between 5–80 safety incidents per 100,000 consultations, which in the UK would translate to between 37–600 incidents per day\n[4]. Another review estimates that there may be a patient safety incident in approximately 2% of family practice consultations\n[5].\nA 2011 report by the American Medical Association on ambulatory patient safety concluded that the introduction of, and research into, patient safety in the primary care environment have lagged behind that of secondary care\n[6]. Understanding the epidemiology of hospital errors was crucial in developing hospital based safety interventions and the media’s reporting of this data ensured public support for efforts to improve safety\n[6]. Some of its authors concluded that there needed to be a similar focus on primary care, because there were ‘virtually no credible studies on how to improve safety’\n[7]. Moreover, a report by the Health Foundation in 2013 emphasised the importance of knowing what methods, tools and indicators are currently being used in primary care to measure patient safety\n[8]. In this paper, patient safety refers to the ‘avoidance, prevention and amelioration of adverse outcomes or injuries stemming from the process of healthcare’\n[8].\nStaff and systems in primary care environments have the potential to contribute to serious error that can cause both morbidity and mortality; which has been demonstrated in the field of prescribing\n[9]. Evidence on primary care error comes mainly from the statistics of the medical defence organisations and from small pilot studies\n[10]. And yet, experts we have consulted in the field were anecdotally aware of a multiplicity of interventions or ‘tools’ from their own and others’ work world-wide, which helped identify grey literature for this study.\nThis paper reports a narrative review of ‘tools’ to improve, measure, and monitor patient safety in the ambulatory settings with a focus on family practice. A narrative review broadly covers a specific topic but does not adhere to strict systematic methods to locate and synthesize articles and enables description and synthesis of qualitative research and categorises studies into more homogenous groups\n[11]. To the authors’ knowledge no such broad-ranging review has been attempted. The context of this study is worldwide including both the US and the UK and throughout the term primary care is used to address the terms general practice and family practice.","Our structured narrative review was planned and conducted according to guidance in the Preferred Reporting Items for Systematic Reviews and Meta- Analyses (PRISMA) guidelines\n[12] but following a more narrative approach (especially with regard to grey literature). The starting point for determining the search terms used in the review was a 3 point definition of our search question and exploration of Medical Subject Heading (MeSH) terms\n[13]. We used a multi-centre team (including leading UK experts on patient safety) at a strategic planning meeting to comment on and finalise the search terms for the review. References were managed in Endnote. Broad ranging search terms were used for developing a search strategy in 3 stems; setting (primary care [i.e. general/family practice, ambulatory care, community care, generalist care], safety synonyms [i.e. error, adverse event, fault, malpractice] and types of tool [i.e. indicator, survey, guideline, scale] (see web Additional file\n1: Appendix 1). The aim was to be as inclusive as possible and address administrative, clinical and patient experience issues. The search was performed on the following databases; Embase, CINAHL, Pubmed, Medline (Ovid 1996 onward), Health Management Information Consortium and Web of Science on the 1/11/2011. We did not limit our search by year of publication or to the English language, in order to capture a world-wide perspective on patient safety. However, only abstracts in English were included due to resource restraints for translation. Grey literature was identified from known internet patient safety sources from the US and UK to expand the scope of the review (see web Additional file:\n1 Appendix 6). In order to fully explore a single tool many resources often had to be read – for example the IHI trigger tool is described in a number of web publications and supporting documents. Care was taken not to count published tools that also appeared in grey literature twice by discussion between the two reviewers and wider team.","Reviewer one was a GP Academic Clinical Fellow with an interest in pharmacology (RS) and reviewer two was a health services researcher with an interest in family practice (SC). Similarly to the strategy used in the AMA’s report\n[6], we were interested in highly generalisable tools so research addressing single drugs or conditions in very specific settings was excluded. Inclusion and exclusion criteria are listed in below. An inclusive strategy was employed such that neither reviewer could exclude studies the other felt were potentially relevant. Disagreements were resolved by regular discussions between the 2 reviewers. The inclusion and exclusion criteria were as follows:\nExclusion criteria:hospital care/setting - unless transferableopinion pieces/editorialssingle drugs/conditions where the focus was felt to be on the specific drug or condition rather than on transferable toolsonly about quality of care without explicit patient safety componentexclude on basis of journal (for example “Health Care Food & Nutrition focus”)economic impact of errors (relevant papers were taken out at this stage for other purposes within the project)Both the abstract and main text were not in EnglishInclusion criteria:if unsure always include - for example, ‘good advice’ which might later inform other toolstools or strategies to improve or analyse safety which are of relevance to Primary Care.\nExclusion criteria:hospital care/setting - unless transferableopinion pieces/editorialssingle drugs/conditions where the focus was felt to be on the specific drug or condition rather than on transferable toolsonly about quality of care without explicit patient safety componentexclude on basis of journal (for example “Health Care Food & Nutrition focus”)economic impact of errors (relevant papers were taken out at this stage for other purposes within the project)Both the abstract and main text were not in English\nhospital care/setting - unless transferable\nopinion pieces/editorials\nsingle drugs/conditions where the focus was felt to be on the specific drug or condition rather than on transferable tools\nonly about quality of care without explicit patient safety component\nexclude on basis of journal (for example “Health Care Food & Nutrition focus”)\neconomic impact of errors (relevant papers were taken out at this stage for other purposes within the project)\nBoth the abstract and main text were not in English\nInclusion criteria:if unsure always include - for example, ‘good advice’ which might later inform other toolstools or strategies to improve or analyse safety which are of relevance to Primary Care.\nif unsure always include - for example, ‘good advice’ which might later inform other tools\ntools or strategies to improve or analyse safety which are of relevance to Primary Care.","Data were extracted independently by the two reviewers (RS and SC). A dual approach was taken to data extraction from published material using both a Word document (Web Additional file:\n1 Appendix 5) and an Excel template. For reporting data from selected papers we used a modified PRISMA\n[12] checklist, which combined aspects of different methodologies (not just systematic reviews and meta-analyses) into a form for all study types (available on application to the authors). For example the PRISMA checklist requires a discussion of limitations which is a highly transferable requirement to all methodologies, but it also requires specific methods based items such as ‘confidence intervals on meta-analyses’. Using information collected on the modified PRISMA form, a numerical data extraction system was agreed by both reviewers in order to present results from the selected papers data for analysis were extracted from that Excel document. A pilot of 10 key papers with differing methodologies was undertaken prior to commencing full data extraction – high levels of agreement were found between the two reviewers. At the end of data extraction differences in rating on the Excel sheet were discussed and analysed across a series of face-to-face and telephone meetings, attempts to reach consensus were almost always possible.","This review is part of a National Institute for Health Research (NIHR), School for Primary Care Research (SPCR: http://www.spcr.nihr.ac.uk/) (UK) project, undertaken with the aim of constructing a Patient Safety Toolkit for English family practice.","Grey literature results are not included in the following calculations and flow diagrams; results are instead included in the list of tools found in web Additional file\n1: Appendix 3 (where they are clearly marked as being from grey sources). Using the process described in Figure \n1, we selected approximately 10% (n = 1311) of the original search total (n = 13,240) for evaluation of abstracts; titles excluded at this stage were clearly not of relevance e.g. relating to non-healthcare safety topics. Abstracts were then analysed for tools, after excluding papers which were from the correct setting but which did not contain any interventions; around 14% of the abstracts were included for full paper analysis (n = 189).Figure 1\n‘Toolkit’ review stages. Graph 1 –a) illustration of the literature base in primary care patient safety 1987-2011from Pubmed b) Papers from the review divided by the annual Pubmed output for the same year.\n\n‘Toolkit’ review stages. Graph 1 –a) illustration of the literature base in primary care patient safety 1987-2011from Pubmed b) Papers from the review divided by the annual Pubmed output for the same year.\nAs Graph 1a) illustrates, the majority of publications in the review have been published in the last decade. These data could be a product of MeSH term development or consistency in the last 10 years. However, as Graph 1b) shows, the same take-off in 2001 occurs even when presented as a proportion of the total literature published on Pubmed. Analysing the MeSH terms of the 11 papers in the review from prior to 2000 revealed that the terms ‘medical error’ and ‘diagnostic error’ were each used twice and ‘risk management’ was used in 3 papers. Keywords using the term ‘drug’ appeared 13 times, family practice and ambulatory care were commonly used keywords.\nThe majority of the literature focused on prescribing (55%), which excludes IT interventions for prescribing that were attributed to the informatics theme instead. Other prominent areas were adverse events in primary care and safety climate (which comprised 8% of the total published literature each). A number of climate measures had been refined from earlier climate surveys for use specifically in primary care (i.e. SAQ (ambulatory)\n[14]. Minor themes included; informatics (4.5%) patient role (3%) and general measures to correct error (5%). The primary/secondary care interface is well described in the literature (5%) but, as yet, only 1 published interface tool specifically for primary care exists. Diagnostic error and results handling appear infrequently (<1% of total literature) despite their relative importance. The remainder of literature (11%) related to referrals, Out-Of-Hours (OOH) care, telephone care, organisational issues, mortality and clerical error. Overall, 114 Tools were identified with 26 accrued from grey literature.\nThe setting of the research uncovered was predominantly family practice (in keeping with our search strategy); the term ‘health system’ was used to describe research such as consensus outputs from multi-disciplinary teams or across the whole healthcare system. Most published literature was US based (41%) followed by UK located studies (23%), with other countries producing no more than 5% of published papers each. The grey literature also reflects the predominance of US and UK sources. A variety of study designs were identified in the review including, for example, consensus techniques (10%) , observational methods (15%) but most were mixed methods studies in patient safety research.\nWe classified the data from the published papers in this review using a taxonomy for primary care patient safety based on previous taxonomies by experts in quality of care and patient safety\n[15–19]. It differs from our data collection form as it was evolved later in the process of our analyses. In our taxonomy (Web Additional file\n1: Appendix 4) there are two principal dimensions of safety: ‘access to safe services’ and ‘effectiveness of safety processes’, which are discussed in terms of the structure of the health care system, processes of safety and health outcomes. This taxonomy is based on previous conceptual work on quality of care\n[15]; in essence, do users get the safe care they need, and is the care safe when they get it?\nIn our review, 88 of the 114 unique tools identified (Web Additional file\n1: Appendix 3) came from the published literature and 26 from grey literature sources – the majority of these being from the websites of known patient safety organisations (see data sources in Methods section). The review identified a wide range of ‘tools’ that cannot be described fully here due to word count constraints. The detailed output from the review will be described across a series of subsequent papers on ambulatory patient safety. However, Table \n1 shows key examples found in each dimension of patient safety: We have presented the most well-known or most often used US or UK tools as illustrative examples. The Table is presented in order of weight of literature with most common topics appearing first.Table 1\nTypes of tools found in the review, where possible well-known US examples of the type of tool are given in order to aid understanding\nType of tool (Explanation of tool)Used in the US?Used in the UK?US ExampleData sourceNumber of primary care tools of this type identifiedPrescribing Indicator PacksYesYesBeers criteria\n[20]EHR15 main sets, much overlap -3(criteria for ‘never events’ in prescribing) - other prescribing toolsGRAM reports\n[21], MAI\n[22] (Geriatric Risk Assessment MedGuide™ Medications Appropriateness Index)EHR, staffTrigger Tools-GeneralYesYesIHI Outpatient AdverseEHR5-MedicationsYesNoEvent Trigger Tool\n[23]3-SurgeryYesYesAdverse drug events among older adults in primary care\n[24] Ambulatory surgery\n[25]1\n(Criteria are screened for in a sample of medical records ‘triggering’ more detailed review)\nEvent Reporting Systems (National systems for informing relevant authorities about safety problems with all aspects of healthcare)YesYesASIPS\n[26] (Applied Strategies for Improving Patient Safety)EHR, Staff and patients6Medicines/device Reporting SystemsYesYesMEADERS\n[27]EHR, Staff and Patients4(National systems for informing relevant authorities about safety problems specific to the above)(Medication Error and Adverse Drug Event Reporting System) VAERS\n[28] (Vaccine Adverse Events Reporting System)Safety Climate/Culture Measures (The practice team rate themselves against safety criteria and discuss the results to make changes)YesYesSafety Attitudes Questionnaire\n[14]Staff10Significant Event Analysis Tools (The practice team discuss untoward events, using a standardised structure, in order to learn from them)NoYesUK example - NHS Education for Scotland\n[29]Staff, EHR and patients5General Primary/Secondary Interface Tools (standardised systems for handling patient care at transition in care level – often electronic discharge summaries)YesYes‘Care Transitions Approach’\n[30]EHR, hospital recordsOnly 3 within the direct control of family doctorsMedication Reconciliation Tools (aligning medication histories after secondary care contact)YesNo formal tool usedPartner’s Post Discharge Tool\n[31]EHR, hospital records3PROMs for safety (questionnaire determining the patient perspective of safety in their practice)YesYesSEAPS\n[32] (Seniors Empowerment and Advocacy in Patient Safety)Patients8Other Patient Involvement Measures (variety of tools including literature for patients, computerised systems and medications specific tools)YesYes‘Speak-Up’ from JCAHO\n[33]Patients4IT MeasuresYesYesSEMI-P\n[34]EHR11(not just CDSS but a variety of measures often tackling systems error, many relate to prescribing safety)(Safety Enhancement and Monitoring Instrument that is Patient centred)Diagnostic Tools (Mainly CDSS designed to improve diagnosis)YesNoDxPlain\n[35]EHR3\nAbbreviations:\nCDSS Computer Decision Support Software.\nEHR Electronic Health Record.\nPROM Patient reported outcome measures.\nUK United Kingsdom.\nUS United States.\n\nTypes of tools found in the review, where possible well-known US examples of the type of tool are given in order to aid understanding\n\n\nAbbreviations:\n\nCDSS Computer Decision Support Software.\n\nEHR Electronic Health Record.\n\nPROM Patient reported outcome measures.\n\nUK United Kingsdom.\n\nUS United States.\n Summary of main findings We have demonstrated that there has been an upsurge in publications on primary care patient safety since 2001 and that most of the literature comes from the USA and the UK, with the pre-eminent topic being prescribing safety. The list of discrete tools (which includes grey literature) has a much more even spread across the dimensions within our conceptual taxonomy (Web Appendices 3 and 4). Using this taxonomy shows that some areas of patient safety are relatively neglected in the published literature on primary care patient safety tools; for example, diagnostic error. Tools for test results and referrals are also poorly represented; there were 5 descriptive papers in total, one un-validated tool for electronic referrals and one indicator set dealing with referrals from OOH care. No tools for investigations management were found.\nWe have demonstrated that there has been an upsurge in publications on primary care patient safety since 2001 and that most of the literature comes from the USA and the UK, with the pre-eminent topic being prescribing safety. The list of discrete tools (which includes grey literature) has a much more even spread across the dimensions within our conceptual taxonomy (Web Appendices 3 and 4). Using this taxonomy shows that some areas of patient safety are relatively neglected in the published literature on primary care patient safety tools; for example, diagnostic error. Tools for test results and referrals are also poorly represented; there were 5 descriptive papers in total, one un-validated tool for electronic referrals and one indicator set dealing with referrals from OOH care. No tools for investigations management were found.\n Comparison to existing literature To the authors’ knowledge no similar review has been undertaken to look specifically at instruments for measuring patient safety in primary care. The AMA report on ambulatory patient safety\n[6] found that the number of reported interventions in primary care is low but their search strategy did not take a worldwide approach and only focused on interventions that reduce error or harm. We designed a more inclusive search strategy to capture measurement tools and strategies and were therefore able to find a wider body of literature. The focus on measurement tools and strategies reflects the importance of knowing what is being used currently in primary care to measure patient safety\n[8]. Many of the 114 tools found are iterations of tools constructed previously and re-designed for other countries. For example, the UK NHS Institute for Innovation and Improvement Primary Care Trigger Tool\n[36] has much in common with the IHI Outpatient Adverse Event Trigger Tool\n[23].\nOur study resonates with the view that there can be no one single measure of patient safety\n[8]. Rather, a framework for patient safety should include for example; past harm, reliability, ‘sensitivity to operations’, anticipation/preparedness and Integration/learning. Vincent C, 2013\n[8] Measures of past harm are prevalent among the tools we found i.e. – adverse event reporting systems. Some tools clearly straddle boundaries within the definition: Significant Event Analyses (SEA) (a technique commonly used in the UK), for example, straddles past events, future anticipation of similar situations and learning in relation to the significant event. Few tools address safety reliability in primary care; practices may set their own standards for audit of patient safety but as yet no formal targets exist for primary care in the UK or US (in direct contrast with hospital mortality data and target dashboards such as HEDIS - http://www.ncqa.org/HEDISQualityMeasurement.aspx). Sensitivity to operations is an umbrella term referring to the information and capacity in clinical systems to monitor safety on an hourly or daily basis, climate measures often address questions to staff in relation to their adaptability to change but there are no other measures of this dynamic in primary care. The work of the defence organisations in advising practices about risks and loop-holes in operating systems comes close to fulfilling this goal and roughly equates to a safety ‘walk-round’\n[37]. The challenge to any ‘toolkit’ is to incorporate prospective measures that prevent and anticipate error. The major elements of the toolkit that address prevention are; trigger tools\n[23–25, 36] (potential rather than actual harm), medicines reconciliation packages\n[30] (prevent harm from changes to prescriptions at the interface of primary and secondary care), safety culture\n[38] and a ‘safe systems’ checklist, which encourages primary care practices to seek out loopholes in their established systems.\nTo the authors’ knowledge no similar review has been undertaken to look specifically at instruments for measuring patient safety in primary care. The AMA report on ambulatory patient safety\n[6] found that the number of reported interventions in primary care is low but their search strategy did not take a worldwide approach and only focused on interventions that reduce error or harm. We designed a more inclusive search strategy to capture measurement tools and strategies and were therefore able to find a wider body of literature. The focus on measurement tools and strategies reflects the importance of knowing what is being used currently in primary care to measure patient safety\n[8]. Many of the 114 tools found are iterations of tools constructed previously and re-designed for other countries. For example, the UK NHS Institute for Innovation and Improvement Primary Care Trigger Tool\n[36] has much in common with the IHI Outpatient Adverse Event Trigger Tool\n[23].\nOur study resonates with the view that there can be no one single measure of patient safety\n[8]. Rather, a framework for patient safety should include for example; past harm, reliability, ‘sensitivity to operations’, anticipation/preparedness and Integration/learning. Vincent C, 2013\n[8] Measures of past harm are prevalent among the tools we found i.e. – adverse event reporting systems. Some tools clearly straddle boundaries within the definition: Significant Event Analyses (SEA) (a technique commonly used in the UK), for example, straddles past events, future anticipation of similar situations and learning in relation to the significant event. Few tools address safety reliability in primary care; practices may set their own standards for audit of patient safety but as yet no formal targets exist for primary care in the UK or US (in direct contrast with hospital mortality data and target dashboards such as HEDIS - http://www.ncqa.org/HEDISQualityMeasurement.aspx). Sensitivity to operations is an umbrella term referring to the information and capacity in clinical systems to monitor safety on an hourly or daily basis, climate measures often address questions to staff in relation to their adaptability to change but there are no other measures of this dynamic in primary care. The work of the defence organisations in advising practices about risks and loop-holes in operating systems comes close to fulfilling this goal and roughly equates to a safety ‘walk-round’\n[37]. The challenge to any ‘toolkit’ is to incorporate prospective measures that prevent and anticipate error. The major elements of the toolkit that address prevention are; trigger tools\n[23–25, 36] (potential rather than actual harm), medicines reconciliation packages\n[30] (prevent harm from changes to prescriptions at the interface of primary and secondary care), safety culture\n[38] and a ‘safe systems’ checklist, which encourages primary care practices to seek out loopholes in their established systems.\n Strengths/limitations This review presented challenges due to the broad nature of our question and that there are no criteria for a standardised definition of, or criteria for classifying, a ‘tool’. Tools can be alerts, scoring systems, order sets, dashboards, questionnaires, educational materials, forms or templates to name but a few and, as such, the output of the review is highly heterogeneous. Therefore, we employed pragmatic ways of handling the large amount of data extracted from the review. Our strengths have included using a two reviewer system and a dual extraction process of both numerically coded data and free text summaries of papers, which enabled us to analyse identified instruments in-depth and an extensive exploration of grey literature with a world-wide perspective. Exclusions due to translation costs were minor – only 6 papers out of 280 were excluded on language basis alone. Time-constraints on the project meant we could not use back and forward citation methods systematically due to the sheer number of papers involved in the review.\nThis review presented challenges due to the broad nature of our question and that there are no criteria for a standardised definition of, or criteria for classifying, a ‘tool’. Tools can be alerts, scoring systems, order sets, dashboards, questionnaires, educational materials, forms or templates to name but a few and, as such, the output of the review is highly heterogeneous. Therefore, we employed pragmatic ways of handling the large amount of data extracted from the review. Our strengths have included using a two reviewer system and a dual extraction process of both numerically coded data and free text summaries of papers, which enabled us to analyse identified instruments in-depth and an extensive exploration of grey literature with a world-wide perspective. Exclusions due to translation costs were minor – only 6 papers out of 280 were excluded on language basis alone. Time-constraints on the project meant we could not use back and forward citation methods systematically due to the sheer number of papers involved in the review.\n Implications for practice, policy, or future research The main aim of the Tools identified is to measure or report safety issues. While measurement and baselines are a prerequisite to improvement, few of the Tools include an embedded implementation strategy that would address improvement or a quality cycle to alter strategies and measure for change\n[39]. It is difficult to estimate the impact the various measurement tools identified in this review would have in improving patient safety; for instance, prescribing indicators would only seem to measure level of harm at surface value but have been found to change harmful prescribing patterns when combined with educational feedback\n[40]. Moreover, standards and consistency of reporting vary and many studies, for example around culture and climate surveys, do not report reliability, validity, details of their study characteristics and participants etc.\nOthers have advocated the need for outcome measures in patient safety\n[41]. However, measurement systems need to be tested to ensure they measure what is claimed, whether they can reliably tell if deterioration or improvement is occurring and what other (untoward and unintended) consequences could occur\n[8]. This adheres to the wider imperative that measures of quality or safety, and the data collected, adhere to key attributes such as reliability and validity and also address issues such as acceptability, implementation issues and possible unintended consequences\n[42, 43]. The aim of future work will be to test the suitability and acceptability of the proposed measures in the toolkit and to test changes within practices after application of the toolkit; as well as intended and unintended (positive and negative) consequences. Measureable outcomes are only one feature of Safety Management Systems, and as such the toolkit should not rely exclusively on them but also develop other areas such as training, policy, culture and feedback of outcomes data in line with other established models of patient safety\n[9, 43–45]. There is a need also to embrace qualitative methodologies to patient safety such as the Manchester Patient Safety Framework (MaPSaF )\n[46].\nThe main aim of the Tools identified is to measure or report safety issues. While measurement and baselines are a prerequisite to improvement, few of the Tools include an embedded implementation strategy that would address improvement or a quality cycle to alter strategies and measure for change\n[39]. It is difficult to estimate the impact the various measurement tools identified in this review would have in improving patient safety; for instance, prescribing indicators would only seem to measure level of harm at surface value but have been found to change harmful prescribing patterns when combined with educational feedback\n[40]. Moreover, standards and consistency of reporting vary and many studies, for example around culture and climate surveys, do not report reliability, validity, details of their study characteristics and participants etc.\nOthers have advocated the need for outcome measures in patient safety\n[41]. However, measurement systems need to be tested to ensure they measure what is claimed, whether they can reliably tell if deterioration or improvement is occurring and what other (untoward and unintended) consequences could occur\n[8]. This adheres to the wider imperative that measures of quality or safety, and the data collected, adhere to key attributes such as reliability and validity and also address issues such as acceptability, implementation issues and possible unintended consequences\n[42, 43]. The aim of future work will be to test the suitability and acceptability of the proposed measures in the toolkit and to test changes within practices after application of the toolkit; as well as intended and unintended (positive and negative) consequences. Measureable outcomes are only one feature of Safety Management Systems, and as such the toolkit should not rely exclusively on them but also develop other areas such as training, policy, culture and feedback of outcomes data in line with other established models of patient safety\n[9, 43–45]. There is a need also to embrace qualitative methodologies to patient safety such as the Manchester Patient Safety Framework (MaPSaF )\n[46].","We have demonstrated that there has been an upsurge in publications on primary care patient safety since 2001 and that most of the literature comes from the USA and the UK, with the pre-eminent topic being prescribing safety. The list of discrete tools (which includes grey literature) has a much more even spread across the dimensions within our conceptual taxonomy (Web Appendices 3 and 4). Using this taxonomy shows that some areas of patient safety are relatively neglected in the published literature on primary care patient safety tools; for example, diagnostic error. Tools for test results and referrals are also poorly represented; there were 5 descriptive papers in total, one un-validated tool for electronic referrals and one indicator set dealing with referrals from OOH care. No tools for investigations management were found.","To the authors’ knowledge no similar review has been undertaken to look specifically at instruments for measuring patient safety in primary care. The AMA report on ambulatory patient safety\n[6] found that the number of reported interventions in primary care is low but their search strategy did not take a worldwide approach and only focused on interventions that reduce error or harm. We designed a more inclusive search strategy to capture measurement tools and strategies and were therefore able to find a wider body of literature. The focus on measurement tools and strategies reflects the importance of knowing what is being used currently in primary care to measure patient safety\n[8]. Many of the 114 tools found are iterations of tools constructed previously and re-designed for other countries. For example, the UK NHS Institute for Innovation and Improvement Primary Care Trigger Tool\n[36] has much in common with the IHI Outpatient Adverse Event Trigger Tool\n[23].\nOur study resonates with the view that there can be no one single measure of patient safety\n[8]. Rather, a framework for patient safety should include for example; past harm, reliability, ‘sensitivity to operations’, anticipation/preparedness and Integration/learning. Vincent C, 2013\n[8] Measures of past harm are prevalent among the tools we found i.e. – adverse event reporting systems. Some tools clearly straddle boundaries within the definition: Significant Event Analyses (SEA) (a technique commonly used in the UK), for example, straddles past events, future anticipation of similar situations and learning in relation to the significant event. Few tools address safety reliability in primary care; practices may set their own standards for audit of patient safety but as yet no formal targets exist for primary care in the UK or US (in direct contrast with hospital mortality data and target dashboards such as HEDIS - http://www.ncqa.org/HEDISQualityMeasurement.aspx). Sensitivity to operations is an umbrella term referring to the information and capacity in clinical systems to monitor safety on an hourly or daily basis, climate measures often address questions to staff in relation to their adaptability to change but there are no other measures of this dynamic in primary care. The work of the defence organisations in advising practices about risks and loop-holes in operating systems comes close to fulfilling this goal and roughly equates to a safety ‘walk-round’\n[37]. The challenge to any ‘toolkit’ is to incorporate prospective measures that prevent and anticipate error. The major elements of the toolkit that address prevention are; trigger tools\n[23–25, 36] (potential rather than actual harm), medicines reconciliation packages\n[30] (prevent harm from changes to prescriptions at the interface of primary and secondary care), safety culture\n[38] and a ‘safe systems’ checklist, which encourages primary care practices to seek out loopholes in their established systems.","This review presented challenges due to the broad nature of our question and that there are no criteria for a standardised definition of, or criteria for classifying, a ‘tool’. Tools can be alerts, scoring systems, order sets, dashboards, questionnaires, educational materials, forms or templates to name but a few and, as such, the output of the review is highly heterogeneous. Therefore, we employed pragmatic ways of handling the large amount of data extracted from the review. Our strengths have included using a two reviewer system and a dual extraction process of both numerically coded data and free text summaries of papers, which enabled us to analyse identified instruments in-depth and an extensive exploration of grey literature with a world-wide perspective. Exclusions due to translation costs were minor – only 6 papers out of 280 were excluded on language basis alone. Time-constraints on the project meant we could not use back and forward citation methods systematically due to the sheer number of papers involved in the review.","The main aim of the Tools identified is to measure or report safety issues. While measurement and baselines are a prerequisite to improvement, few of the Tools include an embedded implementation strategy that would address improvement or a quality cycle to alter strategies and measure for change\n[39]. It is difficult to estimate the impact the various measurement tools identified in this review would have in improving patient safety; for instance, prescribing indicators would only seem to measure level of harm at surface value but have been found to change harmful prescribing patterns when combined with educational feedback\n[40]. Moreover, standards and consistency of reporting vary and many studies, for example around culture and climate surveys, do not report reliability, validity, details of their study characteristics and participants etc.\nOthers have advocated the need for outcome measures in patient safety\n[41]. However, measurement systems need to be tested to ensure they measure what is claimed, whether they can reliably tell if deterioration or improvement is occurring and what other (untoward and unintended) consequences could occur\n[8]. This adheres to the wider imperative that measures of quality or safety, and the data collected, adhere to key attributes such as reliability and validity and also address issues such as acceptability, implementation issues and possible unintended consequences\n[42, 43]. The aim of future work will be to test the suitability and acceptability of the proposed measures in the toolkit and to test changes within practices after application of the toolkit; as well as intended and unintended (positive and negative) consequences. Measureable outcomes are only one feature of Safety Management Systems, and as such the toolkit should not rely exclusively on them but also develop other areas such as training, policy, culture and feedback of outcomes data in line with other established models of patient safety\n[9, 43–45]. There is a need also to embrace qualitative methodologies to patient safety such as the Manchester Patient Safety Framework (MaPSaF )\n[46].","Dr Rachel Spencer is an academic General Practitioner at the University of Nottingham and Professor Stephen Campbell is a health services researcher at the University of Manchester.","Additional file 1:\nTools for Primary Care Patient Safety; a Systematic Review.\n(DOCX 65 KB)"],"string":"[\n \"Patient safety has been on the agenda of hospital physicians since the publication of the Institute of Medicine’s 2000 report, ‘To Err is Human’, revealed that more people were dying in the US as a result of medical error than from road traffic accidents\\n[1]. However, most healthcare interactions in the developed world occur in family medicine: 90% of contacts in the England with the National Health Service take place in primary care\\n[2]. In England there are approximately 1 billion community prescriptions dispensed each year\\n[3]. The potential for adverse events is therefore huge but the knowledge base about primary care patient safety is still sparse. A literature review of the nature and frequency of error in primary care suggested that there are between 5–80 safety incidents per 100,000 consultations, which in the UK would translate to between 37–600 incidents per day\\n[4]. Another review estimates that there may be a patient safety incident in approximately 2% of family practice consultations\\n[5].\\nA 2011 report by the American Medical Association on ambulatory patient safety concluded that the introduction of, and research into, patient safety in the primary care environment have lagged behind that of secondary care\\n[6]. Understanding the epidemiology of hospital errors was crucial in developing hospital based safety interventions and the media’s reporting of this data ensured public support for efforts to improve safety\\n[6]. Some of its authors concluded that there needed to be a similar focus on primary care, because there were ‘virtually no credible studies on how to improve safety’\\n[7]. Moreover, a report by the Health Foundation in 2013 emphasised the importance of knowing what methods, tools and indicators are currently being used in primary care to measure patient safety\\n[8]. In this paper, patient safety refers to the ‘avoidance, prevention and amelioration of adverse outcomes or injuries stemming from the process of healthcare’\\n[8].\\nStaff and systems in primary care environments have the potential to contribute to serious error that can cause both morbidity and mortality; which has been demonstrated in the field of prescribing\\n[9]. Evidence on primary care error comes mainly from the statistics of the medical defence organisations and from small pilot studies\\n[10]. And yet, experts we have consulted in the field were anecdotally aware of a multiplicity of interventions or ‘tools’ from their own and others’ work world-wide, which helped identify grey literature for this study.\\nThis paper reports a narrative review of ‘tools’ to improve, measure, and monitor patient safety in the ambulatory settings with a focus on family practice. A narrative review broadly covers a specific topic but does not adhere to strict systematic methods to locate and synthesize articles and enables description and synthesis of qualitative research and categorises studies into more homogenous groups\\n[11]. To the authors’ knowledge no such broad-ranging review has been attempted. The context of this study is worldwide including both the US and the UK and throughout the term primary care is used to address the terms general practice and family practice.\",\n \"Our structured narrative review was planned and conducted according to guidance in the Preferred Reporting Items for Systematic Reviews and Meta- Analyses (PRISMA) guidelines\\n[12] but following a more narrative approach (especially with regard to grey literature). The starting point for determining the search terms used in the review was a 3 point definition of our search question and exploration of Medical Subject Heading (MeSH) terms\\n[13]. We used a multi-centre team (including leading UK experts on patient safety) at a strategic planning meeting to comment on and finalise the search terms for the review. References were managed in Endnote. Broad ranging search terms were used for developing a search strategy in 3 stems; setting (primary care [i.e. general/family practice, ambulatory care, community care, generalist care], safety synonyms [i.e. error, adverse event, fault, malpractice] and types of tool [i.e. indicator, survey, guideline, scale] (see web Additional file\\n1: Appendix 1). The aim was to be as inclusive as possible and address administrative, clinical and patient experience issues. The search was performed on the following databases; Embase, CINAHL, Pubmed, Medline (Ovid 1996 onward), Health Management Information Consortium and Web of Science on the 1/11/2011. We did not limit our search by year of publication or to the English language, in order to capture a world-wide perspective on patient safety. However, only abstracts in English were included due to resource restraints for translation. Grey literature was identified from known internet patient safety sources from the US and UK to expand the scope of the review (see web Additional file:\\n1 Appendix 6). In order to fully explore a single tool many resources often had to be read – for example the IHI trigger tool is described in a number of web publications and supporting documents. Care was taken not to count published tools that also appeared in grey literature twice by discussion between the two reviewers and wider team.\",\n \"Reviewer one was a GP Academic Clinical Fellow with an interest in pharmacology (RS) and reviewer two was a health services researcher with an interest in family practice (SC). Similarly to the strategy used in the AMA’s report\\n[6], we were interested in highly generalisable tools so research addressing single drugs or conditions in very specific settings was excluded. Inclusion and exclusion criteria are listed in below. An inclusive strategy was employed such that neither reviewer could exclude studies the other felt were potentially relevant. Disagreements were resolved by regular discussions between the 2 reviewers. The inclusion and exclusion criteria were as follows:\\nExclusion criteria:hospital care/setting - unless transferableopinion pieces/editorialssingle drugs/conditions where the focus was felt to be on the specific drug or condition rather than on transferable toolsonly about quality of care without explicit patient safety componentexclude on basis of journal (for example “Health Care Food & Nutrition focus”)economic impact of errors (relevant papers were taken out at this stage for other purposes within the project)Both the abstract and main text were not in EnglishInclusion criteria:if unsure always include - for example, ‘good advice’ which might later inform other toolstools or strategies to improve or analyse safety which are of relevance to Primary Care.\\nExclusion criteria:hospital care/setting - unless transferableopinion pieces/editorialssingle drugs/conditions where the focus was felt to be on the specific drug or condition rather than on transferable toolsonly about quality of care without explicit patient safety componentexclude on basis of journal (for example “Health Care Food & Nutrition focus”)economic impact of errors (relevant papers were taken out at this stage for other purposes within the project)Both the abstract and main text were not in English\\nhospital care/setting - unless transferable\\nopinion pieces/editorials\\nsingle drugs/conditions where the focus was felt to be on the specific drug or condition rather than on transferable tools\\nonly about quality of care without explicit patient safety component\\nexclude on basis of journal (for example “Health Care Food & Nutrition focus”)\\neconomic impact of errors (relevant papers were taken out at this stage for other purposes within the project)\\nBoth the abstract and main text were not in English\\nInclusion criteria:if unsure always include - for example, ‘good advice’ which might later inform other toolstools or strategies to improve or analyse safety which are of relevance to Primary Care.\\nif unsure always include - for example, ‘good advice’ which might later inform other tools\\ntools or strategies to improve or analyse safety which are of relevance to Primary Care.\",\n \"Data were extracted independently by the two reviewers (RS and SC). A dual approach was taken to data extraction from published material using both a Word document (Web Additional file:\\n1 Appendix 5) and an Excel template. For reporting data from selected papers we used a modified PRISMA\\n[12] checklist, which combined aspects of different methodologies (not just systematic reviews and meta-analyses) into a form for all study types (available on application to the authors). For example the PRISMA checklist requires a discussion of limitations which is a highly transferable requirement to all methodologies, but it also requires specific methods based items such as ‘confidence intervals on meta-analyses’. Using information collected on the modified PRISMA form, a numerical data extraction system was agreed by both reviewers in order to present results from the selected papers data for analysis were extracted from that Excel document. A pilot of 10 key papers with differing methodologies was undertaken prior to commencing full data extraction – high levels of agreement were found between the two reviewers. At the end of data extraction differences in rating on the Excel sheet were discussed and analysed across a series of face-to-face and telephone meetings, attempts to reach consensus were almost always possible.\",\n \"This review is part of a National Institute for Health Research (NIHR), School for Primary Care Research (SPCR: http://www.spcr.nihr.ac.uk/) (UK) project, undertaken with the aim of constructing a Patient Safety Toolkit for English family practice.\",\n \"Grey literature results are not included in the following calculations and flow diagrams; results are instead included in the list of tools found in web Additional file\\n1: Appendix 3 (where they are clearly marked as being from grey sources). Using the process described in Figure \\n1, we selected approximately 10% (n = 1311) of the original search total (n = 13,240) for evaluation of abstracts; titles excluded at this stage were clearly not of relevance e.g. relating to non-healthcare safety topics. Abstracts were then analysed for tools, after excluding papers which were from the correct setting but which did not contain any interventions; around 14% of the abstracts were included for full paper analysis (n = 189).Figure 1\\n‘Toolkit’ review stages. Graph 1 –a) illustration of the literature base in primary care patient safety 1987-2011from Pubmed b) Papers from the review divided by the annual Pubmed output for the same year.\\n\\n‘Toolkit’ review stages. Graph 1 –a) illustration of the literature base in primary care patient safety 1987-2011from Pubmed b) Papers from the review divided by the annual Pubmed output for the same year.\\nAs Graph 1a) illustrates, the majority of publications in the review have been published in the last decade. These data could be a product of MeSH term development or consistency in the last 10 years. However, as Graph 1b) shows, the same take-off in 2001 occurs even when presented as a proportion of the total literature published on Pubmed. Analysing the MeSH terms of the 11 papers in the review from prior to 2000 revealed that the terms ‘medical error’ and ‘diagnostic error’ were each used twice and ‘risk management’ was used in 3 papers. Keywords using the term ‘drug’ appeared 13 times, family practice and ambulatory care were commonly used keywords.\\nThe majority of the literature focused on prescribing (55%), which excludes IT interventions for prescribing that were attributed to the informatics theme instead. Other prominent areas were adverse events in primary care and safety climate (which comprised 8% of the total published literature each). A number of climate measures had been refined from earlier climate surveys for use specifically in primary care (i.e. SAQ (ambulatory)\\n[14]. Minor themes included; informatics (4.5%) patient role (3%) and general measures to correct error (5%). The primary/secondary care interface is well described in the literature (5%) but, as yet, only 1 published interface tool specifically for primary care exists. Diagnostic error and results handling appear infrequently (<1% of total literature) despite their relative importance. The remainder of literature (11%) related to referrals, Out-Of-Hours (OOH) care, telephone care, organisational issues, mortality and clerical error. Overall, 114 Tools were identified with 26 accrued from grey literature.\\nThe setting of the research uncovered was predominantly family practice (in keeping with our search strategy); the term ‘health system’ was used to describe research such as consensus outputs from multi-disciplinary teams or across the whole healthcare system. Most published literature was US based (41%) followed by UK located studies (23%), with other countries producing no more than 5% of published papers each. The grey literature also reflects the predominance of US and UK sources. A variety of study designs were identified in the review including, for example, consensus techniques (10%) , observational methods (15%) but most were mixed methods studies in patient safety research.\\nWe classified the data from the published papers in this review using a taxonomy for primary care patient safety based on previous taxonomies by experts in quality of care and patient safety\\n[15–19]. It differs from our data collection form as it was evolved later in the process of our analyses. In our taxonomy (Web Additional file\\n1: Appendix 4) there are two principal dimensions of safety: ‘access to safe services’ and ‘effectiveness of safety processes’, which are discussed in terms of the structure of the health care system, processes of safety and health outcomes. This taxonomy is based on previous conceptual work on quality of care\\n[15]; in essence, do users get the safe care they need, and is the care safe when they get it?\\nIn our review, 88 of the 114 unique tools identified (Web Additional file\\n1: Appendix 3) came from the published literature and 26 from grey literature sources – the majority of these being from the websites of known patient safety organisations (see data sources in Methods section). The review identified a wide range of ‘tools’ that cannot be described fully here due to word count constraints. The detailed output from the review will be described across a series of subsequent papers on ambulatory patient safety. However, Table \\n1 shows key examples found in each dimension of patient safety: We have presented the most well-known or most often used US or UK tools as illustrative examples. The Table is presented in order of weight of literature with most common topics appearing first.Table 1\\nTypes of tools found in the review, where possible well-known US examples of the type of tool are given in order to aid understanding\\nType of tool (Explanation of tool)Used in the US?Used in the UK?US ExampleData sourceNumber of primary care tools of this type identifiedPrescribing Indicator PacksYesYesBeers criteria\\n[20]EHR15 main sets, much overlap -3(criteria for ‘never events’ in prescribing) - other prescribing toolsGRAM reports\\n[21], MAI\\n[22] (Geriatric Risk Assessment MedGuide™ Medications Appropriateness Index)EHR, staffTrigger Tools-GeneralYesYesIHI Outpatient AdverseEHR5-MedicationsYesNoEvent Trigger Tool\\n[23]3-SurgeryYesYesAdverse drug events among older adults in primary care\\n[24] Ambulatory surgery\\n[25]1\\n(Criteria are screened for in a sample of medical records ‘triggering’ more detailed review)\\nEvent Reporting Systems (National systems for informing relevant authorities about safety problems with all aspects of healthcare)YesYesASIPS\\n[26] (Applied Strategies for Improving Patient Safety)EHR, Staff and patients6Medicines/device Reporting SystemsYesYesMEADERS\\n[27]EHR, Staff and Patients4(National systems for informing relevant authorities about safety problems specific to the above)(Medication Error and Adverse Drug Event Reporting System) VAERS\\n[28] (Vaccine Adverse Events Reporting System)Safety Climate/Culture Measures (The practice team rate themselves against safety criteria and discuss the results to make changes)YesYesSafety Attitudes Questionnaire\\n[14]Staff10Significant Event Analysis Tools (The practice team discuss untoward events, using a standardised structure, in order to learn from them)NoYesUK example - NHS Education for Scotland\\n[29]Staff, EHR and patients5General Primary/Secondary Interface Tools (standardised systems for handling patient care at transition in care level – often electronic discharge summaries)YesYes‘Care Transitions Approach’\\n[30]EHR, hospital recordsOnly 3 within the direct control of family doctorsMedication Reconciliation Tools (aligning medication histories after secondary care contact)YesNo formal tool usedPartner’s Post Discharge Tool\\n[31]EHR, hospital records3PROMs for safety (questionnaire determining the patient perspective of safety in their practice)YesYesSEAPS\\n[32] (Seniors Empowerment and Advocacy in Patient Safety)Patients8Other Patient Involvement Measures (variety of tools including literature for patients, computerised systems and medications specific tools)YesYes‘Speak-Up’ from JCAHO\\n[33]Patients4IT MeasuresYesYesSEMI-P\\n[34]EHR11(not just CDSS but a variety of measures often tackling systems error, many relate to prescribing safety)(Safety Enhancement and Monitoring Instrument that is Patient centred)Diagnostic Tools (Mainly CDSS designed to improve diagnosis)YesNoDxPlain\\n[35]EHR3\\nAbbreviations:\\nCDSS Computer Decision Support Software.\\nEHR Electronic Health Record.\\nPROM Patient reported outcome measures.\\nUK United Kingsdom.\\nUS United States.\\n\\nTypes of tools found in the review, where possible well-known US examples of the type of tool are given in order to aid understanding\\n\\n\\nAbbreviations:\\n\\nCDSS Computer Decision Support Software.\\n\\nEHR Electronic Health Record.\\n\\nPROM Patient reported outcome measures.\\n\\nUK United Kingsdom.\\n\\nUS United States.\\n Summary of main findings We have demonstrated that there has been an upsurge in publications on primary care patient safety since 2001 and that most of the literature comes from the USA and the UK, with the pre-eminent topic being prescribing safety. The list of discrete tools (which includes grey literature) has a much more even spread across the dimensions within our conceptual taxonomy (Web Appendices 3 and 4). Using this taxonomy shows that some areas of patient safety are relatively neglected in the published literature on primary care patient safety tools; for example, diagnostic error. Tools for test results and referrals are also poorly represented; there were 5 descriptive papers in total, one un-validated tool for electronic referrals and one indicator set dealing with referrals from OOH care. No tools for investigations management were found.\\nWe have demonstrated that there has been an upsurge in publications on primary care patient safety since 2001 and that most of the literature comes from the USA and the UK, with the pre-eminent topic being prescribing safety. The list of discrete tools (which includes grey literature) has a much more even spread across the dimensions within our conceptual taxonomy (Web Appendices 3 and 4). Using this taxonomy shows that some areas of patient safety are relatively neglected in the published literature on primary care patient safety tools; for example, diagnostic error. Tools for test results and referrals are also poorly represented; there were 5 descriptive papers in total, one un-validated tool for electronic referrals and one indicator set dealing with referrals from OOH care. No tools for investigations management were found.\\n Comparison to existing literature To the authors’ knowledge no similar review has been undertaken to look specifically at instruments for measuring patient safety in primary care. The AMA report on ambulatory patient safety\\n[6] found that the number of reported interventions in primary care is low but their search strategy did not take a worldwide approach and only focused on interventions that reduce error or harm. We designed a more inclusive search strategy to capture measurement tools and strategies and were therefore able to find a wider body of literature. The focus on measurement tools and strategies reflects the importance of knowing what is being used currently in primary care to measure patient safety\\n[8]. Many of the 114 tools found are iterations of tools constructed previously and re-designed for other countries. For example, the UK NHS Institute for Innovation and Improvement Primary Care Trigger Tool\\n[36] has much in common with the IHI Outpatient Adverse Event Trigger Tool\\n[23].\\nOur study resonates with the view that there can be no one single measure of patient safety\\n[8]. Rather, a framework for patient safety should include for example; past harm, reliability, ‘sensitivity to operations’, anticipation/preparedness and Integration/learning. Vincent C, 2013\\n[8] Measures of past harm are prevalent among the tools we found i.e. – adverse event reporting systems. Some tools clearly straddle boundaries within the definition: Significant Event Analyses (SEA) (a technique commonly used in the UK), for example, straddles past events, future anticipation of similar situations and learning in relation to the significant event. Few tools address safety reliability in primary care; practices may set their own standards for audit of patient safety but as yet no formal targets exist for primary care in the UK or US (in direct contrast with hospital mortality data and target dashboards such as HEDIS - http://www.ncqa.org/HEDISQualityMeasurement.aspx). Sensitivity to operations is an umbrella term referring to the information and capacity in clinical systems to monitor safety on an hourly or daily basis, climate measures often address questions to staff in relation to their adaptability to change but there are no other measures of this dynamic in primary care. The work of the defence organisations in advising practices about risks and loop-holes in operating systems comes close to fulfilling this goal and roughly equates to a safety ‘walk-round’\\n[37]. The challenge to any ‘toolkit’ is to incorporate prospective measures that prevent and anticipate error. The major elements of the toolkit that address prevention are; trigger tools\\n[23–25, 36] (potential rather than actual harm), medicines reconciliation packages\\n[30] (prevent harm from changes to prescriptions at the interface of primary and secondary care), safety culture\\n[38] and a ‘safe systems’ checklist, which encourages primary care practices to seek out loopholes in their established systems.\\nTo the authors’ knowledge no similar review has been undertaken to look specifically at instruments for measuring patient safety in primary care. The AMA report on ambulatory patient safety\\n[6] found that the number of reported interventions in primary care is low but their search strategy did not take a worldwide approach and only focused on interventions that reduce error or harm. We designed a more inclusive search strategy to capture measurement tools and strategies and were therefore able to find a wider body of literature. The focus on measurement tools and strategies reflects the importance of knowing what is being used currently in primary care to measure patient safety\\n[8]. Many of the 114 tools found are iterations of tools constructed previously and re-designed for other countries. For example, the UK NHS Institute for Innovation and Improvement Primary Care Trigger Tool\\n[36] has much in common with the IHI Outpatient Adverse Event Trigger Tool\\n[23].\\nOur study resonates with the view that there can be no one single measure of patient safety\\n[8]. Rather, a framework for patient safety should include for example; past harm, reliability, ‘sensitivity to operations’, anticipation/preparedness and Integration/learning. Vincent C, 2013\\n[8] Measures of past harm are prevalent among the tools we found i.e. – adverse event reporting systems. Some tools clearly straddle boundaries within the definition: Significant Event Analyses (SEA) (a technique commonly used in the UK), for example, straddles past events, future anticipation of similar situations and learning in relation to the significant event. Few tools address safety reliability in primary care; practices may set their own standards for audit of patient safety but as yet no formal targets exist for primary care in the UK or US (in direct contrast with hospital mortality data and target dashboards such as HEDIS - http://www.ncqa.org/HEDISQualityMeasurement.aspx). Sensitivity to operations is an umbrella term referring to the information and capacity in clinical systems to monitor safety on an hourly or daily basis, climate measures often address questions to staff in relation to their adaptability to change but there are no other measures of this dynamic in primary care. The work of the defence organisations in advising practices about risks and loop-holes in operating systems comes close to fulfilling this goal and roughly equates to a safety ‘walk-round’\\n[37]. The challenge to any ‘toolkit’ is to incorporate prospective measures that prevent and anticipate error. The major elements of the toolkit that address prevention are; trigger tools\\n[23–25, 36] (potential rather than actual harm), medicines reconciliation packages\\n[30] (prevent harm from changes to prescriptions at the interface of primary and secondary care), safety culture\\n[38] and a ‘safe systems’ checklist, which encourages primary care practices to seek out loopholes in their established systems.\\n Strengths/limitations This review presented challenges due to the broad nature of our question and that there are no criteria for a standardised definition of, or criteria for classifying, a ‘tool’. Tools can be alerts, scoring systems, order sets, dashboards, questionnaires, educational materials, forms or templates to name but a few and, as such, the output of the review is highly heterogeneous. Therefore, we employed pragmatic ways of handling the large amount of data extracted from the review. Our strengths have included using a two reviewer system and a dual extraction process of both numerically coded data and free text summaries of papers, which enabled us to analyse identified instruments in-depth and an extensive exploration of grey literature with a world-wide perspective. Exclusions due to translation costs were minor – only 6 papers out of 280 were excluded on language basis alone. Time-constraints on the project meant we could not use back and forward citation methods systematically due to the sheer number of papers involved in the review.\\nThis review presented challenges due to the broad nature of our question and that there are no criteria for a standardised definition of, or criteria for classifying, a ‘tool’. Tools can be alerts, scoring systems, order sets, dashboards, questionnaires, educational materials, forms or templates to name but a few and, as such, the output of the review is highly heterogeneous. Therefore, we employed pragmatic ways of handling the large amount of data extracted from the review. Our strengths have included using a two reviewer system and a dual extraction process of both numerically coded data and free text summaries of papers, which enabled us to analyse identified instruments in-depth and an extensive exploration of grey literature with a world-wide perspective. Exclusions due to translation costs were minor – only 6 papers out of 280 were excluded on language basis alone. Time-constraints on the project meant we could not use back and forward citation methods systematically due to the sheer number of papers involved in the review.\\n Implications for practice, policy, or future research The main aim of the Tools identified is to measure or report safety issues. While measurement and baselines are a prerequisite to improvement, few of the Tools include an embedded implementation strategy that would address improvement or a quality cycle to alter strategies and measure for change\\n[39]. It is difficult to estimate the impact the various measurement tools identified in this review would have in improving patient safety; for instance, prescribing indicators would only seem to measure level of harm at surface value but have been found to change harmful prescribing patterns when combined with educational feedback\\n[40]. Moreover, standards and consistency of reporting vary and many studies, for example around culture and climate surveys, do not report reliability, validity, details of their study characteristics and participants etc.\\nOthers have advocated the need for outcome measures in patient safety\\n[41]. However, measurement systems need to be tested to ensure they measure what is claimed, whether they can reliably tell if deterioration or improvement is occurring and what other (untoward and unintended) consequences could occur\\n[8]. This adheres to the wider imperative that measures of quality or safety, and the data collected, adhere to key attributes such as reliability and validity and also address issues such as acceptability, implementation issues and possible unintended consequences\\n[42, 43]. The aim of future work will be to test the suitability and acceptability of the proposed measures in the toolkit and to test changes within practices after application of the toolkit; as well as intended and unintended (positive and negative) consequences. Measureable outcomes are only one feature of Safety Management Systems, and as such the toolkit should not rely exclusively on them but also develop other areas such as training, policy, culture and feedback of outcomes data in line with other established models of patient safety\\n[9, 43–45]. There is a need also to embrace qualitative methodologies to patient safety such as the Manchester Patient Safety Framework (MaPSaF )\\n[46].\\nThe main aim of the Tools identified is to measure or report safety issues. While measurement and baselines are a prerequisite to improvement, few of the Tools include an embedded implementation strategy that would address improvement or a quality cycle to alter strategies and measure for change\\n[39]. It is difficult to estimate the impact the various measurement tools identified in this review would have in improving patient safety; for instance, prescribing indicators would only seem to measure level of harm at surface value but have been found to change harmful prescribing patterns when combined with educational feedback\\n[40]. Moreover, standards and consistency of reporting vary and many studies, for example around culture and climate surveys, do not report reliability, validity, details of their study characteristics and participants etc.\\nOthers have advocated the need for outcome measures in patient safety\\n[41]. However, measurement systems need to be tested to ensure they measure what is claimed, whether they can reliably tell if deterioration or improvement is occurring and what other (untoward and unintended) consequences could occur\\n[8]. This adheres to the wider imperative that measures of quality or safety, and the data collected, adhere to key attributes such as reliability and validity and also address issues such as acceptability, implementation issues and possible unintended consequences\\n[42, 43]. The aim of future work will be to test the suitability and acceptability of the proposed measures in the toolkit and to test changes within practices after application of the toolkit; as well as intended and unintended (positive and negative) consequences. Measureable outcomes are only one feature of Safety Management Systems, and as such the toolkit should not rely exclusively on them but also develop other areas such as training, policy, culture and feedback of outcomes data in line with other established models of patient safety\\n[9, 43–45]. There is a need also to embrace qualitative methodologies to patient safety such as the Manchester Patient Safety Framework (MaPSaF )\\n[46].\",\n \"We have demonstrated that there has been an upsurge in publications on primary care patient safety since 2001 and that most of the literature comes from the USA and the UK, with the pre-eminent topic being prescribing safety. The list of discrete tools (which includes grey literature) has a much more even spread across the dimensions within our conceptual taxonomy (Web Appendices 3 and 4). Using this taxonomy shows that some areas of patient safety are relatively neglected in the published literature on primary care patient safety tools; for example, diagnostic error. Tools for test results and referrals are also poorly represented; there were 5 descriptive papers in total, one un-validated tool for electronic referrals and one indicator set dealing with referrals from OOH care. No tools for investigations management were found.\",\n \"To the authors’ knowledge no similar review has been undertaken to look specifically at instruments for measuring patient safety in primary care. The AMA report on ambulatory patient safety\\n[6] found that the number of reported interventions in primary care is low but their search strategy did not take a worldwide approach and only focused on interventions that reduce error or harm. We designed a more inclusive search strategy to capture measurement tools and strategies and were therefore able to find a wider body of literature. The focus on measurement tools and strategies reflects the importance of knowing what is being used currently in primary care to measure patient safety\\n[8]. Many of the 114 tools found are iterations of tools constructed previously and re-designed for other countries. For example, the UK NHS Institute for Innovation and Improvement Primary Care Trigger Tool\\n[36] has much in common with the IHI Outpatient Adverse Event Trigger Tool\\n[23].\\nOur study resonates with the view that there can be no one single measure of patient safety\\n[8]. Rather, a framework for patient safety should include for example; past harm, reliability, ‘sensitivity to operations’, anticipation/preparedness and Integration/learning. Vincent C, 2013\\n[8] Measures of past harm are prevalent among the tools we found i.e. – adverse event reporting systems. Some tools clearly straddle boundaries within the definition: Significant Event Analyses (SEA) (a technique commonly used in the UK), for example, straddles past events, future anticipation of similar situations and learning in relation to the significant event. Few tools address safety reliability in primary care; practices may set their own standards for audit of patient safety but as yet no formal targets exist for primary care in the UK or US (in direct contrast with hospital mortality data and target dashboards such as HEDIS - http://www.ncqa.org/HEDISQualityMeasurement.aspx). Sensitivity to operations is an umbrella term referring to the information and capacity in clinical systems to monitor safety on an hourly or daily basis, climate measures often address questions to staff in relation to their adaptability to change but there are no other measures of this dynamic in primary care. The work of the defence organisations in advising practices about risks and loop-holes in operating systems comes close to fulfilling this goal and roughly equates to a safety ‘walk-round’\\n[37]. The challenge to any ‘toolkit’ is to incorporate prospective measures that prevent and anticipate error. The major elements of the toolkit that address prevention are; trigger tools\\n[23–25, 36] (potential rather than actual harm), medicines reconciliation packages\\n[30] (prevent harm from changes to prescriptions at the interface of primary and secondary care), safety culture\\n[38] and a ‘safe systems’ checklist, which encourages primary care practices to seek out loopholes in their established systems.\",\n \"This review presented challenges due to the broad nature of our question and that there are no criteria for a standardised definition of, or criteria for classifying, a ‘tool’. Tools can be alerts, scoring systems, order sets, dashboards, questionnaires, educational materials, forms or templates to name but a few and, as such, the output of the review is highly heterogeneous. Therefore, we employed pragmatic ways of handling the large amount of data extracted from the review. Our strengths have included using a two reviewer system and a dual extraction process of both numerically coded data and free text summaries of papers, which enabled us to analyse identified instruments in-depth and an extensive exploration of grey literature with a world-wide perspective. Exclusions due to translation costs were minor – only 6 papers out of 280 were excluded on language basis alone. Time-constraints on the project meant we could not use back and forward citation methods systematically due to the sheer number of papers involved in the review.\",\n \"The main aim of the Tools identified is to measure or report safety issues. While measurement and baselines are a prerequisite to improvement, few of the Tools include an embedded implementation strategy that would address improvement or a quality cycle to alter strategies and measure for change\\n[39]. It is difficult to estimate the impact the various measurement tools identified in this review would have in improving patient safety; for instance, prescribing indicators would only seem to measure level of harm at surface value but have been found to change harmful prescribing patterns when combined with educational feedback\\n[40]. Moreover, standards and consistency of reporting vary and many studies, for example around culture and climate surveys, do not report reliability, validity, details of their study characteristics and participants etc.\\nOthers have advocated the need for outcome measures in patient safety\\n[41]. However, measurement systems need to be tested to ensure they measure what is claimed, whether they can reliably tell if deterioration or improvement is occurring and what other (untoward and unintended) consequences could occur\\n[8]. This adheres to the wider imperative that measures of quality or safety, and the data collected, adhere to key attributes such as reliability and validity and also address issues such as acceptability, implementation issues and possible unintended consequences\\n[42, 43]. The aim of future work will be to test the suitability and acceptability of the proposed measures in the toolkit and to test changes within practices after application of the toolkit; as well as intended and unintended (positive and negative) consequences. Measureable outcomes are only one feature of Safety Management Systems, and as such the toolkit should not rely exclusively on them but also develop other areas such as training, policy, culture and feedback of outcomes data in line with other established models of patient safety\\n[9, 43–45]. There is a need also to embrace qualitative methodologies to patient safety such as the Manchester Patient Safety Framework (MaPSaF )\\n[46].\",\n \"Dr Rachel Spencer is an academic General Practitioner at the University of Nottingham and Professor Stephen Campbell is a health services researcher at the University of Manchester.\",\n \"Additional file 1:\\nTools for Primary Care Patient Safety; a Systematic Review.\\n(DOCX 65 KB)\"\n]"},"other_sections_sec_types":{"kind":"list like","value":[null,null,null,null,null,null,null,null,null,null,null,null],"string":"[\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null\n]"},"all_sections_titles":{"kind":"list like","value":["Background","Methods","Data sources and searches","Study selection","Data quality and extraction","Funding","Results and discussion","Summary of main findings","Comparison to existing literature","Strengths/limitations","Implications for practice, policy, or future research","Conclusion","Authors’ information","Electronic supplementary material",""],"string":"[\n \"Background\",\n \"Methods\",\n \"Data sources and searches\",\n \"Study selection\",\n \"Data quality and extraction\",\n \"Funding\",\n \"Results and discussion\",\n \"Summary of main findings\",\n \"Comparison to existing literature\",\n \"Strengths/limitations\",\n \"Implications for practice, policy, or future research\",\n \"Conclusion\",\n \"Authors’ information\",\n \"Electronic supplementary material\",\n \"\"\n]"},"all_sections_texts":{"kind":"list like","value":["Patient safety has been on the agenda of hospital physicians since the publication of the Institute of Medicine’s 2000 report, ‘To Err is Human’, revealed that more people were dying in the US as a result of medical error than from road traffic accidents\n[1]. However, most healthcare interactions in the developed world occur in family medicine: 90% of contacts in the England with the National Health Service take place in primary care\n[2]. In England there are approximately 1 billion community prescriptions dispensed each year\n[3]. The potential for adverse events is therefore huge but the knowledge base about primary care patient safety is still sparse. A literature review of the nature and frequency of error in primary care suggested that there are between 5–80 safety incidents per 100,000 consultations, which in the UK would translate to between 37–600 incidents per day\n[4]. Another review estimates that there may be a patient safety incident in approximately 2% of family practice consultations\n[5].\nA 2011 report by the American Medical Association on ambulatory patient safety concluded that the introduction of, and research into, patient safety in the primary care environment have lagged behind that of secondary care\n[6]. Understanding the epidemiology of hospital errors was crucial in developing hospital based safety interventions and the media’s reporting of this data ensured public support for efforts to improve safety\n[6]. Some of its authors concluded that there needed to be a similar focus on primary care, because there were ‘virtually no credible studies on how to improve safety’\n[7]. Moreover, a report by the Health Foundation in 2013 emphasised the importance of knowing what methods, tools and indicators are currently being used in primary care to measure patient safety\n[8]. In this paper, patient safety refers to the ‘avoidance, prevention and amelioration of adverse outcomes or injuries stemming from the process of healthcare’\n[8].\nStaff and systems in primary care environments have the potential to contribute to serious error that can cause both morbidity and mortality; which has been demonstrated in the field of prescribing\n[9]. Evidence on primary care error comes mainly from the statistics of the medical defence organisations and from small pilot studies\n[10]. And yet, experts we have consulted in the field were anecdotally aware of a multiplicity of interventions or ‘tools’ from their own and others’ work world-wide, which helped identify grey literature for this study.\nThis paper reports a narrative review of ‘tools’ to improve, measure, and monitor patient safety in the ambulatory settings with a focus on family practice. A narrative review broadly covers a specific topic but does not adhere to strict systematic methods to locate and synthesize articles and enables description and synthesis of qualitative research and categorises studies into more homogenous groups\n[11]. To the authors’ knowledge no such broad-ranging review has been attempted. The context of this study is worldwide including both the US and the UK and throughout the term primary care is used to address the terms general practice and family practice."," Data sources and searches Our structured narrative review was planned and conducted according to guidance in the Preferred Reporting Items for Systematic Reviews and Meta- Analyses (PRISMA) guidelines\n[12] but following a more narrative approach (especially with regard to grey literature). The starting point for determining the search terms used in the review was a 3 point definition of our search question and exploration of Medical Subject Heading (MeSH) terms\n[13]. We used a multi-centre team (including leading UK experts on patient safety) at a strategic planning meeting to comment on and finalise the search terms for the review. References were managed in Endnote. Broad ranging search terms were used for developing a search strategy in 3 stems; setting (primary care [i.e. general/family practice, ambulatory care, community care, generalist care], safety synonyms [i.e. error, adverse event, fault, malpractice] and types of tool [i.e. indicator, survey, guideline, scale] (see web Additional file\n1: Appendix 1). The aim was to be as inclusive as possible and address administrative, clinical and patient experience issues. The search was performed on the following databases; Embase, CINAHL, Pubmed, Medline (Ovid 1996 onward), Health Management Information Consortium and Web of Science on the 1/11/2011. We did not limit our search by year of publication or to the English language, in order to capture a world-wide perspective on patient safety. However, only abstracts in English were included due to resource restraints for translation. Grey literature was identified from known internet patient safety sources from the US and UK to expand the scope of the review (see web Additional file:\n1 Appendix 6). In order to fully explore a single tool many resources often had to be read – for example the IHI trigger tool is described in a number of web publications and supporting documents. Care was taken not to count published tools that also appeared in grey literature twice by discussion between the two reviewers and wider team.\nOur structured narrative review was planned and conducted according to guidance in the Preferred Reporting Items for Systematic Reviews and Meta- Analyses (PRISMA) guidelines\n[12] but following a more narrative approach (especially with regard to grey literature). The starting point for determining the search terms used in the review was a 3 point definition of our search question and exploration of Medical Subject Heading (MeSH) terms\n[13]. We used a multi-centre team (including leading UK experts on patient safety) at a strategic planning meeting to comment on and finalise the search terms for the review. References were managed in Endnote. Broad ranging search terms were used for developing a search strategy in 3 stems; setting (primary care [i.e. general/family practice, ambulatory care, community care, generalist care], safety synonyms [i.e. error, adverse event, fault, malpractice] and types of tool [i.e. indicator, survey, guideline, scale] (see web Additional file\n1: Appendix 1). The aim was to be as inclusive as possible and address administrative, clinical and patient experience issues. The search was performed on the following databases; Embase, CINAHL, Pubmed, Medline (Ovid 1996 onward), Health Management Information Consortium and Web of Science on the 1/11/2011. We did not limit our search by year of publication or to the English language, in order to capture a world-wide perspective on patient safety. However, only abstracts in English were included due to resource restraints for translation. Grey literature was identified from known internet patient safety sources from the US and UK to expand the scope of the review (see web Additional file:\n1 Appendix 6). In order to fully explore a single tool many resources often had to be read – for example the IHI trigger tool is described in a number of web publications and supporting documents. Care was taken not to count published tools that also appeared in grey literature twice by discussion between the two reviewers and wider team.\n Study selection Reviewer one was a GP Academic Clinical Fellow with an interest in pharmacology (RS) and reviewer two was a health services researcher with an interest in family practice (SC). Similarly to the strategy used in the AMA’s report\n[6], we were interested in highly generalisable tools so research addressing single drugs or conditions in very specific settings was excluded. Inclusion and exclusion criteria are listed in below. An inclusive strategy was employed such that neither reviewer could exclude studies the other felt were potentially relevant. Disagreements were resolved by regular discussions between the 2 reviewers. The inclusion and exclusion criteria were as follows:\nExclusion criteria:hospital care/setting - unless transferableopinion pieces/editorialssingle drugs/conditions where the focus was felt to be on the specific drug or condition rather than on transferable toolsonly about quality of care without explicit patient safety componentexclude on basis of journal (for example “Health Care Food & Nutrition focus”)economic impact of errors (relevant papers were taken out at this stage for other purposes within the project)Both the abstract and main text were not in EnglishInclusion criteria:if unsure always include - for example, ‘good advice’ which might later inform other toolstools or strategies to improve or analyse safety which are of relevance to Primary Care.\nExclusion criteria:hospital care/setting - unless transferableopinion pieces/editorialssingle drugs/conditions where the focus was felt to be on the specific drug or condition rather than on transferable toolsonly about quality of care without explicit patient safety componentexclude on basis of journal (for example “Health Care Food & Nutrition focus”)economic impact of errors (relevant papers were taken out at this stage for other purposes within the project)Both the abstract and main text were not in English\nhospital care/setting - unless transferable\nopinion pieces/editorials\nsingle drugs/conditions where the focus was felt to be on the specific drug or condition rather than on transferable tools\nonly about quality of care without explicit patient safety component\nexclude on basis of journal (for example “Health Care Food & Nutrition focus”)\neconomic impact of errors (relevant papers were taken out at this stage for other purposes within the project)\nBoth the abstract and main text were not in English\nInclusion criteria:if unsure always include - for example, ‘good advice’ which might later inform other toolstools or strategies to improve or analyse safety which are of relevance to Primary Care.\nif unsure always include - for example, ‘good advice’ which might later inform other tools\ntools or strategies to improve or analyse safety which are of relevance to Primary Care.\nReviewer one was a GP Academic Clinical Fellow with an interest in pharmacology (RS) and reviewer two was a health services researcher with an interest in family practice (SC). Similarly to the strategy used in the AMA’s report\n[6], we were interested in highly generalisable tools so research addressing single drugs or conditions in very specific settings was excluded. Inclusion and exclusion criteria are listed in below. An inclusive strategy was employed such that neither reviewer could exclude studies the other felt were potentially relevant. Disagreements were resolved by regular discussions between the 2 reviewers. The inclusion and exclusion criteria were as follows:\nExclusion criteria:hospital care/setting - unless transferableopinion pieces/editorialssingle drugs/conditions where the focus was felt to be on the specific drug or condition rather than on transferable toolsonly about quality of care without explicit patient safety componentexclude on basis of journal (for example “Health Care Food & Nutrition focus”)economic impact of errors (relevant papers were taken out at this stage for other purposes within the project)Both the abstract and main text were not in EnglishInclusion criteria:if unsure always include - for example, ‘good advice’ which might later inform other toolstools or strategies to improve or analyse safety which are of relevance to Primary Care.\nExclusion criteria:hospital care/setting - unless transferableopinion pieces/editorialssingle drugs/conditions where the focus was felt to be on the specific drug or condition rather than on transferable toolsonly about quality of care without explicit patient safety componentexclude on basis of journal (for example “Health Care Food & Nutrition focus”)economic impact of errors (relevant papers were taken out at this stage for other purposes within the project)Both the abstract and main text were not in English\nhospital care/setting - unless transferable\nopinion pieces/editorials\nsingle drugs/conditions where the focus was felt to be on the specific drug or condition rather than on transferable tools\nonly about quality of care without explicit patient safety component\nexclude on basis of journal (for example “Health Care Food & Nutrition focus”)\neconomic impact of errors (relevant papers were taken out at this stage for other purposes within the project)\nBoth the abstract and main text were not in English\nInclusion criteria:if unsure always include - for example, ‘good advice’ which might later inform other toolstools or strategies to improve or analyse safety which are of relevance to Primary Care.\nif unsure always include - for example, ‘good advice’ which might later inform other tools\ntools or strategies to improve or analyse safety which are of relevance to Primary Care.\n Data quality and extraction Data were extracted independently by the two reviewers (RS and SC). A dual approach was taken to data extraction from published material using both a Word document (Web Additional file:\n1 Appendix 5) and an Excel template. For reporting data from selected papers we used a modified PRISMA\n[12] checklist, which combined aspects of different methodologies (not just systematic reviews and meta-analyses) into a form for all study types (available on application to the authors). For example the PRISMA checklist requires a discussion of limitations which is a highly transferable requirement to all methodologies, but it also requires specific methods based items such as ‘confidence intervals on meta-analyses’. Using information collected on the modified PRISMA form, a numerical data extraction system was agreed by both reviewers in order to present results from the selected papers data for analysis were extracted from that Excel document. A pilot of 10 key papers with differing methodologies was undertaken prior to commencing full data extraction – high levels of agreement were found between the two reviewers. At the end of data extraction differences in rating on the Excel sheet were discussed and analysed across a series of face-to-face and telephone meetings, attempts to reach consensus were almost always possible.\nData were extracted independently by the two reviewers (RS and SC). A dual approach was taken to data extraction from published material using both a Word document (Web Additional file:\n1 Appendix 5) and an Excel template. For reporting data from selected papers we used a modified PRISMA\n[12] checklist, which combined aspects of different methodologies (not just systematic reviews and meta-analyses) into a form for all study types (available on application to the authors). For example the PRISMA checklist requires a discussion of limitations which is a highly transferable requirement to all methodologies, but it also requires specific methods based items such as ‘confidence intervals on meta-analyses’. Using information collected on the modified PRISMA form, a numerical data extraction system was agreed by both reviewers in order to present results from the selected papers data for analysis were extracted from that Excel document. A pilot of 10 key papers with differing methodologies was undertaken prior to commencing full data extraction – high levels of agreement were found between the two reviewers. At the end of data extraction differences in rating on the Excel sheet were discussed and analysed across a series of face-to-face and telephone meetings, attempts to reach consensus were almost always possible.\n Funding This review is part of a National Institute for Health Research (NIHR), School for Primary Care Research (SPCR: http://www.spcr.nihr.ac.uk/) (UK) project, undertaken with the aim of constructing a Patient Safety Toolkit for English family practice.\nThis review is part of a National Institute for Health Research (NIHR), School for Primary Care Research (SPCR: http://www.spcr.nihr.ac.uk/) (UK) project, undertaken with the aim of constructing a Patient Safety Toolkit for English family practice.","Our structured narrative review was planned and conducted according to guidance in the Preferred Reporting Items for Systematic Reviews and Meta- Analyses (PRISMA) guidelines\n[12] but following a more narrative approach (especially with regard to grey literature). The starting point for determining the search terms used in the review was a 3 point definition of our search question and exploration of Medical Subject Heading (MeSH) terms\n[13]. We used a multi-centre team (including leading UK experts on patient safety) at a strategic planning meeting to comment on and finalise the search terms for the review. References were managed in Endnote. Broad ranging search terms were used for developing a search strategy in 3 stems; setting (primary care [i.e. general/family practice, ambulatory care, community care, generalist care], safety synonyms [i.e. error, adverse event, fault, malpractice] and types of tool [i.e. indicator, survey, guideline, scale] (see web Additional file\n1: Appendix 1). The aim was to be as inclusive as possible and address administrative, clinical and patient experience issues. The search was performed on the following databases; Embase, CINAHL, Pubmed, Medline (Ovid 1996 onward), Health Management Information Consortium and Web of Science on the 1/11/2011. We did not limit our search by year of publication or to the English language, in order to capture a world-wide perspective on patient safety. However, only abstracts in English were included due to resource restraints for translation. Grey literature was identified from known internet patient safety sources from the US and UK to expand the scope of the review (see web Additional file:\n1 Appendix 6). In order to fully explore a single tool many resources often had to be read – for example the IHI trigger tool is described in a number of web publications and supporting documents. Care was taken not to count published tools that also appeared in grey literature twice by discussion between the two reviewers and wider team.","Reviewer one was a GP Academic Clinical Fellow with an interest in pharmacology (RS) and reviewer two was a health services researcher with an interest in family practice (SC). Similarly to the strategy used in the AMA’s report\n[6], we were interested in highly generalisable tools so research addressing single drugs or conditions in very specific settings was excluded. Inclusion and exclusion criteria are listed in below. An inclusive strategy was employed such that neither reviewer could exclude studies the other felt were potentially relevant. Disagreements were resolved by regular discussions between the 2 reviewers. The inclusion and exclusion criteria were as follows:\nExclusion criteria:hospital care/setting - unless transferableopinion pieces/editorialssingle drugs/conditions where the focus was felt to be on the specific drug or condition rather than on transferable toolsonly about quality of care without explicit patient safety componentexclude on basis of journal (for example “Health Care Food & Nutrition focus”)economic impact of errors (relevant papers were taken out at this stage for other purposes within the project)Both the abstract and main text were not in EnglishInclusion criteria:if unsure always include - for example, ‘good advice’ which might later inform other toolstools or strategies to improve or analyse safety which are of relevance to Primary Care.\nExclusion criteria:hospital care/setting - unless transferableopinion pieces/editorialssingle drugs/conditions where the focus was felt to be on the specific drug or condition rather than on transferable toolsonly about quality of care without explicit patient safety componentexclude on basis of journal (for example “Health Care Food & Nutrition focus”)economic impact of errors (relevant papers were taken out at this stage for other purposes within the project)Both the abstract and main text were not in English\nhospital care/setting - unless transferable\nopinion pieces/editorials\nsingle drugs/conditions where the focus was felt to be on the specific drug or condition rather than on transferable tools\nonly about quality of care without explicit patient safety component\nexclude on basis of journal (for example “Health Care Food & Nutrition focus”)\neconomic impact of errors (relevant papers were taken out at this stage for other purposes within the project)\nBoth the abstract and main text were not in English\nInclusion criteria:if unsure always include - for example, ‘good advice’ which might later inform other toolstools or strategies to improve or analyse safety which are of relevance to Primary Care.\nif unsure always include - for example, ‘good advice’ which might later inform other tools\ntools or strategies to improve or analyse safety which are of relevance to Primary Care.","Data were extracted independently by the two reviewers (RS and SC). A dual approach was taken to data extraction from published material using both a Word document (Web Additional file:\n1 Appendix 5) and an Excel template. For reporting data from selected papers we used a modified PRISMA\n[12] checklist, which combined aspects of different methodologies (not just systematic reviews and meta-analyses) into a form for all study types (available on application to the authors). For example the PRISMA checklist requires a discussion of limitations which is a highly transferable requirement to all methodologies, but it also requires specific methods based items such as ‘confidence intervals on meta-analyses’. Using information collected on the modified PRISMA form, a numerical data extraction system was agreed by both reviewers in order to present results from the selected papers data for analysis were extracted from that Excel document. A pilot of 10 key papers with differing methodologies was undertaken prior to commencing full data extraction – high levels of agreement were found between the two reviewers. At the end of data extraction differences in rating on the Excel sheet were discussed and analysed across a series of face-to-face and telephone meetings, attempts to reach consensus were almost always possible.","This review is part of a National Institute for Health Research (NIHR), School for Primary Care Research (SPCR: http://www.spcr.nihr.ac.uk/) (UK) project, undertaken with the aim of constructing a Patient Safety Toolkit for English family practice.","Grey literature results are not included in the following calculations and flow diagrams; results are instead included in the list of tools found in web Additional file\n1: Appendix 3 (where they are clearly marked as being from grey sources). Using the process described in Figure \n1, we selected approximately 10% (n = 1311) of the original search total (n = 13,240) for evaluation of abstracts; titles excluded at this stage were clearly not of relevance e.g. relating to non-healthcare safety topics. Abstracts were then analysed for tools, after excluding papers which were from the correct setting but which did not contain any interventions; around 14% of the abstracts were included for full paper analysis (n = 189).Figure 1\n‘Toolkit’ review stages. Graph 1 –a) illustration of the literature base in primary care patient safety 1987-2011from Pubmed b) Papers from the review divided by the annual Pubmed output for the same year.\n\n‘Toolkit’ review stages. Graph 1 –a) illustration of the literature base in primary care patient safety 1987-2011from Pubmed b) Papers from the review divided by the annual Pubmed output for the same year.\nAs Graph 1a) illustrates, the majority of publications in the review have been published in the last decade. These data could be a product of MeSH term development or consistency in the last 10 years. However, as Graph 1b) shows, the same take-off in 2001 occurs even when presented as a proportion of the total literature published on Pubmed. Analysing the MeSH terms of the 11 papers in the review from prior to 2000 revealed that the terms ‘medical error’ and ‘diagnostic error’ were each used twice and ‘risk management’ was used in 3 papers. Keywords using the term ‘drug’ appeared 13 times, family practice and ambulatory care were commonly used keywords.\nThe majority of the literature focused on prescribing (55%), which excludes IT interventions for prescribing that were attributed to the informatics theme instead. Other prominent areas were adverse events in primary care and safety climate (which comprised 8% of the total published literature each). A number of climate measures had been refined from earlier climate surveys for use specifically in primary care (i.e. SAQ (ambulatory)\n[14]. Minor themes included; informatics (4.5%) patient role (3%) and general measures to correct error (5%). The primary/secondary care interface is well described in the literature (5%) but, as yet, only 1 published interface tool specifically for primary care exists. Diagnostic error and results handling appear infrequently (<1% of total literature) despite their relative importance. The remainder of literature (11%) related to referrals, Out-Of-Hours (OOH) care, telephone care, organisational issues, mortality and clerical error. Overall, 114 Tools were identified with 26 accrued from grey literature.\nThe setting of the research uncovered was predominantly family practice (in keeping with our search strategy); the term ‘health system’ was used to describe research such as consensus outputs from multi-disciplinary teams or across the whole healthcare system. Most published literature was US based (41%) followed by UK located studies (23%), with other countries producing no more than 5% of published papers each. The grey literature also reflects the predominance of US and UK sources. A variety of study designs were identified in the review including, for example, consensus techniques (10%) , observational methods (15%) but most were mixed methods studies in patient safety research.\nWe classified the data from the published papers in this review using a taxonomy for primary care patient safety based on previous taxonomies by experts in quality of care and patient safety\n[15–19]. It differs from our data collection form as it was evolved later in the process of our analyses. In our taxonomy (Web Additional file\n1: Appendix 4) there are two principal dimensions of safety: ‘access to safe services’ and ‘effectiveness of safety processes’, which are discussed in terms of the structure of the health care system, processes of safety and health outcomes. This taxonomy is based on previous conceptual work on quality of care\n[15]; in essence, do users get the safe care they need, and is the care safe when they get it?\nIn our review, 88 of the 114 unique tools identified (Web Additional file\n1: Appendix 3) came from the published literature and 26 from grey literature sources – the majority of these being from the websites of known patient safety organisations (see data sources in Methods section). The review identified a wide range of ‘tools’ that cannot be described fully here due to word count constraints. The detailed output from the review will be described across a series of subsequent papers on ambulatory patient safety. However, Table \n1 shows key examples found in each dimension of patient safety: We have presented the most well-known or most often used US or UK tools as illustrative examples. The Table is presented in order of weight of literature with most common topics appearing first.Table 1\nTypes of tools found in the review, where possible well-known US examples of the type of tool are given in order to aid understanding\nType of tool (Explanation of tool)Used in the US?Used in the UK?US ExampleData sourceNumber of primary care tools of this type identifiedPrescribing Indicator PacksYesYesBeers criteria\n[20]EHR15 main sets, much overlap -3(criteria for ‘never events’ in prescribing) - other prescribing toolsGRAM reports\n[21], MAI\n[22] (Geriatric Risk Assessment MedGuide™ Medications Appropriateness Index)EHR, staffTrigger Tools-GeneralYesYesIHI Outpatient AdverseEHR5-MedicationsYesNoEvent Trigger Tool\n[23]3-SurgeryYesYesAdverse drug events among older adults in primary care\n[24] Ambulatory surgery\n[25]1\n(Criteria are screened for in a sample of medical records ‘triggering’ more detailed review)\nEvent Reporting Systems (National systems for informing relevant authorities about safety problems with all aspects of healthcare)YesYesASIPS\n[26] (Applied Strategies for Improving Patient Safety)EHR, Staff and patients6Medicines/device Reporting SystemsYesYesMEADERS\n[27]EHR, Staff and Patients4(National systems for informing relevant authorities about safety problems specific to the above)(Medication Error and Adverse Drug Event Reporting System) VAERS\n[28] (Vaccine Adverse Events Reporting System)Safety Climate/Culture Measures (The practice team rate themselves against safety criteria and discuss the results to make changes)YesYesSafety Attitudes Questionnaire\n[14]Staff10Significant Event Analysis Tools (The practice team discuss untoward events, using a standardised structure, in order to learn from them)NoYesUK example - NHS Education for Scotland\n[29]Staff, EHR and patients5General Primary/Secondary Interface Tools (standardised systems for handling patient care at transition in care level – often electronic discharge summaries)YesYes‘Care Transitions Approach’\n[30]EHR, hospital recordsOnly 3 within the direct control of family doctorsMedication Reconciliation Tools (aligning medication histories after secondary care contact)YesNo formal tool usedPartner’s Post Discharge Tool\n[31]EHR, hospital records3PROMs for safety (questionnaire determining the patient perspective of safety in their practice)YesYesSEAPS\n[32] (Seniors Empowerment and Advocacy in Patient Safety)Patients8Other Patient Involvement Measures (variety of tools including literature for patients, computerised systems and medications specific tools)YesYes‘Speak-Up’ from JCAHO\n[33]Patients4IT MeasuresYesYesSEMI-P\n[34]EHR11(not just CDSS but a variety of measures often tackling systems error, many relate to prescribing safety)(Safety Enhancement and Monitoring Instrument that is Patient centred)Diagnostic Tools (Mainly CDSS designed to improve diagnosis)YesNoDxPlain\n[35]EHR3\nAbbreviations:\nCDSS Computer Decision Support Software.\nEHR Electronic Health Record.\nPROM Patient reported outcome measures.\nUK United Kingsdom.\nUS United States.\n\nTypes of tools found in the review, where possible well-known US examples of the type of tool are given in order to aid understanding\n\n\nAbbreviations:\n\nCDSS Computer Decision Support Software.\n\nEHR Electronic Health Record.\n\nPROM Patient reported outcome measures.\n\nUK United Kingsdom.\n\nUS United States.\n Summary of main findings We have demonstrated that there has been an upsurge in publications on primary care patient safety since 2001 and that most of the literature comes from the USA and the UK, with the pre-eminent topic being prescribing safety. The list of discrete tools (which includes grey literature) has a much more even spread across the dimensions within our conceptual taxonomy (Web Appendices 3 and 4). Using this taxonomy shows that some areas of patient safety are relatively neglected in the published literature on primary care patient safety tools; for example, diagnostic error. Tools for test results and referrals are also poorly represented; there were 5 descriptive papers in total, one un-validated tool for electronic referrals and one indicator set dealing with referrals from OOH care. No tools for investigations management were found.\nWe have demonstrated that there has been an upsurge in publications on primary care patient safety since 2001 and that most of the literature comes from the USA and the UK, with the pre-eminent topic being prescribing safety. The list of discrete tools (which includes grey literature) has a much more even spread across the dimensions within our conceptual taxonomy (Web Appendices 3 and 4). Using this taxonomy shows that some areas of patient safety are relatively neglected in the published literature on primary care patient safety tools; for example, diagnostic error. Tools for test results and referrals are also poorly represented; there were 5 descriptive papers in total, one un-validated tool for electronic referrals and one indicator set dealing with referrals from OOH care. No tools for investigations management were found.\n Comparison to existing literature To the authors’ knowledge no similar review has been undertaken to look specifically at instruments for measuring patient safety in primary care. The AMA report on ambulatory patient safety\n[6] found that the number of reported interventions in primary care is low but their search strategy did not take a worldwide approach and only focused on interventions that reduce error or harm. We designed a more inclusive search strategy to capture measurement tools and strategies and were therefore able to find a wider body of literature. The focus on measurement tools and strategies reflects the importance of knowing what is being used currently in primary care to measure patient safety\n[8]. Many of the 114 tools found are iterations of tools constructed previously and re-designed for other countries. For example, the UK NHS Institute for Innovation and Improvement Primary Care Trigger Tool\n[36] has much in common with the IHI Outpatient Adverse Event Trigger Tool\n[23].\nOur study resonates with the view that there can be no one single measure of patient safety\n[8]. Rather, a framework for patient safety should include for example; past harm, reliability, ‘sensitivity to operations’, anticipation/preparedness and Integration/learning. Vincent C, 2013\n[8] Measures of past harm are prevalent among the tools we found i.e. – adverse event reporting systems. Some tools clearly straddle boundaries within the definition: Significant Event Analyses (SEA) (a technique commonly used in the UK), for example, straddles past events, future anticipation of similar situations and learning in relation to the significant event. Few tools address safety reliability in primary care; practices may set their own standards for audit of patient safety but as yet no formal targets exist for primary care in the UK or US (in direct contrast with hospital mortality data and target dashboards such as HEDIS - http://www.ncqa.org/HEDISQualityMeasurement.aspx). Sensitivity to operations is an umbrella term referring to the information and capacity in clinical systems to monitor safety on an hourly or daily basis, climate measures often address questions to staff in relation to their adaptability to change but there are no other measures of this dynamic in primary care. The work of the defence organisations in advising practices about risks and loop-holes in operating systems comes close to fulfilling this goal and roughly equates to a safety ‘walk-round’\n[37]. The challenge to any ‘toolkit’ is to incorporate prospective measures that prevent and anticipate error. The major elements of the toolkit that address prevention are; trigger tools\n[23–25, 36] (potential rather than actual harm), medicines reconciliation packages\n[30] (prevent harm from changes to prescriptions at the interface of primary and secondary care), safety culture\n[38] and a ‘safe systems’ checklist, which encourages primary care practices to seek out loopholes in their established systems.\nTo the authors’ knowledge no similar review has been undertaken to look specifically at instruments for measuring patient safety in primary care. The AMA report on ambulatory patient safety\n[6] found that the number of reported interventions in primary care is low but their search strategy did not take a worldwide approach and only focused on interventions that reduce error or harm. We designed a more inclusive search strategy to capture measurement tools and strategies and were therefore able to find a wider body of literature. The focus on measurement tools and strategies reflects the importance of knowing what is being used currently in primary care to measure patient safety\n[8]. Many of the 114 tools found are iterations of tools constructed previously and re-designed for other countries. For example, the UK NHS Institute for Innovation and Improvement Primary Care Trigger Tool\n[36] has much in common with the IHI Outpatient Adverse Event Trigger Tool\n[23].\nOur study resonates with the view that there can be no one single measure of patient safety\n[8]. Rather, a framework for patient safety should include for example; past harm, reliability, ‘sensitivity to operations’, anticipation/preparedness and Integration/learning. Vincent C, 2013\n[8] Measures of past harm are prevalent among the tools we found i.e. – adverse event reporting systems. Some tools clearly straddle boundaries within the definition: Significant Event Analyses (SEA) (a technique commonly used in the UK), for example, straddles past events, future anticipation of similar situations and learning in relation to the significant event. Few tools address safety reliability in primary care; practices may set their own standards for audit of patient safety but as yet no formal targets exist for primary care in the UK or US (in direct contrast with hospital mortality data and target dashboards such as HEDIS - http://www.ncqa.org/HEDISQualityMeasurement.aspx). Sensitivity to operations is an umbrella term referring to the information and capacity in clinical systems to monitor safety on an hourly or daily basis, climate measures often address questions to staff in relation to their adaptability to change but there are no other measures of this dynamic in primary care. The work of the defence organisations in advising practices about risks and loop-holes in operating systems comes close to fulfilling this goal and roughly equates to a safety ‘walk-round’\n[37]. The challenge to any ‘toolkit’ is to incorporate prospective measures that prevent and anticipate error. The major elements of the toolkit that address prevention are; trigger tools\n[23–25, 36] (potential rather than actual harm), medicines reconciliation packages\n[30] (prevent harm from changes to prescriptions at the interface of primary and secondary care), safety culture\n[38] and a ‘safe systems’ checklist, which encourages primary care practices to seek out loopholes in their established systems.\n Strengths/limitations This review presented challenges due to the broad nature of our question and that there are no criteria for a standardised definition of, or criteria for classifying, a ‘tool’. Tools can be alerts, scoring systems, order sets, dashboards, questionnaires, educational materials, forms or templates to name but a few and, as such, the output of the review is highly heterogeneous. Therefore, we employed pragmatic ways of handling the large amount of data extracted from the review. Our strengths have included using a two reviewer system and a dual extraction process of both numerically coded data and free text summaries of papers, which enabled us to analyse identified instruments in-depth and an extensive exploration of grey literature with a world-wide perspective. Exclusions due to translation costs were minor – only 6 papers out of 280 were excluded on language basis alone. Time-constraints on the project meant we could not use back and forward citation methods systematically due to the sheer number of papers involved in the review.\nThis review presented challenges due to the broad nature of our question and that there are no criteria for a standardised definition of, or criteria for classifying, a ‘tool’. Tools can be alerts, scoring systems, order sets, dashboards, questionnaires, educational materials, forms or templates to name but a few and, as such, the output of the review is highly heterogeneous. Therefore, we employed pragmatic ways of handling the large amount of data extracted from the review. Our strengths have included using a two reviewer system and a dual extraction process of both numerically coded data and free text summaries of papers, which enabled us to analyse identified instruments in-depth and an extensive exploration of grey literature with a world-wide perspective. Exclusions due to translation costs were minor – only 6 papers out of 280 were excluded on language basis alone. Time-constraints on the project meant we could not use back and forward citation methods systematically due to the sheer number of papers involved in the review.\n Implications for practice, policy, or future research The main aim of the Tools identified is to measure or report safety issues. While measurement and baselines are a prerequisite to improvement, few of the Tools include an embedded implementation strategy that would address improvement or a quality cycle to alter strategies and measure for change\n[39]. It is difficult to estimate the impact the various measurement tools identified in this review would have in improving patient safety; for instance, prescribing indicators would only seem to measure level of harm at surface value but have been found to change harmful prescribing patterns when combined with educational feedback\n[40]. Moreover, standards and consistency of reporting vary and many studies, for example around culture and climate surveys, do not report reliability, validity, details of their study characteristics and participants etc.\nOthers have advocated the need for outcome measures in patient safety\n[41]. However, measurement systems need to be tested to ensure they measure what is claimed, whether they can reliably tell if deterioration or improvement is occurring and what other (untoward and unintended) consequences could occur\n[8]. This adheres to the wider imperative that measures of quality or safety, and the data collected, adhere to key attributes such as reliability and validity and also address issues such as acceptability, implementation issues and possible unintended consequences\n[42, 43]. The aim of future work will be to test the suitability and acceptability of the proposed measures in the toolkit and to test changes within practices after application of the toolkit; as well as intended and unintended (positive and negative) consequences. Measureable outcomes are only one feature of Safety Management Systems, and as such the toolkit should not rely exclusively on them but also develop other areas such as training, policy, culture and feedback of outcomes data in line with other established models of patient safety\n[9, 43–45]. There is a need also to embrace qualitative methodologies to patient safety such as the Manchester Patient Safety Framework (MaPSaF )\n[46].\nThe main aim of the Tools identified is to measure or report safety issues. While measurement and baselines are a prerequisite to improvement, few of the Tools include an embedded implementation strategy that would address improvement or a quality cycle to alter strategies and measure for change\n[39]. It is difficult to estimate the impact the various measurement tools identified in this review would have in improving patient safety; for instance, prescribing indicators would only seem to measure level of harm at surface value but have been found to change harmful prescribing patterns when combined with educational feedback\n[40]. Moreover, standards and consistency of reporting vary and many studies, for example around culture and climate surveys, do not report reliability, validity, details of their study characteristics and participants etc.\nOthers have advocated the need for outcome measures in patient safety\n[41]. However, measurement systems need to be tested to ensure they measure what is claimed, whether they can reliably tell if deterioration or improvement is occurring and what other (untoward and unintended) consequences could occur\n[8]. This adheres to the wider imperative that measures of quality or safety, and the data collected, adhere to key attributes such as reliability and validity and also address issues such as acceptability, implementation issues and possible unintended consequences\n[42, 43]. The aim of future work will be to test the suitability and acceptability of the proposed measures in the toolkit and to test changes within practices after application of the toolkit; as well as intended and unintended (positive and negative) consequences. Measureable outcomes are only one feature of Safety Management Systems, and as such the toolkit should not rely exclusively on them but also develop other areas such as training, policy, culture and feedback of outcomes data in line with other established models of patient safety\n[9, 43–45]. There is a need also to embrace qualitative methodologies to patient safety such as the Manchester Patient Safety Framework (MaPSaF )\n[46].","We have demonstrated that there has been an upsurge in publications on primary care patient safety since 2001 and that most of the literature comes from the USA and the UK, with the pre-eminent topic being prescribing safety. The list of discrete tools (which includes grey literature) has a much more even spread across the dimensions within our conceptual taxonomy (Web Appendices 3 and 4). Using this taxonomy shows that some areas of patient safety are relatively neglected in the published literature on primary care patient safety tools; for example, diagnostic error. Tools for test results and referrals are also poorly represented; there were 5 descriptive papers in total, one un-validated tool for electronic referrals and one indicator set dealing with referrals from OOH care. No tools for investigations management were found.","To the authors’ knowledge no similar review has been undertaken to look specifically at instruments for measuring patient safety in primary care. The AMA report on ambulatory patient safety\n[6] found that the number of reported interventions in primary care is low but their search strategy did not take a worldwide approach and only focused on interventions that reduce error or harm. We designed a more inclusive search strategy to capture measurement tools and strategies and were therefore able to find a wider body of literature. The focus on measurement tools and strategies reflects the importance of knowing what is being used currently in primary care to measure patient safety\n[8]. Many of the 114 tools found are iterations of tools constructed previously and re-designed for other countries. For example, the UK NHS Institute for Innovation and Improvement Primary Care Trigger Tool\n[36] has much in common with the IHI Outpatient Adverse Event Trigger Tool\n[23].\nOur study resonates with the view that there can be no one single measure of patient safety\n[8]. Rather, a framework for patient safety should include for example; past harm, reliability, ‘sensitivity to operations’, anticipation/preparedness and Integration/learning. Vincent C, 2013\n[8] Measures of past harm are prevalent among the tools we found i.e. – adverse event reporting systems. Some tools clearly straddle boundaries within the definition: Significant Event Analyses (SEA) (a technique commonly used in the UK), for example, straddles past events, future anticipation of similar situations and learning in relation to the significant event. Few tools address safety reliability in primary care; practices may set their own standards for audit of patient safety but as yet no formal targets exist for primary care in the UK or US (in direct contrast with hospital mortality data and target dashboards such as HEDIS - http://www.ncqa.org/HEDISQualityMeasurement.aspx). Sensitivity to operations is an umbrella term referring to the information and capacity in clinical systems to monitor safety on an hourly or daily basis, climate measures often address questions to staff in relation to their adaptability to change but there are no other measures of this dynamic in primary care. The work of the defence organisations in advising practices about risks and loop-holes in operating systems comes close to fulfilling this goal and roughly equates to a safety ‘walk-round’\n[37]. The challenge to any ‘toolkit’ is to incorporate prospective measures that prevent and anticipate error. The major elements of the toolkit that address prevention are; trigger tools\n[23–25, 36] (potential rather than actual harm), medicines reconciliation packages\n[30] (prevent harm from changes to prescriptions at the interface of primary and secondary care), safety culture\n[38] and a ‘safe systems’ checklist, which encourages primary care practices to seek out loopholes in their established systems.","This review presented challenges due to the broad nature of our question and that there are no criteria for a standardised definition of, or criteria for classifying, a ‘tool’. Tools can be alerts, scoring systems, order sets, dashboards, questionnaires, educational materials, forms or templates to name but a few and, as such, the output of the review is highly heterogeneous. Therefore, we employed pragmatic ways of handling the large amount of data extracted from the review. Our strengths have included using a two reviewer system and a dual extraction process of both numerically coded data and free text summaries of papers, which enabled us to analyse identified instruments in-depth and an extensive exploration of grey literature with a world-wide perspective. Exclusions due to translation costs were minor – only 6 papers out of 280 were excluded on language basis alone. Time-constraints on the project meant we could not use back and forward citation methods systematically due to the sheer number of papers involved in the review.","The main aim of the Tools identified is to measure or report safety issues. While measurement and baselines are a prerequisite to improvement, few of the Tools include an embedded implementation strategy that would address improvement or a quality cycle to alter strategies and measure for change\n[39]. It is difficult to estimate the impact the various measurement tools identified in this review would have in improving patient safety; for instance, prescribing indicators would only seem to measure level of harm at surface value but have been found to change harmful prescribing patterns when combined with educational feedback\n[40]. Moreover, standards and consistency of reporting vary and many studies, for example around culture and climate surveys, do not report reliability, validity, details of their study characteristics and participants etc.\nOthers have advocated the need for outcome measures in patient safety\n[41]. However, measurement systems need to be tested to ensure they measure what is claimed, whether they can reliably tell if deterioration or improvement is occurring and what other (untoward and unintended) consequences could occur\n[8]. This adheres to the wider imperative that measures of quality or safety, and the data collected, adhere to key attributes such as reliability and validity and also address issues such as acceptability, implementation issues and possible unintended consequences\n[42, 43]. The aim of future work will be to test the suitability and acceptability of the proposed measures in the toolkit and to test changes within practices after application of the toolkit; as well as intended and unintended (positive and negative) consequences. Measureable outcomes are only one feature of Safety Management Systems, and as such the toolkit should not rely exclusively on them but also develop other areas such as training, policy, culture and feedback of outcomes data in line with other established models of patient safety\n[9, 43–45]. There is a need also to embrace qualitative methodologies to patient safety such as the Manchester Patient Safety Framework (MaPSaF )\n[46].","We have identified 114 published and unpublished tools and indicators, which can be used currently in primary care to measure patient safety. However, the AMA concluded that there are virtually no credible studies on how to improve safety in primary care\n[6] andthe challenge is still to turn measurement into improvement as few tools have been used in quality improvement cycles or as part of performance targets for safety in ambulatory care. Having a comprehensive set of tools for tracking and preventing safety events is the first step in fixing that, and this paper clearly shows where our current toolkit is wanting. The results of this review will enable a better understanding of the epidemiology of ambulatory care safety and help underpin the future development of primary care based safety interventions.","Dr Rachel Spencer is an academic General Practitioner at the University of Nottingham and Professor Stephen Campbell is a health services researcher at the University of Manchester."," Additional file 1:\nTools for Primary Care Patient Safety; a Systematic Review.\n(DOCX 65 KB)\nAdditional file 1:\nTools for Primary Care Patient Safety; a Systematic Review.\n(DOCX 65 KB)","Additional file 1:\nTools for Primary Care Patient Safety; a Systematic Review.\n(DOCX 65 KB)"],"string":"[\n \"Patient safety has been on the agenda of hospital physicians since the publication of the Institute of Medicine’s 2000 report, ‘To Err is Human’, revealed that more people were dying in the US as a result of medical error than from road traffic accidents\\n[1]. However, most healthcare interactions in the developed world occur in family medicine: 90% of contacts in the England with the National Health Service take place in primary care\\n[2]. In England there are approximately 1 billion community prescriptions dispensed each year\\n[3]. The potential for adverse events is therefore huge but the knowledge base about primary care patient safety is still sparse. A literature review of the nature and frequency of error in primary care suggested that there are between 5–80 safety incidents per 100,000 consultations, which in the UK would translate to between 37–600 incidents per day\\n[4]. Another review estimates that there may be a patient safety incident in approximately 2% of family practice consultations\\n[5].\\nA 2011 report by the American Medical Association on ambulatory patient safety concluded that the introduction of, and research into, patient safety in the primary care environment have lagged behind that of secondary care\\n[6]. Understanding the epidemiology of hospital errors was crucial in developing hospital based safety interventions and the media’s reporting of this data ensured public support for efforts to improve safety\\n[6]. Some of its authors concluded that there needed to be a similar focus on primary care, because there were ‘virtually no credible studies on how to improve safety’\\n[7]. Moreover, a report by the Health Foundation in 2013 emphasised the importance of knowing what methods, tools and indicators are currently being used in primary care to measure patient safety\\n[8]. In this paper, patient safety refers to the ‘avoidance, prevention and amelioration of adverse outcomes or injuries stemming from the process of healthcare’\\n[8].\\nStaff and systems in primary care environments have the potential to contribute to serious error that can cause both morbidity and mortality; which has been demonstrated in the field of prescribing\\n[9]. Evidence on primary care error comes mainly from the statistics of the medical defence organisations and from small pilot studies\\n[10]. And yet, experts we have consulted in the field were anecdotally aware of a multiplicity of interventions or ‘tools’ from their own and others’ work world-wide, which helped identify grey literature for this study.\\nThis paper reports a narrative review of ‘tools’ to improve, measure, and monitor patient safety in the ambulatory settings with a focus on family practice. A narrative review broadly covers a specific topic but does not adhere to strict systematic methods to locate and synthesize articles and enables description and synthesis of qualitative research and categorises studies into more homogenous groups\\n[11]. To the authors’ knowledge no such broad-ranging review has been attempted. The context of this study is worldwide including both the US and the UK and throughout the term primary care is used to address the terms general practice and family practice.\",\n \" Data sources and searches Our structured narrative review was planned and conducted according to guidance in the Preferred Reporting Items for Systematic Reviews and Meta- Analyses (PRISMA) guidelines\\n[12] but following a more narrative approach (especially with regard to grey literature). The starting point for determining the search terms used in the review was a 3 point definition of our search question and exploration of Medical Subject Heading (MeSH) terms\\n[13]. We used a multi-centre team (including leading UK experts on patient safety) at a strategic planning meeting to comment on and finalise the search terms for the review. References were managed in Endnote. Broad ranging search terms were used for developing a search strategy in 3 stems; setting (primary care [i.e. general/family practice, ambulatory care, community care, generalist care], safety synonyms [i.e. error, adverse event, fault, malpractice] and types of tool [i.e. indicator, survey, guideline, scale] (see web Additional file\\n1: Appendix 1). The aim was to be as inclusive as possible and address administrative, clinical and patient experience issues. The search was performed on the following databases; Embase, CINAHL, Pubmed, Medline (Ovid 1996 onward), Health Management Information Consortium and Web of Science on the 1/11/2011. We did not limit our search by year of publication or to the English language, in order to capture a world-wide perspective on patient safety. However, only abstracts in English were included due to resource restraints for translation. Grey literature was identified from known internet patient safety sources from the US and UK to expand the scope of the review (see web Additional file:\\n1 Appendix 6). In order to fully explore a single tool many resources often had to be read – for example the IHI trigger tool is described in a number of web publications and supporting documents. Care was taken not to count published tools that also appeared in grey literature twice by discussion between the two reviewers and wider team.\\nOur structured narrative review was planned and conducted according to guidance in the Preferred Reporting Items for Systematic Reviews and Meta- Analyses (PRISMA) guidelines\\n[12] but following a more narrative approach (especially with regard to grey literature). The starting point for determining the search terms used in the review was a 3 point definition of our search question and exploration of Medical Subject Heading (MeSH) terms\\n[13]. We used a multi-centre team (including leading UK experts on patient safety) at a strategic planning meeting to comment on and finalise the search terms for the review. References were managed in Endnote. Broad ranging search terms were used for developing a search strategy in 3 stems; setting (primary care [i.e. general/family practice, ambulatory care, community care, generalist care], safety synonyms [i.e. error, adverse event, fault, malpractice] and types of tool [i.e. indicator, survey, guideline, scale] (see web Additional file\\n1: Appendix 1). The aim was to be as inclusive as possible and address administrative, clinical and patient experience issues. The search was performed on the following databases; Embase, CINAHL, Pubmed, Medline (Ovid 1996 onward), Health Management Information Consortium and Web of Science on the 1/11/2011. We did not limit our search by year of publication or to the English language, in order to capture a world-wide perspective on patient safety. However, only abstracts in English were included due to resource restraints for translation. Grey literature was identified from known internet patient safety sources from the US and UK to expand the scope of the review (see web Additional file:\\n1 Appendix 6). In order to fully explore a single tool many resources often had to be read – for example the IHI trigger tool is described in a number of web publications and supporting documents. Care was taken not to count published tools that also appeared in grey literature twice by discussion between the two reviewers and wider team.\\n Study selection Reviewer one was a GP Academic Clinical Fellow with an interest in pharmacology (RS) and reviewer two was a health services researcher with an interest in family practice (SC). Similarly to the strategy used in the AMA’s report\\n[6], we were interested in highly generalisable tools so research addressing single drugs or conditions in very specific settings was excluded. Inclusion and exclusion criteria are listed in below. An inclusive strategy was employed such that neither reviewer could exclude studies the other felt were potentially relevant. Disagreements were resolved by regular discussions between the 2 reviewers. The inclusion and exclusion criteria were as follows:\\nExclusion criteria:hospital care/setting - unless transferableopinion pieces/editorialssingle drugs/conditions where the focus was felt to be on the specific drug or condition rather than on transferable toolsonly about quality of care without explicit patient safety componentexclude on basis of journal (for example “Health Care Food & Nutrition focus”)economic impact of errors (relevant papers were taken out at this stage for other purposes within the project)Both the abstract and main text were not in EnglishInclusion criteria:if unsure always include - for example, ‘good advice’ which might later inform other toolstools or strategies to improve or analyse safety which are of relevance to Primary Care.\\nExclusion criteria:hospital care/setting - unless transferableopinion pieces/editorialssingle drugs/conditions where the focus was felt to be on the specific drug or condition rather than on transferable toolsonly about quality of care without explicit patient safety componentexclude on basis of journal (for example “Health Care Food & Nutrition focus”)economic impact of errors (relevant papers were taken out at this stage for other purposes within the project)Both the abstract and main text were not in English\\nhospital care/setting - unless transferable\\nopinion pieces/editorials\\nsingle drugs/conditions where the focus was felt to be on the specific drug or condition rather than on transferable tools\\nonly about quality of care without explicit patient safety component\\nexclude on basis of journal (for example “Health Care Food & Nutrition focus”)\\neconomic impact of errors (relevant papers were taken out at this stage for other purposes within the project)\\nBoth the abstract and main text were not in English\\nInclusion criteria:if unsure always include - for example, ‘good advice’ which might later inform other toolstools or strategies to improve or analyse safety which are of relevance to Primary Care.\\nif unsure always include - for example, ‘good advice’ which might later inform other tools\\ntools or strategies to improve or analyse safety which are of relevance to Primary Care.\\nReviewer one was a GP Academic Clinical Fellow with an interest in pharmacology (RS) and reviewer two was a health services researcher with an interest in family practice (SC). Similarly to the strategy used in the AMA’s report\\n[6], we were interested in highly generalisable tools so research addressing single drugs or conditions in very specific settings was excluded. Inclusion and exclusion criteria are listed in below. An inclusive strategy was employed such that neither reviewer could exclude studies the other felt were potentially relevant. Disagreements were resolved by regular discussions between the 2 reviewers. The inclusion and exclusion criteria were as follows:\\nExclusion criteria:hospital care/setting - unless transferableopinion pieces/editorialssingle drugs/conditions where the focus was felt to be on the specific drug or condition rather than on transferable toolsonly about quality of care without explicit patient safety componentexclude on basis of journal (for example “Health Care Food & Nutrition focus”)economic impact of errors (relevant papers were taken out at this stage for other purposes within the project)Both the abstract and main text were not in EnglishInclusion criteria:if unsure always include - for example, ‘good advice’ which might later inform other toolstools or strategies to improve or analyse safety which are of relevance to Primary Care.\\nExclusion criteria:hospital care/setting - unless transferableopinion pieces/editorialssingle drugs/conditions where the focus was felt to be on the specific drug or condition rather than on transferable toolsonly about quality of care without explicit patient safety componentexclude on basis of journal (for example “Health Care Food & Nutrition focus”)economic impact of errors (relevant papers were taken out at this stage for other purposes within the project)Both the abstract and main text were not in English\\nhospital care/setting - unless transferable\\nopinion pieces/editorials\\nsingle drugs/conditions where the focus was felt to be on the specific drug or condition rather than on transferable tools\\nonly about quality of care without explicit patient safety component\\nexclude on basis of journal (for example “Health Care Food & Nutrition focus”)\\neconomic impact of errors (relevant papers were taken out at this stage for other purposes within the project)\\nBoth the abstract and main text were not in English\\nInclusion criteria:if unsure always include - for example, ‘good advice’ which might later inform other toolstools or strategies to improve or analyse safety which are of relevance to Primary Care.\\nif unsure always include - for example, ‘good advice’ which might later inform other tools\\ntools or strategies to improve or analyse safety which are of relevance to Primary Care.\\n Data quality and extraction Data were extracted independently by the two reviewers (RS and SC). A dual approach was taken to data extraction from published material using both a Word document (Web Additional file:\\n1 Appendix 5) and an Excel template. For reporting data from selected papers we used a modified PRISMA\\n[12] checklist, which combined aspects of different methodologies (not just systematic reviews and meta-analyses) into a form for all study types (available on application to the authors). For example the PRISMA checklist requires a discussion of limitations which is a highly transferable requirement to all methodologies, but it also requires specific methods based items such as ‘confidence intervals on meta-analyses’. Using information collected on the modified PRISMA form, a numerical data extraction system was agreed by both reviewers in order to present results from the selected papers data for analysis were extracted from that Excel document. A pilot of 10 key papers with differing methodologies was undertaken prior to commencing full data extraction – high levels of agreement were found between the two reviewers. At the end of data extraction differences in rating on the Excel sheet were discussed and analysed across a series of face-to-face and telephone meetings, attempts to reach consensus were almost always possible.\\nData were extracted independently by the two reviewers (RS and SC). A dual approach was taken to data extraction from published material using both a Word document (Web Additional file:\\n1 Appendix 5) and an Excel template. For reporting data from selected papers we used a modified PRISMA\\n[12] checklist, which combined aspects of different methodologies (not just systematic reviews and meta-analyses) into a form for all study types (available on application to the authors). For example the PRISMA checklist requires a discussion of limitations which is a highly transferable requirement to all methodologies, but it also requires specific methods based items such as ‘confidence intervals on meta-analyses’. Using information collected on the modified PRISMA form, a numerical data extraction system was agreed by both reviewers in order to present results from the selected papers data for analysis were extracted from that Excel document. A pilot of 10 key papers with differing methodologies was undertaken prior to commencing full data extraction – high levels of agreement were found between the two reviewers. At the end of data extraction differences in rating on the Excel sheet were discussed and analysed across a series of face-to-face and telephone meetings, attempts to reach consensus were almost always possible.\\n Funding This review is part of a National Institute for Health Research (NIHR), School for Primary Care Research (SPCR: http://www.spcr.nihr.ac.uk/) (UK) project, undertaken with the aim of constructing a Patient Safety Toolkit for English family practice.\\nThis review is part of a National Institute for Health Research (NIHR), School for Primary Care Research (SPCR: http://www.spcr.nihr.ac.uk/) (UK) project, undertaken with the aim of constructing a Patient Safety Toolkit for English family practice.\",\n \"Our structured narrative review was planned and conducted according to guidance in the Preferred Reporting Items for Systematic Reviews and Meta- Analyses (PRISMA) guidelines\\n[12] but following a more narrative approach (especially with regard to grey literature). The starting point for determining the search terms used in the review was a 3 point definition of our search question and exploration of Medical Subject Heading (MeSH) terms\\n[13]. We used a multi-centre team (including leading UK experts on patient safety) at a strategic planning meeting to comment on and finalise the search terms for the review. References were managed in Endnote. Broad ranging search terms were used for developing a search strategy in 3 stems; setting (primary care [i.e. general/family practice, ambulatory care, community care, generalist care], safety synonyms [i.e. error, adverse event, fault, malpractice] and types of tool [i.e. indicator, survey, guideline, scale] (see web Additional file\\n1: Appendix 1). The aim was to be as inclusive as possible and address administrative, clinical and patient experience issues. The search was performed on the following databases; Embase, CINAHL, Pubmed, Medline (Ovid 1996 onward), Health Management Information Consortium and Web of Science on the 1/11/2011. We did not limit our search by year of publication or to the English language, in order to capture a world-wide perspective on patient safety. However, only abstracts in English were included due to resource restraints for translation. Grey literature was identified from known internet patient safety sources from the US and UK to expand the scope of the review (see web Additional file:\\n1 Appendix 6). In order to fully explore a single tool many resources often had to be read – for example the IHI trigger tool is described in a number of web publications and supporting documents. Care was taken not to count published tools that also appeared in grey literature twice by discussion between the two reviewers and wider team.\",\n \"Reviewer one was a GP Academic Clinical Fellow with an interest in pharmacology (RS) and reviewer two was a health services researcher with an interest in family practice (SC). Similarly to the strategy used in the AMA’s report\\n[6], we were interested in highly generalisable tools so research addressing single drugs or conditions in very specific settings was excluded. Inclusion and exclusion criteria are listed in below. An inclusive strategy was employed such that neither reviewer could exclude studies the other felt were potentially relevant. Disagreements were resolved by regular discussions between the 2 reviewers. The inclusion and exclusion criteria were as follows:\\nExclusion criteria:hospital care/setting - unless transferableopinion pieces/editorialssingle drugs/conditions where the focus was felt to be on the specific drug or condition rather than on transferable toolsonly about quality of care without explicit patient safety componentexclude on basis of journal (for example “Health Care Food & Nutrition focus”)economic impact of errors (relevant papers were taken out at this stage for other purposes within the project)Both the abstract and main text were not in EnglishInclusion criteria:if unsure always include - for example, ‘good advice’ which might later inform other toolstools or strategies to improve or analyse safety which are of relevance to Primary Care.\\nExclusion criteria:hospital care/setting - unless transferableopinion pieces/editorialssingle drugs/conditions where the focus was felt to be on the specific drug or condition rather than on transferable toolsonly about quality of care without explicit patient safety componentexclude on basis of journal (for example “Health Care Food & Nutrition focus”)economic impact of errors (relevant papers were taken out at this stage for other purposes within the project)Both the abstract and main text were not in English\\nhospital care/setting - unless transferable\\nopinion pieces/editorials\\nsingle drugs/conditions where the focus was felt to be on the specific drug or condition rather than on transferable tools\\nonly about quality of care without explicit patient safety component\\nexclude on basis of journal (for example “Health Care Food & Nutrition focus”)\\neconomic impact of errors (relevant papers were taken out at this stage for other purposes within the project)\\nBoth the abstract and main text were not in English\\nInclusion criteria:if unsure always include - for example, ‘good advice’ which might later inform other toolstools or strategies to improve or analyse safety which are of relevance to Primary Care.\\nif unsure always include - for example, ‘good advice’ which might later inform other tools\\ntools or strategies to improve or analyse safety which are of relevance to Primary Care.\",\n \"Data were extracted independently by the two reviewers (RS and SC). A dual approach was taken to data extraction from published material using both a Word document (Web Additional file:\\n1 Appendix 5) and an Excel template. For reporting data from selected papers we used a modified PRISMA\\n[12] checklist, which combined aspects of different methodologies (not just systematic reviews and meta-analyses) into a form for all study types (available on application to the authors). For example the PRISMA checklist requires a discussion of limitations which is a highly transferable requirement to all methodologies, but it also requires specific methods based items such as ‘confidence intervals on meta-analyses’. Using information collected on the modified PRISMA form, a numerical data extraction system was agreed by both reviewers in order to present results from the selected papers data for analysis were extracted from that Excel document. A pilot of 10 key papers with differing methodologies was undertaken prior to commencing full data extraction – high levels of agreement were found between the two reviewers. At the end of data extraction differences in rating on the Excel sheet were discussed and analysed across a series of face-to-face and telephone meetings, attempts to reach consensus were almost always possible.\",\n \"This review is part of a National Institute for Health Research (NIHR), School for Primary Care Research (SPCR: http://www.spcr.nihr.ac.uk/) (UK) project, undertaken with the aim of constructing a Patient Safety Toolkit for English family practice.\",\n \"Grey literature results are not included in the following calculations and flow diagrams; results are instead included in the list of tools found in web Additional file\\n1: Appendix 3 (where they are clearly marked as being from grey sources). Using the process described in Figure \\n1, we selected approximately 10% (n = 1311) of the original search total (n = 13,240) for evaluation of abstracts; titles excluded at this stage were clearly not of relevance e.g. relating to non-healthcare safety topics. Abstracts were then analysed for tools, after excluding papers which were from the correct setting but which did not contain any interventions; around 14% of the abstracts were included for full paper analysis (n = 189).Figure 1\\n‘Toolkit’ review stages. Graph 1 –a) illustration of the literature base in primary care patient safety 1987-2011from Pubmed b) Papers from the review divided by the annual Pubmed output for the same year.\\n\\n‘Toolkit’ review stages. Graph 1 –a) illustration of the literature base in primary care patient safety 1987-2011from Pubmed b) Papers from the review divided by the annual Pubmed output for the same year.\\nAs Graph 1a) illustrates, the majority of publications in the review have been published in the last decade. These data could be a product of MeSH term development or consistency in the last 10 years. However, as Graph 1b) shows, the same take-off in 2001 occurs even when presented as a proportion of the total literature published on Pubmed. Analysing the MeSH terms of the 11 papers in the review from prior to 2000 revealed that the terms ‘medical error’ and ‘diagnostic error’ were each used twice and ‘risk management’ was used in 3 papers. Keywords using the term ‘drug’ appeared 13 times, family practice and ambulatory care were commonly used keywords.\\nThe majority of the literature focused on prescribing (55%), which excludes IT interventions for prescribing that were attributed to the informatics theme instead. Other prominent areas were adverse events in primary care and safety climate (which comprised 8% of the total published literature each). A number of climate measures had been refined from earlier climate surveys for use specifically in primary care (i.e. SAQ (ambulatory)\\n[14]. Minor themes included; informatics (4.5%) patient role (3%) and general measures to correct error (5%). The primary/secondary care interface is well described in the literature (5%) but, as yet, only 1 published interface tool specifically for primary care exists. Diagnostic error and results handling appear infrequently (<1% of total literature) despite their relative importance. The remainder of literature (11%) related to referrals, Out-Of-Hours (OOH) care, telephone care, organisational issues, mortality and clerical error. Overall, 114 Tools were identified with 26 accrued from grey literature.\\nThe setting of the research uncovered was predominantly family practice (in keeping with our search strategy); the term ‘health system’ was used to describe research such as consensus outputs from multi-disciplinary teams or across the whole healthcare system. Most published literature was US based (41%) followed by UK located studies (23%), with other countries producing no more than 5% of published papers each. The grey literature also reflects the predominance of US and UK sources. A variety of study designs were identified in the review including, for example, consensus techniques (10%) , observational methods (15%) but most were mixed methods studies in patient safety research.\\nWe classified the data from the published papers in this review using a taxonomy for primary care patient safety based on previous taxonomies by experts in quality of care and patient safety\\n[15–19]. It differs from our data collection form as it was evolved later in the process of our analyses. In our taxonomy (Web Additional file\\n1: Appendix 4) there are two principal dimensions of safety: ‘access to safe services’ and ‘effectiveness of safety processes’, which are discussed in terms of the structure of the health care system, processes of safety and health outcomes. This taxonomy is based on previous conceptual work on quality of care\\n[15]; in essence, do users get the safe care they need, and is the care safe when they get it?\\nIn our review, 88 of the 114 unique tools identified (Web Additional file\\n1: Appendix 3) came from the published literature and 26 from grey literature sources – the majority of these being from the websites of known patient safety organisations (see data sources in Methods section). The review identified a wide range of ‘tools’ that cannot be described fully here due to word count constraints. The detailed output from the review will be described across a series of subsequent papers on ambulatory patient safety. However, Table \\n1 shows key examples found in each dimension of patient safety: We have presented the most well-known or most often used US or UK tools as illustrative examples. The Table is presented in order of weight of literature with most common topics appearing first.Table 1\\nTypes of tools found in the review, where possible well-known US examples of the type of tool are given in order to aid understanding\\nType of tool (Explanation of tool)Used in the US?Used in the UK?US ExampleData sourceNumber of primary care tools of this type identifiedPrescribing Indicator PacksYesYesBeers criteria\\n[20]EHR15 main sets, much overlap -3(criteria for ‘never events’ in prescribing) - other prescribing toolsGRAM reports\\n[21], MAI\\n[22] (Geriatric Risk Assessment MedGuide™ Medications Appropriateness Index)EHR, staffTrigger Tools-GeneralYesYesIHI Outpatient AdverseEHR5-MedicationsYesNoEvent Trigger Tool\\n[23]3-SurgeryYesYesAdverse drug events among older adults in primary care\\n[24] Ambulatory surgery\\n[25]1\\n(Criteria are screened for in a sample of medical records ‘triggering’ more detailed review)\\nEvent Reporting Systems (National systems for informing relevant authorities about safety problems with all aspects of healthcare)YesYesASIPS\\n[26] (Applied Strategies for Improving Patient Safety)EHR, Staff and patients6Medicines/device Reporting SystemsYesYesMEADERS\\n[27]EHR, Staff and Patients4(National systems for informing relevant authorities about safety problems specific to the above)(Medication Error and Adverse Drug Event Reporting System) VAERS\\n[28] (Vaccine Adverse Events Reporting System)Safety Climate/Culture Measures (The practice team rate themselves against safety criteria and discuss the results to make changes)YesYesSafety Attitudes Questionnaire\\n[14]Staff10Significant Event Analysis Tools (The practice team discuss untoward events, using a standardised structure, in order to learn from them)NoYesUK example - NHS Education for Scotland\\n[29]Staff, EHR and patients5General Primary/Secondary Interface Tools (standardised systems for handling patient care at transition in care level – often electronic discharge summaries)YesYes‘Care Transitions Approach’\\n[30]EHR, hospital recordsOnly 3 within the direct control of family doctorsMedication Reconciliation Tools (aligning medication histories after secondary care contact)YesNo formal tool usedPartner’s Post Discharge Tool\\n[31]EHR, hospital records3PROMs for safety (questionnaire determining the patient perspective of safety in their practice)YesYesSEAPS\\n[32] (Seniors Empowerment and Advocacy in Patient Safety)Patients8Other Patient Involvement Measures (variety of tools including literature for patients, computerised systems and medications specific tools)YesYes‘Speak-Up’ from JCAHO\\n[33]Patients4IT MeasuresYesYesSEMI-P\\n[34]EHR11(not just CDSS but a variety of measures often tackling systems error, many relate to prescribing safety)(Safety Enhancement and Monitoring Instrument that is Patient centred)Diagnostic Tools (Mainly CDSS designed to improve diagnosis)YesNoDxPlain\\n[35]EHR3\\nAbbreviations:\\nCDSS Computer Decision Support Software.\\nEHR Electronic Health Record.\\nPROM Patient reported outcome measures.\\nUK United Kingsdom.\\nUS United States.\\n\\nTypes of tools found in the review, where possible well-known US examples of the type of tool are given in order to aid understanding\\n\\n\\nAbbreviations:\\n\\nCDSS Computer Decision Support Software.\\n\\nEHR Electronic Health Record.\\n\\nPROM Patient reported outcome measures.\\n\\nUK United Kingsdom.\\n\\nUS United States.\\n Summary of main findings We have demonstrated that there has been an upsurge in publications on primary care patient safety since 2001 and that most of the literature comes from the USA and the UK, with the pre-eminent topic being prescribing safety. The list of discrete tools (which includes grey literature) has a much more even spread across the dimensions within our conceptual taxonomy (Web Appendices 3 and 4). Using this taxonomy shows that some areas of patient safety are relatively neglected in the published literature on primary care patient safety tools; for example, diagnostic error. Tools for test results and referrals are also poorly represented; there were 5 descriptive papers in total, one un-validated tool for electronic referrals and one indicator set dealing with referrals from OOH care. No tools for investigations management were found.\\nWe have demonstrated that there has been an upsurge in publications on primary care patient safety since 2001 and that most of the literature comes from the USA and the UK, with the pre-eminent topic being prescribing safety. The list of discrete tools (which includes grey literature) has a much more even spread across the dimensions within our conceptual taxonomy (Web Appendices 3 and 4). Using this taxonomy shows that some areas of patient safety are relatively neglected in the published literature on primary care patient safety tools; for example, diagnostic error. Tools for test results and referrals are also poorly represented; there were 5 descriptive papers in total, one un-validated tool for electronic referrals and one indicator set dealing with referrals from OOH care. No tools for investigations management were found.\\n Comparison to existing literature To the authors’ knowledge no similar review has been undertaken to look specifically at instruments for measuring patient safety in primary care. The AMA report on ambulatory patient safety\\n[6] found that the number of reported interventions in primary care is low but their search strategy did not take a worldwide approach and only focused on interventions that reduce error or harm. We designed a more inclusive search strategy to capture measurement tools and strategies and were therefore able to find a wider body of literature. The focus on measurement tools and strategies reflects the importance of knowing what is being used currently in primary care to measure patient safety\\n[8]. Many of the 114 tools found are iterations of tools constructed previously and re-designed for other countries. For example, the UK NHS Institute for Innovation and Improvement Primary Care Trigger Tool\\n[36] has much in common with the IHI Outpatient Adverse Event Trigger Tool\\n[23].\\nOur study resonates with the view that there can be no one single measure of patient safety\\n[8]. Rather, a framework for patient safety should include for example; past harm, reliability, ‘sensitivity to operations’, anticipation/preparedness and Integration/learning. Vincent C, 2013\\n[8] Measures of past harm are prevalent among the tools we found i.e. – adverse event reporting systems. Some tools clearly straddle boundaries within the definition: Significant Event Analyses (SEA) (a technique commonly used in the UK), for example, straddles past events, future anticipation of similar situations and learning in relation to the significant event. Few tools address safety reliability in primary care; practices may set their own standards for audit of patient safety but as yet no formal targets exist for primary care in the UK or US (in direct contrast with hospital mortality data and target dashboards such as HEDIS - http://www.ncqa.org/HEDISQualityMeasurement.aspx). Sensitivity to operations is an umbrella term referring to the information and capacity in clinical systems to monitor safety on an hourly or daily basis, climate measures often address questions to staff in relation to their adaptability to change but there are no other measures of this dynamic in primary care. The work of the defence organisations in advising practices about risks and loop-holes in operating systems comes close to fulfilling this goal and roughly equates to a safety ‘walk-round’\\n[37]. The challenge to any ‘toolkit’ is to incorporate prospective measures that prevent and anticipate error. The major elements of the toolkit that address prevention are; trigger tools\\n[23–25, 36] (potential rather than actual harm), medicines reconciliation packages\\n[30] (prevent harm from changes to prescriptions at the interface of primary and secondary care), safety culture\\n[38] and a ‘safe systems’ checklist, which encourages primary care practices to seek out loopholes in their established systems.\\nTo the authors’ knowledge no similar review has been undertaken to look specifically at instruments for measuring patient safety in primary care. The AMA report on ambulatory patient safety\\n[6] found that the number of reported interventions in primary care is low but their search strategy did not take a worldwide approach and only focused on interventions that reduce error or harm. We designed a more inclusive search strategy to capture measurement tools and strategies and were therefore able to find a wider body of literature. The focus on measurement tools and strategies reflects the importance of knowing what is being used currently in primary care to measure patient safety\\n[8]. Many of the 114 tools found are iterations of tools constructed previously and re-designed for other countries. For example, the UK NHS Institute for Innovation and Improvement Primary Care Trigger Tool\\n[36] has much in common with the IHI Outpatient Adverse Event Trigger Tool\\n[23].\\nOur study resonates with the view that there can be no one single measure of patient safety\\n[8]. Rather, a framework for patient safety should include for example; past harm, reliability, ‘sensitivity to operations’, anticipation/preparedness and Integration/learning. Vincent C, 2013\\n[8] Measures of past harm are prevalent among the tools we found i.e. – adverse event reporting systems. Some tools clearly straddle boundaries within the definition: Significant Event Analyses (SEA) (a technique commonly used in the UK), for example, straddles past events, future anticipation of similar situations and learning in relation to the significant event. Few tools address safety reliability in primary care; practices may set their own standards for audit of patient safety but as yet no formal targets exist for primary care in the UK or US (in direct contrast with hospital mortality data and target dashboards such as HEDIS - http://www.ncqa.org/HEDISQualityMeasurement.aspx). Sensitivity to operations is an umbrella term referring to the information and capacity in clinical systems to monitor safety on an hourly or daily basis, climate measures often address questions to staff in relation to their adaptability to change but there are no other measures of this dynamic in primary care. The work of the defence organisations in advising practices about risks and loop-holes in operating systems comes close to fulfilling this goal and roughly equates to a safety ‘walk-round’\\n[37]. The challenge to any ‘toolkit’ is to incorporate prospective measures that prevent and anticipate error. The major elements of the toolkit that address prevention are; trigger tools\\n[23–25, 36] (potential rather than actual harm), medicines reconciliation packages\\n[30] (prevent harm from changes to prescriptions at the interface of primary and secondary care), safety culture\\n[38] and a ‘safe systems’ checklist, which encourages primary care practices to seek out loopholes in their established systems.\\n Strengths/limitations This review presented challenges due to the broad nature of our question and that there are no criteria for a standardised definition of, or criteria for classifying, a ‘tool’. Tools can be alerts, scoring systems, order sets, dashboards, questionnaires, educational materials, forms or templates to name but a few and, as such, the output of the review is highly heterogeneous. Therefore, we employed pragmatic ways of handling the large amount of data extracted from the review. Our strengths have included using a two reviewer system and a dual extraction process of both numerically coded data and free text summaries of papers, which enabled us to analyse identified instruments in-depth and an extensive exploration of grey literature with a world-wide perspective. Exclusions due to translation costs were minor – only 6 papers out of 280 were excluded on language basis alone. Time-constraints on the project meant we could not use back and forward citation methods systematically due to the sheer number of papers involved in the review.\\nThis review presented challenges due to the broad nature of our question and that there are no criteria for a standardised definition of, or criteria for classifying, a ‘tool’. Tools can be alerts, scoring systems, order sets, dashboards, questionnaires, educational materials, forms or templates to name but a few and, as such, the output of the review is highly heterogeneous. Therefore, we employed pragmatic ways of handling the large amount of data extracted from the review. Our strengths have included using a two reviewer system and a dual extraction process of both numerically coded data and free text summaries of papers, which enabled us to analyse identified instruments in-depth and an extensive exploration of grey literature with a world-wide perspective. Exclusions due to translation costs were minor – only 6 papers out of 280 were excluded on language basis alone. Time-constraints on the project meant we could not use back and forward citation methods systematically due to the sheer number of papers involved in the review.\\n Implications for practice, policy, or future research The main aim of the Tools identified is to measure or report safety issues. While measurement and baselines are a prerequisite to improvement, few of the Tools include an embedded implementation strategy that would address improvement or a quality cycle to alter strategies and measure for change\\n[39]. It is difficult to estimate the impact the various measurement tools identified in this review would have in improving patient safety; for instance, prescribing indicators would only seem to measure level of harm at surface value but have been found to change harmful prescribing patterns when combined with educational feedback\\n[40]. Moreover, standards and consistency of reporting vary and many studies, for example around culture and climate surveys, do not report reliability, validity, details of their study characteristics and participants etc.\\nOthers have advocated the need for outcome measures in patient safety\\n[41]. However, measurement systems need to be tested to ensure they measure what is claimed, whether they can reliably tell if deterioration or improvement is occurring and what other (untoward and unintended) consequences could occur\\n[8]. This adheres to the wider imperative that measures of quality or safety, and the data collected, adhere to key attributes such as reliability and validity and also address issues such as acceptability, implementation issues and possible unintended consequences\\n[42, 43]. The aim of future work will be to test the suitability and acceptability of the proposed measures in the toolkit and to test changes within practices after application of the toolkit; as well as intended and unintended (positive and negative) consequences. Measureable outcomes are only one feature of Safety Management Systems, and as such the toolkit should not rely exclusively on them but also develop other areas such as training, policy, culture and feedback of outcomes data in line with other established models of patient safety\\n[9, 43–45]. There is a need also to embrace qualitative methodologies to patient safety such as the Manchester Patient Safety Framework (MaPSaF )\\n[46].\\nThe main aim of the Tools identified is to measure or report safety issues. While measurement and baselines are a prerequisite to improvement, few of the Tools include an embedded implementation strategy that would address improvement or a quality cycle to alter strategies and measure for change\\n[39]. It is difficult to estimate the impact the various measurement tools identified in this review would have in improving patient safety; for instance, prescribing indicators would only seem to measure level of harm at surface value but have been found to change harmful prescribing patterns when combined with educational feedback\\n[40]. Moreover, standards and consistency of reporting vary and many studies, for example around culture and climate surveys, do not report reliability, validity, details of their study characteristics and participants etc.\\nOthers have advocated the need for outcome measures in patient safety\\n[41]. However, measurement systems need to be tested to ensure they measure what is claimed, whether they can reliably tell if deterioration or improvement is occurring and what other (untoward and unintended) consequences could occur\\n[8]. This adheres to the wider imperative that measures of quality or safety, and the data collected, adhere to key attributes such as reliability and validity and also address issues such as acceptability, implementation issues and possible unintended consequences\\n[42, 43]. The aim of future work will be to test the suitability and acceptability of the proposed measures in the toolkit and to test changes within practices after application of the toolkit; as well as intended and unintended (positive and negative) consequences. Measureable outcomes are only one feature of Safety Management Systems, and as such the toolkit should not rely exclusively on them but also develop other areas such as training, policy, culture and feedback of outcomes data in line with other established models of patient safety\\n[9, 43–45]. There is a need also to embrace qualitative methodologies to patient safety such as the Manchester Patient Safety Framework (MaPSaF )\\n[46].\",\n \"We have demonstrated that there has been an upsurge in publications on primary care patient safety since 2001 and that most of the literature comes from the USA and the UK, with the pre-eminent topic being prescribing safety. The list of discrete tools (which includes grey literature) has a much more even spread across the dimensions within our conceptual taxonomy (Web Appendices 3 and 4). Using this taxonomy shows that some areas of patient safety are relatively neglected in the published literature on primary care patient safety tools; for example, diagnostic error. Tools for test results and referrals are also poorly represented; there were 5 descriptive papers in total, one un-validated tool for electronic referrals and one indicator set dealing with referrals from OOH care. No tools for investigations management were found.\",\n \"To the authors’ knowledge no similar review has been undertaken to look specifically at instruments for measuring patient safety in primary care. The AMA report on ambulatory patient safety\\n[6] found that the number of reported interventions in primary care is low but their search strategy did not take a worldwide approach and only focused on interventions that reduce error or harm. We designed a more inclusive search strategy to capture measurement tools and strategies and were therefore able to find a wider body of literature. The focus on measurement tools and strategies reflects the importance of knowing what is being used currently in primary care to measure patient safety\\n[8]. Many of the 114 tools found are iterations of tools constructed previously and re-designed for other countries. For example, the UK NHS Institute for Innovation and Improvement Primary Care Trigger Tool\\n[36] has much in common with the IHI Outpatient Adverse Event Trigger Tool\\n[23].\\nOur study resonates with the view that there can be no one single measure of patient safety\\n[8]. Rather, a framework for patient safety should include for example; past harm, reliability, ‘sensitivity to operations’, anticipation/preparedness and Integration/learning. Vincent C, 2013\\n[8] Measures of past harm are prevalent among the tools we found i.e. – adverse event reporting systems. Some tools clearly straddle boundaries within the definition: Significant Event Analyses (SEA) (a technique commonly used in the UK), for example, straddles past events, future anticipation of similar situations and learning in relation to the significant event. Few tools address safety reliability in primary care; practices may set their own standards for audit of patient safety but as yet no formal targets exist for primary care in the UK or US (in direct contrast with hospital mortality data and target dashboards such as HEDIS - http://www.ncqa.org/HEDISQualityMeasurement.aspx). Sensitivity to operations is an umbrella term referring to the information and capacity in clinical systems to monitor safety on an hourly or daily basis, climate measures often address questions to staff in relation to their adaptability to change but there are no other measures of this dynamic in primary care. The work of the defence organisations in advising practices about risks and loop-holes in operating systems comes close to fulfilling this goal and roughly equates to a safety ‘walk-round’\\n[37]. The challenge to any ‘toolkit’ is to incorporate prospective measures that prevent and anticipate error. The major elements of the toolkit that address prevention are; trigger tools\\n[23–25, 36] (potential rather than actual harm), medicines reconciliation packages\\n[30] (prevent harm from changes to prescriptions at the interface of primary and secondary care), safety culture\\n[38] and a ‘safe systems’ checklist, which encourages primary care practices to seek out loopholes in their established systems.\",\n \"This review presented challenges due to the broad nature of our question and that there are no criteria for a standardised definition of, or criteria for classifying, a ‘tool’. Tools can be alerts, scoring systems, order sets, dashboards, questionnaires, educational materials, forms or templates to name but a few and, as such, the output of the review is highly heterogeneous. Therefore, we employed pragmatic ways of handling the large amount of data extracted from the review. Our strengths have included using a two reviewer system and a dual extraction process of both numerically coded data and free text summaries of papers, which enabled us to analyse identified instruments in-depth and an extensive exploration of grey literature with a world-wide perspective. Exclusions due to translation costs were minor – only 6 papers out of 280 were excluded on language basis alone. Time-constraints on the project meant we could not use back and forward citation methods systematically due to the sheer number of papers involved in the review.\",\n \"The main aim of the Tools identified is to measure or report safety issues. While measurement and baselines are a prerequisite to improvement, few of the Tools include an embedded implementation strategy that would address improvement or a quality cycle to alter strategies and measure for change\\n[39]. It is difficult to estimate the impact the various measurement tools identified in this review would have in improving patient safety; for instance, prescribing indicators would only seem to measure level of harm at surface value but have been found to change harmful prescribing patterns when combined with educational feedback\\n[40]. Moreover, standards and consistency of reporting vary and many studies, for example around culture and climate surveys, do not report reliability, validity, details of their study characteristics and participants etc.\\nOthers have advocated the need for outcome measures in patient safety\\n[41]. However, measurement systems need to be tested to ensure they measure what is claimed, whether they can reliably tell if deterioration or improvement is occurring and what other (untoward and unintended) consequences could occur\\n[8]. This adheres to the wider imperative that measures of quality or safety, and the data collected, adhere to key attributes such as reliability and validity and also address issues such as acceptability, implementation issues and possible unintended consequences\\n[42, 43]. The aim of future work will be to test the suitability and acceptability of the proposed measures in the toolkit and to test changes within practices after application of the toolkit; as well as intended and unintended (positive and negative) consequences. Measureable outcomes are only one feature of Safety Management Systems, and as such the toolkit should not rely exclusively on them but also develop other areas such as training, policy, culture and feedback of outcomes data in line with other established models of patient safety\\n[9, 43–45]. There is a need also to embrace qualitative methodologies to patient safety such as the Manchester Patient Safety Framework (MaPSaF )\\n[46].\",\n \"We have identified 114 published and unpublished tools and indicators, which can be used currently in primary care to measure patient safety. However, the AMA concluded that there are virtually no credible studies on how to improve safety in primary care\\n[6] andthe challenge is still to turn measurement into improvement as few tools have been used in quality improvement cycles or as part of performance targets for safety in ambulatory care. Having a comprehensive set of tools for tracking and preventing safety events is the first step in fixing that, and this paper clearly shows where our current toolkit is wanting. The results of this review will enable a better understanding of the epidemiology of ambulatory care safety and help underpin the future development of primary care based safety interventions.\",\n \"Dr Rachel Spencer is an academic General Practitioner at the University of Nottingham and Professor Stephen Campbell is a health services researcher at the University of Manchester.\",\n \" Additional file 1:\\nTools for Primary Care Patient Safety; a Systematic Review.\\n(DOCX 65 KB)\\nAdditional file 1:\\nTools for Primary Care Patient Safety; a Systematic Review.\\n(DOCX 65 KB)\",\n \"Additional file 1:\\nTools for Primary Care Patient Safety; a Systematic Review.\\n(DOCX 65 KB)\"\n]"},"all_sections_sec_types":{"kind":"list like","value":[null,"methods",null,null,null,null,null,null,null,null,null,"conclusions",null,"supplementary-material",null],"string":"[\n null,\n \"methods\",\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n \"conclusions\",\n null,\n \"supplementary-material\",\n null\n]"},"keywords":{"kind":"list like","value":["Family practice","Patient safety","Review"],"string":"[\n \"Family practice\",\n \"Patient safety\",\n \"Review\"\n]"},"whole_article_text":{"kind":"string","value":"Background:\nPatient safety has been on the agenda of hospital physicians since the publication of the Institute of Medicine’s 2000 report, ‘To Err is Human’, revealed that more people were dying in the US as a result of medical error than from road traffic accidents\n[1]. However, most healthcare interactions in the developed world occur in family medicine: 90% of contacts in the England with the National Health Service take place in primary care\n[2]. In England there are approximately 1 billion community prescriptions dispensed each year\n[3]. The potential for adverse events is therefore huge but the knowledge base about primary care patient safety is still sparse. A literature review of the nature and frequency of error in primary care suggested that there are between 5–80 safety incidents per 100,000 consultations, which in the UK would translate to between 37–600 incidents per day\n[4]. Another review estimates that there may be a patient safety incident in approximately 2% of family practice consultations\n[5].\nA 2011 report by the American Medical Association on ambulatory patient safety concluded that the introduction of, and research into, patient safety in the primary care environment have lagged behind that of secondary care\n[6]. Understanding the epidemiology of hospital errors was crucial in developing hospital based safety interventions and the media’s reporting of this data ensured public support for efforts to improve safety\n[6]. Some of its authors concluded that there needed to be a similar focus on primary care, because there were ‘virtually no credible studies on how to improve safety’\n[7]. Moreover, a report by the Health Foundation in 2013 emphasised the importance of knowing what methods, tools and indicators are currently being used in primary care to measure patient safety\n[8]. In this paper, patient safety refers to the ‘avoidance, prevention and amelioration of adverse outcomes or injuries stemming from the process of healthcare’\n[8].\nStaff and systems in primary care environments have the potential to contribute to serious error that can cause both morbidity and mortality; which has been demonstrated in the field of prescribing\n[9]. Evidence on primary care error comes mainly from the statistics of the medical defence organisations and from small pilot studies\n[10]. And yet, experts we have consulted in the field were anecdotally aware of a multiplicity of interventions or ‘tools’ from their own and others’ work world-wide, which helped identify grey literature for this study.\nThis paper reports a narrative review of ‘tools’ to improve, measure, and monitor patient safety in the ambulatory settings with a focus on family practice. A narrative review broadly covers a specific topic but does not adhere to strict systematic methods to locate and synthesize articles and enables description and synthesis of qualitative research and categorises studies into more homogenous groups\n[11]. To the authors’ knowledge no such broad-ranging review has been attempted. The context of this study is worldwide including both the US and the UK and throughout the term primary care is used to address the terms general practice and family practice.\n\nMethods:\n Data sources and searches Our structured narrative review was planned and conducted according to guidance in the Preferred Reporting Items for Systematic Reviews and Meta- Analyses (PRISMA) guidelines\n[12] but following a more narrative approach (especially with regard to grey literature). The starting point for determining the search terms used in the review was a 3 point definition of our search question and exploration of Medical Subject Heading (MeSH) terms\n[13]. We used a multi-centre team (including leading UK experts on patient safety) at a strategic planning meeting to comment on and finalise the search terms for the review. References were managed in Endnote. Broad ranging search terms were used for developing a search strategy in 3 stems; setting (primary care [i.e. general/family practice, ambulatory care, community care, generalist care], safety synonyms [i.e. error, adverse event, fault, malpractice] and types of tool [i.e. indicator, survey, guideline, scale] (see web Additional file\n1: Appendix 1). The aim was to be as inclusive as possible and address administrative, clinical and patient experience issues. The search was performed on the following databases; Embase, CINAHL, Pubmed, Medline (Ovid 1996 onward), Health Management Information Consortium and Web of Science on the 1/11/2011. We did not limit our search by year of publication or to the English language, in order to capture a world-wide perspective on patient safety. However, only abstracts in English were included due to resource restraints for translation. Grey literature was identified from known internet patient safety sources from the US and UK to expand the scope of the review (see web Additional file:\n1 Appendix 6). In order to fully explore a single tool many resources often had to be read – for example the IHI trigger tool is described in a number of web publications and supporting documents. Care was taken not to count published tools that also appeared in grey literature twice by discussion between the two reviewers and wider team.\nOur structured narrative review was planned and conducted according to guidance in the Preferred Reporting Items for Systematic Reviews and Meta- Analyses (PRISMA) guidelines\n[12] but following a more narrative approach (especially with regard to grey literature). The starting point for determining the search terms used in the review was a 3 point definition of our search question and exploration of Medical Subject Heading (MeSH) terms\n[13]. We used a multi-centre team (including leading UK experts on patient safety) at a strategic planning meeting to comment on and finalise the search terms for the review. References were managed in Endnote. Broad ranging search terms were used for developing a search strategy in 3 stems; setting (primary care [i.e. general/family practice, ambulatory care, community care, generalist care], safety synonyms [i.e. error, adverse event, fault, malpractice] and types of tool [i.e. indicator, survey, guideline, scale] (see web Additional file\n1: Appendix 1). The aim was to be as inclusive as possible and address administrative, clinical and patient experience issues. The search was performed on the following databases; Embase, CINAHL, Pubmed, Medline (Ovid 1996 onward), Health Management Information Consortium and Web of Science on the 1/11/2011. We did not limit our search by year of publication or to the English language, in order to capture a world-wide perspective on patient safety. However, only abstracts in English were included due to resource restraints for translation. Grey literature was identified from known internet patient safety sources from the US and UK to expand the scope of the review (see web Additional file:\n1 Appendix 6). In order to fully explore a single tool many resources often had to be read – for example the IHI trigger tool is described in a number of web publications and supporting documents. Care was taken not to count published tools that also appeared in grey literature twice by discussion between the two reviewers and wider team.\n Study selection Reviewer one was a GP Academic Clinical Fellow with an interest in pharmacology (RS) and reviewer two was a health services researcher with an interest in family practice (SC). Similarly to the strategy used in the AMA’s report\n[6], we were interested in highly generalisable tools so research addressing single drugs or conditions in very specific settings was excluded. Inclusion and exclusion criteria are listed in below. An inclusive strategy was employed such that neither reviewer could exclude studies the other felt were potentially relevant. Disagreements were resolved by regular discussions between the 2 reviewers. The inclusion and exclusion criteria were as follows:\nExclusion criteria:hospital care/setting - unless transferableopinion pieces/editorialssingle drugs/conditions where the focus was felt to be on the specific drug or condition rather than on transferable toolsonly about quality of care without explicit patient safety componentexclude on basis of journal (for example “Health Care Food & Nutrition focus”)economic impact of errors (relevant papers were taken out at this stage for other purposes within the project)Both the abstract and main text were not in EnglishInclusion criteria:if unsure always include - for example, ‘good advice’ which might later inform other toolstools or strategies to improve or analyse safety which are of relevance to Primary Care.\nExclusion criteria:hospital care/setting - unless transferableopinion pieces/editorialssingle drugs/conditions where the focus was felt to be on the specific drug or condition rather than on transferable toolsonly about quality of care without explicit patient safety componentexclude on basis of journal (for example “Health Care Food & Nutrition focus”)economic impact of errors (relevant papers were taken out at this stage for other purposes within the project)Both the abstract and main text were not in English\nhospital care/setting - unless transferable\nopinion pieces/editorials\nsingle drugs/conditions where the focus was felt to be on the specific drug or condition rather than on transferable tools\nonly about quality of care without explicit patient safety component\nexclude on basis of journal (for example “Health Care Food & Nutrition focus”)\neconomic impact of errors (relevant papers were taken out at this stage for other purposes within the project)\nBoth the abstract and main text were not in English\nInclusion criteria:if unsure always include - for example, ‘good advice’ which might later inform other toolstools or strategies to improve or analyse safety which are of relevance to Primary Care.\nif unsure always include - for example, ‘good advice’ which might later inform other tools\ntools or strategies to improve or analyse safety which are of relevance to Primary Care.\nReviewer one was a GP Academic Clinical Fellow with an interest in pharmacology (RS) and reviewer two was a health services researcher with an interest in family practice (SC). Similarly to the strategy used in the AMA’s report\n[6], we were interested in highly generalisable tools so research addressing single drugs or conditions in very specific settings was excluded. Inclusion and exclusion criteria are listed in below. An inclusive strategy was employed such that neither reviewer could exclude studies the other felt were potentially relevant. Disagreements were resolved by regular discussions between the 2 reviewers. The inclusion and exclusion criteria were as follows:\nExclusion criteria:hospital care/setting - unless transferableopinion pieces/editorialssingle drugs/conditions where the focus was felt to be on the specific drug or condition rather than on transferable toolsonly about quality of care without explicit patient safety componentexclude on basis of journal (for example “Health Care Food & Nutrition focus”)economic impact of errors (relevant papers were taken out at this stage for other purposes within the project)Both the abstract and main text were not in EnglishInclusion criteria:if unsure always include - for example, ‘good advice’ which might later inform other toolstools or strategies to improve or analyse safety which are of relevance to Primary Care.\nExclusion criteria:hospital care/setting - unless transferableopinion pieces/editorialssingle drugs/conditions where the focus was felt to be on the specific drug or condition rather than on transferable toolsonly about quality of care without explicit patient safety componentexclude on basis of journal (for example “Health Care Food & Nutrition focus”)economic impact of errors (relevant papers were taken out at this stage for other purposes within the project)Both the abstract and main text were not in English\nhospital care/setting - unless transferable\nopinion pieces/editorials\nsingle drugs/conditions where the focus was felt to be on the specific drug or condition rather than on transferable tools\nonly about quality of care without explicit patient safety component\nexclude on basis of journal (for example “Health Care Food & Nutrition focus”)\neconomic impact of errors (relevant papers were taken out at this stage for other purposes within the project)\nBoth the abstract and main text were not in English\nInclusion criteria:if unsure always include - for example, ‘good advice’ which might later inform other toolstools or strategies to improve or analyse safety which are of relevance to Primary Care.\nif unsure always include - for example, ‘good advice’ which might later inform other tools\ntools or strategies to improve or analyse safety which are of relevance to Primary Care.\n Data quality and extraction Data were extracted independently by the two reviewers (RS and SC). A dual approach was taken to data extraction from published material using both a Word document (Web Additional file:\n1 Appendix 5) and an Excel template. For reporting data from selected papers we used a modified PRISMA\n[12] checklist, which combined aspects of different methodologies (not just systematic reviews and meta-analyses) into a form for all study types (available on application to the authors). For example the PRISMA checklist requires a discussion of limitations which is a highly transferable requirement to all methodologies, but it also requires specific methods based items such as ‘confidence intervals on meta-analyses’. Using information collected on the modified PRISMA form, a numerical data extraction system was agreed by both reviewers in order to present results from the selected papers data for analysis were extracted from that Excel document. A pilot of 10 key papers with differing methodologies was undertaken prior to commencing full data extraction – high levels of agreement were found between the two reviewers. At the end of data extraction differences in rating on the Excel sheet were discussed and analysed across a series of face-to-face and telephone meetings, attempts to reach consensus were almost always possible.\nData were extracted independently by the two reviewers (RS and SC). A dual approach was taken to data extraction from published material using both a Word document (Web Additional file:\n1 Appendix 5) and an Excel template. For reporting data from selected papers we used a modified PRISMA\n[12] checklist, which combined aspects of different methodologies (not just systematic reviews and meta-analyses) into a form for all study types (available on application to the authors). For example the PRISMA checklist requires a discussion of limitations which is a highly transferable requirement to all methodologies, but it also requires specific methods based items such as ‘confidence intervals on meta-analyses’. Using information collected on the modified PRISMA form, a numerical data extraction system was agreed by both reviewers in order to present results from the selected papers data for analysis were extracted from that Excel document. A pilot of 10 key papers with differing methodologies was undertaken prior to commencing full data extraction – high levels of agreement were found between the two reviewers. At the end of data extraction differences in rating on the Excel sheet were discussed and analysed across a series of face-to-face and telephone meetings, attempts to reach consensus were almost always possible.\n Funding This review is part of a National Institute for Health Research (NIHR), School for Primary Care Research (SPCR: http://www.spcr.nihr.ac.uk/) (UK) project, undertaken with the aim of constructing a Patient Safety Toolkit for English family practice.\nThis review is part of a National Institute for Health Research (NIHR), School for Primary Care Research (SPCR: http://www.spcr.nihr.ac.uk/) (UK) project, undertaken with the aim of constructing a Patient Safety Toolkit for English family practice.\n\nData sources and searches:\nOur structured narrative review was planned and conducted according to guidance in the Preferred Reporting Items for Systematic Reviews and Meta- Analyses (PRISMA) guidelines\n[12] but following a more narrative approach (especially with regard to grey literature). The starting point for determining the search terms used in the review was a 3 point definition of our search question and exploration of Medical Subject Heading (MeSH) terms\n[13]. We used a multi-centre team (including leading UK experts on patient safety) at a strategic planning meeting to comment on and finalise the search terms for the review. References were managed in Endnote. Broad ranging search terms were used for developing a search strategy in 3 stems; setting (primary care [i.e. general/family practice, ambulatory care, community care, generalist care], safety synonyms [i.e. error, adverse event, fault, malpractice] and types of tool [i.e. indicator, survey, guideline, scale] (see web Additional file\n1: Appendix 1). The aim was to be as inclusive as possible and address administrative, clinical and patient experience issues. The search was performed on the following databases; Embase, CINAHL, Pubmed, Medline (Ovid 1996 onward), Health Management Information Consortium and Web of Science on the 1/11/2011. We did not limit our search by year of publication or to the English language, in order to capture a world-wide perspective on patient safety. However, only abstracts in English were included due to resource restraints for translation. Grey literature was identified from known internet patient safety sources from the US and UK to expand the scope of the review (see web Additional file:\n1 Appendix 6). In order to fully explore a single tool many resources often had to be read – for example the IHI trigger tool is described in a number of web publications and supporting documents. Care was taken not to count published tools that also appeared in grey literature twice by discussion between the two reviewers and wider team.\n\nStudy selection:\nReviewer one was a GP Academic Clinical Fellow with an interest in pharmacology (RS) and reviewer two was a health services researcher with an interest in family practice (SC). Similarly to the strategy used in the AMA’s report\n[6], we were interested in highly generalisable tools so research addressing single drugs or conditions in very specific settings was excluded. Inclusion and exclusion criteria are listed in below. An inclusive strategy was employed such that neither reviewer could exclude studies the other felt were potentially relevant. Disagreements were resolved by regular discussions between the 2 reviewers. The inclusion and exclusion criteria were as follows:\nExclusion criteria:hospital care/setting - unless transferableopinion pieces/editorialssingle drugs/conditions where the focus was felt to be on the specific drug or condition rather than on transferable toolsonly about quality of care without explicit patient safety componentexclude on basis of journal (for example “Health Care Food & Nutrition focus”)economic impact of errors (relevant papers were taken out at this stage for other purposes within the project)Both the abstract and main text were not in EnglishInclusion criteria:if unsure always include - for example, ‘good advice’ which might later inform other toolstools or strategies to improve or analyse safety which are of relevance to Primary Care.\nExclusion criteria:hospital care/setting - unless transferableopinion pieces/editorialssingle drugs/conditions where the focus was felt to be on the specific drug or condition rather than on transferable toolsonly about quality of care without explicit patient safety componentexclude on basis of journal (for example “Health Care Food & Nutrition focus”)economic impact of errors (relevant papers were taken out at this stage for other purposes within the project)Both the abstract and main text were not in English\nhospital care/setting - unless transferable\nopinion pieces/editorials\nsingle drugs/conditions where the focus was felt to be on the specific drug or condition rather than on transferable tools\nonly about quality of care without explicit patient safety component\nexclude on basis of journal (for example “Health Care Food & Nutrition focus”)\neconomic impact of errors (relevant papers were taken out at this stage for other purposes within the project)\nBoth the abstract and main text were not in English\nInclusion criteria:if unsure always include - for example, ‘good advice’ which might later inform other toolstools or strategies to improve or analyse safety which are of relevance to Primary Care.\nif unsure always include - for example, ‘good advice’ which might later inform other tools\ntools or strategies to improve or analyse safety which are of relevance to Primary Care.\n\nData quality and extraction:\nData were extracted independently by the two reviewers (RS and SC). A dual approach was taken to data extraction from published material using both a Word document (Web Additional file:\n1 Appendix 5) and an Excel template. For reporting data from selected papers we used a modified PRISMA\n[12] checklist, which combined aspects of different methodologies (not just systematic reviews and meta-analyses) into a form for all study types (available on application to the authors). For example the PRISMA checklist requires a discussion of limitations which is a highly transferable requirement to all methodologies, but it also requires specific methods based items such as ‘confidence intervals on meta-analyses’. Using information collected on the modified PRISMA form, a numerical data extraction system was agreed by both reviewers in order to present results from the selected papers data for analysis were extracted from that Excel document. A pilot of 10 key papers with differing methodologies was undertaken prior to commencing full data extraction – high levels of agreement were found between the two reviewers. At the end of data extraction differences in rating on the Excel sheet were discussed and analysed across a series of face-to-face and telephone meetings, attempts to reach consensus were almost always possible.\n\nFunding:\nThis review is part of a National Institute for Health Research (NIHR), School for Primary Care Research (SPCR: http://www.spcr.nihr.ac.uk/) (UK) project, undertaken with the aim of constructing a Patient Safety Toolkit for English family practice.\n\nResults and discussion:\nGrey literature results are not included in the following calculations and flow diagrams; results are instead included in the list of tools found in web Additional file\n1: Appendix 3 (where they are clearly marked as being from grey sources). Using the process described in Figure \n1, we selected approximately 10% (n = 1311) of the original search total (n = 13,240) for evaluation of abstracts; titles excluded at this stage were clearly not of relevance e.g. relating to non-healthcare safety topics. Abstracts were then analysed for tools, after excluding papers which were from the correct setting but which did not contain any interventions; around 14% of the abstracts were included for full paper analysis (n = 189).Figure 1\n‘Toolkit’ review stages. Graph 1 –a) illustration of the literature base in primary care patient safety 1987-2011from Pubmed b) Papers from the review divided by the annual Pubmed output for the same year.\n\n‘Toolkit’ review stages. Graph 1 –a) illustration of the literature base in primary care patient safety 1987-2011from Pubmed b) Papers from the review divided by the annual Pubmed output for the same year.\nAs Graph 1a) illustrates, the majority of publications in the review have been published in the last decade. These data could be a product of MeSH term development or consistency in the last 10 years. However, as Graph 1b) shows, the same take-off in 2001 occurs even when presented as a proportion of the total literature published on Pubmed. Analysing the MeSH terms of the 11 papers in the review from prior to 2000 revealed that the terms ‘medical error’ and ‘diagnostic error’ were each used twice and ‘risk management’ was used in 3 papers. Keywords using the term ‘drug’ appeared 13 times, family practice and ambulatory care were commonly used keywords.\nThe majority of the literature focused on prescribing (55%), which excludes IT interventions for prescribing that were attributed to the informatics theme instead. Other prominent areas were adverse events in primary care and safety climate (which comprised 8% of the total published literature each). A number of climate measures had been refined from earlier climate surveys for use specifically in primary care (i.e. SAQ (ambulatory)\n[14]. Minor themes included; informatics (4.5%) patient role (3%) and general measures to correct error (5%). The primary/secondary care interface is well described in the literature (5%) but, as yet, only 1 published interface tool specifically for primary care exists. Diagnostic error and results handling appear infrequently (<1% of total literature) despite their relative importance. The remainder of literature (11%) related to referrals, Out-Of-Hours (OOH) care, telephone care, organisational issues, mortality and clerical error. Overall, 114 Tools were identified with 26 accrued from grey literature.\nThe setting of the research uncovered was predominantly family practice (in keeping with our search strategy); the term ‘health system’ was used to describe research such as consensus outputs from multi-disciplinary teams or across the whole healthcare system. Most published literature was US based (41%) followed by UK located studies (23%), with other countries producing no more than 5% of published papers each. The grey literature also reflects the predominance of US and UK sources. A variety of study designs were identified in the review including, for example, consensus techniques (10%) , observational methods (15%) but most were mixed methods studies in patient safety research.\nWe classified the data from the published papers in this review using a taxonomy for primary care patient safety based on previous taxonomies by experts in quality of care and patient safety\n[15–19]. It differs from our data collection form as it was evolved later in the process of our analyses. In our taxonomy (Web Additional file\n1: Appendix 4) there are two principal dimensions of safety: ‘access to safe services’ and ‘effectiveness of safety processes’, which are discussed in terms of the structure of the health care system, processes of safety and health outcomes. This taxonomy is based on previous conceptual work on quality of care\n[15]; in essence, do users get the safe care they need, and is the care safe when they get it?\nIn our review, 88 of the 114 unique tools identified (Web Additional file\n1: Appendix 3) came from the published literature and 26 from grey literature sources – the majority of these being from the websites of known patient safety organisations (see data sources in Methods section). The review identified a wide range of ‘tools’ that cannot be described fully here due to word count constraints. The detailed output from the review will be described across a series of subsequent papers on ambulatory patient safety. However, Table \n1 shows key examples found in each dimension of patient safety: We have presented the most well-known or most often used US or UK tools as illustrative examples. The Table is presented in order of weight of literature with most common topics appearing first.Table 1\nTypes of tools found in the review, where possible well-known US examples of the type of tool are given in order to aid understanding\nType of tool (Explanation of tool)Used in the US?Used in the UK?US ExampleData sourceNumber of primary care tools of this type identifiedPrescribing Indicator PacksYesYesBeers criteria\n[20]EHR15 main sets, much overlap -3(criteria for ‘never events’ in prescribing) - other prescribing toolsGRAM reports\n[21], MAI\n[22] (Geriatric Risk Assessment MedGuide™ Medications Appropriateness Index)EHR, staffTrigger Tools-GeneralYesYesIHI Outpatient AdverseEHR5-MedicationsYesNoEvent Trigger Tool\n[23]3-SurgeryYesYesAdverse drug events among older adults in primary care\n[24] Ambulatory surgery\n[25]1\n(Criteria are screened for in a sample of medical records ‘triggering’ more detailed review)\nEvent Reporting Systems (National systems for informing relevant authorities about safety problems with all aspects of healthcare)YesYesASIPS\n[26] (Applied Strategies for Improving Patient Safety)EHR, Staff and patients6Medicines/device Reporting SystemsYesYesMEADERS\n[27]EHR, Staff and Patients4(National systems for informing relevant authorities about safety problems specific to the above)(Medication Error and Adverse Drug Event Reporting System) VAERS\n[28] (Vaccine Adverse Events Reporting System)Safety Climate/Culture Measures (The practice team rate themselves against safety criteria and discuss the results to make changes)YesYesSafety Attitudes Questionnaire\n[14]Staff10Significant Event Analysis Tools (The practice team discuss untoward events, using a standardised structure, in order to learn from them)NoYesUK example - NHS Education for Scotland\n[29]Staff, EHR and patients5General Primary/Secondary Interface Tools (standardised systems for handling patient care at transition in care level – often electronic discharge summaries)YesYes‘Care Transitions Approach’\n[30]EHR, hospital recordsOnly 3 within the direct control of family doctorsMedication Reconciliation Tools (aligning medication histories after secondary care contact)YesNo formal tool usedPartner’s Post Discharge Tool\n[31]EHR, hospital records3PROMs for safety (questionnaire determining the patient perspective of safety in their practice)YesYesSEAPS\n[32] (Seniors Empowerment and Advocacy in Patient Safety)Patients8Other Patient Involvement Measures (variety of tools including literature for patients, computerised systems and medications specific tools)YesYes‘Speak-Up’ from JCAHO\n[33]Patients4IT MeasuresYesYesSEMI-P\n[34]EHR11(not just CDSS but a variety of measures often tackling systems error, many relate to prescribing safety)(Safety Enhancement and Monitoring Instrument that is Patient centred)Diagnostic Tools (Mainly CDSS designed to improve diagnosis)YesNoDxPlain\n[35]EHR3\nAbbreviations:\nCDSS Computer Decision Support Software.\nEHR Electronic Health Record.\nPROM Patient reported outcome measures.\nUK United Kingsdom.\nUS United States.\n\nTypes of tools found in the review, where possible well-known US examples of the type of tool are given in order to aid understanding\n\n\nAbbreviations:\n\nCDSS Computer Decision Support Software.\n\nEHR Electronic Health Record.\n\nPROM Patient reported outcome measures.\n\nUK United Kingsdom.\n\nUS United States.\n Summary of main findings We have demonstrated that there has been an upsurge in publications on primary care patient safety since 2001 and that most of the literature comes from the USA and the UK, with the pre-eminent topic being prescribing safety. The list of discrete tools (which includes grey literature) has a much more even spread across the dimensions within our conceptual taxonomy (Web Appendices 3 and 4). Using this taxonomy shows that some areas of patient safety are relatively neglected in the published literature on primary care patient safety tools; for example, diagnostic error. Tools for test results and referrals are also poorly represented; there were 5 descriptive papers in total, one un-validated tool for electronic referrals and one indicator set dealing with referrals from OOH care. No tools for investigations management were found.\nWe have demonstrated that there has been an upsurge in publications on primary care patient safety since 2001 and that most of the literature comes from the USA and the UK, with the pre-eminent topic being prescribing safety. The list of discrete tools (which includes grey literature) has a much more even spread across the dimensions within our conceptual taxonomy (Web Appendices 3 and 4). Using this taxonomy shows that some areas of patient safety are relatively neglected in the published literature on primary care patient safety tools; for example, diagnostic error. Tools for test results and referrals are also poorly represented; there were 5 descriptive papers in total, one un-validated tool for electronic referrals and one indicator set dealing with referrals from OOH care. No tools for investigations management were found.\n Comparison to existing literature To the authors’ knowledge no similar review has been undertaken to look specifically at instruments for measuring patient safety in primary care. The AMA report on ambulatory patient safety\n[6] found that the number of reported interventions in primary care is low but their search strategy did not take a worldwide approach and only focused on interventions that reduce error or harm. We designed a more inclusive search strategy to capture measurement tools and strategies and were therefore able to find a wider body of literature. The focus on measurement tools and strategies reflects the importance of knowing what is being used currently in primary care to measure patient safety\n[8]. Many of the 114 tools found are iterations of tools constructed previously and re-designed for other countries. For example, the UK NHS Institute for Innovation and Improvement Primary Care Trigger Tool\n[36] has much in common with the IHI Outpatient Adverse Event Trigger Tool\n[23].\nOur study resonates with the view that there can be no one single measure of patient safety\n[8]. Rather, a framework for patient safety should include for example; past harm, reliability, ‘sensitivity to operations’, anticipation/preparedness and Integration/learning. Vincent C, 2013\n[8] Measures of past harm are prevalent among the tools we found i.e. – adverse event reporting systems. Some tools clearly straddle boundaries within the definition: Significant Event Analyses (SEA) (a technique commonly used in the UK), for example, straddles past events, future anticipation of similar situations and learning in relation to the significant event. Few tools address safety reliability in primary care; practices may set their own standards for audit of patient safety but as yet no formal targets exist for primary care in the UK or US (in direct contrast with hospital mortality data and target dashboards such as HEDIS - http://www.ncqa.org/HEDISQualityMeasurement.aspx). Sensitivity to operations is an umbrella term referring to the information and capacity in clinical systems to monitor safety on an hourly or daily basis, climate measures often address questions to staff in relation to their adaptability to change but there are no other measures of this dynamic in primary care. The work of the defence organisations in advising practices about risks and loop-holes in operating systems comes close to fulfilling this goal and roughly equates to a safety ‘walk-round’\n[37]. The challenge to any ‘toolkit’ is to incorporate prospective measures that prevent and anticipate error. The major elements of the toolkit that address prevention are; trigger tools\n[23–25, 36] (potential rather than actual harm), medicines reconciliation packages\n[30] (prevent harm from changes to prescriptions at the interface of primary and secondary care), safety culture\n[38] and a ‘safe systems’ checklist, which encourages primary care practices to seek out loopholes in their established systems.\nTo the authors’ knowledge no similar review has been undertaken to look specifically at instruments for measuring patient safety in primary care. The AMA report on ambulatory patient safety\n[6] found that the number of reported interventions in primary care is low but their search strategy did not take a worldwide approach and only focused on interventions that reduce error or harm. We designed a more inclusive search strategy to capture measurement tools and strategies and were therefore able to find a wider body of literature. The focus on measurement tools and strategies reflects the importance of knowing what is being used currently in primary care to measure patient safety\n[8]. Many of the 114 tools found are iterations of tools constructed previously and re-designed for other countries. For example, the UK NHS Institute for Innovation and Improvement Primary Care Trigger Tool\n[36] has much in common with the IHI Outpatient Adverse Event Trigger Tool\n[23].\nOur study resonates with the view that there can be no one single measure of patient safety\n[8]. Rather, a framework for patient safety should include for example; past harm, reliability, ‘sensitivity to operations’, anticipation/preparedness and Integration/learning. Vincent C, 2013\n[8] Measures of past harm are prevalent among the tools we found i.e. – adverse event reporting systems. Some tools clearly straddle boundaries within the definition: Significant Event Analyses (SEA) (a technique commonly used in the UK), for example, straddles past events, future anticipation of similar situations and learning in relation to the significant event. Few tools address safety reliability in primary care; practices may set their own standards for audit of patient safety but as yet no formal targets exist for primary care in the UK or US (in direct contrast with hospital mortality data and target dashboards such as HEDIS - http://www.ncqa.org/HEDISQualityMeasurement.aspx). Sensitivity to operations is an umbrella term referring to the information and capacity in clinical systems to monitor safety on an hourly or daily basis, climate measures often address questions to staff in relation to their adaptability to change but there are no other measures of this dynamic in primary care. The work of the defence organisations in advising practices about risks and loop-holes in operating systems comes close to fulfilling this goal and roughly equates to a safety ‘walk-round’\n[37]. The challenge to any ‘toolkit’ is to incorporate prospective measures that prevent and anticipate error. The major elements of the toolkit that address prevention are; trigger tools\n[23–25, 36] (potential rather than actual harm), medicines reconciliation packages\n[30] (prevent harm from changes to prescriptions at the interface of primary and secondary care), safety culture\n[38] and a ‘safe systems’ checklist, which encourages primary care practices to seek out loopholes in their established systems.\n Strengths/limitations This review presented challenges due to the broad nature of our question and that there are no criteria for a standardised definition of, or criteria for classifying, a ‘tool’. Tools can be alerts, scoring systems, order sets, dashboards, questionnaires, educational materials, forms or templates to name but a few and, as such, the output of the review is highly heterogeneous. Therefore, we employed pragmatic ways of handling the large amount of data extracted from the review. Our strengths have included using a two reviewer system and a dual extraction process of both numerically coded data and free text summaries of papers, which enabled us to analyse identified instruments in-depth and an extensive exploration of grey literature with a world-wide perspective. Exclusions due to translation costs were minor – only 6 papers out of 280 were excluded on language basis alone. Time-constraints on the project meant we could not use back and forward citation methods systematically due to the sheer number of papers involved in the review.\nThis review presented challenges due to the broad nature of our question and that there are no criteria for a standardised definition of, or criteria for classifying, a ‘tool’. Tools can be alerts, scoring systems, order sets, dashboards, questionnaires, educational materials, forms or templates to name but a few and, as such, the output of the review is highly heterogeneous. Therefore, we employed pragmatic ways of handling the large amount of data extracted from the review. Our strengths have included using a two reviewer system and a dual extraction process of both numerically coded data and free text summaries of papers, which enabled us to analyse identified instruments in-depth and an extensive exploration of grey literature with a world-wide perspective. Exclusions due to translation costs were minor – only 6 papers out of 280 were excluded on language basis alone. Time-constraints on the project meant we could not use back and forward citation methods systematically due to the sheer number of papers involved in the review.\n Implications for practice, policy, or future research The main aim of the Tools identified is to measure or report safety issues. While measurement and baselines are a prerequisite to improvement, few of the Tools include an embedded implementation strategy that would address improvement or a quality cycle to alter strategies and measure for change\n[39]. It is difficult to estimate the impact the various measurement tools identified in this review would have in improving patient safety; for instance, prescribing indicators would only seem to measure level of harm at surface value but have been found to change harmful prescribing patterns when combined with educational feedback\n[40]. Moreover, standards and consistency of reporting vary and many studies, for example around culture and climate surveys, do not report reliability, validity, details of their study characteristics and participants etc.\nOthers have advocated the need for outcome measures in patient safety\n[41]. However, measurement systems need to be tested to ensure they measure what is claimed, whether they can reliably tell if deterioration or improvement is occurring and what other (untoward and unintended) consequences could occur\n[8]. This adheres to the wider imperative that measures of quality or safety, and the data collected, adhere to key attributes such as reliability and validity and also address issues such as acceptability, implementation issues and possible unintended consequences\n[42, 43]. The aim of future work will be to test the suitability and acceptability of the proposed measures in the toolkit and to test changes within practices after application of the toolkit; as well as intended and unintended (positive and negative) consequences. Measureable outcomes are only one feature of Safety Management Systems, and as such the toolkit should not rely exclusively on them but also develop other areas such as training, policy, culture and feedback of outcomes data in line with other established models of patient safety\n[9, 43–45]. There is a need also to embrace qualitative methodologies to patient safety such as the Manchester Patient Safety Framework (MaPSaF )\n[46].\nThe main aim of the Tools identified is to measure or report safety issues. While measurement and baselines are a prerequisite to improvement, few of the Tools include an embedded implementation strategy that would address improvement or a quality cycle to alter strategies and measure for change\n[39]. It is difficult to estimate the impact the various measurement tools identified in this review would have in improving patient safety; for instance, prescribing indicators would only seem to measure level of harm at surface value but have been found to change harmful prescribing patterns when combined with educational feedback\n[40]. Moreover, standards and consistency of reporting vary and many studies, for example around culture and climate surveys, do not report reliability, validity, details of their study characteristics and participants etc.\nOthers have advocated the need for outcome measures in patient safety\n[41]. However, measurement systems need to be tested to ensure they measure what is claimed, whether they can reliably tell if deterioration or improvement is occurring and what other (untoward and unintended) consequences could occur\n[8]. This adheres to the wider imperative that measures of quality or safety, and the data collected, adhere to key attributes such as reliability and validity and also address issues such as acceptability, implementation issues and possible unintended consequences\n[42, 43]. The aim of future work will be to test the suitability and acceptability of the proposed measures in the toolkit and to test changes within practices after application of the toolkit; as well as intended and unintended (positive and negative) consequences. Measureable outcomes are only one feature of Safety Management Systems, and as such the toolkit should not rely exclusively on them but also develop other areas such as training, policy, culture and feedback of outcomes data in line with other established models of patient safety\n[9, 43–45]. There is a need also to embrace qualitative methodologies to patient safety such as the Manchester Patient Safety Framework (MaPSaF )\n[46].\n\nSummary of main findings:\nWe have demonstrated that there has been an upsurge in publications on primary care patient safety since 2001 and that most of the literature comes from the USA and the UK, with the pre-eminent topic being prescribing safety. The list of discrete tools (which includes grey literature) has a much more even spread across the dimensions within our conceptual taxonomy (Web Appendices 3 and 4). Using this taxonomy shows that some areas of patient safety are relatively neglected in the published literature on primary care patient safety tools; for example, diagnostic error. Tools for test results and referrals are also poorly represented; there were 5 descriptive papers in total, one un-validated tool for electronic referrals and one indicator set dealing with referrals from OOH care. No tools for investigations management were found.\n\nComparison to existing literature:\nTo the authors’ knowledge no similar review has been undertaken to look specifically at instruments for measuring patient safety in primary care. The AMA report on ambulatory patient safety\n[6] found that the number of reported interventions in primary care is low but their search strategy did not take a worldwide approach and only focused on interventions that reduce error or harm. We designed a more inclusive search strategy to capture measurement tools and strategies and were therefore able to find a wider body of literature. The focus on measurement tools and strategies reflects the importance of knowing what is being used currently in primary care to measure patient safety\n[8]. Many of the 114 tools found are iterations of tools constructed previously and re-designed for other countries. For example, the UK NHS Institute for Innovation and Improvement Primary Care Trigger Tool\n[36] has much in common with the IHI Outpatient Adverse Event Trigger Tool\n[23].\nOur study resonates with the view that there can be no one single measure of patient safety\n[8]. Rather, a framework for patient safety should include for example; past harm, reliability, ‘sensitivity to operations’, anticipation/preparedness and Integration/learning. Vincent C, 2013\n[8] Measures of past harm are prevalent among the tools we found i.e. – adverse event reporting systems. Some tools clearly straddle boundaries within the definition: Significant Event Analyses (SEA) (a technique commonly used in the UK), for example, straddles past events, future anticipation of similar situations and learning in relation to the significant event. Few tools address safety reliability in primary care; practices may set their own standards for audit of patient safety but as yet no formal targets exist for primary care in the UK or US (in direct contrast with hospital mortality data and target dashboards such as HEDIS - http://www.ncqa.org/HEDISQualityMeasurement.aspx). Sensitivity to operations is an umbrella term referring to the information and capacity in clinical systems to monitor safety on an hourly or daily basis, climate measures often address questions to staff in relation to their adaptability to change but there are no other measures of this dynamic in primary care. The work of the defence organisations in advising practices about risks and loop-holes in operating systems comes close to fulfilling this goal and roughly equates to a safety ‘walk-round’\n[37]. The challenge to any ‘toolkit’ is to incorporate prospective measures that prevent and anticipate error. The major elements of the toolkit that address prevention are; trigger tools\n[23–25, 36] (potential rather than actual harm), medicines reconciliation packages\n[30] (prevent harm from changes to prescriptions at the interface of primary and secondary care), safety culture\n[38] and a ‘safe systems’ checklist, which encourages primary care practices to seek out loopholes in their established systems.\n\nStrengths/limitations:\nThis review presented challenges due to the broad nature of our question and that there are no criteria for a standardised definition of, or criteria for classifying, a ‘tool’. Tools can be alerts, scoring systems, order sets, dashboards, questionnaires, educational materials, forms or templates to name but a few and, as such, the output of the review is highly heterogeneous. Therefore, we employed pragmatic ways of handling the large amount of data extracted from the review. Our strengths have included using a two reviewer system and a dual extraction process of both numerically coded data and free text summaries of papers, which enabled us to analyse identified instruments in-depth and an extensive exploration of grey literature with a world-wide perspective. Exclusions due to translation costs were minor – only 6 papers out of 280 were excluded on language basis alone. Time-constraints on the project meant we could not use back and forward citation methods systematically due to the sheer number of papers involved in the review.\n\nImplications for practice, policy, or future research:\nThe main aim of the Tools identified is to measure or report safety issues. While measurement and baselines are a prerequisite to improvement, few of the Tools include an embedded implementation strategy that would address improvement or a quality cycle to alter strategies and measure for change\n[39]. It is difficult to estimate the impact the various measurement tools identified in this review would have in improving patient safety; for instance, prescribing indicators would only seem to measure level of harm at surface value but have been found to change harmful prescribing patterns when combined with educational feedback\n[40]. Moreover, standards and consistency of reporting vary and many studies, for example around culture and climate surveys, do not report reliability, validity, details of their study characteristics and participants etc.\nOthers have advocated the need for outcome measures in patient safety\n[41]. However, measurement systems need to be tested to ensure they measure what is claimed, whether they can reliably tell if deterioration or improvement is occurring and what other (untoward and unintended) consequences could occur\n[8]. This adheres to the wider imperative that measures of quality or safety, and the data collected, adhere to key attributes such as reliability and validity and also address issues such as acceptability, implementation issues and possible unintended consequences\n[42, 43]. The aim of future work will be to test the suitability and acceptability of the proposed measures in the toolkit and to test changes within practices after application of the toolkit; as well as intended and unintended (positive and negative) consequences. Measureable outcomes are only one feature of Safety Management Systems, and as such the toolkit should not rely exclusively on them but also develop other areas such as training, policy, culture and feedback of outcomes data in line with other established models of patient safety\n[9, 43–45]. There is a need also to embrace qualitative methodologies to patient safety such as the Manchester Patient Safety Framework (MaPSaF )\n[46].\n\nConclusion:\nWe have identified 114 published and unpublished tools and indicators, which can be used currently in primary care to measure patient safety. However, the AMA concluded that there are virtually no credible studies on how to improve safety in primary care\n[6] andthe challenge is still to turn measurement into improvement as few tools have been used in quality improvement cycles or as part of performance targets for safety in ambulatory care. Having a comprehensive set of tools for tracking and preventing safety events is the first step in fixing that, and this paper clearly shows where our current toolkit is wanting. The results of this review will enable a better understanding of the epidemiology of ambulatory care safety and help underpin the future development of primary care based safety interventions.\n\nAuthors’ information:\nDr Rachel Spencer is an academic General Practitioner at the University of Nottingham and Professor Stephen Campbell is a health services researcher at the University of Manchester.\n\nElectronic supplementary material:\n Additional file 1:\nTools for Primary Care Patient Safety; a Systematic Review.\n(DOCX 65 KB)\nAdditional file 1:\nTools for Primary Care Patient Safety; a Systematic Review.\n(DOCX 65 KB)\n\n:\nAdditional file 1:\nTools for Primary Care Patient Safety; a Systematic Review.\n(DOCX 65 KB)\n\n"},"whole_article_abstract":{"kind":"string","value":"Background:\nPatient safety in primary care is a developing field with an embryonic but evolving evidence base. This narrative review aims to identify tools that can be used by family practitioners as part of a patient safety toolkit to improve the safety of the care and services provided by their practices.\n\nMethods:\nSearches were performed in 6 healthcare databases in 2011 using 3 search stems; location (primary care), patient safety synonyms and outcome measure synonyms. Two reviewers analysed the results using a numerical and thematic analyses. Extensive grey literature exploration was also conducted.\n\nResults:\nOverall, 114 Tools were identified with 26 accrued from grey literature. Most published literature originated from the USA (41%) and the UK (23%) within the last 10 years. Most of the literature addresses the themes of medication error (55%) followed by safety climate (8%) and adverse event reporting (8%). Minor themes included; informatics (4.5%) patient role (3%) and general measures to correct error (5%). The primary/secondary care interface is well described (5%) but few specific tools for primary care exist. Diagnostic error and results handling appear infrequently (<1% of total literature) despite their relative importance. The remainder of literature (11%) related to referrals, Out-Of-Hours (OOH) care, telephone care, organisational issues, mortality and clerical error.\n\nConclusions:\nThis review identified tools and indicators that are available for use in family practice to measure patient safety, which is crucial to improve safety and design a patient safety toolkit. However, many of the tools have yet to be used in quality improvement strategies and cycles such as plan-do-study-act (PDSA) so there is a dearth of evidence of their utility in improving as opposed to measuring and highlighting safety issues. The lack of focus on diagnostics, systems safety and results handling provide direction and priorities for future research."},"background_conclusion_text":{"kind":"string","value":"Background:\nPatient safety has been on the agenda of hospital physicians since the publication of the Institute of Medicine’s 2000 report, ‘To Err is Human’, revealed that more people were dying in the US as a result of medical error than from road traffic accidents\n[1]. However, most healthcare interactions in the developed world occur in family medicine: 90% of contacts in the England with the National Health Service take place in primary care\n[2]. In England there are approximately 1 billion community prescriptions dispensed each year\n[3]. The potential for adverse events is therefore huge but the knowledge base about primary care patient safety is still sparse. A literature review of the nature and frequency of error in primary care suggested that there are between 5–80 safety incidents per 100,000 consultations, which in the UK would translate to between 37–600 incidents per day\n[4]. Another review estimates that there may be a patient safety incident in approximately 2% of family practice consultations\n[5].\nA 2011 report by the American Medical Association on ambulatory patient safety concluded that the introduction of, and research into, patient safety in the primary care environment have lagged behind that of secondary care\n[6]. Understanding the epidemiology of hospital errors was crucial in developing hospital based safety interventions and the media’s reporting of this data ensured public support for efforts to improve safety\n[6]. Some of its authors concluded that there needed to be a similar focus on primary care, because there were ‘virtually no credible studies on how to improve safety’\n[7]. Moreover, a report by the Health Foundation in 2013 emphasised the importance of knowing what methods, tools and indicators are currently being used in primary care to measure patient safety\n[8]. In this paper, patient safety refers to the ‘avoidance, prevention and amelioration of adverse outcomes or injuries stemming from the process of healthcare’\n[8].\nStaff and systems in primary care environments have the potential to contribute to serious error that can cause both morbidity and mortality; which has been demonstrated in the field of prescribing\n[9]. Evidence on primary care error comes mainly from the statistics of the medical defence organisations and from small pilot studies\n[10]. And yet, experts we have consulted in the field were anecdotally aware of a multiplicity of interventions or ‘tools’ from their own and others’ work world-wide, which helped identify grey literature for this study.\nThis paper reports a narrative review of ‘tools’ to improve, measure, and monitor patient safety in the ambulatory settings with a focus on family practice. A narrative review broadly covers a specific topic but does not adhere to strict systematic methods to locate and synthesize articles and enables description and synthesis of qualitative research and categorises studies into more homogenous groups\n[11]. To the authors’ knowledge no such broad-ranging review has been attempted. The context of this study is worldwide including both the US and the UK and throughout the term primary care is used to address the terms general practice and family practice.\n\nConclusion:\nWe have identified 114 published and unpublished tools and indicators, which can be used currently in primary care to measure patient safety. However, the AMA concluded that there are virtually no credible studies on how to improve safety in primary care\n[6] andthe challenge is still to turn measurement into improvement as few tools have been used in quality improvement cycles or as part of performance targets for safety in ambulatory care. Having a comprehensive set of tools for tracking and preventing safety events is the first step in fixing that, and this paper clearly shows where our current toolkit is wanting. The results of this review will enable a better understanding of the epidemiology of ambulatory care safety and help underpin the future development of primary care based safety interventions."},"background_conclusion_abstract":{"kind":"string","value":"Background:\nPatient safety in primary care is a developing field with an embryonic but evolving evidence base. This narrative review aims to identify tools that can be used by family practitioners as part of a patient safety toolkit to improve the safety of the care and services provided by their practices.\n\nMethods:\nSearches were performed in 6 healthcare databases in 2011 using 3 search stems; location (primary care), patient safety synonyms and outcome measure synonyms. Two reviewers analysed the results using a numerical and thematic analyses. Extensive grey literature exploration was also conducted.\n\nResults:\nOverall, 114 Tools were identified with 26 accrued from grey literature. Most published literature originated from the USA (41%) and the UK (23%) within the last 10 years. Most of the literature addresses the themes of medication error (55%) followed by safety climate (8%) and adverse event reporting (8%). Minor themes included; informatics (4.5%) patient role (3%) and general measures to correct error (5%). The primary/secondary care interface is well described (5%) but few specific tools for primary care exist. Diagnostic error and results handling appear infrequently (<1% of total literature) despite their relative importance. The remainder of literature (11%) related to referrals, Out-Of-Hours (OOH) care, telephone care, organisational issues, mortality and clerical error.\n\nConclusions:\nThis review identified tools and indicators that are available for use in family practice to measure patient safety, which is crucial to improve safety and design a patient safety toolkit. However, many of the tools have yet to be used in quality improvement strategies and cycles such as plan-do-study-act (PDSA) so there is a dearth of evidence of their utility in improving as opposed to measuring and highlighting safety issues. The lack of focus on diagnostics, systems safety and results handling provide direction and priorities for future research."},"whole_article_text_length":{"kind":"number","value":9823,"string":"9,823"},"whole_article_abstract_length":{"kind":"number","value":386,"string":"386"},"other_sections_lengths":{"kind":"list like","value":[600,385,493,239,45,4120,150,547,192,382,28,22],"string":"[\n 600,\n 385,\n 493,\n 239,\n 45,\n 4120,\n 150,\n 547,\n 192,\n 382,\n 28,\n 22\n]"},"num_sections":{"kind":"number","value":15,"string":"15"},"most_frequent_words":{"kind":"list like","value":["safety","care","patient","tools","patient safety","primary","primary care","review","data","literature"],"string":"[\n \"safety\",\n \"care\",\n \"patient\",\n \"tools\",\n \"patient safety\",\n \"primary\",\n \"primary care\",\n \"review\",\n \"data\",\n \"literature\"\n]"},"keybert_topics":{"kind":"list like","value":["ambulatory patient safety","patient safety research","patient safety incident","patient 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[SUMMARY]"},"annotated_tf_idf_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] safety | care | patient | patient safety | tools | primary | primary care | review | data | papers [SUMMARY]"},"annotated_entity_plan_background_abstract_prompt":{"kind":"string","value":"[CONTENT] ||| [SUMMARY]"},"annotated_entity_plan_methods_abstract_prompt":{"kind":"string","value":"[CONTENT] 6 | 2011 | 3 ||| Two ||| [SUMMARY]"},"annotated_entity_plan_results_abstract_prompt":{"kind":"null"},"annotated_entity_plan_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] ||| PDSA ||| [SUMMARY]"},"annotated_entity_plan_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] ||| ||| 6 | 2011 | 3 ||| Two ||| ||| ||| 114 | 26 | grey literature ||| USA | 41% | UK | 23% | the last 10 years ||| 55% | 8% | 8% ||| 4.5% | 3% | 5% ||| 5% ||| 1% ||| 11% ||| ||| PDSA ||| [SUMMARY]"},"annotated_entity_plan_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] ||| ||| 6 | 2011 | 3 ||| Two ||| ||| ||| 114 | 26 | grey literature ||| USA | 41% | UK | 23% | the last 10 years ||| 55% | 8% | 8% ||| 4.5% | 3% | 5% ||| 5% ||| 1% ||| 11% ||| ||| PDSA ||| [SUMMARY]"}}},{"rowIdx":261,"cells":{"title":{"kind":"string","value":"Exploring the Suicide Mechanism Path of High-Suicide-Risk Adolescents-Based on Weibo Text Analysis."},"pmid":{"kind":"string","value":"36141767"},"background_abstract":{"kind":"string","value":"Adolescent suicide can have serious consequences for individuals, families and society, so we should pay attention to it. As social media becomes a platform for adolescents to share their daily lives and express their emotions, online identification and intervention of adolescent suicide problems become possible. In order to find the suicide mechanism path of high-suicide-risk adolescents, we explore the factors that influence is, especially the relations between psychological pain, hopelessness and suicide stages."},"background_abstract_label":{"kind":"string","value":"BACKGROUND"},"methods_abstract":{"kind":"string","value":"We identified high-suicide-risk adolescents through machine learning model identification and manual identification, and used the Weibo text analysis method to explore the suicide mechanism path of high-suicide-risk adolescents."},"methods_abstract_label":{"kind":"string","value":"METHODS"},"results_abstract":{"kind":"string","value":"Qualitative analysis showed that 36.2% of high-suicide-risk adolescents suffered from mental illness, and depression accounted for 76.3% of all mental illnesses. The mediating effect analysis showed that hopelessness played a complete mediating role between psychological pain and suicide stages. In addition, hopelessness was significantly negatively correlated with suicide stages."},"results_abstract_label":{"kind":"string","value":"RESULTS"},"conclusions_abstract":{"kind":"string","value":"mental illness (especially depression) in high-suicide-risk adolescents is closely related to suicide stages, the later the suicide stage, the higher the diagnosis rate of mental illness. The suicide mechanism path in high-suicide-risk adolescents is: psychological pain→ hopelessness → suicide stages, indicating that psychological pain mainly affects suicide risk through hopelessness. Adolescents who are later in the suicide stages have fewer expressions of hopelessness in the traditional sense."},"conclusions_abstract_label":{"kind":"string","value":"CONCLUSION"},"mesh_descriptor_names":{"kind":"list like","value":["Adolescent","Emotions","Humans","Pain","Risk Factors","Self Concept","Social Media","Suicide"],"string":"[\n \"Adolescent\",\n \"Emotions\",\n \"Humans\",\n \"Pain\",\n \"Risk Factors\",\n \"Self Concept\",\n \"Social Media\",\n \"Suicide\"\n]"},"pmcid":{"kind":"string","value":"9517096"},"background_title":{"kind":"string","value":"1. Introduction"},"background_text":{"kind":"string","value":"Adolescent suicide has always been a global public health concern to us. Suicide is the fourth leading cause of death globally among people aged 15–19 [1], and in China, it is the second leading cause of death for people aged 20–34 [2]. The consequences of adolescent suicide are the most serious among many mental health problems. It means disability or even a loss of life for individuals, heavy blows and severe trauma for families, and huge expenditures on the health, welfare and justice sectors for society. The study of suicide risk factors and the exploration of suicide mechanisms in high-suicide-risk adolescents are of great significance for the prevention of suicide.\nWith the development of technology, social media has become a platform for people to record their daily life and express their emotions. In China, Sina Weibo as a typical product of the big data era has extensive influence. Sina Weibo is China’s most popular social media platform, just like Twitter in America. Although there are some short video platforms (e.g., Douyin) and forums (e.g., Douban) that are also popular in China, it is only on Sina Weibo that users can share information in real-time and communicate interactively in the forms of text, pictures, videos and other multimedia. According to a report released by Sina Weibo, Weibo users show a younger trend, of which the post-90s and post-00s youth groups account for nearly 80%, and the daily active volume can reach 224 million [3].\nThe user’s emotional expression on social platforms has higher immediacy and less masking. Identifying the user’s psychological state through their expression on social media will be more convenient and quicker, and the obtained data will be more real. Therefore, we use the Weibo text analysis method to explore the suicide mechanism path of high-suicide-risk adolescents. Text analysis refers to the representation of text and the selection of feature items, which is a basic problem in text mining and information retrieval. Text analysis means that we can quantify the feature words extracted from text to represent text information. Therefore, we can extract the psychological indicators we want from the Weibo text by constructing a dictionary of psychological indicators.\nSuicide represents a series of consecutive stages, including suicide ideation, suicide plan, and suicide attempt [4,5]. Suicide ideation, as a high-risk factor for suicide, has a significant predictive effect [6,7]. Approximately 1/3 of suicide ideators ultimately attempt suicide, and the number of suicide attempters is several times higher than those who really die by suicide, so suicide attempt is a greater risk factor for suicide than suicidal ideation [1,8,9]. The later the suicide stage, the closer to suicide death, and the greater risk of suicide. Therefore, in order to quantify the degree of suicide risk and provide accurate information for suicide interventions, we decided to divide suicidal behavior into different stages. At the same time, considering that depressed mood is an important signal of suicide, we finally divided suicide into 4 stages, which the suicide risk gradually increases: depressed mood, suicide ideation, suicide plan and suicide attempt, and defined adolescents at different suicide stages as high-suicide-risk adolescents.\nA meta-analysis of the causes of suicide among adolescents found that suicide is a public health problem involving complex factors, such as biology, psychology, family, society and culture [8]. Among the psychological factors, psychological pain and hopelessness are important risk factors featuring strongly in suicide research [9,10,11]. Although psychological pain and hopelessness are often mentioned together as having an impact on suicidal behavior, as two most common motivations for suicide attempts [12], it is unclear how the two proximal factors of suicide affect the suicide stages.\nPsychological pain is defined as the “introspective experience of negative emotions such as fear, despair, grief, shame, guilt, blocked love, loneliness and loss” [13,14,15,16]. When Shneidman first hypothesized the relationship between psychological pain and suicide, he pointed out that other factors can only affect suicidal behavior through psychological pain, without which suicide would not occur [13]. Evidence suggests that psychological pain plays a central role in suicidal behavior [10,17,18]. Psychological pain is not enough to develop suicide ideation, it also requires hopelessness [19]. Hopelessness is defined as negative expectations or pessimism about oneself or the future [20]. Previous studies had showed that hopelessness was also an important risk factor of suicide [21,22,23].\nTo explore the relationship between psychological pain, hopelessness and suicide stages for high-suicide-risk adolescents, we established a hypothesis for the suicide mechanism path: psychological pain → hopelessness → suicide stages, with hopelessness as the mediating variable. In addition, there is a high correlation between mental illness and suicidal behaviors [24,25], and in order to better clarify the effect of psychological pain and hopelessness on suicide stages, a mediating effect model of our study is proposed with mental illness as a control variable (Figure 1). We used the more ecological Weibo text analysis method in this study, and hoped to further deepen the research on adolescent suicide and to find a new theoretical basis for future suicide interventions drawing on online data."},"methods_title":{"kind":"null"},"methods_text":{"kind":"null"},"results_title":{"kind":"string","value":"3. Results"},"results_text":{"kind":"string","value":"3.1. Group Characteristics Reflected by Qualitative Analysis The mental illness diagnosis of the experimental group was marked; it was found that 38 were diagnosed and 67 were undiagnosed. The proportion of users diagnosed with mental illness was 36.2%. Among the 38 high-suicide-risk adolescents diagnosed with mental illness, 29 users had depression (or depression was included in multiple mental illnesses), accounting for 76.3%; the rest included bipolar disorder (13.16%), anxiety disorder (13.16%), etc.; 9 of them suffered from multiple mental illnesses (e.g., depression and anxiety disorder), accounting for 23.68%.\nAmong the experimental group, 57 were in the stage of depressed mood (54.3%), 21 were in the stage of suicide ideation (20.0%), 19 were in the stage of suicide plan (18.1%), and 8 people were in the stage of suicide attempt (7.6%) (Table 2).\nThe mental illness diagnosis of the experimental group was marked; it was found that 38 were diagnosed and 67 were undiagnosed. The proportion of users diagnosed with mental illness was 36.2%. Among the 38 high-suicide-risk adolescents diagnosed with mental illness, 29 users had depression (or depression was included in multiple mental illnesses), accounting for 76.3%; the rest included bipolar disorder (13.16%), anxiety disorder (13.16%), etc.; 9 of them suffered from multiple mental illnesses (e.g., depression and anxiety disorder), accounting for 23.68%.\nAmong the experimental group, 57 were in the stage of depressed mood (54.3%), 21 were in the stage of suicide ideation (20.0%), 19 were in the stage of suicide plan (18.1%), and 8 people were in the stage of suicide attempt (7.6%) (Table 2).\n3.2. Path Analysis of Psychological Pain, Hopelessness and Suicide Stages A mediation effect test was performed on the path model (psychological pain → hopelessness → suicide stages). The performance of each fit index of the model was good (RMSEA < 0.08; CFI > 0.9; TLI > 0.9), indicating that the model fitting effect is excellent (Table 3).\nAfter controlling for mental illness factors, the total effect c was significant (β = −0.154, p < 0.05), indicating that there was a mediation effect; the path coefficients a and b were significant (β = 0.571, p < 0.001; β = −0.271, p < 0.05), but the direct effect c’ was not significant (β = 0.052, p > 0.05), indicating that there was a complete mediating effect of hopelessness. That is, psychological pain mainly affects suicide risk through hopelessness (Table 4).\nIt was worth noting that the relationship between the expression of hopelessness and the stages of suicide showed a significant negative correlation (β = −0.271, p < 0.05), indicating that the late suicide stage such as suicide attempt, which may be closer to suicide death, included less expressions of hopelessness (Table 4).\nFurthermore, there was a significant positive correlation between diagnosed mental illness and suicide stages (β = 0.398, p < 0.001), indicating the necessity of treating mental illness as a control variable in order to see the relationship between psychological pain, hopelessness and suicide stages more clearly. At the same time, it also showed that the later the suicide stage, the higher the diagnosis rate of mental illness (Table 4).\nA mediation effect test was performed on the path model (psychological pain → hopelessness → suicide stages). The performance of each fit index of the model was good (RMSEA < 0.08; CFI > 0.9; TLI > 0.9), indicating that the model fitting effect is excellent (Table 3).\nAfter controlling for mental illness factors, the total effect c was significant (β = −0.154, p < 0.05), indicating that there was a mediation effect; the path coefficients a and b were significant (β = 0.571, p < 0.001; β = −0.271, p < 0.05), but the direct effect c’ was not significant (β = 0.052, p > 0.05), indicating that there was a complete mediating effect of hopelessness. That is, psychological pain mainly affects suicide risk through hopelessness (Table 4).\nIt was worth noting that the relationship between the expression of hopelessness and the stages of suicide showed a significant negative correlation (β = −0.271, p < 0.05), indicating that the late suicide stage such as suicide attempt, which may be closer to suicide death, included less expressions of hopelessness (Table 4).\nFurthermore, there was a significant positive correlation between diagnosed mental illness and suicide stages (β = 0.398, p < 0.001), indicating the necessity of treating mental illness as a control variable in order to see the relationship between psychological pain, hopelessness and suicide stages more clearly. At the same time, it also showed that the later the suicide stage, the higher the diagnosis rate of mental illness (Table 4)."},"conclusions_title":{"kind":"string","value":"6. Conclusions"},"conclusions_text":{"kind":"string","value":"The qualitative findings suggested that mental illness in high-suicide-risk adolescents was strongly associated with suicide risk, and that depression was the most common mental illness associated with suicide. Another finding was that the later the suicide stage, the smaller was the proportion of high-suicide-risk adolescents.\nThe results of the mediation effect analysis verified the suicide mechanism path: psychological pain → hopelessness → suicide stages, indicating that psychological pain mainly affected suicide stages through hopelessness, and hopelessness may be a better predictor of suicide risk for high-suicide-risk adolescents.\nNotably, although adolescents in the later suicide stages were at greater suicide risk, they had less expression of hopelessness in the traditional sense."},"other_sections_titles":{"kind":"list like","value":["2. Materials and Methods","2.2. Online Psychological Assistance Project","2.3. Data Collection and Processing","2.3.1. Labeling the Interview Records Data","2.3.2. Getting the Weibo Text Data","2.4. Data Analysis","2.4.1. Qualitative Analysis","2.4.2. Analysis of Suicide Path","3.1. Group Characteristics Reflected by Qualitative Analysis","3.2. Path Analysis of Psychological Pain, Hopelessness and Suicide Stages","4.1. The Qualitative Analysis Results","4.2. The Mediating Effect of Hopelessness","4.3. The Hopelessness Expressions and the Suicide Stages","5. Limitations and Future Studies"],"string":"[\n \"2. Materials and Methods\",\n \"2.2. Online Psychological Assistance Project\",\n \"2.3. Data Collection and Processing\",\n \"2.3.1. Labeling the Interview Records Data\",\n \"2.3.2. Getting the Weibo Text Data\",\n \"2.4. Data Analysis\",\n \"2.4.1. Qualitative Analysis\",\n \"2.4.2. Analysis of Suicide Path\",\n \"3.1. Group Characteristics Reflected by Qualitative Analysis\",\n \"3.2. Path Analysis of Psychological Pain, Hopelessness and Suicide Stages\",\n \"4.1. The Qualitative Analysis Results\",\n \"4.2. The Mediating Effect of Hopelessness\",\n \"4.3. The Hopelessness Expressions and the Suicide Stages\",\n \"5. Limitations and Future Studies\"\n]"},"other_sections_texts":{"kind":"list like","value":["2.1. Participants Through the online psychological assistance project, we obtained the interview records data of 3627 Weibo users, and then processed the user’s interview records data and Weibo texts data. Finally, we retained 105 high-suicide-risk adolescents with enough Weibo texts as the experimental group.\nEthical Statement: Participants gave informed consent when agreeing to take part in the online psychological assistance project. The project received ethical approval from the Institution-al Review Board of the Institute of Psychology, Chinese Academy of Sciences, with the ethics approval number H16003.\nThrough the online psychological assistance project, we obtained the interview records data of 3627 Weibo users, and then processed the user’s interview records data and Weibo texts data. Finally, we retained 105 high-suicide-risk adolescents with enough Weibo texts as the experimental group.\nEthical Statement: Participants gave informed consent when agreeing to take part in the online psychological assistance project. The project received ethical approval from the Institution-al Review Board of the Institute of Psychology, Chinese Academy of Sciences, with the ethics approval number H16003.\n2.2. Online Psychological Assistance Project We used the suicide signal recognition model established by the Computational Cyber-Psychology Lab (CCPL) to identify the suicide signal of a single microblog, which can effectively determine whether the single microblog has shown suicidal risk [26].\nSince November 2016, we had used the suicide signal recognition model for online identification of Weibo comments. The last Weibo post before the death of Weibo user “Zoufan”, as a place where people can express their negative emotions, has received more than 1 million comments until now, and the number of comments keep rising (Figure 2).\nFirstly, we used the suicide signal model to identify the suicide signal of the Weibo comments. Then, the comments identified by the model were re-identified by manual recognition. Finally, we used the official Weibo account “Psychological Map Psy” and sent a private message to the Weibo users marked as having a suicide risk. The contents of the private message mainly include inquiries about the user’s emotional state, expressions of concern, links to self-assessment psychological scales, and the hotline of the psychological crisis intervention center, etc. (Figure 3). In addition, users can choose whether to communicate with the voluntary counselor online according to their own wishes, so as to achieve agile psychological crisis intervention.\nWe used the suicide signal recognition model established by the Computational Cyber-Psychology Lab (CCPL) to identify the suicide signal of a single microblog, which can effectively determine whether the single microblog has shown suicidal risk [26].\nSince November 2016, we had used the suicide signal recognition model for online identification of Weibo comments. The last Weibo post before the death of Weibo user “Zoufan”, as a place where people can express their negative emotions, has received more than 1 million comments until now, and the number of comments keep rising (Figure 2).\nFirstly, we used the suicide signal model to identify the suicide signal of the Weibo comments. Then, the comments identified by the model were re-identified by manual recognition. Finally, we used the official Weibo account “Psychological Map Psy” and sent a private message to the Weibo users marked as having a suicide risk. The contents of the private message mainly include inquiries about the user’s emotional state, expressions of concern, links to self-assessment psychological scales, and the hotline of the psychological crisis intervention center, etc. (Figure 3). In addition, users can choose whether to communicate with the voluntary counselor online according to their own wishes, so as to achieve agile psychological crisis intervention.\n2.3. Data Collection and Processing 2.3.1. Labeling the Interview Records Data During the online psychological assistance project period (November 2016 to April 2019), the official Weibo account had communicated with 3627 users online, and 127,336 message records were accumulated. Adhering to the principle of privacy and confidentiality, the use of interview records data was strictly restricted. The experimenter must protect the privacy of the research subjects involved in the project, and should not use any confidential information related to the research subjects (including but not limited to accounts, nicknames, contact information, personal homepage, network IP, location, information records, etc.) outside the scope of the research, and would not violate the privacy of the subjects in any way.\nThe interview records data were encoded by qualitative analysis. The basic content of interview records data were the current status and problems of users. We extracted three indicators from the interview records data, and the specific descriptions of them are shown below:(1)Determining whether the user was in adolescent stages (junior high school, high school, college), and retaining the data labeled as the adolescents.(2)Determining whether the user was diagnosed with mental illness by a psychiatrist, had a history of mental illness, or was taking medication for mental illness, and labeling a user had a mental illness or not as a 0/1 dummy variable.(3)Coding the suicide stages (depressed mood, suicide ideation, suicide plan, suicide attempt) of adolescents, and marking the suicide stages as 1, 2, 3 and 4, an ordered categorical variable.\nDetermining whether the user was in adolescent stages (junior high school, high school, college), and retaining the data labeled as the adolescents.\nDetermining whether the user was diagnosed with mental illness by a psychiatrist, had a history of mental illness, or was taking medication for mental illness, and labeling a user had a mental illness or not as a 0/1 dummy variable.\nCoding the suicide stages (depressed mood, suicide ideation, suicide plan, suicide attempt) of adolescents, and marking the suicide stages as 1, 2, 3 and 4, an ordered categorical variable.\nSuicide stages were coded on the basis of the following criteria: (a) Depressed mood: feeling negative and depressed, but not yet having suicidal ideation. It means participants did not express suicidal intention explicitly or implicitly(whether they actively expressed suicidal intentions, or exposed suicidal intentions under inquiries from the official Weibo account). (b) Suicide ideation: there is suicidal intent, but suicide plan has not formed. Due to being affected by an intense depressive mood, the idea of avoiding pain through suicide arises, but there is no specific time and way to implement it. (c) Suicide plan: on the basis of suicidal ideation, there is a rough or detailed suicide plan, such as when, where, and how to commit suicide. (d) Suicide attempt: at least one suicide attempt with the aim of ending one’s own life.\nDuring the online psychological assistance project period (November 2016 to April 2019), the official Weibo account had communicated with 3627 users online, and 127,336 message records were accumulated. Adhering to the principle of privacy and confidentiality, the use of interview records data was strictly restricted. The experimenter must protect the privacy of the research subjects involved in the project, and should not use any confidential information related to the research subjects (including but not limited to accounts, nicknames, contact information, personal homepage, network IP, location, information records, etc.) outside the scope of the research, and would not violate the privacy of the subjects in any way.\nThe interview records data were encoded by qualitative analysis. The basic content of interview records data were the current status and problems of users. We extracted three indicators from the interview records data, and the specific descriptions of them are shown below:(1)Determining whether the user was in adolescent stages (junior high school, high school, college), and retaining the data labeled as the adolescents.(2)Determining whether the user was diagnosed with mental illness by a psychiatrist, had a history of mental illness, or was taking medication for mental illness, and labeling a user had a mental illness or not as a 0/1 dummy variable.(3)Coding the suicide stages (depressed mood, suicide ideation, suicide plan, suicide attempt) of adolescents, and marking the suicide stages as 1, 2, 3 and 4, an ordered categorical variable.\nDetermining whether the user was in adolescent stages (junior high school, high school, college), and retaining the data labeled as the adolescents.\nDetermining whether the user was diagnosed with mental illness by a psychiatrist, had a history of mental illness, or was taking medication for mental illness, and labeling a user had a mental illness or not as a 0/1 dummy variable.\nCoding the suicide stages (depressed mood, suicide ideation, suicide plan, suicide attempt) of adolescents, and marking the suicide stages as 1, 2, 3 and 4, an ordered categorical variable.\nSuicide stages were coded on the basis of the following criteria: (a) Depressed mood: feeling negative and depressed, but not yet having suicidal ideation. It means participants did not express suicidal intention explicitly or implicitly(whether they actively expressed suicidal intentions, or exposed suicidal intentions under inquiries from the official Weibo account). (b) Suicide ideation: there is suicidal intent, but suicide plan has not formed. Due to being affected by an intense depressive mood, the idea of avoiding pain through suicide arises, but there is no specific time and way to implement it. (c) Suicide plan: on the basis of suicidal ideation, there is a rough or detailed suicide plan, such as when, where, and how to commit suicide. (d) Suicide attempt: at least one suicide attempt with the aim of ending one’s own life.\n2.3.2. Getting the Weibo Text Data We scraped Weibo texts published by the same 3627 users through the Application Programming Interface (API) of Sina Weibo. In order to obtain sufficient Weibo texts for analysis, we took the first time that the official Weibo account sent a private message to the user as the beginning of suicide intervention, downloaded all Weibo texts in the 3 months before the intervention, and filtered the users whose Weibo texts are less than 300 words (Figure 4).\nFinally, 105 high-suicide-risk adolescents (as the experimental group) were included in the analysis. We then removed stop words and performed Chinese word segmentation on the Weibo texts, and calculated word frequency. “The Chinese Suicide Dictionary” [27] was used to measure the word frequency ratio of their psychological pain and hopelessness. The dictionary outline is shown in Table 1. The psychological pain dimension represents the pain and torment of individuals at the psychological level, including 403 keywords; the hopelessness dimension represents the subjective feeling of losing hope, including 188 keywords.\nWe scraped Weibo texts published by the same 3627 users through the Application Programming Interface (API) of Sina Weibo. In order to obtain sufficient Weibo texts for analysis, we took the first time that the official Weibo account sent a private message to the user as the beginning of suicide intervention, downloaded all Weibo texts in the 3 months before the intervention, and filtered the users whose Weibo texts are less than 300 words (Figure 4).\nFinally, 105 high-suicide-risk adolescents (as the experimental group) were included in the analysis. We then removed stop words and performed Chinese word segmentation on the Weibo texts, and calculated word frequency. “The Chinese Suicide Dictionary” [27] was used to measure the word frequency ratio of their psychological pain and hopelessness. The dictionary outline is shown in Table 1. The psychological pain dimension represents the pain and torment of individuals at the psychological level, including 403 keywords; the hopelessness dimension represents the subjective feeling of losing hope, including 188 keywords.\n2.3.1. Labeling the Interview Records Data During the online psychological assistance project period (November 2016 to April 2019), the official Weibo account had communicated with 3627 users online, and 127,336 message records were accumulated. Adhering to the principle of privacy and confidentiality, the use of interview records data was strictly restricted. The experimenter must protect the privacy of the research subjects involved in the project, and should not use any confidential information related to the research subjects (including but not limited to accounts, nicknames, contact information, personal homepage, network IP, location, information records, etc.) outside the scope of the research, and would not violate the privacy of the subjects in any way.\nThe interview records data were encoded by qualitative analysis. The basic content of interview records data were the current status and problems of users. We extracted three indicators from the interview records data, and the specific descriptions of them are shown below:(1)Determining whether the user was in adolescent stages (junior high school, high school, college), and retaining the data labeled as the adolescents.(2)Determining whether the user was diagnosed with mental illness by a psychiatrist, had a history of mental illness, or was taking medication for mental illness, and labeling a user had a mental illness or not as a 0/1 dummy variable.(3)Coding the suicide stages (depressed mood, suicide ideation, suicide plan, suicide attempt) of adolescents, and marking the suicide stages as 1, 2, 3 and 4, an ordered categorical variable.\nDetermining whether the user was in adolescent stages (junior high school, high school, college), and retaining the data labeled as the adolescents.\nDetermining whether the user was diagnosed with mental illness by a psychiatrist, had a history of mental illness, or was taking medication for mental illness, and labeling a user had a mental illness or not as a 0/1 dummy variable.\nCoding the suicide stages (depressed mood, suicide ideation, suicide plan, suicide attempt) of adolescents, and marking the suicide stages as 1, 2, 3 and 4, an ordered categorical variable.\nSuicide stages were coded on the basis of the following criteria: (a) Depressed mood: feeling negative and depressed, but not yet having suicidal ideation. It means participants did not express suicidal intention explicitly or implicitly(whether they actively expressed suicidal intentions, or exposed suicidal intentions under inquiries from the official Weibo account). (b) Suicide ideation: there is suicidal intent, but suicide plan has not formed. Due to being affected by an intense depressive mood, the idea of avoiding pain through suicide arises, but there is no specific time and way to implement it. (c) Suicide plan: on the basis of suicidal ideation, there is a rough or detailed suicide plan, such as when, where, and how to commit suicide. (d) Suicide attempt: at least one suicide attempt with the aim of ending one’s own life.\nDuring the online psychological assistance project period (November 2016 to April 2019), the official Weibo account had communicated with 3627 users online, and 127,336 message records were accumulated. Adhering to the principle of privacy and confidentiality, the use of interview records data was strictly restricted. The experimenter must protect the privacy of the research subjects involved in the project, and should not use any confidential information related to the research subjects (including but not limited to accounts, nicknames, contact information, personal homepage, network IP, location, information records, etc.) outside the scope of the research, and would not violate the privacy of the subjects in any way.\nThe interview records data were encoded by qualitative analysis. The basic content of interview records data were the current status and problems of users. We extracted three indicators from the interview records data, and the specific descriptions of them are shown below:(1)Determining whether the user was in adolescent stages (junior high school, high school, college), and retaining the data labeled as the adolescents.(2)Determining whether the user was diagnosed with mental illness by a psychiatrist, had a history of mental illness, or was taking medication for mental illness, and labeling a user had a mental illness or not as a 0/1 dummy variable.(3)Coding the suicide stages (depressed mood, suicide ideation, suicide plan, suicide attempt) of adolescents, and marking the suicide stages as 1, 2, 3 and 4, an ordered categorical variable.\nDetermining whether the user was in adolescent stages (junior high school, high school, college), and retaining the data labeled as the adolescents.\nDetermining whether the user was diagnosed with mental illness by a psychiatrist, had a history of mental illness, or was taking medication for mental illness, and labeling a user had a mental illness or not as a 0/1 dummy variable.\nCoding the suicide stages (depressed mood, suicide ideation, suicide plan, suicide attempt) of adolescents, and marking the suicide stages as 1, 2, 3 and 4, an ordered categorical variable.\nSuicide stages were coded on the basis of the following criteria: (a) Depressed mood: feeling negative and depressed, but not yet having suicidal ideation. It means participants did not express suicidal intention explicitly or implicitly(whether they actively expressed suicidal intentions, or exposed suicidal intentions under inquiries from the official Weibo account). (b) Suicide ideation: there is suicidal intent, but suicide plan has not formed. Due to being affected by an intense depressive mood, the idea of avoiding pain through suicide arises, but there is no specific time and way to implement it. (c) Suicide plan: on the basis of suicidal ideation, there is a rough or detailed suicide plan, such as when, where, and how to commit suicide. (d) Suicide attempt: at least one suicide attempt with the aim of ending one’s own life.\n2.3.2. Getting the Weibo Text Data We scraped Weibo texts published by the same 3627 users through the Application Programming Interface (API) of Sina Weibo. In order to obtain sufficient Weibo texts for analysis, we took the first time that the official Weibo account sent a private message to the user as the beginning of suicide intervention, downloaded all Weibo texts in the 3 months before the intervention, and filtered the users whose Weibo texts are less than 300 words (Figure 4).\nFinally, 105 high-suicide-risk adolescents (as the experimental group) were included in the analysis. We then removed stop words and performed Chinese word segmentation on the Weibo texts, and calculated word frequency. “The Chinese Suicide Dictionary” [27] was used to measure the word frequency ratio of their psychological pain and hopelessness. The dictionary outline is shown in Table 1. The psychological pain dimension represents the pain and torment of individuals at the psychological level, including 403 keywords; the hopelessness dimension represents the subjective feeling of losing hope, including 188 keywords.\nWe scraped Weibo texts published by the same 3627 users through the Application Programming Interface (API) of Sina Weibo. In order to obtain sufficient Weibo texts for analysis, we took the first time that the official Weibo account sent a private message to the user as the beginning of suicide intervention, downloaded all Weibo texts in the 3 months before the intervention, and filtered the users whose Weibo texts are less than 300 words (Figure 4).\nFinally, 105 high-suicide-risk adolescents (as the experimental group) were included in the analysis. We then removed stop words and performed Chinese word segmentation on the Weibo texts, and calculated word frequency. “The Chinese Suicide Dictionary” [27] was used to measure the word frequency ratio of their psychological pain and hopelessness. The dictionary outline is shown in Table 1. The psychological pain dimension represents the pain and torment of individuals at the psychological level, including 403 keywords; the hopelessness dimension represents the subjective feeling of losing hope, including 188 keywords.\n2.4. Data Analysis 2.4.1. Qualitative Analysis Through the labeling results of interview records data, we investigated the situation of mental illness and the distribution of suicide stages for the experimental group.\nThrough the labeling results of interview records data, we investigated the situation of mental illness and the distribution of suicide stages for the experimental group.\n2.4.2. Analysis of Suicide Path The mediation effect analysis was executed on the software Mplus by using the bootstrap method to test the suicide mechanism path model. The word frequency of psychological pain and hopelessness of the experimental group were treated as independent variable (X) and mediating variable (M), respectively. The suicide stages were treated as dependent variable (Y). The mental illness was included in the model as a control variable (Z).\nThe mediation effect analysis was executed on the software Mplus by using the bootstrap method to test the suicide mechanism path model. The word frequency of psychological pain and hopelessness of the experimental group were treated as independent variable (X) and mediating variable (M), respectively. The suicide stages were treated as dependent variable (Y). The mental illness was included in the model as a control variable (Z).\n2.4.1. Qualitative Analysis Through the labeling results of interview records data, we investigated the situation of mental illness and the distribution of suicide stages for the experimental group.\nThrough the labeling results of interview records data, we investigated the situation of mental illness and the distribution of suicide stages for the experimental group.\n2.4.2. Analysis of Suicide Path The mediation effect analysis was executed on the software Mplus by using the bootstrap method to test the suicide mechanism path model. The word frequency of psychological pain and hopelessness of the experimental group were treated as independent variable (X) and mediating variable (M), respectively. The suicide stages were treated as dependent variable (Y). The mental illness was included in the model as a control variable (Z).\nThe mediation effect analysis was executed on the software Mplus by using the bootstrap method to test the suicide mechanism path model. The word frequency of psychological pain and hopelessness of the experimental group were treated as independent variable (X) and mediating variable (M), respectively. The suicide stages were treated as dependent variable (Y). The mental illness was included in the model as a control variable (Z).","We used the suicide signal recognition model established by the Computational Cyber-Psychology Lab (CCPL) to identify the suicide signal of a single microblog, which can effectively determine whether the single microblog has shown suicidal risk [26].\nSince November 2016, we had used the suicide signal recognition model for online identification of Weibo comments. The last Weibo post before the death of Weibo user “Zoufan”, as a place where people can express their negative emotions, has received more than 1 million comments until now, and the number of comments keep rising (Figure 2).\nFirstly, we used the suicide signal model to identify the suicide signal of the Weibo comments. Then, the comments identified by the model were re-identified by manual recognition. Finally, we used the official Weibo account “Psychological Map Psy” and sent a private message to the Weibo users marked as having a suicide risk. The contents of the private message mainly include inquiries about the user’s emotional state, expressions of concern, links to self-assessment psychological scales, and the hotline of the psychological crisis intervention center, etc. (Figure 3). In addition, users can choose whether to communicate with the voluntary counselor online according to their own wishes, so as to achieve agile psychological crisis intervention.","2.3.1. Labeling the Interview Records Data During the online psychological assistance project period (November 2016 to April 2019), the official Weibo account had communicated with 3627 users online, and 127,336 message records were accumulated. Adhering to the principle of privacy and confidentiality, the use of interview records data was strictly restricted. The experimenter must protect the privacy of the research subjects involved in the project, and should not use any confidential information related to the research subjects (including but not limited to accounts, nicknames, contact information, personal homepage, network IP, location, information records, etc.) outside the scope of the research, and would not violate the privacy of the subjects in any way.\nThe interview records data were encoded by qualitative analysis. The basic content of interview records data were the current status and problems of users. We extracted three indicators from the interview records data, and the specific descriptions of them are shown below:(1)Determining whether the user was in adolescent stages (junior high school, high school, college), and retaining the data labeled as the adolescents.(2)Determining whether the user was diagnosed with mental illness by a psychiatrist, had a history of mental illness, or was taking medication for mental illness, and labeling a user had a mental illness or not as a 0/1 dummy variable.(3)Coding the suicide stages (depressed mood, suicide ideation, suicide plan, suicide attempt) of adolescents, and marking the suicide stages as 1, 2, 3 and 4, an ordered categorical variable.\nDetermining whether the user was in adolescent stages (junior high school, high school, college), and retaining the data labeled as the adolescents.\nDetermining whether the user was diagnosed with mental illness by a psychiatrist, had a history of mental illness, or was taking medication for mental illness, and labeling a user had a mental illness or not as a 0/1 dummy variable.\nCoding the suicide stages (depressed mood, suicide ideation, suicide plan, suicide attempt) of adolescents, and marking the suicide stages as 1, 2, 3 and 4, an ordered categorical variable.\nSuicide stages were coded on the basis of the following criteria: (a) Depressed mood: feeling negative and depressed, but not yet having suicidal ideation. It means participants did not express suicidal intention explicitly or implicitly(whether they actively expressed suicidal intentions, or exposed suicidal intentions under inquiries from the official Weibo account). (b) Suicide ideation: there is suicidal intent, but suicide plan has not formed. Due to being affected by an intense depressive mood, the idea of avoiding pain through suicide arises, but there is no specific time and way to implement it. (c) Suicide plan: on the basis of suicidal ideation, there is a rough or detailed suicide plan, such as when, where, and how to commit suicide. (d) Suicide attempt: at least one suicide attempt with the aim of ending one’s own life.\nDuring the online psychological assistance project period (November 2016 to April 2019), the official Weibo account had communicated with 3627 users online, and 127,336 message records were accumulated. Adhering to the principle of privacy and confidentiality, the use of interview records data was strictly restricted. The experimenter must protect the privacy of the research subjects involved in the project, and should not use any confidential information related to the research subjects (including but not limited to accounts, nicknames, contact information, personal homepage, network IP, location, information records, etc.) outside the scope of the research, and would not violate the privacy of the subjects in any way.\nThe interview records data were encoded by qualitative analysis. The basic content of interview records data were the current status and problems of users. We extracted three indicators from the interview records data, and the specific descriptions of them are shown below:(1)Determining whether the user was in adolescent stages (junior high school, high school, college), and retaining the data labeled as the adolescents.(2)Determining whether the user was diagnosed with mental illness by a psychiatrist, had a history of mental illness, or was taking medication for mental illness, and labeling a user had a mental illness or not as a 0/1 dummy variable.(3)Coding the suicide stages (depressed mood, suicide ideation, suicide plan, suicide attempt) of adolescents, and marking the suicide stages as 1, 2, 3 and 4, an ordered categorical variable.\nDetermining whether the user was in adolescent stages (junior high school, high school, college), and retaining the data labeled as the adolescents.\nDetermining whether the user was diagnosed with mental illness by a psychiatrist, had a history of mental illness, or was taking medication for mental illness, and labeling a user had a mental illness or not as a 0/1 dummy variable.\nCoding the suicide stages (depressed mood, suicide ideation, suicide plan, suicide attempt) of adolescents, and marking the suicide stages as 1, 2, 3 and 4, an ordered categorical variable.\nSuicide stages were coded on the basis of the following criteria: (a) Depressed mood: feeling negative and depressed, but not yet having suicidal ideation. It means participants did not express suicidal intention explicitly or implicitly(whether they actively expressed suicidal intentions, or exposed suicidal intentions under inquiries from the official Weibo account). (b) Suicide ideation: there is suicidal intent, but suicide plan has not formed. Due to being affected by an intense depressive mood, the idea of avoiding pain through suicide arises, but there is no specific time and way to implement it. (c) Suicide plan: on the basis of suicidal ideation, there is a rough or detailed suicide plan, such as when, where, and how to commit suicide. (d) Suicide attempt: at least one suicide attempt with the aim of ending one’s own life.\n2.3.2. Getting the Weibo Text Data We scraped Weibo texts published by the same 3627 users through the Application Programming Interface (API) of Sina Weibo. In order to obtain sufficient Weibo texts for analysis, we took the first time that the official Weibo account sent a private message to the user as the beginning of suicide intervention, downloaded all Weibo texts in the 3 months before the intervention, and filtered the users whose Weibo texts are less than 300 words (Figure 4).\nFinally, 105 high-suicide-risk adolescents (as the experimental group) were included in the analysis. We then removed stop words and performed Chinese word segmentation on the Weibo texts, and calculated word frequency. “The Chinese Suicide Dictionary” [27] was used to measure the word frequency ratio of their psychological pain and hopelessness. The dictionary outline is shown in Table 1. The psychological pain dimension represents the pain and torment of individuals at the psychological level, including 403 keywords; the hopelessness dimension represents the subjective feeling of losing hope, including 188 keywords.\nWe scraped Weibo texts published by the same 3627 users through the Application Programming Interface (API) of Sina Weibo. In order to obtain sufficient Weibo texts for analysis, we took the first time that the official Weibo account sent a private message to the user as the beginning of suicide intervention, downloaded all Weibo texts in the 3 months before the intervention, and filtered the users whose Weibo texts are less than 300 words (Figure 4).\nFinally, 105 high-suicide-risk adolescents (as the experimental group) were included in the analysis. We then removed stop words and performed Chinese word segmentation on the Weibo texts, and calculated word frequency. “The Chinese Suicide Dictionary” [27] was used to measure the word frequency ratio of their psychological pain and hopelessness. The dictionary outline is shown in Table 1. The psychological pain dimension represents the pain and torment of individuals at the psychological level, including 403 keywords; the hopelessness dimension represents the subjective feeling of losing hope, including 188 keywords.","During the online psychological assistance project period (November 2016 to April 2019), the official Weibo account had communicated with 3627 users online, and 127,336 message records were accumulated. Adhering to the principle of privacy and confidentiality, the use of interview records data was strictly restricted. The experimenter must protect the privacy of the research subjects involved in the project, and should not use any confidential information related to the research subjects (including but not limited to accounts, nicknames, contact information, personal homepage, network IP, location, information records, etc.) outside the scope of the research, and would not violate the privacy of the subjects in any way.\nThe interview records data were encoded by qualitative analysis. The basic content of interview records data were the current status and problems of users. We extracted three indicators from the interview records data, and the specific descriptions of them are shown below:(1)Determining whether the user was in adolescent stages (junior high school, high school, college), and retaining the data labeled as the adolescents.(2)Determining whether the user was diagnosed with mental illness by a psychiatrist, had a history of mental illness, or was taking medication for mental illness, and labeling a user had a mental illness or not as a 0/1 dummy variable.(3)Coding the suicide stages (depressed mood, suicide ideation, suicide plan, suicide attempt) of adolescents, and marking the suicide stages as 1, 2, 3 and 4, an ordered categorical variable.\nDetermining whether the user was in adolescent stages (junior high school, high school, college), and retaining the data labeled as the adolescents.\nDetermining whether the user was diagnosed with mental illness by a psychiatrist, had a history of mental illness, or was taking medication for mental illness, and labeling a user had a mental illness or not as a 0/1 dummy variable.\nCoding the suicide stages (depressed mood, suicide ideation, suicide plan, suicide attempt) of adolescents, and marking the suicide stages as 1, 2, 3 and 4, an ordered categorical variable.\nSuicide stages were coded on the basis of the following criteria: (a) Depressed mood: feeling negative and depressed, but not yet having suicidal ideation. It means participants did not express suicidal intention explicitly or implicitly(whether they actively expressed suicidal intentions, or exposed suicidal intentions under inquiries from the official Weibo account). (b) Suicide ideation: there is suicidal intent, but suicide plan has not formed. Due to being affected by an intense depressive mood, the idea of avoiding pain through suicide arises, but there is no specific time and way to implement it. (c) Suicide plan: on the basis of suicidal ideation, there is a rough or detailed suicide plan, such as when, where, and how to commit suicide. (d) Suicide attempt: at least one suicide attempt with the aim of ending one’s own life.","We scraped Weibo texts published by the same 3627 users through the Application Programming Interface (API) of Sina Weibo. In order to obtain sufficient Weibo texts for analysis, we took the first time that the official Weibo account sent a private message to the user as the beginning of suicide intervention, downloaded all Weibo texts in the 3 months before the intervention, and filtered the users whose Weibo texts are less than 300 words (Figure 4).\nFinally, 105 high-suicide-risk adolescents (as the experimental group) were included in the analysis. We then removed stop words and performed Chinese word segmentation on the Weibo texts, and calculated word frequency. “The Chinese Suicide Dictionary” [27] was used to measure the word frequency ratio of their psychological pain and hopelessness. The dictionary outline is shown in Table 1. The psychological pain dimension represents the pain and torment of individuals at the psychological level, including 403 keywords; the hopelessness dimension represents the subjective feeling of losing hope, including 188 keywords.","2.4.1. Qualitative Analysis Through the labeling results of interview records data, we investigated the situation of mental illness and the distribution of suicide stages for the experimental group.\nThrough the labeling results of interview records data, we investigated the situation of mental illness and the distribution of suicide stages for the experimental group.\n2.4.2. Analysis of Suicide Path The mediation effect analysis was executed on the software Mplus by using the bootstrap method to test the suicide mechanism path model. The word frequency of psychological pain and hopelessness of the experimental group were treated as independent variable (X) and mediating variable (M), respectively. The suicide stages were treated as dependent variable (Y). The mental illness was included in the model as a control variable (Z).\nThe mediation effect analysis was executed on the software Mplus by using the bootstrap method to test the suicide mechanism path model. The word frequency of psychological pain and hopelessness of the experimental group were treated as independent variable (X) and mediating variable (M), respectively. The suicide stages were treated as dependent variable (Y). The mental illness was included in the model as a control variable (Z).","Through the labeling results of interview records data, we investigated the situation of mental illness and the distribution of suicide stages for the experimental group.","The mediation effect analysis was executed on the software Mplus by using the bootstrap method to test the suicide mechanism path model. The word frequency of psychological pain and hopelessness of the experimental group were treated as independent variable (X) and mediating variable (M), respectively. The suicide stages were treated as dependent variable (Y). The mental illness was included in the model as a control variable (Z).","The mental illness diagnosis of the experimental group was marked; it was found that 38 were diagnosed and 67 were undiagnosed. The proportion of users diagnosed with mental illness was 36.2%. Among the 38 high-suicide-risk adolescents diagnosed with mental illness, 29 users had depression (or depression was included in multiple mental illnesses), accounting for 76.3%; the rest included bipolar disorder (13.16%), anxiety disorder (13.16%), etc.; 9 of them suffered from multiple mental illnesses (e.g., depression and anxiety disorder), accounting for 23.68%.\nAmong the experimental group, 57 were in the stage of depressed mood (54.3%), 21 were in the stage of suicide ideation (20.0%), 19 were in the stage of suicide plan (18.1%), and 8 people were in the stage of suicide attempt (7.6%) (Table 2).","A mediation effect test was performed on the path model (psychological pain → hopelessness → suicide stages). The performance of each fit index of the model was good (RMSEA < 0.08; CFI > 0.9; TLI > 0.9), indicating that the model fitting effect is excellent (Table 3).\nAfter controlling for mental illness factors, the total effect c was significant (β = −0.154, p < 0.05), indicating that there was a mediation effect; the path coefficients a and b were significant (β = 0.571, p < 0.001; β = −0.271, p < 0.05), but the direct effect c’ was not significant (β = 0.052, p > 0.05), indicating that there was a complete mediating effect of hopelessness. That is, psychological pain mainly affects suicide risk through hopelessness (Table 4).\nIt was worth noting that the relationship between the expression of hopelessness and the stages of suicide showed a significant negative correlation (β = −0.271, p < 0.05), indicating that the late suicide stage such as suicide attempt, which may be closer to suicide death, included less expressions of hopelessness (Table 4).\nFurthermore, there was a significant positive correlation between diagnosed mental illness and suicide stages (β = 0.398, p < 0.001), indicating the necessity of treating mental illness as a control variable in order to see the relationship between psychological pain, hopelessness and suicide stages more clearly. At the same time, it also showed that the later the suicide stage, the higher the diagnosis rate of mental illness (Table 4).","Among the experimental group, 36.2% of users suffered from mental illness. Depression accounted for 76.3% of all mental illness; it is the most common mental illness among the high-suicide-risk adolescents. The incidence of suicide among students whose relatives were affected by psychosis were higher than that of general residents. Suicide and mental illness were closely linked [28]. British doctor Sainsbury conducted a qualitative study on the data of 400 suicides examined by a coroner in London between 1936 and 1938, and found that 37% of the suicides reflected mental illness [29]. In a meta-analysis conducted in 2004, twenty-seven studies comprising 3275 suicides were included, of which 87.3% had been diagnosed with a mental disorder prior to their death [30]. In contrast, the research population in our study was only a high suicide risk group, so the detection rate of mental illness was relatively low. Although the research populations of the other two studies were both suicide decedents, the time interval between the two studies was large. It is very possible that there existed better methods to detect the mental illness of the suicide decedents in later studies, and it is understandable that the detection rate of mental illness has greatly increased. Furthermore, a more recent study has shown that the most common mental illness associated with suicide was depression [31]. The proportion of depression in all mental illnesses in our study also supported this conclusion.\nAnother result of the qualitative analysis showed that the later the suicide stage, the lower the proportion of adolescents. A study on residents’ suicidal ideation, suicide plans and suicide attempts in Dalian, China showed that the detection rate of suicidal ideation, suicide plan and suicide attempt decreased in that order in different groups [32]. This may because not all suicide ideations translate into suicidal actions, and most people with suicide ideation have not attempted suicide [33,34].","Our research found that the effect of psychological pain on suicide stages was totally mediated by hopelessness, confirming our suicide mechanism path hypothesis: psychological pain → hopelessness → suicide stages. Although the first step of suicide ideation starts from psychological pain, psychological pain itself is not enough to generate suicide ideation, and hopelessness is also needed to develop suicide ideation [19]. People make decisions and take actions based in part on their emotional predictions [35,36], and hopelessness makes people at suicide risk overestimate their future emotional pain [37]. If the individual experiences constant pain in life, this may reduce the individual’s desire to live, leading to suicidal thoughts. However, pain alone is not enough to completely lose the belief in life. Only when the individual feels that the terrible situation cannot be improved, and he or she will live in pain forever, suicide will be considered.\nThus, expressions of hopelessness may be a better predictor of suicide risk than psychological pain, and we found a traceable pathway for suicide intervention. Based on the Weibo posts, we can capture whether individual released suicide signals and it is possible to track the suicide stages in the future, making suicide intervention more accurate.","There was a negative predictive relationship between hopelessness expressions and suicide stages, as adolescents at the later suicide stages had less expressions of hopelessness. One possible reason is that adolescents at higher risk of suicide may use less concrete, direct words of hopelessness to express such feelings. From a linguistic point of view, Gao Yihong and Meng Ling analyzed the Weibo texts of “Zoufan” three months before suicide. They found that “Zoufan” often used decontextualized and fragmented ways to express personal depressed mood, and expressed metaphors and intentions about death frequently [38]. This euphemistic and poetic way of venting may be the commonality of high suicide risk adolescents. Another possible reason is “the Presuicidal Syndrome”, which mentioned that there exists an “ominous quiet” in the short period before suicide. This is a sign of “affective restriction”, which represents extreme repression of intolerable feelings [39]. Such “affective restriction” may be why people in the later suicide stage are less likely to express hopelessness. Moreover, adolescents at the later suicide stage may be more accepting and tolerant of psychological pain and hopelessness, which makes them less willing to talk about individual psychological pain and hopelessness, and with lack of supportive survival signals through texts, lose the belief in getting help.\nMany researchers have paid attention to the use of social media for suicide prevention and intervention, and have conducted a series of studies [40,41]. However, little attention has been paid to whether the suicidal signal was from an early suicide ideator or a later suicide attempter. Our study fills this gap, complementing and extending suicide-related research. Our results suggest that adolescents who are at a later suicide stage and have a higher suicide risk often do not express too much hopelessness directly. Therefore, we cannot judge closeness to suicide only by the level of traditional expressions of hopelessness.","Although this study could divide the suicide stages of adolescents through labeling Weibo private message data, it cannot effectively determine the gender of the user. Previous studies had shown that suicidal behaviors were also affected by gender, and that the incidence of suicide attempts in adolescent females was higher than that of males [42]. Compared with males, females were more psychologically sensitive and vulnerable [43], and were more likely to express their pain through suicidal behaviors [44]. The expressions of hopelessness and psychological pain as predictors of suicide may differ by gender. There were only 105 adolescent users with high suicide risk in our study. Caution is thus necessary when generalizing the conclusions to other case.\nOur findings provided a traceable pathway for suicide intervention, finding that the effect of psychological pain on suicide stages was totally mediated by hopelessness, and that adolescents in the later suicide stages had less expressions of hopelessness in the traditional sense. However, the specific language characteristics of each suicide stage are still unknown. If we want to accomplish the precise prediction of suicide stages through the expressions on Weibo posts and provide more accurate and effective information for suicide intervention, we need to further explore the specific language expression characteristics of high suicide risk adolescents in each suicide stage, especially how to identify abstract metaphors that may constitute a suicidal signal. It is thus possible that we can provide more accurate suicide intervention for adolescents at different suicide stages in the future.\nWe mainly focus on the expression on social media of two psychological indicators (psychological pain, hopelessness). However, there are many factors which affect suicide, and we can research suicide intervention from multiple perspectives, not only examining some negative indicators, but also exploring some positive indicators in future research. For example, previous studies had shown that social support [45], psychological resilience [46], and self-compassion [47] were protective factors for adolescent suicide."],"string":"[\n \"2.1. Participants Through the online psychological assistance project, we obtained the interview records data of 3627 Weibo users, and then processed the user’s interview records data and Weibo texts data. Finally, we retained 105 high-suicide-risk adolescents with enough Weibo texts as the experimental group.\\nEthical Statement: Participants gave informed consent when agreeing to take part in the online psychological assistance project. The project received ethical approval from the Institution-al Review Board of the Institute of Psychology, Chinese Academy of Sciences, with the ethics approval number H16003.\\nThrough the online psychological assistance project, we obtained the interview records data of 3627 Weibo users, and then processed the user’s interview records data and Weibo texts data. Finally, we retained 105 high-suicide-risk adolescents with enough Weibo texts as the experimental group.\\nEthical Statement: Participants gave informed consent when agreeing to take part in the online psychological assistance project. The project received ethical approval from the Institution-al Review Board of the Institute of Psychology, Chinese Academy of Sciences, with the ethics approval number H16003.\\n2.2. Online Psychological Assistance Project We used the suicide signal recognition model established by the Computational Cyber-Psychology Lab (CCPL) to identify the suicide signal of a single microblog, which can effectively determine whether the single microblog has shown suicidal risk [26].\\nSince November 2016, we had used the suicide signal recognition model for online identification of Weibo comments. The last Weibo post before the death of Weibo user “Zoufan”, as a place where people can express their negative emotions, has received more than 1 million comments until now, and the number of comments keep rising (Figure 2).\\nFirstly, we used the suicide signal model to identify the suicide signal of the Weibo comments. Then, the comments identified by the model were re-identified by manual recognition. Finally, we used the official Weibo account “Psychological Map Psy” and sent a private message to the Weibo users marked as having a suicide risk. The contents of the private message mainly include inquiries about the user’s emotional state, expressions of concern, links to self-assessment psychological scales, and the hotline of the psychological crisis intervention center, etc. (Figure 3). In addition, users can choose whether to communicate with the voluntary counselor online according to their own wishes, so as to achieve agile psychological crisis intervention.\\nWe used the suicide signal recognition model established by the Computational Cyber-Psychology Lab (CCPL) to identify the suicide signal of a single microblog, which can effectively determine whether the single microblog has shown suicidal risk [26].\\nSince November 2016, we had used the suicide signal recognition model for online identification of Weibo comments. The last Weibo post before the death of Weibo user “Zoufan”, as a place where people can express their negative emotions, has received more than 1 million comments until now, and the number of comments keep rising (Figure 2).\\nFirstly, we used the suicide signal model to identify the suicide signal of the Weibo comments. Then, the comments identified by the model were re-identified by manual recognition. Finally, we used the official Weibo account “Psychological Map Psy” and sent a private message to the Weibo users marked as having a suicide risk. The contents of the private message mainly include inquiries about the user’s emotional state, expressions of concern, links to self-assessment psychological scales, and the hotline of the psychological crisis intervention center, etc. (Figure 3). In addition, users can choose whether to communicate with the voluntary counselor online according to their own wishes, so as to achieve agile psychological crisis intervention.\\n2.3. Data Collection and Processing 2.3.1. Labeling the Interview Records Data During the online psychological assistance project period (November 2016 to April 2019), the official Weibo account had communicated with 3627 users online, and 127,336 message records were accumulated. Adhering to the principle of privacy and confidentiality, the use of interview records data was strictly restricted. The experimenter must protect the privacy of the research subjects involved in the project, and should not use any confidential information related to the research subjects (including but not limited to accounts, nicknames, contact information, personal homepage, network IP, location, information records, etc.) outside the scope of the research, and would not violate the privacy of the subjects in any way.\\nThe interview records data were encoded by qualitative analysis. The basic content of interview records data were the current status and problems of users. We extracted three indicators from the interview records data, and the specific descriptions of them are shown below:(1)Determining whether the user was in adolescent stages (junior high school, high school, college), and retaining the data labeled as the adolescents.(2)Determining whether the user was diagnosed with mental illness by a psychiatrist, had a history of mental illness, or was taking medication for mental illness, and labeling a user had a mental illness or not as a 0/1 dummy variable.(3)Coding the suicide stages (depressed mood, suicide ideation, suicide plan, suicide attempt) of adolescents, and marking the suicide stages as 1, 2, 3 and 4, an ordered categorical variable.\\nDetermining whether the user was in adolescent stages (junior high school, high school, college), and retaining the data labeled as the adolescents.\\nDetermining whether the user was diagnosed with mental illness by a psychiatrist, had a history of mental illness, or was taking medication for mental illness, and labeling a user had a mental illness or not as a 0/1 dummy variable.\\nCoding the suicide stages (depressed mood, suicide ideation, suicide plan, suicide attempt) of adolescents, and marking the suicide stages as 1, 2, 3 and 4, an ordered categorical variable.\\nSuicide stages were coded on the basis of the following criteria: (a) Depressed mood: feeling negative and depressed, but not yet having suicidal ideation. It means participants did not express suicidal intention explicitly or implicitly(whether they actively expressed suicidal intentions, or exposed suicidal intentions under inquiries from the official Weibo account). (b) Suicide ideation: there is suicidal intent, but suicide plan has not formed. Due to being affected by an intense depressive mood, the idea of avoiding pain through suicide arises, but there is no specific time and way to implement it. (c) Suicide plan: on the basis of suicidal ideation, there is a rough or detailed suicide plan, such as when, where, and how to commit suicide. (d) Suicide attempt: at least one suicide attempt with the aim of ending one’s own life.\\nDuring the online psychological assistance project period (November 2016 to April 2019), the official Weibo account had communicated with 3627 users online, and 127,336 message records were accumulated. Adhering to the principle of privacy and confidentiality, the use of interview records data was strictly restricted. The experimenter must protect the privacy of the research subjects involved in the project, and should not use any confidential information related to the research subjects (including but not limited to accounts, nicknames, contact information, personal homepage, network IP, location, information records, etc.) outside the scope of the research, and would not violate the privacy of the subjects in any way.\\nThe interview records data were encoded by qualitative analysis. The basic content of interview records data were the current status and problems of users. We extracted three indicators from the interview records data, and the specific descriptions of them are shown below:(1)Determining whether the user was in adolescent stages (junior high school, high school, college), and retaining the data labeled as the adolescents.(2)Determining whether the user was diagnosed with mental illness by a psychiatrist, had a history of mental illness, or was taking medication for mental illness, and labeling a user had a mental illness or not as a 0/1 dummy variable.(3)Coding the suicide stages (depressed mood, suicide ideation, suicide plan, suicide attempt) of adolescents, and marking the suicide stages as 1, 2, 3 and 4, an ordered categorical variable.\\nDetermining whether the user was in adolescent stages (junior high school, high school, college), and retaining the data labeled as the adolescents.\\nDetermining whether the user was diagnosed with mental illness by a psychiatrist, had a history of mental illness, or was taking medication for mental illness, and labeling a user had a mental illness or not as a 0/1 dummy variable.\\nCoding the suicide stages (depressed mood, suicide ideation, suicide plan, suicide attempt) of adolescents, and marking the suicide stages as 1, 2, 3 and 4, an ordered categorical variable.\\nSuicide stages were coded on the basis of the following criteria: (a) Depressed mood: feeling negative and depressed, but not yet having suicidal ideation. It means participants did not express suicidal intention explicitly or implicitly(whether they actively expressed suicidal intentions, or exposed suicidal intentions under inquiries from the official Weibo account). (b) Suicide ideation: there is suicidal intent, but suicide plan has not formed. Due to being affected by an intense depressive mood, the idea of avoiding pain through suicide arises, but there is no specific time and way to implement it. (c) Suicide plan: on the basis of suicidal ideation, there is a rough or detailed suicide plan, such as when, where, and how to commit suicide. (d) Suicide attempt: at least one suicide attempt with the aim of ending one’s own life.\\n2.3.2. Getting the Weibo Text Data We scraped Weibo texts published by the same 3627 users through the Application Programming Interface (API) of Sina Weibo. In order to obtain sufficient Weibo texts for analysis, we took the first time that the official Weibo account sent a private message to the user as the beginning of suicide intervention, downloaded all Weibo texts in the 3 months before the intervention, and filtered the users whose Weibo texts are less than 300 words (Figure 4).\\nFinally, 105 high-suicide-risk adolescents (as the experimental group) were included in the analysis. We then removed stop words and performed Chinese word segmentation on the Weibo texts, and calculated word frequency. “The Chinese Suicide Dictionary” [27] was used to measure the word frequency ratio of their psychological pain and hopelessness. The dictionary outline is shown in Table 1. The psychological pain dimension represents the pain and torment of individuals at the psychological level, including 403 keywords; the hopelessness dimension represents the subjective feeling of losing hope, including 188 keywords.\\nWe scraped Weibo texts published by the same 3627 users through the Application Programming Interface (API) of Sina Weibo. In order to obtain sufficient Weibo texts for analysis, we took the first time that the official Weibo account sent a private message to the user as the beginning of suicide intervention, downloaded all Weibo texts in the 3 months before the intervention, and filtered the users whose Weibo texts are less than 300 words (Figure 4).\\nFinally, 105 high-suicide-risk adolescents (as the experimental group) were included in the analysis. We then removed stop words and performed Chinese word segmentation on the Weibo texts, and calculated word frequency. “The Chinese Suicide Dictionary” [27] was used to measure the word frequency ratio of their psychological pain and hopelessness. The dictionary outline is shown in Table 1. The psychological pain dimension represents the pain and torment of individuals at the psychological level, including 403 keywords; the hopelessness dimension represents the subjective feeling of losing hope, including 188 keywords.\\n2.3.1. Labeling the Interview Records Data During the online psychological assistance project period (November 2016 to April 2019), the official Weibo account had communicated with 3627 users online, and 127,336 message records were accumulated. Adhering to the principle of privacy and confidentiality, the use of interview records data was strictly restricted. The experimenter must protect the privacy of the research subjects involved in the project, and should not use any confidential information related to the research subjects (including but not limited to accounts, nicknames, contact information, personal homepage, network IP, location, information records, etc.) outside the scope of the research, and would not violate the privacy of the subjects in any way.\\nThe interview records data were encoded by qualitative analysis. The basic content of interview records data were the current status and problems of users. We extracted three indicators from the interview records data, and the specific descriptions of them are shown below:(1)Determining whether the user was in adolescent stages (junior high school, high school, college), and retaining the data labeled as the adolescents.(2)Determining whether the user was diagnosed with mental illness by a psychiatrist, had a history of mental illness, or was taking medication for mental illness, and labeling a user had a mental illness or not as a 0/1 dummy variable.(3)Coding the suicide stages (depressed mood, suicide ideation, suicide plan, suicide attempt) of adolescents, and marking the suicide stages as 1, 2, 3 and 4, an ordered categorical variable.\\nDetermining whether the user was in adolescent stages (junior high school, high school, college), and retaining the data labeled as the adolescents.\\nDetermining whether the user was diagnosed with mental illness by a psychiatrist, had a history of mental illness, or was taking medication for mental illness, and labeling a user had a mental illness or not as a 0/1 dummy variable.\\nCoding the suicide stages (depressed mood, suicide ideation, suicide plan, suicide attempt) of adolescents, and marking the suicide stages as 1, 2, 3 and 4, an ordered categorical variable.\\nSuicide stages were coded on the basis of the following criteria: (a) Depressed mood: feeling negative and depressed, but not yet having suicidal ideation. It means participants did not express suicidal intention explicitly or implicitly(whether they actively expressed suicidal intentions, or exposed suicidal intentions under inquiries from the official Weibo account). (b) Suicide ideation: there is suicidal intent, but suicide plan has not formed. Due to being affected by an intense depressive mood, the idea of avoiding pain through suicide arises, but there is no specific time and way to implement it. (c) Suicide plan: on the basis of suicidal ideation, there is a rough or detailed suicide plan, such as when, where, and how to commit suicide. (d) Suicide attempt: at least one suicide attempt with the aim of ending one’s own life.\\nDuring the online psychological assistance project period (November 2016 to April 2019), the official Weibo account had communicated with 3627 users online, and 127,336 message records were accumulated. Adhering to the principle of privacy and confidentiality, the use of interview records data was strictly restricted. The experimenter must protect the privacy of the research subjects involved in the project, and should not use any confidential information related to the research subjects (including but not limited to accounts, nicknames, contact information, personal homepage, network IP, location, information records, etc.) outside the scope of the research, and would not violate the privacy of the subjects in any way.\\nThe interview records data were encoded by qualitative analysis. The basic content of interview records data were the current status and problems of users. We extracted three indicators from the interview records data, and the specific descriptions of them are shown below:(1)Determining whether the user was in adolescent stages (junior high school, high school, college), and retaining the data labeled as the adolescents.(2)Determining whether the user was diagnosed with mental illness by a psychiatrist, had a history of mental illness, or was taking medication for mental illness, and labeling a user had a mental illness or not as a 0/1 dummy variable.(3)Coding the suicide stages (depressed mood, suicide ideation, suicide plan, suicide attempt) of adolescents, and marking the suicide stages as 1, 2, 3 and 4, an ordered categorical variable.\\nDetermining whether the user was in adolescent stages (junior high school, high school, college), and retaining the data labeled as the adolescents.\\nDetermining whether the user was diagnosed with mental illness by a psychiatrist, had a history of mental illness, or was taking medication for mental illness, and labeling a user had a mental illness or not as a 0/1 dummy variable.\\nCoding the suicide stages (depressed mood, suicide ideation, suicide plan, suicide attempt) of adolescents, and marking the suicide stages as 1, 2, 3 and 4, an ordered categorical variable.\\nSuicide stages were coded on the basis of the following criteria: (a) Depressed mood: feeling negative and depressed, but not yet having suicidal ideation. It means participants did not express suicidal intention explicitly or implicitly(whether they actively expressed suicidal intentions, or exposed suicidal intentions under inquiries from the official Weibo account). (b) Suicide ideation: there is suicidal intent, but suicide plan has not formed. Due to being affected by an intense depressive mood, the idea of avoiding pain through suicide arises, but there is no specific time and way to implement it. (c) Suicide plan: on the basis of suicidal ideation, there is a rough or detailed suicide plan, such as when, where, and how to commit suicide. (d) Suicide attempt: at least one suicide attempt with the aim of ending one’s own life.\\n2.3.2. Getting the Weibo Text Data We scraped Weibo texts published by the same 3627 users through the Application Programming Interface (API) of Sina Weibo. In order to obtain sufficient Weibo texts for analysis, we took the first time that the official Weibo account sent a private message to the user as the beginning of suicide intervention, downloaded all Weibo texts in the 3 months before the intervention, and filtered the users whose Weibo texts are less than 300 words (Figure 4).\\nFinally, 105 high-suicide-risk adolescents (as the experimental group) were included in the analysis. We then removed stop words and performed Chinese word segmentation on the Weibo texts, and calculated word frequency. “The Chinese Suicide Dictionary” [27] was used to measure the word frequency ratio of their psychological pain and hopelessness. The dictionary outline is shown in Table 1. The psychological pain dimension represents the pain and torment of individuals at the psychological level, including 403 keywords; the hopelessness dimension represents the subjective feeling of losing hope, including 188 keywords.\\nWe scraped Weibo texts published by the same 3627 users through the Application Programming Interface (API) of Sina Weibo. In order to obtain sufficient Weibo texts for analysis, we took the first time that the official Weibo account sent a private message to the user as the beginning of suicide intervention, downloaded all Weibo texts in the 3 months before the intervention, and filtered the users whose Weibo texts are less than 300 words (Figure 4).\\nFinally, 105 high-suicide-risk adolescents (as the experimental group) were included in the analysis. We then removed stop words and performed Chinese word segmentation on the Weibo texts, and calculated word frequency. “The Chinese Suicide Dictionary” [27] was used to measure the word frequency ratio of their psychological pain and hopelessness. The dictionary outline is shown in Table 1. The psychological pain dimension represents the pain and torment of individuals at the psychological level, including 403 keywords; the hopelessness dimension represents the subjective feeling of losing hope, including 188 keywords.\\n2.4. Data Analysis 2.4.1. Qualitative Analysis Through the labeling results of interview records data, we investigated the situation of mental illness and the distribution of suicide stages for the experimental group.\\nThrough the labeling results of interview records data, we investigated the situation of mental illness and the distribution of suicide stages for the experimental group.\\n2.4.2. Analysis of Suicide Path The mediation effect analysis was executed on the software Mplus by using the bootstrap method to test the suicide mechanism path model. The word frequency of psychological pain and hopelessness of the experimental group were treated as independent variable (X) and mediating variable (M), respectively. The suicide stages were treated as dependent variable (Y). The mental illness was included in the model as a control variable (Z).\\nThe mediation effect analysis was executed on the software Mplus by using the bootstrap method to test the suicide mechanism path model. The word frequency of psychological pain and hopelessness of the experimental group were treated as independent variable (X) and mediating variable (M), respectively. The suicide stages were treated as dependent variable (Y). The mental illness was included in the model as a control variable (Z).\\n2.4.1. Qualitative Analysis Through the labeling results of interview records data, we investigated the situation of mental illness and the distribution of suicide stages for the experimental group.\\nThrough the labeling results of interview records data, we investigated the situation of mental illness and the distribution of suicide stages for the experimental group.\\n2.4.2. Analysis of Suicide Path The mediation effect analysis was executed on the software Mplus by using the bootstrap method to test the suicide mechanism path model. The word frequency of psychological pain and hopelessness of the experimental group were treated as independent variable (X) and mediating variable (M), respectively. The suicide stages were treated as dependent variable (Y). The mental illness was included in the model as a control variable (Z).\\nThe mediation effect analysis was executed on the software Mplus by using the bootstrap method to test the suicide mechanism path model. The word frequency of psychological pain and hopelessness of the experimental group were treated as independent variable (X) and mediating variable (M), respectively. The suicide stages were treated as dependent variable (Y). The mental illness was included in the model as a control variable (Z).\",\n \"We used the suicide signal recognition model established by the Computational Cyber-Psychology Lab (CCPL) to identify the suicide signal of a single microblog, which can effectively determine whether the single microblog has shown suicidal risk [26].\\nSince November 2016, we had used the suicide signal recognition model for online identification of Weibo comments. The last Weibo post before the death of Weibo user “Zoufan”, as a place where people can express their negative emotions, has received more than 1 million comments until now, and the number of comments keep rising (Figure 2).\\nFirstly, we used the suicide signal model to identify the suicide signal of the Weibo comments. Then, the comments identified by the model were re-identified by manual recognition. Finally, we used the official Weibo account “Psychological Map Psy” and sent a private message to the Weibo users marked as having a suicide risk. The contents of the private message mainly include inquiries about the user’s emotional state, expressions of concern, links to self-assessment psychological scales, and the hotline of the psychological crisis intervention center, etc. (Figure 3). In addition, users can choose whether to communicate with the voluntary counselor online according to their own wishes, so as to achieve agile psychological crisis intervention.\",\n \"2.3.1. Labeling the Interview Records Data During the online psychological assistance project period (November 2016 to April 2019), the official Weibo account had communicated with 3627 users online, and 127,336 message records were accumulated. Adhering to the principle of privacy and confidentiality, the use of interview records data was strictly restricted. The experimenter must protect the privacy of the research subjects involved in the project, and should not use any confidential information related to the research subjects (including but not limited to accounts, nicknames, contact information, personal homepage, network IP, location, information records, etc.) outside the scope of the research, and would not violate the privacy of the subjects in any way.\\nThe interview records data were encoded by qualitative analysis. The basic content of interview records data were the current status and problems of users. We extracted three indicators from the interview records data, and the specific descriptions of them are shown below:(1)Determining whether the user was in adolescent stages (junior high school, high school, college), and retaining the data labeled as the adolescents.(2)Determining whether the user was diagnosed with mental illness by a psychiatrist, had a history of mental illness, or was taking medication for mental illness, and labeling a user had a mental illness or not as a 0/1 dummy variable.(3)Coding the suicide stages (depressed mood, suicide ideation, suicide plan, suicide attempt) of adolescents, and marking the suicide stages as 1, 2, 3 and 4, an ordered categorical variable.\\nDetermining whether the user was in adolescent stages (junior high school, high school, college), and retaining the data labeled as the adolescents.\\nDetermining whether the user was diagnosed with mental illness by a psychiatrist, had a history of mental illness, or was taking medication for mental illness, and labeling a user had a mental illness or not as a 0/1 dummy variable.\\nCoding the suicide stages (depressed mood, suicide ideation, suicide plan, suicide attempt) of adolescents, and marking the suicide stages as 1, 2, 3 and 4, an ordered categorical variable.\\nSuicide stages were coded on the basis of the following criteria: (a) Depressed mood: feeling negative and depressed, but not yet having suicidal ideation. It means participants did not express suicidal intention explicitly or implicitly(whether they actively expressed suicidal intentions, or exposed suicidal intentions under inquiries from the official Weibo account). (b) Suicide ideation: there is suicidal intent, but suicide plan has not formed. Due to being affected by an intense depressive mood, the idea of avoiding pain through suicide arises, but there is no specific time and way to implement it. (c) Suicide plan: on the basis of suicidal ideation, there is a rough or detailed suicide plan, such as when, where, and how to commit suicide. (d) Suicide attempt: at least one suicide attempt with the aim of ending one’s own life.\\nDuring the online psychological assistance project period (November 2016 to April 2019), the official Weibo account had communicated with 3627 users online, and 127,336 message records were accumulated. Adhering to the principle of privacy and confidentiality, the use of interview records data was strictly restricted. The experimenter must protect the privacy of the research subjects involved in the project, and should not use any confidential information related to the research subjects (including but not limited to accounts, nicknames, contact information, personal homepage, network IP, location, information records, etc.) outside the scope of the research, and would not violate the privacy of the subjects in any way.\\nThe interview records data were encoded by qualitative analysis. The basic content of interview records data were the current status and problems of users. We extracted three indicators from the interview records data, and the specific descriptions of them are shown below:(1)Determining whether the user was in adolescent stages (junior high school, high school, college), and retaining the data labeled as the adolescents.(2)Determining whether the user was diagnosed with mental illness by a psychiatrist, had a history of mental illness, or was taking medication for mental illness, and labeling a user had a mental illness or not as a 0/1 dummy variable.(3)Coding the suicide stages (depressed mood, suicide ideation, suicide plan, suicide attempt) of adolescents, and marking the suicide stages as 1, 2, 3 and 4, an ordered categorical variable.\\nDetermining whether the user was in adolescent stages (junior high school, high school, college), and retaining the data labeled as the adolescents.\\nDetermining whether the user was diagnosed with mental illness by a psychiatrist, had a history of mental illness, or was taking medication for mental illness, and labeling a user had a mental illness or not as a 0/1 dummy variable.\\nCoding the suicide stages (depressed mood, suicide ideation, suicide plan, suicide attempt) of adolescents, and marking the suicide stages as 1, 2, 3 and 4, an ordered categorical variable.\\nSuicide stages were coded on the basis of the following criteria: (a) Depressed mood: feeling negative and depressed, but not yet having suicidal ideation. It means participants did not express suicidal intention explicitly or implicitly(whether they actively expressed suicidal intentions, or exposed suicidal intentions under inquiries from the official Weibo account). (b) Suicide ideation: there is suicidal intent, but suicide plan has not formed. Due to being affected by an intense depressive mood, the idea of avoiding pain through suicide arises, but there is no specific time and way to implement it. (c) Suicide plan: on the basis of suicidal ideation, there is a rough or detailed suicide plan, such as when, where, and how to commit suicide. (d) Suicide attempt: at least one suicide attempt with the aim of ending one’s own life.\\n2.3.2. Getting the Weibo Text Data We scraped Weibo texts published by the same 3627 users through the Application Programming Interface (API) of Sina Weibo. In order to obtain sufficient Weibo texts for analysis, we took the first time that the official Weibo account sent a private message to the user as the beginning of suicide intervention, downloaded all Weibo texts in the 3 months before the intervention, and filtered the users whose Weibo texts are less than 300 words (Figure 4).\\nFinally, 105 high-suicide-risk adolescents (as the experimental group) were included in the analysis. We then removed stop words and performed Chinese word segmentation on the Weibo texts, and calculated word frequency. “The Chinese Suicide Dictionary” [27] was used to measure the word frequency ratio of their psychological pain and hopelessness. The dictionary outline is shown in Table 1. The psychological pain dimension represents the pain and torment of individuals at the psychological level, including 403 keywords; the hopelessness dimension represents the subjective feeling of losing hope, including 188 keywords.\\nWe scraped Weibo texts published by the same 3627 users through the Application Programming Interface (API) of Sina Weibo. In order to obtain sufficient Weibo texts for analysis, we took the first time that the official Weibo account sent a private message to the user as the beginning of suicide intervention, downloaded all Weibo texts in the 3 months before the intervention, and filtered the users whose Weibo texts are less than 300 words (Figure 4).\\nFinally, 105 high-suicide-risk adolescents (as the experimental group) were included in the analysis. We then removed stop words and performed Chinese word segmentation on the Weibo texts, and calculated word frequency. “The Chinese Suicide Dictionary” [27] was used to measure the word frequency ratio of their psychological pain and hopelessness. The dictionary outline is shown in Table 1. The psychological pain dimension represents the pain and torment of individuals at the psychological level, including 403 keywords; the hopelessness dimension represents the subjective feeling of losing hope, including 188 keywords.\",\n \"During the online psychological assistance project period (November 2016 to April 2019), the official Weibo account had communicated with 3627 users online, and 127,336 message records were accumulated. Adhering to the principle of privacy and confidentiality, the use of interview records data was strictly restricted. The experimenter must protect the privacy of the research subjects involved in the project, and should not use any confidential information related to the research subjects (including but not limited to accounts, nicknames, contact information, personal homepage, network IP, location, information records, etc.) outside the scope of the research, and would not violate the privacy of the subjects in any way.\\nThe interview records data were encoded by qualitative analysis. The basic content of interview records data were the current status and problems of users. We extracted three indicators from the interview records data, and the specific descriptions of them are shown below:(1)Determining whether the user was in adolescent stages (junior high school, high school, college), and retaining the data labeled as the adolescents.(2)Determining whether the user was diagnosed with mental illness by a psychiatrist, had a history of mental illness, or was taking medication for mental illness, and labeling a user had a mental illness or not as a 0/1 dummy variable.(3)Coding the suicide stages (depressed mood, suicide ideation, suicide plan, suicide attempt) of adolescents, and marking the suicide stages as 1, 2, 3 and 4, an ordered categorical variable.\\nDetermining whether the user was in adolescent stages (junior high school, high school, college), and retaining the data labeled as the adolescents.\\nDetermining whether the user was diagnosed with mental illness by a psychiatrist, had a history of mental illness, or was taking medication for mental illness, and labeling a user had a mental illness or not as a 0/1 dummy variable.\\nCoding the suicide stages (depressed mood, suicide ideation, suicide plan, suicide attempt) of adolescents, and marking the suicide stages as 1, 2, 3 and 4, an ordered categorical variable.\\nSuicide stages were coded on the basis of the following criteria: (a) Depressed mood: feeling negative and depressed, but not yet having suicidal ideation. It means participants did not express suicidal intention explicitly or implicitly(whether they actively expressed suicidal intentions, or exposed suicidal intentions under inquiries from the official Weibo account). (b) Suicide ideation: there is suicidal intent, but suicide plan has not formed. Due to being affected by an intense depressive mood, the idea of avoiding pain through suicide arises, but there is no specific time and way to implement it. (c) Suicide plan: on the basis of suicidal ideation, there is a rough or detailed suicide plan, such as when, where, and how to commit suicide. (d) Suicide attempt: at least one suicide attempt with the aim of ending one’s own life.\",\n \"We scraped Weibo texts published by the same 3627 users through the Application Programming Interface (API) of Sina Weibo. In order to obtain sufficient Weibo texts for analysis, we took the first time that the official Weibo account sent a private message to the user as the beginning of suicide intervention, downloaded all Weibo texts in the 3 months before the intervention, and filtered the users whose Weibo texts are less than 300 words (Figure 4).\\nFinally, 105 high-suicide-risk adolescents (as the experimental group) were included in the analysis. We then removed stop words and performed Chinese word segmentation on the Weibo texts, and calculated word frequency. “The Chinese Suicide Dictionary” [27] was used to measure the word frequency ratio of their psychological pain and hopelessness. The dictionary outline is shown in Table 1. The psychological pain dimension represents the pain and torment of individuals at the psychological level, including 403 keywords; the hopelessness dimension represents the subjective feeling of losing hope, including 188 keywords.\",\n \"2.4.1. Qualitative Analysis Through the labeling results of interview records data, we investigated the situation of mental illness and the distribution of suicide stages for the experimental group.\\nThrough the labeling results of interview records data, we investigated the situation of mental illness and the distribution of suicide stages for the experimental group.\\n2.4.2. Analysis of Suicide Path The mediation effect analysis was executed on the software Mplus by using the bootstrap method to test the suicide mechanism path model. The word frequency of psychological pain and hopelessness of the experimental group were treated as independent variable (X) and mediating variable (M), respectively. The suicide stages were treated as dependent variable (Y). The mental illness was included in the model as a control variable (Z).\\nThe mediation effect analysis was executed on the software Mplus by using the bootstrap method to test the suicide mechanism path model. The word frequency of psychological pain and hopelessness of the experimental group were treated as independent variable (X) and mediating variable (M), respectively. The suicide stages were treated as dependent variable (Y). The mental illness was included in the model as a control variable (Z).\",\n \"Through the labeling results of interview records data, we investigated the situation of mental illness and the distribution of suicide stages for the experimental group.\",\n \"The mediation effect analysis was executed on the software Mplus by using the bootstrap method to test the suicide mechanism path model. The word frequency of psychological pain and hopelessness of the experimental group were treated as independent variable (X) and mediating variable (M), respectively. The suicide stages were treated as dependent variable (Y). The mental illness was included in the model as a control variable (Z).\",\n \"The mental illness diagnosis of the experimental group was marked; it was found that 38 were diagnosed and 67 were undiagnosed. The proportion of users diagnosed with mental illness was 36.2%. Among the 38 high-suicide-risk adolescents diagnosed with mental illness, 29 users had depression (or depression was included in multiple mental illnesses), accounting for 76.3%; the rest included bipolar disorder (13.16%), anxiety disorder (13.16%), etc.; 9 of them suffered from multiple mental illnesses (e.g., depression and anxiety disorder), accounting for 23.68%.\\nAmong the experimental group, 57 were in the stage of depressed mood (54.3%), 21 were in the stage of suicide ideation (20.0%), 19 were in the stage of suicide plan (18.1%), and 8 people were in the stage of suicide attempt (7.6%) (Table 2).\",\n \"A mediation effect test was performed on the path model (psychological pain → hopelessness → suicide stages). The performance of each fit index of the model was good (RMSEA < 0.08; CFI > 0.9; TLI > 0.9), indicating that the model fitting effect is excellent (Table 3).\\nAfter controlling for mental illness factors, the total effect c was significant (β = −0.154, p < 0.05), indicating that there was a mediation effect; the path coefficients a and b were significant (β = 0.571, p < 0.001; β = −0.271, p < 0.05), but the direct effect c’ was not significant (β = 0.052, p > 0.05), indicating that there was a complete mediating effect of hopelessness. That is, psychological pain mainly affects suicide risk through hopelessness (Table 4).\\nIt was worth noting that the relationship between the expression of hopelessness and the stages of suicide showed a significant negative correlation (β = −0.271, p < 0.05), indicating that the late suicide stage such as suicide attempt, which may be closer to suicide death, included less expressions of hopelessness (Table 4).\\nFurthermore, there was a significant positive correlation between diagnosed mental illness and suicide stages (β = 0.398, p < 0.001), indicating the necessity of treating mental illness as a control variable in order to see the relationship between psychological pain, hopelessness and suicide stages more clearly. At the same time, it also showed that the later the suicide stage, the higher the diagnosis rate of mental illness (Table 4).\",\n \"Among the experimental group, 36.2% of users suffered from mental illness. Depression accounted for 76.3% of all mental illness; it is the most common mental illness among the high-suicide-risk adolescents. The incidence of suicide among students whose relatives were affected by psychosis were higher than that of general residents. Suicide and mental illness were closely linked [28]. British doctor Sainsbury conducted a qualitative study on the data of 400 suicides examined by a coroner in London between 1936 and 1938, and found that 37% of the suicides reflected mental illness [29]. In a meta-analysis conducted in 2004, twenty-seven studies comprising 3275 suicides were included, of which 87.3% had been diagnosed with a mental disorder prior to their death [30]. In contrast, the research population in our study was only a high suicide risk group, so the detection rate of mental illness was relatively low. Although the research populations of the other two studies were both suicide decedents, the time interval between the two studies was large. It is very possible that there existed better methods to detect the mental illness of the suicide decedents in later studies, and it is understandable that the detection rate of mental illness has greatly increased. Furthermore, a more recent study has shown that the most common mental illness associated with suicide was depression [31]. The proportion of depression in all mental illnesses in our study also supported this conclusion.\\nAnother result of the qualitative analysis showed that the later the suicide stage, the lower the proportion of adolescents. A study on residents’ suicidal ideation, suicide plans and suicide attempts in Dalian, China showed that the detection rate of suicidal ideation, suicide plan and suicide attempt decreased in that order in different groups [32]. This may because not all suicide ideations translate into suicidal actions, and most people with suicide ideation have not attempted suicide [33,34].\",\n \"Our research found that the effect of psychological pain on suicide stages was totally mediated by hopelessness, confirming our suicide mechanism path hypothesis: psychological pain → hopelessness → suicide stages. Although the first step of suicide ideation starts from psychological pain, psychological pain itself is not enough to generate suicide ideation, and hopelessness is also needed to develop suicide ideation [19]. People make decisions and take actions based in part on their emotional predictions [35,36], and hopelessness makes people at suicide risk overestimate their future emotional pain [37]. If the individual experiences constant pain in life, this may reduce the individual’s desire to live, leading to suicidal thoughts. However, pain alone is not enough to completely lose the belief in life. Only when the individual feels that the terrible situation cannot be improved, and he or she will live in pain forever, suicide will be considered.\\nThus, expressions of hopelessness may be a better predictor of suicide risk than psychological pain, and we found a traceable pathway for suicide intervention. Based on the Weibo posts, we can capture whether individual released suicide signals and it is possible to track the suicide stages in the future, making suicide intervention more accurate.\",\n \"There was a negative predictive relationship between hopelessness expressions and suicide stages, as adolescents at the later suicide stages had less expressions of hopelessness. One possible reason is that adolescents at higher risk of suicide may use less concrete, direct words of hopelessness to express such feelings. From a linguistic point of view, Gao Yihong and Meng Ling analyzed the Weibo texts of “Zoufan” three months before suicide. They found that “Zoufan” often used decontextualized and fragmented ways to express personal depressed mood, and expressed metaphors and intentions about death frequently [38]. This euphemistic and poetic way of venting may be the commonality of high suicide risk adolescents. Another possible reason is “the Presuicidal Syndrome”, which mentioned that there exists an “ominous quiet” in the short period before suicide. This is a sign of “affective restriction”, which represents extreme repression of intolerable feelings [39]. Such “affective restriction” may be why people in the later suicide stage are less likely to express hopelessness. Moreover, adolescents at the later suicide stage may be more accepting and tolerant of psychological pain and hopelessness, which makes them less willing to talk about individual psychological pain and hopelessness, and with lack of supportive survival signals through texts, lose the belief in getting help.\\nMany researchers have paid attention to the use of social media for suicide prevention and intervention, and have conducted a series of studies [40,41]. However, little attention has been paid to whether the suicidal signal was from an early suicide ideator or a later suicide attempter. Our study fills this gap, complementing and extending suicide-related research. Our results suggest that adolescents who are at a later suicide stage and have a higher suicide risk often do not express too much hopelessness directly. Therefore, we cannot judge closeness to suicide only by the level of traditional expressions of hopelessness.\",\n \"Although this study could divide the suicide stages of adolescents through labeling Weibo private message data, it cannot effectively determine the gender of the user. Previous studies had shown that suicidal behaviors were also affected by gender, and that the incidence of suicide attempts in adolescent females was higher than that of males [42]. Compared with males, females were more psychologically sensitive and vulnerable [43], and were more likely to express their pain through suicidal behaviors [44]. The expressions of hopelessness and psychological pain as predictors of suicide may differ by gender. There were only 105 adolescent users with high suicide risk in our study. Caution is thus necessary when generalizing the conclusions to other case.\\nOur findings provided a traceable pathway for suicide intervention, finding that the effect of psychological pain on suicide stages was totally mediated by hopelessness, and that adolescents in the later suicide stages had less expressions of hopelessness in the traditional sense. However, the specific language characteristics of each suicide stage are still unknown. If we want to accomplish the precise prediction of suicide stages through the expressions on Weibo posts and provide more accurate and effective information for suicide intervention, we need to further explore the specific language expression characteristics of high suicide risk adolescents in each suicide stage, especially how to identify abstract metaphors that may constitute a suicidal signal. It is thus possible that we can provide more accurate suicide intervention for adolescents at different suicide stages in the future.\\nWe mainly focus on the expression on social media of two psychological indicators (psychological pain, hopelessness). However, there are many factors which affect suicide, and we can research suicide intervention from multiple perspectives, not only examining some negative indicators, but also exploring some positive indicators in future research. For example, previous studies had shown that social support [45], psychological resilience [46], and self-compassion [47] were protective factors for adolescent suicide.\"\n]"},"other_sections_sec_types":{"kind":"list like","value":[null,null,null,null,null,null,null,null,null,null,null,null,null,null],"string":"[\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null\n]"},"all_sections_titles":{"kind":"list like","value":["1. Introduction","2. Materials and Methods","2.1. Participants","2.2. Online Psychological Assistance Project","2.3. Data Collection and Processing","2.3.1. Labeling the Interview Records Data","2.3.2. Getting the Weibo Text Data","2.4. Data Analysis","2.4.1. Qualitative Analysis","2.4.2. Analysis of Suicide Path","3. Results","3.1. Group Characteristics Reflected by Qualitative Analysis","3.2. Path Analysis of Psychological Pain, Hopelessness and Suicide Stages","4. Discussion","4.1. The Qualitative Analysis Results","4.2. The Mediating Effect of Hopelessness","4.3. The Hopelessness Expressions and the Suicide Stages","5. Limitations and Future Studies","6. Conclusions"],"string":"[\n \"1. Introduction\",\n \"2. Materials and Methods\",\n \"2.1. Participants\",\n \"2.2. Online Psychological Assistance Project\",\n \"2.3. Data Collection and Processing\",\n \"2.3.1. Labeling the Interview Records Data\",\n \"2.3.2. Getting the Weibo Text Data\",\n \"2.4. Data Analysis\",\n \"2.4.1. Qualitative Analysis\",\n \"2.4.2. Analysis of Suicide Path\",\n \"3. Results\",\n \"3.1. Group Characteristics Reflected by Qualitative Analysis\",\n \"3.2. Path Analysis of Psychological Pain, Hopelessness and Suicide Stages\",\n \"4. Discussion\",\n \"4.1. The Qualitative Analysis Results\",\n \"4.2. The Mediating Effect of Hopelessness\",\n \"4.3. The Hopelessness Expressions and the Suicide Stages\",\n \"5. Limitations and Future Studies\",\n \"6. Conclusions\"\n]"},"all_sections_texts":{"kind":"list like","value":["Adolescent suicide has always been a global public health concern to us. Suicide is the fourth leading cause of death globally among people aged 15–19 [1], and in China, it is the second leading cause of death for people aged 20–34 [2]. The consequences of adolescent suicide are the most serious among many mental health problems. It means disability or even a loss of life for individuals, heavy blows and severe trauma for families, and huge expenditures on the health, welfare and justice sectors for society. The study of suicide risk factors and the exploration of suicide mechanisms in high-suicide-risk adolescents are of great significance for the prevention of suicide.\nWith the development of technology, social media has become a platform for people to record their daily life and express their emotions. In China, Sina Weibo as a typical product of the big data era has extensive influence. Sina Weibo is China’s most popular social media platform, just like Twitter in America. Although there are some short video platforms (e.g., Douyin) and forums (e.g., Douban) that are also popular in China, it is only on Sina Weibo that users can share information in real-time and communicate interactively in the forms of text, pictures, videos and other multimedia. According to a report released by Sina Weibo, Weibo users show a younger trend, of which the post-90s and post-00s youth groups account for nearly 80%, and the daily active volume can reach 224 million [3].\nThe user’s emotional expression on social platforms has higher immediacy and less masking. Identifying the user’s psychological state through their expression on social media will be more convenient and quicker, and the obtained data will be more real. Therefore, we use the Weibo text analysis method to explore the suicide mechanism path of high-suicide-risk adolescents. Text analysis refers to the representation of text and the selection of feature items, which is a basic problem in text mining and information retrieval. Text analysis means that we can quantify the feature words extracted from text to represent text information. Therefore, we can extract the psychological indicators we want from the Weibo text by constructing a dictionary of psychological indicators.\nSuicide represents a series of consecutive stages, including suicide ideation, suicide plan, and suicide attempt [4,5]. Suicide ideation, as a high-risk factor for suicide, has a significant predictive effect [6,7]. Approximately 1/3 of suicide ideators ultimately attempt suicide, and the number of suicide attempters is several times higher than those who really die by suicide, so suicide attempt is a greater risk factor for suicide than suicidal ideation [1,8,9]. The later the suicide stage, the closer to suicide death, and the greater risk of suicide. Therefore, in order to quantify the degree of suicide risk and provide accurate information for suicide interventions, we decided to divide suicidal behavior into different stages. At the same time, considering that depressed mood is an important signal of suicide, we finally divided suicide into 4 stages, which the suicide risk gradually increases: depressed mood, suicide ideation, suicide plan and suicide attempt, and defined adolescents at different suicide stages as high-suicide-risk adolescents.\nA meta-analysis of the causes of suicide among adolescents found that suicide is a public health problem involving complex factors, such as biology, psychology, family, society and culture [8]. Among the psychological factors, psychological pain and hopelessness are important risk factors featuring strongly in suicide research [9,10,11]. Although psychological pain and hopelessness are often mentioned together as having an impact on suicidal behavior, as two most common motivations for suicide attempts [12], it is unclear how the two proximal factors of suicide affect the suicide stages.\nPsychological pain is defined as the “introspective experience of negative emotions such as fear, despair, grief, shame, guilt, blocked love, loneliness and loss” [13,14,15,16]. When Shneidman first hypothesized the relationship between psychological pain and suicide, he pointed out that other factors can only affect suicidal behavior through psychological pain, without which suicide would not occur [13]. Evidence suggests that psychological pain plays a central role in suicidal behavior [10,17,18]. Psychological pain is not enough to develop suicide ideation, it also requires hopelessness [19]. Hopelessness is defined as negative expectations or pessimism about oneself or the future [20]. Previous studies had showed that hopelessness was also an important risk factor of suicide [21,22,23].\nTo explore the relationship between psychological pain, hopelessness and suicide stages for high-suicide-risk adolescents, we established a hypothesis for the suicide mechanism path: psychological pain → hopelessness → suicide stages, with hopelessness as the mediating variable. In addition, there is a high correlation between mental illness and suicidal behaviors [24,25], and in order to better clarify the effect of psychological pain and hopelessness on suicide stages, a mediating effect model of our study is proposed with mental illness as a control variable (Figure 1). We used the more ecological Weibo text analysis method in this study, and hoped to further deepen the research on adolescent suicide and to find a new theoretical basis for future suicide interventions drawing on online data.","2.1. Participants Through the online psychological assistance project, we obtained the interview records data of 3627 Weibo users, and then processed the user’s interview records data and Weibo texts data. Finally, we retained 105 high-suicide-risk adolescents with enough Weibo texts as the experimental group.\nEthical Statement: Participants gave informed consent when agreeing to take part in the online psychological assistance project. The project received ethical approval from the Institution-al Review Board of the Institute of Psychology, Chinese Academy of Sciences, with the ethics approval number H16003.\nThrough the online psychological assistance project, we obtained the interview records data of 3627 Weibo users, and then processed the user’s interview records data and Weibo texts data. Finally, we retained 105 high-suicide-risk adolescents with enough Weibo texts as the experimental group.\nEthical Statement: Participants gave informed consent when agreeing to take part in the online psychological assistance project. The project received ethical approval from the Institution-al Review Board of the Institute of Psychology, Chinese Academy of Sciences, with the ethics approval number H16003.\n2.2. Online Psychological Assistance Project We used the suicide signal recognition model established by the Computational Cyber-Psychology Lab (CCPL) to identify the suicide signal of a single microblog, which can effectively determine whether the single microblog has shown suicidal risk [26].\nSince November 2016, we had used the suicide signal recognition model for online identification of Weibo comments. The last Weibo post before the death of Weibo user “Zoufan”, as a place where people can express their negative emotions, has received more than 1 million comments until now, and the number of comments keep rising (Figure 2).\nFirstly, we used the suicide signal model to identify the suicide signal of the Weibo comments. Then, the comments identified by the model were re-identified by manual recognition. Finally, we used the official Weibo account “Psychological Map Psy” and sent a private message to the Weibo users marked as having a suicide risk. The contents of the private message mainly include inquiries about the user’s emotional state, expressions of concern, links to self-assessment psychological scales, and the hotline of the psychological crisis intervention center, etc. (Figure 3). In addition, users can choose whether to communicate with the voluntary counselor online according to their own wishes, so as to achieve agile psychological crisis intervention.\nWe used the suicide signal recognition model established by the Computational Cyber-Psychology Lab (CCPL) to identify the suicide signal of a single microblog, which can effectively determine whether the single microblog has shown suicidal risk [26].\nSince November 2016, we had used the suicide signal recognition model for online identification of Weibo comments. The last Weibo post before the death of Weibo user “Zoufan”, as a place where people can express their negative emotions, has received more than 1 million comments until now, and the number of comments keep rising (Figure 2).\nFirstly, we used the suicide signal model to identify the suicide signal of the Weibo comments. Then, the comments identified by the model were re-identified by manual recognition. Finally, we used the official Weibo account “Psychological Map Psy” and sent a private message to the Weibo users marked as having a suicide risk. The contents of the private message mainly include inquiries about the user’s emotional state, expressions of concern, links to self-assessment psychological scales, and the hotline of the psychological crisis intervention center, etc. (Figure 3). In addition, users can choose whether to communicate with the voluntary counselor online according to their own wishes, so as to achieve agile psychological crisis intervention.\n2.3. Data Collection and Processing 2.3.1. Labeling the Interview Records Data During the online psychological assistance project period (November 2016 to April 2019), the official Weibo account had communicated with 3627 users online, and 127,336 message records were accumulated. Adhering to the principle of privacy and confidentiality, the use of interview records data was strictly restricted. The experimenter must protect the privacy of the research subjects involved in the project, and should not use any confidential information related to the research subjects (including but not limited to accounts, nicknames, contact information, personal homepage, network IP, location, information records, etc.) outside the scope of the research, and would not violate the privacy of the subjects in any way.\nThe interview records data were encoded by qualitative analysis. The basic content of interview records data were the current status and problems of users. We extracted three indicators from the interview records data, and the specific descriptions of them are shown below:(1)Determining whether the user was in adolescent stages (junior high school, high school, college), and retaining the data labeled as the adolescents.(2)Determining whether the user was diagnosed with mental illness by a psychiatrist, had a history of mental illness, or was taking medication for mental illness, and labeling a user had a mental illness or not as a 0/1 dummy variable.(3)Coding the suicide stages (depressed mood, suicide ideation, suicide plan, suicide attempt) of adolescents, and marking the suicide stages as 1, 2, 3 and 4, an ordered categorical variable.\nDetermining whether the user was in adolescent stages (junior high school, high school, college), and retaining the data labeled as the adolescents.\nDetermining whether the user was diagnosed with mental illness by a psychiatrist, had a history of mental illness, or was taking medication for mental illness, and labeling a user had a mental illness or not as a 0/1 dummy variable.\nCoding the suicide stages (depressed mood, suicide ideation, suicide plan, suicide attempt) of adolescents, and marking the suicide stages as 1, 2, 3 and 4, an ordered categorical variable.\nSuicide stages were coded on the basis of the following criteria: (a) Depressed mood: feeling negative and depressed, but not yet having suicidal ideation. It means participants did not express suicidal intention explicitly or implicitly(whether they actively expressed suicidal intentions, or exposed suicidal intentions under inquiries from the official Weibo account). (b) Suicide ideation: there is suicidal intent, but suicide plan has not formed. Due to being affected by an intense depressive mood, the idea of avoiding pain through suicide arises, but there is no specific time and way to implement it. (c) Suicide plan: on the basis of suicidal ideation, there is a rough or detailed suicide plan, such as when, where, and how to commit suicide. (d) Suicide attempt: at least one suicide attempt with the aim of ending one’s own life.\nDuring the online psychological assistance project period (November 2016 to April 2019), the official Weibo account had communicated with 3627 users online, and 127,336 message records were accumulated. Adhering to the principle of privacy and confidentiality, the use of interview records data was strictly restricted. The experimenter must protect the privacy of the research subjects involved in the project, and should not use any confidential information related to the research subjects (including but not limited to accounts, nicknames, contact information, personal homepage, network IP, location, information records, etc.) outside the scope of the research, and would not violate the privacy of the subjects in any way.\nThe interview records data were encoded by qualitative analysis. The basic content of interview records data were the current status and problems of users. We extracted three indicators from the interview records data, and the specific descriptions of them are shown below:(1)Determining whether the user was in adolescent stages (junior high school, high school, college), and retaining the data labeled as the adolescents.(2)Determining whether the user was diagnosed with mental illness by a psychiatrist, had a history of mental illness, or was taking medication for mental illness, and labeling a user had a mental illness or not as a 0/1 dummy variable.(3)Coding the suicide stages (depressed mood, suicide ideation, suicide plan, suicide attempt) of adolescents, and marking the suicide stages as 1, 2, 3 and 4, an ordered categorical variable.\nDetermining whether the user was in adolescent stages (junior high school, high school, college), and retaining the data labeled as the adolescents.\nDetermining whether the user was diagnosed with mental illness by a psychiatrist, had a history of mental illness, or was taking medication for mental illness, and labeling a user had a mental illness or not as a 0/1 dummy variable.\nCoding the suicide stages (depressed mood, suicide ideation, suicide plan, suicide attempt) of adolescents, and marking the suicide stages as 1, 2, 3 and 4, an ordered categorical variable.\nSuicide stages were coded on the basis of the following criteria: (a) Depressed mood: feeling negative and depressed, but not yet having suicidal ideation. It means participants did not express suicidal intention explicitly or implicitly(whether they actively expressed suicidal intentions, or exposed suicidal intentions under inquiries from the official Weibo account). (b) Suicide ideation: there is suicidal intent, but suicide plan has not formed. Due to being affected by an intense depressive mood, the idea of avoiding pain through suicide arises, but there is no specific time and way to implement it. (c) Suicide plan: on the basis of suicidal ideation, there is a rough or detailed suicide plan, such as when, where, and how to commit suicide. (d) Suicide attempt: at least one suicide attempt with the aim of ending one’s own life.\n2.3.2. Getting the Weibo Text Data We scraped Weibo texts published by the same 3627 users through the Application Programming Interface (API) of Sina Weibo. In order to obtain sufficient Weibo texts for analysis, we took the first time that the official Weibo account sent a private message to the user as the beginning of suicide intervention, downloaded all Weibo texts in the 3 months before the intervention, and filtered the users whose Weibo texts are less than 300 words (Figure 4).\nFinally, 105 high-suicide-risk adolescents (as the experimental group) were included in the analysis. We then removed stop words and performed Chinese word segmentation on the Weibo texts, and calculated word frequency. “The Chinese Suicide Dictionary” [27] was used to measure the word frequency ratio of their psychological pain and hopelessness. The dictionary outline is shown in Table 1. The psychological pain dimension represents the pain and torment of individuals at the psychological level, including 403 keywords; the hopelessness dimension represents the subjective feeling of losing hope, including 188 keywords.\nWe scraped Weibo texts published by the same 3627 users through the Application Programming Interface (API) of Sina Weibo. In order to obtain sufficient Weibo texts for analysis, we took the first time that the official Weibo account sent a private message to the user as the beginning of suicide intervention, downloaded all Weibo texts in the 3 months before the intervention, and filtered the users whose Weibo texts are less than 300 words (Figure 4).\nFinally, 105 high-suicide-risk adolescents (as the experimental group) were included in the analysis. We then removed stop words and performed Chinese word segmentation on the Weibo texts, and calculated word frequency. “The Chinese Suicide Dictionary” [27] was used to measure the word frequency ratio of their psychological pain and hopelessness. The dictionary outline is shown in Table 1. The psychological pain dimension represents the pain and torment of individuals at the psychological level, including 403 keywords; the hopelessness dimension represents the subjective feeling of losing hope, including 188 keywords.\n2.3.1. Labeling the Interview Records Data During the online psychological assistance project period (November 2016 to April 2019), the official Weibo account had communicated with 3627 users online, and 127,336 message records were accumulated. Adhering to the principle of privacy and confidentiality, the use of interview records data was strictly restricted. The experimenter must protect the privacy of the research subjects involved in the project, and should not use any confidential information related to the research subjects (including but not limited to accounts, nicknames, contact information, personal homepage, network IP, location, information records, etc.) outside the scope of the research, and would not violate the privacy of the subjects in any way.\nThe interview records data were encoded by qualitative analysis. The basic content of interview records data were the current status and problems of users. We extracted three indicators from the interview records data, and the specific descriptions of them are shown below:(1)Determining whether the user was in adolescent stages (junior high school, high school, college), and retaining the data labeled as the adolescents.(2)Determining whether the user was diagnosed with mental illness by a psychiatrist, had a history of mental illness, or was taking medication for mental illness, and labeling a user had a mental illness or not as a 0/1 dummy variable.(3)Coding the suicide stages (depressed mood, suicide ideation, suicide plan, suicide attempt) of adolescents, and marking the suicide stages as 1, 2, 3 and 4, an ordered categorical variable.\nDetermining whether the user was in adolescent stages (junior high school, high school, college), and retaining the data labeled as the adolescents.\nDetermining whether the user was diagnosed with mental illness by a psychiatrist, had a history of mental illness, or was taking medication for mental illness, and labeling a user had a mental illness or not as a 0/1 dummy variable.\nCoding the suicide stages (depressed mood, suicide ideation, suicide plan, suicide attempt) of adolescents, and marking the suicide stages as 1, 2, 3 and 4, an ordered categorical variable.\nSuicide stages were coded on the basis of the following criteria: (a) Depressed mood: feeling negative and depressed, but not yet having suicidal ideation. It means participants did not express suicidal intention explicitly or implicitly(whether they actively expressed suicidal intentions, or exposed suicidal intentions under inquiries from the official Weibo account). (b) Suicide ideation: there is suicidal intent, but suicide plan has not formed. Due to being affected by an intense depressive mood, the idea of avoiding pain through suicide arises, but there is no specific time and way to implement it. (c) Suicide plan: on the basis of suicidal ideation, there is a rough or detailed suicide plan, such as when, where, and how to commit suicide. (d) Suicide attempt: at least one suicide attempt with the aim of ending one’s own life.\nDuring the online psychological assistance project period (November 2016 to April 2019), the official Weibo account had communicated with 3627 users online, and 127,336 message records were accumulated. Adhering to the principle of privacy and confidentiality, the use of interview records data was strictly restricted. The experimenter must protect the privacy of the research subjects involved in the project, and should not use any confidential information related to the research subjects (including but not limited to accounts, nicknames, contact information, personal homepage, network IP, location, information records, etc.) outside the scope of the research, and would not violate the privacy of the subjects in any way.\nThe interview records data were encoded by qualitative analysis. The basic content of interview records data were the current status and problems of users. We extracted three indicators from the interview records data, and the specific descriptions of them are shown below:(1)Determining whether the user was in adolescent stages (junior high school, high school, college), and retaining the data labeled as the adolescents.(2)Determining whether the user was diagnosed with mental illness by a psychiatrist, had a history of mental illness, or was taking medication for mental illness, and labeling a user had a mental illness or not as a 0/1 dummy variable.(3)Coding the suicide stages (depressed mood, suicide ideation, suicide plan, suicide attempt) of adolescents, and marking the suicide stages as 1, 2, 3 and 4, an ordered categorical variable.\nDetermining whether the user was in adolescent stages (junior high school, high school, college), and retaining the data labeled as the adolescents.\nDetermining whether the user was diagnosed with mental illness by a psychiatrist, had a history of mental illness, or was taking medication for mental illness, and labeling a user had a mental illness or not as a 0/1 dummy variable.\nCoding the suicide stages (depressed mood, suicide ideation, suicide plan, suicide attempt) of adolescents, and marking the suicide stages as 1, 2, 3 and 4, an ordered categorical variable.\nSuicide stages were coded on the basis of the following criteria: (a) Depressed mood: feeling negative and depressed, but not yet having suicidal ideation. It means participants did not express suicidal intention explicitly or implicitly(whether they actively expressed suicidal intentions, or exposed suicidal intentions under inquiries from the official Weibo account). (b) Suicide ideation: there is suicidal intent, but suicide plan has not formed. Due to being affected by an intense depressive mood, the idea of avoiding pain through suicide arises, but there is no specific time and way to implement it. (c) Suicide plan: on the basis of suicidal ideation, there is a rough or detailed suicide plan, such as when, where, and how to commit suicide. (d) Suicide attempt: at least one suicide attempt with the aim of ending one’s own life.\n2.3.2. Getting the Weibo Text Data We scraped Weibo texts published by the same 3627 users through the Application Programming Interface (API) of Sina Weibo. In order to obtain sufficient Weibo texts for analysis, we took the first time that the official Weibo account sent a private message to the user as the beginning of suicide intervention, downloaded all Weibo texts in the 3 months before the intervention, and filtered the users whose Weibo texts are less than 300 words (Figure 4).\nFinally, 105 high-suicide-risk adolescents (as the experimental group) were included in the analysis. We then removed stop words and performed Chinese word segmentation on the Weibo texts, and calculated word frequency. “The Chinese Suicide Dictionary” [27] was used to measure the word frequency ratio of their psychological pain and hopelessness. The dictionary outline is shown in Table 1. The psychological pain dimension represents the pain and torment of individuals at the psychological level, including 403 keywords; the hopelessness dimension represents the subjective feeling of losing hope, including 188 keywords.\nWe scraped Weibo texts published by the same 3627 users through the Application Programming Interface (API) of Sina Weibo. In order to obtain sufficient Weibo texts for analysis, we took the first time that the official Weibo account sent a private message to the user as the beginning of suicide intervention, downloaded all Weibo texts in the 3 months before the intervention, and filtered the users whose Weibo texts are less than 300 words (Figure 4).\nFinally, 105 high-suicide-risk adolescents (as the experimental group) were included in the analysis. We then removed stop words and performed Chinese word segmentation on the Weibo texts, and calculated word frequency. “The Chinese Suicide Dictionary” [27] was used to measure the word frequency ratio of their psychological pain and hopelessness. The dictionary outline is shown in Table 1. The psychological pain dimension represents the pain and torment of individuals at the psychological level, including 403 keywords; the hopelessness dimension represents the subjective feeling of losing hope, including 188 keywords.\n2.4. Data Analysis 2.4.1. Qualitative Analysis Through the labeling results of interview records data, we investigated the situation of mental illness and the distribution of suicide stages for the experimental group.\nThrough the labeling results of interview records data, we investigated the situation of mental illness and the distribution of suicide stages for the experimental group.\n2.4.2. Analysis of Suicide Path The mediation effect analysis was executed on the software Mplus by using the bootstrap method to test the suicide mechanism path model. The word frequency of psychological pain and hopelessness of the experimental group were treated as independent variable (X) and mediating variable (M), respectively. The suicide stages were treated as dependent variable (Y). The mental illness was included in the model as a control variable (Z).\nThe mediation effect analysis was executed on the software Mplus by using the bootstrap method to test the suicide mechanism path model. The word frequency of psychological pain and hopelessness of the experimental group were treated as independent variable (X) and mediating variable (M), respectively. The suicide stages were treated as dependent variable (Y). The mental illness was included in the model as a control variable (Z).\n2.4.1. Qualitative Analysis Through the labeling results of interview records data, we investigated the situation of mental illness and the distribution of suicide stages for the experimental group.\nThrough the labeling results of interview records data, we investigated the situation of mental illness and the distribution of suicide stages for the experimental group.\n2.4.2. Analysis of Suicide Path The mediation effect analysis was executed on the software Mplus by using the bootstrap method to test the suicide mechanism path model. The word frequency of psychological pain and hopelessness of the experimental group were treated as independent variable (X) and mediating variable (M), respectively. The suicide stages were treated as dependent variable (Y). The mental illness was included in the model as a control variable (Z).\nThe mediation effect analysis was executed on the software Mplus by using the bootstrap method to test the suicide mechanism path model. The word frequency of psychological pain and hopelessness of the experimental group were treated as independent variable (X) and mediating variable (M), respectively. The suicide stages were treated as dependent variable (Y). The mental illness was included in the model as a control variable (Z).","Through the online psychological assistance project, we obtained the interview records data of 3627 Weibo users, and then processed the user’s interview records data and Weibo texts data. Finally, we retained 105 high-suicide-risk adolescents with enough Weibo texts as the experimental group.\nEthical Statement: Participants gave informed consent when agreeing to take part in the online psychological assistance project. The project received ethical approval from the Institution-al Review Board of the Institute of Psychology, Chinese Academy of Sciences, with the ethics approval number H16003.","We used the suicide signal recognition model established by the Computational Cyber-Psychology Lab (CCPL) to identify the suicide signal of a single microblog, which can effectively determine whether the single microblog has shown suicidal risk [26].\nSince November 2016, we had used the suicide signal recognition model for online identification of Weibo comments. The last Weibo post before the death of Weibo user “Zoufan”, as a place where people can express their negative emotions, has received more than 1 million comments until now, and the number of comments keep rising (Figure 2).\nFirstly, we used the suicide signal model to identify the suicide signal of the Weibo comments. Then, the comments identified by the model were re-identified by manual recognition. Finally, we used the official Weibo account “Psychological Map Psy” and sent a private message to the Weibo users marked as having a suicide risk. The contents of the private message mainly include inquiries about the user’s emotional state, expressions of concern, links to self-assessment psychological scales, and the hotline of the psychological crisis intervention center, etc. (Figure 3). In addition, users can choose whether to communicate with the voluntary counselor online according to their own wishes, so as to achieve agile psychological crisis intervention.","2.3.1. Labeling the Interview Records Data During the online psychological assistance project period (November 2016 to April 2019), the official Weibo account had communicated with 3627 users online, and 127,336 message records were accumulated. Adhering to the principle of privacy and confidentiality, the use of interview records data was strictly restricted. The experimenter must protect the privacy of the research subjects involved in the project, and should not use any confidential information related to the research subjects (including but not limited to accounts, nicknames, contact information, personal homepage, network IP, location, information records, etc.) outside the scope of the research, and would not violate the privacy of the subjects in any way.\nThe interview records data were encoded by qualitative analysis. The basic content of interview records data were the current status and problems of users. We extracted three indicators from the interview records data, and the specific descriptions of them are shown below:(1)Determining whether the user was in adolescent stages (junior high school, high school, college), and retaining the data labeled as the adolescents.(2)Determining whether the user was diagnosed with mental illness by a psychiatrist, had a history of mental illness, or was taking medication for mental illness, and labeling a user had a mental illness or not as a 0/1 dummy variable.(3)Coding the suicide stages (depressed mood, suicide ideation, suicide plan, suicide attempt) of adolescents, and marking the suicide stages as 1, 2, 3 and 4, an ordered categorical variable.\nDetermining whether the user was in adolescent stages (junior high school, high school, college), and retaining the data labeled as the adolescents.\nDetermining whether the user was diagnosed with mental illness by a psychiatrist, had a history of mental illness, or was taking medication for mental illness, and labeling a user had a mental illness or not as a 0/1 dummy variable.\nCoding the suicide stages (depressed mood, suicide ideation, suicide plan, suicide attempt) of adolescents, and marking the suicide stages as 1, 2, 3 and 4, an ordered categorical variable.\nSuicide stages were coded on the basis of the following criteria: (a) Depressed mood: feeling negative and depressed, but not yet having suicidal ideation. It means participants did not express suicidal intention explicitly or implicitly(whether they actively expressed suicidal intentions, or exposed suicidal intentions under inquiries from the official Weibo account). (b) Suicide ideation: there is suicidal intent, but suicide plan has not formed. Due to being affected by an intense depressive mood, the idea of avoiding pain through suicide arises, but there is no specific time and way to implement it. (c) Suicide plan: on the basis of suicidal ideation, there is a rough or detailed suicide plan, such as when, where, and how to commit suicide. (d) Suicide attempt: at least one suicide attempt with the aim of ending one’s own life.\nDuring the online psychological assistance project period (November 2016 to April 2019), the official Weibo account had communicated with 3627 users online, and 127,336 message records were accumulated. Adhering to the principle of privacy and confidentiality, the use of interview records data was strictly restricted. The experimenter must protect the privacy of the research subjects involved in the project, and should not use any confidential information related to the research subjects (including but not limited to accounts, nicknames, contact information, personal homepage, network IP, location, information records, etc.) outside the scope of the research, and would not violate the privacy of the subjects in any way.\nThe interview records data were encoded by qualitative analysis. The basic content of interview records data were the current status and problems of users. We extracted three indicators from the interview records data, and the specific descriptions of them are shown below:(1)Determining whether the user was in adolescent stages (junior high school, high school, college), and retaining the data labeled as the adolescents.(2)Determining whether the user was diagnosed with mental illness by a psychiatrist, had a history of mental illness, or was taking medication for mental illness, and labeling a user had a mental illness or not as a 0/1 dummy variable.(3)Coding the suicide stages (depressed mood, suicide ideation, suicide plan, suicide attempt) of adolescents, and marking the suicide stages as 1, 2, 3 and 4, an ordered categorical variable.\nDetermining whether the user was in adolescent stages (junior high school, high school, college), and retaining the data labeled as the adolescents.\nDetermining whether the user was diagnosed with mental illness by a psychiatrist, had a history of mental illness, or was taking medication for mental illness, and labeling a user had a mental illness or not as a 0/1 dummy variable.\nCoding the suicide stages (depressed mood, suicide ideation, suicide plan, suicide attempt) of adolescents, and marking the suicide stages as 1, 2, 3 and 4, an ordered categorical variable.\nSuicide stages were coded on the basis of the following criteria: (a) Depressed mood: feeling negative and depressed, but not yet having suicidal ideation. It means participants did not express suicidal intention explicitly or implicitly(whether they actively expressed suicidal intentions, or exposed suicidal intentions under inquiries from the official Weibo account). (b) Suicide ideation: there is suicidal intent, but suicide plan has not formed. Due to being affected by an intense depressive mood, the idea of avoiding pain through suicide arises, but there is no specific time and way to implement it. (c) Suicide plan: on the basis of suicidal ideation, there is a rough or detailed suicide plan, such as when, where, and how to commit suicide. (d) Suicide attempt: at least one suicide attempt with the aim of ending one’s own life.\n2.3.2. Getting the Weibo Text Data We scraped Weibo texts published by the same 3627 users through the Application Programming Interface (API) of Sina Weibo. In order to obtain sufficient Weibo texts for analysis, we took the first time that the official Weibo account sent a private message to the user as the beginning of suicide intervention, downloaded all Weibo texts in the 3 months before the intervention, and filtered the users whose Weibo texts are less than 300 words (Figure 4).\nFinally, 105 high-suicide-risk adolescents (as the experimental group) were included in the analysis. We then removed stop words and performed Chinese word segmentation on the Weibo texts, and calculated word frequency. “The Chinese Suicide Dictionary” [27] was used to measure the word frequency ratio of their psychological pain and hopelessness. The dictionary outline is shown in Table 1. The psychological pain dimension represents the pain and torment of individuals at the psychological level, including 403 keywords; the hopelessness dimension represents the subjective feeling of losing hope, including 188 keywords.\nWe scraped Weibo texts published by the same 3627 users through the Application Programming Interface (API) of Sina Weibo. In order to obtain sufficient Weibo texts for analysis, we took the first time that the official Weibo account sent a private message to the user as the beginning of suicide intervention, downloaded all Weibo texts in the 3 months before the intervention, and filtered the users whose Weibo texts are less than 300 words (Figure 4).\nFinally, 105 high-suicide-risk adolescents (as the experimental group) were included in the analysis. We then removed stop words and performed Chinese word segmentation on the Weibo texts, and calculated word frequency. “The Chinese Suicide Dictionary” [27] was used to measure the word frequency ratio of their psychological pain and hopelessness. The dictionary outline is shown in Table 1. The psychological pain dimension represents the pain and torment of individuals at the psychological level, including 403 keywords; the hopelessness dimension represents the subjective feeling of losing hope, including 188 keywords.","During the online psychological assistance project period (November 2016 to April 2019), the official Weibo account had communicated with 3627 users online, and 127,336 message records were accumulated. Adhering to the principle of privacy and confidentiality, the use of interview records data was strictly restricted. The experimenter must protect the privacy of the research subjects involved in the project, and should not use any confidential information related to the research subjects (including but not limited to accounts, nicknames, contact information, personal homepage, network IP, location, information records, etc.) outside the scope of the research, and would not violate the privacy of the subjects in any way.\nThe interview records data were encoded by qualitative analysis. The basic content of interview records data were the current status and problems of users. We extracted three indicators from the interview records data, and the specific descriptions of them are shown below:(1)Determining whether the user was in adolescent stages (junior high school, high school, college), and retaining the data labeled as the adolescents.(2)Determining whether the user was diagnosed with mental illness by a psychiatrist, had a history of mental illness, or was taking medication for mental illness, and labeling a user had a mental illness or not as a 0/1 dummy variable.(3)Coding the suicide stages (depressed mood, suicide ideation, suicide plan, suicide attempt) of adolescents, and marking the suicide stages as 1, 2, 3 and 4, an ordered categorical variable.\nDetermining whether the user was in adolescent stages (junior high school, high school, college), and retaining the data labeled as the adolescents.\nDetermining whether the user was diagnosed with mental illness by a psychiatrist, had a history of mental illness, or was taking medication for mental illness, and labeling a user had a mental illness or not as a 0/1 dummy variable.\nCoding the suicide stages (depressed mood, suicide ideation, suicide plan, suicide attempt) of adolescents, and marking the suicide stages as 1, 2, 3 and 4, an ordered categorical variable.\nSuicide stages were coded on the basis of the following criteria: (a) Depressed mood: feeling negative and depressed, but not yet having suicidal ideation. It means participants did not express suicidal intention explicitly or implicitly(whether they actively expressed suicidal intentions, or exposed suicidal intentions under inquiries from the official Weibo account). (b) Suicide ideation: there is suicidal intent, but suicide plan has not formed. Due to being affected by an intense depressive mood, the idea of avoiding pain through suicide arises, but there is no specific time and way to implement it. (c) Suicide plan: on the basis of suicidal ideation, there is a rough or detailed suicide plan, such as when, where, and how to commit suicide. (d) Suicide attempt: at least one suicide attempt with the aim of ending one’s own life.","We scraped Weibo texts published by the same 3627 users through the Application Programming Interface (API) of Sina Weibo. In order to obtain sufficient Weibo texts for analysis, we took the first time that the official Weibo account sent a private message to the user as the beginning of suicide intervention, downloaded all Weibo texts in the 3 months before the intervention, and filtered the users whose Weibo texts are less than 300 words (Figure 4).\nFinally, 105 high-suicide-risk adolescents (as the experimental group) were included in the analysis. We then removed stop words and performed Chinese word segmentation on the Weibo texts, and calculated word frequency. “The Chinese Suicide Dictionary” [27] was used to measure the word frequency ratio of their psychological pain and hopelessness. The dictionary outline is shown in Table 1. The psychological pain dimension represents the pain and torment of individuals at the psychological level, including 403 keywords; the hopelessness dimension represents the subjective feeling of losing hope, including 188 keywords.","2.4.1. Qualitative Analysis Through the labeling results of interview records data, we investigated the situation of mental illness and the distribution of suicide stages for the experimental group.\nThrough the labeling results of interview records data, we investigated the situation of mental illness and the distribution of suicide stages for the experimental group.\n2.4.2. Analysis of Suicide Path The mediation effect analysis was executed on the software Mplus by using the bootstrap method to test the suicide mechanism path model. The word frequency of psychological pain and hopelessness of the experimental group were treated as independent variable (X) and mediating variable (M), respectively. The suicide stages were treated as dependent variable (Y). The mental illness was included in the model as a control variable (Z).\nThe mediation effect analysis was executed on the software Mplus by using the bootstrap method to test the suicide mechanism path model. The word frequency of psychological pain and hopelessness of the experimental group were treated as independent variable (X) and mediating variable (M), respectively. The suicide stages were treated as dependent variable (Y). The mental illness was included in the model as a control variable (Z).","Through the labeling results of interview records data, we investigated the situation of mental illness and the distribution of suicide stages for the experimental group.","The mediation effect analysis was executed on the software Mplus by using the bootstrap method to test the suicide mechanism path model. The word frequency of psychological pain and hopelessness of the experimental group were treated as independent variable (X) and mediating variable (M), respectively. The suicide stages were treated as dependent variable (Y). The mental illness was included in the model as a control variable (Z).","3.1. Group Characteristics Reflected by Qualitative Analysis The mental illness diagnosis of the experimental group was marked; it was found that 38 were diagnosed and 67 were undiagnosed. The proportion of users diagnosed with mental illness was 36.2%. Among the 38 high-suicide-risk adolescents diagnosed with mental illness, 29 users had depression (or depression was included in multiple mental illnesses), accounting for 76.3%; the rest included bipolar disorder (13.16%), anxiety disorder (13.16%), etc.; 9 of them suffered from multiple mental illnesses (e.g., depression and anxiety disorder), accounting for 23.68%.\nAmong the experimental group, 57 were in the stage of depressed mood (54.3%), 21 were in the stage of suicide ideation (20.0%), 19 were in the stage of suicide plan (18.1%), and 8 people were in the stage of suicide attempt (7.6%) (Table 2).\nThe mental illness diagnosis of the experimental group was marked; it was found that 38 were diagnosed and 67 were undiagnosed. The proportion of users diagnosed with mental illness was 36.2%. Among the 38 high-suicide-risk adolescents diagnosed with mental illness, 29 users had depression (or depression was included in multiple mental illnesses), accounting for 76.3%; the rest included bipolar disorder (13.16%), anxiety disorder (13.16%), etc.; 9 of them suffered from multiple mental illnesses (e.g., depression and anxiety disorder), accounting for 23.68%.\nAmong the experimental group, 57 were in the stage of depressed mood (54.3%), 21 were in the stage of suicide ideation (20.0%), 19 were in the stage of suicide plan (18.1%), and 8 people were in the stage of suicide attempt (7.6%) (Table 2).\n3.2. Path Analysis of Psychological Pain, Hopelessness and Suicide Stages A mediation effect test was performed on the path model (psychological pain → hopelessness → suicide stages). The performance of each fit index of the model was good (RMSEA < 0.08; CFI > 0.9; TLI > 0.9), indicating that the model fitting effect is excellent (Table 3).\nAfter controlling for mental illness factors, the total effect c was significant (β = −0.154, p < 0.05), indicating that there was a mediation effect; the path coefficients a and b were significant (β = 0.571, p < 0.001; β = −0.271, p < 0.05), but the direct effect c’ was not significant (β = 0.052, p > 0.05), indicating that there was a complete mediating effect of hopelessness. That is, psychological pain mainly affects suicide risk through hopelessness (Table 4).\nIt was worth noting that the relationship between the expression of hopelessness and the stages of suicide showed a significant negative correlation (β = −0.271, p < 0.05), indicating that the late suicide stage such as suicide attempt, which may be closer to suicide death, included less expressions of hopelessness (Table 4).\nFurthermore, there was a significant positive correlation between diagnosed mental illness and suicide stages (β = 0.398, p < 0.001), indicating the necessity of treating mental illness as a control variable in order to see the relationship between psychological pain, hopelessness and suicide stages more clearly. At the same time, it also showed that the later the suicide stage, the higher the diagnosis rate of mental illness (Table 4).\nA mediation effect test was performed on the path model (psychological pain → hopelessness → suicide stages). The performance of each fit index of the model was good (RMSEA < 0.08; CFI > 0.9; TLI > 0.9), indicating that the model fitting effect is excellent (Table 3).\nAfter controlling for mental illness factors, the total effect c was significant (β = −0.154, p < 0.05), indicating that there was a mediation effect; the path coefficients a and b were significant (β = 0.571, p < 0.001; β = −0.271, p < 0.05), but the direct effect c’ was not significant (β = 0.052, p > 0.05), indicating that there was a complete mediating effect of hopelessness. That is, psychological pain mainly affects suicide risk through hopelessness (Table 4).\nIt was worth noting that the relationship between the expression of hopelessness and the stages of suicide showed a significant negative correlation (β = −0.271, p < 0.05), indicating that the late suicide stage such as suicide attempt, which may be closer to suicide death, included less expressions of hopelessness (Table 4).\nFurthermore, there was a significant positive correlation between diagnosed mental illness and suicide stages (β = 0.398, p < 0.001), indicating the necessity of treating mental illness as a control variable in order to see the relationship between psychological pain, hopelessness and suicide stages more clearly. At the same time, it also showed that the later the suicide stage, the higher the diagnosis rate of mental illness (Table 4).","The mental illness diagnosis of the experimental group was marked; it was found that 38 were diagnosed and 67 were undiagnosed. The proportion of users diagnosed with mental illness was 36.2%. Among the 38 high-suicide-risk adolescents diagnosed with mental illness, 29 users had depression (or depression was included in multiple mental illnesses), accounting for 76.3%; the rest included bipolar disorder (13.16%), anxiety disorder (13.16%), etc.; 9 of them suffered from multiple mental illnesses (e.g., depression and anxiety disorder), accounting for 23.68%.\nAmong the experimental group, 57 were in the stage of depressed mood (54.3%), 21 were in the stage of suicide ideation (20.0%), 19 were in the stage of suicide plan (18.1%), and 8 people were in the stage of suicide attempt (7.6%) (Table 2).","A mediation effect test was performed on the path model (psychological pain → hopelessness → suicide stages). The performance of each fit index of the model was good (RMSEA < 0.08; CFI > 0.9; TLI > 0.9), indicating that the model fitting effect is excellent (Table 3).\nAfter controlling for mental illness factors, the total effect c was significant (β = −0.154, p < 0.05), indicating that there was a mediation effect; the path coefficients a and b were significant (β = 0.571, p < 0.001; β = −0.271, p < 0.05), but the direct effect c’ was not significant (β = 0.052, p > 0.05), indicating that there was a complete mediating effect of hopelessness. That is, psychological pain mainly affects suicide risk through hopelessness (Table 4).\nIt was worth noting that the relationship between the expression of hopelessness and the stages of suicide showed a significant negative correlation (β = −0.271, p < 0.05), indicating that the late suicide stage such as suicide attempt, which may be closer to suicide death, included less expressions of hopelessness (Table 4).\nFurthermore, there was a significant positive correlation between diagnosed mental illness and suicide stages (β = 0.398, p < 0.001), indicating the necessity of treating mental illness as a control variable in order to see the relationship between psychological pain, hopelessness and suicide stages more clearly. At the same time, it also showed that the later the suicide stage, the higher the diagnosis rate of mental illness (Table 4).","4.1. The Qualitative Analysis Results Among the experimental group, 36.2% of users suffered from mental illness. Depression accounted for 76.3% of all mental illness; it is the most common mental illness among the high-suicide-risk adolescents. The incidence of suicide among students whose relatives were affected by psychosis were higher than that of general residents. Suicide and mental illness were closely linked [28]. British doctor Sainsbury conducted a qualitative study on the data of 400 suicides examined by a coroner in London between 1936 and 1938, and found that 37% of the suicides reflected mental illness [29]. In a meta-analysis conducted in 2004, twenty-seven studies comprising 3275 suicides were included, of which 87.3% had been diagnosed with a mental disorder prior to their death [30]. In contrast, the research population in our study was only a high suicide risk group, so the detection rate of mental illness was relatively low. Although the research populations of the other two studies were both suicide decedents, the time interval between the two studies was large. It is very possible that there existed better methods to detect the mental illness of the suicide decedents in later studies, and it is understandable that the detection rate of mental illness has greatly increased. Furthermore, a more recent study has shown that the most common mental illness associated with suicide was depression [31]. The proportion of depression in all mental illnesses in our study also supported this conclusion.\nAnother result of the qualitative analysis showed that the later the suicide stage, the lower the proportion of adolescents. A study on residents’ suicidal ideation, suicide plans and suicide attempts in Dalian, China showed that the detection rate of suicidal ideation, suicide plan and suicide attempt decreased in that order in different groups [32]. This may because not all suicide ideations translate into suicidal actions, and most people with suicide ideation have not attempted suicide [33,34].\nAmong the experimental group, 36.2% of users suffered from mental illness. Depression accounted for 76.3% of all mental illness; it is the most common mental illness among the high-suicide-risk adolescents. The incidence of suicide among students whose relatives were affected by psychosis were higher than that of general residents. Suicide and mental illness were closely linked [28]. British doctor Sainsbury conducted a qualitative study on the data of 400 suicides examined by a coroner in London between 1936 and 1938, and found that 37% of the suicides reflected mental illness [29]. In a meta-analysis conducted in 2004, twenty-seven studies comprising 3275 suicides were included, of which 87.3% had been diagnosed with a mental disorder prior to their death [30]. In contrast, the research population in our study was only a high suicide risk group, so the detection rate of mental illness was relatively low. Although the research populations of the other two studies were both suicide decedents, the time interval between the two studies was large. It is very possible that there existed better methods to detect the mental illness of the suicide decedents in later studies, and it is understandable that the detection rate of mental illness has greatly increased. Furthermore, a more recent study has shown that the most common mental illness associated with suicide was depression [31]. The proportion of depression in all mental illnesses in our study also supported this conclusion.\nAnother result of the qualitative analysis showed that the later the suicide stage, the lower the proportion of adolescents. A study on residents’ suicidal ideation, suicide plans and suicide attempts in Dalian, China showed that the detection rate of suicidal ideation, suicide plan and suicide attempt decreased in that order in different groups [32]. This may because not all suicide ideations translate into suicidal actions, and most people with suicide ideation have not attempted suicide [33,34].\n4.2. The Mediating Effect of Hopelessness Our research found that the effect of psychological pain on suicide stages was totally mediated by hopelessness, confirming our suicide mechanism path hypothesis: psychological pain → hopelessness → suicide stages. Although the first step of suicide ideation starts from psychological pain, psychological pain itself is not enough to generate suicide ideation, and hopelessness is also needed to develop suicide ideation [19]. People make decisions and take actions based in part on their emotional predictions [35,36], and hopelessness makes people at suicide risk overestimate their future emotional pain [37]. If the individual experiences constant pain in life, this may reduce the individual’s desire to live, leading to suicidal thoughts. However, pain alone is not enough to completely lose the belief in life. Only when the individual feels that the terrible situation cannot be improved, and he or she will live in pain forever, suicide will be considered.\nThus, expressions of hopelessness may be a better predictor of suicide risk than psychological pain, and we found a traceable pathway for suicide intervention. Based on the Weibo posts, we can capture whether individual released suicide signals and it is possible to track the suicide stages in the future, making suicide intervention more accurate.\nOur research found that the effect of psychological pain on suicide stages was totally mediated by hopelessness, confirming our suicide mechanism path hypothesis: psychological pain → hopelessness → suicide stages. Although the first step of suicide ideation starts from psychological pain, psychological pain itself is not enough to generate suicide ideation, and hopelessness is also needed to develop suicide ideation [19]. People make decisions and take actions based in part on their emotional predictions [35,36], and hopelessness makes people at suicide risk overestimate their future emotional pain [37]. If the individual experiences constant pain in life, this may reduce the individual’s desire to live, leading to suicidal thoughts. However, pain alone is not enough to completely lose the belief in life. Only when the individual feels that the terrible situation cannot be improved, and he or she will live in pain forever, suicide will be considered.\nThus, expressions of hopelessness may be a better predictor of suicide risk than psychological pain, and we found a traceable pathway for suicide intervention. Based on the Weibo posts, we can capture whether individual released suicide signals and it is possible to track the suicide stages in the future, making suicide intervention more accurate.\n4.3. The Hopelessness Expressions and the Suicide Stages There was a negative predictive relationship between hopelessness expressions and suicide stages, as adolescents at the later suicide stages had less expressions of hopelessness. One possible reason is that adolescents at higher risk of suicide may use less concrete, direct words of hopelessness to express such feelings. From a linguistic point of view, Gao Yihong and Meng Ling analyzed the Weibo texts of “Zoufan” three months before suicide. They found that “Zoufan” often used decontextualized and fragmented ways to express personal depressed mood, and expressed metaphors and intentions about death frequently [38]. This euphemistic and poetic way of venting may be the commonality of high suicide risk adolescents. Another possible reason is “the Presuicidal Syndrome”, which mentioned that there exists an “ominous quiet” in the short period before suicide. This is a sign of “affective restriction”, which represents extreme repression of intolerable feelings [39]. Such “affective restriction” may be why people in the later suicide stage are less likely to express hopelessness. Moreover, adolescents at the later suicide stage may be more accepting and tolerant of psychological pain and hopelessness, which makes them less willing to talk about individual psychological pain and hopelessness, and with lack of supportive survival signals through texts, lose the belief in getting help.\nMany researchers have paid attention to the use of social media for suicide prevention and intervention, and have conducted a series of studies [40,41]. However, little attention has been paid to whether the suicidal signal was from an early suicide ideator or a later suicide attempter. Our study fills this gap, complementing and extending suicide-related research. Our results suggest that adolescents who are at a later suicide stage and have a higher suicide risk often do not express too much hopelessness directly. Therefore, we cannot judge closeness to suicide only by the level of traditional expressions of hopelessness.\nThere was a negative predictive relationship between hopelessness expressions and suicide stages, as adolescents at the later suicide stages had less expressions of hopelessness. One possible reason is that adolescents at higher risk of suicide may use less concrete, direct words of hopelessness to express such feelings. From a linguistic point of view, Gao Yihong and Meng Ling analyzed the Weibo texts of “Zoufan” three months before suicide. They found that “Zoufan” often used decontextualized and fragmented ways to express personal depressed mood, and expressed metaphors and intentions about death frequently [38]. This euphemistic and poetic way of venting may be the commonality of high suicide risk adolescents. Another possible reason is “the Presuicidal Syndrome”, which mentioned that there exists an “ominous quiet” in the short period before suicide. This is a sign of “affective restriction”, which represents extreme repression of intolerable feelings [39]. Such “affective restriction” may be why people in the later suicide stage are less likely to express hopelessness. Moreover, adolescents at the later suicide stage may be more accepting and tolerant of psychological pain and hopelessness, which makes them less willing to talk about individual psychological pain and hopelessness, and with lack of supportive survival signals through texts, lose the belief in getting help.\nMany researchers have paid attention to the use of social media for suicide prevention and intervention, and have conducted a series of studies [40,41]. However, little attention has been paid to whether the suicidal signal was from an early suicide ideator or a later suicide attempter. Our study fills this gap, complementing and extending suicide-related research. Our results suggest that adolescents who are at a later suicide stage and have a higher suicide risk often do not express too much hopelessness directly. Therefore, we cannot judge closeness to suicide only by the level of traditional expressions of hopelessness.","Among the experimental group, 36.2% of users suffered from mental illness. Depression accounted for 76.3% of all mental illness; it is the most common mental illness among the high-suicide-risk adolescents. The incidence of suicide among students whose relatives were affected by psychosis were higher than that of general residents. Suicide and mental illness were closely linked [28]. British doctor Sainsbury conducted a qualitative study on the data of 400 suicides examined by a coroner in London between 1936 and 1938, and found that 37% of the suicides reflected mental illness [29]. In a meta-analysis conducted in 2004, twenty-seven studies comprising 3275 suicides were included, of which 87.3% had been diagnosed with a mental disorder prior to their death [30]. In contrast, the research population in our study was only a high suicide risk group, so the detection rate of mental illness was relatively low. Although the research populations of the other two studies were both suicide decedents, the time interval between the two studies was large. It is very possible that there existed better methods to detect the mental illness of the suicide decedents in later studies, and it is understandable that the detection rate of mental illness has greatly increased. Furthermore, a more recent study has shown that the most common mental illness associated with suicide was depression [31]. The proportion of depression in all mental illnesses in our study also supported this conclusion.\nAnother result of the qualitative analysis showed that the later the suicide stage, the lower the proportion of adolescents. A study on residents’ suicidal ideation, suicide plans and suicide attempts in Dalian, China showed that the detection rate of suicidal ideation, suicide plan and suicide attempt decreased in that order in different groups [32]. This may because not all suicide ideations translate into suicidal actions, and most people with suicide ideation have not attempted suicide [33,34].","Our research found that the effect of psychological pain on suicide stages was totally mediated by hopelessness, confirming our suicide mechanism path hypothesis: psychological pain → hopelessness → suicide stages. Although the first step of suicide ideation starts from psychological pain, psychological pain itself is not enough to generate suicide ideation, and hopelessness is also needed to develop suicide ideation [19]. People make decisions and take actions based in part on their emotional predictions [35,36], and hopelessness makes people at suicide risk overestimate their future emotional pain [37]. If the individual experiences constant pain in life, this may reduce the individual’s desire to live, leading to suicidal thoughts. However, pain alone is not enough to completely lose the belief in life. Only when the individual feels that the terrible situation cannot be improved, and he or she will live in pain forever, suicide will be considered.\nThus, expressions of hopelessness may be a better predictor of suicide risk than psychological pain, and we found a traceable pathway for suicide intervention. Based on the Weibo posts, we can capture whether individual released suicide signals and it is possible to track the suicide stages in the future, making suicide intervention more accurate.","There was a negative predictive relationship between hopelessness expressions and suicide stages, as adolescents at the later suicide stages had less expressions of hopelessness. One possible reason is that adolescents at higher risk of suicide may use less concrete, direct words of hopelessness to express such feelings. From a linguistic point of view, Gao Yihong and Meng Ling analyzed the Weibo texts of “Zoufan” three months before suicide. They found that “Zoufan” often used decontextualized and fragmented ways to express personal depressed mood, and expressed metaphors and intentions about death frequently [38]. This euphemistic and poetic way of venting may be the commonality of high suicide risk adolescents. Another possible reason is “the Presuicidal Syndrome”, which mentioned that there exists an “ominous quiet” in the short period before suicide. This is a sign of “affective restriction”, which represents extreme repression of intolerable feelings [39]. Such “affective restriction” may be why people in the later suicide stage are less likely to express hopelessness. Moreover, adolescents at the later suicide stage may be more accepting and tolerant of psychological pain and hopelessness, which makes them less willing to talk about individual psychological pain and hopelessness, and with lack of supportive survival signals through texts, lose the belief in getting help.\nMany researchers have paid attention to the use of social media for suicide prevention and intervention, and have conducted a series of studies [40,41]. However, little attention has been paid to whether the suicidal signal was from an early suicide ideator or a later suicide attempter. Our study fills this gap, complementing and extending suicide-related research. Our results suggest that adolescents who are at a later suicide stage and have a higher suicide risk often do not express too much hopelessness directly. Therefore, we cannot judge closeness to suicide only by the level of traditional expressions of hopelessness.","Although this study could divide the suicide stages of adolescents through labeling Weibo private message data, it cannot effectively determine the gender of the user. Previous studies had shown that suicidal behaviors were also affected by gender, and that the incidence of suicide attempts in adolescent females was higher than that of males [42]. Compared with males, females were more psychologically sensitive and vulnerable [43], and were more likely to express their pain through suicidal behaviors [44]. The expressions of hopelessness and psychological pain as predictors of suicide may differ by gender. There were only 105 adolescent users with high suicide risk in our study. Caution is thus necessary when generalizing the conclusions to other case.\nOur findings provided a traceable pathway for suicide intervention, finding that the effect of psychological pain on suicide stages was totally mediated by hopelessness, and that adolescents in the later suicide stages had less expressions of hopelessness in the traditional sense. However, the specific language characteristics of each suicide stage are still unknown. If we want to accomplish the precise prediction of suicide stages through the expressions on Weibo posts and provide more accurate and effective information for suicide intervention, we need to further explore the specific language expression characteristics of high suicide risk adolescents in each suicide stage, especially how to identify abstract metaphors that may constitute a suicidal signal. It is thus possible that we can provide more accurate suicide intervention for adolescents at different suicide stages in the future.\nWe mainly focus on the expression on social media of two psychological indicators (psychological pain, hopelessness). However, there are many factors which affect suicide, and we can research suicide intervention from multiple perspectives, not only examining some negative indicators, but also exploring some positive indicators in future research. For example, previous studies had shown that social support [45], psychological resilience [46], and self-compassion [47] were protective factors for adolescent suicide.","The qualitative findings suggested that mental illness in high-suicide-risk adolescents was strongly associated with suicide risk, and that depression was the most common mental illness associated with suicide. Another finding was that the later the suicide stage, the smaller was the proportion of high-suicide-risk adolescents.\nThe results of the mediation effect analysis verified the suicide mechanism path: psychological pain → hopelessness → suicide stages, indicating that psychological pain mainly affected suicide stages through hopelessness, and hopelessness may be a better predictor of suicide risk for high-suicide-risk adolescents.\nNotably, although adolescents in the later suicide stages were at greater suicide risk, they had less expression of hopelessness in the traditional sense."],"string":"[\n \"Adolescent suicide has always been a global public health concern to us. Suicide is the fourth leading cause of death globally among people aged 15–19 [1], and in China, it is the second leading cause of death for people aged 20–34 [2]. The consequences of adolescent suicide are the most serious among many mental health problems. It means disability or even a loss of life for individuals, heavy blows and severe trauma for families, and huge expenditures on the health, welfare and justice sectors for society. The study of suicide risk factors and the exploration of suicide mechanisms in high-suicide-risk adolescents are of great significance for the prevention of suicide.\\nWith the development of technology, social media has become a platform for people to record their daily life and express their emotions. In China, Sina Weibo as a typical product of the big data era has extensive influence. Sina Weibo is China’s most popular social media platform, just like Twitter in America. Although there are some short video platforms (e.g., Douyin) and forums (e.g., Douban) that are also popular in China, it is only on Sina Weibo that users can share information in real-time and communicate interactively in the forms of text, pictures, videos and other multimedia. According to a report released by Sina Weibo, Weibo users show a younger trend, of which the post-90s and post-00s youth groups account for nearly 80%, and the daily active volume can reach 224 million [3].\\nThe user’s emotional expression on social platforms has higher immediacy and less masking. Identifying the user’s psychological state through their expression on social media will be more convenient and quicker, and the obtained data will be more real. Therefore, we use the Weibo text analysis method to explore the suicide mechanism path of high-suicide-risk adolescents. Text analysis refers to the representation of text and the selection of feature items, which is a basic problem in text mining and information retrieval. Text analysis means that we can quantify the feature words extracted from text to represent text information. Therefore, we can extract the psychological indicators we want from the Weibo text by constructing a dictionary of psychological indicators.\\nSuicide represents a series of consecutive stages, including suicide ideation, suicide plan, and suicide attempt [4,5]. Suicide ideation, as a high-risk factor for suicide, has a significant predictive effect [6,7]. Approximately 1/3 of suicide ideators ultimately attempt suicide, and the number of suicide attempters is several times higher than those who really die by suicide, so suicide attempt is a greater risk factor for suicide than suicidal ideation [1,8,9]. The later the suicide stage, the closer to suicide death, and the greater risk of suicide. Therefore, in order to quantify the degree of suicide risk and provide accurate information for suicide interventions, we decided to divide suicidal behavior into different stages. At the same time, considering that depressed mood is an important signal of suicide, we finally divided suicide into 4 stages, which the suicide risk gradually increases: depressed mood, suicide ideation, suicide plan and suicide attempt, and defined adolescents at different suicide stages as high-suicide-risk adolescents.\\nA meta-analysis of the causes of suicide among adolescents found that suicide is a public health problem involving complex factors, such as biology, psychology, family, society and culture [8]. Among the psychological factors, psychological pain and hopelessness are important risk factors featuring strongly in suicide research [9,10,11]. Although psychological pain and hopelessness are often mentioned together as having an impact on suicidal behavior, as two most common motivations for suicide attempts [12], it is unclear how the two proximal factors of suicide affect the suicide stages.\\nPsychological pain is defined as the “introspective experience of negative emotions such as fear, despair, grief, shame, guilt, blocked love, loneliness and loss” [13,14,15,16]. When Shneidman first hypothesized the relationship between psychological pain and suicide, he pointed out that other factors can only affect suicidal behavior through psychological pain, without which suicide would not occur [13]. Evidence suggests that psychological pain plays a central role in suicidal behavior [10,17,18]. Psychological pain is not enough to develop suicide ideation, it also requires hopelessness [19]. Hopelessness is defined as negative expectations or pessimism about oneself or the future [20]. Previous studies had showed that hopelessness was also an important risk factor of suicide [21,22,23].\\nTo explore the relationship between psychological pain, hopelessness and suicide stages for high-suicide-risk adolescents, we established a hypothesis for the suicide mechanism path: psychological pain → hopelessness → suicide stages, with hopelessness as the mediating variable. In addition, there is a high correlation between mental illness and suicidal behaviors [24,25], and in order to better clarify the effect of psychological pain and hopelessness on suicide stages, a mediating effect model of our study is proposed with mental illness as a control variable (Figure 1). We used the more ecological Weibo text analysis method in this study, and hoped to further deepen the research on adolescent suicide and to find a new theoretical basis for future suicide interventions drawing on online data.\",\n \"2.1. Participants Through the online psychological assistance project, we obtained the interview records data of 3627 Weibo users, and then processed the user’s interview records data and Weibo texts data. Finally, we retained 105 high-suicide-risk adolescents with enough Weibo texts as the experimental group.\\nEthical Statement: Participants gave informed consent when agreeing to take part in the online psychological assistance project. The project received ethical approval from the Institution-al Review Board of the Institute of Psychology, Chinese Academy of Sciences, with the ethics approval number H16003.\\nThrough the online psychological assistance project, we obtained the interview records data of 3627 Weibo users, and then processed the user’s interview records data and Weibo texts data. Finally, we retained 105 high-suicide-risk adolescents with enough Weibo texts as the experimental group.\\nEthical Statement: Participants gave informed consent when agreeing to take part in the online psychological assistance project. The project received ethical approval from the Institution-al Review Board of the Institute of Psychology, Chinese Academy of Sciences, with the ethics approval number H16003.\\n2.2. Online Psychological Assistance Project We used the suicide signal recognition model established by the Computational Cyber-Psychology Lab (CCPL) to identify the suicide signal of a single microblog, which can effectively determine whether the single microblog has shown suicidal risk [26].\\nSince November 2016, we had used the suicide signal recognition model for online identification of Weibo comments. The last Weibo post before the death of Weibo user “Zoufan”, as a place where people can express their negative emotions, has received more than 1 million comments until now, and the number of comments keep rising (Figure 2).\\nFirstly, we used the suicide signal model to identify the suicide signal of the Weibo comments. Then, the comments identified by the model were re-identified by manual recognition. Finally, we used the official Weibo account “Psychological Map Psy” and sent a private message to the Weibo users marked as having a suicide risk. The contents of the private message mainly include inquiries about the user’s emotional state, expressions of concern, links to self-assessment psychological scales, and the hotline of the psychological crisis intervention center, etc. (Figure 3). In addition, users can choose whether to communicate with the voluntary counselor online according to their own wishes, so as to achieve agile psychological crisis intervention.\\nWe used the suicide signal recognition model established by the Computational Cyber-Psychology Lab (CCPL) to identify the suicide signal of a single microblog, which can effectively determine whether the single microblog has shown suicidal risk [26].\\nSince November 2016, we had used the suicide signal recognition model for online identification of Weibo comments. The last Weibo post before the death of Weibo user “Zoufan”, as a place where people can express their negative emotions, has received more than 1 million comments until now, and the number of comments keep rising (Figure 2).\\nFirstly, we used the suicide signal model to identify the suicide signal of the Weibo comments. Then, the comments identified by the model were re-identified by manual recognition. Finally, we used the official Weibo account “Psychological Map Psy” and sent a private message to the Weibo users marked as having a suicide risk. The contents of the private message mainly include inquiries about the user’s emotional state, expressions of concern, links to self-assessment psychological scales, and the hotline of the psychological crisis intervention center, etc. (Figure 3). In addition, users can choose whether to communicate with the voluntary counselor online according to their own wishes, so as to achieve agile psychological crisis intervention.\\n2.3. Data Collection and Processing 2.3.1. Labeling the Interview Records Data During the online psychological assistance project period (November 2016 to April 2019), the official Weibo account had communicated with 3627 users online, and 127,336 message records were accumulated. Adhering to the principle of privacy and confidentiality, the use of interview records data was strictly restricted. The experimenter must protect the privacy of the research subjects involved in the project, and should not use any confidential information related to the research subjects (including but not limited to accounts, nicknames, contact information, personal homepage, network IP, location, information records, etc.) outside the scope of the research, and would not violate the privacy of the subjects in any way.\\nThe interview records data were encoded by qualitative analysis. The basic content of interview records data were the current status and problems of users. We extracted three indicators from the interview records data, and the specific descriptions of them are shown below:(1)Determining whether the user was in adolescent stages (junior high school, high school, college), and retaining the data labeled as the adolescents.(2)Determining whether the user was diagnosed with mental illness by a psychiatrist, had a history of mental illness, or was taking medication for mental illness, and labeling a user had a mental illness or not as a 0/1 dummy variable.(3)Coding the suicide stages (depressed mood, suicide ideation, suicide plan, suicide attempt) of adolescents, and marking the suicide stages as 1, 2, 3 and 4, an ordered categorical variable.\\nDetermining whether the user was in adolescent stages (junior high school, high school, college), and retaining the data labeled as the adolescents.\\nDetermining whether the user was diagnosed with mental illness by a psychiatrist, had a history of mental illness, or was taking medication for mental illness, and labeling a user had a mental illness or not as a 0/1 dummy variable.\\nCoding the suicide stages (depressed mood, suicide ideation, suicide plan, suicide attempt) of adolescents, and marking the suicide stages as 1, 2, 3 and 4, an ordered categorical variable.\\nSuicide stages were coded on the basis of the following criteria: (a) Depressed mood: feeling negative and depressed, but not yet having suicidal ideation. It means participants did not express suicidal intention explicitly or implicitly(whether they actively expressed suicidal intentions, or exposed suicidal intentions under inquiries from the official Weibo account). (b) Suicide ideation: there is suicidal intent, but suicide plan has not formed. Due to being affected by an intense depressive mood, the idea of avoiding pain through suicide arises, but there is no specific time and way to implement it. (c) Suicide plan: on the basis of suicidal ideation, there is a rough or detailed suicide plan, such as when, where, and how to commit suicide. (d) Suicide attempt: at least one suicide attempt with the aim of ending one’s own life.\\nDuring the online psychological assistance project period (November 2016 to April 2019), the official Weibo account had communicated with 3627 users online, and 127,336 message records were accumulated. Adhering to the principle of privacy and confidentiality, the use of interview records data was strictly restricted. The experimenter must protect the privacy of the research subjects involved in the project, and should not use any confidential information related to the research subjects (including but not limited to accounts, nicknames, contact information, personal homepage, network IP, location, information records, etc.) outside the scope of the research, and would not violate the privacy of the subjects in any way.\\nThe interview records data were encoded by qualitative analysis. The basic content of interview records data were the current status and problems of users. We extracted three indicators from the interview records data, and the specific descriptions of them are shown below:(1)Determining whether the user was in adolescent stages (junior high school, high school, college), and retaining the data labeled as the adolescents.(2)Determining whether the user was diagnosed with mental illness by a psychiatrist, had a history of mental illness, or was taking medication for mental illness, and labeling a user had a mental illness or not as a 0/1 dummy variable.(3)Coding the suicide stages (depressed mood, suicide ideation, suicide plan, suicide attempt) of adolescents, and marking the suicide stages as 1, 2, 3 and 4, an ordered categorical variable.\\nDetermining whether the user was in adolescent stages (junior high school, high school, college), and retaining the data labeled as the adolescents.\\nDetermining whether the user was diagnosed with mental illness by a psychiatrist, had a history of mental illness, or was taking medication for mental illness, and labeling a user had a mental illness or not as a 0/1 dummy variable.\\nCoding the suicide stages (depressed mood, suicide ideation, suicide plan, suicide attempt) of adolescents, and marking the suicide stages as 1, 2, 3 and 4, an ordered categorical variable.\\nSuicide stages were coded on the basis of the following criteria: (a) Depressed mood: feeling negative and depressed, but not yet having suicidal ideation. It means participants did not express suicidal intention explicitly or implicitly(whether they actively expressed suicidal intentions, or exposed suicidal intentions under inquiries from the official Weibo account). (b) Suicide ideation: there is suicidal intent, but suicide plan has not formed. Due to being affected by an intense depressive mood, the idea of avoiding pain through suicide arises, but there is no specific time and way to implement it. (c) Suicide plan: on the basis of suicidal ideation, there is a rough or detailed suicide plan, such as when, where, and how to commit suicide. (d) Suicide attempt: at least one suicide attempt with the aim of ending one’s own life.\\n2.3.2. Getting the Weibo Text Data We scraped Weibo texts published by the same 3627 users through the Application Programming Interface (API) of Sina Weibo. In order to obtain sufficient Weibo texts for analysis, we took the first time that the official Weibo account sent a private message to the user as the beginning of suicide intervention, downloaded all Weibo texts in the 3 months before the intervention, and filtered the users whose Weibo texts are less than 300 words (Figure 4).\\nFinally, 105 high-suicide-risk adolescents (as the experimental group) were included in the analysis. We then removed stop words and performed Chinese word segmentation on the Weibo texts, and calculated word frequency. “The Chinese Suicide Dictionary” [27] was used to measure the word frequency ratio of their psychological pain and hopelessness. The dictionary outline is shown in Table 1. The psychological pain dimension represents the pain and torment of individuals at the psychological level, including 403 keywords; the hopelessness dimension represents the subjective feeling of losing hope, including 188 keywords.\\nWe scraped Weibo texts published by the same 3627 users through the Application Programming Interface (API) of Sina Weibo. In order to obtain sufficient Weibo texts for analysis, we took the first time that the official Weibo account sent a private message to the user as the beginning of suicide intervention, downloaded all Weibo texts in the 3 months before the intervention, and filtered the users whose Weibo texts are less than 300 words (Figure 4).\\nFinally, 105 high-suicide-risk adolescents (as the experimental group) were included in the analysis. We then removed stop words and performed Chinese word segmentation on the Weibo texts, and calculated word frequency. “The Chinese Suicide Dictionary” [27] was used to measure the word frequency ratio of their psychological pain and hopelessness. The dictionary outline is shown in Table 1. The psychological pain dimension represents the pain and torment of individuals at the psychological level, including 403 keywords; the hopelessness dimension represents the subjective feeling of losing hope, including 188 keywords.\\n2.3.1. Labeling the Interview Records Data During the online psychological assistance project period (November 2016 to April 2019), the official Weibo account had communicated with 3627 users online, and 127,336 message records were accumulated. Adhering to the principle of privacy and confidentiality, the use of interview records data was strictly restricted. The experimenter must protect the privacy of the research subjects involved in the project, and should not use any confidential information related to the research subjects (including but not limited to accounts, nicknames, contact information, personal homepage, network IP, location, information records, etc.) outside the scope of the research, and would not violate the privacy of the subjects in any way.\\nThe interview records data were encoded by qualitative analysis. The basic content of interview records data were the current status and problems of users. We extracted three indicators from the interview records data, and the specific descriptions of them are shown below:(1)Determining whether the user was in adolescent stages (junior high school, high school, college), and retaining the data labeled as the adolescents.(2)Determining whether the user was diagnosed with mental illness by a psychiatrist, had a history of mental illness, or was taking medication for mental illness, and labeling a user had a mental illness or not as a 0/1 dummy variable.(3)Coding the suicide stages (depressed mood, suicide ideation, suicide plan, suicide attempt) of adolescents, and marking the suicide stages as 1, 2, 3 and 4, an ordered categorical variable.\\nDetermining whether the user was in adolescent stages (junior high school, high school, college), and retaining the data labeled as the adolescents.\\nDetermining whether the user was diagnosed with mental illness by a psychiatrist, had a history of mental illness, or was taking medication for mental illness, and labeling a user had a mental illness or not as a 0/1 dummy variable.\\nCoding the suicide stages (depressed mood, suicide ideation, suicide plan, suicide attempt) of adolescents, and marking the suicide stages as 1, 2, 3 and 4, an ordered categorical variable.\\nSuicide stages were coded on the basis of the following criteria: (a) Depressed mood: feeling negative and depressed, but not yet having suicidal ideation. It means participants did not express suicidal intention explicitly or implicitly(whether they actively expressed suicidal intentions, or exposed suicidal intentions under inquiries from the official Weibo account). (b) Suicide ideation: there is suicidal intent, but suicide plan has not formed. Due to being affected by an intense depressive mood, the idea of avoiding pain through suicide arises, but there is no specific time and way to implement it. (c) Suicide plan: on the basis of suicidal ideation, there is a rough or detailed suicide plan, such as when, where, and how to commit suicide. (d) Suicide attempt: at least one suicide attempt with the aim of ending one’s own life.\\nDuring the online psychological assistance project period (November 2016 to April 2019), the official Weibo account had communicated with 3627 users online, and 127,336 message records were accumulated. Adhering to the principle of privacy and confidentiality, the use of interview records data was strictly restricted. The experimenter must protect the privacy of the research subjects involved in the project, and should not use any confidential information related to the research subjects (including but not limited to accounts, nicknames, contact information, personal homepage, network IP, location, information records, etc.) outside the scope of the research, and would not violate the privacy of the subjects in any way.\\nThe interview records data were encoded by qualitative analysis. The basic content of interview records data were the current status and problems of users. We extracted three indicators from the interview records data, and the specific descriptions of them are shown below:(1)Determining whether the user was in adolescent stages (junior high school, high school, college), and retaining the data labeled as the adolescents.(2)Determining whether the user was diagnosed with mental illness by a psychiatrist, had a history of mental illness, or was taking medication for mental illness, and labeling a user had a mental illness or not as a 0/1 dummy variable.(3)Coding the suicide stages (depressed mood, suicide ideation, suicide plan, suicide attempt) of adolescents, and marking the suicide stages as 1, 2, 3 and 4, an ordered categorical variable.\\nDetermining whether the user was in adolescent stages (junior high school, high school, college), and retaining the data labeled as the adolescents.\\nDetermining whether the user was diagnosed with mental illness by a psychiatrist, had a history of mental illness, or was taking medication for mental illness, and labeling a user had a mental illness or not as a 0/1 dummy variable.\\nCoding the suicide stages (depressed mood, suicide ideation, suicide plan, suicide attempt) of adolescents, and marking the suicide stages as 1, 2, 3 and 4, an ordered categorical variable.\\nSuicide stages were coded on the basis of the following criteria: (a) Depressed mood: feeling negative and depressed, but not yet having suicidal ideation. It means participants did not express suicidal intention explicitly or implicitly(whether they actively expressed suicidal intentions, or exposed suicidal intentions under inquiries from the official Weibo account). (b) Suicide ideation: there is suicidal intent, but suicide plan has not formed. Due to being affected by an intense depressive mood, the idea of avoiding pain through suicide arises, but there is no specific time and way to implement it. (c) Suicide plan: on the basis of suicidal ideation, there is a rough or detailed suicide plan, such as when, where, and how to commit suicide. (d) Suicide attempt: at least one suicide attempt with the aim of ending one’s own life.\\n2.3.2. Getting the Weibo Text Data We scraped Weibo texts published by the same 3627 users through the Application Programming Interface (API) of Sina Weibo. In order to obtain sufficient Weibo texts for analysis, we took the first time that the official Weibo account sent a private message to the user as the beginning of suicide intervention, downloaded all Weibo texts in the 3 months before the intervention, and filtered the users whose Weibo texts are less than 300 words (Figure 4).\\nFinally, 105 high-suicide-risk adolescents (as the experimental group) were included in the analysis. We then removed stop words and performed Chinese word segmentation on the Weibo texts, and calculated word frequency. “The Chinese Suicide Dictionary” [27] was used to measure the word frequency ratio of their psychological pain and hopelessness. The dictionary outline is shown in Table 1. The psychological pain dimension represents the pain and torment of individuals at the psychological level, including 403 keywords; the hopelessness dimension represents the subjective feeling of losing hope, including 188 keywords.\\nWe scraped Weibo texts published by the same 3627 users through the Application Programming Interface (API) of Sina Weibo. In order to obtain sufficient Weibo texts for analysis, we took the first time that the official Weibo account sent a private message to the user as the beginning of suicide intervention, downloaded all Weibo texts in the 3 months before the intervention, and filtered the users whose Weibo texts are less than 300 words (Figure 4).\\nFinally, 105 high-suicide-risk adolescents (as the experimental group) were included in the analysis. We then removed stop words and performed Chinese word segmentation on the Weibo texts, and calculated word frequency. “The Chinese Suicide Dictionary” [27] was used to measure the word frequency ratio of their psychological pain and hopelessness. The dictionary outline is shown in Table 1. The psychological pain dimension represents the pain and torment of individuals at the psychological level, including 403 keywords; the hopelessness dimension represents the subjective feeling of losing hope, including 188 keywords.\\n2.4. Data Analysis 2.4.1. Qualitative Analysis Through the labeling results of interview records data, we investigated the situation of mental illness and the distribution of suicide stages for the experimental group.\\nThrough the labeling results of interview records data, we investigated the situation of mental illness and the distribution of suicide stages for the experimental group.\\n2.4.2. Analysis of Suicide Path The mediation effect analysis was executed on the software Mplus by using the bootstrap method to test the suicide mechanism path model. The word frequency of psychological pain and hopelessness of the experimental group were treated as independent variable (X) and mediating variable (M), respectively. The suicide stages were treated as dependent variable (Y). The mental illness was included in the model as a control variable (Z).\\nThe mediation effect analysis was executed on the software Mplus by using the bootstrap method to test the suicide mechanism path model. The word frequency of psychological pain and hopelessness of the experimental group were treated as independent variable (X) and mediating variable (M), respectively. The suicide stages were treated as dependent variable (Y). The mental illness was included in the model as a control variable (Z).\\n2.4.1. Qualitative Analysis Through the labeling results of interview records data, we investigated the situation of mental illness and the distribution of suicide stages for the experimental group.\\nThrough the labeling results of interview records data, we investigated the situation of mental illness and the distribution of suicide stages for the experimental group.\\n2.4.2. Analysis of Suicide Path The mediation effect analysis was executed on the software Mplus by using the bootstrap method to test the suicide mechanism path model. The word frequency of psychological pain and hopelessness of the experimental group were treated as independent variable (X) and mediating variable (M), respectively. The suicide stages were treated as dependent variable (Y). The mental illness was included in the model as a control variable (Z).\\nThe mediation effect analysis was executed on the software Mplus by using the bootstrap method to test the suicide mechanism path model. The word frequency of psychological pain and hopelessness of the experimental group were treated as independent variable (X) and mediating variable (M), respectively. The suicide stages were treated as dependent variable (Y). The mental illness was included in the model as a control variable (Z).\",\n \"Through the online psychological assistance project, we obtained the interview records data of 3627 Weibo users, and then processed the user’s interview records data and Weibo texts data. Finally, we retained 105 high-suicide-risk adolescents with enough Weibo texts as the experimental group.\\nEthical Statement: Participants gave informed consent when agreeing to take part in the online psychological assistance project. The project received ethical approval from the Institution-al Review Board of the Institute of Psychology, Chinese Academy of Sciences, with the ethics approval number H16003.\",\n \"We used the suicide signal recognition model established by the Computational Cyber-Psychology Lab (CCPL) to identify the suicide signal of a single microblog, which can effectively determine whether the single microblog has shown suicidal risk [26].\\nSince November 2016, we had used the suicide signal recognition model for online identification of Weibo comments. The last Weibo post before the death of Weibo user “Zoufan”, as a place where people can express their negative emotions, has received more than 1 million comments until now, and the number of comments keep rising (Figure 2).\\nFirstly, we used the suicide signal model to identify the suicide signal of the Weibo comments. Then, the comments identified by the model were re-identified by manual recognition. Finally, we used the official Weibo account “Psychological Map Psy” and sent a private message to the Weibo users marked as having a suicide risk. The contents of the private message mainly include inquiries about the user’s emotional state, expressions of concern, links to self-assessment psychological scales, and the hotline of the psychological crisis intervention center, etc. (Figure 3). In addition, users can choose whether to communicate with the voluntary counselor online according to their own wishes, so as to achieve agile psychological crisis intervention.\",\n \"2.3.1. Labeling the Interview Records Data During the online psychological assistance project period (November 2016 to April 2019), the official Weibo account had communicated with 3627 users online, and 127,336 message records were accumulated. Adhering to the principle of privacy and confidentiality, the use of interview records data was strictly restricted. The experimenter must protect the privacy of the research subjects involved in the project, and should not use any confidential information related to the research subjects (including but not limited to accounts, nicknames, contact information, personal homepage, network IP, location, information records, etc.) outside the scope of the research, and would not violate the privacy of the subjects in any way.\\nThe interview records data were encoded by qualitative analysis. The basic content of interview records data were the current status and problems of users. We extracted three indicators from the interview records data, and the specific descriptions of them are shown below:(1)Determining whether the user was in adolescent stages (junior high school, high school, college), and retaining the data labeled as the adolescents.(2)Determining whether the user was diagnosed with mental illness by a psychiatrist, had a history of mental illness, or was taking medication for mental illness, and labeling a user had a mental illness or not as a 0/1 dummy variable.(3)Coding the suicide stages (depressed mood, suicide ideation, suicide plan, suicide attempt) of adolescents, and marking the suicide stages as 1, 2, 3 and 4, an ordered categorical variable.\\nDetermining whether the user was in adolescent stages (junior high school, high school, college), and retaining the data labeled as the adolescents.\\nDetermining whether the user was diagnosed with mental illness by a psychiatrist, had a history of mental illness, or was taking medication for mental illness, and labeling a user had a mental illness or not as a 0/1 dummy variable.\\nCoding the suicide stages (depressed mood, suicide ideation, suicide plan, suicide attempt) of adolescents, and marking the suicide stages as 1, 2, 3 and 4, an ordered categorical variable.\\nSuicide stages were coded on the basis of the following criteria: (a) Depressed mood: feeling negative and depressed, but not yet having suicidal ideation. It means participants did not express suicidal intention explicitly or implicitly(whether they actively expressed suicidal intentions, or exposed suicidal intentions under inquiries from the official Weibo account). (b) Suicide ideation: there is suicidal intent, but suicide plan has not formed. Due to being affected by an intense depressive mood, the idea of avoiding pain through suicide arises, but there is no specific time and way to implement it. (c) Suicide plan: on the basis of suicidal ideation, there is a rough or detailed suicide plan, such as when, where, and how to commit suicide. (d) Suicide attempt: at least one suicide attempt with the aim of ending one’s own life.\\nDuring the online psychological assistance project period (November 2016 to April 2019), the official Weibo account had communicated with 3627 users online, and 127,336 message records were accumulated. Adhering to the principle of privacy and confidentiality, the use of interview records data was strictly restricted. The experimenter must protect the privacy of the research subjects involved in the project, and should not use any confidential information related to the research subjects (including but not limited to accounts, nicknames, contact information, personal homepage, network IP, location, information records, etc.) outside the scope of the research, and would not violate the privacy of the subjects in any way.\\nThe interview records data were encoded by qualitative analysis. The basic content of interview records data were the current status and problems of users. We extracted three indicators from the interview records data, and the specific descriptions of them are shown below:(1)Determining whether the user was in adolescent stages (junior high school, high school, college), and retaining the data labeled as the adolescents.(2)Determining whether the user was diagnosed with mental illness by a psychiatrist, had a history of mental illness, or was taking medication for mental illness, and labeling a user had a mental illness or not as a 0/1 dummy variable.(3)Coding the suicide stages (depressed mood, suicide ideation, suicide plan, suicide attempt) of adolescents, and marking the suicide stages as 1, 2, 3 and 4, an ordered categorical variable.\\nDetermining whether the user was in adolescent stages (junior high school, high school, college), and retaining the data labeled as the adolescents.\\nDetermining whether the user was diagnosed with mental illness by a psychiatrist, had a history of mental illness, or was taking medication for mental illness, and labeling a user had a mental illness or not as a 0/1 dummy variable.\\nCoding the suicide stages (depressed mood, suicide ideation, suicide plan, suicide attempt) of adolescents, and marking the suicide stages as 1, 2, 3 and 4, an ordered categorical variable.\\nSuicide stages were coded on the basis of the following criteria: (a) Depressed mood: feeling negative and depressed, but not yet having suicidal ideation. It means participants did not express suicidal intention explicitly or implicitly(whether they actively expressed suicidal intentions, or exposed suicidal intentions under inquiries from the official Weibo account). (b) Suicide ideation: there is suicidal intent, but suicide plan has not formed. Due to being affected by an intense depressive mood, the idea of avoiding pain through suicide arises, but there is no specific time and way to implement it. (c) Suicide plan: on the basis of suicidal ideation, there is a rough or detailed suicide plan, such as when, where, and how to commit suicide. (d) Suicide attempt: at least one suicide attempt with the aim of ending one’s own life.\\n2.3.2. Getting the Weibo Text Data We scraped Weibo texts published by the same 3627 users through the Application Programming Interface (API) of Sina Weibo. In order to obtain sufficient Weibo texts for analysis, we took the first time that the official Weibo account sent a private message to the user as the beginning of suicide intervention, downloaded all Weibo texts in the 3 months before the intervention, and filtered the users whose Weibo texts are less than 300 words (Figure 4).\\nFinally, 105 high-suicide-risk adolescents (as the experimental group) were included in the analysis. We then removed stop words and performed Chinese word segmentation on the Weibo texts, and calculated word frequency. “The Chinese Suicide Dictionary” [27] was used to measure the word frequency ratio of their psychological pain and hopelessness. The dictionary outline is shown in Table 1. The psychological pain dimension represents the pain and torment of individuals at the psychological level, including 403 keywords; the hopelessness dimension represents the subjective feeling of losing hope, including 188 keywords.\\nWe scraped Weibo texts published by the same 3627 users through the Application Programming Interface (API) of Sina Weibo. In order to obtain sufficient Weibo texts for analysis, we took the first time that the official Weibo account sent a private message to the user as the beginning of suicide intervention, downloaded all Weibo texts in the 3 months before the intervention, and filtered the users whose Weibo texts are less than 300 words (Figure 4).\\nFinally, 105 high-suicide-risk adolescents (as the experimental group) were included in the analysis. We then removed stop words and performed Chinese word segmentation on the Weibo texts, and calculated word frequency. “The Chinese Suicide Dictionary” [27] was used to measure the word frequency ratio of their psychological pain and hopelessness. The dictionary outline is shown in Table 1. The psychological pain dimension represents the pain and torment of individuals at the psychological level, including 403 keywords; the hopelessness dimension represents the subjective feeling of losing hope, including 188 keywords.\",\n \"During the online psychological assistance project period (November 2016 to April 2019), the official Weibo account had communicated with 3627 users online, and 127,336 message records were accumulated. Adhering to the principle of privacy and confidentiality, the use of interview records data was strictly restricted. The experimenter must protect the privacy of the research subjects involved in the project, and should not use any confidential information related to the research subjects (including but not limited to accounts, nicknames, contact information, personal homepage, network IP, location, information records, etc.) outside the scope of the research, and would not violate the privacy of the subjects in any way.\\nThe interview records data were encoded by qualitative analysis. The basic content of interview records data were the current status and problems of users. We extracted three indicators from the interview records data, and the specific descriptions of them are shown below:(1)Determining whether the user was in adolescent stages (junior high school, high school, college), and retaining the data labeled as the adolescents.(2)Determining whether the user was diagnosed with mental illness by a psychiatrist, had a history of mental illness, or was taking medication for mental illness, and labeling a user had a mental illness or not as a 0/1 dummy variable.(3)Coding the suicide stages (depressed mood, suicide ideation, suicide plan, suicide attempt) of adolescents, and marking the suicide stages as 1, 2, 3 and 4, an ordered categorical variable.\\nDetermining whether the user was in adolescent stages (junior high school, high school, college), and retaining the data labeled as the adolescents.\\nDetermining whether the user was diagnosed with mental illness by a psychiatrist, had a history of mental illness, or was taking medication for mental illness, and labeling a user had a mental illness or not as a 0/1 dummy variable.\\nCoding the suicide stages (depressed mood, suicide ideation, suicide plan, suicide attempt) of adolescents, and marking the suicide stages as 1, 2, 3 and 4, an ordered categorical variable.\\nSuicide stages were coded on the basis of the following criteria: (a) Depressed mood: feeling negative and depressed, but not yet having suicidal ideation. It means participants did not express suicidal intention explicitly or implicitly(whether they actively expressed suicidal intentions, or exposed suicidal intentions under inquiries from the official Weibo account). (b) Suicide ideation: there is suicidal intent, but suicide plan has not formed. Due to being affected by an intense depressive mood, the idea of avoiding pain through suicide arises, but there is no specific time and way to implement it. (c) Suicide plan: on the basis of suicidal ideation, there is a rough or detailed suicide plan, such as when, where, and how to commit suicide. (d) Suicide attempt: at least one suicide attempt with the aim of ending one’s own life.\",\n \"We scraped Weibo texts published by the same 3627 users through the Application Programming Interface (API) of Sina Weibo. In order to obtain sufficient Weibo texts for analysis, we took the first time that the official Weibo account sent a private message to the user as the beginning of suicide intervention, downloaded all Weibo texts in the 3 months before the intervention, and filtered the users whose Weibo texts are less than 300 words (Figure 4).\\nFinally, 105 high-suicide-risk adolescents (as the experimental group) were included in the analysis. We then removed stop words and performed Chinese word segmentation on the Weibo texts, and calculated word frequency. “The Chinese Suicide Dictionary” [27] was used to measure the word frequency ratio of their psychological pain and hopelessness. The dictionary outline is shown in Table 1. The psychological pain dimension represents the pain and torment of individuals at the psychological level, including 403 keywords; the hopelessness dimension represents the subjective feeling of losing hope, including 188 keywords.\",\n \"2.4.1. Qualitative Analysis Through the labeling results of interview records data, we investigated the situation of mental illness and the distribution of suicide stages for the experimental group.\\nThrough the labeling results of interview records data, we investigated the situation of mental illness and the distribution of suicide stages for the experimental group.\\n2.4.2. Analysis of Suicide Path The mediation effect analysis was executed on the software Mplus by using the bootstrap method to test the suicide mechanism path model. The word frequency of psychological pain and hopelessness of the experimental group were treated as independent variable (X) and mediating variable (M), respectively. The suicide stages were treated as dependent variable (Y). The mental illness was included in the model as a control variable (Z).\\nThe mediation effect analysis was executed on the software Mplus by using the bootstrap method to test the suicide mechanism path model. The word frequency of psychological pain and hopelessness of the experimental group were treated as independent variable (X) and mediating variable (M), respectively. The suicide stages were treated as dependent variable (Y). The mental illness was included in the model as a control variable (Z).\",\n \"Through the labeling results of interview records data, we investigated the situation of mental illness and the distribution of suicide stages for the experimental group.\",\n \"The mediation effect analysis was executed on the software Mplus by using the bootstrap method to test the suicide mechanism path model. The word frequency of psychological pain and hopelessness of the experimental group were treated as independent variable (X) and mediating variable (M), respectively. The suicide stages were treated as dependent variable (Y). The mental illness was included in the model as a control variable (Z).\",\n \"3.1. Group Characteristics Reflected by Qualitative Analysis The mental illness diagnosis of the experimental group was marked; it was found that 38 were diagnosed and 67 were undiagnosed. The proportion of users diagnosed with mental illness was 36.2%. Among the 38 high-suicide-risk adolescents diagnosed with mental illness, 29 users had depression (or depression was included in multiple mental illnesses), accounting for 76.3%; the rest included bipolar disorder (13.16%), anxiety disorder (13.16%), etc.; 9 of them suffered from multiple mental illnesses (e.g., depression and anxiety disorder), accounting for 23.68%.\\nAmong the experimental group, 57 were in the stage of depressed mood (54.3%), 21 were in the stage of suicide ideation (20.0%), 19 were in the stage of suicide plan (18.1%), and 8 people were in the stage of suicide attempt (7.6%) (Table 2).\\nThe mental illness diagnosis of the experimental group was marked; it was found that 38 were diagnosed and 67 were undiagnosed. The proportion of users diagnosed with mental illness was 36.2%. Among the 38 high-suicide-risk adolescents diagnosed with mental illness, 29 users had depression (or depression was included in multiple mental illnesses), accounting for 76.3%; the rest included bipolar disorder (13.16%), anxiety disorder (13.16%), etc.; 9 of them suffered from multiple mental illnesses (e.g., depression and anxiety disorder), accounting for 23.68%.\\nAmong the experimental group, 57 were in the stage of depressed mood (54.3%), 21 were in the stage of suicide ideation (20.0%), 19 were in the stage of suicide plan (18.1%), and 8 people were in the stage of suicide attempt (7.6%) (Table 2).\\n3.2. Path Analysis of Psychological Pain, Hopelessness and Suicide Stages A mediation effect test was performed on the path model (psychological pain → hopelessness → suicide stages). The performance of each fit index of the model was good (RMSEA < 0.08; CFI > 0.9; TLI > 0.9), indicating that the model fitting effect is excellent (Table 3).\\nAfter controlling for mental illness factors, the total effect c was significant (β = −0.154, p < 0.05), indicating that there was a mediation effect; the path coefficients a and b were significant (β = 0.571, p < 0.001; β = −0.271, p < 0.05), but the direct effect c’ was not significant (β = 0.052, p > 0.05), indicating that there was a complete mediating effect of hopelessness. That is, psychological pain mainly affects suicide risk through hopelessness (Table 4).\\nIt was worth noting that the relationship between the expression of hopelessness and the stages of suicide showed a significant negative correlation (β = −0.271, p < 0.05), indicating that the late suicide stage such as suicide attempt, which may be closer to suicide death, included less expressions of hopelessness (Table 4).\\nFurthermore, there was a significant positive correlation between diagnosed mental illness and suicide stages (β = 0.398, p < 0.001), indicating the necessity of treating mental illness as a control variable in order to see the relationship between psychological pain, hopelessness and suicide stages more clearly. At the same time, it also showed that the later the suicide stage, the higher the diagnosis rate of mental illness (Table 4).\\nA mediation effect test was performed on the path model (psychological pain → hopelessness → suicide stages). The performance of each fit index of the model was good (RMSEA < 0.08; CFI > 0.9; TLI > 0.9), indicating that the model fitting effect is excellent (Table 3).\\nAfter controlling for mental illness factors, the total effect c was significant (β = −0.154, p < 0.05), indicating that there was a mediation effect; the path coefficients a and b were significant (β = 0.571, p < 0.001; β = −0.271, p < 0.05), but the direct effect c’ was not significant (β = 0.052, p > 0.05), indicating that there was a complete mediating effect of hopelessness. That is, psychological pain mainly affects suicide risk through hopelessness (Table 4).\\nIt was worth noting that the relationship between the expression of hopelessness and the stages of suicide showed a significant negative correlation (β = −0.271, p < 0.05), indicating that the late suicide stage such as suicide attempt, which may be closer to suicide death, included less expressions of hopelessness (Table 4).\\nFurthermore, there was a significant positive correlation between diagnosed mental illness and suicide stages (β = 0.398, p < 0.001), indicating the necessity of treating mental illness as a control variable in order to see the relationship between psychological pain, hopelessness and suicide stages more clearly. At the same time, it also showed that the later the suicide stage, the higher the diagnosis rate of mental illness (Table 4).\",\n \"The mental illness diagnosis of the experimental group was marked; it was found that 38 were diagnosed and 67 were undiagnosed. The proportion of users diagnosed with mental illness was 36.2%. Among the 38 high-suicide-risk adolescents diagnosed with mental illness, 29 users had depression (or depression was included in multiple mental illnesses), accounting for 76.3%; the rest included bipolar disorder (13.16%), anxiety disorder (13.16%), etc.; 9 of them suffered from multiple mental illnesses (e.g., depression and anxiety disorder), accounting for 23.68%.\\nAmong the experimental group, 57 were in the stage of depressed mood (54.3%), 21 were in the stage of suicide ideation (20.0%), 19 were in the stage of suicide plan (18.1%), and 8 people were in the stage of suicide attempt (7.6%) (Table 2).\",\n \"A mediation effect test was performed on the path model (psychological pain → hopelessness → suicide stages). The performance of each fit index of the model was good (RMSEA < 0.08; CFI > 0.9; TLI > 0.9), indicating that the model fitting effect is excellent (Table 3).\\nAfter controlling for mental illness factors, the total effect c was significant (β = −0.154, p < 0.05), indicating that there was a mediation effect; the path coefficients a and b were significant (β = 0.571, p < 0.001; β = −0.271, p < 0.05), but the direct effect c’ was not significant (β = 0.052, p > 0.05), indicating that there was a complete mediating effect of hopelessness. That is, psychological pain mainly affects suicide risk through hopelessness (Table 4).\\nIt was worth noting that the relationship between the expression of hopelessness and the stages of suicide showed a significant negative correlation (β = −0.271, p < 0.05), indicating that the late suicide stage such as suicide attempt, which may be closer to suicide death, included less expressions of hopelessness (Table 4).\\nFurthermore, there was a significant positive correlation between diagnosed mental illness and suicide stages (β = 0.398, p < 0.001), indicating the necessity of treating mental illness as a control variable in order to see the relationship between psychological pain, hopelessness and suicide stages more clearly. At the same time, it also showed that the later the suicide stage, the higher the diagnosis rate of mental illness (Table 4).\",\n \"4.1. The Qualitative Analysis Results Among the experimental group, 36.2% of users suffered from mental illness. Depression accounted for 76.3% of all mental illness; it is the most common mental illness among the high-suicide-risk adolescents. The incidence of suicide among students whose relatives were affected by psychosis were higher than that of general residents. Suicide and mental illness were closely linked [28]. British doctor Sainsbury conducted a qualitative study on the data of 400 suicides examined by a coroner in London between 1936 and 1938, and found that 37% of the suicides reflected mental illness [29]. In a meta-analysis conducted in 2004, twenty-seven studies comprising 3275 suicides were included, of which 87.3% had been diagnosed with a mental disorder prior to their death [30]. In contrast, the research population in our study was only a high suicide risk group, so the detection rate of mental illness was relatively low. Although the research populations of the other two studies were both suicide decedents, the time interval between the two studies was large. It is very possible that there existed better methods to detect the mental illness of the suicide decedents in later studies, and it is understandable that the detection rate of mental illness has greatly increased. Furthermore, a more recent study has shown that the most common mental illness associated with suicide was depression [31]. The proportion of depression in all mental illnesses in our study also supported this conclusion.\\nAnother result of the qualitative analysis showed that the later the suicide stage, the lower the proportion of adolescents. A study on residents’ suicidal ideation, suicide plans and suicide attempts in Dalian, China showed that the detection rate of suicidal ideation, suicide plan and suicide attempt decreased in that order in different groups [32]. This may because not all suicide ideations translate into suicidal actions, and most people with suicide ideation have not attempted suicide [33,34].\\nAmong the experimental group, 36.2% of users suffered from mental illness. Depression accounted for 76.3% of all mental illness; it is the most common mental illness among the high-suicide-risk adolescents. The incidence of suicide among students whose relatives were affected by psychosis were higher than that of general residents. Suicide and mental illness were closely linked [28]. British doctor Sainsbury conducted a qualitative study on the data of 400 suicides examined by a coroner in London between 1936 and 1938, and found that 37% of the suicides reflected mental illness [29]. In a meta-analysis conducted in 2004, twenty-seven studies comprising 3275 suicides were included, of which 87.3% had been diagnosed with a mental disorder prior to their death [30]. In contrast, the research population in our study was only a high suicide risk group, so the detection rate of mental illness was relatively low. Although the research populations of the other two studies were both suicide decedents, the time interval between the two studies was large. It is very possible that there existed better methods to detect the mental illness of the suicide decedents in later studies, and it is understandable that the detection rate of mental illness has greatly increased. Furthermore, a more recent study has shown that the most common mental illness associated with suicide was depression [31]. The proportion of depression in all mental illnesses in our study also supported this conclusion.\\nAnother result of the qualitative analysis showed that the later the suicide stage, the lower the proportion of adolescents. A study on residents’ suicidal ideation, suicide plans and suicide attempts in Dalian, China showed that the detection rate of suicidal ideation, suicide plan and suicide attempt decreased in that order in different groups [32]. This may because not all suicide ideations translate into suicidal actions, and most people with suicide ideation have not attempted suicide [33,34].\\n4.2. The Mediating Effect of Hopelessness Our research found that the effect of psychological pain on suicide stages was totally mediated by hopelessness, confirming our suicide mechanism path hypothesis: psychological pain → hopelessness → suicide stages. Although the first step of suicide ideation starts from psychological pain, psychological pain itself is not enough to generate suicide ideation, and hopelessness is also needed to develop suicide ideation [19]. People make decisions and take actions based in part on their emotional predictions [35,36], and hopelessness makes people at suicide risk overestimate their future emotional pain [37]. If the individual experiences constant pain in life, this may reduce the individual’s desire to live, leading to suicidal thoughts. However, pain alone is not enough to completely lose the belief in life. Only when the individual feels that the terrible situation cannot be improved, and he or she will live in pain forever, suicide will be considered.\\nThus, expressions of hopelessness may be a better predictor of suicide risk than psychological pain, and we found a traceable pathway for suicide intervention. Based on the Weibo posts, we can capture whether individual released suicide signals and it is possible to track the suicide stages in the future, making suicide intervention more accurate.\\nOur research found that the effect of psychological pain on suicide stages was totally mediated by hopelessness, confirming our suicide mechanism path hypothesis: psychological pain → hopelessness → suicide stages. Although the first step of suicide ideation starts from psychological pain, psychological pain itself is not enough to generate suicide ideation, and hopelessness is also needed to develop suicide ideation [19]. People make decisions and take actions based in part on their emotional predictions [35,36], and hopelessness makes people at suicide risk overestimate their future emotional pain [37]. If the individual experiences constant pain in life, this may reduce the individual’s desire to live, leading to suicidal thoughts. However, pain alone is not enough to completely lose the belief in life. Only when the individual feels that the terrible situation cannot be improved, and he or she will live in pain forever, suicide will be considered.\\nThus, expressions of hopelessness may be a better predictor of suicide risk than psychological pain, and we found a traceable pathway for suicide intervention. Based on the Weibo posts, we can capture whether individual released suicide signals and it is possible to track the suicide stages in the future, making suicide intervention more accurate.\\n4.3. The Hopelessness Expressions and the Suicide Stages There was a negative predictive relationship between hopelessness expressions and suicide stages, as adolescents at the later suicide stages had less expressions of hopelessness. One possible reason is that adolescents at higher risk of suicide may use less concrete, direct words of hopelessness to express such feelings. From a linguistic point of view, Gao Yihong and Meng Ling analyzed the Weibo texts of “Zoufan” three months before suicide. They found that “Zoufan” often used decontextualized and fragmented ways to express personal depressed mood, and expressed metaphors and intentions about death frequently [38]. This euphemistic and poetic way of venting may be the commonality of high suicide risk adolescents. Another possible reason is “the Presuicidal Syndrome”, which mentioned that there exists an “ominous quiet” in the short period before suicide. This is a sign of “affective restriction”, which represents extreme repression of intolerable feelings [39]. Such “affective restriction” may be why people in the later suicide stage are less likely to express hopelessness. Moreover, adolescents at the later suicide stage may be more accepting and tolerant of psychological pain and hopelessness, which makes them less willing to talk about individual psychological pain and hopelessness, and with lack of supportive survival signals through texts, lose the belief in getting help.\\nMany researchers have paid attention to the use of social media for suicide prevention and intervention, and have conducted a series of studies [40,41]. However, little attention has been paid to whether the suicidal signal was from an early suicide ideator or a later suicide attempter. Our study fills this gap, complementing and extending suicide-related research. Our results suggest that adolescents who are at a later suicide stage and have a higher suicide risk often do not express too much hopelessness directly. Therefore, we cannot judge closeness to suicide only by the level of traditional expressions of hopelessness.\\nThere was a negative predictive relationship between hopelessness expressions and suicide stages, as adolescents at the later suicide stages had less expressions of hopelessness. One possible reason is that adolescents at higher risk of suicide may use less concrete, direct words of hopelessness to express such feelings. From a linguistic point of view, Gao Yihong and Meng Ling analyzed the Weibo texts of “Zoufan” three months before suicide. They found that “Zoufan” often used decontextualized and fragmented ways to express personal depressed mood, and expressed metaphors and intentions about death frequently [38]. This euphemistic and poetic way of venting may be the commonality of high suicide risk adolescents. Another possible reason is “the Presuicidal Syndrome”, which mentioned that there exists an “ominous quiet” in the short period before suicide. This is a sign of “affective restriction”, which represents extreme repression of intolerable feelings [39]. Such “affective restriction” may be why people in the later suicide stage are less likely to express hopelessness. Moreover, adolescents at the later suicide stage may be more accepting and tolerant of psychological pain and hopelessness, which makes them less willing to talk about individual psychological pain and hopelessness, and with lack of supportive survival signals through texts, lose the belief in getting help.\\nMany researchers have paid attention to the use of social media for suicide prevention and intervention, and have conducted a series of studies [40,41]. However, little attention has been paid to whether the suicidal signal was from an early suicide ideator or a later suicide attempter. Our study fills this gap, complementing and extending suicide-related research. Our results suggest that adolescents who are at a later suicide stage and have a higher suicide risk often do not express too much hopelessness directly. Therefore, we cannot judge closeness to suicide only by the level of traditional expressions of hopelessness.\",\n \"Among the experimental group, 36.2% of users suffered from mental illness. Depression accounted for 76.3% of all mental illness; it is the most common mental illness among the high-suicide-risk adolescents. The incidence of suicide among students whose relatives were affected by psychosis were higher than that of general residents. Suicide and mental illness were closely linked [28]. British doctor Sainsbury conducted a qualitative study on the data of 400 suicides examined by a coroner in London between 1936 and 1938, and found that 37% of the suicides reflected mental illness [29]. In a meta-analysis conducted in 2004, twenty-seven studies comprising 3275 suicides were included, of which 87.3% had been diagnosed with a mental disorder prior to their death [30]. In contrast, the research population in our study was only a high suicide risk group, so the detection rate of mental illness was relatively low. Although the research populations of the other two studies were both suicide decedents, the time interval between the two studies was large. It is very possible that there existed better methods to detect the mental illness of the suicide decedents in later studies, and it is understandable that the detection rate of mental illness has greatly increased. Furthermore, a more recent study has shown that the most common mental illness associated with suicide was depression [31]. The proportion of depression in all mental illnesses in our study also supported this conclusion.\\nAnother result of the qualitative analysis showed that the later the suicide stage, the lower the proportion of adolescents. A study on residents’ suicidal ideation, suicide plans and suicide attempts in Dalian, China showed that the detection rate of suicidal ideation, suicide plan and suicide attempt decreased in that order in different groups [32]. This may because not all suicide ideations translate into suicidal actions, and most people with suicide ideation have not attempted suicide [33,34].\",\n \"Our research found that the effect of psychological pain on suicide stages was totally mediated by hopelessness, confirming our suicide mechanism path hypothesis: psychological pain → hopelessness → suicide stages. Although the first step of suicide ideation starts from psychological pain, psychological pain itself is not enough to generate suicide ideation, and hopelessness is also needed to develop suicide ideation [19]. People make decisions and take actions based in part on their emotional predictions [35,36], and hopelessness makes people at suicide risk overestimate their future emotional pain [37]. If the individual experiences constant pain in life, this may reduce the individual’s desire to live, leading to suicidal thoughts. However, pain alone is not enough to completely lose the belief in life. Only when the individual feels that the terrible situation cannot be improved, and he or she will live in pain forever, suicide will be considered.\\nThus, expressions of hopelessness may be a better predictor of suicide risk than psychological pain, and we found a traceable pathway for suicide intervention. Based on the Weibo posts, we can capture whether individual released suicide signals and it is possible to track the suicide stages in the future, making suicide intervention more accurate.\",\n \"There was a negative predictive relationship between hopelessness expressions and suicide stages, as adolescents at the later suicide stages had less expressions of hopelessness. One possible reason is that adolescents at higher risk of suicide may use less concrete, direct words of hopelessness to express such feelings. From a linguistic point of view, Gao Yihong and Meng Ling analyzed the Weibo texts of “Zoufan” three months before suicide. They found that “Zoufan” often used decontextualized and fragmented ways to express personal depressed mood, and expressed metaphors and intentions about death frequently [38]. This euphemistic and poetic way of venting may be the commonality of high suicide risk adolescents. Another possible reason is “the Presuicidal Syndrome”, which mentioned that there exists an “ominous quiet” in the short period before suicide. This is a sign of “affective restriction”, which represents extreme repression of intolerable feelings [39]. Such “affective restriction” may be why people in the later suicide stage are less likely to express hopelessness. Moreover, adolescents at the later suicide stage may be more accepting and tolerant of psychological pain and hopelessness, which makes them less willing to talk about individual psychological pain and hopelessness, and with lack of supportive survival signals through texts, lose the belief in getting help.\\nMany researchers have paid attention to the use of social media for suicide prevention and intervention, and have conducted a series of studies [40,41]. However, little attention has been paid to whether the suicidal signal was from an early suicide ideator or a later suicide attempter. Our study fills this gap, complementing and extending suicide-related research. Our results suggest that adolescents who are at a later suicide stage and have a higher suicide risk often do not express too much hopelessness directly. Therefore, we cannot judge closeness to suicide only by the level of traditional expressions of hopelessness.\",\n \"Although this study could divide the suicide stages of adolescents through labeling Weibo private message data, it cannot effectively determine the gender of the user. Previous studies had shown that suicidal behaviors were also affected by gender, and that the incidence of suicide attempts in adolescent females was higher than that of males [42]. Compared with males, females were more psychologically sensitive and vulnerable [43], and were more likely to express their pain through suicidal behaviors [44]. The expressions of hopelessness and psychological pain as predictors of suicide may differ by gender. There were only 105 adolescent users with high suicide risk in our study. Caution is thus necessary when generalizing the conclusions to other case.\\nOur findings provided a traceable pathway for suicide intervention, finding that the effect of psychological pain on suicide stages was totally mediated by hopelessness, and that adolescents in the later suicide stages had less expressions of hopelessness in the traditional sense. However, the specific language characteristics of each suicide stage are still unknown. If we want to accomplish the precise prediction of suicide stages through the expressions on Weibo posts and provide more accurate and effective information for suicide intervention, we need to further explore the specific language expression characteristics of high suicide risk adolescents in each suicide stage, especially how to identify abstract metaphors that may constitute a suicidal signal. It is thus possible that we can provide more accurate suicide intervention for adolescents at different suicide stages in the future.\\nWe mainly focus on the expression on social media of two psychological indicators (psychological pain, hopelessness). However, there are many factors which affect suicide, and we can research suicide intervention from multiple perspectives, not only examining some negative indicators, but also exploring some positive indicators in future research. For example, previous studies had shown that social support [45], psychological resilience [46], and self-compassion [47] were protective factors for adolescent suicide.\",\n \"The qualitative findings suggested that mental illness in high-suicide-risk adolescents was strongly associated with suicide risk, and that depression was the most common mental illness associated with suicide. Another finding was that the later the suicide stage, the smaller was the proportion of high-suicide-risk adolescents.\\nThe results of the mediation effect analysis verified the suicide mechanism path: psychological pain → hopelessness → suicide stages, indicating that psychological pain mainly affected suicide stages through hopelessness, and hopelessness may be a better predictor of suicide risk for high-suicide-risk adolescents.\\nNotably, although adolescents in the later suicide stages were at greater suicide risk, they had less expression of hopelessness in the traditional sense.\"\n]"},"all_sections_sec_types":{"kind":"list like","value":["intro",null,"subjects",null,null,null,null,null,null,null,"results",null,null,"discussion",null,null,null,null,"conclusions"],"string":"[\n \"intro\",\n null,\n \"subjects\",\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n \"results\",\n null,\n null,\n \"discussion\",\n null,\n null,\n null,\n null,\n \"conclusions\"\n]"},"keywords":{"kind":"list like","value":["high-suicide-risk adolescents","psychological pain","hopelessness","suicide stages","Weibo text analysis"],"string":"[\n \"high-suicide-risk adolescents\",\n \"psychological pain\",\n \"hopelessness\",\n \"suicide stages\",\n \"Weibo text analysis\"\n]"},"whole_article_text":{"kind":"string","value":"1. Introduction:\nAdolescent suicide has always been a global public health concern to us. Suicide is the fourth leading cause of death globally among people aged 15–19 [1], and in China, it is the second leading cause of death for people aged 20–34 [2]. The consequences of adolescent suicide are the most serious among many mental health problems. It means disability or even a loss of life for individuals, heavy blows and severe trauma for families, and huge expenditures on the health, welfare and justice sectors for society. The study of suicide risk factors and the exploration of suicide mechanisms in high-suicide-risk adolescents are of great significance for the prevention of suicide.\nWith the development of technology, social media has become a platform for people to record their daily life and express their emotions. In China, Sina Weibo as a typical product of the big data era has extensive influence. Sina Weibo is China’s most popular social media platform, just like Twitter in America. Although there are some short video platforms (e.g., Douyin) and forums (e.g., Douban) that are also popular in China, it is only on Sina Weibo that users can share information in real-time and communicate interactively in the forms of text, pictures, videos and other multimedia. According to a report released by Sina Weibo, Weibo users show a younger trend, of which the post-90s and post-00s youth groups account for nearly 80%, and the daily active volume can reach 224 million [3].\nThe user’s emotional expression on social platforms has higher immediacy and less masking. Identifying the user’s psychological state through their expression on social media will be more convenient and quicker, and the obtained data will be more real. Therefore, we use the Weibo text analysis method to explore the suicide mechanism path of high-suicide-risk adolescents. Text analysis refers to the representation of text and the selection of feature items, which is a basic problem in text mining and information retrieval. Text analysis means that we can quantify the feature words extracted from text to represent text information. Therefore, we can extract the psychological indicators we want from the Weibo text by constructing a dictionary of psychological indicators.\nSuicide represents a series of consecutive stages, including suicide ideation, suicide plan, and suicide attempt [4,5]. Suicide ideation, as a high-risk factor for suicide, has a significant predictive effect [6,7]. Approximately 1/3 of suicide ideators ultimately attempt suicide, and the number of suicide attempters is several times higher than those who really die by suicide, so suicide attempt is a greater risk factor for suicide than suicidal ideation [1,8,9]. The later the suicide stage, the closer to suicide death, and the greater risk of suicide. Therefore, in order to quantify the degree of suicide risk and provide accurate information for suicide interventions, we decided to divide suicidal behavior into different stages. At the same time, considering that depressed mood is an important signal of suicide, we finally divided suicide into 4 stages, which the suicide risk gradually increases: depressed mood, suicide ideation, suicide plan and suicide attempt, and defined adolescents at different suicide stages as high-suicide-risk adolescents.\nA meta-analysis of the causes of suicide among adolescents found that suicide is a public health problem involving complex factors, such as biology, psychology, family, society and culture [8]. Among the psychological factors, psychological pain and hopelessness are important risk factors featuring strongly in suicide research [9,10,11]. Although psychological pain and hopelessness are often mentioned together as having an impact on suicidal behavior, as two most common motivations for suicide attempts [12], it is unclear how the two proximal factors of suicide affect the suicide stages.\nPsychological pain is defined as the “introspective experience of negative emotions such as fear, despair, grief, shame, guilt, blocked love, loneliness and loss” [13,14,15,16]. When Shneidman first hypothesized the relationship between psychological pain and suicide, he pointed out that other factors can only affect suicidal behavior through psychological pain, without which suicide would not occur [13]. Evidence suggests that psychological pain plays a central role in suicidal behavior [10,17,18]. Psychological pain is not enough to develop suicide ideation, it also requires hopelessness [19]. Hopelessness is defined as negative expectations or pessimism about oneself or the future [20]. Previous studies had showed that hopelessness was also an important risk factor of suicide [21,22,23].\nTo explore the relationship between psychological pain, hopelessness and suicide stages for high-suicide-risk adolescents, we established a hypothesis for the suicide mechanism path: psychological pain → hopelessness → suicide stages, with hopelessness as the mediating variable. In addition, there is a high correlation between mental illness and suicidal behaviors [24,25], and in order to better clarify the effect of psychological pain and hopelessness on suicide stages, a mediating effect model of our study is proposed with mental illness as a control variable (Figure 1). We used the more ecological Weibo text analysis method in this study, and hoped to further deepen the research on adolescent suicide and to find a new theoretical basis for future suicide interventions drawing on online data.\n\n2. Materials and Methods:\n2.1. Participants Through the online psychological assistance project, we obtained the interview records data of 3627 Weibo users, and then processed the user’s interview records data and Weibo texts data. Finally, we retained 105 high-suicide-risk adolescents with enough Weibo texts as the experimental group.\nEthical Statement: Participants gave informed consent when agreeing to take part in the online psychological assistance project. The project received ethical approval from the Institution-al Review Board of the Institute of Psychology, Chinese Academy of Sciences, with the ethics approval number H16003.\nThrough the online psychological assistance project, we obtained the interview records data of 3627 Weibo users, and then processed the user’s interview records data and Weibo texts data. Finally, we retained 105 high-suicide-risk adolescents with enough Weibo texts as the experimental group.\nEthical Statement: Participants gave informed consent when agreeing to take part in the online psychological assistance project. The project received ethical approval from the Institution-al Review Board of the Institute of Psychology, Chinese Academy of Sciences, with the ethics approval number H16003.\n2.2. Online Psychological Assistance Project We used the suicide signal recognition model established by the Computational Cyber-Psychology Lab (CCPL) to identify the suicide signal of a single microblog, which can effectively determine whether the single microblog has shown suicidal risk [26].\nSince November 2016, we had used the suicide signal recognition model for online identification of Weibo comments. The last Weibo post before the death of Weibo user “Zoufan”, as a place where people can express their negative emotions, has received more than 1 million comments until now, and the number of comments keep rising (Figure 2).\nFirstly, we used the suicide signal model to identify the suicide signal of the Weibo comments. Then, the comments identified by the model were re-identified by manual recognition. Finally, we used the official Weibo account “Psychological Map Psy” and sent a private message to the Weibo users marked as having a suicide risk. The contents of the private message mainly include inquiries about the user’s emotional state, expressions of concern, links to self-assessment psychological scales, and the hotline of the psychological crisis intervention center, etc. (Figure 3). In addition, users can choose whether to communicate with the voluntary counselor online according to their own wishes, so as to achieve agile psychological crisis intervention.\nWe used the suicide signal recognition model established by the Computational Cyber-Psychology Lab (CCPL) to identify the suicide signal of a single microblog, which can effectively determine whether the single microblog has shown suicidal risk [26].\nSince November 2016, we had used the suicide signal recognition model for online identification of Weibo comments. The last Weibo post before the death of Weibo user “Zoufan”, as a place where people can express their negative emotions, has received more than 1 million comments until now, and the number of comments keep rising (Figure 2).\nFirstly, we used the suicide signal model to identify the suicide signal of the Weibo comments. Then, the comments identified by the model were re-identified by manual recognition. Finally, we used the official Weibo account “Psychological Map Psy” and sent a private message to the Weibo users marked as having a suicide risk. The contents of the private message mainly include inquiries about the user’s emotional state, expressions of concern, links to self-assessment psychological scales, and the hotline of the psychological crisis intervention center, etc. (Figure 3). In addition, users can choose whether to communicate with the voluntary counselor online according to their own wishes, so as to achieve agile psychological crisis intervention.\n2.3. Data Collection and Processing 2.3.1. Labeling the Interview Records Data During the online psychological assistance project period (November 2016 to April 2019), the official Weibo account had communicated with 3627 users online, and 127,336 message records were accumulated. Adhering to the principle of privacy and confidentiality, the use of interview records data was strictly restricted. The experimenter must protect the privacy of the research subjects involved in the project, and should not use any confidential information related to the research subjects (including but not limited to accounts, nicknames, contact information, personal homepage, network IP, location, information records, etc.) outside the scope of the research, and would not violate the privacy of the subjects in any way.\nThe interview records data were encoded by qualitative analysis. The basic content of interview records data were the current status and problems of users. We extracted three indicators from the interview records data, and the specific descriptions of them are shown below:(1)Determining whether the user was in adolescent stages (junior high school, high school, college), and retaining the data labeled as the adolescents.(2)Determining whether the user was diagnosed with mental illness by a psychiatrist, had a history of mental illness, or was taking medication for mental illness, and labeling a user had a mental illness or not as a 0/1 dummy variable.(3)Coding the suicide stages (depressed mood, suicide ideation, suicide plan, suicide attempt) of adolescents, and marking the suicide stages as 1, 2, 3 and 4, an ordered categorical variable.\nDetermining whether the user was in adolescent stages (junior high school, high school, college), and retaining the data labeled as the adolescents.\nDetermining whether the user was diagnosed with mental illness by a psychiatrist, had a history of mental illness, or was taking medication for mental illness, and labeling a user had a mental illness or not as a 0/1 dummy variable.\nCoding the suicide stages (depressed mood, suicide ideation, suicide plan, suicide attempt) of adolescents, and marking the suicide stages as 1, 2, 3 and 4, an ordered categorical variable.\nSuicide stages were coded on the basis of the following criteria: (a) Depressed mood: feeling negative and depressed, but not yet having suicidal ideation. It means participants did not express suicidal intention explicitly or implicitly(whether they actively expressed suicidal intentions, or exposed suicidal intentions under inquiries from the official Weibo account). (b) Suicide ideation: there is suicidal intent, but suicide plan has not formed. Due to being affected by an intense depressive mood, the idea of avoiding pain through suicide arises, but there is no specific time and way to implement it. (c) Suicide plan: on the basis of suicidal ideation, there is a rough or detailed suicide plan, such as when, where, and how to commit suicide. (d) Suicide attempt: at least one suicide attempt with the aim of ending one’s own life.\nDuring the online psychological assistance project period (November 2016 to April 2019), the official Weibo account had communicated with 3627 users online, and 127,336 message records were accumulated. Adhering to the principle of privacy and confidentiality, the use of interview records data was strictly restricted. The experimenter must protect the privacy of the research subjects involved in the project, and should not use any confidential information related to the research subjects (including but not limited to accounts, nicknames, contact information, personal homepage, network IP, location, information records, etc.) outside the scope of the research, and would not violate the privacy of the subjects in any way.\nThe interview records data were encoded by qualitative analysis. The basic content of interview records data were the current status and problems of users. We extracted three indicators from the interview records data, and the specific descriptions of them are shown below:(1)Determining whether the user was in adolescent stages (junior high school, high school, college), and retaining the data labeled as the adolescents.(2)Determining whether the user was diagnosed with mental illness by a psychiatrist, had a history of mental illness, or was taking medication for mental illness, and labeling a user had a mental illness or not as a 0/1 dummy variable.(3)Coding the suicide stages (depressed mood, suicide ideation, suicide plan, suicide attempt) of adolescents, and marking the suicide stages as 1, 2, 3 and 4, an ordered categorical variable.\nDetermining whether the user was in adolescent stages (junior high school, high school, college), and retaining the data labeled as the adolescents.\nDetermining whether the user was diagnosed with mental illness by a psychiatrist, had a history of mental illness, or was taking medication for mental illness, and labeling a user had a mental illness or not as a 0/1 dummy variable.\nCoding the suicide stages (depressed mood, suicide ideation, suicide plan, suicide attempt) of adolescents, and marking the suicide stages as 1, 2, 3 and 4, an ordered categorical variable.\nSuicide stages were coded on the basis of the following criteria: (a) Depressed mood: feeling negative and depressed, but not yet having suicidal ideation. It means participants did not express suicidal intention explicitly or implicitly(whether they actively expressed suicidal intentions, or exposed suicidal intentions under inquiries from the official Weibo account). (b) Suicide ideation: there is suicidal intent, but suicide plan has not formed. Due to being affected by an intense depressive mood, the idea of avoiding pain through suicide arises, but there is no specific time and way to implement it. (c) Suicide plan: on the basis of suicidal ideation, there is a rough or detailed suicide plan, such as when, where, and how to commit suicide. (d) Suicide attempt: at least one suicide attempt with the aim of ending one’s own life.\n2.3.2. Getting the Weibo Text Data We scraped Weibo texts published by the same 3627 users through the Application Programming Interface (API) of Sina Weibo. In order to obtain sufficient Weibo texts for analysis, we took the first time that the official Weibo account sent a private message to the user as the beginning of suicide intervention, downloaded all Weibo texts in the 3 months before the intervention, and filtered the users whose Weibo texts are less than 300 words (Figure 4).\nFinally, 105 high-suicide-risk adolescents (as the experimental group) were included in the analysis. We then removed stop words and performed Chinese word segmentation on the Weibo texts, and calculated word frequency. “The Chinese Suicide Dictionary” [27] was used to measure the word frequency ratio of their psychological pain and hopelessness. The dictionary outline is shown in Table 1. The psychological pain dimension represents the pain and torment of individuals at the psychological level, including 403 keywords; the hopelessness dimension represents the subjective feeling of losing hope, including 188 keywords.\nWe scraped Weibo texts published by the same 3627 users through the Application Programming Interface (API) of Sina Weibo. In order to obtain sufficient Weibo texts for analysis, we took the first time that the official Weibo account sent a private message to the user as the beginning of suicide intervention, downloaded all Weibo texts in the 3 months before the intervention, and filtered the users whose Weibo texts are less than 300 words (Figure 4).\nFinally, 105 high-suicide-risk adolescents (as the experimental group) were included in the analysis. We then removed stop words and performed Chinese word segmentation on the Weibo texts, and calculated word frequency. “The Chinese Suicide Dictionary” [27] was used to measure the word frequency ratio of their psychological pain and hopelessness. The dictionary outline is shown in Table 1. The psychological pain dimension represents the pain and torment of individuals at the psychological level, including 403 keywords; the hopelessness dimension represents the subjective feeling of losing hope, including 188 keywords.\n2.3.1. Labeling the Interview Records Data During the online psychological assistance project period (November 2016 to April 2019), the official Weibo account had communicated with 3627 users online, and 127,336 message records were accumulated. Adhering to the principle of privacy and confidentiality, the use of interview records data was strictly restricted. The experimenter must protect the privacy of the research subjects involved in the project, and should not use any confidential information related to the research subjects (including but not limited to accounts, nicknames, contact information, personal homepage, network IP, location, information records, etc.) outside the scope of the research, and would not violate the privacy of the subjects in any way.\nThe interview records data were encoded by qualitative analysis. The basic content of interview records data were the current status and problems of users. We extracted three indicators from the interview records data, and the specific descriptions of them are shown below:(1)Determining whether the user was in adolescent stages (junior high school, high school, college), and retaining the data labeled as the adolescents.(2)Determining whether the user was diagnosed with mental illness by a psychiatrist, had a history of mental illness, or was taking medication for mental illness, and labeling a user had a mental illness or not as a 0/1 dummy variable.(3)Coding the suicide stages (depressed mood, suicide ideation, suicide plan, suicide attempt) of adolescents, and marking the suicide stages as 1, 2, 3 and 4, an ordered categorical variable.\nDetermining whether the user was in adolescent stages (junior high school, high school, college), and retaining the data labeled as the adolescents.\nDetermining whether the user was diagnosed with mental illness by a psychiatrist, had a history of mental illness, or was taking medication for mental illness, and labeling a user had a mental illness or not as a 0/1 dummy variable.\nCoding the suicide stages (depressed mood, suicide ideation, suicide plan, suicide attempt) of adolescents, and marking the suicide stages as 1, 2, 3 and 4, an ordered categorical variable.\nSuicide stages were coded on the basis of the following criteria: (a) Depressed mood: feeling negative and depressed, but not yet having suicidal ideation. It means participants did not express suicidal intention explicitly or implicitly(whether they actively expressed suicidal intentions, or exposed suicidal intentions under inquiries from the official Weibo account). (b) Suicide ideation: there is suicidal intent, but suicide plan has not formed. Due to being affected by an intense depressive mood, the idea of avoiding pain through suicide arises, but there is no specific time and way to implement it. (c) Suicide plan: on the basis of suicidal ideation, there is a rough or detailed suicide plan, such as when, where, and how to commit suicide. (d) Suicide attempt: at least one suicide attempt with the aim of ending one’s own life.\nDuring the online psychological assistance project period (November 2016 to April 2019), the official Weibo account had communicated with 3627 users online, and 127,336 message records were accumulated. Adhering to the principle of privacy and confidentiality, the use of interview records data was strictly restricted. The experimenter must protect the privacy of the research subjects involved in the project, and should not use any confidential information related to the research subjects (including but not limited to accounts, nicknames, contact information, personal homepage, network IP, location, information records, etc.) outside the scope of the research, and would not violate the privacy of the subjects in any way.\nThe interview records data were encoded by qualitative analysis. The basic content of interview records data were the current status and problems of users. We extracted three indicators from the interview records data, and the specific descriptions of them are shown below:(1)Determining whether the user was in adolescent stages (junior high school, high school, college), and retaining the data labeled as the adolescents.(2)Determining whether the user was diagnosed with mental illness by a psychiatrist, had a history of mental illness, or was taking medication for mental illness, and labeling a user had a mental illness or not as a 0/1 dummy variable.(3)Coding the suicide stages (depressed mood, suicide ideation, suicide plan, suicide attempt) of adolescents, and marking the suicide stages as 1, 2, 3 and 4, an ordered categorical variable.\nDetermining whether the user was in adolescent stages (junior high school, high school, college), and retaining the data labeled as the adolescents.\nDetermining whether the user was diagnosed with mental illness by a psychiatrist, had a history of mental illness, or was taking medication for mental illness, and labeling a user had a mental illness or not as a 0/1 dummy variable.\nCoding the suicide stages (depressed mood, suicide ideation, suicide plan, suicide attempt) of adolescents, and marking the suicide stages as 1, 2, 3 and 4, an ordered categorical variable.\nSuicide stages were coded on the basis of the following criteria: (a) Depressed mood: feeling negative and depressed, but not yet having suicidal ideation. It means participants did not express suicidal intention explicitly or implicitly(whether they actively expressed suicidal intentions, or exposed suicidal intentions under inquiries from the official Weibo account). (b) Suicide ideation: there is suicidal intent, but suicide plan has not formed. Due to being affected by an intense depressive mood, the idea of avoiding pain through suicide arises, but there is no specific time and way to implement it. (c) Suicide plan: on the basis of suicidal ideation, there is a rough or detailed suicide plan, such as when, where, and how to commit suicide. (d) Suicide attempt: at least one suicide attempt with the aim of ending one’s own life.\n2.3.2. Getting the Weibo Text Data We scraped Weibo texts published by the same 3627 users through the Application Programming Interface (API) of Sina Weibo. In order to obtain sufficient Weibo texts for analysis, we took the first time that the official Weibo account sent a private message to the user as the beginning of suicide intervention, downloaded all Weibo texts in the 3 months before the intervention, and filtered the users whose Weibo texts are less than 300 words (Figure 4).\nFinally, 105 high-suicide-risk adolescents (as the experimental group) were included in the analysis. We then removed stop words and performed Chinese word segmentation on the Weibo texts, and calculated word frequency. “The Chinese Suicide Dictionary” [27] was used to measure the word frequency ratio of their psychological pain and hopelessness. The dictionary outline is shown in Table 1. The psychological pain dimension represents the pain and torment of individuals at the psychological level, including 403 keywords; the hopelessness dimension represents the subjective feeling of losing hope, including 188 keywords.\nWe scraped Weibo texts published by the same 3627 users through the Application Programming Interface (API) of Sina Weibo. In order to obtain sufficient Weibo texts for analysis, we took the first time that the official Weibo account sent a private message to the user as the beginning of suicide intervention, downloaded all Weibo texts in the 3 months before the intervention, and filtered the users whose Weibo texts are less than 300 words (Figure 4).\nFinally, 105 high-suicide-risk adolescents (as the experimental group) were included in the analysis. We then removed stop words and performed Chinese word segmentation on the Weibo texts, and calculated word frequency. “The Chinese Suicide Dictionary” [27] was used to measure the word frequency ratio of their psychological pain and hopelessness. The dictionary outline is shown in Table 1. The psychological pain dimension represents the pain and torment of individuals at the psychological level, including 403 keywords; the hopelessness dimension represents the subjective feeling of losing hope, including 188 keywords.\n2.4. Data Analysis 2.4.1. Qualitative Analysis Through the labeling results of interview records data, we investigated the situation of mental illness and the distribution of suicide stages for the experimental group.\nThrough the labeling results of interview records data, we investigated the situation of mental illness and the distribution of suicide stages for the experimental group.\n2.4.2. Analysis of Suicide Path The mediation effect analysis was executed on the software Mplus by using the bootstrap method to test the suicide mechanism path model. The word frequency of psychological pain and hopelessness of the experimental group were treated as independent variable (X) and mediating variable (M), respectively. The suicide stages were treated as dependent variable (Y). The mental illness was included in the model as a control variable (Z).\nThe mediation effect analysis was executed on the software Mplus by using the bootstrap method to test the suicide mechanism path model. The word frequency of psychological pain and hopelessness of the experimental group were treated as independent variable (X) and mediating variable (M), respectively. The suicide stages were treated as dependent variable (Y). The mental illness was included in the model as a control variable (Z).\n2.4.1. Qualitative Analysis Through the labeling results of interview records data, we investigated the situation of mental illness and the distribution of suicide stages for the experimental group.\nThrough the labeling results of interview records data, we investigated the situation of mental illness and the distribution of suicide stages for the experimental group.\n2.4.2. Analysis of Suicide Path The mediation effect analysis was executed on the software Mplus by using the bootstrap method to test the suicide mechanism path model. The word frequency of psychological pain and hopelessness of the experimental group were treated as independent variable (X) and mediating variable (M), respectively. The suicide stages were treated as dependent variable (Y). The mental illness was included in the model as a control variable (Z).\nThe mediation effect analysis was executed on the software Mplus by using the bootstrap method to test the suicide mechanism path model. The word frequency of psychological pain and hopelessness of the experimental group were treated as independent variable (X) and mediating variable (M), respectively. The suicide stages were treated as dependent variable (Y). The mental illness was included in the model as a control variable (Z).\n\n2.1. Participants:\nThrough the online psychological assistance project, we obtained the interview records data of 3627 Weibo users, and then processed the user’s interview records data and Weibo texts data. Finally, we retained 105 high-suicide-risk adolescents with enough Weibo texts as the experimental group.\nEthical Statement: Participants gave informed consent when agreeing to take part in the online psychological assistance project. The project received ethical approval from the Institution-al Review Board of the Institute of Psychology, Chinese Academy of Sciences, with the ethics approval number H16003.\n\n2.2. Online Psychological Assistance Project:\nWe used the suicide signal recognition model established by the Computational Cyber-Psychology Lab (CCPL) to identify the suicide signal of a single microblog, which can effectively determine whether the single microblog has shown suicidal risk [26].\nSince November 2016, we had used the suicide signal recognition model for online identification of Weibo comments. The last Weibo post before the death of Weibo user “Zoufan”, as a place where people can express their negative emotions, has received more than 1 million comments until now, and the number of comments keep rising (Figure 2).\nFirstly, we used the suicide signal model to identify the suicide signal of the Weibo comments. Then, the comments identified by the model were re-identified by manual recognition. Finally, we used the official Weibo account “Psychological Map Psy” and sent a private message to the Weibo users marked as having a suicide risk. The contents of the private message mainly include inquiries about the user’s emotional state, expressions of concern, links to self-assessment psychological scales, and the hotline of the psychological crisis intervention center, etc. (Figure 3). In addition, users can choose whether to communicate with the voluntary counselor online according to their own wishes, so as to achieve agile psychological crisis intervention.\n\n2.3. Data Collection and Processing:\n2.3.1. Labeling the Interview Records Data During the online psychological assistance project period (November 2016 to April 2019), the official Weibo account had communicated with 3627 users online, and 127,336 message records were accumulated. Adhering to the principle of privacy and confidentiality, the use of interview records data was strictly restricted. The experimenter must protect the privacy of the research subjects involved in the project, and should not use any confidential information related to the research subjects (including but not limited to accounts, nicknames, contact information, personal homepage, network IP, location, information records, etc.) outside the scope of the research, and would not violate the privacy of the subjects in any way.\nThe interview records data were encoded by qualitative analysis. The basic content of interview records data were the current status and problems of users. We extracted three indicators from the interview records data, and the specific descriptions of them are shown below:(1)Determining whether the user was in adolescent stages (junior high school, high school, college), and retaining the data labeled as the adolescents.(2)Determining whether the user was diagnosed with mental illness by a psychiatrist, had a history of mental illness, or was taking medication for mental illness, and labeling a user had a mental illness or not as a 0/1 dummy variable.(3)Coding the suicide stages (depressed mood, suicide ideation, suicide plan, suicide attempt) of adolescents, and marking the suicide stages as 1, 2, 3 and 4, an ordered categorical variable.\nDetermining whether the user was in adolescent stages (junior high school, high school, college), and retaining the data labeled as the adolescents.\nDetermining whether the user was diagnosed with mental illness by a psychiatrist, had a history of mental illness, or was taking medication for mental illness, and labeling a user had a mental illness or not as a 0/1 dummy variable.\nCoding the suicide stages (depressed mood, suicide ideation, suicide plan, suicide attempt) of adolescents, and marking the suicide stages as 1, 2, 3 and 4, an ordered categorical variable.\nSuicide stages were coded on the basis of the following criteria: (a) Depressed mood: feeling negative and depressed, but not yet having suicidal ideation. It means participants did not express suicidal intention explicitly or implicitly(whether they actively expressed suicidal intentions, or exposed suicidal intentions under inquiries from the official Weibo account). (b) Suicide ideation: there is suicidal intent, but suicide plan has not formed. Due to being affected by an intense depressive mood, the idea of avoiding pain through suicide arises, but there is no specific time and way to implement it. (c) Suicide plan: on the basis of suicidal ideation, there is a rough or detailed suicide plan, such as when, where, and how to commit suicide. (d) Suicide attempt: at least one suicide attempt with the aim of ending one’s own life.\nDuring the online psychological assistance project period (November 2016 to April 2019), the official Weibo account had communicated with 3627 users online, and 127,336 message records were accumulated. Adhering to the principle of privacy and confidentiality, the use of interview records data was strictly restricted. The experimenter must protect the privacy of the research subjects involved in the project, and should not use any confidential information related to the research subjects (including but not limited to accounts, nicknames, contact information, personal homepage, network IP, location, information records, etc.) outside the scope of the research, and would not violate the privacy of the subjects in any way.\nThe interview records data were encoded by qualitative analysis. The basic content of interview records data were the current status and problems of users. We extracted three indicators from the interview records data, and the specific descriptions of them are shown below:(1)Determining whether the user was in adolescent stages (junior high school, high school, college), and retaining the data labeled as the adolescents.(2)Determining whether the user was diagnosed with mental illness by a psychiatrist, had a history of mental illness, or was taking medication for mental illness, and labeling a user had a mental illness or not as a 0/1 dummy variable.(3)Coding the suicide stages (depressed mood, suicide ideation, suicide plan, suicide attempt) of adolescents, and marking the suicide stages as 1, 2, 3 and 4, an ordered categorical variable.\nDetermining whether the user was in adolescent stages (junior high school, high school, college), and retaining the data labeled as the adolescents.\nDetermining whether the user was diagnosed with mental illness by a psychiatrist, had a history of mental illness, or was taking medication for mental illness, and labeling a user had a mental illness or not as a 0/1 dummy variable.\nCoding the suicide stages (depressed mood, suicide ideation, suicide plan, suicide attempt) of adolescents, and marking the suicide stages as 1, 2, 3 and 4, an ordered categorical variable.\nSuicide stages were coded on the basis of the following criteria: (a) Depressed mood: feeling negative and depressed, but not yet having suicidal ideation. It means participants did not express suicidal intention explicitly or implicitly(whether they actively expressed suicidal intentions, or exposed suicidal intentions under inquiries from the official Weibo account). (b) Suicide ideation: there is suicidal intent, but suicide plan has not formed. Due to being affected by an intense depressive mood, the idea of avoiding pain through suicide arises, but there is no specific time and way to implement it. (c) Suicide plan: on the basis of suicidal ideation, there is a rough or detailed suicide plan, such as when, where, and how to commit suicide. (d) Suicide attempt: at least one suicide attempt with the aim of ending one’s own life.\n2.3.2. Getting the Weibo Text Data We scraped Weibo texts published by the same 3627 users through the Application Programming Interface (API) of Sina Weibo. In order to obtain sufficient Weibo texts for analysis, we took the first time that the official Weibo account sent a private message to the user as the beginning of suicide intervention, downloaded all Weibo texts in the 3 months before the intervention, and filtered the users whose Weibo texts are less than 300 words (Figure 4).\nFinally, 105 high-suicide-risk adolescents (as the experimental group) were included in the analysis. We then removed stop words and performed Chinese word segmentation on the Weibo texts, and calculated word frequency. “The Chinese Suicide Dictionary” [27] was used to measure the word frequency ratio of their psychological pain and hopelessness. The dictionary outline is shown in Table 1. The psychological pain dimension represents the pain and torment of individuals at the psychological level, including 403 keywords; the hopelessness dimension represents the subjective feeling of losing hope, including 188 keywords.\nWe scraped Weibo texts published by the same 3627 users through the Application Programming Interface (API) of Sina Weibo. In order to obtain sufficient Weibo texts for analysis, we took the first time that the official Weibo account sent a private message to the user as the beginning of suicide intervention, downloaded all Weibo texts in the 3 months before the intervention, and filtered the users whose Weibo texts are less than 300 words (Figure 4).\nFinally, 105 high-suicide-risk adolescents (as the experimental group) were included in the analysis. We then removed stop words and performed Chinese word segmentation on the Weibo texts, and calculated word frequency. “The Chinese Suicide Dictionary” [27] was used to measure the word frequency ratio of their psychological pain and hopelessness. The dictionary outline is shown in Table 1. The psychological pain dimension represents the pain and torment of individuals at the psychological level, including 403 keywords; the hopelessness dimension represents the subjective feeling of losing hope, including 188 keywords.\n\n2.3.1. Labeling the Interview Records Data:\nDuring the online psychological assistance project period (November 2016 to April 2019), the official Weibo account had communicated with 3627 users online, and 127,336 message records were accumulated. Adhering to the principle of privacy and confidentiality, the use of interview records data was strictly restricted. The experimenter must protect the privacy of the research subjects involved in the project, and should not use any confidential information related to the research subjects (including but not limited to accounts, nicknames, contact information, personal homepage, network IP, location, information records, etc.) outside the scope of the research, and would not violate the privacy of the subjects in any way.\nThe interview records data were encoded by qualitative analysis. The basic content of interview records data were the current status and problems of users. We extracted three indicators from the interview records data, and the specific descriptions of them are shown below:(1)Determining whether the user was in adolescent stages (junior high school, high school, college), and retaining the data labeled as the adolescents.(2)Determining whether the user was diagnosed with mental illness by a psychiatrist, had a history of mental illness, or was taking medication for mental illness, and labeling a user had a mental illness or not as a 0/1 dummy variable.(3)Coding the suicide stages (depressed mood, suicide ideation, suicide plan, suicide attempt) of adolescents, and marking the suicide stages as 1, 2, 3 and 4, an ordered categorical variable.\nDetermining whether the user was in adolescent stages (junior high school, high school, college), and retaining the data labeled as the adolescents.\nDetermining whether the user was diagnosed with mental illness by a psychiatrist, had a history of mental illness, or was taking medication for mental illness, and labeling a user had a mental illness or not as a 0/1 dummy variable.\nCoding the suicide stages (depressed mood, suicide ideation, suicide plan, suicide attempt) of adolescents, and marking the suicide stages as 1, 2, 3 and 4, an ordered categorical variable.\nSuicide stages were coded on the basis of the following criteria: (a) Depressed mood: feeling negative and depressed, but not yet having suicidal ideation. It means participants did not express suicidal intention explicitly or implicitly(whether they actively expressed suicidal intentions, or exposed suicidal intentions under inquiries from the official Weibo account). (b) Suicide ideation: there is suicidal intent, but suicide plan has not formed. Due to being affected by an intense depressive mood, the idea of avoiding pain through suicide arises, but there is no specific time and way to implement it. (c) Suicide plan: on the basis of suicidal ideation, there is a rough or detailed suicide plan, such as when, where, and how to commit suicide. (d) Suicide attempt: at least one suicide attempt with the aim of ending one’s own life.\n\n2.3.2. Getting the Weibo Text Data:\nWe scraped Weibo texts published by the same 3627 users through the Application Programming Interface (API) of Sina Weibo. In order to obtain sufficient Weibo texts for analysis, we took the first time that the official Weibo account sent a private message to the user as the beginning of suicide intervention, downloaded all Weibo texts in the 3 months before the intervention, and filtered the users whose Weibo texts are less than 300 words (Figure 4).\nFinally, 105 high-suicide-risk adolescents (as the experimental group) were included in the analysis. We then removed stop words and performed Chinese word segmentation on the Weibo texts, and calculated word frequency. “The Chinese Suicide Dictionary” [27] was used to measure the word frequency ratio of their psychological pain and hopelessness. The dictionary outline is shown in Table 1. The psychological pain dimension represents the pain and torment of individuals at the psychological level, including 403 keywords; the hopelessness dimension represents the subjective feeling of losing hope, including 188 keywords.\n\n2.4. Data Analysis:\n2.4.1. Qualitative Analysis Through the labeling results of interview records data, we investigated the situation of mental illness and the distribution of suicide stages for the experimental group.\nThrough the labeling results of interview records data, we investigated the situation of mental illness and the distribution of suicide stages for the experimental group.\n2.4.2. Analysis of Suicide Path The mediation effect analysis was executed on the software Mplus by using the bootstrap method to test the suicide mechanism path model. The word frequency of psychological pain and hopelessness of the experimental group were treated as independent variable (X) and mediating variable (M), respectively. The suicide stages were treated as dependent variable (Y). The mental illness was included in the model as a control variable (Z).\nThe mediation effect analysis was executed on the software Mplus by using the bootstrap method to test the suicide mechanism path model. The word frequency of psychological pain and hopelessness of the experimental group were treated as independent variable (X) and mediating variable (M), respectively. The suicide stages were treated as dependent variable (Y). The mental illness was included in the model as a control variable (Z).\n\n2.4.1. Qualitative Analysis:\nThrough the labeling results of interview records data, we investigated the situation of mental illness and the distribution of suicide stages for the experimental group.\n\n2.4.2. Analysis of Suicide Path:\nThe mediation effect analysis was executed on the software Mplus by using the bootstrap method to test the suicide mechanism path model. The word frequency of psychological pain and hopelessness of the experimental group were treated as independent variable (X) and mediating variable (M), respectively. The suicide stages were treated as dependent variable (Y). The mental illness was included in the model as a control variable (Z).\n\n3. Results:\n3.1. Group Characteristics Reflected by Qualitative Analysis The mental illness diagnosis of the experimental group was marked; it was found that 38 were diagnosed and 67 were undiagnosed. The proportion of users diagnosed with mental illness was 36.2%. Among the 38 high-suicide-risk adolescents diagnosed with mental illness, 29 users had depression (or depression was included in multiple mental illnesses), accounting for 76.3%; the rest included bipolar disorder (13.16%), anxiety disorder (13.16%), etc.; 9 of them suffered from multiple mental illnesses (e.g., depression and anxiety disorder), accounting for 23.68%.\nAmong the experimental group, 57 were in the stage of depressed mood (54.3%), 21 were in the stage of suicide ideation (20.0%), 19 were in the stage of suicide plan (18.1%), and 8 people were in the stage of suicide attempt (7.6%) (Table 2).\nThe mental illness diagnosis of the experimental group was marked; it was found that 38 were diagnosed and 67 were undiagnosed. The proportion of users diagnosed with mental illness was 36.2%. Among the 38 high-suicide-risk adolescents diagnosed with mental illness, 29 users had depression (or depression was included in multiple mental illnesses), accounting for 76.3%; the rest included bipolar disorder (13.16%), anxiety disorder (13.16%), etc.; 9 of them suffered from multiple mental illnesses (e.g., depression and anxiety disorder), accounting for 23.68%.\nAmong the experimental group, 57 were in the stage of depressed mood (54.3%), 21 were in the stage of suicide ideation (20.0%), 19 were in the stage of suicide plan (18.1%), and 8 people were in the stage of suicide attempt (7.6%) (Table 2).\n3.2. Path Analysis of Psychological Pain, Hopelessness and Suicide Stages A mediation effect test was performed on the path model (psychological pain → hopelessness → suicide stages). The performance of each fit index of the model was good (RMSEA < 0.08; CFI > 0.9; TLI > 0.9), indicating that the model fitting effect is excellent (Table 3).\nAfter controlling for mental illness factors, the total effect c was significant (β = −0.154, p < 0.05), indicating that there was a mediation effect; the path coefficients a and b were significant (β = 0.571, p < 0.001; β = −0.271, p < 0.05), but the direct effect c’ was not significant (β = 0.052, p > 0.05), indicating that there was a complete mediating effect of hopelessness. That is, psychological pain mainly affects suicide risk through hopelessness (Table 4).\nIt was worth noting that the relationship between the expression of hopelessness and the stages of suicide showed a significant negative correlation (β = −0.271, p < 0.05), indicating that the late suicide stage such as suicide attempt, which may be closer to suicide death, included less expressions of hopelessness (Table 4).\nFurthermore, there was a significant positive correlation between diagnosed mental illness and suicide stages (β = 0.398, p < 0.001), indicating the necessity of treating mental illness as a control variable in order to see the relationship between psychological pain, hopelessness and suicide stages more clearly. At the same time, it also showed that the later the suicide stage, the higher the diagnosis rate of mental illness (Table 4).\nA mediation effect test was performed on the path model (psychological pain → hopelessness → suicide stages). The performance of each fit index of the model was good (RMSEA < 0.08; CFI > 0.9; TLI > 0.9), indicating that the model fitting effect is excellent (Table 3).\nAfter controlling for mental illness factors, the total effect c was significant (β = −0.154, p < 0.05), indicating that there was a mediation effect; the path coefficients a and b were significant (β = 0.571, p < 0.001; β = −0.271, p < 0.05), but the direct effect c’ was not significant (β = 0.052, p > 0.05), indicating that there was a complete mediating effect of hopelessness. That is, psychological pain mainly affects suicide risk through hopelessness (Table 4).\nIt was worth noting that the relationship between the expression of hopelessness and the stages of suicide showed a significant negative correlation (β = −0.271, p < 0.05), indicating that the late suicide stage such as suicide attempt, which may be closer to suicide death, included less expressions of hopelessness (Table 4).\nFurthermore, there was a significant positive correlation between diagnosed mental illness and suicide stages (β = 0.398, p < 0.001), indicating the necessity of treating mental illness as a control variable in order to see the relationship between psychological pain, hopelessness and suicide stages more clearly. At the same time, it also showed that the later the suicide stage, the higher the diagnosis rate of mental illness (Table 4).\n\n3.1. Group Characteristics Reflected by Qualitative Analysis:\nThe mental illness diagnosis of the experimental group was marked; it was found that 38 were diagnosed and 67 were undiagnosed. The proportion of users diagnosed with mental illness was 36.2%. Among the 38 high-suicide-risk adolescents diagnosed with mental illness, 29 users had depression (or depression was included in multiple mental illnesses), accounting for 76.3%; the rest included bipolar disorder (13.16%), anxiety disorder (13.16%), etc.; 9 of them suffered from multiple mental illnesses (e.g., depression and anxiety disorder), accounting for 23.68%.\nAmong the experimental group, 57 were in the stage of depressed mood (54.3%), 21 were in the stage of suicide ideation (20.0%), 19 were in the stage of suicide plan (18.1%), and 8 people were in the stage of suicide attempt (7.6%) (Table 2).\n\n3.2. Path Analysis of Psychological Pain, Hopelessness and Suicide Stages:\nA mediation effect test was performed on the path model (psychological pain → hopelessness → suicide stages). The performance of each fit index of the model was good (RMSEA < 0.08; CFI > 0.9; TLI > 0.9), indicating that the model fitting effect is excellent (Table 3).\nAfter controlling for mental illness factors, the total effect c was significant (β = −0.154, p < 0.05), indicating that there was a mediation effect; the path coefficients a and b were significant (β = 0.571, p < 0.001; β = −0.271, p < 0.05), but the direct effect c’ was not significant (β = 0.052, p > 0.05), indicating that there was a complete mediating effect of hopelessness. That is, psychological pain mainly affects suicide risk through hopelessness (Table 4).\nIt was worth noting that the relationship between the expression of hopelessness and the stages of suicide showed a significant negative correlation (β = −0.271, p < 0.05), indicating that the late suicide stage such as suicide attempt, which may be closer to suicide death, included less expressions of hopelessness (Table 4).\nFurthermore, there was a significant positive correlation between diagnosed mental illness and suicide stages (β = 0.398, p < 0.001), indicating the necessity of treating mental illness as a control variable in order to see the relationship between psychological pain, hopelessness and suicide stages more clearly. At the same time, it also showed that the later the suicide stage, the higher the diagnosis rate of mental illness (Table 4).\n\n4. Discussion:\n4.1. The Qualitative Analysis Results Among the experimental group, 36.2% of users suffered from mental illness. Depression accounted for 76.3% of all mental illness; it is the most common mental illness among the high-suicide-risk adolescents. The incidence of suicide among students whose relatives were affected by psychosis were higher than that of general residents. Suicide and mental illness were closely linked [28]. British doctor Sainsbury conducted a qualitative study on the data of 400 suicides examined by a coroner in London between 1936 and 1938, and found that 37% of the suicides reflected mental illness [29]. In a meta-analysis conducted in 2004, twenty-seven studies comprising 3275 suicides were included, of which 87.3% had been diagnosed with a mental disorder prior to their death [30]. In contrast, the research population in our study was only a high suicide risk group, so the detection rate of mental illness was relatively low. Although the research populations of the other two studies were both suicide decedents, the time interval between the two studies was large. It is very possible that there existed better methods to detect the mental illness of the suicide decedents in later studies, and it is understandable that the detection rate of mental illness has greatly increased. Furthermore, a more recent study has shown that the most common mental illness associated with suicide was depression [31]. The proportion of depression in all mental illnesses in our study also supported this conclusion.\nAnother result of the qualitative analysis showed that the later the suicide stage, the lower the proportion of adolescents. A study on residents’ suicidal ideation, suicide plans and suicide attempts in Dalian, China showed that the detection rate of suicidal ideation, suicide plan and suicide attempt decreased in that order in different groups [32]. This may because not all suicide ideations translate into suicidal actions, and most people with suicide ideation have not attempted suicide [33,34].\nAmong the experimental group, 36.2% of users suffered from mental illness. Depression accounted for 76.3% of all mental illness; it is the most common mental illness among the high-suicide-risk adolescents. The incidence of suicide among students whose relatives were affected by psychosis were higher than that of general residents. Suicide and mental illness were closely linked [28]. British doctor Sainsbury conducted a qualitative study on the data of 400 suicides examined by a coroner in London between 1936 and 1938, and found that 37% of the suicides reflected mental illness [29]. In a meta-analysis conducted in 2004, twenty-seven studies comprising 3275 suicides were included, of which 87.3% had been diagnosed with a mental disorder prior to their death [30]. In contrast, the research population in our study was only a high suicide risk group, so the detection rate of mental illness was relatively low. Although the research populations of the other two studies were both suicide decedents, the time interval between the two studies was large. It is very possible that there existed better methods to detect the mental illness of the suicide decedents in later studies, and it is understandable that the detection rate of mental illness has greatly increased. Furthermore, a more recent study has shown that the most common mental illness associated with suicide was depression [31]. The proportion of depression in all mental illnesses in our study also supported this conclusion.\nAnother result of the qualitative analysis showed that the later the suicide stage, the lower the proportion of adolescents. A study on residents’ suicidal ideation, suicide plans and suicide attempts in Dalian, China showed that the detection rate of suicidal ideation, suicide plan and suicide attempt decreased in that order in different groups [32]. This may because not all suicide ideations translate into suicidal actions, and most people with suicide ideation have not attempted suicide [33,34].\n4.2. The Mediating Effect of Hopelessness Our research found that the effect of psychological pain on suicide stages was totally mediated by hopelessness, confirming our suicide mechanism path hypothesis: psychological pain → hopelessness → suicide stages. Although the first step of suicide ideation starts from psychological pain, psychological pain itself is not enough to generate suicide ideation, and hopelessness is also needed to develop suicide ideation [19]. People make decisions and take actions based in part on their emotional predictions [35,36], and hopelessness makes people at suicide risk overestimate their future emotional pain [37]. If the individual experiences constant pain in life, this may reduce the individual’s desire to live, leading to suicidal thoughts. However, pain alone is not enough to completely lose the belief in life. Only when the individual feels that the terrible situation cannot be improved, and he or she will live in pain forever, suicide will be considered.\nThus, expressions of hopelessness may be a better predictor of suicide risk than psychological pain, and we found a traceable pathway for suicide intervention. Based on the Weibo posts, we can capture whether individual released suicide signals and it is possible to track the suicide stages in the future, making suicide intervention more accurate.\nOur research found that the effect of psychological pain on suicide stages was totally mediated by hopelessness, confirming our suicide mechanism path hypothesis: psychological pain → hopelessness → suicide stages. Although the first step of suicide ideation starts from psychological pain, psychological pain itself is not enough to generate suicide ideation, and hopelessness is also needed to develop suicide ideation [19]. People make decisions and take actions based in part on their emotional predictions [35,36], and hopelessness makes people at suicide risk overestimate their future emotional pain [37]. If the individual experiences constant pain in life, this may reduce the individual’s desire to live, leading to suicidal thoughts. However, pain alone is not enough to completely lose the belief in life. Only when the individual feels that the terrible situation cannot be improved, and he or she will live in pain forever, suicide will be considered.\nThus, expressions of hopelessness may be a better predictor of suicide risk than psychological pain, and we found a traceable pathway for suicide intervention. Based on the Weibo posts, we can capture whether individual released suicide signals and it is possible to track the suicide stages in the future, making suicide intervention more accurate.\n4.3. The Hopelessness Expressions and the Suicide Stages There was a negative predictive relationship between hopelessness expressions and suicide stages, as adolescents at the later suicide stages had less expressions of hopelessness. One possible reason is that adolescents at higher risk of suicide may use less concrete, direct words of hopelessness to express such feelings. From a linguistic point of view, Gao Yihong and Meng Ling analyzed the Weibo texts of “Zoufan” three months before suicide. They found that “Zoufan” often used decontextualized and fragmented ways to express personal depressed mood, and expressed metaphors and intentions about death frequently [38]. This euphemistic and poetic way of venting may be the commonality of high suicide risk adolescents. Another possible reason is “the Presuicidal Syndrome”, which mentioned that there exists an “ominous quiet” in the short period before suicide. This is a sign of “affective restriction”, which represents extreme repression of intolerable feelings [39]. Such “affective restriction” may be why people in the later suicide stage are less likely to express hopelessness. Moreover, adolescents at the later suicide stage may be more accepting and tolerant of psychological pain and hopelessness, which makes them less willing to talk about individual psychological pain and hopelessness, and with lack of supportive survival signals through texts, lose the belief in getting help.\nMany researchers have paid attention to the use of social media for suicide prevention and intervention, and have conducted a series of studies [40,41]. However, little attention has been paid to whether the suicidal signal was from an early suicide ideator or a later suicide attempter. Our study fills this gap, complementing and extending suicide-related research. Our results suggest that adolescents who are at a later suicide stage and have a higher suicide risk often do not express too much hopelessness directly. Therefore, we cannot judge closeness to suicide only by the level of traditional expressions of hopelessness.\nThere was a negative predictive relationship between hopelessness expressions and suicide stages, as adolescents at the later suicide stages had less expressions of hopelessness. One possible reason is that adolescents at higher risk of suicide may use less concrete, direct words of hopelessness to express such feelings. From a linguistic point of view, Gao Yihong and Meng Ling analyzed the Weibo texts of “Zoufan” three months before suicide. They found that “Zoufan” often used decontextualized and fragmented ways to express personal depressed mood, and expressed metaphors and intentions about death frequently [38]. This euphemistic and poetic way of venting may be the commonality of high suicide risk adolescents. Another possible reason is “the Presuicidal Syndrome”, which mentioned that there exists an “ominous quiet” in the short period before suicide. This is a sign of “affective restriction”, which represents extreme repression of intolerable feelings [39]. Such “affective restriction” may be why people in the later suicide stage are less likely to express hopelessness. Moreover, adolescents at the later suicide stage may be more accepting and tolerant of psychological pain and hopelessness, which makes them less willing to talk about individual psychological pain and hopelessness, and with lack of supportive survival signals through texts, lose the belief in getting help.\nMany researchers have paid attention to the use of social media for suicide prevention and intervention, and have conducted a series of studies [40,41]. However, little attention has been paid to whether the suicidal signal was from an early suicide ideator or a later suicide attempter. Our study fills this gap, complementing and extending suicide-related research. Our results suggest that adolescents who are at a later suicide stage and have a higher suicide risk often do not express too much hopelessness directly. Therefore, we cannot judge closeness to suicide only by the level of traditional expressions of hopelessness.\n\n4.1. The Qualitative Analysis Results:\nAmong the experimental group, 36.2% of users suffered from mental illness. Depression accounted for 76.3% of all mental illness; it is the most common mental illness among the high-suicide-risk adolescents. The incidence of suicide among students whose relatives were affected by psychosis were higher than that of general residents. Suicide and mental illness were closely linked [28]. British doctor Sainsbury conducted a qualitative study on the data of 400 suicides examined by a coroner in London between 1936 and 1938, and found that 37% of the suicides reflected mental illness [29]. In a meta-analysis conducted in 2004, twenty-seven studies comprising 3275 suicides were included, of which 87.3% had been diagnosed with a mental disorder prior to their death [30]. In contrast, the research population in our study was only a high suicide risk group, so the detection rate of mental illness was relatively low. Although the research populations of the other two studies were both suicide decedents, the time interval between the two studies was large. It is very possible that there existed better methods to detect the mental illness of the suicide decedents in later studies, and it is understandable that the detection rate of mental illness has greatly increased. Furthermore, a more recent study has shown that the most common mental illness associated with suicide was depression [31]. The proportion of depression in all mental illnesses in our study also supported this conclusion.\nAnother result of the qualitative analysis showed that the later the suicide stage, the lower the proportion of adolescents. A study on residents’ suicidal ideation, suicide plans and suicide attempts in Dalian, China showed that the detection rate of suicidal ideation, suicide plan and suicide attempt decreased in that order in different groups [32]. This may because not all suicide ideations translate into suicidal actions, and most people with suicide ideation have not attempted suicide [33,34].\n\n4.2. The Mediating Effect of Hopelessness:\nOur research found that the effect of psychological pain on suicide stages was totally mediated by hopelessness, confirming our suicide mechanism path hypothesis: psychological pain → hopelessness → suicide stages. Although the first step of suicide ideation starts from psychological pain, psychological pain itself is not enough to generate suicide ideation, and hopelessness is also needed to develop suicide ideation [19]. People make decisions and take actions based in part on their emotional predictions [35,36], and hopelessness makes people at suicide risk overestimate their future emotional pain [37]. If the individual experiences constant pain in life, this may reduce the individual’s desire to live, leading to suicidal thoughts. However, pain alone is not enough to completely lose the belief in life. Only when the individual feels that the terrible situation cannot be improved, and he or she will live in pain forever, suicide will be considered.\nThus, expressions of hopelessness may be a better predictor of suicide risk than psychological pain, and we found a traceable pathway for suicide intervention. Based on the Weibo posts, we can capture whether individual released suicide signals and it is possible to track the suicide stages in the future, making suicide intervention more accurate.\n\n4.3. The Hopelessness Expressions and the Suicide Stages:\nThere was a negative predictive relationship between hopelessness expressions and suicide stages, as adolescents at the later suicide stages had less expressions of hopelessness. One possible reason is that adolescents at higher risk of suicide may use less concrete, direct words of hopelessness to express such feelings. From a linguistic point of view, Gao Yihong and Meng Ling analyzed the Weibo texts of “Zoufan” three months before suicide. They found that “Zoufan” often used decontextualized and fragmented ways to express personal depressed mood, and expressed metaphors and intentions about death frequently [38]. This euphemistic and poetic way of venting may be the commonality of high suicide risk adolescents. Another possible reason is “the Presuicidal Syndrome”, which mentioned that there exists an “ominous quiet” in the short period before suicide. This is a sign of “affective restriction”, which represents extreme repression of intolerable feelings [39]. Such “affective restriction” may be why people in the later suicide stage are less likely to express hopelessness. Moreover, adolescents at the later suicide stage may be more accepting and tolerant of psychological pain and hopelessness, which makes them less willing to talk about individual psychological pain and hopelessness, and with lack of supportive survival signals through texts, lose the belief in getting help.\nMany researchers have paid attention to the use of social media for suicide prevention and intervention, and have conducted a series of studies [40,41]. However, little attention has been paid to whether the suicidal signal was from an early suicide ideator or a later suicide attempter. Our study fills this gap, complementing and extending suicide-related research. Our results suggest that adolescents who are at a later suicide stage and have a higher suicide risk often do not express too much hopelessness directly. Therefore, we cannot judge closeness to suicide only by the level of traditional expressions of hopelessness.\n\n5. Limitations and Future Studies:\nAlthough this study could divide the suicide stages of adolescents through labeling Weibo private message data, it cannot effectively determine the gender of the user. Previous studies had shown that suicidal behaviors were also affected by gender, and that the incidence of suicide attempts in adolescent females was higher than that of males [42]. Compared with males, females were more psychologically sensitive and vulnerable [43], and were more likely to express their pain through suicidal behaviors [44]. The expressions of hopelessness and psychological pain as predictors of suicide may differ by gender. There were only 105 adolescent users with high suicide risk in our study. Caution is thus necessary when generalizing the conclusions to other case.\nOur findings provided a traceable pathway for suicide intervention, finding that the effect of psychological pain on suicide stages was totally mediated by hopelessness, and that adolescents in the later suicide stages had less expressions of hopelessness in the traditional sense. However, the specific language characteristics of each suicide stage are still unknown. If we want to accomplish the precise prediction of suicide stages through the expressions on Weibo posts and provide more accurate and effective information for suicide intervention, we need to further explore the specific language expression characteristics of high suicide risk adolescents in each suicide stage, especially how to identify abstract metaphors that may constitute a suicidal signal. It is thus possible that we can provide more accurate suicide intervention for adolescents at different suicide stages in the future.\nWe mainly focus on the expression on social media of two psychological indicators (psychological pain, hopelessness). However, there are many factors which affect suicide, and we can research suicide intervention from multiple perspectives, not only examining some negative indicators, but also exploring some positive indicators in future research. For example, previous studies had shown that social support [45], psychological resilience [46], and self-compassion [47] were protective factors for adolescent suicide.\n\n6. Conclusions:\nThe qualitative findings suggested that mental illness in high-suicide-risk adolescents was strongly associated with suicide risk, and that depression was the most common mental illness associated with suicide. Another finding was that the later the suicide stage, the smaller was the proportion of high-suicide-risk adolescents.\nThe results of the mediation effect analysis verified the suicide mechanism path: psychological pain → hopelessness → suicide stages, indicating that psychological pain mainly affected suicide stages through hopelessness, and hopelessness may be a better predictor of suicide risk for high-suicide-risk adolescents.\nNotably, although adolescents in the later suicide stages were at greater suicide risk, they had less expression of hopelessness in the traditional sense.\n\n"},"whole_article_abstract":{"kind":"string","value":"Background:\nAdolescent suicide can have serious consequences for individuals, families and society, so we should pay attention to it. As social media becomes a platform for adolescents to share their daily lives and express their emotions, online identification and intervention of adolescent suicide problems become possible. In order to find the suicide mechanism path of high-suicide-risk adolescents, we explore the factors that influence is, especially the relations between psychological pain, hopelessness and suicide stages.\n\nMethods:\nWe identified high-suicide-risk adolescents through machine learning model identification and manual identification, and used the Weibo text analysis method to explore the suicide mechanism path of high-suicide-risk adolescents.\n\nResults:\nQualitative analysis showed that 36.2% of high-suicide-risk adolescents suffered from mental illness, and depression accounted for 76.3% of all mental illnesses. The mediating effect analysis showed that hopelessness played a complete mediating role between psychological pain and suicide stages. In addition, hopelessness was significantly negatively correlated with suicide stages.\n\nConclusions:\nmental illness (especially depression) in high-suicide-risk adolescents is closely related to suicide stages, the later the suicide stage, the higher the diagnosis rate of mental illness. The suicide mechanism path in high-suicide-risk adolescents is: psychological pain→ hopelessness → suicide stages, indicating that psychological pain mainly affects suicide risk through hopelessness. Adolescents who are later in the suicide stages have fewer expressions of hopelessness in the traditional sense."},"background_conclusion_text":{"kind":"string","value":"1. Introduction:\nAdolescent suicide has always been a global public health concern to us. Suicide is the fourth leading cause of death globally among people aged 15–19 [1], and in China, it is the second leading cause of death for people aged 20–34 [2]. The consequences of adolescent suicide are the most serious among many mental health problems. It means disability or even a loss of life for individuals, heavy blows and severe trauma for families, and huge expenditures on the health, welfare and justice sectors for society. The study of suicide risk factors and the exploration of suicide mechanisms in high-suicide-risk adolescents are of great significance for the prevention of suicide.\nWith the development of technology, social media has become a platform for people to record their daily life and express their emotions. In China, Sina Weibo as a typical product of the big data era has extensive influence. Sina Weibo is China’s most popular social media platform, just like Twitter in America. Although there are some short video platforms (e.g., Douyin) and forums (e.g., Douban) that are also popular in China, it is only on Sina Weibo that users can share information in real-time and communicate interactively in the forms of text, pictures, videos and other multimedia. According to a report released by Sina Weibo, Weibo users show a younger trend, of which the post-90s and post-00s youth groups account for nearly 80%, and the daily active volume can reach 224 million [3].\nThe user’s emotional expression on social platforms has higher immediacy and less masking. Identifying the user’s psychological state through their expression on social media will be more convenient and quicker, and the obtained data will be more real. Therefore, we use the Weibo text analysis method to explore the suicide mechanism path of high-suicide-risk adolescents. Text analysis refers to the representation of text and the selection of feature items, which is a basic problem in text mining and information retrieval. Text analysis means that we can quantify the feature words extracted from text to represent text information. Therefore, we can extract the psychological indicators we want from the Weibo text by constructing a dictionary of psychological indicators.\nSuicide represents a series of consecutive stages, including suicide ideation, suicide plan, and suicide attempt [4,5]. Suicide ideation, as a high-risk factor for suicide, has a significant predictive effect [6,7]. Approximately 1/3 of suicide ideators ultimately attempt suicide, and the number of suicide attempters is several times higher than those who really die by suicide, so suicide attempt is a greater risk factor for suicide than suicidal ideation [1,8,9]. The later the suicide stage, the closer to suicide death, and the greater risk of suicide. Therefore, in order to quantify the degree of suicide risk and provide accurate information for suicide interventions, we decided to divide suicidal behavior into different stages. At the same time, considering that depressed mood is an important signal of suicide, we finally divided suicide into 4 stages, which the suicide risk gradually increases: depressed mood, suicide ideation, suicide plan and suicide attempt, and defined adolescents at different suicide stages as high-suicide-risk adolescents.\nA meta-analysis of the causes of suicide among adolescents found that suicide is a public health problem involving complex factors, such as biology, psychology, family, society and culture [8]. Among the psychological factors, psychological pain and hopelessness are important risk factors featuring strongly in suicide research [9,10,11]. Although psychological pain and hopelessness are often mentioned together as having an impact on suicidal behavior, as two most common motivations for suicide attempts [12], it is unclear how the two proximal factors of suicide affect the suicide stages.\nPsychological pain is defined as the “introspective experience of negative emotions such as fear, despair, grief, shame, guilt, blocked love, loneliness and loss” [13,14,15,16]. When Shneidman first hypothesized the relationship between psychological pain and suicide, he pointed out that other factors can only affect suicidal behavior through psychological pain, without which suicide would not occur [13]. Evidence suggests that psychological pain plays a central role in suicidal behavior [10,17,18]. Psychological pain is not enough to develop suicide ideation, it also requires hopelessness [19]. Hopelessness is defined as negative expectations or pessimism about oneself or the future [20]. Previous studies had showed that hopelessness was also an important risk factor of suicide [21,22,23].\nTo explore the relationship between psychological pain, hopelessness and suicide stages for high-suicide-risk adolescents, we established a hypothesis for the suicide mechanism path: psychological pain → hopelessness → suicide stages, with hopelessness as the mediating variable. In addition, there is a high correlation between mental illness and suicidal behaviors [24,25], and in order to better clarify the effect of psychological pain and hopelessness on suicide stages, a mediating effect model of our study is proposed with mental illness as a control variable (Figure 1). We used the more ecological Weibo text analysis method in this study, and hoped to further deepen the research on adolescent suicide and to find a new theoretical basis for future suicide interventions drawing on online data.\n\n6. Conclusions:\nThe qualitative findings suggested that mental illness in high-suicide-risk adolescents was strongly associated with suicide risk, and that depression was the most common mental illness associated with suicide. Another finding was that the later the suicide stage, the smaller was the proportion of high-suicide-risk adolescents.\nThe results of the mediation effect analysis verified the suicide mechanism path: psychological pain → hopelessness → suicide stages, indicating that psychological pain mainly affected suicide stages through hopelessness, and hopelessness may be a better predictor of suicide risk for high-suicide-risk adolescents.\nNotably, although adolescents in the later suicide stages were at greater suicide risk, they had less expression of hopelessness in the traditional sense."},"background_conclusion_abstract":{"kind":"string","value":"Background:\nAdolescent suicide can have serious consequences for individuals, families and society, so we should pay attention to it. As social media becomes a platform for adolescents to share their daily lives and express their emotions, online identification and intervention of adolescent suicide problems become possible. In order to find the suicide mechanism path of high-suicide-risk adolescents, we explore the factors that influence is, especially the relations between psychological pain, hopelessness and suicide stages.\n\nMethods:\nWe identified high-suicide-risk adolescents through machine learning model identification and manual identification, and used the Weibo text analysis method to explore the suicide mechanism path of high-suicide-risk adolescents.\n\nResults:\nQualitative analysis showed that 36.2% of high-suicide-risk adolescents suffered from mental illness, and depression accounted for 76.3% of all mental illnesses. The mediating effect analysis showed that hopelessness played a complete mediating role between psychological pain and suicide stages. In addition, hopelessness was significantly negatively correlated with suicide stages.\n\nConclusions:\nmental illness (especially depression) in high-suicide-risk adolescents is closely related to suicide stages, the later the suicide stage, the higher the diagnosis rate of mental illness. The suicide mechanism path in high-suicide-risk adolescents is: psychological pain→ hopelessness → suicide stages, indicating that psychological pain mainly affects suicide risk through hopelessness. Adolescents who are later in the suicide stages have fewer expressions of hopelessness in the traditional sense."},"whole_article_text_length":{"kind":"number","value":13205,"string":"13,205"},"whole_article_abstract_length":{"kind":"number","value":283,"string":"283"},"other_sections_lengths":{"kind":"list like","value":[4224,249,1521,557,195,227,27,80,175,306,366,231,356,367],"string":"[\n 4224,\n 249,\n 1521,\n 557,\n 195,\n 227,\n 27,\n 80,\n 175,\n 306,\n 366,\n 231,\n 356,\n 367\n]"},"num_sections":{"kind":"number","value":19,"string":"19"},"most_frequent_words":{"kind":"list like","value":["suicide","mental","illness","mental illness","stages","weibo","psychological","hopelessness","pain","suicide stages"],"string":"[\n \"suicide\",\n \"mental\",\n \"illness\",\n \"mental illness\",\n \"stages\",\n \"weibo\",\n \"psychological\",\n \"hopelessness\",\n \"pain\",\n \"suicide stages\"\n]"},"keybert_topics":{"kind":"list like","value":["china sina weibo","frequency chinese suicide","media suicide prevention","adolescent suicide global","adolescents weibo texts"],"string":"[\n \"china sina weibo\",\n \"frequency chinese suicide\",\n \"media suicide prevention\",\n \"adolescent suicide global\",\n \"adolescents weibo texts\"\n]"},"annotated_base_background_abstract_prompt":{"kind":"string","value":""},"annotated_base_methods_abstract_prompt":{"kind":"null"},"annotated_base_results_abstract_prompt":{"kind":"string","value":""},"annotated_base_conclusions_abstract_prompt":{"kind":"string","value":""},"annotated_base_whole_article_abstract_prompt":{"kind":"string","value":""},"annotated_base_background_conclusion_abstract_prompt":{"kind":"string","value":""},"annotated_keywords_background_abstract_prompt":{"kind":"string","value":"[CONTENT] high-suicide-risk adolescents | psychological pain | hopelessness | suicide stages | Weibo text analysis [SUMMARY]"},"annotated_keywords_methods_abstract_prompt":{"kind":"null"},"annotated_keywords_results_abstract_prompt":{"kind":"string","value":"[CONTENT] high-suicide-risk adolescents | psychological pain | hopelessness | suicide stages | Weibo text analysis [SUMMARY]"},"annotated_keywords_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] high-suicide-risk adolescents | psychological pain | hopelessness | suicide stages | Weibo text analysis [SUMMARY]"},"annotated_keywords_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] high-suicide-risk adolescents | psychological pain | hopelessness | suicide stages | Weibo text analysis [SUMMARY]"},"annotated_keywords_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] high-suicide-risk adolescents | psychological pain | hopelessness | suicide stages | Weibo text analysis [SUMMARY]"},"annotated_mesh_background_abstract_prompt":{"kind":"string","value":"[CONTENT] Adolescent | Emotions | Humans | Pain | Risk Factors | Self Concept | Social Media | Suicide [SUMMARY]"},"annotated_mesh_methods_abstract_prompt":{"kind":"null"},"annotated_mesh_results_abstract_prompt":{"kind":"string","value":"[CONTENT] Adolescent | Emotions | Humans | Pain | Risk Factors | Self Concept | Social Media | Suicide [SUMMARY]"},"annotated_mesh_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] Adolescent | Emotions | Humans | Pain | Risk Factors | Self Concept | Social Media | Suicide [SUMMARY]"},"annotated_mesh_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] Adolescent | Emotions | Humans | Pain | Risk Factors | Self Concept | Social Media | Suicide [SUMMARY]"},"annotated_mesh_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] Adolescent | Emotions | Humans | Pain | Risk Factors | Self Concept | Social Media | Suicide [SUMMARY]"},"annotated_keybert_background_abstract_prompt":{"kind":"string","value":"[CONTENT] china sina weibo | frequency chinese suicide | media suicide prevention | adolescent suicide global | adolescents weibo texts [SUMMARY]"},"annotated_keybert_methods_abstract_prompt":{"kind":"null"},"annotated_keybert_results_abstract_prompt":{"kind":"string","value":"[CONTENT] china sina weibo | frequency chinese suicide | media suicide prevention | adolescent suicide global | adolescents weibo texts [SUMMARY]"},"annotated_keybert_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] china sina weibo | frequency chinese suicide | media suicide prevention | adolescent suicide global | adolescents weibo texts [SUMMARY]"},"annotated_keybert_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] china sina weibo | frequency chinese suicide | media suicide prevention | adolescent suicide global | adolescents weibo texts [SUMMARY]"},"annotated_keybert_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] china sina weibo | frequency chinese suicide | media suicide prevention | adolescent suicide global | adolescents weibo texts [SUMMARY]"},"annotated_most_frequent_background_abstract_prompt":{"kind":"string","value":"[CONTENT] suicide | mental | illness | mental illness | stages | weibo | psychological | hopelessness | pain | suicide stages [SUMMARY]"},"annotated_most_frequent_methods_abstract_prompt":{"kind":"null"},"annotated_most_frequent_results_abstract_prompt":{"kind":"string","value":"[CONTENT] suicide | mental | illness | mental illness | stages | weibo | psychological | hopelessness | pain | suicide stages [SUMMARY]"},"annotated_most_frequent_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] suicide | mental | illness | mental illness | stages | weibo | psychological | hopelessness | pain | suicide stages [SUMMARY]"},"annotated_most_frequent_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] suicide | mental | illness | mental illness | stages | weibo | psychological | hopelessness | pain | suicide stages [SUMMARY]"},"annotated_most_frequent_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] suicide | mental | illness | mental illness | stages | weibo | psychological | hopelessness | pain | suicide stages [SUMMARY]"},"annotated_tf_idf_background_abstract_prompt":{"kind":"string","value":"[CONTENT] suicide | text | psychological | risk | factors | psychological pain | suicidal behavior | behavior | text analysis | health [SUMMARY]"},"annotated_tf_idf_methods_abstract_prompt":{"kind":"null"},"annotated_tf_idf_results_abstract_prompt":{"kind":"string","value":"[CONTENT] suicide | indicating | significant | mental | 05 | stage suicide | table | stage | effect | illness [SUMMARY]"},"annotated_tf_idf_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] suicide | suicide risk | risk | adolescents | hopelessness | associated | associated suicide | suicide risk adolescents | risk adolescents | high suicide [SUMMARY]"},"annotated_tf_idf_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] suicide | mental | illness | mental illness | hopelessness | stages | weibo | pain | psychological | suicide stages [SUMMARY]"},"annotated_tf_idf_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] suicide | mental | illness | mental illness | hopelessness | stages | weibo | pain | psychological | suicide stages [SUMMARY]"},"annotated_entity_plan_background_abstract_prompt":{"kind":"string","value":"[CONTENT] ||| ||| [SUMMARY]"},"annotated_entity_plan_methods_abstract_prompt":{"kind":"null"},"annotated_entity_plan_results_abstract_prompt":{"kind":"string","value":"[CONTENT] 36.2% | 76.3% ||| ||| [SUMMARY]"},"annotated_entity_plan_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] ||| pain→ ||| [SUMMARY]"},"annotated_entity_plan_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] ||| ||| ||| Weibo ||| ||| 36.2% | 76.3% ||| ||| ||| ||| pain→ ||| [SUMMARY]"},"annotated_entity_plan_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] ||| ||| ||| Weibo ||| ||| 36.2% | 76.3% ||| ||| ||| ||| pain→ ||| [SUMMARY]"}}},{"rowIdx":262,"cells":{"title":{"kind":"string","value":"A pilot randomized controlled trial of dialectical behavior therapy (DBT) for reducing craving and achieving cessation in patients with marijuana use disorder: feasibility, acceptability, and appropriateness."},"pmid":{"kind":"string","value":"33844901"},"background_abstract":{"kind":"string","value":"Thailand Registry of Clinical Trials, TCTR20200319007."},"background_abstract_label":{"kind":"string","value":"CLINICAL TRIAL REGISTRATION"},"methods_abstract":{"kind":"string","value":"Sixty-one patients with marijuana use disorder diagnoses were randomly assigned to a DBT group or a control group (psycho-education). Patients completed measures at pre-intervention, post-intervention, and at two-month follow-up. The Marijuana Craving Questionnaire (MCQ) and marijuana urine test kits were used to assess craving and abstinence respectively."},"methods_abstract_label":{"kind":"string","value":"METHODS"},"results_abstract":{"kind":"string","value":"The feasibility of DBT was significantly higher than control group feasibility. In the DBT 29/30 participants completed all sessions (96% retention) and 24/31 control group participants completed all sessions (77% retention) (χ2 = 4.95, p = 0.02). Moreover, 29/30 (96%) participants in the DBT group completed the two-month follow-up and 20/31 (64.5%) control group members completed the two-month follow-up (χ2 = 9.97, p = 0.002). The results showed that patients in the DBT group had significantly higher intervention acceptability rates (16.57 vs. 9.6) than those in the control group. This pattern was repeated for appropriateness rates (p < 0.05). The overall results for craving showed that there was no significant difference between the groups (F = 3.52, p > 0.05), although DBT showed a significant reduction in the \"emotionality\" subscale compared to the control group (F = 19.94, p < 0.05). To analyze cessation rates, DBT was compared to the control group at the posttest (46% vs. 16%) and follow-up (40% vs. 9.5%) and the results confirmed higher effectiveness in the DBT group for cessation (p < 0.05). Furthermore, among those who had lapsed, participants in the DBT group had fewer consumption days than those in the control group (p < 0.05)."},"results_abstract_label":{"kind":"string","value":"RESULTS"},"conclusions_abstract":{"kind":"string","value":"DBT showed feasibility, acceptability, and promising efficacy in terms of the marijuana cessation rate."},"conclusions_abstract_label":{"kind":"string","value":"CONCLUSIONS"},"mesh_descriptor_names":{"kind":"list like","value":["Behavior Therapy","Craving","Dialectical Behavior Therapy","Feasibility Studies","Humans","Marijuana Use","Pilot Projects","Treatment Outcome"],"string":"[\n \"Behavior Therapy\",\n \"Craving\",\n \"Dialectical Behavior Therapy\",\n \"Feasibility Studies\",\n \"Humans\",\n \"Marijuana Use\",\n \"Pilot Projects\",\n \"Treatment Outcome\"\n]"},"pmcid":{"kind":"string","value":"8835386"},"background_title":{"kind":"string","value":"Introduction"},"background_text":{"kind":"string","value":"Marijuana is the most prevalent substance among those reported to be a significant problem among people seeking treatment for substance abuse. 1 According to WHO reports, more than 140 million people consume marijuana every year. 2 With regard to Iran, recent evidence shows that more than 5% of people consume marijuana every year, predominantly young males. However, in view of the harsh marijuana prohibition policy of the Iranian government, most clinicians estimate that these rates have been hugely underestimated. 3 Marijuana, as an illegal drug, is associated with significant physical, psychological, and social consequences. 4 Studies have shown that regular and heavy marijuana use patterns correlate with increased risk of mood disorders, anxiety, and psychotic episodes and although causality has not been demonstrated, these patterns can increase the course of mental health problems. 5 Also, several medical problems such as respiratory system deficits, stroke, myocardial infarction, and digestive tract cancers are associated with marijuana use patterns, especially among those with marijuana use disorder (MUD). 6 , 7 Approximately one in three marijuana users meet the criteria for MUD based on the DSM-5, and this proportion is rising. 8 One of the most important psychological problems in substance use disorder treatment is craving. Craving is a factor identified as the root cause of relapses and treatment failures. 9 , 10 MUD patients report visual, tactile, and olfactory cues related to craving and compulsivity sensations. 11 Based on these results, clinicians have tried to treat patients with marijuana use disorder.\nTo date, the Food and Drug Administration (FDA) in the United States has not approved any psycho-pharmacotherapy for MUD, and therefore psycho-social interventions have received particular attention. 12 The most widely used psychological treatment in the substance use disorder (SUD) context is cognitive-behavioral therapy (CBT). 13 Results showed that CBT is somewhat effective for SUD, but that most patients with MUD do not achieve cessation and are not motivated to continue skills training during follow-up. Relapse rates therefore remain a considerable limitation of treatment. 10 , 13 One of the main reasons for this low success rate lies in the limitations of CBT. First, CBT protocols do not focus on comorbid problems, whereas most patients with SUD have at least one psychiatric or psychological problem. Secondly, cognitive restructuring may not be useful for all SUD patients. Some patients may be unable to restructure their dysfunctional cognition and core beliefs despite receiving CBT. 10 , 12 , 13 Furthermore, emotion regulation deficits are strongly associated with increased addictive behaviors such as SUD. With emotion regulation, people adjust their emotional experiences related to distressing and unpleasant events. Emotion regulation is essential for successful coping with environmental demands and personal welfare. 14 On the behavioral level, studies have found that marijuana craving cues are strongly associated with deficits in regulation of negative affect and emotions. 14 , 15 Also, on the neural level, during reappraisal of negative stimuli, patients with MUD and regular users have shown altered neural activity and functional connectivity. Moreover, marijuana use is related to dysfunctions in the amygdala and in amygdala-dorsolateral prefrontal cortex (DLPFC) coupling activity. 15 Together, these findings demonstrate that emotion-based psychotherapy must manage comorbid problems and eliminate the limitations of CBT.\nOne of the psychotherapies in the third wave behavioral therapy cluster is dialectical behavior therapy (DBT). DBT has been described as intervention in emotion regulation deficits by focusing on dangerous impulses in borderline personality disorder and substance use disorder. The goals of DBT include improving and regulating emotions as a primary mechanism of change. DBT is a trans-diagnostic treatment and suitable for comorbid problems. DBT trains skills including distress tolerance, interpersonal effectiveness, emotion regulation, and mindfulness. Overall, in the context of SUD, DBT teaches emotion regulation skills to decrease engagement in pathological emotion regulation strategies. It also intervenes in low quality of life situations, reduces drug-seeking behavior, and helps patients function adaptively by accepting unpleasant emotions such as craving. 9 , 10 , 14\nResearch literature shows the efficacy and effectiveness of DBT in various comorbid problems and diseases such as suicide, 16 forensic psychiatric patients, 17 and irritable bowel syndrome. 18 Nevertheless, studies have reported contradictory results for the effectiveness of implementing DBT in various SUD populations. 19 , 20 Furthermore, the literature has recommended using larger samples, clearer instruments to measure outcome variables, and specific and integrated protocols. 21\nAdditionally, according to our investigations, no DBT randomized clinical trials have been conducted that investigated cessation in MUD patients (with or without comorbid problems). A DBT intervention aimed at increasing the cessation rate and reducing craving among MUD patients was developed for this study.\nThis pilot trial investigated the feasibility and preliminary efficacy of DBT relative to a psycho-education intervention that was controlled for time duration and attention. Feasibility was assessed via satisfaction and session completion rates. Preliminary efficacy was evaluated via the impact of DBT on cessation rate and reduction of consumption rates, compared to the psycho-education intervention. Although craving and acceptance of craving are not the primary goals of DBT, they were also compared across the two interventions."},"methods_title":{"kind":"string","value":"Methods"},"methods_text":{"kind":"string","value":" Trial design This study was designed as a controlled randomized clinical trial, including pretest, post-test, and two-month follow-up phases.\nThis study was designed as a controlled randomized clinical trial, including pretest, post-test, and two-month follow-up phases.\n Sample size Since the sampling method comprises snowball sampling and strict eligibility criteria were applied, on the basis of data from similar studies 10 it was determined that at least 20 participants were needed in each group. However, in view of the predicted retention rates, we selected 30 patients for each group.\nSince the sampling method comprises snowball sampling and strict eligibility criteria were applied, on the basis of data from similar studies 10 it was determined that at least 20 participants were needed in each group. However, in view of the predicted retention rates, we selected 30 patients for each group.\n Selection criteria The inclusion criteria were as follows: 1) diagnosis of marijuana use disorder; 2) age 18 years or over; 3) no current or past history of major psychiatric disorders; 4) no other concurrent SUD treatment; and 5) willingness to attend intervention sessions, complete surveys, and take tests (questionnaires and urine test kits).\nExclusion criteria were as follows: 1) unwillingness to participate; 2) not participating in intervention sessions for more than two weeks; 3) starting secondary psychotherapy; and 4) consuming methamphetamine, amphetamine, cannabis, methadone, benzodiazepines, or morphine during the research stages.\nThe inclusion criteria were as follows: 1) diagnosis of marijuana use disorder; 2) age 18 years or over; 3) no current or past history of major psychiatric disorders; 4) no other concurrent SUD treatment; and 5) willingness to attend intervention sessions, complete surveys, and take tests (questionnaires and urine test kits).\nExclusion criteria were as follows: 1) unwillingness to participate; 2) not participating in intervention sessions for more than two weeks; 3) starting secondary psychotherapy; and 4) consuming methamphetamine, amphetamine, cannabis, methadone, benzodiazepines, or morphine during the research stages.\n Participants, procedures, and randomization Since there are no cannabis use disorder treatment centers in Iran, there is no specific place to select patients. Furthermore, patients at drug treatment centers are referred for treatment of other substance use disorders and comorbidity of drug use is one of the exclusion criteria for this study, since it could lead to misleading results. Therefore, the relatives and acquaintances of those who had been referred to the drug treatment center were interviewed. From November 1, 2019, to November 5, 2019, 15 relatives and family members of drug users referred to drug treatment centers were diagnosed with MUD at this stage. Then, using snowball sampling, after 15 days of investigation, a total of 83 patients were diagnosed with MUD. Seventy-five of the 83 MUD patients who were approached consented, eight declined to participate, and 14 were ineligible. The primary reasons for declining were anxiety about addiction stigma and time constraints. Most of the ineligible patients had multiple illicit use disorders, so they did not meet the study criteria. Therefore, 61 patients completed the baseline assessment and were included in the current analyses. These patients were randomly assigned to each group using a random number table. The interventions were implemented from December 1, 2019, to March 20, 2020. The follow-up phase started on March 21, 2020, and ended on May 20, 2020, (at two months’ follow-up). In order to test for exclusion criteria before each session, a six-drug test kit for methamphetamine, amphetamine, cannabis, methadone, benzodiazepines, and morphine was administered to individuals using urine samples.\nSince there are no cannabis use disorder treatment centers in Iran, there is no specific place to select patients. Furthermore, patients at drug treatment centers are referred for treatment of other substance use disorders and comorbidity of drug use is one of the exclusion criteria for this study, since it could lead to misleading results. Therefore, the relatives and acquaintances of those who had been referred to the drug treatment center were interviewed. From November 1, 2019, to November 5, 2019, 15 relatives and family members of drug users referred to drug treatment centers were diagnosed with MUD at this stage. Then, using snowball sampling, after 15 days of investigation, a total of 83 patients were diagnosed with MUD. Seventy-five of the 83 MUD patients who were approached consented, eight declined to participate, and 14 were ineligible. The primary reasons for declining were anxiety about addiction stigma and time constraints. Most of the ineligible patients had multiple illicit use disorders, so they did not meet the study criteria. Therefore, 61 patients completed the baseline assessment and were included in the current analyses. These patients were randomly assigned to each group using a random number table. The interventions were implemented from December 1, 2019, to March 20, 2020. The follow-up phase started on March 21, 2020, and ended on May 20, 2020, (at two months’ follow-up). In order to test for exclusion criteria before each session, a six-drug test kit for methamphetamine, amphetamine, cannabis, methadone, benzodiazepines, and morphine was administered to individuals using urine samples.\n Blinding Both groups were blind to the existence of another group in the study. However, patients were informed about participating in research but not about another group. One day after the end of treatment, the post-test was carried out by mental health technicians with a master’s degree in psychology.\nBoth groups were blind to the existence of another group in the study. However, patients were informed about participating in research but not about another group. One day after the end of treatment, the post-test was carried out by mental health technicians with a master’s degree in psychology.\n Outcome measures Abstinence A marijuana urine test kit prepared by Kian Teb Company (officially licensed by the National Medical Device Directorate IR. IRAN) was used to identify abstainers.\nA marijuana urine test kit prepared by Kian Teb Company (officially licensed by the National Medical Device Directorate IR. IRAN) was used to identify abstainers.\n Marijuana smoking A self-report scale was designed for patients who had lapsed during the post-test follow-up. On this scale, patients indicate the number of days of consumption over 30-day periods. The first thirty days after the last intervention session was considered the post-test smoking period and the second-month follow-up was considered as the follow-up marijuana use period.\nA self-report scale was designed for patients who had lapsed during the post-test follow-up. On this scale, patients indicate the number of days of consumption over 30-day periods. The first thirty days after the last intervention session was considered the post-test smoking period and the second-month follow-up was considered as the follow-up marijuana use period.\n Craving The Marijuana Craving Questionnaire (MCQ) short-form is a 12-item self-report questionnaire with ten items for subjective assessment of cannabis craving. The scale covers 4 factors: compulsivity, emotionality, expectancy, and purposefulness. According to how patients were thinking or feeling ‘’right now,’’ they placed checkmarks on the questionnaire to endorse responses ranging from 1 or strongly disagree to 7 or strongly agree. Results showed that this questionnaire’s internal consistency is adequate (α = 0.90). The measure was administered following a 12-hour deprivation period. The typical onset of marijuana craving and withdrawal symptoms is observed within approximately one day of cessation and so the current paper’s questionnaire scores can be conceptualized as an index of the propensity to experience marijuana craving following deprivation. 22 In Iranian MUD patients, the MCQ had internal consistency of α = 0.87. Details of the MCQ’s psychometrics properties will be published as a separate study as soon as possible.\nThe Marijuana Craving Questionnaire (MCQ) short-form is a 12-item self-report questionnaire with ten items for subjective assessment of cannabis craving. The scale covers 4 factors: compulsivity, emotionality, expectancy, and purposefulness. According to how patients were thinking or feeling ‘’right now,’’ they placed checkmarks on the questionnaire to endorse responses ranging from 1 or strongly disagree to 7 or strongly agree. Results showed that this questionnaire’s internal consistency is adequate (α = 0.90). The measure was administered following a 12-hour deprivation period. The typical onset of marijuana craving and withdrawal symptoms is observed within approximately one day of cessation and so the current paper’s questionnaire scores can be conceptualized as an index of the propensity to experience marijuana craving following deprivation. 22 In Iranian MUD patients, the MCQ had internal consistency of α = 0.87. Details of the MCQ’s psychometrics properties will be published as a separate study as soon as possible.\n Acceptability The Acceptability of Intervention Measure (AIM) was employed to measure the acceptability of interventions. AIM response items are measured on a 5-point Likert scale (from Completely Disagree with 1 point to Completely Agree with 5 points). The mean of points scored for each item is taken as the final score. This questionnaire developed by Weiner et al., and they reported Cronbach’s ɑ = 85 for internal consistency. 23\nThe Acceptability of Intervention Measure (AIM) was employed to measure the acceptability of interventions. AIM response items are measured on a 5-point Likert scale (from Completely Disagree with 1 point to Completely Agree with 5 points). The mean of points scored for each item is taken as the final score. This questionnaire developed by Weiner et al., and they reported Cronbach’s ɑ = 85 for internal consistency. 23\n Appropriateness The Intervention Appropriateness Measure (IAM) was used for Appropriateness. The IAM consist of a four-item scale that measures perceived intervention appropriateness. Items are measured on a five-point Likert scale (Completely Disagree to Completely Agree), and the mean of points scored for each item is taken as the final score. Higher scores mean that the participant feels this intervention is more appropriate for him/her. For this tool, Cronbach’s ɑ = 0.91 and all Factor Loadings are reported as higher than 0.8. 23\nThe Intervention Appropriateness Measure (IAM) was used for Appropriateness. The IAM consist of a four-item scale that measures perceived intervention appropriateness. Items are measured on a five-point Likert scale (Completely Disagree to Completely Agree), and the mean of points scored for each item is taken as the final score. Higher scores mean that the participant feels this intervention is more appropriate for him/her. For this tool, Cronbach’s ɑ = 0.91 and all Factor Loadings are reported as higher than 0.8. 23\n Abstinence A marijuana urine test kit prepared by Kian Teb Company (officially licensed by the National Medical Device Directorate IR. IRAN) was used to identify abstainers.\nA marijuana urine test kit prepared by Kian Teb Company (officially licensed by the National Medical Device Directorate IR. IRAN) was used to identify abstainers.\n Marijuana smoking A self-report scale was designed for patients who had lapsed during the post-test follow-up. On this scale, patients indicate the number of days of consumption over 30-day periods. The first thirty days after the last intervention session was considered the post-test smoking period and the second-month follow-up was considered as the follow-up marijuana use period.\nA self-report scale was designed for patients who had lapsed during the post-test follow-up. On this scale, patients indicate the number of days of consumption over 30-day periods. The first thirty days after the last intervention session was considered the post-test smoking period and the second-month follow-up was considered as the follow-up marijuana use period.\n Craving The Marijuana Craving Questionnaire (MCQ) short-form is a 12-item self-report questionnaire with ten items for subjective assessment of cannabis craving. The scale covers 4 factors: compulsivity, emotionality, expectancy, and purposefulness. According to how patients were thinking or feeling ‘’right now,’’ they placed checkmarks on the questionnaire to endorse responses ranging from 1 or strongly disagree to 7 or strongly agree. Results showed that this questionnaire’s internal consistency is adequate (α = 0.90). The measure was administered following a 12-hour deprivation period. The typical onset of marijuana craving and withdrawal symptoms is observed within approximately one day of cessation and so the current paper’s questionnaire scores can be conceptualized as an index of the propensity to experience marijuana craving following deprivation. 22 In Iranian MUD patients, the MCQ had internal consistency of α = 0.87. Details of the MCQ’s psychometrics properties will be published as a separate study as soon as possible.\nThe Marijuana Craving Questionnaire (MCQ) short-form is a 12-item self-report questionnaire with ten items for subjective assessment of cannabis craving. The scale covers 4 factors: compulsivity, emotionality, expectancy, and purposefulness. According to how patients were thinking or feeling ‘’right now,’’ they placed checkmarks on the questionnaire to endorse responses ranging from 1 or strongly disagree to 7 or strongly agree. Results showed that this questionnaire’s internal consistency is adequate (α = 0.90). The measure was administered following a 12-hour deprivation period. The typical onset of marijuana craving and withdrawal symptoms is observed within approximately one day of cessation and so the current paper’s questionnaire scores can be conceptualized as an index of the propensity to experience marijuana craving following deprivation. 22 In Iranian MUD patients, the MCQ had internal consistency of α = 0.87. Details of the MCQ’s psychometrics properties will be published as a separate study as soon as possible.\n Acceptability The Acceptability of Intervention Measure (AIM) was employed to measure the acceptability of interventions. AIM response items are measured on a 5-point Likert scale (from Completely Disagree with 1 point to Completely Agree with 5 points). The mean of points scored for each item is taken as the final score. This questionnaire developed by Weiner et al., and they reported Cronbach’s ɑ = 85 for internal consistency. 23\nThe Acceptability of Intervention Measure (AIM) was employed to measure the acceptability of interventions. AIM response items are measured on a 5-point Likert scale (from Completely Disagree with 1 point to Completely Agree with 5 points). The mean of points scored for each item is taken as the final score. This questionnaire developed by Weiner et al., and they reported Cronbach’s ɑ = 85 for internal consistency. 23\n Appropriateness The Intervention Appropriateness Measure (IAM) was used for Appropriateness. The IAM consist of a four-item scale that measures perceived intervention appropriateness. Items are measured on a five-point Likert scale (Completely Disagree to Completely Agree), and the mean of points scored for each item is taken as the final score. Higher scores mean that the participant feels this intervention is more appropriate for him/her. For this tool, Cronbach’s ɑ = 0.91 and all Factor Loadings are reported as higher than 0.8. 23\nThe Intervention Appropriateness Measure (IAM) was used for Appropriateness. The IAM consist of a four-item scale that measures perceived intervention appropriateness. Items are measured on a five-point Likert scale (Completely Disagree to Completely Agree), and the mean of points scored for each item is taken as the final score. Higher scores mean that the participant feels this intervention is more appropriate for him/her. For this tool, Cronbach’s ɑ = 0.91 and all Factor Loadings are reported as higher than 0.8. 23"},"results_title":{"kind":"string","value":"Results"},"results_text":{"kind":"string","value":" Feasibility In the psycho-education group, 24/31 participants completed all sessions, compared to 29/30 members of the DBT group (retention rates: 77% in the control group vs. 96% in the DBT group). Additionally, 96% (29/30) of the DBT group members completed the two-month follow-up, whereas 64.5% (20/31) of the control group completed follow-up ( Figure 1 ). The chi-square test was applied, showing associations between group and retention, with χ2= 4.95, p = 0.02 for post-treatment and, χ2= 9.97, p = 0.002 for the follow-up phase. Consequently, DBT retention rates were significantly higher than psycho-education retention rates at post-treatment and follow-up.\n\nFigure 1Consort diagram.\n\nIn the psycho-education group, 24/31 participants completed all sessions, compared to 29/30 members of the DBT group (retention rates: 77% in the control group vs. 96% in the DBT group). Additionally, 96% (29/30) of the DBT group members completed the two-month follow-up, whereas 64.5% (20/31) of the control group completed follow-up ( Figure 1 ). The chi-square test was applied, showing associations between group and retention, with χ2= 4.95, p = 0.02 for post-treatment and, χ2= 9.97, p = 0.002 for the follow-up phase. Consequently, DBT retention rates were significantly higher than psycho-education retention rates at post-treatment and follow-up.\n\nFigure 1Consort diagram.\n\n Acceptability and appropriateness To enable assessment of the acceptability and appropriateness of intervention, patients completed the AIM and IAM scales in the post-treatment phase. The acceptability scores were 16.57 for DBT and 9.6 for the control group (p < 0.05). The appropriateness scores were17.03 for DBT and 10.7 for the psychoeducation group (p < 0.05). Since there are no standards for these measures, these points were transferred to Likert-based questionnaire scales. For acceptability, the results equated to ‘’agree’’ for the DBT group versus ‘’neither agree nor disagree’’ for the psychoeducation group. For appropriateness, the results equated to ‘’completely agree’’ for the DBT group versus ‘’neither agree nor disagree’’ for the psychoeducation group.\nTo enable assessment of the acceptability and appropriateness of intervention, patients completed the AIM and IAM scales in the post-treatment phase. The acceptability scores were 16.57 for DBT and 9.6 for the control group (p < 0.05). The appropriateness scores were17.03 for DBT and 10.7 for the psychoeducation group (p < 0.05). Since there are no standards for these measures, these points were transferred to Likert-based questionnaire scales. For acceptability, the results equated to ‘’agree’’ for the DBT group versus ‘’neither agree nor disagree’’ for the psychoeducation group. For appropriateness, the results equated to ‘’completely agree’’ for the DBT group versus ‘’neither agree nor disagree’’ for the psychoeducation group.\n Participant characteristics Participants’ demographic variables are shown in Table 2 . Analyses showed that there were no significant differences between the two groups regarding these variables. It should be noted that since over 97% of the participants were male from the beginning, the results were reported only for men.\n\nTable 2Mean and standard deviation of demographic variables in the intervention and control groups at the test phaseVariableIntervention groupControl groupp-valueEducational level* 0.2No higher education, n (%)2 (6)6 (19)Diploma, n (%)14 (46)15 (48)University student or graduate, n (%)14 (46)10 (33)Age †25.6 (5.67)27.19 (7.48)0.3Months of marijuana use19.53 (5.9)17.48 (6.03)0.1Craving (total) † Pre-test45.2 (8.3)47.9 (10.2)0.2Post-test42.13 (7.7)44.48 (8.1)0.1Follow-up42.66 (9.25)45.8 (8.4)0.1Data presented as mean (standard deviation), unless otherwise specified.* Chi-square test.† Independent t test.\n\nData presented as mean (standard deviation), unless otherwise specified.\n* Chi-square test.\n† Independent t test.\nParticipants’ demographic variables are shown in Table 2 . Analyses showed that there were no significant differences between the two groups regarding these variables. It should be noted that since over 97% of the participants were male from the beginning, the results were reported only for men.\n\nTable 2Mean and standard deviation of demographic variables in the intervention and control groups at the test phaseVariableIntervention groupControl groupp-valueEducational level* 0.2No higher education, n (%)2 (6)6 (19)Diploma, n (%)14 (46)15 (48)University student or graduate, n (%)14 (46)10 (33)Age †25.6 (5.67)27.19 (7.48)0.3Months of marijuana use19.53 (5.9)17.48 (6.03)0.1Craving (total) † Pre-test45.2 (8.3)47.9 (10.2)0.2Post-test42.13 (7.7)44.48 (8.1)0.1Follow-up42.66 (9.25)45.8 (8.4)0.1Data presented as mean (standard deviation), unless otherwise specified.* Chi-square test.† Independent t test.\n\nData presented as mean (standard deviation), unless otherwise specified.\n* Chi-square test.\n† Independent t test.\n Efficacy outcomes The hypothesis of equal covariance matrices was examined for craving (Box’s M = 3.63, P = 0.74). The results of this test indicate homogeneity of covariance matrices. Mauchly’s test of sphericity also showed that the sphericity assumption was not violated (p = 0.42 and Mauchly’s W = 0.97).\nThe results of the intergroup test and intergroup relations are also presented in Table 3 . As shown in Table 3 , the effect levels for craving (F = 3.52, p > 0.05) suggest that there is no significant difference between groups. These results were repeated for three of the subscales of craving: compulsivity, expectancy, and purposefulness. However, the emotionality subscale results showed a significant reduction in the DBT group compared to the control group 10.6 vs. 14.4 in the post-test and 10.43 vs. 13.26 in the follow-up phase (F = 19.94, p < 0.05).\n\nTable 3Repeated measures ANOVA for variables for the DBT group and control group in the pre-test, post-test, and follow-upVariable/sourceType III sum of squaresdfMean squareFSigPartial eta squaredObserved powerCraving Tests of within-subjects effects Factor1347.6962173.842.60.07*0.040.512Factor1 × group4.7622.380.0360.110.0010.055Error (factor1)7845.15411866.48 Tests of between-subjects effects Group341.0841341.083.520.06 †0.0560.455Error5708.795996.75 Emotionality Tests of within-subjects effects Factor1257.332128.6511.690.00 †0.1650.9Factor1 × group74.70237.350.370.03*0.050.63Error (factor1)1297.8111810.99 Tests of between-subjects effects Group283.891283.8919.940.00 †0.250.9Error839.9065914.23 * Significant to 0.05.† Significant to 0.01.\n\n* Significant to 0.05.\n† Significant to 0.01.\nWith regard to cessation, the results indicated that DBT achieved a higher rate of cessation than the control treatment in both the post-test and at follow-up, ( Table 4 ) (p < 0.05). It was also found that among those who continued to use the drug, the number of use days per month in the post-test and the follow-up periods (two months) was significantly lower in the intervention group than the control group.\n\nTable 4Cessation and consumption between groups DBTControlp-valueCessation* Post-test14 (46%)5 (16%)0.01\n‡Follow-up12 (40%)3 (9.5%)0.006\n‡Number of days use † Post-test2.43±1.87.5±5.030.00\n‡Follow-up3.44±1.918.75±3.270.00\n‡Bold p-values are significant at critical levels.* The chi-square test was applied.† T test for independent samples.‡ Significant to 0.01.\n\nBold p-values are significant at critical levels.\n* The chi-square test was applied.\n† T test for independent samples.\n‡ Significant to 0.01.\nThe hypothesis of equal covariance matrices was examined for craving (Box’s M = 3.63, P = 0.74). The results of this test indicate homogeneity of covariance matrices. Mauchly’s test of sphericity also showed that the sphericity assumption was not violated (p = 0.42 and Mauchly’s W = 0.97).\nThe results of the intergroup test and intergroup relations are also presented in Table 3 . As shown in Table 3 , the effect levels for craving (F = 3.52, p > 0.05) suggest that there is no significant difference between groups. These results were repeated for three of the subscales of craving: compulsivity, expectancy, and purposefulness. However, the emotionality subscale results showed a significant reduction in the DBT group compared to the control group 10.6 vs. 14.4 in the post-test and 10.43 vs. 13.26 in the follow-up phase (F = 19.94, p < 0.05).\n\nTable 3Repeated measures ANOVA for variables for the DBT group and control group in the pre-test, post-test, and follow-upVariable/sourceType III sum of squaresdfMean squareFSigPartial eta squaredObserved powerCraving Tests of within-subjects effects Factor1347.6962173.842.60.07*0.040.512Factor1 × group4.7622.380.0360.110.0010.055Error (factor1)7845.15411866.48 Tests of between-subjects effects Group341.0841341.083.520.06 †0.0560.455Error5708.795996.75 Emotionality Tests of within-subjects effects Factor1257.332128.6511.690.00 †0.1650.9Factor1 × group74.70237.350.370.03*0.050.63Error (factor1)1297.8111810.99 Tests of between-subjects effects Group283.891283.8919.940.00 †0.250.9Error839.9065914.23 * Significant to 0.05.† Significant to 0.01.\n\n* Significant to 0.05.\n† Significant to 0.01.\nWith regard to cessation, the results indicated that DBT achieved a higher rate of cessation than the control treatment in both the post-test and at follow-up, ( Table 4 ) (p < 0.05). It was also found that among those who continued to use the drug, the number of use days per month in the post-test and the follow-up periods (two months) was significantly lower in the intervention group than the control group.\n\nTable 4Cessation and consumption between groups DBTControlp-valueCessation* Post-test14 (46%)5 (16%)0.01\n‡Follow-up12 (40%)3 (9.5%)0.006\n‡Number of days use † Post-test2.43±1.87.5±5.030.00\n‡Follow-up3.44±1.918.75±3.270.00\n‡Bold p-values are significant at critical levels.* The chi-square test was applied.† T test for independent samples.‡ Significant to 0.01.\n\nBold p-values are significant at critical levels.\n* The chi-square test was applied.\n† T test for independent samples.\n‡ Significant to 0.01."},"conclusions_title":{"kind":"string","value":"Conclusion"},"conclusions_text":{"kind":"string","value":"To conclude, DBT demonstrated adequate feasibility, acceptability, and appropriateness for patients with marijuana use disorder. Moreover, DBT also exhibited significant efficacy compared to the control group for achieving cessation and reducing emotion-related craving. Even in patients who could not achieve abstinence, DBT led to a reduction in marijuana consumption rates. These findings persisted at two-month follow-up.\n Limitations and future directions Despite these positive results, the present study also has some limitations. First, in order to evaluate the most significant treatment components (such as mindfulness and distress tolerance), no groups received the third wave versions of other therapies (ACT or MBSR). This study only had a two-month follow-up period and could not conduct long-term evaluation due to the study site’s medical and infrastructure conditions. It is recommended that future research should examine mediating and confounding variables to investigate the results of similar research to the present study. Other factors affecting relapse and recurrence could also be examined. Moreover, women should be investigated so that gender-related implications can be determined.\nDespite these positive results, the present study also has some limitations. First, in order to evaluate the most significant treatment components (such as mindfulness and distress tolerance), no groups received the third wave versions of other therapies (ACT or MBSR). This study only had a two-month follow-up period and could not conduct long-term evaluation due to the study site’s medical and infrastructure conditions. It is recommended that future research should examine mediating and confounding variables to investigate the results of similar research to the present study. Other factors affecting relapse and recurrence could also be examined. Moreover, women should be investigated so that gender-related implications can be determined."},"other_sections_titles":{"kind":"list like","value":["Trial design","Sample size","Selection criteria","Participants, procedures, and randomization","Blinding","Outcome measures","Abstinence","Marijuana smoking","Craving","Acceptability","Appropriateness","Intervention","Dialectical behavior therapy","Psychoeducation","Therapists and treatment adherence","Statistical method","Ethical considerations","Feasibility","Acceptability and appropriateness","Participant characteristics","Efficacy outcomes","Limitations and future directions"],"string":"[\n \"Trial design\",\n \"Sample size\",\n \"Selection criteria\",\n \"Participants, procedures, and randomization\",\n \"Blinding\",\n \"Outcome measures\",\n \"Abstinence\",\n \"Marijuana smoking\",\n \"Craving\",\n \"Acceptability\",\n \"Appropriateness\",\n \"Intervention\",\n \"Dialectical behavior therapy\",\n \"Psychoeducation\",\n \"Therapists and treatment adherence\",\n \"Statistical method\",\n \"Ethical considerations\",\n \"Feasibility\",\n \"Acceptability and appropriateness\",\n \"Participant characteristics\",\n \"Efficacy outcomes\",\n \"Limitations and future directions\"\n]"},"other_sections_texts":{"kind":"list like","value":["This study was designed as a controlled randomized clinical trial, including pretest, post-test, and two-month follow-up phases.","Since the sampling method comprises snowball sampling and strict eligibility criteria were applied, on the basis of data from similar studies 10 it was determined that at least 20 participants were needed in each group. However, in view of the predicted retention rates, we selected 30 patients for each group.","The inclusion criteria were as follows: 1) diagnosis of marijuana use disorder; 2) age 18 years or over; 3) no current or past history of major psychiatric disorders; 4) no other concurrent SUD treatment; and 5) willingness to attend intervention sessions, complete surveys, and take tests (questionnaires and urine test kits).\nExclusion criteria were as follows: 1) unwillingness to participate; 2) not participating in intervention sessions for more than two weeks; 3) starting secondary psychotherapy; and 4) consuming methamphetamine, amphetamine, cannabis, methadone, benzodiazepines, or morphine during the research stages.","Since there are no cannabis use disorder treatment centers in Iran, there is no specific place to select patients. Furthermore, patients at drug treatment centers are referred for treatment of other substance use disorders and comorbidity of drug use is one of the exclusion criteria for this study, since it could lead to misleading results. Therefore, the relatives and acquaintances of those who had been referred to the drug treatment center were interviewed. From November 1, 2019, to November 5, 2019, 15 relatives and family members of drug users referred to drug treatment centers were diagnosed with MUD at this stage. Then, using snowball sampling, after 15 days of investigation, a total of 83 patients were diagnosed with MUD. Seventy-five of the 83 MUD patients who were approached consented, eight declined to participate, and 14 were ineligible. The primary reasons for declining were anxiety about addiction stigma and time constraints. Most of the ineligible patients had multiple illicit use disorders, so they did not meet the study criteria. Therefore, 61 patients completed the baseline assessment and were included in the current analyses. These patients were randomly assigned to each group using a random number table. The interventions were implemented from December 1, 2019, to March 20, 2020. The follow-up phase started on March 21, 2020, and ended on May 20, 2020, (at two months’ follow-up). In order to test for exclusion criteria before each session, a six-drug test kit for methamphetamine, amphetamine, cannabis, methadone, benzodiazepines, and morphine was administered to individuals using urine samples.","Both groups were blind to the existence of another group in the study. However, patients were informed about participating in research but not about another group. One day after the end of treatment, the post-test was carried out by mental health technicians with a master’s degree in psychology."," Abstinence A marijuana urine test kit prepared by Kian Teb Company (officially licensed by the National Medical Device Directorate IR. IRAN) was used to identify abstainers.\nA marijuana urine test kit prepared by Kian Teb Company (officially licensed by the National Medical Device Directorate IR. IRAN) was used to identify abstainers.\n Marijuana smoking A self-report scale was designed for patients who had lapsed during the post-test follow-up. On this scale, patients indicate the number of days of consumption over 30-day periods. The first thirty days after the last intervention session was considered the post-test smoking period and the second-month follow-up was considered as the follow-up marijuana use period.\nA self-report scale was designed for patients who had lapsed during the post-test follow-up. On this scale, patients indicate the number of days of consumption over 30-day periods. The first thirty days after the last intervention session was considered the post-test smoking period and the second-month follow-up was considered as the follow-up marijuana use period.\n Craving The Marijuana Craving Questionnaire (MCQ) short-form is a 12-item self-report questionnaire with ten items for subjective assessment of cannabis craving. The scale covers 4 factors: compulsivity, emotionality, expectancy, and purposefulness. According to how patients were thinking or feeling ‘’right now,’’ they placed checkmarks on the questionnaire to endorse responses ranging from 1 or strongly disagree to 7 or strongly agree. Results showed that this questionnaire’s internal consistency is adequate (α = 0.90). The measure was administered following a 12-hour deprivation period. The typical onset of marijuana craving and withdrawal symptoms is observed within approximately one day of cessation and so the current paper’s questionnaire scores can be conceptualized as an index of the propensity to experience marijuana craving following deprivation. 22 In Iranian MUD patients, the MCQ had internal consistency of α = 0.87. Details of the MCQ’s psychometrics properties will be published as a separate study as soon as possible.\nThe Marijuana Craving Questionnaire (MCQ) short-form is a 12-item self-report questionnaire with ten items for subjective assessment of cannabis craving. The scale covers 4 factors: compulsivity, emotionality, expectancy, and purposefulness. According to how patients were thinking or feeling ‘’right now,’’ they placed checkmarks on the questionnaire to endorse responses ranging from 1 or strongly disagree to 7 or strongly agree. Results showed that this questionnaire’s internal consistency is adequate (α = 0.90). The measure was administered following a 12-hour deprivation period. The typical onset of marijuana craving and withdrawal symptoms is observed within approximately one day of cessation and so the current paper’s questionnaire scores can be conceptualized as an index of the propensity to experience marijuana craving following deprivation. 22 In Iranian MUD patients, the MCQ had internal consistency of α = 0.87. Details of the MCQ’s psychometrics properties will be published as a separate study as soon as possible.\n Acceptability The Acceptability of Intervention Measure (AIM) was employed to measure the acceptability of interventions. AIM response items are measured on a 5-point Likert scale (from Completely Disagree with 1 point to Completely Agree with 5 points). The mean of points scored for each item is taken as the final score. This questionnaire developed by Weiner et al., and they reported Cronbach’s ɑ = 85 for internal consistency. 23\nThe Acceptability of Intervention Measure (AIM) was employed to measure the acceptability of interventions. AIM response items are measured on a 5-point Likert scale (from Completely Disagree with 1 point to Completely Agree with 5 points). The mean of points scored for each item is taken as the final score. This questionnaire developed by Weiner et al., and they reported Cronbach’s ɑ = 85 for internal consistency. 23\n Appropriateness The Intervention Appropriateness Measure (IAM) was used for Appropriateness. The IAM consist of a four-item scale that measures perceived intervention appropriateness. Items are measured on a five-point Likert scale (Completely Disagree to Completely Agree), and the mean of points scored for each item is taken as the final score. Higher scores mean that the participant feels this intervention is more appropriate for him/her. For this tool, Cronbach’s ɑ = 0.91 and all Factor Loadings are reported as higher than 0.8. 23\nThe Intervention Appropriateness Measure (IAM) was used for Appropriateness. The IAM consist of a four-item scale that measures perceived intervention appropriateness. Items are measured on a five-point Likert scale (Completely Disagree to Completely Agree), and the mean of points scored for each item is taken as the final score. Higher scores mean that the participant feels this intervention is more appropriate for him/her. For this tool, Cronbach’s ɑ = 0.91 and all Factor Loadings are reported as higher than 0.8. 23","A marijuana urine test kit prepared by Kian Teb Company (officially licensed by the National Medical Device Directorate IR. IRAN) was used to identify abstainers.","A self-report scale was designed for patients who had lapsed during the post-test follow-up. On this scale, patients indicate the number of days of consumption over 30-day periods. The first thirty days after the last intervention session was considered the post-test smoking period and the second-month follow-up was considered as the follow-up marijuana use period.","The Marijuana Craving Questionnaire (MCQ) short-form is a 12-item self-report questionnaire with ten items for subjective assessment of cannabis craving. The scale covers 4 factors: compulsivity, emotionality, expectancy, and purposefulness. According to how patients were thinking or feeling ‘’right now,’’ they placed checkmarks on the questionnaire to endorse responses ranging from 1 or strongly disagree to 7 or strongly agree. Results showed that this questionnaire’s internal consistency is adequate (α = 0.90). The measure was administered following a 12-hour deprivation period. The typical onset of marijuana craving and withdrawal symptoms is observed within approximately one day of cessation and so the current paper’s questionnaire scores can be conceptualized as an index of the propensity to experience marijuana craving following deprivation. 22 In Iranian MUD patients, the MCQ had internal consistency of α = 0.87. Details of the MCQ’s psychometrics properties will be published as a separate study as soon as possible.","The Acceptability of Intervention Measure (AIM) was employed to measure the acceptability of interventions. AIM response items are measured on a 5-point Likert scale (from Completely Disagree with 1 point to Completely Agree with 5 points). The mean of points scored for each item is taken as the final score. This questionnaire developed by Weiner et al., and they reported Cronbach’s ɑ = 85 for internal consistency. 23","The Intervention Appropriateness Measure (IAM) was used for Appropriateness. The IAM consist of a four-item scale that measures perceived intervention appropriateness. Items are measured on a five-point Likert scale (Completely Disagree to Completely Agree), and the mean of points scored for each item is taken as the final score. Higher scores mean that the participant feels this intervention is more appropriate for him/her. For this tool, Cronbach’s ɑ = 0.91 and all Factor Loadings are reported as higher than 0.8. 23"," Dialectical behavior therapy DBT is a group intervention consisting of 16 sessions (meeting once a week for 90 minutes) with one psychotherapist and her co-therapist. The intervention protocol is an adaptation of DBT for SUD based on three basic manuals. 10 , 24 , 25 The primary objective of the DBT is to reduce dysfunction in emotion regulation and craving via increasing cessation rates and improving skills. A psychotherapist with a PhD delivered the intervention sessions (with a psychologist as co-therapist) and they were blind both to the existence of another group and to the study objectives. Table 1 shows the content covered in each DBT session.\n\nTable 1DBT content per sessionCessationContentPre-sessionExplanation of dialectical behavioral therapy, principles, and goals. Brief introduction to the content of each session. Familiarity with participants. Participants are given an intervention booklet to read at home.1st session (mindfulness 1)Introduce the concept of mindfulness and three mental states (wise, reasonable, emotional) and their relations with substance use.2nd session (mindfulness 2)Teach two clusters of mindfulness skills. The first includes viewing, participation, and description. The second includes a non-judgmental stance and inclusive self-consciousness.3rd sessionSummarize the mindfulness sessions – definition of addiction, standard therapies of addiction, introduction to and teaching of dialectical avoidance technique. Review the positive and negative aspects of abstinence. Explanation and investigation of relapse and its causes. Explaining the skill of the pure mind, the addicted mind, the types of behaviors related to the pure mentality and the addicted mentality, and preparing a list of supporters.4th-5th sessions (Distress tolerance)Teaching distraction strategies with five skills include activities, comparisons, emotions, thoughts, and enjoyment. Through enjoyable activities, focusing on work or other topics, counting, leaving the situation, paying attention to daily tasks, distracting from thoughts, and self-harm behaviors – teaching and training self-soothing with five senses.6th-7th sessions (Emotion regulation)Definition of emotion, how emotions work, familiarity with emotion regulation skills. Emotion Identification Exercise, Emotion Registration Exercise. Identifying barriers to experiencing emotion in a healthy way and ways to overcome these barriers. Teaching creating short-term positive emotional experiences for experiencing positive emotional states.8th-10th sessions (Emotion regulation and distress tolerance in an MUD context)Explain craving and its connection to the experience of emotions. Introducing methods for identifying values. Importance of committed action based on a list of essential values in life. Develop new coping strategies in response to unpleasant emotions, sensations, and cognitions, especially craving as a multidimensional problem and teaching problem solving and behavior analysis.11th sessionBasic acceptance technique training. Introduce living in the present moment techniques.12th-13th sessionsInterpersonal effectiveness training. Participants learn assertiveness skills about substance users. Other skills include non-verbal communication, verbal communication, and problem-solving, decision-making, and listening skills.14th-16th sessionsReview of sessions. Elimination of ambiguities. Exercising skills in the presence of other people.\n\nDBT is a group intervention consisting of 16 sessions (meeting once a week for 90 minutes) with one psychotherapist and her co-therapist. The intervention protocol is an adaptation of DBT for SUD based on three basic manuals. 10 , 24 , 25 The primary objective of the DBT is to reduce dysfunction in emotion regulation and craving via increasing cessation rates and improving skills. A psychotherapist with a PhD delivered the intervention sessions (with a psychologist as co-therapist) and they were blind both to the existence of another group and to the study objectives. Table 1 shows the content covered in each DBT session.\n\nTable 1DBT content per sessionCessationContentPre-sessionExplanation of dialectical behavioral therapy, principles, and goals. Brief introduction to the content of each session. Familiarity with participants. Participants are given an intervention booklet to read at home.1st session (mindfulness 1)Introduce the concept of mindfulness and three mental states (wise, reasonable, emotional) and their relations with substance use.2nd session (mindfulness 2)Teach two clusters of mindfulness skills. The first includes viewing, participation, and description. The second includes a non-judgmental stance and inclusive self-consciousness.3rd sessionSummarize the mindfulness sessions – definition of addiction, standard therapies of addiction, introduction to and teaching of dialectical avoidance technique. Review the positive and negative aspects of abstinence. Explanation and investigation of relapse and its causes. Explaining the skill of the pure mind, the addicted mind, the types of behaviors related to the pure mentality and the addicted mentality, and preparing a list of supporters.4th-5th sessions (Distress tolerance)Teaching distraction strategies with five skills include activities, comparisons, emotions, thoughts, and enjoyment. Through enjoyable activities, focusing on work or other topics, counting, leaving the situation, paying attention to daily tasks, distracting from thoughts, and self-harm behaviors – teaching and training self-soothing with five senses.6th-7th sessions (Emotion regulation)Definition of emotion, how emotions work, familiarity with emotion regulation skills. Emotion Identification Exercise, Emotion Registration Exercise. Identifying barriers to experiencing emotion in a healthy way and ways to overcome these barriers. Teaching creating short-term positive emotional experiences for experiencing positive emotional states.8th-10th sessions (Emotion regulation and distress tolerance in an MUD context)Explain craving and its connection to the experience of emotions. Introducing methods for identifying values. Importance of committed action based on a list of essential values in life. Develop new coping strategies in response to unpleasant emotions, sensations, and cognitions, especially craving as a multidimensional problem and teaching problem solving and behavior analysis.11th sessionBasic acceptance technique training. Introduce living in the present moment techniques.12th-13th sessionsInterpersonal effectiveness training. Participants learn assertiveness skills about substance users. Other skills include non-verbal communication, verbal communication, and problem-solving, decision-making, and listening skills.14th-16th sessionsReview of sessions. Elimination of ambiguities. Exercising skills in the presence of other people.\n\n Psychoeducation This option is more ethical than not offering any intervention to the control group. A psychiatrist with five years of experience in addiction psychotherapy implemented this intervention. This intervention includes problem-solving skills, assertiveness, and craving management in eight sessions. Thepsychoeducation intervention was used to provide a basis for comparison with only those elements of the DBT intervention that are different from other psychotherapies. This intervention is utilized for MUD and health-related problems. The intention of this intervention is to provide individuals struggling with cravings and substance use disorder the knowledge needed to comprehensively appreciate their problems and the empowerment needed to cope with them. The psychoeducation intervention included information about the dangers of marijuana and the therapists also provided a pamphlet containing techniques for reduction of craving. 14 , 26 , 27\nThis option is more ethical than not offering any intervention to the control group. A psychiatrist with five years of experience in addiction psychotherapy implemented this intervention. This intervention includes problem-solving skills, assertiveness, and craving management in eight sessions. Thepsychoeducation intervention was used to provide a basis for comparison with only those elements of the DBT intervention that are different from other psychotherapies. This intervention is utilized for MUD and health-related problems. The intention of this intervention is to provide individuals struggling with cravings and substance use disorder the knowledge needed to comprehensively appreciate their problems and the empowerment needed to cope with them. The psychoeducation intervention included information about the dangers of marijuana and the therapists also provided a pamphlet containing techniques for reduction of craving. 14 , 26 , 27\n Therapists and treatment adherence To enable adherence to the principles of DBT to be checked, audios of the sessions were recorded with the consent of all participants. A DBT researcher and psychotherapist who was not involved in the treatment groups checked session content afterwards. Sessions were divided into 15-minute modules that were chosen for adherence checks at random. Treatment stance and occurrence and depth of DBT processes were appraised. Based on the treatment manual, modules were rated for adherence level as either adequate or not adequate. The majority (83%) were judged to have been conducted adequately.\nTo enable adherence to the principles of DBT to be checked, audios of the sessions were recorded with the consent of all participants. A DBT researcher and psychotherapist who was not involved in the treatment groups checked session content afterwards. Sessions were divided into 15-minute modules that were chosen for adherence checks at random. Treatment stance and occurrence and depth of DBT processes were appraised. Based on the treatment manual, modules were rated for adherence level as either adequate or not adequate. The majority (83%) were judged to have been conducted adequately.\n Statistical method Demographic information was gathered and reported as frequencies, means, and standard deviations and repeated measures ANOVA and chi-square tests were conducted for the outcomes using SPSS software, version 26.\nDemographic information was gathered and reported as frequencies, means, and standard deviations and repeated measures ANOVA and chi-square tests were conducted for the outcomes using SPSS software, version 26.","DBT is a group intervention consisting of 16 sessions (meeting once a week for 90 minutes) with one psychotherapist and her co-therapist. The intervention protocol is an adaptation of DBT for SUD based on three basic manuals. 10 , 24 , 25 The primary objective of the DBT is to reduce dysfunction in emotion regulation and craving via increasing cessation rates and improving skills. A psychotherapist with a PhD delivered the intervention sessions (with a psychologist as co-therapist) and they were blind both to the existence of another group and to the study objectives. Table 1 shows the content covered in each DBT session.\n\nTable 1DBT content per sessionCessationContentPre-sessionExplanation of dialectical behavioral therapy, principles, and goals. Brief introduction to the content of each session. Familiarity with participants. Participants are given an intervention booklet to read at home.1st session (mindfulness 1)Introduce the concept of mindfulness and three mental states (wise, reasonable, emotional) and their relations with substance use.2nd session (mindfulness 2)Teach two clusters of mindfulness skills. The first includes viewing, participation, and description. The second includes a non-judgmental stance and inclusive self-consciousness.3rd sessionSummarize the mindfulness sessions – definition of addiction, standard therapies of addiction, introduction to and teaching of dialectical avoidance technique. Review the positive and negative aspects of abstinence. Explanation and investigation of relapse and its causes. Explaining the skill of the pure mind, the addicted mind, the types of behaviors related to the pure mentality and the addicted mentality, and preparing a list of supporters.4th-5th sessions (Distress tolerance)Teaching distraction strategies with five skills include activities, comparisons, emotions, thoughts, and enjoyment. Through enjoyable activities, focusing on work or other topics, counting, leaving the situation, paying attention to daily tasks, distracting from thoughts, and self-harm behaviors – teaching and training self-soothing with five senses.6th-7th sessions (Emotion regulation)Definition of emotion, how emotions work, familiarity with emotion regulation skills. Emotion Identification Exercise, Emotion Registration Exercise. Identifying barriers to experiencing emotion in a healthy way and ways to overcome these barriers. Teaching creating short-term positive emotional experiences for experiencing positive emotional states.8th-10th sessions (Emotion regulation and distress tolerance in an MUD context)Explain craving and its connection to the experience of emotions. Introducing methods for identifying values. Importance of committed action based on a list of essential values in life. Develop new coping strategies in response to unpleasant emotions, sensations, and cognitions, especially craving as a multidimensional problem and teaching problem solving and behavior analysis.11th sessionBasic acceptance technique training. Introduce living in the present moment techniques.12th-13th sessionsInterpersonal effectiveness training. Participants learn assertiveness skills about substance users. Other skills include non-verbal communication, verbal communication, and problem-solving, decision-making, and listening skills.14th-16th sessionsReview of sessions. Elimination of ambiguities. Exercising skills in the presence of other people.\n","This option is more ethical than not offering any intervention to the control group. A psychiatrist with five years of experience in addiction psychotherapy implemented this intervention. This intervention includes problem-solving skills, assertiveness, and craving management in eight sessions. Thepsychoeducation intervention was used to provide a basis for comparison with only those elements of the DBT intervention that are different from other psychotherapies. This intervention is utilized for MUD and health-related problems. The intention of this intervention is to provide individuals struggling with cravings and substance use disorder the knowledge needed to comprehensively appreciate their problems and the empowerment needed to cope with them. The psychoeducation intervention included information about the dangers of marijuana and the therapists also provided a pamphlet containing techniques for reduction of craving. 14 , 26 , 27","To enable adherence to the principles of DBT to be checked, audios of the sessions were recorded with the consent of all participants. A DBT researcher and psychotherapist who was not involved in the treatment groups checked session content afterwards. Sessions were divided into 15-minute modules that were chosen for adherence checks at random. Treatment stance and occurrence and depth of DBT processes were appraised. Based on the treatment manual, modules were rated for adherence level as either adequate or not adequate. The majority (83%) were judged to have been conducted adequately.","Demographic information was gathered and reported as frequencies, means, and standard deviations and repeated measures ANOVA and chi-square tests were conducted for the outcomes using SPSS software, version 26.","Written informed consent was obtained from all participants before initiation of the research. The tools used in this study were all filled-out anonymously, and an ID code was used to maintain the confidentiality of personal information (Ir.kums.rce.1398.1203). At the end of the research process, dialectical behavior therapy was also provided to the control group. This study is registered with the Thailand Registry of Clinical Trials (TCTR20200319007).","In the psycho-education group, 24/31 participants completed all sessions, compared to 29/30 members of the DBT group (retention rates: 77% in the control group vs. 96% in the DBT group). Additionally, 96% (29/30) of the DBT group members completed the two-month follow-up, whereas 64.5% (20/31) of the control group completed follow-up ( Figure 1 ). The chi-square test was applied, showing associations between group and retention, with χ2= 4.95, p = 0.02 for post-treatment and, χ2= 9.97, p = 0.002 for the follow-up phase. Consequently, DBT retention rates were significantly higher than psycho-education retention rates at post-treatment and follow-up.\n\nFigure 1Consort diagram.\n","To enable assessment of the acceptability and appropriateness of intervention, patients completed the AIM and IAM scales in the post-treatment phase. The acceptability scores were 16.57 for DBT and 9.6 for the control group (p < 0.05). The appropriateness scores were17.03 for DBT and 10.7 for the psychoeducation group (p < 0.05). Since there are no standards for these measures, these points were transferred to Likert-based questionnaire scales. For acceptability, the results equated to ‘’agree’’ for the DBT group versus ‘’neither agree nor disagree’’ for the psychoeducation group. For appropriateness, the results equated to ‘’completely agree’’ for the DBT group versus ‘’neither agree nor disagree’’ for the psychoeducation group.","Participants’ demographic variables are shown in Table 2 . Analyses showed that there were no significant differences between the two groups regarding these variables. It should be noted that since over 97% of the participants were male from the beginning, the results were reported only for men.\n\nTable 2Mean and standard deviation of demographic variables in the intervention and control groups at the test phaseVariableIntervention groupControl groupp-valueEducational level* 0.2No higher education, n (%)2 (6)6 (19)Diploma, n (%)14 (46)15 (48)University student or graduate, n (%)14 (46)10 (33)Age †25.6 (5.67)27.19 (7.48)0.3Months of marijuana use19.53 (5.9)17.48 (6.03)0.1Craving (total) † Pre-test45.2 (8.3)47.9 (10.2)0.2Post-test42.13 (7.7)44.48 (8.1)0.1Follow-up42.66 (9.25)45.8 (8.4)0.1Data presented as mean (standard deviation), unless otherwise specified.* Chi-square test.† Independent t test.\n\nData presented as mean (standard deviation), unless otherwise specified.\n* Chi-square test.\n† Independent t test.","The hypothesis of equal covariance matrices was examined for craving (Box’s M = 3.63, P = 0.74). The results of this test indicate homogeneity of covariance matrices. Mauchly’s test of sphericity also showed that the sphericity assumption was not violated (p = 0.42 and Mauchly’s W = 0.97).\nThe results of the intergroup test and intergroup relations are also presented in Table 3 . As shown in Table 3 , the effect levels for craving (F = 3.52, p > 0.05) suggest that there is no significant difference between groups. These results were repeated for three of the subscales of craving: compulsivity, expectancy, and purposefulness. However, the emotionality subscale results showed a significant reduction in the DBT group compared to the control group 10.6 vs. 14.4 in the post-test and 10.43 vs. 13.26 in the follow-up phase (F = 19.94, p < 0.05).\n\nTable 3Repeated measures ANOVA for variables for the DBT group and control group in the pre-test, post-test, and follow-upVariable/sourceType III sum of squaresdfMean squareFSigPartial eta squaredObserved powerCraving Tests of within-subjects effects Factor1347.6962173.842.60.07*0.040.512Factor1 × group4.7622.380.0360.110.0010.055Error (factor1)7845.15411866.48 Tests of between-subjects effects Group341.0841341.083.520.06 †0.0560.455Error5708.795996.75 Emotionality Tests of within-subjects effects Factor1257.332128.6511.690.00 †0.1650.9Factor1 × group74.70237.350.370.03*0.050.63Error (factor1)1297.8111810.99 Tests of between-subjects effects Group283.891283.8919.940.00 †0.250.9Error839.9065914.23 * Significant to 0.05.† Significant to 0.01.\n\n* Significant to 0.05.\n† Significant to 0.01.\nWith regard to cessation, the results indicated that DBT achieved a higher rate of cessation than the control treatment in both the post-test and at follow-up, ( Table 4 ) (p < 0.05). It was also found that among those who continued to use the drug, the number of use days per month in the post-test and the follow-up periods (two months) was significantly lower in the intervention group than the control group.\n\nTable 4Cessation and consumption between groups DBTControlp-valueCessation* Post-test14 (46%)5 (16%)0.01\n‡Follow-up12 (40%)3 (9.5%)0.006\n‡Number of days use † Post-test2.43±1.87.5±5.030.00\n‡Follow-up3.44±1.918.75±3.270.00\n‡Bold p-values are significant at critical levels.* The chi-square test was applied.† T test for independent samples.‡ Significant to 0.01.\n\nBold p-values are significant at critical levels.\n* The chi-square test was applied.\n† T test for independent samples.\n‡ Significant to 0.01.","Despite these positive results, the present study also has some limitations. First, in order to evaluate the most significant treatment components (such as mindfulness and distress tolerance), no groups received the third wave versions of other therapies (ACT or MBSR). This study only had a two-month follow-up period and could not conduct long-term evaluation due to the study site’s medical and infrastructure conditions. It is recommended that future research should examine mediating and confounding variables to investigate the results of similar research to the present study. Other factors affecting relapse and recurrence could also be examined. Moreover, women should be investigated so that gender-related implications can be determined."],"string":"[\n \"This study was designed as a controlled randomized clinical trial, including pretest, post-test, and two-month follow-up phases.\",\n \"Since the sampling method comprises snowball sampling and strict eligibility criteria were applied, on the basis of data from similar studies 10 it was determined that at least 20 participants were needed in each group. However, in view of the predicted retention rates, we selected 30 patients for each group.\",\n \"The inclusion criteria were as follows: 1) diagnosis of marijuana use disorder; 2) age 18 years or over; 3) no current or past history of major psychiatric disorders; 4) no other concurrent SUD treatment; and 5) willingness to attend intervention sessions, complete surveys, and take tests (questionnaires and urine test kits).\\nExclusion criteria were as follows: 1) unwillingness to participate; 2) not participating in intervention sessions for more than two weeks; 3) starting secondary psychotherapy; and 4) consuming methamphetamine, amphetamine, cannabis, methadone, benzodiazepines, or morphine during the research stages.\",\n \"Since there are no cannabis use disorder treatment centers in Iran, there is no specific place to select patients. Furthermore, patients at drug treatment centers are referred for treatment of other substance use disorders and comorbidity of drug use is one of the exclusion criteria for this study, since it could lead to misleading results. Therefore, the relatives and acquaintances of those who had been referred to the drug treatment center were interviewed. From November 1, 2019, to November 5, 2019, 15 relatives and family members of drug users referred to drug treatment centers were diagnosed with MUD at this stage. Then, using snowball sampling, after 15 days of investigation, a total of 83 patients were diagnosed with MUD. Seventy-five of the 83 MUD patients who were approached consented, eight declined to participate, and 14 were ineligible. The primary reasons for declining were anxiety about addiction stigma and time constraints. Most of the ineligible patients had multiple illicit use disorders, so they did not meet the study criteria. Therefore, 61 patients completed the baseline assessment and were included in the current analyses. These patients were randomly assigned to each group using a random number table. The interventions were implemented from December 1, 2019, to March 20, 2020. The follow-up phase started on March 21, 2020, and ended on May 20, 2020, (at two months’ follow-up). In order to test for exclusion criteria before each session, a six-drug test kit for methamphetamine, amphetamine, cannabis, methadone, benzodiazepines, and morphine was administered to individuals using urine samples.\",\n \"Both groups were blind to the existence of another group in the study. However, patients were informed about participating in research but not about another group. One day after the end of treatment, the post-test was carried out by mental health technicians with a master’s degree in psychology.\",\n \" Abstinence A marijuana urine test kit prepared by Kian Teb Company (officially licensed by the National Medical Device Directorate IR. IRAN) was used to identify abstainers.\\nA marijuana urine test kit prepared by Kian Teb Company (officially licensed by the National Medical Device Directorate IR. IRAN) was used to identify abstainers.\\n Marijuana smoking A self-report scale was designed for patients who had lapsed during the post-test follow-up. On this scale, patients indicate the number of days of consumption over 30-day periods. The first thirty days after the last intervention session was considered the post-test smoking period and the second-month follow-up was considered as the follow-up marijuana use period.\\nA self-report scale was designed for patients who had lapsed during the post-test follow-up. On this scale, patients indicate the number of days of consumption over 30-day periods. The first thirty days after the last intervention session was considered the post-test smoking period and the second-month follow-up was considered as the follow-up marijuana use period.\\n Craving The Marijuana Craving Questionnaire (MCQ) short-form is a 12-item self-report questionnaire with ten items for subjective assessment of cannabis craving. The scale covers 4 factors: compulsivity, emotionality, expectancy, and purposefulness. According to how patients were thinking or feeling ‘’right now,’’ they placed checkmarks on the questionnaire to endorse responses ranging from 1 or strongly disagree to 7 or strongly agree. Results showed that this questionnaire’s internal consistency is adequate (α = 0.90). The measure was administered following a 12-hour deprivation period. The typical onset of marijuana craving and withdrawal symptoms is observed within approximately one day of cessation and so the current paper’s questionnaire scores can be conceptualized as an index of the propensity to experience marijuana craving following deprivation. 22 In Iranian MUD patients, the MCQ had internal consistency of α = 0.87. Details of the MCQ’s psychometrics properties will be published as a separate study as soon as possible.\\nThe Marijuana Craving Questionnaire (MCQ) short-form is a 12-item self-report questionnaire with ten items for subjective assessment of cannabis craving. The scale covers 4 factors: compulsivity, emotionality, expectancy, and purposefulness. According to how patients were thinking or feeling ‘’right now,’’ they placed checkmarks on the questionnaire to endorse responses ranging from 1 or strongly disagree to 7 or strongly agree. Results showed that this questionnaire’s internal consistency is adequate (α = 0.90). The measure was administered following a 12-hour deprivation period. The typical onset of marijuana craving and withdrawal symptoms is observed within approximately one day of cessation and so the current paper’s questionnaire scores can be conceptualized as an index of the propensity to experience marijuana craving following deprivation. 22 In Iranian MUD patients, the MCQ had internal consistency of α = 0.87. Details of the MCQ’s psychometrics properties will be published as a separate study as soon as possible.\\n Acceptability The Acceptability of Intervention Measure (AIM) was employed to measure the acceptability of interventions. AIM response items are measured on a 5-point Likert scale (from Completely Disagree with 1 point to Completely Agree with 5 points). The mean of points scored for each item is taken as the final score. This questionnaire developed by Weiner et al., and they reported Cronbach’s ɑ = 85 for internal consistency. 23\\nThe Acceptability of Intervention Measure (AIM) was employed to measure the acceptability of interventions. AIM response items are measured on a 5-point Likert scale (from Completely Disagree with 1 point to Completely Agree with 5 points). The mean of points scored for each item is taken as the final score. This questionnaire developed by Weiner et al., and they reported Cronbach’s ɑ = 85 for internal consistency. 23\\n Appropriateness The Intervention Appropriateness Measure (IAM) was used for Appropriateness. The IAM consist of a four-item scale that measures perceived intervention appropriateness. Items are measured on a five-point Likert scale (Completely Disagree to Completely Agree), and the mean of points scored for each item is taken as the final score. Higher scores mean that the participant feels this intervention is more appropriate for him/her. For this tool, Cronbach’s ɑ = 0.91 and all Factor Loadings are reported as higher than 0.8. 23\\nThe Intervention Appropriateness Measure (IAM) was used for Appropriateness. The IAM consist of a four-item scale that measures perceived intervention appropriateness. Items are measured on a five-point Likert scale (Completely Disagree to Completely Agree), and the mean of points scored for each item is taken as the final score. Higher scores mean that the participant feels this intervention is more appropriate for him/her. For this tool, Cronbach’s ɑ = 0.91 and all Factor Loadings are reported as higher than 0.8. 23\",\n \"A marijuana urine test kit prepared by Kian Teb Company (officially licensed by the National Medical Device Directorate IR. IRAN) was used to identify abstainers.\",\n \"A self-report scale was designed for patients who had lapsed during the post-test follow-up. On this scale, patients indicate the number of days of consumption over 30-day periods. The first thirty days after the last intervention session was considered the post-test smoking period and the second-month follow-up was considered as the follow-up marijuana use period.\",\n \"The Marijuana Craving Questionnaire (MCQ) short-form is a 12-item self-report questionnaire with ten items for subjective assessment of cannabis craving. The scale covers 4 factors: compulsivity, emotionality, expectancy, and purposefulness. According to how patients were thinking or feeling ‘’right now,’’ they placed checkmarks on the questionnaire to endorse responses ranging from 1 or strongly disagree to 7 or strongly agree. Results showed that this questionnaire’s internal consistency is adequate (α = 0.90). The measure was administered following a 12-hour deprivation period. The typical onset of marijuana craving and withdrawal symptoms is observed within approximately one day of cessation and so the current paper’s questionnaire scores can be conceptualized as an index of the propensity to experience marijuana craving following deprivation. 22 In Iranian MUD patients, the MCQ had internal consistency of α = 0.87. Details of the MCQ’s psychometrics properties will be published as a separate study as soon as possible.\",\n \"The Acceptability of Intervention Measure (AIM) was employed to measure the acceptability of interventions. AIM response items are measured on a 5-point Likert scale (from Completely Disagree with 1 point to Completely Agree with 5 points). The mean of points scored for each item is taken as the final score. This questionnaire developed by Weiner et al., and they reported Cronbach’s ɑ = 85 for internal consistency. 23\",\n \"The Intervention Appropriateness Measure (IAM) was used for Appropriateness. The IAM consist of a four-item scale that measures perceived intervention appropriateness. Items are measured on a five-point Likert scale (Completely Disagree to Completely Agree), and the mean of points scored for each item is taken as the final score. Higher scores mean that the participant feels this intervention is more appropriate for him/her. For this tool, Cronbach’s ɑ = 0.91 and all Factor Loadings are reported as higher than 0.8. 23\",\n \" Dialectical behavior therapy DBT is a group intervention consisting of 16 sessions (meeting once a week for 90 minutes) with one psychotherapist and her co-therapist. The intervention protocol is an adaptation of DBT for SUD based on three basic manuals. 10 , 24 , 25 The primary objective of the DBT is to reduce dysfunction in emotion regulation and craving via increasing cessation rates and improving skills. A psychotherapist with a PhD delivered the intervention sessions (with a psychologist as co-therapist) and they were blind both to the existence of another group and to the study objectives. Table 1 shows the content covered in each DBT session.\\n\\nTable 1DBT content per sessionCessationContentPre-sessionExplanation of dialectical behavioral therapy, principles, and goals. Brief introduction to the content of each session. Familiarity with participants. Participants are given an intervention booklet to read at home.1st session (mindfulness 1)Introduce the concept of mindfulness and three mental states (wise, reasonable, emotional) and their relations with substance use.2nd session (mindfulness 2)Teach two clusters of mindfulness skills. The first includes viewing, participation, and description. The second includes a non-judgmental stance and inclusive self-consciousness.3rd sessionSummarize the mindfulness sessions – definition of addiction, standard therapies of addiction, introduction to and teaching of dialectical avoidance technique. Review the positive and negative aspects of abstinence. Explanation and investigation of relapse and its causes. Explaining the skill of the pure mind, the addicted mind, the types of behaviors related to the pure mentality and the addicted mentality, and preparing a list of supporters.4th-5th sessions (Distress tolerance)Teaching distraction strategies with five skills include activities, comparisons, emotions, thoughts, and enjoyment. Through enjoyable activities, focusing on work or other topics, counting, leaving the situation, paying attention to daily tasks, distracting from thoughts, and self-harm behaviors – teaching and training self-soothing with five senses.6th-7th sessions (Emotion regulation)Definition of emotion, how emotions work, familiarity with emotion regulation skills. Emotion Identification Exercise, Emotion Registration Exercise. Identifying barriers to experiencing emotion in a healthy way and ways to overcome these barriers. Teaching creating short-term positive emotional experiences for experiencing positive emotional states.8th-10th sessions (Emotion regulation and distress tolerance in an MUD context)Explain craving and its connection to the experience of emotions. Introducing methods for identifying values. Importance of committed action based on a list of essential values in life. Develop new coping strategies in response to unpleasant emotions, sensations, and cognitions, especially craving as a multidimensional problem and teaching problem solving and behavior analysis.11th sessionBasic acceptance technique training. Introduce living in the present moment techniques.12th-13th sessionsInterpersonal effectiveness training. Participants learn assertiveness skills about substance users. Other skills include non-verbal communication, verbal communication, and problem-solving, decision-making, and listening skills.14th-16th sessionsReview of sessions. Elimination of ambiguities. Exercising skills in the presence of other people.\\n\\nDBT is a group intervention consisting of 16 sessions (meeting once a week for 90 minutes) with one psychotherapist and her co-therapist. The intervention protocol is an adaptation of DBT for SUD based on three basic manuals. 10 , 24 , 25 The primary objective of the DBT is to reduce dysfunction in emotion regulation and craving via increasing cessation rates and improving skills. A psychotherapist with a PhD delivered the intervention sessions (with a psychologist as co-therapist) and they were blind both to the existence of another group and to the study objectives. Table 1 shows the content covered in each DBT session.\\n\\nTable 1DBT content per sessionCessationContentPre-sessionExplanation of dialectical behavioral therapy, principles, and goals. Brief introduction to the content of each session. Familiarity with participants. Participants are given an intervention booklet to read at home.1st session (mindfulness 1)Introduce the concept of mindfulness and three mental states (wise, reasonable, emotional) and their relations with substance use.2nd session (mindfulness 2)Teach two clusters of mindfulness skills. The first includes viewing, participation, and description. The second includes a non-judgmental stance and inclusive self-consciousness.3rd sessionSummarize the mindfulness sessions – definition of addiction, standard therapies of addiction, introduction to and teaching of dialectical avoidance technique. Review the positive and negative aspects of abstinence. Explanation and investigation of relapse and its causes. Explaining the skill of the pure mind, the addicted mind, the types of behaviors related to the pure mentality and the addicted mentality, and preparing a list of supporters.4th-5th sessions (Distress tolerance)Teaching distraction strategies with five skills include activities, comparisons, emotions, thoughts, and enjoyment. Through enjoyable activities, focusing on work or other topics, counting, leaving the situation, paying attention to daily tasks, distracting from thoughts, and self-harm behaviors – teaching and training self-soothing with five senses.6th-7th sessions (Emotion regulation)Definition of emotion, how emotions work, familiarity with emotion regulation skills. Emotion Identification Exercise, Emotion Registration Exercise. Identifying barriers to experiencing emotion in a healthy way and ways to overcome these barriers. Teaching creating short-term positive emotional experiences for experiencing positive emotional states.8th-10th sessions (Emotion regulation and distress tolerance in an MUD context)Explain craving and its connection to the experience of emotions. Introducing methods for identifying values. Importance of committed action based on a list of essential values in life. Develop new coping strategies in response to unpleasant emotions, sensations, and cognitions, especially craving as a multidimensional problem and teaching problem solving and behavior analysis.11th sessionBasic acceptance technique training. Introduce living in the present moment techniques.12th-13th sessionsInterpersonal effectiveness training. Participants learn assertiveness skills about substance users. Other skills include non-verbal communication, verbal communication, and problem-solving, decision-making, and listening skills.14th-16th sessionsReview of sessions. Elimination of ambiguities. Exercising skills in the presence of other people.\\n\\n Psychoeducation This option is more ethical than not offering any intervention to the control group. A psychiatrist with five years of experience in addiction psychotherapy implemented this intervention. This intervention includes problem-solving skills, assertiveness, and craving management in eight sessions. Thepsychoeducation intervention was used to provide a basis for comparison with only those elements of the DBT intervention that are different from other psychotherapies. This intervention is utilized for MUD and health-related problems. The intention of this intervention is to provide individuals struggling with cravings and substance use disorder the knowledge needed to comprehensively appreciate their problems and the empowerment needed to cope with them. The psychoeducation intervention included information about the dangers of marijuana and the therapists also provided a pamphlet containing techniques for reduction of craving. 14 , 26 , 27\\nThis option is more ethical than not offering any intervention to the control group. A psychiatrist with five years of experience in addiction psychotherapy implemented this intervention. This intervention includes problem-solving skills, assertiveness, and craving management in eight sessions. Thepsychoeducation intervention was used to provide a basis for comparison with only those elements of the DBT intervention that are different from other psychotherapies. This intervention is utilized for MUD and health-related problems. The intention of this intervention is to provide individuals struggling with cravings and substance use disorder the knowledge needed to comprehensively appreciate their problems and the empowerment needed to cope with them. The psychoeducation intervention included information about the dangers of marijuana and the therapists also provided a pamphlet containing techniques for reduction of craving. 14 , 26 , 27\\n Therapists and treatment adherence To enable adherence to the principles of DBT to be checked, audios of the sessions were recorded with the consent of all participants. A DBT researcher and psychotherapist who was not involved in the treatment groups checked session content afterwards. Sessions were divided into 15-minute modules that were chosen for adherence checks at random. Treatment stance and occurrence and depth of DBT processes were appraised. Based on the treatment manual, modules were rated for adherence level as either adequate or not adequate. The majority (83%) were judged to have been conducted adequately.\\nTo enable adherence to the principles of DBT to be checked, audios of the sessions were recorded with the consent of all participants. A DBT researcher and psychotherapist who was not involved in the treatment groups checked session content afterwards. Sessions were divided into 15-minute modules that were chosen for adherence checks at random. Treatment stance and occurrence and depth of DBT processes were appraised. Based on the treatment manual, modules were rated for adherence level as either adequate or not adequate. The majority (83%) were judged to have been conducted adequately.\\n Statistical method Demographic information was gathered and reported as frequencies, means, and standard deviations and repeated measures ANOVA and chi-square tests were conducted for the outcomes using SPSS software, version 26.\\nDemographic information was gathered and reported as frequencies, means, and standard deviations and repeated measures ANOVA and chi-square tests were conducted for the outcomes using SPSS software, version 26.\",\n \"DBT is a group intervention consisting of 16 sessions (meeting once a week for 90 minutes) with one psychotherapist and her co-therapist. The intervention protocol is an adaptation of DBT for SUD based on three basic manuals. 10 , 24 , 25 The primary objective of the DBT is to reduce dysfunction in emotion regulation and craving via increasing cessation rates and improving skills. A psychotherapist with a PhD delivered the intervention sessions (with a psychologist as co-therapist) and they were blind both to the existence of another group and to the study objectives. Table 1 shows the content covered in each DBT session.\\n\\nTable 1DBT content per sessionCessationContentPre-sessionExplanation of dialectical behavioral therapy, principles, and goals. Brief introduction to the content of each session. Familiarity with participants. Participants are given an intervention booklet to read at home.1st session (mindfulness 1)Introduce the concept of mindfulness and three mental states (wise, reasonable, emotional) and their relations with substance use.2nd session (mindfulness 2)Teach two clusters of mindfulness skills. The first includes viewing, participation, and description. The second includes a non-judgmental stance and inclusive self-consciousness.3rd sessionSummarize the mindfulness sessions – definition of addiction, standard therapies of addiction, introduction to and teaching of dialectical avoidance technique. Review the positive and negative aspects of abstinence. Explanation and investigation of relapse and its causes. Explaining the skill of the pure mind, the addicted mind, the types of behaviors related to the pure mentality and the addicted mentality, and preparing a list of supporters.4th-5th sessions (Distress tolerance)Teaching distraction strategies with five skills include activities, comparisons, emotions, thoughts, and enjoyment. Through enjoyable activities, focusing on work or other topics, counting, leaving the situation, paying attention to daily tasks, distracting from thoughts, and self-harm behaviors – teaching and training self-soothing with five senses.6th-7th sessions (Emotion regulation)Definition of emotion, how emotions work, familiarity with emotion regulation skills. Emotion Identification Exercise, Emotion Registration Exercise. Identifying barriers to experiencing emotion in a healthy way and ways to overcome these barriers. Teaching creating short-term positive emotional experiences for experiencing positive emotional states.8th-10th sessions (Emotion regulation and distress tolerance in an MUD context)Explain craving and its connection to the experience of emotions. Introducing methods for identifying values. Importance of committed action based on a list of essential values in life. Develop new coping strategies in response to unpleasant emotions, sensations, and cognitions, especially craving as a multidimensional problem and teaching problem solving and behavior analysis.11th sessionBasic acceptance technique training. Introduce living in the present moment techniques.12th-13th sessionsInterpersonal effectiveness training. Participants learn assertiveness skills about substance users. Other skills include non-verbal communication, verbal communication, and problem-solving, decision-making, and listening skills.14th-16th sessionsReview of sessions. Elimination of ambiguities. Exercising skills in the presence of other people.\\n\",\n \"This option is more ethical than not offering any intervention to the control group. A psychiatrist with five years of experience in addiction psychotherapy implemented this intervention. This intervention includes problem-solving skills, assertiveness, and craving management in eight sessions. Thepsychoeducation intervention was used to provide a basis for comparison with only those elements of the DBT intervention that are different from other psychotherapies. This intervention is utilized for MUD and health-related problems. The intention of this intervention is to provide individuals struggling with cravings and substance use disorder the knowledge needed to comprehensively appreciate their problems and the empowerment needed to cope with them. The psychoeducation intervention included information about the dangers of marijuana and the therapists also provided a pamphlet containing techniques for reduction of craving. 14 , 26 , 27\",\n \"To enable adherence to the principles of DBT to be checked, audios of the sessions were recorded with the consent of all participants. A DBT researcher and psychotherapist who was not involved in the treatment groups checked session content afterwards. Sessions were divided into 15-minute modules that were chosen for adherence checks at random. Treatment stance and occurrence and depth of DBT processes were appraised. Based on the treatment manual, modules were rated for adherence level as either adequate or not adequate. The majority (83%) were judged to have been conducted adequately.\",\n \"Demographic information was gathered and reported as frequencies, means, and standard deviations and repeated measures ANOVA and chi-square tests were conducted for the outcomes using SPSS software, version 26.\",\n \"Written informed consent was obtained from all participants before initiation of the research. The tools used in this study were all filled-out anonymously, and an ID code was used to maintain the confidentiality of personal information (Ir.kums.rce.1398.1203). At the end of the research process, dialectical behavior therapy was also provided to the control group. This study is registered with the Thailand Registry of Clinical Trials (TCTR20200319007).\",\n \"In the psycho-education group, 24/31 participants completed all sessions, compared to 29/30 members of the DBT group (retention rates: 77% in the control group vs. 96% in the DBT group). Additionally, 96% (29/30) of the DBT group members completed the two-month follow-up, whereas 64.5% (20/31) of the control group completed follow-up ( Figure 1 ). The chi-square test was applied, showing associations between group and retention, with χ2= 4.95, p = 0.02 for post-treatment and, χ2= 9.97, p = 0.002 for the follow-up phase. Consequently, DBT retention rates were significantly higher than psycho-education retention rates at post-treatment and follow-up.\\n\\nFigure 1Consort diagram.\\n\",\n \"To enable assessment of the acceptability and appropriateness of intervention, patients completed the AIM and IAM scales in the post-treatment phase. The acceptability scores were 16.57 for DBT and 9.6 for the control group (p < 0.05). The appropriateness scores were17.03 for DBT and 10.7 for the psychoeducation group (p < 0.05). Since there are no standards for these measures, these points were transferred to Likert-based questionnaire scales. For acceptability, the results equated to ‘’agree’’ for the DBT group versus ‘’neither agree nor disagree’’ for the psychoeducation group. For appropriateness, the results equated to ‘’completely agree’’ for the DBT group versus ‘’neither agree nor disagree’’ for the psychoeducation group.\",\n \"Participants’ demographic variables are shown in Table 2 . Analyses showed that there were no significant differences between the two groups regarding these variables. It should be noted that since over 97% of the participants were male from the beginning, the results were reported only for men.\\n\\nTable 2Mean and standard deviation of demographic variables in the intervention and control groups at the test phaseVariableIntervention groupControl groupp-valueEducational level* 0.2No higher education, n (%)2 (6)6 (19)Diploma, n (%)14 (46)15 (48)University student or graduate, n (%)14 (46)10 (33)Age †25.6 (5.67)27.19 (7.48)0.3Months of marijuana use19.53 (5.9)17.48 (6.03)0.1Craving (total) † Pre-test45.2 (8.3)47.9 (10.2)0.2Post-test42.13 (7.7)44.48 (8.1)0.1Follow-up42.66 (9.25)45.8 (8.4)0.1Data presented as mean (standard deviation), unless otherwise specified.* Chi-square test.† Independent t test.\\n\\nData presented as mean (standard deviation), unless otherwise specified.\\n* Chi-square test.\\n† Independent t test.\",\n \"The hypothesis of equal covariance matrices was examined for craving (Box’s M = 3.63, P = 0.74). The results of this test indicate homogeneity of covariance matrices. Mauchly’s test of sphericity also showed that the sphericity assumption was not violated (p = 0.42 and Mauchly’s W = 0.97).\\nThe results of the intergroup test and intergroup relations are also presented in Table 3 . As shown in Table 3 , the effect levels for craving (F = 3.52, p > 0.05) suggest that there is no significant difference between groups. These results were repeated for three of the subscales of craving: compulsivity, expectancy, and purposefulness. However, the emotionality subscale results showed a significant reduction in the DBT group compared to the control group 10.6 vs. 14.4 in the post-test and 10.43 vs. 13.26 in the follow-up phase (F = 19.94, p < 0.05).\\n\\nTable 3Repeated measures ANOVA for variables for the DBT group and control group in the pre-test, post-test, and follow-upVariable/sourceType III sum of squaresdfMean squareFSigPartial eta squaredObserved powerCraving Tests of within-subjects effects Factor1347.6962173.842.60.07*0.040.512Factor1 × group4.7622.380.0360.110.0010.055Error (factor1)7845.15411866.48 Tests of between-subjects effects Group341.0841341.083.520.06 †0.0560.455Error5708.795996.75 Emotionality Tests of within-subjects effects Factor1257.332128.6511.690.00 †0.1650.9Factor1 × group74.70237.350.370.03*0.050.63Error (factor1)1297.8111810.99 Tests of between-subjects effects Group283.891283.8919.940.00 †0.250.9Error839.9065914.23 * Significant to 0.05.† Significant to 0.01.\\n\\n* Significant to 0.05.\\n† Significant to 0.01.\\nWith regard to cessation, the results indicated that DBT achieved a higher rate of cessation than the control treatment in both the post-test and at follow-up, ( Table 4 ) (p < 0.05). It was also found that among those who continued to use the drug, the number of use days per month in the post-test and the follow-up periods (two months) was significantly lower in the intervention group than the control group.\\n\\nTable 4Cessation and consumption between groups DBTControlp-valueCessation* Post-test14 (46%)5 (16%)0.01\\n‡Follow-up12 (40%)3 (9.5%)0.006\\n‡Number of days use † Post-test2.43±1.87.5±5.030.00\\n‡Follow-up3.44±1.918.75±3.270.00\\n‡Bold p-values are significant at critical levels.* The chi-square test was applied.† T test for independent samples.‡ Significant to 0.01.\\n\\nBold p-values are significant at critical levels.\\n* The chi-square test was applied.\\n† T test for independent samples.\\n‡ Significant to 0.01.\",\n \"Despite these positive results, the present study also has some limitations. First, in order to evaluate the most significant treatment components (such as mindfulness and distress tolerance), no groups received the third wave versions of other therapies (ACT or MBSR). This study only had a two-month follow-up period and could not conduct long-term evaluation due to the study site’s medical and infrastructure conditions. It is recommended that future research should examine mediating and confounding variables to investigate the results of similar research to the present study. Other factors affecting relapse and recurrence could also be examined. Moreover, women should be investigated so that gender-related implications can be determined.\"\n]"},"other_sections_sec_types":{"kind":"list like","value":[null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null],"string":"[\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null\n]"},"all_sections_titles":{"kind":"list like","value":["Introduction","Methods","Trial design","Sample size","Selection criteria","Participants, procedures, and randomization","Blinding","Outcome measures","Abstinence","Marijuana smoking","Craving","Acceptability","Appropriateness","Intervention","Dialectical behavior therapy","Psychoeducation","Therapists and treatment adherence","Statistical method","Ethical considerations","Results","Feasibility","Acceptability and appropriateness","Participant characteristics","Efficacy outcomes","Discussion and conclusion","Conclusion","Limitations and future directions"],"string":"[\n \"Introduction\",\n \"Methods\",\n \"Trial design\",\n \"Sample size\",\n \"Selection criteria\",\n \"Participants, procedures, and randomization\",\n \"Blinding\",\n \"Outcome measures\",\n \"Abstinence\",\n \"Marijuana smoking\",\n \"Craving\",\n \"Acceptability\",\n \"Appropriateness\",\n \"Intervention\",\n \"Dialectical behavior therapy\",\n \"Psychoeducation\",\n \"Therapists and treatment adherence\",\n \"Statistical method\",\n \"Ethical considerations\",\n \"Results\",\n \"Feasibility\",\n \"Acceptability and appropriateness\",\n \"Participant characteristics\",\n \"Efficacy outcomes\",\n \"Discussion and conclusion\",\n \"Conclusion\",\n \"Limitations and future directions\"\n]"},"all_sections_texts":{"kind":"list like","value":["Marijuana is the most prevalent substance among those reported to be a significant problem among people seeking treatment for substance abuse. 1 According to WHO reports, more than 140 million people consume marijuana every year. 2 With regard to Iran, recent evidence shows that more than 5% of people consume marijuana every year, predominantly young males. However, in view of the harsh marijuana prohibition policy of the Iranian government, most clinicians estimate that these rates have been hugely underestimated. 3 Marijuana, as an illegal drug, is associated with significant physical, psychological, and social consequences. 4 Studies have shown that regular and heavy marijuana use patterns correlate with increased risk of mood disorders, anxiety, and psychotic episodes and although causality has not been demonstrated, these patterns can increase the course of mental health problems. 5 Also, several medical problems such as respiratory system deficits, stroke, myocardial infarction, and digestive tract cancers are associated with marijuana use patterns, especially among those with marijuana use disorder (MUD). 6 , 7 Approximately one in three marijuana users meet the criteria for MUD based on the DSM-5, and this proportion is rising. 8 One of the most important psychological problems in substance use disorder treatment is craving. Craving is a factor identified as the root cause of relapses and treatment failures. 9 , 10 MUD patients report visual, tactile, and olfactory cues related to craving and compulsivity sensations. 11 Based on these results, clinicians have tried to treat patients with marijuana use disorder.\nTo date, the Food and Drug Administration (FDA) in the United States has not approved any psycho-pharmacotherapy for MUD, and therefore psycho-social interventions have received particular attention. 12 The most widely used psychological treatment in the substance use disorder (SUD) context is cognitive-behavioral therapy (CBT). 13 Results showed that CBT is somewhat effective for SUD, but that most patients with MUD do not achieve cessation and are not motivated to continue skills training during follow-up. Relapse rates therefore remain a considerable limitation of treatment. 10 , 13 One of the main reasons for this low success rate lies in the limitations of CBT. First, CBT protocols do not focus on comorbid problems, whereas most patients with SUD have at least one psychiatric or psychological problem. Secondly, cognitive restructuring may not be useful for all SUD patients. Some patients may be unable to restructure their dysfunctional cognition and core beliefs despite receiving CBT. 10 , 12 , 13 Furthermore, emotion regulation deficits are strongly associated with increased addictive behaviors such as SUD. With emotion regulation, people adjust their emotional experiences related to distressing and unpleasant events. Emotion regulation is essential for successful coping with environmental demands and personal welfare. 14 On the behavioral level, studies have found that marijuana craving cues are strongly associated with deficits in regulation of negative affect and emotions. 14 , 15 Also, on the neural level, during reappraisal of negative stimuli, patients with MUD and regular users have shown altered neural activity and functional connectivity. Moreover, marijuana use is related to dysfunctions in the amygdala and in amygdala-dorsolateral prefrontal cortex (DLPFC) coupling activity. 15 Together, these findings demonstrate that emotion-based psychotherapy must manage comorbid problems and eliminate the limitations of CBT.\nOne of the psychotherapies in the third wave behavioral therapy cluster is dialectical behavior therapy (DBT). DBT has been described as intervention in emotion regulation deficits by focusing on dangerous impulses in borderline personality disorder and substance use disorder. The goals of DBT include improving and regulating emotions as a primary mechanism of change. DBT is a trans-diagnostic treatment and suitable for comorbid problems. DBT trains skills including distress tolerance, interpersonal effectiveness, emotion regulation, and mindfulness. Overall, in the context of SUD, DBT teaches emotion regulation skills to decrease engagement in pathological emotion regulation strategies. It also intervenes in low quality of life situations, reduces drug-seeking behavior, and helps patients function adaptively by accepting unpleasant emotions such as craving. 9 , 10 , 14\nResearch literature shows the efficacy and effectiveness of DBT in various comorbid problems and diseases such as suicide, 16 forensic psychiatric patients, 17 and irritable bowel syndrome. 18 Nevertheless, studies have reported contradictory results for the effectiveness of implementing DBT in various SUD populations. 19 , 20 Furthermore, the literature has recommended using larger samples, clearer instruments to measure outcome variables, and specific and integrated protocols. 21\nAdditionally, according to our investigations, no DBT randomized clinical trials have been conducted that investigated cessation in MUD patients (with or without comorbid problems). A DBT intervention aimed at increasing the cessation rate and reducing craving among MUD patients was developed for this study.\nThis pilot trial investigated the feasibility and preliminary efficacy of DBT relative to a psycho-education intervention that was controlled for time duration and attention. Feasibility was assessed via satisfaction and session completion rates. Preliminary efficacy was evaluated via the impact of DBT on cessation rate and reduction of consumption rates, compared to the psycho-education intervention. Although craving and acceptance of craving are not the primary goals of DBT, they were also compared across the two interventions."," Trial design This study was designed as a controlled randomized clinical trial, including pretest, post-test, and two-month follow-up phases.\nThis study was designed as a controlled randomized clinical trial, including pretest, post-test, and two-month follow-up phases.\n Sample size Since the sampling method comprises snowball sampling and strict eligibility criteria were applied, on the basis of data from similar studies 10 it was determined that at least 20 participants were needed in each group. However, in view of the predicted retention rates, we selected 30 patients for each group.\nSince the sampling method comprises snowball sampling and strict eligibility criteria were applied, on the basis of data from similar studies 10 it was determined that at least 20 participants were needed in each group. However, in view of the predicted retention rates, we selected 30 patients for each group.\n Selection criteria The inclusion criteria were as follows: 1) diagnosis of marijuana use disorder; 2) age 18 years or over; 3) no current or past history of major psychiatric disorders; 4) no other concurrent SUD treatment; and 5) willingness to attend intervention sessions, complete surveys, and take tests (questionnaires and urine test kits).\nExclusion criteria were as follows: 1) unwillingness to participate; 2) not participating in intervention sessions for more than two weeks; 3) starting secondary psychotherapy; and 4) consuming methamphetamine, amphetamine, cannabis, methadone, benzodiazepines, or morphine during the research stages.\nThe inclusion criteria were as follows: 1) diagnosis of marijuana use disorder; 2) age 18 years or over; 3) no current or past history of major psychiatric disorders; 4) no other concurrent SUD treatment; and 5) willingness to attend intervention sessions, complete surveys, and take tests (questionnaires and urine test kits).\nExclusion criteria were as follows: 1) unwillingness to participate; 2) not participating in intervention sessions for more than two weeks; 3) starting secondary psychotherapy; and 4) consuming methamphetamine, amphetamine, cannabis, methadone, benzodiazepines, or morphine during the research stages.\n Participants, procedures, and randomization Since there are no cannabis use disorder treatment centers in Iran, there is no specific place to select patients. Furthermore, patients at drug treatment centers are referred for treatment of other substance use disorders and comorbidity of drug use is one of the exclusion criteria for this study, since it could lead to misleading results. Therefore, the relatives and acquaintances of those who had been referred to the drug treatment center were interviewed. From November 1, 2019, to November 5, 2019, 15 relatives and family members of drug users referred to drug treatment centers were diagnosed with MUD at this stage. Then, using snowball sampling, after 15 days of investigation, a total of 83 patients were diagnosed with MUD. Seventy-five of the 83 MUD patients who were approached consented, eight declined to participate, and 14 were ineligible. The primary reasons for declining were anxiety about addiction stigma and time constraints. Most of the ineligible patients had multiple illicit use disorders, so they did not meet the study criteria. Therefore, 61 patients completed the baseline assessment and were included in the current analyses. These patients were randomly assigned to each group using a random number table. The interventions were implemented from December 1, 2019, to March 20, 2020. The follow-up phase started on March 21, 2020, and ended on May 20, 2020, (at two months’ follow-up). In order to test for exclusion criteria before each session, a six-drug test kit for methamphetamine, amphetamine, cannabis, methadone, benzodiazepines, and morphine was administered to individuals using urine samples.\nSince there are no cannabis use disorder treatment centers in Iran, there is no specific place to select patients. Furthermore, patients at drug treatment centers are referred for treatment of other substance use disorders and comorbidity of drug use is one of the exclusion criteria for this study, since it could lead to misleading results. Therefore, the relatives and acquaintances of those who had been referred to the drug treatment center were interviewed. From November 1, 2019, to November 5, 2019, 15 relatives and family members of drug users referred to drug treatment centers were diagnosed with MUD at this stage. Then, using snowball sampling, after 15 days of investigation, a total of 83 patients were diagnosed with MUD. Seventy-five of the 83 MUD patients who were approached consented, eight declined to participate, and 14 were ineligible. The primary reasons for declining were anxiety about addiction stigma and time constraints. Most of the ineligible patients had multiple illicit use disorders, so they did not meet the study criteria. Therefore, 61 patients completed the baseline assessment and were included in the current analyses. These patients were randomly assigned to each group using a random number table. The interventions were implemented from December 1, 2019, to March 20, 2020. The follow-up phase started on March 21, 2020, and ended on May 20, 2020, (at two months’ follow-up). In order to test for exclusion criteria before each session, a six-drug test kit for methamphetamine, amphetamine, cannabis, methadone, benzodiazepines, and morphine was administered to individuals using urine samples.\n Blinding Both groups were blind to the existence of another group in the study. However, patients were informed about participating in research but not about another group. One day after the end of treatment, the post-test was carried out by mental health technicians with a master’s degree in psychology.\nBoth groups were blind to the existence of another group in the study. However, patients were informed about participating in research but not about another group. One day after the end of treatment, the post-test was carried out by mental health technicians with a master’s degree in psychology.\n Outcome measures Abstinence A marijuana urine test kit prepared by Kian Teb Company (officially licensed by the National Medical Device Directorate IR. IRAN) was used to identify abstainers.\nA marijuana urine test kit prepared by Kian Teb Company (officially licensed by the National Medical Device Directorate IR. IRAN) was used to identify abstainers.\n Marijuana smoking A self-report scale was designed for patients who had lapsed during the post-test follow-up. On this scale, patients indicate the number of days of consumption over 30-day periods. The first thirty days after the last intervention session was considered the post-test smoking period and the second-month follow-up was considered as the follow-up marijuana use period.\nA self-report scale was designed for patients who had lapsed during the post-test follow-up. On this scale, patients indicate the number of days of consumption over 30-day periods. The first thirty days after the last intervention session was considered the post-test smoking period and the second-month follow-up was considered as the follow-up marijuana use period.\n Craving The Marijuana Craving Questionnaire (MCQ) short-form is a 12-item self-report questionnaire with ten items for subjective assessment of cannabis craving. The scale covers 4 factors: compulsivity, emotionality, expectancy, and purposefulness. According to how patients were thinking or feeling ‘’right now,’’ they placed checkmarks on the questionnaire to endorse responses ranging from 1 or strongly disagree to 7 or strongly agree. Results showed that this questionnaire’s internal consistency is adequate (α = 0.90). The measure was administered following a 12-hour deprivation period. The typical onset of marijuana craving and withdrawal symptoms is observed within approximately one day of cessation and so the current paper’s questionnaire scores can be conceptualized as an index of the propensity to experience marijuana craving following deprivation. 22 In Iranian MUD patients, the MCQ had internal consistency of α = 0.87. Details of the MCQ’s psychometrics properties will be published as a separate study as soon as possible.\nThe Marijuana Craving Questionnaire (MCQ) short-form is a 12-item self-report questionnaire with ten items for subjective assessment of cannabis craving. The scale covers 4 factors: compulsivity, emotionality, expectancy, and purposefulness. According to how patients were thinking or feeling ‘’right now,’’ they placed checkmarks on the questionnaire to endorse responses ranging from 1 or strongly disagree to 7 or strongly agree. Results showed that this questionnaire’s internal consistency is adequate (α = 0.90). The measure was administered following a 12-hour deprivation period. The typical onset of marijuana craving and withdrawal symptoms is observed within approximately one day of cessation and so the current paper’s questionnaire scores can be conceptualized as an index of the propensity to experience marijuana craving following deprivation. 22 In Iranian MUD patients, the MCQ had internal consistency of α = 0.87. Details of the MCQ’s psychometrics properties will be published as a separate study as soon as possible.\n Acceptability The Acceptability of Intervention Measure (AIM) was employed to measure the acceptability of interventions. AIM response items are measured on a 5-point Likert scale (from Completely Disagree with 1 point to Completely Agree with 5 points). The mean of points scored for each item is taken as the final score. This questionnaire developed by Weiner et al., and they reported Cronbach’s ɑ = 85 for internal consistency. 23\nThe Acceptability of Intervention Measure (AIM) was employed to measure the acceptability of interventions. AIM response items are measured on a 5-point Likert scale (from Completely Disagree with 1 point to Completely Agree with 5 points). The mean of points scored for each item is taken as the final score. This questionnaire developed by Weiner et al., and they reported Cronbach’s ɑ = 85 for internal consistency. 23\n Appropriateness The Intervention Appropriateness Measure (IAM) was used for Appropriateness. The IAM consist of a four-item scale that measures perceived intervention appropriateness. Items are measured on a five-point Likert scale (Completely Disagree to Completely Agree), and the mean of points scored for each item is taken as the final score. Higher scores mean that the participant feels this intervention is more appropriate for him/her. For this tool, Cronbach’s ɑ = 0.91 and all Factor Loadings are reported as higher than 0.8. 23\nThe Intervention Appropriateness Measure (IAM) was used for Appropriateness. The IAM consist of a four-item scale that measures perceived intervention appropriateness. Items are measured on a five-point Likert scale (Completely Disagree to Completely Agree), and the mean of points scored for each item is taken as the final score. Higher scores mean that the participant feels this intervention is more appropriate for him/her. For this tool, Cronbach’s ɑ = 0.91 and all Factor Loadings are reported as higher than 0.8. 23\n Abstinence A marijuana urine test kit prepared by Kian Teb Company (officially licensed by the National Medical Device Directorate IR. IRAN) was used to identify abstainers.\nA marijuana urine test kit prepared by Kian Teb Company (officially licensed by the National Medical Device Directorate IR. IRAN) was used to identify abstainers.\n Marijuana smoking A self-report scale was designed for patients who had lapsed during the post-test follow-up. On this scale, patients indicate the number of days of consumption over 30-day periods. The first thirty days after the last intervention session was considered the post-test smoking period and the second-month follow-up was considered as the follow-up marijuana use period.\nA self-report scale was designed for patients who had lapsed during the post-test follow-up. On this scale, patients indicate the number of days of consumption over 30-day periods. The first thirty days after the last intervention session was considered the post-test smoking period and the second-month follow-up was considered as the follow-up marijuana use period.\n Craving The Marijuana Craving Questionnaire (MCQ) short-form is a 12-item self-report questionnaire with ten items for subjective assessment of cannabis craving. The scale covers 4 factors: compulsivity, emotionality, expectancy, and purposefulness. According to how patients were thinking or feeling ‘’right now,’’ they placed checkmarks on the questionnaire to endorse responses ranging from 1 or strongly disagree to 7 or strongly agree. Results showed that this questionnaire’s internal consistency is adequate (α = 0.90). The measure was administered following a 12-hour deprivation period. The typical onset of marijuana craving and withdrawal symptoms is observed within approximately one day of cessation and so the current paper’s questionnaire scores can be conceptualized as an index of the propensity to experience marijuana craving following deprivation. 22 In Iranian MUD patients, the MCQ had internal consistency of α = 0.87. Details of the MCQ’s psychometrics properties will be published as a separate study as soon as possible.\nThe Marijuana Craving Questionnaire (MCQ) short-form is a 12-item self-report questionnaire with ten items for subjective assessment of cannabis craving. The scale covers 4 factors: compulsivity, emotionality, expectancy, and purposefulness. According to how patients were thinking or feeling ‘’right now,’’ they placed checkmarks on the questionnaire to endorse responses ranging from 1 or strongly disagree to 7 or strongly agree. Results showed that this questionnaire’s internal consistency is adequate (α = 0.90). The measure was administered following a 12-hour deprivation period. The typical onset of marijuana craving and withdrawal symptoms is observed within approximately one day of cessation and so the current paper’s questionnaire scores can be conceptualized as an index of the propensity to experience marijuana craving following deprivation. 22 In Iranian MUD patients, the MCQ had internal consistency of α = 0.87. Details of the MCQ’s psychometrics properties will be published as a separate study as soon as possible.\n Acceptability The Acceptability of Intervention Measure (AIM) was employed to measure the acceptability of interventions. AIM response items are measured on a 5-point Likert scale (from Completely Disagree with 1 point to Completely Agree with 5 points). The mean of points scored for each item is taken as the final score. This questionnaire developed by Weiner et al., and they reported Cronbach’s ɑ = 85 for internal consistency. 23\nThe Acceptability of Intervention Measure (AIM) was employed to measure the acceptability of interventions. AIM response items are measured on a 5-point Likert scale (from Completely Disagree with 1 point to Completely Agree with 5 points). The mean of points scored for each item is taken as the final score. This questionnaire developed by Weiner et al., and they reported Cronbach’s ɑ = 85 for internal consistency. 23\n Appropriateness The Intervention Appropriateness Measure (IAM) was used for Appropriateness. The IAM consist of a four-item scale that measures perceived intervention appropriateness. Items are measured on a five-point Likert scale (Completely Disagree to Completely Agree), and the mean of points scored for each item is taken as the final score. Higher scores mean that the participant feels this intervention is more appropriate for him/her. For this tool, Cronbach’s ɑ = 0.91 and all Factor Loadings are reported as higher than 0.8. 23\nThe Intervention Appropriateness Measure (IAM) was used for Appropriateness. The IAM consist of a four-item scale that measures perceived intervention appropriateness. Items are measured on a five-point Likert scale (Completely Disagree to Completely Agree), and the mean of points scored for each item is taken as the final score. Higher scores mean that the participant feels this intervention is more appropriate for him/her. For this tool, Cronbach’s ɑ = 0.91 and all Factor Loadings are reported as higher than 0.8. 23","This study was designed as a controlled randomized clinical trial, including pretest, post-test, and two-month follow-up phases.","Since the sampling method comprises snowball sampling and strict eligibility criteria were applied, on the basis of data from similar studies 10 it was determined that at least 20 participants were needed in each group. However, in view of the predicted retention rates, we selected 30 patients for each group.","The inclusion criteria were as follows: 1) diagnosis of marijuana use disorder; 2) age 18 years or over; 3) no current or past history of major psychiatric disorders; 4) no other concurrent SUD treatment; and 5) willingness to attend intervention sessions, complete surveys, and take tests (questionnaires and urine test kits).\nExclusion criteria were as follows: 1) unwillingness to participate; 2) not participating in intervention sessions for more than two weeks; 3) starting secondary psychotherapy; and 4) consuming methamphetamine, amphetamine, cannabis, methadone, benzodiazepines, or morphine during the research stages.","Since there are no cannabis use disorder treatment centers in Iran, there is no specific place to select patients. Furthermore, patients at drug treatment centers are referred for treatment of other substance use disorders and comorbidity of drug use is one of the exclusion criteria for this study, since it could lead to misleading results. Therefore, the relatives and acquaintances of those who had been referred to the drug treatment center were interviewed. From November 1, 2019, to November 5, 2019, 15 relatives and family members of drug users referred to drug treatment centers were diagnosed with MUD at this stage. Then, using snowball sampling, after 15 days of investigation, a total of 83 patients were diagnosed with MUD. Seventy-five of the 83 MUD patients who were approached consented, eight declined to participate, and 14 were ineligible. The primary reasons for declining were anxiety about addiction stigma and time constraints. Most of the ineligible patients had multiple illicit use disorders, so they did not meet the study criteria. Therefore, 61 patients completed the baseline assessment and were included in the current analyses. These patients were randomly assigned to each group using a random number table. The interventions were implemented from December 1, 2019, to March 20, 2020. The follow-up phase started on March 21, 2020, and ended on May 20, 2020, (at two months’ follow-up). In order to test for exclusion criteria before each session, a six-drug test kit for methamphetamine, amphetamine, cannabis, methadone, benzodiazepines, and morphine was administered to individuals using urine samples.","Both groups were blind to the existence of another group in the study. However, patients were informed about participating in research but not about another group. One day after the end of treatment, the post-test was carried out by mental health technicians with a master’s degree in psychology."," Abstinence A marijuana urine test kit prepared by Kian Teb Company (officially licensed by the National Medical Device Directorate IR. IRAN) was used to identify abstainers.\nA marijuana urine test kit prepared by Kian Teb Company (officially licensed by the National Medical Device Directorate IR. IRAN) was used to identify abstainers.\n Marijuana smoking A self-report scale was designed for patients who had lapsed during the post-test follow-up. On this scale, patients indicate the number of days of consumption over 30-day periods. The first thirty days after the last intervention session was considered the post-test smoking period and the second-month follow-up was considered as the follow-up marijuana use period.\nA self-report scale was designed for patients who had lapsed during the post-test follow-up. On this scale, patients indicate the number of days of consumption over 30-day periods. The first thirty days after the last intervention session was considered the post-test smoking period and the second-month follow-up was considered as the follow-up marijuana use period.\n Craving The Marijuana Craving Questionnaire (MCQ) short-form is a 12-item self-report questionnaire with ten items for subjective assessment of cannabis craving. The scale covers 4 factors: compulsivity, emotionality, expectancy, and purposefulness. According to how patients were thinking or feeling ‘’right now,’’ they placed checkmarks on the questionnaire to endorse responses ranging from 1 or strongly disagree to 7 or strongly agree. Results showed that this questionnaire’s internal consistency is adequate (α = 0.90). The measure was administered following a 12-hour deprivation period. The typical onset of marijuana craving and withdrawal symptoms is observed within approximately one day of cessation and so the current paper’s questionnaire scores can be conceptualized as an index of the propensity to experience marijuana craving following deprivation. 22 In Iranian MUD patients, the MCQ had internal consistency of α = 0.87. Details of the MCQ’s psychometrics properties will be published as a separate study as soon as possible.\nThe Marijuana Craving Questionnaire (MCQ) short-form is a 12-item self-report questionnaire with ten items for subjective assessment of cannabis craving. The scale covers 4 factors: compulsivity, emotionality, expectancy, and purposefulness. According to how patients were thinking or feeling ‘’right now,’’ they placed checkmarks on the questionnaire to endorse responses ranging from 1 or strongly disagree to 7 or strongly agree. Results showed that this questionnaire’s internal consistency is adequate (α = 0.90). The measure was administered following a 12-hour deprivation period. The typical onset of marijuana craving and withdrawal symptoms is observed within approximately one day of cessation and so the current paper’s questionnaire scores can be conceptualized as an index of the propensity to experience marijuana craving following deprivation. 22 In Iranian MUD patients, the MCQ had internal consistency of α = 0.87. Details of the MCQ’s psychometrics properties will be published as a separate study as soon as possible.\n Acceptability The Acceptability of Intervention Measure (AIM) was employed to measure the acceptability of interventions. AIM response items are measured on a 5-point Likert scale (from Completely Disagree with 1 point to Completely Agree with 5 points). The mean of points scored for each item is taken as the final score. This questionnaire developed by Weiner et al., and they reported Cronbach’s ɑ = 85 for internal consistency. 23\nThe Acceptability of Intervention Measure (AIM) was employed to measure the acceptability of interventions. AIM response items are measured on a 5-point Likert scale (from Completely Disagree with 1 point to Completely Agree with 5 points). The mean of points scored for each item is taken as the final score. This questionnaire developed by Weiner et al., and they reported Cronbach’s ɑ = 85 for internal consistency. 23\n Appropriateness The Intervention Appropriateness Measure (IAM) was used for Appropriateness. The IAM consist of a four-item scale that measures perceived intervention appropriateness. Items are measured on a five-point Likert scale (Completely Disagree to Completely Agree), and the mean of points scored for each item is taken as the final score. Higher scores mean that the participant feels this intervention is more appropriate for him/her. For this tool, Cronbach’s ɑ = 0.91 and all Factor Loadings are reported as higher than 0.8. 23\nThe Intervention Appropriateness Measure (IAM) was used for Appropriateness. The IAM consist of a four-item scale that measures perceived intervention appropriateness. Items are measured on a five-point Likert scale (Completely Disagree to Completely Agree), and the mean of points scored for each item is taken as the final score. Higher scores mean that the participant feels this intervention is more appropriate for him/her. For this tool, Cronbach’s ɑ = 0.91 and all Factor Loadings are reported as higher than 0.8. 23","A marijuana urine test kit prepared by Kian Teb Company (officially licensed by the National Medical Device Directorate IR. IRAN) was used to identify abstainers.","A self-report scale was designed for patients who had lapsed during the post-test follow-up. On this scale, patients indicate the number of days of consumption over 30-day periods. The first thirty days after the last intervention session was considered the post-test smoking period and the second-month follow-up was considered as the follow-up marijuana use period.","The Marijuana Craving Questionnaire (MCQ) short-form is a 12-item self-report questionnaire with ten items for subjective assessment of cannabis craving. The scale covers 4 factors: compulsivity, emotionality, expectancy, and purposefulness. According to how patients were thinking or feeling ‘’right now,’’ they placed checkmarks on the questionnaire to endorse responses ranging from 1 or strongly disagree to 7 or strongly agree. Results showed that this questionnaire’s internal consistency is adequate (α = 0.90). The measure was administered following a 12-hour deprivation period. The typical onset of marijuana craving and withdrawal symptoms is observed within approximately one day of cessation and so the current paper’s questionnaire scores can be conceptualized as an index of the propensity to experience marijuana craving following deprivation. 22 In Iranian MUD patients, the MCQ had internal consistency of α = 0.87. Details of the MCQ’s psychometrics properties will be published as a separate study as soon as possible.","The Acceptability of Intervention Measure (AIM) was employed to measure the acceptability of interventions. AIM response items are measured on a 5-point Likert scale (from Completely Disagree with 1 point to Completely Agree with 5 points). The mean of points scored for each item is taken as the final score. This questionnaire developed by Weiner et al., and they reported Cronbach’s ɑ = 85 for internal consistency. 23","The Intervention Appropriateness Measure (IAM) was used for Appropriateness. The IAM consist of a four-item scale that measures perceived intervention appropriateness. Items are measured on a five-point Likert scale (Completely Disagree to Completely Agree), and the mean of points scored for each item is taken as the final score. Higher scores mean that the participant feels this intervention is more appropriate for him/her. For this tool, Cronbach’s ɑ = 0.91 and all Factor Loadings are reported as higher than 0.8. 23"," Dialectical behavior therapy DBT is a group intervention consisting of 16 sessions (meeting once a week for 90 minutes) with one psychotherapist and her co-therapist. The intervention protocol is an adaptation of DBT for SUD based on three basic manuals. 10 , 24 , 25 The primary objective of the DBT is to reduce dysfunction in emotion regulation and craving via increasing cessation rates and improving skills. A psychotherapist with a PhD delivered the intervention sessions (with a psychologist as co-therapist) and they were blind both to the existence of another group and to the study objectives. Table 1 shows the content covered in each DBT session.\n\nTable 1DBT content per sessionCessationContentPre-sessionExplanation of dialectical behavioral therapy, principles, and goals. Brief introduction to the content of each session. Familiarity with participants. Participants are given an intervention booklet to read at home.1st session (mindfulness 1)Introduce the concept of mindfulness and three mental states (wise, reasonable, emotional) and their relations with substance use.2nd session (mindfulness 2)Teach two clusters of mindfulness skills. The first includes viewing, participation, and description. The second includes a non-judgmental stance and inclusive self-consciousness.3rd sessionSummarize the mindfulness sessions – definition of addiction, standard therapies of addiction, introduction to and teaching of dialectical avoidance technique. Review the positive and negative aspects of abstinence. Explanation and investigation of relapse and its causes. Explaining the skill of the pure mind, the addicted mind, the types of behaviors related to the pure mentality and the addicted mentality, and preparing a list of supporters.4th-5th sessions (Distress tolerance)Teaching distraction strategies with five skills include activities, comparisons, emotions, thoughts, and enjoyment. Through enjoyable activities, focusing on work or other topics, counting, leaving the situation, paying attention to daily tasks, distracting from thoughts, and self-harm behaviors – teaching and training self-soothing with five senses.6th-7th sessions (Emotion regulation)Definition of emotion, how emotions work, familiarity with emotion regulation skills. Emotion Identification Exercise, Emotion Registration Exercise. Identifying barriers to experiencing emotion in a healthy way and ways to overcome these barriers. Teaching creating short-term positive emotional experiences for experiencing positive emotional states.8th-10th sessions (Emotion regulation and distress tolerance in an MUD context)Explain craving and its connection to the experience of emotions. Introducing methods for identifying values. Importance of committed action based on a list of essential values in life. Develop new coping strategies in response to unpleasant emotions, sensations, and cognitions, especially craving as a multidimensional problem and teaching problem solving and behavior analysis.11th sessionBasic acceptance technique training. Introduce living in the present moment techniques.12th-13th sessionsInterpersonal effectiveness training. Participants learn assertiveness skills about substance users. Other skills include non-verbal communication, verbal communication, and problem-solving, decision-making, and listening skills.14th-16th sessionsReview of sessions. Elimination of ambiguities. Exercising skills in the presence of other people.\n\nDBT is a group intervention consisting of 16 sessions (meeting once a week for 90 minutes) with one psychotherapist and her co-therapist. The intervention protocol is an adaptation of DBT for SUD based on three basic manuals. 10 , 24 , 25 The primary objective of the DBT is to reduce dysfunction in emotion regulation and craving via increasing cessation rates and improving skills. A psychotherapist with a PhD delivered the intervention sessions (with a psychologist as co-therapist) and they were blind both to the existence of another group and to the study objectives. Table 1 shows the content covered in each DBT session.\n\nTable 1DBT content per sessionCessationContentPre-sessionExplanation of dialectical behavioral therapy, principles, and goals. Brief introduction to the content of each session. Familiarity with participants. Participants are given an intervention booklet to read at home.1st session (mindfulness 1)Introduce the concept of mindfulness and three mental states (wise, reasonable, emotional) and their relations with substance use.2nd session (mindfulness 2)Teach two clusters of mindfulness skills. The first includes viewing, participation, and description. The second includes a non-judgmental stance and inclusive self-consciousness.3rd sessionSummarize the mindfulness sessions – definition of addiction, standard therapies of addiction, introduction to and teaching of dialectical avoidance technique. Review the positive and negative aspects of abstinence. Explanation and investigation of relapse and its causes. Explaining the skill of the pure mind, the addicted mind, the types of behaviors related to the pure mentality and the addicted mentality, and preparing a list of supporters.4th-5th sessions (Distress tolerance)Teaching distraction strategies with five skills include activities, comparisons, emotions, thoughts, and enjoyment. Through enjoyable activities, focusing on work or other topics, counting, leaving the situation, paying attention to daily tasks, distracting from thoughts, and self-harm behaviors – teaching and training self-soothing with five senses.6th-7th sessions (Emotion regulation)Definition of emotion, how emotions work, familiarity with emotion regulation skills. Emotion Identification Exercise, Emotion Registration Exercise. Identifying barriers to experiencing emotion in a healthy way and ways to overcome these barriers. Teaching creating short-term positive emotional experiences for experiencing positive emotional states.8th-10th sessions (Emotion regulation and distress tolerance in an MUD context)Explain craving and its connection to the experience of emotions. Introducing methods for identifying values. Importance of committed action based on a list of essential values in life. Develop new coping strategies in response to unpleasant emotions, sensations, and cognitions, especially craving as a multidimensional problem and teaching problem solving and behavior analysis.11th sessionBasic acceptance technique training. Introduce living in the present moment techniques.12th-13th sessionsInterpersonal effectiveness training. Participants learn assertiveness skills about substance users. Other skills include non-verbal communication, verbal communication, and problem-solving, decision-making, and listening skills.14th-16th sessionsReview of sessions. Elimination of ambiguities. Exercising skills in the presence of other people.\n\n Psychoeducation This option is more ethical than not offering any intervention to the control group. A psychiatrist with five years of experience in addiction psychotherapy implemented this intervention. This intervention includes problem-solving skills, assertiveness, and craving management in eight sessions. Thepsychoeducation intervention was used to provide a basis for comparison with only those elements of the DBT intervention that are different from other psychotherapies. This intervention is utilized for MUD and health-related problems. The intention of this intervention is to provide individuals struggling with cravings and substance use disorder the knowledge needed to comprehensively appreciate their problems and the empowerment needed to cope with them. The psychoeducation intervention included information about the dangers of marijuana and the therapists also provided a pamphlet containing techniques for reduction of craving. 14 , 26 , 27\nThis option is more ethical than not offering any intervention to the control group. A psychiatrist with five years of experience in addiction psychotherapy implemented this intervention. This intervention includes problem-solving skills, assertiveness, and craving management in eight sessions. Thepsychoeducation intervention was used to provide a basis for comparison with only those elements of the DBT intervention that are different from other psychotherapies. This intervention is utilized for MUD and health-related problems. The intention of this intervention is to provide individuals struggling with cravings and substance use disorder the knowledge needed to comprehensively appreciate their problems and the empowerment needed to cope with them. The psychoeducation intervention included information about the dangers of marijuana and the therapists also provided a pamphlet containing techniques for reduction of craving. 14 , 26 , 27\n Therapists and treatment adherence To enable adherence to the principles of DBT to be checked, audios of the sessions were recorded with the consent of all participants. A DBT researcher and psychotherapist who was not involved in the treatment groups checked session content afterwards. Sessions were divided into 15-minute modules that were chosen for adherence checks at random. Treatment stance and occurrence and depth of DBT processes were appraised. Based on the treatment manual, modules were rated for adherence level as either adequate or not adequate. The majority (83%) were judged to have been conducted adequately.\nTo enable adherence to the principles of DBT to be checked, audios of the sessions were recorded with the consent of all participants. A DBT researcher and psychotherapist who was not involved in the treatment groups checked session content afterwards. Sessions were divided into 15-minute modules that were chosen for adherence checks at random. Treatment stance and occurrence and depth of DBT processes were appraised. Based on the treatment manual, modules were rated for adherence level as either adequate or not adequate. The majority (83%) were judged to have been conducted adequately.\n Statistical method Demographic information was gathered and reported as frequencies, means, and standard deviations and repeated measures ANOVA and chi-square tests were conducted for the outcomes using SPSS software, version 26.\nDemographic information was gathered and reported as frequencies, means, and standard deviations and repeated measures ANOVA and chi-square tests were conducted for the outcomes using SPSS software, version 26.","DBT is a group intervention consisting of 16 sessions (meeting once a week for 90 minutes) with one psychotherapist and her co-therapist. The intervention protocol is an adaptation of DBT for SUD based on three basic manuals. 10 , 24 , 25 The primary objective of the DBT is to reduce dysfunction in emotion regulation and craving via increasing cessation rates and improving skills. A psychotherapist with a PhD delivered the intervention sessions (with a psychologist as co-therapist) and they were blind both to the existence of another group and to the study objectives. Table 1 shows the content covered in each DBT session.\n\nTable 1DBT content per sessionCessationContentPre-sessionExplanation of dialectical behavioral therapy, principles, and goals. Brief introduction to the content of each session. Familiarity with participants. Participants are given an intervention booklet to read at home.1st session (mindfulness 1)Introduce the concept of mindfulness and three mental states (wise, reasonable, emotional) and their relations with substance use.2nd session (mindfulness 2)Teach two clusters of mindfulness skills. The first includes viewing, participation, and description. The second includes a non-judgmental stance and inclusive self-consciousness.3rd sessionSummarize the mindfulness sessions – definition of addiction, standard therapies of addiction, introduction to and teaching of dialectical avoidance technique. Review the positive and negative aspects of abstinence. Explanation and investigation of relapse and its causes. Explaining the skill of the pure mind, the addicted mind, the types of behaviors related to the pure mentality and the addicted mentality, and preparing a list of supporters.4th-5th sessions (Distress tolerance)Teaching distraction strategies with five skills include activities, comparisons, emotions, thoughts, and enjoyment. Through enjoyable activities, focusing on work or other topics, counting, leaving the situation, paying attention to daily tasks, distracting from thoughts, and self-harm behaviors – teaching and training self-soothing with five senses.6th-7th sessions (Emotion regulation)Definition of emotion, how emotions work, familiarity with emotion regulation skills. Emotion Identification Exercise, Emotion Registration Exercise. Identifying barriers to experiencing emotion in a healthy way and ways to overcome these barriers. Teaching creating short-term positive emotional experiences for experiencing positive emotional states.8th-10th sessions (Emotion regulation and distress tolerance in an MUD context)Explain craving and its connection to the experience of emotions. Introducing methods for identifying values. Importance of committed action based on a list of essential values in life. Develop new coping strategies in response to unpleasant emotions, sensations, and cognitions, especially craving as a multidimensional problem and teaching problem solving and behavior analysis.11th sessionBasic acceptance technique training. Introduce living in the present moment techniques.12th-13th sessionsInterpersonal effectiveness training. Participants learn assertiveness skills about substance users. Other skills include non-verbal communication, verbal communication, and problem-solving, decision-making, and listening skills.14th-16th sessionsReview of sessions. Elimination of ambiguities. Exercising skills in the presence of other people.\n","This option is more ethical than not offering any intervention to the control group. A psychiatrist with five years of experience in addiction psychotherapy implemented this intervention. This intervention includes problem-solving skills, assertiveness, and craving management in eight sessions. Thepsychoeducation intervention was used to provide a basis for comparison with only those elements of the DBT intervention that are different from other psychotherapies. This intervention is utilized for MUD and health-related problems. The intention of this intervention is to provide individuals struggling with cravings and substance use disorder the knowledge needed to comprehensively appreciate their problems and the empowerment needed to cope with them. The psychoeducation intervention included information about the dangers of marijuana and the therapists also provided a pamphlet containing techniques for reduction of craving. 14 , 26 , 27","To enable adherence to the principles of DBT to be checked, audios of the sessions were recorded with the consent of all participants. A DBT researcher and psychotherapist who was not involved in the treatment groups checked session content afterwards. Sessions were divided into 15-minute modules that were chosen for adherence checks at random. Treatment stance and occurrence and depth of DBT processes were appraised. Based on the treatment manual, modules were rated for adherence level as either adequate or not adequate. The majority (83%) were judged to have been conducted adequately.","Demographic information was gathered and reported as frequencies, means, and standard deviations and repeated measures ANOVA and chi-square tests were conducted for the outcomes using SPSS software, version 26.","Written informed consent was obtained from all participants before initiation of the research. The tools used in this study were all filled-out anonymously, and an ID code was used to maintain the confidentiality of personal information (Ir.kums.rce.1398.1203). At the end of the research process, dialectical behavior therapy was also provided to the control group. This study is registered with the Thailand Registry of Clinical Trials (TCTR20200319007)."," Feasibility In the psycho-education group, 24/31 participants completed all sessions, compared to 29/30 members of the DBT group (retention rates: 77% in the control group vs. 96% in the DBT group). Additionally, 96% (29/30) of the DBT group members completed the two-month follow-up, whereas 64.5% (20/31) of the control group completed follow-up ( Figure 1 ). The chi-square test was applied, showing associations between group and retention, with χ2= 4.95, p = 0.02 for post-treatment and, χ2= 9.97, p = 0.002 for the follow-up phase. Consequently, DBT retention rates were significantly higher than psycho-education retention rates at post-treatment and follow-up.\n\nFigure 1Consort diagram.\n\nIn the psycho-education group, 24/31 participants completed all sessions, compared to 29/30 members of the DBT group (retention rates: 77% in the control group vs. 96% in the DBT group). Additionally, 96% (29/30) of the DBT group members completed the two-month follow-up, whereas 64.5% (20/31) of the control group completed follow-up ( Figure 1 ). The chi-square test was applied, showing associations between group and retention, with χ2= 4.95, p = 0.02 for post-treatment and, χ2= 9.97, p = 0.002 for the follow-up phase. Consequently, DBT retention rates were significantly higher than psycho-education retention rates at post-treatment and follow-up.\n\nFigure 1Consort diagram.\n\n Acceptability and appropriateness To enable assessment of the acceptability and appropriateness of intervention, patients completed the AIM and IAM scales in the post-treatment phase. The acceptability scores were 16.57 for DBT and 9.6 for the control group (p < 0.05). The appropriateness scores were17.03 for DBT and 10.7 for the psychoeducation group (p < 0.05). Since there are no standards for these measures, these points were transferred to Likert-based questionnaire scales. For acceptability, the results equated to ‘’agree’’ for the DBT group versus ‘’neither agree nor disagree’’ for the psychoeducation group. For appropriateness, the results equated to ‘’completely agree’’ for the DBT group versus ‘’neither agree nor disagree’’ for the psychoeducation group.\nTo enable assessment of the acceptability and appropriateness of intervention, patients completed the AIM and IAM scales in the post-treatment phase. The acceptability scores were 16.57 for DBT and 9.6 for the control group (p < 0.05). The appropriateness scores were17.03 for DBT and 10.7 for the psychoeducation group (p < 0.05). Since there are no standards for these measures, these points were transferred to Likert-based questionnaire scales. For acceptability, the results equated to ‘’agree’’ for the DBT group versus ‘’neither agree nor disagree’’ for the psychoeducation group. For appropriateness, the results equated to ‘’completely agree’’ for the DBT group versus ‘’neither agree nor disagree’’ for the psychoeducation group.\n Participant characteristics Participants’ demographic variables are shown in Table 2 . Analyses showed that there were no significant differences between the two groups regarding these variables. It should be noted that since over 97% of the participants were male from the beginning, the results were reported only for men.\n\nTable 2Mean and standard deviation of demographic variables in the intervention and control groups at the test phaseVariableIntervention groupControl groupp-valueEducational level* 0.2No higher education, n (%)2 (6)6 (19)Diploma, n (%)14 (46)15 (48)University student or graduate, n (%)14 (46)10 (33)Age †25.6 (5.67)27.19 (7.48)0.3Months of marijuana use19.53 (5.9)17.48 (6.03)0.1Craving (total) † Pre-test45.2 (8.3)47.9 (10.2)0.2Post-test42.13 (7.7)44.48 (8.1)0.1Follow-up42.66 (9.25)45.8 (8.4)0.1Data presented as mean (standard deviation), unless otherwise specified.* Chi-square test.† Independent t test.\n\nData presented as mean (standard deviation), unless otherwise specified.\n* Chi-square test.\n† Independent t test.\nParticipants’ demographic variables are shown in Table 2 . Analyses showed that there were no significant differences between the two groups regarding these variables. It should be noted that since over 97% of the participants were male from the beginning, the results were reported only for men.\n\nTable 2Mean and standard deviation of demographic variables in the intervention and control groups at the test phaseVariableIntervention groupControl groupp-valueEducational level* 0.2No higher education, n (%)2 (6)6 (19)Diploma, n (%)14 (46)15 (48)University student or graduate, n (%)14 (46)10 (33)Age †25.6 (5.67)27.19 (7.48)0.3Months of marijuana use19.53 (5.9)17.48 (6.03)0.1Craving (total) † Pre-test45.2 (8.3)47.9 (10.2)0.2Post-test42.13 (7.7)44.48 (8.1)0.1Follow-up42.66 (9.25)45.8 (8.4)0.1Data presented as mean (standard deviation), unless otherwise specified.* Chi-square test.† Independent t test.\n\nData presented as mean (standard deviation), unless otherwise specified.\n* Chi-square test.\n† Independent t test.\n Efficacy outcomes The hypothesis of equal covariance matrices was examined for craving (Box’s M = 3.63, P = 0.74). The results of this test indicate homogeneity of covariance matrices. Mauchly’s test of sphericity also showed that the sphericity assumption was not violated (p = 0.42 and Mauchly’s W = 0.97).\nThe results of the intergroup test and intergroup relations are also presented in Table 3 . As shown in Table 3 , the effect levels for craving (F = 3.52, p > 0.05) suggest that there is no significant difference between groups. These results were repeated for three of the subscales of craving: compulsivity, expectancy, and purposefulness. However, the emotionality subscale results showed a significant reduction in the DBT group compared to the control group 10.6 vs. 14.4 in the post-test and 10.43 vs. 13.26 in the follow-up phase (F = 19.94, p < 0.05).\n\nTable 3Repeated measures ANOVA for variables for the DBT group and control group in the pre-test, post-test, and follow-upVariable/sourceType III sum of squaresdfMean squareFSigPartial eta squaredObserved powerCraving Tests of within-subjects effects Factor1347.6962173.842.60.07*0.040.512Factor1 × group4.7622.380.0360.110.0010.055Error (factor1)7845.15411866.48 Tests of between-subjects effects Group341.0841341.083.520.06 †0.0560.455Error5708.795996.75 Emotionality Tests of within-subjects effects Factor1257.332128.6511.690.00 †0.1650.9Factor1 × group74.70237.350.370.03*0.050.63Error (factor1)1297.8111810.99 Tests of between-subjects effects Group283.891283.8919.940.00 †0.250.9Error839.9065914.23 * Significant to 0.05.† Significant to 0.01.\n\n* Significant to 0.05.\n† Significant to 0.01.\nWith regard to cessation, the results indicated that DBT achieved a higher rate of cessation than the control treatment in both the post-test and at follow-up, ( Table 4 ) (p < 0.05). It was also found that among those who continued to use the drug, the number of use days per month in the post-test and the follow-up periods (two months) was significantly lower in the intervention group than the control group.\n\nTable 4Cessation and consumption between groups DBTControlp-valueCessation* Post-test14 (46%)5 (16%)0.01\n‡Follow-up12 (40%)3 (9.5%)0.006\n‡Number of days use † Post-test2.43±1.87.5±5.030.00\n‡Follow-up3.44±1.918.75±3.270.00\n‡Bold p-values are significant at critical levels.* The chi-square test was applied.† T test for independent samples.‡ Significant to 0.01.\n\nBold p-values are significant at critical levels.\n* The chi-square test was applied.\n† T test for independent samples.\n‡ Significant to 0.01.\nThe hypothesis of equal covariance matrices was examined for craving (Box’s M = 3.63, P = 0.74). The results of this test indicate homogeneity of covariance matrices. Mauchly’s test of sphericity also showed that the sphericity assumption was not violated (p = 0.42 and Mauchly’s W = 0.97).\nThe results of the intergroup test and intergroup relations are also presented in Table 3 . As shown in Table 3 , the effect levels for craving (F = 3.52, p > 0.05) suggest that there is no significant difference between groups. These results were repeated for three of the subscales of craving: compulsivity, expectancy, and purposefulness. However, the emotionality subscale results showed a significant reduction in the DBT group compared to the control group 10.6 vs. 14.4 in the post-test and 10.43 vs. 13.26 in the follow-up phase (F = 19.94, p < 0.05).\n\nTable 3Repeated measures ANOVA for variables for the DBT group and control group in the pre-test, post-test, and follow-upVariable/sourceType III sum of squaresdfMean squareFSigPartial eta squaredObserved powerCraving Tests of within-subjects effects Factor1347.6962173.842.60.07*0.040.512Factor1 × group4.7622.380.0360.110.0010.055Error (factor1)7845.15411866.48 Tests of between-subjects effects Group341.0841341.083.520.06 †0.0560.455Error5708.795996.75 Emotionality Tests of within-subjects effects Factor1257.332128.6511.690.00 †0.1650.9Factor1 × group74.70237.350.370.03*0.050.63Error (factor1)1297.8111810.99 Tests of between-subjects effects Group283.891283.8919.940.00 †0.250.9Error839.9065914.23 * Significant to 0.05.† Significant to 0.01.\n\n* Significant to 0.05.\n† Significant to 0.01.\nWith regard to cessation, the results indicated that DBT achieved a higher rate of cessation than the control treatment in both the post-test and at follow-up, ( Table 4 ) (p < 0.05). It was also found that among those who continued to use the drug, the number of use days per month in the post-test and the follow-up periods (two months) was significantly lower in the intervention group than the control group.\n\nTable 4Cessation and consumption between groups DBTControlp-valueCessation* Post-test14 (46%)5 (16%)0.01\n‡Follow-up12 (40%)3 (9.5%)0.006\n‡Number of days use † Post-test2.43±1.87.5±5.030.00\n‡Follow-up3.44±1.918.75±3.270.00\n‡Bold p-values are significant at critical levels.* The chi-square test was applied.† T test for independent samples.‡ Significant to 0.01.\n\nBold p-values are significant at critical levels.\n* The chi-square test was applied.\n† T test for independent samples.\n‡ Significant to 0.01.","In the psycho-education group, 24/31 participants completed all sessions, compared to 29/30 members of the DBT group (retention rates: 77% in the control group vs. 96% in the DBT group). Additionally, 96% (29/30) of the DBT group members completed the two-month follow-up, whereas 64.5% (20/31) of the control group completed follow-up ( Figure 1 ). The chi-square test was applied, showing associations between group and retention, with χ2= 4.95, p = 0.02 for post-treatment and, χ2= 9.97, p = 0.002 for the follow-up phase. Consequently, DBT retention rates were significantly higher than psycho-education retention rates at post-treatment and follow-up.\n\nFigure 1Consort diagram.\n","To enable assessment of the acceptability and appropriateness of intervention, patients completed the AIM and IAM scales in the post-treatment phase. The acceptability scores were 16.57 for DBT and 9.6 for the control group (p < 0.05). The appropriateness scores were17.03 for DBT and 10.7 for the psychoeducation group (p < 0.05). Since there are no standards for these measures, these points were transferred to Likert-based questionnaire scales. For acceptability, the results equated to ‘’agree’’ for the DBT group versus ‘’neither agree nor disagree’’ for the psychoeducation group. For appropriateness, the results equated to ‘’completely agree’’ for the DBT group versus ‘’neither agree nor disagree’’ for the psychoeducation group.","Participants’ demographic variables are shown in Table 2 . Analyses showed that there were no significant differences between the two groups regarding these variables. It should be noted that since over 97% of the participants were male from the beginning, the results were reported only for men.\n\nTable 2Mean and standard deviation of demographic variables in the intervention and control groups at the test phaseVariableIntervention groupControl groupp-valueEducational level* 0.2No higher education, n (%)2 (6)6 (19)Diploma, n (%)14 (46)15 (48)University student or graduate, n (%)14 (46)10 (33)Age †25.6 (5.67)27.19 (7.48)0.3Months of marijuana use19.53 (5.9)17.48 (6.03)0.1Craving (total) † Pre-test45.2 (8.3)47.9 (10.2)0.2Post-test42.13 (7.7)44.48 (8.1)0.1Follow-up42.66 (9.25)45.8 (8.4)0.1Data presented as mean (standard deviation), unless otherwise specified.* Chi-square test.† Independent t test.\n\nData presented as mean (standard deviation), unless otherwise specified.\n* Chi-square test.\n† Independent t test.","The hypothesis of equal covariance matrices was examined for craving (Box’s M = 3.63, P = 0.74). The results of this test indicate homogeneity of covariance matrices. Mauchly’s test of sphericity also showed that the sphericity assumption was not violated (p = 0.42 and Mauchly’s W = 0.97).\nThe results of the intergroup test and intergroup relations are also presented in Table 3 . As shown in Table 3 , the effect levels for craving (F = 3.52, p > 0.05) suggest that there is no significant difference between groups. These results were repeated for three of the subscales of craving: compulsivity, expectancy, and purposefulness. However, the emotionality subscale results showed a significant reduction in the DBT group compared to the control group 10.6 vs. 14.4 in the post-test and 10.43 vs. 13.26 in the follow-up phase (F = 19.94, p < 0.05).\n\nTable 3Repeated measures ANOVA for variables for the DBT group and control group in the pre-test, post-test, and follow-upVariable/sourceType III sum of squaresdfMean squareFSigPartial eta squaredObserved powerCraving Tests of within-subjects effects Factor1347.6962173.842.60.07*0.040.512Factor1 × group4.7622.380.0360.110.0010.055Error (factor1)7845.15411866.48 Tests of between-subjects effects Group341.0841341.083.520.06 †0.0560.455Error5708.795996.75 Emotionality Tests of within-subjects effects Factor1257.332128.6511.690.00 †0.1650.9Factor1 × group74.70237.350.370.03*0.050.63Error (factor1)1297.8111810.99 Tests of between-subjects effects Group283.891283.8919.940.00 †0.250.9Error839.9065914.23 * Significant to 0.05.† Significant to 0.01.\n\n* Significant to 0.05.\n† Significant to 0.01.\nWith regard to cessation, the results indicated that DBT achieved a higher rate of cessation than the control treatment in both the post-test and at follow-up, ( Table 4 ) (p < 0.05). It was also found that among those who continued to use the drug, the number of use days per month in the post-test and the follow-up periods (two months) was significantly lower in the intervention group than the control group.\n\nTable 4Cessation and consumption between groups DBTControlp-valueCessation* Post-test14 (46%)5 (16%)0.01\n‡Follow-up12 (40%)3 (9.5%)0.006\n‡Number of days use † Post-test2.43±1.87.5±5.030.00\n‡Follow-up3.44±1.918.75±3.270.00\n‡Bold p-values are significant at critical levels.* The chi-square test was applied.† T test for independent samples.‡ Significant to 0.01.\n\nBold p-values are significant at critical levels.\n* The chi-square test was applied.\n† T test for independent samples.\n‡ Significant to 0.01.","This study examined the feasibility, acceptability, and preliminary efficacy of a 16-session DBT intervention to address craving and achieve cessation in MUD patients. This intervention showed strong evidence of feasibility. Moreover, acceptability and appropriateness rates in the DBT group were high and adequate. The results showed that DBT is a promising intervention for marijuana cessation in patients with MUD. Although this study is the first RCT of DBT for MUD, the scientific literature about DBT for other addictive behaviors reports similar results.\nRezaei et al. found that DBT significantly improved craving among methadone users. Their result showed that DBT could reduce methadone usage and improve emotion regulation. 28 Moreover, another study showed that implementation of DBT with alcohol-dependent patients improved alcohol-related behavior and emotional deficits, which is similar to the results of the present study. 29 However, the results for craving showed there was no significant difference between groups. With regard to this finding, our result differs from the majority of other research findings. For example, Rezaei et al. found that DBT significantly improved craving among methadone users. 10 This result was also repeated in Rabinovitz’s paper. 30 One of the main reasons for this difference lies in the finding in the present study that DBT had greater improvement in the emotionality subscale of craving (p < 0.5). Since the most important structure of marijuana craving is its emotional dimensions, 5 , 14 the lack of changes in other subscales resulted in non-significance for the overall craving scale score. The results of the present study with relation to craving are therefore somewhat co-directional with the findings of previous studies. On the behavioral level, other studies found that Marijuana craving cues were strongly associated with deficits in regulation of negative affect and emotions. 14 , 15 Also, when neural levels were assessed during reappraisal of negative stimuli, patients with MUD and regular users showed altered neural activity and functional connectivity. Furthermore, being a marijuana user was related to dysfunctions in the amygdala and in amygdala-DLPFC coupling activity. 14 Taken together, these findings demonstrate that emotion regulation problems and craving are prevalent in MUD patients and can interfere with the cessation process. Since DBT is a third-wave behavioral therapy, it has a strong emotional basis. This therapy encompasses three emotion-based goals: understanding emotions, reducing emotional vulnerability, and reducing emotional suffering. Patients are helped to understand that unpleasant emotions are a normal part of life and that accepting their existence is more healthy than trying to avoid controlling them. 10 , 28 Overall, DBT is an emotion regulation method that helps patients learn, understand, and label emotions, reducing emotional vulnerability and emotional suffering. These skills help MUD patients to label emotions related to craving. This improvement in emotional states can improve dysfunctions in the amygdala and in amygdala-DLPFC coupling activity. 9 , 31 , 32\nAlong the same lines, it also improves emotional craving-related brain structures and reduces impulsive behavior (e.g., lapses). Also, with the ‘’distress tolerance’’ component of DBT, patients learn to live with destructive emotions to accept unpleasant craving situations. 32 , 33 Therefore, by increasing craving, they no longer consume marijuana immediately. 22 , 24 Similarly, other DBT components teach MUD patients reinforcement management and problem-solving skills that can help them to reduce marijuana consumption. 34 , 35","To conclude, DBT demonstrated adequate feasibility, acceptability, and appropriateness for patients with marijuana use disorder. Moreover, DBT also exhibited significant efficacy compared to the control group for achieving cessation and reducing emotion-related craving. Even in patients who could not achieve abstinence, DBT led to a reduction in marijuana consumption rates. These findings persisted at two-month follow-up.\n Limitations and future directions Despite these positive results, the present study also has some limitations. First, in order to evaluate the most significant treatment components (such as mindfulness and distress tolerance), no groups received the third wave versions of other therapies (ACT or MBSR). This study only had a two-month follow-up period and could not conduct long-term evaluation due to the study site’s medical and infrastructure conditions. It is recommended that future research should examine mediating and confounding variables to investigate the results of similar research to the present study. Other factors affecting relapse and recurrence could also be examined. Moreover, women should be investigated so that gender-related implications can be determined.\nDespite these positive results, the present study also has some limitations. First, in order to evaluate the most significant treatment components (such as mindfulness and distress tolerance), no groups received the third wave versions of other therapies (ACT or MBSR). This study only had a two-month follow-up period and could not conduct long-term evaluation due to the study site’s medical and infrastructure conditions. It is recommended that future research should examine mediating and confounding variables to investigate the results of similar research to the present study. Other factors affecting relapse and recurrence could also be examined. Moreover, women should be investigated so that gender-related implications can be determined.","Despite these positive results, the present study also has some limitations. First, in order to evaluate the most significant treatment components (such as mindfulness and distress tolerance), no groups received the third wave versions of other therapies (ACT or MBSR). This study only had a two-month follow-up period and could not conduct long-term evaluation due to the study site’s medical and infrastructure conditions. It is recommended that future research should examine mediating and confounding variables to investigate the results of similar research to the present study. Other factors affecting relapse and recurrence could also be examined. Moreover, women should be investigated so that gender-related implications can be determined."],"string":"[\n \"Marijuana is the most prevalent substance among those reported to be a significant problem among people seeking treatment for substance abuse. 1 According to WHO reports, more than 140 million people consume marijuana every year. 2 With regard to Iran, recent evidence shows that more than 5% of people consume marijuana every year, predominantly young males. However, in view of the harsh marijuana prohibition policy of the Iranian government, most clinicians estimate that these rates have been hugely underestimated. 3 Marijuana, as an illegal drug, is associated with significant physical, psychological, and social consequences. 4 Studies have shown that regular and heavy marijuana use patterns correlate with increased risk of mood disorders, anxiety, and psychotic episodes and although causality has not been demonstrated, these patterns can increase the course of mental health problems. 5 Also, several medical problems such as respiratory system deficits, stroke, myocardial infarction, and digestive tract cancers are associated with marijuana use patterns, especially among those with marijuana use disorder (MUD). 6 , 7 Approximately one in three marijuana users meet the criteria for MUD based on the DSM-5, and this proportion is rising. 8 One of the most important psychological problems in substance use disorder treatment is craving. Craving is a factor identified as the root cause of relapses and treatment failures. 9 , 10 MUD patients report visual, tactile, and olfactory cues related to craving and compulsivity sensations. 11 Based on these results, clinicians have tried to treat patients with marijuana use disorder.\\nTo date, the Food and Drug Administration (FDA) in the United States has not approved any psycho-pharmacotherapy for MUD, and therefore psycho-social interventions have received particular attention. 12 The most widely used psychological treatment in the substance use disorder (SUD) context is cognitive-behavioral therapy (CBT). 13 Results showed that CBT is somewhat effective for SUD, but that most patients with MUD do not achieve cessation and are not motivated to continue skills training during follow-up. Relapse rates therefore remain a considerable limitation of treatment. 10 , 13 One of the main reasons for this low success rate lies in the limitations of CBT. First, CBT protocols do not focus on comorbid problems, whereas most patients with SUD have at least one psychiatric or psychological problem. Secondly, cognitive restructuring may not be useful for all SUD patients. Some patients may be unable to restructure their dysfunctional cognition and core beliefs despite receiving CBT. 10 , 12 , 13 Furthermore, emotion regulation deficits are strongly associated with increased addictive behaviors such as SUD. With emotion regulation, people adjust their emotional experiences related to distressing and unpleasant events. Emotion regulation is essential for successful coping with environmental demands and personal welfare. 14 On the behavioral level, studies have found that marijuana craving cues are strongly associated with deficits in regulation of negative affect and emotions. 14 , 15 Also, on the neural level, during reappraisal of negative stimuli, patients with MUD and regular users have shown altered neural activity and functional connectivity. Moreover, marijuana use is related to dysfunctions in the amygdala and in amygdala-dorsolateral prefrontal cortex (DLPFC) coupling activity. 15 Together, these findings demonstrate that emotion-based psychotherapy must manage comorbid problems and eliminate the limitations of CBT.\\nOne of the psychotherapies in the third wave behavioral therapy cluster is dialectical behavior therapy (DBT). DBT has been described as intervention in emotion regulation deficits by focusing on dangerous impulses in borderline personality disorder and substance use disorder. The goals of DBT include improving and regulating emotions as a primary mechanism of change. DBT is a trans-diagnostic treatment and suitable for comorbid problems. DBT trains skills including distress tolerance, interpersonal effectiveness, emotion regulation, and mindfulness. Overall, in the context of SUD, DBT teaches emotion regulation skills to decrease engagement in pathological emotion regulation strategies. It also intervenes in low quality of life situations, reduces drug-seeking behavior, and helps patients function adaptively by accepting unpleasant emotions such as craving. 9 , 10 , 14\\nResearch literature shows the efficacy and effectiveness of DBT in various comorbid problems and diseases such as suicide, 16 forensic psychiatric patients, 17 and irritable bowel syndrome. 18 Nevertheless, studies have reported contradictory results for the effectiveness of implementing DBT in various SUD populations. 19 , 20 Furthermore, the literature has recommended using larger samples, clearer instruments to measure outcome variables, and specific and integrated protocols. 21\\nAdditionally, according to our investigations, no DBT randomized clinical trials have been conducted that investigated cessation in MUD patients (with or without comorbid problems). A DBT intervention aimed at increasing the cessation rate and reducing craving among MUD patients was developed for this study.\\nThis pilot trial investigated the feasibility and preliminary efficacy of DBT relative to a psycho-education intervention that was controlled for time duration and attention. Feasibility was assessed via satisfaction and session completion rates. Preliminary efficacy was evaluated via the impact of DBT on cessation rate and reduction of consumption rates, compared to the psycho-education intervention. Although craving and acceptance of craving are not the primary goals of DBT, they were also compared across the two interventions.\",\n \" Trial design This study was designed as a controlled randomized clinical trial, including pretest, post-test, and two-month follow-up phases.\\nThis study was designed as a controlled randomized clinical trial, including pretest, post-test, and two-month follow-up phases.\\n Sample size Since the sampling method comprises snowball sampling and strict eligibility criteria were applied, on the basis of data from similar studies 10 it was determined that at least 20 participants were needed in each group. However, in view of the predicted retention rates, we selected 30 patients for each group.\\nSince the sampling method comprises snowball sampling and strict eligibility criteria were applied, on the basis of data from similar studies 10 it was determined that at least 20 participants were needed in each group. However, in view of the predicted retention rates, we selected 30 patients for each group.\\n Selection criteria The inclusion criteria were as follows: 1) diagnosis of marijuana use disorder; 2) age 18 years or over; 3) no current or past history of major psychiatric disorders; 4) no other concurrent SUD treatment; and 5) willingness to attend intervention sessions, complete surveys, and take tests (questionnaires and urine test kits).\\nExclusion criteria were as follows: 1) unwillingness to participate; 2) not participating in intervention sessions for more than two weeks; 3) starting secondary psychotherapy; and 4) consuming methamphetamine, amphetamine, cannabis, methadone, benzodiazepines, or morphine during the research stages.\\nThe inclusion criteria were as follows: 1) diagnosis of marijuana use disorder; 2) age 18 years or over; 3) no current or past history of major psychiatric disorders; 4) no other concurrent SUD treatment; and 5) willingness to attend intervention sessions, complete surveys, and take tests (questionnaires and urine test kits).\\nExclusion criteria were as follows: 1) unwillingness to participate; 2) not participating in intervention sessions for more than two weeks; 3) starting secondary psychotherapy; and 4) consuming methamphetamine, amphetamine, cannabis, methadone, benzodiazepines, or morphine during the research stages.\\n Participants, procedures, and randomization Since there are no cannabis use disorder treatment centers in Iran, there is no specific place to select patients. Furthermore, patients at drug treatment centers are referred for treatment of other substance use disorders and comorbidity of drug use is one of the exclusion criteria for this study, since it could lead to misleading results. Therefore, the relatives and acquaintances of those who had been referred to the drug treatment center were interviewed. From November 1, 2019, to November 5, 2019, 15 relatives and family members of drug users referred to drug treatment centers were diagnosed with MUD at this stage. Then, using snowball sampling, after 15 days of investigation, a total of 83 patients were diagnosed with MUD. Seventy-five of the 83 MUD patients who were approached consented, eight declined to participate, and 14 were ineligible. The primary reasons for declining were anxiety about addiction stigma and time constraints. Most of the ineligible patients had multiple illicit use disorders, so they did not meet the study criteria. Therefore, 61 patients completed the baseline assessment and were included in the current analyses. These patients were randomly assigned to each group using a random number table. The interventions were implemented from December 1, 2019, to March 20, 2020. The follow-up phase started on March 21, 2020, and ended on May 20, 2020, (at two months’ follow-up). In order to test for exclusion criteria before each session, a six-drug test kit for methamphetamine, amphetamine, cannabis, methadone, benzodiazepines, and morphine was administered to individuals using urine samples.\\nSince there are no cannabis use disorder treatment centers in Iran, there is no specific place to select patients. Furthermore, patients at drug treatment centers are referred for treatment of other substance use disorders and comorbidity of drug use is one of the exclusion criteria for this study, since it could lead to misleading results. Therefore, the relatives and acquaintances of those who had been referred to the drug treatment center were interviewed. From November 1, 2019, to November 5, 2019, 15 relatives and family members of drug users referred to drug treatment centers were diagnosed with MUD at this stage. Then, using snowball sampling, after 15 days of investigation, a total of 83 patients were diagnosed with MUD. Seventy-five of the 83 MUD patients who were approached consented, eight declined to participate, and 14 were ineligible. The primary reasons for declining were anxiety about addiction stigma and time constraints. Most of the ineligible patients had multiple illicit use disorders, so they did not meet the study criteria. Therefore, 61 patients completed the baseline assessment and were included in the current analyses. These patients were randomly assigned to each group using a random number table. The interventions were implemented from December 1, 2019, to March 20, 2020. The follow-up phase started on March 21, 2020, and ended on May 20, 2020, (at two months’ follow-up). In order to test for exclusion criteria before each session, a six-drug test kit for methamphetamine, amphetamine, cannabis, methadone, benzodiazepines, and morphine was administered to individuals using urine samples.\\n Blinding Both groups were blind to the existence of another group in the study. However, patients were informed about participating in research but not about another group. One day after the end of treatment, the post-test was carried out by mental health technicians with a master’s degree in psychology.\\nBoth groups were blind to the existence of another group in the study. However, patients were informed about participating in research but not about another group. One day after the end of treatment, the post-test was carried out by mental health technicians with a master’s degree in psychology.\\n Outcome measures Abstinence A marijuana urine test kit prepared by Kian Teb Company (officially licensed by the National Medical Device Directorate IR. IRAN) was used to identify abstainers.\\nA marijuana urine test kit prepared by Kian Teb Company (officially licensed by the National Medical Device Directorate IR. IRAN) was used to identify abstainers.\\n Marijuana smoking A self-report scale was designed for patients who had lapsed during the post-test follow-up. On this scale, patients indicate the number of days of consumption over 30-day periods. The first thirty days after the last intervention session was considered the post-test smoking period and the second-month follow-up was considered as the follow-up marijuana use period.\\nA self-report scale was designed for patients who had lapsed during the post-test follow-up. On this scale, patients indicate the number of days of consumption over 30-day periods. The first thirty days after the last intervention session was considered the post-test smoking period and the second-month follow-up was considered as the follow-up marijuana use period.\\n Craving The Marijuana Craving Questionnaire (MCQ) short-form is a 12-item self-report questionnaire with ten items for subjective assessment of cannabis craving. The scale covers 4 factors: compulsivity, emotionality, expectancy, and purposefulness. According to how patients were thinking or feeling ‘’right now,’’ they placed checkmarks on the questionnaire to endorse responses ranging from 1 or strongly disagree to 7 or strongly agree. Results showed that this questionnaire’s internal consistency is adequate (α = 0.90). The measure was administered following a 12-hour deprivation period. The typical onset of marijuana craving and withdrawal symptoms is observed within approximately one day of cessation and so the current paper’s questionnaire scores can be conceptualized as an index of the propensity to experience marijuana craving following deprivation. 22 In Iranian MUD patients, the MCQ had internal consistency of α = 0.87. Details of the MCQ’s psychometrics properties will be published as a separate study as soon as possible.\\nThe Marijuana Craving Questionnaire (MCQ) short-form is a 12-item self-report questionnaire with ten items for subjective assessment of cannabis craving. The scale covers 4 factors: compulsivity, emotionality, expectancy, and purposefulness. According to how patients were thinking or feeling ‘’right now,’’ they placed checkmarks on the questionnaire to endorse responses ranging from 1 or strongly disagree to 7 or strongly agree. Results showed that this questionnaire’s internal consistency is adequate (α = 0.90). The measure was administered following a 12-hour deprivation period. The typical onset of marijuana craving and withdrawal symptoms is observed within approximately one day of cessation and so the current paper’s questionnaire scores can be conceptualized as an index of the propensity to experience marijuana craving following deprivation. 22 In Iranian MUD patients, the MCQ had internal consistency of α = 0.87. Details of the MCQ’s psychometrics properties will be published as a separate study as soon as possible.\\n Acceptability The Acceptability of Intervention Measure (AIM) was employed to measure the acceptability of interventions. AIM response items are measured on a 5-point Likert scale (from Completely Disagree with 1 point to Completely Agree with 5 points). The mean of points scored for each item is taken as the final score. This questionnaire developed by Weiner et al., and they reported Cronbach’s ɑ = 85 for internal consistency. 23\\nThe Acceptability of Intervention Measure (AIM) was employed to measure the acceptability of interventions. AIM response items are measured on a 5-point Likert scale (from Completely Disagree with 1 point to Completely Agree with 5 points). The mean of points scored for each item is taken as the final score. This questionnaire developed by Weiner et al., and they reported Cronbach’s ɑ = 85 for internal consistency. 23\\n Appropriateness The Intervention Appropriateness Measure (IAM) was used for Appropriateness. The IAM consist of a four-item scale that measures perceived intervention appropriateness. Items are measured on a five-point Likert scale (Completely Disagree to Completely Agree), and the mean of points scored for each item is taken as the final score. Higher scores mean that the participant feels this intervention is more appropriate for him/her. For this tool, Cronbach’s ɑ = 0.91 and all Factor Loadings are reported as higher than 0.8. 23\\nThe Intervention Appropriateness Measure (IAM) was used for Appropriateness. The IAM consist of a four-item scale that measures perceived intervention appropriateness. Items are measured on a five-point Likert scale (Completely Disagree to Completely Agree), and the mean of points scored for each item is taken as the final score. Higher scores mean that the participant feels this intervention is more appropriate for him/her. For this tool, Cronbach’s ɑ = 0.91 and all Factor Loadings are reported as higher than 0.8. 23\\n Abstinence A marijuana urine test kit prepared by Kian Teb Company (officially licensed by the National Medical Device Directorate IR. IRAN) was used to identify abstainers.\\nA marijuana urine test kit prepared by Kian Teb Company (officially licensed by the National Medical Device Directorate IR. IRAN) was used to identify abstainers.\\n Marijuana smoking A self-report scale was designed for patients who had lapsed during the post-test follow-up. On this scale, patients indicate the number of days of consumption over 30-day periods. The first thirty days after the last intervention session was considered the post-test smoking period and the second-month follow-up was considered as the follow-up marijuana use period.\\nA self-report scale was designed for patients who had lapsed during the post-test follow-up. On this scale, patients indicate the number of days of consumption over 30-day periods. The first thirty days after the last intervention session was considered the post-test smoking period and the second-month follow-up was considered as the follow-up marijuana use period.\\n Craving The Marijuana Craving Questionnaire (MCQ) short-form is a 12-item self-report questionnaire with ten items for subjective assessment of cannabis craving. The scale covers 4 factors: compulsivity, emotionality, expectancy, and purposefulness. According to how patients were thinking or feeling ‘’right now,’’ they placed checkmarks on the questionnaire to endorse responses ranging from 1 or strongly disagree to 7 or strongly agree. Results showed that this questionnaire’s internal consistency is adequate (α = 0.90). The measure was administered following a 12-hour deprivation period. The typical onset of marijuana craving and withdrawal symptoms is observed within approximately one day of cessation and so the current paper’s questionnaire scores can be conceptualized as an index of the propensity to experience marijuana craving following deprivation. 22 In Iranian MUD patients, the MCQ had internal consistency of α = 0.87. Details of the MCQ’s psychometrics properties will be published as a separate study as soon as possible.\\nThe Marijuana Craving Questionnaire (MCQ) short-form is a 12-item self-report questionnaire with ten items for subjective assessment of cannabis craving. The scale covers 4 factors: compulsivity, emotionality, expectancy, and purposefulness. According to how patients were thinking or feeling ‘’right now,’’ they placed checkmarks on the questionnaire to endorse responses ranging from 1 or strongly disagree to 7 or strongly agree. Results showed that this questionnaire’s internal consistency is adequate (α = 0.90). The measure was administered following a 12-hour deprivation period. The typical onset of marijuana craving and withdrawal symptoms is observed within approximately one day of cessation and so the current paper’s questionnaire scores can be conceptualized as an index of the propensity to experience marijuana craving following deprivation. 22 In Iranian MUD patients, the MCQ had internal consistency of α = 0.87. Details of the MCQ’s psychometrics properties will be published as a separate study as soon as possible.\\n Acceptability The Acceptability of Intervention Measure (AIM) was employed to measure the acceptability of interventions. AIM response items are measured on a 5-point Likert scale (from Completely Disagree with 1 point to Completely Agree with 5 points). The mean of points scored for each item is taken as the final score. This questionnaire developed by Weiner et al., and they reported Cronbach’s ɑ = 85 for internal consistency. 23\\nThe Acceptability of Intervention Measure (AIM) was employed to measure the acceptability of interventions. AIM response items are measured on a 5-point Likert scale (from Completely Disagree with 1 point to Completely Agree with 5 points). The mean of points scored for each item is taken as the final score. This questionnaire developed by Weiner et al., and they reported Cronbach’s ɑ = 85 for internal consistency. 23\\n Appropriateness The Intervention Appropriateness Measure (IAM) was used for Appropriateness. The IAM consist of a four-item scale that measures perceived intervention appropriateness. Items are measured on a five-point Likert scale (Completely Disagree to Completely Agree), and the mean of points scored for each item is taken as the final score. Higher scores mean that the participant feels this intervention is more appropriate for him/her. For this tool, Cronbach’s ɑ = 0.91 and all Factor Loadings are reported as higher than 0.8. 23\\nThe Intervention Appropriateness Measure (IAM) was used for Appropriateness. The IAM consist of a four-item scale that measures perceived intervention appropriateness. Items are measured on a five-point Likert scale (Completely Disagree to Completely Agree), and the mean of points scored for each item is taken as the final score. Higher scores mean that the participant feels this intervention is more appropriate for him/her. For this tool, Cronbach’s ɑ = 0.91 and all Factor Loadings are reported as higher than 0.8. 23\",\n \"This study was designed as a controlled randomized clinical trial, including pretest, post-test, and two-month follow-up phases.\",\n \"Since the sampling method comprises snowball sampling and strict eligibility criteria were applied, on the basis of data from similar studies 10 it was determined that at least 20 participants were needed in each group. However, in view of the predicted retention rates, we selected 30 patients for each group.\",\n \"The inclusion criteria were as follows: 1) diagnosis of marijuana use disorder; 2) age 18 years or over; 3) no current or past history of major psychiatric disorders; 4) no other concurrent SUD treatment; and 5) willingness to attend intervention sessions, complete surveys, and take tests (questionnaires and urine test kits).\\nExclusion criteria were as follows: 1) unwillingness to participate; 2) not participating in intervention sessions for more than two weeks; 3) starting secondary psychotherapy; and 4) consuming methamphetamine, amphetamine, cannabis, methadone, benzodiazepines, or morphine during the research stages.\",\n \"Since there are no cannabis use disorder treatment centers in Iran, there is no specific place to select patients. Furthermore, patients at drug treatment centers are referred for treatment of other substance use disorders and comorbidity of drug use is one of the exclusion criteria for this study, since it could lead to misleading results. Therefore, the relatives and acquaintances of those who had been referred to the drug treatment center were interviewed. From November 1, 2019, to November 5, 2019, 15 relatives and family members of drug users referred to drug treatment centers were diagnosed with MUD at this stage. Then, using snowball sampling, after 15 days of investigation, a total of 83 patients were diagnosed with MUD. Seventy-five of the 83 MUD patients who were approached consented, eight declined to participate, and 14 were ineligible. The primary reasons for declining were anxiety about addiction stigma and time constraints. Most of the ineligible patients had multiple illicit use disorders, so they did not meet the study criteria. Therefore, 61 patients completed the baseline assessment and were included in the current analyses. These patients were randomly assigned to each group using a random number table. The interventions were implemented from December 1, 2019, to March 20, 2020. The follow-up phase started on March 21, 2020, and ended on May 20, 2020, (at two months’ follow-up). In order to test for exclusion criteria before each session, a six-drug test kit for methamphetamine, amphetamine, cannabis, methadone, benzodiazepines, and morphine was administered to individuals using urine samples.\",\n \"Both groups were blind to the existence of another group in the study. However, patients were informed about participating in research but not about another group. One day after the end of treatment, the post-test was carried out by mental health technicians with a master’s degree in psychology.\",\n \" Abstinence A marijuana urine test kit prepared by Kian Teb Company (officially licensed by the National Medical Device Directorate IR. IRAN) was used to identify abstainers.\\nA marijuana urine test kit prepared by Kian Teb Company (officially licensed by the National Medical Device Directorate IR. IRAN) was used to identify abstainers.\\n Marijuana smoking A self-report scale was designed for patients who had lapsed during the post-test follow-up. On this scale, patients indicate the number of days of consumption over 30-day periods. The first thirty days after the last intervention session was considered the post-test smoking period and the second-month follow-up was considered as the follow-up marijuana use period.\\nA self-report scale was designed for patients who had lapsed during the post-test follow-up. On this scale, patients indicate the number of days of consumption over 30-day periods. The first thirty days after the last intervention session was considered the post-test smoking period and the second-month follow-up was considered as the follow-up marijuana use period.\\n Craving The Marijuana Craving Questionnaire (MCQ) short-form is a 12-item self-report questionnaire with ten items for subjective assessment of cannabis craving. The scale covers 4 factors: compulsivity, emotionality, expectancy, and purposefulness. According to how patients were thinking or feeling ‘’right now,’’ they placed checkmarks on the questionnaire to endorse responses ranging from 1 or strongly disagree to 7 or strongly agree. Results showed that this questionnaire’s internal consistency is adequate (α = 0.90). The measure was administered following a 12-hour deprivation period. The typical onset of marijuana craving and withdrawal symptoms is observed within approximately one day of cessation and so the current paper’s questionnaire scores can be conceptualized as an index of the propensity to experience marijuana craving following deprivation. 22 In Iranian MUD patients, the MCQ had internal consistency of α = 0.87. Details of the MCQ’s psychometrics properties will be published as a separate study as soon as possible.\\nThe Marijuana Craving Questionnaire (MCQ) short-form is a 12-item self-report questionnaire with ten items for subjective assessment of cannabis craving. The scale covers 4 factors: compulsivity, emotionality, expectancy, and purposefulness. According to how patients were thinking or feeling ‘’right now,’’ they placed checkmarks on the questionnaire to endorse responses ranging from 1 or strongly disagree to 7 or strongly agree. Results showed that this questionnaire’s internal consistency is adequate (α = 0.90). The measure was administered following a 12-hour deprivation period. The typical onset of marijuana craving and withdrawal symptoms is observed within approximately one day of cessation and so the current paper’s questionnaire scores can be conceptualized as an index of the propensity to experience marijuana craving following deprivation. 22 In Iranian MUD patients, the MCQ had internal consistency of α = 0.87. Details of the MCQ’s psychometrics properties will be published as a separate study as soon as possible.\\n Acceptability The Acceptability of Intervention Measure (AIM) was employed to measure the acceptability of interventions. AIM response items are measured on a 5-point Likert scale (from Completely Disagree with 1 point to Completely Agree with 5 points). The mean of points scored for each item is taken as the final score. This questionnaire developed by Weiner et al., and they reported Cronbach’s ɑ = 85 for internal consistency. 23\\nThe Acceptability of Intervention Measure (AIM) was employed to measure the acceptability of interventions. AIM response items are measured on a 5-point Likert scale (from Completely Disagree with 1 point to Completely Agree with 5 points). The mean of points scored for each item is taken as the final score. This questionnaire developed by Weiner et al., and they reported Cronbach’s ɑ = 85 for internal consistency. 23\\n Appropriateness The Intervention Appropriateness Measure (IAM) was used for Appropriateness. The IAM consist of a four-item scale that measures perceived intervention appropriateness. Items are measured on a five-point Likert scale (Completely Disagree to Completely Agree), and the mean of points scored for each item is taken as the final score. Higher scores mean that the participant feels this intervention is more appropriate for him/her. For this tool, Cronbach’s ɑ = 0.91 and all Factor Loadings are reported as higher than 0.8. 23\\nThe Intervention Appropriateness Measure (IAM) was used for Appropriateness. The IAM consist of a four-item scale that measures perceived intervention appropriateness. Items are measured on a five-point Likert scale (Completely Disagree to Completely Agree), and the mean of points scored for each item is taken as the final score. Higher scores mean that the participant feels this intervention is more appropriate for him/her. For this tool, Cronbach’s ɑ = 0.91 and all Factor Loadings are reported as higher than 0.8. 23\",\n \"A marijuana urine test kit prepared by Kian Teb Company (officially licensed by the National Medical Device Directorate IR. IRAN) was used to identify abstainers.\",\n \"A self-report scale was designed for patients who had lapsed during the post-test follow-up. On this scale, patients indicate the number of days of consumption over 30-day periods. The first thirty days after the last intervention session was considered the post-test smoking period and the second-month follow-up was considered as the follow-up marijuana use period.\",\n \"The Marijuana Craving Questionnaire (MCQ) short-form is a 12-item self-report questionnaire with ten items for subjective assessment of cannabis craving. The scale covers 4 factors: compulsivity, emotionality, expectancy, and purposefulness. According to how patients were thinking or feeling ‘’right now,’’ they placed checkmarks on the questionnaire to endorse responses ranging from 1 or strongly disagree to 7 or strongly agree. Results showed that this questionnaire’s internal consistency is adequate (α = 0.90). The measure was administered following a 12-hour deprivation period. The typical onset of marijuana craving and withdrawal symptoms is observed within approximately one day of cessation and so the current paper’s questionnaire scores can be conceptualized as an index of the propensity to experience marijuana craving following deprivation. 22 In Iranian MUD patients, the MCQ had internal consistency of α = 0.87. Details of the MCQ’s psychometrics properties will be published as a separate study as soon as possible.\",\n \"The Acceptability of Intervention Measure (AIM) was employed to measure the acceptability of interventions. AIM response items are measured on a 5-point Likert scale (from Completely Disagree with 1 point to Completely Agree with 5 points). The mean of points scored for each item is taken as the final score. This questionnaire developed by Weiner et al., and they reported Cronbach’s ɑ = 85 for internal consistency. 23\",\n \"The Intervention Appropriateness Measure (IAM) was used for Appropriateness. The IAM consist of a four-item scale that measures perceived intervention appropriateness. Items are measured on a five-point Likert scale (Completely Disagree to Completely Agree), and the mean of points scored for each item is taken as the final score. Higher scores mean that the participant feels this intervention is more appropriate for him/her. For this tool, Cronbach’s ɑ = 0.91 and all Factor Loadings are reported as higher than 0.8. 23\",\n \" Dialectical behavior therapy DBT is a group intervention consisting of 16 sessions (meeting once a week for 90 minutes) with one psychotherapist and her co-therapist. The intervention protocol is an adaptation of DBT for SUD based on three basic manuals. 10 , 24 , 25 The primary objective of the DBT is to reduce dysfunction in emotion regulation and craving via increasing cessation rates and improving skills. A psychotherapist with a PhD delivered the intervention sessions (with a psychologist as co-therapist) and they were blind both to the existence of another group and to the study objectives. Table 1 shows the content covered in each DBT session.\\n\\nTable 1DBT content per sessionCessationContentPre-sessionExplanation of dialectical behavioral therapy, principles, and goals. Brief introduction to the content of each session. Familiarity with participants. Participants are given an intervention booklet to read at home.1st session (mindfulness 1)Introduce the concept of mindfulness and three mental states (wise, reasonable, emotional) and their relations with substance use.2nd session (mindfulness 2)Teach two clusters of mindfulness skills. The first includes viewing, participation, and description. The second includes a non-judgmental stance and inclusive self-consciousness.3rd sessionSummarize the mindfulness sessions – definition of addiction, standard therapies of addiction, introduction to and teaching of dialectical avoidance technique. Review the positive and negative aspects of abstinence. Explanation and investigation of relapse and its causes. Explaining the skill of the pure mind, the addicted mind, the types of behaviors related to the pure mentality and the addicted mentality, and preparing a list of supporters.4th-5th sessions (Distress tolerance)Teaching distraction strategies with five skills include activities, comparisons, emotions, thoughts, and enjoyment. Through enjoyable activities, focusing on work or other topics, counting, leaving the situation, paying attention to daily tasks, distracting from thoughts, and self-harm behaviors – teaching and training self-soothing with five senses.6th-7th sessions (Emotion regulation)Definition of emotion, how emotions work, familiarity with emotion regulation skills. Emotion Identification Exercise, Emotion Registration Exercise. Identifying barriers to experiencing emotion in a healthy way and ways to overcome these barriers. Teaching creating short-term positive emotional experiences for experiencing positive emotional states.8th-10th sessions (Emotion regulation and distress tolerance in an MUD context)Explain craving and its connection to the experience of emotions. Introducing methods for identifying values. Importance of committed action based on a list of essential values in life. Develop new coping strategies in response to unpleasant emotions, sensations, and cognitions, especially craving as a multidimensional problem and teaching problem solving and behavior analysis.11th sessionBasic acceptance technique training. Introduce living in the present moment techniques.12th-13th sessionsInterpersonal effectiveness training. Participants learn assertiveness skills about substance users. Other skills include non-verbal communication, verbal communication, and problem-solving, decision-making, and listening skills.14th-16th sessionsReview of sessions. Elimination of ambiguities. Exercising skills in the presence of other people.\\n\\nDBT is a group intervention consisting of 16 sessions (meeting once a week for 90 minutes) with one psychotherapist and her co-therapist. The intervention protocol is an adaptation of DBT for SUD based on three basic manuals. 10 , 24 , 25 The primary objective of the DBT is to reduce dysfunction in emotion regulation and craving via increasing cessation rates and improving skills. A psychotherapist with a PhD delivered the intervention sessions (with a psychologist as co-therapist) and they were blind both to the existence of another group and to the study objectives. Table 1 shows the content covered in each DBT session.\\n\\nTable 1DBT content per sessionCessationContentPre-sessionExplanation of dialectical behavioral therapy, principles, and goals. Brief introduction to the content of each session. Familiarity with participants. Participants are given an intervention booklet to read at home.1st session (mindfulness 1)Introduce the concept of mindfulness and three mental states (wise, reasonable, emotional) and their relations with substance use.2nd session (mindfulness 2)Teach two clusters of mindfulness skills. The first includes viewing, participation, and description. The second includes a non-judgmental stance and inclusive self-consciousness.3rd sessionSummarize the mindfulness sessions – definition of addiction, standard therapies of addiction, introduction to and teaching of dialectical avoidance technique. Review the positive and negative aspects of abstinence. Explanation and investigation of relapse and its causes. Explaining the skill of the pure mind, the addicted mind, the types of behaviors related to the pure mentality and the addicted mentality, and preparing a list of supporters.4th-5th sessions (Distress tolerance)Teaching distraction strategies with five skills include activities, comparisons, emotions, thoughts, and enjoyment. Through enjoyable activities, focusing on work or other topics, counting, leaving the situation, paying attention to daily tasks, distracting from thoughts, and self-harm behaviors – teaching and training self-soothing with five senses.6th-7th sessions (Emotion regulation)Definition of emotion, how emotions work, familiarity with emotion regulation skills. Emotion Identification Exercise, Emotion Registration Exercise. Identifying barriers to experiencing emotion in a healthy way and ways to overcome these barriers. Teaching creating short-term positive emotional experiences for experiencing positive emotional states.8th-10th sessions (Emotion regulation and distress tolerance in an MUD context)Explain craving and its connection to the experience of emotions. Introducing methods for identifying values. Importance of committed action based on a list of essential values in life. Develop new coping strategies in response to unpleasant emotions, sensations, and cognitions, especially craving as a multidimensional problem and teaching problem solving and behavior analysis.11th sessionBasic acceptance technique training. Introduce living in the present moment techniques.12th-13th sessionsInterpersonal effectiveness training. Participants learn assertiveness skills about substance users. Other skills include non-verbal communication, verbal communication, and problem-solving, decision-making, and listening skills.14th-16th sessionsReview of sessions. Elimination of ambiguities. Exercising skills in the presence of other people.\\n\\n Psychoeducation This option is more ethical than not offering any intervention to the control group. A psychiatrist with five years of experience in addiction psychotherapy implemented this intervention. This intervention includes problem-solving skills, assertiveness, and craving management in eight sessions. Thepsychoeducation intervention was used to provide a basis for comparison with only those elements of the DBT intervention that are different from other psychotherapies. This intervention is utilized for MUD and health-related problems. The intention of this intervention is to provide individuals struggling with cravings and substance use disorder the knowledge needed to comprehensively appreciate their problems and the empowerment needed to cope with them. The psychoeducation intervention included information about the dangers of marijuana and the therapists also provided a pamphlet containing techniques for reduction of craving. 14 , 26 , 27\\nThis option is more ethical than not offering any intervention to the control group. A psychiatrist with five years of experience in addiction psychotherapy implemented this intervention. This intervention includes problem-solving skills, assertiveness, and craving management in eight sessions. Thepsychoeducation intervention was used to provide a basis for comparison with only those elements of the DBT intervention that are different from other psychotherapies. This intervention is utilized for MUD and health-related problems. The intention of this intervention is to provide individuals struggling with cravings and substance use disorder the knowledge needed to comprehensively appreciate their problems and the empowerment needed to cope with them. The psychoeducation intervention included information about the dangers of marijuana and the therapists also provided a pamphlet containing techniques for reduction of craving. 14 , 26 , 27\\n Therapists and treatment adherence To enable adherence to the principles of DBT to be checked, audios of the sessions were recorded with the consent of all participants. A DBT researcher and psychotherapist who was not involved in the treatment groups checked session content afterwards. Sessions were divided into 15-minute modules that were chosen for adherence checks at random. Treatment stance and occurrence and depth of DBT processes were appraised. Based on the treatment manual, modules were rated for adherence level as either adequate or not adequate. The majority (83%) were judged to have been conducted adequately.\\nTo enable adherence to the principles of DBT to be checked, audios of the sessions were recorded with the consent of all participants. A DBT researcher and psychotherapist who was not involved in the treatment groups checked session content afterwards. Sessions were divided into 15-minute modules that were chosen for adherence checks at random. Treatment stance and occurrence and depth of DBT processes were appraised. Based on the treatment manual, modules were rated for adherence level as either adequate or not adequate. The majority (83%) were judged to have been conducted adequately.\\n Statistical method Demographic information was gathered and reported as frequencies, means, and standard deviations and repeated measures ANOVA and chi-square tests were conducted for the outcomes using SPSS software, version 26.\\nDemographic information was gathered and reported as frequencies, means, and standard deviations and repeated measures ANOVA and chi-square tests were conducted for the outcomes using SPSS software, version 26.\",\n \"DBT is a group intervention consisting of 16 sessions (meeting once a week for 90 minutes) with one psychotherapist and her co-therapist. The intervention protocol is an adaptation of DBT for SUD based on three basic manuals. 10 , 24 , 25 The primary objective of the DBT is to reduce dysfunction in emotion regulation and craving via increasing cessation rates and improving skills. A psychotherapist with a PhD delivered the intervention sessions (with a psychologist as co-therapist) and they were blind both to the existence of another group and to the study objectives. Table 1 shows the content covered in each DBT session.\\n\\nTable 1DBT content per sessionCessationContentPre-sessionExplanation of dialectical behavioral therapy, principles, and goals. Brief introduction to the content of each session. Familiarity with participants. Participants are given an intervention booklet to read at home.1st session (mindfulness 1)Introduce the concept of mindfulness and three mental states (wise, reasonable, emotional) and their relations with substance use.2nd session (mindfulness 2)Teach two clusters of mindfulness skills. The first includes viewing, participation, and description. The second includes a non-judgmental stance and inclusive self-consciousness.3rd sessionSummarize the mindfulness sessions – definition of addiction, standard therapies of addiction, introduction to and teaching of dialectical avoidance technique. Review the positive and negative aspects of abstinence. Explanation and investigation of relapse and its causes. Explaining the skill of the pure mind, the addicted mind, the types of behaviors related to the pure mentality and the addicted mentality, and preparing a list of supporters.4th-5th sessions (Distress tolerance)Teaching distraction strategies with five skills include activities, comparisons, emotions, thoughts, and enjoyment. Through enjoyable activities, focusing on work or other topics, counting, leaving the situation, paying attention to daily tasks, distracting from thoughts, and self-harm behaviors – teaching and training self-soothing with five senses.6th-7th sessions (Emotion regulation)Definition of emotion, how emotions work, familiarity with emotion regulation skills. Emotion Identification Exercise, Emotion Registration Exercise. Identifying barriers to experiencing emotion in a healthy way and ways to overcome these barriers. Teaching creating short-term positive emotional experiences for experiencing positive emotional states.8th-10th sessions (Emotion regulation and distress tolerance in an MUD context)Explain craving and its connection to the experience of emotions. Introducing methods for identifying values. Importance of committed action based on a list of essential values in life. Develop new coping strategies in response to unpleasant emotions, sensations, and cognitions, especially craving as a multidimensional problem and teaching problem solving and behavior analysis.11th sessionBasic acceptance technique training. Introduce living in the present moment techniques.12th-13th sessionsInterpersonal effectiveness training. Participants learn assertiveness skills about substance users. Other skills include non-verbal communication, verbal communication, and problem-solving, decision-making, and listening skills.14th-16th sessionsReview of sessions. Elimination of ambiguities. Exercising skills in the presence of other people.\\n\",\n \"This option is more ethical than not offering any intervention to the control group. A psychiatrist with five years of experience in addiction psychotherapy implemented this intervention. This intervention includes problem-solving skills, assertiveness, and craving management in eight sessions. Thepsychoeducation intervention was used to provide a basis for comparison with only those elements of the DBT intervention that are different from other psychotherapies. This intervention is utilized for MUD and health-related problems. The intention of this intervention is to provide individuals struggling with cravings and substance use disorder the knowledge needed to comprehensively appreciate their problems and the empowerment needed to cope with them. The psychoeducation intervention included information about the dangers of marijuana and the therapists also provided a pamphlet containing techniques for reduction of craving. 14 , 26 , 27\",\n \"To enable adherence to the principles of DBT to be checked, audios of the sessions were recorded with the consent of all participants. A DBT researcher and psychotherapist who was not involved in the treatment groups checked session content afterwards. Sessions were divided into 15-minute modules that were chosen for adherence checks at random. Treatment stance and occurrence and depth of DBT processes were appraised. Based on the treatment manual, modules were rated for adherence level as either adequate or not adequate. The majority (83%) were judged to have been conducted adequately.\",\n \"Demographic information was gathered and reported as frequencies, means, and standard deviations and repeated measures ANOVA and chi-square tests were conducted for the outcomes using SPSS software, version 26.\",\n \"Written informed consent was obtained from all participants before initiation of the research. The tools used in this study were all filled-out anonymously, and an ID code was used to maintain the confidentiality of personal information (Ir.kums.rce.1398.1203). At the end of the research process, dialectical behavior therapy was also provided to the control group. This study is registered with the Thailand Registry of Clinical Trials (TCTR20200319007).\",\n \" Feasibility In the psycho-education group, 24/31 participants completed all sessions, compared to 29/30 members of the DBT group (retention rates: 77% in the control group vs. 96% in the DBT group). Additionally, 96% (29/30) of the DBT group members completed the two-month follow-up, whereas 64.5% (20/31) of the control group completed follow-up ( Figure 1 ). The chi-square test was applied, showing associations between group and retention, with χ2= 4.95, p = 0.02 for post-treatment and, χ2= 9.97, p = 0.002 for the follow-up phase. Consequently, DBT retention rates were significantly higher than psycho-education retention rates at post-treatment and follow-up.\\n\\nFigure 1Consort diagram.\\n\\nIn the psycho-education group, 24/31 participants completed all sessions, compared to 29/30 members of the DBT group (retention rates: 77% in the control group vs. 96% in the DBT group). Additionally, 96% (29/30) of the DBT group members completed the two-month follow-up, whereas 64.5% (20/31) of the control group completed follow-up ( Figure 1 ). The chi-square test was applied, showing associations between group and retention, with χ2= 4.95, p = 0.02 for post-treatment and, χ2= 9.97, p = 0.002 for the follow-up phase. Consequently, DBT retention rates were significantly higher than psycho-education retention rates at post-treatment and follow-up.\\n\\nFigure 1Consort diagram.\\n\\n Acceptability and appropriateness To enable assessment of the acceptability and appropriateness of intervention, patients completed the AIM and IAM scales in the post-treatment phase. The acceptability scores were 16.57 for DBT and 9.6 for the control group (p < 0.05). The appropriateness scores were17.03 for DBT and 10.7 for the psychoeducation group (p < 0.05). Since there are no standards for these measures, these points were transferred to Likert-based questionnaire scales. For acceptability, the results equated to ‘’agree’’ for the DBT group versus ‘’neither agree nor disagree’’ for the psychoeducation group. For appropriateness, the results equated to ‘’completely agree’’ for the DBT group versus ‘’neither agree nor disagree’’ for the psychoeducation group.\\nTo enable assessment of the acceptability and appropriateness of intervention, patients completed the AIM and IAM scales in the post-treatment phase. The acceptability scores were 16.57 for DBT and 9.6 for the control group (p < 0.05). The appropriateness scores were17.03 for DBT and 10.7 for the psychoeducation group (p < 0.05). Since there are no standards for these measures, these points were transferred to Likert-based questionnaire scales. For acceptability, the results equated to ‘’agree’’ for the DBT group versus ‘’neither agree nor disagree’’ for the psychoeducation group. For appropriateness, the results equated to ‘’completely agree’’ for the DBT group versus ‘’neither agree nor disagree’’ for the psychoeducation group.\\n Participant characteristics Participants’ demographic variables are shown in Table 2 . Analyses showed that there were no significant differences between the two groups regarding these variables. It should be noted that since over 97% of the participants were male from the beginning, the results were reported only for men.\\n\\nTable 2Mean and standard deviation of demographic variables in the intervention and control groups at the test phaseVariableIntervention groupControl groupp-valueEducational level* 0.2No higher education, n (%)2 (6)6 (19)Diploma, n (%)14 (46)15 (48)University student or graduate, n (%)14 (46)10 (33)Age †25.6 (5.67)27.19 (7.48)0.3Months of marijuana use19.53 (5.9)17.48 (6.03)0.1Craving (total) † Pre-test45.2 (8.3)47.9 (10.2)0.2Post-test42.13 (7.7)44.48 (8.1)0.1Follow-up42.66 (9.25)45.8 (8.4)0.1Data presented as mean (standard deviation), unless otherwise specified.* Chi-square test.† Independent t test.\\n\\nData presented as mean (standard deviation), unless otherwise specified.\\n* Chi-square test.\\n† Independent t test.\\nParticipants’ demographic variables are shown in Table 2 . Analyses showed that there were no significant differences between the two groups regarding these variables. It should be noted that since over 97% of the participants were male from the beginning, the results were reported only for men.\\n\\nTable 2Mean and standard deviation of demographic variables in the intervention and control groups at the test phaseVariableIntervention groupControl groupp-valueEducational level* 0.2No higher education, n (%)2 (6)6 (19)Diploma, n (%)14 (46)15 (48)University student or graduate, n (%)14 (46)10 (33)Age †25.6 (5.67)27.19 (7.48)0.3Months of marijuana use19.53 (5.9)17.48 (6.03)0.1Craving (total) † Pre-test45.2 (8.3)47.9 (10.2)0.2Post-test42.13 (7.7)44.48 (8.1)0.1Follow-up42.66 (9.25)45.8 (8.4)0.1Data presented as mean (standard deviation), unless otherwise specified.* Chi-square test.† Independent t test.\\n\\nData presented as mean (standard deviation), unless otherwise specified.\\n* Chi-square test.\\n† Independent t test.\\n Efficacy outcomes The hypothesis of equal covariance matrices was examined for craving (Box’s M = 3.63, P = 0.74). The results of this test indicate homogeneity of covariance matrices. Mauchly’s test of sphericity also showed that the sphericity assumption was not violated (p = 0.42 and Mauchly’s W = 0.97).\\nThe results of the intergroup test and intergroup relations are also presented in Table 3 . As shown in Table 3 , the effect levels for craving (F = 3.52, p > 0.05) suggest that there is no significant difference between groups. These results were repeated for three of the subscales of craving: compulsivity, expectancy, and purposefulness. However, the emotionality subscale results showed a significant reduction in the DBT group compared to the control group 10.6 vs. 14.4 in the post-test and 10.43 vs. 13.26 in the follow-up phase (F = 19.94, p < 0.05).\\n\\nTable 3Repeated measures ANOVA for variables for the DBT group and control group in the pre-test, post-test, and follow-upVariable/sourceType III sum of squaresdfMean squareFSigPartial eta squaredObserved powerCraving Tests of within-subjects effects Factor1347.6962173.842.60.07*0.040.512Factor1 × group4.7622.380.0360.110.0010.055Error (factor1)7845.15411866.48 Tests of between-subjects effects Group341.0841341.083.520.06 †0.0560.455Error5708.795996.75 Emotionality Tests of within-subjects effects Factor1257.332128.6511.690.00 †0.1650.9Factor1 × group74.70237.350.370.03*0.050.63Error (factor1)1297.8111810.99 Tests of between-subjects effects Group283.891283.8919.940.00 †0.250.9Error839.9065914.23 * Significant to 0.05.† Significant to 0.01.\\n\\n* Significant to 0.05.\\n† Significant to 0.01.\\nWith regard to cessation, the results indicated that DBT achieved a higher rate of cessation than the control treatment in both the post-test and at follow-up, ( Table 4 ) (p < 0.05). It was also found that among those who continued to use the drug, the number of use days per month in the post-test and the follow-up periods (two months) was significantly lower in the intervention group than the control group.\\n\\nTable 4Cessation and consumption between groups DBTControlp-valueCessation* Post-test14 (46%)5 (16%)0.01\\n‡Follow-up12 (40%)3 (9.5%)0.006\\n‡Number of days use † Post-test2.43±1.87.5±5.030.00\\n‡Follow-up3.44±1.918.75±3.270.00\\n‡Bold p-values are significant at critical levels.* The chi-square test was applied.† T test for independent samples.‡ Significant to 0.01.\\n\\nBold p-values are significant at critical levels.\\n* The chi-square test was applied.\\n† T test for independent samples.\\n‡ Significant to 0.01.\\nThe hypothesis of equal covariance matrices was examined for craving (Box’s M = 3.63, P = 0.74). The results of this test indicate homogeneity of covariance matrices. Mauchly’s test of sphericity also showed that the sphericity assumption was not violated (p = 0.42 and Mauchly’s W = 0.97).\\nThe results of the intergroup test and intergroup relations are also presented in Table 3 . As shown in Table 3 , the effect levels for craving (F = 3.52, p > 0.05) suggest that there is no significant difference between groups. These results were repeated for three of the subscales of craving: compulsivity, expectancy, and purposefulness. However, the emotionality subscale results showed a significant reduction in the DBT group compared to the control group 10.6 vs. 14.4 in the post-test and 10.43 vs. 13.26 in the follow-up phase (F = 19.94, p < 0.05).\\n\\nTable 3Repeated measures ANOVA for variables for the DBT group and control group in the pre-test, post-test, and follow-upVariable/sourceType III sum of squaresdfMean squareFSigPartial eta squaredObserved powerCraving Tests of within-subjects effects Factor1347.6962173.842.60.07*0.040.512Factor1 × group4.7622.380.0360.110.0010.055Error (factor1)7845.15411866.48 Tests of between-subjects effects Group341.0841341.083.520.06 †0.0560.455Error5708.795996.75 Emotionality Tests of within-subjects effects Factor1257.332128.6511.690.00 †0.1650.9Factor1 × group74.70237.350.370.03*0.050.63Error (factor1)1297.8111810.99 Tests of between-subjects effects Group283.891283.8919.940.00 †0.250.9Error839.9065914.23 * Significant to 0.05.† Significant to 0.01.\\n\\n* Significant to 0.05.\\n† Significant to 0.01.\\nWith regard to cessation, the results indicated that DBT achieved a higher rate of cessation than the control treatment in both the post-test and at follow-up, ( Table 4 ) (p < 0.05). It was also found that among those who continued to use the drug, the number of use days per month in the post-test and the follow-up periods (two months) was significantly lower in the intervention group than the control group.\\n\\nTable 4Cessation and consumption between groups DBTControlp-valueCessation* Post-test14 (46%)5 (16%)0.01\\n‡Follow-up12 (40%)3 (9.5%)0.006\\n‡Number of days use † Post-test2.43±1.87.5±5.030.00\\n‡Follow-up3.44±1.918.75±3.270.00\\n‡Bold p-values are significant at critical levels.* The chi-square test was applied.† T test for independent samples.‡ Significant to 0.01.\\n\\nBold p-values are significant at critical levels.\\n* The chi-square test was applied.\\n† T test for independent samples.\\n‡ Significant to 0.01.\",\n \"In the psycho-education group, 24/31 participants completed all sessions, compared to 29/30 members of the DBT group (retention rates: 77% in the control group vs. 96% in the DBT group). Additionally, 96% (29/30) of the DBT group members completed the two-month follow-up, whereas 64.5% (20/31) of the control group completed follow-up ( Figure 1 ). The chi-square test was applied, showing associations between group and retention, with χ2= 4.95, p = 0.02 for post-treatment and, χ2= 9.97, p = 0.002 for the follow-up phase. Consequently, DBT retention rates were significantly higher than psycho-education retention rates at post-treatment and follow-up.\\n\\nFigure 1Consort diagram.\\n\",\n \"To enable assessment of the acceptability and appropriateness of intervention, patients completed the AIM and IAM scales in the post-treatment phase. The acceptability scores were 16.57 for DBT and 9.6 for the control group (p < 0.05). The appropriateness scores were17.03 for DBT and 10.7 for the psychoeducation group (p < 0.05). Since there are no standards for these measures, these points were transferred to Likert-based questionnaire scales. For acceptability, the results equated to ‘’agree’’ for the DBT group versus ‘’neither agree nor disagree’’ for the psychoeducation group. For appropriateness, the results equated to ‘’completely agree’’ for the DBT group versus ‘’neither agree nor disagree’’ for the psychoeducation group.\",\n \"Participants’ demographic variables are shown in Table 2 . Analyses showed that there were no significant differences between the two groups regarding these variables. It should be noted that since over 97% of the participants were male from the beginning, the results were reported only for men.\\n\\nTable 2Mean and standard deviation of demographic variables in the intervention and control groups at the test phaseVariableIntervention groupControl groupp-valueEducational level* 0.2No higher education, n (%)2 (6)6 (19)Diploma, n (%)14 (46)15 (48)University student or graduate, n (%)14 (46)10 (33)Age †25.6 (5.67)27.19 (7.48)0.3Months of marijuana use19.53 (5.9)17.48 (6.03)0.1Craving (total) † Pre-test45.2 (8.3)47.9 (10.2)0.2Post-test42.13 (7.7)44.48 (8.1)0.1Follow-up42.66 (9.25)45.8 (8.4)0.1Data presented as mean (standard deviation), unless otherwise specified.* Chi-square test.† Independent t test.\\n\\nData presented as mean (standard deviation), unless otherwise specified.\\n* Chi-square test.\\n† Independent t test.\",\n \"The hypothesis of equal covariance matrices was examined for craving (Box’s M = 3.63, P = 0.74). The results of this test indicate homogeneity of covariance matrices. Mauchly’s test of sphericity also showed that the sphericity assumption was not violated (p = 0.42 and Mauchly’s W = 0.97).\\nThe results of the intergroup test and intergroup relations are also presented in Table 3 . As shown in Table 3 , the effect levels for craving (F = 3.52, p > 0.05) suggest that there is no significant difference between groups. These results were repeated for three of the subscales of craving: compulsivity, expectancy, and purposefulness. However, the emotionality subscale results showed a significant reduction in the DBT group compared to the control group 10.6 vs. 14.4 in the post-test and 10.43 vs. 13.26 in the follow-up phase (F = 19.94, p < 0.05).\\n\\nTable 3Repeated measures ANOVA for variables for the DBT group and control group in the pre-test, post-test, and follow-upVariable/sourceType III sum of squaresdfMean squareFSigPartial eta squaredObserved powerCraving Tests of within-subjects effects Factor1347.6962173.842.60.07*0.040.512Factor1 × group4.7622.380.0360.110.0010.055Error (factor1)7845.15411866.48 Tests of between-subjects effects Group341.0841341.083.520.06 †0.0560.455Error5708.795996.75 Emotionality Tests of within-subjects effects Factor1257.332128.6511.690.00 †0.1650.9Factor1 × group74.70237.350.370.03*0.050.63Error (factor1)1297.8111810.99 Tests of between-subjects effects Group283.891283.8919.940.00 †0.250.9Error839.9065914.23 * Significant to 0.05.† Significant to 0.01.\\n\\n* Significant to 0.05.\\n† Significant to 0.01.\\nWith regard to cessation, the results indicated that DBT achieved a higher rate of cessation than the control treatment in both the post-test and at follow-up, ( Table 4 ) (p < 0.05). It was also found that among those who continued to use the drug, the number of use days per month in the post-test and the follow-up periods (two months) was significantly lower in the intervention group than the control group.\\n\\nTable 4Cessation and consumption between groups DBTControlp-valueCessation* Post-test14 (46%)5 (16%)0.01\\n‡Follow-up12 (40%)3 (9.5%)0.006\\n‡Number of days use † Post-test2.43±1.87.5±5.030.00\\n‡Follow-up3.44±1.918.75±3.270.00\\n‡Bold p-values are significant at critical levels.* The chi-square test was applied.† T test for independent samples.‡ Significant to 0.01.\\n\\nBold p-values are significant at critical levels.\\n* The chi-square test was applied.\\n† T test for independent samples.\\n‡ Significant to 0.01.\",\n \"This study examined the feasibility, acceptability, and preliminary efficacy of a 16-session DBT intervention to address craving and achieve cessation in MUD patients. This intervention showed strong evidence of feasibility. Moreover, acceptability and appropriateness rates in the DBT group were high and adequate. The results showed that DBT is a promising intervention for marijuana cessation in patients with MUD. Although this study is the first RCT of DBT for MUD, the scientific literature about DBT for other addictive behaviors reports similar results.\\nRezaei et al. found that DBT significantly improved craving among methadone users. Their result showed that DBT could reduce methadone usage and improve emotion regulation. 28 Moreover, another study showed that implementation of DBT with alcohol-dependent patients improved alcohol-related behavior and emotional deficits, which is similar to the results of the present study. 29 However, the results for craving showed there was no significant difference between groups. With regard to this finding, our result differs from the majority of other research findings. For example, Rezaei et al. found that DBT significantly improved craving among methadone users. 10 This result was also repeated in Rabinovitz’s paper. 30 One of the main reasons for this difference lies in the finding in the present study that DBT had greater improvement in the emotionality subscale of craving (p < 0.5). Since the most important structure of marijuana craving is its emotional dimensions, 5 , 14 the lack of changes in other subscales resulted in non-significance for the overall craving scale score. The results of the present study with relation to craving are therefore somewhat co-directional with the findings of previous studies. On the behavioral level, other studies found that Marijuana craving cues were strongly associated with deficits in regulation of negative affect and emotions. 14 , 15 Also, when neural levels were assessed during reappraisal of negative stimuli, patients with MUD and regular users showed altered neural activity and functional connectivity. Furthermore, being a marijuana user was related to dysfunctions in the amygdala and in amygdala-DLPFC coupling activity. 14 Taken together, these findings demonstrate that emotion regulation problems and craving are prevalent in MUD patients and can interfere with the cessation process. Since DBT is a third-wave behavioral therapy, it has a strong emotional basis. This therapy encompasses three emotion-based goals: understanding emotions, reducing emotional vulnerability, and reducing emotional suffering. Patients are helped to understand that unpleasant emotions are a normal part of life and that accepting their existence is more healthy than trying to avoid controlling them. 10 , 28 Overall, DBT is an emotion regulation method that helps patients learn, understand, and label emotions, reducing emotional vulnerability and emotional suffering. These skills help MUD patients to label emotions related to craving. This improvement in emotional states can improve dysfunctions in the amygdala and in amygdala-DLPFC coupling activity. 9 , 31 , 32\\nAlong the same lines, it also improves emotional craving-related brain structures and reduces impulsive behavior (e.g., lapses). Also, with the ‘’distress tolerance’’ component of DBT, patients learn to live with destructive emotions to accept unpleasant craving situations. 32 , 33 Therefore, by increasing craving, they no longer consume marijuana immediately. 22 , 24 Similarly, other DBT components teach MUD patients reinforcement management and problem-solving skills that can help them to reduce marijuana consumption. 34 , 35\",\n \"To conclude, DBT demonstrated adequate feasibility, acceptability, and appropriateness for patients with marijuana use disorder. Moreover, DBT also exhibited significant efficacy compared to the control group for achieving cessation and reducing emotion-related craving. Even in patients who could not achieve abstinence, DBT led to a reduction in marijuana consumption rates. These findings persisted at two-month follow-up.\\n Limitations and future directions Despite these positive results, the present study also has some limitations. First, in order to evaluate the most significant treatment components (such as mindfulness and distress tolerance), no groups received the third wave versions of other therapies (ACT or MBSR). This study only had a two-month follow-up period and could not conduct long-term evaluation due to the study site’s medical and infrastructure conditions. It is recommended that future research should examine mediating and confounding variables to investigate the results of similar research to the present study. Other factors affecting relapse and recurrence could also be examined. Moreover, women should be investigated so that gender-related implications can be determined.\\nDespite these positive results, the present study also has some limitations. First, in order to evaluate the most significant treatment components (such as mindfulness and distress tolerance), no groups received the third wave versions of other therapies (ACT or MBSR). This study only had a two-month follow-up period and could not conduct long-term evaluation due to the study site’s medical and infrastructure conditions. It is recommended that future research should examine mediating and confounding variables to investigate the results of similar research to the present study. Other factors affecting relapse and recurrence could also be examined. Moreover, women should be investigated so that gender-related implications can be determined.\",\n \"Despite these positive results, the present study also has some limitations. First, in order to evaluate the most significant treatment components (such as mindfulness and distress tolerance), no groups received the third wave versions of other therapies (ACT or MBSR). This study only had a two-month follow-up period and could not conduct long-term evaluation due to the study site’s medical and infrastructure conditions. It is recommended that future research should examine mediating and confounding variables to investigate the results of similar research to the present study. Other factors affecting relapse and recurrence could also be examined. Moreover, women should be investigated so that gender-related implications can be determined.\"\n]"},"all_sections_sec_types":{"kind":"list like","value":["intro","methods",null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,"results",null,null,null,null,"discussion","conclusions",null],"string":"[\n \"intro\",\n \"methods\",\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n \"results\",\n null,\n null,\n null,\n null,\n \"discussion\",\n \"conclusions\",\n null\n]"},"keywords":{"kind":"list like","value":["Dialectical behavior therapy","marijuana use","feasibility studies","craving","lapse"],"string":"[\n \"Dialectical behavior therapy\",\n \"marijuana use\",\n \"feasibility studies\",\n \"craving\",\n \"lapse\"\n]"},"whole_article_text":{"kind":"string","value":"Introduction:\nMarijuana is the most prevalent substance among those reported to be a significant problem among people seeking treatment for substance abuse. 1 According to WHO reports, more than 140 million people consume marijuana every year. 2 With regard to Iran, recent evidence shows that more than 5% of people consume marijuana every year, predominantly young males. However, in view of the harsh marijuana prohibition policy of the Iranian government, most clinicians estimate that these rates have been hugely underestimated. 3 Marijuana, as an illegal drug, is associated with significant physical, psychological, and social consequences. 4 Studies have shown that regular and heavy marijuana use patterns correlate with increased risk of mood disorders, anxiety, and psychotic episodes and although causality has not been demonstrated, these patterns can increase the course of mental health problems. 5 Also, several medical problems such as respiratory system deficits, stroke, myocardial infarction, and digestive tract cancers are associated with marijuana use patterns, especially among those with marijuana use disorder (MUD). 6 , 7 Approximately one in three marijuana users meet the criteria for MUD based on the DSM-5, and this proportion is rising. 8 One of the most important psychological problems in substance use disorder treatment is craving. Craving is a factor identified as the root cause of relapses and treatment failures. 9 , 10 MUD patients report visual, tactile, and olfactory cues related to craving and compulsivity sensations. 11 Based on these results, clinicians have tried to treat patients with marijuana use disorder.\nTo date, the Food and Drug Administration (FDA) in the United States has not approved any psycho-pharmacotherapy for MUD, and therefore psycho-social interventions have received particular attention. 12 The most widely used psychological treatment in the substance use disorder (SUD) context is cognitive-behavioral therapy (CBT). 13 Results showed that CBT is somewhat effective for SUD, but that most patients with MUD do not achieve cessation and are not motivated to continue skills training during follow-up. Relapse rates therefore remain a considerable limitation of treatment. 10 , 13 One of the main reasons for this low success rate lies in the limitations of CBT. First, CBT protocols do not focus on comorbid problems, whereas most patients with SUD have at least one psychiatric or psychological problem. Secondly, cognitive restructuring may not be useful for all SUD patients. Some patients may be unable to restructure their dysfunctional cognition and core beliefs despite receiving CBT. 10 , 12 , 13 Furthermore, emotion regulation deficits are strongly associated with increased addictive behaviors such as SUD. With emotion regulation, people adjust their emotional experiences related to distressing and unpleasant events. Emotion regulation is essential for successful coping with environmental demands and personal welfare. 14 On the behavioral level, studies have found that marijuana craving cues are strongly associated with deficits in regulation of negative affect and emotions. 14 , 15 Also, on the neural level, during reappraisal of negative stimuli, patients with MUD and regular users have shown altered neural activity and functional connectivity. Moreover, marijuana use is related to dysfunctions in the amygdala and in amygdala-dorsolateral prefrontal cortex (DLPFC) coupling activity. 15 Together, these findings demonstrate that emotion-based psychotherapy must manage comorbid problems and eliminate the limitations of CBT.\nOne of the psychotherapies in the third wave behavioral therapy cluster is dialectical behavior therapy (DBT). DBT has been described as intervention in emotion regulation deficits by focusing on dangerous impulses in borderline personality disorder and substance use disorder. The goals of DBT include improving and regulating emotions as a primary mechanism of change. DBT is a trans-diagnostic treatment and suitable for comorbid problems. DBT trains skills including distress tolerance, interpersonal effectiveness, emotion regulation, and mindfulness. Overall, in the context of SUD, DBT teaches emotion regulation skills to decrease engagement in pathological emotion regulation strategies. It also intervenes in low quality of life situations, reduces drug-seeking behavior, and helps patients function adaptively by accepting unpleasant emotions such as craving. 9 , 10 , 14\nResearch literature shows the efficacy and effectiveness of DBT in various comorbid problems and diseases such as suicide, 16 forensic psychiatric patients, 17 and irritable bowel syndrome. 18 Nevertheless, studies have reported contradictory results for the effectiveness of implementing DBT in various SUD populations. 19 , 20 Furthermore, the literature has recommended using larger samples, clearer instruments to measure outcome variables, and specific and integrated protocols. 21\nAdditionally, according to our investigations, no DBT randomized clinical trials have been conducted that investigated cessation in MUD patients (with or without comorbid problems). A DBT intervention aimed at increasing the cessation rate and reducing craving among MUD patients was developed for this study.\nThis pilot trial investigated the feasibility and preliminary efficacy of DBT relative to a psycho-education intervention that was controlled for time duration and attention. Feasibility was assessed via satisfaction and session completion rates. Preliminary efficacy was evaluated via the impact of DBT on cessation rate and reduction of consumption rates, compared to the psycho-education intervention. Although craving and acceptance of craving are not the primary goals of DBT, they were also compared across the two interventions.\n\nMethods:\n Trial design This study was designed as a controlled randomized clinical trial, including pretest, post-test, and two-month follow-up phases.\nThis study was designed as a controlled randomized clinical trial, including pretest, post-test, and two-month follow-up phases.\n Sample size Since the sampling method comprises snowball sampling and strict eligibility criteria were applied, on the basis of data from similar studies 10 it was determined that at least 20 participants were needed in each group. However, in view of the predicted retention rates, we selected 30 patients for each group.\nSince the sampling method comprises snowball sampling and strict eligibility criteria were applied, on the basis of data from similar studies 10 it was determined that at least 20 participants were needed in each group. However, in view of the predicted retention rates, we selected 30 patients for each group.\n Selection criteria The inclusion criteria were as follows: 1) diagnosis of marijuana use disorder; 2) age 18 years or over; 3) no current or past history of major psychiatric disorders; 4) no other concurrent SUD treatment; and 5) willingness to attend intervention sessions, complete surveys, and take tests (questionnaires and urine test kits).\nExclusion criteria were as follows: 1) unwillingness to participate; 2) not participating in intervention sessions for more than two weeks; 3) starting secondary psychotherapy; and 4) consuming methamphetamine, amphetamine, cannabis, methadone, benzodiazepines, or morphine during the research stages.\nThe inclusion criteria were as follows: 1) diagnosis of marijuana use disorder; 2) age 18 years or over; 3) no current or past history of major psychiatric disorders; 4) no other concurrent SUD treatment; and 5) willingness to attend intervention sessions, complete surveys, and take tests (questionnaires and urine test kits).\nExclusion criteria were as follows: 1) unwillingness to participate; 2) not participating in intervention sessions for more than two weeks; 3) starting secondary psychotherapy; and 4) consuming methamphetamine, amphetamine, cannabis, methadone, benzodiazepines, or morphine during the research stages.\n Participants, procedures, and randomization Since there are no cannabis use disorder treatment centers in Iran, there is no specific place to select patients. Furthermore, patients at drug treatment centers are referred for treatment of other substance use disorders and comorbidity of drug use is one of the exclusion criteria for this study, since it could lead to misleading results. Therefore, the relatives and acquaintances of those who had been referred to the drug treatment center were interviewed. From November 1, 2019, to November 5, 2019, 15 relatives and family members of drug users referred to drug treatment centers were diagnosed with MUD at this stage. Then, using snowball sampling, after 15 days of investigation, a total of 83 patients were diagnosed with MUD. Seventy-five of the 83 MUD patients who were approached consented, eight declined to participate, and 14 were ineligible. The primary reasons for declining were anxiety about addiction stigma and time constraints. Most of the ineligible patients had multiple illicit use disorders, so they did not meet the study criteria. Therefore, 61 patients completed the baseline assessment and were included in the current analyses. These patients were randomly assigned to each group using a random number table. The interventions were implemented from December 1, 2019, to March 20, 2020. The follow-up phase started on March 21, 2020, and ended on May 20, 2020, (at two months’ follow-up). In order to test for exclusion criteria before each session, a six-drug test kit for methamphetamine, amphetamine, cannabis, methadone, benzodiazepines, and morphine was administered to individuals using urine samples.\nSince there are no cannabis use disorder treatment centers in Iran, there is no specific place to select patients. Furthermore, patients at drug treatment centers are referred for treatment of other substance use disorders and comorbidity of drug use is one of the exclusion criteria for this study, since it could lead to misleading results. Therefore, the relatives and acquaintances of those who had been referred to the drug treatment center were interviewed. From November 1, 2019, to November 5, 2019, 15 relatives and family members of drug users referred to drug treatment centers were diagnosed with MUD at this stage. Then, using snowball sampling, after 15 days of investigation, a total of 83 patients were diagnosed with MUD. Seventy-five of the 83 MUD patients who were approached consented, eight declined to participate, and 14 were ineligible. The primary reasons for declining were anxiety about addiction stigma and time constraints. Most of the ineligible patients had multiple illicit use disorders, so they did not meet the study criteria. Therefore, 61 patients completed the baseline assessment and were included in the current analyses. These patients were randomly assigned to each group using a random number table. The interventions were implemented from December 1, 2019, to March 20, 2020. The follow-up phase started on March 21, 2020, and ended on May 20, 2020, (at two months’ follow-up). In order to test for exclusion criteria before each session, a six-drug test kit for methamphetamine, amphetamine, cannabis, methadone, benzodiazepines, and morphine was administered to individuals using urine samples.\n Blinding Both groups were blind to the existence of another group in the study. However, patients were informed about participating in research but not about another group. One day after the end of treatment, the post-test was carried out by mental health technicians with a master’s degree in psychology.\nBoth groups were blind to the existence of another group in the study. However, patients were informed about participating in research but not about another group. One day after the end of treatment, the post-test was carried out by mental health technicians with a master’s degree in psychology.\n Outcome measures Abstinence A marijuana urine test kit prepared by Kian Teb Company (officially licensed by the National Medical Device Directorate IR. IRAN) was used to identify abstainers.\nA marijuana urine test kit prepared by Kian Teb Company (officially licensed by the National Medical Device Directorate IR. IRAN) was used to identify abstainers.\n Marijuana smoking A self-report scale was designed for patients who had lapsed during the post-test follow-up. On this scale, patients indicate the number of days of consumption over 30-day periods. The first thirty days after the last intervention session was considered the post-test smoking period and the second-month follow-up was considered as the follow-up marijuana use period.\nA self-report scale was designed for patients who had lapsed during the post-test follow-up. On this scale, patients indicate the number of days of consumption over 30-day periods. The first thirty days after the last intervention session was considered the post-test smoking period and the second-month follow-up was considered as the follow-up marijuana use period.\n Craving The Marijuana Craving Questionnaire (MCQ) short-form is a 12-item self-report questionnaire with ten items for subjective assessment of cannabis craving. The scale covers 4 factors: compulsivity, emotionality, expectancy, and purposefulness. According to how patients were thinking or feeling ‘’right now,’’ they placed checkmarks on the questionnaire to endorse responses ranging from 1 or strongly disagree to 7 or strongly agree. Results showed that this questionnaire’s internal consistency is adequate (α = 0.90). The measure was administered following a 12-hour deprivation period. The typical onset of marijuana craving and withdrawal symptoms is observed within approximately one day of cessation and so the current paper’s questionnaire scores can be conceptualized as an index of the propensity to experience marijuana craving following deprivation. 22 In Iranian MUD patients, the MCQ had internal consistency of α = 0.87. Details of the MCQ’s psychometrics properties will be published as a separate study as soon as possible.\nThe Marijuana Craving Questionnaire (MCQ) short-form is a 12-item self-report questionnaire with ten items for subjective assessment of cannabis craving. The scale covers 4 factors: compulsivity, emotionality, expectancy, and purposefulness. According to how patients were thinking or feeling ‘’right now,’’ they placed checkmarks on the questionnaire to endorse responses ranging from 1 or strongly disagree to 7 or strongly agree. Results showed that this questionnaire’s internal consistency is adequate (α = 0.90). The measure was administered following a 12-hour deprivation period. The typical onset of marijuana craving and withdrawal symptoms is observed within approximately one day of cessation and so the current paper’s questionnaire scores can be conceptualized as an index of the propensity to experience marijuana craving following deprivation. 22 In Iranian MUD patients, the MCQ had internal consistency of α = 0.87. Details of the MCQ’s psychometrics properties will be published as a separate study as soon as possible.\n Acceptability The Acceptability of Intervention Measure (AIM) was employed to measure the acceptability of interventions. AIM response items are measured on a 5-point Likert scale (from Completely Disagree with 1 point to Completely Agree with 5 points). The mean of points scored for each item is taken as the final score. This questionnaire developed by Weiner et al., and they reported Cronbach’s ɑ = 85 for internal consistency. 23\nThe Acceptability of Intervention Measure (AIM) was employed to measure the acceptability of interventions. AIM response items are measured on a 5-point Likert scale (from Completely Disagree with 1 point to Completely Agree with 5 points). The mean of points scored for each item is taken as the final score. This questionnaire developed by Weiner et al., and they reported Cronbach’s ɑ = 85 for internal consistency. 23\n Appropriateness The Intervention Appropriateness Measure (IAM) was used for Appropriateness. The IAM consist of a four-item scale that measures perceived intervention appropriateness. Items are measured on a five-point Likert scale (Completely Disagree to Completely Agree), and the mean of points scored for each item is taken as the final score. Higher scores mean that the participant feels this intervention is more appropriate for him/her. For this tool, Cronbach’s ɑ = 0.91 and all Factor Loadings are reported as higher than 0.8. 23\nThe Intervention Appropriateness Measure (IAM) was used for Appropriateness. The IAM consist of a four-item scale that measures perceived intervention appropriateness. Items are measured on a five-point Likert scale (Completely Disagree to Completely Agree), and the mean of points scored for each item is taken as the final score. Higher scores mean that the participant feels this intervention is more appropriate for him/her. For this tool, Cronbach’s ɑ = 0.91 and all Factor Loadings are reported as higher than 0.8. 23\n Abstinence A marijuana urine test kit prepared by Kian Teb Company (officially licensed by the National Medical Device Directorate IR. IRAN) was used to identify abstainers.\nA marijuana urine test kit prepared by Kian Teb Company (officially licensed by the National Medical Device Directorate IR. IRAN) was used to identify abstainers.\n Marijuana smoking A self-report scale was designed for patients who had lapsed during the post-test follow-up. On this scale, patients indicate the number of days of consumption over 30-day periods. The first thirty days after the last intervention session was considered the post-test smoking period and the second-month follow-up was considered as the follow-up marijuana use period.\nA self-report scale was designed for patients who had lapsed during the post-test follow-up. On this scale, patients indicate the number of days of consumption over 30-day periods. The first thirty days after the last intervention session was considered the post-test smoking period and the second-month follow-up was considered as the follow-up marijuana use period.\n Craving The Marijuana Craving Questionnaire (MCQ) short-form is a 12-item self-report questionnaire with ten items for subjective assessment of cannabis craving. The scale covers 4 factors: compulsivity, emotionality, expectancy, and purposefulness. According to how patients were thinking or feeling ‘’right now,’’ they placed checkmarks on the questionnaire to endorse responses ranging from 1 or strongly disagree to 7 or strongly agree. Results showed that this questionnaire’s internal consistency is adequate (α = 0.90). The measure was administered following a 12-hour deprivation period. The typical onset of marijuana craving and withdrawal symptoms is observed within approximately one day of cessation and so the current paper’s questionnaire scores can be conceptualized as an index of the propensity to experience marijuana craving following deprivation. 22 In Iranian MUD patients, the MCQ had internal consistency of α = 0.87. Details of the MCQ’s psychometrics properties will be published as a separate study as soon as possible.\nThe Marijuana Craving Questionnaire (MCQ) short-form is a 12-item self-report questionnaire with ten items for subjective assessment of cannabis craving. The scale covers 4 factors: compulsivity, emotionality, expectancy, and purposefulness. According to how patients were thinking or feeling ‘’right now,’’ they placed checkmarks on the questionnaire to endorse responses ranging from 1 or strongly disagree to 7 or strongly agree. Results showed that this questionnaire’s internal consistency is adequate (α = 0.90). The measure was administered following a 12-hour deprivation period. The typical onset of marijuana craving and withdrawal symptoms is observed within approximately one day of cessation and so the current paper’s questionnaire scores can be conceptualized as an index of the propensity to experience marijuana craving following deprivation. 22 In Iranian MUD patients, the MCQ had internal consistency of α = 0.87. Details of the MCQ’s psychometrics properties will be published as a separate study as soon as possible.\n Acceptability The Acceptability of Intervention Measure (AIM) was employed to measure the acceptability of interventions. AIM response items are measured on a 5-point Likert scale (from Completely Disagree with 1 point to Completely Agree with 5 points). The mean of points scored for each item is taken as the final score. This questionnaire developed by Weiner et al., and they reported Cronbach’s ɑ = 85 for internal consistency. 23\nThe Acceptability of Intervention Measure (AIM) was employed to measure the acceptability of interventions. AIM response items are measured on a 5-point Likert scale (from Completely Disagree with 1 point to Completely Agree with 5 points). The mean of points scored for each item is taken as the final score. This questionnaire developed by Weiner et al., and they reported Cronbach’s ɑ = 85 for internal consistency. 23\n Appropriateness The Intervention Appropriateness Measure (IAM) was used for Appropriateness. The IAM consist of a four-item scale that measures perceived intervention appropriateness. Items are measured on a five-point Likert scale (Completely Disagree to Completely Agree), and the mean of points scored for each item is taken as the final score. Higher scores mean that the participant feels this intervention is more appropriate for him/her. For this tool, Cronbach’s ɑ = 0.91 and all Factor Loadings are reported as higher than 0.8. 23\nThe Intervention Appropriateness Measure (IAM) was used for Appropriateness. The IAM consist of a four-item scale that measures perceived intervention appropriateness. Items are measured on a five-point Likert scale (Completely Disagree to Completely Agree), and the mean of points scored for each item is taken as the final score. Higher scores mean that the participant feels this intervention is more appropriate for him/her. For this tool, Cronbach’s ɑ = 0.91 and all Factor Loadings are reported as higher than 0.8. 23\n\nTrial design:\nThis study was designed as a controlled randomized clinical trial, including pretest, post-test, and two-month follow-up phases.\n\nSample size:\nSince the sampling method comprises snowball sampling and strict eligibility criteria were applied, on the basis of data from similar studies 10 it was determined that at least 20 participants were needed in each group. However, in view of the predicted retention rates, we selected 30 patients for each group.\n\nSelection criteria:\nThe inclusion criteria were as follows: 1) diagnosis of marijuana use disorder; 2) age 18 years or over; 3) no current or past history of major psychiatric disorders; 4) no other concurrent SUD treatment; and 5) willingness to attend intervention sessions, complete surveys, and take tests (questionnaires and urine test kits).\nExclusion criteria were as follows: 1) unwillingness to participate; 2) not participating in intervention sessions for more than two weeks; 3) starting secondary psychotherapy; and 4) consuming methamphetamine, amphetamine, cannabis, methadone, benzodiazepines, or morphine during the research stages.\n\nParticipants, procedures, and randomization:\nSince there are no cannabis use disorder treatment centers in Iran, there is no specific place to select patients. Furthermore, patients at drug treatment centers are referred for treatment of other substance use disorders and comorbidity of drug use is one of the exclusion criteria for this study, since it could lead to misleading results. Therefore, the relatives and acquaintances of those who had been referred to the drug treatment center were interviewed. From November 1, 2019, to November 5, 2019, 15 relatives and family members of drug users referred to drug treatment centers were diagnosed with MUD at this stage. Then, using snowball sampling, after 15 days of investigation, a total of 83 patients were diagnosed with MUD. Seventy-five of the 83 MUD patients who were approached consented, eight declined to participate, and 14 were ineligible. The primary reasons for declining were anxiety about addiction stigma and time constraints. Most of the ineligible patients had multiple illicit use disorders, so they did not meet the study criteria. Therefore, 61 patients completed the baseline assessment and were included in the current analyses. These patients were randomly assigned to each group using a random number table. The interventions were implemented from December 1, 2019, to March 20, 2020. The follow-up phase started on March 21, 2020, and ended on May 20, 2020, (at two months’ follow-up). In order to test for exclusion criteria before each session, a six-drug test kit for methamphetamine, amphetamine, cannabis, methadone, benzodiazepines, and morphine was administered to individuals using urine samples.\n\nBlinding:\nBoth groups were blind to the existence of another group in the study. However, patients were informed about participating in research but not about another group. One day after the end of treatment, the post-test was carried out by mental health technicians with a master’s degree in psychology.\n\nOutcome measures:\n Abstinence A marijuana urine test kit prepared by Kian Teb Company (officially licensed by the National Medical Device Directorate IR. IRAN) was used to identify abstainers.\nA marijuana urine test kit prepared by Kian Teb Company (officially licensed by the National Medical Device Directorate IR. IRAN) was used to identify abstainers.\n Marijuana smoking A self-report scale was designed for patients who had lapsed during the post-test follow-up. On this scale, patients indicate the number of days of consumption over 30-day periods. The first thirty days after the last intervention session was considered the post-test smoking period and the second-month follow-up was considered as the follow-up marijuana use period.\nA self-report scale was designed for patients who had lapsed during the post-test follow-up. On this scale, patients indicate the number of days of consumption over 30-day periods. The first thirty days after the last intervention session was considered the post-test smoking period and the second-month follow-up was considered as the follow-up marijuana use period.\n Craving The Marijuana Craving Questionnaire (MCQ) short-form is a 12-item self-report questionnaire with ten items for subjective assessment of cannabis craving. The scale covers 4 factors: compulsivity, emotionality, expectancy, and purposefulness. According to how patients were thinking or feeling ‘’right now,’’ they placed checkmarks on the questionnaire to endorse responses ranging from 1 or strongly disagree to 7 or strongly agree. Results showed that this questionnaire’s internal consistency is adequate (α = 0.90). The measure was administered following a 12-hour deprivation period. The typical onset of marijuana craving and withdrawal symptoms is observed within approximately one day of cessation and so the current paper’s questionnaire scores can be conceptualized as an index of the propensity to experience marijuana craving following deprivation. 22 In Iranian MUD patients, the MCQ had internal consistency of α = 0.87. Details of the MCQ’s psychometrics properties will be published as a separate study as soon as possible.\nThe Marijuana Craving Questionnaire (MCQ) short-form is a 12-item self-report questionnaire with ten items for subjective assessment of cannabis craving. The scale covers 4 factors: compulsivity, emotionality, expectancy, and purposefulness. According to how patients were thinking or feeling ‘’right now,’’ they placed checkmarks on the questionnaire to endorse responses ranging from 1 or strongly disagree to 7 or strongly agree. Results showed that this questionnaire’s internal consistency is adequate (α = 0.90). The measure was administered following a 12-hour deprivation period. The typical onset of marijuana craving and withdrawal symptoms is observed within approximately one day of cessation and so the current paper’s questionnaire scores can be conceptualized as an index of the propensity to experience marijuana craving following deprivation. 22 In Iranian MUD patients, the MCQ had internal consistency of α = 0.87. Details of the MCQ’s psychometrics properties will be published as a separate study as soon as possible.\n Acceptability The Acceptability of Intervention Measure (AIM) was employed to measure the acceptability of interventions. AIM response items are measured on a 5-point Likert scale (from Completely Disagree with 1 point to Completely Agree with 5 points). The mean of points scored for each item is taken as the final score. This questionnaire developed by Weiner et al., and they reported Cronbach’s ɑ = 85 for internal consistency. 23\nThe Acceptability of Intervention Measure (AIM) was employed to measure the acceptability of interventions. AIM response items are measured on a 5-point Likert scale (from Completely Disagree with 1 point to Completely Agree with 5 points). The mean of points scored for each item is taken as the final score. This questionnaire developed by Weiner et al., and they reported Cronbach’s ɑ = 85 for internal consistency. 23\n Appropriateness The Intervention Appropriateness Measure (IAM) was used for Appropriateness. The IAM consist of a four-item scale that measures perceived intervention appropriateness. Items are measured on a five-point Likert scale (Completely Disagree to Completely Agree), and the mean of points scored for each item is taken as the final score. Higher scores mean that the participant feels this intervention is more appropriate for him/her. For this tool, Cronbach’s ɑ = 0.91 and all Factor Loadings are reported as higher than 0.8. 23\nThe Intervention Appropriateness Measure (IAM) was used for Appropriateness. The IAM consist of a four-item scale that measures perceived intervention appropriateness. Items are measured on a five-point Likert scale (Completely Disagree to Completely Agree), and the mean of points scored for each item is taken as the final score. Higher scores mean that the participant feels this intervention is more appropriate for him/her. For this tool, Cronbach’s ɑ = 0.91 and all Factor Loadings are reported as higher than 0.8. 23\n\nAbstinence:\nA marijuana urine test kit prepared by Kian Teb Company (officially licensed by the National Medical Device Directorate IR. IRAN) was used to identify abstainers.\n\nMarijuana smoking:\nA self-report scale was designed for patients who had lapsed during the post-test follow-up. On this scale, patients indicate the number of days of consumption over 30-day periods. The first thirty days after the last intervention session was considered the post-test smoking period and the second-month follow-up was considered as the follow-up marijuana use period.\n\nCraving:\nThe Marijuana Craving Questionnaire (MCQ) short-form is a 12-item self-report questionnaire with ten items for subjective assessment of cannabis craving. The scale covers 4 factors: compulsivity, emotionality, expectancy, and purposefulness. According to how patients were thinking or feeling ‘’right now,’’ they placed checkmarks on the questionnaire to endorse responses ranging from 1 or strongly disagree to 7 or strongly agree. Results showed that this questionnaire’s internal consistency is adequate (α = 0.90). The measure was administered following a 12-hour deprivation period. The typical onset of marijuana craving and withdrawal symptoms is observed within approximately one day of cessation and so the current paper’s questionnaire scores can be conceptualized as an index of the propensity to experience marijuana craving following deprivation. 22 In Iranian MUD patients, the MCQ had internal consistency of α = 0.87. Details of the MCQ’s psychometrics properties will be published as a separate study as soon as possible.\n\nAcceptability:\nThe Acceptability of Intervention Measure (AIM) was employed to measure the acceptability of interventions. AIM response items are measured on a 5-point Likert scale (from Completely Disagree with 1 point to Completely Agree with 5 points). The mean of points scored for each item is taken as the final score. This questionnaire developed by Weiner et al., and they reported Cronbach’s ɑ = 85 for internal consistency. 23\n\nAppropriateness:\nThe Intervention Appropriateness Measure (IAM) was used for Appropriateness. The IAM consist of a four-item scale that measures perceived intervention appropriateness. Items are measured on a five-point Likert scale (Completely Disagree to Completely Agree), and the mean of points scored for each item is taken as the final score. Higher scores mean that the participant feels this intervention is more appropriate for him/her. For this tool, Cronbach’s ɑ = 0.91 and all Factor Loadings are reported as higher than 0.8. 23\n\nIntervention:\n Dialectical behavior therapy DBT is a group intervention consisting of 16 sessions (meeting once a week for 90 minutes) with one psychotherapist and her co-therapist. The intervention protocol is an adaptation of DBT for SUD based on three basic manuals. 10 , 24 , 25 The primary objective of the DBT is to reduce dysfunction in emotion regulation and craving via increasing cessation rates and improving skills. A psychotherapist with a PhD delivered the intervention sessions (with a psychologist as co-therapist) and they were blind both to the existence of another group and to the study objectives. Table 1 shows the content covered in each DBT session.\n\nTable 1DBT content per sessionCessationContentPre-sessionExplanation of dialectical behavioral therapy, principles, and goals. Brief introduction to the content of each session. Familiarity with participants. Participants are given an intervention booklet to read at home.1st session (mindfulness 1)Introduce the concept of mindfulness and three mental states (wise, reasonable, emotional) and their relations with substance use.2nd session (mindfulness 2)Teach two clusters of mindfulness skills. The first includes viewing, participation, and description. The second includes a non-judgmental stance and inclusive self-consciousness.3rd sessionSummarize the mindfulness sessions – definition of addiction, standard therapies of addiction, introduction to and teaching of dialectical avoidance technique. Review the positive and negative aspects of abstinence. Explanation and investigation of relapse and its causes. Explaining the skill of the pure mind, the addicted mind, the types of behaviors related to the pure mentality and the addicted mentality, and preparing a list of supporters.4th-5th sessions (Distress tolerance)Teaching distraction strategies with five skills include activities, comparisons, emotions, thoughts, and enjoyment. Through enjoyable activities, focusing on work or other topics, counting, leaving the situation, paying attention to daily tasks, distracting from thoughts, and self-harm behaviors – teaching and training self-soothing with five senses.6th-7th sessions (Emotion regulation)Definition of emotion, how emotions work, familiarity with emotion regulation skills. Emotion Identification Exercise, Emotion Registration Exercise. Identifying barriers to experiencing emotion in a healthy way and ways to overcome these barriers. Teaching creating short-term positive emotional experiences for experiencing positive emotional states.8th-10th sessions (Emotion regulation and distress tolerance in an MUD context)Explain craving and its connection to the experience of emotions. Introducing methods for identifying values. Importance of committed action based on a list of essential values in life. Develop new coping strategies in response to unpleasant emotions, sensations, and cognitions, especially craving as a multidimensional problem and teaching problem solving and behavior analysis.11th sessionBasic acceptance technique training. Introduce living in the present moment techniques.12th-13th sessionsInterpersonal effectiveness training. Participants learn assertiveness skills about substance users. Other skills include non-verbal communication, verbal communication, and problem-solving, decision-making, and listening skills.14th-16th sessionsReview of sessions. Elimination of ambiguities. Exercising skills in the presence of other people.\n\nDBT is a group intervention consisting of 16 sessions (meeting once a week for 90 minutes) with one psychotherapist and her co-therapist. The intervention protocol is an adaptation of DBT for SUD based on three basic manuals. 10 , 24 , 25 The primary objective of the DBT is to reduce dysfunction in emotion regulation and craving via increasing cessation rates and improving skills. A psychotherapist with a PhD delivered the intervention sessions (with a psychologist as co-therapist) and they were blind both to the existence of another group and to the study objectives. Table 1 shows the content covered in each DBT session.\n\nTable 1DBT content per sessionCessationContentPre-sessionExplanation of dialectical behavioral therapy, principles, and goals. Brief introduction to the content of each session. Familiarity with participants. Participants are given an intervention booklet to read at home.1st session (mindfulness 1)Introduce the concept of mindfulness and three mental states (wise, reasonable, emotional) and their relations with substance use.2nd session (mindfulness 2)Teach two clusters of mindfulness skills. The first includes viewing, participation, and description. The second includes a non-judgmental stance and inclusive self-consciousness.3rd sessionSummarize the mindfulness sessions – definition of addiction, standard therapies of addiction, introduction to and teaching of dialectical avoidance technique. Review the positive and negative aspects of abstinence. Explanation and investigation of relapse and its causes. Explaining the skill of the pure mind, the addicted mind, the types of behaviors related to the pure mentality and the addicted mentality, and preparing a list of supporters.4th-5th sessions (Distress tolerance)Teaching distraction strategies with five skills include activities, comparisons, emotions, thoughts, and enjoyment. Through enjoyable activities, focusing on work or other topics, counting, leaving the situation, paying attention to daily tasks, distracting from thoughts, and self-harm behaviors – teaching and training self-soothing with five senses.6th-7th sessions (Emotion regulation)Definition of emotion, how emotions work, familiarity with emotion regulation skills. Emotion Identification Exercise, Emotion Registration Exercise. Identifying barriers to experiencing emotion in a healthy way and ways to overcome these barriers. Teaching creating short-term positive emotional experiences for experiencing positive emotional states.8th-10th sessions (Emotion regulation and distress tolerance in an MUD context)Explain craving and its connection to the experience of emotions. Introducing methods for identifying values. Importance of committed action based on a list of essential values in life. Develop new coping strategies in response to unpleasant emotions, sensations, and cognitions, especially craving as a multidimensional problem and teaching problem solving and behavior analysis.11th sessionBasic acceptance technique training. Introduce living in the present moment techniques.12th-13th sessionsInterpersonal effectiveness training. Participants learn assertiveness skills about substance users. Other skills include non-verbal communication, verbal communication, and problem-solving, decision-making, and listening skills.14th-16th sessionsReview of sessions. Elimination of ambiguities. Exercising skills in the presence of other people.\n\n Psychoeducation This option is more ethical than not offering any intervention to the control group. A psychiatrist with five years of experience in addiction psychotherapy implemented this intervention. This intervention includes problem-solving skills, assertiveness, and craving management in eight sessions. Thepsychoeducation intervention was used to provide a basis for comparison with only those elements of the DBT intervention that are different from other psychotherapies. This intervention is utilized for MUD and health-related problems. The intention of this intervention is to provide individuals struggling with cravings and substance use disorder the knowledge needed to comprehensively appreciate their problems and the empowerment needed to cope with them. The psychoeducation intervention included information about the dangers of marijuana and the therapists also provided a pamphlet containing techniques for reduction of craving. 14 , 26 , 27\nThis option is more ethical than not offering any intervention to the control group. A psychiatrist with five years of experience in addiction psychotherapy implemented this intervention. This intervention includes problem-solving skills, assertiveness, and craving management in eight sessions. Thepsychoeducation intervention was used to provide a basis for comparison with only those elements of the DBT intervention that are different from other psychotherapies. This intervention is utilized for MUD and health-related problems. The intention of this intervention is to provide individuals struggling with cravings and substance use disorder the knowledge needed to comprehensively appreciate their problems and the empowerment needed to cope with them. The psychoeducation intervention included information about the dangers of marijuana and the therapists also provided a pamphlet containing techniques for reduction of craving. 14 , 26 , 27\n Therapists and treatment adherence To enable adherence to the principles of DBT to be checked, audios of the sessions were recorded with the consent of all participants. A DBT researcher and psychotherapist who was not involved in the treatment groups checked session content afterwards. Sessions were divided into 15-minute modules that were chosen for adherence checks at random. Treatment stance and occurrence and depth of DBT processes were appraised. Based on the treatment manual, modules were rated for adherence level as either adequate or not adequate. The majority (83%) were judged to have been conducted adequately.\nTo enable adherence to the principles of DBT to be checked, audios of the sessions were recorded with the consent of all participants. A DBT researcher and psychotherapist who was not involved in the treatment groups checked session content afterwards. Sessions were divided into 15-minute modules that were chosen for adherence checks at random. Treatment stance and occurrence and depth of DBT processes were appraised. Based on the treatment manual, modules were rated for adherence level as either adequate or not adequate. The majority (83%) were judged to have been conducted adequately.\n Statistical method Demographic information was gathered and reported as frequencies, means, and standard deviations and repeated measures ANOVA and chi-square tests were conducted for the outcomes using SPSS software, version 26.\nDemographic information was gathered and reported as frequencies, means, and standard deviations and repeated measures ANOVA and chi-square tests were conducted for the outcomes using SPSS software, version 26.\n\nDialectical behavior therapy:\nDBT is a group intervention consisting of 16 sessions (meeting once a week for 90 minutes) with one psychotherapist and her co-therapist. The intervention protocol is an adaptation of DBT for SUD based on three basic manuals. 10 , 24 , 25 The primary objective of the DBT is to reduce dysfunction in emotion regulation and craving via increasing cessation rates and improving skills. A psychotherapist with a PhD delivered the intervention sessions (with a psychologist as co-therapist) and they were blind both to the existence of another group and to the study objectives. Table 1 shows the content covered in each DBT session.\n\nTable 1DBT content per sessionCessationContentPre-sessionExplanation of dialectical behavioral therapy, principles, and goals. Brief introduction to the content of each session. Familiarity with participants. Participants are given an intervention booklet to read at home.1st session (mindfulness 1)Introduce the concept of mindfulness and three mental states (wise, reasonable, emotional) and their relations with substance use.2nd session (mindfulness 2)Teach two clusters of mindfulness skills. The first includes viewing, participation, and description. The second includes a non-judgmental stance and inclusive self-consciousness.3rd sessionSummarize the mindfulness sessions – definition of addiction, standard therapies of addiction, introduction to and teaching of dialectical avoidance technique. Review the positive and negative aspects of abstinence. Explanation and investigation of relapse and its causes. Explaining the skill of the pure mind, the addicted mind, the types of behaviors related to the pure mentality and the addicted mentality, and preparing a list of supporters.4th-5th sessions (Distress tolerance)Teaching distraction strategies with five skills include activities, comparisons, emotions, thoughts, and enjoyment. Through enjoyable activities, focusing on work or other topics, counting, leaving the situation, paying attention to daily tasks, distracting from thoughts, and self-harm behaviors – teaching and training self-soothing with five senses.6th-7th sessions (Emotion regulation)Definition of emotion, how emotions work, familiarity with emotion regulation skills. Emotion Identification Exercise, Emotion Registration Exercise. Identifying barriers to experiencing emotion in a healthy way and ways to overcome these barriers. Teaching creating short-term positive emotional experiences for experiencing positive emotional states.8th-10th sessions (Emotion regulation and distress tolerance in an MUD context)Explain craving and its connection to the experience of emotions. Introducing methods for identifying values. Importance of committed action based on a list of essential values in life. Develop new coping strategies in response to unpleasant emotions, sensations, and cognitions, especially craving as a multidimensional problem and teaching problem solving and behavior analysis.11th sessionBasic acceptance technique training. Introduce living in the present moment techniques.12th-13th sessionsInterpersonal effectiveness training. Participants learn assertiveness skills about substance users. Other skills include non-verbal communication, verbal communication, and problem-solving, decision-making, and listening skills.14th-16th sessionsReview of sessions. Elimination of ambiguities. Exercising skills in the presence of other people.\n\n\nPsychoeducation:\nThis option is more ethical than not offering any intervention to the control group. A psychiatrist with five years of experience in addiction psychotherapy implemented this intervention. This intervention includes problem-solving skills, assertiveness, and craving management in eight sessions. Thepsychoeducation intervention was used to provide a basis for comparison with only those elements of the DBT intervention that are different from other psychotherapies. This intervention is utilized for MUD and health-related problems. The intention of this intervention is to provide individuals struggling with cravings and substance use disorder the knowledge needed to comprehensively appreciate their problems and the empowerment needed to cope with them. The psychoeducation intervention included information about the dangers of marijuana and the therapists also provided a pamphlet containing techniques for reduction of craving. 14 , 26 , 27\n\nTherapists and treatment adherence:\nTo enable adherence to the principles of DBT to be checked, audios of the sessions were recorded with the consent of all participants. A DBT researcher and psychotherapist who was not involved in the treatment groups checked session content afterwards. Sessions were divided into 15-minute modules that were chosen for adherence checks at random. Treatment stance and occurrence and depth of DBT processes were appraised. Based on the treatment manual, modules were rated for adherence level as either adequate or not adequate. The majority (83%) were judged to have been conducted adequately.\n\nStatistical method:\nDemographic information was gathered and reported as frequencies, means, and standard deviations and repeated measures ANOVA and chi-square tests were conducted for the outcomes using SPSS software, version 26.\n\nEthical considerations:\nWritten informed consent was obtained from all participants before initiation of the research. The tools used in this study were all filled-out anonymously, and an ID code was used to maintain the confidentiality of personal information (Ir.kums.rce.1398.1203). At the end of the research process, dialectical behavior therapy was also provided to the control group. This study is registered with the Thailand Registry of Clinical Trials (TCTR20200319007).\n\nResults:\n Feasibility In the psycho-education group, 24/31 participants completed all sessions, compared to 29/30 members of the DBT group (retention rates: 77% in the control group vs. 96% in the DBT group). Additionally, 96% (29/30) of the DBT group members completed the two-month follow-up, whereas 64.5% (20/31) of the control group completed follow-up ( Figure 1 ). The chi-square test was applied, showing associations between group and retention, with χ2= 4.95, p = 0.02 for post-treatment and, χ2= 9.97, p = 0.002 for the follow-up phase. Consequently, DBT retention rates were significantly higher than psycho-education retention rates at post-treatment and follow-up.\n\nFigure 1Consort diagram.\n\nIn the psycho-education group, 24/31 participants completed all sessions, compared to 29/30 members of the DBT group (retention rates: 77% in the control group vs. 96% in the DBT group). Additionally, 96% (29/30) of the DBT group members completed the two-month follow-up, whereas 64.5% (20/31) of the control group completed follow-up ( Figure 1 ). The chi-square test was applied, showing associations between group and retention, with χ2= 4.95, p = 0.02 for post-treatment and, χ2= 9.97, p = 0.002 for the follow-up phase. Consequently, DBT retention rates were significantly higher than psycho-education retention rates at post-treatment and follow-up.\n\nFigure 1Consort diagram.\n\n Acceptability and appropriateness To enable assessment of the acceptability and appropriateness of intervention, patients completed the AIM and IAM scales in the post-treatment phase. The acceptability scores were 16.57 for DBT and 9.6 for the control group (p < 0.05). The appropriateness scores were17.03 for DBT and 10.7 for the psychoeducation group (p < 0.05). Since there are no standards for these measures, these points were transferred to Likert-based questionnaire scales. For acceptability, the results equated to ‘’agree’’ for the DBT group versus ‘’neither agree nor disagree’’ for the psychoeducation group. For appropriateness, the results equated to ‘’completely agree’’ for the DBT group versus ‘’neither agree nor disagree’’ for the psychoeducation group.\nTo enable assessment of the acceptability and appropriateness of intervention, patients completed the AIM and IAM scales in the post-treatment phase. The acceptability scores were 16.57 for DBT and 9.6 for the control group (p < 0.05). The appropriateness scores were17.03 for DBT and 10.7 for the psychoeducation group (p < 0.05). Since there are no standards for these measures, these points were transferred to Likert-based questionnaire scales. For acceptability, the results equated to ‘’agree’’ for the DBT group versus ‘’neither agree nor disagree’’ for the psychoeducation group. For appropriateness, the results equated to ‘’completely agree’’ for the DBT group versus ‘’neither agree nor disagree’’ for the psychoeducation group.\n Participant characteristics Participants’ demographic variables are shown in Table 2 . Analyses showed that there were no significant differences between the two groups regarding these variables. It should be noted that since over 97% of the participants were male from the beginning, the results were reported only for men.\n\nTable 2Mean and standard deviation of demographic variables in the intervention and control groups at the test phaseVariableIntervention groupControl groupp-valueEducational level* 0.2No higher education, n (%)2 (6)6 (19)Diploma, n (%)14 (46)15 (48)University student or graduate, n (%)14 (46)10 (33)Age †25.6 (5.67)27.19 (7.48)0.3Months of marijuana use19.53 (5.9)17.48 (6.03)0.1Craving (total) † Pre-test45.2 (8.3)47.9 (10.2)0.2Post-test42.13 (7.7)44.48 (8.1)0.1Follow-up42.66 (9.25)45.8 (8.4)0.1Data presented as mean (standard deviation), unless otherwise specified.* Chi-square test.† Independent t test.\n\nData presented as mean (standard deviation), unless otherwise specified.\n* Chi-square test.\n† Independent t test.\nParticipants’ demographic variables are shown in Table 2 . Analyses showed that there were no significant differences between the two groups regarding these variables. It should be noted that since over 97% of the participants were male from the beginning, the results were reported only for men.\n\nTable 2Mean and standard deviation of demographic variables in the intervention and control groups at the test phaseVariableIntervention groupControl groupp-valueEducational level* 0.2No higher education, n (%)2 (6)6 (19)Diploma, n (%)14 (46)15 (48)University student or graduate, n (%)14 (46)10 (33)Age †25.6 (5.67)27.19 (7.48)0.3Months of marijuana use19.53 (5.9)17.48 (6.03)0.1Craving (total) † Pre-test45.2 (8.3)47.9 (10.2)0.2Post-test42.13 (7.7)44.48 (8.1)0.1Follow-up42.66 (9.25)45.8 (8.4)0.1Data presented as mean (standard deviation), unless otherwise specified.* Chi-square test.† Independent t test.\n\nData presented as mean (standard deviation), unless otherwise specified.\n* Chi-square test.\n† Independent t test.\n Efficacy outcomes The hypothesis of equal covariance matrices was examined for craving (Box’s M = 3.63, P = 0.74). The results of this test indicate homogeneity of covariance matrices. Mauchly’s test of sphericity also showed that the sphericity assumption was not violated (p = 0.42 and Mauchly’s W = 0.97).\nThe results of the intergroup test and intergroup relations are also presented in Table 3 . As shown in Table 3 , the effect levels for craving (F = 3.52, p > 0.05) suggest that there is no significant difference between groups. These results were repeated for three of the subscales of craving: compulsivity, expectancy, and purposefulness. However, the emotionality subscale results showed a significant reduction in the DBT group compared to the control group 10.6 vs. 14.4 in the post-test and 10.43 vs. 13.26 in the follow-up phase (F = 19.94, p < 0.05).\n\nTable 3Repeated measures ANOVA for variables for the DBT group and control group in the pre-test, post-test, and follow-upVariable/sourceType III sum of squaresdfMean squareFSigPartial eta squaredObserved powerCraving Tests of within-subjects effects Factor1347.6962173.842.60.07*0.040.512Factor1 × group4.7622.380.0360.110.0010.055Error (factor1)7845.15411866.48 Tests of between-subjects effects Group341.0841341.083.520.06 †0.0560.455Error5708.795996.75 Emotionality Tests of within-subjects effects Factor1257.332128.6511.690.00 †0.1650.9Factor1 × group74.70237.350.370.03*0.050.63Error (factor1)1297.8111810.99 Tests of between-subjects effects Group283.891283.8919.940.00 †0.250.9Error839.9065914.23 * Significant to 0.05.† Significant to 0.01.\n\n* Significant to 0.05.\n† Significant to 0.01.\nWith regard to cessation, the results indicated that DBT achieved a higher rate of cessation than the control treatment in both the post-test and at follow-up, ( Table 4 ) (p < 0.05). It was also found that among those who continued to use the drug, the number of use days per month in the post-test and the follow-up periods (two months) was significantly lower in the intervention group than the control group.\n\nTable 4Cessation and consumption between groups DBTControlp-valueCessation* Post-test14 (46%)5 (16%)0.01\n‡Follow-up12 (40%)3 (9.5%)0.006\n‡Number of days use † Post-test2.43±1.87.5±5.030.00\n‡Follow-up3.44±1.918.75±3.270.00\n‡Bold p-values are significant at critical levels.* The chi-square test was applied.† T test for independent samples.‡ Significant to 0.01.\n\nBold p-values are significant at critical levels.\n* The chi-square test was applied.\n† T test for independent samples.\n‡ Significant to 0.01.\nThe hypothesis of equal covariance matrices was examined for craving (Box’s M = 3.63, P = 0.74). The results of this test indicate homogeneity of covariance matrices. Mauchly’s test of sphericity also showed that the sphericity assumption was not violated (p = 0.42 and Mauchly’s W = 0.97).\nThe results of the intergroup test and intergroup relations are also presented in Table 3 . As shown in Table 3 , the effect levels for craving (F = 3.52, p > 0.05) suggest that there is no significant difference between groups. These results were repeated for three of the subscales of craving: compulsivity, expectancy, and purposefulness. However, the emotionality subscale results showed a significant reduction in the DBT group compared to the control group 10.6 vs. 14.4 in the post-test and 10.43 vs. 13.26 in the follow-up phase (F = 19.94, p < 0.05).\n\nTable 3Repeated measures ANOVA for variables for the DBT group and control group in the pre-test, post-test, and follow-upVariable/sourceType III sum of squaresdfMean squareFSigPartial eta squaredObserved powerCraving Tests of within-subjects effects Factor1347.6962173.842.60.07*0.040.512Factor1 × group4.7622.380.0360.110.0010.055Error (factor1)7845.15411866.48 Tests of between-subjects effects Group341.0841341.083.520.06 †0.0560.455Error5708.795996.75 Emotionality Tests of within-subjects effects Factor1257.332128.6511.690.00 †0.1650.9Factor1 × group74.70237.350.370.03*0.050.63Error (factor1)1297.8111810.99 Tests of between-subjects effects Group283.891283.8919.940.00 †0.250.9Error839.9065914.23 * Significant to 0.05.† Significant to 0.01.\n\n* Significant to 0.05.\n† Significant to 0.01.\nWith regard to cessation, the results indicated that DBT achieved a higher rate of cessation than the control treatment in both the post-test and at follow-up, ( Table 4 ) (p < 0.05). It was also found that among those who continued to use the drug, the number of use days per month in the post-test and the follow-up periods (two months) was significantly lower in the intervention group than the control group.\n\nTable 4Cessation and consumption between groups DBTControlp-valueCessation* Post-test14 (46%)5 (16%)0.01\n‡Follow-up12 (40%)3 (9.5%)0.006\n‡Number of days use † Post-test2.43±1.87.5±5.030.00\n‡Follow-up3.44±1.918.75±3.270.00\n‡Bold p-values are significant at critical levels.* The chi-square test was applied.† T test for independent samples.‡ Significant to 0.01.\n\nBold p-values are significant at critical levels.\n* The chi-square test was applied.\n† T test for independent samples.\n‡ Significant to 0.01.\n\nFeasibility:\nIn the psycho-education group, 24/31 participants completed all sessions, compared to 29/30 members of the DBT group (retention rates: 77% in the control group vs. 96% in the DBT group). Additionally, 96% (29/30) of the DBT group members completed the two-month follow-up, whereas 64.5% (20/31) of the control group completed follow-up ( Figure 1 ). The chi-square test was applied, showing associations between group and retention, with χ2= 4.95, p = 0.02 for post-treatment and, χ2= 9.97, p = 0.002 for the follow-up phase. Consequently, DBT retention rates were significantly higher than psycho-education retention rates at post-treatment and follow-up.\n\nFigure 1Consort diagram.\n\n\nAcceptability and appropriateness:\nTo enable assessment of the acceptability and appropriateness of intervention, patients completed the AIM and IAM scales in the post-treatment phase. The acceptability scores were 16.57 for DBT and 9.6 for the control group (p < 0.05). The appropriateness scores were17.03 for DBT and 10.7 for the psychoeducation group (p < 0.05). Since there are no standards for these measures, these points were transferred to Likert-based questionnaire scales. For acceptability, the results equated to ‘’agree’’ for the DBT group versus ‘’neither agree nor disagree’’ for the psychoeducation group. For appropriateness, the results equated to ‘’completely agree’’ for the DBT group versus ‘’neither agree nor disagree’’ for the psychoeducation group.\n\nParticipant characteristics:\nParticipants’ demographic variables are shown in Table 2 . Analyses showed that there were no significant differences between the two groups regarding these variables. It should be noted that since over 97% of the participants were male from the beginning, the results were reported only for men.\n\nTable 2Mean and standard deviation of demographic variables in the intervention and control groups at the test phaseVariableIntervention groupControl groupp-valueEducational level* 0.2No higher education, n (%)2 (6)6 (19)Diploma, n (%)14 (46)15 (48)University student or graduate, n (%)14 (46)10 (33)Age †25.6 (5.67)27.19 (7.48)0.3Months of marijuana use19.53 (5.9)17.48 (6.03)0.1Craving (total) † Pre-test45.2 (8.3)47.9 (10.2)0.2Post-test42.13 (7.7)44.48 (8.1)0.1Follow-up42.66 (9.25)45.8 (8.4)0.1Data presented as mean (standard deviation), unless otherwise specified.* Chi-square test.† Independent t test.\n\nData presented as mean (standard deviation), unless otherwise specified.\n* Chi-square test.\n† Independent t test.\n\nEfficacy outcomes:\nThe hypothesis of equal covariance matrices was examined for craving (Box’s M = 3.63, P = 0.74). The results of this test indicate homogeneity of covariance matrices. Mauchly’s test of sphericity also showed that the sphericity assumption was not violated (p = 0.42 and Mauchly’s W = 0.97).\nThe results of the intergroup test and intergroup relations are also presented in Table 3 . As shown in Table 3 , the effect levels for craving (F = 3.52, p > 0.05) suggest that there is no significant difference between groups. These results were repeated for three of the subscales of craving: compulsivity, expectancy, and purposefulness. However, the emotionality subscale results showed a significant reduction in the DBT group compared to the control group 10.6 vs. 14.4 in the post-test and 10.43 vs. 13.26 in the follow-up phase (F = 19.94, p < 0.05).\n\nTable 3Repeated measures ANOVA for variables for the DBT group and control group in the pre-test, post-test, and follow-upVariable/sourceType III sum of squaresdfMean squareFSigPartial eta squaredObserved powerCraving Tests of within-subjects effects Factor1347.6962173.842.60.07*0.040.512Factor1 × group4.7622.380.0360.110.0010.055Error (factor1)7845.15411866.48 Tests of between-subjects effects Group341.0841341.083.520.06 †0.0560.455Error5708.795996.75 Emotionality Tests of within-subjects effects Factor1257.332128.6511.690.00 †0.1650.9Factor1 × group74.70237.350.370.03*0.050.63Error (factor1)1297.8111810.99 Tests of between-subjects effects Group283.891283.8919.940.00 †0.250.9Error839.9065914.23 * Significant to 0.05.† Significant to 0.01.\n\n* Significant to 0.05.\n† Significant to 0.01.\nWith regard to cessation, the results indicated that DBT achieved a higher rate of cessation than the control treatment in both the post-test and at follow-up, ( Table 4 ) (p < 0.05). It was also found that among those who continued to use the drug, the number of use days per month in the post-test and the follow-up periods (two months) was significantly lower in the intervention group than the control group.\n\nTable 4Cessation and consumption between groups DBTControlp-valueCessation* Post-test14 (46%)5 (16%)0.01\n‡Follow-up12 (40%)3 (9.5%)0.006\n‡Number of days use † Post-test2.43±1.87.5±5.030.00\n‡Follow-up3.44±1.918.75±3.270.00\n‡Bold p-values are significant at critical levels.* The chi-square test was applied.† T test for independent samples.‡ Significant to 0.01.\n\nBold p-values are significant at critical levels.\n* The chi-square test was applied.\n† T test for independent samples.\n‡ Significant to 0.01.\n\nDiscussion and conclusion:\nThis study examined the feasibility, acceptability, and preliminary efficacy of a 16-session DBT intervention to address craving and achieve cessation in MUD patients. This intervention showed strong evidence of feasibility. Moreover, acceptability and appropriateness rates in the DBT group were high and adequate. The results showed that DBT is a promising intervention for marijuana cessation in patients with MUD. Although this study is the first RCT of DBT for MUD, the scientific literature about DBT for other addictive behaviors reports similar results.\nRezaei et al. found that DBT significantly improved craving among methadone users. Their result showed that DBT could reduce methadone usage and improve emotion regulation. 28 Moreover, another study showed that implementation of DBT with alcohol-dependent patients improved alcohol-related behavior and emotional deficits, which is similar to the results of the present study. 29 However, the results for craving showed there was no significant difference between groups. With regard to this finding, our result differs from the majority of other research findings. For example, Rezaei et al. found that DBT significantly improved craving among methadone users. 10 This result was also repeated in Rabinovitz’s paper. 30 One of the main reasons for this difference lies in the finding in the present study that DBT had greater improvement in the emotionality subscale of craving (p < 0.5). Since the most important structure of marijuana craving is its emotional dimensions, 5 , 14 the lack of changes in other subscales resulted in non-significance for the overall craving scale score. The results of the present study with relation to craving are therefore somewhat co-directional with the findings of previous studies. On the behavioral level, other studies found that Marijuana craving cues were strongly associated with deficits in regulation of negative affect and emotions. 14 , 15 Also, when neural levels were assessed during reappraisal of negative stimuli, patients with MUD and regular users showed altered neural activity and functional connectivity. Furthermore, being a marijuana user was related to dysfunctions in the amygdala and in amygdala-DLPFC coupling activity. 14 Taken together, these findings demonstrate that emotion regulation problems and craving are prevalent in MUD patients and can interfere with the cessation process. Since DBT is a third-wave behavioral therapy, it has a strong emotional basis. This therapy encompasses three emotion-based goals: understanding emotions, reducing emotional vulnerability, and reducing emotional suffering. Patients are helped to understand that unpleasant emotions are a normal part of life and that accepting their existence is more healthy than trying to avoid controlling them. 10 , 28 Overall, DBT is an emotion regulation method that helps patients learn, understand, and label emotions, reducing emotional vulnerability and emotional suffering. These skills help MUD patients to label emotions related to craving. This improvement in emotional states can improve dysfunctions in the amygdala and in amygdala-DLPFC coupling activity. 9 , 31 , 32\nAlong the same lines, it also improves emotional craving-related brain structures and reduces impulsive behavior (e.g., lapses). Also, with the ‘’distress tolerance’’ component of DBT, patients learn to live with destructive emotions to accept unpleasant craving situations. 32 , 33 Therefore, by increasing craving, they no longer consume marijuana immediately. 22 , 24 Similarly, other DBT components teach MUD patients reinforcement management and problem-solving skills that can help them to reduce marijuana consumption. 34 , 35\n\nConclusion:\nTo conclude, DBT demonstrated adequate feasibility, acceptability, and appropriateness for patients with marijuana use disorder. Moreover, DBT also exhibited significant efficacy compared to the control group for achieving cessation and reducing emotion-related craving. Even in patients who could not achieve abstinence, DBT led to a reduction in marijuana consumption rates. These findings persisted at two-month follow-up.\n Limitations and future directions Despite these positive results, the present study also has some limitations. First, in order to evaluate the most significant treatment components (such as mindfulness and distress tolerance), no groups received the third wave versions of other therapies (ACT or MBSR). This study only had a two-month follow-up period and could not conduct long-term evaluation due to the study site’s medical and infrastructure conditions. It is recommended that future research should examine mediating and confounding variables to investigate the results of similar research to the present study. Other factors affecting relapse and recurrence could also be examined. Moreover, women should be investigated so that gender-related implications can be determined.\nDespite these positive results, the present study also has some limitations. First, in order to evaluate the most significant treatment components (such as mindfulness and distress tolerance), no groups received the third wave versions of other therapies (ACT or MBSR). This study only had a two-month follow-up period and could not conduct long-term evaluation due to the study site’s medical and infrastructure conditions. It is recommended that future research should examine mediating and confounding variables to investigate the results of similar research to the present study. Other factors affecting relapse and recurrence could also be examined. Moreover, women should be investigated so that gender-related implications can be determined.\n\nLimitations and future directions:\nDespite these positive results, the present study also has some limitations. First, in order to evaluate the most significant treatment components (such as mindfulness and distress tolerance), no groups received the third wave versions of other therapies (ACT or MBSR). This study only had a two-month follow-up period and could not conduct long-term evaluation due to the study site’s medical and infrastructure conditions. It is recommended that future research should examine mediating and confounding variables to investigate the results of similar research to the present study. Other factors affecting relapse and recurrence could also be examined. Moreover, women should be investigated so that gender-related implications can be determined.\n\n"},"whole_article_abstract":{"kind":"string","value":"Background:\nThailand Registry of Clinical Trials, TCTR20200319007.\n\nMethods:\nSixty-one patients with marijuana use disorder diagnoses were randomly assigned to a DBT group or a control group (psycho-education). Patients completed measures at pre-intervention, post-intervention, and at two-month follow-up. The Marijuana Craving Questionnaire (MCQ) and marijuana urine test kits were used to assess craving and abstinence respectively.\n\nResults:\nThe feasibility of DBT was significantly higher than control group feasibility. In the DBT 29/30 participants completed all sessions (96% retention) and 24/31 control group participants completed all sessions (77% retention) (χ2 = 4.95, p = 0.02). Moreover, 29/30 (96%) participants in the DBT group completed the two-month follow-up and 20/31 (64.5%) control group members completed the two-month follow-up (χ2 = 9.97, p = 0.002). The results showed that patients in the DBT group had significantly higher intervention acceptability rates (16.57 vs. 9.6) than those in the control group. This pattern was repeated for appropriateness rates (p < 0.05). The overall results for craving showed that there was no significant difference between the groups (F = 3.52, p > 0.05), although DBT showed a significant reduction in the \"emotionality\" subscale compared to the control group (F = 19.94, p < 0.05). To analyze cessation rates, DBT was compared to the control group at the posttest (46% vs. 16%) and follow-up (40% vs. 9.5%) and the results confirmed higher effectiveness in the DBT group for cessation (p < 0.05). Furthermore, among those who had lapsed, participants in the DBT group had fewer consumption days than those in the control group (p < 0.05).\n\nConclusions:\nDBT showed feasibility, acceptability, and promising efficacy in terms of the marijuana cessation rate."},"background_conclusion_text":{"kind":"string","value":"Introduction:\nMarijuana is the most prevalent substance among those reported to be a significant problem among people seeking treatment for substance abuse. 1 According to WHO reports, more than 140 million people consume marijuana every year. 2 With regard to Iran, recent evidence shows that more than 5% of people consume marijuana every year, predominantly young males. However, in view of the harsh marijuana prohibition policy of the Iranian government, most clinicians estimate that these rates have been hugely underestimated. 3 Marijuana, as an illegal drug, is associated with significant physical, psychological, and social consequences. 4 Studies have shown that regular and heavy marijuana use patterns correlate with increased risk of mood disorders, anxiety, and psychotic episodes and although causality has not been demonstrated, these patterns can increase the course of mental health problems. 5 Also, several medical problems such as respiratory system deficits, stroke, myocardial infarction, and digestive tract cancers are associated with marijuana use patterns, especially among those with marijuana use disorder (MUD). 6 , 7 Approximately one in three marijuana users meet the criteria for MUD based on the DSM-5, and this proportion is rising. 8 One of the most important psychological problems in substance use disorder treatment is craving. Craving is a factor identified as the root cause of relapses and treatment failures. 9 , 10 MUD patients report visual, tactile, and olfactory cues related to craving and compulsivity sensations. 11 Based on these results, clinicians have tried to treat patients with marijuana use disorder.\nTo date, the Food and Drug Administration (FDA) in the United States has not approved any psycho-pharmacotherapy for MUD, and therefore psycho-social interventions have received particular attention. 12 The most widely used psychological treatment in the substance use disorder (SUD) context is cognitive-behavioral therapy (CBT). 13 Results showed that CBT is somewhat effective for SUD, but that most patients with MUD do not achieve cessation and are not motivated to continue skills training during follow-up. Relapse rates therefore remain a considerable limitation of treatment. 10 , 13 One of the main reasons for this low success rate lies in the limitations of CBT. First, CBT protocols do not focus on comorbid problems, whereas most patients with SUD have at least one psychiatric or psychological problem. Secondly, cognitive restructuring may not be useful for all SUD patients. Some patients may be unable to restructure their dysfunctional cognition and core beliefs despite receiving CBT. 10 , 12 , 13 Furthermore, emotion regulation deficits are strongly associated with increased addictive behaviors such as SUD. With emotion regulation, people adjust their emotional experiences related to distressing and unpleasant events. Emotion regulation is essential for successful coping with environmental demands and personal welfare. 14 On the behavioral level, studies have found that marijuana craving cues are strongly associated with deficits in regulation of negative affect and emotions. 14 , 15 Also, on the neural level, during reappraisal of negative stimuli, patients with MUD and regular users have shown altered neural activity and functional connectivity. Moreover, marijuana use is related to dysfunctions in the amygdala and in amygdala-dorsolateral prefrontal cortex (DLPFC) coupling activity. 15 Together, these findings demonstrate that emotion-based psychotherapy must manage comorbid problems and eliminate the limitations of CBT.\nOne of the psychotherapies in the third wave behavioral therapy cluster is dialectical behavior therapy (DBT). DBT has been described as intervention in emotion regulation deficits by focusing on dangerous impulses in borderline personality disorder and substance use disorder. The goals of DBT include improving and regulating emotions as a primary mechanism of change. DBT is a trans-diagnostic treatment and suitable for comorbid problems. DBT trains skills including distress tolerance, interpersonal effectiveness, emotion regulation, and mindfulness. Overall, in the context of SUD, DBT teaches emotion regulation skills to decrease engagement in pathological emotion regulation strategies. It also intervenes in low quality of life situations, reduces drug-seeking behavior, and helps patients function adaptively by accepting unpleasant emotions such as craving. 9 , 10 , 14\nResearch literature shows the efficacy and effectiveness of DBT in various comorbid problems and diseases such as suicide, 16 forensic psychiatric patients, 17 and irritable bowel syndrome. 18 Nevertheless, studies have reported contradictory results for the effectiveness of implementing DBT in various SUD populations. 19 , 20 Furthermore, the literature has recommended using larger samples, clearer instruments to measure outcome variables, and specific and integrated protocols. 21\nAdditionally, according to our investigations, no DBT randomized clinical trials have been conducted that investigated cessation in MUD patients (with or without comorbid problems). A DBT intervention aimed at increasing the cessation rate and reducing craving among MUD patients was developed for this study.\nThis pilot trial investigated the feasibility and preliminary efficacy of DBT relative to a psycho-education intervention that was controlled for time duration and attention. Feasibility was assessed via satisfaction and session completion rates. Preliminary efficacy was evaluated via the impact of DBT on cessation rate and reduction of consumption rates, compared to the psycho-education intervention. Although craving and acceptance of craving are not the primary goals of DBT, they were also compared across the two interventions.\n\nConclusion:\nTo conclude, DBT demonstrated adequate feasibility, acceptability, and appropriateness for patients with marijuana use disorder. Moreover, DBT also exhibited significant efficacy compared to the control group for achieving cessation and reducing emotion-related craving. Even in patients who could not achieve abstinence, DBT led to a reduction in marijuana consumption rates. These findings persisted at two-month follow-up.\n Limitations and future directions Despite these positive results, the present study also has some limitations. First, in order to evaluate the most significant treatment components (such as mindfulness and distress tolerance), no groups received the third wave versions of other therapies (ACT or MBSR). This study only had a two-month follow-up period and could not conduct long-term evaluation due to the study site’s medical and infrastructure conditions. It is recommended that future research should examine mediating and confounding variables to investigate the results of similar research to the present study. Other factors affecting relapse and recurrence could also be examined. Moreover, women should be investigated so that gender-related implications can be determined.\nDespite these positive results, the present study also has some limitations. First, in order to evaluate the most significant treatment components (such as mindfulness and distress tolerance), no groups received the third wave versions of other therapies (ACT or MBSR). This study only had a two-month follow-up period and could not conduct long-term evaluation due to the study site’s medical and infrastructure conditions. It is recommended that future research should examine mediating and confounding variables to investigate the results of similar research to the present study. Other factors affecting relapse and recurrence could also be examined. Moreover, women should be investigated so that gender-related implications can be determined."},"background_conclusion_abstract":{"kind":"string","value":"Background:\nThailand Registry of Clinical Trials, TCTR20200319007.\n\nMethods:\nSixty-one patients with marijuana use disorder diagnoses were randomly assigned to a DBT group or a control group (psycho-education). Patients completed measures at pre-intervention, post-intervention, and at two-month follow-up. The Marijuana Craving Questionnaire (MCQ) and marijuana urine test kits were used to assess craving and abstinence respectively.\n\nResults:\nThe feasibility of DBT was significantly higher than control group feasibility. In the DBT 29/30 participants completed all sessions (96% retention) and 24/31 control group participants completed all sessions (77% retention) (χ2 = 4.95, p = 0.02). Moreover, 29/30 (96%) participants in the DBT group completed the two-month follow-up and 20/31 (64.5%) control group members completed the two-month follow-up (χ2 = 9.97, p = 0.002). The results showed that patients in the DBT group had significantly higher intervention acceptability rates (16.57 vs. 9.6) than those in the control group. This pattern was repeated for appropriateness rates (p < 0.05). The overall results for craving showed that there was no significant difference between the groups (F = 3.52, p > 0.05), although DBT showed a significant reduction in the \"emotionality\" subscale compared to the control group (F = 19.94, p < 0.05). To analyze cessation rates, DBT was compared to the control group at the posttest (46% vs. 16%) and follow-up (40% vs. 9.5%) and the results confirmed higher effectiveness in the DBT group for cessation (p < 0.05). Furthermore, among those who had lapsed, participants in the DBT group had fewer consumption days than those in the control group (p < 0.05).\n\nConclusions:\nDBT showed feasibility, acceptability, and promising efficacy in terms of the marijuana cessation rate."},"whole_article_text_length":{"kind":"number","value":12763,"string":"12,763"},"whole_article_abstract_length":{"kind":"number","value":375,"string":"375"},"other_sections_lengths":{"kind":"list like","value":[27,55,119,306,56,955,29,75,183,81,99,1670,540,145,105,35,78,150,139,198,480,131],"string":"[\n 27,\n 55,\n 119,\n 306,\n 56,\n 955,\n 29,\n 75,\n 183,\n 81,\n 99,\n 1670,\n 540,\n 145,\n 105,\n 35,\n 78,\n 150,\n 139,\n 198,\n 480,\n 131\n]"},"num_sections":{"kind":"number","value":27,"string":"27"},"most_frequent_words":{"kind":"list like","value":["intervention","test","dbt","group","patients","craving","marijuana","follow","treatment","results"],"string":"[\n \"intervention\",\n \"test\",\n \"dbt\",\n \"group\",\n 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Dialectical behavior therapy | marijuana use | feasibility studies | craving | lapse [SUMMARY]"},"annotated_keywords_methods_abstract_prompt":{"kind":"string","value":"[CONTENT] Dialectical behavior therapy | marijuana use | feasibility studies | craving | lapse [SUMMARY]"},"annotated_keywords_results_abstract_prompt":{"kind":"string","value":"[CONTENT] Dialectical behavior therapy | marijuana use | feasibility studies | craving | lapse [SUMMARY]"},"annotated_keywords_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] Dialectical behavior therapy | marijuana use | feasibility studies | craving | lapse [SUMMARY]"},"annotated_keywords_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] Dialectical behavior therapy | marijuana use | feasibility studies | craving | lapse [SUMMARY]"},"annotated_keywords_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] Dialectical behavior therapy | marijuana use | feasibility studies | craving | lapse [SUMMARY]"},"annotated_mesh_background_abstract_prompt":{"kind":"string","value":"[CONTENT] Behavior Therapy | Craving | Dialectical Behavior Therapy | Feasibility Studies | Humans | Marijuana Use | Pilot Projects | Treatment Outcome [SUMMARY]"},"annotated_mesh_methods_abstract_prompt":{"kind":"string","value":"[CONTENT] Behavior Therapy | Craving | Dialectical Behavior Therapy | Feasibility Studies | Humans | Marijuana Use | Pilot Projects | Treatment Outcome [SUMMARY]"},"annotated_mesh_results_abstract_prompt":{"kind":"string","value":"[CONTENT] Behavior Therapy | Craving | Dialectical Behavior Therapy | Feasibility Studies | Humans | Marijuana Use | Pilot Projects | Treatment Outcome [SUMMARY]"},"annotated_mesh_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] Behavior Therapy | Craving | Dialectical Behavior Therapy | Feasibility Studies | Humans | Marijuana Use | Pilot Projects | Treatment Outcome 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[SUMMARY]"},"annotated_most_frequent_background_abstract_prompt":{"kind":"string","value":"[CONTENT] intervention | test | dbt | group | patients | craving | marijuana | follow | treatment | results [SUMMARY]"},"annotated_most_frequent_methods_abstract_prompt":{"kind":"string","value":"[CONTENT] intervention | test | dbt | group | patients | craving | marijuana | follow | treatment | results [SUMMARY]"},"annotated_most_frequent_results_abstract_prompt":{"kind":"string","value":"[CONTENT] intervention | test | dbt | group | patients | craving | marijuana | follow | treatment | results [SUMMARY]"},"annotated_most_frequent_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] intervention | test | dbt | group | patients | craving | marijuana | follow | treatment | results [SUMMARY]"},"annotated_most_frequent_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] intervention | test | dbt | group | patients | craving | marijuana | follow | treatment | results [SUMMARY]"},"annotated_most_frequent_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] intervention | test | dbt | group | patients | craving | marijuana | follow | treatment | results [SUMMARY]"},"annotated_tf_idf_background_abstract_prompt":{"kind":"string","value":"[CONTENT] dbt | cbt | regulation | problems | emotion | marijuana | patients | emotion regulation | comorbid problems | comorbid [SUMMARY]"},"annotated_tf_idf_methods_abstract_prompt":{"kind":"string","value":"[CONTENT] patients | questionnaire | scale | item | marijuana | test | completely | measure | intervention | mcq [SUMMARY]"},"annotated_tf_idf_results_abstract_prompt":{"kind":"string","value":"[CONTENT] test | group | significant | 05 | dbt | follow | post | control | 01 | table [SUMMARY]"},"annotated_tf_idf_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] study | present study | present | future | limitations | research | results | significant | related | implications determined [SUMMARY]"},"annotated_tf_idf_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] dbt | test | group | intervention | patients | follow | craving | study | marijuana | post [SUMMARY]"},"annotated_tf_idf_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] dbt | test | group | intervention | patients | follow | craving | study | marijuana | post [SUMMARY]"},"annotated_entity_plan_background_abstract_prompt":{"kind":"string","value":"[CONTENT] Thailand Registry of Clinical Trials [SUMMARY]"},"annotated_entity_plan_methods_abstract_prompt":{"kind":"string","value":"[CONTENT] Sixty-one | marijuana | DBT ||| two-month ||| The Marijuana Craving Questionnaire | marijuana [SUMMARY]"},"annotated_entity_plan_results_abstract_prompt":{"kind":"string","value":"[CONTENT] DBT ||| DBT | 29/30 | 96% | 24/31 | 77% | 4.95 | 0.02 ||| 29/30 | 96% | DBT | two-month | 64.5% | two-month | 9.97 | 0.002 ||| DBT | 16.57 | 9.6 ||| ||| 3.52 | 0.05 | DBT | F = | 19.94 ||| DBT | 46% | 16% | 40% | 9.5% | DBT ||| DBT [SUMMARY]"},"annotated_entity_plan_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] DBT | marijuana [SUMMARY]"},"annotated_entity_plan_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] Thailand Registry of Clinical Trials ||| Sixty-one | marijuana | DBT ||| two-month ||| The Marijuana Craving Questionnaire | marijuana ||| ||| DBT ||| DBT | 29/30 | 96% | 24/31 | 77% | 4.95 | 0.02 ||| 29/30 | 96% | DBT | two-month | 64.5% | two-month | 9.97 | 0.002 ||| DBT | 16.57 | 9.6 ||| ||| 3.52 | 0.05 | DBT | F = | 19.94 ||| DBT | 46% | 16% | 40% | 9.5% | DBT ||| DBT ||| DBT | marijuana [SUMMARY]"},"annotated_entity_plan_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] Thailand Registry of Clinical Trials ||| Sixty-one | marijuana | DBT ||| two-month ||| The Marijuana Craving Questionnaire | marijuana ||| ||| DBT ||| DBT | 29/30 | 96% | 24/31 | 77% | 4.95 | 0.02 ||| 29/30 | 96% | DBT | two-month | 64.5% | two-month | 9.97 | 0.002 ||| DBT | 16.57 | 9.6 ||| ||| 3.52 | 0.05 | DBT | F = | 19.94 ||| DBT | 46% | 16% | 40% | 9.5% | DBT ||| DBT ||| DBT | marijuana [SUMMARY]"}}},{"rowIdx":263,"cells":{"title":{"kind":"string","value":"Incidence of seed migration to the chest, abdomen, and pelvis after transperineal interstitial prostate brachytherapy with loose (125)I seeds."},"pmid":{"kind":"string","value":"21974959"},"background_abstract":{"kind":"string","value":"The aim was to determine the incidence of seed migration not only to the chest, but also to the abdomen and pelvis after transperineal interstitial prostate brachytherapy with loose (125)I seeds."},"background_abstract_label":{"kind":"string","value":"BACKGROUND"},"methods_abstract":{"kind":"string","value":"We reviewed the records of 267 patients who underwent prostate brachytherapy with loose (125)I seeds. After seed implantation, orthogonal chest radiographs, an abdominal radiograph, and a pelvic radiograph were undertaken routinely to document the occurrence and sites of seed migration. The incidence of seed migration to the chest, abdomen, and pelvis was calculated. All patients who had seed migration to the abdomen and pelvis subsequently underwent a computed tomography scan to identify the exact location of the migrated seeds. Postimplant dosimetric analysis was undertaken, and dosimetric results were compared between patients with and without seed migration."},"methods_abstract_label":{"kind":"string","value":"METHODS"},"results_abstract":{"kind":"string","value":"A total of 19,236 seeds were implanted in 267 patients. Overall, 91 of 19,236 (0.47%) seeds migrated in 66 of 267 (24.7%) patients. Sixty-nine (0.36%) seeds migrated to the chest in 54 (20.2%) patients. Seven (0.036%) seeds migrated to the abdomen in six (2.2%) patients. Fifteen (0.078%) seeds migrated to the pelvis in 15 (5.6%) patients. Seed migration occurred predominantly within two weeks after seed implantation. None of the 66 patients had symptoms related to the migrated seeds. Postimplant prostate D90 was not significantly different between patients with and without seed migration."},"results_abstract_label":{"kind":"string","value":"RESULTS"},"conclusions_abstract":{"kind":"string","value":"We showed the incidence of seed migration to the chest, abdomen and pelvis. Seed migration did not have a significant effect on postimplant prostate D90."},"conclusions_abstract_label":{"kind":"string","value":"CONCLUSION"},"mesh_descriptor_names":{"kind":"list like","value":["Aged","Aged, 80 and over","Brachytherapy","Humans","Iodine Radioisotopes","Male","Middle Aged","Movement","Pelvis","Prostatic Neoplasms","Radiography, Abdominal","Radiography, Thoracic","Radiotherapy"],"string":"[\n \"Aged\",\n \"Aged, 80 and over\",\n \"Brachytherapy\",\n \"Humans\",\n \"Iodine Radioisotopes\",\n \"Male\",\n \"Middle Aged\",\n \"Movement\",\n \"Pelvis\",\n \"Prostatic Neoplasms\",\n \"Radiography, Abdominal\",\n \"Radiography, Thoracic\",\n \"Radiotherapy\"\n]"},"pmcid":{"kind":"string","value":"3206434"},"background_title":{"kind":"string","value":"Background"},"background_text":{"kind":"string","value":"Seed migration is a well-recognized event that occurs after transperineal interstitial prostate brachytherapy, and it is observed more often with loose seeds than with linked seeds [1-5].\nIt is well known that the most frequent site of seed migration is the chest. The American Brachytherapy Society has advised that a chest radiograph should be undertaken at the first follow-up visit to scan the lungs for embolized seeds [6]. Consequently, the incidence of seed migration to the chest has been well reported [1,2,4,5,7-19]. However, documentation of the incidence of seed migration to the abdomen and pelvis is rare. Rare cases of seed migration to a coronary artery, the right ventricle, the liver, the kidneys, Batson's vertebral venous plexus, and the left testicular vein have been reported [20-26]. However, it has never been fully determined whether seed migration to these locations is really rare.\nThe primary purposes of the present study were to determine the incidence of seed migration not only to the chest, but also to the abdomen and the pelvis at our institution and to identify the exact location of the seeds that had migrated to the abdomen and pelvis with computed tomography (CT). The secondary purpose was to determine the impact of seed migration on postimplant dosimetry."},"methods_title":{"kind":"string","value":"Methods"},"methods_text":{"kind":"string","value":"We reviewed the records of 267 patients who underwent transperineal interstitial prostate brachytherapy with loose 125I seeds for clinical T1/T2 prostate cancer at our institution. Table 1 details the characteristics of all 267 patients. Two patients (0.75%) received brachytherapy plus external beam radiotherapy (45 Gy in 1.8 Gy fractions). One hundred twenty-three of the 267 (46.1%) patients also underwent neoadjuvant hormonal therapy (NHT), which consisted of luteinizing hormone-releasing hormone agonist and antiandrogens. NHT was generally undertaken in patients with a prostate volume >40 cc or those with pubic arch interference by transrectal ultrasound (TRUS) at the preimplant volume study [27].\nPatient Characteristics (N = 267)\nData are presented as mean ± standard error (range) or number (percent) of patients.\nAbbreviations: PSA = prostate-specific antigen; TRUS = transrectal ultrasound\nOne month before seed implantation, a preplan was obtained with TRUS images taken at 5 mm intervals from the base to the apex of the prostate with the patient in the dorsal lithotomy position. The planning target volume included the prostate gland, with a margin of 3 mm anteriorly and laterally and 5 mm in the cranial and caudal directions. No margin was added posteriorly at the rectal interface. Treatment planning used a peripheral or a modified peripheral approach. For the 265 patients who received brachytherapy alone, the prescribed brachytherapy dose was 145 Gy and 160 Gy for the first 163 patients and the subsequent 102 patients, respectively. For the remaining two patients who received brachytherapy plus external beam radiotherapy, the prescribed brachytherapy dose was 110 Gy. TG 43 formalism was used in the preplanning and postimplant dosimetry analyses [28]. All 267 patients were treated with loose 125I radioactive seeds with a Mick applicator (Mick Radio-Nuclear Instruments, Bronx, NY). To ensure that no seeds were left in the bladder, postoperative fluoroscopic images were obtained. Prior to discharge, postoperative surveys of voided urine were conducted to detect voided seeds.\nOrthogonal chest radiographs, an abdominal radiograph, and a pelvic radiograph were undertaken to document the occurrence and sites of seed migration one day after seed implantation. These follow-up radiographs were undertaken routinely at each outpatient visit. Patients returned to our outpatient clinic two weeks and three months after seed implantation, then at three-month intervals for the first three years and at six-month intervals thereafter. Seed migration to the chest and the abdomen was recorded when one or more seeds were visualized on orthogonal chest radiographs and the anteroposterior (AP) abdominal radiograph, respectively. Seed migration to the pelvis was recorded when one or more seeds were separated from the main seed cluster on an AP pelvic radiograph. However, seeds placed into the bladder and the seminal vesicles or seeds placed inferior to the prostate by mistake were not scored as migrated. Seeds voided in the urine were not scored as migrated. Subsequently, all patients who had seed migration to the abdomen and pelvis underwent a CT scan to identify the exact location of the migrated seeds. The incidence of seed migration to the chest, abdomen, and pelvis was calculated.\nPostimplant dosimetric analysis by CT was performed one month after seed implantation. The seed count in the region of the prostate gland was determined on the AP pelvic radiographs obtained two weeks after seed implantation. The postimplant prostate D90 (the dose received by 90% of the volume of the prostate) value was compared between patients with and without seed migration. Statistical analysis was performed with Student's t-test. A p value of <0.05 was considered statistically significant."},"results_title":{"kind":"null"},"results_text":{"kind":"null"},"conclusions_title":{"kind":"string","value":"Conclusions"},"conclusions_text":{"kind":"string","value":"AS and JN designed the study, collected the data, interpreted the results, performed the statistical analysis, drafted the manuscript, and oversaw the project's completion. EK, HN, RM, SS, YS and RK participated in data acquisition. MO and NS contributed to data analysis. All authors read and approved the manuscript."},"other_sections_titles":{"kind":"list like","value":["Background","Results","Seeds that migrated to the abdomen (seven seeds in six patients)","Seeds that migrated to the pelvis (15 seeds in 15 patients)","Seed relocation four years after seed implantation","Postimplant dosimetric analysis","Discussion","Incidence of seed migration","The dynamics of seed migration to the chest","Seed migration to the kidneys and Batson's vertebral plexus is not very rare","In some cases of seed migration to the kidneys, the mechanism is difficult to explain","Other possible mechanisms of seed migration","Influence of seed migration on postimplant dosimetry","Seed migration-related sequelae","Conclusions"],"string":"[\n \"Background\",\n \"Results\",\n \"Seeds that migrated to the abdomen (seven seeds in six patients)\",\n \"Seeds that migrated to the pelvis (15 seeds in 15 patients)\",\n \"Seed relocation four years after seed implantation\",\n \"Postimplant dosimetric analysis\",\n \"Discussion\",\n \"Incidence of seed migration\",\n \"The dynamics of seed migration to the chest\",\n \"Seed migration to the kidneys and Batson's vertebral plexus is not very rare\",\n \"In some cases of seed migration to the kidneys, the mechanism is difficult to explain\",\n \"Other possible mechanisms of seed migration\",\n \"Influence of seed migration on postimplant dosimetry\",\n \"Seed migration-related sequelae\",\n \"Conclusions\"\n]"},"other_sections_texts":{"kind":"list like","value":["Seed migration is a well-recognized event that occurs after transperineal interstitial prostate brachytherapy, and it is observed more often with loose seeds than with linked seeds [1-5].\nIt is well known that the most frequent site of seed migration is the chest. The American Brachytherapy Society has advised that a chest radiograph should be undertaken at the first follow-up visit to scan the lungs for embolized seeds [6]. Consequently, the incidence of seed migration to the chest has been well reported [1,2,4,5,7-19]. However, documentation of the incidence of seed migration to the abdomen and pelvis is rare. Rare cases of seed migration to a coronary artery, the right ventricle, the liver, the kidneys, Batson's vertebral venous plexus, and the left testicular vein have been reported [20-26]. However, it has never been fully determined whether seed migration to these locations is really rare.\nThe primary purposes of the present study were to determine the incidence of seed migration not only to the chest, but also to the abdomen and the pelvis at our institution and to identify the exact location of the seeds that had migrated to the abdomen and pelvis with computed tomography (CT). The secondary purpose was to determine the impact of seed migration on postimplant dosimetry.","In total, 19,236 seeds were implanted in 267 patients. All 267 patients underwent follow-up radiographs. Median follow-up was 41 months (range, 8.5-76 months).\nAt one day after seed implantation, follow-up radiographs demonstrated that 41 of the 19,236 (0.21%) seeds migrated in 37 of the 267 (13.9%) patients: three seeds in one patient, two seeds in each of two patients, and a single seed in each of the remaining 34 patients. Fifteen (0.078%) seeds migrated to the chest in 15 (5.6%) patients. One (0.0052%) seed migrated to the abdomen in one (0.37%) patient. Twenty-five (0.13%) seeds migrated to the pelvis in 23 (8.6%) patients.\nAt two weeks after seed implantation, 85 of the 19,236 (0.44%) seeds migrated in 61 of the 267 (22.8%) patients: seven seeds in one patient, three seeds in each of four patients, two seeds in each of 10 patients, and a single seed in each of the remaining 46 patients. Sixty-one (0.32%) seeds migrated to the chest in 48 (18.0%) patients. Seven (0.036%) seeds migrated to the abdomen in six (2.2%) patients. Seventeen (0.088%) seeds migrated to the pelvis in 16 (6.0%) patients.\nAt three months after seed implantation, 87 of the 19,236 (0.45%) seeds migrated in 63 of the 267 (23.6%) patients: seven seeds in one patient, three seeds in each of four patients, two seeds in each of 10 patients, and a single seed in each of the remaining 48 patients. Sixty-three (0.33%) seeds migrated to the chest in 50 (18.7%) patients. Seven (0.036%) seeds migrated to the abdomen in six (2.2%) patients. Seventeen (0.088%) seeds migrated to the pelvis in 16 (6.0%) patients.\nAlthough seed migration occurred predominantly within two weeks after seed implantation, eventually, six seeds were found to migrate or relocate to the chest long after seed implantation: four seeds were found to have migrated to the chest at a median of 27 months (range 9.1-16 months), and two seeds were found to have relocated from the pelvis to the chest at 6.1 and 48 months after seed implantation, respectively. In these patients, follow-up radiographs were undertaken routinely at every outpatient visit; however, migration or relocation of these seeds was not found at the previous visit. Meanwhile, no seed relocation from the chest to other sites was observed in the present study.\nEventually, 91 of the 19,236 (0.47%) seeds migrated in 66 of the 267 (24.7%) patients: seven seeds in one patient, three seeds in each of five patients, two seeds in each of nine patients, and a single seed in each of the remaining 51 patients. Sixty-nine (0.36%) seeds migrated to the chest in 54 (20.2%) patients. Seven (0.036%) seeds migrated to the abdomen in six (2.2%) patients. Fifteen (0.078%) seeds migrated to the pelvis in 15 (5.6%) patients. All 66 patients were informed of seed migration; none of these patients had symptoms related to the migrated seeds.\n Seeds that migrated to the abdomen (seven seeds in six patients) Two of the 19,236 (0.010%) seeds migrated to the liver in two of the 267 (0.75%) patients: a single seed migrated to the liver in each of two patients. Five (0.026%) seeds migrated to the kidneys in four (1.5%) patients: two seeds migrated to the same kidney in one patient, and a single seed migrated to the kidney in each of the remaining three patients. In one patient (Case 1), one day after seed implantation, an abdominal radiograph showed that a seed had migrated to the right side of the middle abdomen, which was considered to be separated from the inferior vena cava (IVC) (Figure 1A). However, two weeks after seed implantation, an abdominal radiograph showed that the seed had disappeared from the right side of the middle abdomen, and showed a seed that had migrated to the left side of the middle abdomen (Figure 1B). On pelvic radiographs, there were no changes in number of seeds that had been implanted into the prostate between one day and two weeks after seed implantation. It was concluded that the seed had relocated from the right side of the middle abdomen to the left side of the middle abdomen. A subsequent abdominal CT demonstrated that the seed had migrated to the left kidney (Figure 1C). In another patient (Case 2), two weeks after seed implantation, an abdominal radiograph showed that two seeds had migrated to the same right kidney (Figure 2A-C).\nCase 1: Relocation from the right side of the middle abdomen to the left kidney. One day after seed implantation, a follow-up abdominal radiograph showed that a seed had migrated to the right side of the middle abdomen (solid arrow) (A). Two weeks later, the seed had relocated from the right side of the middle abdomen to the left side of the middle abdomen (solid arrow) (B). Subsequent computed tomography showed that the seed had migrated to the left kidney (solid arrow) (C).\nCase 2: Migration of two seeds to the same right kidney. Two weeks after seed implantation, a follow-up abdominal radiograph showed that two seeds had migrated to the right side of the middle abdomen (solid arrows) (A). Subsequent computed tomography showed that these two seeds had migrated to the same right kidney (solid arrows) (B,C).\nTwo of the 19,236 (0.010%) seeds migrated to the liver in two of the 267 (0.75%) patients: a single seed migrated to the liver in each of two patients. Five (0.026%) seeds migrated to the kidneys in four (1.5%) patients: two seeds migrated to the same kidney in one patient, and a single seed migrated to the kidney in each of the remaining three patients. In one patient (Case 1), one day after seed implantation, an abdominal radiograph showed that a seed had migrated to the right side of the middle abdomen, which was considered to be separated from the inferior vena cava (IVC) (Figure 1A). However, two weeks after seed implantation, an abdominal radiograph showed that the seed had disappeared from the right side of the middle abdomen, and showed a seed that had migrated to the left side of the middle abdomen (Figure 1B). On pelvic radiographs, there were no changes in number of seeds that had been implanted into the prostate between one day and two weeks after seed implantation. It was concluded that the seed had relocated from the right side of the middle abdomen to the left side of the middle abdomen. A subsequent abdominal CT demonstrated that the seed had migrated to the left kidney (Figure 1C). In another patient (Case 2), two weeks after seed implantation, an abdominal radiograph showed that two seeds had migrated to the same right kidney (Figure 2A-C).\nCase 1: Relocation from the right side of the middle abdomen to the left kidney. One day after seed implantation, a follow-up abdominal radiograph showed that a seed had migrated to the right side of the middle abdomen (solid arrow) (A). Two weeks later, the seed had relocated from the right side of the middle abdomen to the left side of the middle abdomen (solid arrow) (B). Subsequent computed tomography showed that the seed had migrated to the left kidney (solid arrow) (C).\nCase 2: Migration of two seeds to the same right kidney. Two weeks after seed implantation, a follow-up abdominal radiograph showed that two seeds had migrated to the right side of the middle abdomen (solid arrows) (A). Subsequent computed tomography showed that these two seeds had migrated to the same right kidney (solid arrows) (B,C).\n Seeds that migrated to the pelvis (15 seeds in 15 patients) A single seed migrated to the pelvis in each of 15 patients. Five of the 19,236 (0.026%) seeds migrated to Batson's vertebral venous plexus in five of the 267 (1.9%) patients. Four (0.021%) seeds migrated to the sacral venous plexus in four (1.5%) patients. Two (0.010%) seeds migrated to the iliac veins in two (0.75%) patients. Two (0.010%) seeds migrated to the right ischial bone in two (0.75%) patients. Two (0.010%) seeds migrated to the obturator internus muscles in two (0.75%) patients.\nA single seed migrated to the pelvis in each of 15 patients. Five of the 19,236 (0.026%) seeds migrated to Batson's vertebral venous plexus in five of the 267 (1.9%) patients. Four (0.021%) seeds migrated to the sacral venous plexus in four (1.5%) patients. Two (0.010%) seeds migrated to the iliac veins in two (0.75%) patients. Two (0.010%) seeds migrated to the right ischial bone in two (0.75%) patients. Two (0.010%) seeds migrated to the obturator internus muscles in two (0.75%) patients.\n Seed relocation four years after seed implantation In one patient (Case 3), seed relocation was found four years after seed implantation. A seed had migrated to the right groin area one day after seed implantation (Figure 3A-B). Three years and six months after seed implantation, a pelvic radiograph showed that the seed was in the same location. However, four years after seed implantation, a pelvic radiograph showed that the seed had disappeared from the right groin area (Figure 3C), and a chest radiograph showed a seed that had migrated to the left lung, which was not found at three years and six months after seed implantation. It was concluded that the seed had initially lodged in a branch of the right femoral vein, and then relocated to the left lung through the IVC, long after seed implantation.\nCase 3: Seed disappearance from the right groin area four years after seed implantation. One day after seed implantation, a follow-up pelvic radiograph showed that a seed was located inferior to the right side of the pelvis (solid arrow) (A). Subsequent computed tomography showed that the seed had migrated to the right groin area (solid arrow) (B). Four years after seed implantation, a follow-up pelvic radiograph showed that the seed had disappeared from the pelvic area (C).\nIn one patient (Case 3), seed relocation was found four years after seed implantation. A seed had migrated to the right groin area one day after seed implantation (Figure 3A-B). Three years and six months after seed implantation, a pelvic radiograph showed that the seed was in the same location. However, four years after seed implantation, a pelvic radiograph showed that the seed had disappeared from the right groin area (Figure 3C), and a chest radiograph showed a seed that had migrated to the left lung, which was not found at three years and six months after seed implantation. It was concluded that the seed had initially lodged in a branch of the right femoral vein, and then relocated to the left lung through the IVC, long after seed implantation.\nCase 3: Seed disappearance from the right groin area four years after seed implantation. One day after seed implantation, a follow-up pelvic radiograph showed that a seed was located inferior to the right side of the pelvis (solid arrow) (A). Subsequent computed tomography showed that the seed had migrated to the right groin area (solid arrow) (B). Four years after seed implantation, a follow-up pelvic radiograph showed that the seed had disappeared from the pelvic area (C).\n Postimplant dosimetric analysis In the 265 patients who received brachytherapy alone, the postimplant prostate D90 was 175.0 ± 1.3 Gy (mean ± standard error [SE]). In these 265 patients, the postimplant prostate D90 value was not significantly different between patients with and without seed migration (mean ± SE, 175.1 ± 2.3 Gy vs. 175.0 ± 1.5 Gy, respectively, p = 0.992). In 15 patients who had multiple migrated seeds, the postimplant prostate D90 ranged from 137.2 to 198.5 Gy (mean ± SE, 171.8 ± 5.5 Gy), which was not significantly different from that in patients without seed migration (p = 0.573).\nIn the remaining two patients who received brachytherapy plus external beam radiotherapy, the postimplant prostate D90 values were 116.4 Gy and 132.6 Gy. These two patients had no seed migration.\nIn the 265 patients who received brachytherapy alone, the postimplant prostate D90 was 175.0 ± 1.3 Gy (mean ± standard error [SE]). In these 265 patients, the postimplant prostate D90 value was not significantly different between patients with and without seed migration (mean ± SE, 175.1 ± 2.3 Gy vs. 175.0 ± 1.5 Gy, respectively, p = 0.992). In 15 patients who had multiple migrated seeds, the postimplant prostate D90 ranged from 137.2 to 198.5 Gy (mean ± SE, 171.8 ± 5.5 Gy), which was not significantly different from that in patients without seed migration (p = 0.573).\nIn the remaining two patients who received brachytherapy plus external beam radiotherapy, the postimplant prostate D90 values were 116.4 Gy and 132.6 Gy. These two patients had no seed migration.","Two of the 19,236 (0.010%) seeds migrated to the liver in two of the 267 (0.75%) patients: a single seed migrated to the liver in each of two patients. Five (0.026%) seeds migrated to the kidneys in four (1.5%) patients: two seeds migrated to the same kidney in one patient, and a single seed migrated to the kidney in each of the remaining three patients. In one patient (Case 1), one day after seed implantation, an abdominal radiograph showed that a seed had migrated to the right side of the middle abdomen, which was considered to be separated from the inferior vena cava (IVC) (Figure 1A). However, two weeks after seed implantation, an abdominal radiograph showed that the seed had disappeared from the right side of the middle abdomen, and showed a seed that had migrated to the left side of the middle abdomen (Figure 1B). On pelvic radiographs, there were no changes in number of seeds that had been implanted into the prostate between one day and two weeks after seed implantation. It was concluded that the seed had relocated from the right side of the middle abdomen to the left side of the middle abdomen. A subsequent abdominal CT demonstrated that the seed had migrated to the left kidney (Figure 1C). In another patient (Case 2), two weeks after seed implantation, an abdominal radiograph showed that two seeds had migrated to the same right kidney (Figure 2A-C).\nCase 1: Relocation from the right side of the middle abdomen to the left kidney. One day after seed implantation, a follow-up abdominal radiograph showed that a seed had migrated to the right side of the middle abdomen (solid arrow) (A). Two weeks later, the seed had relocated from the right side of the middle abdomen to the left side of the middle abdomen (solid arrow) (B). Subsequent computed tomography showed that the seed had migrated to the left kidney (solid arrow) (C).\nCase 2: Migration of two seeds to the same right kidney. Two weeks after seed implantation, a follow-up abdominal radiograph showed that two seeds had migrated to the right side of the middle abdomen (solid arrows) (A). Subsequent computed tomography showed that these two seeds had migrated to the same right kidney (solid arrows) (B,C).","A single seed migrated to the pelvis in each of 15 patients. Five of the 19,236 (0.026%) seeds migrated to Batson's vertebral venous plexus in five of the 267 (1.9%) patients. Four (0.021%) seeds migrated to the sacral venous plexus in four (1.5%) patients. Two (0.010%) seeds migrated to the iliac veins in two (0.75%) patients. Two (0.010%) seeds migrated to the right ischial bone in two (0.75%) patients. Two (0.010%) seeds migrated to the obturator internus muscles in two (0.75%) patients.","In one patient (Case 3), seed relocation was found four years after seed implantation. A seed had migrated to the right groin area one day after seed implantation (Figure 3A-B). Three years and six months after seed implantation, a pelvic radiograph showed that the seed was in the same location. However, four years after seed implantation, a pelvic radiograph showed that the seed had disappeared from the right groin area (Figure 3C), and a chest radiograph showed a seed that had migrated to the left lung, which was not found at three years and six months after seed implantation. It was concluded that the seed had initially lodged in a branch of the right femoral vein, and then relocated to the left lung through the IVC, long after seed implantation.\nCase 3: Seed disappearance from the right groin area four years after seed implantation. One day after seed implantation, a follow-up pelvic radiograph showed that a seed was located inferior to the right side of the pelvis (solid arrow) (A). Subsequent computed tomography showed that the seed had migrated to the right groin area (solid arrow) (B). Four years after seed implantation, a follow-up pelvic radiograph showed that the seed had disappeared from the pelvic area (C).","In the 265 patients who received brachytherapy alone, the postimplant prostate D90 was 175.0 ± 1.3 Gy (mean ± standard error [SE]). In these 265 patients, the postimplant prostate D90 value was not significantly different between patients with and without seed migration (mean ± SE, 175.1 ± 2.3 Gy vs. 175.0 ± 1.5 Gy, respectively, p = 0.992). In 15 patients who had multiple migrated seeds, the postimplant prostate D90 ranged from 137.2 to 198.5 Gy (mean ± SE, 171.8 ± 5.5 Gy), which was not significantly different from that in patients without seed migration (p = 0.573).\nIn the remaining two patients who received brachytherapy plus external beam radiotherapy, the postimplant prostate D90 values were 116.4 Gy and 132.6 Gy. These two patients had no seed migration."," Incidence of seed migration We found that 0.36% of implanted seeds migrated to the chest in 20% of our patient population, similar to previous reports (Table 2). It has been reported that the incidence of seed migration to the chest can be as high as 55% per patient population and 0.98% per number of implanted seeds (Table 2) [9]. The variability of the incidence of seed migration to the chest among the reported results is considered to be attributed to different types of seeds (linked or loose), different designs of seed placement (intraprostatic or extraprostatic), different timings of follow-up radiographs, and different protocols of follow-up chest radiographs (orthogonal or AP alone) (Table 2) [1-5,9,13].\nLiterature Survey of Seed Migration\n*Linked seeds were restricted to needles placed at the periphery of the prostate implant. All seeds placed centrally, close to the urethra, were loose seeds. On average, 8-10 loose seeds were used.\n†Of the total 125I seeds, 64% were linked seeds. Linked seeds were limited to the region of the capsule/periprostatic region. Loose seeds were utilized in the central aspect of the gland.\n††9 patients, within 14 days; 75 patients, more than 30 days.\n§11patients within 14 days; 61patients, more than 30 days.\n||Of the 20 patients who had postimplant chest radiographs obtained within 14 days of brachytherapy, none had seeds detected in the lungs. Categorizing patients by isotope, for 125I, 18/75 (24.0%) and for 103Pd, 16/61 (26.2%) with chest radiographs beyond 30 days had seeds found in the lungs.\n¶A mixture of linked and free seeds, with a median 74% of needles per case containing seeds in linked, with central and posterior needles typically containing loose seeds.\nIn contrast, the incidence of seed migration to the abdomen and pelvis has been reported rarely (Table 2). A possible reason is that the American Brachytherapy Society does not specifically recommend follow-up abdominal and pelvic radiographs after seed implantation [6]. Therefore, in most institutions, follow-up abdominal and pelvic radiographs would not be undertaken routinely. However, we found that seed migration to the abdomen and pelvis occurred in 2.2% and 5.6%, respectively, of our patient population. Although the incidence of seed migration to the abdomen and pelvis is lower than that of seed migration to the chest, we would consider it advisable to undertake follow-up abdominal and pelvic radiographs after seed implantation.\nWe found that 0.36% of implanted seeds migrated to the chest in 20% of our patient population, similar to previous reports (Table 2). It has been reported that the incidence of seed migration to the chest can be as high as 55% per patient population and 0.98% per number of implanted seeds (Table 2) [9]. The variability of the incidence of seed migration to the chest among the reported results is considered to be attributed to different types of seeds (linked or loose), different designs of seed placement (intraprostatic or extraprostatic), different timings of follow-up radiographs, and different protocols of follow-up chest radiographs (orthogonal or AP alone) (Table 2) [1-5,9,13].\nLiterature Survey of Seed Migration\n*Linked seeds were restricted to needles placed at the periphery of the prostate implant. All seeds placed centrally, close to the urethra, were loose seeds. On average, 8-10 loose seeds were used.\n†Of the total 125I seeds, 64% were linked seeds. Linked seeds were limited to the region of the capsule/periprostatic region. Loose seeds were utilized in the central aspect of the gland.\n††9 patients, within 14 days; 75 patients, more than 30 days.\n§11patients within 14 days; 61patients, more than 30 days.\n||Of the 20 patients who had postimplant chest radiographs obtained within 14 days of brachytherapy, none had seeds detected in the lungs. Categorizing patients by isotope, for 125I, 18/75 (24.0%) and for 103Pd, 16/61 (26.2%) with chest radiographs beyond 30 days had seeds found in the lungs.\n¶A mixture of linked and free seeds, with a median 74% of needles per case containing seeds in linked, with central and posterior needles typically containing loose seeds.\nIn contrast, the incidence of seed migration to the abdomen and pelvis has been reported rarely (Table 2). A possible reason is that the American Brachytherapy Society does not specifically recommend follow-up abdominal and pelvic radiographs after seed implantation [6]. Therefore, in most institutions, follow-up abdominal and pelvic radiographs would not be undertaken routinely. However, we found that seed migration to the abdomen and pelvis occurred in 2.2% and 5.6%, respectively, of our patient population. Although the incidence of seed migration to the abdomen and pelvis is lower than that of seed migration to the chest, we would consider it advisable to undertake follow-up abdominal and pelvic radiographs after seed implantation.\n The dynamics of seed migration to the chest One day, two weeks, and three months after seed implantation, follow-up chest radiographs showed 22%, 88%, and 91%, respectively, of 69 seeds that eventually migrated to the chest. These results mean that 22%, 66%, and 2.9% of these 69 seeds migrated to the chest within one day, between one day and two weeks, and between two weeks and three months after seed implantation, respectively. About 90% of these 69 seeds migrated to the chest within two weeks after seed implantation. These results are similar to a previous report [13]. Merrick et al. have speculated that seed migration to the chest may be most likely to occur between 14 and 28 days after seed implantation [13]. Although we observed that several seeds migrated to the chest more than six months after seed implantation, this finding should be considered exceptional. Therefore, it is suggested that follow-up chest radiographs should be undertaken two weeks, or preferably a few months, after seed implantation to detect most seeds that will migrate to the chest.\nOne day, two weeks, and three months after seed implantation, follow-up chest radiographs showed 22%, 88%, and 91%, respectively, of 69 seeds that eventually migrated to the chest. These results mean that 22%, 66%, and 2.9% of these 69 seeds migrated to the chest within one day, between one day and two weeks, and between two weeks and three months after seed implantation, respectively. About 90% of these 69 seeds migrated to the chest within two weeks after seed implantation. These results are similar to a previous report [13]. Merrick et al. have speculated that seed migration to the chest may be most likely to occur between 14 and 28 days after seed implantation [13]. Although we observed that several seeds migrated to the chest more than six months after seed implantation, this finding should be considered exceptional. Therefore, it is suggested that follow-up chest radiographs should be undertaken two weeks, or preferably a few months, after seed implantation to detect most seeds that will migrate to the chest.\n Seed migration to the kidneys and Batson's vertebral plexus is not very rare The results of the present study show a total of four and five cases of seed migration to the kidneys and Batson's vertebral venous plexus, respectively, at only one institution, which suggests that such cases are not very rare. Meanwhile, in previous studies, a total of only four and four cases of seed migration to the kidneys and Batson's vertebral venous plexus, respectively, have been reported as rare cases, which is in disagreement with our conclusion [22,23,26,29]. The same number or more cases of seed migration to these areas were found in our single study compared with all previous studies. A possible explanation is that, in the present study, orthogonal chest radiographs, an abdominal radiograph, and a pelvic radiograph were undertaken routinely to detect seed migration to the chest, abdomen, and pelvis at several time points after seed implantation. Moreover, in all patients who had seed migration to the abdomen and pelvis, a CT scan was undertaken to identify the exact location of the migrated seeds. Consequently, more cases of seed migration to the kidneys and Batson's vertebral venous plexus were found in the present study. We speculate that some seed migration to the kidneys and Batson's vertebral venous plexus might have gone undetected in other institutions.\nThe results of the present study show a total of four and five cases of seed migration to the kidneys and Batson's vertebral venous plexus, respectively, at only one institution, which suggests that such cases are not very rare. Meanwhile, in previous studies, a total of only four and four cases of seed migration to the kidneys and Batson's vertebral venous plexus, respectively, have been reported as rare cases, which is in disagreement with our conclusion [22,23,26,29]. The same number or more cases of seed migration to these areas were found in our single study compared with all previous studies. A possible explanation is that, in the present study, orthogonal chest radiographs, an abdominal radiograph, and a pelvic radiograph were undertaken routinely to detect seed migration to the chest, abdomen, and pelvis at several time points after seed implantation. Moreover, in all patients who had seed migration to the abdomen and pelvis, a CT scan was undertaken to identify the exact location of the migrated seeds. Consequently, more cases of seed migration to the kidneys and Batson's vertebral venous plexus were found in the present study. We speculate that some seed migration to the kidneys and Batson's vertebral venous plexus might have gone undetected in other institutions.\n In some cases of seed migration to the kidneys, the mechanism is difficult to explain Previous investigators have explained seed migration to the kidneys as follows: migrated seeds in venous vessels would enter the systemic circulation through a right-to-left shunt, such as a patent foramen ovale or pulmonary arteriovenous malformation, and then embolize to a branch of the renal arteries [26]. We call this route of seed migration through a right-to-left shunt \"a paradoxical route.\"\nThe present study has provided some interesting cases of seed migration to the kidneys, the mechanism of which is difficult to explain. In Case 1 of the present study, a seed had migrated to the right side of the middle abdomen, which was considered to be separated from the IVC, and then had relocated to the left kidney (Figure 1A-C). If the seed had initially embolized to an artery of the right side of the middle abdomen, such as a branch of the right renal artery, through a paradoxical route, it is difficult to explain how the seed would have relocated to the left kidney. In Case 2, two seeds had migrated to the same right kidney (Figure 2A-C). The mechanism of seed migration to the kidney through a paradoxical route is too complicated. Therefore, it is highly unlikely that two seeds would happen to migrate to the same right kidney through a paradoxical route by chance, although the possibility cannot be completely excluded. Other possible mechanisms should be proposed to explain how seed migration to the kidneys would have occurred in the two cases mentioned above.\nPrevious investigators have explained seed migration to the kidneys as follows: migrated seeds in venous vessels would enter the systemic circulation through a right-to-left shunt, such as a patent foramen ovale or pulmonary arteriovenous malformation, and then embolize to a branch of the renal arteries [26]. We call this route of seed migration through a right-to-left shunt \"a paradoxical route.\"\nThe present study has provided some interesting cases of seed migration to the kidneys, the mechanism of which is difficult to explain. In Case 1 of the present study, a seed had migrated to the right side of the middle abdomen, which was considered to be separated from the IVC, and then had relocated to the left kidney (Figure 1A-C). If the seed had initially embolized to an artery of the right side of the middle abdomen, such as a branch of the right renal artery, through a paradoxical route, it is difficult to explain how the seed would have relocated to the left kidney. In Case 2, two seeds had migrated to the same right kidney (Figure 2A-C). The mechanism of seed migration to the kidney through a paradoxical route is too complicated. Therefore, it is highly unlikely that two seeds would happen to migrate to the same right kidney through a paradoxical route by chance, although the possibility cannot be completely excluded. Other possible mechanisms should be proposed to explain how seed migration to the kidneys would have occurred in the two cases mentioned above.\n Other possible mechanisms of seed migration We assume that seed movement in venous vessels would reflect not only the force of the blood flow but also the force of gravity, and that a seed sometimes would move in venous vessels against the blood flow, because the gravity could overcome the blood flow. The explanation is as follows. Intravascular missile migration following gunshot injury has been reported [30-34]. A missile sometimes moves in a retrograde fashion against the normal blood flow in major venous vessels including the IVC, and sometimes lodges in the renal vein and the hepatic vein [30-34]. It has been postulated that missile migration against the blood flow in venous vessels could occur because of gravity, the patient's position (upright) at the moment of wounding and/or positional changes of the body, the weight and shape of the missile, and possible low flow states [30]. Although the size and the weight of an 125I radioactive seed (4.5 mm in length, 0.8 mm in diameter, and about 10 mg in weight per seed) are smaller than those of a bullet, it is considered that a seed could move against the blood flow in major venous vessels because of gravity. The reasons are as follows. The specific gravity of the seed (about 4 g/mL) is much higher than that of blood, and the seed is less water resistant because of its rod-shaped structure. In addition, a seed would usually be located near the wall of major venous vessels due to gravity and the patient's position, and therefore, a seed would be affected by a relatively slow stream of blood near the vascular wall compared with that in the center of major venous vessels. It is known that in major venous vessels, the blood flow is generally laminar, and the distribution of velocity across the tube is parabolic [35]. Therefore, the velocity of blood flow decreases from the center toward the wall of the venous vessel tube [35].\nThe mechanism of seed migration to various sites could be explained by the assumption that a seed could move in major venous vessels against the blood flow by gravity. In the present cases, seed migration to the kidneys, the liver, and the right groin area (probably a branch of the right femoral vein) (Case 3) would have occurred in venous vessels, partially in a retrograde fashion by gravity, without being passed through a paradoxical route. This explanation does not require the assumption that a patient would have a right-to-left shunt.\nWe assume that seed movement in venous vessels would reflect not only the force of the blood flow but also the force of gravity, and that a seed sometimes would move in venous vessels against the blood flow, because the gravity could overcome the blood flow. The explanation is as follows. Intravascular missile migration following gunshot injury has been reported [30-34]. A missile sometimes moves in a retrograde fashion against the normal blood flow in major venous vessels including the IVC, and sometimes lodges in the renal vein and the hepatic vein [30-34]. It has been postulated that missile migration against the blood flow in venous vessels could occur because of gravity, the patient's position (upright) at the moment of wounding and/or positional changes of the body, the weight and shape of the missile, and possible low flow states [30]. Although the size and the weight of an 125I radioactive seed (4.5 mm in length, 0.8 mm in diameter, and about 10 mg in weight per seed) are smaller than those of a bullet, it is considered that a seed could move against the blood flow in major venous vessels because of gravity. The reasons are as follows. The specific gravity of the seed (about 4 g/mL) is much higher than that of blood, and the seed is less water resistant because of its rod-shaped structure. In addition, a seed would usually be located near the wall of major venous vessels due to gravity and the patient's position, and therefore, a seed would be affected by a relatively slow stream of blood near the vascular wall compared with that in the center of major venous vessels. It is known that in major venous vessels, the blood flow is generally laminar, and the distribution of velocity across the tube is parabolic [35]. Therefore, the velocity of blood flow decreases from the center toward the wall of the venous vessel tube [35].\nThe mechanism of seed migration to various sites could be explained by the assumption that a seed could move in major venous vessels against the blood flow by gravity. In the present cases, seed migration to the kidneys, the liver, and the right groin area (probably a branch of the right femoral vein) (Case 3) would have occurred in venous vessels, partially in a retrograde fashion by gravity, without being passed through a paradoxical route. This explanation does not require the assumption that a patient would have a right-to-left shunt.\n Influence of seed migration on postimplant dosimetry The postimplant prostate D90 was not significantly different between patients with and without seed migration. Moreover, in 15 patients who had multiple migrated seeds, the postimplant prostate D90 was relatively acceptable, and no supplemental seed implantation was required. These results indicate that seed migration did not have a significant effect on postimplant prostate dosimetry in the present study. Possible reasons are as follows. First, in most patients with seed migration, only one or two seeds had migrated, which would have less effect on the dosimetry of the prostate. Tapen et al. have suggested that the loss of a few seeds may not have a significant effect on dose homogeneity or total dose to the prostate [5]. Second, seed migration would have much less effect on the dosimetry of the prostate than other mechanisms of seed loss, such as seed misplacement to the seminal vesicle or perineum and being voided in the urine postoperatively. Merrick et al. have reported that seed migration to the chest accounted for only 10% of total seed loss from the prostate region, highlighting the importance of other mechanisms of loss [13].\nThe postimplant prostate D90 was not significantly different between patients with and without seed migration. Moreover, in 15 patients who had multiple migrated seeds, the postimplant prostate D90 was relatively acceptable, and no supplemental seed implantation was required. These results indicate that seed migration did not have a significant effect on postimplant prostate dosimetry in the present study. Possible reasons are as follows. First, in most patients with seed migration, only one or two seeds had migrated, which would have less effect on the dosimetry of the prostate. Tapen et al. have suggested that the loss of a few seeds may not have a significant effect on dose homogeneity or total dose to the prostate [5]. Second, seed migration would have much less effect on the dosimetry of the prostate than other mechanisms of seed loss, such as seed misplacement to the seminal vesicle or perineum and being voided in the urine postoperatively. Merrick et al. have reported that seed migration to the chest accounted for only 10% of total seed loss from the prostate region, highlighting the importance of other mechanisms of loss [13].\n Seed migration-related sequelae In the present study, none of the 66 patients had symptoms related to the migrated seeds. Although it has been reported that most patients with seed migration are asymptomatic, there have been a few reports of seed migration-related sequelae, such as a few anecdotal instances of cardiac arrhythmia, myocardial infarction, and radiation pneumonitis [21,36,37]. Therefore, it is important to try to reduce the incidence of seed migration. The use of seeds linked with an absorbable suture material has been associated with a dramatically decreased rate of seed migration, although a potentially higher risk of radiotoxicity to the urethra and rectum has been pointed out [1,3,5,38]. In our study, linked seeds were not administered, because they are not commercially available in our country at present.\nIn the present study, none of the 66 patients had symptoms related to the migrated seeds. Although it has been reported that most patients with seed migration are asymptomatic, there have been a few reports of seed migration-related sequelae, such as a few anecdotal instances of cardiac arrhythmia, myocardial infarction, and radiation pneumonitis [21,36,37]. Therefore, it is important to try to reduce the incidence of seed migration. The use of seeds linked with an absorbable suture material has been associated with a dramatically decreased rate of seed migration, although a potentially higher risk of radiotoxicity to the urethra and rectum has been pointed out [1,3,5,38]. In our study, linked seeds were not administered, because they are not commercially available in our country at present.","We found that 0.36% of implanted seeds migrated to the chest in 20% of our patient population, similar to previous reports (Table 2). It has been reported that the incidence of seed migration to the chest can be as high as 55% per patient population and 0.98% per number of implanted seeds (Table 2) [9]. The variability of the incidence of seed migration to the chest among the reported results is considered to be attributed to different types of seeds (linked or loose), different designs of seed placement (intraprostatic or extraprostatic), different timings of follow-up radiographs, and different protocols of follow-up chest radiographs (orthogonal or AP alone) (Table 2) [1-5,9,13].\nLiterature Survey of Seed Migration\n*Linked seeds were restricted to needles placed at the periphery of the prostate implant. All seeds placed centrally, close to the urethra, were loose seeds. On average, 8-10 loose seeds were used.\n†Of the total 125I seeds, 64% were linked seeds. Linked seeds were limited to the region of the capsule/periprostatic region. Loose seeds were utilized in the central aspect of the gland.\n††9 patients, within 14 days; 75 patients, more than 30 days.\n§11patients within 14 days; 61patients, more than 30 days.\n||Of the 20 patients who had postimplant chest radiographs obtained within 14 days of brachytherapy, none had seeds detected in the lungs. Categorizing patients by isotope, for 125I, 18/75 (24.0%) and for 103Pd, 16/61 (26.2%) with chest radiographs beyond 30 days had seeds found in the lungs.\n¶A mixture of linked and free seeds, with a median 74% of needles per case containing seeds in linked, with central and posterior needles typically containing loose seeds.\nIn contrast, the incidence of seed migration to the abdomen and pelvis has been reported rarely (Table 2). A possible reason is that the American Brachytherapy Society does not specifically recommend follow-up abdominal and pelvic radiographs after seed implantation [6]. Therefore, in most institutions, follow-up abdominal and pelvic radiographs would not be undertaken routinely. However, we found that seed migration to the abdomen and pelvis occurred in 2.2% and 5.6%, respectively, of our patient population. Although the incidence of seed migration to the abdomen and pelvis is lower than that of seed migration to the chest, we would consider it advisable to undertake follow-up abdominal and pelvic radiographs after seed implantation.","One day, two weeks, and three months after seed implantation, follow-up chest radiographs showed 22%, 88%, and 91%, respectively, of 69 seeds that eventually migrated to the chest. These results mean that 22%, 66%, and 2.9% of these 69 seeds migrated to the chest within one day, between one day and two weeks, and between two weeks and three months after seed implantation, respectively. About 90% of these 69 seeds migrated to the chest within two weeks after seed implantation. These results are similar to a previous report [13]. Merrick et al. have speculated that seed migration to the chest may be most likely to occur between 14 and 28 days after seed implantation [13]. Although we observed that several seeds migrated to the chest more than six months after seed implantation, this finding should be considered exceptional. Therefore, it is suggested that follow-up chest radiographs should be undertaken two weeks, or preferably a few months, after seed implantation to detect most seeds that will migrate to the chest.","The results of the present study show a total of four and five cases of seed migration to the kidneys and Batson's vertebral venous plexus, respectively, at only one institution, which suggests that such cases are not very rare. Meanwhile, in previous studies, a total of only four and four cases of seed migration to the kidneys and Batson's vertebral venous plexus, respectively, have been reported as rare cases, which is in disagreement with our conclusion [22,23,26,29]. The same number or more cases of seed migration to these areas were found in our single study compared with all previous studies. A possible explanation is that, in the present study, orthogonal chest radiographs, an abdominal radiograph, and a pelvic radiograph were undertaken routinely to detect seed migration to the chest, abdomen, and pelvis at several time points after seed implantation. Moreover, in all patients who had seed migration to the abdomen and pelvis, a CT scan was undertaken to identify the exact location of the migrated seeds. Consequently, more cases of seed migration to the kidneys and Batson's vertebral venous plexus were found in the present study. We speculate that some seed migration to the kidneys and Batson's vertebral venous plexus might have gone undetected in other institutions.","Previous investigators have explained seed migration to the kidneys as follows: migrated seeds in venous vessels would enter the systemic circulation through a right-to-left shunt, such as a patent foramen ovale or pulmonary arteriovenous malformation, and then embolize to a branch of the renal arteries [26]. We call this route of seed migration through a right-to-left shunt \"a paradoxical route.\"\nThe present study has provided some interesting cases of seed migration to the kidneys, the mechanism of which is difficult to explain. In Case 1 of the present study, a seed had migrated to the right side of the middle abdomen, which was considered to be separated from the IVC, and then had relocated to the left kidney (Figure 1A-C). If the seed had initially embolized to an artery of the right side of the middle abdomen, such as a branch of the right renal artery, through a paradoxical route, it is difficult to explain how the seed would have relocated to the left kidney. In Case 2, two seeds had migrated to the same right kidney (Figure 2A-C). The mechanism of seed migration to the kidney through a paradoxical route is too complicated. Therefore, it is highly unlikely that two seeds would happen to migrate to the same right kidney through a paradoxical route by chance, although the possibility cannot be completely excluded. Other possible mechanisms should be proposed to explain how seed migration to the kidneys would have occurred in the two cases mentioned above.","We assume that seed movement in venous vessels would reflect not only the force of the blood flow but also the force of gravity, and that a seed sometimes would move in venous vessels against the blood flow, because the gravity could overcome the blood flow. The explanation is as follows. Intravascular missile migration following gunshot injury has been reported [30-34]. A missile sometimes moves in a retrograde fashion against the normal blood flow in major venous vessels including the IVC, and sometimes lodges in the renal vein and the hepatic vein [30-34]. It has been postulated that missile migration against the blood flow in venous vessels could occur because of gravity, the patient's position (upright) at the moment of wounding and/or positional changes of the body, the weight and shape of the missile, and possible low flow states [30]. Although the size and the weight of an 125I radioactive seed (4.5 mm in length, 0.8 mm in diameter, and about 10 mg in weight per seed) are smaller than those of a bullet, it is considered that a seed could move against the blood flow in major venous vessels because of gravity. The reasons are as follows. The specific gravity of the seed (about 4 g/mL) is much higher than that of blood, and the seed is less water resistant because of its rod-shaped structure. In addition, a seed would usually be located near the wall of major venous vessels due to gravity and the patient's position, and therefore, a seed would be affected by a relatively slow stream of blood near the vascular wall compared with that in the center of major venous vessels. It is known that in major venous vessels, the blood flow is generally laminar, and the distribution of velocity across the tube is parabolic [35]. Therefore, the velocity of blood flow decreases from the center toward the wall of the venous vessel tube [35].\nThe mechanism of seed migration to various sites could be explained by the assumption that a seed could move in major venous vessels against the blood flow by gravity. In the present cases, seed migration to the kidneys, the liver, and the right groin area (probably a branch of the right femoral vein) (Case 3) would have occurred in venous vessels, partially in a retrograde fashion by gravity, without being passed through a paradoxical route. This explanation does not require the assumption that a patient would have a right-to-left shunt.","The postimplant prostate D90 was not significantly different between patients with and without seed migration. Moreover, in 15 patients who had multiple migrated seeds, the postimplant prostate D90 was relatively acceptable, and no supplemental seed implantation was required. These results indicate that seed migration did not have a significant effect on postimplant prostate dosimetry in the present study. Possible reasons are as follows. First, in most patients with seed migration, only one or two seeds had migrated, which would have less effect on the dosimetry of the prostate. Tapen et al. have suggested that the loss of a few seeds may not have a significant effect on dose homogeneity or total dose to the prostate [5]. Second, seed migration would have much less effect on the dosimetry of the prostate than other mechanisms of seed loss, such as seed misplacement to the seminal vesicle or perineum and being voided in the urine postoperatively. Merrick et al. have reported that seed migration to the chest accounted for only 10% of total seed loss from the prostate region, highlighting the importance of other mechanisms of loss [13].","In the present study, none of the 66 patients had symptoms related to the migrated seeds. Although it has been reported that most patients with seed migration are asymptomatic, there have been a few reports of seed migration-related sequelae, such as a few anecdotal instances of cardiac arrhythmia, myocardial infarction, and radiation pneumonitis [21,36,37]. Therefore, it is important to try to reduce the incidence of seed migration. The use of seeds linked with an absorbable suture material has been associated with a dramatically decreased rate of seed migration, although a potentially higher risk of radiotoxicity to the urethra and rectum has been pointed out [1,3,5,38]. In our study, linked seeds were not administered, because they are not commercially available in our country at present.","In the present study, we determined the incidence of seed migration not only to the chest, but also to the abdomen and pelvis. Although the incidence of seed migration to the abdomen and pelvis was lower than that of seed migration to the chest, it would be advisable to undertake follow-up abdominal and pelvic radiographs after seed implantation. Seed migration to the chest occurred predominantly within two weeks and, therefore, it is suggested that follow-up chest radiographs should be undertaken two weeks or later after seed implantation. Seed migration to the kidneys and Batson's vertebral venous plexus was not very rare. Seed migration did not significantly affect postimplant prostate D90."],"string":"[\n \"Seed migration is a well-recognized event that occurs after transperineal interstitial prostate brachytherapy, and it is observed more often with loose seeds than with linked seeds [1-5].\\nIt is well known that the most frequent site of seed migration is the chest. The American Brachytherapy Society has advised that a chest radiograph should be undertaken at the first follow-up visit to scan the lungs for embolized seeds [6]. Consequently, the incidence of seed migration to the chest has been well reported [1,2,4,5,7-19]. However, documentation of the incidence of seed migration to the abdomen and pelvis is rare. Rare cases of seed migration to a coronary artery, the right ventricle, the liver, the kidneys, Batson's vertebral venous plexus, and the left testicular vein have been reported [20-26]. However, it has never been fully determined whether seed migration to these locations is really rare.\\nThe primary purposes of the present study were to determine the incidence of seed migration not only to the chest, but also to the abdomen and the pelvis at our institution and to identify the exact location of the seeds that had migrated to the abdomen and pelvis with computed tomography (CT). The secondary purpose was to determine the impact of seed migration on postimplant dosimetry.\",\n \"In total, 19,236 seeds were implanted in 267 patients. All 267 patients underwent follow-up radiographs. Median follow-up was 41 months (range, 8.5-76 months).\\nAt one day after seed implantation, follow-up radiographs demonstrated that 41 of the 19,236 (0.21%) seeds migrated in 37 of the 267 (13.9%) patients: three seeds in one patient, two seeds in each of two patients, and a single seed in each of the remaining 34 patients. Fifteen (0.078%) seeds migrated to the chest in 15 (5.6%) patients. One (0.0052%) seed migrated to the abdomen in one (0.37%) patient. Twenty-five (0.13%) seeds migrated to the pelvis in 23 (8.6%) patients.\\nAt two weeks after seed implantation, 85 of the 19,236 (0.44%) seeds migrated in 61 of the 267 (22.8%) patients: seven seeds in one patient, three seeds in each of four patients, two seeds in each of 10 patients, and a single seed in each of the remaining 46 patients. Sixty-one (0.32%) seeds migrated to the chest in 48 (18.0%) patients. Seven (0.036%) seeds migrated to the abdomen in six (2.2%) patients. Seventeen (0.088%) seeds migrated to the pelvis in 16 (6.0%) patients.\\nAt three months after seed implantation, 87 of the 19,236 (0.45%) seeds migrated in 63 of the 267 (23.6%) patients: seven seeds in one patient, three seeds in each of four patients, two seeds in each of 10 patients, and a single seed in each of the remaining 48 patients. Sixty-three (0.33%) seeds migrated to the chest in 50 (18.7%) patients. Seven (0.036%) seeds migrated to the abdomen in six (2.2%) patients. Seventeen (0.088%) seeds migrated to the pelvis in 16 (6.0%) patients.\\nAlthough seed migration occurred predominantly within two weeks after seed implantation, eventually, six seeds were found to migrate or relocate to the chest long after seed implantation: four seeds were found to have migrated to the chest at a median of 27 months (range 9.1-16 months), and two seeds were found to have relocated from the pelvis to the chest at 6.1 and 48 months after seed implantation, respectively. In these patients, follow-up radiographs were undertaken routinely at every outpatient visit; however, migration or relocation of these seeds was not found at the previous visit. Meanwhile, no seed relocation from the chest to other sites was observed in the present study.\\nEventually, 91 of the 19,236 (0.47%) seeds migrated in 66 of the 267 (24.7%) patients: seven seeds in one patient, three seeds in each of five patients, two seeds in each of nine patients, and a single seed in each of the remaining 51 patients. Sixty-nine (0.36%) seeds migrated to the chest in 54 (20.2%) patients. Seven (0.036%) seeds migrated to the abdomen in six (2.2%) patients. Fifteen (0.078%) seeds migrated to the pelvis in 15 (5.6%) patients. All 66 patients were informed of seed migration; none of these patients had symptoms related to the migrated seeds.\\n Seeds that migrated to the abdomen (seven seeds in six patients) Two of the 19,236 (0.010%) seeds migrated to the liver in two of the 267 (0.75%) patients: a single seed migrated to the liver in each of two patients. Five (0.026%) seeds migrated to the kidneys in four (1.5%) patients: two seeds migrated to the same kidney in one patient, and a single seed migrated to the kidney in each of the remaining three patients. In one patient (Case 1), one day after seed implantation, an abdominal radiograph showed that a seed had migrated to the right side of the middle abdomen, which was considered to be separated from the inferior vena cava (IVC) (Figure 1A). However, two weeks after seed implantation, an abdominal radiograph showed that the seed had disappeared from the right side of the middle abdomen, and showed a seed that had migrated to the left side of the middle abdomen (Figure 1B). On pelvic radiographs, there were no changes in number of seeds that had been implanted into the prostate between one day and two weeks after seed implantation. It was concluded that the seed had relocated from the right side of the middle abdomen to the left side of the middle abdomen. A subsequent abdominal CT demonstrated that the seed had migrated to the left kidney (Figure 1C). In another patient (Case 2), two weeks after seed implantation, an abdominal radiograph showed that two seeds had migrated to the same right kidney (Figure 2A-C).\\nCase 1: Relocation from the right side of the middle abdomen to the left kidney. One day after seed implantation, a follow-up abdominal radiograph showed that a seed had migrated to the right side of the middle abdomen (solid arrow) (A). Two weeks later, the seed had relocated from the right side of the middle abdomen to the left side of the middle abdomen (solid arrow) (B). Subsequent computed tomography showed that the seed had migrated to the left kidney (solid arrow) (C).\\nCase 2: Migration of two seeds to the same right kidney. Two weeks after seed implantation, a follow-up abdominal radiograph showed that two seeds had migrated to the right side of the middle abdomen (solid arrows) (A). Subsequent computed tomography showed that these two seeds had migrated to the same right kidney (solid arrows) (B,C).\\nTwo of the 19,236 (0.010%) seeds migrated to the liver in two of the 267 (0.75%) patients: a single seed migrated to the liver in each of two patients. Five (0.026%) seeds migrated to the kidneys in four (1.5%) patients: two seeds migrated to the same kidney in one patient, and a single seed migrated to the kidney in each of the remaining three patients. In one patient (Case 1), one day after seed implantation, an abdominal radiograph showed that a seed had migrated to the right side of the middle abdomen, which was considered to be separated from the inferior vena cava (IVC) (Figure 1A). However, two weeks after seed implantation, an abdominal radiograph showed that the seed had disappeared from the right side of the middle abdomen, and showed a seed that had migrated to the left side of the middle abdomen (Figure 1B). On pelvic radiographs, there were no changes in number of seeds that had been implanted into the prostate between one day and two weeks after seed implantation. It was concluded that the seed had relocated from the right side of the middle abdomen to the left side of the middle abdomen. A subsequent abdominal CT demonstrated that the seed had migrated to the left kidney (Figure 1C). In another patient (Case 2), two weeks after seed implantation, an abdominal radiograph showed that two seeds had migrated to the same right kidney (Figure 2A-C).\\nCase 1: Relocation from the right side of the middle abdomen to the left kidney. One day after seed implantation, a follow-up abdominal radiograph showed that a seed had migrated to the right side of the middle abdomen (solid arrow) (A). Two weeks later, the seed had relocated from the right side of the middle abdomen to the left side of the middle abdomen (solid arrow) (B). Subsequent computed tomography showed that the seed had migrated to the left kidney (solid arrow) (C).\\nCase 2: Migration of two seeds to the same right kidney. Two weeks after seed implantation, a follow-up abdominal radiograph showed that two seeds had migrated to the right side of the middle abdomen (solid arrows) (A). Subsequent computed tomography showed that these two seeds had migrated to the same right kidney (solid arrows) (B,C).\\n Seeds that migrated to the pelvis (15 seeds in 15 patients) A single seed migrated to the pelvis in each of 15 patients. Five of the 19,236 (0.026%) seeds migrated to Batson's vertebral venous plexus in five of the 267 (1.9%) patients. Four (0.021%) seeds migrated to the sacral venous plexus in four (1.5%) patients. Two (0.010%) seeds migrated to the iliac veins in two (0.75%) patients. Two (0.010%) seeds migrated to the right ischial bone in two (0.75%) patients. Two (0.010%) seeds migrated to the obturator internus muscles in two (0.75%) patients.\\nA single seed migrated to the pelvis in each of 15 patients. Five of the 19,236 (0.026%) seeds migrated to Batson's vertebral venous plexus in five of the 267 (1.9%) patients. Four (0.021%) seeds migrated to the sacral venous plexus in four (1.5%) patients. Two (0.010%) seeds migrated to the iliac veins in two (0.75%) patients. Two (0.010%) seeds migrated to the right ischial bone in two (0.75%) patients. Two (0.010%) seeds migrated to the obturator internus muscles in two (0.75%) patients.\\n Seed relocation four years after seed implantation In one patient (Case 3), seed relocation was found four years after seed implantation. A seed had migrated to the right groin area one day after seed implantation (Figure 3A-B). Three years and six months after seed implantation, a pelvic radiograph showed that the seed was in the same location. However, four years after seed implantation, a pelvic radiograph showed that the seed had disappeared from the right groin area (Figure 3C), and a chest radiograph showed a seed that had migrated to the left lung, which was not found at three years and six months after seed implantation. It was concluded that the seed had initially lodged in a branch of the right femoral vein, and then relocated to the left lung through the IVC, long after seed implantation.\\nCase 3: Seed disappearance from the right groin area four years after seed implantation. One day after seed implantation, a follow-up pelvic radiograph showed that a seed was located inferior to the right side of the pelvis (solid arrow) (A). Subsequent computed tomography showed that the seed had migrated to the right groin area (solid arrow) (B). Four years after seed implantation, a follow-up pelvic radiograph showed that the seed had disappeared from the pelvic area (C).\\nIn one patient (Case 3), seed relocation was found four years after seed implantation. A seed had migrated to the right groin area one day after seed implantation (Figure 3A-B). Three years and six months after seed implantation, a pelvic radiograph showed that the seed was in the same location. However, four years after seed implantation, a pelvic radiograph showed that the seed had disappeared from the right groin area (Figure 3C), and a chest radiograph showed a seed that had migrated to the left lung, which was not found at three years and six months after seed implantation. It was concluded that the seed had initially lodged in a branch of the right femoral vein, and then relocated to the left lung through the IVC, long after seed implantation.\\nCase 3: Seed disappearance from the right groin area four years after seed implantation. One day after seed implantation, a follow-up pelvic radiograph showed that a seed was located inferior to the right side of the pelvis (solid arrow) (A). Subsequent computed tomography showed that the seed had migrated to the right groin area (solid arrow) (B). Four years after seed implantation, a follow-up pelvic radiograph showed that the seed had disappeared from the pelvic area (C).\\n Postimplant dosimetric analysis In the 265 patients who received brachytherapy alone, the postimplant prostate D90 was 175.0 ± 1.3 Gy (mean ± standard error [SE]). In these 265 patients, the postimplant prostate D90 value was not significantly different between patients with and without seed migration (mean ± SE, 175.1 ± 2.3 Gy vs. 175.0 ± 1.5 Gy, respectively, p = 0.992). In 15 patients who had multiple migrated seeds, the postimplant prostate D90 ranged from 137.2 to 198.5 Gy (mean ± SE, 171.8 ± 5.5 Gy), which was not significantly different from that in patients without seed migration (p = 0.573).\\nIn the remaining two patients who received brachytherapy plus external beam radiotherapy, the postimplant prostate D90 values were 116.4 Gy and 132.6 Gy. These two patients had no seed migration.\\nIn the 265 patients who received brachytherapy alone, the postimplant prostate D90 was 175.0 ± 1.3 Gy (mean ± standard error [SE]). In these 265 patients, the postimplant prostate D90 value was not significantly different between patients with and without seed migration (mean ± SE, 175.1 ± 2.3 Gy vs. 175.0 ± 1.5 Gy, respectively, p = 0.992). In 15 patients who had multiple migrated seeds, the postimplant prostate D90 ranged from 137.2 to 198.5 Gy (mean ± SE, 171.8 ± 5.5 Gy), which was not significantly different from that in patients without seed migration (p = 0.573).\\nIn the remaining two patients who received brachytherapy plus external beam radiotherapy, the postimplant prostate D90 values were 116.4 Gy and 132.6 Gy. These two patients had no seed migration.\",\n \"Two of the 19,236 (0.010%) seeds migrated to the liver in two of the 267 (0.75%) patients: a single seed migrated to the liver in each of two patients. Five (0.026%) seeds migrated to the kidneys in four (1.5%) patients: two seeds migrated to the same kidney in one patient, and a single seed migrated to the kidney in each of the remaining three patients. In one patient (Case 1), one day after seed implantation, an abdominal radiograph showed that a seed had migrated to the right side of the middle abdomen, which was considered to be separated from the inferior vena cava (IVC) (Figure 1A). However, two weeks after seed implantation, an abdominal radiograph showed that the seed had disappeared from the right side of the middle abdomen, and showed a seed that had migrated to the left side of the middle abdomen (Figure 1B). On pelvic radiographs, there were no changes in number of seeds that had been implanted into the prostate between one day and two weeks after seed implantation. It was concluded that the seed had relocated from the right side of the middle abdomen to the left side of the middle abdomen. A subsequent abdominal CT demonstrated that the seed had migrated to the left kidney (Figure 1C). In another patient (Case 2), two weeks after seed implantation, an abdominal radiograph showed that two seeds had migrated to the same right kidney (Figure 2A-C).\\nCase 1: Relocation from the right side of the middle abdomen to the left kidney. One day after seed implantation, a follow-up abdominal radiograph showed that a seed had migrated to the right side of the middle abdomen (solid arrow) (A). Two weeks later, the seed had relocated from the right side of the middle abdomen to the left side of the middle abdomen (solid arrow) (B). Subsequent computed tomography showed that the seed had migrated to the left kidney (solid arrow) (C).\\nCase 2: Migration of two seeds to the same right kidney. Two weeks after seed implantation, a follow-up abdominal radiograph showed that two seeds had migrated to the right side of the middle abdomen (solid arrows) (A). Subsequent computed tomography showed that these two seeds had migrated to the same right kidney (solid arrows) (B,C).\",\n \"A single seed migrated to the pelvis in each of 15 patients. Five of the 19,236 (0.026%) seeds migrated to Batson's vertebral venous plexus in five of the 267 (1.9%) patients. Four (0.021%) seeds migrated to the sacral venous plexus in four (1.5%) patients. Two (0.010%) seeds migrated to the iliac veins in two (0.75%) patients. Two (0.010%) seeds migrated to the right ischial bone in two (0.75%) patients. Two (0.010%) seeds migrated to the obturator internus muscles in two (0.75%) patients.\",\n \"In one patient (Case 3), seed relocation was found four years after seed implantation. A seed had migrated to the right groin area one day after seed implantation (Figure 3A-B). Three years and six months after seed implantation, a pelvic radiograph showed that the seed was in the same location. However, four years after seed implantation, a pelvic radiograph showed that the seed had disappeared from the right groin area (Figure 3C), and a chest radiograph showed a seed that had migrated to the left lung, which was not found at three years and six months after seed implantation. It was concluded that the seed had initially lodged in a branch of the right femoral vein, and then relocated to the left lung through the IVC, long after seed implantation.\\nCase 3: Seed disappearance from the right groin area four years after seed implantation. One day after seed implantation, a follow-up pelvic radiograph showed that a seed was located inferior to the right side of the pelvis (solid arrow) (A). Subsequent computed tomography showed that the seed had migrated to the right groin area (solid arrow) (B). Four years after seed implantation, a follow-up pelvic radiograph showed that the seed had disappeared from the pelvic area (C).\",\n \"In the 265 patients who received brachytherapy alone, the postimplant prostate D90 was 175.0 ± 1.3 Gy (mean ± standard error [SE]). In these 265 patients, the postimplant prostate D90 value was not significantly different between patients with and without seed migration (mean ± SE, 175.1 ± 2.3 Gy vs. 175.0 ± 1.5 Gy, respectively, p = 0.992). In 15 patients who had multiple migrated seeds, the postimplant prostate D90 ranged from 137.2 to 198.5 Gy (mean ± SE, 171.8 ± 5.5 Gy), which was not significantly different from that in patients without seed migration (p = 0.573).\\nIn the remaining two patients who received brachytherapy plus external beam radiotherapy, the postimplant prostate D90 values were 116.4 Gy and 132.6 Gy. These two patients had no seed migration.\",\n \" Incidence of seed migration We found that 0.36% of implanted seeds migrated to the chest in 20% of our patient population, similar to previous reports (Table 2). It has been reported that the incidence of seed migration to the chest can be as high as 55% per patient population and 0.98% per number of implanted seeds (Table 2) [9]. The variability of the incidence of seed migration to the chest among the reported results is considered to be attributed to different types of seeds (linked or loose), different designs of seed placement (intraprostatic or extraprostatic), different timings of follow-up radiographs, and different protocols of follow-up chest radiographs (orthogonal or AP alone) (Table 2) [1-5,9,13].\\nLiterature Survey of Seed Migration\\n*Linked seeds were restricted to needles placed at the periphery of the prostate implant. All seeds placed centrally, close to the urethra, were loose seeds. On average, 8-10 loose seeds were used.\\n†Of the total 125I seeds, 64% were linked seeds. Linked seeds were limited to the region of the capsule/periprostatic region. Loose seeds were utilized in the central aspect of the gland.\\n††9 patients, within 14 days; 75 patients, more than 30 days.\\n§11patients within 14 days; 61patients, more than 30 days.\\n||Of the 20 patients who had postimplant chest radiographs obtained within 14 days of brachytherapy, none had seeds detected in the lungs. Categorizing patients by isotope, for 125I, 18/75 (24.0%) and for 103Pd, 16/61 (26.2%) with chest radiographs beyond 30 days had seeds found in the lungs.\\n¶A mixture of linked and free seeds, with a median 74% of needles per case containing seeds in linked, with central and posterior needles typically containing loose seeds.\\nIn contrast, the incidence of seed migration to the abdomen and pelvis has been reported rarely (Table 2). A possible reason is that the American Brachytherapy Society does not specifically recommend follow-up abdominal and pelvic radiographs after seed implantation [6]. Therefore, in most institutions, follow-up abdominal and pelvic radiographs would not be undertaken routinely. However, we found that seed migration to the abdomen and pelvis occurred in 2.2% and 5.6%, respectively, of our patient population. Although the incidence of seed migration to the abdomen and pelvis is lower than that of seed migration to the chest, we would consider it advisable to undertake follow-up abdominal and pelvic radiographs after seed implantation.\\nWe found that 0.36% of implanted seeds migrated to the chest in 20% of our patient population, similar to previous reports (Table 2). It has been reported that the incidence of seed migration to the chest can be as high as 55% per patient population and 0.98% per number of implanted seeds (Table 2) [9]. The variability of the incidence of seed migration to the chest among the reported results is considered to be attributed to different types of seeds (linked or loose), different designs of seed placement (intraprostatic or extraprostatic), different timings of follow-up radiographs, and different protocols of follow-up chest radiographs (orthogonal or AP alone) (Table 2) [1-5,9,13].\\nLiterature Survey of Seed Migration\\n*Linked seeds were restricted to needles placed at the periphery of the prostate implant. All seeds placed centrally, close to the urethra, were loose seeds. On average, 8-10 loose seeds were used.\\n†Of the total 125I seeds, 64% were linked seeds. Linked seeds were limited to the region of the capsule/periprostatic region. Loose seeds were utilized in the central aspect of the gland.\\n††9 patients, within 14 days; 75 patients, more than 30 days.\\n§11patients within 14 days; 61patients, more than 30 days.\\n||Of the 20 patients who had postimplant chest radiographs obtained within 14 days of brachytherapy, none had seeds detected in the lungs. Categorizing patients by isotope, for 125I, 18/75 (24.0%) and for 103Pd, 16/61 (26.2%) with chest radiographs beyond 30 days had seeds found in the lungs.\\n¶A mixture of linked and free seeds, with a median 74% of needles per case containing seeds in linked, with central and posterior needles typically containing loose seeds.\\nIn contrast, the incidence of seed migration to the abdomen and pelvis has been reported rarely (Table 2). A possible reason is that the American Brachytherapy Society does not specifically recommend follow-up abdominal and pelvic radiographs after seed implantation [6]. Therefore, in most institutions, follow-up abdominal and pelvic radiographs would not be undertaken routinely. However, we found that seed migration to the abdomen and pelvis occurred in 2.2% and 5.6%, respectively, of our patient population. Although the incidence of seed migration to the abdomen and pelvis is lower than that of seed migration to the chest, we would consider it advisable to undertake follow-up abdominal and pelvic radiographs after seed implantation.\\n The dynamics of seed migration to the chest One day, two weeks, and three months after seed implantation, follow-up chest radiographs showed 22%, 88%, and 91%, respectively, of 69 seeds that eventually migrated to the chest. These results mean that 22%, 66%, and 2.9% of these 69 seeds migrated to the chest within one day, between one day and two weeks, and between two weeks and three months after seed implantation, respectively. About 90% of these 69 seeds migrated to the chest within two weeks after seed implantation. These results are similar to a previous report [13]. Merrick et al. have speculated that seed migration to the chest may be most likely to occur between 14 and 28 days after seed implantation [13]. Although we observed that several seeds migrated to the chest more than six months after seed implantation, this finding should be considered exceptional. Therefore, it is suggested that follow-up chest radiographs should be undertaken two weeks, or preferably a few months, after seed implantation to detect most seeds that will migrate to the chest.\\nOne day, two weeks, and three months after seed implantation, follow-up chest radiographs showed 22%, 88%, and 91%, respectively, of 69 seeds that eventually migrated to the chest. These results mean that 22%, 66%, and 2.9% of these 69 seeds migrated to the chest within one day, between one day and two weeks, and between two weeks and three months after seed implantation, respectively. About 90% of these 69 seeds migrated to the chest within two weeks after seed implantation. These results are similar to a previous report [13]. Merrick et al. have speculated that seed migration to the chest may be most likely to occur between 14 and 28 days after seed implantation [13]. Although we observed that several seeds migrated to the chest more than six months after seed implantation, this finding should be considered exceptional. Therefore, it is suggested that follow-up chest radiographs should be undertaken two weeks, or preferably a few months, after seed implantation to detect most seeds that will migrate to the chest.\\n Seed migration to the kidneys and Batson's vertebral plexus is not very rare The results of the present study show a total of four and five cases of seed migration to the kidneys and Batson's vertebral venous plexus, respectively, at only one institution, which suggests that such cases are not very rare. Meanwhile, in previous studies, a total of only four and four cases of seed migration to the kidneys and Batson's vertebral venous plexus, respectively, have been reported as rare cases, which is in disagreement with our conclusion [22,23,26,29]. The same number or more cases of seed migration to these areas were found in our single study compared with all previous studies. A possible explanation is that, in the present study, orthogonal chest radiographs, an abdominal radiograph, and a pelvic radiograph were undertaken routinely to detect seed migration to the chest, abdomen, and pelvis at several time points after seed implantation. Moreover, in all patients who had seed migration to the abdomen and pelvis, a CT scan was undertaken to identify the exact location of the migrated seeds. Consequently, more cases of seed migration to the kidneys and Batson's vertebral venous plexus were found in the present study. We speculate that some seed migration to the kidneys and Batson's vertebral venous plexus might have gone undetected in other institutions.\\nThe results of the present study show a total of four and five cases of seed migration to the kidneys and Batson's vertebral venous plexus, respectively, at only one institution, which suggests that such cases are not very rare. Meanwhile, in previous studies, a total of only four and four cases of seed migration to the kidneys and Batson's vertebral venous plexus, respectively, have been reported as rare cases, which is in disagreement with our conclusion [22,23,26,29]. The same number or more cases of seed migration to these areas were found in our single study compared with all previous studies. A possible explanation is that, in the present study, orthogonal chest radiographs, an abdominal radiograph, and a pelvic radiograph were undertaken routinely to detect seed migration to the chest, abdomen, and pelvis at several time points after seed implantation. Moreover, in all patients who had seed migration to the abdomen and pelvis, a CT scan was undertaken to identify the exact location of the migrated seeds. Consequently, more cases of seed migration to the kidneys and Batson's vertebral venous plexus were found in the present study. We speculate that some seed migration to the kidneys and Batson's vertebral venous plexus might have gone undetected in other institutions.\\n In some cases of seed migration to the kidneys, the mechanism is difficult to explain Previous investigators have explained seed migration to the kidneys as follows: migrated seeds in venous vessels would enter the systemic circulation through a right-to-left shunt, such as a patent foramen ovale or pulmonary arteriovenous malformation, and then embolize to a branch of the renal arteries [26]. We call this route of seed migration through a right-to-left shunt \\\"a paradoxical route.\\\"\\nThe present study has provided some interesting cases of seed migration to the kidneys, the mechanism of which is difficult to explain. In Case 1 of the present study, a seed had migrated to the right side of the middle abdomen, which was considered to be separated from the IVC, and then had relocated to the left kidney (Figure 1A-C). If the seed had initially embolized to an artery of the right side of the middle abdomen, such as a branch of the right renal artery, through a paradoxical route, it is difficult to explain how the seed would have relocated to the left kidney. In Case 2, two seeds had migrated to the same right kidney (Figure 2A-C). The mechanism of seed migration to the kidney through a paradoxical route is too complicated. Therefore, it is highly unlikely that two seeds would happen to migrate to the same right kidney through a paradoxical route by chance, although the possibility cannot be completely excluded. Other possible mechanisms should be proposed to explain how seed migration to the kidneys would have occurred in the two cases mentioned above.\\nPrevious investigators have explained seed migration to the kidneys as follows: migrated seeds in venous vessels would enter the systemic circulation through a right-to-left shunt, such as a patent foramen ovale or pulmonary arteriovenous malformation, and then embolize to a branch of the renal arteries [26]. We call this route of seed migration through a right-to-left shunt \\\"a paradoxical route.\\\"\\nThe present study has provided some interesting cases of seed migration to the kidneys, the mechanism of which is difficult to explain. In Case 1 of the present study, a seed had migrated to the right side of the middle abdomen, which was considered to be separated from the IVC, and then had relocated to the left kidney (Figure 1A-C). If the seed had initially embolized to an artery of the right side of the middle abdomen, such as a branch of the right renal artery, through a paradoxical route, it is difficult to explain how the seed would have relocated to the left kidney. In Case 2, two seeds had migrated to the same right kidney (Figure 2A-C). The mechanism of seed migration to the kidney through a paradoxical route is too complicated. Therefore, it is highly unlikely that two seeds would happen to migrate to the same right kidney through a paradoxical route by chance, although the possibility cannot be completely excluded. Other possible mechanisms should be proposed to explain how seed migration to the kidneys would have occurred in the two cases mentioned above.\\n Other possible mechanisms of seed migration We assume that seed movement in venous vessels would reflect not only the force of the blood flow but also the force of gravity, and that a seed sometimes would move in venous vessels against the blood flow, because the gravity could overcome the blood flow. The explanation is as follows. Intravascular missile migration following gunshot injury has been reported [30-34]. A missile sometimes moves in a retrograde fashion against the normal blood flow in major venous vessels including the IVC, and sometimes lodges in the renal vein and the hepatic vein [30-34]. It has been postulated that missile migration against the blood flow in venous vessels could occur because of gravity, the patient's position (upright) at the moment of wounding and/or positional changes of the body, the weight and shape of the missile, and possible low flow states [30]. Although the size and the weight of an 125I radioactive seed (4.5 mm in length, 0.8 mm in diameter, and about 10 mg in weight per seed) are smaller than those of a bullet, it is considered that a seed could move against the blood flow in major venous vessels because of gravity. The reasons are as follows. The specific gravity of the seed (about 4 g/mL) is much higher than that of blood, and the seed is less water resistant because of its rod-shaped structure. In addition, a seed would usually be located near the wall of major venous vessels due to gravity and the patient's position, and therefore, a seed would be affected by a relatively slow stream of blood near the vascular wall compared with that in the center of major venous vessels. It is known that in major venous vessels, the blood flow is generally laminar, and the distribution of velocity across the tube is parabolic [35]. Therefore, the velocity of blood flow decreases from the center toward the wall of the venous vessel tube [35].\\nThe mechanism of seed migration to various sites could be explained by the assumption that a seed could move in major venous vessels against the blood flow by gravity. In the present cases, seed migration to the kidneys, the liver, and the right groin area (probably a branch of the right femoral vein) (Case 3) would have occurred in venous vessels, partially in a retrograde fashion by gravity, without being passed through a paradoxical route. This explanation does not require the assumption that a patient would have a right-to-left shunt.\\nWe assume that seed movement in venous vessels would reflect not only the force of the blood flow but also the force of gravity, and that a seed sometimes would move in venous vessels against the blood flow, because the gravity could overcome the blood flow. The explanation is as follows. Intravascular missile migration following gunshot injury has been reported [30-34]. A missile sometimes moves in a retrograde fashion against the normal blood flow in major venous vessels including the IVC, and sometimes lodges in the renal vein and the hepatic vein [30-34]. It has been postulated that missile migration against the blood flow in venous vessels could occur because of gravity, the patient's position (upright) at the moment of wounding and/or positional changes of the body, the weight and shape of the missile, and possible low flow states [30]. Although the size and the weight of an 125I radioactive seed (4.5 mm in length, 0.8 mm in diameter, and about 10 mg in weight per seed) are smaller than those of a bullet, it is considered that a seed could move against the blood flow in major venous vessels because of gravity. The reasons are as follows. The specific gravity of the seed (about 4 g/mL) is much higher than that of blood, and the seed is less water resistant because of its rod-shaped structure. In addition, a seed would usually be located near the wall of major venous vessels due to gravity and the patient's position, and therefore, a seed would be affected by a relatively slow stream of blood near the vascular wall compared with that in the center of major venous vessels. It is known that in major venous vessels, the blood flow is generally laminar, and the distribution of velocity across the tube is parabolic [35]. Therefore, the velocity of blood flow decreases from the center toward the wall of the venous vessel tube [35].\\nThe mechanism of seed migration to various sites could be explained by the assumption that a seed could move in major venous vessels against the blood flow by gravity. In the present cases, seed migration to the kidneys, the liver, and the right groin area (probably a branch of the right femoral vein) (Case 3) would have occurred in venous vessels, partially in a retrograde fashion by gravity, without being passed through a paradoxical route. This explanation does not require the assumption that a patient would have a right-to-left shunt.\\n Influence of seed migration on postimplant dosimetry The postimplant prostate D90 was not significantly different between patients with and without seed migration. Moreover, in 15 patients who had multiple migrated seeds, the postimplant prostate D90 was relatively acceptable, and no supplemental seed implantation was required. These results indicate that seed migration did not have a significant effect on postimplant prostate dosimetry in the present study. Possible reasons are as follows. First, in most patients with seed migration, only one or two seeds had migrated, which would have less effect on the dosimetry of the prostate. Tapen et al. have suggested that the loss of a few seeds may not have a significant effect on dose homogeneity or total dose to the prostate [5]. Second, seed migration would have much less effect on the dosimetry of the prostate than other mechanisms of seed loss, such as seed misplacement to the seminal vesicle or perineum and being voided in the urine postoperatively. Merrick et al. have reported that seed migration to the chest accounted for only 10% of total seed loss from the prostate region, highlighting the importance of other mechanisms of loss [13].\\nThe postimplant prostate D90 was not significantly different between patients with and without seed migration. Moreover, in 15 patients who had multiple migrated seeds, the postimplant prostate D90 was relatively acceptable, and no supplemental seed implantation was required. These results indicate that seed migration did not have a significant effect on postimplant prostate dosimetry in the present study. Possible reasons are as follows. First, in most patients with seed migration, only one or two seeds had migrated, which would have less effect on the dosimetry of the prostate. Tapen et al. have suggested that the loss of a few seeds may not have a significant effect on dose homogeneity or total dose to the prostate [5]. Second, seed migration would have much less effect on the dosimetry of the prostate than other mechanisms of seed loss, such as seed misplacement to the seminal vesicle or perineum and being voided in the urine postoperatively. Merrick et al. have reported that seed migration to the chest accounted for only 10% of total seed loss from the prostate region, highlighting the importance of other mechanisms of loss [13].\\n Seed migration-related sequelae In the present study, none of the 66 patients had symptoms related to the migrated seeds. Although it has been reported that most patients with seed migration are asymptomatic, there have been a few reports of seed migration-related sequelae, such as a few anecdotal instances of cardiac arrhythmia, myocardial infarction, and radiation pneumonitis [21,36,37]. Therefore, it is important to try to reduce the incidence of seed migration. The use of seeds linked with an absorbable suture material has been associated with a dramatically decreased rate of seed migration, although a potentially higher risk of radiotoxicity to the urethra and rectum has been pointed out [1,3,5,38]. In our study, linked seeds were not administered, because they are not commercially available in our country at present.\\nIn the present study, none of the 66 patients had symptoms related to the migrated seeds. Although it has been reported that most patients with seed migration are asymptomatic, there have been a few reports of seed migration-related sequelae, such as a few anecdotal instances of cardiac arrhythmia, myocardial infarction, and radiation pneumonitis [21,36,37]. Therefore, it is important to try to reduce the incidence of seed migration. The use of seeds linked with an absorbable suture material has been associated with a dramatically decreased rate of seed migration, although a potentially higher risk of radiotoxicity to the urethra and rectum has been pointed out [1,3,5,38]. In our study, linked seeds were not administered, because they are not commercially available in our country at present.\",\n \"We found that 0.36% of implanted seeds migrated to the chest in 20% of our patient population, similar to previous reports (Table 2). It has been reported that the incidence of seed migration to the chest can be as high as 55% per patient population and 0.98% per number of implanted seeds (Table 2) [9]. The variability of the incidence of seed migration to the chest among the reported results is considered to be attributed to different types of seeds (linked or loose), different designs of seed placement (intraprostatic or extraprostatic), different timings of follow-up radiographs, and different protocols of follow-up chest radiographs (orthogonal or AP alone) (Table 2) [1-5,9,13].\\nLiterature Survey of Seed Migration\\n*Linked seeds were restricted to needles placed at the periphery of the prostate implant. All seeds placed centrally, close to the urethra, were loose seeds. On average, 8-10 loose seeds were used.\\n†Of the total 125I seeds, 64% were linked seeds. Linked seeds were limited to the region of the capsule/periprostatic region. Loose seeds were utilized in the central aspect of the gland.\\n††9 patients, within 14 days; 75 patients, more than 30 days.\\n§11patients within 14 days; 61patients, more than 30 days.\\n||Of the 20 patients who had postimplant chest radiographs obtained within 14 days of brachytherapy, none had seeds detected in the lungs. Categorizing patients by isotope, for 125I, 18/75 (24.0%) and for 103Pd, 16/61 (26.2%) with chest radiographs beyond 30 days had seeds found in the lungs.\\n¶A mixture of linked and free seeds, with a median 74% of needles per case containing seeds in linked, with central and posterior needles typically containing loose seeds.\\nIn contrast, the incidence of seed migration to the abdomen and pelvis has been reported rarely (Table 2). A possible reason is that the American Brachytherapy Society does not specifically recommend follow-up abdominal and pelvic radiographs after seed implantation [6]. Therefore, in most institutions, follow-up abdominal and pelvic radiographs would not be undertaken routinely. However, we found that seed migration to the abdomen and pelvis occurred in 2.2% and 5.6%, respectively, of our patient population. Although the incidence of seed migration to the abdomen and pelvis is lower than that of seed migration to the chest, we would consider it advisable to undertake follow-up abdominal and pelvic radiographs after seed implantation.\",\n \"One day, two weeks, and three months after seed implantation, follow-up chest radiographs showed 22%, 88%, and 91%, respectively, of 69 seeds that eventually migrated to the chest. These results mean that 22%, 66%, and 2.9% of these 69 seeds migrated to the chest within one day, between one day and two weeks, and between two weeks and three months after seed implantation, respectively. About 90% of these 69 seeds migrated to the chest within two weeks after seed implantation. These results are similar to a previous report [13]. Merrick et al. have speculated that seed migration to the chest may be most likely to occur between 14 and 28 days after seed implantation [13]. Although we observed that several seeds migrated to the chest more than six months after seed implantation, this finding should be considered exceptional. Therefore, it is suggested that follow-up chest radiographs should be undertaken two weeks, or preferably a few months, after seed implantation to detect most seeds that will migrate to the chest.\",\n \"The results of the present study show a total of four and five cases of seed migration to the kidneys and Batson's vertebral venous plexus, respectively, at only one institution, which suggests that such cases are not very rare. Meanwhile, in previous studies, a total of only four and four cases of seed migration to the kidneys and Batson's vertebral venous plexus, respectively, have been reported as rare cases, which is in disagreement with our conclusion [22,23,26,29]. The same number or more cases of seed migration to these areas were found in our single study compared with all previous studies. A possible explanation is that, in the present study, orthogonal chest radiographs, an abdominal radiograph, and a pelvic radiograph were undertaken routinely to detect seed migration to the chest, abdomen, and pelvis at several time points after seed implantation. Moreover, in all patients who had seed migration to the abdomen and pelvis, a CT scan was undertaken to identify the exact location of the migrated seeds. Consequently, more cases of seed migration to the kidneys and Batson's vertebral venous plexus were found in the present study. We speculate that some seed migration to the kidneys and Batson's vertebral venous plexus might have gone undetected in other institutions.\",\n \"Previous investigators have explained seed migration to the kidneys as follows: migrated seeds in venous vessels would enter the systemic circulation through a right-to-left shunt, such as a patent foramen ovale or pulmonary arteriovenous malformation, and then embolize to a branch of the renal arteries [26]. We call this route of seed migration through a right-to-left shunt \\\"a paradoxical route.\\\"\\nThe present study has provided some interesting cases of seed migration to the kidneys, the mechanism of which is difficult to explain. In Case 1 of the present study, a seed had migrated to the right side of the middle abdomen, which was considered to be separated from the IVC, and then had relocated to the left kidney (Figure 1A-C). If the seed had initially embolized to an artery of the right side of the middle abdomen, such as a branch of the right renal artery, through a paradoxical route, it is difficult to explain how the seed would have relocated to the left kidney. In Case 2, two seeds had migrated to the same right kidney (Figure 2A-C). The mechanism of seed migration to the kidney through a paradoxical route is too complicated. Therefore, it is highly unlikely that two seeds would happen to migrate to the same right kidney through a paradoxical route by chance, although the possibility cannot be completely excluded. Other possible mechanisms should be proposed to explain how seed migration to the kidneys would have occurred in the two cases mentioned above.\",\n \"We assume that seed movement in venous vessels would reflect not only the force of the blood flow but also the force of gravity, and that a seed sometimes would move in venous vessels against the blood flow, because the gravity could overcome the blood flow. The explanation is as follows. Intravascular missile migration following gunshot injury has been reported [30-34]. A missile sometimes moves in a retrograde fashion against the normal blood flow in major venous vessels including the IVC, and sometimes lodges in the renal vein and the hepatic vein [30-34]. It has been postulated that missile migration against the blood flow in venous vessels could occur because of gravity, the patient's position (upright) at the moment of wounding and/or positional changes of the body, the weight and shape of the missile, and possible low flow states [30]. Although the size and the weight of an 125I radioactive seed (4.5 mm in length, 0.8 mm in diameter, and about 10 mg in weight per seed) are smaller than those of a bullet, it is considered that a seed could move against the blood flow in major venous vessels because of gravity. The reasons are as follows. The specific gravity of the seed (about 4 g/mL) is much higher than that of blood, and the seed is less water resistant because of its rod-shaped structure. In addition, a seed would usually be located near the wall of major venous vessels due to gravity and the patient's position, and therefore, a seed would be affected by a relatively slow stream of blood near the vascular wall compared with that in the center of major venous vessels. It is known that in major venous vessels, the blood flow is generally laminar, and the distribution of velocity across the tube is parabolic [35]. Therefore, the velocity of blood flow decreases from the center toward the wall of the venous vessel tube [35].\\nThe mechanism of seed migration to various sites could be explained by the assumption that a seed could move in major venous vessels against the blood flow by gravity. In the present cases, seed migration to the kidneys, the liver, and the right groin area (probably a branch of the right femoral vein) (Case 3) would have occurred in venous vessels, partially in a retrograde fashion by gravity, without being passed through a paradoxical route. This explanation does not require the assumption that a patient would have a right-to-left shunt.\",\n \"The postimplant prostate D90 was not significantly different between patients with and without seed migration. Moreover, in 15 patients who had multiple migrated seeds, the postimplant prostate D90 was relatively acceptable, and no supplemental seed implantation was required. These results indicate that seed migration did not have a significant effect on postimplant prostate dosimetry in the present study. Possible reasons are as follows. First, in most patients with seed migration, only one or two seeds had migrated, which would have less effect on the dosimetry of the prostate. Tapen et al. have suggested that the loss of a few seeds may not have a significant effect on dose homogeneity or total dose to the prostate [5]. Second, seed migration would have much less effect on the dosimetry of the prostate than other mechanisms of seed loss, such as seed misplacement to the seminal vesicle or perineum and being voided in the urine postoperatively. Merrick et al. have reported that seed migration to the chest accounted for only 10% of total seed loss from the prostate region, highlighting the importance of other mechanisms of loss [13].\",\n \"In the present study, none of the 66 patients had symptoms related to the migrated seeds. Although it has been reported that most patients with seed migration are asymptomatic, there have been a few reports of seed migration-related sequelae, such as a few anecdotal instances of cardiac arrhythmia, myocardial infarction, and radiation pneumonitis [21,36,37]. Therefore, it is important to try to reduce the incidence of seed migration. The use of seeds linked with an absorbable suture material has been associated with a dramatically decreased rate of seed migration, although a potentially higher risk of radiotoxicity to the urethra and rectum has been pointed out [1,3,5,38]. In our study, linked seeds were not administered, because they are not commercially available in our country at present.\",\n \"In the present study, we determined the incidence of seed migration not only to the chest, but also to the abdomen and pelvis. Although the incidence of seed migration to the abdomen and pelvis was lower than that of seed migration to the chest, it would be advisable to undertake follow-up abdominal and pelvic radiographs after seed implantation. Seed migration to the chest occurred predominantly within two weeks and, therefore, it is suggested that follow-up chest radiographs should be undertaken two weeks or later after seed implantation. Seed migration to the kidneys and Batson's vertebral venous plexus was not very rare. Seed migration did not significantly affect postimplant prostate D90.\"\n]"},"other_sections_sec_types":{"kind":"list like","value":[null,null,null,null,null,null,null,null,null,null,null,null,null,null,null],"string":"[\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null\n]"},"all_sections_titles":{"kind":"list like","value":["Background","Methods","Results","Seeds that migrated to the abdomen (seven seeds in six patients)","Seeds that migrated to the pelvis (15 seeds in 15 patients)","Seed relocation four years after seed implantation","Postimplant dosimetric analysis","Discussion","Incidence of seed migration","The dynamics of seed migration to the chest","Seed migration to the kidneys and Batson's vertebral plexus is not very rare","In some cases of seed migration to the kidneys, the mechanism is difficult to explain","Other possible mechanisms of seed migration","Influence of seed migration on postimplant dosimetry","Seed migration-related sequelae","Conclusions"],"string":"[\n \"Background\",\n \"Methods\",\n \"Results\",\n \"Seeds that migrated to the abdomen (seven seeds in six patients)\",\n \"Seeds that migrated to the pelvis (15 seeds in 15 patients)\",\n \"Seed relocation four years after seed implantation\",\n \"Postimplant dosimetric analysis\",\n \"Discussion\",\n \"Incidence of seed migration\",\n \"The dynamics of seed migration to the chest\",\n \"Seed migration to the kidneys and Batson's vertebral plexus is not very rare\",\n \"In some cases of seed migration to the kidneys, the mechanism is difficult to explain\",\n \"Other possible mechanisms of seed migration\",\n \"Influence of seed migration on postimplant dosimetry\",\n \"Seed migration-related sequelae\",\n \"Conclusions\"\n]"},"all_sections_texts":{"kind":"list like","value":["Seed migration is a well-recognized event that occurs after transperineal interstitial prostate brachytherapy, and it is observed more often with loose seeds than with linked seeds [1-5].\nIt is well known that the most frequent site of seed migration is the chest. The American Brachytherapy Society has advised that a chest radiograph should be undertaken at the first follow-up visit to scan the lungs for embolized seeds [6]. Consequently, the incidence of seed migration to the chest has been well reported [1,2,4,5,7-19]. However, documentation of the incidence of seed migration to the abdomen and pelvis is rare. Rare cases of seed migration to a coronary artery, the right ventricle, the liver, the kidneys, Batson's vertebral venous plexus, and the left testicular vein have been reported [20-26]. However, it has never been fully determined whether seed migration to these locations is really rare.\nThe primary purposes of the present study were to determine the incidence of seed migration not only to the chest, but also to the abdomen and the pelvis at our institution and to identify the exact location of the seeds that had migrated to the abdomen and pelvis with computed tomography (CT). The secondary purpose was to determine the impact of seed migration on postimplant dosimetry.","We reviewed the records of 267 patients who underwent transperineal interstitial prostate brachytherapy with loose 125I seeds for clinical T1/T2 prostate cancer at our institution. Table 1 details the characteristics of all 267 patients. Two patients (0.75%) received brachytherapy plus external beam radiotherapy (45 Gy in 1.8 Gy fractions). One hundred twenty-three of the 267 (46.1%) patients also underwent neoadjuvant hormonal therapy (NHT), which consisted of luteinizing hormone-releasing hormone agonist and antiandrogens. NHT was generally undertaken in patients with a prostate volume >40 cc or those with pubic arch interference by transrectal ultrasound (TRUS) at the preimplant volume study [27].\nPatient Characteristics (N = 267)\nData are presented as mean ± standard error (range) or number (percent) of patients.\nAbbreviations: PSA = prostate-specific antigen; TRUS = transrectal ultrasound\nOne month before seed implantation, a preplan was obtained with TRUS images taken at 5 mm intervals from the base to the apex of the prostate with the patient in the dorsal lithotomy position. The planning target volume included the prostate gland, with a margin of 3 mm anteriorly and laterally and 5 mm in the cranial and caudal directions. No margin was added posteriorly at the rectal interface. Treatment planning used a peripheral or a modified peripheral approach. For the 265 patients who received brachytherapy alone, the prescribed brachytherapy dose was 145 Gy and 160 Gy for the first 163 patients and the subsequent 102 patients, respectively. For the remaining two patients who received brachytherapy plus external beam radiotherapy, the prescribed brachytherapy dose was 110 Gy. TG 43 formalism was used in the preplanning and postimplant dosimetry analyses [28]. All 267 patients were treated with loose 125I radioactive seeds with a Mick applicator (Mick Radio-Nuclear Instruments, Bronx, NY). To ensure that no seeds were left in the bladder, postoperative fluoroscopic images were obtained. Prior to discharge, postoperative surveys of voided urine were conducted to detect voided seeds.\nOrthogonal chest radiographs, an abdominal radiograph, and a pelvic radiograph were undertaken to document the occurrence and sites of seed migration one day after seed implantation. These follow-up radiographs were undertaken routinely at each outpatient visit. Patients returned to our outpatient clinic two weeks and three months after seed implantation, then at three-month intervals for the first three years and at six-month intervals thereafter. Seed migration to the chest and the abdomen was recorded when one or more seeds were visualized on orthogonal chest radiographs and the anteroposterior (AP) abdominal radiograph, respectively. Seed migration to the pelvis was recorded when one or more seeds were separated from the main seed cluster on an AP pelvic radiograph. However, seeds placed into the bladder and the seminal vesicles or seeds placed inferior to the prostate by mistake were not scored as migrated. Seeds voided in the urine were not scored as migrated. Subsequently, all patients who had seed migration to the abdomen and pelvis underwent a CT scan to identify the exact location of the migrated seeds. The incidence of seed migration to the chest, abdomen, and pelvis was calculated.\nPostimplant dosimetric analysis by CT was performed one month after seed implantation. The seed count in the region of the prostate gland was determined on the AP pelvic radiographs obtained two weeks after seed implantation. The postimplant prostate D90 (the dose received by 90% of the volume of the prostate) value was compared between patients with and without seed migration. Statistical analysis was performed with Student's t-test. A p value of <0.05 was considered statistically significant.","In total, 19,236 seeds were implanted in 267 patients. All 267 patients underwent follow-up radiographs. Median follow-up was 41 months (range, 8.5-76 months).\nAt one day after seed implantation, follow-up radiographs demonstrated that 41 of the 19,236 (0.21%) seeds migrated in 37 of the 267 (13.9%) patients: three seeds in one patient, two seeds in each of two patients, and a single seed in each of the remaining 34 patients. Fifteen (0.078%) seeds migrated to the chest in 15 (5.6%) patients. One (0.0052%) seed migrated to the abdomen in one (0.37%) patient. Twenty-five (0.13%) seeds migrated to the pelvis in 23 (8.6%) patients.\nAt two weeks after seed implantation, 85 of the 19,236 (0.44%) seeds migrated in 61 of the 267 (22.8%) patients: seven seeds in one patient, three seeds in each of four patients, two seeds in each of 10 patients, and a single seed in each of the remaining 46 patients. Sixty-one (0.32%) seeds migrated to the chest in 48 (18.0%) patients. Seven (0.036%) seeds migrated to the abdomen in six (2.2%) patients. Seventeen (0.088%) seeds migrated to the pelvis in 16 (6.0%) patients.\nAt three months after seed implantation, 87 of the 19,236 (0.45%) seeds migrated in 63 of the 267 (23.6%) patients: seven seeds in one patient, three seeds in each of four patients, two seeds in each of 10 patients, and a single seed in each of the remaining 48 patients. Sixty-three (0.33%) seeds migrated to the chest in 50 (18.7%) patients. Seven (0.036%) seeds migrated to the abdomen in six (2.2%) patients. Seventeen (0.088%) seeds migrated to the pelvis in 16 (6.0%) patients.\nAlthough seed migration occurred predominantly within two weeks after seed implantation, eventually, six seeds were found to migrate or relocate to the chest long after seed implantation: four seeds were found to have migrated to the chest at a median of 27 months (range 9.1-16 months), and two seeds were found to have relocated from the pelvis to the chest at 6.1 and 48 months after seed implantation, respectively. In these patients, follow-up radiographs were undertaken routinely at every outpatient visit; however, migration or relocation of these seeds was not found at the previous visit. Meanwhile, no seed relocation from the chest to other sites was observed in the present study.\nEventually, 91 of the 19,236 (0.47%) seeds migrated in 66 of the 267 (24.7%) patients: seven seeds in one patient, three seeds in each of five patients, two seeds in each of nine patients, and a single seed in each of the remaining 51 patients. Sixty-nine (0.36%) seeds migrated to the chest in 54 (20.2%) patients. Seven (0.036%) seeds migrated to the abdomen in six (2.2%) patients. Fifteen (0.078%) seeds migrated to the pelvis in 15 (5.6%) patients. All 66 patients were informed of seed migration; none of these patients had symptoms related to the migrated seeds.\n Seeds that migrated to the abdomen (seven seeds in six patients) Two of the 19,236 (0.010%) seeds migrated to the liver in two of the 267 (0.75%) patients: a single seed migrated to the liver in each of two patients. Five (0.026%) seeds migrated to the kidneys in four (1.5%) patients: two seeds migrated to the same kidney in one patient, and a single seed migrated to the kidney in each of the remaining three patients. In one patient (Case 1), one day after seed implantation, an abdominal radiograph showed that a seed had migrated to the right side of the middle abdomen, which was considered to be separated from the inferior vena cava (IVC) (Figure 1A). However, two weeks after seed implantation, an abdominal radiograph showed that the seed had disappeared from the right side of the middle abdomen, and showed a seed that had migrated to the left side of the middle abdomen (Figure 1B). On pelvic radiographs, there were no changes in number of seeds that had been implanted into the prostate between one day and two weeks after seed implantation. It was concluded that the seed had relocated from the right side of the middle abdomen to the left side of the middle abdomen. A subsequent abdominal CT demonstrated that the seed had migrated to the left kidney (Figure 1C). In another patient (Case 2), two weeks after seed implantation, an abdominal radiograph showed that two seeds had migrated to the same right kidney (Figure 2A-C).\nCase 1: Relocation from the right side of the middle abdomen to the left kidney. One day after seed implantation, a follow-up abdominal radiograph showed that a seed had migrated to the right side of the middle abdomen (solid arrow) (A). Two weeks later, the seed had relocated from the right side of the middle abdomen to the left side of the middle abdomen (solid arrow) (B). Subsequent computed tomography showed that the seed had migrated to the left kidney (solid arrow) (C).\nCase 2: Migration of two seeds to the same right kidney. Two weeks after seed implantation, a follow-up abdominal radiograph showed that two seeds had migrated to the right side of the middle abdomen (solid arrows) (A). Subsequent computed tomography showed that these two seeds had migrated to the same right kidney (solid arrows) (B,C).\nTwo of the 19,236 (0.010%) seeds migrated to the liver in two of the 267 (0.75%) patients: a single seed migrated to the liver in each of two patients. Five (0.026%) seeds migrated to the kidneys in four (1.5%) patients: two seeds migrated to the same kidney in one patient, and a single seed migrated to the kidney in each of the remaining three patients. In one patient (Case 1), one day after seed implantation, an abdominal radiograph showed that a seed had migrated to the right side of the middle abdomen, which was considered to be separated from the inferior vena cava (IVC) (Figure 1A). However, two weeks after seed implantation, an abdominal radiograph showed that the seed had disappeared from the right side of the middle abdomen, and showed a seed that had migrated to the left side of the middle abdomen (Figure 1B). On pelvic radiographs, there were no changes in number of seeds that had been implanted into the prostate between one day and two weeks after seed implantation. It was concluded that the seed had relocated from the right side of the middle abdomen to the left side of the middle abdomen. A subsequent abdominal CT demonstrated that the seed had migrated to the left kidney (Figure 1C). In another patient (Case 2), two weeks after seed implantation, an abdominal radiograph showed that two seeds had migrated to the same right kidney (Figure 2A-C).\nCase 1: Relocation from the right side of the middle abdomen to the left kidney. One day after seed implantation, a follow-up abdominal radiograph showed that a seed had migrated to the right side of the middle abdomen (solid arrow) (A). Two weeks later, the seed had relocated from the right side of the middle abdomen to the left side of the middle abdomen (solid arrow) (B). Subsequent computed tomography showed that the seed had migrated to the left kidney (solid arrow) (C).\nCase 2: Migration of two seeds to the same right kidney. Two weeks after seed implantation, a follow-up abdominal radiograph showed that two seeds had migrated to the right side of the middle abdomen (solid arrows) (A). Subsequent computed tomography showed that these two seeds had migrated to the same right kidney (solid arrows) (B,C).\n Seeds that migrated to the pelvis (15 seeds in 15 patients) A single seed migrated to the pelvis in each of 15 patients. Five of the 19,236 (0.026%) seeds migrated to Batson's vertebral venous plexus in five of the 267 (1.9%) patients. Four (0.021%) seeds migrated to the sacral venous plexus in four (1.5%) patients. Two (0.010%) seeds migrated to the iliac veins in two (0.75%) patients. Two (0.010%) seeds migrated to the right ischial bone in two (0.75%) patients. Two (0.010%) seeds migrated to the obturator internus muscles in two (0.75%) patients.\nA single seed migrated to the pelvis in each of 15 patients. Five of the 19,236 (0.026%) seeds migrated to Batson's vertebral venous plexus in five of the 267 (1.9%) patients. Four (0.021%) seeds migrated to the sacral venous plexus in four (1.5%) patients. Two (0.010%) seeds migrated to the iliac veins in two (0.75%) patients. Two (0.010%) seeds migrated to the right ischial bone in two (0.75%) patients. Two (0.010%) seeds migrated to the obturator internus muscles in two (0.75%) patients.\n Seed relocation four years after seed implantation In one patient (Case 3), seed relocation was found four years after seed implantation. A seed had migrated to the right groin area one day after seed implantation (Figure 3A-B). Three years and six months after seed implantation, a pelvic radiograph showed that the seed was in the same location. However, four years after seed implantation, a pelvic radiograph showed that the seed had disappeared from the right groin area (Figure 3C), and a chest radiograph showed a seed that had migrated to the left lung, which was not found at three years and six months after seed implantation. It was concluded that the seed had initially lodged in a branch of the right femoral vein, and then relocated to the left lung through the IVC, long after seed implantation.\nCase 3: Seed disappearance from the right groin area four years after seed implantation. One day after seed implantation, a follow-up pelvic radiograph showed that a seed was located inferior to the right side of the pelvis (solid arrow) (A). Subsequent computed tomography showed that the seed had migrated to the right groin area (solid arrow) (B). Four years after seed implantation, a follow-up pelvic radiograph showed that the seed had disappeared from the pelvic area (C).\nIn one patient (Case 3), seed relocation was found four years after seed implantation. A seed had migrated to the right groin area one day after seed implantation (Figure 3A-B). Three years and six months after seed implantation, a pelvic radiograph showed that the seed was in the same location. However, four years after seed implantation, a pelvic radiograph showed that the seed had disappeared from the right groin area (Figure 3C), and a chest radiograph showed a seed that had migrated to the left lung, which was not found at three years and six months after seed implantation. It was concluded that the seed had initially lodged in a branch of the right femoral vein, and then relocated to the left lung through the IVC, long after seed implantation.\nCase 3: Seed disappearance from the right groin area four years after seed implantation. One day after seed implantation, a follow-up pelvic radiograph showed that a seed was located inferior to the right side of the pelvis (solid arrow) (A). Subsequent computed tomography showed that the seed had migrated to the right groin area (solid arrow) (B). Four years after seed implantation, a follow-up pelvic radiograph showed that the seed had disappeared from the pelvic area (C).\n Postimplant dosimetric analysis In the 265 patients who received brachytherapy alone, the postimplant prostate D90 was 175.0 ± 1.3 Gy (mean ± standard error [SE]). In these 265 patients, the postimplant prostate D90 value was not significantly different between patients with and without seed migration (mean ± SE, 175.1 ± 2.3 Gy vs. 175.0 ± 1.5 Gy, respectively, p = 0.992). In 15 patients who had multiple migrated seeds, the postimplant prostate D90 ranged from 137.2 to 198.5 Gy (mean ± SE, 171.8 ± 5.5 Gy), which was not significantly different from that in patients without seed migration (p = 0.573).\nIn the remaining two patients who received brachytherapy plus external beam radiotherapy, the postimplant prostate D90 values were 116.4 Gy and 132.6 Gy. These two patients had no seed migration.\nIn the 265 patients who received brachytherapy alone, the postimplant prostate D90 was 175.0 ± 1.3 Gy (mean ± standard error [SE]). In these 265 patients, the postimplant prostate D90 value was not significantly different between patients with and without seed migration (mean ± SE, 175.1 ± 2.3 Gy vs. 175.0 ± 1.5 Gy, respectively, p = 0.992). In 15 patients who had multiple migrated seeds, the postimplant prostate D90 ranged from 137.2 to 198.5 Gy (mean ± SE, 171.8 ± 5.5 Gy), which was not significantly different from that in patients without seed migration (p = 0.573).\nIn the remaining two patients who received brachytherapy plus external beam radiotherapy, the postimplant prostate D90 values were 116.4 Gy and 132.6 Gy. These two patients had no seed migration.","Two of the 19,236 (0.010%) seeds migrated to the liver in two of the 267 (0.75%) patients: a single seed migrated to the liver in each of two patients. Five (0.026%) seeds migrated to the kidneys in four (1.5%) patients: two seeds migrated to the same kidney in one patient, and a single seed migrated to the kidney in each of the remaining three patients. In one patient (Case 1), one day after seed implantation, an abdominal radiograph showed that a seed had migrated to the right side of the middle abdomen, which was considered to be separated from the inferior vena cava (IVC) (Figure 1A). However, two weeks after seed implantation, an abdominal radiograph showed that the seed had disappeared from the right side of the middle abdomen, and showed a seed that had migrated to the left side of the middle abdomen (Figure 1B). On pelvic radiographs, there were no changes in number of seeds that had been implanted into the prostate between one day and two weeks after seed implantation. It was concluded that the seed had relocated from the right side of the middle abdomen to the left side of the middle abdomen. A subsequent abdominal CT demonstrated that the seed had migrated to the left kidney (Figure 1C). In another patient (Case 2), two weeks after seed implantation, an abdominal radiograph showed that two seeds had migrated to the same right kidney (Figure 2A-C).\nCase 1: Relocation from the right side of the middle abdomen to the left kidney. One day after seed implantation, a follow-up abdominal radiograph showed that a seed had migrated to the right side of the middle abdomen (solid arrow) (A). Two weeks later, the seed had relocated from the right side of the middle abdomen to the left side of the middle abdomen (solid arrow) (B). Subsequent computed tomography showed that the seed had migrated to the left kidney (solid arrow) (C).\nCase 2: Migration of two seeds to the same right kidney. Two weeks after seed implantation, a follow-up abdominal radiograph showed that two seeds had migrated to the right side of the middle abdomen (solid arrows) (A). Subsequent computed tomography showed that these two seeds had migrated to the same right kidney (solid arrows) (B,C).","A single seed migrated to the pelvis in each of 15 patients. Five of the 19,236 (0.026%) seeds migrated to Batson's vertebral venous plexus in five of the 267 (1.9%) patients. Four (0.021%) seeds migrated to the sacral venous plexus in four (1.5%) patients. Two (0.010%) seeds migrated to the iliac veins in two (0.75%) patients. Two (0.010%) seeds migrated to the right ischial bone in two (0.75%) patients. Two (0.010%) seeds migrated to the obturator internus muscles in two (0.75%) patients.","In one patient (Case 3), seed relocation was found four years after seed implantation. A seed had migrated to the right groin area one day after seed implantation (Figure 3A-B). Three years and six months after seed implantation, a pelvic radiograph showed that the seed was in the same location. However, four years after seed implantation, a pelvic radiograph showed that the seed had disappeared from the right groin area (Figure 3C), and a chest radiograph showed a seed that had migrated to the left lung, which was not found at three years and six months after seed implantation. It was concluded that the seed had initially lodged in a branch of the right femoral vein, and then relocated to the left lung through the IVC, long after seed implantation.\nCase 3: Seed disappearance from the right groin area four years after seed implantation. One day after seed implantation, a follow-up pelvic radiograph showed that a seed was located inferior to the right side of the pelvis (solid arrow) (A). Subsequent computed tomography showed that the seed had migrated to the right groin area (solid arrow) (B). Four years after seed implantation, a follow-up pelvic radiograph showed that the seed had disappeared from the pelvic area (C).","In the 265 patients who received brachytherapy alone, the postimplant prostate D90 was 175.0 ± 1.3 Gy (mean ± standard error [SE]). In these 265 patients, the postimplant prostate D90 value was not significantly different between patients with and without seed migration (mean ± SE, 175.1 ± 2.3 Gy vs. 175.0 ± 1.5 Gy, respectively, p = 0.992). In 15 patients who had multiple migrated seeds, the postimplant prostate D90 ranged from 137.2 to 198.5 Gy (mean ± SE, 171.8 ± 5.5 Gy), which was not significantly different from that in patients without seed migration (p = 0.573).\nIn the remaining two patients who received brachytherapy plus external beam radiotherapy, the postimplant prostate D90 values were 116.4 Gy and 132.6 Gy. These two patients had no seed migration."," Incidence of seed migration We found that 0.36% of implanted seeds migrated to the chest in 20% of our patient population, similar to previous reports (Table 2). It has been reported that the incidence of seed migration to the chest can be as high as 55% per patient population and 0.98% per number of implanted seeds (Table 2) [9]. The variability of the incidence of seed migration to the chest among the reported results is considered to be attributed to different types of seeds (linked or loose), different designs of seed placement (intraprostatic or extraprostatic), different timings of follow-up radiographs, and different protocols of follow-up chest radiographs (orthogonal or AP alone) (Table 2) [1-5,9,13].\nLiterature Survey of Seed Migration\n*Linked seeds were restricted to needles placed at the periphery of the prostate implant. All seeds placed centrally, close to the urethra, were loose seeds. On average, 8-10 loose seeds were used.\n†Of the total 125I seeds, 64% were linked seeds. Linked seeds were limited to the region of the capsule/periprostatic region. Loose seeds were utilized in the central aspect of the gland.\n††9 patients, within 14 days; 75 patients, more than 30 days.\n§11patients within 14 days; 61patients, more than 30 days.\n||Of the 20 patients who had postimplant chest radiographs obtained within 14 days of brachytherapy, none had seeds detected in the lungs. Categorizing patients by isotope, for 125I, 18/75 (24.0%) and for 103Pd, 16/61 (26.2%) with chest radiographs beyond 30 days had seeds found in the lungs.\n¶A mixture of linked and free seeds, with a median 74% of needles per case containing seeds in linked, with central and posterior needles typically containing loose seeds.\nIn contrast, the incidence of seed migration to the abdomen and pelvis has been reported rarely (Table 2). A possible reason is that the American Brachytherapy Society does not specifically recommend follow-up abdominal and pelvic radiographs after seed implantation [6]. Therefore, in most institutions, follow-up abdominal and pelvic radiographs would not be undertaken routinely. However, we found that seed migration to the abdomen and pelvis occurred in 2.2% and 5.6%, respectively, of our patient population. Although the incidence of seed migration to the abdomen and pelvis is lower than that of seed migration to the chest, we would consider it advisable to undertake follow-up abdominal and pelvic radiographs after seed implantation.\nWe found that 0.36% of implanted seeds migrated to the chest in 20% of our patient population, similar to previous reports (Table 2). It has been reported that the incidence of seed migration to the chest can be as high as 55% per patient population and 0.98% per number of implanted seeds (Table 2) [9]. The variability of the incidence of seed migration to the chest among the reported results is considered to be attributed to different types of seeds (linked or loose), different designs of seed placement (intraprostatic or extraprostatic), different timings of follow-up radiographs, and different protocols of follow-up chest radiographs (orthogonal or AP alone) (Table 2) [1-5,9,13].\nLiterature Survey of Seed Migration\n*Linked seeds were restricted to needles placed at the periphery of the prostate implant. All seeds placed centrally, close to the urethra, were loose seeds. On average, 8-10 loose seeds were used.\n†Of the total 125I seeds, 64% were linked seeds. Linked seeds were limited to the region of the capsule/periprostatic region. Loose seeds were utilized in the central aspect of the gland.\n††9 patients, within 14 days; 75 patients, more than 30 days.\n§11patients within 14 days; 61patients, more than 30 days.\n||Of the 20 patients who had postimplant chest radiographs obtained within 14 days of brachytherapy, none had seeds detected in the lungs. Categorizing patients by isotope, for 125I, 18/75 (24.0%) and for 103Pd, 16/61 (26.2%) with chest radiographs beyond 30 days had seeds found in the lungs.\n¶A mixture of linked and free seeds, with a median 74% of needles per case containing seeds in linked, with central and posterior needles typically containing loose seeds.\nIn contrast, the incidence of seed migration to the abdomen and pelvis has been reported rarely (Table 2). A possible reason is that the American Brachytherapy Society does not specifically recommend follow-up abdominal and pelvic radiographs after seed implantation [6]. Therefore, in most institutions, follow-up abdominal and pelvic radiographs would not be undertaken routinely. However, we found that seed migration to the abdomen and pelvis occurred in 2.2% and 5.6%, respectively, of our patient population. Although the incidence of seed migration to the abdomen and pelvis is lower than that of seed migration to the chest, we would consider it advisable to undertake follow-up abdominal and pelvic radiographs after seed implantation.\n The dynamics of seed migration to the chest One day, two weeks, and three months after seed implantation, follow-up chest radiographs showed 22%, 88%, and 91%, respectively, of 69 seeds that eventually migrated to the chest. These results mean that 22%, 66%, and 2.9% of these 69 seeds migrated to the chest within one day, between one day and two weeks, and between two weeks and three months after seed implantation, respectively. About 90% of these 69 seeds migrated to the chest within two weeks after seed implantation. These results are similar to a previous report [13]. Merrick et al. have speculated that seed migration to the chest may be most likely to occur between 14 and 28 days after seed implantation [13]. Although we observed that several seeds migrated to the chest more than six months after seed implantation, this finding should be considered exceptional. Therefore, it is suggested that follow-up chest radiographs should be undertaken two weeks, or preferably a few months, after seed implantation to detect most seeds that will migrate to the chest.\nOne day, two weeks, and three months after seed implantation, follow-up chest radiographs showed 22%, 88%, and 91%, respectively, of 69 seeds that eventually migrated to the chest. These results mean that 22%, 66%, and 2.9% of these 69 seeds migrated to the chest within one day, between one day and two weeks, and between two weeks and three months after seed implantation, respectively. About 90% of these 69 seeds migrated to the chest within two weeks after seed implantation. These results are similar to a previous report [13]. Merrick et al. have speculated that seed migration to the chest may be most likely to occur between 14 and 28 days after seed implantation [13]. Although we observed that several seeds migrated to the chest more than six months after seed implantation, this finding should be considered exceptional. Therefore, it is suggested that follow-up chest radiographs should be undertaken two weeks, or preferably a few months, after seed implantation to detect most seeds that will migrate to the chest.\n Seed migration to the kidneys and Batson's vertebral plexus is not very rare The results of the present study show a total of four and five cases of seed migration to the kidneys and Batson's vertebral venous plexus, respectively, at only one institution, which suggests that such cases are not very rare. Meanwhile, in previous studies, a total of only four and four cases of seed migration to the kidneys and Batson's vertebral venous plexus, respectively, have been reported as rare cases, which is in disagreement with our conclusion [22,23,26,29]. The same number or more cases of seed migration to these areas were found in our single study compared with all previous studies. A possible explanation is that, in the present study, orthogonal chest radiographs, an abdominal radiograph, and a pelvic radiograph were undertaken routinely to detect seed migration to the chest, abdomen, and pelvis at several time points after seed implantation. Moreover, in all patients who had seed migration to the abdomen and pelvis, a CT scan was undertaken to identify the exact location of the migrated seeds. Consequently, more cases of seed migration to the kidneys and Batson's vertebral venous plexus were found in the present study. We speculate that some seed migration to the kidneys and Batson's vertebral venous plexus might have gone undetected in other institutions.\nThe results of the present study show a total of four and five cases of seed migration to the kidneys and Batson's vertebral venous plexus, respectively, at only one institution, which suggests that such cases are not very rare. Meanwhile, in previous studies, a total of only four and four cases of seed migration to the kidneys and Batson's vertebral venous plexus, respectively, have been reported as rare cases, which is in disagreement with our conclusion [22,23,26,29]. The same number or more cases of seed migration to these areas were found in our single study compared with all previous studies. A possible explanation is that, in the present study, orthogonal chest radiographs, an abdominal radiograph, and a pelvic radiograph were undertaken routinely to detect seed migration to the chest, abdomen, and pelvis at several time points after seed implantation. Moreover, in all patients who had seed migration to the abdomen and pelvis, a CT scan was undertaken to identify the exact location of the migrated seeds. Consequently, more cases of seed migration to the kidneys and Batson's vertebral venous plexus were found in the present study. We speculate that some seed migration to the kidneys and Batson's vertebral venous plexus might have gone undetected in other institutions.\n In some cases of seed migration to the kidneys, the mechanism is difficult to explain Previous investigators have explained seed migration to the kidneys as follows: migrated seeds in venous vessels would enter the systemic circulation through a right-to-left shunt, such as a patent foramen ovale or pulmonary arteriovenous malformation, and then embolize to a branch of the renal arteries [26]. We call this route of seed migration through a right-to-left shunt \"a paradoxical route.\"\nThe present study has provided some interesting cases of seed migration to the kidneys, the mechanism of which is difficult to explain. In Case 1 of the present study, a seed had migrated to the right side of the middle abdomen, which was considered to be separated from the IVC, and then had relocated to the left kidney (Figure 1A-C). If the seed had initially embolized to an artery of the right side of the middle abdomen, such as a branch of the right renal artery, through a paradoxical route, it is difficult to explain how the seed would have relocated to the left kidney. In Case 2, two seeds had migrated to the same right kidney (Figure 2A-C). The mechanism of seed migration to the kidney through a paradoxical route is too complicated. Therefore, it is highly unlikely that two seeds would happen to migrate to the same right kidney through a paradoxical route by chance, although the possibility cannot be completely excluded. Other possible mechanisms should be proposed to explain how seed migration to the kidneys would have occurred in the two cases mentioned above.\nPrevious investigators have explained seed migration to the kidneys as follows: migrated seeds in venous vessels would enter the systemic circulation through a right-to-left shunt, such as a patent foramen ovale or pulmonary arteriovenous malformation, and then embolize to a branch of the renal arteries [26]. We call this route of seed migration through a right-to-left shunt \"a paradoxical route.\"\nThe present study has provided some interesting cases of seed migration to the kidneys, the mechanism of which is difficult to explain. In Case 1 of the present study, a seed had migrated to the right side of the middle abdomen, which was considered to be separated from the IVC, and then had relocated to the left kidney (Figure 1A-C). If the seed had initially embolized to an artery of the right side of the middle abdomen, such as a branch of the right renal artery, through a paradoxical route, it is difficult to explain how the seed would have relocated to the left kidney. In Case 2, two seeds had migrated to the same right kidney (Figure 2A-C). The mechanism of seed migration to the kidney through a paradoxical route is too complicated. Therefore, it is highly unlikely that two seeds would happen to migrate to the same right kidney through a paradoxical route by chance, although the possibility cannot be completely excluded. Other possible mechanisms should be proposed to explain how seed migration to the kidneys would have occurred in the two cases mentioned above.\n Other possible mechanisms of seed migration We assume that seed movement in venous vessels would reflect not only the force of the blood flow but also the force of gravity, and that a seed sometimes would move in venous vessels against the blood flow, because the gravity could overcome the blood flow. The explanation is as follows. Intravascular missile migration following gunshot injury has been reported [30-34]. A missile sometimes moves in a retrograde fashion against the normal blood flow in major venous vessels including the IVC, and sometimes lodges in the renal vein and the hepatic vein [30-34]. It has been postulated that missile migration against the blood flow in venous vessels could occur because of gravity, the patient's position (upright) at the moment of wounding and/or positional changes of the body, the weight and shape of the missile, and possible low flow states [30]. Although the size and the weight of an 125I radioactive seed (4.5 mm in length, 0.8 mm in diameter, and about 10 mg in weight per seed) are smaller than those of a bullet, it is considered that a seed could move against the blood flow in major venous vessels because of gravity. The reasons are as follows. The specific gravity of the seed (about 4 g/mL) is much higher than that of blood, and the seed is less water resistant because of its rod-shaped structure. In addition, a seed would usually be located near the wall of major venous vessels due to gravity and the patient's position, and therefore, a seed would be affected by a relatively slow stream of blood near the vascular wall compared with that in the center of major venous vessels. It is known that in major venous vessels, the blood flow is generally laminar, and the distribution of velocity across the tube is parabolic [35]. Therefore, the velocity of blood flow decreases from the center toward the wall of the venous vessel tube [35].\nThe mechanism of seed migration to various sites could be explained by the assumption that a seed could move in major venous vessels against the blood flow by gravity. In the present cases, seed migration to the kidneys, the liver, and the right groin area (probably a branch of the right femoral vein) (Case 3) would have occurred in venous vessels, partially in a retrograde fashion by gravity, without being passed through a paradoxical route. This explanation does not require the assumption that a patient would have a right-to-left shunt.\nWe assume that seed movement in venous vessels would reflect not only the force of the blood flow but also the force of gravity, and that a seed sometimes would move in venous vessels against the blood flow, because the gravity could overcome the blood flow. The explanation is as follows. Intravascular missile migration following gunshot injury has been reported [30-34]. A missile sometimes moves in a retrograde fashion against the normal blood flow in major venous vessels including the IVC, and sometimes lodges in the renal vein and the hepatic vein [30-34]. It has been postulated that missile migration against the blood flow in venous vessels could occur because of gravity, the patient's position (upright) at the moment of wounding and/or positional changes of the body, the weight and shape of the missile, and possible low flow states [30]. Although the size and the weight of an 125I radioactive seed (4.5 mm in length, 0.8 mm in diameter, and about 10 mg in weight per seed) are smaller than those of a bullet, it is considered that a seed could move against the blood flow in major venous vessels because of gravity. The reasons are as follows. The specific gravity of the seed (about 4 g/mL) is much higher than that of blood, and the seed is less water resistant because of its rod-shaped structure. In addition, a seed would usually be located near the wall of major venous vessels due to gravity and the patient's position, and therefore, a seed would be affected by a relatively slow stream of blood near the vascular wall compared with that in the center of major venous vessels. It is known that in major venous vessels, the blood flow is generally laminar, and the distribution of velocity across the tube is parabolic [35]. Therefore, the velocity of blood flow decreases from the center toward the wall of the venous vessel tube [35].\nThe mechanism of seed migration to various sites could be explained by the assumption that a seed could move in major venous vessels against the blood flow by gravity. In the present cases, seed migration to the kidneys, the liver, and the right groin area (probably a branch of the right femoral vein) (Case 3) would have occurred in venous vessels, partially in a retrograde fashion by gravity, without being passed through a paradoxical route. This explanation does not require the assumption that a patient would have a right-to-left shunt.\n Influence of seed migration on postimplant dosimetry The postimplant prostate D90 was not significantly different between patients with and without seed migration. Moreover, in 15 patients who had multiple migrated seeds, the postimplant prostate D90 was relatively acceptable, and no supplemental seed implantation was required. These results indicate that seed migration did not have a significant effect on postimplant prostate dosimetry in the present study. Possible reasons are as follows. First, in most patients with seed migration, only one or two seeds had migrated, which would have less effect on the dosimetry of the prostate. Tapen et al. have suggested that the loss of a few seeds may not have a significant effect on dose homogeneity or total dose to the prostate [5]. Second, seed migration would have much less effect on the dosimetry of the prostate than other mechanisms of seed loss, such as seed misplacement to the seminal vesicle or perineum and being voided in the urine postoperatively. Merrick et al. have reported that seed migration to the chest accounted for only 10% of total seed loss from the prostate region, highlighting the importance of other mechanisms of loss [13].\nThe postimplant prostate D90 was not significantly different between patients with and without seed migration. Moreover, in 15 patients who had multiple migrated seeds, the postimplant prostate D90 was relatively acceptable, and no supplemental seed implantation was required. These results indicate that seed migration did not have a significant effect on postimplant prostate dosimetry in the present study. Possible reasons are as follows. First, in most patients with seed migration, only one or two seeds had migrated, which would have less effect on the dosimetry of the prostate. Tapen et al. have suggested that the loss of a few seeds may not have a significant effect on dose homogeneity or total dose to the prostate [5]. Second, seed migration would have much less effect on the dosimetry of the prostate than other mechanisms of seed loss, such as seed misplacement to the seminal vesicle or perineum and being voided in the urine postoperatively. Merrick et al. have reported that seed migration to the chest accounted for only 10% of total seed loss from the prostate region, highlighting the importance of other mechanisms of loss [13].\n Seed migration-related sequelae In the present study, none of the 66 patients had symptoms related to the migrated seeds. Although it has been reported that most patients with seed migration are asymptomatic, there have been a few reports of seed migration-related sequelae, such as a few anecdotal instances of cardiac arrhythmia, myocardial infarction, and radiation pneumonitis [21,36,37]. Therefore, it is important to try to reduce the incidence of seed migration. The use of seeds linked with an absorbable suture material has been associated with a dramatically decreased rate of seed migration, although a potentially higher risk of radiotoxicity to the urethra and rectum has been pointed out [1,3,5,38]. In our study, linked seeds were not administered, because they are not commercially available in our country at present.\nIn the present study, none of the 66 patients had symptoms related to the migrated seeds. Although it has been reported that most patients with seed migration are asymptomatic, there have been a few reports of seed migration-related sequelae, such as a few anecdotal instances of cardiac arrhythmia, myocardial infarction, and radiation pneumonitis [21,36,37]. Therefore, it is important to try to reduce the incidence of seed migration. The use of seeds linked with an absorbable suture material has been associated with a dramatically decreased rate of seed migration, although a potentially higher risk of radiotoxicity to the urethra and rectum has been pointed out [1,3,5,38]. In our study, linked seeds were not administered, because they are not commercially available in our country at present.","We found that 0.36% of implanted seeds migrated to the chest in 20% of our patient population, similar to previous reports (Table 2). It has been reported that the incidence of seed migration to the chest can be as high as 55% per patient population and 0.98% per number of implanted seeds (Table 2) [9]. The variability of the incidence of seed migration to the chest among the reported results is considered to be attributed to different types of seeds (linked or loose), different designs of seed placement (intraprostatic or extraprostatic), different timings of follow-up radiographs, and different protocols of follow-up chest radiographs (orthogonal or AP alone) (Table 2) [1-5,9,13].\nLiterature Survey of Seed Migration\n*Linked seeds were restricted to needles placed at the periphery of the prostate implant. All seeds placed centrally, close to the urethra, were loose seeds. On average, 8-10 loose seeds were used.\n†Of the total 125I seeds, 64% were linked seeds. Linked seeds were limited to the region of the capsule/periprostatic region. Loose seeds were utilized in the central aspect of the gland.\n††9 patients, within 14 days; 75 patients, more than 30 days.\n§11patients within 14 days; 61patients, more than 30 days.\n||Of the 20 patients who had postimplant chest radiographs obtained within 14 days of brachytherapy, none had seeds detected in the lungs. Categorizing patients by isotope, for 125I, 18/75 (24.0%) and for 103Pd, 16/61 (26.2%) with chest radiographs beyond 30 days had seeds found in the lungs.\n¶A mixture of linked and free seeds, with a median 74% of needles per case containing seeds in linked, with central and posterior needles typically containing loose seeds.\nIn contrast, the incidence of seed migration to the abdomen and pelvis has been reported rarely (Table 2). A possible reason is that the American Brachytherapy Society does not specifically recommend follow-up abdominal and pelvic radiographs after seed implantation [6]. Therefore, in most institutions, follow-up abdominal and pelvic radiographs would not be undertaken routinely. However, we found that seed migration to the abdomen and pelvis occurred in 2.2% and 5.6%, respectively, of our patient population. Although the incidence of seed migration to the abdomen and pelvis is lower than that of seed migration to the chest, we would consider it advisable to undertake follow-up abdominal and pelvic radiographs after seed implantation.","One day, two weeks, and three months after seed implantation, follow-up chest radiographs showed 22%, 88%, and 91%, respectively, of 69 seeds that eventually migrated to the chest. These results mean that 22%, 66%, and 2.9% of these 69 seeds migrated to the chest within one day, between one day and two weeks, and between two weeks and three months after seed implantation, respectively. About 90% of these 69 seeds migrated to the chest within two weeks after seed implantation. These results are similar to a previous report [13]. Merrick et al. have speculated that seed migration to the chest may be most likely to occur between 14 and 28 days after seed implantation [13]. Although we observed that several seeds migrated to the chest more than six months after seed implantation, this finding should be considered exceptional. Therefore, it is suggested that follow-up chest radiographs should be undertaken two weeks, or preferably a few months, after seed implantation to detect most seeds that will migrate to the chest.","The results of the present study show a total of four and five cases of seed migration to the kidneys and Batson's vertebral venous plexus, respectively, at only one institution, which suggests that such cases are not very rare. Meanwhile, in previous studies, a total of only four and four cases of seed migration to the kidneys and Batson's vertebral venous plexus, respectively, have been reported as rare cases, which is in disagreement with our conclusion [22,23,26,29]. The same number or more cases of seed migration to these areas were found in our single study compared with all previous studies. A possible explanation is that, in the present study, orthogonal chest radiographs, an abdominal radiograph, and a pelvic radiograph were undertaken routinely to detect seed migration to the chest, abdomen, and pelvis at several time points after seed implantation. Moreover, in all patients who had seed migration to the abdomen and pelvis, a CT scan was undertaken to identify the exact location of the migrated seeds. Consequently, more cases of seed migration to the kidneys and Batson's vertebral venous plexus were found in the present study. We speculate that some seed migration to the kidneys and Batson's vertebral venous plexus might have gone undetected in other institutions.","Previous investigators have explained seed migration to the kidneys as follows: migrated seeds in venous vessels would enter the systemic circulation through a right-to-left shunt, such as a patent foramen ovale or pulmonary arteriovenous malformation, and then embolize to a branch of the renal arteries [26]. We call this route of seed migration through a right-to-left shunt \"a paradoxical route.\"\nThe present study has provided some interesting cases of seed migration to the kidneys, the mechanism of which is difficult to explain. In Case 1 of the present study, a seed had migrated to the right side of the middle abdomen, which was considered to be separated from the IVC, and then had relocated to the left kidney (Figure 1A-C). If the seed had initially embolized to an artery of the right side of the middle abdomen, such as a branch of the right renal artery, through a paradoxical route, it is difficult to explain how the seed would have relocated to the left kidney. In Case 2, two seeds had migrated to the same right kidney (Figure 2A-C). The mechanism of seed migration to the kidney through a paradoxical route is too complicated. Therefore, it is highly unlikely that two seeds would happen to migrate to the same right kidney through a paradoxical route by chance, although the possibility cannot be completely excluded. Other possible mechanisms should be proposed to explain how seed migration to the kidneys would have occurred in the two cases mentioned above.","We assume that seed movement in venous vessels would reflect not only the force of the blood flow but also the force of gravity, and that a seed sometimes would move in venous vessels against the blood flow, because the gravity could overcome the blood flow. The explanation is as follows. Intravascular missile migration following gunshot injury has been reported [30-34]. A missile sometimes moves in a retrograde fashion against the normal blood flow in major venous vessels including the IVC, and sometimes lodges in the renal vein and the hepatic vein [30-34]. It has been postulated that missile migration against the blood flow in venous vessels could occur because of gravity, the patient's position (upright) at the moment of wounding and/or positional changes of the body, the weight and shape of the missile, and possible low flow states [30]. Although the size and the weight of an 125I radioactive seed (4.5 mm in length, 0.8 mm in diameter, and about 10 mg in weight per seed) are smaller than those of a bullet, it is considered that a seed could move against the blood flow in major venous vessels because of gravity. The reasons are as follows. The specific gravity of the seed (about 4 g/mL) is much higher than that of blood, and the seed is less water resistant because of its rod-shaped structure. In addition, a seed would usually be located near the wall of major venous vessels due to gravity and the patient's position, and therefore, a seed would be affected by a relatively slow stream of blood near the vascular wall compared with that in the center of major venous vessels. It is known that in major venous vessels, the blood flow is generally laminar, and the distribution of velocity across the tube is parabolic [35]. Therefore, the velocity of blood flow decreases from the center toward the wall of the venous vessel tube [35].\nThe mechanism of seed migration to various sites could be explained by the assumption that a seed could move in major venous vessels against the blood flow by gravity. In the present cases, seed migration to the kidneys, the liver, and the right groin area (probably a branch of the right femoral vein) (Case 3) would have occurred in venous vessels, partially in a retrograde fashion by gravity, without being passed through a paradoxical route. This explanation does not require the assumption that a patient would have a right-to-left shunt.","The postimplant prostate D90 was not significantly different between patients with and without seed migration. Moreover, in 15 patients who had multiple migrated seeds, the postimplant prostate D90 was relatively acceptable, and no supplemental seed implantation was required. These results indicate that seed migration did not have a significant effect on postimplant prostate dosimetry in the present study. Possible reasons are as follows. First, in most patients with seed migration, only one or two seeds had migrated, which would have less effect on the dosimetry of the prostate. Tapen et al. have suggested that the loss of a few seeds may not have a significant effect on dose homogeneity or total dose to the prostate [5]. Second, seed migration would have much less effect on the dosimetry of the prostate than other mechanisms of seed loss, such as seed misplacement to the seminal vesicle or perineum and being voided in the urine postoperatively. Merrick et al. have reported that seed migration to the chest accounted for only 10% of total seed loss from the prostate region, highlighting the importance of other mechanisms of loss [13].","In the present study, none of the 66 patients had symptoms related to the migrated seeds. Although it has been reported that most patients with seed migration are asymptomatic, there have been a few reports of seed migration-related sequelae, such as a few anecdotal instances of cardiac arrhythmia, myocardial infarction, and radiation pneumonitis [21,36,37]. Therefore, it is important to try to reduce the incidence of seed migration. The use of seeds linked with an absorbable suture material has been associated with a dramatically decreased rate of seed migration, although a potentially higher risk of radiotoxicity to the urethra and rectum has been pointed out [1,3,5,38]. In our study, linked seeds were not administered, because they are not commercially available in our country at present.","In the present study, we determined the incidence of seed migration not only to the chest, but also to the abdomen and pelvis. Although the incidence of seed migration to the abdomen and pelvis was lower than that of seed migration to the chest, it would be advisable to undertake follow-up abdominal and pelvic radiographs after seed implantation. Seed migration to the chest occurred predominantly within two weeks and, therefore, it is suggested that follow-up chest radiographs should be undertaken two weeks or later after seed implantation. Seed migration to the kidneys and Batson's vertebral venous plexus was not very rare. Seed migration did not significantly affect postimplant prostate D90."],"string":"[\n \"Seed migration is a well-recognized event that occurs after transperineal interstitial prostate brachytherapy, and it is observed more often with loose seeds than with linked seeds [1-5].\\nIt is well known that the most frequent site of seed migration is the chest. The American Brachytherapy Society has advised that a chest radiograph should be undertaken at the first follow-up visit to scan the lungs for embolized seeds [6]. Consequently, the incidence of seed migration to the chest has been well reported [1,2,4,5,7-19]. However, documentation of the incidence of seed migration to the abdomen and pelvis is rare. Rare cases of seed migration to a coronary artery, the right ventricle, the liver, the kidneys, Batson's vertebral venous plexus, and the left testicular vein have been reported [20-26]. However, it has never been fully determined whether seed migration to these locations is really rare.\\nThe primary purposes of the present study were to determine the incidence of seed migration not only to the chest, but also to the abdomen and the pelvis at our institution and to identify the exact location of the seeds that had migrated to the abdomen and pelvis with computed tomography (CT). The secondary purpose was to determine the impact of seed migration on postimplant dosimetry.\",\n \"We reviewed the records of 267 patients who underwent transperineal interstitial prostate brachytherapy with loose 125I seeds for clinical T1/T2 prostate cancer at our institution. Table 1 details the characteristics of all 267 patients. Two patients (0.75%) received brachytherapy plus external beam radiotherapy (45 Gy in 1.8 Gy fractions). One hundred twenty-three of the 267 (46.1%) patients also underwent neoadjuvant hormonal therapy (NHT), which consisted of luteinizing hormone-releasing hormone agonist and antiandrogens. NHT was generally undertaken in patients with a prostate volume >40 cc or those with pubic arch interference by transrectal ultrasound (TRUS) at the preimplant volume study [27].\\nPatient Characteristics (N = 267)\\nData are presented as mean ± standard error (range) or number (percent) of patients.\\nAbbreviations: PSA = prostate-specific antigen; TRUS = transrectal ultrasound\\nOne month before seed implantation, a preplan was obtained with TRUS images taken at 5 mm intervals from the base to the apex of the prostate with the patient in the dorsal lithotomy position. The planning target volume included the prostate gland, with a margin of 3 mm anteriorly and laterally and 5 mm in the cranial and caudal directions. No margin was added posteriorly at the rectal interface. Treatment planning used a peripheral or a modified peripheral approach. For the 265 patients who received brachytherapy alone, the prescribed brachytherapy dose was 145 Gy and 160 Gy for the first 163 patients and the subsequent 102 patients, respectively. For the remaining two patients who received brachytherapy plus external beam radiotherapy, the prescribed brachytherapy dose was 110 Gy. TG 43 formalism was used in the preplanning and postimplant dosimetry analyses [28]. All 267 patients were treated with loose 125I radioactive seeds with a Mick applicator (Mick Radio-Nuclear Instruments, Bronx, NY). To ensure that no seeds were left in the bladder, postoperative fluoroscopic images were obtained. Prior to discharge, postoperative surveys of voided urine were conducted to detect voided seeds.\\nOrthogonal chest radiographs, an abdominal radiograph, and a pelvic radiograph were undertaken to document the occurrence and sites of seed migration one day after seed implantation. These follow-up radiographs were undertaken routinely at each outpatient visit. Patients returned to our outpatient clinic two weeks and three months after seed implantation, then at three-month intervals for the first three years and at six-month intervals thereafter. Seed migration to the chest and the abdomen was recorded when one or more seeds were visualized on orthogonal chest radiographs and the anteroposterior (AP) abdominal radiograph, respectively. Seed migration to the pelvis was recorded when one or more seeds were separated from the main seed cluster on an AP pelvic radiograph. However, seeds placed into the bladder and the seminal vesicles or seeds placed inferior to the prostate by mistake were not scored as migrated. Seeds voided in the urine were not scored as migrated. Subsequently, all patients who had seed migration to the abdomen and pelvis underwent a CT scan to identify the exact location of the migrated seeds. The incidence of seed migration to the chest, abdomen, and pelvis was calculated.\\nPostimplant dosimetric analysis by CT was performed one month after seed implantation. The seed count in the region of the prostate gland was determined on the AP pelvic radiographs obtained two weeks after seed implantation. The postimplant prostate D90 (the dose received by 90% of the volume of the prostate) value was compared between patients with and without seed migration. Statistical analysis was performed with Student's t-test. A p value of <0.05 was considered statistically significant.\",\n \"In total, 19,236 seeds were implanted in 267 patients. All 267 patients underwent follow-up radiographs. Median follow-up was 41 months (range, 8.5-76 months).\\nAt one day after seed implantation, follow-up radiographs demonstrated that 41 of the 19,236 (0.21%) seeds migrated in 37 of the 267 (13.9%) patients: three seeds in one patient, two seeds in each of two patients, and a single seed in each of the remaining 34 patients. Fifteen (0.078%) seeds migrated to the chest in 15 (5.6%) patients. One (0.0052%) seed migrated to the abdomen in one (0.37%) patient. Twenty-five (0.13%) seeds migrated to the pelvis in 23 (8.6%) patients.\\nAt two weeks after seed implantation, 85 of the 19,236 (0.44%) seeds migrated in 61 of the 267 (22.8%) patients: seven seeds in one patient, three seeds in each of four patients, two seeds in each of 10 patients, and a single seed in each of the remaining 46 patients. Sixty-one (0.32%) seeds migrated to the chest in 48 (18.0%) patients. Seven (0.036%) seeds migrated to the abdomen in six (2.2%) patients. Seventeen (0.088%) seeds migrated to the pelvis in 16 (6.0%) patients.\\nAt three months after seed implantation, 87 of the 19,236 (0.45%) seeds migrated in 63 of the 267 (23.6%) patients: seven seeds in one patient, three seeds in each of four patients, two seeds in each of 10 patients, and a single seed in each of the remaining 48 patients. Sixty-three (0.33%) seeds migrated to the chest in 50 (18.7%) patients. Seven (0.036%) seeds migrated to the abdomen in six (2.2%) patients. Seventeen (0.088%) seeds migrated to the pelvis in 16 (6.0%) patients.\\nAlthough seed migration occurred predominantly within two weeks after seed implantation, eventually, six seeds were found to migrate or relocate to the chest long after seed implantation: four seeds were found to have migrated to the chest at a median of 27 months (range 9.1-16 months), and two seeds were found to have relocated from the pelvis to the chest at 6.1 and 48 months after seed implantation, respectively. In these patients, follow-up radiographs were undertaken routinely at every outpatient visit; however, migration or relocation of these seeds was not found at the previous visit. Meanwhile, no seed relocation from the chest to other sites was observed in the present study.\\nEventually, 91 of the 19,236 (0.47%) seeds migrated in 66 of the 267 (24.7%) patients: seven seeds in one patient, three seeds in each of five patients, two seeds in each of nine patients, and a single seed in each of the remaining 51 patients. Sixty-nine (0.36%) seeds migrated to the chest in 54 (20.2%) patients. Seven (0.036%) seeds migrated to the abdomen in six (2.2%) patients. Fifteen (0.078%) seeds migrated to the pelvis in 15 (5.6%) patients. All 66 patients were informed of seed migration; none of these patients had symptoms related to the migrated seeds.\\n Seeds that migrated to the abdomen (seven seeds in six patients) Two of the 19,236 (0.010%) seeds migrated to the liver in two of the 267 (0.75%) patients: a single seed migrated to the liver in each of two patients. Five (0.026%) seeds migrated to the kidneys in four (1.5%) patients: two seeds migrated to the same kidney in one patient, and a single seed migrated to the kidney in each of the remaining three patients. In one patient (Case 1), one day after seed implantation, an abdominal radiograph showed that a seed had migrated to the right side of the middle abdomen, which was considered to be separated from the inferior vena cava (IVC) (Figure 1A). However, two weeks after seed implantation, an abdominal radiograph showed that the seed had disappeared from the right side of the middle abdomen, and showed a seed that had migrated to the left side of the middle abdomen (Figure 1B). On pelvic radiographs, there were no changes in number of seeds that had been implanted into the prostate between one day and two weeks after seed implantation. It was concluded that the seed had relocated from the right side of the middle abdomen to the left side of the middle abdomen. A subsequent abdominal CT demonstrated that the seed had migrated to the left kidney (Figure 1C). In another patient (Case 2), two weeks after seed implantation, an abdominal radiograph showed that two seeds had migrated to the same right kidney (Figure 2A-C).\\nCase 1: Relocation from the right side of the middle abdomen to the left kidney. One day after seed implantation, a follow-up abdominal radiograph showed that a seed had migrated to the right side of the middle abdomen (solid arrow) (A). Two weeks later, the seed had relocated from the right side of the middle abdomen to the left side of the middle abdomen (solid arrow) (B). Subsequent computed tomography showed that the seed had migrated to the left kidney (solid arrow) (C).\\nCase 2: Migration of two seeds to the same right kidney. Two weeks after seed implantation, a follow-up abdominal radiograph showed that two seeds had migrated to the right side of the middle abdomen (solid arrows) (A). Subsequent computed tomography showed that these two seeds had migrated to the same right kidney (solid arrows) (B,C).\\nTwo of the 19,236 (0.010%) seeds migrated to the liver in two of the 267 (0.75%) patients: a single seed migrated to the liver in each of two patients. Five (0.026%) seeds migrated to the kidneys in four (1.5%) patients: two seeds migrated to the same kidney in one patient, and a single seed migrated to the kidney in each of the remaining three patients. In one patient (Case 1), one day after seed implantation, an abdominal radiograph showed that a seed had migrated to the right side of the middle abdomen, which was considered to be separated from the inferior vena cava (IVC) (Figure 1A). However, two weeks after seed implantation, an abdominal radiograph showed that the seed had disappeared from the right side of the middle abdomen, and showed a seed that had migrated to the left side of the middle abdomen (Figure 1B). On pelvic radiographs, there were no changes in number of seeds that had been implanted into the prostate between one day and two weeks after seed implantation. It was concluded that the seed had relocated from the right side of the middle abdomen to the left side of the middle abdomen. A subsequent abdominal CT demonstrated that the seed had migrated to the left kidney (Figure 1C). In another patient (Case 2), two weeks after seed implantation, an abdominal radiograph showed that two seeds had migrated to the same right kidney (Figure 2A-C).\\nCase 1: Relocation from the right side of the middle abdomen to the left kidney. One day after seed implantation, a follow-up abdominal radiograph showed that a seed had migrated to the right side of the middle abdomen (solid arrow) (A). Two weeks later, the seed had relocated from the right side of the middle abdomen to the left side of the middle abdomen (solid arrow) (B). Subsequent computed tomography showed that the seed had migrated to the left kidney (solid arrow) (C).\\nCase 2: Migration of two seeds to the same right kidney. Two weeks after seed implantation, a follow-up abdominal radiograph showed that two seeds had migrated to the right side of the middle abdomen (solid arrows) (A). Subsequent computed tomography showed that these two seeds had migrated to the same right kidney (solid arrows) (B,C).\\n Seeds that migrated to the pelvis (15 seeds in 15 patients) A single seed migrated to the pelvis in each of 15 patients. Five of the 19,236 (0.026%) seeds migrated to Batson's vertebral venous plexus in five of the 267 (1.9%) patients. Four (0.021%) seeds migrated to the sacral venous plexus in four (1.5%) patients. Two (0.010%) seeds migrated to the iliac veins in two (0.75%) patients. Two (0.010%) seeds migrated to the right ischial bone in two (0.75%) patients. Two (0.010%) seeds migrated to the obturator internus muscles in two (0.75%) patients.\\nA single seed migrated to the pelvis in each of 15 patients. Five of the 19,236 (0.026%) seeds migrated to Batson's vertebral venous plexus in five of the 267 (1.9%) patients. Four (0.021%) seeds migrated to the sacral venous plexus in four (1.5%) patients. Two (0.010%) seeds migrated to the iliac veins in two (0.75%) patients. Two (0.010%) seeds migrated to the right ischial bone in two (0.75%) patients. Two (0.010%) seeds migrated to the obturator internus muscles in two (0.75%) patients.\\n Seed relocation four years after seed implantation In one patient (Case 3), seed relocation was found four years after seed implantation. A seed had migrated to the right groin area one day after seed implantation (Figure 3A-B). Three years and six months after seed implantation, a pelvic radiograph showed that the seed was in the same location. However, four years after seed implantation, a pelvic radiograph showed that the seed had disappeared from the right groin area (Figure 3C), and a chest radiograph showed a seed that had migrated to the left lung, which was not found at three years and six months after seed implantation. It was concluded that the seed had initially lodged in a branch of the right femoral vein, and then relocated to the left lung through the IVC, long after seed implantation.\\nCase 3: Seed disappearance from the right groin area four years after seed implantation. One day after seed implantation, a follow-up pelvic radiograph showed that a seed was located inferior to the right side of the pelvis (solid arrow) (A). Subsequent computed tomography showed that the seed had migrated to the right groin area (solid arrow) (B). Four years after seed implantation, a follow-up pelvic radiograph showed that the seed had disappeared from the pelvic area (C).\\nIn one patient (Case 3), seed relocation was found four years after seed implantation. A seed had migrated to the right groin area one day after seed implantation (Figure 3A-B). Three years and six months after seed implantation, a pelvic radiograph showed that the seed was in the same location. However, four years after seed implantation, a pelvic radiograph showed that the seed had disappeared from the right groin area (Figure 3C), and a chest radiograph showed a seed that had migrated to the left lung, which was not found at three years and six months after seed implantation. It was concluded that the seed had initially lodged in a branch of the right femoral vein, and then relocated to the left lung through the IVC, long after seed implantation.\\nCase 3: Seed disappearance from the right groin area four years after seed implantation. One day after seed implantation, a follow-up pelvic radiograph showed that a seed was located inferior to the right side of the pelvis (solid arrow) (A). Subsequent computed tomography showed that the seed had migrated to the right groin area (solid arrow) (B). Four years after seed implantation, a follow-up pelvic radiograph showed that the seed had disappeared from the pelvic area (C).\\n Postimplant dosimetric analysis In the 265 patients who received brachytherapy alone, the postimplant prostate D90 was 175.0 ± 1.3 Gy (mean ± standard error [SE]). In these 265 patients, the postimplant prostate D90 value was not significantly different between patients with and without seed migration (mean ± SE, 175.1 ± 2.3 Gy vs. 175.0 ± 1.5 Gy, respectively, p = 0.992). In 15 patients who had multiple migrated seeds, the postimplant prostate D90 ranged from 137.2 to 198.5 Gy (mean ± SE, 171.8 ± 5.5 Gy), which was not significantly different from that in patients without seed migration (p = 0.573).\\nIn the remaining two patients who received brachytherapy plus external beam radiotherapy, the postimplant prostate D90 values were 116.4 Gy and 132.6 Gy. These two patients had no seed migration.\\nIn the 265 patients who received brachytherapy alone, the postimplant prostate D90 was 175.0 ± 1.3 Gy (mean ± standard error [SE]). In these 265 patients, the postimplant prostate D90 value was not significantly different between patients with and without seed migration (mean ± SE, 175.1 ± 2.3 Gy vs. 175.0 ± 1.5 Gy, respectively, p = 0.992). In 15 patients who had multiple migrated seeds, the postimplant prostate D90 ranged from 137.2 to 198.5 Gy (mean ± SE, 171.8 ± 5.5 Gy), which was not significantly different from that in patients without seed migration (p = 0.573).\\nIn the remaining two patients who received brachytherapy plus external beam radiotherapy, the postimplant prostate D90 values were 116.4 Gy and 132.6 Gy. These two patients had no seed migration.\",\n \"Two of the 19,236 (0.010%) seeds migrated to the liver in two of the 267 (0.75%) patients: a single seed migrated to the liver in each of two patients. Five (0.026%) seeds migrated to the kidneys in four (1.5%) patients: two seeds migrated to the same kidney in one patient, and a single seed migrated to the kidney in each of the remaining three patients. In one patient (Case 1), one day after seed implantation, an abdominal radiograph showed that a seed had migrated to the right side of the middle abdomen, which was considered to be separated from the inferior vena cava (IVC) (Figure 1A). However, two weeks after seed implantation, an abdominal radiograph showed that the seed had disappeared from the right side of the middle abdomen, and showed a seed that had migrated to the left side of the middle abdomen (Figure 1B). On pelvic radiographs, there were no changes in number of seeds that had been implanted into the prostate between one day and two weeks after seed implantation. It was concluded that the seed had relocated from the right side of the middle abdomen to the left side of the middle abdomen. A subsequent abdominal CT demonstrated that the seed had migrated to the left kidney (Figure 1C). In another patient (Case 2), two weeks after seed implantation, an abdominal radiograph showed that two seeds had migrated to the same right kidney (Figure 2A-C).\\nCase 1: Relocation from the right side of the middle abdomen to the left kidney. One day after seed implantation, a follow-up abdominal radiograph showed that a seed had migrated to the right side of the middle abdomen (solid arrow) (A). Two weeks later, the seed had relocated from the right side of the middle abdomen to the left side of the middle abdomen (solid arrow) (B). Subsequent computed tomography showed that the seed had migrated to the left kidney (solid arrow) (C).\\nCase 2: Migration of two seeds to the same right kidney. Two weeks after seed implantation, a follow-up abdominal radiograph showed that two seeds had migrated to the right side of the middle abdomen (solid arrows) (A). Subsequent computed tomography showed that these two seeds had migrated to the same right kidney (solid arrows) (B,C).\",\n \"A single seed migrated to the pelvis in each of 15 patients. Five of the 19,236 (0.026%) seeds migrated to Batson's vertebral venous plexus in five of the 267 (1.9%) patients. Four (0.021%) seeds migrated to the sacral venous plexus in four (1.5%) patients. Two (0.010%) seeds migrated to the iliac veins in two (0.75%) patients. Two (0.010%) seeds migrated to the right ischial bone in two (0.75%) patients. Two (0.010%) seeds migrated to the obturator internus muscles in two (0.75%) patients.\",\n \"In one patient (Case 3), seed relocation was found four years after seed implantation. A seed had migrated to the right groin area one day after seed implantation (Figure 3A-B). Three years and six months after seed implantation, a pelvic radiograph showed that the seed was in the same location. However, four years after seed implantation, a pelvic radiograph showed that the seed had disappeared from the right groin area (Figure 3C), and a chest radiograph showed a seed that had migrated to the left lung, which was not found at three years and six months after seed implantation. It was concluded that the seed had initially lodged in a branch of the right femoral vein, and then relocated to the left lung through the IVC, long after seed implantation.\\nCase 3: Seed disappearance from the right groin area four years after seed implantation. One day after seed implantation, a follow-up pelvic radiograph showed that a seed was located inferior to the right side of the pelvis (solid arrow) (A). Subsequent computed tomography showed that the seed had migrated to the right groin area (solid arrow) (B). Four years after seed implantation, a follow-up pelvic radiograph showed that the seed had disappeared from the pelvic area (C).\",\n \"In the 265 patients who received brachytherapy alone, the postimplant prostate D90 was 175.0 ± 1.3 Gy (mean ± standard error [SE]). In these 265 patients, the postimplant prostate D90 value was not significantly different between patients with and without seed migration (mean ± SE, 175.1 ± 2.3 Gy vs. 175.0 ± 1.5 Gy, respectively, p = 0.992). In 15 patients who had multiple migrated seeds, the postimplant prostate D90 ranged from 137.2 to 198.5 Gy (mean ± SE, 171.8 ± 5.5 Gy), which was not significantly different from that in patients without seed migration (p = 0.573).\\nIn the remaining two patients who received brachytherapy plus external beam radiotherapy, the postimplant prostate D90 values were 116.4 Gy and 132.6 Gy. These two patients had no seed migration.\",\n \" Incidence of seed migration We found that 0.36% of implanted seeds migrated to the chest in 20% of our patient population, similar to previous reports (Table 2). It has been reported that the incidence of seed migration to the chest can be as high as 55% per patient population and 0.98% per number of implanted seeds (Table 2) [9]. The variability of the incidence of seed migration to the chest among the reported results is considered to be attributed to different types of seeds (linked or loose), different designs of seed placement (intraprostatic or extraprostatic), different timings of follow-up radiographs, and different protocols of follow-up chest radiographs (orthogonal or AP alone) (Table 2) [1-5,9,13].\\nLiterature Survey of Seed Migration\\n*Linked seeds were restricted to needles placed at the periphery of the prostate implant. All seeds placed centrally, close to the urethra, were loose seeds. On average, 8-10 loose seeds were used.\\n†Of the total 125I seeds, 64% were linked seeds. Linked seeds were limited to the region of the capsule/periprostatic region. Loose seeds were utilized in the central aspect of the gland.\\n††9 patients, within 14 days; 75 patients, more than 30 days.\\n§11patients within 14 days; 61patients, more than 30 days.\\n||Of the 20 patients who had postimplant chest radiographs obtained within 14 days of brachytherapy, none had seeds detected in the lungs. Categorizing patients by isotope, for 125I, 18/75 (24.0%) and for 103Pd, 16/61 (26.2%) with chest radiographs beyond 30 days had seeds found in the lungs.\\n¶A mixture of linked and free seeds, with a median 74% of needles per case containing seeds in linked, with central and posterior needles typically containing loose seeds.\\nIn contrast, the incidence of seed migration to the abdomen and pelvis has been reported rarely (Table 2). A possible reason is that the American Brachytherapy Society does not specifically recommend follow-up abdominal and pelvic radiographs after seed implantation [6]. Therefore, in most institutions, follow-up abdominal and pelvic radiographs would not be undertaken routinely. However, we found that seed migration to the abdomen and pelvis occurred in 2.2% and 5.6%, respectively, of our patient population. Although the incidence of seed migration to the abdomen and pelvis is lower than that of seed migration to the chest, we would consider it advisable to undertake follow-up abdominal and pelvic radiographs after seed implantation.\\nWe found that 0.36% of implanted seeds migrated to the chest in 20% of our patient population, similar to previous reports (Table 2). It has been reported that the incidence of seed migration to the chest can be as high as 55% per patient population and 0.98% per number of implanted seeds (Table 2) [9]. The variability of the incidence of seed migration to the chest among the reported results is considered to be attributed to different types of seeds (linked or loose), different designs of seed placement (intraprostatic or extraprostatic), different timings of follow-up radiographs, and different protocols of follow-up chest radiographs (orthogonal or AP alone) (Table 2) [1-5,9,13].\\nLiterature Survey of Seed Migration\\n*Linked seeds were restricted to needles placed at the periphery of the prostate implant. All seeds placed centrally, close to the urethra, were loose seeds. On average, 8-10 loose seeds were used.\\n†Of the total 125I seeds, 64% were linked seeds. Linked seeds were limited to the region of the capsule/periprostatic region. Loose seeds were utilized in the central aspect of the gland.\\n††9 patients, within 14 days; 75 patients, more than 30 days.\\n§11patients within 14 days; 61patients, more than 30 days.\\n||Of the 20 patients who had postimplant chest radiographs obtained within 14 days of brachytherapy, none had seeds detected in the lungs. Categorizing patients by isotope, for 125I, 18/75 (24.0%) and for 103Pd, 16/61 (26.2%) with chest radiographs beyond 30 days had seeds found in the lungs.\\n¶A mixture of linked and free seeds, with a median 74% of needles per case containing seeds in linked, with central and posterior needles typically containing loose seeds.\\nIn contrast, the incidence of seed migration to the abdomen and pelvis has been reported rarely (Table 2). A possible reason is that the American Brachytherapy Society does not specifically recommend follow-up abdominal and pelvic radiographs after seed implantation [6]. Therefore, in most institutions, follow-up abdominal and pelvic radiographs would not be undertaken routinely. However, we found that seed migration to the abdomen and pelvis occurred in 2.2% and 5.6%, respectively, of our patient population. Although the incidence of seed migration to the abdomen and pelvis is lower than that of seed migration to the chest, we would consider it advisable to undertake follow-up abdominal and pelvic radiographs after seed implantation.\\n The dynamics of seed migration to the chest One day, two weeks, and three months after seed implantation, follow-up chest radiographs showed 22%, 88%, and 91%, respectively, of 69 seeds that eventually migrated to the chest. These results mean that 22%, 66%, and 2.9% of these 69 seeds migrated to the chest within one day, between one day and two weeks, and between two weeks and three months after seed implantation, respectively. About 90% of these 69 seeds migrated to the chest within two weeks after seed implantation. These results are similar to a previous report [13]. Merrick et al. have speculated that seed migration to the chest may be most likely to occur between 14 and 28 days after seed implantation [13]. Although we observed that several seeds migrated to the chest more than six months after seed implantation, this finding should be considered exceptional. Therefore, it is suggested that follow-up chest radiographs should be undertaken two weeks, or preferably a few months, after seed implantation to detect most seeds that will migrate to the chest.\\nOne day, two weeks, and three months after seed implantation, follow-up chest radiographs showed 22%, 88%, and 91%, respectively, of 69 seeds that eventually migrated to the chest. These results mean that 22%, 66%, and 2.9% of these 69 seeds migrated to the chest within one day, between one day and two weeks, and between two weeks and three months after seed implantation, respectively. About 90% of these 69 seeds migrated to the chest within two weeks after seed implantation. These results are similar to a previous report [13]. Merrick et al. have speculated that seed migration to the chest may be most likely to occur between 14 and 28 days after seed implantation [13]. Although we observed that several seeds migrated to the chest more than six months after seed implantation, this finding should be considered exceptional. Therefore, it is suggested that follow-up chest radiographs should be undertaken two weeks, or preferably a few months, after seed implantation to detect most seeds that will migrate to the chest.\\n Seed migration to the kidneys and Batson's vertebral plexus is not very rare The results of the present study show a total of four and five cases of seed migration to the kidneys and Batson's vertebral venous plexus, respectively, at only one institution, which suggests that such cases are not very rare. Meanwhile, in previous studies, a total of only four and four cases of seed migration to the kidneys and Batson's vertebral venous plexus, respectively, have been reported as rare cases, which is in disagreement with our conclusion [22,23,26,29]. The same number or more cases of seed migration to these areas were found in our single study compared with all previous studies. A possible explanation is that, in the present study, orthogonal chest radiographs, an abdominal radiograph, and a pelvic radiograph were undertaken routinely to detect seed migration to the chest, abdomen, and pelvis at several time points after seed implantation. Moreover, in all patients who had seed migration to the abdomen and pelvis, a CT scan was undertaken to identify the exact location of the migrated seeds. Consequently, more cases of seed migration to the kidneys and Batson's vertebral venous plexus were found in the present study. We speculate that some seed migration to the kidneys and Batson's vertebral venous plexus might have gone undetected in other institutions.\\nThe results of the present study show a total of four and five cases of seed migration to the kidneys and Batson's vertebral venous plexus, respectively, at only one institution, which suggests that such cases are not very rare. Meanwhile, in previous studies, a total of only four and four cases of seed migration to the kidneys and Batson's vertebral venous plexus, respectively, have been reported as rare cases, which is in disagreement with our conclusion [22,23,26,29]. The same number or more cases of seed migration to these areas were found in our single study compared with all previous studies. A possible explanation is that, in the present study, orthogonal chest radiographs, an abdominal radiograph, and a pelvic radiograph were undertaken routinely to detect seed migration to the chest, abdomen, and pelvis at several time points after seed implantation. Moreover, in all patients who had seed migration to the abdomen and pelvis, a CT scan was undertaken to identify the exact location of the migrated seeds. Consequently, more cases of seed migration to the kidneys and Batson's vertebral venous plexus were found in the present study. We speculate that some seed migration to the kidneys and Batson's vertebral venous plexus might have gone undetected in other institutions.\\n In some cases of seed migration to the kidneys, the mechanism is difficult to explain Previous investigators have explained seed migration to the kidneys as follows: migrated seeds in venous vessels would enter the systemic circulation through a right-to-left shunt, such as a patent foramen ovale or pulmonary arteriovenous malformation, and then embolize to a branch of the renal arteries [26]. We call this route of seed migration through a right-to-left shunt \\\"a paradoxical route.\\\"\\nThe present study has provided some interesting cases of seed migration to the kidneys, the mechanism of which is difficult to explain. In Case 1 of the present study, a seed had migrated to the right side of the middle abdomen, which was considered to be separated from the IVC, and then had relocated to the left kidney (Figure 1A-C). If the seed had initially embolized to an artery of the right side of the middle abdomen, such as a branch of the right renal artery, through a paradoxical route, it is difficult to explain how the seed would have relocated to the left kidney. In Case 2, two seeds had migrated to the same right kidney (Figure 2A-C). The mechanism of seed migration to the kidney through a paradoxical route is too complicated. Therefore, it is highly unlikely that two seeds would happen to migrate to the same right kidney through a paradoxical route by chance, although the possibility cannot be completely excluded. Other possible mechanisms should be proposed to explain how seed migration to the kidneys would have occurred in the two cases mentioned above.\\nPrevious investigators have explained seed migration to the kidneys as follows: migrated seeds in venous vessels would enter the systemic circulation through a right-to-left shunt, such as a patent foramen ovale or pulmonary arteriovenous malformation, and then embolize to a branch of the renal arteries [26]. We call this route of seed migration through a right-to-left shunt \\\"a paradoxical route.\\\"\\nThe present study has provided some interesting cases of seed migration to the kidneys, the mechanism of which is difficult to explain. In Case 1 of the present study, a seed had migrated to the right side of the middle abdomen, which was considered to be separated from the IVC, and then had relocated to the left kidney (Figure 1A-C). If the seed had initially embolized to an artery of the right side of the middle abdomen, such as a branch of the right renal artery, through a paradoxical route, it is difficult to explain how the seed would have relocated to the left kidney. In Case 2, two seeds had migrated to the same right kidney (Figure 2A-C). The mechanism of seed migration to the kidney through a paradoxical route is too complicated. Therefore, it is highly unlikely that two seeds would happen to migrate to the same right kidney through a paradoxical route by chance, although the possibility cannot be completely excluded. Other possible mechanisms should be proposed to explain how seed migration to the kidneys would have occurred in the two cases mentioned above.\\n Other possible mechanisms of seed migration We assume that seed movement in venous vessels would reflect not only the force of the blood flow but also the force of gravity, and that a seed sometimes would move in venous vessels against the blood flow, because the gravity could overcome the blood flow. The explanation is as follows. Intravascular missile migration following gunshot injury has been reported [30-34]. A missile sometimes moves in a retrograde fashion against the normal blood flow in major venous vessels including the IVC, and sometimes lodges in the renal vein and the hepatic vein [30-34]. It has been postulated that missile migration against the blood flow in venous vessels could occur because of gravity, the patient's position (upright) at the moment of wounding and/or positional changes of the body, the weight and shape of the missile, and possible low flow states [30]. Although the size and the weight of an 125I radioactive seed (4.5 mm in length, 0.8 mm in diameter, and about 10 mg in weight per seed) are smaller than those of a bullet, it is considered that a seed could move against the blood flow in major venous vessels because of gravity. The reasons are as follows. The specific gravity of the seed (about 4 g/mL) is much higher than that of blood, and the seed is less water resistant because of its rod-shaped structure. In addition, a seed would usually be located near the wall of major venous vessels due to gravity and the patient's position, and therefore, a seed would be affected by a relatively slow stream of blood near the vascular wall compared with that in the center of major venous vessels. It is known that in major venous vessels, the blood flow is generally laminar, and the distribution of velocity across the tube is parabolic [35]. Therefore, the velocity of blood flow decreases from the center toward the wall of the venous vessel tube [35].\\nThe mechanism of seed migration to various sites could be explained by the assumption that a seed could move in major venous vessels against the blood flow by gravity. In the present cases, seed migration to the kidneys, the liver, and the right groin area (probably a branch of the right femoral vein) (Case 3) would have occurred in venous vessels, partially in a retrograde fashion by gravity, without being passed through a paradoxical route. This explanation does not require the assumption that a patient would have a right-to-left shunt.\\nWe assume that seed movement in venous vessels would reflect not only the force of the blood flow but also the force of gravity, and that a seed sometimes would move in venous vessels against the blood flow, because the gravity could overcome the blood flow. The explanation is as follows. Intravascular missile migration following gunshot injury has been reported [30-34]. A missile sometimes moves in a retrograde fashion against the normal blood flow in major venous vessels including the IVC, and sometimes lodges in the renal vein and the hepatic vein [30-34]. It has been postulated that missile migration against the blood flow in venous vessels could occur because of gravity, the patient's position (upright) at the moment of wounding and/or positional changes of the body, the weight and shape of the missile, and possible low flow states [30]. Although the size and the weight of an 125I radioactive seed (4.5 mm in length, 0.8 mm in diameter, and about 10 mg in weight per seed) are smaller than those of a bullet, it is considered that a seed could move against the blood flow in major venous vessels because of gravity. The reasons are as follows. The specific gravity of the seed (about 4 g/mL) is much higher than that of blood, and the seed is less water resistant because of its rod-shaped structure. In addition, a seed would usually be located near the wall of major venous vessels due to gravity and the patient's position, and therefore, a seed would be affected by a relatively slow stream of blood near the vascular wall compared with that in the center of major venous vessels. It is known that in major venous vessels, the blood flow is generally laminar, and the distribution of velocity across the tube is parabolic [35]. Therefore, the velocity of blood flow decreases from the center toward the wall of the venous vessel tube [35].\\nThe mechanism of seed migration to various sites could be explained by the assumption that a seed could move in major venous vessels against the blood flow by gravity. In the present cases, seed migration to the kidneys, the liver, and the right groin area (probably a branch of the right femoral vein) (Case 3) would have occurred in venous vessels, partially in a retrograde fashion by gravity, without being passed through a paradoxical route. This explanation does not require the assumption that a patient would have a right-to-left shunt.\\n Influence of seed migration on postimplant dosimetry The postimplant prostate D90 was not significantly different between patients with and without seed migration. Moreover, in 15 patients who had multiple migrated seeds, the postimplant prostate D90 was relatively acceptable, and no supplemental seed implantation was required. These results indicate that seed migration did not have a significant effect on postimplant prostate dosimetry in the present study. Possible reasons are as follows. First, in most patients with seed migration, only one or two seeds had migrated, which would have less effect on the dosimetry of the prostate. Tapen et al. have suggested that the loss of a few seeds may not have a significant effect on dose homogeneity or total dose to the prostate [5]. Second, seed migration would have much less effect on the dosimetry of the prostate than other mechanisms of seed loss, such as seed misplacement to the seminal vesicle or perineum and being voided in the urine postoperatively. Merrick et al. have reported that seed migration to the chest accounted for only 10% of total seed loss from the prostate region, highlighting the importance of other mechanisms of loss [13].\\nThe postimplant prostate D90 was not significantly different between patients with and without seed migration. Moreover, in 15 patients who had multiple migrated seeds, the postimplant prostate D90 was relatively acceptable, and no supplemental seed implantation was required. These results indicate that seed migration did not have a significant effect on postimplant prostate dosimetry in the present study. Possible reasons are as follows. First, in most patients with seed migration, only one or two seeds had migrated, which would have less effect on the dosimetry of the prostate. Tapen et al. have suggested that the loss of a few seeds may not have a significant effect on dose homogeneity or total dose to the prostate [5]. Second, seed migration would have much less effect on the dosimetry of the prostate than other mechanisms of seed loss, such as seed misplacement to the seminal vesicle or perineum and being voided in the urine postoperatively. Merrick et al. have reported that seed migration to the chest accounted for only 10% of total seed loss from the prostate region, highlighting the importance of other mechanisms of loss [13].\\n Seed migration-related sequelae In the present study, none of the 66 patients had symptoms related to the migrated seeds. Although it has been reported that most patients with seed migration are asymptomatic, there have been a few reports of seed migration-related sequelae, such as a few anecdotal instances of cardiac arrhythmia, myocardial infarction, and radiation pneumonitis [21,36,37]. Therefore, it is important to try to reduce the incidence of seed migration. The use of seeds linked with an absorbable suture material has been associated with a dramatically decreased rate of seed migration, although a potentially higher risk of radiotoxicity to the urethra and rectum has been pointed out [1,3,5,38]. In our study, linked seeds were not administered, because they are not commercially available in our country at present.\\nIn the present study, none of the 66 patients had symptoms related to the migrated seeds. Although it has been reported that most patients with seed migration are asymptomatic, there have been a few reports of seed migration-related sequelae, such as a few anecdotal instances of cardiac arrhythmia, myocardial infarction, and radiation pneumonitis [21,36,37]. Therefore, it is important to try to reduce the incidence of seed migration. The use of seeds linked with an absorbable suture material has been associated with a dramatically decreased rate of seed migration, although a potentially higher risk of radiotoxicity to the urethra and rectum has been pointed out [1,3,5,38]. In our study, linked seeds were not administered, because they are not commercially available in our country at present.\",\n \"We found that 0.36% of implanted seeds migrated to the chest in 20% of our patient population, similar to previous reports (Table 2). It has been reported that the incidence of seed migration to the chest can be as high as 55% per patient population and 0.98% per number of implanted seeds (Table 2) [9]. The variability of the incidence of seed migration to the chest among the reported results is considered to be attributed to different types of seeds (linked or loose), different designs of seed placement (intraprostatic or extraprostatic), different timings of follow-up radiographs, and different protocols of follow-up chest radiographs (orthogonal or AP alone) (Table 2) [1-5,9,13].\\nLiterature Survey of Seed Migration\\n*Linked seeds were restricted to needles placed at the periphery of the prostate implant. All seeds placed centrally, close to the urethra, were loose seeds. On average, 8-10 loose seeds were used.\\n†Of the total 125I seeds, 64% were linked seeds. Linked seeds were limited to the region of the capsule/periprostatic region. Loose seeds were utilized in the central aspect of the gland.\\n††9 patients, within 14 days; 75 patients, more than 30 days.\\n§11patients within 14 days; 61patients, more than 30 days.\\n||Of the 20 patients who had postimplant chest radiographs obtained within 14 days of brachytherapy, none had seeds detected in the lungs. Categorizing patients by isotope, for 125I, 18/75 (24.0%) and for 103Pd, 16/61 (26.2%) with chest radiographs beyond 30 days had seeds found in the lungs.\\n¶A mixture of linked and free seeds, with a median 74% of needles per case containing seeds in linked, with central and posterior needles typically containing loose seeds.\\nIn contrast, the incidence of seed migration to the abdomen and pelvis has been reported rarely (Table 2). A possible reason is that the American Brachytherapy Society does not specifically recommend follow-up abdominal and pelvic radiographs after seed implantation [6]. Therefore, in most institutions, follow-up abdominal and pelvic radiographs would not be undertaken routinely. However, we found that seed migration to the abdomen and pelvis occurred in 2.2% and 5.6%, respectively, of our patient population. Although the incidence of seed migration to the abdomen and pelvis is lower than that of seed migration to the chest, we would consider it advisable to undertake follow-up abdominal and pelvic radiographs after seed implantation.\",\n \"One day, two weeks, and three months after seed implantation, follow-up chest radiographs showed 22%, 88%, and 91%, respectively, of 69 seeds that eventually migrated to the chest. These results mean that 22%, 66%, and 2.9% of these 69 seeds migrated to the chest within one day, between one day and two weeks, and between two weeks and three months after seed implantation, respectively. About 90% of these 69 seeds migrated to the chest within two weeks after seed implantation. These results are similar to a previous report [13]. Merrick et al. have speculated that seed migration to the chest may be most likely to occur between 14 and 28 days after seed implantation [13]. Although we observed that several seeds migrated to the chest more than six months after seed implantation, this finding should be considered exceptional. Therefore, it is suggested that follow-up chest radiographs should be undertaken two weeks, or preferably a few months, after seed implantation to detect most seeds that will migrate to the chest.\",\n \"The results of the present study show a total of four and five cases of seed migration to the kidneys and Batson's vertebral venous plexus, respectively, at only one institution, which suggests that such cases are not very rare. Meanwhile, in previous studies, a total of only four and four cases of seed migration to the kidneys and Batson's vertebral venous plexus, respectively, have been reported as rare cases, which is in disagreement with our conclusion [22,23,26,29]. The same number or more cases of seed migration to these areas were found in our single study compared with all previous studies. A possible explanation is that, in the present study, orthogonal chest radiographs, an abdominal radiograph, and a pelvic radiograph were undertaken routinely to detect seed migration to the chest, abdomen, and pelvis at several time points after seed implantation. Moreover, in all patients who had seed migration to the abdomen and pelvis, a CT scan was undertaken to identify the exact location of the migrated seeds. Consequently, more cases of seed migration to the kidneys and Batson's vertebral venous plexus were found in the present study. We speculate that some seed migration to the kidneys and Batson's vertebral venous plexus might have gone undetected in other institutions.\",\n \"Previous investigators have explained seed migration to the kidneys as follows: migrated seeds in venous vessels would enter the systemic circulation through a right-to-left shunt, such as a patent foramen ovale or pulmonary arteriovenous malformation, and then embolize to a branch of the renal arteries [26]. We call this route of seed migration through a right-to-left shunt \\\"a paradoxical route.\\\"\\nThe present study has provided some interesting cases of seed migration to the kidneys, the mechanism of which is difficult to explain. In Case 1 of the present study, a seed had migrated to the right side of the middle abdomen, which was considered to be separated from the IVC, and then had relocated to the left kidney (Figure 1A-C). If the seed had initially embolized to an artery of the right side of the middle abdomen, such as a branch of the right renal artery, through a paradoxical route, it is difficult to explain how the seed would have relocated to the left kidney. In Case 2, two seeds had migrated to the same right kidney (Figure 2A-C). The mechanism of seed migration to the kidney through a paradoxical route is too complicated. Therefore, it is highly unlikely that two seeds would happen to migrate to the same right kidney through a paradoxical route by chance, although the possibility cannot be completely excluded. Other possible mechanisms should be proposed to explain how seed migration to the kidneys would have occurred in the two cases mentioned above.\",\n \"We assume that seed movement in venous vessels would reflect not only the force of the blood flow but also the force of gravity, and that a seed sometimes would move in venous vessels against the blood flow, because the gravity could overcome the blood flow. The explanation is as follows. Intravascular missile migration following gunshot injury has been reported [30-34]. A missile sometimes moves in a retrograde fashion against the normal blood flow in major venous vessels including the IVC, and sometimes lodges in the renal vein and the hepatic vein [30-34]. It has been postulated that missile migration against the blood flow in venous vessels could occur because of gravity, the patient's position (upright) at the moment of wounding and/or positional changes of the body, the weight and shape of the missile, and possible low flow states [30]. Although the size and the weight of an 125I radioactive seed (4.5 mm in length, 0.8 mm in diameter, and about 10 mg in weight per seed) are smaller than those of a bullet, it is considered that a seed could move against the blood flow in major venous vessels because of gravity. The reasons are as follows. The specific gravity of the seed (about 4 g/mL) is much higher than that of blood, and the seed is less water resistant because of its rod-shaped structure. In addition, a seed would usually be located near the wall of major venous vessels due to gravity and the patient's position, and therefore, a seed would be affected by a relatively slow stream of blood near the vascular wall compared with that in the center of major venous vessels. It is known that in major venous vessels, the blood flow is generally laminar, and the distribution of velocity across the tube is parabolic [35]. Therefore, the velocity of blood flow decreases from the center toward the wall of the venous vessel tube [35].\\nThe mechanism of seed migration to various sites could be explained by the assumption that a seed could move in major venous vessels against the blood flow by gravity. In the present cases, seed migration to the kidneys, the liver, and the right groin area (probably a branch of the right femoral vein) (Case 3) would have occurred in venous vessels, partially in a retrograde fashion by gravity, without being passed through a paradoxical route. This explanation does not require the assumption that a patient would have a right-to-left shunt.\",\n \"The postimplant prostate D90 was not significantly different between patients with and without seed migration. Moreover, in 15 patients who had multiple migrated seeds, the postimplant prostate D90 was relatively acceptable, and no supplemental seed implantation was required. These results indicate that seed migration did not have a significant effect on postimplant prostate dosimetry in the present study. Possible reasons are as follows. First, in most patients with seed migration, only one or two seeds had migrated, which would have less effect on the dosimetry of the prostate. Tapen et al. have suggested that the loss of a few seeds may not have a significant effect on dose homogeneity or total dose to the prostate [5]. Second, seed migration would have much less effect on the dosimetry of the prostate than other mechanisms of seed loss, such as seed misplacement to the seminal vesicle or perineum and being voided in the urine postoperatively. Merrick et al. have reported that seed migration to the chest accounted for only 10% of total seed loss from the prostate region, highlighting the importance of other mechanisms of loss [13].\",\n \"In the present study, none of the 66 patients had symptoms related to the migrated seeds. Although it has been reported that most patients with seed migration are asymptomatic, there have been a few reports of seed migration-related sequelae, such as a few anecdotal instances of cardiac arrhythmia, myocardial infarction, and radiation pneumonitis [21,36,37]. Therefore, it is important to try to reduce the incidence of seed migration. The use of seeds linked with an absorbable suture material has been associated with a dramatically decreased rate of seed migration, although a potentially higher risk of radiotoxicity to the urethra and rectum has been pointed out [1,3,5,38]. In our study, linked seeds were not administered, because they are not commercially available in our country at present.\",\n \"In the present study, we determined the incidence of seed migration not only to the chest, but also to the abdomen and pelvis. Although the incidence of seed migration to the abdomen and pelvis was lower than that of seed migration to the chest, it would be advisable to undertake follow-up abdominal and pelvic radiographs after seed implantation. Seed migration to the chest occurred predominantly within two weeks and, therefore, it is suggested that follow-up chest radiographs should be undertaken two weeks or later after seed implantation. Seed migration to the kidneys and Batson's vertebral venous plexus was not very rare. Seed migration did not significantly affect postimplant prostate D90.\"\n]"},"all_sections_sec_types":{"kind":"list like","value":[null,"methods",null,null,null,null,null,null,null,null,null,null,null,null,null,null],"string":"[\n null,\n \"methods\",\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null\n]"},"keywords":{"kind":"list like","value":["Brachytherapy","125I","Migration","Prostate cancer","Seed"],"string":"[\n \"Brachytherapy\",\n \"125I\",\n \"Migration\",\n \"Prostate cancer\",\n \"Seed\"\n]"},"whole_article_text":{"kind":"string","value":"Background:\nSeed migration is a well-recognized event that occurs after transperineal interstitial prostate brachytherapy, and it is observed more often with loose seeds than with linked seeds [1-5].\nIt is well known that the most frequent site of seed migration is the chest. The American Brachytherapy Society has advised that a chest radiograph should be undertaken at the first follow-up visit to scan the lungs for embolized seeds [6]. Consequently, the incidence of seed migration to the chest has been well reported [1,2,4,5,7-19]. However, documentation of the incidence of seed migration to the abdomen and pelvis is rare. Rare cases of seed migration to a coronary artery, the right ventricle, the liver, the kidneys, Batson's vertebral venous plexus, and the left testicular vein have been reported [20-26]. However, it has never been fully determined whether seed migration to these locations is really rare.\nThe primary purposes of the present study were to determine the incidence of seed migration not only to the chest, but also to the abdomen and the pelvis at our institution and to identify the exact location of the seeds that had migrated to the abdomen and pelvis with computed tomography (CT). The secondary purpose was to determine the impact of seed migration on postimplant dosimetry.\n\nMethods:\nWe reviewed the records of 267 patients who underwent transperineal interstitial prostate brachytherapy with loose 125I seeds for clinical T1/T2 prostate cancer at our institution. Table 1 details the characteristics of all 267 patients. Two patients (0.75%) received brachytherapy plus external beam radiotherapy (45 Gy in 1.8 Gy fractions). One hundred twenty-three of the 267 (46.1%) patients also underwent neoadjuvant hormonal therapy (NHT), which consisted of luteinizing hormone-releasing hormone agonist and antiandrogens. NHT was generally undertaken in patients with a prostate volume >40 cc or those with pubic arch interference by transrectal ultrasound (TRUS) at the preimplant volume study [27].\nPatient Characteristics (N = 267)\nData are presented as mean ± standard error (range) or number (percent) of patients.\nAbbreviations: PSA = prostate-specific antigen; TRUS = transrectal ultrasound\nOne month before seed implantation, a preplan was obtained with TRUS images taken at 5 mm intervals from the base to the apex of the prostate with the patient in the dorsal lithotomy position. The planning target volume included the prostate gland, with a margin of 3 mm anteriorly and laterally and 5 mm in the cranial and caudal directions. No margin was added posteriorly at the rectal interface. Treatment planning used a peripheral or a modified peripheral approach. For the 265 patients who received brachytherapy alone, the prescribed brachytherapy dose was 145 Gy and 160 Gy for the first 163 patients and the subsequent 102 patients, respectively. For the remaining two patients who received brachytherapy plus external beam radiotherapy, the prescribed brachytherapy dose was 110 Gy. TG 43 formalism was used in the preplanning and postimplant dosimetry analyses [28]. All 267 patients were treated with loose 125I radioactive seeds with a Mick applicator (Mick Radio-Nuclear Instruments, Bronx, NY). To ensure that no seeds were left in the bladder, postoperative fluoroscopic images were obtained. Prior to discharge, postoperative surveys of voided urine were conducted to detect voided seeds.\nOrthogonal chest radiographs, an abdominal radiograph, and a pelvic radiograph were undertaken to document the occurrence and sites of seed migration one day after seed implantation. These follow-up radiographs were undertaken routinely at each outpatient visit. Patients returned to our outpatient clinic two weeks and three months after seed implantation, then at three-month intervals for the first three years and at six-month intervals thereafter. Seed migration to the chest and the abdomen was recorded when one or more seeds were visualized on orthogonal chest radiographs and the anteroposterior (AP) abdominal radiograph, respectively. Seed migration to the pelvis was recorded when one or more seeds were separated from the main seed cluster on an AP pelvic radiograph. However, seeds placed into the bladder and the seminal vesicles or seeds placed inferior to the prostate by mistake were not scored as migrated. Seeds voided in the urine were not scored as migrated. Subsequently, all patients who had seed migration to the abdomen and pelvis underwent a CT scan to identify the exact location of the migrated seeds. The incidence of seed migration to the chest, abdomen, and pelvis was calculated.\nPostimplant dosimetric analysis by CT was performed one month after seed implantation. The seed count in the region of the prostate gland was determined on the AP pelvic radiographs obtained two weeks after seed implantation. The postimplant prostate D90 (the dose received by 90% of the volume of the prostate) value was compared between patients with and without seed migration. Statistical analysis was performed with Student's t-test. A p value of <0.05 was considered statistically significant.\n\nResults:\nIn total, 19,236 seeds were implanted in 267 patients. All 267 patients underwent follow-up radiographs. Median follow-up was 41 months (range, 8.5-76 months).\nAt one day after seed implantation, follow-up radiographs demonstrated that 41 of the 19,236 (0.21%) seeds migrated in 37 of the 267 (13.9%) patients: three seeds in one patient, two seeds in each of two patients, and a single seed in each of the remaining 34 patients. Fifteen (0.078%) seeds migrated to the chest in 15 (5.6%) patients. One (0.0052%) seed migrated to the abdomen in one (0.37%) patient. Twenty-five (0.13%) seeds migrated to the pelvis in 23 (8.6%) patients.\nAt two weeks after seed implantation, 85 of the 19,236 (0.44%) seeds migrated in 61 of the 267 (22.8%) patients: seven seeds in one patient, three seeds in each of four patients, two seeds in each of 10 patients, and a single seed in each of the remaining 46 patients. Sixty-one (0.32%) seeds migrated to the chest in 48 (18.0%) patients. Seven (0.036%) seeds migrated to the abdomen in six (2.2%) patients. Seventeen (0.088%) seeds migrated to the pelvis in 16 (6.0%) patients.\nAt three months after seed implantation, 87 of the 19,236 (0.45%) seeds migrated in 63 of the 267 (23.6%) patients: seven seeds in one patient, three seeds in each of four patients, two seeds in each of 10 patients, and a single seed in each of the remaining 48 patients. Sixty-three (0.33%) seeds migrated to the chest in 50 (18.7%) patients. Seven (0.036%) seeds migrated to the abdomen in six (2.2%) patients. Seventeen (0.088%) seeds migrated to the pelvis in 16 (6.0%) patients.\nAlthough seed migration occurred predominantly within two weeks after seed implantation, eventually, six seeds were found to migrate or relocate to the chest long after seed implantation: four seeds were found to have migrated to the chest at a median of 27 months (range 9.1-16 months), and two seeds were found to have relocated from the pelvis to the chest at 6.1 and 48 months after seed implantation, respectively. In these patients, follow-up radiographs were undertaken routinely at every outpatient visit; however, migration or relocation of these seeds was not found at the previous visit. Meanwhile, no seed relocation from the chest to other sites was observed in the present study.\nEventually, 91 of the 19,236 (0.47%) seeds migrated in 66 of the 267 (24.7%) patients: seven seeds in one patient, three seeds in each of five patients, two seeds in each of nine patients, and a single seed in each of the remaining 51 patients. Sixty-nine (0.36%) seeds migrated to the chest in 54 (20.2%) patients. Seven (0.036%) seeds migrated to the abdomen in six (2.2%) patients. Fifteen (0.078%) seeds migrated to the pelvis in 15 (5.6%) patients. All 66 patients were informed of seed migration; none of these patients had symptoms related to the migrated seeds.\n Seeds that migrated to the abdomen (seven seeds in six patients) Two of the 19,236 (0.010%) seeds migrated to the liver in two of the 267 (0.75%) patients: a single seed migrated to the liver in each of two patients. Five (0.026%) seeds migrated to the kidneys in four (1.5%) patients: two seeds migrated to the same kidney in one patient, and a single seed migrated to the kidney in each of the remaining three patients. In one patient (Case 1), one day after seed implantation, an abdominal radiograph showed that a seed had migrated to the right side of the middle abdomen, which was considered to be separated from the inferior vena cava (IVC) (Figure 1A). However, two weeks after seed implantation, an abdominal radiograph showed that the seed had disappeared from the right side of the middle abdomen, and showed a seed that had migrated to the left side of the middle abdomen (Figure 1B). On pelvic radiographs, there were no changes in number of seeds that had been implanted into the prostate between one day and two weeks after seed implantation. It was concluded that the seed had relocated from the right side of the middle abdomen to the left side of the middle abdomen. A subsequent abdominal CT demonstrated that the seed had migrated to the left kidney (Figure 1C). In another patient (Case 2), two weeks after seed implantation, an abdominal radiograph showed that two seeds had migrated to the same right kidney (Figure 2A-C).\nCase 1: Relocation from the right side of the middle abdomen to the left kidney. One day after seed implantation, a follow-up abdominal radiograph showed that a seed had migrated to the right side of the middle abdomen (solid arrow) (A). Two weeks later, the seed had relocated from the right side of the middle abdomen to the left side of the middle abdomen (solid arrow) (B). Subsequent computed tomography showed that the seed had migrated to the left kidney (solid arrow) (C).\nCase 2: Migration of two seeds to the same right kidney. Two weeks after seed implantation, a follow-up abdominal radiograph showed that two seeds had migrated to the right side of the middle abdomen (solid arrows) (A). Subsequent computed tomography showed that these two seeds had migrated to the same right kidney (solid arrows) (B,C).\nTwo of the 19,236 (0.010%) seeds migrated to the liver in two of the 267 (0.75%) patients: a single seed migrated to the liver in each of two patients. Five (0.026%) seeds migrated to the kidneys in four (1.5%) patients: two seeds migrated to the same kidney in one patient, and a single seed migrated to the kidney in each of the remaining three patients. In one patient (Case 1), one day after seed implantation, an abdominal radiograph showed that a seed had migrated to the right side of the middle abdomen, which was considered to be separated from the inferior vena cava (IVC) (Figure 1A). However, two weeks after seed implantation, an abdominal radiograph showed that the seed had disappeared from the right side of the middle abdomen, and showed a seed that had migrated to the left side of the middle abdomen (Figure 1B). On pelvic radiographs, there were no changes in number of seeds that had been implanted into the prostate between one day and two weeks after seed implantation. It was concluded that the seed had relocated from the right side of the middle abdomen to the left side of the middle abdomen. A subsequent abdominal CT demonstrated that the seed had migrated to the left kidney (Figure 1C). In another patient (Case 2), two weeks after seed implantation, an abdominal radiograph showed that two seeds had migrated to the same right kidney (Figure 2A-C).\nCase 1: Relocation from the right side of the middle abdomen to the left kidney. One day after seed implantation, a follow-up abdominal radiograph showed that a seed had migrated to the right side of the middle abdomen (solid arrow) (A). Two weeks later, the seed had relocated from the right side of the middle abdomen to the left side of the middle abdomen (solid arrow) (B). Subsequent computed tomography showed that the seed had migrated to the left kidney (solid arrow) (C).\nCase 2: Migration of two seeds to the same right kidney. Two weeks after seed implantation, a follow-up abdominal radiograph showed that two seeds had migrated to the right side of the middle abdomen (solid arrows) (A). Subsequent computed tomography showed that these two seeds had migrated to the same right kidney (solid arrows) (B,C).\n Seeds that migrated to the pelvis (15 seeds in 15 patients) A single seed migrated to the pelvis in each of 15 patients. Five of the 19,236 (0.026%) seeds migrated to Batson's vertebral venous plexus in five of the 267 (1.9%) patients. Four (0.021%) seeds migrated to the sacral venous plexus in four (1.5%) patients. Two (0.010%) seeds migrated to the iliac veins in two (0.75%) patients. Two (0.010%) seeds migrated to the right ischial bone in two (0.75%) patients. Two (0.010%) seeds migrated to the obturator internus muscles in two (0.75%) patients.\nA single seed migrated to the pelvis in each of 15 patients. Five of the 19,236 (0.026%) seeds migrated to Batson's vertebral venous plexus in five of the 267 (1.9%) patients. Four (0.021%) seeds migrated to the sacral venous plexus in four (1.5%) patients. Two (0.010%) seeds migrated to the iliac veins in two (0.75%) patients. Two (0.010%) seeds migrated to the right ischial bone in two (0.75%) patients. Two (0.010%) seeds migrated to the obturator internus muscles in two (0.75%) patients.\n Seed relocation four years after seed implantation In one patient (Case 3), seed relocation was found four years after seed implantation. A seed had migrated to the right groin area one day after seed implantation (Figure 3A-B). Three years and six months after seed implantation, a pelvic radiograph showed that the seed was in the same location. However, four years after seed implantation, a pelvic radiograph showed that the seed had disappeared from the right groin area (Figure 3C), and a chest radiograph showed a seed that had migrated to the left lung, which was not found at three years and six months after seed implantation. It was concluded that the seed had initially lodged in a branch of the right femoral vein, and then relocated to the left lung through the IVC, long after seed implantation.\nCase 3: Seed disappearance from the right groin area four years after seed implantation. One day after seed implantation, a follow-up pelvic radiograph showed that a seed was located inferior to the right side of the pelvis (solid arrow) (A). Subsequent computed tomography showed that the seed had migrated to the right groin area (solid arrow) (B). Four years after seed implantation, a follow-up pelvic radiograph showed that the seed had disappeared from the pelvic area (C).\nIn one patient (Case 3), seed relocation was found four years after seed implantation. A seed had migrated to the right groin area one day after seed implantation (Figure 3A-B). Three years and six months after seed implantation, a pelvic radiograph showed that the seed was in the same location. However, four years after seed implantation, a pelvic radiograph showed that the seed had disappeared from the right groin area (Figure 3C), and a chest radiograph showed a seed that had migrated to the left lung, which was not found at three years and six months after seed implantation. It was concluded that the seed had initially lodged in a branch of the right femoral vein, and then relocated to the left lung through the IVC, long after seed implantation.\nCase 3: Seed disappearance from the right groin area four years after seed implantation. One day after seed implantation, a follow-up pelvic radiograph showed that a seed was located inferior to the right side of the pelvis (solid arrow) (A). Subsequent computed tomography showed that the seed had migrated to the right groin area (solid arrow) (B). Four years after seed implantation, a follow-up pelvic radiograph showed that the seed had disappeared from the pelvic area (C).\n Postimplant dosimetric analysis In the 265 patients who received brachytherapy alone, the postimplant prostate D90 was 175.0 ± 1.3 Gy (mean ± standard error [SE]). In these 265 patients, the postimplant prostate D90 value was not significantly different between patients with and without seed migration (mean ± SE, 175.1 ± 2.3 Gy vs. 175.0 ± 1.5 Gy, respectively, p = 0.992). In 15 patients who had multiple migrated seeds, the postimplant prostate D90 ranged from 137.2 to 198.5 Gy (mean ± SE, 171.8 ± 5.5 Gy), which was not significantly different from that in patients without seed migration (p = 0.573).\nIn the remaining two patients who received brachytherapy plus external beam radiotherapy, the postimplant prostate D90 values were 116.4 Gy and 132.6 Gy. These two patients had no seed migration.\nIn the 265 patients who received brachytherapy alone, the postimplant prostate D90 was 175.0 ± 1.3 Gy (mean ± standard error [SE]). In these 265 patients, the postimplant prostate D90 value was not significantly different between patients with and without seed migration (mean ± SE, 175.1 ± 2.3 Gy vs. 175.0 ± 1.5 Gy, respectively, p = 0.992). In 15 patients who had multiple migrated seeds, the postimplant prostate D90 ranged from 137.2 to 198.5 Gy (mean ± SE, 171.8 ± 5.5 Gy), which was not significantly different from that in patients without seed migration (p = 0.573).\nIn the remaining two patients who received brachytherapy plus external beam radiotherapy, the postimplant prostate D90 values were 116.4 Gy and 132.6 Gy. These two patients had no seed migration.\n\nSeeds that migrated to the abdomen (seven seeds in six patients):\nTwo of the 19,236 (0.010%) seeds migrated to the liver in two of the 267 (0.75%) patients: a single seed migrated to the liver in each of two patients. Five (0.026%) seeds migrated to the kidneys in four (1.5%) patients: two seeds migrated to the same kidney in one patient, and a single seed migrated to the kidney in each of the remaining three patients. In one patient (Case 1), one day after seed implantation, an abdominal radiograph showed that a seed had migrated to the right side of the middle abdomen, which was considered to be separated from the inferior vena cava (IVC) (Figure 1A). However, two weeks after seed implantation, an abdominal radiograph showed that the seed had disappeared from the right side of the middle abdomen, and showed a seed that had migrated to the left side of the middle abdomen (Figure 1B). On pelvic radiographs, there were no changes in number of seeds that had been implanted into the prostate between one day and two weeks after seed implantation. It was concluded that the seed had relocated from the right side of the middle abdomen to the left side of the middle abdomen. A subsequent abdominal CT demonstrated that the seed had migrated to the left kidney (Figure 1C). In another patient (Case 2), two weeks after seed implantation, an abdominal radiograph showed that two seeds had migrated to the same right kidney (Figure 2A-C).\nCase 1: Relocation from the right side of the middle abdomen to the left kidney. One day after seed implantation, a follow-up abdominal radiograph showed that a seed had migrated to the right side of the middle abdomen (solid arrow) (A). Two weeks later, the seed had relocated from the right side of the middle abdomen to the left side of the middle abdomen (solid arrow) (B). Subsequent computed tomography showed that the seed had migrated to the left kidney (solid arrow) (C).\nCase 2: Migration of two seeds to the same right kidney. Two weeks after seed implantation, a follow-up abdominal radiograph showed that two seeds had migrated to the right side of the middle abdomen (solid arrows) (A). Subsequent computed tomography showed that these two seeds had migrated to the same right kidney (solid arrows) (B,C).\n\nSeeds that migrated to the pelvis (15 seeds in 15 patients):\nA single seed migrated to the pelvis in each of 15 patients. Five of the 19,236 (0.026%) seeds migrated to Batson's vertebral venous plexus in five of the 267 (1.9%) patients. Four (0.021%) seeds migrated to the sacral venous plexus in four (1.5%) patients. Two (0.010%) seeds migrated to the iliac veins in two (0.75%) patients. Two (0.010%) seeds migrated to the right ischial bone in two (0.75%) patients. Two (0.010%) seeds migrated to the obturator internus muscles in two (0.75%) patients.\n\nSeed relocation four years after seed implantation:\nIn one patient (Case 3), seed relocation was found four years after seed implantation. A seed had migrated to the right groin area one day after seed implantation (Figure 3A-B). Three years and six months after seed implantation, a pelvic radiograph showed that the seed was in the same location. However, four years after seed implantation, a pelvic radiograph showed that the seed had disappeared from the right groin area (Figure 3C), and a chest radiograph showed a seed that had migrated to the left lung, which was not found at three years and six months after seed implantation. It was concluded that the seed had initially lodged in a branch of the right femoral vein, and then relocated to the left lung through the IVC, long after seed implantation.\nCase 3: Seed disappearance from the right groin area four years after seed implantation. One day after seed implantation, a follow-up pelvic radiograph showed that a seed was located inferior to the right side of the pelvis (solid arrow) (A). Subsequent computed tomography showed that the seed had migrated to the right groin area (solid arrow) (B). Four years after seed implantation, a follow-up pelvic radiograph showed that the seed had disappeared from the pelvic area (C).\n\nPostimplant dosimetric analysis:\nIn the 265 patients who received brachytherapy alone, the postimplant prostate D90 was 175.0 ± 1.3 Gy (mean ± standard error [SE]). In these 265 patients, the postimplant prostate D90 value was not significantly different between patients with and without seed migration (mean ± SE, 175.1 ± 2.3 Gy vs. 175.0 ± 1.5 Gy, respectively, p = 0.992). In 15 patients who had multiple migrated seeds, the postimplant prostate D90 ranged from 137.2 to 198.5 Gy (mean ± SE, 171.8 ± 5.5 Gy), which was not significantly different from that in patients without seed migration (p = 0.573).\nIn the remaining two patients who received brachytherapy plus external beam radiotherapy, the postimplant prostate D90 values were 116.4 Gy and 132.6 Gy. These two patients had no seed migration.\n\nDiscussion:\n Incidence of seed migration We found that 0.36% of implanted seeds migrated to the chest in 20% of our patient population, similar to previous reports (Table 2). It has been reported that the incidence of seed migration to the chest can be as high as 55% per patient population and 0.98% per number of implanted seeds (Table 2) [9]. The variability of the incidence of seed migration to the chest among the reported results is considered to be attributed to different types of seeds (linked or loose), different designs of seed placement (intraprostatic or extraprostatic), different timings of follow-up radiographs, and different protocols of follow-up chest radiographs (orthogonal or AP alone) (Table 2) [1-5,9,13].\nLiterature Survey of Seed Migration\n*Linked seeds were restricted to needles placed at the periphery of the prostate implant. All seeds placed centrally, close to the urethra, were loose seeds. On average, 8-10 loose seeds were used.\n†Of the total 125I seeds, 64% were linked seeds. Linked seeds were limited to the region of the capsule/periprostatic region. Loose seeds were utilized in the central aspect of the gland.\n††9 patients, within 14 days; 75 patients, more than 30 days.\n§11patients within 14 days; 61patients, more than 30 days.\n||Of the 20 patients who had postimplant chest radiographs obtained within 14 days of brachytherapy, none had seeds detected in the lungs. Categorizing patients by isotope, for 125I, 18/75 (24.0%) and for 103Pd, 16/61 (26.2%) with chest radiographs beyond 30 days had seeds found in the lungs.\n¶A mixture of linked and free seeds, with a median 74% of needles per case containing seeds in linked, with central and posterior needles typically containing loose seeds.\nIn contrast, the incidence of seed migration to the abdomen and pelvis has been reported rarely (Table 2). A possible reason is that the American Brachytherapy Society does not specifically recommend follow-up abdominal and pelvic radiographs after seed implantation [6]. Therefore, in most institutions, follow-up abdominal and pelvic radiographs would not be undertaken routinely. However, we found that seed migration to the abdomen and pelvis occurred in 2.2% and 5.6%, respectively, of our patient population. Although the incidence of seed migration to the abdomen and pelvis is lower than that of seed migration to the chest, we would consider it advisable to undertake follow-up abdominal and pelvic radiographs after seed implantation.\nWe found that 0.36% of implanted seeds migrated to the chest in 20% of our patient population, similar to previous reports (Table 2). It has been reported that the incidence of seed migration to the chest can be as high as 55% per patient population and 0.98% per number of implanted seeds (Table 2) [9]. The variability of the incidence of seed migration to the chest among the reported results is considered to be attributed to different types of seeds (linked or loose), different designs of seed placement (intraprostatic or extraprostatic), different timings of follow-up radiographs, and different protocols of follow-up chest radiographs (orthogonal or AP alone) (Table 2) [1-5,9,13].\nLiterature Survey of Seed Migration\n*Linked seeds were restricted to needles placed at the periphery of the prostate implant. All seeds placed centrally, close to the urethra, were loose seeds. On average, 8-10 loose seeds were used.\n†Of the total 125I seeds, 64% were linked seeds. Linked seeds were limited to the region of the capsule/periprostatic region. Loose seeds were utilized in the central aspect of the gland.\n††9 patients, within 14 days; 75 patients, more than 30 days.\n§11patients within 14 days; 61patients, more than 30 days.\n||Of the 20 patients who had postimplant chest radiographs obtained within 14 days of brachytherapy, none had seeds detected in the lungs. Categorizing patients by isotope, for 125I, 18/75 (24.0%) and for 103Pd, 16/61 (26.2%) with chest radiographs beyond 30 days had seeds found in the lungs.\n¶A mixture of linked and free seeds, with a median 74% of needles per case containing seeds in linked, with central and posterior needles typically containing loose seeds.\nIn contrast, the incidence of seed migration to the abdomen and pelvis has been reported rarely (Table 2). A possible reason is that the American Brachytherapy Society does not specifically recommend follow-up abdominal and pelvic radiographs after seed implantation [6]. Therefore, in most institutions, follow-up abdominal and pelvic radiographs would not be undertaken routinely. However, we found that seed migration to the abdomen and pelvis occurred in 2.2% and 5.6%, respectively, of our patient population. Although the incidence of seed migration to the abdomen and pelvis is lower than that of seed migration to the chest, we would consider it advisable to undertake follow-up abdominal and pelvic radiographs after seed implantation.\n The dynamics of seed migration to the chest One day, two weeks, and three months after seed implantation, follow-up chest radiographs showed 22%, 88%, and 91%, respectively, of 69 seeds that eventually migrated to the chest. These results mean that 22%, 66%, and 2.9% of these 69 seeds migrated to the chest within one day, between one day and two weeks, and between two weeks and three months after seed implantation, respectively. About 90% of these 69 seeds migrated to the chest within two weeks after seed implantation. These results are similar to a previous report [13]. Merrick et al. have speculated that seed migration to the chest may be most likely to occur between 14 and 28 days after seed implantation [13]. Although we observed that several seeds migrated to the chest more than six months after seed implantation, this finding should be considered exceptional. Therefore, it is suggested that follow-up chest radiographs should be undertaken two weeks, or preferably a few months, after seed implantation to detect most seeds that will migrate to the chest.\nOne day, two weeks, and three months after seed implantation, follow-up chest radiographs showed 22%, 88%, and 91%, respectively, of 69 seeds that eventually migrated to the chest. These results mean that 22%, 66%, and 2.9% of these 69 seeds migrated to the chest within one day, between one day and two weeks, and between two weeks and three months after seed implantation, respectively. About 90% of these 69 seeds migrated to the chest within two weeks after seed implantation. These results are similar to a previous report [13]. Merrick et al. have speculated that seed migration to the chest may be most likely to occur between 14 and 28 days after seed implantation [13]. Although we observed that several seeds migrated to the chest more than six months after seed implantation, this finding should be considered exceptional. Therefore, it is suggested that follow-up chest radiographs should be undertaken two weeks, or preferably a few months, after seed implantation to detect most seeds that will migrate to the chest.\n Seed migration to the kidneys and Batson's vertebral plexus is not very rare The results of the present study show a total of four and five cases of seed migration to the kidneys and Batson's vertebral venous plexus, respectively, at only one institution, which suggests that such cases are not very rare. Meanwhile, in previous studies, a total of only four and four cases of seed migration to the kidneys and Batson's vertebral venous plexus, respectively, have been reported as rare cases, which is in disagreement with our conclusion [22,23,26,29]. The same number or more cases of seed migration to these areas were found in our single study compared with all previous studies. A possible explanation is that, in the present study, orthogonal chest radiographs, an abdominal radiograph, and a pelvic radiograph were undertaken routinely to detect seed migration to the chest, abdomen, and pelvis at several time points after seed implantation. Moreover, in all patients who had seed migration to the abdomen and pelvis, a CT scan was undertaken to identify the exact location of the migrated seeds. Consequently, more cases of seed migration to the kidneys and Batson's vertebral venous plexus were found in the present study. We speculate that some seed migration to the kidneys and Batson's vertebral venous plexus might have gone undetected in other institutions.\nThe results of the present study show a total of four and five cases of seed migration to the kidneys and Batson's vertebral venous plexus, respectively, at only one institution, which suggests that such cases are not very rare. Meanwhile, in previous studies, a total of only four and four cases of seed migration to the kidneys and Batson's vertebral venous plexus, respectively, have been reported as rare cases, which is in disagreement with our conclusion [22,23,26,29]. The same number or more cases of seed migration to these areas were found in our single study compared with all previous studies. A possible explanation is that, in the present study, orthogonal chest radiographs, an abdominal radiograph, and a pelvic radiograph were undertaken routinely to detect seed migration to the chest, abdomen, and pelvis at several time points after seed implantation. Moreover, in all patients who had seed migration to the abdomen and pelvis, a CT scan was undertaken to identify the exact location of the migrated seeds. Consequently, more cases of seed migration to the kidneys and Batson's vertebral venous plexus were found in the present study. We speculate that some seed migration to the kidneys and Batson's vertebral venous plexus might have gone undetected in other institutions.\n In some cases of seed migration to the kidneys, the mechanism is difficult to explain Previous investigators have explained seed migration to the kidneys as follows: migrated seeds in venous vessels would enter the systemic circulation through a right-to-left shunt, such as a patent foramen ovale or pulmonary arteriovenous malformation, and then embolize to a branch of the renal arteries [26]. We call this route of seed migration through a right-to-left shunt \"a paradoxical route.\"\nThe present study has provided some interesting cases of seed migration to the kidneys, the mechanism of which is difficult to explain. In Case 1 of the present study, a seed had migrated to the right side of the middle abdomen, which was considered to be separated from the IVC, and then had relocated to the left kidney (Figure 1A-C). If the seed had initially embolized to an artery of the right side of the middle abdomen, such as a branch of the right renal artery, through a paradoxical route, it is difficult to explain how the seed would have relocated to the left kidney. In Case 2, two seeds had migrated to the same right kidney (Figure 2A-C). The mechanism of seed migration to the kidney through a paradoxical route is too complicated. Therefore, it is highly unlikely that two seeds would happen to migrate to the same right kidney through a paradoxical route by chance, although the possibility cannot be completely excluded. Other possible mechanisms should be proposed to explain how seed migration to the kidneys would have occurred in the two cases mentioned above.\nPrevious investigators have explained seed migration to the kidneys as follows: migrated seeds in venous vessels would enter the systemic circulation through a right-to-left shunt, such as a patent foramen ovale or pulmonary arteriovenous malformation, and then embolize to a branch of the renal arteries [26]. We call this route of seed migration through a right-to-left shunt \"a paradoxical route.\"\nThe present study has provided some interesting cases of seed migration to the kidneys, the mechanism of which is difficult to explain. In Case 1 of the present study, a seed had migrated to the right side of the middle abdomen, which was considered to be separated from the IVC, and then had relocated to the left kidney (Figure 1A-C). If the seed had initially embolized to an artery of the right side of the middle abdomen, such as a branch of the right renal artery, through a paradoxical route, it is difficult to explain how the seed would have relocated to the left kidney. In Case 2, two seeds had migrated to the same right kidney (Figure 2A-C). The mechanism of seed migration to the kidney through a paradoxical route is too complicated. Therefore, it is highly unlikely that two seeds would happen to migrate to the same right kidney through a paradoxical route by chance, although the possibility cannot be completely excluded. Other possible mechanisms should be proposed to explain how seed migration to the kidneys would have occurred in the two cases mentioned above.\n Other possible mechanisms of seed migration We assume that seed movement in venous vessels would reflect not only the force of the blood flow but also the force of gravity, and that a seed sometimes would move in venous vessels against the blood flow, because the gravity could overcome the blood flow. The explanation is as follows. Intravascular missile migration following gunshot injury has been reported [30-34]. A missile sometimes moves in a retrograde fashion against the normal blood flow in major venous vessels including the IVC, and sometimes lodges in the renal vein and the hepatic vein [30-34]. It has been postulated that missile migration against the blood flow in venous vessels could occur because of gravity, the patient's position (upright) at the moment of wounding and/or positional changes of the body, the weight and shape of the missile, and possible low flow states [30]. Although the size and the weight of an 125I radioactive seed (4.5 mm in length, 0.8 mm in diameter, and about 10 mg in weight per seed) are smaller than those of a bullet, it is considered that a seed could move against the blood flow in major venous vessels because of gravity. The reasons are as follows. The specific gravity of the seed (about 4 g/mL) is much higher than that of blood, and the seed is less water resistant because of its rod-shaped structure. In addition, a seed would usually be located near the wall of major venous vessels due to gravity and the patient's position, and therefore, a seed would be affected by a relatively slow stream of blood near the vascular wall compared with that in the center of major venous vessels. It is known that in major venous vessels, the blood flow is generally laminar, and the distribution of velocity across the tube is parabolic [35]. Therefore, the velocity of blood flow decreases from the center toward the wall of the venous vessel tube [35].\nThe mechanism of seed migration to various sites could be explained by the assumption that a seed could move in major venous vessels against the blood flow by gravity. In the present cases, seed migration to the kidneys, the liver, and the right groin area (probably a branch of the right femoral vein) (Case 3) would have occurred in venous vessels, partially in a retrograde fashion by gravity, without being passed through a paradoxical route. This explanation does not require the assumption that a patient would have a right-to-left shunt.\nWe assume that seed movement in venous vessels would reflect not only the force of the blood flow but also the force of gravity, and that a seed sometimes would move in venous vessels against the blood flow, because the gravity could overcome the blood flow. The explanation is as follows. Intravascular missile migration following gunshot injury has been reported [30-34]. A missile sometimes moves in a retrograde fashion against the normal blood flow in major venous vessels including the IVC, and sometimes lodges in the renal vein and the hepatic vein [30-34]. It has been postulated that missile migration against the blood flow in venous vessels could occur because of gravity, the patient's position (upright) at the moment of wounding and/or positional changes of the body, the weight and shape of the missile, and possible low flow states [30]. Although the size and the weight of an 125I radioactive seed (4.5 mm in length, 0.8 mm in diameter, and about 10 mg in weight per seed) are smaller than those of a bullet, it is considered that a seed could move against the blood flow in major venous vessels because of gravity. The reasons are as follows. The specific gravity of the seed (about 4 g/mL) is much higher than that of blood, and the seed is less water resistant because of its rod-shaped structure. In addition, a seed would usually be located near the wall of major venous vessels due to gravity and the patient's position, and therefore, a seed would be affected by a relatively slow stream of blood near the vascular wall compared with that in the center of major venous vessels. It is known that in major venous vessels, the blood flow is generally laminar, and the distribution of velocity across the tube is parabolic [35]. Therefore, the velocity of blood flow decreases from the center toward the wall of the venous vessel tube [35].\nThe mechanism of seed migration to various sites could be explained by the assumption that a seed could move in major venous vessels against the blood flow by gravity. In the present cases, seed migration to the kidneys, the liver, and the right groin area (probably a branch of the right femoral vein) (Case 3) would have occurred in venous vessels, partially in a retrograde fashion by gravity, without being passed through a paradoxical route. This explanation does not require the assumption that a patient would have a right-to-left shunt.\n Influence of seed migration on postimplant dosimetry The postimplant prostate D90 was not significantly different between patients with and without seed migration. Moreover, in 15 patients who had multiple migrated seeds, the postimplant prostate D90 was relatively acceptable, and no supplemental seed implantation was required. These results indicate that seed migration did not have a significant effect on postimplant prostate dosimetry in the present study. Possible reasons are as follows. First, in most patients with seed migration, only one or two seeds had migrated, which would have less effect on the dosimetry of the prostate. Tapen et al. have suggested that the loss of a few seeds may not have a significant effect on dose homogeneity or total dose to the prostate [5]. Second, seed migration would have much less effect on the dosimetry of the prostate than other mechanisms of seed loss, such as seed misplacement to the seminal vesicle or perineum and being voided in the urine postoperatively. Merrick et al. have reported that seed migration to the chest accounted for only 10% of total seed loss from the prostate region, highlighting the importance of other mechanisms of loss [13].\nThe postimplant prostate D90 was not significantly different between patients with and without seed migration. Moreover, in 15 patients who had multiple migrated seeds, the postimplant prostate D90 was relatively acceptable, and no supplemental seed implantation was required. These results indicate that seed migration did not have a significant effect on postimplant prostate dosimetry in the present study. Possible reasons are as follows. First, in most patients with seed migration, only one or two seeds had migrated, which would have less effect on the dosimetry of the prostate. Tapen et al. have suggested that the loss of a few seeds may not have a significant effect on dose homogeneity or total dose to the prostate [5]. Second, seed migration would have much less effect on the dosimetry of the prostate than other mechanisms of seed loss, such as seed misplacement to the seminal vesicle or perineum and being voided in the urine postoperatively. Merrick et al. have reported that seed migration to the chest accounted for only 10% of total seed loss from the prostate region, highlighting the importance of other mechanisms of loss [13].\n Seed migration-related sequelae In the present study, none of the 66 patients had symptoms related to the migrated seeds. Although it has been reported that most patients with seed migration are asymptomatic, there have been a few reports of seed migration-related sequelae, such as a few anecdotal instances of cardiac arrhythmia, myocardial infarction, and radiation pneumonitis [21,36,37]. Therefore, it is important to try to reduce the incidence of seed migration. The use of seeds linked with an absorbable suture material has been associated with a dramatically decreased rate of seed migration, although a potentially higher risk of radiotoxicity to the urethra and rectum has been pointed out [1,3,5,38]. In our study, linked seeds were not administered, because they are not commercially available in our country at present.\nIn the present study, none of the 66 patients had symptoms related to the migrated seeds. Although it has been reported that most patients with seed migration are asymptomatic, there have been a few reports of seed migration-related sequelae, such as a few anecdotal instances of cardiac arrhythmia, myocardial infarction, and radiation pneumonitis [21,36,37]. Therefore, it is important to try to reduce the incidence of seed migration. The use of seeds linked with an absorbable suture material has been associated with a dramatically decreased rate of seed migration, although a potentially higher risk of radiotoxicity to the urethra and rectum has been pointed out [1,3,5,38]. In our study, linked seeds were not administered, because they are not commercially available in our country at present.\n\nIncidence of seed migration:\nWe found that 0.36% of implanted seeds migrated to the chest in 20% of our patient population, similar to previous reports (Table 2). It has been reported that the incidence of seed migration to the chest can be as high as 55% per patient population and 0.98% per number of implanted seeds (Table 2) [9]. The variability of the incidence of seed migration to the chest among the reported results is considered to be attributed to different types of seeds (linked or loose), different designs of seed placement (intraprostatic or extraprostatic), different timings of follow-up radiographs, and different protocols of follow-up chest radiographs (orthogonal or AP alone) (Table 2) [1-5,9,13].\nLiterature Survey of Seed Migration\n*Linked seeds were restricted to needles placed at the periphery of the prostate implant. All seeds placed centrally, close to the urethra, were loose seeds. On average, 8-10 loose seeds were used.\n†Of the total 125I seeds, 64% were linked seeds. Linked seeds were limited to the region of the capsule/periprostatic region. Loose seeds were utilized in the central aspect of the gland.\n††9 patients, within 14 days; 75 patients, more than 30 days.\n§11patients within 14 days; 61patients, more than 30 days.\n||Of the 20 patients who had postimplant chest radiographs obtained within 14 days of brachytherapy, none had seeds detected in the lungs. Categorizing patients by isotope, for 125I, 18/75 (24.0%) and for 103Pd, 16/61 (26.2%) with chest radiographs beyond 30 days had seeds found in the lungs.\n¶A mixture of linked and free seeds, with a median 74% of needles per case containing seeds in linked, with central and posterior needles typically containing loose seeds.\nIn contrast, the incidence of seed migration to the abdomen and pelvis has been reported rarely (Table 2). A possible reason is that the American Brachytherapy Society does not specifically recommend follow-up abdominal and pelvic radiographs after seed implantation [6]. Therefore, in most institutions, follow-up abdominal and pelvic radiographs would not be undertaken routinely. However, we found that seed migration to the abdomen and pelvis occurred in 2.2% and 5.6%, respectively, of our patient population. Although the incidence of seed migration to the abdomen and pelvis is lower than that of seed migration to the chest, we would consider it advisable to undertake follow-up abdominal and pelvic radiographs after seed implantation.\n\nThe dynamics of seed migration to the chest:\nOne day, two weeks, and three months after seed implantation, follow-up chest radiographs showed 22%, 88%, and 91%, respectively, of 69 seeds that eventually migrated to the chest. These results mean that 22%, 66%, and 2.9% of these 69 seeds migrated to the chest within one day, between one day and two weeks, and between two weeks and three months after seed implantation, respectively. About 90% of these 69 seeds migrated to the chest within two weeks after seed implantation. These results are similar to a previous report [13]. Merrick et al. have speculated that seed migration to the chest may be most likely to occur between 14 and 28 days after seed implantation [13]. Although we observed that several seeds migrated to the chest more than six months after seed implantation, this finding should be considered exceptional. Therefore, it is suggested that follow-up chest radiographs should be undertaken two weeks, or preferably a few months, after seed implantation to detect most seeds that will migrate to the chest.\n\nSeed migration to the kidneys and Batson's vertebral plexus is not very rare:\nThe results of the present study show a total of four and five cases of seed migration to the kidneys and Batson's vertebral venous plexus, respectively, at only one institution, which suggests that such cases are not very rare. Meanwhile, in previous studies, a total of only four and four cases of seed migration to the kidneys and Batson's vertebral venous plexus, respectively, have been reported as rare cases, which is in disagreement with our conclusion [22,23,26,29]. The same number or more cases of seed migration to these areas were found in our single study compared with all previous studies. A possible explanation is that, in the present study, orthogonal chest radiographs, an abdominal radiograph, and a pelvic radiograph were undertaken routinely to detect seed migration to the chest, abdomen, and pelvis at several time points after seed implantation. Moreover, in all patients who had seed migration to the abdomen and pelvis, a CT scan was undertaken to identify the exact location of the migrated seeds. Consequently, more cases of seed migration to the kidneys and Batson's vertebral venous plexus were found in the present study. We speculate that some seed migration to the kidneys and Batson's vertebral venous plexus might have gone undetected in other institutions.\n\nIn some cases of seed migration to the kidneys, the mechanism is difficult to explain:\nPrevious investigators have explained seed migration to the kidneys as follows: migrated seeds in venous vessels would enter the systemic circulation through a right-to-left shunt, such as a patent foramen ovale or pulmonary arteriovenous malformation, and then embolize to a branch of the renal arteries [26]. We call this route of seed migration through a right-to-left shunt \"a paradoxical route.\"\nThe present study has provided some interesting cases of seed migration to the kidneys, the mechanism of which is difficult to explain. In Case 1 of the present study, a seed had migrated to the right side of the middle abdomen, which was considered to be separated from the IVC, and then had relocated to the left kidney (Figure 1A-C). If the seed had initially embolized to an artery of the right side of the middle abdomen, such as a branch of the right renal artery, through a paradoxical route, it is difficult to explain how the seed would have relocated to the left kidney. In Case 2, two seeds had migrated to the same right kidney (Figure 2A-C). The mechanism of seed migration to the kidney through a paradoxical route is too complicated. Therefore, it is highly unlikely that two seeds would happen to migrate to the same right kidney through a paradoxical route by chance, although the possibility cannot be completely excluded. Other possible mechanisms should be proposed to explain how seed migration to the kidneys would have occurred in the two cases mentioned above.\n\nOther possible mechanisms of seed migration:\nWe assume that seed movement in venous vessels would reflect not only the force of the blood flow but also the force of gravity, and that a seed sometimes would move in venous vessels against the blood flow, because the gravity could overcome the blood flow. The explanation is as follows. Intravascular missile migration following gunshot injury has been reported [30-34]. A missile sometimes moves in a retrograde fashion against the normal blood flow in major venous vessels including the IVC, and sometimes lodges in the renal vein and the hepatic vein [30-34]. It has been postulated that missile migration against the blood flow in venous vessels could occur because of gravity, the patient's position (upright) at the moment of wounding and/or positional changes of the body, the weight and shape of the missile, and possible low flow states [30]. Although the size and the weight of an 125I radioactive seed (4.5 mm in length, 0.8 mm in diameter, and about 10 mg in weight per seed) are smaller than those of a bullet, it is considered that a seed could move against the blood flow in major venous vessels because of gravity. The reasons are as follows. The specific gravity of the seed (about 4 g/mL) is much higher than that of blood, and the seed is less water resistant because of its rod-shaped structure. In addition, a seed would usually be located near the wall of major venous vessels due to gravity and the patient's position, and therefore, a seed would be affected by a relatively slow stream of blood near the vascular wall compared with that in the center of major venous vessels. It is known that in major venous vessels, the blood flow is generally laminar, and the distribution of velocity across the tube is parabolic [35]. Therefore, the velocity of blood flow decreases from the center toward the wall of the venous vessel tube [35].\nThe mechanism of seed migration to various sites could be explained by the assumption that a seed could move in major venous vessels against the blood flow by gravity. In the present cases, seed migration to the kidneys, the liver, and the right groin area (probably a branch of the right femoral vein) (Case 3) would have occurred in venous vessels, partially in a retrograde fashion by gravity, without being passed through a paradoxical route. This explanation does not require the assumption that a patient would have a right-to-left shunt.\n\nInfluence of seed migration on postimplant dosimetry:\nThe postimplant prostate D90 was not significantly different between patients with and without seed migration. Moreover, in 15 patients who had multiple migrated seeds, the postimplant prostate D90 was relatively acceptable, and no supplemental seed implantation was required. These results indicate that seed migration did not have a significant effect on postimplant prostate dosimetry in the present study. Possible reasons are as follows. First, in most patients with seed migration, only one or two seeds had migrated, which would have less effect on the dosimetry of the prostate. Tapen et al. have suggested that the loss of a few seeds may not have a significant effect on dose homogeneity or total dose to the prostate [5]. Second, seed migration would have much less effect on the dosimetry of the prostate than other mechanisms of seed loss, such as seed misplacement to the seminal vesicle or perineum and being voided in the urine postoperatively. Merrick et al. have reported that seed migration to the chest accounted for only 10% of total seed loss from the prostate region, highlighting the importance of other mechanisms of loss [13].\n\nSeed migration-related sequelae:\nIn the present study, none of the 66 patients had symptoms related to the migrated seeds. Although it has been reported that most patients with seed migration are asymptomatic, there have been a few reports of seed migration-related sequelae, such as a few anecdotal instances of cardiac arrhythmia, myocardial infarction, and radiation pneumonitis [21,36,37]. Therefore, it is important to try to reduce the incidence of seed migration. The use of seeds linked with an absorbable suture material has been associated with a dramatically decreased rate of seed migration, although a potentially higher risk of radiotoxicity to the urethra and rectum has been pointed out [1,3,5,38]. In our study, linked seeds were not administered, because they are not commercially available in our country at present.\n\nConclusions:\nIn the present study, we determined the incidence of seed migration not only to the chest, but also to the abdomen and pelvis. Although the incidence of seed migration to the abdomen and pelvis was lower than that of seed migration to the chest, it would be advisable to undertake follow-up abdominal and pelvic radiographs after seed implantation. Seed migration to the chest occurred predominantly within two weeks and, therefore, it is suggested that follow-up chest radiographs should be undertaken two weeks or later after seed implantation. Seed migration to the kidneys and Batson's vertebral venous plexus was not very rare. Seed migration did not significantly affect postimplant prostate D90.\n\n"},"whole_article_abstract":{"kind":"string","value":"Background:\nThe aim was to determine the incidence of seed migration not only to the chest, but also to the abdomen and pelvis after transperineal interstitial prostate brachytherapy with loose (125)I seeds.\n\nMethods:\nWe reviewed the records of 267 patients who underwent prostate brachytherapy with loose (125)I seeds. After seed implantation, orthogonal chest radiographs, an abdominal radiograph, and a pelvic radiograph were undertaken routinely to document the occurrence and sites of seed migration. The incidence of seed migration to the chest, abdomen, and pelvis was calculated. All patients who had seed migration to the abdomen and pelvis subsequently underwent a computed tomography scan to identify the exact location of the migrated seeds. Postimplant dosimetric analysis was undertaken, and dosimetric results were compared between patients with and without seed migration.\n\nResults:\nA total of 19,236 seeds were implanted in 267 patients. Overall, 91 of 19,236 (0.47%) seeds migrated in 66 of 267 (24.7%) patients. Sixty-nine (0.36%) seeds migrated to the chest in 54 (20.2%) patients. Seven (0.036%) seeds migrated to the abdomen in six (2.2%) patients. Fifteen (0.078%) seeds migrated to the pelvis in 15 (5.6%) patients. Seed migration occurred predominantly within two weeks after seed implantation. None of the 66 patients had symptoms related to the migrated seeds. Postimplant prostate D90 was not significantly different between patients with and without seed migration.\n\nConclusions:\nWe showed the incidence of seed migration to the chest, abdomen and pelvis. Seed migration did not have a significant effect on postimplant prostate D90."},"background_conclusion_text":{"kind":"string","value":"Background:\nSeed migration is a well-recognized event that occurs after transperineal interstitial prostate brachytherapy, and it is observed more often with loose seeds than with linked seeds [1-5].\nIt is well known that the most frequent site of seed migration is the chest. The American Brachytherapy Society has advised that a chest radiograph should be undertaken at the first follow-up visit to scan the lungs for embolized seeds [6]. Consequently, the incidence of seed migration to the chest has been well reported [1,2,4,5,7-19]. However, documentation of the incidence of seed migration to the abdomen and pelvis is rare. Rare cases of seed migration to a coronary artery, the right ventricle, the liver, the kidneys, Batson's vertebral venous plexus, and the left testicular vein have been reported [20-26]. However, it has never been fully determined whether seed migration to these locations is really rare.\nThe primary purposes of the present study were to determine the incidence of seed migration not only to the chest, but also to the abdomen and the pelvis at our institution and to identify the exact location of the seeds that had migrated to the abdomen and pelvis with computed tomography (CT). The secondary purpose was to determine the impact of seed migration on postimplant dosimetry.\n\nConclusions:\nAS and JN designed the study, collected the data, interpreted the results, performed the statistical analysis, drafted the manuscript, and oversaw the project's completion. EK, HN, RM, SS, YS and RK participated in data acquisition. MO and NS contributed to data analysis. All authors read and approved the manuscript."},"background_conclusion_abstract":{"kind":"string","value":"Background:\nThe aim was to determine the incidence of seed migration not only to the chest, but also to the abdomen and pelvis after transperineal interstitial prostate brachytherapy with loose (125)I seeds.\n\nMethods:\nWe reviewed the records of 267 patients who underwent prostate brachytherapy with loose (125)I seeds. After seed implantation, orthogonal chest radiographs, an abdominal radiograph, and a pelvic radiograph were undertaken routinely to document the occurrence and sites of seed migration. The incidence of seed migration to the chest, abdomen, and pelvis was calculated. All patients who had seed migration to the abdomen and pelvis subsequently underwent a computed tomography scan to identify the exact location of the migrated seeds. Postimplant dosimetric analysis was undertaken, and dosimetric results were compared between patients with and without seed migration.\n\nResults:\nA total of 19,236 seeds were implanted in 267 patients. Overall, 91 of 19,236 (0.47%) seeds migrated in 66 of 267 (24.7%) patients. Sixty-nine (0.36%) seeds migrated to the chest in 54 (20.2%) patients. Seven (0.036%) seeds migrated to the abdomen in six (2.2%) patients. Fifteen (0.078%) seeds migrated to the pelvis in 15 (5.6%) patients. Seed migration occurred predominantly within two weeks after seed implantation. None of the 66 patients had symptoms related to the migrated seeds. Postimplant prostate D90 was not significantly different between patients with and without seed migration.\n\nConclusions:\nWe showed the incidence of seed migration to the chest, abdomen and pelvis. Seed migration did not have a significant effect on postimplant prostate D90."},"whole_article_text_length":{"kind":"number","value":11127,"string":"11,127"},"whole_article_abstract_length":{"kind":"number","value":312,"string":"312"},"other_sections_lengths":{"kind":"list like","value":[250,2678,465,119,250,153,4197,489,208,237,291,480,209,144,125],"string":"[\n 250,\n 2678,\n 465,\n 119,\n 250,\n 153,\n 4197,\n 489,\n 208,\n 237,\n 291,\n 480,\n 209,\n 144,\n 125\n]"},"num_sections":{"kind":"number","value":16,"string":"16"},"most_frequent_words":{"kind":"list like","value":["seed","seeds","migration","seed migration","migrated","patients","seed implantation","implantation","right","chest"],"string":"[\n \"seed\",\n \"seeds\",\n \"migration\",\n \"seed migration\",\n \"migrated\",\n \"patients\",\n \"seed implantation\",\n \"implantation\",\n \"right\",\n \"chest\"\n]"},"keybert_topics":{"kind":"list like","value":["seed relocation chest","seed migration pelvis","seed migration abdomen","seeds migrated chest","seed migration coronary"],"string":"[\n \"seed relocation chest\",\n \"seed migration pelvis\",\n \"seed migration abdomen\",\n \"seeds migrated chest\",\n \"seed migration coronary\"\n]"},"annotated_base_background_abstract_prompt":{"kind":"string","value":""},"annotated_base_methods_abstract_prompt":{"kind":"string","value":""},"annotated_base_results_abstract_prompt":{"kind":"null"},"annotated_base_conclusions_abstract_prompt":{"kind":"string","value":""},"annotated_base_whole_article_abstract_prompt":{"kind":"string","value":""},"annotated_base_background_conclusion_abstract_prompt":{"kind":"string","value":""},"annotated_keywords_background_abstract_prompt":{"kind":"string","value":"[CONTENT] Brachytherapy | 125I | Migration | Prostate cancer | Seed [SUMMARY]"},"annotated_keywords_methods_abstract_prompt":{"kind":"string","value":"[CONTENT] Brachytherapy | 125I | Migration | Prostate cancer | Seed [SUMMARY]"},"annotated_keywords_results_abstract_prompt":{"kind":"null"},"annotated_keywords_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] Brachytherapy | 125I | Migration | Prostate cancer | Seed [SUMMARY]"},"annotated_keywords_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] Brachytherapy | 125I | Migration | Prostate cancer | Seed [SUMMARY]"},"annotated_keywords_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] Brachytherapy | 125I | Migration | Prostate cancer | Seed [SUMMARY]"},"annotated_mesh_background_abstract_prompt":{"kind":"string","value":"[CONTENT] Aged | Aged, 80 and over | Brachytherapy | Humans | Iodine Radioisotopes | Male | Middle Aged | Movement | Pelvis | Prostatic Neoplasms | Radiography, Abdominal | Radiography, Thoracic | Radiotherapy [SUMMARY]"},"annotated_mesh_methods_abstract_prompt":{"kind":"string","value":"[CONTENT] Aged | Aged, 80 and over | Brachytherapy | Humans | Iodine Radioisotopes | Male | Middle Aged | Movement | Pelvis | Prostatic Neoplasms | Radiography, Abdominal | Radiography, Thoracic | Radiotherapy [SUMMARY]"},"annotated_mesh_results_abstract_prompt":{"kind":"null"},"annotated_mesh_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] Aged | Aged, 80 and over | Brachytherapy | Humans | Iodine Radioisotopes | Male | Middle Aged | Movement | Pelvis | Prostatic Neoplasms | Radiography, Abdominal | Radiography, Thoracic | Radiotherapy [SUMMARY]"},"annotated_mesh_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] Aged | Aged, 80 and over | Brachytherapy | Humans | Iodine Radioisotopes | Male | Middle Aged | Movement | Pelvis | Prostatic Neoplasms | Radiography, Abdominal | Radiography, Thoracic | Radiotherapy [SUMMARY]"},"annotated_mesh_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] Aged | Aged, 80 and over | Brachytherapy | Humans | Iodine Radioisotopes | Male | Middle Aged | Movement | Pelvis | Prostatic Neoplasms | Radiography, Abdominal | Radiography, Thoracic | Radiotherapy [SUMMARY]"},"annotated_keybert_background_abstract_prompt":{"kind":"string","value":"[CONTENT] seed relocation chest | seed migration pelvis | seed migration abdomen | seeds migrated chest | seed migration coronary [SUMMARY]"},"annotated_keybert_methods_abstract_prompt":{"kind":"string","value":"[CONTENT] seed relocation chest | seed migration pelvis | seed migration abdomen | seeds migrated chest | seed migration coronary [SUMMARY]"},"annotated_keybert_results_abstract_prompt":{"kind":"null"},"annotated_keybert_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] seed relocation chest | seed migration pelvis | seed migration abdomen | seeds migrated chest | seed migration coronary [SUMMARY]"},"annotated_keybert_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] seed relocation chest | seed migration pelvis | seed migration abdomen | seeds migrated chest | seed migration coronary [SUMMARY]"},"annotated_keybert_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] seed relocation chest | seed migration pelvis | seed migration abdomen | seeds migrated chest | seed migration coronary [SUMMARY]"},"annotated_most_frequent_background_abstract_prompt":{"kind":"string","value":"[CONTENT] seed | seeds | migration | seed migration | migrated | patients | seed implantation | implantation | right | chest [SUMMARY]"},"annotated_most_frequent_methods_abstract_prompt":{"kind":"string","value":"[CONTENT] seed | seeds | migration | seed migration | migrated | patients | seed implantation | implantation | right | chest [SUMMARY]"},"annotated_most_frequent_results_abstract_prompt":{"kind":"null"},"annotated_most_frequent_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] seed | seeds | migration | seed migration | migrated | patients | seed implantation | implantation | right | chest [SUMMARY]"},"annotated_most_frequent_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] seed | seeds | migration | seed migration | migrated | patients | seed implantation | implantation | right | chest [SUMMARY]"},"annotated_most_frequent_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] seed | seeds | migration | seed migration | migrated | patients | seed implantation | implantation | right | chest [SUMMARY]"},"annotated_tf_idf_background_abstract_prompt":{"kind":"string","value":"[CONTENT] seed migration | migration | seed | rare | determine | chest | incidence | abdomen pelvis | incidence seed migration | incidence seed [SUMMARY]"},"annotated_tf_idf_methods_abstract_prompt":{"kind":"string","value":"[CONTENT] patients | prostate | seed | volume | month | gy | seeds | brachytherapy | 267 | received [SUMMARY]"},"annotated_tf_idf_results_abstract_prompt":{"kind":"null"},"annotated_tf_idf_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] seed | seed migration | migration | seed implantation seed migration | implantation seed migration | chest | migration chest | seed migration chest | seed implantation seed | implantation seed [SUMMARY]"},"annotated_tf_idf_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] seed | seeds | seed migration | migration | patients | migrated | chest | implantation | seed implantation | right [SUMMARY]"},"annotated_tf_idf_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] seed | seeds | seed migration | migration | patients | migrated | chest | implantation | seed implantation | right [SUMMARY]"},"annotated_entity_plan_background_abstract_prompt":{"kind":"string","value":"[CONTENT] 125)I [SUMMARY]"},"annotated_entity_plan_methods_abstract_prompt":{"kind":"string","value":"[CONTENT] 267 | 125)I ||| ||| ||| ||| [SUMMARY]"},"annotated_entity_plan_results_abstract_prompt":{"kind":"null"},"annotated_entity_plan_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] ||| [SUMMARY]"},"annotated_entity_plan_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] ||| 125)I ||| 267 | 125)I ||| ||| ||| ||| ||| 19,236 | 267 ||| 91 | 19,236 | 0.47% | 66 | 267 | 24.7% ||| Sixty-nine | 0.36% | 54 | 20.2% ||| Seven | 0.036% | six | 2.2% ||| Fifteen | 0.078% ||| 15 | 5.6% ||| two weeks ||| 66 ||| D90 ||| ||| [SUMMARY]"},"annotated_entity_plan_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] ||| 125)I ||| 267 | 125)I ||| ||| ||| ||| ||| 19,236 | 267 ||| 91 | 19,236 | 0.47% | 66 | 267 | 24.7% ||| Sixty-nine | 0.36% | 54 | 20.2% ||| Seven | 0.036% | six | 2.2% ||| Fifteen | 0.078% ||| 15 | 5.6% ||| two weeks ||| 66 ||| D90 ||| ||| [SUMMARY]"}}},{"rowIdx":264,"cells":{"title":{"kind":"string","value":"Immediate processing of erotic stimuli in paedophilia and controls: a case control study."},"pmid":{"kind":"string","value":"23510246"},"background_abstract":{"kind":"string","value":"Most neuroimaging studies investigating sexual arousal in paedophilia used erotic pictures together with a blocked fMRI design and long stimulus presentation time. While this approach allows the detection of sexual arousal, it does not enable the assessment of the immediate processing of erotically salient stimuli. Our study aimed to identify neuronal networks related to the immediate processing of erotic stimuli in heterosexual male paedophiles and healthy age-matched controls."},"background_abstract_label":{"kind":"string","value":"BACKGROUND"},"methods_abstract":{"kind":"string","value":"We presented erotic pictures of prepubescent children and adults in an event related fMRI-design to eight paedophilic subjects and age-matched controls."},"methods_abstract_label":{"kind":"string","value":"METHODS"},"results_abstract":{"kind":"string","value":"Erotic pictures of females elicited more activation in the right temporal lobe, the right parietal lobe and both occipital lobes and erotic pictures of children activated the right dorsomedial prefrontal cortex in both groups. An interaction of sex, age and group was present in the right anteriolateral oribitofrontal cortex."},"results_abstract_label":{"kind":"string","value":"RESULTS"},"conclusions_abstract":{"kind":"string","value":"Our event related study design confirmed that erotic pictures activate some of the brain regions already known to be involved in the processing of erotic pictures when these are presented in blocks. In addition, it revealed that erotic pictures of prepubescent children activate brain regions critical for choosing response strategies in both groups, and that erotically salient stimuli selectively activate a brain region in paedophilic subjects that had previously been attributed to reward and punishment, and that had been shown to be implicated in the suppression of erotic response and deception."},"conclusions_abstract_label":{"kind":"string","value":"CONCLUSIONS"},"mesh_descriptor_names":{"kind":"list like","value":["Adult","Brain","Case-Control Studies","Emotions","Erotica","Evoked Potentials","Humans","Image Processing, Computer-Assisted","Magnetic Resonance Imaging","Male","Middle Aged","Pedophilia","Photic Stimulation"],"string":"[\n \"Adult\",\n \"Brain\",\n \"Case-Control Studies\",\n \"Emotions\",\n \"Erotica\",\n \"Evoked Potentials\",\n \"Humans\",\n \"Image Processing, Computer-Assisted\",\n \"Magnetic Resonance Imaging\",\n \"Male\",\n \"Middle Aged\",\n \"Pedophilia\",\n \"Photic Stimulation\"\n]"},"pmcid":{"kind":"string","value":"3610191"},"background_title":{"kind":"string","value":"Background"},"background_text":{"kind":"string","value":"Paedophilia is a matter of great public interest and often evokes emotional discussions in mass media. It is difficult to estimate the prevalence of paedophilia as both paedophilic offenders and victims often prefer not to identify themselves. From surveys of the general population we know that approximately 12% of men and 17% of women report experiencing sexual abuse in their childhood [1]. These figures underline the potential impact of this disease on society. According to DSM IV-TR, paedophilia is defined by two main criteria: firstly persistent sexual fantasies, urges or behaviour involving sexual activity with prepubescent children and secondly that these people have acted on these urges, or that these urges or fantasies caused marked distress or interpersonal difficulties [2].\nFindings from neuropsychological studies on paedophilia are heterogeneous. Whilst a lower IQ [3], educational difficulties [4] and a higher rate of left-handedness [5] indicate rather generalised brain dysfunction, other studies suggest more specific alterations like focal weaknesses in frontal-executive [6,7] and/or temporal-verbal [8] skills or even a more deliberate response style and greater self-monitoring in paedophilic subjects [9,10]. Furthermore revealed research on personality traits in paedophilia various findings like impaired interpersonal functions, impaired self-awareness, disinhibitory traits, sociopathy and a propensity for cognitive distortions [11]. In summary, it can be stated that the neurobiology of paedophilia remains incomplete.\nIn recent years, researchers have increasingly addressed the neuronal underpinnings of sexual arousal in order to better understand sexual behaviour. Stoléru et al. [12,13] and Redouté et al. [14,15] were the first to propose a neurophenomological model that disentangled the cognitive, emotional, motivational and physiological components of sexual arousal. Two recently published quantitative meta-analyses on sexual cue reactivity [16,17] underline that distinctive subcomponents of sexual arousal can be reliably localised by neuroimaging techniques.\nBased on the fMRI results about sexual arousal in healthy controls it was natural to transfer that approach to paedophilic subjects in order to better understand the cognitive and behavioural processes in paedophilia. In heterosexual paedophilia fMRI studies have demonstrated the altered processing of erotic visual stimuli in the dorsomedial prefrontal cortex (DMPFC), amygdala and hippocampus [18]. The latter study also reports that erotic pictures of adults induced a stronger activation of the hypothalmus, the periaqueductal grey and the dorsolateral prefrontal cortex (DLPFC) in healthy controls than in heterosexual paedophilic subjects. While this study highlights differences between controls and paedophilic subjects other studies report similar activation patterns for the respective erotic conditions. For example Schiffer et al. [19] show that erotic pictures of girls induced the same activation of limbic structures such as the amygdala, the cingulate gyrus or the hippocampus in heterosexual paedophilia as erotic pictures of women in a control group. However this study also found an additional activation of the DLPFC in the heterosexual paedophilic group alone. Research has also been carried out on homosexual paedophilia. Again in both paedophilic and control subjects, a common network relating to sexual activation was found, comprising the occipitotemporal and prefrontal cortex [20]. However, only paedophilic subjects showed activated subcortical regions such as the thalamus, the globus pallidus and the striatum during erotic stimulation. Another study on homosexual paedophilia describes differential activation of the right orbitofrontal cortex [21].\nMost of the above-mentioned studies indicate that prefrontal brain regions might be related to paedophilia, but the results and structures involved differ from study to study. Considering these rather heterogeneous results, the findings from a recent study by Ponseti et al. [22] appear surprising. These authors propose an fMRI based classification procedure for heterosexual and homosexual non-paedophilic and paedophilic subjects with 95% accuracy. Unlike previous studies, these authors describe activation in regions known to be involved in the processing of erotic stimuli such as the caudate nucleus, cingulated cortex, insula, fusiform gyrus, temporal cortex, occipital cortex, thalamus, amygdala and cerebellum but not in the prefrontal cortex.\nExcept for the study of Walter et al. [18] all of the above-mentioned studies that describe a prefrontal involvement in paedophilia used relatively long presentation times ranging from 19.2 - 38.5 s and blocked fMRI designs.\nOne critical shortcoming of long presentation times is that different cognitive processes, such as sustained attention or self-referential processes might also take place and somehow interfere with the target process. Earlier fMRI studies on sexual arousal in normal subjects showed that with shorter presentation times of sexually arousing pictures specific neuronal networks and brain processes can be addressed. Static presentation for 8.75 s as used by Moulier et al. [23] demonstrated for example that the initiation and low levels of penile tumescence are controlled by frontal, parietal, insular and cingular cortical areas. In accordance with these results, Ferretti et al. [24] showed that longer presentation times (> 30 s) induce sexual arousal and penile erection whilst shorter presentation times (< 3 s) induce arousal without erection. A stimulation time of 5 s even allowed to distinguish specific sexual emotional effects from more general emotional effects [25]. A study comparing both types of fMRI-designs in the visual processing of erotic stimuli in healthy volunteers [26] provides strong support for the use of fast stimulation time, suggesting an event related design. The authors proposed that event related designs might be an alternative to blocked designs if the core interest is the detection of networks associated with the immediate processing of erotic stimuli.\nBesides being more useful for the detection of these networks, short presentation times also offer other potential benefits as long presentation times in paedophilia are even more problematic than in healthy controls. Paedophilic subjects are mostly recruited after coming into conflict with the legal authorities as a result of their sexual preferences and this may increase a tendency to suppress erotic arousal or dissimulate sexual attraction to the pictures of children presented. The latter point has been used to explain some of the differences between ratings of erotic salience and brain response observed in some of the aforementioned studies [19,21]. As presentation time becomes shorter, deliberate manipulation may become more difficult and the immediate processes following the perception of target stimuli could become visible.\nIn our study we aim to identify the neuronal networks related to immediate processing of erotic stimuli in heterosexual paedophilic subjects recruited from a forensic outpatient setting. Based on previous neuropsychological research and recent fMRI studies we predicted a differential activation in the prefrontal cortex in paedophilic subjects."},"methods_title":{"kind":"string","value":"Methods"},"methods_text":{"kind":"string","value":" Subjects Behavioural and functional magnetic resonance imaging data were acquired from 16 male right-handed subjects. All participants in this study were adults. Written informed consent was obtained from all subjects before participation in the study.\nThe subjects in the paedophilic (N = 8) group were recruited from an outpatient cognitive behavioural group therapy at the department of forensic psychiatry in Basel, Switzerland. The heterosexual paedophilic subjects fulfilled DSM-IV-TR criteria for exclusive type (attracted only to prepubescent children) not limited to incest heterosexual paedophilia. Three of the subjects had previously molested prepubescent children, the other five had been convicted because of the possession of large quantities of explicit internet child pornography. Each participant’s sexual orientation and preference for prepubescent erotic stimuli was assessed in a clinical interview, the Multiphasic Sex Inventory (MSI) [27] and additionally verified by the clinical record and the court file. For all subjects, neither the interview nor the record indicated other comorbid paraphilias. None of the subjects had admitted paedophilia prior to the contact with the legal authorities.\nHeterosexual control subjects (N = 8) were recruited from an advert on the University Hospital notice board. A clinical evaluation of all subjects revealed no other psychiatric, neurological or medical conditions. The local Ethics Committee of the University of Basel, Switzerland approved the study.\nBehavioural and functional magnetic resonance imaging data were acquired from 16 male right-handed subjects. All participants in this study were adults. Written informed consent was obtained from all subjects before participation in the study.\nThe subjects in the paedophilic (N = 8) group were recruited from an outpatient cognitive behavioural group therapy at the department of forensic psychiatry in Basel, Switzerland. The heterosexual paedophilic subjects fulfilled DSM-IV-TR criteria for exclusive type (attracted only to prepubescent children) not limited to incest heterosexual paedophilia. Three of the subjects had previously molested prepubescent children, the other five had been convicted because of the possession of large quantities of explicit internet child pornography. Each participant’s sexual orientation and preference for prepubescent erotic stimuli was assessed in a clinical interview, the Multiphasic Sex Inventory (MSI) [27] and additionally verified by the clinical record and the court file. For all subjects, neither the interview nor the record indicated other comorbid paraphilias. None of the subjects had admitted paedophilia prior to the contact with the legal authorities.\nHeterosexual control subjects (N = 8) were recruited from an advert on the University Hospital notice board. A clinical evaluation of all subjects revealed no other psychiatric, neurological or medical conditions. The local Ethics Committee of the University of Basel, Switzerland approved the study.\n Stimulation and paradigm The study design was adopted from Bühler et al. [26]. Visual stimuli were generated and displayed on a personal computer using the Neurobehavioral Systems (NBS) software package Presentation® 12.2. Stimuli were presented via fMRI-compatible digital video goggles (NordicNeuroLab, Bergen, Norway).\nWhile inside the scanner, subjects were presented with various types of pictures in an event related design. We presented 10 pictures from each category (erotic pictures of boys, girls, men, women or neutral control pictures) for 750 ms with a jittered interstimulus interval that varied randomly between 10 – 20 s in steps of full seconds (Figure 1).\nFMRI paradigm. We presented erotic pictures of adults (women/men) and children (girls/boys) interspersed with neutral control pictures. Pictures from each category were presented for 750 ms with a jittered interstimulus interval ranging from 10–20 s.\nIn each picture, only one person of one of the above-mentioned categories was displayed in bathing clothes in front of a plain-coloured background. All pictures showed faces and the complete corpus. Secondary sexual characteristics were clearly visible in the pictures showing adults but were clearly absent in the pictures depicting prepubescent children. We avoided photographs of adolescents and applied a biological rather than a legal cut off to make the pictures of adults easily distinguishable from those of children. Before inclusion into the paradigm rated the study subjects the pictures and only the pictures with the highest ratings on a visual analogue scale were included. Neutral pictures showed simple objects like e.g. a small boot in front of the same background.\nIn order to control attention during the passive fMRI task we used a previously applied procedure to assure attentive observation of the presented pictures [28]: Before the scanning procedure, subjects were instructed to attentively observe the pictures and told that we would check their attention by asking them to identify the pictures after the scanning session. Immediately after the fMRI session we presented the slides that they had seen interspersed with similar pictures, which had not been presented during the experiment. Subjects then had to decide which pictures they had seen during the fMRI stimulation.\nThe study design was adopted from Bühler et al. [26]. Visual stimuli were generated and displayed on a personal computer using the Neurobehavioral Systems (NBS) software package Presentation® 12.2. Stimuli were presented via fMRI-compatible digital video goggles (NordicNeuroLab, Bergen, Norway).\nWhile inside the scanner, subjects were presented with various types of pictures in an event related design. We presented 10 pictures from each category (erotic pictures of boys, girls, men, women or neutral control pictures) for 750 ms with a jittered interstimulus interval that varied randomly between 10 – 20 s in steps of full seconds (Figure 1).\nFMRI paradigm. We presented erotic pictures of adults (women/men) and children (girls/boys) interspersed with neutral control pictures. Pictures from each category were presented for 750 ms with a jittered interstimulus interval ranging from 10–20 s.\nIn each picture, only one person of one of the above-mentioned categories was displayed in bathing clothes in front of a plain-coloured background. All pictures showed faces and the complete corpus. Secondary sexual characteristics were clearly visible in the pictures showing adults but were clearly absent in the pictures depicting prepubescent children. We avoided photographs of adolescents and applied a biological rather than a legal cut off to make the pictures of adults easily distinguishable from those of children. Before inclusion into the paradigm rated the study subjects the pictures and only the pictures with the highest ratings on a visual analogue scale were included. Neutral pictures showed simple objects like e.g. a small boot in front of the same background.\nIn order to control attention during the passive fMRI task we used a previously applied procedure to assure attentive observation of the presented pictures [28]: Before the scanning procedure, subjects were instructed to attentively observe the pictures and told that we would check their attention by asking them to identify the pictures after the scanning session. Immediately after the fMRI session we presented the slides that they had seen interspersed with similar pictures, which had not been presented during the experiment. Subjects then had to decide which pictures they had seen during the fMRI stimulation.\n FMRI acquisition and analysis Images were acquired using a 3 T MRI scanner (Verio, Siemens Healthcare, Erlangen, Germany) equipped with a standard radio frequency head coil. First a T1-weighted high-resolution data set that covered the whole brain was acquired using a three-dimensional MPRAGE (magnetization-prepared rapid acquisition gradient echo) sequence with a repetition time (TR) of 2.00, an isotropic spatial resolution of 1.0 mm3, and an echo time (TE) of 3.4 ms. T2* weighted functional images were recorded using echoplanar imaging with a TR of 2500 ms, an isotropic spatial resolution of 3×3×3 mm3 and a TE of 30 ms (FoV 228; matrix 76; spacing between slices: 0.51 mm; interslice time: 69 ms). Altogether 152 volumes with 36 image slices with a thickness of 3 mm were obtained.\nImages were acquired using a 3 T MRI scanner (Verio, Siemens Healthcare, Erlangen, Germany) equipped with a standard radio frequency head coil. First a T1-weighted high-resolution data set that covered the whole brain was acquired using a three-dimensional MPRAGE (magnetization-prepared rapid acquisition gradient echo) sequence with a repetition time (TR) of 2.00, an isotropic spatial resolution of 1.0 mm3, and an echo time (TE) of 3.4 ms. T2* weighted functional images were recorded using echoplanar imaging with a TR of 2500 ms, an isotropic spatial resolution of 3×3×3 mm3 and a TE of 30 ms (FoV 228; matrix 76; spacing between slices: 0.51 mm; interslice time: 69 ms). Altogether 152 volumes with 36 image slices with a thickness of 3 mm were obtained.\n Image pre-processing and statistical analysis Image time-series were processed using the BrainVoyager QX 2.3.0 software package (Brain Innovation, Maastricht, The Netherlands). Pre-processing included head motion correction, slice scan time correction, temporal high pass filtering and removal of linear trends. Using the results of the image registration with anatomical scans, the functional image-time series were then warped into Talairach space and resampled into 3 mm isotropic voxel time-series. Normalized images were smoothed using a 6.00 mm isotropic Gaussian kernel.\nFor first-level analysis, a General Linear Model (GLM) analysis was applied with separate subject z-normalized predictors fitted to z-normalized voxel time-courses from all data sets. The orthogonal predictors of interest in the design matrix were: “boys”, “girls”, “men”, “women” and “neutral”. For second-level analysis, the GLM fits (beta weights) were assessed in a 3-way-ANOVA with two within subject factors (sex: female vs. male; age: child vs. adult) and one between subject factor group (paedophilia vs. control). We assessed the main effects of, and the interaction between, these factors at the voxel level and obtained maps that thresholded at the significance level of 5% (corrected for multiple comparison). To obtain this correction, an uncorrected statistical threshold was initially applied to each of the F-maps at p = 0.005, and a cluster-level threshold correction procedure based on Monte Carlo simulations [29] was applied in order to determine the minimum cluster size below which any activation was to be discarded. This procedure yielded a minimum cluster size of 10 voxels (corresponding to a minimum cluster extent of 280 mm) for the F-maps of the interaction of all factors, 12 voxel (297 mm) for the factor age and 19 voxels (482 mm) for the factor sex.\nThe anatomical labelling of active clusters was defined on the basis of the peak voxel coordinates, using the Talairach Daemon (http://www.talairach.org) [30,31].\nActive clusters resulting from the voxel-level ANOVA were further defined as Regions of Interests (ROI). Data from those ROIs were extracted and analysed in order to understand the direction of the effects of the ANOVA. To that end, we calculated linear contrasts for the different factors, i.e. sex (female > male), age (child > adult) and the interaction of the factors (girl > woman) separately for both groups. In order to further specify the interaction of all factors (sex, age, group) we extracted BOLD %-signal-change from the respective ROI and averaged the BOLD%-signal-change separately for the groups.\nImage time-series were processed using the BrainVoyager QX 2.3.0 software package (Brain Innovation, Maastricht, The Netherlands). Pre-processing included head motion correction, slice scan time correction, temporal high pass filtering and removal of linear trends. Using the results of the image registration with anatomical scans, the functional image-time series were then warped into Talairach space and resampled into 3 mm isotropic voxel time-series. Normalized images were smoothed using a 6.00 mm isotropic Gaussian kernel.\nFor first-level analysis, a General Linear Model (GLM) analysis was applied with separate subject z-normalized predictors fitted to z-normalized voxel time-courses from all data sets. The orthogonal predictors of interest in the design matrix were: “boys”, “girls”, “men”, “women” and “neutral”. For second-level analysis, the GLM fits (beta weights) were assessed in a 3-way-ANOVA with two within subject factors (sex: female vs. male; age: child vs. adult) and one between subject factor group (paedophilia vs. control). We assessed the main effects of, and the interaction between, these factors at the voxel level and obtained maps that thresholded at the significance level of 5% (corrected for multiple comparison). To obtain this correction, an uncorrected statistical threshold was initially applied to each of the F-maps at p = 0.005, and a cluster-level threshold correction procedure based on Monte Carlo simulations [29] was applied in order to determine the minimum cluster size below which any activation was to be discarded. This procedure yielded a minimum cluster size of 10 voxels (corresponding to a minimum cluster extent of 280 mm) for the F-maps of the interaction of all factors, 12 voxel (297 mm) for the factor age and 19 voxels (482 mm) for the factor sex.\nThe anatomical labelling of active clusters was defined on the basis of the peak voxel coordinates, using the Talairach Daemon (http://www.talairach.org) [30,31].\nActive clusters resulting from the voxel-level ANOVA were further defined as Regions of Interests (ROI). Data from those ROIs were extracted and analysed in order to understand the direction of the effects of the ANOVA. To that end, we calculated linear contrasts for the different factors, i.e. sex (female > male), age (child > adult) and the interaction of the factors (girl > woman) separately for both groups. In order to further specify the interaction of all factors (sex, age, group) we extracted BOLD %-signal-change from the respective ROI and averaged the BOLD%-signal-change separately for the groups."},"results_title":{"kind":"string","value":"Results"},"results_text":{"kind":"string","value":" Demographics Sexual orientation in the control group was heterosexual with a preference for mature women. In the paedophilia group all subjects were heterosexual and attracted exclusively by prepubescent stimuli. The mean age of the paedophilic subjects was 48.25 (standard deviation: 9.15) years and mean IQ 116 (12.12). In the control groups, the mean age was 46.25 (8.35) years and IQ 124.88 (13.91). Student t-tests showed no significant difference between these groups (Table 1). In the paedophilic group 75.4% of the pictures were correctly identified immediately after the scanning session. In the control group 77.5% of the pictures were correctly identified. Again a group difference was excluded by t-test.\nMean age, IQ and rate of the correctly indentified pictures (behavioural control) with standard deviation (SD) and T-test for both groups\nNone of the results reaches statistically significant differences.\nSexual orientation in the control group was heterosexual with a preference for mature women. In the paedophilia group all subjects were heterosexual and attracted exclusively by prepubescent stimuli. The mean age of the paedophilic subjects was 48.25 (standard deviation: 9.15) years and mean IQ 116 (12.12). In the control groups, the mean age was 46.25 (8.35) years and IQ 124.88 (13.91). Student t-tests showed no significant difference between these groups (Table 1). In the paedophilic group 75.4% of the pictures were correctly identified immediately after the scanning session. In the control group 77.5% of the pictures were correctly identified. Again a group difference was excluded by t-test.\nMean age, IQ and rate of the correctly indentified pictures (behavioural control) with standard deviation (SD) and T-test for both groups\nNone of the results reaches statistically significant differences.\n FMRI Voxel level ANOVA We found a significant interaction of the factors sex, age and group (p < 0.05, corrected) in the middle frontal gyrus (Figure 2, Table 2). We furthermore found significant main effects for the factors sex and age outside of the middle frontal gyrus, i. e. in brain areas that didn`t show a significant interaction of all factors.\nWhole brain ANOVA. Active voxels showed a main effect of the factor sex (female/male) in the right middle temporal gyrus, right fusiform gyrus, right inferior and middle occipital gyrus and right parietal lobe. In the left hemisphere the inferior occipital gyrus displayed active voxels (A). Furthermore we found bilateral activation in the cerebellum. The factor age (adult/child) showed an activation of the right dorsomedial prefrontal cortex (B). In the right lateral orbitofrontal cortex a significant interaction of sex, age and group was detected (C). Significant voxels in A-C represent effects at a corrected p-level of p < 0.05. X, Y, Z corresponds to the Tailarach coordinates.\nRegions that showed a significant effect in the voxel wise ANOVA\nX, Y, Z corresponds to the Tailarach coordinates (BA = Brodmann area; R = right, L = left).\nA significant main effect of the factor sex (female, male) was present at a corrected threshold of p < 0.05 in the right middle temporal gyrus, right fusiform gyrus, right inferior and middle occipital gyrus and right parietal lobe. In the left hemisphere, the inferior occipital gyrus was also active. We also found bilateral activation in the cerebellum.\nThe factor age revealed an activation of the right dorsomedial prefrontal cortex (p < 0.05, corrected).\nFor the factor group, no significant activation was found at the latter threshold, but again this finding is restricted to brain areas that did not show a significant interaction of all factors.\nWe found a significant interaction of the factors sex, age and group (p < 0.05, corrected) in the middle frontal gyrus (Figure 2, Table 2). We furthermore found significant main effects for the factors sex and age outside of the middle frontal gyrus, i. e. in brain areas that didn`t show a significant interaction of all factors.\nWhole brain ANOVA. Active voxels showed a main effect of the factor sex (female/male) in the right middle temporal gyrus, right fusiform gyrus, right inferior and middle occipital gyrus and right parietal lobe. In the left hemisphere the inferior occipital gyrus displayed active voxels (A). Furthermore we found bilateral activation in the cerebellum. The factor age (adult/child) showed an activation of the right dorsomedial prefrontal cortex (B). In the right lateral orbitofrontal cortex a significant interaction of sex, age and group was detected (C). Significant voxels in A-C represent effects at a corrected p-level of p < 0.05. X, Y, Z corresponds to the Tailarach coordinates.\nRegions that showed a significant effect in the voxel wise ANOVA\nX, Y, Z corresponds to the Tailarach coordinates (BA = Brodmann area; R = right, L = left).\nA significant main effect of the factor sex (female, male) was present at a corrected threshold of p < 0.05 in the right middle temporal gyrus, right fusiform gyrus, right inferior and middle occipital gyrus and right parietal lobe. In the left hemisphere, the inferior occipital gyrus was also active. We also found bilateral activation in the cerebellum.\nThe factor age revealed an activation of the right dorsomedial prefrontal cortex (p < 0.05, corrected).\nFor the factor group, no significant activation was found at the latter threshold, but again this finding is restricted to brain areas that did not show a significant interaction of all factors.\n Voxel level ANOVA We found a significant interaction of the factors sex, age and group (p < 0.05, corrected) in the middle frontal gyrus (Figure 2, Table 2). We furthermore found significant main effects for the factors sex and age outside of the middle frontal gyrus, i. e. in brain areas that didn`t show a significant interaction of all factors.\nWhole brain ANOVA. Active voxels showed a main effect of the factor sex (female/male) in the right middle temporal gyrus, right fusiform gyrus, right inferior and middle occipital gyrus and right parietal lobe. In the left hemisphere the inferior occipital gyrus displayed active voxels (A). Furthermore we found bilateral activation in the cerebellum. The factor age (adult/child) showed an activation of the right dorsomedial prefrontal cortex (B). In the right lateral orbitofrontal cortex a significant interaction of sex, age and group was detected (C). Significant voxels in A-C represent effects at a corrected p-level of p < 0.05. X, Y, Z corresponds to the Tailarach coordinates.\nRegions that showed a significant effect in the voxel wise ANOVA\nX, Y, Z corresponds to the Tailarach coordinates (BA = Brodmann area; R = right, L = left).\nA significant main effect of the factor sex (female, male) was present at a corrected threshold of p < 0.05 in the right middle temporal gyrus, right fusiform gyrus, right inferior and middle occipital gyrus and right parietal lobe. In the left hemisphere, the inferior occipital gyrus was also active. We also found bilateral activation in the cerebellum.\nThe factor age revealed an activation of the right dorsomedial prefrontal cortex (p < 0.05, corrected).\nFor the factor group, no significant activation was found at the latter threshold, but again this finding is restricted to brain areas that did not show a significant interaction of all factors.\nWe found a significant interaction of the factors sex, age and group (p < 0.05, corrected) in the middle frontal gyrus (Figure 2, Table 2). We furthermore found significant main effects for the factors sex and age outside of the middle frontal gyrus, i. e. in brain areas that didn`t show a significant interaction of all factors.\nWhole brain ANOVA. Active voxels showed a main effect of the factor sex (female/male) in the right middle temporal gyrus, right fusiform gyrus, right inferior and middle occipital gyrus and right parietal lobe. In the left hemisphere the inferior occipital gyrus displayed active voxels (A). Furthermore we found bilateral activation in the cerebellum. The factor age (adult/child) showed an activation of the right dorsomedial prefrontal cortex (B). In the right lateral orbitofrontal cortex a significant interaction of sex, age and group was detected (C). Significant voxels in A-C represent effects at a corrected p-level of p < 0.05. X, Y, Z corresponds to the Tailarach coordinates.\nRegions that showed a significant effect in the voxel wise ANOVA\nX, Y, Z corresponds to the Tailarach coordinates (BA = Brodmann area; R = right, L = left).\nA significant main effect of the factor sex (female, male) was present at a corrected threshold of p < 0.05 in the right middle temporal gyrus, right fusiform gyrus, right inferior and middle occipital gyrus and right parietal lobe. In the left hemisphere, the inferior occipital gyrus was also active. We also found bilateral activation in the cerebellum.\nThe factor age revealed an activation of the right dorsomedial prefrontal cortex (p < 0.05, corrected).\nFor the factor group, no significant activation was found at the latter threshold, but again this finding is restricted to brain areas that did not show a significant interaction of all factors.\n ROI analysis The linear contrasts of these ROIs confirmed that, in both groups, the main effect of the factor sex was based on a higher activation due to female pictures and the main effect of the factor age on a stronger activation for pictures of children. ROI analysis clarified that in the ROI that became significant for the interaction of all factors, only paedophilic subjects showed activation in the girl condition while controls showed a deactivation in this condition (Figure 3A).\nROIs analysis (A). The ROI female > male contrast level for the ROIs extracted from the ANOVA of the main effect of sex displayed a greater activation for female pictures in all active ROIs (rTL = right temporal lobe, rPL = right parietal lobe, rOL = right occipital lobe, lOL = left occipital lobe). The child > adult contrast confirmed that the main effect of age is based on a stronger activation due to pictures of children in both groups (rDMPFC = right dorsomedial prefrontal cortex). ROI analysis of the cluster showing an interaction of all factors revealed a significant difference (* = p < 0.05) for the contrast girl > woman, with activation in paedphilic subjects and deactivation in control subjects (rlOFC = right lateral orbitofrontal cortex). Event related averaging (B). BOLD %-signal-change in the rlOFC only illustrated a strong activation due to erotic pictures of girls in paedophilia.\nEvent related averaging of BOLD %-signal-change for all conditions and all groups in that ROI illustrated a strong activation due to erotic pictures of girls in paedophilic subjects that was absent for all other picture conditions in both groups (Figure 3B).\nThe linear contrasts of these ROIs confirmed that, in both groups, the main effect of the factor sex was based on a higher activation due to female pictures and the main effect of the factor age on a stronger activation for pictures of children. ROI analysis clarified that in the ROI that became significant for the interaction of all factors, only paedophilic subjects showed activation in the girl condition while controls showed a deactivation in this condition (Figure 3A).\nROIs analysis (A). The ROI female > male contrast level for the ROIs extracted from the ANOVA of the main effect of sex displayed a greater activation for female pictures in all active ROIs (rTL = right temporal lobe, rPL = right parietal lobe, rOL = right occipital lobe, lOL = left occipital lobe). The child > adult contrast confirmed that the main effect of age is based on a stronger activation due to pictures of children in both groups (rDMPFC = right dorsomedial prefrontal cortex). ROI analysis of the cluster showing an interaction of all factors revealed a significant difference (* = p < 0.05) for the contrast girl > woman, with activation in paedphilic subjects and deactivation in control subjects (rlOFC = right lateral orbitofrontal cortex). Event related averaging (B). BOLD %-signal-change in the rlOFC only illustrated a strong activation due to erotic pictures of girls in paedophilia.\nEvent related averaging of BOLD %-signal-change for all conditions and all groups in that ROI illustrated a strong activation due to erotic pictures of girls in paedophilic subjects that was absent for all other picture conditions in both groups (Figure 3B)."},"conclusions_title":{"kind":"string","value":"Conclusion"},"conclusions_text":{"kind":"string","value":"Our fMRI study confirms the findings of previous studies concerning the processing of visual erotic stimuli. Furthermore we were able to demonstrate that the dorsomedial prefrontal cortex is specifically engaged in processing erotic pictures of children regardless of the study group. This activation appears to represent the evaluation of relevance, which is apparently not linked to sexual orientation per se but rather to the study context. In addition we have made some new findings regarding the immediate processing of visual erotic stimulation in paedophilia, as we found an immediate activation of the brain regions involved in evaluating emotional salience and reward, and engaged in the regulation of emotional responses. This activation might be related to the suppression or deception of erotic responses."},"other_sections_titles":{"kind":"list like","value":["Background","Subjects","Stimulation and paradigm","FMRI acquisition and analysis","Image pre-processing and statistical analysis","Demographics","FMRI","Voxel level ANOVA","ROI analysis","Abbreviations","Competing interests","Authors’ contributions","Pre-publication history"],"string":"[\n \"Background\",\n \"Subjects\",\n \"Stimulation and paradigm\",\n \"FMRI acquisition and analysis\",\n \"Image pre-processing and statistical analysis\",\n \"Demographics\",\n \"FMRI\",\n \"Voxel level ANOVA\",\n \"ROI analysis\",\n \"Abbreviations\",\n \"Competing interests\",\n \"Authors’ contributions\",\n \"Pre-publication history\"\n]"},"other_sections_texts":{"kind":"list like","value":["Paedophilia is a matter of great public interest and often evokes emotional discussions in mass media. It is difficult to estimate the prevalence of paedophilia as both paedophilic offenders and victims often prefer not to identify themselves. From surveys of the general population we know that approximately 12% of men and 17% of women report experiencing sexual abuse in their childhood [1]. These figures underline the potential impact of this disease on society. According to DSM IV-TR, paedophilia is defined by two main criteria: firstly persistent sexual fantasies, urges or behaviour involving sexual activity with prepubescent children and secondly that these people have acted on these urges, or that these urges or fantasies caused marked distress or interpersonal difficulties [2].\nFindings from neuropsychological studies on paedophilia are heterogeneous. Whilst a lower IQ [3], educational difficulties [4] and a higher rate of left-handedness [5] indicate rather generalised brain dysfunction, other studies suggest more specific alterations like focal weaknesses in frontal-executive [6,7] and/or temporal-verbal [8] skills or even a more deliberate response style and greater self-monitoring in paedophilic subjects [9,10]. Furthermore revealed research on personality traits in paedophilia various findings like impaired interpersonal functions, impaired self-awareness, disinhibitory traits, sociopathy and a propensity for cognitive distortions [11]. In summary, it can be stated that the neurobiology of paedophilia remains incomplete.\nIn recent years, researchers have increasingly addressed the neuronal underpinnings of sexual arousal in order to better understand sexual behaviour. Stoléru et al. [12,13] and Redouté et al. [14,15] were the first to propose a neurophenomological model that disentangled the cognitive, emotional, motivational and physiological components of sexual arousal. Two recently published quantitative meta-analyses on sexual cue reactivity [16,17] underline that distinctive subcomponents of sexual arousal can be reliably localised by neuroimaging techniques.\nBased on the fMRI results about sexual arousal in healthy controls it was natural to transfer that approach to paedophilic subjects in order to better understand the cognitive and behavioural processes in paedophilia. In heterosexual paedophilia fMRI studies have demonstrated the altered processing of erotic visual stimuli in the dorsomedial prefrontal cortex (DMPFC), amygdala and hippocampus [18]. The latter study also reports that erotic pictures of adults induced a stronger activation of the hypothalmus, the periaqueductal grey and the dorsolateral prefrontal cortex (DLPFC) in healthy controls than in heterosexual paedophilic subjects. While this study highlights differences between controls and paedophilic subjects other studies report similar activation patterns for the respective erotic conditions. For example Schiffer et al. [19] show that erotic pictures of girls induced the same activation of limbic structures such as the amygdala, the cingulate gyrus or the hippocampus in heterosexual paedophilia as erotic pictures of women in a control group. However this study also found an additional activation of the DLPFC in the heterosexual paedophilic group alone. Research has also been carried out on homosexual paedophilia. Again in both paedophilic and control subjects, a common network relating to sexual activation was found, comprising the occipitotemporal and prefrontal cortex [20]. However, only paedophilic subjects showed activated subcortical regions such as the thalamus, the globus pallidus and the striatum during erotic stimulation. Another study on homosexual paedophilia describes differential activation of the right orbitofrontal cortex [21].\nMost of the above-mentioned studies indicate that prefrontal brain regions might be related to paedophilia, but the results and structures involved differ from study to study. Considering these rather heterogeneous results, the findings from a recent study by Ponseti et al. [22] appear surprising. These authors propose an fMRI based classification procedure for heterosexual and homosexual non-paedophilic and paedophilic subjects with 95% accuracy. Unlike previous studies, these authors describe activation in regions known to be involved in the processing of erotic stimuli such as the caudate nucleus, cingulated cortex, insula, fusiform gyrus, temporal cortex, occipital cortex, thalamus, amygdala and cerebellum but not in the prefrontal cortex.\nExcept for the study of Walter et al. [18] all of the above-mentioned studies that describe a prefrontal involvement in paedophilia used relatively long presentation times ranging from 19.2 - 38.5 s and blocked fMRI designs.\nOne critical shortcoming of long presentation times is that different cognitive processes, such as sustained attention or self-referential processes might also take place and somehow interfere with the target process. Earlier fMRI studies on sexual arousal in normal subjects showed that with shorter presentation times of sexually arousing pictures specific neuronal networks and brain processes can be addressed. Static presentation for 8.75 s as used by Moulier et al. [23] demonstrated for example that the initiation and low levels of penile tumescence are controlled by frontal, parietal, insular and cingular cortical areas. In accordance with these results, Ferretti et al. [24] showed that longer presentation times (> 30 s) induce sexual arousal and penile erection whilst shorter presentation times (< 3 s) induce arousal without erection. A stimulation time of 5 s even allowed to distinguish specific sexual emotional effects from more general emotional effects [25]. A study comparing both types of fMRI-designs in the visual processing of erotic stimuli in healthy volunteers [26] provides strong support for the use of fast stimulation time, suggesting an event related design. The authors proposed that event related designs might be an alternative to blocked designs if the core interest is the detection of networks associated with the immediate processing of erotic stimuli.\nBesides being more useful for the detection of these networks, short presentation times also offer other potential benefits as long presentation times in paedophilia are even more problematic than in healthy controls. Paedophilic subjects are mostly recruited after coming into conflict with the legal authorities as a result of their sexual preferences and this may increase a tendency to suppress erotic arousal or dissimulate sexual attraction to the pictures of children presented. The latter point has been used to explain some of the differences between ratings of erotic salience and brain response observed in some of the aforementioned studies [19,21]. As presentation time becomes shorter, deliberate manipulation may become more difficult and the immediate processes following the perception of target stimuli could become visible.\nIn our study we aim to identify the neuronal networks related to immediate processing of erotic stimuli in heterosexual paedophilic subjects recruited from a forensic outpatient setting. Based on previous neuropsychological research and recent fMRI studies we predicted a differential activation in the prefrontal cortex in paedophilic subjects.","Behavioural and functional magnetic resonance imaging data were acquired from 16 male right-handed subjects. All participants in this study were adults. Written informed consent was obtained from all subjects before participation in the study.\nThe subjects in the paedophilic (N = 8) group were recruited from an outpatient cognitive behavioural group therapy at the department of forensic psychiatry in Basel, Switzerland. The heterosexual paedophilic subjects fulfilled DSM-IV-TR criteria for exclusive type (attracted only to prepubescent children) not limited to incest heterosexual paedophilia. Three of the subjects had previously molested prepubescent children, the other five had been convicted because of the possession of large quantities of explicit internet child pornography. Each participant’s sexual orientation and preference for prepubescent erotic stimuli was assessed in a clinical interview, the Multiphasic Sex Inventory (MSI) [27] and additionally verified by the clinical record and the court file. For all subjects, neither the interview nor the record indicated other comorbid paraphilias. None of the subjects had admitted paedophilia prior to the contact with the legal authorities.\nHeterosexual control subjects (N = 8) were recruited from an advert on the University Hospital notice board. A clinical evaluation of all subjects revealed no other psychiatric, neurological or medical conditions. The local Ethics Committee of the University of Basel, Switzerland approved the study.","The study design was adopted from Bühler et al. [26]. Visual stimuli were generated and displayed on a personal computer using the Neurobehavioral Systems (NBS) software package Presentation® 12.2. Stimuli were presented via fMRI-compatible digital video goggles (NordicNeuroLab, Bergen, Norway).\nWhile inside the scanner, subjects were presented with various types of pictures in an event related design. We presented 10 pictures from each category (erotic pictures of boys, girls, men, women or neutral control pictures) for 750 ms with a jittered interstimulus interval that varied randomly between 10 – 20 s in steps of full seconds (Figure 1).\nFMRI paradigm. We presented erotic pictures of adults (women/men) and children (girls/boys) interspersed with neutral control pictures. Pictures from each category were presented for 750 ms with a jittered interstimulus interval ranging from 10–20 s.\nIn each picture, only one person of one of the above-mentioned categories was displayed in bathing clothes in front of a plain-coloured background. All pictures showed faces and the complete corpus. Secondary sexual characteristics were clearly visible in the pictures showing adults but were clearly absent in the pictures depicting prepubescent children. We avoided photographs of adolescents and applied a biological rather than a legal cut off to make the pictures of adults easily distinguishable from those of children. Before inclusion into the paradigm rated the study subjects the pictures and only the pictures with the highest ratings on a visual analogue scale were included. Neutral pictures showed simple objects like e.g. a small boot in front of the same background.\nIn order to control attention during the passive fMRI task we used a previously applied procedure to assure attentive observation of the presented pictures [28]: Before the scanning procedure, subjects were instructed to attentively observe the pictures and told that we would check their attention by asking them to identify the pictures after the scanning session. Immediately after the fMRI session we presented the slides that they had seen interspersed with similar pictures, which had not been presented during the experiment. Subjects then had to decide which pictures they had seen during the fMRI stimulation.","Images were acquired using a 3 T MRI scanner (Verio, Siemens Healthcare, Erlangen, Germany) equipped with a standard radio frequency head coil. First a T1-weighted high-resolution data set that covered the whole brain was acquired using a three-dimensional MPRAGE (magnetization-prepared rapid acquisition gradient echo) sequence with a repetition time (TR) of 2.00, an isotropic spatial resolution of 1.0 mm3, and an echo time (TE) of 3.4 ms. T2* weighted functional images were recorded using echoplanar imaging with a TR of 2500 ms, an isotropic spatial resolution of 3×3×3 mm3 and a TE of 30 ms (FoV 228; matrix 76; spacing between slices: 0.51 mm; interslice time: 69 ms). Altogether 152 volumes with 36 image slices with a thickness of 3 mm were obtained.","Image time-series were processed using the BrainVoyager QX 2.3.0 software package (Brain Innovation, Maastricht, The Netherlands). Pre-processing included head motion correction, slice scan time correction, temporal high pass filtering and removal of linear trends. Using the results of the image registration with anatomical scans, the functional image-time series were then warped into Talairach space and resampled into 3 mm isotropic voxel time-series. Normalized images were smoothed using a 6.00 mm isotropic Gaussian kernel.\nFor first-level analysis, a General Linear Model (GLM) analysis was applied with separate subject z-normalized predictors fitted to z-normalized voxel time-courses from all data sets. The orthogonal predictors of interest in the design matrix were: “boys”, “girls”, “men”, “women” and “neutral”. For second-level analysis, the GLM fits (beta weights) were assessed in a 3-way-ANOVA with two within subject factors (sex: female vs. male; age: child vs. adult) and one between subject factor group (paedophilia vs. control). We assessed the main effects of, and the interaction between, these factors at the voxel level and obtained maps that thresholded at the significance level of 5% (corrected for multiple comparison). To obtain this correction, an uncorrected statistical threshold was initially applied to each of the F-maps at p = 0.005, and a cluster-level threshold correction procedure based on Monte Carlo simulations [29] was applied in order to determine the minimum cluster size below which any activation was to be discarded. This procedure yielded a minimum cluster size of 10 voxels (corresponding to a minimum cluster extent of 280 mm) for the F-maps of the interaction of all factors, 12 voxel (297 mm) for the factor age and 19 voxels (482 mm) for the factor sex.\nThe anatomical labelling of active clusters was defined on the basis of the peak voxel coordinates, using the Talairach Daemon (http://www.talairach.org) [30,31].\nActive clusters resulting from the voxel-level ANOVA were further defined as Regions of Interests (ROI). Data from those ROIs were extracted and analysed in order to understand the direction of the effects of the ANOVA. To that end, we calculated linear contrasts for the different factors, i.e. sex (female > male), age (child > adult) and the interaction of the factors (girl > woman) separately for both groups. In order to further specify the interaction of all factors (sex, age, group) we extracted BOLD %-signal-change from the respective ROI and averaged the BOLD%-signal-change separately for the groups.","Sexual orientation in the control group was heterosexual with a preference for mature women. In the paedophilia group all subjects were heterosexual and attracted exclusively by prepubescent stimuli. The mean age of the paedophilic subjects was 48.25 (standard deviation: 9.15) years and mean IQ 116 (12.12). In the control groups, the mean age was 46.25 (8.35) years and IQ 124.88 (13.91). Student t-tests showed no significant difference between these groups (Table 1). In the paedophilic group 75.4% of the pictures were correctly identified immediately after the scanning session. In the control group 77.5% of the pictures were correctly identified. Again a group difference was excluded by t-test.\nMean age, IQ and rate of the correctly indentified pictures (behavioural control) with standard deviation (SD) and T-test for both groups\nNone of the results reaches statistically significant differences."," Voxel level ANOVA We found a significant interaction of the factors sex, age and group (p < 0.05, corrected) in the middle frontal gyrus (Figure 2, Table 2). We furthermore found significant main effects for the factors sex and age outside of the middle frontal gyrus, i. e. in brain areas that didn`t show a significant interaction of all factors.\nWhole brain ANOVA. Active voxels showed a main effect of the factor sex (female/male) in the right middle temporal gyrus, right fusiform gyrus, right inferior and middle occipital gyrus and right parietal lobe. In the left hemisphere the inferior occipital gyrus displayed active voxels (A). Furthermore we found bilateral activation in the cerebellum. The factor age (adult/child) showed an activation of the right dorsomedial prefrontal cortex (B). In the right lateral orbitofrontal cortex a significant interaction of sex, age and group was detected (C). Significant voxels in A-C represent effects at a corrected p-level of p < 0.05. X, Y, Z corresponds to the Tailarach coordinates.\nRegions that showed a significant effect in the voxel wise ANOVA\nX, Y, Z corresponds to the Tailarach coordinates (BA = Brodmann area; R = right, L = left).\nA significant main effect of the factor sex (female, male) was present at a corrected threshold of p < 0.05 in the right middle temporal gyrus, right fusiform gyrus, right inferior and middle occipital gyrus and right parietal lobe. In the left hemisphere, the inferior occipital gyrus was also active. We also found bilateral activation in the cerebellum.\nThe factor age revealed an activation of the right dorsomedial prefrontal cortex (p < 0.05, corrected).\nFor the factor group, no significant activation was found at the latter threshold, but again this finding is restricted to brain areas that did not show a significant interaction of all factors.\nWe found a significant interaction of the factors sex, age and group (p < 0.05, corrected) in the middle frontal gyrus (Figure 2, Table 2). We furthermore found significant main effects for the factors sex and age outside of the middle frontal gyrus, i. e. in brain areas that didn`t show a significant interaction of all factors.\nWhole brain ANOVA. Active voxels showed a main effect of the factor sex (female/male) in the right middle temporal gyrus, right fusiform gyrus, right inferior and middle occipital gyrus and right parietal lobe. In the left hemisphere the inferior occipital gyrus displayed active voxels (A). Furthermore we found bilateral activation in the cerebellum. The factor age (adult/child) showed an activation of the right dorsomedial prefrontal cortex (B). In the right lateral orbitofrontal cortex a significant interaction of sex, age and group was detected (C). Significant voxels in A-C represent effects at a corrected p-level of p < 0.05. X, Y, Z corresponds to the Tailarach coordinates.\nRegions that showed a significant effect in the voxel wise ANOVA\nX, Y, Z corresponds to the Tailarach coordinates (BA = Brodmann area; R = right, L = left).\nA significant main effect of the factor sex (female, male) was present at a corrected threshold of p < 0.05 in the right middle temporal gyrus, right fusiform gyrus, right inferior and middle occipital gyrus and right parietal lobe. In the left hemisphere, the inferior occipital gyrus was also active. We also found bilateral activation in the cerebellum.\nThe factor age revealed an activation of the right dorsomedial prefrontal cortex (p < 0.05, corrected).\nFor the factor group, no significant activation was found at the latter threshold, but again this finding is restricted to brain areas that did not show a significant interaction of all factors.","We found a significant interaction of the factors sex, age and group (p < 0.05, corrected) in the middle frontal gyrus (Figure 2, Table 2). We furthermore found significant main effects for the factors sex and age outside of the middle frontal gyrus, i. e. in brain areas that didn`t show a significant interaction of all factors.\nWhole brain ANOVA. Active voxels showed a main effect of the factor sex (female/male) in the right middle temporal gyrus, right fusiform gyrus, right inferior and middle occipital gyrus and right parietal lobe. In the left hemisphere the inferior occipital gyrus displayed active voxels (A). Furthermore we found bilateral activation in the cerebellum. The factor age (adult/child) showed an activation of the right dorsomedial prefrontal cortex (B). In the right lateral orbitofrontal cortex a significant interaction of sex, age and group was detected (C). Significant voxels in A-C represent effects at a corrected p-level of p < 0.05. X, Y, Z corresponds to the Tailarach coordinates.\nRegions that showed a significant effect in the voxel wise ANOVA\nX, Y, Z corresponds to the Tailarach coordinates (BA = Brodmann area; R = right, L = left).\nA significant main effect of the factor sex (female, male) was present at a corrected threshold of p < 0.05 in the right middle temporal gyrus, right fusiform gyrus, right inferior and middle occipital gyrus and right parietal lobe. In the left hemisphere, the inferior occipital gyrus was also active. We also found bilateral activation in the cerebellum.\nThe factor age revealed an activation of the right dorsomedial prefrontal cortex (p < 0.05, corrected).\nFor the factor group, no significant activation was found at the latter threshold, but again this finding is restricted to brain areas that did not show a significant interaction of all factors.","The linear contrasts of these ROIs confirmed that, in both groups, the main effect of the factor sex was based on a higher activation due to female pictures and the main effect of the factor age on a stronger activation for pictures of children. ROI analysis clarified that in the ROI that became significant for the interaction of all factors, only paedophilic subjects showed activation in the girl condition while controls showed a deactivation in this condition (Figure 3A).\nROIs analysis (A). The ROI female > male contrast level for the ROIs extracted from the ANOVA of the main effect of sex displayed a greater activation for female pictures in all active ROIs (rTL = right temporal lobe, rPL = right parietal lobe, rOL = right occipital lobe, lOL = left occipital lobe). The child > adult contrast confirmed that the main effect of age is based on a stronger activation due to pictures of children in both groups (rDMPFC = right dorsomedial prefrontal cortex). ROI analysis of the cluster showing an interaction of all factors revealed a significant difference (* = p < 0.05) for the contrast girl > woman, with activation in paedphilic subjects and deactivation in control subjects (rlOFC = right lateral orbitofrontal cortex). Event related averaging (B). BOLD %-signal-change in the rlOFC only illustrated a strong activation due to erotic pictures of girls in paedophilia.\nEvent related averaging of BOLD %-signal-change for all conditions and all groups in that ROI illustrated a strong activation due to erotic pictures of girls in paedophilic subjects that was absent for all other picture conditions in both groups (Figure 3B).","OFC: Orbitofrontal cortex; DMPFC: Dorsomedial prefrontal cortex; DLPFC: Dorsolateral prefrontal cortex; MPRAGE: Magnetization prepared rapid acquisistion gradient echo; GLM: General linear model, ROI: region of interest.","The authors declare that they have no competing interests.","BH, RM, MG, VD and ES conceived and designed the study. BH, NH, RM, PL and MK realized the study design and acquired the data. BH, FE, NH performed data analyses. BH and FE interpreted data and wrote the manuscript. BH, FE, NH, PL, MK, RM, VD, ES and MG contributed in the drafting and revision of the manuscript for important intellectual content. All authors read and approved the final manuscript.","The pre-publication history for this paper can be accessed here:\n\nhttp://www.biomedcentral.com/1471-244X/13/88/prepub\n"],"string":"[\n \"Paedophilia is a matter of great public interest and often evokes emotional discussions in mass media. It is difficult to estimate the prevalence of paedophilia as both paedophilic offenders and victims often prefer not to identify themselves. From surveys of the general population we know that approximately 12% of men and 17% of women report experiencing sexual abuse in their childhood [1]. These figures underline the potential impact of this disease on society. According to DSM IV-TR, paedophilia is defined by two main criteria: firstly persistent sexual fantasies, urges or behaviour involving sexual activity with prepubescent children and secondly that these people have acted on these urges, or that these urges or fantasies caused marked distress or interpersonal difficulties [2].\\nFindings from neuropsychological studies on paedophilia are heterogeneous. Whilst a lower IQ [3], educational difficulties [4] and a higher rate of left-handedness [5] indicate rather generalised brain dysfunction, other studies suggest more specific alterations like focal weaknesses in frontal-executive [6,7] and/or temporal-verbal [8] skills or even a more deliberate response style and greater self-monitoring in paedophilic subjects [9,10]. Furthermore revealed research on personality traits in paedophilia various findings like impaired interpersonal functions, impaired self-awareness, disinhibitory traits, sociopathy and a propensity for cognitive distortions [11]. In summary, it can be stated that the neurobiology of paedophilia remains incomplete.\\nIn recent years, researchers have increasingly addressed the neuronal underpinnings of sexual arousal in order to better understand sexual behaviour. Stoléru et al. [12,13] and Redouté et al. [14,15] were the first to propose a neurophenomological model that disentangled the cognitive, emotional, motivational and physiological components of sexual arousal. Two recently published quantitative meta-analyses on sexual cue reactivity [16,17] underline that distinctive subcomponents of sexual arousal can be reliably localised by neuroimaging techniques.\\nBased on the fMRI results about sexual arousal in healthy controls it was natural to transfer that approach to paedophilic subjects in order to better understand the cognitive and behavioural processes in paedophilia. In heterosexual paedophilia fMRI studies have demonstrated the altered processing of erotic visual stimuli in the dorsomedial prefrontal cortex (DMPFC), amygdala and hippocampus [18]. The latter study also reports that erotic pictures of adults induced a stronger activation of the hypothalmus, the periaqueductal grey and the dorsolateral prefrontal cortex (DLPFC) in healthy controls than in heterosexual paedophilic subjects. While this study highlights differences between controls and paedophilic subjects other studies report similar activation patterns for the respective erotic conditions. For example Schiffer et al. [19] show that erotic pictures of girls induced the same activation of limbic structures such as the amygdala, the cingulate gyrus or the hippocampus in heterosexual paedophilia as erotic pictures of women in a control group. However this study also found an additional activation of the DLPFC in the heterosexual paedophilic group alone. Research has also been carried out on homosexual paedophilia. Again in both paedophilic and control subjects, a common network relating to sexual activation was found, comprising the occipitotemporal and prefrontal cortex [20]. However, only paedophilic subjects showed activated subcortical regions such as the thalamus, the globus pallidus and the striatum during erotic stimulation. Another study on homosexual paedophilia describes differential activation of the right orbitofrontal cortex [21].\\nMost of the above-mentioned studies indicate that prefrontal brain regions might be related to paedophilia, but the results and structures involved differ from study to study. Considering these rather heterogeneous results, the findings from a recent study by Ponseti et al. [22] appear surprising. These authors propose an fMRI based classification procedure for heterosexual and homosexual non-paedophilic and paedophilic subjects with 95% accuracy. Unlike previous studies, these authors describe activation in regions known to be involved in the processing of erotic stimuli such as the caudate nucleus, cingulated cortex, insula, fusiform gyrus, temporal cortex, occipital cortex, thalamus, amygdala and cerebellum but not in the prefrontal cortex.\\nExcept for the study of Walter et al. [18] all of the above-mentioned studies that describe a prefrontal involvement in paedophilia used relatively long presentation times ranging from 19.2 - 38.5 s and blocked fMRI designs.\\nOne critical shortcoming of long presentation times is that different cognitive processes, such as sustained attention or self-referential processes might also take place and somehow interfere with the target process. Earlier fMRI studies on sexual arousal in normal subjects showed that with shorter presentation times of sexually arousing pictures specific neuronal networks and brain processes can be addressed. Static presentation for 8.75 s as used by Moulier et al. [23] demonstrated for example that the initiation and low levels of penile tumescence are controlled by frontal, parietal, insular and cingular cortical areas. In accordance with these results, Ferretti et al. [24] showed that longer presentation times (> 30 s) induce sexual arousal and penile erection whilst shorter presentation times (< 3 s) induce arousal without erection. A stimulation time of 5 s even allowed to distinguish specific sexual emotional effects from more general emotional effects [25]. A study comparing both types of fMRI-designs in the visual processing of erotic stimuli in healthy volunteers [26] provides strong support for the use of fast stimulation time, suggesting an event related design. The authors proposed that event related designs might be an alternative to blocked designs if the core interest is the detection of networks associated with the immediate processing of erotic stimuli.\\nBesides being more useful for the detection of these networks, short presentation times also offer other potential benefits as long presentation times in paedophilia are even more problematic than in healthy controls. Paedophilic subjects are mostly recruited after coming into conflict with the legal authorities as a result of their sexual preferences and this may increase a tendency to suppress erotic arousal or dissimulate sexual attraction to the pictures of children presented. The latter point has been used to explain some of the differences between ratings of erotic salience and brain response observed in some of the aforementioned studies [19,21]. As presentation time becomes shorter, deliberate manipulation may become more difficult and the immediate processes following the perception of target stimuli could become visible.\\nIn our study we aim to identify the neuronal networks related to immediate processing of erotic stimuli in heterosexual paedophilic subjects recruited from a forensic outpatient setting. Based on previous neuropsychological research and recent fMRI studies we predicted a differential activation in the prefrontal cortex in paedophilic subjects.\",\n \"Behavioural and functional magnetic resonance imaging data were acquired from 16 male right-handed subjects. All participants in this study were adults. Written informed consent was obtained from all subjects before participation in the study.\\nThe subjects in the paedophilic (N = 8) group were recruited from an outpatient cognitive behavioural group therapy at the department of forensic psychiatry in Basel, Switzerland. The heterosexual paedophilic subjects fulfilled DSM-IV-TR criteria for exclusive type (attracted only to prepubescent children) not limited to incest heterosexual paedophilia. Three of the subjects had previously molested prepubescent children, the other five had been convicted because of the possession of large quantities of explicit internet child pornography. Each participant’s sexual orientation and preference for prepubescent erotic stimuli was assessed in a clinical interview, the Multiphasic Sex Inventory (MSI) [27] and additionally verified by the clinical record and the court file. For all subjects, neither the interview nor the record indicated other comorbid paraphilias. None of the subjects had admitted paedophilia prior to the contact with the legal authorities.\\nHeterosexual control subjects (N = 8) were recruited from an advert on the University Hospital notice board. A clinical evaluation of all subjects revealed no other psychiatric, neurological or medical conditions. The local Ethics Committee of the University of Basel, Switzerland approved the study.\",\n \"The study design was adopted from Bühler et al. [26]. Visual stimuli were generated and displayed on a personal computer using the Neurobehavioral Systems (NBS) software package Presentation® 12.2. Stimuli were presented via fMRI-compatible digital video goggles (NordicNeuroLab, Bergen, Norway).\\nWhile inside the scanner, subjects were presented with various types of pictures in an event related design. We presented 10 pictures from each category (erotic pictures of boys, girls, men, women or neutral control pictures) for 750 ms with a jittered interstimulus interval that varied randomly between 10 – 20 s in steps of full seconds (Figure 1).\\nFMRI paradigm. We presented erotic pictures of adults (women/men) and children (girls/boys) interspersed with neutral control pictures. Pictures from each category were presented for 750 ms with a jittered interstimulus interval ranging from 10–20 s.\\nIn each picture, only one person of one of the above-mentioned categories was displayed in bathing clothes in front of a plain-coloured background. All pictures showed faces and the complete corpus. Secondary sexual characteristics were clearly visible in the pictures showing adults but were clearly absent in the pictures depicting prepubescent children. We avoided photographs of adolescents and applied a biological rather than a legal cut off to make the pictures of adults easily distinguishable from those of children. Before inclusion into the paradigm rated the study subjects the pictures and only the pictures with the highest ratings on a visual analogue scale were included. Neutral pictures showed simple objects like e.g. a small boot in front of the same background.\\nIn order to control attention during the passive fMRI task we used a previously applied procedure to assure attentive observation of the presented pictures [28]: Before the scanning procedure, subjects were instructed to attentively observe the pictures and told that we would check their attention by asking them to identify the pictures after the scanning session. Immediately after the fMRI session we presented the slides that they had seen interspersed with similar pictures, which had not been presented during the experiment. Subjects then had to decide which pictures they had seen during the fMRI stimulation.\",\n \"Images were acquired using a 3 T MRI scanner (Verio, Siemens Healthcare, Erlangen, Germany) equipped with a standard radio frequency head coil. First a T1-weighted high-resolution data set that covered the whole brain was acquired using a three-dimensional MPRAGE (magnetization-prepared rapid acquisition gradient echo) sequence with a repetition time (TR) of 2.00, an isotropic spatial resolution of 1.0 mm3, and an echo time (TE) of 3.4 ms. T2* weighted functional images were recorded using echoplanar imaging with a TR of 2500 ms, an isotropic spatial resolution of 3×3×3 mm3 and a TE of 30 ms (FoV 228; matrix 76; spacing between slices: 0.51 mm; interslice time: 69 ms). Altogether 152 volumes with 36 image slices with a thickness of 3 mm were obtained.\",\n \"Image time-series were processed using the BrainVoyager QX 2.3.0 software package (Brain Innovation, Maastricht, The Netherlands). Pre-processing included head motion correction, slice scan time correction, temporal high pass filtering and removal of linear trends. Using the results of the image registration with anatomical scans, the functional image-time series were then warped into Talairach space and resampled into 3 mm isotropic voxel time-series. Normalized images were smoothed using a 6.00 mm isotropic Gaussian kernel.\\nFor first-level analysis, a General Linear Model (GLM) analysis was applied with separate subject z-normalized predictors fitted to z-normalized voxel time-courses from all data sets. The orthogonal predictors of interest in the design matrix were: “boys”, “girls”, “men”, “women” and “neutral”. For second-level analysis, the GLM fits (beta weights) were assessed in a 3-way-ANOVA with two within subject factors (sex: female vs. male; age: child vs. adult) and one between subject factor group (paedophilia vs. control). We assessed the main effects of, and the interaction between, these factors at the voxel level and obtained maps that thresholded at the significance level of 5% (corrected for multiple comparison). To obtain this correction, an uncorrected statistical threshold was initially applied to each of the F-maps at p = 0.005, and a cluster-level threshold correction procedure based on Monte Carlo simulations [29] was applied in order to determine the minimum cluster size below which any activation was to be discarded. This procedure yielded a minimum cluster size of 10 voxels (corresponding to a minimum cluster extent of 280 mm) for the F-maps of the interaction of all factors, 12 voxel (297 mm) for the factor age and 19 voxels (482 mm) for the factor sex.\\nThe anatomical labelling of active clusters was defined on the basis of the peak voxel coordinates, using the Talairach Daemon (http://www.talairach.org) [30,31].\\nActive clusters resulting from the voxel-level ANOVA were further defined as Regions of Interests (ROI). Data from those ROIs were extracted and analysed in order to understand the direction of the effects of the ANOVA. To that end, we calculated linear contrasts for the different factors, i.e. sex (female > male), age (child > adult) and the interaction of the factors (girl > woman) separately for both groups. In order to further specify the interaction of all factors (sex, age, group) we extracted BOLD %-signal-change from the respective ROI and averaged the BOLD%-signal-change separately for the groups.\",\n \"Sexual orientation in the control group was heterosexual with a preference for mature women. In the paedophilia group all subjects were heterosexual and attracted exclusively by prepubescent stimuli. The mean age of the paedophilic subjects was 48.25 (standard deviation: 9.15) years and mean IQ 116 (12.12). In the control groups, the mean age was 46.25 (8.35) years and IQ 124.88 (13.91). Student t-tests showed no significant difference between these groups (Table 1). In the paedophilic group 75.4% of the pictures were correctly identified immediately after the scanning session. In the control group 77.5% of the pictures were correctly identified. Again a group difference was excluded by t-test.\\nMean age, IQ and rate of the correctly indentified pictures (behavioural control) with standard deviation (SD) and T-test for both groups\\nNone of the results reaches statistically significant differences.\",\n \" Voxel level ANOVA We found a significant interaction of the factors sex, age and group (p < 0.05, corrected) in the middle frontal gyrus (Figure 2, Table 2). We furthermore found significant main effects for the factors sex and age outside of the middle frontal gyrus, i. e. in brain areas that didn`t show a significant interaction of all factors.\\nWhole brain ANOVA. Active voxels showed a main effect of the factor sex (female/male) in the right middle temporal gyrus, right fusiform gyrus, right inferior and middle occipital gyrus and right parietal lobe. In the left hemisphere the inferior occipital gyrus displayed active voxels (A). Furthermore we found bilateral activation in the cerebellum. The factor age (adult/child) showed an activation of the right dorsomedial prefrontal cortex (B). In the right lateral orbitofrontal cortex a significant interaction of sex, age and group was detected (C). Significant voxels in A-C represent effects at a corrected p-level of p < 0.05. X, Y, Z corresponds to the Tailarach coordinates.\\nRegions that showed a significant effect in the voxel wise ANOVA\\nX, Y, Z corresponds to the Tailarach coordinates (BA = Brodmann area; R = right, L = left).\\nA significant main effect of the factor sex (female, male) was present at a corrected threshold of p < 0.05 in the right middle temporal gyrus, right fusiform gyrus, right inferior and middle occipital gyrus and right parietal lobe. In the left hemisphere, the inferior occipital gyrus was also active. We also found bilateral activation in the cerebellum.\\nThe factor age revealed an activation of the right dorsomedial prefrontal cortex (p < 0.05, corrected).\\nFor the factor group, no significant activation was found at the latter threshold, but again this finding is restricted to brain areas that did not show a significant interaction of all factors.\\nWe found a significant interaction of the factors sex, age and group (p < 0.05, corrected) in the middle frontal gyrus (Figure 2, Table 2). We furthermore found significant main effects for the factors sex and age outside of the middle frontal gyrus, i. e. in brain areas that didn`t show a significant interaction of all factors.\\nWhole brain ANOVA. Active voxels showed a main effect of the factor sex (female/male) in the right middle temporal gyrus, right fusiform gyrus, right inferior and middle occipital gyrus and right parietal lobe. In the left hemisphere the inferior occipital gyrus displayed active voxels (A). Furthermore we found bilateral activation in the cerebellum. The factor age (adult/child) showed an activation of the right dorsomedial prefrontal cortex (B). In the right lateral orbitofrontal cortex a significant interaction of sex, age and group was detected (C). Significant voxels in A-C represent effects at a corrected p-level of p < 0.05. X, Y, Z corresponds to the Tailarach coordinates.\\nRegions that showed a significant effect in the voxel wise ANOVA\\nX, Y, Z corresponds to the Tailarach coordinates (BA = Brodmann area; R = right, L = left).\\nA significant main effect of the factor sex (female, male) was present at a corrected threshold of p < 0.05 in the right middle temporal gyrus, right fusiform gyrus, right inferior and middle occipital gyrus and right parietal lobe. In the left hemisphere, the inferior occipital gyrus was also active. We also found bilateral activation in the cerebellum.\\nThe factor age revealed an activation of the right dorsomedial prefrontal cortex (p < 0.05, corrected).\\nFor the factor group, no significant activation was found at the latter threshold, but again this finding is restricted to brain areas that did not show a significant interaction of all factors.\",\n \"We found a significant interaction of the factors sex, age and group (p < 0.05, corrected) in the middle frontal gyrus (Figure 2, Table 2). We furthermore found significant main effects for the factors sex and age outside of the middle frontal gyrus, i. e. in brain areas that didn`t show a significant interaction of all factors.\\nWhole brain ANOVA. Active voxels showed a main effect of the factor sex (female/male) in the right middle temporal gyrus, right fusiform gyrus, right inferior and middle occipital gyrus and right parietal lobe. In the left hemisphere the inferior occipital gyrus displayed active voxels (A). Furthermore we found bilateral activation in the cerebellum. The factor age (adult/child) showed an activation of the right dorsomedial prefrontal cortex (B). In the right lateral orbitofrontal cortex a significant interaction of sex, age and group was detected (C). Significant voxels in A-C represent effects at a corrected p-level of p < 0.05. X, Y, Z corresponds to the Tailarach coordinates.\\nRegions that showed a significant effect in the voxel wise ANOVA\\nX, Y, Z corresponds to the Tailarach coordinates (BA = Brodmann area; R = right, L = left).\\nA significant main effect of the factor sex (female, male) was present at a corrected threshold of p < 0.05 in the right middle temporal gyrus, right fusiform gyrus, right inferior and middle occipital gyrus and right parietal lobe. In the left hemisphere, the inferior occipital gyrus was also active. We also found bilateral activation in the cerebellum.\\nThe factor age revealed an activation of the right dorsomedial prefrontal cortex (p < 0.05, corrected).\\nFor the factor group, no significant activation was found at the latter threshold, but again this finding is restricted to brain areas that did not show a significant interaction of all factors.\",\n \"The linear contrasts of these ROIs confirmed that, in both groups, the main effect of the factor sex was based on a higher activation due to female pictures and the main effect of the factor age on a stronger activation for pictures of children. ROI analysis clarified that in the ROI that became significant for the interaction of all factors, only paedophilic subjects showed activation in the girl condition while controls showed a deactivation in this condition (Figure 3A).\\nROIs analysis (A). The ROI female > male contrast level for the ROIs extracted from the ANOVA of the main effect of sex displayed a greater activation for female pictures in all active ROIs (rTL = right temporal lobe, rPL = right parietal lobe, rOL = right occipital lobe, lOL = left occipital lobe). The child > adult contrast confirmed that the main effect of age is based on a stronger activation due to pictures of children in both groups (rDMPFC = right dorsomedial prefrontal cortex). ROI analysis of the cluster showing an interaction of all factors revealed a significant difference (* = p < 0.05) for the contrast girl > woman, with activation in paedphilic subjects and deactivation in control subjects (rlOFC = right lateral orbitofrontal cortex). Event related averaging (B). BOLD %-signal-change in the rlOFC only illustrated a strong activation due to erotic pictures of girls in paedophilia.\\nEvent related averaging of BOLD %-signal-change for all conditions and all groups in that ROI illustrated a strong activation due to erotic pictures of girls in paedophilic subjects that was absent for all other picture conditions in both groups (Figure 3B).\",\n \"OFC: Orbitofrontal cortex; DMPFC: Dorsomedial prefrontal cortex; DLPFC: Dorsolateral prefrontal cortex; MPRAGE: Magnetization prepared rapid acquisistion gradient echo; GLM: General linear model, ROI: region of interest.\",\n \"The authors declare that they have no competing interests.\",\n \"BH, RM, MG, VD and ES conceived and designed the study. BH, NH, RM, PL and MK realized the study design and acquired the data. BH, FE, NH performed data analyses. BH and FE interpreted data and wrote the manuscript. BH, FE, NH, PL, MK, RM, VD, ES and MG contributed in the drafting and revision of the manuscript for important intellectual content. All authors read and approved the final manuscript.\",\n \"The pre-publication history for this paper can be accessed here:\\n\\nhttp://www.biomedcentral.com/1471-244X/13/88/prepub\\n\"\n]"},"other_sections_sec_types":{"kind":"list like","value":[null,null,null,null,null,null,null,null,null,null,null,null,null],"string":"[\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null\n]"},"all_sections_titles":{"kind":"list like","value":["Background","Methods","Subjects","Stimulation and paradigm","FMRI acquisition and analysis","Image pre-processing and statistical analysis","Results","Demographics","FMRI","Voxel level ANOVA","ROI analysis","Discussion","Conclusion","Abbreviations","Competing interests","Authors’ contributions","Pre-publication history"],"string":"[\n \"Background\",\n \"Methods\",\n \"Subjects\",\n \"Stimulation and paradigm\",\n \"FMRI acquisition and analysis\",\n \"Image pre-processing and statistical analysis\",\n \"Results\",\n \"Demographics\",\n \"FMRI\",\n \"Voxel level ANOVA\",\n \"ROI analysis\",\n \"Discussion\",\n \"Conclusion\",\n \"Abbreviations\",\n \"Competing interests\",\n \"Authors’ contributions\",\n \"Pre-publication history\"\n]"},"all_sections_texts":{"kind":"list like","value":["Paedophilia is a matter of great public interest and often evokes emotional discussions in mass media. It is difficult to estimate the prevalence of paedophilia as both paedophilic offenders and victims often prefer not to identify themselves. From surveys of the general population we know that approximately 12% of men and 17% of women report experiencing sexual abuse in their childhood [1]. These figures underline the potential impact of this disease on society. According to DSM IV-TR, paedophilia is defined by two main criteria: firstly persistent sexual fantasies, urges or behaviour involving sexual activity with prepubescent children and secondly that these people have acted on these urges, or that these urges or fantasies caused marked distress or interpersonal difficulties [2].\nFindings from neuropsychological studies on paedophilia are heterogeneous. Whilst a lower IQ [3], educational difficulties [4] and a higher rate of left-handedness [5] indicate rather generalised brain dysfunction, other studies suggest more specific alterations like focal weaknesses in frontal-executive [6,7] and/or temporal-verbal [8] skills or even a more deliberate response style and greater self-monitoring in paedophilic subjects [9,10]. Furthermore revealed research on personality traits in paedophilia various findings like impaired interpersonal functions, impaired self-awareness, disinhibitory traits, sociopathy and a propensity for cognitive distortions [11]. In summary, it can be stated that the neurobiology of paedophilia remains incomplete.\nIn recent years, researchers have increasingly addressed the neuronal underpinnings of sexual arousal in order to better understand sexual behaviour. Stoléru et al. [12,13] and Redouté et al. [14,15] were the first to propose a neurophenomological model that disentangled the cognitive, emotional, motivational and physiological components of sexual arousal. Two recently published quantitative meta-analyses on sexual cue reactivity [16,17] underline that distinctive subcomponents of sexual arousal can be reliably localised by neuroimaging techniques.\nBased on the fMRI results about sexual arousal in healthy controls it was natural to transfer that approach to paedophilic subjects in order to better understand the cognitive and behavioural processes in paedophilia. In heterosexual paedophilia fMRI studies have demonstrated the altered processing of erotic visual stimuli in the dorsomedial prefrontal cortex (DMPFC), amygdala and hippocampus [18]. The latter study also reports that erotic pictures of adults induced a stronger activation of the hypothalmus, the periaqueductal grey and the dorsolateral prefrontal cortex (DLPFC) in healthy controls than in heterosexual paedophilic subjects. While this study highlights differences between controls and paedophilic subjects other studies report similar activation patterns for the respective erotic conditions. For example Schiffer et al. [19] show that erotic pictures of girls induced the same activation of limbic structures such as the amygdala, the cingulate gyrus or the hippocampus in heterosexual paedophilia as erotic pictures of women in a control group. However this study also found an additional activation of the DLPFC in the heterosexual paedophilic group alone. Research has also been carried out on homosexual paedophilia. Again in both paedophilic and control subjects, a common network relating to sexual activation was found, comprising the occipitotemporal and prefrontal cortex [20]. However, only paedophilic subjects showed activated subcortical regions such as the thalamus, the globus pallidus and the striatum during erotic stimulation. Another study on homosexual paedophilia describes differential activation of the right orbitofrontal cortex [21].\nMost of the above-mentioned studies indicate that prefrontal brain regions might be related to paedophilia, but the results and structures involved differ from study to study. Considering these rather heterogeneous results, the findings from a recent study by Ponseti et al. [22] appear surprising. These authors propose an fMRI based classification procedure for heterosexual and homosexual non-paedophilic and paedophilic subjects with 95% accuracy. Unlike previous studies, these authors describe activation in regions known to be involved in the processing of erotic stimuli such as the caudate nucleus, cingulated cortex, insula, fusiform gyrus, temporal cortex, occipital cortex, thalamus, amygdala and cerebellum but not in the prefrontal cortex.\nExcept for the study of Walter et al. [18] all of the above-mentioned studies that describe a prefrontal involvement in paedophilia used relatively long presentation times ranging from 19.2 - 38.5 s and blocked fMRI designs.\nOne critical shortcoming of long presentation times is that different cognitive processes, such as sustained attention or self-referential processes might also take place and somehow interfere with the target process. Earlier fMRI studies on sexual arousal in normal subjects showed that with shorter presentation times of sexually arousing pictures specific neuronal networks and brain processes can be addressed. Static presentation for 8.75 s as used by Moulier et al. [23] demonstrated for example that the initiation and low levels of penile tumescence are controlled by frontal, parietal, insular and cingular cortical areas. In accordance with these results, Ferretti et al. [24] showed that longer presentation times (> 30 s) induce sexual arousal and penile erection whilst shorter presentation times (< 3 s) induce arousal without erection. A stimulation time of 5 s even allowed to distinguish specific sexual emotional effects from more general emotional effects [25]. A study comparing both types of fMRI-designs in the visual processing of erotic stimuli in healthy volunteers [26] provides strong support for the use of fast stimulation time, suggesting an event related design. The authors proposed that event related designs might be an alternative to blocked designs if the core interest is the detection of networks associated with the immediate processing of erotic stimuli.\nBesides being more useful for the detection of these networks, short presentation times also offer other potential benefits as long presentation times in paedophilia are even more problematic than in healthy controls. Paedophilic subjects are mostly recruited after coming into conflict with the legal authorities as a result of their sexual preferences and this may increase a tendency to suppress erotic arousal or dissimulate sexual attraction to the pictures of children presented. The latter point has been used to explain some of the differences between ratings of erotic salience and brain response observed in some of the aforementioned studies [19,21]. As presentation time becomes shorter, deliberate manipulation may become more difficult and the immediate processes following the perception of target stimuli could become visible.\nIn our study we aim to identify the neuronal networks related to immediate processing of erotic stimuli in heterosexual paedophilic subjects recruited from a forensic outpatient setting. Based on previous neuropsychological research and recent fMRI studies we predicted a differential activation in the prefrontal cortex in paedophilic subjects."," Subjects Behavioural and functional magnetic resonance imaging data were acquired from 16 male right-handed subjects. All participants in this study were adults. Written informed consent was obtained from all subjects before participation in the study.\nThe subjects in the paedophilic (N = 8) group were recruited from an outpatient cognitive behavioural group therapy at the department of forensic psychiatry in Basel, Switzerland. The heterosexual paedophilic subjects fulfilled DSM-IV-TR criteria for exclusive type (attracted only to prepubescent children) not limited to incest heterosexual paedophilia. Three of the subjects had previously molested prepubescent children, the other five had been convicted because of the possession of large quantities of explicit internet child pornography. Each participant’s sexual orientation and preference for prepubescent erotic stimuli was assessed in a clinical interview, the Multiphasic Sex Inventory (MSI) [27] and additionally verified by the clinical record and the court file. For all subjects, neither the interview nor the record indicated other comorbid paraphilias. None of the subjects had admitted paedophilia prior to the contact with the legal authorities.\nHeterosexual control subjects (N = 8) were recruited from an advert on the University Hospital notice board. A clinical evaluation of all subjects revealed no other psychiatric, neurological or medical conditions. The local Ethics Committee of the University of Basel, Switzerland approved the study.\nBehavioural and functional magnetic resonance imaging data were acquired from 16 male right-handed subjects. All participants in this study were adults. Written informed consent was obtained from all subjects before participation in the study.\nThe subjects in the paedophilic (N = 8) group were recruited from an outpatient cognitive behavioural group therapy at the department of forensic psychiatry in Basel, Switzerland. The heterosexual paedophilic subjects fulfilled DSM-IV-TR criteria for exclusive type (attracted only to prepubescent children) not limited to incest heterosexual paedophilia. Three of the subjects had previously molested prepubescent children, the other five had been convicted because of the possession of large quantities of explicit internet child pornography. Each participant’s sexual orientation and preference for prepubescent erotic stimuli was assessed in a clinical interview, the Multiphasic Sex Inventory (MSI) [27] and additionally verified by the clinical record and the court file. For all subjects, neither the interview nor the record indicated other comorbid paraphilias. None of the subjects had admitted paedophilia prior to the contact with the legal authorities.\nHeterosexual control subjects (N = 8) were recruited from an advert on the University Hospital notice board. A clinical evaluation of all subjects revealed no other psychiatric, neurological or medical conditions. The local Ethics Committee of the University of Basel, Switzerland approved the study.\n Stimulation and paradigm The study design was adopted from Bühler et al. [26]. Visual stimuli were generated and displayed on a personal computer using the Neurobehavioral Systems (NBS) software package Presentation® 12.2. Stimuli were presented via fMRI-compatible digital video goggles (NordicNeuroLab, Bergen, Norway).\nWhile inside the scanner, subjects were presented with various types of pictures in an event related design. We presented 10 pictures from each category (erotic pictures of boys, girls, men, women or neutral control pictures) for 750 ms with a jittered interstimulus interval that varied randomly between 10 – 20 s in steps of full seconds (Figure 1).\nFMRI paradigm. We presented erotic pictures of adults (women/men) and children (girls/boys) interspersed with neutral control pictures. Pictures from each category were presented for 750 ms with a jittered interstimulus interval ranging from 10–20 s.\nIn each picture, only one person of one of the above-mentioned categories was displayed in bathing clothes in front of a plain-coloured background. All pictures showed faces and the complete corpus. Secondary sexual characteristics were clearly visible in the pictures showing adults but were clearly absent in the pictures depicting prepubescent children. We avoided photographs of adolescents and applied a biological rather than a legal cut off to make the pictures of adults easily distinguishable from those of children. Before inclusion into the paradigm rated the study subjects the pictures and only the pictures with the highest ratings on a visual analogue scale were included. Neutral pictures showed simple objects like e.g. a small boot in front of the same background.\nIn order to control attention during the passive fMRI task we used a previously applied procedure to assure attentive observation of the presented pictures [28]: Before the scanning procedure, subjects were instructed to attentively observe the pictures and told that we would check their attention by asking them to identify the pictures after the scanning session. Immediately after the fMRI session we presented the slides that they had seen interspersed with similar pictures, which had not been presented during the experiment. Subjects then had to decide which pictures they had seen during the fMRI stimulation.\nThe study design was adopted from Bühler et al. [26]. Visual stimuli were generated and displayed on a personal computer using the Neurobehavioral Systems (NBS) software package Presentation® 12.2. Stimuli were presented via fMRI-compatible digital video goggles (NordicNeuroLab, Bergen, Norway).\nWhile inside the scanner, subjects were presented with various types of pictures in an event related design. We presented 10 pictures from each category (erotic pictures of boys, girls, men, women or neutral control pictures) for 750 ms with a jittered interstimulus interval that varied randomly between 10 – 20 s in steps of full seconds (Figure 1).\nFMRI paradigm. We presented erotic pictures of adults (women/men) and children (girls/boys) interspersed with neutral control pictures. Pictures from each category were presented for 750 ms with a jittered interstimulus interval ranging from 10–20 s.\nIn each picture, only one person of one of the above-mentioned categories was displayed in bathing clothes in front of a plain-coloured background. All pictures showed faces and the complete corpus. Secondary sexual characteristics were clearly visible in the pictures showing adults but were clearly absent in the pictures depicting prepubescent children. We avoided photographs of adolescents and applied a biological rather than a legal cut off to make the pictures of adults easily distinguishable from those of children. Before inclusion into the paradigm rated the study subjects the pictures and only the pictures with the highest ratings on a visual analogue scale were included. Neutral pictures showed simple objects like e.g. a small boot in front of the same background.\nIn order to control attention during the passive fMRI task we used a previously applied procedure to assure attentive observation of the presented pictures [28]: Before the scanning procedure, subjects were instructed to attentively observe the pictures and told that we would check their attention by asking them to identify the pictures after the scanning session. Immediately after the fMRI session we presented the slides that they had seen interspersed with similar pictures, which had not been presented during the experiment. Subjects then had to decide which pictures they had seen during the fMRI stimulation.\n FMRI acquisition and analysis Images were acquired using a 3 T MRI scanner (Verio, Siemens Healthcare, Erlangen, Germany) equipped with a standard radio frequency head coil. First a T1-weighted high-resolution data set that covered the whole brain was acquired using a three-dimensional MPRAGE (magnetization-prepared rapid acquisition gradient echo) sequence with a repetition time (TR) of 2.00, an isotropic spatial resolution of 1.0 mm3, and an echo time (TE) of 3.4 ms. T2* weighted functional images were recorded using echoplanar imaging with a TR of 2500 ms, an isotropic spatial resolution of 3×3×3 mm3 and a TE of 30 ms (FoV 228; matrix 76; spacing between slices: 0.51 mm; interslice time: 69 ms). Altogether 152 volumes with 36 image slices with a thickness of 3 mm were obtained.\nImages were acquired using a 3 T MRI scanner (Verio, Siemens Healthcare, Erlangen, Germany) equipped with a standard radio frequency head coil. First a T1-weighted high-resolution data set that covered the whole brain was acquired using a three-dimensional MPRAGE (magnetization-prepared rapid acquisition gradient echo) sequence with a repetition time (TR) of 2.00, an isotropic spatial resolution of 1.0 mm3, and an echo time (TE) of 3.4 ms. T2* weighted functional images were recorded using echoplanar imaging with a TR of 2500 ms, an isotropic spatial resolution of 3×3×3 mm3 and a TE of 30 ms (FoV 228; matrix 76; spacing between slices: 0.51 mm; interslice time: 69 ms). Altogether 152 volumes with 36 image slices with a thickness of 3 mm were obtained.\n Image pre-processing and statistical analysis Image time-series were processed using the BrainVoyager QX 2.3.0 software package (Brain Innovation, Maastricht, The Netherlands). Pre-processing included head motion correction, slice scan time correction, temporal high pass filtering and removal of linear trends. Using the results of the image registration with anatomical scans, the functional image-time series were then warped into Talairach space and resampled into 3 mm isotropic voxel time-series. Normalized images were smoothed using a 6.00 mm isotropic Gaussian kernel.\nFor first-level analysis, a General Linear Model (GLM) analysis was applied with separate subject z-normalized predictors fitted to z-normalized voxel time-courses from all data sets. The orthogonal predictors of interest in the design matrix were: “boys”, “girls”, “men”, “women” and “neutral”. For second-level analysis, the GLM fits (beta weights) were assessed in a 3-way-ANOVA with two within subject factors (sex: female vs. male; age: child vs. adult) and one between subject factor group (paedophilia vs. control). We assessed the main effects of, and the interaction between, these factors at the voxel level and obtained maps that thresholded at the significance level of 5% (corrected for multiple comparison). To obtain this correction, an uncorrected statistical threshold was initially applied to each of the F-maps at p = 0.005, and a cluster-level threshold correction procedure based on Monte Carlo simulations [29] was applied in order to determine the minimum cluster size below which any activation was to be discarded. This procedure yielded a minimum cluster size of 10 voxels (corresponding to a minimum cluster extent of 280 mm) for the F-maps of the interaction of all factors, 12 voxel (297 mm) for the factor age and 19 voxels (482 mm) for the factor sex.\nThe anatomical labelling of active clusters was defined on the basis of the peak voxel coordinates, using the Talairach Daemon (http://www.talairach.org) [30,31].\nActive clusters resulting from the voxel-level ANOVA were further defined as Regions of Interests (ROI). Data from those ROIs were extracted and analysed in order to understand the direction of the effects of the ANOVA. To that end, we calculated linear contrasts for the different factors, i.e. sex (female > male), age (child > adult) and the interaction of the factors (girl > woman) separately for both groups. In order to further specify the interaction of all factors (sex, age, group) we extracted BOLD %-signal-change from the respective ROI and averaged the BOLD%-signal-change separately for the groups.\nImage time-series were processed using the BrainVoyager QX 2.3.0 software package (Brain Innovation, Maastricht, The Netherlands). Pre-processing included head motion correction, slice scan time correction, temporal high pass filtering and removal of linear trends. Using the results of the image registration with anatomical scans, the functional image-time series were then warped into Talairach space and resampled into 3 mm isotropic voxel time-series. Normalized images were smoothed using a 6.00 mm isotropic Gaussian kernel.\nFor first-level analysis, a General Linear Model (GLM) analysis was applied with separate subject z-normalized predictors fitted to z-normalized voxel time-courses from all data sets. The orthogonal predictors of interest in the design matrix were: “boys”, “girls”, “men”, “women” and “neutral”. For second-level analysis, the GLM fits (beta weights) were assessed in a 3-way-ANOVA with two within subject factors (sex: female vs. male; age: child vs. adult) and one between subject factor group (paedophilia vs. control). We assessed the main effects of, and the interaction between, these factors at the voxel level and obtained maps that thresholded at the significance level of 5% (corrected for multiple comparison). To obtain this correction, an uncorrected statistical threshold was initially applied to each of the F-maps at p = 0.005, and a cluster-level threshold correction procedure based on Monte Carlo simulations [29] was applied in order to determine the minimum cluster size below which any activation was to be discarded. This procedure yielded a minimum cluster size of 10 voxels (corresponding to a minimum cluster extent of 280 mm) for the F-maps of the interaction of all factors, 12 voxel (297 mm) for the factor age and 19 voxels (482 mm) for the factor sex.\nThe anatomical labelling of active clusters was defined on the basis of the peak voxel coordinates, using the Talairach Daemon (http://www.talairach.org) [30,31].\nActive clusters resulting from the voxel-level ANOVA were further defined as Regions of Interests (ROI). Data from those ROIs were extracted and analysed in order to understand the direction of the effects of the ANOVA. To that end, we calculated linear contrasts for the different factors, i.e. sex (female > male), age (child > adult) and the interaction of the factors (girl > woman) separately for both groups. In order to further specify the interaction of all factors (sex, age, group) we extracted BOLD %-signal-change from the respective ROI and averaged the BOLD%-signal-change separately for the groups.","Behavioural and functional magnetic resonance imaging data were acquired from 16 male right-handed subjects. All participants in this study were adults. Written informed consent was obtained from all subjects before participation in the study.\nThe subjects in the paedophilic (N = 8) group were recruited from an outpatient cognitive behavioural group therapy at the department of forensic psychiatry in Basel, Switzerland. The heterosexual paedophilic subjects fulfilled DSM-IV-TR criteria for exclusive type (attracted only to prepubescent children) not limited to incest heterosexual paedophilia. Three of the subjects had previously molested prepubescent children, the other five had been convicted because of the possession of large quantities of explicit internet child pornography. Each participant’s sexual orientation and preference for prepubescent erotic stimuli was assessed in a clinical interview, the Multiphasic Sex Inventory (MSI) [27] and additionally verified by the clinical record and the court file. For all subjects, neither the interview nor the record indicated other comorbid paraphilias. None of the subjects had admitted paedophilia prior to the contact with the legal authorities.\nHeterosexual control subjects (N = 8) were recruited from an advert on the University Hospital notice board. A clinical evaluation of all subjects revealed no other psychiatric, neurological or medical conditions. The local Ethics Committee of the University of Basel, Switzerland approved the study.","The study design was adopted from Bühler et al. [26]. Visual stimuli were generated and displayed on a personal computer using the Neurobehavioral Systems (NBS) software package Presentation® 12.2. Stimuli were presented via fMRI-compatible digital video goggles (NordicNeuroLab, Bergen, Norway).\nWhile inside the scanner, subjects were presented with various types of pictures in an event related design. We presented 10 pictures from each category (erotic pictures of boys, girls, men, women or neutral control pictures) for 750 ms with a jittered interstimulus interval that varied randomly between 10 – 20 s in steps of full seconds (Figure 1).\nFMRI paradigm. We presented erotic pictures of adults (women/men) and children (girls/boys) interspersed with neutral control pictures. Pictures from each category were presented for 750 ms with a jittered interstimulus interval ranging from 10–20 s.\nIn each picture, only one person of one of the above-mentioned categories was displayed in bathing clothes in front of a plain-coloured background. All pictures showed faces and the complete corpus. Secondary sexual characteristics were clearly visible in the pictures showing adults but were clearly absent in the pictures depicting prepubescent children. We avoided photographs of adolescents and applied a biological rather than a legal cut off to make the pictures of adults easily distinguishable from those of children. Before inclusion into the paradigm rated the study subjects the pictures and only the pictures with the highest ratings on a visual analogue scale were included. Neutral pictures showed simple objects like e.g. a small boot in front of the same background.\nIn order to control attention during the passive fMRI task we used a previously applied procedure to assure attentive observation of the presented pictures [28]: Before the scanning procedure, subjects were instructed to attentively observe the pictures and told that we would check their attention by asking them to identify the pictures after the scanning session. Immediately after the fMRI session we presented the slides that they had seen interspersed with similar pictures, which had not been presented during the experiment. Subjects then had to decide which pictures they had seen during the fMRI stimulation.","Images were acquired using a 3 T MRI scanner (Verio, Siemens Healthcare, Erlangen, Germany) equipped with a standard radio frequency head coil. First a T1-weighted high-resolution data set that covered the whole brain was acquired using a three-dimensional MPRAGE (magnetization-prepared rapid acquisition gradient echo) sequence with a repetition time (TR) of 2.00, an isotropic spatial resolution of 1.0 mm3, and an echo time (TE) of 3.4 ms. T2* weighted functional images were recorded using echoplanar imaging with a TR of 2500 ms, an isotropic spatial resolution of 3×3×3 mm3 and a TE of 30 ms (FoV 228; matrix 76; spacing between slices: 0.51 mm; interslice time: 69 ms). Altogether 152 volumes with 36 image slices with a thickness of 3 mm were obtained.","Image time-series were processed using the BrainVoyager QX 2.3.0 software package (Brain Innovation, Maastricht, The Netherlands). Pre-processing included head motion correction, slice scan time correction, temporal high pass filtering and removal of linear trends. Using the results of the image registration with anatomical scans, the functional image-time series were then warped into Talairach space and resampled into 3 mm isotropic voxel time-series. Normalized images were smoothed using a 6.00 mm isotropic Gaussian kernel.\nFor first-level analysis, a General Linear Model (GLM) analysis was applied with separate subject z-normalized predictors fitted to z-normalized voxel time-courses from all data sets. The orthogonal predictors of interest in the design matrix were: “boys”, “girls”, “men”, “women” and “neutral”. For second-level analysis, the GLM fits (beta weights) were assessed in a 3-way-ANOVA with two within subject factors (sex: female vs. male; age: child vs. adult) and one between subject factor group (paedophilia vs. control). We assessed the main effects of, and the interaction between, these factors at the voxel level and obtained maps that thresholded at the significance level of 5% (corrected for multiple comparison). To obtain this correction, an uncorrected statistical threshold was initially applied to each of the F-maps at p = 0.005, and a cluster-level threshold correction procedure based on Monte Carlo simulations [29] was applied in order to determine the minimum cluster size below which any activation was to be discarded. This procedure yielded a minimum cluster size of 10 voxels (corresponding to a minimum cluster extent of 280 mm) for the F-maps of the interaction of all factors, 12 voxel (297 mm) for the factor age and 19 voxels (482 mm) for the factor sex.\nThe anatomical labelling of active clusters was defined on the basis of the peak voxel coordinates, using the Talairach Daemon (http://www.talairach.org) [30,31].\nActive clusters resulting from the voxel-level ANOVA were further defined as Regions of Interests (ROI). Data from those ROIs were extracted and analysed in order to understand the direction of the effects of the ANOVA. To that end, we calculated linear contrasts for the different factors, i.e. sex (female > male), age (child > adult) and the interaction of the factors (girl > woman) separately for both groups. In order to further specify the interaction of all factors (sex, age, group) we extracted BOLD %-signal-change from the respective ROI and averaged the BOLD%-signal-change separately for the groups."," Demographics Sexual orientation in the control group was heterosexual with a preference for mature women. In the paedophilia group all subjects were heterosexual and attracted exclusively by prepubescent stimuli. The mean age of the paedophilic subjects was 48.25 (standard deviation: 9.15) years and mean IQ 116 (12.12). In the control groups, the mean age was 46.25 (8.35) years and IQ 124.88 (13.91). Student t-tests showed no significant difference between these groups (Table 1). In the paedophilic group 75.4% of the pictures were correctly identified immediately after the scanning session. In the control group 77.5% of the pictures were correctly identified. Again a group difference was excluded by t-test.\nMean age, IQ and rate of the correctly indentified pictures (behavioural control) with standard deviation (SD) and T-test for both groups\nNone of the results reaches statistically significant differences.\nSexual orientation in the control group was heterosexual with a preference for mature women. In the paedophilia group all subjects were heterosexual and attracted exclusively by prepubescent stimuli. The mean age of the paedophilic subjects was 48.25 (standard deviation: 9.15) years and mean IQ 116 (12.12). In the control groups, the mean age was 46.25 (8.35) years and IQ 124.88 (13.91). Student t-tests showed no significant difference between these groups (Table 1). In the paedophilic group 75.4% of the pictures were correctly identified immediately after the scanning session. In the control group 77.5% of the pictures were correctly identified. Again a group difference was excluded by t-test.\nMean age, IQ and rate of the correctly indentified pictures (behavioural control) with standard deviation (SD) and T-test for both groups\nNone of the results reaches statistically significant differences.\n FMRI Voxel level ANOVA We found a significant interaction of the factors sex, age and group (p < 0.05, corrected) in the middle frontal gyrus (Figure 2, Table 2). We furthermore found significant main effects for the factors sex and age outside of the middle frontal gyrus, i. e. in brain areas that didn`t show a significant interaction of all factors.\nWhole brain ANOVA. Active voxels showed a main effect of the factor sex (female/male) in the right middle temporal gyrus, right fusiform gyrus, right inferior and middle occipital gyrus and right parietal lobe. In the left hemisphere the inferior occipital gyrus displayed active voxels (A). Furthermore we found bilateral activation in the cerebellum. The factor age (adult/child) showed an activation of the right dorsomedial prefrontal cortex (B). In the right lateral orbitofrontal cortex a significant interaction of sex, age and group was detected (C). Significant voxels in A-C represent effects at a corrected p-level of p < 0.05. X, Y, Z corresponds to the Tailarach coordinates.\nRegions that showed a significant effect in the voxel wise ANOVA\nX, Y, Z corresponds to the Tailarach coordinates (BA = Brodmann area; R = right, L = left).\nA significant main effect of the factor sex (female, male) was present at a corrected threshold of p < 0.05 in the right middle temporal gyrus, right fusiform gyrus, right inferior and middle occipital gyrus and right parietal lobe. In the left hemisphere, the inferior occipital gyrus was also active. We also found bilateral activation in the cerebellum.\nThe factor age revealed an activation of the right dorsomedial prefrontal cortex (p < 0.05, corrected).\nFor the factor group, no significant activation was found at the latter threshold, but again this finding is restricted to brain areas that did not show a significant interaction of all factors.\nWe found a significant interaction of the factors sex, age and group (p < 0.05, corrected) in the middle frontal gyrus (Figure 2, Table 2). We furthermore found significant main effects for the factors sex and age outside of the middle frontal gyrus, i. e. in brain areas that didn`t show a significant interaction of all factors.\nWhole brain ANOVA. Active voxels showed a main effect of the factor sex (female/male) in the right middle temporal gyrus, right fusiform gyrus, right inferior and middle occipital gyrus and right parietal lobe. In the left hemisphere the inferior occipital gyrus displayed active voxels (A). Furthermore we found bilateral activation in the cerebellum. The factor age (adult/child) showed an activation of the right dorsomedial prefrontal cortex (B). In the right lateral orbitofrontal cortex a significant interaction of sex, age and group was detected (C). Significant voxels in A-C represent effects at a corrected p-level of p < 0.05. X, Y, Z corresponds to the Tailarach coordinates.\nRegions that showed a significant effect in the voxel wise ANOVA\nX, Y, Z corresponds to the Tailarach coordinates (BA = Brodmann area; R = right, L = left).\nA significant main effect of the factor sex (female, male) was present at a corrected threshold of p < 0.05 in the right middle temporal gyrus, right fusiform gyrus, right inferior and middle occipital gyrus and right parietal lobe. In the left hemisphere, the inferior occipital gyrus was also active. We also found bilateral activation in the cerebellum.\nThe factor age revealed an activation of the right dorsomedial prefrontal cortex (p < 0.05, corrected).\nFor the factor group, no significant activation was found at the latter threshold, but again this finding is restricted to brain areas that did not show a significant interaction of all factors.\n Voxel level ANOVA We found a significant interaction of the factors sex, age and group (p < 0.05, corrected) in the middle frontal gyrus (Figure 2, Table 2). We furthermore found significant main effects for the factors sex and age outside of the middle frontal gyrus, i. e. in brain areas that didn`t show a significant interaction of all factors.\nWhole brain ANOVA. Active voxels showed a main effect of the factor sex (female/male) in the right middle temporal gyrus, right fusiform gyrus, right inferior and middle occipital gyrus and right parietal lobe. In the left hemisphere the inferior occipital gyrus displayed active voxels (A). Furthermore we found bilateral activation in the cerebellum. The factor age (adult/child) showed an activation of the right dorsomedial prefrontal cortex (B). In the right lateral orbitofrontal cortex a significant interaction of sex, age and group was detected (C). Significant voxels in A-C represent effects at a corrected p-level of p < 0.05. X, Y, Z corresponds to the Tailarach coordinates.\nRegions that showed a significant effect in the voxel wise ANOVA\nX, Y, Z corresponds to the Tailarach coordinates (BA = Brodmann area; R = right, L = left).\nA significant main effect of the factor sex (female, male) was present at a corrected threshold of p < 0.05 in the right middle temporal gyrus, right fusiform gyrus, right inferior and middle occipital gyrus and right parietal lobe. In the left hemisphere, the inferior occipital gyrus was also active. We also found bilateral activation in the cerebellum.\nThe factor age revealed an activation of the right dorsomedial prefrontal cortex (p < 0.05, corrected).\nFor the factor group, no significant activation was found at the latter threshold, but again this finding is restricted to brain areas that did not show a significant interaction of all factors.\nWe found a significant interaction of the factors sex, age and group (p < 0.05, corrected) in the middle frontal gyrus (Figure 2, Table 2). We furthermore found significant main effects for the factors sex and age outside of the middle frontal gyrus, i. e. in brain areas that didn`t show a significant interaction of all factors.\nWhole brain ANOVA. Active voxels showed a main effect of the factor sex (female/male) in the right middle temporal gyrus, right fusiform gyrus, right inferior and middle occipital gyrus and right parietal lobe. In the left hemisphere the inferior occipital gyrus displayed active voxels (A). Furthermore we found bilateral activation in the cerebellum. The factor age (adult/child) showed an activation of the right dorsomedial prefrontal cortex (B). In the right lateral orbitofrontal cortex a significant interaction of sex, age and group was detected (C). Significant voxels in A-C represent effects at a corrected p-level of p < 0.05. X, Y, Z corresponds to the Tailarach coordinates.\nRegions that showed a significant effect in the voxel wise ANOVA\nX, Y, Z corresponds to the Tailarach coordinates (BA = Brodmann area; R = right, L = left).\nA significant main effect of the factor sex (female, male) was present at a corrected threshold of p < 0.05 in the right middle temporal gyrus, right fusiform gyrus, right inferior and middle occipital gyrus and right parietal lobe. In the left hemisphere, the inferior occipital gyrus was also active. We also found bilateral activation in the cerebellum.\nThe factor age revealed an activation of the right dorsomedial prefrontal cortex (p < 0.05, corrected).\nFor the factor group, no significant activation was found at the latter threshold, but again this finding is restricted to brain areas that did not show a significant interaction of all factors.\n ROI analysis The linear contrasts of these ROIs confirmed that, in both groups, the main effect of the factor sex was based on a higher activation due to female pictures and the main effect of the factor age on a stronger activation for pictures of children. ROI analysis clarified that in the ROI that became significant for the interaction of all factors, only paedophilic subjects showed activation in the girl condition while controls showed a deactivation in this condition (Figure 3A).\nROIs analysis (A). The ROI female > male contrast level for the ROIs extracted from the ANOVA of the main effect of sex displayed a greater activation for female pictures in all active ROIs (rTL = right temporal lobe, rPL = right parietal lobe, rOL = right occipital lobe, lOL = left occipital lobe). The child > adult contrast confirmed that the main effect of age is based on a stronger activation due to pictures of children in both groups (rDMPFC = right dorsomedial prefrontal cortex). ROI analysis of the cluster showing an interaction of all factors revealed a significant difference (* = p < 0.05) for the contrast girl > woman, with activation in paedphilic subjects and deactivation in control subjects (rlOFC = right lateral orbitofrontal cortex). Event related averaging (B). BOLD %-signal-change in the rlOFC only illustrated a strong activation due to erotic pictures of girls in paedophilia.\nEvent related averaging of BOLD %-signal-change for all conditions and all groups in that ROI illustrated a strong activation due to erotic pictures of girls in paedophilic subjects that was absent for all other picture conditions in both groups (Figure 3B).\nThe linear contrasts of these ROIs confirmed that, in both groups, the main effect of the factor sex was based on a higher activation due to female pictures and the main effect of the factor age on a stronger activation for pictures of children. ROI analysis clarified that in the ROI that became significant for the interaction of all factors, only paedophilic subjects showed activation in the girl condition while controls showed a deactivation in this condition (Figure 3A).\nROIs analysis (A). The ROI female > male contrast level for the ROIs extracted from the ANOVA of the main effect of sex displayed a greater activation for female pictures in all active ROIs (rTL = right temporal lobe, rPL = right parietal lobe, rOL = right occipital lobe, lOL = left occipital lobe). The child > adult contrast confirmed that the main effect of age is based on a stronger activation due to pictures of children in both groups (rDMPFC = right dorsomedial prefrontal cortex). ROI analysis of the cluster showing an interaction of all factors revealed a significant difference (* = p < 0.05) for the contrast girl > woman, with activation in paedphilic subjects and deactivation in control subjects (rlOFC = right lateral orbitofrontal cortex). Event related averaging (B). BOLD %-signal-change in the rlOFC only illustrated a strong activation due to erotic pictures of girls in paedophilia.\nEvent related averaging of BOLD %-signal-change for all conditions and all groups in that ROI illustrated a strong activation due to erotic pictures of girls in paedophilic subjects that was absent for all other picture conditions in both groups (Figure 3B).","Sexual orientation in the control group was heterosexual with a preference for mature women. In the paedophilia group all subjects were heterosexual and attracted exclusively by prepubescent stimuli. The mean age of the paedophilic subjects was 48.25 (standard deviation: 9.15) years and mean IQ 116 (12.12). In the control groups, the mean age was 46.25 (8.35) years and IQ 124.88 (13.91). Student t-tests showed no significant difference between these groups (Table 1). In the paedophilic group 75.4% of the pictures were correctly identified immediately after the scanning session. In the control group 77.5% of the pictures were correctly identified. Again a group difference was excluded by t-test.\nMean age, IQ and rate of the correctly indentified pictures (behavioural control) with standard deviation (SD) and T-test for both groups\nNone of the results reaches statistically significant differences."," Voxel level ANOVA We found a significant interaction of the factors sex, age and group (p < 0.05, corrected) in the middle frontal gyrus (Figure 2, Table 2). We furthermore found significant main effects for the factors sex and age outside of the middle frontal gyrus, i. e. in brain areas that didn`t show a significant interaction of all factors.\nWhole brain ANOVA. Active voxels showed a main effect of the factor sex (female/male) in the right middle temporal gyrus, right fusiform gyrus, right inferior and middle occipital gyrus and right parietal lobe. In the left hemisphere the inferior occipital gyrus displayed active voxels (A). Furthermore we found bilateral activation in the cerebellum. The factor age (adult/child) showed an activation of the right dorsomedial prefrontal cortex (B). In the right lateral orbitofrontal cortex a significant interaction of sex, age and group was detected (C). Significant voxels in A-C represent effects at a corrected p-level of p < 0.05. X, Y, Z corresponds to the Tailarach coordinates.\nRegions that showed a significant effect in the voxel wise ANOVA\nX, Y, Z corresponds to the Tailarach coordinates (BA = Brodmann area; R = right, L = left).\nA significant main effect of the factor sex (female, male) was present at a corrected threshold of p < 0.05 in the right middle temporal gyrus, right fusiform gyrus, right inferior and middle occipital gyrus and right parietal lobe. In the left hemisphere, the inferior occipital gyrus was also active. We also found bilateral activation in the cerebellum.\nThe factor age revealed an activation of the right dorsomedial prefrontal cortex (p < 0.05, corrected).\nFor the factor group, no significant activation was found at the latter threshold, but again this finding is restricted to brain areas that did not show a significant interaction of all factors.\nWe found a significant interaction of the factors sex, age and group (p < 0.05, corrected) in the middle frontal gyrus (Figure 2, Table 2). We furthermore found significant main effects for the factors sex and age outside of the middle frontal gyrus, i. e. in brain areas that didn`t show a significant interaction of all factors.\nWhole brain ANOVA. Active voxels showed a main effect of the factor sex (female/male) in the right middle temporal gyrus, right fusiform gyrus, right inferior and middle occipital gyrus and right parietal lobe. In the left hemisphere the inferior occipital gyrus displayed active voxels (A). Furthermore we found bilateral activation in the cerebellum. The factor age (adult/child) showed an activation of the right dorsomedial prefrontal cortex (B). In the right lateral orbitofrontal cortex a significant interaction of sex, age and group was detected (C). Significant voxels in A-C represent effects at a corrected p-level of p < 0.05. X, Y, Z corresponds to the Tailarach coordinates.\nRegions that showed a significant effect in the voxel wise ANOVA\nX, Y, Z corresponds to the Tailarach coordinates (BA = Brodmann area; R = right, L = left).\nA significant main effect of the factor sex (female, male) was present at a corrected threshold of p < 0.05 in the right middle temporal gyrus, right fusiform gyrus, right inferior and middle occipital gyrus and right parietal lobe. In the left hemisphere, the inferior occipital gyrus was also active. We also found bilateral activation in the cerebellum.\nThe factor age revealed an activation of the right dorsomedial prefrontal cortex (p < 0.05, corrected).\nFor the factor group, no significant activation was found at the latter threshold, but again this finding is restricted to brain areas that did not show a significant interaction of all factors.","We found a significant interaction of the factors sex, age and group (p < 0.05, corrected) in the middle frontal gyrus (Figure 2, Table 2). We furthermore found significant main effects for the factors sex and age outside of the middle frontal gyrus, i. e. in brain areas that didn`t show a significant interaction of all factors.\nWhole brain ANOVA. Active voxels showed a main effect of the factor sex (female/male) in the right middle temporal gyrus, right fusiform gyrus, right inferior and middle occipital gyrus and right parietal lobe. In the left hemisphere the inferior occipital gyrus displayed active voxels (A). Furthermore we found bilateral activation in the cerebellum. The factor age (adult/child) showed an activation of the right dorsomedial prefrontal cortex (B). In the right lateral orbitofrontal cortex a significant interaction of sex, age and group was detected (C). Significant voxels in A-C represent effects at a corrected p-level of p < 0.05. X, Y, Z corresponds to the Tailarach coordinates.\nRegions that showed a significant effect in the voxel wise ANOVA\nX, Y, Z corresponds to the Tailarach coordinates (BA = Brodmann area; R = right, L = left).\nA significant main effect of the factor sex (female, male) was present at a corrected threshold of p < 0.05 in the right middle temporal gyrus, right fusiform gyrus, right inferior and middle occipital gyrus and right parietal lobe. In the left hemisphere, the inferior occipital gyrus was also active. We also found bilateral activation in the cerebellum.\nThe factor age revealed an activation of the right dorsomedial prefrontal cortex (p < 0.05, corrected).\nFor the factor group, no significant activation was found at the latter threshold, but again this finding is restricted to brain areas that did not show a significant interaction of all factors.","The linear contrasts of these ROIs confirmed that, in both groups, the main effect of the factor sex was based on a higher activation due to female pictures and the main effect of the factor age on a stronger activation for pictures of children. ROI analysis clarified that in the ROI that became significant for the interaction of all factors, only paedophilic subjects showed activation in the girl condition while controls showed a deactivation in this condition (Figure 3A).\nROIs analysis (A). The ROI female > male contrast level for the ROIs extracted from the ANOVA of the main effect of sex displayed a greater activation for female pictures in all active ROIs (rTL = right temporal lobe, rPL = right parietal lobe, rOL = right occipital lobe, lOL = left occipital lobe). The child > adult contrast confirmed that the main effect of age is based on a stronger activation due to pictures of children in both groups (rDMPFC = right dorsomedial prefrontal cortex). ROI analysis of the cluster showing an interaction of all factors revealed a significant difference (* = p < 0.05) for the contrast girl > woman, with activation in paedphilic subjects and deactivation in control subjects (rlOFC = right lateral orbitofrontal cortex). Event related averaging (B). BOLD %-signal-change in the rlOFC only illustrated a strong activation due to erotic pictures of girls in paedophilia.\nEvent related averaging of BOLD %-signal-change for all conditions and all groups in that ROI illustrated a strong activation due to erotic pictures of girls in paedophilic subjects that was absent for all other picture conditions in both groups (Figure 3B).","Visual presentation of erotic pictures has been used to address the underpinnings of erotic stimulation in paedophilia [19-22,28,32]. Results proved heterogeneous, which might partially be explained by the detection of non-specific secondary processes in blocked designs with long stimulus presentation time.\nOur study focuses on the immediate response to erotic pictures. Like previous studies using a short presentation time and an event-related fMRI design [22,26,33] we also found that female erotic pictures elicited more activation in the right temporal lobe, the right parietal lobe, both occipital lobes and the cerebellum in both heterosexual groups. This finding indicates that these regions respond to erotic visual stimulation in an immediate fashion, e.g. activation can be detected even after a short presentation time. This finding highlights that this activation seems independent from non-specific processes such as sustained attention. Furthermore there was no difference between BOLD response to female erotic pictures in both heterosexual groups.\nPictures of children elicited a differential activation regardless of sex in the right DMPFC. This is appears to be a very interesting finding as a crucial role in the critical evaluation of stimuli and for the preparation of response strategies has been attributed to the DMPFC [34]. All study subjects were well aware of the fact that our study was about paedophilia. We would expect that erotic pictures of children would be recognised as a stimulus of special interest in that context. Both groups are also likely to be aware that this condition is specifically attached to socially unaccepted sexual preference and behaviour. These assumptions might explain why we found a specific activation of the DMPFC in both groups, independent from the sex of the presented pictures. In the DMPFC these pictures are apparently not differentiated by preferred sex or specific erotic salience but are rather perceived as a category requiring specific attention. This assumption would nicely fit with the findings from Walter et al. [35] indicating that activation of the DMPFC is not related to specific processes such as the processing of erotic content, but rather to more general processes like attention. In line with our findings, Ponseti et al. reported [33] that the medial orbitofrontal cortex showed a stronger response to erotic pictures than neutral pictures, but this response was also independent from individual sexual preference, as it was present in heterosexual male subjects with both pictures of male and female erotic stimuli. In contrast to earlier studies showing a group effect and a different activation pattern in the prefrontal cortex after presentation of erotic pictures of children, our findings indicate that in the context of immediate processing of visual erotic stimuli a considerable overlap in the processing of these stimuli is present. Considering that both groups rely on the same neuronal stimulus evaluation and response preparation networks, this finding seems rather less surprising.\nIn contrast to the earlier studies that presented pictures for more than 30 s, we focus on the immediate response to visual stimuli. Many of the previous studies were conducted with forensic inpatients while we only included outpatients and most of our paedophilic study group were convicted due to their consumption of illegal internet pornography. Other studies [36,37] already demonstrated that social functioning and neurocognitive capabilities are apparently better in these subjects than in forensic inpatients with hands-on delinquency. In this context it has to be stressed that the IQ of the paedophilic participants can be considered extremely high especially in view of the already mentioned findings from Cantor et al. [3]. Hence we cannot exclude that selection of the study subjects contributed to our findings and that the generalisability of our results might be limited.\nBrain response in the right lateral oribitofrontal cortex (OFC) was shown to interact with the factors sex, preferred age and group. Mean group betas and event related averaging confirmed a specific activation of the right lateral OFC due to erotic pictures of prepubescent girls in heterosexual paedophilic subjects only whereas all other conditions in the paedophilic group and all conditions in the control group displayed a deactivation of the lateral OFC. Further support for a deviant activation in the right lateral OFC can be found in an fMRI study by Walter et al. [18] that revealed a reduced activation for nude stimuli of female adults in paedophilic subjects.\nFMRI research has already confirmed that OFC can be activated by visual inputs [38]. According to Kringelbach et al. [39] the OFC is a nexus for sensory integration. Phillips et al. [40] stated that OFC is related to the identification of the emotional significance of a stimulus and to the production of affective states in response to that stimulus. These authors furthermore suggested that, together with the amygdala, insula, ventral striatum and ventral anterior cingulated, the OFC is also involved in the automatic regulation of emotional responses. Hence, different processes such as the modulation of autonomic reactions, learning, prediction, decision making and emotional and reward-related behaviours are processed in the OFC. According to an fMRI study by Sescousse et al. [41] the anterior part of the lateral OFC responds more strongly to more abstract rewards such as monetary gain, while the posterior lateral OFC responds more strongly to more basic rewards, for example erotic stimulation. These findings were in line with a study by Kringelbach and Rolls [42] describing a posterior anterior distribution of primary (e.g. sexual stimulation) and more abstract secondary (monetary gain) reinforcers. They suggested that the anterior part of the lateral OFC is considered to be a more recent structure, phylogentically speaking, and might therefore be related to the processing of more abstract reinforcers like money.\nFindings from our study underline the notion that the OFC plays a critical role in evaluating reinforcers that might provoke behavioural changes [42,43]. This is even more interesting, as it has been shown that the anterior lateral OFC responds more to punishment while the anterior medial part of the OFC responds to reward [44,45]. In line with this idea, the specific group effect in the right lateral OFC might indicate that the specific emotional significance of the stimuli was only detected in paedophilic subjects. Erotic pictures of children provoked a brain response resembling that of punishment in paedophilic subjects. According to the literature, the right anterior lateral OFC is not only associated with punishment but also with cues indicating a behavioural shift. This significance for behavioural adoption has been highlighted in recent reviews on the OFC [39,43,46]. This is interesting as healthy controls have demonstrated that the lateral OFC became active when subjects tried to suppress erotic responses to visual erotic stimulation [47] or in deception [48]. Our results would therefore indicate that immediately after the presentation of erotic pictures of children, a brain region known to be involved in evaluating emotional salience became active in paedophilic subjects. This activation might be related not only to the regulation of emotional states or autonomic responses but also to behavioural changes like suppression or deception of emotional responses.\nThe findings from our study point to a network perspective of the immediate processing of visual erotic stimulation. This notion would be in line with the so-called orbital and medial prefrontal cortex [49]. In this network two likely interacting systems became active or inactive while processing the visual stimuli. Even after a short presentation of visual stimuli the DMPFC showed an increased activation in response to the target condition of our study (erotic pictures of children). It is most likely that this finding is related to attentional processes, which are evoked in both study groups. We further found a specific activation due to the respective erotic condition in the anterior lateral OFC in paedophilic subjects. Activation in that region might indicate evaluation of emotional salience and the reward value of that stimulus. Furthermore this activation might indicate the intent to modify behaviour and to dissimulate or deceive erotic responses.\nAssessing the immediate processing of erotic pictures using fMRI might be a useful tool for probing emotional engagement towards the presented stimuli in future studies. These results could also be therapeutically relevant as most cognitive behavioural treatment approaches aim to reduce cognitive distortions and the denial of the implications of paedophilic behaviours and to increase the awareness of problematic attraction to children [50] rather than to change the sexual preference. The often-reported tendency of paedophilic subjects to dissimulate and minimise the dramatic impact on potential victims of child abuse, might arise from similar processes, which aim at suppressing emotional distress. Interestingly Ponseti et al. [22] did not find such an activation in their study sample, which in contrast to our sample deliberately stated paedophilic sexual deviance.","Our fMRI study confirms the findings of previous studies concerning the processing of visual erotic stimuli. Furthermore we were able to demonstrate that the dorsomedial prefrontal cortex is specifically engaged in processing erotic pictures of children regardless of the study group. This activation appears to represent the evaluation of relevance, which is apparently not linked to sexual orientation per se but rather to the study context. In addition we have made some new findings regarding the immediate processing of visual erotic stimulation in paedophilia, as we found an immediate activation of the brain regions involved in evaluating emotional salience and reward, and engaged in the regulation of emotional responses. This activation might be related to the suppression or deception of erotic responses.","OFC: Orbitofrontal cortex; DMPFC: Dorsomedial prefrontal cortex; DLPFC: Dorsolateral prefrontal cortex; MPRAGE: Magnetization prepared rapid acquisistion gradient echo; GLM: General linear model, ROI: region of interest.","The authors declare that they have no competing interests.","BH, RM, MG, VD and ES conceived and designed the study. BH, NH, RM, PL and MK realized the study design and acquired the data. BH, FE, NH performed data analyses. BH and FE interpreted data and wrote the manuscript. BH, FE, NH, PL, MK, RM, VD, ES and MG contributed in the drafting and revision of the manuscript for important intellectual content. All authors read and approved the final manuscript.","The pre-publication history for this paper can be accessed here:\n\nhttp://www.biomedcentral.com/1471-244X/13/88/prepub\n"],"string":"[\n \"Paedophilia is a matter of great public interest and often evokes emotional discussions in mass media. It is difficult to estimate the prevalence of paedophilia as both paedophilic offenders and victims often prefer not to identify themselves. From surveys of the general population we know that approximately 12% of men and 17% of women report experiencing sexual abuse in their childhood [1]. These figures underline the potential impact of this disease on society. According to DSM IV-TR, paedophilia is defined by two main criteria: firstly persistent sexual fantasies, urges or behaviour involving sexual activity with prepubescent children and secondly that these people have acted on these urges, or that these urges or fantasies caused marked distress or interpersonal difficulties [2].\\nFindings from neuropsychological studies on paedophilia are heterogeneous. Whilst a lower IQ [3], educational difficulties [4] and a higher rate of left-handedness [5] indicate rather generalised brain dysfunction, other studies suggest more specific alterations like focal weaknesses in frontal-executive [6,7] and/or temporal-verbal [8] skills or even a more deliberate response style and greater self-monitoring in paedophilic subjects [9,10]. Furthermore revealed research on personality traits in paedophilia various findings like impaired interpersonal functions, impaired self-awareness, disinhibitory traits, sociopathy and a propensity for cognitive distortions [11]. In summary, it can be stated that the neurobiology of paedophilia remains incomplete.\\nIn recent years, researchers have increasingly addressed the neuronal underpinnings of sexual arousal in order to better understand sexual behaviour. Stoléru et al. [12,13] and Redouté et al. [14,15] were the first to propose a neurophenomological model that disentangled the cognitive, emotional, motivational and physiological components of sexual arousal. Two recently published quantitative meta-analyses on sexual cue reactivity [16,17] underline that distinctive subcomponents of sexual arousal can be reliably localised by neuroimaging techniques.\\nBased on the fMRI results about sexual arousal in healthy controls it was natural to transfer that approach to paedophilic subjects in order to better understand the cognitive and behavioural processes in paedophilia. In heterosexual paedophilia fMRI studies have demonstrated the altered processing of erotic visual stimuli in the dorsomedial prefrontal cortex (DMPFC), amygdala and hippocampus [18]. The latter study also reports that erotic pictures of adults induced a stronger activation of the hypothalmus, the periaqueductal grey and the dorsolateral prefrontal cortex (DLPFC) in healthy controls than in heterosexual paedophilic subjects. While this study highlights differences between controls and paedophilic subjects other studies report similar activation patterns for the respective erotic conditions. For example Schiffer et al. [19] show that erotic pictures of girls induced the same activation of limbic structures such as the amygdala, the cingulate gyrus or the hippocampus in heterosexual paedophilia as erotic pictures of women in a control group. However this study also found an additional activation of the DLPFC in the heterosexual paedophilic group alone. Research has also been carried out on homosexual paedophilia. Again in both paedophilic and control subjects, a common network relating to sexual activation was found, comprising the occipitotemporal and prefrontal cortex [20]. However, only paedophilic subjects showed activated subcortical regions such as the thalamus, the globus pallidus and the striatum during erotic stimulation. Another study on homosexual paedophilia describes differential activation of the right orbitofrontal cortex [21].\\nMost of the above-mentioned studies indicate that prefrontal brain regions might be related to paedophilia, but the results and structures involved differ from study to study. Considering these rather heterogeneous results, the findings from a recent study by Ponseti et al. [22] appear surprising. These authors propose an fMRI based classification procedure for heterosexual and homosexual non-paedophilic and paedophilic subjects with 95% accuracy. Unlike previous studies, these authors describe activation in regions known to be involved in the processing of erotic stimuli such as the caudate nucleus, cingulated cortex, insula, fusiform gyrus, temporal cortex, occipital cortex, thalamus, amygdala and cerebellum but not in the prefrontal cortex.\\nExcept for the study of Walter et al. [18] all of the above-mentioned studies that describe a prefrontal involvement in paedophilia used relatively long presentation times ranging from 19.2 - 38.5 s and blocked fMRI designs.\\nOne critical shortcoming of long presentation times is that different cognitive processes, such as sustained attention or self-referential processes might also take place and somehow interfere with the target process. Earlier fMRI studies on sexual arousal in normal subjects showed that with shorter presentation times of sexually arousing pictures specific neuronal networks and brain processes can be addressed. Static presentation for 8.75 s as used by Moulier et al. [23] demonstrated for example that the initiation and low levels of penile tumescence are controlled by frontal, parietal, insular and cingular cortical areas. In accordance with these results, Ferretti et al. [24] showed that longer presentation times (> 30 s) induce sexual arousal and penile erection whilst shorter presentation times (< 3 s) induce arousal without erection. A stimulation time of 5 s even allowed to distinguish specific sexual emotional effects from more general emotional effects [25]. A study comparing both types of fMRI-designs in the visual processing of erotic stimuli in healthy volunteers [26] provides strong support for the use of fast stimulation time, suggesting an event related design. The authors proposed that event related designs might be an alternative to blocked designs if the core interest is the detection of networks associated with the immediate processing of erotic stimuli.\\nBesides being more useful for the detection of these networks, short presentation times also offer other potential benefits as long presentation times in paedophilia are even more problematic than in healthy controls. Paedophilic subjects are mostly recruited after coming into conflict with the legal authorities as a result of their sexual preferences and this may increase a tendency to suppress erotic arousal or dissimulate sexual attraction to the pictures of children presented. The latter point has been used to explain some of the differences between ratings of erotic salience and brain response observed in some of the aforementioned studies [19,21]. As presentation time becomes shorter, deliberate manipulation may become more difficult and the immediate processes following the perception of target stimuli could become visible.\\nIn our study we aim to identify the neuronal networks related to immediate processing of erotic stimuli in heterosexual paedophilic subjects recruited from a forensic outpatient setting. Based on previous neuropsychological research and recent fMRI studies we predicted a differential activation in the prefrontal cortex in paedophilic subjects.\",\n \" Subjects Behavioural and functional magnetic resonance imaging data were acquired from 16 male right-handed subjects. All participants in this study were adults. Written informed consent was obtained from all subjects before participation in the study.\\nThe subjects in the paedophilic (N = 8) group were recruited from an outpatient cognitive behavioural group therapy at the department of forensic psychiatry in Basel, Switzerland. The heterosexual paedophilic subjects fulfilled DSM-IV-TR criteria for exclusive type (attracted only to prepubescent children) not limited to incest heterosexual paedophilia. Three of the subjects had previously molested prepubescent children, the other five had been convicted because of the possession of large quantities of explicit internet child pornography. Each participant’s sexual orientation and preference for prepubescent erotic stimuli was assessed in a clinical interview, the Multiphasic Sex Inventory (MSI) [27] and additionally verified by the clinical record and the court file. For all subjects, neither the interview nor the record indicated other comorbid paraphilias. None of the subjects had admitted paedophilia prior to the contact with the legal authorities.\\nHeterosexual control subjects (N = 8) were recruited from an advert on the University Hospital notice board. A clinical evaluation of all subjects revealed no other psychiatric, neurological or medical conditions. The local Ethics Committee of the University of Basel, Switzerland approved the study.\\nBehavioural and functional magnetic resonance imaging data were acquired from 16 male right-handed subjects. All participants in this study were adults. Written informed consent was obtained from all subjects before participation in the study.\\nThe subjects in the paedophilic (N = 8) group were recruited from an outpatient cognitive behavioural group therapy at the department of forensic psychiatry in Basel, Switzerland. The heterosexual paedophilic subjects fulfilled DSM-IV-TR criteria for exclusive type (attracted only to prepubescent children) not limited to incest heterosexual paedophilia. Three of the subjects had previously molested prepubescent children, the other five had been convicted because of the possession of large quantities of explicit internet child pornography. Each participant’s sexual orientation and preference for prepubescent erotic stimuli was assessed in a clinical interview, the Multiphasic Sex Inventory (MSI) [27] and additionally verified by the clinical record and the court file. For all subjects, neither the interview nor the record indicated other comorbid paraphilias. None of the subjects had admitted paedophilia prior to the contact with the legal authorities.\\nHeterosexual control subjects (N = 8) were recruited from an advert on the University Hospital notice board. A clinical evaluation of all subjects revealed no other psychiatric, neurological or medical conditions. The local Ethics Committee of the University of Basel, Switzerland approved the study.\\n Stimulation and paradigm The study design was adopted from Bühler et al. [26]. Visual stimuli were generated and displayed on a personal computer using the Neurobehavioral Systems (NBS) software package Presentation® 12.2. Stimuli were presented via fMRI-compatible digital video goggles (NordicNeuroLab, Bergen, Norway).\\nWhile inside the scanner, subjects were presented with various types of pictures in an event related design. We presented 10 pictures from each category (erotic pictures of boys, girls, men, women or neutral control pictures) for 750 ms with a jittered interstimulus interval that varied randomly between 10 – 20 s in steps of full seconds (Figure 1).\\nFMRI paradigm. We presented erotic pictures of adults (women/men) and children (girls/boys) interspersed with neutral control pictures. Pictures from each category were presented for 750 ms with a jittered interstimulus interval ranging from 10–20 s.\\nIn each picture, only one person of one of the above-mentioned categories was displayed in bathing clothes in front of a plain-coloured background. All pictures showed faces and the complete corpus. Secondary sexual characteristics were clearly visible in the pictures showing adults but were clearly absent in the pictures depicting prepubescent children. We avoided photographs of adolescents and applied a biological rather than a legal cut off to make the pictures of adults easily distinguishable from those of children. Before inclusion into the paradigm rated the study subjects the pictures and only the pictures with the highest ratings on a visual analogue scale were included. Neutral pictures showed simple objects like e.g. a small boot in front of the same background.\\nIn order to control attention during the passive fMRI task we used a previously applied procedure to assure attentive observation of the presented pictures [28]: Before the scanning procedure, subjects were instructed to attentively observe the pictures and told that we would check their attention by asking them to identify the pictures after the scanning session. Immediately after the fMRI session we presented the slides that they had seen interspersed with similar pictures, which had not been presented during the experiment. Subjects then had to decide which pictures they had seen during the fMRI stimulation.\\nThe study design was adopted from Bühler et al. [26]. Visual stimuli were generated and displayed on a personal computer using the Neurobehavioral Systems (NBS) software package Presentation® 12.2. Stimuli were presented via fMRI-compatible digital video goggles (NordicNeuroLab, Bergen, Norway).\\nWhile inside the scanner, subjects were presented with various types of pictures in an event related design. We presented 10 pictures from each category (erotic pictures of boys, girls, men, women or neutral control pictures) for 750 ms with a jittered interstimulus interval that varied randomly between 10 – 20 s in steps of full seconds (Figure 1).\\nFMRI paradigm. We presented erotic pictures of adults (women/men) and children (girls/boys) interspersed with neutral control pictures. Pictures from each category were presented for 750 ms with a jittered interstimulus interval ranging from 10–20 s.\\nIn each picture, only one person of one of the above-mentioned categories was displayed in bathing clothes in front of a plain-coloured background. All pictures showed faces and the complete corpus. Secondary sexual characteristics were clearly visible in the pictures showing adults but were clearly absent in the pictures depicting prepubescent children. We avoided photographs of adolescents and applied a biological rather than a legal cut off to make the pictures of adults easily distinguishable from those of children. Before inclusion into the paradigm rated the study subjects the pictures and only the pictures with the highest ratings on a visual analogue scale were included. Neutral pictures showed simple objects like e.g. a small boot in front of the same background.\\nIn order to control attention during the passive fMRI task we used a previously applied procedure to assure attentive observation of the presented pictures [28]: Before the scanning procedure, subjects were instructed to attentively observe the pictures and told that we would check their attention by asking them to identify the pictures after the scanning session. Immediately after the fMRI session we presented the slides that they had seen interspersed with similar pictures, which had not been presented during the experiment. Subjects then had to decide which pictures they had seen during the fMRI stimulation.\\n FMRI acquisition and analysis Images were acquired using a 3 T MRI scanner (Verio, Siemens Healthcare, Erlangen, Germany) equipped with a standard radio frequency head coil. First a T1-weighted high-resolution data set that covered the whole brain was acquired using a three-dimensional MPRAGE (magnetization-prepared rapid acquisition gradient echo) sequence with a repetition time (TR) of 2.00, an isotropic spatial resolution of 1.0 mm3, and an echo time (TE) of 3.4 ms. T2* weighted functional images were recorded using echoplanar imaging with a TR of 2500 ms, an isotropic spatial resolution of 3×3×3 mm3 and a TE of 30 ms (FoV 228; matrix 76; spacing between slices: 0.51 mm; interslice time: 69 ms). Altogether 152 volumes with 36 image slices with a thickness of 3 mm were obtained.\\nImages were acquired using a 3 T MRI scanner (Verio, Siemens Healthcare, Erlangen, Germany) equipped with a standard radio frequency head coil. First a T1-weighted high-resolution data set that covered the whole brain was acquired using a three-dimensional MPRAGE (magnetization-prepared rapid acquisition gradient echo) sequence with a repetition time (TR) of 2.00, an isotropic spatial resolution of 1.0 mm3, and an echo time (TE) of 3.4 ms. T2* weighted functional images were recorded using echoplanar imaging with a TR of 2500 ms, an isotropic spatial resolution of 3×3×3 mm3 and a TE of 30 ms (FoV 228; matrix 76; spacing between slices: 0.51 mm; interslice time: 69 ms). Altogether 152 volumes with 36 image slices with a thickness of 3 mm were obtained.\\n Image pre-processing and statistical analysis Image time-series were processed using the BrainVoyager QX 2.3.0 software package (Brain Innovation, Maastricht, The Netherlands). Pre-processing included head motion correction, slice scan time correction, temporal high pass filtering and removal of linear trends. Using the results of the image registration with anatomical scans, the functional image-time series were then warped into Talairach space and resampled into 3 mm isotropic voxel time-series. Normalized images were smoothed using a 6.00 mm isotropic Gaussian kernel.\\nFor first-level analysis, a General Linear Model (GLM) analysis was applied with separate subject z-normalized predictors fitted to z-normalized voxel time-courses from all data sets. The orthogonal predictors of interest in the design matrix were: “boys”, “girls”, “men”, “women” and “neutral”. For second-level analysis, the GLM fits (beta weights) were assessed in a 3-way-ANOVA with two within subject factors (sex: female vs. male; age: child vs. adult) and one between subject factor group (paedophilia vs. control). We assessed the main effects of, and the interaction between, these factors at the voxel level and obtained maps that thresholded at the significance level of 5% (corrected for multiple comparison). To obtain this correction, an uncorrected statistical threshold was initially applied to each of the F-maps at p = 0.005, and a cluster-level threshold correction procedure based on Monte Carlo simulations [29] was applied in order to determine the minimum cluster size below which any activation was to be discarded. This procedure yielded a minimum cluster size of 10 voxels (corresponding to a minimum cluster extent of 280 mm) for the F-maps of the interaction of all factors, 12 voxel (297 mm) for the factor age and 19 voxels (482 mm) for the factor sex.\\nThe anatomical labelling of active clusters was defined on the basis of the peak voxel coordinates, using the Talairach Daemon (http://www.talairach.org) [30,31].\\nActive clusters resulting from the voxel-level ANOVA were further defined as Regions of Interests (ROI). Data from those ROIs were extracted and analysed in order to understand the direction of the effects of the ANOVA. To that end, we calculated linear contrasts for the different factors, i.e. sex (female > male), age (child > adult) and the interaction of the factors (girl > woman) separately for both groups. In order to further specify the interaction of all factors (sex, age, group) we extracted BOLD %-signal-change from the respective ROI and averaged the BOLD%-signal-change separately for the groups.\\nImage time-series were processed using the BrainVoyager QX 2.3.0 software package (Brain Innovation, Maastricht, The Netherlands). Pre-processing included head motion correction, slice scan time correction, temporal high pass filtering and removal of linear trends. Using the results of the image registration with anatomical scans, the functional image-time series were then warped into Talairach space and resampled into 3 mm isotropic voxel time-series. Normalized images were smoothed using a 6.00 mm isotropic Gaussian kernel.\\nFor first-level analysis, a General Linear Model (GLM) analysis was applied with separate subject z-normalized predictors fitted to z-normalized voxel time-courses from all data sets. The orthogonal predictors of interest in the design matrix were: “boys”, “girls”, “men”, “women” and “neutral”. For second-level analysis, the GLM fits (beta weights) were assessed in a 3-way-ANOVA with two within subject factors (sex: female vs. male; age: child vs. adult) and one between subject factor group (paedophilia vs. control). We assessed the main effects of, and the interaction between, these factors at the voxel level and obtained maps that thresholded at the significance level of 5% (corrected for multiple comparison). To obtain this correction, an uncorrected statistical threshold was initially applied to each of the F-maps at p = 0.005, and a cluster-level threshold correction procedure based on Monte Carlo simulations [29] was applied in order to determine the minimum cluster size below which any activation was to be discarded. This procedure yielded a minimum cluster size of 10 voxels (corresponding to a minimum cluster extent of 280 mm) for the F-maps of the interaction of all factors, 12 voxel (297 mm) for the factor age and 19 voxels (482 mm) for the factor sex.\\nThe anatomical labelling of active clusters was defined on the basis of the peak voxel coordinates, using the Talairach Daemon (http://www.talairach.org) [30,31].\\nActive clusters resulting from the voxel-level ANOVA were further defined as Regions of Interests (ROI). Data from those ROIs were extracted and analysed in order to understand the direction of the effects of the ANOVA. To that end, we calculated linear contrasts for the different factors, i.e. sex (female > male), age (child > adult) and the interaction of the factors (girl > woman) separately for both groups. In order to further specify the interaction of all factors (sex, age, group) we extracted BOLD %-signal-change from the respective ROI and averaged the BOLD%-signal-change separately for the groups.\",\n \"Behavioural and functional magnetic resonance imaging data were acquired from 16 male right-handed subjects. All participants in this study were adults. Written informed consent was obtained from all subjects before participation in the study.\\nThe subjects in the paedophilic (N = 8) group were recruited from an outpatient cognitive behavioural group therapy at the department of forensic psychiatry in Basel, Switzerland. The heterosexual paedophilic subjects fulfilled DSM-IV-TR criteria for exclusive type (attracted only to prepubescent children) not limited to incest heterosexual paedophilia. Three of the subjects had previously molested prepubescent children, the other five had been convicted because of the possession of large quantities of explicit internet child pornography. Each participant’s sexual orientation and preference for prepubescent erotic stimuli was assessed in a clinical interview, the Multiphasic Sex Inventory (MSI) [27] and additionally verified by the clinical record and the court file. For all subjects, neither the interview nor the record indicated other comorbid paraphilias. None of the subjects had admitted paedophilia prior to the contact with the legal authorities.\\nHeterosexual control subjects (N = 8) were recruited from an advert on the University Hospital notice board. A clinical evaluation of all subjects revealed no other psychiatric, neurological or medical conditions. The local Ethics Committee of the University of Basel, Switzerland approved the study.\",\n \"The study design was adopted from Bühler et al. [26]. Visual stimuli were generated and displayed on a personal computer using the Neurobehavioral Systems (NBS) software package Presentation® 12.2. Stimuli were presented via fMRI-compatible digital video goggles (NordicNeuroLab, Bergen, Norway).\\nWhile inside the scanner, subjects were presented with various types of pictures in an event related design. We presented 10 pictures from each category (erotic pictures of boys, girls, men, women or neutral control pictures) for 750 ms with a jittered interstimulus interval that varied randomly between 10 – 20 s in steps of full seconds (Figure 1).\\nFMRI paradigm. We presented erotic pictures of adults (women/men) and children (girls/boys) interspersed with neutral control pictures. Pictures from each category were presented for 750 ms with a jittered interstimulus interval ranging from 10–20 s.\\nIn each picture, only one person of one of the above-mentioned categories was displayed in bathing clothes in front of a plain-coloured background. All pictures showed faces and the complete corpus. Secondary sexual characteristics were clearly visible in the pictures showing adults but were clearly absent in the pictures depicting prepubescent children. We avoided photographs of adolescents and applied a biological rather than a legal cut off to make the pictures of adults easily distinguishable from those of children. Before inclusion into the paradigm rated the study subjects the pictures and only the pictures with the highest ratings on a visual analogue scale were included. Neutral pictures showed simple objects like e.g. a small boot in front of the same background.\\nIn order to control attention during the passive fMRI task we used a previously applied procedure to assure attentive observation of the presented pictures [28]: Before the scanning procedure, subjects were instructed to attentively observe the pictures and told that we would check their attention by asking them to identify the pictures after the scanning session. Immediately after the fMRI session we presented the slides that they had seen interspersed with similar pictures, which had not been presented during the experiment. Subjects then had to decide which pictures they had seen during the fMRI stimulation.\",\n \"Images were acquired using a 3 T MRI scanner (Verio, Siemens Healthcare, Erlangen, Germany) equipped with a standard radio frequency head coil. First a T1-weighted high-resolution data set that covered the whole brain was acquired using a three-dimensional MPRAGE (magnetization-prepared rapid acquisition gradient echo) sequence with a repetition time (TR) of 2.00, an isotropic spatial resolution of 1.0 mm3, and an echo time (TE) of 3.4 ms. T2* weighted functional images were recorded using echoplanar imaging with a TR of 2500 ms, an isotropic spatial resolution of 3×3×3 mm3 and a TE of 30 ms (FoV 228; matrix 76; spacing between slices: 0.51 mm; interslice time: 69 ms). Altogether 152 volumes with 36 image slices with a thickness of 3 mm were obtained.\",\n \"Image time-series were processed using the BrainVoyager QX 2.3.0 software package (Brain Innovation, Maastricht, The Netherlands). Pre-processing included head motion correction, slice scan time correction, temporal high pass filtering and removal of linear trends. Using the results of the image registration with anatomical scans, the functional image-time series were then warped into Talairach space and resampled into 3 mm isotropic voxel time-series. Normalized images were smoothed using a 6.00 mm isotropic Gaussian kernel.\\nFor first-level analysis, a General Linear Model (GLM) analysis was applied with separate subject z-normalized predictors fitted to z-normalized voxel time-courses from all data sets. The orthogonal predictors of interest in the design matrix were: “boys”, “girls”, “men”, “women” and “neutral”. For second-level analysis, the GLM fits (beta weights) were assessed in a 3-way-ANOVA with two within subject factors (sex: female vs. male; age: child vs. adult) and one between subject factor group (paedophilia vs. control). We assessed the main effects of, and the interaction between, these factors at the voxel level and obtained maps that thresholded at the significance level of 5% (corrected for multiple comparison). To obtain this correction, an uncorrected statistical threshold was initially applied to each of the F-maps at p = 0.005, and a cluster-level threshold correction procedure based on Monte Carlo simulations [29] was applied in order to determine the minimum cluster size below which any activation was to be discarded. This procedure yielded a minimum cluster size of 10 voxels (corresponding to a minimum cluster extent of 280 mm) for the F-maps of the interaction of all factors, 12 voxel (297 mm) for the factor age and 19 voxels (482 mm) for the factor sex.\\nThe anatomical labelling of active clusters was defined on the basis of the peak voxel coordinates, using the Talairach Daemon (http://www.talairach.org) [30,31].\\nActive clusters resulting from the voxel-level ANOVA were further defined as Regions of Interests (ROI). Data from those ROIs were extracted and analysed in order to understand the direction of the effects of the ANOVA. To that end, we calculated linear contrasts for the different factors, i.e. sex (female > male), age (child > adult) and the interaction of the factors (girl > woman) separately for both groups. In order to further specify the interaction of all factors (sex, age, group) we extracted BOLD %-signal-change from the respective ROI and averaged the BOLD%-signal-change separately for the groups.\",\n \" Demographics Sexual orientation in the control group was heterosexual with a preference for mature women. In the paedophilia group all subjects were heterosexual and attracted exclusively by prepubescent stimuli. The mean age of the paedophilic subjects was 48.25 (standard deviation: 9.15) years and mean IQ 116 (12.12). In the control groups, the mean age was 46.25 (8.35) years and IQ 124.88 (13.91). Student t-tests showed no significant difference between these groups (Table 1). In the paedophilic group 75.4% of the pictures were correctly identified immediately after the scanning session. In the control group 77.5% of the pictures were correctly identified. Again a group difference was excluded by t-test.\\nMean age, IQ and rate of the correctly indentified pictures (behavioural control) with standard deviation (SD) and T-test for both groups\\nNone of the results reaches statistically significant differences.\\nSexual orientation in the control group was heterosexual with a preference for mature women. In the paedophilia group all subjects were heterosexual and attracted exclusively by prepubescent stimuli. The mean age of the paedophilic subjects was 48.25 (standard deviation: 9.15) years and mean IQ 116 (12.12). In the control groups, the mean age was 46.25 (8.35) years and IQ 124.88 (13.91). Student t-tests showed no significant difference between these groups (Table 1). In the paedophilic group 75.4% of the pictures were correctly identified immediately after the scanning session. In the control group 77.5% of the pictures were correctly identified. Again a group difference was excluded by t-test.\\nMean age, IQ and rate of the correctly indentified pictures (behavioural control) with standard deviation (SD) and T-test for both groups\\nNone of the results reaches statistically significant differences.\\n FMRI Voxel level ANOVA We found a significant interaction of the factors sex, age and group (p < 0.05, corrected) in the middle frontal gyrus (Figure 2, Table 2). We furthermore found significant main effects for the factors sex and age outside of the middle frontal gyrus, i. e. in brain areas that didn`t show a significant interaction of all factors.\\nWhole brain ANOVA. Active voxels showed a main effect of the factor sex (female/male) in the right middle temporal gyrus, right fusiform gyrus, right inferior and middle occipital gyrus and right parietal lobe. In the left hemisphere the inferior occipital gyrus displayed active voxels (A). Furthermore we found bilateral activation in the cerebellum. The factor age (adult/child) showed an activation of the right dorsomedial prefrontal cortex (B). In the right lateral orbitofrontal cortex a significant interaction of sex, age and group was detected (C). Significant voxels in A-C represent effects at a corrected p-level of p < 0.05. X, Y, Z corresponds to the Tailarach coordinates.\\nRegions that showed a significant effect in the voxel wise ANOVA\\nX, Y, Z corresponds to the Tailarach coordinates (BA = Brodmann area; R = right, L = left).\\nA significant main effect of the factor sex (female, male) was present at a corrected threshold of p < 0.05 in the right middle temporal gyrus, right fusiform gyrus, right inferior and middle occipital gyrus and right parietal lobe. In the left hemisphere, the inferior occipital gyrus was also active. We also found bilateral activation in the cerebellum.\\nThe factor age revealed an activation of the right dorsomedial prefrontal cortex (p < 0.05, corrected).\\nFor the factor group, no significant activation was found at the latter threshold, but again this finding is restricted to brain areas that did not show a significant interaction of all factors.\\nWe found a significant interaction of the factors sex, age and group (p < 0.05, corrected) in the middle frontal gyrus (Figure 2, Table 2). We furthermore found significant main effects for the factors sex and age outside of the middle frontal gyrus, i. e. in brain areas that didn`t show a significant interaction of all factors.\\nWhole brain ANOVA. Active voxels showed a main effect of the factor sex (female/male) in the right middle temporal gyrus, right fusiform gyrus, right inferior and middle occipital gyrus and right parietal lobe. In the left hemisphere the inferior occipital gyrus displayed active voxels (A). Furthermore we found bilateral activation in the cerebellum. The factor age (adult/child) showed an activation of the right dorsomedial prefrontal cortex (B). In the right lateral orbitofrontal cortex a significant interaction of sex, age and group was detected (C). Significant voxels in A-C represent effects at a corrected p-level of p < 0.05. X, Y, Z corresponds to the Tailarach coordinates.\\nRegions that showed a significant effect in the voxel wise ANOVA\\nX, Y, Z corresponds to the Tailarach coordinates (BA = Brodmann area; R = right, L = left).\\nA significant main effect of the factor sex (female, male) was present at a corrected threshold of p < 0.05 in the right middle temporal gyrus, right fusiform gyrus, right inferior and middle occipital gyrus and right parietal lobe. In the left hemisphere, the inferior occipital gyrus was also active. We also found bilateral activation in the cerebellum.\\nThe factor age revealed an activation of the right dorsomedial prefrontal cortex (p < 0.05, corrected).\\nFor the factor group, no significant activation was found at the latter threshold, but again this finding is restricted to brain areas that did not show a significant interaction of all factors.\\n Voxel level ANOVA We found a significant interaction of the factors sex, age and group (p < 0.05, corrected) in the middle frontal gyrus (Figure 2, Table 2). We furthermore found significant main effects for the factors sex and age outside of the middle frontal gyrus, i. e. in brain areas that didn`t show a significant interaction of all factors.\\nWhole brain ANOVA. Active voxels showed a main effect of the factor sex (female/male) in the right middle temporal gyrus, right fusiform gyrus, right inferior and middle occipital gyrus and right parietal lobe. In the left hemisphere the inferior occipital gyrus displayed active voxels (A). Furthermore we found bilateral activation in the cerebellum. The factor age (adult/child) showed an activation of the right dorsomedial prefrontal cortex (B). In the right lateral orbitofrontal cortex a significant interaction of sex, age and group was detected (C). Significant voxels in A-C represent effects at a corrected p-level of p < 0.05. X, Y, Z corresponds to the Tailarach coordinates.\\nRegions that showed a significant effect in the voxel wise ANOVA\\nX, Y, Z corresponds to the Tailarach coordinates (BA = Brodmann area; R = right, L = left).\\nA significant main effect of the factor sex (female, male) was present at a corrected threshold of p < 0.05 in the right middle temporal gyrus, right fusiform gyrus, right inferior and middle occipital gyrus and right parietal lobe. In the left hemisphere, the inferior occipital gyrus was also active. We also found bilateral activation in the cerebellum.\\nThe factor age revealed an activation of the right dorsomedial prefrontal cortex (p < 0.05, corrected).\\nFor the factor group, no significant activation was found at the latter threshold, but again this finding is restricted to brain areas that did not show a significant interaction of all factors.\\nWe found a significant interaction of the factors sex, age and group (p < 0.05, corrected) in the middle frontal gyrus (Figure 2, Table 2). We furthermore found significant main effects for the factors sex and age outside of the middle frontal gyrus, i. e. in brain areas that didn`t show a significant interaction of all factors.\\nWhole brain ANOVA. Active voxels showed a main effect of the factor sex (female/male) in the right middle temporal gyrus, right fusiform gyrus, right inferior and middle occipital gyrus and right parietal lobe. In the left hemisphere the inferior occipital gyrus displayed active voxels (A). Furthermore we found bilateral activation in the cerebellum. The factor age (adult/child) showed an activation of the right dorsomedial prefrontal cortex (B). In the right lateral orbitofrontal cortex a significant interaction of sex, age and group was detected (C). Significant voxels in A-C represent effects at a corrected p-level of p < 0.05. X, Y, Z corresponds to the Tailarach coordinates.\\nRegions that showed a significant effect in the voxel wise ANOVA\\nX, Y, Z corresponds to the Tailarach coordinates (BA = Brodmann area; R = right, L = left).\\nA significant main effect of the factor sex (female, male) was present at a corrected threshold of p < 0.05 in the right middle temporal gyrus, right fusiform gyrus, right inferior and middle occipital gyrus and right parietal lobe. In the left hemisphere, the inferior occipital gyrus was also active. We also found bilateral activation in the cerebellum.\\nThe factor age revealed an activation of the right dorsomedial prefrontal cortex (p < 0.05, corrected).\\nFor the factor group, no significant activation was found at the latter threshold, but again this finding is restricted to brain areas that did not show a significant interaction of all factors.\\n ROI analysis The linear contrasts of these ROIs confirmed that, in both groups, the main effect of the factor sex was based on a higher activation due to female pictures and the main effect of the factor age on a stronger activation for pictures of children. ROI analysis clarified that in the ROI that became significant for the interaction of all factors, only paedophilic subjects showed activation in the girl condition while controls showed a deactivation in this condition (Figure 3A).\\nROIs analysis (A). The ROI female > male contrast level for the ROIs extracted from the ANOVA of the main effect of sex displayed a greater activation for female pictures in all active ROIs (rTL = right temporal lobe, rPL = right parietal lobe, rOL = right occipital lobe, lOL = left occipital lobe). The child > adult contrast confirmed that the main effect of age is based on a stronger activation due to pictures of children in both groups (rDMPFC = right dorsomedial prefrontal cortex). ROI analysis of the cluster showing an interaction of all factors revealed a significant difference (* = p < 0.05) for the contrast girl > woman, with activation in paedphilic subjects and deactivation in control subjects (rlOFC = right lateral orbitofrontal cortex). Event related averaging (B). BOLD %-signal-change in the rlOFC only illustrated a strong activation due to erotic pictures of girls in paedophilia.\\nEvent related averaging of BOLD %-signal-change for all conditions and all groups in that ROI illustrated a strong activation due to erotic pictures of girls in paedophilic subjects that was absent for all other picture conditions in both groups (Figure 3B).\\nThe linear contrasts of these ROIs confirmed that, in both groups, the main effect of the factor sex was based on a higher activation due to female pictures and the main effect of the factor age on a stronger activation for pictures of children. ROI analysis clarified that in the ROI that became significant for the interaction of all factors, only paedophilic subjects showed activation in the girl condition while controls showed a deactivation in this condition (Figure 3A).\\nROIs analysis (A). The ROI female > male contrast level for the ROIs extracted from the ANOVA of the main effect of sex displayed a greater activation for female pictures in all active ROIs (rTL = right temporal lobe, rPL = right parietal lobe, rOL = right occipital lobe, lOL = left occipital lobe). The child > adult contrast confirmed that the main effect of age is based on a stronger activation due to pictures of children in both groups (rDMPFC = right dorsomedial prefrontal cortex). ROI analysis of the cluster showing an interaction of all factors revealed a significant difference (* = p < 0.05) for the contrast girl > woman, with activation in paedphilic subjects and deactivation in control subjects (rlOFC = right lateral orbitofrontal cortex). Event related averaging (B). BOLD %-signal-change in the rlOFC only illustrated a strong activation due to erotic pictures of girls in paedophilia.\\nEvent related averaging of BOLD %-signal-change for all conditions and all groups in that ROI illustrated a strong activation due to erotic pictures of girls in paedophilic subjects that was absent for all other picture conditions in both groups (Figure 3B).\",\n \"Sexual orientation in the control group was heterosexual with a preference for mature women. In the paedophilia group all subjects were heterosexual and attracted exclusively by prepubescent stimuli. The mean age of the paedophilic subjects was 48.25 (standard deviation: 9.15) years and mean IQ 116 (12.12). In the control groups, the mean age was 46.25 (8.35) years and IQ 124.88 (13.91). Student t-tests showed no significant difference between these groups (Table 1). In the paedophilic group 75.4% of the pictures were correctly identified immediately after the scanning session. In the control group 77.5% of the pictures were correctly identified. Again a group difference was excluded by t-test.\\nMean age, IQ and rate of the correctly indentified pictures (behavioural control) with standard deviation (SD) and T-test for both groups\\nNone of the results reaches statistically significant differences.\",\n \" Voxel level ANOVA We found a significant interaction of the factors sex, age and group (p < 0.05, corrected) in the middle frontal gyrus (Figure 2, Table 2). We furthermore found significant main effects for the factors sex and age outside of the middle frontal gyrus, i. e. in brain areas that didn`t show a significant interaction of all factors.\\nWhole brain ANOVA. Active voxels showed a main effect of the factor sex (female/male) in the right middle temporal gyrus, right fusiform gyrus, right inferior and middle occipital gyrus and right parietal lobe. In the left hemisphere the inferior occipital gyrus displayed active voxels (A). Furthermore we found bilateral activation in the cerebellum. The factor age (adult/child) showed an activation of the right dorsomedial prefrontal cortex (B). In the right lateral orbitofrontal cortex a significant interaction of sex, age and group was detected (C). Significant voxels in A-C represent effects at a corrected p-level of p < 0.05. X, Y, Z corresponds to the Tailarach coordinates.\\nRegions that showed a significant effect in the voxel wise ANOVA\\nX, Y, Z corresponds to the Tailarach coordinates (BA = Brodmann area; R = right, L = left).\\nA significant main effect of the factor sex (female, male) was present at a corrected threshold of p < 0.05 in the right middle temporal gyrus, right fusiform gyrus, right inferior and middle occipital gyrus and right parietal lobe. In the left hemisphere, the inferior occipital gyrus was also active. We also found bilateral activation in the cerebellum.\\nThe factor age revealed an activation of the right dorsomedial prefrontal cortex (p < 0.05, corrected).\\nFor the factor group, no significant activation was found at the latter threshold, but again this finding is restricted to brain areas that did not show a significant interaction of all factors.\\nWe found a significant interaction of the factors sex, age and group (p < 0.05, corrected) in the middle frontal gyrus (Figure 2, Table 2). We furthermore found significant main effects for the factors sex and age outside of the middle frontal gyrus, i. e. in brain areas that didn`t show a significant interaction of all factors.\\nWhole brain ANOVA. Active voxels showed a main effect of the factor sex (female/male) in the right middle temporal gyrus, right fusiform gyrus, right inferior and middle occipital gyrus and right parietal lobe. In the left hemisphere the inferior occipital gyrus displayed active voxels (A). Furthermore we found bilateral activation in the cerebellum. The factor age (adult/child) showed an activation of the right dorsomedial prefrontal cortex (B). In the right lateral orbitofrontal cortex a significant interaction of sex, age and group was detected (C). Significant voxels in A-C represent effects at a corrected p-level of p < 0.05. X, Y, Z corresponds to the Tailarach coordinates.\\nRegions that showed a significant effect in the voxel wise ANOVA\\nX, Y, Z corresponds to the Tailarach coordinates (BA = Brodmann area; R = right, L = left).\\nA significant main effect of the factor sex (female, male) was present at a corrected threshold of p < 0.05 in the right middle temporal gyrus, right fusiform gyrus, right inferior and middle occipital gyrus and right parietal lobe. In the left hemisphere, the inferior occipital gyrus was also active. We also found bilateral activation in the cerebellum.\\nThe factor age revealed an activation of the right dorsomedial prefrontal cortex (p < 0.05, corrected).\\nFor the factor group, no significant activation was found at the latter threshold, but again this finding is restricted to brain areas that did not show a significant interaction of all factors.\",\n \"We found a significant interaction of the factors sex, age and group (p < 0.05, corrected) in the middle frontal gyrus (Figure 2, Table 2). We furthermore found significant main effects for the factors sex and age outside of the middle frontal gyrus, i. e. in brain areas that didn`t show a significant interaction of all factors.\\nWhole brain ANOVA. Active voxels showed a main effect of the factor sex (female/male) in the right middle temporal gyrus, right fusiform gyrus, right inferior and middle occipital gyrus and right parietal lobe. In the left hemisphere the inferior occipital gyrus displayed active voxels (A). Furthermore we found bilateral activation in the cerebellum. The factor age (adult/child) showed an activation of the right dorsomedial prefrontal cortex (B). In the right lateral orbitofrontal cortex a significant interaction of sex, age and group was detected (C). Significant voxels in A-C represent effects at a corrected p-level of p < 0.05. X, Y, Z corresponds to the Tailarach coordinates.\\nRegions that showed a significant effect in the voxel wise ANOVA\\nX, Y, Z corresponds to the Tailarach coordinates (BA = Brodmann area; R = right, L = left).\\nA significant main effect of the factor sex (female, male) was present at a corrected threshold of p < 0.05 in the right middle temporal gyrus, right fusiform gyrus, right inferior and middle occipital gyrus and right parietal lobe. In the left hemisphere, the inferior occipital gyrus was also active. We also found bilateral activation in the cerebellum.\\nThe factor age revealed an activation of the right dorsomedial prefrontal cortex (p < 0.05, corrected).\\nFor the factor group, no significant activation was found at the latter threshold, but again this finding is restricted to brain areas that did not show a significant interaction of all factors.\",\n \"The linear contrasts of these ROIs confirmed that, in both groups, the main effect of the factor sex was based on a higher activation due to female pictures and the main effect of the factor age on a stronger activation for pictures of children. ROI analysis clarified that in the ROI that became significant for the interaction of all factors, only paedophilic subjects showed activation in the girl condition while controls showed a deactivation in this condition (Figure 3A).\\nROIs analysis (A). The ROI female > male contrast level for the ROIs extracted from the ANOVA of the main effect of sex displayed a greater activation for female pictures in all active ROIs (rTL = right temporal lobe, rPL = right parietal lobe, rOL = right occipital lobe, lOL = left occipital lobe). The child > adult contrast confirmed that the main effect of age is based on a stronger activation due to pictures of children in both groups (rDMPFC = right dorsomedial prefrontal cortex). ROI analysis of the cluster showing an interaction of all factors revealed a significant difference (* = p < 0.05) for the contrast girl > woman, with activation in paedphilic subjects and deactivation in control subjects (rlOFC = right lateral orbitofrontal cortex). Event related averaging (B). BOLD %-signal-change in the rlOFC only illustrated a strong activation due to erotic pictures of girls in paedophilia.\\nEvent related averaging of BOLD %-signal-change for all conditions and all groups in that ROI illustrated a strong activation due to erotic pictures of girls in paedophilic subjects that was absent for all other picture conditions in both groups (Figure 3B).\",\n \"Visual presentation of erotic pictures has been used to address the underpinnings of erotic stimulation in paedophilia [19-22,28,32]. Results proved heterogeneous, which might partially be explained by the detection of non-specific secondary processes in blocked designs with long stimulus presentation time.\\nOur study focuses on the immediate response to erotic pictures. Like previous studies using a short presentation time and an event-related fMRI design [22,26,33] we also found that female erotic pictures elicited more activation in the right temporal lobe, the right parietal lobe, both occipital lobes and the cerebellum in both heterosexual groups. This finding indicates that these regions respond to erotic visual stimulation in an immediate fashion, e.g. activation can be detected even after a short presentation time. This finding highlights that this activation seems independent from non-specific processes such as sustained attention. Furthermore there was no difference between BOLD response to female erotic pictures in both heterosexual groups.\\nPictures of children elicited a differential activation regardless of sex in the right DMPFC. This is appears to be a very interesting finding as a crucial role in the critical evaluation of stimuli and for the preparation of response strategies has been attributed to the DMPFC [34]. All study subjects were well aware of the fact that our study was about paedophilia. We would expect that erotic pictures of children would be recognised as a stimulus of special interest in that context. Both groups are also likely to be aware that this condition is specifically attached to socially unaccepted sexual preference and behaviour. These assumptions might explain why we found a specific activation of the DMPFC in both groups, independent from the sex of the presented pictures. In the DMPFC these pictures are apparently not differentiated by preferred sex or specific erotic salience but are rather perceived as a category requiring specific attention. This assumption would nicely fit with the findings from Walter et al. [35] indicating that activation of the DMPFC is not related to specific processes such as the processing of erotic content, but rather to more general processes like attention. In line with our findings, Ponseti et al. reported [33] that the medial orbitofrontal cortex showed a stronger response to erotic pictures than neutral pictures, but this response was also independent from individual sexual preference, as it was present in heterosexual male subjects with both pictures of male and female erotic stimuli. In contrast to earlier studies showing a group effect and a different activation pattern in the prefrontal cortex after presentation of erotic pictures of children, our findings indicate that in the context of immediate processing of visual erotic stimuli a considerable overlap in the processing of these stimuli is present. Considering that both groups rely on the same neuronal stimulus evaluation and response preparation networks, this finding seems rather less surprising.\\nIn contrast to the earlier studies that presented pictures for more than 30 s, we focus on the immediate response to visual stimuli. Many of the previous studies were conducted with forensic inpatients while we only included outpatients and most of our paedophilic study group were convicted due to their consumption of illegal internet pornography. Other studies [36,37] already demonstrated that social functioning and neurocognitive capabilities are apparently better in these subjects than in forensic inpatients with hands-on delinquency. In this context it has to be stressed that the IQ of the paedophilic participants can be considered extremely high especially in view of the already mentioned findings from Cantor et al. [3]. Hence we cannot exclude that selection of the study subjects contributed to our findings and that the generalisability of our results might be limited.\\nBrain response in the right lateral oribitofrontal cortex (OFC) was shown to interact with the factors sex, preferred age and group. Mean group betas and event related averaging confirmed a specific activation of the right lateral OFC due to erotic pictures of prepubescent girls in heterosexual paedophilic subjects only whereas all other conditions in the paedophilic group and all conditions in the control group displayed a deactivation of the lateral OFC. Further support for a deviant activation in the right lateral OFC can be found in an fMRI study by Walter et al. [18] that revealed a reduced activation for nude stimuli of female adults in paedophilic subjects.\\nFMRI research has already confirmed that OFC can be activated by visual inputs [38]. According to Kringelbach et al. [39] the OFC is a nexus for sensory integration. Phillips et al. [40] stated that OFC is related to the identification of the emotional significance of a stimulus and to the production of affective states in response to that stimulus. These authors furthermore suggested that, together with the amygdala, insula, ventral striatum and ventral anterior cingulated, the OFC is also involved in the automatic regulation of emotional responses. Hence, different processes such as the modulation of autonomic reactions, learning, prediction, decision making and emotional and reward-related behaviours are processed in the OFC. According to an fMRI study by Sescousse et al. [41] the anterior part of the lateral OFC responds more strongly to more abstract rewards such as monetary gain, while the posterior lateral OFC responds more strongly to more basic rewards, for example erotic stimulation. These findings were in line with a study by Kringelbach and Rolls [42] describing a posterior anterior distribution of primary (e.g. sexual stimulation) and more abstract secondary (monetary gain) reinforcers. They suggested that the anterior part of the lateral OFC is considered to be a more recent structure, phylogentically speaking, and might therefore be related to the processing of more abstract reinforcers like money.\\nFindings from our study underline the notion that the OFC plays a critical role in evaluating reinforcers that might provoke behavioural changes [42,43]. This is even more interesting, as it has been shown that the anterior lateral OFC responds more to punishment while the anterior medial part of the OFC responds to reward [44,45]. In line with this idea, the specific group effect in the right lateral OFC might indicate that the specific emotional significance of the stimuli was only detected in paedophilic subjects. Erotic pictures of children provoked a brain response resembling that of punishment in paedophilic subjects. According to the literature, the right anterior lateral OFC is not only associated with punishment but also with cues indicating a behavioural shift. This significance for behavioural adoption has been highlighted in recent reviews on the OFC [39,43,46]. This is interesting as healthy controls have demonstrated that the lateral OFC became active when subjects tried to suppress erotic responses to visual erotic stimulation [47] or in deception [48]. Our results would therefore indicate that immediately after the presentation of erotic pictures of children, a brain region known to be involved in evaluating emotional salience became active in paedophilic subjects. This activation might be related not only to the regulation of emotional states or autonomic responses but also to behavioural changes like suppression or deception of emotional responses.\\nThe findings from our study point to a network perspective of the immediate processing of visual erotic stimulation. This notion would be in line with the so-called orbital and medial prefrontal cortex [49]. In this network two likely interacting systems became active or inactive while processing the visual stimuli. Even after a short presentation of visual stimuli the DMPFC showed an increased activation in response to the target condition of our study (erotic pictures of children). It is most likely that this finding is related to attentional processes, which are evoked in both study groups. We further found a specific activation due to the respective erotic condition in the anterior lateral OFC in paedophilic subjects. Activation in that region might indicate evaluation of emotional salience and the reward value of that stimulus. Furthermore this activation might indicate the intent to modify behaviour and to dissimulate or deceive erotic responses.\\nAssessing the immediate processing of erotic pictures using fMRI might be a useful tool for probing emotional engagement towards the presented stimuli in future studies. These results could also be therapeutically relevant as most cognitive behavioural treatment approaches aim to reduce cognitive distortions and the denial of the implications of paedophilic behaviours and to increase the awareness of problematic attraction to children [50] rather than to change the sexual preference. The often-reported tendency of paedophilic subjects to dissimulate and minimise the dramatic impact on potential victims of child abuse, might arise from similar processes, which aim at suppressing emotional distress. Interestingly Ponseti et al. [22] did not find such an activation in their study sample, which in contrast to our sample deliberately stated paedophilic sexual deviance.\",\n \"Our fMRI study confirms the findings of previous studies concerning the processing of visual erotic stimuli. Furthermore we were able to demonstrate that the dorsomedial prefrontal cortex is specifically engaged in processing erotic pictures of children regardless of the study group. This activation appears to represent the evaluation of relevance, which is apparently not linked to sexual orientation per se but rather to the study context. In addition we have made some new findings regarding the immediate processing of visual erotic stimulation in paedophilia, as we found an immediate activation of the brain regions involved in evaluating emotional salience and reward, and engaged in the regulation of emotional responses. This activation might be related to the suppression or deception of erotic responses.\",\n \"OFC: Orbitofrontal cortex; DMPFC: Dorsomedial prefrontal cortex; DLPFC: Dorsolateral prefrontal cortex; MPRAGE: Magnetization prepared rapid acquisistion gradient echo; GLM: General linear model, ROI: region of interest.\",\n \"The authors declare that they have no competing interests.\",\n \"BH, RM, MG, VD and ES conceived and designed the study. BH, NH, RM, PL and MK realized the study design and acquired the data. BH, FE, NH performed data analyses. BH and FE interpreted data and wrote the manuscript. BH, FE, NH, PL, MK, RM, VD, ES and MG contributed in the drafting and revision of the manuscript for important intellectual content. All authors read and approved the final manuscript.\",\n \"The pre-publication history for this paper can be accessed here:\\n\\nhttp://www.biomedcentral.com/1471-244X/13/88/prepub\\n\"\n]"},"all_sections_sec_types":{"kind":"list like","value":[null,"methods",null,null,null,null,"results",null,null,null,null,"discussion","conclusions",null,null,null,null],"string":"[\n null,\n \"methods\",\n null,\n null,\n null,\n null,\n \"results\",\n null,\n null,\n null,\n null,\n \"discussion\",\n \"conclusions\",\n null,\n null,\n null,\n null\n]"},"keywords":{"kind":"list like","value":["Paedophilia","Event related fMRI","Erotic stimulation"],"string":"[\n \"Paedophilia\",\n \"Event related fMRI\",\n \"Erotic stimulation\"\n]"},"whole_article_text":{"kind":"string","value":"Background:\nPaedophilia is a matter of great public interest and often evokes emotional discussions in mass media. It is difficult to estimate the prevalence of paedophilia as both paedophilic offenders and victims often prefer not to identify themselves. From surveys of the general population we know that approximately 12% of men and 17% of women report experiencing sexual abuse in their childhood [1]. These figures underline the potential impact of this disease on society. According to DSM IV-TR, paedophilia is defined by two main criteria: firstly persistent sexual fantasies, urges or behaviour involving sexual activity with prepubescent children and secondly that these people have acted on these urges, or that these urges or fantasies caused marked distress or interpersonal difficulties [2].\nFindings from neuropsychological studies on paedophilia are heterogeneous. Whilst a lower IQ [3], educational difficulties [4] and a higher rate of left-handedness [5] indicate rather generalised brain dysfunction, other studies suggest more specific alterations like focal weaknesses in frontal-executive [6,7] and/or temporal-verbal [8] skills or even a more deliberate response style and greater self-monitoring in paedophilic subjects [9,10]. Furthermore revealed research on personality traits in paedophilia various findings like impaired interpersonal functions, impaired self-awareness, disinhibitory traits, sociopathy and a propensity for cognitive distortions [11]. In summary, it can be stated that the neurobiology of paedophilia remains incomplete.\nIn recent years, researchers have increasingly addressed the neuronal underpinnings of sexual arousal in order to better understand sexual behaviour. Stoléru et al. [12,13] and Redouté et al. [14,15] were the first to propose a neurophenomological model that disentangled the cognitive, emotional, motivational and physiological components of sexual arousal. Two recently published quantitative meta-analyses on sexual cue reactivity [16,17] underline that distinctive subcomponents of sexual arousal can be reliably localised by neuroimaging techniques.\nBased on the fMRI results about sexual arousal in healthy controls it was natural to transfer that approach to paedophilic subjects in order to better understand the cognitive and behavioural processes in paedophilia. In heterosexual paedophilia fMRI studies have demonstrated the altered processing of erotic visual stimuli in the dorsomedial prefrontal cortex (DMPFC), amygdala and hippocampus [18]. The latter study also reports that erotic pictures of adults induced a stronger activation of the hypothalmus, the periaqueductal grey and the dorsolateral prefrontal cortex (DLPFC) in healthy controls than in heterosexual paedophilic subjects. While this study highlights differences between controls and paedophilic subjects other studies report similar activation patterns for the respective erotic conditions. For example Schiffer et al. [19] show that erotic pictures of girls induced the same activation of limbic structures such as the amygdala, the cingulate gyrus or the hippocampus in heterosexual paedophilia as erotic pictures of women in a control group. However this study also found an additional activation of the DLPFC in the heterosexual paedophilic group alone. Research has also been carried out on homosexual paedophilia. Again in both paedophilic and control subjects, a common network relating to sexual activation was found, comprising the occipitotemporal and prefrontal cortex [20]. However, only paedophilic subjects showed activated subcortical regions such as the thalamus, the globus pallidus and the striatum during erotic stimulation. Another study on homosexual paedophilia describes differential activation of the right orbitofrontal cortex [21].\nMost of the above-mentioned studies indicate that prefrontal brain regions might be related to paedophilia, but the results and structures involved differ from study to study. Considering these rather heterogeneous results, the findings from a recent study by Ponseti et al. [22] appear surprising. These authors propose an fMRI based classification procedure for heterosexual and homosexual non-paedophilic and paedophilic subjects with 95% accuracy. Unlike previous studies, these authors describe activation in regions known to be involved in the processing of erotic stimuli such as the caudate nucleus, cingulated cortex, insula, fusiform gyrus, temporal cortex, occipital cortex, thalamus, amygdala and cerebellum but not in the prefrontal cortex.\nExcept for the study of Walter et al. [18] all of the above-mentioned studies that describe a prefrontal involvement in paedophilia used relatively long presentation times ranging from 19.2 - 38.5 s and blocked fMRI designs.\nOne critical shortcoming of long presentation times is that different cognitive processes, such as sustained attention or self-referential processes might also take place and somehow interfere with the target process. Earlier fMRI studies on sexual arousal in normal subjects showed that with shorter presentation times of sexually arousing pictures specific neuronal networks and brain processes can be addressed. Static presentation for 8.75 s as used by Moulier et al. [23] demonstrated for example that the initiation and low levels of penile tumescence are controlled by frontal, parietal, insular and cingular cortical areas. In accordance with these results, Ferretti et al. [24] showed that longer presentation times (> 30 s) induce sexual arousal and penile erection whilst shorter presentation times (< 3 s) induce arousal without erection. A stimulation time of 5 s even allowed to distinguish specific sexual emotional effects from more general emotional effects [25]. A study comparing both types of fMRI-designs in the visual processing of erotic stimuli in healthy volunteers [26] provides strong support for the use of fast stimulation time, suggesting an event related design. The authors proposed that event related designs might be an alternative to blocked designs if the core interest is the detection of networks associated with the immediate processing of erotic stimuli.\nBesides being more useful for the detection of these networks, short presentation times also offer other potential benefits as long presentation times in paedophilia are even more problematic than in healthy controls. Paedophilic subjects are mostly recruited after coming into conflict with the legal authorities as a result of their sexual preferences and this may increase a tendency to suppress erotic arousal or dissimulate sexual attraction to the pictures of children presented. The latter point has been used to explain some of the differences between ratings of erotic salience and brain response observed in some of the aforementioned studies [19,21]. As presentation time becomes shorter, deliberate manipulation may become more difficult and the immediate processes following the perception of target stimuli could become visible.\nIn our study we aim to identify the neuronal networks related to immediate processing of erotic stimuli in heterosexual paedophilic subjects recruited from a forensic outpatient setting. Based on previous neuropsychological research and recent fMRI studies we predicted a differential activation in the prefrontal cortex in paedophilic subjects.\n\nMethods:\n Subjects Behavioural and functional magnetic resonance imaging data were acquired from 16 male right-handed subjects. All participants in this study were adults. Written informed consent was obtained from all subjects before participation in the study.\nThe subjects in the paedophilic (N = 8) group were recruited from an outpatient cognitive behavioural group therapy at the department of forensic psychiatry in Basel, Switzerland. The heterosexual paedophilic subjects fulfilled DSM-IV-TR criteria for exclusive type (attracted only to prepubescent children) not limited to incest heterosexual paedophilia. Three of the subjects had previously molested prepubescent children, the other five had been convicted because of the possession of large quantities of explicit internet child pornography. Each participant’s sexual orientation and preference for prepubescent erotic stimuli was assessed in a clinical interview, the Multiphasic Sex Inventory (MSI) [27] and additionally verified by the clinical record and the court file. For all subjects, neither the interview nor the record indicated other comorbid paraphilias. None of the subjects had admitted paedophilia prior to the contact with the legal authorities.\nHeterosexual control subjects (N = 8) were recruited from an advert on the University Hospital notice board. A clinical evaluation of all subjects revealed no other psychiatric, neurological or medical conditions. The local Ethics Committee of the University of Basel, Switzerland approved the study.\nBehavioural and functional magnetic resonance imaging data were acquired from 16 male right-handed subjects. All participants in this study were adults. Written informed consent was obtained from all subjects before participation in the study.\nThe subjects in the paedophilic (N = 8) group were recruited from an outpatient cognitive behavioural group therapy at the department of forensic psychiatry in Basel, Switzerland. The heterosexual paedophilic subjects fulfilled DSM-IV-TR criteria for exclusive type (attracted only to prepubescent children) not limited to incest heterosexual paedophilia. Three of the subjects had previously molested prepubescent children, the other five had been convicted because of the possession of large quantities of explicit internet child pornography. Each participant’s sexual orientation and preference for prepubescent erotic stimuli was assessed in a clinical interview, the Multiphasic Sex Inventory (MSI) [27] and additionally verified by the clinical record and the court file. For all subjects, neither the interview nor the record indicated other comorbid paraphilias. None of the subjects had admitted paedophilia prior to the contact with the legal authorities.\nHeterosexual control subjects (N = 8) were recruited from an advert on the University Hospital notice board. A clinical evaluation of all subjects revealed no other psychiatric, neurological or medical conditions. The local Ethics Committee of the University of Basel, Switzerland approved the study.\n Stimulation and paradigm The study design was adopted from Bühler et al. [26]. Visual stimuli were generated and displayed on a personal computer using the Neurobehavioral Systems (NBS) software package Presentation® 12.2. Stimuli were presented via fMRI-compatible digital video goggles (NordicNeuroLab, Bergen, Norway).\nWhile inside the scanner, subjects were presented with various types of pictures in an event related design. We presented 10 pictures from each category (erotic pictures of boys, girls, men, women or neutral control pictures) for 750 ms with a jittered interstimulus interval that varied randomly between 10 – 20 s in steps of full seconds (Figure 1).\nFMRI paradigm. We presented erotic pictures of adults (women/men) and children (girls/boys) interspersed with neutral control pictures. Pictures from each category were presented for 750 ms with a jittered interstimulus interval ranging from 10–20 s.\nIn each picture, only one person of one of the above-mentioned categories was displayed in bathing clothes in front of a plain-coloured background. All pictures showed faces and the complete corpus. Secondary sexual characteristics were clearly visible in the pictures showing adults but were clearly absent in the pictures depicting prepubescent children. We avoided photographs of adolescents and applied a biological rather than a legal cut off to make the pictures of adults easily distinguishable from those of children. Before inclusion into the paradigm rated the study subjects the pictures and only the pictures with the highest ratings on a visual analogue scale were included. Neutral pictures showed simple objects like e.g. a small boot in front of the same background.\nIn order to control attention during the passive fMRI task we used a previously applied procedure to assure attentive observation of the presented pictures [28]: Before the scanning procedure, subjects were instructed to attentively observe the pictures and told that we would check their attention by asking them to identify the pictures after the scanning session. Immediately after the fMRI session we presented the slides that they had seen interspersed with similar pictures, which had not been presented during the experiment. Subjects then had to decide which pictures they had seen during the fMRI stimulation.\nThe study design was adopted from Bühler et al. [26]. Visual stimuli were generated and displayed on a personal computer using the Neurobehavioral Systems (NBS) software package Presentation® 12.2. Stimuli were presented via fMRI-compatible digital video goggles (NordicNeuroLab, Bergen, Norway).\nWhile inside the scanner, subjects were presented with various types of pictures in an event related design. We presented 10 pictures from each category (erotic pictures of boys, girls, men, women or neutral control pictures) for 750 ms with a jittered interstimulus interval that varied randomly between 10 – 20 s in steps of full seconds (Figure 1).\nFMRI paradigm. We presented erotic pictures of adults (women/men) and children (girls/boys) interspersed with neutral control pictures. Pictures from each category were presented for 750 ms with a jittered interstimulus interval ranging from 10–20 s.\nIn each picture, only one person of one of the above-mentioned categories was displayed in bathing clothes in front of a plain-coloured background. All pictures showed faces and the complete corpus. Secondary sexual characteristics were clearly visible in the pictures showing adults but were clearly absent in the pictures depicting prepubescent children. We avoided photographs of adolescents and applied a biological rather than a legal cut off to make the pictures of adults easily distinguishable from those of children. Before inclusion into the paradigm rated the study subjects the pictures and only the pictures with the highest ratings on a visual analogue scale were included. Neutral pictures showed simple objects like e.g. a small boot in front of the same background.\nIn order to control attention during the passive fMRI task we used a previously applied procedure to assure attentive observation of the presented pictures [28]: Before the scanning procedure, subjects were instructed to attentively observe the pictures and told that we would check their attention by asking them to identify the pictures after the scanning session. Immediately after the fMRI session we presented the slides that they had seen interspersed with similar pictures, which had not been presented during the experiment. Subjects then had to decide which pictures they had seen during the fMRI stimulation.\n FMRI acquisition and analysis Images were acquired using a 3 T MRI scanner (Verio, Siemens Healthcare, Erlangen, Germany) equipped with a standard radio frequency head coil. First a T1-weighted high-resolution data set that covered the whole brain was acquired using a three-dimensional MPRAGE (magnetization-prepared rapid acquisition gradient echo) sequence with a repetition time (TR) of 2.00, an isotropic spatial resolution of 1.0 mm3, and an echo time (TE) of 3.4 ms. T2* weighted functional images were recorded using echoplanar imaging with a TR of 2500 ms, an isotropic spatial resolution of 3×3×3 mm3 and a TE of 30 ms (FoV 228; matrix 76; spacing between slices: 0.51 mm; interslice time: 69 ms). Altogether 152 volumes with 36 image slices with a thickness of 3 mm were obtained.\nImages were acquired using a 3 T MRI scanner (Verio, Siemens Healthcare, Erlangen, Germany) equipped with a standard radio frequency head coil. First a T1-weighted high-resolution data set that covered the whole brain was acquired using a three-dimensional MPRAGE (magnetization-prepared rapid acquisition gradient echo) sequence with a repetition time (TR) of 2.00, an isotropic spatial resolution of 1.0 mm3, and an echo time (TE) of 3.4 ms. T2* weighted functional images were recorded using echoplanar imaging with a TR of 2500 ms, an isotropic spatial resolution of 3×3×3 mm3 and a TE of 30 ms (FoV 228; matrix 76; spacing between slices: 0.51 mm; interslice time: 69 ms). Altogether 152 volumes with 36 image slices with a thickness of 3 mm were obtained.\n Image pre-processing and statistical analysis Image time-series were processed using the BrainVoyager QX 2.3.0 software package (Brain Innovation, Maastricht, The Netherlands). Pre-processing included head motion correction, slice scan time correction, temporal high pass filtering and removal of linear trends. Using the results of the image registration with anatomical scans, the functional image-time series were then warped into Talairach space and resampled into 3 mm isotropic voxel time-series. Normalized images were smoothed using a 6.00 mm isotropic Gaussian kernel.\nFor first-level analysis, a General Linear Model (GLM) analysis was applied with separate subject z-normalized predictors fitted to z-normalized voxel time-courses from all data sets. The orthogonal predictors of interest in the design matrix were: “boys”, “girls”, “men”, “women” and “neutral”. For second-level analysis, the GLM fits (beta weights) were assessed in a 3-way-ANOVA with two within subject factors (sex: female vs. male; age: child vs. adult) and one between subject factor group (paedophilia vs. control). We assessed the main effects of, and the interaction between, these factors at the voxel level and obtained maps that thresholded at the significance level of 5% (corrected for multiple comparison). To obtain this correction, an uncorrected statistical threshold was initially applied to each of the F-maps at p = 0.005, and a cluster-level threshold correction procedure based on Monte Carlo simulations [29] was applied in order to determine the minimum cluster size below which any activation was to be discarded. This procedure yielded a minimum cluster size of 10 voxels (corresponding to a minimum cluster extent of 280 mm) for the F-maps of the interaction of all factors, 12 voxel (297 mm) for the factor age and 19 voxels (482 mm) for the factor sex.\nThe anatomical labelling of active clusters was defined on the basis of the peak voxel coordinates, using the Talairach Daemon (http://www.talairach.org) [30,31].\nActive clusters resulting from the voxel-level ANOVA were further defined as Regions of Interests (ROI). Data from those ROIs were extracted and analysed in order to understand the direction of the effects of the ANOVA. To that end, we calculated linear contrasts for the different factors, i.e. sex (female > male), age (child > adult) and the interaction of the factors (girl > woman) separately for both groups. In order to further specify the interaction of all factors (sex, age, group) we extracted BOLD %-signal-change from the respective ROI and averaged the BOLD%-signal-change separately for the groups.\nImage time-series were processed using the BrainVoyager QX 2.3.0 software package (Brain Innovation, Maastricht, The Netherlands). Pre-processing included head motion correction, slice scan time correction, temporal high pass filtering and removal of linear trends. Using the results of the image registration with anatomical scans, the functional image-time series were then warped into Talairach space and resampled into 3 mm isotropic voxel time-series. Normalized images were smoothed using a 6.00 mm isotropic Gaussian kernel.\nFor first-level analysis, a General Linear Model (GLM) analysis was applied with separate subject z-normalized predictors fitted to z-normalized voxel time-courses from all data sets. The orthogonal predictors of interest in the design matrix were: “boys”, “girls”, “men”, “women” and “neutral”. For second-level analysis, the GLM fits (beta weights) were assessed in a 3-way-ANOVA with two within subject factors (sex: female vs. male; age: child vs. adult) and one between subject factor group (paedophilia vs. control). We assessed the main effects of, and the interaction between, these factors at the voxel level and obtained maps that thresholded at the significance level of 5% (corrected for multiple comparison). To obtain this correction, an uncorrected statistical threshold was initially applied to each of the F-maps at p = 0.005, and a cluster-level threshold correction procedure based on Monte Carlo simulations [29] was applied in order to determine the minimum cluster size below which any activation was to be discarded. This procedure yielded a minimum cluster size of 10 voxels (corresponding to a minimum cluster extent of 280 mm) for the F-maps of the interaction of all factors, 12 voxel (297 mm) for the factor age and 19 voxels (482 mm) for the factor sex.\nThe anatomical labelling of active clusters was defined on the basis of the peak voxel coordinates, using the Talairach Daemon (http://www.talairach.org) [30,31].\nActive clusters resulting from the voxel-level ANOVA were further defined as Regions of Interests (ROI). Data from those ROIs were extracted and analysed in order to understand the direction of the effects of the ANOVA. To that end, we calculated linear contrasts for the different factors, i.e. sex (female > male), age (child > adult) and the interaction of the factors (girl > woman) separately for both groups. In order to further specify the interaction of all factors (sex, age, group) we extracted BOLD %-signal-change from the respective ROI and averaged the BOLD%-signal-change separately for the groups.\n\nSubjects:\nBehavioural and functional magnetic resonance imaging data were acquired from 16 male right-handed subjects. All participants in this study were adults. Written informed consent was obtained from all subjects before participation in the study.\nThe subjects in the paedophilic (N = 8) group were recruited from an outpatient cognitive behavioural group therapy at the department of forensic psychiatry in Basel, Switzerland. The heterosexual paedophilic subjects fulfilled DSM-IV-TR criteria for exclusive type (attracted only to prepubescent children) not limited to incest heterosexual paedophilia. Three of the subjects had previously molested prepubescent children, the other five had been convicted because of the possession of large quantities of explicit internet child pornography. Each participant’s sexual orientation and preference for prepubescent erotic stimuli was assessed in a clinical interview, the Multiphasic Sex Inventory (MSI) [27] and additionally verified by the clinical record and the court file. For all subjects, neither the interview nor the record indicated other comorbid paraphilias. None of the subjects had admitted paedophilia prior to the contact with the legal authorities.\nHeterosexual control subjects (N = 8) were recruited from an advert on the University Hospital notice board. A clinical evaluation of all subjects revealed no other psychiatric, neurological or medical conditions. The local Ethics Committee of the University of Basel, Switzerland approved the study.\n\nStimulation and paradigm:\nThe study design was adopted from Bühler et al. [26]. Visual stimuli were generated and displayed on a personal computer using the Neurobehavioral Systems (NBS) software package Presentation® 12.2. Stimuli were presented via fMRI-compatible digital video goggles (NordicNeuroLab, Bergen, Norway).\nWhile inside the scanner, subjects were presented with various types of pictures in an event related design. We presented 10 pictures from each category (erotic pictures of boys, girls, men, women or neutral control pictures) for 750 ms with a jittered interstimulus interval that varied randomly between 10 – 20 s in steps of full seconds (Figure 1).\nFMRI paradigm. We presented erotic pictures of adults (women/men) and children (girls/boys) interspersed with neutral control pictures. Pictures from each category were presented for 750 ms with a jittered interstimulus interval ranging from 10–20 s.\nIn each picture, only one person of one of the above-mentioned categories was displayed in bathing clothes in front of a plain-coloured background. All pictures showed faces and the complete corpus. Secondary sexual characteristics were clearly visible in the pictures showing adults but were clearly absent in the pictures depicting prepubescent children. We avoided photographs of adolescents and applied a biological rather than a legal cut off to make the pictures of adults easily distinguishable from those of children. Before inclusion into the paradigm rated the study subjects the pictures and only the pictures with the highest ratings on a visual analogue scale were included. Neutral pictures showed simple objects like e.g. a small boot in front of the same background.\nIn order to control attention during the passive fMRI task we used a previously applied procedure to assure attentive observation of the presented pictures [28]: Before the scanning procedure, subjects were instructed to attentively observe the pictures and told that we would check their attention by asking them to identify the pictures after the scanning session. Immediately after the fMRI session we presented the slides that they had seen interspersed with similar pictures, which had not been presented during the experiment. Subjects then had to decide which pictures they had seen during the fMRI stimulation.\n\nFMRI acquisition and analysis:\nImages were acquired using a 3 T MRI scanner (Verio, Siemens Healthcare, Erlangen, Germany) equipped with a standard radio frequency head coil. First a T1-weighted high-resolution data set that covered the whole brain was acquired using a three-dimensional MPRAGE (magnetization-prepared rapid acquisition gradient echo) sequence with a repetition time (TR) of 2.00, an isotropic spatial resolution of 1.0 mm3, and an echo time (TE) of 3.4 ms. T2* weighted functional images were recorded using echoplanar imaging with a TR of 2500 ms, an isotropic spatial resolution of 3×3×3 mm3 and a TE of 30 ms (FoV 228; matrix 76; spacing between slices: 0.51 mm; interslice time: 69 ms). Altogether 152 volumes with 36 image slices with a thickness of 3 mm were obtained.\n\nImage pre-processing and statistical analysis:\nImage time-series were processed using the BrainVoyager QX 2.3.0 software package (Brain Innovation, Maastricht, The Netherlands). Pre-processing included head motion correction, slice scan time correction, temporal high pass filtering and removal of linear trends. Using the results of the image registration with anatomical scans, the functional image-time series were then warped into Talairach space and resampled into 3 mm isotropic voxel time-series. Normalized images were smoothed using a 6.00 mm isotropic Gaussian kernel.\nFor first-level analysis, a General Linear Model (GLM) analysis was applied with separate subject z-normalized predictors fitted to z-normalized voxel time-courses from all data sets. The orthogonal predictors of interest in the design matrix were: “boys”, “girls”, “men”, “women” and “neutral”. For second-level analysis, the GLM fits (beta weights) were assessed in a 3-way-ANOVA with two within subject factors (sex: female vs. male; age: child vs. adult) and one between subject factor group (paedophilia vs. control). We assessed the main effects of, and the interaction between, these factors at the voxel level and obtained maps that thresholded at the significance level of 5% (corrected for multiple comparison). To obtain this correction, an uncorrected statistical threshold was initially applied to each of the F-maps at p = 0.005, and a cluster-level threshold correction procedure based on Monte Carlo simulations [29] was applied in order to determine the minimum cluster size below which any activation was to be discarded. This procedure yielded a minimum cluster size of 10 voxels (corresponding to a minimum cluster extent of 280 mm) for the F-maps of the interaction of all factors, 12 voxel (297 mm) for the factor age and 19 voxels (482 mm) for the factor sex.\nThe anatomical labelling of active clusters was defined on the basis of the peak voxel coordinates, using the Talairach Daemon (http://www.talairach.org) [30,31].\nActive clusters resulting from the voxel-level ANOVA were further defined as Regions of Interests (ROI). Data from those ROIs were extracted and analysed in order to understand the direction of the effects of the ANOVA. To that end, we calculated linear contrasts for the different factors, i.e. sex (female > male), age (child > adult) and the interaction of the factors (girl > woman) separately for both groups. In order to further specify the interaction of all factors (sex, age, group) we extracted BOLD %-signal-change from the respective ROI and averaged the BOLD%-signal-change separately for the groups.\n\nResults:\n Demographics Sexual orientation in the control group was heterosexual with a preference for mature women. In the paedophilia group all subjects were heterosexual and attracted exclusively by prepubescent stimuli. The mean age of the paedophilic subjects was 48.25 (standard deviation: 9.15) years and mean IQ 116 (12.12). In the control groups, the mean age was 46.25 (8.35) years and IQ 124.88 (13.91). Student t-tests showed no significant difference between these groups (Table 1). In the paedophilic group 75.4% of the pictures were correctly identified immediately after the scanning session. In the control group 77.5% of the pictures were correctly identified. Again a group difference was excluded by t-test.\nMean age, IQ and rate of the correctly indentified pictures (behavioural control) with standard deviation (SD) and T-test for both groups\nNone of the results reaches statistically significant differences.\nSexual orientation in the control group was heterosexual with a preference for mature women. In the paedophilia group all subjects were heterosexual and attracted exclusively by prepubescent stimuli. The mean age of the paedophilic subjects was 48.25 (standard deviation: 9.15) years and mean IQ 116 (12.12). In the control groups, the mean age was 46.25 (8.35) years and IQ 124.88 (13.91). Student t-tests showed no significant difference between these groups (Table 1). In the paedophilic group 75.4% of the pictures were correctly identified immediately after the scanning session. In the control group 77.5% of the pictures were correctly identified. Again a group difference was excluded by t-test.\nMean age, IQ and rate of the correctly indentified pictures (behavioural control) with standard deviation (SD) and T-test for both groups\nNone of the results reaches statistically significant differences.\n FMRI Voxel level ANOVA We found a significant interaction of the factors sex, age and group (p < 0.05, corrected) in the middle frontal gyrus (Figure 2, Table 2). We furthermore found significant main effects for the factors sex and age outside of the middle frontal gyrus, i. e. in brain areas that didn`t show a significant interaction of all factors.\nWhole brain ANOVA. Active voxels showed a main effect of the factor sex (female/male) in the right middle temporal gyrus, right fusiform gyrus, right inferior and middle occipital gyrus and right parietal lobe. In the left hemisphere the inferior occipital gyrus displayed active voxels (A). Furthermore we found bilateral activation in the cerebellum. The factor age (adult/child) showed an activation of the right dorsomedial prefrontal cortex (B). In the right lateral orbitofrontal cortex a significant interaction of sex, age and group was detected (C). Significant voxels in A-C represent effects at a corrected p-level of p < 0.05. X, Y, Z corresponds to the Tailarach coordinates.\nRegions that showed a significant effect in the voxel wise ANOVA\nX, Y, Z corresponds to the Tailarach coordinates (BA = Brodmann area; R = right, L = left).\nA significant main effect of the factor sex (female, male) was present at a corrected threshold of p < 0.05 in the right middle temporal gyrus, right fusiform gyrus, right inferior and middle occipital gyrus and right parietal lobe. In the left hemisphere, the inferior occipital gyrus was also active. We also found bilateral activation in the cerebellum.\nThe factor age revealed an activation of the right dorsomedial prefrontal cortex (p < 0.05, corrected).\nFor the factor group, no significant activation was found at the latter threshold, but again this finding is restricted to brain areas that did not show a significant interaction of all factors.\nWe found a significant interaction of the factors sex, age and group (p < 0.05, corrected) in the middle frontal gyrus (Figure 2, Table 2). We furthermore found significant main effects for the factors sex and age outside of the middle frontal gyrus, i. e. in brain areas that didn`t show a significant interaction of all factors.\nWhole brain ANOVA. Active voxels showed a main effect of the factor sex (female/male) in the right middle temporal gyrus, right fusiform gyrus, right inferior and middle occipital gyrus and right parietal lobe. In the left hemisphere the inferior occipital gyrus displayed active voxels (A). Furthermore we found bilateral activation in the cerebellum. The factor age (adult/child) showed an activation of the right dorsomedial prefrontal cortex (B). In the right lateral orbitofrontal cortex a significant interaction of sex, age and group was detected (C). Significant voxels in A-C represent effects at a corrected p-level of p < 0.05. X, Y, Z corresponds to the Tailarach coordinates.\nRegions that showed a significant effect in the voxel wise ANOVA\nX, Y, Z corresponds to the Tailarach coordinates (BA = Brodmann area; R = right, L = left).\nA significant main effect of the factor sex (female, male) was present at a corrected threshold of p < 0.05 in the right middle temporal gyrus, right fusiform gyrus, right inferior and middle occipital gyrus and right parietal lobe. In the left hemisphere, the inferior occipital gyrus was also active. We also found bilateral activation in the cerebellum.\nThe factor age revealed an activation of the right dorsomedial prefrontal cortex (p < 0.05, corrected).\nFor the factor group, no significant activation was found at the latter threshold, but again this finding is restricted to brain areas that did not show a significant interaction of all factors.\n Voxel level ANOVA We found a significant interaction of the factors sex, age and group (p < 0.05, corrected) in the middle frontal gyrus (Figure 2, Table 2). We furthermore found significant main effects for the factors sex and age outside of the middle frontal gyrus, i. e. in brain areas that didn`t show a significant interaction of all factors.\nWhole brain ANOVA. Active voxels showed a main effect of the factor sex (female/male) in the right middle temporal gyrus, right fusiform gyrus, right inferior and middle occipital gyrus and right parietal lobe. In the left hemisphere the inferior occipital gyrus displayed active voxels (A). Furthermore we found bilateral activation in the cerebellum. The factor age (adult/child) showed an activation of the right dorsomedial prefrontal cortex (B). In the right lateral orbitofrontal cortex a significant interaction of sex, age and group was detected (C). Significant voxels in A-C represent effects at a corrected p-level of p < 0.05. X, Y, Z corresponds to the Tailarach coordinates.\nRegions that showed a significant effect in the voxel wise ANOVA\nX, Y, Z corresponds to the Tailarach coordinates (BA = Brodmann area; R = right, L = left).\nA significant main effect of the factor sex (female, male) was present at a corrected threshold of p < 0.05 in the right middle temporal gyrus, right fusiform gyrus, right inferior and middle occipital gyrus and right parietal lobe. In the left hemisphere, the inferior occipital gyrus was also active. We also found bilateral activation in the cerebellum.\nThe factor age revealed an activation of the right dorsomedial prefrontal cortex (p < 0.05, corrected).\nFor the factor group, no significant activation was found at the latter threshold, but again this finding is restricted to brain areas that did not show a significant interaction of all factors.\nWe found a significant interaction of the factors sex, age and group (p < 0.05, corrected) in the middle frontal gyrus (Figure 2, Table 2). We furthermore found significant main effects for the factors sex and age outside of the middle frontal gyrus, i. e. in brain areas that didn`t show a significant interaction of all factors.\nWhole brain ANOVA. Active voxels showed a main effect of the factor sex (female/male) in the right middle temporal gyrus, right fusiform gyrus, right inferior and middle occipital gyrus and right parietal lobe. In the left hemisphere the inferior occipital gyrus displayed active voxels (A). Furthermore we found bilateral activation in the cerebellum. The factor age (adult/child) showed an activation of the right dorsomedial prefrontal cortex (B). In the right lateral orbitofrontal cortex a significant interaction of sex, age and group was detected (C). Significant voxels in A-C represent effects at a corrected p-level of p < 0.05. X, Y, Z corresponds to the Tailarach coordinates.\nRegions that showed a significant effect in the voxel wise ANOVA\nX, Y, Z corresponds to the Tailarach coordinates (BA = Brodmann area; R = right, L = left).\nA significant main effect of the factor sex (female, male) was present at a corrected threshold of p < 0.05 in the right middle temporal gyrus, right fusiform gyrus, right inferior and middle occipital gyrus and right parietal lobe. In the left hemisphere, the inferior occipital gyrus was also active. We also found bilateral activation in the cerebellum.\nThe factor age revealed an activation of the right dorsomedial prefrontal cortex (p < 0.05, corrected).\nFor the factor group, no significant activation was found at the latter threshold, but again this finding is restricted to brain areas that did not show a significant interaction of all factors.\n ROI analysis The linear contrasts of these ROIs confirmed that, in both groups, the main effect of the factor sex was based on a higher activation due to female pictures and the main effect of the factor age on a stronger activation for pictures of children. ROI analysis clarified that in the ROI that became significant for the interaction of all factors, only paedophilic subjects showed activation in the girl condition while controls showed a deactivation in this condition (Figure 3A).\nROIs analysis (A). The ROI female > male contrast level for the ROIs extracted from the ANOVA of the main effect of sex displayed a greater activation for female pictures in all active ROIs (rTL = right temporal lobe, rPL = right parietal lobe, rOL = right occipital lobe, lOL = left occipital lobe). The child > adult contrast confirmed that the main effect of age is based on a stronger activation due to pictures of children in both groups (rDMPFC = right dorsomedial prefrontal cortex). ROI analysis of the cluster showing an interaction of all factors revealed a significant difference (* = p < 0.05) for the contrast girl > woman, with activation in paedphilic subjects and deactivation in control subjects (rlOFC = right lateral orbitofrontal cortex). Event related averaging (B). BOLD %-signal-change in the rlOFC only illustrated a strong activation due to erotic pictures of girls in paedophilia.\nEvent related averaging of BOLD %-signal-change for all conditions and all groups in that ROI illustrated a strong activation due to erotic pictures of girls in paedophilic subjects that was absent for all other picture conditions in both groups (Figure 3B).\nThe linear contrasts of these ROIs confirmed that, in both groups, the main effect of the factor sex was based on a higher activation due to female pictures and the main effect of the factor age on a stronger activation for pictures of children. ROI analysis clarified that in the ROI that became significant for the interaction of all factors, only paedophilic subjects showed activation in the girl condition while controls showed a deactivation in this condition (Figure 3A).\nROIs analysis (A). The ROI female > male contrast level for the ROIs extracted from the ANOVA of the main effect of sex displayed a greater activation for female pictures in all active ROIs (rTL = right temporal lobe, rPL = right parietal lobe, rOL = right occipital lobe, lOL = left occipital lobe). The child > adult contrast confirmed that the main effect of age is based on a stronger activation due to pictures of children in both groups (rDMPFC = right dorsomedial prefrontal cortex). ROI analysis of the cluster showing an interaction of all factors revealed a significant difference (* = p < 0.05) for the contrast girl > woman, with activation in paedphilic subjects and deactivation in control subjects (rlOFC = right lateral orbitofrontal cortex). Event related averaging (B). BOLD %-signal-change in the rlOFC only illustrated a strong activation due to erotic pictures of girls in paedophilia.\nEvent related averaging of BOLD %-signal-change for all conditions and all groups in that ROI illustrated a strong activation due to erotic pictures of girls in paedophilic subjects that was absent for all other picture conditions in both groups (Figure 3B).\n\nDemographics:\nSexual orientation in the control group was heterosexual with a preference for mature women. In the paedophilia group all subjects were heterosexual and attracted exclusively by prepubescent stimuli. The mean age of the paedophilic subjects was 48.25 (standard deviation: 9.15) years and mean IQ 116 (12.12). In the control groups, the mean age was 46.25 (8.35) years and IQ 124.88 (13.91). Student t-tests showed no significant difference between these groups (Table 1). In the paedophilic group 75.4% of the pictures were correctly identified immediately after the scanning session. In the control group 77.5% of the pictures were correctly identified. Again a group difference was excluded by t-test.\nMean age, IQ and rate of the correctly indentified pictures (behavioural control) with standard deviation (SD) and T-test for both groups\nNone of the results reaches statistically significant differences.\n\nFMRI:\n Voxel level ANOVA We found a significant interaction of the factors sex, age and group (p < 0.05, corrected) in the middle frontal gyrus (Figure 2, Table 2). We furthermore found significant main effects for the factors sex and age outside of the middle frontal gyrus, i. e. in brain areas that didn`t show a significant interaction of all factors.\nWhole brain ANOVA. Active voxels showed a main effect of the factor sex (female/male) in the right middle temporal gyrus, right fusiform gyrus, right inferior and middle occipital gyrus and right parietal lobe. In the left hemisphere the inferior occipital gyrus displayed active voxels (A). Furthermore we found bilateral activation in the cerebellum. The factor age (adult/child) showed an activation of the right dorsomedial prefrontal cortex (B). In the right lateral orbitofrontal cortex a significant interaction of sex, age and group was detected (C). Significant voxels in A-C represent effects at a corrected p-level of p < 0.05. X, Y, Z corresponds to the Tailarach coordinates.\nRegions that showed a significant effect in the voxel wise ANOVA\nX, Y, Z corresponds to the Tailarach coordinates (BA = Brodmann area; R = right, L = left).\nA significant main effect of the factor sex (female, male) was present at a corrected threshold of p < 0.05 in the right middle temporal gyrus, right fusiform gyrus, right inferior and middle occipital gyrus and right parietal lobe. In the left hemisphere, the inferior occipital gyrus was also active. We also found bilateral activation in the cerebellum.\nThe factor age revealed an activation of the right dorsomedial prefrontal cortex (p < 0.05, corrected).\nFor the factor group, no significant activation was found at the latter threshold, but again this finding is restricted to brain areas that did not show a significant interaction of all factors.\nWe found a significant interaction of the factors sex, age and group (p < 0.05, corrected) in the middle frontal gyrus (Figure 2, Table 2). We furthermore found significant main effects for the factors sex and age outside of the middle frontal gyrus, i. e. in brain areas that didn`t show a significant interaction of all factors.\nWhole brain ANOVA. Active voxels showed a main effect of the factor sex (female/male) in the right middle temporal gyrus, right fusiform gyrus, right inferior and middle occipital gyrus and right parietal lobe. In the left hemisphere the inferior occipital gyrus displayed active voxels (A). Furthermore we found bilateral activation in the cerebellum. The factor age (adult/child) showed an activation of the right dorsomedial prefrontal cortex (B). In the right lateral orbitofrontal cortex a significant interaction of sex, age and group was detected (C). Significant voxels in A-C represent effects at a corrected p-level of p < 0.05. X, Y, Z corresponds to the Tailarach coordinates.\nRegions that showed a significant effect in the voxel wise ANOVA\nX, Y, Z corresponds to the Tailarach coordinates (BA = Brodmann area; R = right, L = left).\nA significant main effect of the factor sex (female, male) was present at a corrected threshold of p < 0.05 in the right middle temporal gyrus, right fusiform gyrus, right inferior and middle occipital gyrus and right parietal lobe. In the left hemisphere, the inferior occipital gyrus was also active. We also found bilateral activation in the cerebellum.\nThe factor age revealed an activation of the right dorsomedial prefrontal cortex (p < 0.05, corrected).\nFor the factor group, no significant activation was found at the latter threshold, but again this finding is restricted to brain areas that did not show a significant interaction of all factors.\n\nVoxel level ANOVA:\nWe found a significant interaction of the factors sex, age and group (p < 0.05, corrected) in the middle frontal gyrus (Figure 2, Table 2). We furthermore found significant main effects for the factors sex and age outside of the middle frontal gyrus, i. e. in brain areas that didn`t show a significant interaction of all factors.\nWhole brain ANOVA. Active voxels showed a main effect of the factor sex (female/male) in the right middle temporal gyrus, right fusiform gyrus, right inferior and middle occipital gyrus and right parietal lobe. In the left hemisphere the inferior occipital gyrus displayed active voxels (A). Furthermore we found bilateral activation in the cerebellum. The factor age (adult/child) showed an activation of the right dorsomedial prefrontal cortex (B). In the right lateral orbitofrontal cortex a significant interaction of sex, age and group was detected (C). Significant voxels in A-C represent effects at a corrected p-level of p < 0.05. X, Y, Z corresponds to the Tailarach coordinates.\nRegions that showed a significant effect in the voxel wise ANOVA\nX, Y, Z corresponds to the Tailarach coordinates (BA = Brodmann area; R = right, L = left).\nA significant main effect of the factor sex (female, male) was present at a corrected threshold of p < 0.05 in the right middle temporal gyrus, right fusiform gyrus, right inferior and middle occipital gyrus and right parietal lobe. In the left hemisphere, the inferior occipital gyrus was also active. We also found bilateral activation in the cerebellum.\nThe factor age revealed an activation of the right dorsomedial prefrontal cortex (p < 0.05, corrected).\nFor the factor group, no significant activation was found at the latter threshold, but again this finding is restricted to brain areas that did not show a significant interaction of all factors.\n\nROI analysis:\nThe linear contrasts of these ROIs confirmed that, in both groups, the main effect of the factor sex was based on a higher activation due to female pictures and the main effect of the factor age on a stronger activation for pictures of children. ROI analysis clarified that in the ROI that became significant for the interaction of all factors, only paedophilic subjects showed activation in the girl condition while controls showed a deactivation in this condition (Figure 3A).\nROIs analysis (A). The ROI female > male contrast level for the ROIs extracted from the ANOVA of the main effect of sex displayed a greater activation for female pictures in all active ROIs (rTL = right temporal lobe, rPL = right parietal lobe, rOL = right occipital lobe, lOL = left occipital lobe). The child > adult contrast confirmed that the main effect of age is based on a stronger activation due to pictures of children in both groups (rDMPFC = right dorsomedial prefrontal cortex). ROI analysis of the cluster showing an interaction of all factors revealed a significant difference (* = p < 0.05) for the contrast girl > woman, with activation in paedphilic subjects and deactivation in control subjects (rlOFC = right lateral orbitofrontal cortex). Event related averaging (B). BOLD %-signal-change in the rlOFC only illustrated a strong activation due to erotic pictures of girls in paedophilia.\nEvent related averaging of BOLD %-signal-change for all conditions and all groups in that ROI illustrated a strong activation due to erotic pictures of girls in paedophilic subjects that was absent for all other picture conditions in both groups (Figure 3B).\n\nDiscussion:\nVisual presentation of erotic pictures has been used to address the underpinnings of erotic stimulation in paedophilia [19-22,28,32]. Results proved heterogeneous, which might partially be explained by the detection of non-specific secondary processes in blocked designs with long stimulus presentation time.\nOur study focuses on the immediate response to erotic pictures. Like previous studies using a short presentation time and an event-related fMRI design [22,26,33] we also found that female erotic pictures elicited more activation in the right temporal lobe, the right parietal lobe, both occipital lobes and the cerebellum in both heterosexual groups. This finding indicates that these regions respond to erotic visual stimulation in an immediate fashion, e.g. activation can be detected even after a short presentation time. This finding highlights that this activation seems independent from non-specific processes such as sustained attention. Furthermore there was no difference between BOLD response to female erotic pictures in both heterosexual groups.\nPictures of children elicited a differential activation regardless of sex in the right DMPFC. This is appears to be a very interesting finding as a crucial role in the critical evaluation of stimuli and for the preparation of response strategies has been attributed to the DMPFC [34]. All study subjects were well aware of the fact that our study was about paedophilia. We would expect that erotic pictures of children would be recognised as a stimulus of special interest in that context. Both groups are also likely to be aware that this condition is specifically attached to socially unaccepted sexual preference and behaviour. These assumptions might explain why we found a specific activation of the DMPFC in both groups, independent from the sex of the presented pictures. In the DMPFC these pictures are apparently not differentiated by preferred sex or specific erotic salience but are rather perceived as a category requiring specific attention. This assumption would nicely fit with the findings from Walter et al. [35] indicating that activation of the DMPFC is not related to specific processes such as the processing of erotic content, but rather to more general processes like attention. In line with our findings, Ponseti et al. reported [33] that the medial orbitofrontal cortex showed a stronger response to erotic pictures than neutral pictures, but this response was also independent from individual sexual preference, as it was present in heterosexual male subjects with both pictures of male and female erotic stimuli. In contrast to earlier studies showing a group effect and a different activation pattern in the prefrontal cortex after presentation of erotic pictures of children, our findings indicate that in the context of immediate processing of visual erotic stimuli a considerable overlap in the processing of these stimuli is present. Considering that both groups rely on the same neuronal stimulus evaluation and response preparation networks, this finding seems rather less surprising.\nIn contrast to the earlier studies that presented pictures for more than 30 s, we focus on the immediate response to visual stimuli. Many of the previous studies were conducted with forensic inpatients while we only included outpatients and most of our paedophilic study group were convicted due to their consumption of illegal internet pornography. Other studies [36,37] already demonstrated that social functioning and neurocognitive capabilities are apparently better in these subjects than in forensic inpatients with hands-on delinquency. In this context it has to be stressed that the IQ of the paedophilic participants can be considered extremely high especially in view of the already mentioned findings from Cantor et al. [3]. Hence we cannot exclude that selection of the study subjects contributed to our findings and that the generalisability of our results might be limited.\nBrain response in the right lateral oribitofrontal cortex (OFC) was shown to interact with the factors sex, preferred age and group. Mean group betas and event related averaging confirmed a specific activation of the right lateral OFC due to erotic pictures of prepubescent girls in heterosexual paedophilic subjects only whereas all other conditions in the paedophilic group and all conditions in the control group displayed a deactivation of the lateral OFC. Further support for a deviant activation in the right lateral OFC can be found in an fMRI study by Walter et al. [18] that revealed a reduced activation for nude stimuli of female adults in paedophilic subjects.\nFMRI research has already confirmed that OFC can be activated by visual inputs [38]. According to Kringelbach et al. [39] the OFC is a nexus for sensory integration. Phillips et al. [40] stated that OFC is related to the identification of the emotional significance of a stimulus and to the production of affective states in response to that stimulus. These authors furthermore suggested that, together with the amygdala, insula, ventral striatum and ventral anterior cingulated, the OFC is also involved in the automatic regulation of emotional responses. Hence, different processes such as the modulation of autonomic reactions, learning, prediction, decision making and emotional and reward-related behaviours are processed in the OFC. According to an fMRI study by Sescousse et al. [41] the anterior part of the lateral OFC responds more strongly to more abstract rewards such as monetary gain, while the posterior lateral OFC responds more strongly to more basic rewards, for example erotic stimulation. These findings were in line with a study by Kringelbach and Rolls [42] describing a posterior anterior distribution of primary (e.g. sexual stimulation) and more abstract secondary (monetary gain) reinforcers. They suggested that the anterior part of the lateral OFC is considered to be a more recent structure, phylogentically speaking, and might therefore be related to the processing of more abstract reinforcers like money.\nFindings from our study underline the notion that the OFC plays a critical role in evaluating reinforcers that might provoke behavioural changes [42,43]. This is even more interesting, as it has been shown that the anterior lateral OFC responds more to punishment while the anterior medial part of the OFC responds to reward [44,45]. In line with this idea, the specific group effect in the right lateral OFC might indicate that the specific emotional significance of the stimuli was only detected in paedophilic subjects. Erotic pictures of children provoked a brain response resembling that of punishment in paedophilic subjects. According to the literature, the right anterior lateral OFC is not only associated with punishment but also with cues indicating a behavioural shift. This significance for behavioural adoption has been highlighted in recent reviews on the OFC [39,43,46]. This is interesting as healthy controls have demonstrated that the lateral OFC became active when subjects tried to suppress erotic responses to visual erotic stimulation [47] or in deception [48]. Our results would therefore indicate that immediately after the presentation of erotic pictures of children, a brain region known to be involved in evaluating emotional salience became active in paedophilic subjects. This activation might be related not only to the regulation of emotional states or autonomic responses but also to behavioural changes like suppression or deception of emotional responses.\nThe findings from our study point to a network perspective of the immediate processing of visual erotic stimulation. This notion would be in line with the so-called orbital and medial prefrontal cortex [49]. In this network two likely interacting systems became active or inactive while processing the visual stimuli. Even after a short presentation of visual stimuli the DMPFC showed an increased activation in response to the target condition of our study (erotic pictures of children). It is most likely that this finding is related to attentional processes, which are evoked in both study groups. We further found a specific activation due to the respective erotic condition in the anterior lateral OFC in paedophilic subjects. Activation in that region might indicate evaluation of emotional salience and the reward value of that stimulus. Furthermore this activation might indicate the intent to modify behaviour and to dissimulate or deceive erotic responses.\nAssessing the immediate processing of erotic pictures using fMRI might be a useful tool for probing emotional engagement towards the presented stimuli in future studies. These results could also be therapeutically relevant as most cognitive behavioural treatment approaches aim to reduce cognitive distortions and the denial of the implications of paedophilic behaviours and to increase the awareness of problematic attraction to children [50] rather than to change the sexual preference. The often-reported tendency of paedophilic subjects to dissimulate and minimise the dramatic impact on potential victims of child abuse, might arise from similar processes, which aim at suppressing emotional distress. Interestingly Ponseti et al. [22] did not find such an activation in their study sample, which in contrast to our sample deliberately stated paedophilic sexual deviance.\n\nConclusion:\nOur fMRI study confirms the findings of previous studies concerning the processing of visual erotic stimuli. Furthermore we were able to demonstrate that the dorsomedial prefrontal cortex is specifically engaged in processing erotic pictures of children regardless of the study group. This activation appears to represent the evaluation of relevance, which is apparently not linked to sexual orientation per se but rather to the study context. In addition we have made some new findings regarding the immediate processing of visual erotic stimulation in paedophilia, as we found an immediate activation of the brain regions involved in evaluating emotional salience and reward, and engaged in the regulation of emotional responses. This activation might be related to the suppression or deception of erotic responses.\n\nAbbreviations:\nOFC: Orbitofrontal cortex; DMPFC: Dorsomedial prefrontal cortex; DLPFC: Dorsolateral prefrontal cortex; MPRAGE: Magnetization prepared rapid acquisistion gradient echo; GLM: General linear model, ROI: region of interest.\n\nCompeting interests:\nThe authors declare that they have no competing interests.\n\nAuthors’ contributions:\nBH, RM, MG, VD and ES conceived and designed the study. BH, NH, RM, PL and MK realized the study design and acquired the data. BH, FE, NH performed data analyses. BH and FE interpreted data and wrote the manuscript. BH, FE, NH, PL, MK, RM, VD, ES and MG contributed in the drafting and revision of the manuscript for important intellectual content. All authors read and approved the final manuscript.\n\nPre-publication history:\nThe pre-publication history for this paper can be accessed here:\n\nhttp://www.biomedcentral.com/1471-244X/13/88/prepub\n\n\n"},"whole_article_abstract":{"kind":"string","value":"Background:\nMost neuroimaging studies investigating sexual arousal in paedophilia used erotic pictures together with a blocked fMRI design and long stimulus presentation time. While this approach allows the detection of sexual arousal, it does not enable the assessment of the immediate processing of erotically salient stimuli. Our study aimed to identify neuronal networks related to the immediate processing of erotic stimuli in heterosexual male paedophiles and healthy age-matched controls.\n\nMethods:\nWe presented erotic pictures of prepubescent children and adults in an event related fMRI-design to eight paedophilic subjects and age-matched controls.\n\nResults:\nErotic pictures of females elicited more activation in the right temporal lobe, the right parietal lobe and both occipital lobes and erotic pictures of children activated the right dorsomedial prefrontal cortex in both groups. An interaction of sex, age and group was present in the right anteriolateral oribitofrontal cortex.\n\nConclusions:\nOur event related study design confirmed that erotic pictures activate some of the brain regions already known to be involved in the processing of erotic pictures when these are presented in blocks. In addition, it revealed that erotic pictures of prepubescent children activate brain regions critical for choosing response strategies in both groups, and that erotically salient stimuli selectively activate a brain region in paedophilic subjects that had previously been attributed to reward and punishment, and that had been shown to be implicated in the suppression of erotic response and deception."},"background_conclusion_text":{"kind":"string","value":"Background:\nPaedophilia is a matter of great public interest and often evokes emotional discussions in mass media. It is difficult to estimate the prevalence of paedophilia as both paedophilic offenders and victims often prefer not to identify themselves. From surveys of the general population we know that approximately 12% of men and 17% of women report experiencing sexual abuse in their childhood [1]. These figures underline the potential impact of this disease on society. According to DSM IV-TR, paedophilia is defined by two main criteria: firstly persistent sexual fantasies, urges or behaviour involving sexual activity with prepubescent children and secondly that these people have acted on these urges, or that these urges or fantasies caused marked distress or interpersonal difficulties [2].\nFindings from neuropsychological studies on paedophilia are heterogeneous. Whilst a lower IQ [3], educational difficulties [4] and a higher rate of left-handedness [5] indicate rather generalised brain dysfunction, other studies suggest more specific alterations like focal weaknesses in frontal-executive [6,7] and/or temporal-verbal [8] skills or even a more deliberate response style and greater self-monitoring in paedophilic subjects [9,10]. Furthermore revealed research on personality traits in paedophilia various findings like impaired interpersonal functions, impaired self-awareness, disinhibitory traits, sociopathy and a propensity for cognitive distortions [11]. In summary, it can be stated that the neurobiology of paedophilia remains incomplete.\nIn recent years, researchers have increasingly addressed the neuronal underpinnings of sexual arousal in order to better understand sexual behaviour. Stoléru et al. [12,13] and Redouté et al. [14,15] were the first to propose a neurophenomological model that disentangled the cognitive, emotional, motivational and physiological components of sexual arousal. Two recently published quantitative meta-analyses on sexual cue reactivity [16,17] underline that distinctive subcomponents of sexual arousal can be reliably localised by neuroimaging techniques.\nBased on the fMRI results about sexual arousal in healthy controls it was natural to transfer that approach to paedophilic subjects in order to better understand the cognitive and behavioural processes in paedophilia. In heterosexual paedophilia fMRI studies have demonstrated the altered processing of erotic visual stimuli in the dorsomedial prefrontal cortex (DMPFC), amygdala and hippocampus [18]. The latter study also reports that erotic pictures of adults induced a stronger activation of the hypothalmus, the periaqueductal grey and the dorsolateral prefrontal cortex (DLPFC) in healthy controls than in heterosexual paedophilic subjects. While this study highlights differences between controls and paedophilic subjects other studies report similar activation patterns for the respective erotic conditions. For example Schiffer et al. [19] show that erotic pictures of girls induced the same activation of limbic structures such as the amygdala, the cingulate gyrus or the hippocampus in heterosexual paedophilia as erotic pictures of women in a control group. However this study also found an additional activation of the DLPFC in the heterosexual paedophilic group alone. Research has also been carried out on homosexual paedophilia. Again in both paedophilic and control subjects, a common network relating to sexual activation was found, comprising the occipitotemporal and prefrontal cortex [20]. However, only paedophilic subjects showed activated subcortical regions such as the thalamus, the globus pallidus and the striatum during erotic stimulation. Another study on homosexual paedophilia describes differential activation of the right orbitofrontal cortex [21].\nMost of the above-mentioned studies indicate that prefrontal brain regions might be related to paedophilia, but the results and structures involved differ from study to study. Considering these rather heterogeneous results, the findings from a recent study by Ponseti et al. [22] appear surprising. These authors propose an fMRI based classification procedure for heterosexual and homosexual non-paedophilic and paedophilic subjects with 95% accuracy. Unlike previous studies, these authors describe activation in regions known to be involved in the processing of erotic stimuli such as the caudate nucleus, cingulated cortex, insula, fusiform gyrus, temporal cortex, occipital cortex, thalamus, amygdala and cerebellum but not in the prefrontal cortex.\nExcept for the study of Walter et al. [18] all of the above-mentioned studies that describe a prefrontal involvement in paedophilia used relatively long presentation times ranging from 19.2 - 38.5 s and blocked fMRI designs.\nOne critical shortcoming of long presentation times is that different cognitive processes, such as sustained attention or self-referential processes might also take place and somehow interfere with the target process. Earlier fMRI studies on sexual arousal in normal subjects showed that with shorter presentation times of sexually arousing pictures specific neuronal networks and brain processes can be addressed. Static presentation for 8.75 s as used by Moulier et al. [23] demonstrated for example that the initiation and low levels of penile tumescence are controlled by frontal, parietal, insular and cingular cortical areas. In accordance with these results, Ferretti et al. [24] showed that longer presentation times (> 30 s) induce sexual arousal and penile erection whilst shorter presentation times (< 3 s) induce arousal without erection. A stimulation time of 5 s even allowed to distinguish specific sexual emotional effects from more general emotional effects [25]. A study comparing both types of fMRI-designs in the visual processing of erotic stimuli in healthy volunteers [26] provides strong support for the use of fast stimulation time, suggesting an event related design. The authors proposed that event related designs might be an alternative to blocked designs if the core interest is the detection of networks associated with the immediate processing of erotic stimuli.\nBesides being more useful for the detection of these networks, short presentation times also offer other potential benefits as long presentation times in paedophilia are even more problematic than in healthy controls. Paedophilic subjects are mostly recruited after coming into conflict with the legal authorities as a result of their sexual preferences and this may increase a tendency to suppress erotic arousal or dissimulate sexual attraction to the pictures of children presented. The latter point has been used to explain some of the differences between ratings of erotic salience and brain response observed in some of the aforementioned studies [19,21]. As presentation time becomes shorter, deliberate manipulation may become more difficult and the immediate processes following the perception of target stimuli could become visible.\nIn our study we aim to identify the neuronal networks related to immediate processing of erotic stimuli in heterosexual paedophilic subjects recruited from a forensic outpatient setting. Based on previous neuropsychological research and recent fMRI studies we predicted a differential activation in the prefrontal cortex in paedophilic subjects.\n\nConclusion:\nOur fMRI study confirms the findings of previous studies concerning the processing of visual erotic stimuli. Furthermore we were able to demonstrate that the dorsomedial prefrontal cortex is specifically engaged in processing erotic pictures of children regardless of the study group. This activation appears to represent the evaluation of relevance, which is apparently not linked to sexual orientation per se but rather to the study context. In addition we have made some new findings regarding the immediate processing of visual erotic stimulation in paedophilia, as we found an immediate activation of the brain regions involved in evaluating emotional salience and reward, and engaged in the regulation of emotional responses. This activation might be related to the suppression or deception of erotic responses."},"background_conclusion_abstract":{"kind":"string","value":"Background:\nMost neuroimaging studies investigating sexual arousal in paedophilia used erotic pictures together with a blocked fMRI design and long stimulus presentation time. While this approach allows the detection of sexual arousal, it does not enable the assessment of the immediate processing of erotically salient stimuli. Our study aimed to identify neuronal networks related to the immediate processing of erotic stimuli in heterosexual male paedophiles and healthy age-matched controls.\n\nMethods:\nWe presented erotic pictures of prepubescent children and adults in an event related fMRI-design to eight paedophilic subjects and age-matched controls.\n\nResults:\nErotic pictures of females elicited more activation in the right temporal lobe, the right parietal lobe and both occipital lobes and erotic pictures of children activated the right dorsomedial prefrontal cortex in both groups. An interaction of sex, age and group was present in the right anteriolateral oribitofrontal cortex.\n\nConclusions:\nOur event related study design confirmed that erotic pictures activate some of the brain regions already known to be involved in the processing of erotic pictures when these are presented in blocks. In addition, it revealed that erotic pictures of prepubescent children activate brain regions critical for choosing response strategies in both groups, and that erotically salient stimuli selectively activate a brain region in paedophilic subjects that had previously been attributed to reward and punishment, and that had been shown to be implicated in the suppression of erotic response and deception."},"whole_article_text_length":{"kind":"number","value":11290,"string":"11,290"},"whole_article_abstract_length":{"kind":"number","value":264,"string":"264"},"other_sections_lengths":{"kind":"list like","value":[1224,249,408,157,517,173,746,370,312,38,10,92,16],"string":"[\n 1224,\n 249,\n 408,\n 157,\n 517,\n 173,\n 746,\n 370,\n 312,\n 38,\n 10,\n 92,\n 16\n]"},"num_sections":{"kind":"number","value":17,"string":"17"},"most_frequent_words":{"kind":"list like","value":["right","pictures","activation","subjects","significant","gyrus","age","sex","group","erotic"],"string":"[\n \"right\",\n 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[SUMMARY]"}}},{"rowIdx":265,"cells":{"title":{"kind":"string","value":"Quadratic relationship between systolic blood pressure and white matter lesions in individuals with hypertension."},"pmid":{"kind":"string","value":"36204999"},"background_abstract":{"kind":"string","value":"There is a well documented relationship between cardiovascular risk factors and the development of brain injury, which can lead to cognitive dysfunction. Hypertension (HTN) is a condition increasing the risk of silent and symptomatic ischemic brain lesions. Although benefits of hypertension treatment are indisputable, the target blood pressure value where the possibility of tissue damage is most reduced remains under debate."},"background_abstract_label":{"kind":"string","value":"BACKGROUND"},"methods_abstract":{"kind":"string","value":"Our group performed a cross-sectional ( n = 376) and longitudinal ( n = 188) study of individuals without dementia or stroke (60% women n = 228, age 68.5 ± 7.4 years; men n = 148, age 70.7 ± 6.9 years). Participants were split into hypertensive ( n = 169) and normotensive ( n = 207) groups. MR images were obtained on a 3T system. Linear modeling was performed in hypertensive and normotensive cohorts to investigate the relationship between systolic (SBP) and diastolic (DBP) blood pressure, white matter lesion (WML), and brain volumes."},"methods_abstract_label":{"kind":"string","value":"METHOD"},"results_abstract":{"kind":"string","value":"Participants in the hypertensive cohort showed a quadratic relationship between SBP and WML, with the lowest amounts of WML being measured in participants with readings at approximately 124 mmHg. Additionally, the hypertensive cohort also exhibited a quadratic relationship between DBP and mean hippocampal volume; participants with readings at approximately 77 mmHg showing the largest volumes. Longitudinally, all groups experienced WML growth, despite different BP trajectories, further suggesting that WML expansion may occur despite or because of BP reduction in individuals with compromised vascular system."},"results_abstract_label":{"kind":"string","value":"RESULTS"},"conclusions_abstract":{"kind":"string","value":"Overall, our study suggests that in the hypertensive group there is a valley of mid-range blood pressures displaying less pathology in the brain."},"conclusions_abstract_label":{"kind":"string","value":"CONCLUSION"},"mesh_descriptor_names":{"kind":"list like","value":["Male","Female","Humans","Middle Aged","Aged","Blood Pressure","White Matter","Cross-Sectional Studies","Magnetic Resonance Imaging","Hypertension"],"string":"[\n \"Male\",\n \"Female\",\n \"Humans\",\n \"Middle Aged\",\n \"Aged\",\n \"Blood Pressure\",\n \"White Matter\",\n \"Cross-Sectional Studies\",\n \"Magnetic Resonance Imaging\",\n \"Hypertension\"\n]"},"pmcid":{"kind":"string","value":"9794123"},"background_title":{"kind":"string","value":"INTRODUCTION"},"background_text":{"kind":"string","value":"The impact of cardiovascular disease on the United States’ population cannot be overstated. One out of every three Americans is hypertensive, with 66% of adults aged 60 or older exhibiting above average blood pressure readings [1,2]. It was estimated in 2018 that 11 million individuals in the United States were living with undiagnosed hypertension (HTN), placing them at an increased risk for adverse cardiovascular events that can directly affect the central nervous system as well as cognition [1,2].\nHigh blood pressure (BP) can cause damage and dysfunction to organ systems with perhaps the most significant of such being the brain and the heart [3–5]. Hypertensive patients develop atherosclerotic plaques within the large blood vessels supplying blood flow both into and out of the brain at an accelerated rate, increasing their risk of an ischemic event [6,7]. HTN can cause a remodeling of the brain microvasculature with vessels exhibiting rarefication and narrowing lumen [4,6,8]. Such changes in vessel architecture can have negative effects on cerebral blood flow (CBF) and autoregulation. Reduction in CBF has been shown to cause brain atrophy both in animal models [9] and in clinical population [10], and has been consistently linked with white matter damage [11,12]. White matter lesions (WML) are very common in HTN participants [13–15]. Indeed, several studies have confirmed that two major risk factors for WML development are increased age and high BP, with smoking, high cholesterol, and heart disease also being significant contributors to lesion formation [14,16,17].\nAlthough benefits of HTN treatment are indisputable for major cardiovascular outcome, the target blood pressure for the brain remains under debate. While some argue for aggressive blood pressure reductions [18], others point to possible danger of this approach [19], as marked lowering of BP in longstanding HTN may cause relative hypoperfusion. This suggested to us the existence of an optimum BP range.\nA recent metanalysis found a U-shape relationship between SBP and a risk of dementia in participants older than 75, as well as U-shape relationship between SBP and combined dementia and mortality across all age groups starting at 60 [20]. Our group has previously reported a quadratic (inverted U-shape) relationship between CBF and SBP in hypertensive patients, pointing to the optimum lying between 120 and 130 mmHg where CBF was the highest [21]. It was consistently shown that WML volumes are inversely related to global and local indices of CBF [22]. In this observational report, we investigated whether in hypertensive participants WML and atrophy, structural markers of HTN-related brain damage, show relationship with SBP, which is parallel to the one we observed for CBF and SBP: namely is there a quadratic association between them."},"methods_title":{"kind":"string","value":"METHODS"},"methods_text":{"kind":"string","value":"The data of this study are available from the corresponding author upon request.\nParticipants recruitment Study participants were recruited at the NYU School of Medicine, at the former Center for Brain Health. Out of 376 individuals described in this report, 369 (98%) were previously a part of a study assessing the relationship between BP and CBF. Here, we report on participants with quantitative assessment of white matter pathology. Baseline assessment was performed in a larger group of n = 376 participants, and n = 188 of them had follow-up examinations. The reasons for lack of follow-up are described in the Supplement. Participants were enrolled between November 2010 and November 2017. All participants signed Institutional Review Board (ethics committee)-approved consent forms and were free of neocortical stroke, brain tumor, life-long psychotic disorders and dementia at baseline. Information pertaining to recruitment, exclusion criteria, and participant flow is presented in the Supplement.\nStudy participants were recruited at the NYU School of Medicine, at the former Center for Brain Health. Out of 376 individuals described in this report, 369 (98%) were previously a part of a study assessing the relationship between BP and CBF. Here, we report on participants with quantitative assessment of white matter pathology. Baseline assessment was performed in a larger group of n = 376 participants, and n = 188 of them had follow-up examinations. The reasons for lack of follow-up are described in the Supplement. Participants were enrolled between November 2010 and November 2017. All participants signed Institutional Review Board (ethics committee)-approved consent forms and were free of neocortical stroke, brain tumor, life-long psychotic disorders and dementia at baseline. Information pertaining to recruitment, exclusion criteria, and participant flow is presented in the Supplement.\nClinical assessment All participants underwent medical, psychiatric, and neurological assessments, blood tests, electrocardiogram (ECG), and magnetic resonance imaging (MRI) evaluation examinations at baseline, and the longitudinal group also at follow-up. Participants exhibiting dementia, based on a physician-administered interview using the Brief Cognitive Rating Scale, rating on the Global Deterioration Scale [23], and Clinical Dementia Rating were excluded [24]. Fasting blood was tested for complete blood count, liver function, as well as metabolic and lipid panel.\nBlood pressure was taken once in a sitting position, after 5 min of rest. It was measured on the left upper arm using a manual sphygmomanometer. Body mass index (BMI) was calculated as weight/height (kg/m) [2].\nMedication: We recoded the use of antihypertensive medications (angiotensin receptor blockers (ARBs), angiotensin converting enzyme inhibitors (ACE), beta-blockers, diuretics, and calcium channel blockers), statins and glucose lowering drugs.\nDiabetes mellitus was defined as fasting glucose level of 126 mg/dl and higher and/or current use of glucose lowering medication [25]. Smoking status was defined as positive if participants was a current smoker or smoked within last 10 years.\nAll participants underwent medical, psychiatric, and neurological assessments, blood tests, electrocardiogram (ECG), and magnetic resonance imaging (MRI) evaluation examinations at baseline, and the longitudinal group also at follow-up. Participants exhibiting dementia, based on a physician-administered interview using the Brief Cognitive Rating Scale, rating on the Global Deterioration Scale [23], and Clinical Dementia Rating were excluded [24]. Fasting blood was tested for complete blood count, liver function, as well as metabolic and lipid panel.\nBlood pressure was taken once in a sitting position, after 5 min of rest. It was measured on the left upper arm using a manual sphygmomanometer. Body mass index (BMI) was calculated as weight/height (kg/m) [2].\nMedication: We recoded the use of antihypertensive medications (angiotensin receptor blockers (ARBs), angiotensin converting enzyme inhibitors (ACE), beta-blockers, diuretics, and calcium channel blockers), statins and glucose lowering drugs.\nDiabetes mellitus was defined as fasting glucose level of 126 mg/dl and higher and/or current use of glucose lowering medication [25]. Smoking status was defined as positive if participants was a current smoker or smoked within last 10 years.\nStudy groups For consistency with our previous study hypertension was defined as current antihypertensive treatment or BP ≥140/90 mmHg [26]. One hundred and sixty-nine participants were classified as hypertensive; 136 participants were taking antihypertensive medication and 42 were not taking any antihypertensive medication with high BP recorded during their in-office visit. Normotension (NTN) was defined as BP <140/90 mmHg and no antihypertensive treatment.\nFor descriptive purpose and secondary analyses, we described hypertensive participants as untreated: no antihypertensive treatment and BP ≥140/90 mmHg; controlled: antihypertensive treatment and BP <140/90 mmHg, uncontrolled: antihypertensive treatment and BP ≥140/90 mmHg. We also divided our normotensive group into Stage 1 hypertension based on new guidelines: [27] no treatment and BP between 130–139/80–89 mmHg, and normotensive BP <130/80 mmHg.\nFor consistency with our previous study hypertension was defined as current antihypertensive treatment or BP ≥140/90 mmHg [26]. One hundred and sixty-nine participants were classified as hypertensive; 136 participants were taking antihypertensive medication and 42 were not taking any antihypertensive medication with high BP recorded during their in-office visit. Normotension (NTN) was defined as BP <140/90 mmHg and no antihypertensive treatment.\nFor descriptive purpose and secondary analyses, we described hypertensive participants as untreated: no antihypertensive treatment and BP ≥140/90 mmHg; controlled: antihypertensive treatment and BP <140/90 mmHg, uncontrolled: antihypertensive treatment and BP ≥140/90 mmHg. We also divided our normotensive group into Stage 1 hypertension based on new guidelines: [27] no treatment and BP between 130–139/80–89 mmHg, and normotensive BP <130/80 mmHg.\nMR imaging All MR imaging was performed on the 3T system (Siemens, Erlangen, Germany). Parameters for Magnetization Prepared Rapid Acquisition Gradient Echo (MPRAGE) images were: repetition time (TR) = 2250 ms, echo time (TE) = 2.7 ms, inversion time (TI) = 900 ms, flip angle (FA) = 8°, slice thickness: 1.0 mm, field of view (FOV) = 200 mm, acquisition matrix = 256 × 192 × 124, reconstructed as 256 × 256 × 124. Axial fluid attenuation inversion recovery (FLAIR) images were acquired with TR/TE/TI 9000/99/2500 ms; FA 130°, slice thickness: 3.3 mm, field of view (FOV) 220 mm, matrix = 256 × 192 reconstructed as thirty 256 × 256 images.\nAll MR imaging was performed on the 3T system (Siemens, Erlangen, Germany). Parameters for Magnetization Prepared Rapid Acquisition Gradient Echo (MPRAGE) images were: repetition time (TR) = 2250 ms, echo time (TE) = 2.7 ms, inversion time (TI) = 900 ms, flip angle (FA) = 8°, slice thickness: 1.0 mm, field of view (FOV) = 200 mm, acquisition matrix = 256 × 192 × 124, reconstructed as 256 × 256 × 124. Axial fluid attenuation inversion recovery (FLAIR) images were acquired with TR/TE/TI 9000/99/2500 ms; FA 130°, slice thickness: 3.3 mm, field of view (FOV) 220 mm, matrix = 256 × 192 reconstructed as thirty 256 × 256 images.\nMRI processing White matter lesions were segmented on FLAIR MRI with freely available image analysis software FireVoxel (https://firevoxel.org/) using a previously validated workflow [28]. FLAIR images were first corrected for signal nonuniformity using the N3 algorithm. A 3D mask M was then created over the entire brain parenchyma, that is, the gray and white matter, with cerebrospinal fluid (CSF) excluded [29]. Voxels at the external surface S of M were then identified. Next was creation of set L, containing voxels in M with signal intensity 2.5 standard deviations above the mean FLAIR signal. Finally, L was filtered to remove (a) cortical voxels, those located within 3 mm of S, (b) small clusters of volume <12 mm3 (presumed to represent image noise), and (c) connected regions having >50% CSF border (presumed to represent the choroid plexus and the septum). The resulting mask represents our estimate of WML. Its volume VWML is presented as percentage of the total brain volume.\nGray matter (GM), white matter (WM) and CSF volumes were estimated using Statistical Parametric Mapping segmentation procedure (SPM, version 8, with ‘New-Segment’ extension) [30]. Total brain volume was the sum of GM and WM volumes. Intracranial volume (ICV) was the sum of all tissue types. Left and right hippocampal volumes were obtained with FreeSurfer version 6.0 [31], and averaged. They are referred to from now on as ‘mean hippocampal volume’. GM, WM and hippocampal volumes are presented as a fraction of the ICV. MRI data were acquired over the span of 9 years a 3T magnet that underwent hardware and software upgrades. We detected the inter-epoch variability in GM measurements. To avoid this fixed bias we z-scored GM values separately by epoch, recentered and rescaled them. Even though no other structural measure was affected, for consistency, we used rescaled values for hippocampal and WM volumes.\nWhite matter lesions were segmented on FLAIR MRI with freely available image analysis software FireVoxel (https://firevoxel.org/) using a previously validated workflow [28]. FLAIR images were first corrected for signal nonuniformity using the N3 algorithm. A 3D mask M was then created over the entire brain parenchyma, that is, the gray and white matter, with cerebrospinal fluid (CSF) excluded [29]. Voxels at the external surface S of M were then identified. Next was creation of set L, containing voxels in M with signal intensity 2.5 standard deviations above the mean FLAIR signal. Finally, L was filtered to remove (a) cortical voxels, those located within 3 mm of S, (b) small clusters of volume <12 mm3 (presumed to represent image noise), and (c) connected regions having >50% CSF border (presumed to represent the choroid plexus and the septum). The resulting mask represents our estimate of WML. Its volume VWML is presented as percentage of the total brain volume.\nGray matter (GM), white matter (WM) and CSF volumes were estimated using Statistical Parametric Mapping segmentation procedure (SPM, version 8, with ‘New-Segment’ extension) [30]. Total brain volume was the sum of GM and WM volumes. Intracranial volume (ICV) was the sum of all tissue types. Left and right hippocampal volumes were obtained with FreeSurfer version 6.0 [31], and averaged. They are referred to from now on as ‘mean hippocampal volume’. GM, WM and hippocampal volumes are presented as a fraction of the ICV. MRI data were acquired over the span of 9 years a 3T magnet that underwent hardware and software upgrades. We detected the inter-epoch variability in GM measurements. To avoid this fixed bias we z-scored GM values separately by epoch, recentered and rescaled them. Even though no other structural measure was affected, for consistency, we used rescaled values for hippocampal and WM volumes.\nStatistics Categorical variables were compared with χ2 tests. T-test, Mann–Whitney U-test, and analysis of covariance (ANCOVA) were used to compare group means for continuous variables, depending on the data distribution and the need to adjust for age and sex. Normality was tested with Shapiro–Wilk test.\nRelationships between BP, WML volume and brain volumes (hippocampal, GM, WM) at baseline were examined with linear regression. Volumes were dependent variables; age, sex, body mass index, smoking, diabetes, systolic blood pressure and diastolic blood pressure were independent predictors. These relationships were tested in participants with and without hypertension separately. We present here F-statistics for the regression models, as well unstandardized (B) and standardized (β) coefficients for individual predictors. Again, we hypothesized that in the HTN group an optimal BP may exist [21], which minimizes WML volume and maximizes brain volumes. Thus, as before, we tested for the existence of the peak, by introducing both the linear BP and quadratic (BP [2]) terms into the regression models.\nAfter a quadratic relationship between the volume and BP was confirmed, the critical BP at which volume reaches its maximum or minimum value was determined as:\nTo examine whether BP, WML and brain volumes changed over time, we used mixed models for repeated measures (MMRM), where BP or volume was a dependent variable and time, group (HTN vs. NTN), and group × time interaction were predictors. For models with WML or brain volumes age as added as a covariate. Both intercept and slope were treated as random.\nTo test whether baseline BP predicted change in WML and brain volumes, we created volume rates of change by regressing volume on time for each participant and tested whether baseline SBP and DBP was associated with volume slope. Baseline volumes, BMI, age, sex, smoking and diabetes status were initially included in the models, and retained only when significant. The relationships were tested in the normo- and hypertensive participants separately. For all the analyses, the most parsimonious model was chosen, defined as the model that included only significant or necessary (main effects when interaction was present) terms.\nTo test whether change in BP (independent variable) was related to change in volume (dependent) we used MMRM. WML or brain volumes were a dependent variable and BP (SBP or DBP), group (HTN vs. NTN), and group × BP interaction were predictors. Both intercept and slope were treated as random. SBP and DBP were predictors in separate models.\nWe performed supplementary analyses using pulse pressure (PP) as a predictor in all the models.\nWML volumes were log transformed. We checked the linear models for violations of models assumptions. For all the analyses, the most parsimonious model was chosen, defined as the model that included only significant or necessary (main effects when interaction was present) terms. Statistical significance was defined as a P-value <0.05. SPSS (version 25, SPSS, Inc., Chicago, Illinois, USA) software was used for all analyses.\nCategorical variables were compared with χ2 tests. T-test, Mann–Whitney U-test, and analysis of covariance (ANCOVA) were used to compare group means for continuous variables, depending on the data distribution and the need to adjust for age and sex. Normality was tested with Shapiro–Wilk test.\nRelationships between BP, WML volume and brain volumes (hippocampal, GM, WM) at baseline were examined with linear regression. Volumes were dependent variables; age, sex, body mass index, smoking, diabetes, systolic blood pressure and diastolic blood pressure were independent predictors. These relationships were tested in participants with and without hypertension separately. We present here F-statistics for the regression models, as well unstandardized (B) and standardized (β) coefficients for individual predictors. Again, we hypothesized that in the HTN group an optimal BP may exist [21], which minimizes WML volume and maximizes brain volumes. Thus, as before, we tested for the existence of the peak, by introducing both the linear BP and quadratic (BP [2]) terms into the regression models.\nAfter a quadratic relationship between the volume and BP was confirmed, the critical BP at which volume reaches its maximum or minimum value was determined as:\nTo examine whether BP, WML and brain volumes changed over time, we used mixed models for repeated measures (MMRM), where BP or volume was a dependent variable and time, group (HTN vs. NTN), and group × time interaction were predictors. For models with WML or brain volumes age as added as a covariate. Both intercept and slope were treated as random.\nTo test whether baseline BP predicted change in WML and brain volumes, we created volume rates of change by regressing volume on time for each participant and tested whether baseline SBP and DBP was associated with volume slope. Baseline volumes, BMI, age, sex, smoking and diabetes status were initially included in the models, and retained only when significant. The relationships were tested in the normo- and hypertensive participants separately. For all the analyses, the most parsimonious model was chosen, defined as the model that included only significant or necessary (main effects when interaction was present) terms.\nTo test whether change in BP (independent variable) was related to change in volume (dependent) we used MMRM. WML or brain volumes were a dependent variable and BP (SBP or DBP), group (HTN vs. NTN), and group × BP interaction were predictors. Both intercept and slope were treated as random. SBP and DBP were predictors in separate models.\nWe performed supplementary analyses using pulse pressure (PP) as a predictor in all the models.\nWML volumes were log transformed. We checked the linear models for violations of models assumptions. For all the analyses, the most parsimonious model was chosen, defined as the model that included only significant or necessary (main effects when interaction was present) terms. Statistical significance was defined as a P-value <0.05. SPSS (version 25, SPSS, Inc., Chicago, Illinois, USA) software was used for all analyses."},"results_title":{"kind":"string","value":"RESULTS"},"results_text":{"kind":"string","value":"Our group of 376 participants consisted of HTN (n = 207) and NTN (n = 169) individuals. The characteristics of the groups are given in Table 1. Two hundred and twenty-eight (60.6%) were women (age, 68.5 ± 7.4 years (mean ± standard deviation); education, 16.7 ± 2.3 years); and 148 (39.4%) men (age, 70.7 ± 6.9 years; education, 17.0 ± 2.4 years). The majority of the participants were Caucasian (86%), 10% African American, 4% other races.\nBaseline characteristics of the study group (n = 376)\nData is presented as mean ± standard deviation unless otherwise indicated. P-values come from t-test (total cholesterol, LDL) or Mann–Whitney U-test (age, education, BMI, HDL, triglycerides, glucose). For categorical variables, χ2 was used.\nn = 371 (NTN = 204, HTN = 167)\nn = 369 (NTN = 205, HTN = 164)\nn = 363 (NTN = 200, HTN = 163)\nn = 371 (NTN = 206, HTN = 165)\nValues are presented as mean ± standard errors (SE), P-values from ANCOVA after accounting for age.\nHippocampal volume is a mean of left and right, values are presented as mean ± standard errors (SE), P-values from ANCOVA after accounting for age and gender. Since the residuals from GM model were not normally distributed, the values were reanalyzed using ranked ANCOVA. Results remained the same.\nICV, intracranial volume; HTN, hypertension; NTN, normotensive.\nIn the longitudinal group of 188 participants, 116 (61.7%) were women (age, 69.6 ± 7.5 years; education, 16.7 ± 2.1 years) and 72 (38.3%) men (age, 71.4 ± 6.1 years; education, 17.2 ± 2.3 years). The mean time of follow-up was 2.3 ± 0.70 years. The longitudinal group did not differ from the participants with only one examination in terms of sex, HTN prevalence, SBP, DBP, BMI, total cholesterol, LDL and HDL cholesterol, triglycerides, education and WML volume. Prevalence of smoking and diabetes, as well as the number of participants with normotension, controlled, uncontrolled and untreated hypertension were also not different between the two groups. However, participants with longitudinal observation were slightly older (age, 70.3 ± 7.0 vs. 68.4 ± 7.4 years, P = 0.005) and had lower glucose levels (glucose, 82.5 ± 16.0 vs. 85.6 ± 14 mg/dl) than the individuals with only one exam. The clinical characteristics, as well as baseline brain and WML volumes of HTN and NTN participants in the longitudinal subgroup are given in Table S1. WML volumes by five baseline study group are shown in Tables S2 and S3.\nBaseline analyses BP and white matter lesion volumes In the HTN group, SBP and quadratic SBP term were predictors of WML volume at baseline (regression model included age, F3,168 = 14.4, P < 0.001) (Table 2, Fig. 1). The results were similar with log-transformed data (F3,168 = 13.1, P < 0.001) (Table 3). The critical SBP value calculated from equation [2] was x = 124 mmHg.\nLinear regression model predicting WML volume in the HTN group at baseline (raw data)\nCritical value (Eq. [2]): x = –(−0.07317/(2 × 0.0003)) = 122 mmHg.\nHTN, hypertension; WML, white matter lesion.\nQuadratic relationship between systolic blood pressure readings and white matter lesion measurements in hypertensive participants.\nLinear regression model predicting WML volume in the HTN group at baseline (log transformed data: log(WML volume))\nCritical value (Eq. [2]): x = –(−0.02741/(2 × 0.00011)) = 124.6 mmHg.\nHTN, hypertension; WML, white matter lesion.\nIn the NTN group, neither SBP nor DBP were related to WML.\nIn the HTN group, SBP and quadratic SBP term were predictors of WML volume at baseline (regression model included age, F3,168 = 14.4, P < 0.001) (Table 2, Fig. 1). The results were similar with log-transformed data (F3,168 = 13.1, P < 0.001) (Table 3). The critical SBP value calculated from equation [2] was x = 124 mmHg.\nLinear regression model predicting WML volume in the HTN group at baseline (raw data)\nCritical value (Eq. [2]): x = –(−0.07317/(2 × 0.0003)) = 122 mmHg.\nHTN, hypertension; WML, white matter lesion.\nQuadratic relationship between systolic blood pressure readings and white matter lesion measurements in hypertensive participants.\nLinear regression model predicting WML volume in the HTN group at baseline (log transformed data: log(WML volume))\nCritical value (Eq. [2]): x = –(−0.02741/(2 × 0.00011)) = 124.6 mmHg.\nHTN, hypertension; WML, white matter lesion.\nIn the NTN group, neither SBP nor DBP were related to WML.\nBP and brain volumes In HTN, the quadratic DBP term was a predictor of the mean hippocampal volume at baseline (the model included age and sex, F4,168 = 11.6, P < 0.001) (Table 4, Fig. 2). The critical DBP value calculated from equation [2] was x = 77 mmHg.\nModel predicting mean hippocampal volume in the HTN group at baseline\nCritical value (Eq. [2]): x = −(0.00399/(2 × (−0.000026))) = 77 mmHg.\nHTN, hypertension; WML, white matter lesion.\nRelationship between diastolic blood pressure readings and mean hippocampal brain volume measurements in hypertensive participants.\nGM was associated with SBP (β = −0.16, P = 0.02) and the model (F4,166 = 14.0, P < 0.001) included BMI, smoking and age.\nIn NTN, the mean hippocampal volume was inversely related to SBP (β = −0.17, P < 0.008), (regression model included age and sex, F3,206 = 22.4, P < 0.001). GM volume was inversely related to DBP (β = −0.15, P = 0.01), (the model included age and sex, F3,206 = 32.3, P < 0.001).\nIn HTN, the quadratic DBP term was a predictor of the mean hippocampal volume at baseline (the model included age and sex, F4,168 = 11.6, P < 0.001) (Table 4, Fig. 2). The critical DBP value calculated from equation [2] was x = 77 mmHg.\nModel predicting mean hippocampal volume in the HTN group at baseline\nCritical value (Eq. [2]): x = −(0.00399/(2 × (−0.000026))) = 77 mmHg.\nHTN, hypertension; WML, white matter lesion.\nRelationship between diastolic blood pressure readings and mean hippocampal brain volume measurements in hypertensive participants.\nGM was associated with SBP (β = −0.16, P = 0.02) and the model (F4,166 = 14.0, P < 0.001) included BMI, smoking and age.\nIn NTN, the mean hippocampal volume was inversely related to SBP (β = −0.17, P < 0.008), (regression model included age and sex, F3,206 = 22.4, P < 0.001). GM volume was inversely related to DBP (β = −0.15, P = 0.01), (the model included age and sex, F3,206 = 32.3, P < 0.001).\nBP and white matter lesion volumes In the HTN group, SBP and quadratic SBP term were predictors of WML volume at baseline (regression model included age, F3,168 = 14.4, P < 0.001) (Table 2, Fig. 1). The results were similar with log-transformed data (F3,168 = 13.1, P < 0.001) (Table 3). The critical SBP value calculated from equation [2] was x = 124 mmHg.\nLinear regression model predicting WML volume in the HTN group at baseline (raw data)\nCritical value (Eq. [2]): x = –(−0.07317/(2 × 0.0003)) = 122 mmHg.\nHTN, hypertension; WML, white matter lesion.\nQuadratic relationship between systolic blood pressure readings and white matter lesion measurements in hypertensive participants.\nLinear regression model predicting WML volume in the HTN group at baseline (log transformed data: log(WML volume))\nCritical value (Eq. [2]): x = –(−0.02741/(2 × 0.00011)) = 124.6 mmHg.\nHTN, hypertension; WML, white matter lesion.\nIn the NTN group, neither SBP nor DBP were related to WML.\nIn the HTN group, SBP and quadratic SBP term were predictors of WML volume at baseline (regression model included age, F3,168 = 14.4, P < 0.001) (Table 2, Fig. 1). The results were similar with log-transformed data (F3,168 = 13.1, P < 0.001) (Table 3). The critical SBP value calculated from equation [2] was x = 124 mmHg.\nLinear regression model predicting WML volume in the HTN group at baseline (raw data)\nCritical value (Eq. [2]): x = –(−0.07317/(2 × 0.0003)) = 122 mmHg.\nHTN, hypertension; WML, white matter lesion.\nQuadratic relationship between systolic blood pressure readings and white matter lesion measurements in hypertensive participants.\nLinear regression model predicting WML volume in the HTN group at baseline (log transformed data: log(WML volume))\nCritical value (Eq. [2]): x = –(−0.02741/(2 × 0.00011)) = 124.6 mmHg.\nHTN, hypertension; WML, white matter lesion.\nIn the NTN group, neither SBP nor DBP were related to WML.\nBP and brain volumes In HTN, the quadratic DBP term was a predictor of the mean hippocampal volume at baseline (the model included age and sex, F4,168 = 11.6, P < 0.001) (Table 4, Fig. 2). The critical DBP value calculated from equation [2] was x = 77 mmHg.\nModel predicting mean hippocampal volume in the HTN group at baseline\nCritical value (Eq. [2]): x = −(0.00399/(2 × (−0.000026))) = 77 mmHg.\nHTN, hypertension; WML, white matter lesion.\nRelationship between diastolic blood pressure readings and mean hippocampal brain volume measurements in hypertensive participants.\nGM was associated with SBP (β = −0.16, P = 0.02) and the model (F4,166 = 14.0, P < 0.001) included BMI, smoking and age.\nIn NTN, the mean hippocampal volume was inversely related to SBP (β = −0.17, P < 0.008), (regression model included age and sex, F3,206 = 22.4, P < 0.001). GM volume was inversely related to DBP (β = −0.15, P = 0.01), (the model included age and sex, F3,206 = 32.3, P < 0.001).\nIn HTN, the quadratic DBP term was a predictor of the mean hippocampal volume at baseline (the model included age and sex, F4,168 = 11.6, P < 0.001) (Table 4, Fig. 2). The critical DBP value calculated from equation [2] was x = 77 mmHg.\nModel predicting mean hippocampal volume in the HTN group at baseline\nCritical value (Eq. [2]): x = −(0.00399/(2 × (−0.000026))) = 77 mmHg.\nHTN, hypertension; WML, white matter lesion.\nRelationship between diastolic blood pressure readings and mean hippocampal brain volume measurements in hypertensive participants.\nGM was associated with SBP (β = −0.16, P = 0.02) and the model (F4,166 = 14.0, P < 0.001) included BMI, smoking and age.\nIn NTN, the mean hippocampal volume was inversely related to SBP (β = −0.17, P < 0.008), (regression model included age and sex, F3,206 = 22.4, P < 0.001). GM volume was inversely related to DBP (β = −0.15, P = 0.01), (the model included age and sex, F3,206 = 32.3, P < 0.001).\nLongitudinal analyses Changes in BP and volumes over time SBP did not change significantly in the HTN or NTN group, but the interaction term was significant (P = 0.01) indicating divergent pattern of SBP dynamics (the estimate = −1.6, P = 0.07 for HTN and 1.4, P = 0.07 for NTN). Similarly, DBP patterns were different (P = 0.03 for interaction term) with a lack of change in the HTN participants (estimate −0.6, P = 0.31) and a significant increase in the NTN group (the estimate = 1.1, P = 0.03). In both groups, WML volumes increased with time, (the estimate = 0.025, P < 0.001) for HTN and (0.028, P < 0.001) for NTN. In both groups, brain volumes decreased with time. An estimate for the fixed effect of time for the mean hippocampal volumes was (−0.0044, P < 0.001) for HTN and (−0.0038, P < 0.001) among NTN participants. For GM, they were (−0.48, P < 0.001) and (−0.45, P < 0.001); and finally, for WM (−0.30, P < 0.001) and (−0.22, P = 0.001), for the HTN and NTN groups, respectively. Group × time interaction terms were not significant for models with brain or WML volumes, indicating that rates of change did not differ between groups. Figure S2 presents BP and WML changes in both groups.\nSBP did not change significantly in the HTN or NTN group, but the interaction term was significant (P = 0.01) indicating divergent pattern of SBP dynamics (the estimate = −1.6, P = 0.07 for HTN and 1.4, P = 0.07 for NTN). Similarly, DBP patterns were different (P = 0.03 for interaction term) with a lack of change in the HTN participants (estimate −0.6, P = 0.31) and a significant increase in the NTN group (the estimate = 1.1, P = 0.03). In both groups, WML volumes increased with time, (the estimate = 0.025, P < 0.001) for HTN and (0.028, P < 0.001) for NTN. In both groups, brain volumes decreased with time. An estimate for the fixed effect of time for the mean hippocampal volumes was (−0.0044, P < 0.001) for HTN and (−0.0038, P < 0.001) among NTN participants. For GM, they were (−0.48, P < 0.001) and (−0.45, P < 0.001); and finally, for WM (−0.30, P < 0.001) and (−0.22, P = 0.001), for the HTN and NTN groups, respectively. Group × time interaction terms were not significant for models with brain or WML volumes, indicating that rates of change did not differ between groups. Figure S2 presents BP and WML changes in both groups.\nChanges in BP and volumes over time SBP did not change significantly in the HTN or NTN group, but the interaction term was significant (P = 0.01) indicating divergent pattern of SBP dynamics (the estimate = −1.6, P = 0.07 for HTN and 1.4, P = 0.07 for NTN). Similarly, DBP patterns were different (P = 0.03 for interaction term) with a lack of change in the HTN participants (estimate −0.6, P = 0.31) and a significant increase in the NTN group (the estimate = 1.1, P = 0.03). In both groups, WML volumes increased with time, (the estimate = 0.025, P < 0.001) for HTN and (0.028, P < 0.001) for NTN. In both groups, brain volumes decreased with time. An estimate for the fixed effect of time for the mean hippocampal volumes was (−0.0044, P < 0.001) for HTN and (−0.0038, P < 0.001) among NTN participants. For GM, they were (−0.48, P < 0.001) and (−0.45, P < 0.001); and finally, for WM (−0.30, P < 0.001) and (−0.22, P = 0.001), for the HTN and NTN groups, respectively. Group × time interaction terms were not significant for models with brain or WML volumes, indicating that rates of change did not differ between groups. Figure S2 presents BP and WML changes in both groups.\nSBP did not change significantly in the HTN or NTN group, but the interaction term was significant (P = 0.01) indicating divergent pattern of SBP dynamics (the estimate = −1.6, P = 0.07 for HTN and 1.4, P = 0.07 for NTN). Similarly, DBP patterns were different (P = 0.03 for interaction term) with a lack of change in the HTN participants (estimate −0.6, P = 0.31) and a significant increase in the NTN group (the estimate = 1.1, P = 0.03). In both groups, WML volumes increased with time, (the estimate = 0.025, P < 0.001) for HTN and (0.028, P < 0.001) for NTN. In both groups, brain volumes decreased with time. An estimate for the fixed effect of time for the mean hippocampal volumes was (−0.0044, P < 0.001) for HTN and (−0.0038, P < 0.001) among NTN participants. For GM, they were (−0.48, P < 0.001) and (−0.45, P < 0.001); and finally, for WM (−0.30, P < 0.001) and (−0.22, P = 0.001), for the HTN and NTN groups, respectively. Group × time interaction terms were not significant for models with brain or WML volumes, indicating that rates of change did not differ between groups. Figure S2 presents BP and WML changes in both groups.\nSecondary analyses Table S4 shows the stability of secondary group definition (untreated, uncontrolled and controlled HTN, Stage 1 HTN, and normotension). Table S5 presents the results from MMRM analyses of WML volumes and BP changes conducted with all five subgroups defined at baseline based on HTN control status. One can appreciate that WML volume increased significantly in untreated and controlled hypertension, as well as in normotensive participants.\nTable S6 and Figure S3 shows that participants who at baseline were classified as untreated (n = 19) or uncontrolled hypertension (n = 20) and reclassified at the last visit as good outcome (improved BP control) experience the same increase in WML volumes as participants with bad outcome (BP remained untreated or uncontrolled).\nBaseline BP and changes in white matter lesion and brain volumes In the hypertensive group both baseline SBP (β = 0.33, P = 0.007) and DBP (β = −0.30, P = 0.008) were related to WML changes over time (entire model F4,83 = 10.5, P < 0.001, included also baseline WML volumes and smoking). Baseline BP was not related to changes in hippocampal, GM, or WM volumes.\nIn normotensive participants baseline BP was not related to change in any examined volumes.\nIn the hypertensive group both baseline SBP (β = 0.33, P = 0.007) and DBP (β = −0.30, P = 0.008) were related to WML changes over time (entire model F4,83 = 10.5, P < 0.001, included also baseline WML volumes and smoking). Baseline BP was not related to changes in hippocampal, GM, or WM volumes.\nIn normotensive participants baseline BP was not related to change in any examined volumes.\nLongitudinal changes in BP and changes in white matter lesion and brain volumes In the HTN group, neither change in SBP nor DBP were associated with a change in WML, hippocampal, GM or WM volumes (Tables S7 and S8).\nIn the NTN group, both increase in SBP (an estimate for the fixed effect of SBP = 0.0023, P = 0.01) and DBP (an estimate for the fixed effect of DBP = 0.0035, P = 0.01) over time were related to the increase in WML volume. The significant interaction term indicated that these dynamics were meaningfully different from the HTN group. Moreover, in the normotensive group both increase in SBP (an estimate for the fixed effect of SBP = −0.0002, P = 0.03) and DBP (an estimate for the fixed effect of DBP = −0.0004, P = 0.01) over time were related to reduction in the mean hippocampal volume. However, only for SBP the interaction term was significant, indicating that these changes were different from HTN group.\nResults for pulse pressure are presented in the Supplement p. 8–9.\nIn the HTN group, neither change in SBP nor DBP were associated with a change in WML, hippocampal, GM or WM volumes (Tables S7 and S8).\nIn the NTN group, both increase in SBP (an estimate for the fixed effect of SBP = 0.0023, P = 0.01) and DBP (an estimate for the fixed effect of DBP = 0.0035, P = 0.01) over time were related to the increase in WML volume. The significant interaction term indicated that these dynamics were meaningfully different from the HTN group. Moreover, in the normotensive group both increase in SBP (an estimate for the fixed effect of SBP = −0.0002, P = 0.03) and DBP (an estimate for the fixed effect of DBP = −0.0004, P = 0.01) over time were related to reduction in the mean hippocampal volume. However, only for SBP the interaction term was significant, indicating that these changes were different from HTN group.\nResults for pulse pressure are presented in the Supplement p. 8–9.\nTable S4 shows the stability of secondary group definition (untreated, uncontrolled and controlled HTN, Stage 1 HTN, and normotension). Table S5 presents the results from MMRM analyses of WML volumes and BP changes conducted with all five subgroups defined at baseline based on HTN control status. One can appreciate that WML volume increased significantly in untreated and controlled hypertension, as well as in normotensive participants.\nTable S6 and Figure S3 shows that participants who at baseline were classified as untreated (n = 19) or uncontrolled hypertension (n = 20) and reclassified at the last visit as good outcome (improved BP control) experience the same increase in WML volumes as participants with bad outcome (BP remained untreated or uncontrolled).\nBaseline BP and changes in white matter lesion and brain volumes In the hypertensive group both baseline SBP (β = 0.33, P = 0.007) and DBP (β = −0.30, P = 0.008) were related to WML changes over time (entire model F4,83 = 10.5, P < 0.001, included also baseline WML volumes and smoking). Baseline BP was not related to changes in hippocampal, GM, or WM volumes.\nIn normotensive participants baseline BP was not related to change in any examined volumes.\nIn the hypertensive group both baseline SBP (β = 0.33, P = 0.007) and DBP (β = −0.30, P = 0.008) were related to WML changes over time (entire model F4,83 = 10.5, P < 0.001, included also baseline WML volumes and smoking). Baseline BP was not related to changes in hippocampal, GM, or WM volumes.\nIn normotensive participants baseline BP was not related to change in any examined volumes.\nLongitudinal changes in BP and changes in white matter lesion and brain volumes In the HTN group, neither change in SBP nor DBP were associated with a change in WML, hippocampal, GM or WM volumes (Tables S7 and S8).\nIn the NTN group, both increase in SBP (an estimate for the fixed effect of SBP = 0.0023, P = 0.01) and DBP (an estimate for the fixed effect of DBP = 0.0035, P = 0.01) over time were related to the increase in WML volume. The significant interaction term indicated that these dynamics were meaningfully different from the HTN group. Moreover, in the normotensive group both increase in SBP (an estimate for the fixed effect of SBP = −0.0002, P = 0.03) and DBP (an estimate for the fixed effect of DBP = −0.0004, P = 0.01) over time were related to reduction in the mean hippocampal volume. However, only for SBP the interaction term was significant, indicating that these changes were different from HTN group.\nResults for pulse pressure are presented in the Supplement p. 8–9.\nIn the HTN group, neither change in SBP nor DBP were associated with a change in WML, hippocampal, GM or WM volumes (Tables S7 and S8).\nIn the NTN group, both increase in SBP (an estimate for the fixed effect of SBP = 0.0023, P = 0.01) and DBP (an estimate for the fixed effect of DBP = 0.0035, P = 0.01) over time were related to the increase in WML volume. The significant interaction term indicated that these dynamics were meaningfully different from the HTN group. Moreover, in the normotensive group both increase in SBP (an estimate for the fixed effect of SBP = −0.0002, P = 0.03) and DBP (an estimate for the fixed effect of DBP = −0.0004, P = 0.01) over time were related to reduction in the mean hippocampal volume. However, only for SBP the interaction term was significant, indicating that these changes were different from HTN group.\nResults for pulse pressure are presented in the Supplement p. 8–9."},"conclusions_title":{"kind":"null"},"conclusions_text":{"kind":"null"},"other_sections_titles":{"kind":"list like","value":["Clinical assessment","Study groups","MR imaging","MRI processing","Statistics","Baseline analyses","BP and white matter lesion volumes","BP and brain volumes","Longitudinal analyses","Changes in BP and volumes over time","Secondary analyses","Baseline BP and changes in white matter lesion and brain volumes","Longitudinal changes in BP and changes in white matter lesion and brain volumes","ACKNOWLEDGEMENTS"],"string":"[\n \"Clinical assessment\",\n \"Study groups\",\n \"MR imaging\",\n \"MRI processing\",\n \"Statistics\",\n \"Baseline analyses\",\n \"BP and white matter lesion volumes\",\n \"BP and brain volumes\",\n \"Longitudinal analyses\",\n \"Changes in BP and volumes over time\",\n \"Secondary analyses\",\n \"Baseline BP and changes in white matter lesion and brain volumes\",\n \"Longitudinal changes in BP and changes in white matter lesion and brain volumes\",\n \"ACKNOWLEDGEMENTS\"\n]"},"other_sections_texts":{"kind":"list like","value":["All participants underwent medical, psychiatric, and neurological assessments, blood tests, electrocardiogram (ECG), and magnetic resonance imaging (MRI) evaluation examinations at baseline, and the longitudinal group also at follow-up. Participants exhibiting dementia, based on a physician-administered interview using the Brief Cognitive Rating Scale, rating on the Global Deterioration Scale [23], and Clinical Dementia Rating were excluded [24]. Fasting blood was tested for complete blood count, liver function, as well as metabolic and lipid panel.\nBlood pressure was taken once in a sitting position, after 5 min of rest. It was measured on the left upper arm using a manual sphygmomanometer. Body mass index (BMI) was calculated as weight/height (kg/m) [2].\nMedication: We recoded the use of antihypertensive medications (angiotensin receptor blockers (ARBs), angiotensin converting enzyme inhibitors (ACE), beta-blockers, diuretics, and calcium channel blockers), statins and glucose lowering drugs.\nDiabetes mellitus was defined as fasting glucose level of 126 mg/dl and higher and/or current use of glucose lowering medication [25]. Smoking status was defined as positive if participants was a current smoker or smoked within last 10 years.","For consistency with our previous study hypertension was defined as current antihypertensive treatment or BP ≥140/90 mmHg [26]. One hundred and sixty-nine participants were classified as hypertensive; 136 participants were taking antihypertensive medication and 42 were not taking any antihypertensive medication with high BP recorded during their in-office visit. Normotension (NTN) was defined as BP <140/90 mmHg and no antihypertensive treatment.\nFor descriptive purpose and secondary analyses, we described hypertensive participants as untreated: no antihypertensive treatment and BP ≥140/90 mmHg; controlled: antihypertensive treatment and BP <140/90 mmHg, uncontrolled: antihypertensive treatment and BP ≥140/90 mmHg. We also divided our normotensive group into Stage 1 hypertension based on new guidelines: [27] no treatment and BP between 130–139/80–89 mmHg, and normotensive BP <130/80 mmHg.","All MR imaging was performed on the 3T system (Siemens, Erlangen, Germany). Parameters for Magnetization Prepared Rapid Acquisition Gradient Echo (MPRAGE) images were: repetition time (TR) = 2250 ms, echo time (TE) = 2.7 ms, inversion time (TI) = 900 ms, flip angle (FA) = 8°, slice thickness: 1.0 mm, field of view (FOV) = 200 mm, acquisition matrix = 256 × 192 × 124, reconstructed as 256 × 256 × 124. Axial fluid attenuation inversion recovery (FLAIR) images were acquired with TR/TE/TI 9000/99/2500 ms; FA 130°, slice thickness: 3.3 mm, field of view (FOV) 220 mm, matrix = 256 × 192 reconstructed as thirty 256 × 256 images.","White matter lesions were segmented on FLAIR MRI with freely available image analysis software FireVoxel (https://firevoxel.org/) using a previously validated workflow [28]. FLAIR images were first corrected for signal nonuniformity using the N3 algorithm. A 3D mask M was then created over the entire brain parenchyma, that is, the gray and white matter, with cerebrospinal fluid (CSF) excluded [29]. Voxels at the external surface S of M were then identified. Next was creation of set L, containing voxels in M with signal intensity 2.5 standard deviations above the mean FLAIR signal. Finally, L was filtered to remove (a) cortical voxels, those located within 3 mm of S, (b) small clusters of volume <12 mm3 (presumed to represent image noise), and (c) connected regions having >50% CSF border (presumed to represent the choroid plexus and the septum). The resulting mask represents our estimate of WML. Its volume VWML is presented as percentage of the total brain volume.\nGray matter (GM), white matter (WM) and CSF volumes were estimated using Statistical Parametric Mapping segmentation procedure (SPM, version 8, with ‘New-Segment’ extension) [30]. Total brain volume was the sum of GM and WM volumes. Intracranial volume (ICV) was the sum of all tissue types. Left and right hippocampal volumes were obtained with FreeSurfer version 6.0 [31], and averaged. They are referred to from now on as ‘mean hippocampal volume’. GM, WM and hippocampal volumes are presented as a fraction of the ICV. MRI data were acquired over the span of 9 years a 3T magnet that underwent hardware and software upgrades. We detected the inter-epoch variability in GM measurements. To avoid this fixed bias we z-scored GM values separately by epoch, recentered and rescaled them. Even though no other structural measure was affected, for consistency, we used rescaled values for hippocampal and WM volumes.","Categorical variables were compared with χ2 tests. T-test, Mann–Whitney U-test, and analysis of covariance (ANCOVA) were used to compare group means for continuous variables, depending on the data distribution and the need to adjust for age and sex. Normality was tested with Shapiro–Wilk test.\nRelationships between BP, WML volume and brain volumes (hippocampal, GM, WM) at baseline were examined with linear regression. Volumes were dependent variables; age, sex, body mass index, smoking, diabetes, systolic blood pressure and diastolic blood pressure were independent predictors. These relationships were tested in participants with and without hypertension separately. We present here F-statistics for the regression models, as well unstandardized (B) and standardized (β) coefficients for individual predictors. Again, we hypothesized that in the HTN group an optimal BP may exist [21], which minimizes WML volume and maximizes brain volumes. Thus, as before, we tested for the existence of the peak, by introducing both the linear BP and quadratic (BP [2]) terms into the regression models.\nAfter a quadratic relationship between the volume and BP was confirmed, the critical BP at which volume reaches its maximum or minimum value was determined as:\nTo examine whether BP, WML and brain volumes changed over time, we used mixed models for repeated measures (MMRM), where BP or volume was a dependent variable and time, group (HTN vs. NTN), and group × time interaction were predictors. For models with WML or brain volumes age as added as a covariate. Both intercept and slope were treated as random.\nTo test whether baseline BP predicted change in WML and brain volumes, we created volume rates of change by regressing volume on time for each participant and tested whether baseline SBP and DBP was associated with volume slope. Baseline volumes, BMI, age, sex, smoking and diabetes status were initially included in the models, and retained only when significant. The relationships were tested in the normo- and hypertensive participants separately. For all the analyses, the most parsimonious model was chosen, defined as the model that included only significant or necessary (main effects when interaction was present) terms.\nTo test whether change in BP (independent variable) was related to change in volume (dependent) we used MMRM. WML or brain volumes were a dependent variable and BP (SBP or DBP), group (HTN vs. NTN), and group × BP interaction were predictors. Both intercept and slope were treated as random. SBP and DBP were predictors in separate models.\nWe performed supplementary analyses using pulse pressure (PP) as a predictor in all the models.\nWML volumes were log transformed. We checked the linear models for violations of models assumptions. For all the analyses, the most parsimonious model was chosen, defined as the model that included only significant or necessary (main effects when interaction was present) terms. Statistical significance was defined as a P-value <0.05. SPSS (version 25, SPSS, Inc., Chicago, Illinois, USA) software was used for all analyses.","BP and white matter lesion volumes In the HTN group, SBP and quadratic SBP term were predictors of WML volume at baseline (regression model included age, F3,168 = 14.4, P < 0.001) (Table 2, Fig. 1). The results were similar with log-transformed data (F3,168 = 13.1, P < 0.001) (Table 3). The critical SBP value calculated from equation [2] was x = 124 mmHg.\nLinear regression model predicting WML volume in the HTN group at baseline (raw data)\nCritical value (Eq. [2]): x = –(−0.07317/(2 × 0.0003)) = 122 mmHg.\nHTN, hypertension; WML, white matter lesion.\nQuadratic relationship between systolic blood pressure readings and white matter lesion measurements in hypertensive participants.\nLinear regression model predicting WML volume in the HTN group at baseline (log transformed data: log(WML volume))\nCritical value (Eq. [2]): x = –(−0.02741/(2 × 0.00011)) = 124.6 mmHg.\nHTN, hypertension; WML, white matter lesion.\nIn the NTN group, neither SBP nor DBP were related to WML.\nIn the HTN group, SBP and quadratic SBP term were predictors of WML volume at baseline (regression model included age, F3,168 = 14.4, P < 0.001) (Table 2, Fig. 1). The results were similar with log-transformed data (F3,168 = 13.1, P < 0.001) (Table 3). The critical SBP value calculated from equation [2] was x = 124 mmHg.\nLinear regression model predicting WML volume in the HTN group at baseline (raw data)\nCritical value (Eq. [2]): x = –(−0.07317/(2 × 0.0003)) = 122 mmHg.\nHTN, hypertension; WML, white matter lesion.\nQuadratic relationship between systolic blood pressure readings and white matter lesion measurements in hypertensive participants.\nLinear regression model predicting WML volume in the HTN group at baseline (log transformed data: log(WML volume))\nCritical value (Eq. [2]): x = –(−0.02741/(2 × 0.00011)) = 124.6 mmHg.\nHTN, hypertension; WML, white matter lesion.\nIn the NTN group, neither SBP nor DBP were related to WML.\nBP and brain volumes In HTN, the quadratic DBP term was a predictor of the mean hippocampal volume at baseline (the model included age and sex, F4,168 = 11.6, P < 0.001) (Table 4, Fig. 2). The critical DBP value calculated from equation [2] was x = 77 mmHg.\nModel predicting mean hippocampal volume in the HTN group at baseline\nCritical value (Eq. [2]): x = −(0.00399/(2 × (−0.000026))) = 77 mmHg.\nHTN, hypertension; WML, white matter lesion.\nRelationship between diastolic blood pressure readings and mean hippocampal brain volume measurements in hypertensive participants.\nGM was associated with SBP (β = −0.16, P = 0.02) and the model (F4,166 = 14.0, P < 0.001) included BMI, smoking and age.\nIn NTN, the mean hippocampal volume was inversely related to SBP (β = −0.17, P < 0.008), (regression model included age and sex, F3,206 = 22.4, P < 0.001). GM volume was inversely related to DBP (β = −0.15, P = 0.01), (the model included age and sex, F3,206 = 32.3, P < 0.001).\nIn HTN, the quadratic DBP term was a predictor of the mean hippocampal volume at baseline (the model included age and sex, F4,168 = 11.6, P < 0.001) (Table 4, Fig. 2). The critical DBP value calculated from equation [2] was x = 77 mmHg.\nModel predicting mean hippocampal volume in the HTN group at baseline\nCritical value (Eq. [2]): x = −(0.00399/(2 × (−0.000026))) = 77 mmHg.\nHTN, hypertension; WML, white matter lesion.\nRelationship between diastolic blood pressure readings and mean hippocampal brain volume measurements in hypertensive participants.\nGM was associated with SBP (β = −0.16, P = 0.02) and the model (F4,166 = 14.0, P < 0.001) included BMI, smoking and age.\nIn NTN, the mean hippocampal volume was inversely related to SBP (β = −0.17, P < 0.008), (regression model included age and sex, F3,206 = 22.4, P < 0.001). GM volume was inversely related to DBP (β = −0.15, P = 0.01), (the model included age and sex, F3,206 = 32.3, P < 0.001).","In the HTN group, SBP and quadratic SBP term were predictors of WML volume at baseline (regression model included age, F3,168 = 14.4, P < 0.001) (Table 2, Fig. 1). The results were similar with log-transformed data (F3,168 = 13.1, P < 0.001) (Table 3). The critical SBP value calculated from equation [2] was x = 124 mmHg.\nLinear regression model predicting WML volume in the HTN group at baseline (raw data)\nCritical value (Eq. [2]): x = –(−0.07317/(2 × 0.0003)) = 122 mmHg.\nHTN, hypertension; WML, white matter lesion.\nQuadratic relationship between systolic blood pressure readings and white matter lesion measurements in hypertensive participants.\nLinear regression model predicting WML volume in the HTN group at baseline (log transformed data: log(WML volume))\nCritical value (Eq. [2]): x = –(−0.02741/(2 × 0.00011)) = 124.6 mmHg.\nHTN, hypertension; WML, white matter lesion.\nIn the NTN group, neither SBP nor DBP were related to WML.","In HTN, the quadratic DBP term was a predictor of the mean hippocampal volume at baseline (the model included age and sex, F4,168 = 11.6, P < 0.001) (Table 4, Fig. 2). The critical DBP value calculated from equation [2] was x = 77 mmHg.\nModel predicting mean hippocampal volume in the HTN group at baseline\nCritical value (Eq. [2]): x = −(0.00399/(2 × (−0.000026))) = 77 mmHg.\nHTN, hypertension; WML, white matter lesion.\nRelationship between diastolic blood pressure readings and mean hippocampal brain volume measurements in hypertensive participants.\nGM was associated with SBP (β = −0.16, P = 0.02) and the model (F4,166 = 14.0, P < 0.001) included BMI, smoking and age.\nIn NTN, the mean hippocampal volume was inversely related to SBP (β = −0.17, P < 0.008), (regression model included age and sex, F3,206 = 22.4, P < 0.001). GM volume was inversely related to DBP (β = −0.15, P = 0.01), (the model included age and sex, F3,206 = 32.3, P < 0.001).","Changes in BP and volumes over time SBP did not change significantly in the HTN or NTN group, but the interaction term was significant (P = 0.01) indicating divergent pattern of SBP dynamics (the estimate = −1.6, P = 0.07 for HTN and 1.4, P = 0.07 for NTN). Similarly, DBP patterns were different (P = 0.03 for interaction term) with a lack of change in the HTN participants (estimate −0.6, P = 0.31) and a significant increase in the NTN group (the estimate = 1.1, P = 0.03). In both groups, WML volumes increased with time, (the estimate = 0.025, P < 0.001) for HTN and (0.028, P < 0.001) for NTN. In both groups, brain volumes decreased with time. An estimate for the fixed effect of time for the mean hippocampal volumes was (−0.0044, P < 0.001) for HTN and (−0.0038, P < 0.001) among NTN participants. For GM, they were (−0.48, P < 0.001) and (−0.45, P < 0.001); and finally, for WM (−0.30, P < 0.001) and (−0.22, P = 0.001), for the HTN and NTN groups, respectively. Group × time interaction terms were not significant for models with brain or WML volumes, indicating that rates of change did not differ between groups. Figure S2 presents BP and WML changes in both groups.\nSBP did not change significantly in the HTN or NTN group, but the interaction term was significant (P = 0.01) indicating divergent pattern of SBP dynamics (the estimate = −1.6, P = 0.07 for HTN and 1.4, P = 0.07 for NTN). Similarly, DBP patterns were different (P = 0.03 for interaction term) with a lack of change in the HTN participants (estimate −0.6, P = 0.31) and a significant increase in the NTN group (the estimate = 1.1, P = 0.03). In both groups, WML volumes increased with time, (the estimate = 0.025, P < 0.001) for HTN and (0.028, P < 0.001) for NTN. In both groups, brain volumes decreased with time. An estimate for the fixed effect of time for the mean hippocampal volumes was (−0.0044, P < 0.001) for HTN and (−0.0038, P < 0.001) among NTN participants. For GM, they were (−0.48, P < 0.001) and (−0.45, P < 0.001); and finally, for WM (−0.30, P < 0.001) and (−0.22, P = 0.001), for the HTN and NTN groups, respectively. Group × time interaction terms were not significant for models with brain or WML volumes, indicating that rates of change did not differ between groups. Figure S2 presents BP and WML changes in both groups.","SBP did not change significantly in the HTN or NTN group, but the interaction term was significant (P = 0.01) indicating divergent pattern of SBP dynamics (the estimate = −1.6, P = 0.07 for HTN and 1.4, P = 0.07 for NTN). Similarly, DBP patterns were different (P = 0.03 for interaction term) with a lack of change in the HTN participants (estimate −0.6, P = 0.31) and a significant increase in the NTN group (the estimate = 1.1, P = 0.03). In both groups, WML volumes increased with time, (the estimate = 0.025, P < 0.001) for HTN and (0.028, P < 0.001) for NTN. In both groups, brain volumes decreased with time. An estimate for the fixed effect of time for the mean hippocampal volumes was (−0.0044, P < 0.001) for HTN and (−0.0038, P < 0.001) among NTN participants. For GM, they were (−0.48, P < 0.001) and (−0.45, P < 0.001); and finally, for WM (−0.30, P < 0.001) and (−0.22, P = 0.001), for the HTN and NTN groups, respectively. Group × time interaction terms were not significant for models with brain or WML volumes, indicating that rates of change did not differ between groups. Figure S2 presents BP and WML changes in both groups.","Table S4 shows the stability of secondary group definition (untreated, uncontrolled and controlled HTN, Stage 1 HTN, and normotension). Table S5 presents the results from MMRM analyses of WML volumes and BP changes conducted with all five subgroups defined at baseline based on HTN control status. One can appreciate that WML volume increased significantly in untreated and controlled hypertension, as well as in normotensive participants.\nTable S6 and Figure S3 shows that participants who at baseline were classified as untreated (n = 19) or uncontrolled hypertension (n = 20) and reclassified at the last visit as good outcome (improved BP control) experience the same increase in WML volumes as participants with bad outcome (BP remained untreated or uncontrolled).\nBaseline BP and changes in white matter lesion and brain volumes In the hypertensive group both baseline SBP (β = 0.33, P = 0.007) and DBP (β = −0.30, P = 0.008) were related to WML changes over time (entire model F4,83 = 10.5, P < 0.001, included also baseline WML volumes and smoking). Baseline BP was not related to changes in hippocampal, GM, or WM volumes.\nIn normotensive participants baseline BP was not related to change in any examined volumes.\nIn the hypertensive group both baseline SBP (β = 0.33, P = 0.007) and DBP (β = −0.30, P = 0.008) were related to WML changes over time (entire model F4,83 = 10.5, P < 0.001, included also baseline WML volumes and smoking). Baseline BP was not related to changes in hippocampal, GM, or WM volumes.\nIn normotensive participants baseline BP was not related to change in any examined volumes.\nLongitudinal changes in BP and changes in white matter lesion and brain volumes In the HTN group, neither change in SBP nor DBP were associated with a change in WML, hippocampal, GM or WM volumes (Tables S7 and S8).\nIn the NTN group, both increase in SBP (an estimate for the fixed effect of SBP = 0.0023, P = 0.01) and DBP (an estimate for the fixed effect of DBP = 0.0035, P = 0.01) over time were related to the increase in WML volume. The significant interaction term indicated that these dynamics were meaningfully different from the HTN group. Moreover, in the normotensive group both increase in SBP (an estimate for the fixed effect of SBP = −0.0002, P = 0.03) and DBP (an estimate for the fixed effect of DBP = −0.0004, P = 0.01) over time were related to reduction in the mean hippocampal volume. However, only for SBP the interaction term was significant, indicating that these changes were different from HTN group.\nResults for pulse pressure are presented in the Supplement p. 8–9.\nIn the HTN group, neither change in SBP nor DBP were associated with a change in WML, hippocampal, GM or WM volumes (Tables S7 and S8).\nIn the NTN group, both increase in SBP (an estimate for the fixed effect of SBP = 0.0023, P = 0.01) and DBP (an estimate for the fixed effect of DBP = 0.0035, P = 0.01) over time were related to the increase in WML volume. The significant interaction term indicated that these dynamics were meaningfully different from the HTN group. Moreover, in the normotensive group both increase in SBP (an estimate for the fixed effect of SBP = −0.0002, P = 0.03) and DBP (an estimate for the fixed effect of DBP = −0.0004, P = 0.01) over time were related to reduction in the mean hippocampal volume. However, only for SBP the interaction term was significant, indicating that these changes were different from HTN group.\nResults for pulse pressure are presented in the Supplement p. 8–9.","In the hypertensive group both baseline SBP (β = 0.33, P = 0.007) and DBP (β = −0.30, P = 0.008) were related to WML changes over time (entire model F4,83 = 10.5, P < 0.001, included also baseline WML volumes and smoking). Baseline BP was not related to changes in hippocampal, GM, or WM volumes.\nIn normotensive participants baseline BP was not related to change in any examined volumes.","In the HTN group, neither change in SBP nor DBP were associated with a change in WML, hippocampal, GM or WM volumes (Tables S7 and S8).\nIn the NTN group, both increase in SBP (an estimate for the fixed effect of SBP = 0.0023, P = 0.01) and DBP (an estimate for the fixed effect of DBP = 0.0035, P = 0.01) over time were related to the increase in WML volume. The significant interaction term indicated that these dynamics were meaningfully different from the HTN group. Moreover, in the normotensive group both increase in SBP (an estimate for the fixed effect of SBP = −0.0002, P = 0.03) and DBP (an estimate for the fixed effect of DBP = −0.0004, P = 0.01) over time were related to reduction in the mean hippocampal volume. However, only for SBP the interaction term was significant, indicating that these changes were different from HTN group.\nResults for pulse pressure are presented in the Supplement p. 8–9.","A special thanks to Ke Xi for her help with the statistical modeling as well as assistance in the production of regression plots for this manuscript.\nSources of funding: Study funding comes from NIH grants HL111724, NS104364, AG022374, AG12101, AG08051, and Alzheimer's Association NIRG-09-132490.\nDisclosures: None\nConflicts of interest There are no conflicts of interest.\nThere are no conflicts of interest."],"string":"[\n \"All participants underwent medical, psychiatric, and neurological assessments, blood tests, electrocardiogram (ECG), and magnetic resonance imaging (MRI) evaluation examinations at baseline, and the longitudinal group also at follow-up. Participants exhibiting dementia, based on a physician-administered interview using the Brief Cognitive Rating Scale, rating on the Global Deterioration Scale [23], and Clinical Dementia Rating were excluded [24]. Fasting blood was tested for complete blood count, liver function, as well as metabolic and lipid panel.\\nBlood pressure was taken once in a sitting position, after 5 min of rest. It was measured on the left upper arm using a manual sphygmomanometer. Body mass index (BMI) was calculated as weight/height (kg/m) [2].\\nMedication: We recoded the use of antihypertensive medications (angiotensin receptor blockers (ARBs), angiotensin converting enzyme inhibitors (ACE), beta-blockers, diuretics, and calcium channel blockers), statins and glucose lowering drugs.\\nDiabetes mellitus was defined as fasting glucose level of 126 mg/dl and higher and/or current use of glucose lowering medication [25]. Smoking status was defined as positive if participants was a current smoker or smoked within last 10 years.\",\n \"For consistency with our previous study hypertension was defined as current antihypertensive treatment or BP ≥140/90 mmHg [26]. One hundred and sixty-nine participants were classified as hypertensive; 136 participants were taking antihypertensive medication and 42 were not taking any antihypertensive medication with high BP recorded during their in-office visit. Normotension (NTN) was defined as BP <140/90 mmHg and no antihypertensive treatment.\\nFor descriptive purpose and secondary analyses, we described hypertensive participants as untreated: no antihypertensive treatment and BP ≥140/90 mmHg; controlled: antihypertensive treatment and BP <140/90 mmHg, uncontrolled: antihypertensive treatment and BP ≥140/90 mmHg. We also divided our normotensive group into Stage 1 hypertension based on new guidelines: [27] no treatment and BP between 130–139/80–89 mmHg, and normotensive BP <130/80 mmHg.\",\n \"All MR imaging was performed on the 3T system (Siemens, Erlangen, Germany). Parameters for Magnetization Prepared Rapid Acquisition Gradient Echo (MPRAGE) images were: repetition time (TR) = 2250 ms, echo time (TE) = 2.7 ms, inversion time (TI) = 900 ms, flip angle (FA) = 8°, slice thickness: 1.0 mm, field of view (FOV) = 200 mm, acquisition matrix = 256 × 192 × 124, reconstructed as 256 × 256 × 124. Axial fluid attenuation inversion recovery (FLAIR) images were acquired with TR/TE/TI 9000/99/2500 ms; FA 130°, slice thickness: 3.3 mm, field of view (FOV) 220 mm, matrix = 256 × 192 reconstructed as thirty 256 × 256 images.\",\n \"White matter lesions were segmented on FLAIR MRI with freely available image analysis software FireVoxel (https://firevoxel.org/) using a previously validated workflow [28]. FLAIR images were first corrected for signal nonuniformity using the N3 algorithm. A 3D mask M was then created over the entire brain parenchyma, that is, the gray and white matter, with cerebrospinal fluid (CSF) excluded [29]. Voxels at the external surface S of M were then identified. Next was creation of set L, containing voxels in M with signal intensity 2.5 standard deviations above the mean FLAIR signal. Finally, L was filtered to remove (a) cortical voxels, those located within 3 mm of S, (b) small clusters of volume <12 mm3 (presumed to represent image noise), and (c) connected regions having >50% CSF border (presumed to represent the choroid plexus and the septum). The resulting mask represents our estimate of WML. Its volume VWML is presented as percentage of the total brain volume.\\nGray matter (GM), white matter (WM) and CSF volumes were estimated using Statistical Parametric Mapping segmentation procedure (SPM, version 8, with ‘New-Segment’ extension) [30]. Total brain volume was the sum of GM and WM volumes. Intracranial volume (ICV) was the sum of all tissue types. Left and right hippocampal volumes were obtained with FreeSurfer version 6.0 [31], and averaged. They are referred to from now on as ‘mean hippocampal volume’. GM, WM and hippocampal volumes are presented as a fraction of the ICV. MRI data were acquired over the span of 9 years a 3T magnet that underwent hardware and software upgrades. We detected the inter-epoch variability in GM measurements. To avoid this fixed bias we z-scored GM values separately by epoch, recentered and rescaled them. Even though no other structural measure was affected, for consistency, we used rescaled values for hippocampal and WM volumes.\",\n \"Categorical variables were compared with χ2 tests. T-test, Mann–Whitney U-test, and analysis of covariance (ANCOVA) were used to compare group means for continuous variables, depending on the data distribution and the need to adjust for age and sex. Normality was tested with Shapiro–Wilk test.\\nRelationships between BP, WML volume and brain volumes (hippocampal, GM, WM) at baseline were examined with linear regression. Volumes were dependent variables; age, sex, body mass index, smoking, diabetes, systolic blood pressure and diastolic blood pressure were independent predictors. These relationships were tested in participants with and without hypertension separately. We present here F-statistics for the regression models, as well unstandardized (B) and standardized (β) coefficients for individual predictors. Again, we hypothesized that in the HTN group an optimal BP may exist [21], which minimizes WML volume and maximizes brain volumes. Thus, as before, we tested for the existence of the peak, by introducing both the linear BP and quadratic (BP [2]) terms into the regression models.\\nAfter a quadratic relationship between the volume and BP was confirmed, the critical BP at which volume reaches its maximum or minimum value was determined as:\\nTo examine whether BP, WML and brain volumes changed over time, we used mixed models for repeated measures (MMRM), where BP or volume was a dependent variable and time, group (HTN vs. NTN), and group × time interaction were predictors. For models with WML or brain volumes age as added as a covariate. Both intercept and slope were treated as random.\\nTo test whether baseline BP predicted change in WML and brain volumes, we created volume rates of change by regressing volume on time for each participant and tested whether baseline SBP and DBP was associated with volume slope. Baseline volumes, BMI, age, sex, smoking and diabetes status were initially included in the models, and retained only when significant. The relationships were tested in the normo- and hypertensive participants separately. For all the analyses, the most parsimonious model was chosen, defined as the model that included only significant or necessary (main effects when interaction was present) terms.\\nTo test whether change in BP (independent variable) was related to change in volume (dependent) we used MMRM. WML or brain volumes were a dependent variable and BP (SBP or DBP), group (HTN vs. NTN), and group × BP interaction were predictors. Both intercept and slope were treated as random. SBP and DBP were predictors in separate models.\\nWe performed supplementary analyses using pulse pressure (PP) as a predictor in all the models.\\nWML volumes were log transformed. We checked the linear models for violations of models assumptions. For all the analyses, the most parsimonious model was chosen, defined as the model that included only significant or necessary (main effects when interaction was present) terms. Statistical significance was defined as a P-value <0.05. SPSS (version 25, SPSS, Inc., Chicago, Illinois, USA) software was used for all analyses.\",\n \"BP and white matter lesion volumes In the HTN group, SBP and quadratic SBP term were predictors of WML volume at baseline (regression model included age, F3,168 = 14.4, P < 0.001) (Table 2, Fig. 1). The results were similar with log-transformed data (F3,168 = 13.1, P < 0.001) (Table 3). The critical SBP value calculated from equation [2] was x = 124 mmHg.\\nLinear regression model predicting WML volume in the HTN group at baseline (raw data)\\nCritical value (Eq. [2]): x = –(−0.07317/(2 × 0.0003)) = 122 mmHg.\\nHTN, hypertension; WML, white matter lesion.\\nQuadratic relationship between systolic blood pressure readings and white matter lesion measurements in hypertensive participants.\\nLinear regression model predicting WML volume in the HTN group at baseline (log transformed data: log(WML volume))\\nCritical value (Eq. [2]): x = –(−0.02741/(2 × 0.00011)) = 124.6 mmHg.\\nHTN, hypertension; WML, white matter lesion.\\nIn the NTN group, neither SBP nor DBP were related to WML.\\nIn the HTN group, SBP and quadratic SBP term were predictors of WML volume at baseline (regression model included age, F3,168 = 14.4, P < 0.001) (Table 2, Fig. 1). The results were similar with log-transformed data (F3,168 = 13.1, P < 0.001) (Table 3). The critical SBP value calculated from equation [2] was x = 124 mmHg.\\nLinear regression model predicting WML volume in the HTN group at baseline (raw data)\\nCritical value (Eq. [2]): x = –(−0.07317/(2 × 0.0003)) = 122 mmHg.\\nHTN, hypertension; WML, white matter lesion.\\nQuadratic relationship between systolic blood pressure readings and white matter lesion measurements in hypertensive participants.\\nLinear regression model predicting WML volume in the HTN group at baseline (log transformed data: log(WML volume))\\nCritical value (Eq. [2]): x = –(−0.02741/(2 × 0.00011)) = 124.6 mmHg.\\nHTN, hypertension; WML, white matter lesion.\\nIn the NTN group, neither SBP nor DBP were related to WML.\\nBP and brain volumes In HTN, the quadratic DBP term was a predictor of the mean hippocampal volume at baseline (the model included age and sex, F4,168 = 11.6, P < 0.001) (Table 4, Fig. 2). The critical DBP value calculated from equation [2] was x = 77 mmHg.\\nModel predicting mean hippocampal volume in the HTN group at baseline\\nCritical value (Eq. [2]): x = −(0.00399/(2 × (−0.000026))) = 77 mmHg.\\nHTN, hypertension; WML, white matter lesion.\\nRelationship between diastolic blood pressure readings and mean hippocampal brain volume measurements in hypertensive participants.\\nGM was associated with SBP (β = −0.16, P = 0.02) and the model (F4,166 = 14.0, P < 0.001) included BMI, smoking and age.\\nIn NTN, the mean hippocampal volume was inversely related to SBP (β = −0.17, P < 0.008), (regression model included age and sex, F3,206 = 22.4, P < 0.001). GM volume was inversely related to DBP (β = −0.15, P = 0.01), (the model included age and sex, F3,206 = 32.3, P < 0.001).\\nIn HTN, the quadratic DBP term was a predictor of the mean hippocampal volume at baseline (the model included age and sex, F4,168 = 11.6, P < 0.001) (Table 4, Fig. 2). The critical DBP value calculated from equation [2] was x = 77 mmHg.\\nModel predicting mean hippocampal volume in the HTN group at baseline\\nCritical value (Eq. [2]): x = −(0.00399/(2 × (−0.000026))) = 77 mmHg.\\nHTN, hypertension; WML, white matter lesion.\\nRelationship between diastolic blood pressure readings and mean hippocampal brain volume measurements in hypertensive participants.\\nGM was associated with SBP (β = −0.16, P = 0.02) and the model (F4,166 = 14.0, P < 0.001) included BMI, smoking and age.\\nIn NTN, the mean hippocampal volume was inversely related to SBP (β = −0.17, P < 0.008), (regression model included age and sex, F3,206 = 22.4, P < 0.001). GM volume was inversely related to DBP (β = −0.15, P = 0.01), (the model included age and sex, F3,206 = 32.3, P < 0.001).\",\n \"In the HTN group, SBP and quadratic SBP term were predictors of WML volume at baseline (regression model included age, F3,168 = 14.4, P < 0.001) (Table 2, Fig. 1). The results were similar with log-transformed data (F3,168 = 13.1, P < 0.001) (Table 3). The critical SBP value calculated from equation [2] was x = 124 mmHg.\\nLinear regression model predicting WML volume in the HTN group at baseline (raw data)\\nCritical value (Eq. [2]): x = –(−0.07317/(2 × 0.0003)) = 122 mmHg.\\nHTN, hypertension; WML, white matter lesion.\\nQuadratic relationship between systolic blood pressure readings and white matter lesion measurements in hypertensive participants.\\nLinear regression model predicting WML volume in the HTN group at baseline (log transformed data: log(WML volume))\\nCritical value (Eq. [2]): x = –(−0.02741/(2 × 0.00011)) = 124.6 mmHg.\\nHTN, hypertension; WML, white matter lesion.\\nIn the NTN group, neither SBP nor DBP were related to WML.\",\n \"In HTN, the quadratic DBP term was a predictor of the mean hippocampal volume at baseline (the model included age and sex, F4,168 = 11.6, P < 0.001) (Table 4, Fig. 2). The critical DBP value calculated from equation [2] was x = 77 mmHg.\\nModel predicting mean hippocampal volume in the HTN group at baseline\\nCritical value (Eq. [2]): x = −(0.00399/(2 × (−0.000026))) = 77 mmHg.\\nHTN, hypertension; WML, white matter lesion.\\nRelationship between diastolic blood pressure readings and mean hippocampal brain volume measurements in hypertensive participants.\\nGM was associated with SBP (β = −0.16, P = 0.02) and the model (F4,166 = 14.0, P < 0.001) included BMI, smoking and age.\\nIn NTN, the mean hippocampal volume was inversely related to SBP (β = −0.17, P < 0.008), (regression model included age and sex, F3,206 = 22.4, P < 0.001). GM volume was inversely related to DBP (β = −0.15, P = 0.01), (the model included age and sex, F3,206 = 32.3, P < 0.001).\",\n \"Changes in BP and volumes over time SBP did not change significantly in the HTN or NTN group, but the interaction term was significant (P = 0.01) indicating divergent pattern of SBP dynamics (the estimate = −1.6, P = 0.07 for HTN and 1.4, P = 0.07 for NTN). Similarly, DBP patterns were different (P = 0.03 for interaction term) with a lack of change in the HTN participants (estimate −0.6, P = 0.31) and a significant increase in the NTN group (the estimate = 1.1, P = 0.03). In both groups, WML volumes increased with time, (the estimate = 0.025, P < 0.001) for HTN and (0.028, P < 0.001) for NTN. In both groups, brain volumes decreased with time. An estimate for the fixed effect of time for the mean hippocampal volumes was (−0.0044, P < 0.001) for HTN and (−0.0038, P < 0.001) among NTN participants. For GM, they were (−0.48, P < 0.001) and (−0.45, P < 0.001); and finally, for WM (−0.30, P < 0.001) and (−0.22, P = 0.001), for the HTN and NTN groups, respectively. Group × time interaction terms were not significant for models with brain or WML volumes, indicating that rates of change did not differ between groups. Figure S2 presents BP and WML changes in both groups.\\nSBP did not change significantly in the HTN or NTN group, but the interaction term was significant (P = 0.01) indicating divergent pattern of SBP dynamics (the estimate = −1.6, P = 0.07 for HTN and 1.4, P = 0.07 for NTN). Similarly, DBP patterns were different (P = 0.03 for interaction term) with a lack of change in the HTN participants (estimate −0.6, P = 0.31) and a significant increase in the NTN group (the estimate = 1.1, P = 0.03). In both groups, WML volumes increased with time, (the estimate = 0.025, P < 0.001) for HTN and (0.028, P < 0.001) for NTN. In both groups, brain volumes decreased with time. An estimate for the fixed effect of time for the mean hippocampal volumes was (−0.0044, P < 0.001) for HTN and (−0.0038, P < 0.001) among NTN participants. For GM, they were (−0.48, P < 0.001) and (−0.45, P < 0.001); and finally, for WM (−0.30, P < 0.001) and (−0.22, P = 0.001), for the HTN and NTN groups, respectively. Group × time interaction terms were not significant for models with brain or WML volumes, indicating that rates of change did not differ between groups. Figure S2 presents BP and WML changes in both groups.\",\n \"SBP did not change significantly in the HTN or NTN group, but the interaction term was significant (P = 0.01) indicating divergent pattern of SBP dynamics (the estimate = −1.6, P = 0.07 for HTN and 1.4, P = 0.07 for NTN). Similarly, DBP patterns were different (P = 0.03 for interaction term) with a lack of change in the HTN participants (estimate −0.6, P = 0.31) and a significant increase in the NTN group (the estimate = 1.1, P = 0.03). In both groups, WML volumes increased with time, (the estimate = 0.025, P < 0.001) for HTN and (0.028, P < 0.001) for NTN. In both groups, brain volumes decreased with time. An estimate for the fixed effect of time for the mean hippocampal volumes was (−0.0044, P < 0.001) for HTN and (−0.0038, P < 0.001) among NTN participants. For GM, they were (−0.48, P < 0.001) and (−0.45, P < 0.001); and finally, for WM (−0.30, P < 0.001) and (−0.22, P = 0.001), for the HTN and NTN groups, respectively. Group × time interaction terms were not significant for models with brain or WML volumes, indicating that rates of change did not differ between groups. Figure S2 presents BP and WML changes in both groups.\",\n \"Table S4 shows the stability of secondary group definition (untreated, uncontrolled and controlled HTN, Stage 1 HTN, and normotension). Table S5 presents the results from MMRM analyses of WML volumes and BP changes conducted with all five subgroups defined at baseline based on HTN control status. One can appreciate that WML volume increased significantly in untreated and controlled hypertension, as well as in normotensive participants.\\nTable S6 and Figure S3 shows that participants who at baseline were classified as untreated (n = 19) or uncontrolled hypertension (n = 20) and reclassified at the last visit as good outcome (improved BP control) experience the same increase in WML volumes as participants with bad outcome (BP remained untreated or uncontrolled).\\nBaseline BP and changes in white matter lesion and brain volumes In the hypertensive group both baseline SBP (β = 0.33, P = 0.007) and DBP (β = −0.30, P = 0.008) were related to WML changes over time (entire model F4,83 = 10.5, P < 0.001, included also baseline WML volumes and smoking). Baseline BP was not related to changes in hippocampal, GM, or WM volumes.\\nIn normotensive participants baseline BP was not related to change in any examined volumes.\\nIn the hypertensive group both baseline SBP (β = 0.33, P = 0.007) and DBP (β = −0.30, P = 0.008) were related to WML changes over time (entire model F4,83 = 10.5, P < 0.001, included also baseline WML volumes and smoking). Baseline BP was not related to changes in hippocampal, GM, or WM volumes.\\nIn normotensive participants baseline BP was not related to change in any examined volumes.\\nLongitudinal changes in BP and changes in white matter lesion and brain volumes In the HTN group, neither change in SBP nor DBP were associated with a change in WML, hippocampal, GM or WM volumes (Tables S7 and S8).\\nIn the NTN group, both increase in SBP (an estimate for the fixed effect of SBP = 0.0023, P = 0.01) and DBP (an estimate for the fixed effect of DBP = 0.0035, P = 0.01) over time were related to the increase in WML volume. The significant interaction term indicated that these dynamics were meaningfully different from the HTN group. Moreover, in the normotensive group both increase in SBP (an estimate for the fixed effect of SBP = −0.0002, P = 0.03) and DBP (an estimate for the fixed effect of DBP = −0.0004, P = 0.01) over time were related to reduction in the mean hippocampal volume. However, only for SBP the interaction term was significant, indicating that these changes were different from HTN group.\\nResults for pulse pressure are presented in the Supplement p. 8–9.\\nIn the HTN group, neither change in SBP nor DBP were associated with a change in WML, hippocampal, GM or WM volumes (Tables S7 and S8).\\nIn the NTN group, both increase in SBP (an estimate for the fixed effect of SBP = 0.0023, P = 0.01) and DBP (an estimate for the fixed effect of DBP = 0.0035, P = 0.01) over time were related to the increase in WML volume. The significant interaction term indicated that these dynamics were meaningfully different from the HTN group. Moreover, in the normotensive group both increase in SBP (an estimate for the fixed effect of SBP = −0.0002, P = 0.03) and DBP (an estimate for the fixed effect of DBP = −0.0004, P = 0.01) over time were related to reduction in the mean hippocampal volume. However, only for SBP the interaction term was significant, indicating that these changes were different from HTN group.\\nResults for pulse pressure are presented in the Supplement p. 8–9.\",\n \"In the hypertensive group both baseline SBP (β = 0.33, P = 0.007) and DBP (β = −0.30, P = 0.008) were related to WML changes over time (entire model F4,83 = 10.5, P < 0.001, included also baseline WML volumes and smoking). Baseline BP was not related to changes in hippocampal, GM, or WM volumes.\\nIn normotensive participants baseline BP was not related to change in any examined volumes.\",\n \"In the HTN group, neither change in SBP nor DBP were associated with a change in WML, hippocampal, GM or WM volumes (Tables S7 and S8).\\nIn the NTN group, both increase in SBP (an estimate for the fixed effect of SBP = 0.0023, P = 0.01) and DBP (an estimate for the fixed effect of DBP = 0.0035, P = 0.01) over time were related to the increase in WML volume. The significant interaction term indicated that these dynamics were meaningfully different from the HTN group. Moreover, in the normotensive group both increase in SBP (an estimate for the fixed effect of SBP = −0.0002, P = 0.03) and DBP (an estimate for the fixed effect of DBP = −0.0004, P = 0.01) over time were related to reduction in the mean hippocampal volume. However, only for SBP the interaction term was significant, indicating that these changes were different from HTN group.\\nResults for pulse pressure are presented in the Supplement p. 8–9.\",\n \"A special thanks to Ke Xi for her help with the statistical modeling as well as assistance in the production of regression plots for this manuscript.\\nSources of funding: Study funding comes from NIH grants HL111724, NS104364, AG022374, AG12101, AG08051, and Alzheimer's Association NIRG-09-132490.\\nDisclosures: None\\nConflicts of interest There are no conflicts of interest.\\nThere are no conflicts of interest.\"\n]"},"other_sections_sec_types":{"kind":"list like","value":[null,null,null,null,null,null,null,null,null,null,null,null,null,null],"string":"[\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null\n]"},"all_sections_titles":{"kind":"list like","value":["INTRODUCTION","METHODS","Participants recruitment","Clinical assessment","Study groups","MR imaging","MRI processing","Statistics","RESULTS","Baseline analyses","BP and white matter lesion volumes","BP and brain volumes","Longitudinal analyses","Changes in BP and volumes over time","Secondary analyses","Baseline BP and changes in white matter lesion and brain volumes","Longitudinal changes in BP and changes in white matter lesion and brain volumes","DISCUSSION","ACKNOWLEDGEMENTS","Conflicts of interest","Supplementary Material"],"string":"[\n \"INTRODUCTION\",\n \"METHODS\",\n \"Participants recruitment\",\n \"Clinical assessment\",\n \"Study groups\",\n \"MR imaging\",\n \"MRI processing\",\n \"Statistics\",\n \"RESULTS\",\n \"Baseline analyses\",\n \"BP and white matter lesion volumes\",\n \"BP and brain volumes\",\n \"Longitudinal analyses\",\n \"Changes in BP and volumes over time\",\n \"Secondary analyses\",\n \"Baseline BP and changes in white matter lesion and brain volumes\",\n \"Longitudinal changes in BP and changes in white matter lesion and brain volumes\",\n \"DISCUSSION\",\n \"ACKNOWLEDGEMENTS\",\n \"Conflicts of interest\",\n \"Supplementary Material\"\n]"},"all_sections_texts":{"kind":"list like","value":["The impact of cardiovascular disease on the United States’ population cannot be overstated. One out of every three Americans is hypertensive, with 66% of adults aged 60 or older exhibiting above average blood pressure readings [1,2]. It was estimated in 2018 that 11 million individuals in the United States were living with undiagnosed hypertension (HTN), placing them at an increased risk for adverse cardiovascular events that can directly affect the central nervous system as well as cognition [1,2].\nHigh blood pressure (BP) can cause damage and dysfunction to organ systems with perhaps the most significant of such being the brain and the heart [3–5]. Hypertensive patients develop atherosclerotic plaques within the large blood vessels supplying blood flow both into and out of the brain at an accelerated rate, increasing their risk of an ischemic event [6,7]. HTN can cause a remodeling of the brain microvasculature with vessels exhibiting rarefication and narrowing lumen [4,6,8]. Such changes in vessel architecture can have negative effects on cerebral blood flow (CBF) and autoregulation. Reduction in CBF has been shown to cause brain atrophy both in animal models [9] and in clinical population [10], and has been consistently linked with white matter damage [11,12]. White matter lesions (WML) are very common in HTN participants [13–15]. Indeed, several studies have confirmed that two major risk factors for WML development are increased age and high BP, with smoking, high cholesterol, and heart disease also being significant contributors to lesion formation [14,16,17].\nAlthough benefits of HTN treatment are indisputable for major cardiovascular outcome, the target blood pressure for the brain remains under debate. While some argue for aggressive blood pressure reductions [18], others point to possible danger of this approach [19], as marked lowering of BP in longstanding HTN may cause relative hypoperfusion. This suggested to us the existence of an optimum BP range.\nA recent metanalysis found a U-shape relationship between SBP and a risk of dementia in participants older than 75, as well as U-shape relationship between SBP and combined dementia and mortality across all age groups starting at 60 [20]. Our group has previously reported a quadratic (inverted U-shape) relationship between CBF and SBP in hypertensive patients, pointing to the optimum lying between 120 and 130 mmHg where CBF was the highest [21]. It was consistently shown that WML volumes are inversely related to global and local indices of CBF [22]. In this observational report, we investigated whether in hypertensive participants WML and atrophy, structural markers of HTN-related brain damage, show relationship with SBP, which is parallel to the one we observed for CBF and SBP: namely is there a quadratic association between them.","The data of this study are available from the corresponding author upon request.\nParticipants recruitment Study participants were recruited at the NYU School of Medicine, at the former Center for Brain Health. Out of 376 individuals described in this report, 369 (98%) were previously a part of a study assessing the relationship between BP and CBF. Here, we report on participants with quantitative assessment of white matter pathology. Baseline assessment was performed in a larger group of n = 376 participants, and n = 188 of them had follow-up examinations. The reasons for lack of follow-up are described in the Supplement. Participants were enrolled between November 2010 and November 2017. All participants signed Institutional Review Board (ethics committee)-approved consent forms and were free of neocortical stroke, brain tumor, life-long psychotic disorders and dementia at baseline. Information pertaining to recruitment, exclusion criteria, and participant flow is presented in the Supplement.\nStudy participants were recruited at the NYU School of Medicine, at the former Center for Brain Health. Out of 376 individuals described in this report, 369 (98%) were previously a part of a study assessing the relationship between BP and CBF. Here, we report on participants with quantitative assessment of white matter pathology. Baseline assessment was performed in a larger group of n = 376 participants, and n = 188 of them had follow-up examinations. The reasons for lack of follow-up are described in the Supplement. Participants were enrolled between November 2010 and November 2017. All participants signed Institutional Review Board (ethics committee)-approved consent forms and were free of neocortical stroke, brain tumor, life-long psychotic disorders and dementia at baseline. Information pertaining to recruitment, exclusion criteria, and participant flow is presented in the Supplement.\nClinical assessment All participants underwent medical, psychiatric, and neurological assessments, blood tests, electrocardiogram (ECG), and magnetic resonance imaging (MRI) evaluation examinations at baseline, and the longitudinal group also at follow-up. Participants exhibiting dementia, based on a physician-administered interview using the Brief Cognitive Rating Scale, rating on the Global Deterioration Scale [23], and Clinical Dementia Rating were excluded [24]. Fasting blood was tested for complete blood count, liver function, as well as metabolic and lipid panel.\nBlood pressure was taken once in a sitting position, after 5 min of rest. It was measured on the left upper arm using a manual sphygmomanometer. Body mass index (BMI) was calculated as weight/height (kg/m) [2].\nMedication: We recoded the use of antihypertensive medications (angiotensin receptor blockers (ARBs), angiotensin converting enzyme inhibitors (ACE), beta-blockers, diuretics, and calcium channel blockers), statins and glucose lowering drugs.\nDiabetes mellitus was defined as fasting glucose level of 126 mg/dl and higher and/or current use of glucose lowering medication [25]. Smoking status was defined as positive if participants was a current smoker or smoked within last 10 years.\nAll participants underwent medical, psychiatric, and neurological assessments, blood tests, electrocardiogram (ECG), and magnetic resonance imaging (MRI) evaluation examinations at baseline, and the longitudinal group also at follow-up. Participants exhibiting dementia, based on a physician-administered interview using the Brief Cognitive Rating Scale, rating on the Global Deterioration Scale [23], and Clinical Dementia Rating were excluded [24]. Fasting blood was tested for complete blood count, liver function, as well as metabolic and lipid panel.\nBlood pressure was taken once in a sitting position, after 5 min of rest. It was measured on the left upper arm using a manual sphygmomanometer. Body mass index (BMI) was calculated as weight/height (kg/m) [2].\nMedication: We recoded the use of antihypertensive medications (angiotensin receptor blockers (ARBs), angiotensin converting enzyme inhibitors (ACE), beta-blockers, diuretics, and calcium channel blockers), statins and glucose lowering drugs.\nDiabetes mellitus was defined as fasting glucose level of 126 mg/dl and higher and/or current use of glucose lowering medication [25]. Smoking status was defined as positive if participants was a current smoker or smoked within last 10 years.\nStudy groups For consistency with our previous study hypertension was defined as current antihypertensive treatment or BP ≥140/90 mmHg [26]. One hundred and sixty-nine participants were classified as hypertensive; 136 participants were taking antihypertensive medication and 42 were not taking any antihypertensive medication with high BP recorded during their in-office visit. Normotension (NTN) was defined as BP <140/90 mmHg and no antihypertensive treatment.\nFor descriptive purpose and secondary analyses, we described hypertensive participants as untreated: no antihypertensive treatment and BP ≥140/90 mmHg; controlled: antihypertensive treatment and BP <140/90 mmHg, uncontrolled: antihypertensive treatment and BP ≥140/90 mmHg. We also divided our normotensive group into Stage 1 hypertension based on new guidelines: [27] no treatment and BP between 130–139/80–89 mmHg, and normotensive BP <130/80 mmHg.\nFor consistency with our previous study hypertension was defined as current antihypertensive treatment or BP ≥140/90 mmHg [26]. One hundred and sixty-nine participants were classified as hypertensive; 136 participants were taking antihypertensive medication and 42 were not taking any antihypertensive medication with high BP recorded during their in-office visit. Normotension (NTN) was defined as BP <140/90 mmHg and no antihypertensive treatment.\nFor descriptive purpose and secondary analyses, we described hypertensive participants as untreated: no antihypertensive treatment and BP ≥140/90 mmHg; controlled: antihypertensive treatment and BP <140/90 mmHg, uncontrolled: antihypertensive treatment and BP ≥140/90 mmHg. We also divided our normotensive group into Stage 1 hypertension based on new guidelines: [27] no treatment and BP between 130–139/80–89 mmHg, and normotensive BP <130/80 mmHg.\nMR imaging All MR imaging was performed on the 3T system (Siemens, Erlangen, Germany). Parameters for Magnetization Prepared Rapid Acquisition Gradient Echo (MPRAGE) images were: repetition time (TR) = 2250 ms, echo time (TE) = 2.7 ms, inversion time (TI) = 900 ms, flip angle (FA) = 8°, slice thickness: 1.0 mm, field of view (FOV) = 200 mm, acquisition matrix = 256 × 192 × 124, reconstructed as 256 × 256 × 124. Axial fluid attenuation inversion recovery (FLAIR) images were acquired with TR/TE/TI 9000/99/2500 ms; FA 130°, slice thickness: 3.3 mm, field of view (FOV) 220 mm, matrix = 256 × 192 reconstructed as thirty 256 × 256 images.\nAll MR imaging was performed on the 3T system (Siemens, Erlangen, Germany). Parameters for Magnetization Prepared Rapid Acquisition Gradient Echo (MPRAGE) images were: repetition time (TR) = 2250 ms, echo time (TE) = 2.7 ms, inversion time (TI) = 900 ms, flip angle (FA) = 8°, slice thickness: 1.0 mm, field of view (FOV) = 200 mm, acquisition matrix = 256 × 192 × 124, reconstructed as 256 × 256 × 124. Axial fluid attenuation inversion recovery (FLAIR) images were acquired with TR/TE/TI 9000/99/2500 ms; FA 130°, slice thickness: 3.3 mm, field of view (FOV) 220 mm, matrix = 256 × 192 reconstructed as thirty 256 × 256 images.\nMRI processing White matter lesions were segmented on FLAIR MRI with freely available image analysis software FireVoxel (https://firevoxel.org/) using a previously validated workflow [28]. FLAIR images were first corrected for signal nonuniformity using the N3 algorithm. A 3D mask M was then created over the entire brain parenchyma, that is, the gray and white matter, with cerebrospinal fluid (CSF) excluded [29]. Voxels at the external surface S of M were then identified. Next was creation of set L, containing voxels in M with signal intensity 2.5 standard deviations above the mean FLAIR signal. Finally, L was filtered to remove (a) cortical voxels, those located within 3 mm of S, (b) small clusters of volume <12 mm3 (presumed to represent image noise), and (c) connected regions having >50% CSF border (presumed to represent the choroid plexus and the septum). The resulting mask represents our estimate of WML. Its volume VWML is presented as percentage of the total brain volume.\nGray matter (GM), white matter (WM) and CSF volumes were estimated using Statistical Parametric Mapping segmentation procedure (SPM, version 8, with ‘New-Segment’ extension) [30]. Total brain volume was the sum of GM and WM volumes. Intracranial volume (ICV) was the sum of all tissue types. Left and right hippocampal volumes were obtained with FreeSurfer version 6.0 [31], and averaged. They are referred to from now on as ‘mean hippocampal volume’. GM, WM and hippocampal volumes are presented as a fraction of the ICV. MRI data were acquired over the span of 9 years a 3T magnet that underwent hardware and software upgrades. We detected the inter-epoch variability in GM measurements. To avoid this fixed bias we z-scored GM values separately by epoch, recentered and rescaled them. Even though no other structural measure was affected, for consistency, we used rescaled values for hippocampal and WM volumes.\nWhite matter lesions were segmented on FLAIR MRI with freely available image analysis software FireVoxel (https://firevoxel.org/) using a previously validated workflow [28]. FLAIR images were first corrected for signal nonuniformity using the N3 algorithm. A 3D mask M was then created over the entire brain parenchyma, that is, the gray and white matter, with cerebrospinal fluid (CSF) excluded [29]. Voxels at the external surface S of M were then identified. Next was creation of set L, containing voxels in M with signal intensity 2.5 standard deviations above the mean FLAIR signal. Finally, L was filtered to remove (a) cortical voxels, those located within 3 mm of S, (b) small clusters of volume <12 mm3 (presumed to represent image noise), and (c) connected regions having >50% CSF border (presumed to represent the choroid plexus and the septum). The resulting mask represents our estimate of WML. Its volume VWML is presented as percentage of the total brain volume.\nGray matter (GM), white matter (WM) and CSF volumes were estimated using Statistical Parametric Mapping segmentation procedure (SPM, version 8, with ‘New-Segment’ extension) [30]. Total brain volume was the sum of GM and WM volumes. Intracranial volume (ICV) was the sum of all tissue types. Left and right hippocampal volumes were obtained with FreeSurfer version 6.0 [31], and averaged. They are referred to from now on as ‘mean hippocampal volume’. GM, WM and hippocampal volumes are presented as a fraction of the ICV. MRI data were acquired over the span of 9 years a 3T magnet that underwent hardware and software upgrades. We detected the inter-epoch variability in GM measurements. To avoid this fixed bias we z-scored GM values separately by epoch, recentered and rescaled them. Even though no other structural measure was affected, for consistency, we used rescaled values for hippocampal and WM volumes.\nStatistics Categorical variables were compared with χ2 tests. T-test, Mann–Whitney U-test, and analysis of covariance (ANCOVA) were used to compare group means for continuous variables, depending on the data distribution and the need to adjust for age and sex. Normality was tested with Shapiro–Wilk test.\nRelationships between BP, WML volume and brain volumes (hippocampal, GM, WM) at baseline were examined with linear regression. Volumes were dependent variables; age, sex, body mass index, smoking, diabetes, systolic blood pressure and diastolic blood pressure were independent predictors. These relationships were tested in participants with and without hypertension separately. We present here F-statistics for the regression models, as well unstandardized (B) and standardized (β) coefficients for individual predictors. Again, we hypothesized that in the HTN group an optimal BP may exist [21], which minimizes WML volume and maximizes brain volumes. Thus, as before, we tested for the existence of the peak, by introducing both the linear BP and quadratic (BP [2]) terms into the regression models.\nAfter a quadratic relationship between the volume and BP was confirmed, the critical BP at which volume reaches its maximum or minimum value was determined as:\nTo examine whether BP, WML and brain volumes changed over time, we used mixed models for repeated measures (MMRM), where BP or volume was a dependent variable and time, group (HTN vs. NTN), and group × time interaction were predictors. For models with WML or brain volumes age as added as a covariate. Both intercept and slope were treated as random.\nTo test whether baseline BP predicted change in WML and brain volumes, we created volume rates of change by regressing volume on time for each participant and tested whether baseline SBP and DBP was associated with volume slope. Baseline volumes, BMI, age, sex, smoking and diabetes status were initially included in the models, and retained only when significant. The relationships were tested in the normo- and hypertensive participants separately. For all the analyses, the most parsimonious model was chosen, defined as the model that included only significant or necessary (main effects when interaction was present) terms.\nTo test whether change in BP (independent variable) was related to change in volume (dependent) we used MMRM. WML or brain volumes were a dependent variable and BP (SBP or DBP), group (HTN vs. NTN), and group × BP interaction were predictors. Both intercept and slope were treated as random. SBP and DBP were predictors in separate models.\nWe performed supplementary analyses using pulse pressure (PP) as a predictor in all the models.\nWML volumes were log transformed. We checked the linear models for violations of models assumptions. For all the analyses, the most parsimonious model was chosen, defined as the model that included only significant or necessary (main effects when interaction was present) terms. Statistical significance was defined as a P-value <0.05. SPSS (version 25, SPSS, Inc., Chicago, Illinois, USA) software was used for all analyses.\nCategorical variables were compared with χ2 tests. T-test, Mann–Whitney U-test, and analysis of covariance (ANCOVA) were used to compare group means for continuous variables, depending on the data distribution and the need to adjust for age and sex. Normality was tested with Shapiro–Wilk test.\nRelationships between BP, WML volume and brain volumes (hippocampal, GM, WM) at baseline were examined with linear regression. Volumes were dependent variables; age, sex, body mass index, smoking, diabetes, systolic blood pressure and diastolic blood pressure were independent predictors. These relationships were tested in participants with and without hypertension separately. We present here F-statistics for the regression models, as well unstandardized (B) and standardized (β) coefficients for individual predictors. Again, we hypothesized that in the HTN group an optimal BP may exist [21], which minimizes WML volume and maximizes brain volumes. Thus, as before, we tested for the existence of the peak, by introducing both the linear BP and quadratic (BP [2]) terms into the regression models.\nAfter a quadratic relationship between the volume and BP was confirmed, the critical BP at which volume reaches its maximum or minimum value was determined as:\nTo examine whether BP, WML and brain volumes changed over time, we used mixed models for repeated measures (MMRM), where BP or volume was a dependent variable and time, group (HTN vs. NTN), and group × time interaction were predictors. For models with WML or brain volumes age as added as a covariate. Both intercept and slope were treated as random.\nTo test whether baseline BP predicted change in WML and brain volumes, we created volume rates of change by regressing volume on time for each participant and tested whether baseline SBP and DBP was associated with volume slope. Baseline volumes, BMI, age, sex, smoking and diabetes status were initially included in the models, and retained only when significant. The relationships were tested in the normo- and hypertensive participants separately. For all the analyses, the most parsimonious model was chosen, defined as the model that included only significant or necessary (main effects when interaction was present) terms.\nTo test whether change in BP (independent variable) was related to change in volume (dependent) we used MMRM. WML or brain volumes were a dependent variable and BP (SBP or DBP), group (HTN vs. NTN), and group × BP interaction were predictors. Both intercept and slope were treated as random. SBP and DBP were predictors in separate models.\nWe performed supplementary analyses using pulse pressure (PP) as a predictor in all the models.\nWML volumes were log transformed. We checked the linear models for violations of models assumptions. For all the analyses, the most parsimonious model was chosen, defined as the model that included only significant or necessary (main effects when interaction was present) terms. Statistical significance was defined as a P-value <0.05. SPSS (version 25, SPSS, Inc., Chicago, Illinois, USA) software was used for all analyses.","Study participants were recruited at the NYU School of Medicine, at the former Center for Brain Health. Out of 376 individuals described in this report, 369 (98%) were previously a part of a study assessing the relationship between BP and CBF. Here, we report on participants with quantitative assessment of white matter pathology. Baseline assessment was performed in a larger group of n = 376 participants, and n = 188 of them had follow-up examinations. The reasons for lack of follow-up are described in the Supplement. Participants were enrolled between November 2010 and November 2017. All participants signed Institutional Review Board (ethics committee)-approved consent forms and were free of neocortical stroke, brain tumor, life-long psychotic disorders and dementia at baseline. Information pertaining to recruitment, exclusion criteria, and participant flow is presented in the Supplement.","All participants underwent medical, psychiatric, and neurological assessments, blood tests, electrocardiogram (ECG), and magnetic resonance imaging (MRI) evaluation examinations at baseline, and the longitudinal group also at follow-up. Participants exhibiting dementia, based on a physician-administered interview using the Brief Cognitive Rating Scale, rating on the Global Deterioration Scale [23], and Clinical Dementia Rating were excluded [24]. Fasting blood was tested for complete blood count, liver function, as well as metabolic and lipid panel.\nBlood pressure was taken once in a sitting position, after 5 min of rest. It was measured on the left upper arm using a manual sphygmomanometer. Body mass index (BMI) was calculated as weight/height (kg/m) [2].\nMedication: We recoded the use of antihypertensive medications (angiotensin receptor blockers (ARBs), angiotensin converting enzyme inhibitors (ACE), beta-blockers, diuretics, and calcium channel blockers), statins and glucose lowering drugs.\nDiabetes mellitus was defined as fasting glucose level of 126 mg/dl and higher and/or current use of glucose lowering medication [25]. Smoking status was defined as positive if participants was a current smoker or smoked within last 10 years.","For consistency with our previous study hypertension was defined as current antihypertensive treatment or BP ≥140/90 mmHg [26]. One hundred and sixty-nine participants were classified as hypertensive; 136 participants were taking antihypertensive medication and 42 were not taking any antihypertensive medication with high BP recorded during their in-office visit. Normotension (NTN) was defined as BP <140/90 mmHg and no antihypertensive treatment.\nFor descriptive purpose and secondary analyses, we described hypertensive participants as untreated: no antihypertensive treatment and BP ≥140/90 mmHg; controlled: antihypertensive treatment and BP <140/90 mmHg, uncontrolled: antihypertensive treatment and BP ≥140/90 mmHg. We also divided our normotensive group into Stage 1 hypertension based on new guidelines: [27] no treatment and BP between 130–139/80–89 mmHg, and normotensive BP <130/80 mmHg.","All MR imaging was performed on the 3T system (Siemens, Erlangen, Germany). Parameters for Magnetization Prepared Rapid Acquisition Gradient Echo (MPRAGE) images were: repetition time (TR) = 2250 ms, echo time (TE) = 2.7 ms, inversion time (TI) = 900 ms, flip angle (FA) = 8°, slice thickness: 1.0 mm, field of view (FOV) = 200 mm, acquisition matrix = 256 × 192 × 124, reconstructed as 256 × 256 × 124. Axial fluid attenuation inversion recovery (FLAIR) images were acquired with TR/TE/TI 9000/99/2500 ms; FA 130°, slice thickness: 3.3 mm, field of view (FOV) 220 mm, matrix = 256 × 192 reconstructed as thirty 256 × 256 images.","White matter lesions were segmented on FLAIR MRI with freely available image analysis software FireVoxel (https://firevoxel.org/) using a previously validated workflow [28]. FLAIR images were first corrected for signal nonuniformity using the N3 algorithm. A 3D mask M was then created over the entire brain parenchyma, that is, the gray and white matter, with cerebrospinal fluid (CSF) excluded [29]. Voxels at the external surface S of M were then identified. Next was creation of set L, containing voxels in M with signal intensity 2.5 standard deviations above the mean FLAIR signal. Finally, L was filtered to remove (a) cortical voxels, those located within 3 mm of S, (b) small clusters of volume <12 mm3 (presumed to represent image noise), and (c) connected regions having >50% CSF border (presumed to represent the choroid plexus and the septum). The resulting mask represents our estimate of WML. Its volume VWML is presented as percentage of the total brain volume.\nGray matter (GM), white matter (WM) and CSF volumes were estimated using Statistical Parametric Mapping segmentation procedure (SPM, version 8, with ‘New-Segment’ extension) [30]. Total brain volume was the sum of GM and WM volumes. Intracranial volume (ICV) was the sum of all tissue types. Left and right hippocampal volumes were obtained with FreeSurfer version 6.0 [31], and averaged. They are referred to from now on as ‘mean hippocampal volume’. GM, WM and hippocampal volumes are presented as a fraction of the ICV. MRI data were acquired over the span of 9 years a 3T magnet that underwent hardware and software upgrades. We detected the inter-epoch variability in GM measurements. To avoid this fixed bias we z-scored GM values separately by epoch, recentered and rescaled them. Even though no other structural measure was affected, for consistency, we used rescaled values for hippocampal and WM volumes.","Categorical variables were compared with χ2 tests. T-test, Mann–Whitney U-test, and analysis of covariance (ANCOVA) were used to compare group means for continuous variables, depending on the data distribution and the need to adjust for age and sex. Normality was tested with Shapiro–Wilk test.\nRelationships between BP, WML volume and brain volumes (hippocampal, GM, WM) at baseline were examined with linear regression. Volumes were dependent variables; age, sex, body mass index, smoking, diabetes, systolic blood pressure and diastolic blood pressure were independent predictors. These relationships were tested in participants with and without hypertension separately. We present here F-statistics for the regression models, as well unstandardized (B) and standardized (β) coefficients for individual predictors. Again, we hypothesized that in the HTN group an optimal BP may exist [21], which minimizes WML volume and maximizes brain volumes. Thus, as before, we tested for the existence of the peak, by introducing both the linear BP and quadratic (BP [2]) terms into the regression models.\nAfter a quadratic relationship between the volume and BP was confirmed, the critical BP at which volume reaches its maximum or minimum value was determined as:\nTo examine whether BP, WML and brain volumes changed over time, we used mixed models for repeated measures (MMRM), where BP or volume was a dependent variable and time, group (HTN vs. NTN), and group × time interaction were predictors. For models with WML or brain volumes age as added as a covariate. Both intercept and slope were treated as random.\nTo test whether baseline BP predicted change in WML and brain volumes, we created volume rates of change by regressing volume on time for each participant and tested whether baseline SBP and DBP was associated with volume slope. Baseline volumes, BMI, age, sex, smoking and diabetes status were initially included in the models, and retained only when significant. The relationships were tested in the normo- and hypertensive participants separately. For all the analyses, the most parsimonious model was chosen, defined as the model that included only significant or necessary (main effects when interaction was present) terms.\nTo test whether change in BP (independent variable) was related to change in volume (dependent) we used MMRM. WML or brain volumes were a dependent variable and BP (SBP or DBP), group (HTN vs. NTN), and group × BP interaction were predictors. Both intercept and slope were treated as random. SBP and DBP were predictors in separate models.\nWe performed supplementary analyses using pulse pressure (PP) as a predictor in all the models.\nWML volumes were log transformed. We checked the linear models for violations of models assumptions. For all the analyses, the most parsimonious model was chosen, defined as the model that included only significant or necessary (main effects when interaction was present) terms. Statistical significance was defined as a P-value <0.05. SPSS (version 25, SPSS, Inc., Chicago, Illinois, USA) software was used for all analyses.","Our group of 376 participants consisted of HTN (n = 207) and NTN (n = 169) individuals. The characteristics of the groups are given in Table 1. Two hundred and twenty-eight (60.6%) were women (age, 68.5 ± 7.4 years (mean ± standard deviation); education, 16.7 ± 2.3 years); and 148 (39.4%) men (age, 70.7 ± 6.9 years; education, 17.0 ± 2.4 years). The majority of the participants were Caucasian (86%), 10% African American, 4% other races.\nBaseline characteristics of the study group (n = 376)\nData is presented as mean ± standard deviation unless otherwise indicated. P-values come from t-test (total cholesterol, LDL) or Mann–Whitney U-test (age, education, BMI, HDL, triglycerides, glucose). For categorical variables, χ2 was used.\nn = 371 (NTN = 204, HTN = 167)\nn = 369 (NTN = 205, HTN = 164)\nn = 363 (NTN = 200, HTN = 163)\nn = 371 (NTN = 206, HTN = 165)\nValues are presented as mean ± standard errors (SE), P-values from ANCOVA after accounting for age.\nHippocampal volume is a mean of left and right, values are presented as mean ± standard errors (SE), P-values from ANCOVA after accounting for age and gender. Since the residuals from GM model were not normally distributed, the values were reanalyzed using ranked ANCOVA. Results remained the same.\nICV, intracranial volume; HTN, hypertension; NTN, normotensive.\nIn the longitudinal group of 188 participants, 116 (61.7%) were women (age, 69.6 ± 7.5 years; education, 16.7 ± 2.1 years) and 72 (38.3%) men (age, 71.4 ± 6.1 years; education, 17.2 ± 2.3 years). The mean time of follow-up was 2.3 ± 0.70 years. The longitudinal group did not differ from the participants with only one examination in terms of sex, HTN prevalence, SBP, DBP, BMI, total cholesterol, LDL and HDL cholesterol, triglycerides, education and WML volume. Prevalence of smoking and diabetes, as well as the number of participants with normotension, controlled, uncontrolled and untreated hypertension were also not different between the two groups. However, participants with longitudinal observation were slightly older (age, 70.3 ± 7.0 vs. 68.4 ± 7.4 years, P = 0.005) and had lower glucose levels (glucose, 82.5 ± 16.0 vs. 85.6 ± 14 mg/dl) than the individuals with only one exam. The clinical characteristics, as well as baseline brain and WML volumes of HTN and NTN participants in the longitudinal subgroup are given in Table S1. WML volumes by five baseline study group are shown in Tables S2 and S3.\nBaseline analyses BP and white matter lesion volumes In the HTN group, SBP and quadratic SBP term were predictors of WML volume at baseline (regression model included age, F3,168 = 14.4, P < 0.001) (Table 2, Fig. 1). The results were similar with log-transformed data (F3,168 = 13.1, P < 0.001) (Table 3). The critical SBP value calculated from equation [2] was x = 124 mmHg.\nLinear regression model predicting WML volume in the HTN group at baseline (raw data)\nCritical value (Eq. [2]): x = –(−0.07317/(2 × 0.0003)) = 122 mmHg.\nHTN, hypertension; WML, white matter lesion.\nQuadratic relationship between systolic blood pressure readings and white matter lesion measurements in hypertensive participants.\nLinear regression model predicting WML volume in the HTN group at baseline (log transformed data: log(WML volume))\nCritical value (Eq. [2]): x = –(−0.02741/(2 × 0.00011)) = 124.6 mmHg.\nHTN, hypertension; WML, white matter lesion.\nIn the NTN group, neither SBP nor DBP were related to WML.\nIn the HTN group, SBP and quadratic SBP term were predictors of WML volume at baseline (regression model included age, F3,168 = 14.4, P < 0.001) (Table 2, Fig. 1). The results were similar with log-transformed data (F3,168 = 13.1, P < 0.001) (Table 3). The critical SBP value calculated from equation [2] was x = 124 mmHg.\nLinear regression model predicting WML volume in the HTN group at baseline (raw data)\nCritical value (Eq. [2]): x = –(−0.07317/(2 × 0.0003)) = 122 mmHg.\nHTN, hypertension; WML, white matter lesion.\nQuadratic relationship between systolic blood pressure readings and white matter lesion measurements in hypertensive participants.\nLinear regression model predicting WML volume in the HTN group at baseline (log transformed data: log(WML volume))\nCritical value (Eq. [2]): x = –(−0.02741/(2 × 0.00011)) = 124.6 mmHg.\nHTN, hypertension; WML, white matter lesion.\nIn the NTN group, neither SBP nor DBP were related to WML.\nBP and brain volumes In HTN, the quadratic DBP term was a predictor of the mean hippocampal volume at baseline (the model included age and sex, F4,168 = 11.6, P < 0.001) (Table 4, Fig. 2). The critical DBP value calculated from equation [2] was x = 77 mmHg.\nModel predicting mean hippocampal volume in the HTN group at baseline\nCritical value (Eq. [2]): x = −(0.00399/(2 × (−0.000026))) = 77 mmHg.\nHTN, hypertension; WML, white matter lesion.\nRelationship between diastolic blood pressure readings and mean hippocampal brain volume measurements in hypertensive participants.\nGM was associated with SBP (β = −0.16, P = 0.02) and the model (F4,166 = 14.0, P < 0.001) included BMI, smoking and age.\nIn NTN, the mean hippocampal volume was inversely related to SBP (β = −0.17, P < 0.008), (regression model included age and sex, F3,206 = 22.4, P < 0.001). GM volume was inversely related to DBP (β = −0.15, P = 0.01), (the model included age and sex, F3,206 = 32.3, P < 0.001).\nIn HTN, the quadratic DBP term was a predictor of the mean hippocampal volume at baseline (the model included age and sex, F4,168 = 11.6, P < 0.001) (Table 4, Fig. 2). The critical DBP value calculated from equation [2] was x = 77 mmHg.\nModel predicting mean hippocampal volume in the HTN group at baseline\nCritical value (Eq. [2]): x = −(0.00399/(2 × (−0.000026))) = 77 mmHg.\nHTN, hypertension; WML, white matter lesion.\nRelationship between diastolic blood pressure readings and mean hippocampal brain volume measurements in hypertensive participants.\nGM was associated with SBP (β = −0.16, P = 0.02) and the model (F4,166 = 14.0, P < 0.001) included BMI, smoking and age.\nIn NTN, the mean hippocampal volume was inversely related to SBP (β = −0.17, P < 0.008), (regression model included age and sex, F3,206 = 22.4, P < 0.001). GM volume was inversely related to DBP (β = −0.15, P = 0.01), (the model included age and sex, F3,206 = 32.3, P < 0.001).\nBP and white matter lesion volumes In the HTN group, SBP and quadratic SBP term were predictors of WML volume at baseline (regression model included age, F3,168 = 14.4, P < 0.001) (Table 2, Fig. 1). The results were similar with log-transformed data (F3,168 = 13.1, P < 0.001) (Table 3). The critical SBP value calculated from equation [2] was x = 124 mmHg.\nLinear regression model predicting WML volume in the HTN group at baseline (raw data)\nCritical value (Eq. [2]): x = –(−0.07317/(2 × 0.0003)) = 122 mmHg.\nHTN, hypertension; WML, white matter lesion.\nQuadratic relationship between systolic blood pressure readings and white matter lesion measurements in hypertensive participants.\nLinear regression model predicting WML volume in the HTN group at baseline (log transformed data: log(WML volume))\nCritical value (Eq. [2]): x = –(−0.02741/(2 × 0.00011)) = 124.6 mmHg.\nHTN, hypertension; WML, white matter lesion.\nIn the NTN group, neither SBP nor DBP were related to WML.\nIn the HTN group, SBP and quadratic SBP term were predictors of WML volume at baseline (regression model included age, F3,168 = 14.4, P < 0.001) (Table 2, Fig. 1). The results were similar with log-transformed data (F3,168 = 13.1, P < 0.001) (Table 3). The critical SBP value calculated from equation [2] was x = 124 mmHg.\nLinear regression model predicting WML volume in the HTN group at baseline (raw data)\nCritical value (Eq. [2]): x = –(−0.07317/(2 × 0.0003)) = 122 mmHg.\nHTN, hypertension; WML, white matter lesion.\nQuadratic relationship between systolic blood pressure readings and white matter lesion measurements in hypertensive participants.\nLinear regression model predicting WML volume in the HTN group at baseline (log transformed data: log(WML volume))\nCritical value (Eq. [2]): x = –(−0.02741/(2 × 0.00011)) = 124.6 mmHg.\nHTN, hypertension; WML, white matter lesion.\nIn the NTN group, neither SBP nor DBP were related to WML.\nBP and brain volumes In HTN, the quadratic DBP term was a predictor of the mean hippocampal volume at baseline (the model included age and sex, F4,168 = 11.6, P < 0.001) (Table 4, Fig. 2). The critical DBP value calculated from equation [2] was x = 77 mmHg.\nModel predicting mean hippocampal volume in the HTN group at baseline\nCritical value (Eq. [2]): x = −(0.00399/(2 × (−0.000026))) = 77 mmHg.\nHTN, hypertension; WML, white matter lesion.\nRelationship between diastolic blood pressure readings and mean hippocampal brain volume measurements in hypertensive participants.\nGM was associated with SBP (β = −0.16, P = 0.02) and the model (F4,166 = 14.0, P < 0.001) included BMI, smoking and age.\nIn NTN, the mean hippocampal volume was inversely related to SBP (β = −0.17, P < 0.008), (regression model included age and sex, F3,206 = 22.4, P < 0.001). GM volume was inversely related to DBP (β = −0.15, P = 0.01), (the model included age and sex, F3,206 = 32.3, P < 0.001).\nIn HTN, the quadratic DBP term was a predictor of the mean hippocampal volume at baseline (the model included age and sex, F4,168 = 11.6, P < 0.001) (Table 4, Fig. 2). The critical DBP value calculated from equation [2] was x = 77 mmHg.\nModel predicting mean hippocampal volume in the HTN group at baseline\nCritical value (Eq. [2]): x = −(0.00399/(2 × (−0.000026))) = 77 mmHg.\nHTN, hypertension; WML, white matter lesion.\nRelationship between diastolic blood pressure readings and mean hippocampal brain volume measurements in hypertensive participants.\nGM was associated with SBP (β = −0.16, P = 0.02) and the model (F4,166 = 14.0, P < 0.001) included BMI, smoking and age.\nIn NTN, the mean hippocampal volume was inversely related to SBP (β = −0.17, P < 0.008), (regression model included age and sex, F3,206 = 22.4, P < 0.001). GM volume was inversely related to DBP (β = −0.15, P = 0.01), (the model included age and sex, F3,206 = 32.3, P < 0.001).\nLongitudinal analyses Changes in BP and volumes over time SBP did not change significantly in the HTN or NTN group, but the interaction term was significant (P = 0.01) indicating divergent pattern of SBP dynamics (the estimate = −1.6, P = 0.07 for HTN and 1.4, P = 0.07 for NTN). Similarly, DBP patterns were different (P = 0.03 for interaction term) with a lack of change in the HTN participants (estimate −0.6, P = 0.31) and a significant increase in the NTN group (the estimate = 1.1, P = 0.03). In both groups, WML volumes increased with time, (the estimate = 0.025, P < 0.001) for HTN and (0.028, P < 0.001) for NTN. In both groups, brain volumes decreased with time. An estimate for the fixed effect of time for the mean hippocampal volumes was (−0.0044, P < 0.001) for HTN and (−0.0038, P < 0.001) among NTN participants. For GM, they were (−0.48, P < 0.001) and (−0.45, P < 0.001); and finally, for WM (−0.30, P < 0.001) and (−0.22, P = 0.001), for the HTN and NTN groups, respectively. Group × time interaction terms were not significant for models with brain or WML volumes, indicating that rates of change did not differ between groups. Figure S2 presents BP and WML changes in both groups.\nSBP did not change significantly in the HTN or NTN group, but the interaction term was significant (P = 0.01) indicating divergent pattern of SBP dynamics (the estimate = −1.6, P = 0.07 for HTN and 1.4, P = 0.07 for NTN). Similarly, DBP patterns were different (P = 0.03 for interaction term) with a lack of change in the HTN participants (estimate −0.6, P = 0.31) and a significant increase in the NTN group (the estimate = 1.1, P = 0.03). In both groups, WML volumes increased with time, (the estimate = 0.025, P < 0.001) for HTN and (0.028, P < 0.001) for NTN. In both groups, brain volumes decreased with time. An estimate for the fixed effect of time for the mean hippocampal volumes was (−0.0044, P < 0.001) for HTN and (−0.0038, P < 0.001) among NTN participants. For GM, they were (−0.48, P < 0.001) and (−0.45, P < 0.001); and finally, for WM (−0.30, P < 0.001) and (−0.22, P = 0.001), for the HTN and NTN groups, respectively. Group × time interaction terms were not significant for models with brain or WML volumes, indicating that rates of change did not differ between groups. Figure S2 presents BP and WML changes in both groups.\nChanges in BP and volumes over time SBP did not change significantly in the HTN or NTN group, but the interaction term was significant (P = 0.01) indicating divergent pattern of SBP dynamics (the estimate = −1.6, P = 0.07 for HTN and 1.4, P = 0.07 for NTN). Similarly, DBP patterns were different (P = 0.03 for interaction term) with a lack of change in the HTN participants (estimate −0.6, P = 0.31) and a significant increase in the NTN group (the estimate = 1.1, P = 0.03). In both groups, WML volumes increased with time, (the estimate = 0.025, P < 0.001) for HTN and (0.028, P < 0.001) for NTN. In both groups, brain volumes decreased with time. An estimate for the fixed effect of time for the mean hippocampal volumes was (−0.0044, P < 0.001) for HTN and (−0.0038, P < 0.001) among NTN participants. For GM, they were (−0.48, P < 0.001) and (−0.45, P < 0.001); and finally, for WM (−0.30, P < 0.001) and (−0.22, P = 0.001), for the HTN and NTN groups, respectively. Group × time interaction terms were not significant for models with brain or WML volumes, indicating that rates of change did not differ between groups. Figure S2 presents BP and WML changes in both groups.\nSBP did not change significantly in the HTN or NTN group, but the interaction term was significant (P = 0.01) indicating divergent pattern of SBP dynamics (the estimate = −1.6, P = 0.07 for HTN and 1.4, P = 0.07 for NTN). Similarly, DBP patterns were different (P = 0.03 for interaction term) with a lack of change in the HTN participants (estimate −0.6, P = 0.31) and a significant increase in the NTN group (the estimate = 1.1, P = 0.03). In both groups, WML volumes increased with time, (the estimate = 0.025, P < 0.001) for HTN and (0.028, P < 0.001) for NTN. In both groups, brain volumes decreased with time. An estimate for the fixed effect of time for the mean hippocampal volumes was (−0.0044, P < 0.001) for HTN and (−0.0038, P < 0.001) among NTN participants. For GM, they were (−0.48, P < 0.001) and (−0.45, P < 0.001); and finally, for WM (−0.30, P < 0.001) and (−0.22, P = 0.001), for the HTN and NTN groups, respectively. Group × time interaction terms were not significant for models with brain or WML volumes, indicating that rates of change did not differ between groups. Figure S2 presents BP and WML changes in both groups.\nSecondary analyses Table S4 shows the stability of secondary group definition (untreated, uncontrolled and controlled HTN, Stage 1 HTN, and normotension). Table S5 presents the results from MMRM analyses of WML volumes and BP changes conducted with all five subgroups defined at baseline based on HTN control status. One can appreciate that WML volume increased significantly in untreated and controlled hypertension, as well as in normotensive participants.\nTable S6 and Figure S3 shows that participants who at baseline were classified as untreated (n = 19) or uncontrolled hypertension (n = 20) and reclassified at the last visit as good outcome (improved BP control) experience the same increase in WML volumes as participants with bad outcome (BP remained untreated or uncontrolled).\nBaseline BP and changes in white matter lesion and brain volumes In the hypertensive group both baseline SBP (β = 0.33, P = 0.007) and DBP (β = −0.30, P = 0.008) were related to WML changes over time (entire model F4,83 = 10.5, P < 0.001, included also baseline WML volumes and smoking). Baseline BP was not related to changes in hippocampal, GM, or WM volumes.\nIn normotensive participants baseline BP was not related to change in any examined volumes.\nIn the hypertensive group both baseline SBP (β = 0.33, P = 0.007) and DBP (β = −0.30, P = 0.008) were related to WML changes over time (entire model F4,83 = 10.5, P < 0.001, included also baseline WML volumes and smoking). Baseline BP was not related to changes in hippocampal, GM, or WM volumes.\nIn normotensive participants baseline BP was not related to change in any examined volumes.\nLongitudinal changes in BP and changes in white matter lesion and brain volumes In the HTN group, neither change in SBP nor DBP were associated with a change in WML, hippocampal, GM or WM volumes (Tables S7 and S8).\nIn the NTN group, both increase in SBP (an estimate for the fixed effect of SBP = 0.0023, P = 0.01) and DBP (an estimate for the fixed effect of DBP = 0.0035, P = 0.01) over time were related to the increase in WML volume. The significant interaction term indicated that these dynamics were meaningfully different from the HTN group. Moreover, in the normotensive group both increase in SBP (an estimate for the fixed effect of SBP = −0.0002, P = 0.03) and DBP (an estimate for the fixed effect of DBP = −0.0004, P = 0.01) over time were related to reduction in the mean hippocampal volume. However, only for SBP the interaction term was significant, indicating that these changes were different from HTN group.\nResults for pulse pressure are presented in the Supplement p. 8–9.\nIn the HTN group, neither change in SBP nor DBP were associated with a change in WML, hippocampal, GM or WM volumes (Tables S7 and S8).\nIn the NTN group, both increase in SBP (an estimate for the fixed effect of SBP = 0.0023, P = 0.01) and DBP (an estimate for the fixed effect of DBP = 0.0035, P = 0.01) over time were related to the increase in WML volume. The significant interaction term indicated that these dynamics were meaningfully different from the HTN group. Moreover, in the normotensive group both increase in SBP (an estimate for the fixed effect of SBP = −0.0002, P = 0.03) and DBP (an estimate for the fixed effect of DBP = −0.0004, P = 0.01) over time were related to reduction in the mean hippocampal volume. However, only for SBP the interaction term was significant, indicating that these changes were different from HTN group.\nResults for pulse pressure are presented in the Supplement p. 8–9.\nTable S4 shows the stability of secondary group definition (untreated, uncontrolled and controlled HTN, Stage 1 HTN, and normotension). Table S5 presents the results from MMRM analyses of WML volumes and BP changes conducted with all five subgroups defined at baseline based on HTN control status. One can appreciate that WML volume increased significantly in untreated and controlled hypertension, as well as in normotensive participants.\nTable S6 and Figure S3 shows that participants who at baseline were classified as untreated (n = 19) or uncontrolled hypertension (n = 20) and reclassified at the last visit as good outcome (improved BP control) experience the same increase in WML volumes as participants with bad outcome (BP remained untreated or uncontrolled).\nBaseline BP and changes in white matter lesion and brain volumes In the hypertensive group both baseline SBP (β = 0.33, P = 0.007) and DBP (β = −0.30, P = 0.008) were related to WML changes over time (entire model F4,83 = 10.5, P < 0.001, included also baseline WML volumes and smoking). Baseline BP was not related to changes in hippocampal, GM, or WM volumes.\nIn normotensive participants baseline BP was not related to change in any examined volumes.\nIn the hypertensive group both baseline SBP (β = 0.33, P = 0.007) and DBP (β = −0.30, P = 0.008) were related to WML changes over time (entire model F4,83 = 10.5, P < 0.001, included also baseline WML volumes and smoking). Baseline BP was not related to changes in hippocampal, GM, or WM volumes.\nIn normotensive participants baseline BP was not related to change in any examined volumes.\nLongitudinal changes in BP and changes in white matter lesion and brain volumes In the HTN group, neither change in SBP nor DBP were associated with a change in WML, hippocampal, GM or WM volumes (Tables S7 and S8).\nIn the NTN group, both increase in SBP (an estimate for the fixed effect of SBP = 0.0023, P = 0.01) and DBP (an estimate for the fixed effect of DBP = 0.0035, P = 0.01) over time were related to the increase in WML volume. The significant interaction term indicated that these dynamics were meaningfully different from the HTN group. Moreover, in the normotensive group both increase in SBP (an estimate for the fixed effect of SBP = −0.0002, P = 0.03) and DBP (an estimate for the fixed effect of DBP = −0.0004, P = 0.01) over time were related to reduction in the mean hippocampal volume. However, only for SBP the interaction term was significant, indicating that these changes were different from HTN group.\nResults for pulse pressure are presented in the Supplement p. 8–9.\nIn the HTN group, neither change in SBP nor DBP were associated with a change in WML, hippocampal, GM or WM volumes (Tables S7 and S8).\nIn the NTN group, both increase in SBP (an estimate for the fixed effect of SBP = 0.0023, P = 0.01) and DBP (an estimate for the fixed effect of DBP = 0.0035, P = 0.01) over time were related to the increase in WML volume. The significant interaction term indicated that these dynamics were meaningfully different from the HTN group. Moreover, in the normotensive group both increase in SBP (an estimate for the fixed effect of SBP = −0.0002, P = 0.03) and DBP (an estimate for the fixed effect of DBP = −0.0004, P = 0.01) over time were related to reduction in the mean hippocampal volume. However, only for SBP the interaction term was significant, indicating that these changes were different from HTN group.\nResults for pulse pressure are presented in the Supplement p. 8–9.","BP and white matter lesion volumes In the HTN group, SBP and quadratic SBP term were predictors of WML volume at baseline (regression model included age, F3,168 = 14.4, P < 0.001) (Table 2, Fig. 1). The results were similar with log-transformed data (F3,168 = 13.1, P < 0.001) (Table 3). The critical SBP value calculated from equation [2] was x = 124 mmHg.\nLinear regression model predicting WML volume in the HTN group at baseline (raw data)\nCritical value (Eq. [2]): x = –(−0.07317/(2 × 0.0003)) = 122 mmHg.\nHTN, hypertension; WML, white matter lesion.\nQuadratic relationship between systolic blood pressure readings and white matter lesion measurements in hypertensive participants.\nLinear regression model predicting WML volume in the HTN group at baseline (log transformed data: log(WML volume))\nCritical value (Eq. [2]): x = –(−0.02741/(2 × 0.00011)) = 124.6 mmHg.\nHTN, hypertension; WML, white matter lesion.\nIn the NTN group, neither SBP nor DBP were related to WML.\nIn the HTN group, SBP and quadratic SBP term were predictors of WML volume at baseline (regression model included age, F3,168 = 14.4, P < 0.001) (Table 2, Fig. 1). The results were similar with log-transformed data (F3,168 = 13.1, P < 0.001) (Table 3). The critical SBP value calculated from equation [2] was x = 124 mmHg.\nLinear regression model predicting WML volume in the HTN group at baseline (raw data)\nCritical value (Eq. [2]): x = –(−0.07317/(2 × 0.0003)) = 122 mmHg.\nHTN, hypertension; WML, white matter lesion.\nQuadratic relationship between systolic blood pressure readings and white matter lesion measurements in hypertensive participants.\nLinear regression model predicting WML volume in the HTN group at baseline (log transformed data: log(WML volume))\nCritical value (Eq. [2]): x = –(−0.02741/(2 × 0.00011)) = 124.6 mmHg.\nHTN, hypertension; WML, white matter lesion.\nIn the NTN group, neither SBP nor DBP were related to WML.\nBP and brain volumes In HTN, the quadratic DBP term was a predictor of the mean hippocampal volume at baseline (the model included age and sex, F4,168 = 11.6, P < 0.001) (Table 4, Fig. 2). The critical DBP value calculated from equation [2] was x = 77 mmHg.\nModel predicting mean hippocampal volume in the HTN group at baseline\nCritical value (Eq. [2]): x = −(0.00399/(2 × (−0.000026))) = 77 mmHg.\nHTN, hypertension; WML, white matter lesion.\nRelationship between diastolic blood pressure readings and mean hippocampal brain volume measurements in hypertensive participants.\nGM was associated with SBP (β = −0.16, P = 0.02) and the model (F4,166 = 14.0, P < 0.001) included BMI, smoking and age.\nIn NTN, the mean hippocampal volume was inversely related to SBP (β = −0.17, P < 0.008), (regression model included age and sex, F3,206 = 22.4, P < 0.001). GM volume was inversely related to DBP (β = −0.15, P = 0.01), (the model included age and sex, F3,206 = 32.3, P < 0.001).\nIn HTN, the quadratic DBP term was a predictor of the mean hippocampal volume at baseline (the model included age and sex, F4,168 = 11.6, P < 0.001) (Table 4, Fig. 2). The critical DBP value calculated from equation [2] was x = 77 mmHg.\nModel predicting mean hippocampal volume in the HTN group at baseline\nCritical value (Eq. [2]): x = −(0.00399/(2 × (−0.000026))) = 77 mmHg.\nHTN, hypertension; WML, white matter lesion.\nRelationship between diastolic blood pressure readings and mean hippocampal brain volume measurements in hypertensive participants.\nGM was associated with SBP (β = −0.16, P = 0.02) and the model (F4,166 = 14.0, P < 0.001) included BMI, smoking and age.\nIn NTN, the mean hippocampal volume was inversely related to SBP (β = −0.17, P < 0.008), (regression model included age and sex, F3,206 = 22.4, P < 0.001). GM volume was inversely related to DBP (β = −0.15, P = 0.01), (the model included age and sex, F3,206 = 32.3, P < 0.001).","In the HTN group, SBP and quadratic SBP term were predictors of WML volume at baseline (regression model included age, F3,168 = 14.4, P < 0.001) (Table 2, Fig. 1). The results were similar with log-transformed data (F3,168 = 13.1, P < 0.001) (Table 3). The critical SBP value calculated from equation [2] was x = 124 mmHg.\nLinear regression model predicting WML volume in the HTN group at baseline (raw data)\nCritical value (Eq. [2]): x = –(−0.07317/(2 × 0.0003)) = 122 mmHg.\nHTN, hypertension; WML, white matter lesion.\nQuadratic relationship between systolic blood pressure readings and white matter lesion measurements in hypertensive participants.\nLinear regression model predicting WML volume in the HTN group at baseline (log transformed data: log(WML volume))\nCritical value (Eq. [2]): x = –(−0.02741/(2 × 0.00011)) = 124.6 mmHg.\nHTN, hypertension; WML, white matter lesion.\nIn the NTN group, neither SBP nor DBP were related to WML.","In HTN, the quadratic DBP term was a predictor of the mean hippocampal volume at baseline (the model included age and sex, F4,168 = 11.6, P < 0.001) (Table 4, Fig. 2). The critical DBP value calculated from equation [2] was x = 77 mmHg.\nModel predicting mean hippocampal volume in the HTN group at baseline\nCritical value (Eq. [2]): x = −(0.00399/(2 × (−0.000026))) = 77 mmHg.\nHTN, hypertension; WML, white matter lesion.\nRelationship between diastolic blood pressure readings and mean hippocampal brain volume measurements in hypertensive participants.\nGM was associated with SBP (β = −0.16, P = 0.02) and the model (F4,166 = 14.0, P < 0.001) included BMI, smoking and age.\nIn NTN, the mean hippocampal volume was inversely related to SBP (β = −0.17, P < 0.008), (regression model included age and sex, F3,206 = 22.4, P < 0.001). GM volume was inversely related to DBP (β = −0.15, P = 0.01), (the model included age and sex, F3,206 = 32.3, P < 0.001).","Changes in BP and volumes over time SBP did not change significantly in the HTN or NTN group, but the interaction term was significant (P = 0.01) indicating divergent pattern of SBP dynamics (the estimate = −1.6, P = 0.07 for HTN and 1.4, P = 0.07 for NTN). Similarly, DBP patterns were different (P = 0.03 for interaction term) with a lack of change in the HTN participants (estimate −0.6, P = 0.31) and a significant increase in the NTN group (the estimate = 1.1, P = 0.03). In both groups, WML volumes increased with time, (the estimate = 0.025, P < 0.001) for HTN and (0.028, P < 0.001) for NTN. In both groups, brain volumes decreased with time. An estimate for the fixed effect of time for the mean hippocampal volumes was (−0.0044, P < 0.001) for HTN and (−0.0038, P < 0.001) among NTN participants. For GM, they were (−0.48, P < 0.001) and (−0.45, P < 0.001); and finally, for WM (−0.30, P < 0.001) and (−0.22, P = 0.001), for the HTN and NTN groups, respectively. Group × time interaction terms were not significant for models with brain or WML volumes, indicating that rates of change did not differ between groups. Figure S2 presents BP and WML changes in both groups.\nSBP did not change significantly in the HTN or NTN group, but the interaction term was significant (P = 0.01) indicating divergent pattern of SBP dynamics (the estimate = −1.6, P = 0.07 for HTN and 1.4, P = 0.07 for NTN). Similarly, DBP patterns were different (P = 0.03 for interaction term) with a lack of change in the HTN participants (estimate −0.6, P = 0.31) and a significant increase in the NTN group (the estimate = 1.1, P = 0.03). In both groups, WML volumes increased with time, (the estimate = 0.025, P < 0.001) for HTN and (0.028, P < 0.001) for NTN. In both groups, brain volumes decreased with time. An estimate for the fixed effect of time for the mean hippocampal volumes was (−0.0044, P < 0.001) for HTN and (−0.0038, P < 0.001) among NTN participants. For GM, they were (−0.48, P < 0.001) and (−0.45, P < 0.001); and finally, for WM (−0.30, P < 0.001) and (−0.22, P = 0.001), for the HTN and NTN groups, respectively. Group × time interaction terms were not significant for models with brain or WML volumes, indicating that rates of change did not differ between groups. Figure S2 presents BP and WML changes in both groups.","SBP did not change significantly in the HTN or NTN group, but the interaction term was significant (P = 0.01) indicating divergent pattern of SBP dynamics (the estimate = −1.6, P = 0.07 for HTN and 1.4, P = 0.07 for NTN). Similarly, DBP patterns were different (P = 0.03 for interaction term) with a lack of change in the HTN participants (estimate −0.6, P = 0.31) and a significant increase in the NTN group (the estimate = 1.1, P = 0.03). In both groups, WML volumes increased with time, (the estimate = 0.025, P < 0.001) for HTN and (0.028, P < 0.001) for NTN. In both groups, brain volumes decreased with time. An estimate for the fixed effect of time for the mean hippocampal volumes was (−0.0044, P < 0.001) for HTN and (−0.0038, P < 0.001) among NTN participants. For GM, they were (−0.48, P < 0.001) and (−0.45, P < 0.001); and finally, for WM (−0.30, P < 0.001) and (−0.22, P = 0.001), for the HTN and NTN groups, respectively. Group × time interaction terms were not significant for models with brain or WML volumes, indicating that rates of change did not differ between groups. Figure S2 presents BP and WML changes in both groups.","Table S4 shows the stability of secondary group definition (untreated, uncontrolled and controlled HTN, Stage 1 HTN, and normotension). Table S5 presents the results from MMRM analyses of WML volumes and BP changes conducted with all five subgroups defined at baseline based on HTN control status. One can appreciate that WML volume increased significantly in untreated and controlled hypertension, as well as in normotensive participants.\nTable S6 and Figure S3 shows that participants who at baseline were classified as untreated (n = 19) or uncontrolled hypertension (n = 20) and reclassified at the last visit as good outcome (improved BP control) experience the same increase in WML volumes as participants with bad outcome (BP remained untreated or uncontrolled).\nBaseline BP and changes in white matter lesion and brain volumes In the hypertensive group both baseline SBP (β = 0.33, P = 0.007) and DBP (β = −0.30, P = 0.008) were related to WML changes over time (entire model F4,83 = 10.5, P < 0.001, included also baseline WML volumes and smoking). Baseline BP was not related to changes in hippocampal, GM, or WM volumes.\nIn normotensive participants baseline BP was not related to change in any examined volumes.\nIn the hypertensive group both baseline SBP (β = 0.33, P = 0.007) and DBP (β = −0.30, P = 0.008) were related to WML changes over time (entire model F4,83 = 10.5, P < 0.001, included also baseline WML volumes and smoking). Baseline BP was not related to changes in hippocampal, GM, or WM volumes.\nIn normotensive participants baseline BP was not related to change in any examined volumes.\nLongitudinal changes in BP and changes in white matter lesion and brain volumes In the HTN group, neither change in SBP nor DBP were associated with a change in WML, hippocampal, GM or WM volumes (Tables S7 and S8).\nIn the NTN group, both increase in SBP (an estimate for the fixed effect of SBP = 0.0023, P = 0.01) and DBP (an estimate for the fixed effect of DBP = 0.0035, P = 0.01) over time were related to the increase in WML volume. The significant interaction term indicated that these dynamics were meaningfully different from the HTN group. Moreover, in the normotensive group both increase in SBP (an estimate for the fixed effect of SBP = −0.0002, P = 0.03) and DBP (an estimate for the fixed effect of DBP = −0.0004, P = 0.01) over time were related to reduction in the mean hippocampal volume. However, only for SBP the interaction term was significant, indicating that these changes were different from HTN group.\nResults for pulse pressure are presented in the Supplement p. 8–9.\nIn the HTN group, neither change in SBP nor DBP were associated with a change in WML, hippocampal, GM or WM volumes (Tables S7 and S8).\nIn the NTN group, both increase in SBP (an estimate for the fixed effect of SBP = 0.0023, P = 0.01) and DBP (an estimate for the fixed effect of DBP = 0.0035, P = 0.01) over time were related to the increase in WML volume. The significant interaction term indicated that these dynamics were meaningfully different from the HTN group. Moreover, in the normotensive group both increase in SBP (an estimate for the fixed effect of SBP = −0.0002, P = 0.03) and DBP (an estimate for the fixed effect of DBP = −0.0004, P = 0.01) over time were related to reduction in the mean hippocampal volume. However, only for SBP the interaction term was significant, indicating that these changes were different from HTN group.\nResults for pulse pressure are presented in the Supplement p. 8–9.","In the hypertensive group both baseline SBP (β = 0.33, P = 0.007) and DBP (β = −0.30, P = 0.008) were related to WML changes over time (entire model F4,83 = 10.5, P < 0.001, included also baseline WML volumes and smoking). Baseline BP was not related to changes in hippocampal, GM, or WM volumes.\nIn normotensive participants baseline BP was not related to change in any examined volumes.","In the HTN group, neither change in SBP nor DBP were associated with a change in WML, hippocampal, GM or WM volumes (Tables S7 and S8).\nIn the NTN group, both increase in SBP (an estimate for the fixed effect of SBP = 0.0023, P = 0.01) and DBP (an estimate for the fixed effect of DBP = 0.0035, P = 0.01) over time were related to the increase in WML volume. The significant interaction term indicated that these dynamics were meaningfully different from the HTN group. Moreover, in the normotensive group both increase in SBP (an estimate for the fixed effect of SBP = −0.0002, P = 0.03) and DBP (an estimate for the fixed effect of DBP = −0.0004, P = 0.01) over time were related to reduction in the mean hippocampal volume. However, only for SBP the interaction term was significant, indicating that these changes were different from HTN group.\nResults for pulse pressure are presented in the Supplement p. 8–9.","Longitudinal observations of brain health in aging have continuously concluded that BP has a significant impact on the nervous system. It is well known that increased cardiovascular burden, as represented by an elevated systolic or diastolic BP, can substantially increase the risk of developing hemorrhagic and ischemic stroke [1,2], white matter lesions and atrophy [14,32], and cognitive dysfunction [33,34].\nIn our examination of a cohort of normotensive and hypertensive participants, we posit that adequate BP is vital for a healthy brain. We have previously observed that in hypertension, there appears to be a window of mid-range SBP around 125 mmHg which maximizes perfusion [21]. In the present study, we show that hypertensive participants with a similar SBP (124 mmHg) have lower WML volumes that participants with SBP further away from this optimum. This is a novel finding further supporting the notion of an ideal blood pressure range that promotes optimal brain function and minimizes damage. Opposite to our results, the SPRINT-Mind study found that the group where BP was lowered aggressively to less than 120 experienced smaller WML progression over time [18]. However our findings are consistent with a report showing that in hypertensive participants low BP was related to higher PWML volume [35] and an earlier study where an increase in WML volume over time was most pronounced in participants with the highest BP who also had the greatest BP fluctuations [36].\nWe also find that among hypertensive participants there was a quadratic relationship between the mean hippocampal volume at baseline and DBP, such as both the participants with low and high DBP had lower volume. This observation, although interesting, is not completely novel. Others have previously described a parallel U-shape association between brain atrophy and DBP, where both low and high concurrent DBP were related to greater cortical volume reduction [37]. The SPRINT MIND study showed that intensive BP treatment group experienced significantly greater hippocampal volume reduction [38]. It further confirms the hypothesis indicating the existence of a BP optimum, and reconciles reports showing that both high [39] and low BP [40] are related to lower hippocampal volumes.\nAmong normotensive participants relationships between BP and brain volumes were linear such as both higher SBP and DBP were related to lower brain volumes. It is congruent with previous reports and meta-analyses showing both BP components associated with volumes [3]. Notably, no quadratic relationship was observed in this group, further strengthening our initial assumption that optimum exists in the group with preexisting impairment of the vascular system.\nReduction in the volume of specific brain regions over time is another parameter indicative of abnormal aging and is often seen reported in HTN [3,41]. Our HTN group had more global and hippocampal atrophy at baseline (Table 1) than their normotensive counterparts. However, after adjustment for age, the WML volume did not differ between HTN and NTN groups. It is possible that substantial percentage (55%) of participants with controlled BP attenuated the differences. Comparisons across five groups showed that participants with untreated and uncontrolled hypertension tended to have higher WML volumes (Table S2).\nIn our hypertensive participants, higher baseline SBP and lower DBP were both independently related to the growth of WML over time. While the first finding is not surprising, we offer two possible explanations for the latter. First, as the mean arterial pressure, the major driver of perfusion pressure, depends mostly on DBP, the low DBP in HTN may indicate the propensity to hypoperfusion. Second, this association would be consistent with widening of pulse pressure, indicative of arterial stiffness. Indeed, an earlier analysis of the Lothian Birth Cohort 1936 showed that the sequence of DBP reduction, PP increase and rising internal carotid artery pulsatility index leads to WM damage [42]. Our supplemental analyses also confirm that baseline PP was related to WML growth.\nIt is worth noting that both normo and hypertensive groups experienced reductions in brain volumes and WML growth over time. This was present despite dissimilar trajectories of blood pressure: increases in the NTN and apparent lack of change in the HTN participants. A more fine-grained picture emerged from analyzing five groups (Table S5): WML lesions grew despite BP reductions in the untreated group and similar trend was observed among individuals with uncontrolled HTN, yet lesion volume increased concomitantly to BP increases in participants with controlled HTN and among normotensive participants. Even more interestingly we show that WML growth was similar in participants with baseline untreated/ uncontrolled HTN who at the last visit had disparate outcomes: improved or unchanged BP control (Table S6). It stands in opposition to the results of SPRINT-MIND trial where smaller increase in WML volume was observed in participants with more aggressive BP lowering [18]. We believe that the main reason for this discrepancy is that our groups were much smaller and follow-up time shorter than in SPRINT study. In addition, the difference in WML volume change between SPRINT groups amounted only to 0.5 cm3.\nHowever, since BP load over time is unknown, it is also likely that in both groups BP was not controlled for a long time, and one group showed improvement only on the last measurement. Both groups could have also experienced similar, strong longitudinal BP fluctuation, which increase the probability of WML progression [36]. Then again, our results confirm that baseline WML volumes are good predictor of lesion progression, consistent with previous reports [43], and suggest that once the pathological process is set in motion, there is limited room for its modification. Such view has important implications for patient management, further strengthening the notion that early intervention (close monitoring of BP in normotensives and early treatment) would be most successful. Finally, the findings are also consistent with our hypothesis that in the setting of impaired cerebral flow regulation in participants with more severe form of HTN BP reductions are harmful.\nNot surprisingly, in the light of the above considerations, only in the group of normotensive individuals, longitudinal increases of SBP and DBP were related to lesion growth and reduction is mean hippocampal volume. It is to be expected, as rising BP would contribute to brain deterioration. This finding is also in agreement with a previous report of community residing participants over the age of 75, where the 2-year change in ambulatory SBP was associated with the 2-year WML increase [44].\nOur study suffered from a few limitations. First, only one measurement of blood pressure was taken at the time, instead of the average of three consecutive ones. It could have resulted in misclassification of cases. This was especially true for ‘untreated’: only 63% of participants defined at baseline as untreated were still classified as hypertensive at follow-up. However, high baseline WML volume in this group (Table S2) speaks against the possibility of misclassification. Almost everybody in the controlled and uncontrolled HTN groups remained hypertensive at follow-up, and 88% (91/104) of participants normotensive (<140/90) at baseline were normotensive at follow-up. Although, the method for BP measurement was not optimal it matched closely a common real-life practice. Second, the interval between assessment was short, and since WML progression is slow it might have precluded us from observing significant differences between groups. Analysis of participants WML volume did not include a differentiation of periventricular lesions from deep lesions. It is possible that the effects of hypertension of WML development could vary by lesion location. We did not perform manual segmentation of silent infractions. It may have artificially inflated lesion volumes, as some infarcts might have been erroneously classified as WML. Wang et al.[45] previously reported that including stroke lesions could bias WML volume estimation by as much as 20%. Although, in our case the error would have been likely smaller, since their study included patients recruited soon after presenting with stroke symptoms, while we excluded participants with overt infarcts, the misclassification cannot be excluded.\nOnly a half of participants had follow-up. Although participants studied only once and those with additional examinations did not differ in most of the characteristics, it still could have caused a bias. Another limitation was that our group was predominantly Caucasian, with high level of educational achievement thus generalizability to a more diverse cohort is uncertain. We did not have data on the duration of hypertension, which can modify the relationships we examined. Normotensive individuals were slightly (on average 3 years) but statistically significantly younger than hypertensive participants. It is plausible that had they been older, we might have seen stronger associations between BP and WML. A recent meta-analysis showed that the optimal blood pressure value changes depending on age [20]. We did not have a group large enough to address whether this is the case also for white matter lesions. Finally, since our study was observational, results cannot carry the same weight a these derived from clinical trial, and causal relations cannot be inferred.\nIn conclusion, we find that among hypertensive participants there was a quadratic relationship between BP and WML as well as mean hippocampal volume. The WML burden was the smallest when SBP was close to 124 mmHg. The mean hippocampal volume was the highest when DBP was in high 70-mmHg range. Such phenomena were not observed in the normotensive group, suggesting the existence of a BP optimum in hypertension. The current report extends our earlier findings showing that cerebral perfusion is maximized when systolic BP is in 120–130 mmHg range. Longitudinally, all groups experienced lesion growth, despite different BP trajectories, further suggesting the possibility that WML expansion may occur despite or because of BP reduction in individuals with compromised vascular system. Considering growing evidence showing nonlinear association between blood pressure an outcomes further clinical trials are needed.","A special thanks to Ke Xi for her help with the statistical modeling as well as assistance in the production of regression plots for this manuscript.\nSources of funding: Study funding comes from NIH grants HL111724, NS104364, AG022374, AG12101, AG08051, and Alzheimer's Association NIRG-09-132490.\nDisclosures: None\nConflicts of interest There are no conflicts of interest.\nThere are no conflicts of interest.","There are no conflicts of interest.",""],"string":"[\n \"The impact of cardiovascular disease on the United States’ population cannot be overstated. One out of every three Americans is hypertensive, with 66% of adults aged 60 or older exhibiting above average blood pressure readings [1,2]. It was estimated in 2018 that 11 million individuals in the United States were living with undiagnosed hypertension (HTN), placing them at an increased risk for adverse cardiovascular events that can directly affect the central nervous system as well as cognition [1,2].\\nHigh blood pressure (BP) can cause damage and dysfunction to organ systems with perhaps the most significant of such being the brain and the heart [3–5]. Hypertensive patients develop atherosclerotic plaques within the large blood vessels supplying blood flow both into and out of the brain at an accelerated rate, increasing their risk of an ischemic event [6,7]. HTN can cause a remodeling of the brain microvasculature with vessels exhibiting rarefication and narrowing lumen [4,6,8]. Such changes in vessel architecture can have negative effects on cerebral blood flow (CBF) and autoregulation. Reduction in CBF has been shown to cause brain atrophy both in animal models [9] and in clinical population [10], and has been consistently linked with white matter damage [11,12]. White matter lesions (WML) are very common in HTN participants [13–15]. Indeed, several studies have confirmed that two major risk factors for WML development are increased age and high BP, with smoking, high cholesterol, and heart disease also being significant contributors to lesion formation [14,16,17].\\nAlthough benefits of HTN treatment are indisputable for major cardiovascular outcome, the target blood pressure for the brain remains under debate. While some argue for aggressive blood pressure reductions [18], others point to possible danger of this approach [19], as marked lowering of BP in longstanding HTN may cause relative hypoperfusion. This suggested to us the existence of an optimum BP range.\\nA recent metanalysis found a U-shape relationship between SBP and a risk of dementia in participants older than 75, as well as U-shape relationship between SBP and combined dementia and mortality across all age groups starting at 60 [20]. Our group has previously reported a quadratic (inverted U-shape) relationship between CBF and SBP in hypertensive patients, pointing to the optimum lying between 120 and 130 mmHg where CBF was the highest [21]. It was consistently shown that WML volumes are inversely related to global and local indices of CBF [22]. In this observational report, we investigated whether in hypertensive participants WML and atrophy, structural markers of HTN-related brain damage, show relationship with SBP, which is parallel to the one we observed for CBF and SBP: namely is there a quadratic association between them.\",\n \"The data of this study are available from the corresponding author upon request.\\nParticipants recruitment Study participants were recruited at the NYU School of Medicine, at the former Center for Brain Health. Out of 376 individuals described in this report, 369 (98%) were previously a part of a study assessing the relationship between BP and CBF. Here, we report on participants with quantitative assessment of white matter pathology. Baseline assessment was performed in a larger group of n = 376 participants, and n = 188 of them had follow-up examinations. The reasons for lack of follow-up are described in the Supplement. Participants were enrolled between November 2010 and November 2017. All participants signed Institutional Review Board (ethics committee)-approved consent forms and were free of neocortical stroke, brain tumor, life-long psychotic disorders and dementia at baseline. Information pertaining to recruitment, exclusion criteria, and participant flow is presented in the Supplement.\\nStudy participants were recruited at the NYU School of Medicine, at the former Center for Brain Health. Out of 376 individuals described in this report, 369 (98%) were previously a part of a study assessing the relationship between BP and CBF. Here, we report on participants with quantitative assessment of white matter pathology. Baseline assessment was performed in a larger group of n = 376 participants, and n = 188 of them had follow-up examinations. The reasons for lack of follow-up are described in the Supplement. Participants were enrolled between November 2010 and November 2017. All participants signed Institutional Review Board (ethics committee)-approved consent forms and were free of neocortical stroke, brain tumor, life-long psychotic disorders and dementia at baseline. Information pertaining to recruitment, exclusion criteria, and participant flow is presented in the Supplement.\\nClinical assessment All participants underwent medical, psychiatric, and neurological assessments, blood tests, electrocardiogram (ECG), and magnetic resonance imaging (MRI) evaluation examinations at baseline, and the longitudinal group also at follow-up. Participants exhibiting dementia, based on a physician-administered interview using the Brief Cognitive Rating Scale, rating on the Global Deterioration Scale [23], and Clinical Dementia Rating were excluded [24]. Fasting blood was tested for complete blood count, liver function, as well as metabolic and lipid panel.\\nBlood pressure was taken once in a sitting position, after 5 min of rest. It was measured on the left upper arm using a manual sphygmomanometer. Body mass index (BMI) was calculated as weight/height (kg/m) [2].\\nMedication: We recoded the use of antihypertensive medications (angiotensin receptor blockers (ARBs), angiotensin converting enzyme inhibitors (ACE), beta-blockers, diuretics, and calcium channel blockers), statins and glucose lowering drugs.\\nDiabetes mellitus was defined as fasting glucose level of 126 mg/dl and higher and/or current use of glucose lowering medication [25]. Smoking status was defined as positive if participants was a current smoker or smoked within last 10 years.\\nAll participants underwent medical, psychiatric, and neurological assessments, blood tests, electrocardiogram (ECG), and magnetic resonance imaging (MRI) evaluation examinations at baseline, and the longitudinal group also at follow-up. Participants exhibiting dementia, based on a physician-administered interview using the Brief Cognitive Rating Scale, rating on the Global Deterioration Scale [23], and Clinical Dementia Rating were excluded [24]. Fasting blood was tested for complete blood count, liver function, as well as metabolic and lipid panel.\\nBlood pressure was taken once in a sitting position, after 5 min of rest. It was measured on the left upper arm using a manual sphygmomanometer. Body mass index (BMI) was calculated as weight/height (kg/m) [2].\\nMedication: We recoded the use of antihypertensive medications (angiotensin receptor blockers (ARBs), angiotensin converting enzyme inhibitors (ACE), beta-blockers, diuretics, and calcium channel blockers), statins and glucose lowering drugs.\\nDiabetes mellitus was defined as fasting glucose level of 126 mg/dl and higher and/or current use of glucose lowering medication [25]. Smoking status was defined as positive if participants was a current smoker or smoked within last 10 years.\\nStudy groups For consistency with our previous study hypertension was defined as current antihypertensive treatment or BP ≥140/90 mmHg [26]. One hundred and sixty-nine participants were classified as hypertensive; 136 participants were taking antihypertensive medication and 42 were not taking any antihypertensive medication with high BP recorded during their in-office visit. Normotension (NTN) was defined as BP <140/90 mmHg and no antihypertensive treatment.\\nFor descriptive purpose and secondary analyses, we described hypertensive participants as untreated: no antihypertensive treatment and BP ≥140/90 mmHg; controlled: antihypertensive treatment and BP <140/90 mmHg, uncontrolled: antihypertensive treatment and BP ≥140/90 mmHg. We also divided our normotensive group into Stage 1 hypertension based on new guidelines: [27] no treatment and BP between 130–139/80–89 mmHg, and normotensive BP <130/80 mmHg.\\nFor consistency with our previous study hypertension was defined as current antihypertensive treatment or BP ≥140/90 mmHg [26]. One hundred and sixty-nine participants were classified as hypertensive; 136 participants were taking antihypertensive medication and 42 were not taking any antihypertensive medication with high BP recorded during their in-office visit. Normotension (NTN) was defined as BP <140/90 mmHg and no antihypertensive treatment.\\nFor descriptive purpose and secondary analyses, we described hypertensive participants as untreated: no antihypertensive treatment and BP ≥140/90 mmHg; controlled: antihypertensive treatment and BP <140/90 mmHg, uncontrolled: antihypertensive treatment and BP ≥140/90 mmHg. We also divided our normotensive group into Stage 1 hypertension based on new guidelines: [27] no treatment and BP between 130–139/80–89 mmHg, and normotensive BP <130/80 mmHg.\\nMR imaging All MR imaging was performed on the 3T system (Siemens, Erlangen, Germany). Parameters for Magnetization Prepared Rapid Acquisition Gradient Echo (MPRAGE) images were: repetition time (TR) = 2250 ms, echo time (TE) = 2.7 ms, inversion time (TI) = 900 ms, flip angle (FA) = 8°, slice thickness: 1.0 mm, field of view (FOV) = 200 mm, acquisition matrix = 256 × 192 × 124, reconstructed as 256 × 256 × 124. Axial fluid attenuation inversion recovery (FLAIR) images were acquired with TR/TE/TI 9000/99/2500 ms; FA 130°, slice thickness: 3.3 mm, field of view (FOV) 220 mm, matrix = 256 × 192 reconstructed as thirty 256 × 256 images.\\nAll MR imaging was performed on the 3T system (Siemens, Erlangen, Germany). Parameters for Magnetization Prepared Rapid Acquisition Gradient Echo (MPRAGE) images were: repetition time (TR) = 2250 ms, echo time (TE) = 2.7 ms, inversion time (TI) = 900 ms, flip angle (FA) = 8°, slice thickness: 1.0 mm, field of view (FOV) = 200 mm, acquisition matrix = 256 × 192 × 124, reconstructed as 256 × 256 × 124. Axial fluid attenuation inversion recovery (FLAIR) images were acquired with TR/TE/TI 9000/99/2500 ms; FA 130°, slice thickness: 3.3 mm, field of view (FOV) 220 mm, matrix = 256 × 192 reconstructed as thirty 256 × 256 images.\\nMRI processing White matter lesions were segmented on FLAIR MRI with freely available image analysis software FireVoxel (https://firevoxel.org/) using a previously validated workflow [28]. FLAIR images were first corrected for signal nonuniformity using the N3 algorithm. A 3D mask M was then created over the entire brain parenchyma, that is, the gray and white matter, with cerebrospinal fluid (CSF) excluded [29]. Voxels at the external surface S of M were then identified. Next was creation of set L, containing voxels in M with signal intensity 2.5 standard deviations above the mean FLAIR signal. Finally, L was filtered to remove (a) cortical voxels, those located within 3 mm of S, (b) small clusters of volume <12 mm3 (presumed to represent image noise), and (c) connected regions having >50% CSF border (presumed to represent the choroid plexus and the septum). The resulting mask represents our estimate of WML. Its volume VWML is presented as percentage of the total brain volume.\\nGray matter (GM), white matter (WM) and CSF volumes were estimated using Statistical Parametric Mapping segmentation procedure (SPM, version 8, with ‘New-Segment’ extension) [30]. Total brain volume was the sum of GM and WM volumes. Intracranial volume (ICV) was the sum of all tissue types. Left and right hippocampal volumes were obtained with FreeSurfer version 6.0 [31], and averaged. They are referred to from now on as ‘mean hippocampal volume’. GM, WM and hippocampal volumes are presented as a fraction of the ICV. MRI data were acquired over the span of 9 years a 3T magnet that underwent hardware and software upgrades. We detected the inter-epoch variability in GM measurements. To avoid this fixed bias we z-scored GM values separately by epoch, recentered and rescaled them. Even though no other structural measure was affected, for consistency, we used rescaled values for hippocampal and WM volumes.\\nWhite matter lesions were segmented on FLAIR MRI with freely available image analysis software FireVoxel (https://firevoxel.org/) using a previously validated workflow [28]. FLAIR images were first corrected for signal nonuniformity using the N3 algorithm. A 3D mask M was then created over the entire brain parenchyma, that is, the gray and white matter, with cerebrospinal fluid (CSF) excluded [29]. Voxels at the external surface S of M were then identified. Next was creation of set L, containing voxels in M with signal intensity 2.5 standard deviations above the mean FLAIR signal. Finally, L was filtered to remove (a) cortical voxels, those located within 3 mm of S, (b) small clusters of volume <12 mm3 (presumed to represent image noise), and (c) connected regions having >50% CSF border (presumed to represent the choroid plexus and the septum). The resulting mask represents our estimate of WML. Its volume VWML is presented as percentage of the total brain volume.\\nGray matter (GM), white matter (WM) and CSF volumes were estimated using Statistical Parametric Mapping segmentation procedure (SPM, version 8, with ‘New-Segment’ extension) [30]. Total brain volume was the sum of GM and WM volumes. Intracranial volume (ICV) was the sum of all tissue types. Left and right hippocampal volumes were obtained with FreeSurfer version 6.0 [31], and averaged. They are referred to from now on as ‘mean hippocampal volume’. GM, WM and hippocampal volumes are presented as a fraction of the ICV. MRI data were acquired over the span of 9 years a 3T magnet that underwent hardware and software upgrades. We detected the inter-epoch variability in GM measurements. To avoid this fixed bias we z-scored GM values separately by epoch, recentered and rescaled them. Even though no other structural measure was affected, for consistency, we used rescaled values for hippocampal and WM volumes.\\nStatistics Categorical variables were compared with χ2 tests. T-test, Mann–Whitney U-test, and analysis of covariance (ANCOVA) were used to compare group means for continuous variables, depending on the data distribution and the need to adjust for age and sex. Normality was tested with Shapiro–Wilk test.\\nRelationships between BP, WML volume and brain volumes (hippocampal, GM, WM) at baseline were examined with linear regression. Volumes were dependent variables; age, sex, body mass index, smoking, diabetes, systolic blood pressure and diastolic blood pressure were independent predictors. These relationships were tested in participants with and without hypertension separately. We present here F-statistics for the regression models, as well unstandardized (B) and standardized (β) coefficients for individual predictors. Again, we hypothesized that in the HTN group an optimal BP may exist [21], which minimizes WML volume and maximizes brain volumes. Thus, as before, we tested for the existence of the peak, by introducing both the linear BP and quadratic (BP [2]) terms into the regression models.\\nAfter a quadratic relationship between the volume and BP was confirmed, the critical BP at which volume reaches its maximum or minimum value was determined as:\\nTo examine whether BP, WML and brain volumes changed over time, we used mixed models for repeated measures (MMRM), where BP or volume was a dependent variable and time, group (HTN vs. NTN), and group × time interaction were predictors. For models with WML or brain volumes age as added as a covariate. Both intercept and slope were treated as random.\\nTo test whether baseline BP predicted change in WML and brain volumes, we created volume rates of change by regressing volume on time for each participant and tested whether baseline SBP and DBP was associated with volume slope. Baseline volumes, BMI, age, sex, smoking and diabetes status were initially included in the models, and retained only when significant. The relationships were tested in the normo- and hypertensive participants separately. For all the analyses, the most parsimonious model was chosen, defined as the model that included only significant or necessary (main effects when interaction was present) terms.\\nTo test whether change in BP (independent variable) was related to change in volume (dependent) we used MMRM. WML or brain volumes were a dependent variable and BP (SBP or DBP), group (HTN vs. NTN), and group × BP interaction were predictors. Both intercept and slope were treated as random. SBP and DBP were predictors in separate models.\\nWe performed supplementary analyses using pulse pressure (PP) as a predictor in all the models.\\nWML volumes were log transformed. We checked the linear models for violations of models assumptions. For all the analyses, the most parsimonious model was chosen, defined as the model that included only significant or necessary (main effects when interaction was present) terms. Statistical significance was defined as a P-value <0.05. SPSS (version 25, SPSS, Inc., Chicago, Illinois, USA) software was used for all analyses.\\nCategorical variables were compared with χ2 tests. T-test, Mann–Whitney U-test, and analysis of covariance (ANCOVA) were used to compare group means for continuous variables, depending on the data distribution and the need to adjust for age and sex. Normality was tested with Shapiro–Wilk test.\\nRelationships between BP, WML volume and brain volumes (hippocampal, GM, WM) at baseline were examined with linear regression. Volumes were dependent variables; age, sex, body mass index, smoking, diabetes, systolic blood pressure and diastolic blood pressure were independent predictors. These relationships were tested in participants with and without hypertension separately. We present here F-statistics for the regression models, as well unstandardized (B) and standardized (β) coefficients for individual predictors. Again, we hypothesized that in the HTN group an optimal BP may exist [21], which minimizes WML volume and maximizes brain volumes. Thus, as before, we tested for the existence of the peak, by introducing both the linear BP and quadratic (BP [2]) terms into the regression models.\\nAfter a quadratic relationship between the volume and BP was confirmed, the critical BP at which volume reaches its maximum or minimum value was determined as:\\nTo examine whether BP, WML and brain volumes changed over time, we used mixed models for repeated measures (MMRM), where BP or volume was a dependent variable and time, group (HTN vs. NTN), and group × time interaction were predictors. For models with WML or brain volumes age as added as a covariate. Both intercept and slope were treated as random.\\nTo test whether baseline BP predicted change in WML and brain volumes, we created volume rates of change by regressing volume on time for each participant and tested whether baseline SBP and DBP was associated with volume slope. Baseline volumes, BMI, age, sex, smoking and diabetes status were initially included in the models, and retained only when significant. The relationships were tested in the normo- and hypertensive participants separately. For all the analyses, the most parsimonious model was chosen, defined as the model that included only significant or necessary (main effects when interaction was present) terms.\\nTo test whether change in BP (independent variable) was related to change in volume (dependent) we used MMRM. WML or brain volumes were a dependent variable and BP (SBP or DBP), group (HTN vs. NTN), and group × BP interaction were predictors. Both intercept and slope were treated as random. SBP and DBP were predictors in separate models.\\nWe performed supplementary analyses using pulse pressure (PP) as a predictor in all the models.\\nWML volumes were log transformed. We checked the linear models for violations of models assumptions. For all the analyses, the most parsimonious model was chosen, defined as the model that included only significant or necessary (main effects when interaction was present) terms. Statistical significance was defined as a P-value <0.05. SPSS (version 25, SPSS, Inc., Chicago, Illinois, USA) software was used for all analyses.\",\n \"Study participants were recruited at the NYU School of Medicine, at the former Center for Brain Health. Out of 376 individuals described in this report, 369 (98%) were previously a part of a study assessing the relationship between BP and CBF. Here, we report on participants with quantitative assessment of white matter pathology. Baseline assessment was performed in a larger group of n = 376 participants, and n = 188 of them had follow-up examinations. The reasons for lack of follow-up are described in the Supplement. Participants were enrolled between November 2010 and November 2017. All participants signed Institutional Review Board (ethics committee)-approved consent forms and were free of neocortical stroke, brain tumor, life-long psychotic disorders and dementia at baseline. Information pertaining to recruitment, exclusion criteria, and participant flow is presented in the Supplement.\",\n \"All participants underwent medical, psychiatric, and neurological assessments, blood tests, electrocardiogram (ECG), and magnetic resonance imaging (MRI) evaluation examinations at baseline, and the longitudinal group also at follow-up. Participants exhibiting dementia, based on a physician-administered interview using the Brief Cognitive Rating Scale, rating on the Global Deterioration Scale [23], and Clinical Dementia Rating were excluded [24]. Fasting blood was tested for complete blood count, liver function, as well as metabolic and lipid panel.\\nBlood pressure was taken once in a sitting position, after 5 min of rest. It was measured on the left upper arm using a manual sphygmomanometer. Body mass index (BMI) was calculated as weight/height (kg/m) [2].\\nMedication: We recoded the use of antihypertensive medications (angiotensin receptor blockers (ARBs), angiotensin converting enzyme inhibitors (ACE), beta-blockers, diuretics, and calcium channel blockers), statins and glucose lowering drugs.\\nDiabetes mellitus was defined as fasting glucose level of 126 mg/dl and higher and/or current use of glucose lowering medication [25]. Smoking status was defined as positive if participants was a current smoker or smoked within last 10 years.\",\n \"For consistency with our previous study hypertension was defined as current antihypertensive treatment or BP ≥140/90 mmHg [26]. One hundred and sixty-nine participants were classified as hypertensive; 136 participants were taking antihypertensive medication and 42 were not taking any antihypertensive medication with high BP recorded during their in-office visit. Normotension (NTN) was defined as BP <140/90 mmHg and no antihypertensive treatment.\\nFor descriptive purpose and secondary analyses, we described hypertensive participants as untreated: no antihypertensive treatment and BP ≥140/90 mmHg; controlled: antihypertensive treatment and BP <140/90 mmHg, uncontrolled: antihypertensive treatment and BP ≥140/90 mmHg. We also divided our normotensive group into Stage 1 hypertension based on new guidelines: [27] no treatment and BP between 130–139/80–89 mmHg, and normotensive BP <130/80 mmHg.\",\n \"All MR imaging was performed on the 3T system (Siemens, Erlangen, Germany). Parameters for Magnetization Prepared Rapid Acquisition Gradient Echo (MPRAGE) images were: repetition time (TR) = 2250 ms, echo time (TE) = 2.7 ms, inversion time (TI) = 900 ms, flip angle (FA) = 8°, slice thickness: 1.0 mm, field of view (FOV) = 200 mm, acquisition matrix = 256 × 192 × 124, reconstructed as 256 × 256 × 124. Axial fluid attenuation inversion recovery (FLAIR) images were acquired with TR/TE/TI 9000/99/2500 ms; FA 130°, slice thickness: 3.3 mm, field of view (FOV) 220 mm, matrix = 256 × 192 reconstructed as thirty 256 × 256 images.\",\n \"White matter lesions were segmented on FLAIR MRI with freely available image analysis software FireVoxel (https://firevoxel.org/) using a previously validated workflow [28]. FLAIR images were first corrected for signal nonuniformity using the N3 algorithm. A 3D mask M was then created over the entire brain parenchyma, that is, the gray and white matter, with cerebrospinal fluid (CSF) excluded [29]. Voxels at the external surface S of M were then identified. Next was creation of set L, containing voxels in M with signal intensity 2.5 standard deviations above the mean FLAIR signal. Finally, L was filtered to remove (a) cortical voxels, those located within 3 mm of S, (b) small clusters of volume <12 mm3 (presumed to represent image noise), and (c) connected regions having >50% CSF border (presumed to represent the choroid plexus and the septum). The resulting mask represents our estimate of WML. Its volume VWML is presented as percentage of the total brain volume.\\nGray matter (GM), white matter (WM) and CSF volumes were estimated using Statistical Parametric Mapping segmentation procedure (SPM, version 8, with ‘New-Segment’ extension) [30]. Total brain volume was the sum of GM and WM volumes. Intracranial volume (ICV) was the sum of all tissue types. Left and right hippocampal volumes were obtained with FreeSurfer version 6.0 [31], and averaged. They are referred to from now on as ‘mean hippocampal volume’. GM, WM and hippocampal volumes are presented as a fraction of the ICV. MRI data were acquired over the span of 9 years a 3T magnet that underwent hardware and software upgrades. We detected the inter-epoch variability in GM measurements. To avoid this fixed bias we z-scored GM values separately by epoch, recentered and rescaled them. Even though no other structural measure was affected, for consistency, we used rescaled values for hippocampal and WM volumes.\",\n \"Categorical variables were compared with χ2 tests. T-test, Mann–Whitney U-test, and analysis of covariance (ANCOVA) were used to compare group means for continuous variables, depending on the data distribution and the need to adjust for age and sex. Normality was tested with Shapiro–Wilk test.\\nRelationships between BP, WML volume and brain volumes (hippocampal, GM, WM) at baseline were examined with linear regression. Volumes were dependent variables; age, sex, body mass index, smoking, diabetes, systolic blood pressure and diastolic blood pressure were independent predictors. These relationships were tested in participants with and without hypertension separately. We present here F-statistics for the regression models, as well unstandardized (B) and standardized (β) coefficients for individual predictors. Again, we hypothesized that in the HTN group an optimal BP may exist [21], which minimizes WML volume and maximizes brain volumes. Thus, as before, we tested for the existence of the peak, by introducing both the linear BP and quadratic (BP [2]) terms into the regression models.\\nAfter a quadratic relationship between the volume and BP was confirmed, the critical BP at which volume reaches its maximum or minimum value was determined as:\\nTo examine whether BP, WML and brain volumes changed over time, we used mixed models for repeated measures (MMRM), where BP or volume was a dependent variable and time, group (HTN vs. NTN), and group × time interaction were predictors. For models with WML or brain volumes age as added as a covariate. Both intercept and slope were treated as random.\\nTo test whether baseline BP predicted change in WML and brain volumes, we created volume rates of change by regressing volume on time for each participant and tested whether baseline SBP and DBP was associated with volume slope. Baseline volumes, BMI, age, sex, smoking and diabetes status were initially included in the models, and retained only when significant. The relationships were tested in the normo- and hypertensive participants separately. For all the analyses, the most parsimonious model was chosen, defined as the model that included only significant or necessary (main effects when interaction was present) terms.\\nTo test whether change in BP (independent variable) was related to change in volume (dependent) we used MMRM. WML or brain volumes were a dependent variable and BP (SBP or DBP), group (HTN vs. NTN), and group × BP interaction were predictors. Both intercept and slope were treated as random. SBP and DBP were predictors in separate models.\\nWe performed supplementary analyses using pulse pressure (PP) as a predictor in all the models.\\nWML volumes were log transformed. We checked the linear models for violations of models assumptions. For all the analyses, the most parsimonious model was chosen, defined as the model that included only significant or necessary (main effects when interaction was present) terms. Statistical significance was defined as a P-value <0.05. SPSS (version 25, SPSS, Inc., Chicago, Illinois, USA) software was used for all analyses.\",\n \"Our group of 376 participants consisted of HTN (n = 207) and NTN (n = 169) individuals. The characteristics of the groups are given in Table 1. Two hundred and twenty-eight (60.6%) were women (age, 68.5 ± 7.4 years (mean ± standard deviation); education, 16.7 ± 2.3 years); and 148 (39.4%) men (age, 70.7 ± 6.9 years; education, 17.0 ± 2.4 years). The majority of the participants were Caucasian (86%), 10% African American, 4% other races.\\nBaseline characteristics of the study group (n = 376)\\nData is presented as mean ± standard deviation unless otherwise indicated. P-values come from t-test (total cholesterol, LDL) or Mann–Whitney U-test (age, education, BMI, HDL, triglycerides, glucose). For categorical variables, χ2 was used.\\nn = 371 (NTN = 204, HTN = 167)\\nn = 369 (NTN = 205, HTN = 164)\\nn = 363 (NTN = 200, HTN = 163)\\nn = 371 (NTN = 206, HTN = 165)\\nValues are presented as mean ± standard errors (SE), P-values from ANCOVA after accounting for age.\\nHippocampal volume is a mean of left and right, values are presented as mean ± standard errors (SE), P-values from ANCOVA after accounting for age and gender. Since the residuals from GM model were not normally distributed, the values were reanalyzed using ranked ANCOVA. Results remained the same.\\nICV, intracranial volume; HTN, hypertension; NTN, normotensive.\\nIn the longitudinal group of 188 participants, 116 (61.7%) were women (age, 69.6 ± 7.5 years; education, 16.7 ± 2.1 years) and 72 (38.3%) men (age, 71.4 ± 6.1 years; education, 17.2 ± 2.3 years). The mean time of follow-up was 2.3 ± 0.70 years. The longitudinal group did not differ from the participants with only one examination in terms of sex, HTN prevalence, SBP, DBP, BMI, total cholesterol, LDL and HDL cholesterol, triglycerides, education and WML volume. Prevalence of smoking and diabetes, as well as the number of participants with normotension, controlled, uncontrolled and untreated hypertension were also not different between the two groups. However, participants with longitudinal observation were slightly older (age, 70.3 ± 7.0 vs. 68.4 ± 7.4 years, P = 0.005) and had lower glucose levels (glucose, 82.5 ± 16.0 vs. 85.6 ± 14 mg/dl) than the individuals with only one exam. The clinical characteristics, as well as baseline brain and WML volumes of HTN and NTN participants in the longitudinal subgroup are given in Table S1. WML volumes by five baseline study group are shown in Tables S2 and S3.\\nBaseline analyses BP and white matter lesion volumes In the HTN group, SBP and quadratic SBP term were predictors of WML volume at baseline (regression model included age, F3,168 = 14.4, P < 0.001) (Table 2, Fig. 1). The results were similar with log-transformed data (F3,168 = 13.1, P < 0.001) (Table 3). The critical SBP value calculated from equation [2] was x = 124 mmHg.\\nLinear regression model predicting WML volume in the HTN group at baseline (raw data)\\nCritical value (Eq. [2]): x = –(−0.07317/(2 × 0.0003)) = 122 mmHg.\\nHTN, hypertension; WML, white matter lesion.\\nQuadratic relationship between systolic blood pressure readings and white matter lesion measurements in hypertensive participants.\\nLinear regression model predicting WML volume in the HTN group at baseline (log transformed data: log(WML volume))\\nCritical value (Eq. [2]): x = –(−0.02741/(2 × 0.00011)) = 124.6 mmHg.\\nHTN, hypertension; WML, white matter lesion.\\nIn the NTN group, neither SBP nor DBP were related to WML.\\nIn the HTN group, SBP and quadratic SBP term were predictors of WML volume at baseline (regression model included age, F3,168 = 14.4, P < 0.001) (Table 2, Fig. 1). The results were similar with log-transformed data (F3,168 = 13.1, P < 0.001) (Table 3). The critical SBP value calculated from equation [2] was x = 124 mmHg.\\nLinear regression model predicting WML volume in the HTN group at baseline (raw data)\\nCritical value (Eq. [2]): x = –(−0.07317/(2 × 0.0003)) = 122 mmHg.\\nHTN, hypertension; WML, white matter lesion.\\nQuadratic relationship between systolic blood pressure readings and white matter lesion measurements in hypertensive participants.\\nLinear regression model predicting WML volume in the HTN group at baseline (log transformed data: log(WML volume))\\nCritical value (Eq. [2]): x = –(−0.02741/(2 × 0.00011)) = 124.6 mmHg.\\nHTN, hypertension; WML, white matter lesion.\\nIn the NTN group, neither SBP nor DBP were related to WML.\\nBP and brain volumes In HTN, the quadratic DBP term was a predictor of the mean hippocampal volume at baseline (the model included age and sex, F4,168 = 11.6, P < 0.001) (Table 4, Fig. 2). The critical DBP value calculated from equation [2] was x = 77 mmHg.\\nModel predicting mean hippocampal volume in the HTN group at baseline\\nCritical value (Eq. [2]): x = −(0.00399/(2 × (−0.000026))) = 77 mmHg.\\nHTN, hypertension; WML, white matter lesion.\\nRelationship between diastolic blood pressure readings and mean hippocampal brain volume measurements in hypertensive participants.\\nGM was associated with SBP (β = −0.16, P = 0.02) and the model (F4,166 = 14.0, P < 0.001) included BMI, smoking and age.\\nIn NTN, the mean hippocampal volume was inversely related to SBP (β = −0.17, P < 0.008), (regression model included age and sex, F3,206 = 22.4, P < 0.001). GM volume was inversely related to DBP (β = −0.15, P = 0.01), (the model included age and sex, F3,206 = 32.3, P < 0.001).\\nIn HTN, the quadratic DBP term was a predictor of the mean hippocampal volume at baseline (the model included age and sex, F4,168 = 11.6, P < 0.001) (Table 4, Fig. 2). The critical DBP value calculated from equation [2] was x = 77 mmHg.\\nModel predicting mean hippocampal volume in the HTN group at baseline\\nCritical value (Eq. [2]): x = −(0.00399/(2 × (−0.000026))) = 77 mmHg.\\nHTN, hypertension; WML, white matter lesion.\\nRelationship between diastolic blood pressure readings and mean hippocampal brain volume measurements in hypertensive participants.\\nGM was associated with SBP (β = −0.16, P = 0.02) and the model (F4,166 = 14.0, P < 0.001) included BMI, smoking and age.\\nIn NTN, the mean hippocampal volume was inversely related to SBP (β = −0.17, P < 0.008), (regression model included age and sex, F3,206 = 22.4, P < 0.001). GM volume was inversely related to DBP (β = −0.15, P = 0.01), (the model included age and sex, F3,206 = 32.3, P < 0.001).\\nBP and white matter lesion volumes In the HTN group, SBP and quadratic SBP term were predictors of WML volume at baseline (regression model included age, F3,168 = 14.4, P < 0.001) (Table 2, Fig. 1). The results were similar with log-transformed data (F3,168 = 13.1, P < 0.001) (Table 3). The critical SBP value calculated from equation [2] was x = 124 mmHg.\\nLinear regression model predicting WML volume in the HTN group at baseline (raw data)\\nCritical value (Eq. [2]): x = –(−0.07317/(2 × 0.0003)) = 122 mmHg.\\nHTN, hypertension; WML, white matter lesion.\\nQuadratic relationship between systolic blood pressure readings and white matter lesion measurements in hypertensive participants.\\nLinear regression model predicting WML volume in the HTN group at baseline (log transformed data: log(WML volume))\\nCritical value (Eq. [2]): x = –(−0.02741/(2 × 0.00011)) = 124.6 mmHg.\\nHTN, hypertension; WML, white matter lesion.\\nIn the NTN group, neither SBP nor DBP were related to WML.\\nIn the HTN group, SBP and quadratic SBP term were predictors of WML volume at baseline (regression model included age, F3,168 = 14.4, P < 0.001) (Table 2, Fig. 1). The results were similar with log-transformed data (F3,168 = 13.1, P < 0.001) (Table 3). The critical SBP value calculated from equation [2] was x = 124 mmHg.\\nLinear regression model predicting WML volume in the HTN group at baseline (raw data)\\nCritical value (Eq. [2]): x = –(−0.07317/(2 × 0.0003)) = 122 mmHg.\\nHTN, hypertension; WML, white matter lesion.\\nQuadratic relationship between systolic blood pressure readings and white matter lesion measurements in hypertensive participants.\\nLinear regression model predicting WML volume in the HTN group at baseline (log transformed data: log(WML volume))\\nCritical value (Eq. [2]): x = –(−0.02741/(2 × 0.00011)) = 124.6 mmHg.\\nHTN, hypertension; WML, white matter lesion.\\nIn the NTN group, neither SBP nor DBP were related to WML.\\nBP and brain volumes In HTN, the quadratic DBP term was a predictor of the mean hippocampal volume at baseline (the model included age and sex, F4,168 = 11.6, P < 0.001) (Table 4, Fig. 2). The critical DBP value calculated from equation [2] was x = 77 mmHg.\\nModel predicting mean hippocampal volume in the HTN group at baseline\\nCritical value (Eq. [2]): x = −(0.00399/(2 × (−0.000026))) = 77 mmHg.\\nHTN, hypertension; WML, white matter lesion.\\nRelationship between diastolic blood pressure readings and mean hippocampal brain volume measurements in hypertensive participants.\\nGM was associated with SBP (β = −0.16, P = 0.02) and the model (F4,166 = 14.0, P < 0.001) included BMI, smoking and age.\\nIn NTN, the mean hippocampal volume was inversely related to SBP (β = −0.17, P < 0.008), (regression model included age and sex, F3,206 = 22.4, P < 0.001). GM volume was inversely related to DBP (β = −0.15, P = 0.01), (the model included age and sex, F3,206 = 32.3, P < 0.001).\\nIn HTN, the quadratic DBP term was a predictor of the mean hippocampal volume at baseline (the model included age and sex, F4,168 = 11.6, P < 0.001) (Table 4, Fig. 2). The critical DBP value calculated from equation [2] was x = 77 mmHg.\\nModel predicting mean hippocampal volume in the HTN group at baseline\\nCritical value (Eq. [2]): x = −(0.00399/(2 × (−0.000026))) = 77 mmHg.\\nHTN, hypertension; WML, white matter lesion.\\nRelationship between diastolic blood pressure readings and mean hippocampal brain volume measurements in hypertensive participants.\\nGM was associated with SBP (β = −0.16, P = 0.02) and the model (F4,166 = 14.0, P < 0.001) included BMI, smoking and age.\\nIn NTN, the mean hippocampal volume was inversely related to SBP (β = −0.17, P < 0.008), (regression model included age and sex, F3,206 = 22.4, P < 0.001). GM volume was inversely related to DBP (β = −0.15, P = 0.01), (the model included age and sex, F3,206 = 32.3, P < 0.001).\\nLongitudinal analyses Changes in BP and volumes over time SBP did not change significantly in the HTN or NTN group, but the interaction term was significant (P = 0.01) indicating divergent pattern of SBP dynamics (the estimate = −1.6, P = 0.07 for HTN and 1.4, P = 0.07 for NTN). Similarly, DBP patterns were different (P = 0.03 for interaction term) with a lack of change in the HTN participants (estimate −0.6, P = 0.31) and a significant increase in the NTN group (the estimate = 1.1, P = 0.03). In both groups, WML volumes increased with time, (the estimate = 0.025, P < 0.001) for HTN and (0.028, P < 0.001) for NTN. In both groups, brain volumes decreased with time. An estimate for the fixed effect of time for the mean hippocampal volumes was (−0.0044, P < 0.001) for HTN and (−0.0038, P < 0.001) among NTN participants. For GM, they were (−0.48, P < 0.001) and (−0.45, P < 0.001); and finally, for WM (−0.30, P < 0.001) and (−0.22, P = 0.001), for the HTN and NTN groups, respectively. Group × time interaction terms were not significant for models with brain or WML volumes, indicating that rates of change did not differ between groups. Figure S2 presents BP and WML changes in both groups.\\nSBP did not change significantly in the HTN or NTN group, but the interaction term was significant (P = 0.01) indicating divergent pattern of SBP dynamics (the estimate = −1.6, P = 0.07 for HTN and 1.4, P = 0.07 for NTN). Similarly, DBP patterns were different (P = 0.03 for interaction term) with a lack of change in the HTN participants (estimate −0.6, P = 0.31) and a significant increase in the NTN group (the estimate = 1.1, P = 0.03). In both groups, WML volumes increased with time, (the estimate = 0.025, P < 0.001) for HTN and (0.028, P < 0.001) for NTN. In both groups, brain volumes decreased with time. An estimate for the fixed effect of time for the mean hippocampal volumes was (−0.0044, P < 0.001) for HTN and (−0.0038, P < 0.001) among NTN participants. For GM, they were (−0.48, P < 0.001) and (−0.45, P < 0.001); and finally, for WM (−0.30, P < 0.001) and (−0.22, P = 0.001), for the HTN and NTN groups, respectively. Group × time interaction terms were not significant for models with brain or WML volumes, indicating that rates of change did not differ between groups. Figure S2 presents BP and WML changes in both groups.\\nChanges in BP and volumes over time SBP did not change significantly in the HTN or NTN group, but the interaction term was significant (P = 0.01) indicating divergent pattern of SBP dynamics (the estimate = −1.6, P = 0.07 for HTN and 1.4, P = 0.07 for NTN). Similarly, DBP patterns were different (P = 0.03 for interaction term) with a lack of change in the HTN participants (estimate −0.6, P = 0.31) and a significant increase in the NTN group (the estimate = 1.1, P = 0.03). In both groups, WML volumes increased with time, (the estimate = 0.025, P < 0.001) for HTN and (0.028, P < 0.001) for NTN. In both groups, brain volumes decreased with time. An estimate for the fixed effect of time for the mean hippocampal volumes was (−0.0044, P < 0.001) for HTN and (−0.0038, P < 0.001) among NTN participants. For GM, they were (−0.48, P < 0.001) and (−0.45, P < 0.001); and finally, for WM (−0.30, P < 0.001) and (−0.22, P = 0.001), for the HTN and NTN groups, respectively. Group × time interaction terms were not significant for models with brain or WML volumes, indicating that rates of change did not differ between groups. Figure S2 presents BP and WML changes in both groups.\\nSBP did not change significantly in the HTN or NTN group, but the interaction term was significant (P = 0.01) indicating divergent pattern of SBP dynamics (the estimate = −1.6, P = 0.07 for HTN and 1.4, P = 0.07 for NTN). Similarly, DBP patterns were different (P = 0.03 for interaction term) with a lack of change in the HTN participants (estimate −0.6, P = 0.31) and a significant increase in the NTN group (the estimate = 1.1, P = 0.03). In both groups, WML volumes increased with time, (the estimate = 0.025, P < 0.001) for HTN and (0.028, P < 0.001) for NTN. In both groups, brain volumes decreased with time. An estimate for the fixed effect of time for the mean hippocampal volumes was (−0.0044, P < 0.001) for HTN and (−0.0038, P < 0.001) among NTN participants. For GM, they were (−0.48, P < 0.001) and (−0.45, P < 0.001); and finally, for WM (−0.30, P < 0.001) and (−0.22, P = 0.001), for the HTN and NTN groups, respectively. Group × time interaction terms were not significant for models with brain or WML volumes, indicating that rates of change did not differ between groups. Figure S2 presents BP and WML changes in both groups.\\nSecondary analyses Table S4 shows the stability of secondary group definition (untreated, uncontrolled and controlled HTN, Stage 1 HTN, and normotension). Table S5 presents the results from MMRM analyses of WML volumes and BP changes conducted with all five subgroups defined at baseline based on HTN control status. One can appreciate that WML volume increased significantly in untreated and controlled hypertension, as well as in normotensive participants.\\nTable S6 and Figure S3 shows that participants who at baseline were classified as untreated (n = 19) or uncontrolled hypertension (n = 20) and reclassified at the last visit as good outcome (improved BP control) experience the same increase in WML volumes as participants with bad outcome (BP remained untreated or uncontrolled).\\nBaseline BP and changes in white matter lesion and brain volumes In the hypertensive group both baseline SBP (β = 0.33, P = 0.007) and DBP (β = −0.30, P = 0.008) were related to WML changes over time (entire model F4,83 = 10.5, P < 0.001, included also baseline WML volumes and smoking). Baseline BP was not related to changes in hippocampal, GM, or WM volumes.\\nIn normotensive participants baseline BP was not related to change in any examined volumes.\\nIn the hypertensive group both baseline SBP (β = 0.33, P = 0.007) and DBP (β = −0.30, P = 0.008) were related to WML changes over time (entire model F4,83 = 10.5, P < 0.001, included also baseline WML volumes and smoking). Baseline BP was not related to changes in hippocampal, GM, or WM volumes.\\nIn normotensive participants baseline BP was not related to change in any examined volumes.\\nLongitudinal changes in BP and changes in white matter lesion and brain volumes In the HTN group, neither change in SBP nor DBP were associated with a change in WML, hippocampal, GM or WM volumes (Tables S7 and S8).\\nIn the NTN group, both increase in SBP (an estimate for the fixed effect of SBP = 0.0023, P = 0.01) and DBP (an estimate for the fixed effect of DBP = 0.0035, P = 0.01) over time were related to the increase in WML volume. The significant interaction term indicated that these dynamics were meaningfully different from the HTN group. Moreover, in the normotensive group both increase in SBP (an estimate for the fixed effect of SBP = −0.0002, P = 0.03) and DBP (an estimate for the fixed effect of DBP = −0.0004, P = 0.01) over time were related to reduction in the mean hippocampal volume. However, only for SBP the interaction term was significant, indicating that these changes were different from HTN group.\\nResults for pulse pressure are presented in the Supplement p. 8–9.\\nIn the HTN group, neither change in SBP nor DBP were associated with a change in WML, hippocampal, GM or WM volumes (Tables S7 and S8).\\nIn the NTN group, both increase in SBP (an estimate for the fixed effect of SBP = 0.0023, P = 0.01) and DBP (an estimate for the fixed effect of DBP = 0.0035, P = 0.01) over time were related to the increase in WML volume. The significant interaction term indicated that these dynamics were meaningfully different from the HTN group. Moreover, in the normotensive group both increase in SBP (an estimate for the fixed effect of SBP = −0.0002, P = 0.03) and DBP (an estimate for the fixed effect of DBP = −0.0004, P = 0.01) over time were related to reduction in the mean hippocampal volume. However, only for SBP the interaction term was significant, indicating that these changes were different from HTN group.\\nResults for pulse pressure are presented in the Supplement p. 8–9.\\nTable S4 shows the stability of secondary group definition (untreated, uncontrolled and controlled HTN, Stage 1 HTN, and normotension). Table S5 presents the results from MMRM analyses of WML volumes and BP changes conducted with all five subgroups defined at baseline based on HTN control status. One can appreciate that WML volume increased significantly in untreated and controlled hypertension, as well as in normotensive participants.\\nTable S6 and Figure S3 shows that participants who at baseline were classified as untreated (n = 19) or uncontrolled hypertension (n = 20) and reclassified at the last visit as good outcome (improved BP control) experience the same increase in WML volumes as participants with bad outcome (BP remained untreated or uncontrolled).\\nBaseline BP and changes in white matter lesion and brain volumes In the hypertensive group both baseline SBP (β = 0.33, P = 0.007) and DBP (β = −0.30, P = 0.008) were related to WML changes over time (entire model F4,83 = 10.5, P < 0.001, included also baseline WML volumes and smoking). Baseline BP was not related to changes in hippocampal, GM, or WM volumes.\\nIn normotensive participants baseline BP was not related to change in any examined volumes.\\nIn the hypertensive group both baseline SBP (β = 0.33, P = 0.007) and DBP (β = −0.30, P = 0.008) were related to WML changes over time (entire model F4,83 = 10.5, P < 0.001, included also baseline WML volumes and smoking). Baseline BP was not related to changes in hippocampal, GM, or WM volumes.\\nIn normotensive participants baseline BP was not related to change in any examined volumes.\\nLongitudinal changes in BP and changes in white matter lesion and brain volumes In the HTN group, neither change in SBP nor DBP were associated with a change in WML, hippocampal, GM or WM volumes (Tables S7 and S8).\\nIn the NTN group, both increase in SBP (an estimate for the fixed effect of SBP = 0.0023, P = 0.01) and DBP (an estimate for the fixed effect of DBP = 0.0035, P = 0.01) over time were related to the increase in WML volume. The significant interaction term indicated that these dynamics were meaningfully different from the HTN group. Moreover, in the normotensive group both increase in SBP (an estimate for the fixed effect of SBP = −0.0002, P = 0.03) and DBP (an estimate for the fixed effect of DBP = −0.0004, P = 0.01) over time were related to reduction in the mean hippocampal volume. However, only for SBP the interaction term was significant, indicating that these changes were different from HTN group.\\nResults for pulse pressure are presented in the Supplement p. 8–9.\\nIn the HTN group, neither change in SBP nor DBP were associated with a change in WML, hippocampal, GM or WM volumes (Tables S7 and S8).\\nIn the NTN group, both increase in SBP (an estimate for the fixed effect of SBP = 0.0023, P = 0.01) and DBP (an estimate for the fixed effect of DBP = 0.0035, P = 0.01) over time were related to the increase in WML volume. The significant interaction term indicated that these dynamics were meaningfully different from the HTN group. Moreover, in the normotensive group both increase in SBP (an estimate for the fixed effect of SBP = −0.0002, P = 0.03) and DBP (an estimate for the fixed effect of DBP = −0.0004, P = 0.01) over time were related to reduction in the mean hippocampal volume. However, only for SBP the interaction term was significant, indicating that these changes were different from HTN group.\\nResults for pulse pressure are presented in the Supplement p. 8–9.\",\n \"BP and white matter lesion volumes In the HTN group, SBP and quadratic SBP term were predictors of WML volume at baseline (regression model included age, F3,168 = 14.4, P < 0.001) (Table 2, Fig. 1). The results were similar with log-transformed data (F3,168 = 13.1, P < 0.001) (Table 3). The critical SBP value calculated from equation [2] was x = 124 mmHg.\\nLinear regression model predicting WML volume in the HTN group at baseline (raw data)\\nCritical value (Eq. [2]): x = –(−0.07317/(2 × 0.0003)) = 122 mmHg.\\nHTN, hypertension; WML, white matter lesion.\\nQuadratic relationship between systolic blood pressure readings and white matter lesion measurements in hypertensive participants.\\nLinear regression model predicting WML volume in the HTN group at baseline (log transformed data: log(WML volume))\\nCritical value (Eq. [2]): x = –(−0.02741/(2 × 0.00011)) = 124.6 mmHg.\\nHTN, hypertension; WML, white matter lesion.\\nIn the NTN group, neither SBP nor DBP were related to WML.\\nIn the HTN group, SBP and quadratic SBP term were predictors of WML volume at baseline (regression model included age, F3,168 = 14.4, P < 0.001) (Table 2, Fig. 1). The results were similar with log-transformed data (F3,168 = 13.1, P < 0.001) (Table 3). The critical SBP value calculated from equation [2] was x = 124 mmHg.\\nLinear regression model predicting WML volume in the HTN group at baseline (raw data)\\nCritical value (Eq. [2]): x = –(−0.07317/(2 × 0.0003)) = 122 mmHg.\\nHTN, hypertension; WML, white matter lesion.\\nQuadratic relationship between systolic blood pressure readings and white matter lesion measurements in hypertensive participants.\\nLinear regression model predicting WML volume in the HTN group at baseline (log transformed data: log(WML volume))\\nCritical value (Eq. [2]): x = –(−0.02741/(2 × 0.00011)) = 124.6 mmHg.\\nHTN, hypertension; WML, white matter lesion.\\nIn the NTN group, neither SBP nor DBP were related to WML.\\nBP and brain volumes In HTN, the quadratic DBP term was a predictor of the mean hippocampal volume at baseline (the model included age and sex, F4,168 = 11.6, P < 0.001) (Table 4, Fig. 2). The critical DBP value calculated from equation [2] was x = 77 mmHg.\\nModel predicting mean hippocampal volume in the HTN group at baseline\\nCritical value (Eq. [2]): x = −(0.00399/(2 × (−0.000026))) = 77 mmHg.\\nHTN, hypertension; WML, white matter lesion.\\nRelationship between diastolic blood pressure readings and mean hippocampal brain volume measurements in hypertensive participants.\\nGM was associated with SBP (β = −0.16, P = 0.02) and the model (F4,166 = 14.0, P < 0.001) included BMI, smoking and age.\\nIn NTN, the mean hippocampal volume was inversely related to SBP (β = −0.17, P < 0.008), (regression model included age and sex, F3,206 = 22.4, P < 0.001). GM volume was inversely related to DBP (β = −0.15, P = 0.01), (the model included age and sex, F3,206 = 32.3, P < 0.001).\\nIn HTN, the quadratic DBP term was a predictor of the mean hippocampal volume at baseline (the model included age and sex, F4,168 = 11.6, P < 0.001) (Table 4, Fig. 2). The critical DBP value calculated from equation [2] was x = 77 mmHg.\\nModel predicting mean hippocampal volume in the HTN group at baseline\\nCritical value (Eq. [2]): x = −(0.00399/(2 × (−0.000026))) = 77 mmHg.\\nHTN, hypertension; WML, white matter lesion.\\nRelationship between diastolic blood pressure readings and mean hippocampal brain volume measurements in hypertensive participants.\\nGM was associated with SBP (β = −0.16, P = 0.02) and the model (F4,166 = 14.0, P < 0.001) included BMI, smoking and age.\\nIn NTN, the mean hippocampal volume was inversely related to SBP (β = −0.17, P < 0.008), (regression model included age and sex, F3,206 = 22.4, P < 0.001). GM volume was inversely related to DBP (β = −0.15, P = 0.01), (the model included age and sex, F3,206 = 32.3, P < 0.001).\",\n \"In the HTN group, SBP and quadratic SBP term were predictors of WML volume at baseline (regression model included age, F3,168 = 14.4, P < 0.001) (Table 2, Fig. 1). The results were similar with log-transformed data (F3,168 = 13.1, P < 0.001) (Table 3). The critical SBP value calculated from equation [2] was x = 124 mmHg.\\nLinear regression model predicting WML volume in the HTN group at baseline (raw data)\\nCritical value (Eq. [2]): x = –(−0.07317/(2 × 0.0003)) = 122 mmHg.\\nHTN, hypertension; WML, white matter lesion.\\nQuadratic relationship between systolic blood pressure readings and white matter lesion measurements in hypertensive participants.\\nLinear regression model predicting WML volume in the HTN group at baseline (log transformed data: log(WML volume))\\nCritical value (Eq. [2]): x = –(−0.02741/(2 × 0.00011)) = 124.6 mmHg.\\nHTN, hypertension; WML, white matter lesion.\\nIn the NTN group, neither SBP nor DBP were related to WML.\",\n \"In HTN, the quadratic DBP term was a predictor of the mean hippocampal volume at baseline (the model included age and sex, F4,168 = 11.6, P < 0.001) (Table 4, Fig. 2). The critical DBP value calculated from equation [2] was x = 77 mmHg.\\nModel predicting mean hippocampal volume in the HTN group at baseline\\nCritical value (Eq. [2]): x = −(0.00399/(2 × (−0.000026))) = 77 mmHg.\\nHTN, hypertension; WML, white matter lesion.\\nRelationship between diastolic blood pressure readings and mean hippocampal brain volume measurements in hypertensive participants.\\nGM was associated with SBP (β = −0.16, P = 0.02) and the model (F4,166 = 14.0, P < 0.001) included BMI, smoking and age.\\nIn NTN, the mean hippocampal volume was inversely related to SBP (β = −0.17, P < 0.008), (regression model included age and sex, F3,206 = 22.4, P < 0.001). GM volume was inversely related to DBP (β = −0.15, P = 0.01), (the model included age and sex, F3,206 = 32.3, P < 0.001).\",\n \"Changes in BP and volumes over time SBP did not change significantly in the HTN or NTN group, but the interaction term was significant (P = 0.01) indicating divergent pattern of SBP dynamics (the estimate = −1.6, P = 0.07 for HTN and 1.4, P = 0.07 for NTN). Similarly, DBP patterns were different (P = 0.03 for interaction term) with a lack of change in the HTN participants (estimate −0.6, P = 0.31) and a significant increase in the NTN group (the estimate = 1.1, P = 0.03). In both groups, WML volumes increased with time, (the estimate = 0.025, P < 0.001) for HTN and (0.028, P < 0.001) for NTN. In both groups, brain volumes decreased with time. An estimate for the fixed effect of time for the mean hippocampal volumes was (−0.0044, P < 0.001) for HTN and (−0.0038, P < 0.001) among NTN participants. For GM, they were (−0.48, P < 0.001) and (−0.45, P < 0.001); and finally, for WM (−0.30, P < 0.001) and (−0.22, P = 0.001), for the HTN and NTN groups, respectively. Group × time interaction terms were not significant for models with brain or WML volumes, indicating that rates of change did not differ between groups. Figure S2 presents BP and WML changes in both groups.\\nSBP did not change significantly in the HTN or NTN group, but the interaction term was significant (P = 0.01) indicating divergent pattern of SBP dynamics (the estimate = −1.6, P = 0.07 for HTN and 1.4, P = 0.07 for NTN). Similarly, DBP patterns were different (P = 0.03 for interaction term) with a lack of change in the HTN participants (estimate −0.6, P = 0.31) and a significant increase in the NTN group (the estimate = 1.1, P = 0.03). In both groups, WML volumes increased with time, (the estimate = 0.025, P < 0.001) for HTN and (0.028, P < 0.001) for NTN. In both groups, brain volumes decreased with time. An estimate for the fixed effect of time for the mean hippocampal volumes was (−0.0044, P < 0.001) for HTN and (−0.0038, P < 0.001) among NTN participants. For GM, they were (−0.48, P < 0.001) and (−0.45, P < 0.001); and finally, for WM (−0.30, P < 0.001) and (−0.22, P = 0.001), for the HTN and NTN groups, respectively. Group × time interaction terms were not significant for models with brain or WML volumes, indicating that rates of change did not differ between groups. Figure S2 presents BP and WML changes in both groups.\",\n \"SBP did not change significantly in the HTN or NTN group, but the interaction term was significant (P = 0.01) indicating divergent pattern of SBP dynamics (the estimate = −1.6, P = 0.07 for HTN and 1.4, P = 0.07 for NTN). Similarly, DBP patterns were different (P = 0.03 for interaction term) with a lack of change in the HTN participants (estimate −0.6, P = 0.31) and a significant increase in the NTN group (the estimate = 1.1, P = 0.03). In both groups, WML volumes increased with time, (the estimate = 0.025, P < 0.001) for HTN and (0.028, P < 0.001) for NTN. In both groups, brain volumes decreased with time. An estimate for the fixed effect of time for the mean hippocampal volumes was (−0.0044, P < 0.001) for HTN and (−0.0038, P < 0.001) among NTN participants. For GM, they were (−0.48, P < 0.001) and (−0.45, P < 0.001); and finally, for WM (−0.30, P < 0.001) and (−0.22, P = 0.001), for the HTN and NTN groups, respectively. Group × time interaction terms were not significant for models with brain or WML volumes, indicating that rates of change did not differ between groups. Figure S2 presents BP and WML changes in both groups.\",\n \"Table S4 shows the stability of secondary group definition (untreated, uncontrolled and controlled HTN, Stage 1 HTN, and normotension). Table S5 presents the results from MMRM analyses of WML volumes and BP changes conducted with all five subgroups defined at baseline based on HTN control status. One can appreciate that WML volume increased significantly in untreated and controlled hypertension, as well as in normotensive participants.\\nTable S6 and Figure S3 shows that participants who at baseline were classified as untreated (n = 19) or uncontrolled hypertension (n = 20) and reclassified at the last visit as good outcome (improved BP control) experience the same increase in WML volumes as participants with bad outcome (BP remained untreated or uncontrolled).\\nBaseline BP and changes in white matter lesion and brain volumes In the hypertensive group both baseline SBP (β = 0.33, P = 0.007) and DBP (β = −0.30, P = 0.008) were related to WML changes over time (entire model F4,83 = 10.5, P < 0.001, included also baseline WML volumes and smoking). Baseline BP was not related to changes in hippocampal, GM, or WM volumes.\\nIn normotensive participants baseline BP was not related to change in any examined volumes.\\nIn the hypertensive group both baseline SBP (β = 0.33, P = 0.007) and DBP (β = −0.30, P = 0.008) were related to WML changes over time (entire model F4,83 = 10.5, P < 0.001, included also baseline WML volumes and smoking). Baseline BP was not related to changes in hippocampal, GM, or WM volumes.\\nIn normotensive participants baseline BP was not related to change in any examined volumes.\\nLongitudinal changes in BP and changes in white matter lesion and brain volumes In the HTN group, neither change in SBP nor DBP were associated with a change in WML, hippocampal, GM or WM volumes (Tables S7 and S8).\\nIn the NTN group, both increase in SBP (an estimate for the fixed effect of SBP = 0.0023, P = 0.01) and DBP (an estimate for the fixed effect of DBP = 0.0035, P = 0.01) over time were related to the increase in WML volume. The significant interaction term indicated that these dynamics were meaningfully different from the HTN group. Moreover, in the normotensive group both increase in SBP (an estimate for the fixed effect of SBP = −0.0002, P = 0.03) and DBP (an estimate for the fixed effect of DBP = −0.0004, P = 0.01) over time were related to reduction in the mean hippocampal volume. However, only for SBP the interaction term was significant, indicating that these changes were different from HTN group.\\nResults for pulse pressure are presented in the Supplement p. 8–9.\\nIn the HTN group, neither change in SBP nor DBP were associated with a change in WML, hippocampal, GM or WM volumes (Tables S7 and S8).\\nIn the NTN group, both increase in SBP (an estimate for the fixed effect of SBP = 0.0023, P = 0.01) and DBP (an estimate for the fixed effect of DBP = 0.0035, P = 0.01) over time were related to the increase in WML volume. The significant interaction term indicated that these dynamics were meaningfully different from the HTN group. Moreover, in the normotensive group both increase in SBP (an estimate for the fixed effect of SBP = −0.0002, P = 0.03) and DBP (an estimate for the fixed effect of DBP = −0.0004, P = 0.01) over time were related to reduction in the mean hippocampal volume. However, only for SBP the interaction term was significant, indicating that these changes were different from HTN group.\\nResults for pulse pressure are presented in the Supplement p. 8–9.\",\n \"In the hypertensive group both baseline SBP (β = 0.33, P = 0.007) and DBP (β = −0.30, P = 0.008) were related to WML changes over time (entire model F4,83 = 10.5, P < 0.001, included also baseline WML volumes and smoking). Baseline BP was not related to changes in hippocampal, GM, or WM volumes.\\nIn normotensive participants baseline BP was not related to change in any examined volumes.\",\n \"In the HTN group, neither change in SBP nor DBP were associated with a change in WML, hippocampal, GM or WM volumes (Tables S7 and S8).\\nIn the NTN group, both increase in SBP (an estimate for the fixed effect of SBP = 0.0023, P = 0.01) and DBP (an estimate for the fixed effect of DBP = 0.0035, P = 0.01) over time were related to the increase in WML volume. The significant interaction term indicated that these dynamics were meaningfully different from the HTN group. Moreover, in the normotensive group both increase in SBP (an estimate for the fixed effect of SBP = −0.0002, P = 0.03) and DBP (an estimate for the fixed effect of DBP = −0.0004, P = 0.01) over time were related to reduction in the mean hippocampal volume. However, only for SBP the interaction term was significant, indicating that these changes were different from HTN group.\\nResults for pulse pressure are presented in the Supplement p. 8–9.\",\n \"Longitudinal observations of brain health in aging have continuously concluded that BP has a significant impact on the nervous system. It is well known that increased cardiovascular burden, as represented by an elevated systolic or diastolic BP, can substantially increase the risk of developing hemorrhagic and ischemic stroke [1,2], white matter lesions and atrophy [14,32], and cognitive dysfunction [33,34].\\nIn our examination of a cohort of normotensive and hypertensive participants, we posit that adequate BP is vital for a healthy brain. We have previously observed that in hypertension, there appears to be a window of mid-range SBP around 125 mmHg which maximizes perfusion [21]. In the present study, we show that hypertensive participants with a similar SBP (124 mmHg) have lower WML volumes that participants with SBP further away from this optimum. This is a novel finding further supporting the notion of an ideal blood pressure range that promotes optimal brain function and minimizes damage. Opposite to our results, the SPRINT-Mind study found that the group where BP was lowered aggressively to less than 120 experienced smaller WML progression over time [18]. However our findings are consistent with a report showing that in hypertensive participants low BP was related to higher PWML volume [35] and an earlier study where an increase in WML volume over time was most pronounced in participants with the highest BP who also had the greatest BP fluctuations [36].\\nWe also find that among hypertensive participants there was a quadratic relationship between the mean hippocampal volume at baseline and DBP, such as both the participants with low and high DBP had lower volume. This observation, although interesting, is not completely novel. Others have previously described a parallel U-shape association between brain atrophy and DBP, where both low and high concurrent DBP were related to greater cortical volume reduction [37]. The SPRINT MIND study showed that intensive BP treatment group experienced significantly greater hippocampal volume reduction [38]. It further confirms the hypothesis indicating the existence of a BP optimum, and reconciles reports showing that both high [39] and low BP [40] are related to lower hippocampal volumes.\\nAmong normotensive participants relationships between BP and brain volumes were linear such as both higher SBP and DBP were related to lower brain volumes. It is congruent with previous reports and meta-analyses showing both BP components associated with volumes [3]. Notably, no quadratic relationship was observed in this group, further strengthening our initial assumption that optimum exists in the group with preexisting impairment of the vascular system.\\nReduction in the volume of specific brain regions over time is another parameter indicative of abnormal aging and is often seen reported in HTN [3,41]. Our HTN group had more global and hippocampal atrophy at baseline (Table 1) than their normotensive counterparts. However, after adjustment for age, the WML volume did not differ between HTN and NTN groups. It is possible that substantial percentage (55%) of participants with controlled BP attenuated the differences. Comparisons across five groups showed that participants with untreated and uncontrolled hypertension tended to have higher WML volumes (Table S2).\\nIn our hypertensive participants, higher baseline SBP and lower DBP were both independently related to the growth of WML over time. While the first finding is not surprising, we offer two possible explanations for the latter. First, as the mean arterial pressure, the major driver of perfusion pressure, depends mostly on DBP, the low DBP in HTN may indicate the propensity to hypoperfusion. Second, this association would be consistent with widening of pulse pressure, indicative of arterial stiffness. Indeed, an earlier analysis of the Lothian Birth Cohort 1936 showed that the sequence of DBP reduction, PP increase and rising internal carotid artery pulsatility index leads to WM damage [42]. Our supplemental analyses also confirm that baseline PP was related to WML growth.\\nIt is worth noting that both normo and hypertensive groups experienced reductions in brain volumes and WML growth over time. This was present despite dissimilar trajectories of blood pressure: increases in the NTN and apparent lack of change in the HTN participants. A more fine-grained picture emerged from analyzing five groups (Table S5): WML lesions grew despite BP reductions in the untreated group and similar trend was observed among individuals with uncontrolled HTN, yet lesion volume increased concomitantly to BP increases in participants with controlled HTN and among normotensive participants. Even more interestingly we show that WML growth was similar in participants with baseline untreated/ uncontrolled HTN who at the last visit had disparate outcomes: improved or unchanged BP control (Table S6). It stands in opposition to the results of SPRINT-MIND trial where smaller increase in WML volume was observed in participants with more aggressive BP lowering [18]. We believe that the main reason for this discrepancy is that our groups were much smaller and follow-up time shorter than in SPRINT study. In addition, the difference in WML volume change between SPRINT groups amounted only to 0.5 cm3.\\nHowever, since BP load over time is unknown, it is also likely that in both groups BP was not controlled for a long time, and one group showed improvement only on the last measurement. Both groups could have also experienced similar, strong longitudinal BP fluctuation, which increase the probability of WML progression [36]. Then again, our results confirm that baseline WML volumes are good predictor of lesion progression, consistent with previous reports [43], and suggest that once the pathological process is set in motion, there is limited room for its modification. Such view has important implications for patient management, further strengthening the notion that early intervention (close monitoring of BP in normotensives and early treatment) would be most successful. Finally, the findings are also consistent with our hypothesis that in the setting of impaired cerebral flow regulation in participants with more severe form of HTN BP reductions are harmful.\\nNot surprisingly, in the light of the above considerations, only in the group of normotensive individuals, longitudinal increases of SBP and DBP were related to lesion growth and reduction is mean hippocampal volume. It is to be expected, as rising BP would contribute to brain deterioration. This finding is also in agreement with a previous report of community residing participants over the age of 75, where the 2-year change in ambulatory SBP was associated with the 2-year WML increase [44].\\nOur study suffered from a few limitations. First, only one measurement of blood pressure was taken at the time, instead of the average of three consecutive ones. It could have resulted in misclassification of cases. This was especially true for ‘untreated’: only 63% of participants defined at baseline as untreated were still classified as hypertensive at follow-up. However, high baseline WML volume in this group (Table S2) speaks against the possibility of misclassification. Almost everybody in the controlled and uncontrolled HTN groups remained hypertensive at follow-up, and 88% (91/104) of participants normotensive (<140/90) at baseline were normotensive at follow-up. Although, the method for BP measurement was not optimal it matched closely a common real-life practice. Second, the interval between assessment was short, and since WML progression is slow it might have precluded us from observing significant differences between groups. Analysis of participants WML volume did not include a differentiation of periventricular lesions from deep lesions. It is possible that the effects of hypertension of WML development could vary by lesion location. We did not perform manual segmentation of silent infractions. It may have artificially inflated lesion volumes, as some infarcts might have been erroneously classified as WML. Wang et al.[45] previously reported that including stroke lesions could bias WML volume estimation by as much as 20%. Although, in our case the error would have been likely smaller, since their study included patients recruited soon after presenting with stroke symptoms, while we excluded participants with overt infarcts, the misclassification cannot be excluded.\\nOnly a half of participants had follow-up. Although participants studied only once and those with additional examinations did not differ in most of the characteristics, it still could have caused a bias. Another limitation was that our group was predominantly Caucasian, with high level of educational achievement thus generalizability to a more diverse cohort is uncertain. We did not have data on the duration of hypertension, which can modify the relationships we examined. Normotensive individuals were slightly (on average 3 years) but statistically significantly younger than hypertensive participants. It is plausible that had they been older, we might have seen stronger associations between BP and WML. A recent meta-analysis showed that the optimal blood pressure value changes depending on age [20]. We did not have a group large enough to address whether this is the case also for white matter lesions. Finally, since our study was observational, results cannot carry the same weight a these derived from clinical trial, and causal relations cannot be inferred.\\nIn conclusion, we find that among hypertensive participants there was a quadratic relationship between BP and WML as well as mean hippocampal volume. The WML burden was the smallest when SBP was close to 124 mmHg. The mean hippocampal volume was the highest when DBP was in high 70-mmHg range. Such phenomena were not observed in the normotensive group, suggesting the existence of a BP optimum in hypertension. The current report extends our earlier findings showing that cerebral perfusion is maximized when systolic BP is in 120–130 mmHg range. Longitudinally, all groups experienced lesion growth, despite different BP trajectories, further suggesting the possibility that WML expansion may occur despite or because of BP reduction in individuals with compromised vascular system. Considering growing evidence showing nonlinear association between blood pressure an outcomes further clinical trials are needed.\",\n \"A special thanks to Ke Xi for her help with the statistical modeling as well as assistance in the production of regression plots for this manuscript.\\nSources of funding: Study funding comes from NIH grants HL111724, NS104364, AG022374, AG12101, AG08051, and Alzheimer's Association NIRG-09-132490.\\nDisclosures: None\\nConflicts of interest There are no conflicts of interest.\\nThere are no conflicts of interest.\",\n \"There are no conflicts of interest.\",\n \"\"\n]"},"all_sections_sec_types":{"kind":"list like","value":["intro","methods","subjects",null,null,null,null,null,"results",null,null,null,null,null,null,null,null,"discussion",null,"COI-statement","supplementary-material"],"string":"[\n \"intro\",\n \"methods\",\n \"subjects\",\n null,\n null,\n null,\n null,\n null,\n \"results\",\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n \"discussion\",\n null,\n \"COI-statement\",\n \"supplementary-material\"\n]"},"keywords":{"kind":"list like","value":["hypertension","magnetic resonance imaging","neuro-imaging","radiology","systolic and diastolic blood pressure","white matter lesions"],"string":"[\n \"hypertension\",\n \"magnetic resonance imaging\",\n \"neuro-imaging\",\n \"radiology\",\n \"systolic and diastolic blood pressure\",\n \"white matter lesions\"\n]"},"whole_article_text":{"kind":"string","value":"INTRODUCTION:\nThe impact of cardiovascular disease on the United States’ population cannot be overstated. One out of every three Americans is hypertensive, with 66% of adults aged 60 or older exhibiting above average blood pressure readings [1,2]. It was estimated in 2018 that 11 million individuals in the United States were living with undiagnosed hypertension (HTN), placing them at an increased risk for adverse cardiovascular events that can directly affect the central nervous system as well as cognition [1,2].\nHigh blood pressure (BP) can cause damage and dysfunction to organ systems with perhaps the most significant of such being the brain and the heart [3–5]. Hypertensive patients develop atherosclerotic plaques within the large blood vessels supplying blood flow both into and out of the brain at an accelerated rate, increasing their risk of an ischemic event [6,7]. HTN can cause a remodeling of the brain microvasculature with vessels exhibiting rarefication and narrowing lumen [4,6,8]. Such changes in vessel architecture can have negative effects on cerebral blood flow (CBF) and autoregulation. Reduction in CBF has been shown to cause brain atrophy both in animal models [9] and in clinical population [10], and has been consistently linked with white matter damage [11,12]. White matter lesions (WML) are very common in HTN participants [13–15]. Indeed, several studies have confirmed that two major risk factors for WML development are increased age and high BP, with smoking, high cholesterol, and heart disease also being significant contributors to lesion formation [14,16,17].\nAlthough benefits of HTN treatment are indisputable for major cardiovascular outcome, the target blood pressure for the brain remains under debate. While some argue for aggressive blood pressure reductions [18], others point to possible danger of this approach [19], as marked lowering of BP in longstanding HTN may cause relative hypoperfusion. This suggested to us the existence of an optimum BP range.\nA recent metanalysis found a U-shape relationship between SBP and a risk of dementia in participants older than 75, as well as U-shape relationship between SBP and combined dementia and mortality across all age groups starting at 60 [20]. Our group has previously reported a quadratic (inverted U-shape) relationship between CBF and SBP in hypertensive patients, pointing to the optimum lying between 120 and 130 mmHg where CBF was the highest [21]. It was consistently shown that WML volumes are inversely related to global and local indices of CBF [22]. In this observational report, we investigated whether in hypertensive participants WML and atrophy, structural markers of HTN-related brain damage, show relationship with SBP, which is parallel to the one we observed for CBF and SBP: namely is there a quadratic association between them.\n\nMETHODS:\nThe data of this study are available from the corresponding author upon request.\nParticipants recruitment Study participants were recruited at the NYU School of Medicine, at the former Center for Brain Health. Out of 376 individuals described in this report, 369 (98%) were previously a part of a study assessing the relationship between BP and CBF. Here, we report on participants with quantitative assessment of white matter pathology. Baseline assessment was performed in a larger group of n = 376 participants, and n = 188 of them had follow-up examinations. The reasons for lack of follow-up are described in the Supplement. Participants were enrolled between November 2010 and November 2017. All participants signed Institutional Review Board (ethics committee)-approved consent forms and were free of neocortical stroke, brain tumor, life-long psychotic disorders and dementia at baseline. Information pertaining to recruitment, exclusion criteria, and participant flow is presented in the Supplement.\nStudy participants were recruited at the NYU School of Medicine, at the former Center for Brain Health. Out of 376 individuals described in this report, 369 (98%) were previously a part of a study assessing the relationship between BP and CBF. Here, we report on participants with quantitative assessment of white matter pathology. Baseline assessment was performed in a larger group of n = 376 participants, and n = 188 of them had follow-up examinations. The reasons for lack of follow-up are described in the Supplement. Participants were enrolled between November 2010 and November 2017. All participants signed Institutional Review Board (ethics committee)-approved consent forms and were free of neocortical stroke, brain tumor, life-long psychotic disorders and dementia at baseline. Information pertaining to recruitment, exclusion criteria, and participant flow is presented in the Supplement.\nClinical assessment All participants underwent medical, psychiatric, and neurological assessments, blood tests, electrocardiogram (ECG), and magnetic resonance imaging (MRI) evaluation examinations at baseline, and the longitudinal group also at follow-up. Participants exhibiting dementia, based on a physician-administered interview using the Brief Cognitive Rating Scale, rating on the Global Deterioration Scale [23], and Clinical Dementia Rating were excluded [24]. Fasting blood was tested for complete blood count, liver function, as well as metabolic and lipid panel.\nBlood pressure was taken once in a sitting position, after 5 min of rest. It was measured on the left upper arm using a manual sphygmomanometer. Body mass index (BMI) was calculated as weight/height (kg/m) [2].\nMedication: We recoded the use of antihypertensive medications (angiotensin receptor blockers (ARBs), angiotensin converting enzyme inhibitors (ACE), beta-blockers, diuretics, and calcium channel blockers), statins and glucose lowering drugs.\nDiabetes mellitus was defined as fasting glucose level of 126 mg/dl and higher and/or current use of glucose lowering medication [25]. Smoking status was defined as positive if participants was a current smoker or smoked within last 10 years.\nAll participants underwent medical, psychiatric, and neurological assessments, blood tests, electrocardiogram (ECG), and magnetic resonance imaging (MRI) evaluation examinations at baseline, and the longitudinal group also at follow-up. Participants exhibiting dementia, based on a physician-administered interview using the Brief Cognitive Rating Scale, rating on the Global Deterioration Scale [23], and Clinical Dementia Rating were excluded [24]. Fasting blood was tested for complete blood count, liver function, as well as metabolic and lipid panel.\nBlood pressure was taken once in a sitting position, after 5 min of rest. It was measured on the left upper arm using a manual sphygmomanometer. Body mass index (BMI) was calculated as weight/height (kg/m) [2].\nMedication: We recoded the use of antihypertensive medications (angiotensin receptor blockers (ARBs), angiotensin converting enzyme inhibitors (ACE), beta-blockers, diuretics, and calcium channel blockers), statins and glucose lowering drugs.\nDiabetes mellitus was defined as fasting glucose level of 126 mg/dl and higher and/or current use of glucose lowering medication [25]. Smoking status was defined as positive if participants was a current smoker or smoked within last 10 years.\nStudy groups For consistency with our previous study hypertension was defined as current antihypertensive treatment or BP ≥140/90 mmHg [26]. One hundred and sixty-nine participants were classified as hypertensive; 136 participants were taking antihypertensive medication and 42 were not taking any antihypertensive medication with high BP recorded during their in-office visit. Normotension (NTN) was defined as BP <140/90 mmHg and no antihypertensive treatment.\nFor descriptive purpose and secondary analyses, we described hypertensive participants as untreated: no antihypertensive treatment and BP ≥140/90 mmHg; controlled: antihypertensive treatment and BP <140/90 mmHg, uncontrolled: antihypertensive treatment and BP ≥140/90 mmHg. We also divided our normotensive group into Stage 1 hypertension based on new guidelines: [27] no treatment and BP between 130–139/80–89 mmHg, and normotensive BP <130/80 mmHg.\nFor consistency with our previous study hypertension was defined as current antihypertensive treatment or BP ≥140/90 mmHg [26]. One hundred and sixty-nine participants were classified as hypertensive; 136 participants were taking antihypertensive medication and 42 were not taking any antihypertensive medication with high BP recorded during their in-office visit. Normotension (NTN) was defined as BP <140/90 mmHg and no antihypertensive treatment.\nFor descriptive purpose and secondary analyses, we described hypertensive participants as untreated: no antihypertensive treatment and BP ≥140/90 mmHg; controlled: antihypertensive treatment and BP <140/90 mmHg, uncontrolled: antihypertensive treatment and BP ≥140/90 mmHg. We also divided our normotensive group into Stage 1 hypertension based on new guidelines: [27] no treatment and BP between 130–139/80–89 mmHg, and normotensive BP <130/80 mmHg.\nMR imaging All MR imaging was performed on the 3T system (Siemens, Erlangen, Germany). Parameters for Magnetization Prepared Rapid Acquisition Gradient Echo (MPRAGE) images were: repetition time (TR) = 2250 ms, echo time (TE) = 2.7 ms, inversion time (TI) = 900 ms, flip angle (FA) = 8°, slice thickness: 1.0 mm, field of view (FOV) = 200 mm, acquisition matrix = 256 × 192 × 124, reconstructed as 256 × 256 × 124. Axial fluid attenuation inversion recovery (FLAIR) images were acquired with TR/TE/TI 9000/99/2500 ms; FA 130°, slice thickness: 3.3 mm, field of view (FOV) 220 mm, matrix = 256 × 192 reconstructed as thirty 256 × 256 images.\nAll MR imaging was performed on the 3T system (Siemens, Erlangen, Germany). Parameters for Magnetization Prepared Rapid Acquisition Gradient Echo (MPRAGE) images were: repetition time (TR) = 2250 ms, echo time (TE) = 2.7 ms, inversion time (TI) = 900 ms, flip angle (FA) = 8°, slice thickness: 1.0 mm, field of view (FOV) = 200 mm, acquisition matrix = 256 × 192 × 124, reconstructed as 256 × 256 × 124. Axial fluid attenuation inversion recovery (FLAIR) images were acquired with TR/TE/TI 9000/99/2500 ms; FA 130°, slice thickness: 3.3 mm, field of view (FOV) 220 mm, matrix = 256 × 192 reconstructed as thirty 256 × 256 images.\nMRI processing White matter lesions were segmented on FLAIR MRI with freely available image analysis software FireVoxel (https://firevoxel.org/) using a previously validated workflow [28]. FLAIR images were first corrected for signal nonuniformity using the N3 algorithm. A 3D mask M was then created over the entire brain parenchyma, that is, the gray and white matter, with cerebrospinal fluid (CSF) excluded [29]. Voxels at the external surface S of M were then identified. Next was creation of set L, containing voxels in M with signal intensity 2.5 standard deviations above the mean FLAIR signal. Finally, L was filtered to remove (a) cortical voxels, those located within 3 mm of S, (b) small clusters of volume <12 mm3 (presumed to represent image noise), and (c) connected regions having >50% CSF border (presumed to represent the choroid plexus and the septum). The resulting mask represents our estimate of WML. Its volume VWML is presented as percentage of the total brain volume.\nGray matter (GM), white matter (WM) and CSF volumes were estimated using Statistical Parametric Mapping segmentation procedure (SPM, version 8, with ‘New-Segment’ extension) [30]. Total brain volume was the sum of GM and WM volumes. Intracranial volume (ICV) was the sum of all tissue types. Left and right hippocampal volumes were obtained with FreeSurfer version 6.0 [31], and averaged. They are referred to from now on as ‘mean hippocampal volume’. GM, WM and hippocampal volumes are presented as a fraction of the ICV. MRI data were acquired over the span of 9 years a 3T magnet that underwent hardware and software upgrades. We detected the inter-epoch variability in GM measurements. To avoid this fixed bias we z-scored GM values separately by epoch, recentered and rescaled them. Even though no other structural measure was affected, for consistency, we used rescaled values for hippocampal and WM volumes.\nWhite matter lesions were segmented on FLAIR MRI with freely available image analysis software FireVoxel (https://firevoxel.org/) using a previously validated workflow [28]. FLAIR images were first corrected for signal nonuniformity using the N3 algorithm. A 3D mask M was then created over the entire brain parenchyma, that is, the gray and white matter, with cerebrospinal fluid (CSF) excluded [29]. Voxels at the external surface S of M were then identified. Next was creation of set L, containing voxels in M with signal intensity 2.5 standard deviations above the mean FLAIR signal. Finally, L was filtered to remove (a) cortical voxels, those located within 3 mm of S, (b) small clusters of volume <12 mm3 (presumed to represent image noise), and (c) connected regions having >50% CSF border (presumed to represent the choroid plexus and the septum). The resulting mask represents our estimate of WML. Its volume VWML is presented as percentage of the total brain volume.\nGray matter (GM), white matter (WM) and CSF volumes were estimated using Statistical Parametric Mapping segmentation procedure (SPM, version 8, with ‘New-Segment’ extension) [30]. Total brain volume was the sum of GM and WM volumes. Intracranial volume (ICV) was the sum of all tissue types. Left and right hippocampal volumes were obtained with FreeSurfer version 6.0 [31], and averaged. They are referred to from now on as ‘mean hippocampal volume’. GM, WM and hippocampal volumes are presented as a fraction of the ICV. MRI data were acquired over the span of 9 years a 3T magnet that underwent hardware and software upgrades. We detected the inter-epoch variability in GM measurements. To avoid this fixed bias we z-scored GM values separately by epoch, recentered and rescaled them. Even though no other structural measure was affected, for consistency, we used rescaled values for hippocampal and WM volumes.\nStatistics Categorical variables were compared with χ2 tests. T-test, Mann–Whitney U-test, and analysis of covariance (ANCOVA) were used to compare group means for continuous variables, depending on the data distribution and the need to adjust for age and sex. Normality was tested with Shapiro–Wilk test.\nRelationships between BP, WML volume and brain volumes (hippocampal, GM, WM) at baseline were examined with linear regression. Volumes were dependent variables; age, sex, body mass index, smoking, diabetes, systolic blood pressure and diastolic blood pressure were independent predictors. These relationships were tested in participants with and without hypertension separately. We present here F-statistics for the regression models, as well unstandardized (B) and standardized (β) coefficients for individual predictors. Again, we hypothesized that in the HTN group an optimal BP may exist [21], which minimizes WML volume and maximizes brain volumes. Thus, as before, we tested for the existence of the peak, by introducing both the linear BP and quadratic (BP [2]) terms into the regression models.\nAfter a quadratic relationship between the volume and BP was confirmed, the critical BP at which volume reaches its maximum or minimum value was determined as:\nTo examine whether BP, WML and brain volumes changed over time, we used mixed models for repeated measures (MMRM), where BP or volume was a dependent variable and time, group (HTN vs. NTN), and group × time interaction were predictors. For models with WML or brain volumes age as added as a covariate. Both intercept and slope were treated as random.\nTo test whether baseline BP predicted change in WML and brain volumes, we created volume rates of change by regressing volume on time for each participant and tested whether baseline SBP and DBP was associated with volume slope. Baseline volumes, BMI, age, sex, smoking and diabetes status were initially included in the models, and retained only when significant. The relationships were tested in the normo- and hypertensive participants separately. For all the analyses, the most parsimonious model was chosen, defined as the model that included only significant or necessary (main effects when interaction was present) terms.\nTo test whether change in BP (independent variable) was related to change in volume (dependent) we used MMRM. WML or brain volumes were a dependent variable and BP (SBP or DBP), group (HTN vs. NTN), and group × BP interaction were predictors. Both intercept and slope were treated as random. SBP and DBP were predictors in separate models.\nWe performed supplementary analyses using pulse pressure (PP) as a predictor in all the models.\nWML volumes were log transformed. We checked the linear models for violations of models assumptions. For all the analyses, the most parsimonious model was chosen, defined as the model that included only significant or necessary (main effects when interaction was present) terms. Statistical significance was defined as a P-value <0.05. SPSS (version 25, SPSS, Inc., Chicago, Illinois, USA) software was used for all analyses.\nCategorical variables were compared with χ2 tests. T-test, Mann–Whitney U-test, and analysis of covariance (ANCOVA) were used to compare group means for continuous variables, depending on the data distribution and the need to adjust for age and sex. Normality was tested with Shapiro–Wilk test.\nRelationships between BP, WML volume and brain volumes (hippocampal, GM, WM) at baseline were examined with linear regression. Volumes were dependent variables; age, sex, body mass index, smoking, diabetes, systolic blood pressure and diastolic blood pressure were independent predictors. These relationships were tested in participants with and without hypertension separately. We present here F-statistics for the regression models, as well unstandardized (B) and standardized (β) coefficients for individual predictors. Again, we hypothesized that in the HTN group an optimal BP may exist [21], which minimizes WML volume and maximizes brain volumes. Thus, as before, we tested for the existence of the peak, by introducing both the linear BP and quadratic (BP [2]) terms into the regression models.\nAfter a quadratic relationship between the volume and BP was confirmed, the critical BP at which volume reaches its maximum or minimum value was determined as:\nTo examine whether BP, WML and brain volumes changed over time, we used mixed models for repeated measures (MMRM), where BP or volume was a dependent variable and time, group (HTN vs. NTN), and group × time interaction were predictors. For models with WML or brain volumes age as added as a covariate. Both intercept and slope were treated as random.\nTo test whether baseline BP predicted change in WML and brain volumes, we created volume rates of change by regressing volume on time for each participant and tested whether baseline SBP and DBP was associated with volume slope. Baseline volumes, BMI, age, sex, smoking and diabetes status were initially included in the models, and retained only when significant. The relationships were tested in the normo- and hypertensive participants separately. For all the analyses, the most parsimonious model was chosen, defined as the model that included only significant or necessary (main effects when interaction was present) terms.\nTo test whether change in BP (independent variable) was related to change in volume (dependent) we used MMRM. WML or brain volumes were a dependent variable and BP (SBP or DBP), group (HTN vs. NTN), and group × BP interaction were predictors. Both intercept and slope were treated as random. SBP and DBP were predictors in separate models.\nWe performed supplementary analyses using pulse pressure (PP) as a predictor in all the models.\nWML volumes were log transformed. We checked the linear models for violations of models assumptions. For all the analyses, the most parsimonious model was chosen, defined as the model that included only significant or necessary (main effects when interaction was present) terms. Statistical significance was defined as a P-value <0.05. SPSS (version 25, SPSS, Inc., Chicago, Illinois, USA) software was used for all analyses.\n\nParticipants recruitment:\nStudy participants were recruited at the NYU School of Medicine, at the former Center for Brain Health. Out of 376 individuals described in this report, 369 (98%) were previously a part of a study assessing the relationship between BP and CBF. Here, we report on participants with quantitative assessment of white matter pathology. Baseline assessment was performed in a larger group of n = 376 participants, and n = 188 of them had follow-up examinations. The reasons for lack of follow-up are described in the Supplement. Participants were enrolled between November 2010 and November 2017. All participants signed Institutional Review Board (ethics committee)-approved consent forms and were free of neocortical stroke, brain tumor, life-long psychotic disorders and dementia at baseline. Information pertaining to recruitment, exclusion criteria, and participant flow is presented in the Supplement.\n\nClinical assessment:\nAll participants underwent medical, psychiatric, and neurological assessments, blood tests, electrocardiogram (ECG), and magnetic resonance imaging (MRI) evaluation examinations at baseline, and the longitudinal group also at follow-up. Participants exhibiting dementia, based on a physician-administered interview using the Brief Cognitive Rating Scale, rating on the Global Deterioration Scale [23], and Clinical Dementia Rating were excluded [24]. Fasting blood was tested for complete blood count, liver function, as well as metabolic and lipid panel.\nBlood pressure was taken once in a sitting position, after 5 min of rest. It was measured on the left upper arm using a manual sphygmomanometer. Body mass index (BMI) was calculated as weight/height (kg/m) [2].\nMedication: We recoded the use of antihypertensive medications (angiotensin receptor blockers (ARBs), angiotensin converting enzyme inhibitors (ACE), beta-blockers, diuretics, and calcium channel blockers), statins and glucose lowering drugs.\nDiabetes mellitus was defined as fasting glucose level of 126 mg/dl and higher and/or current use of glucose lowering medication [25]. Smoking status was defined as positive if participants was a current smoker or smoked within last 10 years.\n\nStudy groups:\nFor consistency with our previous study hypertension was defined as current antihypertensive treatment or BP ≥140/90 mmHg [26]. One hundred and sixty-nine participants were classified as hypertensive; 136 participants were taking antihypertensive medication and 42 were not taking any antihypertensive medication with high BP recorded during their in-office visit. Normotension (NTN) was defined as BP <140/90 mmHg and no antihypertensive treatment.\nFor descriptive purpose and secondary analyses, we described hypertensive participants as untreated: no antihypertensive treatment and BP ≥140/90 mmHg; controlled: antihypertensive treatment and BP <140/90 mmHg, uncontrolled: antihypertensive treatment and BP ≥140/90 mmHg. We also divided our normotensive group into Stage 1 hypertension based on new guidelines: [27] no treatment and BP between 130–139/80–89 mmHg, and normotensive BP <130/80 mmHg.\n\nMR imaging:\nAll MR imaging was performed on the 3T system (Siemens, Erlangen, Germany). Parameters for Magnetization Prepared Rapid Acquisition Gradient Echo (MPRAGE) images were: repetition time (TR) = 2250 ms, echo time (TE) = 2.7 ms, inversion time (TI) = 900 ms, flip angle (FA) = 8°, slice thickness: 1.0 mm, field of view (FOV) = 200 mm, acquisition matrix = 256 × 192 × 124, reconstructed as 256 × 256 × 124. Axial fluid attenuation inversion recovery (FLAIR) images were acquired with TR/TE/TI 9000/99/2500 ms; FA 130°, slice thickness: 3.3 mm, field of view (FOV) 220 mm, matrix = 256 × 192 reconstructed as thirty 256 × 256 images.\n\nMRI processing:\nWhite matter lesions were segmented on FLAIR MRI with freely available image analysis software FireVoxel (https://firevoxel.org/) using a previously validated workflow [28]. FLAIR images were first corrected for signal nonuniformity using the N3 algorithm. A 3D mask M was then created over the entire brain parenchyma, that is, the gray and white matter, with cerebrospinal fluid (CSF) excluded [29]. Voxels at the external surface S of M were then identified. Next was creation of set L, containing voxels in M with signal intensity 2.5 standard deviations above the mean FLAIR signal. Finally, L was filtered to remove (a) cortical voxels, those located within 3 mm of S, (b) small clusters of volume <12 mm3 (presumed to represent image noise), and (c) connected regions having >50% CSF border (presumed to represent the choroid plexus and the septum). The resulting mask represents our estimate of WML. Its volume VWML is presented as percentage of the total brain volume.\nGray matter (GM), white matter (WM) and CSF volumes were estimated using Statistical Parametric Mapping segmentation procedure (SPM, version 8, with ‘New-Segment’ extension) [30]. Total brain volume was the sum of GM and WM volumes. Intracranial volume (ICV) was the sum of all tissue types. Left and right hippocampal volumes were obtained with FreeSurfer version 6.0 [31], and averaged. They are referred to from now on as ‘mean hippocampal volume’. GM, WM and hippocampal volumes are presented as a fraction of the ICV. MRI data were acquired over the span of 9 years a 3T magnet that underwent hardware and software upgrades. We detected the inter-epoch variability in GM measurements. To avoid this fixed bias we z-scored GM values separately by epoch, recentered and rescaled them. Even though no other structural measure was affected, for consistency, we used rescaled values for hippocampal and WM volumes.\n\nStatistics:\nCategorical variables were compared with χ2 tests. T-test, Mann–Whitney U-test, and analysis of covariance (ANCOVA) were used to compare group means for continuous variables, depending on the data distribution and the need to adjust for age and sex. Normality was tested with Shapiro–Wilk test.\nRelationships between BP, WML volume and brain volumes (hippocampal, GM, WM) at baseline were examined with linear regression. Volumes were dependent variables; age, sex, body mass index, smoking, diabetes, systolic blood pressure and diastolic blood pressure were independent predictors. These relationships were tested in participants with and without hypertension separately. We present here F-statistics for the regression models, as well unstandardized (B) and standardized (β) coefficients for individual predictors. Again, we hypothesized that in the HTN group an optimal BP may exist [21], which minimizes WML volume and maximizes brain volumes. Thus, as before, we tested for the existence of the peak, by introducing both the linear BP and quadratic (BP [2]) terms into the regression models.\nAfter a quadratic relationship between the volume and BP was confirmed, the critical BP at which volume reaches its maximum or minimum value was determined as:\nTo examine whether BP, WML and brain volumes changed over time, we used mixed models for repeated measures (MMRM), where BP or volume was a dependent variable and time, group (HTN vs. NTN), and group × time interaction were predictors. For models with WML or brain volumes age as added as a covariate. Both intercept and slope were treated as random.\nTo test whether baseline BP predicted change in WML and brain volumes, we created volume rates of change by regressing volume on time for each participant and tested whether baseline SBP and DBP was associated with volume slope. Baseline volumes, BMI, age, sex, smoking and diabetes status were initially included in the models, and retained only when significant. The relationships were tested in the normo- and hypertensive participants separately. For all the analyses, the most parsimonious model was chosen, defined as the model that included only significant or necessary (main effects when interaction was present) terms.\nTo test whether change in BP (independent variable) was related to change in volume (dependent) we used MMRM. WML or brain volumes were a dependent variable and BP (SBP or DBP), group (HTN vs. NTN), and group × BP interaction were predictors. Both intercept and slope were treated as random. SBP and DBP were predictors in separate models.\nWe performed supplementary analyses using pulse pressure (PP) as a predictor in all the models.\nWML volumes were log transformed. We checked the linear models for violations of models assumptions. For all the analyses, the most parsimonious model was chosen, defined as the model that included only significant or necessary (main effects when interaction was present) terms. Statistical significance was defined as a P-value <0.05. SPSS (version 25, SPSS, Inc., Chicago, Illinois, USA) software was used for all analyses.\n\nRESULTS:\nOur group of 376 participants consisted of HTN (n = 207) and NTN (n = 169) individuals. The characteristics of the groups are given in Table 1. Two hundred and twenty-eight (60.6%) were women (age, 68.5 ± 7.4 years (mean ± standard deviation); education, 16.7 ± 2.3 years); and 148 (39.4%) men (age, 70.7 ± 6.9 years; education, 17.0 ± 2.4 years). The majority of the participants were Caucasian (86%), 10% African American, 4% other races.\nBaseline characteristics of the study group (n = 376)\nData is presented as mean ± standard deviation unless otherwise indicated. P-values come from t-test (total cholesterol, LDL) or Mann–Whitney U-test (age, education, BMI, HDL, triglycerides, glucose). For categorical variables, χ2 was used.\nn = 371 (NTN = 204, HTN = 167)\nn = 369 (NTN = 205, HTN = 164)\nn = 363 (NTN = 200, HTN = 163)\nn = 371 (NTN = 206, HTN = 165)\nValues are presented as mean ± standard errors (SE), P-values from ANCOVA after accounting for age.\nHippocampal volume is a mean of left and right, values are presented as mean ± standard errors (SE), P-values from ANCOVA after accounting for age and gender. Since the residuals from GM model were not normally distributed, the values were reanalyzed using ranked ANCOVA. Results remained the same.\nICV, intracranial volume; HTN, hypertension; NTN, normotensive.\nIn the longitudinal group of 188 participants, 116 (61.7%) were women (age, 69.6 ± 7.5 years; education, 16.7 ± 2.1 years) and 72 (38.3%) men (age, 71.4 ± 6.1 years; education, 17.2 ± 2.3 years). The mean time of follow-up was 2.3 ± 0.70 years. The longitudinal group did not differ from the participants with only one examination in terms of sex, HTN prevalence, SBP, DBP, BMI, total cholesterol, LDL and HDL cholesterol, triglycerides, education and WML volume. Prevalence of smoking and diabetes, as well as the number of participants with normotension, controlled, uncontrolled and untreated hypertension were also not different between the two groups. However, participants with longitudinal observation were slightly older (age, 70.3 ± 7.0 vs. 68.4 ± 7.4 years, P = 0.005) and had lower glucose levels (glucose, 82.5 ± 16.0 vs. 85.6 ± 14 mg/dl) than the individuals with only one exam. The clinical characteristics, as well as baseline brain and WML volumes of HTN and NTN participants in the longitudinal subgroup are given in Table S1. WML volumes by five baseline study group are shown in Tables S2 and S3.\nBaseline analyses BP and white matter lesion volumes In the HTN group, SBP and quadratic SBP term were predictors of WML volume at baseline (regression model included age, F3,168 = 14.4, P < 0.001) (Table 2, Fig. 1). The results were similar with log-transformed data (F3,168 = 13.1, P < 0.001) (Table 3). The critical SBP value calculated from equation [2] was x = 124 mmHg.\nLinear regression model predicting WML volume in the HTN group at baseline (raw data)\nCritical value (Eq. [2]): x = –(−0.07317/(2 × 0.0003)) = 122 mmHg.\nHTN, hypertension; WML, white matter lesion.\nQuadratic relationship between systolic blood pressure readings and white matter lesion measurements in hypertensive participants.\nLinear regression model predicting WML volume in the HTN group at baseline (log transformed data: log(WML volume))\nCritical value (Eq. [2]): x = –(−0.02741/(2 × 0.00011)) = 124.6 mmHg.\nHTN, hypertension; WML, white matter lesion.\nIn the NTN group, neither SBP nor DBP were related to WML.\nIn the HTN group, SBP and quadratic SBP term were predictors of WML volume at baseline (regression model included age, F3,168 = 14.4, P < 0.001) (Table 2, Fig. 1). The results were similar with log-transformed data (F3,168 = 13.1, P < 0.001) (Table 3). The critical SBP value calculated from equation [2] was x = 124 mmHg.\nLinear regression model predicting WML volume in the HTN group at baseline (raw data)\nCritical value (Eq. [2]): x = –(−0.07317/(2 × 0.0003)) = 122 mmHg.\nHTN, hypertension; WML, white matter lesion.\nQuadratic relationship between systolic blood pressure readings and white matter lesion measurements in hypertensive participants.\nLinear regression model predicting WML volume in the HTN group at baseline (log transformed data: log(WML volume))\nCritical value (Eq. [2]): x = –(−0.02741/(2 × 0.00011)) = 124.6 mmHg.\nHTN, hypertension; WML, white matter lesion.\nIn the NTN group, neither SBP nor DBP were related to WML.\nBP and brain volumes In HTN, the quadratic DBP term was a predictor of the mean hippocampal volume at baseline (the model included age and sex, F4,168 = 11.6, P < 0.001) (Table 4, Fig. 2). The critical DBP value calculated from equation [2] was x = 77 mmHg.\nModel predicting mean hippocampal volume in the HTN group at baseline\nCritical value (Eq. [2]): x = −(0.00399/(2 × (−0.000026))) = 77 mmHg.\nHTN, hypertension; WML, white matter lesion.\nRelationship between diastolic blood pressure readings and mean hippocampal brain volume measurements in hypertensive participants.\nGM was associated with SBP (β = −0.16, P = 0.02) and the model (F4,166 = 14.0, P < 0.001) included BMI, smoking and age.\nIn NTN, the mean hippocampal volume was inversely related to SBP (β = −0.17, P < 0.008), (regression model included age and sex, F3,206 = 22.4, P < 0.001). GM volume was inversely related to DBP (β = −0.15, P = 0.01), (the model included age and sex, F3,206 = 32.3, P < 0.001).\nIn HTN, the quadratic DBP term was a predictor of the mean hippocampal volume at baseline (the model included age and sex, F4,168 = 11.6, P < 0.001) (Table 4, Fig. 2). The critical DBP value calculated from equation [2] was x = 77 mmHg.\nModel predicting mean hippocampal volume in the HTN group at baseline\nCritical value (Eq. [2]): x = −(0.00399/(2 × (−0.000026))) = 77 mmHg.\nHTN, hypertension; WML, white matter lesion.\nRelationship between diastolic blood pressure readings and mean hippocampal brain volume measurements in hypertensive participants.\nGM was associated with SBP (β = −0.16, P = 0.02) and the model (F4,166 = 14.0, P < 0.001) included BMI, smoking and age.\nIn NTN, the mean hippocampal volume was inversely related to SBP (β = −0.17, P < 0.008), (regression model included age and sex, F3,206 = 22.4, P < 0.001). GM volume was inversely related to DBP (β = −0.15, P = 0.01), (the model included age and sex, F3,206 = 32.3, P < 0.001).\nBP and white matter lesion volumes In the HTN group, SBP and quadratic SBP term were predictors of WML volume at baseline (regression model included age, F3,168 = 14.4, P < 0.001) (Table 2, Fig. 1). The results were similar with log-transformed data (F3,168 = 13.1, P < 0.001) (Table 3). The critical SBP value calculated from equation [2] was x = 124 mmHg.\nLinear regression model predicting WML volume in the HTN group at baseline (raw data)\nCritical value (Eq. [2]): x = –(−0.07317/(2 × 0.0003)) = 122 mmHg.\nHTN, hypertension; WML, white matter lesion.\nQuadratic relationship between systolic blood pressure readings and white matter lesion measurements in hypertensive participants.\nLinear regression model predicting WML volume in the HTN group at baseline (log transformed data: log(WML volume))\nCritical value (Eq. [2]): x = –(−0.02741/(2 × 0.00011)) = 124.6 mmHg.\nHTN, hypertension; WML, white matter lesion.\nIn the NTN group, neither SBP nor DBP were related to WML.\nIn the HTN group, SBP and quadratic SBP term were predictors of WML volume at baseline (regression model included age, F3,168 = 14.4, P < 0.001) (Table 2, Fig. 1). The results were similar with log-transformed data (F3,168 = 13.1, P < 0.001) (Table 3). The critical SBP value calculated from equation [2] was x = 124 mmHg.\nLinear regression model predicting WML volume in the HTN group at baseline (raw data)\nCritical value (Eq. [2]): x = –(−0.07317/(2 × 0.0003)) = 122 mmHg.\nHTN, hypertension; WML, white matter lesion.\nQuadratic relationship between systolic blood pressure readings and white matter lesion measurements in hypertensive participants.\nLinear regression model predicting WML volume in the HTN group at baseline (log transformed data: log(WML volume))\nCritical value (Eq. [2]): x = –(−0.02741/(2 × 0.00011)) = 124.6 mmHg.\nHTN, hypertension; WML, white matter lesion.\nIn the NTN group, neither SBP nor DBP were related to WML.\nBP and brain volumes In HTN, the quadratic DBP term was a predictor of the mean hippocampal volume at baseline (the model included age and sex, F4,168 = 11.6, P < 0.001) (Table 4, Fig. 2). The critical DBP value calculated from equation [2] was x = 77 mmHg.\nModel predicting mean hippocampal volume in the HTN group at baseline\nCritical value (Eq. [2]): x = −(0.00399/(2 × (−0.000026))) = 77 mmHg.\nHTN, hypertension; WML, white matter lesion.\nRelationship between diastolic blood pressure readings and mean hippocampal brain volume measurements in hypertensive participants.\nGM was associated with SBP (β = −0.16, P = 0.02) and the model (F4,166 = 14.0, P < 0.001) included BMI, smoking and age.\nIn NTN, the mean hippocampal volume was inversely related to SBP (β = −0.17, P < 0.008), (regression model included age and sex, F3,206 = 22.4, P < 0.001). GM volume was inversely related to DBP (β = −0.15, P = 0.01), (the model included age and sex, F3,206 = 32.3, P < 0.001).\nIn HTN, the quadratic DBP term was a predictor of the mean hippocampal volume at baseline (the model included age and sex, F4,168 = 11.6, P < 0.001) (Table 4, Fig. 2). The critical DBP value calculated from equation [2] was x = 77 mmHg.\nModel predicting mean hippocampal volume in the HTN group at baseline\nCritical value (Eq. [2]): x = −(0.00399/(2 × (−0.000026))) = 77 mmHg.\nHTN, hypertension; WML, white matter lesion.\nRelationship between diastolic blood pressure readings and mean hippocampal brain volume measurements in hypertensive participants.\nGM was associated with SBP (β = −0.16, P = 0.02) and the model (F4,166 = 14.0, P < 0.001) included BMI, smoking and age.\nIn NTN, the mean hippocampal volume was inversely related to SBP (β = −0.17, P < 0.008), (regression model included age and sex, F3,206 = 22.4, P < 0.001). GM volume was inversely related to DBP (β = −0.15, P = 0.01), (the model included age and sex, F3,206 = 32.3, P < 0.001).\nLongitudinal analyses Changes in BP and volumes over time SBP did not change significantly in the HTN or NTN group, but the interaction term was significant (P = 0.01) indicating divergent pattern of SBP dynamics (the estimate = −1.6, P = 0.07 for HTN and 1.4, P = 0.07 for NTN). Similarly, DBP patterns were different (P = 0.03 for interaction term) with a lack of change in the HTN participants (estimate −0.6, P = 0.31) and a significant increase in the NTN group (the estimate = 1.1, P = 0.03). In both groups, WML volumes increased with time, (the estimate = 0.025, P < 0.001) for HTN and (0.028, P < 0.001) for NTN. In both groups, brain volumes decreased with time. An estimate for the fixed effect of time for the mean hippocampal volumes was (−0.0044, P < 0.001) for HTN and (−0.0038, P < 0.001) among NTN participants. For GM, they were (−0.48, P < 0.001) and (−0.45, P < 0.001); and finally, for WM (−0.30, P < 0.001) and (−0.22, P = 0.001), for the HTN and NTN groups, respectively. Group × time interaction terms were not significant for models with brain or WML volumes, indicating that rates of change did not differ between groups. Figure S2 presents BP and WML changes in both groups.\nSBP did not change significantly in the HTN or NTN group, but the interaction term was significant (P = 0.01) indicating divergent pattern of SBP dynamics (the estimate = −1.6, P = 0.07 for HTN and 1.4, P = 0.07 for NTN). Similarly, DBP patterns were different (P = 0.03 for interaction term) with a lack of change in the HTN participants (estimate −0.6, P = 0.31) and a significant increase in the NTN group (the estimate = 1.1, P = 0.03). In both groups, WML volumes increased with time, (the estimate = 0.025, P < 0.001) for HTN and (0.028, P < 0.001) for NTN. In both groups, brain volumes decreased with time. An estimate for the fixed effect of time for the mean hippocampal volumes was (−0.0044, P < 0.001) for HTN and (−0.0038, P < 0.001) among NTN participants. For GM, they were (−0.48, P < 0.001) and (−0.45, P < 0.001); and finally, for WM (−0.30, P < 0.001) and (−0.22, P = 0.001), for the HTN and NTN groups, respectively. Group × time interaction terms were not significant for models with brain or WML volumes, indicating that rates of change did not differ between groups. Figure S2 presents BP and WML changes in both groups.\nChanges in BP and volumes over time SBP did not change significantly in the HTN or NTN group, but the interaction term was significant (P = 0.01) indicating divergent pattern of SBP dynamics (the estimate = −1.6, P = 0.07 for HTN and 1.4, P = 0.07 for NTN). Similarly, DBP patterns were different (P = 0.03 for interaction term) with a lack of change in the HTN participants (estimate −0.6, P = 0.31) and a significant increase in the NTN group (the estimate = 1.1, P = 0.03). In both groups, WML volumes increased with time, (the estimate = 0.025, P < 0.001) for HTN and (0.028, P < 0.001) for NTN. In both groups, brain volumes decreased with time. An estimate for the fixed effect of time for the mean hippocampal volumes was (−0.0044, P < 0.001) for HTN and (−0.0038, P < 0.001) among NTN participants. For GM, they were (−0.48, P < 0.001) and (−0.45, P < 0.001); and finally, for WM (−0.30, P < 0.001) and (−0.22, P = 0.001), for the HTN and NTN groups, respectively. Group × time interaction terms were not significant for models with brain or WML volumes, indicating that rates of change did not differ between groups. Figure S2 presents BP and WML changes in both groups.\nSBP did not change significantly in the HTN or NTN group, but the interaction term was significant (P = 0.01) indicating divergent pattern of SBP dynamics (the estimate = −1.6, P = 0.07 for HTN and 1.4, P = 0.07 for NTN). Similarly, DBP patterns were different (P = 0.03 for interaction term) with a lack of change in the HTN participants (estimate −0.6, P = 0.31) and a significant increase in the NTN group (the estimate = 1.1, P = 0.03). In both groups, WML volumes increased with time, (the estimate = 0.025, P < 0.001) for HTN and (0.028, P < 0.001) for NTN. In both groups, brain volumes decreased with time. An estimate for the fixed effect of time for the mean hippocampal volumes was (−0.0044, P < 0.001) for HTN and (−0.0038, P < 0.001) among NTN participants. For GM, they were (−0.48, P < 0.001) and (−0.45, P < 0.001); and finally, for WM (−0.30, P < 0.001) and (−0.22, P = 0.001), for the HTN and NTN groups, respectively. Group × time interaction terms were not significant for models with brain or WML volumes, indicating that rates of change did not differ between groups. Figure S2 presents BP and WML changes in both groups.\nSecondary analyses Table S4 shows the stability of secondary group definition (untreated, uncontrolled and controlled HTN, Stage 1 HTN, and normotension). Table S5 presents the results from MMRM analyses of WML volumes and BP changes conducted with all five subgroups defined at baseline based on HTN control status. One can appreciate that WML volume increased significantly in untreated and controlled hypertension, as well as in normotensive participants.\nTable S6 and Figure S3 shows that participants who at baseline were classified as untreated (n = 19) or uncontrolled hypertension (n = 20) and reclassified at the last visit as good outcome (improved BP control) experience the same increase in WML volumes as participants with bad outcome (BP remained untreated or uncontrolled).\nBaseline BP and changes in white matter lesion and brain volumes In the hypertensive group both baseline SBP (β = 0.33, P = 0.007) and DBP (β = −0.30, P = 0.008) were related to WML changes over time (entire model F4,83 = 10.5, P < 0.001, included also baseline WML volumes and smoking). Baseline BP was not related to changes in hippocampal, GM, or WM volumes.\nIn normotensive participants baseline BP was not related to change in any examined volumes.\nIn the hypertensive group both baseline SBP (β = 0.33, P = 0.007) and DBP (β = −0.30, P = 0.008) were related to WML changes over time (entire model F4,83 = 10.5, P < 0.001, included also baseline WML volumes and smoking). Baseline BP was not related to changes in hippocampal, GM, or WM volumes.\nIn normotensive participants baseline BP was not related to change in any examined volumes.\nLongitudinal changes in BP and changes in white matter lesion and brain volumes In the HTN group, neither change in SBP nor DBP were associated with a change in WML, hippocampal, GM or WM volumes (Tables S7 and S8).\nIn the NTN group, both increase in SBP (an estimate for the fixed effect of SBP = 0.0023, P = 0.01) and DBP (an estimate for the fixed effect of DBP = 0.0035, P = 0.01) over time were related to the increase in WML volume. The significant interaction term indicated that these dynamics were meaningfully different from the HTN group. Moreover, in the normotensive group both increase in SBP (an estimate for the fixed effect of SBP = −0.0002, P = 0.03) and DBP (an estimate for the fixed effect of DBP = −0.0004, P = 0.01) over time were related to reduction in the mean hippocampal volume. However, only for SBP the interaction term was significant, indicating that these changes were different from HTN group.\nResults for pulse pressure are presented in the Supplement p. 8–9.\nIn the HTN group, neither change in SBP nor DBP were associated with a change in WML, hippocampal, GM or WM volumes (Tables S7 and S8).\nIn the NTN group, both increase in SBP (an estimate for the fixed effect of SBP = 0.0023, P = 0.01) and DBP (an estimate for the fixed effect of DBP = 0.0035, P = 0.01) over time were related to the increase in WML volume. The significant interaction term indicated that these dynamics were meaningfully different from the HTN group. Moreover, in the normotensive group both increase in SBP (an estimate for the fixed effect of SBP = −0.0002, P = 0.03) and DBP (an estimate for the fixed effect of DBP = −0.0004, P = 0.01) over time were related to reduction in the mean hippocampal volume. However, only for SBP the interaction term was significant, indicating that these changes were different from HTN group.\nResults for pulse pressure are presented in the Supplement p. 8–9.\nTable S4 shows the stability of secondary group definition (untreated, uncontrolled and controlled HTN, Stage 1 HTN, and normotension). Table S5 presents the results from MMRM analyses of WML volumes and BP changes conducted with all five subgroups defined at baseline based on HTN control status. One can appreciate that WML volume increased significantly in untreated and controlled hypertension, as well as in normotensive participants.\nTable S6 and Figure S3 shows that participants who at baseline were classified as untreated (n = 19) or uncontrolled hypertension (n = 20) and reclassified at the last visit as good outcome (improved BP control) experience the same increase in WML volumes as participants with bad outcome (BP remained untreated or uncontrolled).\nBaseline BP and changes in white matter lesion and brain volumes In the hypertensive group both baseline SBP (β = 0.33, P = 0.007) and DBP (β = −0.30, P = 0.008) were related to WML changes over time (entire model F4,83 = 10.5, P < 0.001, included also baseline WML volumes and smoking). Baseline BP was not related to changes in hippocampal, GM, or WM volumes.\nIn normotensive participants baseline BP was not related to change in any examined volumes.\nIn the hypertensive group both baseline SBP (β = 0.33, P = 0.007) and DBP (β = −0.30, P = 0.008) were related to WML changes over time (entire model F4,83 = 10.5, P < 0.001, included also baseline WML volumes and smoking). Baseline BP was not related to changes in hippocampal, GM, or WM volumes.\nIn normotensive participants baseline BP was not related to change in any examined volumes.\nLongitudinal changes in BP and changes in white matter lesion and brain volumes In the HTN group, neither change in SBP nor DBP were associated with a change in WML, hippocampal, GM or WM volumes (Tables S7 and S8).\nIn the NTN group, both increase in SBP (an estimate for the fixed effect of SBP = 0.0023, P = 0.01) and DBP (an estimate for the fixed effect of DBP = 0.0035, P = 0.01) over time were related to the increase in WML volume. The significant interaction term indicated that these dynamics were meaningfully different from the HTN group. Moreover, in the normotensive group both increase in SBP (an estimate for the fixed effect of SBP = −0.0002, P = 0.03) and DBP (an estimate for the fixed effect of DBP = −0.0004, P = 0.01) over time were related to reduction in the mean hippocampal volume. However, only for SBP the interaction term was significant, indicating that these changes were different from HTN group.\nResults for pulse pressure are presented in the Supplement p. 8–9.\nIn the HTN group, neither change in SBP nor DBP were associated with a change in WML, hippocampal, GM or WM volumes (Tables S7 and S8).\nIn the NTN group, both increase in SBP (an estimate for the fixed effect of SBP = 0.0023, P = 0.01) and DBP (an estimate for the fixed effect of DBP = 0.0035, P = 0.01) over time were related to the increase in WML volume. The significant interaction term indicated that these dynamics were meaningfully different from the HTN group. Moreover, in the normotensive group both increase in SBP (an estimate for the fixed effect of SBP = −0.0002, P = 0.03) and DBP (an estimate for the fixed effect of DBP = −0.0004, P = 0.01) over time were related to reduction in the mean hippocampal volume. However, only for SBP the interaction term was significant, indicating that these changes were different from HTN group.\nResults for pulse pressure are presented in the Supplement p. 8–9.\n\nBaseline analyses:\nBP and white matter lesion volumes In the HTN group, SBP and quadratic SBP term were predictors of WML volume at baseline (regression model included age, F3,168 = 14.4, P < 0.001) (Table 2, Fig. 1). The results were similar with log-transformed data (F3,168 = 13.1, P < 0.001) (Table 3). The critical SBP value calculated from equation [2] was x = 124 mmHg.\nLinear regression model predicting WML volume in the HTN group at baseline (raw data)\nCritical value (Eq. [2]): x = –(−0.07317/(2 × 0.0003)) = 122 mmHg.\nHTN, hypertension; WML, white matter lesion.\nQuadratic relationship between systolic blood pressure readings and white matter lesion measurements in hypertensive participants.\nLinear regression model predicting WML volume in the HTN group at baseline (log transformed data: log(WML volume))\nCritical value (Eq. [2]): x = –(−0.02741/(2 × 0.00011)) = 124.6 mmHg.\nHTN, hypertension; WML, white matter lesion.\nIn the NTN group, neither SBP nor DBP were related to WML.\nIn the HTN group, SBP and quadratic SBP term were predictors of WML volume at baseline (regression model included age, F3,168 = 14.4, P < 0.001) (Table 2, Fig. 1). The results were similar with log-transformed data (F3,168 = 13.1, P < 0.001) (Table 3). The critical SBP value calculated from equation [2] was x = 124 mmHg.\nLinear regression model predicting WML volume in the HTN group at baseline (raw data)\nCritical value (Eq. [2]): x = –(−0.07317/(2 × 0.0003)) = 122 mmHg.\nHTN, hypertension; WML, white matter lesion.\nQuadratic relationship between systolic blood pressure readings and white matter lesion measurements in hypertensive participants.\nLinear regression model predicting WML volume in the HTN group at baseline (log transformed data: log(WML volume))\nCritical value (Eq. [2]): x = –(−0.02741/(2 × 0.00011)) = 124.6 mmHg.\nHTN, hypertension; WML, white matter lesion.\nIn the NTN group, neither SBP nor DBP were related to WML.\nBP and brain volumes In HTN, the quadratic DBP term was a predictor of the mean hippocampal volume at baseline (the model included age and sex, F4,168 = 11.6, P < 0.001) (Table 4, Fig. 2). The critical DBP value calculated from equation [2] was x = 77 mmHg.\nModel predicting mean hippocampal volume in the HTN group at baseline\nCritical value (Eq. [2]): x = −(0.00399/(2 × (−0.000026))) = 77 mmHg.\nHTN, hypertension; WML, white matter lesion.\nRelationship between diastolic blood pressure readings and mean hippocampal brain volume measurements in hypertensive participants.\nGM was associated with SBP (β = −0.16, P = 0.02) and the model (F4,166 = 14.0, P < 0.001) included BMI, smoking and age.\nIn NTN, the mean hippocampal volume was inversely related to SBP (β = −0.17, P < 0.008), (regression model included age and sex, F3,206 = 22.4, P < 0.001). GM volume was inversely related to DBP (β = −0.15, P = 0.01), (the model included age and sex, F3,206 = 32.3, P < 0.001).\nIn HTN, the quadratic DBP term was a predictor of the mean hippocampal volume at baseline (the model included age and sex, F4,168 = 11.6, P < 0.001) (Table 4, Fig. 2). The critical DBP value calculated from equation [2] was x = 77 mmHg.\nModel predicting mean hippocampal volume in the HTN group at baseline\nCritical value (Eq. [2]): x = −(0.00399/(2 × (−0.000026))) = 77 mmHg.\nHTN, hypertension; WML, white matter lesion.\nRelationship between diastolic blood pressure readings and mean hippocampal brain volume measurements in hypertensive participants.\nGM was associated with SBP (β = −0.16, P = 0.02) and the model (F4,166 = 14.0, P < 0.001) included BMI, smoking and age.\nIn NTN, the mean hippocampal volume was inversely related to SBP (β = −0.17, P < 0.008), (regression model included age and sex, F3,206 = 22.4, P < 0.001). GM volume was inversely related to DBP (β = −0.15, P = 0.01), (the model included age and sex, F3,206 = 32.3, P < 0.001).\n\nBP and white matter lesion volumes:\nIn the HTN group, SBP and quadratic SBP term were predictors of WML volume at baseline (regression model included age, F3,168 = 14.4, P < 0.001) (Table 2, Fig. 1). The results were similar with log-transformed data (F3,168 = 13.1, P < 0.001) (Table 3). The critical SBP value calculated from equation [2] was x = 124 mmHg.\nLinear regression model predicting WML volume in the HTN group at baseline (raw data)\nCritical value (Eq. [2]): x = –(−0.07317/(2 × 0.0003)) = 122 mmHg.\nHTN, hypertension; WML, white matter lesion.\nQuadratic relationship between systolic blood pressure readings and white matter lesion measurements in hypertensive participants.\nLinear regression model predicting WML volume in the HTN group at baseline (log transformed data: log(WML volume))\nCritical value (Eq. [2]): x = –(−0.02741/(2 × 0.00011)) = 124.6 mmHg.\nHTN, hypertension; WML, white matter lesion.\nIn the NTN group, neither SBP nor DBP were related to WML.\n\nBP and brain volumes:\nIn HTN, the quadratic DBP term was a predictor of the mean hippocampal volume at baseline (the model included age and sex, F4,168 = 11.6, P < 0.001) (Table 4, Fig. 2). The critical DBP value calculated from equation [2] was x = 77 mmHg.\nModel predicting mean hippocampal volume in the HTN group at baseline\nCritical value (Eq. [2]): x = −(0.00399/(2 × (−0.000026))) = 77 mmHg.\nHTN, hypertension; WML, white matter lesion.\nRelationship between diastolic blood pressure readings and mean hippocampal brain volume measurements in hypertensive participants.\nGM was associated with SBP (β = −0.16, P = 0.02) and the model (F4,166 = 14.0, P < 0.001) included BMI, smoking and age.\nIn NTN, the mean hippocampal volume was inversely related to SBP (β = −0.17, P < 0.008), (regression model included age and sex, F3,206 = 22.4, P < 0.001). GM volume was inversely related to DBP (β = −0.15, P = 0.01), (the model included age and sex, F3,206 = 32.3, P < 0.001).\n\nLongitudinal analyses:\nChanges in BP and volumes over time SBP did not change significantly in the HTN or NTN group, but the interaction term was significant (P = 0.01) indicating divergent pattern of SBP dynamics (the estimate = −1.6, P = 0.07 for HTN and 1.4, P = 0.07 for NTN). Similarly, DBP patterns were different (P = 0.03 for interaction term) with a lack of change in the HTN participants (estimate −0.6, P = 0.31) and a significant increase in the NTN group (the estimate = 1.1, P = 0.03). In both groups, WML volumes increased with time, (the estimate = 0.025, P < 0.001) for HTN and (0.028, P < 0.001) for NTN. In both groups, brain volumes decreased with time. An estimate for the fixed effect of time for the mean hippocampal volumes was (−0.0044, P < 0.001) for HTN and (−0.0038, P < 0.001) among NTN participants. For GM, they were (−0.48, P < 0.001) and (−0.45, P < 0.001); and finally, for WM (−0.30, P < 0.001) and (−0.22, P = 0.001), for the HTN and NTN groups, respectively. Group × time interaction terms were not significant for models with brain or WML volumes, indicating that rates of change did not differ between groups. Figure S2 presents BP and WML changes in both groups.\nSBP did not change significantly in the HTN or NTN group, but the interaction term was significant (P = 0.01) indicating divergent pattern of SBP dynamics (the estimate = −1.6, P = 0.07 for HTN and 1.4, P = 0.07 for NTN). Similarly, DBP patterns were different (P = 0.03 for interaction term) with a lack of change in the HTN participants (estimate −0.6, P = 0.31) and a significant increase in the NTN group (the estimate = 1.1, P = 0.03). In both groups, WML volumes increased with time, (the estimate = 0.025, P < 0.001) for HTN and (0.028, P < 0.001) for NTN. In both groups, brain volumes decreased with time. An estimate for the fixed effect of time for the mean hippocampal volumes was (−0.0044, P < 0.001) for HTN and (−0.0038, P < 0.001) among NTN participants. For GM, they were (−0.48, P < 0.001) and (−0.45, P < 0.001); and finally, for WM (−0.30, P < 0.001) and (−0.22, P = 0.001), for the HTN and NTN groups, respectively. Group × time interaction terms were not significant for models with brain or WML volumes, indicating that rates of change did not differ between groups. Figure S2 presents BP and WML changes in both groups.\n\nChanges in BP and volumes over time:\nSBP did not change significantly in the HTN or NTN group, but the interaction term was significant (P = 0.01) indicating divergent pattern of SBP dynamics (the estimate = −1.6, P = 0.07 for HTN and 1.4, P = 0.07 for NTN). Similarly, DBP patterns were different (P = 0.03 for interaction term) with a lack of change in the HTN participants (estimate −0.6, P = 0.31) and a significant increase in the NTN group (the estimate = 1.1, P = 0.03). In both groups, WML volumes increased with time, (the estimate = 0.025, P < 0.001) for HTN and (0.028, P < 0.001) for NTN. In both groups, brain volumes decreased with time. An estimate for the fixed effect of time for the mean hippocampal volumes was (−0.0044, P < 0.001) for HTN and (−0.0038, P < 0.001) among NTN participants. For GM, they were (−0.48, P < 0.001) and (−0.45, P < 0.001); and finally, for WM (−0.30, P < 0.001) and (−0.22, P = 0.001), for the HTN and NTN groups, respectively. Group × time interaction terms were not significant for models with brain or WML volumes, indicating that rates of change did not differ between groups. Figure S2 presents BP and WML changes in both groups.\n\nSecondary analyses:\nTable S4 shows the stability of secondary group definition (untreated, uncontrolled and controlled HTN, Stage 1 HTN, and normotension). Table S5 presents the results from MMRM analyses of WML volumes and BP changes conducted with all five subgroups defined at baseline based on HTN control status. One can appreciate that WML volume increased significantly in untreated and controlled hypertension, as well as in normotensive participants.\nTable S6 and Figure S3 shows that participants who at baseline were classified as untreated (n = 19) or uncontrolled hypertension (n = 20) and reclassified at the last visit as good outcome (improved BP control) experience the same increase in WML volumes as participants with bad outcome (BP remained untreated or uncontrolled).\nBaseline BP and changes in white matter lesion and brain volumes In the hypertensive group both baseline SBP (β = 0.33, P = 0.007) and DBP (β = −0.30, P = 0.008) were related to WML changes over time (entire model F4,83 = 10.5, P < 0.001, included also baseline WML volumes and smoking). Baseline BP was not related to changes in hippocampal, GM, or WM volumes.\nIn normotensive participants baseline BP was not related to change in any examined volumes.\nIn the hypertensive group both baseline SBP (β = 0.33, P = 0.007) and DBP (β = −0.30, P = 0.008) were related to WML changes over time (entire model F4,83 = 10.5, P < 0.001, included also baseline WML volumes and smoking). Baseline BP was not related to changes in hippocampal, GM, or WM volumes.\nIn normotensive participants baseline BP was not related to change in any examined volumes.\nLongitudinal changes in BP and changes in white matter lesion and brain volumes In the HTN group, neither change in SBP nor DBP were associated with a change in WML, hippocampal, GM or WM volumes (Tables S7 and S8).\nIn the NTN group, both increase in SBP (an estimate for the fixed effect of SBP = 0.0023, P = 0.01) and DBP (an estimate for the fixed effect of DBP = 0.0035, P = 0.01) over time were related to the increase in WML volume. The significant interaction term indicated that these dynamics were meaningfully different from the HTN group. Moreover, in the normotensive group both increase in SBP (an estimate for the fixed effect of SBP = −0.0002, P = 0.03) and DBP (an estimate for the fixed effect of DBP = −0.0004, P = 0.01) over time were related to reduction in the mean hippocampal volume. However, only for SBP the interaction term was significant, indicating that these changes were different from HTN group.\nResults for pulse pressure are presented in the Supplement p. 8–9.\nIn the HTN group, neither change in SBP nor DBP were associated with a change in WML, hippocampal, GM or WM volumes (Tables S7 and S8).\nIn the NTN group, both increase in SBP (an estimate for the fixed effect of SBP = 0.0023, P = 0.01) and DBP (an estimate for the fixed effect of DBP = 0.0035, P = 0.01) over time were related to the increase in WML volume. The significant interaction term indicated that these dynamics were meaningfully different from the HTN group. Moreover, in the normotensive group both increase in SBP (an estimate for the fixed effect of SBP = −0.0002, P = 0.03) and DBP (an estimate for the fixed effect of DBP = −0.0004, P = 0.01) over time were related to reduction in the mean hippocampal volume. However, only for SBP the interaction term was significant, indicating that these changes were different from HTN group.\nResults for pulse pressure are presented in the Supplement p. 8–9.\n\nBaseline BP and changes in white matter lesion and brain volumes:\nIn the hypertensive group both baseline SBP (β = 0.33, P = 0.007) and DBP (β = −0.30, P = 0.008) were related to WML changes over time (entire model F4,83 = 10.5, P < 0.001, included also baseline WML volumes and smoking). Baseline BP was not related to changes in hippocampal, GM, or WM volumes.\nIn normotensive participants baseline BP was not related to change in any examined volumes.\n\nLongitudinal changes in BP and changes in white matter lesion and brain volumes:\nIn the HTN group, neither change in SBP nor DBP were associated with a change in WML, hippocampal, GM or WM volumes (Tables S7 and S8).\nIn the NTN group, both increase in SBP (an estimate for the fixed effect of SBP = 0.0023, P = 0.01) and DBP (an estimate for the fixed effect of DBP = 0.0035, P = 0.01) over time were related to the increase in WML volume. The significant interaction term indicated that these dynamics were meaningfully different from the HTN group. Moreover, in the normotensive group both increase in SBP (an estimate for the fixed effect of SBP = −0.0002, P = 0.03) and DBP (an estimate for the fixed effect of DBP = −0.0004, P = 0.01) over time were related to reduction in the mean hippocampal volume. However, only for SBP the interaction term was significant, indicating that these changes were different from HTN group.\nResults for pulse pressure are presented in the Supplement p. 8–9.\n\nDISCUSSION:\nLongitudinal observations of brain health in aging have continuously concluded that BP has a significant impact on the nervous system. It is well known that increased cardiovascular burden, as represented by an elevated systolic or diastolic BP, can substantially increase the risk of developing hemorrhagic and ischemic stroke [1,2], white matter lesions and atrophy [14,32], and cognitive dysfunction [33,34].\nIn our examination of a cohort of normotensive and hypertensive participants, we posit that adequate BP is vital for a healthy brain. We have previously observed that in hypertension, there appears to be a window of mid-range SBP around 125 mmHg which maximizes perfusion [21]. In the present study, we show that hypertensive participants with a similar SBP (124 mmHg) have lower WML volumes that participants with SBP further away from this optimum. This is a novel finding further supporting the notion of an ideal blood pressure range that promotes optimal brain function and minimizes damage. Opposite to our results, the SPRINT-Mind study found that the group where BP was lowered aggressively to less than 120 experienced smaller WML progression over time [18]. However our findings are consistent with a report showing that in hypertensive participants low BP was related to higher PWML volume [35] and an earlier study where an increase in WML volume over time was most pronounced in participants with the highest BP who also had the greatest BP fluctuations [36].\nWe also find that among hypertensive participants there was a quadratic relationship between the mean hippocampal volume at baseline and DBP, such as both the participants with low and high DBP had lower volume. This observation, although interesting, is not completely novel. Others have previously described a parallel U-shape association between brain atrophy and DBP, where both low and high concurrent DBP were related to greater cortical volume reduction [37]. The SPRINT MIND study showed that intensive BP treatment group experienced significantly greater hippocampal volume reduction [38]. It further confirms the hypothesis indicating the existence of a BP optimum, and reconciles reports showing that both high [39] and low BP [40] are related to lower hippocampal volumes.\nAmong normotensive participants relationships between BP and brain volumes were linear such as both higher SBP and DBP were related to lower brain volumes. It is congruent with previous reports and meta-analyses showing both BP components associated with volumes [3]. Notably, no quadratic relationship was observed in this group, further strengthening our initial assumption that optimum exists in the group with preexisting impairment of the vascular system.\nReduction in the volume of specific brain regions over time is another parameter indicative of abnormal aging and is often seen reported in HTN [3,41]. Our HTN group had more global and hippocampal atrophy at baseline (Table 1) than their normotensive counterparts. However, after adjustment for age, the WML volume did not differ between HTN and NTN groups. It is possible that substantial percentage (55%) of participants with controlled BP attenuated the differences. Comparisons across five groups showed that participants with untreated and uncontrolled hypertension tended to have higher WML volumes (Table S2).\nIn our hypertensive participants, higher baseline SBP and lower DBP were both independently related to the growth of WML over time. While the first finding is not surprising, we offer two possible explanations for the latter. First, as the mean arterial pressure, the major driver of perfusion pressure, depends mostly on DBP, the low DBP in HTN may indicate the propensity to hypoperfusion. Second, this association would be consistent with widening of pulse pressure, indicative of arterial stiffness. Indeed, an earlier analysis of the Lothian Birth Cohort 1936 showed that the sequence of DBP reduction, PP increase and rising internal carotid artery pulsatility index leads to WM damage [42]. Our supplemental analyses also confirm that baseline PP was related to WML growth.\nIt is worth noting that both normo and hypertensive groups experienced reductions in brain volumes and WML growth over time. This was present despite dissimilar trajectories of blood pressure: increases in the NTN and apparent lack of change in the HTN participants. A more fine-grained picture emerged from analyzing five groups (Table S5): WML lesions grew despite BP reductions in the untreated group and similar trend was observed among individuals with uncontrolled HTN, yet lesion volume increased concomitantly to BP increases in participants with controlled HTN and among normotensive participants. Even more interestingly we show that WML growth was similar in participants with baseline untreated/ uncontrolled HTN who at the last visit had disparate outcomes: improved or unchanged BP control (Table S6). It stands in opposition to the results of SPRINT-MIND trial where smaller increase in WML volume was observed in participants with more aggressive BP lowering [18]. We believe that the main reason for this discrepancy is that our groups were much smaller and follow-up time shorter than in SPRINT study. In addition, the difference in WML volume change between SPRINT groups amounted only to 0.5 cm3.\nHowever, since BP load over time is unknown, it is also likely that in both groups BP was not controlled for a long time, and one group showed improvement only on the last measurement. Both groups could have also experienced similar, strong longitudinal BP fluctuation, which increase the probability of WML progression [36]. Then again, our results confirm that baseline WML volumes are good predictor of lesion progression, consistent with previous reports [43], and suggest that once the pathological process is set in motion, there is limited room for its modification. Such view has important implications for patient management, further strengthening the notion that early intervention (close monitoring of BP in normotensives and early treatment) would be most successful. Finally, the findings are also consistent with our hypothesis that in the setting of impaired cerebral flow regulation in participants with more severe form of HTN BP reductions are harmful.\nNot surprisingly, in the light of the above considerations, only in the group of normotensive individuals, longitudinal increases of SBP and DBP were related to lesion growth and reduction is mean hippocampal volume. It is to be expected, as rising BP would contribute to brain deterioration. This finding is also in agreement with a previous report of community residing participants over the age of 75, where the 2-year change in ambulatory SBP was associated with the 2-year WML increase [44].\nOur study suffered from a few limitations. First, only one measurement of blood pressure was taken at the time, instead of the average of three consecutive ones. It could have resulted in misclassification of cases. This was especially true for ‘untreated’: only 63% of participants defined at baseline as untreated were still classified as hypertensive at follow-up. However, high baseline WML volume in this group (Table S2) speaks against the possibility of misclassification. Almost everybody in the controlled and uncontrolled HTN groups remained hypertensive at follow-up, and 88% (91/104) of participants normotensive (<140/90) at baseline were normotensive at follow-up. Although, the method for BP measurement was not optimal it matched closely a common real-life practice. Second, the interval between assessment was short, and since WML progression is slow it might have precluded us from observing significant differences between groups. Analysis of participants WML volume did not include a differentiation of periventricular lesions from deep lesions. It is possible that the effects of hypertension of WML development could vary by lesion location. We did not perform manual segmentation of silent infractions. It may have artificially inflated lesion volumes, as some infarcts might have been erroneously classified as WML. Wang et al.[45] previously reported that including stroke lesions could bias WML volume estimation by as much as 20%. Although, in our case the error would have been likely smaller, since their study included patients recruited soon after presenting with stroke symptoms, while we excluded participants with overt infarcts, the misclassification cannot be excluded.\nOnly a half of participants had follow-up. Although participants studied only once and those with additional examinations did not differ in most of the characteristics, it still could have caused a bias. Another limitation was that our group was predominantly Caucasian, with high level of educational achievement thus generalizability to a more diverse cohort is uncertain. We did not have data on the duration of hypertension, which can modify the relationships we examined. Normotensive individuals were slightly (on average 3 years) but statistically significantly younger than hypertensive participants. It is plausible that had they been older, we might have seen stronger associations between BP and WML. A recent meta-analysis showed that the optimal blood pressure value changes depending on age [20]. We did not have a group large enough to address whether this is the case also for white matter lesions. Finally, since our study was observational, results cannot carry the same weight a these derived from clinical trial, and causal relations cannot be inferred.\nIn conclusion, we find that among hypertensive participants there was a quadratic relationship between BP and WML as well as mean hippocampal volume. The WML burden was the smallest when SBP was close to 124 mmHg. The mean hippocampal volume was the highest when DBP was in high 70-mmHg range. Such phenomena were not observed in the normotensive group, suggesting the existence of a BP optimum in hypertension. The current report extends our earlier findings showing that cerebral perfusion is maximized when systolic BP is in 120–130 mmHg range. Longitudinally, all groups experienced lesion growth, despite different BP trajectories, further suggesting the possibility that WML expansion may occur despite or because of BP reduction in individuals with compromised vascular system. Considering growing evidence showing nonlinear association between blood pressure an outcomes further clinical trials are needed.\n\nACKNOWLEDGEMENTS:\nA special thanks to Ke Xi for her help with the statistical modeling as well as assistance in the production of regression plots for this manuscript.\nSources of funding: Study funding comes from NIH grants HL111724, NS104364, AG022374, AG12101, AG08051, and Alzheimer's Association NIRG-09-132490.\nDisclosures: None\nConflicts of interest There are no conflicts of interest.\nThere are no conflicts of interest.\n\nConflicts of interest:\nThere are no conflicts of interest.\n\nSupplementary Material:\n\n\n"},"whole_article_abstract":{"kind":"string","value":"Background:\nThere is a well documented relationship between cardiovascular risk factors and the development of brain injury, which can lead to cognitive dysfunction. Hypertension (HTN) is a condition increasing the risk of silent and symptomatic ischemic brain lesions. Although benefits of hypertension treatment are indisputable, the target blood pressure value where the possibility of tissue damage is most reduced remains under debate.\n\nMethods:\nOur group performed a cross-sectional ( n = 376) and longitudinal ( n = 188) study of individuals without dementia or stroke (60% women n = 228, age 68.5 ± 7.4 years; men n = 148, age 70.7 ± 6.9 years). Participants were split into hypertensive ( n = 169) and normotensive ( n = 207) groups. MR images were obtained on a 3T system. Linear modeling was performed in hypertensive and normotensive cohorts to investigate the relationship between systolic (SBP) and diastolic (DBP) blood pressure, white matter lesion (WML), and brain volumes.\n\nResults:\nParticipants in the hypertensive cohort showed a quadratic relationship between SBP and WML, with the lowest amounts of WML being measured in participants with readings at approximately 124 mmHg. Additionally, the hypertensive cohort also exhibited a quadratic relationship between DBP and mean hippocampal volume; participants with readings at approximately 77 mmHg showing the largest volumes. Longitudinally, all groups experienced WML growth, despite different BP trajectories, further suggesting that WML expansion may occur despite or because of BP reduction in individuals with compromised vascular system.\n\nConclusions:\nOverall, our study suggests that in the hypertensive group there is a valley of mid-range blood pressures displaying less pathology in the brain."},"background_conclusion_text":{"kind":"null"},"background_conclusion_abstract":{"kind":"null"},"whole_article_text_length":{"kind":"number","value":16795,"string":"16,795"},"whole_article_abstract_length":{"kind":"number","value":341,"string":"341"},"other_sections_lengths":{"kind":"list like","value":[242,156,182,382,607,1009,239,259,614,303,775,94,209,79],"string":"[\n 242,\n 156,\n 182,\n 382,\n 607,\n 1009,\n 239,\n 259,\n 614,\n 303,\n 775,\n 94,\n 209,\n 79\n]"},"num_sections":{"kind":"number","value":21,"string":"21"},"most_frequent_words":{"kind":"list like","value":["wml","htn","bp","group","volume","volumes","sbp","participants","baseline","001"],"string":"[\n \"wml\",\n \"htn\",\n \"bp\",\n \"group\",\n \"volume\",\n \"volumes\",\n \"sbp\",\n \"participants\",\n \"baseline\",\n \"001\"\n]"},"keybert_topics":{"kind":"list like","value":["effects cerebral blood","high blood pressure","impact cardiovascular disease","previously observed hypertension","brain volumes hypertensive"],"string":"[\n \"effects cerebral blood\",\n \"high blood pressure\",\n \"impact cardiovascular disease\",\n \"previously observed hypertension\",\n \"brain volumes hypertensive\"\n]"},"annotated_base_background_abstract_prompt":{"kind":"string","value":""},"annotated_base_methods_abstract_prompt":{"kind":"string","value":""},"annotated_base_results_abstract_prompt":{"kind":"string","value":""},"annotated_base_conclusions_abstract_prompt":{"kind":"null"},"annotated_base_whole_article_abstract_prompt":{"kind":"string","value":""},"annotated_base_background_conclusion_abstract_prompt":{"kind":"null"},"annotated_keywords_background_abstract_prompt":{"kind":"string","value":"[CONTENT] hypertension | magnetic resonance imaging | neuro-imaging | radiology | systolic and diastolic blood pressure | white matter lesions [SUMMARY]"},"annotated_keywords_methods_abstract_prompt":{"kind":"string","value":"[CONTENT] hypertension | magnetic resonance imaging | neuro-imaging | radiology | systolic and diastolic blood pressure | white matter lesions [SUMMARY]"},"annotated_keywords_results_abstract_prompt":{"kind":"string","value":"[CONTENT] hypertension | magnetic resonance imaging | neuro-imaging | radiology | systolic and diastolic blood pressure | white matter lesions [SUMMARY]"},"annotated_keywords_conclusions_abstract_prompt":{"kind":"null"},"annotated_keywords_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] hypertension | magnetic resonance imaging | neuro-imaging | radiology | systolic and diastolic blood pressure | white matter lesions [SUMMARY]"},"annotated_keywords_background_conclusion_abstract_prompt":{"kind":"null"},"annotated_mesh_background_abstract_prompt":{"kind":"string","value":"[CONTENT] Male | Female | Humans | Middle Aged | Aged | Blood Pressure | White Matter | Cross-Sectional Studies | Magnetic Resonance Imaging | Hypertension [SUMMARY]"},"annotated_mesh_methods_abstract_prompt":{"kind":"string","value":"[CONTENT] Male | Female | Humans | Middle Aged | Aged | Blood Pressure | White Matter | Cross-Sectional Studies | Magnetic Resonance Imaging | Hypertension [SUMMARY]"},"annotated_mesh_results_abstract_prompt":{"kind":"string","value":"[CONTENT] Male | Female | Humans | Middle Aged | Aged | Blood Pressure | White Matter | Cross-Sectional Studies | Magnetic Resonance Imaging | Hypertension [SUMMARY]"},"annotated_mesh_conclusions_abstract_prompt":{"kind":"null"},"annotated_mesh_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] Male | Female | Humans | Middle Aged | Aged | Blood Pressure | White Matter | Cross-Sectional Studies | Magnetic Resonance Imaging | Hypertension [SUMMARY]"},"annotated_mesh_background_conclusion_abstract_prompt":{"kind":"null"},"annotated_keybert_background_abstract_prompt":{"kind":"string","value":"[CONTENT] effects cerebral blood | high blood pressure | impact cardiovascular disease | previously observed hypertension | brain volumes hypertensive [SUMMARY]"},"annotated_keybert_methods_abstract_prompt":{"kind":"string","value":"[CONTENT] effects cerebral blood | high blood pressure | impact cardiovascular disease | previously observed hypertension | brain volumes hypertensive [SUMMARY]"},"annotated_keybert_results_abstract_prompt":{"kind":"string","value":"[CONTENT] effects cerebral blood | high blood pressure | impact cardiovascular disease | previously observed hypertension | brain volumes hypertensive [SUMMARY]"},"annotated_keybert_conclusions_abstract_prompt":{"kind":"null"},"annotated_keybert_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] effects cerebral blood | high blood pressure | impact cardiovascular disease | previously observed hypertension | brain volumes hypertensive [SUMMARY]"},"annotated_keybert_background_conclusion_abstract_prompt":{"kind":"null"},"annotated_most_frequent_background_abstract_prompt":{"kind":"string","value":"[CONTENT] wml | htn | bp | group | volume | volumes | sbp | participants | baseline | 001 [SUMMARY]"},"annotated_most_frequent_methods_abstract_prompt":{"kind":"string","value":"[CONTENT] wml | htn | bp | group | volume | volumes | sbp | participants | baseline | 001 [SUMMARY]"},"annotated_most_frequent_results_abstract_prompt":{"kind":"string","value":"[CONTENT] wml | htn | bp | group | volume | volumes | sbp | participants | baseline | 001 [SUMMARY]"},"annotated_most_frequent_conclusions_abstract_prompt":{"kind":"null"},"annotated_most_frequent_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] wml | htn | bp | group | volume | volumes | sbp | participants | baseline | 001 [SUMMARY]"},"annotated_most_frequent_background_conclusion_abstract_prompt":{"kind":"null"},"annotated_tf_idf_background_abstract_prompt":{"kind":"string","value":"[CONTENT] cbf | cause | blood | risk | relationship sbp | shape relationship | htn | brain | shape | damage [SUMMARY]"},"annotated_tf_idf_methods_abstract_prompt":{"kind":"string","value":"[CONTENT] bp | volume | antihypertensive | volumes | models | 256 | participants | brain | tested | 140 90 mmhg [SUMMARY]"},"annotated_tf_idf_results_abstract_prompt":{"kind":"string","value":"[CONTENT] htn | 001 | wml | sbp | volume | group | baseline | volumes | estimate | model [SUMMARY]"},"annotated_tf_idf_conclusions_abstract_prompt":{"kind":"null"},"annotated_tf_idf_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] htn | wml | volume | bp | 001 | volumes | sbp | group | baseline | participants [SUMMARY]"},"annotated_tf_idf_background_conclusion_abstract_prompt":{"kind":"null"},"annotated_entity_plan_background_abstract_prompt":{"kind":"string","value":"[CONTENT] ||| Hypertension (HTN ||| [SUMMARY]"},"annotated_entity_plan_methods_abstract_prompt":{"kind":"string","value":"[CONTENT] 376 | 188 | 60% | 228 | age 68.5 | 7.4 years | 148 | age 70.7 | 6.9 years ||| 169 | 207 ||| 3 ||| Linear | SBP | DBP | WML [SUMMARY]"},"annotated_entity_plan_results_abstract_prompt":{"kind":"string","value":"[CONTENT] SBP | WML | WML | approximately 124 ||| DBP | approximately 77 ||| WML | BP | WML | BP [SUMMARY]"},"annotated_entity_plan_conclusions_abstract_prompt":{"kind":"null"},"annotated_entity_plan_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] ||| Hypertension (HTN ||| ||| 376 | 188 | 60% | 228 | age 68.5 | 7.4 years | 148 | age 70.7 | 6.9 years ||| 169 | 207 ||| 3 ||| Linear | SBP | DBP | WML ||| SBP | WML | WML | approximately 124 ||| DBP | approximately 77 ||| WML | BP | WML | BP ||| [SUMMARY]"},"annotated_entity_plan_background_conclusion_abstract_prompt":{"kind":"null"}}},{"rowIdx":266,"cells":{"title":{"kind":"string","value":"Feasibility of assessing public health impacts of air pollution reduction programs on a local scale: New Haven case study."},"pmid":{"kind":"string","value":"21335318"},"background_abstract":{"kind":"string","value":"New approaches to link health surveillance data with environmental and population exposure information are needed to examine the health benefits of risk management decisions."},"background_abstract_label":{"kind":"string","value":"BACKGROUND"},"methods_abstract":{"kind":"string","value":"Using a hybrid modeling approach that combines regional and local-scale air quality data, we estimated ambient concentrations for multiple air pollutants [e.g., PM2.5 (particulate matter ≤ 2.5 μm in aerodynamic diameter), NOx (nitrogen oxides)] for baseline year 2001 and projected emissions for 2010, 2020, and 2030. We assessed the feasibility of detecting health improvements in relation to reductions in air pollution for 26 different pollutant-health outcome linkages using both sample size and exploratory epidemiological simulations to further inform decision-making needs."},"methods_abstract_label":{"kind":"string","value":"METHODS"},"results_abstract":{"kind":"string","value":"Model projections suggested decreases (~10-60%) in pollutant concentrations, mainly attributable to decreases in pollutants from local sources between 2001 and 2010. Models indicated considerable spatial variability in the concentrations of most pollutants. Sample size analyses supported the feasibility of identifying linkages between reductions in NOx and improvements in all-cause mortality, prevalence of asthma in children and adults, and cardiovascular and respiratory hospitalizations."},"results_abstract_label":{"kind":"string","value":"RESULTS"},"conclusions_abstract":{"kind":"string","value":"Substantial reductions in air pollution (e.g., ~60% for NOx) are needed to detect health impacts of environmental actions using traditional epidemiological study designs in small communities like New Haven. In contrast, exploratory epidemiological simulations suggest that it may be possible to demonstrate the health impacts of PM reductions by predicting intraurban pollution gradients within New Haven using coupled models."},"conclusions_abstract_label":{"kind":"string","value":"CONCLUSION"},"mesh_descriptor_names":{"kind":"list like","value":["Adolescent","Adult","Aged","Aged, 80 and over","Air Pollutants","Air Pollution","Cardiovascular Diseases","Child","Child, Preschool","Cities","Connecticut","Conservation of Natural Resources","Environmental Policy","Feasibility Studies","Health Status","Humans","Infant","Infant, Newborn","Linear Models","Middle Aged","Models, Chemical","Public Health","Respiratory Tract Diseases","Young Adult"],"string":"[\n \"Adolescent\",\n \"Adult\",\n \"Aged\",\n \"Aged, 80 and over\",\n \"Air Pollutants\",\n \"Air Pollution\",\n \"Cardiovascular Diseases\",\n \"Child\",\n \"Child, Preschool\",\n \"Cities\",\n \"Connecticut\",\n \"Conservation of Natural Resources\",\n \"Environmental Policy\",\n \"Feasibility Studies\",\n \"Health Status\",\n \"Humans\",\n \"Infant\",\n \"Infant, Newborn\",\n \"Linear Models\",\n \"Middle Aged\",\n \"Models, Chemical\",\n \"Public Health\",\n \"Respiratory Tract Diseases\",\n \"Young Adult\"\n]"},"pmcid":{"kind":"string","value":"3080930"},"background_title":{"kind":"null"},"background_text":{"kind":"null"},"methods_title":{"kind":"string","value":"Simulation-based epidemiological feasibility analysis"},"methods_text":{"kind":"string","value":"The average number of hospitalizations for CHD among those ≥ 65 years of age in each census block group decreased between 2001 and 2010, whereas average numbers of asthma hospitalizations increased (Table 3). We were unable to detect associations between small reductions in PM2.5 pollution concentrations and health outcomes, so we restricted our analysis to census block groups with PM2.5 reductions of > 4 μg/m3 (n = 30). For these census block groups, numbers of CHD hospitalizations were inversely associated with the estimated reduction in PM2.5 concentrations, indicating that numbers of hospitalizations decreased as the reduction in PM2.5 increased (p < 0.1; Table 4, Figure 3A). Asthma hospitalizations were also inversely associated with reductions in PM2.5 concentrations based on our simulations, suggesting that greater reductions in PM2.5 may slow the increase in asthma hospitalizations over time (Table 4, Figure 3B). However, the inverse association was weaker than for CHD hospitalizations. Finally, an exploratory analysis we conducted by including additional surrogate variables in the regression models aimed at capturing neighborhood effects caused substantial increases in model R2 values, whereas the PM2.5 effect estimates were attenuated somewhat depending on the outcome chosen (data not shown)."},"results_title":{"kind":"string","value":"Results"},"results_text":{"kind":"string","value":" Air quality modeling We produced combined CMAQ and AERMOD model results for the study area. Model estimates for NOx and PM2.5 were consistent with measured Air Quality System (AQS) monitoring data for the area (Johnson et al. 2010). Figure 1 shows maps of modeled PM2.5 and NOx concentrations for the baseline year (2001) and projections for 2010, 2020, and 2030. PM2.5 maps for all four time points show a wide range of concentrations in the study area, with high concentrations in the city center, near the port areas, and near major roadways such as I-95, whereas PM2.5 concentrations in suburban areas were much lower than in central parts of the study area. Finally, PM2.5 concentrations are projected to decrease over time, with the most pronounced decreases in areas with the highest estimated concentrations in 2001.\nSpatial patterns in ambient air quality concentrations for NOx relates strongly to the sources of mobile emissions such as major highways. NOx concentrations shown here depict strong spatial gradients because many of the locations for which we estimated concentrations (population-weighted centroids of the 318 census block groups) are near major roadways. Thus, contrasts between those areas and suburban areas are very pronounced. Finally, NOx concentrations are also projected to decrease considerably over time, particularly in locations close to roadways, because of the implementation of federal emission standards for mobile source emissions.\nFigure 2 shows distributions of annual and daily average modeled PM2.5 and NOx concentrations for the baseline model year 2001 and projections (2010, 2020, and 2030). We sorted the annual average concentrations for 2001 in order to group the 318 study area locations (census block groups) for which we divided estimates into three groups according to average pollutant concentrations at baseline: low (locations in the lowest 25% of the distribution), medium (locations in the 2nd and 3rd quartiles), and high (locations in the highest quartile of the distribution). As expected, daily averages are more variable than annual averages for both PM2.5 and NOx, which indicates the importance of temporal variability in pollutant concentrations. Downward trends in PM2.5 concentrations were evident for areas with medium and high concentrations, but not for low-concentration areas. Declines in NOx concentrations were evident over time for all three groups, but the decrease was also much sharper in the high-concentration areas. The models predicted large percentage decreases for NOx between 2001 and 2010 (61%) and with less pronounced decreases from 2001 to 2020 and 2030 (overall decreases of 78% and 81%, respectively). For PM2.5 the models predicted smaller percentage decreases from 2001 to 2010 (8%), 2020 (9%), and 2030 (9%).\nWe produced combined CMAQ and AERMOD model results for the study area. Model estimates for NOx and PM2.5 were consistent with measured Air Quality System (AQS) monitoring data for the area (Johnson et al. 2010). Figure 1 shows maps of modeled PM2.5 and NOx concentrations for the baseline year (2001) and projections for 2010, 2020, and 2030. PM2.5 maps for all four time points show a wide range of concentrations in the study area, with high concentrations in the city center, near the port areas, and near major roadways such as I-95, whereas PM2.5 concentrations in suburban areas were much lower than in central parts of the study area. Finally, PM2.5 concentrations are projected to decrease over time, with the most pronounced decreases in areas with the highest estimated concentrations in 2001.\nSpatial patterns in ambient air quality concentrations for NOx relates strongly to the sources of mobile emissions such as major highways. NOx concentrations shown here depict strong spatial gradients because many of the locations for which we estimated concentrations (population-weighted centroids of the 318 census block groups) are near major roadways. Thus, contrasts between those areas and suburban areas are very pronounced. Finally, NOx concentrations are also projected to decrease considerably over time, particularly in locations close to roadways, because of the implementation of federal emission standards for mobile source emissions.\nFigure 2 shows distributions of annual and daily average modeled PM2.5 and NOx concentrations for the baseline model year 2001 and projections (2010, 2020, and 2030). We sorted the annual average concentrations for 2001 in order to group the 318 study area locations (census block groups) for which we divided estimates into three groups according to average pollutant concentrations at baseline: low (locations in the lowest 25% of the distribution), medium (locations in the 2nd and 3rd quartiles), and high (locations in the highest quartile of the distribution). As expected, daily averages are more variable than annual averages for both PM2.5 and NOx, which indicates the importance of temporal variability in pollutant concentrations. Downward trends in PM2.5 concentrations were evident for areas with medium and high concentrations, but not for low-concentration areas. Declines in NOx concentrations were evident over time for all three groups, but the decrease was also much sharper in the high-concentration areas. The models predicted large percentage decreases for NOx between 2001 and 2010 (61%) and with less pronounced decreases from 2001 to 2020 and 2030 (overall decreases of 78% and 81%, respectively). For PM2.5 the models predicted smaller percentage decreases from 2001 to 2010 (8%), 2020 (9%), and 2030 (9%).\n Sample-size–based feasibility analysis Table 1 exhibits the percent reduction in concentrations for a given pollutant that would be needed if we assumed a specific RR (1.01, 1.05, 1.10, 1.15, and 1.20) and an estimated reduction in adverse health outcome (2.5%, 5%, 10%, 15%, and 20%) using the assumptions stated above. Table 2 lists the health outcomes that we explored with corresponding minimum statistically detectable decreases in each outcome for the New Haven study population given the baseline rate of the outcome. The New Haven study population area is sufficient in size to examine reductions in adverse health outcomes ranging from a low of 2.5% (adult asthma prevalence) to a high of 10% (all-cause mortality and hospital discharge for cardiovascular diseases and respiratory causes).\nBased on the percentage decrease air pollution projected for 2010, 2020, and 2030 within New Haven for NOx (61%, 2010; 78%, 2020; 81%, 2030) and PM2.5 (8%, 2010; 9%, 2020 and 2030), the percent reduction in air pollution needed to produce a given change in the outcome (Table 1), and the minimum statistically significant percent decrease in each health outcome that can be detected in the New Haven study population (Table 2), we can assess the feasibility for detecting beneficial health effects of air pollution reductions. Of the 26 different air pollution–health outcome linkages assessed, only five, all NOx related, are potentially feasible (Table 2, last column): all-cause mortality, cardiovascular disease hospitalization, respiratory disease hospitalization discharge, current prevalence of asthma in children, and current prevalence of asthma in adults.\nTable 1 exhibits the percent reduction in concentrations for a given pollutant that would be needed if we assumed a specific RR (1.01, 1.05, 1.10, 1.15, and 1.20) and an estimated reduction in adverse health outcome (2.5%, 5%, 10%, 15%, and 20%) using the assumptions stated above. Table 2 lists the health outcomes that we explored with corresponding minimum statistically detectable decreases in each outcome for the New Haven study population given the baseline rate of the outcome. The New Haven study population area is sufficient in size to examine reductions in adverse health outcomes ranging from a low of 2.5% (adult asthma prevalence) to a high of 10% (all-cause mortality and hospital discharge for cardiovascular diseases and respiratory causes).\nBased on the percentage decrease air pollution projected for 2010, 2020, and 2030 within New Haven for NOx (61%, 2010; 78%, 2020; 81%, 2030) and PM2.5 (8%, 2010; 9%, 2020 and 2030), the percent reduction in air pollution needed to produce a given change in the outcome (Table 1), and the minimum statistically significant percent decrease in each health outcome that can be detected in the New Haven study population (Table 2), we can assess the feasibility for detecting beneficial health effects of air pollution reductions. Of the 26 different air pollution–health outcome linkages assessed, only five, all NOx related, are potentially feasible (Table 2, last column): all-cause mortality, cardiovascular disease hospitalization, respiratory disease hospitalization discharge, current prevalence of asthma in children, and current prevalence of asthma in adults.\n Simulation-based epidemiological feasibility analysis The average number of hospitalizations for CHD among those ≥ 65 years of age in each census block group decreased between 2001 and 2010, whereas average numbers of asthma hospitalizations increased (Table 3). We were unable to detect associations between small reductions in PM2.5 pollution concentrations and health outcomes, so we restricted our analysis to census block groups with PM2.5 reductions of > 4 μg/m3 (n = 30). For these census block groups, numbers of CHD hospitalizations were inversely associated with the estimated reduction in PM2.5 concentrations, indicating that numbers of hospitalizations decreased as the reduction in PM2.5 increased (p < 0.1; Table 4, Figure 3A). Asthma hospitalizations were also inversely associated with reductions in PM2.5 concentrations based on our simulations, suggesting that greater reductions in PM2.5 may slow the increase in asthma hospitalizations over time (Table 4, Figure 3B). However, the inverse association was weaker than for CHD hospitalizations. Finally, an exploratory analysis we conducted by including additional surrogate variables in the regression models aimed at capturing neighborhood effects caused substantial increases in model R2 values, whereas the PM2.5 effect estimates were attenuated somewhat depending on the outcome chosen (data not shown).\nThe average number of hospitalizations for CHD among those ≥ 65 years of age in each census block group decreased between 2001 and 2010, whereas average numbers of asthma hospitalizations increased (Table 3). We were unable to detect associations between small reductions in PM2.5 pollution concentrations and health outcomes, so we restricted our analysis to census block groups with PM2.5 reductions of > 4 μg/m3 (n = 30). For these census block groups, numbers of CHD hospitalizations were inversely associated with the estimated reduction in PM2.5 concentrations, indicating that numbers of hospitalizations decreased as the reduction in PM2.5 increased (p < 0.1; Table 4, Figure 3A). Asthma hospitalizations were also inversely associated with reductions in PM2.5 concentrations based on our simulations, suggesting that greater reductions in PM2.5 may slow the increase in asthma hospitalizations over time (Table 4, Figure 3B). However, the inverse association was weaker than for CHD hospitalizations. Finally, an exploratory analysis we conducted by including additional surrogate variables in the regression models aimed at capturing neighborhood effects caused substantial increases in model R2 values, whereas the PM2.5 effect estimates were attenuated somewhat depending on the outcome chosen (data not shown)."},"conclusions_title":{"kind":"string","value":"Conclusions"},"conclusions_text":{"kind":"string","value":"In this project we successfully applied, compared, and evaluated exposure assessment and epidemiological modeling tools in the context of observed public health status in a relatively small community, New Haven, Connecticut, and provided the U.S. EPA and local, state, and city organizations with a new modeling-based methodology to measure the impact of collective risk mitigation approaches and regulations. Furthermore, because no single regulation or program that affects air quality can be isolated to track its effect on health, this project provided critical findings on how regulatory agencies may better examine the complex interactions of cumulative impacts on air quality and health effects from multiple actions in other urban communities."},"other_sections_titles":{"kind":"list like","value":["Air quality modeling","Sample-size–based feasibility analysis"],"string":"[\n \"Air quality modeling\",\n \"Sample-size–based feasibility analysis\"\n]"},"other_sections_texts":{"kind":"list like","value":["We produced combined CMAQ and AERMOD model results for the study area. Model estimates for NOx and PM2.5 were consistent with measured Air Quality System (AQS) monitoring data for the area (Johnson et al. 2010). Figure 1 shows maps of modeled PM2.5 and NOx concentrations for the baseline year (2001) and projections for 2010, 2020, and 2030. PM2.5 maps for all four time points show a wide range of concentrations in the study area, with high concentrations in the city center, near the port areas, and near major roadways such as I-95, whereas PM2.5 concentrations in suburban areas were much lower than in central parts of the study area. Finally, PM2.5 concentrations are projected to decrease over time, with the most pronounced decreases in areas with the highest estimated concentrations in 2001.\nSpatial patterns in ambient air quality concentrations for NOx relates strongly to the sources of mobile emissions such as major highways. NOx concentrations shown here depict strong spatial gradients because many of the locations for which we estimated concentrations (population-weighted centroids of the 318 census block groups) are near major roadways. Thus, contrasts between those areas and suburban areas are very pronounced. Finally, NOx concentrations are also projected to decrease considerably over time, particularly in locations close to roadways, because of the implementation of federal emission standards for mobile source emissions.\nFigure 2 shows distributions of annual and daily average modeled PM2.5 and NOx concentrations for the baseline model year 2001 and projections (2010, 2020, and 2030). We sorted the annual average concentrations for 2001 in order to group the 318 study area locations (census block groups) for which we divided estimates into three groups according to average pollutant concentrations at baseline: low (locations in the lowest 25% of the distribution), medium (locations in the 2nd and 3rd quartiles), and high (locations in the highest quartile of the distribution). As expected, daily averages are more variable than annual averages for both PM2.5 and NOx, which indicates the importance of temporal variability in pollutant concentrations. Downward trends in PM2.5 concentrations were evident for areas with medium and high concentrations, but not for low-concentration areas. Declines in NOx concentrations were evident over time for all three groups, but the decrease was also much sharper in the high-concentration areas. The models predicted large percentage decreases for NOx between 2001 and 2010 (61%) and with less pronounced decreases from 2001 to 2020 and 2030 (overall decreases of 78% and 81%, respectively). For PM2.5 the models predicted smaller percentage decreases from 2001 to 2010 (8%), 2020 (9%), and 2030 (9%).","Table 1 exhibits the percent reduction in concentrations for a given pollutant that would be needed if we assumed a specific RR (1.01, 1.05, 1.10, 1.15, and 1.20) and an estimated reduction in adverse health outcome (2.5%, 5%, 10%, 15%, and 20%) using the assumptions stated above. Table 2 lists the health outcomes that we explored with corresponding minimum statistically detectable decreases in each outcome for the New Haven study population given the baseline rate of the outcome. The New Haven study population area is sufficient in size to examine reductions in adverse health outcomes ranging from a low of 2.5% (adult asthma prevalence) to a high of 10% (all-cause mortality and hospital discharge for cardiovascular diseases and respiratory causes).\nBased on the percentage decrease air pollution projected for 2010, 2020, and 2030 within New Haven for NOx (61%, 2010; 78%, 2020; 81%, 2030) and PM2.5 (8%, 2010; 9%, 2020 and 2030), the percent reduction in air pollution needed to produce a given change in the outcome (Table 1), and the minimum statistically significant percent decrease in each health outcome that can be detected in the New Haven study population (Table 2), we can assess the feasibility for detecting beneficial health effects of air pollution reductions. Of the 26 different air pollution–health outcome linkages assessed, only five, all NOx related, are potentially feasible (Table 2, last column): all-cause mortality, cardiovascular disease hospitalization, respiratory disease hospitalization discharge, current prevalence of asthma in children, and current prevalence of asthma in adults."],"string":"[\n \"We produced combined CMAQ and AERMOD model results for the study area. Model estimates for NOx and PM2.5 were consistent with measured Air Quality System (AQS) monitoring data for the area (Johnson et al. 2010). Figure 1 shows maps of modeled PM2.5 and NOx concentrations for the baseline year (2001) and projections for 2010, 2020, and 2030. PM2.5 maps for all four time points show a wide range of concentrations in the study area, with high concentrations in the city center, near the port areas, and near major roadways such as I-95, whereas PM2.5 concentrations in suburban areas were much lower than in central parts of the study area. Finally, PM2.5 concentrations are projected to decrease over time, with the most pronounced decreases in areas with the highest estimated concentrations in 2001.\\nSpatial patterns in ambient air quality concentrations for NOx relates strongly to the sources of mobile emissions such as major highways. NOx concentrations shown here depict strong spatial gradients because many of the locations for which we estimated concentrations (population-weighted centroids of the 318 census block groups) are near major roadways. Thus, contrasts between those areas and suburban areas are very pronounced. Finally, NOx concentrations are also projected to decrease considerably over time, particularly in locations close to roadways, because of the implementation of federal emission standards for mobile source emissions.\\nFigure 2 shows distributions of annual and daily average modeled PM2.5 and NOx concentrations for the baseline model year 2001 and projections (2010, 2020, and 2030). We sorted the annual average concentrations for 2001 in order to group the 318 study area locations (census block groups) for which we divided estimates into three groups according to average pollutant concentrations at baseline: low (locations in the lowest 25% of the distribution), medium (locations in the 2nd and 3rd quartiles), and high (locations in the highest quartile of the distribution). As expected, daily averages are more variable than annual averages for both PM2.5 and NOx, which indicates the importance of temporal variability in pollutant concentrations. Downward trends in PM2.5 concentrations were evident for areas with medium and high concentrations, but not for low-concentration areas. Declines in NOx concentrations were evident over time for all three groups, but the decrease was also much sharper in the high-concentration areas. The models predicted large percentage decreases for NOx between 2001 and 2010 (61%) and with less pronounced decreases from 2001 to 2020 and 2030 (overall decreases of 78% and 81%, respectively). For PM2.5 the models predicted smaller percentage decreases from 2001 to 2010 (8%), 2020 (9%), and 2030 (9%).\",\n \"Table 1 exhibits the percent reduction in concentrations for a given pollutant that would be needed if we assumed a specific RR (1.01, 1.05, 1.10, 1.15, and 1.20) and an estimated reduction in adverse health outcome (2.5%, 5%, 10%, 15%, and 20%) using the assumptions stated above. Table 2 lists the health outcomes that we explored with corresponding minimum statistically detectable decreases in each outcome for the New Haven study population given the baseline rate of the outcome. The New Haven study population area is sufficient in size to examine reductions in adverse health outcomes ranging from a low of 2.5% (adult asthma prevalence) to a high of 10% (all-cause mortality and hospital discharge for cardiovascular diseases and respiratory causes).\\nBased on the percentage decrease air pollution projected for 2010, 2020, and 2030 within New Haven for NOx (61%, 2010; 78%, 2020; 81%, 2030) and PM2.5 (8%, 2010; 9%, 2020 and 2030), the percent reduction in air pollution needed to produce a given change in the outcome (Table 1), and the minimum statistically significant percent decrease in each health outcome that can be detected in the New Haven study population (Table 2), we can assess the feasibility for detecting beneficial health effects of air pollution reductions. Of the 26 different air pollution–health outcome linkages assessed, only five, all NOx related, are potentially feasible (Table 2, last column): all-cause mortality, cardiovascular disease hospitalization, respiratory disease hospitalization discharge, current prevalence of asthma in children, and current prevalence of asthma in adults.\"\n]"},"other_sections_sec_types":{"kind":"list like","value":[null,"methods"],"string":"[\n null,\n \"methods\"\n]"},"all_sections_titles":{"kind":"list like","value":["Materials and Methods","Results","Air quality modeling","Sample-size–based feasibility analysis","Simulation-based epidemiological feasibility analysis","Discussion","Conclusions"],"string":"[\n \"Materials and Methods\",\n \"Results\",\n \"Air quality modeling\",\n \"Sample-size–based feasibility analysis\",\n \"Simulation-based epidemiological feasibility analysis\",\n \"Discussion\",\n \"Conclusions\"\n]"},"all_sections_texts":{"kind":"list like","value":["The New Haven Study Area is centered in the City of New Haven, Connecticut (population ~ 127,000), and extends to a 20-km radius, encompassing 318 census block groups in New Haven County with an estimated population in 2007 of more than 367,000 people. The City of New Haven is located on the southern coast of Connecticut on New Haven Harbor, which is fed by three rivers (the West, Mill, and Quinnipiac) that discharge into northern Long Island Sound. New Haven lies at the intersection of interstates I-91 and I-95, both major regional expressways that are often congested. In addition, several surface arteries pass through or around New Haven, including Routes 1, 10, 17, 34, and 63. Seaborne traffic passes through the Port of New Haven, a deep-water seaport that attracts a considerable number of barges and associated truck and rail traffic. In addition to several institutional power plants, one power generation facility serves the community. This wide range of emission source categories allows for testing of multipollutant emission control strategies.\nWe evaluated the overall feasibility of assessing the public health impact of air pollution reduction programs in the City of New Haven by linking projected emissions reductions from overall regulatory actions to estimated detectable health outcome changes. We began by identifying pollutants of interest for New Haven based on the local emissions inventory for the baseline year of 2001 (Weil 2004) and criteria air pollutants. For the present study, we focused on two air pollutants: NOx and PM2.5. We also identified health outcomes that have been associated with these pollutants: cardiovascular disease hospitalization and mortality; respiratory disease hospitalization and mortality; chronic obstructive pulmonary disease mortality and hospitalization; and asthma prevalence, diagnosis, and hospitalization.\nWe then evaluated existing data on ambient level air pollution, emission data, personal exposure data, and health outcome data for the New Haven area. As part of this data inventory evaluation, we assessed the relevance and completeness of data, as well as verification of locations and quantities of emissions from local sources. We then generated emission estimates for NOx and PM2.5 based on local emissions sources and the projected impacts of federal, state, and local regulatory reduction activities. We also applied an improved methodology to predict mobile source emissions (Cook et al. 2008).\nWe first estimated pollutant specific local-scale air concentrations using the U.S. EPA’s AERMOD dispersion model (Cimorelli et al. 2005). This model used information on local emission sources and local meteorological conditions to provide hourly and annual average concentrations at multiple locations corresponding to the weighted centroids of each of the 318 census block groups in the study area. We estimated total NOx and PM2.5 concentrations by combining regional background levels, chemically reactive pollutant estimates from the CMAQ (Community Multiscale Air Quality) model, and the AERMOD estimates. We estimated emissions using the baseline year (2001) emissions rates and projected emissions in 2010, 2020, and 2030 based on planned and anticipated pollution control programs.\nTo assess feasibility using a sample size approach, we first determined the minimum detectable decrease in each outcome relative to its baseline incidence rate [tests of two independent proportions for a (one-sided) likelihood ratio chi-square test with an α of 0.05 and power of 0.80] for a study population of 367,173 (i.e., the 2007 Census estimate for the New Haven population within the 318 block groups included in the study area). For some of the health outcomes, we made additional study area subpopulation calculations for different age groups (< 18 years, ≥ 18 years).\nThere is general consensus that RRs associated with air pollution exposure for a wide variety of health outcomes are typically less than 1.50, and usually within the range of 1.01–1.20, often for a 10-μg/m3 change in PM2.5 or an interquartile range change in gaseous pollutant concentrations. Jerrett et al. (2008) and Wellenius et al. (2005) found risk ratios or a RR in this range for NO2. RRs for PM2.5 and various outcomes in this range were found by Pope et al. (2002) and Sheppard et al. (1999), whereas Laden et al. (2006) found higher PM2.5 and mortality RRs of 1.16–1.28, and Peters et al. (2001) found an RR of 1.69 for PM2.5 and acute myocardial infarction.\nNext we determined the percent reduction in exposure that would be required to produce a given reduction in the outcome assuming a range of possible effect sizes for concentration–outcome associations. Specifically, we considered air pollution RR values of 1.01, 1.05, 1.10, 1.15, and 1.20 representing the increase in outcome (y) associated with an incremental increase in a given pollutant exposure equal to the level of the average value of the ambient pollution concentration (c) in the study population. The change in the outcome (Δy) associated with a change in exposure (Δc) is a function of the baseline incidence rate (y) and the risk coefficient (β) for a one-unit increase in exposure:\nwhere β = [ln(RR)]/c. The percent decrease in exposure (Δcreq) required to produce a particular reduction in the outcome for a given RR is calculated as\nValues of Δcreq < 100 indicate the percent reduction in exposure that would be required to produce a specific reduction in the outcome (Δy) assuming a given RR for the exposure–outcome association. Values of Δcreq ≥ 100 indicate that the corresponding value for Δy is not feasible, because exposure would have to be reduced by more than 100% to achieve it.\nFinally, we combined data on projected changes in mean annual ambient concentrations of air pollutants for 2010, 2020, and 2030 with the information on minimum detectable effect estimates and the percent reduction in exposure required to produce a given effect estimate to identify which air pollutant–health outcome associations (out of 26 possible combinations) would be most feasible for assessment.\nFor pollutants such as PM where projected reductions were relatively modest (~ 8%), we used an exploratory epidemiological methodology similar to that presented in Pope et al. (2009). Specifically, we used the simulated health data at census block group level (derived from county-specific health data and census information on demographics) to evaluate different strategies for demonstrating impacts of relatively small changes in ambient pollution (compared to NOx) over multiple years.\nThe outcomes for this analysis were the differences between the number of hospitalizations for 2001 and 2010, as illustrated by Equation 3:\nwhere ΔHC is the change in the number of hospitalizations at census block group C and H2010 and H2001 are the number of hospitalizations for the years 2010 and 2001, respectively.\nWe calculated the number of hospitalizations due to congestive heart disease [CHD; International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9CM; World Health Organization 2004), code 428] and asthma (ICD-9CM, code 493) for each census block group for the years 2010 and 2001. We chose these end points based on significant associations (1.28% increase in risk per 10-μg/m3 increase in same-day PM2.5) reported by Dominici et al. (2006) between PM and CHD hospitalizations for the Medicare cohort. We restricted hospitalizations due to CHD to the population > 65 years of age, and we calculated asthma hospitalizations separately for all ages and for the population < 25 years of age, because of known age-dependent differences [Connecticut Department of Public Health (CDPH) 2007]. We calculated the number of hospitalizations for the years 2001 and 2010 as\nwhere Hc is the number of hospitalizations for census block group C, Rate is the rate of hospitalization for females (F) and males (M) of race R (white, Hispanic, black), and PopC is the population of each subgroup (e.g., white female, black male, etc.).\nWe used 2000 U.S. Census Bureau (2001) data to estimate the size of each population subgroup in 2001. We used county-level population projections for 2010 to estimate the proportional change in each population subgroup from 2000 to 2010 and applied this to the 2000 census block group population to estimate 2010 census block populations for each subgroup.\nHospitalization rates for both outcomes are available for all of New Haven County for 2001 (CDPH 2001) and 2007 (CDPH 2007), and we used 2007 data for 2010 hospitalizations. The rates are broken down by age and sex, and age and race, but not by age, sex, and race. We therefore assumed constant ratios of rates of hospitalizations for males and females for all races. We calculated hospitalization rates according to sex, race, age (> 65 years of age for CHD, all ages, and < 25 years of age for asthma), health outcome (CHD or asthma), and year (2001 or 2010) as\nFor example, RateFR is the county-level hospitalization for females of race R; RateR is the county-level hospitalization rate for race R; Ratio of ratesM/F is the ratio of hospitalization rates between males and females; and PopFR and PopMR are county-level population sizes for females and males of race R, respectively. We then calculated RateMR by multiplying RateFR by Ratio of ratesM/F.\nReductions of PM2.5 at each census block group were then regressed against the changes in hospitalizations from 2001 to 2010 at each census block group. All regression analyses were performed using SAS (version 9.1; SAS Institute Inc., Cary, NC).\nOur analysis indirectly accounted for the effects due to changes in key ethnic/racial demographic profile by using group or sex relevant hospitalization rates as part of the health data and feasibility simulations. We explored the influence of introducing an additional explanatory variable in our health effects regressions to indirectly account for both neighborhood effects and the missing determinants of observed hospital admissions by computing average admissions either within a 3- or 4-km radius around each census tract (similarly considered by Özkaynak and Thurston 1987)."," Air quality modeling We produced combined CMAQ and AERMOD model results for the study area. Model estimates for NOx and PM2.5 were consistent with measured Air Quality System (AQS) monitoring data for the area (Johnson et al. 2010). Figure 1 shows maps of modeled PM2.5 and NOx concentrations for the baseline year (2001) and projections for 2010, 2020, and 2030. PM2.5 maps for all four time points show a wide range of concentrations in the study area, with high concentrations in the city center, near the port areas, and near major roadways such as I-95, whereas PM2.5 concentrations in suburban areas were much lower than in central parts of the study area. Finally, PM2.5 concentrations are projected to decrease over time, with the most pronounced decreases in areas with the highest estimated concentrations in 2001.\nSpatial patterns in ambient air quality concentrations for NOx relates strongly to the sources of mobile emissions such as major highways. NOx concentrations shown here depict strong spatial gradients because many of the locations for which we estimated concentrations (population-weighted centroids of the 318 census block groups) are near major roadways. Thus, contrasts between those areas and suburban areas are very pronounced. Finally, NOx concentrations are also projected to decrease considerably over time, particularly in locations close to roadways, because of the implementation of federal emission standards for mobile source emissions.\nFigure 2 shows distributions of annual and daily average modeled PM2.5 and NOx concentrations for the baseline model year 2001 and projections (2010, 2020, and 2030). We sorted the annual average concentrations for 2001 in order to group the 318 study area locations (census block groups) for which we divided estimates into three groups according to average pollutant concentrations at baseline: low (locations in the lowest 25% of the distribution), medium (locations in the 2nd and 3rd quartiles), and high (locations in the highest quartile of the distribution). As expected, daily averages are more variable than annual averages for both PM2.5 and NOx, which indicates the importance of temporal variability in pollutant concentrations. Downward trends in PM2.5 concentrations were evident for areas with medium and high concentrations, but not for low-concentration areas. Declines in NOx concentrations were evident over time for all three groups, but the decrease was also much sharper in the high-concentration areas. The models predicted large percentage decreases for NOx between 2001 and 2010 (61%) and with less pronounced decreases from 2001 to 2020 and 2030 (overall decreases of 78% and 81%, respectively). For PM2.5 the models predicted smaller percentage decreases from 2001 to 2010 (8%), 2020 (9%), and 2030 (9%).\nWe produced combined CMAQ and AERMOD model results for the study area. Model estimates for NOx and PM2.5 were consistent with measured Air Quality System (AQS) monitoring data for the area (Johnson et al. 2010). Figure 1 shows maps of modeled PM2.5 and NOx concentrations for the baseline year (2001) and projections for 2010, 2020, and 2030. PM2.5 maps for all four time points show a wide range of concentrations in the study area, with high concentrations in the city center, near the port areas, and near major roadways such as I-95, whereas PM2.5 concentrations in suburban areas were much lower than in central parts of the study area. Finally, PM2.5 concentrations are projected to decrease over time, with the most pronounced decreases in areas with the highest estimated concentrations in 2001.\nSpatial patterns in ambient air quality concentrations for NOx relates strongly to the sources of mobile emissions such as major highways. NOx concentrations shown here depict strong spatial gradients because many of the locations for which we estimated concentrations (population-weighted centroids of the 318 census block groups) are near major roadways. Thus, contrasts between those areas and suburban areas are very pronounced. Finally, NOx concentrations are also projected to decrease considerably over time, particularly in locations close to roadways, because of the implementation of federal emission standards for mobile source emissions.\nFigure 2 shows distributions of annual and daily average modeled PM2.5 and NOx concentrations for the baseline model year 2001 and projections (2010, 2020, and 2030). We sorted the annual average concentrations for 2001 in order to group the 318 study area locations (census block groups) for which we divided estimates into three groups according to average pollutant concentrations at baseline: low (locations in the lowest 25% of the distribution), medium (locations in the 2nd and 3rd quartiles), and high (locations in the highest quartile of the distribution). As expected, daily averages are more variable than annual averages for both PM2.5 and NOx, which indicates the importance of temporal variability in pollutant concentrations. Downward trends in PM2.5 concentrations were evident for areas with medium and high concentrations, but not for low-concentration areas. Declines in NOx concentrations were evident over time for all three groups, but the decrease was also much sharper in the high-concentration areas. The models predicted large percentage decreases for NOx between 2001 and 2010 (61%) and with less pronounced decreases from 2001 to 2020 and 2030 (overall decreases of 78% and 81%, respectively). For PM2.5 the models predicted smaller percentage decreases from 2001 to 2010 (8%), 2020 (9%), and 2030 (9%).\n Sample-size–based feasibility analysis Table 1 exhibits the percent reduction in concentrations for a given pollutant that would be needed if we assumed a specific RR (1.01, 1.05, 1.10, 1.15, and 1.20) and an estimated reduction in adverse health outcome (2.5%, 5%, 10%, 15%, and 20%) using the assumptions stated above. Table 2 lists the health outcomes that we explored with corresponding minimum statistically detectable decreases in each outcome for the New Haven study population given the baseline rate of the outcome. The New Haven study population area is sufficient in size to examine reductions in adverse health outcomes ranging from a low of 2.5% (adult asthma prevalence) to a high of 10% (all-cause mortality and hospital discharge for cardiovascular diseases and respiratory causes).\nBased on the percentage decrease air pollution projected for 2010, 2020, and 2030 within New Haven for NOx (61%, 2010; 78%, 2020; 81%, 2030) and PM2.5 (8%, 2010; 9%, 2020 and 2030), the percent reduction in air pollution needed to produce a given change in the outcome (Table 1), and the minimum statistically significant percent decrease in each health outcome that can be detected in the New Haven study population (Table 2), we can assess the feasibility for detecting beneficial health effects of air pollution reductions. Of the 26 different air pollution–health outcome linkages assessed, only five, all NOx related, are potentially feasible (Table 2, last column): all-cause mortality, cardiovascular disease hospitalization, respiratory disease hospitalization discharge, current prevalence of asthma in children, and current prevalence of asthma in adults.\nTable 1 exhibits the percent reduction in concentrations for a given pollutant that would be needed if we assumed a specific RR (1.01, 1.05, 1.10, 1.15, and 1.20) and an estimated reduction in adverse health outcome (2.5%, 5%, 10%, 15%, and 20%) using the assumptions stated above. Table 2 lists the health outcomes that we explored with corresponding minimum statistically detectable decreases in each outcome for the New Haven study population given the baseline rate of the outcome. The New Haven study population area is sufficient in size to examine reductions in adverse health outcomes ranging from a low of 2.5% (adult asthma prevalence) to a high of 10% (all-cause mortality and hospital discharge for cardiovascular diseases and respiratory causes).\nBased on the percentage decrease air pollution projected for 2010, 2020, and 2030 within New Haven for NOx (61%, 2010; 78%, 2020; 81%, 2030) and PM2.5 (8%, 2010; 9%, 2020 and 2030), the percent reduction in air pollution needed to produce a given change in the outcome (Table 1), and the minimum statistically significant percent decrease in each health outcome that can be detected in the New Haven study population (Table 2), we can assess the feasibility for detecting beneficial health effects of air pollution reductions. Of the 26 different air pollution–health outcome linkages assessed, only five, all NOx related, are potentially feasible (Table 2, last column): all-cause mortality, cardiovascular disease hospitalization, respiratory disease hospitalization discharge, current prevalence of asthma in children, and current prevalence of asthma in adults.\n Simulation-based epidemiological feasibility analysis The average number of hospitalizations for CHD among those ≥ 65 years of age in each census block group decreased between 2001 and 2010, whereas average numbers of asthma hospitalizations increased (Table 3). We were unable to detect associations between small reductions in PM2.5 pollution concentrations and health outcomes, so we restricted our analysis to census block groups with PM2.5 reductions of > 4 μg/m3 (n = 30). For these census block groups, numbers of CHD hospitalizations were inversely associated with the estimated reduction in PM2.5 concentrations, indicating that numbers of hospitalizations decreased as the reduction in PM2.5 increased (p < 0.1; Table 4, Figure 3A). Asthma hospitalizations were also inversely associated with reductions in PM2.5 concentrations based on our simulations, suggesting that greater reductions in PM2.5 may slow the increase in asthma hospitalizations over time (Table 4, Figure 3B). However, the inverse association was weaker than for CHD hospitalizations. Finally, an exploratory analysis we conducted by including additional surrogate variables in the regression models aimed at capturing neighborhood effects caused substantial increases in model R2 values, whereas the PM2.5 effect estimates were attenuated somewhat depending on the outcome chosen (data not shown).\nThe average number of hospitalizations for CHD among those ≥ 65 years of age in each census block group decreased between 2001 and 2010, whereas average numbers of asthma hospitalizations increased (Table 3). We were unable to detect associations between small reductions in PM2.5 pollution concentrations and health outcomes, so we restricted our analysis to census block groups with PM2.5 reductions of > 4 μg/m3 (n = 30). For these census block groups, numbers of CHD hospitalizations were inversely associated with the estimated reduction in PM2.5 concentrations, indicating that numbers of hospitalizations decreased as the reduction in PM2.5 increased (p < 0.1; Table 4, Figure 3A). Asthma hospitalizations were also inversely associated with reductions in PM2.5 concentrations based on our simulations, suggesting that greater reductions in PM2.5 may slow the increase in asthma hospitalizations over time (Table 4, Figure 3B). However, the inverse association was weaker than for CHD hospitalizations. Finally, an exploratory analysis we conducted by including additional surrogate variables in the regression models aimed at capturing neighborhood effects caused substantial increases in model R2 values, whereas the PM2.5 effect estimates were attenuated somewhat depending on the outcome chosen (data not shown).","We produced combined CMAQ and AERMOD model results for the study area. Model estimates for NOx and PM2.5 were consistent with measured Air Quality System (AQS) monitoring data for the area (Johnson et al. 2010). Figure 1 shows maps of modeled PM2.5 and NOx concentrations for the baseline year (2001) and projections for 2010, 2020, and 2030. PM2.5 maps for all four time points show a wide range of concentrations in the study area, with high concentrations in the city center, near the port areas, and near major roadways such as I-95, whereas PM2.5 concentrations in suburban areas were much lower than in central parts of the study area. Finally, PM2.5 concentrations are projected to decrease over time, with the most pronounced decreases in areas with the highest estimated concentrations in 2001.\nSpatial patterns in ambient air quality concentrations for NOx relates strongly to the sources of mobile emissions such as major highways. NOx concentrations shown here depict strong spatial gradients because many of the locations for which we estimated concentrations (population-weighted centroids of the 318 census block groups) are near major roadways. Thus, contrasts between those areas and suburban areas are very pronounced. Finally, NOx concentrations are also projected to decrease considerably over time, particularly in locations close to roadways, because of the implementation of federal emission standards for mobile source emissions.\nFigure 2 shows distributions of annual and daily average modeled PM2.5 and NOx concentrations for the baseline model year 2001 and projections (2010, 2020, and 2030). We sorted the annual average concentrations for 2001 in order to group the 318 study area locations (census block groups) for which we divided estimates into three groups according to average pollutant concentrations at baseline: low (locations in the lowest 25% of the distribution), medium (locations in the 2nd and 3rd quartiles), and high (locations in the highest quartile of the distribution). As expected, daily averages are more variable than annual averages for both PM2.5 and NOx, which indicates the importance of temporal variability in pollutant concentrations. Downward trends in PM2.5 concentrations were evident for areas with medium and high concentrations, but not for low-concentration areas. Declines in NOx concentrations were evident over time for all three groups, but the decrease was also much sharper in the high-concentration areas. The models predicted large percentage decreases for NOx between 2001 and 2010 (61%) and with less pronounced decreases from 2001 to 2020 and 2030 (overall decreases of 78% and 81%, respectively). For PM2.5 the models predicted smaller percentage decreases from 2001 to 2010 (8%), 2020 (9%), and 2030 (9%).","Table 1 exhibits the percent reduction in concentrations for a given pollutant that would be needed if we assumed a specific RR (1.01, 1.05, 1.10, 1.15, and 1.20) and an estimated reduction in adverse health outcome (2.5%, 5%, 10%, 15%, and 20%) using the assumptions stated above. Table 2 lists the health outcomes that we explored with corresponding minimum statistically detectable decreases in each outcome for the New Haven study population given the baseline rate of the outcome. The New Haven study population area is sufficient in size to examine reductions in adverse health outcomes ranging from a low of 2.5% (adult asthma prevalence) to a high of 10% (all-cause mortality and hospital discharge for cardiovascular diseases and respiratory causes).\nBased on the percentage decrease air pollution projected for 2010, 2020, and 2030 within New Haven for NOx (61%, 2010; 78%, 2020; 81%, 2030) and PM2.5 (8%, 2010; 9%, 2020 and 2030), the percent reduction in air pollution needed to produce a given change in the outcome (Table 1), and the minimum statistically significant percent decrease in each health outcome that can be detected in the New Haven study population (Table 2), we can assess the feasibility for detecting beneficial health effects of air pollution reductions. Of the 26 different air pollution–health outcome linkages assessed, only five, all NOx related, are potentially feasible (Table 2, last column): all-cause mortality, cardiovascular disease hospitalization, respiratory disease hospitalization discharge, current prevalence of asthma in children, and current prevalence of asthma in adults.","The average number of hospitalizations for CHD among those ≥ 65 years of age in each census block group decreased between 2001 and 2010, whereas average numbers of asthma hospitalizations increased (Table 3). We were unable to detect associations between small reductions in PM2.5 pollution concentrations and health outcomes, so we restricted our analysis to census block groups with PM2.5 reductions of > 4 μg/m3 (n = 30). For these census block groups, numbers of CHD hospitalizations were inversely associated with the estimated reduction in PM2.5 concentrations, indicating that numbers of hospitalizations decreased as the reduction in PM2.5 increased (p < 0.1; Table 4, Figure 3A). Asthma hospitalizations were also inversely associated with reductions in PM2.5 concentrations based on our simulations, suggesting that greater reductions in PM2.5 may slow the increase in asthma hospitalizations over time (Table 4, Figure 3B). However, the inverse association was weaker than for CHD hospitalizations. Finally, an exploratory analysis we conducted by including additional surrogate variables in the regression models aimed at capturing neighborhood effects caused substantial increases in model R2 values, whereas the PM2.5 effect estimates were attenuated somewhat depending on the outcome chosen (data not shown).","We used detailed information on local health and exposure-related data to assess the feasibility of identifying an impact of cumulative air pollution programs on environmental public health in New Haven for 26 different pollutant–health outcome linkages. Combined regional (CMAQ) and local-scale (AERMOD) air quality modeling analysis showed a small overall decrease for PM2.5 (~ 8–9%) in mean pollutant concentrations mostly from local sources and between 2001 and 2010; in contrast, we projected that NOx would decrease by > 60%. Most NOx reductions can be attributed to mobile source emission reduction programs. Thus, it is important to accurately characterize near-road impacts. Local reductions in PM2.5 are modest relative to high background PM concentrations. Statistical power calculations suggest that projected decreases in NOx may result in statistically significant improvements in health outcomes, including all-cause mortality, asthma prevalence in children and adults, and cardiovascular and respiratory hospitalizations. For other pollutants with more modest reductions, including PM, we determined the likelihood of performing a successful traditional air pollution reduction–health reduction analysis in New Haven to be poor. Alternative epidemiological study designs that use spatially and temporally resolved air quality and exposure models to characterize intraurban gradients were promising based on exploratory epidemiological simulations. However, health outcomes with low baseline rates would have to be strongly associated with air pollution exposures in order for exposure reductions to result in identifiable improvements and thus would not be ideal for examining risk management decisions.\nThis study illustrates the advantages of using air quality models over traditional epidemiological approaches using ambient measurements. For example, central-site data are especially problematic for certain PM components and species (e.g., elemental carbon, organic carbon, coarse and ultrafine PM) that exhibit significant spatial heterogeneity. Also, for many pollutants (e.g., toxic pollutants), ambient monitoring data are often nonexistent or limited. Appropriately verified air quality models, on the other hand, can provide the needed spatial and temporal resolution for multiple air pollutant concentrations at many locations. These same models can also be used to estimate the projected air quality and inputs for exposure models for future years, dependent on air pollution reduction activities, or due to the addition of new sources in a community (Isakov et al. 2006). For example, this model can address what happens if emissions from some specific stationary or mobile sources are reduced by certain amounts and what the associated impacts of these local controls versus regional controls may be. This model application helps determine which control options are most effective in reducing ambient concentrations.\nBoth the air quality modeling and feasibility analysis methodologies we used in this research have certain shortcomings. For instance, despite their advantages of being able to provide temporal (hourly) and spatial (at hundreds of locations) estimates, and having a long history of use by regulatory agencies in multipollutant mitigation strategies, models have uncertainties due to model inputs, algorithms, and model parameters (Sax and Isakov 2003). Therefore, in order to reduce uncertainty due to model inputs, detailed emissions and meteorological information should be provided for each model application. In the simulation-based epidemiological feasibility analyses we considered only single-pollutant models and did not include ecological covariates (e.g., income, poverty status, smoking) typically used in cross-sectional, ecological analysis (Özkaynak and Thurston 1987; Pope et al. 2009), because of a lack of complete information. Moreover, it is possible that some of the covariates may change over time, but presumably this may be less of an issue in local-scale assessments than in national-scale analyses. We did not perform joint optimizations with NOx and PM, which could be used to examine more complicated alternative study designs such as census block groups with low reduction levels in NOx but intermediate to high reductions in PM. Clearly, accounting for multipollutant strategies in future assessments will be important in implementing enhanced air pollution–health outcome risk management studies (Mauderly et al. 2010).\nThe linkages between air quality and exposure models (e.g., with the Stochastic Human Exposure and Dose Simulation Model and Hazardous Air Pollution Exposure Model) in the context of the New Haven study have been examined elsewhere (Isakov et al. 2009). Our biggest challenge has been with accessing geographically and temporally resolved health data in New Haven. Of course, this data gap is often a major challenge in other urban areas as well. Although there was strong local cooperation and local, state, and federal interest in working with the project, better research access to locally relevant health data should be both facilitated and encouraged. Given that the 2010 census has recently been collected and the air quality modeling for 2010 can be performed soon, we hope that the methodology we tested can be implemented in the near future using the actual 2010 local air quality modeling, census, and health data, in order to evaluate the results obtained from this feasibility study by using better databases and more robust models.\nBolstered by the findings from our study, the City of New Haven has been working to find better solutions for reducing air pollution burden and for understanding the impacts from air emissions. We presented the results from this analysis to the New Haven departments of Health, City Planning, and Economic Development and to the city chief executive officer. These results have been used by New Haven in finalizing their negotiations to obtain zero emissions from a proposed new power plant unit to meet peak demand operations, which will be achieved through offsets by the local power plant company and proposed retrofits of garbage trucks and some port operations and additional community benefits. Moreover, the city is also evaluating what can be done to reduce impacts from port operations and mitigate exposures at city schools located near busy roads and highways, in light of the detailed air quality modeling results and health risk evaluations presented here.","In this project we successfully applied, compared, and evaluated exposure assessment and epidemiological modeling tools in the context of observed public health status in a relatively small community, New Haven, Connecticut, and provided the U.S. EPA and local, state, and city organizations with a new modeling-based methodology to measure the impact of collective risk mitigation approaches and regulations. Furthermore, because no single regulation or program that affects air quality can be isolated to track its effect on health, this project provided critical findings on how regulatory agencies may better examine the complex interactions of cumulative impacts on air quality and health effects from multiple actions in other urban communities."],"string":"[\n \"The New Haven Study Area is centered in the City of New Haven, Connecticut (population ~ 127,000), and extends to a 20-km radius, encompassing 318 census block groups in New Haven County with an estimated population in 2007 of more than 367,000 people. The City of New Haven is located on the southern coast of Connecticut on New Haven Harbor, which is fed by three rivers (the West, Mill, and Quinnipiac) that discharge into northern Long Island Sound. New Haven lies at the intersection of interstates I-91 and I-95, both major regional expressways that are often congested. In addition, several surface arteries pass through or around New Haven, including Routes 1, 10, 17, 34, and 63. Seaborne traffic passes through the Port of New Haven, a deep-water seaport that attracts a considerable number of barges and associated truck and rail traffic. In addition to several institutional power plants, one power generation facility serves the community. This wide range of emission source categories allows for testing of multipollutant emission control strategies.\\nWe evaluated the overall feasibility of assessing the public health impact of air pollution reduction programs in the City of New Haven by linking projected emissions reductions from overall regulatory actions to estimated detectable health outcome changes. We began by identifying pollutants of interest for New Haven based on the local emissions inventory for the baseline year of 2001 (Weil 2004) and criteria air pollutants. For the present study, we focused on two air pollutants: NOx and PM2.5. We also identified health outcomes that have been associated with these pollutants: cardiovascular disease hospitalization and mortality; respiratory disease hospitalization and mortality; chronic obstructive pulmonary disease mortality and hospitalization; and asthma prevalence, diagnosis, and hospitalization.\\nWe then evaluated existing data on ambient level air pollution, emission data, personal exposure data, and health outcome data for the New Haven area. As part of this data inventory evaluation, we assessed the relevance and completeness of data, as well as verification of locations and quantities of emissions from local sources. We then generated emission estimates for NOx and PM2.5 based on local emissions sources and the projected impacts of federal, state, and local regulatory reduction activities. We also applied an improved methodology to predict mobile source emissions (Cook et al. 2008).\\nWe first estimated pollutant specific local-scale air concentrations using the U.S. EPA’s AERMOD dispersion model (Cimorelli et al. 2005). This model used information on local emission sources and local meteorological conditions to provide hourly and annual average concentrations at multiple locations corresponding to the weighted centroids of each of the 318 census block groups in the study area. We estimated total NOx and PM2.5 concentrations by combining regional background levels, chemically reactive pollutant estimates from the CMAQ (Community Multiscale Air Quality) model, and the AERMOD estimates. We estimated emissions using the baseline year (2001) emissions rates and projected emissions in 2010, 2020, and 2030 based on planned and anticipated pollution control programs.\\nTo assess feasibility using a sample size approach, we first determined the minimum detectable decrease in each outcome relative to its baseline incidence rate [tests of two independent proportions for a (one-sided) likelihood ratio chi-square test with an α of 0.05 and power of 0.80] for a study population of 367,173 (i.e., the 2007 Census estimate for the New Haven population within the 318 block groups included in the study area). For some of the health outcomes, we made additional study area subpopulation calculations for different age groups (< 18 years, ≥ 18 years).\\nThere is general consensus that RRs associated with air pollution exposure for a wide variety of health outcomes are typically less than 1.50, and usually within the range of 1.01–1.20, often for a 10-μg/m3 change in PM2.5 or an interquartile range change in gaseous pollutant concentrations. Jerrett et al. (2008) and Wellenius et al. (2005) found risk ratios or a RR in this range for NO2. RRs for PM2.5 and various outcomes in this range were found by Pope et al. (2002) and Sheppard et al. (1999), whereas Laden et al. (2006) found higher PM2.5 and mortality RRs of 1.16–1.28, and Peters et al. (2001) found an RR of 1.69 for PM2.5 and acute myocardial infarction.\\nNext we determined the percent reduction in exposure that would be required to produce a given reduction in the outcome assuming a range of possible effect sizes for concentration–outcome associations. Specifically, we considered air pollution RR values of 1.01, 1.05, 1.10, 1.15, and 1.20 representing the increase in outcome (y) associated with an incremental increase in a given pollutant exposure equal to the level of the average value of the ambient pollution concentration (c) in the study population. The change in the outcome (Δy) associated with a change in exposure (Δc) is a function of the baseline incidence rate (y) and the risk coefficient (β) for a one-unit increase in exposure:\\nwhere β = [ln(RR)]/c. The percent decrease in exposure (Δcreq) required to produce a particular reduction in the outcome for a given RR is calculated as\\nValues of Δcreq < 100 indicate the percent reduction in exposure that would be required to produce a specific reduction in the outcome (Δy) assuming a given RR for the exposure–outcome association. Values of Δcreq ≥ 100 indicate that the corresponding value for Δy is not feasible, because exposure would have to be reduced by more than 100% to achieve it.\\nFinally, we combined data on projected changes in mean annual ambient concentrations of air pollutants for 2010, 2020, and 2030 with the information on minimum detectable effect estimates and the percent reduction in exposure required to produce a given effect estimate to identify which air pollutant–health outcome associations (out of 26 possible combinations) would be most feasible for assessment.\\nFor pollutants such as PM where projected reductions were relatively modest (~ 8%), we used an exploratory epidemiological methodology similar to that presented in Pope et al. (2009). Specifically, we used the simulated health data at census block group level (derived from county-specific health data and census information on demographics) to evaluate different strategies for demonstrating impacts of relatively small changes in ambient pollution (compared to NOx) over multiple years.\\nThe outcomes for this analysis were the differences between the number of hospitalizations for 2001 and 2010, as illustrated by Equation 3:\\nwhere ΔHC is the change in the number of hospitalizations at census block group C and H2010 and H2001 are the number of hospitalizations for the years 2010 and 2001, respectively.\\nWe calculated the number of hospitalizations due to congestive heart disease [CHD; International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9CM; World Health Organization 2004), code 428] and asthma (ICD-9CM, code 493) for each census block group for the years 2010 and 2001. We chose these end points based on significant associations (1.28% increase in risk per 10-μg/m3 increase in same-day PM2.5) reported by Dominici et al. (2006) between PM and CHD hospitalizations for the Medicare cohort. We restricted hospitalizations due to CHD to the population > 65 years of age, and we calculated asthma hospitalizations separately for all ages and for the population < 25 years of age, because of known age-dependent differences [Connecticut Department of Public Health (CDPH) 2007]. We calculated the number of hospitalizations for the years 2001 and 2010 as\\nwhere Hc is the number of hospitalizations for census block group C, Rate is the rate of hospitalization for females (F) and males (M) of race R (white, Hispanic, black), and PopC is the population of each subgroup (e.g., white female, black male, etc.).\\nWe used 2000 U.S. Census Bureau (2001) data to estimate the size of each population subgroup in 2001. We used county-level population projections for 2010 to estimate the proportional change in each population subgroup from 2000 to 2010 and applied this to the 2000 census block group population to estimate 2010 census block populations for each subgroup.\\nHospitalization rates for both outcomes are available for all of New Haven County for 2001 (CDPH 2001) and 2007 (CDPH 2007), and we used 2007 data for 2010 hospitalizations. The rates are broken down by age and sex, and age and race, but not by age, sex, and race. We therefore assumed constant ratios of rates of hospitalizations for males and females for all races. We calculated hospitalization rates according to sex, race, age (> 65 years of age for CHD, all ages, and < 25 years of age for asthma), health outcome (CHD or asthma), and year (2001 or 2010) as\\nFor example, RateFR is the county-level hospitalization for females of race R; RateR is the county-level hospitalization rate for race R; Ratio of ratesM/F is the ratio of hospitalization rates between males and females; and PopFR and PopMR are county-level population sizes for females and males of race R, respectively. We then calculated RateMR by multiplying RateFR by Ratio of ratesM/F.\\nReductions of PM2.5 at each census block group were then regressed against the changes in hospitalizations from 2001 to 2010 at each census block group. All regression analyses were performed using SAS (version 9.1; SAS Institute Inc., Cary, NC).\\nOur analysis indirectly accounted for the effects due to changes in key ethnic/racial demographic profile by using group or sex relevant hospitalization rates as part of the health data and feasibility simulations. We explored the influence of introducing an additional explanatory variable in our health effects regressions to indirectly account for both neighborhood effects and the missing determinants of observed hospital admissions by computing average admissions either within a 3- or 4-km radius around each census tract (similarly considered by Özkaynak and Thurston 1987).\",\n \" Air quality modeling We produced combined CMAQ and AERMOD model results for the study area. Model estimates for NOx and PM2.5 were consistent with measured Air Quality System (AQS) monitoring data for the area (Johnson et al. 2010). Figure 1 shows maps of modeled PM2.5 and NOx concentrations for the baseline year (2001) and projections for 2010, 2020, and 2030. PM2.5 maps for all four time points show a wide range of concentrations in the study area, with high concentrations in the city center, near the port areas, and near major roadways such as I-95, whereas PM2.5 concentrations in suburban areas were much lower than in central parts of the study area. Finally, PM2.5 concentrations are projected to decrease over time, with the most pronounced decreases in areas with the highest estimated concentrations in 2001.\\nSpatial patterns in ambient air quality concentrations for NOx relates strongly to the sources of mobile emissions such as major highways. NOx concentrations shown here depict strong spatial gradients because many of the locations for which we estimated concentrations (population-weighted centroids of the 318 census block groups) are near major roadways. Thus, contrasts between those areas and suburban areas are very pronounced. Finally, NOx concentrations are also projected to decrease considerably over time, particularly in locations close to roadways, because of the implementation of federal emission standards for mobile source emissions.\\nFigure 2 shows distributions of annual and daily average modeled PM2.5 and NOx concentrations for the baseline model year 2001 and projections (2010, 2020, and 2030). We sorted the annual average concentrations for 2001 in order to group the 318 study area locations (census block groups) for which we divided estimates into three groups according to average pollutant concentrations at baseline: low (locations in the lowest 25% of the distribution), medium (locations in the 2nd and 3rd quartiles), and high (locations in the highest quartile of the distribution). As expected, daily averages are more variable than annual averages for both PM2.5 and NOx, which indicates the importance of temporal variability in pollutant concentrations. Downward trends in PM2.5 concentrations were evident for areas with medium and high concentrations, but not for low-concentration areas. Declines in NOx concentrations were evident over time for all three groups, but the decrease was also much sharper in the high-concentration areas. The models predicted large percentage decreases for NOx between 2001 and 2010 (61%) and with less pronounced decreases from 2001 to 2020 and 2030 (overall decreases of 78% and 81%, respectively). For PM2.5 the models predicted smaller percentage decreases from 2001 to 2010 (8%), 2020 (9%), and 2030 (9%).\\nWe produced combined CMAQ and AERMOD model results for the study area. Model estimates for NOx and PM2.5 were consistent with measured Air Quality System (AQS) monitoring data for the area (Johnson et al. 2010). Figure 1 shows maps of modeled PM2.5 and NOx concentrations for the baseline year (2001) and projections for 2010, 2020, and 2030. PM2.5 maps for all four time points show a wide range of concentrations in the study area, with high concentrations in the city center, near the port areas, and near major roadways such as I-95, whereas PM2.5 concentrations in suburban areas were much lower than in central parts of the study area. Finally, PM2.5 concentrations are projected to decrease over time, with the most pronounced decreases in areas with the highest estimated concentrations in 2001.\\nSpatial patterns in ambient air quality concentrations for NOx relates strongly to the sources of mobile emissions such as major highways. NOx concentrations shown here depict strong spatial gradients because many of the locations for which we estimated concentrations (population-weighted centroids of the 318 census block groups) are near major roadways. Thus, contrasts between those areas and suburban areas are very pronounced. Finally, NOx concentrations are also projected to decrease considerably over time, particularly in locations close to roadways, because of the implementation of federal emission standards for mobile source emissions.\\nFigure 2 shows distributions of annual and daily average modeled PM2.5 and NOx concentrations for the baseline model year 2001 and projections (2010, 2020, and 2030). We sorted the annual average concentrations for 2001 in order to group the 318 study area locations (census block groups) for which we divided estimates into three groups according to average pollutant concentrations at baseline: low (locations in the lowest 25% of the distribution), medium (locations in the 2nd and 3rd quartiles), and high (locations in the highest quartile of the distribution). As expected, daily averages are more variable than annual averages for both PM2.5 and NOx, which indicates the importance of temporal variability in pollutant concentrations. Downward trends in PM2.5 concentrations were evident for areas with medium and high concentrations, but not for low-concentration areas. Declines in NOx concentrations were evident over time for all three groups, but the decrease was also much sharper in the high-concentration areas. The models predicted large percentage decreases for NOx between 2001 and 2010 (61%) and with less pronounced decreases from 2001 to 2020 and 2030 (overall decreases of 78% and 81%, respectively). For PM2.5 the models predicted smaller percentage decreases from 2001 to 2010 (8%), 2020 (9%), and 2030 (9%).\\n Sample-size–based feasibility analysis Table 1 exhibits the percent reduction in concentrations for a given pollutant that would be needed if we assumed a specific RR (1.01, 1.05, 1.10, 1.15, and 1.20) and an estimated reduction in adverse health outcome (2.5%, 5%, 10%, 15%, and 20%) using the assumptions stated above. Table 2 lists the health outcomes that we explored with corresponding minimum statistically detectable decreases in each outcome for the New Haven study population given the baseline rate of the outcome. The New Haven study population area is sufficient in size to examine reductions in adverse health outcomes ranging from a low of 2.5% (adult asthma prevalence) to a high of 10% (all-cause mortality and hospital discharge for cardiovascular diseases and respiratory causes).\\nBased on the percentage decrease air pollution projected for 2010, 2020, and 2030 within New Haven for NOx (61%, 2010; 78%, 2020; 81%, 2030) and PM2.5 (8%, 2010; 9%, 2020 and 2030), the percent reduction in air pollution needed to produce a given change in the outcome (Table 1), and the minimum statistically significant percent decrease in each health outcome that can be detected in the New Haven study population (Table 2), we can assess the feasibility for detecting beneficial health effects of air pollution reductions. Of the 26 different air pollution–health outcome linkages assessed, only five, all NOx related, are potentially feasible (Table 2, last column): all-cause mortality, cardiovascular disease hospitalization, respiratory disease hospitalization discharge, current prevalence of asthma in children, and current prevalence of asthma in adults.\\nTable 1 exhibits the percent reduction in concentrations for a given pollutant that would be needed if we assumed a specific RR (1.01, 1.05, 1.10, 1.15, and 1.20) and an estimated reduction in adverse health outcome (2.5%, 5%, 10%, 15%, and 20%) using the assumptions stated above. Table 2 lists the health outcomes that we explored with corresponding minimum statistically detectable decreases in each outcome for the New Haven study population given the baseline rate of the outcome. The New Haven study population area is sufficient in size to examine reductions in adverse health outcomes ranging from a low of 2.5% (adult asthma prevalence) to a high of 10% (all-cause mortality and hospital discharge for cardiovascular diseases and respiratory causes).\\nBased on the percentage decrease air pollution projected for 2010, 2020, and 2030 within New Haven for NOx (61%, 2010; 78%, 2020; 81%, 2030) and PM2.5 (8%, 2010; 9%, 2020 and 2030), the percent reduction in air pollution needed to produce a given change in the outcome (Table 1), and the minimum statistically significant percent decrease in each health outcome that can be detected in the New Haven study population (Table 2), we can assess the feasibility for detecting beneficial health effects of air pollution reductions. Of the 26 different air pollution–health outcome linkages assessed, only five, all NOx related, are potentially feasible (Table 2, last column): all-cause mortality, cardiovascular disease hospitalization, respiratory disease hospitalization discharge, current prevalence of asthma in children, and current prevalence of asthma in adults.\\n Simulation-based epidemiological feasibility analysis The average number of hospitalizations for CHD among those ≥ 65 years of age in each census block group decreased between 2001 and 2010, whereas average numbers of asthma hospitalizations increased (Table 3). We were unable to detect associations between small reductions in PM2.5 pollution concentrations and health outcomes, so we restricted our analysis to census block groups with PM2.5 reductions of > 4 μg/m3 (n = 30). For these census block groups, numbers of CHD hospitalizations were inversely associated with the estimated reduction in PM2.5 concentrations, indicating that numbers of hospitalizations decreased as the reduction in PM2.5 increased (p < 0.1; Table 4, Figure 3A). Asthma hospitalizations were also inversely associated with reductions in PM2.5 concentrations based on our simulations, suggesting that greater reductions in PM2.5 may slow the increase in asthma hospitalizations over time (Table 4, Figure 3B). However, the inverse association was weaker than for CHD hospitalizations. Finally, an exploratory analysis we conducted by including additional surrogate variables in the regression models aimed at capturing neighborhood effects caused substantial increases in model R2 values, whereas the PM2.5 effect estimates were attenuated somewhat depending on the outcome chosen (data not shown).\\nThe average number of hospitalizations for CHD among those ≥ 65 years of age in each census block group decreased between 2001 and 2010, whereas average numbers of asthma hospitalizations increased (Table 3). We were unable to detect associations between small reductions in PM2.5 pollution concentrations and health outcomes, so we restricted our analysis to census block groups with PM2.5 reductions of > 4 μg/m3 (n = 30). For these census block groups, numbers of CHD hospitalizations were inversely associated with the estimated reduction in PM2.5 concentrations, indicating that numbers of hospitalizations decreased as the reduction in PM2.5 increased (p < 0.1; Table 4, Figure 3A). Asthma hospitalizations were also inversely associated with reductions in PM2.5 concentrations based on our simulations, suggesting that greater reductions in PM2.5 may slow the increase in asthma hospitalizations over time (Table 4, Figure 3B). However, the inverse association was weaker than for CHD hospitalizations. Finally, an exploratory analysis we conducted by including additional surrogate variables in the regression models aimed at capturing neighborhood effects caused substantial increases in model R2 values, whereas the PM2.5 effect estimates were attenuated somewhat depending on the outcome chosen (data not shown).\",\n \"We produced combined CMAQ and AERMOD model results for the study area. Model estimates for NOx and PM2.5 were consistent with measured Air Quality System (AQS) monitoring data for the area (Johnson et al. 2010). Figure 1 shows maps of modeled PM2.5 and NOx concentrations for the baseline year (2001) and projections for 2010, 2020, and 2030. PM2.5 maps for all four time points show a wide range of concentrations in the study area, with high concentrations in the city center, near the port areas, and near major roadways such as I-95, whereas PM2.5 concentrations in suburban areas were much lower than in central parts of the study area. Finally, PM2.5 concentrations are projected to decrease over time, with the most pronounced decreases in areas with the highest estimated concentrations in 2001.\\nSpatial patterns in ambient air quality concentrations for NOx relates strongly to the sources of mobile emissions such as major highways. NOx concentrations shown here depict strong spatial gradients because many of the locations for which we estimated concentrations (population-weighted centroids of the 318 census block groups) are near major roadways. Thus, contrasts between those areas and suburban areas are very pronounced. Finally, NOx concentrations are also projected to decrease considerably over time, particularly in locations close to roadways, because of the implementation of federal emission standards for mobile source emissions.\\nFigure 2 shows distributions of annual and daily average modeled PM2.5 and NOx concentrations for the baseline model year 2001 and projections (2010, 2020, and 2030). We sorted the annual average concentrations for 2001 in order to group the 318 study area locations (census block groups) for which we divided estimates into three groups according to average pollutant concentrations at baseline: low (locations in the lowest 25% of the distribution), medium (locations in the 2nd and 3rd quartiles), and high (locations in the highest quartile of the distribution). As expected, daily averages are more variable than annual averages for both PM2.5 and NOx, which indicates the importance of temporal variability in pollutant concentrations. Downward trends in PM2.5 concentrations were evident for areas with medium and high concentrations, but not for low-concentration areas. Declines in NOx concentrations were evident over time for all three groups, but the decrease was also much sharper in the high-concentration areas. The models predicted large percentage decreases for NOx between 2001 and 2010 (61%) and with less pronounced decreases from 2001 to 2020 and 2030 (overall decreases of 78% and 81%, respectively). For PM2.5 the models predicted smaller percentage decreases from 2001 to 2010 (8%), 2020 (9%), and 2030 (9%).\",\n \"Table 1 exhibits the percent reduction in concentrations for a given pollutant that would be needed if we assumed a specific RR (1.01, 1.05, 1.10, 1.15, and 1.20) and an estimated reduction in adverse health outcome (2.5%, 5%, 10%, 15%, and 20%) using the assumptions stated above. Table 2 lists the health outcomes that we explored with corresponding minimum statistically detectable decreases in each outcome for the New Haven study population given the baseline rate of the outcome. The New Haven study population area is sufficient in size to examine reductions in adverse health outcomes ranging from a low of 2.5% (adult asthma prevalence) to a high of 10% (all-cause mortality and hospital discharge for cardiovascular diseases and respiratory causes).\\nBased on the percentage decrease air pollution projected for 2010, 2020, and 2030 within New Haven for NOx (61%, 2010; 78%, 2020; 81%, 2030) and PM2.5 (8%, 2010; 9%, 2020 and 2030), the percent reduction in air pollution needed to produce a given change in the outcome (Table 1), and the minimum statistically significant percent decrease in each health outcome that can be detected in the New Haven study population (Table 2), we can assess the feasibility for detecting beneficial health effects of air pollution reductions. Of the 26 different air pollution–health outcome linkages assessed, only five, all NOx related, are potentially feasible (Table 2, last column): all-cause mortality, cardiovascular disease hospitalization, respiratory disease hospitalization discharge, current prevalence of asthma in children, and current prevalence of asthma in adults.\",\n \"The average number of hospitalizations for CHD among those ≥ 65 years of age in each census block group decreased between 2001 and 2010, whereas average numbers of asthma hospitalizations increased (Table 3). We were unable to detect associations between small reductions in PM2.5 pollution concentrations and health outcomes, so we restricted our analysis to census block groups with PM2.5 reductions of > 4 μg/m3 (n = 30). For these census block groups, numbers of CHD hospitalizations were inversely associated with the estimated reduction in PM2.5 concentrations, indicating that numbers of hospitalizations decreased as the reduction in PM2.5 increased (p < 0.1; Table 4, Figure 3A). Asthma hospitalizations were also inversely associated with reductions in PM2.5 concentrations based on our simulations, suggesting that greater reductions in PM2.5 may slow the increase in asthma hospitalizations over time (Table 4, Figure 3B). However, the inverse association was weaker than for CHD hospitalizations. Finally, an exploratory analysis we conducted by including additional surrogate variables in the regression models aimed at capturing neighborhood effects caused substantial increases in model R2 values, whereas the PM2.5 effect estimates were attenuated somewhat depending on the outcome chosen (data not shown).\",\n \"We used detailed information on local health and exposure-related data to assess the feasibility of identifying an impact of cumulative air pollution programs on environmental public health in New Haven for 26 different pollutant–health outcome linkages. Combined regional (CMAQ) and local-scale (AERMOD) air quality modeling analysis showed a small overall decrease for PM2.5 (~ 8–9%) in mean pollutant concentrations mostly from local sources and between 2001 and 2010; in contrast, we projected that NOx would decrease by > 60%. Most NOx reductions can be attributed to mobile source emission reduction programs. Thus, it is important to accurately characterize near-road impacts. Local reductions in PM2.5 are modest relative to high background PM concentrations. Statistical power calculations suggest that projected decreases in NOx may result in statistically significant improvements in health outcomes, including all-cause mortality, asthma prevalence in children and adults, and cardiovascular and respiratory hospitalizations. For other pollutants with more modest reductions, including PM, we determined the likelihood of performing a successful traditional air pollution reduction–health reduction analysis in New Haven to be poor. Alternative epidemiological study designs that use spatially and temporally resolved air quality and exposure models to characterize intraurban gradients were promising based on exploratory epidemiological simulations. However, health outcomes with low baseline rates would have to be strongly associated with air pollution exposures in order for exposure reductions to result in identifiable improvements and thus would not be ideal for examining risk management decisions.\\nThis study illustrates the advantages of using air quality models over traditional epidemiological approaches using ambient measurements. For example, central-site data are especially problematic for certain PM components and species (e.g., elemental carbon, organic carbon, coarse and ultrafine PM) that exhibit significant spatial heterogeneity. Also, for many pollutants (e.g., toxic pollutants), ambient monitoring data are often nonexistent or limited. Appropriately verified air quality models, on the other hand, can provide the needed spatial and temporal resolution for multiple air pollutant concentrations at many locations. These same models can also be used to estimate the projected air quality and inputs for exposure models for future years, dependent on air pollution reduction activities, or due to the addition of new sources in a community (Isakov et al. 2006). For example, this model can address what happens if emissions from some specific stationary or mobile sources are reduced by certain amounts and what the associated impacts of these local controls versus regional controls may be. This model application helps determine which control options are most effective in reducing ambient concentrations.\\nBoth the air quality modeling and feasibility analysis methodologies we used in this research have certain shortcomings. For instance, despite their advantages of being able to provide temporal (hourly) and spatial (at hundreds of locations) estimates, and having a long history of use by regulatory agencies in multipollutant mitigation strategies, models have uncertainties due to model inputs, algorithms, and model parameters (Sax and Isakov 2003). Therefore, in order to reduce uncertainty due to model inputs, detailed emissions and meteorological information should be provided for each model application. In the simulation-based epidemiological feasibility analyses we considered only single-pollutant models and did not include ecological covariates (e.g., income, poverty status, smoking) typically used in cross-sectional, ecological analysis (Özkaynak and Thurston 1987; Pope et al. 2009), because of a lack of complete information. Moreover, it is possible that some of the covariates may change over time, but presumably this may be less of an issue in local-scale assessments than in national-scale analyses. We did not perform joint optimizations with NOx and PM, which could be used to examine more complicated alternative study designs such as census block groups with low reduction levels in NOx but intermediate to high reductions in PM. Clearly, accounting for multipollutant strategies in future assessments will be important in implementing enhanced air pollution–health outcome risk management studies (Mauderly et al. 2010).\\nThe linkages between air quality and exposure models (e.g., with the Stochastic Human Exposure and Dose Simulation Model and Hazardous Air Pollution Exposure Model) in the context of the New Haven study have been examined elsewhere (Isakov et al. 2009). Our biggest challenge has been with accessing geographically and temporally resolved health data in New Haven. Of course, this data gap is often a major challenge in other urban areas as well. Although there was strong local cooperation and local, state, and federal interest in working with the project, better research access to locally relevant health data should be both facilitated and encouraged. Given that the 2010 census has recently been collected and the air quality modeling for 2010 can be performed soon, we hope that the methodology we tested can be implemented in the near future using the actual 2010 local air quality modeling, census, and health data, in order to evaluate the results obtained from this feasibility study by using better databases and more robust models.\\nBolstered by the findings from our study, the City of New Haven has been working to find better solutions for reducing air pollution burden and for understanding the impacts from air emissions. We presented the results from this analysis to the New Haven departments of Health, City Planning, and Economic Development and to the city chief executive officer. These results have been used by New Haven in finalizing their negotiations to obtain zero emissions from a proposed new power plant unit to meet peak demand operations, which will be achieved through offsets by the local power plant company and proposed retrofits of garbage trucks and some port operations and additional community benefits. Moreover, the city is also evaluating what can be done to reduce impacts from port operations and mitigate exposures at city schools located near busy roads and highways, in light of the detailed air quality modeling results and health risk evaluations presented here.\",\n \"In this project we successfully applied, compared, and evaluated exposure assessment and epidemiological modeling tools in the context of observed public health status in a relatively small community, New Haven, Connecticut, and provided the U.S. EPA and local, state, and city organizations with a new modeling-based methodology to measure the impact of collective risk mitigation approaches and regulations. Furthermore, because no single regulation or program that affects air quality can be isolated to track its effect on health, this project provided critical findings on how regulatory agencies may better examine the complex interactions of cumulative impacts on air quality and health effects from multiple actions in other urban communities.\"\n]"},"all_sections_sec_types":{"kind":"list like","value":["materials|methods","results",null,"methods","methods","discussion","conclusions"],"string":"[\n \"materials|methods\",\n \"results\",\n null,\n \"methods\",\n \"methods\",\n \"discussion\",\n \"conclusions\"\n]"},"keywords":{"kind":"list like","value":["air pollution","feasibility analysis","health effects","nitrogen oxides","particulate matter"],"string":"[\n \"air pollution\",\n \"feasibility analysis\",\n \"health effects\",\n \"nitrogen oxides\",\n \"particulate matter\"\n]"},"whole_article_text":{"kind":"string","value":"Materials and Methods:\nThe New Haven Study Area is centered in the City of New Haven, Connecticut (population ~ 127,000), and extends to a 20-km radius, encompassing 318 census block groups in New Haven County with an estimated population in 2007 of more than 367,000 people. The City of New Haven is located on the southern coast of Connecticut on New Haven Harbor, which is fed by three rivers (the West, Mill, and Quinnipiac) that discharge into northern Long Island Sound. New Haven lies at the intersection of interstates I-91 and I-95, both major regional expressways that are often congested. In addition, several surface arteries pass through or around New Haven, including Routes 1, 10, 17, 34, and 63. Seaborne traffic passes through the Port of New Haven, a deep-water seaport that attracts a considerable number of barges and associated truck and rail traffic. In addition to several institutional power plants, one power generation facility serves the community. This wide range of emission source categories allows for testing of multipollutant emission control strategies.\nWe evaluated the overall feasibility of assessing the public health impact of air pollution reduction programs in the City of New Haven by linking projected emissions reductions from overall regulatory actions to estimated detectable health outcome changes. We began by identifying pollutants of interest for New Haven based on the local emissions inventory for the baseline year of 2001 (Weil 2004) and criteria air pollutants. For the present study, we focused on two air pollutants: NOx and PM2.5. We also identified health outcomes that have been associated with these pollutants: cardiovascular disease hospitalization and mortality; respiratory disease hospitalization and mortality; chronic obstructive pulmonary disease mortality and hospitalization; and asthma prevalence, diagnosis, and hospitalization.\nWe then evaluated existing data on ambient level air pollution, emission data, personal exposure data, and health outcome data for the New Haven area. As part of this data inventory evaluation, we assessed the relevance and completeness of data, as well as verification of locations and quantities of emissions from local sources. We then generated emission estimates for NOx and PM2.5 based on local emissions sources and the projected impacts of federal, state, and local regulatory reduction activities. We also applied an improved methodology to predict mobile source emissions (Cook et al. 2008).\nWe first estimated pollutant specific local-scale air concentrations using the U.S. EPA’s AERMOD dispersion model (Cimorelli et al. 2005). This model used information on local emission sources and local meteorological conditions to provide hourly and annual average concentrations at multiple locations corresponding to the weighted centroids of each of the 318 census block groups in the study area. We estimated total NOx and PM2.5 concentrations by combining regional background levels, chemically reactive pollutant estimates from the CMAQ (Community Multiscale Air Quality) model, and the AERMOD estimates. We estimated emissions using the baseline year (2001) emissions rates and projected emissions in 2010, 2020, and 2030 based on planned and anticipated pollution control programs.\nTo assess feasibility using a sample size approach, we first determined the minimum detectable decrease in each outcome relative to its baseline incidence rate [tests of two independent proportions for a (one-sided) likelihood ratio chi-square test with an α of 0.05 and power of 0.80] for a study population of 367,173 (i.e., the 2007 Census estimate for the New Haven population within the 318 block groups included in the study area). For some of the health outcomes, we made additional study area subpopulation calculations for different age groups (< 18 years, ≥ 18 years).\nThere is general consensus that RRs associated with air pollution exposure for a wide variety of health outcomes are typically less than 1.50, and usually within the range of 1.01–1.20, often for a 10-μg/m3 change in PM2.5 or an interquartile range change in gaseous pollutant concentrations. Jerrett et al. (2008) and Wellenius et al. (2005) found risk ratios or a RR in this range for NO2. RRs for PM2.5 and various outcomes in this range were found by Pope et al. (2002) and Sheppard et al. (1999), whereas Laden et al. (2006) found higher PM2.5 and mortality RRs of 1.16–1.28, and Peters et al. (2001) found an RR of 1.69 for PM2.5 and acute myocardial infarction.\nNext we determined the percent reduction in exposure that would be required to produce a given reduction in the outcome assuming a range of possible effect sizes for concentration–outcome associations. Specifically, we considered air pollution RR values of 1.01, 1.05, 1.10, 1.15, and 1.20 representing the increase in outcome (y) associated with an incremental increase in a given pollutant exposure equal to the level of the average value of the ambient pollution concentration (c) in the study population. The change in the outcome (Δy) associated with a change in exposure (Δc) is a function of the baseline incidence rate (y) and the risk coefficient (β) for a one-unit increase in exposure:\nwhere β = [ln(RR)]/c. The percent decrease in exposure (Δcreq) required to produce a particular reduction in the outcome for a given RR is calculated as\nValues of Δcreq < 100 indicate the percent reduction in exposure that would be required to produce a specific reduction in the outcome (Δy) assuming a given RR for the exposure–outcome association. Values of Δcreq ≥ 100 indicate that the corresponding value for Δy is not feasible, because exposure would have to be reduced by more than 100% to achieve it.\nFinally, we combined data on projected changes in mean annual ambient concentrations of air pollutants for 2010, 2020, and 2030 with the information on minimum detectable effect estimates and the percent reduction in exposure required to produce a given effect estimate to identify which air pollutant–health outcome associations (out of 26 possible combinations) would be most feasible for assessment.\nFor pollutants such as PM where projected reductions were relatively modest (~ 8%), we used an exploratory epidemiological methodology similar to that presented in Pope et al. (2009). Specifically, we used the simulated health data at census block group level (derived from county-specific health data and census information on demographics) to evaluate different strategies for demonstrating impacts of relatively small changes in ambient pollution (compared to NOx) over multiple years.\nThe outcomes for this analysis were the differences between the number of hospitalizations for 2001 and 2010, as illustrated by Equation 3:\nwhere ΔHC is the change in the number of hospitalizations at census block group C and H2010 and H2001 are the number of hospitalizations for the years 2010 and 2001, respectively.\nWe calculated the number of hospitalizations due to congestive heart disease [CHD; International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9CM; World Health Organization 2004), code 428] and asthma (ICD-9CM, code 493) for each census block group for the years 2010 and 2001. We chose these end points based on significant associations (1.28% increase in risk per 10-μg/m3 increase in same-day PM2.5) reported by Dominici et al. (2006) between PM and CHD hospitalizations for the Medicare cohort. We restricted hospitalizations due to CHD to the population > 65 years of age, and we calculated asthma hospitalizations separately for all ages and for the population < 25 years of age, because of known age-dependent differences [Connecticut Department of Public Health (CDPH) 2007]. We calculated the number of hospitalizations for the years 2001 and 2010 as\nwhere Hc is the number of hospitalizations for census block group C, Rate is the rate of hospitalization for females (F) and males (M) of race R (white, Hispanic, black), and PopC is the population of each subgroup (e.g., white female, black male, etc.).\nWe used 2000 U.S. Census Bureau (2001) data to estimate the size of each population subgroup in 2001. We used county-level population projections for 2010 to estimate the proportional change in each population subgroup from 2000 to 2010 and applied this to the 2000 census block group population to estimate 2010 census block populations for each subgroup.\nHospitalization rates for both outcomes are available for all of New Haven County for 2001 (CDPH 2001) and 2007 (CDPH 2007), and we used 2007 data for 2010 hospitalizations. The rates are broken down by age and sex, and age and race, but not by age, sex, and race. We therefore assumed constant ratios of rates of hospitalizations for males and females for all races. We calculated hospitalization rates according to sex, race, age (> 65 years of age for CHD, all ages, and < 25 years of age for asthma), health outcome (CHD or asthma), and year (2001 or 2010) as\nFor example, RateFR is the county-level hospitalization for females of race R; RateR is the county-level hospitalization rate for race R; Ratio of ratesM/F is the ratio of hospitalization rates between males and females; and PopFR and PopMR are county-level population sizes for females and males of race R, respectively. We then calculated RateMR by multiplying RateFR by Ratio of ratesM/F.\nReductions of PM2.5 at each census block group were then regressed against the changes in hospitalizations from 2001 to 2010 at each census block group. All regression analyses were performed using SAS (version 9.1; SAS Institute Inc., Cary, NC).\nOur analysis indirectly accounted for the effects due to changes in key ethnic/racial demographic profile by using group or sex relevant hospitalization rates as part of the health data and feasibility simulations. We explored the influence of introducing an additional explanatory variable in our health effects regressions to indirectly account for both neighborhood effects and the missing determinants of observed hospital admissions by computing average admissions either within a 3- or 4-km radius around each census tract (similarly considered by Özkaynak and Thurston 1987).\n\nResults:\n Air quality modeling We produced combined CMAQ and AERMOD model results for the study area. Model estimates for NOx and PM2.5 were consistent with measured Air Quality System (AQS) monitoring data for the area (Johnson et al. 2010). Figure 1 shows maps of modeled PM2.5 and NOx concentrations for the baseline year (2001) and projections for 2010, 2020, and 2030. PM2.5 maps for all four time points show a wide range of concentrations in the study area, with high concentrations in the city center, near the port areas, and near major roadways such as I-95, whereas PM2.5 concentrations in suburban areas were much lower than in central parts of the study area. Finally, PM2.5 concentrations are projected to decrease over time, with the most pronounced decreases in areas with the highest estimated concentrations in 2001.\nSpatial patterns in ambient air quality concentrations for NOx relates strongly to the sources of mobile emissions such as major highways. NOx concentrations shown here depict strong spatial gradients because many of the locations for which we estimated concentrations (population-weighted centroids of the 318 census block groups) are near major roadways. Thus, contrasts between those areas and suburban areas are very pronounced. Finally, NOx concentrations are also projected to decrease considerably over time, particularly in locations close to roadways, because of the implementation of federal emission standards for mobile source emissions.\nFigure 2 shows distributions of annual and daily average modeled PM2.5 and NOx concentrations for the baseline model year 2001 and projections (2010, 2020, and 2030). We sorted the annual average concentrations for 2001 in order to group the 318 study area locations (census block groups) for which we divided estimates into three groups according to average pollutant concentrations at baseline: low (locations in the lowest 25% of the distribution), medium (locations in the 2nd and 3rd quartiles), and high (locations in the highest quartile of the distribution). As expected, daily averages are more variable than annual averages for both PM2.5 and NOx, which indicates the importance of temporal variability in pollutant concentrations. Downward trends in PM2.5 concentrations were evident for areas with medium and high concentrations, but not for low-concentration areas. Declines in NOx concentrations were evident over time for all three groups, but the decrease was also much sharper in the high-concentration areas. The models predicted large percentage decreases for NOx between 2001 and 2010 (61%) and with less pronounced decreases from 2001 to 2020 and 2030 (overall decreases of 78% and 81%, respectively). For PM2.5 the models predicted smaller percentage decreases from 2001 to 2010 (8%), 2020 (9%), and 2030 (9%).\nWe produced combined CMAQ and AERMOD model results for the study area. Model estimates for NOx and PM2.5 were consistent with measured Air Quality System (AQS) monitoring data for the area (Johnson et al. 2010). Figure 1 shows maps of modeled PM2.5 and NOx concentrations for the baseline year (2001) and projections for 2010, 2020, and 2030. PM2.5 maps for all four time points show a wide range of concentrations in the study area, with high concentrations in the city center, near the port areas, and near major roadways such as I-95, whereas PM2.5 concentrations in suburban areas were much lower than in central parts of the study area. Finally, PM2.5 concentrations are projected to decrease over time, with the most pronounced decreases in areas with the highest estimated concentrations in 2001.\nSpatial patterns in ambient air quality concentrations for NOx relates strongly to the sources of mobile emissions such as major highways. NOx concentrations shown here depict strong spatial gradients because many of the locations for which we estimated concentrations (population-weighted centroids of the 318 census block groups) are near major roadways. Thus, contrasts between those areas and suburban areas are very pronounced. Finally, NOx concentrations are also projected to decrease considerably over time, particularly in locations close to roadways, because of the implementation of federal emission standards for mobile source emissions.\nFigure 2 shows distributions of annual and daily average modeled PM2.5 and NOx concentrations for the baseline model year 2001 and projections (2010, 2020, and 2030). We sorted the annual average concentrations for 2001 in order to group the 318 study area locations (census block groups) for which we divided estimates into three groups according to average pollutant concentrations at baseline: low (locations in the lowest 25% of the distribution), medium (locations in the 2nd and 3rd quartiles), and high (locations in the highest quartile of the distribution). As expected, daily averages are more variable than annual averages for both PM2.5 and NOx, which indicates the importance of temporal variability in pollutant concentrations. Downward trends in PM2.5 concentrations were evident for areas with medium and high concentrations, but not for low-concentration areas. Declines in NOx concentrations were evident over time for all three groups, but the decrease was also much sharper in the high-concentration areas. The models predicted large percentage decreases for NOx between 2001 and 2010 (61%) and with less pronounced decreases from 2001 to 2020 and 2030 (overall decreases of 78% and 81%, respectively). For PM2.5 the models predicted smaller percentage decreases from 2001 to 2010 (8%), 2020 (9%), and 2030 (9%).\n Sample-size–based feasibility analysis Table 1 exhibits the percent reduction in concentrations for a given pollutant that would be needed if we assumed a specific RR (1.01, 1.05, 1.10, 1.15, and 1.20) and an estimated reduction in adverse health outcome (2.5%, 5%, 10%, 15%, and 20%) using the assumptions stated above. Table 2 lists the health outcomes that we explored with corresponding minimum statistically detectable decreases in each outcome for the New Haven study population given the baseline rate of the outcome. The New Haven study population area is sufficient in size to examine reductions in adverse health outcomes ranging from a low of 2.5% (adult asthma prevalence) to a high of 10% (all-cause mortality and hospital discharge for cardiovascular diseases and respiratory causes).\nBased on the percentage decrease air pollution projected for 2010, 2020, and 2030 within New Haven for NOx (61%, 2010; 78%, 2020; 81%, 2030) and PM2.5 (8%, 2010; 9%, 2020 and 2030), the percent reduction in air pollution needed to produce a given change in the outcome (Table 1), and the minimum statistically significant percent decrease in each health outcome that can be detected in the New Haven study population (Table 2), we can assess the feasibility for detecting beneficial health effects of air pollution reductions. Of the 26 different air pollution–health outcome linkages assessed, only five, all NOx related, are potentially feasible (Table 2, last column): all-cause mortality, cardiovascular disease hospitalization, respiratory disease hospitalization discharge, current prevalence of asthma in children, and current prevalence of asthma in adults.\nTable 1 exhibits the percent reduction in concentrations for a given pollutant that would be needed if we assumed a specific RR (1.01, 1.05, 1.10, 1.15, and 1.20) and an estimated reduction in adverse health outcome (2.5%, 5%, 10%, 15%, and 20%) using the assumptions stated above. Table 2 lists the health outcomes that we explored with corresponding minimum statistically detectable decreases in each outcome for the New Haven study population given the baseline rate of the outcome. The New Haven study population area is sufficient in size to examine reductions in adverse health outcomes ranging from a low of 2.5% (adult asthma prevalence) to a high of 10% (all-cause mortality and hospital discharge for cardiovascular diseases and respiratory causes).\nBased on the percentage decrease air pollution projected for 2010, 2020, and 2030 within New Haven for NOx (61%, 2010; 78%, 2020; 81%, 2030) and PM2.5 (8%, 2010; 9%, 2020 and 2030), the percent reduction in air pollution needed to produce a given change in the outcome (Table 1), and the minimum statistically significant percent decrease in each health outcome that can be detected in the New Haven study population (Table 2), we can assess the feasibility for detecting beneficial health effects of air pollution reductions. Of the 26 different air pollution–health outcome linkages assessed, only five, all NOx related, are potentially feasible (Table 2, last column): all-cause mortality, cardiovascular disease hospitalization, respiratory disease hospitalization discharge, current prevalence of asthma in children, and current prevalence of asthma in adults.\n Simulation-based epidemiological feasibility analysis The average number of hospitalizations for CHD among those ≥ 65 years of age in each census block group decreased between 2001 and 2010, whereas average numbers of asthma hospitalizations increased (Table 3). We were unable to detect associations between small reductions in PM2.5 pollution concentrations and health outcomes, so we restricted our analysis to census block groups with PM2.5 reductions of > 4 μg/m3 (n = 30). For these census block groups, numbers of CHD hospitalizations were inversely associated with the estimated reduction in PM2.5 concentrations, indicating that numbers of hospitalizations decreased as the reduction in PM2.5 increased (p < 0.1; Table 4, Figure 3A). Asthma hospitalizations were also inversely associated with reductions in PM2.5 concentrations based on our simulations, suggesting that greater reductions in PM2.5 may slow the increase in asthma hospitalizations over time (Table 4, Figure 3B). However, the inverse association was weaker than for CHD hospitalizations. Finally, an exploratory analysis we conducted by including additional surrogate variables in the regression models aimed at capturing neighborhood effects caused substantial increases in model R2 values, whereas the PM2.5 effect estimates were attenuated somewhat depending on the outcome chosen (data not shown).\nThe average number of hospitalizations for CHD among those ≥ 65 years of age in each census block group decreased between 2001 and 2010, whereas average numbers of asthma hospitalizations increased (Table 3). We were unable to detect associations between small reductions in PM2.5 pollution concentrations and health outcomes, so we restricted our analysis to census block groups with PM2.5 reductions of > 4 μg/m3 (n = 30). For these census block groups, numbers of CHD hospitalizations were inversely associated with the estimated reduction in PM2.5 concentrations, indicating that numbers of hospitalizations decreased as the reduction in PM2.5 increased (p < 0.1; Table 4, Figure 3A). Asthma hospitalizations were also inversely associated with reductions in PM2.5 concentrations based on our simulations, suggesting that greater reductions in PM2.5 may slow the increase in asthma hospitalizations over time (Table 4, Figure 3B). However, the inverse association was weaker than for CHD hospitalizations. Finally, an exploratory analysis we conducted by including additional surrogate variables in the regression models aimed at capturing neighborhood effects caused substantial increases in model R2 values, whereas the PM2.5 effect estimates were attenuated somewhat depending on the outcome chosen (data not shown).\n\nAir quality modeling:\nWe produced combined CMAQ and AERMOD model results for the study area. Model estimates for NOx and PM2.5 were consistent with measured Air Quality System (AQS) monitoring data for the area (Johnson et al. 2010). Figure 1 shows maps of modeled PM2.5 and NOx concentrations for the baseline year (2001) and projections for 2010, 2020, and 2030. PM2.5 maps for all four time points show a wide range of concentrations in the study area, with high concentrations in the city center, near the port areas, and near major roadways such as I-95, whereas PM2.5 concentrations in suburban areas were much lower than in central parts of the study area. Finally, PM2.5 concentrations are projected to decrease over time, with the most pronounced decreases in areas with the highest estimated concentrations in 2001.\nSpatial patterns in ambient air quality concentrations for NOx relates strongly to the sources of mobile emissions such as major highways. NOx concentrations shown here depict strong spatial gradients because many of the locations for which we estimated concentrations (population-weighted centroids of the 318 census block groups) are near major roadways. Thus, contrasts between those areas and suburban areas are very pronounced. Finally, NOx concentrations are also projected to decrease considerably over time, particularly in locations close to roadways, because of the implementation of federal emission standards for mobile source emissions.\nFigure 2 shows distributions of annual and daily average modeled PM2.5 and NOx concentrations for the baseline model year 2001 and projections (2010, 2020, and 2030). We sorted the annual average concentrations for 2001 in order to group the 318 study area locations (census block groups) for which we divided estimates into three groups according to average pollutant concentrations at baseline: low (locations in the lowest 25% of the distribution), medium (locations in the 2nd and 3rd quartiles), and high (locations in the highest quartile of the distribution). As expected, daily averages are more variable than annual averages for both PM2.5 and NOx, which indicates the importance of temporal variability in pollutant concentrations. Downward trends in PM2.5 concentrations were evident for areas with medium and high concentrations, but not for low-concentration areas. Declines in NOx concentrations were evident over time for all three groups, but the decrease was also much sharper in the high-concentration areas. The models predicted large percentage decreases for NOx between 2001 and 2010 (61%) and with less pronounced decreases from 2001 to 2020 and 2030 (overall decreases of 78% and 81%, respectively). For PM2.5 the models predicted smaller percentage decreases from 2001 to 2010 (8%), 2020 (9%), and 2030 (9%).\n\nSample-size–based feasibility analysis:\nTable 1 exhibits the percent reduction in concentrations for a given pollutant that would be needed if we assumed a specific RR (1.01, 1.05, 1.10, 1.15, and 1.20) and an estimated reduction in adverse health outcome (2.5%, 5%, 10%, 15%, and 20%) using the assumptions stated above. Table 2 lists the health outcomes that we explored with corresponding minimum statistically detectable decreases in each outcome for the New Haven study population given the baseline rate of the outcome. The New Haven study population area is sufficient in size to examine reductions in adverse health outcomes ranging from a low of 2.5% (adult asthma prevalence) to a high of 10% (all-cause mortality and hospital discharge for cardiovascular diseases and respiratory causes).\nBased on the percentage decrease air pollution projected for 2010, 2020, and 2030 within New Haven for NOx (61%, 2010; 78%, 2020; 81%, 2030) and PM2.5 (8%, 2010; 9%, 2020 and 2030), the percent reduction in air pollution needed to produce a given change in the outcome (Table 1), and the minimum statistically significant percent decrease in each health outcome that can be detected in the New Haven study population (Table 2), we can assess the feasibility for detecting beneficial health effects of air pollution reductions. Of the 26 different air pollution–health outcome linkages assessed, only five, all NOx related, are potentially feasible (Table 2, last column): all-cause mortality, cardiovascular disease hospitalization, respiratory disease hospitalization discharge, current prevalence of asthma in children, and current prevalence of asthma in adults.\n\nSimulation-based epidemiological feasibility analysis:\nThe average number of hospitalizations for CHD among those ≥ 65 years of age in each census block group decreased between 2001 and 2010, whereas average numbers of asthma hospitalizations increased (Table 3). We were unable to detect associations between small reductions in PM2.5 pollution concentrations and health outcomes, so we restricted our analysis to census block groups with PM2.5 reductions of > 4 μg/m3 (n = 30). For these census block groups, numbers of CHD hospitalizations were inversely associated with the estimated reduction in PM2.5 concentrations, indicating that numbers of hospitalizations decreased as the reduction in PM2.5 increased (p < 0.1; Table 4, Figure 3A). Asthma hospitalizations were also inversely associated with reductions in PM2.5 concentrations based on our simulations, suggesting that greater reductions in PM2.5 may slow the increase in asthma hospitalizations over time (Table 4, Figure 3B). However, the inverse association was weaker than for CHD hospitalizations. Finally, an exploratory analysis we conducted by including additional surrogate variables in the regression models aimed at capturing neighborhood effects caused substantial increases in model R2 values, whereas the PM2.5 effect estimates were attenuated somewhat depending on the outcome chosen (data not shown).\n\nDiscussion:\nWe used detailed information on local health and exposure-related data to assess the feasibility of identifying an impact of cumulative air pollution programs on environmental public health in New Haven for 26 different pollutant–health outcome linkages. Combined regional (CMAQ) and local-scale (AERMOD) air quality modeling analysis showed a small overall decrease for PM2.5 (~ 8–9%) in mean pollutant concentrations mostly from local sources and between 2001 and 2010; in contrast, we projected that NOx would decrease by > 60%. Most NOx reductions can be attributed to mobile source emission reduction programs. Thus, it is important to accurately characterize near-road impacts. Local reductions in PM2.5 are modest relative to high background PM concentrations. Statistical power calculations suggest that projected decreases in NOx may result in statistically significant improvements in health outcomes, including all-cause mortality, asthma prevalence in children and adults, and cardiovascular and respiratory hospitalizations. For other pollutants with more modest reductions, including PM, we determined the likelihood of performing a successful traditional air pollution reduction–health reduction analysis in New Haven to be poor. Alternative epidemiological study designs that use spatially and temporally resolved air quality and exposure models to characterize intraurban gradients were promising based on exploratory epidemiological simulations. However, health outcomes with low baseline rates would have to be strongly associated with air pollution exposures in order for exposure reductions to result in identifiable improvements and thus would not be ideal for examining risk management decisions.\nThis study illustrates the advantages of using air quality models over traditional epidemiological approaches using ambient measurements. For example, central-site data are especially problematic for certain PM components and species (e.g., elemental carbon, organic carbon, coarse and ultrafine PM) that exhibit significant spatial heterogeneity. Also, for many pollutants (e.g., toxic pollutants), ambient monitoring data are often nonexistent or limited. Appropriately verified air quality models, on the other hand, can provide the needed spatial and temporal resolution for multiple air pollutant concentrations at many locations. These same models can also be used to estimate the projected air quality and inputs for exposure models for future years, dependent on air pollution reduction activities, or due to the addition of new sources in a community (Isakov et al. 2006). For example, this model can address what happens if emissions from some specific stationary or mobile sources are reduced by certain amounts and what the associated impacts of these local controls versus regional controls may be. This model application helps determine which control options are most effective in reducing ambient concentrations.\nBoth the air quality modeling and feasibility analysis methodologies we used in this research have certain shortcomings. For instance, despite their advantages of being able to provide temporal (hourly) and spatial (at hundreds of locations) estimates, and having a long history of use by regulatory agencies in multipollutant mitigation strategies, models have uncertainties due to model inputs, algorithms, and model parameters (Sax and Isakov 2003). Therefore, in order to reduce uncertainty due to model inputs, detailed emissions and meteorological information should be provided for each model application. In the simulation-based epidemiological feasibility analyses we considered only single-pollutant models and did not include ecological covariates (e.g., income, poverty status, smoking) typically used in cross-sectional, ecological analysis (Özkaynak and Thurston 1987; Pope et al. 2009), because of a lack of complete information. Moreover, it is possible that some of the covariates may change over time, but presumably this may be less of an issue in local-scale assessments than in national-scale analyses. We did not perform joint optimizations with NOx and PM, which could be used to examine more complicated alternative study designs such as census block groups with low reduction levels in NOx but intermediate to high reductions in PM. Clearly, accounting for multipollutant strategies in future assessments will be important in implementing enhanced air pollution–health outcome risk management studies (Mauderly et al. 2010).\nThe linkages between air quality and exposure models (e.g., with the Stochastic Human Exposure and Dose Simulation Model and Hazardous Air Pollution Exposure Model) in the context of the New Haven study have been examined elsewhere (Isakov et al. 2009). Our biggest challenge has been with accessing geographically and temporally resolved health data in New Haven. Of course, this data gap is often a major challenge in other urban areas as well. Although there was strong local cooperation and local, state, and federal interest in working with the project, better research access to locally relevant health data should be both facilitated and encouraged. Given that the 2010 census has recently been collected and the air quality modeling for 2010 can be performed soon, we hope that the methodology we tested can be implemented in the near future using the actual 2010 local air quality modeling, census, and health data, in order to evaluate the results obtained from this feasibility study by using better databases and more robust models.\nBolstered by the findings from our study, the City of New Haven has been working to find better solutions for reducing air pollution burden and for understanding the impacts from air emissions. We presented the results from this analysis to the New Haven departments of Health, City Planning, and Economic Development and to the city chief executive officer. These results have been used by New Haven in finalizing their negotiations to obtain zero emissions from a proposed new power plant unit to meet peak demand operations, which will be achieved through offsets by the local power plant company and proposed retrofits of garbage trucks and some port operations and additional community benefits. Moreover, the city is also evaluating what can be done to reduce impacts from port operations and mitigate exposures at city schools located near busy roads and highways, in light of the detailed air quality modeling results and health risk evaluations presented here.\n\nConclusions:\nIn this project we successfully applied, compared, and evaluated exposure assessment and epidemiological modeling tools in the context of observed public health status in a relatively small community, New Haven, Connecticut, and provided the U.S. EPA and local, state, and city organizations with a new modeling-based methodology to measure the impact of collective risk mitigation approaches and regulations. Furthermore, because no single regulation or program that affects air quality can be isolated to track its effect on health, this project provided critical findings on how regulatory agencies may better examine the complex interactions of cumulative impacts on air quality and health effects from multiple actions in other urban communities.\n\n"},"whole_article_abstract":{"kind":"string","value":"Background:\nNew approaches to link health surveillance data with environmental and population exposure information are needed to examine the health benefits of risk management decisions.\n\nMethods:\nUsing a hybrid modeling approach that combines regional and local-scale air quality data, we estimated ambient concentrations for multiple air pollutants [e.g., PM2.5 (particulate matter ≤ 2.5 μm in aerodynamic diameter), NOx (nitrogen oxides)] for baseline year 2001 and projected emissions for 2010, 2020, and 2030. We assessed the feasibility of detecting health improvements in relation to reductions in air pollution for 26 different pollutant-health outcome linkages using both sample size and exploratory epidemiological simulations to further inform decision-making needs.\n\nResults:\nModel projections suggested decreases (~10-60%) in pollutant concentrations, mainly attributable to decreases in pollutants from local sources between 2001 and 2010. Models indicated considerable spatial variability in the concentrations of most pollutants. Sample size analyses supported the feasibility of identifying linkages between reductions in NOx and improvements in all-cause mortality, prevalence of asthma in children and adults, and cardiovascular and respiratory hospitalizations.\n\nConclusions:\nSubstantial reductions in air pollution (e.g., ~60% for NOx) are needed to detect health impacts of environmental actions using traditional epidemiological study designs in small communities like New Haven. In contrast, exploratory epidemiological simulations suggest that it may be possible to demonstrate the health impacts of PM reductions by predicting intraurban pollution gradients within New Haven using coupled models."},"background_conclusion_text":{"kind":"null"},"background_conclusion_abstract":{"kind":"null"},"whole_article_text_length":{"kind":"number","value":6374,"string":"6,374"},"whole_article_abstract_length":{"kind":"number","value":280,"string":"280"},"other_sections_lengths":{"kind":"list like","value":[511,320],"string":"[\n 511,\n 320\n]"},"num_sections":{"kind":"number","value":7,"string":"7"},"most_frequent_words":{"kind":"list like","value":["concentrations","pm2","air","health","2010","nox","2001","new","outcome","hospitalizations"],"string":"[\n \"concentrations\",\n \"pm2\",\n \"air\",\n \"health\",\n \"2010\",\n \"nox\",\n \"2001\",\n \"new\",\n \"outcome\",\n \"hospitalizations\"\n]"},"keybert_topics":{"kind":"list like","value":["new haven connecticut","air 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[SUMMARY]"},"annotated_keywords_results_abstract_prompt":{"kind":"string","value":"[CONTENT] air pollution | feasibility analysis | health effects | nitrogen oxides | particulate matter [SUMMARY]"},"annotated_keywords_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] air pollution | feasibility analysis | health effects | nitrogen oxides | particulate matter [SUMMARY]"},"annotated_keywords_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] air pollution | feasibility analysis | health effects | nitrogen oxides | particulate matter [SUMMARY]"},"annotated_keywords_background_conclusion_abstract_prompt":{"kind":"null"},"annotated_mesh_background_abstract_prompt":{"kind":"null"},"annotated_mesh_methods_abstract_prompt":{"kind":"string","value":"[CONTENT] Adolescent | Adult | Aged | Aged, 80 and over | Air Pollutants | Air Pollution | Cardiovascular Diseases | Child | Child, Preschool | Cities | Connecticut | Conservation of Natural Resources | Environmental Policy | Feasibility Studies | Health Status | Humans | Infant | Infant, Newborn | Linear Models | Middle Aged | Models, Chemical | Public Health | Respiratory Tract Diseases | Young Adult [SUMMARY]"},"annotated_mesh_results_abstract_prompt":{"kind":"string","value":"[CONTENT] Adolescent | Adult | Aged | Aged, 80 and over | Air Pollutants | Air Pollution | Cardiovascular Diseases | Child | Child, Preschool | Cities | Connecticut | Conservation of Natural Resources | Environmental Policy | Feasibility Studies | Health Status | Humans | Infant | Infant, Newborn | Linear Models | Middle Aged | Models, Chemical | Public Health | Respiratory Tract Diseases | Young Adult [SUMMARY]"},"annotated_mesh_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] Adolescent | Adult | Aged | Aged, 80 and over | Air Pollutants | Air Pollution | Cardiovascular Diseases | Child | Child, Preschool | Cities | Connecticut | Conservation of Natural Resources | Environmental Policy | Feasibility Studies | Health Status | Humans | Infant | Infant, Newborn | Linear Models | Middle Aged | Models, Chemical | Public Health | Respiratory Tract Diseases | Young Adult [SUMMARY]"},"annotated_mesh_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] Adolescent | Adult | Aged | Aged, 80 and over | Air Pollutants | Air Pollution | Cardiovascular Diseases | Child | Child, Preschool | Cities | Connecticut | Conservation of Natural Resources | Environmental Policy | Feasibility Studies | Health Status | Humans | Infant | Infant, Newborn | Linear Models | Middle Aged | Models, Chemical | Public Health | Respiratory Tract Diseases | Young Adult [SUMMARY]"},"annotated_mesh_background_conclusion_abstract_prompt":{"kind":"null"},"annotated_keybert_background_abstract_prompt":{"kind":"null"},"annotated_keybert_methods_abstract_prompt":{"kind":"string","value":"[CONTENT] new haven connecticut | air pollution programs | emissions proposed new | estimate new haven | emissions major highways [SUMMARY]"},"annotated_keybert_results_abstract_prompt":{"kind":"string","value":"[CONTENT] new haven connecticut | air pollution programs | emissions proposed new | estimate new haven | emissions major highways [SUMMARY]"},"annotated_keybert_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] new haven connecticut | air pollution programs | emissions proposed new | estimate new haven | emissions major highways [SUMMARY]"},"annotated_keybert_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] new haven connecticut | air pollution programs | emissions proposed new | estimate new haven | emissions major highways [SUMMARY]"},"annotated_keybert_background_conclusion_abstract_prompt":{"kind":"null"},"annotated_most_frequent_background_abstract_prompt":{"kind":"null"},"annotated_most_frequent_methods_abstract_prompt":{"kind":"string","value":"[CONTENT] concentrations | pm2 | air | health | 2010 | nox | 2001 | new | outcome | hospitalizations [SUMMARY]"},"annotated_most_frequent_results_abstract_prompt":{"kind":"string","value":"[CONTENT] concentrations | pm2 | air | health | 2010 | nox | 2001 | new | outcome | hospitalizations [SUMMARY]"},"annotated_most_frequent_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] concentrations | pm2 | air | health | 2010 | nox | 2001 | new | outcome | hospitalizations [SUMMARY]"},"annotated_most_frequent_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] concentrations | pm2 | air | health | 2010 | nox | 2001 | new | outcome | hospitalizations [SUMMARY]"},"annotated_most_frequent_background_conclusion_abstract_prompt":{"kind":"null"},"annotated_tf_idf_background_abstract_prompt":{"kind":"null"},"annotated_tf_idf_methods_abstract_prompt":{"kind":"string","value":"[CONTENT] hospitalizations | pm2 | numbers | reductions | table | asthma hospitalizations | chd | reductions pm2 | increased | increased table [SUMMARY]"},"annotated_tf_idf_results_abstract_prompt":{"kind":"string","value":"[CONTENT] concentrations | pm2 | nox | table | areas | 2001 | 2030 | hospitalizations | 2020 | 2010 [SUMMARY]"},"annotated_tf_idf_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] provided | project | health | modeling | air quality | quality | new | risk mitigation approaches | evaluated exposure | evaluated exposure assessment epidemiological [SUMMARY]"},"annotated_tf_idf_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] concentrations | pm2 | health | air | hospitalizations | nox | new | table | 2010 | outcome [SUMMARY]"},"annotated_tf_idf_background_conclusion_abstract_prompt":{"kind":"null"},"annotated_entity_plan_background_abstract_prompt":{"kind":"null"},"annotated_entity_plan_methods_abstract_prompt":{"kind":"string","value":"[CONTENT] ||| 2.5 | year 2001 | 2010 | 2020 | 2030 ||| 26 [SUMMARY]"},"annotated_entity_plan_results_abstract_prompt":{"kind":"string","value":"[CONTENT] 60% | between 2001 and 2010 ||| ||| [SUMMARY]"},"annotated_entity_plan_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] ~60% | New Haven ||| New Haven [SUMMARY]"},"annotated_entity_plan_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] ||| 2.5 | year 2001 | 2010 | 2020 | 2030 ||| 26 ||| ||| 60% | between 2001 and 2010 ||| ||| ||| ~60% | New Haven ||| New Haven [SUMMARY]"},"annotated_entity_plan_background_conclusion_abstract_prompt":{"kind":"null"}}},{"rowIdx":267,"cells":{"title":{"kind":"string","value":"Current status on health sciences research productivity pertaining to Angola up to 2014."},"pmid":{"kind":"string","value":"26126605"},"background_abstract":{"kind":"string","value":"Health research driven by the healthcare demands of the population can provide an informative evidence base to support decision-making processes on health policies, programmes, and practices. This paper surveyed the production of scientific research concerning health in Angola, specifically to access the publication rate over time, the main research topics and scientific fields, and the contribution of Angolan researchers and institutions."},"background_abstract_label":{"kind":"string","value":"BACKGROUND"},"methods_abstract":{"kind":"string","value":"The study focused on data collected in a retrospective literature search in Biblioteca Virtual em Saúde (BVS) as of June 8, 2014, with the keyword \"Angola\" and on content information in correspondent publications deposited in PubMed."},"methods_abstract_label":{"kind":"string","value":"METHODS"},"results_abstract":{"kind":"string","value":"BVS generated 1,029 hits, 74.6 % of which were deposited in PubMed where 301 abstracts were described. From 1979 to 2003, there were 62 publications and in 2004-2013 the quantity increased four-fold (n = 232); malaria was the most frequent topic (n = 42). Angola was the country with the largest number of publications, taking into account the primary affiliation of the first author (n = 45). Universities, institutes, or research centres accounted for 65 % of the publications and in descending order Portugal, Brazil, and the United States of America occupied the three first positions. Epidemiology was by far the most frequent field of research (n = 165)."},"results_abstract_label":{"kind":"string","value":"RESULTS"},"conclusions_abstract":{"kind":"string","value":"The number of publications has increased steadily over the past 10 years, with predominance on malaria topics. Angola was the country with the largest number of major affiliations of the first author, but the contribution of Angolan institutions was relatively low, indicating a need to reinforce academic research institutions in the country."},"conclusions_abstract_label":{"kind":"string","value":"CONCLUSIONS"},"mesh_descriptor_names":{"kind":"list like","value":["Academies and Institutes","Angola","Authorship","Bibliometrics","Biomedical Research","Brazil","Epidemiologic Studies","Humans","Malaria","Portugal","Publishing","United States","Universities"],"string":"[\n \"Academies and Institutes\",\n \"Angola\",\n \"Authorship\",\n \"Bibliometrics\",\n \"Biomedical Research\",\n \"Brazil\",\n \"Epidemiologic Studies\",\n \"Humans\",\n \"Malaria\",\n \"Portugal\",\n \"Publishing\",\n \"United States\",\n \"Universities\"\n]"},"pmcid":{"kind":"string","value":"4486127"},"background_title":{"kind":"string","value":"Background"},"background_text":{"kind":"string","value":"The concept of research for health, expressed on the World Health Report 2013, covers a broader range of investigations than health research. This wider view of research will become increasingly important in the transition from the United Nations Millennium Development Goals to a post-2015 sustainable development agenda [1].\nIn a more restricted concept, health research has the potential to contribute to the identification of social and economic determinants of health. In the context of implementation research, it is a major basis for decision-making processes on health policies, programmes, and practices, especially when driven by the demand of the health problems of the population [2, 3].\nMoreover, conducting more health research can stimulate countries to strengthen their capacity to produce and use such research to guide decision-making, improve the efficiency of the investments, increase accountability in the work of researchers, and contribute substantially to the research training of human resources in developing countries [4, 5]. In the perspective of the World Health Organization (WHO), health research must be founded on building capacity to step up health research systems and support demand-driven health problems, particularly in low- and middle-income countries. Additionally, health research must be embedded with standards for best practices and ensure that quality evidence is turned into affordable health technologies and evidence-informed policy [6].\nHowever, at the global level, the proportion of health research that considers these aspects is negligible [7]. In particular, evidence shows that the research output in developing countries is considerably less than in developed countries [8]. Therefore, it is crucial to get an accurate picture of the landscape of the scientific research produced in developing countries in order to identify gaps critical for social development [9]. In sub-Saharan Africa, in particular, the production of health research can affect teaching, the quality of undergraduate training, career promotion, and translational research relevant to the country, which is comparable to developed countries [5].\nThere have been documented bibliometric studies of research production in Africa, as a whole, as well as in other continents. This then motivated studies of the chronological progression of research within individual African countries [10–18]. These publications demonstrate the feebleness of African authorship in the most cited scientific publications in health [19].\nAngola, a Portuguese-speaking country, is independent since 1975 and has spent 27 years in progressive socio-political instability, with the drawback of the civil war that ended 12 years ago. Nevertheless, Angola is making progress towards the achievement of the health Millennium Development Goals but is still far from achieving them [1]. The reconstruction of the national health system and policy will benefit from the knowledge of the factors influencing the success of certain interventions and the measure of their impact on populations. Therefore, health research will contribute to increased efficiency and effectiveness of health policy and decision making processes in Angola on programmes and practices. Support for science in Angola is less than 0.1 % of its GDP, compared to a world average of 1.7 % [20]. Consequently, a more serious commitment would be needed to build local and national capacity to accelerate the improvement of human resources training for health research. In this context, it becomes even more appropriate to carry out a survey on what has been produced as the result of health research. When searching the literature on health research in Angola, we found only one publication dated from 1953 which linked research to health, entitled “Scientific Research and the Health Status of Angola” [21]. The lack of studies on health research produced in Angola in the last 61 years motivated this work.\nWe carried out a non-systematic literature survey to evaluate the progression of health research pertaining to Angola, answering specifically the following questions: 1) how has the number of publications evolved over the years? 2) what are the most published topics? 3) what is the level of involvement of Angolan researchers and institutions in this published research? and 4) what are the most represented research fields?\nWe expect that this study will contribute to increasing the current state of knowledge on health research related to Angola and identify gaps that may guide the WHO recommendations on health research and consequently the adoption of effective interventions that will strengthen health research in Angola."},"methods_title":{"kind":"null"},"methods_text":{"kind":"null"},"results_title":{"kind":"string","value":"Results and discussion"},"results_text":{"kind":"string","value":"Searching the BVS on June 8, 2014, with the keyword “Angola”, showed 1,029 results, of which 74.6 % were also in MEDLINE/PubMed database. Consequently, it was considered that further search in MEDLINE/PubMed would produce representative research publication data on health in Angola. Therefore, we undertook a further search for relevant Angolan publications in MEDLINE/PubMed, keeping the keyword “Angola”. To describe the number of publications across time we included all publications retrieved from BVS without the abstract filter. All other variables were taken into account using the availability of an abstract as the main inclusion criteria, and this yielded 658 abstracts. Applying the exclusion criteria we selected 301 abstracts for further description.\n Publication rate As shown in Fig. 1, the publications in MEDLINE/PubMed addressing health issues related to Angola date back to 1909. MEDLINE/PubMed, the first bibliographic database in the life sciences, with a focus on biomedicine, mainly covers the literature published from 1946 onwards, but it also includes more dated publications [22]. There has been a remarkable, albeit irregular, increase in the number of publications during the 1990s, with a more sustained increase from 2004 onwards, and a peak in 2013. From 1979 to 2003 (34 years) there were 62 publications, but in the last 10 years (2004–2013) the number increased four-fold (n = 232), suggesting a rampant increment in publication rate (Fig. 1). Additionally, it is noteworthy that the number of publications by the beginning of June 2014 already reached half the figure recorded in 2013, supporting the notion that Angola is motivating a steady increase in health-related scientific publications. This exponential trend was also observed in Palestine, a war zone, where a total of 770 publications were retrieved in the medical and biomedical field across a 10-year period (01 January 2002 to 31 December 2011), averaging approximately 80 articles per year. Interestingly, the number of publications has also increased four-fold during the period 2002–2011, with a stabilization in the last 3 years of the study period [23].Fig. 1Between 1909 and 1973, none of the publications deposited in MEDLINE/PubMed up to June 8, 2014, complied with the inclusion criteria for abstracts about health research in Angola\nBetween 1909 and 1973, none of the publications deposited in MEDLINE/PubMed up to June 8, 2014, complied with the inclusion criteria for abstracts about health research in Angola\nA study published in 2007, which analyzed the geography of the PubMed biomedical publications in Africa between 1996 and 2005, concluded that the contribution of Africa, in particular, was considerably less than that of other continents. Indeed, it was evident that there was a continuous increase in publication output during this period in all African sub-regions, although Angola was located in the lowest quintile with less than two publications per year [17]. In the present study, we found, in the same period, five publications that included researchers that were affiliated with Angolan institutions. However, this did not change the lower position of Angola. Another publication about the scientific production in Public Health and Epidemiology in the WHO African region, in the period 1991–2010, obtained very similar results for Angola, placing it in the lowest quintile, with 11–50 publications [18]. This increase in health research in Angola matches with a recent study about the WHO African Region between 2000 and 2014 [24]. In this study, Angola was located in quintile three, with 100 to 499 articles.\nAs shown in Fig. 1, the publications in MEDLINE/PubMed addressing health issues related to Angola date back to 1909. MEDLINE/PubMed, the first bibliographic database in the life sciences, with a focus on biomedicine, mainly covers the literature published from 1946 onwards, but it also includes more dated publications [22]. There has been a remarkable, albeit irregular, increase in the number of publications during the 1990s, with a more sustained increase from 2004 onwards, and a peak in 2013. From 1979 to 2003 (34 years) there were 62 publications, but in the last 10 years (2004–2013) the number increased four-fold (n = 232), suggesting a rampant increment in publication rate (Fig. 1). Additionally, it is noteworthy that the number of publications by the beginning of June 2014 already reached half the figure recorded in 2013, supporting the notion that Angola is motivating a steady increase in health-related scientific publications. This exponential trend was also observed in Palestine, a war zone, where a total of 770 publications were retrieved in the medical and biomedical field across a 10-year period (01 January 2002 to 31 December 2011), averaging approximately 80 articles per year. Interestingly, the number of publications has also increased four-fold during the period 2002–2011, with a stabilization in the last 3 years of the study period [23].Fig. 1Between 1909 and 1973, none of the publications deposited in MEDLINE/PubMed up to June 8, 2014, complied with the inclusion criteria for abstracts about health research in Angola\nBetween 1909 and 1973, none of the publications deposited in MEDLINE/PubMed up to June 8, 2014, complied with the inclusion criteria for abstracts about health research in Angola\nA study published in 2007, which analyzed the geography of the PubMed biomedical publications in Africa between 1996 and 2005, concluded that the contribution of Africa, in particular, was considerably less than that of other continents. Indeed, it was evident that there was a continuous increase in publication output during this period in all African sub-regions, although Angola was located in the lowest quintile with less than two publications per year [17]. In the present study, we found, in the same period, five publications that included researchers that were affiliated with Angolan institutions. However, this did not change the lower position of Angola. Another publication about the scientific production in Public Health and Epidemiology in the WHO African region, in the period 1991–2010, obtained very similar results for Angola, placing it in the lowest quintile, with 11–50 publications [18]. This increase in health research in Angola matches with a recent study about the WHO African Region between 2000 and 2014 [24]. In this study, Angola was located in quintile three, with 100 to 499 articles.\n Health research topics Malaria was the disease that stood out, with 45 publications, followed by HIV infection (n = 26), trypanosomiasis (n = 24), and themes of epidemiology/public health (n = 24). Tuberculosis occupied the tenth position with nine papers (Fig. 2). As a whole, infectious diseases was a topic of 59 % of the publications under analysis (n = 178).Fig. 2Publications are deposited in MEDLINE/PubMed up to June 8, 2014\nPublications are deposited in MEDLINE/PubMed up to June 8, 2014\nThe most investigated topics are aligned with the epidemiological profile of Angola, regarding the main causes of morbidity and mortality (communicable diseases), with a large emphasis on malaria [25]. Interestingly, there was a disproportionate volume of publications on HIV/AIDS relative to tuberculosis, which is unusual given the frequent association of both morbidities [26]. In an assessment of scientific output in public health and epidemiology in Africa, in the period 1991–2010, HIV/AIDS infection was the predominant topic (11.3 %) while malaria accounted for 8.6 % and tuberculosis for 7.1 % [16]. This analysis also considers the issue of the importance of research funding concerning infectious diseases [27]. The trend towards epidemiological transition in sub-Saharan Africa, owing to the increase of chronic non-communicable diseases, is not yet reflected at the scientific publication level, neither in the present study nor in the aforementioned African publications [16, 28–30].\nMalaria was the disease that stood out, with 45 publications, followed by HIV infection (n = 26), trypanosomiasis (n = 24), and themes of epidemiology/public health (n = 24). Tuberculosis occupied the tenth position with nine papers (Fig. 2). As a whole, infectious diseases was a topic of 59 % of the publications under analysis (n = 178).Fig. 2Publications are deposited in MEDLINE/PubMed up to June 8, 2014\nPublications are deposited in MEDLINE/PubMed up to June 8, 2014\nThe most investigated topics are aligned with the epidemiological profile of Angola, regarding the main causes of morbidity and mortality (communicable diseases), with a large emphasis on malaria [25]. Interestingly, there was a disproportionate volume of publications on HIV/AIDS relative to tuberculosis, which is unusual given the frequent association of both morbidities [26]. In an assessment of scientific output in public health and epidemiology in Africa, in the period 1991–2010, HIV/AIDS infection was the predominant topic (11.3 %) while malaria accounted for 8.6 % and tuberculosis for 7.1 % [16]. This analysis also considers the issue of the importance of research funding concerning infectious diseases [27]. The trend towards epidemiological transition in sub-Saharan Africa, owing to the increase of chronic non-communicable diseases, is not yet reflected at the scientific publication level, neither in the present study nor in the aforementioned African publications [16, 28–30].\n Authorship and affiliation The primary affiliation of the first author was most frequently in an Angolan institution (45 publications) (Fig. 3). A total of 150 institutions were listed as the first author’s primary affiliation, revealing a high institutional dispersion; 14.7 % were Angolan institutions. Furthermore, 65 % of publications represented research conducted in universities and institutes or research centres (Fig. 4). The majority of publications by first author affiliation country and the number of academic research institutions were located in Portugal, United States of America, or Brazil (Fig. 5). The high number of publications with first author affiliation in Angola denotes the concern of researchers in linking their work to institutions from the country of study. Nevertheless, this involvement is dispersed across many different institutions, many of which have relatively low track records in publications, namely hospitals and non-governmental organizations. Furthermore, academic research in universities and institutes (or research centres) related to institutional affiliations abroad indicate the need for reinforcing the academic research capacity in Angola.Fig. 3Country denotes the location in which the primary institution of the first author affiliation was based in publications up to June 8, 2014Fig. 4Publications deposited in MEDLINE/PubMed up to June 8, 2014Fig. 5Country denotes the location in which the primary institution of the first author affiliation was based concerning publications on health research in Angola deposited in MEDLINE/PubMed up to June 8, 2014\nCountry denotes the location in which the primary institution of the first author affiliation was based in publications up to June 8, 2014\nPublications deposited in MEDLINE/PubMed up to June 8, 2014\nCountry denotes the location in which the primary institution of the first author affiliation was based concerning publications on health research in Angola deposited in MEDLINE/PubMed up to June 8, 2014\nThe primary affiliation of the first author was most frequently in an Angolan institution (45 publications) (Fig. 3). A total of 150 institutions were listed as the first author’s primary affiliation, revealing a high institutional dispersion; 14.7 % were Angolan institutions. Furthermore, 65 % of publications represented research conducted in universities and institutes or research centres (Fig. 4). The majority of publications by first author affiliation country and the number of academic research institutions were located in Portugal, United States of America, or Brazil (Fig. 5). The high number of publications with first author affiliation in Angola denotes the concern of researchers in linking their work to institutions from the country of study. Nevertheless, this involvement is dispersed across many different institutions, many of which have relatively low track records in publications, namely hospitals and non-governmental organizations. Furthermore, academic research in universities and institutes (or research centres) related to institutional affiliations abroad indicate the need for reinforcing the academic research capacity in Angola.Fig. 3Country denotes the location in which the primary institution of the first author affiliation was based in publications up to June 8, 2014Fig. 4Publications deposited in MEDLINE/PubMed up to June 8, 2014Fig. 5Country denotes the location in which the primary institution of the first author affiliation was based concerning publications on health research in Angola deposited in MEDLINE/PubMed up to June 8, 2014\nCountry denotes the location in which the primary institution of the first author affiliation was based in publications up to June 8, 2014\nPublications deposited in MEDLINE/PubMed up to June 8, 2014\nCountry denotes the location in which the primary institution of the first author affiliation was based concerning publications on health research in Angola deposited in MEDLINE/PubMed up to June 8, 2014\n Publications by Angolan researchers An Angolan researcher was the first author in 58 (19 %) abstracts. The frequency of Angolan first authors or co-authors increased when the first author was affiliated to an Angolan institution (Fig. 6). In the present study, it is noteworthy that one-fifth of all publications had an Angolan as first author. The number of African first authors on publications relating to Africa between 1991 and 2010 has been increasing [16]. This finding was reinforced in the period between 2000 and 2014 in the WHO African Region, and it was found that the contributions of first authors from Africa doubled in this period, although it is recognized that it is still minimal (0.7 % in 2000 and 1.3 % in 2014) [24].Fig. 6Publications on health research in Angola deposited in MEDLINE/PubMed up to June 8, 2014, according to country in which the primary institution of the first author affiliation was based\nPublications on health research in Angola deposited in MEDLINE/PubMed up to June 8, 2014, according to country in which the primary institution of the first author affiliation was based\nAn Angolan researcher was the first author in 58 (19 %) abstracts. The frequency of Angolan first authors or co-authors increased when the first author was affiliated to an Angolan institution (Fig. 6). In the present study, it is noteworthy that one-fifth of all publications had an Angolan as first author. The number of African first authors on publications relating to Africa between 1991 and 2010 has been increasing [16]. This finding was reinforced in the period between 2000 and 2014 in the WHO African Region, and it was found that the contributions of first authors from Africa doubled in this period, although it is recognized that it is still minimal (0.7 % in 2000 and 1.3 % in 2014) [24].Fig. 6Publications on health research in Angola deposited in MEDLINE/PubMed up to June 8, 2014, according to country in which the primary institution of the first author affiliation was based\nPublications on health research in Angola deposited in MEDLINE/PubMed up to June 8, 2014, according to country in which the primary institution of the first author affiliation was based\n Scientific fields Epidemiological research was by far the most common research area (n = 165) (Fig. 7). Although a few population genetics studies (data not shown) are included in this topic, this finding is in accordance to the burden of infectious diseases in the country. However, the increasing frequency of non-communicable diseases in sub-Saharan Africa is likely to be a matter of concern for research in Africa in coming years.Fig. 7Publications deposited in MEDLINE/PubMed up to June 8, 2014\nPublications deposited in MEDLINE/PubMed up to June 8, 2014\nOn the other hand, a small number of publications (n = 32; 10.6 %) on socioeconomic and professional research, including governance, health policies, health systems management, and human resources is noted. Considering that Angola was a war zone for more than 50 % of its existence as an independent state, we might have expected a larger number of research publications on relevant topics such as the socioeconomic determinants of health (e.g., unemployment, poverty, education, family dissolution, and lifestyles) and the performance of health unities and health systems. However, we recognize the possibility that studies concerning some of those socioeconomic determinants of health may be part of research in social and economic sciences and, therefore, more accessible in other bibliographic databases. Further, the lack of studies on human resources in the various health professions, including studies in medical education, is also noticeable.\nWe expect that the increasing number of health professional schools in Angola, including six new public medical schools, particularly since 2009, will raise interest in these topics, especially if educational curricula at the undergraduate and postgraduate levels in Angola incorporate more programs focused on improving research skills and providing on-the-job training in epidemiology [31].\nEpidemiological research was by far the most common research area (n = 165) (Fig. 7). Although a few population genetics studies (data not shown) are included in this topic, this finding is in accordance to the burden of infectious diseases in the country. However, the increasing frequency of non-communicable diseases in sub-Saharan Africa is likely to be a matter of concern for research in Africa in coming years.Fig. 7Publications deposited in MEDLINE/PubMed up to June 8, 2014\nPublications deposited in MEDLINE/PubMed up to June 8, 2014\nOn the other hand, a small number of publications (n = 32; 10.6 %) on socioeconomic and professional research, including governance, health policies, health systems management, and human resources is noted. Considering that Angola was a war zone for more than 50 % of its existence as an independent state, we might have expected a larger number of research publications on relevant topics such as the socioeconomic determinants of health (e.g., unemployment, poverty, education, family dissolution, and lifestyles) and the performance of health unities and health systems. However, we recognize the possibility that studies concerning some of those socioeconomic determinants of health may be part of research in social and economic sciences and, therefore, more accessible in other bibliographic databases. Further, the lack of studies on human resources in the various health professions, including studies in medical education, is also noticeable.\nWe expect that the increasing number of health professional schools in Angola, including six new public medical schools, particularly since 2009, will raise interest in these topics, especially if educational curricula at the undergraduate and postgraduate levels in Angola incorporate more programs focused on improving research skills and providing on-the-job training in epidemiology [31].\n Journals choice We also found that, despite publications being widely spread in a variety of journals, 11.4 % concentrated in PLoS ONE and Malaria Journal, both of which are free access journals indexed by common databases (Fig. 8). This finding is not unusual given that malaria was the most common research topic among Angolan publications observed in the present study. Notwithstanding the debate around open access publishing, our findings reinforce the importance of providing readers in financially disadvantaged countries with a wide range of relevant research and information [32, 33].Fig. 8Publications on health research in Angola deposited in MEDLINE/PubMed up to June 8, 2014\nPublications on health research in Angola deposited in MEDLINE/PubMed up to June 8, 2014\nWe also found that, despite publications being widely spread in a variety of journals, 11.4 % concentrated in PLoS ONE and Malaria Journal, both of which are free access journals indexed by common databases (Fig. 8). This finding is not unusual given that malaria was the most common research topic among Angolan publications observed in the present study. Notwithstanding the debate around open access publishing, our findings reinforce the importance of providing readers in financially disadvantaged countries with a wide range of relevant research and information [32, 33].Fig. 8Publications on health research in Angola deposited in MEDLINE/PubMed up to June 8, 2014\nPublications on health research in Angola deposited in MEDLINE/PubMed up to June 8, 2014\n Limitations of the study The fact that Angola is a Portuguese speaking country and the majority of the journals indexed by MEDLINE/PubMed are in English may have introduced a selection bias due to idiom barriers. Though MEDLINE/PubMed represented the vast majority of the publications of the study and the search began at BVS, which included papers published in journals indexed by Scielo, it does not embody all the scientific and biomedical journals. Furthermore, we did not search “grey literature”. Thus, the number of Angolan authors and coauthors might be underestimated. Nevertheless, Angola still does not have an exhaustive electronic database of researchers containing individual publication records. In addition, the incorrect reporting of authors’ nationality and affiliation, observed in a few papers in the present study, may further contribute to underestimation of Angolan-authored publications. Finally, the study did not include the analysis of journal impact factors and the authors did not have access to any citation index database, which would allow the performance of bibliometric citation analyses.\nThe fact that Angola is a Portuguese speaking country and the majority of the journals indexed by MEDLINE/PubMed are in English may have introduced a selection bias due to idiom barriers. Though MEDLINE/PubMed represented the vast majority of the publications of the study and the search began at BVS, which included papers published in journals indexed by Scielo, it does not embody all the scientific and biomedical journals. Furthermore, we did not search “grey literature”. Thus, the number of Angolan authors and coauthors might be underestimated. Nevertheless, Angola still does not have an exhaustive electronic database of researchers containing individual publication records. In addition, the incorrect reporting of authors’ nationality and affiliation, observed in a few papers in the present study, may further contribute to underestimation of Angolan-authored publications. Finally, the study did not include the analysis of journal impact factors and the authors did not have access to any citation index database, which would allow the performance of bibliometric citation analyses."},"conclusions_title":{"kind":"string","value":"Conclusions"},"conclusions_text":{"kind":"string","value":"This initial contribution characterizing the productivity of health research pertaining to Angola showed that the number of publications has increased steadily over the past 10 years and was mainly focused on infectious diseases, namely malaria. Angola, as the country in which the primary institution of the first author affiliation was based, evidenced the largest number of publications, and about 20 % of the publications included an Angolan as the first author. However, academic institutions in Portugal, the United States of America, and Brazil contributed more than universities and research centres in Angola to research publication. The research was largely epidemiological, and remarkably, with a very small number of publications on economic and professional issues, involving themes such as health policy, governance, and management of health systems and human resources.\nIn Angola, with the large expansion of higher education since 2009, universities should build and strengthen their capacities in research and human resources training. Including research training in the curricula of pre-graduate courses in health sciences will contribute to graduate professionals able to act as agents responsible for change, competent human resource managers, and promoters of evidence-based policies [34]. The alignment of the national health research agenda with the national program of health development and the wide use of multilateral international cooperation are critical to develop the operational tools towards universal health coverage in Angola as conceived by the WHO [1]. In summary, this work highlights the rapid increase of scientific publications related to Angola but also a need for reinforcing academic-driven research in this country."},"other_sections_titles":{"kind":"list like","value":["Publication rate","Health research topics","Authorship and affiliation","Publications by Angolan researchers","Scientific fields","Journals choice","Limitations of the study"],"string":"[\n \"Publication rate\",\n \"Health research topics\",\n \"Authorship and affiliation\",\n \"Publications by Angolan researchers\",\n \"Scientific fields\",\n \"Journals choice\",\n \"Limitations of the study\"\n]"},"other_sections_texts":{"kind":"list like","value":["As shown in Fig. 1, the publications in MEDLINE/PubMed addressing health issues related to Angola date back to 1909. MEDLINE/PubMed, the first bibliographic database in the life sciences, with a focus on biomedicine, mainly covers the literature published from 1946 onwards, but it also includes more dated publications [22]. There has been a remarkable, albeit irregular, increase in the number of publications during the 1990s, with a more sustained increase from 2004 onwards, and a peak in 2013. From 1979 to 2003 (34 years) there were 62 publications, but in the last 10 years (2004–2013) the number increased four-fold (n = 232), suggesting a rampant increment in publication rate (Fig. 1). Additionally, it is noteworthy that the number of publications by the beginning of June 2014 already reached half the figure recorded in 2013, supporting the notion that Angola is motivating a steady increase in health-related scientific publications. This exponential trend was also observed in Palestine, a war zone, where a total of 770 publications were retrieved in the medical and biomedical field across a 10-year period (01 January 2002 to 31 December 2011), averaging approximately 80 articles per year. Interestingly, the number of publications has also increased four-fold during the period 2002–2011, with a stabilization in the last 3 years of the study period [23].Fig. 1Between 1909 and 1973, none of the publications deposited in MEDLINE/PubMed up to June 8, 2014, complied with the inclusion criteria for abstracts about health research in Angola\nBetween 1909 and 1973, none of the publications deposited in MEDLINE/PubMed up to June 8, 2014, complied with the inclusion criteria for abstracts about health research in Angola\nA study published in 2007, which analyzed the geography of the PubMed biomedical publications in Africa between 1996 and 2005, concluded that the contribution of Africa, in particular, was considerably less than that of other continents. Indeed, it was evident that there was a continuous increase in publication output during this period in all African sub-regions, although Angola was located in the lowest quintile with less than two publications per year [17]. In the present study, we found, in the same period, five publications that included researchers that were affiliated with Angolan institutions. However, this did not change the lower position of Angola. Another publication about the scientific production in Public Health and Epidemiology in the WHO African region, in the period 1991–2010, obtained very similar results for Angola, placing it in the lowest quintile, with 11–50 publications [18]. This increase in health research in Angola matches with a recent study about the WHO African Region between 2000 and 2014 [24]. In this study, Angola was located in quintile three, with 100 to 499 articles.","Malaria was the disease that stood out, with 45 publications, followed by HIV infection (n = 26), trypanosomiasis (n = 24), and themes of epidemiology/public health (n = 24). Tuberculosis occupied the tenth position with nine papers (Fig. 2). As a whole, infectious diseases was a topic of 59 % of the publications under analysis (n = 178).Fig. 2Publications are deposited in MEDLINE/PubMed up to June 8, 2014\nPublications are deposited in MEDLINE/PubMed up to June 8, 2014\nThe most investigated topics are aligned with the epidemiological profile of Angola, regarding the main causes of morbidity and mortality (communicable diseases), with a large emphasis on malaria [25]. Interestingly, there was a disproportionate volume of publications on HIV/AIDS relative to tuberculosis, which is unusual given the frequent association of both morbidities [26]. In an assessment of scientific output in public health and epidemiology in Africa, in the period 1991–2010, HIV/AIDS infection was the predominant topic (11.3 %) while malaria accounted for 8.6 % and tuberculosis for 7.1 % [16]. This analysis also considers the issue of the importance of research funding concerning infectious diseases [27]. The trend towards epidemiological transition in sub-Saharan Africa, owing to the increase of chronic non-communicable diseases, is not yet reflected at the scientific publication level, neither in the present study nor in the aforementioned African publications [16, 28–30].","The primary affiliation of the first author was most frequently in an Angolan institution (45 publications) (Fig. 3). A total of 150 institutions were listed as the first author’s primary affiliation, revealing a high institutional dispersion; 14.7 % were Angolan institutions. Furthermore, 65 % of publications represented research conducted in universities and institutes or research centres (Fig. 4). The majority of publications by first author affiliation country and the number of academic research institutions were located in Portugal, United States of America, or Brazil (Fig. 5). The high number of publications with first author affiliation in Angola denotes the concern of researchers in linking their work to institutions from the country of study. Nevertheless, this involvement is dispersed across many different institutions, many of which have relatively low track records in publications, namely hospitals and non-governmental organizations. Furthermore, academic research in universities and institutes (or research centres) related to institutional affiliations abroad indicate the need for reinforcing the academic research capacity in Angola.Fig. 3Country denotes the location in which the primary institution of the first author affiliation was based in publications up to June 8, 2014Fig. 4Publications deposited in MEDLINE/PubMed up to June 8, 2014Fig. 5Country denotes the location in which the primary institution of the first author affiliation was based concerning publications on health research in Angola deposited in MEDLINE/PubMed up to June 8, 2014\nCountry denotes the location in which the primary institution of the first author affiliation was based in publications up to June 8, 2014\nPublications deposited in MEDLINE/PubMed up to June 8, 2014\nCountry denotes the location in which the primary institution of the first author affiliation was based concerning publications on health research in Angola deposited in MEDLINE/PubMed up to June 8, 2014","An Angolan researcher was the first author in 58 (19 %) abstracts. The frequency of Angolan first authors or co-authors increased when the first author was affiliated to an Angolan institution (Fig. 6). In the present study, it is noteworthy that one-fifth of all publications had an Angolan as first author. The number of African first authors on publications relating to Africa between 1991 and 2010 has been increasing [16]. This finding was reinforced in the period between 2000 and 2014 in the WHO African Region, and it was found that the contributions of first authors from Africa doubled in this period, although it is recognized that it is still minimal (0.7 % in 2000 and 1.3 % in 2014) [24].Fig. 6Publications on health research in Angola deposited in MEDLINE/PubMed up to June 8, 2014, according to country in which the primary institution of the first author affiliation was based\nPublications on health research in Angola deposited in MEDLINE/PubMed up to June 8, 2014, according to country in which the primary institution of the first author affiliation was based","Epidemiological research was by far the most common research area (n = 165) (Fig. 7). Although a few population genetics studies (data not shown) are included in this topic, this finding is in accordance to the burden of infectious diseases in the country. However, the increasing frequency of non-communicable diseases in sub-Saharan Africa is likely to be a matter of concern for research in Africa in coming years.Fig. 7Publications deposited in MEDLINE/PubMed up to June 8, 2014\nPublications deposited in MEDLINE/PubMed up to June 8, 2014\nOn the other hand, a small number of publications (n = 32; 10.6 %) on socioeconomic and professional research, including governance, health policies, health systems management, and human resources is noted. Considering that Angola was a war zone for more than 50 % of its existence as an independent state, we might have expected a larger number of research publications on relevant topics such as the socioeconomic determinants of health (e.g., unemployment, poverty, education, family dissolution, and lifestyles) and the performance of health unities and health systems. However, we recognize the possibility that studies concerning some of those socioeconomic determinants of health may be part of research in social and economic sciences and, therefore, more accessible in other bibliographic databases. Further, the lack of studies on human resources in the various health professions, including studies in medical education, is also noticeable.\nWe expect that the increasing number of health professional schools in Angola, including six new public medical schools, particularly since 2009, will raise interest in these topics, especially if educational curricula at the undergraduate and postgraduate levels in Angola incorporate more programs focused on improving research skills and providing on-the-job training in epidemiology [31].","We also found that, despite publications being widely spread in a variety of journals, 11.4 % concentrated in PLoS ONE and Malaria Journal, both of which are free access journals indexed by common databases (Fig. 8). This finding is not unusual given that malaria was the most common research topic among Angolan publications observed in the present study. Notwithstanding the debate around open access publishing, our findings reinforce the importance of providing readers in financially disadvantaged countries with a wide range of relevant research and information [32, 33].Fig. 8Publications on health research in Angola deposited in MEDLINE/PubMed up to June 8, 2014\nPublications on health research in Angola deposited in MEDLINE/PubMed up to June 8, 2014","The fact that Angola is a Portuguese speaking country and the majority of the journals indexed by MEDLINE/PubMed are in English may have introduced a selection bias due to idiom barriers. Though MEDLINE/PubMed represented the vast majority of the publications of the study and the search began at BVS, which included papers published in journals indexed by Scielo, it does not embody all the scientific and biomedical journals. Furthermore, we did not search “grey literature”. Thus, the number of Angolan authors and coauthors might be underestimated. Nevertheless, Angola still does not have an exhaustive electronic database of researchers containing individual publication records. In addition, the incorrect reporting of authors’ nationality and affiliation, observed in a few papers in the present study, may further contribute to underestimation of Angolan-authored publications. Finally, the study did not include the analysis of journal impact factors and the authors did not have access to any citation index database, which would allow the performance of bibliometric citation analyses."],"string":"[\n \"As shown in Fig. 1, the publications in MEDLINE/PubMed addressing health issues related to Angola date back to 1909. MEDLINE/PubMed, the first bibliographic database in the life sciences, with a focus on biomedicine, mainly covers the literature published from 1946 onwards, but it also includes more dated publications [22]. There has been a remarkable, albeit irregular, increase in the number of publications during the 1990s, with a more sustained increase from 2004 onwards, and a peak in 2013. From 1979 to 2003 (34 years) there were 62 publications, but in the last 10 years (2004–2013) the number increased four-fold (n = 232), suggesting a rampant increment in publication rate (Fig. 1). Additionally, it is noteworthy that the number of publications by the beginning of June 2014 already reached half the figure recorded in 2013, supporting the notion that Angola is motivating a steady increase in health-related scientific publications. This exponential trend was also observed in Palestine, a war zone, where a total of 770 publications were retrieved in the medical and biomedical field across a 10-year period (01 January 2002 to 31 December 2011), averaging approximately 80 articles per year. Interestingly, the number of publications has also increased four-fold during the period 2002–2011, with a stabilization in the last 3 years of the study period [23].Fig. 1Between 1909 and 1973, none of the publications deposited in MEDLINE/PubMed up to June 8, 2014, complied with the inclusion criteria for abstracts about health research in Angola\\nBetween 1909 and 1973, none of the publications deposited in MEDLINE/PubMed up to June 8, 2014, complied with the inclusion criteria for abstracts about health research in Angola\\nA study published in 2007, which analyzed the geography of the PubMed biomedical publications in Africa between 1996 and 2005, concluded that the contribution of Africa, in particular, was considerably less than that of other continents. Indeed, it was evident that there was a continuous increase in publication output during this period in all African sub-regions, although Angola was located in the lowest quintile with less than two publications per year [17]. In the present study, we found, in the same period, five publications that included researchers that were affiliated with Angolan institutions. However, this did not change the lower position of Angola. Another publication about the scientific production in Public Health and Epidemiology in the WHO African region, in the period 1991–2010, obtained very similar results for Angola, placing it in the lowest quintile, with 11–50 publications [18]. This increase in health research in Angola matches with a recent study about the WHO African Region between 2000 and 2014 [24]. In this study, Angola was located in quintile three, with 100 to 499 articles.\",\n \"Malaria was the disease that stood out, with 45 publications, followed by HIV infection (n = 26), trypanosomiasis (n = 24), and themes of epidemiology/public health (n = 24). Tuberculosis occupied the tenth position with nine papers (Fig. 2). As a whole, infectious diseases was a topic of 59 % of the publications under analysis (n = 178).Fig. 2Publications are deposited in MEDLINE/PubMed up to June 8, 2014\\nPublications are deposited in MEDLINE/PubMed up to June 8, 2014\\nThe most investigated topics are aligned with the epidemiological profile of Angola, regarding the main causes of morbidity and mortality (communicable diseases), with a large emphasis on malaria [25]. Interestingly, there was a disproportionate volume of publications on HIV/AIDS relative to tuberculosis, which is unusual given the frequent association of both morbidities [26]. In an assessment of scientific output in public health and epidemiology in Africa, in the period 1991–2010, HIV/AIDS infection was the predominant topic (11.3 %) while malaria accounted for 8.6 % and tuberculosis for 7.1 % [16]. This analysis also considers the issue of the importance of research funding concerning infectious diseases [27]. The trend towards epidemiological transition in sub-Saharan Africa, owing to the increase of chronic non-communicable diseases, is not yet reflected at the scientific publication level, neither in the present study nor in the aforementioned African publications [16, 28–30].\",\n \"The primary affiliation of the first author was most frequently in an Angolan institution (45 publications) (Fig. 3). A total of 150 institutions were listed as the first author’s primary affiliation, revealing a high institutional dispersion; 14.7 % were Angolan institutions. Furthermore, 65 % of publications represented research conducted in universities and institutes or research centres (Fig. 4). The majority of publications by first author affiliation country and the number of academic research institutions were located in Portugal, United States of America, or Brazil (Fig. 5). The high number of publications with first author affiliation in Angola denotes the concern of researchers in linking their work to institutions from the country of study. Nevertheless, this involvement is dispersed across many different institutions, many of which have relatively low track records in publications, namely hospitals and non-governmental organizations. Furthermore, academic research in universities and institutes (or research centres) related to institutional affiliations abroad indicate the need for reinforcing the academic research capacity in Angola.Fig. 3Country denotes the location in which the primary institution of the first author affiliation was based in publications up to June 8, 2014Fig. 4Publications deposited in MEDLINE/PubMed up to June 8, 2014Fig. 5Country denotes the location in which the primary institution of the first author affiliation was based concerning publications on health research in Angola deposited in MEDLINE/PubMed up to June 8, 2014\\nCountry denotes the location in which the primary institution of the first author affiliation was based in publications up to June 8, 2014\\nPublications deposited in MEDLINE/PubMed up to June 8, 2014\\nCountry denotes the location in which the primary institution of the first author affiliation was based concerning publications on health research in Angola deposited in MEDLINE/PubMed up to June 8, 2014\",\n \"An Angolan researcher was the first author in 58 (19 %) abstracts. The frequency of Angolan first authors or co-authors increased when the first author was affiliated to an Angolan institution (Fig. 6). In the present study, it is noteworthy that one-fifth of all publications had an Angolan as first author. The number of African first authors on publications relating to Africa between 1991 and 2010 has been increasing [16]. This finding was reinforced in the period between 2000 and 2014 in the WHO African Region, and it was found that the contributions of first authors from Africa doubled in this period, although it is recognized that it is still minimal (0.7 % in 2000 and 1.3 % in 2014) [24].Fig. 6Publications on health research in Angola deposited in MEDLINE/PubMed up to June 8, 2014, according to country in which the primary institution of the first author affiliation was based\\nPublications on health research in Angola deposited in MEDLINE/PubMed up to June 8, 2014, according to country in which the primary institution of the first author affiliation was based\",\n \"Epidemiological research was by far the most common research area (n = 165) (Fig. 7). Although a few population genetics studies (data not shown) are included in this topic, this finding is in accordance to the burden of infectious diseases in the country. However, the increasing frequency of non-communicable diseases in sub-Saharan Africa is likely to be a matter of concern for research in Africa in coming years.Fig. 7Publications deposited in MEDLINE/PubMed up to June 8, 2014\\nPublications deposited in MEDLINE/PubMed up to June 8, 2014\\nOn the other hand, a small number of publications (n = 32; 10.6 %) on socioeconomic and professional research, including governance, health policies, health systems management, and human resources is noted. Considering that Angola was a war zone for more than 50 % of its existence as an independent state, we might have expected a larger number of research publications on relevant topics such as the socioeconomic determinants of health (e.g., unemployment, poverty, education, family dissolution, and lifestyles) and the performance of health unities and health systems. However, we recognize the possibility that studies concerning some of those socioeconomic determinants of health may be part of research in social and economic sciences and, therefore, more accessible in other bibliographic databases. Further, the lack of studies on human resources in the various health professions, including studies in medical education, is also noticeable.\\nWe expect that the increasing number of health professional schools in Angola, including six new public medical schools, particularly since 2009, will raise interest in these topics, especially if educational curricula at the undergraduate and postgraduate levels in Angola incorporate more programs focused on improving research skills and providing on-the-job training in epidemiology [31].\",\n \"We also found that, despite publications being widely spread in a variety of journals, 11.4 % concentrated in PLoS ONE and Malaria Journal, both of which are free access journals indexed by common databases (Fig. 8). This finding is not unusual given that malaria was the most common research topic among Angolan publications observed in the present study. Notwithstanding the debate around open access publishing, our findings reinforce the importance of providing readers in financially disadvantaged countries with a wide range of relevant research and information [32, 33].Fig. 8Publications on health research in Angola deposited in MEDLINE/PubMed up to June 8, 2014\\nPublications on health research in Angola deposited in MEDLINE/PubMed up to June 8, 2014\",\n \"The fact that Angola is a Portuguese speaking country and the majority of the journals indexed by MEDLINE/PubMed are in English may have introduced a selection bias due to idiom barriers. Though MEDLINE/PubMed represented the vast majority of the publications of the study and the search began at BVS, which included papers published in journals indexed by Scielo, it does not embody all the scientific and biomedical journals. Furthermore, we did not search “grey literature”. Thus, the number of Angolan authors and coauthors might be underestimated. Nevertheless, Angola still does not have an exhaustive electronic database of researchers containing individual publication records. In addition, the incorrect reporting of authors’ nationality and affiliation, observed in a few papers in the present study, may further contribute to underestimation of Angolan-authored publications. Finally, the study did not include the analysis of journal impact factors and the authors did not have access to any citation index database, which would allow the performance of bibliometric citation analyses.\"\n]"},"other_sections_sec_types":{"kind":"list like","value":[null,null,null,null,null,null,null],"string":"[\n null,\n null,\n null,\n null,\n null,\n null,\n null\n]"},"all_sections_titles":{"kind":"list like","value":["Background","Methods","Results and discussion","Publication rate","Health research topics","Authorship and affiliation","Publications by Angolan researchers","Scientific fields","Journals choice","Limitations of the study","Conclusions"],"string":"[\n \"Background\",\n \"Methods\",\n \"Results and discussion\",\n \"Publication rate\",\n \"Health research topics\",\n \"Authorship and affiliation\",\n \"Publications by Angolan researchers\",\n \"Scientific fields\",\n \"Journals choice\",\n \"Limitations of the study\",\n \"Conclusions\"\n]"},"all_sections_texts":{"kind":"list like","value":["The concept of research for health, expressed on the World Health Report 2013, covers a broader range of investigations than health research. This wider view of research will become increasingly important in the transition from the United Nations Millennium Development Goals to a post-2015 sustainable development agenda [1].\nIn a more restricted concept, health research has the potential to contribute to the identification of social and economic determinants of health. In the context of implementation research, it is a major basis for decision-making processes on health policies, programmes, and practices, especially when driven by the demand of the health problems of the population [2, 3].\nMoreover, conducting more health research can stimulate countries to strengthen their capacity to produce and use such research to guide decision-making, improve the efficiency of the investments, increase accountability in the work of researchers, and contribute substantially to the research training of human resources in developing countries [4, 5]. In the perspective of the World Health Organization (WHO), health research must be founded on building capacity to step up health research systems and support demand-driven health problems, particularly in low- and middle-income countries. Additionally, health research must be embedded with standards for best practices and ensure that quality evidence is turned into affordable health technologies and evidence-informed policy [6].\nHowever, at the global level, the proportion of health research that considers these aspects is negligible [7]. In particular, evidence shows that the research output in developing countries is considerably less than in developed countries [8]. Therefore, it is crucial to get an accurate picture of the landscape of the scientific research produced in developing countries in order to identify gaps critical for social development [9]. In sub-Saharan Africa, in particular, the production of health research can affect teaching, the quality of undergraduate training, career promotion, and translational research relevant to the country, which is comparable to developed countries [5].\nThere have been documented bibliometric studies of research production in Africa, as a whole, as well as in other continents. This then motivated studies of the chronological progression of research within individual African countries [10–18]. These publications demonstrate the feebleness of African authorship in the most cited scientific publications in health [19].\nAngola, a Portuguese-speaking country, is independent since 1975 and has spent 27 years in progressive socio-political instability, with the drawback of the civil war that ended 12 years ago. Nevertheless, Angola is making progress towards the achievement of the health Millennium Development Goals but is still far from achieving them [1]. The reconstruction of the national health system and policy will benefit from the knowledge of the factors influencing the success of certain interventions and the measure of their impact on populations. Therefore, health research will contribute to increased efficiency and effectiveness of health policy and decision making processes in Angola on programmes and practices. Support for science in Angola is less than 0.1 % of its GDP, compared to a world average of 1.7 % [20]. Consequently, a more serious commitment would be needed to build local and national capacity to accelerate the improvement of human resources training for health research. In this context, it becomes even more appropriate to carry out a survey on what has been produced as the result of health research. When searching the literature on health research in Angola, we found only one publication dated from 1953 which linked research to health, entitled “Scientific Research and the Health Status of Angola” [21]. The lack of studies on health research produced in Angola in the last 61 years motivated this work.\nWe carried out a non-systematic literature survey to evaluate the progression of health research pertaining to Angola, answering specifically the following questions: 1) how has the number of publications evolved over the years? 2) what are the most published topics? 3) what is the level of involvement of Angolan researchers and institutions in this published research? and 4) what are the most represented research fields?\nWe expect that this study will contribute to increasing the current state of knowledge on health research related to Angola and identify gaps that may guide the WHO recommendations on health research and consequently the adoption of effective interventions that will strengthen health research in Angola.","We used the reference database Biblioteca Virtual em Saúde (BVS) to perform a non-systematic scientific literature survey aiming to count all existing publications up to June 8, 2014, with the keyword “Angola” and to produce a scientific publications dataset to be compared with other databases. The BVS was chosen because it is a platform that covers human health sciences issues, information sources published in countries of the Portuguese-speaking community, and a set of international databases. Moreover, resource constraints did not allow access to other biomedical databases such as those using the OVID platform. Thereafter, our content information study was based on the public database MEDLINE/PubMed disclosing the best match with the BVS dataset using the same keyword.\nPublications with no abstract available were excluded from the MEDLINE/PubMed dataset. To focus our study in human health research relevant for Angola, we revised the abstracts using the following exclusion criteria: 1) the word Angola did not mean the country; 2) the topic did not concern physical and mental health; 3) the topic reported exclusively to biological vectors; 4) Angola was not the focus of a specific study, but was instead reported as part of a group of countries (common for poliomyelitis and Marburg disease) or just as a reference; 5) case reports published outside Angola regarding citizens who temporarily resided in Angola.\nFrom each publication included in the study we extracted the following variables: year of publication; topic of health or disease, according to the revised International Classification of Diseases (ICD-10); country in which the primary institution of the first author affiliation was based; type of institution of the first author primary affiliation (university, institute or centre of scientific research, hospital, non-governmental organization, other); presence of an Angolan citizen as first author; number of Angolan co-authors; field of research (basic biomedical, clinical, epidemiological or socioeconomic and professional); and publishing journal. The Angolan authors and co-authors were identified by consulting their registration on the National Medical Council and in the universities. Socioeconomic and professional research includes health policy, human resources, and governance and management of health systems. The year of publication was the single variable that did not require the existence of the abstract. All the references of the abstracts included in the study were stored in a collection in the Zotero library. Collection of abstract content data was performed by manual curation and data were entered into an excel spreadsheet (Microsoft™ Excel® 2007) for statistical processing and plots generation.","Searching the BVS on June 8, 2014, with the keyword “Angola”, showed 1,029 results, of which 74.6 % were also in MEDLINE/PubMed database. Consequently, it was considered that further search in MEDLINE/PubMed would produce representative research publication data on health in Angola. Therefore, we undertook a further search for relevant Angolan publications in MEDLINE/PubMed, keeping the keyword “Angola”. To describe the number of publications across time we included all publications retrieved from BVS without the abstract filter. All other variables were taken into account using the availability of an abstract as the main inclusion criteria, and this yielded 658 abstracts. Applying the exclusion criteria we selected 301 abstracts for further description.\n Publication rate As shown in Fig. 1, the publications in MEDLINE/PubMed addressing health issues related to Angola date back to 1909. MEDLINE/PubMed, the first bibliographic database in the life sciences, with a focus on biomedicine, mainly covers the literature published from 1946 onwards, but it also includes more dated publications [22]. There has been a remarkable, albeit irregular, increase in the number of publications during the 1990s, with a more sustained increase from 2004 onwards, and a peak in 2013. From 1979 to 2003 (34 years) there were 62 publications, but in the last 10 years (2004–2013) the number increased four-fold (n = 232), suggesting a rampant increment in publication rate (Fig. 1). Additionally, it is noteworthy that the number of publications by the beginning of June 2014 already reached half the figure recorded in 2013, supporting the notion that Angola is motivating a steady increase in health-related scientific publications. This exponential trend was also observed in Palestine, a war zone, where a total of 770 publications were retrieved in the medical and biomedical field across a 10-year period (01 January 2002 to 31 December 2011), averaging approximately 80 articles per year. Interestingly, the number of publications has also increased four-fold during the period 2002–2011, with a stabilization in the last 3 years of the study period [23].Fig. 1Between 1909 and 1973, none of the publications deposited in MEDLINE/PubMed up to June 8, 2014, complied with the inclusion criteria for abstracts about health research in Angola\nBetween 1909 and 1973, none of the publications deposited in MEDLINE/PubMed up to June 8, 2014, complied with the inclusion criteria for abstracts about health research in Angola\nA study published in 2007, which analyzed the geography of the PubMed biomedical publications in Africa between 1996 and 2005, concluded that the contribution of Africa, in particular, was considerably less than that of other continents. Indeed, it was evident that there was a continuous increase in publication output during this period in all African sub-regions, although Angola was located in the lowest quintile with less than two publications per year [17]. In the present study, we found, in the same period, five publications that included researchers that were affiliated with Angolan institutions. However, this did not change the lower position of Angola. Another publication about the scientific production in Public Health and Epidemiology in the WHO African region, in the period 1991–2010, obtained very similar results for Angola, placing it in the lowest quintile, with 11–50 publications [18]. This increase in health research in Angola matches with a recent study about the WHO African Region between 2000 and 2014 [24]. In this study, Angola was located in quintile three, with 100 to 499 articles.\nAs shown in Fig. 1, the publications in MEDLINE/PubMed addressing health issues related to Angola date back to 1909. MEDLINE/PubMed, the first bibliographic database in the life sciences, with a focus on biomedicine, mainly covers the literature published from 1946 onwards, but it also includes more dated publications [22]. There has been a remarkable, albeit irregular, increase in the number of publications during the 1990s, with a more sustained increase from 2004 onwards, and a peak in 2013. From 1979 to 2003 (34 years) there were 62 publications, but in the last 10 years (2004–2013) the number increased four-fold (n = 232), suggesting a rampant increment in publication rate (Fig. 1). Additionally, it is noteworthy that the number of publications by the beginning of June 2014 already reached half the figure recorded in 2013, supporting the notion that Angola is motivating a steady increase in health-related scientific publications. This exponential trend was also observed in Palestine, a war zone, where a total of 770 publications were retrieved in the medical and biomedical field across a 10-year period (01 January 2002 to 31 December 2011), averaging approximately 80 articles per year. Interestingly, the number of publications has also increased four-fold during the period 2002–2011, with a stabilization in the last 3 years of the study period [23].Fig. 1Between 1909 and 1973, none of the publications deposited in MEDLINE/PubMed up to June 8, 2014, complied with the inclusion criteria for abstracts about health research in Angola\nBetween 1909 and 1973, none of the publications deposited in MEDLINE/PubMed up to June 8, 2014, complied with the inclusion criteria for abstracts about health research in Angola\nA study published in 2007, which analyzed the geography of the PubMed biomedical publications in Africa between 1996 and 2005, concluded that the contribution of Africa, in particular, was considerably less than that of other continents. Indeed, it was evident that there was a continuous increase in publication output during this period in all African sub-regions, although Angola was located in the lowest quintile with less than two publications per year [17]. In the present study, we found, in the same period, five publications that included researchers that were affiliated with Angolan institutions. However, this did not change the lower position of Angola. Another publication about the scientific production in Public Health and Epidemiology in the WHO African region, in the period 1991–2010, obtained very similar results for Angola, placing it in the lowest quintile, with 11–50 publications [18]. This increase in health research in Angola matches with a recent study about the WHO African Region between 2000 and 2014 [24]. In this study, Angola was located in quintile three, with 100 to 499 articles.\n Health research topics Malaria was the disease that stood out, with 45 publications, followed by HIV infection (n = 26), trypanosomiasis (n = 24), and themes of epidemiology/public health (n = 24). Tuberculosis occupied the tenth position with nine papers (Fig. 2). As a whole, infectious diseases was a topic of 59 % of the publications under analysis (n = 178).Fig. 2Publications are deposited in MEDLINE/PubMed up to June 8, 2014\nPublications are deposited in MEDLINE/PubMed up to June 8, 2014\nThe most investigated topics are aligned with the epidemiological profile of Angola, regarding the main causes of morbidity and mortality (communicable diseases), with a large emphasis on malaria [25]. Interestingly, there was a disproportionate volume of publications on HIV/AIDS relative to tuberculosis, which is unusual given the frequent association of both morbidities [26]. In an assessment of scientific output in public health and epidemiology in Africa, in the period 1991–2010, HIV/AIDS infection was the predominant topic (11.3 %) while malaria accounted for 8.6 % and tuberculosis for 7.1 % [16]. This analysis also considers the issue of the importance of research funding concerning infectious diseases [27]. The trend towards epidemiological transition in sub-Saharan Africa, owing to the increase of chronic non-communicable diseases, is not yet reflected at the scientific publication level, neither in the present study nor in the aforementioned African publications [16, 28–30].\nMalaria was the disease that stood out, with 45 publications, followed by HIV infection (n = 26), trypanosomiasis (n = 24), and themes of epidemiology/public health (n = 24). Tuberculosis occupied the tenth position with nine papers (Fig. 2). As a whole, infectious diseases was a topic of 59 % of the publications under analysis (n = 178).Fig. 2Publications are deposited in MEDLINE/PubMed up to June 8, 2014\nPublications are deposited in MEDLINE/PubMed up to June 8, 2014\nThe most investigated topics are aligned with the epidemiological profile of Angola, regarding the main causes of morbidity and mortality (communicable diseases), with a large emphasis on malaria [25]. Interestingly, there was a disproportionate volume of publications on HIV/AIDS relative to tuberculosis, which is unusual given the frequent association of both morbidities [26]. In an assessment of scientific output in public health and epidemiology in Africa, in the period 1991–2010, HIV/AIDS infection was the predominant topic (11.3 %) while malaria accounted for 8.6 % and tuberculosis for 7.1 % [16]. This analysis also considers the issue of the importance of research funding concerning infectious diseases [27]. The trend towards epidemiological transition in sub-Saharan Africa, owing to the increase of chronic non-communicable diseases, is not yet reflected at the scientific publication level, neither in the present study nor in the aforementioned African publications [16, 28–30].\n Authorship and affiliation The primary affiliation of the first author was most frequently in an Angolan institution (45 publications) (Fig. 3). A total of 150 institutions were listed as the first author’s primary affiliation, revealing a high institutional dispersion; 14.7 % were Angolan institutions. Furthermore, 65 % of publications represented research conducted in universities and institutes or research centres (Fig. 4). The majority of publications by first author affiliation country and the number of academic research institutions were located in Portugal, United States of America, or Brazil (Fig. 5). The high number of publications with first author affiliation in Angola denotes the concern of researchers in linking their work to institutions from the country of study. Nevertheless, this involvement is dispersed across many different institutions, many of which have relatively low track records in publications, namely hospitals and non-governmental organizations. Furthermore, academic research in universities and institutes (or research centres) related to institutional affiliations abroad indicate the need for reinforcing the academic research capacity in Angola.Fig. 3Country denotes the location in which the primary institution of the first author affiliation was based in publications up to June 8, 2014Fig. 4Publications deposited in MEDLINE/PubMed up to June 8, 2014Fig. 5Country denotes the location in which the primary institution of the first author affiliation was based concerning publications on health research in Angola deposited in MEDLINE/PubMed up to June 8, 2014\nCountry denotes the location in which the primary institution of the first author affiliation was based in publications up to June 8, 2014\nPublications deposited in MEDLINE/PubMed up to June 8, 2014\nCountry denotes the location in which the primary institution of the first author affiliation was based concerning publications on health research in Angola deposited in MEDLINE/PubMed up to June 8, 2014\nThe primary affiliation of the first author was most frequently in an Angolan institution (45 publications) (Fig. 3). A total of 150 institutions were listed as the first author’s primary affiliation, revealing a high institutional dispersion; 14.7 % were Angolan institutions. Furthermore, 65 % of publications represented research conducted in universities and institutes or research centres (Fig. 4). The majority of publications by first author affiliation country and the number of academic research institutions were located in Portugal, United States of America, or Brazil (Fig. 5). The high number of publications with first author affiliation in Angola denotes the concern of researchers in linking their work to institutions from the country of study. Nevertheless, this involvement is dispersed across many different institutions, many of which have relatively low track records in publications, namely hospitals and non-governmental organizations. Furthermore, academic research in universities and institutes (or research centres) related to institutional affiliations abroad indicate the need for reinforcing the academic research capacity in Angola.Fig. 3Country denotes the location in which the primary institution of the first author affiliation was based in publications up to June 8, 2014Fig. 4Publications deposited in MEDLINE/PubMed up to June 8, 2014Fig. 5Country denotes the location in which the primary institution of the first author affiliation was based concerning publications on health research in Angola deposited in MEDLINE/PubMed up to June 8, 2014\nCountry denotes the location in which the primary institution of the first author affiliation was based in publications up to June 8, 2014\nPublications deposited in MEDLINE/PubMed up to June 8, 2014\nCountry denotes the location in which the primary institution of the first author affiliation was based concerning publications on health research in Angola deposited in MEDLINE/PubMed up to June 8, 2014\n Publications by Angolan researchers An Angolan researcher was the first author in 58 (19 %) abstracts. The frequency of Angolan first authors or co-authors increased when the first author was affiliated to an Angolan institution (Fig. 6). In the present study, it is noteworthy that one-fifth of all publications had an Angolan as first author. The number of African first authors on publications relating to Africa between 1991 and 2010 has been increasing [16]. This finding was reinforced in the period between 2000 and 2014 in the WHO African Region, and it was found that the contributions of first authors from Africa doubled in this period, although it is recognized that it is still minimal (0.7 % in 2000 and 1.3 % in 2014) [24].Fig. 6Publications on health research in Angola deposited in MEDLINE/PubMed up to June 8, 2014, according to country in which the primary institution of the first author affiliation was based\nPublications on health research in Angola deposited in MEDLINE/PubMed up to June 8, 2014, according to country in which the primary institution of the first author affiliation was based\nAn Angolan researcher was the first author in 58 (19 %) abstracts. The frequency of Angolan first authors or co-authors increased when the first author was affiliated to an Angolan institution (Fig. 6). In the present study, it is noteworthy that one-fifth of all publications had an Angolan as first author. The number of African first authors on publications relating to Africa between 1991 and 2010 has been increasing [16]. This finding was reinforced in the period between 2000 and 2014 in the WHO African Region, and it was found that the contributions of first authors from Africa doubled in this period, although it is recognized that it is still minimal (0.7 % in 2000 and 1.3 % in 2014) [24].Fig. 6Publications on health research in Angola deposited in MEDLINE/PubMed up to June 8, 2014, according to country in which the primary institution of the first author affiliation was based\nPublications on health research in Angola deposited in MEDLINE/PubMed up to June 8, 2014, according to country in which the primary institution of the first author affiliation was based\n Scientific fields Epidemiological research was by far the most common research area (n = 165) (Fig. 7). Although a few population genetics studies (data not shown) are included in this topic, this finding is in accordance to the burden of infectious diseases in the country. However, the increasing frequency of non-communicable diseases in sub-Saharan Africa is likely to be a matter of concern for research in Africa in coming years.Fig. 7Publications deposited in MEDLINE/PubMed up to June 8, 2014\nPublications deposited in MEDLINE/PubMed up to June 8, 2014\nOn the other hand, a small number of publications (n = 32; 10.6 %) on socioeconomic and professional research, including governance, health policies, health systems management, and human resources is noted. Considering that Angola was a war zone for more than 50 % of its existence as an independent state, we might have expected a larger number of research publications on relevant topics such as the socioeconomic determinants of health (e.g., unemployment, poverty, education, family dissolution, and lifestyles) and the performance of health unities and health systems. However, we recognize the possibility that studies concerning some of those socioeconomic determinants of health may be part of research in social and economic sciences and, therefore, more accessible in other bibliographic databases. Further, the lack of studies on human resources in the various health professions, including studies in medical education, is also noticeable.\nWe expect that the increasing number of health professional schools in Angola, including six new public medical schools, particularly since 2009, will raise interest in these topics, especially if educational curricula at the undergraduate and postgraduate levels in Angola incorporate more programs focused on improving research skills and providing on-the-job training in epidemiology [31].\nEpidemiological research was by far the most common research area (n = 165) (Fig. 7). Although a few population genetics studies (data not shown) are included in this topic, this finding is in accordance to the burden of infectious diseases in the country. However, the increasing frequency of non-communicable diseases in sub-Saharan Africa is likely to be a matter of concern for research in Africa in coming years.Fig. 7Publications deposited in MEDLINE/PubMed up to June 8, 2014\nPublications deposited in MEDLINE/PubMed up to June 8, 2014\nOn the other hand, a small number of publications (n = 32; 10.6 %) on socioeconomic and professional research, including governance, health policies, health systems management, and human resources is noted. Considering that Angola was a war zone for more than 50 % of its existence as an independent state, we might have expected a larger number of research publications on relevant topics such as the socioeconomic determinants of health (e.g., unemployment, poverty, education, family dissolution, and lifestyles) and the performance of health unities and health systems. However, we recognize the possibility that studies concerning some of those socioeconomic determinants of health may be part of research in social and economic sciences and, therefore, more accessible in other bibliographic databases. Further, the lack of studies on human resources in the various health professions, including studies in medical education, is also noticeable.\nWe expect that the increasing number of health professional schools in Angola, including six new public medical schools, particularly since 2009, will raise interest in these topics, especially if educational curricula at the undergraduate and postgraduate levels in Angola incorporate more programs focused on improving research skills and providing on-the-job training in epidemiology [31].\n Journals choice We also found that, despite publications being widely spread in a variety of journals, 11.4 % concentrated in PLoS ONE and Malaria Journal, both of which are free access journals indexed by common databases (Fig. 8). This finding is not unusual given that malaria was the most common research topic among Angolan publications observed in the present study. Notwithstanding the debate around open access publishing, our findings reinforce the importance of providing readers in financially disadvantaged countries with a wide range of relevant research and information [32, 33].Fig. 8Publications on health research in Angola deposited in MEDLINE/PubMed up to June 8, 2014\nPublications on health research in Angola deposited in MEDLINE/PubMed up to June 8, 2014\nWe also found that, despite publications being widely spread in a variety of journals, 11.4 % concentrated in PLoS ONE and Malaria Journal, both of which are free access journals indexed by common databases (Fig. 8). This finding is not unusual given that malaria was the most common research topic among Angolan publications observed in the present study. Notwithstanding the debate around open access publishing, our findings reinforce the importance of providing readers in financially disadvantaged countries with a wide range of relevant research and information [32, 33].Fig. 8Publications on health research in Angola deposited in MEDLINE/PubMed up to June 8, 2014\nPublications on health research in Angola deposited in MEDLINE/PubMed up to June 8, 2014\n Limitations of the study The fact that Angola is a Portuguese speaking country and the majority of the journals indexed by MEDLINE/PubMed are in English may have introduced a selection bias due to idiom barriers. Though MEDLINE/PubMed represented the vast majority of the publications of the study and the search began at BVS, which included papers published in journals indexed by Scielo, it does not embody all the scientific and biomedical journals. Furthermore, we did not search “grey literature”. Thus, the number of Angolan authors and coauthors might be underestimated. Nevertheless, Angola still does not have an exhaustive electronic database of researchers containing individual publication records. In addition, the incorrect reporting of authors’ nationality and affiliation, observed in a few papers in the present study, may further contribute to underestimation of Angolan-authored publications. Finally, the study did not include the analysis of journal impact factors and the authors did not have access to any citation index database, which would allow the performance of bibliometric citation analyses.\nThe fact that Angola is a Portuguese speaking country and the majority of the journals indexed by MEDLINE/PubMed are in English may have introduced a selection bias due to idiom barriers. Though MEDLINE/PubMed represented the vast majority of the publications of the study and the search began at BVS, which included papers published in journals indexed by Scielo, it does not embody all the scientific and biomedical journals. Furthermore, we did not search “grey literature”. Thus, the number of Angolan authors and coauthors might be underestimated. Nevertheless, Angola still does not have an exhaustive electronic database of researchers containing individual publication records. In addition, the incorrect reporting of authors’ nationality and affiliation, observed in a few papers in the present study, may further contribute to underestimation of Angolan-authored publications. Finally, the study did not include the analysis of journal impact factors and the authors did not have access to any citation index database, which would allow the performance of bibliometric citation analyses.","As shown in Fig. 1, the publications in MEDLINE/PubMed addressing health issues related to Angola date back to 1909. MEDLINE/PubMed, the first bibliographic database in the life sciences, with a focus on biomedicine, mainly covers the literature published from 1946 onwards, but it also includes more dated publications [22]. There has been a remarkable, albeit irregular, increase in the number of publications during the 1990s, with a more sustained increase from 2004 onwards, and a peak in 2013. From 1979 to 2003 (34 years) there were 62 publications, but in the last 10 years (2004–2013) the number increased four-fold (n = 232), suggesting a rampant increment in publication rate (Fig. 1). Additionally, it is noteworthy that the number of publications by the beginning of June 2014 already reached half the figure recorded in 2013, supporting the notion that Angola is motivating a steady increase in health-related scientific publications. This exponential trend was also observed in Palestine, a war zone, where a total of 770 publications were retrieved in the medical and biomedical field across a 10-year period (01 January 2002 to 31 December 2011), averaging approximately 80 articles per year. Interestingly, the number of publications has also increased four-fold during the period 2002–2011, with a stabilization in the last 3 years of the study period [23].Fig. 1Between 1909 and 1973, none of the publications deposited in MEDLINE/PubMed up to June 8, 2014, complied with the inclusion criteria for abstracts about health research in Angola\nBetween 1909 and 1973, none of the publications deposited in MEDLINE/PubMed up to June 8, 2014, complied with the inclusion criteria for abstracts about health research in Angola\nA study published in 2007, which analyzed the geography of the PubMed biomedical publications in Africa between 1996 and 2005, concluded that the contribution of Africa, in particular, was considerably less than that of other continents. Indeed, it was evident that there was a continuous increase in publication output during this period in all African sub-regions, although Angola was located in the lowest quintile with less than two publications per year [17]. In the present study, we found, in the same period, five publications that included researchers that were affiliated with Angolan institutions. However, this did not change the lower position of Angola. Another publication about the scientific production in Public Health and Epidemiology in the WHO African region, in the period 1991–2010, obtained very similar results for Angola, placing it in the lowest quintile, with 11–50 publications [18]. This increase in health research in Angola matches with a recent study about the WHO African Region between 2000 and 2014 [24]. In this study, Angola was located in quintile three, with 100 to 499 articles.","Malaria was the disease that stood out, with 45 publications, followed by HIV infection (n = 26), trypanosomiasis (n = 24), and themes of epidemiology/public health (n = 24). Tuberculosis occupied the tenth position with nine papers (Fig. 2). As a whole, infectious diseases was a topic of 59 % of the publications under analysis (n = 178).Fig. 2Publications are deposited in MEDLINE/PubMed up to June 8, 2014\nPublications are deposited in MEDLINE/PubMed up to June 8, 2014\nThe most investigated topics are aligned with the epidemiological profile of Angola, regarding the main causes of morbidity and mortality (communicable diseases), with a large emphasis on malaria [25]. Interestingly, there was a disproportionate volume of publications on HIV/AIDS relative to tuberculosis, which is unusual given the frequent association of both morbidities [26]. In an assessment of scientific output in public health and epidemiology in Africa, in the period 1991–2010, HIV/AIDS infection was the predominant topic (11.3 %) while malaria accounted for 8.6 % and tuberculosis for 7.1 % [16]. This analysis also considers the issue of the importance of research funding concerning infectious diseases [27]. The trend towards epidemiological transition in sub-Saharan Africa, owing to the increase of chronic non-communicable diseases, is not yet reflected at the scientific publication level, neither in the present study nor in the aforementioned African publications [16, 28–30].","The primary affiliation of the first author was most frequently in an Angolan institution (45 publications) (Fig. 3). A total of 150 institutions were listed as the first author’s primary affiliation, revealing a high institutional dispersion; 14.7 % were Angolan institutions. Furthermore, 65 % of publications represented research conducted in universities and institutes or research centres (Fig. 4). The majority of publications by first author affiliation country and the number of academic research institutions were located in Portugal, United States of America, or Brazil (Fig. 5). The high number of publications with first author affiliation in Angola denotes the concern of researchers in linking their work to institutions from the country of study. Nevertheless, this involvement is dispersed across many different institutions, many of which have relatively low track records in publications, namely hospitals and non-governmental organizations. Furthermore, academic research in universities and institutes (or research centres) related to institutional affiliations abroad indicate the need for reinforcing the academic research capacity in Angola.Fig. 3Country denotes the location in which the primary institution of the first author affiliation was based in publications up to June 8, 2014Fig. 4Publications deposited in MEDLINE/PubMed up to June 8, 2014Fig. 5Country denotes the location in which the primary institution of the first author affiliation was based concerning publications on health research in Angola deposited in MEDLINE/PubMed up to June 8, 2014\nCountry denotes the location in which the primary institution of the first author affiliation was based in publications up to June 8, 2014\nPublications deposited in MEDLINE/PubMed up to June 8, 2014\nCountry denotes the location in which the primary institution of the first author affiliation was based concerning publications on health research in Angola deposited in MEDLINE/PubMed up to June 8, 2014","An Angolan researcher was the first author in 58 (19 %) abstracts. The frequency of Angolan first authors or co-authors increased when the first author was affiliated to an Angolan institution (Fig. 6). In the present study, it is noteworthy that one-fifth of all publications had an Angolan as first author. The number of African first authors on publications relating to Africa between 1991 and 2010 has been increasing [16]. This finding was reinforced in the period between 2000 and 2014 in the WHO African Region, and it was found that the contributions of first authors from Africa doubled in this period, although it is recognized that it is still minimal (0.7 % in 2000 and 1.3 % in 2014) [24].Fig. 6Publications on health research in Angola deposited in MEDLINE/PubMed up to June 8, 2014, according to country in which the primary institution of the first author affiliation was based\nPublications on health research in Angola deposited in MEDLINE/PubMed up to June 8, 2014, according to country in which the primary institution of the first author affiliation was based","Epidemiological research was by far the most common research area (n = 165) (Fig. 7). Although a few population genetics studies (data not shown) are included in this topic, this finding is in accordance to the burden of infectious diseases in the country. However, the increasing frequency of non-communicable diseases in sub-Saharan Africa is likely to be a matter of concern for research in Africa in coming years.Fig. 7Publications deposited in MEDLINE/PubMed up to June 8, 2014\nPublications deposited in MEDLINE/PubMed up to June 8, 2014\nOn the other hand, a small number of publications (n = 32; 10.6 %) on socioeconomic and professional research, including governance, health policies, health systems management, and human resources is noted. Considering that Angola was a war zone for more than 50 % of its existence as an independent state, we might have expected a larger number of research publications on relevant topics such as the socioeconomic determinants of health (e.g., unemployment, poverty, education, family dissolution, and lifestyles) and the performance of health unities and health systems. However, we recognize the possibility that studies concerning some of those socioeconomic determinants of health may be part of research in social and economic sciences and, therefore, more accessible in other bibliographic databases. Further, the lack of studies on human resources in the various health professions, including studies in medical education, is also noticeable.\nWe expect that the increasing number of health professional schools in Angola, including six new public medical schools, particularly since 2009, will raise interest in these topics, especially if educational curricula at the undergraduate and postgraduate levels in Angola incorporate more programs focused on improving research skills and providing on-the-job training in epidemiology [31].","We also found that, despite publications being widely spread in a variety of journals, 11.4 % concentrated in PLoS ONE and Malaria Journal, both of which are free access journals indexed by common databases (Fig. 8). This finding is not unusual given that malaria was the most common research topic among Angolan publications observed in the present study. Notwithstanding the debate around open access publishing, our findings reinforce the importance of providing readers in financially disadvantaged countries with a wide range of relevant research and information [32, 33].Fig. 8Publications on health research in Angola deposited in MEDLINE/PubMed up to June 8, 2014\nPublications on health research in Angola deposited in MEDLINE/PubMed up to June 8, 2014","The fact that Angola is a Portuguese speaking country and the majority of the journals indexed by MEDLINE/PubMed are in English may have introduced a selection bias due to idiom barriers. Though MEDLINE/PubMed represented the vast majority of the publications of the study and the search began at BVS, which included papers published in journals indexed by Scielo, it does not embody all the scientific and biomedical journals. Furthermore, we did not search “grey literature”. Thus, the number of Angolan authors and coauthors might be underestimated. Nevertheless, Angola still does not have an exhaustive electronic database of researchers containing individual publication records. In addition, the incorrect reporting of authors’ nationality and affiliation, observed in a few papers in the present study, may further contribute to underestimation of Angolan-authored publications. Finally, the study did not include the analysis of journal impact factors and the authors did not have access to any citation index database, which would allow the performance of bibliometric citation analyses.","This initial contribution characterizing the productivity of health research pertaining to Angola showed that the number of publications has increased steadily over the past 10 years and was mainly focused on infectious diseases, namely malaria. Angola, as the country in which the primary institution of the first author affiliation was based, evidenced the largest number of publications, and about 20 % of the publications included an Angolan as the first author. However, academic institutions in Portugal, the United States of America, and Brazil contributed more than universities and research centres in Angola to research publication. The research was largely epidemiological, and remarkably, with a very small number of publications on economic and professional issues, involving themes such as health policy, governance, and management of health systems and human resources.\nIn Angola, with the large expansion of higher education since 2009, universities should build and strengthen their capacities in research and human resources training. Including research training in the curricula of pre-graduate courses in health sciences will contribute to graduate professionals able to act as agents responsible for change, competent human resource managers, and promoters of evidence-based policies [34]. The alignment of the national health research agenda with the national program of health development and the wide use of multilateral international cooperation are critical to develop the operational tools towards universal health coverage in Angola as conceived by the WHO [1]. In summary, this work highlights the rapid increase of scientific publications related to Angola but also a need for reinforcing academic-driven research in this country."],"string":"[\n \"The concept of research for health, expressed on the World Health Report 2013, covers a broader range of investigations than health research. This wider view of research will become increasingly important in the transition from the United Nations Millennium Development Goals to a post-2015 sustainable development agenda [1].\\nIn a more restricted concept, health research has the potential to contribute to the identification of social and economic determinants of health. In the context of implementation research, it is a major basis for decision-making processes on health policies, programmes, and practices, especially when driven by the demand of the health problems of the population [2, 3].\\nMoreover, conducting more health research can stimulate countries to strengthen their capacity to produce and use such research to guide decision-making, improve the efficiency of the investments, increase accountability in the work of researchers, and contribute substantially to the research training of human resources in developing countries [4, 5]. In the perspective of the World Health Organization (WHO), health research must be founded on building capacity to step up health research systems and support demand-driven health problems, particularly in low- and middle-income countries. Additionally, health research must be embedded with standards for best practices and ensure that quality evidence is turned into affordable health technologies and evidence-informed policy [6].\\nHowever, at the global level, the proportion of health research that considers these aspects is negligible [7]. In particular, evidence shows that the research output in developing countries is considerably less than in developed countries [8]. Therefore, it is crucial to get an accurate picture of the landscape of the scientific research produced in developing countries in order to identify gaps critical for social development [9]. In sub-Saharan Africa, in particular, the production of health research can affect teaching, the quality of undergraduate training, career promotion, and translational research relevant to the country, which is comparable to developed countries [5].\\nThere have been documented bibliometric studies of research production in Africa, as a whole, as well as in other continents. This then motivated studies of the chronological progression of research within individual African countries [10–18]. These publications demonstrate the feebleness of African authorship in the most cited scientific publications in health [19].\\nAngola, a Portuguese-speaking country, is independent since 1975 and has spent 27 years in progressive socio-political instability, with the drawback of the civil war that ended 12 years ago. Nevertheless, Angola is making progress towards the achievement of the health Millennium Development Goals but is still far from achieving them [1]. The reconstruction of the national health system and policy will benefit from the knowledge of the factors influencing the success of certain interventions and the measure of their impact on populations. Therefore, health research will contribute to increased efficiency and effectiveness of health policy and decision making processes in Angola on programmes and practices. Support for science in Angola is less than 0.1 % of its GDP, compared to a world average of 1.7 % [20]. Consequently, a more serious commitment would be needed to build local and national capacity to accelerate the improvement of human resources training for health research. In this context, it becomes even more appropriate to carry out a survey on what has been produced as the result of health research. When searching the literature on health research in Angola, we found only one publication dated from 1953 which linked research to health, entitled “Scientific Research and the Health Status of Angola” [21]. The lack of studies on health research produced in Angola in the last 61 years motivated this work.\\nWe carried out a non-systematic literature survey to evaluate the progression of health research pertaining to Angola, answering specifically the following questions: 1) how has the number of publications evolved over the years? 2) what are the most published topics? 3) what is the level of involvement of Angolan researchers and institutions in this published research? and 4) what are the most represented research fields?\\nWe expect that this study will contribute to increasing the current state of knowledge on health research related to Angola and identify gaps that may guide the WHO recommendations on health research and consequently the adoption of effective interventions that will strengthen health research in Angola.\",\n \"We used the reference database Biblioteca Virtual em Saúde (BVS) to perform a non-systematic scientific literature survey aiming to count all existing publications up to June 8, 2014, with the keyword “Angola” and to produce a scientific publications dataset to be compared with other databases. The BVS was chosen because it is a platform that covers human health sciences issues, information sources published in countries of the Portuguese-speaking community, and a set of international databases. Moreover, resource constraints did not allow access to other biomedical databases such as those using the OVID platform. Thereafter, our content information study was based on the public database MEDLINE/PubMed disclosing the best match with the BVS dataset using the same keyword.\\nPublications with no abstract available were excluded from the MEDLINE/PubMed dataset. To focus our study in human health research relevant for Angola, we revised the abstracts using the following exclusion criteria: 1) the word Angola did not mean the country; 2) the topic did not concern physical and mental health; 3) the topic reported exclusively to biological vectors; 4) Angola was not the focus of a specific study, but was instead reported as part of a group of countries (common for poliomyelitis and Marburg disease) or just as a reference; 5) case reports published outside Angola regarding citizens who temporarily resided in Angola.\\nFrom each publication included in the study we extracted the following variables: year of publication; topic of health or disease, according to the revised International Classification of Diseases (ICD-10); country in which the primary institution of the first author affiliation was based; type of institution of the first author primary affiliation (university, institute or centre of scientific research, hospital, non-governmental organization, other); presence of an Angolan citizen as first author; number of Angolan co-authors; field of research (basic biomedical, clinical, epidemiological or socioeconomic and professional); and publishing journal. The Angolan authors and co-authors were identified by consulting their registration on the National Medical Council and in the universities. Socioeconomic and professional research includes health policy, human resources, and governance and management of health systems. The year of publication was the single variable that did not require the existence of the abstract. All the references of the abstracts included in the study were stored in a collection in the Zotero library. Collection of abstract content data was performed by manual curation and data were entered into an excel spreadsheet (Microsoft™ Excel® 2007) for statistical processing and plots generation.\",\n \"Searching the BVS on June 8, 2014, with the keyword “Angola”, showed 1,029 results, of which 74.6 % were also in MEDLINE/PubMed database. Consequently, it was considered that further search in MEDLINE/PubMed would produce representative research publication data on health in Angola. Therefore, we undertook a further search for relevant Angolan publications in MEDLINE/PubMed, keeping the keyword “Angola”. To describe the number of publications across time we included all publications retrieved from BVS without the abstract filter. All other variables were taken into account using the availability of an abstract as the main inclusion criteria, and this yielded 658 abstracts. Applying the exclusion criteria we selected 301 abstracts for further description.\\n Publication rate As shown in Fig. 1, the publications in MEDLINE/PubMed addressing health issues related to Angola date back to 1909. MEDLINE/PubMed, the first bibliographic database in the life sciences, with a focus on biomedicine, mainly covers the literature published from 1946 onwards, but it also includes more dated publications [22]. There has been a remarkable, albeit irregular, increase in the number of publications during the 1990s, with a more sustained increase from 2004 onwards, and a peak in 2013. From 1979 to 2003 (34 years) there were 62 publications, but in the last 10 years (2004–2013) the number increased four-fold (n = 232), suggesting a rampant increment in publication rate (Fig. 1). Additionally, it is noteworthy that the number of publications by the beginning of June 2014 already reached half the figure recorded in 2013, supporting the notion that Angola is motivating a steady increase in health-related scientific publications. This exponential trend was also observed in Palestine, a war zone, where a total of 770 publications were retrieved in the medical and biomedical field across a 10-year period (01 January 2002 to 31 December 2011), averaging approximately 80 articles per year. Interestingly, the number of publications has also increased four-fold during the period 2002–2011, with a stabilization in the last 3 years of the study period [23].Fig. 1Between 1909 and 1973, none of the publications deposited in MEDLINE/PubMed up to June 8, 2014, complied with the inclusion criteria for abstracts about health research in Angola\\nBetween 1909 and 1973, none of the publications deposited in MEDLINE/PubMed up to June 8, 2014, complied with the inclusion criteria for abstracts about health research in Angola\\nA study published in 2007, which analyzed the geography of the PubMed biomedical publications in Africa between 1996 and 2005, concluded that the contribution of Africa, in particular, was considerably less than that of other continents. Indeed, it was evident that there was a continuous increase in publication output during this period in all African sub-regions, although Angola was located in the lowest quintile with less than two publications per year [17]. In the present study, we found, in the same period, five publications that included researchers that were affiliated with Angolan institutions. However, this did not change the lower position of Angola. Another publication about the scientific production in Public Health and Epidemiology in the WHO African region, in the period 1991–2010, obtained very similar results for Angola, placing it in the lowest quintile, with 11–50 publications [18]. This increase in health research in Angola matches with a recent study about the WHO African Region between 2000 and 2014 [24]. In this study, Angola was located in quintile three, with 100 to 499 articles.\\nAs shown in Fig. 1, the publications in MEDLINE/PubMed addressing health issues related to Angola date back to 1909. MEDLINE/PubMed, the first bibliographic database in the life sciences, with a focus on biomedicine, mainly covers the literature published from 1946 onwards, but it also includes more dated publications [22]. There has been a remarkable, albeit irregular, increase in the number of publications during the 1990s, with a more sustained increase from 2004 onwards, and a peak in 2013. From 1979 to 2003 (34 years) there were 62 publications, but in the last 10 years (2004–2013) the number increased four-fold (n = 232), suggesting a rampant increment in publication rate (Fig. 1). Additionally, it is noteworthy that the number of publications by the beginning of June 2014 already reached half the figure recorded in 2013, supporting the notion that Angola is motivating a steady increase in health-related scientific publications. This exponential trend was also observed in Palestine, a war zone, where a total of 770 publications were retrieved in the medical and biomedical field across a 10-year period (01 January 2002 to 31 December 2011), averaging approximately 80 articles per year. Interestingly, the number of publications has also increased four-fold during the period 2002–2011, with a stabilization in the last 3 years of the study period [23].Fig. 1Between 1909 and 1973, none of the publications deposited in MEDLINE/PubMed up to June 8, 2014, complied with the inclusion criteria for abstracts about health research in Angola\\nBetween 1909 and 1973, none of the publications deposited in MEDLINE/PubMed up to June 8, 2014, complied with the inclusion criteria for abstracts about health research in Angola\\nA study published in 2007, which analyzed the geography of the PubMed biomedical publications in Africa between 1996 and 2005, concluded that the contribution of Africa, in particular, was considerably less than that of other continents. Indeed, it was evident that there was a continuous increase in publication output during this period in all African sub-regions, although Angola was located in the lowest quintile with less than two publications per year [17]. In the present study, we found, in the same period, five publications that included researchers that were affiliated with Angolan institutions. However, this did not change the lower position of Angola. Another publication about the scientific production in Public Health and Epidemiology in the WHO African region, in the period 1991–2010, obtained very similar results for Angola, placing it in the lowest quintile, with 11–50 publications [18]. This increase in health research in Angola matches with a recent study about the WHO African Region between 2000 and 2014 [24]. In this study, Angola was located in quintile three, with 100 to 499 articles.\\n Health research topics Malaria was the disease that stood out, with 45 publications, followed by HIV infection (n = 26), trypanosomiasis (n = 24), and themes of epidemiology/public health (n = 24). Tuberculosis occupied the tenth position with nine papers (Fig. 2). As a whole, infectious diseases was a topic of 59 % of the publications under analysis (n = 178).Fig. 2Publications are deposited in MEDLINE/PubMed up to June 8, 2014\\nPublications are deposited in MEDLINE/PubMed up to June 8, 2014\\nThe most investigated topics are aligned with the epidemiological profile of Angola, regarding the main causes of morbidity and mortality (communicable diseases), with a large emphasis on malaria [25]. Interestingly, there was a disproportionate volume of publications on HIV/AIDS relative to tuberculosis, which is unusual given the frequent association of both morbidities [26]. In an assessment of scientific output in public health and epidemiology in Africa, in the period 1991–2010, HIV/AIDS infection was the predominant topic (11.3 %) while malaria accounted for 8.6 % and tuberculosis for 7.1 % [16]. This analysis also considers the issue of the importance of research funding concerning infectious diseases [27]. The trend towards epidemiological transition in sub-Saharan Africa, owing to the increase of chronic non-communicable diseases, is not yet reflected at the scientific publication level, neither in the present study nor in the aforementioned African publications [16, 28–30].\\nMalaria was the disease that stood out, with 45 publications, followed by HIV infection (n = 26), trypanosomiasis (n = 24), and themes of epidemiology/public health (n = 24). Tuberculosis occupied the tenth position with nine papers (Fig. 2). As a whole, infectious diseases was a topic of 59 % of the publications under analysis (n = 178).Fig. 2Publications are deposited in MEDLINE/PubMed up to June 8, 2014\\nPublications are deposited in MEDLINE/PubMed up to June 8, 2014\\nThe most investigated topics are aligned with the epidemiological profile of Angola, regarding the main causes of morbidity and mortality (communicable diseases), with a large emphasis on malaria [25]. Interestingly, there was a disproportionate volume of publications on HIV/AIDS relative to tuberculosis, which is unusual given the frequent association of both morbidities [26]. In an assessment of scientific output in public health and epidemiology in Africa, in the period 1991–2010, HIV/AIDS infection was the predominant topic (11.3 %) while malaria accounted for 8.6 % and tuberculosis for 7.1 % [16]. This analysis also considers the issue of the importance of research funding concerning infectious diseases [27]. The trend towards epidemiological transition in sub-Saharan Africa, owing to the increase of chronic non-communicable diseases, is not yet reflected at the scientific publication level, neither in the present study nor in the aforementioned African publications [16, 28–30].\\n Authorship and affiliation The primary affiliation of the first author was most frequently in an Angolan institution (45 publications) (Fig. 3). A total of 150 institutions were listed as the first author’s primary affiliation, revealing a high institutional dispersion; 14.7 % were Angolan institutions. Furthermore, 65 % of publications represented research conducted in universities and institutes or research centres (Fig. 4). The majority of publications by first author affiliation country and the number of academic research institutions were located in Portugal, United States of America, or Brazil (Fig. 5). The high number of publications with first author affiliation in Angola denotes the concern of researchers in linking their work to institutions from the country of study. Nevertheless, this involvement is dispersed across many different institutions, many of which have relatively low track records in publications, namely hospitals and non-governmental organizations. Furthermore, academic research in universities and institutes (or research centres) related to institutional affiliations abroad indicate the need for reinforcing the academic research capacity in Angola.Fig. 3Country denotes the location in which the primary institution of the first author affiliation was based in publications up to June 8, 2014Fig. 4Publications deposited in MEDLINE/PubMed up to June 8, 2014Fig. 5Country denotes the location in which the primary institution of the first author affiliation was based concerning publications on health research in Angola deposited in MEDLINE/PubMed up to June 8, 2014\\nCountry denotes the location in which the primary institution of the first author affiliation was based in publications up to June 8, 2014\\nPublications deposited in MEDLINE/PubMed up to June 8, 2014\\nCountry denotes the location in which the primary institution of the first author affiliation was based concerning publications on health research in Angola deposited in MEDLINE/PubMed up to June 8, 2014\\nThe primary affiliation of the first author was most frequently in an Angolan institution (45 publications) (Fig. 3). A total of 150 institutions were listed as the first author’s primary affiliation, revealing a high institutional dispersion; 14.7 % were Angolan institutions. Furthermore, 65 % of publications represented research conducted in universities and institutes or research centres (Fig. 4). The majority of publications by first author affiliation country and the number of academic research institutions were located in Portugal, United States of America, or Brazil (Fig. 5). The high number of publications with first author affiliation in Angola denotes the concern of researchers in linking their work to institutions from the country of study. Nevertheless, this involvement is dispersed across many different institutions, many of which have relatively low track records in publications, namely hospitals and non-governmental organizations. Furthermore, academic research in universities and institutes (or research centres) related to institutional affiliations abroad indicate the need for reinforcing the academic research capacity in Angola.Fig. 3Country denotes the location in which the primary institution of the first author affiliation was based in publications up to June 8, 2014Fig. 4Publications deposited in MEDLINE/PubMed up to June 8, 2014Fig. 5Country denotes the location in which the primary institution of the first author affiliation was based concerning publications on health research in Angola deposited in MEDLINE/PubMed up to June 8, 2014\\nCountry denotes the location in which the primary institution of the first author affiliation was based in publications up to June 8, 2014\\nPublications deposited in MEDLINE/PubMed up to June 8, 2014\\nCountry denotes the location in which the primary institution of the first author affiliation was based concerning publications on health research in Angola deposited in MEDLINE/PubMed up to June 8, 2014\\n Publications by Angolan researchers An Angolan researcher was the first author in 58 (19 %) abstracts. The frequency of Angolan first authors or co-authors increased when the first author was affiliated to an Angolan institution (Fig. 6). In the present study, it is noteworthy that one-fifth of all publications had an Angolan as first author. The number of African first authors on publications relating to Africa between 1991 and 2010 has been increasing [16]. This finding was reinforced in the period between 2000 and 2014 in the WHO African Region, and it was found that the contributions of first authors from Africa doubled in this period, although it is recognized that it is still minimal (0.7 % in 2000 and 1.3 % in 2014) [24].Fig. 6Publications on health research in Angola deposited in MEDLINE/PubMed up to June 8, 2014, according to country in which the primary institution of the first author affiliation was based\\nPublications on health research in Angola deposited in MEDLINE/PubMed up to June 8, 2014, according to country in which the primary institution of the first author affiliation was based\\nAn Angolan researcher was the first author in 58 (19 %) abstracts. The frequency of Angolan first authors or co-authors increased when the first author was affiliated to an Angolan institution (Fig. 6). In the present study, it is noteworthy that one-fifth of all publications had an Angolan as first author. The number of African first authors on publications relating to Africa between 1991 and 2010 has been increasing [16]. This finding was reinforced in the period between 2000 and 2014 in the WHO African Region, and it was found that the contributions of first authors from Africa doubled in this period, although it is recognized that it is still minimal (0.7 % in 2000 and 1.3 % in 2014) [24].Fig. 6Publications on health research in Angola deposited in MEDLINE/PubMed up to June 8, 2014, according to country in which the primary institution of the first author affiliation was based\\nPublications on health research in Angola deposited in MEDLINE/PubMed up to June 8, 2014, according to country in which the primary institution of the first author affiliation was based\\n Scientific fields Epidemiological research was by far the most common research area (n = 165) (Fig. 7). Although a few population genetics studies (data not shown) are included in this topic, this finding is in accordance to the burden of infectious diseases in the country. However, the increasing frequency of non-communicable diseases in sub-Saharan Africa is likely to be a matter of concern for research in Africa in coming years.Fig. 7Publications deposited in MEDLINE/PubMed up to June 8, 2014\\nPublications deposited in MEDLINE/PubMed up to June 8, 2014\\nOn the other hand, a small number of publications (n = 32; 10.6 %) on socioeconomic and professional research, including governance, health policies, health systems management, and human resources is noted. Considering that Angola was a war zone for more than 50 % of its existence as an independent state, we might have expected a larger number of research publications on relevant topics such as the socioeconomic determinants of health (e.g., unemployment, poverty, education, family dissolution, and lifestyles) and the performance of health unities and health systems. However, we recognize the possibility that studies concerning some of those socioeconomic determinants of health may be part of research in social and economic sciences and, therefore, more accessible in other bibliographic databases. Further, the lack of studies on human resources in the various health professions, including studies in medical education, is also noticeable.\\nWe expect that the increasing number of health professional schools in Angola, including six new public medical schools, particularly since 2009, will raise interest in these topics, especially if educational curricula at the undergraduate and postgraduate levels in Angola incorporate more programs focused on improving research skills and providing on-the-job training in epidemiology [31].\\nEpidemiological research was by far the most common research area (n = 165) (Fig. 7). Although a few population genetics studies (data not shown) are included in this topic, this finding is in accordance to the burden of infectious diseases in the country. However, the increasing frequency of non-communicable diseases in sub-Saharan Africa is likely to be a matter of concern for research in Africa in coming years.Fig. 7Publications deposited in MEDLINE/PubMed up to June 8, 2014\\nPublications deposited in MEDLINE/PubMed up to June 8, 2014\\nOn the other hand, a small number of publications (n = 32; 10.6 %) on socioeconomic and professional research, including governance, health policies, health systems management, and human resources is noted. Considering that Angola was a war zone for more than 50 % of its existence as an independent state, we might have expected a larger number of research publications on relevant topics such as the socioeconomic determinants of health (e.g., unemployment, poverty, education, family dissolution, and lifestyles) and the performance of health unities and health systems. However, we recognize the possibility that studies concerning some of those socioeconomic determinants of health may be part of research in social and economic sciences and, therefore, more accessible in other bibliographic databases. Further, the lack of studies on human resources in the various health professions, including studies in medical education, is also noticeable.\\nWe expect that the increasing number of health professional schools in Angola, including six new public medical schools, particularly since 2009, will raise interest in these topics, especially if educational curricula at the undergraduate and postgraduate levels in Angola incorporate more programs focused on improving research skills and providing on-the-job training in epidemiology [31].\\n Journals choice We also found that, despite publications being widely spread in a variety of journals, 11.4 % concentrated in PLoS ONE and Malaria Journal, both of which are free access journals indexed by common databases (Fig. 8). This finding is not unusual given that malaria was the most common research topic among Angolan publications observed in the present study. Notwithstanding the debate around open access publishing, our findings reinforce the importance of providing readers in financially disadvantaged countries with a wide range of relevant research and information [32, 33].Fig. 8Publications on health research in Angola deposited in MEDLINE/PubMed up to June 8, 2014\\nPublications on health research in Angola deposited in MEDLINE/PubMed up to June 8, 2014\\nWe also found that, despite publications being widely spread in a variety of journals, 11.4 % concentrated in PLoS ONE and Malaria Journal, both of which are free access journals indexed by common databases (Fig. 8). This finding is not unusual given that malaria was the most common research topic among Angolan publications observed in the present study. Notwithstanding the debate around open access publishing, our findings reinforce the importance of providing readers in financially disadvantaged countries with a wide range of relevant research and information [32, 33].Fig. 8Publications on health research in Angola deposited in MEDLINE/PubMed up to June 8, 2014\\nPublications on health research in Angola deposited in MEDLINE/PubMed up to June 8, 2014\\n Limitations of the study The fact that Angola is a Portuguese speaking country and the majority of the journals indexed by MEDLINE/PubMed are in English may have introduced a selection bias due to idiom barriers. Though MEDLINE/PubMed represented the vast majority of the publications of the study and the search began at BVS, which included papers published in journals indexed by Scielo, it does not embody all the scientific and biomedical journals. Furthermore, we did not search “grey literature”. Thus, the number of Angolan authors and coauthors might be underestimated. Nevertheless, Angola still does not have an exhaustive electronic database of researchers containing individual publication records. In addition, the incorrect reporting of authors’ nationality and affiliation, observed in a few papers in the present study, may further contribute to underestimation of Angolan-authored publications. Finally, the study did not include the analysis of journal impact factors and the authors did not have access to any citation index database, which would allow the performance of bibliometric citation analyses.\\nThe fact that Angola is a Portuguese speaking country and the majority of the journals indexed by MEDLINE/PubMed are in English may have introduced a selection bias due to idiom barriers. Though MEDLINE/PubMed represented the vast majority of the publications of the study and the search began at BVS, which included papers published in journals indexed by Scielo, it does not embody all the scientific and biomedical journals. Furthermore, we did not search “grey literature”. Thus, the number of Angolan authors and coauthors might be underestimated. Nevertheless, Angola still does not have an exhaustive electronic database of researchers containing individual publication records. In addition, the incorrect reporting of authors’ nationality and affiliation, observed in a few papers in the present study, may further contribute to underestimation of Angolan-authored publications. Finally, the study did not include the analysis of journal impact factors and the authors did not have access to any citation index database, which would allow the performance of bibliometric citation analyses.\",\n \"As shown in Fig. 1, the publications in MEDLINE/PubMed addressing health issues related to Angola date back to 1909. MEDLINE/PubMed, the first bibliographic database in the life sciences, with a focus on biomedicine, mainly covers the literature published from 1946 onwards, but it also includes more dated publications [22]. There has been a remarkable, albeit irregular, increase in the number of publications during the 1990s, with a more sustained increase from 2004 onwards, and a peak in 2013. From 1979 to 2003 (34 years) there were 62 publications, but in the last 10 years (2004–2013) the number increased four-fold (n = 232), suggesting a rampant increment in publication rate (Fig. 1). Additionally, it is noteworthy that the number of publications by the beginning of June 2014 already reached half the figure recorded in 2013, supporting the notion that Angola is motivating a steady increase in health-related scientific publications. This exponential trend was also observed in Palestine, a war zone, where a total of 770 publications were retrieved in the medical and biomedical field across a 10-year period (01 January 2002 to 31 December 2011), averaging approximately 80 articles per year. Interestingly, the number of publications has also increased four-fold during the period 2002–2011, with a stabilization in the last 3 years of the study period [23].Fig. 1Between 1909 and 1973, none of the publications deposited in MEDLINE/PubMed up to June 8, 2014, complied with the inclusion criteria for abstracts about health research in Angola\\nBetween 1909 and 1973, none of the publications deposited in MEDLINE/PubMed up to June 8, 2014, complied with the inclusion criteria for abstracts about health research in Angola\\nA study published in 2007, which analyzed the geography of the PubMed biomedical publications in Africa between 1996 and 2005, concluded that the contribution of Africa, in particular, was considerably less than that of other continents. Indeed, it was evident that there was a continuous increase in publication output during this period in all African sub-regions, although Angola was located in the lowest quintile with less than two publications per year [17]. In the present study, we found, in the same period, five publications that included researchers that were affiliated with Angolan institutions. However, this did not change the lower position of Angola. Another publication about the scientific production in Public Health and Epidemiology in the WHO African region, in the period 1991–2010, obtained very similar results for Angola, placing it in the lowest quintile, with 11–50 publications [18]. This increase in health research in Angola matches with a recent study about the WHO African Region between 2000 and 2014 [24]. In this study, Angola was located in quintile three, with 100 to 499 articles.\",\n \"Malaria was the disease that stood out, with 45 publications, followed by HIV infection (n = 26), trypanosomiasis (n = 24), and themes of epidemiology/public health (n = 24). Tuberculosis occupied the tenth position with nine papers (Fig. 2). As a whole, infectious diseases was a topic of 59 % of the publications under analysis (n = 178).Fig. 2Publications are deposited in MEDLINE/PubMed up to June 8, 2014\\nPublications are deposited in MEDLINE/PubMed up to June 8, 2014\\nThe most investigated topics are aligned with the epidemiological profile of Angola, regarding the main causes of morbidity and mortality (communicable diseases), with a large emphasis on malaria [25]. Interestingly, there was a disproportionate volume of publications on HIV/AIDS relative to tuberculosis, which is unusual given the frequent association of both morbidities [26]. In an assessment of scientific output in public health and epidemiology in Africa, in the period 1991–2010, HIV/AIDS infection was the predominant topic (11.3 %) while malaria accounted for 8.6 % and tuberculosis for 7.1 % [16]. This analysis also considers the issue of the importance of research funding concerning infectious diseases [27]. The trend towards epidemiological transition in sub-Saharan Africa, owing to the increase of chronic non-communicable diseases, is not yet reflected at the scientific publication level, neither in the present study nor in the aforementioned African publications [16, 28–30].\",\n \"The primary affiliation of the first author was most frequently in an Angolan institution (45 publications) (Fig. 3). A total of 150 institutions were listed as the first author’s primary affiliation, revealing a high institutional dispersion; 14.7 % were Angolan institutions. Furthermore, 65 % of publications represented research conducted in universities and institutes or research centres (Fig. 4). The majority of publications by first author affiliation country and the number of academic research institutions were located in Portugal, United States of America, or Brazil (Fig. 5). The high number of publications with first author affiliation in Angola denotes the concern of researchers in linking their work to institutions from the country of study. Nevertheless, this involvement is dispersed across many different institutions, many of which have relatively low track records in publications, namely hospitals and non-governmental organizations. Furthermore, academic research in universities and institutes (or research centres) related to institutional affiliations abroad indicate the need for reinforcing the academic research capacity in Angola.Fig. 3Country denotes the location in which the primary institution of the first author affiliation was based in publications up to June 8, 2014Fig. 4Publications deposited in MEDLINE/PubMed up to June 8, 2014Fig. 5Country denotes the location in which the primary institution of the first author affiliation was based concerning publications on health research in Angola deposited in MEDLINE/PubMed up to June 8, 2014\\nCountry denotes the location in which the primary institution of the first author affiliation was based in publications up to June 8, 2014\\nPublications deposited in MEDLINE/PubMed up to June 8, 2014\\nCountry denotes the location in which the primary institution of the first author affiliation was based concerning publications on health research in Angola deposited in MEDLINE/PubMed up to June 8, 2014\",\n \"An Angolan researcher was the first author in 58 (19 %) abstracts. The frequency of Angolan first authors or co-authors increased when the first author was affiliated to an Angolan institution (Fig. 6). In the present study, it is noteworthy that one-fifth of all publications had an Angolan as first author. The number of African first authors on publications relating to Africa between 1991 and 2010 has been increasing [16]. This finding was reinforced in the period between 2000 and 2014 in the WHO African Region, and it was found that the contributions of first authors from Africa doubled in this period, although it is recognized that it is still minimal (0.7 % in 2000 and 1.3 % in 2014) [24].Fig. 6Publications on health research in Angola deposited in MEDLINE/PubMed up to June 8, 2014, according to country in which the primary institution of the first author affiliation was based\\nPublications on health research in Angola deposited in MEDLINE/PubMed up to June 8, 2014, according to country in which the primary institution of the first author affiliation was based\",\n \"Epidemiological research was by far the most common research area (n = 165) (Fig. 7). Although a few population genetics studies (data not shown) are included in this topic, this finding is in accordance to the burden of infectious diseases in the country. However, the increasing frequency of non-communicable diseases in sub-Saharan Africa is likely to be a matter of concern for research in Africa in coming years.Fig. 7Publications deposited in MEDLINE/PubMed up to June 8, 2014\\nPublications deposited in MEDLINE/PubMed up to June 8, 2014\\nOn the other hand, a small number of publications (n = 32; 10.6 %) on socioeconomic and professional research, including governance, health policies, health systems management, and human resources is noted. Considering that Angola was a war zone for more than 50 % of its existence as an independent state, we might have expected a larger number of research publications on relevant topics such as the socioeconomic determinants of health (e.g., unemployment, poverty, education, family dissolution, and lifestyles) and the performance of health unities and health systems. However, we recognize the possibility that studies concerning some of those socioeconomic determinants of health may be part of research in social and economic sciences and, therefore, more accessible in other bibliographic databases. Further, the lack of studies on human resources in the various health professions, including studies in medical education, is also noticeable.\\nWe expect that the increasing number of health professional schools in Angola, including six new public medical schools, particularly since 2009, will raise interest in these topics, especially if educational curricula at the undergraduate and postgraduate levels in Angola incorporate more programs focused on improving research skills and providing on-the-job training in epidemiology [31].\",\n \"We also found that, despite publications being widely spread in a variety of journals, 11.4 % concentrated in PLoS ONE and Malaria Journal, both of which are free access journals indexed by common databases (Fig. 8). This finding is not unusual given that malaria was the most common research topic among Angolan publications observed in the present study. Notwithstanding the debate around open access publishing, our findings reinforce the importance of providing readers in financially disadvantaged countries with a wide range of relevant research and information [32, 33].Fig. 8Publications on health research in Angola deposited in MEDLINE/PubMed up to June 8, 2014\\nPublications on health research in Angola deposited in MEDLINE/PubMed up to June 8, 2014\",\n \"The fact that Angola is a Portuguese speaking country and the majority of the journals indexed by MEDLINE/PubMed are in English may have introduced a selection bias due to idiom barriers. Though MEDLINE/PubMed represented the vast majority of the publications of the study and the search began at BVS, which included papers published in journals indexed by Scielo, it does not embody all the scientific and biomedical journals. Furthermore, we did not search “grey literature”. Thus, the number of Angolan authors and coauthors might be underestimated. Nevertheless, Angola still does not have an exhaustive electronic database of researchers containing individual publication records. In addition, the incorrect reporting of authors’ nationality and affiliation, observed in a few papers in the present study, may further contribute to underestimation of Angolan-authored publications. Finally, the study did not include the analysis of journal impact factors and the authors did not have access to any citation index database, which would allow the performance of bibliometric citation analyses.\",\n \"This initial contribution characterizing the productivity of health research pertaining to Angola showed that the number of publications has increased steadily over the past 10 years and was mainly focused on infectious diseases, namely malaria. Angola, as the country in which the primary institution of the first author affiliation was based, evidenced the largest number of publications, and about 20 % of the publications included an Angolan as the first author. However, academic institutions in Portugal, the United States of America, and Brazil contributed more than universities and research centres in Angola to research publication. The research was largely epidemiological, and remarkably, with a very small number of publications on economic and professional issues, involving themes such as health policy, governance, and management of health systems and human resources.\\nIn Angola, with the large expansion of higher education since 2009, universities should build and strengthen their capacities in research and human resources training. Including research training in the curricula of pre-graduate courses in health sciences will contribute to graduate professionals able to act as agents responsible for change, competent human resource managers, and promoters of evidence-based policies [34]. The alignment of the national health research agenda with the national program of health development and the wide use of multilateral international cooperation are critical to develop the operational tools towards universal health coverage in Angola as conceived by the WHO [1]. In summary, this work highlights the rapid increase of scientific publications related to Angola but also a need for reinforcing academic-driven research in this country.\"\n]"},"all_sections_sec_types":{"kind":"list like","value":["introduction","materials|methods","results",null,null,null,null,null,null,null,"conclusion"],"string":"[\n \"introduction\",\n \"materials|methods\",\n \"results\",\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n \"conclusion\"\n]"},"keywords":{"kind":"list like","value":["Angola","Health research","Health research centres","Universities"],"string":"[\n \"Angola\",\n \"Health research\",\n \"Health research centres\",\n \"Universities\"\n]"},"whole_article_text":{"kind":"string","value":"Background:\nThe concept of research for health, expressed on the World Health Report 2013, covers a broader range of investigations than health research. This wider view of research will become increasingly important in the transition from the United Nations Millennium Development Goals to a post-2015 sustainable development agenda [1].\nIn a more restricted concept, health research has the potential to contribute to the identification of social and economic determinants of health. In the context of implementation research, it is a major basis for decision-making processes on health policies, programmes, and practices, especially when driven by the demand of the health problems of the population [2, 3].\nMoreover, conducting more health research can stimulate countries to strengthen their capacity to produce and use such research to guide decision-making, improve the efficiency of the investments, increase accountability in the work of researchers, and contribute substantially to the research training of human resources in developing countries [4, 5]. In the perspective of the World Health Organization (WHO), health research must be founded on building capacity to step up health research systems and support demand-driven health problems, particularly in low- and middle-income countries. Additionally, health research must be embedded with standards for best practices and ensure that quality evidence is turned into affordable health technologies and evidence-informed policy [6].\nHowever, at the global level, the proportion of health research that considers these aspects is negligible [7]. In particular, evidence shows that the research output in developing countries is considerably less than in developed countries [8]. Therefore, it is crucial to get an accurate picture of the landscape of the scientific research produced in developing countries in order to identify gaps critical for social development [9]. In sub-Saharan Africa, in particular, the production of health research can affect teaching, the quality of undergraduate training, career promotion, and translational research relevant to the country, which is comparable to developed countries [5].\nThere have been documented bibliometric studies of research production in Africa, as a whole, as well as in other continents. This then motivated studies of the chronological progression of research within individual African countries [10–18]. These publications demonstrate the feebleness of African authorship in the most cited scientific publications in health [19].\nAngola, a Portuguese-speaking country, is independent since 1975 and has spent 27 years in progressive socio-political instability, with the drawback of the civil war that ended 12 years ago. Nevertheless, Angola is making progress towards the achievement of the health Millennium Development Goals but is still far from achieving them [1]. The reconstruction of the national health system and policy will benefit from the knowledge of the factors influencing the success of certain interventions and the measure of their impact on populations. Therefore, health research will contribute to increased efficiency and effectiveness of health policy and decision making processes in Angola on programmes and practices. Support for science in Angola is less than 0.1 % of its GDP, compared to a world average of 1.7 % [20]. Consequently, a more serious commitment would be needed to build local and national capacity to accelerate the improvement of human resources training for health research. In this context, it becomes even more appropriate to carry out a survey on what has been produced as the result of health research. When searching the literature on health research in Angola, we found only one publication dated from 1953 which linked research to health, entitled “Scientific Research and the Health Status of Angola” [21]. The lack of studies on health research produced in Angola in the last 61 years motivated this work.\nWe carried out a non-systematic literature survey to evaluate the progression of health research pertaining to Angola, answering specifically the following questions: 1) how has the number of publications evolved over the years? 2) what are the most published topics? 3) what is the level of involvement of Angolan researchers and institutions in this published research? and 4) what are the most represented research fields?\nWe expect that this study will contribute to increasing the current state of knowledge on health research related to Angola and identify gaps that may guide the WHO recommendations on health research and consequently the adoption of effective interventions that will strengthen health research in Angola.\n\nMethods:\nWe used the reference database Biblioteca Virtual em Saúde (BVS) to perform a non-systematic scientific literature survey aiming to count all existing publications up to June 8, 2014, with the keyword “Angola” and to produce a scientific publications dataset to be compared with other databases. The BVS was chosen because it is a platform that covers human health sciences issues, information sources published in countries of the Portuguese-speaking community, and a set of international databases. Moreover, resource constraints did not allow access to other biomedical databases such as those using the OVID platform. Thereafter, our content information study was based on the public database MEDLINE/PubMed disclosing the best match with the BVS dataset using the same keyword.\nPublications with no abstract available were excluded from the MEDLINE/PubMed dataset. To focus our study in human health research relevant for Angola, we revised the abstracts using the following exclusion criteria: 1) the word Angola did not mean the country; 2) the topic did not concern physical and mental health; 3) the topic reported exclusively to biological vectors; 4) Angola was not the focus of a specific study, but was instead reported as part of a group of countries (common for poliomyelitis and Marburg disease) or just as a reference; 5) case reports published outside Angola regarding citizens who temporarily resided in Angola.\nFrom each publication included in the study we extracted the following variables: year of publication; topic of health or disease, according to the revised International Classification of Diseases (ICD-10); country in which the primary institution of the first author affiliation was based; type of institution of the first author primary affiliation (university, institute or centre of scientific research, hospital, non-governmental organization, other); presence of an Angolan citizen as first author; number of Angolan co-authors; field of research (basic biomedical, clinical, epidemiological or socioeconomic and professional); and publishing journal. The Angolan authors and co-authors were identified by consulting their registration on the National Medical Council and in the universities. Socioeconomic and professional research includes health policy, human resources, and governance and management of health systems. The year of publication was the single variable that did not require the existence of the abstract. All the references of the abstracts included in the study were stored in a collection in the Zotero library. Collection of abstract content data was performed by manual curation and data were entered into an excel spreadsheet (Microsoft™ Excel® 2007) for statistical processing and plots generation.\n\nResults and discussion:\nSearching the BVS on June 8, 2014, with the keyword “Angola”, showed 1,029 results, of which 74.6 % were also in MEDLINE/PubMed database. Consequently, it was considered that further search in MEDLINE/PubMed would produce representative research publication data on health in Angola. Therefore, we undertook a further search for relevant Angolan publications in MEDLINE/PubMed, keeping the keyword “Angola”. To describe the number of publications across time we included all publications retrieved from BVS without the abstract filter. All other variables were taken into account using the availability of an abstract as the main inclusion criteria, and this yielded 658 abstracts. Applying the exclusion criteria we selected 301 abstracts for further description.\n Publication rate As shown in Fig. 1, the publications in MEDLINE/PubMed addressing health issues related to Angola date back to 1909. MEDLINE/PubMed, the first bibliographic database in the life sciences, with a focus on biomedicine, mainly covers the literature published from 1946 onwards, but it also includes more dated publications [22]. There has been a remarkable, albeit irregular, increase in the number of publications during the 1990s, with a more sustained increase from 2004 onwards, and a peak in 2013. From 1979 to 2003 (34 years) there were 62 publications, but in the last 10 years (2004–2013) the number increased four-fold (n = 232), suggesting a rampant increment in publication rate (Fig. 1). Additionally, it is noteworthy that the number of publications by the beginning of June 2014 already reached half the figure recorded in 2013, supporting the notion that Angola is motivating a steady increase in health-related scientific publications. This exponential trend was also observed in Palestine, a war zone, where a total of 770 publications were retrieved in the medical and biomedical field across a 10-year period (01 January 2002 to 31 December 2011), averaging approximately 80 articles per year. Interestingly, the number of publications has also increased four-fold during the period 2002–2011, with a stabilization in the last 3 years of the study period [23].Fig. 1Between 1909 and 1973, none of the publications deposited in MEDLINE/PubMed up to June 8, 2014, complied with the inclusion criteria for abstracts about health research in Angola\nBetween 1909 and 1973, none of the publications deposited in MEDLINE/PubMed up to June 8, 2014, complied with the inclusion criteria for abstracts about health research in Angola\nA study published in 2007, which analyzed the geography of the PubMed biomedical publications in Africa between 1996 and 2005, concluded that the contribution of Africa, in particular, was considerably less than that of other continents. Indeed, it was evident that there was a continuous increase in publication output during this period in all African sub-regions, although Angola was located in the lowest quintile with less than two publications per year [17]. In the present study, we found, in the same period, five publications that included researchers that were affiliated with Angolan institutions. However, this did not change the lower position of Angola. Another publication about the scientific production in Public Health and Epidemiology in the WHO African region, in the period 1991–2010, obtained very similar results for Angola, placing it in the lowest quintile, with 11–50 publications [18]. This increase in health research in Angola matches with a recent study about the WHO African Region between 2000 and 2014 [24]. In this study, Angola was located in quintile three, with 100 to 499 articles.\nAs shown in Fig. 1, the publications in MEDLINE/PubMed addressing health issues related to Angola date back to 1909. MEDLINE/PubMed, the first bibliographic database in the life sciences, with a focus on biomedicine, mainly covers the literature published from 1946 onwards, but it also includes more dated publications [22]. There has been a remarkable, albeit irregular, increase in the number of publications during the 1990s, with a more sustained increase from 2004 onwards, and a peak in 2013. From 1979 to 2003 (34 years) there were 62 publications, but in the last 10 years (2004–2013) the number increased four-fold (n = 232), suggesting a rampant increment in publication rate (Fig. 1). Additionally, it is noteworthy that the number of publications by the beginning of June 2014 already reached half the figure recorded in 2013, supporting the notion that Angola is motivating a steady increase in health-related scientific publications. This exponential trend was also observed in Palestine, a war zone, where a total of 770 publications were retrieved in the medical and biomedical field across a 10-year period (01 January 2002 to 31 December 2011), averaging approximately 80 articles per year. Interestingly, the number of publications has also increased four-fold during the period 2002–2011, with a stabilization in the last 3 years of the study period [23].Fig. 1Between 1909 and 1973, none of the publications deposited in MEDLINE/PubMed up to June 8, 2014, complied with the inclusion criteria for abstracts about health research in Angola\nBetween 1909 and 1973, none of the publications deposited in MEDLINE/PubMed up to June 8, 2014, complied with the inclusion criteria for abstracts about health research in Angola\nA study published in 2007, which analyzed the geography of the PubMed biomedical publications in Africa between 1996 and 2005, concluded that the contribution of Africa, in particular, was considerably less than that of other continents. Indeed, it was evident that there was a continuous increase in publication output during this period in all African sub-regions, although Angola was located in the lowest quintile with less than two publications per year [17]. In the present study, we found, in the same period, five publications that included researchers that were affiliated with Angolan institutions. However, this did not change the lower position of Angola. Another publication about the scientific production in Public Health and Epidemiology in the WHO African region, in the period 1991–2010, obtained very similar results for Angola, placing it in the lowest quintile, with 11–50 publications [18]. This increase in health research in Angola matches with a recent study about the WHO African Region between 2000 and 2014 [24]. In this study, Angola was located in quintile three, with 100 to 499 articles.\n Health research topics Malaria was the disease that stood out, with 45 publications, followed by HIV infection (n = 26), trypanosomiasis (n = 24), and themes of epidemiology/public health (n = 24). Tuberculosis occupied the tenth position with nine papers (Fig. 2). As a whole, infectious diseases was a topic of 59 % of the publications under analysis (n = 178).Fig. 2Publications are deposited in MEDLINE/PubMed up to June 8, 2014\nPublications are deposited in MEDLINE/PubMed up to June 8, 2014\nThe most investigated topics are aligned with the epidemiological profile of Angola, regarding the main causes of morbidity and mortality (communicable diseases), with a large emphasis on malaria [25]. Interestingly, there was a disproportionate volume of publications on HIV/AIDS relative to tuberculosis, which is unusual given the frequent association of both morbidities [26]. In an assessment of scientific output in public health and epidemiology in Africa, in the period 1991–2010, HIV/AIDS infection was the predominant topic (11.3 %) while malaria accounted for 8.6 % and tuberculosis for 7.1 % [16]. This analysis also considers the issue of the importance of research funding concerning infectious diseases [27]. The trend towards epidemiological transition in sub-Saharan Africa, owing to the increase of chronic non-communicable diseases, is not yet reflected at the scientific publication level, neither in the present study nor in the aforementioned African publications [16, 28–30].\nMalaria was the disease that stood out, with 45 publications, followed by HIV infection (n = 26), trypanosomiasis (n = 24), and themes of epidemiology/public health (n = 24). Tuberculosis occupied the tenth position with nine papers (Fig. 2). As a whole, infectious diseases was a topic of 59 % of the publications under analysis (n = 178).Fig. 2Publications are deposited in MEDLINE/PubMed up to June 8, 2014\nPublications are deposited in MEDLINE/PubMed up to June 8, 2014\nThe most investigated topics are aligned with the epidemiological profile of Angola, regarding the main causes of morbidity and mortality (communicable diseases), with a large emphasis on malaria [25]. Interestingly, there was a disproportionate volume of publications on HIV/AIDS relative to tuberculosis, which is unusual given the frequent association of both morbidities [26]. In an assessment of scientific output in public health and epidemiology in Africa, in the period 1991–2010, HIV/AIDS infection was the predominant topic (11.3 %) while malaria accounted for 8.6 % and tuberculosis for 7.1 % [16]. This analysis also considers the issue of the importance of research funding concerning infectious diseases [27]. The trend towards epidemiological transition in sub-Saharan Africa, owing to the increase of chronic non-communicable diseases, is not yet reflected at the scientific publication level, neither in the present study nor in the aforementioned African publications [16, 28–30].\n Authorship and affiliation The primary affiliation of the first author was most frequently in an Angolan institution (45 publications) (Fig. 3). A total of 150 institutions were listed as the first author’s primary affiliation, revealing a high institutional dispersion; 14.7 % were Angolan institutions. Furthermore, 65 % of publications represented research conducted in universities and institutes or research centres (Fig. 4). The majority of publications by first author affiliation country and the number of academic research institutions were located in Portugal, United States of America, or Brazil (Fig. 5). The high number of publications with first author affiliation in Angola denotes the concern of researchers in linking their work to institutions from the country of study. Nevertheless, this involvement is dispersed across many different institutions, many of which have relatively low track records in publications, namely hospitals and non-governmental organizations. Furthermore, academic research in universities and institutes (or research centres) related to institutional affiliations abroad indicate the need for reinforcing the academic research capacity in Angola.Fig. 3Country denotes the location in which the primary institution of the first author affiliation was based in publications up to June 8, 2014Fig. 4Publications deposited in MEDLINE/PubMed up to June 8, 2014Fig. 5Country denotes the location in which the primary institution of the first author affiliation was based concerning publications on health research in Angola deposited in MEDLINE/PubMed up to June 8, 2014\nCountry denotes the location in which the primary institution of the first author affiliation was based in publications up to June 8, 2014\nPublications deposited in MEDLINE/PubMed up to June 8, 2014\nCountry denotes the location in which the primary institution of the first author affiliation was based concerning publications on health research in Angola deposited in MEDLINE/PubMed up to June 8, 2014\nThe primary affiliation of the first author was most frequently in an Angolan institution (45 publications) (Fig. 3). A total of 150 institutions were listed as the first author’s primary affiliation, revealing a high institutional dispersion; 14.7 % were Angolan institutions. Furthermore, 65 % of publications represented research conducted in universities and institutes or research centres (Fig. 4). The majority of publications by first author affiliation country and the number of academic research institutions were located in Portugal, United States of America, or Brazil (Fig. 5). The high number of publications with first author affiliation in Angola denotes the concern of researchers in linking their work to institutions from the country of study. Nevertheless, this involvement is dispersed across many different institutions, many of which have relatively low track records in publications, namely hospitals and non-governmental organizations. Furthermore, academic research in universities and institutes (or research centres) related to institutional affiliations abroad indicate the need for reinforcing the academic research capacity in Angola.Fig. 3Country denotes the location in which the primary institution of the first author affiliation was based in publications up to June 8, 2014Fig. 4Publications deposited in MEDLINE/PubMed up to June 8, 2014Fig. 5Country denotes the location in which the primary institution of the first author affiliation was based concerning publications on health research in Angola deposited in MEDLINE/PubMed up to June 8, 2014\nCountry denotes the location in which the primary institution of the first author affiliation was based in publications up to June 8, 2014\nPublications deposited in MEDLINE/PubMed up to June 8, 2014\nCountry denotes the location in which the primary institution of the first author affiliation was based concerning publications on health research in Angola deposited in MEDLINE/PubMed up to June 8, 2014\n Publications by Angolan researchers An Angolan researcher was the first author in 58 (19 %) abstracts. The frequency of Angolan first authors or co-authors increased when the first author was affiliated to an Angolan institution (Fig. 6). In the present study, it is noteworthy that one-fifth of all publications had an Angolan as first author. The number of African first authors on publications relating to Africa between 1991 and 2010 has been increasing [16]. This finding was reinforced in the period between 2000 and 2014 in the WHO African Region, and it was found that the contributions of first authors from Africa doubled in this period, although it is recognized that it is still minimal (0.7 % in 2000 and 1.3 % in 2014) [24].Fig. 6Publications on health research in Angola deposited in MEDLINE/PubMed up to June 8, 2014, according to country in which the primary institution of the first author affiliation was based\nPublications on health research in Angola deposited in MEDLINE/PubMed up to June 8, 2014, according to country in which the primary institution of the first author affiliation was based\nAn Angolan researcher was the first author in 58 (19 %) abstracts. The frequency of Angolan first authors or co-authors increased when the first author was affiliated to an Angolan institution (Fig. 6). In the present study, it is noteworthy that one-fifth of all publications had an Angolan as first author. The number of African first authors on publications relating to Africa between 1991 and 2010 has been increasing [16]. This finding was reinforced in the period between 2000 and 2014 in the WHO African Region, and it was found that the contributions of first authors from Africa doubled in this period, although it is recognized that it is still minimal (0.7 % in 2000 and 1.3 % in 2014) [24].Fig. 6Publications on health research in Angola deposited in MEDLINE/PubMed up to June 8, 2014, according to country in which the primary institution of the first author affiliation was based\nPublications on health research in Angola deposited in MEDLINE/PubMed up to June 8, 2014, according to country in which the primary institution of the first author affiliation was based\n Scientific fields Epidemiological research was by far the most common research area (n = 165) (Fig. 7). Although a few population genetics studies (data not shown) are included in this topic, this finding is in accordance to the burden of infectious diseases in the country. However, the increasing frequency of non-communicable diseases in sub-Saharan Africa is likely to be a matter of concern for research in Africa in coming years.Fig. 7Publications deposited in MEDLINE/PubMed up to June 8, 2014\nPublications deposited in MEDLINE/PubMed up to June 8, 2014\nOn the other hand, a small number of publications (n = 32; 10.6 %) on socioeconomic and professional research, including governance, health policies, health systems management, and human resources is noted. Considering that Angola was a war zone for more than 50 % of its existence as an independent state, we might have expected a larger number of research publications on relevant topics such as the socioeconomic determinants of health (e.g., unemployment, poverty, education, family dissolution, and lifestyles) and the performance of health unities and health systems. However, we recognize the possibility that studies concerning some of those socioeconomic determinants of health may be part of research in social and economic sciences and, therefore, more accessible in other bibliographic databases. Further, the lack of studies on human resources in the various health professions, including studies in medical education, is also noticeable.\nWe expect that the increasing number of health professional schools in Angola, including six new public medical schools, particularly since 2009, will raise interest in these topics, especially if educational curricula at the undergraduate and postgraduate levels in Angola incorporate more programs focused on improving research skills and providing on-the-job training in epidemiology [31].\nEpidemiological research was by far the most common research area (n = 165) (Fig. 7). Although a few population genetics studies (data not shown) are included in this topic, this finding is in accordance to the burden of infectious diseases in the country. However, the increasing frequency of non-communicable diseases in sub-Saharan Africa is likely to be a matter of concern for research in Africa in coming years.Fig. 7Publications deposited in MEDLINE/PubMed up to June 8, 2014\nPublications deposited in MEDLINE/PubMed up to June 8, 2014\nOn the other hand, a small number of publications (n = 32; 10.6 %) on socioeconomic and professional research, including governance, health policies, health systems management, and human resources is noted. Considering that Angola was a war zone for more than 50 % of its existence as an independent state, we might have expected a larger number of research publications on relevant topics such as the socioeconomic determinants of health (e.g., unemployment, poverty, education, family dissolution, and lifestyles) and the performance of health unities and health systems. However, we recognize the possibility that studies concerning some of those socioeconomic determinants of health may be part of research in social and economic sciences and, therefore, more accessible in other bibliographic databases. Further, the lack of studies on human resources in the various health professions, including studies in medical education, is also noticeable.\nWe expect that the increasing number of health professional schools in Angola, including six new public medical schools, particularly since 2009, will raise interest in these topics, especially if educational curricula at the undergraduate and postgraduate levels in Angola incorporate more programs focused on improving research skills and providing on-the-job training in epidemiology [31].\n Journals choice We also found that, despite publications being widely spread in a variety of journals, 11.4 % concentrated in PLoS ONE and Malaria Journal, both of which are free access journals indexed by common databases (Fig. 8). This finding is not unusual given that malaria was the most common research topic among Angolan publications observed in the present study. Notwithstanding the debate around open access publishing, our findings reinforce the importance of providing readers in financially disadvantaged countries with a wide range of relevant research and information [32, 33].Fig. 8Publications on health research in Angola deposited in MEDLINE/PubMed up to June 8, 2014\nPublications on health research in Angola deposited in MEDLINE/PubMed up to June 8, 2014\nWe also found that, despite publications being widely spread in a variety of journals, 11.4 % concentrated in PLoS ONE and Malaria Journal, both of which are free access journals indexed by common databases (Fig. 8). This finding is not unusual given that malaria was the most common research topic among Angolan publications observed in the present study. Notwithstanding the debate around open access publishing, our findings reinforce the importance of providing readers in financially disadvantaged countries with a wide range of relevant research and information [32, 33].Fig. 8Publications on health research in Angola deposited in MEDLINE/PubMed up to June 8, 2014\nPublications on health research in Angola deposited in MEDLINE/PubMed up to June 8, 2014\n Limitations of the study The fact that Angola is a Portuguese speaking country and the majority of the journals indexed by MEDLINE/PubMed are in English may have introduced a selection bias due to idiom barriers. Though MEDLINE/PubMed represented the vast majority of the publications of the study and the search began at BVS, which included papers published in journals indexed by Scielo, it does not embody all the scientific and biomedical journals. Furthermore, we did not search “grey literature”. Thus, the number of Angolan authors and coauthors might be underestimated. Nevertheless, Angola still does not have an exhaustive electronic database of researchers containing individual publication records. In addition, the incorrect reporting of authors’ nationality and affiliation, observed in a few papers in the present study, may further contribute to underestimation of Angolan-authored publications. Finally, the study did not include the analysis of journal impact factors and the authors did not have access to any citation index database, which would allow the performance of bibliometric citation analyses.\nThe fact that Angola is a Portuguese speaking country and the majority of the journals indexed by MEDLINE/PubMed are in English may have introduced a selection bias due to idiom barriers. Though MEDLINE/PubMed represented the vast majority of the publications of the study and the search began at BVS, which included papers published in journals indexed by Scielo, it does not embody all the scientific and biomedical journals. Furthermore, we did not search “grey literature”. Thus, the number of Angolan authors and coauthors might be underestimated. Nevertheless, Angola still does not have an exhaustive electronic database of researchers containing individual publication records. In addition, the incorrect reporting of authors’ nationality and affiliation, observed in a few papers in the present study, may further contribute to underestimation of Angolan-authored publications. Finally, the study did not include the analysis of journal impact factors and the authors did not have access to any citation index database, which would allow the performance of bibliometric citation analyses.\n\nPublication rate:\nAs shown in Fig. 1, the publications in MEDLINE/PubMed addressing health issues related to Angola date back to 1909. MEDLINE/PubMed, the first bibliographic database in the life sciences, with a focus on biomedicine, mainly covers the literature published from 1946 onwards, but it also includes more dated publications [22]. There has been a remarkable, albeit irregular, increase in the number of publications during the 1990s, with a more sustained increase from 2004 onwards, and a peak in 2013. From 1979 to 2003 (34 years) there were 62 publications, but in the last 10 years (2004–2013) the number increased four-fold (n = 232), suggesting a rampant increment in publication rate (Fig. 1). Additionally, it is noteworthy that the number of publications by the beginning of June 2014 already reached half the figure recorded in 2013, supporting the notion that Angola is motivating a steady increase in health-related scientific publications. This exponential trend was also observed in Palestine, a war zone, where a total of 770 publications were retrieved in the medical and biomedical field across a 10-year period (01 January 2002 to 31 December 2011), averaging approximately 80 articles per year. Interestingly, the number of publications has also increased four-fold during the period 2002–2011, with a stabilization in the last 3 years of the study period [23].Fig. 1Between 1909 and 1973, none of the publications deposited in MEDLINE/PubMed up to June 8, 2014, complied with the inclusion criteria for abstracts about health research in Angola\nBetween 1909 and 1973, none of the publications deposited in MEDLINE/PubMed up to June 8, 2014, complied with the inclusion criteria for abstracts about health research in Angola\nA study published in 2007, which analyzed the geography of the PubMed biomedical publications in Africa between 1996 and 2005, concluded that the contribution of Africa, in particular, was considerably less than that of other continents. Indeed, it was evident that there was a continuous increase in publication output during this period in all African sub-regions, although Angola was located in the lowest quintile with less than two publications per year [17]. In the present study, we found, in the same period, five publications that included researchers that were affiliated with Angolan institutions. However, this did not change the lower position of Angola. Another publication about the scientific production in Public Health and Epidemiology in the WHO African region, in the period 1991–2010, obtained very similar results for Angola, placing it in the lowest quintile, with 11–50 publications [18]. This increase in health research in Angola matches with a recent study about the WHO African Region between 2000 and 2014 [24]. In this study, Angola was located in quintile three, with 100 to 499 articles.\n\nHealth research topics:\nMalaria was the disease that stood out, with 45 publications, followed by HIV infection (n = 26), trypanosomiasis (n = 24), and themes of epidemiology/public health (n = 24). Tuberculosis occupied the tenth position with nine papers (Fig. 2). As a whole, infectious diseases was a topic of 59 % of the publications under analysis (n = 178).Fig. 2Publications are deposited in MEDLINE/PubMed up to June 8, 2014\nPublications are deposited in MEDLINE/PubMed up to June 8, 2014\nThe most investigated topics are aligned with the epidemiological profile of Angola, regarding the main causes of morbidity and mortality (communicable diseases), with a large emphasis on malaria [25]. Interestingly, there was a disproportionate volume of publications on HIV/AIDS relative to tuberculosis, which is unusual given the frequent association of both morbidities [26]. In an assessment of scientific output in public health and epidemiology in Africa, in the period 1991–2010, HIV/AIDS infection was the predominant topic (11.3 %) while malaria accounted for 8.6 % and tuberculosis for 7.1 % [16]. This analysis also considers the issue of the importance of research funding concerning infectious diseases [27]. The trend towards epidemiological transition in sub-Saharan Africa, owing to the increase of chronic non-communicable diseases, is not yet reflected at the scientific publication level, neither in the present study nor in the aforementioned African publications [16, 28–30].\n\nAuthorship and affiliation:\nThe primary affiliation of the first author was most frequently in an Angolan institution (45 publications) (Fig. 3). A total of 150 institutions were listed as the first author’s primary affiliation, revealing a high institutional dispersion; 14.7 % were Angolan institutions. Furthermore, 65 % of publications represented research conducted in universities and institutes or research centres (Fig. 4). The majority of publications by first author affiliation country and the number of academic research institutions were located in Portugal, United States of America, or Brazil (Fig. 5). The high number of publications with first author affiliation in Angola denotes the concern of researchers in linking their work to institutions from the country of study. Nevertheless, this involvement is dispersed across many different institutions, many of which have relatively low track records in publications, namely hospitals and non-governmental organizations. Furthermore, academic research in universities and institutes (or research centres) related to institutional affiliations abroad indicate the need for reinforcing the academic research capacity in Angola.Fig. 3Country denotes the location in which the primary institution of the first author affiliation was based in publications up to June 8, 2014Fig. 4Publications deposited in MEDLINE/PubMed up to June 8, 2014Fig. 5Country denotes the location in which the primary institution of the first author affiliation was based concerning publications on health research in Angola deposited in MEDLINE/PubMed up to June 8, 2014\nCountry denotes the location in which the primary institution of the first author affiliation was based in publications up to June 8, 2014\nPublications deposited in MEDLINE/PubMed up to June 8, 2014\nCountry denotes the location in which the primary institution of the first author affiliation was based concerning publications on health research in Angola deposited in MEDLINE/PubMed up to June 8, 2014\n\nPublications by Angolan researchers:\nAn Angolan researcher was the first author in 58 (19 %) abstracts. The frequency of Angolan first authors or co-authors increased when the first author was affiliated to an Angolan institution (Fig. 6). In the present study, it is noteworthy that one-fifth of all publications had an Angolan as first author. The number of African first authors on publications relating to Africa between 1991 and 2010 has been increasing [16]. This finding was reinforced in the period between 2000 and 2014 in the WHO African Region, and it was found that the contributions of first authors from Africa doubled in this period, although it is recognized that it is still minimal (0.7 % in 2000 and 1.3 % in 2014) [24].Fig. 6Publications on health research in Angola deposited in MEDLINE/PubMed up to June 8, 2014, according to country in which the primary institution of the first author affiliation was based\nPublications on health research in Angola deposited in MEDLINE/PubMed up to June 8, 2014, according to country in which the primary institution of the first author affiliation was based\n\nScientific fields:\nEpidemiological research was by far the most common research area (n = 165) (Fig. 7). Although a few population genetics studies (data not shown) are included in this topic, this finding is in accordance to the burden of infectious diseases in the country. However, the increasing frequency of non-communicable diseases in sub-Saharan Africa is likely to be a matter of concern for research in Africa in coming years.Fig. 7Publications deposited in MEDLINE/PubMed up to June 8, 2014\nPublications deposited in MEDLINE/PubMed up to June 8, 2014\nOn the other hand, a small number of publications (n = 32; 10.6 %) on socioeconomic and professional research, including governance, health policies, health systems management, and human resources is noted. Considering that Angola was a war zone for more than 50 % of its existence as an independent state, we might have expected a larger number of research publications on relevant topics such as the socioeconomic determinants of health (e.g., unemployment, poverty, education, family dissolution, and lifestyles) and the performance of health unities and health systems. However, we recognize the possibility that studies concerning some of those socioeconomic determinants of health may be part of research in social and economic sciences and, therefore, more accessible in other bibliographic databases. Further, the lack of studies on human resources in the various health professions, including studies in medical education, is also noticeable.\nWe expect that the increasing number of health professional schools in Angola, including six new public medical schools, particularly since 2009, will raise interest in these topics, especially if educational curricula at the undergraduate and postgraduate levels in Angola incorporate more programs focused on improving research skills and providing on-the-job training in epidemiology [31].\n\nJournals choice:\nWe also found that, despite publications being widely spread in a variety of journals, 11.4 % concentrated in PLoS ONE and Malaria Journal, both of which are free access journals indexed by common databases (Fig. 8). This finding is not unusual given that malaria was the most common research topic among Angolan publications observed in the present study. Notwithstanding the debate around open access publishing, our findings reinforce the importance of providing readers in financially disadvantaged countries with a wide range of relevant research and information [32, 33].Fig. 8Publications on health research in Angola deposited in MEDLINE/PubMed up to June 8, 2014\nPublications on health research in Angola deposited in MEDLINE/PubMed up to June 8, 2014\n\nLimitations of the study:\nThe fact that Angola is a Portuguese speaking country and the majority of the journals indexed by MEDLINE/PubMed are in English may have introduced a selection bias due to idiom barriers. Though MEDLINE/PubMed represented the vast majority of the publications of the study and the search began at BVS, which included papers published in journals indexed by Scielo, it does not embody all the scientific and biomedical journals. Furthermore, we did not search “grey literature”. Thus, the number of Angolan authors and coauthors might be underestimated. Nevertheless, Angola still does not have an exhaustive electronic database of researchers containing individual publication records. In addition, the incorrect reporting of authors’ nationality and affiliation, observed in a few papers in the present study, may further contribute to underestimation of Angolan-authored publications. Finally, the study did not include the analysis of journal impact factors and the authors did not have access to any citation index database, which would allow the performance of bibliometric citation analyses.\n\nConclusions:\nThis initial contribution characterizing the productivity of health research pertaining to Angola showed that the number of publications has increased steadily over the past 10 years and was mainly focused on infectious diseases, namely malaria. Angola, as the country in which the primary institution of the first author affiliation was based, evidenced the largest number of publications, and about 20 % of the publications included an Angolan as the first author. However, academic institutions in Portugal, the United States of America, and Brazil contributed more than universities and research centres in Angola to research publication. The research was largely epidemiological, and remarkably, with a very small number of publications on economic and professional issues, involving themes such as health policy, governance, and management of health systems and human resources.\nIn Angola, with the large expansion of higher education since 2009, universities should build and strengthen their capacities in research and human resources training. Including research training in the curricula of pre-graduate courses in health sciences will contribute to graduate professionals able to act as agents responsible for change, competent human resource managers, and promoters of evidence-based policies [34]. The alignment of the national health research agenda with the national program of health development and the wide use of multilateral international cooperation are critical to develop the operational tools towards universal health coverage in Angola as conceived by the WHO [1]. In summary, this work highlights the rapid increase of scientific publications related to Angola but also a need for reinforcing academic-driven research in this country.\n\n"},"whole_article_abstract":{"kind":"string","value":"Background:\nHealth research driven by the healthcare demands of the population can provide an informative evidence base to support decision-making processes on health policies, programmes, and practices. This paper surveyed the production of scientific research concerning health in Angola, specifically to access the publication rate over time, the main research topics and scientific fields, and the contribution of Angolan researchers and institutions.\n\nMethods:\nThe study focused on data collected in a retrospective literature search in Biblioteca Virtual em Saúde (BVS) as of June 8, 2014, with the keyword \"Angola\" and on content information in correspondent publications deposited in PubMed.\n\nResults:\nBVS generated 1,029 hits, 74.6 % of which were deposited in PubMed where 301 abstracts were described. From 1979 to 2003, there were 62 publications and in 2004-2013 the quantity increased four-fold (n = 232); malaria was the most frequent topic (n = 42). Angola was the country with the largest number of publications, taking into account the primary affiliation of the first author (n = 45). Universities, institutes, or research centres accounted for 65 % of the publications and in descending order Portugal, Brazil, and the United States of America occupied the three first positions. Epidemiology was by far the most frequent field of research (n = 165).\n\nConclusions:\nThe number of publications has increased steadily over the past 10 years, with predominance on malaria topics. Angola was the country with the largest number of major affiliations of the first author, but the contribution of Angolan institutions was relatively low, indicating a need to reinforce academic research institutions in the country."},"background_conclusion_text":{"kind":"string","value":"Background:\nThe concept of research for health, expressed on the World Health Report 2013, covers a broader range of investigations than health research. This wider view of research will become increasingly important in the transition from the United Nations Millennium Development Goals to a post-2015 sustainable development agenda [1].\nIn a more restricted concept, health research has the potential to contribute to the identification of social and economic determinants of health. In the context of implementation research, it is a major basis for decision-making processes on health policies, programmes, and practices, especially when driven by the demand of the health problems of the population [2, 3].\nMoreover, conducting more health research can stimulate countries to strengthen their capacity to produce and use such research to guide decision-making, improve the efficiency of the investments, increase accountability in the work of researchers, and contribute substantially to the research training of human resources in developing countries [4, 5]. In the perspective of the World Health Organization (WHO), health research must be founded on building capacity to step up health research systems and support demand-driven health problems, particularly in low- and middle-income countries. Additionally, health research must be embedded with standards for best practices and ensure that quality evidence is turned into affordable health technologies and evidence-informed policy [6].\nHowever, at the global level, the proportion of health research that considers these aspects is negligible [7]. In particular, evidence shows that the research output in developing countries is considerably less than in developed countries [8]. Therefore, it is crucial to get an accurate picture of the landscape of the scientific research produced in developing countries in order to identify gaps critical for social development [9]. In sub-Saharan Africa, in particular, the production of health research can affect teaching, the quality of undergraduate training, career promotion, and translational research relevant to the country, which is comparable to developed countries [5].\nThere have been documented bibliometric studies of research production in Africa, as a whole, as well as in other continents. This then motivated studies of the chronological progression of research within individual African countries [10–18]. These publications demonstrate the feebleness of African authorship in the most cited scientific publications in health [19].\nAngola, a Portuguese-speaking country, is independent since 1975 and has spent 27 years in progressive socio-political instability, with the drawback of the civil war that ended 12 years ago. Nevertheless, Angola is making progress towards the achievement of the health Millennium Development Goals but is still far from achieving them [1]. The reconstruction of the national health system and policy will benefit from the knowledge of the factors influencing the success of certain interventions and the measure of their impact on populations. Therefore, health research will contribute to increased efficiency and effectiveness of health policy and decision making processes in Angola on programmes and practices. Support for science in Angola is less than 0.1 % of its GDP, compared to a world average of 1.7 % [20]. Consequently, a more serious commitment would be needed to build local and national capacity to accelerate the improvement of human resources training for health research. In this context, it becomes even more appropriate to carry out a survey on what has been produced as the result of health research. When searching the literature on health research in Angola, we found only one publication dated from 1953 which linked research to health, entitled “Scientific Research and the Health Status of Angola” [21]. The lack of studies on health research produced in Angola in the last 61 years motivated this work.\nWe carried out a non-systematic literature survey to evaluate the progression of health research pertaining to Angola, answering specifically the following questions: 1) how has the number of publications evolved over the years? 2) what are the most published topics? 3) what is the level of involvement of Angolan researchers and institutions in this published research? and 4) what are the most represented research fields?\nWe expect that this study will contribute to increasing the current state of knowledge on health research related to Angola and identify gaps that may guide the WHO recommendations on health research and consequently the adoption of effective interventions that will strengthen health research in Angola.\n\nConclusions:\nThis initial contribution characterizing the productivity of health research pertaining to Angola showed that the number of publications has increased steadily over the past 10 years and was mainly focused on infectious diseases, namely malaria. Angola, as the country in which the primary institution of the first author affiliation was based, evidenced the largest number of publications, and about 20 % of the publications included an Angolan as the first author. However, academic institutions in Portugal, the United States of America, and Brazil contributed more than universities and research centres in Angola to research publication. The research was largely epidemiological, and remarkably, with a very small number of publications on economic and professional issues, involving themes such as health policy, governance, and management of health systems and human resources.\nIn Angola, with the large expansion of higher education since 2009, universities should build and strengthen their capacities in research and human resources training. Including research training in the curricula of pre-graduate courses in health sciences will contribute to graduate professionals able to act as agents responsible for change, competent human resource managers, and promoters of evidence-based policies [34]. The alignment of the national health research agenda with the national program of health development and the wide use of multilateral international cooperation are critical to develop the operational tools towards universal health coverage in Angola as conceived by the WHO [1]. In summary, this work highlights the rapid increase of scientific publications related to Angola but also a need for reinforcing academic-driven research in this country."},"background_conclusion_abstract":{"kind":"string","value":"Background:\nHealth research driven by the healthcare demands of the population can provide an informative evidence base to support decision-making processes on health policies, programmes, and practices. This paper surveyed the production of scientific research concerning health in Angola, specifically to access the publication rate over time, the main research topics and scientific fields, and the contribution of Angolan researchers and institutions.\n\nMethods:\nThe study focused on data collected in a retrospective literature search in Biblioteca Virtual em Saúde (BVS) as of June 8, 2014, with the keyword \"Angola\" and on content information in correspondent publications deposited in PubMed.\n\nResults:\nBVS generated 1,029 hits, 74.6 % of which were deposited in PubMed where 301 abstracts were described. From 1979 to 2003, there were 62 publications and in 2004-2013 the quantity increased four-fold (n = 232); malaria was the most frequent topic (n = 42). Angola was the country with the largest number of publications, taking into account the primary affiliation of the first author (n = 45). Universities, institutes, or research centres accounted for 65 % of the publications and in descending order Portugal, Brazil, and the United States of America occupied the three first positions. Epidemiology was by far the most frequent field of research (n = 165).\n\nConclusions:\nThe number of publications has increased steadily over the past 10 years, with predominance on malaria topics. Angola was the country with the largest number of major affiliations of the first author, but the contribution of Angolan institutions was relatively low, indicating a need to reinforce academic research institutions in the country."},"whole_article_text_length":{"kind":"number","value":8108,"string":"8,108"},"whole_article_abstract_length":{"kind":"number","value":330,"string":"330"},"other_sections_lengths":{"kind":"list like","value":[549,300,347,214,352,136,188],"string":"[\n 549,\n 300,\n 347,\n 214,\n 352,\n 136,\n 188\n]"},"num_sections":{"kind":"number","value":11,"string":"11"},"most_frequent_words":{"kind":"list like","value":["publications","research","health","angola","pubmed","medline","medline pubmed","2014","june","health research"],"string":"[\n \"publications\",\n \"research\",\n \"health\",\n \"angola\",\n \"pubmed\",\n \"medline\",\n \"medline pubmed\",\n \"2014\",\n \"june\",\n \"health research\"\n]"},"keybert_topics":{"kind":"list like","value":["research includes health","health research produced","national health research","conducting health research","health research agenda"],"string":"[\n \"research includes health\",\n \"health research produced\",\n \"national health research\",\n \"conducting health research\",\n \"health research agenda\"\n]"},"annotated_base_background_abstract_prompt":{"kind":"string","value":""},"annotated_base_methods_abstract_prompt":{"kind":"null"},"annotated_base_results_abstract_prompt":{"kind":"string","value":""},"annotated_base_conclusions_abstract_prompt":{"kind":"string","value":""},"annotated_base_whole_article_abstract_prompt":{"kind":"string","value":""},"annotated_base_background_conclusion_abstract_prompt":{"kind":"string","value":""},"annotated_keywords_background_abstract_prompt":{"kind":"string","value":"[CONTENT] Angola | Health research | Health research centres | Universities [SUMMARY]"},"annotated_keywords_methods_abstract_prompt":{"kind":"null"},"annotated_keywords_results_abstract_prompt":{"kind":"string","value":"[CONTENT] Angola | Health research | Health research centres | Universities [SUMMARY]"},"annotated_keywords_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] Angola | Health research | Health research centres | Universities [SUMMARY]"},"annotated_keywords_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] Angola | Health research | Health research centres | Universities [SUMMARY]"},"annotated_keywords_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] Angola | Health research | Health research centres | Universities [SUMMARY]"},"annotated_mesh_background_abstract_prompt":{"kind":"string","value":"[CONTENT] Academies and Institutes | Angola | Authorship | Bibliometrics | Biomedical Research | Brazil | Epidemiologic Studies | Humans | Malaria | Portugal | Publishing | United States | Universities [SUMMARY]"},"annotated_mesh_methods_abstract_prompt":{"kind":"null"},"annotated_mesh_results_abstract_prompt":{"kind":"string","value":"[CONTENT] Academies and Institutes | Angola | Authorship | Bibliometrics | Biomedical Research | Brazil | Epidemiologic Studies | Humans | Malaria | Portugal | Publishing | United States | Universities [SUMMARY]"},"annotated_mesh_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] Academies and Institutes | Angola | Authorship | Bibliometrics | Biomedical Research | Brazil | Epidemiologic Studies | Humans | Malaria | Portugal | Publishing | United States | Universities [SUMMARY]"},"annotated_mesh_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] Academies and Institutes | Angola | Authorship | Bibliometrics | Biomedical Research | Brazil | Epidemiologic Studies | Humans | Malaria | Portugal | Publishing | United States | Universities [SUMMARY]"},"annotated_mesh_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] Academies and Institutes | Angola | Authorship | Bibliometrics | Biomedical Research | Brazil | Epidemiologic Studies | Humans | Malaria | Portugal | Publishing | United States | Universities [SUMMARY]"},"annotated_keybert_background_abstract_prompt":{"kind":"string","value":"[CONTENT] research includes health | health research produced | national health research | conducting health research | health research agenda [SUMMARY]"},"annotated_keybert_methods_abstract_prompt":{"kind":"null"},"annotated_keybert_results_abstract_prompt":{"kind":"string","value":"[CONTENT] research includes health | health research produced | national health research | conducting health research | health research agenda [SUMMARY]"},"annotated_keybert_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] research includes health | health research produced | national health research | conducting 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[SUMMARY]"},"annotated_tf_idf_methods_abstract_prompt":{"kind":"null"},"annotated_tf_idf_results_abstract_prompt":{"kind":"string","value":"[CONTENT] publications | research | health | angola | pubmed | 2014 | medline | medline pubmed | june | author [SUMMARY]"},"annotated_tf_idf_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] research | health | angola | graduate | human | publications | number publications | national | academic | training [SUMMARY]"},"annotated_tf_idf_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] research | health | publications | angola | author | 2014 | pubmed | medline | medline pubmed | june [SUMMARY]"},"annotated_tf_idf_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] research | health | publications | angola | author | 2014 | pubmed | medline | medline pubmed | june [SUMMARY]"},"annotated_entity_plan_background_abstract_prompt":{"kind":"string","value":"[CONTENT] ||| Angola | Angolan [SUMMARY]"},"annotated_entity_plan_methods_abstract_prompt":{"kind":"null"},"annotated_entity_plan_results_abstract_prompt":{"kind":"string","value":"[CONTENT] 1,029 | 74.6 % | PubMed | 301 ||| 1979 | 2003 | 62 | 2004-2013 | four-fold | 232 | 42 ||| Angola | first | 45 ||| 65 % | Portugal | Brazil | the United States of America | three ||| 165 [SUMMARY]"},"annotated_entity_plan_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] the past 10 years ||| Angola | first | Angolan [SUMMARY]"},"annotated_entity_plan_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] ||| Angola | Angolan ||| Biblioteca Virtual | Saúde | June 8, 2014 | Angola | PubMed ||| 1,029 | 74.6 % | PubMed | 301 ||| 1979 | 2003 | 62 | 2004-2013 | four-fold | 232 | 42 ||| Angola | first | 45 ||| 65 % | Portugal | Brazil | the United States of America | three ||| 165 ||| the past 10 years ||| Angola | first | Angolan [SUMMARY]"},"annotated_entity_plan_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] ||| Angola | Angolan ||| Biblioteca Virtual | Saúde | June 8, 2014 | Angola | PubMed ||| 1,029 | 74.6 % | PubMed | 301 ||| 1979 | 2003 | 62 | 2004-2013 | four-fold | 232 | 42 ||| Angola | first | 45 ||| 65 % | Portugal | Brazil | the United States of America | three ||| 165 ||| the past 10 years ||| Angola | first | Angolan [SUMMARY]"}}},{"rowIdx":268,"cells":{"title":{"kind":"string","value":"Medial Ball and Socket Total Knee Arthroplasty in Indian Population: 5-Year Clinical Results."},"pmid":{"kind":"string","value":"35251545"},"background_abstract":{"kind":"string","value":"Medial pivot total knee arthroplasty aims to restore native knee kinematics through highly conforming medial tibiofemoral articulation with survival comparable to contemporary knee designs. The aim of this study was to report preliminary clinical results of medial pivot total knee arthroplasty in an Indian population."},"background_abstract_label":{"kind":"string","value":"BACKGROUND"},"methods_abstract":{"kind":"string","value":"A retrospective analysis of 45 patients (average age, 62 years; 40 women and 5 men) with end-stage arthritis (Kellgren-Lawrence grade 4) operated with a medial pivot prosthesis was done. All patients were assessed using Knee Society Score (satisfaction, expectation, and functional scores) and Oxford Knee Score, and range of motion was recorded at the end of 5-year postoperative follow-up. In addition, all patients underwent standardized radiological assessment."},"methods_abstract_label":{"kind":"string","value":"METHODS"},"results_abstract":{"kind":"string","value":"At the final follow-up, patients reported significant improvement in mean Knee Society Score (satisfaction, expectation, and functional scores) and Oxford Knee Score (p < 0.05). The mean range of motion achieved at the end of 5 years ranged from 0° (extension) to 118.4° (further flexion). There was no evidence of loosening or osteolysis at a minimum follow-up of 5 years."},"results_abstract_label":{"kind":"string","value":"RESULTS"},"conclusions_abstract":{"kind":"string","value":"These results demonstrated satisfactory clinical and radiological outcomes at 5 years after total knee arthroplasty with a medial pivot design, which may be related to better replication of natural knee kinematics with the medial pivot knee and inherent advantages of this design."},"conclusions_abstract_label":{"kind":"string","value":"CONCLUSIONS"},"mesh_descriptor_names":{"kind":"list like","value":["Arthroplasty, Replacement, Knee","Female","Follow-Up Studies","Humans","Knee Joint","Knee Prosthesis","Male","Middle Aged","Osteoarthritis, Knee","Prosthesis Design","Range of Motion, Articular","Retrospective Studies"],"string":"[\n \"Arthroplasty, Replacement, Knee\",\n \"Female\",\n \"Follow-Up Studies\",\n \"Humans\",\n \"Knee Joint\",\n \"Knee Prosthesis\",\n \"Male\",\n \"Middle Aged\",\n \"Osteoarthritis, Knee\",\n \"Prosthesis Design\",\n \"Range of Motion, Articular\",\n \"Retrospective Studies\"\n]"},"pmcid":{"kind":"string","value":"8858900"},"background_title":{"kind":"null"},"background_text":{"kind":"null"},"methods_title":{"kind":"string","value":"METHODS"},"methods_text":{"kind":"string","value":"A retrospective analysis of prospectively collected data of adult patients with end-stage knee arthritis (Kellgren-Lawrence grade 4) operated in a University Teaching Hospital from January 2015 to June 2015 was performed. A total of 245 patients were identified. Only patients (45 patients) who underwent MP TKA (ADVANCE Medial Pivot, MicroPort Orthopedics, Arlington, TN, USA) were included in the study. MP TKA was introduced to our system in 2014. Institutional Review Board approval was taken for the study (IRB No. IEC-486).\nThe primary outcome measures for this study were patient satisfaction and met-expectations with the MP prosthesis using Knee Society Score (KSS) satisfaction, expectation, and functional scores. The secondary outcome measures were Oxford Knee Score (OKS), range of motion, and radiological assessment.\nAll TKAs were performed by the same surgeon (RM) using the standard medial parapatellar approach. All patients were given an antibiotic (Cefuroxime axetil 1.5 g) 30–45 minutes prior to skin incision. All patients were given intravenous tranexamic acid (15 mg/kg) 10–15 minutes prior to tourniquet deflation. The posterior cruciate ligament (PCL) was sacrificed in all cases and implants were fixed with a single mix of Palacos bone cement with gentamicin (Heraeus Medical, Warsaw, IN, USA). Whenever there was mediolateral overhang as per the anteroposterior size of the femoral component, the authors used a stature femoral component (narrow mediolateral) available with this prosthesis. The stature femoral component was used in 70% of the patients and the most common size of femur and tibia used in the study was 2. Patelloplasty was done. Surgical and rehabilitation protocol was standardized for all patients.\nFor each patient, demographic data, type of arthritis, body mass index (BMI), and preoperative scores were documented from the patient record and postoperative evaluation was done at the latest follow-up using KSS (satisfaction, expectation, and functional scores), OKS, and range of motion (measured using a goniometer). Flexion deformity, if any, was also recorded. Serial standard preoperative and postoperative radiographs at baseline and the latest follow-up were evaluated by an independent radiologist (NS) as per the protocol defined by the modern knee society radiographic evaluation system (Figs. 1, 2, 3).14) TKA loosening was defined as a complete radiolucent line of more than 2 mm in width, a visible cement mantle fracture around the component, or a change in component position. The interclass correlation coefficient was excellent (0.92) for clinical evaluation and good (0.84) for radiographic evaluation.15)\n Statistical Analysis Baseline characteristics were described for each patient using mean ± standard deviation, median (range), or frequency/percentage. KSS (satisfaction, expectation, and functional scores) and OKS were compared using a generalized estimating equation because the observations were correlated preoperatively and postoperatively. Within group comparison for all other parameters was performed using paired t-test or Wilcoxon rank-sum test (Mann-Whitney U-test). Correlation between two continuous variables was analysed using Pearson correlation coefficients. All analyses were conducted using Stata ver. 13.0 (Stata Corp., College Station, TX, USA). A p-value < 0.05 was considered statistically significant.\nBaseline characteristics were described for each patient using mean ± standard deviation, median (range), or frequency/percentage. KSS (satisfaction, expectation, and functional scores) and OKS were compared using a generalized estimating equation because the observations were correlated preoperatively and postoperatively. Within group comparison for all other parameters was performed using paired t-test or Wilcoxon rank-sum test (Mann-Whitney U-test). Correlation between two continuous variables was analysed using Pearson correlation coefficients. All analyses were conducted using Stata ver. 13.0 (Stata Corp., College Station, TX, USA). A p-value < 0.05 was considered statistically significant."},"results_title":{"kind":"string","value":"RESULTS"},"results_text":{"kind":"string","value":"All patients reported improved satisfaction and functional outcome after TKA at a minimum follow-up of 5 years. The average age at the time of TKA was 62 years and 88.9% of the patients were women. There were 26 left knees and 24 right knees. The average BMI was 28.4 kg/m2. Five patients underwent bilateral TKA (total 50 MP TKAs). There were 4 cases of inflammatory arthritis, while the rest were degenerative osteoarthritis (Table 1).\nThe mean preoperative KSS was 4.4 ± 1.7 (range, 0–6) for satisfaction score, 11.8 ± 1.6 (range, 7–15) for expectation score, and 19.6 ± 4.4 (range, 6–27) for functional score. The mean postoperative KSS was 34.6 ± 3.0 (range, 26–38) for satisfaction score, 12.5 ± 1.4 (range, 10–15) for expectation score, and 61.3 ± 6.7 (range, 42–69) for functional score at the latest follow-up. The mean OKS was 9.5 ± 2.5 (range, 4–12) preoperatively and 44.2 ± 2.2 (range, 40–48) at the latest follow-up. Patients reported clinically and statistically significant improvements (p < 0.001) for all patient-reported outcome measures (Table 2). The mean range of motion showed a statistically significant improvement from 97.6° ± 11.2° (70°–120°) to 118.4° ± 8.4° (100°–130°). Three patients had flexion deformity of less than 10° at the final follow-up.\nThere was no evidence of implant failure or subsidence in any of the postoperative radiographs. Nonprogressive radiolucent lines (< 2 mm) were seen in 5 TKAs. The average tibiofemoral angle improved from a mean of 4.8° varus (2.2°–7.4°) preoperatively to a mean of 4.1° valgus (3.3°–4.9°) at the latest follow-up (Table 3). None of the patients were lost to follow-up or died due to surgery-related or unrelated causes. One patient with rheumatoid arthritis developed mid-substance quadriceps tendon rupture, which was managed with mesh repair. The patient did well and was able to perform activities of daily living."},"conclusions_title":{"kind":"null"},"conclusions_text":{"kind":"null"},"other_sections_titles":{"kind":"list like","value":["Statistical Analysis"],"string":"[\n \"Statistical Analysis\"\n]"},"other_sections_texts":{"kind":"list like","value":["Baseline characteristics were described for each patient using mean ± standard deviation, median (range), or frequency/percentage. KSS (satisfaction, expectation, and functional scores) and OKS were compared using a generalized estimating equation because the observations were correlated preoperatively and postoperatively. Within group comparison for all other parameters was performed using paired t-test or Wilcoxon rank-sum test (Mann-Whitney U-test). Correlation between two continuous variables was analysed using Pearson correlation coefficients. All analyses were conducted using Stata ver. 13.0 (Stata Corp., College Station, TX, USA). A p-value < 0.05 was considered statistically significant."],"string":"[\n \"Baseline characteristics were described for each patient using mean ± standard deviation, median (range), or frequency/percentage. KSS (satisfaction, expectation, and functional scores) and OKS were compared using a generalized estimating equation because the observations were correlated preoperatively and postoperatively. Within group comparison for all other parameters was performed using paired t-test or Wilcoxon rank-sum test (Mann-Whitney U-test). Correlation between two continuous variables was analysed using Pearson correlation coefficients. All analyses were conducted using Stata ver. 13.0 (Stata Corp., College Station, TX, USA). A p-value < 0.05 was considered statistically significant.\"\n]"},"other_sections_sec_types":{"kind":"list like","value":[null],"string":"[\n null\n]"},"all_sections_titles":{"kind":"list like","value":["METHODS","Statistical Analysis","RESULTS","DISCUSSION"],"string":"[\n \"METHODS\",\n \"Statistical Analysis\",\n \"RESULTS\",\n \"DISCUSSION\"\n]"},"all_sections_texts":{"kind":"list like","value":["A retrospective analysis of prospectively collected data of adult patients with end-stage knee arthritis (Kellgren-Lawrence grade 4) operated in a University Teaching Hospital from January 2015 to June 2015 was performed. A total of 245 patients were identified. Only patients (45 patients) who underwent MP TKA (ADVANCE Medial Pivot, MicroPort Orthopedics, Arlington, TN, USA) were included in the study. MP TKA was introduced to our system in 2014. Institutional Review Board approval was taken for the study (IRB No. IEC-486).\nThe primary outcome measures for this study were patient satisfaction and met-expectations with the MP prosthesis using Knee Society Score (KSS) satisfaction, expectation, and functional scores. The secondary outcome measures were Oxford Knee Score (OKS), range of motion, and radiological assessment.\nAll TKAs were performed by the same surgeon (RM) using the standard medial parapatellar approach. All patients were given an antibiotic (Cefuroxime axetil 1.5 g) 30–45 minutes prior to skin incision. All patients were given intravenous tranexamic acid (15 mg/kg) 10–15 minutes prior to tourniquet deflation. The posterior cruciate ligament (PCL) was sacrificed in all cases and implants were fixed with a single mix of Palacos bone cement with gentamicin (Heraeus Medical, Warsaw, IN, USA). Whenever there was mediolateral overhang as per the anteroposterior size of the femoral component, the authors used a stature femoral component (narrow mediolateral) available with this prosthesis. The stature femoral component was used in 70% of the patients and the most common size of femur and tibia used in the study was 2. Patelloplasty was done. Surgical and rehabilitation protocol was standardized for all patients.\nFor each patient, demographic data, type of arthritis, body mass index (BMI), and preoperative scores were documented from the patient record and postoperative evaluation was done at the latest follow-up using KSS (satisfaction, expectation, and functional scores), OKS, and range of motion (measured using a goniometer). Flexion deformity, if any, was also recorded. Serial standard preoperative and postoperative radiographs at baseline and the latest follow-up were evaluated by an independent radiologist (NS) as per the protocol defined by the modern knee society radiographic evaluation system (Figs. 1, 2, 3).14) TKA loosening was defined as a complete radiolucent line of more than 2 mm in width, a visible cement mantle fracture around the component, or a change in component position. The interclass correlation coefficient was excellent (0.92) for clinical evaluation and good (0.84) for radiographic evaluation.15)\n Statistical Analysis Baseline characteristics were described for each patient using mean ± standard deviation, median (range), or frequency/percentage. KSS (satisfaction, expectation, and functional scores) and OKS were compared using a generalized estimating equation because the observations were correlated preoperatively and postoperatively. Within group comparison for all other parameters was performed using paired t-test or Wilcoxon rank-sum test (Mann-Whitney U-test). Correlation between two continuous variables was analysed using Pearson correlation coefficients. All analyses were conducted using Stata ver. 13.0 (Stata Corp., College Station, TX, USA). A p-value < 0.05 was considered statistically significant.\nBaseline characteristics were described for each patient using mean ± standard deviation, median (range), or frequency/percentage. KSS (satisfaction, expectation, and functional scores) and OKS were compared using a generalized estimating equation because the observations were correlated preoperatively and postoperatively. Within group comparison for all other parameters was performed using paired t-test or Wilcoxon rank-sum test (Mann-Whitney U-test). Correlation between two continuous variables was analysed using Pearson correlation coefficients. All analyses were conducted using Stata ver. 13.0 (Stata Corp., College Station, TX, USA). A p-value < 0.05 was considered statistically significant.","Baseline characteristics were described for each patient using mean ± standard deviation, median (range), or frequency/percentage. KSS (satisfaction, expectation, and functional scores) and OKS were compared using a generalized estimating equation because the observations were correlated preoperatively and postoperatively. Within group comparison for all other parameters was performed using paired t-test or Wilcoxon rank-sum test (Mann-Whitney U-test). Correlation between two continuous variables was analysed using Pearson correlation coefficients. All analyses were conducted using Stata ver. 13.0 (Stata Corp., College Station, TX, USA). A p-value < 0.05 was considered statistically significant.","All patients reported improved satisfaction and functional outcome after TKA at a minimum follow-up of 5 years. The average age at the time of TKA was 62 years and 88.9% of the patients were women. There were 26 left knees and 24 right knees. The average BMI was 28.4 kg/m2. Five patients underwent bilateral TKA (total 50 MP TKAs). There were 4 cases of inflammatory arthritis, while the rest were degenerative osteoarthritis (Table 1).\nThe mean preoperative KSS was 4.4 ± 1.7 (range, 0–6) for satisfaction score, 11.8 ± 1.6 (range, 7–15) for expectation score, and 19.6 ± 4.4 (range, 6–27) for functional score. The mean postoperative KSS was 34.6 ± 3.0 (range, 26–38) for satisfaction score, 12.5 ± 1.4 (range, 10–15) for expectation score, and 61.3 ± 6.7 (range, 42–69) for functional score at the latest follow-up. The mean OKS was 9.5 ± 2.5 (range, 4–12) preoperatively and 44.2 ± 2.2 (range, 40–48) at the latest follow-up. Patients reported clinically and statistically significant improvements (p < 0.001) for all patient-reported outcome measures (Table 2). The mean range of motion showed a statistically significant improvement from 97.6° ± 11.2° (70°–120°) to 118.4° ± 8.4° (100°–130°). Three patients had flexion deformity of less than 10° at the final follow-up.\nThere was no evidence of implant failure or subsidence in any of the postoperative radiographs. Nonprogressive radiolucent lines (< 2 mm) were seen in 5 TKAs. The average tibiofemoral angle improved from a mean of 4.8° varus (2.2°–7.4°) preoperatively to a mean of 4.1° valgus (3.3°–4.9°) at the latest follow-up (Table 3). None of the patients were lost to follow-up or died due to surgery-related or unrelated causes. One patient with rheumatoid arthritis developed mid-substance quadriceps tendon rupture, which was managed with mesh repair. The patient did well and was able to perform activities of daily living.","Traditional methods of TKA have failed to recreate normal knee kinematics, which may contribute to up to 20% suboptimal outcomes.16) Attempts have been made to refine prostheses to achieve normal kinematics and elongate implant survivorship. The most important finding of this study is satisfactory outcome with MP-TKA without any major complications at a minimum follow-up of 5 years in the Indian population. This may be related to better replication of natural knee kinematics with MP knees and inherent advantages of this design.\nThe clinical results of our study are encouraging and satisfactory with all the patients reporting significant improvement in the patient-reported outcome measures. These results are in agreement with other studies in the literature.7891011) All patients in the present study felt their knees stable. None of the patients developed any symptomatic anteroposterior instability. Single radius curvature of the femoral component in MP TKA maximizes quadriceps efficiency, limits paradoxical roll forward, and provides potential advantages to the extensor apparatus.2) Pritchett17) reported that 77% of patients preferred MP to the posterior-stabilized (PS) design. Macheras et al.10) reported excellent long-term clinical results with an MP design and survival of 98.8% at 17 years of follow-up.\nThe average flexion reported in the present study was 118.4°, which is comparable to other studies with similar knee designs.7891011171819) Although the maximum flexion was less than that in other high-flex knees and mobile-bearing designs, this may be related to late presentation with advanced arthritis in the Indian population.20) The advantage of the MP design is that patients feel their knees are stable in activities requiring deep flexion due to its medial conformity. Furthermore, it has been reported that the increased range of motion correlates with increased satisfaction after TKA in Asian patients.21)\nThe medial conformity of this design also obviates the need for ligament release for balancing the knee, which is required in severe varus deformity; severe deformities necessitate PCL release to balance the knees. The posterior-stabilized type of MP TKA does not require a box cut for post-cam mechanism; instead, its ultracongruent polyethylene with anterior lip provides anteroposterior stability without any post. This also preserves bone stock in already smaller bones in Asian patients and, if required, for future revision surgery.18) Bae et al.22) showed that there was no difference in clinical and radiological results of TKA with an MP design regardless of whether the PCL was retained (67 knees) or sacrificed (70 knees).\nThe radiographic results were similar to those of other studies.7891011171819) There was no evidence of loosening or osteolysis in the present study, which may be attributed to the fact that the MP design produces fewer wear particles due to its ultracongruent nature.23) We note certain inherent limitations of the study, such as the single center study, small number of patients, retrospective study design, absence of control group, and relatively short follow-up. The current study has demonstrated satisfactory clinical and radiological outcomes at 5 years following MP-TKA in an Indian population."],"string":"[\n \"A retrospective analysis of prospectively collected data of adult patients with end-stage knee arthritis (Kellgren-Lawrence grade 4) operated in a University Teaching Hospital from January 2015 to June 2015 was performed. A total of 245 patients were identified. Only patients (45 patients) who underwent MP TKA (ADVANCE Medial Pivot, MicroPort Orthopedics, Arlington, TN, USA) were included in the study. MP TKA was introduced to our system in 2014. Institutional Review Board approval was taken for the study (IRB No. IEC-486).\\nThe primary outcome measures for this study were patient satisfaction and met-expectations with the MP prosthesis using Knee Society Score (KSS) satisfaction, expectation, and functional scores. The secondary outcome measures were Oxford Knee Score (OKS), range of motion, and radiological assessment.\\nAll TKAs were performed by the same surgeon (RM) using the standard medial parapatellar approach. All patients were given an antibiotic (Cefuroxime axetil 1.5 g) 30–45 minutes prior to skin incision. All patients were given intravenous tranexamic acid (15 mg/kg) 10–15 minutes prior to tourniquet deflation. The posterior cruciate ligament (PCL) was sacrificed in all cases and implants were fixed with a single mix of Palacos bone cement with gentamicin (Heraeus Medical, Warsaw, IN, USA). Whenever there was mediolateral overhang as per the anteroposterior size of the femoral component, the authors used a stature femoral component (narrow mediolateral) available with this prosthesis. The stature femoral component was used in 70% of the patients and the most common size of femur and tibia used in the study was 2. Patelloplasty was done. Surgical and rehabilitation protocol was standardized for all patients.\\nFor each patient, demographic data, type of arthritis, body mass index (BMI), and preoperative scores were documented from the patient record and postoperative evaluation was done at the latest follow-up using KSS (satisfaction, expectation, and functional scores), OKS, and range of motion (measured using a goniometer). Flexion deformity, if any, was also recorded. Serial standard preoperative and postoperative radiographs at baseline and the latest follow-up were evaluated by an independent radiologist (NS) as per the protocol defined by the modern knee society radiographic evaluation system (Figs. 1, 2, 3).14) TKA loosening was defined as a complete radiolucent line of more than 2 mm in width, a visible cement mantle fracture around the component, or a change in component position. The interclass correlation coefficient was excellent (0.92) for clinical evaluation and good (0.84) for radiographic evaluation.15)\\n Statistical Analysis Baseline characteristics were described for each patient using mean ± standard deviation, median (range), or frequency/percentage. KSS (satisfaction, expectation, and functional scores) and OKS were compared using a generalized estimating equation because the observations were correlated preoperatively and postoperatively. Within group comparison for all other parameters was performed using paired t-test or Wilcoxon rank-sum test (Mann-Whitney U-test). Correlation between two continuous variables was analysed using Pearson correlation coefficients. All analyses were conducted using Stata ver. 13.0 (Stata Corp., College Station, TX, USA). A p-value < 0.05 was considered statistically significant.\\nBaseline characteristics were described for each patient using mean ± standard deviation, median (range), or frequency/percentage. KSS (satisfaction, expectation, and functional scores) and OKS were compared using a generalized estimating equation because the observations were correlated preoperatively and postoperatively. Within group comparison for all other parameters was performed using paired t-test or Wilcoxon rank-sum test (Mann-Whitney U-test). Correlation between two continuous variables was analysed using Pearson correlation coefficients. All analyses were conducted using Stata ver. 13.0 (Stata Corp., College Station, TX, USA). A p-value < 0.05 was considered statistically significant.\",\n \"Baseline characteristics were described for each patient using mean ± standard deviation, median (range), or frequency/percentage. KSS (satisfaction, expectation, and functional scores) and OKS were compared using a generalized estimating equation because the observations were correlated preoperatively and postoperatively. Within group comparison for all other parameters was performed using paired t-test or Wilcoxon rank-sum test (Mann-Whitney U-test). Correlation between two continuous variables was analysed using Pearson correlation coefficients. All analyses were conducted using Stata ver. 13.0 (Stata Corp., College Station, TX, USA). A p-value < 0.05 was considered statistically significant.\",\n \"All patients reported improved satisfaction and functional outcome after TKA at a minimum follow-up of 5 years. The average age at the time of TKA was 62 years and 88.9% of the patients were women. There were 26 left knees and 24 right knees. The average BMI was 28.4 kg/m2. Five patients underwent bilateral TKA (total 50 MP TKAs). There were 4 cases of inflammatory arthritis, while the rest were degenerative osteoarthritis (Table 1).\\nThe mean preoperative KSS was 4.4 ± 1.7 (range, 0–6) for satisfaction score, 11.8 ± 1.6 (range, 7–15) for expectation score, and 19.6 ± 4.4 (range, 6–27) for functional score. The mean postoperative KSS was 34.6 ± 3.0 (range, 26–38) for satisfaction score, 12.5 ± 1.4 (range, 10–15) for expectation score, and 61.3 ± 6.7 (range, 42–69) for functional score at the latest follow-up. The mean OKS was 9.5 ± 2.5 (range, 4–12) preoperatively and 44.2 ± 2.2 (range, 40–48) at the latest follow-up. Patients reported clinically and statistically significant improvements (p < 0.001) for all patient-reported outcome measures (Table 2). The mean range of motion showed a statistically significant improvement from 97.6° ± 11.2° (70°–120°) to 118.4° ± 8.4° (100°–130°). Three patients had flexion deformity of less than 10° at the final follow-up.\\nThere was no evidence of implant failure or subsidence in any of the postoperative radiographs. Nonprogressive radiolucent lines (< 2 mm) were seen in 5 TKAs. The average tibiofemoral angle improved from a mean of 4.8° varus (2.2°–7.4°) preoperatively to a mean of 4.1° valgus (3.3°–4.9°) at the latest follow-up (Table 3). None of the patients were lost to follow-up or died due to surgery-related or unrelated causes. One patient with rheumatoid arthritis developed mid-substance quadriceps tendon rupture, which was managed with mesh repair. The patient did well and was able to perform activities of daily living.\",\n \"Traditional methods of TKA have failed to recreate normal knee kinematics, which may contribute to up to 20% suboptimal outcomes.16) Attempts have been made to refine prostheses to achieve normal kinematics and elongate implant survivorship. The most important finding of this study is satisfactory outcome with MP-TKA without any major complications at a minimum follow-up of 5 years in the Indian population. This may be related to better replication of natural knee kinematics with MP knees and inherent advantages of this design.\\nThe clinical results of our study are encouraging and satisfactory with all the patients reporting significant improvement in the patient-reported outcome measures. These results are in agreement with other studies in the literature.7891011) All patients in the present study felt their knees stable. None of the patients developed any symptomatic anteroposterior instability. Single radius curvature of the femoral component in MP TKA maximizes quadriceps efficiency, limits paradoxical roll forward, and provides potential advantages to the extensor apparatus.2) Pritchett17) reported that 77% of patients preferred MP to the posterior-stabilized (PS) design. Macheras et al.10) reported excellent long-term clinical results with an MP design and survival of 98.8% at 17 years of follow-up.\\nThe average flexion reported in the present study was 118.4°, which is comparable to other studies with similar knee designs.7891011171819) Although the maximum flexion was less than that in other high-flex knees and mobile-bearing designs, this may be related to late presentation with advanced arthritis in the Indian population.20) The advantage of the MP design is that patients feel their knees are stable in activities requiring deep flexion due to its medial conformity. Furthermore, it has been reported that the increased range of motion correlates with increased satisfaction after TKA in Asian patients.21)\\nThe medial conformity of this design also obviates the need for ligament release for balancing the knee, which is required in severe varus deformity; severe deformities necessitate PCL release to balance the knees. The posterior-stabilized type of MP TKA does not require a box cut for post-cam mechanism; instead, its ultracongruent polyethylene with anterior lip provides anteroposterior stability without any post. This also preserves bone stock in already smaller bones in Asian patients and, if required, for future revision surgery.18) Bae et al.22) showed that there was no difference in clinical and radiological results of TKA with an MP design regardless of whether the PCL was retained (67 knees) or sacrificed (70 knees).\\nThe radiographic results were similar to those of other studies.7891011171819) There was no evidence of loosening or osteolysis in the present study, which may be attributed to the fact that the MP design produces fewer wear particles due to its ultracongruent nature.23) We note certain inherent limitations of the study, such as the single center study, small number of patients, retrospective study design, absence of control group, and relatively short follow-up. The current study has demonstrated satisfactory clinical and radiological outcomes at 5 years following MP-TKA in an Indian population.\"\n]"},"all_sections_sec_types":{"kind":"list like","value":["methods",null,"results","discussion"],"string":"[\n \"methods\",\n null,\n \"results\",\n \"discussion\"\n]"},"keywords":{"kind":"list like","value":["Medial","Knee","Arthroplasty","Clinical","Radiological"],"string":"[\n \"Medial\",\n \"Knee\",\n \"Arthroplasty\",\n \"Clinical\",\n \"Radiological\"\n]"},"whole_article_text":{"kind":"string","value":"METHODS:\nA retrospective analysis of prospectively collected data of adult patients with end-stage knee arthritis (Kellgren-Lawrence grade 4) operated in a University Teaching Hospital from January 2015 to June 2015 was performed. A total of 245 patients were identified. Only patients (45 patients) who underwent MP TKA (ADVANCE Medial Pivot, MicroPort Orthopedics, Arlington, TN, USA) were included in the study. MP TKA was introduced to our system in 2014. Institutional Review Board approval was taken for the study (IRB No. IEC-486).\nThe primary outcome measures for this study were patient satisfaction and met-expectations with the MP prosthesis using Knee Society Score (KSS) satisfaction, expectation, and functional scores. The secondary outcome measures were Oxford Knee Score (OKS), range of motion, and radiological assessment.\nAll TKAs were performed by the same surgeon (RM) using the standard medial parapatellar approach. All patients were given an antibiotic (Cefuroxime axetil 1.5 g) 30–45 minutes prior to skin incision. All patients were given intravenous tranexamic acid (15 mg/kg) 10–15 minutes prior to tourniquet deflation. The posterior cruciate ligament (PCL) was sacrificed in all cases and implants were fixed with a single mix of Palacos bone cement with gentamicin (Heraeus Medical, Warsaw, IN, USA). Whenever there was mediolateral overhang as per the anteroposterior size of the femoral component, the authors used a stature femoral component (narrow mediolateral) available with this prosthesis. The stature femoral component was used in 70% of the patients and the most common size of femur and tibia used in the study was 2. Patelloplasty was done. Surgical and rehabilitation protocol was standardized for all patients.\nFor each patient, demographic data, type of arthritis, body mass index (BMI), and preoperative scores were documented from the patient record and postoperative evaluation was done at the latest follow-up using KSS (satisfaction, expectation, and functional scores), OKS, and range of motion (measured using a goniometer). Flexion deformity, if any, was also recorded. Serial standard preoperative and postoperative radiographs at baseline and the latest follow-up were evaluated by an independent radiologist (NS) as per the protocol defined by the modern knee society radiographic evaluation system (Figs. 1, 2, 3).14) TKA loosening was defined as a complete radiolucent line of more than 2 mm in width, a visible cement mantle fracture around the component, or a change in component position. The interclass correlation coefficient was excellent (0.92) for clinical evaluation and good (0.84) for radiographic evaluation.15)\n Statistical Analysis Baseline characteristics were described for each patient using mean ± standard deviation, median (range), or frequency/percentage. KSS (satisfaction, expectation, and functional scores) and OKS were compared using a generalized estimating equation because the observations were correlated preoperatively and postoperatively. Within group comparison for all other parameters was performed using paired t-test or Wilcoxon rank-sum test (Mann-Whitney U-test). Correlation between two continuous variables was analysed using Pearson correlation coefficients. All analyses were conducted using Stata ver. 13.0 (Stata Corp., College Station, TX, USA). A p-value < 0.05 was considered statistically significant.\nBaseline characteristics were described for each patient using mean ± standard deviation, median (range), or frequency/percentage. KSS (satisfaction, expectation, and functional scores) and OKS were compared using a generalized estimating equation because the observations were correlated preoperatively and postoperatively. Within group comparison for all other parameters was performed using paired t-test or Wilcoxon rank-sum test (Mann-Whitney U-test). Correlation between two continuous variables was analysed using Pearson correlation coefficients. All analyses were conducted using Stata ver. 13.0 (Stata Corp., College Station, TX, USA). A p-value < 0.05 was considered statistically significant.\n\nStatistical Analysis:\nBaseline characteristics were described for each patient using mean ± standard deviation, median (range), or frequency/percentage. KSS (satisfaction, expectation, and functional scores) and OKS were compared using a generalized estimating equation because the observations were correlated preoperatively and postoperatively. Within group comparison for all other parameters was performed using paired t-test or Wilcoxon rank-sum test (Mann-Whitney U-test). Correlation between two continuous variables was analysed using Pearson correlation coefficients. All analyses were conducted using Stata ver. 13.0 (Stata Corp., College Station, TX, USA). A p-value < 0.05 was considered statistically significant.\n\nRESULTS:\nAll patients reported improved satisfaction and functional outcome after TKA at a minimum follow-up of 5 years. The average age at the time of TKA was 62 years and 88.9% of the patients were women. There were 26 left knees and 24 right knees. The average BMI was 28.4 kg/m2. Five patients underwent bilateral TKA (total 50 MP TKAs). There were 4 cases of inflammatory arthritis, while the rest were degenerative osteoarthritis (Table 1).\nThe mean preoperative KSS was 4.4 ± 1.7 (range, 0–6) for satisfaction score, 11.8 ± 1.6 (range, 7–15) for expectation score, and 19.6 ± 4.4 (range, 6–27) for functional score. The mean postoperative KSS was 34.6 ± 3.0 (range, 26–38) for satisfaction score, 12.5 ± 1.4 (range, 10–15) for expectation score, and 61.3 ± 6.7 (range, 42–69) for functional score at the latest follow-up. The mean OKS was 9.5 ± 2.5 (range, 4–12) preoperatively and 44.2 ± 2.2 (range, 40–48) at the latest follow-up. Patients reported clinically and statistically significant improvements (p < 0.001) for all patient-reported outcome measures (Table 2). The mean range of motion showed a statistically significant improvement from 97.6° ± 11.2° (70°–120°) to 118.4° ± 8.4° (100°–130°). Three patients had flexion deformity of less than 10° at the final follow-up.\nThere was no evidence of implant failure or subsidence in any of the postoperative radiographs. Nonprogressive radiolucent lines (< 2 mm) were seen in 5 TKAs. The average tibiofemoral angle improved from a mean of 4.8° varus (2.2°–7.4°) preoperatively to a mean of 4.1° valgus (3.3°–4.9°) at the latest follow-up (Table 3). None of the patients were lost to follow-up or died due to surgery-related or unrelated causes. One patient with rheumatoid arthritis developed mid-substance quadriceps tendon rupture, which was managed with mesh repair. The patient did well and was able to perform activities of daily living.\n\nDISCUSSION:\nTraditional methods of TKA have failed to recreate normal knee kinematics, which may contribute to up to 20% suboptimal outcomes.16) Attempts have been made to refine prostheses to achieve normal kinematics and elongate implant survivorship. The most important finding of this study is satisfactory outcome with MP-TKA without any major complications at a minimum follow-up of 5 years in the Indian population. This may be related to better replication of natural knee kinematics with MP knees and inherent advantages of this design.\nThe clinical results of our study are encouraging and satisfactory with all the patients reporting significant improvement in the patient-reported outcome measures. These results are in agreement with other studies in the literature.7891011) All patients in the present study felt their knees stable. None of the patients developed any symptomatic anteroposterior instability. Single radius curvature of the femoral component in MP TKA maximizes quadriceps efficiency, limits paradoxical roll forward, and provides potential advantages to the extensor apparatus.2) Pritchett17) reported that 77% of patients preferred MP to the posterior-stabilized (PS) design. Macheras et al.10) reported excellent long-term clinical results with an MP design and survival of 98.8% at 17 years of follow-up.\nThe average flexion reported in the present study was 118.4°, which is comparable to other studies with similar knee designs.7891011171819) Although the maximum flexion was less than that in other high-flex knees and mobile-bearing designs, this may be related to late presentation with advanced arthritis in the Indian population.20) The advantage of the MP design is that patients feel their knees are stable in activities requiring deep flexion due to its medial conformity. Furthermore, it has been reported that the increased range of motion correlates with increased satisfaction after TKA in Asian patients.21)\nThe medial conformity of this design also obviates the need for ligament release for balancing the knee, which is required in severe varus deformity; severe deformities necessitate PCL release to balance the knees. The posterior-stabilized type of MP TKA does not require a box cut for post-cam mechanism; instead, its ultracongruent polyethylene with anterior lip provides anteroposterior stability without any post. This also preserves bone stock in already smaller bones in Asian patients and, if required, for future revision surgery.18) Bae et al.22) showed that there was no difference in clinical and radiological results of TKA with an MP design regardless of whether the PCL was retained (67 knees) or sacrificed (70 knees).\nThe radiographic results were similar to those of other studies.7891011171819) There was no evidence of loosening or osteolysis in the present study, which may be attributed to the fact that the MP design produces fewer wear particles due to its ultracongruent nature.23) We note certain inherent limitations of the study, such as the single center study, small number of patients, retrospective study design, absence of control group, and relatively short follow-up. The current study has demonstrated satisfactory clinical and radiological outcomes at 5 years following MP-TKA in an Indian population.\n\n"},"whole_article_abstract":{"kind":"string","value":"Background:\nMedial pivot total knee arthroplasty aims to restore native knee kinematics through highly conforming medial tibiofemoral articulation with survival comparable to contemporary knee designs. The aim of this study was to report preliminary clinical results of medial pivot total knee arthroplasty in an Indian population.\n\nMethods:\nA retrospective analysis of 45 patients (average age, 62 years; 40 women and 5 men) with end-stage arthritis (Kellgren-Lawrence grade 4) operated with a medial pivot prosthesis was done. All patients were assessed using Knee Society Score (satisfaction, expectation, and functional scores) and Oxford Knee Score, and range of motion was recorded at the end of 5-year postoperative follow-up. In addition, all patients underwent standardized radiological assessment.\n\nResults:\nAt the final follow-up, patients reported significant improvement in mean Knee Society Score (satisfaction, expectation, and functional scores) and Oxford Knee Score (p < 0.05). The mean range of motion achieved at the end of 5 years ranged from 0° (extension) to 118.4° (further flexion). There was no evidence of loosening or osteolysis at a minimum follow-up of 5 years.\n\nConclusions:\nThese results demonstrated satisfactory clinical and radiological outcomes at 5 years after total knee arthroplasty with a medial pivot design, which may be related to better replication of natural knee kinematics with the medial pivot knee and inherent advantages of this design."},"background_conclusion_text":{"kind":"null"},"background_conclusion_abstract":{"kind":"null"},"whole_article_text_length":{"kind":"number","value":1877,"string":"1,877"},"whole_article_abstract_length":{"kind":"number","value":276,"string":"276"},"other_sections_lengths":{"kind":"list like","value":[124],"string":"[\n 124\n]"},"num_sections":{"kind":"number","value":4,"string":"4"},"most_frequent_words":{"kind":"list like","value":["patients","range","mp","study","tka","follow","satisfaction","patient","test","mean"],"string":"[\n \"patients\",\n \"range\",\n \"mp\",\n \"study\",\n \"tka\",\n \"follow\",\n \"satisfaction\",\n \"patient\",\n \"test\",\n \"mean\"\n]"},"keybert_topics":{"kind":"list like","value":["stage knee arthritis","knee score","kinematics mp knees","knees radiographic results","prosthesis knee society"],"string":"[\n \"stage knee arthritis\",\n \"knee score\",\n \"kinematics mp knees\",\n \"knees radiographic results\",\n \"prosthesis knee society\"\n]"},"annotated_base_background_abstract_prompt":{"kind":"null"},"annotated_base_methods_abstract_prompt":{"kind":"string","value":""},"annotated_base_results_abstract_prompt":{"kind":"string","value":""},"annotated_base_conclusions_abstract_prompt":{"kind":"null"},"annotated_base_whole_article_abstract_prompt":{"kind":"string","value":""},"annotated_base_background_conclusion_abstract_prompt":{"kind":"null"},"annotated_keywords_background_abstract_prompt":{"kind":"null"},"annotated_keywords_methods_abstract_prompt":{"kind":"string","value":"[CONTENT] Medial | Knee | Arthroplasty | Clinical | Radiological [SUMMARY]"},"annotated_keywords_results_abstract_prompt":{"kind":"string","value":"[CONTENT] Medial | Knee | Arthroplasty | Clinical | Radiological [SUMMARY]"},"annotated_keywords_conclusions_abstract_prompt":{"kind":"null"},"annotated_keywords_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] Medial | Knee | Arthroplasty | Clinical | Radiological [SUMMARY]"},"annotated_keywords_background_conclusion_abstract_prompt":{"kind":"null"},"annotated_mesh_background_abstract_prompt":{"kind":"null"},"annotated_mesh_methods_abstract_prompt":{"kind":"string","value":"[CONTENT] Arthroplasty, Replacement, Knee | Female | Follow-Up Studies | Humans | Knee Joint | Knee Prosthesis | Male | Middle Aged | Osteoarthritis, Knee | Prosthesis Design | Range of Motion, Articular | Retrospective Studies [SUMMARY]"},"annotated_mesh_results_abstract_prompt":{"kind":"string","value":"[CONTENT] Arthroplasty, Replacement, Knee | Female | Follow-Up Studies | Humans | Knee Joint | Knee Prosthesis | Male | Middle Aged | Osteoarthritis, Knee | Prosthesis Design | Range of Motion, Articular | Retrospective Studies [SUMMARY]"},"annotated_mesh_conclusions_abstract_prompt":{"kind":"null"},"annotated_mesh_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] Arthroplasty, Replacement, Knee | Female | Follow-Up Studies | Humans | Knee Joint | Knee Prosthesis | Male | Middle Aged | Osteoarthritis, Knee | Prosthesis Design | Range of Motion, Articular | Retrospective Studies [SUMMARY]"},"annotated_mesh_background_conclusion_abstract_prompt":{"kind":"null"},"annotated_keybert_background_abstract_prompt":{"kind":"null"},"annotated_keybert_methods_abstract_prompt":{"kind":"string","value":"[CONTENT] stage knee arthritis | knee score | kinematics mp knees | knees radiographic results | prosthesis knee society [SUMMARY]"},"annotated_keybert_results_abstract_prompt":{"kind":"string","value":"[CONTENT] stage knee arthritis | knee score | kinematics mp knees | knees radiographic results | prosthesis knee society [SUMMARY]"},"annotated_keybert_conclusions_abstract_prompt":{"kind":"null"},"annotated_keybert_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] stage knee arthritis | knee score | kinematics mp knees | knees radiographic results | prosthesis knee society [SUMMARY]"},"annotated_keybert_background_conclusion_abstract_prompt":{"kind":"null"},"annotated_most_frequent_background_abstract_prompt":{"kind":"null"},"annotated_most_frequent_methods_abstract_prompt":{"kind":"string","value":"[CONTENT] patients | range | mp | study | tka | follow | satisfaction | patient | test | mean [SUMMARY]"},"annotated_most_frequent_results_abstract_prompt":{"kind":"string","value":"[CONTENT] patients | range | mp | study | tka | follow | satisfaction | patient | test | mean [SUMMARY]"},"annotated_most_frequent_conclusions_abstract_prompt":{"kind":"null"},"annotated_most_frequent_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] patients | range | mp | study | tka | follow | satisfaction | patient | test | mean [SUMMARY]"},"annotated_most_frequent_background_conclusion_abstract_prompt":{"kind":"null"},"annotated_tf_idf_background_abstract_prompt":{"kind":"null"},"annotated_tf_idf_methods_abstract_prompt":{"kind":"string","value":"[CONTENT] patients | test | evaluation | component | scores | correlation | kss satisfaction expectation functional | kss satisfaction expectation | kss satisfaction | satisfaction expectation functional scores [SUMMARY]"},"annotated_tf_idf_results_abstract_prompt":{"kind":"string","value":"[CONTENT] score | range | patients | follow | mean | table | latest follow | latest | reported | average [SUMMARY]"},"annotated_tf_idf_conclusions_abstract_prompt":{"kind":"null"},"annotated_tf_idf_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] patients | test | range | study | mp | tka | correlation | mean | follow | score [SUMMARY]"},"annotated_tf_idf_background_conclusion_abstract_prompt":{"kind":"null"},"annotated_entity_plan_background_abstract_prompt":{"kind":"null"},"annotated_entity_plan_methods_abstract_prompt":{"kind":"string","value":"[CONTENT] 45 | 62 years | 40 | 5 | Kellgren-Lawrence | 4 ||| Knee Society Score | Oxford Knee Score | the end of 5-year ||| [SUMMARY]"},"annotated_entity_plan_results_abstract_prompt":{"kind":"string","value":"[CONTENT] Knee Society Score | Oxford Knee Score ||| the end of 5 years | 118.4 ||| 5 years [SUMMARY]"},"annotated_entity_plan_conclusions_abstract_prompt":{"kind":"null"},"annotated_entity_plan_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] ||| Indian ||| 45 | 62 years | 40 | 5 | Kellgren-Lawrence | 4 ||| Knee Society Score | Oxford Knee Score | the end of 5-year ||| ||| ||| Knee Society Score | Oxford Knee Score ||| the end of 5 years | 118.4 ||| 5 years ||| 5 years [SUMMARY]"},"annotated_entity_plan_background_conclusion_abstract_prompt":{"kind":"null"}}},{"rowIdx":269,"cells":{"title":{"kind":"string","value":"Acute-phase protein concentrations in serum of clinically healthy and diseased European bison (Bison bonasus) - preliminary study."},"pmid":{"kind":"string","value":"35012560"},"background_abstract":{"kind":"string","value":"This is the first report describing levels of APPs in European bison. Serum concentration of acute phase proteins (APPs) may be helpful to assess general health status in wildlife and potentially useful in selecting animals for elimination. Since there is a lack of literature data regarding concentration of APPs in European bisons, establishment of the reference values is also needed."},"background_abstract_label":{"kind":"string","value":"BACKGROUND"},"methods_abstract":{"kind":"string","value":"A total of 87 European bison from Polish populations were divided into two groups: (1) healthy: immobilized for transportation, placing a telemetry collar and routine diagnostic purposes; and (2) selectively culled due to the poor health condition. The serum concentration of haptoglobin, serum amyloid A and α1-acid-glycoprotein were determined using commercial quantitative ELISA assays. Since none of the variables met the normality assumptions, non-parametric Mann-Whitney U test was used for all comparisons. Statistical significance was set at p < 0.05. Statistical analyses were performed using Statistica 13.3 (Tibco, USA)."},"methods_abstract_label":{"kind":"string","value":"METHODS"},"results_abstract":{"kind":"string","value":"The concentration of haptoglobin and serum amyloid A was significantly higher in animals culled (euthanised) due to the poor condition in respect to the clinically healthy European bison. The levels of α1-acid-glycoprotein did not show statistical difference between healthy and sick animals."},"results_abstract_label":{"kind":"string","value":"RESULTS"},"conclusions_abstract":{"kind":"string","value":"Correlation between APPs concertation and health status was proven, therefore the determination of selected APPs may be considered in future as auxiliary predictive tool in assessing European bison health condition."},"conclusions_abstract_label":{"kind":"string","value":"CONCLUSIONS"},"mesh_descriptor_names":{"kind":"list like","value":["Acute-Phase Proteins","Animals","Bison","Haptoglobins","Serum Amyloid A Protein"],"string":"[\n \"Acute-Phase Proteins\",\n \"Animals\",\n \"Bison\",\n \"Haptoglobins\",\n \"Serum Amyloid A Protein\"\n]"},"pmcid":{"kind":"string","value":"8744219"},"background_title":{"kind":"string","value":"Background"},"background_text":{"kind":"string","value":"At the beginning of XX century, European bison (Bison bonasus) extinct from wild, when the last surviving animals were killed in the Białowieża Primeval Forest after World War I. Fortunately, the biggest wild herbivore in Europe have survived and now, 70 years after the first European bison had been released into the wild again, its world population is estimated at more than 9100 heads [1]. Unfortunately, growing numbers and density of the population exceeding capacity of the available habitats, increasing anthropopression and limited gene pool increase nowadays the risk of health threats such as exposure to infectious and invasive pathogens [2]. Therefore, it is crucial to support European bison population development by proper wildlife management. One of the tools that can be used to manage wildlife species is selective elimination (culling) of individuals, which genotype (and/or lineage) or health impairment make them undesirable in a herd. Apart from that, diseased individuals may threaten other animals in the population in case of contagious diseases, which, to make things worse, may also have zoonotic potential and be a challenge for public health [3, 4]. This applies especially to tuberculosis, which is currently a real zoonotic health hazard in wild ruminant populations including European bison [5]. The question which arises is how to accurately select individuals to be eliminated. The potential solution may be taken from farm animals medicine, in particular cattle, which is relatively closely related to European bison. It involves determining serum acute phase proteins (APPs) concentration, which reflects animal health status under different pathological conditions [6–8]. It could also be used as a predictive tool describing the chance of recovery based on the severity of the disease. Besides, determination of APPs can be applied as a reliable solution to assess general health status, which is crucial in case of endangered animal species, particularly with limited genes pool. The concentrations and type of APPs differ within the species [9, 10]. In cattle, the most important APPs are haptoglobin (Hp), serum amyloid A (SAA) and α1-acid-glycoprotein (AGP) [11], which levels are used as biomarkers of various pathological conditions and as predictive values for mastitis [12], respiratory diseases [13], and lameness [14]. However, no reports investigating concentration of APPs in European bison are available. Although, some studies have been described in other wildlife non-bovid species, nevertheless the data is very limited [15–17]. The secretion of APPs is associated with the acute phase reaction (APR) [18]. The APR is an innate response of the body to the disturbance of homeostasis which may be associated with various factors i.e. infection, damage of tissues, neoplastic hyperplasia, immunological disorders [19–21]. The goal of the acute phase response is to recover homeostasis and eliminate the causative factor. It comprises numerous hormonal, metabolic and neurological changes which occur within a short period of time after the injury, at the beginning of infection or inflammation [18]. Referring to cattle (as closely related to European bison domesticated species), the Hp, SAA and AGP have been described as the most significant markers of APR and are considered the major APPs used in the diagnostics. The level of Hp in cattle increases significantly in the course of APR (from nearly zero to about 2 g/l within 48 h from stimulation. The major biologic function of Hp is to bind haemoglobin in an equimolar ratio with very high affinity to prevent haemoglobin-mediated renal parenchymal injury and loss of iron following intravascular haemolysis [22]. Serum amyloid A seems also to be a good indicator of acute phase reaction in cattle. The synthesis of certain members of the family SAA proteins is significantly increased during inflammation [23]. SAA proteins can be considered as apolipoproteins because they associate with plasma lipoproteins mainly within the high-density range. Physiological role of SAA in the immune response during inflammation is not well understood, but various effects have been described. These include e.g., inhibition of lymphocyte proliferation, detoxification of endotoxin, inhibition of platelet aggregation, inhibition of thrombocytes aggregation, and inhibition of oxidative reaction in neutrophils [23]. Therefore, the aim of our preliminary study was to determine serum concentration of selected APPs (haptoglobin, serum amyloid A and α1-acid-glycoprotein) in clinically healthy European bison and animals eliminated under the clinical suspicion of different pathological conditions, followed by post-mortem evaluation [24]. We hypothesised that concentrations of selected APPs were higher in the eliminated European bison (ELIM) comparing to clinically healthy animals (HEAL)."},"methods_title":{"kind":"null"},"methods_text":{"kind":"null"},"results_title":{"kind":"string","value":"Results"},"results_text":{"kind":"string","value":" Analytical validation of the assays For Hp mean within-run and between-run CVs were 8.11% and 7.11%, respectively. Dilution studies resulted in linear regression equations with a correlation coefficient of 0.98 showing that the method measures the protein in a linear manner. The detection limit of the assay was 0.005 mg/ml.\nFor SAA mean within-run and between-run CVs were 5.15% and 5.35%, respectively. Dilution studies resulted in linear regression equations with a correlation coefficient of 0.99 showing that the method measures the protein in a linear manner. The detection limit of the assay was 3.49 µg/ml.\nFor AGP mean within-run and between-run CVs were 4.08% and 4.37%, respectively. Dilution studies resulted in linear regression equations with a correlation coefficient of 0.99 showing that the method measures the protein in a linear manner. The detection limit of the assay was 15.6 µg/ml.\nFor Hp mean within-run and between-run CVs were 8.11% and 7.11%, respectively. Dilution studies resulted in linear regression equations with a correlation coefficient of 0.98 showing that the method measures the protein in a linear manner. The detection limit of the assay was 0.005 mg/ml.\nFor SAA mean within-run and between-run CVs were 5.15% and 5.35%, respectively. Dilution studies resulted in linear regression equations with a correlation coefficient of 0.99 showing that the method measures the protein in a linear manner. The detection limit of the assay was 3.49 µg/ml.\nFor AGP mean within-run and between-run CVs were 4.08% and 4.37%, respectively. Dilution studies resulted in linear regression equations with a correlation coefficient of 0.99 showing that the method measures the protein in a linear manner. The detection limit of the assay was 15.6 µg/ml.\n Acute phase protein concentrations The median concentration of SAA in ELIM animals was almost 4 times higher than the median level in clinically healthy group. In ELIM, the median concentration was 70.81 µg/ml while in HEAL, the median concentration of SAA was 18.95 µg/ml. The interquartile range (IQR) equals 12.38-30.11 µg/ml for SAA concentration in HEAL, while in ELIM 50.50-112.46 µg/ml (Fig. 1). On other hand, the median concentration of Hp in HEAL group was over 2 times lower than in the eliminated animals with IQR equal to 0.1-0.214 mg/ml in HEAL and 0.270-0.592 mg/ml in ELIM. The median concentration of Hp in HEAL was 0.176 mg/ml, while in ELIM the median concentration of Hp was equal to 0.305 mg/ml (Fig. 2). The concentration of SAA and Hp was significantly higher in ELIM comparing to HEAL (p <0.01). Similar differences were also observed for AGP levels, however they were not statistically significant (p = 0.3950). The median levels of AGP in HEAL and ELIM were equal to 207.25 and 271.25 µg/ml, respectively. The IQR for AGP was 146.65-259.1 µg/ml in HEAL and 178.55-407.25 in ELIM µg/ml (Fig. 3).Fig. 1 A boxplot for the concentration of serum amyloid A in serum of European bison. The line inside the box is the median. The top and bottom lines of the box are the first and third quartiles, respectively. The top and bottom whiskers are the minimum and maximum concentrations, circles represent outliers. * - statistically significant differences between HEAL and ELIM (p < 0.01)Fig. 2 A boxplot for the concentration of haptoglobin in serum of European bison. The line inside the box is the median. The top and bottom lines of the box are the first and third quartiles, respectively. The top and bottom whiskers are the minimum and maximum concentrations. * - statistically significant differences between HEAL and ELIM (p < 0.01)Fig. 3 A boxplot for the concentration of α-1-acid glikoprotein in serum of European bison. The line inside the box is the median. The top and bottom lines of the box are the first and third quartiles, respectively. The top and bottom whiskers are the minimum and maximum concentrations, circles represent outliers, asterics represent far outliers\n A boxplot for the concentration of serum amyloid A in serum of European bison. The line inside the box is the median. The top and bottom lines of the box are the first and third quartiles, respectively. The top and bottom whiskers are the minimum and maximum concentrations, circles represent outliers. * - statistically significant differences between HEAL and ELIM (p < 0.01)\n A boxplot for the concentration of haptoglobin in serum of European bison. The line inside the box is the median. The top and bottom lines of the box are the first and third quartiles, respectively. The top and bottom whiskers are the minimum and maximum concentrations. * - statistically significant differences between HEAL and ELIM (p < 0.01)\n A boxplot for the concentration of α-1-acid glikoprotein in serum of European bison. The line inside the box is the median. The top and bottom lines of the box are the first and third quartiles, respectively. The top and bottom whiskers are the minimum and maximum concentrations, circles represent outliers, asterics represent far outliers\nThe median concentration of SAA in ELIM animals was almost 4 times higher than the median level in clinically healthy group. In ELIM, the median concentration was 70.81 µg/ml while in HEAL, the median concentration of SAA was 18.95 µg/ml. The interquartile range (IQR) equals 12.38-30.11 µg/ml for SAA concentration in HEAL, while in ELIM 50.50-112.46 µg/ml (Fig. 1). On other hand, the median concentration of Hp in HEAL group was over 2 times lower than in the eliminated animals with IQR equal to 0.1-0.214 mg/ml in HEAL and 0.270-0.592 mg/ml in ELIM. The median concentration of Hp in HEAL was 0.176 mg/ml, while in ELIM the median concentration of Hp was equal to 0.305 mg/ml (Fig. 2). The concentration of SAA and Hp was significantly higher in ELIM comparing to HEAL (p <0.01). Similar differences were also observed for AGP levels, however they were not statistically significant (p = 0.3950). The median levels of AGP in HEAL and ELIM were equal to 207.25 and 271.25 µg/ml, respectively. The IQR for AGP was 146.65-259.1 µg/ml in HEAL and 178.55-407.25 in ELIM µg/ml (Fig. 3).Fig. 1 A boxplot for the concentration of serum amyloid A in serum of European bison. The line inside the box is the median. The top and bottom lines of the box are the first and third quartiles, respectively. The top and bottom whiskers are the minimum and maximum concentrations, circles represent outliers. * - statistically significant differences between HEAL and ELIM (p < 0.01)Fig. 2 A boxplot for the concentration of haptoglobin in serum of European bison. The line inside the box is the median. The top and bottom lines of the box are the first and third quartiles, respectively. The top and bottom whiskers are the minimum and maximum concentrations. * - statistically significant differences between HEAL and ELIM (p < 0.01)Fig. 3 A boxplot for the concentration of α-1-acid glikoprotein in serum of European bison. The line inside the box is the median. The top and bottom lines of the box are the first and third quartiles, respectively. The top and bottom whiskers are the minimum and maximum concentrations, circles represent outliers, asterics represent far outliers\n A boxplot for the concentration of serum amyloid A in serum of European bison. The line inside the box is the median. The top and bottom lines of the box are the first and third quartiles, respectively. The top and bottom whiskers are the minimum and maximum concentrations, circles represent outliers. * - statistically significant differences between HEAL and ELIM (p < 0.01)\n A boxplot for the concentration of haptoglobin in serum of European bison. The line inside the box is the median. The top and bottom lines of the box are the first and third quartiles, respectively. The top and bottom whiskers are the minimum and maximum concentrations. * - statistically significant differences between HEAL and ELIM (p < 0.01)\n A boxplot for the concentration of α-1-acid glikoprotein in serum of European bison. The line inside the box is the median. The top and bottom lines of the box are the first and third quartiles, respectively. The top and bottom whiskers are the minimum and maximum concentrations, circles represent outliers, asterics represent far outliers"},"conclusions_title":{"kind":"string","value":"Conclusions"},"conclusions_text":{"kind":"string","value":"To conclude, our preliminary study is the first report describing concentrations of the selected APPs (Hp, SAA and AGP) in European bison. Subsequently the study suggest that concentration of APPs may be used as a supportive tool, monitoring the health status (at individual and/or population level) and when making decisions regarding undesired individuals to be eliminated due to health reasons. The most significant limitations of our research was the moderate number of animals involved in the study. Further studies will include greater samples size in order to associate APPs levels with the most frequent pathologies of European bison from different populations, variable in exposure to pathogens or differences in herd management. Most importantly, the study confirmed the selection of animals for culling was diligent and thoughtful and could have been confirmed by APP up-regulation in sick animals."},"other_sections_titles":{"kind":"list like","value":["Background","Methods","Samples","Determination of APPs","Statistical analysis","Analytical validation of the assays","Acute phase protein concentrations"],"string":"[\n \"Background\",\n \"Methods\",\n \"Samples\",\n \"Determination of APPs\",\n \"Statistical analysis\",\n \"Analytical validation of the assays\",\n \"Acute phase protein concentrations\"\n]"},"other_sections_texts":{"kind":"list like","value":["At the beginning of XX century, European bison (Bison bonasus) extinct from wild, when the last surviving animals were killed in the Białowieża Primeval Forest after World War I. Fortunately, the biggest wild herbivore in Europe have survived and now, 70 years after the first European bison had been released into the wild again, its world population is estimated at more than 9100 heads [1]. Unfortunately, growing numbers and density of the population exceeding capacity of the available habitats, increasing anthropopression and limited gene pool increase nowadays the risk of health threats such as exposure to infectious and invasive pathogens [2]. Therefore, it is crucial to support European bison population development by proper wildlife management. One of the tools that can be used to manage wildlife species is selective elimination (culling) of individuals, which genotype (and/or lineage) or health impairment make them undesirable in a herd. Apart from that, diseased individuals may threaten other animals in the population in case of contagious diseases, which, to make things worse, may also have zoonotic potential and be a challenge for public health [3, 4]. This applies especially to tuberculosis, which is currently a real zoonotic health hazard in wild ruminant populations including European bison [5]. The question which arises is how to accurately select individuals to be eliminated. The potential solution may be taken from farm animals medicine, in particular cattle, which is relatively closely related to European bison. It involves determining serum acute phase proteins (APPs) concentration, which reflects animal health status under different pathological conditions [6–8]. It could also be used as a predictive tool describing the chance of recovery based on the severity of the disease. Besides, determination of APPs can be applied as a reliable solution to assess general health status, which is crucial in case of endangered animal species, particularly with limited genes pool. The concentrations and type of APPs differ within the species [9, 10]. In cattle, the most important APPs are haptoglobin (Hp), serum amyloid A (SAA) and α1-acid-glycoprotein (AGP) [11], which levels are used as biomarkers of various pathological conditions and as predictive values for mastitis [12], respiratory diseases [13], and lameness [14]. However, no reports investigating concentration of APPs in European bison are available. Although, some studies have been described in other wildlife non-bovid species, nevertheless the data is very limited [15–17]. The secretion of APPs is associated with the acute phase reaction (APR) [18]. The APR is an innate response of the body to the disturbance of homeostasis which may be associated with various factors i.e. infection, damage of tissues, neoplastic hyperplasia, immunological disorders [19–21]. The goal of the acute phase response is to recover homeostasis and eliminate the causative factor. It comprises numerous hormonal, metabolic and neurological changes which occur within a short period of time after the injury, at the beginning of infection or inflammation [18]. Referring to cattle (as closely related to European bison domesticated species), the Hp, SAA and AGP have been described as the most significant markers of APR and are considered the major APPs used in the diagnostics. The level of Hp in cattle increases significantly in the course of APR (from nearly zero to about 2 g/l within 48 h from stimulation. The major biologic function of Hp is to bind haemoglobin in an equimolar ratio with very high affinity to prevent haemoglobin-mediated renal parenchymal injury and loss of iron following intravascular haemolysis [22]. Serum amyloid A seems also to be a good indicator of acute phase reaction in cattle. The synthesis of certain members of the family SAA proteins is significantly increased during inflammation [23]. SAA proteins can be considered as apolipoproteins because they associate with plasma lipoproteins mainly within the high-density range. Physiological role of SAA in the immune response during inflammation is not well understood, but various effects have been described. These include e.g., inhibition of lymphocyte proliferation, detoxification of endotoxin, inhibition of platelet aggregation, inhibition of thrombocytes aggregation, and inhibition of oxidative reaction in neutrophils [23]. Therefore, the aim of our preliminary study was to determine serum concentration of selected APPs (haptoglobin, serum amyloid A and α1-acid-glycoprotein) in clinically healthy European bison and animals eliminated under the clinical suspicion of different pathological conditions, followed by post-mortem evaluation [24]. We hypothesised that concentrations of selected APPs were higher in the eliminated European bison (ELIM) comparing to clinically healthy animals (HEAL)."," Samples Blood samples were collected from 87 European bison, including 40 samples from clinically healthy individuals (HEAL) and 47 samples from individuals eliminated due to different pathological conditions (ELIM). In HEAL, there were 27 females and 13 males in the average age of 6.17 (range: 1-16 years). While in ELIM there were 30 females and 17 males in the average age of 6.5 (range: 1-20 years). The clinically healthy European bison were pharmacologically immobilized for transportation, placing a telemetry collar, or routine diagnostic purposes according to the previously described protocols [25]. Briefly, the combination of xylazine and etorphine were used to immobilization. The preparations were administered with specialized pneumatic Dan-Inject applicators. Following a successful shot, the animal is immobilized within 15 min and thereafter veterinary and animal husbandry manipulations are possible. In order to awake the European bisons the mixture of atipamezole, naloxone and diprenorphine were used. The selected individuals were culled after the health assessment (ELIM) by the herd supervising veterinarian and under the decision of Ministry of Environment. Briefly, the health status assessment includes integumentary system examination, orifices inspection (check for any discharges), reproductive organs examination, sight organ examination as well as body condition assessment. The animals were sampled in accordance with the appropriate regulations and permits (Polish General Directorate for Environmental Protection: Regulations DOP-OZGIZ.6401.06.7.2012.1 s and DOPOZ. 6401.06.7.2012.1 s1.Warsaw, 2012; Polish General Directorate for Environmental Protection: Regulations DZPWG.6401.06.23.2014.km2.; and Polish Ministry of the Environment, Regulation: DLP-III-0771-5/42,173/14/ZK). The blood samples were taken at capture and collected from the jugular vein into sterile tubes with clot activator for serum separation. The poor quality samples were excluded prior to the study for ensuring the high reliability of results. Serum was harvested from the blood samples by centrifugation (3000 × g for 15 min at room temperature) and stored at -80° C for further analysis (not longer than one month).\nBlood samples were collected from 87 European bison, including 40 samples from clinically healthy individuals (HEAL) and 47 samples from individuals eliminated due to different pathological conditions (ELIM). In HEAL, there were 27 females and 13 males in the average age of 6.17 (range: 1-16 years). While in ELIM there were 30 females and 17 males in the average age of 6.5 (range: 1-20 years). The clinically healthy European bison were pharmacologically immobilized for transportation, placing a telemetry collar, or routine diagnostic purposes according to the previously described protocols [25]. Briefly, the combination of xylazine and etorphine were used to immobilization. The preparations were administered with specialized pneumatic Dan-Inject applicators. Following a successful shot, the animal is immobilized within 15 min and thereafter veterinary and animal husbandry manipulations are possible. In order to awake the European bisons the mixture of atipamezole, naloxone and diprenorphine were used. The selected individuals were culled after the health assessment (ELIM) by the herd supervising veterinarian and under the decision of Ministry of Environment. Briefly, the health status assessment includes integumentary system examination, orifices inspection (check for any discharges), reproductive organs examination, sight organ examination as well as body condition assessment. The animals were sampled in accordance with the appropriate regulations and permits (Polish General Directorate for Environmental Protection: Regulations DOP-OZGIZ.6401.06.7.2012.1 s and DOPOZ. 6401.06.7.2012.1 s1.Warsaw, 2012; Polish General Directorate for Environmental Protection: Regulations DZPWG.6401.06.23.2014.km2.; and Polish Ministry of the Environment, Regulation: DLP-III-0771-5/42,173/14/ZK). The blood samples were taken at capture and collected from the jugular vein into sterile tubes with clot activator for serum separation. The poor quality samples were excluded prior to the study for ensuring the high reliability of results. Serum was harvested from the blood samples by centrifugation (3000 × g for 15 min at room temperature) and stored at -80° C for further analysis (not longer than one month).\n Determination of APPs The concentrations of haptoglobin, serum amyloid A and α1-acid-glycoprotein in serum were measured using commercial ELISAs and colorimetric assays according to manufacturer’s guidelines, which included Haptoglobin Kit Phase Range and Phase Serum Amyloid A assays (Tridelta Development Ltd County Kildare, Ireland) and Cow α1-acid glycoprotein (AGP) ELISA (Life Diagnostic, West Chester, USA). Serum samples were tested in duplicate. Prior to AGP and SAA analyses samples were diluted as follows: 1:10,000 for AGP and 1:500 for SAA. The concentrations of the APP was calculated based on standard curve for each protein using the FindGraph computer software (UNIPHIZ Lab, Vancouver, Canada). All assays were preliminarily validated in our laboratory before being applied in European bison samples. For this purpose, precision, accuracy and limit of detection were calculated. Within-run coefficients of variation (CVs) were calculated after the analysis of 2 serum samples with low and high proteins concentrations eight times in a single run. Between-run CVs were obtained by measuring the same samples in eight separate runs carried out on three different days. All samples used were frozen in aliquots and only the vials needed for each run were used. Accuracy was investigated by linearity under dilution; in brief, two European bison serum samples were diluted (1:2; 1:4; 1:8; 1:16, 1:32) with sample diluents. The limit of detection was calculated as the lowest concentration of APP that could be distinguished from a zero sample, and was taken as the mean + 3 standard deviations (SD) of 12 replicates of blank sample, tested in one analytical run.\nThe concentrations of haptoglobin, serum amyloid A and α1-acid-glycoprotein in serum were measured using commercial ELISAs and colorimetric assays according to manufacturer’s guidelines, which included Haptoglobin Kit Phase Range and Phase Serum Amyloid A assays (Tridelta Development Ltd County Kildare, Ireland) and Cow α1-acid glycoprotein (AGP) ELISA (Life Diagnostic, West Chester, USA). Serum samples were tested in duplicate. Prior to AGP and SAA analyses samples were diluted as follows: 1:10,000 for AGP and 1:500 for SAA. The concentrations of the APP was calculated based on standard curve for each protein using the FindGraph computer software (UNIPHIZ Lab, Vancouver, Canada). All assays were preliminarily validated in our laboratory before being applied in European bison samples. For this purpose, precision, accuracy and limit of detection were calculated. Within-run coefficients of variation (CVs) were calculated after the analysis of 2 serum samples with low and high proteins concentrations eight times in a single run. Between-run CVs were obtained by measuring the same samples in eight separate runs carried out on three different days. All samples used were frozen in aliquots and only the vials needed for each run were used. Accuracy was investigated by linearity under dilution; in brief, two European bison serum samples were diluted (1:2; 1:4; 1:8; 1:16, 1:32) with sample diluents. The limit of detection was calculated as the lowest concentration of APP that could be distinguished from a zero sample, and was taken as the mean + 3 standard deviations (SD) of 12 replicates of blank sample, tested in one analytical run.\n Statistical analysis In order to select proper statistical tools to evaluate the differences between healthy (HEAL) and European bison eliminated due to poor health conditions (sick animals) (ELIM), the normality of data was verified by using Shapiro-Wilk test. Since none of the variables met the normality assumptions, non-parametric Mann-Whitney U test was used for all comparisons. Statistical significance was set at p < 0.05. Statistical analyses were performed using Statistica 13.3 (Tibco, USA).\nIn order to select proper statistical tools to evaluate the differences between healthy (HEAL) and European bison eliminated due to poor health conditions (sick animals) (ELIM), the normality of data was verified by using Shapiro-Wilk test. Since none of the variables met the normality assumptions, non-parametric Mann-Whitney U test was used for all comparisons. Statistical significance was set at p < 0.05. Statistical analyses were performed using Statistica 13.3 (Tibco, USA).","Blood samples were collected from 87 European bison, including 40 samples from clinically healthy individuals (HEAL) and 47 samples from individuals eliminated due to different pathological conditions (ELIM). In HEAL, there were 27 females and 13 males in the average age of 6.17 (range: 1-16 years). While in ELIM there were 30 females and 17 males in the average age of 6.5 (range: 1-20 years). The clinically healthy European bison were pharmacologically immobilized for transportation, placing a telemetry collar, or routine diagnostic purposes according to the previously described protocols [25]. Briefly, the combination of xylazine and etorphine were used to immobilization. The preparations were administered with specialized pneumatic Dan-Inject applicators. Following a successful shot, the animal is immobilized within 15 min and thereafter veterinary and animal husbandry manipulations are possible. In order to awake the European bisons the mixture of atipamezole, naloxone and diprenorphine were used. The selected individuals were culled after the health assessment (ELIM) by the herd supervising veterinarian and under the decision of Ministry of Environment. Briefly, the health status assessment includes integumentary system examination, orifices inspection (check for any discharges), reproductive organs examination, sight organ examination as well as body condition assessment. The animals were sampled in accordance with the appropriate regulations and permits (Polish General Directorate for Environmental Protection: Regulations DOP-OZGIZ.6401.06.7.2012.1 s and DOPOZ. 6401.06.7.2012.1 s1.Warsaw, 2012; Polish General Directorate for Environmental Protection: Regulations DZPWG.6401.06.23.2014.km2.; and Polish Ministry of the Environment, Regulation: DLP-III-0771-5/42,173/14/ZK). The blood samples were taken at capture and collected from the jugular vein into sterile tubes with clot activator for serum separation. The poor quality samples were excluded prior to the study for ensuring the high reliability of results. Serum was harvested from the blood samples by centrifugation (3000 × g for 15 min at room temperature) and stored at -80° C for further analysis (not longer than one month).","The concentrations of haptoglobin, serum amyloid A and α1-acid-glycoprotein in serum were measured using commercial ELISAs and colorimetric assays according to manufacturer’s guidelines, which included Haptoglobin Kit Phase Range and Phase Serum Amyloid A assays (Tridelta Development Ltd County Kildare, Ireland) and Cow α1-acid glycoprotein (AGP) ELISA (Life Diagnostic, West Chester, USA). Serum samples were tested in duplicate. Prior to AGP and SAA analyses samples were diluted as follows: 1:10,000 for AGP and 1:500 for SAA. The concentrations of the APP was calculated based on standard curve for each protein using the FindGraph computer software (UNIPHIZ Lab, Vancouver, Canada). All assays were preliminarily validated in our laboratory before being applied in European bison samples. For this purpose, precision, accuracy and limit of detection were calculated. Within-run coefficients of variation (CVs) were calculated after the analysis of 2 serum samples with low and high proteins concentrations eight times in a single run. Between-run CVs were obtained by measuring the same samples in eight separate runs carried out on three different days. All samples used were frozen in aliquots and only the vials needed for each run were used. Accuracy was investigated by linearity under dilution; in brief, two European bison serum samples were diluted (1:2; 1:4; 1:8; 1:16, 1:32) with sample diluents. The limit of detection was calculated as the lowest concentration of APP that could be distinguished from a zero sample, and was taken as the mean + 3 standard deviations (SD) of 12 replicates of blank sample, tested in one analytical run.","In order to select proper statistical tools to evaluate the differences between healthy (HEAL) and European bison eliminated due to poor health conditions (sick animals) (ELIM), the normality of data was verified by using Shapiro-Wilk test. Since none of the variables met the normality assumptions, non-parametric Mann-Whitney U test was used for all comparisons. Statistical significance was set at p < 0.05. Statistical analyses were performed using Statistica 13.3 (Tibco, USA).","For Hp mean within-run and between-run CVs were 8.11% and 7.11%, respectively. Dilution studies resulted in linear regression equations with a correlation coefficient of 0.98 showing that the method measures the protein in a linear manner. The detection limit of the assay was 0.005 mg/ml.\nFor SAA mean within-run and between-run CVs were 5.15% and 5.35%, respectively. Dilution studies resulted in linear regression equations with a correlation coefficient of 0.99 showing that the method measures the protein in a linear manner. The detection limit of the assay was 3.49 µg/ml.\nFor AGP mean within-run and between-run CVs were 4.08% and 4.37%, respectively. Dilution studies resulted in linear regression equations with a correlation coefficient of 0.99 showing that the method measures the protein in a linear manner. The detection limit of the assay was 15.6 µg/ml.","The median concentration of SAA in ELIM animals was almost 4 times higher than the median level in clinically healthy group. In ELIM, the median concentration was 70.81 µg/ml while in HEAL, the median concentration of SAA was 18.95 µg/ml. The interquartile range (IQR) equals 12.38-30.11 µg/ml for SAA concentration in HEAL, while in ELIM 50.50-112.46 µg/ml (Fig. 1). On other hand, the median concentration of Hp in HEAL group was over 2 times lower than in the eliminated animals with IQR equal to 0.1-0.214 mg/ml in HEAL and 0.270-0.592 mg/ml in ELIM. The median concentration of Hp in HEAL was 0.176 mg/ml, while in ELIM the median concentration of Hp was equal to 0.305 mg/ml (Fig. 2). The concentration of SAA and Hp was significantly higher in ELIM comparing to HEAL (p <0.01). Similar differences were also observed for AGP levels, however they were not statistically significant (p = 0.3950). The median levels of AGP in HEAL and ELIM were equal to 207.25 and 271.25 µg/ml, respectively. The IQR for AGP was 146.65-259.1 µg/ml in HEAL and 178.55-407.25 in ELIM µg/ml (Fig. 3).Fig. 1 A boxplot for the concentration of serum amyloid A in serum of European bison. The line inside the box is the median. The top and bottom lines of the box are the first and third quartiles, respectively. The top and bottom whiskers are the minimum and maximum concentrations, circles represent outliers. * - statistically significant differences between HEAL and ELIM (p < 0.01)Fig. 2 A boxplot for the concentration of haptoglobin in serum of European bison. The line inside the box is the median. The top and bottom lines of the box are the first and third quartiles, respectively. The top and bottom whiskers are the minimum and maximum concentrations. * - statistically significant differences between HEAL and ELIM (p < 0.01)Fig. 3 A boxplot for the concentration of α-1-acid glikoprotein in serum of European bison. The line inside the box is the median. The top and bottom lines of the box are the first and third quartiles, respectively. The top and bottom whiskers are the minimum and maximum concentrations, circles represent outliers, asterics represent far outliers\n A boxplot for the concentration of serum amyloid A in serum of European bison. The line inside the box is the median. The top and bottom lines of the box are the first and third quartiles, respectively. The top and bottom whiskers are the minimum and maximum concentrations, circles represent outliers. * - statistically significant differences between HEAL and ELIM (p < 0.01)\n A boxplot for the concentration of haptoglobin in serum of European bison. The line inside the box is the median. The top and bottom lines of the box are the first and third quartiles, respectively. The top and bottom whiskers are the minimum and maximum concentrations. * - statistically significant differences between HEAL and ELIM (p < 0.01)\n A boxplot for the concentration of α-1-acid glikoprotein in serum of European bison. The line inside the box is the median. The top and bottom lines of the box are the first and third quartiles, respectively. The top and bottom whiskers are the minimum and maximum concentrations, circles represent outliers, asterics represent far outliers"],"string":"[\n \"At the beginning of XX century, European bison (Bison bonasus) extinct from wild, when the last surviving animals were killed in the Białowieża Primeval Forest after World War I. Fortunately, the biggest wild herbivore in Europe have survived and now, 70 years after the first European bison had been released into the wild again, its world population is estimated at more than 9100 heads [1]. Unfortunately, growing numbers and density of the population exceeding capacity of the available habitats, increasing anthropopression and limited gene pool increase nowadays the risk of health threats such as exposure to infectious and invasive pathogens [2]. Therefore, it is crucial to support European bison population development by proper wildlife management. One of the tools that can be used to manage wildlife species is selective elimination (culling) of individuals, which genotype (and/or lineage) or health impairment make them undesirable in a herd. Apart from that, diseased individuals may threaten other animals in the population in case of contagious diseases, which, to make things worse, may also have zoonotic potential and be a challenge for public health [3, 4]. This applies especially to tuberculosis, which is currently a real zoonotic health hazard in wild ruminant populations including European bison [5]. The question which arises is how to accurately select individuals to be eliminated. The potential solution may be taken from farm animals medicine, in particular cattle, which is relatively closely related to European bison. It involves determining serum acute phase proteins (APPs) concentration, which reflects animal health status under different pathological conditions [6–8]. It could also be used as a predictive tool describing the chance of recovery based on the severity of the disease. Besides, determination of APPs can be applied as a reliable solution to assess general health status, which is crucial in case of endangered animal species, particularly with limited genes pool. The concentrations and type of APPs differ within the species [9, 10]. In cattle, the most important APPs are haptoglobin (Hp), serum amyloid A (SAA) and α1-acid-glycoprotein (AGP) [11], which levels are used as biomarkers of various pathological conditions and as predictive values for mastitis [12], respiratory diseases [13], and lameness [14]. However, no reports investigating concentration of APPs in European bison are available. Although, some studies have been described in other wildlife non-bovid species, nevertheless the data is very limited [15–17]. The secretion of APPs is associated with the acute phase reaction (APR) [18]. The APR is an innate response of the body to the disturbance of homeostasis which may be associated with various factors i.e. infection, damage of tissues, neoplastic hyperplasia, immunological disorders [19–21]. The goal of the acute phase response is to recover homeostasis and eliminate the causative factor. It comprises numerous hormonal, metabolic and neurological changes which occur within a short period of time after the injury, at the beginning of infection or inflammation [18]. Referring to cattle (as closely related to European bison domesticated species), the Hp, SAA and AGP have been described as the most significant markers of APR and are considered the major APPs used in the diagnostics. The level of Hp in cattle increases significantly in the course of APR (from nearly zero to about 2 g/l within 48 h from stimulation. The major biologic function of Hp is to bind haemoglobin in an equimolar ratio with very high affinity to prevent haemoglobin-mediated renal parenchymal injury and loss of iron following intravascular haemolysis [22]. Serum amyloid A seems also to be a good indicator of acute phase reaction in cattle. The synthesis of certain members of the family SAA proteins is significantly increased during inflammation [23]. SAA proteins can be considered as apolipoproteins because they associate with plasma lipoproteins mainly within the high-density range. Physiological role of SAA in the immune response during inflammation is not well understood, but various effects have been described. These include e.g., inhibition of lymphocyte proliferation, detoxification of endotoxin, inhibition of platelet aggregation, inhibition of thrombocytes aggregation, and inhibition of oxidative reaction in neutrophils [23]. Therefore, the aim of our preliminary study was to determine serum concentration of selected APPs (haptoglobin, serum amyloid A and α1-acid-glycoprotein) in clinically healthy European bison and animals eliminated under the clinical suspicion of different pathological conditions, followed by post-mortem evaluation [24]. We hypothesised that concentrations of selected APPs were higher in the eliminated European bison (ELIM) comparing to clinically healthy animals (HEAL).\",\n \" Samples Blood samples were collected from 87 European bison, including 40 samples from clinically healthy individuals (HEAL) and 47 samples from individuals eliminated due to different pathological conditions (ELIM). In HEAL, there were 27 females and 13 males in the average age of 6.17 (range: 1-16 years). While in ELIM there were 30 females and 17 males in the average age of 6.5 (range: 1-20 years). The clinically healthy European bison were pharmacologically immobilized for transportation, placing a telemetry collar, or routine diagnostic purposes according to the previously described protocols [25]. Briefly, the combination of xylazine and etorphine were used to immobilization. The preparations were administered with specialized pneumatic Dan-Inject applicators. Following a successful shot, the animal is immobilized within 15 min and thereafter veterinary and animal husbandry manipulations are possible. In order to awake the European bisons the mixture of atipamezole, naloxone and diprenorphine were used. The selected individuals were culled after the health assessment (ELIM) by the herd supervising veterinarian and under the decision of Ministry of Environment. Briefly, the health status assessment includes integumentary system examination, orifices inspection (check for any discharges), reproductive organs examination, sight organ examination as well as body condition assessment. The animals were sampled in accordance with the appropriate regulations and permits (Polish General Directorate for Environmental Protection: Regulations DOP-OZGIZ.6401.06.7.2012.1 s and DOPOZ. 6401.06.7.2012.1 s1.Warsaw, 2012; Polish General Directorate for Environmental Protection: Regulations DZPWG.6401.06.23.2014.km2.; and Polish Ministry of the Environment, Regulation: DLP-III-0771-5/42,173/14/ZK). The blood samples were taken at capture and collected from the jugular vein into sterile tubes with clot activator for serum separation. The poor quality samples were excluded prior to the study for ensuring the high reliability of results. Serum was harvested from the blood samples by centrifugation (3000 × g for 15 min at room temperature) and stored at -80° C for further analysis (not longer than one month).\\nBlood samples were collected from 87 European bison, including 40 samples from clinically healthy individuals (HEAL) and 47 samples from individuals eliminated due to different pathological conditions (ELIM). In HEAL, there were 27 females and 13 males in the average age of 6.17 (range: 1-16 years). While in ELIM there were 30 females and 17 males in the average age of 6.5 (range: 1-20 years). The clinically healthy European bison were pharmacologically immobilized for transportation, placing a telemetry collar, or routine diagnostic purposes according to the previously described protocols [25]. Briefly, the combination of xylazine and etorphine were used to immobilization. The preparations were administered with specialized pneumatic Dan-Inject applicators. Following a successful shot, the animal is immobilized within 15 min and thereafter veterinary and animal husbandry manipulations are possible. In order to awake the European bisons the mixture of atipamezole, naloxone and diprenorphine were used. The selected individuals were culled after the health assessment (ELIM) by the herd supervising veterinarian and under the decision of Ministry of Environment. Briefly, the health status assessment includes integumentary system examination, orifices inspection (check for any discharges), reproductive organs examination, sight organ examination as well as body condition assessment. The animals were sampled in accordance with the appropriate regulations and permits (Polish General Directorate for Environmental Protection: Regulations DOP-OZGIZ.6401.06.7.2012.1 s and DOPOZ. 6401.06.7.2012.1 s1.Warsaw, 2012; Polish General Directorate for Environmental Protection: Regulations DZPWG.6401.06.23.2014.km2.; and Polish Ministry of the Environment, Regulation: DLP-III-0771-5/42,173/14/ZK). The blood samples were taken at capture and collected from the jugular vein into sterile tubes with clot activator for serum separation. The poor quality samples were excluded prior to the study for ensuring the high reliability of results. Serum was harvested from the blood samples by centrifugation (3000 × g for 15 min at room temperature) and stored at -80° C for further analysis (not longer than one month).\\n Determination of APPs The concentrations of haptoglobin, serum amyloid A and α1-acid-glycoprotein in serum were measured using commercial ELISAs and colorimetric assays according to manufacturer’s guidelines, which included Haptoglobin Kit Phase Range and Phase Serum Amyloid A assays (Tridelta Development Ltd County Kildare, Ireland) and Cow α1-acid glycoprotein (AGP) ELISA (Life Diagnostic, West Chester, USA). Serum samples were tested in duplicate. Prior to AGP and SAA analyses samples were diluted as follows: 1:10,000 for AGP and 1:500 for SAA. The concentrations of the APP was calculated based on standard curve for each protein using the FindGraph computer software (UNIPHIZ Lab, Vancouver, Canada). All assays were preliminarily validated in our laboratory before being applied in European bison samples. For this purpose, precision, accuracy and limit of detection were calculated. Within-run coefficients of variation (CVs) were calculated after the analysis of 2 serum samples with low and high proteins concentrations eight times in a single run. Between-run CVs were obtained by measuring the same samples in eight separate runs carried out on three different days. All samples used were frozen in aliquots and only the vials needed for each run were used. Accuracy was investigated by linearity under dilution; in brief, two European bison serum samples were diluted (1:2; 1:4; 1:8; 1:16, 1:32) with sample diluents. The limit of detection was calculated as the lowest concentration of APP that could be distinguished from a zero sample, and was taken as the mean + 3 standard deviations (SD) of 12 replicates of blank sample, tested in one analytical run.\\nThe concentrations of haptoglobin, serum amyloid A and α1-acid-glycoprotein in serum were measured using commercial ELISAs and colorimetric assays according to manufacturer’s guidelines, which included Haptoglobin Kit Phase Range and Phase Serum Amyloid A assays (Tridelta Development Ltd County Kildare, Ireland) and Cow α1-acid glycoprotein (AGP) ELISA (Life Diagnostic, West Chester, USA). Serum samples were tested in duplicate. Prior to AGP and SAA analyses samples were diluted as follows: 1:10,000 for AGP and 1:500 for SAA. The concentrations of the APP was calculated based on standard curve for each protein using the FindGraph computer software (UNIPHIZ Lab, Vancouver, Canada). All assays were preliminarily validated in our laboratory before being applied in European bison samples. For this purpose, precision, accuracy and limit of detection were calculated. Within-run coefficients of variation (CVs) were calculated after the analysis of 2 serum samples with low and high proteins concentrations eight times in a single run. Between-run CVs were obtained by measuring the same samples in eight separate runs carried out on three different days. All samples used were frozen in aliquots and only the vials needed for each run were used. Accuracy was investigated by linearity under dilution; in brief, two European bison serum samples were diluted (1:2; 1:4; 1:8; 1:16, 1:32) with sample diluents. The limit of detection was calculated as the lowest concentration of APP that could be distinguished from a zero sample, and was taken as the mean + 3 standard deviations (SD) of 12 replicates of blank sample, tested in one analytical run.\\n Statistical analysis In order to select proper statistical tools to evaluate the differences between healthy (HEAL) and European bison eliminated due to poor health conditions (sick animals) (ELIM), the normality of data was verified by using Shapiro-Wilk test. Since none of the variables met the normality assumptions, non-parametric Mann-Whitney U test was used for all comparisons. Statistical significance was set at p < 0.05. Statistical analyses were performed using Statistica 13.3 (Tibco, USA).\\nIn order to select proper statistical tools to evaluate the differences between healthy (HEAL) and European bison eliminated due to poor health conditions (sick animals) (ELIM), the normality of data was verified by using Shapiro-Wilk test. Since none of the variables met the normality assumptions, non-parametric Mann-Whitney U test was used for all comparisons. Statistical significance was set at p < 0.05. Statistical analyses were performed using Statistica 13.3 (Tibco, USA).\",\n \"Blood samples were collected from 87 European bison, including 40 samples from clinically healthy individuals (HEAL) and 47 samples from individuals eliminated due to different pathological conditions (ELIM). In HEAL, there were 27 females and 13 males in the average age of 6.17 (range: 1-16 years). While in ELIM there were 30 females and 17 males in the average age of 6.5 (range: 1-20 years). The clinically healthy European bison were pharmacologically immobilized for transportation, placing a telemetry collar, or routine diagnostic purposes according to the previously described protocols [25]. Briefly, the combination of xylazine and etorphine were used to immobilization. The preparations were administered with specialized pneumatic Dan-Inject applicators. Following a successful shot, the animal is immobilized within 15 min and thereafter veterinary and animal husbandry manipulations are possible. In order to awake the European bisons the mixture of atipamezole, naloxone and diprenorphine were used. The selected individuals were culled after the health assessment (ELIM) by the herd supervising veterinarian and under the decision of Ministry of Environment. Briefly, the health status assessment includes integumentary system examination, orifices inspection (check for any discharges), reproductive organs examination, sight organ examination as well as body condition assessment. The animals were sampled in accordance with the appropriate regulations and permits (Polish General Directorate for Environmental Protection: Regulations DOP-OZGIZ.6401.06.7.2012.1 s and DOPOZ. 6401.06.7.2012.1 s1.Warsaw, 2012; Polish General Directorate for Environmental Protection: Regulations DZPWG.6401.06.23.2014.km2.; and Polish Ministry of the Environment, Regulation: DLP-III-0771-5/42,173/14/ZK). The blood samples were taken at capture and collected from the jugular vein into sterile tubes with clot activator for serum separation. The poor quality samples were excluded prior to the study for ensuring the high reliability of results. Serum was harvested from the blood samples by centrifugation (3000 × g for 15 min at room temperature) and stored at -80° C for further analysis (not longer than one month).\",\n \"The concentrations of haptoglobin, serum amyloid A and α1-acid-glycoprotein in serum were measured using commercial ELISAs and colorimetric assays according to manufacturer’s guidelines, which included Haptoglobin Kit Phase Range and Phase Serum Amyloid A assays (Tridelta Development Ltd County Kildare, Ireland) and Cow α1-acid glycoprotein (AGP) ELISA (Life Diagnostic, West Chester, USA). Serum samples were tested in duplicate. Prior to AGP and SAA analyses samples were diluted as follows: 1:10,000 for AGP and 1:500 for SAA. The concentrations of the APP was calculated based on standard curve for each protein using the FindGraph computer software (UNIPHIZ Lab, Vancouver, Canada). All assays were preliminarily validated in our laboratory before being applied in European bison samples. For this purpose, precision, accuracy and limit of detection were calculated. Within-run coefficients of variation (CVs) were calculated after the analysis of 2 serum samples with low and high proteins concentrations eight times in a single run. Between-run CVs were obtained by measuring the same samples in eight separate runs carried out on three different days. All samples used were frozen in aliquots and only the vials needed for each run were used. Accuracy was investigated by linearity under dilution; in brief, two European bison serum samples were diluted (1:2; 1:4; 1:8; 1:16, 1:32) with sample diluents. The limit of detection was calculated as the lowest concentration of APP that could be distinguished from a zero sample, and was taken as the mean + 3 standard deviations (SD) of 12 replicates of blank sample, tested in one analytical run.\",\n \"In order to select proper statistical tools to evaluate the differences between healthy (HEAL) and European bison eliminated due to poor health conditions (sick animals) (ELIM), the normality of data was verified by using Shapiro-Wilk test. Since none of the variables met the normality assumptions, non-parametric Mann-Whitney U test was used for all comparisons. Statistical significance was set at p < 0.05. Statistical analyses were performed using Statistica 13.3 (Tibco, USA).\",\n \"For Hp mean within-run and between-run CVs were 8.11% and 7.11%, respectively. Dilution studies resulted in linear regression equations with a correlation coefficient of 0.98 showing that the method measures the protein in a linear manner. The detection limit of the assay was 0.005 mg/ml.\\nFor SAA mean within-run and between-run CVs were 5.15% and 5.35%, respectively. Dilution studies resulted in linear regression equations with a correlation coefficient of 0.99 showing that the method measures the protein in a linear manner. The detection limit of the assay was 3.49 µg/ml.\\nFor AGP mean within-run and between-run CVs were 4.08% and 4.37%, respectively. Dilution studies resulted in linear regression equations with a correlation coefficient of 0.99 showing that the method measures the protein in a linear manner. The detection limit of the assay was 15.6 µg/ml.\",\n \"The median concentration of SAA in ELIM animals was almost 4 times higher than the median level in clinically healthy group. In ELIM, the median concentration was 70.81 µg/ml while in HEAL, the median concentration of SAA was 18.95 µg/ml. The interquartile range (IQR) equals 12.38-30.11 µg/ml for SAA concentration in HEAL, while in ELIM 50.50-112.46 µg/ml (Fig. 1). On other hand, the median concentration of Hp in HEAL group was over 2 times lower than in the eliminated animals with IQR equal to 0.1-0.214 mg/ml in HEAL and 0.270-0.592 mg/ml in ELIM. The median concentration of Hp in HEAL was 0.176 mg/ml, while in ELIM the median concentration of Hp was equal to 0.305 mg/ml (Fig. 2). The concentration of SAA and Hp was significantly higher in ELIM comparing to HEAL (p <0.01). Similar differences were also observed for AGP levels, however they were not statistically significant (p = 0.3950). The median levels of AGP in HEAL and ELIM were equal to 207.25 and 271.25 µg/ml, respectively. The IQR for AGP was 146.65-259.1 µg/ml in HEAL and 178.55-407.25 in ELIM µg/ml (Fig. 3).Fig. 1 A boxplot for the concentration of serum amyloid A in serum of European bison. The line inside the box is the median. The top and bottom lines of the box are the first and third quartiles, respectively. The top and bottom whiskers are the minimum and maximum concentrations, circles represent outliers. * - statistically significant differences between HEAL and ELIM (p < 0.01)Fig. 2 A boxplot for the concentration of haptoglobin in serum of European bison. The line inside the box is the median. The top and bottom lines of the box are the first and third quartiles, respectively. The top and bottom whiskers are the minimum and maximum concentrations. * - statistically significant differences between HEAL and ELIM (p < 0.01)Fig. 3 A boxplot for the concentration of α-1-acid glikoprotein in serum of European bison. The line inside the box is the median. The top and bottom lines of the box are the first and third quartiles, respectively. The top and bottom whiskers are the minimum and maximum concentrations, circles represent outliers, asterics represent far outliers\\n A boxplot for the concentration of serum amyloid A in serum of European bison. The line inside the box is the median. The top and bottom lines of the box are the first and third quartiles, respectively. The top and bottom whiskers are the minimum and maximum concentrations, circles represent outliers. * - statistically significant differences between HEAL and ELIM (p < 0.01)\\n A boxplot for the concentration of haptoglobin in serum of European bison. The line inside the box is the median. The top and bottom lines of the box are the first and third quartiles, respectively. The top and bottom whiskers are the minimum and maximum concentrations. * - statistically significant differences between HEAL and ELIM (p < 0.01)\\n A boxplot for the concentration of α-1-acid glikoprotein in serum of European bison. The line inside the box is the median. The top and bottom lines of the box are the first and third quartiles, respectively. The top and bottom whiskers are the minimum and maximum concentrations, circles represent outliers, asterics represent far outliers\"\n]"},"other_sections_sec_types":{"kind":"list like","value":[null,null,null,null,null,null,null],"string":"[\n null,\n null,\n null,\n null,\n null,\n null,\n null\n]"},"all_sections_titles":{"kind":"list like","value":["Background","Methods","Samples","Determination of APPs","Statistical analysis","Results","Analytical validation of the assays","Acute phase protein concentrations","Discussion","Conclusions"],"string":"[\n \"Background\",\n \"Methods\",\n \"Samples\",\n \"Determination of APPs\",\n \"Statistical analysis\",\n \"Results\",\n \"Analytical validation of the assays\",\n \"Acute phase protein concentrations\",\n \"Discussion\",\n \"Conclusions\"\n]"},"all_sections_texts":{"kind":"list like","value":["At the beginning of XX century, European bison (Bison bonasus) extinct from wild, when the last surviving animals were killed in the Białowieża Primeval Forest after World War I. Fortunately, the biggest wild herbivore in Europe have survived and now, 70 years after the first European bison had been released into the wild again, its world population is estimated at more than 9100 heads [1]. Unfortunately, growing numbers and density of the population exceeding capacity of the available habitats, increasing anthropopression and limited gene pool increase nowadays the risk of health threats such as exposure to infectious and invasive pathogens [2]. Therefore, it is crucial to support European bison population development by proper wildlife management. One of the tools that can be used to manage wildlife species is selective elimination (culling) of individuals, which genotype (and/or lineage) or health impairment make them undesirable in a herd. Apart from that, diseased individuals may threaten other animals in the population in case of contagious diseases, which, to make things worse, may also have zoonotic potential and be a challenge for public health [3, 4]. This applies especially to tuberculosis, which is currently a real zoonotic health hazard in wild ruminant populations including European bison [5]. The question which arises is how to accurately select individuals to be eliminated. The potential solution may be taken from farm animals medicine, in particular cattle, which is relatively closely related to European bison. It involves determining serum acute phase proteins (APPs) concentration, which reflects animal health status under different pathological conditions [6–8]. It could also be used as a predictive tool describing the chance of recovery based on the severity of the disease. Besides, determination of APPs can be applied as a reliable solution to assess general health status, which is crucial in case of endangered animal species, particularly with limited genes pool. The concentrations and type of APPs differ within the species [9, 10]. In cattle, the most important APPs are haptoglobin (Hp), serum amyloid A (SAA) and α1-acid-glycoprotein (AGP) [11], which levels are used as biomarkers of various pathological conditions and as predictive values for mastitis [12], respiratory diseases [13], and lameness [14]. However, no reports investigating concentration of APPs in European bison are available. Although, some studies have been described in other wildlife non-bovid species, nevertheless the data is very limited [15–17]. The secretion of APPs is associated with the acute phase reaction (APR) [18]. The APR is an innate response of the body to the disturbance of homeostasis which may be associated with various factors i.e. infection, damage of tissues, neoplastic hyperplasia, immunological disorders [19–21]. The goal of the acute phase response is to recover homeostasis and eliminate the causative factor. It comprises numerous hormonal, metabolic and neurological changes which occur within a short period of time after the injury, at the beginning of infection or inflammation [18]. Referring to cattle (as closely related to European bison domesticated species), the Hp, SAA and AGP have been described as the most significant markers of APR and are considered the major APPs used in the diagnostics. The level of Hp in cattle increases significantly in the course of APR (from nearly zero to about 2 g/l within 48 h from stimulation. The major biologic function of Hp is to bind haemoglobin in an equimolar ratio with very high affinity to prevent haemoglobin-mediated renal parenchymal injury and loss of iron following intravascular haemolysis [22]. Serum amyloid A seems also to be a good indicator of acute phase reaction in cattle. The synthesis of certain members of the family SAA proteins is significantly increased during inflammation [23]. SAA proteins can be considered as apolipoproteins because they associate with plasma lipoproteins mainly within the high-density range. Physiological role of SAA in the immune response during inflammation is not well understood, but various effects have been described. These include e.g., inhibition of lymphocyte proliferation, detoxification of endotoxin, inhibition of platelet aggregation, inhibition of thrombocytes aggregation, and inhibition of oxidative reaction in neutrophils [23]. Therefore, the aim of our preliminary study was to determine serum concentration of selected APPs (haptoglobin, serum amyloid A and α1-acid-glycoprotein) in clinically healthy European bison and animals eliminated under the clinical suspicion of different pathological conditions, followed by post-mortem evaluation [24]. We hypothesised that concentrations of selected APPs were higher in the eliminated European bison (ELIM) comparing to clinically healthy animals (HEAL)."," Samples Blood samples were collected from 87 European bison, including 40 samples from clinically healthy individuals (HEAL) and 47 samples from individuals eliminated due to different pathological conditions (ELIM). In HEAL, there were 27 females and 13 males in the average age of 6.17 (range: 1-16 years). While in ELIM there were 30 females and 17 males in the average age of 6.5 (range: 1-20 years). The clinically healthy European bison were pharmacologically immobilized for transportation, placing a telemetry collar, or routine diagnostic purposes according to the previously described protocols [25]. Briefly, the combination of xylazine and etorphine were used to immobilization. The preparations were administered with specialized pneumatic Dan-Inject applicators. Following a successful shot, the animal is immobilized within 15 min and thereafter veterinary and animal husbandry manipulations are possible. In order to awake the European bisons the mixture of atipamezole, naloxone and diprenorphine were used. The selected individuals were culled after the health assessment (ELIM) by the herd supervising veterinarian and under the decision of Ministry of Environment. Briefly, the health status assessment includes integumentary system examination, orifices inspection (check for any discharges), reproductive organs examination, sight organ examination as well as body condition assessment. The animals were sampled in accordance with the appropriate regulations and permits (Polish General Directorate for Environmental Protection: Regulations DOP-OZGIZ.6401.06.7.2012.1 s and DOPOZ. 6401.06.7.2012.1 s1.Warsaw, 2012; Polish General Directorate for Environmental Protection: Regulations DZPWG.6401.06.23.2014.km2.; and Polish Ministry of the Environment, Regulation: DLP-III-0771-5/42,173/14/ZK). The blood samples were taken at capture and collected from the jugular vein into sterile tubes with clot activator for serum separation. The poor quality samples were excluded prior to the study for ensuring the high reliability of results. Serum was harvested from the blood samples by centrifugation (3000 × g for 15 min at room temperature) and stored at -80° C for further analysis (not longer than one month).\nBlood samples were collected from 87 European bison, including 40 samples from clinically healthy individuals (HEAL) and 47 samples from individuals eliminated due to different pathological conditions (ELIM). In HEAL, there were 27 females and 13 males in the average age of 6.17 (range: 1-16 years). While in ELIM there were 30 females and 17 males in the average age of 6.5 (range: 1-20 years). The clinically healthy European bison were pharmacologically immobilized for transportation, placing a telemetry collar, or routine diagnostic purposes according to the previously described protocols [25]. Briefly, the combination of xylazine and etorphine were used to immobilization. The preparations were administered with specialized pneumatic Dan-Inject applicators. Following a successful shot, the animal is immobilized within 15 min and thereafter veterinary and animal husbandry manipulations are possible. In order to awake the European bisons the mixture of atipamezole, naloxone and diprenorphine were used. The selected individuals were culled after the health assessment (ELIM) by the herd supervising veterinarian and under the decision of Ministry of Environment. Briefly, the health status assessment includes integumentary system examination, orifices inspection (check for any discharges), reproductive organs examination, sight organ examination as well as body condition assessment. The animals were sampled in accordance with the appropriate regulations and permits (Polish General Directorate for Environmental Protection: Regulations DOP-OZGIZ.6401.06.7.2012.1 s and DOPOZ. 6401.06.7.2012.1 s1.Warsaw, 2012; Polish General Directorate for Environmental Protection: Regulations DZPWG.6401.06.23.2014.km2.; and Polish Ministry of the Environment, Regulation: DLP-III-0771-5/42,173/14/ZK). The blood samples were taken at capture and collected from the jugular vein into sterile tubes with clot activator for serum separation. The poor quality samples were excluded prior to the study for ensuring the high reliability of results. Serum was harvested from the blood samples by centrifugation (3000 × g for 15 min at room temperature) and stored at -80° C for further analysis (not longer than one month).\n Determination of APPs The concentrations of haptoglobin, serum amyloid A and α1-acid-glycoprotein in serum were measured using commercial ELISAs and colorimetric assays according to manufacturer’s guidelines, which included Haptoglobin Kit Phase Range and Phase Serum Amyloid A assays (Tridelta Development Ltd County Kildare, Ireland) and Cow α1-acid glycoprotein (AGP) ELISA (Life Diagnostic, West Chester, USA). Serum samples were tested in duplicate. Prior to AGP and SAA analyses samples were diluted as follows: 1:10,000 for AGP and 1:500 for SAA. The concentrations of the APP was calculated based on standard curve for each protein using the FindGraph computer software (UNIPHIZ Lab, Vancouver, Canada). All assays were preliminarily validated in our laboratory before being applied in European bison samples. For this purpose, precision, accuracy and limit of detection were calculated. Within-run coefficients of variation (CVs) were calculated after the analysis of 2 serum samples with low and high proteins concentrations eight times in a single run. Between-run CVs were obtained by measuring the same samples in eight separate runs carried out on three different days. All samples used were frozen in aliquots and only the vials needed for each run were used. Accuracy was investigated by linearity under dilution; in brief, two European bison serum samples were diluted (1:2; 1:4; 1:8; 1:16, 1:32) with sample diluents. The limit of detection was calculated as the lowest concentration of APP that could be distinguished from a zero sample, and was taken as the mean + 3 standard deviations (SD) of 12 replicates of blank sample, tested in one analytical run.\nThe concentrations of haptoglobin, serum amyloid A and α1-acid-glycoprotein in serum were measured using commercial ELISAs and colorimetric assays according to manufacturer’s guidelines, which included Haptoglobin Kit Phase Range and Phase Serum Amyloid A assays (Tridelta Development Ltd County Kildare, Ireland) and Cow α1-acid glycoprotein (AGP) ELISA (Life Diagnostic, West Chester, USA). Serum samples were tested in duplicate. Prior to AGP and SAA analyses samples were diluted as follows: 1:10,000 for AGP and 1:500 for SAA. The concentrations of the APP was calculated based on standard curve for each protein using the FindGraph computer software (UNIPHIZ Lab, Vancouver, Canada). All assays were preliminarily validated in our laboratory before being applied in European bison samples. For this purpose, precision, accuracy and limit of detection were calculated. Within-run coefficients of variation (CVs) were calculated after the analysis of 2 serum samples with low and high proteins concentrations eight times in a single run. Between-run CVs were obtained by measuring the same samples in eight separate runs carried out on three different days. All samples used were frozen in aliquots and only the vials needed for each run were used. Accuracy was investigated by linearity under dilution; in brief, two European bison serum samples were diluted (1:2; 1:4; 1:8; 1:16, 1:32) with sample diluents. The limit of detection was calculated as the lowest concentration of APP that could be distinguished from a zero sample, and was taken as the mean + 3 standard deviations (SD) of 12 replicates of blank sample, tested in one analytical run.\n Statistical analysis In order to select proper statistical tools to evaluate the differences between healthy (HEAL) and European bison eliminated due to poor health conditions (sick animals) (ELIM), the normality of data was verified by using Shapiro-Wilk test. Since none of the variables met the normality assumptions, non-parametric Mann-Whitney U test was used for all comparisons. Statistical significance was set at p < 0.05. Statistical analyses were performed using Statistica 13.3 (Tibco, USA).\nIn order to select proper statistical tools to evaluate the differences between healthy (HEAL) and European bison eliminated due to poor health conditions (sick animals) (ELIM), the normality of data was verified by using Shapiro-Wilk test. Since none of the variables met the normality assumptions, non-parametric Mann-Whitney U test was used for all comparisons. Statistical significance was set at p < 0.05. Statistical analyses were performed using Statistica 13.3 (Tibco, USA).","Blood samples were collected from 87 European bison, including 40 samples from clinically healthy individuals (HEAL) and 47 samples from individuals eliminated due to different pathological conditions (ELIM). In HEAL, there were 27 females and 13 males in the average age of 6.17 (range: 1-16 years). While in ELIM there were 30 females and 17 males in the average age of 6.5 (range: 1-20 years). The clinically healthy European bison were pharmacologically immobilized for transportation, placing a telemetry collar, or routine diagnostic purposes according to the previously described protocols [25]. Briefly, the combination of xylazine and etorphine were used to immobilization. The preparations were administered with specialized pneumatic Dan-Inject applicators. Following a successful shot, the animal is immobilized within 15 min and thereafter veterinary and animal husbandry manipulations are possible. In order to awake the European bisons the mixture of atipamezole, naloxone and diprenorphine were used. The selected individuals were culled after the health assessment (ELIM) by the herd supervising veterinarian and under the decision of Ministry of Environment. Briefly, the health status assessment includes integumentary system examination, orifices inspection (check for any discharges), reproductive organs examination, sight organ examination as well as body condition assessment. The animals were sampled in accordance with the appropriate regulations and permits (Polish General Directorate for Environmental Protection: Regulations DOP-OZGIZ.6401.06.7.2012.1 s and DOPOZ. 6401.06.7.2012.1 s1.Warsaw, 2012; Polish General Directorate for Environmental Protection: Regulations DZPWG.6401.06.23.2014.km2.; and Polish Ministry of the Environment, Regulation: DLP-III-0771-5/42,173/14/ZK). The blood samples were taken at capture and collected from the jugular vein into sterile tubes with clot activator for serum separation. The poor quality samples were excluded prior to the study for ensuring the high reliability of results. Serum was harvested from the blood samples by centrifugation (3000 × g for 15 min at room temperature) and stored at -80° C for further analysis (not longer than one month).","The concentrations of haptoglobin, serum amyloid A and α1-acid-glycoprotein in serum were measured using commercial ELISAs and colorimetric assays according to manufacturer’s guidelines, which included Haptoglobin Kit Phase Range and Phase Serum Amyloid A assays (Tridelta Development Ltd County Kildare, Ireland) and Cow α1-acid glycoprotein (AGP) ELISA (Life Diagnostic, West Chester, USA). Serum samples were tested in duplicate. Prior to AGP and SAA analyses samples were diluted as follows: 1:10,000 for AGP and 1:500 for SAA. The concentrations of the APP was calculated based on standard curve for each protein using the FindGraph computer software (UNIPHIZ Lab, Vancouver, Canada). All assays were preliminarily validated in our laboratory before being applied in European bison samples. For this purpose, precision, accuracy and limit of detection were calculated. Within-run coefficients of variation (CVs) were calculated after the analysis of 2 serum samples with low and high proteins concentrations eight times in a single run. Between-run CVs were obtained by measuring the same samples in eight separate runs carried out on three different days. All samples used were frozen in aliquots and only the vials needed for each run were used. Accuracy was investigated by linearity under dilution; in brief, two European bison serum samples were diluted (1:2; 1:4; 1:8; 1:16, 1:32) with sample diluents. The limit of detection was calculated as the lowest concentration of APP that could be distinguished from a zero sample, and was taken as the mean + 3 standard deviations (SD) of 12 replicates of blank sample, tested in one analytical run.","In order to select proper statistical tools to evaluate the differences between healthy (HEAL) and European bison eliminated due to poor health conditions (sick animals) (ELIM), the normality of data was verified by using Shapiro-Wilk test. Since none of the variables met the normality assumptions, non-parametric Mann-Whitney U test was used for all comparisons. Statistical significance was set at p < 0.05. Statistical analyses were performed using Statistica 13.3 (Tibco, USA)."," Analytical validation of the assays For Hp mean within-run and between-run CVs were 8.11% and 7.11%, respectively. Dilution studies resulted in linear regression equations with a correlation coefficient of 0.98 showing that the method measures the protein in a linear manner. The detection limit of the assay was 0.005 mg/ml.\nFor SAA mean within-run and between-run CVs were 5.15% and 5.35%, respectively. Dilution studies resulted in linear regression equations with a correlation coefficient of 0.99 showing that the method measures the protein in a linear manner. The detection limit of the assay was 3.49 µg/ml.\nFor AGP mean within-run and between-run CVs were 4.08% and 4.37%, respectively. Dilution studies resulted in linear regression equations with a correlation coefficient of 0.99 showing that the method measures the protein in a linear manner. The detection limit of the assay was 15.6 µg/ml.\nFor Hp mean within-run and between-run CVs were 8.11% and 7.11%, respectively. Dilution studies resulted in linear regression equations with a correlation coefficient of 0.98 showing that the method measures the protein in a linear manner. The detection limit of the assay was 0.005 mg/ml.\nFor SAA mean within-run and between-run CVs were 5.15% and 5.35%, respectively. Dilution studies resulted in linear regression equations with a correlation coefficient of 0.99 showing that the method measures the protein in a linear manner. The detection limit of the assay was 3.49 µg/ml.\nFor AGP mean within-run and between-run CVs were 4.08% and 4.37%, respectively. Dilution studies resulted in linear regression equations with a correlation coefficient of 0.99 showing that the method measures the protein in a linear manner. The detection limit of the assay was 15.6 µg/ml.\n Acute phase protein concentrations The median concentration of SAA in ELIM animals was almost 4 times higher than the median level in clinically healthy group. In ELIM, the median concentration was 70.81 µg/ml while in HEAL, the median concentration of SAA was 18.95 µg/ml. The interquartile range (IQR) equals 12.38-30.11 µg/ml for SAA concentration in HEAL, while in ELIM 50.50-112.46 µg/ml (Fig. 1). On other hand, the median concentration of Hp in HEAL group was over 2 times lower than in the eliminated animals with IQR equal to 0.1-0.214 mg/ml in HEAL and 0.270-0.592 mg/ml in ELIM. The median concentration of Hp in HEAL was 0.176 mg/ml, while in ELIM the median concentration of Hp was equal to 0.305 mg/ml (Fig. 2). The concentration of SAA and Hp was significantly higher in ELIM comparing to HEAL (p <0.01). Similar differences were also observed for AGP levels, however they were not statistically significant (p = 0.3950). The median levels of AGP in HEAL and ELIM were equal to 207.25 and 271.25 µg/ml, respectively. The IQR for AGP was 146.65-259.1 µg/ml in HEAL and 178.55-407.25 in ELIM µg/ml (Fig. 3).Fig. 1 A boxplot for the concentration of serum amyloid A in serum of European bison. The line inside the box is the median. The top and bottom lines of the box are the first and third quartiles, respectively. The top and bottom whiskers are the minimum and maximum concentrations, circles represent outliers. * - statistically significant differences between HEAL and ELIM (p < 0.01)Fig. 2 A boxplot for the concentration of haptoglobin in serum of European bison. The line inside the box is the median. The top and bottom lines of the box are the first and third quartiles, respectively. The top and bottom whiskers are the minimum and maximum concentrations. * - statistically significant differences between HEAL and ELIM (p < 0.01)Fig. 3 A boxplot for the concentration of α-1-acid glikoprotein in serum of European bison. The line inside the box is the median. The top and bottom lines of the box are the first and third quartiles, respectively. The top and bottom whiskers are the minimum and maximum concentrations, circles represent outliers, asterics represent far outliers\n A boxplot for the concentration of serum amyloid A in serum of European bison. The line inside the box is the median. The top and bottom lines of the box are the first and third quartiles, respectively. The top and bottom whiskers are the minimum and maximum concentrations, circles represent outliers. * - statistically significant differences between HEAL and ELIM (p < 0.01)\n A boxplot for the concentration of haptoglobin in serum of European bison. The line inside the box is the median. The top and bottom lines of the box are the first and third quartiles, respectively. The top and bottom whiskers are the minimum and maximum concentrations. * - statistically significant differences between HEAL and ELIM (p < 0.01)\n A boxplot for the concentration of α-1-acid glikoprotein in serum of European bison. The line inside the box is the median. The top and bottom lines of the box are the first and third quartiles, respectively. The top and bottom whiskers are the minimum and maximum concentrations, circles represent outliers, asterics represent far outliers\nThe median concentration of SAA in ELIM animals was almost 4 times higher than the median level in clinically healthy group. In ELIM, the median concentration was 70.81 µg/ml while in HEAL, the median concentration of SAA was 18.95 µg/ml. The interquartile range (IQR) equals 12.38-30.11 µg/ml for SAA concentration in HEAL, while in ELIM 50.50-112.46 µg/ml (Fig. 1). On other hand, the median concentration of Hp in HEAL group was over 2 times lower than in the eliminated animals with IQR equal to 0.1-0.214 mg/ml in HEAL and 0.270-0.592 mg/ml in ELIM. The median concentration of Hp in HEAL was 0.176 mg/ml, while in ELIM the median concentration of Hp was equal to 0.305 mg/ml (Fig. 2). The concentration of SAA and Hp was significantly higher in ELIM comparing to HEAL (p <0.01). Similar differences were also observed for AGP levels, however they were not statistically significant (p = 0.3950). The median levels of AGP in HEAL and ELIM were equal to 207.25 and 271.25 µg/ml, respectively. The IQR for AGP was 146.65-259.1 µg/ml in HEAL and 178.55-407.25 in ELIM µg/ml (Fig. 3).Fig. 1 A boxplot for the concentration of serum amyloid A in serum of European bison. The line inside the box is the median. The top and bottom lines of the box are the first and third quartiles, respectively. The top and bottom whiskers are the minimum and maximum concentrations, circles represent outliers. * - statistically significant differences between HEAL and ELIM (p < 0.01)Fig. 2 A boxplot for the concentration of haptoglobin in serum of European bison. The line inside the box is the median. The top and bottom lines of the box are the first and third quartiles, respectively. The top and bottom whiskers are the minimum and maximum concentrations. * - statistically significant differences between HEAL and ELIM (p < 0.01)Fig. 3 A boxplot for the concentration of α-1-acid glikoprotein in serum of European bison. The line inside the box is the median. The top and bottom lines of the box are the first and third quartiles, respectively. The top and bottom whiskers are the minimum and maximum concentrations, circles represent outliers, asterics represent far outliers\n A boxplot for the concentration of serum amyloid A in serum of European bison. The line inside the box is the median. The top and bottom lines of the box are the first and third quartiles, respectively. The top and bottom whiskers are the minimum and maximum concentrations, circles represent outliers. * - statistically significant differences between HEAL and ELIM (p < 0.01)\n A boxplot for the concentration of haptoglobin in serum of European bison. The line inside the box is the median. The top and bottom lines of the box are the first and third quartiles, respectively. The top and bottom whiskers are the minimum and maximum concentrations. * - statistically significant differences between HEAL and ELIM (p < 0.01)\n A boxplot for the concentration of α-1-acid glikoprotein in serum of European bison. The line inside the box is the median. The top and bottom lines of the box are the first and third quartiles, respectively. The top and bottom whiskers are the minimum and maximum concentrations, circles represent outliers, asterics represent far outliers","For Hp mean within-run and between-run CVs were 8.11% and 7.11%, respectively. Dilution studies resulted in linear regression equations with a correlation coefficient of 0.98 showing that the method measures the protein in a linear manner. The detection limit of the assay was 0.005 mg/ml.\nFor SAA mean within-run and between-run CVs were 5.15% and 5.35%, respectively. Dilution studies resulted in linear regression equations with a correlation coefficient of 0.99 showing that the method measures the protein in a linear manner. The detection limit of the assay was 3.49 µg/ml.\nFor AGP mean within-run and between-run CVs were 4.08% and 4.37%, respectively. Dilution studies resulted in linear regression equations with a correlation coefficient of 0.99 showing that the method measures the protein in a linear manner. The detection limit of the assay was 15.6 µg/ml.","The median concentration of SAA in ELIM animals was almost 4 times higher than the median level in clinically healthy group. In ELIM, the median concentration was 70.81 µg/ml while in HEAL, the median concentration of SAA was 18.95 µg/ml. The interquartile range (IQR) equals 12.38-30.11 µg/ml for SAA concentration in HEAL, while in ELIM 50.50-112.46 µg/ml (Fig. 1). On other hand, the median concentration of Hp in HEAL group was over 2 times lower than in the eliminated animals with IQR equal to 0.1-0.214 mg/ml in HEAL and 0.270-0.592 mg/ml in ELIM. The median concentration of Hp in HEAL was 0.176 mg/ml, while in ELIM the median concentration of Hp was equal to 0.305 mg/ml (Fig. 2). The concentration of SAA and Hp was significantly higher in ELIM comparing to HEAL (p <0.01). Similar differences were also observed for AGP levels, however they were not statistically significant (p = 0.3950). The median levels of AGP in HEAL and ELIM were equal to 207.25 and 271.25 µg/ml, respectively. The IQR for AGP was 146.65-259.1 µg/ml in HEAL and 178.55-407.25 in ELIM µg/ml (Fig. 3).Fig. 1 A boxplot for the concentration of serum amyloid A in serum of European bison. The line inside the box is the median. The top and bottom lines of the box are the first and third quartiles, respectively. The top and bottom whiskers are the minimum and maximum concentrations, circles represent outliers. * - statistically significant differences between HEAL and ELIM (p < 0.01)Fig. 2 A boxplot for the concentration of haptoglobin in serum of European bison. The line inside the box is the median. The top and bottom lines of the box are the first and third quartiles, respectively. The top and bottom whiskers are the minimum and maximum concentrations. * - statistically significant differences between HEAL and ELIM (p < 0.01)Fig. 3 A boxplot for the concentration of α-1-acid glikoprotein in serum of European bison. The line inside the box is the median. The top and bottom lines of the box are the first and third quartiles, respectively. The top and bottom whiskers are the minimum and maximum concentrations, circles represent outliers, asterics represent far outliers\n A boxplot for the concentration of serum amyloid A in serum of European bison. The line inside the box is the median. The top and bottom lines of the box are the first and third quartiles, respectively. The top and bottom whiskers are the minimum and maximum concentrations, circles represent outliers. * - statistically significant differences between HEAL and ELIM (p < 0.01)\n A boxplot for the concentration of haptoglobin in serum of European bison. The line inside the box is the median. The top and bottom lines of the box are the first and third quartiles, respectively. The top and bottom whiskers are the minimum and maximum concentrations. * - statistically significant differences between HEAL and ELIM (p < 0.01)\n A boxplot for the concentration of α-1-acid glikoprotein in serum of European bison. The line inside the box is the median. The top and bottom lines of the box are the first and third quartiles, respectively. The top and bottom whiskers are the minimum and maximum concentrations, circles represent outliers, asterics represent far outliers","In 2020, International Union for Conservation of Nature (IUCN) has changed European bison status from “Vulnerable” to “Near Threatened”, which proved that proper wildlife management is effective [26]. However, taking into account that modern European bison population was recreated from several individuals, continuous actions should be carried out to protect the species of the largest land European mammal. On-going health surveillance of European bison should be an important element of European bison protection strategy [2]. Our study suggest that determination of APPs might be a useful tool to assess health status of the individual or at the population level. In European bison, in the other study the most prevalent pathologies observed postmortally included: pneumonia, emphysema, nephritis, body traumas, posthitis/balanoposthitis in males and infestations of Fasciola hepatica and Dictyocaulus viviparus [27]. However, to our best knowledge there are no studies investigating APPs in European bison and their relation with different pathological conditions.\nRegarding European bison`s immunology, there is only a report describing changes of immunoglobulins within the age [27]. Possibly, further studies may describe dynamics of APPs in different European bison disease. In the current preliminary study, we reported higher concentration of two (Hp, SAA) out of three investigated APPs in the eliminated due to poor health condition European bison. However, we cannot reject AGP as a potential marker for future studies using larger number of animals, since its concentrations were generally higher in animals from ELIM group, even though not statistically proven. The knowledge on usage of APPs as markers of physiological and pathological processes in wildlife species is lacking, even though they appear as desirable candidates for monitoring wildlife health and disease burden in the changing environment. Similar in the assumptions, the study of Glidden et al. [28] have demonstrated that Hp levels may be used to detect infections non-specifically and potential as a surveillance marker in African buffalo. Nevertheless, broaden experience may be drawn from cattle medicine, especially considering genetic relatedness among bovids. For example, Bagga et al. [14] have found APPs useful in diagnostics of lameness in cows, reporting SAA and Hp concentrations approximately 3 and 20, respectively times higher in lame cows comparing to non-lame cows. Similarly in our study the median concentration of SAA was almost 4 times higher in ELIM than in HEAL. The SAA levels obtained by Bagga in lame and non-lame cows (22.19 µg/ml and 8.89 µg/ml, respectively) are numerically lower that median SAA concentration in ELIM and HEAL (70.81 µg/ml and 18.95 µg/ml, respectively). Similarly the Hp concentrations in lame and non-lame cows (0.217 mg/ml and 0.012 mg/ml, respectively) are numerically lower than Hp concentration in ELIM and HEAL (0.305 mg/ml and 0.176 mg/ml, respectively. Dalanezi et al. [12] described increase in milk APPs in course of mastitis caused by different pathogens and suggested pathogen-specific APPs profiles. On the other hand, Moisa et al. [13] state that Hp concentration can be useful biomarker of respiratory diseases in dairy calves. The researcher reports that 0.195 mg/ml is the cut-off value for Hp as biomarker for bovine respiratory diseases. Comparing to our study, the European bisons from HEAL were below this value (0.176 mg/ml), while the animals from ELIM were above the cut-off point (0.305 mg/ml). Yet, Kęsik-Maliszewska et al. [29] have not proved differences in serum APPs excretion in experimentally infected with Schmallenberg virus calves, suggesting that not all infections induce measurable response. A team of Ansari-Lari et al. [30] analysed changes of Hp, Fb, SAA and albumin (Ab) levels in the course of post-traumatic reticulitis and peritonitis in cattle. Their study showed that the analysed proteins respond in a similar way and the particularly significant increase of their level was observed in acute diffuse peritonitis as compared to other forms of disease. Reduction of Ab level was observed in acute local peritonitis whereas serum concentration of Ab increased in a diffuse form of this condition. Moreover, APPs may be a useful indicators of the cattle welfare and stress [31, 32]. In the study by Saco et al. [31] serum levels of SAA and Hp in cattle kept under various environmental conditions were measured. They showed higher concentration of SAA in animals exposed to stress, whereas Hp values remained at similar level regardless of the animal maintenance. Comparing to our results, the SAA concentration both in ELIM and HEAL (70.81 µg/ml and 18.95 µg/ml, respectively) were significantly higher than cows kept under hardy conditions (3.46 µg/ml and 4.50 µg/ml). However, the levels of APPs may also vary in physiological conditions such as pregnancy, lactation [33, 34].","To conclude, our preliminary study is the first report describing concentrations of the selected APPs (Hp, SAA and AGP) in European bison. Subsequently the study suggest that concentration of APPs may be used as a supportive tool, monitoring the health status (at individual and/or population level) and when making decisions regarding undesired individuals to be eliminated due to health reasons. The most significant limitations of our research was the moderate number of animals involved in the study. Further studies will include greater samples size in order to associate APPs levels with the most frequent pathologies of European bison from different populations, variable in exposure to pathogens or differences in herd management. Most importantly, the study confirmed the selection of animals for culling was diligent and thoughtful and could have been confirmed by APP up-regulation in sick animals."],"string":"[\n \"At the beginning of XX century, European bison (Bison bonasus) extinct from wild, when the last surviving animals were killed in the Białowieża Primeval Forest after World War I. Fortunately, the biggest wild herbivore in Europe have survived and now, 70 years after the first European bison had been released into the wild again, its world population is estimated at more than 9100 heads [1]. Unfortunately, growing numbers and density of the population exceeding capacity of the available habitats, increasing anthropopression and limited gene pool increase nowadays the risk of health threats such as exposure to infectious and invasive pathogens [2]. Therefore, it is crucial to support European bison population development by proper wildlife management. One of the tools that can be used to manage wildlife species is selective elimination (culling) of individuals, which genotype (and/or lineage) or health impairment make them undesirable in a herd. Apart from that, diseased individuals may threaten other animals in the population in case of contagious diseases, which, to make things worse, may also have zoonotic potential and be a challenge for public health [3, 4]. This applies especially to tuberculosis, which is currently a real zoonotic health hazard in wild ruminant populations including European bison [5]. The question which arises is how to accurately select individuals to be eliminated. The potential solution may be taken from farm animals medicine, in particular cattle, which is relatively closely related to European bison. It involves determining serum acute phase proteins (APPs) concentration, which reflects animal health status under different pathological conditions [6–8]. It could also be used as a predictive tool describing the chance of recovery based on the severity of the disease. Besides, determination of APPs can be applied as a reliable solution to assess general health status, which is crucial in case of endangered animal species, particularly with limited genes pool. The concentrations and type of APPs differ within the species [9, 10]. In cattle, the most important APPs are haptoglobin (Hp), serum amyloid A (SAA) and α1-acid-glycoprotein (AGP) [11], which levels are used as biomarkers of various pathological conditions and as predictive values for mastitis [12], respiratory diseases [13], and lameness [14]. However, no reports investigating concentration of APPs in European bison are available. Although, some studies have been described in other wildlife non-bovid species, nevertheless the data is very limited [15–17]. The secretion of APPs is associated with the acute phase reaction (APR) [18]. The APR is an innate response of the body to the disturbance of homeostasis which may be associated with various factors i.e. infection, damage of tissues, neoplastic hyperplasia, immunological disorders [19–21]. The goal of the acute phase response is to recover homeostasis and eliminate the causative factor. It comprises numerous hormonal, metabolic and neurological changes which occur within a short period of time after the injury, at the beginning of infection or inflammation [18]. Referring to cattle (as closely related to European bison domesticated species), the Hp, SAA and AGP have been described as the most significant markers of APR and are considered the major APPs used in the diagnostics. The level of Hp in cattle increases significantly in the course of APR (from nearly zero to about 2 g/l within 48 h from stimulation. The major biologic function of Hp is to bind haemoglobin in an equimolar ratio with very high affinity to prevent haemoglobin-mediated renal parenchymal injury and loss of iron following intravascular haemolysis [22]. Serum amyloid A seems also to be a good indicator of acute phase reaction in cattle. The synthesis of certain members of the family SAA proteins is significantly increased during inflammation [23]. SAA proteins can be considered as apolipoproteins because they associate with plasma lipoproteins mainly within the high-density range. Physiological role of SAA in the immune response during inflammation is not well understood, but various effects have been described. These include e.g., inhibition of lymphocyte proliferation, detoxification of endotoxin, inhibition of platelet aggregation, inhibition of thrombocytes aggregation, and inhibition of oxidative reaction in neutrophils [23]. Therefore, the aim of our preliminary study was to determine serum concentration of selected APPs (haptoglobin, serum amyloid A and α1-acid-glycoprotein) in clinically healthy European bison and animals eliminated under the clinical suspicion of different pathological conditions, followed by post-mortem evaluation [24]. We hypothesised that concentrations of selected APPs were higher in the eliminated European bison (ELIM) comparing to clinically healthy animals (HEAL).\",\n \" Samples Blood samples were collected from 87 European bison, including 40 samples from clinically healthy individuals (HEAL) and 47 samples from individuals eliminated due to different pathological conditions (ELIM). In HEAL, there were 27 females and 13 males in the average age of 6.17 (range: 1-16 years). While in ELIM there were 30 females and 17 males in the average age of 6.5 (range: 1-20 years). The clinically healthy European bison were pharmacologically immobilized for transportation, placing a telemetry collar, or routine diagnostic purposes according to the previously described protocols [25]. Briefly, the combination of xylazine and etorphine were used to immobilization. The preparations were administered with specialized pneumatic Dan-Inject applicators. Following a successful shot, the animal is immobilized within 15 min and thereafter veterinary and animal husbandry manipulations are possible. In order to awake the European bisons the mixture of atipamezole, naloxone and diprenorphine were used. The selected individuals were culled after the health assessment (ELIM) by the herd supervising veterinarian and under the decision of Ministry of Environment. Briefly, the health status assessment includes integumentary system examination, orifices inspection (check for any discharges), reproductive organs examination, sight organ examination as well as body condition assessment. The animals were sampled in accordance with the appropriate regulations and permits (Polish General Directorate for Environmental Protection: Regulations DOP-OZGIZ.6401.06.7.2012.1 s and DOPOZ. 6401.06.7.2012.1 s1.Warsaw, 2012; Polish General Directorate for Environmental Protection: Regulations DZPWG.6401.06.23.2014.km2.; and Polish Ministry of the Environment, Regulation: DLP-III-0771-5/42,173/14/ZK). The blood samples were taken at capture and collected from the jugular vein into sterile tubes with clot activator for serum separation. The poor quality samples were excluded prior to the study for ensuring the high reliability of results. Serum was harvested from the blood samples by centrifugation (3000 × g for 15 min at room temperature) and stored at -80° C for further analysis (not longer than one month).\\nBlood samples were collected from 87 European bison, including 40 samples from clinically healthy individuals (HEAL) and 47 samples from individuals eliminated due to different pathological conditions (ELIM). In HEAL, there were 27 females and 13 males in the average age of 6.17 (range: 1-16 years). While in ELIM there were 30 females and 17 males in the average age of 6.5 (range: 1-20 years). The clinically healthy European bison were pharmacologically immobilized for transportation, placing a telemetry collar, or routine diagnostic purposes according to the previously described protocols [25]. Briefly, the combination of xylazine and etorphine were used to immobilization. The preparations were administered with specialized pneumatic Dan-Inject applicators. Following a successful shot, the animal is immobilized within 15 min and thereafter veterinary and animal husbandry manipulations are possible. In order to awake the European bisons the mixture of atipamezole, naloxone and diprenorphine were used. The selected individuals were culled after the health assessment (ELIM) by the herd supervising veterinarian and under the decision of Ministry of Environment. Briefly, the health status assessment includes integumentary system examination, orifices inspection (check for any discharges), reproductive organs examination, sight organ examination as well as body condition assessment. The animals were sampled in accordance with the appropriate regulations and permits (Polish General Directorate for Environmental Protection: Regulations DOP-OZGIZ.6401.06.7.2012.1 s and DOPOZ. 6401.06.7.2012.1 s1.Warsaw, 2012; Polish General Directorate for Environmental Protection: Regulations DZPWG.6401.06.23.2014.km2.; and Polish Ministry of the Environment, Regulation: DLP-III-0771-5/42,173/14/ZK). The blood samples were taken at capture and collected from the jugular vein into sterile tubes with clot activator for serum separation. The poor quality samples were excluded prior to the study for ensuring the high reliability of results. Serum was harvested from the blood samples by centrifugation (3000 × g for 15 min at room temperature) and stored at -80° C for further analysis (not longer than one month).\\n Determination of APPs The concentrations of haptoglobin, serum amyloid A and α1-acid-glycoprotein in serum were measured using commercial ELISAs and colorimetric assays according to manufacturer’s guidelines, which included Haptoglobin Kit Phase Range and Phase Serum Amyloid A assays (Tridelta Development Ltd County Kildare, Ireland) and Cow α1-acid glycoprotein (AGP) ELISA (Life Diagnostic, West Chester, USA). Serum samples were tested in duplicate. Prior to AGP and SAA analyses samples were diluted as follows: 1:10,000 for AGP and 1:500 for SAA. The concentrations of the APP was calculated based on standard curve for each protein using the FindGraph computer software (UNIPHIZ Lab, Vancouver, Canada). All assays were preliminarily validated in our laboratory before being applied in European bison samples. For this purpose, precision, accuracy and limit of detection were calculated. Within-run coefficients of variation (CVs) were calculated after the analysis of 2 serum samples with low and high proteins concentrations eight times in a single run. Between-run CVs were obtained by measuring the same samples in eight separate runs carried out on three different days. All samples used were frozen in aliquots and only the vials needed for each run were used. Accuracy was investigated by linearity under dilution; in brief, two European bison serum samples were diluted (1:2; 1:4; 1:8; 1:16, 1:32) with sample diluents. The limit of detection was calculated as the lowest concentration of APP that could be distinguished from a zero sample, and was taken as the mean + 3 standard deviations (SD) of 12 replicates of blank sample, tested in one analytical run.\\nThe concentrations of haptoglobin, serum amyloid A and α1-acid-glycoprotein in serum were measured using commercial ELISAs and colorimetric assays according to manufacturer’s guidelines, which included Haptoglobin Kit Phase Range and Phase Serum Amyloid A assays (Tridelta Development Ltd County Kildare, Ireland) and Cow α1-acid glycoprotein (AGP) ELISA (Life Diagnostic, West Chester, USA). Serum samples were tested in duplicate. Prior to AGP and SAA analyses samples were diluted as follows: 1:10,000 for AGP and 1:500 for SAA. The concentrations of the APP was calculated based on standard curve for each protein using the FindGraph computer software (UNIPHIZ Lab, Vancouver, Canada). All assays were preliminarily validated in our laboratory before being applied in European bison samples. For this purpose, precision, accuracy and limit of detection were calculated. Within-run coefficients of variation (CVs) were calculated after the analysis of 2 serum samples with low and high proteins concentrations eight times in a single run. Between-run CVs were obtained by measuring the same samples in eight separate runs carried out on three different days. All samples used were frozen in aliquots and only the vials needed for each run were used. Accuracy was investigated by linearity under dilution; in brief, two European bison serum samples were diluted (1:2; 1:4; 1:8; 1:16, 1:32) with sample diluents. The limit of detection was calculated as the lowest concentration of APP that could be distinguished from a zero sample, and was taken as the mean + 3 standard deviations (SD) of 12 replicates of blank sample, tested in one analytical run.\\n Statistical analysis In order to select proper statistical tools to evaluate the differences between healthy (HEAL) and European bison eliminated due to poor health conditions (sick animals) (ELIM), the normality of data was verified by using Shapiro-Wilk test. Since none of the variables met the normality assumptions, non-parametric Mann-Whitney U test was used for all comparisons. Statistical significance was set at p < 0.05. Statistical analyses were performed using Statistica 13.3 (Tibco, USA).\\nIn order to select proper statistical tools to evaluate the differences between healthy (HEAL) and European bison eliminated due to poor health conditions (sick animals) (ELIM), the normality of data was verified by using Shapiro-Wilk test. Since none of the variables met the normality assumptions, non-parametric Mann-Whitney U test was used for all comparisons. Statistical significance was set at p < 0.05. Statistical analyses were performed using Statistica 13.3 (Tibco, USA).\",\n \"Blood samples were collected from 87 European bison, including 40 samples from clinically healthy individuals (HEAL) and 47 samples from individuals eliminated due to different pathological conditions (ELIM). In HEAL, there were 27 females and 13 males in the average age of 6.17 (range: 1-16 years). While in ELIM there were 30 females and 17 males in the average age of 6.5 (range: 1-20 years). The clinically healthy European bison were pharmacologically immobilized for transportation, placing a telemetry collar, or routine diagnostic purposes according to the previously described protocols [25]. Briefly, the combination of xylazine and etorphine were used to immobilization. The preparations were administered with specialized pneumatic Dan-Inject applicators. Following a successful shot, the animal is immobilized within 15 min and thereafter veterinary and animal husbandry manipulations are possible. In order to awake the European bisons the mixture of atipamezole, naloxone and diprenorphine were used. The selected individuals were culled after the health assessment (ELIM) by the herd supervising veterinarian and under the decision of Ministry of Environment. Briefly, the health status assessment includes integumentary system examination, orifices inspection (check for any discharges), reproductive organs examination, sight organ examination as well as body condition assessment. The animals were sampled in accordance with the appropriate regulations and permits (Polish General Directorate for Environmental Protection: Regulations DOP-OZGIZ.6401.06.7.2012.1 s and DOPOZ. 6401.06.7.2012.1 s1.Warsaw, 2012; Polish General Directorate for Environmental Protection: Regulations DZPWG.6401.06.23.2014.km2.; and Polish Ministry of the Environment, Regulation: DLP-III-0771-5/42,173/14/ZK). The blood samples were taken at capture and collected from the jugular vein into sterile tubes with clot activator for serum separation. The poor quality samples were excluded prior to the study for ensuring the high reliability of results. Serum was harvested from the blood samples by centrifugation (3000 × g for 15 min at room temperature) and stored at -80° C for further analysis (not longer than one month).\",\n \"The concentrations of haptoglobin, serum amyloid A and α1-acid-glycoprotein in serum were measured using commercial ELISAs and colorimetric assays according to manufacturer’s guidelines, which included Haptoglobin Kit Phase Range and Phase Serum Amyloid A assays (Tridelta Development Ltd County Kildare, Ireland) and Cow α1-acid glycoprotein (AGP) ELISA (Life Diagnostic, West Chester, USA). Serum samples were tested in duplicate. Prior to AGP and SAA analyses samples were diluted as follows: 1:10,000 for AGP and 1:500 for SAA. The concentrations of the APP was calculated based on standard curve for each protein using the FindGraph computer software (UNIPHIZ Lab, Vancouver, Canada). All assays were preliminarily validated in our laboratory before being applied in European bison samples. For this purpose, precision, accuracy and limit of detection were calculated. Within-run coefficients of variation (CVs) were calculated after the analysis of 2 serum samples with low and high proteins concentrations eight times in a single run. Between-run CVs were obtained by measuring the same samples in eight separate runs carried out on three different days. All samples used were frozen in aliquots and only the vials needed for each run were used. Accuracy was investigated by linearity under dilution; in brief, two European bison serum samples were diluted (1:2; 1:4; 1:8; 1:16, 1:32) with sample diluents. The limit of detection was calculated as the lowest concentration of APP that could be distinguished from a zero sample, and was taken as the mean + 3 standard deviations (SD) of 12 replicates of blank sample, tested in one analytical run.\",\n \"In order to select proper statistical tools to evaluate the differences between healthy (HEAL) and European bison eliminated due to poor health conditions (sick animals) (ELIM), the normality of data was verified by using Shapiro-Wilk test. Since none of the variables met the normality assumptions, non-parametric Mann-Whitney U test was used for all comparisons. Statistical significance was set at p < 0.05. Statistical analyses were performed using Statistica 13.3 (Tibco, USA).\",\n \" Analytical validation of the assays For Hp mean within-run and between-run CVs were 8.11% and 7.11%, respectively. Dilution studies resulted in linear regression equations with a correlation coefficient of 0.98 showing that the method measures the protein in a linear manner. The detection limit of the assay was 0.005 mg/ml.\\nFor SAA mean within-run and between-run CVs were 5.15% and 5.35%, respectively. Dilution studies resulted in linear regression equations with a correlation coefficient of 0.99 showing that the method measures the protein in a linear manner. The detection limit of the assay was 3.49 µg/ml.\\nFor AGP mean within-run and between-run CVs were 4.08% and 4.37%, respectively. Dilution studies resulted in linear regression equations with a correlation coefficient of 0.99 showing that the method measures the protein in a linear manner. The detection limit of the assay was 15.6 µg/ml.\\nFor Hp mean within-run and between-run CVs were 8.11% and 7.11%, respectively. Dilution studies resulted in linear regression equations with a correlation coefficient of 0.98 showing that the method measures the protein in a linear manner. The detection limit of the assay was 0.005 mg/ml.\\nFor SAA mean within-run and between-run CVs were 5.15% and 5.35%, respectively. Dilution studies resulted in linear regression equations with a correlation coefficient of 0.99 showing that the method measures the protein in a linear manner. The detection limit of the assay was 3.49 µg/ml.\\nFor AGP mean within-run and between-run CVs were 4.08% and 4.37%, respectively. Dilution studies resulted in linear regression equations with a correlation coefficient of 0.99 showing that the method measures the protein in a linear manner. The detection limit of the assay was 15.6 µg/ml.\\n Acute phase protein concentrations The median concentration of SAA in ELIM animals was almost 4 times higher than the median level in clinically healthy group. In ELIM, the median concentration was 70.81 µg/ml while in HEAL, the median concentration of SAA was 18.95 µg/ml. The interquartile range (IQR) equals 12.38-30.11 µg/ml for SAA concentration in HEAL, while in ELIM 50.50-112.46 µg/ml (Fig. 1). On other hand, the median concentration of Hp in HEAL group was over 2 times lower than in the eliminated animals with IQR equal to 0.1-0.214 mg/ml in HEAL and 0.270-0.592 mg/ml in ELIM. The median concentration of Hp in HEAL was 0.176 mg/ml, while in ELIM the median concentration of Hp was equal to 0.305 mg/ml (Fig. 2). The concentration of SAA and Hp was significantly higher in ELIM comparing to HEAL (p <0.01). Similar differences were also observed for AGP levels, however they were not statistically significant (p = 0.3950). The median levels of AGP in HEAL and ELIM were equal to 207.25 and 271.25 µg/ml, respectively. The IQR for AGP was 146.65-259.1 µg/ml in HEAL and 178.55-407.25 in ELIM µg/ml (Fig. 3).Fig. 1 A boxplot for the concentration of serum amyloid A in serum of European bison. The line inside the box is the median. The top and bottom lines of the box are the first and third quartiles, respectively. The top and bottom whiskers are the minimum and maximum concentrations, circles represent outliers. * - statistically significant differences between HEAL and ELIM (p < 0.01)Fig. 2 A boxplot for the concentration of haptoglobin in serum of European bison. The line inside the box is the median. The top and bottom lines of the box are the first and third quartiles, respectively. The top and bottom whiskers are the minimum and maximum concentrations. * - statistically significant differences between HEAL and ELIM (p < 0.01)Fig. 3 A boxplot for the concentration of α-1-acid glikoprotein in serum of European bison. The line inside the box is the median. The top and bottom lines of the box are the first and third quartiles, respectively. The top and bottom whiskers are the minimum and maximum concentrations, circles represent outliers, asterics represent far outliers\\n A boxplot for the concentration of serum amyloid A in serum of European bison. The line inside the box is the median. The top and bottom lines of the box are the first and third quartiles, respectively. The top and bottom whiskers are the minimum and maximum concentrations, circles represent outliers. * - statistically significant differences between HEAL and ELIM (p < 0.01)\\n A boxplot for the concentration of haptoglobin in serum of European bison. The line inside the box is the median. The top and bottom lines of the box are the first and third quartiles, respectively. The top and bottom whiskers are the minimum and maximum concentrations. * - statistically significant differences between HEAL and ELIM (p < 0.01)\\n A boxplot for the concentration of α-1-acid glikoprotein in serum of European bison. The line inside the box is the median. The top and bottom lines of the box are the first and third quartiles, respectively. The top and bottom whiskers are the minimum and maximum concentrations, circles represent outliers, asterics represent far outliers\\nThe median concentration of SAA in ELIM animals was almost 4 times higher than the median level in clinically healthy group. In ELIM, the median concentration was 70.81 µg/ml while in HEAL, the median concentration of SAA was 18.95 µg/ml. The interquartile range (IQR) equals 12.38-30.11 µg/ml for SAA concentration in HEAL, while in ELIM 50.50-112.46 µg/ml (Fig. 1). On other hand, the median concentration of Hp in HEAL group was over 2 times lower than in the eliminated animals with IQR equal to 0.1-0.214 mg/ml in HEAL and 0.270-0.592 mg/ml in ELIM. The median concentration of Hp in HEAL was 0.176 mg/ml, while in ELIM the median concentration of Hp was equal to 0.305 mg/ml (Fig. 2). The concentration of SAA and Hp was significantly higher in ELIM comparing to HEAL (p <0.01). Similar differences were also observed for AGP levels, however they were not statistically significant (p = 0.3950). The median levels of AGP in HEAL and ELIM were equal to 207.25 and 271.25 µg/ml, respectively. The IQR for AGP was 146.65-259.1 µg/ml in HEAL and 178.55-407.25 in ELIM µg/ml (Fig. 3).Fig. 1 A boxplot for the concentration of serum amyloid A in serum of European bison. The line inside the box is the median. The top and bottom lines of the box are the first and third quartiles, respectively. The top and bottom whiskers are the minimum and maximum concentrations, circles represent outliers. * - statistically significant differences between HEAL and ELIM (p < 0.01)Fig. 2 A boxplot for the concentration of haptoglobin in serum of European bison. The line inside the box is the median. The top and bottom lines of the box are the first and third quartiles, respectively. The top and bottom whiskers are the minimum and maximum concentrations. * - statistically significant differences between HEAL and ELIM (p < 0.01)Fig. 3 A boxplot for the concentration of α-1-acid glikoprotein in serum of European bison. The line inside the box is the median. The top and bottom lines of the box are the first and third quartiles, respectively. The top and bottom whiskers are the minimum and maximum concentrations, circles represent outliers, asterics represent far outliers\\n A boxplot for the concentration of serum amyloid A in serum of European bison. The line inside the box is the median. The top and bottom lines of the box are the first and third quartiles, respectively. The top and bottom whiskers are the minimum and maximum concentrations, circles represent outliers. * - statistically significant differences between HEAL and ELIM (p < 0.01)\\n A boxplot for the concentration of haptoglobin in serum of European bison. The line inside the box is the median. The top and bottom lines of the box are the first and third quartiles, respectively. The top and bottom whiskers are the minimum and maximum concentrations. * - statistically significant differences between HEAL and ELIM (p < 0.01)\\n A boxplot for the concentration of α-1-acid glikoprotein in serum of European bison. The line inside the box is the median. The top and bottom lines of the box are the first and third quartiles, respectively. The top and bottom whiskers are the minimum and maximum concentrations, circles represent outliers, asterics represent far outliers\",\n \"For Hp mean within-run and between-run CVs were 8.11% and 7.11%, respectively. Dilution studies resulted in linear regression equations with a correlation coefficient of 0.98 showing that the method measures the protein in a linear manner. The detection limit of the assay was 0.005 mg/ml.\\nFor SAA mean within-run and between-run CVs were 5.15% and 5.35%, respectively. Dilution studies resulted in linear regression equations with a correlation coefficient of 0.99 showing that the method measures the protein in a linear manner. The detection limit of the assay was 3.49 µg/ml.\\nFor AGP mean within-run and between-run CVs were 4.08% and 4.37%, respectively. Dilution studies resulted in linear regression equations with a correlation coefficient of 0.99 showing that the method measures the protein in a linear manner. The detection limit of the assay was 15.6 µg/ml.\",\n \"The median concentration of SAA in ELIM animals was almost 4 times higher than the median level in clinically healthy group. In ELIM, the median concentration was 70.81 µg/ml while in HEAL, the median concentration of SAA was 18.95 µg/ml. The interquartile range (IQR) equals 12.38-30.11 µg/ml for SAA concentration in HEAL, while in ELIM 50.50-112.46 µg/ml (Fig. 1). On other hand, the median concentration of Hp in HEAL group was over 2 times lower than in the eliminated animals with IQR equal to 0.1-0.214 mg/ml in HEAL and 0.270-0.592 mg/ml in ELIM. The median concentration of Hp in HEAL was 0.176 mg/ml, while in ELIM the median concentration of Hp was equal to 0.305 mg/ml (Fig. 2). The concentration of SAA and Hp was significantly higher in ELIM comparing to HEAL (p <0.01). Similar differences were also observed for AGP levels, however they were not statistically significant (p = 0.3950). The median levels of AGP in HEAL and ELIM were equal to 207.25 and 271.25 µg/ml, respectively. The IQR for AGP was 146.65-259.1 µg/ml in HEAL and 178.55-407.25 in ELIM µg/ml (Fig. 3).Fig. 1 A boxplot for the concentration of serum amyloid A in serum of European bison. The line inside the box is the median. The top and bottom lines of the box are the first and third quartiles, respectively. The top and bottom whiskers are the minimum and maximum concentrations, circles represent outliers. * - statistically significant differences between HEAL and ELIM (p < 0.01)Fig. 2 A boxplot for the concentration of haptoglobin in serum of European bison. The line inside the box is the median. The top and bottom lines of the box are the first and third quartiles, respectively. The top and bottom whiskers are the minimum and maximum concentrations. * - statistically significant differences between HEAL and ELIM (p < 0.01)Fig. 3 A boxplot for the concentration of α-1-acid glikoprotein in serum of European bison. The line inside the box is the median. The top and bottom lines of the box are the first and third quartiles, respectively. The top and bottom whiskers are the minimum and maximum concentrations, circles represent outliers, asterics represent far outliers\\n A boxplot for the concentration of serum amyloid A in serum of European bison. The line inside the box is the median. The top and bottom lines of the box are the first and third quartiles, respectively. The top and bottom whiskers are the minimum and maximum concentrations, circles represent outliers. * - statistically significant differences between HEAL and ELIM (p < 0.01)\\n A boxplot for the concentration of haptoglobin in serum of European bison. The line inside the box is the median. The top and bottom lines of the box are the first and third quartiles, respectively. The top and bottom whiskers are the minimum and maximum concentrations. * - statistically significant differences between HEAL and ELIM (p < 0.01)\\n A boxplot for the concentration of α-1-acid glikoprotein in serum of European bison. The line inside the box is the median. The top and bottom lines of the box are the first and third quartiles, respectively. The top and bottom whiskers are the minimum and maximum concentrations, circles represent outliers, asterics represent far outliers\",\n \"In 2020, International Union for Conservation of Nature (IUCN) has changed European bison status from “Vulnerable” to “Near Threatened”, which proved that proper wildlife management is effective [26]. However, taking into account that modern European bison population was recreated from several individuals, continuous actions should be carried out to protect the species of the largest land European mammal. On-going health surveillance of European bison should be an important element of European bison protection strategy [2]. Our study suggest that determination of APPs might be a useful tool to assess health status of the individual or at the population level. In European bison, in the other study the most prevalent pathologies observed postmortally included: pneumonia, emphysema, nephritis, body traumas, posthitis/balanoposthitis in males and infestations of Fasciola hepatica and Dictyocaulus viviparus [27]. However, to our best knowledge there are no studies investigating APPs in European bison and their relation with different pathological conditions.\\nRegarding European bison`s immunology, there is only a report describing changes of immunoglobulins within the age [27]. Possibly, further studies may describe dynamics of APPs in different European bison disease. In the current preliminary study, we reported higher concentration of two (Hp, SAA) out of three investigated APPs in the eliminated due to poor health condition European bison. However, we cannot reject AGP as a potential marker for future studies using larger number of animals, since its concentrations were generally higher in animals from ELIM group, even though not statistically proven. The knowledge on usage of APPs as markers of physiological and pathological processes in wildlife species is lacking, even though they appear as desirable candidates for monitoring wildlife health and disease burden in the changing environment. Similar in the assumptions, the study of Glidden et al. [28] have demonstrated that Hp levels may be used to detect infections non-specifically and potential as a surveillance marker in African buffalo. Nevertheless, broaden experience may be drawn from cattle medicine, especially considering genetic relatedness among bovids. For example, Bagga et al. [14] have found APPs useful in diagnostics of lameness in cows, reporting SAA and Hp concentrations approximately 3 and 20, respectively times higher in lame cows comparing to non-lame cows. Similarly in our study the median concentration of SAA was almost 4 times higher in ELIM than in HEAL. The SAA levels obtained by Bagga in lame and non-lame cows (22.19 µg/ml and 8.89 µg/ml, respectively) are numerically lower that median SAA concentration in ELIM and HEAL (70.81 µg/ml and 18.95 µg/ml, respectively). Similarly the Hp concentrations in lame and non-lame cows (0.217 mg/ml and 0.012 mg/ml, respectively) are numerically lower than Hp concentration in ELIM and HEAL (0.305 mg/ml and 0.176 mg/ml, respectively. Dalanezi et al. [12] described increase in milk APPs in course of mastitis caused by different pathogens and suggested pathogen-specific APPs profiles. On the other hand, Moisa et al. [13] state that Hp concentration can be useful biomarker of respiratory diseases in dairy calves. The researcher reports that 0.195 mg/ml is the cut-off value for Hp as biomarker for bovine respiratory diseases. Comparing to our study, the European bisons from HEAL were below this value (0.176 mg/ml), while the animals from ELIM were above the cut-off point (0.305 mg/ml). Yet, Kęsik-Maliszewska et al. [29] have not proved differences in serum APPs excretion in experimentally infected with Schmallenberg virus calves, suggesting that not all infections induce measurable response. A team of Ansari-Lari et al. [30] analysed changes of Hp, Fb, SAA and albumin (Ab) levels in the course of post-traumatic reticulitis and peritonitis in cattle. Their study showed that the analysed proteins respond in a similar way and the particularly significant increase of their level was observed in acute diffuse peritonitis as compared to other forms of disease. Reduction of Ab level was observed in acute local peritonitis whereas serum concentration of Ab increased in a diffuse form of this condition. Moreover, APPs may be a useful indicators of the cattle welfare and stress [31, 32]. In the study by Saco et al. [31] serum levels of SAA and Hp in cattle kept under various environmental conditions were measured. They showed higher concentration of SAA in animals exposed to stress, whereas Hp values remained at similar level regardless of the animal maintenance. Comparing to our results, the SAA concentration both in ELIM and HEAL (70.81 µg/ml and 18.95 µg/ml, respectively) were significantly higher than cows kept under hardy conditions (3.46 µg/ml and 4.50 µg/ml). However, the levels of APPs may also vary in physiological conditions such as pregnancy, lactation [33, 34].\",\n \"To conclude, our preliminary study is the first report describing concentrations of the selected APPs (Hp, SAA and AGP) in European bison. Subsequently the study suggest that concentration of APPs may be used as a supportive tool, monitoring the health status (at individual and/or population level) and when making decisions regarding undesired individuals to be eliminated due to health reasons. The most significant limitations of our research was the moderate number of animals involved in the study. Further studies will include greater samples size in order to associate APPs levels with the most frequent pathologies of European bison from different populations, variable in exposure to pathogens or differences in herd management. Most importantly, the study confirmed the selection of animals for culling was diligent and thoughtful and could have been confirmed by APP up-regulation in sick animals.\"\n]"},"all_sections_sec_types":{"kind":"list like","value":[null,null,null,null,null,"results",null,null,"discussion","conclusion"],"string":"[\n null,\n null,\n null,\n null,\n null,\n \"results\",\n null,\n null,\n \"discussion\",\n \"conclusion\"\n]"},"keywords":{"kind":"list like","value":["European bison","Acute phase proteins","Wildlife management","Hp","SAA","AGP"],"string":"[\n \"European bison\",\n \"Acute phase proteins\",\n \"Wildlife management\",\n \"Hp\",\n \"SAA\",\n \"AGP\"\n]"},"whole_article_text":{"kind":"string","value":"Background:\nAt the beginning of XX century, European bison (Bison bonasus) extinct from wild, when the last surviving animals were killed in the Białowieża Primeval Forest after World War I. Fortunately, the biggest wild herbivore in Europe have survived and now, 70 years after the first European bison had been released into the wild again, its world population is estimated at more than 9100 heads [1]. Unfortunately, growing numbers and density of the population exceeding capacity of the available habitats, increasing anthropopression and limited gene pool increase nowadays the risk of health threats such as exposure to infectious and invasive pathogens [2]. Therefore, it is crucial to support European bison population development by proper wildlife management. One of the tools that can be used to manage wildlife species is selective elimination (culling) of individuals, which genotype (and/or lineage) or health impairment make them undesirable in a herd. Apart from that, diseased individuals may threaten other animals in the population in case of contagious diseases, which, to make things worse, may also have zoonotic potential and be a challenge for public health [3, 4]. This applies especially to tuberculosis, which is currently a real zoonotic health hazard in wild ruminant populations including European bison [5]. The question which arises is how to accurately select individuals to be eliminated. The potential solution may be taken from farm animals medicine, in particular cattle, which is relatively closely related to European bison. It involves determining serum acute phase proteins (APPs) concentration, which reflects animal health status under different pathological conditions [6–8]. It could also be used as a predictive tool describing the chance of recovery based on the severity of the disease. Besides, determination of APPs can be applied as a reliable solution to assess general health status, which is crucial in case of endangered animal species, particularly with limited genes pool. The concentrations and type of APPs differ within the species [9, 10]. In cattle, the most important APPs are haptoglobin (Hp), serum amyloid A (SAA) and α1-acid-glycoprotein (AGP) [11], which levels are used as biomarkers of various pathological conditions and as predictive values for mastitis [12], respiratory diseases [13], and lameness [14]. However, no reports investigating concentration of APPs in European bison are available. Although, some studies have been described in other wildlife non-bovid species, nevertheless the data is very limited [15–17]. The secretion of APPs is associated with the acute phase reaction (APR) [18]. The APR is an innate response of the body to the disturbance of homeostasis which may be associated with various factors i.e. infection, damage of tissues, neoplastic hyperplasia, immunological disorders [19–21]. The goal of the acute phase response is to recover homeostasis and eliminate the causative factor. It comprises numerous hormonal, metabolic and neurological changes which occur within a short period of time after the injury, at the beginning of infection or inflammation [18]. Referring to cattle (as closely related to European bison domesticated species), the Hp, SAA and AGP have been described as the most significant markers of APR and are considered the major APPs used in the diagnostics. The level of Hp in cattle increases significantly in the course of APR (from nearly zero to about 2 g/l within 48 h from stimulation. The major biologic function of Hp is to bind haemoglobin in an equimolar ratio with very high affinity to prevent haemoglobin-mediated renal parenchymal injury and loss of iron following intravascular haemolysis [22]. Serum amyloid A seems also to be a good indicator of acute phase reaction in cattle. The synthesis of certain members of the family SAA proteins is significantly increased during inflammation [23]. SAA proteins can be considered as apolipoproteins because they associate with plasma lipoproteins mainly within the high-density range. Physiological role of SAA in the immune response during inflammation is not well understood, but various effects have been described. These include e.g., inhibition of lymphocyte proliferation, detoxification of endotoxin, inhibition of platelet aggregation, inhibition of thrombocytes aggregation, and inhibition of oxidative reaction in neutrophils [23]. Therefore, the aim of our preliminary study was to determine serum concentration of selected APPs (haptoglobin, serum amyloid A and α1-acid-glycoprotein) in clinically healthy European bison and animals eliminated under the clinical suspicion of different pathological conditions, followed by post-mortem evaluation [24]. We hypothesised that concentrations of selected APPs were higher in the eliminated European bison (ELIM) comparing to clinically healthy animals (HEAL).\n\nMethods:\n Samples Blood samples were collected from 87 European bison, including 40 samples from clinically healthy individuals (HEAL) and 47 samples from individuals eliminated due to different pathological conditions (ELIM). In HEAL, there were 27 females and 13 males in the average age of 6.17 (range: 1-16 years). While in ELIM there were 30 females and 17 males in the average age of 6.5 (range: 1-20 years). The clinically healthy European bison were pharmacologically immobilized for transportation, placing a telemetry collar, or routine diagnostic purposes according to the previously described protocols [25]. Briefly, the combination of xylazine and etorphine were used to immobilization. The preparations were administered with specialized pneumatic Dan-Inject applicators. Following a successful shot, the animal is immobilized within 15 min and thereafter veterinary and animal husbandry manipulations are possible. In order to awake the European bisons the mixture of atipamezole, naloxone and diprenorphine were used. The selected individuals were culled after the health assessment (ELIM) by the herd supervising veterinarian and under the decision of Ministry of Environment. Briefly, the health status assessment includes integumentary system examination, orifices inspection (check for any discharges), reproductive organs examination, sight organ examination as well as body condition assessment. The animals were sampled in accordance with the appropriate regulations and permits (Polish General Directorate for Environmental Protection: Regulations DOP-OZGIZ.6401.06.7.2012.1 s and DOPOZ. 6401.06.7.2012.1 s1.Warsaw, 2012; Polish General Directorate for Environmental Protection: Regulations DZPWG.6401.06.23.2014.km2.; and Polish Ministry of the Environment, Regulation: DLP-III-0771-5/42,173/14/ZK). The blood samples were taken at capture and collected from the jugular vein into sterile tubes with clot activator for serum separation. The poor quality samples were excluded prior to the study for ensuring the high reliability of results. Serum was harvested from the blood samples by centrifugation (3000 × g for 15 min at room temperature) and stored at -80° C for further analysis (not longer than one month).\nBlood samples were collected from 87 European bison, including 40 samples from clinically healthy individuals (HEAL) and 47 samples from individuals eliminated due to different pathological conditions (ELIM). In HEAL, there were 27 females and 13 males in the average age of 6.17 (range: 1-16 years). While in ELIM there were 30 females and 17 males in the average age of 6.5 (range: 1-20 years). The clinically healthy European bison were pharmacologically immobilized for transportation, placing a telemetry collar, or routine diagnostic purposes according to the previously described protocols [25]. Briefly, the combination of xylazine and etorphine were used to immobilization. The preparations were administered with specialized pneumatic Dan-Inject applicators. Following a successful shot, the animal is immobilized within 15 min and thereafter veterinary and animal husbandry manipulations are possible. In order to awake the European bisons the mixture of atipamezole, naloxone and diprenorphine were used. The selected individuals were culled after the health assessment (ELIM) by the herd supervising veterinarian and under the decision of Ministry of Environment. Briefly, the health status assessment includes integumentary system examination, orifices inspection (check for any discharges), reproductive organs examination, sight organ examination as well as body condition assessment. The animals were sampled in accordance with the appropriate regulations and permits (Polish General Directorate for Environmental Protection: Regulations DOP-OZGIZ.6401.06.7.2012.1 s and DOPOZ. 6401.06.7.2012.1 s1.Warsaw, 2012; Polish General Directorate for Environmental Protection: Regulations DZPWG.6401.06.23.2014.km2.; and Polish Ministry of the Environment, Regulation: DLP-III-0771-5/42,173/14/ZK). The blood samples were taken at capture and collected from the jugular vein into sterile tubes with clot activator for serum separation. The poor quality samples were excluded prior to the study for ensuring the high reliability of results. Serum was harvested from the blood samples by centrifugation (3000 × g for 15 min at room temperature) and stored at -80° C for further analysis (not longer than one month).\n Determination of APPs The concentrations of haptoglobin, serum amyloid A and α1-acid-glycoprotein in serum were measured using commercial ELISAs and colorimetric assays according to manufacturer’s guidelines, which included Haptoglobin Kit Phase Range and Phase Serum Amyloid A assays (Tridelta Development Ltd County Kildare, Ireland) and Cow α1-acid glycoprotein (AGP) ELISA (Life Diagnostic, West Chester, USA). Serum samples were tested in duplicate. Prior to AGP and SAA analyses samples were diluted as follows: 1:10,000 for AGP and 1:500 for SAA. The concentrations of the APP was calculated based on standard curve for each protein using the FindGraph computer software (UNIPHIZ Lab, Vancouver, Canada). All assays were preliminarily validated in our laboratory before being applied in European bison samples. For this purpose, precision, accuracy and limit of detection were calculated. Within-run coefficients of variation (CVs) were calculated after the analysis of 2 serum samples with low and high proteins concentrations eight times in a single run. Between-run CVs were obtained by measuring the same samples in eight separate runs carried out on three different days. All samples used were frozen in aliquots and only the vials needed for each run were used. Accuracy was investigated by linearity under dilution; in brief, two European bison serum samples were diluted (1:2; 1:4; 1:8; 1:16, 1:32) with sample diluents. The limit of detection was calculated as the lowest concentration of APP that could be distinguished from a zero sample, and was taken as the mean + 3 standard deviations (SD) of 12 replicates of blank sample, tested in one analytical run.\nThe concentrations of haptoglobin, serum amyloid A and α1-acid-glycoprotein in serum were measured using commercial ELISAs and colorimetric assays according to manufacturer’s guidelines, which included Haptoglobin Kit Phase Range and Phase Serum Amyloid A assays (Tridelta Development Ltd County Kildare, Ireland) and Cow α1-acid glycoprotein (AGP) ELISA (Life Diagnostic, West Chester, USA). Serum samples were tested in duplicate. Prior to AGP and SAA analyses samples were diluted as follows: 1:10,000 for AGP and 1:500 for SAA. The concentrations of the APP was calculated based on standard curve for each protein using the FindGraph computer software (UNIPHIZ Lab, Vancouver, Canada). All assays were preliminarily validated in our laboratory before being applied in European bison samples. For this purpose, precision, accuracy and limit of detection were calculated. Within-run coefficients of variation (CVs) were calculated after the analysis of 2 serum samples with low and high proteins concentrations eight times in a single run. Between-run CVs were obtained by measuring the same samples in eight separate runs carried out on three different days. All samples used were frozen in aliquots and only the vials needed for each run were used. Accuracy was investigated by linearity under dilution; in brief, two European bison serum samples were diluted (1:2; 1:4; 1:8; 1:16, 1:32) with sample diluents. The limit of detection was calculated as the lowest concentration of APP that could be distinguished from a zero sample, and was taken as the mean + 3 standard deviations (SD) of 12 replicates of blank sample, tested in one analytical run.\n Statistical analysis In order to select proper statistical tools to evaluate the differences between healthy (HEAL) and European bison eliminated due to poor health conditions (sick animals) (ELIM), the normality of data was verified by using Shapiro-Wilk test. Since none of the variables met the normality assumptions, non-parametric Mann-Whitney U test was used for all comparisons. Statistical significance was set at p < 0.05. Statistical analyses were performed using Statistica 13.3 (Tibco, USA).\nIn order to select proper statistical tools to evaluate the differences between healthy (HEAL) and European bison eliminated due to poor health conditions (sick animals) (ELIM), the normality of data was verified by using Shapiro-Wilk test. Since none of the variables met the normality assumptions, non-parametric Mann-Whitney U test was used for all comparisons. Statistical significance was set at p < 0.05. Statistical analyses were performed using Statistica 13.3 (Tibco, USA).\n\nSamples:\nBlood samples were collected from 87 European bison, including 40 samples from clinically healthy individuals (HEAL) and 47 samples from individuals eliminated due to different pathological conditions (ELIM). In HEAL, there were 27 females and 13 males in the average age of 6.17 (range: 1-16 years). While in ELIM there were 30 females and 17 males in the average age of 6.5 (range: 1-20 years). The clinically healthy European bison were pharmacologically immobilized for transportation, placing a telemetry collar, or routine diagnostic purposes according to the previously described protocols [25]. Briefly, the combination of xylazine and etorphine were used to immobilization. The preparations were administered with specialized pneumatic Dan-Inject applicators. Following a successful shot, the animal is immobilized within 15 min and thereafter veterinary and animal husbandry manipulations are possible. In order to awake the European bisons the mixture of atipamezole, naloxone and diprenorphine were used. The selected individuals were culled after the health assessment (ELIM) by the herd supervising veterinarian and under the decision of Ministry of Environment. Briefly, the health status assessment includes integumentary system examination, orifices inspection (check for any discharges), reproductive organs examination, sight organ examination as well as body condition assessment. The animals were sampled in accordance with the appropriate regulations and permits (Polish General Directorate for Environmental Protection: Regulations DOP-OZGIZ.6401.06.7.2012.1 s and DOPOZ. 6401.06.7.2012.1 s1.Warsaw, 2012; Polish General Directorate for Environmental Protection: Regulations DZPWG.6401.06.23.2014.km2.; and Polish Ministry of the Environment, Regulation: DLP-III-0771-5/42,173/14/ZK). The blood samples were taken at capture and collected from the jugular vein into sterile tubes with clot activator for serum separation. The poor quality samples were excluded prior to the study for ensuring the high reliability of results. Serum was harvested from the blood samples by centrifugation (3000 × g for 15 min at room temperature) and stored at -80° C for further analysis (not longer than one month).\n\nDetermination of APPs:\nThe concentrations of haptoglobin, serum amyloid A and α1-acid-glycoprotein in serum were measured using commercial ELISAs and colorimetric assays according to manufacturer’s guidelines, which included Haptoglobin Kit Phase Range and Phase Serum Amyloid A assays (Tridelta Development Ltd County Kildare, Ireland) and Cow α1-acid glycoprotein (AGP) ELISA (Life Diagnostic, West Chester, USA). Serum samples were tested in duplicate. Prior to AGP and SAA analyses samples were diluted as follows: 1:10,000 for AGP and 1:500 for SAA. The concentrations of the APP was calculated based on standard curve for each protein using the FindGraph computer software (UNIPHIZ Lab, Vancouver, Canada). All assays were preliminarily validated in our laboratory before being applied in European bison samples. For this purpose, precision, accuracy and limit of detection were calculated. Within-run coefficients of variation (CVs) were calculated after the analysis of 2 serum samples with low and high proteins concentrations eight times in a single run. Between-run CVs were obtained by measuring the same samples in eight separate runs carried out on three different days. All samples used were frozen in aliquots and only the vials needed for each run were used. Accuracy was investigated by linearity under dilution; in brief, two European bison serum samples were diluted (1:2; 1:4; 1:8; 1:16, 1:32) with sample diluents. The limit of detection was calculated as the lowest concentration of APP that could be distinguished from a zero sample, and was taken as the mean + 3 standard deviations (SD) of 12 replicates of blank sample, tested in one analytical run.\n\nStatistical analysis:\nIn order to select proper statistical tools to evaluate the differences between healthy (HEAL) and European bison eliminated due to poor health conditions (sick animals) (ELIM), the normality of data was verified by using Shapiro-Wilk test. Since none of the variables met the normality assumptions, non-parametric Mann-Whitney U test was used for all comparisons. Statistical significance was set at p < 0.05. Statistical analyses were performed using Statistica 13.3 (Tibco, USA).\n\nResults:\n Analytical validation of the assays For Hp mean within-run and between-run CVs were 8.11% and 7.11%, respectively. Dilution studies resulted in linear regression equations with a correlation coefficient of 0.98 showing that the method measures the protein in a linear manner. The detection limit of the assay was 0.005 mg/ml.\nFor SAA mean within-run and between-run CVs were 5.15% and 5.35%, respectively. Dilution studies resulted in linear regression equations with a correlation coefficient of 0.99 showing that the method measures the protein in a linear manner. The detection limit of the assay was 3.49 µg/ml.\nFor AGP mean within-run and between-run CVs were 4.08% and 4.37%, respectively. Dilution studies resulted in linear regression equations with a correlation coefficient of 0.99 showing that the method measures the protein in a linear manner. The detection limit of the assay was 15.6 µg/ml.\nFor Hp mean within-run and between-run CVs were 8.11% and 7.11%, respectively. Dilution studies resulted in linear regression equations with a correlation coefficient of 0.98 showing that the method measures the protein in a linear manner. The detection limit of the assay was 0.005 mg/ml.\nFor SAA mean within-run and between-run CVs were 5.15% and 5.35%, respectively. Dilution studies resulted in linear regression equations with a correlation coefficient of 0.99 showing that the method measures the protein in a linear manner. The detection limit of the assay was 3.49 µg/ml.\nFor AGP mean within-run and between-run CVs were 4.08% and 4.37%, respectively. Dilution studies resulted in linear regression equations with a correlation coefficient of 0.99 showing that the method measures the protein in a linear manner. The detection limit of the assay was 15.6 µg/ml.\n Acute phase protein concentrations The median concentration of SAA in ELIM animals was almost 4 times higher than the median level in clinically healthy group. In ELIM, the median concentration was 70.81 µg/ml while in HEAL, the median concentration of SAA was 18.95 µg/ml. The interquartile range (IQR) equals 12.38-30.11 µg/ml for SAA concentration in HEAL, while in ELIM 50.50-112.46 µg/ml (Fig. 1). On other hand, the median concentration of Hp in HEAL group was over 2 times lower than in the eliminated animals with IQR equal to 0.1-0.214 mg/ml in HEAL and 0.270-0.592 mg/ml in ELIM. The median concentration of Hp in HEAL was 0.176 mg/ml, while in ELIM the median concentration of Hp was equal to 0.305 mg/ml (Fig. 2). The concentration of SAA and Hp was significantly higher in ELIM comparing to HEAL (p <0.01). Similar differences were also observed for AGP levels, however they were not statistically significant (p = 0.3950). The median levels of AGP in HEAL and ELIM were equal to 207.25 and 271.25 µg/ml, respectively. The IQR for AGP was 146.65-259.1 µg/ml in HEAL and 178.55-407.25 in ELIM µg/ml (Fig. 3).Fig. 1 A boxplot for the concentration of serum amyloid A in serum of European bison. The line inside the box is the median. The top and bottom lines of the box are the first and third quartiles, respectively. The top and bottom whiskers are the minimum and maximum concentrations, circles represent outliers. * - statistically significant differences between HEAL and ELIM (p < 0.01)Fig. 2 A boxplot for the concentration of haptoglobin in serum of European bison. The line inside the box is the median. The top and bottom lines of the box are the first and third quartiles, respectively. The top and bottom whiskers are the minimum and maximum concentrations. * - statistically significant differences between HEAL and ELIM (p < 0.01)Fig. 3 A boxplot for the concentration of α-1-acid glikoprotein in serum of European bison. The line inside the box is the median. The top and bottom lines of the box are the first and third quartiles, respectively. The top and bottom whiskers are the minimum and maximum concentrations, circles represent outliers, asterics represent far outliers\n A boxplot for the concentration of serum amyloid A in serum of European bison. The line inside the box is the median. The top and bottom lines of the box are the first and third quartiles, respectively. The top and bottom whiskers are the minimum and maximum concentrations, circles represent outliers. * - statistically significant differences between HEAL and ELIM (p < 0.01)\n A boxplot for the concentration of haptoglobin in serum of European bison. The line inside the box is the median. The top and bottom lines of the box are the first and third quartiles, respectively. The top and bottom whiskers are the minimum and maximum concentrations. * - statistically significant differences between HEAL and ELIM (p < 0.01)\n A boxplot for the concentration of α-1-acid glikoprotein in serum of European bison. The line inside the box is the median. The top and bottom lines of the box are the first and third quartiles, respectively. The top and bottom whiskers are the minimum and maximum concentrations, circles represent outliers, asterics represent far outliers\nThe median concentration of SAA in ELIM animals was almost 4 times higher than the median level in clinically healthy group. In ELIM, the median concentration was 70.81 µg/ml while in HEAL, the median concentration of SAA was 18.95 µg/ml. The interquartile range (IQR) equals 12.38-30.11 µg/ml for SAA concentration in HEAL, while in ELIM 50.50-112.46 µg/ml (Fig. 1). On other hand, the median concentration of Hp in HEAL group was over 2 times lower than in the eliminated animals with IQR equal to 0.1-0.214 mg/ml in HEAL and 0.270-0.592 mg/ml in ELIM. The median concentration of Hp in HEAL was 0.176 mg/ml, while in ELIM the median concentration of Hp was equal to 0.305 mg/ml (Fig. 2). The concentration of SAA and Hp was significantly higher in ELIM comparing to HEAL (p <0.01). Similar differences were also observed for AGP levels, however they were not statistically significant (p = 0.3950). The median levels of AGP in HEAL and ELIM were equal to 207.25 and 271.25 µg/ml, respectively. The IQR for AGP was 146.65-259.1 µg/ml in HEAL and 178.55-407.25 in ELIM µg/ml (Fig. 3).Fig. 1 A boxplot for the concentration of serum amyloid A in serum of European bison. The line inside the box is the median. The top and bottom lines of the box are the first and third quartiles, respectively. The top and bottom whiskers are the minimum and maximum concentrations, circles represent outliers. * - statistically significant differences between HEAL and ELIM (p < 0.01)Fig. 2 A boxplot for the concentration of haptoglobin in serum of European bison. The line inside the box is the median. The top and bottom lines of the box are the first and third quartiles, respectively. The top and bottom whiskers are the minimum and maximum concentrations. * - statistically significant differences between HEAL and ELIM (p < 0.01)Fig. 3 A boxplot for the concentration of α-1-acid glikoprotein in serum of European bison. The line inside the box is the median. The top and bottom lines of the box are the first and third quartiles, respectively. The top and bottom whiskers are the minimum and maximum concentrations, circles represent outliers, asterics represent far outliers\n A boxplot for the concentration of serum amyloid A in serum of European bison. The line inside the box is the median. The top and bottom lines of the box are the first and third quartiles, respectively. The top and bottom whiskers are the minimum and maximum concentrations, circles represent outliers. * - statistically significant differences between HEAL and ELIM (p < 0.01)\n A boxplot for the concentration of haptoglobin in serum of European bison. The line inside the box is the median. The top and bottom lines of the box are the first and third quartiles, respectively. The top and bottom whiskers are the minimum and maximum concentrations. * - statistically significant differences between HEAL and ELIM (p < 0.01)\n A boxplot for the concentration of α-1-acid glikoprotein in serum of European bison. The line inside the box is the median. The top and bottom lines of the box are the first and third quartiles, respectively. The top and bottom whiskers are the minimum and maximum concentrations, circles represent outliers, asterics represent far outliers\n\nAnalytical validation of the assays:\nFor Hp mean within-run and between-run CVs were 8.11% and 7.11%, respectively. Dilution studies resulted in linear regression equations with a correlation coefficient of 0.98 showing that the method measures the protein in a linear manner. The detection limit of the assay was 0.005 mg/ml.\nFor SAA mean within-run and between-run CVs were 5.15% and 5.35%, respectively. Dilution studies resulted in linear regression equations with a correlation coefficient of 0.99 showing that the method measures the protein in a linear manner. The detection limit of the assay was 3.49 µg/ml.\nFor AGP mean within-run and between-run CVs were 4.08% and 4.37%, respectively. Dilution studies resulted in linear regression equations with a correlation coefficient of 0.99 showing that the method measures the protein in a linear manner. The detection limit of the assay was 15.6 µg/ml.\n\nAcute phase protein concentrations:\nThe median concentration of SAA in ELIM animals was almost 4 times higher than the median level in clinically healthy group. In ELIM, the median concentration was 70.81 µg/ml while in HEAL, the median concentration of SAA was 18.95 µg/ml. The interquartile range (IQR) equals 12.38-30.11 µg/ml for SAA concentration in HEAL, while in ELIM 50.50-112.46 µg/ml (Fig. 1). On other hand, the median concentration of Hp in HEAL group was over 2 times lower than in the eliminated animals with IQR equal to 0.1-0.214 mg/ml in HEAL and 0.270-0.592 mg/ml in ELIM. The median concentration of Hp in HEAL was 0.176 mg/ml, while in ELIM the median concentration of Hp was equal to 0.305 mg/ml (Fig. 2). The concentration of SAA and Hp was significantly higher in ELIM comparing to HEAL (p <0.01). Similar differences were also observed for AGP levels, however they were not statistically significant (p = 0.3950). The median levels of AGP in HEAL and ELIM were equal to 207.25 and 271.25 µg/ml, respectively. The IQR for AGP was 146.65-259.1 µg/ml in HEAL and 178.55-407.25 in ELIM µg/ml (Fig. 3).Fig. 1 A boxplot for the concentration of serum amyloid A in serum of European bison. The line inside the box is the median. The top and bottom lines of the box are the first and third quartiles, respectively. The top and bottom whiskers are the minimum and maximum concentrations, circles represent outliers. * - statistically significant differences between HEAL and ELIM (p < 0.01)Fig. 2 A boxplot for the concentration of haptoglobin in serum of European bison. The line inside the box is the median. The top and bottom lines of the box are the first and third quartiles, respectively. The top and bottom whiskers are the minimum and maximum concentrations. * - statistically significant differences between HEAL and ELIM (p < 0.01)Fig. 3 A boxplot for the concentration of α-1-acid glikoprotein in serum of European bison. The line inside the box is the median. The top and bottom lines of the box are the first and third quartiles, respectively. The top and bottom whiskers are the minimum and maximum concentrations, circles represent outliers, asterics represent far outliers\n A boxplot for the concentration of serum amyloid A in serum of European bison. The line inside the box is the median. The top and bottom lines of the box are the first and third quartiles, respectively. The top and bottom whiskers are the minimum and maximum concentrations, circles represent outliers. * - statistically significant differences between HEAL and ELIM (p < 0.01)\n A boxplot for the concentration of haptoglobin in serum of European bison. The line inside the box is the median. The top and bottom lines of the box are the first and third quartiles, respectively. The top and bottom whiskers are the minimum and maximum concentrations. * - statistically significant differences between HEAL and ELIM (p < 0.01)\n A boxplot for the concentration of α-1-acid glikoprotein in serum of European bison. The line inside the box is the median. The top and bottom lines of the box are the first and third quartiles, respectively. The top and bottom whiskers are the minimum and maximum concentrations, circles represent outliers, asterics represent far outliers\n\nDiscussion:\nIn 2020, International Union for Conservation of Nature (IUCN) has changed European bison status from “Vulnerable” to “Near Threatened”, which proved that proper wildlife management is effective [26]. However, taking into account that modern European bison population was recreated from several individuals, continuous actions should be carried out to protect the species of the largest land European mammal. On-going health surveillance of European bison should be an important element of European bison protection strategy [2]. Our study suggest that determination of APPs might be a useful tool to assess health status of the individual or at the population level. In European bison, in the other study the most prevalent pathologies observed postmortally included: pneumonia, emphysema, nephritis, body traumas, posthitis/balanoposthitis in males and infestations of Fasciola hepatica and Dictyocaulus viviparus [27]. However, to our best knowledge there are no studies investigating APPs in European bison and their relation with different pathological conditions.\nRegarding European bison`s immunology, there is only a report describing changes of immunoglobulins within the age [27]. Possibly, further studies may describe dynamics of APPs in different European bison disease. In the current preliminary study, we reported higher concentration of two (Hp, SAA) out of three investigated APPs in the eliminated due to poor health condition European bison. However, we cannot reject AGP as a potential marker for future studies using larger number of animals, since its concentrations were generally higher in animals from ELIM group, even though not statistically proven. The knowledge on usage of APPs as markers of physiological and pathological processes in wildlife species is lacking, even though they appear as desirable candidates for monitoring wildlife health and disease burden in the changing environment. Similar in the assumptions, the study of Glidden et al. [28] have demonstrated that Hp levels may be used to detect infections non-specifically and potential as a surveillance marker in African buffalo. Nevertheless, broaden experience may be drawn from cattle medicine, especially considering genetic relatedness among bovids. For example, Bagga et al. [14] have found APPs useful in diagnostics of lameness in cows, reporting SAA and Hp concentrations approximately 3 and 20, respectively times higher in lame cows comparing to non-lame cows. Similarly in our study the median concentration of SAA was almost 4 times higher in ELIM than in HEAL. The SAA levels obtained by Bagga in lame and non-lame cows (22.19 µg/ml and 8.89 µg/ml, respectively) are numerically lower that median SAA concentration in ELIM and HEAL (70.81 µg/ml and 18.95 µg/ml, respectively). Similarly the Hp concentrations in lame and non-lame cows (0.217 mg/ml and 0.012 mg/ml, respectively) are numerically lower than Hp concentration in ELIM and HEAL (0.305 mg/ml and 0.176 mg/ml, respectively. Dalanezi et al. [12] described increase in milk APPs in course of mastitis caused by different pathogens and suggested pathogen-specific APPs profiles. On the other hand, Moisa et al. [13] state that Hp concentration can be useful biomarker of respiratory diseases in dairy calves. The researcher reports that 0.195 mg/ml is the cut-off value for Hp as biomarker for bovine respiratory diseases. Comparing to our study, the European bisons from HEAL were below this value (0.176 mg/ml), while the animals from ELIM were above the cut-off point (0.305 mg/ml). Yet, Kęsik-Maliszewska et al. [29] have not proved differences in serum APPs excretion in experimentally infected with Schmallenberg virus calves, suggesting that not all infections induce measurable response. A team of Ansari-Lari et al. [30] analysed changes of Hp, Fb, SAA and albumin (Ab) levels in the course of post-traumatic reticulitis and peritonitis in cattle. Their study showed that the analysed proteins respond in a similar way and the particularly significant increase of their level was observed in acute diffuse peritonitis as compared to other forms of disease. Reduction of Ab level was observed in acute local peritonitis whereas serum concentration of Ab increased in a diffuse form of this condition. Moreover, APPs may be a useful indicators of the cattle welfare and stress [31, 32]. In the study by Saco et al. [31] serum levels of SAA and Hp in cattle kept under various environmental conditions were measured. They showed higher concentration of SAA in animals exposed to stress, whereas Hp values remained at similar level regardless of the animal maintenance. Comparing to our results, the SAA concentration both in ELIM and HEAL (70.81 µg/ml and 18.95 µg/ml, respectively) were significantly higher than cows kept under hardy conditions (3.46 µg/ml and 4.50 µg/ml). However, the levels of APPs may also vary in physiological conditions such as pregnancy, lactation [33, 34].\n\nConclusions:\nTo conclude, our preliminary study is the first report describing concentrations of the selected APPs (Hp, SAA and AGP) in European bison. Subsequently the study suggest that concentration of APPs may be used as a supportive tool, monitoring the health status (at individual and/or population level) and when making decisions regarding undesired individuals to be eliminated due to health reasons. The most significant limitations of our research was the moderate number of animals involved in the study. Further studies will include greater samples size in order to associate APPs levels with the most frequent pathologies of European bison from different populations, variable in exposure to pathogens or differences in herd management. Most importantly, the study confirmed the selection of animals for culling was diligent and thoughtful and could have been confirmed by APP up-regulation in sick animals.\n\n"},"whole_article_abstract":{"kind":"string","value":"Background:\nThis is the first report describing levels of APPs in European bison. Serum concentration of acute phase proteins (APPs) may be helpful to assess general health status in wildlife and potentially useful in selecting animals for elimination. Since there is a lack of literature data regarding concentration of APPs in European bisons, establishment of the reference values is also needed.\n\nMethods:\nA total of 87 European bison from Polish populations were divided into two groups: (1) healthy: immobilized for transportation, placing a telemetry collar and routine diagnostic purposes; and (2) selectively culled due to the poor health condition. The serum concentration of haptoglobin, serum amyloid A and α1-acid-glycoprotein were determined using commercial quantitative ELISA assays. Since none of the variables met the normality assumptions, non-parametric Mann-Whitney U test was used for all comparisons. Statistical significance was set at p < 0.05. Statistical analyses were performed using Statistica 13.3 (Tibco, USA).\n\nResults:\nThe concentration of haptoglobin and serum amyloid A was significantly higher in animals culled (euthanised) due to the poor condition in respect to the clinically healthy European bison. The levels of α1-acid-glycoprotein did not show statistical difference between healthy and sick animals.\n\nConclusions:\nCorrelation between APPs concertation and health status was proven, therefore the determination of selected APPs may be considered in future as auxiliary predictive tool in assessing European bison health condition."},"background_conclusion_text":{"kind":"string","value":"Background:\nAt the beginning of XX century, European bison (Bison bonasus) extinct from wild, when the last surviving animals were killed in the Białowieża Primeval Forest after World War I. Fortunately, the biggest wild herbivore in Europe have survived and now, 70 years after the first European bison had been released into the wild again, its world population is estimated at more than 9100 heads [1]. Unfortunately, growing numbers and density of the population exceeding capacity of the available habitats, increasing anthropopression and limited gene pool increase nowadays the risk of health threats such as exposure to infectious and invasive pathogens [2]. Therefore, it is crucial to support European bison population development by proper wildlife management. One of the tools that can be used to manage wildlife species is selective elimination (culling) of individuals, which genotype (and/or lineage) or health impairment make them undesirable in a herd. Apart from that, diseased individuals may threaten other animals in the population in case of contagious diseases, which, to make things worse, may also have zoonotic potential and be a challenge for public health [3, 4]. This applies especially to tuberculosis, which is currently a real zoonotic health hazard in wild ruminant populations including European bison [5]. The question which arises is how to accurately select individuals to be eliminated. The potential solution may be taken from farm animals medicine, in particular cattle, which is relatively closely related to European bison. It involves determining serum acute phase proteins (APPs) concentration, which reflects animal health status under different pathological conditions [6–8]. It could also be used as a predictive tool describing the chance of recovery based on the severity of the disease. Besides, determination of APPs can be applied as a reliable solution to assess general health status, which is crucial in case of endangered animal species, particularly with limited genes pool. The concentrations and type of APPs differ within the species [9, 10]. In cattle, the most important APPs are haptoglobin (Hp), serum amyloid A (SAA) and α1-acid-glycoprotein (AGP) [11], which levels are used as biomarkers of various pathological conditions and as predictive values for mastitis [12], respiratory diseases [13], and lameness [14]. However, no reports investigating concentration of APPs in European bison are available. Although, some studies have been described in other wildlife non-bovid species, nevertheless the data is very limited [15–17]. The secretion of APPs is associated with the acute phase reaction (APR) [18]. The APR is an innate response of the body to the disturbance of homeostasis which may be associated with various factors i.e. infection, damage of tissues, neoplastic hyperplasia, immunological disorders [19–21]. The goal of the acute phase response is to recover homeostasis and eliminate the causative factor. It comprises numerous hormonal, metabolic and neurological changes which occur within a short period of time after the injury, at the beginning of infection or inflammation [18]. Referring to cattle (as closely related to European bison domesticated species), the Hp, SAA and AGP have been described as the most significant markers of APR and are considered the major APPs used in the diagnostics. The level of Hp in cattle increases significantly in the course of APR (from nearly zero to about 2 g/l within 48 h from stimulation. The major biologic function of Hp is to bind haemoglobin in an equimolar ratio with very high affinity to prevent haemoglobin-mediated renal parenchymal injury and loss of iron following intravascular haemolysis [22]. Serum amyloid A seems also to be a good indicator of acute phase reaction in cattle. The synthesis of certain members of the family SAA proteins is significantly increased during inflammation [23]. SAA proteins can be considered as apolipoproteins because they associate with plasma lipoproteins mainly within the high-density range. Physiological role of SAA in the immune response during inflammation is not well understood, but various effects have been described. These include e.g., inhibition of lymphocyte proliferation, detoxification of endotoxin, inhibition of platelet aggregation, inhibition of thrombocytes aggregation, and inhibition of oxidative reaction in neutrophils [23]. Therefore, the aim of our preliminary study was to determine serum concentration of selected APPs (haptoglobin, serum amyloid A and α1-acid-glycoprotein) in clinically healthy European bison and animals eliminated under the clinical suspicion of different pathological conditions, followed by post-mortem evaluation [24]. We hypothesised that concentrations of selected APPs were higher in the eliminated European bison (ELIM) comparing to clinically healthy animals (HEAL).\n\nConclusions:\nTo conclude, our preliminary study is the first report describing concentrations of the selected APPs (Hp, SAA and AGP) in European bison. Subsequently the study suggest that concentration of APPs may be used as a supportive tool, monitoring the health status (at individual and/or population level) and when making decisions regarding undesired individuals to be eliminated due to health reasons. The most significant limitations of our research was the moderate number of animals involved in the study. Further studies will include greater samples size in order to associate APPs levels with the most frequent pathologies of European bison from different populations, variable in exposure to pathogens or differences in herd management. Most importantly, the study confirmed the selection of animals for culling was diligent and thoughtful and could have been confirmed by APP up-regulation in sick animals."},"background_conclusion_abstract":{"kind":"string","value":"Background:\nThis is the first report describing levels of APPs in European bison. Serum concentration of acute phase proteins (APPs) may be helpful to assess general health status in wildlife and potentially useful in selecting animals for elimination. Since there is a lack of literature data regarding concentration of APPs in European bisons, establishment of the reference values is also needed.\n\nMethods:\nA total of 87 European bison from Polish populations were divided into two groups: (1) healthy: immobilized for transportation, placing a telemetry collar and routine diagnostic purposes; and (2) selectively culled due to the poor health condition. The serum concentration of haptoglobin, serum amyloid A and α1-acid-glycoprotein were determined using commercial quantitative ELISA assays. Since none of the variables met the normality assumptions, non-parametric Mann-Whitney U test was used for all comparisons. Statistical significance was set at p < 0.05. Statistical analyses were performed using Statistica 13.3 (Tibco, USA).\n\nResults:\nThe concentration of haptoglobin and serum amyloid A was significantly higher in animals culled (euthanised) due to the poor condition in respect to the clinically healthy European bison. The levels of α1-acid-glycoprotein did not show statistical difference between healthy and sick animals.\n\nConclusions:\nCorrelation between APPs concertation and health status was proven, therefore the determination of selected APPs may be considered in future as auxiliary predictive tool in assessing European bison health condition."},"whole_article_text_length":{"kind":"number","value":6953,"string":"6,953"},"whole_article_abstract_length":{"kind":"number","value":278,"string":"278"},"other_sections_lengths":{"kind":"list like","value":[884,1577,382,307,92,176,670],"string":"[\n 884,\n 1577,\n 382,\n 307,\n 92,\n 176,\n 670\n]"},"num_sections":{"kind":"number","value":10,"string":"10"},"most_frequent_words":{"kind":"list like","value":["european","concentration","ml","serum","elim","bison","european bison","heal","median","samples"],"string":"[\n \"european\",\n \"concentration\",\n \"ml\",\n \"serum\",\n \"elim\",\n \"bison\",\n \"european bison\",\n \"heal\",\n \"median\",\n 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apps [SUMMARY]"},"annotated_tf_idf_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] samples | ml | serum | european | run | bison | european bison | elim | concentration | apps [SUMMARY]"},"annotated_entity_plan_background_abstract_prompt":{"kind":"string","value":"[CONTENT] first | European ||| ||| European [SUMMARY]"},"annotated_entity_plan_methods_abstract_prompt":{"kind":"null"},"annotated_entity_plan_results_abstract_prompt":{"kind":"string","value":"[CONTENT] European ||| [SUMMARY]"},"annotated_entity_plan_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] European [SUMMARY]"},"annotated_entity_plan_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] ||| first | European ||| ||| European ||| 87 | European | Polish | two | 1 | 2 | the poor health condition ||| A and α1 | ELISA ||| Mann-Whitney U ||| p < 0.05 ||| Statistica | Tibco | USA ||| ||| European ||| ||| European [SUMMARY]"},"annotated_entity_plan_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] ||| first | European ||| ||| European ||| 87 | European | Polish | two | 1 | 2 | the poor health condition ||| A and α1 | ELISA ||| Mann-Whitney U ||| p < 0.05 ||| Statistica | Tibco | USA ||| ||| European ||| ||| European [SUMMARY]"}}},{"rowIdx":270,"cells":{"title":{"kind":"string","value":"Low serum level of apolipoprotein A1 may predict the severity of COVID-19: A retrospective study."},"pmid":{"kind":"string","value":"34260764"},"background_abstract":{"kind":"string","value":"Dyslipidemia has been observed in patients with coronavirus disease 2019 (COVID-19). This study aimed to investigate blood lipid profiles in patients with COVID-19 and to explore their predictive values for COVID-19 severity."},"background_abstract_label":{"kind":"string","value":"BACKGROUND"},"methods_abstract":{"kind":"string","value":"A total of 142 consecutive patients with COVID-19 were included in this single-center retrospective study. Blood lipid profile characteristics were investigated in patients with COVID-19 in comparison with 77 age- and gender-matched healthy subjects, their predictive values for COVID-19 severity were analyzed by using multivariable logistic regression analysis, and their prediction efficiencies were evaluated by using receiver operator characteristic (ROC) curves."},"methods_abstract_label":{"kind":"string","value":"METHODS"},"results_abstract":{"kind":"string","value":"There were 125 and 17 cases in the non-severe and severe groups, respectively. Total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and apolipoprotein A1 (ApoA1) gradually decreased across the groups in the following order: healthy controls, non-severe group, and severe group. ApoA1 was identified as an independent risk factor for COVID-19 severity (adjusted odds ratio [OR]: 0.865, 95% confidence interval [CI]: 0.800-0.935, p < 0.001), along with interleukin-6 (IL-6) (adjusted OR: 1.097, 95% CI: 1.034-1.165, p = 0.002). ApoA1 exhibited the highest area under the ROC curve (AUC) among all single markers (AUC: 0.896, 95% CI: 0.834-0.941); moreover, the risk model established using ApoA1 and IL-6 enhanced prediction efficiency (AUC: 0.977, 95% CI: 0.932-0.995)."},"results_abstract_label":{"kind":"string","value":"RESULTS"},"conclusions_abstract":{"kind":"string","value":"Blood lipid profiles in patients with COVID-19 are quite abnormal compared with those in healthy subjects, especially in severe cases. Serum ApoA1 may represent a good indicator for predicting the severity of COVID-19."},"conclusions_abstract_label":{"kind":"string","value":"CONCLUSION"},"mesh_descriptor_names":{"kind":"list like","value":["Adult","Aged","Apolipoprotein A-I","Area Under Curve","Biomarkers","COVID-19","Case-Control Studies","Cholesterol, HDL","Cholesterol, LDL","Comorbidity","Female","Humans","Lipids","Male","Middle Aged","Retrospective Studies","Risk Factors","Severity of Illness Index"],"string":"[\n \"Adult\",\n \"Aged\",\n \"Apolipoprotein A-I\",\n \"Area Under Curve\",\n \"Biomarkers\",\n \"COVID-19\",\n \"Case-Control Studies\",\n \"Cholesterol, HDL\",\n \"Cholesterol, LDL\",\n \"Comorbidity\",\n \"Female\",\n \"Humans\",\n \"Lipids\",\n \"Male\",\n \"Middle Aged\",\n \"Retrospective Studies\",\n \"Risk Factors\",\n \"Severity of Illness Index\"\n]"},"pmcid":{"kind":"string","value":"8373354"},"background_title":{"kind":"string","value":"INTRODUCTION"},"background_text":{"kind":"string","value":"Severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) is a highly transmissible coronavirus that has caused an ever‐increasing number of coronavirus disease 2019 (COVID‐19) infections since December 2019 and spread rapidly worldwide. Although approximately 80% of patients infected with SARS‐CoV‐2 exhibit mild symptoms,1 the remaining severe cases may experience acute respiratory distress syndrome (ARDS), multiorgan failure (MOF), and death.2 Therefore, it is necessary to discriminate between severe and mild cases.\nPrevious studies have found that the development of severe COVID‐19 is associated with age and underlying diseases, and patients who develop severe disease are likely to suffer from aberrant inflammatory reactions and cytokine storms.1, 3 Consequently, several clinical characteristics, the inflammation index, and cytokine levels have been used as indicators for predicting the severity of COVID‐19 by us and others.4, 5 Emerging evidence suggests that lipid metabolism dysregulation might promote the progression of COVID‐19, as revealed by mass spectrometry (MS)‐based proteomics analysis.6, 7 Although MS analysis is not commonly performed, blood lipids are routinely examined using automatic biochemical instruments in clinical laboratories. Additionally, dyslipidemia has also been observed in other respiratory infectious diseases.8, 9, 10 Therefore, blood lipids may be considered potential and available indicators of COVID‐19 severity.\nIn a former study, serum hypolipidemia was identified in patients with COVID‐19.11 However, that study did not analyze other blood lipid components, such as apolipoprotein A1 (ApoA1), ApoB, and lipoprotein (a), and their predictive values for COVID‐19 severity are not fully understood. Therefore, to more comprehensively investigate blood lipid profiles in patients with COVID‐19 and determine their predictive value for disease severity, a retrospective study was performed."},"methods_title":{"kind":"null"},"methods_text":{"kind":"null"},"results_title":{"kind":"string","value":"RESULTS"},"results_text":{"kind":"string","value":"General clinical characteristics In total, 142 consecutive patients with confirmed COVID‐19 were included in this study. The mean age was 49.10 ± 16.36 years, and 38.73% of the patients were male. Hypertension (37, 26.06%), diabetes (12, 8.45%), hepatic disease (10, 7.04%), and chronic lung disease (9, 6.34%) were the most common comorbidities. Fever (84, 59.15%) was the leading initial symptom, followed by cough (61, 42.96%), expectoration (32, 22.54%) and fatigue (27, 19.01%).\nAmong the 142 patients, 17 (11.97%) and 125 (88.03%) patients were classified into the severe and non‐severe groups during admission, respectively. Significant differences in age, body mass index (BMI), hypertension, hepatic disease, and fever were noted between the severe and non‐severe groups. Regarding clinical treatment, a greater proportion of patients in the severe group received glucocorticoids, antibiotics, oxygen, invasive mechanical ventilation, and intensive care unit treatment (Table 1).\nGeneral clinical characteristics of patients with COVID‐19\nData are presented as mean ± standard deviation or n (%). p‐value indicates the comparison between the non‐severe group and severe group.\nAbbreviations: COVID‐19, coronavirus disease 2019; ECMO, extracorporeal membrane oxygenation.\nIn total, 142 consecutive patients with confirmed COVID‐19 were included in this study. The mean age was 49.10 ± 16.36 years, and 38.73% of the patients were male. Hypertension (37, 26.06%), diabetes (12, 8.45%), hepatic disease (10, 7.04%), and chronic lung disease (9, 6.34%) were the most common comorbidities. Fever (84, 59.15%) was the leading initial symptom, followed by cough (61, 42.96%), expectoration (32, 22.54%) and fatigue (27, 19.01%).\nAmong the 142 patients, 17 (11.97%) and 125 (88.03%) patients were classified into the severe and non‐severe groups during admission, respectively. Significant differences in age, body mass index (BMI), hypertension, hepatic disease, and fever were noted between the severe and non‐severe groups. Regarding clinical treatment, a greater proportion of patients in the severe group received glucocorticoids, antibiotics, oxygen, invasive mechanical ventilation, and intensive care unit treatment (Table 1).\nGeneral clinical characteristics of patients with COVID‐19\nData are presented as mean ± standard deviation or n (%). p‐value indicates the comparison between the non‐severe group and severe group.\nAbbreviations: COVID‐19, coronavirus disease 2019; ECMO, extracorporeal membrane oxygenation.\nLaboratory findings We first compared the general laboratory parameters between healthy controls and COVID‐19 patients. Neutrophil%, C‐reactive protein (CRP), aspartate aminotransferase (AST), and lactic dehydrogenase (LDH) were elevated, while white blood cell (WBC) count, lymphocyte%, lymphocyte count, platelet count, red blood cell (RBC) count, hemoglobin, albumin, total bilirubin (TBil), blood urea nitrogen (BUN), and blood uric acid (BUA) were decreased in patients with COVID‐19 compared with healthy controls (Table 2).\nComparisons of demographic and laboratory variables between healthy controls and COVID‐19 patients\nData are presented as mean ± standard deviation, n (%), or medians (interquartile ranges).\nAbbreviations: ALT, alanine aminotransferase; AST, aspartate aminotransferase; BUA, blood uric acid; BUN, blood urea nitrogen; COVID‐19, coronavirus disease 2019; CRP, C‐reactive protein; DBil, direct bilirubin; LDH, Lactic dehydrogenase; RBC, red blood cell; Scr, serum creatinine; TBil, total bilirubin; WBC, white blood cell.\nWe next compared general laboratory parameters, coagulation tests, and cytokine levels between severe and non‐severe COVID‐19 patients. Compared with those in the non‐severe group, patients in the severe group exhibited increased neutrophil%, fibrinogen, activated partial thromboplastin time (aPTT), CRP, interleukin‐10 (IL‐10), interleukin‐6 (IL‐6), interferon‐γ (INF‐γ), AST, and LDH levels, as well as reduced lymphocyte count, platelet count, lymphocyte%, and albumin levels (Table 3).\nBaseline laboratory parameters in patients with COVID‐19\nData are presented as medians (interquartile ranges).\nAbbreviations: COVID‐19, coronavirus disease 2019; WBC, white blood cell; RBC, red blood cell; PT, prothrombin time; aPTT, activated partial thromboplastin time; CRP, C‐reactive protein; IL‐2, interleukin‐2; IL‐4, interleukin‐4; IL‐6, interleukin‐6; IL‐10, interleukin‐10; IFN‐γ, interferon‐γ; TNF‐α, tumor necrosis factor‐α; TBil, total bilirubin; DBil, direct bilirubin; AST, aspartate aminotransferase; ALT, alanine aminotransferase; LDH, Lactic dehydrogenase; BUN, blood urea nitrogen; BUA, blood uric acid; Scr, serum creatinine.\nWe first compared the general laboratory parameters between healthy controls and COVID‐19 patients. Neutrophil%, C‐reactive protein (CRP), aspartate aminotransferase (AST), and lactic dehydrogenase (LDH) were elevated, while white blood cell (WBC) count, lymphocyte%, lymphocyte count, platelet count, red blood cell (RBC) count, hemoglobin, albumin, total bilirubin (TBil), blood urea nitrogen (BUN), and blood uric acid (BUA) were decreased in patients with COVID‐19 compared with healthy controls (Table 2).\nComparisons of demographic and laboratory variables between healthy controls and COVID‐19 patients\nData are presented as mean ± standard deviation, n (%), or medians (interquartile ranges).\nAbbreviations: ALT, alanine aminotransferase; AST, aspartate aminotransferase; BUA, blood uric acid; BUN, blood urea nitrogen; COVID‐19, coronavirus disease 2019; CRP, C‐reactive protein; DBil, direct bilirubin; LDH, Lactic dehydrogenase; RBC, red blood cell; Scr, serum creatinine; TBil, total bilirubin; WBC, white blood cell.\nWe next compared general laboratory parameters, coagulation tests, and cytokine levels between severe and non‐severe COVID‐19 patients. Compared with those in the non‐severe group, patients in the severe group exhibited increased neutrophil%, fibrinogen, activated partial thromboplastin time (aPTT), CRP, interleukin‐10 (IL‐10), interleukin‐6 (IL‐6), interferon‐γ (INF‐γ), AST, and LDH levels, as well as reduced lymphocyte count, platelet count, lymphocyte%, and albumin levels (Table 3).\nBaseline laboratory parameters in patients with COVID‐19\nData are presented as medians (interquartile ranges).\nAbbreviations: COVID‐19, coronavirus disease 2019; WBC, white blood cell; RBC, red blood cell; PT, prothrombin time; aPTT, activated partial thromboplastin time; CRP, C‐reactive protein; IL‐2, interleukin‐2; IL‐4, interleukin‐4; IL‐6, interleukin‐6; IL‐10, interleukin‐10; IFN‐γ, interferon‐γ; TNF‐α, tumor necrosis factor‐α; TBil, total bilirubin; DBil, direct bilirubin; AST, aspartate aminotransferase; ALT, alanine aminotransferase; LDH, Lactic dehydrogenase; BUN, blood urea nitrogen; BUA, blood uric acid; Scr, serum creatinine.\nBaseline blood lipids Baseline blood lipids were obtained within 5 days of admission. TC, HDL‐C, LDL‐C, and ApoA1 gradually decreased from the healthy controls to the non‐severe group and the severe group. TG was higher in the non‐severe group than in the healthy controls; however, no significant differences were found between the severe and non‐severe groups or between the severe group and the healthy controls. There were no significant differences in ApoB or lipoprotein (a) among the three groups (Figure 1).\nComparisons of blood lipids among the healthy controls, non‐severe group, and severe group. Data are presented as medians (interquartile ranges). (A) The total cholesterol levels in the healthy controls, non‐severe group, and severe group were 4.97 (4.55–5.79), 4.08 (3.69–4.63), and 3.58 (3.06–3.85) mmol/L, respectively. (B) The triglyceride levels in the healthy controls, non‐severe group, and severe group were 1.04 (0.85–1.43), 1.44 (0.98–2.00), and 1.22 (0.83–2.29) mmol/L, respectively. (C) The HDL‐C levels in the healthy controls, non‐severe group, and severe group were 1.62 (1.35–1.92), 1.09 (0.91–1.29), and 0.93 (0.82–1.00) mmol/L, respectively. (D) The LDL‐C levels in the healthy controls, non‐severe group, and severe group were 2.77 (2.40–3.42), 2.54 (2.18–2.85), and 2.21 (1.93–2.49) mmol/L, respectively. (E) The ApoA1 levels in the healthy controls, non‐severe group, and severe group were 1.45 (1.31–1.65), 1.22 (1.12–1.34), and 0.98 (0.89–1.08) g/L, respectively. (F) The ApoB levels in the healthy controls, non‐severe group, and severe group were 0.86 (0.72–1.01), 0.81 (0.71–0.94), and 0.76 (0.66–0.86) g/L, respectively. (G) The lipoprotein (a) levels in the healthy controls, non‐severe group, and severe group were 114.80 (62.20–202.90), 87.15 (47.75–161.15), 94.45 (36.55–135.40) mg/L, respectively. The Kruskal‐Wallis test was used to compare differences among the three groups, and post hoc pairwise comparisons were performed using the Nemenyi test. HDL‐C, high‐density lipoprotein cholesterol; LDL‐C, low‐density lipoprotein cholesterol; ApoA1, apolipoprotein A1; ApoB, apolipoprotein B\nBaseline blood lipids were obtained within 5 days of admission. TC, HDL‐C, LDL‐C, and ApoA1 gradually decreased from the healthy controls to the non‐severe group and the severe group. TG was higher in the non‐severe group than in the healthy controls; however, no significant differences were found between the severe and non‐severe groups or between the severe group and the healthy controls. There were no significant differences in ApoB or lipoprotein (a) among the three groups (Figure 1).\nComparisons of blood lipids among the healthy controls, non‐severe group, and severe group. Data are presented as medians (interquartile ranges). (A) The total cholesterol levels in the healthy controls, non‐severe group, and severe group were 4.97 (4.55–5.79), 4.08 (3.69–4.63), and 3.58 (3.06–3.85) mmol/L, respectively. (B) The triglyceride levels in the healthy controls, non‐severe group, and severe group were 1.04 (0.85–1.43), 1.44 (0.98–2.00), and 1.22 (0.83–2.29) mmol/L, respectively. (C) The HDL‐C levels in the healthy controls, non‐severe group, and severe group were 1.62 (1.35–1.92), 1.09 (0.91–1.29), and 0.93 (0.82–1.00) mmol/L, respectively. (D) The LDL‐C levels in the healthy controls, non‐severe group, and severe group were 2.77 (2.40–3.42), 2.54 (2.18–2.85), and 2.21 (1.93–2.49) mmol/L, respectively. (E) The ApoA1 levels in the healthy controls, non‐severe group, and severe group were 1.45 (1.31–1.65), 1.22 (1.12–1.34), and 0.98 (0.89–1.08) g/L, respectively. (F) The ApoB levels in the healthy controls, non‐severe group, and severe group were 0.86 (0.72–1.01), 0.81 (0.71–0.94), and 0.76 (0.66–0.86) g/L, respectively. (G) The lipoprotein (a) levels in the healthy controls, non‐severe group, and severe group were 114.80 (62.20–202.90), 87.15 (47.75–161.15), 94.45 (36.55–135.40) mg/L, respectively. The Kruskal‐Wallis test was used to compare differences among the three groups, and post hoc pairwise comparisons were performed using the Nemenyi test. HDL‐C, high‐density lipoprotein cholesterol; LDL‐C, low‐density lipoprotein cholesterol; ApoA1, apolipoprotein A1; ApoB, apolipoprotein B\nRisk factors for COVID‐19 severity Potential risk factors, including several general clinical characteristics, immunoinflammatory markers, and blood lipids, were first identified by univariate logistic analysis. Age, BMI, hypertension, neutrophil%, lymphocyte%, lymphocyte count, platelet count, fibrinogen, CRP, IL‐6, IL‐10, HDL‐C, ApoA1, ALB, AST, and LDH were associated with the severity of COVID‐19 (p < 0.05). However, aPTT, interferon‐γ (IFN‐γ), TC, and LDL‐C were unrelated to COVID‐19 severity (p>0.05) (Figure 2). Next, variables with p < 0.1 in the univariate logistic analysis were entered into the multivariate logistic analysis. However, after adjusting for other potential risk factors, only IL‐6 (adjusted odds ratio [OR]: 1.097, 95% confidence interval [CI]: 1.034–1.165, p = 0.002) and ApoA1 (adjusted OR: 0.865, 95% CI: 0.800–0.935, p < 0.001) were identified as independent risk factors by multivariate logistic analysis (Figure 3). Therefore, a risk model was built using the combination of ApoA1 and IL‐6. The mathematical formula of the risk model was log (P)=12.303+0.093*IL‐6–0.145*ApoA1.\nUnivariate logistic regression of risk factors for COVID‐19 severity. Old age, high BMI, hypertension, increased neutrophil%, fibrinogen, C‐reactive protein, interleukin‐6, interleukin‐10, aspartate aminotransferase, and lactic dehydrogenase and decreased lymphocyte%, lymphocyte count, platelet count, HDL‐C, apolipoprotein A1, and albumin were associated with the severity of COVID‐19 in univariate logistic regression analysis (all p < 0.05). COVID‐19: coronavirus disease 2019, OR, odds ratio; CI, confidence interval; HDL‐C, high‐density lipoprotein cholesterol; LDL‐C, low‐density lipoprotein cholesterol\nMultivariate logistic regression of independent risk factors for COVID‐19 severity. After adjusting for other potential risk factors, increased IL‐6 (OR: 1.097, 95% CI: 1.034–1.165, p = 0.002) and decreased ApoA1 (OR: 0.865, 95% CI: 0.800–0.935, p < 0.001) were recognized as independent risk factors for COVID‐19 severity. COVID‐19: coronavirus disease 2019, OR: odds ratio, CI: confidence interval\nIn the prediction of COVID‐19 severity, the AUCs (95% CI) of TC, HDL‐C, LDL‐C, ApoA1, IL‐6, and the risk model were 0.726 (0.645–0.798), 0.674 (0.590–0.750), 0.669 (0.585–0.746), 0.896 (0.834–0.941), 0.855 (0.786–0.908), and 0.977 (0.932–0.995), respectively (Figure 4, Table 4). In particular, the sensitivity and specificity of Apo A1 were 94.12% (95% CI: 71.20%–99.00%) and 80.80% (95% CI: 72.80%–87.30%), respectively, which were both the highest among the above single markers. Moreover, the risk model increased the levels of sensitivity and specificity to 100.00% (95% CI: 80.30%–100.00%) and 89.89% (95% CI: 81.40%–94.10%), respectively.\nReceiver operator characteristic curves of total cholesterol, HDL‐C, LDL‐C, ApoA1, IL‐6, and risk model for the severity of COVID‐19. COVID‐19: coronavirus disease 2019, HDL‐C: high‐density lipoprotein cholesterol, LDL‐C: low‐density lipoprotein cholesterol, ApoA1: apolipoprotein A1, IL‐6: interleukin‐6\nPredictive performance of blood lipids, interleukin‐6, and the risk model for COVID‐19 severity\nAbbreviations: COVID‐19, coronavirus disease 2019; HDL‐C, high‐density lipoprotein cholesterol; LDL‐C, low‐density lipoprotein cholesterol; ApoA1, apolipoprotein A1; IL‐6, Interleukin‐6; CI, confidence interval; PPV, positive predictive value; NPV, negative predictive value.\nPotential risk factors, including several general clinical characteristics, immunoinflammatory markers, and blood lipids, were first identified by univariate logistic analysis. Age, BMI, hypertension, neutrophil%, lymphocyte%, lymphocyte count, platelet count, fibrinogen, CRP, IL‐6, IL‐10, HDL‐C, ApoA1, ALB, AST, and LDH were associated with the severity of COVID‐19 (p < 0.05). However, aPTT, interferon‐γ (IFN‐γ), TC, and LDL‐C were unrelated to COVID‐19 severity (p>0.05) (Figure 2). Next, variables with p < 0.1 in the univariate logistic analysis were entered into the multivariate logistic analysis. However, after adjusting for other potential risk factors, only IL‐6 (adjusted odds ratio [OR]: 1.097, 95% confidence interval [CI]: 1.034–1.165, p = 0.002) and ApoA1 (adjusted OR: 0.865, 95% CI: 0.800–0.935, p < 0.001) were identified as independent risk factors by multivariate logistic analysis (Figure 3). Therefore, a risk model was built using the combination of ApoA1 and IL‐6. The mathematical formula of the risk model was log (P)=12.303+0.093*IL‐6–0.145*ApoA1.\nUnivariate logistic regression of risk factors for COVID‐19 severity. Old age, high BMI, hypertension, increased neutrophil%, fibrinogen, C‐reactive protein, interleukin‐6, interleukin‐10, aspartate aminotransferase, and lactic dehydrogenase and decreased lymphocyte%, lymphocyte count, platelet count, HDL‐C, apolipoprotein A1, and albumin were associated with the severity of COVID‐19 in univariate logistic regression analysis (all p < 0.05). COVID‐19: coronavirus disease 2019, OR, odds ratio; CI, confidence interval; HDL‐C, high‐density lipoprotein cholesterol; LDL‐C, low‐density lipoprotein cholesterol\nMultivariate logistic regression of independent risk factors for COVID‐19 severity. After adjusting for other potential risk factors, increased IL‐6 (OR: 1.097, 95% CI: 1.034–1.165, p = 0.002) and decreased ApoA1 (OR: 0.865, 95% CI: 0.800–0.935, p < 0.001) were recognized as independent risk factors for COVID‐19 severity. COVID‐19: coronavirus disease 2019, OR: odds ratio, CI: confidence interval\nIn the prediction of COVID‐19 severity, the AUCs (95% CI) of TC, HDL‐C, LDL‐C, ApoA1, IL‐6, and the risk model were 0.726 (0.645–0.798), 0.674 (0.590–0.750), 0.669 (0.585–0.746), 0.896 (0.834–0.941), 0.855 (0.786–0.908), and 0.977 (0.932–0.995), respectively (Figure 4, Table 4). In particular, the sensitivity and specificity of Apo A1 were 94.12% (95% CI: 71.20%–99.00%) and 80.80% (95% CI: 72.80%–87.30%), respectively, which were both the highest among the above single markers. Moreover, the risk model increased the levels of sensitivity and specificity to 100.00% (95% CI: 80.30%–100.00%) and 89.89% (95% CI: 81.40%–94.10%), respectively.\nReceiver operator characteristic curves of total cholesterol, HDL‐C, LDL‐C, ApoA1, IL‐6, and risk model for the severity of COVID‐19. COVID‐19: coronavirus disease 2019, HDL‐C: high‐density lipoprotein cholesterol, LDL‐C: low‐density lipoprotein cholesterol, ApoA1: apolipoprotein A1, IL‐6: interleukin‐6\nPredictive performance of blood lipids, interleukin‐6, and the risk model for COVID‐19 severity\nAbbreviations: COVID‐19, coronavirus disease 2019; HDL‐C, high‐density lipoprotein cholesterol; LDL‐C, low‐density lipoprotein cholesterol; ApoA1, apolipoprotein A1; IL‐6, Interleukin‐6; CI, confidence interval; PPV, positive predictive value; NPV, negative predictive value."},"conclusions_title":{"kind":"null"},"conclusions_text":{"kind":"null"},"other_sections_titles":{"kind":"list like","value":["INTRODUCTION","Study design and participants selection","Determination of blood lipids","Statistical analysis","General clinical characteristics","Laboratory findings","Baseline blood lipids","Risk factors for COVID‐19 severity","AUTHOR CONTRIBUTIONS","ETHICAL APPROVAL"],"string":"[\n \"INTRODUCTION\",\n \"Study design and participants selection\",\n \"Determination of blood lipids\",\n \"Statistical analysis\",\n \"General clinical characteristics\",\n \"Laboratory findings\",\n \"Baseline blood lipids\",\n \"Risk factors for COVID‐19 severity\",\n \"AUTHOR CONTRIBUTIONS\",\n \"ETHICAL APPROVAL\"\n]"},"other_sections_texts":{"kind":"list like","value":["Severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) is a highly transmissible coronavirus that has caused an ever‐increasing number of coronavirus disease 2019 (COVID‐19) infections since December 2019 and spread rapidly worldwide. Although approximately 80% of patients infected with SARS‐CoV‐2 exhibit mild symptoms,1 the remaining severe cases may experience acute respiratory distress syndrome (ARDS), multiorgan failure (MOF), and death.2 Therefore, it is necessary to discriminate between severe and mild cases.\nPrevious studies have found that the development of severe COVID‐19 is associated with age and underlying diseases, and patients who develop severe disease are likely to suffer from aberrant inflammatory reactions and cytokine storms.1, 3 Consequently, several clinical characteristics, the inflammation index, and cytokine levels have been used as indicators for predicting the severity of COVID‐19 by us and others.4, 5 Emerging evidence suggests that lipid metabolism dysregulation might promote the progression of COVID‐19, as revealed by mass spectrometry (MS)‐based proteomics analysis.6, 7 Although MS analysis is not commonly performed, blood lipids are routinely examined using automatic biochemical instruments in clinical laboratories. Additionally, dyslipidemia has also been observed in other respiratory infectious diseases.8, 9, 10 Therefore, blood lipids may be considered potential and available indicators of COVID‐19 severity.\nIn a former study, serum hypolipidemia was identified in patients with COVID‐19.11 However, that study did not analyze other blood lipid components, such as apolipoprotein A1 (ApoA1), ApoB, and lipoprotein (a), and their predictive values for COVID‐19 severity are not fully understood. Therefore, to more comprehensively investigate blood lipid profiles in patients with COVID‐19 and determine their predictive value for disease severity, a retrospective study was performed.","This was a single‐center retrospective study approved by the institutional ethics board (PJ‐NBEY‐KY‐2020–061–01). A total of 142 consecutive patients with COVID‐19 were included from January 23 to April 20, 2020. In addition, 77 age‐ and gender‐matched healthy subjects were selected for evaluating the characteristics of blood lipid profiles in patients with COVID‐19.\nThe diagnosis of COVID‐19 and its severity were determined according to the National Diagnosis and Treatment Protocol for Novel Coronavirus Infection‐Induced Pneumonia (6th Trial Version). Patients with confirmed COVID‐19 were diagnosed based on a positive SARS‐CoV‐2 nucleic acid RT‐PCR result using specimens derived from sputum, throat swabs, or nasopharyngeal swabs. Severe patients exhibited one of the following features: (a) respiratory distress with respiration rate (RR) greater than 30 breaths per minute; (b) blood oxygen saturation less than 93% at a state of rest; (c) arterial blood oxygen partial pressure/inhaled oxygen concentration less than 300 mmHg (1 mmHg = 0.133 kPa); or (d) lesion rapidly progressed by more than 50% within one or two days as determined by pulmonary imaging.\nGeneral clinical characteristics, including gender, age, comorbidities, initial symptoms, treatment, and laboratory test data, were collected from the electronic medical records (EMRs).","Blood lipids were assessed using a fully automatic biochemical analyzer (ADVIA2400, Siemens) according to the manufacturer's instructions (Purebio Biotechnology Co., Ltd). Briefly, total cholesterol (TC) was measured using the cholesterol oxidase‐p‐aminophenazone (CHOD‐PAP) method; triglyceride (TG) was assessed using the glycerol phosphate oxidase‐p‐aminophenazone (GPO‐PAP) method; high‐density lipoprotein cholesterol (HDL‐C) was assessed using the direct‐hydrogen peroxide method; low‐density lipoprotein cholesterol (LDL‐C) was assessed using the direct‐surfactant removal method; and ApoA1, ApoB, and lipoprotein (a) were assessed using the immunoturbidimetric method.","SPSS software, version 16.0 (IBM) was used for statistical analysis. Normally and non‐normally distributed continuous data were expressed as the mean ± SD (standard deviation) and median (interquartile range [IQR]), respectively. Categorical variables were reported as numbers (%). The Kruskal‐Wallis test was used to compare blood lipids among the severe group, non‐severe group, and healthy subjects, and post hoc pairwise comparisons were performed using the Nemenyi test. Differences between the two groups were assessed using Student's t‐test and Mann‐Whitney U test for normally and non‐normally distributed continuous data, respectively, and chi‐square or Fisher's exact tests were used for categorical variables. Multivariate logistic regression analysis was adopted to explore independent risk factors for COVID‐19 severity, receiver operator characteristic (ROC) curves were generated, and the areas under ROC curves (AUCs) were calculated to evaluate prediction efficiency. A p‐value <0.05 indicates statistical significance.","In total, 142 consecutive patients with confirmed COVID‐19 were included in this study. The mean age was 49.10 ± 16.36 years, and 38.73% of the patients were male. Hypertension (37, 26.06%), diabetes (12, 8.45%), hepatic disease (10, 7.04%), and chronic lung disease (9, 6.34%) were the most common comorbidities. Fever (84, 59.15%) was the leading initial symptom, followed by cough (61, 42.96%), expectoration (32, 22.54%) and fatigue (27, 19.01%).\nAmong the 142 patients, 17 (11.97%) and 125 (88.03%) patients were classified into the severe and non‐severe groups during admission, respectively. Significant differences in age, body mass index (BMI), hypertension, hepatic disease, and fever were noted between the severe and non‐severe groups. Regarding clinical treatment, a greater proportion of patients in the severe group received glucocorticoids, antibiotics, oxygen, invasive mechanical ventilation, and intensive care unit treatment (Table 1).\nGeneral clinical characteristics of patients with COVID‐19\nData are presented as mean ± standard deviation or n (%). p‐value indicates the comparison between the non‐severe group and severe group.\nAbbreviations: COVID‐19, coronavirus disease 2019; ECMO, extracorporeal membrane oxygenation.","We first compared the general laboratory parameters between healthy controls and COVID‐19 patients. Neutrophil%, C‐reactive protein (CRP), aspartate aminotransferase (AST), and lactic dehydrogenase (LDH) were elevated, while white blood cell (WBC) count, lymphocyte%, lymphocyte count, platelet count, red blood cell (RBC) count, hemoglobin, albumin, total bilirubin (TBil), blood urea nitrogen (BUN), and blood uric acid (BUA) were decreased in patients with COVID‐19 compared with healthy controls (Table 2).\nComparisons of demographic and laboratory variables between healthy controls and COVID‐19 patients\nData are presented as mean ± standard deviation, n (%), or medians (interquartile ranges).\nAbbreviations: ALT, alanine aminotransferase; AST, aspartate aminotransferase; BUA, blood uric acid; BUN, blood urea nitrogen; COVID‐19, coronavirus disease 2019; CRP, C‐reactive protein; DBil, direct bilirubin; LDH, Lactic dehydrogenase; RBC, red blood cell; Scr, serum creatinine; TBil, total bilirubin; WBC, white blood cell.\nWe next compared general laboratory parameters, coagulation tests, and cytokine levels between severe and non‐severe COVID‐19 patients. Compared with those in the non‐severe group, patients in the severe group exhibited increased neutrophil%, fibrinogen, activated partial thromboplastin time (aPTT), CRP, interleukin‐10 (IL‐10), interleukin‐6 (IL‐6), interferon‐γ (INF‐γ), AST, and LDH levels, as well as reduced lymphocyte count, platelet count, lymphocyte%, and albumin levels (Table 3).\nBaseline laboratory parameters in patients with COVID‐19\nData are presented as medians (interquartile ranges).\nAbbreviations: COVID‐19, coronavirus disease 2019; WBC, white blood cell; RBC, red blood cell; PT, prothrombin time; aPTT, activated partial thromboplastin time; CRP, C‐reactive protein; IL‐2, interleukin‐2; IL‐4, interleukin‐4; IL‐6, interleukin‐6; IL‐10, interleukin‐10; IFN‐γ, interferon‐γ; TNF‐α, tumor necrosis factor‐α; TBil, total bilirubin; DBil, direct bilirubin; AST, aspartate aminotransferase; ALT, alanine aminotransferase; LDH, Lactic dehydrogenase; BUN, blood urea nitrogen; BUA, blood uric acid; Scr, serum creatinine.","Baseline blood lipids were obtained within 5 days of admission. TC, HDL‐C, LDL‐C, and ApoA1 gradually decreased from the healthy controls to the non‐severe group and the severe group. TG was higher in the non‐severe group than in the healthy controls; however, no significant differences were found between the severe and non‐severe groups or between the severe group and the healthy controls. There were no significant differences in ApoB or lipoprotein (a) among the three groups (Figure 1).\nComparisons of blood lipids among the healthy controls, non‐severe group, and severe group. Data are presented as medians (interquartile ranges). (A) The total cholesterol levels in the healthy controls, non‐severe group, and severe group were 4.97 (4.55–5.79), 4.08 (3.69–4.63), and 3.58 (3.06–3.85) mmol/L, respectively. (B) The triglyceride levels in the healthy controls, non‐severe group, and severe group were 1.04 (0.85–1.43), 1.44 (0.98–2.00), and 1.22 (0.83–2.29) mmol/L, respectively. (C) The HDL‐C levels in the healthy controls, non‐severe group, and severe group were 1.62 (1.35–1.92), 1.09 (0.91–1.29), and 0.93 (0.82–1.00) mmol/L, respectively. (D) The LDL‐C levels in the healthy controls, non‐severe group, and severe group were 2.77 (2.40–3.42), 2.54 (2.18–2.85), and 2.21 (1.93–2.49) mmol/L, respectively. (E) The ApoA1 levels in the healthy controls, non‐severe group, and severe group were 1.45 (1.31–1.65), 1.22 (1.12–1.34), and 0.98 (0.89–1.08) g/L, respectively. (F) The ApoB levels in the healthy controls, non‐severe group, and severe group were 0.86 (0.72–1.01), 0.81 (0.71–0.94), and 0.76 (0.66–0.86) g/L, respectively. (G) The lipoprotein (a) levels in the healthy controls, non‐severe group, and severe group were 114.80 (62.20–202.90), 87.15 (47.75–161.15), 94.45 (36.55–135.40) mg/L, respectively. The Kruskal‐Wallis test was used to compare differences among the three groups, and post hoc pairwise comparisons were performed using the Nemenyi test. HDL‐C, high‐density lipoprotein cholesterol; LDL‐C, low‐density lipoprotein cholesterol; ApoA1, apolipoprotein A1; ApoB, apolipoprotein B","Potential risk factors, including several general clinical characteristics, immunoinflammatory markers, and blood lipids, were first identified by univariate logistic analysis. Age, BMI, hypertension, neutrophil%, lymphocyte%, lymphocyte count, platelet count, fibrinogen, CRP, IL‐6, IL‐10, HDL‐C, ApoA1, ALB, AST, and LDH were associated with the severity of COVID‐19 (p < 0.05). However, aPTT, interferon‐γ (IFN‐γ), TC, and LDL‐C were unrelated to COVID‐19 severity (p>0.05) (Figure 2). Next, variables with p < 0.1 in the univariate logistic analysis were entered into the multivariate logistic analysis. However, after adjusting for other potential risk factors, only IL‐6 (adjusted odds ratio [OR]: 1.097, 95% confidence interval [CI]: 1.034–1.165, p = 0.002) and ApoA1 (adjusted OR: 0.865, 95% CI: 0.800–0.935, p < 0.001) were identified as independent risk factors by multivariate logistic analysis (Figure 3). Therefore, a risk model was built using the combination of ApoA1 and IL‐6. The mathematical formula of the risk model was log (P)=12.303+0.093*IL‐6–0.145*ApoA1.\nUnivariate logistic regression of risk factors for COVID‐19 severity. Old age, high BMI, hypertension, increased neutrophil%, fibrinogen, C‐reactive protein, interleukin‐6, interleukin‐10, aspartate aminotransferase, and lactic dehydrogenase and decreased lymphocyte%, lymphocyte count, platelet count, HDL‐C, apolipoprotein A1, and albumin were associated with the severity of COVID‐19 in univariate logistic regression analysis (all p < 0.05). COVID‐19: coronavirus disease 2019, OR, odds ratio; CI, confidence interval; HDL‐C, high‐density lipoprotein cholesterol; LDL‐C, low‐density lipoprotein cholesterol\nMultivariate logistic regression of independent risk factors for COVID‐19 severity. After adjusting for other potential risk factors, increased IL‐6 (OR: 1.097, 95% CI: 1.034–1.165, p = 0.002) and decreased ApoA1 (OR: 0.865, 95% CI: 0.800–0.935, p < 0.001) were recognized as independent risk factors for COVID‐19 severity. COVID‐19: coronavirus disease 2019, OR: odds ratio, CI: confidence interval\nIn the prediction of COVID‐19 severity, the AUCs (95% CI) of TC, HDL‐C, LDL‐C, ApoA1, IL‐6, and the risk model were 0.726 (0.645–0.798), 0.674 (0.590–0.750), 0.669 (0.585–0.746), 0.896 (0.834–0.941), 0.855 (0.786–0.908), and 0.977 (0.932–0.995), respectively (Figure 4, Table 4). In particular, the sensitivity and specificity of Apo A1 were 94.12% (95% CI: 71.20%–99.00%) and 80.80% (95% CI: 72.80%–87.30%), respectively, which were both the highest among the above single markers. Moreover, the risk model increased the levels of sensitivity and specificity to 100.00% (95% CI: 80.30%–100.00%) and 89.89% (95% CI: 81.40%–94.10%), respectively.\nReceiver operator characteristic curves of total cholesterol, HDL‐C, LDL‐C, ApoA1, IL‐6, and risk model for the severity of COVID‐19. COVID‐19: coronavirus disease 2019, HDL‐C: high‐density lipoprotein cholesterol, LDL‐C: low‐density lipoprotein cholesterol, ApoA1: apolipoprotein A1, IL‐6: interleukin‐6\nPredictive performance of blood lipids, interleukin‐6, and the risk model for COVID‐19 severity\nAbbreviations: COVID‐19, coronavirus disease 2019; HDL‐C, high‐density lipoprotein cholesterol; LDL‐C, low‐density lipoprotein cholesterol; ApoA1, apolipoprotein A1; IL‐6, Interleukin‐6; CI, confidence interval; PPV, positive predictive value; NPV, negative predictive value.","Zhe Zhu interpreted the data and wrote the paper. Yayun Yang, Linyan Fan, Shuyuan Ye, and Kehong Lou collected and analyzed the data. Xin Hua, Zuoan Huang, and Qiaoyun Shi performed laboratory analysis. Guosheng Gao designed the study and revised the paper.","This study was approved by the institutional ethics board of HwaMei Hospital, University of Chinese Academy of Science (PJ‐NBEY‐KY‐2020–061–01)."],"string":"[\n \"Severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) is a highly transmissible coronavirus that has caused an ever‐increasing number of coronavirus disease 2019 (COVID‐19) infections since December 2019 and spread rapidly worldwide. Although approximately 80% of patients infected with SARS‐CoV‐2 exhibit mild symptoms,1 the remaining severe cases may experience acute respiratory distress syndrome (ARDS), multiorgan failure (MOF), and death.2 Therefore, it is necessary to discriminate between severe and mild cases.\\nPrevious studies have found that the development of severe COVID‐19 is associated with age and underlying diseases, and patients who develop severe disease are likely to suffer from aberrant inflammatory reactions and cytokine storms.1, 3 Consequently, several clinical characteristics, the inflammation index, and cytokine levels have been used as indicators for predicting the severity of COVID‐19 by us and others.4, 5 Emerging evidence suggests that lipid metabolism dysregulation might promote the progression of COVID‐19, as revealed by mass spectrometry (MS)‐based proteomics analysis.6, 7 Although MS analysis is not commonly performed, blood lipids are routinely examined using automatic biochemical instruments in clinical laboratories. Additionally, dyslipidemia has also been observed in other respiratory infectious diseases.8, 9, 10 Therefore, blood lipids may be considered potential and available indicators of COVID‐19 severity.\\nIn a former study, serum hypolipidemia was identified in patients with COVID‐19.11 However, that study did not analyze other blood lipid components, such as apolipoprotein A1 (ApoA1), ApoB, and lipoprotein (a), and their predictive values for COVID‐19 severity are not fully understood. Therefore, to more comprehensively investigate blood lipid profiles in patients with COVID‐19 and determine their predictive value for disease severity, a retrospective study was performed.\",\n \"This was a single‐center retrospective study approved by the institutional ethics board (PJ‐NBEY‐KY‐2020–061–01). A total of 142 consecutive patients with COVID‐19 were included from January 23 to April 20, 2020. In addition, 77 age‐ and gender‐matched healthy subjects were selected for evaluating the characteristics of blood lipid profiles in patients with COVID‐19.\\nThe diagnosis of COVID‐19 and its severity were determined according to the National Diagnosis and Treatment Protocol for Novel Coronavirus Infection‐Induced Pneumonia (6th Trial Version). Patients with confirmed COVID‐19 were diagnosed based on a positive SARS‐CoV‐2 nucleic acid RT‐PCR result using specimens derived from sputum, throat swabs, or nasopharyngeal swabs. Severe patients exhibited one of the following features: (a) respiratory distress with respiration rate (RR) greater than 30 breaths per minute; (b) blood oxygen saturation less than 93% at a state of rest; (c) arterial blood oxygen partial pressure/inhaled oxygen concentration less than 300 mmHg (1 mmHg = 0.133 kPa); or (d) lesion rapidly progressed by more than 50% within one or two days as determined by pulmonary imaging.\\nGeneral clinical characteristics, including gender, age, comorbidities, initial symptoms, treatment, and laboratory test data, were collected from the electronic medical records (EMRs).\",\n \"Blood lipids were assessed using a fully automatic biochemical analyzer (ADVIA2400, Siemens) according to the manufacturer's instructions (Purebio Biotechnology Co., Ltd). Briefly, total cholesterol (TC) was measured using the cholesterol oxidase‐p‐aminophenazone (CHOD‐PAP) method; triglyceride (TG) was assessed using the glycerol phosphate oxidase‐p‐aminophenazone (GPO‐PAP) method; high‐density lipoprotein cholesterol (HDL‐C) was assessed using the direct‐hydrogen peroxide method; low‐density lipoprotein cholesterol (LDL‐C) was assessed using the direct‐surfactant removal method; and ApoA1, ApoB, and lipoprotein (a) were assessed using the immunoturbidimetric method.\",\n \"SPSS software, version 16.0 (IBM) was used for statistical analysis. Normally and non‐normally distributed continuous data were expressed as the mean ± SD (standard deviation) and median (interquartile range [IQR]), respectively. Categorical variables were reported as numbers (%). The Kruskal‐Wallis test was used to compare blood lipids among the severe group, non‐severe group, and healthy subjects, and post hoc pairwise comparisons were performed using the Nemenyi test. Differences between the two groups were assessed using Student's t‐test and Mann‐Whitney U test for normally and non‐normally distributed continuous data, respectively, and chi‐square or Fisher's exact tests were used for categorical variables. Multivariate logistic regression analysis was adopted to explore independent risk factors for COVID‐19 severity, receiver operator characteristic (ROC) curves were generated, and the areas under ROC curves (AUCs) were calculated to evaluate prediction efficiency. A p‐value <0.05 indicates statistical significance.\",\n \"In total, 142 consecutive patients with confirmed COVID‐19 were included in this study. The mean age was 49.10 ± 16.36 years, and 38.73% of the patients were male. Hypertension (37, 26.06%), diabetes (12, 8.45%), hepatic disease (10, 7.04%), and chronic lung disease (9, 6.34%) were the most common comorbidities. Fever (84, 59.15%) was the leading initial symptom, followed by cough (61, 42.96%), expectoration (32, 22.54%) and fatigue (27, 19.01%).\\nAmong the 142 patients, 17 (11.97%) and 125 (88.03%) patients were classified into the severe and non‐severe groups during admission, respectively. Significant differences in age, body mass index (BMI), hypertension, hepatic disease, and fever were noted between the severe and non‐severe groups. Regarding clinical treatment, a greater proportion of patients in the severe group received glucocorticoids, antibiotics, oxygen, invasive mechanical ventilation, and intensive care unit treatment (Table 1).\\nGeneral clinical characteristics of patients with COVID‐19\\nData are presented as mean ± standard deviation or n (%). p‐value indicates the comparison between the non‐severe group and severe group.\\nAbbreviations: COVID‐19, coronavirus disease 2019; ECMO, extracorporeal membrane oxygenation.\",\n \"We first compared the general laboratory parameters between healthy controls and COVID‐19 patients. Neutrophil%, C‐reactive protein (CRP), aspartate aminotransferase (AST), and lactic dehydrogenase (LDH) were elevated, while white blood cell (WBC) count, lymphocyte%, lymphocyte count, platelet count, red blood cell (RBC) count, hemoglobin, albumin, total bilirubin (TBil), blood urea nitrogen (BUN), and blood uric acid (BUA) were decreased in patients with COVID‐19 compared with healthy controls (Table 2).\\nComparisons of demographic and laboratory variables between healthy controls and COVID‐19 patients\\nData are presented as mean ± standard deviation, n (%), or medians (interquartile ranges).\\nAbbreviations: ALT, alanine aminotransferase; AST, aspartate aminotransferase; BUA, blood uric acid; BUN, blood urea nitrogen; COVID‐19, coronavirus disease 2019; CRP, C‐reactive protein; DBil, direct bilirubin; LDH, Lactic dehydrogenase; RBC, red blood cell; Scr, serum creatinine; TBil, total bilirubin; WBC, white blood cell.\\nWe next compared general laboratory parameters, coagulation tests, and cytokine levels between severe and non‐severe COVID‐19 patients. Compared with those in the non‐severe group, patients in the severe group exhibited increased neutrophil%, fibrinogen, activated partial thromboplastin time (aPTT), CRP, interleukin‐10 (IL‐10), interleukin‐6 (IL‐6), interferon‐γ (INF‐γ), AST, and LDH levels, as well as reduced lymphocyte count, platelet count, lymphocyte%, and albumin levels (Table 3).\\nBaseline laboratory parameters in patients with COVID‐19\\nData are presented as medians (interquartile ranges).\\nAbbreviations: COVID‐19, coronavirus disease 2019; WBC, white blood cell; RBC, red blood cell; PT, prothrombin time; aPTT, activated partial thromboplastin time; CRP, C‐reactive protein; IL‐2, interleukin‐2; IL‐4, interleukin‐4; IL‐6, interleukin‐6; IL‐10, interleukin‐10; IFN‐γ, interferon‐γ; TNF‐α, tumor necrosis factor‐α; TBil, total bilirubin; DBil, direct bilirubin; AST, aspartate aminotransferase; ALT, alanine aminotransferase; LDH, Lactic dehydrogenase; BUN, blood urea nitrogen; BUA, blood uric acid; Scr, serum creatinine.\",\n \"Baseline blood lipids were obtained within 5 days of admission. TC, HDL‐C, LDL‐C, and ApoA1 gradually decreased from the healthy controls to the non‐severe group and the severe group. TG was higher in the non‐severe group than in the healthy controls; however, no significant differences were found between the severe and non‐severe groups or between the severe group and the healthy controls. There were no significant differences in ApoB or lipoprotein (a) among the three groups (Figure 1).\\nComparisons of blood lipids among the healthy controls, non‐severe group, and severe group. Data are presented as medians (interquartile ranges). (A) The total cholesterol levels in the healthy controls, non‐severe group, and severe group were 4.97 (4.55–5.79), 4.08 (3.69–4.63), and 3.58 (3.06–3.85) mmol/L, respectively. (B) The triglyceride levels in the healthy controls, non‐severe group, and severe group were 1.04 (0.85–1.43), 1.44 (0.98–2.00), and 1.22 (0.83–2.29) mmol/L, respectively. (C) The HDL‐C levels in the healthy controls, non‐severe group, and severe group were 1.62 (1.35–1.92), 1.09 (0.91–1.29), and 0.93 (0.82–1.00) mmol/L, respectively. (D) The LDL‐C levels in the healthy controls, non‐severe group, and severe group were 2.77 (2.40–3.42), 2.54 (2.18–2.85), and 2.21 (1.93–2.49) mmol/L, respectively. (E) The ApoA1 levels in the healthy controls, non‐severe group, and severe group were 1.45 (1.31–1.65), 1.22 (1.12–1.34), and 0.98 (0.89–1.08) g/L, respectively. (F) The ApoB levels in the healthy controls, non‐severe group, and severe group were 0.86 (0.72–1.01), 0.81 (0.71–0.94), and 0.76 (0.66–0.86) g/L, respectively. (G) The lipoprotein (a) levels in the healthy controls, non‐severe group, and severe group were 114.80 (62.20–202.90), 87.15 (47.75–161.15), 94.45 (36.55–135.40) mg/L, respectively. The Kruskal‐Wallis test was used to compare differences among the three groups, and post hoc pairwise comparisons were performed using the Nemenyi test. HDL‐C, high‐density lipoprotein cholesterol; LDL‐C, low‐density lipoprotein cholesterol; ApoA1, apolipoprotein A1; ApoB, apolipoprotein B\",\n \"Potential risk factors, including several general clinical characteristics, immunoinflammatory markers, and blood lipids, were first identified by univariate logistic analysis. Age, BMI, hypertension, neutrophil%, lymphocyte%, lymphocyte count, platelet count, fibrinogen, CRP, IL‐6, IL‐10, HDL‐C, ApoA1, ALB, AST, and LDH were associated with the severity of COVID‐19 (p < 0.05). However, aPTT, interferon‐γ (IFN‐γ), TC, and LDL‐C were unrelated to COVID‐19 severity (p>0.05) (Figure 2). Next, variables with p < 0.1 in the univariate logistic analysis were entered into the multivariate logistic analysis. However, after adjusting for other potential risk factors, only IL‐6 (adjusted odds ratio [OR]: 1.097, 95% confidence interval [CI]: 1.034–1.165, p = 0.002) and ApoA1 (adjusted OR: 0.865, 95% CI: 0.800–0.935, p < 0.001) were identified as independent risk factors by multivariate logistic analysis (Figure 3). Therefore, a risk model was built using the combination of ApoA1 and IL‐6. The mathematical formula of the risk model was log (P)=12.303+0.093*IL‐6–0.145*ApoA1.\\nUnivariate logistic regression of risk factors for COVID‐19 severity. Old age, high BMI, hypertension, increased neutrophil%, fibrinogen, C‐reactive protein, interleukin‐6, interleukin‐10, aspartate aminotransferase, and lactic dehydrogenase and decreased lymphocyte%, lymphocyte count, platelet count, HDL‐C, apolipoprotein A1, and albumin were associated with the severity of COVID‐19 in univariate logistic regression analysis (all p < 0.05). COVID‐19: coronavirus disease 2019, OR, odds ratio; CI, confidence interval; HDL‐C, high‐density lipoprotein cholesterol; LDL‐C, low‐density lipoprotein cholesterol\\nMultivariate logistic regression of independent risk factors for COVID‐19 severity. After adjusting for other potential risk factors, increased IL‐6 (OR: 1.097, 95% CI: 1.034–1.165, p = 0.002) and decreased ApoA1 (OR: 0.865, 95% CI: 0.800–0.935, p < 0.001) were recognized as independent risk factors for COVID‐19 severity. COVID‐19: coronavirus disease 2019, OR: odds ratio, CI: confidence interval\\nIn the prediction of COVID‐19 severity, the AUCs (95% CI) of TC, HDL‐C, LDL‐C, ApoA1, IL‐6, and the risk model were 0.726 (0.645–0.798), 0.674 (0.590–0.750), 0.669 (0.585–0.746), 0.896 (0.834–0.941), 0.855 (0.786–0.908), and 0.977 (0.932–0.995), respectively (Figure 4, Table 4). In particular, the sensitivity and specificity of Apo A1 were 94.12% (95% CI: 71.20%–99.00%) and 80.80% (95% CI: 72.80%–87.30%), respectively, which were both the highest among the above single markers. Moreover, the risk model increased the levels of sensitivity and specificity to 100.00% (95% CI: 80.30%–100.00%) and 89.89% (95% CI: 81.40%–94.10%), respectively.\\nReceiver operator characteristic curves of total cholesterol, HDL‐C, LDL‐C, ApoA1, IL‐6, and risk model for the severity of COVID‐19. COVID‐19: coronavirus disease 2019, HDL‐C: high‐density lipoprotein cholesterol, LDL‐C: low‐density lipoprotein cholesterol, ApoA1: apolipoprotein A1, IL‐6: interleukin‐6\\nPredictive performance of blood lipids, interleukin‐6, and the risk model for COVID‐19 severity\\nAbbreviations: COVID‐19, coronavirus disease 2019; HDL‐C, high‐density lipoprotein cholesterol; LDL‐C, low‐density lipoprotein cholesterol; ApoA1, apolipoprotein A1; IL‐6, Interleukin‐6; CI, confidence interval; PPV, positive predictive value; NPV, negative predictive value.\",\n \"Zhe Zhu interpreted the data and wrote the paper. Yayun Yang, Linyan Fan, Shuyuan Ye, and Kehong Lou collected and analyzed the data. Xin Hua, Zuoan Huang, and Qiaoyun Shi performed laboratory analysis. Guosheng Gao designed the study and revised the paper.\",\n \"This study was approved by the institutional ethics board of HwaMei Hospital, University of Chinese Academy of Science (PJ‐NBEY‐KY‐2020–061–01).\"\n]"},"other_sections_sec_types":{"kind":"list like","value":[null,null,null,null,null,null,null,null,null,null],"string":"[\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null\n]"},"all_sections_titles":{"kind":"list like","value":["INTRODUCTION","MATERIAL AND METHODS","Study design and participants selection","Determination of blood lipids","Statistical analysis","RESULTS","General clinical characteristics","Laboratory findings","Baseline blood lipids","Risk factors for COVID‐19 severity","DISCUSSION","CONFLICT OF INTEREST","AUTHOR CONTRIBUTIONS","ETHICAL APPROVAL"],"string":"[\n \"INTRODUCTION\",\n \"MATERIAL AND METHODS\",\n \"Study design and participants selection\",\n \"Determination of blood lipids\",\n \"Statistical analysis\",\n \"RESULTS\",\n \"General clinical characteristics\",\n \"Laboratory findings\",\n \"Baseline blood lipids\",\n \"Risk factors for COVID‐19 severity\",\n \"DISCUSSION\",\n \"CONFLICT OF INTEREST\",\n \"AUTHOR CONTRIBUTIONS\",\n \"ETHICAL APPROVAL\"\n]"},"all_sections_texts":{"kind":"list like","value":["Severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) is a highly transmissible coronavirus that has caused an ever‐increasing number of coronavirus disease 2019 (COVID‐19) infections since December 2019 and spread rapidly worldwide. Although approximately 80% of patients infected with SARS‐CoV‐2 exhibit mild symptoms,1 the remaining severe cases may experience acute respiratory distress syndrome (ARDS), multiorgan failure (MOF), and death.2 Therefore, it is necessary to discriminate between severe and mild cases.\nPrevious studies have found that the development of severe COVID‐19 is associated with age and underlying diseases, and patients who develop severe disease are likely to suffer from aberrant inflammatory reactions and cytokine storms.1, 3 Consequently, several clinical characteristics, the inflammation index, and cytokine levels have been used as indicators for predicting the severity of COVID‐19 by us and others.4, 5 Emerging evidence suggests that lipid metabolism dysregulation might promote the progression of COVID‐19, as revealed by mass spectrometry (MS)‐based proteomics analysis.6, 7 Although MS analysis is not commonly performed, blood lipids are routinely examined using automatic biochemical instruments in clinical laboratories. Additionally, dyslipidemia has also been observed in other respiratory infectious diseases.8, 9, 10 Therefore, blood lipids may be considered potential and available indicators of COVID‐19 severity.\nIn a former study, serum hypolipidemia was identified in patients with COVID‐19.11 However, that study did not analyze other blood lipid components, such as apolipoprotein A1 (ApoA1), ApoB, and lipoprotein (a), and their predictive values for COVID‐19 severity are not fully understood. Therefore, to more comprehensively investigate blood lipid profiles in patients with COVID‐19 and determine their predictive value for disease severity, a retrospective study was performed.","Study design and participants selection This was a single‐center retrospective study approved by the institutional ethics board (PJ‐NBEY‐KY‐2020–061–01). A total of 142 consecutive patients with COVID‐19 were included from January 23 to April 20, 2020. In addition, 77 age‐ and gender‐matched healthy subjects were selected for evaluating the characteristics of blood lipid profiles in patients with COVID‐19.\nThe diagnosis of COVID‐19 and its severity were determined according to the National Diagnosis and Treatment Protocol for Novel Coronavirus Infection‐Induced Pneumonia (6th Trial Version). Patients with confirmed COVID‐19 were diagnosed based on a positive SARS‐CoV‐2 nucleic acid RT‐PCR result using specimens derived from sputum, throat swabs, or nasopharyngeal swabs. Severe patients exhibited one of the following features: (a) respiratory distress with respiration rate (RR) greater than 30 breaths per minute; (b) blood oxygen saturation less than 93% at a state of rest; (c) arterial blood oxygen partial pressure/inhaled oxygen concentration less than 300 mmHg (1 mmHg = 0.133 kPa); or (d) lesion rapidly progressed by more than 50% within one or two days as determined by pulmonary imaging.\nGeneral clinical characteristics, including gender, age, comorbidities, initial symptoms, treatment, and laboratory test data, were collected from the electronic medical records (EMRs).\nThis was a single‐center retrospective study approved by the institutional ethics board (PJ‐NBEY‐KY‐2020–061–01). A total of 142 consecutive patients with COVID‐19 were included from January 23 to April 20, 2020. In addition, 77 age‐ and gender‐matched healthy subjects were selected for evaluating the characteristics of blood lipid profiles in patients with COVID‐19.\nThe diagnosis of COVID‐19 and its severity were determined according to the National Diagnosis and Treatment Protocol for Novel Coronavirus Infection‐Induced Pneumonia (6th Trial Version). Patients with confirmed COVID‐19 were diagnosed based on a positive SARS‐CoV‐2 nucleic acid RT‐PCR result using specimens derived from sputum, throat swabs, or nasopharyngeal swabs. Severe patients exhibited one of the following features: (a) respiratory distress with respiration rate (RR) greater than 30 breaths per minute; (b) blood oxygen saturation less than 93% at a state of rest; (c) arterial blood oxygen partial pressure/inhaled oxygen concentration less than 300 mmHg (1 mmHg = 0.133 kPa); or (d) lesion rapidly progressed by more than 50% within one or two days as determined by pulmonary imaging.\nGeneral clinical characteristics, including gender, age, comorbidities, initial symptoms, treatment, and laboratory test data, were collected from the electronic medical records (EMRs).\nDetermination of blood lipids Blood lipids were assessed using a fully automatic biochemical analyzer (ADVIA2400, Siemens) according to the manufacturer's instructions (Purebio Biotechnology Co., Ltd). Briefly, total cholesterol (TC) was measured using the cholesterol oxidase‐p‐aminophenazone (CHOD‐PAP) method; triglyceride (TG) was assessed using the glycerol phosphate oxidase‐p‐aminophenazone (GPO‐PAP) method; high‐density lipoprotein cholesterol (HDL‐C) was assessed using the direct‐hydrogen peroxide method; low‐density lipoprotein cholesterol (LDL‐C) was assessed using the direct‐surfactant removal method; and ApoA1, ApoB, and lipoprotein (a) were assessed using the immunoturbidimetric method.\nBlood lipids were assessed using a fully automatic biochemical analyzer (ADVIA2400, Siemens) according to the manufacturer's instructions (Purebio Biotechnology Co., Ltd). Briefly, total cholesterol (TC) was measured using the cholesterol oxidase‐p‐aminophenazone (CHOD‐PAP) method; triglyceride (TG) was assessed using the glycerol phosphate oxidase‐p‐aminophenazone (GPO‐PAP) method; high‐density lipoprotein cholesterol (HDL‐C) was assessed using the direct‐hydrogen peroxide method; low‐density lipoprotein cholesterol (LDL‐C) was assessed using the direct‐surfactant removal method; and ApoA1, ApoB, and lipoprotein (a) were assessed using the immunoturbidimetric method.\nStatistical analysis SPSS software, version 16.0 (IBM) was used for statistical analysis. Normally and non‐normally distributed continuous data were expressed as the mean ± SD (standard deviation) and median (interquartile range [IQR]), respectively. Categorical variables were reported as numbers (%). The Kruskal‐Wallis test was used to compare blood lipids among the severe group, non‐severe group, and healthy subjects, and post hoc pairwise comparisons were performed using the Nemenyi test. Differences between the two groups were assessed using Student's t‐test and Mann‐Whitney U test for normally and non‐normally distributed continuous data, respectively, and chi‐square or Fisher's exact tests were used for categorical variables. Multivariate logistic regression analysis was adopted to explore independent risk factors for COVID‐19 severity, receiver operator characteristic (ROC) curves were generated, and the areas under ROC curves (AUCs) were calculated to evaluate prediction efficiency. A p‐value <0.05 indicates statistical significance.\nSPSS software, version 16.0 (IBM) was used for statistical analysis. Normally and non‐normally distributed continuous data were expressed as the mean ± SD (standard deviation) and median (interquartile range [IQR]), respectively. Categorical variables were reported as numbers (%). The Kruskal‐Wallis test was used to compare blood lipids among the severe group, non‐severe group, and healthy subjects, and post hoc pairwise comparisons were performed using the Nemenyi test. Differences between the two groups were assessed using Student's t‐test and Mann‐Whitney U test for normally and non‐normally distributed continuous data, respectively, and chi‐square or Fisher's exact tests were used for categorical variables. Multivariate logistic regression analysis was adopted to explore independent risk factors for COVID‐19 severity, receiver operator characteristic (ROC) curves were generated, and the areas under ROC curves (AUCs) were calculated to evaluate prediction efficiency. A p‐value <0.05 indicates statistical significance.","This was a single‐center retrospective study approved by the institutional ethics board (PJ‐NBEY‐KY‐2020–061–01). A total of 142 consecutive patients with COVID‐19 were included from January 23 to April 20, 2020. In addition, 77 age‐ and gender‐matched healthy subjects were selected for evaluating the characteristics of blood lipid profiles in patients with COVID‐19.\nThe diagnosis of COVID‐19 and its severity were determined according to the National Diagnosis and Treatment Protocol for Novel Coronavirus Infection‐Induced Pneumonia (6th Trial Version). Patients with confirmed COVID‐19 were diagnosed based on a positive SARS‐CoV‐2 nucleic acid RT‐PCR result using specimens derived from sputum, throat swabs, or nasopharyngeal swabs. Severe patients exhibited one of the following features: (a) respiratory distress with respiration rate (RR) greater than 30 breaths per minute; (b) blood oxygen saturation less than 93% at a state of rest; (c) arterial blood oxygen partial pressure/inhaled oxygen concentration less than 300 mmHg (1 mmHg = 0.133 kPa); or (d) lesion rapidly progressed by more than 50% within one or two days as determined by pulmonary imaging.\nGeneral clinical characteristics, including gender, age, comorbidities, initial symptoms, treatment, and laboratory test data, were collected from the electronic medical records (EMRs).","Blood lipids were assessed using a fully automatic biochemical analyzer (ADVIA2400, Siemens) according to the manufacturer's instructions (Purebio Biotechnology Co., Ltd). Briefly, total cholesterol (TC) was measured using the cholesterol oxidase‐p‐aminophenazone (CHOD‐PAP) method; triglyceride (TG) was assessed using the glycerol phosphate oxidase‐p‐aminophenazone (GPO‐PAP) method; high‐density lipoprotein cholesterol (HDL‐C) was assessed using the direct‐hydrogen peroxide method; low‐density lipoprotein cholesterol (LDL‐C) was assessed using the direct‐surfactant removal method; and ApoA1, ApoB, and lipoprotein (a) were assessed using the immunoturbidimetric method.","SPSS software, version 16.0 (IBM) was used for statistical analysis. Normally and non‐normally distributed continuous data were expressed as the mean ± SD (standard deviation) and median (interquartile range [IQR]), respectively. Categorical variables were reported as numbers (%). The Kruskal‐Wallis test was used to compare blood lipids among the severe group, non‐severe group, and healthy subjects, and post hoc pairwise comparisons were performed using the Nemenyi test. Differences between the two groups were assessed using Student's t‐test and Mann‐Whitney U test for normally and non‐normally distributed continuous data, respectively, and chi‐square or Fisher's exact tests were used for categorical variables. Multivariate logistic regression analysis was adopted to explore independent risk factors for COVID‐19 severity, receiver operator characteristic (ROC) curves were generated, and the areas under ROC curves (AUCs) were calculated to evaluate prediction efficiency. A p‐value <0.05 indicates statistical significance.","General clinical characteristics In total, 142 consecutive patients with confirmed COVID‐19 were included in this study. The mean age was 49.10 ± 16.36 years, and 38.73% of the patients were male. Hypertension (37, 26.06%), diabetes (12, 8.45%), hepatic disease (10, 7.04%), and chronic lung disease (9, 6.34%) were the most common comorbidities. Fever (84, 59.15%) was the leading initial symptom, followed by cough (61, 42.96%), expectoration (32, 22.54%) and fatigue (27, 19.01%).\nAmong the 142 patients, 17 (11.97%) and 125 (88.03%) patients were classified into the severe and non‐severe groups during admission, respectively. Significant differences in age, body mass index (BMI), hypertension, hepatic disease, and fever were noted between the severe and non‐severe groups. Regarding clinical treatment, a greater proportion of patients in the severe group received glucocorticoids, antibiotics, oxygen, invasive mechanical ventilation, and intensive care unit treatment (Table 1).\nGeneral clinical characteristics of patients with COVID‐19\nData are presented as mean ± standard deviation or n (%). p‐value indicates the comparison between the non‐severe group and severe group.\nAbbreviations: COVID‐19, coronavirus disease 2019; ECMO, extracorporeal membrane oxygenation.\nIn total, 142 consecutive patients with confirmed COVID‐19 were included in this study. The mean age was 49.10 ± 16.36 years, and 38.73% of the patients were male. Hypertension (37, 26.06%), diabetes (12, 8.45%), hepatic disease (10, 7.04%), and chronic lung disease (9, 6.34%) were the most common comorbidities. Fever (84, 59.15%) was the leading initial symptom, followed by cough (61, 42.96%), expectoration (32, 22.54%) and fatigue (27, 19.01%).\nAmong the 142 patients, 17 (11.97%) and 125 (88.03%) patients were classified into the severe and non‐severe groups during admission, respectively. Significant differences in age, body mass index (BMI), hypertension, hepatic disease, and fever were noted between the severe and non‐severe groups. Regarding clinical treatment, a greater proportion of patients in the severe group received glucocorticoids, antibiotics, oxygen, invasive mechanical ventilation, and intensive care unit treatment (Table 1).\nGeneral clinical characteristics of patients with COVID‐19\nData are presented as mean ± standard deviation or n (%). p‐value indicates the comparison between the non‐severe group and severe group.\nAbbreviations: COVID‐19, coronavirus disease 2019; ECMO, extracorporeal membrane oxygenation.\nLaboratory findings We first compared the general laboratory parameters between healthy controls and COVID‐19 patients. Neutrophil%, C‐reactive protein (CRP), aspartate aminotransferase (AST), and lactic dehydrogenase (LDH) were elevated, while white blood cell (WBC) count, lymphocyte%, lymphocyte count, platelet count, red blood cell (RBC) count, hemoglobin, albumin, total bilirubin (TBil), blood urea nitrogen (BUN), and blood uric acid (BUA) were decreased in patients with COVID‐19 compared with healthy controls (Table 2).\nComparisons of demographic and laboratory variables between healthy controls and COVID‐19 patients\nData are presented as mean ± standard deviation, n (%), or medians (interquartile ranges).\nAbbreviations: ALT, alanine aminotransferase; AST, aspartate aminotransferase; BUA, blood uric acid; BUN, blood urea nitrogen; COVID‐19, coronavirus disease 2019; CRP, C‐reactive protein; DBil, direct bilirubin; LDH, Lactic dehydrogenase; RBC, red blood cell; Scr, serum creatinine; TBil, total bilirubin; WBC, white blood cell.\nWe next compared general laboratory parameters, coagulation tests, and cytokine levels between severe and non‐severe COVID‐19 patients. Compared with those in the non‐severe group, patients in the severe group exhibited increased neutrophil%, fibrinogen, activated partial thromboplastin time (aPTT), CRP, interleukin‐10 (IL‐10), interleukin‐6 (IL‐6), interferon‐γ (INF‐γ), AST, and LDH levels, as well as reduced lymphocyte count, platelet count, lymphocyte%, and albumin levels (Table 3).\nBaseline laboratory parameters in patients with COVID‐19\nData are presented as medians (interquartile ranges).\nAbbreviations: COVID‐19, coronavirus disease 2019; WBC, white blood cell; RBC, red blood cell; PT, prothrombin time; aPTT, activated partial thromboplastin time; CRP, C‐reactive protein; IL‐2, interleukin‐2; IL‐4, interleukin‐4; IL‐6, interleukin‐6; IL‐10, interleukin‐10; IFN‐γ, interferon‐γ; TNF‐α, tumor necrosis factor‐α; TBil, total bilirubin; DBil, direct bilirubin; AST, aspartate aminotransferase; ALT, alanine aminotransferase; LDH, Lactic dehydrogenase; BUN, blood urea nitrogen; BUA, blood uric acid; Scr, serum creatinine.\nWe first compared the general laboratory parameters between healthy controls and COVID‐19 patients. Neutrophil%, C‐reactive protein (CRP), aspartate aminotransferase (AST), and lactic dehydrogenase (LDH) were elevated, while white blood cell (WBC) count, lymphocyte%, lymphocyte count, platelet count, red blood cell (RBC) count, hemoglobin, albumin, total bilirubin (TBil), blood urea nitrogen (BUN), and blood uric acid (BUA) were decreased in patients with COVID‐19 compared with healthy controls (Table 2).\nComparisons of demographic and laboratory variables between healthy controls and COVID‐19 patients\nData are presented as mean ± standard deviation, n (%), or medians (interquartile ranges).\nAbbreviations: ALT, alanine aminotransferase; AST, aspartate aminotransferase; BUA, blood uric acid; BUN, blood urea nitrogen; COVID‐19, coronavirus disease 2019; CRP, C‐reactive protein; DBil, direct bilirubin; LDH, Lactic dehydrogenase; RBC, red blood cell; Scr, serum creatinine; TBil, total bilirubin; WBC, white blood cell.\nWe next compared general laboratory parameters, coagulation tests, and cytokine levels between severe and non‐severe COVID‐19 patients. Compared with those in the non‐severe group, patients in the severe group exhibited increased neutrophil%, fibrinogen, activated partial thromboplastin time (aPTT), CRP, interleukin‐10 (IL‐10), interleukin‐6 (IL‐6), interferon‐γ (INF‐γ), AST, and LDH levels, as well as reduced lymphocyte count, platelet count, lymphocyte%, and albumin levels (Table 3).\nBaseline laboratory parameters in patients with COVID‐19\nData are presented as medians (interquartile ranges).\nAbbreviations: COVID‐19, coronavirus disease 2019; WBC, white blood cell; RBC, red blood cell; PT, prothrombin time; aPTT, activated partial thromboplastin time; CRP, C‐reactive protein; IL‐2, interleukin‐2; IL‐4, interleukin‐4; IL‐6, interleukin‐6; IL‐10, interleukin‐10; IFN‐γ, interferon‐γ; TNF‐α, tumor necrosis factor‐α; TBil, total bilirubin; DBil, direct bilirubin; AST, aspartate aminotransferase; ALT, alanine aminotransferase; LDH, Lactic dehydrogenase; BUN, blood urea nitrogen; BUA, blood uric acid; Scr, serum creatinine.\nBaseline blood lipids Baseline blood lipids were obtained within 5 days of admission. TC, HDL‐C, LDL‐C, and ApoA1 gradually decreased from the healthy controls to the non‐severe group and the severe group. TG was higher in the non‐severe group than in the healthy controls; however, no significant differences were found between the severe and non‐severe groups or between the severe group and the healthy controls. There were no significant differences in ApoB or lipoprotein (a) among the three groups (Figure 1).\nComparisons of blood lipids among the healthy controls, non‐severe group, and severe group. Data are presented as medians (interquartile ranges). (A) The total cholesterol levels in the healthy controls, non‐severe group, and severe group were 4.97 (4.55–5.79), 4.08 (3.69–4.63), and 3.58 (3.06–3.85) mmol/L, respectively. (B) The triglyceride levels in the healthy controls, non‐severe group, and severe group were 1.04 (0.85–1.43), 1.44 (0.98–2.00), and 1.22 (0.83–2.29) mmol/L, respectively. (C) The HDL‐C levels in the healthy controls, non‐severe group, and severe group were 1.62 (1.35–1.92), 1.09 (0.91–1.29), and 0.93 (0.82–1.00) mmol/L, respectively. (D) The LDL‐C levels in the healthy controls, non‐severe group, and severe group were 2.77 (2.40–3.42), 2.54 (2.18–2.85), and 2.21 (1.93–2.49) mmol/L, respectively. (E) The ApoA1 levels in the healthy controls, non‐severe group, and severe group were 1.45 (1.31–1.65), 1.22 (1.12–1.34), and 0.98 (0.89–1.08) g/L, respectively. (F) The ApoB levels in the healthy controls, non‐severe group, and severe group were 0.86 (0.72–1.01), 0.81 (0.71–0.94), and 0.76 (0.66–0.86) g/L, respectively. (G) The lipoprotein (a) levels in the healthy controls, non‐severe group, and severe group were 114.80 (62.20–202.90), 87.15 (47.75–161.15), 94.45 (36.55–135.40) mg/L, respectively. The Kruskal‐Wallis test was used to compare differences among the three groups, and post hoc pairwise comparisons were performed using the Nemenyi test. HDL‐C, high‐density lipoprotein cholesterol; LDL‐C, low‐density lipoprotein cholesterol; ApoA1, apolipoprotein A1; ApoB, apolipoprotein B\nBaseline blood lipids were obtained within 5 days of admission. TC, HDL‐C, LDL‐C, and ApoA1 gradually decreased from the healthy controls to the non‐severe group and the severe group. TG was higher in the non‐severe group than in the healthy controls; however, no significant differences were found between the severe and non‐severe groups or between the severe group and the healthy controls. There were no significant differences in ApoB or lipoprotein (a) among the three groups (Figure 1).\nComparisons of blood lipids among the healthy controls, non‐severe group, and severe group. Data are presented as medians (interquartile ranges). (A) The total cholesterol levels in the healthy controls, non‐severe group, and severe group were 4.97 (4.55–5.79), 4.08 (3.69–4.63), and 3.58 (3.06–3.85) mmol/L, respectively. (B) The triglyceride levels in the healthy controls, non‐severe group, and severe group were 1.04 (0.85–1.43), 1.44 (0.98–2.00), and 1.22 (0.83–2.29) mmol/L, respectively. (C) The HDL‐C levels in the healthy controls, non‐severe group, and severe group were 1.62 (1.35–1.92), 1.09 (0.91–1.29), and 0.93 (0.82–1.00) mmol/L, respectively. (D) The LDL‐C levels in the healthy controls, non‐severe group, and severe group were 2.77 (2.40–3.42), 2.54 (2.18–2.85), and 2.21 (1.93–2.49) mmol/L, respectively. (E) The ApoA1 levels in the healthy controls, non‐severe group, and severe group were 1.45 (1.31–1.65), 1.22 (1.12–1.34), and 0.98 (0.89–1.08) g/L, respectively. (F) The ApoB levels in the healthy controls, non‐severe group, and severe group were 0.86 (0.72–1.01), 0.81 (0.71–0.94), and 0.76 (0.66–0.86) g/L, respectively. (G) The lipoprotein (a) levels in the healthy controls, non‐severe group, and severe group were 114.80 (62.20–202.90), 87.15 (47.75–161.15), 94.45 (36.55–135.40) mg/L, respectively. The Kruskal‐Wallis test was used to compare differences among the three groups, and post hoc pairwise comparisons were performed using the Nemenyi test. HDL‐C, high‐density lipoprotein cholesterol; LDL‐C, low‐density lipoprotein cholesterol; ApoA1, apolipoprotein A1; ApoB, apolipoprotein B\nRisk factors for COVID‐19 severity Potential risk factors, including several general clinical characteristics, immunoinflammatory markers, and blood lipids, were first identified by univariate logistic analysis. Age, BMI, hypertension, neutrophil%, lymphocyte%, lymphocyte count, platelet count, fibrinogen, CRP, IL‐6, IL‐10, HDL‐C, ApoA1, ALB, AST, and LDH were associated with the severity of COVID‐19 (p < 0.05). However, aPTT, interferon‐γ (IFN‐γ), TC, and LDL‐C were unrelated to COVID‐19 severity (p>0.05) (Figure 2). Next, variables with p < 0.1 in the univariate logistic analysis were entered into the multivariate logistic analysis. However, after adjusting for other potential risk factors, only IL‐6 (adjusted odds ratio [OR]: 1.097, 95% confidence interval [CI]: 1.034–1.165, p = 0.002) and ApoA1 (adjusted OR: 0.865, 95% CI: 0.800–0.935, p < 0.001) were identified as independent risk factors by multivariate logistic analysis (Figure 3). Therefore, a risk model was built using the combination of ApoA1 and IL‐6. The mathematical formula of the risk model was log (P)=12.303+0.093*IL‐6–0.145*ApoA1.\nUnivariate logistic regression of risk factors for COVID‐19 severity. Old age, high BMI, hypertension, increased neutrophil%, fibrinogen, C‐reactive protein, interleukin‐6, interleukin‐10, aspartate aminotransferase, and lactic dehydrogenase and decreased lymphocyte%, lymphocyte count, platelet count, HDL‐C, apolipoprotein A1, and albumin were associated with the severity of COVID‐19 in univariate logistic regression analysis (all p < 0.05). COVID‐19: coronavirus disease 2019, OR, odds ratio; CI, confidence interval; HDL‐C, high‐density lipoprotein cholesterol; LDL‐C, low‐density lipoprotein cholesterol\nMultivariate logistic regression of independent risk factors for COVID‐19 severity. After adjusting for other potential risk factors, increased IL‐6 (OR: 1.097, 95% CI: 1.034–1.165, p = 0.002) and decreased ApoA1 (OR: 0.865, 95% CI: 0.800–0.935, p < 0.001) were recognized as independent risk factors for COVID‐19 severity. COVID‐19: coronavirus disease 2019, OR: odds ratio, CI: confidence interval\nIn the prediction of COVID‐19 severity, the AUCs (95% CI) of TC, HDL‐C, LDL‐C, ApoA1, IL‐6, and the risk model were 0.726 (0.645–0.798), 0.674 (0.590–0.750), 0.669 (0.585–0.746), 0.896 (0.834–0.941), 0.855 (0.786–0.908), and 0.977 (0.932–0.995), respectively (Figure 4, Table 4). In particular, the sensitivity and specificity of Apo A1 were 94.12% (95% CI: 71.20%–99.00%) and 80.80% (95% CI: 72.80%–87.30%), respectively, which were both the highest among the above single markers. Moreover, the risk model increased the levels of sensitivity and specificity to 100.00% (95% CI: 80.30%–100.00%) and 89.89% (95% CI: 81.40%–94.10%), respectively.\nReceiver operator characteristic curves of total cholesterol, HDL‐C, LDL‐C, ApoA1, IL‐6, and risk model for the severity of COVID‐19. COVID‐19: coronavirus disease 2019, HDL‐C: high‐density lipoprotein cholesterol, LDL‐C: low‐density lipoprotein cholesterol, ApoA1: apolipoprotein A1, IL‐6: interleukin‐6\nPredictive performance of blood lipids, interleukin‐6, and the risk model for COVID‐19 severity\nAbbreviations: COVID‐19, coronavirus disease 2019; HDL‐C, high‐density lipoprotein cholesterol; LDL‐C, low‐density lipoprotein cholesterol; ApoA1, apolipoprotein A1; IL‐6, Interleukin‐6; CI, confidence interval; PPV, positive predictive value; NPV, negative predictive value.\nPotential risk factors, including several general clinical characteristics, immunoinflammatory markers, and blood lipids, were first identified by univariate logistic analysis. Age, BMI, hypertension, neutrophil%, lymphocyte%, lymphocyte count, platelet count, fibrinogen, CRP, IL‐6, IL‐10, HDL‐C, ApoA1, ALB, AST, and LDH were associated with the severity of COVID‐19 (p < 0.05). However, aPTT, interferon‐γ (IFN‐γ), TC, and LDL‐C were unrelated to COVID‐19 severity (p>0.05) (Figure 2). Next, variables with p < 0.1 in the univariate logistic analysis were entered into the multivariate logistic analysis. However, after adjusting for other potential risk factors, only IL‐6 (adjusted odds ratio [OR]: 1.097, 95% confidence interval [CI]: 1.034–1.165, p = 0.002) and ApoA1 (adjusted OR: 0.865, 95% CI: 0.800–0.935, p < 0.001) were identified as independent risk factors by multivariate logistic analysis (Figure 3). Therefore, a risk model was built using the combination of ApoA1 and IL‐6. The mathematical formula of the risk model was log (P)=12.303+0.093*IL‐6–0.145*ApoA1.\nUnivariate logistic regression of risk factors for COVID‐19 severity. Old age, high BMI, hypertension, increased neutrophil%, fibrinogen, C‐reactive protein, interleukin‐6, interleukin‐10, aspartate aminotransferase, and lactic dehydrogenase and decreased lymphocyte%, lymphocyte count, platelet count, HDL‐C, apolipoprotein A1, and albumin were associated with the severity of COVID‐19 in univariate logistic regression analysis (all p < 0.05). COVID‐19: coronavirus disease 2019, OR, odds ratio; CI, confidence interval; HDL‐C, high‐density lipoprotein cholesterol; LDL‐C, low‐density lipoprotein cholesterol\nMultivariate logistic regression of independent risk factors for COVID‐19 severity. After adjusting for other potential risk factors, increased IL‐6 (OR: 1.097, 95% CI: 1.034–1.165, p = 0.002) and decreased ApoA1 (OR: 0.865, 95% CI: 0.800–0.935, p < 0.001) were recognized as independent risk factors for COVID‐19 severity. COVID‐19: coronavirus disease 2019, OR: odds ratio, CI: confidence interval\nIn the prediction of COVID‐19 severity, the AUCs (95% CI) of TC, HDL‐C, LDL‐C, ApoA1, IL‐6, and the risk model were 0.726 (0.645–0.798), 0.674 (0.590–0.750), 0.669 (0.585–0.746), 0.896 (0.834–0.941), 0.855 (0.786–0.908), and 0.977 (0.932–0.995), respectively (Figure 4, Table 4). In particular, the sensitivity and specificity of Apo A1 were 94.12% (95% CI: 71.20%–99.00%) and 80.80% (95% CI: 72.80%–87.30%), respectively, which were both the highest among the above single markers. Moreover, the risk model increased the levels of sensitivity and specificity to 100.00% (95% CI: 80.30%–100.00%) and 89.89% (95% CI: 81.40%–94.10%), respectively.\nReceiver operator characteristic curves of total cholesterol, HDL‐C, LDL‐C, ApoA1, IL‐6, and risk model for the severity of COVID‐19. COVID‐19: coronavirus disease 2019, HDL‐C: high‐density lipoprotein cholesterol, LDL‐C: low‐density lipoprotein cholesterol, ApoA1: apolipoprotein A1, IL‐6: interleukin‐6\nPredictive performance of blood lipids, interleukin‐6, and the risk model for COVID‐19 severity\nAbbreviations: COVID‐19, coronavirus disease 2019; HDL‐C, high‐density lipoprotein cholesterol; LDL‐C, low‐density lipoprotein cholesterol; ApoA1, apolipoprotein A1; IL‐6, Interleukin‐6; CI, confidence interval; PPV, positive predictive value; NPV, negative predictive value.","In total, 142 consecutive patients with confirmed COVID‐19 were included in this study. The mean age was 49.10 ± 16.36 years, and 38.73% of the patients were male. Hypertension (37, 26.06%), diabetes (12, 8.45%), hepatic disease (10, 7.04%), and chronic lung disease (9, 6.34%) were the most common comorbidities. Fever (84, 59.15%) was the leading initial symptom, followed by cough (61, 42.96%), expectoration (32, 22.54%) and fatigue (27, 19.01%).\nAmong the 142 patients, 17 (11.97%) and 125 (88.03%) patients were classified into the severe and non‐severe groups during admission, respectively. Significant differences in age, body mass index (BMI), hypertension, hepatic disease, and fever were noted between the severe and non‐severe groups. Regarding clinical treatment, a greater proportion of patients in the severe group received glucocorticoids, antibiotics, oxygen, invasive mechanical ventilation, and intensive care unit treatment (Table 1).\nGeneral clinical characteristics of patients with COVID‐19\nData are presented as mean ± standard deviation or n (%). p‐value indicates the comparison between the non‐severe group and severe group.\nAbbreviations: COVID‐19, coronavirus disease 2019; ECMO, extracorporeal membrane oxygenation.","We first compared the general laboratory parameters between healthy controls and COVID‐19 patients. Neutrophil%, C‐reactive protein (CRP), aspartate aminotransferase (AST), and lactic dehydrogenase (LDH) were elevated, while white blood cell (WBC) count, lymphocyte%, lymphocyte count, platelet count, red blood cell (RBC) count, hemoglobin, albumin, total bilirubin (TBil), blood urea nitrogen (BUN), and blood uric acid (BUA) were decreased in patients with COVID‐19 compared with healthy controls (Table 2).\nComparisons of demographic and laboratory variables between healthy controls and COVID‐19 patients\nData are presented as mean ± standard deviation, n (%), or medians (interquartile ranges).\nAbbreviations: ALT, alanine aminotransferase; AST, aspartate aminotransferase; BUA, blood uric acid; BUN, blood urea nitrogen; COVID‐19, coronavirus disease 2019; CRP, C‐reactive protein; DBil, direct bilirubin; LDH, Lactic dehydrogenase; RBC, red blood cell; Scr, serum creatinine; TBil, total bilirubin; WBC, white blood cell.\nWe next compared general laboratory parameters, coagulation tests, and cytokine levels between severe and non‐severe COVID‐19 patients. Compared with those in the non‐severe group, patients in the severe group exhibited increased neutrophil%, fibrinogen, activated partial thromboplastin time (aPTT), CRP, interleukin‐10 (IL‐10), interleukin‐6 (IL‐6), interferon‐γ (INF‐γ), AST, and LDH levels, as well as reduced lymphocyte count, platelet count, lymphocyte%, and albumin levels (Table 3).\nBaseline laboratory parameters in patients with COVID‐19\nData are presented as medians (interquartile ranges).\nAbbreviations: COVID‐19, coronavirus disease 2019; WBC, white blood cell; RBC, red blood cell; PT, prothrombin time; aPTT, activated partial thromboplastin time; CRP, C‐reactive protein; IL‐2, interleukin‐2; IL‐4, interleukin‐4; IL‐6, interleukin‐6; IL‐10, interleukin‐10; IFN‐γ, interferon‐γ; TNF‐α, tumor necrosis factor‐α; TBil, total bilirubin; DBil, direct bilirubin; AST, aspartate aminotransferase; ALT, alanine aminotransferase; LDH, Lactic dehydrogenase; BUN, blood urea nitrogen; BUA, blood uric acid; Scr, serum creatinine.","Baseline blood lipids were obtained within 5 days of admission. TC, HDL‐C, LDL‐C, and ApoA1 gradually decreased from the healthy controls to the non‐severe group and the severe group. TG was higher in the non‐severe group than in the healthy controls; however, no significant differences were found between the severe and non‐severe groups or between the severe group and the healthy controls. There were no significant differences in ApoB or lipoprotein (a) among the three groups (Figure 1).\nComparisons of blood lipids among the healthy controls, non‐severe group, and severe group. Data are presented as medians (interquartile ranges). (A) The total cholesterol levels in the healthy controls, non‐severe group, and severe group were 4.97 (4.55–5.79), 4.08 (3.69–4.63), and 3.58 (3.06–3.85) mmol/L, respectively. (B) The triglyceride levels in the healthy controls, non‐severe group, and severe group were 1.04 (0.85–1.43), 1.44 (0.98–2.00), and 1.22 (0.83–2.29) mmol/L, respectively. (C) The HDL‐C levels in the healthy controls, non‐severe group, and severe group were 1.62 (1.35–1.92), 1.09 (0.91–1.29), and 0.93 (0.82–1.00) mmol/L, respectively. (D) The LDL‐C levels in the healthy controls, non‐severe group, and severe group were 2.77 (2.40–3.42), 2.54 (2.18–2.85), and 2.21 (1.93–2.49) mmol/L, respectively. (E) The ApoA1 levels in the healthy controls, non‐severe group, and severe group were 1.45 (1.31–1.65), 1.22 (1.12–1.34), and 0.98 (0.89–1.08) g/L, respectively. (F) The ApoB levels in the healthy controls, non‐severe group, and severe group were 0.86 (0.72–1.01), 0.81 (0.71–0.94), and 0.76 (0.66–0.86) g/L, respectively. (G) The lipoprotein (a) levels in the healthy controls, non‐severe group, and severe group were 114.80 (62.20–202.90), 87.15 (47.75–161.15), 94.45 (36.55–135.40) mg/L, respectively. The Kruskal‐Wallis test was used to compare differences among the three groups, and post hoc pairwise comparisons were performed using the Nemenyi test. HDL‐C, high‐density lipoprotein cholesterol; LDL‐C, low‐density lipoprotein cholesterol; ApoA1, apolipoprotein A1; ApoB, apolipoprotein B","Potential risk factors, including several general clinical characteristics, immunoinflammatory markers, and blood lipids, were first identified by univariate logistic analysis. Age, BMI, hypertension, neutrophil%, lymphocyte%, lymphocyte count, platelet count, fibrinogen, CRP, IL‐6, IL‐10, HDL‐C, ApoA1, ALB, AST, and LDH were associated with the severity of COVID‐19 (p < 0.05). However, aPTT, interferon‐γ (IFN‐γ), TC, and LDL‐C were unrelated to COVID‐19 severity (p>0.05) (Figure 2). Next, variables with p < 0.1 in the univariate logistic analysis were entered into the multivariate logistic analysis. However, after adjusting for other potential risk factors, only IL‐6 (adjusted odds ratio [OR]: 1.097, 95% confidence interval [CI]: 1.034–1.165, p = 0.002) and ApoA1 (adjusted OR: 0.865, 95% CI: 0.800–0.935, p < 0.001) were identified as independent risk factors by multivariate logistic analysis (Figure 3). Therefore, a risk model was built using the combination of ApoA1 and IL‐6. The mathematical formula of the risk model was log (P)=12.303+0.093*IL‐6–0.145*ApoA1.\nUnivariate logistic regression of risk factors for COVID‐19 severity. Old age, high BMI, hypertension, increased neutrophil%, fibrinogen, C‐reactive protein, interleukin‐6, interleukin‐10, aspartate aminotransferase, and lactic dehydrogenase and decreased lymphocyte%, lymphocyte count, platelet count, HDL‐C, apolipoprotein A1, and albumin were associated with the severity of COVID‐19 in univariate logistic regression analysis (all p < 0.05). COVID‐19: coronavirus disease 2019, OR, odds ratio; CI, confidence interval; HDL‐C, high‐density lipoprotein cholesterol; LDL‐C, low‐density lipoprotein cholesterol\nMultivariate logistic regression of independent risk factors for COVID‐19 severity. After adjusting for other potential risk factors, increased IL‐6 (OR: 1.097, 95% CI: 1.034–1.165, p = 0.002) and decreased ApoA1 (OR: 0.865, 95% CI: 0.800–0.935, p < 0.001) were recognized as independent risk factors for COVID‐19 severity. COVID‐19: coronavirus disease 2019, OR: odds ratio, CI: confidence interval\nIn the prediction of COVID‐19 severity, the AUCs (95% CI) of TC, HDL‐C, LDL‐C, ApoA1, IL‐6, and the risk model were 0.726 (0.645–0.798), 0.674 (0.590–0.750), 0.669 (0.585–0.746), 0.896 (0.834–0.941), 0.855 (0.786–0.908), and 0.977 (0.932–0.995), respectively (Figure 4, Table 4). In particular, the sensitivity and specificity of Apo A1 were 94.12% (95% CI: 71.20%–99.00%) and 80.80% (95% CI: 72.80%–87.30%), respectively, which were both the highest among the above single markers. Moreover, the risk model increased the levels of sensitivity and specificity to 100.00% (95% CI: 80.30%–100.00%) and 89.89% (95% CI: 81.40%–94.10%), respectively.\nReceiver operator characteristic curves of total cholesterol, HDL‐C, LDL‐C, ApoA1, IL‐6, and risk model for the severity of COVID‐19. COVID‐19: coronavirus disease 2019, HDL‐C: high‐density lipoprotein cholesterol, LDL‐C: low‐density lipoprotein cholesterol, ApoA1: apolipoprotein A1, IL‐6: interleukin‐6\nPredictive performance of blood lipids, interleukin‐6, and the risk model for COVID‐19 severity\nAbbreviations: COVID‐19, coronavirus disease 2019; HDL‐C, high‐density lipoprotein cholesterol; LDL‐C, low‐density lipoprotein cholesterol; ApoA1, apolipoprotein A1; IL‐6, Interleukin‐6; CI, confidence interval; PPV, positive predictive value; NPV, negative predictive value.","In this study, baseline TC, HDL‐C, LDL‐C, and ApoA1 gradually decreased across the groups in the following order: healthy controls, non‐severe group, and severe group, indicating that they had potential roles in predicting the severity of COVID‐19. Other blood lipids, including TG, ApoB, and lipoprotein (a), had little value in distinguishing COVID‐19 severity. Additionally, we found that ApoA1 was most obviously decreased among the altered lipids in this study, representing an independent risk factor for COVID‐19 severity. The combination of ApoA1 and IL‐6 yielded even higher prediction efficiency.\nIn a recent study, Wei et al.11 observed that serum levels of TC, HDL‐C, and LDL‐C in patients with COVID‐19 were significantly lower than those in healthy subjects, especially in severe and critical cases, which was similar to our study. Another study also found that LDL‐C performed well in discriminating COVID‐19 severity, it was gradually decreased across moderate, severe, and critical cases; however, HDL‐C exhibited no significant differences among the three groups.12 These small discrepancies may be related to the heterogeneity of disease severity, different sample sizes, and testing methods. However, our study and others11, 12 all showed that blood lipids have potential auxiliary value in distinguishing severe COVID‐19 patients.\nApoA1, a major protein component of the HDL complex, is involved in “reverse cholesterol transport” by transporting excess cholesterol from peripheral cells back to the liver for excretion. In addition, ApoA1 has an anti‐inflammatory characteristic,13 suggesting its role in inflammatory diseases. Previous studies have revealed that serum ApoA1 is associated with the outcome of patients with sepsis and acute respiratory distress syndrome induced by pneumonia, as well as critically ill patients.14, 15, 16, 17 In acute inflammatory disease, serum amyloid A (SAA), an acute‐phase protein, displaces ApoA1 from the HDL complex; then, free ApoA1 is easily eliminated by the kidney, resulting in low levels in the peripheral blood.18 On the other hand, the liver is susceptible to attack by SARS‐CoV‐2, especially in severe cases19; therefore, reduced synthesis by the injured liver may also play a role.\nIL‐6 plays a key role in the development of COVID‐19, and its predictive value for disease severity has been revealed previously by us and others.4, 20, 21, 22 It was also found that increased IL‐6 was associated with poor outcomes.22, 23 In this study, IL‐6 and ApoA1 were identified as independent risk factors for COVID‐19 severity. The risk model established using these two markers exhibited the highest predictive value, with an AUC of 0.977 (95% CI: 0.932–0.995).\nApoA1 and its mimetic peptide D‐amino acids (D‐4F) exhibit therapeutic potential for treating cancer, influenza, sepsis, and ARDS, primarily due to their anti‐inflammatory, anti‐oxidant, and anti‐apoptotic properties.13, 24, 25, 26, 27 In addition, it is noteworthy that ApoA1 inhibits IL‐6 release and reduces macrophage activation.25 IL‐6 is the main participant in the cytokine storm, and macrophages are the primary source of IL‐6. Therefore, ApoA1 may exhibit therapeutic potential in treating patients with COVID‐19. It might be worthwhile to test the efficacy and safety of ApoA1 and its mimetics in treating these patients.\nThe main strength of this study is that the patients included in this study were treated without delay when infected with SARS‐CoV‐2, which may represent an early stage of the disease. Second, this study enrolled healthy controls to analyze trends in blood lipids among healthy subjects, non‐severe cases, and severe cases. Third, the predictive values of verified clinical characteristics and laboratory parameters were selected for comparison with blood lipids, making the results more credible. Finally, blood lipids were routinely tested by using an automatic biochemical analyzer, with clinical application value.\nA limitation of this study is that it was a single‐center retrospective study with a relatively small sample size that was not validated in internal or external cohorts. Therefore, a prospective study with a larger sample size is strongly encouraged.\nIn conclusion, this study sheds light on abnormal blood lipid profiles in patients with COVID‐19 compared with healthy subjects, especially in severe cases. Specifically, TC, HDL‐C, LDL‐C, and ApoA1 gradually decreased from the healthy controls to non‐severe and severe groups. Additionally, ApoA1 is a good indicator of COVID‐19 severity, and the combination of ApoA1 and IL‐6 enhances model predictability. These findings might be helpful in disclosing the pathogenesis of COVID‐19 and developing novel therapeutic strategies for COVID‐19.","The authors declare that they have no competing interests.","Zhe Zhu interpreted the data and wrote the paper. Yayun Yang, Linyan Fan, Shuyuan Ye, and Kehong Lou collected and analyzed the data. Xin Hua, Zuoan Huang, and Qiaoyun Shi performed laboratory analysis. Guosheng Gao designed the study and revised the paper.","This study was approved by the institutional ethics board of HwaMei Hospital, University of Chinese Academy of Science (PJ‐NBEY‐KY‐2020–061–01)."],"string":"[\n \"Severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) is a highly transmissible coronavirus that has caused an ever‐increasing number of coronavirus disease 2019 (COVID‐19) infections since December 2019 and spread rapidly worldwide. Although approximately 80% of patients infected with SARS‐CoV‐2 exhibit mild symptoms,1 the remaining severe cases may experience acute respiratory distress syndrome (ARDS), multiorgan failure (MOF), and death.2 Therefore, it is necessary to discriminate between severe and mild cases.\\nPrevious studies have found that the development of severe COVID‐19 is associated with age and underlying diseases, and patients who develop severe disease are likely to suffer from aberrant inflammatory reactions and cytokine storms.1, 3 Consequently, several clinical characteristics, the inflammation index, and cytokine levels have been used as indicators for predicting the severity of COVID‐19 by us and others.4, 5 Emerging evidence suggests that lipid metabolism dysregulation might promote the progression of COVID‐19, as revealed by mass spectrometry (MS)‐based proteomics analysis.6, 7 Although MS analysis is not commonly performed, blood lipids are routinely examined using automatic biochemical instruments in clinical laboratories. Additionally, dyslipidemia has also been observed in other respiratory infectious diseases.8, 9, 10 Therefore, blood lipids may be considered potential and available indicators of COVID‐19 severity.\\nIn a former study, serum hypolipidemia was identified in patients with COVID‐19.11 However, that study did not analyze other blood lipid components, such as apolipoprotein A1 (ApoA1), ApoB, and lipoprotein (a), and their predictive values for COVID‐19 severity are not fully understood. Therefore, to more comprehensively investigate blood lipid profiles in patients with COVID‐19 and determine their predictive value for disease severity, a retrospective study was performed.\",\n \"Study design and participants selection This was a single‐center retrospective study approved by the institutional ethics board (PJ‐NBEY‐KY‐2020–061–01). A total of 142 consecutive patients with COVID‐19 were included from January 23 to April 20, 2020. In addition, 77 age‐ and gender‐matched healthy subjects were selected for evaluating the characteristics of blood lipid profiles in patients with COVID‐19.\\nThe diagnosis of COVID‐19 and its severity were determined according to the National Diagnosis and Treatment Protocol for Novel Coronavirus Infection‐Induced Pneumonia (6th Trial Version). Patients with confirmed COVID‐19 were diagnosed based on a positive SARS‐CoV‐2 nucleic acid RT‐PCR result using specimens derived from sputum, throat swabs, or nasopharyngeal swabs. Severe patients exhibited one of the following features: (a) respiratory distress with respiration rate (RR) greater than 30 breaths per minute; (b) blood oxygen saturation less than 93% at a state of rest; (c) arterial blood oxygen partial pressure/inhaled oxygen concentration less than 300 mmHg (1 mmHg = 0.133 kPa); or (d) lesion rapidly progressed by more than 50% within one or two days as determined by pulmonary imaging.\\nGeneral clinical characteristics, including gender, age, comorbidities, initial symptoms, treatment, and laboratory test data, were collected from the electronic medical records (EMRs).\\nThis was a single‐center retrospective study approved by the institutional ethics board (PJ‐NBEY‐KY‐2020–061–01). A total of 142 consecutive patients with COVID‐19 were included from January 23 to April 20, 2020. In addition, 77 age‐ and gender‐matched healthy subjects were selected for evaluating the characteristics of blood lipid profiles in patients with COVID‐19.\\nThe diagnosis of COVID‐19 and its severity were determined according to the National Diagnosis and Treatment Protocol for Novel Coronavirus Infection‐Induced Pneumonia (6th Trial Version). Patients with confirmed COVID‐19 were diagnosed based on a positive SARS‐CoV‐2 nucleic acid RT‐PCR result using specimens derived from sputum, throat swabs, or nasopharyngeal swabs. Severe patients exhibited one of the following features: (a) respiratory distress with respiration rate (RR) greater than 30 breaths per minute; (b) blood oxygen saturation less than 93% at a state of rest; (c) arterial blood oxygen partial pressure/inhaled oxygen concentration less than 300 mmHg (1 mmHg = 0.133 kPa); or (d) lesion rapidly progressed by more than 50% within one or two days as determined by pulmonary imaging.\\nGeneral clinical characteristics, including gender, age, comorbidities, initial symptoms, treatment, and laboratory test data, were collected from the electronic medical records (EMRs).\\nDetermination of blood lipids Blood lipids were assessed using a fully automatic biochemical analyzer (ADVIA2400, Siemens) according to the manufacturer's instructions (Purebio Biotechnology Co., Ltd). Briefly, total cholesterol (TC) was measured using the cholesterol oxidase‐p‐aminophenazone (CHOD‐PAP) method; triglyceride (TG) was assessed using the glycerol phosphate oxidase‐p‐aminophenazone (GPO‐PAP) method; high‐density lipoprotein cholesterol (HDL‐C) was assessed using the direct‐hydrogen peroxide method; low‐density lipoprotein cholesterol (LDL‐C) was assessed using the direct‐surfactant removal method; and ApoA1, ApoB, and lipoprotein (a) were assessed using the immunoturbidimetric method.\\nBlood lipids were assessed using a fully automatic biochemical analyzer (ADVIA2400, Siemens) according to the manufacturer's instructions (Purebio Biotechnology Co., Ltd). Briefly, total cholesterol (TC) was measured using the cholesterol oxidase‐p‐aminophenazone (CHOD‐PAP) method; triglyceride (TG) was assessed using the glycerol phosphate oxidase‐p‐aminophenazone (GPO‐PAP) method; high‐density lipoprotein cholesterol (HDL‐C) was assessed using the direct‐hydrogen peroxide method; low‐density lipoprotein cholesterol (LDL‐C) was assessed using the direct‐surfactant removal method; and ApoA1, ApoB, and lipoprotein (a) were assessed using the immunoturbidimetric method.\\nStatistical analysis SPSS software, version 16.0 (IBM) was used for statistical analysis. Normally and non‐normally distributed continuous data were expressed as the mean ± SD (standard deviation) and median (interquartile range [IQR]), respectively. Categorical variables were reported as numbers (%). The Kruskal‐Wallis test was used to compare blood lipids among the severe group, non‐severe group, and healthy subjects, and post hoc pairwise comparisons were performed using the Nemenyi test. Differences between the two groups were assessed using Student's t‐test and Mann‐Whitney U test for normally and non‐normally distributed continuous data, respectively, and chi‐square or Fisher's exact tests were used for categorical variables. Multivariate logistic regression analysis was adopted to explore independent risk factors for COVID‐19 severity, receiver operator characteristic (ROC) curves were generated, and the areas under ROC curves (AUCs) were calculated to evaluate prediction efficiency. A p‐value <0.05 indicates statistical significance.\\nSPSS software, version 16.0 (IBM) was used for statistical analysis. Normally and non‐normally distributed continuous data were expressed as the mean ± SD (standard deviation) and median (interquartile range [IQR]), respectively. Categorical variables were reported as numbers (%). The Kruskal‐Wallis test was used to compare blood lipids among the severe group, non‐severe group, and healthy subjects, and post hoc pairwise comparisons were performed using the Nemenyi test. Differences between the two groups were assessed using Student's t‐test and Mann‐Whitney U test for normally and non‐normally distributed continuous data, respectively, and chi‐square or Fisher's exact tests were used for categorical variables. Multivariate logistic regression analysis was adopted to explore independent risk factors for COVID‐19 severity, receiver operator characteristic (ROC) curves were generated, and the areas under ROC curves (AUCs) were calculated to evaluate prediction efficiency. A p‐value <0.05 indicates statistical significance.\",\n \"This was a single‐center retrospective study approved by the institutional ethics board (PJ‐NBEY‐KY‐2020–061–01). A total of 142 consecutive patients with COVID‐19 were included from January 23 to April 20, 2020. In addition, 77 age‐ and gender‐matched healthy subjects were selected for evaluating the characteristics of blood lipid profiles in patients with COVID‐19.\\nThe diagnosis of COVID‐19 and its severity were determined according to the National Diagnosis and Treatment Protocol for Novel Coronavirus Infection‐Induced Pneumonia (6th Trial Version). Patients with confirmed COVID‐19 were diagnosed based on a positive SARS‐CoV‐2 nucleic acid RT‐PCR result using specimens derived from sputum, throat swabs, or nasopharyngeal swabs. Severe patients exhibited one of the following features: (a) respiratory distress with respiration rate (RR) greater than 30 breaths per minute; (b) blood oxygen saturation less than 93% at a state of rest; (c) arterial blood oxygen partial pressure/inhaled oxygen concentration less than 300 mmHg (1 mmHg = 0.133 kPa); or (d) lesion rapidly progressed by more than 50% within one or two days as determined by pulmonary imaging.\\nGeneral clinical characteristics, including gender, age, comorbidities, initial symptoms, treatment, and laboratory test data, were collected from the electronic medical records (EMRs).\",\n \"Blood lipids were assessed using a fully automatic biochemical analyzer (ADVIA2400, Siemens) according to the manufacturer's instructions (Purebio Biotechnology Co., Ltd). Briefly, total cholesterol (TC) was measured using the cholesterol oxidase‐p‐aminophenazone (CHOD‐PAP) method; triglyceride (TG) was assessed using the glycerol phosphate oxidase‐p‐aminophenazone (GPO‐PAP) method; high‐density lipoprotein cholesterol (HDL‐C) was assessed using the direct‐hydrogen peroxide method; low‐density lipoprotein cholesterol (LDL‐C) was assessed using the direct‐surfactant removal method; and ApoA1, ApoB, and lipoprotein (a) were assessed using the immunoturbidimetric method.\",\n \"SPSS software, version 16.0 (IBM) was used for statistical analysis. Normally and non‐normally distributed continuous data were expressed as the mean ± SD (standard deviation) and median (interquartile range [IQR]), respectively. Categorical variables were reported as numbers (%). The Kruskal‐Wallis test was used to compare blood lipids among the severe group, non‐severe group, and healthy subjects, and post hoc pairwise comparisons were performed using the Nemenyi test. Differences between the two groups were assessed using Student's t‐test and Mann‐Whitney U test for normally and non‐normally distributed continuous data, respectively, and chi‐square or Fisher's exact tests were used for categorical variables. Multivariate logistic regression analysis was adopted to explore independent risk factors for COVID‐19 severity, receiver operator characteristic (ROC) curves were generated, and the areas under ROC curves (AUCs) were calculated to evaluate prediction efficiency. A p‐value <0.05 indicates statistical significance.\",\n \"General clinical characteristics In total, 142 consecutive patients with confirmed COVID‐19 were included in this study. The mean age was 49.10 ± 16.36 years, and 38.73% of the patients were male. Hypertension (37, 26.06%), diabetes (12, 8.45%), hepatic disease (10, 7.04%), and chronic lung disease (9, 6.34%) were the most common comorbidities. Fever (84, 59.15%) was the leading initial symptom, followed by cough (61, 42.96%), expectoration (32, 22.54%) and fatigue (27, 19.01%).\\nAmong the 142 patients, 17 (11.97%) and 125 (88.03%) patients were classified into the severe and non‐severe groups during admission, respectively. Significant differences in age, body mass index (BMI), hypertension, hepatic disease, and fever were noted between the severe and non‐severe groups. Regarding clinical treatment, a greater proportion of patients in the severe group received glucocorticoids, antibiotics, oxygen, invasive mechanical ventilation, and intensive care unit treatment (Table 1).\\nGeneral clinical characteristics of patients with COVID‐19\\nData are presented as mean ± standard deviation or n (%). p‐value indicates the comparison between the non‐severe group and severe group.\\nAbbreviations: COVID‐19, coronavirus disease 2019; ECMO, extracorporeal membrane oxygenation.\\nIn total, 142 consecutive patients with confirmed COVID‐19 were included in this study. The mean age was 49.10 ± 16.36 years, and 38.73% of the patients were male. Hypertension (37, 26.06%), diabetes (12, 8.45%), hepatic disease (10, 7.04%), and chronic lung disease (9, 6.34%) were the most common comorbidities. Fever (84, 59.15%) was the leading initial symptom, followed by cough (61, 42.96%), expectoration (32, 22.54%) and fatigue (27, 19.01%).\\nAmong the 142 patients, 17 (11.97%) and 125 (88.03%) patients were classified into the severe and non‐severe groups during admission, respectively. Significant differences in age, body mass index (BMI), hypertension, hepatic disease, and fever were noted between the severe and non‐severe groups. Regarding clinical treatment, a greater proportion of patients in the severe group received glucocorticoids, antibiotics, oxygen, invasive mechanical ventilation, and intensive care unit treatment (Table 1).\\nGeneral clinical characteristics of patients with COVID‐19\\nData are presented as mean ± standard deviation or n (%). p‐value indicates the comparison between the non‐severe group and severe group.\\nAbbreviations: COVID‐19, coronavirus disease 2019; ECMO, extracorporeal membrane oxygenation.\\nLaboratory findings We first compared the general laboratory parameters between healthy controls and COVID‐19 patients. Neutrophil%, C‐reactive protein (CRP), aspartate aminotransferase (AST), and lactic dehydrogenase (LDH) were elevated, while white blood cell (WBC) count, lymphocyte%, lymphocyte count, platelet count, red blood cell (RBC) count, hemoglobin, albumin, total bilirubin (TBil), blood urea nitrogen (BUN), and blood uric acid (BUA) were decreased in patients with COVID‐19 compared with healthy controls (Table 2).\\nComparisons of demographic and laboratory variables between healthy controls and COVID‐19 patients\\nData are presented as mean ± standard deviation, n (%), or medians (interquartile ranges).\\nAbbreviations: ALT, alanine aminotransferase; AST, aspartate aminotransferase; BUA, blood uric acid; BUN, blood urea nitrogen; COVID‐19, coronavirus disease 2019; CRP, C‐reactive protein; DBil, direct bilirubin; LDH, Lactic dehydrogenase; RBC, red blood cell; Scr, serum creatinine; TBil, total bilirubin; WBC, white blood cell.\\nWe next compared general laboratory parameters, coagulation tests, and cytokine levels between severe and non‐severe COVID‐19 patients. Compared with those in the non‐severe group, patients in the severe group exhibited increased neutrophil%, fibrinogen, activated partial thromboplastin time (aPTT), CRP, interleukin‐10 (IL‐10), interleukin‐6 (IL‐6), interferon‐γ (INF‐γ), AST, and LDH levels, as well as reduced lymphocyte count, platelet count, lymphocyte%, and albumin levels (Table 3).\\nBaseline laboratory parameters in patients with COVID‐19\\nData are presented as medians (interquartile ranges).\\nAbbreviations: COVID‐19, coronavirus disease 2019; WBC, white blood cell; RBC, red blood cell; PT, prothrombin time; aPTT, activated partial thromboplastin time; CRP, C‐reactive protein; IL‐2, interleukin‐2; IL‐4, interleukin‐4; IL‐6, interleukin‐6; IL‐10, interleukin‐10; IFN‐γ, interferon‐γ; TNF‐α, tumor necrosis factor‐α; TBil, total bilirubin; DBil, direct bilirubin; AST, aspartate aminotransferase; ALT, alanine aminotransferase; LDH, Lactic dehydrogenase; BUN, blood urea nitrogen; BUA, blood uric acid; Scr, serum creatinine.\\nWe first compared the general laboratory parameters between healthy controls and COVID‐19 patients. Neutrophil%, C‐reactive protein (CRP), aspartate aminotransferase (AST), and lactic dehydrogenase (LDH) were elevated, while white blood cell (WBC) count, lymphocyte%, lymphocyte count, platelet count, red blood cell (RBC) count, hemoglobin, albumin, total bilirubin (TBil), blood urea nitrogen (BUN), and blood uric acid (BUA) were decreased in patients with COVID‐19 compared with healthy controls (Table 2).\\nComparisons of demographic and laboratory variables between healthy controls and COVID‐19 patients\\nData are presented as mean ± standard deviation, n (%), or medians (interquartile ranges).\\nAbbreviations: ALT, alanine aminotransferase; AST, aspartate aminotransferase; BUA, blood uric acid; BUN, blood urea nitrogen; COVID‐19, coronavirus disease 2019; CRP, C‐reactive protein; DBil, direct bilirubin; LDH, Lactic dehydrogenase; RBC, red blood cell; Scr, serum creatinine; TBil, total bilirubin; WBC, white blood cell.\\nWe next compared general laboratory parameters, coagulation tests, and cytokine levels between severe and non‐severe COVID‐19 patients. Compared with those in the non‐severe group, patients in the severe group exhibited increased neutrophil%, fibrinogen, activated partial thromboplastin time (aPTT), CRP, interleukin‐10 (IL‐10), interleukin‐6 (IL‐6), interferon‐γ (INF‐γ), AST, and LDH levels, as well as reduced lymphocyte count, platelet count, lymphocyte%, and albumin levels (Table 3).\\nBaseline laboratory parameters in patients with COVID‐19\\nData are presented as medians (interquartile ranges).\\nAbbreviations: COVID‐19, coronavirus disease 2019; WBC, white blood cell; RBC, red blood cell; PT, prothrombin time; aPTT, activated partial thromboplastin time; CRP, C‐reactive protein; IL‐2, interleukin‐2; IL‐4, interleukin‐4; IL‐6, interleukin‐6; IL‐10, interleukin‐10; IFN‐γ, interferon‐γ; TNF‐α, tumor necrosis factor‐α; TBil, total bilirubin; DBil, direct bilirubin; AST, aspartate aminotransferase; ALT, alanine aminotransferase; LDH, Lactic dehydrogenase; BUN, blood urea nitrogen; BUA, blood uric acid; Scr, serum creatinine.\\nBaseline blood lipids Baseline blood lipids were obtained within 5 days of admission. TC, HDL‐C, LDL‐C, and ApoA1 gradually decreased from the healthy controls to the non‐severe group and the severe group. TG was higher in the non‐severe group than in the healthy controls; however, no significant differences were found between the severe and non‐severe groups or between the severe group and the healthy controls. There were no significant differences in ApoB or lipoprotein (a) among the three groups (Figure 1).\\nComparisons of blood lipids among the healthy controls, non‐severe group, and severe group. Data are presented as medians (interquartile ranges). (A) The total cholesterol levels in the healthy controls, non‐severe group, and severe group were 4.97 (4.55–5.79), 4.08 (3.69–4.63), and 3.58 (3.06–3.85) mmol/L, respectively. (B) The triglyceride levels in the healthy controls, non‐severe group, and severe group were 1.04 (0.85–1.43), 1.44 (0.98–2.00), and 1.22 (0.83–2.29) mmol/L, respectively. (C) The HDL‐C levels in the healthy controls, non‐severe group, and severe group were 1.62 (1.35–1.92), 1.09 (0.91–1.29), and 0.93 (0.82–1.00) mmol/L, respectively. (D) The LDL‐C levels in the healthy controls, non‐severe group, and severe group were 2.77 (2.40–3.42), 2.54 (2.18–2.85), and 2.21 (1.93–2.49) mmol/L, respectively. (E) The ApoA1 levels in the healthy controls, non‐severe group, and severe group were 1.45 (1.31–1.65), 1.22 (1.12–1.34), and 0.98 (0.89–1.08) g/L, respectively. (F) The ApoB levels in the healthy controls, non‐severe group, and severe group were 0.86 (0.72–1.01), 0.81 (0.71–0.94), and 0.76 (0.66–0.86) g/L, respectively. (G) The lipoprotein (a) levels in the healthy controls, non‐severe group, and severe group were 114.80 (62.20–202.90), 87.15 (47.75–161.15), 94.45 (36.55–135.40) mg/L, respectively. The Kruskal‐Wallis test was used to compare differences among the three groups, and post hoc pairwise comparisons were performed using the Nemenyi test. HDL‐C, high‐density lipoprotein cholesterol; LDL‐C, low‐density lipoprotein cholesterol; ApoA1, apolipoprotein A1; ApoB, apolipoprotein B\\nBaseline blood lipids were obtained within 5 days of admission. TC, HDL‐C, LDL‐C, and ApoA1 gradually decreased from the healthy controls to the non‐severe group and the severe group. TG was higher in the non‐severe group than in the healthy controls; however, no significant differences were found between the severe and non‐severe groups or between the severe group and the healthy controls. There were no significant differences in ApoB or lipoprotein (a) among the three groups (Figure 1).\\nComparisons of blood lipids among the healthy controls, non‐severe group, and severe group. Data are presented as medians (interquartile ranges). (A) The total cholesterol levels in the healthy controls, non‐severe group, and severe group were 4.97 (4.55–5.79), 4.08 (3.69–4.63), and 3.58 (3.06–3.85) mmol/L, respectively. (B) The triglyceride levels in the healthy controls, non‐severe group, and severe group were 1.04 (0.85–1.43), 1.44 (0.98–2.00), and 1.22 (0.83–2.29) mmol/L, respectively. (C) The HDL‐C levels in the healthy controls, non‐severe group, and severe group were 1.62 (1.35–1.92), 1.09 (0.91–1.29), and 0.93 (0.82–1.00) mmol/L, respectively. (D) The LDL‐C levels in the healthy controls, non‐severe group, and severe group were 2.77 (2.40–3.42), 2.54 (2.18–2.85), and 2.21 (1.93–2.49) mmol/L, respectively. (E) The ApoA1 levels in the healthy controls, non‐severe group, and severe group were 1.45 (1.31–1.65), 1.22 (1.12–1.34), and 0.98 (0.89–1.08) g/L, respectively. (F) The ApoB levels in the healthy controls, non‐severe group, and severe group were 0.86 (0.72–1.01), 0.81 (0.71–0.94), and 0.76 (0.66–0.86) g/L, respectively. (G) The lipoprotein (a) levels in the healthy controls, non‐severe group, and severe group were 114.80 (62.20–202.90), 87.15 (47.75–161.15), 94.45 (36.55–135.40) mg/L, respectively. The Kruskal‐Wallis test was used to compare differences among the three groups, and post hoc pairwise comparisons were performed using the Nemenyi test. HDL‐C, high‐density lipoprotein cholesterol; LDL‐C, low‐density lipoprotein cholesterol; ApoA1, apolipoprotein A1; ApoB, apolipoprotein B\\nRisk factors for COVID‐19 severity Potential risk factors, including several general clinical characteristics, immunoinflammatory markers, and blood lipids, were first identified by univariate logistic analysis. Age, BMI, hypertension, neutrophil%, lymphocyte%, lymphocyte count, platelet count, fibrinogen, CRP, IL‐6, IL‐10, HDL‐C, ApoA1, ALB, AST, and LDH were associated with the severity of COVID‐19 (p < 0.05). However, aPTT, interferon‐γ (IFN‐γ), TC, and LDL‐C were unrelated to COVID‐19 severity (p>0.05) (Figure 2). Next, variables with p < 0.1 in the univariate logistic analysis were entered into the multivariate logistic analysis. However, after adjusting for other potential risk factors, only IL‐6 (adjusted odds ratio [OR]: 1.097, 95% confidence interval [CI]: 1.034–1.165, p = 0.002) and ApoA1 (adjusted OR: 0.865, 95% CI: 0.800–0.935, p < 0.001) were identified as independent risk factors by multivariate logistic analysis (Figure 3). Therefore, a risk model was built using the combination of ApoA1 and IL‐6. The mathematical formula of the risk model was log (P)=12.303+0.093*IL‐6–0.145*ApoA1.\\nUnivariate logistic regression of risk factors for COVID‐19 severity. Old age, high BMI, hypertension, increased neutrophil%, fibrinogen, C‐reactive protein, interleukin‐6, interleukin‐10, aspartate aminotransferase, and lactic dehydrogenase and decreased lymphocyte%, lymphocyte count, platelet count, HDL‐C, apolipoprotein A1, and albumin were associated with the severity of COVID‐19 in univariate logistic regression analysis (all p < 0.05). COVID‐19: coronavirus disease 2019, OR, odds ratio; CI, confidence interval; HDL‐C, high‐density lipoprotein cholesterol; LDL‐C, low‐density lipoprotein cholesterol\\nMultivariate logistic regression of independent risk factors for COVID‐19 severity. After adjusting for other potential risk factors, increased IL‐6 (OR: 1.097, 95% CI: 1.034–1.165, p = 0.002) and decreased ApoA1 (OR: 0.865, 95% CI: 0.800–0.935, p < 0.001) were recognized as independent risk factors for COVID‐19 severity. COVID‐19: coronavirus disease 2019, OR: odds ratio, CI: confidence interval\\nIn the prediction of COVID‐19 severity, the AUCs (95% CI) of TC, HDL‐C, LDL‐C, ApoA1, IL‐6, and the risk model were 0.726 (0.645–0.798), 0.674 (0.590–0.750), 0.669 (0.585–0.746), 0.896 (0.834–0.941), 0.855 (0.786–0.908), and 0.977 (0.932–0.995), respectively (Figure 4, Table 4). In particular, the sensitivity and specificity of Apo A1 were 94.12% (95% CI: 71.20%–99.00%) and 80.80% (95% CI: 72.80%–87.30%), respectively, which were both the highest among the above single markers. Moreover, the risk model increased the levels of sensitivity and specificity to 100.00% (95% CI: 80.30%–100.00%) and 89.89% (95% CI: 81.40%–94.10%), respectively.\\nReceiver operator characteristic curves of total cholesterol, HDL‐C, LDL‐C, ApoA1, IL‐6, and risk model for the severity of COVID‐19. COVID‐19: coronavirus disease 2019, HDL‐C: high‐density lipoprotein cholesterol, LDL‐C: low‐density lipoprotein cholesterol, ApoA1: apolipoprotein A1, IL‐6: interleukin‐6\\nPredictive performance of blood lipids, interleukin‐6, and the risk model for COVID‐19 severity\\nAbbreviations: COVID‐19, coronavirus disease 2019; HDL‐C, high‐density lipoprotein cholesterol; LDL‐C, low‐density lipoprotein cholesterol; ApoA1, apolipoprotein A1; IL‐6, Interleukin‐6; CI, confidence interval; PPV, positive predictive value; NPV, negative predictive value.\\nPotential risk factors, including several general clinical characteristics, immunoinflammatory markers, and blood lipids, were first identified by univariate logistic analysis. Age, BMI, hypertension, neutrophil%, lymphocyte%, lymphocyte count, platelet count, fibrinogen, CRP, IL‐6, IL‐10, HDL‐C, ApoA1, ALB, AST, and LDH were associated with the severity of COVID‐19 (p < 0.05). However, aPTT, interferon‐γ (IFN‐γ), TC, and LDL‐C were unrelated to COVID‐19 severity (p>0.05) (Figure 2). Next, variables with p < 0.1 in the univariate logistic analysis were entered into the multivariate logistic analysis. However, after adjusting for other potential risk factors, only IL‐6 (adjusted odds ratio [OR]: 1.097, 95% confidence interval [CI]: 1.034–1.165, p = 0.002) and ApoA1 (adjusted OR: 0.865, 95% CI: 0.800–0.935, p < 0.001) were identified as independent risk factors by multivariate logistic analysis (Figure 3). Therefore, a risk model was built using the combination of ApoA1 and IL‐6. The mathematical formula of the risk model was log (P)=12.303+0.093*IL‐6–0.145*ApoA1.\\nUnivariate logistic regression of risk factors for COVID‐19 severity. Old age, high BMI, hypertension, increased neutrophil%, fibrinogen, C‐reactive protein, interleukin‐6, interleukin‐10, aspartate aminotransferase, and lactic dehydrogenase and decreased lymphocyte%, lymphocyte count, platelet count, HDL‐C, apolipoprotein A1, and albumin were associated with the severity of COVID‐19 in univariate logistic regression analysis (all p < 0.05). COVID‐19: coronavirus disease 2019, OR, odds ratio; CI, confidence interval; HDL‐C, high‐density lipoprotein cholesterol; LDL‐C, low‐density lipoprotein cholesterol\\nMultivariate logistic regression of independent risk factors for COVID‐19 severity. After adjusting for other potential risk factors, increased IL‐6 (OR: 1.097, 95% CI: 1.034–1.165, p = 0.002) and decreased ApoA1 (OR: 0.865, 95% CI: 0.800–0.935, p < 0.001) were recognized as independent risk factors for COVID‐19 severity. COVID‐19: coronavirus disease 2019, OR: odds ratio, CI: confidence interval\\nIn the prediction of COVID‐19 severity, the AUCs (95% CI) of TC, HDL‐C, LDL‐C, ApoA1, IL‐6, and the risk model were 0.726 (0.645–0.798), 0.674 (0.590–0.750), 0.669 (0.585–0.746), 0.896 (0.834–0.941), 0.855 (0.786–0.908), and 0.977 (0.932–0.995), respectively (Figure 4, Table 4). In particular, the sensitivity and specificity of Apo A1 were 94.12% (95% CI: 71.20%–99.00%) and 80.80% (95% CI: 72.80%–87.30%), respectively, which were both the highest among the above single markers. Moreover, the risk model increased the levels of sensitivity and specificity to 100.00% (95% CI: 80.30%–100.00%) and 89.89% (95% CI: 81.40%–94.10%), respectively.\\nReceiver operator characteristic curves of total cholesterol, HDL‐C, LDL‐C, ApoA1, IL‐6, and risk model for the severity of COVID‐19. COVID‐19: coronavirus disease 2019, HDL‐C: high‐density lipoprotein cholesterol, LDL‐C: low‐density lipoprotein cholesterol, ApoA1: apolipoprotein A1, IL‐6: interleukin‐6\\nPredictive performance of blood lipids, interleukin‐6, and the risk model for COVID‐19 severity\\nAbbreviations: COVID‐19, coronavirus disease 2019; HDL‐C, high‐density lipoprotein cholesterol; LDL‐C, low‐density lipoprotein cholesterol; ApoA1, apolipoprotein A1; IL‐6, Interleukin‐6; CI, confidence interval; PPV, positive predictive value; NPV, negative predictive value.\",\n \"In total, 142 consecutive patients with confirmed COVID‐19 were included in this study. The mean age was 49.10 ± 16.36 years, and 38.73% of the patients were male. Hypertension (37, 26.06%), diabetes (12, 8.45%), hepatic disease (10, 7.04%), and chronic lung disease (9, 6.34%) were the most common comorbidities. Fever (84, 59.15%) was the leading initial symptom, followed by cough (61, 42.96%), expectoration (32, 22.54%) and fatigue (27, 19.01%).\\nAmong the 142 patients, 17 (11.97%) and 125 (88.03%) patients were classified into the severe and non‐severe groups during admission, respectively. Significant differences in age, body mass index (BMI), hypertension, hepatic disease, and fever were noted between the severe and non‐severe groups. Regarding clinical treatment, a greater proportion of patients in the severe group received glucocorticoids, antibiotics, oxygen, invasive mechanical ventilation, and intensive care unit treatment (Table 1).\\nGeneral clinical characteristics of patients with COVID‐19\\nData are presented as mean ± standard deviation or n (%). p‐value indicates the comparison between the non‐severe group and severe group.\\nAbbreviations: COVID‐19, coronavirus disease 2019; ECMO, extracorporeal membrane oxygenation.\",\n \"We first compared the general laboratory parameters between healthy controls and COVID‐19 patients. Neutrophil%, C‐reactive protein (CRP), aspartate aminotransferase (AST), and lactic dehydrogenase (LDH) were elevated, while white blood cell (WBC) count, lymphocyte%, lymphocyte count, platelet count, red blood cell (RBC) count, hemoglobin, albumin, total bilirubin (TBil), blood urea nitrogen (BUN), and blood uric acid (BUA) were decreased in patients with COVID‐19 compared with healthy controls (Table 2).\\nComparisons of demographic and laboratory variables between healthy controls and COVID‐19 patients\\nData are presented as mean ± standard deviation, n (%), or medians (interquartile ranges).\\nAbbreviations: ALT, alanine aminotransferase; AST, aspartate aminotransferase; BUA, blood uric acid; BUN, blood urea nitrogen; COVID‐19, coronavirus disease 2019; CRP, C‐reactive protein; DBil, direct bilirubin; LDH, Lactic dehydrogenase; RBC, red blood cell; Scr, serum creatinine; TBil, total bilirubin; WBC, white blood cell.\\nWe next compared general laboratory parameters, coagulation tests, and cytokine levels between severe and non‐severe COVID‐19 patients. Compared with those in the non‐severe group, patients in the severe group exhibited increased neutrophil%, fibrinogen, activated partial thromboplastin time (aPTT), CRP, interleukin‐10 (IL‐10), interleukin‐6 (IL‐6), interferon‐γ (INF‐γ), AST, and LDH levels, as well as reduced lymphocyte count, platelet count, lymphocyte%, and albumin levels (Table 3).\\nBaseline laboratory parameters in patients with COVID‐19\\nData are presented as medians (interquartile ranges).\\nAbbreviations: COVID‐19, coronavirus disease 2019; WBC, white blood cell; RBC, red blood cell; PT, prothrombin time; aPTT, activated partial thromboplastin time; CRP, C‐reactive protein; IL‐2, interleukin‐2; IL‐4, interleukin‐4; IL‐6, interleukin‐6; IL‐10, interleukin‐10; IFN‐γ, interferon‐γ; TNF‐α, tumor necrosis factor‐α; TBil, total bilirubin; DBil, direct bilirubin; AST, aspartate aminotransferase; ALT, alanine aminotransferase; LDH, Lactic dehydrogenase; BUN, blood urea nitrogen; BUA, blood uric acid; Scr, serum creatinine.\",\n \"Baseline blood lipids were obtained within 5 days of admission. TC, HDL‐C, LDL‐C, and ApoA1 gradually decreased from the healthy controls to the non‐severe group and the severe group. TG was higher in the non‐severe group than in the healthy controls; however, no significant differences were found between the severe and non‐severe groups or between the severe group and the healthy controls. There were no significant differences in ApoB or lipoprotein (a) among the three groups (Figure 1).\\nComparisons of blood lipids among the healthy controls, non‐severe group, and severe group. Data are presented as medians (interquartile ranges). (A) The total cholesterol levels in the healthy controls, non‐severe group, and severe group were 4.97 (4.55–5.79), 4.08 (3.69–4.63), and 3.58 (3.06–3.85) mmol/L, respectively. (B) The triglyceride levels in the healthy controls, non‐severe group, and severe group were 1.04 (0.85–1.43), 1.44 (0.98–2.00), and 1.22 (0.83–2.29) mmol/L, respectively. (C) The HDL‐C levels in the healthy controls, non‐severe group, and severe group were 1.62 (1.35–1.92), 1.09 (0.91–1.29), and 0.93 (0.82–1.00) mmol/L, respectively. (D) The LDL‐C levels in the healthy controls, non‐severe group, and severe group were 2.77 (2.40–3.42), 2.54 (2.18–2.85), and 2.21 (1.93–2.49) mmol/L, respectively. (E) The ApoA1 levels in the healthy controls, non‐severe group, and severe group were 1.45 (1.31–1.65), 1.22 (1.12–1.34), and 0.98 (0.89–1.08) g/L, respectively. (F) The ApoB levels in the healthy controls, non‐severe group, and severe group were 0.86 (0.72–1.01), 0.81 (0.71–0.94), and 0.76 (0.66–0.86) g/L, respectively. (G) The lipoprotein (a) levels in the healthy controls, non‐severe group, and severe group were 114.80 (62.20–202.90), 87.15 (47.75–161.15), 94.45 (36.55–135.40) mg/L, respectively. The Kruskal‐Wallis test was used to compare differences among the three groups, and post hoc pairwise comparisons were performed using the Nemenyi test. HDL‐C, high‐density lipoprotein cholesterol; LDL‐C, low‐density lipoprotein cholesterol; ApoA1, apolipoprotein A1; ApoB, apolipoprotein B\",\n \"Potential risk factors, including several general clinical characteristics, immunoinflammatory markers, and blood lipids, were first identified by univariate logistic analysis. Age, BMI, hypertension, neutrophil%, lymphocyte%, lymphocyte count, platelet count, fibrinogen, CRP, IL‐6, IL‐10, HDL‐C, ApoA1, ALB, AST, and LDH were associated with the severity of COVID‐19 (p < 0.05). However, aPTT, interferon‐γ (IFN‐γ), TC, and LDL‐C were unrelated to COVID‐19 severity (p>0.05) (Figure 2). Next, variables with p < 0.1 in the univariate logistic analysis were entered into the multivariate logistic analysis. However, after adjusting for other potential risk factors, only IL‐6 (adjusted odds ratio [OR]: 1.097, 95% confidence interval [CI]: 1.034–1.165, p = 0.002) and ApoA1 (adjusted OR: 0.865, 95% CI: 0.800–0.935, p < 0.001) were identified as independent risk factors by multivariate logistic analysis (Figure 3). Therefore, a risk model was built using the combination of ApoA1 and IL‐6. The mathematical formula of the risk model was log (P)=12.303+0.093*IL‐6–0.145*ApoA1.\\nUnivariate logistic regression of risk factors for COVID‐19 severity. Old age, high BMI, hypertension, increased neutrophil%, fibrinogen, C‐reactive protein, interleukin‐6, interleukin‐10, aspartate aminotransferase, and lactic dehydrogenase and decreased lymphocyte%, lymphocyte count, platelet count, HDL‐C, apolipoprotein A1, and albumin were associated with the severity of COVID‐19 in univariate logistic regression analysis (all p < 0.05). COVID‐19: coronavirus disease 2019, OR, odds ratio; CI, confidence interval; HDL‐C, high‐density lipoprotein cholesterol; LDL‐C, low‐density lipoprotein cholesterol\\nMultivariate logistic regression of independent risk factors for COVID‐19 severity. After adjusting for other potential risk factors, increased IL‐6 (OR: 1.097, 95% CI: 1.034–1.165, p = 0.002) and decreased ApoA1 (OR: 0.865, 95% CI: 0.800–0.935, p < 0.001) were recognized as independent risk factors for COVID‐19 severity. COVID‐19: coronavirus disease 2019, OR: odds ratio, CI: confidence interval\\nIn the prediction of COVID‐19 severity, the AUCs (95% CI) of TC, HDL‐C, LDL‐C, ApoA1, IL‐6, and the risk model were 0.726 (0.645–0.798), 0.674 (0.590–0.750), 0.669 (0.585–0.746), 0.896 (0.834–0.941), 0.855 (0.786–0.908), and 0.977 (0.932–0.995), respectively (Figure 4, Table 4). In particular, the sensitivity and specificity of Apo A1 were 94.12% (95% CI: 71.20%–99.00%) and 80.80% (95% CI: 72.80%–87.30%), respectively, which were both the highest among the above single markers. Moreover, the risk model increased the levels of sensitivity and specificity to 100.00% (95% CI: 80.30%–100.00%) and 89.89% (95% CI: 81.40%–94.10%), respectively.\\nReceiver operator characteristic curves of total cholesterol, HDL‐C, LDL‐C, ApoA1, IL‐6, and risk model for the severity of COVID‐19. COVID‐19: coronavirus disease 2019, HDL‐C: high‐density lipoprotein cholesterol, LDL‐C: low‐density lipoprotein cholesterol, ApoA1: apolipoprotein A1, IL‐6: interleukin‐6\\nPredictive performance of blood lipids, interleukin‐6, and the risk model for COVID‐19 severity\\nAbbreviations: COVID‐19, coronavirus disease 2019; HDL‐C, high‐density lipoprotein cholesterol; LDL‐C, low‐density lipoprotein cholesterol; ApoA1, apolipoprotein A1; IL‐6, Interleukin‐6; CI, confidence interval; PPV, positive predictive value; NPV, negative predictive value.\",\n \"In this study, baseline TC, HDL‐C, LDL‐C, and ApoA1 gradually decreased across the groups in the following order: healthy controls, non‐severe group, and severe group, indicating that they had potential roles in predicting the severity of COVID‐19. Other blood lipids, including TG, ApoB, and lipoprotein (a), had little value in distinguishing COVID‐19 severity. Additionally, we found that ApoA1 was most obviously decreased among the altered lipids in this study, representing an independent risk factor for COVID‐19 severity. The combination of ApoA1 and IL‐6 yielded even higher prediction efficiency.\\nIn a recent study, Wei et al.11 observed that serum levels of TC, HDL‐C, and LDL‐C in patients with COVID‐19 were significantly lower than those in healthy subjects, especially in severe and critical cases, which was similar to our study. Another study also found that LDL‐C performed well in discriminating COVID‐19 severity, it was gradually decreased across moderate, severe, and critical cases; however, HDL‐C exhibited no significant differences among the three groups.12 These small discrepancies may be related to the heterogeneity of disease severity, different sample sizes, and testing methods. However, our study and others11, 12 all showed that blood lipids have potential auxiliary value in distinguishing severe COVID‐19 patients.\\nApoA1, a major protein component of the HDL complex, is involved in “reverse cholesterol transport” by transporting excess cholesterol from peripheral cells back to the liver for excretion. In addition, ApoA1 has an anti‐inflammatory characteristic,13 suggesting its role in inflammatory diseases. Previous studies have revealed that serum ApoA1 is associated with the outcome of patients with sepsis and acute respiratory distress syndrome induced by pneumonia, as well as critically ill patients.14, 15, 16, 17 In acute inflammatory disease, serum amyloid A (SAA), an acute‐phase protein, displaces ApoA1 from the HDL complex; then, free ApoA1 is easily eliminated by the kidney, resulting in low levels in the peripheral blood.18 On the other hand, the liver is susceptible to attack by SARS‐CoV‐2, especially in severe cases19; therefore, reduced synthesis by the injured liver may also play a role.\\nIL‐6 plays a key role in the development of COVID‐19, and its predictive value for disease severity has been revealed previously by us and others.4, 20, 21, 22 It was also found that increased IL‐6 was associated with poor outcomes.22, 23 In this study, IL‐6 and ApoA1 were identified as independent risk factors for COVID‐19 severity. The risk model established using these two markers exhibited the highest predictive value, with an AUC of 0.977 (95% CI: 0.932–0.995).\\nApoA1 and its mimetic peptide D‐amino acids (D‐4F) exhibit therapeutic potential for treating cancer, influenza, sepsis, and ARDS, primarily due to their anti‐inflammatory, anti‐oxidant, and anti‐apoptotic properties.13, 24, 25, 26, 27 In addition, it is noteworthy that ApoA1 inhibits IL‐6 release and reduces macrophage activation.25 IL‐6 is the main participant in the cytokine storm, and macrophages are the primary source of IL‐6. Therefore, ApoA1 may exhibit therapeutic potential in treating patients with COVID‐19. It might be worthwhile to test the efficacy and safety of ApoA1 and its mimetics in treating these patients.\\nThe main strength of this study is that the patients included in this study were treated without delay when infected with SARS‐CoV‐2, which may represent an early stage of the disease. Second, this study enrolled healthy controls to analyze trends in blood lipids among healthy subjects, non‐severe cases, and severe cases. Third, the predictive values of verified clinical characteristics and laboratory parameters were selected for comparison with blood lipids, making the results more credible. Finally, blood lipids were routinely tested by using an automatic biochemical analyzer, with clinical application value.\\nA limitation of this study is that it was a single‐center retrospective study with a relatively small sample size that was not validated in internal or external cohorts. Therefore, a prospective study with a larger sample size is strongly encouraged.\\nIn conclusion, this study sheds light on abnormal blood lipid profiles in patients with COVID‐19 compared with healthy subjects, especially in severe cases. Specifically, TC, HDL‐C, LDL‐C, and ApoA1 gradually decreased from the healthy controls to non‐severe and severe groups. Additionally, ApoA1 is a good indicator of COVID‐19 severity, and the combination of ApoA1 and IL‐6 enhances model predictability. These findings might be helpful in disclosing the pathogenesis of COVID‐19 and developing novel therapeutic strategies for COVID‐19.\",\n \"The authors declare that they have no competing interests.\",\n \"Zhe Zhu interpreted the data and wrote the paper. Yayun Yang, Linyan Fan, Shuyuan Ye, and Kehong Lou collected and analyzed the data. Xin Hua, Zuoan Huang, and Qiaoyun Shi performed laboratory analysis. Guosheng Gao designed the study and revised the paper.\",\n \"This study was approved by the institutional ethics board of HwaMei Hospital, University of Chinese Academy of Science (PJ‐NBEY‐KY‐2020–061–01).\"\n]"},"all_sections_sec_types":{"kind":"list like","value":[null,"materials-and-methods",null,null,null,"results",null,null,null,null,"discussion","COI-statement",null,null],"string":"[\n null,\n \"materials-and-methods\",\n null,\n null,\n null,\n \"results\",\n null,\n null,\n null,\n null,\n \"discussion\",\n \"COI-statement\",\n null,\n null\n]"},"keywords":{"kind":"list like","value":["ApoA1","blood lipid","COVID‐19","disease severity","SARS‐CoV‐2"],"string":"[\n \"ApoA1\",\n \"blood lipid\",\n \"COVID‐19\",\n \"disease severity\",\n \"SARS‐CoV‐2\"\n]"},"whole_article_text":{"kind":"string","value":"INTRODUCTION:\nSevere acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) is a highly transmissible coronavirus that has caused an ever‐increasing number of coronavirus disease 2019 (COVID‐19) infections since December 2019 and spread rapidly worldwide. Although approximately 80% of patients infected with SARS‐CoV‐2 exhibit mild symptoms,1 the remaining severe cases may experience acute respiratory distress syndrome (ARDS), multiorgan failure (MOF), and death.2 Therefore, it is necessary to discriminate between severe and mild cases.\nPrevious studies have found that the development of severe COVID‐19 is associated with age and underlying diseases, and patients who develop severe disease are likely to suffer from aberrant inflammatory reactions and cytokine storms.1, 3 Consequently, several clinical characteristics, the inflammation index, and cytokine levels have been used as indicators for predicting the severity of COVID‐19 by us and others.4, 5 Emerging evidence suggests that lipid metabolism dysregulation might promote the progression of COVID‐19, as revealed by mass spectrometry (MS)‐based proteomics analysis.6, 7 Although MS analysis is not commonly performed, blood lipids are routinely examined using automatic biochemical instruments in clinical laboratories. Additionally, dyslipidemia has also been observed in other respiratory infectious diseases.8, 9, 10 Therefore, blood lipids may be considered potential and available indicators of COVID‐19 severity.\nIn a former study, serum hypolipidemia was identified in patients with COVID‐19.11 However, that study did not analyze other blood lipid components, such as apolipoprotein A1 (ApoA1), ApoB, and lipoprotein (a), and their predictive values for COVID‐19 severity are not fully understood. Therefore, to more comprehensively investigate blood lipid profiles in patients with COVID‐19 and determine their predictive value for disease severity, a retrospective study was performed.\n\nMATERIAL AND METHODS:\nStudy design and participants selection This was a single‐center retrospective study approved by the institutional ethics board (PJ‐NBEY‐KY‐2020–061–01). A total of 142 consecutive patients with COVID‐19 were included from January 23 to April 20, 2020. In addition, 77 age‐ and gender‐matched healthy subjects were selected for evaluating the characteristics of blood lipid profiles in patients with COVID‐19.\nThe diagnosis of COVID‐19 and its severity were determined according to the National Diagnosis and Treatment Protocol for Novel Coronavirus Infection‐Induced Pneumonia (6th Trial Version). Patients with confirmed COVID‐19 were diagnosed based on a positive SARS‐CoV‐2 nucleic acid RT‐PCR result using specimens derived from sputum, throat swabs, or nasopharyngeal swabs. Severe patients exhibited one of the following features: (a) respiratory distress with respiration rate (RR) greater than 30 breaths per minute; (b) blood oxygen saturation less than 93% at a state of rest; (c) arterial blood oxygen partial pressure/inhaled oxygen concentration less than 300 mmHg (1 mmHg = 0.133 kPa); or (d) lesion rapidly progressed by more than 50% within one or two days as determined by pulmonary imaging.\nGeneral clinical characteristics, including gender, age, comorbidities, initial symptoms, treatment, and laboratory test data, were collected from the electronic medical records (EMRs).\nThis was a single‐center retrospective study approved by the institutional ethics board (PJ‐NBEY‐KY‐2020–061–01). A total of 142 consecutive patients with COVID‐19 were included from January 23 to April 20, 2020. In addition, 77 age‐ and gender‐matched healthy subjects were selected for evaluating the characteristics of blood lipid profiles in patients with COVID‐19.\nThe diagnosis of COVID‐19 and its severity were determined according to the National Diagnosis and Treatment Protocol for Novel Coronavirus Infection‐Induced Pneumonia (6th Trial Version). Patients with confirmed COVID‐19 were diagnosed based on a positive SARS‐CoV‐2 nucleic acid RT‐PCR result using specimens derived from sputum, throat swabs, or nasopharyngeal swabs. Severe patients exhibited one of the following features: (a) respiratory distress with respiration rate (RR) greater than 30 breaths per minute; (b) blood oxygen saturation less than 93% at a state of rest; (c) arterial blood oxygen partial pressure/inhaled oxygen concentration less than 300 mmHg (1 mmHg = 0.133 kPa); or (d) lesion rapidly progressed by more than 50% within one or two days as determined by pulmonary imaging.\nGeneral clinical characteristics, including gender, age, comorbidities, initial symptoms, treatment, and laboratory test data, were collected from the electronic medical records (EMRs).\nDetermination of blood lipids Blood lipids were assessed using a fully automatic biochemical analyzer (ADVIA2400, Siemens) according to the manufacturer's instructions (Purebio Biotechnology Co., Ltd). Briefly, total cholesterol (TC) was measured using the cholesterol oxidase‐p‐aminophenazone (CHOD‐PAP) method; triglyceride (TG) was assessed using the glycerol phosphate oxidase‐p‐aminophenazone (GPO‐PAP) method; high‐density lipoprotein cholesterol (HDL‐C) was assessed using the direct‐hydrogen peroxide method; low‐density lipoprotein cholesterol (LDL‐C) was assessed using the direct‐surfactant removal method; and ApoA1, ApoB, and lipoprotein (a) were assessed using the immunoturbidimetric method.\nBlood lipids were assessed using a fully automatic biochemical analyzer (ADVIA2400, Siemens) according to the manufacturer's instructions (Purebio Biotechnology Co., Ltd). Briefly, total cholesterol (TC) was measured using the cholesterol oxidase‐p‐aminophenazone (CHOD‐PAP) method; triglyceride (TG) was assessed using the glycerol phosphate oxidase‐p‐aminophenazone (GPO‐PAP) method; high‐density lipoprotein cholesterol (HDL‐C) was assessed using the direct‐hydrogen peroxide method; low‐density lipoprotein cholesterol (LDL‐C) was assessed using the direct‐surfactant removal method; and ApoA1, ApoB, and lipoprotein (a) were assessed using the immunoturbidimetric method.\nStatistical analysis SPSS software, version 16.0 (IBM) was used for statistical analysis. Normally and non‐normally distributed continuous data were expressed as the mean ± SD (standard deviation) and median (interquartile range [IQR]), respectively. Categorical variables were reported as numbers (%). The Kruskal‐Wallis test was used to compare blood lipids among the severe group, non‐severe group, and healthy subjects, and post hoc pairwise comparisons were performed using the Nemenyi test. Differences between the two groups were assessed using Student's t‐test and Mann‐Whitney U test for normally and non‐normally distributed continuous data, respectively, and chi‐square or Fisher's exact tests were used for categorical variables. Multivariate logistic regression analysis was adopted to explore independent risk factors for COVID‐19 severity, receiver operator characteristic (ROC) curves were generated, and the areas under ROC curves (AUCs) were calculated to evaluate prediction efficiency. A p‐value <0.05 indicates statistical significance.\nSPSS software, version 16.0 (IBM) was used for statistical analysis. Normally and non‐normally distributed continuous data were expressed as the mean ± SD (standard deviation) and median (interquartile range [IQR]), respectively. Categorical variables were reported as numbers (%). The Kruskal‐Wallis test was used to compare blood lipids among the severe group, non‐severe group, and healthy subjects, and post hoc pairwise comparisons were performed using the Nemenyi test. Differences between the two groups were assessed using Student's t‐test and Mann‐Whitney U test for normally and non‐normally distributed continuous data, respectively, and chi‐square or Fisher's exact tests were used for categorical variables. Multivariate logistic regression analysis was adopted to explore independent risk factors for COVID‐19 severity, receiver operator characteristic (ROC) curves were generated, and the areas under ROC curves (AUCs) were calculated to evaluate prediction efficiency. A p‐value <0.05 indicates statistical significance.\n\nStudy design and participants selection:\nThis was a single‐center retrospective study approved by the institutional ethics board (PJ‐NBEY‐KY‐2020–061–01). A total of 142 consecutive patients with COVID‐19 were included from January 23 to April 20, 2020. In addition, 77 age‐ and gender‐matched healthy subjects were selected for evaluating the characteristics of blood lipid profiles in patients with COVID‐19.\nThe diagnosis of COVID‐19 and its severity were determined according to the National Diagnosis and Treatment Protocol for Novel Coronavirus Infection‐Induced Pneumonia (6th Trial Version). Patients with confirmed COVID‐19 were diagnosed based on a positive SARS‐CoV‐2 nucleic acid RT‐PCR result using specimens derived from sputum, throat swabs, or nasopharyngeal swabs. Severe patients exhibited one of the following features: (a) respiratory distress with respiration rate (RR) greater than 30 breaths per minute; (b) blood oxygen saturation less than 93% at a state of rest; (c) arterial blood oxygen partial pressure/inhaled oxygen concentration less than 300 mmHg (1 mmHg = 0.133 kPa); or (d) lesion rapidly progressed by more than 50% within one or two days as determined by pulmonary imaging.\nGeneral clinical characteristics, including gender, age, comorbidities, initial symptoms, treatment, and laboratory test data, were collected from the electronic medical records (EMRs).\n\nDetermination of blood lipids:\nBlood lipids were assessed using a fully automatic biochemical analyzer (ADVIA2400, Siemens) according to the manufacturer's instructions (Purebio Biotechnology Co., Ltd). Briefly, total cholesterol (TC) was measured using the cholesterol oxidase‐p‐aminophenazone (CHOD‐PAP) method; triglyceride (TG) was assessed using the glycerol phosphate oxidase‐p‐aminophenazone (GPO‐PAP) method; high‐density lipoprotein cholesterol (HDL‐C) was assessed using the direct‐hydrogen peroxide method; low‐density lipoprotein cholesterol (LDL‐C) was assessed using the direct‐surfactant removal method; and ApoA1, ApoB, and lipoprotein (a) were assessed using the immunoturbidimetric method.\n\nStatistical analysis:\nSPSS software, version 16.0 (IBM) was used for statistical analysis. Normally and non‐normally distributed continuous data were expressed as the mean ± SD (standard deviation) and median (interquartile range [IQR]), respectively. Categorical variables were reported as numbers (%). The Kruskal‐Wallis test was used to compare blood lipids among the severe group, non‐severe group, and healthy subjects, and post hoc pairwise comparisons were performed using the Nemenyi test. Differences between the two groups were assessed using Student's t‐test and Mann‐Whitney U test for normally and non‐normally distributed continuous data, respectively, and chi‐square or Fisher's exact tests were used for categorical variables. Multivariate logistic regression analysis was adopted to explore independent risk factors for COVID‐19 severity, receiver operator characteristic (ROC) curves were generated, and the areas under ROC curves (AUCs) were calculated to evaluate prediction efficiency. A p‐value <0.05 indicates statistical significance.\n\nRESULTS:\nGeneral clinical characteristics In total, 142 consecutive patients with confirmed COVID‐19 were included in this study. The mean age was 49.10 ± 16.36 years, and 38.73% of the patients were male. Hypertension (37, 26.06%), diabetes (12, 8.45%), hepatic disease (10, 7.04%), and chronic lung disease (9, 6.34%) were the most common comorbidities. Fever (84, 59.15%) was the leading initial symptom, followed by cough (61, 42.96%), expectoration (32, 22.54%) and fatigue (27, 19.01%).\nAmong the 142 patients, 17 (11.97%) and 125 (88.03%) patients were classified into the severe and non‐severe groups during admission, respectively. Significant differences in age, body mass index (BMI), hypertension, hepatic disease, and fever were noted between the severe and non‐severe groups. Regarding clinical treatment, a greater proportion of patients in the severe group received glucocorticoids, antibiotics, oxygen, invasive mechanical ventilation, and intensive care unit treatment (Table 1).\nGeneral clinical characteristics of patients with COVID‐19\nData are presented as mean ± standard deviation or n (%). p‐value indicates the comparison between the non‐severe group and severe group.\nAbbreviations: COVID‐19, coronavirus disease 2019; ECMO, extracorporeal membrane oxygenation.\nIn total, 142 consecutive patients with confirmed COVID‐19 were included in this study. The mean age was 49.10 ± 16.36 years, and 38.73% of the patients were male. Hypertension (37, 26.06%), diabetes (12, 8.45%), hepatic disease (10, 7.04%), and chronic lung disease (9, 6.34%) were the most common comorbidities. Fever (84, 59.15%) was the leading initial symptom, followed by cough (61, 42.96%), expectoration (32, 22.54%) and fatigue (27, 19.01%).\nAmong the 142 patients, 17 (11.97%) and 125 (88.03%) patients were classified into the severe and non‐severe groups during admission, respectively. Significant differences in age, body mass index (BMI), hypertension, hepatic disease, and fever were noted between the severe and non‐severe groups. Regarding clinical treatment, a greater proportion of patients in the severe group received glucocorticoids, antibiotics, oxygen, invasive mechanical ventilation, and intensive care unit treatment (Table 1).\nGeneral clinical characteristics of patients with COVID‐19\nData are presented as mean ± standard deviation or n (%). p‐value indicates the comparison between the non‐severe group and severe group.\nAbbreviations: COVID‐19, coronavirus disease 2019; ECMO, extracorporeal membrane oxygenation.\nLaboratory findings We first compared the general laboratory parameters between healthy controls and COVID‐19 patients. Neutrophil%, C‐reactive protein (CRP), aspartate aminotransferase (AST), and lactic dehydrogenase (LDH) were elevated, while white blood cell (WBC) count, lymphocyte%, lymphocyte count, platelet count, red blood cell (RBC) count, hemoglobin, albumin, total bilirubin (TBil), blood urea nitrogen (BUN), and blood uric acid (BUA) were decreased in patients with COVID‐19 compared with healthy controls (Table 2).\nComparisons of demographic and laboratory variables between healthy controls and COVID‐19 patients\nData are presented as mean ± standard deviation, n (%), or medians (interquartile ranges).\nAbbreviations: ALT, alanine aminotransferase; AST, aspartate aminotransferase; BUA, blood uric acid; BUN, blood urea nitrogen; COVID‐19, coronavirus disease 2019; CRP, C‐reactive protein; DBil, direct bilirubin; LDH, Lactic dehydrogenase; RBC, red blood cell; Scr, serum creatinine; TBil, total bilirubin; WBC, white blood cell.\nWe next compared general laboratory parameters, coagulation tests, and cytokine levels between severe and non‐severe COVID‐19 patients. Compared with those in the non‐severe group, patients in the severe group exhibited increased neutrophil%, fibrinogen, activated partial thromboplastin time (aPTT), CRP, interleukin‐10 (IL‐10), interleukin‐6 (IL‐6), interferon‐γ (INF‐γ), AST, and LDH levels, as well as reduced lymphocyte count, platelet count, lymphocyte%, and albumin levels (Table 3).\nBaseline laboratory parameters in patients with COVID‐19\nData are presented as medians (interquartile ranges).\nAbbreviations: COVID‐19, coronavirus disease 2019; WBC, white blood cell; RBC, red blood cell; PT, prothrombin time; aPTT, activated partial thromboplastin time; CRP, C‐reactive protein; IL‐2, interleukin‐2; IL‐4, interleukin‐4; IL‐6, interleukin‐6; IL‐10, interleukin‐10; IFN‐γ, interferon‐γ; TNF‐α, tumor necrosis factor‐α; TBil, total bilirubin; DBil, direct bilirubin; AST, aspartate aminotransferase; ALT, alanine aminotransferase; LDH, Lactic dehydrogenase; BUN, blood urea nitrogen; BUA, blood uric acid; Scr, serum creatinine.\nWe first compared the general laboratory parameters between healthy controls and COVID‐19 patients. Neutrophil%, C‐reactive protein (CRP), aspartate aminotransferase (AST), and lactic dehydrogenase (LDH) were elevated, while white blood cell (WBC) count, lymphocyte%, lymphocyte count, platelet count, red blood cell (RBC) count, hemoglobin, albumin, total bilirubin (TBil), blood urea nitrogen (BUN), and blood uric acid (BUA) were decreased in patients with COVID‐19 compared with healthy controls (Table 2).\nComparisons of demographic and laboratory variables between healthy controls and COVID‐19 patients\nData are presented as mean ± standard deviation, n (%), or medians (interquartile ranges).\nAbbreviations: ALT, alanine aminotransferase; AST, aspartate aminotransferase; BUA, blood uric acid; BUN, blood urea nitrogen; COVID‐19, coronavirus disease 2019; CRP, C‐reactive protein; DBil, direct bilirubin; LDH, Lactic dehydrogenase; RBC, red blood cell; Scr, serum creatinine; TBil, total bilirubin; WBC, white blood cell.\nWe next compared general laboratory parameters, coagulation tests, and cytokine levels between severe and non‐severe COVID‐19 patients. Compared with those in the non‐severe group, patients in the severe group exhibited increased neutrophil%, fibrinogen, activated partial thromboplastin time (aPTT), CRP, interleukin‐10 (IL‐10), interleukin‐6 (IL‐6), interferon‐γ (INF‐γ), AST, and LDH levels, as well as reduced lymphocyte count, platelet count, lymphocyte%, and albumin levels (Table 3).\nBaseline laboratory parameters in patients with COVID‐19\nData are presented as medians (interquartile ranges).\nAbbreviations: COVID‐19, coronavirus disease 2019; WBC, white blood cell; RBC, red blood cell; PT, prothrombin time; aPTT, activated partial thromboplastin time; CRP, C‐reactive protein; IL‐2, interleukin‐2; IL‐4, interleukin‐4; IL‐6, interleukin‐6; IL‐10, interleukin‐10; IFN‐γ, interferon‐γ; TNF‐α, tumor necrosis factor‐α; TBil, total bilirubin; DBil, direct bilirubin; AST, aspartate aminotransferase; ALT, alanine aminotransferase; LDH, Lactic dehydrogenase; BUN, blood urea nitrogen; BUA, blood uric acid; Scr, serum creatinine.\nBaseline blood lipids Baseline blood lipids were obtained within 5 days of admission. TC, HDL‐C, LDL‐C, and ApoA1 gradually decreased from the healthy controls to the non‐severe group and the severe group. TG was higher in the non‐severe group than in the healthy controls; however, no significant differences were found between the severe and non‐severe groups or between the severe group and the healthy controls. There were no significant differences in ApoB or lipoprotein (a) among the three groups (Figure 1).\nComparisons of blood lipids among the healthy controls, non‐severe group, and severe group. Data are presented as medians (interquartile ranges). (A) The total cholesterol levels in the healthy controls, non‐severe group, and severe group were 4.97 (4.55–5.79), 4.08 (3.69–4.63), and 3.58 (3.06–3.85) mmol/L, respectively. (B) The triglyceride levels in the healthy controls, non‐severe group, and severe group were 1.04 (0.85–1.43), 1.44 (0.98–2.00), and 1.22 (0.83–2.29) mmol/L, respectively. (C) The HDL‐C levels in the healthy controls, non‐severe group, and severe group were 1.62 (1.35–1.92), 1.09 (0.91–1.29), and 0.93 (0.82–1.00) mmol/L, respectively. (D) The LDL‐C levels in the healthy controls, non‐severe group, and severe group were 2.77 (2.40–3.42), 2.54 (2.18–2.85), and 2.21 (1.93–2.49) mmol/L, respectively. (E) The ApoA1 levels in the healthy controls, non‐severe group, and severe group were 1.45 (1.31–1.65), 1.22 (1.12–1.34), and 0.98 (0.89–1.08) g/L, respectively. (F) The ApoB levels in the healthy controls, non‐severe group, and severe group were 0.86 (0.72–1.01), 0.81 (0.71–0.94), and 0.76 (0.66–0.86) g/L, respectively. (G) The lipoprotein (a) levels in the healthy controls, non‐severe group, and severe group were 114.80 (62.20–202.90), 87.15 (47.75–161.15), 94.45 (36.55–135.40) mg/L, respectively. The Kruskal‐Wallis test was used to compare differences among the three groups, and post hoc pairwise comparisons were performed using the Nemenyi test. HDL‐C, high‐density lipoprotein cholesterol; LDL‐C, low‐density lipoprotein cholesterol; ApoA1, apolipoprotein A1; ApoB, apolipoprotein B\nBaseline blood lipids were obtained within 5 days of admission. TC, HDL‐C, LDL‐C, and ApoA1 gradually decreased from the healthy controls to the non‐severe group and the severe group. TG was higher in the non‐severe group than in the healthy controls; however, no significant differences were found between the severe and non‐severe groups or between the severe group and the healthy controls. There were no significant differences in ApoB or lipoprotein (a) among the three groups (Figure 1).\nComparisons of blood lipids among the healthy controls, non‐severe group, and severe group. Data are presented as medians (interquartile ranges). (A) The total cholesterol levels in the healthy controls, non‐severe group, and severe group were 4.97 (4.55–5.79), 4.08 (3.69–4.63), and 3.58 (3.06–3.85) mmol/L, respectively. (B) The triglyceride levels in the healthy controls, non‐severe group, and severe group were 1.04 (0.85–1.43), 1.44 (0.98–2.00), and 1.22 (0.83–2.29) mmol/L, respectively. (C) The HDL‐C levels in the healthy controls, non‐severe group, and severe group were 1.62 (1.35–1.92), 1.09 (0.91–1.29), and 0.93 (0.82–1.00) mmol/L, respectively. (D) The LDL‐C levels in the healthy controls, non‐severe group, and severe group were 2.77 (2.40–3.42), 2.54 (2.18–2.85), and 2.21 (1.93–2.49) mmol/L, respectively. (E) The ApoA1 levels in the healthy controls, non‐severe group, and severe group were 1.45 (1.31–1.65), 1.22 (1.12–1.34), and 0.98 (0.89–1.08) g/L, respectively. (F) The ApoB levels in the healthy controls, non‐severe group, and severe group were 0.86 (0.72–1.01), 0.81 (0.71–0.94), and 0.76 (0.66–0.86) g/L, respectively. (G) The lipoprotein (a) levels in the healthy controls, non‐severe group, and severe group were 114.80 (62.20–202.90), 87.15 (47.75–161.15), 94.45 (36.55–135.40) mg/L, respectively. The Kruskal‐Wallis test was used to compare differences among the three groups, and post hoc pairwise comparisons were performed using the Nemenyi test. HDL‐C, high‐density lipoprotein cholesterol; LDL‐C, low‐density lipoprotein cholesterol; ApoA1, apolipoprotein A1; ApoB, apolipoprotein B\nRisk factors for COVID‐19 severity Potential risk factors, including several general clinical characteristics, immunoinflammatory markers, and blood lipids, were first identified by univariate logistic analysis. Age, BMI, hypertension, neutrophil%, lymphocyte%, lymphocyte count, platelet count, fibrinogen, CRP, IL‐6, IL‐10, HDL‐C, ApoA1, ALB, AST, and LDH were associated with the severity of COVID‐19 (p < 0.05). However, aPTT, interferon‐γ (IFN‐γ), TC, and LDL‐C were unrelated to COVID‐19 severity (p>0.05) (Figure 2). Next, variables with p < 0.1 in the univariate logistic analysis were entered into the multivariate logistic analysis. However, after adjusting for other potential risk factors, only IL‐6 (adjusted odds ratio [OR]: 1.097, 95% confidence interval [CI]: 1.034–1.165, p = 0.002) and ApoA1 (adjusted OR: 0.865, 95% CI: 0.800–0.935, p < 0.001) were identified as independent risk factors by multivariate logistic analysis (Figure 3). Therefore, a risk model was built using the combination of ApoA1 and IL‐6. The mathematical formula of the risk model was log (P)=12.303+0.093*IL‐6–0.145*ApoA1.\nUnivariate logistic regression of risk factors for COVID‐19 severity. Old age, high BMI, hypertension, increased neutrophil%, fibrinogen, C‐reactive protein, interleukin‐6, interleukin‐10, aspartate aminotransferase, and lactic dehydrogenase and decreased lymphocyte%, lymphocyte count, platelet count, HDL‐C, apolipoprotein A1, and albumin were associated with the severity of COVID‐19 in univariate logistic regression analysis (all p < 0.05). COVID‐19: coronavirus disease 2019, OR, odds ratio; CI, confidence interval; HDL‐C, high‐density lipoprotein cholesterol; LDL‐C, low‐density lipoprotein cholesterol\nMultivariate logistic regression of independent risk factors for COVID‐19 severity. After adjusting for other potential risk factors, increased IL‐6 (OR: 1.097, 95% CI: 1.034–1.165, p = 0.002) and decreased ApoA1 (OR: 0.865, 95% CI: 0.800–0.935, p < 0.001) were recognized as independent risk factors for COVID‐19 severity. COVID‐19: coronavirus disease 2019, OR: odds ratio, CI: confidence interval\nIn the prediction of COVID‐19 severity, the AUCs (95% CI) of TC, HDL‐C, LDL‐C, ApoA1, IL‐6, and the risk model were 0.726 (0.645–0.798), 0.674 (0.590–0.750), 0.669 (0.585–0.746), 0.896 (0.834–0.941), 0.855 (0.786–0.908), and 0.977 (0.932–0.995), respectively (Figure 4, Table 4). In particular, the sensitivity and specificity of Apo A1 were 94.12% (95% CI: 71.20%–99.00%) and 80.80% (95% CI: 72.80%–87.30%), respectively, which were both the highest among the above single markers. Moreover, the risk model increased the levels of sensitivity and specificity to 100.00% (95% CI: 80.30%–100.00%) and 89.89% (95% CI: 81.40%–94.10%), respectively.\nReceiver operator characteristic curves of total cholesterol, HDL‐C, LDL‐C, ApoA1, IL‐6, and risk model for the severity of COVID‐19. COVID‐19: coronavirus disease 2019, HDL‐C: high‐density lipoprotein cholesterol, LDL‐C: low‐density lipoprotein cholesterol, ApoA1: apolipoprotein A1, IL‐6: interleukin‐6\nPredictive performance of blood lipids, interleukin‐6, and the risk model for COVID‐19 severity\nAbbreviations: COVID‐19, coronavirus disease 2019; HDL‐C, high‐density lipoprotein cholesterol; LDL‐C, low‐density lipoprotein cholesterol; ApoA1, apolipoprotein A1; IL‐6, Interleukin‐6; CI, confidence interval; PPV, positive predictive value; NPV, negative predictive value.\nPotential risk factors, including several general clinical characteristics, immunoinflammatory markers, and blood lipids, were first identified by univariate logistic analysis. Age, BMI, hypertension, neutrophil%, lymphocyte%, lymphocyte count, platelet count, fibrinogen, CRP, IL‐6, IL‐10, HDL‐C, ApoA1, ALB, AST, and LDH were associated with the severity of COVID‐19 (p < 0.05). However, aPTT, interferon‐γ (IFN‐γ), TC, and LDL‐C were unrelated to COVID‐19 severity (p>0.05) (Figure 2). Next, variables with p < 0.1 in the univariate logistic analysis were entered into the multivariate logistic analysis. However, after adjusting for other potential risk factors, only IL‐6 (adjusted odds ratio [OR]: 1.097, 95% confidence interval [CI]: 1.034–1.165, p = 0.002) and ApoA1 (adjusted OR: 0.865, 95% CI: 0.800–0.935, p < 0.001) were identified as independent risk factors by multivariate logistic analysis (Figure 3). Therefore, a risk model was built using the combination of ApoA1 and IL‐6. The mathematical formula of the risk model was log (P)=12.303+0.093*IL‐6–0.145*ApoA1.\nUnivariate logistic regression of risk factors for COVID‐19 severity. Old age, high BMI, hypertension, increased neutrophil%, fibrinogen, C‐reactive protein, interleukin‐6, interleukin‐10, aspartate aminotransferase, and lactic dehydrogenase and decreased lymphocyte%, lymphocyte count, platelet count, HDL‐C, apolipoprotein A1, and albumin were associated with the severity of COVID‐19 in univariate logistic regression analysis (all p < 0.05). COVID‐19: coronavirus disease 2019, OR, odds ratio; CI, confidence interval; HDL‐C, high‐density lipoprotein cholesterol; LDL‐C, low‐density lipoprotein cholesterol\nMultivariate logistic regression of independent risk factors for COVID‐19 severity. After adjusting for other potential risk factors, increased IL‐6 (OR: 1.097, 95% CI: 1.034–1.165, p = 0.002) and decreased ApoA1 (OR: 0.865, 95% CI: 0.800–0.935, p < 0.001) were recognized as independent risk factors for COVID‐19 severity. COVID‐19: coronavirus disease 2019, OR: odds ratio, CI: confidence interval\nIn the prediction of COVID‐19 severity, the AUCs (95% CI) of TC, HDL‐C, LDL‐C, ApoA1, IL‐6, and the risk model were 0.726 (0.645–0.798), 0.674 (0.590–0.750), 0.669 (0.585–0.746), 0.896 (0.834–0.941), 0.855 (0.786–0.908), and 0.977 (0.932–0.995), respectively (Figure 4, Table 4). In particular, the sensitivity and specificity of Apo A1 were 94.12% (95% CI: 71.20%–99.00%) and 80.80% (95% CI: 72.80%–87.30%), respectively, which were both the highest among the above single markers. Moreover, the risk model increased the levels of sensitivity and specificity to 100.00% (95% CI: 80.30%–100.00%) and 89.89% (95% CI: 81.40%–94.10%), respectively.\nReceiver operator characteristic curves of total cholesterol, HDL‐C, LDL‐C, ApoA1, IL‐6, and risk model for the severity of COVID‐19. COVID‐19: coronavirus disease 2019, HDL‐C: high‐density lipoprotein cholesterol, LDL‐C: low‐density lipoprotein cholesterol, ApoA1: apolipoprotein A1, IL‐6: interleukin‐6\nPredictive performance of blood lipids, interleukin‐6, and the risk model for COVID‐19 severity\nAbbreviations: COVID‐19, coronavirus disease 2019; HDL‐C, high‐density lipoprotein cholesterol; LDL‐C, low‐density lipoprotein cholesterol; ApoA1, apolipoprotein A1; IL‐6, Interleukin‐6; CI, confidence interval; PPV, positive predictive value; NPV, negative predictive value.\n\nGeneral clinical characteristics:\nIn total, 142 consecutive patients with confirmed COVID‐19 were included in this study. The mean age was 49.10 ± 16.36 years, and 38.73% of the patients were male. Hypertension (37, 26.06%), diabetes (12, 8.45%), hepatic disease (10, 7.04%), and chronic lung disease (9, 6.34%) were the most common comorbidities. Fever (84, 59.15%) was the leading initial symptom, followed by cough (61, 42.96%), expectoration (32, 22.54%) and fatigue (27, 19.01%).\nAmong the 142 patients, 17 (11.97%) and 125 (88.03%) patients were classified into the severe and non‐severe groups during admission, respectively. Significant differences in age, body mass index (BMI), hypertension, hepatic disease, and fever were noted between the severe and non‐severe groups. Regarding clinical treatment, a greater proportion of patients in the severe group received glucocorticoids, antibiotics, oxygen, invasive mechanical ventilation, and intensive care unit treatment (Table 1).\nGeneral clinical characteristics of patients with COVID‐19\nData are presented as mean ± standard deviation or n (%). p‐value indicates the comparison between the non‐severe group and severe group.\nAbbreviations: COVID‐19, coronavirus disease 2019; ECMO, extracorporeal membrane oxygenation.\n\nLaboratory findings:\nWe first compared the general laboratory parameters between healthy controls and COVID‐19 patients. Neutrophil%, C‐reactive protein (CRP), aspartate aminotransferase (AST), and lactic dehydrogenase (LDH) were elevated, while white blood cell (WBC) count, lymphocyte%, lymphocyte count, platelet count, red blood cell (RBC) count, hemoglobin, albumin, total bilirubin (TBil), blood urea nitrogen (BUN), and blood uric acid (BUA) were decreased in patients with COVID‐19 compared with healthy controls (Table 2).\nComparisons of demographic and laboratory variables between healthy controls and COVID‐19 patients\nData are presented as mean ± standard deviation, n (%), or medians (interquartile ranges).\nAbbreviations: ALT, alanine aminotransferase; AST, aspartate aminotransferase; BUA, blood uric acid; BUN, blood urea nitrogen; COVID‐19, coronavirus disease 2019; CRP, C‐reactive protein; DBil, direct bilirubin; LDH, Lactic dehydrogenase; RBC, red blood cell; Scr, serum creatinine; TBil, total bilirubin; WBC, white blood cell.\nWe next compared general laboratory parameters, coagulation tests, and cytokine levels between severe and non‐severe COVID‐19 patients. Compared with those in the non‐severe group, patients in the severe group exhibited increased neutrophil%, fibrinogen, activated partial thromboplastin time (aPTT), CRP, interleukin‐10 (IL‐10), interleukin‐6 (IL‐6), interferon‐γ (INF‐γ), AST, and LDH levels, as well as reduced lymphocyte count, platelet count, lymphocyte%, and albumin levels (Table 3).\nBaseline laboratory parameters in patients with COVID‐19\nData are presented as medians (interquartile ranges).\nAbbreviations: COVID‐19, coronavirus disease 2019; WBC, white blood cell; RBC, red blood cell; PT, prothrombin time; aPTT, activated partial thromboplastin time; CRP, C‐reactive protein; IL‐2, interleukin‐2; IL‐4, interleukin‐4; IL‐6, interleukin‐6; IL‐10, interleukin‐10; IFN‐γ, interferon‐γ; TNF‐α, tumor necrosis factor‐α; TBil, total bilirubin; DBil, direct bilirubin; AST, aspartate aminotransferase; ALT, alanine aminotransferase; LDH, Lactic dehydrogenase; BUN, blood urea nitrogen; BUA, blood uric acid; Scr, serum creatinine.\n\nBaseline blood lipids:\nBaseline blood lipids were obtained within 5 days of admission. TC, HDL‐C, LDL‐C, and ApoA1 gradually decreased from the healthy controls to the non‐severe group and the severe group. TG was higher in the non‐severe group than in the healthy controls; however, no significant differences were found between the severe and non‐severe groups or between the severe group and the healthy controls. There were no significant differences in ApoB or lipoprotein (a) among the three groups (Figure 1).\nComparisons of blood lipids among the healthy controls, non‐severe group, and severe group. Data are presented as medians (interquartile ranges). (A) The total cholesterol levels in the healthy controls, non‐severe group, and severe group were 4.97 (4.55–5.79), 4.08 (3.69–4.63), and 3.58 (3.06–3.85) mmol/L, respectively. (B) The triglyceride levels in the healthy controls, non‐severe group, and severe group were 1.04 (0.85–1.43), 1.44 (0.98–2.00), and 1.22 (0.83–2.29) mmol/L, respectively. (C) The HDL‐C levels in the healthy controls, non‐severe group, and severe group were 1.62 (1.35–1.92), 1.09 (0.91–1.29), and 0.93 (0.82–1.00) mmol/L, respectively. (D) The LDL‐C levels in the healthy controls, non‐severe group, and severe group were 2.77 (2.40–3.42), 2.54 (2.18–2.85), and 2.21 (1.93–2.49) mmol/L, respectively. (E) The ApoA1 levels in the healthy controls, non‐severe group, and severe group were 1.45 (1.31–1.65), 1.22 (1.12–1.34), and 0.98 (0.89–1.08) g/L, respectively. (F) The ApoB levels in the healthy controls, non‐severe group, and severe group were 0.86 (0.72–1.01), 0.81 (0.71–0.94), and 0.76 (0.66–0.86) g/L, respectively. (G) The lipoprotein (a) levels in the healthy controls, non‐severe group, and severe group were 114.80 (62.20–202.90), 87.15 (47.75–161.15), 94.45 (36.55–135.40) mg/L, respectively. The Kruskal‐Wallis test was used to compare differences among the three groups, and post hoc pairwise comparisons were performed using the Nemenyi test. HDL‐C, high‐density lipoprotein cholesterol; LDL‐C, low‐density lipoprotein cholesterol; ApoA1, apolipoprotein A1; ApoB, apolipoprotein B\n\nRisk factors for COVID‐19 severity:\nPotential risk factors, including several general clinical characteristics, immunoinflammatory markers, and blood lipids, were first identified by univariate logistic analysis. Age, BMI, hypertension, neutrophil%, lymphocyte%, lymphocyte count, platelet count, fibrinogen, CRP, IL‐6, IL‐10, HDL‐C, ApoA1, ALB, AST, and LDH were associated with the severity of COVID‐19 (p < 0.05). However, aPTT, interferon‐γ (IFN‐γ), TC, and LDL‐C were unrelated to COVID‐19 severity (p>0.05) (Figure 2). Next, variables with p < 0.1 in the univariate logistic analysis were entered into the multivariate logistic analysis. However, after adjusting for other potential risk factors, only IL‐6 (adjusted odds ratio [OR]: 1.097, 95% confidence interval [CI]: 1.034–1.165, p = 0.002) and ApoA1 (adjusted OR: 0.865, 95% CI: 0.800–0.935, p < 0.001) were identified as independent risk factors by multivariate logistic analysis (Figure 3). Therefore, a risk model was built using the combination of ApoA1 and IL‐6. The mathematical formula of the risk model was log (P)=12.303+0.093*IL‐6–0.145*ApoA1.\nUnivariate logistic regression of risk factors for COVID‐19 severity. Old age, high BMI, hypertension, increased neutrophil%, fibrinogen, C‐reactive protein, interleukin‐6, interleukin‐10, aspartate aminotransferase, and lactic dehydrogenase and decreased lymphocyte%, lymphocyte count, platelet count, HDL‐C, apolipoprotein A1, and albumin were associated with the severity of COVID‐19 in univariate logistic regression analysis (all p < 0.05). COVID‐19: coronavirus disease 2019, OR, odds ratio; CI, confidence interval; HDL‐C, high‐density lipoprotein cholesterol; LDL‐C, low‐density lipoprotein cholesterol\nMultivariate logistic regression of independent risk factors for COVID‐19 severity. After adjusting for other potential risk factors, increased IL‐6 (OR: 1.097, 95% CI: 1.034–1.165, p = 0.002) and decreased ApoA1 (OR: 0.865, 95% CI: 0.800–0.935, p < 0.001) were recognized as independent risk factors for COVID‐19 severity. COVID‐19: coronavirus disease 2019, OR: odds ratio, CI: confidence interval\nIn the prediction of COVID‐19 severity, the AUCs (95% CI) of TC, HDL‐C, LDL‐C, ApoA1, IL‐6, and the risk model were 0.726 (0.645–0.798), 0.674 (0.590–0.750), 0.669 (0.585–0.746), 0.896 (0.834–0.941), 0.855 (0.786–0.908), and 0.977 (0.932–0.995), respectively (Figure 4, Table 4). In particular, the sensitivity and specificity of Apo A1 were 94.12% (95% CI: 71.20%–99.00%) and 80.80% (95% CI: 72.80%–87.30%), respectively, which were both the highest among the above single markers. Moreover, the risk model increased the levels of sensitivity and specificity to 100.00% (95% CI: 80.30%–100.00%) and 89.89% (95% CI: 81.40%–94.10%), respectively.\nReceiver operator characteristic curves of total cholesterol, HDL‐C, LDL‐C, ApoA1, IL‐6, and risk model for the severity of COVID‐19. COVID‐19: coronavirus disease 2019, HDL‐C: high‐density lipoprotein cholesterol, LDL‐C: low‐density lipoprotein cholesterol, ApoA1: apolipoprotein A1, IL‐6: interleukin‐6\nPredictive performance of blood lipids, interleukin‐6, and the risk model for COVID‐19 severity\nAbbreviations: COVID‐19, coronavirus disease 2019; HDL‐C, high‐density lipoprotein cholesterol; LDL‐C, low‐density lipoprotein cholesterol; ApoA1, apolipoprotein A1; IL‐6, Interleukin‐6; CI, confidence interval; PPV, positive predictive value; NPV, negative predictive value.\n\nDISCUSSION:\nIn this study, baseline TC, HDL‐C, LDL‐C, and ApoA1 gradually decreased across the groups in the following order: healthy controls, non‐severe group, and severe group, indicating that they had potential roles in predicting the severity of COVID‐19. Other blood lipids, including TG, ApoB, and lipoprotein (a), had little value in distinguishing COVID‐19 severity. Additionally, we found that ApoA1 was most obviously decreased among the altered lipids in this study, representing an independent risk factor for COVID‐19 severity. The combination of ApoA1 and IL‐6 yielded even higher prediction efficiency.\nIn a recent study, Wei et al.11 observed that serum levels of TC, HDL‐C, and LDL‐C in patients with COVID‐19 were significantly lower than those in healthy subjects, especially in severe and critical cases, which was similar to our study. Another study also found that LDL‐C performed well in discriminating COVID‐19 severity, it was gradually decreased across moderate, severe, and critical cases; however, HDL‐C exhibited no significant differences among the three groups.12 These small discrepancies may be related to the heterogeneity of disease severity, different sample sizes, and testing methods. However, our study and others11, 12 all showed that blood lipids have potential auxiliary value in distinguishing severe COVID‐19 patients.\nApoA1, a major protein component of the HDL complex, is involved in “reverse cholesterol transport” by transporting excess cholesterol from peripheral cells back to the liver for excretion. In addition, ApoA1 has an anti‐inflammatory characteristic,13 suggesting its role in inflammatory diseases. Previous studies have revealed that serum ApoA1 is associated with the outcome of patients with sepsis and acute respiratory distress syndrome induced by pneumonia, as well as critically ill patients.14, 15, 16, 17 In acute inflammatory disease, serum amyloid A (SAA), an acute‐phase protein, displaces ApoA1 from the HDL complex; then, free ApoA1 is easily eliminated by the kidney, resulting in low levels in the peripheral blood.18 On the other hand, the liver is susceptible to attack by SARS‐CoV‐2, especially in severe cases19; therefore, reduced synthesis by the injured liver may also play a role.\nIL‐6 plays a key role in the development of COVID‐19, and its predictive value for disease severity has been revealed previously by us and others.4, 20, 21, 22 It was also found that increased IL‐6 was associated with poor outcomes.22, 23 In this study, IL‐6 and ApoA1 were identified as independent risk factors for COVID‐19 severity. The risk model established using these two markers exhibited the highest predictive value, with an AUC of 0.977 (95% CI: 0.932–0.995).\nApoA1 and its mimetic peptide D‐amino acids (D‐4F) exhibit therapeutic potential for treating cancer, influenza, sepsis, and ARDS, primarily due to their anti‐inflammatory, anti‐oxidant, and anti‐apoptotic properties.13, 24, 25, 26, 27 In addition, it is noteworthy that ApoA1 inhibits IL‐6 release and reduces macrophage activation.25 IL‐6 is the main participant in the cytokine storm, and macrophages are the primary source of IL‐6. Therefore, ApoA1 may exhibit therapeutic potential in treating patients with COVID‐19. It might be worthwhile to test the efficacy and safety of ApoA1 and its mimetics in treating these patients.\nThe main strength of this study is that the patients included in this study were treated without delay when infected with SARS‐CoV‐2, which may represent an early stage of the disease. Second, this study enrolled healthy controls to analyze trends in blood lipids among healthy subjects, non‐severe cases, and severe cases. Third, the predictive values of verified clinical characteristics and laboratory parameters were selected for comparison with blood lipids, making the results more credible. Finally, blood lipids were routinely tested by using an automatic biochemical analyzer, with clinical application value.\nA limitation of this study is that it was a single‐center retrospective study with a relatively small sample size that was not validated in internal or external cohorts. Therefore, a prospective study with a larger sample size is strongly encouraged.\nIn conclusion, this study sheds light on abnormal blood lipid profiles in patients with COVID‐19 compared with healthy subjects, especially in severe cases. Specifically, TC, HDL‐C, LDL‐C, and ApoA1 gradually decreased from the healthy controls to non‐severe and severe groups. Additionally, ApoA1 is a good indicator of COVID‐19 severity, and the combination of ApoA1 and IL‐6 enhances model predictability. These findings might be helpful in disclosing the pathogenesis of COVID‐19 and developing novel therapeutic strategies for COVID‐19.\n\nCONFLICT OF INTEREST:\nThe authors declare that they have no competing interests.\n\nAUTHOR CONTRIBUTIONS:\nZhe Zhu interpreted the data and wrote the paper. Yayun Yang, Linyan Fan, Shuyuan Ye, and Kehong Lou collected and analyzed the data. Xin Hua, Zuoan Huang, and Qiaoyun Shi performed laboratory analysis. Guosheng Gao designed the study and revised the paper.\n\nETHICAL APPROVAL:\nThis study was approved by the institutional ethics board of HwaMei Hospital, University of Chinese Academy of Science (PJ‐NBEY‐KY‐2020–061–01).\n\n"},"whole_article_abstract":{"kind":"string","value":"Background:\nDyslipidemia has been observed in patients with coronavirus disease 2019 (COVID-19). This study aimed to investigate blood lipid profiles in patients with COVID-19 and to explore their predictive values for COVID-19 severity.\n\nMethods:\nA total of 142 consecutive patients with COVID-19 were included in this single-center retrospective study. Blood lipid profile characteristics were investigated in patients with COVID-19 in comparison with 77 age- and gender-matched healthy subjects, their predictive values for COVID-19 severity were analyzed by using multivariable logistic regression analysis, and their prediction efficiencies were evaluated by using receiver operator characteristic (ROC) curves.\n\nResults:\nThere were 125 and 17 cases in the non-severe and severe groups, respectively. Total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and apolipoprotein A1 (ApoA1) gradually decreased across the groups in the following order: healthy controls, non-severe group, and severe group. ApoA1 was identified as an independent risk factor for COVID-19 severity (adjusted odds ratio [OR]: 0.865, 95% confidence interval [CI]: 0.800-0.935, p < 0.001), along with interleukin-6 (IL-6) (adjusted OR: 1.097, 95% CI: 1.034-1.165, p = 0.002). ApoA1 exhibited the highest area under the ROC curve (AUC) among all single markers (AUC: 0.896, 95% CI: 0.834-0.941); moreover, the risk model established using ApoA1 and IL-6 enhanced prediction efficiency (AUC: 0.977, 95% CI: 0.932-0.995).\n\nConclusions:\nBlood lipid profiles in patients with COVID-19 are quite abnormal compared with those in healthy subjects, especially in severe cases. Serum ApoA1 may represent a good indicator for predicting the severity of COVID-19."},"background_conclusion_text":{"kind":"null"},"background_conclusion_abstract":{"kind":"null"},"whole_article_text_length":{"kind":"number","value":8410,"string":"8,410"},"whole_article_abstract_length":{"kind":"number","value":359,"string":"359"},"other_sections_lengths":{"kind":"list like","value":[312,244,108,176,262,427,444,689,51,23],"string":"[\n 312,\n 244,\n 108,\n 176,\n 262,\n 427,\n 444,\n 689,\n 51,\n 23\n]"},"num_sections":{"kind":"number","value":14,"string":"14"},"most_frequent_words":{"kind":"list like","value":["severe","19","covid","covid 19","severe group","group","blood","patients","non","apoa1"],"string":"[\n \"severe\",\n \"19\",\n \"covid\",\n \"covid 19\",\n \"severe group\",\n \"group\",\n \"blood\",\n \"patients\",\n \"non\",\n \"apoa1\"\n]"},"keybert_topics":{"kind":"list like","value":["novel coronavirus infection","syndrome coronavirus sars","19 coronavirus","coronavirus 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[SUMMARY]"},"annotated_most_frequent_conclusions_abstract_prompt":{"kind":"null"},"annotated_most_frequent_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] severe | 19 | covid | covid 19 | severe group | group | blood | patients | non | apoa1 [SUMMARY]"},"annotated_most_frequent_background_conclusion_abstract_prompt":{"kind":"null"},"annotated_tf_idf_background_abstract_prompt":{"kind":"string","value":"[CONTENT] covid | 19 | covid 19 | severe | patients | severity | lipid | respiratory | mild | indicators [SUMMARY]"},"annotated_tf_idf_methods_abstract_prompt":{"kind":"null"},"annotated_tf_idf_results_abstract_prompt":{"kind":"string","value":"[CONTENT] severe | severe group | group | 19 | il | covid | covid 19 | controls | healthy controls | ci [SUMMARY]"},"annotated_tf_idf_conclusions_abstract_prompt":{"kind":"null"},"annotated_tf_idf_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] severe | 19 | covid 19 | covid | patients | group | severe group | blood | non | il [SUMMARY]"},"annotated_tf_idf_background_conclusion_abstract_prompt":{"kind":"null"},"annotated_entity_plan_background_abstract_prompt":{"kind":"string","value":"[CONTENT] 2019 | COVID-19 ||| COVID-19 | COVID-19 [SUMMARY]"},"annotated_entity_plan_methods_abstract_prompt":{"kind":"null"},"annotated_entity_plan_results_abstract_prompt":{"kind":"string","value":"[CONTENT] 125 | 17 ||| HDL-C | A1 ||| COVID-19 | 0.865 | 95% ||| CI | 0.800-0.935 | 1.097 | 95% | CI | 1.034-1.165 | 0.002 ||| ROC | 0.896 | 95% | CI | 0.834-0.941 | IL-6 | 0.977 | 95% | CI | 0.932 [SUMMARY]"},"annotated_entity_plan_conclusions_abstract_prompt":{"kind":"null"},"annotated_entity_plan_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] 2019 | COVID-19 ||| COVID-19 | COVID-19 ||| 142 | COVID-19 ||| COVID-19 | 77 | COVID-19 | ROC ||| 125 | 17 ||| HDL-C | A1 ||| COVID-19 | 0.865 | 95% ||| CI | 0.800-0.935 | 1.097 | 95% | CI | 1.034-1.165 | 0.002 ||| ROC | 0.896 | 95% | CI | 0.834-0.941 | IL-6 | 0.977 | 95% | CI | 0.932 ||| COVID-19 ||| Serum ApoA1 | COVID-19 [SUMMARY]"},"annotated_entity_plan_background_conclusion_abstract_prompt":{"kind":"null"}}},{"rowIdx":271,"cells":{"title":{"kind":"string","value":"PREOPERATIVE COMPUTED TOMOGRAPHY ANGIOGRAPHY IN MULTIDISCIPLINARY PERSONALIZED ASSESSMENT OF PATIENT WITH RIGHT-SIDED COLON CANCER: SURGEON AND RADIOLOGIST POINT OF VIEW."},"pmid":{"kind":"string","value":"36043651"},"background_abstract":{"kind":"string","value":"3D-CT angiography has made it possible to reach a qualitatively new level in the determination of treatment tactics for patients with colorectal cancer."},"background_abstract_label":{"kind":"string","value":"BACKGROUND"},"methods_abstract":{"kind":"string","value":"This study involved 103 patients with colorectal cancer who underwent preoperative 3D-CT angiography from 2016 to 2021."},"methods_abstract_label":{"kind":"string","value":"METHODS"},"results_abstract":{"kind":"string","value":"All patients underwent radical D3 right hemicolectomy. The median quantity of removal lymph nodes were 24.71±10.04. Anastomotic leakage was diagnosed in one patient. We have identified eight most common types of superior mesenteric artery. The ileocolic artery crossed the superior mesenteric vein on the anterior surface in 64 (62.1%) patients and on the posterior surface in 39 (37.9%). In 58 (56.3%) patients, the right colic artery was either absent or was a nonindependent branch of superior mesenteric artery. The distance from the root of the superior mesenteric artery to the root of the middle colic artery was 37.8±12.8 mm and that from the root of the middle colic artery to the root of the ileocolic artery was 29.5±15.7 mm. The trunk of Henle was above the root of the middle colic artery in 66 (64.1%) patients, at the same level with the middle colic artery in 16 (15.5%), and below the middle colic artery in 18 (17.5%) patients."},"results_abstract_label":{"kind":"string","value":"RESULTS"},"conclusions_abstract":{"kind":"string","value":"Preoperative analysis of 3D-CT angiography is a key pattern in assessment of vascular anatomy and can potentially show the complexity of future lymphadenectomy and reduce the risk of anastomotic leakage."},"conclusions_abstract_label":{"kind":"string","value":"CONCLUSIONS"},"mesh_descriptor_names":{"kind":"list like","value":["Anastomotic Leak","Colectomy","Colonic Neoplasms","Computed Tomography Angiography","Humans","Laparoscopy","Radiologists","Surgeons"],"string":"[\n \"Anastomotic Leak\",\n \"Colectomy\",\n \"Colonic Neoplasms\",\n \"Computed Tomography Angiography\",\n \"Humans\",\n \"Laparoscopy\",\n \"Radiologists\",\n \"Surgeons\"\n]"},"pmcid":{"kind":"string","value":"9423717"},"background_title":{"kind":"string","value":"INTRODUCTION"},"background_text":{"kind":"string","value":"The incidence of anastomotic leak (AL) after right hemicolectomy is relatively low,\nin comparison with left-sided/rectal colorectal cancer (CRC). In 2015, the European\nSociety of Coloproctology (ESCP) audited right colectomy and ileocecal resection,\ncollecting prospective data on 3,208 patients across 284 centers in 39 countries.\nThe overall AL rate was 8.1%\n11\n. This is due to a more stable blood supply. However, different anatomical\nvariations can have a significant impact on the duration of surgery and cause the\ntechnical complexity of its implementation. The need for standardization is still\ndebated in the literature of Eastern D3 lymphadenectomy and Western embryologically\noriented complete mesocolic excision with central vascular ligation (CME/CVL)\n5,8\n.\nIn contrast to left and rectal cancer surgery, where the inferior mesenteric artery\nis the most important reference point, there are several such points, including the\nsuperior mesenteric vein (SMV), truncus Henle (TH), and the branches of the superior\nmesenteric artery (SMA). Not uncommon anatomical variability of the abovementioned\nvessels causes a higher percentage of conversions to open operations and increases\nintraoperative time and intraoperative blood loss\n9,12\n. It is difficult, in the scientific world of the 21st century, if not\nimpossible, to say anything new in surgical anatomy of the abdominal cavity.\nHowever, with widely used in clinical practice, contrast-enhanced computed\ntomography (CT) has made it possible to reach a qualitatively new level in\npreoperative diagnosis and determination of treatment tactics for patients with CRC.\nRoutine use of CT angiography allows a detailed analysis of each clinical case in\nthe preoperative stage and identifies various anatomical nuances that may affect the operation\n6,7\n.\nThe aim of this article was to analyze the clinical and radiological aspects that\nusually need to be discussed before surgery by a multidisciplinary team in patients\nwith right-sided colon cancer."},"methods_title":{"kind":"string","value":"METHODS"},"methods_text":{"kind":"string","value":"This study was carried out a comparative analysis of 3D-CT angiography data with\nintraoperative data. A detailed analysis of the anatomy of the branches of the SMA\nand its relationship with the surrounding structures was done in order to explore\nthe nuances that may complicate and increase the time during right hemicolectomy\nwith CME/CVL. The relationship between the anatomical features of the structure of\nSMA and postoperative complications was also investigated.\nDescription of patients We included 103 patients (56 males and 47 females; mean age 64.2±11.6) with CRC\nwho underwent preoperative 3D-CT angiography at Ternopil University Hospital\nfrom 2016 to 2021. The exclusion criteria were stage IV process and locally\nadvanced forms of cancer. The informed consents were obtained from all patients.\nThis study was passed by the Ethics Commission of Ternopil National Medical\nUniversity (no. 43).\nWe included 103 patients (56 males and 47 females; mean age 64.2±11.6) with CRC\nwho underwent preoperative 3D-CT angiography at Ternopil University Hospital\nfrom 2016 to 2021. The exclusion criteria were stage IV process and locally\nadvanced forms of cancer. The informed consents were obtained from all patients.\nThis study was passed by the Ethics Commission of Ternopil National Medical\nUniversity (no. 43).\nMeasurements In this study, the following objectives were set: determine the type of SMA;\ndetermine the distance from the root of the SMA to the root of the middle colic\nartery (MCA), the distance from the root of the MCA to the root of the ileocolic\nartery (ICA); variant structure of the right colic artery (RCA); and the\nrelationship between MCA and gastrocolic TH; and explore different variants of\nTH confluence.\nThe distance between the vascular structures was measured in the frontal plane\nusing a linear measurement. Anatomical features of the structure of SMA branches\nwere determined in the arterial phase and venous structures of TH in the venous\nphase and compared the ratio of MCA and TH using Fusion.\nIn this study, the following objectives were set: determine the type of SMA;\ndetermine the distance from the root of the SMA to the root of the middle colic\nartery (MCA), the distance from the root of the MCA to the root of the ileocolic\nartery (ICA); variant structure of the right colic artery (RCA); and the\nrelationship between MCA and gastrocolic TH; and explore different variants of\nTH confluence.\nThe distance between the vascular structures was measured in the frontal plane\nusing a linear measurement. Anatomical features of the structure of SMA branches\nwere determined in the arterial phase and venous structures of TH in the venous\nphase and compared the ratio of MCA and TH using Fusion.\nScan protocol 3D-CT angiography was performed using a Philips Brilliance 64 CT machine with IV\ncontrast (100 mL of iodinated contrast agent [370 mg/mL]). Contrast was injected\ninto the ulnar vein at a rate of 4.5 mL/s. The bolus tracking method was used\nfor scanning. Arterial phase scanning automatically began when the contrast in\nthe abdominal aorta at the level of the abdominal trunk reached 180 HU. The\n64-slice multidetector CT scanner (MDCT) can generate 0.75-mm slices that can be\nreconstructed into a 0.5-mm image. Therefore, in order to obtain high-quality CT\nangiography for preoperative analysis, a scanning protocol should be maintained:\nsublingual nitrate intake, high contrast rate (4–5 mL/s), early arterial phase\n(20–30 ‘), stress reduction (80–100 kV), and doubling the mAs. Image processing\nwas performed using 3D volume rendering technique (VRT).\n3D-CT angiography was performed using a Philips Brilliance 64 CT machine with IV\ncontrast (100 mL of iodinated contrast agent [370 mg/mL]). Contrast was injected\ninto the ulnar vein at a rate of 4.5 mL/s. The bolus tracking method was used\nfor scanning. Arterial phase scanning automatically began when the contrast in\nthe abdominal aorta at the level of the abdominal trunk reached 180 HU. The\n64-slice multidetector CT scanner (MDCT) can generate 0.75-mm slices that can be\nreconstructed into a 0.5-mm image. Therefore, in order to obtain high-quality CT\nangiography for preoperative analysis, a scanning protocol should be maintained:\nsublingual nitrate intake, high contrast rate (4–5 mL/s), early arterial phase\n(20–30 ‘), stress reduction (80–100 kV), and doubling the mAs. Image processing\nwas performed using 3D volume rendering technique (VRT).\nStatistical analysis Ordinal data were calculated using the median. All calculations were performed\nusing the Statistica version 64 software.\nOrdinal data were calculated using the median. All calculations were performed\nusing the Statistica version 64 software."},"results_title":{"kind":"string","value":"RESULTS"},"results_text":{"kind":"string","value":"All patients underwent local radical right hemicolectomy with CME/CVL and R0\nresection.\nThe median quantity of removal lymph nodes was 24.71±10.04 (range 13–58). Positive\nlymph nodes were revealed in 38.7% of cases. The incidence of metastatic lymph nodes\nwas 38.7% in D1 zone, 3.2% in D2 zone, and 9.7% in D3 zone. Mean operative time was\n82 min (range 63–130). Median intraoperative blood loss was 70 mL (range 32–280). No\npatients required intraoperative blood transfusion. Postoperative complications were\ndeveloped in seven patients. AL was diagnosed in one patient on postoperative day 8\nfor whom relaparotomy, lavage, and end stoma were performed (Figure 5). Unfortunately, on the first day after patient\ndischarge from the hospital, he died from massive thromboembolic complication,\ndespite maintaining prophylaxis therapy. One patient suffered from paralytic ileus\nin an early postoperative period. Median staying in hospital after operation was 8.4\ndays.\nThe SMA was present in 100% of cases. Compared with the widely used Zebrowski\nclassification of the inferior mesenteric artery, we could not find a common\nclassification of anatomical variations of SMA. We have identified eight types that\nare most common in practice: \nType A – MCA, RCA, and ICA deviate classically\nindependently of each other from the main SMA trunk.\nType B – RCA is absent.\nType C – RCA deviates from ICA.\nType D – RCA deviates from the right branch of the MCA or\nthe main trunk of the MCA.\nType E – Classical type A + the presence of additional MCA\n(AMCA)\nType F – Right and left MCA branches deviate separately\nfrom the main SMA trunk.\nType G – MCA and ICA have a common trunk and RCA is\nabsent.\nType H – RCA deviates from ICA + AMCA.\n\n\nType A – MCA, RCA, and ICA deviate classically\nindependently of each other from the main SMA trunk.\n\nType B – RCA is absent.\n\nType C – RCA deviates from ICA.\n\nType D – RCA deviates from the right branch of the MCA or\nthe main trunk of the MCA.\n\nType E – Classical type A + the presence of additional MCA\n(AMCA)\n\nType F – Right and left MCA branches deviate separately\nfrom the main SMA trunk.\n\nType G – MCA and ICA have a common trunk and RCA is\nabsent.\n\nType H – RCA deviates from ICA + AMCA.\nOur analysis showed that type A was detected in 27 (25.9%) patients, type B in 22\n(21.4%), type C in 20 (19.2%), type D in 12 (11.6%), type E in 9 (8.7%), type F in 9\n(8.7%), type G in 3 (2.9%), and type H in 1 (0.9%) patient (Figures 1 and 2). The\nanalysis also showed that in 12 (11.6%) patients, the right hepatic artery deviates\nfrom SMA.\nThe ICA was present in 103 (100%) cases. The ICA crossed the SMV on the anterior\nsurface in 64 (62.1%) cases and on the posterior surface of the SMV in 39 (37.9%)\ncases.\nThe RCA is one of the most volatile arterial structures of the SMA system (literature\ndata indicate that it is present in 11–40% of cases)\n1,4,12\n. According to our selected types of SMA, RCA was absent in 25 (24.3%) and in\n33 (32%) patients and deviated from ICA and MCA/RMCA. Accordingly, in 58 (56.3%)\npatients, RCA was either absent or was a nonindependent branch of SMA.\nThe MCA was present and originated directly from SMA in 103 (100%) cases. AMCA was\npresent in 10 (9.7%) cases.\nThe distance from the root of the SMA to the root of the MCA was 37.8±12.8 mm (range\n13–65).\nThe distance from the root of the MCA to the root of the ICA was 29.5±15.7 mm (range\n0–80).\nGastrocolic TH was present in 100 (97.1%) cases and located on the lower edge of the\nmesentery of the transverse colon, along the head of the pancreas, and flows into\nthe right lateral part of the SMV wall. Our analysis showed that the caliber of TH\nvaried from 3 to 10 mm and its length was 11.5±4.8 mm (range 2–33). Usually, the\nconfluence of TH formed: middle colic vein (MCV), right colic vein (RCV), additional\nmiddle colic vein (AMCV), right gastroepiploic vein (RGEV), and anterior superior\npancreaticoduodenal vein (ASPDV). Also, we observed a very interesting case where\none of the veins which create confluence of TH was ileocolic vein (ICV) (Figure 3). Our analysis of 3D-CT angiograms\nshowed the following type combinations of TH confluence: MCV+RCVRCV+RGEV+ASPDVICV+RCV+RGEVAbsence of THMCV+RGEVMCV+RGEV+ASPDV+AMCV\n\nMCV+RCV\nRCV+RGEV+ASPDV\nICV+RCV+RGEV\nAbsence of TH\nMCV+RGEV\nMCV+RGEV+ASPDV+AMCV\nRespectively, type 1 was observed in 17 (16.5%) patients, type 2 in 55 (53.4%), type\n3 in 1 (1%), type 4 in 3 (2.9%), type 5 in 15 (14.6%), and type 6 in 12 (11.6%)\npatients (Figure 3). Unfortunately, it was\nimpossible in some cases to create 3D-CT reconstruction of some types of TH due to\nlack of contrast, incorrect scanning, and various technical features.\nAn important point of preoperative planning is understanding the relationship between\nTH and MCA. In 66 (64.1%) patients, TH was located above the root of the MCA, in\nwhich case the distance between them was 12.38±5.41 mm (range 3–29). In 18 (17.5%)\npatients, TH was located below the root of the MCA, in which case the distance\nbetween them was 10.95±7.1 mm. In 16 (15.5%) patients, TH was located at the same\nlevel with the root of the MCA (Figure 4)."},"conclusions_title":{"kind":"string","value":"CONCLUSION"},"conclusions_text":{"kind":"string","value":"Personalized preoperative analysis of 3D-CT angiography is a key pattern in\nassessment of vascular anatomy and can potentially show the complexity of future\nlymphadenectomy, reduce intraoperative time for identifying key landmarks, and\ndevelop an individualized surgical strategy. Personalized 3D-CT assessment can\npotentially significantly reduce the risk of AL. To solve this problem, new studies\nand further standardization are needed."},"other_sections_titles":{"kind":"list like","value":["Description of patients","Measurements","Scan protocol","Statistical analysis"],"string":"[\n \"Description of patients\",\n \"Measurements\",\n \"Scan protocol\",\n \"Statistical analysis\"\n]"},"other_sections_texts":{"kind":"list like","value":["We included 103 patients (56 males and 47 females; mean age 64.2±11.6) with CRC\nwho underwent preoperative 3D-CT angiography at Ternopil University Hospital\nfrom 2016 to 2021. The exclusion criteria were stage IV process and locally\nadvanced forms of cancer. The informed consents were obtained from all patients.\nThis study was passed by the Ethics Commission of Ternopil National Medical\nUniversity (no. 43).","In this study, the following objectives were set: determine the type of SMA;\ndetermine the distance from the root of the SMA to the root of the middle colic\nartery (MCA), the distance from the root of the MCA to the root of the ileocolic\nartery (ICA); variant structure of the right colic artery (RCA); and the\nrelationship between MCA and gastrocolic TH; and explore different variants of\nTH confluence.\nThe distance between the vascular structures was measured in the frontal plane\nusing a linear measurement. Anatomical features of the structure of SMA branches\nwere determined in the arterial phase and venous structures of TH in the venous\nphase and compared the ratio of MCA and TH using Fusion.","3D-CT angiography was performed using a Philips Brilliance 64 CT machine with IV\ncontrast (100 mL of iodinated contrast agent [370 mg/mL]). Contrast was injected\ninto the ulnar vein at a rate of 4.5 mL/s. The bolus tracking method was used\nfor scanning. Arterial phase scanning automatically began when the contrast in\nthe abdominal aorta at the level of the abdominal trunk reached 180 HU. The\n64-slice multidetector CT scanner (MDCT) can generate 0.75-mm slices that can be\nreconstructed into a 0.5-mm image. Therefore, in order to obtain high-quality CT\nangiography for preoperative analysis, a scanning protocol should be maintained:\nsublingual nitrate intake, high contrast rate (4–5 mL/s), early arterial phase\n(20–30 ‘), stress reduction (80–100 kV), and doubling the mAs. Image processing\nwas performed using 3D volume rendering technique (VRT).","Ordinal data were calculated using the median. All calculations were performed\nusing the Statistica version 64 software."],"string":"[\n \"We included 103 patients (56 males and 47 females; mean age 64.2±11.6) with CRC\\nwho underwent preoperative 3D-CT angiography at Ternopil University Hospital\\nfrom 2016 to 2021. The exclusion criteria were stage IV process and locally\\nadvanced forms of cancer. The informed consents were obtained from all patients.\\nThis study was passed by the Ethics Commission of Ternopil National Medical\\nUniversity (no. 43).\",\n \"In this study, the following objectives were set: determine the type of SMA;\\ndetermine the distance from the root of the SMA to the root of the middle colic\\nartery (MCA), the distance from the root of the MCA to the root of the ileocolic\\nartery (ICA); variant structure of the right colic artery (RCA); and the\\nrelationship between MCA and gastrocolic TH; and explore different variants of\\nTH confluence.\\nThe distance between the vascular structures was measured in the frontal plane\\nusing a linear measurement. Anatomical features of the structure of SMA branches\\nwere determined in the arterial phase and venous structures of TH in the venous\\nphase and compared the ratio of MCA and TH using Fusion.\",\n \"3D-CT angiography was performed using a Philips Brilliance 64 CT machine with IV\\ncontrast (100 mL of iodinated contrast agent [370 mg/mL]). Contrast was injected\\ninto the ulnar vein at a rate of 4.5 mL/s. The bolus tracking method was used\\nfor scanning. Arterial phase scanning automatically began when the contrast in\\nthe abdominal aorta at the level of the abdominal trunk reached 180 HU. The\\n64-slice multidetector CT scanner (MDCT) can generate 0.75-mm slices that can be\\nreconstructed into a 0.5-mm image. Therefore, in order to obtain high-quality CT\\nangiography for preoperative analysis, a scanning protocol should be maintained:\\nsublingual nitrate intake, high contrast rate (4–5 mL/s), early arterial phase\\n(20–30 ‘), stress reduction (80–100 kV), and doubling the mAs. Image processing\\nwas performed using 3D volume rendering technique (VRT).\",\n \"Ordinal data were calculated using the median. All calculations were performed\\nusing the Statistica version 64 software.\"\n]"},"other_sections_sec_types":{"kind":"list like","value":[null,null,null,null],"string":"[\n null,\n null,\n null,\n null\n]"},"all_sections_titles":{"kind":"list like","value":["INTRODUCTION","METHODS","Description of patients","Measurements","Scan protocol","Statistical analysis","RESULTS","DISCUSSION","CONCLUSION"],"string":"[\n \"INTRODUCTION\",\n \"METHODS\",\n \"Description of patients\",\n \"Measurements\",\n \"Scan protocol\",\n \"Statistical analysis\",\n \"RESULTS\",\n \"DISCUSSION\",\n \"CONCLUSION\"\n]"},"all_sections_texts":{"kind":"list like","value":["The incidence of anastomotic leak (AL) after right hemicolectomy is relatively low,\nin comparison with left-sided/rectal colorectal cancer (CRC). In 2015, the European\nSociety of Coloproctology (ESCP) audited right colectomy and ileocecal resection,\ncollecting prospective data on 3,208 patients across 284 centers in 39 countries.\nThe overall AL rate was 8.1%\n11\n. This is due to a more stable blood supply. However, different anatomical\nvariations can have a significant impact on the duration of surgery and cause the\ntechnical complexity of its implementation. The need for standardization is still\ndebated in the literature of Eastern D3 lymphadenectomy and Western embryologically\noriented complete mesocolic excision with central vascular ligation (CME/CVL)\n5,8\n.\nIn contrast to left and rectal cancer surgery, where the inferior mesenteric artery\nis the most important reference point, there are several such points, including the\nsuperior mesenteric vein (SMV), truncus Henle (TH), and the branches of the superior\nmesenteric artery (SMA). Not uncommon anatomical variability of the abovementioned\nvessels causes a higher percentage of conversions to open operations and increases\nintraoperative time and intraoperative blood loss\n9,12\n. It is difficult, in the scientific world of the 21st century, if not\nimpossible, to say anything new in surgical anatomy of the abdominal cavity.\nHowever, with widely used in clinical practice, contrast-enhanced computed\ntomography (CT) has made it possible to reach a qualitatively new level in\npreoperative diagnosis and determination of treatment tactics for patients with CRC.\nRoutine use of CT angiography allows a detailed analysis of each clinical case in\nthe preoperative stage and identifies various anatomical nuances that may affect the operation\n6,7\n.\nThe aim of this article was to analyze the clinical and radiological aspects that\nusually need to be discussed before surgery by a multidisciplinary team in patients\nwith right-sided colon cancer.","This study was carried out a comparative analysis of 3D-CT angiography data with\nintraoperative data. A detailed analysis of the anatomy of the branches of the SMA\nand its relationship with the surrounding structures was done in order to explore\nthe nuances that may complicate and increase the time during right hemicolectomy\nwith CME/CVL. The relationship between the anatomical features of the structure of\nSMA and postoperative complications was also investigated.\nDescription of patients We included 103 patients (56 males and 47 females; mean age 64.2±11.6) with CRC\nwho underwent preoperative 3D-CT angiography at Ternopil University Hospital\nfrom 2016 to 2021. The exclusion criteria were stage IV process and locally\nadvanced forms of cancer. The informed consents were obtained from all patients.\nThis study was passed by the Ethics Commission of Ternopil National Medical\nUniversity (no. 43).\nWe included 103 patients (56 males and 47 females; mean age 64.2±11.6) with CRC\nwho underwent preoperative 3D-CT angiography at Ternopil University Hospital\nfrom 2016 to 2021. The exclusion criteria were stage IV process and locally\nadvanced forms of cancer. The informed consents were obtained from all patients.\nThis study was passed by the Ethics Commission of Ternopil National Medical\nUniversity (no. 43).\nMeasurements In this study, the following objectives were set: determine the type of SMA;\ndetermine the distance from the root of the SMA to the root of the middle colic\nartery (MCA), the distance from the root of the MCA to the root of the ileocolic\nartery (ICA); variant structure of the right colic artery (RCA); and the\nrelationship between MCA and gastrocolic TH; and explore different variants of\nTH confluence.\nThe distance between the vascular structures was measured in the frontal plane\nusing a linear measurement. Anatomical features of the structure of SMA branches\nwere determined in the arterial phase and venous structures of TH in the venous\nphase and compared the ratio of MCA and TH using Fusion.\nIn this study, the following objectives were set: determine the type of SMA;\ndetermine the distance from the root of the SMA to the root of the middle colic\nartery (MCA), the distance from the root of the MCA to the root of the ileocolic\nartery (ICA); variant structure of the right colic artery (RCA); and the\nrelationship between MCA and gastrocolic TH; and explore different variants of\nTH confluence.\nThe distance between the vascular structures was measured in the frontal plane\nusing a linear measurement. Anatomical features of the structure of SMA branches\nwere determined in the arterial phase and venous structures of TH in the venous\nphase and compared the ratio of MCA and TH using Fusion.\nScan protocol 3D-CT angiography was performed using a Philips Brilliance 64 CT machine with IV\ncontrast (100 mL of iodinated contrast agent [370 mg/mL]). Contrast was injected\ninto the ulnar vein at a rate of 4.5 mL/s. The bolus tracking method was used\nfor scanning. Arterial phase scanning automatically began when the contrast in\nthe abdominal aorta at the level of the abdominal trunk reached 180 HU. The\n64-slice multidetector CT scanner (MDCT) can generate 0.75-mm slices that can be\nreconstructed into a 0.5-mm image. Therefore, in order to obtain high-quality CT\nangiography for preoperative analysis, a scanning protocol should be maintained:\nsublingual nitrate intake, high contrast rate (4–5 mL/s), early arterial phase\n(20–30 ‘), stress reduction (80–100 kV), and doubling the mAs. Image processing\nwas performed using 3D volume rendering technique (VRT).\n3D-CT angiography was performed using a Philips Brilliance 64 CT machine with IV\ncontrast (100 mL of iodinated contrast agent [370 mg/mL]). Contrast was injected\ninto the ulnar vein at a rate of 4.5 mL/s. The bolus tracking method was used\nfor scanning. Arterial phase scanning automatically began when the contrast in\nthe abdominal aorta at the level of the abdominal trunk reached 180 HU. The\n64-slice multidetector CT scanner (MDCT) can generate 0.75-mm slices that can be\nreconstructed into a 0.5-mm image. Therefore, in order to obtain high-quality CT\nangiography for preoperative analysis, a scanning protocol should be maintained:\nsublingual nitrate intake, high contrast rate (4–5 mL/s), early arterial phase\n(20–30 ‘), stress reduction (80–100 kV), and doubling the mAs. Image processing\nwas performed using 3D volume rendering technique (VRT).\nStatistical analysis Ordinal data were calculated using the median. All calculations were performed\nusing the Statistica version 64 software.\nOrdinal data were calculated using the median. All calculations were performed\nusing the Statistica version 64 software.","We included 103 patients (56 males and 47 females; mean age 64.2±11.6) with CRC\nwho underwent preoperative 3D-CT angiography at Ternopil University Hospital\nfrom 2016 to 2021. The exclusion criteria were stage IV process and locally\nadvanced forms of cancer. The informed consents were obtained from all patients.\nThis study was passed by the Ethics Commission of Ternopil National Medical\nUniversity (no. 43).","In this study, the following objectives were set: determine the type of SMA;\ndetermine the distance from the root of the SMA to the root of the middle colic\nartery (MCA), the distance from the root of the MCA to the root of the ileocolic\nartery (ICA); variant structure of the right colic artery (RCA); and the\nrelationship between MCA and gastrocolic TH; and explore different variants of\nTH confluence.\nThe distance between the vascular structures was measured in the frontal plane\nusing a linear measurement. Anatomical features of the structure of SMA branches\nwere determined in the arterial phase and venous structures of TH in the venous\nphase and compared the ratio of MCA and TH using Fusion.","3D-CT angiography was performed using a Philips Brilliance 64 CT machine with IV\ncontrast (100 mL of iodinated contrast agent [370 mg/mL]). Contrast was injected\ninto the ulnar vein at a rate of 4.5 mL/s. The bolus tracking method was used\nfor scanning. Arterial phase scanning automatically began when the contrast in\nthe abdominal aorta at the level of the abdominal trunk reached 180 HU. The\n64-slice multidetector CT scanner (MDCT) can generate 0.75-mm slices that can be\nreconstructed into a 0.5-mm image. Therefore, in order to obtain high-quality CT\nangiography for preoperative analysis, a scanning protocol should be maintained:\nsublingual nitrate intake, high contrast rate (4–5 mL/s), early arterial phase\n(20–30 ‘), stress reduction (80–100 kV), and doubling the mAs. Image processing\nwas performed using 3D volume rendering technique (VRT).","Ordinal data were calculated using the median. All calculations were performed\nusing the Statistica version 64 software.","All patients underwent local radical right hemicolectomy with CME/CVL and R0\nresection.\nThe median quantity of removal lymph nodes was 24.71±10.04 (range 13–58). Positive\nlymph nodes were revealed in 38.7% of cases. The incidence of metastatic lymph nodes\nwas 38.7% in D1 zone, 3.2% in D2 zone, and 9.7% in D3 zone. Mean operative time was\n82 min (range 63–130). Median intraoperative blood loss was 70 mL (range 32–280). No\npatients required intraoperative blood transfusion. Postoperative complications were\ndeveloped in seven patients. AL was diagnosed in one patient on postoperative day 8\nfor whom relaparotomy, lavage, and end stoma were performed (Figure 5). Unfortunately, on the first day after patient\ndischarge from the hospital, he died from massive thromboembolic complication,\ndespite maintaining prophylaxis therapy. One patient suffered from paralytic ileus\nin an early postoperative period. Median staying in hospital after operation was 8.4\ndays.\nThe SMA was present in 100% of cases. Compared with the widely used Zebrowski\nclassification of the inferior mesenteric artery, we could not find a common\nclassification of anatomical variations of SMA. We have identified eight types that\nare most common in practice: \nType A – MCA, RCA, and ICA deviate classically\nindependently of each other from the main SMA trunk.\nType B – RCA is absent.\nType C – RCA deviates from ICA.\nType D – RCA deviates from the right branch of the MCA or\nthe main trunk of the MCA.\nType E – Classical type A + the presence of additional MCA\n(AMCA)\nType F – Right and left MCA branches deviate separately\nfrom the main SMA trunk.\nType G – MCA and ICA have a common trunk and RCA is\nabsent.\nType H – RCA deviates from ICA + AMCA.\n\n\nType A – MCA, RCA, and ICA deviate classically\nindependently of each other from the main SMA trunk.\n\nType B – RCA is absent.\n\nType C – RCA deviates from ICA.\n\nType D – RCA deviates from the right branch of the MCA or\nthe main trunk of the MCA.\n\nType E – Classical type A + the presence of additional MCA\n(AMCA)\n\nType F – Right and left MCA branches deviate separately\nfrom the main SMA trunk.\n\nType G – MCA and ICA have a common trunk and RCA is\nabsent.\n\nType H – RCA deviates from ICA + AMCA.\nOur analysis showed that type A was detected in 27 (25.9%) patients, type B in 22\n(21.4%), type C in 20 (19.2%), type D in 12 (11.6%), type E in 9 (8.7%), type F in 9\n(8.7%), type G in 3 (2.9%), and type H in 1 (0.9%) patient (Figures 1 and 2). The\nanalysis also showed that in 12 (11.6%) patients, the right hepatic artery deviates\nfrom SMA.\nThe ICA was present in 103 (100%) cases. The ICA crossed the SMV on the anterior\nsurface in 64 (62.1%) cases and on the posterior surface of the SMV in 39 (37.9%)\ncases.\nThe RCA is one of the most volatile arterial structures of the SMA system (literature\ndata indicate that it is present in 11–40% of cases)\n1,4,12\n. According to our selected types of SMA, RCA was absent in 25 (24.3%) and in\n33 (32%) patients and deviated from ICA and MCA/RMCA. Accordingly, in 58 (56.3%)\npatients, RCA was either absent or was a nonindependent branch of SMA.\nThe MCA was present and originated directly from SMA in 103 (100%) cases. AMCA was\npresent in 10 (9.7%) cases.\nThe distance from the root of the SMA to the root of the MCA was 37.8±12.8 mm (range\n13–65).\nThe distance from the root of the MCA to the root of the ICA was 29.5±15.7 mm (range\n0–80).\nGastrocolic TH was present in 100 (97.1%) cases and located on the lower edge of the\nmesentery of the transverse colon, along the head of the pancreas, and flows into\nthe right lateral part of the SMV wall. Our analysis showed that the caliber of TH\nvaried from 3 to 10 mm and its length was 11.5±4.8 mm (range 2–33). Usually, the\nconfluence of TH formed: middle colic vein (MCV), right colic vein (RCV), additional\nmiddle colic vein (AMCV), right gastroepiploic vein (RGEV), and anterior superior\npancreaticoduodenal vein (ASPDV). Also, we observed a very interesting case where\none of the veins which create confluence of TH was ileocolic vein (ICV) (Figure 3). Our analysis of 3D-CT angiograms\nshowed the following type combinations of TH confluence: MCV+RCVRCV+RGEV+ASPDVICV+RCV+RGEVAbsence of THMCV+RGEVMCV+RGEV+ASPDV+AMCV\n\nMCV+RCV\nRCV+RGEV+ASPDV\nICV+RCV+RGEV\nAbsence of TH\nMCV+RGEV\nMCV+RGEV+ASPDV+AMCV\nRespectively, type 1 was observed in 17 (16.5%) patients, type 2 in 55 (53.4%), type\n3 in 1 (1%), type 4 in 3 (2.9%), type 5 in 15 (14.6%), and type 6 in 12 (11.6%)\npatients (Figure 3). Unfortunately, it was\nimpossible in some cases to create 3D-CT reconstruction of some types of TH due to\nlack of contrast, incorrect scanning, and various technical features.\nAn important point of preoperative planning is understanding the relationship between\nTH and MCA. In 66 (64.1%) patients, TH was located above the root of the MCA, in\nwhich case the distance between them was 12.38±5.41 mm (range 3–29). In 18 (17.5%)\npatients, TH was located below the root of the MCA, in which case the distance\nbetween them was 10.95±7.1 mm. In 16 (15.5%) patients, TH was located at the same\nlevel with the root of the MCA (Figure 4).","The well-known concept of right hemicolectomy with CME/CVL, in the past decade, has\nsupplanted the traditional old notion of colon cancer surgery and has improved\npatients’ 5-year survival\n1,5,8\n. AL is the most devastating complication in colorectal surgery. Patients who\ndeveloped an AL had a higher mortality than those who did not, a longer median\nhospital stay, and a higher 30-day reoperation and 30-day readmission rate\n11\n. In our study, we observed AL in one patient, which resulted in 30-day\nmortality (Figure 5). Retrospective analysis of\nthis case showed our mistake. According to the oncology canons, the operation was\nperformed correctly (CME/CVL), but we did not perform an analysis of vascular\nanatomy before surgery, resulting in irreversible ischemic changes in the\nanastomotic area and the actual AL.\nIn the left-sided CRC, the inferior mesenteric artery is the most important landmark\nto perform D3 lymphadenectomy, while in the right-sided colon cancer surgery, there\nare several such “central” landmarks: SMV, SMA, ICA, MCA, and TH.\nEach right hemicolectomy with CME/CVL started from dissection of SMV and\nidentification of ICA and ICV. Here we do not have any problem. However, interesting\nis the effect of the course of ICA in relation to SMV on disease-free survival with\ncorrespondingly better results in the group of patients where ICA is ahead of SMV\n6\n. Therefore, a potential group of patients with an ICA course, behind the SMV,\nrequires more precision lymphadenectomy of the apical area. In our study, we found\nthat ICA crossed the SMV on the anterior surface in 64 (62.1%) cases and on the\nposterior surface of the SMV in 39 (37.9%) cases.\nRCA is one of the most volatile arterial structures of the SMA system. Literature\ndata indicate that it is present in 11–40% of cases\n1,4,12\n. In our study, we found that the weighted mean incidence of RCA was 43.7%\nfrom the SMA, 20.4% from the ICA, and 11.6% from the root of the MCA or rMCA, and\nRCA was absent in 25 (24.3%) cases. Usually, we do not encounter any problems with\nRCA when implementing the Western concept of CME/CVL and do not pay much attention\nto it if it is not an independent branch. However, the abovementioned anatomical\nvariants of RCA should be considered when performing the Eastern concept of D3 lymph\nnode dissection (segmental resections — 10 cm from the edge of the tumor)\n10\n.\nThe next one key point for performing right hemicolectomy with CME/CVL is TH,\nespecially due to being a special area of the apical lymph nodes. TH is a\nthin-walled venous trunk that has many different combinations of formation.\nTraditional TH branches are MCV, RGEV, ASPDV, and aMCV\n1,2\n. Very often, it is in the TH area due to excessive traction of the mesentery\nduring the allocation of MCA surgeons get bleeding. It is critical to understand the\nrelationship between TH and MCA to prevent damage of this trunk (Figure 4). In our study, we found that TH was\nlocated above the root of the MCA (12.38±5.41 mm) in 66 (64.1%) patients, at the\nsame level with the root of the MCA in 16 (15.5%) patients, and was located below\nthe root of the MCA (10.95±7.1 mm) in 18 (17.5%) patients. In the situation if the\nroot of the MCA is above TH, it is safer to start mobilization from the cranial part\nof the root of transverse colon mesentery, and in cases where the root of the MCA is\nbelow TH, then the dissection of MCA should begin from the caudal part of transverse\ncolon mesentery\n7\n.\nCT is a modality of choice for staging of colon cancer and distant metastasis.\nMagnetic resonance angiography is an expensive method to perform it routinely and\npreoperatively for every patient. Therefore, CT is optimal for staging and\nevaluating mesenteric vasculature\n8\n. However, 3D-CT angiography has several limitations. First, the preoperative\nCT protocol for patients with colon cancer usually does not include the early\narterial phase and, therefore, results in some difficulty in performing adequate 3D\nreconstruction. Second, the caliber of SMA branches is usually small in diameter and\nthey are not always well visualized on 3D-CT angiograms. In the abovementioned\ncases, the use of CT in the preoperative analysis of anatomical variants of the\nstructure of SMA branches cannot be performed in 3D mode and should be performed in\nnormal 2D mode\n1\n.\nNew era of development personalized strategy could be achieved by virtual reality\nexploration and planning for precision colorectal surgery, which can provide an\nenhanced understanding of crucial anatomical details\n3\n.\nOur study has some limitation. This study is partly retrospective (observation period\nfrom 2016 to 2018) and partly prospective (observation period from 2019 to 2021), so\nwe cannot fully conduct an effective analysis between anatomical variations of\nvascular anatomy with postoperative complications in a group of cases that were\nretrospectively analyzed.","Personalized preoperative analysis of 3D-CT angiography is a key pattern in\nassessment of vascular anatomy and can potentially show the complexity of future\nlymphadenectomy, reduce intraoperative time for identifying key landmarks, and\ndevelop an individualized surgical strategy. Personalized 3D-CT assessment can\npotentially significantly reduce the risk of AL. To solve this problem, new studies\nand further standardization are needed."],"string":"[\n \"The incidence of anastomotic leak (AL) after right hemicolectomy is relatively low,\\nin comparison with left-sided/rectal colorectal cancer (CRC). In 2015, the European\\nSociety of Coloproctology (ESCP) audited right colectomy and ileocecal resection,\\ncollecting prospective data on 3,208 patients across 284 centers in 39 countries.\\nThe overall AL rate was 8.1%\\n11\\n. This is due to a more stable blood supply. However, different anatomical\\nvariations can have a significant impact on the duration of surgery and cause the\\ntechnical complexity of its implementation. The need for standardization is still\\ndebated in the literature of Eastern D3 lymphadenectomy and Western embryologically\\noriented complete mesocolic excision with central vascular ligation (CME/CVL)\\n5,8\\n.\\nIn contrast to left and rectal cancer surgery, where the inferior mesenteric artery\\nis the most important reference point, there are several such points, including the\\nsuperior mesenteric vein (SMV), truncus Henle (TH), and the branches of the superior\\nmesenteric artery (SMA). Not uncommon anatomical variability of the abovementioned\\nvessels causes a higher percentage of conversions to open operations and increases\\nintraoperative time and intraoperative blood loss\\n9,12\\n. It is difficult, in the scientific world of the 21st century, if not\\nimpossible, to say anything new in surgical anatomy of the abdominal cavity.\\nHowever, with widely used in clinical practice, contrast-enhanced computed\\ntomography (CT) has made it possible to reach a qualitatively new level in\\npreoperative diagnosis and determination of treatment tactics for patients with CRC.\\nRoutine use of CT angiography allows a detailed analysis of each clinical case in\\nthe preoperative stage and identifies various anatomical nuances that may affect the operation\\n6,7\\n.\\nThe aim of this article was to analyze the clinical and radiological aspects that\\nusually need to be discussed before surgery by a multidisciplinary team in patients\\nwith right-sided colon cancer.\",\n \"This study was carried out a comparative analysis of 3D-CT angiography data with\\nintraoperative data. A detailed analysis of the anatomy of the branches of the SMA\\nand its relationship with the surrounding structures was done in order to explore\\nthe nuances that may complicate and increase the time during right hemicolectomy\\nwith CME/CVL. The relationship between the anatomical features of the structure of\\nSMA and postoperative complications was also investigated.\\nDescription of patients We included 103 patients (56 males and 47 females; mean age 64.2±11.6) with CRC\\nwho underwent preoperative 3D-CT angiography at Ternopil University Hospital\\nfrom 2016 to 2021. The exclusion criteria were stage IV process and locally\\nadvanced forms of cancer. The informed consents were obtained from all patients.\\nThis study was passed by the Ethics Commission of Ternopil National Medical\\nUniversity (no. 43).\\nWe included 103 patients (56 males and 47 females; mean age 64.2±11.6) with CRC\\nwho underwent preoperative 3D-CT angiography at Ternopil University Hospital\\nfrom 2016 to 2021. The exclusion criteria were stage IV process and locally\\nadvanced forms of cancer. The informed consents were obtained from all patients.\\nThis study was passed by the Ethics Commission of Ternopil National Medical\\nUniversity (no. 43).\\nMeasurements In this study, the following objectives were set: determine the type of SMA;\\ndetermine the distance from the root of the SMA to the root of the middle colic\\nartery (MCA), the distance from the root of the MCA to the root of the ileocolic\\nartery (ICA); variant structure of the right colic artery (RCA); and the\\nrelationship between MCA and gastrocolic TH; and explore different variants of\\nTH confluence.\\nThe distance between the vascular structures was measured in the frontal plane\\nusing a linear measurement. Anatomical features of the structure of SMA branches\\nwere determined in the arterial phase and venous structures of TH in the venous\\nphase and compared the ratio of MCA and TH using Fusion.\\nIn this study, the following objectives were set: determine the type of SMA;\\ndetermine the distance from the root of the SMA to the root of the middle colic\\nartery (MCA), the distance from the root of the MCA to the root of the ileocolic\\nartery (ICA); variant structure of the right colic artery (RCA); and the\\nrelationship between MCA and gastrocolic TH; and explore different variants of\\nTH confluence.\\nThe distance between the vascular structures was measured in the frontal plane\\nusing a linear measurement. Anatomical features of the structure of SMA branches\\nwere determined in the arterial phase and venous structures of TH in the venous\\nphase and compared the ratio of MCA and TH using Fusion.\\nScan protocol 3D-CT angiography was performed using a Philips Brilliance 64 CT machine with IV\\ncontrast (100 mL of iodinated contrast agent [370 mg/mL]). Contrast was injected\\ninto the ulnar vein at a rate of 4.5 mL/s. The bolus tracking method was used\\nfor scanning. Arterial phase scanning automatically began when the contrast in\\nthe abdominal aorta at the level of the abdominal trunk reached 180 HU. The\\n64-slice multidetector CT scanner (MDCT) can generate 0.75-mm slices that can be\\nreconstructed into a 0.5-mm image. Therefore, in order to obtain high-quality CT\\nangiography for preoperative analysis, a scanning protocol should be maintained:\\nsublingual nitrate intake, high contrast rate (4–5 mL/s), early arterial phase\\n(20–30 ‘), stress reduction (80–100 kV), and doubling the mAs. Image processing\\nwas performed using 3D volume rendering technique (VRT).\\n3D-CT angiography was performed using a Philips Brilliance 64 CT machine with IV\\ncontrast (100 mL of iodinated contrast agent [370 mg/mL]). Contrast was injected\\ninto the ulnar vein at a rate of 4.5 mL/s. The bolus tracking method was used\\nfor scanning. Arterial phase scanning automatically began when the contrast in\\nthe abdominal aorta at the level of the abdominal trunk reached 180 HU. The\\n64-slice multidetector CT scanner (MDCT) can generate 0.75-mm slices that can be\\nreconstructed into a 0.5-mm image. Therefore, in order to obtain high-quality CT\\nangiography for preoperative analysis, a scanning protocol should be maintained:\\nsublingual nitrate intake, high contrast rate (4–5 mL/s), early arterial phase\\n(20–30 ‘), stress reduction (80–100 kV), and doubling the mAs. Image processing\\nwas performed using 3D volume rendering technique (VRT).\\nStatistical analysis Ordinal data were calculated using the median. All calculations were performed\\nusing the Statistica version 64 software.\\nOrdinal data were calculated using the median. All calculations were performed\\nusing the Statistica version 64 software.\",\n \"We included 103 patients (56 males and 47 females; mean age 64.2±11.6) with CRC\\nwho underwent preoperative 3D-CT angiography at Ternopil University Hospital\\nfrom 2016 to 2021. The exclusion criteria were stage IV process and locally\\nadvanced forms of cancer. The informed consents were obtained from all patients.\\nThis study was passed by the Ethics Commission of Ternopil National Medical\\nUniversity (no. 43).\",\n \"In this study, the following objectives were set: determine the type of SMA;\\ndetermine the distance from the root of the SMA to the root of the middle colic\\nartery (MCA), the distance from the root of the MCA to the root of the ileocolic\\nartery (ICA); variant structure of the right colic artery (RCA); and the\\nrelationship between MCA and gastrocolic TH; and explore different variants of\\nTH confluence.\\nThe distance between the vascular structures was measured in the frontal plane\\nusing a linear measurement. Anatomical features of the structure of SMA branches\\nwere determined in the arterial phase and venous structures of TH in the venous\\nphase and compared the ratio of MCA and TH using Fusion.\",\n \"3D-CT angiography was performed using a Philips Brilliance 64 CT machine with IV\\ncontrast (100 mL of iodinated contrast agent [370 mg/mL]). Contrast was injected\\ninto the ulnar vein at a rate of 4.5 mL/s. The bolus tracking method was used\\nfor scanning. Arterial phase scanning automatically began when the contrast in\\nthe abdominal aorta at the level of the abdominal trunk reached 180 HU. The\\n64-slice multidetector CT scanner (MDCT) can generate 0.75-mm slices that can be\\nreconstructed into a 0.5-mm image. Therefore, in order to obtain high-quality CT\\nangiography for preoperative analysis, a scanning protocol should be maintained:\\nsublingual nitrate intake, high contrast rate (4–5 mL/s), early arterial phase\\n(20–30 ‘), stress reduction (80–100 kV), and doubling the mAs. Image processing\\nwas performed using 3D volume rendering technique (VRT).\",\n \"Ordinal data were calculated using the median. All calculations were performed\\nusing the Statistica version 64 software.\",\n \"All patients underwent local radical right hemicolectomy with CME/CVL and R0\\nresection.\\nThe median quantity of removal lymph nodes was 24.71±10.04 (range 13–58). Positive\\nlymph nodes were revealed in 38.7% of cases. The incidence of metastatic lymph nodes\\nwas 38.7% in D1 zone, 3.2% in D2 zone, and 9.7% in D3 zone. Mean operative time was\\n82 min (range 63–130). Median intraoperative blood loss was 70 mL (range 32–280). No\\npatients required intraoperative blood transfusion. Postoperative complications were\\ndeveloped in seven patients. AL was diagnosed in one patient on postoperative day 8\\nfor whom relaparotomy, lavage, and end stoma were performed (Figure 5). Unfortunately, on the first day after patient\\ndischarge from the hospital, he died from massive thromboembolic complication,\\ndespite maintaining prophylaxis therapy. One patient suffered from paralytic ileus\\nin an early postoperative period. Median staying in hospital after operation was 8.4\\ndays.\\nThe SMA was present in 100% of cases. Compared with the widely used Zebrowski\\nclassification of the inferior mesenteric artery, we could not find a common\\nclassification of anatomical variations of SMA. We have identified eight types that\\nare most common in practice: \\nType A – MCA, RCA, and ICA deviate classically\\nindependently of each other from the main SMA trunk.\\nType B – RCA is absent.\\nType C – RCA deviates from ICA.\\nType D – RCA deviates from the right branch of the MCA or\\nthe main trunk of the MCA.\\nType E – Classical type A + the presence of additional MCA\\n(AMCA)\\nType F – Right and left MCA branches deviate separately\\nfrom the main SMA trunk.\\nType G – MCA and ICA have a common trunk and RCA is\\nabsent.\\nType H – RCA deviates from ICA + AMCA.\\n\\n\\nType A – MCA, RCA, and ICA deviate classically\\nindependently of each other from the main SMA trunk.\\n\\nType B – RCA is absent.\\n\\nType C – RCA deviates from ICA.\\n\\nType D – RCA deviates from the right branch of the MCA or\\nthe main trunk of the MCA.\\n\\nType E – Classical type A + the presence of additional MCA\\n(AMCA)\\n\\nType F – Right and left MCA branches deviate separately\\nfrom the main SMA trunk.\\n\\nType G – MCA and ICA have a common trunk and RCA is\\nabsent.\\n\\nType H – RCA deviates from ICA + AMCA.\\nOur analysis showed that type A was detected in 27 (25.9%) patients, type B in 22\\n(21.4%), type C in 20 (19.2%), type D in 12 (11.6%), type E in 9 (8.7%), type F in 9\\n(8.7%), type G in 3 (2.9%), and type H in 1 (0.9%) patient (Figures 1 and 2). The\\nanalysis also showed that in 12 (11.6%) patients, the right hepatic artery deviates\\nfrom SMA.\\nThe ICA was present in 103 (100%) cases. The ICA crossed the SMV on the anterior\\nsurface in 64 (62.1%) cases and on the posterior surface of the SMV in 39 (37.9%)\\ncases.\\nThe RCA is one of the most volatile arterial structures of the SMA system (literature\\ndata indicate that it is present in 11–40% of cases)\\n1,4,12\\n. According to our selected types of SMA, RCA was absent in 25 (24.3%) and in\\n33 (32%) patients and deviated from ICA and MCA/RMCA. Accordingly, in 58 (56.3%)\\npatients, RCA was either absent or was a nonindependent branch of SMA.\\nThe MCA was present and originated directly from SMA in 103 (100%) cases. AMCA was\\npresent in 10 (9.7%) cases.\\nThe distance from the root of the SMA to the root of the MCA was 37.8±12.8 mm (range\\n13–65).\\nThe distance from the root of the MCA to the root of the ICA was 29.5±15.7 mm (range\\n0–80).\\nGastrocolic TH was present in 100 (97.1%) cases and located on the lower edge of the\\nmesentery of the transverse colon, along the head of the pancreas, and flows into\\nthe right lateral part of the SMV wall. Our analysis showed that the caliber of TH\\nvaried from 3 to 10 mm and its length was 11.5±4.8 mm (range 2–33). Usually, the\\nconfluence of TH formed: middle colic vein (MCV), right colic vein (RCV), additional\\nmiddle colic vein (AMCV), right gastroepiploic vein (RGEV), and anterior superior\\npancreaticoduodenal vein (ASPDV). Also, we observed a very interesting case where\\none of the veins which create confluence of TH was ileocolic vein (ICV) (Figure 3). Our analysis of 3D-CT angiograms\\nshowed the following type combinations of TH confluence: MCV+RCVRCV+RGEV+ASPDVICV+RCV+RGEVAbsence of THMCV+RGEVMCV+RGEV+ASPDV+AMCV\\n\\nMCV+RCV\\nRCV+RGEV+ASPDV\\nICV+RCV+RGEV\\nAbsence of TH\\nMCV+RGEV\\nMCV+RGEV+ASPDV+AMCV\\nRespectively, type 1 was observed in 17 (16.5%) patients, type 2 in 55 (53.4%), type\\n3 in 1 (1%), type 4 in 3 (2.9%), type 5 in 15 (14.6%), and type 6 in 12 (11.6%)\\npatients (Figure 3). Unfortunately, it was\\nimpossible in some cases to create 3D-CT reconstruction of some types of TH due to\\nlack of contrast, incorrect scanning, and various technical features.\\nAn important point of preoperative planning is understanding the relationship between\\nTH and MCA. In 66 (64.1%) patients, TH was located above the root of the MCA, in\\nwhich case the distance between them was 12.38±5.41 mm (range 3–29). In 18 (17.5%)\\npatients, TH was located below the root of the MCA, in which case the distance\\nbetween them was 10.95±7.1 mm. In 16 (15.5%) patients, TH was located at the same\\nlevel with the root of the MCA (Figure 4).\",\n \"The well-known concept of right hemicolectomy with CME/CVL, in the past decade, has\\nsupplanted the traditional old notion of colon cancer surgery and has improved\\npatients’ 5-year survival\\n1,5,8\\n. AL is the most devastating complication in colorectal surgery. Patients who\\ndeveloped an AL had a higher mortality than those who did not, a longer median\\nhospital stay, and a higher 30-day reoperation and 30-day readmission rate\\n11\\n. In our study, we observed AL in one patient, which resulted in 30-day\\nmortality (Figure 5). Retrospective analysis of\\nthis case showed our mistake. According to the oncology canons, the operation was\\nperformed correctly (CME/CVL), but we did not perform an analysis of vascular\\nanatomy before surgery, resulting in irreversible ischemic changes in the\\nanastomotic area and the actual AL.\\nIn the left-sided CRC, the inferior mesenteric artery is the most important landmark\\nto perform D3 lymphadenectomy, while in the right-sided colon cancer surgery, there\\nare several such “central” landmarks: SMV, SMA, ICA, MCA, and TH.\\nEach right hemicolectomy with CME/CVL started from dissection of SMV and\\nidentification of ICA and ICV. Here we do not have any problem. However, interesting\\nis the effect of the course of ICA in relation to SMV on disease-free survival with\\ncorrespondingly better results in the group of patients where ICA is ahead of SMV\\n6\\n. Therefore, a potential group of patients with an ICA course, behind the SMV,\\nrequires more precision lymphadenectomy of the apical area. In our study, we found\\nthat ICA crossed the SMV on the anterior surface in 64 (62.1%) cases and on the\\nposterior surface of the SMV in 39 (37.9%) cases.\\nRCA is one of the most volatile arterial structures of the SMA system. Literature\\ndata indicate that it is present in 11–40% of cases\\n1,4,12\\n. In our study, we found that the weighted mean incidence of RCA was 43.7%\\nfrom the SMA, 20.4% from the ICA, and 11.6% from the root of the MCA or rMCA, and\\nRCA was absent in 25 (24.3%) cases. Usually, we do not encounter any problems with\\nRCA when implementing the Western concept of CME/CVL and do not pay much attention\\nto it if it is not an independent branch. However, the abovementioned anatomical\\nvariants of RCA should be considered when performing the Eastern concept of D3 lymph\\nnode dissection (segmental resections — 10 cm from the edge of the tumor)\\n10\\n.\\nThe next one key point for performing right hemicolectomy with CME/CVL is TH,\\nespecially due to being a special area of the apical lymph nodes. TH is a\\nthin-walled venous trunk that has many different combinations of formation.\\nTraditional TH branches are MCV, RGEV, ASPDV, and aMCV\\n1,2\\n. Very often, it is in the TH area due to excessive traction of the mesentery\\nduring the allocation of MCA surgeons get bleeding. It is critical to understand the\\nrelationship between TH and MCA to prevent damage of this trunk (Figure 4). In our study, we found that TH was\\nlocated above the root of the MCA (12.38±5.41 mm) in 66 (64.1%) patients, at the\\nsame level with the root of the MCA in 16 (15.5%) patients, and was located below\\nthe root of the MCA (10.95±7.1 mm) in 18 (17.5%) patients. In the situation if the\\nroot of the MCA is above TH, it is safer to start mobilization from the cranial part\\nof the root of transverse colon mesentery, and in cases where the root of the MCA is\\nbelow TH, then the dissection of MCA should begin from the caudal part of transverse\\ncolon mesentery\\n7\\n.\\nCT is a modality of choice for staging of colon cancer and distant metastasis.\\nMagnetic resonance angiography is an expensive method to perform it routinely and\\npreoperatively for every patient. Therefore, CT is optimal for staging and\\nevaluating mesenteric vasculature\\n8\\n. However, 3D-CT angiography has several limitations. First, the preoperative\\nCT protocol for patients with colon cancer usually does not include the early\\narterial phase and, therefore, results in some difficulty in performing adequate 3D\\nreconstruction. Second, the caliber of SMA branches is usually small in diameter and\\nthey are not always well visualized on 3D-CT angiograms. In the abovementioned\\ncases, the use of CT in the preoperative analysis of anatomical variants of the\\nstructure of SMA branches cannot be performed in 3D mode and should be performed in\\nnormal 2D mode\\n1\\n.\\nNew era of development personalized strategy could be achieved by virtual reality\\nexploration and planning for precision colorectal surgery, which can provide an\\nenhanced understanding of crucial anatomical details\\n3\\n.\\nOur study has some limitation. This study is partly retrospective (observation period\\nfrom 2016 to 2018) and partly prospective (observation period from 2019 to 2021), so\\nwe cannot fully conduct an effective analysis between anatomical variations of\\nvascular anatomy with postoperative complications in a group of cases that were\\nretrospectively analyzed.\",\n \"Personalized preoperative analysis of 3D-CT angiography is a key pattern in\\nassessment of vascular anatomy and can potentially show the complexity of future\\nlymphadenectomy, reduce intraoperative time for identifying key landmarks, and\\ndevelop an individualized surgical strategy. Personalized 3D-CT assessment can\\npotentially significantly reduce the risk of AL. To solve this problem, new studies\\nand further standardization are needed.\"\n]"},"all_sections_sec_types":{"kind":"list like","value":["intro","methods",null,null,null,null,"results","discussion","conclusions"],"string":"[\n \"intro\",\n \"methods\",\n null,\n null,\n null,\n null,\n \"results\",\n \"discussion\",\n \"conclusions\"\n]"},"keywords":{"kind":"list like","value":["Colonic Neoplasms","Anastomotic Leak","Tomography","X-Ray Computed","Angiography","Neoplasias do Colo","Fístula Anastomótica","Tomografia Computadorizada por Rx","Angiografia"],"string":"[\n \"Colonic Neoplasms\",\n \"Anastomotic Leak\",\n \"Tomography\",\n \"X-Ray Computed\",\n \"Angiography\",\n \"Neoplasias do Colo\",\n \"Fístula Anastomótica\",\n \"Tomografia Computadorizada por Rx\",\n \"Angiografia\"\n]"},"whole_article_text":{"kind":"string","value":"INTRODUCTION:\nThe incidence of anastomotic leak (AL) after right hemicolectomy is relatively low,\nin comparison with left-sided/rectal colorectal cancer (CRC). In 2015, the European\nSociety of Coloproctology (ESCP) audited right colectomy and ileocecal resection,\ncollecting prospective data on 3,208 patients across 284 centers in 39 countries.\nThe overall AL rate was 8.1%\n11\n. This is due to a more stable blood supply. However, different anatomical\nvariations can have a significant impact on the duration of surgery and cause the\ntechnical complexity of its implementation. The need for standardization is still\ndebated in the literature of Eastern D3 lymphadenectomy and Western embryologically\noriented complete mesocolic excision with central vascular ligation (CME/CVL)\n5,8\n.\nIn contrast to left and rectal cancer surgery, where the inferior mesenteric artery\nis the most important reference point, there are several such points, including the\nsuperior mesenteric vein (SMV), truncus Henle (TH), and the branches of the superior\nmesenteric artery (SMA). Not uncommon anatomical variability of the abovementioned\nvessels causes a higher percentage of conversions to open operations and increases\nintraoperative time and intraoperative blood loss\n9,12\n. It is difficult, in the scientific world of the 21st century, if not\nimpossible, to say anything new in surgical anatomy of the abdominal cavity.\nHowever, with widely used in clinical practice, contrast-enhanced computed\ntomography (CT) has made it possible to reach a qualitatively new level in\npreoperative diagnosis and determination of treatment tactics for patients with CRC.\nRoutine use of CT angiography allows a detailed analysis of each clinical case in\nthe preoperative stage and identifies various anatomical nuances that may affect the operation\n6,7\n.\nThe aim of this article was to analyze the clinical and radiological aspects that\nusually need to be discussed before surgery by a multidisciplinary team in patients\nwith right-sided colon cancer.\n\nMETHODS:\nThis study was carried out a comparative analysis of 3D-CT angiography data with\nintraoperative data. A detailed analysis of the anatomy of the branches of the SMA\nand its relationship with the surrounding structures was done in order to explore\nthe nuances that may complicate and increase the time during right hemicolectomy\nwith CME/CVL. The relationship between the anatomical features of the structure of\nSMA and postoperative complications was also investigated.\nDescription of patients We included 103 patients (56 males and 47 females; mean age 64.2±11.6) with CRC\nwho underwent preoperative 3D-CT angiography at Ternopil University Hospital\nfrom 2016 to 2021. The exclusion criteria were stage IV process and locally\nadvanced forms of cancer. The informed consents were obtained from all patients.\nThis study was passed by the Ethics Commission of Ternopil National Medical\nUniversity (no. 43).\nWe included 103 patients (56 males and 47 females; mean age 64.2±11.6) with CRC\nwho underwent preoperative 3D-CT angiography at Ternopil University Hospital\nfrom 2016 to 2021. The exclusion criteria were stage IV process and locally\nadvanced forms of cancer. The informed consents were obtained from all patients.\nThis study was passed by the Ethics Commission of Ternopil National Medical\nUniversity (no. 43).\nMeasurements In this study, the following objectives were set: determine the type of SMA;\ndetermine the distance from the root of the SMA to the root of the middle colic\nartery (MCA), the distance from the root of the MCA to the root of the ileocolic\nartery (ICA); variant structure of the right colic artery (RCA); and the\nrelationship between MCA and gastrocolic TH; and explore different variants of\nTH confluence.\nThe distance between the vascular structures was measured in the frontal plane\nusing a linear measurement. Anatomical features of the structure of SMA branches\nwere determined in the arterial phase and venous structures of TH in the venous\nphase and compared the ratio of MCA and TH using Fusion.\nIn this study, the following objectives were set: determine the type of SMA;\ndetermine the distance from the root of the SMA to the root of the middle colic\nartery (MCA), the distance from the root of the MCA to the root of the ileocolic\nartery (ICA); variant structure of the right colic artery (RCA); and the\nrelationship between MCA and gastrocolic TH; and explore different variants of\nTH confluence.\nThe distance between the vascular structures was measured in the frontal plane\nusing a linear measurement. Anatomical features of the structure of SMA branches\nwere determined in the arterial phase and venous structures of TH in the venous\nphase and compared the ratio of MCA and TH using Fusion.\nScan protocol 3D-CT angiography was performed using a Philips Brilliance 64 CT machine with IV\ncontrast (100 mL of iodinated contrast agent [370 mg/mL]). Contrast was injected\ninto the ulnar vein at a rate of 4.5 mL/s. The bolus tracking method was used\nfor scanning. Arterial phase scanning automatically began when the contrast in\nthe abdominal aorta at the level of the abdominal trunk reached 180 HU. The\n64-slice multidetector CT scanner (MDCT) can generate 0.75-mm slices that can be\nreconstructed into a 0.5-mm image. Therefore, in order to obtain high-quality CT\nangiography for preoperative analysis, a scanning protocol should be maintained:\nsublingual nitrate intake, high contrast rate (4–5 mL/s), early arterial phase\n(20–30 ‘), stress reduction (80–100 kV), and doubling the mAs. Image processing\nwas performed using 3D volume rendering technique (VRT).\n3D-CT angiography was performed using a Philips Brilliance 64 CT machine with IV\ncontrast (100 mL of iodinated contrast agent [370 mg/mL]). Contrast was injected\ninto the ulnar vein at a rate of 4.5 mL/s. The bolus tracking method was used\nfor scanning. Arterial phase scanning automatically began when the contrast in\nthe abdominal aorta at the level of the abdominal trunk reached 180 HU. The\n64-slice multidetector CT scanner (MDCT) can generate 0.75-mm slices that can be\nreconstructed into a 0.5-mm image. Therefore, in order to obtain high-quality CT\nangiography for preoperative analysis, a scanning protocol should be maintained:\nsublingual nitrate intake, high contrast rate (4–5 mL/s), early arterial phase\n(20–30 ‘), stress reduction (80–100 kV), and doubling the mAs. Image processing\nwas performed using 3D volume rendering technique (VRT).\nStatistical analysis Ordinal data were calculated using the median. All calculations were performed\nusing the Statistica version 64 software.\nOrdinal data were calculated using the median. All calculations were performed\nusing the Statistica version 64 software.\n\nDescription of patients:\nWe included 103 patients (56 males and 47 females; mean age 64.2±11.6) with CRC\nwho underwent preoperative 3D-CT angiography at Ternopil University Hospital\nfrom 2016 to 2021. The exclusion criteria were stage IV process and locally\nadvanced forms of cancer. The informed consents were obtained from all patients.\nThis study was passed by the Ethics Commission of Ternopil National Medical\nUniversity (no. 43).\n\nMeasurements:\nIn this study, the following objectives were set: determine the type of SMA;\ndetermine the distance from the root of the SMA to the root of the middle colic\nartery (MCA), the distance from the root of the MCA to the root of the ileocolic\nartery (ICA); variant structure of the right colic artery (RCA); and the\nrelationship between MCA and gastrocolic TH; and explore different variants of\nTH confluence.\nThe distance between the vascular structures was measured in the frontal plane\nusing a linear measurement. Anatomical features of the structure of SMA branches\nwere determined in the arterial phase and venous structures of TH in the venous\nphase and compared the ratio of MCA and TH using Fusion.\n\nScan protocol:\n3D-CT angiography was performed using a Philips Brilliance 64 CT machine with IV\ncontrast (100 mL of iodinated contrast agent [370 mg/mL]). Contrast was injected\ninto the ulnar vein at a rate of 4.5 mL/s. The bolus tracking method was used\nfor scanning. Arterial phase scanning automatically began when the contrast in\nthe abdominal aorta at the level of the abdominal trunk reached 180 HU. The\n64-slice multidetector CT scanner (MDCT) can generate 0.75-mm slices that can be\nreconstructed into a 0.5-mm image. Therefore, in order to obtain high-quality CT\nangiography for preoperative analysis, a scanning protocol should be maintained:\nsublingual nitrate intake, high contrast rate (4–5 mL/s), early arterial phase\n(20–30 ‘), stress reduction (80–100 kV), and doubling the mAs. Image processing\nwas performed using 3D volume rendering technique (VRT).\n\nStatistical analysis:\nOrdinal data were calculated using the median. All calculations were performed\nusing the Statistica version 64 software.\n\nRESULTS:\nAll patients underwent local radical right hemicolectomy with CME/CVL and R0\nresection.\nThe median quantity of removal lymph nodes was 24.71±10.04 (range 13–58). Positive\nlymph nodes were revealed in 38.7% of cases. The incidence of metastatic lymph nodes\nwas 38.7% in D1 zone, 3.2% in D2 zone, and 9.7% in D3 zone. Mean operative time was\n82 min (range 63–130). Median intraoperative blood loss was 70 mL (range 32–280). No\npatients required intraoperative blood transfusion. Postoperative complications were\ndeveloped in seven patients. AL was diagnosed in one patient on postoperative day 8\nfor whom relaparotomy, lavage, and end stoma were performed (Figure 5). Unfortunately, on the first day after patient\ndischarge from the hospital, he died from massive thromboembolic complication,\ndespite maintaining prophylaxis therapy. One patient suffered from paralytic ileus\nin an early postoperative period. Median staying in hospital after operation was 8.4\ndays.\nThe SMA was present in 100% of cases. Compared with the widely used Zebrowski\nclassification of the inferior mesenteric artery, we could not find a common\nclassification of anatomical variations of SMA. We have identified eight types that\nare most common in practice: \nType A – MCA, RCA, and ICA deviate classically\nindependently of each other from the main SMA trunk.\nType B – RCA is absent.\nType C – RCA deviates from ICA.\nType D – RCA deviates from the right branch of the MCA or\nthe main trunk of the MCA.\nType E – Classical type A + the presence of additional MCA\n(AMCA)\nType F – Right and left MCA branches deviate separately\nfrom the main SMA trunk.\nType G – MCA and ICA have a common trunk and RCA is\nabsent.\nType H – RCA deviates from ICA + AMCA.\n\n\nType A – MCA, RCA, and ICA deviate classically\nindependently of each other from the main SMA trunk.\n\nType B – RCA is absent.\n\nType C – RCA deviates from ICA.\n\nType D – RCA deviates from the right branch of the MCA or\nthe main trunk of the MCA.\n\nType E – Classical type A + the presence of additional MCA\n(AMCA)\n\nType F – Right and left MCA branches deviate separately\nfrom the main SMA trunk.\n\nType G – MCA and ICA have a common trunk and RCA is\nabsent.\n\nType H – RCA deviates from ICA + AMCA.\nOur analysis showed that type A was detected in 27 (25.9%) patients, type B in 22\n(21.4%), type C in 20 (19.2%), type D in 12 (11.6%), type E in 9 (8.7%), type F in 9\n(8.7%), type G in 3 (2.9%), and type H in 1 (0.9%) patient (Figures 1 and 2). The\nanalysis also showed that in 12 (11.6%) patients, the right hepatic artery deviates\nfrom SMA.\nThe ICA was present in 103 (100%) cases. The ICA crossed the SMV on the anterior\nsurface in 64 (62.1%) cases and on the posterior surface of the SMV in 39 (37.9%)\ncases.\nThe RCA is one of the most volatile arterial structures of the SMA system (literature\ndata indicate that it is present in 11–40% of cases)\n1,4,12\n. According to our selected types of SMA, RCA was absent in 25 (24.3%) and in\n33 (32%) patients and deviated from ICA and MCA/RMCA. Accordingly, in 58 (56.3%)\npatients, RCA was either absent or was a nonindependent branch of SMA.\nThe MCA was present and originated directly from SMA in 103 (100%) cases. AMCA was\npresent in 10 (9.7%) cases.\nThe distance from the root of the SMA to the root of the MCA was 37.8±12.8 mm (range\n13–65).\nThe distance from the root of the MCA to the root of the ICA was 29.5±15.7 mm (range\n0–80).\nGastrocolic TH was present in 100 (97.1%) cases and located on the lower edge of the\nmesentery of the transverse colon, along the head of the pancreas, and flows into\nthe right lateral part of the SMV wall. Our analysis showed that the caliber of TH\nvaried from 3 to 10 mm and its length was 11.5±4.8 mm (range 2–33). Usually, the\nconfluence of TH formed: middle colic vein (MCV), right colic vein (RCV), additional\nmiddle colic vein (AMCV), right gastroepiploic vein (RGEV), and anterior superior\npancreaticoduodenal vein (ASPDV). Also, we observed a very interesting case where\none of the veins which create confluence of TH was ileocolic vein (ICV) (Figure 3). Our analysis of 3D-CT angiograms\nshowed the following type combinations of TH confluence: MCV+RCVRCV+RGEV+ASPDVICV+RCV+RGEVAbsence of THMCV+RGEVMCV+RGEV+ASPDV+AMCV\n\nMCV+RCV\nRCV+RGEV+ASPDV\nICV+RCV+RGEV\nAbsence of TH\nMCV+RGEV\nMCV+RGEV+ASPDV+AMCV\nRespectively, type 1 was observed in 17 (16.5%) patients, type 2 in 55 (53.4%), type\n3 in 1 (1%), type 4 in 3 (2.9%), type 5 in 15 (14.6%), and type 6 in 12 (11.6%)\npatients (Figure 3). Unfortunately, it was\nimpossible in some cases to create 3D-CT reconstruction of some types of TH due to\nlack of contrast, incorrect scanning, and various technical features.\nAn important point of preoperative planning is understanding the relationship between\nTH and MCA. In 66 (64.1%) patients, TH was located above the root of the MCA, in\nwhich case the distance between them was 12.38±5.41 mm (range 3–29). In 18 (17.5%)\npatients, TH was located below the root of the MCA, in which case the distance\nbetween them was 10.95±7.1 mm. In 16 (15.5%) patients, TH was located at the same\nlevel with the root of the MCA (Figure 4).\n\nDISCUSSION:\nThe well-known concept of right hemicolectomy with CME/CVL, in the past decade, has\nsupplanted the traditional old notion of colon cancer surgery and has improved\npatients’ 5-year survival\n1,5,8\n. AL is the most devastating complication in colorectal surgery. Patients who\ndeveloped an AL had a higher mortality than those who did not, a longer median\nhospital stay, and a higher 30-day reoperation and 30-day readmission rate\n11\n. In our study, we observed AL in one patient, which resulted in 30-day\nmortality (Figure 5). Retrospective analysis of\nthis case showed our mistake. According to the oncology canons, the operation was\nperformed correctly (CME/CVL), but we did not perform an analysis of vascular\nanatomy before surgery, resulting in irreversible ischemic changes in the\nanastomotic area and the actual AL.\nIn the left-sided CRC, the inferior mesenteric artery is the most important landmark\nto perform D3 lymphadenectomy, while in the right-sided colon cancer surgery, there\nare several such “central” landmarks: SMV, SMA, ICA, MCA, and TH.\nEach right hemicolectomy with CME/CVL started from dissection of SMV and\nidentification of ICA and ICV. Here we do not have any problem. However, interesting\nis the effect of the course of ICA in relation to SMV on disease-free survival with\ncorrespondingly better results in the group of patients where ICA is ahead of SMV\n6\n. Therefore, a potential group of patients with an ICA course, behind the SMV,\nrequires more precision lymphadenectomy of the apical area. In our study, we found\nthat ICA crossed the SMV on the anterior surface in 64 (62.1%) cases and on the\nposterior surface of the SMV in 39 (37.9%) cases.\nRCA is one of the most volatile arterial structures of the SMA system. Literature\ndata indicate that it is present in 11–40% of cases\n1,4,12\n. In our study, we found that the weighted mean incidence of RCA was 43.7%\nfrom the SMA, 20.4% from the ICA, and 11.6% from the root of the MCA or rMCA, and\nRCA was absent in 25 (24.3%) cases. Usually, we do not encounter any problems with\nRCA when implementing the Western concept of CME/CVL and do not pay much attention\nto it if it is not an independent branch. However, the abovementioned anatomical\nvariants of RCA should be considered when performing the Eastern concept of D3 lymph\nnode dissection (segmental resections — 10 cm from the edge of the tumor)\n10\n.\nThe next one key point for performing right hemicolectomy with CME/CVL is TH,\nespecially due to being a special area of the apical lymph nodes. TH is a\nthin-walled venous trunk that has many different combinations of formation.\nTraditional TH branches are MCV, RGEV, ASPDV, and aMCV\n1,2\n. Very often, it is in the TH area due to excessive traction of the mesentery\nduring the allocation of MCA surgeons get bleeding. It is critical to understand the\nrelationship between TH and MCA to prevent damage of this trunk (Figure 4). In our study, we found that TH was\nlocated above the root of the MCA (12.38±5.41 mm) in 66 (64.1%) patients, at the\nsame level with the root of the MCA in 16 (15.5%) patients, and was located below\nthe root of the MCA (10.95±7.1 mm) in 18 (17.5%) patients. In the situation if the\nroot of the MCA is above TH, it is safer to start mobilization from the cranial part\nof the root of transverse colon mesentery, and in cases where the root of the MCA is\nbelow TH, then the dissection of MCA should begin from the caudal part of transverse\ncolon mesentery\n7\n.\nCT is a modality of choice for staging of colon cancer and distant metastasis.\nMagnetic resonance angiography is an expensive method to perform it routinely and\npreoperatively for every patient. Therefore, CT is optimal for staging and\nevaluating mesenteric vasculature\n8\n. However, 3D-CT angiography has several limitations. First, the preoperative\nCT protocol for patients with colon cancer usually does not include the early\narterial phase and, therefore, results in some difficulty in performing adequate 3D\nreconstruction. Second, the caliber of SMA branches is usually small in diameter and\nthey are not always well visualized on 3D-CT angiograms. In the abovementioned\ncases, the use of CT in the preoperative analysis of anatomical variants of the\nstructure of SMA branches cannot be performed in 3D mode and should be performed in\nnormal 2D mode\n1\n.\nNew era of development personalized strategy could be achieved by virtual reality\nexploration and planning for precision colorectal surgery, which can provide an\nenhanced understanding of crucial anatomical details\n3\n.\nOur study has some limitation. This study is partly retrospective (observation period\nfrom 2016 to 2018) and partly prospective (observation period from 2019 to 2021), so\nwe cannot fully conduct an effective analysis between anatomical variations of\nvascular anatomy with postoperative complications in a group of cases that were\nretrospectively analyzed.\n\nCONCLUSION:\nPersonalized preoperative analysis of 3D-CT angiography is a key pattern in\nassessment of vascular anatomy and can potentially show the complexity of future\nlymphadenectomy, reduce intraoperative time for identifying key landmarks, and\ndevelop an individualized surgical strategy. Personalized 3D-CT assessment can\npotentially significantly reduce the risk of AL. To solve this problem, new studies\nand further standardization are needed.\n\n"},"whole_article_abstract":{"kind":"string","value":"Background:\n3D-CT angiography has made it possible to reach a qualitatively new level in the determination of treatment tactics for patients with colorectal cancer.\n\nMethods:\nThis study involved 103 patients with colorectal cancer who underwent preoperative 3D-CT angiography from 2016 to 2021.\n\nResults:\nAll patients underwent radical D3 right hemicolectomy. The median quantity of removal lymph nodes were 24.71±10.04. Anastomotic leakage was diagnosed in one patient. We have identified eight most common types of superior mesenteric artery. The ileocolic artery crossed the superior mesenteric vein on the anterior surface in 64 (62.1%) patients and on the posterior surface in 39 (37.9%). In 58 (56.3%) patients, the right colic artery was either absent or was a nonindependent branch of superior mesenteric artery. The distance from the root of the superior mesenteric artery to the root of the middle colic artery was 37.8±12.8 mm and that from the root of the middle colic artery to the root of the ileocolic artery was 29.5±15.7 mm. The trunk of Henle was above the root of the middle colic artery in 66 (64.1%) patients, at the same level with the middle colic artery in 16 (15.5%), and below the middle colic artery in 18 (17.5%) patients.\n\nConclusions:\nPreoperative analysis of 3D-CT angiography is a key pattern in assessment of vascular anatomy and can potentially show the complexity of future lymphadenectomy and reduce the risk of anastomotic leakage."},"background_conclusion_text":{"kind":"string","value":"INTRODUCTION:\nThe incidence of anastomotic leak (AL) after right hemicolectomy is relatively low,\nin comparison with left-sided/rectal colorectal cancer (CRC). In 2015, the European\nSociety of Coloproctology (ESCP) audited right colectomy and ileocecal resection,\ncollecting prospective data on 3,208 patients across 284 centers in 39 countries.\nThe overall AL rate was 8.1%\n11\n. This is due to a more stable blood supply. However, different anatomical\nvariations can have a significant impact on the duration of surgery and cause the\ntechnical complexity of its implementation. The need for standardization is still\ndebated in the literature of Eastern D3 lymphadenectomy and Western embryologically\noriented complete mesocolic excision with central vascular ligation (CME/CVL)\n5,8\n.\nIn contrast to left and rectal cancer surgery, where the inferior mesenteric artery\nis the most important reference point, there are several such points, including the\nsuperior mesenteric vein (SMV), truncus Henle (TH), and the branches of the superior\nmesenteric artery (SMA). Not uncommon anatomical variability of the abovementioned\nvessels causes a higher percentage of conversions to open operations and increases\nintraoperative time and intraoperative blood loss\n9,12\n. It is difficult, in the scientific world of the 21st century, if not\nimpossible, to say anything new in surgical anatomy of the abdominal cavity.\nHowever, with widely used in clinical practice, contrast-enhanced computed\ntomography (CT) has made it possible to reach a qualitatively new level in\npreoperative diagnosis and determination of treatment tactics for patients with CRC.\nRoutine use of CT angiography allows a detailed analysis of each clinical case in\nthe preoperative stage and identifies various anatomical nuances that may affect the operation\n6,7\n.\nThe aim of this article was to analyze the clinical and radiological aspects that\nusually need to be discussed before surgery by a multidisciplinary team in patients\nwith right-sided colon cancer.\n\nCONCLUSION:\nPersonalized preoperative analysis of 3D-CT angiography is a key pattern in\nassessment of vascular anatomy and can potentially show the complexity of future\nlymphadenectomy, reduce intraoperative time for identifying key landmarks, and\ndevelop an individualized surgical strategy. Personalized 3D-CT assessment can\npotentially significantly reduce the risk of AL. To solve this problem, new studies\nand further standardization are needed."},"background_conclusion_abstract":{"kind":"string","value":"Background:\n3D-CT angiography has made it possible to reach a qualitatively new level in the determination of treatment tactics for patients with colorectal cancer.\n\nMethods:\nThis study involved 103 patients with colorectal cancer who underwent preoperative 3D-CT angiography from 2016 to 2021.\n\nResults:\nAll patients underwent radical D3 right hemicolectomy. The median quantity of removal lymph nodes were 24.71±10.04. Anastomotic leakage was diagnosed in one patient. We have identified eight most common types of superior mesenteric artery. The ileocolic artery crossed the superior mesenteric vein on the anterior surface in 64 (62.1%) patients and on the posterior surface in 39 (37.9%). In 58 (56.3%) patients, the right colic artery was either absent or was a nonindependent branch of superior mesenteric artery. The distance from the root of the superior mesenteric artery to the root of the middle colic artery was 37.8±12.8 mm and that from the root of the middle colic artery to the root of the ileocolic artery was 29.5±15.7 mm. The trunk of Henle was above the root of the middle colic artery in 66 (64.1%) patients, at the same level with the middle colic artery in 16 (15.5%), and below the middle colic artery in 18 (17.5%) patients.\n\nConclusions:\nPreoperative analysis of 3D-CT angiography is a key pattern in assessment of vascular anatomy and can potentially show the complexity of future lymphadenectomy and reduce the risk of anastomotic leakage."},"whole_article_text_length":{"kind":"number","value":4139,"string":"4,139"},"whole_article_abstract_length":{"kind":"number","value":281,"string":"281"},"other_sections_lengths":{"kind":"list like","value":[80,144,184,20],"string":"[\n 80,\n 144,\n 184,\n 20\n]"},"num_sections":{"kind":"number","value":9,"string":"9"},"most_frequent_words":{"kind":"list like","value":["mca","type","th","patients","sma","ct","root","rca","ica","right"],"string":"[\n \"mca\",\n \"type\",\n \"th\",\n \"patients\",\n \"sma\",\n \"ct\",\n \"root\",\n \"rca\",\n \"ica\",\n \"right\"\n]"},"keybert_topics":{"kind":"list like","value":["precision colorectal surgery","colectomy ileocecal resection","cancer surgery central","leak right hemicolectomy","rectal cancer surgery"],"string":"[\n \"precision colorectal surgery\",\n \"colectomy ileocecal resection\",\n \"cancer surgery central\",\n \"leak right hemicolectomy\",\n \"rectal cancer surgery\"\n]"},"annotated_base_background_abstract_prompt":{"kind":"string","value":""},"annotated_base_methods_abstract_prompt":{"kind":"string","value":""},"annotated_base_results_abstract_prompt":{"kind":"string","value":""},"annotated_base_conclusions_abstract_prompt":{"kind":"string","value":""},"annotated_base_whole_article_abstract_prompt":{"kind":"string","value":""},"annotated_base_background_conclusion_abstract_prompt":{"kind":"string","value":""},"annotated_keywords_background_abstract_prompt":{"kind":"string","value":"[CONTENT] Colonic Neoplasms | Anastomotic Leak | Tomography | X-Ray Computed | Angiography | Neoplasias do Colo | Fístula Anastomótica | Tomografia Computadorizada por Rx | Angiografia [SUMMARY]"},"annotated_keywords_methods_abstract_prompt":{"kind":"string","value":"[CONTENT] Colonic Neoplasms | Anastomotic Leak | Tomography | X-Ray Computed | Angiography | Neoplasias do Colo | Fístula Anastomótica | Tomografia Computadorizada por Rx | Angiografia [SUMMARY]"},"annotated_keywords_results_abstract_prompt":{"kind":"string","value":"[CONTENT] Colonic Neoplasms | Anastomotic Leak | Tomography | X-Ray Computed | Angiography | Neoplasias do Colo | Fístula Anastomótica | Tomografia Computadorizada por Rx | Angiografia [SUMMARY]"},"annotated_keywords_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] Colonic Neoplasms | Anastomotic Leak | Tomography | X-Ray Computed | Angiography | Neoplasias do Colo | Fístula Anastomótica | Tomografia Computadorizada por Rx | Angiografia [SUMMARY]"},"annotated_keywords_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] Colonic Neoplasms | Anastomotic Leak | Tomography | X-Ray Computed | Angiography | Neoplasias do Colo | Fístula Anastomótica | Tomografia Computadorizada por Rx | Angiografia [SUMMARY]"},"annotated_keywords_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] Colonic Neoplasms | Anastomotic Leak | Tomography | X-Ray Computed | Angiography | Neoplasias do Colo | Fístula Anastomótica | Tomografia Computadorizada por Rx | Angiografia [SUMMARY]"},"annotated_mesh_background_abstract_prompt":{"kind":"string","value":"[CONTENT] Anastomotic Leak | Colectomy | Colonic Neoplasms | Computed Tomography Angiography | Humans | Laparoscopy | Radiologists | Surgeons [SUMMARY]"},"annotated_mesh_methods_abstract_prompt":{"kind":"string","value":"[CONTENT] Anastomotic Leak | Colectomy | Colonic Neoplasms | Computed Tomography Angiography | Humans | Laparoscopy | Radiologists | Surgeons [SUMMARY]"},"annotated_mesh_results_abstract_prompt":{"kind":"string","value":"[CONTENT] Anastomotic Leak | Colectomy | Colonic Neoplasms | Computed Tomography Angiography | Humans | Laparoscopy | Radiologists | Surgeons [SUMMARY]"},"annotated_mesh_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] Anastomotic Leak | Colectomy | Colonic Neoplasms | Computed Tomography Angiography | Humans | Laparoscopy | Radiologists | Surgeons [SUMMARY]"},"annotated_mesh_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] Anastomotic Leak | Colectomy | Colonic Neoplasms | Computed Tomography Angiography | Humans | Laparoscopy | Radiologists | Surgeons [SUMMARY]"},"annotated_mesh_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] Anastomotic Leak | Colectomy | Colonic Neoplasms | Computed Tomography Angiography | Humans | Laparoscopy | Radiologists | Surgeons [SUMMARY]"},"annotated_keybert_background_abstract_prompt":{"kind":"string","value":"[CONTENT] precision colorectal surgery | colectomy ileocecal resection | cancer surgery central | leak right hemicolectomy | rectal cancer surgery [SUMMARY]"},"annotated_keybert_methods_abstract_prompt":{"kind":"string","value":"[CONTENT] precision colorectal surgery | colectomy ileocecal resection | cancer surgery central | leak right hemicolectomy | rectal cancer surgery [SUMMARY]"},"annotated_keybert_results_abstract_prompt":{"kind":"string","value":"[CONTENT] precision colorectal surgery | colectomy ileocecal resection | cancer surgery central | leak right hemicolectomy | rectal cancer surgery [SUMMARY]"},"annotated_keybert_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] precision colorectal surgery | colectomy ileocecal resection | cancer surgery central | leak right hemicolectomy | rectal cancer surgery [SUMMARY]"},"annotated_keybert_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] precision colorectal surgery | colectomy ileocecal resection | cancer surgery central | leak right hemicolectomy | rectal cancer surgery [SUMMARY]"},"annotated_keybert_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] precision colorectal surgery | colectomy ileocecal resection | cancer surgery central | leak right hemicolectomy | rectal cancer surgery [SUMMARY]"},"annotated_most_frequent_background_abstract_prompt":{"kind":"string","value":"[CONTENT] mca | type | th | patients | sma | ct | root | rca | ica | right [SUMMARY]"},"annotated_most_frequent_methods_abstract_prompt":{"kind":"string","value":"[CONTENT] mca | type | th | patients | sma | ct | root | rca | ica | right [SUMMARY]"},"annotated_most_frequent_results_abstract_prompt":{"kind":"string","value":"[CONTENT] mca | type | th | patients | sma | ct | root | rca | ica | right [SUMMARY]"},"annotated_most_frequent_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] mca | type | th | patients | sma | ct | root | rca | ica | right [SUMMARY]"},"annotated_most_frequent_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] mca | type | th | patients | sma | ct | root | rca | ica | right [SUMMARY]"},"annotated_most_frequent_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] mca | type | th | patients | sma | ct | root | rca | ica | right [SUMMARY]"},"annotated_tf_idf_background_abstract_prompt":{"kind":"string","value":"[CONTENT] clinical | surgery | mesenteric | need | rectal | superior mesenteric | cancer | anatomical | patients | right [SUMMARY]"},"annotated_tf_idf_methods_abstract_prompt":{"kind":"string","value":"[CONTENT] contrast | ml | mca | root | phase | ct | th | sma | distance | scanning [SUMMARY]"},"annotated_tf_idf_results_abstract_prompt":{"kind":"string","value":"[CONTENT] type | mca | rca | cases | type rca | ica | range | deviates | sma | patients [SUMMARY]"},"annotated_tf_idf_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] reduce | assessment | potentially | key | personalized | 3d ct | 3d | lymphadenectomy reduce intraoperative time | anatomy potentially complexity future | ct assessment potentially significantly [SUMMARY]"},"annotated_tf_idf_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] mca | th | root | type | ct | patients | sma | contrast | 64 | 3d [SUMMARY]"},"annotated_tf_idf_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] mca | th | root | type | ct | patients | sma | contrast | 64 | 3d [SUMMARY]"},"annotated_entity_plan_background_abstract_prompt":{"kind":"string","value":"[CONTENT] [SUMMARY]"},"annotated_entity_plan_methods_abstract_prompt":{"kind":"string","value":"[CONTENT] 103 | 2016 | 2021 [SUMMARY]"},"annotated_entity_plan_results_abstract_prompt":{"kind":"string","value":"[CONTENT] D3 ||| 24.71±10.04 ||| one ||| eight ||| 64 | 62.1% | 39 | 37.9% ||| 58 | 56.3% ||| 37.8±12.8 mm | 29.5±15.7 mm ||| Henle | 66 | 64.1% | 16 | 15.5% | 18 | 17.5% [SUMMARY]"},"annotated_entity_plan_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] [SUMMARY]"},"annotated_entity_plan_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] ||| 103 | 2016 | 2021 ||| ||| D3 ||| 24.71±10.04 ||| one ||| eight ||| 64 | 62.1% | 39 | 37.9% ||| 58 | 56.3% ||| 37.8±12.8 mm | 29.5±15.7 mm ||| Henle | 66 | 64.1% | 16 | 15.5% | 18 | 17.5% ||| [SUMMARY]"},"annotated_entity_plan_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] ||| 103 | 2016 | 2021 ||| ||| D3 ||| 24.71±10.04 ||| one ||| eight ||| 64 | 62.1% | 39 | 37.9% ||| 58 | 56.3% ||| 37.8±12.8 mm | 29.5±15.7 mm ||| Henle | 66 | 64.1% | 16 | 15.5% | 18 | 17.5% ||| [SUMMARY]"}}},{"rowIdx":272,"cells":{"title":{"kind":"string","value":"Methylphenidate normalizes frontocingulate underactivation during error processing in attention-deficit/hyperactivity disorder."},"pmid":{"kind":"string","value":"21664605"},"background_abstract":{"kind":"string","value":"Children with attention-deficit/hyperactivity disorder (ADHD) have deficits in performance monitoring often improved with the indirect catecholamine agonist methylphenidate (MPH). We used functional magnetic resonance imaging to investigate the effects of single-dose MPH on activation of error processing brain areas in medication-naive boys with ADHD during a stop task that elicits 50% error rates."},"background_abstract_label":{"kind":"string","value":"BACKGROUND"},"methods_abstract":{"kind":"string","value":"Twelve medication-naive boys with ADHD were scanned twice, under either a single clinical dose of MPH or placebo, in a randomized, double-blind design while they performed an individually adjusted tracking stop task, designed to elicit 50% failures. Brain activation was compared within patients under either drug condition. To test for potential normalization effects of MPH, brain activation in ADHD patients under either drug condition was compared with that of 13 healthy age-matched boys."},"methods_abstract_label":{"kind":"string","value":"METHODS"},"results_abstract":{"kind":"string","value":"During failed inhibition, boys with ADHD under placebo relative to control subjects showed reduced brain activation in performance monitoring areas of dorsomedial and left ventrolateral prefrontal cortices, thalamus, cingulate, and parietal regions. MPH, relative to placebo, upregulated activation in these brain regions within patients and normalized all activation differences between patients and control subjects. During successful inhibition, MPH normalized reduced activation observed in patients under placebo compared with control subjects in parietotemporal and cerebellar regions."},"results_abstract_label":{"kind":"string","value":"RESULTS"},"conclusions_abstract":{"kind":"string","value":"MPH normalized brain dysfunction in medication-naive ADHD boys relative to control subjects in typical brain areas of performance monitoring, comprising left ventrolateral and dorsomedial frontal and parietal cortices. This could underlie the amelioration of MPH of attention and academic performance in ADHD."},"conclusions_abstract_label":{"kind":"string","value":"CONCLUSIONS"},"mesh_descriptor_names":{"kind":"list like","value":["Adolescent","Attention","Attention Deficit Disorder with Hyperactivity","Central Nervous System Stimulants","Child","Double-Blind Method","Frontal Lobe","Gyrus Cinguli","Humans","Magnetic Resonance Imaging","Male","Methylphenidate","Neuropsychological Tests","Psychomotor Performance"],"string":"[\n \"Adolescent\",\n \"Attention\",\n \"Attention Deficit Disorder with Hyperactivity\",\n \"Central Nervous System Stimulants\",\n \"Child\",\n \"Double-Blind Method\",\n \"Frontal Lobe\",\n \"Gyrus Cinguli\",\n \"Humans\",\n \"Magnetic Resonance Imaging\",\n \"Male\",\n \"Methylphenidate\",\n \"Neuropsychological Tests\",\n \"Psychomotor Performance\"\n]"},"pmcid":{"kind":"string","value":"3139835"},"background_title":{"kind":"null"},"background_text":{"kind":"null"},"methods_title":{"kind":"null"},"methods_text":{"kind":"null"},"results_title":{"kind":"string","value":"Results"},"results_text":{"kind":"string","value":" Performance The probability of inhibition was about 50% in all subjects with no significant group differences, showing that the task algorithm worked [F(1,38) = 1; p < .3; Table 1].\nA multivariate ANOVA between control subjects and ADHD patients under either drug condition showed a trend for a significant group effect [F(8,62) = 2, p < .09] due to a significant univariate group effect in the standard deviation to go trials [F(2,34) = 5, p < .02], which were higher in patients under either medication condition compared with control subjects (p < .05). Post hoc tests furthermore revealed a trend for MPH compared with placebo to slow down reaction times within ADHD patients to both go (p < .06) and post-error go trials (both p < .07) (Table 1).\nThe probability of inhibition was about 50% in all subjects with no significant group differences, showing that the task algorithm worked [F(1,38) = 1; p < .3; Table 1].\nA multivariate ANOVA between control subjects and ADHD patients under either drug condition showed a trend for a significant group effect [F(8,62) = 2, p < .09] due to a significant univariate group effect in the standard deviation to go trials [F(2,34) = 5, p < .02], which were higher in patients under either medication condition compared with control subjects (p < .05). Post hoc tests furthermore revealed a trend for MPH compared with placebo to slow down reaction times within ADHD patients to both go (p < .06) and post-error go trials (both p < .07) (Table 1)."},"conclusions_title":{"kind":"null"},"conclusions_text":{"kind":"null"},"other_sections_titles":{"kind":"list like","value":["Subjects","fMRI Paradigm: Stop Task","fMRI Image Acquisition","fMRI Image Analysis","Performance","Brain Activation","Motion","Within-Group Brain Activations","Failed Stop–Go Contrast","Successful Stop–Go Contrast","ANOVA Within-Patient Comparisons in Brain Activation Between the Placebo and the MPH Conditions","Failed Stop–Go Contrast","Successful Stop–Go Contrast","ANOVA Between-Group Comparisons in Brain Activation Between Control Subjects and Boys with ADHD Under Either the Placebo or the MPH Conditions","Failed Stop–Go Contrast","Successful Stop–Go Contrast","Conjunction Analysis Between Within-Group and Between-Group ANOVAs"],"string":"[\n \"Subjects\",\n \"fMRI Paradigm: Stop Task\",\n \"fMRI Image Acquisition\",\n \"fMRI Image Analysis\",\n \"Performance\",\n \"Brain Activation\",\n \"Motion\",\n \"Within-Group Brain Activations\",\n \"Failed Stop–Go Contrast\",\n \"Successful Stop–Go Contrast\",\n \"ANOVA Within-Patient Comparisons in Brain Activation Between the Placebo and the MPH Conditions\",\n \"Failed Stop–Go Contrast\",\n \"Successful Stop–Go Contrast\",\n \"ANOVA Between-Group Comparisons in Brain Activation Between Control Subjects and Boys with ADHD Under Either the Placebo or the MPH Conditions\",\n \"Failed Stop–Go Contrast\",\n \"Successful Stop–Go Contrast\",\n \"Conjunction Analysis Between Within-Group and Between-Group ANOVAs\"\n]"},"other_sections_texts":{"kind":"list like","value":["Twelve medication-naïve, right-handed boys aged 10 to 15 years (mean age = 13, SD = 1) who met clinical diagnostic criteria for the combined (inattentive/hyperactive) subtype of ADHD (DSM-IV) were recruited through clinics. Clinical diagnosis of ADHD was established through interviews with an experienced child psychiatrist (A-MM) using the standardized Maudsley Diagnostic Interview to check for presence or absence of diagnostic criteria for any mental disorder as set out by DSM-IV (30). Exclusion criteria were lifetime comorbidity with any other psychiatric disorder, except for conduct/oppositional defiant disorder (present in one patient), as well as learning disability and specific reading disorder, neurological abnormalities, epilepsy, drug or substance abuse, and previous exposure to stimulant medication. Patients with ADHD also had to score above cutoff for hyperactive/inattentive symptoms on the Strengths and Difficulties Questionnaire for Parents (SDQ) (31). Patients were scanned twice, in a randomized, counterbalanced fashion, 1 week apart, 1 hour after either .3 mg/kg of MPH administration or placebo (vitamin C, 100 mg).\nThirteen male right-handed adolescent boys in the age range of 11 to 16 years (mean age = 13, SD = 1) were recruited through advertisements in the same geographic areas of South London to ensure similar socioeconomic status and were scanned once. They scored below cutoff for behavioral problems in the SDQ and had no history of psychiatric disorder.\nAll participants were above the fifth percentile on the Raven progressive matrices performance IQ (32) (IQ mean estimate controls = 100, SD = 14; ADHD = 91, SD = 9) and paid £30 for participation. Parental and child informed consent/assent and approval from the local ethical committee was obtained.\nUnivariate analyses of variance (ANOVAs) showed no group differences between boys with and without ADHD for age [F(1,25) = 2, p = .2] but did for IQ [F(1,25) = 8, p < .009]. IQ is associated with ADHD in the general population (33,34). We purposely did not match groups for IQ because matching ADHD and control groups for IQ would have created unrepresentative groups and therefore be misguided (35). Furthermore, IQ was significantly negatively correlated with the SDQ scores for inattention and hyperactivity (r = −.5, p < .001). We did not covary for IQ because when groups are not randomly selected, covarying for a variable that differs between groups violates the standard assumptions for analysis of covariance. When the covariate is intrinsic to the condition, it becomes meaningless to “adjust” group effects for differences in the covariate because it would alter the group effect in potentially problematic ways, leading to spurious results (35,36).","The rapid, mixed-trial, event-related fMRI design was practiced by subjects once before scanning. The visual tracking stop task requires withholding of a motor response to a go stimulus when it is followed unpredictably by a stop signal (8,37,38). The basic task is a choice reaction time task (left and right pointing arrows: go signals) with a mean intertrial-interval of 1.8 sec (156 go trials). In 20% of trials, pseudo-randomly interspersed, the go signals are followed (about 250 ms later) by arrows pointing upwards (stop signals), and subjects have to inhibit their motor responses (40 stop trials). A tracking algorithm changes the time interval between go-signal and stop-signal onsets according to each subject's inhibitory performance to ensure that the task is equally challenging for each individual and to provide 50% successful and 50% unsuccessful inhibition trials at every moment of the task.","Gradient-echo echoplanar magnetic resonance imaging data were acquired on a GE Signa 1.5-T Horizon LX System (General Electric, Milwaukee, Wisconsin) at the Maudsley Hospital, London. A quadrature birdcage head coil was used for radio-frequency transmission and reception. During the 6-min run of the stop task, in each of 16 noncontiguous planes parallel to the anterior–posterior commissural, 196 T2*-weighted magnetic resonance images depicting blood oxygen–level dependent (BOLD) contrast covering the whole brain were acquired with echo time = 40 msec, repetition time = 1.8 sec, flip angle = 90°, in-plane resolution = 3.1 mm, slice thickness = 7 mm, slice skip = .7 mm, providing complete brain coverage.","At the individual subject level, a standard general linear modeling approach was used to obtain estimates of the response size (beta) to each of the two stop task conditions (successful and unsuccessful stop trials) against an implicit baseline (go trials). Following transformation of the fMRI data for each individual into standard space and smoothing with a three-dimensional 7-mm full width at half maximum Gaussian filter, the experimental model was convolved for each condition with gamma variate functions having peak responses at 4 and 8 sec following stimulus onset to accommodate variability in BOLD response timing. By fitting these convolved model components to the time series at each voxel, beta estimates were obtained for each effect of interest. The standard errors of these beta estimates were computed nonparametrically using a bootstrap procedure designed to operate on time series data, containing serial dependencies, with repeated deterministic (experimentally determined) effects. This method is outlined in detail in a previous work (39). Two hundred bootstraps at each voxel were used to estimate parameter standard errors. Using the combined parameter estimates over all conditions, the mean fitted time series was also computed and, from the combined bootstrap parameter estimates for each bootstrap, the 95% confidence limits on the fitted time series was computed.\nThe second-level analysis proceeded by computing either the group differences (patients and controls) or the drug condition differences (placebo, MPH) within patients at each voxel and the standard error of this difference (using the bootstrap estimates derived earlier). The significance of these differences was then tested in three ways: 1) a simple parametric random effects (paired t) test, using only the group difference/placebo-MPH effect size differences; 2) a permutation test of the same random effects t statistic in which the null distribution was estimated by randomly swapping the signs of the differences (we used 40,000 permutations per voxel to obtain a confidence limit of .0007–.0013 for p value of .001); and 3) a mixed-effects test using both the effect size differences and their subject-level standard errors to accommodate first (subject) level heteroscedasticity (40). This was also conducted using 40,000 permutations per voxel.\nIn addition to voxelwise maps, cluster-level inference on the contrast (beta) values was performed at a family-wise error corrected threshold of p < .05 using the Threshold-Free Cluster Enhancement method proposed by Smith and Nichols (41). This cluster-level inference was also used for the within-group maps for each experimental condition.","The probability of inhibition was about 50% in all subjects with no significant group differences, showing that the task algorithm worked [F(1,38) = 1; p < .3; Table 1].\nA multivariate ANOVA between control subjects and ADHD patients under either drug condition showed a trend for a significant group effect [F(8,62) = 2, p < .09] due to a significant univariate group effect in the standard deviation to go trials [F(2,34) = 5, p < .02], which were higher in patients under either medication condition compared with control subjects (p < .05). Post hoc tests furthermore revealed a trend for MPH compared with placebo to slow down reaction times within ADHD patients to both go (p < .06) and post-error go trials (both p < .07) (Table 1)."," Motion Multivariate ANOVA showed no significant group differences between control subjects and ADHD patients under either drug condition in mean or maximum rotation or translation parameters in the x, y, or z dimensions [F(2,38) = .9, p < .5].\nMultivariate ANOVA showed no significant group differences between control subjects and ADHD patients under either drug condition in mean or maximum rotation or translation parameters in the x, y, or z dimensions [F(2,38) = .9, p < .5].\n Within-Group Brain Activations Failed Stop–Go Contrast Control subjects activated relatively large clusters in left and right inferior frontal cortex (IFC)/anterior insula, medial frontal/anterior cingulate cortex (MFC/ACC), precentral, inferior parietal, middle and superior temporal areas, posterior thalamus and caudate, parahippocampal gyri, precuneus/posterior cingulate, occipital, and cerebellar areas.\nActivation in ADHD patients under placebo was in MFC and superior temporal cortex reaching into anterior and posterior insula, caudate, and inferior parietal and occipital cortex.\nActivation in ADHD patients under MPH was in a large cluster of ACC and MFC, in left and right IFC/anterior insula, left premotor cortex, right basal ganglia, bilateral middle and superior temporal, inferior parietal and occipital areas, hippocampal gyri, posterior cingulate, precuneus, and cerebellum (Figure S1 in Supplement 1).\nControl subjects activated relatively large clusters in left and right inferior frontal cortex (IFC)/anterior insula, medial frontal/anterior cingulate cortex (MFC/ACC), precentral, inferior parietal, middle and superior temporal areas, posterior thalamus and caudate, parahippocampal gyri, precuneus/posterior cingulate, occipital, and cerebellar areas.\nActivation in ADHD patients under placebo was in MFC and superior temporal cortex reaching into anterior and posterior insula, caudate, and inferior parietal and occipital cortex.\nActivation in ADHD patients under MPH was in a large cluster of ACC and MFC, in left and right IFC/anterior insula, left premotor cortex, right basal ganglia, bilateral middle and superior temporal, inferior parietal and occipital areas, hippocampal gyri, posterior cingulate, precuneus, and cerebellum (Figure S1 in Supplement 1).\n Successful Stop–Go Contrast Activation in healthy control boys was in a large cluster comprising left and right orbital and IFC, dorsolateral and MFC, insula, basal ganglia, hippocampus, posterior thalamic regions, pre- and postcentral gyri, inferior and superior parietal, temporal and occipital cortices, precuneus, and posterior cingulate.\nActivation in ADHD patients under placebo was in small clusters in right MFC, supplementary motor area (SMA), right superior temporal, postcentral, left inferior and superior parietal, and occipital cortices.\nActivation in ADHD patients under the MPH condition was in superior and MFC/ACC, right globus pallidus and putamen, right superior temporal and superior and inferior parietal cortex, posterior insula, and left cerebellum (Figure S1 in Supplement 1).\nActivation in healthy control boys was in a large cluster comprising left and right orbital and IFC, dorsolateral and MFC, insula, basal ganglia, hippocampus, posterior thalamic regions, pre- and postcentral gyri, inferior and superior parietal, temporal and occipital cortices, precuneus, and posterior cingulate.\nActivation in ADHD patients under placebo was in small clusters in right MFC, supplementary motor area (SMA), right superior temporal, postcentral, left inferior and superior parietal, and occipital cortices.\nActivation in ADHD patients under the MPH condition was in superior and MFC/ACC, right globus pallidus and putamen, right superior temporal and superior and inferior parietal cortex, posterior insula, and left cerebellum (Figure S1 in Supplement 1).\n Failed Stop–Go Contrast Control subjects activated relatively large clusters in left and right inferior frontal cortex (IFC)/anterior insula, medial frontal/anterior cingulate cortex (MFC/ACC), precentral, inferior parietal, middle and superior temporal areas, posterior thalamus and caudate, parahippocampal gyri, precuneus/posterior cingulate, occipital, and cerebellar areas.\nActivation in ADHD patients under placebo was in MFC and superior temporal cortex reaching into anterior and posterior insula, caudate, and inferior parietal and occipital cortex.\nActivation in ADHD patients under MPH was in a large cluster of ACC and MFC, in left and right IFC/anterior insula, left premotor cortex, right basal ganglia, bilateral middle and superior temporal, inferior parietal and occipital areas, hippocampal gyri, posterior cingulate, precuneus, and cerebellum (Figure S1 in Supplement 1).\nControl subjects activated relatively large clusters in left and right inferior frontal cortex (IFC)/anterior insula, medial frontal/anterior cingulate cortex (MFC/ACC), precentral, inferior parietal, middle and superior temporal areas, posterior thalamus and caudate, parahippocampal gyri, precuneus/posterior cingulate, occipital, and cerebellar areas.\nActivation in ADHD patients under placebo was in MFC and superior temporal cortex reaching into anterior and posterior insula, caudate, and inferior parietal and occipital cortex.\nActivation in ADHD patients under MPH was in a large cluster of ACC and MFC, in left and right IFC/anterior insula, left premotor cortex, right basal ganglia, bilateral middle and superior temporal, inferior parietal and occipital areas, hippocampal gyri, posterior cingulate, precuneus, and cerebellum (Figure S1 in Supplement 1).\n Successful Stop–Go Contrast Activation in healthy control boys was in a large cluster comprising left and right orbital and IFC, dorsolateral and MFC, insula, basal ganglia, hippocampus, posterior thalamic regions, pre- and postcentral gyri, inferior and superior parietal, temporal and occipital cortices, precuneus, and posterior cingulate.\nActivation in ADHD patients under placebo was in small clusters in right MFC, supplementary motor area (SMA), right superior temporal, postcentral, left inferior and superior parietal, and occipital cortices.\nActivation in ADHD patients under the MPH condition was in superior and MFC/ACC, right globus pallidus and putamen, right superior temporal and superior and inferior parietal cortex, posterior insula, and left cerebellum (Figure S1 in Supplement 1).\nActivation in healthy control boys was in a large cluster comprising left and right orbital and IFC, dorsolateral and MFC, insula, basal ganglia, hippocampus, posterior thalamic regions, pre- and postcentral gyri, inferior and superior parietal, temporal and occipital cortices, precuneus, and posterior cingulate.\nActivation in ADHD patients under placebo was in small clusters in right MFC, supplementary motor area (SMA), right superior temporal, postcentral, left inferior and superior parietal, and occipital cortices.\nActivation in ADHD patients under the MPH condition was in superior and MFC/ACC, right globus pallidus and putamen, right superior temporal and superior and inferior parietal cortex, posterior insula, and left cerebellum (Figure S1 in Supplement 1).\n ANOVA Within-Patient Comparisons in Brain Activation Between the Placebo and the MPH Conditions Failed Stop–Go Contrast MPH contrasted with placebo elicited enhanced activation in left IFC, reaching into insula and putamen; in right IFC reaching into insula, putamen, and caudate; in left medial frontal lobe; and in left inferior parietal, precuneus, and occipital regions (Figure 1, Table 2). The placebo condition elicited no enhanced activation over MPH.\nTo investigate whether brain regions that differed with MPH were associated with task performance, statistical measures of the BOLD response were extracted for each subject in each ANOVA cluster and then correlated with performance variables. There was a significant positive correlation in patients between post-error reaction times and left and right IFC activation (r = .7, p < .02) and between go reaction times and right IFC activation (r = .6, p < .05) and a negative correlation between response variability and right inferior parietal activation (r = −.7, p < .02).\nMPH contrasted with placebo elicited enhanced activation in left IFC, reaching into insula and putamen; in right IFC reaching into insula, putamen, and caudate; in left medial frontal lobe; and in left inferior parietal, precuneus, and occipital regions (Figure 1, Table 2). The placebo condition elicited no enhanced activation over MPH.\nTo investigate whether brain regions that differed with MPH were associated with task performance, statistical measures of the BOLD response were extracted for each subject in each ANOVA cluster and then correlated with performance variables. There was a significant positive correlation in patients between post-error reaction times and left and right IFC activation (r = .7, p < .02) and between go reaction times and right IFC activation (r = .6, p < .05) and a negative correlation between response variability and right inferior parietal activation (r = −.7, p < .02).\n Successful Stop–Go Contrast No significant activation differences were observed between medication conditions.\nNo significant activation differences were observed between medication conditions.\n Failed Stop–Go Contrast MPH contrasted with placebo elicited enhanced activation in left IFC, reaching into insula and putamen; in right IFC reaching into insula, putamen, and caudate; in left medial frontal lobe; and in left inferior parietal, precuneus, and occipital regions (Figure 1, Table 2). The placebo condition elicited no enhanced activation over MPH.\nTo investigate whether brain regions that differed with MPH were associated with task performance, statistical measures of the BOLD response were extracted for each subject in each ANOVA cluster and then correlated with performance variables. There was a significant positive correlation in patients between post-error reaction times and left and right IFC activation (r = .7, p < .02) and between go reaction times and right IFC activation (r = .6, p < .05) and a negative correlation between response variability and right inferior parietal activation (r = −.7, p < .02).\nMPH contrasted with placebo elicited enhanced activation in left IFC, reaching into insula and putamen; in right IFC reaching into insula, putamen, and caudate; in left medial frontal lobe; and in left inferior parietal, precuneus, and occipital regions (Figure 1, Table 2). The placebo condition elicited no enhanced activation over MPH.\nTo investigate whether brain regions that differed with MPH were associated with task performance, statistical measures of the BOLD response were extracted for each subject in each ANOVA cluster and then correlated with performance variables. There was a significant positive correlation in patients between post-error reaction times and left and right IFC activation (r = .7, p < .02) and between go reaction times and right IFC activation (r = .6, p < .05) and a negative correlation between response variability and right inferior parietal activation (r = −.7, p < .02).\n Successful Stop–Go Contrast No significant activation differences were observed between medication conditions.\nNo significant activation differences were observed between medication conditions.\n ANOVA Between-Group Comparisons in Brain Activation Between Control Subjects and Boys with ADHD Under Either the Placebo or the MPH Conditions Failed Stop–Go Contrast Relative to control subjects, ADHD patients under the placebo condition showed underactivation in left IFC and dorsomedial prefrontal cortices (dMFC) (including pre-SMA), right premotor, superior and inferior parietal cortices, posterior cingulate/precuneus, posterior thalamus, and bilateral inferior temporo-occipital areas (Figure 2A, Table 3).\nUnder the MPH condition, ADHD patients did not differ from controls in any of these regions.\nPost-error slowing in ADHD patients under placebo, but not in control subjects, was significantly positively correlated with activation in left IFC, premotor, dMFC, and thalamic underactivation clusters (r > .6 for all clusters, p < .05) as well as with superior parietal, occipital, and cerebellar activation (r < .4, p < .05). Standard deviation of reaction times was correlated with dMFC activation in controls (r = .6, p < .02).\nRelative to control subjects, ADHD patients under the placebo condition showed underactivation in left IFC and dorsomedial prefrontal cortices (dMFC) (including pre-SMA), right premotor, superior and inferior parietal cortices, posterior cingulate/precuneus, posterior thalamus, and bilateral inferior temporo-occipital areas (Figure 2A, Table 3).\nUnder the MPH condition, ADHD patients did not differ from controls in any of these regions.\nPost-error slowing in ADHD patients under placebo, but not in control subjects, was significantly positively correlated with activation in left IFC, premotor, dMFC, and thalamic underactivation clusters (r > .6 for all clusters, p < .05) as well as with superior parietal, occipital, and cerebellar activation (r < .4, p < .05). Standard deviation of reaction times was correlated with dMFC activation in controls (r = .6, p < .02).\n Successful Stop–Go Contrast Relative to control subjects, ADHD patients under the placebo condition showed underactivation in a right hemispheric network of medial temporal and inferior parietal lobes, precuneus/posterior cingulate and cerebellum (Figure 2B, Table 3). To test our hypothesis of IFC underactivation, we reanalyzed the data at a more lenient p value of p < .002 for voxelwise comparison. This elicited additional underactivation in right IFC, left and right subthalamic nuclei, and the pre-SMA (Table 3 and Figure S2 in Supplement 1).\nUnder the MPH condition, ADHD patients did not differ from control subjects in any of these regions.\nActivation in posterior cingulate and lingual gyrus correlated significantly positively with post-error go reaction times in ADHD boys under placebo (r > .6, p < .3).\nAll group difference findings for both contrasts remained essentially unchanged when IQ was covaried.\nRelative to control subjects, ADHD patients under the placebo condition showed underactivation in a right hemispheric network of medial temporal and inferior parietal lobes, precuneus/posterior cingulate and cerebellum (Figure 2B, Table 3). To test our hypothesis of IFC underactivation, we reanalyzed the data at a more lenient p value of p < .002 for voxelwise comparison. This elicited additional underactivation in right IFC, left and right subthalamic nuclei, and the pre-SMA (Table 3 and Figure S2 in Supplement 1).\nUnder the MPH condition, ADHD patients did not differ from control subjects in any of these regions.\nActivation in posterior cingulate and lingual gyrus correlated significantly positively with post-error go reaction times in ADHD boys under placebo (r > .6, p < .3).\nAll group difference findings for both contrasts remained essentially unchanged when IQ was covaried.\n Failed Stop–Go Contrast Relative to control subjects, ADHD patients under the placebo condition showed underactivation in left IFC and dorsomedial prefrontal cortices (dMFC) (including pre-SMA), right premotor, superior and inferior parietal cortices, posterior cingulate/precuneus, posterior thalamus, and bilateral inferior temporo-occipital areas (Figure 2A, Table 3).\nUnder the MPH condition, ADHD patients did not differ from controls in any of these regions.\nPost-error slowing in ADHD patients under placebo, but not in control subjects, was significantly positively correlated with activation in left IFC, premotor, dMFC, and thalamic underactivation clusters (r > .6 for all clusters, p < .05) as well as with superior parietal, occipital, and cerebellar activation (r < .4, p < .05). Standard deviation of reaction times was correlated with dMFC activation in controls (r = .6, p < .02).\nRelative to control subjects, ADHD patients under the placebo condition showed underactivation in left IFC and dorsomedial prefrontal cortices (dMFC) (including pre-SMA), right premotor, superior and inferior parietal cortices, posterior cingulate/precuneus, posterior thalamus, and bilateral inferior temporo-occipital areas (Figure 2A, Table 3).\nUnder the MPH condition, ADHD patients did not differ from controls in any of these regions.\nPost-error slowing in ADHD patients under placebo, but not in control subjects, was significantly positively correlated with activation in left IFC, premotor, dMFC, and thalamic underactivation clusters (r > .6 for all clusters, p < .05) as well as with superior parietal, occipital, and cerebellar activation (r < .4, p < .05). Standard deviation of reaction times was correlated with dMFC activation in controls (r = .6, p < .02).\n Successful Stop–Go Contrast Relative to control subjects, ADHD patients under the placebo condition showed underactivation in a right hemispheric network of medial temporal and inferior parietal lobes, precuneus/posterior cingulate and cerebellum (Figure 2B, Table 3). To test our hypothesis of IFC underactivation, we reanalyzed the data at a more lenient p value of p < .002 for voxelwise comparison. This elicited additional underactivation in right IFC, left and right subthalamic nuclei, and the pre-SMA (Table 3 and Figure S2 in Supplement 1).\nUnder the MPH condition, ADHD patients did not differ from control subjects in any of these regions.\nActivation in posterior cingulate and lingual gyrus correlated significantly positively with post-error go reaction times in ADHD boys under placebo (r > .6, p < .3).\nAll group difference findings for both contrasts remained essentially unchanged when IQ was covaried.\nRelative to control subjects, ADHD patients under the placebo condition showed underactivation in a right hemispheric network of medial temporal and inferior parietal lobes, precuneus/posterior cingulate and cerebellum (Figure 2B, Table 3). To test our hypothesis of IFC underactivation, we reanalyzed the data at a more lenient p value of p < .002 for voxelwise comparison. This elicited additional underactivation in right IFC, left and right subthalamic nuclei, and the pre-SMA (Table 3 and Figure S2 in Supplement 1).\nUnder the MPH condition, ADHD patients did not differ from control subjects in any of these regions.\nActivation in posterior cingulate and lingual gyrus correlated significantly positively with post-error go reaction times in ADHD boys under placebo (r > .6, p < .3).\nAll group difference findings for both contrasts remained essentially unchanged when IQ was covaried.\n Conjunction Analysis Between Within-Group and Between-Group ANOVAs To test whether brain regions that were upregulated with MPH relative to placebo within patients overlapped with brain regions that were reduced in patients under placebo relative to controls and then normalized with MPH, we performed a conjunction analysis by determining the voxels where the within-group ANOVA (MPH > placebo in ADHD) and the between-group ANOVA (control subjects > ADHD placebo) were both significant (42). Three clusters emerged, in left IFC (Talairach coordinates: −43, 7, 4), right SMA (Talairach coordinates: 7, 4, 59) and right inferior parietal lobe (Talairach coordinates: 32, −63, 42). Overlapping clusters are also indicated in bold in Table 3 and shown in Figure 3.\nTo test whether brain regions that were upregulated with MPH relative to placebo within patients overlapped with brain regions that were reduced in patients under placebo relative to controls and then normalized with MPH, we performed a conjunction analysis by determining the voxels where the within-group ANOVA (MPH > placebo in ADHD) and the between-group ANOVA (control subjects > ADHD placebo) were both significant (42). Three clusters emerged, in left IFC (Talairach coordinates: −43, 7, 4), right SMA (Talairach coordinates: 7, 4, 59) and right inferior parietal lobe (Talairach coordinates: 32, −63, 42). Overlapping clusters are also indicated in bold in Table 3 and shown in Figure 3.","Multivariate ANOVA showed no significant group differences between control subjects and ADHD patients under either drug condition in mean or maximum rotation or translation parameters in the x, y, or z dimensions [F(2,38) = .9, p < .5]."," Failed Stop–Go Contrast Control subjects activated relatively large clusters in left and right inferior frontal cortex (IFC)/anterior insula, medial frontal/anterior cingulate cortex (MFC/ACC), precentral, inferior parietal, middle and superior temporal areas, posterior thalamus and caudate, parahippocampal gyri, precuneus/posterior cingulate, occipital, and cerebellar areas.\nActivation in ADHD patients under placebo was in MFC and superior temporal cortex reaching into anterior and posterior insula, caudate, and inferior parietal and occipital cortex.\nActivation in ADHD patients under MPH was in a large cluster of ACC and MFC, in left and right IFC/anterior insula, left premotor cortex, right basal ganglia, bilateral middle and superior temporal, inferior parietal and occipital areas, hippocampal gyri, posterior cingulate, precuneus, and cerebellum (Figure S1 in Supplement 1).\nControl subjects activated relatively large clusters in left and right inferior frontal cortex (IFC)/anterior insula, medial frontal/anterior cingulate cortex (MFC/ACC), precentral, inferior parietal, middle and superior temporal areas, posterior thalamus and caudate, parahippocampal gyri, precuneus/posterior cingulate, occipital, and cerebellar areas.\nActivation in ADHD patients under placebo was in MFC and superior temporal cortex reaching into anterior and posterior insula, caudate, and inferior parietal and occipital cortex.\nActivation in ADHD patients under MPH was in a large cluster of ACC and MFC, in left and right IFC/anterior insula, left premotor cortex, right basal ganglia, bilateral middle and superior temporal, inferior parietal and occipital areas, hippocampal gyri, posterior cingulate, precuneus, and cerebellum (Figure S1 in Supplement 1).\n Successful Stop–Go Contrast Activation in healthy control boys was in a large cluster comprising left and right orbital and IFC, dorsolateral and MFC, insula, basal ganglia, hippocampus, posterior thalamic regions, pre- and postcentral gyri, inferior and superior parietal, temporal and occipital cortices, precuneus, and posterior cingulate.\nActivation in ADHD patients under placebo was in small clusters in right MFC, supplementary motor area (SMA), right superior temporal, postcentral, left inferior and superior parietal, and occipital cortices.\nActivation in ADHD patients under the MPH condition was in superior and MFC/ACC, right globus pallidus and putamen, right superior temporal and superior and inferior parietal cortex, posterior insula, and left cerebellum (Figure S1 in Supplement 1).\nActivation in healthy control boys was in a large cluster comprising left and right orbital and IFC, dorsolateral and MFC, insula, basal ganglia, hippocampus, posterior thalamic regions, pre- and postcentral gyri, inferior and superior parietal, temporal and occipital cortices, precuneus, and posterior cingulate.\nActivation in ADHD patients under placebo was in small clusters in right MFC, supplementary motor area (SMA), right superior temporal, postcentral, left inferior and superior parietal, and occipital cortices.\nActivation in ADHD patients under the MPH condition was in superior and MFC/ACC, right globus pallidus and putamen, right superior temporal and superior and inferior parietal cortex, posterior insula, and left cerebellum (Figure S1 in Supplement 1).","Control subjects activated relatively large clusters in left and right inferior frontal cortex (IFC)/anterior insula, medial frontal/anterior cingulate cortex (MFC/ACC), precentral, inferior parietal, middle and superior temporal areas, posterior thalamus and caudate, parahippocampal gyri, precuneus/posterior cingulate, occipital, and cerebellar areas.\nActivation in ADHD patients under placebo was in MFC and superior temporal cortex reaching into anterior and posterior insula, caudate, and inferior parietal and occipital cortex.\nActivation in ADHD patients under MPH was in a large cluster of ACC and MFC, in left and right IFC/anterior insula, left premotor cortex, right basal ganglia, bilateral middle and superior temporal, inferior parietal and occipital areas, hippocampal gyri, posterior cingulate, precuneus, and cerebellum (Figure S1 in Supplement 1).","Activation in healthy control boys was in a large cluster comprising left and right orbital and IFC, dorsolateral and MFC, insula, basal ganglia, hippocampus, posterior thalamic regions, pre- and postcentral gyri, inferior and superior parietal, temporal and occipital cortices, precuneus, and posterior cingulate.\nActivation in ADHD patients under placebo was in small clusters in right MFC, supplementary motor area (SMA), right superior temporal, postcentral, left inferior and superior parietal, and occipital cortices.\nActivation in ADHD patients under the MPH condition was in superior and MFC/ACC, right globus pallidus and putamen, right superior temporal and superior and inferior parietal cortex, posterior insula, and left cerebellum (Figure S1 in Supplement 1)."," Failed Stop–Go Contrast MPH contrasted with placebo elicited enhanced activation in left IFC, reaching into insula and putamen; in right IFC reaching into insula, putamen, and caudate; in left medial frontal lobe; and in left inferior parietal, precuneus, and occipital regions (Figure 1, Table 2). The placebo condition elicited no enhanced activation over MPH.\nTo investigate whether brain regions that differed with MPH were associated with task performance, statistical measures of the BOLD response were extracted for each subject in each ANOVA cluster and then correlated with performance variables. There was a significant positive correlation in patients between post-error reaction times and left and right IFC activation (r = .7, p < .02) and between go reaction times and right IFC activation (r = .6, p < .05) and a negative correlation between response variability and right inferior parietal activation (r = −.7, p < .02).\nMPH contrasted with placebo elicited enhanced activation in left IFC, reaching into insula and putamen; in right IFC reaching into insula, putamen, and caudate; in left medial frontal lobe; and in left inferior parietal, precuneus, and occipital regions (Figure 1, Table 2). The placebo condition elicited no enhanced activation over MPH.\nTo investigate whether brain regions that differed with MPH were associated with task performance, statistical measures of the BOLD response were extracted for each subject in each ANOVA cluster and then correlated with performance variables. There was a significant positive correlation in patients between post-error reaction times and left and right IFC activation (r = .7, p < .02) and between go reaction times and right IFC activation (r = .6, p < .05) and a negative correlation between response variability and right inferior parietal activation (r = −.7, p < .02).\n Successful Stop–Go Contrast No significant activation differences were observed between medication conditions.\nNo significant activation differences were observed between medication conditions.","MPH contrasted with placebo elicited enhanced activation in left IFC, reaching into insula and putamen; in right IFC reaching into insula, putamen, and caudate; in left medial frontal lobe; and in left inferior parietal, precuneus, and occipital regions (Figure 1, Table 2). The placebo condition elicited no enhanced activation over MPH.\nTo investigate whether brain regions that differed with MPH were associated with task performance, statistical measures of the BOLD response were extracted for each subject in each ANOVA cluster and then correlated with performance variables. There was a significant positive correlation in patients between post-error reaction times and left and right IFC activation (r = .7, p < .02) and between go reaction times and right IFC activation (r = .6, p < .05) and a negative correlation between response variability and right inferior parietal activation (r = −.7, p < .02).","No significant activation differences were observed between medication conditions."," Failed Stop–Go Contrast Relative to control subjects, ADHD patients under the placebo condition showed underactivation in left IFC and dorsomedial prefrontal cortices (dMFC) (including pre-SMA), right premotor, superior and inferior parietal cortices, posterior cingulate/precuneus, posterior thalamus, and bilateral inferior temporo-occipital areas (Figure 2A, Table 3).\nUnder the MPH condition, ADHD patients did not differ from controls in any of these regions.\nPost-error slowing in ADHD patients under placebo, but not in control subjects, was significantly positively correlated with activation in left IFC, premotor, dMFC, and thalamic underactivation clusters (r > .6 for all clusters, p < .05) as well as with superior parietal, occipital, and cerebellar activation (r < .4, p < .05). Standard deviation of reaction times was correlated with dMFC activation in controls (r = .6, p < .02).\nRelative to control subjects, ADHD patients under the placebo condition showed underactivation in left IFC and dorsomedial prefrontal cortices (dMFC) (including pre-SMA), right premotor, superior and inferior parietal cortices, posterior cingulate/precuneus, posterior thalamus, and bilateral inferior temporo-occipital areas (Figure 2A, Table 3).\nUnder the MPH condition, ADHD patients did not differ from controls in any of these regions.\nPost-error slowing in ADHD patients under placebo, but not in control subjects, was significantly positively correlated with activation in left IFC, premotor, dMFC, and thalamic underactivation clusters (r > .6 for all clusters, p < .05) as well as with superior parietal, occipital, and cerebellar activation (r < .4, p < .05). Standard deviation of reaction times was correlated with dMFC activation in controls (r = .6, p < .02).\n Successful Stop–Go Contrast Relative to control subjects, ADHD patients under the placebo condition showed underactivation in a right hemispheric network of medial temporal and inferior parietal lobes, precuneus/posterior cingulate and cerebellum (Figure 2B, Table 3). To test our hypothesis of IFC underactivation, we reanalyzed the data at a more lenient p value of p < .002 for voxelwise comparison. This elicited additional underactivation in right IFC, left and right subthalamic nuclei, and the pre-SMA (Table 3 and Figure S2 in Supplement 1).\nUnder the MPH condition, ADHD patients did not differ from control subjects in any of these regions.\nActivation in posterior cingulate and lingual gyrus correlated significantly positively with post-error go reaction times in ADHD boys under placebo (r > .6, p < .3).\nAll group difference findings for both contrasts remained essentially unchanged when IQ was covaried.\nRelative to control subjects, ADHD patients under the placebo condition showed underactivation in a right hemispheric network of medial temporal and inferior parietal lobes, precuneus/posterior cingulate and cerebellum (Figure 2B, Table 3). To test our hypothesis of IFC underactivation, we reanalyzed the data at a more lenient p value of p < .002 for voxelwise comparison. This elicited additional underactivation in right IFC, left and right subthalamic nuclei, and the pre-SMA (Table 3 and Figure S2 in Supplement 1).\nUnder the MPH condition, ADHD patients did not differ from control subjects in any of these regions.\nActivation in posterior cingulate and lingual gyrus correlated significantly positively with post-error go reaction times in ADHD boys under placebo (r > .6, p < .3).\nAll group difference findings for both contrasts remained essentially unchanged when IQ was covaried.","Relative to control subjects, ADHD patients under the placebo condition showed underactivation in left IFC and dorsomedial prefrontal cortices (dMFC) (including pre-SMA), right premotor, superior and inferior parietal cortices, posterior cingulate/precuneus, posterior thalamus, and bilateral inferior temporo-occipital areas (Figure 2A, Table 3).\nUnder the MPH condition, ADHD patients did not differ from controls in any of these regions.\nPost-error slowing in ADHD patients under placebo, but not in control subjects, was significantly positively correlated with activation in left IFC, premotor, dMFC, and thalamic underactivation clusters (r > .6 for all clusters, p < .05) as well as with superior parietal, occipital, and cerebellar activation (r < .4, p < .05). Standard deviation of reaction times was correlated with dMFC activation in controls (r = .6, p < .02).","Relative to control subjects, ADHD patients under the placebo condition showed underactivation in a right hemispheric network of medial temporal and inferior parietal lobes, precuneus/posterior cingulate and cerebellum (Figure 2B, Table 3). To test our hypothesis of IFC underactivation, we reanalyzed the data at a more lenient p value of p < .002 for voxelwise comparison. This elicited additional underactivation in right IFC, left and right subthalamic nuclei, and the pre-SMA (Table 3 and Figure S2 in Supplement 1).\nUnder the MPH condition, ADHD patients did not differ from control subjects in any of these regions.\nActivation in posterior cingulate and lingual gyrus correlated significantly positively with post-error go reaction times in ADHD boys under placebo (r > .6, p < .3).\nAll group difference findings for both contrasts remained essentially unchanged when IQ was covaried.","To test whether brain regions that were upregulated with MPH relative to placebo within patients overlapped with brain regions that were reduced in patients under placebo relative to controls and then normalized with MPH, we performed a conjunction analysis by determining the voxels where the within-group ANOVA (MPH > placebo in ADHD) and the between-group ANOVA (control subjects > ADHD placebo) were both significant (42). Three clusters emerged, in left IFC (Talairach coordinates: −43, 7, 4), right SMA (Talairach coordinates: 7, 4, 59) and right inferior parietal lobe (Talairach coordinates: 32, −63, 42). Overlapping clusters are also indicated in bold in Table 3 and shown in Figure 3."],"string":"[\n \"Twelve medication-naïve, right-handed boys aged 10 to 15 years (mean age = 13, SD = 1) who met clinical diagnostic criteria for the combined (inattentive/hyperactive) subtype of ADHD (DSM-IV) were recruited through clinics. Clinical diagnosis of ADHD was established through interviews with an experienced child psychiatrist (A-MM) using the standardized Maudsley Diagnostic Interview to check for presence or absence of diagnostic criteria for any mental disorder as set out by DSM-IV (30). Exclusion criteria were lifetime comorbidity with any other psychiatric disorder, except for conduct/oppositional defiant disorder (present in one patient), as well as learning disability and specific reading disorder, neurological abnormalities, epilepsy, drug or substance abuse, and previous exposure to stimulant medication. Patients with ADHD also had to score above cutoff for hyperactive/inattentive symptoms on the Strengths and Difficulties Questionnaire for Parents (SDQ) (31). Patients were scanned twice, in a randomized, counterbalanced fashion, 1 week apart, 1 hour after either .3 mg/kg of MPH administration or placebo (vitamin C, 100 mg).\\nThirteen male right-handed adolescent boys in the age range of 11 to 16 years (mean age = 13, SD = 1) were recruited through advertisements in the same geographic areas of South London to ensure similar socioeconomic status and were scanned once. They scored below cutoff for behavioral problems in the SDQ and had no history of psychiatric disorder.\\nAll participants were above the fifth percentile on the Raven progressive matrices performance IQ (32) (IQ mean estimate controls = 100, SD = 14; ADHD = 91, SD = 9) and paid £30 for participation. Parental and child informed consent/assent and approval from the local ethical committee was obtained.\\nUnivariate analyses of variance (ANOVAs) showed no group differences between boys with and without ADHD for age [F(1,25) = 2, p = .2] but did for IQ [F(1,25) = 8, p < .009]. IQ is associated with ADHD in the general population (33,34). We purposely did not match groups for IQ because matching ADHD and control groups for IQ would have created unrepresentative groups and therefore be misguided (35). Furthermore, IQ was significantly negatively correlated with the SDQ scores for inattention and hyperactivity (r = −.5, p < .001). We did not covary for IQ because when groups are not randomly selected, covarying for a variable that differs between groups violates the standard assumptions for analysis of covariance. When the covariate is intrinsic to the condition, it becomes meaningless to “adjust” group effects for differences in the covariate because it would alter the group effect in potentially problematic ways, leading to spurious results (35,36).\",\n \"The rapid, mixed-trial, event-related fMRI design was practiced by subjects once before scanning. The visual tracking stop task requires withholding of a motor response to a go stimulus when it is followed unpredictably by a stop signal (8,37,38). The basic task is a choice reaction time task (left and right pointing arrows: go signals) with a mean intertrial-interval of 1.8 sec (156 go trials). In 20% of trials, pseudo-randomly interspersed, the go signals are followed (about 250 ms later) by arrows pointing upwards (stop signals), and subjects have to inhibit their motor responses (40 stop trials). A tracking algorithm changes the time interval between go-signal and stop-signal onsets according to each subject's inhibitory performance to ensure that the task is equally challenging for each individual and to provide 50% successful and 50% unsuccessful inhibition trials at every moment of the task.\",\n \"Gradient-echo echoplanar magnetic resonance imaging data were acquired on a GE Signa 1.5-T Horizon LX System (General Electric, Milwaukee, Wisconsin) at the Maudsley Hospital, London. A quadrature birdcage head coil was used for radio-frequency transmission and reception. During the 6-min run of the stop task, in each of 16 noncontiguous planes parallel to the anterior–posterior commissural, 196 T2*-weighted magnetic resonance images depicting blood oxygen–level dependent (BOLD) contrast covering the whole brain were acquired with echo time = 40 msec, repetition time = 1.8 sec, flip angle = 90°, in-plane resolution = 3.1 mm, slice thickness = 7 mm, slice skip = .7 mm, providing complete brain coverage.\",\n \"At the individual subject level, a standard general linear modeling approach was used to obtain estimates of the response size (beta) to each of the two stop task conditions (successful and unsuccessful stop trials) against an implicit baseline (go trials). Following transformation of the fMRI data for each individual into standard space and smoothing with a three-dimensional 7-mm full width at half maximum Gaussian filter, the experimental model was convolved for each condition with gamma variate functions having peak responses at 4 and 8 sec following stimulus onset to accommodate variability in BOLD response timing. By fitting these convolved model components to the time series at each voxel, beta estimates were obtained for each effect of interest. The standard errors of these beta estimates were computed nonparametrically using a bootstrap procedure designed to operate on time series data, containing serial dependencies, with repeated deterministic (experimentally determined) effects. This method is outlined in detail in a previous work (39). Two hundred bootstraps at each voxel were used to estimate parameter standard errors. Using the combined parameter estimates over all conditions, the mean fitted time series was also computed and, from the combined bootstrap parameter estimates for each bootstrap, the 95% confidence limits on the fitted time series was computed.\\nThe second-level analysis proceeded by computing either the group differences (patients and controls) or the drug condition differences (placebo, MPH) within patients at each voxel and the standard error of this difference (using the bootstrap estimates derived earlier). The significance of these differences was then tested in three ways: 1) a simple parametric random effects (paired t) test, using only the group difference/placebo-MPH effect size differences; 2) a permutation test of the same random effects t statistic in which the null distribution was estimated by randomly swapping the signs of the differences (we used 40,000 permutations per voxel to obtain a confidence limit of .0007–.0013 for p value of .001); and 3) a mixed-effects test using both the effect size differences and their subject-level standard errors to accommodate first (subject) level heteroscedasticity (40). This was also conducted using 40,000 permutations per voxel.\\nIn addition to voxelwise maps, cluster-level inference on the contrast (beta) values was performed at a family-wise error corrected threshold of p < .05 using the Threshold-Free Cluster Enhancement method proposed by Smith and Nichols (41). This cluster-level inference was also used for the within-group maps for each experimental condition.\",\n \"The probability of inhibition was about 50% in all subjects with no significant group differences, showing that the task algorithm worked [F(1,38) = 1; p < .3; Table 1].\\nA multivariate ANOVA between control subjects and ADHD patients under either drug condition showed a trend for a significant group effect [F(8,62) = 2, p < .09] due to a significant univariate group effect in the standard deviation to go trials [F(2,34) = 5, p < .02], which were higher in patients under either medication condition compared with control subjects (p < .05). Post hoc tests furthermore revealed a trend for MPH compared with placebo to slow down reaction times within ADHD patients to both go (p < .06) and post-error go trials (both p < .07) (Table 1).\",\n \" Motion Multivariate ANOVA showed no significant group differences between control subjects and ADHD patients under either drug condition in mean or maximum rotation or translation parameters in the x, y, or z dimensions [F(2,38) = .9, p < .5].\\nMultivariate ANOVA showed no significant group differences between control subjects and ADHD patients under either drug condition in mean or maximum rotation or translation parameters in the x, y, or z dimensions [F(2,38) = .9, p < .5].\\n Within-Group Brain Activations Failed Stop–Go Contrast Control subjects activated relatively large clusters in left and right inferior frontal cortex (IFC)/anterior insula, medial frontal/anterior cingulate cortex (MFC/ACC), precentral, inferior parietal, middle and superior temporal areas, posterior thalamus and caudate, parahippocampal gyri, precuneus/posterior cingulate, occipital, and cerebellar areas.\\nActivation in ADHD patients under placebo was in MFC and superior temporal cortex reaching into anterior and posterior insula, caudate, and inferior parietal and occipital cortex.\\nActivation in ADHD patients under MPH was in a large cluster of ACC and MFC, in left and right IFC/anterior insula, left premotor cortex, right basal ganglia, bilateral middle and superior temporal, inferior parietal and occipital areas, hippocampal gyri, posterior cingulate, precuneus, and cerebellum (Figure S1 in Supplement 1).\\nControl subjects activated relatively large clusters in left and right inferior frontal cortex (IFC)/anterior insula, medial frontal/anterior cingulate cortex (MFC/ACC), precentral, inferior parietal, middle and superior temporal areas, posterior thalamus and caudate, parahippocampal gyri, precuneus/posterior cingulate, occipital, and cerebellar areas.\\nActivation in ADHD patients under placebo was in MFC and superior temporal cortex reaching into anterior and posterior insula, caudate, and inferior parietal and occipital cortex.\\nActivation in ADHD patients under MPH was in a large cluster of ACC and MFC, in left and right IFC/anterior insula, left premotor cortex, right basal ganglia, bilateral middle and superior temporal, inferior parietal and occipital areas, hippocampal gyri, posterior cingulate, precuneus, and cerebellum (Figure S1 in Supplement 1).\\n Successful Stop–Go Contrast Activation in healthy control boys was in a large cluster comprising left and right orbital and IFC, dorsolateral and MFC, insula, basal ganglia, hippocampus, posterior thalamic regions, pre- and postcentral gyri, inferior and superior parietal, temporal and occipital cortices, precuneus, and posterior cingulate.\\nActivation in ADHD patients under placebo was in small clusters in right MFC, supplementary motor area (SMA), right superior temporal, postcentral, left inferior and superior parietal, and occipital cortices.\\nActivation in ADHD patients under the MPH condition was in superior and MFC/ACC, right globus pallidus and putamen, right superior temporal and superior and inferior parietal cortex, posterior insula, and left cerebellum (Figure S1 in Supplement 1).\\nActivation in healthy control boys was in a large cluster comprising left and right orbital and IFC, dorsolateral and MFC, insula, basal ganglia, hippocampus, posterior thalamic regions, pre- and postcentral gyri, inferior and superior parietal, temporal and occipital cortices, precuneus, and posterior cingulate.\\nActivation in ADHD patients under placebo was in small clusters in right MFC, supplementary motor area (SMA), right superior temporal, postcentral, left inferior and superior parietal, and occipital cortices.\\nActivation in ADHD patients under the MPH condition was in superior and MFC/ACC, right globus pallidus and putamen, right superior temporal and superior and inferior parietal cortex, posterior insula, and left cerebellum (Figure S1 in Supplement 1).\\n Failed Stop–Go Contrast Control subjects activated relatively large clusters in left and right inferior frontal cortex (IFC)/anterior insula, medial frontal/anterior cingulate cortex (MFC/ACC), precentral, inferior parietal, middle and superior temporal areas, posterior thalamus and caudate, parahippocampal gyri, precuneus/posterior cingulate, occipital, and cerebellar areas.\\nActivation in ADHD patients under placebo was in MFC and superior temporal cortex reaching into anterior and posterior insula, caudate, and inferior parietal and occipital cortex.\\nActivation in ADHD patients under MPH was in a large cluster of ACC and MFC, in left and right IFC/anterior insula, left premotor cortex, right basal ganglia, bilateral middle and superior temporal, inferior parietal and occipital areas, hippocampal gyri, posterior cingulate, precuneus, and cerebellum (Figure S1 in Supplement 1).\\nControl subjects activated relatively large clusters in left and right inferior frontal cortex (IFC)/anterior insula, medial frontal/anterior cingulate cortex (MFC/ACC), precentral, inferior parietal, middle and superior temporal areas, posterior thalamus and caudate, parahippocampal gyri, precuneus/posterior cingulate, occipital, and cerebellar areas.\\nActivation in ADHD patients under placebo was in MFC and superior temporal cortex reaching into anterior and posterior insula, caudate, and inferior parietal and occipital cortex.\\nActivation in ADHD patients under MPH was in a large cluster of ACC and MFC, in left and right IFC/anterior insula, left premotor cortex, right basal ganglia, bilateral middle and superior temporal, inferior parietal and occipital areas, hippocampal gyri, posterior cingulate, precuneus, and cerebellum (Figure S1 in Supplement 1).\\n Successful Stop–Go Contrast Activation in healthy control boys was in a large cluster comprising left and right orbital and IFC, dorsolateral and MFC, insula, basal ganglia, hippocampus, posterior thalamic regions, pre- and postcentral gyri, inferior and superior parietal, temporal and occipital cortices, precuneus, and posterior cingulate.\\nActivation in ADHD patients under placebo was in small clusters in right MFC, supplementary motor area (SMA), right superior temporal, postcentral, left inferior and superior parietal, and occipital cortices.\\nActivation in ADHD patients under the MPH condition was in superior and MFC/ACC, right globus pallidus and putamen, right superior temporal and superior and inferior parietal cortex, posterior insula, and left cerebellum (Figure S1 in Supplement 1).\\nActivation in healthy control boys was in a large cluster comprising left and right orbital and IFC, dorsolateral and MFC, insula, basal ganglia, hippocampus, posterior thalamic regions, pre- and postcentral gyri, inferior and superior parietal, temporal and occipital cortices, precuneus, and posterior cingulate.\\nActivation in ADHD patients under placebo was in small clusters in right MFC, supplementary motor area (SMA), right superior temporal, postcentral, left inferior and superior parietal, and occipital cortices.\\nActivation in ADHD patients under the MPH condition was in superior and MFC/ACC, right globus pallidus and putamen, right superior temporal and superior and inferior parietal cortex, posterior insula, and left cerebellum (Figure S1 in Supplement 1).\\n ANOVA Within-Patient Comparisons in Brain Activation Between the Placebo and the MPH Conditions Failed Stop–Go Contrast MPH contrasted with placebo elicited enhanced activation in left IFC, reaching into insula and putamen; in right IFC reaching into insula, putamen, and caudate; in left medial frontal lobe; and in left inferior parietal, precuneus, and occipital regions (Figure 1, Table 2). The placebo condition elicited no enhanced activation over MPH.\\nTo investigate whether brain regions that differed with MPH were associated with task performance, statistical measures of the BOLD response were extracted for each subject in each ANOVA cluster and then correlated with performance variables. There was a significant positive correlation in patients between post-error reaction times and left and right IFC activation (r = .7, p < .02) and between go reaction times and right IFC activation (r = .6, p < .05) and a negative correlation between response variability and right inferior parietal activation (r = −.7, p < .02).\\nMPH contrasted with placebo elicited enhanced activation in left IFC, reaching into insula and putamen; in right IFC reaching into insula, putamen, and caudate; in left medial frontal lobe; and in left inferior parietal, precuneus, and occipital regions (Figure 1, Table 2). The placebo condition elicited no enhanced activation over MPH.\\nTo investigate whether brain regions that differed with MPH were associated with task performance, statistical measures of the BOLD response were extracted for each subject in each ANOVA cluster and then correlated with performance variables. There was a significant positive correlation in patients between post-error reaction times and left and right IFC activation (r = .7, p < .02) and between go reaction times and right IFC activation (r = .6, p < .05) and a negative correlation between response variability and right inferior parietal activation (r = −.7, p < .02).\\n Successful Stop–Go Contrast No significant activation differences were observed between medication conditions.\\nNo significant activation differences were observed between medication conditions.\\n Failed Stop–Go Contrast MPH contrasted with placebo elicited enhanced activation in left IFC, reaching into insula and putamen; in right IFC reaching into insula, putamen, and caudate; in left medial frontal lobe; and in left inferior parietal, precuneus, and occipital regions (Figure 1, Table 2). The placebo condition elicited no enhanced activation over MPH.\\nTo investigate whether brain regions that differed with MPH were associated with task performance, statistical measures of the BOLD response were extracted for each subject in each ANOVA cluster and then correlated with performance variables. There was a significant positive correlation in patients between post-error reaction times and left and right IFC activation (r = .7, p < .02) and between go reaction times and right IFC activation (r = .6, p < .05) and a negative correlation between response variability and right inferior parietal activation (r = −.7, p < .02).\\nMPH contrasted with placebo elicited enhanced activation in left IFC, reaching into insula and putamen; in right IFC reaching into insula, putamen, and caudate; in left medial frontal lobe; and in left inferior parietal, precuneus, and occipital regions (Figure 1, Table 2). The placebo condition elicited no enhanced activation over MPH.\\nTo investigate whether brain regions that differed with MPH were associated with task performance, statistical measures of the BOLD response were extracted for each subject in each ANOVA cluster and then correlated with performance variables. There was a significant positive correlation in patients between post-error reaction times and left and right IFC activation (r = .7, p < .02) and between go reaction times and right IFC activation (r = .6, p < .05) and a negative correlation between response variability and right inferior parietal activation (r = −.7, p < .02).\\n Successful Stop–Go Contrast No significant activation differences were observed between medication conditions.\\nNo significant activation differences were observed between medication conditions.\\n ANOVA Between-Group Comparisons in Brain Activation Between Control Subjects and Boys with ADHD Under Either the Placebo or the MPH Conditions Failed Stop–Go Contrast Relative to control subjects, ADHD patients under the placebo condition showed underactivation in left IFC and dorsomedial prefrontal cortices (dMFC) (including pre-SMA), right premotor, superior and inferior parietal cortices, posterior cingulate/precuneus, posterior thalamus, and bilateral inferior temporo-occipital areas (Figure 2A, Table 3).\\nUnder the MPH condition, ADHD patients did not differ from controls in any of these regions.\\nPost-error slowing in ADHD patients under placebo, but not in control subjects, was significantly positively correlated with activation in left IFC, premotor, dMFC, and thalamic underactivation clusters (r > .6 for all clusters, p < .05) as well as with superior parietal, occipital, and cerebellar activation (r < .4, p < .05). Standard deviation of reaction times was correlated with dMFC activation in controls (r = .6, p < .02).\\nRelative to control subjects, ADHD patients under the placebo condition showed underactivation in left IFC and dorsomedial prefrontal cortices (dMFC) (including pre-SMA), right premotor, superior and inferior parietal cortices, posterior cingulate/precuneus, posterior thalamus, and bilateral inferior temporo-occipital areas (Figure 2A, Table 3).\\nUnder the MPH condition, ADHD patients did not differ from controls in any of these regions.\\nPost-error slowing in ADHD patients under placebo, but not in control subjects, was significantly positively correlated with activation in left IFC, premotor, dMFC, and thalamic underactivation clusters (r > .6 for all clusters, p < .05) as well as with superior parietal, occipital, and cerebellar activation (r < .4, p < .05). Standard deviation of reaction times was correlated with dMFC activation in controls (r = .6, p < .02).\\n Successful Stop–Go Contrast Relative to control subjects, ADHD patients under the placebo condition showed underactivation in a right hemispheric network of medial temporal and inferior parietal lobes, precuneus/posterior cingulate and cerebellum (Figure 2B, Table 3). To test our hypothesis of IFC underactivation, we reanalyzed the data at a more lenient p value of p < .002 for voxelwise comparison. This elicited additional underactivation in right IFC, left and right subthalamic nuclei, and the pre-SMA (Table 3 and Figure S2 in Supplement 1).\\nUnder the MPH condition, ADHD patients did not differ from control subjects in any of these regions.\\nActivation in posterior cingulate and lingual gyrus correlated significantly positively with post-error go reaction times in ADHD boys under placebo (r > .6, p < .3).\\nAll group difference findings for both contrasts remained essentially unchanged when IQ was covaried.\\nRelative to control subjects, ADHD patients under the placebo condition showed underactivation in a right hemispheric network of medial temporal and inferior parietal lobes, precuneus/posterior cingulate and cerebellum (Figure 2B, Table 3). To test our hypothesis of IFC underactivation, we reanalyzed the data at a more lenient p value of p < .002 for voxelwise comparison. This elicited additional underactivation in right IFC, left and right subthalamic nuclei, and the pre-SMA (Table 3 and Figure S2 in Supplement 1).\\nUnder the MPH condition, ADHD patients did not differ from control subjects in any of these regions.\\nActivation in posterior cingulate and lingual gyrus correlated significantly positively with post-error go reaction times in ADHD boys under placebo (r > .6, p < .3).\\nAll group difference findings for both contrasts remained essentially unchanged when IQ was covaried.\\n Failed Stop–Go Contrast Relative to control subjects, ADHD patients under the placebo condition showed underactivation in left IFC and dorsomedial prefrontal cortices (dMFC) (including pre-SMA), right premotor, superior and inferior parietal cortices, posterior cingulate/precuneus, posterior thalamus, and bilateral inferior temporo-occipital areas (Figure 2A, Table 3).\\nUnder the MPH condition, ADHD patients did not differ from controls in any of these regions.\\nPost-error slowing in ADHD patients under placebo, but not in control subjects, was significantly positively correlated with activation in left IFC, premotor, dMFC, and thalamic underactivation clusters (r > .6 for all clusters, p < .05) as well as with superior parietal, occipital, and cerebellar activation (r < .4, p < .05). Standard deviation of reaction times was correlated with dMFC activation in controls (r = .6, p < .02).\\nRelative to control subjects, ADHD patients under the placebo condition showed underactivation in left IFC and dorsomedial prefrontal cortices (dMFC) (including pre-SMA), right premotor, superior and inferior parietal cortices, posterior cingulate/precuneus, posterior thalamus, and bilateral inferior temporo-occipital areas (Figure 2A, Table 3).\\nUnder the MPH condition, ADHD patients did not differ from controls in any of these regions.\\nPost-error slowing in ADHD patients under placebo, but not in control subjects, was significantly positively correlated with activation in left IFC, premotor, dMFC, and thalamic underactivation clusters (r > .6 for all clusters, p < .05) as well as with superior parietal, occipital, and cerebellar activation (r < .4, p < .05). Standard deviation of reaction times was correlated with dMFC activation in controls (r = .6, p < .02).\\n Successful Stop–Go Contrast Relative to control subjects, ADHD patients under the placebo condition showed underactivation in a right hemispheric network of medial temporal and inferior parietal lobes, precuneus/posterior cingulate and cerebellum (Figure 2B, Table 3). To test our hypothesis of IFC underactivation, we reanalyzed the data at a more lenient p value of p < .002 for voxelwise comparison. This elicited additional underactivation in right IFC, left and right subthalamic nuclei, and the pre-SMA (Table 3 and Figure S2 in Supplement 1).\\nUnder the MPH condition, ADHD patients did not differ from control subjects in any of these regions.\\nActivation in posterior cingulate and lingual gyrus correlated significantly positively with post-error go reaction times in ADHD boys under placebo (r > .6, p < .3).\\nAll group difference findings for both contrasts remained essentially unchanged when IQ was covaried.\\nRelative to control subjects, ADHD patients under the placebo condition showed underactivation in a right hemispheric network of medial temporal and inferior parietal lobes, precuneus/posterior cingulate and cerebellum (Figure 2B, Table 3). To test our hypothesis of IFC underactivation, we reanalyzed the data at a more lenient p value of p < .002 for voxelwise comparison. This elicited additional underactivation in right IFC, left and right subthalamic nuclei, and the pre-SMA (Table 3 and Figure S2 in Supplement 1).\\nUnder the MPH condition, ADHD patients did not differ from control subjects in any of these regions.\\nActivation in posterior cingulate and lingual gyrus correlated significantly positively with post-error go reaction times in ADHD boys under placebo (r > .6, p < .3).\\nAll group difference findings for both contrasts remained essentially unchanged when IQ was covaried.\\n Conjunction Analysis Between Within-Group and Between-Group ANOVAs To test whether brain regions that were upregulated with MPH relative to placebo within patients overlapped with brain regions that were reduced in patients under placebo relative to controls and then normalized with MPH, we performed a conjunction analysis by determining the voxels where the within-group ANOVA (MPH > placebo in ADHD) and the between-group ANOVA (control subjects > ADHD placebo) were both significant (42). Three clusters emerged, in left IFC (Talairach coordinates: −43, 7, 4), right SMA (Talairach coordinates: 7, 4, 59) and right inferior parietal lobe (Talairach coordinates: 32, −63, 42). Overlapping clusters are also indicated in bold in Table 3 and shown in Figure 3.\\nTo test whether brain regions that were upregulated with MPH relative to placebo within patients overlapped with brain regions that were reduced in patients under placebo relative to controls and then normalized with MPH, we performed a conjunction analysis by determining the voxels where the within-group ANOVA (MPH > placebo in ADHD) and the between-group ANOVA (control subjects > ADHD placebo) were both significant (42). Three clusters emerged, in left IFC (Talairach coordinates: −43, 7, 4), right SMA (Talairach coordinates: 7, 4, 59) and right inferior parietal lobe (Talairach coordinates: 32, −63, 42). Overlapping clusters are also indicated in bold in Table 3 and shown in Figure 3.\",\n \"Multivariate ANOVA showed no significant group differences between control subjects and ADHD patients under either drug condition in mean or maximum rotation or translation parameters in the x, y, or z dimensions [F(2,38) = .9, p < .5].\",\n \" Failed Stop–Go Contrast Control subjects activated relatively large clusters in left and right inferior frontal cortex (IFC)/anterior insula, medial frontal/anterior cingulate cortex (MFC/ACC), precentral, inferior parietal, middle and superior temporal areas, posterior thalamus and caudate, parahippocampal gyri, precuneus/posterior cingulate, occipital, and cerebellar areas.\\nActivation in ADHD patients under placebo was in MFC and superior temporal cortex reaching into anterior and posterior insula, caudate, and inferior parietal and occipital cortex.\\nActivation in ADHD patients under MPH was in a large cluster of ACC and MFC, in left and right IFC/anterior insula, left premotor cortex, right basal ganglia, bilateral middle and superior temporal, inferior parietal and occipital areas, hippocampal gyri, posterior cingulate, precuneus, and cerebellum (Figure S1 in Supplement 1).\\nControl subjects activated relatively large clusters in left and right inferior frontal cortex (IFC)/anterior insula, medial frontal/anterior cingulate cortex (MFC/ACC), precentral, inferior parietal, middle and superior temporal areas, posterior thalamus and caudate, parahippocampal gyri, precuneus/posterior cingulate, occipital, and cerebellar areas.\\nActivation in ADHD patients under placebo was in MFC and superior temporal cortex reaching into anterior and posterior insula, caudate, and inferior parietal and occipital cortex.\\nActivation in ADHD patients under MPH was in a large cluster of ACC and MFC, in left and right IFC/anterior insula, left premotor cortex, right basal ganglia, bilateral middle and superior temporal, inferior parietal and occipital areas, hippocampal gyri, posterior cingulate, precuneus, and cerebellum (Figure S1 in Supplement 1).\\n Successful Stop–Go Contrast Activation in healthy control boys was in a large cluster comprising left and right orbital and IFC, dorsolateral and MFC, insula, basal ganglia, hippocampus, posterior thalamic regions, pre- and postcentral gyri, inferior and superior parietal, temporal and occipital cortices, precuneus, and posterior cingulate.\\nActivation in ADHD patients under placebo was in small clusters in right MFC, supplementary motor area (SMA), right superior temporal, postcentral, left inferior and superior parietal, and occipital cortices.\\nActivation in ADHD patients under the MPH condition was in superior and MFC/ACC, right globus pallidus and putamen, right superior temporal and superior and inferior parietal cortex, posterior insula, and left cerebellum (Figure S1 in Supplement 1).\\nActivation in healthy control boys was in a large cluster comprising left and right orbital and IFC, dorsolateral and MFC, insula, basal ganglia, hippocampus, posterior thalamic regions, pre- and postcentral gyri, inferior and superior parietal, temporal and occipital cortices, precuneus, and posterior cingulate.\\nActivation in ADHD patients under placebo was in small clusters in right MFC, supplementary motor area (SMA), right superior temporal, postcentral, left inferior and superior parietal, and occipital cortices.\\nActivation in ADHD patients under the MPH condition was in superior and MFC/ACC, right globus pallidus and putamen, right superior temporal and superior and inferior parietal cortex, posterior insula, and left cerebellum (Figure S1 in Supplement 1).\",\n \"Control subjects activated relatively large clusters in left and right inferior frontal cortex (IFC)/anterior insula, medial frontal/anterior cingulate cortex (MFC/ACC), precentral, inferior parietal, middle and superior temporal areas, posterior thalamus and caudate, parahippocampal gyri, precuneus/posterior cingulate, occipital, and cerebellar areas.\\nActivation in ADHD patients under placebo was in MFC and superior temporal cortex reaching into anterior and posterior insula, caudate, and inferior parietal and occipital cortex.\\nActivation in ADHD patients under MPH was in a large cluster of ACC and MFC, in left and right IFC/anterior insula, left premotor cortex, right basal ganglia, bilateral middle and superior temporal, inferior parietal and occipital areas, hippocampal gyri, posterior cingulate, precuneus, and cerebellum (Figure S1 in Supplement 1).\",\n \"Activation in healthy control boys was in a large cluster comprising left and right orbital and IFC, dorsolateral and MFC, insula, basal ganglia, hippocampus, posterior thalamic regions, pre- and postcentral gyri, inferior and superior parietal, temporal and occipital cortices, precuneus, and posterior cingulate.\\nActivation in ADHD patients under placebo was in small clusters in right MFC, supplementary motor area (SMA), right superior temporal, postcentral, left inferior and superior parietal, and occipital cortices.\\nActivation in ADHD patients under the MPH condition was in superior and MFC/ACC, right globus pallidus and putamen, right superior temporal and superior and inferior parietal cortex, posterior insula, and left cerebellum (Figure S1 in Supplement 1).\",\n \" Failed Stop–Go Contrast MPH contrasted with placebo elicited enhanced activation in left IFC, reaching into insula and putamen; in right IFC reaching into insula, putamen, and caudate; in left medial frontal lobe; and in left inferior parietal, precuneus, and occipital regions (Figure 1, Table 2). The placebo condition elicited no enhanced activation over MPH.\\nTo investigate whether brain regions that differed with MPH were associated with task performance, statistical measures of the BOLD response were extracted for each subject in each ANOVA cluster and then correlated with performance variables. There was a significant positive correlation in patients between post-error reaction times and left and right IFC activation (r = .7, p < .02) and between go reaction times and right IFC activation (r = .6, p < .05) and a negative correlation between response variability and right inferior parietal activation (r = −.7, p < .02).\\nMPH contrasted with placebo elicited enhanced activation in left IFC, reaching into insula and putamen; in right IFC reaching into insula, putamen, and caudate; in left medial frontal lobe; and in left inferior parietal, precuneus, and occipital regions (Figure 1, Table 2). The placebo condition elicited no enhanced activation over MPH.\\nTo investigate whether brain regions that differed with MPH were associated with task performance, statistical measures of the BOLD response were extracted for each subject in each ANOVA cluster and then correlated with performance variables. There was a significant positive correlation in patients between post-error reaction times and left and right IFC activation (r = .7, p < .02) and between go reaction times and right IFC activation (r = .6, p < .05) and a negative correlation between response variability and right inferior parietal activation (r = −.7, p < .02).\\n Successful Stop–Go Contrast No significant activation differences were observed between medication conditions.\\nNo significant activation differences were observed between medication conditions.\",\n \"MPH contrasted with placebo elicited enhanced activation in left IFC, reaching into insula and putamen; in right IFC reaching into insula, putamen, and caudate; in left medial frontal lobe; and in left inferior parietal, precuneus, and occipital regions (Figure 1, Table 2). The placebo condition elicited no enhanced activation over MPH.\\nTo investigate whether brain regions that differed with MPH were associated with task performance, statistical measures of the BOLD response were extracted for each subject in each ANOVA cluster and then correlated with performance variables. There was a significant positive correlation in patients between post-error reaction times and left and right IFC activation (r = .7, p < .02) and between go reaction times and right IFC activation (r = .6, p < .05) and a negative correlation between response variability and right inferior parietal activation (r = −.7, p < .02).\",\n \"No significant activation differences were observed between medication conditions.\",\n \" Failed Stop–Go Contrast Relative to control subjects, ADHD patients under the placebo condition showed underactivation in left IFC and dorsomedial prefrontal cortices (dMFC) (including pre-SMA), right premotor, superior and inferior parietal cortices, posterior cingulate/precuneus, posterior thalamus, and bilateral inferior temporo-occipital areas (Figure 2A, Table 3).\\nUnder the MPH condition, ADHD patients did not differ from controls in any of these regions.\\nPost-error slowing in ADHD patients under placebo, but not in control subjects, was significantly positively correlated with activation in left IFC, premotor, dMFC, and thalamic underactivation clusters (r > .6 for all clusters, p < .05) as well as with superior parietal, occipital, and cerebellar activation (r < .4, p < .05). Standard deviation of reaction times was correlated with dMFC activation in controls (r = .6, p < .02).\\nRelative to control subjects, ADHD patients under the placebo condition showed underactivation in left IFC and dorsomedial prefrontal cortices (dMFC) (including pre-SMA), right premotor, superior and inferior parietal cortices, posterior cingulate/precuneus, posterior thalamus, and bilateral inferior temporo-occipital areas (Figure 2A, Table 3).\\nUnder the MPH condition, ADHD patients did not differ from controls in any of these regions.\\nPost-error slowing in ADHD patients under placebo, but not in control subjects, was significantly positively correlated with activation in left IFC, premotor, dMFC, and thalamic underactivation clusters (r > .6 for all clusters, p < .05) as well as with superior parietal, occipital, and cerebellar activation (r < .4, p < .05). Standard deviation of reaction times was correlated with dMFC activation in controls (r = .6, p < .02).\\n Successful Stop–Go Contrast Relative to control subjects, ADHD patients under the placebo condition showed underactivation in a right hemispheric network of medial temporal and inferior parietal lobes, precuneus/posterior cingulate and cerebellum (Figure 2B, Table 3). To test our hypothesis of IFC underactivation, we reanalyzed the data at a more lenient p value of p < .002 for voxelwise comparison. This elicited additional underactivation in right IFC, left and right subthalamic nuclei, and the pre-SMA (Table 3 and Figure S2 in Supplement 1).\\nUnder the MPH condition, ADHD patients did not differ from control subjects in any of these regions.\\nActivation in posterior cingulate and lingual gyrus correlated significantly positively with post-error go reaction times in ADHD boys under placebo (r > .6, p < .3).\\nAll group difference findings for both contrasts remained essentially unchanged when IQ was covaried.\\nRelative to control subjects, ADHD patients under the placebo condition showed underactivation in a right hemispheric network of medial temporal and inferior parietal lobes, precuneus/posterior cingulate and cerebellum (Figure 2B, Table 3). To test our hypothesis of IFC underactivation, we reanalyzed the data at a more lenient p value of p < .002 for voxelwise comparison. This elicited additional underactivation in right IFC, left and right subthalamic nuclei, and the pre-SMA (Table 3 and Figure S2 in Supplement 1).\\nUnder the MPH condition, ADHD patients did not differ from control subjects in any of these regions.\\nActivation in posterior cingulate and lingual gyrus correlated significantly positively with post-error go reaction times in ADHD boys under placebo (r > .6, p < .3).\\nAll group difference findings for both contrasts remained essentially unchanged when IQ was covaried.\",\n \"Relative to control subjects, ADHD patients under the placebo condition showed underactivation in left IFC and dorsomedial prefrontal cortices (dMFC) (including pre-SMA), right premotor, superior and inferior parietal cortices, posterior cingulate/precuneus, posterior thalamus, and bilateral inferior temporo-occipital areas (Figure 2A, Table 3).\\nUnder the MPH condition, ADHD patients did not differ from controls in any of these regions.\\nPost-error slowing in ADHD patients under placebo, but not in control subjects, was significantly positively correlated with activation in left IFC, premotor, dMFC, and thalamic underactivation clusters (r > .6 for all clusters, p < .05) as well as with superior parietal, occipital, and cerebellar activation (r < .4, p < .05). Standard deviation of reaction times was correlated with dMFC activation in controls (r = .6, p < .02).\",\n \"Relative to control subjects, ADHD patients under the placebo condition showed underactivation in a right hemispheric network of medial temporal and inferior parietal lobes, precuneus/posterior cingulate and cerebellum (Figure 2B, Table 3). To test our hypothesis of IFC underactivation, we reanalyzed the data at a more lenient p value of p < .002 for voxelwise comparison. This elicited additional underactivation in right IFC, left and right subthalamic nuclei, and the pre-SMA (Table 3 and Figure S2 in Supplement 1).\\nUnder the MPH condition, ADHD patients did not differ from control subjects in any of these regions.\\nActivation in posterior cingulate and lingual gyrus correlated significantly positively with post-error go reaction times in ADHD boys under placebo (r > .6, p < .3).\\nAll group difference findings for both contrasts remained essentially unchanged when IQ was covaried.\",\n \"To test whether brain regions that were upregulated with MPH relative to placebo within patients overlapped with brain regions that were reduced in patients under placebo relative to controls and then normalized with MPH, we performed a conjunction analysis by determining the voxels where the within-group ANOVA (MPH > placebo in ADHD) and the between-group ANOVA (control subjects > ADHD placebo) were both significant (42). Three clusters emerged, in left IFC (Talairach coordinates: −43, 7, 4), right SMA (Talairach coordinates: 7, 4, 59) and right inferior parietal lobe (Talairach coordinates: 32, −63, 42). Overlapping clusters are also indicated in bold in Table 3 and shown in Figure 3.\"\n]"},"other_sections_sec_types":{"kind":"list like","value":[null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null],"string":"[\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null\n]"},"all_sections_titles":{"kind":"list like","value":["Methods and Materials","Subjects","fMRI Paradigm: Stop Task","fMRI Image Acquisition","fMRI Image Analysis","Results","Performance","Brain Activation","Motion","Within-Group Brain Activations","Failed Stop–Go Contrast","Successful Stop–Go Contrast","ANOVA Within-Patient Comparisons in Brain Activation Between the Placebo and the MPH Conditions","Failed Stop–Go Contrast","Successful Stop–Go Contrast","ANOVA Between-Group Comparisons in Brain Activation Between Control Subjects and Boys with ADHD Under Either the Placebo or the MPH Conditions","Failed Stop–Go Contrast","Successful Stop–Go Contrast","Conjunction Analysis Between Within-Group and Between-Group ANOVAs","Discussion"],"string":"[\n \"Methods and Materials\",\n \"Subjects\",\n \"fMRI Paradigm: Stop Task\",\n \"fMRI Image Acquisition\",\n \"fMRI Image Analysis\",\n \"Results\",\n \"Performance\",\n \"Brain Activation\",\n \"Motion\",\n \"Within-Group Brain Activations\",\n \"Failed Stop–Go Contrast\",\n \"Successful Stop–Go Contrast\",\n \"ANOVA Within-Patient Comparisons in Brain Activation Between the Placebo and the MPH Conditions\",\n \"Failed Stop–Go Contrast\",\n \"Successful Stop–Go Contrast\",\n \"ANOVA Between-Group Comparisons in Brain Activation Between Control Subjects and Boys with ADHD Under Either the Placebo or the MPH Conditions\",\n \"Failed Stop–Go Contrast\",\n \"Successful Stop–Go Contrast\",\n \"Conjunction Analysis Between Within-Group and Between-Group ANOVAs\",\n \"Discussion\"\n]"},"all_sections_texts":{"kind":"list like","value":[" Subjects Twelve medication-naïve, right-handed boys aged 10 to 15 years (mean age = 13, SD = 1) who met clinical diagnostic criteria for the combined (inattentive/hyperactive) subtype of ADHD (DSM-IV) were recruited through clinics. Clinical diagnosis of ADHD was established through interviews with an experienced child psychiatrist (A-MM) using the standardized Maudsley Diagnostic Interview to check for presence or absence of diagnostic criteria for any mental disorder as set out by DSM-IV (30). Exclusion criteria were lifetime comorbidity with any other psychiatric disorder, except for conduct/oppositional defiant disorder (present in one patient), as well as learning disability and specific reading disorder, neurological abnormalities, epilepsy, drug or substance abuse, and previous exposure to stimulant medication. Patients with ADHD also had to score above cutoff for hyperactive/inattentive symptoms on the Strengths and Difficulties Questionnaire for Parents (SDQ) (31). Patients were scanned twice, in a randomized, counterbalanced fashion, 1 week apart, 1 hour after either .3 mg/kg of MPH administration or placebo (vitamin C, 100 mg).\nThirteen male right-handed adolescent boys in the age range of 11 to 16 years (mean age = 13, SD = 1) were recruited through advertisements in the same geographic areas of South London to ensure similar socioeconomic status and were scanned once. They scored below cutoff for behavioral problems in the SDQ and had no history of psychiatric disorder.\nAll participants were above the fifth percentile on the Raven progressive matrices performance IQ (32) (IQ mean estimate controls = 100, SD = 14; ADHD = 91, SD = 9) and paid £30 for participation. Parental and child informed consent/assent and approval from the local ethical committee was obtained.\nUnivariate analyses of variance (ANOVAs) showed no group differences between boys with and without ADHD for age [F(1,25) = 2, p = .2] but did for IQ [F(1,25) = 8, p < .009]. IQ is associated with ADHD in the general population (33,34). We purposely did not match groups for IQ because matching ADHD and control groups for IQ would have created unrepresentative groups and therefore be misguided (35). Furthermore, IQ was significantly negatively correlated with the SDQ scores for inattention and hyperactivity (r = −.5, p < .001). We did not covary for IQ because when groups are not randomly selected, covarying for a variable that differs between groups violates the standard assumptions for analysis of covariance. When the covariate is intrinsic to the condition, it becomes meaningless to “adjust” group effects for differences in the covariate because it would alter the group effect in potentially problematic ways, leading to spurious results (35,36).\nTwelve medication-naïve, right-handed boys aged 10 to 15 years (mean age = 13, SD = 1) who met clinical diagnostic criteria for the combined (inattentive/hyperactive) subtype of ADHD (DSM-IV) were recruited through clinics. Clinical diagnosis of ADHD was established through interviews with an experienced child psychiatrist (A-MM) using the standardized Maudsley Diagnostic Interview to check for presence or absence of diagnostic criteria for any mental disorder as set out by DSM-IV (30). Exclusion criteria were lifetime comorbidity with any other psychiatric disorder, except for conduct/oppositional defiant disorder (present in one patient), as well as learning disability and specific reading disorder, neurological abnormalities, epilepsy, drug or substance abuse, and previous exposure to stimulant medication. Patients with ADHD also had to score above cutoff for hyperactive/inattentive symptoms on the Strengths and Difficulties Questionnaire for Parents (SDQ) (31). Patients were scanned twice, in a randomized, counterbalanced fashion, 1 week apart, 1 hour after either .3 mg/kg of MPH administration or placebo (vitamin C, 100 mg).\nThirteen male right-handed adolescent boys in the age range of 11 to 16 years (mean age = 13, SD = 1) were recruited through advertisements in the same geographic areas of South London to ensure similar socioeconomic status and were scanned once. They scored below cutoff for behavioral problems in the SDQ and had no history of psychiatric disorder.\nAll participants were above the fifth percentile on the Raven progressive matrices performance IQ (32) (IQ mean estimate controls = 100, SD = 14; ADHD = 91, SD = 9) and paid £30 for participation. Parental and child informed consent/assent and approval from the local ethical committee was obtained.\nUnivariate analyses of variance (ANOVAs) showed no group differences between boys with and without ADHD for age [F(1,25) = 2, p = .2] but did for IQ [F(1,25) = 8, p < .009]. IQ is associated with ADHD in the general population (33,34). We purposely did not match groups for IQ because matching ADHD and control groups for IQ would have created unrepresentative groups and therefore be misguided (35). Furthermore, IQ was significantly negatively correlated with the SDQ scores for inattention and hyperactivity (r = −.5, p < .001). We did not covary for IQ because when groups are not randomly selected, covarying for a variable that differs between groups violates the standard assumptions for analysis of covariance. When the covariate is intrinsic to the condition, it becomes meaningless to “adjust” group effects for differences in the covariate because it would alter the group effect in potentially problematic ways, leading to spurious results (35,36).\n fMRI Paradigm: Stop Task The rapid, mixed-trial, event-related fMRI design was practiced by subjects once before scanning. The visual tracking stop task requires withholding of a motor response to a go stimulus when it is followed unpredictably by a stop signal (8,37,38). The basic task is a choice reaction time task (left and right pointing arrows: go signals) with a mean intertrial-interval of 1.8 sec (156 go trials). In 20% of trials, pseudo-randomly interspersed, the go signals are followed (about 250 ms later) by arrows pointing upwards (stop signals), and subjects have to inhibit their motor responses (40 stop trials). A tracking algorithm changes the time interval between go-signal and stop-signal onsets according to each subject's inhibitory performance to ensure that the task is equally challenging for each individual and to provide 50% successful and 50% unsuccessful inhibition trials at every moment of the task.\nThe rapid, mixed-trial, event-related fMRI design was practiced by subjects once before scanning. The visual tracking stop task requires withholding of a motor response to a go stimulus when it is followed unpredictably by a stop signal (8,37,38). The basic task is a choice reaction time task (left and right pointing arrows: go signals) with a mean intertrial-interval of 1.8 sec (156 go trials). In 20% of trials, pseudo-randomly interspersed, the go signals are followed (about 250 ms later) by arrows pointing upwards (stop signals), and subjects have to inhibit their motor responses (40 stop trials). A tracking algorithm changes the time interval between go-signal and stop-signal onsets according to each subject's inhibitory performance to ensure that the task is equally challenging for each individual and to provide 50% successful and 50% unsuccessful inhibition trials at every moment of the task.\n fMRI Image Acquisition Gradient-echo echoplanar magnetic resonance imaging data were acquired on a GE Signa 1.5-T Horizon LX System (General Electric, Milwaukee, Wisconsin) at the Maudsley Hospital, London. A quadrature birdcage head coil was used for radio-frequency transmission and reception. During the 6-min run of the stop task, in each of 16 noncontiguous planes parallel to the anterior–posterior commissural, 196 T2*-weighted magnetic resonance images depicting blood oxygen–level dependent (BOLD) contrast covering the whole brain were acquired with echo time = 40 msec, repetition time = 1.8 sec, flip angle = 90°, in-plane resolution = 3.1 mm, slice thickness = 7 mm, slice skip = .7 mm, providing complete brain coverage.\nGradient-echo echoplanar magnetic resonance imaging data were acquired on a GE Signa 1.5-T Horizon LX System (General Electric, Milwaukee, Wisconsin) at the Maudsley Hospital, London. A quadrature birdcage head coil was used for radio-frequency transmission and reception. During the 6-min run of the stop task, in each of 16 noncontiguous planes parallel to the anterior–posterior commissural, 196 T2*-weighted magnetic resonance images depicting blood oxygen–level dependent (BOLD) contrast covering the whole brain were acquired with echo time = 40 msec, repetition time = 1.8 sec, flip angle = 90°, in-plane resolution = 3.1 mm, slice thickness = 7 mm, slice skip = .7 mm, providing complete brain coverage.\n fMRI Image Analysis At the individual subject level, a standard general linear modeling approach was used to obtain estimates of the response size (beta) to each of the two stop task conditions (successful and unsuccessful stop trials) against an implicit baseline (go trials). Following transformation of the fMRI data for each individual into standard space and smoothing with a three-dimensional 7-mm full width at half maximum Gaussian filter, the experimental model was convolved for each condition with gamma variate functions having peak responses at 4 and 8 sec following stimulus onset to accommodate variability in BOLD response timing. By fitting these convolved model components to the time series at each voxel, beta estimates were obtained for each effect of interest. The standard errors of these beta estimates were computed nonparametrically using a bootstrap procedure designed to operate on time series data, containing serial dependencies, with repeated deterministic (experimentally determined) effects. This method is outlined in detail in a previous work (39). Two hundred bootstraps at each voxel were used to estimate parameter standard errors. Using the combined parameter estimates over all conditions, the mean fitted time series was also computed and, from the combined bootstrap parameter estimates for each bootstrap, the 95% confidence limits on the fitted time series was computed.\nThe second-level analysis proceeded by computing either the group differences (patients and controls) or the drug condition differences (placebo, MPH) within patients at each voxel and the standard error of this difference (using the bootstrap estimates derived earlier). The significance of these differences was then tested in three ways: 1) a simple parametric random effects (paired t) test, using only the group difference/placebo-MPH effect size differences; 2) a permutation test of the same random effects t statistic in which the null distribution was estimated by randomly swapping the signs of the differences (we used 40,000 permutations per voxel to obtain a confidence limit of .0007–.0013 for p value of .001); and 3) a mixed-effects test using both the effect size differences and their subject-level standard errors to accommodate first (subject) level heteroscedasticity (40). This was also conducted using 40,000 permutations per voxel.\nIn addition to voxelwise maps, cluster-level inference on the contrast (beta) values was performed at a family-wise error corrected threshold of p < .05 using the Threshold-Free Cluster Enhancement method proposed by Smith and Nichols (41). This cluster-level inference was also used for the within-group maps for each experimental condition.\nAt the individual subject level, a standard general linear modeling approach was used to obtain estimates of the response size (beta) to each of the two stop task conditions (successful and unsuccessful stop trials) against an implicit baseline (go trials). Following transformation of the fMRI data for each individual into standard space and smoothing with a three-dimensional 7-mm full width at half maximum Gaussian filter, the experimental model was convolved for each condition with gamma variate functions having peak responses at 4 and 8 sec following stimulus onset to accommodate variability in BOLD response timing. By fitting these convolved model components to the time series at each voxel, beta estimates were obtained for each effect of interest. The standard errors of these beta estimates were computed nonparametrically using a bootstrap procedure designed to operate on time series data, containing serial dependencies, with repeated deterministic (experimentally determined) effects. This method is outlined in detail in a previous work (39). Two hundred bootstraps at each voxel were used to estimate parameter standard errors. Using the combined parameter estimates over all conditions, the mean fitted time series was also computed and, from the combined bootstrap parameter estimates for each bootstrap, the 95% confidence limits on the fitted time series was computed.\nThe second-level analysis proceeded by computing either the group differences (patients and controls) or the drug condition differences (placebo, MPH) within patients at each voxel and the standard error of this difference (using the bootstrap estimates derived earlier). The significance of these differences was then tested in three ways: 1) a simple parametric random effects (paired t) test, using only the group difference/placebo-MPH effect size differences; 2) a permutation test of the same random effects t statistic in which the null distribution was estimated by randomly swapping the signs of the differences (we used 40,000 permutations per voxel to obtain a confidence limit of .0007–.0013 for p value of .001); and 3) a mixed-effects test using both the effect size differences and their subject-level standard errors to accommodate first (subject) level heteroscedasticity (40). This was also conducted using 40,000 permutations per voxel.\nIn addition to voxelwise maps, cluster-level inference on the contrast (beta) values was performed at a family-wise error corrected threshold of p < .05 using the Threshold-Free Cluster Enhancement method proposed by Smith and Nichols (41). This cluster-level inference was also used for the within-group maps for each experimental condition.","Twelve medication-naïve, right-handed boys aged 10 to 15 years (mean age = 13, SD = 1) who met clinical diagnostic criteria for the combined (inattentive/hyperactive) subtype of ADHD (DSM-IV) were recruited through clinics. Clinical diagnosis of ADHD was established through interviews with an experienced child psychiatrist (A-MM) using the standardized Maudsley Diagnostic Interview to check for presence or absence of diagnostic criteria for any mental disorder as set out by DSM-IV (30). Exclusion criteria were lifetime comorbidity with any other psychiatric disorder, except for conduct/oppositional defiant disorder (present in one patient), as well as learning disability and specific reading disorder, neurological abnormalities, epilepsy, drug or substance abuse, and previous exposure to stimulant medication. Patients with ADHD also had to score above cutoff for hyperactive/inattentive symptoms on the Strengths and Difficulties Questionnaire for Parents (SDQ) (31). Patients were scanned twice, in a randomized, counterbalanced fashion, 1 week apart, 1 hour after either .3 mg/kg of MPH administration or placebo (vitamin C, 100 mg).\nThirteen male right-handed adolescent boys in the age range of 11 to 16 years (mean age = 13, SD = 1) were recruited through advertisements in the same geographic areas of South London to ensure similar socioeconomic status and were scanned once. They scored below cutoff for behavioral problems in the SDQ and had no history of psychiatric disorder.\nAll participants were above the fifth percentile on the Raven progressive matrices performance IQ (32) (IQ mean estimate controls = 100, SD = 14; ADHD = 91, SD = 9) and paid £30 for participation. Parental and child informed consent/assent and approval from the local ethical committee was obtained.\nUnivariate analyses of variance (ANOVAs) showed no group differences between boys with and without ADHD for age [F(1,25) = 2, p = .2] but did for IQ [F(1,25) = 8, p < .009]. IQ is associated with ADHD in the general population (33,34). We purposely did not match groups for IQ because matching ADHD and control groups for IQ would have created unrepresentative groups and therefore be misguided (35). Furthermore, IQ was significantly negatively correlated with the SDQ scores for inattention and hyperactivity (r = −.5, p < .001). We did not covary for IQ because when groups are not randomly selected, covarying for a variable that differs between groups violates the standard assumptions for analysis of covariance. When the covariate is intrinsic to the condition, it becomes meaningless to “adjust” group effects for differences in the covariate because it would alter the group effect in potentially problematic ways, leading to spurious results (35,36).","The rapid, mixed-trial, event-related fMRI design was practiced by subjects once before scanning. The visual tracking stop task requires withholding of a motor response to a go stimulus when it is followed unpredictably by a stop signal (8,37,38). The basic task is a choice reaction time task (left and right pointing arrows: go signals) with a mean intertrial-interval of 1.8 sec (156 go trials). In 20% of trials, pseudo-randomly interspersed, the go signals are followed (about 250 ms later) by arrows pointing upwards (stop signals), and subjects have to inhibit their motor responses (40 stop trials). A tracking algorithm changes the time interval between go-signal and stop-signal onsets according to each subject's inhibitory performance to ensure that the task is equally challenging for each individual and to provide 50% successful and 50% unsuccessful inhibition trials at every moment of the task.","Gradient-echo echoplanar magnetic resonance imaging data were acquired on a GE Signa 1.5-T Horizon LX System (General Electric, Milwaukee, Wisconsin) at the Maudsley Hospital, London. A quadrature birdcage head coil was used for radio-frequency transmission and reception. During the 6-min run of the stop task, in each of 16 noncontiguous planes parallel to the anterior–posterior commissural, 196 T2*-weighted magnetic resonance images depicting blood oxygen–level dependent (BOLD) contrast covering the whole brain were acquired with echo time = 40 msec, repetition time = 1.8 sec, flip angle = 90°, in-plane resolution = 3.1 mm, slice thickness = 7 mm, slice skip = .7 mm, providing complete brain coverage.","At the individual subject level, a standard general linear modeling approach was used to obtain estimates of the response size (beta) to each of the two stop task conditions (successful and unsuccessful stop trials) against an implicit baseline (go trials). Following transformation of the fMRI data for each individual into standard space and smoothing with a three-dimensional 7-mm full width at half maximum Gaussian filter, the experimental model was convolved for each condition with gamma variate functions having peak responses at 4 and 8 sec following stimulus onset to accommodate variability in BOLD response timing. By fitting these convolved model components to the time series at each voxel, beta estimates were obtained for each effect of interest. The standard errors of these beta estimates were computed nonparametrically using a bootstrap procedure designed to operate on time series data, containing serial dependencies, with repeated deterministic (experimentally determined) effects. This method is outlined in detail in a previous work (39). Two hundred bootstraps at each voxel were used to estimate parameter standard errors. Using the combined parameter estimates over all conditions, the mean fitted time series was also computed and, from the combined bootstrap parameter estimates for each bootstrap, the 95% confidence limits on the fitted time series was computed.\nThe second-level analysis proceeded by computing either the group differences (patients and controls) or the drug condition differences (placebo, MPH) within patients at each voxel and the standard error of this difference (using the bootstrap estimates derived earlier). The significance of these differences was then tested in three ways: 1) a simple parametric random effects (paired t) test, using only the group difference/placebo-MPH effect size differences; 2) a permutation test of the same random effects t statistic in which the null distribution was estimated by randomly swapping the signs of the differences (we used 40,000 permutations per voxel to obtain a confidence limit of .0007–.0013 for p value of .001); and 3) a mixed-effects test using both the effect size differences and their subject-level standard errors to accommodate first (subject) level heteroscedasticity (40). This was also conducted using 40,000 permutations per voxel.\nIn addition to voxelwise maps, cluster-level inference on the contrast (beta) values was performed at a family-wise error corrected threshold of p < .05 using the Threshold-Free Cluster Enhancement method proposed by Smith and Nichols (41). This cluster-level inference was also used for the within-group maps for each experimental condition."," Performance The probability of inhibition was about 50% in all subjects with no significant group differences, showing that the task algorithm worked [F(1,38) = 1; p < .3; Table 1].\nA multivariate ANOVA between control subjects and ADHD patients under either drug condition showed a trend for a significant group effect [F(8,62) = 2, p < .09] due to a significant univariate group effect in the standard deviation to go trials [F(2,34) = 5, p < .02], which were higher in patients under either medication condition compared with control subjects (p < .05). Post hoc tests furthermore revealed a trend for MPH compared with placebo to slow down reaction times within ADHD patients to both go (p < .06) and post-error go trials (both p < .07) (Table 1).\nThe probability of inhibition was about 50% in all subjects with no significant group differences, showing that the task algorithm worked [F(1,38) = 1; p < .3; Table 1].\nA multivariate ANOVA between control subjects and ADHD patients under either drug condition showed a trend for a significant group effect [F(8,62) = 2, p < .09] due to a significant univariate group effect in the standard deviation to go trials [F(2,34) = 5, p < .02], which were higher in patients under either medication condition compared with control subjects (p < .05). Post hoc tests furthermore revealed a trend for MPH compared with placebo to slow down reaction times within ADHD patients to both go (p < .06) and post-error go trials (both p < .07) (Table 1).","The probability of inhibition was about 50% in all subjects with no significant group differences, showing that the task algorithm worked [F(1,38) = 1; p < .3; Table 1].\nA multivariate ANOVA between control subjects and ADHD patients under either drug condition showed a trend for a significant group effect [F(8,62) = 2, p < .09] due to a significant univariate group effect in the standard deviation to go trials [F(2,34) = 5, p < .02], which were higher in patients under either medication condition compared with control subjects (p < .05). Post hoc tests furthermore revealed a trend for MPH compared with placebo to slow down reaction times within ADHD patients to both go (p < .06) and post-error go trials (both p < .07) (Table 1)."," Motion Multivariate ANOVA showed no significant group differences between control subjects and ADHD patients under either drug condition in mean or maximum rotation or translation parameters in the x, y, or z dimensions [F(2,38) = .9, p < .5].\nMultivariate ANOVA showed no significant group differences between control subjects and ADHD patients under either drug condition in mean or maximum rotation or translation parameters in the x, y, or z dimensions [F(2,38) = .9, p < .5].\n Within-Group Brain Activations Failed Stop–Go Contrast Control subjects activated relatively large clusters in left and right inferior frontal cortex (IFC)/anterior insula, medial frontal/anterior cingulate cortex (MFC/ACC), precentral, inferior parietal, middle and superior temporal areas, posterior thalamus and caudate, parahippocampal gyri, precuneus/posterior cingulate, occipital, and cerebellar areas.\nActivation in ADHD patients under placebo was in MFC and superior temporal cortex reaching into anterior and posterior insula, caudate, and inferior parietal and occipital cortex.\nActivation in ADHD patients under MPH was in a large cluster of ACC and MFC, in left and right IFC/anterior insula, left premotor cortex, right basal ganglia, bilateral middle and superior temporal, inferior parietal and occipital areas, hippocampal gyri, posterior cingulate, precuneus, and cerebellum (Figure S1 in Supplement 1).\nControl subjects activated relatively large clusters in left and right inferior frontal cortex (IFC)/anterior insula, medial frontal/anterior cingulate cortex (MFC/ACC), precentral, inferior parietal, middle and superior temporal areas, posterior thalamus and caudate, parahippocampal gyri, precuneus/posterior cingulate, occipital, and cerebellar areas.\nActivation in ADHD patients under placebo was in MFC and superior temporal cortex reaching into anterior and posterior insula, caudate, and inferior parietal and occipital cortex.\nActivation in ADHD patients under MPH was in a large cluster of ACC and MFC, in left and right IFC/anterior insula, left premotor cortex, right basal ganglia, bilateral middle and superior temporal, inferior parietal and occipital areas, hippocampal gyri, posterior cingulate, precuneus, and cerebellum (Figure S1 in Supplement 1).\n Successful Stop–Go Contrast Activation in healthy control boys was in a large cluster comprising left and right orbital and IFC, dorsolateral and MFC, insula, basal ganglia, hippocampus, posterior thalamic regions, pre- and postcentral gyri, inferior and superior parietal, temporal and occipital cortices, precuneus, and posterior cingulate.\nActivation in ADHD patients under placebo was in small clusters in right MFC, supplementary motor area (SMA), right superior temporal, postcentral, left inferior and superior parietal, and occipital cortices.\nActivation in ADHD patients under the MPH condition was in superior and MFC/ACC, right globus pallidus and putamen, right superior temporal and superior and inferior parietal cortex, posterior insula, and left cerebellum (Figure S1 in Supplement 1).\nActivation in healthy control boys was in a large cluster comprising left and right orbital and IFC, dorsolateral and MFC, insula, basal ganglia, hippocampus, posterior thalamic regions, pre- and postcentral gyri, inferior and superior parietal, temporal and occipital cortices, precuneus, and posterior cingulate.\nActivation in ADHD patients under placebo was in small clusters in right MFC, supplementary motor area (SMA), right superior temporal, postcentral, left inferior and superior parietal, and occipital cortices.\nActivation in ADHD patients under the MPH condition was in superior and MFC/ACC, right globus pallidus and putamen, right superior temporal and superior and inferior parietal cortex, posterior insula, and left cerebellum (Figure S1 in Supplement 1).\n Failed Stop–Go Contrast Control subjects activated relatively large clusters in left and right inferior frontal cortex (IFC)/anterior insula, medial frontal/anterior cingulate cortex (MFC/ACC), precentral, inferior parietal, middle and superior temporal areas, posterior thalamus and caudate, parahippocampal gyri, precuneus/posterior cingulate, occipital, and cerebellar areas.\nActivation in ADHD patients under placebo was in MFC and superior temporal cortex reaching into anterior and posterior insula, caudate, and inferior parietal and occipital cortex.\nActivation in ADHD patients under MPH was in a large cluster of ACC and MFC, in left and right IFC/anterior insula, left premotor cortex, right basal ganglia, bilateral middle and superior temporal, inferior parietal and occipital areas, hippocampal gyri, posterior cingulate, precuneus, and cerebellum (Figure S1 in Supplement 1).\nControl subjects activated relatively large clusters in left and right inferior frontal cortex (IFC)/anterior insula, medial frontal/anterior cingulate cortex (MFC/ACC), precentral, inferior parietal, middle and superior temporal areas, posterior thalamus and caudate, parahippocampal gyri, precuneus/posterior cingulate, occipital, and cerebellar areas.\nActivation in ADHD patients under placebo was in MFC and superior temporal cortex reaching into anterior and posterior insula, caudate, and inferior parietal and occipital cortex.\nActivation in ADHD patients under MPH was in a large cluster of ACC and MFC, in left and right IFC/anterior insula, left premotor cortex, right basal ganglia, bilateral middle and superior temporal, inferior parietal and occipital areas, hippocampal gyri, posterior cingulate, precuneus, and cerebellum (Figure S1 in Supplement 1).\n Successful Stop–Go Contrast Activation in healthy control boys was in a large cluster comprising left and right orbital and IFC, dorsolateral and MFC, insula, basal ganglia, hippocampus, posterior thalamic regions, pre- and postcentral gyri, inferior and superior parietal, temporal and occipital cortices, precuneus, and posterior cingulate.\nActivation in ADHD patients under placebo was in small clusters in right MFC, supplementary motor area (SMA), right superior temporal, postcentral, left inferior and superior parietal, and occipital cortices.\nActivation in ADHD patients under the MPH condition was in superior and MFC/ACC, right globus pallidus and putamen, right superior temporal and superior and inferior parietal cortex, posterior insula, and left cerebellum (Figure S1 in Supplement 1).\nActivation in healthy control boys was in a large cluster comprising left and right orbital and IFC, dorsolateral and MFC, insula, basal ganglia, hippocampus, posterior thalamic regions, pre- and postcentral gyri, inferior and superior parietal, temporal and occipital cortices, precuneus, and posterior cingulate.\nActivation in ADHD patients under placebo was in small clusters in right MFC, supplementary motor area (SMA), right superior temporal, postcentral, left inferior and superior parietal, and occipital cortices.\nActivation in ADHD patients under the MPH condition was in superior and MFC/ACC, right globus pallidus and putamen, right superior temporal and superior and inferior parietal cortex, posterior insula, and left cerebellum (Figure S1 in Supplement 1).\n ANOVA Within-Patient Comparisons in Brain Activation Between the Placebo and the MPH Conditions Failed Stop–Go Contrast MPH contrasted with placebo elicited enhanced activation in left IFC, reaching into insula and putamen; in right IFC reaching into insula, putamen, and caudate; in left medial frontal lobe; and in left inferior parietal, precuneus, and occipital regions (Figure 1, Table 2). The placebo condition elicited no enhanced activation over MPH.\nTo investigate whether brain regions that differed with MPH were associated with task performance, statistical measures of the BOLD response were extracted for each subject in each ANOVA cluster and then correlated with performance variables. There was a significant positive correlation in patients between post-error reaction times and left and right IFC activation (r = .7, p < .02) and between go reaction times and right IFC activation (r = .6, p < .05) and a negative correlation between response variability and right inferior parietal activation (r = −.7, p < .02).\nMPH contrasted with placebo elicited enhanced activation in left IFC, reaching into insula and putamen; in right IFC reaching into insula, putamen, and caudate; in left medial frontal lobe; and in left inferior parietal, precuneus, and occipital regions (Figure 1, Table 2). The placebo condition elicited no enhanced activation over MPH.\nTo investigate whether brain regions that differed with MPH were associated with task performance, statistical measures of the BOLD response were extracted for each subject in each ANOVA cluster and then correlated with performance variables. There was a significant positive correlation in patients between post-error reaction times and left and right IFC activation (r = .7, p < .02) and between go reaction times and right IFC activation (r = .6, p < .05) and a negative correlation between response variability and right inferior parietal activation (r = −.7, p < .02).\n Successful Stop–Go Contrast No significant activation differences were observed between medication conditions.\nNo significant activation differences were observed between medication conditions.\n Failed Stop–Go Contrast MPH contrasted with placebo elicited enhanced activation in left IFC, reaching into insula and putamen; in right IFC reaching into insula, putamen, and caudate; in left medial frontal lobe; and in left inferior parietal, precuneus, and occipital regions (Figure 1, Table 2). The placebo condition elicited no enhanced activation over MPH.\nTo investigate whether brain regions that differed with MPH were associated with task performance, statistical measures of the BOLD response were extracted for each subject in each ANOVA cluster and then correlated with performance variables. There was a significant positive correlation in patients between post-error reaction times and left and right IFC activation (r = .7, p < .02) and between go reaction times and right IFC activation (r = .6, p < .05) and a negative correlation between response variability and right inferior parietal activation (r = −.7, p < .02).\nMPH contrasted with placebo elicited enhanced activation in left IFC, reaching into insula and putamen; in right IFC reaching into insula, putamen, and caudate; in left medial frontal lobe; and in left inferior parietal, precuneus, and occipital regions (Figure 1, Table 2). The placebo condition elicited no enhanced activation over MPH.\nTo investigate whether brain regions that differed with MPH were associated with task performance, statistical measures of the BOLD response were extracted for each subject in each ANOVA cluster and then correlated with performance variables. There was a significant positive correlation in patients between post-error reaction times and left and right IFC activation (r = .7, p < .02) and between go reaction times and right IFC activation (r = .6, p < .05) and a negative correlation between response variability and right inferior parietal activation (r = −.7, p < .02).\n Successful Stop–Go Contrast No significant activation differences were observed between medication conditions.\nNo significant activation differences were observed between medication conditions.\n ANOVA Between-Group Comparisons in Brain Activation Between Control Subjects and Boys with ADHD Under Either the Placebo or the MPH Conditions Failed Stop–Go Contrast Relative to control subjects, ADHD patients under the placebo condition showed underactivation in left IFC and dorsomedial prefrontal cortices (dMFC) (including pre-SMA), right premotor, superior and inferior parietal cortices, posterior cingulate/precuneus, posterior thalamus, and bilateral inferior temporo-occipital areas (Figure 2A, Table 3).\nUnder the MPH condition, ADHD patients did not differ from controls in any of these regions.\nPost-error slowing in ADHD patients under placebo, but not in control subjects, was significantly positively correlated with activation in left IFC, premotor, dMFC, and thalamic underactivation clusters (r > .6 for all clusters, p < .05) as well as with superior parietal, occipital, and cerebellar activation (r < .4, p < .05). Standard deviation of reaction times was correlated with dMFC activation in controls (r = .6, p < .02).\nRelative to control subjects, ADHD patients under the placebo condition showed underactivation in left IFC and dorsomedial prefrontal cortices (dMFC) (including pre-SMA), right premotor, superior and inferior parietal cortices, posterior cingulate/precuneus, posterior thalamus, and bilateral inferior temporo-occipital areas (Figure 2A, Table 3).\nUnder the MPH condition, ADHD patients did not differ from controls in any of these regions.\nPost-error slowing in ADHD patients under placebo, but not in control subjects, was significantly positively correlated with activation in left IFC, premotor, dMFC, and thalamic underactivation clusters (r > .6 for all clusters, p < .05) as well as with superior parietal, occipital, and cerebellar activation (r < .4, p < .05). Standard deviation of reaction times was correlated with dMFC activation in controls (r = .6, p < .02).\n Successful Stop–Go Contrast Relative to control subjects, ADHD patients under the placebo condition showed underactivation in a right hemispheric network of medial temporal and inferior parietal lobes, precuneus/posterior cingulate and cerebellum (Figure 2B, Table 3). To test our hypothesis of IFC underactivation, we reanalyzed the data at a more lenient p value of p < .002 for voxelwise comparison. This elicited additional underactivation in right IFC, left and right subthalamic nuclei, and the pre-SMA (Table 3 and Figure S2 in Supplement 1).\nUnder the MPH condition, ADHD patients did not differ from control subjects in any of these regions.\nActivation in posterior cingulate and lingual gyrus correlated significantly positively with post-error go reaction times in ADHD boys under placebo (r > .6, p < .3).\nAll group difference findings for both contrasts remained essentially unchanged when IQ was covaried.\nRelative to control subjects, ADHD patients under the placebo condition showed underactivation in a right hemispheric network of medial temporal and inferior parietal lobes, precuneus/posterior cingulate and cerebellum (Figure 2B, Table 3). To test our hypothesis of IFC underactivation, we reanalyzed the data at a more lenient p value of p < .002 for voxelwise comparison. This elicited additional underactivation in right IFC, left and right subthalamic nuclei, and the pre-SMA (Table 3 and Figure S2 in Supplement 1).\nUnder the MPH condition, ADHD patients did not differ from control subjects in any of these regions.\nActivation in posterior cingulate and lingual gyrus correlated significantly positively with post-error go reaction times in ADHD boys under placebo (r > .6, p < .3).\nAll group difference findings for both contrasts remained essentially unchanged when IQ was covaried.\n Failed Stop–Go Contrast Relative to control subjects, ADHD patients under the placebo condition showed underactivation in left IFC and dorsomedial prefrontal cortices (dMFC) (including pre-SMA), right premotor, superior and inferior parietal cortices, posterior cingulate/precuneus, posterior thalamus, and bilateral inferior temporo-occipital areas (Figure 2A, Table 3).\nUnder the MPH condition, ADHD patients did not differ from controls in any of these regions.\nPost-error slowing in ADHD patients under placebo, but not in control subjects, was significantly positively correlated with activation in left IFC, premotor, dMFC, and thalamic underactivation clusters (r > .6 for all clusters, p < .05) as well as with superior parietal, occipital, and cerebellar activation (r < .4, p < .05). Standard deviation of reaction times was correlated with dMFC activation in controls (r = .6, p < .02).\nRelative to control subjects, ADHD patients under the placebo condition showed underactivation in left IFC and dorsomedial prefrontal cortices (dMFC) (including pre-SMA), right premotor, superior and inferior parietal cortices, posterior cingulate/precuneus, posterior thalamus, and bilateral inferior temporo-occipital areas (Figure 2A, Table 3).\nUnder the MPH condition, ADHD patients did not differ from controls in any of these regions.\nPost-error slowing in ADHD patients under placebo, but not in control subjects, was significantly positively correlated with activation in left IFC, premotor, dMFC, and thalamic underactivation clusters (r > .6 for all clusters, p < .05) as well as with superior parietal, occipital, and cerebellar activation (r < .4, p < .05). Standard deviation of reaction times was correlated with dMFC activation in controls (r = .6, p < .02).\n Successful Stop–Go Contrast Relative to control subjects, ADHD patients under the placebo condition showed underactivation in a right hemispheric network of medial temporal and inferior parietal lobes, precuneus/posterior cingulate and cerebellum (Figure 2B, Table 3). To test our hypothesis of IFC underactivation, we reanalyzed the data at a more lenient p value of p < .002 for voxelwise comparison. This elicited additional underactivation in right IFC, left and right subthalamic nuclei, and the pre-SMA (Table 3 and Figure S2 in Supplement 1).\nUnder the MPH condition, ADHD patients did not differ from control subjects in any of these regions.\nActivation in posterior cingulate and lingual gyrus correlated significantly positively with post-error go reaction times in ADHD boys under placebo (r > .6, p < .3).\nAll group difference findings for both contrasts remained essentially unchanged when IQ was covaried.\nRelative to control subjects, ADHD patients under the placebo condition showed underactivation in a right hemispheric network of medial temporal and inferior parietal lobes, precuneus/posterior cingulate and cerebellum (Figure 2B, Table 3). To test our hypothesis of IFC underactivation, we reanalyzed the data at a more lenient p value of p < .002 for voxelwise comparison. This elicited additional underactivation in right IFC, left and right subthalamic nuclei, and the pre-SMA (Table 3 and Figure S2 in Supplement 1).\nUnder the MPH condition, ADHD patients did not differ from control subjects in any of these regions.\nActivation in posterior cingulate and lingual gyrus correlated significantly positively with post-error go reaction times in ADHD boys under placebo (r > .6, p < .3).\nAll group difference findings for both contrasts remained essentially unchanged when IQ was covaried.\n Conjunction Analysis Between Within-Group and Between-Group ANOVAs To test whether brain regions that were upregulated with MPH relative to placebo within patients overlapped with brain regions that were reduced in patients under placebo relative to controls and then normalized with MPH, we performed a conjunction analysis by determining the voxels where the within-group ANOVA (MPH > placebo in ADHD) and the between-group ANOVA (control subjects > ADHD placebo) were both significant (42). Three clusters emerged, in left IFC (Talairach coordinates: −43, 7, 4), right SMA (Talairach coordinates: 7, 4, 59) and right inferior parietal lobe (Talairach coordinates: 32, −63, 42). Overlapping clusters are also indicated in bold in Table 3 and shown in Figure 3.\nTo test whether brain regions that were upregulated with MPH relative to placebo within patients overlapped with brain regions that were reduced in patients under placebo relative to controls and then normalized with MPH, we performed a conjunction analysis by determining the voxels where the within-group ANOVA (MPH > placebo in ADHD) and the between-group ANOVA (control subjects > ADHD placebo) were both significant (42). Three clusters emerged, in left IFC (Talairach coordinates: −43, 7, 4), right SMA (Talairach coordinates: 7, 4, 59) and right inferior parietal lobe (Talairach coordinates: 32, −63, 42). Overlapping clusters are also indicated in bold in Table 3 and shown in Figure 3.","Multivariate ANOVA showed no significant group differences between control subjects and ADHD patients under either drug condition in mean or maximum rotation or translation parameters in the x, y, or z dimensions [F(2,38) = .9, p < .5]."," Failed Stop–Go Contrast Control subjects activated relatively large clusters in left and right inferior frontal cortex (IFC)/anterior insula, medial frontal/anterior cingulate cortex (MFC/ACC), precentral, inferior parietal, middle and superior temporal areas, posterior thalamus and caudate, parahippocampal gyri, precuneus/posterior cingulate, occipital, and cerebellar areas.\nActivation in ADHD patients under placebo was in MFC and superior temporal cortex reaching into anterior and posterior insula, caudate, and inferior parietal and occipital cortex.\nActivation in ADHD patients under MPH was in a large cluster of ACC and MFC, in left and right IFC/anterior insula, left premotor cortex, right basal ganglia, bilateral middle and superior temporal, inferior parietal and occipital areas, hippocampal gyri, posterior cingulate, precuneus, and cerebellum (Figure S1 in Supplement 1).\nControl subjects activated relatively large clusters in left and right inferior frontal cortex (IFC)/anterior insula, medial frontal/anterior cingulate cortex (MFC/ACC), precentral, inferior parietal, middle and superior temporal areas, posterior thalamus and caudate, parahippocampal gyri, precuneus/posterior cingulate, occipital, and cerebellar areas.\nActivation in ADHD patients under placebo was in MFC and superior temporal cortex reaching into anterior and posterior insula, caudate, and inferior parietal and occipital cortex.\nActivation in ADHD patients under MPH was in a large cluster of ACC and MFC, in left and right IFC/anterior insula, left premotor cortex, right basal ganglia, bilateral middle and superior temporal, inferior parietal and occipital areas, hippocampal gyri, posterior cingulate, precuneus, and cerebellum (Figure S1 in Supplement 1).\n Successful Stop–Go Contrast Activation in healthy control boys was in a large cluster comprising left and right orbital and IFC, dorsolateral and MFC, insula, basal ganglia, hippocampus, posterior thalamic regions, pre- and postcentral gyri, inferior and superior parietal, temporal and occipital cortices, precuneus, and posterior cingulate.\nActivation in ADHD patients under placebo was in small clusters in right MFC, supplementary motor area (SMA), right superior temporal, postcentral, left inferior and superior parietal, and occipital cortices.\nActivation in ADHD patients under the MPH condition was in superior and MFC/ACC, right globus pallidus and putamen, right superior temporal and superior and inferior parietal cortex, posterior insula, and left cerebellum (Figure S1 in Supplement 1).\nActivation in healthy control boys was in a large cluster comprising left and right orbital and IFC, dorsolateral and MFC, insula, basal ganglia, hippocampus, posterior thalamic regions, pre- and postcentral gyri, inferior and superior parietal, temporal and occipital cortices, precuneus, and posterior cingulate.\nActivation in ADHD patients under placebo was in small clusters in right MFC, supplementary motor area (SMA), right superior temporal, postcentral, left inferior and superior parietal, and occipital cortices.\nActivation in ADHD patients under the MPH condition was in superior and MFC/ACC, right globus pallidus and putamen, right superior temporal and superior and inferior parietal cortex, posterior insula, and left cerebellum (Figure S1 in Supplement 1).","Control subjects activated relatively large clusters in left and right inferior frontal cortex (IFC)/anterior insula, medial frontal/anterior cingulate cortex (MFC/ACC), precentral, inferior parietal, middle and superior temporal areas, posterior thalamus and caudate, parahippocampal gyri, precuneus/posterior cingulate, occipital, and cerebellar areas.\nActivation in ADHD patients under placebo was in MFC and superior temporal cortex reaching into anterior and posterior insula, caudate, and inferior parietal and occipital cortex.\nActivation in ADHD patients under MPH was in a large cluster of ACC and MFC, in left and right IFC/anterior insula, left premotor cortex, right basal ganglia, bilateral middle and superior temporal, inferior parietal and occipital areas, hippocampal gyri, posterior cingulate, precuneus, and cerebellum (Figure S1 in Supplement 1).","Activation in healthy control boys was in a large cluster comprising left and right orbital and IFC, dorsolateral and MFC, insula, basal ganglia, hippocampus, posterior thalamic regions, pre- and postcentral gyri, inferior and superior parietal, temporal and occipital cortices, precuneus, and posterior cingulate.\nActivation in ADHD patients under placebo was in small clusters in right MFC, supplementary motor area (SMA), right superior temporal, postcentral, left inferior and superior parietal, and occipital cortices.\nActivation in ADHD patients under the MPH condition was in superior and MFC/ACC, right globus pallidus and putamen, right superior temporal and superior and inferior parietal cortex, posterior insula, and left cerebellum (Figure S1 in Supplement 1)."," Failed Stop–Go Contrast MPH contrasted with placebo elicited enhanced activation in left IFC, reaching into insula and putamen; in right IFC reaching into insula, putamen, and caudate; in left medial frontal lobe; and in left inferior parietal, precuneus, and occipital regions (Figure 1, Table 2). The placebo condition elicited no enhanced activation over MPH.\nTo investigate whether brain regions that differed with MPH were associated with task performance, statistical measures of the BOLD response were extracted for each subject in each ANOVA cluster and then correlated with performance variables. There was a significant positive correlation in patients between post-error reaction times and left and right IFC activation (r = .7, p < .02) and between go reaction times and right IFC activation (r = .6, p < .05) and a negative correlation between response variability and right inferior parietal activation (r = −.7, p < .02).\nMPH contrasted with placebo elicited enhanced activation in left IFC, reaching into insula and putamen; in right IFC reaching into insula, putamen, and caudate; in left medial frontal lobe; and in left inferior parietal, precuneus, and occipital regions (Figure 1, Table 2). The placebo condition elicited no enhanced activation over MPH.\nTo investigate whether brain regions that differed with MPH were associated with task performance, statistical measures of the BOLD response were extracted for each subject in each ANOVA cluster and then correlated with performance variables. There was a significant positive correlation in patients between post-error reaction times and left and right IFC activation (r = .7, p < .02) and between go reaction times and right IFC activation (r = .6, p < .05) and a negative correlation between response variability and right inferior parietal activation (r = −.7, p < .02).\n Successful Stop–Go Contrast No significant activation differences were observed between medication conditions.\nNo significant activation differences were observed between medication conditions.","MPH contrasted with placebo elicited enhanced activation in left IFC, reaching into insula and putamen; in right IFC reaching into insula, putamen, and caudate; in left medial frontal lobe; and in left inferior parietal, precuneus, and occipital regions (Figure 1, Table 2). The placebo condition elicited no enhanced activation over MPH.\nTo investigate whether brain regions that differed with MPH were associated with task performance, statistical measures of the BOLD response were extracted for each subject in each ANOVA cluster and then correlated with performance variables. There was a significant positive correlation in patients between post-error reaction times and left and right IFC activation (r = .7, p < .02) and between go reaction times and right IFC activation (r = .6, p < .05) and a negative correlation between response variability and right inferior parietal activation (r = −.7, p < .02).","No significant activation differences were observed between medication conditions."," Failed Stop–Go Contrast Relative to control subjects, ADHD patients under the placebo condition showed underactivation in left IFC and dorsomedial prefrontal cortices (dMFC) (including pre-SMA), right premotor, superior and inferior parietal cortices, posterior cingulate/precuneus, posterior thalamus, and bilateral inferior temporo-occipital areas (Figure 2A, Table 3).\nUnder the MPH condition, ADHD patients did not differ from controls in any of these regions.\nPost-error slowing in ADHD patients under placebo, but not in control subjects, was significantly positively correlated with activation in left IFC, premotor, dMFC, and thalamic underactivation clusters (r > .6 for all clusters, p < .05) as well as with superior parietal, occipital, and cerebellar activation (r < .4, p < .05). Standard deviation of reaction times was correlated with dMFC activation in controls (r = .6, p < .02).\nRelative to control subjects, ADHD patients under the placebo condition showed underactivation in left IFC and dorsomedial prefrontal cortices (dMFC) (including pre-SMA), right premotor, superior and inferior parietal cortices, posterior cingulate/precuneus, posterior thalamus, and bilateral inferior temporo-occipital areas (Figure 2A, Table 3).\nUnder the MPH condition, ADHD patients did not differ from controls in any of these regions.\nPost-error slowing in ADHD patients under placebo, but not in control subjects, was significantly positively correlated with activation in left IFC, premotor, dMFC, and thalamic underactivation clusters (r > .6 for all clusters, p < .05) as well as with superior parietal, occipital, and cerebellar activation (r < .4, p < .05). Standard deviation of reaction times was correlated with dMFC activation in controls (r = .6, p < .02).\n Successful Stop–Go Contrast Relative to control subjects, ADHD patients under the placebo condition showed underactivation in a right hemispheric network of medial temporal and inferior parietal lobes, precuneus/posterior cingulate and cerebellum (Figure 2B, Table 3). To test our hypothesis of IFC underactivation, we reanalyzed the data at a more lenient p value of p < .002 for voxelwise comparison. This elicited additional underactivation in right IFC, left and right subthalamic nuclei, and the pre-SMA (Table 3 and Figure S2 in Supplement 1).\nUnder the MPH condition, ADHD patients did not differ from control subjects in any of these regions.\nActivation in posterior cingulate and lingual gyrus correlated significantly positively with post-error go reaction times in ADHD boys under placebo (r > .6, p < .3).\nAll group difference findings for both contrasts remained essentially unchanged when IQ was covaried.\nRelative to control subjects, ADHD patients under the placebo condition showed underactivation in a right hemispheric network of medial temporal and inferior parietal lobes, precuneus/posterior cingulate and cerebellum (Figure 2B, Table 3). To test our hypothesis of IFC underactivation, we reanalyzed the data at a more lenient p value of p < .002 for voxelwise comparison. This elicited additional underactivation in right IFC, left and right subthalamic nuclei, and the pre-SMA (Table 3 and Figure S2 in Supplement 1).\nUnder the MPH condition, ADHD patients did not differ from control subjects in any of these regions.\nActivation in posterior cingulate and lingual gyrus correlated significantly positively with post-error go reaction times in ADHD boys under placebo (r > .6, p < .3).\nAll group difference findings for both contrasts remained essentially unchanged when IQ was covaried.","Relative to control subjects, ADHD patients under the placebo condition showed underactivation in left IFC and dorsomedial prefrontal cortices (dMFC) (including pre-SMA), right premotor, superior and inferior parietal cortices, posterior cingulate/precuneus, posterior thalamus, and bilateral inferior temporo-occipital areas (Figure 2A, Table 3).\nUnder the MPH condition, ADHD patients did not differ from controls in any of these regions.\nPost-error slowing in ADHD patients under placebo, but not in control subjects, was significantly positively correlated with activation in left IFC, premotor, dMFC, and thalamic underactivation clusters (r > .6 for all clusters, p < .05) as well as with superior parietal, occipital, and cerebellar activation (r < .4, p < .05). Standard deviation of reaction times was correlated with dMFC activation in controls (r = .6, p < .02).","Relative to control subjects, ADHD patients under the placebo condition showed underactivation in a right hemispheric network of medial temporal and inferior parietal lobes, precuneus/posterior cingulate and cerebellum (Figure 2B, Table 3). To test our hypothesis of IFC underactivation, we reanalyzed the data at a more lenient p value of p < .002 for voxelwise comparison. This elicited additional underactivation in right IFC, left and right subthalamic nuclei, and the pre-SMA (Table 3 and Figure S2 in Supplement 1).\nUnder the MPH condition, ADHD patients did not differ from control subjects in any of these regions.\nActivation in posterior cingulate and lingual gyrus correlated significantly positively with post-error go reaction times in ADHD boys under placebo (r > .6, p < .3).\nAll group difference findings for both contrasts remained essentially unchanged when IQ was covaried.","To test whether brain regions that were upregulated with MPH relative to placebo within patients overlapped with brain regions that were reduced in patients under placebo relative to controls and then normalized with MPH, we performed a conjunction analysis by determining the voxels where the within-group ANOVA (MPH > placebo in ADHD) and the between-group ANOVA (control subjects > ADHD placebo) were both significant (42). Three clusters emerged, in left IFC (Talairach coordinates: −43, 7, 4), right SMA (Talairach coordinates: 7, 4, 59) and right inferior parietal lobe (Talairach coordinates: 32, −63, 42). Overlapping clusters are also indicated in bold in Table 3 and shown in Figure 3.","During error trials, ADHD boys under placebo compared with healthy control subjects showed significant underactivation in a typical error processing and performance monitoring network comprising dMFC, left IFC, thalamus, posterior cingulate/precuneus, and inferior temporoparietal regions. Among patients, MPH compared with placebo significantly upregulated activation in overlapping medial frontal, IFC, and parietal regions as well as the lenticular nucleus. Under MPH, brain activation differences between control subjects and ADHD patients were no longer observed. Reduced fronto-thalamo-parietal activation that was normalized with MPH was, furthermore, negatively associated with faster post-error reaction times in patients, which were trendwise slowed with MPH.\nDuring successful stop trials, ADHD boys showed underactivation in a right hemispheric network of medial temporal and inferior parietal brain regions and, at a more lenient threshold, in small clusters of bilateral IFC, thalamus, and pre-SMA. Although within-patient comparison between MPH and placebo did not show significant activation differences, all underactivations in patients relative to control subjects under placebo were normalized with a single dose of MPH.\nThe dMFC, comprising Brodmann areas 8, 6, and 32, including pre-SMA and ACC, is a typical region of error processing and performance monitoring in adults (37,43–48) and children (29,38,49). We have previously found this region to be underactivated in children with ADHD during oddball (50) and switch tasks (4). Errors indicate violation of a reward prediction (i.e., positive performance) and have been linked to midbrain dopamine (51). Normalization with MPH of underfunctioning of this region in ADHD is in line with the notion that phasic dopamine response modulates error-related mesial frontal activation (52,53). These findings extend evidence for upregulation with acute and chronic doses of MPH in previously medicated patients with ADHD in a more rostral ACC location during tasks of cognitive control (22,28,54).\nActivation in dMFC during errors triggers additional activation in functionally interconnected left IFC, as well as striatal, premotor, and parietal components of the error monitoring system, leading to post-error performance adjustments (43–45,48). IFC underactivation is one of the most consistent findings in fMRI studies in patients with ADHD, with right IFC dysfunction typically observed during inhibitory performance (7,8,11,55), in line with its role in inhibition (37,56), and left IFC during stop errors (4,29) as well as during flexible, selective, or sustained attention (4,9,26,50,57), in line with its role for performance monitoring (44,45,48,49) and saliency processing (58,59). IFC dysfunction is furthermore a disorder-specific neurofunctional deficit compared with patients with conduct (6,50,57,60) and obsessive compulsive (4) disorders. MPH thus appears to modulate an important neurofunctional biomarker of ADHD. The more predominantly left-hemispheric upregulation effect during errors may suggest a stronger effect of MPH on performance monitoring than inhibitory function in ADHD. Left IFC upregulation has previously been observed in ADHD patients in the context of an attention-demanding time discrimination task after acute (27) and 6 weeks of MPH treatment during interference inhibition (54). Structural studies have shown more normal cortical thinning in left IFC in psychostimulant-medicated compared with unmedicated ADHD children (61). Together, this raises the speculation that MPH may have a lateralized upregulating effect on left IFC structure and function.\nPosterior thalamic regions have been associated both with motor response inhibition (62) and performance monitoring (48,63,64). The finding that MPH normalizes activation in this region is in line with speculation of this region's involvement in the modulation of the dopaminergic error signal (63,65,66).\nThe fact that lower dMFC, IFC, and thalamic activation in ADHD patients was associated with faster post-error slowing, both of which were enhanced by MPH, reinforces the role of this network for abnormal error monitoring in ADHD. Posterior cingulate and precuneus are connected with MFC and parietal areas and form part of the performance monitoring network (47,49,67,68), mediating visual spatial attention to saliency (69,70) and the integration of performance outcome with attentional modulation (48). The fact that these regions were underactivated during both inhibition and its failure is in line with a generic attention role of these areas. In line with this, we and others have previously observed underactivation in ADHD patients in these regions during inhibition errors (4,6,8,60), as well as during other salient stimuli such as oddball, novel or incongruent targets (10,26,50,71,72).\nNormalization with MPH of reduced activation in typical frontoparietal regions of saliency processing and performance monitoring is consistent with the dopamine-deficiency hypothesis of ADHD given that dopamine agonists enhance stimulus salience (73). It is also in line with our previous findings of upregulation with MPH of posterior cingulate/precuneus in the same group of medication naive boys with ADHD during a target detection task, resulting in improved attention (26), and during an attention demanding time discrimination task (27). To our knowledge, normalization of inferior parietal activation with MPH has only recently been observed in ADHD patients, in the context of sustained attention (26) and interference inhibition (54).\nDuring successful stop trials, MPH also normalized underactivation in the cerebellum, which, together with subthalamic nucleus, caudate, and IFG, forms a neurofunctional network of motor response inhibition (38). These findings extend previous evidence for cerebellum upregulation with MPH in ADHD patients during interference inhibition (54) and time estimation (27).\nWithin patients, MPH also enhanced activation of caudate and putamen. This is in line with previous fMRI findings of caudate upregulation in ADHD patients after acute and chronic doses of MPH during inhibition and attention tasks (22,24,54) and is likely associated with the known effect of MPH on striatal dopamine transporter blockage (14,15).\nThe findings of more pronounced normalization effects of MPH on abnormal performance monitoring than inhibition networks could suggest that MPH enhances generic attention and performance monitoring functions more than inhibitory capacity. This would be in line with the behavioral effect of MPH of modulating go and post-error reaction times, but not inhibition speed, which, furthermore were correlated with the reduced frontothalamic error-processing activation that was normalized with MPH. Relative to control subjects, patients only significantly differed in intrasubject response variability. Small subject numbers, a relatively older child group, and fMRI task design restrictions may have been responsible for minor behavior differences. The findings of brain dysfunctions in boys with ADHD and their normalization under the clinical dose of MPH despite minor performance differences and only trend-level improvements with MPH show that brain activation is more sensitive than performance to detect both abnormalities and pharmacologic effects. This is in line with previous findings of marked brain dysfunctions in ADHD adolescents despite no stop task impairment (7,8,50) and higher sensitivity of brain activation than behavior to show pharmacologic effects of MPH in ADHD (24,26–28,54,74).\nMPH prevents the reuptake of catecholamines from the synaptic cleft by blocking dopamine and norepinephrine transporters (DAT/NET) (75,76), with higher affinity for the former (77,78). In healthy adults, MPH blocks 60% to 70% of striatal DAT in a dose-dependent manner, increasing extracellular levels of dopamine in striatum (75,79–82), as well as in frontal, thalamic, and temporal regions (83). The upregulating effects on basal ganglia, thalamic, and anterior cingulate activation were therefore likely mediated by mesolimbic striatocingulate dopaminergic pathways known to modulate error monitoring systems (63,64). In frontal regions, however, MPH upregulates noradrenaline to the same or greater extent than dopamine (84–86), via reuptake inhibition of NET that clear up both dopamine and noradrenaline (85,87–89). The upregulating effects on frontal activation, therefore, may have been mediated by enhanced catecholamine neurotransmission, in line with recent evidence that noradrenaline also plays a role in error monitoring (66,90).\nA limitation of the study is that patients were tested twice, whereas control subjects were only scanned once, for ethical and financial reasons. Practice effects, however, were overcome by the counterbalanced design. Another limitation is the relatively small sample size. Minimum numbers of 15 to 20 participants have been suggested for fMRI studies (91). Repeated-measures designs, however, are statistically more powerful than independent data sets, which makes the within-subject ANOVA more robust.\nTo our knowledge, this is the first study to show that a single dose of MPH in ADHD upregulates and normalizes the underfunctioning of dMFC, left IFC, posterior cingulate, and parietal regions that in concert play an important role in error processing. The normalization findings of these key regions of both performance monitoring and ADHD dysfunction reinforce the association between dopaminergic neurotransmission abnormalities, ADHD, and poor performance monitoring and may underlie the behavioral effects of improving attention and school performance in boys with ADHD."],"string":"[\n \" Subjects Twelve medication-naïve, right-handed boys aged 10 to 15 years (mean age = 13, SD = 1) who met clinical diagnostic criteria for the combined (inattentive/hyperactive) subtype of ADHD (DSM-IV) were recruited through clinics. Clinical diagnosis of ADHD was established through interviews with an experienced child psychiatrist (A-MM) using the standardized Maudsley Diagnostic Interview to check for presence or absence of diagnostic criteria for any mental disorder as set out by DSM-IV (30). Exclusion criteria were lifetime comorbidity with any other psychiatric disorder, except for conduct/oppositional defiant disorder (present in one patient), as well as learning disability and specific reading disorder, neurological abnormalities, epilepsy, drug or substance abuse, and previous exposure to stimulant medication. Patients with ADHD also had to score above cutoff for hyperactive/inattentive symptoms on the Strengths and Difficulties Questionnaire for Parents (SDQ) (31). Patients were scanned twice, in a randomized, counterbalanced fashion, 1 week apart, 1 hour after either .3 mg/kg of MPH administration or placebo (vitamin C, 100 mg).\\nThirteen male right-handed adolescent boys in the age range of 11 to 16 years (mean age = 13, SD = 1) were recruited through advertisements in the same geographic areas of South London to ensure similar socioeconomic status and were scanned once. They scored below cutoff for behavioral problems in the SDQ and had no history of psychiatric disorder.\\nAll participants were above the fifth percentile on the Raven progressive matrices performance IQ (32) (IQ mean estimate controls = 100, SD = 14; ADHD = 91, SD = 9) and paid £30 for participation. Parental and child informed consent/assent and approval from the local ethical committee was obtained.\\nUnivariate analyses of variance (ANOVAs) showed no group differences between boys with and without ADHD for age [F(1,25) = 2, p = .2] but did for IQ [F(1,25) = 8, p < .009]. IQ is associated with ADHD in the general population (33,34). We purposely did not match groups for IQ because matching ADHD and control groups for IQ would have created unrepresentative groups and therefore be misguided (35). Furthermore, IQ was significantly negatively correlated with the SDQ scores for inattention and hyperactivity (r = −.5, p < .001). We did not covary for IQ because when groups are not randomly selected, covarying for a variable that differs between groups violates the standard assumptions for analysis of covariance. When the covariate is intrinsic to the condition, it becomes meaningless to “adjust” group effects for differences in the covariate because it would alter the group effect in potentially problematic ways, leading to spurious results (35,36).\\nTwelve medication-naïve, right-handed boys aged 10 to 15 years (mean age = 13, SD = 1) who met clinical diagnostic criteria for the combined (inattentive/hyperactive) subtype of ADHD (DSM-IV) were recruited through clinics. Clinical diagnosis of ADHD was established through interviews with an experienced child psychiatrist (A-MM) using the standardized Maudsley Diagnostic Interview to check for presence or absence of diagnostic criteria for any mental disorder as set out by DSM-IV (30). Exclusion criteria were lifetime comorbidity with any other psychiatric disorder, except for conduct/oppositional defiant disorder (present in one patient), as well as learning disability and specific reading disorder, neurological abnormalities, epilepsy, drug or substance abuse, and previous exposure to stimulant medication. Patients with ADHD also had to score above cutoff for hyperactive/inattentive symptoms on the Strengths and Difficulties Questionnaire for Parents (SDQ) (31). Patients were scanned twice, in a randomized, counterbalanced fashion, 1 week apart, 1 hour after either .3 mg/kg of MPH administration or placebo (vitamin C, 100 mg).\\nThirteen male right-handed adolescent boys in the age range of 11 to 16 years (mean age = 13, SD = 1) were recruited through advertisements in the same geographic areas of South London to ensure similar socioeconomic status and were scanned once. They scored below cutoff for behavioral problems in the SDQ and had no history of psychiatric disorder.\\nAll participants were above the fifth percentile on the Raven progressive matrices performance IQ (32) (IQ mean estimate controls = 100, SD = 14; ADHD = 91, SD = 9) and paid £30 for participation. Parental and child informed consent/assent and approval from the local ethical committee was obtained.\\nUnivariate analyses of variance (ANOVAs) showed no group differences between boys with and without ADHD for age [F(1,25) = 2, p = .2] but did for IQ [F(1,25) = 8, p < .009]. IQ is associated with ADHD in the general population (33,34). We purposely did not match groups for IQ because matching ADHD and control groups for IQ would have created unrepresentative groups and therefore be misguided (35). Furthermore, IQ was significantly negatively correlated with the SDQ scores for inattention and hyperactivity (r = −.5, p < .001). We did not covary for IQ because when groups are not randomly selected, covarying for a variable that differs between groups violates the standard assumptions for analysis of covariance. When the covariate is intrinsic to the condition, it becomes meaningless to “adjust” group effects for differences in the covariate because it would alter the group effect in potentially problematic ways, leading to spurious results (35,36).\\n fMRI Paradigm: Stop Task The rapid, mixed-trial, event-related fMRI design was practiced by subjects once before scanning. The visual tracking stop task requires withholding of a motor response to a go stimulus when it is followed unpredictably by a stop signal (8,37,38). The basic task is a choice reaction time task (left and right pointing arrows: go signals) with a mean intertrial-interval of 1.8 sec (156 go trials). In 20% of trials, pseudo-randomly interspersed, the go signals are followed (about 250 ms later) by arrows pointing upwards (stop signals), and subjects have to inhibit their motor responses (40 stop trials). A tracking algorithm changes the time interval between go-signal and stop-signal onsets according to each subject's inhibitory performance to ensure that the task is equally challenging for each individual and to provide 50% successful and 50% unsuccessful inhibition trials at every moment of the task.\\nThe rapid, mixed-trial, event-related fMRI design was practiced by subjects once before scanning. The visual tracking stop task requires withholding of a motor response to a go stimulus when it is followed unpredictably by a stop signal (8,37,38). The basic task is a choice reaction time task (left and right pointing arrows: go signals) with a mean intertrial-interval of 1.8 sec (156 go trials). In 20% of trials, pseudo-randomly interspersed, the go signals are followed (about 250 ms later) by arrows pointing upwards (stop signals), and subjects have to inhibit their motor responses (40 stop trials). A tracking algorithm changes the time interval between go-signal and stop-signal onsets according to each subject's inhibitory performance to ensure that the task is equally challenging for each individual and to provide 50% successful and 50% unsuccessful inhibition trials at every moment of the task.\\n fMRI Image Acquisition Gradient-echo echoplanar magnetic resonance imaging data were acquired on a GE Signa 1.5-T Horizon LX System (General Electric, Milwaukee, Wisconsin) at the Maudsley Hospital, London. A quadrature birdcage head coil was used for radio-frequency transmission and reception. During the 6-min run of the stop task, in each of 16 noncontiguous planes parallel to the anterior–posterior commissural, 196 T2*-weighted magnetic resonance images depicting blood oxygen–level dependent (BOLD) contrast covering the whole brain were acquired with echo time = 40 msec, repetition time = 1.8 sec, flip angle = 90°, in-plane resolution = 3.1 mm, slice thickness = 7 mm, slice skip = .7 mm, providing complete brain coverage.\\nGradient-echo echoplanar magnetic resonance imaging data were acquired on a GE Signa 1.5-T Horizon LX System (General Electric, Milwaukee, Wisconsin) at the Maudsley Hospital, London. A quadrature birdcage head coil was used for radio-frequency transmission and reception. During the 6-min run of the stop task, in each of 16 noncontiguous planes parallel to the anterior–posterior commissural, 196 T2*-weighted magnetic resonance images depicting blood oxygen–level dependent (BOLD) contrast covering the whole brain were acquired with echo time = 40 msec, repetition time = 1.8 sec, flip angle = 90°, in-plane resolution = 3.1 mm, slice thickness = 7 mm, slice skip = .7 mm, providing complete brain coverage.\\n fMRI Image Analysis At the individual subject level, a standard general linear modeling approach was used to obtain estimates of the response size (beta) to each of the two stop task conditions (successful and unsuccessful stop trials) against an implicit baseline (go trials). Following transformation of the fMRI data for each individual into standard space and smoothing with a three-dimensional 7-mm full width at half maximum Gaussian filter, the experimental model was convolved for each condition with gamma variate functions having peak responses at 4 and 8 sec following stimulus onset to accommodate variability in BOLD response timing. By fitting these convolved model components to the time series at each voxel, beta estimates were obtained for each effect of interest. The standard errors of these beta estimates were computed nonparametrically using a bootstrap procedure designed to operate on time series data, containing serial dependencies, with repeated deterministic (experimentally determined) effects. This method is outlined in detail in a previous work (39). Two hundred bootstraps at each voxel were used to estimate parameter standard errors. Using the combined parameter estimates over all conditions, the mean fitted time series was also computed and, from the combined bootstrap parameter estimates for each bootstrap, the 95% confidence limits on the fitted time series was computed.\\nThe second-level analysis proceeded by computing either the group differences (patients and controls) or the drug condition differences (placebo, MPH) within patients at each voxel and the standard error of this difference (using the bootstrap estimates derived earlier). The significance of these differences was then tested in three ways: 1) a simple parametric random effects (paired t) test, using only the group difference/placebo-MPH effect size differences; 2) a permutation test of the same random effects t statistic in which the null distribution was estimated by randomly swapping the signs of the differences (we used 40,000 permutations per voxel to obtain a confidence limit of .0007–.0013 for p value of .001); and 3) a mixed-effects test using both the effect size differences and their subject-level standard errors to accommodate first (subject) level heteroscedasticity (40). This was also conducted using 40,000 permutations per voxel.\\nIn addition to voxelwise maps, cluster-level inference on the contrast (beta) values was performed at a family-wise error corrected threshold of p < .05 using the Threshold-Free Cluster Enhancement method proposed by Smith and Nichols (41). This cluster-level inference was also used for the within-group maps for each experimental condition.\\nAt the individual subject level, a standard general linear modeling approach was used to obtain estimates of the response size (beta) to each of the two stop task conditions (successful and unsuccessful stop trials) against an implicit baseline (go trials). Following transformation of the fMRI data for each individual into standard space and smoothing with a three-dimensional 7-mm full width at half maximum Gaussian filter, the experimental model was convolved for each condition with gamma variate functions having peak responses at 4 and 8 sec following stimulus onset to accommodate variability in BOLD response timing. By fitting these convolved model components to the time series at each voxel, beta estimates were obtained for each effect of interest. The standard errors of these beta estimates were computed nonparametrically using a bootstrap procedure designed to operate on time series data, containing serial dependencies, with repeated deterministic (experimentally determined) effects. This method is outlined in detail in a previous work (39). Two hundred bootstraps at each voxel were used to estimate parameter standard errors. Using the combined parameter estimates over all conditions, the mean fitted time series was also computed and, from the combined bootstrap parameter estimates for each bootstrap, the 95% confidence limits on the fitted time series was computed.\\nThe second-level analysis proceeded by computing either the group differences (patients and controls) or the drug condition differences (placebo, MPH) within patients at each voxel and the standard error of this difference (using the bootstrap estimates derived earlier). The significance of these differences was then tested in three ways: 1) a simple parametric random effects (paired t) test, using only the group difference/placebo-MPH effect size differences; 2) a permutation test of the same random effects t statistic in which the null distribution was estimated by randomly swapping the signs of the differences (we used 40,000 permutations per voxel to obtain a confidence limit of .0007–.0013 for p value of .001); and 3) a mixed-effects test using both the effect size differences and their subject-level standard errors to accommodate first (subject) level heteroscedasticity (40). This was also conducted using 40,000 permutations per voxel.\\nIn addition to voxelwise maps, cluster-level inference on the contrast (beta) values was performed at a family-wise error corrected threshold of p < .05 using the Threshold-Free Cluster Enhancement method proposed by Smith and Nichols (41). This cluster-level inference was also used for the within-group maps for each experimental condition.\",\n \"Twelve medication-naïve, right-handed boys aged 10 to 15 years (mean age = 13, SD = 1) who met clinical diagnostic criteria for the combined (inattentive/hyperactive) subtype of ADHD (DSM-IV) were recruited through clinics. Clinical diagnosis of ADHD was established through interviews with an experienced child psychiatrist (A-MM) using the standardized Maudsley Diagnostic Interview to check for presence or absence of diagnostic criteria for any mental disorder as set out by DSM-IV (30). Exclusion criteria were lifetime comorbidity with any other psychiatric disorder, except for conduct/oppositional defiant disorder (present in one patient), as well as learning disability and specific reading disorder, neurological abnormalities, epilepsy, drug or substance abuse, and previous exposure to stimulant medication. Patients with ADHD also had to score above cutoff for hyperactive/inattentive symptoms on the Strengths and Difficulties Questionnaire for Parents (SDQ) (31). Patients were scanned twice, in a randomized, counterbalanced fashion, 1 week apart, 1 hour after either .3 mg/kg of MPH administration or placebo (vitamin C, 100 mg).\\nThirteen male right-handed adolescent boys in the age range of 11 to 16 years (mean age = 13, SD = 1) were recruited through advertisements in the same geographic areas of South London to ensure similar socioeconomic status and were scanned once. They scored below cutoff for behavioral problems in the SDQ and had no history of psychiatric disorder.\\nAll participants were above the fifth percentile on the Raven progressive matrices performance IQ (32) (IQ mean estimate controls = 100, SD = 14; ADHD = 91, SD = 9) and paid £30 for participation. Parental and child informed consent/assent and approval from the local ethical committee was obtained.\\nUnivariate analyses of variance (ANOVAs) showed no group differences between boys with and without ADHD for age [F(1,25) = 2, p = .2] but did for IQ [F(1,25) = 8, p < .009]. IQ is associated with ADHD in the general population (33,34). We purposely did not match groups for IQ because matching ADHD and control groups for IQ would have created unrepresentative groups and therefore be misguided (35). Furthermore, IQ was significantly negatively correlated with the SDQ scores for inattention and hyperactivity (r = −.5, p < .001). We did not covary for IQ because when groups are not randomly selected, covarying for a variable that differs between groups violates the standard assumptions for analysis of covariance. When the covariate is intrinsic to the condition, it becomes meaningless to “adjust” group effects for differences in the covariate because it would alter the group effect in potentially problematic ways, leading to spurious results (35,36).\",\n \"The rapid, mixed-trial, event-related fMRI design was practiced by subjects once before scanning. The visual tracking stop task requires withholding of a motor response to a go stimulus when it is followed unpredictably by a stop signal (8,37,38). The basic task is a choice reaction time task (left and right pointing arrows: go signals) with a mean intertrial-interval of 1.8 sec (156 go trials). In 20% of trials, pseudo-randomly interspersed, the go signals are followed (about 250 ms later) by arrows pointing upwards (stop signals), and subjects have to inhibit their motor responses (40 stop trials). A tracking algorithm changes the time interval between go-signal and stop-signal onsets according to each subject's inhibitory performance to ensure that the task is equally challenging for each individual and to provide 50% successful and 50% unsuccessful inhibition trials at every moment of the task.\",\n \"Gradient-echo echoplanar magnetic resonance imaging data were acquired on a GE Signa 1.5-T Horizon LX System (General Electric, Milwaukee, Wisconsin) at the Maudsley Hospital, London. A quadrature birdcage head coil was used for radio-frequency transmission and reception. During the 6-min run of the stop task, in each of 16 noncontiguous planes parallel to the anterior–posterior commissural, 196 T2*-weighted magnetic resonance images depicting blood oxygen–level dependent (BOLD) contrast covering the whole brain were acquired with echo time = 40 msec, repetition time = 1.8 sec, flip angle = 90°, in-plane resolution = 3.1 mm, slice thickness = 7 mm, slice skip = .7 mm, providing complete brain coverage.\",\n \"At the individual subject level, a standard general linear modeling approach was used to obtain estimates of the response size (beta) to each of the two stop task conditions (successful and unsuccessful stop trials) against an implicit baseline (go trials). Following transformation of the fMRI data for each individual into standard space and smoothing with a three-dimensional 7-mm full width at half maximum Gaussian filter, the experimental model was convolved for each condition with gamma variate functions having peak responses at 4 and 8 sec following stimulus onset to accommodate variability in BOLD response timing. By fitting these convolved model components to the time series at each voxel, beta estimates were obtained for each effect of interest. The standard errors of these beta estimates were computed nonparametrically using a bootstrap procedure designed to operate on time series data, containing serial dependencies, with repeated deterministic (experimentally determined) effects. This method is outlined in detail in a previous work (39). Two hundred bootstraps at each voxel were used to estimate parameter standard errors. Using the combined parameter estimates over all conditions, the mean fitted time series was also computed and, from the combined bootstrap parameter estimates for each bootstrap, the 95% confidence limits on the fitted time series was computed.\\nThe second-level analysis proceeded by computing either the group differences (patients and controls) or the drug condition differences (placebo, MPH) within patients at each voxel and the standard error of this difference (using the bootstrap estimates derived earlier). The significance of these differences was then tested in three ways: 1) a simple parametric random effects (paired t) test, using only the group difference/placebo-MPH effect size differences; 2) a permutation test of the same random effects t statistic in which the null distribution was estimated by randomly swapping the signs of the differences (we used 40,000 permutations per voxel to obtain a confidence limit of .0007–.0013 for p value of .001); and 3) a mixed-effects test using both the effect size differences and their subject-level standard errors to accommodate first (subject) level heteroscedasticity (40). This was also conducted using 40,000 permutations per voxel.\\nIn addition to voxelwise maps, cluster-level inference on the contrast (beta) values was performed at a family-wise error corrected threshold of p < .05 using the Threshold-Free Cluster Enhancement method proposed by Smith and Nichols (41). This cluster-level inference was also used for the within-group maps for each experimental condition.\",\n \" Performance The probability of inhibition was about 50% in all subjects with no significant group differences, showing that the task algorithm worked [F(1,38) = 1; p < .3; Table 1].\\nA multivariate ANOVA between control subjects and ADHD patients under either drug condition showed a trend for a significant group effect [F(8,62) = 2, p < .09] due to a significant univariate group effect in the standard deviation to go trials [F(2,34) = 5, p < .02], which were higher in patients under either medication condition compared with control subjects (p < .05). Post hoc tests furthermore revealed a trend for MPH compared with placebo to slow down reaction times within ADHD patients to both go (p < .06) and post-error go trials (both p < .07) (Table 1).\\nThe probability of inhibition was about 50% in all subjects with no significant group differences, showing that the task algorithm worked [F(1,38) = 1; p < .3; Table 1].\\nA multivariate ANOVA between control subjects and ADHD patients under either drug condition showed a trend for a significant group effect [F(8,62) = 2, p < .09] due to a significant univariate group effect in the standard deviation to go trials [F(2,34) = 5, p < .02], which were higher in patients under either medication condition compared with control subjects (p < .05). Post hoc tests furthermore revealed a trend for MPH compared with placebo to slow down reaction times within ADHD patients to both go (p < .06) and post-error go trials (both p < .07) (Table 1).\",\n \"The probability of inhibition was about 50% in all subjects with no significant group differences, showing that the task algorithm worked [F(1,38) = 1; p < .3; Table 1].\\nA multivariate ANOVA between control subjects and ADHD patients under either drug condition showed a trend for a significant group effect [F(8,62) = 2, p < .09] due to a significant univariate group effect in the standard deviation to go trials [F(2,34) = 5, p < .02], which were higher in patients under either medication condition compared with control subjects (p < .05). Post hoc tests furthermore revealed a trend for MPH compared with placebo to slow down reaction times within ADHD patients to both go (p < .06) and post-error go trials (both p < .07) (Table 1).\",\n \" Motion Multivariate ANOVA showed no significant group differences between control subjects and ADHD patients under either drug condition in mean or maximum rotation or translation parameters in the x, y, or z dimensions [F(2,38) = .9, p < .5].\\nMultivariate ANOVA showed no significant group differences between control subjects and ADHD patients under either drug condition in mean or maximum rotation or translation parameters in the x, y, or z dimensions [F(2,38) = .9, p < .5].\\n Within-Group Brain Activations Failed Stop–Go Contrast Control subjects activated relatively large clusters in left and right inferior frontal cortex (IFC)/anterior insula, medial frontal/anterior cingulate cortex (MFC/ACC), precentral, inferior parietal, middle and superior temporal areas, posterior thalamus and caudate, parahippocampal gyri, precuneus/posterior cingulate, occipital, and cerebellar areas.\\nActivation in ADHD patients under placebo was in MFC and superior temporal cortex reaching into anterior and posterior insula, caudate, and inferior parietal and occipital cortex.\\nActivation in ADHD patients under MPH was in a large cluster of ACC and MFC, in left and right IFC/anterior insula, left premotor cortex, right basal ganglia, bilateral middle and superior temporal, inferior parietal and occipital areas, hippocampal gyri, posterior cingulate, precuneus, and cerebellum (Figure S1 in Supplement 1).\\nControl subjects activated relatively large clusters in left and right inferior frontal cortex (IFC)/anterior insula, medial frontal/anterior cingulate cortex (MFC/ACC), precentral, inferior parietal, middle and superior temporal areas, posterior thalamus and caudate, parahippocampal gyri, precuneus/posterior cingulate, occipital, and cerebellar areas.\\nActivation in ADHD patients under placebo was in MFC and superior temporal cortex reaching into anterior and posterior insula, caudate, and inferior parietal and occipital cortex.\\nActivation in ADHD patients under MPH was in a large cluster of ACC and MFC, in left and right IFC/anterior insula, left premotor cortex, right basal ganglia, bilateral middle and superior temporal, inferior parietal and occipital areas, hippocampal gyri, posterior cingulate, precuneus, and cerebellum (Figure S1 in Supplement 1).\\n Successful Stop–Go Contrast Activation in healthy control boys was in a large cluster comprising left and right orbital and IFC, dorsolateral and MFC, insula, basal ganglia, hippocampus, posterior thalamic regions, pre- and postcentral gyri, inferior and superior parietal, temporal and occipital cortices, precuneus, and posterior cingulate.\\nActivation in ADHD patients under placebo was in small clusters in right MFC, supplementary motor area (SMA), right superior temporal, postcentral, left inferior and superior parietal, and occipital cortices.\\nActivation in ADHD patients under the MPH condition was in superior and MFC/ACC, right globus pallidus and putamen, right superior temporal and superior and inferior parietal cortex, posterior insula, and left cerebellum (Figure S1 in Supplement 1).\\nActivation in healthy control boys was in a large cluster comprising left and right orbital and IFC, dorsolateral and MFC, insula, basal ganglia, hippocampus, posterior thalamic regions, pre- and postcentral gyri, inferior and superior parietal, temporal and occipital cortices, precuneus, and posterior cingulate.\\nActivation in ADHD patients under placebo was in small clusters in right MFC, supplementary motor area (SMA), right superior temporal, postcentral, left inferior and superior parietal, and occipital cortices.\\nActivation in ADHD patients under the MPH condition was in superior and MFC/ACC, right globus pallidus and putamen, right superior temporal and superior and inferior parietal cortex, posterior insula, and left cerebellum (Figure S1 in Supplement 1).\\n Failed Stop–Go Contrast Control subjects activated relatively large clusters in left and right inferior frontal cortex (IFC)/anterior insula, medial frontal/anterior cingulate cortex (MFC/ACC), precentral, inferior parietal, middle and superior temporal areas, posterior thalamus and caudate, parahippocampal gyri, precuneus/posterior cingulate, occipital, and cerebellar areas.\\nActivation in ADHD patients under placebo was in MFC and superior temporal cortex reaching into anterior and posterior insula, caudate, and inferior parietal and occipital cortex.\\nActivation in ADHD patients under MPH was in a large cluster of ACC and MFC, in left and right IFC/anterior insula, left premotor cortex, right basal ganglia, bilateral middle and superior temporal, inferior parietal and occipital areas, hippocampal gyri, posterior cingulate, precuneus, and cerebellum (Figure S1 in Supplement 1).\\nControl subjects activated relatively large clusters in left and right inferior frontal cortex (IFC)/anterior insula, medial frontal/anterior cingulate cortex (MFC/ACC), precentral, inferior parietal, middle and superior temporal areas, posterior thalamus and caudate, parahippocampal gyri, precuneus/posterior cingulate, occipital, and cerebellar areas.\\nActivation in ADHD patients under placebo was in MFC and superior temporal cortex reaching into anterior and posterior insula, caudate, and inferior parietal and occipital cortex.\\nActivation in ADHD patients under MPH was in a large cluster of ACC and MFC, in left and right IFC/anterior insula, left premotor cortex, right basal ganglia, bilateral middle and superior temporal, inferior parietal and occipital areas, hippocampal gyri, posterior cingulate, precuneus, and cerebellum (Figure S1 in Supplement 1).\\n Successful Stop–Go Contrast Activation in healthy control boys was in a large cluster comprising left and right orbital and IFC, dorsolateral and MFC, insula, basal ganglia, hippocampus, posterior thalamic regions, pre- and postcentral gyri, inferior and superior parietal, temporal and occipital cortices, precuneus, and posterior cingulate.\\nActivation in ADHD patients under placebo was in small clusters in right MFC, supplementary motor area (SMA), right superior temporal, postcentral, left inferior and superior parietal, and occipital cortices.\\nActivation in ADHD patients under the MPH condition was in superior and MFC/ACC, right globus pallidus and putamen, right superior temporal and superior and inferior parietal cortex, posterior insula, and left cerebellum (Figure S1 in Supplement 1).\\nActivation in healthy control boys was in a large cluster comprising left and right orbital and IFC, dorsolateral and MFC, insula, basal ganglia, hippocampus, posterior thalamic regions, pre- and postcentral gyri, inferior and superior parietal, temporal and occipital cortices, precuneus, and posterior cingulate.\\nActivation in ADHD patients under placebo was in small clusters in right MFC, supplementary motor area (SMA), right superior temporal, postcentral, left inferior and superior parietal, and occipital cortices.\\nActivation in ADHD patients under the MPH condition was in superior and MFC/ACC, right globus pallidus and putamen, right superior temporal and superior and inferior parietal cortex, posterior insula, and left cerebellum (Figure S1 in Supplement 1).\\n ANOVA Within-Patient Comparisons in Brain Activation Between the Placebo and the MPH Conditions Failed Stop–Go Contrast MPH contrasted with placebo elicited enhanced activation in left IFC, reaching into insula and putamen; in right IFC reaching into insula, putamen, and caudate; in left medial frontal lobe; and in left inferior parietal, precuneus, and occipital regions (Figure 1, Table 2). The placebo condition elicited no enhanced activation over MPH.\\nTo investigate whether brain regions that differed with MPH were associated with task performance, statistical measures of the BOLD response were extracted for each subject in each ANOVA cluster and then correlated with performance variables. There was a significant positive correlation in patients between post-error reaction times and left and right IFC activation (r = .7, p < .02) and between go reaction times and right IFC activation (r = .6, p < .05) and a negative correlation between response variability and right inferior parietal activation (r = −.7, p < .02).\\nMPH contrasted with placebo elicited enhanced activation in left IFC, reaching into insula and putamen; in right IFC reaching into insula, putamen, and caudate; in left medial frontal lobe; and in left inferior parietal, precuneus, and occipital regions (Figure 1, Table 2). The placebo condition elicited no enhanced activation over MPH.\\nTo investigate whether brain regions that differed with MPH were associated with task performance, statistical measures of the BOLD response were extracted for each subject in each ANOVA cluster and then correlated with performance variables. There was a significant positive correlation in patients between post-error reaction times and left and right IFC activation (r = .7, p < .02) and between go reaction times and right IFC activation (r = .6, p < .05) and a negative correlation between response variability and right inferior parietal activation (r = −.7, p < .02).\\n Successful Stop–Go Contrast No significant activation differences were observed between medication conditions.\\nNo significant activation differences were observed between medication conditions.\\n Failed Stop–Go Contrast MPH contrasted with placebo elicited enhanced activation in left IFC, reaching into insula and putamen; in right IFC reaching into insula, putamen, and caudate; in left medial frontal lobe; and in left inferior parietal, precuneus, and occipital regions (Figure 1, Table 2). The placebo condition elicited no enhanced activation over MPH.\\nTo investigate whether brain regions that differed with MPH were associated with task performance, statistical measures of the BOLD response were extracted for each subject in each ANOVA cluster and then correlated with performance variables. There was a significant positive correlation in patients between post-error reaction times and left and right IFC activation (r = .7, p < .02) and between go reaction times and right IFC activation (r = .6, p < .05) and a negative correlation between response variability and right inferior parietal activation (r = −.7, p < .02).\\nMPH contrasted with placebo elicited enhanced activation in left IFC, reaching into insula and putamen; in right IFC reaching into insula, putamen, and caudate; in left medial frontal lobe; and in left inferior parietal, precuneus, and occipital regions (Figure 1, Table 2). The placebo condition elicited no enhanced activation over MPH.\\nTo investigate whether brain regions that differed with MPH were associated with task performance, statistical measures of the BOLD response were extracted for each subject in each ANOVA cluster and then correlated with performance variables. There was a significant positive correlation in patients between post-error reaction times and left and right IFC activation (r = .7, p < .02) and between go reaction times and right IFC activation (r = .6, p < .05) and a negative correlation between response variability and right inferior parietal activation (r = −.7, p < .02).\\n Successful Stop–Go Contrast No significant activation differences were observed between medication conditions.\\nNo significant activation differences were observed between medication conditions.\\n ANOVA Between-Group Comparisons in Brain Activation Between Control Subjects and Boys with ADHD Under Either the Placebo or the MPH Conditions Failed Stop–Go Contrast Relative to control subjects, ADHD patients under the placebo condition showed underactivation in left IFC and dorsomedial prefrontal cortices (dMFC) (including pre-SMA), right premotor, superior and inferior parietal cortices, posterior cingulate/precuneus, posterior thalamus, and bilateral inferior temporo-occipital areas (Figure 2A, Table 3).\\nUnder the MPH condition, ADHD patients did not differ from controls in any of these regions.\\nPost-error slowing in ADHD patients under placebo, but not in control subjects, was significantly positively correlated with activation in left IFC, premotor, dMFC, and thalamic underactivation clusters (r > .6 for all clusters, p < .05) as well as with superior parietal, occipital, and cerebellar activation (r < .4, p < .05). Standard deviation of reaction times was correlated with dMFC activation in controls (r = .6, p < .02).\\nRelative to control subjects, ADHD patients under the placebo condition showed underactivation in left IFC and dorsomedial prefrontal cortices (dMFC) (including pre-SMA), right premotor, superior and inferior parietal cortices, posterior cingulate/precuneus, posterior thalamus, and bilateral inferior temporo-occipital areas (Figure 2A, Table 3).\\nUnder the MPH condition, ADHD patients did not differ from controls in any of these regions.\\nPost-error slowing in ADHD patients under placebo, but not in control subjects, was significantly positively correlated with activation in left IFC, premotor, dMFC, and thalamic underactivation clusters (r > .6 for all clusters, p < .05) as well as with superior parietal, occipital, and cerebellar activation (r < .4, p < .05). Standard deviation of reaction times was correlated with dMFC activation in controls (r = .6, p < .02).\\n Successful Stop–Go Contrast Relative to control subjects, ADHD patients under the placebo condition showed underactivation in a right hemispheric network of medial temporal and inferior parietal lobes, precuneus/posterior cingulate and cerebellum (Figure 2B, Table 3). To test our hypothesis of IFC underactivation, we reanalyzed the data at a more lenient p value of p < .002 for voxelwise comparison. This elicited additional underactivation in right IFC, left and right subthalamic nuclei, and the pre-SMA (Table 3 and Figure S2 in Supplement 1).\\nUnder the MPH condition, ADHD patients did not differ from control subjects in any of these regions.\\nActivation in posterior cingulate and lingual gyrus correlated significantly positively with post-error go reaction times in ADHD boys under placebo (r > .6, p < .3).\\nAll group difference findings for both contrasts remained essentially unchanged when IQ was covaried.\\nRelative to control subjects, ADHD patients under the placebo condition showed underactivation in a right hemispheric network of medial temporal and inferior parietal lobes, precuneus/posterior cingulate and cerebellum (Figure 2B, Table 3). To test our hypothesis of IFC underactivation, we reanalyzed the data at a more lenient p value of p < .002 for voxelwise comparison. This elicited additional underactivation in right IFC, left and right subthalamic nuclei, and the pre-SMA (Table 3 and Figure S2 in Supplement 1).\\nUnder the MPH condition, ADHD patients did not differ from control subjects in any of these regions.\\nActivation in posterior cingulate and lingual gyrus correlated significantly positively with post-error go reaction times in ADHD boys under placebo (r > .6, p < .3).\\nAll group difference findings for both contrasts remained essentially unchanged when IQ was covaried.\\n Failed Stop–Go Contrast Relative to control subjects, ADHD patients under the placebo condition showed underactivation in left IFC and dorsomedial prefrontal cortices (dMFC) (including pre-SMA), right premotor, superior and inferior parietal cortices, posterior cingulate/precuneus, posterior thalamus, and bilateral inferior temporo-occipital areas (Figure 2A, Table 3).\\nUnder the MPH condition, ADHD patients did not differ from controls in any of these regions.\\nPost-error slowing in ADHD patients under placebo, but not in control subjects, was significantly positively correlated with activation in left IFC, premotor, dMFC, and thalamic underactivation clusters (r > .6 for all clusters, p < .05) as well as with superior parietal, occipital, and cerebellar activation (r < .4, p < .05). Standard deviation of reaction times was correlated with dMFC activation in controls (r = .6, p < .02).\\nRelative to control subjects, ADHD patients under the placebo condition showed underactivation in left IFC and dorsomedial prefrontal cortices (dMFC) (including pre-SMA), right premotor, superior and inferior parietal cortices, posterior cingulate/precuneus, posterior thalamus, and bilateral inferior temporo-occipital areas (Figure 2A, Table 3).\\nUnder the MPH condition, ADHD patients did not differ from controls in any of these regions.\\nPost-error slowing in ADHD patients under placebo, but not in control subjects, was significantly positively correlated with activation in left IFC, premotor, dMFC, and thalamic underactivation clusters (r > .6 for all clusters, p < .05) as well as with superior parietal, occipital, and cerebellar activation (r < .4, p < .05). Standard deviation of reaction times was correlated with dMFC activation in controls (r = .6, p < .02).\\n Successful Stop–Go Contrast Relative to control subjects, ADHD patients under the placebo condition showed underactivation in a right hemispheric network of medial temporal and inferior parietal lobes, precuneus/posterior cingulate and cerebellum (Figure 2B, Table 3). To test our hypothesis of IFC underactivation, we reanalyzed the data at a more lenient p value of p < .002 for voxelwise comparison. This elicited additional underactivation in right IFC, left and right subthalamic nuclei, and the pre-SMA (Table 3 and Figure S2 in Supplement 1).\\nUnder the MPH condition, ADHD patients did not differ from control subjects in any of these regions.\\nActivation in posterior cingulate and lingual gyrus correlated significantly positively with post-error go reaction times in ADHD boys under placebo (r > .6, p < .3).\\nAll group difference findings for both contrasts remained essentially unchanged when IQ was covaried.\\nRelative to control subjects, ADHD patients under the placebo condition showed underactivation in a right hemispheric network of medial temporal and inferior parietal lobes, precuneus/posterior cingulate and cerebellum (Figure 2B, Table 3). To test our hypothesis of IFC underactivation, we reanalyzed the data at a more lenient p value of p < .002 for voxelwise comparison. This elicited additional underactivation in right IFC, left and right subthalamic nuclei, and the pre-SMA (Table 3 and Figure S2 in Supplement 1).\\nUnder the MPH condition, ADHD patients did not differ from control subjects in any of these regions.\\nActivation in posterior cingulate and lingual gyrus correlated significantly positively with post-error go reaction times in ADHD boys under placebo (r > .6, p < .3).\\nAll group difference findings for both contrasts remained essentially unchanged when IQ was covaried.\\n Conjunction Analysis Between Within-Group and Between-Group ANOVAs To test whether brain regions that were upregulated with MPH relative to placebo within patients overlapped with brain regions that were reduced in patients under placebo relative to controls and then normalized with MPH, we performed a conjunction analysis by determining the voxels where the within-group ANOVA (MPH > placebo in ADHD) and the between-group ANOVA (control subjects > ADHD placebo) were both significant (42). Three clusters emerged, in left IFC (Talairach coordinates: −43, 7, 4), right SMA (Talairach coordinates: 7, 4, 59) and right inferior parietal lobe (Talairach coordinates: 32, −63, 42). Overlapping clusters are also indicated in bold in Table 3 and shown in Figure 3.\\nTo test whether brain regions that were upregulated with MPH relative to placebo within patients overlapped with brain regions that were reduced in patients under placebo relative to controls and then normalized with MPH, we performed a conjunction analysis by determining the voxels where the within-group ANOVA (MPH > placebo in ADHD) and the between-group ANOVA (control subjects > ADHD placebo) were both significant (42). Three clusters emerged, in left IFC (Talairach coordinates: −43, 7, 4), right SMA (Talairach coordinates: 7, 4, 59) and right inferior parietal lobe (Talairach coordinates: 32, −63, 42). Overlapping clusters are also indicated in bold in Table 3 and shown in Figure 3.\",\n \"Multivariate ANOVA showed no significant group differences between control subjects and ADHD patients under either drug condition in mean or maximum rotation or translation parameters in the x, y, or z dimensions [F(2,38) = .9, p < .5].\",\n \" Failed Stop–Go Contrast Control subjects activated relatively large clusters in left and right inferior frontal cortex (IFC)/anterior insula, medial frontal/anterior cingulate cortex (MFC/ACC), precentral, inferior parietal, middle and superior temporal areas, posterior thalamus and caudate, parahippocampal gyri, precuneus/posterior cingulate, occipital, and cerebellar areas.\\nActivation in ADHD patients under placebo was in MFC and superior temporal cortex reaching into anterior and posterior insula, caudate, and inferior parietal and occipital cortex.\\nActivation in ADHD patients under MPH was in a large cluster of ACC and MFC, in left and right IFC/anterior insula, left premotor cortex, right basal ganglia, bilateral middle and superior temporal, inferior parietal and occipital areas, hippocampal gyri, posterior cingulate, precuneus, and cerebellum (Figure S1 in Supplement 1).\\nControl subjects activated relatively large clusters in left and right inferior frontal cortex (IFC)/anterior insula, medial frontal/anterior cingulate cortex (MFC/ACC), precentral, inferior parietal, middle and superior temporal areas, posterior thalamus and caudate, parahippocampal gyri, precuneus/posterior cingulate, occipital, and cerebellar areas.\\nActivation in ADHD patients under placebo was in MFC and superior temporal cortex reaching into anterior and posterior insula, caudate, and inferior parietal and occipital cortex.\\nActivation in ADHD patients under MPH was in a large cluster of ACC and MFC, in left and right IFC/anterior insula, left premotor cortex, right basal ganglia, bilateral middle and superior temporal, inferior parietal and occipital areas, hippocampal gyri, posterior cingulate, precuneus, and cerebellum (Figure S1 in Supplement 1).\\n Successful Stop–Go Contrast Activation in healthy control boys was in a large cluster comprising left and right orbital and IFC, dorsolateral and MFC, insula, basal ganglia, hippocampus, posterior thalamic regions, pre- and postcentral gyri, inferior and superior parietal, temporal and occipital cortices, precuneus, and posterior cingulate.\\nActivation in ADHD patients under placebo was in small clusters in right MFC, supplementary motor area (SMA), right superior temporal, postcentral, left inferior and superior parietal, and occipital cortices.\\nActivation in ADHD patients under the MPH condition was in superior and MFC/ACC, right globus pallidus and putamen, right superior temporal and superior and inferior parietal cortex, posterior insula, and left cerebellum (Figure S1 in Supplement 1).\\nActivation in healthy control boys was in a large cluster comprising left and right orbital and IFC, dorsolateral and MFC, insula, basal ganglia, hippocampus, posterior thalamic regions, pre- and postcentral gyri, inferior and superior parietal, temporal and occipital cortices, precuneus, and posterior cingulate.\\nActivation in ADHD patients under placebo was in small clusters in right MFC, supplementary motor area (SMA), right superior temporal, postcentral, left inferior and superior parietal, and occipital cortices.\\nActivation in ADHD patients under the MPH condition was in superior and MFC/ACC, right globus pallidus and putamen, right superior temporal and superior and inferior parietal cortex, posterior insula, and left cerebellum (Figure S1 in Supplement 1).\",\n \"Control subjects activated relatively large clusters in left and right inferior frontal cortex (IFC)/anterior insula, medial frontal/anterior cingulate cortex (MFC/ACC), precentral, inferior parietal, middle and superior temporal areas, posterior thalamus and caudate, parahippocampal gyri, precuneus/posterior cingulate, occipital, and cerebellar areas.\\nActivation in ADHD patients under placebo was in MFC and superior temporal cortex reaching into anterior and posterior insula, caudate, and inferior parietal and occipital cortex.\\nActivation in ADHD patients under MPH was in a large cluster of ACC and MFC, in left and right IFC/anterior insula, left premotor cortex, right basal ganglia, bilateral middle and superior temporal, inferior parietal and occipital areas, hippocampal gyri, posterior cingulate, precuneus, and cerebellum (Figure S1 in Supplement 1).\",\n \"Activation in healthy control boys was in a large cluster comprising left and right orbital and IFC, dorsolateral and MFC, insula, basal ganglia, hippocampus, posterior thalamic regions, pre- and postcentral gyri, inferior and superior parietal, temporal and occipital cortices, precuneus, and posterior cingulate.\\nActivation in ADHD patients under placebo was in small clusters in right MFC, supplementary motor area (SMA), right superior temporal, postcentral, left inferior and superior parietal, and occipital cortices.\\nActivation in ADHD patients under the MPH condition was in superior and MFC/ACC, right globus pallidus and putamen, right superior temporal and superior and inferior parietal cortex, posterior insula, and left cerebellum (Figure S1 in Supplement 1).\",\n \" Failed Stop–Go Contrast MPH contrasted with placebo elicited enhanced activation in left IFC, reaching into insula and putamen; in right IFC reaching into insula, putamen, and caudate; in left medial frontal lobe; and in left inferior parietal, precuneus, and occipital regions (Figure 1, Table 2). The placebo condition elicited no enhanced activation over MPH.\\nTo investigate whether brain regions that differed with MPH were associated with task performance, statistical measures of the BOLD response were extracted for each subject in each ANOVA cluster and then correlated with performance variables. There was a significant positive correlation in patients between post-error reaction times and left and right IFC activation (r = .7, p < .02) and between go reaction times and right IFC activation (r = .6, p < .05) and a negative correlation between response variability and right inferior parietal activation (r = −.7, p < .02).\\nMPH contrasted with placebo elicited enhanced activation in left IFC, reaching into insula and putamen; in right IFC reaching into insula, putamen, and caudate; in left medial frontal lobe; and in left inferior parietal, precuneus, and occipital regions (Figure 1, Table 2). The placebo condition elicited no enhanced activation over MPH.\\nTo investigate whether brain regions that differed with MPH were associated with task performance, statistical measures of the BOLD response were extracted for each subject in each ANOVA cluster and then correlated with performance variables. There was a significant positive correlation in patients between post-error reaction times and left and right IFC activation (r = .7, p < .02) and between go reaction times and right IFC activation (r = .6, p < .05) and a negative correlation between response variability and right inferior parietal activation (r = −.7, p < .02).\\n Successful Stop–Go Contrast No significant activation differences were observed between medication conditions.\\nNo significant activation differences were observed between medication conditions.\",\n \"MPH contrasted with placebo elicited enhanced activation in left IFC, reaching into insula and putamen; in right IFC reaching into insula, putamen, and caudate; in left medial frontal lobe; and in left inferior parietal, precuneus, and occipital regions (Figure 1, Table 2). The placebo condition elicited no enhanced activation over MPH.\\nTo investigate whether brain regions that differed with MPH were associated with task performance, statistical measures of the BOLD response were extracted for each subject in each ANOVA cluster and then correlated with performance variables. There was a significant positive correlation in patients between post-error reaction times and left and right IFC activation (r = .7, p < .02) and between go reaction times and right IFC activation (r = .6, p < .05) and a negative correlation between response variability and right inferior parietal activation (r = −.7, p < .02).\",\n \"No significant activation differences were observed between medication conditions.\",\n \" Failed Stop–Go Contrast Relative to control subjects, ADHD patients under the placebo condition showed underactivation in left IFC and dorsomedial prefrontal cortices (dMFC) (including pre-SMA), right premotor, superior and inferior parietal cortices, posterior cingulate/precuneus, posterior thalamus, and bilateral inferior temporo-occipital areas (Figure 2A, Table 3).\\nUnder the MPH condition, ADHD patients did not differ from controls in any of these regions.\\nPost-error slowing in ADHD patients under placebo, but not in control subjects, was significantly positively correlated with activation in left IFC, premotor, dMFC, and thalamic underactivation clusters (r > .6 for all clusters, p < .05) as well as with superior parietal, occipital, and cerebellar activation (r < .4, p < .05). Standard deviation of reaction times was correlated with dMFC activation in controls (r = .6, p < .02).\\nRelative to control subjects, ADHD patients under the placebo condition showed underactivation in left IFC and dorsomedial prefrontal cortices (dMFC) (including pre-SMA), right premotor, superior and inferior parietal cortices, posterior cingulate/precuneus, posterior thalamus, and bilateral inferior temporo-occipital areas (Figure 2A, Table 3).\\nUnder the MPH condition, ADHD patients did not differ from controls in any of these regions.\\nPost-error slowing in ADHD patients under placebo, but not in control subjects, was significantly positively correlated with activation in left IFC, premotor, dMFC, and thalamic underactivation clusters (r > .6 for all clusters, p < .05) as well as with superior parietal, occipital, and cerebellar activation (r < .4, p < .05). Standard deviation of reaction times was correlated with dMFC activation in controls (r = .6, p < .02).\\n Successful Stop–Go Contrast Relative to control subjects, ADHD patients under the placebo condition showed underactivation in a right hemispheric network of medial temporal and inferior parietal lobes, precuneus/posterior cingulate and cerebellum (Figure 2B, Table 3). To test our hypothesis of IFC underactivation, we reanalyzed the data at a more lenient p value of p < .002 for voxelwise comparison. This elicited additional underactivation in right IFC, left and right subthalamic nuclei, and the pre-SMA (Table 3 and Figure S2 in Supplement 1).\\nUnder the MPH condition, ADHD patients did not differ from control subjects in any of these regions.\\nActivation in posterior cingulate and lingual gyrus correlated significantly positively with post-error go reaction times in ADHD boys under placebo (r > .6, p < .3).\\nAll group difference findings for both contrasts remained essentially unchanged when IQ was covaried.\\nRelative to control subjects, ADHD patients under the placebo condition showed underactivation in a right hemispheric network of medial temporal and inferior parietal lobes, precuneus/posterior cingulate and cerebellum (Figure 2B, Table 3). To test our hypothesis of IFC underactivation, we reanalyzed the data at a more lenient p value of p < .002 for voxelwise comparison. This elicited additional underactivation in right IFC, left and right subthalamic nuclei, and the pre-SMA (Table 3 and Figure S2 in Supplement 1).\\nUnder the MPH condition, ADHD patients did not differ from control subjects in any of these regions.\\nActivation in posterior cingulate and lingual gyrus correlated significantly positively with post-error go reaction times in ADHD boys under placebo (r > .6, p < .3).\\nAll group difference findings for both contrasts remained essentially unchanged when IQ was covaried.\",\n \"Relative to control subjects, ADHD patients under the placebo condition showed underactivation in left IFC and dorsomedial prefrontal cortices (dMFC) (including pre-SMA), right premotor, superior and inferior parietal cortices, posterior cingulate/precuneus, posterior thalamus, and bilateral inferior temporo-occipital areas (Figure 2A, Table 3).\\nUnder the MPH condition, ADHD patients did not differ from controls in any of these regions.\\nPost-error slowing in ADHD patients under placebo, but not in control subjects, was significantly positively correlated with activation in left IFC, premotor, dMFC, and thalamic underactivation clusters (r > .6 for all clusters, p < .05) as well as with superior parietal, occipital, and cerebellar activation (r < .4, p < .05). Standard deviation of reaction times was correlated with dMFC activation in controls (r = .6, p < .02).\",\n \"Relative to control subjects, ADHD patients under the placebo condition showed underactivation in a right hemispheric network of medial temporal and inferior parietal lobes, precuneus/posterior cingulate and cerebellum (Figure 2B, Table 3). To test our hypothesis of IFC underactivation, we reanalyzed the data at a more lenient p value of p < .002 for voxelwise comparison. This elicited additional underactivation in right IFC, left and right subthalamic nuclei, and the pre-SMA (Table 3 and Figure S2 in Supplement 1).\\nUnder the MPH condition, ADHD patients did not differ from control subjects in any of these regions.\\nActivation in posterior cingulate and lingual gyrus correlated significantly positively with post-error go reaction times in ADHD boys under placebo (r > .6, p < .3).\\nAll group difference findings for both contrasts remained essentially unchanged when IQ was covaried.\",\n \"To test whether brain regions that were upregulated with MPH relative to placebo within patients overlapped with brain regions that were reduced in patients under placebo relative to controls and then normalized with MPH, we performed a conjunction analysis by determining the voxels where the within-group ANOVA (MPH > placebo in ADHD) and the between-group ANOVA (control subjects > ADHD placebo) were both significant (42). Three clusters emerged, in left IFC (Talairach coordinates: −43, 7, 4), right SMA (Talairach coordinates: 7, 4, 59) and right inferior parietal lobe (Talairach coordinates: 32, −63, 42). Overlapping clusters are also indicated in bold in Table 3 and shown in Figure 3.\",\n \"During error trials, ADHD boys under placebo compared with healthy control subjects showed significant underactivation in a typical error processing and performance monitoring network comprising dMFC, left IFC, thalamus, posterior cingulate/precuneus, and inferior temporoparietal regions. Among patients, MPH compared with placebo significantly upregulated activation in overlapping medial frontal, IFC, and parietal regions as well as the lenticular nucleus. Under MPH, brain activation differences between control subjects and ADHD patients were no longer observed. Reduced fronto-thalamo-parietal activation that was normalized with MPH was, furthermore, negatively associated with faster post-error reaction times in patients, which were trendwise slowed with MPH.\\nDuring successful stop trials, ADHD boys showed underactivation in a right hemispheric network of medial temporal and inferior parietal brain regions and, at a more lenient threshold, in small clusters of bilateral IFC, thalamus, and pre-SMA. Although within-patient comparison between MPH and placebo did not show significant activation differences, all underactivations in patients relative to control subjects under placebo were normalized with a single dose of MPH.\\nThe dMFC, comprising Brodmann areas 8, 6, and 32, including pre-SMA and ACC, is a typical region of error processing and performance monitoring in adults (37,43–48) and children (29,38,49). We have previously found this region to be underactivated in children with ADHD during oddball (50) and switch tasks (4). Errors indicate violation of a reward prediction (i.e., positive performance) and have been linked to midbrain dopamine (51). Normalization with MPH of underfunctioning of this region in ADHD is in line with the notion that phasic dopamine response modulates error-related mesial frontal activation (52,53). These findings extend evidence for upregulation with acute and chronic doses of MPH in previously medicated patients with ADHD in a more rostral ACC location during tasks of cognitive control (22,28,54).\\nActivation in dMFC during errors triggers additional activation in functionally interconnected left IFC, as well as striatal, premotor, and parietal components of the error monitoring system, leading to post-error performance adjustments (43–45,48). IFC underactivation is one of the most consistent findings in fMRI studies in patients with ADHD, with right IFC dysfunction typically observed during inhibitory performance (7,8,11,55), in line with its role in inhibition (37,56), and left IFC during stop errors (4,29) as well as during flexible, selective, or sustained attention (4,9,26,50,57), in line with its role for performance monitoring (44,45,48,49) and saliency processing (58,59). IFC dysfunction is furthermore a disorder-specific neurofunctional deficit compared with patients with conduct (6,50,57,60) and obsessive compulsive (4) disorders. MPH thus appears to modulate an important neurofunctional biomarker of ADHD. The more predominantly left-hemispheric upregulation effect during errors may suggest a stronger effect of MPH on performance monitoring than inhibitory function in ADHD. Left IFC upregulation has previously been observed in ADHD patients in the context of an attention-demanding time discrimination task after acute (27) and 6 weeks of MPH treatment during interference inhibition (54). Structural studies have shown more normal cortical thinning in left IFC in psychostimulant-medicated compared with unmedicated ADHD children (61). Together, this raises the speculation that MPH may have a lateralized upregulating effect on left IFC structure and function.\\nPosterior thalamic regions have been associated both with motor response inhibition (62) and performance monitoring (48,63,64). The finding that MPH normalizes activation in this region is in line with speculation of this region's involvement in the modulation of the dopaminergic error signal (63,65,66).\\nThe fact that lower dMFC, IFC, and thalamic activation in ADHD patients was associated with faster post-error slowing, both of which were enhanced by MPH, reinforces the role of this network for abnormal error monitoring in ADHD. Posterior cingulate and precuneus are connected with MFC and parietal areas and form part of the performance monitoring network (47,49,67,68), mediating visual spatial attention to saliency (69,70) and the integration of performance outcome with attentional modulation (48). The fact that these regions were underactivated during both inhibition and its failure is in line with a generic attention role of these areas. In line with this, we and others have previously observed underactivation in ADHD patients in these regions during inhibition errors (4,6,8,60), as well as during other salient stimuli such as oddball, novel or incongruent targets (10,26,50,71,72).\\nNormalization with MPH of reduced activation in typical frontoparietal regions of saliency processing and performance monitoring is consistent with the dopamine-deficiency hypothesis of ADHD given that dopamine agonists enhance stimulus salience (73). It is also in line with our previous findings of upregulation with MPH of posterior cingulate/precuneus in the same group of medication naive boys with ADHD during a target detection task, resulting in improved attention (26), and during an attention demanding time discrimination task (27). To our knowledge, normalization of inferior parietal activation with MPH has only recently been observed in ADHD patients, in the context of sustained attention (26) and interference inhibition (54).\\nDuring successful stop trials, MPH also normalized underactivation in the cerebellum, which, together with subthalamic nucleus, caudate, and IFG, forms a neurofunctional network of motor response inhibition (38). These findings extend previous evidence for cerebellum upregulation with MPH in ADHD patients during interference inhibition (54) and time estimation (27).\\nWithin patients, MPH also enhanced activation of caudate and putamen. This is in line with previous fMRI findings of caudate upregulation in ADHD patients after acute and chronic doses of MPH during inhibition and attention tasks (22,24,54) and is likely associated with the known effect of MPH on striatal dopamine transporter blockage (14,15).\\nThe findings of more pronounced normalization effects of MPH on abnormal performance monitoring than inhibition networks could suggest that MPH enhances generic attention and performance monitoring functions more than inhibitory capacity. This would be in line with the behavioral effect of MPH of modulating go and post-error reaction times, but not inhibition speed, which, furthermore were correlated with the reduced frontothalamic error-processing activation that was normalized with MPH. Relative to control subjects, patients only significantly differed in intrasubject response variability. Small subject numbers, a relatively older child group, and fMRI task design restrictions may have been responsible for minor behavior differences. The findings of brain dysfunctions in boys with ADHD and their normalization under the clinical dose of MPH despite minor performance differences and only trend-level improvements with MPH show that brain activation is more sensitive than performance to detect both abnormalities and pharmacologic effects. This is in line with previous findings of marked brain dysfunctions in ADHD adolescents despite no stop task impairment (7,8,50) and higher sensitivity of brain activation than behavior to show pharmacologic effects of MPH in ADHD (24,26–28,54,74).\\nMPH prevents the reuptake of catecholamines from the synaptic cleft by blocking dopamine and norepinephrine transporters (DAT/NET) (75,76), with higher affinity for the former (77,78). In healthy adults, MPH blocks 60% to 70% of striatal DAT in a dose-dependent manner, increasing extracellular levels of dopamine in striatum (75,79–82), as well as in frontal, thalamic, and temporal regions (83). The upregulating effects on basal ganglia, thalamic, and anterior cingulate activation were therefore likely mediated by mesolimbic striatocingulate dopaminergic pathways known to modulate error monitoring systems (63,64). In frontal regions, however, MPH upregulates noradrenaline to the same or greater extent than dopamine (84–86), via reuptake inhibition of NET that clear up both dopamine and noradrenaline (85,87–89). The upregulating effects on frontal activation, therefore, may have been mediated by enhanced catecholamine neurotransmission, in line with recent evidence that noradrenaline also plays a role in error monitoring (66,90).\\nA limitation of the study is that patients were tested twice, whereas control subjects were only scanned once, for ethical and financial reasons. Practice effects, however, were overcome by the counterbalanced design. Another limitation is the relatively small sample size. Minimum numbers of 15 to 20 participants have been suggested for fMRI studies (91). Repeated-measures designs, however, are statistically more powerful than independent data sets, which makes the within-subject ANOVA more robust.\\nTo our knowledge, this is the first study to show that a single dose of MPH in ADHD upregulates and normalizes the underfunctioning of dMFC, left IFC, posterior cingulate, and parietal regions that in concert play an important role in error processing. The normalization findings of these key regions of both performance monitoring and ADHD dysfunction reinforce the association between dopaminergic neurotransmission abnormalities, ADHD, and poor performance monitoring and may underlie the behavioral effects of improving attention and school performance in boys with ADHD.\"\n]"},"all_sections_sec_types":{"kind":"list like","value":["materials|methods",null,null,null,null,"results",null,null,null,null,null,null,null,null,null,null,null,null,null,"discussion"],"string":"[\n \"materials|methods\",\n null,\n null,\n null,\n null,\n \"results\",\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n \"discussion\"\n]"},"keywords":{"kind":"list like","value":["Attention-deficit/hyperactivity disorder (ADHD)","error processing","methylphenidate","motor response inhibition","performance monitoring","stop task"],"string":"[\n \"Attention-deficit/hyperactivity disorder (ADHD)\",\n \"error processing\",\n \"methylphenidate\",\n \"motor response inhibition\",\n \"performance monitoring\",\n \"stop task\"\n]"},"whole_article_text":{"kind":"string","value":"Methods and Materials:\n Subjects Twelve medication-naïve, right-handed boys aged 10 to 15 years (mean age = 13, SD = 1) who met clinical diagnostic criteria for the combined (inattentive/hyperactive) subtype of ADHD (DSM-IV) were recruited through clinics. Clinical diagnosis of ADHD was established through interviews with an experienced child psychiatrist (A-MM) using the standardized Maudsley Diagnostic Interview to check for presence or absence of diagnostic criteria for any mental disorder as set out by DSM-IV (30). Exclusion criteria were lifetime comorbidity with any other psychiatric disorder, except for conduct/oppositional defiant disorder (present in one patient), as well as learning disability and specific reading disorder, neurological abnormalities, epilepsy, drug or substance abuse, and previous exposure to stimulant medication. Patients with ADHD also had to score above cutoff for hyperactive/inattentive symptoms on the Strengths and Difficulties Questionnaire for Parents (SDQ) (31). Patients were scanned twice, in a randomized, counterbalanced fashion, 1 week apart, 1 hour after either .3 mg/kg of MPH administration or placebo (vitamin C, 100 mg).\nThirteen male right-handed adolescent boys in the age range of 11 to 16 years (mean age = 13, SD = 1) were recruited through advertisements in the same geographic areas of South London to ensure similar socioeconomic status and were scanned once. They scored below cutoff for behavioral problems in the SDQ and had no history of psychiatric disorder.\nAll participants were above the fifth percentile on the Raven progressive matrices performance IQ (32) (IQ mean estimate controls = 100, SD = 14; ADHD = 91, SD = 9) and paid £30 for participation. Parental and child informed consent/assent and approval from the local ethical committee was obtained.\nUnivariate analyses of variance (ANOVAs) showed no group differences between boys with and without ADHD for age [F(1,25) = 2, p = .2] but did for IQ [F(1,25) = 8, p < .009]. IQ is associated with ADHD in the general population (33,34). We purposely did not match groups for IQ because matching ADHD and control groups for IQ would have created unrepresentative groups and therefore be misguided (35). Furthermore, IQ was significantly negatively correlated with the SDQ scores for inattention and hyperactivity (r = −.5, p < .001). We did not covary for IQ because when groups are not randomly selected, covarying for a variable that differs between groups violates the standard assumptions for analysis of covariance. When the covariate is intrinsic to the condition, it becomes meaningless to “adjust” group effects for differences in the covariate because it would alter the group effect in potentially problematic ways, leading to spurious results (35,36).\nTwelve medication-naïve, right-handed boys aged 10 to 15 years (mean age = 13, SD = 1) who met clinical diagnostic criteria for the combined (inattentive/hyperactive) subtype of ADHD (DSM-IV) were recruited through clinics. Clinical diagnosis of ADHD was established through interviews with an experienced child psychiatrist (A-MM) using the standardized Maudsley Diagnostic Interview to check for presence or absence of diagnostic criteria for any mental disorder as set out by DSM-IV (30). Exclusion criteria were lifetime comorbidity with any other psychiatric disorder, except for conduct/oppositional defiant disorder (present in one patient), as well as learning disability and specific reading disorder, neurological abnormalities, epilepsy, drug or substance abuse, and previous exposure to stimulant medication. Patients with ADHD also had to score above cutoff for hyperactive/inattentive symptoms on the Strengths and Difficulties Questionnaire for Parents (SDQ) (31). Patients were scanned twice, in a randomized, counterbalanced fashion, 1 week apart, 1 hour after either .3 mg/kg of MPH administration or placebo (vitamin C, 100 mg).\nThirteen male right-handed adolescent boys in the age range of 11 to 16 years (mean age = 13, SD = 1) were recruited through advertisements in the same geographic areas of South London to ensure similar socioeconomic status and were scanned once. They scored below cutoff for behavioral problems in the SDQ and had no history of psychiatric disorder.\nAll participants were above the fifth percentile on the Raven progressive matrices performance IQ (32) (IQ mean estimate controls = 100, SD = 14; ADHD = 91, SD = 9) and paid £30 for participation. Parental and child informed consent/assent and approval from the local ethical committee was obtained.\nUnivariate analyses of variance (ANOVAs) showed no group differences between boys with and without ADHD for age [F(1,25) = 2, p = .2] but did for IQ [F(1,25) = 8, p < .009]. IQ is associated with ADHD in the general population (33,34). We purposely did not match groups for IQ because matching ADHD and control groups for IQ would have created unrepresentative groups and therefore be misguided (35). Furthermore, IQ was significantly negatively correlated with the SDQ scores for inattention and hyperactivity (r = −.5, p < .001). We did not covary for IQ because when groups are not randomly selected, covarying for a variable that differs between groups violates the standard assumptions for analysis of covariance. When the covariate is intrinsic to the condition, it becomes meaningless to “adjust” group effects for differences in the covariate because it would alter the group effect in potentially problematic ways, leading to spurious results (35,36).\n fMRI Paradigm: Stop Task The rapid, mixed-trial, event-related fMRI design was practiced by subjects once before scanning. The visual tracking stop task requires withholding of a motor response to a go stimulus when it is followed unpredictably by a stop signal (8,37,38). The basic task is a choice reaction time task (left and right pointing arrows: go signals) with a mean intertrial-interval of 1.8 sec (156 go trials). In 20% of trials, pseudo-randomly interspersed, the go signals are followed (about 250 ms later) by arrows pointing upwards (stop signals), and subjects have to inhibit their motor responses (40 stop trials). A tracking algorithm changes the time interval between go-signal and stop-signal onsets according to each subject's inhibitory performance to ensure that the task is equally challenging for each individual and to provide 50% successful and 50% unsuccessful inhibition trials at every moment of the task.\nThe rapid, mixed-trial, event-related fMRI design was practiced by subjects once before scanning. The visual tracking stop task requires withholding of a motor response to a go stimulus when it is followed unpredictably by a stop signal (8,37,38). The basic task is a choice reaction time task (left and right pointing arrows: go signals) with a mean intertrial-interval of 1.8 sec (156 go trials). In 20% of trials, pseudo-randomly interspersed, the go signals are followed (about 250 ms later) by arrows pointing upwards (stop signals), and subjects have to inhibit their motor responses (40 stop trials). A tracking algorithm changes the time interval between go-signal and stop-signal onsets according to each subject's inhibitory performance to ensure that the task is equally challenging for each individual and to provide 50% successful and 50% unsuccessful inhibition trials at every moment of the task.\n fMRI Image Acquisition Gradient-echo echoplanar magnetic resonance imaging data were acquired on a GE Signa 1.5-T Horizon LX System (General Electric, Milwaukee, Wisconsin) at the Maudsley Hospital, London. A quadrature birdcage head coil was used for radio-frequency transmission and reception. During the 6-min run of the stop task, in each of 16 noncontiguous planes parallel to the anterior–posterior commissural, 196 T2*-weighted magnetic resonance images depicting blood oxygen–level dependent (BOLD) contrast covering the whole brain were acquired with echo time = 40 msec, repetition time = 1.8 sec, flip angle = 90°, in-plane resolution = 3.1 mm, slice thickness = 7 mm, slice skip = .7 mm, providing complete brain coverage.\nGradient-echo echoplanar magnetic resonance imaging data were acquired on a GE Signa 1.5-T Horizon LX System (General Electric, Milwaukee, Wisconsin) at the Maudsley Hospital, London. A quadrature birdcage head coil was used for radio-frequency transmission and reception. During the 6-min run of the stop task, in each of 16 noncontiguous planes parallel to the anterior–posterior commissural, 196 T2*-weighted magnetic resonance images depicting blood oxygen–level dependent (BOLD) contrast covering the whole brain were acquired with echo time = 40 msec, repetition time = 1.8 sec, flip angle = 90°, in-plane resolution = 3.1 mm, slice thickness = 7 mm, slice skip = .7 mm, providing complete brain coverage.\n fMRI Image Analysis At the individual subject level, a standard general linear modeling approach was used to obtain estimates of the response size (beta) to each of the two stop task conditions (successful and unsuccessful stop trials) against an implicit baseline (go trials). Following transformation of the fMRI data for each individual into standard space and smoothing with a three-dimensional 7-mm full width at half maximum Gaussian filter, the experimental model was convolved for each condition with gamma variate functions having peak responses at 4 and 8 sec following stimulus onset to accommodate variability in BOLD response timing. By fitting these convolved model components to the time series at each voxel, beta estimates were obtained for each effect of interest. The standard errors of these beta estimates were computed nonparametrically using a bootstrap procedure designed to operate on time series data, containing serial dependencies, with repeated deterministic (experimentally determined) effects. This method is outlined in detail in a previous work (39). Two hundred bootstraps at each voxel were used to estimate parameter standard errors. Using the combined parameter estimates over all conditions, the mean fitted time series was also computed and, from the combined bootstrap parameter estimates for each bootstrap, the 95% confidence limits on the fitted time series was computed.\nThe second-level analysis proceeded by computing either the group differences (patients and controls) or the drug condition differences (placebo, MPH) within patients at each voxel and the standard error of this difference (using the bootstrap estimates derived earlier). The significance of these differences was then tested in three ways: 1) a simple parametric random effects (paired t) test, using only the group difference/placebo-MPH effect size differences; 2) a permutation test of the same random effects t statistic in which the null distribution was estimated by randomly swapping the signs of the differences (we used 40,000 permutations per voxel to obtain a confidence limit of .0007–.0013 for p value of .001); and 3) a mixed-effects test using both the effect size differences and their subject-level standard errors to accommodate first (subject) level heteroscedasticity (40). This was also conducted using 40,000 permutations per voxel.\nIn addition to voxelwise maps, cluster-level inference on the contrast (beta) values was performed at a family-wise error corrected threshold of p < .05 using the Threshold-Free Cluster Enhancement method proposed by Smith and Nichols (41). This cluster-level inference was also used for the within-group maps for each experimental condition.\nAt the individual subject level, a standard general linear modeling approach was used to obtain estimates of the response size (beta) to each of the two stop task conditions (successful and unsuccessful stop trials) against an implicit baseline (go trials). Following transformation of the fMRI data for each individual into standard space and smoothing with a three-dimensional 7-mm full width at half maximum Gaussian filter, the experimental model was convolved for each condition with gamma variate functions having peak responses at 4 and 8 sec following stimulus onset to accommodate variability in BOLD response timing. By fitting these convolved model components to the time series at each voxel, beta estimates were obtained for each effect of interest. The standard errors of these beta estimates were computed nonparametrically using a bootstrap procedure designed to operate on time series data, containing serial dependencies, with repeated deterministic (experimentally determined) effects. This method is outlined in detail in a previous work (39). Two hundred bootstraps at each voxel were used to estimate parameter standard errors. Using the combined parameter estimates over all conditions, the mean fitted time series was also computed and, from the combined bootstrap parameter estimates for each bootstrap, the 95% confidence limits on the fitted time series was computed.\nThe second-level analysis proceeded by computing either the group differences (patients and controls) or the drug condition differences (placebo, MPH) within patients at each voxel and the standard error of this difference (using the bootstrap estimates derived earlier). The significance of these differences was then tested in three ways: 1) a simple parametric random effects (paired t) test, using only the group difference/placebo-MPH effect size differences; 2) a permutation test of the same random effects t statistic in which the null distribution was estimated by randomly swapping the signs of the differences (we used 40,000 permutations per voxel to obtain a confidence limit of .0007–.0013 for p value of .001); and 3) a mixed-effects test using both the effect size differences and their subject-level standard errors to accommodate first (subject) level heteroscedasticity (40). This was also conducted using 40,000 permutations per voxel.\nIn addition to voxelwise maps, cluster-level inference on the contrast (beta) values was performed at a family-wise error corrected threshold of p < .05 using the Threshold-Free Cluster Enhancement method proposed by Smith and Nichols (41). This cluster-level inference was also used for the within-group maps for each experimental condition.\n\nSubjects:\nTwelve medication-naïve, right-handed boys aged 10 to 15 years (mean age = 13, SD = 1) who met clinical diagnostic criteria for the combined (inattentive/hyperactive) subtype of ADHD (DSM-IV) were recruited through clinics. Clinical diagnosis of ADHD was established through interviews with an experienced child psychiatrist (A-MM) using the standardized Maudsley Diagnostic Interview to check for presence or absence of diagnostic criteria for any mental disorder as set out by DSM-IV (30). Exclusion criteria were lifetime comorbidity with any other psychiatric disorder, except for conduct/oppositional defiant disorder (present in one patient), as well as learning disability and specific reading disorder, neurological abnormalities, epilepsy, drug or substance abuse, and previous exposure to stimulant medication. Patients with ADHD also had to score above cutoff for hyperactive/inattentive symptoms on the Strengths and Difficulties Questionnaire for Parents (SDQ) (31). Patients were scanned twice, in a randomized, counterbalanced fashion, 1 week apart, 1 hour after either .3 mg/kg of MPH administration or placebo (vitamin C, 100 mg).\nThirteen male right-handed adolescent boys in the age range of 11 to 16 years (mean age = 13, SD = 1) were recruited through advertisements in the same geographic areas of South London to ensure similar socioeconomic status and were scanned once. They scored below cutoff for behavioral problems in the SDQ and had no history of psychiatric disorder.\nAll participants were above the fifth percentile on the Raven progressive matrices performance IQ (32) (IQ mean estimate controls = 100, SD = 14; ADHD = 91, SD = 9) and paid £30 for participation. Parental and child informed consent/assent and approval from the local ethical committee was obtained.\nUnivariate analyses of variance (ANOVAs) showed no group differences between boys with and without ADHD for age [F(1,25) = 2, p = .2] but did for IQ [F(1,25) = 8, p < .009]. IQ is associated with ADHD in the general population (33,34). We purposely did not match groups for IQ because matching ADHD and control groups for IQ would have created unrepresentative groups and therefore be misguided (35). Furthermore, IQ was significantly negatively correlated with the SDQ scores for inattention and hyperactivity (r = −.5, p < .001). We did not covary for IQ because when groups are not randomly selected, covarying for a variable that differs between groups violates the standard assumptions for analysis of covariance. When the covariate is intrinsic to the condition, it becomes meaningless to “adjust” group effects for differences in the covariate because it would alter the group effect in potentially problematic ways, leading to spurious results (35,36).\n\nfMRI Paradigm: Stop Task:\nThe rapid, mixed-trial, event-related fMRI design was practiced by subjects once before scanning. The visual tracking stop task requires withholding of a motor response to a go stimulus when it is followed unpredictably by a stop signal (8,37,38). The basic task is a choice reaction time task (left and right pointing arrows: go signals) with a mean intertrial-interval of 1.8 sec (156 go trials). In 20% of trials, pseudo-randomly interspersed, the go signals are followed (about 250 ms later) by arrows pointing upwards (stop signals), and subjects have to inhibit their motor responses (40 stop trials). A tracking algorithm changes the time interval between go-signal and stop-signal onsets according to each subject's inhibitory performance to ensure that the task is equally challenging for each individual and to provide 50% successful and 50% unsuccessful inhibition trials at every moment of the task.\n\nfMRI Image Acquisition:\nGradient-echo echoplanar magnetic resonance imaging data were acquired on a GE Signa 1.5-T Horizon LX System (General Electric, Milwaukee, Wisconsin) at the Maudsley Hospital, London. A quadrature birdcage head coil was used for radio-frequency transmission and reception. During the 6-min run of the stop task, in each of 16 noncontiguous planes parallel to the anterior–posterior commissural, 196 T2*-weighted magnetic resonance images depicting blood oxygen–level dependent (BOLD) contrast covering the whole brain were acquired with echo time = 40 msec, repetition time = 1.8 sec, flip angle = 90°, in-plane resolution = 3.1 mm, slice thickness = 7 mm, slice skip = .7 mm, providing complete brain coverage.\n\nfMRI Image Analysis:\nAt the individual subject level, a standard general linear modeling approach was used to obtain estimates of the response size (beta) to each of the two stop task conditions (successful and unsuccessful stop trials) against an implicit baseline (go trials). Following transformation of the fMRI data for each individual into standard space and smoothing with a three-dimensional 7-mm full width at half maximum Gaussian filter, the experimental model was convolved for each condition with gamma variate functions having peak responses at 4 and 8 sec following stimulus onset to accommodate variability in BOLD response timing. By fitting these convolved model components to the time series at each voxel, beta estimates were obtained for each effect of interest. The standard errors of these beta estimates were computed nonparametrically using a bootstrap procedure designed to operate on time series data, containing serial dependencies, with repeated deterministic (experimentally determined) effects. This method is outlined in detail in a previous work (39). Two hundred bootstraps at each voxel were used to estimate parameter standard errors. Using the combined parameter estimates over all conditions, the mean fitted time series was also computed and, from the combined bootstrap parameter estimates for each bootstrap, the 95% confidence limits on the fitted time series was computed.\nThe second-level analysis proceeded by computing either the group differences (patients and controls) or the drug condition differences (placebo, MPH) within patients at each voxel and the standard error of this difference (using the bootstrap estimates derived earlier). The significance of these differences was then tested in three ways: 1) a simple parametric random effects (paired t) test, using only the group difference/placebo-MPH effect size differences; 2) a permutation test of the same random effects t statistic in which the null distribution was estimated by randomly swapping the signs of the differences (we used 40,000 permutations per voxel to obtain a confidence limit of .0007–.0013 for p value of .001); and 3) a mixed-effects test using both the effect size differences and their subject-level standard errors to accommodate first (subject) level heteroscedasticity (40). This was also conducted using 40,000 permutations per voxel.\nIn addition to voxelwise maps, cluster-level inference on the contrast (beta) values was performed at a family-wise error corrected threshold of p < .05 using the Threshold-Free Cluster Enhancement method proposed by Smith and Nichols (41). This cluster-level inference was also used for the within-group maps for each experimental condition.\n\nResults:\n Performance The probability of inhibition was about 50% in all subjects with no significant group differences, showing that the task algorithm worked [F(1,38) = 1; p < .3; Table 1].\nA multivariate ANOVA between control subjects and ADHD patients under either drug condition showed a trend for a significant group effect [F(8,62) = 2, p < .09] due to a significant univariate group effect in the standard deviation to go trials [F(2,34) = 5, p < .02], which were higher in patients under either medication condition compared with control subjects (p < .05). Post hoc tests furthermore revealed a trend for MPH compared with placebo to slow down reaction times within ADHD patients to both go (p < .06) and post-error go trials (both p < .07) (Table 1).\nThe probability of inhibition was about 50% in all subjects with no significant group differences, showing that the task algorithm worked [F(1,38) = 1; p < .3; Table 1].\nA multivariate ANOVA between control subjects and ADHD patients under either drug condition showed a trend for a significant group effect [F(8,62) = 2, p < .09] due to a significant univariate group effect in the standard deviation to go trials [F(2,34) = 5, p < .02], which were higher in patients under either medication condition compared with control subjects (p < .05). Post hoc tests furthermore revealed a trend for MPH compared with placebo to slow down reaction times within ADHD patients to both go (p < .06) and post-error go trials (both p < .07) (Table 1).\n\nPerformance:\nThe probability of inhibition was about 50% in all subjects with no significant group differences, showing that the task algorithm worked [F(1,38) = 1; p < .3; Table 1].\nA multivariate ANOVA between control subjects and ADHD patients under either drug condition showed a trend for a significant group effect [F(8,62) = 2, p < .09] due to a significant univariate group effect in the standard deviation to go trials [F(2,34) = 5, p < .02], which were higher in patients under either medication condition compared with control subjects (p < .05). Post hoc tests furthermore revealed a trend for MPH compared with placebo to slow down reaction times within ADHD patients to both go (p < .06) and post-error go trials (both p < .07) (Table 1).\n\nBrain Activation:\n Motion Multivariate ANOVA showed no significant group differences between control subjects and ADHD patients under either drug condition in mean or maximum rotation or translation parameters in the x, y, or z dimensions [F(2,38) = .9, p < .5].\nMultivariate ANOVA showed no significant group differences between control subjects and ADHD patients under either drug condition in mean or maximum rotation or translation parameters in the x, y, or z dimensions [F(2,38) = .9, p < .5].\n Within-Group Brain Activations Failed Stop–Go Contrast Control subjects activated relatively large clusters in left and right inferior frontal cortex (IFC)/anterior insula, medial frontal/anterior cingulate cortex (MFC/ACC), precentral, inferior parietal, middle and superior temporal areas, posterior thalamus and caudate, parahippocampal gyri, precuneus/posterior cingulate, occipital, and cerebellar areas.\nActivation in ADHD patients under placebo was in MFC and superior temporal cortex reaching into anterior and posterior insula, caudate, and inferior parietal and occipital cortex.\nActivation in ADHD patients under MPH was in a large cluster of ACC and MFC, in left and right IFC/anterior insula, left premotor cortex, right basal ganglia, bilateral middle and superior temporal, inferior parietal and occipital areas, hippocampal gyri, posterior cingulate, precuneus, and cerebellum (Figure S1 in Supplement 1).\nControl subjects activated relatively large clusters in left and right inferior frontal cortex (IFC)/anterior insula, medial frontal/anterior cingulate cortex (MFC/ACC), precentral, inferior parietal, middle and superior temporal areas, posterior thalamus and caudate, parahippocampal gyri, precuneus/posterior cingulate, occipital, and cerebellar areas.\nActivation in ADHD patients under placebo was in MFC and superior temporal cortex reaching into anterior and posterior insula, caudate, and inferior parietal and occipital cortex.\nActivation in ADHD patients under MPH was in a large cluster of ACC and MFC, in left and right IFC/anterior insula, left premotor cortex, right basal ganglia, bilateral middle and superior temporal, inferior parietal and occipital areas, hippocampal gyri, posterior cingulate, precuneus, and cerebellum (Figure S1 in Supplement 1).\n Successful Stop–Go Contrast Activation in healthy control boys was in a large cluster comprising left and right orbital and IFC, dorsolateral and MFC, insula, basal ganglia, hippocampus, posterior thalamic regions, pre- and postcentral gyri, inferior and superior parietal, temporal and occipital cortices, precuneus, and posterior cingulate.\nActivation in ADHD patients under placebo was in small clusters in right MFC, supplementary motor area (SMA), right superior temporal, postcentral, left inferior and superior parietal, and occipital cortices.\nActivation in ADHD patients under the MPH condition was in superior and MFC/ACC, right globus pallidus and putamen, right superior temporal and superior and inferior parietal cortex, posterior insula, and left cerebellum (Figure S1 in Supplement 1).\nActivation in healthy control boys was in a large cluster comprising left and right orbital and IFC, dorsolateral and MFC, insula, basal ganglia, hippocampus, posterior thalamic regions, pre- and postcentral gyri, inferior and superior parietal, temporal and occipital cortices, precuneus, and posterior cingulate.\nActivation in ADHD patients under placebo was in small clusters in right MFC, supplementary motor area (SMA), right superior temporal, postcentral, left inferior and superior parietal, and occipital cortices.\nActivation in ADHD patients under the MPH condition was in superior and MFC/ACC, right globus pallidus and putamen, right superior temporal and superior and inferior parietal cortex, posterior insula, and left cerebellum (Figure S1 in Supplement 1).\n Failed Stop–Go Contrast Control subjects activated relatively large clusters in left and right inferior frontal cortex (IFC)/anterior insula, medial frontal/anterior cingulate cortex (MFC/ACC), precentral, inferior parietal, middle and superior temporal areas, posterior thalamus and caudate, parahippocampal gyri, precuneus/posterior cingulate, occipital, and cerebellar areas.\nActivation in ADHD patients under placebo was in MFC and superior temporal cortex reaching into anterior and posterior insula, caudate, and inferior parietal and occipital cortex.\nActivation in ADHD patients under MPH was in a large cluster of ACC and MFC, in left and right IFC/anterior insula, left premotor cortex, right basal ganglia, bilateral middle and superior temporal, inferior parietal and occipital areas, hippocampal gyri, posterior cingulate, precuneus, and cerebellum (Figure S1 in Supplement 1).\nControl subjects activated relatively large clusters in left and right inferior frontal cortex (IFC)/anterior insula, medial frontal/anterior cingulate cortex (MFC/ACC), precentral, inferior parietal, middle and superior temporal areas, posterior thalamus and caudate, parahippocampal gyri, precuneus/posterior cingulate, occipital, and cerebellar areas.\nActivation in ADHD patients under placebo was in MFC and superior temporal cortex reaching into anterior and posterior insula, caudate, and inferior parietal and occipital cortex.\nActivation in ADHD patients under MPH was in a large cluster of ACC and MFC, in left and right IFC/anterior insula, left premotor cortex, right basal ganglia, bilateral middle and superior temporal, inferior parietal and occipital areas, hippocampal gyri, posterior cingulate, precuneus, and cerebellum (Figure S1 in Supplement 1).\n Successful Stop–Go Contrast Activation in healthy control boys was in a large cluster comprising left and right orbital and IFC, dorsolateral and MFC, insula, basal ganglia, hippocampus, posterior thalamic regions, pre- and postcentral gyri, inferior and superior parietal, temporal and occipital cortices, precuneus, and posterior cingulate.\nActivation in ADHD patients under placebo was in small clusters in right MFC, supplementary motor area (SMA), right superior temporal, postcentral, left inferior and superior parietal, and occipital cortices.\nActivation in ADHD patients under the MPH condition was in superior and MFC/ACC, right globus pallidus and putamen, right superior temporal and superior and inferior parietal cortex, posterior insula, and left cerebellum (Figure S1 in Supplement 1).\nActivation in healthy control boys was in a large cluster comprising left and right orbital and IFC, dorsolateral and MFC, insula, basal ganglia, hippocampus, posterior thalamic regions, pre- and postcentral gyri, inferior and superior parietal, temporal and occipital cortices, precuneus, and posterior cingulate.\nActivation in ADHD patients under placebo was in small clusters in right MFC, supplementary motor area (SMA), right superior temporal, postcentral, left inferior and superior parietal, and occipital cortices.\nActivation in ADHD patients under the MPH condition was in superior and MFC/ACC, right globus pallidus and putamen, right superior temporal and superior and inferior parietal cortex, posterior insula, and left cerebellum (Figure S1 in Supplement 1).\n ANOVA Within-Patient Comparisons in Brain Activation Between the Placebo and the MPH Conditions Failed Stop–Go Contrast MPH contrasted with placebo elicited enhanced activation in left IFC, reaching into insula and putamen; in right IFC reaching into insula, putamen, and caudate; in left medial frontal lobe; and in left inferior parietal, precuneus, and occipital regions (Figure 1, Table 2). The placebo condition elicited no enhanced activation over MPH.\nTo investigate whether brain regions that differed with MPH were associated with task performance, statistical measures of the BOLD response were extracted for each subject in each ANOVA cluster and then correlated with performance variables. There was a significant positive correlation in patients between post-error reaction times and left and right IFC activation (r = .7, p < .02) and between go reaction times and right IFC activation (r = .6, p < .05) and a negative correlation between response variability and right inferior parietal activation (r = −.7, p < .02).\nMPH contrasted with placebo elicited enhanced activation in left IFC, reaching into insula and putamen; in right IFC reaching into insula, putamen, and caudate; in left medial frontal lobe; and in left inferior parietal, precuneus, and occipital regions (Figure 1, Table 2). The placebo condition elicited no enhanced activation over MPH.\nTo investigate whether brain regions that differed with MPH were associated with task performance, statistical measures of the BOLD response were extracted for each subject in each ANOVA cluster and then correlated with performance variables. There was a significant positive correlation in patients between post-error reaction times and left and right IFC activation (r = .7, p < .02) and between go reaction times and right IFC activation (r = .6, p < .05) and a negative correlation between response variability and right inferior parietal activation (r = −.7, p < .02).\n Successful Stop–Go Contrast No significant activation differences were observed between medication conditions.\nNo significant activation differences were observed between medication conditions.\n Failed Stop–Go Contrast MPH contrasted with placebo elicited enhanced activation in left IFC, reaching into insula and putamen; in right IFC reaching into insula, putamen, and caudate; in left medial frontal lobe; and in left inferior parietal, precuneus, and occipital regions (Figure 1, Table 2). The placebo condition elicited no enhanced activation over MPH.\nTo investigate whether brain regions that differed with MPH were associated with task performance, statistical measures of the BOLD response were extracted for each subject in each ANOVA cluster and then correlated with performance variables. There was a significant positive correlation in patients between post-error reaction times and left and right IFC activation (r = .7, p < .02) and between go reaction times and right IFC activation (r = .6, p < .05) and a negative correlation between response variability and right inferior parietal activation (r = −.7, p < .02).\nMPH contrasted with placebo elicited enhanced activation in left IFC, reaching into insula and putamen; in right IFC reaching into insula, putamen, and caudate; in left medial frontal lobe; and in left inferior parietal, precuneus, and occipital regions (Figure 1, Table 2). The placebo condition elicited no enhanced activation over MPH.\nTo investigate whether brain regions that differed with MPH were associated with task performance, statistical measures of the BOLD response were extracted for each subject in each ANOVA cluster and then correlated with performance variables. There was a significant positive correlation in patients between post-error reaction times and left and right IFC activation (r = .7, p < .02) and between go reaction times and right IFC activation (r = .6, p < .05) and a negative correlation between response variability and right inferior parietal activation (r = −.7, p < .02).\n Successful Stop–Go Contrast No significant activation differences were observed between medication conditions.\nNo significant activation differences were observed between medication conditions.\n ANOVA Between-Group Comparisons in Brain Activation Between Control Subjects and Boys with ADHD Under Either the Placebo or the MPH Conditions Failed Stop–Go Contrast Relative to control subjects, ADHD patients under the placebo condition showed underactivation in left IFC and dorsomedial prefrontal cortices (dMFC) (including pre-SMA), right premotor, superior and inferior parietal cortices, posterior cingulate/precuneus, posterior thalamus, and bilateral inferior temporo-occipital areas (Figure 2A, Table 3).\nUnder the MPH condition, ADHD patients did not differ from controls in any of these regions.\nPost-error slowing in ADHD patients under placebo, but not in control subjects, was significantly positively correlated with activation in left IFC, premotor, dMFC, and thalamic underactivation clusters (r > .6 for all clusters, p < .05) as well as with superior parietal, occipital, and cerebellar activation (r < .4, p < .05). Standard deviation of reaction times was correlated with dMFC activation in controls (r = .6, p < .02).\nRelative to control subjects, ADHD patients under the placebo condition showed underactivation in left IFC and dorsomedial prefrontal cortices (dMFC) (including pre-SMA), right premotor, superior and inferior parietal cortices, posterior cingulate/precuneus, posterior thalamus, and bilateral inferior temporo-occipital areas (Figure 2A, Table 3).\nUnder the MPH condition, ADHD patients did not differ from controls in any of these regions.\nPost-error slowing in ADHD patients under placebo, but not in control subjects, was significantly positively correlated with activation in left IFC, premotor, dMFC, and thalamic underactivation clusters (r > .6 for all clusters, p < .05) as well as with superior parietal, occipital, and cerebellar activation (r < .4, p < .05). Standard deviation of reaction times was correlated with dMFC activation in controls (r = .6, p < .02).\n Successful Stop–Go Contrast Relative to control subjects, ADHD patients under the placebo condition showed underactivation in a right hemispheric network of medial temporal and inferior parietal lobes, precuneus/posterior cingulate and cerebellum (Figure 2B, Table 3). To test our hypothesis of IFC underactivation, we reanalyzed the data at a more lenient p value of p < .002 for voxelwise comparison. This elicited additional underactivation in right IFC, left and right subthalamic nuclei, and the pre-SMA (Table 3 and Figure S2 in Supplement 1).\nUnder the MPH condition, ADHD patients did not differ from control subjects in any of these regions.\nActivation in posterior cingulate and lingual gyrus correlated significantly positively with post-error go reaction times in ADHD boys under placebo (r > .6, p < .3).\nAll group difference findings for both contrasts remained essentially unchanged when IQ was covaried.\nRelative to control subjects, ADHD patients under the placebo condition showed underactivation in a right hemispheric network of medial temporal and inferior parietal lobes, precuneus/posterior cingulate and cerebellum (Figure 2B, Table 3). To test our hypothesis of IFC underactivation, we reanalyzed the data at a more lenient p value of p < .002 for voxelwise comparison. This elicited additional underactivation in right IFC, left and right subthalamic nuclei, and the pre-SMA (Table 3 and Figure S2 in Supplement 1).\nUnder the MPH condition, ADHD patients did not differ from control subjects in any of these regions.\nActivation in posterior cingulate and lingual gyrus correlated significantly positively with post-error go reaction times in ADHD boys under placebo (r > .6, p < .3).\nAll group difference findings for both contrasts remained essentially unchanged when IQ was covaried.\n Failed Stop–Go Contrast Relative to control subjects, ADHD patients under the placebo condition showed underactivation in left IFC and dorsomedial prefrontal cortices (dMFC) (including pre-SMA), right premotor, superior and inferior parietal cortices, posterior cingulate/precuneus, posterior thalamus, and bilateral inferior temporo-occipital areas (Figure 2A, Table 3).\nUnder the MPH condition, ADHD patients did not differ from controls in any of these regions.\nPost-error slowing in ADHD patients under placebo, but not in control subjects, was significantly positively correlated with activation in left IFC, premotor, dMFC, and thalamic underactivation clusters (r > .6 for all clusters, p < .05) as well as with superior parietal, occipital, and cerebellar activation (r < .4, p < .05). Standard deviation of reaction times was correlated with dMFC activation in controls (r = .6, p < .02).\nRelative to control subjects, ADHD patients under the placebo condition showed underactivation in left IFC and dorsomedial prefrontal cortices (dMFC) (including pre-SMA), right premotor, superior and inferior parietal cortices, posterior cingulate/precuneus, posterior thalamus, and bilateral inferior temporo-occipital areas (Figure 2A, Table 3).\nUnder the MPH condition, ADHD patients did not differ from controls in any of these regions.\nPost-error slowing in ADHD patients under placebo, but not in control subjects, was significantly positively correlated with activation in left IFC, premotor, dMFC, and thalamic underactivation clusters (r > .6 for all clusters, p < .05) as well as with superior parietal, occipital, and cerebellar activation (r < .4, p < .05). Standard deviation of reaction times was correlated with dMFC activation in controls (r = .6, p < .02).\n Successful Stop–Go Contrast Relative to control subjects, ADHD patients under the placebo condition showed underactivation in a right hemispheric network of medial temporal and inferior parietal lobes, precuneus/posterior cingulate and cerebellum (Figure 2B, Table 3). To test our hypothesis of IFC underactivation, we reanalyzed the data at a more lenient p value of p < .002 for voxelwise comparison. This elicited additional underactivation in right IFC, left and right subthalamic nuclei, and the pre-SMA (Table 3 and Figure S2 in Supplement 1).\nUnder the MPH condition, ADHD patients did not differ from control subjects in any of these regions.\nActivation in posterior cingulate and lingual gyrus correlated significantly positively with post-error go reaction times in ADHD boys under placebo (r > .6, p < .3).\nAll group difference findings for both contrasts remained essentially unchanged when IQ was covaried.\nRelative to control subjects, ADHD patients under the placebo condition showed underactivation in a right hemispheric network of medial temporal and inferior parietal lobes, precuneus/posterior cingulate and cerebellum (Figure 2B, Table 3). To test our hypothesis of IFC underactivation, we reanalyzed the data at a more lenient p value of p < .002 for voxelwise comparison. This elicited additional underactivation in right IFC, left and right subthalamic nuclei, and the pre-SMA (Table 3 and Figure S2 in Supplement 1).\nUnder the MPH condition, ADHD patients did not differ from control subjects in any of these regions.\nActivation in posterior cingulate and lingual gyrus correlated significantly positively with post-error go reaction times in ADHD boys under placebo (r > .6, p < .3).\nAll group difference findings for both contrasts remained essentially unchanged when IQ was covaried.\n Conjunction Analysis Between Within-Group and Between-Group ANOVAs To test whether brain regions that were upregulated with MPH relative to placebo within patients overlapped with brain regions that were reduced in patients under placebo relative to controls and then normalized with MPH, we performed a conjunction analysis by determining the voxels where the within-group ANOVA (MPH > placebo in ADHD) and the between-group ANOVA (control subjects > ADHD placebo) were both significant (42). Three clusters emerged, in left IFC (Talairach coordinates: −43, 7, 4), right SMA (Talairach coordinates: 7, 4, 59) and right inferior parietal lobe (Talairach coordinates: 32, −63, 42). Overlapping clusters are also indicated in bold in Table 3 and shown in Figure 3.\nTo test whether brain regions that were upregulated with MPH relative to placebo within patients overlapped with brain regions that were reduced in patients under placebo relative to controls and then normalized with MPH, we performed a conjunction analysis by determining the voxels where the within-group ANOVA (MPH > placebo in ADHD) and the between-group ANOVA (control subjects > ADHD placebo) were both significant (42). Three clusters emerged, in left IFC (Talairach coordinates: −43, 7, 4), right SMA (Talairach coordinates: 7, 4, 59) and right inferior parietal lobe (Talairach coordinates: 32, −63, 42). Overlapping clusters are also indicated in bold in Table 3 and shown in Figure 3.\n\nMotion:\nMultivariate ANOVA showed no significant group differences between control subjects and ADHD patients under either drug condition in mean or maximum rotation or translation parameters in the x, y, or z dimensions [F(2,38) = .9, p < .5].\n\nWithin-Group Brain Activations:\n Failed Stop–Go Contrast Control subjects activated relatively large clusters in left and right inferior frontal cortex (IFC)/anterior insula, medial frontal/anterior cingulate cortex (MFC/ACC), precentral, inferior parietal, middle and superior temporal areas, posterior thalamus and caudate, parahippocampal gyri, precuneus/posterior cingulate, occipital, and cerebellar areas.\nActivation in ADHD patients under placebo was in MFC and superior temporal cortex reaching into anterior and posterior insula, caudate, and inferior parietal and occipital cortex.\nActivation in ADHD patients under MPH was in a large cluster of ACC and MFC, in left and right IFC/anterior insula, left premotor cortex, right basal ganglia, bilateral middle and superior temporal, inferior parietal and occipital areas, hippocampal gyri, posterior cingulate, precuneus, and cerebellum (Figure S1 in Supplement 1).\nControl subjects activated relatively large clusters in left and right inferior frontal cortex (IFC)/anterior insula, medial frontal/anterior cingulate cortex (MFC/ACC), precentral, inferior parietal, middle and superior temporal areas, posterior thalamus and caudate, parahippocampal gyri, precuneus/posterior cingulate, occipital, and cerebellar areas.\nActivation in ADHD patients under placebo was in MFC and superior temporal cortex reaching into anterior and posterior insula, caudate, and inferior parietal and occipital cortex.\nActivation in ADHD patients under MPH was in a large cluster of ACC and MFC, in left and right IFC/anterior insula, left premotor cortex, right basal ganglia, bilateral middle and superior temporal, inferior parietal and occipital areas, hippocampal gyri, posterior cingulate, precuneus, and cerebellum (Figure S1 in Supplement 1).\n Successful Stop–Go Contrast Activation in healthy control boys was in a large cluster comprising left and right orbital and IFC, dorsolateral and MFC, insula, basal ganglia, hippocampus, posterior thalamic regions, pre- and postcentral gyri, inferior and superior parietal, temporal and occipital cortices, precuneus, and posterior cingulate.\nActivation in ADHD patients under placebo was in small clusters in right MFC, supplementary motor area (SMA), right superior temporal, postcentral, left inferior and superior parietal, and occipital cortices.\nActivation in ADHD patients under the MPH condition was in superior and MFC/ACC, right globus pallidus and putamen, right superior temporal and superior and inferior parietal cortex, posterior insula, and left cerebellum (Figure S1 in Supplement 1).\nActivation in healthy control boys was in a large cluster comprising left and right orbital and IFC, dorsolateral and MFC, insula, basal ganglia, hippocampus, posterior thalamic regions, pre- and postcentral gyri, inferior and superior parietal, temporal and occipital cortices, precuneus, and posterior cingulate.\nActivation in ADHD patients under placebo was in small clusters in right MFC, supplementary motor area (SMA), right superior temporal, postcentral, left inferior and superior parietal, and occipital cortices.\nActivation in ADHD patients under the MPH condition was in superior and MFC/ACC, right globus pallidus and putamen, right superior temporal and superior and inferior parietal cortex, posterior insula, and left cerebellum (Figure S1 in Supplement 1).\n\nFailed Stop–Go Contrast:\nControl subjects activated relatively large clusters in left and right inferior frontal cortex (IFC)/anterior insula, medial frontal/anterior cingulate cortex (MFC/ACC), precentral, inferior parietal, middle and superior temporal areas, posterior thalamus and caudate, parahippocampal gyri, precuneus/posterior cingulate, occipital, and cerebellar areas.\nActivation in ADHD patients under placebo was in MFC and superior temporal cortex reaching into anterior and posterior insula, caudate, and inferior parietal and occipital cortex.\nActivation in ADHD patients under MPH was in a large cluster of ACC and MFC, in left and right IFC/anterior insula, left premotor cortex, right basal ganglia, bilateral middle and superior temporal, inferior parietal and occipital areas, hippocampal gyri, posterior cingulate, precuneus, and cerebellum (Figure S1 in Supplement 1).\n\nSuccessful Stop–Go Contrast:\nActivation in healthy control boys was in a large cluster comprising left and right orbital and IFC, dorsolateral and MFC, insula, basal ganglia, hippocampus, posterior thalamic regions, pre- and postcentral gyri, inferior and superior parietal, temporal and occipital cortices, precuneus, and posterior cingulate.\nActivation in ADHD patients under placebo was in small clusters in right MFC, supplementary motor area (SMA), right superior temporal, postcentral, left inferior and superior parietal, and occipital cortices.\nActivation in ADHD patients under the MPH condition was in superior and MFC/ACC, right globus pallidus and putamen, right superior temporal and superior and inferior parietal cortex, posterior insula, and left cerebellum (Figure S1 in Supplement 1).\n\nANOVA Within-Patient Comparisons in Brain Activation Between the Placebo and the MPH Conditions:\n Failed Stop–Go Contrast MPH contrasted with placebo elicited enhanced activation in left IFC, reaching into insula and putamen; in right IFC reaching into insula, putamen, and caudate; in left medial frontal lobe; and in left inferior parietal, precuneus, and occipital regions (Figure 1, Table 2). The placebo condition elicited no enhanced activation over MPH.\nTo investigate whether brain regions that differed with MPH were associated with task performance, statistical measures of the BOLD response were extracted for each subject in each ANOVA cluster and then correlated with performance variables. There was a significant positive correlation in patients between post-error reaction times and left and right IFC activation (r = .7, p < .02) and between go reaction times and right IFC activation (r = .6, p < .05) and a negative correlation between response variability and right inferior parietal activation (r = −.7, p < .02).\nMPH contrasted with placebo elicited enhanced activation in left IFC, reaching into insula and putamen; in right IFC reaching into insula, putamen, and caudate; in left medial frontal lobe; and in left inferior parietal, precuneus, and occipital regions (Figure 1, Table 2). The placebo condition elicited no enhanced activation over MPH.\nTo investigate whether brain regions that differed with MPH were associated with task performance, statistical measures of the BOLD response were extracted for each subject in each ANOVA cluster and then correlated with performance variables. There was a significant positive correlation in patients between post-error reaction times and left and right IFC activation (r = .7, p < .02) and between go reaction times and right IFC activation (r = .6, p < .05) and a negative correlation between response variability and right inferior parietal activation (r = −.7, p < .02).\n Successful Stop–Go Contrast No significant activation differences were observed between medication conditions.\nNo significant activation differences were observed between medication conditions.\n\nFailed Stop–Go Contrast:\nMPH contrasted with placebo elicited enhanced activation in left IFC, reaching into insula and putamen; in right IFC reaching into insula, putamen, and caudate; in left medial frontal lobe; and in left inferior parietal, precuneus, and occipital regions (Figure 1, Table 2). The placebo condition elicited no enhanced activation over MPH.\nTo investigate whether brain regions that differed with MPH were associated with task performance, statistical measures of the BOLD response were extracted for each subject in each ANOVA cluster and then correlated with performance variables. There was a significant positive correlation in patients between post-error reaction times and left and right IFC activation (r = .7, p < .02) and between go reaction times and right IFC activation (r = .6, p < .05) and a negative correlation between response variability and right inferior parietal activation (r = −.7, p < .02).\n\nSuccessful Stop–Go Contrast:\nNo significant activation differences were observed between medication conditions.\n\nANOVA Between-Group Comparisons in Brain Activation Between Control Subjects and Boys with ADHD Under Either the Placebo or the MPH Conditions:\n Failed Stop–Go Contrast Relative to control subjects, ADHD patients under the placebo condition showed underactivation in left IFC and dorsomedial prefrontal cortices (dMFC) (including pre-SMA), right premotor, superior and inferior parietal cortices, posterior cingulate/precuneus, posterior thalamus, and bilateral inferior temporo-occipital areas (Figure 2A, Table 3).\nUnder the MPH condition, ADHD patients did not differ from controls in any of these regions.\nPost-error slowing in ADHD patients under placebo, but not in control subjects, was significantly positively correlated with activation in left IFC, premotor, dMFC, and thalamic underactivation clusters (r > .6 for all clusters, p < .05) as well as with superior parietal, occipital, and cerebellar activation (r < .4, p < .05). Standard deviation of reaction times was correlated with dMFC activation in controls (r = .6, p < .02).\nRelative to control subjects, ADHD patients under the placebo condition showed underactivation in left IFC and dorsomedial prefrontal cortices (dMFC) (including pre-SMA), right premotor, superior and inferior parietal cortices, posterior cingulate/precuneus, posterior thalamus, and bilateral inferior temporo-occipital areas (Figure 2A, Table 3).\nUnder the MPH condition, ADHD patients did not differ from controls in any of these regions.\nPost-error slowing in ADHD patients under placebo, but not in control subjects, was significantly positively correlated with activation in left IFC, premotor, dMFC, and thalamic underactivation clusters (r > .6 for all clusters, p < .05) as well as with superior parietal, occipital, and cerebellar activation (r < .4, p < .05). Standard deviation of reaction times was correlated with dMFC activation in controls (r = .6, p < .02).\n Successful Stop–Go Contrast Relative to control subjects, ADHD patients under the placebo condition showed underactivation in a right hemispheric network of medial temporal and inferior parietal lobes, precuneus/posterior cingulate and cerebellum (Figure 2B, Table 3). To test our hypothesis of IFC underactivation, we reanalyzed the data at a more lenient p value of p < .002 for voxelwise comparison. This elicited additional underactivation in right IFC, left and right subthalamic nuclei, and the pre-SMA (Table 3 and Figure S2 in Supplement 1).\nUnder the MPH condition, ADHD patients did not differ from control subjects in any of these regions.\nActivation in posterior cingulate and lingual gyrus correlated significantly positively with post-error go reaction times in ADHD boys under placebo (r > .6, p < .3).\nAll group difference findings for both contrasts remained essentially unchanged when IQ was covaried.\nRelative to control subjects, ADHD patients under the placebo condition showed underactivation in a right hemispheric network of medial temporal and inferior parietal lobes, precuneus/posterior cingulate and cerebellum (Figure 2B, Table 3). To test our hypothesis of IFC underactivation, we reanalyzed the data at a more lenient p value of p < .002 for voxelwise comparison. This elicited additional underactivation in right IFC, left and right subthalamic nuclei, and the pre-SMA (Table 3 and Figure S2 in Supplement 1).\nUnder the MPH condition, ADHD patients did not differ from control subjects in any of these regions.\nActivation in posterior cingulate and lingual gyrus correlated significantly positively with post-error go reaction times in ADHD boys under placebo (r > .6, p < .3).\nAll group difference findings for both contrasts remained essentially unchanged when IQ was covaried.\n\nFailed Stop–Go Contrast:\nRelative to control subjects, ADHD patients under the placebo condition showed underactivation in left IFC and dorsomedial prefrontal cortices (dMFC) (including pre-SMA), right premotor, superior and inferior parietal cortices, posterior cingulate/precuneus, posterior thalamus, and bilateral inferior temporo-occipital areas (Figure 2A, Table 3).\nUnder the MPH condition, ADHD patients did not differ from controls in any of these regions.\nPost-error slowing in ADHD patients under placebo, but not in control subjects, was significantly positively correlated with activation in left IFC, premotor, dMFC, and thalamic underactivation clusters (r > .6 for all clusters, p < .05) as well as with superior parietal, occipital, and cerebellar activation (r < .4, p < .05). Standard deviation of reaction times was correlated with dMFC activation in controls (r = .6, p < .02).\n\nSuccessful Stop–Go Contrast:\nRelative to control subjects, ADHD patients under the placebo condition showed underactivation in a right hemispheric network of medial temporal and inferior parietal lobes, precuneus/posterior cingulate and cerebellum (Figure 2B, Table 3). To test our hypothesis of IFC underactivation, we reanalyzed the data at a more lenient p value of p < .002 for voxelwise comparison. This elicited additional underactivation in right IFC, left and right subthalamic nuclei, and the pre-SMA (Table 3 and Figure S2 in Supplement 1).\nUnder the MPH condition, ADHD patients did not differ from control subjects in any of these regions.\nActivation in posterior cingulate and lingual gyrus correlated significantly positively with post-error go reaction times in ADHD boys under placebo (r > .6, p < .3).\nAll group difference findings for both contrasts remained essentially unchanged when IQ was covaried.\n\nConjunction Analysis Between Within-Group and Between-Group ANOVAs:\nTo test whether brain regions that were upregulated with MPH relative to placebo within patients overlapped with brain regions that were reduced in patients under placebo relative to controls and then normalized with MPH, we performed a conjunction analysis by determining the voxels where the within-group ANOVA (MPH > placebo in ADHD) and the between-group ANOVA (control subjects > ADHD placebo) were both significant (42). Three clusters emerged, in left IFC (Talairach coordinates: −43, 7, 4), right SMA (Talairach coordinates: 7, 4, 59) and right inferior parietal lobe (Talairach coordinates: 32, −63, 42). Overlapping clusters are also indicated in bold in Table 3 and shown in Figure 3.\n\nDiscussion:\nDuring error trials, ADHD boys under placebo compared with healthy control subjects showed significant underactivation in a typical error processing and performance monitoring network comprising dMFC, left IFC, thalamus, posterior cingulate/precuneus, and inferior temporoparietal regions. Among patients, MPH compared with placebo significantly upregulated activation in overlapping medial frontal, IFC, and parietal regions as well as the lenticular nucleus. Under MPH, brain activation differences between control subjects and ADHD patients were no longer observed. Reduced fronto-thalamo-parietal activation that was normalized with MPH was, furthermore, negatively associated with faster post-error reaction times in patients, which were trendwise slowed with MPH.\nDuring successful stop trials, ADHD boys showed underactivation in a right hemispheric network of medial temporal and inferior parietal brain regions and, at a more lenient threshold, in small clusters of bilateral IFC, thalamus, and pre-SMA. Although within-patient comparison between MPH and placebo did not show significant activation differences, all underactivations in patients relative to control subjects under placebo were normalized with a single dose of MPH.\nThe dMFC, comprising Brodmann areas 8, 6, and 32, including pre-SMA and ACC, is a typical region of error processing and performance monitoring in adults (37,43–48) and children (29,38,49). We have previously found this region to be underactivated in children with ADHD during oddball (50) and switch tasks (4). Errors indicate violation of a reward prediction (i.e., positive performance) and have been linked to midbrain dopamine (51). Normalization with MPH of underfunctioning of this region in ADHD is in line with the notion that phasic dopamine response modulates error-related mesial frontal activation (52,53). These findings extend evidence for upregulation with acute and chronic doses of MPH in previously medicated patients with ADHD in a more rostral ACC location during tasks of cognitive control (22,28,54).\nActivation in dMFC during errors triggers additional activation in functionally interconnected left IFC, as well as striatal, premotor, and parietal components of the error monitoring system, leading to post-error performance adjustments (43–45,48). IFC underactivation is one of the most consistent findings in fMRI studies in patients with ADHD, with right IFC dysfunction typically observed during inhibitory performance (7,8,11,55), in line with its role in inhibition (37,56), and left IFC during stop errors (4,29) as well as during flexible, selective, or sustained attention (4,9,26,50,57), in line with its role for performance monitoring (44,45,48,49) and saliency processing (58,59). IFC dysfunction is furthermore a disorder-specific neurofunctional deficit compared with patients with conduct (6,50,57,60) and obsessive compulsive (4) disorders. MPH thus appears to modulate an important neurofunctional biomarker of ADHD. The more predominantly left-hemispheric upregulation effect during errors may suggest a stronger effect of MPH on performance monitoring than inhibitory function in ADHD. Left IFC upregulation has previously been observed in ADHD patients in the context of an attention-demanding time discrimination task after acute (27) and 6 weeks of MPH treatment during interference inhibition (54). Structural studies have shown more normal cortical thinning in left IFC in psychostimulant-medicated compared with unmedicated ADHD children (61). Together, this raises the speculation that MPH may have a lateralized upregulating effect on left IFC structure and function.\nPosterior thalamic regions have been associated both with motor response inhibition (62) and performance monitoring (48,63,64). The finding that MPH normalizes activation in this region is in line with speculation of this region's involvement in the modulation of the dopaminergic error signal (63,65,66).\nThe fact that lower dMFC, IFC, and thalamic activation in ADHD patients was associated with faster post-error slowing, both of which were enhanced by MPH, reinforces the role of this network for abnormal error monitoring in ADHD. Posterior cingulate and precuneus are connected with MFC and parietal areas and form part of the performance monitoring network (47,49,67,68), mediating visual spatial attention to saliency (69,70) and the integration of performance outcome with attentional modulation (48). The fact that these regions were underactivated during both inhibition and its failure is in line with a generic attention role of these areas. In line with this, we and others have previously observed underactivation in ADHD patients in these regions during inhibition errors (4,6,8,60), as well as during other salient stimuli such as oddball, novel or incongruent targets (10,26,50,71,72).\nNormalization with MPH of reduced activation in typical frontoparietal regions of saliency processing and performance monitoring is consistent with the dopamine-deficiency hypothesis of ADHD given that dopamine agonists enhance stimulus salience (73). It is also in line with our previous findings of upregulation with MPH of posterior cingulate/precuneus in the same group of medication naive boys with ADHD during a target detection task, resulting in improved attention (26), and during an attention demanding time discrimination task (27). To our knowledge, normalization of inferior parietal activation with MPH has only recently been observed in ADHD patients, in the context of sustained attention (26) and interference inhibition (54).\nDuring successful stop trials, MPH also normalized underactivation in the cerebellum, which, together with subthalamic nucleus, caudate, and IFG, forms a neurofunctional network of motor response inhibition (38). These findings extend previous evidence for cerebellum upregulation with MPH in ADHD patients during interference inhibition (54) and time estimation (27).\nWithin patients, MPH also enhanced activation of caudate and putamen. This is in line with previous fMRI findings of caudate upregulation in ADHD patients after acute and chronic doses of MPH during inhibition and attention tasks (22,24,54) and is likely associated with the known effect of MPH on striatal dopamine transporter blockage (14,15).\nThe findings of more pronounced normalization effects of MPH on abnormal performance monitoring than inhibition networks could suggest that MPH enhances generic attention and performance monitoring functions more than inhibitory capacity. This would be in line with the behavioral effect of MPH of modulating go and post-error reaction times, but not inhibition speed, which, furthermore were correlated with the reduced frontothalamic error-processing activation that was normalized with MPH. Relative to control subjects, patients only significantly differed in intrasubject response variability. Small subject numbers, a relatively older child group, and fMRI task design restrictions may have been responsible for minor behavior differences. The findings of brain dysfunctions in boys with ADHD and their normalization under the clinical dose of MPH despite minor performance differences and only trend-level improvements with MPH show that brain activation is more sensitive than performance to detect both abnormalities and pharmacologic effects. This is in line with previous findings of marked brain dysfunctions in ADHD adolescents despite no stop task impairment (7,8,50) and higher sensitivity of brain activation than behavior to show pharmacologic effects of MPH in ADHD (24,26–28,54,74).\nMPH prevents the reuptake of catecholamines from the synaptic cleft by blocking dopamine and norepinephrine transporters (DAT/NET) (75,76), with higher affinity for the former (77,78). In healthy adults, MPH blocks 60% to 70% of striatal DAT in a dose-dependent manner, increasing extracellular levels of dopamine in striatum (75,79–82), as well as in frontal, thalamic, and temporal regions (83). The upregulating effects on basal ganglia, thalamic, and anterior cingulate activation were therefore likely mediated by mesolimbic striatocingulate dopaminergic pathways known to modulate error monitoring systems (63,64). In frontal regions, however, MPH upregulates noradrenaline to the same or greater extent than dopamine (84–86), via reuptake inhibition of NET that clear up both dopamine and noradrenaline (85,87–89). The upregulating effects on frontal activation, therefore, may have been mediated by enhanced catecholamine neurotransmission, in line with recent evidence that noradrenaline also plays a role in error monitoring (66,90).\nA limitation of the study is that patients were tested twice, whereas control subjects were only scanned once, for ethical and financial reasons. Practice effects, however, were overcome by the counterbalanced design. Another limitation is the relatively small sample size. Minimum numbers of 15 to 20 participants have been suggested for fMRI studies (91). Repeated-measures designs, however, are statistically more powerful than independent data sets, which makes the within-subject ANOVA more robust.\nTo our knowledge, this is the first study to show that a single dose of MPH in ADHD upregulates and normalizes the underfunctioning of dMFC, left IFC, posterior cingulate, and parietal regions that in concert play an important role in error processing. The normalization findings of these key regions of both performance monitoring and ADHD dysfunction reinforce the association between dopaminergic neurotransmission abnormalities, ADHD, and poor performance monitoring and may underlie the behavioral effects of improving attention and school performance in boys with ADHD.\n\n"},"whole_article_abstract":{"kind":"string","value":"Background:\nChildren with attention-deficit/hyperactivity disorder (ADHD) have deficits in performance monitoring often improved with the indirect catecholamine agonist methylphenidate (MPH). We used functional magnetic resonance imaging to investigate the effects of single-dose MPH on activation of error processing brain areas in medication-naive boys with ADHD during a stop task that elicits 50% error rates.\n\nMethods:\nTwelve medication-naive boys with ADHD were scanned twice, under either a single clinical dose of MPH or placebo, in a randomized, double-blind design while they performed an individually adjusted tracking stop task, designed to elicit 50% failures. Brain activation was compared within patients under either drug condition. To test for potential normalization effects of MPH, brain activation in ADHD patients under either drug condition was compared with that of 13 healthy age-matched boys.\n\nResults:\nDuring failed inhibition, boys with ADHD under placebo relative to control subjects showed reduced brain activation in performance monitoring areas of dorsomedial and left ventrolateral prefrontal cortices, thalamus, cingulate, and parietal regions. MPH, relative to placebo, upregulated activation in these brain regions within patients and normalized all activation differences between patients and control subjects. During successful inhibition, MPH normalized reduced activation observed in patients under placebo compared with control subjects in parietotemporal and cerebellar regions.\n\nConclusions:\nMPH normalized brain dysfunction in medication-naive ADHD boys relative to control subjects in typical brain areas of performance monitoring, comprising left ventrolateral and dorsomedial frontal and parietal cortices. This could underlie the amelioration of MPH of attention and academic performance in ADHD."},"background_conclusion_text":{"kind":"null"},"background_conclusion_abstract":{"kind":"null"},"whole_article_text_length":{"kind":"number","value":12775,"string":"12,775"},"whole_article_abstract_length":{"kind":"number","value":303,"string":"303"},"other_sections_lengths":{"kind":"list like","value":[531,180,142,484,158,3768,45,600,153,139,378,171,10,690,172,165,139],"string":"[\n 531,\n 180,\n 142,\n 484,\n 158,\n 3768,\n 45,\n 600,\n 153,\n 139,\n 378,\n 171,\n 10,\n 690,\n 172,\n 165,\n 139\n]"},"num_sections":{"kind":"number","value":20,"string":"20"},"most_frequent_words":{"kind":"list like","value":["adhd","right","activation","patients","mph","left","ifc","inferior","placebo","parietal"],"string":"[\n \"adhd\",\n \"right\",\n \"activation\",\n \"patients\",\n \"mph\",\n \"left\",\n \"ifc\",\n \"inferior\",\n \"placebo\",\n \"parietal\"\n]"},"keybert_topics":{"kind":"list like","value":["adhd patients differ","medication patients adhd","adhd patients acute","adhd adolescents despite","diagnosis adhd established"],"string":"[\n \"adhd patients differ\",\n \"medication patients adhd\",\n \"adhd patients acute\",\n \"adhd adolescents despite\",\n \"diagnosis adhd established\"\n]"},"annotated_base_background_abstract_prompt":{"kind":"null"},"annotated_base_methods_abstract_prompt":{"kind":"null"},"annotated_base_results_abstract_prompt":{"kind":"string","value":""},"annotated_base_conclusions_abstract_prompt":{"kind":"null"},"annotated_base_whole_article_abstract_prompt":{"kind":"string","value":""},"annotated_base_background_conclusion_abstract_prompt":{"kind":"null"},"annotated_keywords_background_abstract_prompt":{"kind":"null"},"annotated_keywords_methods_abstract_prompt":{"kind":"null"},"annotated_keywords_results_abstract_prompt":{"kind":"string","value":"[CONTENT] Attention-deficit/hyperactivity disorder (ADHD) | error processing | methylphenidate | motor response inhibition | performance monitoring | stop task [SUMMARY]"},"annotated_keywords_conclusions_abstract_prompt":{"kind":"null"},"annotated_keywords_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] Attention-deficit/hyperactivity disorder (ADHD) | error processing | methylphenidate | motor response inhibition | performance monitoring | stop task [SUMMARY]"},"annotated_keywords_background_conclusion_abstract_prompt":{"kind":"null"},"annotated_mesh_background_abstract_prompt":{"kind":"null"},"annotated_mesh_methods_abstract_prompt":{"kind":"null"},"annotated_mesh_results_abstract_prompt":{"kind":"string","value":"[CONTENT] Adolescent | Attention | Attention Deficit Disorder with Hyperactivity | Central Nervous System Stimulants | Child | Double-Blind Method | Frontal Lobe | Gyrus Cinguli | Humans | Magnetic Resonance Imaging | Male | Methylphenidate | Neuropsychological Tests | Psychomotor Performance 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[SUMMARY]"},"annotated_keybert_conclusions_abstract_prompt":{"kind":"null"},"annotated_keybert_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] adhd patients differ | medication patients adhd | adhd patients acute | adhd adolescents despite | diagnosis adhd established [SUMMARY]"},"annotated_keybert_background_conclusion_abstract_prompt":{"kind":"null"},"annotated_most_frequent_background_abstract_prompt":{"kind":"null"},"annotated_most_frequent_methods_abstract_prompt":{"kind":"null"},"annotated_most_frequent_results_abstract_prompt":{"kind":"string","value":"[CONTENT] adhd | right | activation | patients | mph | left | ifc | inferior | placebo | parietal [SUMMARY]"},"annotated_most_frequent_conclusions_abstract_prompt":{"kind":"null"},"annotated_most_frequent_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] adhd | right | activation | patients | mph | left | ifc | inferior | placebo | parietal [SUMMARY]"},"annotated_most_frequent_background_conclusion_abstract_prompt":{"kind":"null"},"annotated_tf_idf_background_abstract_prompt":{"kind":"null"},"annotated_tf_idf_methods_abstract_prompt":{"kind":"null"},"annotated_tf_idf_results_abstract_prompt":{"kind":"string","value":"[CONTENT] compared | trend | significant | group effect | significant group | group | subjects | effect | trials | table [SUMMARY]"},"annotated_tf_idf_conclusions_abstract_prompt":{"kind":"null"},"annotated_tf_idf_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] activation | adhd | right | superior | patients | left | ifc | adhd patients | inferior | posterior [SUMMARY]"},"annotated_tf_idf_background_conclusion_abstract_prompt":{"kind":"null"},"annotated_entity_plan_background_abstract_prompt":{"kind":"null"},"annotated_entity_plan_methods_abstract_prompt":{"kind":"null"},"annotated_entity_plan_results_abstract_prompt":{"kind":"string","value":"[CONTENT] ||| MPH ||| MPH [SUMMARY]"},"annotated_entity_plan_conclusions_abstract_prompt":{"kind":"null"},"annotated_entity_plan_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] MPH ||| MPH | 50% ||| Twelve | MPH | 50% ||| ||| MPH | 13 ||| ||| MPH ||| MPH ||| MPH ||| MPH | ADHD [SUMMARY]"},"annotated_entity_plan_background_conclusion_abstract_prompt":{"kind":"null"}}},{"rowIdx":273,"cells":{"title":{"kind":"string","value":"Aloperine Inhibits Proliferation and Promotes Apoptosis in Colorectal Cancer Cells by Regulating the circNSUN2/miR-296-5p/STAT3 Pathway."},"pmid":{"kind":"string","value":"33664565"},"background_abstract":{"kind":"string","value":"Aloperine can regulate miR-296-5p/Signal Transducer and Activator of Transcription 3 (STAT3) pathway to inhibit the malignant development of colorectal cancer (CRC), but the regulatory mechanism is unclear. This study explored the upstream mechanism of Aloperine in reducing CRC damage from the perspective of the circRNA-miRNA-mRNA regulatory network."},"background_abstract_label":{"kind":"string","value":"BACKGROUND"},"methods_abstract":{"kind":"string","value":"After treatment with gradient concentrations of Aloperine (0.1 mmol/L, 0.2 mmol/L, 0.4 mmol/L, 0.8 mmol/L and 1 mmol/L) for 24 hours, changes in CRC cell proliferation and apoptosis were detected by functional experiments. Data of the differential expression of miR-296-5p in CRC patients and healthy people were obtained from Starbase. The effects of Aloperine on 12 differentially expressed circRNAs were detected. The binding of miR-296-5p with NOP2/Sun RNA methyltransferase 2 (circNSUN2) and STAT3 was predicted by TargetScan and confirmed through dual-luciferase experiments. The expressions of circNSUN2, miR-296-5p and STAT3 as well as apoptosis-related genes in CRC cells were detected by qRT-PCR and Western blot as needed. Rescue experiments were conducted to test the regulatory effects of circNSUN2, miR-296-5p and STAT3 on CRC cells."},"methods_abstract_label":{"kind":"string","value":"METHODS"},"results_abstract":{"kind":"string","value":"Aloperine at a concentration gradient inhibited proliferation and promoted apoptosis in CRC cells. The abnormally low expression of miR-296-5p in CRC could be upregulated by Aloperine. Among the differentially expressed circRNAs in CRC, only circNSUN2 not only targets miR-296-5p, but also can be regulated by Aloperine. The up-regulation of circNSUN2 offset the inhibitory effect of Aloperine on cancer cells. The rescue experiments finally confirmed the regulation of circNSUN2/miR-296-5p/STAT3 axis in CRC cells."},"results_abstract_label":{"kind":"string","value":"RESULTS"},"conclusions_abstract":{"kind":"string","value":"By regulating the circNSUN2/miR-296-5p/STAT3 pathway, Aloperine prevents the malignant development of CRC cells."},"conclusions_abstract_label":{"kind":"string","value":"CONCLUSION"},"mesh_descriptor_names":{"kind":"list like","value":["Antineoplastic Agents","Apoptosis","Cell Line, Tumor","Cell Proliferation","Colorectal Neoplasms","Computational Biology","Dose-Response Relationship, Drug","Drug Screening Assays, Antitumor","Humans","Methyltransferases","MicroRNAs","Molecular Structure","Quinolizidines","RNA, Circular","STAT3 Transcription Factor","Structure-Activity Relationship"],"string":"[\n \"Antineoplastic Agents\",\n \"Apoptosis\",\n \"Cell Line, Tumor\",\n \"Cell Proliferation\",\n \"Colorectal Neoplasms\",\n \"Computational Biology\",\n \"Dose-Response Relationship, Drug\",\n \"Drug Screening Assays, Antitumor\",\n \"Humans\",\n \"Methyltransferases\",\n \"MicroRNAs\",\n \"Molecular Structure\",\n \"Quinolizidines\",\n \"RNA, Circular\",\n \"STAT3 Transcription Factor\",\n \"Structure-Activity Relationship\"\n]"},"pmcid":{"kind":"string","value":"7924259"},"background_title":{"kind":"string","value":"Introduction"},"background_text":{"kind":"string","value":"Cancer has become the first killer in the world and the biggest obstacle for extending human life expectancy. It is estimated that there were 18.1 million new cancer cases and 9.6 million cancer deaths in 2018.1 Colorectal cancer (CRC) is one of the most common gastrointestinal tumors.2 According to the data from the International Agency for Research on Cancer (IARC),1 the number of new CRC cases worldwide in 2018 was approximately 1.09 million, making CRC the fourth most prevalent malignant tumor after lung, breast and prostate cancers; meantime, CRC had a high mortality rate of about 9.2%, ranking only after lung cancer. Epidemiological studies have shown that the incidence of CRC varies significantly in different countries, and is higher in developed areas.3 More importantly, as the incidence of CRC rises rapidly in people under 50, the age of onset of the disease is getting lower.4,5 Therefore, it is urgent to improve the diagnosis rate and treatment effect of CRC.\nCurrently, the main clinical treatment of CRC is surgery combined with adjuvant radiotherapy, chemotherapy or molecular targeted therapy. Surgery is generally considered as the first choice for comprehensive treatment of CRC. This method is suitable for patients whose tumors are confined to the intestinal wall while penetrating the intestinal wall and invading the serous or extraserous membrane without lymph node metastasis.6,7 However, the availability of radical resection is limited as most patients with CRC have adenocarcinoma, which generally develops from polyps and is metastatic by nature.8 As a result, CRC patients tend to undergo simple resection, which leads to a high recurrence rate. Chemotherapy, therefore, is still necessary for postoperative and late-stage CRC patients.9,10 At present, the commonly used chemotherapeutic drugs in clinic mainly include 5-fluorouracil (5-Fu), oxaliplatin and its derivatives.9 Chemotherapy is highly risky because it indiscriminately kills tumor cells and immune cells and thereby reduces the anti-tumor immune effect.11 Likewise, radiotherapy greatly weakens the body’s immunity.12 Hence, researchers have proposed to find effective treatments or drugs that cause less damage to patients.\nWith advances in the research and development of traditional Chinese medicine, more attention has been paid to the roles of traditional Chinese medicine and its active ingredients in disease prevention and treatment. At the same time, new targets for molecular targeted therapy of diseases have been discovered in the exploration of the molecular mechanisms of traditional Chinese medicine and its monomers. Aloperine (ALO) has been confirmed to have a significant anti-cancer activity. ALO is a component of the traditional Chinese medicine Sophora alopecuroides L.13 It is also one of the main alkaloids separated and extracted in the lab, with a molecular formula of C15H24N2.14 Recent studies have found that ALO has the effects of anti-inflammation, immunosuppression, redox suppression, cardiovascular protection and anti-cancer, among which its tumor-suppressing activity has been extensively reported. For example, Yu et al revealed that ALO inhibited the carcinogenesis process by regulating excessive autophagy in thyroid cancer cells;15 Liu et al demonstrated that ALO inhibited the PI3K/Akt pathway, increased apoptosis and caused cell cycle block in liver cancer cells;16 besides, ALO was also found to exert an anti-cancer effect in prostate cancer and breast cancer.17,18 In the previous study, our research group found that ALO up-regulated miR-296-5p and inhibited the activity of its target gene STAT3, thereby inhibiting the proliferation and inducing the apoptosis of CRC cells. However, the activation pathway of miR-296-5p is unclear. In order to clarify the upstream activation pathway of ALO-upregulated miR-296-5p, this study will explore the mechanism of ALO in inhibiting CRC from the perspective of the circRNA-miRNA-mRNA regulatory network."},"methods_title":{"kind":"null"},"methods_text":{"kind":"null"},"results_title":{"kind":"null"},"results_text":{"kind":"null"},"conclusions_title":{"kind":"string","value":"Discussion"},"conclusions_text":{"kind":"string","value":"CRC is a malignant lesion of the mucosal epithelium of the colon or rectum under the action of various carcinogenic factors such as environment or heredity.2 Most CRC patients suffer from adenocarcinoma, which usually develops from polyps. Tumors developed from polyps can infiltrate in a circular shape along the horizontal axis of the intestinal tube, develop into the deep layer of the intestinal wall, and finally penetrate the intestinal wall and metastasize to blood vessels or lymphatic vessels.20 This is one of the important reasons for the high recurrence and metastasis rates of CRC. The ultimate goals of CRC research are to improve the current treatment of CRC, save patients’ lives and improve their quality of life. Based on the role of traditional Chinese medicine in disease prevention and treatment, this study explored the effects of ALO, an alkaloid exhibiting strong anticancer activity, on the proliferation and apoptosis of CRC cells. The results showed that after ALO treatment, the viability and proliferation of CRC cells were significantly inhibited; on the contrary, the number of apoptotic cancer cells was significantly increased. This is consistent with the previous experimental results.21 In order to probe deeper into the upstream mechanism of ALO in alleviating CRC carcinogenesis through the miR-296-5p/STAT3 axis, we screened and verified the upstream targeting circRNA of miR-296-5p.\nCircRNAs are a type of covalently closed circular non-coding RNA formed by back-splicing of mRNA precursors (pre-mRNA).22 CircRNA was considered to be a useless splicing by-product in early research. With the deepening of research, it is found that circRNA has a wide range of sources and plays a variety of functional roles in the growth and development of organisms, with the characteristics of conservative, stable, and tissue-specific.22,23 The most familiar and extensively studied function of circRNA is its sponging effect on miRNA.24 For example, circRNA ciRS-7 which contains more than 70 miR-7 binding sites can achieve competitive adsorption of miR-7 through the AGO2 protein.25 Besides, increasing reports on cancer indicate that circRNAs, such as circRNA-cTFRC, circPSMC3 and circSETD3, act as sponges of miRNAs and participate in transcriptional regulation.26–28 Based on these literature reports, we screened circRNAs that were reported to be abnormally expressed in CRC, and finally obtained 12 circRNAs. After detecting their expressions in ALO-treated cancer cells and predicting their binding sequence for miR-296-5p, circNSUN2 was finally identified as the research object of this study.\nCircNSUN2, which maps to the 5p15 amplicon in CRCs, was confirmed by Chen et al to regulate cytoplasmic output and promote liver metastasis of CRC through N 6-methyladenosine modification.29 Similarly, our experimental results uncovered that knockdown of circNSUN2 inhibited proliferation and accelerated apoptosis in CRC cells. More importantly, we for the first time revealed that ALO can prevent the activation of circNSUN2 and offset the promotion effect of overexpression of circNSUN2 on esophageal cancer progression. Since circNSUN2 and miR-296-5p have targeting sequences, we verified the cellular regulatory effects of circNSUN2 and miR-296-5p/STAT3 through dual luciferase experiments and rescue experiments. The final result confirmed our conjecture that regulating the circNSUN2/miR-296-5p/STAT3 axis can reduce the proliferation rate and increase the apoptosis rate of CRC cells.\nAt present, blocking proliferation and increasing apoptosis in cancer cells are the intensively discussed mechanisms in CRC study. Tumor cells can proliferate quickly and unrestrictedly, and thus, inhibiting their proliferation can produce an anti-tumor effect.30 Apoptosis, alternatively called programmed cell death, is an autonomous cell death process strictly controlled by multiple genes. Apoptosis is an important part of cell life cycle, and it is also an important link in regulating the development of the body and maintaining the stability of the internal environment in organisms.31,32 Our research shows that ALO inhibits the proliferation and promotes the apoptosis of CRC cells by regulating the circNSUN2/miR-296-5p/STAT3 pathway, and ultimately prevents the tumorigenesis of CRC.\nThere are still certain limitations in our research. Although we have confirmed the effect of ALO on inducing apoptosis and reducing proliferation in CRC cells and clarified the underlying mechanism, the circNSUN2/miR-296-5p/STAT3 axis and the pathways related to proliferation and apoptosis have not been analyzed yet, which will be further explored in future research. In addition, the anticancer effect of ALO on CRC and its experimental concentration needs to be further tested in animal and clinical experiments. Whether ALO has an impact on drug resistance in CRC is also a direction of future research."},"other_sections_titles":{"kind":"list like","value":["Method","Cell Purchase and ALO Processing","MTT Assay","EdU (5-Ethynyl-2ʹ-Deoxyuridine) Cell Proliferation Experiment","Colony Formation Assay","Apoptosis Experiment","Bioinformatics Analysis and Target Gene Binding Verification","Quantitative Real-Time Polymerase Chain Reaction (qRT-PCR)","Cell Transfection","Western Blot","Statistical Analysis","Result","ALO at a Concentration Gradient Inhibited Proliferation Yet Promoted Apoptosis in CRC Cells","The Abnormally Low Expression of miR-296-5p in Colon Cancer Could Be Up-Regulated by ALO","Among the Differentially Expressed circRNAs in CRC, Only circNSUN2 Not Only Had a Target Site for miR-296-5p, but Also Could Be Regulated by ALO","Overexpression of circNSUN2 Partially Offset the Suppression of ALO on the Biological Behavior of Cancer Cells","Sh-circNSUN2 Inhibited the Malignant Development of CRC Cells, Which Was Neutralized by miR-296-5p Inhibitor","MiR-296-5p Bound to STAT3 and Upregulation of Mir-296-5p Inhibited the Expression of STAT3 in CRC Cells","MiR-296-5p Mimic Inhibited Proliferation and Promoted Apoptosis in CRC Cells by Regulating Apoptosis-Related Genes, Which Was Partially Reversed by Overexpressed STAT3","Discussion"],"string":"[\n \"Method\",\n \"Cell Purchase and ALO Processing\",\n \"MTT Assay\",\n \"EdU (5-Ethynyl-2ʹ-Deoxyuridine) Cell Proliferation Experiment\",\n \"Colony Formation Assay\",\n \"Apoptosis Experiment\",\n \"Bioinformatics Analysis and Target Gene Binding Verification\",\n \"Quantitative Real-Time Polymerase Chain Reaction (qRT-PCR)\",\n \"Cell Transfection\",\n \"Western Blot\",\n \"Statistical Analysis\",\n \"Result\",\n \"ALO at a Concentration Gradient Inhibited Proliferation Yet Promoted Apoptosis in CRC Cells\",\n \"The Abnormally Low Expression of miR-296-5p in Colon Cancer Could Be Up-Regulated by ALO\",\n \"Among the Differentially Expressed circRNAs in CRC, Only circNSUN2 Not Only Had a Target Site for miR-296-5p, but Also Could Be Regulated by ALO\",\n \"Overexpression of circNSUN2 Partially Offset the Suppression of ALO on the Biological Behavior of Cancer Cells\",\n \"Sh-circNSUN2 Inhibited the Malignant Development of CRC Cells, Which Was Neutralized by miR-296-5p Inhibitor\",\n \"MiR-296-5p Bound to STAT3 and Upregulation of Mir-296-5p Inhibited the Expression of STAT3 in CRC Cells\",\n \"MiR-296-5p Mimic Inhibited Proliferation and Promoted Apoptosis in CRC Cells by Regulating Apoptosis-Related Genes, Which Was Partially Reversed by Overexpressed STAT3\",\n \"Discussion\"\n]"},"other_sections_texts":{"kind":"list like","value":["Cell Purchase and ALO Processing CCD-18Co and CRC cell lines SW480 (CCL-228™) and HT29 (HTB-38™) used in this research were provided by the American Type Culture Collection (ATCC). According to the culture requirements, CCD-18Co, SW480 and HT29 cells were separately inoculated in DMEM medium (30–2007, ATCC) containing 10% fetal bovine serum and incubated in a D180-P cell incubator (RWD, China) containing 5% CO2 at 37 °C.\nALO (DK0052, CAS NO: 56293-29-9, HPLC≥98%), the research object of this study, was provided by Chengdu DESITE Biological Company, China. ALO was dissolved in fresh DMEM to prepare 0.1 mmol/L, 0.2 mmol/L, 0.4 mmol/L, 0.8 mmol/L and 1 mmol/L ALO solutions, which were separately used to treat the CCD-18Co, SW480 and HT29 cells for 24 hours (h).\nCCD-18Co and CRC cell lines SW480 (CCL-228™) and HT29 (HTB-38™) used in this research were provided by the American Type Culture Collection (ATCC). According to the culture requirements, CCD-18Co, SW480 and HT29 cells were separately inoculated in DMEM medium (30–2007, ATCC) containing 10% fetal bovine serum and incubated in a D180-P cell incubator (RWD, China) containing 5% CO2 at 37 °C.\nALO (DK0052, CAS NO: 56293-29-9, HPLC≥98%), the research object of this study, was provided by Chengdu DESITE Biological Company, China. ALO was dissolved in fresh DMEM to prepare 0.1 mmol/L, 0.2 mmol/L, 0.4 mmol/L, 0.8 mmol/L and 1 mmol/L ALO solutions, which were separately used to treat the CCD-18Co, SW480 and HT29 cells for 24 hours (h).\nMTT Assay One hundred µL of SW480 or HT29 cell suspension (1×104 cells/mL) was transferred to each well of a 96-well plate. Of note, three holes were added to the same treatment group. After treatment with the ALO solutions for 24 h, SW480 or HT29 cells were added with MTT reagent (10 µL/well, PB180519, Procell, USA), which could react with mitochondria in living cells. After 4 h of binding, a HBS-1096A enzyme label analyzer (DeTie, China) was used to detect the absorbance of SW480 or HT29 cells in the mixed solutions at 490 nm, and then cell viability was calculated based on the absorbance.\nOne hundred µL of SW480 or HT29 cell suspension (1×104 cells/mL) was transferred to each well of a 96-well plate. Of note, three holes were added to the same treatment group. After treatment with the ALO solutions for 24 h, SW480 or HT29 cells were added with MTT reagent (10 µL/well, PB180519, Procell, USA), which could react with mitochondria in living cells. After 4 h of binding, a HBS-1096A enzyme label analyzer (DeTie, China) was used to detect the absorbance of SW480 or HT29 cells in the mixed solutions at 490 nm, and then cell viability was calculated based on the absorbance.\nEdU (5-Ethynyl-2ʹ-Deoxyuridine) Cell Proliferation Experiment SW480 or HT29 cells in logarithmic growth phase were seeded in 96-well plates at 1×104 cells/well. EdU reagent (ST067) purchased from Beyotime was diluted to 50 μmol/L with fresh DMEM medium. The EdU dilution was then used to incubate the cells at 100 μL/well for 2 h. After removing the culture medium, the SW480 or HT29 cells were fixed with methanol at room temperature for 15 minutes (min). Positive fluorescence expression (red) in the collected SW480 or HT29 cells were observed, photographed and recorded under a Leica DMi8 inverted fluorescence microscope (magnification 100 ×). Then DAPI reagent was used to stain the nuclei, and the stains were observed under a microscope.\nSW480 or HT29 cells in logarithmic growth phase were seeded in 96-well plates at 1×104 cells/well. EdU reagent (ST067) purchased from Beyotime was diluted to 50 μmol/L with fresh DMEM medium. The EdU dilution was then used to incubate the cells at 100 μL/well for 2 h. After removing the culture medium, the SW480 or HT29 cells were fixed with methanol at room temperature for 15 minutes (min). Positive fluorescence expression (red) in the collected SW480 or HT29 cells were observed, photographed and recorded under a Leica DMi8 inverted fluorescence microscope (magnification 100 ×). Then DAPI reagent was used to stain the nuclei, and the stains were observed under a microscope.\nColony Formation Assay Two hundred SW480 or HT29 cells were transferred to petri dishes containing complete medium (10 mL). In order to distribute the cells evenly, the petri dishes were rotated slowly for 1 min after cell inoculation. Next, the culture dishes containing cells were placed in an incubator for routine culture. During the culture, the medium was changed every 2 days (d). After 14 d, the SW480 or HT29 cells were soaked with Giemsa dye for 20 min and then the colony formation of the cells was observed under a microscope.\nTwo hundred SW480 or HT29 cells were transferred to petri dishes containing complete medium (10 mL). In order to distribute the cells evenly, the petri dishes were rotated slowly for 1 min after cell inoculation. Next, the culture dishes containing cells were placed in an incubator for routine culture. During the culture, the medium was changed every 2 days (d). After 14 d, the SW480 or HT29 cells were soaked with Giemsa dye for 20 min and then the colony formation of the cells was observed under a microscope.\nApoptosis Experiment An Annexin V-FITC Apoptosis Detection Kit (CA1020, Solarbio, China) and a flow cytometer (CytoFLEX, BECKMAN COULTER, USA) were used to detect changes in the apoptosis of SW480 or HT29 cells. In brief, SW480 or HT29 cells were prepared into 1×106 cells/mL cell suspension with 1 mL 1× Binding Buffer. One hundred μL of SW480 or HT29 cells and 5 μL of Annexin V-FITC reagent were added to a centrifuge tube and mixed for 10 min in the dark at room temperature. Next, 5 μL of PI reagent was added to the centrifuge tube and incubated in the dark at room temperature for 5 min. Afterwards, PBS was added to adjust the mixture to a final volume of 500 μL. After half an hour, the cells in the centrifuge tube were transferred to the detection instrument to calculate the number of apoptotic cells.\nAn Annexin V-FITC Apoptosis Detection Kit (CA1020, Solarbio, China) and a flow cytometer (CytoFLEX, BECKMAN COULTER, USA) were used to detect changes in the apoptosis of SW480 or HT29 cells. In brief, SW480 or HT29 cells were prepared into 1×106 cells/mL cell suspension with 1 mL 1× Binding Buffer. One hundred μL of SW480 or HT29 cells and 5 μL of Annexin V-FITC reagent were added to a centrifuge tube and mixed for 10 min in the dark at room temperature. Next, 5 μL of PI reagent was added to the centrifuge tube and incubated in the dark at room temperature for 5 min. Afterwards, PBS was added to adjust the mixture to a final volume of 500 μL. After half an hour, the cells in the centrifuge tube were transferred to the detection instrument to calculate the number of apoptotic cells.\nBioinformatics Analysis and Target Gene Binding Verification Starbase (http://starbase.sysu.edu.cn/index.php) was used to retrieve and analyze information of differential expression of miR-296-5p in Colon adenocarcinoma (COAD) patients and healthy volunteers. TargetScan (http://www.targetscan.org) was used to predict the circRNA that contained a binding sequence for miR-296-5p and the downstream target genes of miR-296-5p. The binding sequences predicted by TargetScan were designed by COBIOER (China) and used to construct the following reporter plasmids for dual luciferase experiments: NOP2/Sun RNA methyltransferase 2 wild type (circNSUN2-WT), circNSUN2-mutant type (MUT), Signal Transducer and Activator of Transcription 3 (STAT3)-WT and STAT3-MUT. The reporter plasmids were separately co-transfected with miR-296-5p mimic or mimic control into SW480 and HT29 cells using liposome for 48 h. Luciferase activity in the transfected SW480 and HT29 cells was detected using Dual-Luciferase® Reporter Assay System (E1960, Promega, USA) with a GloMax 20/20 luminometer (Promega, USA).\nStarbase (http://starbase.sysu.edu.cn/index.php) was used to retrieve and analyze information of differential expression of miR-296-5p in Colon adenocarcinoma (COAD) patients and healthy volunteers. TargetScan (http://www.targetscan.org) was used to predict the circRNA that contained a binding sequence for miR-296-5p and the downstream target genes of miR-296-5p. The binding sequences predicted by TargetScan were designed by COBIOER (China) and used to construct the following reporter plasmids for dual luciferase experiments: NOP2/Sun RNA methyltransferase 2 wild type (circNSUN2-WT), circNSUN2-mutant type (MUT), Signal Transducer and Activator of Transcription 3 (STAT3)-WT and STAT3-MUT. The reporter plasmids were separately co-transfected with miR-296-5p mimic or mimic control into SW480 and HT29 cells using liposome for 48 h. Luciferase activity in the transfected SW480 and HT29 cells was detected using Dual-Luciferase® Reporter Assay System (E1960, Promega, USA) with a GloMax 20/20 luminometer (Promega, USA).\nQuantitative Real-Time Polymerase Chain Reaction (qRT-PCR) qRT-PCR is a reliable method for rapid detection of gene mRNA levels. Briefly, total RNA of SW480 and HT29 cells was fully isolated by the conventional RNA extraction reagent TRIzol (15,596,018, Invitrogen, USA), and subsequently reverse transcribed into cDNA using the RNA reverse transcription reagent (RR047A) developed by TaKaRa (Japan). The qRT-PCR reaction system which consisted of cDNA, gene primers (Sangon synthesis), SYBR® Green (S4438-20RXN, Sigma-Aldrich, Germany) and DEPC water was added to a 96-well plate and put into the detection instrument (Bio-Rad thermal cycler T100). The reaction conditions were set as follows: pre-denaturation at 95 °C for 10 min, denaturation at 95 °C for 15 seconds (s), and annealing at 58 °C for 1 min, for a total of 40 cycles. According to Anita Ciesielska’s report,19 gene mRNA levels were quantified by the 2−ΔΔCT method. GAPDH and U6 were internal references in the experiment. The specific sequences of synthetic primers were shown in Table 1.Table 1Primers for qRT-PCRGeneForward Primer (5ʹ-3ʹ)Reverse Primer (5ʹ-3ʹ)miR-296-5pCCTGTGTCGTATCCAGTGCAAGTCGTATCCAGTGCGTGTCGcircDDX17TGCCAACCACAACATCCTCCACGCTCCCCAGGATTACCAAATcircHIPK3TATGTTGGTGGATCCTGTTCGGCATGGTGGGTAGACCAAGACTTGTGAcircZFRAACCACCACAGATTCACTATAACCACCACAGATTCACTATcircRNA_100290GTCATTCCCTCTTTAATGGTGCAGAACTTCCGCTCTAACATACHas_circ_0026344CTCAGCCTCTAGCATAAGCTCAGGCAAGAGAATGATTTGAACcircVAPATGGATTCCAAATTGAGATGCGTATTCACTTTTCTATCCGATGGATTTCGCHsa_circ_0009361AGAACCAGATTCGAGACGCCGTGCTCTTCAATGCCACCTTCcircNSUN2CTTGAGAAAATCGCCACACTTGTTGAGGAGCAGTGGTGGcircACAP2TCAGAGCATCTGCCCAAAGTTAAATGAACCCCAAGGCTCCGcircITGA7GTGTGCACAGGTCCTTCCAATGGAAGTTCTGTGAGGGACGcircEIF4G3CCTACCCCATCCCCTTATTCACCGTGCTGTAGACTGCTGAGSTAT3CCCCATACCTGAAGACCAAGGGACTCAAACTGCCCTCCTU6CTCGCTTCGGCAGCACAAACGCTTCACGAATTTGCGTGAPDHTGTGGGCATCAATGGATTTGGACACCATGTATTCCGGGTCAAT\n\nPrimers for qRT-PCR\nqRT-PCR is a reliable method for rapid detection of gene mRNA levels. Briefly, total RNA of SW480 and HT29 cells was fully isolated by the conventional RNA extraction reagent TRIzol (15,596,018, Invitrogen, USA), and subsequently reverse transcribed into cDNA using the RNA reverse transcription reagent (RR047A) developed by TaKaRa (Japan). The qRT-PCR reaction system which consisted of cDNA, gene primers (Sangon synthesis), SYBR® Green (S4438-20RXN, Sigma-Aldrich, Germany) and DEPC water was added to a 96-well plate and put into the detection instrument (Bio-Rad thermal cycler T100). The reaction conditions were set as follows: pre-denaturation at 95 °C for 10 min, denaturation at 95 °C for 15 seconds (s), and annealing at 58 °C for 1 min, for a total of 40 cycles. According to Anita Ciesielska’s report,19 gene mRNA levels were quantified by the 2−ΔΔCT method. GAPDH and U6 were internal references in the experiment. The specific sequences of synthetic primers were shown in Table 1.Table 1Primers for qRT-PCRGeneForward Primer (5ʹ-3ʹ)Reverse Primer (5ʹ-3ʹ)miR-296-5pCCTGTGTCGTATCCAGTGCAAGTCGTATCCAGTGCGTGTCGcircDDX17TGCCAACCACAACATCCTCCACGCTCCCCAGGATTACCAAATcircHIPK3TATGTTGGTGGATCCTGTTCGGCATGGTGGGTAGACCAAGACTTGTGAcircZFRAACCACCACAGATTCACTATAACCACCACAGATTCACTATcircRNA_100290GTCATTCCCTCTTTAATGGTGCAGAACTTCCGCTCTAACATACHas_circ_0026344CTCAGCCTCTAGCATAAGCTCAGGCAAGAGAATGATTTGAACcircVAPATGGATTCCAAATTGAGATGCGTATTCACTTTTCTATCCGATGGATTTCGCHsa_circ_0009361AGAACCAGATTCGAGACGCCGTGCTCTTCAATGCCACCTTCcircNSUN2CTTGAGAAAATCGCCACACTTGTTGAGGAGCAGTGGTGGcircACAP2TCAGAGCATCTGCCCAAAGTTAAATGAACCCCAAGGCTCCGcircITGA7GTGTGCACAGGTCCTTCCAATGGAAGTTCTGTGAGGGACGcircEIF4G3CCTACCCCATCCCCTTATTCACCGTGCTGTAGACTGCTGAGSTAT3CCCCATACCTGAAGACCAAGGGACTCAAACTGCCCTCCTU6CTCGCTTCGGCAGCACAAACGCTTCACGAATTTGCGTGAPDHTGTGGGCATCAATGGATTTGGACACCATGTATTCCGGGTCAAT\n\nPrimers for qRT-PCR\nCell Transfection Exogenous up-regulation or knockdown of test genes is a common method to observe their regulatory effects on cells or tissues. Liposome transfection with Lipofectamine 3000 (L3000008, ThermoFisher Scientific, USA) is the most common and convenient method for exogenous interference. Therefore, we commissioned Guangzhou GENESEED Biological Company to synthesize circNSUN2 overexpressed plasmid, circNSUN2 silenced plasmid (sh-circNSUN2), STAT3 overexpressed plasmid and their respective negative controls. MiR-296-5p mimic (miR10000690-1-5) and mimic control (miR1N0000001-1-5), as well as miR-296-5p inhibitor (miR20000690-1-5) and inhibitor control (miR2N0000001-1-5), were purchased directly from Guangzhou RIOBOBIO Company. The plasmids were transfected into SW480 and HT29 cells according to the grouping using Lipofectamine 3000. After 48 h, the success rate of transfection of the cells was determined by qRT-PCR.\nExogenous up-regulation or knockdown of test genes is a common method to observe their regulatory effects on cells or tissues. Liposome transfection with Lipofectamine 3000 (L3000008, ThermoFisher Scientific, USA) is the most common and convenient method for exogenous interference. Therefore, we commissioned Guangzhou GENESEED Biological Company to synthesize circNSUN2 overexpressed plasmid, circNSUN2 silenced plasmid (sh-circNSUN2), STAT3 overexpressed plasmid and their respective negative controls. MiR-296-5p mimic (miR10000690-1-5) and mimic control (miR1N0000001-1-5), as well as miR-296-5p inhibitor (miR20000690-1-5) and inhibitor control (miR2N0000001-1-5), were purchased directly from Guangzhou RIOBOBIO Company. The plasmids were transfected into SW480 and HT29 cells according to the grouping using Lipofectamine 3000. After 48 h, the success rate of transfection of the cells was determined by qRT-PCR.\nWestern Blot RIPA lysate (tissue/cell, R0010) produced by Solarbio (China) was used to separate protein from SW480 and HT29 cells, and its concentration was determined with BCA protein concentration determination reagent (PC0020, Solarbio, China). Then the protein was subjected to high temperature denaturation to maintain a relatively stable state. After being electrophoresed by SDS-PAGE, the protein was transferred to the membrane carrier (PVDF membrane). The membrane was soaked with the same BSA sealant (SW3015) produced by Solarbio for 2 h at room temperature. Next, the membrane was incubated with Abcam (USA) antibodies (anti-STAT3, ab68153, 88 kDa; Bax, ab32503, 21 kDa; Bcl-2, ab59348, 26 kDa) that could bind to the corresponding antigens in the protein on the membrane overnight (4 °C), with GAPDH (ab8245, 36KD) as an internal reference. To identify the antigen-antibody complex, a secondary antibody labeled with HRP (Goat Anti-Rabbit antibody, 1:10,000, ab6721; Goat Anti-Mouse antibody, 1:10000, ab205719) was used to treat the PVDF membrane for 1.5 h at room temperature. Fluorescent signal of the complex was enhanced using the ECL Western Blotting Substrate (PE0010, Solarbio) and detected using the Gel Doc™ XR+ imaging system (BIO-RAD, USA). The gray value of the bands was calculated by Image Lab software to determine the final gene protein level.\nRIPA lysate (tissue/cell, R0010) produced by Solarbio (China) was used to separate protein from SW480 and HT29 cells, and its concentration was determined with BCA protein concentration determination reagent (PC0020, Solarbio, China). Then the protein was subjected to high temperature denaturation to maintain a relatively stable state. After being electrophoresed by SDS-PAGE, the protein was transferred to the membrane carrier (PVDF membrane). The membrane was soaked with the same BSA sealant (SW3015) produced by Solarbio for 2 h at room temperature. Next, the membrane was incubated with Abcam (USA) antibodies (anti-STAT3, ab68153, 88 kDa; Bax, ab32503, 21 kDa; Bcl-2, ab59348, 26 kDa) that could bind to the corresponding antigens in the protein on the membrane overnight (4 °C), with GAPDH (ab8245, 36KD) as an internal reference. To identify the antigen-antibody complex, a secondary antibody labeled with HRP (Goat Anti-Rabbit antibody, 1:10,000, ab6721; Goat Anti-Mouse antibody, 1:10000, ab205719) was used to treat the PVDF membrane for 1.5 h at room temperature. Fluorescent signal of the complex was enhanced using the ECL Western Blotting Substrate (PE0010, Solarbio) and detected using the Gel Doc™ XR+ imaging system (BIO-RAD, USA). The gray value of the bands was calculated by Image Lab software to determine the final gene protein level.\nStatistical Analysis SPSS 20.0 software (IBM, USA) was employed for statistical analysis of the data obtained in the experiments. Differences between two groups were compared by independent sample t-test, and those between multiple groups were compared by one-way ANOVA followed by Tukey’s test. When p<0.05, the difference was considered statistically significant.\nSPSS 20.0 software (IBM, USA) was employed for statistical analysis of the data obtained in the experiments. Differences between two groups were compared by independent sample t-test, and those between multiple groups were compared by one-way ANOVA followed by Tukey’s test. When p<0.05, the difference was considered statistically significant.","CCD-18Co and CRC cell lines SW480 (CCL-228™) and HT29 (HTB-38™) used in this research were provided by the American Type Culture Collection (ATCC). According to the culture requirements, CCD-18Co, SW480 and HT29 cells were separately inoculated in DMEM medium (30–2007, ATCC) containing 10% fetal bovine serum and incubated in a D180-P cell incubator (RWD, China) containing 5% CO2 at 37 °C.\nALO (DK0052, CAS NO: 56293-29-9, HPLC≥98%), the research object of this study, was provided by Chengdu DESITE Biological Company, China. ALO was dissolved in fresh DMEM to prepare 0.1 mmol/L, 0.2 mmol/L, 0.4 mmol/L, 0.8 mmol/L and 1 mmol/L ALO solutions, which were separately used to treat the CCD-18Co, SW480 and HT29 cells for 24 hours (h).","One hundred µL of SW480 or HT29 cell suspension (1×104 cells/mL) was transferred to each well of a 96-well plate. Of note, three holes were added to the same treatment group. After treatment with the ALO solutions for 24 h, SW480 or HT29 cells were added with MTT reagent (10 µL/well, PB180519, Procell, USA), which could react with mitochondria in living cells. After 4 h of binding, a HBS-1096A enzyme label analyzer (DeTie, China) was used to detect the absorbance of SW480 or HT29 cells in the mixed solutions at 490 nm, and then cell viability was calculated based on the absorbance.","SW480 or HT29 cells in logarithmic growth phase were seeded in 96-well plates at 1×104 cells/well. EdU reagent (ST067) purchased from Beyotime was diluted to 50 μmol/L with fresh DMEM medium. The EdU dilution was then used to incubate the cells at 100 μL/well for 2 h. After removing the culture medium, the SW480 or HT29 cells were fixed with methanol at room temperature for 15 minutes (min). Positive fluorescence expression (red) in the collected SW480 or HT29 cells were observed, photographed and recorded under a Leica DMi8 inverted fluorescence microscope (magnification 100 ×). Then DAPI reagent was used to stain the nuclei, and the stains were observed under a microscope.","Two hundred SW480 or HT29 cells were transferred to petri dishes containing complete medium (10 mL). In order to distribute the cells evenly, the petri dishes were rotated slowly for 1 min after cell inoculation. Next, the culture dishes containing cells were placed in an incubator for routine culture. During the culture, the medium was changed every 2 days (d). After 14 d, the SW480 or HT29 cells were soaked with Giemsa dye for 20 min and then the colony formation of the cells was observed under a microscope.","An Annexin V-FITC Apoptosis Detection Kit (CA1020, Solarbio, China) and a flow cytometer (CytoFLEX, BECKMAN COULTER, USA) were used to detect changes in the apoptosis of SW480 or HT29 cells. In brief, SW480 or HT29 cells were prepared into 1×106 cells/mL cell suspension with 1 mL 1× Binding Buffer. One hundred μL of SW480 or HT29 cells and 5 μL of Annexin V-FITC reagent were added to a centrifuge tube and mixed for 10 min in the dark at room temperature. Next, 5 μL of PI reagent was added to the centrifuge tube and incubated in the dark at room temperature for 5 min. Afterwards, PBS was added to adjust the mixture to a final volume of 500 μL. After half an hour, the cells in the centrifuge tube were transferred to the detection instrument to calculate the number of apoptotic cells.","Starbase (http://starbase.sysu.edu.cn/index.php) was used to retrieve and analyze information of differential expression of miR-296-5p in Colon adenocarcinoma (COAD) patients and healthy volunteers. TargetScan (http://www.targetscan.org) was used to predict the circRNA that contained a binding sequence for miR-296-5p and the downstream target genes of miR-296-5p. The binding sequences predicted by TargetScan were designed by COBIOER (China) and used to construct the following reporter plasmids for dual luciferase experiments: NOP2/Sun RNA methyltransferase 2 wild type (circNSUN2-WT), circNSUN2-mutant type (MUT), Signal Transducer and Activator of Transcription 3 (STAT3)-WT and STAT3-MUT. The reporter plasmids were separately co-transfected with miR-296-5p mimic or mimic control into SW480 and HT29 cells using liposome for 48 h. Luciferase activity in the transfected SW480 and HT29 cells was detected using Dual-Luciferase® Reporter Assay System (E1960, Promega, USA) with a GloMax 20/20 luminometer (Promega, USA).","qRT-PCR is a reliable method for rapid detection of gene mRNA levels. Briefly, total RNA of SW480 and HT29 cells was fully isolated by the conventional RNA extraction reagent TRIzol (15,596,018, Invitrogen, USA), and subsequently reverse transcribed into cDNA using the RNA reverse transcription reagent (RR047A) developed by TaKaRa (Japan). The qRT-PCR reaction system which consisted of cDNA, gene primers (Sangon synthesis), SYBR® Green (S4438-20RXN, Sigma-Aldrich, Germany) and DEPC water was added to a 96-well plate and put into the detection instrument (Bio-Rad thermal cycler T100). The reaction conditions were set as follows: pre-denaturation at 95 °C for 10 min, denaturation at 95 °C for 15 seconds (s), and annealing at 58 °C for 1 min, for a total of 40 cycles. According to Anita Ciesielska’s report,19 gene mRNA levels were quantified by the 2−ΔΔCT method. GAPDH and U6 were internal references in the experiment. The specific sequences of synthetic primers were shown in Table 1.Table 1Primers for qRT-PCRGeneForward Primer (5ʹ-3ʹ)Reverse Primer (5ʹ-3ʹ)miR-296-5pCCTGTGTCGTATCCAGTGCAAGTCGTATCCAGTGCGTGTCGcircDDX17TGCCAACCACAACATCCTCCACGCTCCCCAGGATTACCAAATcircHIPK3TATGTTGGTGGATCCTGTTCGGCATGGTGGGTAGACCAAGACTTGTGAcircZFRAACCACCACAGATTCACTATAACCACCACAGATTCACTATcircRNA_100290GTCATTCCCTCTTTAATGGTGCAGAACTTCCGCTCTAACATACHas_circ_0026344CTCAGCCTCTAGCATAAGCTCAGGCAAGAGAATGATTTGAACcircVAPATGGATTCCAAATTGAGATGCGTATTCACTTTTCTATCCGATGGATTTCGCHsa_circ_0009361AGAACCAGATTCGAGACGCCGTGCTCTTCAATGCCACCTTCcircNSUN2CTTGAGAAAATCGCCACACTTGTTGAGGAGCAGTGGTGGcircACAP2TCAGAGCATCTGCCCAAAGTTAAATGAACCCCAAGGCTCCGcircITGA7GTGTGCACAGGTCCTTCCAATGGAAGTTCTGTGAGGGACGcircEIF4G3CCTACCCCATCCCCTTATTCACCGTGCTGTAGACTGCTGAGSTAT3CCCCATACCTGAAGACCAAGGGACTCAAACTGCCCTCCTU6CTCGCTTCGGCAGCACAAACGCTTCACGAATTTGCGTGAPDHTGTGGGCATCAATGGATTTGGACACCATGTATTCCGGGTCAAT\n\nPrimers for qRT-PCR","Exogenous up-regulation or knockdown of test genes is a common method to observe their regulatory effects on cells or tissues. Liposome transfection with Lipofectamine 3000 (L3000008, ThermoFisher Scientific, USA) is the most common and convenient method for exogenous interference. Therefore, we commissioned Guangzhou GENESEED Biological Company to synthesize circNSUN2 overexpressed plasmid, circNSUN2 silenced plasmid (sh-circNSUN2), STAT3 overexpressed plasmid and their respective negative controls. MiR-296-5p mimic (miR10000690-1-5) and mimic control (miR1N0000001-1-5), as well as miR-296-5p inhibitor (miR20000690-1-5) and inhibitor control (miR2N0000001-1-5), were purchased directly from Guangzhou RIOBOBIO Company. The plasmids were transfected into SW480 and HT29 cells according to the grouping using Lipofectamine 3000. After 48 h, the success rate of transfection of the cells was determined by qRT-PCR.","RIPA lysate (tissue/cell, R0010) produced by Solarbio (China) was used to separate protein from SW480 and HT29 cells, and its concentration was determined with BCA protein concentration determination reagent (PC0020, Solarbio, China). Then the protein was subjected to high temperature denaturation to maintain a relatively stable state. After being electrophoresed by SDS-PAGE, the protein was transferred to the membrane carrier (PVDF membrane). The membrane was soaked with the same BSA sealant (SW3015) produced by Solarbio for 2 h at room temperature. Next, the membrane was incubated with Abcam (USA) antibodies (anti-STAT3, ab68153, 88 kDa; Bax, ab32503, 21 kDa; Bcl-2, ab59348, 26 kDa) that could bind to the corresponding antigens in the protein on the membrane overnight (4 °C), with GAPDH (ab8245, 36KD) as an internal reference. To identify the antigen-antibody complex, a secondary antibody labeled with HRP (Goat Anti-Rabbit antibody, 1:10,000, ab6721; Goat Anti-Mouse antibody, 1:10000, ab205719) was used to treat the PVDF membrane for 1.5 h at room temperature. Fluorescent signal of the complex was enhanced using the ECL Western Blotting Substrate (PE0010, Solarbio) and detected using the Gel Doc™ XR+ imaging system (BIO-RAD, USA). The gray value of the bands was calculated by Image Lab software to determine the final gene protein level.","SPSS 20.0 software (IBM, USA) was employed for statistical analysis of the data obtained in the experiments. Differences between two groups were compared by independent sample t-test, and those between multiple groups were compared by one-way ANOVA followed by Tukey’s test. When p<0.05, the difference was considered statistically significant.","ALO at a Concentration Gradient Inhibited Proliferation Yet Promoted Apoptosis in CRC Cells In this study, the toxicity of ALO in normal cells (CCD-18Co) was detected, ALO showed no significant toxicity to CCD-18Co cells (Figure 1A). In order to study the effects of ALO on the growth and basic physiological functions of CRC cells, we used different concentrations of ALO to treat SW480 and HT29 cells. The results showed that 0.2 mmol/L, 0.4 mmol/L, 0.8 mmol/L and 1 mmol/L ALO solutions all inhibited the viability of SW480 and HT29 cells (p<0.05, Figure 1B and C). Moreover, the viability of SW480 and HT29 cells was close to 50% under the treatment of 0.8 mmol/L ALO solution, but was reduced to less than 50% when the ALO concentration was increased to 1 mmol/L (Figure 1B and C). In the following EdU cell proliferation experiment, the red fluorescence expression was reduced in SW480 and HT29 cells under the treatment of 0.8 mmol/L ALO (Figure 1D and E). Consistently, the clone formation experiment also showed that different concentrations of ALO (0.2 mmol/L, 0.4 mmol/L, 0.8 mmol/L) reduced the number of cell clones and inhibited the proliferation of SW480 and HT29 cells (p<0.05, Figure 1F–H). At the same time, the increase in the concentration of the ALO solution accelerated the death of CRC cells. As shown in Figure 1I–K, the number of apoptotic SW480 and HT29 cells increased significantly after treatment with different concentrations of ALO (p<0.01, Figure 1I–K).Figure 1ALO at a concentration gradient inhibited proliferation yet promoted apoptosis in CRC cells. (A) The effect of ALO on the normal colonic tissue cells (CCD-18Co) was detected by MTT experiments. (B and C) MTT experiments showed that ALO at a concentration gradient (0.1 mmol/L, 0.2 mmol/L, 0.4 mmol/L, 0.8 mmol/L and 1 mmol/L) inhibited the activity of SW480 and HT29 cells. (D and E) EdU cell proliferation experiments showed that 0.8 mmol/L ALO inhibited the proliferation of SW480 and HT29 cells. The magnification was 100×. (F–H) The clone formation experiment showed that ALO at a concentration gradient (0.2 mmol/L, 0.4 mmol/L and 0.8 mmol/L) inhibited the proliferation of SW480 and HT29 cells. (I–K) Flow cytometry experiments showed that ALO at a concentration gradient (0.2 mmol/L, 0.4 mmol/L and 0.8 mmol/L) promoted the apoptosis of SW480 and HT29 cells. All experiments were repeated three times to obtain average values. *p<0.05, **p<0.01, ***p<0.001 vs Control.Abbreviations: ALO, aloperine; CRC, colorectal cancer.\nALO at a concentration gradient inhibited proliferation yet promoted apoptosis in CRC cells. (A) The effect of ALO on the normal colonic tissue cells (CCD-18Co) was detected by MTT experiments. (B and C) MTT experiments showed that ALO at a concentration gradient (0.1 mmol/L, 0.2 mmol/L, 0.4 mmol/L, 0.8 mmol/L and 1 mmol/L) inhibited the activity of SW480 and HT29 cells. (D and E) EdU cell proliferation experiments showed that 0.8 mmol/L ALO inhibited the proliferation of SW480 and HT29 cells. The magnification was 100×. (F–H) The clone formation experiment showed that ALO at a concentration gradient (0.2 mmol/L, 0.4 mmol/L and 0.8 mmol/L) inhibited the proliferation of SW480 and HT29 cells. (I–K) Flow cytometry experiments showed that ALO at a concentration gradient (0.2 mmol/L, 0.4 mmol/L and 0.8 mmol/L) promoted the apoptosis of SW480 and HT29 cells. All experiments were repeated three times to obtain average values. *p<0.05, **p<0.01, ***p<0.001 vs Control.\nIn this study, the toxicity of ALO in normal cells (CCD-18Co) was detected, ALO showed no significant toxicity to CCD-18Co cells (Figure 1A). In order to study the effects of ALO on the growth and basic physiological functions of CRC cells, we used different concentrations of ALO to treat SW480 and HT29 cells. The results showed that 0.2 mmol/L, 0.4 mmol/L, 0.8 mmol/L and 1 mmol/L ALO solutions all inhibited the viability of SW480 and HT29 cells (p<0.05, Figure 1B and C). Moreover, the viability of SW480 and HT29 cells was close to 50% under the treatment of 0.8 mmol/L ALO solution, but was reduced to less than 50% when the ALO concentration was increased to 1 mmol/L (Figure 1B and C). In the following EdU cell proliferation experiment, the red fluorescence expression was reduced in SW480 and HT29 cells under the treatment of 0.8 mmol/L ALO (Figure 1D and E). Consistently, the clone formation experiment also showed that different concentrations of ALO (0.2 mmol/L, 0.4 mmol/L, 0.8 mmol/L) reduced the number of cell clones and inhibited the proliferation of SW480 and HT29 cells (p<0.05, Figure 1F–H). At the same time, the increase in the concentration of the ALO solution accelerated the death of CRC cells. As shown in Figure 1I–K, the number of apoptotic SW480 and HT29 cells increased significantly after treatment with different concentrations of ALO (p<0.01, Figure 1I–K).Figure 1ALO at a concentration gradient inhibited proliferation yet promoted apoptosis in CRC cells. (A) The effect of ALO on the normal colonic tissue cells (CCD-18Co) was detected by MTT experiments. (B and C) MTT experiments showed that ALO at a concentration gradient (0.1 mmol/L, 0.2 mmol/L, 0.4 mmol/L, 0.8 mmol/L and 1 mmol/L) inhibited the activity of SW480 and HT29 cells. (D and E) EdU cell proliferation experiments showed that 0.8 mmol/L ALO inhibited the proliferation of SW480 and HT29 cells. The magnification was 100×. (F–H) The clone formation experiment showed that ALO at a concentration gradient (0.2 mmol/L, 0.4 mmol/L and 0.8 mmol/L) inhibited the proliferation of SW480 and HT29 cells. (I–K) Flow cytometry experiments showed that ALO at a concentration gradient (0.2 mmol/L, 0.4 mmol/L and 0.8 mmol/L) promoted the apoptosis of SW480 and HT29 cells. All experiments were repeated three times to obtain average values. *p<0.05, **p<0.01, ***p<0.001 vs Control.Abbreviations: ALO, aloperine; CRC, colorectal cancer.\nALO at a concentration gradient inhibited proliferation yet promoted apoptosis in CRC cells. (A) The effect of ALO on the normal colonic tissue cells (CCD-18Co) was detected by MTT experiments. (B and C) MTT experiments showed that ALO at a concentration gradient (0.1 mmol/L, 0.2 mmol/L, 0.4 mmol/L, 0.8 mmol/L and 1 mmol/L) inhibited the activity of SW480 and HT29 cells. (D and E) EdU cell proliferation experiments showed that 0.8 mmol/L ALO inhibited the proliferation of SW480 and HT29 cells. The magnification was 100×. (F–H) The clone formation experiment showed that ALO at a concentration gradient (0.2 mmol/L, 0.4 mmol/L and 0.8 mmol/L) inhibited the proliferation of SW480 and HT29 cells. (I–K) Flow cytometry experiments showed that ALO at a concentration gradient (0.2 mmol/L, 0.4 mmol/L and 0.8 mmol/L) promoted the apoptosis of SW480 and HT29 cells. All experiments were repeated three times to obtain average values. *p<0.05, **p<0.01, ***p<0.001 vs Control.\nThe Abnormally Low Expression of miR-296-5p in Colon Cancer Could Be Up-Regulated by ALO Data from the Starbase database showed that the expression of miR-296-5p in colon cancer patients was significantly lower than that in healthy people (p=2.5e-6, Figure 2A). In this study, however, after treatment with different concentrations of ALO for 24 h, miR-296-5p activity in SW480 and HT29 cells was significantly increased (p<0.01, Figure 2B and C), suggesting that ALO can stimulate the up-regulation of miR-296-5p.Figure 2The abnormally low expression of miR-296-5p in colon cancer could be upregulated by ALO. (A) Starbase (http://starbase.sysu.edu.cn/index.php) was used to retrieve the information of differential expression of miR-296-5p in colon adenocarcinoma (COAD, n=450) patients and healthy volunteers (n=8) in vivo. P=2.5e-6. (B and C) The qRT-PCR experiment showed that ALO at a concentration gradient (0.1 mmol/L, 0.2 mmol/L, 0.4 mmol/L, 0.8 mmol/L and 1 mmol/L) up-regulated miR-296-5p expression in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. **p<0.01, ***p<0.001 vs Control.Abbreviation: qRT-PCR, quantitative real-time polymerase chain reaction.\nThe abnormally low expression of miR-296-5p in colon cancer could be upregulated by ALO. (A) Starbase (http://starbase.sysu.edu.cn/index.php) was used to retrieve the information of differential expression of miR-296-5p in colon adenocarcinoma (COAD, n=450) patients and healthy volunteers (n=8) in vivo. P=2.5e-6. (B and C) The qRT-PCR experiment showed that ALO at a concentration gradient (0.1 mmol/L, 0.2 mmol/L, 0.4 mmol/L, 0.8 mmol/L and 1 mmol/L) up-regulated miR-296-5p expression in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. **p<0.01, ***p<0.001 vs Control.\nData from the Starbase database showed that the expression of miR-296-5p in colon cancer patients was significantly lower than that in healthy people (p=2.5e-6, Figure 2A). In this study, however, after treatment with different concentrations of ALO for 24 h, miR-296-5p activity in SW480 and HT29 cells was significantly increased (p<0.01, Figure 2B and C), suggesting that ALO can stimulate the up-regulation of miR-296-5p.Figure 2The abnormally low expression of miR-296-5p in colon cancer could be upregulated by ALO. (A) Starbase (http://starbase.sysu.edu.cn/index.php) was used to retrieve the information of differential expression of miR-296-5p in colon adenocarcinoma (COAD, n=450) patients and healthy volunteers (n=8) in vivo. P=2.5e-6. (B and C) The qRT-PCR experiment showed that ALO at a concentration gradient (0.1 mmol/L, 0.2 mmol/L, 0.4 mmol/L, 0.8 mmol/L and 1 mmol/L) up-regulated miR-296-5p expression in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. **p<0.01, ***p<0.001 vs Control.Abbreviation: qRT-PCR, quantitative real-time polymerase chain reaction.\nThe abnormally low expression of miR-296-5p in colon cancer could be upregulated by ALO. (A) Starbase (http://starbase.sysu.edu.cn/index.php) was used to retrieve the information of differential expression of miR-296-5p in colon adenocarcinoma (COAD, n=450) patients and healthy volunteers (n=8) in vivo. P=2.5e-6. (B and C) The qRT-PCR experiment showed that ALO at a concentration gradient (0.1 mmol/L, 0.2 mmol/L, 0.4 mmol/L, 0.8 mmol/L and 1 mmol/L) up-regulated miR-296-5p expression in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. **p<0.01, ***p<0.001 vs Control.\nAmong the Differentially Expressed circRNAs in CRC, Only circNSUN2 Not Only Had a Target Site for miR-296-5p, but Also Could Be Regulated by ALO Through literature review, we screened 12 circRNAs that were reported to be associated with CRC: circDDX17, circHIPK3, circZFR, circRNA_100290, Has_circ_0026344, circVAPA, Hsa_circ_0009361, circNSUN2, circACAP2, circACVRL1, circITGA7 and circEIF4G3. We first detected the regulatory effect of ALO on these differentially expressed circRNAs by qRT-PCR to perform preliminary screening (Figure 3A). The results showed that the expressions of circDDX17, Has_circ_0026344, circVAPA, circNSUN2, circACAP2, circACVRL1 and circEIF4G3 were regulated by 0.8 mmol/L ALO (p<0.001, Figure 3A). Through subsequent screening of target genes, we found that only circNSUN2 and miR-296-5p have targeted binding sites (Figure 3B). After further verification, it was confirmed that circNSUN2-WT and miR-296-5p mimic did reduce the luciferase activity in SW480 and HT29 cells (p<0.001, Figure 3C and D). Therefore, we chose circNSUN2 as the next research object.Figure 3Among the differentially expressed circRNAs in CRC, only circNSUN2 not only had a target site for miR-296-5p, but also could be regulated by ALO. (A) qRT-PCR detected the regulation of 0.8 mmol/L ALO on the differentially expressed circRNAs in CRC. GAPDH played the role of internal reference. (B) The TargetScan database (http://www.targetscan.org) was used to predict the target sites of circNSUN2 and miR-296-5p. (C and D) The dual luciferase experiment confirmed that circNSUN2 can bind to miR-296-5p in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. ***p<0.001 vs Control; +++p<0.001 vs MC.Abbreviation: MC, miR-296-5p mimic control.\nAmong the differentially expressed circRNAs in CRC, only circNSUN2 not only had a target site for miR-296-5p, but also could be regulated by ALO. (A) qRT-PCR detected the regulation of 0.8 mmol/L ALO on the differentially expressed circRNAs in CRC. GAPDH played the role of internal reference. (B) The TargetScan database (http://www.targetscan.org) was used to predict the target sites of circNSUN2 and miR-296-5p. (C and D) The dual luciferase experiment confirmed that circNSUN2 can bind to miR-296-5p in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. ***p<0.001 vs Control; +++p<0.001 vs MC.\nThrough literature review, we screened 12 circRNAs that were reported to be associated with CRC: circDDX17, circHIPK3, circZFR, circRNA_100290, Has_circ_0026344, circVAPA, Hsa_circ_0009361, circNSUN2, circACAP2, circACVRL1, circITGA7 and circEIF4G3. We first detected the regulatory effect of ALO on these differentially expressed circRNAs by qRT-PCR to perform preliminary screening (Figure 3A). The results showed that the expressions of circDDX17, Has_circ_0026344, circVAPA, circNSUN2, circACAP2, circACVRL1 and circEIF4G3 were regulated by 0.8 mmol/L ALO (p<0.001, Figure 3A). Through subsequent screening of target genes, we found that only circNSUN2 and miR-296-5p have targeted binding sites (Figure 3B). After further verification, it was confirmed that circNSUN2-WT and miR-296-5p mimic did reduce the luciferase activity in SW480 and HT29 cells (p<0.001, Figure 3C and D). Therefore, we chose circNSUN2 as the next research object.Figure 3Among the differentially expressed circRNAs in CRC, only circNSUN2 not only had a target site for miR-296-5p, but also could be regulated by ALO. (A) qRT-PCR detected the regulation of 0.8 mmol/L ALO on the differentially expressed circRNAs in CRC. GAPDH played the role of internal reference. (B) The TargetScan database (http://www.targetscan.org) was used to predict the target sites of circNSUN2 and miR-296-5p. (C and D) The dual luciferase experiment confirmed that circNSUN2 can bind to miR-296-5p in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. ***p<0.001 vs Control; +++p<0.001 vs MC.Abbreviation: MC, miR-296-5p mimic control.\nAmong the differentially expressed circRNAs in CRC, only circNSUN2 not only had a target site for miR-296-5p, but also could be regulated by ALO. (A) qRT-PCR detected the regulation of 0.8 mmol/L ALO on the differentially expressed circRNAs in CRC. GAPDH played the role of internal reference. (B) The TargetScan database (http://www.targetscan.org) was used to predict the target sites of circNSUN2 and miR-296-5p. (C and D) The dual luciferase experiment confirmed that circNSUN2 can bind to miR-296-5p in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. ***p<0.001 vs Control; +++p<0.001 vs MC.\nOverexpression of circNSUN2 Partially Offset the Suppression of ALO on the Biological Behavior of Cancer Cells In SW480 and HT29 cells, ALO suppressed the mRNA level of circNSUN2, whereas transfection of circNSUN2 overexpression significantly increased the expression of circNSUN2 (p<0.001, Figure 4A and B). The following basic cell function experiments showed that the upregulation of circNSUN2 partially offset the inhibitory effect of ALO on CRC cell viability, proliferation and apoptosis (p<0.001, Figure 4C–L).Figure 4Overexpression of circNSUN2 partially offset the suppression of ALO on the biological behavior of cancer cells. (A and B) The qRT-PCR experiment showed that ALO (0.8 mmol/L) inhibited the expression of circNSUN2 in SW480 and HT29 cells. GAPDH played the role of internal reference. (C and D) The MTT experiment showed that overexpression of circNSUN2 partially offset the inhibition of ALO on cell viability in SW480 and HT29 cells. (E and F) EdU cell proliferation experiments showed that overexpression of circNSUN2 partially offset the inhibition of ALO on proliferation in SW480 and HT29 cells. The magnification was 100×. (G–I) The clone formation experiment showed that overexpression of circNSUN2 partially offset the inhibition of ALO on proliferation in SW480 and HT29 cells. (J–L) Flow cytometry experiments showed that overexpression of circNSUN2 partially offset the promotion of ALO on apoptosis in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. ***p<0.001 vs Control; ^p<0.05, ^^p<0.01, ^^^p<0.001 vs ALO+NC.Abbreviations: CircNSUN2, NOP2/Sun RNA methyltransferase 2; NC, negative control.\nOverexpression of circNSUN2 partially offset the suppression of ALO on the biological behavior of cancer cells. (A and B) The qRT-PCR experiment showed that ALO (0.8 mmol/L) inhibited the expression of circNSUN2 in SW480 and HT29 cells. GAPDH played the role of internal reference. (C and D) The MTT experiment showed that overexpression of circNSUN2 partially offset the inhibition of ALO on cell viability in SW480 and HT29 cells. (E and F) EdU cell proliferation experiments showed that overexpression of circNSUN2 partially offset the inhibition of ALO on proliferation in SW480 and HT29 cells. The magnification was 100×. (G–I) The clone formation experiment showed that overexpression of circNSUN2 partially offset the inhibition of ALO on proliferation in SW480 and HT29 cells. (J–L) Flow cytometry experiments showed that overexpression of circNSUN2 partially offset the promotion of ALO on apoptosis in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. ***p<0.001 vs Control; ^p<0.05, ^^p<0.01, ^^^p<0.001 vs ALO+NC.\nIn SW480 and HT29 cells, ALO suppressed the mRNA level of circNSUN2, whereas transfection of circNSUN2 overexpression significantly increased the expression of circNSUN2 (p<0.001, Figure 4A and B). The following basic cell function experiments showed that the upregulation of circNSUN2 partially offset the inhibitory effect of ALO on CRC cell viability, proliferation and apoptosis (p<0.001, Figure 4C–L).Figure 4Overexpression of circNSUN2 partially offset the suppression of ALO on the biological behavior of cancer cells. (A and B) The qRT-PCR experiment showed that ALO (0.8 mmol/L) inhibited the expression of circNSUN2 in SW480 and HT29 cells. GAPDH played the role of internal reference. (C and D) The MTT experiment showed that overexpression of circNSUN2 partially offset the inhibition of ALO on cell viability in SW480 and HT29 cells. (E and F) EdU cell proliferation experiments showed that overexpression of circNSUN2 partially offset the inhibition of ALO on proliferation in SW480 and HT29 cells. The magnification was 100×. (G–I) The clone formation experiment showed that overexpression of circNSUN2 partially offset the inhibition of ALO on proliferation in SW480 and HT29 cells. (J–L) Flow cytometry experiments showed that overexpression of circNSUN2 partially offset the promotion of ALO on apoptosis in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. ***p<0.001 vs Control; ^p<0.05, ^^p<0.01, ^^^p<0.001 vs ALO+NC.Abbreviations: CircNSUN2, NOP2/Sun RNA methyltransferase 2; NC, negative control.\nOverexpression of circNSUN2 partially offset the suppression of ALO on the biological behavior of cancer cells. (A and B) The qRT-PCR experiment showed that ALO (0.8 mmol/L) inhibited the expression of circNSUN2 in SW480 and HT29 cells. GAPDH played the role of internal reference. (C and D) The MTT experiment showed that overexpression of circNSUN2 partially offset the inhibition of ALO on cell viability in SW480 and HT29 cells. (E and F) EdU cell proliferation experiments showed that overexpression of circNSUN2 partially offset the inhibition of ALO on proliferation in SW480 and HT29 cells. The magnification was 100×. (G–I) The clone formation experiment showed that overexpression of circNSUN2 partially offset the inhibition of ALO on proliferation in SW480 and HT29 cells. (J–L) Flow cytometry experiments showed that overexpression of circNSUN2 partially offset the promotion of ALO on apoptosis in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. ***p<0.001 vs Control; ^p<0.05, ^^p<0.01, ^^^p<0.001 vs ALO+NC.\nSh-circNSUN2 Inhibited the Malignant Development of CRC Cells, Which Was Neutralized by miR-296-5p Inhibitor We also tested the mutual regulation of circNSUN2 and miR-296-5p in SW480 and HT29 cells. Sh-circNSUN2 could down-regulate the expression of circNSUN2, and besides, it inhibited the proliferation of SW480 and HT29 cells, while promoting their apoptosis and the expression of miR-296-5p (p<0.001, Figure 5A–J). However, the inhibitory effect of sh-circNSUN2 on the malignant development of the two cancer cells was neutralized by miR-296-5p inhibitor (p<0.001, Figure 5A–J).Figure 5Sh-circNSUN2 inhibited the malignant development of CRC cells, which was neutralized by miR-296-5p inhibitor. (A and B) The qRT-PCR experiment showed that miR-296-5p inhibitor neutralized the inhibitory effect of sh-circNSUN2 on circNSUN2 expression. GAPDH played the role of internal reference. (C and D) The qRT-PCR experiment showed that sh-circNSUN2 up-regulated the mRNA level of miR-296-5p. U6 played the role of internal reference. (E–G) The clone formation experiment showed that the effect of sh-circNSUN2 on the proliferation of SW480 and HT29 cells was neutralized by miR-296-5p inhibitor. (H-J) Flow cytometry experiments showed that the effect of sh-circNSUN2 on promoting the apoptosis of SW480 and HT29 cells was neutralized by miR-296-5p inhibitor. All experiments were repeated three times to obtain average values. ***p<0.001 vs sh-NC+IC; ^^^p<0.001 vs sh-circNSUN2+IC; ###p<0.001 vs sh-NC+I.Abbreviations: Sh-circNSUN2, silent circNSUN2; sh-NC, silent negative control; IC, miR-296-5p inhibitor control; I, miR-296-5p inhibitor.\nSh-circNSUN2 inhibited the malignant development of CRC cells, which was neutralized by miR-296-5p inhibitor. (A and B) The qRT-PCR experiment showed that miR-296-5p inhibitor neutralized the inhibitory effect of sh-circNSUN2 on circNSUN2 expression. GAPDH played the role of internal reference. (C and D) The qRT-PCR experiment showed that sh-circNSUN2 up-regulated the mRNA level of miR-296-5p. U6 played the role of internal reference. (E–G) The clone formation experiment showed that the effect of sh-circNSUN2 on the proliferation of SW480 and HT29 cells was neutralized by miR-296-5p inhibitor. (H-J) Flow cytometry experiments showed that the effect of sh-circNSUN2 on promoting the apoptosis of SW480 and HT29 cells was neutralized by miR-296-5p inhibitor. All experiments were repeated three times to obtain average values. ***p<0.001 vs sh-NC+IC; ^^^p<0.001 vs sh-circNSUN2+IC; ###p<0.001 vs sh-NC+I.\nWe also tested the mutual regulation of circNSUN2 and miR-296-5p in SW480 and HT29 cells. Sh-circNSUN2 could down-regulate the expression of circNSUN2, and besides, it inhibited the proliferation of SW480 and HT29 cells, while promoting their apoptosis and the expression of miR-296-5p (p<0.001, Figure 5A–J). However, the inhibitory effect of sh-circNSUN2 on the malignant development of the two cancer cells was neutralized by miR-296-5p inhibitor (p<0.001, Figure 5A–J).Figure 5Sh-circNSUN2 inhibited the malignant development of CRC cells, which was neutralized by miR-296-5p inhibitor. (A and B) The qRT-PCR experiment showed that miR-296-5p inhibitor neutralized the inhibitory effect of sh-circNSUN2 on circNSUN2 expression. GAPDH played the role of internal reference. (C and D) The qRT-PCR experiment showed that sh-circNSUN2 up-regulated the mRNA level of miR-296-5p. U6 played the role of internal reference. (E–G) The clone formation experiment showed that the effect of sh-circNSUN2 on the proliferation of SW480 and HT29 cells was neutralized by miR-296-5p inhibitor. (H-J) Flow cytometry experiments showed that the effect of sh-circNSUN2 on promoting the apoptosis of SW480 and HT29 cells was neutralized by miR-296-5p inhibitor. All experiments were repeated three times to obtain average values. ***p<0.001 vs sh-NC+IC; ^^^p<0.001 vs sh-circNSUN2+IC; ###p<0.001 vs sh-NC+I.Abbreviations: Sh-circNSUN2, silent circNSUN2; sh-NC, silent negative control; IC, miR-296-5p inhibitor control; I, miR-296-5p inhibitor.\nSh-circNSUN2 inhibited the malignant development of CRC cells, which was neutralized by miR-296-5p inhibitor. (A and B) The qRT-PCR experiment showed that miR-296-5p inhibitor neutralized the inhibitory effect of sh-circNSUN2 on circNSUN2 expression. GAPDH played the role of internal reference. (C and D) The qRT-PCR experiment showed that sh-circNSUN2 up-regulated the mRNA level of miR-296-5p. U6 played the role of internal reference. (E–G) The clone formation experiment showed that the effect of sh-circNSUN2 on the proliferation of SW480 and HT29 cells was neutralized by miR-296-5p inhibitor. (H-J) Flow cytometry experiments showed that the effect of sh-circNSUN2 on promoting the apoptosis of SW480 and HT29 cells was neutralized by miR-296-5p inhibitor. All experiments were repeated three times to obtain average values. ***p<0.001 vs sh-NC+IC; ^^^p<0.001 vs sh-circNSUN2+IC; ###p<0.001 vs sh-NC+I.\nMiR-296-5p Bound to STAT3 and Upregulation of Mir-296-5p Inhibited the Expression of STAT3 in CRC Cells We screened the target genes of miR-296-5p, and found that miR-296-5p and STAT3 have targeted binding sites (Figure 6A), and this binding relation was further confirmed by a dual-luciferase verification test (p<0.001, Figure 6B and C). Next, we used qRT-PCR and Western blot to analyze the mRNA and protein levels of miR-296-5p and STAT. It was found that miR-296-5p mimic up-regulated miR-296-5p expression while preventing the activation of STAT3 (p<0.001, Figure 7A–G). However, the overexpression of STAT3 reversed the regulation of miR-296-5p mimic on the two genes (p<0.001, Figure 7A–G).Figure 6The targeted binding of miR-296-5p to STAT3. (A) TargetScan was used to predict the binding sequence of miR-296-5p and STAT3. (B and C) Dual luciferase experiment confirmed that miR-296-5p can bind to STAT3 in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. +++p<0.001 vs MC.Figure 7Up-regulation of miR-296-5p inhibited the expression of STAT3 in CRC cells. (A and B) The qRT-PCR experiment showed that miR-296-5p mimic up-regulated the mRNA level of miR-296-5p. U6 played the role of internal reference. (C and D) The qRT-PCR experiment showed that miR-296-5p mimic suppressed the mRNA level of STAT3, while overexpression of STAT3 reversed this effect. GAPDH played the role of internal reference. (E–G) The Western blot experiment showed that miR-296-5p mimic inhibited the protein level of STAT3, while over-expression of STAT3 reversed this effect. GAPDH played the role of internal reference. All experiments were repeated three times to obtain average values. ***p<0.001 vs MC+NC; ^^^p<0.001 vs M+NC; ###p<0.001 vs MC+STAT3.Abbreviation: STAT3, signal transducer and activator of transcription 3.\nThe targeted binding of miR-296-5p to STAT3. (A) TargetScan was used to predict the binding sequence of miR-296-5p and STAT3. (B and C) Dual luciferase experiment confirmed that miR-296-5p can bind to STAT3 in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. +++p<0.001 vs MC.\nUp-regulation of miR-296-5p inhibited the expression of STAT3 in CRC cells. (A and B) The qRT-PCR experiment showed that miR-296-5p mimic up-regulated the mRNA level of miR-296-5p. U6 played the role of internal reference. (C and D) The qRT-PCR experiment showed that miR-296-5p mimic suppressed the mRNA level of STAT3, while overexpression of STAT3 reversed this effect. GAPDH played the role of internal reference. (E–G) The Western blot experiment showed that miR-296-5p mimic inhibited the protein level of STAT3, while over-expression of STAT3 reversed this effect. GAPDH played the role of internal reference. All experiments were repeated three times to obtain average values. ***p<0.001 vs MC+NC; ^^^p<0.001 vs M+NC; ###p<0.001 vs MC+STAT3.\nWe screened the target genes of miR-296-5p, and found that miR-296-5p and STAT3 have targeted binding sites (Figure 6A), and this binding relation was further confirmed by a dual-luciferase verification test (p<0.001, Figure 6B and C). Next, we used qRT-PCR and Western blot to analyze the mRNA and protein levels of miR-296-5p and STAT. It was found that miR-296-5p mimic up-regulated miR-296-5p expression while preventing the activation of STAT3 (p<0.001, Figure 7A–G). However, the overexpression of STAT3 reversed the regulation of miR-296-5p mimic on the two genes (p<0.001, Figure 7A–G).Figure 6The targeted binding of miR-296-5p to STAT3. (A) TargetScan was used to predict the binding sequence of miR-296-5p and STAT3. (B and C) Dual luciferase experiment confirmed that miR-296-5p can bind to STAT3 in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. +++p<0.001 vs MC.Figure 7Up-regulation of miR-296-5p inhibited the expression of STAT3 in CRC cells. (A and B) The qRT-PCR experiment showed that miR-296-5p mimic up-regulated the mRNA level of miR-296-5p. U6 played the role of internal reference. (C and D) The qRT-PCR experiment showed that miR-296-5p mimic suppressed the mRNA level of STAT3, while overexpression of STAT3 reversed this effect. GAPDH played the role of internal reference. (E–G) The Western blot experiment showed that miR-296-5p mimic inhibited the protein level of STAT3, while over-expression of STAT3 reversed this effect. GAPDH played the role of internal reference. All experiments were repeated three times to obtain average values. ***p<0.001 vs MC+NC; ^^^p<0.001 vs M+NC; ###p<0.001 vs MC+STAT3.Abbreviation: STAT3, signal transducer and activator of transcription 3.\nThe targeted binding of miR-296-5p to STAT3. (A) TargetScan was used to predict the binding sequence of miR-296-5p and STAT3. (B and C) Dual luciferase experiment confirmed that miR-296-5p can bind to STAT3 in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. +++p<0.001 vs MC.\nUp-regulation of miR-296-5p inhibited the expression of STAT3 in CRC cells. (A and B) The qRT-PCR experiment showed that miR-296-5p mimic up-regulated the mRNA level of miR-296-5p. U6 played the role of internal reference. (C and D) The qRT-PCR experiment showed that miR-296-5p mimic suppressed the mRNA level of STAT3, while overexpression of STAT3 reversed this effect. GAPDH played the role of internal reference. (E–G) The Western blot experiment showed that miR-296-5p mimic inhibited the protein level of STAT3, while over-expression of STAT3 reversed this effect. GAPDH played the role of internal reference. All experiments were repeated three times to obtain average values. ***p<0.001 vs MC+NC; ^^^p<0.001 vs M+NC; ###p<0.001 vs MC+STAT3.\nMiR-296-5p Mimic Inhibited Proliferation and Promoted Apoptosis in CRC Cells by Regulating Apoptosis-Related Genes, Which Was Partially Reversed by Overexpressed STAT3 The following cell experiments showed that by activating Bax and blocking the protein activity of Bcl-2, miR-296-5p mimic inhibited proliferation yet accelerated apoptosis in cancer cells (p<0.001, Figure 8A–I). However, up-regulation of STAT3 produced a completely opposite regulatory effect (p<0.001, Figure 8A–I). More importantly, overexpression of STAT3 neutralized the regulation of miR-296-5p mimic on CRC cells (p<0.001, Figure 8A–I).Figure 8MiR-296-5p mimic inhibited proliferation and promoted apoptosis in CRC cells by regulating apoptosis-related genes, which was partially reversed by overexpression of STAT3. (A–C) The clone formation experiment showed that the inhibitory effect of miR-296-5p mimic on the proliferation of SW480 and HT29 cells was neutralized by overexpressed STAT3. (D–F) Flow cytometry experiments showed that miR-296-5p mimic promoted the apoptosis of SW480 and HT29 cells, which was neutralized by overexpressed STAT3. (G–I) Western blot experiments showed that the regulation of apoptosis-related protein expression by miR-296-5p mimic was neutralized by overexpressed STAT3. GAPDH played the role of internal reference. All experiments were repeated three times to obtain average values. *p<0.05, **p<0.01, ***p<0.001 vs MC+NC; ^^p<0.01, ^^^p<0.001 vs M+NC; ##p<0.01, ###p<0.001 vs MC+STAT3.\nMiR-296-5p mimic inhibited proliferation and promoted apoptosis in CRC cells by regulating apoptosis-related genes, which was partially reversed by overexpression of STAT3. (A–C) The clone formation experiment showed that the inhibitory effect of miR-296-5p mimic on the proliferation of SW480 and HT29 cells was neutralized by overexpressed STAT3. (D–F) Flow cytometry experiments showed that miR-296-5p mimic promoted the apoptosis of SW480 and HT29 cells, which was neutralized by overexpressed STAT3. (G–I) Western blot experiments showed that the regulation of apoptosis-related protein expression by miR-296-5p mimic was neutralized by overexpressed STAT3. GAPDH played the role of internal reference. All experiments were repeated three times to obtain average values. *p<0.05, **p<0.01, ***p<0.001 vs MC+NC; ^^p<0.01, ^^^p<0.001 vs M+NC; ##p<0.01, ###p<0.001 vs MC+STAT3.\nThe following cell experiments showed that by activating Bax and blocking the protein activity of Bcl-2, miR-296-5p mimic inhibited proliferation yet accelerated apoptosis in cancer cells (p<0.001, Figure 8A–I). However, up-regulation of STAT3 produced a completely opposite regulatory effect (p<0.001, Figure 8A–I). More importantly, overexpression of STAT3 neutralized the regulation of miR-296-5p mimic on CRC cells (p<0.001, Figure 8A–I).Figure 8MiR-296-5p mimic inhibited proliferation and promoted apoptosis in CRC cells by regulating apoptosis-related genes, which was partially reversed by overexpression of STAT3. (A–C) The clone formation experiment showed that the inhibitory effect of miR-296-5p mimic on the proliferation of SW480 and HT29 cells was neutralized by overexpressed STAT3. (D–F) Flow cytometry experiments showed that miR-296-5p mimic promoted the apoptosis of SW480 and HT29 cells, which was neutralized by overexpressed STAT3. (G–I) Western blot experiments showed that the regulation of apoptosis-related protein expression by miR-296-5p mimic was neutralized by overexpressed STAT3. GAPDH played the role of internal reference. All experiments were repeated three times to obtain average values. *p<0.05, **p<0.01, ***p<0.001 vs MC+NC; ^^p<0.01, ^^^p<0.001 vs M+NC; ##p<0.01, ###p<0.001 vs MC+STAT3.\nMiR-296-5p mimic inhibited proliferation and promoted apoptosis in CRC cells by regulating apoptosis-related genes, which was partially reversed by overexpression of STAT3. (A–C) The clone formation experiment showed that the inhibitory effect of miR-296-5p mimic on the proliferation of SW480 and HT29 cells was neutralized by overexpressed STAT3. (D–F) Flow cytometry experiments showed that miR-296-5p mimic promoted the apoptosis of SW480 and HT29 cells, which was neutralized by overexpressed STAT3. (G–I) Western blot experiments showed that the regulation of apoptosis-related protein expression by miR-296-5p mimic was neutralized by overexpressed STAT3. GAPDH played the role of internal reference. All experiments were repeated three times to obtain average values. *p<0.05, **p<0.01, ***p<0.001 vs MC+NC; ^^p<0.01, ^^^p<0.001 vs M+NC; ##p<0.01, ###p<0.001 vs MC+STAT3.","In this study, the toxicity of ALO in normal cells (CCD-18Co) was detected, ALO showed no significant toxicity to CCD-18Co cells (Figure 1A). In order to study the effects of ALO on the growth and basic physiological functions of CRC cells, we used different concentrations of ALO to treat SW480 and HT29 cells. The results showed that 0.2 mmol/L, 0.4 mmol/L, 0.8 mmol/L and 1 mmol/L ALO solutions all inhibited the viability of SW480 and HT29 cells (p<0.05, Figure 1B and C). Moreover, the viability of SW480 and HT29 cells was close to 50% under the treatment of 0.8 mmol/L ALO solution, but was reduced to less than 50% when the ALO concentration was increased to 1 mmol/L (Figure 1B and C). In the following EdU cell proliferation experiment, the red fluorescence expression was reduced in SW480 and HT29 cells under the treatment of 0.8 mmol/L ALO (Figure 1D and E). Consistently, the clone formation experiment also showed that different concentrations of ALO (0.2 mmol/L, 0.4 mmol/L, 0.8 mmol/L) reduced the number of cell clones and inhibited the proliferation of SW480 and HT29 cells (p<0.05, Figure 1F–H). At the same time, the increase in the concentration of the ALO solution accelerated the death of CRC cells. As shown in Figure 1I–K, the number of apoptotic SW480 and HT29 cells increased significantly after treatment with different concentrations of ALO (p<0.01, Figure 1I–K).Figure 1ALO at a concentration gradient inhibited proliferation yet promoted apoptosis in CRC cells. (A) The effect of ALO on the normal colonic tissue cells (CCD-18Co) was detected by MTT experiments. (B and C) MTT experiments showed that ALO at a concentration gradient (0.1 mmol/L, 0.2 mmol/L, 0.4 mmol/L, 0.8 mmol/L and 1 mmol/L) inhibited the activity of SW480 and HT29 cells. (D and E) EdU cell proliferation experiments showed that 0.8 mmol/L ALO inhibited the proliferation of SW480 and HT29 cells. The magnification was 100×. (F–H) The clone formation experiment showed that ALO at a concentration gradient (0.2 mmol/L, 0.4 mmol/L and 0.8 mmol/L) inhibited the proliferation of SW480 and HT29 cells. (I–K) Flow cytometry experiments showed that ALO at a concentration gradient (0.2 mmol/L, 0.4 mmol/L and 0.8 mmol/L) promoted the apoptosis of SW480 and HT29 cells. All experiments were repeated three times to obtain average values. *p<0.05, **p<0.01, ***p<0.001 vs Control.Abbreviations: ALO, aloperine; CRC, colorectal cancer.\nALO at a concentration gradient inhibited proliferation yet promoted apoptosis in CRC cells. (A) The effect of ALO on the normal colonic tissue cells (CCD-18Co) was detected by MTT experiments. (B and C) MTT experiments showed that ALO at a concentration gradient (0.1 mmol/L, 0.2 mmol/L, 0.4 mmol/L, 0.8 mmol/L and 1 mmol/L) inhibited the activity of SW480 and HT29 cells. (D and E) EdU cell proliferation experiments showed that 0.8 mmol/L ALO inhibited the proliferation of SW480 and HT29 cells. The magnification was 100×. (F–H) The clone formation experiment showed that ALO at a concentration gradient (0.2 mmol/L, 0.4 mmol/L and 0.8 mmol/L) inhibited the proliferation of SW480 and HT29 cells. (I–K) Flow cytometry experiments showed that ALO at a concentration gradient (0.2 mmol/L, 0.4 mmol/L and 0.8 mmol/L) promoted the apoptosis of SW480 and HT29 cells. All experiments were repeated three times to obtain average values. *p<0.05, **p<0.01, ***p<0.001 vs Control.","Data from the Starbase database showed that the expression of miR-296-5p in colon cancer patients was significantly lower than that in healthy people (p=2.5e-6, Figure 2A). In this study, however, after treatment with different concentrations of ALO for 24 h, miR-296-5p activity in SW480 and HT29 cells was significantly increased (p<0.01, Figure 2B and C), suggesting that ALO can stimulate the up-regulation of miR-296-5p.Figure 2The abnormally low expression of miR-296-5p in colon cancer could be upregulated by ALO. (A) Starbase (http://starbase.sysu.edu.cn/index.php) was used to retrieve the information of differential expression of miR-296-5p in colon adenocarcinoma (COAD, n=450) patients and healthy volunteers (n=8) in vivo. P=2.5e-6. (B and C) The qRT-PCR experiment showed that ALO at a concentration gradient (0.1 mmol/L, 0.2 mmol/L, 0.4 mmol/L, 0.8 mmol/L and 1 mmol/L) up-regulated miR-296-5p expression in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. **p<0.01, ***p<0.001 vs Control.Abbreviation: qRT-PCR, quantitative real-time polymerase chain reaction.\nThe abnormally low expression of miR-296-5p in colon cancer could be upregulated by ALO. (A) Starbase (http://starbase.sysu.edu.cn/index.php) was used to retrieve the information of differential expression of miR-296-5p in colon adenocarcinoma (COAD, n=450) patients and healthy volunteers (n=8) in vivo. P=2.5e-6. (B and C) The qRT-PCR experiment showed that ALO at a concentration gradient (0.1 mmol/L, 0.2 mmol/L, 0.4 mmol/L, 0.8 mmol/L and 1 mmol/L) up-regulated miR-296-5p expression in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. **p<0.01, ***p<0.001 vs Control.","Through literature review, we screened 12 circRNAs that were reported to be associated with CRC: circDDX17, circHIPK3, circZFR, circRNA_100290, Has_circ_0026344, circVAPA, Hsa_circ_0009361, circNSUN2, circACAP2, circACVRL1, circITGA7 and circEIF4G3. We first detected the regulatory effect of ALO on these differentially expressed circRNAs by qRT-PCR to perform preliminary screening (Figure 3A). The results showed that the expressions of circDDX17, Has_circ_0026344, circVAPA, circNSUN2, circACAP2, circACVRL1 and circEIF4G3 were regulated by 0.8 mmol/L ALO (p<0.001, Figure 3A). Through subsequent screening of target genes, we found that only circNSUN2 and miR-296-5p have targeted binding sites (Figure 3B). After further verification, it was confirmed that circNSUN2-WT and miR-296-5p mimic did reduce the luciferase activity in SW480 and HT29 cells (p<0.001, Figure 3C and D). Therefore, we chose circNSUN2 as the next research object.Figure 3Among the differentially expressed circRNAs in CRC, only circNSUN2 not only had a target site for miR-296-5p, but also could be regulated by ALO. (A) qRT-PCR detected the regulation of 0.8 mmol/L ALO on the differentially expressed circRNAs in CRC. GAPDH played the role of internal reference. (B) The TargetScan database (http://www.targetscan.org) was used to predict the target sites of circNSUN2 and miR-296-5p. (C and D) The dual luciferase experiment confirmed that circNSUN2 can bind to miR-296-5p in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. ***p<0.001 vs Control; +++p<0.001 vs MC.Abbreviation: MC, miR-296-5p mimic control.\nAmong the differentially expressed circRNAs in CRC, only circNSUN2 not only had a target site for miR-296-5p, but also could be regulated by ALO. (A) qRT-PCR detected the regulation of 0.8 mmol/L ALO on the differentially expressed circRNAs in CRC. GAPDH played the role of internal reference. (B) The TargetScan database (http://www.targetscan.org) was used to predict the target sites of circNSUN2 and miR-296-5p. (C and D) The dual luciferase experiment confirmed that circNSUN2 can bind to miR-296-5p in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. ***p<0.001 vs Control; +++p<0.001 vs MC.","In SW480 and HT29 cells, ALO suppressed the mRNA level of circNSUN2, whereas transfection of circNSUN2 overexpression significantly increased the expression of circNSUN2 (p<0.001, Figure 4A and B). The following basic cell function experiments showed that the upregulation of circNSUN2 partially offset the inhibitory effect of ALO on CRC cell viability, proliferation and apoptosis (p<0.001, Figure 4C–L).Figure 4Overexpression of circNSUN2 partially offset the suppression of ALO on the biological behavior of cancer cells. (A and B) The qRT-PCR experiment showed that ALO (0.8 mmol/L) inhibited the expression of circNSUN2 in SW480 and HT29 cells. GAPDH played the role of internal reference. (C and D) The MTT experiment showed that overexpression of circNSUN2 partially offset the inhibition of ALO on cell viability in SW480 and HT29 cells. (E and F) EdU cell proliferation experiments showed that overexpression of circNSUN2 partially offset the inhibition of ALO on proliferation in SW480 and HT29 cells. The magnification was 100×. (G–I) The clone formation experiment showed that overexpression of circNSUN2 partially offset the inhibition of ALO on proliferation in SW480 and HT29 cells. (J–L) Flow cytometry experiments showed that overexpression of circNSUN2 partially offset the promotion of ALO on apoptosis in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. ***p<0.001 vs Control; ^p<0.05, ^^p<0.01, ^^^p<0.001 vs ALO+NC.Abbreviations: CircNSUN2, NOP2/Sun RNA methyltransferase 2; NC, negative control.\nOverexpression of circNSUN2 partially offset the suppression of ALO on the biological behavior of cancer cells. (A and B) The qRT-PCR experiment showed that ALO (0.8 mmol/L) inhibited the expression of circNSUN2 in SW480 and HT29 cells. GAPDH played the role of internal reference. (C and D) The MTT experiment showed that overexpression of circNSUN2 partially offset the inhibition of ALO on cell viability in SW480 and HT29 cells. (E and F) EdU cell proliferation experiments showed that overexpression of circNSUN2 partially offset the inhibition of ALO on proliferation in SW480 and HT29 cells. The magnification was 100×. (G–I) The clone formation experiment showed that overexpression of circNSUN2 partially offset the inhibition of ALO on proliferation in SW480 and HT29 cells. (J–L) Flow cytometry experiments showed that overexpression of circNSUN2 partially offset the promotion of ALO on apoptosis in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. ***p<0.001 vs Control; ^p<0.05, ^^p<0.01, ^^^p<0.001 vs ALO+NC.","We also tested the mutual regulation of circNSUN2 and miR-296-5p in SW480 and HT29 cells. Sh-circNSUN2 could down-regulate the expression of circNSUN2, and besides, it inhibited the proliferation of SW480 and HT29 cells, while promoting their apoptosis and the expression of miR-296-5p (p<0.001, Figure 5A–J). However, the inhibitory effect of sh-circNSUN2 on the malignant development of the two cancer cells was neutralized by miR-296-5p inhibitor (p<0.001, Figure 5A–J).Figure 5Sh-circNSUN2 inhibited the malignant development of CRC cells, which was neutralized by miR-296-5p inhibitor. (A and B) The qRT-PCR experiment showed that miR-296-5p inhibitor neutralized the inhibitory effect of sh-circNSUN2 on circNSUN2 expression. GAPDH played the role of internal reference. (C and D) The qRT-PCR experiment showed that sh-circNSUN2 up-regulated the mRNA level of miR-296-5p. U6 played the role of internal reference. (E–G) The clone formation experiment showed that the effect of sh-circNSUN2 on the proliferation of SW480 and HT29 cells was neutralized by miR-296-5p inhibitor. (H-J) Flow cytometry experiments showed that the effect of sh-circNSUN2 on promoting the apoptosis of SW480 and HT29 cells was neutralized by miR-296-5p inhibitor. All experiments were repeated three times to obtain average values. ***p<0.001 vs sh-NC+IC; ^^^p<0.001 vs sh-circNSUN2+IC; ###p<0.001 vs sh-NC+I.Abbreviations: Sh-circNSUN2, silent circNSUN2; sh-NC, silent negative control; IC, miR-296-5p inhibitor control; I, miR-296-5p inhibitor.\nSh-circNSUN2 inhibited the malignant development of CRC cells, which was neutralized by miR-296-5p inhibitor. (A and B) The qRT-PCR experiment showed that miR-296-5p inhibitor neutralized the inhibitory effect of sh-circNSUN2 on circNSUN2 expression. GAPDH played the role of internal reference. (C and D) The qRT-PCR experiment showed that sh-circNSUN2 up-regulated the mRNA level of miR-296-5p. U6 played the role of internal reference. (E–G) The clone formation experiment showed that the effect of sh-circNSUN2 on the proliferation of SW480 and HT29 cells was neutralized by miR-296-5p inhibitor. (H-J) Flow cytometry experiments showed that the effect of sh-circNSUN2 on promoting the apoptosis of SW480 and HT29 cells was neutralized by miR-296-5p inhibitor. All experiments were repeated three times to obtain average values. ***p<0.001 vs sh-NC+IC; ^^^p<0.001 vs sh-circNSUN2+IC; ###p<0.001 vs sh-NC+I.","We screened the target genes of miR-296-5p, and found that miR-296-5p and STAT3 have targeted binding sites (Figure 6A), and this binding relation was further confirmed by a dual-luciferase verification test (p<0.001, Figure 6B and C). Next, we used qRT-PCR and Western blot to analyze the mRNA and protein levels of miR-296-5p and STAT. It was found that miR-296-5p mimic up-regulated miR-296-5p expression while preventing the activation of STAT3 (p<0.001, Figure 7A–G). However, the overexpression of STAT3 reversed the regulation of miR-296-5p mimic on the two genes (p<0.001, Figure 7A–G).Figure 6The targeted binding of miR-296-5p to STAT3. (A) TargetScan was used to predict the binding sequence of miR-296-5p and STAT3. (B and C) Dual luciferase experiment confirmed that miR-296-5p can bind to STAT3 in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. +++p<0.001 vs MC.Figure 7Up-regulation of miR-296-5p inhibited the expression of STAT3 in CRC cells. (A and B) The qRT-PCR experiment showed that miR-296-5p mimic up-regulated the mRNA level of miR-296-5p. U6 played the role of internal reference. (C and D) The qRT-PCR experiment showed that miR-296-5p mimic suppressed the mRNA level of STAT3, while overexpression of STAT3 reversed this effect. GAPDH played the role of internal reference. (E–G) The Western blot experiment showed that miR-296-5p mimic inhibited the protein level of STAT3, while over-expression of STAT3 reversed this effect. GAPDH played the role of internal reference. All experiments were repeated three times to obtain average values. ***p<0.001 vs MC+NC; ^^^p<0.001 vs M+NC; ###p<0.001 vs MC+STAT3.Abbreviation: STAT3, signal transducer and activator of transcription 3.\nThe targeted binding of miR-296-5p to STAT3. (A) TargetScan was used to predict the binding sequence of miR-296-5p and STAT3. (B and C) Dual luciferase experiment confirmed that miR-296-5p can bind to STAT3 in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. +++p<0.001 vs MC.\nUp-regulation of miR-296-5p inhibited the expression of STAT3 in CRC cells. (A and B) The qRT-PCR experiment showed that miR-296-5p mimic up-regulated the mRNA level of miR-296-5p. U6 played the role of internal reference. (C and D) The qRT-PCR experiment showed that miR-296-5p mimic suppressed the mRNA level of STAT3, while overexpression of STAT3 reversed this effect. GAPDH played the role of internal reference. (E–G) The Western blot experiment showed that miR-296-5p mimic inhibited the protein level of STAT3, while over-expression of STAT3 reversed this effect. GAPDH played the role of internal reference. All experiments were repeated three times to obtain average values. ***p<0.001 vs MC+NC; ^^^p<0.001 vs M+NC; ###p<0.001 vs MC+STAT3.","The following cell experiments showed that by activating Bax and blocking the protein activity of Bcl-2, miR-296-5p mimic inhibited proliferation yet accelerated apoptosis in cancer cells (p<0.001, Figure 8A–I). However, up-regulation of STAT3 produced a completely opposite regulatory effect (p<0.001, Figure 8A–I). More importantly, overexpression of STAT3 neutralized the regulation of miR-296-5p mimic on CRC cells (p<0.001, Figure 8A–I).Figure 8MiR-296-5p mimic inhibited proliferation and promoted apoptosis in CRC cells by regulating apoptosis-related genes, which was partially reversed by overexpression of STAT3. (A–C) The clone formation experiment showed that the inhibitory effect of miR-296-5p mimic on the proliferation of SW480 and HT29 cells was neutralized by overexpressed STAT3. (D–F) Flow cytometry experiments showed that miR-296-5p mimic promoted the apoptosis of SW480 and HT29 cells, which was neutralized by overexpressed STAT3. (G–I) Western blot experiments showed that the regulation of apoptosis-related protein expression by miR-296-5p mimic was neutralized by overexpressed STAT3. GAPDH played the role of internal reference. All experiments were repeated three times to obtain average values. *p<0.05, **p<0.01, ***p<0.001 vs MC+NC; ^^p<0.01, ^^^p<0.001 vs M+NC; ##p<0.01, ###p<0.001 vs MC+STAT3.\nMiR-296-5p mimic inhibited proliferation and promoted apoptosis in CRC cells by regulating apoptosis-related genes, which was partially reversed by overexpression of STAT3. (A–C) The clone formation experiment showed that the inhibitory effect of miR-296-5p mimic on the proliferation of SW480 and HT29 cells was neutralized by overexpressed STAT3. (D–F) Flow cytometry experiments showed that miR-296-5p mimic promoted the apoptosis of SW480 and HT29 cells, which was neutralized by overexpressed STAT3. (G–I) Western blot experiments showed that the regulation of apoptosis-related protein expression by miR-296-5p mimic was neutralized by overexpressed STAT3. GAPDH played the role of internal reference. All experiments were repeated three times to obtain average values. *p<0.05, **p<0.01, ***p<0.001 vs MC+NC; ^^p<0.01, ^^^p<0.001 vs M+NC; ##p<0.01, ###p<0.001 vs MC+STAT3.","CRC is a malignant lesion of the mucosal epithelium of the colon or rectum under the action of various carcinogenic factors such as environment or heredity.2 Most CRC patients suffer from adenocarcinoma, which usually develops from polyps. Tumors developed from polyps can infiltrate in a circular shape along the horizontal axis of the intestinal tube, develop into the deep layer of the intestinal wall, and finally penetrate the intestinal wall and metastasize to blood vessels or lymphatic vessels.20 This is one of the important reasons for the high recurrence and metastasis rates of CRC. The ultimate goals of CRC research are to improve the current treatment of CRC, save patients’ lives and improve their quality of life. Based on the role of traditional Chinese medicine in disease prevention and treatment, this study explored the effects of ALO, an alkaloid exhibiting strong anticancer activity, on the proliferation and apoptosis of CRC cells. The results showed that after ALO treatment, the viability and proliferation of CRC cells were significantly inhibited; on the contrary, the number of apoptotic cancer cells was significantly increased. This is consistent with the previous experimental results.21 In order to probe deeper into the upstream mechanism of ALO in alleviating CRC carcinogenesis through the miR-296-5p/STAT3 axis, we screened and verified the upstream targeting circRNA of miR-296-5p.\nCircRNAs are a type of covalently closed circular non-coding RNA formed by back-splicing of mRNA precursors (pre-mRNA).22 CircRNA was considered to be a useless splicing by-product in early research. With the deepening of research, it is found that circRNA has a wide range of sources and plays a variety of functional roles in the growth and development of organisms, with the characteristics of conservative, stable, and tissue-specific.22,23 The most familiar and extensively studied function of circRNA is its sponging effect on miRNA.24 For example, circRNA ciRS-7 which contains more than 70 miR-7 binding sites can achieve competitive adsorption of miR-7 through the AGO2 protein.25 Besides, increasing reports on cancer indicate that circRNAs, such as circRNA-cTFRC, circPSMC3 and circSETD3, act as sponges of miRNAs and participate in transcriptional regulation.26–28 Based on these literature reports, we screened circRNAs that were reported to be abnormally expressed in CRC, and finally obtained 12 circRNAs. After detecting their expressions in ALO-treated cancer cells and predicting their binding sequence for miR-296-5p, circNSUN2 was finally identified as the research object of this study.\nCircNSUN2, which maps to the 5p15 amplicon in CRCs, was confirmed by Chen et al to regulate cytoplasmic output and promote liver metastasis of CRC through N 6-methyladenosine modification.29 Similarly, our experimental results uncovered that knockdown of circNSUN2 inhibited proliferation and accelerated apoptosis in CRC cells. More importantly, we for the first time revealed that ALO can prevent the activation of circNSUN2 and offset the promotion effect of overexpression of circNSUN2 on esophageal cancer progression. Since circNSUN2 and miR-296-5p have targeting sequences, we verified the cellular regulatory effects of circNSUN2 and miR-296-5p/STAT3 through dual luciferase experiments and rescue experiments. The final result confirmed our conjecture that regulating the circNSUN2/miR-296-5p/STAT3 axis can reduce the proliferation rate and increase the apoptosis rate of CRC cells.\nAt present, blocking proliferation and increasing apoptosis in cancer cells are the intensively discussed mechanisms in CRC study. Tumor cells can proliferate quickly and unrestrictedly, and thus, inhibiting their proliferation can produce an anti-tumor effect.30 Apoptosis, alternatively called programmed cell death, is an autonomous cell death process strictly controlled by multiple genes. Apoptosis is an important part of cell life cycle, and it is also an important link in regulating the development of the body and maintaining the stability of the internal environment in organisms.31,32 Our research shows that ALO inhibits the proliferation and promotes the apoptosis of CRC cells by regulating the circNSUN2/miR-296-5p/STAT3 pathway, and ultimately prevents the tumorigenesis of CRC.\nThere are still certain limitations in our research. Although we have confirmed the effect of ALO on inducing apoptosis and reducing proliferation in CRC cells and clarified the underlying mechanism, the circNSUN2/miR-296-5p/STAT3 axis and the pathways related to proliferation and apoptosis have not been analyzed yet, which will be further explored in future research. In addition, the anticancer effect of ALO on CRC and its experimental concentration needs to be further tested in animal and clinical experiments. Whether ALO has an impact on drug resistance in CRC is also a direction of future research."],"string":"[\n \"Cell Purchase and ALO Processing CCD-18Co and CRC cell lines SW480 (CCL-228™) and HT29 (HTB-38™) used in this research were provided by the American Type Culture Collection (ATCC). According to the culture requirements, CCD-18Co, SW480 and HT29 cells were separately inoculated in DMEM medium (30–2007, ATCC) containing 10% fetal bovine serum and incubated in a D180-P cell incubator (RWD, China) containing 5% CO2 at 37 °C.\\nALO (DK0052, CAS NO: 56293-29-9, HPLC≥98%), the research object of this study, was provided by Chengdu DESITE Biological Company, China. ALO was dissolved in fresh DMEM to prepare 0.1 mmol/L, 0.2 mmol/L, 0.4 mmol/L, 0.8 mmol/L and 1 mmol/L ALO solutions, which were separately used to treat the CCD-18Co, SW480 and HT29 cells for 24 hours (h).\\nCCD-18Co and CRC cell lines SW480 (CCL-228™) and HT29 (HTB-38™) used in this research were provided by the American Type Culture Collection (ATCC). According to the culture requirements, CCD-18Co, SW480 and HT29 cells were separately inoculated in DMEM medium (30–2007, ATCC) containing 10% fetal bovine serum and incubated in a D180-P cell incubator (RWD, China) containing 5% CO2 at 37 °C.\\nALO (DK0052, CAS NO: 56293-29-9, HPLC≥98%), the research object of this study, was provided by Chengdu DESITE Biological Company, China. ALO was dissolved in fresh DMEM to prepare 0.1 mmol/L, 0.2 mmol/L, 0.4 mmol/L, 0.8 mmol/L and 1 mmol/L ALO solutions, which were separately used to treat the CCD-18Co, SW480 and HT29 cells for 24 hours (h).\\nMTT Assay One hundred µL of SW480 or HT29 cell suspension (1×104 cells/mL) was transferred to each well of a 96-well plate. Of note, three holes were added to the same treatment group. After treatment with the ALO solutions for 24 h, SW480 or HT29 cells were added with MTT reagent (10 µL/well, PB180519, Procell, USA), which could react with mitochondria in living cells. After 4 h of binding, a HBS-1096A enzyme label analyzer (DeTie, China) was used to detect the absorbance of SW480 or HT29 cells in the mixed solutions at 490 nm, and then cell viability was calculated based on the absorbance.\\nOne hundred µL of SW480 or HT29 cell suspension (1×104 cells/mL) was transferred to each well of a 96-well plate. Of note, three holes were added to the same treatment group. After treatment with the ALO solutions for 24 h, SW480 or HT29 cells were added with MTT reagent (10 µL/well, PB180519, Procell, USA), which could react with mitochondria in living cells. After 4 h of binding, a HBS-1096A enzyme label analyzer (DeTie, China) was used to detect the absorbance of SW480 or HT29 cells in the mixed solutions at 490 nm, and then cell viability was calculated based on the absorbance.\\nEdU (5-Ethynyl-2ʹ-Deoxyuridine) Cell Proliferation Experiment SW480 or HT29 cells in logarithmic growth phase were seeded in 96-well plates at 1×104 cells/well. EdU reagent (ST067) purchased from Beyotime was diluted to 50 μmol/L with fresh DMEM medium. The EdU dilution was then used to incubate the cells at 100 μL/well for 2 h. After removing the culture medium, the SW480 or HT29 cells were fixed with methanol at room temperature for 15 minutes (min). Positive fluorescence expression (red) in the collected SW480 or HT29 cells were observed, photographed and recorded under a Leica DMi8 inverted fluorescence microscope (magnification 100 ×). Then DAPI reagent was used to stain the nuclei, and the stains were observed under a microscope.\\nSW480 or HT29 cells in logarithmic growth phase were seeded in 96-well plates at 1×104 cells/well. EdU reagent (ST067) purchased from Beyotime was diluted to 50 μmol/L with fresh DMEM medium. The EdU dilution was then used to incubate the cells at 100 μL/well for 2 h. After removing the culture medium, the SW480 or HT29 cells were fixed with methanol at room temperature for 15 minutes (min). Positive fluorescence expression (red) in the collected SW480 or HT29 cells were observed, photographed and recorded under a Leica DMi8 inverted fluorescence microscope (magnification 100 ×). Then DAPI reagent was used to stain the nuclei, and the stains were observed under a microscope.\\nColony Formation Assay Two hundred SW480 or HT29 cells were transferred to petri dishes containing complete medium (10 mL). In order to distribute the cells evenly, the petri dishes were rotated slowly for 1 min after cell inoculation. Next, the culture dishes containing cells were placed in an incubator for routine culture. During the culture, the medium was changed every 2 days (d). After 14 d, the SW480 or HT29 cells were soaked with Giemsa dye for 20 min and then the colony formation of the cells was observed under a microscope.\\nTwo hundred SW480 or HT29 cells were transferred to petri dishes containing complete medium (10 mL). In order to distribute the cells evenly, the petri dishes were rotated slowly for 1 min after cell inoculation. Next, the culture dishes containing cells were placed in an incubator for routine culture. During the culture, the medium was changed every 2 days (d). After 14 d, the SW480 or HT29 cells were soaked with Giemsa dye for 20 min and then the colony formation of the cells was observed under a microscope.\\nApoptosis Experiment An Annexin V-FITC Apoptosis Detection Kit (CA1020, Solarbio, China) and a flow cytometer (CytoFLEX, BECKMAN COULTER, USA) were used to detect changes in the apoptosis of SW480 or HT29 cells. In brief, SW480 or HT29 cells were prepared into 1×106 cells/mL cell suspension with 1 mL 1× Binding Buffer. One hundred μL of SW480 or HT29 cells and 5 μL of Annexin V-FITC reagent were added to a centrifuge tube and mixed for 10 min in the dark at room temperature. Next, 5 μL of PI reagent was added to the centrifuge tube and incubated in the dark at room temperature for 5 min. Afterwards, PBS was added to adjust the mixture to a final volume of 500 μL. After half an hour, the cells in the centrifuge tube were transferred to the detection instrument to calculate the number of apoptotic cells.\\nAn Annexin V-FITC Apoptosis Detection Kit (CA1020, Solarbio, China) and a flow cytometer (CytoFLEX, BECKMAN COULTER, USA) were used to detect changes in the apoptosis of SW480 or HT29 cells. In brief, SW480 or HT29 cells were prepared into 1×106 cells/mL cell suspension with 1 mL 1× Binding Buffer. One hundred μL of SW480 or HT29 cells and 5 μL of Annexin V-FITC reagent were added to a centrifuge tube and mixed for 10 min in the dark at room temperature. Next, 5 μL of PI reagent was added to the centrifuge tube and incubated in the dark at room temperature for 5 min. Afterwards, PBS was added to adjust the mixture to a final volume of 500 μL. After half an hour, the cells in the centrifuge tube were transferred to the detection instrument to calculate the number of apoptotic cells.\\nBioinformatics Analysis and Target Gene Binding Verification Starbase (http://starbase.sysu.edu.cn/index.php) was used to retrieve and analyze information of differential expression of miR-296-5p in Colon adenocarcinoma (COAD) patients and healthy volunteers. TargetScan (http://www.targetscan.org) was used to predict the circRNA that contained a binding sequence for miR-296-5p and the downstream target genes of miR-296-5p. The binding sequences predicted by TargetScan were designed by COBIOER (China) and used to construct the following reporter plasmids for dual luciferase experiments: NOP2/Sun RNA methyltransferase 2 wild type (circNSUN2-WT), circNSUN2-mutant type (MUT), Signal Transducer and Activator of Transcription 3 (STAT3)-WT and STAT3-MUT. The reporter plasmids were separately co-transfected with miR-296-5p mimic or mimic control into SW480 and HT29 cells using liposome for 48 h. Luciferase activity in the transfected SW480 and HT29 cells was detected using Dual-Luciferase® Reporter Assay System (E1960, Promega, USA) with a GloMax 20/20 luminometer (Promega, USA).\\nStarbase (http://starbase.sysu.edu.cn/index.php) was used to retrieve and analyze information of differential expression of miR-296-5p in Colon adenocarcinoma (COAD) patients and healthy volunteers. TargetScan (http://www.targetscan.org) was used to predict the circRNA that contained a binding sequence for miR-296-5p and the downstream target genes of miR-296-5p. The binding sequences predicted by TargetScan were designed by COBIOER (China) and used to construct the following reporter plasmids for dual luciferase experiments: NOP2/Sun RNA methyltransferase 2 wild type (circNSUN2-WT), circNSUN2-mutant type (MUT), Signal Transducer and Activator of Transcription 3 (STAT3)-WT and STAT3-MUT. The reporter plasmids were separately co-transfected with miR-296-5p mimic or mimic control into SW480 and HT29 cells using liposome for 48 h. Luciferase activity in the transfected SW480 and HT29 cells was detected using Dual-Luciferase® Reporter Assay System (E1960, Promega, USA) with a GloMax 20/20 luminometer (Promega, USA).\\nQuantitative Real-Time Polymerase Chain Reaction (qRT-PCR) qRT-PCR is a reliable method for rapid detection of gene mRNA levels. Briefly, total RNA of SW480 and HT29 cells was fully isolated by the conventional RNA extraction reagent TRIzol (15,596,018, Invitrogen, USA), and subsequently reverse transcribed into cDNA using the RNA reverse transcription reagent (RR047A) developed by TaKaRa (Japan). The qRT-PCR reaction system which consisted of cDNA, gene primers (Sangon synthesis), SYBR® Green (S4438-20RXN, Sigma-Aldrich, Germany) and DEPC water was added to a 96-well plate and put into the detection instrument (Bio-Rad thermal cycler T100). The reaction conditions were set as follows: pre-denaturation at 95 °C for 10 min, denaturation at 95 °C for 15 seconds (s), and annealing at 58 °C for 1 min, for a total of 40 cycles. According to Anita Ciesielska’s report,19 gene mRNA levels were quantified by the 2−ΔΔCT method. GAPDH and U6 were internal references in the experiment. The specific sequences of synthetic primers were shown in Table 1.Table 1Primers for qRT-PCRGeneForward Primer (5ʹ-3ʹ)Reverse Primer (5ʹ-3ʹ)miR-296-5pCCTGTGTCGTATCCAGTGCAAGTCGTATCCAGTGCGTGTCGcircDDX17TGCCAACCACAACATCCTCCACGCTCCCCAGGATTACCAAATcircHIPK3TATGTTGGTGGATCCTGTTCGGCATGGTGGGTAGACCAAGACTTGTGAcircZFRAACCACCACAGATTCACTATAACCACCACAGATTCACTATcircRNA_100290GTCATTCCCTCTTTAATGGTGCAGAACTTCCGCTCTAACATACHas_circ_0026344CTCAGCCTCTAGCATAAGCTCAGGCAAGAGAATGATTTGAACcircVAPATGGATTCCAAATTGAGATGCGTATTCACTTTTCTATCCGATGGATTTCGCHsa_circ_0009361AGAACCAGATTCGAGACGCCGTGCTCTTCAATGCCACCTTCcircNSUN2CTTGAGAAAATCGCCACACTTGTTGAGGAGCAGTGGTGGcircACAP2TCAGAGCATCTGCCCAAAGTTAAATGAACCCCAAGGCTCCGcircITGA7GTGTGCACAGGTCCTTCCAATGGAAGTTCTGTGAGGGACGcircEIF4G3CCTACCCCATCCCCTTATTCACCGTGCTGTAGACTGCTGAGSTAT3CCCCATACCTGAAGACCAAGGGACTCAAACTGCCCTCCTU6CTCGCTTCGGCAGCACAAACGCTTCACGAATTTGCGTGAPDHTGTGGGCATCAATGGATTTGGACACCATGTATTCCGGGTCAAT\\n\\nPrimers for qRT-PCR\\nqRT-PCR is a reliable method for rapid detection of gene mRNA levels. Briefly, total RNA of SW480 and HT29 cells was fully isolated by the conventional RNA extraction reagent TRIzol (15,596,018, Invitrogen, USA), and subsequently reverse transcribed into cDNA using the RNA reverse transcription reagent (RR047A) developed by TaKaRa (Japan). The qRT-PCR reaction system which consisted of cDNA, gene primers (Sangon synthesis), SYBR® Green (S4438-20RXN, Sigma-Aldrich, Germany) and DEPC water was added to a 96-well plate and put into the detection instrument (Bio-Rad thermal cycler T100). The reaction conditions were set as follows: pre-denaturation at 95 °C for 10 min, denaturation at 95 °C for 15 seconds (s), and annealing at 58 °C for 1 min, for a total of 40 cycles. According to Anita Ciesielska’s report,19 gene mRNA levels were quantified by the 2−ΔΔCT method. GAPDH and U6 were internal references in the experiment. The specific sequences of synthetic primers were shown in Table 1.Table 1Primers for qRT-PCRGeneForward Primer (5ʹ-3ʹ)Reverse Primer (5ʹ-3ʹ)miR-296-5pCCTGTGTCGTATCCAGTGCAAGTCGTATCCAGTGCGTGTCGcircDDX17TGCCAACCACAACATCCTCCACGCTCCCCAGGATTACCAAATcircHIPK3TATGTTGGTGGATCCTGTTCGGCATGGTGGGTAGACCAAGACTTGTGAcircZFRAACCACCACAGATTCACTATAACCACCACAGATTCACTATcircRNA_100290GTCATTCCCTCTTTAATGGTGCAGAACTTCCGCTCTAACATACHas_circ_0026344CTCAGCCTCTAGCATAAGCTCAGGCAAGAGAATGATTTGAACcircVAPATGGATTCCAAATTGAGATGCGTATTCACTTTTCTATCCGATGGATTTCGCHsa_circ_0009361AGAACCAGATTCGAGACGCCGTGCTCTTCAATGCCACCTTCcircNSUN2CTTGAGAAAATCGCCACACTTGTTGAGGAGCAGTGGTGGcircACAP2TCAGAGCATCTGCCCAAAGTTAAATGAACCCCAAGGCTCCGcircITGA7GTGTGCACAGGTCCTTCCAATGGAAGTTCTGTGAGGGACGcircEIF4G3CCTACCCCATCCCCTTATTCACCGTGCTGTAGACTGCTGAGSTAT3CCCCATACCTGAAGACCAAGGGACTCAAACTGCCCTCCTU6CTCGCTTCGGCAGCACAAACGCTTCACGAATTTGCGTGAPDHTGTGGGCATCAATGGATTTGGACACCATGTATTCCGGGTCAAT\\n\\nPrimers for qRT-PCR\\nCell Transfection Exogenous up-regulation or knockdown of test genes is a common method to observe their regulatory effects on cells or tissues. Liposome transfection with Lipofectamine 3000 (L3000008, ThermoFisher Scientific, USA) is the most common and convenient method for exogenous interference. Therefore, we commissioned Guangzhou GENESEED Biological Company to synthesize circNSUN2 overexpressed plasmid, circNSUN2 silenced plasmid (sh-circNSUN2), STAT3 overexpressed plasmid and their respective negative controls. MiR-296-5p mimic (miR10000690-1-5) and mimic control (miR1N0000001-1-5), as well as miR-296-5p inhibitor (miR20000690-1-5) and inhibitor control (miR2N0000001-1-5), were purchased directly from Guangzhou RIOBOBIO Company. The plasmids were transfected into SW480 and HT29 cells according to the grouping using Lipofectamine 3000. After 48 h, the success rate of transfection of the cells was determined by qRT-PCR.\\nExogenous up-regulation or knockdown of test genes is a common method to observe their regulatory effects on cells or tissues. Liposome transfection with Lipofectamine 3000 (L3000008, ThermoFisher Scientific, USA) is the most common and convenient method for exogenous interference. Therefore, we commissioned Guangzhou GENESEED Biological Company to synthesize circNSUN2 overexpressed plasmid, circNSUN2 silenced plasmid (sh-circNSUN2), STAT3 overexpressed plasmid and their respective negative controls. MiR-296-5p mimic (miR10000690-1-5) and mimic control (miR1N0000001-1-5), as well as miR-296-5p inhibitor (miR20000690-1-5) and inhibitor control (miR2N0000001-1-5), were purchased directly from Guangzhou RIOBOBIO Company. The plasmids were transfected into SW480 and HT29 cells according to the grouping using Lipofectamine 3000. After 48 h, the success rate of transfection of the cells was determined by qRT-PCR.\\nWestern Blot RIPA lysate (tissue/cell, R0010) produced by Solarbio (China) was used to separate protein from SW480 and HT29 cells, and its concentration was determined with BCA protein concentration determination reagent (PC0020, Solarbio, China). Then the protein was subjected to high temperature denaturation to maintain a relatively stable state. After being electrophoresed by SDS-PAGE, the protein was transferred to the membrane carrier (PVDF membrane). The membrane was soaked with the same BSA sealant (SW3015) produced by Solarbio for 2 h at room temperature. Next, the membrane was incubated with Abcam (USA) antibodies (anti-STAT3, ab68153, 88 kDa; Bax, ab32503, 21 kDa; Bcl-2, ab59348, 26 kDa) that could bind to the corresponding antigens in the protein on the membrane overnight (4 °C), with GAPDH (ab8245, 36KD) as an internal reference. To identify the antigen-antibody complex, a secondary antibody labeled with HRP (Goat Anti-Rabbit antibody, 1:10,000, ab6721; Goat Anti-Mouse antibody, 1:10000, ab205719) was used to treat the PVDF membrane for 1.5 h at room temperature. Fluorescent signal of the complex was enhanced using the ECL Western Blotting Substrate (PE0010, Solarbio) and detected using the Gel Doc™ XR+ imaging system (BIO-RAD, USA). The gray value of the bands was calculated by Image Lab software to determine the final gene protein level.\\nRIPA lysate (tissue/cell, R0010) produced by Solarbio (China) was used to separate protein from SW480 and HT29 cells, and its concentration was determined with BCA protein concentration determination reagent (PC0020, Solarbio, China). Then the protein was subjected to high temperature denaturation to maintain a relatively stable state. After being electrophoresed by SDS-PAGE, the protein was transferred to the membrane carrier (PVDF membrane). The membrane was soaked with the same BSA sealant (SW3015) produced by Solarbio for 2 h at room temperature. Next, the membrane was incubated with Abcam (USA) antibodies (anti-STAT3, ab68153, 88 kDa; Bax, ab32503, 21 kDa; Bcl-2, ab59348, 26 kDa) that could bind to the corresponding antigens in the protein on the membrane overnight (4 °C), with GAPDH (ab8245, 36KD) as an internal reference. To identify the antigen-antibody complex, a secondary antibody labeled with HRP (Goat Anti-Rabbit antibody, 1:10,000, ab6721; Goat Anti-Mouse antibody, 1:10000, ab205719) was used to treat the PVDF membrane for 1.5 h at room temperature. Fluorescent signal of the complex was enhanced using the ECL Western Blotting Substrate (PE0010, Solarbio) and detected using the Gel Doc™ XR+ imaging system (BIO-RAD, USA). The gray value of the bands was calculated by Image Lab software to determine the final gene protein level.\\nStatistical Analysis SPSS 20.0 software (IBM, USA) was employed for statistical analysis of the data obtained in the experiments. Differences between two groups were compared by independent sample t-test, and those between multiple groups were compared by one-way ANOVA followed by Tukey’s test. When p<0.05, the difference was considered statistically significant.\\nSPSS 20.0 software (IBM, USA) was employed for statistical analysis of the data obtained in the experiments. Differences between two groups were compared by independent sample t-test, and those between multiple groups were compared by one-way ANOVA followed by Tukey’s test. When p<0.05, the difference was considered statistically significant.\",\n \"CCD-18Co and CRC cell lines SW480 (CCL-228™) and HT29 (HTB-38™) used in this research were provided by the American Type Culture Collection (ATCC). According to the culture requirements, CCD-18Co, SW480 and HT29 cells were separately inoculated in DMEM medium (30–2007, ATCC) containing 10% fetal bovine serum and incubated in a D180-P cell incubator (RWD, China) containing 5% CO2 at 37 °C.\\nALO (DK0052, CAS NO: 56293-29-9, HPLC≥98%), the research object of this study, was provided by Chengdu DESITE Biological Company, China. ALO was dissolved in fresh DMEM to prepare 0.1 mmol/L, 0.2 mmol/L, 0.4 mmol/L, 0.8 mmol/L and 1 mmol/L ALO solutions, which were separately used to treat the CCD-18Co, SW480 and HT29 cells for 24 hours (h).\",\n \"One hundred µL of SW480 or HT29 cell suspension (1×104 cells/mL) was transferred to each well of a 96-well plate. Of note, three holes were added to the same treatment group. After treatment with the ALO solutions for 24 h, SW480 or HT29 cells were added with MTT reagent (10 µL/well, PB180519, Procell, USA), which could react with mitochondria in living cells. After 4 h of binding, a HBS-1096A enzyme label analyzer (DeTie, China) was used to detect the absorbance of SW480 or HT29 cells in the mixed solutions at 490 nm, and then cell viability was calculated based on the absorbance.\",\n \"SW480 or HT29 cells in logarithmic growth phase were seeded in 96-well plates at 1×104 cells/well. EdU reagent (ST067) purchased from Beyotime was diluted to 50 μmol/L with fresh DMEM medium. The EdU dilution was then used to incubate the cells at 100 μL/well for 2 h. After removing the culture medium, the SW480 or HT29 cells were fixed with methanol at room temperature for 15 minutes (min). Positive fluorescence expression (red) in the collected SW480 or HT29 cells were observed, photographed and recorded under a Leica DMi8 inverted fluorescence microscope (magnification 100 ×). Then DAPI reagent was used to stain the nuclei, and the stains were observed under a microscope.\",\n \"Two hundred SW480 or HT29 cells were transferred to petri dishes containing complete medium (10 mL). In order to distribute the cells evenly, the petri dishes were rotated slowly for 1 min after cell inoculation. Next, the culture dishes containing cells were placed in an incubator for routine culture. During the culture, the medium was changed every 2 days (d). After 14 d, the SW480 or HT29 cells were soaked with Giemsa dye for 20 min and then the colony formation of the cells was observed under a microscope.\",\n \"An Annexin V-FITC Apoptosis Detection Kit (CA1020, Solarbio, China) and a flow cytometer (CytoFLEX, BECKMAN COULTER, USA) were used to detect changes in the apoptosis of SW480 or HT29 cells. In brief, SW480 or HT29 cells were prepared into 1×106 cells/mL cell suspension with 1 mL 1× Binding Buffer. One hundred μL of SW480 or HT29 cells and 5 μL of Annexin V-FITC reagent were added to a centrifuge tube and mixed for 10 min in the dark at room temperature. Next, 5 μL of PI reagent was added to the centrifuge tube and incubated in the dark at room temperature for 5 min. Afterwards, PBS was added to adjust the mixture to a final volume of 500 μL. After half an hour, the cells in the centrifuge tube were transferred to the detection instrument to calculate the number of apoptotic cells.\",\n \"Starbase (http://starbase.sysu.edu.cn/index.php) was used to retrieve and analyze information of differential expression of miR-296-5p in Colon adenocarcinoma (COAD) patients and healthy volunteers. TargetScan (http://www.targetscan.org) was used to predict the circRNA that contained a binding sequence for miR-296-5p and the downstream target genes of miR-296-5p. The binding sequences predicted by TargetScan were designed by COBIOER (China) and used to construct the following reporter plasmids for dual luciferase experiments: NOP2/Sun RNA methyltransferase 2 wild type (circNSUN2-WT), circNSUN2-mutant type (MUT), Signal Transducer and Activator of Transcription 3 (STAT3)-WT and STAT3-MUT. The reporter plasmids were separately co-transfected with miR-296-5p mimic or mimic control into SW480 and HT29 cells using liposome for 48 h. Luciferase activity in the transfected SW480 and HT29 cells was detected using Dual-Luciferase® Reporter Assay System (E1960, Promega, USA) with a GloMax 20/20 luminometer (Promega, USA).\",\n \"qRT-PCR is a reliable method for rapid detection of gene mRNA levels. Briefly, total RNA of SW480 and HT29 cells was fully isolated by the conventional RNA extraction reagent TRIzol (15,596,018, Invitrogen, USA), and subsequently reverse transcribed into cDNA using the RNA reverse transcription reagent (RR047A) developed by TaKaRa (Japan). The qRT-PCR reaction system which consisted of cDNA, gene primers (Sangon synthesis), SYBR® Green (S4438-20RXN, Sigma-Aldrich, Germany) and DEPC water was added to a 96-well plate and put into the detection instrument (Bio-Rad thermal cycler T100). The reaction conditions were set as follows: pre-denaturation at 95 °C for 10 min, denaturation at 95 °C for 15 seconds (s), and annealing at 58 °C for 1 min, for a total of 40 cycles. According to Anita Ciesielska’s report,19 gene mRNA levels were quantified by the 2−ΔΔCT method. GAPDH and U6 were internal references in the experiment. The specific sequences of synthetic primers were shown in Table 1.Table 1Primers for qRT-PCRGeneForward Primer (5ʹ-3ʹ)Reverse Primer (5ʹ-3ʹ)miR-296-5pCCTGTGTCGTATCCAGTGCAAGTCGTATCCAGTGCGTGTCGcircDDX17TGCCAACCACAACATCCTCCACGCTCCCCAGGATTACCAAATcircHIPK3TATGTTGGTGGATCCTGTTCGGCATGGTGGGTAGACCAAGACTTGTGAcircZFRAACCACCACAGATTCACTATAACCACCACAGATTCACTATcircRNA_100290GTCATTCCCTCTTTAATGGTGCAGAACTTCCGCTCTAACATACHas_circ_0026344CTCAGCCTCTAGCATAAGCTCAGGCAAGAGAATGATTTGAACcircVAPATGGATTCCAAATTGAGATGCGTATTCACTTTTCTATCCGATGGATTTCGCHsa_circ_0009361AGAACCAGATTCGAGACGCCGTGCTCTTCAATGCCACCTTCcircNSUN2CTTGAGAAAATCGCCACACTTGTTGAGGAGCAGTGGTGGcircACAP2TCAGAGCATCTGCCCAAAGTTAAATGAACCCCAAGGCTCCGcircITGA7GTGTGCACAGGTCCTTCCAATGGAAGTTCTGTGAGGGACGcircEIF4G3CCTACCCCATCCCCTTATTCACCGTGCTGTAGACTGCTGAGSTAT3CCCCATACCTGAAGACCAAGGGACTCAAACTGCCCTCCTU6CTCGCTTCGGCAGCACAAACGCTTCACGAATTTGCGTGAPDHTGTGGGCATCAATGGATTTGGACACCATGTATTCCGGGTCAAT\\n\\nPrimers for qRT-PCR\",\n \"Exogenous up-regulation or knockdown of test genes is a common method to observe their regulatory effects on cells or tissues. Liposome transfection with Lipofectamine 3000 (L3000008, ThermoFisher Scientific, USA) is the most common and convenient method for exogenous interference. Therefore, we commissioned Guangzhou GENESEED Biological Company to synthesize circNSUN2 overexpressed plasmid, circNSUN2 silenced plasmid (sh-circNSUN2), STAT3 overexpressed plasmid and their respective negative controls. MiR-296-5p mimic (miR10000690-1-5) and mimic control (miR1N0000001-1-5), as well as miR-296-5p inhibitor (miR20000690-1-5) and inhibitor control (miR2N0000001-1-5), were purchased directly from Guangzhou RIOBOBIO Company. The plasmids were transfected into SW480 and HT29 cells according to the grouping using Lipofectamine 3000. After 48 h, the success rate of transfection of the cells was determined by qRT-PCR.\",\n \"RIPA lysate (tissue/cell, R0010) produced by Solarbio (China) was used to separate protein from SW480 and HT29 cells, and its concentration was determined with BCA protein concentration determination reagent (PC0020, Solarbio, China). Then the protein was subjected to high temperature denaturation to maintain a relatively stable state. After being electrophoresed by SDS-PAGE, the protein was transferred to the membrane carrier (PVDF membrane). The membrane was soaked with the same BSA sealant (SW3015) produced by Solarbio for 2 h at room temperature. Next, the membrane was incubated with Abcam (USA) antibodies (anti-STAT3, ab68153, 88 kDa; Bax, ab32503, 21 kDa; Bcl-2, ab59348, 26 kDa) that could bind to the corresponding antigens in the protein on the membrane overnight (4 °C), with GAPDH (ab8245, 36KD) as an internal reference. To identify the antigen-antibody complex, a secondary antibody labeled with HRP (Goat Anti-Rabbit antibody, 1:10,000, ab6721; Goat Anti-Mouse antibody, 1:10000, ab205719) was used to treat the PVDF membrane for 1.5 h at room temperature. Fluorescent signal of the complex was enhanced using the ECL Western Blotting Substrate (PE0010, Solarbio) and detected using the Gel Doc™ XR+ imaging system (BIO-RAD, USA). The gray value of the bands was calculated by Image Lab software to determine the final gene protein level.\",\n \"SPSS 20.0 software (IBM, USA) was employed for statistical analysis of the data obtained in the experiments. Differences between two groups were compared by independent sample t-test, and those between multiple groups were compared by one-way ANOVA followed by Tukey’s test. When p<0.05, the difference was considered statistically significant.\",\n \"ALO at a Concentration Gradient Inhibited Proliferation Yet Promoted Apoptosis in CRC Cells In this study, the toxicity of ALO in normal cells (CCD-18Co) was detected, ALO showed no significant toxicity to CCD-18Co cells (Figure 1A). In order to study the effects of ALO on the growth and basic physiological functions of CRC cells, we used different concentrations of ALO to treat SW480 and HT29 cells. The results showed that 0.2 mmol/L, 0.4 mmol/L, 0.8 mmol/L and 1 mmol/L ALO solutions all inhibited the viability of SW480 and HT29 cells (p<0.05, Figure 1B and C). Moreover, the viability of SW480 and HT29 cells was close to 50% under the treatment of 0.8 mmol/L ALO solution, but was reduced to less than 50% when the ALO concentration was increased to 1 mmol/L (Figure 1B and C). In the following EdU cell proliferation experiment, the red fluorescence expression was reduced in SW480 and HT29 cells under the treatment of 0.8 mmol/L ALO (Figure 1D and E). Consistently, the clone formation experiment also showed that different concentrations of ALO (0.2 mmol/L, 0.4 mmol/L, 0.8 mmol/L) reduced the number of cell clones and inhibited the proliferation of SW480 and HT29 cells (p<0.05, Figure 1F–H). At the same time, the increase in the concentration of the ALO solution accelerated the death of CRC cells. As shown in Figure 1I–K, the number of apoptotic SW480 and HT29 cells increased significantly after treatment with different concentrations of ALO (p<0.01, Figure 1I–K).Figure 1ALO at a concentration gradient inhibited proliferation yet promoted apoptosis in CRC cells. (A) The effect of ALO on the normal colonic tissue cells (CCD-18Co) was detected by MTT experiments. (B and C) MTT experiments showed that ALO at a concentration gradient (0.1 mmol/L, 0.2 mmol/L, 0.4 mmol/L, 0.8 mmol/L and 1 mmol/L) inhibited the activity of SW480 and HT29 cells. (D and E) EdU cell proliferation experiments showed that 0.8 mmol/L ALO inhibited the proliferation of SW480 and HT29 cells. The magnification was 100×. (F–H) The clone formation experiment showed that ALO at a concentration gradient (0.2 mmol/L, 0.4 mmol/L and 0.8 mmol/L) inhibited the proliferation of SW480 and HT29 cells. (I–K) Flow cytometry experiments showed that ALO at a concentration gradient (0.2 mmol/L, 0.4 mmol/L and 0.8 mmol/L) promoted the apoptosis of SW480 and HT29 cells. All experiments were repeated three times to obtain average values. *p<0.05, **p<0.01, ***p<0.001 vs Control.Abbreviations: ALO, aloperine; CRC, colorectal cancer.\\nALO at a concentration gradient inhibited proliferation yet promoted apoptosis in CRC cells. (A) The effect of ALO on the normal colonic tissue cells (CCD-18Co) was detected by MTT experiments. (B and C) MTT experiments showed that ALO at a concentration gradient (0.1 mmol/L, 0.2 mmol/L, 0.4 mmol/L, 0.8 mmol/L and 1 mmol/L) inhibited the activity of SW480 and HT29 cells. (D and E) EdU cell proliferation experiments showed that 0.8 mmol/L ALO inhibited the proliferation of SW480 and HT29 cells. The magnification was 100×. (F–H) The clone formation experiment showed that ALO at a concentration gradient (0.2 mmol/L, 0.4 mmol/L and 0.8 mmol/L) inhibited the proliferation of SW480 and HT29 cells. (I–K) Flow cytometry experiments showed that ALO at a concentration gradient (0.2 mmol/L, 0.4 mmol/L and 0.8 mmol/L) promoted the apoptosis of SW480 and HT29 cells. All experiments were repeated three times to obtain average values. *p<0.05, **p<0.01, ***p<0.001 vs Control.\\nIn this study, the toxicity of ALO in normal cells (CCD-18Co) was detected, ALO showed no significant toxicity to CCD-18Co cells (Figure 1A). In order to study the effects of ALO on the growth and basic physiological functions of CRC cells, we used different concentrations of ALO to treat SW480 and HT29 cells. The results showed that 0.2 mmol/L, 0.4 mmol/L, 0.8 mmol/L and 1 mmol/L ALO solutions all inhibited the viability of SW480 and HT29 cells (p<0.05, Figure 1B and C). Moreover, the viability of SW480 and HT29 cells was close to 50% under the treatment of 0.8 mmol/L ALO solution, but was reduced to less than 50% when the ALO concentration was increased to 1 mmol/L (Figure 1B and C). In the following EdU cell proliferation experiment, the red fluorescence expression was reduced in SW480 and HT29 cells under the treatment of 0.8 mmol/L ALO (Figure 1D and E). Consistently, the clone formation experiment also showed that different concentrations of ALO (0.2 mmol/L, 0.4 mmol/L, 0.8 mmol/L) reduced the number of cell clones and inhibited the proliferation of SW480 and HT29 cells (p<0.05, Figure 1F–H). At the same time, the increase in the concentration of the ALO solution accelerated the death of CRC cells. As shown in Figure 1I–K, the number of apoptotic SW480 and HT29 cells increased significantly after treatment with different concentrations of ALO (p<0.01, Figure 1I–K).Figure 1ALO at a concentration gradient inhibited proliferation yet promoted apoptosis in CRC cells. (A) The effect of ALO on the normal colonic tissue cells (CCD-18Co) was detected by MTT experiments. (B and C) MTT experiments showed that ALO at a concentration gradient (0.1 mmol/L, 0.2 mmol/L, 0.4 mmol/L, 0.8 mmol/L and 1 mmol/L) inhibited the activity of SW480 and HT29 cells. (D and E) EdU cell proliferation experiments showed that 0.8 mmol/L ALO inhibited the proliferation of SW480 and HT29 cells. The magnification was 100×. (F–H) The clone formation experiment showed that ALO at a concentration gradient (0.2 mmol/L, 0.4 mmol/L and 0.8 mmol/L) inhibited the proliferation of SW480 and HT29 cells. (I–K) Flow cytometry experiments showed that ALO at a concentration gradient (0.2 mmol/L, 0.4 mmol/L and 0.8 mmol/L) promoted the apoptosis of SW480 and HT29 cells. All experiments were repeated three times to obtain average values. *p<0.05, **p<0.01, ***p<0.001 vs Control.Abbreviations: ALO, aloperine; CRC, colorectal cancer.\\nALO at a concentration gradient inhibited proliferation yet promoted apoptosis in CRC cells. (A) The effect of ALO on the normal colonic tissue cells (CCD-18Co) was detected by MTT experiments. (B and C) MTT experiments showed that ALO at a concentration gradient (0.1 mmol/L, 0.2 mmol/L, 0.4 mmol/L, 0.8 mmol/L and 1 mmol/L) inhibited the activity of SW480 and HT29 cells. (D and E) EdU cell proliferation experiments showed that 0.8 mmol/L ALO inhibited the proliferation of SW480 and HT29 cells. The magnification was 100×. (F–H) The clone formation experiment showed that ALO at a concentration gradient (0.2 mmol/L, 0.4 mmol/L and 0.8 mmol/L) inhibited the proliferation of SW480 and HT29 cells. (I–K) Flow cytometry experiments showed that ALO at a concentration gradient (0.2 mmol/L, 0.4 mmol/L and 0.8 mmol/L) promoted the apoptosis of SW480 and HT29 cells. All experiments were repeated three times to obtain average values. *p<0.05, **p<0.01, ***p<0.001 vs Control.\\nThe Abnormally Low Expression of miR-296-5p in Colon Cancer Could Be Up-Regulated by ALO Data from the Starbase database showed that the expression of miR-296-5p in colon cancer patients was significantly lower than that in healthy people (p=2.5e-6, Figure 2A). In this study, however, after treatment with different concentrations of ALO for 24 h, miR-296-5p activity in SW480 and HT29 cells was significantly increased (p<0.01, Figure 2B and C), suggesting that ALO can stimulate the up-regulation of miR-296-5p.Figure 2The abnormally low expression of miR-296-5p in colon cancer could be upregulated by ALO. (A) Starbase (http://starbase.sysu.edu.cn/index.php) was used to retrieve the information of differential expression of miR-296-5p in colon adenocarcinoma (COAD, n=450) patients and healthy volunteers (n=8) in vivo. P=2.5e-6. (B and C) The qRT-PCR experiment showed that ALO at a concentration gradient (0.1 mmol/L, 0.2 mmol/L, 0.4 mmol/L, 0.8 mmol/L and 1 mmol/L) up-regulated miR-296-5p expression in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. **p<0.01, ***p<0.001 vs Control.Abbreviation: qRT-PCR, quantitative real-time polymerase chain reaction.\\nThe abnormally low expression of miR-296-5p in colon cancer could be upregulated by ALO. (A) Starbase (http://starbase.sysu.edu.cn/index.php) was used to retrieve the information of differential expression of miR-296-5p in colon adenocarcinoma (COAD, n=450) patients and healthy volunteers (n=8) in vivo. P=2.5e-6. (B and C) The qRT-PCR experiment showed that ALO at a concentration gradient (0.1 mmol/L, 0.2 mmol/L, 0.4 mmol/L, 0.8 mmol/L and 1 mmol/L) up-regulated miR-296-5p expression in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. **p<0.01, ***p<0.001 vs Control.\\nData from the Starbase database showed that the expression of miR-296-5p in colon cancer patients was significantly lower than that in healthy people (p=2.5e-6, Figure 2A). In this study, however, after treatment with different concentrations of ALO for 24 h, miR-296-5p activity in SW480 and HT29 cells was significantly increased (p<0.01, Figure 2B and C), suggesting that ALO can stimulate the up-regulation of miR-296-5p.Figure 2The abnormally low expression of miR-296-5p in colon cancer could be upregulated by ALO. (A) Starbase (http://starbase.sysu.edu.cn/index.php) was used to retrieve the information of differential expression of miR-296-5p in colon adenocarcinoma (COAD, n=450) patients and healthy volunteers (n=8) in vivo. P=2.5e-6. (B and C) The qRT-PCR experiment showed that ALO at a concentration gradient (0.1 mmol/L, 0.2 mmol/L, 0.4 mmol/L, 0.8 mmol/L and 1 mmol/L) up-regulated miR-296-5p expression in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. **p<0.01, ***p<0.001 vs Control.Abbreviation: qRT-PCR, quantitative real-time polymerase chain reaction.\\nThe abnormally low expression of miR-296-5p in colon cancer could be upregulated by ALO. (A) Starbase (http://starbase.sysu.edu.cn/index.php) was used to retrieve the information of differential expression of miR-296-5p in colon adenocarcinoma (COAD, n=450) patients and healthy volunteers (n=8) in vivo. P=2.5e-6. (B and C) The qRT-PCR experiment showed that ALO at a concentration gradient (0.1 mmol/L, 0.2 mmol/L, 0.4 mmol/L, 0.8 mmol/L and 1 mmol/L) up-regulated miR-296-5p expression in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. **p<0.01, ***p<0.001 vs Control.\\nAmong the Differentially Expressed circRNAs in CRC, Only circNSUN2 Not Only Had a Target Site for miR-296-5p, but Also Could Be Regulated by ALO Through literature review, we screened 12 circRNAs that were reported to be associated with CRC: circDDX17, circHIPK3, circZFR, circRNA_100290, Has_circ_0026344, circVAPA, Hsa_circ_0009361, circNSUN2, circACAP2, circACVRL1, circITGA7 and circEIF4G3. We first detected the regulatory effect of ALO on these differentially expressed circRNAs by qRT-PCR to perform preliminary screening (Figure 3A). The results showed that the expressions of circDDX17, Has_circ_0026344, circVAPA, circNSUN2, circACAP2, circACVRL1 and circEIF4G3 were regulated by 0.8 mmol/L ALO (p<0.001, Figure 3A). Through subsequent screening of target genes, we found that only circNSUN2 and miR-296-5p have targeted binding sites (Figure 3B). After further verification, it was confirmed that circNSUN2-WT and miR-296-5p mimic did reduce the luciferase activity in SW480 and HT29 cells (p<0.001, Figure 3C and D). Therefore, we chose circNSUN2 as the next research object.Figure 3Among the differentially expressed circRNAs in CRC, only circNSUN2 not only had a target site for miR-296-5p, but also could be regulated by ALO. (A) qRT-PCR detected the regulation of 0.8 mmol/L ALO on the differentially expressed circRNAs in CRC. GAPDH played the role of internal reference. (B) The TargetScan database (http://www.targetscan.org) was used to predict the target sites of circNSUN2 and miR-296-5p. (C and D) The dual luciferase experiment confirmed that circNSUN2 can bind to miR-296-5p in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. ***p<0.001 vs Control; +++p<0.001 vs MC.Abbreviation: MC, miR-296-5p mimic control.\\nAmong the differentially expressed circRNAs in CRC, only circNSUN2 not only had a target site for miR-296-5p, but also could be regulated by ALO. (A) qRT-PCR detected the regulation of 0.8 mmol/L ALO on the differentially expressed circRNAs in CRC. GAPDH played the role of internal reference. (B) The TargetScan database (http://www.targetscan.org) was used to predict the target sites of circNSUN2 and miR-296-5p. (C and D) The dual luciferase experiment confirmed that circNSUN2 can bind to miR-296-5p in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. ***p<0.001 vs Control; +++p<0.001 vs MC.\\nThrough literature review, we screened 12 circRNAs that were reported to be associated with CRC: circDDX17, circHIPK3, circZFR, circRNA_100290, Has_circ_0026344, circVAPA, Hsa_circ_0009361, circNSUN2, circACAP2, circACVRL1, circITGA7 and circEIF4G3. We first detected the regulatory effect of ALO on these differentially expressed circRNAs by qRT-PCR to perform preliminary screening (Figure 3A). The results showed that the expressions of circDDX17, Has_circ_0026344, circVAPA, circNSUN2, circACAP2, circACVRL1 and circEIF4G3 were regulated by 0.8 mmol/L ALO (p<0.001, Figure 3A). Through subsequent screening of target genes, we found that only circNSUN2 and miR-296-5p have targeted binding sites (Figure 3B). After further verification, it was confirmed that circNSUN2-WT and miR-296-5p mimic did reduce the luciferase activity in SW480 and HT29 cells (p<0.001, Figure 3C and D). Therefore, we chose circNSUN2 as the next research object.Figure 3Among the differentially expressed circRNAs in CRC, only circNSUN2 not only had a target site for miR-296-5p, but also could be regulated by ALO. (A) qRT-PCR detected the regulation of 0.8 mmol/L ALO on the differentially expressed circRNAs in CRC. GAPDH played the role of internal reference. (B) The TargetScan database (http://www.targetscan.org) was used to predict the target sites of circNSUN2 and miR-296-5p. (C and D) The dual luciferase experiment confirmed that circNSUN2 can bind to miR-296-5p in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. ***p<0.001 vs Control; +++p<0.001 vs MC.Abbreviation: MC, miR-296-5p mimic control.\\nAmong the differentially expressed circRNAs in CRC, only circNSUN2 not only had a target site for miR-296-5p, but also could be regulated by ALO. (A) qRT-PCR detected the regulation of 0.8 mmol/L ALO on the differentially expressed circRNAs in CRC. GAPDH played the role of internal reference. (B) The TargetScan database (http://www.targetscan.org) was used to predict the target sites of circNSUN2 and miR-296-5p. (C and D) The dual luciferase experiment confirmed that circNSUN2 can bind to miR-296-5p in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. ***p<0.001 vs Control; +++p<0.001 vs MC.\\nOverexpression of circNSUN2 Partially Offset the Suppression of ALO on the Biological Behavior of Cancer Cells In SW480 and HT29 cells, ALO suppressed the mRNA level of circNSUN2, whereas transfection of circNSUN2 overexpression significantly increased the expression of circNSUN2 (p<0.001, Figure 4A and B). The following basic cell function experiments showed that the upregulation of circNSUN2 partially offset the inhibitory effect of ALO on CRC cell viability, proliferation and apoptosis (p<0.001, Figure 4C–L).Figure 4Overexpression of circNSUN2 partially offset the suppression of ALO on the biological behavior of cancer cells. (A and B) The qRT-PCR experiment showed that ALO (0.8 mmol/L) inhibited the expression of circNSUN2 in SW480 and HT29 cells. GAPDH played the role of internal reference. (C and D) The MTT experiment showed that overexpression of circNSUN2 partially offset the inhibition of ALO on cell viability in SW480 and HT29 cells. (E and F) EdU cell proliferation experiments showed that overexpression of circNSUN2 partially offset the inhibition of ALO on proliferation in SW480 and HT29 cells. The magnification was 100×. (G–I) The clone formation experiment showed that overexpression of circNSUN2 partially offset the inhibition of ALO on proliferation in SW480 and HT29 cells. (J–L) Flow cytometry experiments showed that overexpression of circNSUN2 partially offset the promotion of ALO on apoptosis in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. ***p<0.001 vs Control; ^p<0.05, ^^p<0.01, ^^^p<0.001 vs ALO+NC.Abbreviations: CircNSUN2, NOP2/Sun RNA methyltransferase 2; NC, negative control.\\nOverexpression of circNSUN2 partially offset the suppression of ALO on the biological behavior of cancer cells. (A and B) The qRT-PCR experiment showed that ALO (0.8 mmol/L) inhibited the expression of circNSUN2 in SW480 and HT29 cells. GAPDH played the role of internal reference. (C and D) The MTT experiment showed that overexpression of circNSUN2 partially offset the inhibition of ALO on cell viability in SW480 and HT29 cells. (E and F) EdU cell proliferation experiments showed that overexpression of circNSUN2 partially offset the inhibition of ALO on proliferation in SW480 and HT29 cells. The magnification was 100×. (G–I) The clone formation experiment showed that overexpression of circNSUN2 partially offset the inhibition of ALO on proliferation in SW480 and HT29 cells. (J–L) Flow cytometry experiments showed that overexpression of circNSUN2 partially offset the promotion of ALO on apoptosis in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. ***p<0.001 vs Control; ^p<0.05, ^^p<0.01, ^^^p<0.001 vs ALO+NC.\\nIn SW480 and HT29 cells, ALO suppressed the mRNA level of circNSUN2, whereas transfection of circNSUN2 overexpression significantly increased the expression of circNSUN2 (p<0.001, Figure 4A and B). The following basic cell function experiments showed that the upregulation of circNSUN2 partially offset the inhibitory effect of ALO on CRC cell viability, proliferation and apoptosis (p<0.001, Figure 4C–L).Figure 4Overexpression of circNSUN2 partially offset the suppression of ALO on the biological behavior of cancer cells. (A and B) The qRT-PCR experiment showed that ALO (0.8 mmol/L) inhibited the expression of circNSUN2 in SW480 and HT29 cells. GAPDH played the role of internal reference. (C and D) The MTT experiment showed that overexpression of circNSUN2 partially offset the inhibition of ALO on cell viability in SW480 and HT29 cells. (E and F) EdU cell proliferation experiments showed that overexpression of circNSUN2 partially offset the inhibition of ALO on proliferation in SW480 and HT29 cells. The magnification was 100×. (G–I) The clone formation experiment showed that overexpression of circNSUN2 partially offset the inhibition of ALO on proliferation in SW480 and HT29 cells. (J–L) Flow cytometry experiments showed that overexpression of circNSUN2 partially offset the promotion of ALO on apoptosis in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. ***p<0.001 vs Control; ^p<0.05, ^^p<0.01, ^^^p<0.001 vs ALO+NC.Abbreviations: CircNSUN2, NOP2/Sun RNA methyltransferase 2; NC, negative control.\\nOverexpression of circNSUN2 partially offset the suppression of ALO on the biological behavior of cancer cells. (A and B) The qRT-PCR experiment showed that ALO (0.8 mmol/L) inhibited the expression of circNSUN2 in SW480 and HT29 cells. GAPDH played the role of internal reference. (C and D) The MTT experiment showed that overexpression of circNSUN2 partially offset the inhibition of ALO on cell viability in SW480 and HT29 cells. (E and F) EdU cell proliferation experiments showed that overexpression of circNSUN2 partially offset the inhibition of ALO on proliferation in SW480 and HT29 cells. The magnification was 100×. (G–I) The clone formation experiment showed that overexpression of circNSUN2 partially offset the inhibition of ALO on proliferation in SW480 and HT29 cells. (J–L) Flow cytometry experiments showed that overexpression of circNSUN2 partially offset the promotion of ALO on apoptosis in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. ***p<0.001 vs Control; ^p<0.05, ^^p<0.01, ^^^p<0.001 vs ALO+NC.\\nSh-circNSUN2 Inhibited the Malignant Development of CRC Cells, Which Was Neutralized by miR-296-5p Inhibitor We also tested the mutual regulation of circNSUN2 and miR-296-5p in SW480 and HT29 cells. Sh-circNSUN2 could down-regulate the expression of circNSUN2, and besides, it inhibited the proliferation of SW480 and HT29 cells, while promoting their apoptosis and the expression of miR-296-5p (p<0.001, Figure 5A–J). However, the inhibitory effect of sh-circNSUN2 on the malignant development of the two cancer cells was neutralized by miR-296-5p inhibitor (p<0.001, Figure 5A–J).Figure 5Sh-circNSUN2 inhibited the malignant development of CRC cells, which was neutralized by miR-296-5p inhibitor. (A and B) The qRT-PCR experiment showed that miR-296-5p inhibitor neutralized the inhibitory effect of sh-circNSUN2 on circNSUN2 expression. GAPDH played the role of internal reference. (C and D) The qRT-PCR experiment showed that sh-circNSUN2 up-regulated the mRNA level of miR-296-5p. U6 played the role of internal reference. (E–G) The clone formation experiment showed that the effect of sh-circNSUN2 on the proliferation of SW480 and HT29 cells was neutralized by miR-296-5p inhibitor. (H-J) Flow cytometry experiments showed that the effect of sh-circNSUN2 on promoting the apoptosis of SW480 and HT29 cells was neutralized by miR-296-5p inhibitor. All experiments were repeated three times to obtain average values. ***p<0.001 vs sh-NC+IC; ^^^p<0.001 vs sh-circNSUN2+IC; ###p<0.001 vs sh-NC+I.Abbreviations: Sh-circNSUN2, silent circNSUN2; sh-NC, silent negative control; IC, miR-296-5p inhibitor control; I, miR-296-5p inhibitor.\\nSh-circNSUN2 inhibited the malignant development of CRC cells, which was neutralized by miR-296-5p inhibitor. (A and B) The qRT-PCR experiment showed that miR-296-5p inhibitor neutralized the inhibitory effect of sh-circNSUN2 on circNSUN2 expression. GAPDH played the role of internal reference. (C and D) The qRT-PCR experiment showed that sh-circNSUN2 up-regulated the mRNA level of miR-296-5p. U6 played the role of internal reference. (E–G) The clone formation experiment showed that the effect of sh-circNSUN2 on the proliferation of SW480 and HT29 cells was neutralized by miR-296-5p inhibitor. (H-J) Flow cytometry experiments showed that the effect of sh-circNSUN2 on promoting the apoptosis of SW480 and HT29 cells was neutralized by miR-296-5p inhibitor. All experiments were repeated three times to obtain average values. ***p<0.001 vs sh-NC+IC; ^^^p<0.001 vs sh-circNSUN2+IC; ###p<0.001 vs sh-NC+I.\\nWe also tested the mutual regulation of circNSUN2 and miR-296-5p in SW480 and HT29 cells. Sh-circNSUN2 could down-regulate the expression of circNSUN2, and besides, it inhibited the proliferation of SW480 and HT29 cells, while promoting their apoptosis and the expression of miR-296-5p (p<0.001, Figure 5A–J). However, the inhibitory effect of sh-circNSUN2 on the malignant development of the two cancer cells was neutralized by miR-296-5p inhibitor (p<0.001, Figure 5A–J).Figure 5Sh-circNSUN2 inhibited the malignant development of CRC cells, which was neutralized by miR-296-5p inhibitor. (A and B) The qRT-PCR experiment showed that miR-296-5p inhibitor neutralized the inhibitory effect of sh-circNSUN2 on circNSUN2 expression. GAPDH played the role of internal reference. (C and D) The qRT-PCR experiment showed that sh-circNSUN2 up-regulated the mRNA level of miR-296-5p. U6 played the role of internal reference. (E–G) The clone formation experiment showed that the effect of sh-circNSUN2 on the proliferation of SW480 and HT29 cells was neutralized by miR-296-5p inhibitor. (H-J) Flow cytometry experiments showed that the effect of sh-circNSUN2 on promoting the apoptosis of SW480 and HT29 cells was neutralized by miR-296-5p inhibitor. All experiments were repeated three times to obtain average values. ***p<0.001 vs sh-NC+IC; ^^^p<0.001 vs sh-circNSUN2+IC; ###p<0.001 vs sh-NC+I.Abbreviations: Sh-circNSUN2, silent circNSUN2; sh-NC, silent negative control; IC, miR-296-5p inhibitor control; I, miR-296-5p inhibitor.\\nSh-circNSUN2 inhibited the malignant development of CRC cells, which was neutralized by miR-296-5p inhibitor. (A and B) The qRT-PCR experiment showed that miR-296-5p inhibitor neutralized the inhibitory effect of sh-circNSUN2 on circNSUN2 expression. GAPDH played the role of internal reference. (C and D) The qRT-PCR experiment showed that sh-circNSUN2 up-regulated the mRNA level of miR-296-5p. U6 played the role of internal reference. (E–G) The clone formation experiment showed that the effect of sh-circNSUN2 on the proliferation of SW480 and HT29 cells was neutralized by miR-296-5p inhibitor. (H-J) Flow cytometry experiments showed that the effect of sh-circNSUN2 on promoting the apoptosis of SW480 and HT29 cells was neutralized by miR-296-5p inhibitor. All experiments were repeated three times to obtain average values. ***p<0.001 vs sh-NC+IC; ^^^p<0.001 vs sh-circNSUN2+IC; ###p<0.001 vs sh-NC+I.\\nMiR-296-5p Bound to STAT3 and Upregulation of Mir-296-5p Inhibited the Expression of STAT3 in CRC Cells We screened the target genes of miR-296-5p, and found that miR-296-5p and STAT3 have targeted binding sites (Figure 6A), and this binding relation was further confirmed by a dual-luciferase verification test (p<0.001, Figure 6B and C). Next, we used qRT-PCR and Western blot to analyze the mRNA and protein levels of miR-296-5p and STAT. It was found that miR-296-5p mimic up-regulated miR-296-5p expression while preventing the activation of STAT3 (p<0.001, Figure 7A–G). However, the overexpression of STAT3 reversed the regulation of miR-296-5p mimic on the two genes (p<0.001, Figure 7A–G).Figure 6The targeted binding of miR-296-5p to STAT3. (A) TargetScan was used to predict the binding sequence of miR-296-5p and STAT3. (B and C) Dual luciferase experiment confirmed that miR-296-5p can bind to STAT3 in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. +++p<0.001 vs MC.Figure 7Up-regulation of miR-296-5p inhibited the expression of STAT3 in CRC cells. (A and B) The qRT-PCR experiment showed that miR-296-5p mimic up-regulated the mRNA level of miR-296-5p. U6 played the role of internal reference. (C and D) The qRT-PCR experiment showed that miR-296-5p mimic suppressed the mRNA level of STAT3, while overexpression of STAT3 reversed this effect. GAPDH played the role of internal reference. (E–G) The Western blot experiment showed that miR-296-5p mimic inhibited the protein level of STAT3, while over-expression of STAT3 reversed this effect. GAPDH played the role of internal reference. All experiments were repeated three times to obtain average values. ***p<0.001 vs MC+NC; ^^^p<0.001 vs M+NC; ###p<0.001 vs MC+STAT3.Abbreviation: STAT3, signal transducer and activator of transcription 3.\\nThe targeted binding of miR-296-5p to STAT3. (A) TargetScan was used to predict the binding sequence of miR-296-5p and STAT3. (B and C) Dual luciferase experiment confirmed that miR-296-5p can bind to STAT3 in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. +++p<0.001 vs MC.\\nUp-regulation of miR-296-5p inhibited the expression of STAT3 in CRC cells. (A and B) The qRT-PCR experiment showed that miR-296-5p mimic up-regulated the mRNA level of miR-296-5p. U6 played the role of internal reference. (C and D) The qRT-PCR experiment showed that miR-296-5p mimic suppressed the mRNA level of STAT3, while overexpression of STAT3 reversed this effect. GAPDH played the role of internal reference. (E–G) The Western blot experiment showed that miR-296-5p mimic inhibited the protein level of STAT3, while over-expression of STAT3 reversed this effect. GAPDH played the role of internal reference. All experiments were repeated three times to obtain average values. ***p<0.001 vs MC+NC; ^^^p<0.001 vs M+NC; ###p<0.001 vs MC+STAT3.\\nWe screened the target genes of miR-296-5p, and found that miR-296-5p and STAT3 have targeted binding sites (Figure 6A), and this binding relation was further confirmed by a dual-luciferase verification test (p<0.001, Figure 6B and C). Next, we used qRT-PCR and Western blot to analyze the mRNA and protein levels of miR-296-5p and STAT. It was found that miR-296-5p mimic up-regulated miR-296-5p expression while preventing the activation of STAT3 (p<0.001, Figure 7A–G). However, the overexpression of STAT3 reversed the regulation of miR-296-5p mimic on the two genes (p<0.001, Figure 7A–G).Figure 6The targeted binding of miR-296-5p to STAT3. (A) TargetScan was used to predict the binding sequence of miR-296-5p and STAT3. (B and C) Dual luciferase experiment confirmed that miR-296-5p can bind to STAT3 in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. +++p<0.001 vs MC.Figure 7Up-regulation of miR-296-5p inhibited the expression of STAT3 in CRC cells. (A and B) The qRT-PCR experiment showed that miR-296-5p mimic up-regulated the mRNA level of miR-296-5p. U6 played the role of internal reference. (C and D) The qRT-PCR experiment showed that miR-296-5p mimic suppressed the mRNA level of STAT3, while overexpression of STAT3 reversed this effect. GAPDH played the role of internal reference. (E–G) The Western blot experiment showed that miR-296-5p mimic inhibited the protein level of STAT3, while over-expression of STAT3 reversed this effect. GAPDH played the role of internal reference. All experiments were repeated three times to obtain average values. ***p<0.001 vs MC+NC; ^^^p<0.001 vs M+NC; ###p<0.001 vs MC+STAT3.Abbreviation: STAT3, signal transducer and activator of transcription 3.\\nThe targeted binding of miR-296-5p to STAT3. (A) TargetScan was used to predict the binding sequence of miR-296-5p and STAT3. (B and C) Dual luciferase experiment confirmed that miR-296-5p can bind to STAT3 in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. +++p<0.001 vs MC.\\nUp-regulation of miR-296-5p inhibited the expression of STAT3 in CRC cells. (A and B) The qRT-PCR experiment showed that miR-296-5p mimic up-regulated the mRNA level of miR-296-5p. U6 played the role of internal reference. (C and D) The qRT-PCR experiment showed that miR-296-5p mimic suppressed the mRNA level of STAT3, while overexpression of STAT3 reversed this effect. GAPDH played the role of internal reference. (E–G) The Western blot experiment showed that miR-296-5p mimic inhibited the protein level of STAT3, while over-expression of STAT3 reversed this effect. GAPDH played the role of internal reference. All experiments were repeated three times to obtain average values. ***p<0.001 vs MC+NC; ^^^p<0.001 vs M+NC; ###p<0.001 vs MC+STAT3.\\nMiR-296-5p Mimic Inhibited Proliferation and Promoted Apoptosis in CRC Cells by Regulating Apoptosis-Related Genes, Which Was Partially Reversed by Overexpressed STAT3 The following cell experiments showed that by activating Bax and blocking the protein activity of Bcl-2, miR-296-5p mimic inhibited proliferation yet accelerated apoptosis in cancer cells (p<0.001, Figure 8A–I). However, up-regulation of STAT3 produced a completely opposite regulatory effect (p<0.001, Figure 8A–I). More importantly, overexpression of STAT3 neutralized the regulation of miR-296-5p mimic on CRC cells (p<0.001, Figure 8A–I).Figure 8MiR-296-5p mimic inhibited proliferation and promoted apoptosis in CRC cells by regulating apoptosis-related genes, which was partially reversed by overexpression of STAT3. (A–C) The clone formation experiment showed that the inhibitory effect of miR-296-5p mimic on the proliferation of SW480 and HT29 cells was neutralized by overexpressed STAT3. (D–F) Flow cytometry experiments showed that miR-296-5p mimic promoted the apoptosis of SW480 and HT29 cells, which was neutralized by overexpressed STAT3. (G–I) Western blot experiments showed that the regulation of apoptosis-related protein expression by miR-296-5p mimic was neutralized by overexpressed STAT3. GAPDH played the role of internal reference. All experiments were repeated three times to obtain average values. *p<0.05, **p<0.01, ***p<0.001 vs MC+NC; ^^p<0.01, ^^^p<0.001 vs M+NC; ##p<0.01, ###p<0.001 vs MC+STAT3.\\nMiR-296-5p mimic inhibited proliferation and promoted apoptosis in CRC cells by regulating apoptosis-related genes, which was partially reversed by overexpression of STAT3. (A–C) The clone formation experiment showed that the inhibitory effect of miR-296-5p mimic on the proliferation of SW480 and HT29 cells was neutralized by overexpressed STAT3. (D–F) Flow cytometry experiments showed that miR-296-5p mimic promoted the apoptosis of SW480 and HT29 cells, which was neutralized by overexpressed STAT3. (G–I) Western blot experiments showed that the regulation of apoptosis-related protein expression by miR-296-5p mimic was neutralized by overexpressed STAT3. GAPDH played the role of internal reference. All experiments were repeated three times to obtain average values. *p<0.05, **p<0.01, ***p<0.001 vs MC+NC; ^^p<0.01, ^^^p<0.001 vs M+NC; ##p<0.01, ###p<0.001 vs MC+STAT3.\\nThe following cell experiments showed that by activating Bax and blocking the protein activity of Bcl-2, miR-296-5p mimic inhibited proliferation yet accelerated apoptosis in cancer cells (p<0.001, Figure 8A–I). However, up-regulation of STAT3 produced a completely opposite regulatory effect (p<0.001, Figure 8A–I). More importantly, overexpression of STAT3 neutralized the regulation of miR-296-5p mimic on CRC cells (p<0.001, Figure 8A–I).Figure 8MiR-296-5p mimic inhibited proliferation and promoted apoptosis in CRC cells by regulating apoptosis-related genes, which was partially reversed by overexpression of STAT3. (A–C) The clone formation experiment showed that the inhibitory effect of miR-296-5p mimic on the proliferation of SW480 and HT29 cells was neutralized by overexpressed STAT3. (D–F) Flow cytometry experiments showed that miR-296-5p mimic promoted the apoptosis of SW480 and HT29 cells, which was neutralized by overexpressed STAT3. (G–I) Western blot experiments showed that the regulation of apoptosis-related protein expression by miR-296-5p mimic was neutralized by overexpressed STAT3. GAPDH played the role of internal reference. All experiments were repeated three times to obtain average values. *p<0.05, **p<0.01, ***p<0.001 vs MC+NC; ^^p<0.01, ^^^p<0.001 vs M+NC; ##p<0.01, ###p<0.001 vs MC+STAT3.\\nMiR-296-5p mimic inhibited proliferation and promoted apoptosis in CRC cells by regulating apoptosis-related genes, which was partially reversed by overexpression of STAT3. (A–C) The clone formation experiment showed that the inhibitory effect of miR-296-5p mimic on the proliferation of SW480 and HT29 cells was neutralized by overexpressed STAT3. (D–F) Flow cytometry experiments showed that miR-296-5p mimic promoted the apoptosis of SW480 and HT29 cells, which was neutralized by overexpressed STAT3. (G–I) Western blot experiments showed that the regulation of apoptosis-related protein expression by miR-296-5p mimic was neutralized by overexpressed STAT3. GAPDH played the role of internal reference. All experiments were repeated three times to obtain average values. *p<0.05, **p<0.01, ***p<0.001 vs MC+NC; ^^p<0.01, ^^^p<0.001 vs M+NC; ##p<0.01, ###p<0.001 vs MC+STAT3.\",\n \"In this study, the toxicity of ALO in normal cells (CCD-18Co) was detected, ALO showed no significant toxicity to CCD-18Co cells (Figure 1A). In order to study the effects of ALO on the growth and basic physiological functions of CRC cells, we used different concentrations of ALO to treat SW480 and HT29 cells. The results showed that 0.2 mmol/L, 0.4 mmol/L, 0.8 mmol/L and 1 mmol/L ALO solutions all inhibited the viability of SW480 and HT29 cells (p<0.05, Figure 1B and C). Moreover, the viability of SW480 and HT29 cells was close to 50% under the treatment of 0.8 mmol/L ALO solution, but was reduced to less than 50% when the ALO concentration was increased to 1 mmol/L (Figure 1B and C). In the following EdU cell proliferation experiment, the red fluorescence expression was reduced in SW480 and HT29 cells under the treatment of 0.8 mmol/L ALO (Figure 1D and E). Consistently, the clone formation experiment also showed that different concentrations of ALO (0.2 mmol/L, 0.4 mmol/L, 0.8 mmol/L) reduced the number of cell clones and inhibited the proliferation of SW480 and HT29 cells (p<0.05, Figure 1F–H). At the same time, the increase in the concentration of the ALO solution accelerated the death of CRC cells. As shown in Figure 1I–K, the number of apoptotic SW480 and HT29 cells increased significantly after treatment with different concentrations of ALO (p<0.01, Figure 1I–K).Figure 1ALO at a concentration gradient inhibited proliferation yet promoted apoptosis in CRC cells. (A) The effect of ALO on the normal colonic tissue cells (CCD-18Co) was detected by MTT experiments. (B and C) MTT experiments showed that ALO at a concentration gradient (0.1 mmol/L, 0.2 mmol/L, 0.4 mmol/L, 0.8 mmol/L and 1 mmol/L) inhibited the activity of SW480 and HT29 cells. (D and E) EdU cell proliferation experiments showed that 0.8 mmol/L ALO inhibited the proliferation of SW480 and HT29 cells. The magnification was 100×. (F–H) The clone formation experiment showed that ALO at a concentration gradient (0.2 mmol/L, 0.4 mmol/L and 0.8 mmol/L) inhibited the proliferation of SW480 and HT29 cells. (I–K) Flow cytometry experiments showed that ALO at a concentration gradient (0.2 mmol/L, 0.4 mmol/L and 0.8 mmol/L) promoted the apoptosis of SW480 and HT29 cells. All experiments were repeated three times to obtain average values. *p<0.05, **p<0.01, ***p<0.001 vs Control.Abbreviations: ALO, aloperine; CRC, colorectal cancer.\\nALO at a concentration gradient inhibited proliferation yet promoted apoptosis in CRC cells. (A) The effect of ALO on the normal colonic tissue cells (CCD-18Co) was detected by MTT experiments. (B and C) MTT experiments showed that ALO at a concentration gradient (0.1 mmol/L, 0.2 mmol/L, 0.4 mmol/L, 0.8 mmol/L and 1 mmol/L) inhibited the activity of SW480 and HT29 cells. (D and E) EdU cell proliferation experiments showed that 0.8 mmol/L ALO inhibited the proliferation of SW480 and HT29 cells. The magnification was 100×. (F–H) The clone formation experiment showed that ALO at a concentration gradient (0.2 mmol/L, 0.4 mmol/L and 0.8 mmol/L) inhibited the proliferation of SW480 and HT29 cells. (I–K) Flow cytometry experiments showed that ALO at a concentration gradient (0.2 mmol/L, 0.4 mmol/L and 0.8 mmol/L) promoted the apoptosis of SW480 and HT29 cells. All experiments were repeated three times to obtain average values. *p<0.05, **p<0.01, ***p<0.001 vs Control.\",\n \"Data from the Starbase database showed that the expression of miR-296-5p in colon cancer patients was significantly lower than that in healthy people (p=2.5e-6, Figure 2A). In this study, however, after treatment with different concentrations of ALO for 24 h, miR-296-5p activity in SW480 and HT29 cells was significantly increased (p<0.01, Figure 2B and C), suggesting that ALO can stimulate the up-regulation of miR-296-5p.Figure 2The abnormally low expression of miR-296-5p in colon cancer could be upregulated by ALO. (A) Starbase (http://starbase.sysu.edu.cn/index.php) was used to retrieve the information of differential expression of miR-296-5p in colon adenocarcinoma (COAD, n=450) patients and healthy volunteers (n=8) in vivo. P=2.5e-6. (B and C) The qRT-PCR experiment showed that ALO at a concentration gradient (0.1 mmol/L, 0.2 mmol/L, 0.4 mmol/L, 0.8 mmol/L and 1 mmol/L) up-regulated miR-296-5p expression in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. **p<0.01, ***p<0.001 vs Control.Abbreviation: qRT-PCR, quantitative real-time polymerase chain reaction.\\nThe abnormally low expression of miR-296-5p in colon cancer could be upregulated by ALO. (A) Starbase (http://starbase.sysu.edu.cn/index.php) was used to retrieve the information of differential expression of miR-296-5p in colon adenocarcinoma (COAD, n=450) patients and healthy volunteers (n=8) in vivo. P=2.5e-6. (B and C) The qRT-PCR experiment showed that ALO at a concentration gradient (0.1 mmol/L, 0.2 mmol/L, 0.4 mmol/L, 0.8 mmol/L and 1 mmol/L) up-regulated miR-296-5p expression in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. **p<0.01, ***p<0.001 vs Control.\",\n \"Through literature review, we screened 12 circRNAs that were reported to be associated with CRC: circDDX17, circHIPK3, circZFR, circRNA_100290, Has_circ_0026344, circVAPA, Hsa_circ_0009361, circNSUN2, circACAP2, circACVRL1, circITGA7 and circEIF4G3. We first detected the regulatory effect of ALO on these differentially expressed circRNAs by qRT-PCR to perform preliminary screening (Figure 3A). The results showed that the expressions of circDDX17, Has_circ_0026344, circVAPA, circNSUN2, circACAP2, circACVRL1 and circEIF4G3 were regulated by 0.8 mmol/L ALO (p<0.001, Figure 3A). Through subsequent screening of target genes, we found that only circNSUN2 and miR-296-5p have targeted binding sites (Figure 3B). After further verification, it was confirmed that circNSUN2-WT and miR-296-5p mimic did reduce the luciferase activity in SW480 and HT29 cells (p<0.001, Figure 3C and D). Therefore, we chose circNSUN2 as the next research object.Figure 3Among the differentially expressed circRNAs in CRC, only circNSUN2 not only had a target site for miR-296-5p, but also could be regulated by ALO. (A) qRT-PCR detected the regulation of 0.8 mmol/L ALO on the differentially expressed circRNAs in CRC. GAPDH played the role of internal reference. (B) The TargetScan database (http://www.targetscan.org) was used to predict the target sites of circNSUN2 and miR-296-5p. (C and D) The dual luciferase experiment confirmed that circNSUN2 can bind to miR-296-5p in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. ***p<0.001 vs Control; +++p<0.001 vs MC.Abbreviation: MC, miR-296-5p mimic control.\\nAmong the differentially expressed circRNAs in CRC, only circNSUN2 not only had a target site for miR-296-5p, but also could be regulated by ALO. (A) qRT-PCR detected the regulation of 0.8 mmol/L ALO on the differentially expressed circRNAs in CRC. GAPDH played the role of internal reference. (B) The TargetScan database (http://www.targetscan.org) was used to predict the target sites of circNSUN2 and miR-296-5p. (C and D) The dual luciferase experiment confirmed that circNSUN2 can bind to miR-296-5p in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. ***p<0.001 vs Control; +++p<0.001 vs MC.\",\n \"In SW480 and HT29 cells, ALO suppressed the mRNA level of circNSUN2, whereas transfection of circNSUN2 overexpression significantly increased the expression of circNSUN2 (p<0.001, Figure 4A and B). The following basic cell function experiments showed that the upregulation of circNSUN2 partially offset the inhibitory effect of ALO on CRC cell viability, proliferation and apoptosis (p<0.001, Figure 4C–L).Figure 4Overexpression of circNSUN2 partially offset the suppression of ALO on the biological behavior of cancer cells. (A and B) The qRT-PCR experiment showed that ALO (0.8 mmol/L) inhibited the expression of circNSUN2 in SW480 and HT29 cells. GAPDH played the role of internal reference. (C and D) The MTT experiment showed that overexpression of circNSUN2 partially offset the inhibition of ALO on cell viability in SW480 and HT29 cells. (E and F) EdU cell proliferation experiments showed that overexpression of circNSUN2 partially offset the inhibition of ALO on proliferation in SW480 and HT29 cells. The magnification was 100×. (G–I) The clone formation experiment showed that overexpression of circNSUN2 partially offset the inhibition of ALO on proliferation in SW480 and HT29 cells. (J–L) Flow cytometry experiments showed that overexpression of circNSUN2 partially offset the promotion of ALO on apoptosis in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. ***p<0.001 vs Control; ^p<0.05, ^^p<0.01, ^^^p<0.001 vs ALO+NC.Abbreviations: CircNSUN2, NOP2/Sun RNA methyltransferase 2; NC, negative control.\\nOverexpression of circNSUN2 partially offset the suppression of ALO on the biological behavior of cancer cells. (A and B) The qRT-PCR experiment showed that ALO (0.8 mmol/L) inhibited the expression of circNSUN2 in SW480 and HT29 cells. GAPDH played the role of internal reference. (C and D) The MTT experiment showed that overexpression of circNSUN2 partially offset the inhibition of ALO on cell viability in SW480 and HT29 cells. (E and F) EdU cell proliferation experiments showed that overexpression of circNSUN2 partially offset the inhibition of ALO on proliferation in SW480 and HT29 cells. The magnification was 100×. (G–I) The clone formation experiment showed that overexpression of circNSUN2 partially offset the inhibition of ALO on proliferation in SW480 and HT29 cells. (J–L) Flow cytometry experiments showed that overexpression of circNSUN2 partially offset the promotion of ALO on apoptosis in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. ***p<0.001 vs Control; ^p<0.05, ^^p<0.01, ^^^p<0.001 vs ALO+NC.\",\n \"We also tested the mutual regulation of circNSUN2 and miR-296-5p in SW480 and HT29 cells. Sh-circNSUN2 could down-regulate the expression of circNSUN2, and besides, it inhibited the proliferation of SW480 and HT29 cells, while promoting their apoptosis and the expression of miR-296-5p (p<0.001, Figure 5A–J). However, the inhibitory effect of sh-circNSUN2 on the malignant development of the two cancer cells was neutralized by miR-296-5p inhibitor (p<0.001, Figure 5A–J).Figure 5Sh-circNSUN2 inhibited the malignant development of CRC cells, which was neutralized by miR-296-5p inhibitor. (A and B) The qRT-PCR experiment showed that miR-296-5p inhibitor neutralized the inhibitory effect of sh-circNSUN2 on circNSUN2 expression. GAPDH played the role of internal reference. (C and D) The qRT-PCR experiment showed that sh-circNSUN2 up-regulated the mRNA level of miR-296-5p. U6 played the role of internal reference. (E–G) The clone formation experiment showed that the effect of sh-circNSUN2 on the proliferation of SW480 and HT29 cells was neutralized by miR-296-5p inhibitor. (H-J) Flow cytometry experiments showed that the effect of sh-circNSUN2 on promoting the apoptosis of SW480 and HT29 cells was neutralized by miR-296-5p inhibitor. All experiments were repeated three times to obtain average values. ***p<0.001 vs sh-NC+IC; ^^^p<0.001 vs sh-circNSUN2+IC; ###p<0.001 vs sh-NC+I.Abbreviations: Sh-circNSUN2, silent circNSUN2; sh-NC, silent negative control; IC, miR-296-5p inhibitor control; I, miR-296-5p inhibitor.\\nSh-circNSUN2 inhibited the malignant development of CRC cells, which was neutralized by miR-296-5p inhibitor. (A and B) The qRT-PCR experiment showed that miR-296-5p inhibitor neutralized the inhibitory effect of sh-circNSUN2 on circNSUN2 expression. GAPDH played the role of internal reference. (C and D) The qRT-PCR experiment showed that sh-circNSUN2 up-regulated the mRNA level of miR-296-5p. U6 played the role of internal reference. (E–G) The clone formation experiment showed that the effect of sh-circNSUN2 on the proliferation of SW480 and HT29 cells was neutralized by miR-296-5p inhibitor. (H-J) Flow cytometry experiments showed that the effect of sh-circNSUN2 on promoting the apoptosis of SW480 and HT29 cells was neutralized by miR-296-5p inhibitor. All experiments were repeated three times to obtain average values. ***p<0.001 vs sh-NC+IC; ^^^p<0.001 vs sh-circNSUN2+IC; ###p<0.001 vs sh-NC+I.\",\n \"We screened the target genes of miR-296-5p, and found that miR-296-5p and STAT3 have targeted binding sites (Figure 6A), and this binding relation was further confirmed by a dual-luciferase verification test (p<0.001, Figure 6B and C). Next, we used qRT-PCR and Western blot to analyze the mRNA and protein levels of miR-296-5p and STAT. It was found that miR-296-5p mimic up-regulated miR-296-5p expression while preventing the activation of STAT3 (p<0.001, Figure 7A–G). However, the overexpression of STAT3 reversed the regulation of miR-296-5p mimic on the two genes (p<0.001, Figure 7A–G).Figure 6The targeted binding of miR-296-5p to STAT3. (A) TargetScan was used to predict the binding sequence of miR-296-5p and STAT3. (B and C) Dual luciferase experiment confirmed that miR-296-5p can bind to STAT3 in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. +++p<0.001 vs MC.Figure 7Up-regulation of miR-296-5p inhibited the expression of STAT3 in CRC cells. (A and B) The qRT-PCR experiment showed that miR-296-5p mimic up-regulated the mRNA level of miR-296-5p. U6 played the role of internal reference. (C and D) The qRT-PCR experiment showed that miR-296-5p mimic suppressed the mRNA level of STAT3, while overexpression of STAT3 reversed this effect. GAPDH played the role of internal reference. (E–G) The Western blot experiment showed that miR-296-5p mimic inhibited the protein level of STAT3, while over-expression of STAT3 reversed this effect. GAPDH played the role of internal reference. All experiments were repeated three times to obtain average values. ***p<0.001 vs MC+NC; ^^^p<0.001 vs M+NC; ###p<0.001 vs MC+STAT3.Abbreviation: STAT3, signal transducer and activator of transcription 3.\\nThe targeted binding of miR-296-5p to STAT3. (A) TargetScan was used to predict the binding sequence of miR-296-5p and STAT3. (B and C) Dual luciferase experiment confirmed that miR-296-5p can bind to STAT3 in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. +++p<0.001 vs MC.\\nUp-regulation of miR-296-5p inhibited the expression of STAT3 in CRC cells. (A and B) The qRT-PCR experiment showed that miR-296-5p mimic up-regulated the mRNA level of miR-296-5p. U6 played the role of internal reference. (C and D) The qRT-PCR experiment showed that miR-296-5p mimic suppressed the mRNA level of STAT3, while overexpression of STAT3 reversed this effect. GAPDH played the role of internal reference. (E–G) The Western blot experiment showed that miR-296-5p mimic inhibited the protein level of STAT3, while over-expression of STAT3 reversed this effect. GAPDH played the role of internal reference. All experiments were repeated three times to obtain average values. ***p<0.001 vs MC+NC; ^^^p<0.001 vs M+NC; ###p<0.001 vs MC+STAT3.\",\n \"The following cell experiments showed that by activating Bax and blocking the protein activity of Bcl-2, miR-296-5p mimic inhibited proliferation yet accelerated apoptosis in cancer cells (p<0.001, Figure 8A–I). However, up-regulation of STAT3 produced a completely opposite regulatory effect (p<0.001, Figure 8A–I). More importantly, overexpression of STAT3 neutralized the regulation of miR-296-5p mimic on CRC cells (p<0.001, Figure 8A–I).Figure 8MiR-296-5p mimic inhibited proliferation and promoted apoptosis in CRC cells by regulating apoptosis-related genes, which was partially reversed by overexpression of STAT3. (A–C) The clone formation experiment showed that the inhibitory effect of miR-296-5p mimic on the proliferation of SW480 and HT29 cells was neutralized by overexpressed STAT3. (D–F) Flow cytometry experiments showed that miR-296-5p mimic promoted the apoptosis of SW480 and HT29 cells, which was neutralized by overexpressed STAT3. (G–I) Western blot experiments showed that the regulation of apoptosis-related protein expression by miR-296-5p mimic was neutralized by overexpressed STAT3. GAPDH played the role of internal reference. All experiments were repeated three times to obtain average values. *p<0.05, **p<0.01, ***p<0.001 vs MC+NC; ^^p<0.01, ^^^p<0.001 vs M+NC; ##p<0.01, ###p<0.001 vs MC+STAT3.\\nMiR-296-5p mimic inhibited proliferation and promoted apoptosis in CRC cells by regulating apoptosis-related genes, which was partially reversed by overexpression of STAT3. (A–C) The clone formation experiment showed that the inhibitory effect of miR-296-5p mimic on the proliferation of SW480 and HT29 cells was neutralized by overexpressed STAT3. (D–F) Flow cytometry experiments showed that miR-296-5p mimic promoted the apoptosis of SW480 and HT29 cells, which was neutralized by overexpressed STAT3. (G–I) Western blot experiments showed that the regulation of apoptosis-related protein expression by miR-296-5p mimic was neutralized by overexpressed STAT3. GAPDH played the role of internal reference. All experiments were repeated three times to obtain average values. *p<0.05, **p<0.01, ***p<0.001 vs MC+NC; ^^p<0.01, ^^^p<0.001 vs M+NC; ##p<0.01, ###p<0.001 vs MC+STAT3.\",\n \"CRC is a malignant lesion of the mucosal epithelium of the colon or rectum under the action of various carcinogenic factors such as environment or heredity.2 Most CRC patients suffer from adenocarcinoma, which usually develops from polyps. Tumors developed from polyps can infiltrate in a circular shape along the horizontal axis of the intestinal tube, develop into the deep layer of the intestinal wall, and finally penetrate the intestinal wall and metastasize to blood vessels or lymphatic vessels.20 This is one of the important reasons for the high recurrence and metastasis rates of CRC. The ultimate goals of CRC research are to improve the current treatment of CRC, save patients’ lives and improve their quality of life. Based on the role of traditional Chinese medicine in disease prevention and treatment, this study explored the effects of ALO, an alkaloid exhibiting strong anticancer activity, on the proliferation and apoptosis of CRC cells. The results showed that after ALO treatment, the viability and proliferation of CRC cells were significantly inhibited; on the contrary, the number of apoptotic cancer cells was significantly increased. This is consistent with the previous experimental results.21 In order to probe deeper into the upstream mechanism of ALO in alleviating CRC carcinogenesis through the miR-296-5p/STAT3 axis, we screened and verified the upstream targeting circRNA of miR-296-5p.\\nCircRNAs are a type of covalently closed circular non-coding RNA formed by back-splicing of mRNA precursors (pre-mRNA).22 CircRNA was considered to be a useless splicing by-product in early research. With the deepening of research, it is found that circRNA has a wide range of sources and plays a variety of functional roles in the growth and development of organisms, with the characteristics of conservative, stable, and tissue-specific.22,23 The most familiar and extensively studied function of circRNA is its sponging effect on miRNA.24 For example, circRNA ciRS-7 which contains more than 70 miR-7 binding sites can achieve competitive adsorption of miR-7 through the AGO2 protein.25 Besides, increasing reports on cancer indicate that circRNAs, such as circRNA-cTFRC, circPSMC3 and circSETD3, act as sponges of miRNAs and participate in transcriptional regulation.26–28 Based on these literature reports, we screened circRNAs that were reported to be abnormally expressed in CRC, and finally obtained 12 circRNAs. After detecting their expressions in ALO-treated cancer cells and predicting their binding sequence for miR-296-5p, circNSUN2 was finally identified as the research object of this study.\\nCircNSUN2, which maps to the 5p15 amplicon in CRCs, was confirmed by Chen et al to regulate cytoplasmic output and promote liver metastasis of CRC through N 6-methyladenosine modification.29 Similarly, our experimental results uncovered that knockdown of circNSUN2 inhibited proliferation and accelerated apoptosis in CRC cells. More importantly, we for the first time revealed that ALO can prevent the activation of circNSUN2 and offset the promotion effect of overexpression of circNSUN2 on esophageal cancer progression. Since circNSUN2 and miR-296-5p have targeting sequences, we verified the cellular regulatory effects of circNSUN2 and miR-296-5p/STAT3 through dual luciferase experiments and rescue experiments. The final result confirmed our conjecture that regulating the circNSUN2/miR-296-5p/STAT3 axis can reduce the proliferation rate and increase the apoptosis rate of CRC cells.\\nAt present, blocking proliferation and increasing apoptosis in cancer cells are the intensively discussed mechanisms in CRC study. Tumor cells can proliferate quickly and unrestrictedly, and thus, inhibiting their proliferation can produce an anti-tumor effect.30 Apoptosis, alternatively called programmed cell death, is an autonomous cell death process strictly controlled by multiple genes. Apoptosis is an important part of cell life cycle, and it is also an important link in regulating the development of the body and maintaining the stability of the internal environment in organisms.31,32 Our research shows that ALO inhibits the proliferation and promotes the apoptosis of CRC cells by regulating the circNSUN2/miR-296-5p/STAT3 pathway, and ultimately prevents the tumorigenesis of CRC.\\nThere are still certain limitations in our research. Although we have confirmed the effect of ALO on inducing apoptosis and reducing proliferation in CRC cells and clarified the underlying mechanism, the circNSUN2/miR-296-5p/STAT3 axis and the pathways related to proliferation and apoptosis have not been analyzed yet, which will be further explored in future research. In addition, the anticancer effect of ALO on CRC and its experimental concentration needs to be further tested in animal and clinical experiments. Whether ALO has an impact on drug resistance in CRC is also a direction of future research.\"\n]"},"other_sections_sec_types":{"kind":"list like","value":[null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null],"string":"[\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null\n]"},"all_sections_titles":{"kind":"list like","value":["Introduction","Method","Cell Purchase and ALO Processing","MTT Assay","EdU (5-Ethynyl-2ʹ-Deoxyuridine) Cell Proliferation Experiment","Colony Formation Assay","Apoptosis Experiment","Bioinformatics Analysis and Target Gene Binding Verification","Quantitative Real-Time Polymerase Chain Reaction (qRT-PCR)","Cell Transfection","Western Blot","Statistical Analysis","Result","ALO at a Concentration Gradient Inhibited Proliferation Yet Promoted Apoptosis in CRC Cells","The Abnormally Low Expression of miR-296-5p in Colon Cancer Could Be Up-Regulated by ALO","Among the Differentially Expressed circRNAs in CRC, Only circNSUN2 Not Only Had a Target Site for miR-296-5p, but Also Could Be Regulated by ALO","Overexpression of circNSUN2 Partially Offset the Suppression of ALO on the Biological Behavior of Cancer Cells","Sh-circNSUN2 Inhibited the Malignant Development of CRC Cells, Which Was Neutralized by miR-296-5p Inhibitor","MiR-296-5p Bound to STAT3 and Upregulation of Mir-296-5p Inhibited the Expression of STAT3 in CRC Cells","MiR-296-5p Mimic Inhibited Proliferation and Promoted Apoptosis in CRC Cells by Regulating Apoptosis-Related Genes, Which Was Partially Reversed by Overexpressed STAT3","Discussion"],"string":"[\n \"Introduction\",\n \"Method\",\n \"Cell Purchase and ALO Processing\",\n \"MTT Assay\",\n \"EdU (5-Ethynyl-2ʹ-Deoxyuridine) Cell Proliferation Experiment\",\n \"Colony Formation Assay\",\n \"Apoptosis Experiment\",\n \"Bioinformatics Analysis and Target Gene Binding Verification\",\n \"Quantitative Real-Time Polymerase Chain Reaction (qRT-PCR)\",\n \"Cell Transfection\",\n \"Western Blot\",\n \"Statistical Analysis\",\n \"Result\",\n \"ALO at a Concentration Gradient Inhibited Proliferation Yet Promoted Apoptosis in CRC Cells\",\n \"The Abnormally Low Expression of miR-296-5p in Colon Cancer Could Be Up-Regulated by ALO\",\n \"Among the Differentially Expressed circRNAs in CRC, Only circNSUN2 Not Only Had a Target Site for miR-296-5p, but Also Could Be Regulated by ALO\",\n \"Overexpression of circNSUN2 Partially Offset the Suppression of ALO on the Biological Behavior of Cancer Cells\",\n \"Sh-circNSUN2 Inhibited the Malignant Development of CRC Cells, Which Was Neutralized by miR-296-5p Inhibitor\",\n \"MiR-296-5p Bound to STAT3 and Upregulation of Mir-296-5p Inhibited the Expression of STAT3 in CRC Cells\",\n \"MiR-296-5p Mimic Inhibited Proliferation and Promoted Apoptosis in CRC Cells by Regulating Apoptosis-Related Genes, Which Was Partially Reversed by Overexpressed STAT3\",\n \"Discussion\"\n]"},"all_sections_texts":{"kind":"list like","value":["Cancer has become the first killer in the world and the biggest obstacle for extending human life expectancy. It is estimated that there were 18.1 million new cancer cases and 9.6 million cancer deaths in 2018.1 Colorectal cancer (CRC) is one of the most common gastrointestinal tumors.2 According to the data from the International Agency for Research on Cancer (IARC),1 the number of new CRC cases worldwide in 2018 was approximately 1.09 million, making CRC the fourth most prevalent malignant tumor after lung, breast and prostate cancers; meantime, CRC had a high mortality rate of about 9.2%, ranking only after lung cancer. Epidemiological studies have shown that the incidence of CRC varies significantly in different countries, and is higher in developed areas.3 More importantly, as the incidence of CRC rises rapidly in people under 50, the age of onset of the disease is getting lower.4,5 Therefore, it is urgent to improve the diagnosis rate and treatment effect of CRC.\nCurrently, the main clinical treatment of CRC is surgery combined with adjuvant radiotherapy, chemotherapy or molecular targeted therapy. Surgery is generally considered as the first choice for comprehensive treatment of CRC. This method is suitable for patients whose tumors are confined to the intestinal wall while penetrating the intestinal wall and invading the serous or extraserous membrane without lymph node metastasis.6,7 However, the availability of radical resection is limited as most patients with CRC have adenocarcinoma, which generally develops from polyps and is metastatic by nature.8 As a result, CRC patients tend to undergo simple resection, which leads to a high recurrence rate. Chemotherapy, therefore, is still necessary for postoperative and late-stage CRC patients.9,10 At present, the commonly used chemotherapeutic drugs in clinic mainly include 5-fluorouracil (5-Fu), oxaliplatin and its derivatives.9 Chemotherapy is highly risky because it indiscriminately kills tumor cells and immune cells and thereby reduces the anti-tumor immune effect.11 Likewise, radiotherapy greatly weakens the body’s immunity.12 Hence, researchers have proposed to find effective treatments or drugs that cause less damage to patients.\nWith advances in the research and development of traditional Chinese medicine, more attention has been paid to the roles of traditional Chinese medicine and its active ingredients in disease prevention and treatment. At the same time, new targets for molecular targeted therapy of diseases have been discovered in the exploration of the molecular mechanisms of traditional Chinese medicine and its monomers. Aloperine (ALO) has been confirmed to have a significant anti-cancer activity. ALO is a component of the traditional Chinese medicine Sophora alopecuroides L.13 It is also one of the main alkaloids separated and extracted in the lab, with a molecular formula of C15H24N2.14 Recent studies have found that ALO has the effects of anti-inflammation, immunosuppression, redox suppression, cardiovascular protection and anti-cancer, among which its tumor-suppressing activity has been extensively reported. For example, Yu et al revealed that ALO inhibited the carcinogenesis process by regulating excessive autophagy in thyroid cancer cells;15 Liu et al demonstrated that ALO inhibited the PI3K/Akt pathway, increased apoptosis and caused cell cycle block in liver cancer cells;16 besides, ALO was also found to exert an anti-cancer effect in prostate cancer and breast cancer.17,18 In the previous study, our research group found that ALO up-regulated miR-296-5p and inhibited the activity of its target gene STAT3, thereby inhibiting the proliferation and inducing the apoptosis of CRC cells. However, the activation pathway of miR-296-5p is unclear. In order to clarify the upstream activation pathway of ALO-upregulated miR-296-5p, this study will explore the mechanism of ALO in inhibiting CRC from the perspective of the circRNA-miRNA-mRNA regulatory network.","Cell Purchase and ALO Processing CCD-18Co and CRC cell lines SW480 (CCL-228™) and HT29 (HTB-38™) used in this research were provided by the American Type Culture Collection (ATCC). According to the culture requirements, CCD-18Co, SW480 and HT29 cells were separately inoculated in DMEM medium (30–2007, ATCC) containing 10% fetal bovine serum and incubated in a D180-P cell incubator (RWD, China) containing 5% CO2 at 37 °C.\nALO (DK0052, CAS NO: 56293-29-9, HPLC≥98%), the research object of this study, was provided by Chengdu DESITE Biological Company, China. ALO was dissolved in fresh DMEM to prepare 0.1 mmol/L, 0.2 mmol/L, 0.4 mmol/L, 0.8 mmol/L and 1 mmol/L ALO solutions, which were separately used to treat the CCD-18Co, SW480 and HT29 cells for 24 hours (h).\nCCD-18Co and CRC cell lines SW480 (CCL-228™) and HT29 (HTB-38™) used in this research were provided by the American Type Culture Collection (ATCC). According to the culture requirements, CCD-18Co, SW480 and HT29 cells were separately inoculated in DMEM medium (30–2007, ATCC) containing 10% fetal bovine serum and incubated in a D180-P cell incubator (RWD, China) containing 5% CO2 at 37 °C.\nALO (DK0052, CAS NO: 56293-29-9, HPLC≥98%), the research object of this study, was provided by Chengdu DESITE Biological Company, China. ALO was dissolved in fresh DMEM to prepare 0.1 mmol/L, 0.2 mmol/L, 0.4 mmol/L, 0.8 mmol/L and 1 mmol/L ALO solutions, which were separately used to treat the CCD-18Co, SW480 and HT29 cells for 24 hours (h).\nMTT Assay One hundred µL of SW480 or HT29 cell suspension (1×104 cells/mL) was transferred to each well of a 96-well plate. Of note, three holes were added to the same treatment group. After treatment with the ALO solutions for 24 h, SW480 or HT29 cells were added with MTT reagent (10 µL/well, PB180519, Procell, USA), which could react with mitochondria in living cells. After 4 h of binding, a HBS-1096A enzyme label analyzer (DeTie, China) was used to detect the absorbance of SW480 or HT29 cells in the mixed solutions at 490 nm, and then cell viability was calculated based on the absorbance.\nOne hundred µL of SW480 or HT29 cell suspension (1×104 cells/mL) was transferred to each well of a 96-well plate. Of note, three holes were added to the same treatment group. After treatment with the ALO solutions for 24 h, SW480 or HT29 cells were added with MTT reagent (10 µL/well, PB180519, Procell, USA), which could react with mitochondria in living cells. After 4 h of binding, a HBS-1096A enzyme label analyzer (DeTie, China) was used to detect the absorbance of SW480 or HT29 cells in the mixed solutions at 490 nm, and then cell viability was calculated based on the absorbance.\nEdU (5-Ethynyl-2ʹ-Deoxyuridine) Cell Proliferation Experiment SW480 or HT29 cells in logarithmic growth phase were seeded in 96-well plates at 1×104 cells/well. EdU reagent (ST067) purchased from Beyotime was diluted to 50 μmol/L with fresh DMEM medium. The EdU dilution was then used to incubate the cells at 100 μL/well for 2 h. After removing the culture medium, the SW480 or HT29 cells were fixed with methanol at room temperature for 15 minutes (min). Positive fluorescence expression (red) in the collected SW480 or HT29 cells were observed, photographed and recorded under a Leica DMi8 inverted fluorescence microscope (magnification 100 ×). Then DAPI reagent was used to stain the nuclei, and the stains were observed under a microscope.\nSW480 or HT29 cells in logarithmic growth phase were seeded in 96-well plates at 1×104 cells/well. EdU reagent (ST067) purchased from Beyotime was diluted to 50 μmol/L with fresh DMEM medium. The EdU dilution was then used to incubate the cells at 100 μL/well for 2 h. After removing the culture medium, the SW480 or HT29 cells were fixed with methanol at room temperature for 15 minutes (min). Positive fluorescence expression (red) in the collected SW480 or HT29 cells were observed, photographed and recorded under a Leica DMi8 inverted fluorescence microscope (magnification 100 ×). Then DAPI reagent was used to stain the nuclei, and the stains were observed under a microscope.\nColony Formation Assay Two hundred SW480 or HT29 cells were transferred to petri dishes containing complete medium (10 mL). In order to distribute the cells evenly, the petri dishes were rotated slowly for 1 min after cell inoculation. Next, the culture dishes containing cells were placed in an incubator for routine culture. During the culture, the medium was changed every 2 days (d). After 14 d, the SW480 or HT29 cells were soaked with Giemsa dye for 20 min and then the colony formation of the cells was observed under a microscope.\nTwo hundred SW480 or HT29 cells were transferred to petri dishes containing complete medium (10 mL). In order to distribute the cells evenly, the petri dishes were rotated slowly for 1 min after cell inoculation. Next, the culture dishes containing cells were placed in an incubator for routine culture. During the culture, the medium was changed every 2 days (d). After 14 d, the SW480 or HT29 cells were soaked with Giemsa dye for 20 min and then the colony formation of the cells was observed under a microscope.\nApoptosis Experiment An Annexin V-FITC Apoptosis Detection Kit (CA1020, Solarbio, China) and a flow cytometer (CytoFLEX, BECKMAN COULTER, USA) were used to detect changes in the apoptosis of SW480 or HT29 cells. In brief, SW480 or HT29 cells were prepared into 1×106 cells/mL cell suspension with 1 mL 1× Binding Buffer. One hundred μL of SW480 or HT29 cells and 5 μL of Annexin V-FITC reagent were added to a centrifuge tube and mixed for 10 min in the dark at room temperature. Next, 5 μL of PI reagent was added to the centrifuge tube and incubated in the dark at room temperature for 5 min. Afterwards, PBS was added to adjust the mixture to a final volume of 500 μL. After half an hour, the cells in the centrifuge tube were transferred to the detection instrument to calculate the number of apoptotic cells.\nAn Annexin V-FITC Apoptosis Detection Kit (CA1020, Solarbio, China) and a flow cytometer (CytoFLEX, BECKMAN COULTER, USA) were used to detect changes in the apoptosis of SW480 or HT29 cells. In brief, SW480 or HT29 cells were prepared into 1×106 cells/mL cell suspension with 1 mL 1× Binding Buffer. One hundred μL of SW480 or HT29 cells and 5 μL of Annexin V-FITC reagent were added to a centrifuge tube and mixed for 10 min in the dark at room temperature. Next, 5 μL of PI reagent was added to the centrifuge tube and incubated in the dark at room temperature for 5 min. Afterwards, PBS was added to adjust the mixture to a final volume of 500 μL. After half an hour, the cells in the centrifuge tube were transferred to the detection instrument to calculate the number of apoptotic cells.\nBioinformatics Analysis and Target Gene Binding Verification Starbase (http://starbase.sysu.edu.cn/index.php) was used to retrieve and analyze information of differential expression of miR-296-5p in Colon adenocarcinoma (COAD) patients and healthy volunteers. TargetScan (http://www.targetscan.org) was used to predict the circRNA that contained a binding sequence for miR-296-5p and the downstream target genes of miR-296-5p. The binding sequences predicted by TargetScan were designed by COBIOER (China) and used to construct the following reporter plasmids for dual luciferase experiments: NOP2/Sun RNA methyltransferase 2 wild type (circNSUN2-WT), circNSUN2-mutant type (MUT), Signal Transducer and Activator of Transcription 3 (STAT3)-WT and STAT3-MUT. The reporter plasmids were separately co-transfected with miR-296-5p mimic or mimic control into SW480 and HT29 cells using liposome for 48 h. Luciferase activity in the transfected SW480 and HT29 cells was detected using Dual-Luciferase® Reporter Assay System (E1960, Promega, USA) with a GloMax 20/20 luminometer (Promega, USA).\nStarbase (http://starbase.sysu.edu.cn/index.php) was used to retrieve and analyze information of differential expression of miR-296-5p in Colon adenocarcinoma (COAD) patients and healthy volunteers. TargetScan (http://www.targetscan.org) was used to predict the circRNA that contained a binding sequence for miR-296-5p and the downstream target genes of miR-296-5p. The binding sequences predicted by TargetScan were designed by COBIOER (China) and used to construct the following reporter plasmids for dual luciferase experiments: NOP2/Sun RNA methyltransferase 2 wild type (circNSUN2-WT), circNSUN2-mutant type (MUT), Signal Transducer and Activator of Transcription 3 (STAT3)-WT and STAT3-MUT. The reporter plasmids were separately co-transfected with miR-296-5p mimic or mimic control into SW480 and HT29 cells using liposome for 48 h. Luciferase activity in the transfected SW480 and HT29 cells was detected using Dual-Luciferase® Reporter Assay System (E1960, Promega, USA) with a GloMax 20/20 luminometer (Promega, USA).\nQuantitative Real-Time Polymerase Chain Reaction (qRT-PCR) qRT-PCR is a reliable method for rapid detection of gene mRNA levels. Briefly, total RNA of SW480 and HT29 cells was fully isolated by the conventional RNA extraction reagent TRIzol (15,596,018, Invitrogen, USA), and subsequently reverse transcribed into cDNA using the RNA reverse transcription reagent (RR047A) developed by TaKaRa (Japan). The qRT-PCR reaction system which consisted of cDNA, gene primers (Sangon synthesis), SYBR® Green (S4438-20RXN, Sigma-Aldrich, Germany) and DEPC water was added to a 96-well plate and put into the detection instrument (Bio-Rad thermal cycler T100). The reaction conditions were set as follows: pre-denaturation at 95 °C for 10 min, denaturation at 95 °C for 15 seconds (s), and annealing at 58 °C for 1 min, for a total of 40 cycles. According to Anita Ciesielska’s report,19 gene mRNA levels were quantified by the 2−ΔΔCT method. GAPDH and U6 were internal references in the experiment. The specific sequences of synthetic primers were shown in Table 1.Table 1Primers for qRT-PCRGeneForward Primer (5ʹ-3ʹ)Reverse Primer (5ʹ-3ʹ)miR-296-5pCCTGTGTCGTATCCAGTGCAAGTCGTATCCAGTGCGTGTCGcircDDX17TGCCAACCACAACATCCTCCACGCTCCCCAGGATTACCAAATcircHIPK3TATGTTGGTGGATCCTGTTCGGCATGGTGGGTAGACCAAGACTTGTGAcircZFRAACCACCACAGATTCACTATAACCACCACAGATTCACTATcircRNA_100290GTCATTCCCTCTTTAATGGTGCAGAACTTCCGCTCTAACATACHas_circ_0026344CTCAGCCTCTAGCATAAGCTCAGGCAAGAGAATGATTTGAACcircVAPATGGATTCCAAATTGAGATGCGTATTCACTTTTCTATCCGATGGATTTCGCHsa_circ_0009361AGAACCAGATTCGAGACGCCGTGCTCTTCAATGCCACCTTCcircNSUN2CTTGAGAAAATCGCCACACTTGTTGAGGAGCAGTGGTGGcircACAP2TCAGAGCATCTGCCCAAAGTTAAATGAACCCCAAGGCTCCGcircITGA7GTGTGCACAGGTCCTTCCAATGGAAGTTCTGTGAGGGACGcircEIF4G3CCTACCCCATCCCCTTATTCACCGTGCTGTAGACTGCTGAGSTAT3CCCCATACCTGAAGACCAAGGGACTCAAACTGCCCTCCTU6CTCGCTTCGGCAGCACAAACGCTTCACGAATTTGCGTGAPDHTGTGGGCATCAATGGATTTGGACACCATGTATTCCGGGTCAAT\n\nPrimers for qRT-PCR\nqRT-PCR is a reliable method for rapid detection of gene mRNA levels. Briefly, total RNA of SW480 and HT29 cells was fully isolated by the conventional RNA extraction reagent TRIzol (15,596,018, Invitrogen, USA), and subsequently reverse transcribed into cDNA using the RNA reverse transcription reagent (RR047A) developed by TaKaRa (Japan). The qRT-PCR reaction system which consisted of cDNA, gene primers (Sangon synthesis), SYBR® Green (S4438-20RXN, Sigma-Aldrich, Germany) and DEPC water was added to a 96-well plate and put into the detection instrument (Bio-Rad thermal cycler T100). The reaction conditions were set as follows: pre-denaturation at 95 °C for 10 min, denaturation at 95 °C for 15 seconds (s), and annealing at 58 °C for 1 min, for a total of 40 cycles. According to Anita Ciesielska’s report,19 gene mRNA levels were quantified by the 2−ΔΔCT method. GAPDH and U6 were internal references in the experiment. The specific sequences of synthetic primers were shown in Table 1.Table 1Primers for qRT-PCRGeneForward Primer (5ʹ-3ʹ)Reverse Primer (5ʹ-3ʹ)miR-296-5pCCTGTGTCGTATCCAGTGCAAGTCGTATCCAGTGCGTGTCGcircDDX17TGCCAACCACAACATCCTCCACGCTCCCCAGGATTACCAAATcircHIPK3TATGTTGGTGGATCCTGTTCGGCATGGTGGGTAGACCAAGACTTGTGAcircZFRAACCACCACAGATTCACTATAACCACCACAGATTCACTATcircRNA_100290GTCATTCCCTCTTTAATGGTGCAGAACTTCCGCTCTAACATACHas_circ_0026344CTCAGCCTCTAGCATAAGCTCAGGCAAGAGAATGATTTGAACcircVAPATGGATTCCAAATTGAGATGCGTATTCACTTTTCTATCCGATGGATTTCGCHsa_circ_0009361AGAACCAGATTCGAGACGCCGTGCTCTTCAATGCCACCTTCcircNSUN2CTTGAGAAAATCGCCACACTTGTTGAGGAGCAGTGGTGGcircACAP2TCAGAGCATCTGCCCAAAGTTAAATGAACCCCAAGGCTCCGcircITGA7GTGTGCACAGGTCCTTCCAATGGAAGTTCTGTGAGGGACGcircEIF4G3CCTACCCCATCCCCTTATTCACCGTGCTGTAGACTGCTGAGSTAT3CCCCATACCTGAAGACCAAGGGACTCAAACTGCCCTCCTU6CTCGCTTCGGCAGCACAAACGCTTCACGAATTTGCGTGAPDHTGTGGGCATCAATGGATTTGGACACCATGTATTCCGGGTCAAT\n\nPrimers for qRT-PCR\nCell Transfection Exogenous up-regulation or knockdown of test genes is a common method to observe their regulatory effects on cells or tissues. Liposome transfection with Lipofectamine 3000 (L3000008, ThermoFisher Scientific, USA) is the most common and convenient method for exogenous interference. Therefore, we commissioned Guangzhou GENESEED Biological Company to synthesize circNSUN2 overexpressed plasmid, circNSUN2 silenced plasmid (sh-circNSUN2), STAT3 overexpressed plasmid and their respective negative controls. MiR-296-5p mimic (miR10000690-1-5) and mimic control (miR1N0000001-1-5), as well as miR-296-5p inhibitor (miR20000690-1-5) and inhibitor control (miR2N0000001-1-5), were purchased directly from Guangzhou RIOBOBIO Company. The plasmids were transfected into SW480 and HT29 cells according to the grouping using Lipofectamine 3000. After 48 h, the success rate of transfection of the cells was determined by qRT-PCR.\nExogenous up-regulation or knockdown of test genes is a common method to observe their regulatory effects on cells or tissues. Liposome transfection with Lipofectamine 3000 (L3000008, ThermoFisher Scientific, USA) is the most common and convenient method for exogenous interference. Therefore, we commissioned Guangzhou GENESEED Biological Company to synthesize circNSUN2 overexpressed plasmid, circNSUN2 silenced plasmid (sh-circNSUN2), STAT3 overexpressed plasmid and their respective negative controls. MiR-296-5p mimic (miR10000690-1-5) and mimic control (miR1N0000001-1-5), as well as miR-296-5p inhibitor (miR20000690-1-5) and inhibitor control (miR2N0000001-1-5), were purchased directly from Guangzhou RIOBOBIO Company. The plasmids were transfected into SW480 and HT29 cells according to the grouping using Lipofectamine 3000. After 48 h, the success rate of transfection of the cells was determined by qRT-PCR.\nWestern Blot RIPA lysate (tissue/cell, R0010) produced by Solarbio (China) was used to separate protein from SW480 and HT29 cells, and its concentration was determined with BCA protein concentration determination reagent (PC0020, Solarbio, China). Then the protein was subjected to high temperature denaturation to maintain a relatively stable state. After being electrophoresed by SDS-PAGE, the protein was transferred to the membrane carrier (PVDF membrane). The membrane was soaked with the same BSA sealant (SW3015) produced by Solarbio for 2 h at room temperature. Next, the membrane was incubated with Abcam (USA) antibodies (anti-STAT3, ab68153, 88 kDa; Bax, ab32503, 21 kDa; Bcl-2, ab59348, 26 kDa) that could bind to the corresponding antigens in the protein on the membrane overnight (4 °C), with GAPDH (ab8245, 36KD) as an internal reference. To identify the antigen-antibody complex, a secondary antibody labeled with HRP (Goat Anti-Rabbit antibody, 1:10,000, ab6721; Goat Anti-Mouse antibody, 1:10000, ab205719) was used to treat the PVDF membrane for 1.5 h at room temperature. Fluorescent signal of the complex was enhanced using the ECL Western Blotting Substrate (PE0010, Solarbio) and detected using the Gel Doc™ XR+ imaging system (BIO-RAD, USA). The gray value of the bands was calculated by Image Lab software to determine the final gene protein level.\nRIPA lysate (tissue/cell, R0010) produced by Solarbio (China) was used to separate protein from SW480 and HT29 cells, and its concentration was determined with BCA protein concentration determination reagent (PC0020, Solarbio, China). Then the protein was subjected to high temperature denaturation to maintain a relatively stable state. After being electrophoresed by SDS-PAGE, the protein was transferred to the membrane carrier (PVDF membrane). The membrane was soaked with the same BSA sealant (SW3015) produced by Solarbio for 2 h at room temperature. Next, the membrane was incubated with Abcam (USA) antibodies (anti-STAT3, ab68153, 88 kDa; Bax, ab32503, 21 kDa; Bcl-2, ab59348, 26 kDa) that could bind to the corresponding antigens in the protein on the membrane overnight (4 °C), with GAPDH (ab8245, 36KD) as an internal reference. To identify the antigen-antibody complex, a secondary antibody labeled with HRP (Goat Anti-Rabbit antibody, 1:10,000, ab6721; Goat Anti-Mouse antibody, 1:10000, ab205719) was used to treat the PVDF membrane for 1.5 h at room temperature. Fluorescent signal of the complex was enhanced using the ECL Western Blotting Substrate (PE0010, Solarbio) and detected using the Gel Doc™ XR+ imaging system (BIO-RAD, USA). The gray value of the bands was calculated by Image Lab software to determine the final gene protein level.\nStatistical Analysis SPSS 20.0 software (IBM, USA) was employed for statistical analysis of the data obtained in the experiments. Differences between two groups were compared by independent sample t-test, and those between multiple groups were compared by one-way ANOVA followed by Tukey’s test. When p<0.05, the difference was considered statistically significant.\nSPSS 20.0 software (IBM, USA) was employed for statistical analysis of the data obtained in the experiments. Differences between two groups were compared by independent sample t-test, and those between multiple groups were compared by one-way ANOVA followed by Tukey’s test. When p<0.05, the difference was considered statistically significant.","CCD-18Co and CRC cell lines SW480 (CCL-228™) and HT29 (HTB-38™) used in this research were provided by the American Type Culture Collection (ATCC). According to the culture requirements, CCD-18Co, SW480 and HT29 cells were separately inoculated in DMEM medium (30–2007, ATCC) containing 10% fetal bovine serum and incubated in a D180-P cell incubator (RWD, China) containing 5% CO2 at 37 °C.\nALO (DK0052, CAS NO: 56293-29-9, HPLC≥98%), the research object of this study, was provided by Chengdu DESITE Biological Company, China. ALO was dissolved in fresh DMEM to prepare 0.1 mmol/L, 0.2 mmol/L, 0.4 mmol/L, 0.8 mmol/L and 1 mmol/L ALO solutions, which were separately used to treat the CCD-18Co, SW480 and HT29 cells for 24 hours (h).","One hundred µL of SW480 or HT29 cell suspension (1×104 cells/mL) was transferred to each well of a 96-well plate. Of note, three holes were added to the same treatment group. After treatment with the ALO solutions for 24 h, SW480 or HT29 cells were added with MTT reagent (10 µL/well, PB180519, Procell, USA), which could react with mitochondria in living cells. After 4 h of binding, a HBS-1096A enzyme label analyzer (DeTie, China) was used to detect the absorbance of SW480 or HT29 cells in the mixed solutions at 490 nm, and then cell viability was calculated based on the absorbance.","SW480 or HT29 cells in logarithmic growth phase were seeded in 96-well plates at 1×104 cells/well. EdU reagent (ST067) purchased from Beyotime was diluted to 50 μmol/L with fresh DMEM medium. The EdU dilution was then used to incubate the cells at 100 μL/well for 2 h. After removing the culture medium, the SW480 or HT29 cells were fixed with methanol at room temperature for 15 minutes (min). Positive fluorescence expression (red) in the collected SW480 or HT29 cells were observed, photographed and recorded under a Leica DMi8 inverted fluorescence microscope (magnification 100 ×). Then DAPI reagent was used to stain the nuclei, and the stains were observed under a microscope.","Two hundred SW480 or HT29 cells were transferred to petri dishes containing complete medium (10 mL). In order to distribute the cells evenly, the petri dishes were rotated slowly for 1 min after cell inoculation. Next, the culture dishes containing cells were placed in an incubator for routine culture. During the culture, the medium was changed every 2 days (d). After 14 d, the SW480 or HT29 cells were soaked with Giemsa dye for 20 min and then the colony formation of the cells was observed under a microscope.","An Annexin V-FITC Apoptosis Detection Kit (CA1020, Solarbio, China) and a flow cytometer (CytoFLEX, BECKMAN COULTER, USA) were used to detect changes in the apoptosis of SW480 or HT29 cells. In brief, SW480 or HT29 cells were prepared into 1×106 cells/mL cell suspension with 1 mL 1× Binding Buffer. One hundred μL of SW480 or HT29 cells and 5 μL of Annexin V-FITC reagent were added to a centrifuge tube and mixed for 10 min in the dark at room temperature. Next, 5 μL of PI reagent was added to the centrifuge tube and incubated in the dark at room temperature for 5 min. Afterwards, PBS was added to adjust the mixture to a final volume of 500 μL. After half an hour, the cells in the centrifuge tube were transferred to the detection instrument to calculate the number of apoptotic cells.","Starbase (http://starbase.sysu.edu.cn/index.php) was used to retrieve and analyze information of differential expression of miR-296-5p in Colon adenocarcinoma (COAD) patients and healthy volunteers. TargetScan (http://www.targetscan.org) was used to predict the circRNA that contained a binding sequence for miR-296-5p and the downstream target genes of miR-296-5p. The binding sequences predicted by TargetScan were designed by COBIOER (China) and used to construct the following reporter plasmids for dual luciferase experiments: NOP2/Sun RNA methyltransferase 2 wild type (circNSUN2-WT), circNSUN2-mutant type (MUT), Signal Transducer and Activator of Transcription 3 (STAT3)-WT and STAT3-MUT. The reporter plasmids were separately co-transfected with miR-296-5p mimic or mimic control into SW480 and HT29 cells using liposome for 48 h. Luciferase activity in the transfected SW480 and HT29 cells was detected using Dual-Luciferase® Reporter Assay System (E1960, Promega, USA) with a GloMax 20/20 luminometer (Promega, USA).","qRT-PCR is a reliable method for rapid detection of gene mRNA levels. Briefly, total RNA of SW480 and HT29 cells was fully isolated by the conventional RNA extraction reagent TRIzol (15,596,018, Invitrogen, USA), and subsequently reverse transcribed into cDNA using the RNA reverse transcription reagent (RR047A) developed by TaKaRa (Japan). The qRT-PCR reaction system which consisted of cDNA, gene primers (Sangon synthesis), SYBR® Green (S4438-20RXN, Sigma-Aldrich, Germany) and DEPC water was added to a 96-well plate and put into the detection instrument (Bio-Rad thermal cycler T100). The reaction conditions were set as follows: pre-denaturation at 95 °C for 10 min, denaturation at 95 °C for 15 seconds (s), and annealing at 58 °C for 1 min, for a total of 40 cycles. According to Anita Ciesielska’s report,19 gene mRNA levels were quantified by the 2−ΔΔCT method. GAPDH and U6 were internal references in the experiment. The specific sequences of synthetic primers were shown in Table 1.Table 1Primers for qRT-PCRGeneForward Primer (5ʹ-3ʹ)Reverse Primer (5ʹ-3ʹ)miR-296-5pCCTGTGTCGTATCCAGTGCAAGTCGTATCCAGTGCGTGTCGcircDDX17TGCCAACCACAACATCCTCCACGCTCCCCAGGATTACCAAATcircHIPK3TATGTTGGTGGATCCTGTTCGGCATGGTGGGTAGACCAAGACTTGTGAcircZFRAACCACCACAGATTCACTATAACCACCACAGATTCACTATcircRNA_100290GTCATTCCCTCTTTAATGGTGCAGAACTTCCGCTCTAACATACHas_circ_0026344CTCAGCCTCTAGCATAAGCTCAGGCAAGAGAATGATTTGAACcircVAPATGGATTCCAAATTGAGATGCGTATTCACTTTTCTATCCGATGGATTTCGCHsa_circ_0009361AGAACCAGATTCGAGACGCCGTGCTCTTCAATGCCACCTTCcircNSUN2CTTGAGAAAATCGCCACACTTGTTGAGGAGCAGTGGTGGcircACAP2TCAGAGCATCTGCCCAAAGTTAAATGAACCCCAAGGCTCCGcircITGA7GTGTGCACAGGTCCTTCCAATGGAAGTTCTGTGAGGGACGcircEIF4G3CCTACCCCATCCCCTTATTCACCGTGCTGTAGACTGCTGAGSTAT3CCCCATACCTGAAGACCAAGGGACTCAAACTGCCCTCCTU6CTCGCTTCGGCAGCACAAACGCTTCACGAATTTGCGTGAPDHTGTGGGCATCAATGGATTTGGACACCATGTATTCCGGGTCAAT\n\nPrimers for qRT-PCR","Exogenous up-regulation or knockdown of test genes is a common method to observe their regulatory effects on cells or tissues. Liposome transfection with Lipofectamine 3000 (L3000008, ThermoFisher Scientific, USA) is the most common and convenient method for exogenous interference. Therefore, we commissioned Guangzhou GENESEED Biological Company to synthesize circNSUN2 overexpressed plasmid, circNSUN2 silenced plasmid (sh-circNSUN2), STAT3 overexpressed plasmid and their respective negative controls. MiR-296-5p mimic (miR10000690-1-5) and mimic control (miR1N0000001-1-5), as well as miR-296-5p inhibitor (miR20000690-1-5) and inhibitor control (miR2N0000001-1-5), were purchased directly from Guangzhou RIOBOBIO Company. The plasmids were transfected into SW480 and HT29 cells according to the grouping using Lipofectamine 3000. After 48 h, the success rate of transfection of the cells was determined by qRT-PCR.","RIPA lysate (tissue/cell, R0010) produced by Solarbio (China) was used to separate protein from SW480 and HT29 cells, and its concentration was determined with BCA protein concentration determination reagent (PC0020, Solarbio, China). Then the protein was subjected to high temperature denaturation to maintain a relatively stable state. After being electrophoresed by SDS-PAGE, the protein was transferred to the membrane carrier (PVDF membrane). The membrane was soaked with the same BSA sealant (SW3015) produced by Solarbio for 2 h at room temperature. Next, the membrane was incubated with Abcam (USA) antibodies (anti-STAT3, ab68153, 88 kDa; Bax, ab32503, 21 kDa; Bcl-2, ab59348, 26 kDa) that could bind to the corresponding antigens in the protein on the membrane overnight (4 °C), with GAPDH (ab8245, 36KD) as an internal reference. To identify the antigen-antibody complex, a secondary antibody labeled with HRP (Goat Anti-Rabbit antibody, 1:10,000, ab6721; Goat Anti-Mouse antibody, 1:10000, ab205719) was used to treat the PVDF membrane for 1.5 h at room temperature. Fluorescent signal of the complex was enhanced using the ECL Western Blotting Substrate (PE0010, Solarbio) and detected using the Gel Doc™ XR+ imaging system (BIO-RAD, USA). The gray value of the bands was calculated by Image Lab software to determine the final gene protein level.","SPSS 20.0 software (IBM, USA) was employed for statistical analysis of the data obtained in the experiments. Differences between two groups were compared by independent sample t-test, and those between multiple groups were compared by one-way ANOVA followed by Tukey’s test. When p<0.05, the difference was considered statistically significant.","ALO at a Concentration Gradient Inhibited Proliferation Yet Promoted Apoptosis in CRC Cells In this study, the toxicity of ALO in normal cells (CCD-18Co) was detected, ALO showed no significant toxicity to CCD-18Co cells (Figure 1A). In order to study the effects of ALO on the growth and basic physiological functions of CRC cells, we used different concentrations of ALO to treat SW480 and HT29 cells. The results showed that 0.2 mmol/L, 0.4 mmol/L, 0.8 mmol/L and 1 mmol/L ALO solutions all inhibited the viability of SW480 and HT29 cells (p<0.05, Figure 1B and C). Moreover, the viability of SW480 and HT29 cells was close to 50% under the treatment of 0.8 mmol/L ALO solution, but was reduced to less than 50% when the ALO concentration was increased to 1 mmol/L (Figure 1B and C). In the following EdU cell proliferation experiment, the red fluorescence expression was reduced in SW480 and HT29 cells under the treatment of 0.8 mmol/L ALO (Figure 1D and E). Consistently, the clone formation experiment also showed that different concentrations of ALO (0.2 mmol/L, 0.4 mmol/L, 0.8 mmol/L) reduced the number of cell clones and inhibited the proliferation of SW480 and HT29 cells (p<0.05, Figure 1F–H). At the same time, the increase in the concentration of the ALO solution accelerated the death of CRC cells. As shown in Figure 1I–K, the number of apoptotic SW480 and HT29 cells increased significantly after treatment with different concentrations of ALO (p<0.01, Figure 1I–K).Figure 1ALO at a concentration gradient inhibited proliferation yet promoted apoptosis in CRC cells. (A) The effect of ALO on the normal colonic tissue cells (CCD-18Co) was detected by MTT experiments. (B and C) MTT experiments showed that ALO at a concentration gradient (0.1 mmol/L, 0.2 mmol/L, 0.4 mmol/L, 0.8 mmol/L and 1 mmol/L) inhibited the activity of SW480 and HT29 cells. (D and E) EdU cell proliferation experiments showed that 0.8 mmol/L ALO inhibited the proliferation of SW480 and HT29 cells. The magnification was 100×. (F–H) The clone formation experiment showed that ALO at a concentration gradient (0.2 mmol/L, 0.4 mmol/L and 0.8 mmol/L) inhibited the proliferation of SW480 and HT29 cells. (I–K) Flow cytometry experiments showed that ALO at a concentration gradient (0.2 mmol/L, 0.4 mmol/L and 0.8 mmol/L) promoted the apoptosis of SW480 and HT29 cells. All experiments were repeated three times to obtain average values. *p<0.05, **p<0.01, ***p<0.001 vs Control.Abbreviations: ALO, aloperine; CRC, colorectal cancer.\nALO at a concentration gradient inhibited proliferation yet promoted apoptosis in CRC cells. (A) The effect of ALO on the normal colonic tissue cells (CCD-18Co) was detected by MTT experiments. (B and C) MTT experiments showed that ALO at a concentration gradient (0.1 mmol/L, 0.2 mmol/L, 0.4 mmol/L, 0.8 mmol/L and 1 mmol/L) inhibited the activity of SW480 and HT29 cells. (D and E) EdU cell proliferation experiments showed that 0.8 mmol/L ALO inhibited the proliferation of SW480 and HT29 cells. The magnification was 100×. (F–H) The clone formation experiment showed that ALO at a concentration gradient (0.2 mmol/L, 0.4 mmol/L and 0.8 mmol/L) inhibited the proliferation of SW480 and HT29 cells. (I–K) Flow cytometry experiments showed that ALO at a concentration gradient (0.2 mmol/L, 0.4 mmol/L and 0.8 mmol/L) promoted the apoptosis of SW480 and HT29 cells. All experiments were repeated three times to obtain average values. *p<0.05, **p<0.01, ***p<0.001 vs Control.\nIn this study, the toxicity of ALO in normal cells (CCD-18Co) was detected, ALO showed no significant toxicity to CCD-18Co cells (Figure 1A). In order to study the effects of ALO on the growth and basic physiological functions of CRC cells, we used different concentrations of ALO to treat SW480 and HT29 cells. The results showed that 0.2 mmol/L, 0.4 mmol/L, 0.8 mmol/L and 1 mmol/L ALO solutions all inhibited the viability of SW480 and HT29 cells (p<0.05, Figure 1B and C). Moreover, the viability of SW480 and HT29 cells was close to 50% under the treatment of 0.8 mmol/L ALO solution, but was reduced to less than 50% when the ALO concentration was increased to 1 mmol/L (Figure 1B and C). In the following EdU cell proliferation experiment, the red fluorescence expression was reduced in SW480 and HT29 cells under the treatment of 0.8 mmol/L ALO (Figure 1D and E). Consistently, the clone formation experiment also showed that different concentrations of ALO (0.2 mmol/L, 0.4 mmol/L, 0.8 mmol/L) reduced the number of cell clones and inhibited the proliferation of SW480 and HT29 cells (p<0.05, Figure 1F–H). At the same time, the increase in the concentration of the ALO solution accelerated the death of CRC cells. As shown in Figure 1I–K, the number of apoptotic SW480 and HT29 cells increased significantly after treatment with different concentrations of ALO (p<0.01, Figure 1I–K).Figure 1ALO at a concentration gradient inhibited proliferation yet promoted apoptosis in CRC cells. (A) The effect of ALO on the normal colonic tissue cells (CCD-18Co) was detected by MTT experiments. (B and C) MTT experiments showed that ALO at a concentration gradient (0.1 mmol/L, 0.2 mmol/L, 0.4 mmol/L, 0.8 mmol/L and 1 mmol/L) inhibited the activity of SW480 and HT29 cells. (D and E) EdU cell proliferation experiments showed that 0.8 mmol/L ALO inhibited the proliferation of SW480 and HT29 cells. The magnification was 100×. (F–H) The clone formation experiment showed that ALO at a concentration gradient (0.2 mmol/L, 0.4 mmol/L and 0.8 mmol/L) inhibited the proliferation of SW480 and HT29 cells. (I–K) Flow cytometry experiments showed that ALO at a concentration gradient (0.2 mmol/L, 0.4 mmol/L and 0.8 mmol/L) promoted the apoptosis of SW480 and HT29 cells. All experiments were repeated three times to obtain average values. *p<0.05, **p<0.01, ***p<0.001 vs Control.Abbreviations: ALO, aloperine; CRC, colorectal cancer.\nALO at a concentration gradient inhibited proliferation yet promoted apoptosis in CRC cells. (A) The effect of ALO on the normal colonic tissue cells (CCD-18Co) was detected by MTT experiments. (B and C) MTT experiments showed that ALO at a concentration gradient (0.1 mmol/L, 0.2 mmol/L, 0.4 mmol/L, 0.8 mmol/L and 1 mmol/L) inhibited the activity of SW480 and HT29 cells. (D and E) EdU cell proliferation experiments showed that 0.8 mmol/L ALO inhibited the proliferation of SW480 and HT29 cells. The magnification was 100×. (F–H) The clone formation experiment showed that ALO at a concentration gradient (0.2 mmol/L, 0.4 mmol/L and 0.8 mmol/L) inhibited the proliferation of SW480 and HT29 cells. (I–K) Flow cytometry experiments showed that ALO at a concentration gradient (0.2 mmol/L, 0.4 mmol/L and 0.8 mmol/L) promoted the apoptosis of SW480 and HT29 cells. All experiments were repeated three times to obtain average values. *p<0.05, **p<0.01, ***p<0.001 vs Control.\nThe Abnormally Low Expression of miR-296-5p in Colon Cancer Could Be Up-Regulated by ALO Data from the Starbase database showed that the expression of miR-296-5p in colon cancer patients was significantly lower than that in healthy people (p=2.5e-6, Figure 2A). In this study, however, after treatment with different concentrations of ALO for 24 h, miR-296-5p activity in SW480 and HT29 cells was significantly increased (p<0.01, Figure 2B and C), suggesting that ALO can stimulate the up-regulation of miR-296-5p.Figure 2The abnormally low expression of miR-296-5p in colon cancer could be upregulated by ALO. (A) Starbase (http://starbase.sysu.edu.cn/index.php) was used to retrieve the information of differential expression of miR-296-5p in colon adenocarcinoma (COAD, n=450) patients and healthy volunteers (n=8) in vivo. P=2.5e-6. (B and C) The qRT-PCR experiment showed that ALO at a concentration gradient (0.1 mmol/L, 0.2 mmol/L, 0.4 mmol/L, 0.8 mmol/L and 1 mmol/L) up-regulated miR-296-5p expression in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. **p<0.01, ***p<0.001 vs Control.Abbreviation: qRT-PCR, quantitative real-time polymerase chain reaction.\nThe abnormally low expression of miR-296-5p in colon cancer could be upregulated by ALO. (A) Starbase (http://starbase.sysu.edu.cn/index.php) was used to retrieve the information of differential expression of miR-296-5p in colon adenocarcinoma (COAD, n=450) patients and healthy volunteers (n=8) in vivo. P=2.5e-6. (B and C) The qRT-PCR experiment showed that ALO at a concentration gradient (0.1 mmol/L, 0.2 mmol/L, 0.4 mmol/L, 0.8 mmol/L and 1 mmol/L) up-regulated miR-296-5p expression in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. **p<0.01, ***p<0.001 vs Control.\nData from the Starbase database showed that the expression of miR-296-5p in colon cancer patients was significantly lower than that in healthy people (p=2.5e-6, Figure 2A). In this study, however, after treatment with different concentrations of ALO for 24 h, miR-296-5p activity in SW480 and HT29 cells was significantly increased (p<0.01, Figure 2B and C), suggesting that ALO can stimulate the up-regulation of miR-296-5p.Figure 2The abnormally low expression of miR-296-5p in colon cancer could be upregulated by ALO. (A) Starbase (http://starbase.sysu.edu.cn/index.php) was used to retrieve the information of differential expression of miR-296-5p in colon adenocarcinoma (COAD, n=450) patients and healthy volunteers (n=8) in vivo. P=2.5e-6. (B and C) The qRT-PCR experiment showed that ALO at a concentration gradient (0.1 mmol/L, 0.2 mmol/L, 0.4 mmol/L, 0.8 mmol/L and 1 mmol/L) up-regulated miR-296-5p expression in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. **p<0.01, ***p<0.001 vs Control.Abbreviation: qRT-PCR, quantitative real-time polymerase chain reaction.\nThe abnormally low expression of miR-296-5p in colon cancer could be upregulated by ALO. (A) Starbase (http://starbase.sysu.edu.cn/index.php) was used to retrieve the information of differential expression of miR-296-5p in colon adenocarcinoma (COAD, n=450) patients and healthy volunteers (n=8) in vivo. P=2.5e-6. (B and C) The qRT-PCR experiment showed that ALO at a concentration gradient (0.1 mmol/L, 0.2 mmol/L, 0.4 mmol/L, 0.8 mmol/L and 1 mmol/L) up-regulated miR-296-5p expression in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. **p<0.01, ***p<0.001 vs Control.\nAmong the Differentially Expressed circRNAs in CRC, Only circNSUN2 Not Only Had a Target Site for miR-296-5p, but Also Could Be Regulated by ALO Through literature review, we screened 12 circRNAs that were reported to be associated with CRC: circDDX17, circHIPK3, circZFR, circRNA_100290, Has_circ_0026344, circVAPA, Hsa_circ_0009361, circNSUN2, circACAP2, circACVRL1, circITGA7 and circEIF4G3. We first detected the regulatory effect of ALO on these differentially expressed circRNAs by qRT-PCR to perform preliminary screening (Figure 3A). The results showed that the expressions of circDDX17, Has_circ_0026344, circVAPA, circNSUN2, circACAP2, circACVRL1 and circEIF4G3 were regulated by 0.8 mmol/L ALO (p<0.001, Figure 3A). Through subsequent screening of target genes, we found that only circNSUN2 and miR-296-5p have targeted binding sites (Figure 3B). After further verification, it was confirmed that circNSUN2-WT and miR-296-5p mimic did reduce the luciferase activity in SW480 and HT29 cells (p<0.001, Figure 3C and D). Therefore, we chose circNSUN2 as the next research object.Figure 3Among the differentially expressed circRNAs in CRC, only circNSUN2 not only had a target site for miR-296-5p, but also could be regulated by ALO. (A) qRT-PCR detected the regulation of 0.8 mmol/L ALO on the differentially expressed circRNAs in CRC. GAPDH played the role of internal reference. (B) The TargetScan database (http://www.targetscan.org) was used to predict the target sites of circNSUN2 and miR-296-5p. (C and D) The dual luciferase experiment confirmed that circNSUN2 can bind to miR-296-5p in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. ***p<0.001 vs Control; +++p<0.001 vs MC.Abbreviation: MC, miR-296-5p mimic control.\nAmong the differentially expressed circRNAs in CRC, only circNSUN2 not only had a target site for miR-296-5p, but also could be regulated by ALO. (A) qRT-PCR detected the regulation of 0.8 mmol/L ALO on the differentially expressed circRNAs in CRC. GAPDH played the role of internal reference. (B) The TargetScan database (http://www.targetscan.org) was used to predict the target sites of circNSUN2 and miR-296-5p. (C and D) The dual luciferase experiment confirmed that circNSUN2 can bind to miR-296-5p in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. ***p<0.001 vs Control; +++p<0.001 vs MC.\nThrough literature review, we screened 12 circRNAs that were reported to be associated with CRC: circDDX17, circHIPK3, circZFR, circRNA_100290, Has_circ_0026344, circVAPA, Hsa_circ_0009361, circNSUN2, circACAP2, circACVRL1, circITGA7 and circEIF4G3. We first detected the regulatory effect of ALO on these differentially expressed circRNAs by qRT-PCR to perform preliminary screening (Figure 3A). The results showed that the expressions of circDDX17, Has_circ_0026344, circVAPA, circNSUN2, circACAP2, circACVRL1 and circEIF4G3 were regulated by 0.8 mmol/L ALO (p<0.001, Figure 3A). Through subsequent screening of target genes, we found that only circNSUN2 and miR-296-5p have targeted binding sites (Figure 3B). After further verification, it was confirmed that circNSUN2-WT and miR-296-5p mimic did reduce the luciferase activity in SW480 and HT29 cells (p<0.001, Figure 3C and D). Therefore, we chose circNSUN2 as the next research object.Figure 3Among the differentially expressed circRNAs in CRC, only circNSUN2 not only had a target site for miR-296-5p, but also could be regulated by ALO. (A) qRT-PCR detected the regulation of 0.8 mmol/L ALO on the differentially expressed circRNAs in CRC. GAPDH played the role of internal reference. (B) The TargetScan database (http://www.targetscan.org) was used to predict the target sites of circNSUN2 and miR-296-5p. (C and D) The dual luciferase experiment confirmed that circNSUN2 can bind to miR-296-5p in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. ***p<0.001 vs Control; +++p<0.001 vs MC.Abbreviation: MC, miR-296-5p mimic control.\nAmong the differentially expressed circRNAs in CRC, only circNSUN2 not only had a target site for miR-296-5p, but also could be regulated by ALO. (A) qRT-PCR detected the regulation of 0.8 mmol/L ALO on the differentially expressed circRNAs in CRC. GAPDH played the role of internal reference. (B) The TargetScan database (http://www.targetscan.org) was used to predict the target sites of circNSUN2 and miR-296-5p. (C and D) The dual luciferase experiment confirmed that circNSUN2 can bind to miR-296-5p in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. ***p<0.001 vs Control; +++p<0.001 vs MC.\nOverexpression of circNSUN2 Partially Offset the Suppression of ALO on the Biological Behavior of Cancer Cells In SW480 and HT29 cells, ALO suppressed the mRNA level of circNSUN2, whereas transfection of circNSUN2 overexpression significantly increased the expression of circNSUN2 (p<0.001, Figure 4A and B). The following basic cell function experiments showed that the upregulation of circNSUN2 partially offset the inhibitory effect of ALO on CRC cell viability, proliferation and apoptosis (p<0.001, Figure 4C–L).Figure 4Overexpression of circNSUN2 partially offset the suppression of ALO on the biological behavior of cancer cells. (A and B) The qRT-PCR experiment showed that ALO (0.8 mmol/L) inhibited the expression of circNSUN2 in SW480 and HT29 cells. GAPDH played the role of internal reference. (C and D) The MTT experiment showed that overexpression of circNSUN2 partially offset the inhibition of ALO on cell viability in SW480 and HT29 cells. (E and F) EdU cell proliferation experiments showed that overexpression of circNSUN2 partially offset the inhibition of ALO on proliferation in SW480 and HT29 cells. The magnification was 100×. (G–I) The clone formation experiment showed that overexpression of circNSUN2 partially offset the inhibition of ALO on proliferation in SW480 and HT29 cells. (J–L) Flow cytometry experiments showed that overexpression of circNSUN2 partially offset the promotion of ALO on apoptosis in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. ***p<0.001 vs Control; ^p<0.05, ^^p<0.01, ^^^p<0.001 vs ALO+NC.Abbreviations: CircNSUN2, NOP2/Sun RNA methyltransferase 2; NC, negative control.\nOverexpression of circNSUN2 partially offset the suppression of ALO on the biological behavior of cancer cells. (A and B) The qRT-PCR experiment showed that ALO (0.8 mmol/L) inhibited the expression of circNSUN2 in SW480 and HT29 cells. GAPDH played the role of internal reference. (C and D) The MTT experiment showed that overexpression of circNSUN2 partially offset the inhibition of ALO on cell viability in SW480 and HT29 cells. (E and F) EdU cell proliferation experiments showed that overexpression of circNSUN2 partially offset the inhibition of ALO on proliferation in SW480 and HT29 cells. The magnification was 100×. (G–I) The clone formation experiment showed that overexpression of circNSUN2 partially offset the inhibition of ALO on proliferation in SW480 and HT29 cells. (J–L) Flow cytometry experiments showed that overexpression of circNSUN2 partially offset the promotion of ALO on apoptosis in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. ***p<0.001 vs Control; ^p<0.05, ^^p<0.01, ^^^p<0.001 vs ALO+NC.\nIn SW480 and HT29 cells, ALO suppressed the mRNA level of circNSUN2, whereas transfection of circNSUN2 overexpression significantly increased the expression of circNSUN2 (p<0.001, Figure 4A and B). The following basic cell function experiments showed that the upregulation of circNSUN2 partially offset the inhibitory effect of ALO on CRC cell viability, proliferation and apoptosis (p<0.001, Figure 4C–L).Figure 4Overexpression of circNSUN2 partially offset the suppression of ALO on the biological behavior of cancer cells. (A and B) The qRT-PCR experiment showed that ALO (0.8 mmol/L) inhibited the expression of circNSUN2 in SW480 and HT29 cells. GAPDH played the role of internal reference. (C and D) The MTT experiment showed that overexpression of circNSUN2 partially offset the inhibition of ALO on cell viability in SW480 and HT29 cells. (E and F) EdU cell proliferation experiments showed that overexpression of circNSUN2 partially offset the inhibition of ALO on proliferation in SW480 and HT29 cells. The magnification was 100×. (G–I) The clone formation experiment showed that overexpression of circNSUN2 partially offset the inhibition of ALO on proliferation in SW480 and HT29 cells. (J–L) Flow cytometry experiments showed that overexpression of circNSUN2 partially offset the promotion of ALO on apoptosis in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. ***p<0.001 vs Control; ^p<0.05, ^^p<0.01, ^^^p<0.001 vs ALO+NC.Abbreviations: CircNSUN2, NOP2/Sun RNA methyltransferase 2; NC, negative control.\nOverexpression of circNSUN2 partially offset the suppression of ALO on the biological behavior of cancer cells. (A and B) The qRT-PCR experiment showed that ALO (0.8 mmol/L) inhibited the expression of circNSUN2 in SW480 and HT29 cells. GAPDH played the role of internal reference. (C and D) The MTT experiment showed that overexpression of circNSUN2 partially offset the inhibition of ALO on cell viability in SW480 and HT29 cells. (E and F) EdU cell proliferation experiments showed that overexpression of circNSUN2 partially offset the inhibition of ALO on proliferation in SW480 and HT29 cells. The magnification was 100×. (G–I) The clone formation experiment showed that overexpression of circNSUN2 partially offset the inhibition of ALO on proliferation in SW480 and HT29 cells. (J–L) Flow cytometry experiments showed that overexpression of circNSUN2 partially offset the promotion of ALO on apoptosis in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. ***p<0.001 vs Control; ^p<0.05, ^^p<0.01, ^^^p<0.001 vs ALO+NC.\nSh-circNSUN2 Inhibited the Malignant Development of CRC Cells, Which Was Neutralized by miR-296-5p Inhibitor We also tested the mutual regulation of circNSUN2 and miR-296-5p in SW480 and HT29 cells. Sh-circNSUN2 could down-regulate the expression of circNSUN2, and besides, it inhibited the proliferation of SW480 and HT29 cells, while promoting their apoptosis and the expression of miR-296-5p (p<0.001, Figure 5A–J). However, the inhibitory effect of sh-circNSUN2 on the malignant development of the two cancer cells was neutralized by miR-296-5p inhibitor (p<0.001, Figure 5A–J).Figure 5Sh-circNSUN2 inhibited the malignant development of CRC cells, which was neutralized by miR-296-5p inhibitor. (A and B) The qRT-PCR experiment showed that miR-296-5p inhibitor neutralized the inhibitory effect of sh-circNSUN2 on circNSUN2 expression. GAPDH played the role of internal reference. (C and D) The qRT-PCR experiment showed that sh-circNSUN2 up-regulated the mRNA level of miR-296-5p. U6 played the role of internal reference. (E–G) The clone formation experiment showed that the effect of sh-circNSUN2 on the proliferation of SW480 and HT29 cells was neutralized by miR-296-5p inhibitor. (H-J) Flow cytometry experiments showed that the effect of sh-circNSUN2 on promoting the apoptosis of SW480 and HT29 cells was neutralized by miR-296-5p inhibitor. All experiments were repeated three times to obtain average values. ***p<0.001 vs sh-NC+IC; ^^^p<0.001 vs sh-circNSUN2+IC; ###p<0.001 vs sh-NC+I.Abbreviations: Sh-circNSUN2, silent circNSUN2; sh-NC, silent negative control; IC, miR-296-5p inhibitor control; I, miR-296-5p inhibitor.\nSh-circNSUN2 inhibited the malignant development of CRC cells, which was neutralized by miR-296-5p inhibitor. (A and B) The qRT-PCR experiment showed that miR-296-5p inhibitor neutralized the inhibitory effect of sh-circNSUN2 on circNSUN2 expression. GAPDH played the role of internal reference. (C and D) The qRT-PCR experiment showed that sh-circNSUN2 up-regulated the mRNA level of miR-296-5p. U6 played the role of internal reference. (E–G) The clone formation experiment showed that the effect of sh-circNSUN2 on the proliferation of SW480 and HT29 cells was neutralized by miR-296-5p inhibitor. (H-J) Flow cytometry experiments showed that the effect of sh-circNSUN2 on promoting the apoptosis of SW480 and HT29 cells was neutralized by miR-296-5p inhibitor. All experiments were repeated three times to obtain average values. ***p<0.001 vs sh-NC+IC; ^^^p<0.001 vs sh-circNSUN2+IC; ###p<0.001 vs sh-NC+I.\nWe also tested the mutual regulation of circNSUN2 and miR-296-5p in SW480 and HT29 cells. Sh-circNSUN2 could down-regulate the expression of circNSUN2, and besides, it inhibited the proliferation of SW480 and HT29 cells, while promoting their apoptosis and the expression of miR-296-5p (p<0.001, Figure 5A–J). However, the inhibitory effect of sh-circNSUN2 on the malignant development of the two cancer cells was neutralized by miR-296-5p inhibitor (p<0.001, Figure 5A–J).Figure 5Sh-circNSUN2 inhibited the malignant development of CRC cells, which was neutralized by miR-296-5p inhibitor. (A and B) The qRT-PCR experiment showed that miR-296-5p inhibitor neutralized the inhibitory effect of sh-circNSUN2 on circNSUN2 expression. GAPDH played the role of internal reference. (C and D) The qRT-PCR experiment showed that sh-circNSUN2 up-regulated the mRNA level of miR-296-5p. U6 played the role of internal reference. (E–G) The clone formation experiment showed that the effect of sh-circNSUN2 on the proliferation of SW480 and HT29 cells was neutralized by miR-296-5p inhibitor. (H-J) Flow cytometry experiments showed that the effect of sh-circNSUN2 on promoting the apoptosis of SW480 and HT29 cells was neutralized by miR-296-5p inhibitor. All experiments were repeated three times to obtain average values. ***p<0.001 vs sh-NC+IC; ^^^p<0.001 vs sh-circNSUN2+IC; ###p<0.001 vs sh-NC+I.Abbreviations: Sh-circNSUN2, silent circNSUN2; sh-NC, silent negative control; IC, miR-296-5p inhibitor control; I, miR-296-5p inhibitor.\nSh-circNSUN2 inhibited the malignant development of CRC cells, which was neutralized by miR-296-5p inhibitor. (A and B) The qRT-PCR experiment showed that miR-296-5p inhibitor neutralized the inhibitory effect of sh-circNSUN2 on circNSUN2 expression. GAPDH played the role of internal reference. (C and D) The qRT-PCR experiment showed that sh-circNSUN2 up-regulated the mRNA level of miR-296-5p. U6 played the role of internal reference. (E–G) The clone formation experiment showed that the effect of sh-circNSUN2 on the proliferation of SW480 and HT29 cells was neutralized by miR-296-5p inhibitor. (H-J) Flow cytometry experiments showed that the effect of sh-circNSUN2 on promoting the apoptosis of SW480 and HT29 cells was neutralized by miR-296-5p inhibitor. All experiments were repeated three times to obtain average values. ***p<0.001 vs sh-NC+IC; ^^^p<0.001 vs sh-circNSUN2+IC; ###p<0.001 vs sh-NC+I.\nMiR-296-5p Bound to STAT3 and Upregulation of Mir-296-5p Inhibited the Expression of STAT3 in CRC Cells We screened the target genes of miR-296-5p, and found that miR-296-5p and STAT3 have targeted binding sites (Figure 6A), and this binding relation was further confirmed by a dual-luciferase verification test (p<0.001, Figure 6B and C). Next, we used qRT-PCR and Western blot to analyze the mRNA and protein levels of miR-296-5p and STAT. It was found that miR-296-5p mimic up-regulated miR-296-5p expression while preventing the activation of STAT3 (p<0.001, Figure 7A–G). However, the overexpression of STAT3 reversed the regulation of miR-296-5p mimic on the two genes (p<0.001, Figure 7A–G).Figure 6The targeted binding of miR-296-5p to STAT3. (A) TargetScan was used to predict the binding sequence of miR-296-5p and STAT3. (B and C) Dual luciferase experiment confirmed that miR-296-5p can bind to STAT3 in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. +++p<0.001 vs MC.Figure 7Up-regulation of miR-296-5p inhibited the expression of STAT3 in CRC cells. (A and B) The qRT-PCR experiment showed that miR-296-5p mimic up-regulated the mRNA level of miR-296-5p. U6 played the role of internal reference. (C and D) The qRT-PCR experiment showed that miR-296-5p mimic suppressed the mRNA level of STAT3, while overexpression of STAT3 reversed this effect. GAPDH played the role of internal reference. (E–G) The Western blot experiment showed that miR-296-5p mimic inhibited the protein level of STAT3, while over-expression of STAT3 reversed this effect. GAPDH played the role of internal reference. All experiments were repeated three times to obtain average values. ***p<0.001 vs MC+NC; ^^^p<0.001 vs M+NC; ###p<0.001 vs MC+STAT3.Abbreviation: STAT3, signal transducer and activator of transcription 3.\nThe targeted binding of miR-296-5p to STAT3. (A) TargetScan was used to predict the binding sequence of miR-296-5p and STAT3. (B and C) Dual luciferase experiment confirmed that miR-296-5p can bind to STAT3 in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. +++p<0.001 vs MC.\nUp-regulation of miR-296-5p inhibited the expression of STAT3 in CRC cells. (A and B) The qRT-PCR experiment showed that miR-296-5p mimic up-regulated the mRNA level of miR-296-5p. U6 played the role of internal reference. (C and D) The qRT-PCR experiment showed that miR-296-5p mimic suppressed the mRNA level of STAT3, while overexpression of STAT3 reversed this effect. GAPDH played the role of internal reference. (E–G) The Western blot experiment showed that miR-296-5p mimic inhibited the protein level of STAT3, while over-expression of STAT3 reversed this effect. GAPDH played the role of internal reference. All experiments were repeated three times to obtain average values. ***p<0.001 vs MC+NC; ^^^p<0.001 vs M+NC; ###p<0.001 vs MC+STAT3.\nWe screened the target genes of miR-296-5p, and found that miR-296-5p and STAT3 have targeted binding sites (Figure 6A), and this binding relation was further confirmed by a dual-luciferase verification test (p<0.001, Figure 6B and C). Next, we used qRT-PCR and Western blot to analyze the mRNA and protein levels of miR-296-5p and STAT. It was found that miR-296-5p mimic up-regulated miR-296-5p expression while preventing the activation of STAT3 (p<0.001, Figure 7A–G). However, the overexpression of STAT3 reversed the regulation of miR-296-5p mimic on the two genes (p<0.001, Figure 7A–G).Figure 6The targeted binding of miR-296-5p to STAT3. (A) TargetScan was used to predict the binding sequence of miR-296-5p and STAT3. (B and C) Dual luciferase experiment confirmed that miR-296-5p can bind to STAT3 in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. +++p<0.001 vs MC.Figure 7Up-regulation of miR-296-5p inhibited the expression of STAT3 in CRC cells. (A and B) The qRT-PCR experiment showed that miR-296-5p mimic up-regulated the mRNA level of miR-296-5p. U6 played the role of internal reference. (C and D) The qRT-PCR experiment showed that miR-296-5p mimic suppressed the mRNA level of STAT3, while overexpression of STAT3 reversed this effect. GAPDH played the role of internal reference. (E–G) The Western blot experiment showed that miR-296-5p mimic inhibited the protein level of STAT3, while over-expression of STAT3 reversed this effect. GAPDH played the role of internal reference. All experiments were repeated three times to obtain average values. ***p<0.001 vs MC+NC; ^^^p<0.001 vs M+NC; ###p<0.001 vs MC+STAT3.Abbreviation: STAT3, signal transducer and activator of transcription 3.\nThe targeted binding of miR-296-5p to STAT3. (A) TargetScan was used to predict the binding sequence of miR-296-5p and STAT3. (B and C) Dual luciferase experiment confirmed that miR-296-5p can bind to STAT3 in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. +++p<0.001 vs MC.\nUp-regulation of miR-296-5p inhibited the expression of STAT3 in CRC cells. (A and B) The qRT-PCR experiment showed that miR-296-5p mimic up-regulated the mRNA level of miR-296-5p. U6 played the role of internal reference. (C and D) The qRT-PCR experiment showed that miR-296-5p mimic suppressed the mRNA level of STAT3, while overexpression of STAT3 reversed this effect. GAPDH played the role of internal reference. (E–G) The Western blot experiment showed that miR-296-5p mimic inhibited the protein level of STAT3, while over-expression of STAT3 reversed this effect. GAPDH played the role of internal reference. All experiments were repeated three times to obtain average values. ***p<0.001 vs MC+NC; ^^^p<0.001 vs M+NC; ###p<0.001 vs MC+STAT3.\nMiR-296-5p Mimic Inhibited Proliferation and Promoted Apoptosis in CRC Cells by Regulating Apoptosis-Related Genes, Which Was Partially Reversed by Overexpressed STAT3 The following cell experiments showed that by activating Bax and blocking the protein activity of Bcl-2, miR-296-5p mimic inhibited proliferation yet accelerated apoptosis in cancer cells (p<0.001, Figure 8A–I). However, up-regulation of STAT3 produced a completely opposite regulatory effect (p<0.001, Figure 8A–I). More importantly, overexpression of STAT3 neutralized the regulation of miR-296-5p mimic on CRC cells (p<0.001, Figure 8A–I).Figure 8MiR-296-5p mimic inhibited proliferation and promoted apoptosis in CRC cells by regulating apoptosis-related genes, which was partially reversed by overexpression of STAT3. (A–C) The clone formation experiment showed that the inhibitory effect of miR-296-5p mimic on the proliferation of SW480 and HT29 cells was neutralized by overexpressed STAT3. (D–F) Flow cytometry experiments showed that miR-296-5p mimic promoted the apoptosis of SW480 and HT29 cells, which was neutralized by overexpressed STAT3. (G–I) Western blot experiments showed that the regulation of apoptosis-related protein expression by miR-296-5p mimic was neutralized by overexpressed STAT3. GAPDH played the role of internal reference. All experiments were repeated three times to obtain average values. *p<0.05, **p<0.01, ***p<0.001 vs MC+NC; ^^p<0.01, ^^^p<0.001 vs M+NC; ##p<0.01, ###p<0.001 vs MC+STAT3.\nMiR-296-5p mimic inhibited proliferation and promoted apoptosis in CRC cells by regulating apoptosis-related genes, which was partially reversed by overexpression of STAT3. (A–C) The clone formation experiment showed that the inhibitory effect of miR-296-5p mimic on the proliferation of SW480 and HT29 cells was neutralized by overexpressed STAT3. (D–F) Flow cytometry experiments showed that miR-296-5p mimic promoted the apoptosis of SW480 and HT29 cells, which was neutralized by overexpressed STAT3. (G–I) Western blot experiments showed that the regulation of apoptosis-related protein expression by miR-296-5p mimic was neutralized by overexpressed STAT3. GAPDH played the role of internal reference. All experiments were repeated three times to obtain average values. *p<0.05, **p<0.01, ***p<0.001 vs MC+NC; ^^p<0.01, ^^^p<0.001 vs M+NC; ##p<0.01, ###p<0.001 vs MC+STAT3.\nThe following cell experiments showed that by activating Bax and blocking the protein activity of Bcl-2, miR-296-5p mimic inhibited proliferation yet accelerated apoptosis in cancer cells (p<0.001, Figure 8A–I). However, up-regulation of STAT3 produced a completely opposite regulatory effect (p<0.001, Figure 8A–I). More importantly, overexpression of STAT3 neutralized the regulation of miR-296-5p mimic on CRC cells (p<0.001, Figure 8A–I).Figure 8MiR-296-5p mimic inhibited proliferation and promoted apoptosis in CRC cells by regulating apoptosis-related genes, which was partially reversed by overexpression of STAT3. (A–C) The clone formation experiment showed that the inhibitory effect of miR-296-5p mimic on the proliferation of SW480 and HT29 cells was neutralized by overexpressed STAT3. (D–F) Flow cytometry experiments showed that miR-296-5p mimic promoted the apoptosis of SW480 and HT29 cells, which was neutralized by overexpressed STAT3. (G–I) Western blot experiments showed that the regulation of apoptosis-related protein expression by miR-296-5p mimic was neutralized by overexpressed STAT3. GAPDH played the role of internal reference. All experiments were repeated three times to obtain average values. *p<0.05, **p<0.01, ***p<0.001 vs MC+NC; ^^p<0.01, ^^^p<0.001 vs M+NC; ##p<0.01, ###p<0.001 vs MC+STAT3.\nMiR-296-5p mimic inhibited proliferation and promoted apoptosis in CRC cells by regulating apoptosis-related genes, which was partially reversed by overexpression of STAT3. (A–C) The clone formation experiment showed that the inhibitory effect of miR-296-5p mimic on the proliferation of SW480 and HT29 cells was neutralized by overexpressed STAT3. (D–F) Flow cytometry experiments showed that miR-296-5p mimic promoted the apoptosis of SW480 and HT29 cells, which was neutralized by overexpressed STAT3. (G–I) Western blot experiments showed that the regulation of apoptosis-related protein expression by miR-296-5p mimic was neutralized by overexpressed STAT3. GAPDH played the role of internal reference. All experiments were repeated three times to obtain average values. *p<0.05, **p<0.01, ***p<0.001 vs MC+NC; ^^p<0.01, ^^^p<0.001 vs M+NC; ##p<0.01, ###p<0.001 vs MC+STAT3.","In this study, the toxicity of ALO in normal cells (CCD-18Co) was detected, ALO showed no significant toxicity to CCD-18Co cells (Figure 1A). In order to study the effects of ALO on the growth and basic physiological functions of CRC cells, we used different concentrations of ALO to treat SW480 and HT29 cells. The results showed that 0.2 mmol/L, 0.4 mmol/L, 0.8 mmol/L and 1 mmol/L ALO solutions all inhibited the viability of SW480 and HT29 cells (p<0.05, Figure 1B and C). Moreover, the viability of SW480 and HT29 cells was close to 50% under the treatment of 0.8 mmol/L ALO solution, but was reduced to less than 50% when the ALO concentration was increased to 1 mmol/L (Figure 1B and C). In the following EdU cell proliferation experiment, the red fluorescence expression was reduced in SW480 and HT29 cells under the treatment of 0.8 mmol/L ALO (Figure 1D and E). Consistently, the clone formation experiment also showed that different concentrations of ALO (0.2 mmol/L, 0.4 mmol/L, 0.8 mmol/L) reduced the number of cell clones and inhibited the proliferation of SW480 and HT29 cells (p<0.05, Figure 1F–H). At the same time, the increase in the concentration of the ALO solution accelerated the death of CRC cells. As shown in Figure 1I–K, the number of apoptotic SW480 and HT29 cells increased significantly after treatment with different concentrations of ALO (p<0.01, Figure 1I–K).Figure 1ALO at a concentration gradient inhibited proliferation yet promoted apoptosis in CRC cells. (A) The effect of ALO on the normal colonic tissue cells (CCD-18Co) was detected by MTT experiments. (B and C) MTT experiments showed that ALO at a concentration gradient (0.1 mmol/L, 0.2 mmol/L, 0.4 mmol/L, 0.8 mmol/L and 1 mmol/L) inhibited the activity of SW480 and HT29 cells. (D and E) EdU cell proliferation experiments showed that 0.8 mmol/L ALO inhibited the proliferation of SW480 and HT29 cells. The magnification was 100×. (F–H) The clone formation experiment showed that ALO at a concentration gradient (0.2 mmol/L, 0.4 mmol/L and 0.8 mmol/L) inhibited the proliferation of SW480 and HT29 cells. (I–K) Flow cytometry experiments showed that ALO at a concentration gradient (0.2 mmol/L, 0.4 mmol/L and 0.8 mmol/L) promoted the apoptosis of SW480 and HT29 cells. All experiments were repeated three times to obtain average values. *p<0.05, **p<0.01, ***p<0.001 vs Control.Abbreviations: ALO, aloperine; CRC, colorectal cancer.\nALO at a concentration gradient inhibited proliferation yet promoted apoptosis in CRC cells. (A) The effect of ALO on the normal colonic tissue cells (CCD-18Co) was detected by MTT experiments. (B and C) MTT experiments showed that ALO at a concentration gradient (0.1 mmol/L, 0.2 mmol/L, 0.4 mmol/L, 0.8 mmol/L and 1 mmol/L) inhibited the activity of SW480 and HT29 cells. (D and E) EdU cell proliferation experiments showed that 0.8 mmol/L ALO inhibited the proliferation of SW480 and HT29 cells. The magnification was 100×. (F–H) The clone formation experiment showed that ALO at a concentration gradient (0.2 mmol/L, 0.4 mmol/L and 0.8 mmol/L) inhibited the proliferation of SW480 and HT29 cells. (I–K) Flow cytometry experiments showed that ALO at a concentration gradient (0.2 mmol/L, 0.4 mmol/L and 0.8 mmol/L) promoted the apoptosis of SW480 and HT29 cells. All experiments were repeated three times to obtain average values. *p<0.05, **p<0.01, ***p<0.001 vs Control.","Data from the Starbase database showed that the expression of miR-296-5p in colon cancer patients was significantly lower than that in healthy people (p=2.5e-6, Figure 2A). In this study, however, after treatment with different concentrations of ALO for 24 h, miR-296-5p activity in SW480 and HT29 cells was significantly increased (p<0.01, Figure 2B and C), suggesting that ALO can stimulate the up-regulation of miR-296-5p.Figure 2The abnormally low expression of miR-296-5p in colon cancer could be upregulated by ALO. (A) Starbase (http://starbase.sysu.edu.cn/index.php) was used to retrieve the information of differential expression of miR-296-5p in colon adenocarcinoma (COAD, n=450) patients and healthy volunteers (n=8) in vivo. P=2.5e-6. (B and C) The qRT-PCR experiment showed that ALO at a concentration gradient (0.1 mmol/L, 0.2 mmol/L, 0.4 mmol/L, 0.8 mmol/L and 1 mmol/L) up-regulated miR-296-5p expression in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. **p<0.01, ***p<0.001 vs Control.Abbreviation: qRT-PCR, quantitative real-time polymerase chain reaction.\nThe abnormally low expression of miR-296-5p in colon cancer could be upregulated by ALO. (A) Starbase (http://starbase.sysu.edu.cn/index.php) was used to retrieve the information of differential expression of miR-296-5p in colon adenocarcinoma (COAD, n=450) patients and healthy volunteers (n=8) in vivo. P=2.5e-6. (B and C) The qRT-PCR experiment showed that ALO at a concentration gradient (0.1 mmol/L, 0.2 mmol/L, 0.4 mmol/L, 0.8 mmol/L and 1 mmol/L) up-regulated miR-296-5p expression in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. **p<0.01, ***p<0.001 vs Control.","Through literature review, we screened 12 circRNAs that were reported to be associated with CRC: circDDX17, circHIPK3, circZFR, circRNA_100290, Has_circ_0026344, circVAPA, Hsa_circ_0009361, circNSUN2, circACAP2, circACVRL1, circITGA7 and circEIF4G3. We first detected the regulatory effect of ALO on these differentially expressed circRNAs by qRT-PCR to perform preliminary screening (Figure 3A). The results showed that the expressions of circDDX17, Has_circ_0026344, circVAPA, circNSUN2, circACAP2, circACVRL1 and circEIF4G3 were regulated by 0.8 mmol/L ALO (p<0.001, Figure 3A). Through subsequent screening of target genes, we found that only circNSUN2 and miR-296-5p have targeted binding sites (Figure 3B). After further verification, it was confirmed that circNSUN2-WT and miR-296-5p mimic did reduce the luciferase activity in SW480 and HT29 cells (p<0.001, Figure 3C and D). Therefore, we chose circNSUN2 as the next research object.Figure 3Among the differentially expressed circRNAs in CRC, only circNSUN2 not only had a target site for miR-296-5p, but also could be regulated by ALO. (A) qRT-PCR detected the regulation of 0.8 mmol/L ALO on the differentially expressed circRNAs in CRC. GAPDH played the role of internal reference. (B) The TargetScan database (http://www.targetscan.org) was used to predict the target sites of circNSUN2 and miR-296-5p. (C and D) The dual luciferase experiment confirmed that circNSUN2 can bind to miR-296-5p in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. ***p<0.001 vs Control; +++p<0.001 vs MC.Abbreviation: MC, miR-296-5p mimic control.\nAmong the differentially expressed circRNAs in CRC, only circNSUN2 not only had a target site for miR-296-5p, but also could be regulated by ALO. (A) qRT-PCR detected the regulation of 0.8 mmol/L ALO on the differentially expressed circRNAs in CRC. GAPDH played the role of internal reference. (B) The TargetScan database (http://www.targetscan.org) was used to predict the target sites of circNSUN2 and miR-296-5p. (C and D) The dual luciferase experiment confirmed that circNSUN2 can bind to miR-296-5p in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. ***p<0.001 vs Control; +++p<0.001 vs MC.","In SW480 and HT29 cells, ALO suppressed the mRNA level of circNSUN2, whereas transfection of circNSUN2 overexpression significantly increased the expression of circNSUN2 (p<0.001, Figure 4A and B). The following basic cell function experiments showed that the upregulation of circNSUN2 partially offset the inhibitory effect of ALO on CRC cell viability, proliferation and apoptosis (p<0.001, Figure 4C–L).Figure 4Overexpression of circNSUN2 partially offset the suppression of ALO on the biological behavior of cancer cells. (A and B) The qRT-PCR experiment showed that ALO (0.8 mmol/L) inhibited the expression of circNSUN2 in SW480 and HT29 cells. GAPDH played the role of internal reference. (C and D) The MTT experiment showed that overexpression of circNSUN2 partially offset the inhibition of ALO on cell viability in SW480 and HT29 cells. (E and F) EdU cell proliferation experiments showed that overexpression of circNSUN2 partially offset the inhibition of ALO on proliferation in SW480 and HT29 cells. The magnification was 100×. (G–I) The clone formation experiment showed that overexpression of circNSUN2 partially offset the inhibition of ALO on proliferation in SW480 and HT29 cells. (J–L) Flow cytometry experiments showed that overexpression of circNSUN2 partially offset the promotion of ALO on apoptosis in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. ***p<0.001 vs Control; ^p<0.05, ^^p<0.01, ^^^p<0.001 vs ALO+NC.Abbreviations: CircNSUN2, NOP2/Sun RNA methyltransferase 2; NC, negative control.\nOverexpression of circNSUN2 partially offset the suppression of ALO on the biological behavior of cancer cells. (A and B) The qRT-PCR experiment showed that ALO (0.8 mmol/L) inhibited the expression of circNSUN2 in SW480 and HT29 cells. GAPDH played the role of internal reference. (C and D) The MTT experiment showed that overexpression of circNSUN2 partially offset the inhibition of ALO on cell viability in SW480 and HT29 cells. (E and F) EdU cell proliferation experiments showed that overexpression of circNSUN2 partially offset the inhibition of ALO on proliferation in SW480 and HT29 cells. The magnification was 100×. (G–I) The clone formation experiment showed that overexpression of circNSUN2 partially offset the inhibition of ALO on proliferation in SW480 and HT29 cells. (J–L) Flow cytometry experiments showed that overexpression of circNSUN2 partially offset the promotion of ALO on apoptosis in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. ***p<0.001 vs Control; ^p<0.05, ^^p<0.01, ^^^p<0.001 vs ALO+NC.","We also tested the mutual regulation of circNSUN2 and miR-296-5p in SW480 and HT29 cells. Sh-circNSUN2 could down-regulate the expression of circNSUN2, and besides, it inhibited the proliferation of SW480 and HT29 cells, while promoting their apoptosis and the expression of miR-296-5p (p<0.001, Figure 5A–J). However, the inhibitory effect of sh-circNSUN2 on the malignant development of the two cancer cells was neutralized by miR-296-5p inhibitor (p<0.001, Figure 5A–J).Figure 5Sh-circNSUN2 inhibited the malignant development of CRC cells, which was neutralized by miR-296-5p inhibitor. (A and B) The qRT-PCR experiment showed that miR-296-5p inhibitor neutralized the inhibitory effect of sh-circNSUN2 on circNSUN2 expression. GAPDH played the role of internal reference. (C and D) The qRT-PCR experiment showed that sh-circNSUN2 up-regulated the mRNA level of miR-296-5p. U6 played the role of internal reference. (E–G) The clone formation experiment showed that the effect of sh-circNSUN2 on the proliferation of SW480 and HT29 cells was neutralized by miR-296-5p inhibitor. (H-J) Flow cytometry experiments showed that the effect of sh-circNSUN2 on promoting the apoptosis of SW480 and HT29 cells was neutralized by miR-296-5p inhibitor. All experiments were repeated three times to obtain average values. ***p<0.001 vs sh-NC+IC; ^^^p<0.001 vs sh-circNSUN2+IC; ###p<0.001 vs sh-NC+I.Abbreviations: Sh-circNSUN2, silent circNSUN2; sh-NC, silent negative control; IC, miR-296-5p inhibitor control; I, miR-296-5p inhibitor.\nSh-circNSUN2 inhibited the malignant development of CRC cells, which was neutralized by miR-296-5p inhibitor. (A and B) The qRT-PCR experiment showed that miR-296-5p inhibitor neutralized the inhibitory effect of sh-circNSUN2 on circNSUN2 expression. GAPDH played the role of internal reference. (C and D) The qRT-PCR experiment showed that sh-circNSUN2 up-regulated the mRNA level of miR-296-5p. U6 played the role of internal reference. (E–G) The clone formation experiment showed that the effect of sh-circNSUN2 on the proliferation of SW480 and HT29 cells was neutralized by miR-296-5p inhibitor. (H-J) Flow cytometry experiments showed that the effect of sh-circNSUN2 on promoting the apoptosis of SW480 and HT29 cells was neutralized by miR-296-5p inhibitor. All experiments were repeated three times to obtain average values. ***p<0.001 vs sh-NC+IC; ^^^p<0.001 vs sh-circNSUN2+IC; ###p<0.001 vs sh-NC+I.","We screened the target genes of miR-296-5p, and found that miR-296-5p and STAT3 have targeted binding sites (Figure 6A), and this binding relation was further confirmed by a dual-luciferase verification test (p<0.001, Figure 6B and C). Next, we used qRT-PCR and Western blot to analyze the mRNA and protein levels of miR-296-5p and STAT. It was found that miR-296-5p mimic up-regulated miR-296-5p expression while preventing the activation of STAT3 (p<0.001, Figure 7A–G). However, the overexpression of STAT3 reversed the regulation of miR-296-5p mimic on the two genes (p<0.001, Figure 7A–G).Figure 6The targeted binding of miR-296-5p to STAT3. (A) TargetScan was used to predict the binding sequence of miR-296-5p and STAT3. (B and C) Dual luciferase experiment confirmed that miR-296-5p can bind to STAT3 in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. +++p<0.001 vs MC.Figure 7Up-regulation of miR-296-5p inhibited the expression of STAT3 in CRC cells. (A and B) The qRT-PCR experiment showed that miR-296-5p mimic up-regulated the mRNA level of miR-296-5p. U6 played the role of internal reference. (C and D) The qRT-PCR experiment showed that miR-296-5p mimic suppressed the mRNA level of STAT3, while overexpression of STAT3 reversed this effect. GAPDH played the role of internal reference. (E–G) The Western blot experiment showed that miR-296-5p mimic inhibited the protein level of STAT3, while over-expression of STAT3 reversed this effect. GAPDH played the role of internal reference. All experiments were repeated three times to obtain average values. ***p<0.001 vs MC+NC; ^^^p<0.001 vs M+NC; ###p<0.001 vs MC+STAT3.Abbreviation: STAT3, signal transducer and activator of transcription 3.\nThe targeted binding of miR-296-5p to STAT3. (A) TargetScan was used to predict the binding sequence of miR-296-5p and STAT3. (B and C) Dual luciferase experiment confirmed that miR-296-5p can bind to STAT3 in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. +++p<0.001 vs MC.\nUp-regulation of miR-296-5p inhibited the expression of STAT3 in CRC cells. (A and B) The qRT-PCR experiment showed that miR-296-5p mimic up-regulated the mRNA level of miR-296-5p. U6 played the role of internal reference. (C and D) The qRT-PCR experiment showed that miR-296-5p mimic suppressed the mRNA level of STAT3, while overexpression of STAT3 reversed this effect. GAPDH played the role of internal reference. (E–G) The Western blot experiment showed that miR-296-5p mimic inhibited the protein level of STAT3, while over-expression of STAT3 reversed this effect. GAPDH played the role of internal reference. All experiments were repeated three times to obtain average values. ***p<0.001 vs MC+NC; ^^^p<0.001 vs M+NC; ###p<0.001 vs MC+STAT3.","The following cell experiments showed that by activating Bax and blocking the protein activity of Bcl-2, miR-296-5p mimic inhibited proliferation yet accelerated apoptosis in cancer cells (p<0.001, Figure 8A–I). However, up-regulation of STAT3 produced a completely opposite regulatory effect (p<0.001, Figure 8A–I). More importantly, overexpression of STAT3 neutralized the regulation of miR-296-5p mimic on CRC cells (p<0.001, Figure 8A–I).Figure 8MiR-296-5p mimic inhibited proliferation and promoted apoptosis in CRC cells by regulating apoptosis-related genes, which was partially reversed by overexpression of STAT3. (A–C) The clone formation experiment showed that the inhibitory effect of miR-296-5p mimic on the proliferation of SW480 and HT29 cells was neutralized by overexpressed STAT3. (D–F) Flow cytometry experiments showed that miR-296-5p mimic promoted the apoptosis of SW480 and HT29 cells, which was neutralized by overexpressed STAT3. (G–I) Western blot experiments showed that the regulation of apoptosis-related protein expression by miR-296-5p mimic was neutralized by overexpressed STAT3. GAPDH played the role of internal reference. All experiments were repeated three times to obtain average values. *p<0.05, **p<0.01, ***p<0.001 vs MC+NC; ^^p<0.01, ^^^p<0.001 vs M+NC; ##p<0.01, ###p<0.001 vs MC+STAT3.\nMiR-296-5p mimic inhibited proliferation and promoted apoptosis in CRC cells by regulating apoptosis-related genes, which was partially reversed by overexpression of STAT3. (A–C) The clone formation experiment showed that the inhibitory effect of miR-296-5p mimic on the proliferation of SW480 and HT29 cells was neutralized by overexpressed STAT3. (D–F) Flow cytometry experiments showed that miR-296-5p mimic promoted the apoptosis of SW480 and HT29 cells, which was neutralized by overexpressed STAT3. (G–I) Western blot experiments showed that the regulation of apoptosis-related protein expression by miR-296-5p mimic was neutralized by overexpressed STAT3. GAPDH played the role of internal reference. All experiments were repeated three times to obtain average values. *p<0.05, **p<0.01, ***p<0.001 vs MC+NC; ^^p<0.01, ^^^p<0.001 vs M+NC; ##p<0.01, ###p<0.001 vs MC+STAT3.","CRC is a malignant lesion of the mucosal epithelium of the colon or rectum under the action of various carcinogenic factors such as environment or heredity.2 Most CRC patients suffer from adenocarcinoma, which usually develops from polyps. Tumors developed from polyps can infiltrate in a circular shape along the horizontal axis of the intestinal tube, develop into the deep layer of the intestinal wall, and finally penetrate the intestinal wall and metastasize to blood vessels or lymphatic vessels.20 This is one of the important reasons for the high recurrence and metastasis rates of CRC. The ultimate goals of CRC research are to improve the current treatment of CRC, save patients’ lives and improve their quality of life. Based on the role of traditional Chinese medicine in disease prevention and treatment, this study explored the effects of ALO, an alkaloid exhibiting strong anticancer activity, on the proliferation and apoptosis of CRC cells. The results showed that after ALO treatment, the viability and proliferation of CRC cells were significantly inhibited; on the contrary, the number of apoptotic cancer cells was significantly increased. This is consistent with the previous experimental results.21 In order to probe deeper into the upstream mechanism of ALO in alleviating CRC carcinogenesis through the miR-296-5p/STAT3 axis, we screened and verified the upstream targeting circRNA of miR-296-5p.\nCircRNAs are a type of covalently closed circular non-coding RNA formed by back-splicing of mRNA precursors (pre-mRNA).22 CircRNA was considered to be a useless splicing by-product in early research. With the deepening of research, it is found that circRNA has a wide range of sources and plays a variety of functional roles in the growth and development of organisms, with the characteristics of conservative, stable, and tissue-specific.22,23 The most familiar and extensively studied function of circRNA is its sponging effect on miRNA.24 For example, circRNA ciRS-7 which contains more than 70 miR-7 binding sites can achieve competitive adsorption of miR-7 through the AGO2 protein.25 Besides, increasing reports on cancer indicate that circRNAs, such as circRNA-cTFRC, circPSMC3 and circSETD3, act as sponges of miRNAs and participate in transcriptional regulation.26–28 Based on these literature reports, we screened circRNAs that were reported to be abnormally expressed in CRC, and finally obtained 12 circRNAs. After detecting their expressions in ALO-treated cancer cells and predicting their binding sequence for miR-296-5p, circNSUN2 was finally identified as the research object of this study.\nCircNSUN2, which maps to the 5p15 amplicon in CRCs, was confirmed by Chen et al to regulate cytoplasmic output and promote liver metastasis of CRC through N 6-methyladenosine modification.29 Similarly, our experimental results uncovered that knockdown of circNSUN2 inhibited proliferation and accelerated apoptosis in CRC cells. More importantly, we for the first time revealed that ALO can prevent the activation of circNSUN2 and offset the promotion effect of overexpression of circNSUN2 on esophageal cancer progression. Since circNSUN2 and miR-296-5p have targeting sequences, we verified the cellular regulatory effects of circNSUN2 and miR-296-5p/STAT3 through dual luciferase experiments and rescue experiments. The final result confirmed our conjecture that regulating the circNSUN2/miR-296-5p/STAT3 axis can reduce the proliferation rate and increase the apoptosis rate of CRC cells.\nAt present, blocking proliferation and increasing apoptosis in cancer cells are the intensively discussed mechanisms in CRC study. Tumor cells can proliferate quickly and unrestrictedly, and thus, inhibiting their proliferation can produce an anti-tumor effect.30 Apoptosis, alternatively called programmed cell death, is an autonomous cell death process strictly controlled by multiple genes. Apoptosis is an important part of cell life cycle, and it is also an important link in regulating the development of the body and maintaining the stability of the internal environment in organisms.31,32 Our research shows that ALO inhibits the proliferation and promotes the apoptosis of CRC cells by regulating the circNSUN2/miR-296-5p/STAT3 pathway, and ultimately prevents the tumorigenesis of CRC.\nThere are still certain limitations in our research. Although we have confirmed the effect of ALO on inducing apoptosis and reducing proliferation in CRC cells and clarified the underlying mechanism, the circNSUN2/miR-296-5p/STAT3 axis and the pathways related to proliferation and apoptosis have not been analyzed yet, which will be further explored in future research. In addition, the anticancer effect of ALO on CRC and its experimental concentration needs to be further tested in animal and clinical experiments. Whether ALO has an impact on drug resistance in CRC is also a direction of future research."],"string":"[\n \"Cancer has become the first killer in the world and the biggest obstacle for extending human life expectancy. It is estimated that there were 18.1 million new cancer cases and 9.6 million cancer deaths in 2018.1 Colorectal cancer (CRC) is one of the most common gastrointestinal tumors.2 According to the data from the International Agency for Research on Cancer (IARC),1 the number of new CRC cases worldwide in 2018 was approximately 1.09 million, making CRC the fourth most prevalent malignant tumor after lung, breast and prostate cancers; meantime, CRC had a high mortality rate of about 9.2%, ranking only after lung cancer. Epidemiological studies have shown that the incidence of CRC varies significantly in different countries, and is higher in developed areas.3 More importantly, as the incidence of CRC rises rapidly in people under 50, the age of onset of the disease is getting lower.4,5 Therefore, it is urgent to improve the diagnosis rate and treatment effect of CRC.\\nCurrently, the main clinical treatment of CRC is surgery combined with adjuvant radiotherapy, chemotherapy or molecular targeted therapy. Surgery is generally considered as the first choice for comprehensive treatment of CRC. This method is suitable for patients whose tumors are confined to the intestinal wall while penetrating the intestinal wall and invading the serous or extraserous membrane without lymph node metastasis.6,7 However, the availability of radical resection is limited as most patients with CRC have adenocarcinoma, which generally develops from polyps and is metastatic by nature.8 As a result, CRC patients tend to undergo simple resection, which leads to a high recurrence rate. Chemotherapy, therefore, is still necessary for postoperative and late-stage CRC patients.9,10 At present, the commonly used chemotherapeutic drugs in clinic mainly include 5-fluorouracil (5-Fu), oxaliplatin and its derivatives.9 Chemotherapy is highly risky because it indiscriminately kills tumor cells and immune cells and thereby reduces the anti-tumor immune effect.11 Likewise, radiotherapy greatly weakens the body’s immunity.12 Hence, researchers have proposed to find effective treatments or drugs that cause less damage to patients.\\nWith advances in the research and development of traditional Chinese medicine, more attention has been paid to the roles of traditional Chinese medicine and its active ingredients in disease prevention and treatment. At the same time, new targets for molecular targeted therapy of diseases have been discovered in the exploration of the molecular mechanisms of traditional Chinese medicine and its monomers. Aloperine (ALO) has been confirmed to have a significant anti-cancer activity. ALO is a component of the traditional Chinese medicine Sophora alopecuroides L.13 It is also one of the main alkaloids separated and extracted in the lab, with a molecular formula of C15H24N2.14 Recent studies have found that ALO has the effects of anti-inflammation, immunosuppression, redox suppression, cardiovascular protection and anti-cancer, among which its tumor-suppressing activity has been extensively reported. For example, Yu et al revealed that ALO inhibited the carcinogenesis process by regulating excessive autophagy in thyroid cancer cells;15 Liu et al demonstrated that ALO inhibited the PI3K/Akt pathway, increased apoptosis and caused cell cycle block in liver cancer cells;16 besides, ALO was also found to exert an anti-cancer effect in prostate cancer and breast cancer.17,18 In the previous study, our research group found that ALO up-regulated miR-296-5p and inhibited the activity of its target gene STAT3, thereby inhibiting the proliferation and inducing the apoptosis of CRC cells. However, the activation pathway of miR-296-5p is unclear. In order to clarify the upstream activation pathway of ALO-upregulated miR-296-5p, this study will explore the mechanism of ALO in inhibiting CRC from the perspective of the circRNA-miRNA-mRNA regulatory network.\",\n \"Cell Purchase and ALO Processing CCD-18Co and CRC cell lines SW480 (CCL-228™) and HT29 (HTB-38™) used in this research were provided by the American Type Culture Collection (ATCC). According to the culture requirements, CCD-18Co, SW480 and HT29 cells were separately inoculated in DMEM medium (30–2007, ATCC) containing 10% fetal bovine serum and incubated in a D180-P cell incubator (RWD, China) containing 5% CO2 at 37 °C.\\nALO (DK0052, CAS NO: 56293-29-9, HPLC≥98%), the research object of this study, was provided by Chengdu DESITE Biological Company, China. ALO was dissolved in fresh DMEM to prepare 0.1 mmol/L, 0.2 mmol/L, 0.4 mmol/L, 0.8 mmol/L and 1 mmol/L ALO solutions, which were separately used to treat the CCD-18Co, SW480 and HT29 cells for 24 hours (h).\\nCCD-18Co and CRC cell lines SW480 (CCL-228™) and HT29 (HTB-38™) used in this research were provided by the American Type Culture Collection (ATCC). According to the culture requirements, CCD-18Co, SW480 and HT29 cells were separately inoculated in DMEM medium (30–2007, ATCC) containing 10% fetal bovine serum and incubated in a D180-P cell incubator (RWD, China) containing 5% CO2 at 37 °C.\\nALO (DK0052, CAS NO: 56293-29-9, HPLC≥98%), the research object of this study, was provided by Chengdu DESITE Biological Company, China. ALO was dissolved in fresh DMEM to prepare 0.1 mmol/L, 0.2 mmol/L, 0.4 mmol/L, 0.8 mmol/L and 1 mmol/L ALO solutions, which were separately used to treat the CCD-18Co, SW480 and HT29 cells for 24 hours (h).\\nMTT Assay One hundred µL of SW480 or HT29 cell suspension (1×104 cells/mL) was transferred to each well of a 96-well plate. Of note, three holes were added to the same treatment group. After treatment with the ALO solutions for 24 h, SW480 or HT29 cells were added with MTT reagent (10 µL/well, PB180519, Procell, USA), which could react with mitochondria in living cells. After 4 h of binding, a HBS-1096A enzyme label analyzer (DeTie, China) was used to detect the absorbance of SW480 or HT29 cells in the mixed solutions at 490 nm, and then cell viability was calculated based on the absorbance.\\nOne hundred µL of SW480 or HT29 cell suspension (1×104 cells/mL) was transferred to each well of a 96-well plate. Of note, three holes were added to the same treatment group. After treatment with the ALO solutions for 24 h, SW480 or HT29 cells were added with MTT reagent (10 µL/well, PB180519, Procell, USA), which could react with mitochondria in living cells. After 4 h of binding, a HBS-1096A enzyme label analyzer (DeTie, China) was used to detect the absorbance of SW480 or HT29 cells in the mixed solutions at 490 nm, and then cell viability was calculated based on the absorbance.\\nEdU (5-Ethynyl-2ʹ-Deoxyuridine) Cell Proliferation Experiment SW480 or HT29 cells in logarithmic growth phase were seeded in 96-well plates at 1×104 cells/well. EdU reagent (ST067) purchased from Beyotime was diluted to 50 μmol/L with fresh DMEM medium. The EdU dilution was then used to incubate the cells at 100 μL/well for 2 h. After removing the culture medium, the SW480 or HT29 cells were fixed with methanol at room temperature for 15 minutes (min). Positive fluorescence expression (red) in the collected SW480 or HT29 cells were observed, photographed and recorded under a Leica DMi8 inverted fluorescence microscope (magnification 100 ×). Then DAPI reagent was used to stain the nuclei, and the stains were observed under a microscope.\\nSW480 or HT29 cells in logarithmic growth phase were seeded in 96-well plates at 1×104 cells/well. EdU reagent (ST067) purchased from Beyotime was diluted to 50 μmol/L with fresh DMEM medium. The EdU dilution was then used to incubate the cells at 100 μL/well for 2 h. After removing the culture medium, the SW480 or HT29 cells were fixed with methanol at room temperature for 15 minutes (min). Positive fluorescence expression (red) in the collected SW480 or HT29 cells were observed, photographed and recorded under a Leica DMi8 inverted fluorescence microscope (magnification 100 ×). Then DAPI reagent was used to stain the nuclei, and the stains were observed under a microscope.\\nColony Formation Assay Two hundred SW480 or HT29 cells were transferred to petri dishes containing complete medium (10 mL). In order to distribute the cells evenly, the petri dishes were rotated slowly for 1 min after cell inoculation. Next, the culture dishes containing cells were placed in an incubator for routine culture. During the culture, the medium was changed every 2 days (d). After 14 d, the SW480 or HT29 cells were soaked with Giemsa dye for 20 min and then the colony formation of the cells was observed under a microscope.\\nTwo hundred SW480 or HT29 cells were transferred to petri dishes containing complete medium (10 mL). In order to distribute the cells evenly, the petri dishes were rotated slowly for 1 min after cell inoculation. Next, the culture dishes containing cells were placed in an incubator for routine culture. During the culture, the medium was changed every 2 days (d). After 14 d, the SW480 or HT29 cells were soaked with Giemsa dye for 20 min and then the colony formation of the cells was observed under a microscope.\\nApoptosis Experiment An Annexin V-FITC Apoptosis Detection Kit (CA1020, Solarbio, China) and a flow cytometer (CytoFLEX, BECKMAN COULTER, USA) were used to detect changes in the apoptosis of SW480 or HT29 cells. In brief, SW480 or HT29 cells were prepared into 1×106 cells/mL cell suspension with 1 mL 1× Binding Buffer. One hundred μL of SW480 or HT29 cells and 5 μL of Annexin V-FITC reagent were added to a centrifuge tube and mixed for 10 min in the dark at room temperature. Next, 5 μL of PI reagent was added to the centrifuge tube and incubated in the dark at room temperature for 5 min. Afterwards, PBS was added to adjust the mixture to a final volume of 500 μL. After half an hour, the cells in the centrifuge tube were transferred to the detection instrument to calculate the number of apoptotic cells.\\nAn Annexin V-FITC Apoptosis Detection Kit (CA1020, Solarbio, China) and a flow cytometer (CytoFLEX, BECKMAN COULTER, USA) were used to detect changes in the apoptosis of SW480 or HT29 cells. In brief, SW480 or HT29 cells were prepared into 1×106 cells/mL cell suspension with 1 mL 1× Binding Buffer. One hundred μL of SW480 or HT29 cells and 5 μL of Annexin V-FITC reagent were added to a centrifuge tube and mixed for 10 min in the dark at room temperature. Next, 5 μL of PI reagent was added to the centrifuge tube and incubated in the dark at room temperature for 5 min. Afterwards, PBS was added to adjust the mixture to a final volume of 500 μL. After half an hour, the cells in the centrifuge tube were transferred to the detection instrument to calculate the number of apoptotic cells.\\nBioinformatics Analysis and Target Gene Binding Verification Starbase (http://starbase.sysu.edu.cn/index.php) was used to retrieve and analyze information of differential expression of miR-296-5p in Colon adenocarcinoma (COAD) patients and healthy volunteers. TargetScan (http://www.targetscan.org) was used to predict the circRNA that contained a binding sequence for miR-296-5p and the downstream target genes of miR-296-5p. The binding sequences predicted by TargetScan were designed by COBIOER (China) and used to construct the following reporter plasmids for dual luciferase experiments: NOP2/Sun RNA methyltransferase 2 wild type (circNSUN2-WT), circNSUN2-mutant type (MUT), Signal Transducer and Activator of Transcription 3 (STAT3)-WT and STAT3-MUT. The reporter plasmids were separately co-transfected with miR-296-5p mimic or mimic control into SW480 and HT29 cells using liposome for 48 h. Luciferase activity in the transfected SW480 and HT29 cells was detected using Dual-Luciferase® Reporter Assay System (E1960, Promega, USA) with a GloMax 20/20 luminometer (Promega, USA).\\nStarbase (http://starbase.sysu.edu.cn/index.php) was used to retrieve and analyze information of differential expression of miR-296-5p in Colon adenocarcinoma (COAD) patients and healthy volunteers. TargetScan (http://www.targetscan.org) was used to predict the circRNA that contained a binding sequence for miR-296-5p and the downstream target genes of miR-296-5p. The binding sequences predicted by TargetScan were designed by COBIOER (China) and used to construct the following reporter plasmids for dual luciferase experiments: NOP2/Sun RNA methyltransferase 2 wild type (circNSUN2-WT), circNSUN2-mutant type (MUT), Signal Transducer and Activator of Transcription 3 (STAT3)-WT and STAT3-MUT. The reporter plasmids were separately co-transfected with miR-296-5p mimic or mimic control into SW480 and HT29 cells using liposome for 48 h. Luciferase activity in the transfected SW480 and HT29 cells was detected using Dual-Luciferase® Reporter Assay System (E1960, Promega, USA) with a GloMax 20/20 luminometer (Promega, USA).\\nQuantitative Real-Time Polymerase Chain Reaction (qRT-PCR) qRT-PCR is a reliable method for rapid detection of gene mRNA levels. Briefly, total RNA of SW480 and HT29 cells was fully isolated by the conventional RNA extraction reagent TRIzol (15,596,018, Invitrogen, USA), and subsequently reverse transcribed into cDNA using the RNA reverse transcription reagent (RR047A) developed by TaKaRa (Japan). The qRT-PCR reaction system which consisted of cDNA, gene primers (Sangon synthesis), SYBR® Green (S4438-20RXN, Sigma-Aldrich, Germany) and DEPC water was added to a 96-well plate and put into the detection instrument (Bio-Rad thermal cycler T100). The reaction conditions were set as follows: pre-denaturation at 95 °C for 10 min, denaturation at 95 °C for 15 seconds (s), and annealing at 58 °C for 1 min, for a total of 40 cycles. According to Anita Ciesielska’s report,19 gene mRNA levels were quantified by the 2−ΔΔCT method. GAPDH and U6 were internal references in the experiment. The specific sequences of synthetic primers were shown in Table 1.Table 1Primers for qRT-PCRGeneForward Primer (5ʹ-3ʹ)Reverse Primer (5ʹ-3ʹ)miR-296-5pCCTGTGTCGTATCCAGTGCAAGTCGTATCCAGTGCGTGTCGcircDDX17TGCCAACCACAACATCCTCCACGCTCCCCAGGATTACCAAATcircHIPK3TATGTTGGTGGATCCTGTTCGGCATGGTGGGTAGACCAAGACTTGTGAcircZFRAACCACCACAGATTCACTATAACCACCACAGATTCACTATcircRNA_100290GTCATTCCCTCTTTAATGGTGCAGAACTTCCGCTCTAACATACHas_circ_0026344CTCAGCCTCTAGCATAAGCTCAGGCAAGAGAATGATTTGAACcircVAPATGGATTCCAAATTGAGATGCGTATTCACTTTTCTATCCGATGGATTTCGCHsa_circ_0009361AGAACCAGATTCGAGACGCCGTGCTCTTCAATGCCACCTTCcircNSUN2CTTGAGAAAATCGCCACACTTGTTGAGGAGCAGTGGTGGcircACAP2TCAGAGCATCTGCCCAAAGTTAAATGAACCCCAAGGCTCCGcircITGA7GTGTGCACAGGTCCTTCCAATGGAAGTTCTGTGAGGGACGcircEIF4G3CCTACCCCATCCCCTTATTCACCGTGCTGTAGACTGCTGAGSTAT3CCCCATACCTGAAGACCAAGGGACTCAAACTGCCCTCCTU6CTCGCTTCGGCAGCACAAACGCTTCACGAATTTGCGTGAPDHTGTGGGCATCAATGGATTTGGACACCATGTATTCCGGGTCAAT\\n\\nPrimers for qRT-PCR\\nqRT-PCR is a reliable method for rapid detection of gene mRNA levels. Briefly, total RNA of SW480 and HT29 cells was fully isolated by the conventional RNA extraction reagent TRIzol (15,596,018, Invitrogen, USA), and subsequently reverse transcribed into cDNA using the RNA reverse transcription reagent (RR047A) developed by TaKaRa (Japan). The qRT-PCR reaction system which consisted of cDNA, gene primers (Sangon synthesis), SYBR® Green (S4438-20RXN, Sigma-Aldrich, Germany) and DEPC water was added to a 96-well plate and put into the detection instrument (Bio-Rad thermal cycler T100). The reaction conditions were set as follows: pre-denaturation at 95 °C for 10 min, denaturation at 95 °C for 15 seconds (s), and annealing at 58 °C for 1 min, for a total of 40 cycles. According to Anita Ciesielska’s report,19 gene mRNA levels were quantified by the 2−ΔΔCT method. GAPDH and U6 were internal references in the experiment. The specific sequences of synthetic primers were shown in Table 1.Table 1Primers for qRT-PCRGeneForward Primer (5ʹ-3ʹ)Reverse Primer (5ʹ-3ʹ)miR-296-5pCCTGTGTCGTATCCAGTGCAAGTCGTATCCAGTGCGTGTCGcircDDX17TGCCAACCACAACATCCTCCACGCTCCCCAGGATTACCAAATcircHIPK3TATGTTGGTGGATCCTGTTCGGCATGGTGGGTAGACCAAGACTTGTGAcircZFRAACCACCACAGATTCACTATAACCACCACAGATTCACTATcircRNA_100290GTCATTCCCTCTTTAATGGTGCAGAACTTCCGCTCTAACATACHas_circ_0026344CTCAGCCTCTAGCATAAGCTCAGGCAAGAGAATGATTTGAACcircVAPATGGATTCCAAATTGAGATGCGTATTCACTTTTCTATCCGATGGATTTCGCHsa_circ_0009361AGAACCAGATTCGAGACGCCGTGCTCTTCAATGCCACCTTCcircNSUN2CTTGAGAAAATCGCCACACTTGTTGAGGAGCAGTGGTGGcircACAP2TCAGAGCATCTGCCCAAAGTTAAATGAACCCCAAGGCTCCGcircITGA7GTGTGCACAGGTCCTTCCAATGGAAGTTCTGTGAGGGACGcircEIF4G3CCTACCCCATCCCCTTATTCACCGTGCTGTAGACTGCTGAGSTAT3CCCCATACCTGAAGACCAAGGGACTCAAACTGCCCTCCTU6CTCGCTTCGGCAGCACAAACGCTTCACGAATTTGCGTGAPDHTGTGGGCATCAATGGATTTGGACACCATGTATTCCGGGTCAAT\\n\\nPrimers for qRT-PCR\\nCell Transfection Exogenous up-regulation or knockdown of test genes is a common method to observe their regulatory effects on cells or tissues. Liposome transfection with Lipofectamine 3000 (L3000008, ThermoFisher Scientific, USA) is the most common and convenient method for exogenous interference. Therefore, we commissioned Guangzhou GENESEED Biological Company to synthesize circNSUN2 overexpressed plasmid, circNSUN2 silenced plasmid (sh-circNSUN2), STAT3 overexpressed plasmid and their respective negative controls. MiR-296-5p mimic (miR10000690-1-5) and mimic control (miR1N0000001-1-5), as well as miR-296-5p inhibitor (miR20000690-1-5) and inhibitor control (miR2N0000001-1-5), were purchased directly from Guangzhou RIOBOBIO Company. The plasmids were transfected into SW480 and HT29 cells according to the grouping using Lipofectamine 3000. After 48 h, the success rate of transfection of the cells was determined by qRT-PCR.\\nExogenous up-regulation or knockdown of test genes is a common method to observe their regulatory effects on cells or tissues. Liposome transfection with Lipofectamine 3000 (L3000008, ThermoFisher Scientific, USA) is the most common and convenient method for exogenous interference. Therefore, we commissioned Guangzhou GENESEED Biological Company to synthesize circNSUN2 overexpressed plasmid, circNSUN2 silenced plasmid (sh-circNSUN2), STAT3 overexpressed plasmid and their respective negative controls. MiR-296-5p mimic (miR10000690-1-5) and mimic control (miR1N0000001-1-5), as well as miR-296-5p inhibitor (miR20000690-1-5) and inhibitor control (miR2N0000001-1-5), were purchased directly from Guangzhou RIOBOBIO Company. The plasmids were transfected into SW480 and HT29 cells according to the grouping using Lipofectamine 3000. After 48 h, the success rate of transfection of the cells was determined by qRT-PCR.\\nWestern Blot RIPA lysate (tissue/cell, R0010) produced by Solarbio (China) was used to separate protein from SW480 and HT29 cells, and its concentration was determined with BCA protein concentration determination reagent (PC0020, Solarbio, China). Then the protein was subjected to high temperature denaturation to maintain a relatively stable state. After being electrophoresed by SDS-PAGE, the protein was transferred to the membrane carrier (PVDF membrane). The membrane was soaked with the same BSA sealant (SW3015) produced by Solarbio for 2 h at room temperature. Next, the membrane was incubated with Abcam (USA) antibodies (anti-STAT3, ab68153, 88 kDa; Bax, ab32503, 21 kDa; Bcl-2, ab59348, 26 kDa) that could bind to the corresponding antigens in the protein on the membrane overnight (4 °C), with GAPDH (ab8245, 36KD) as an internal reference. To identify the antigen-antibody complex, a secondary antibody labeled with HRP (Goat Anti-Rabbit antibody, 1:10,000, ab6721; Goat Anti-Mouse antibody, 1:10000, ab205719) was used to treat the PVDF membrane for 1.5 h at room temperature. Fluorescent signal of the complex was enhanced using the ECL Western Blotting Substrate (PE0010, Solarbio) and detected using the Gel Doc™ XR+ imaging system (BIO-RAD, USA). The gray value of the bands was calculated by Image Lab software to determine the final gene protein level.\\nRIPA lysate (tissue/cell, R0010) produced by Solarbio (China) was used to separate protein from SW480 and HT29 cells, and its concentration was determined with BCA protein concentration determination reagent (PC0020, Solarbio, China). Then the protein was subjected to high temperature denaturation to maintain a relatively stable state. After being electrophoresed by SDS-PAGE, the protein was transferred to the membrane carrier (PVDF membrane). The membrane was soaked with the same BSA sealant (SW3015) produced by Solarbio for 2 h at room temperature. Next, the membrane was incubated with Abcam (USA) antibodies (anti-STAT3, ab68153, 88 kDa; Bax, ab32503, 21 kDa; Bcl-2, ab59348, 26 kDa) that could bind to the corresponding antigens in the protein on the membrane overnight (4 °C), with GAPDH (ab8245, 36KD) as an internal reference. To identify the antigen-antibody complex, a secondary antibody labeled with HRP (Goat Anti-Rabbit antibody, 1:10,000, ab6721; Goat Anti-Mouse antibody, 1:10000, ab205719) was used to treat the PVDF membrane for 1.5 h at room temperature. Fluorescent signal of the complex was enhanced using the ECL Western Blotting Substrate (PE0010, Solarbio) and detected using the Gel Doc™ XR+ imaging system (BIO-RAD, USA). The gray value of the bands was calculated by Image Lab software to determine the final gene protein level.\\nStatistical Analysis SPSS 20.0 software (IBM, USA) was employed for statistical analysis of the data obtained in the experiments. Differences between two groups were compared by independent sample t-test, and those between multiple groups were compared by one-way ANOVA followed by Tukey’s test. When p<0.05, the difference was considered statistically significant.\\nSPSS 20.0 software (IBM, USA) was employed for statistical analysis of the data obtained in the experiments. Differences between two groups were compared by independent sample t-test, and those between multiple groups were compared by one-way ANOVA followed by Tukey’s test. When p<0.05, the difference was considered statistically significant.\",\n \"CCD-18Co and CRC cell lines SW480 (CCL-228™) and HT29 (HTB-38™) used in this research were provided by the American Type Culture Collection (ATCC). According to the culture requirements, CCD-18Co, SW480 and HT29 cells were separately inoculated in DMEM medium (30–2007, ATCC) containing 10% fetal bovine serum and incubated in a D180-P cell incubator (RWD, China) containing 5% CO2 at 37 °C.\\nALO (DK0052, CAS NO: 56293-29-9, HPLC≥98%), the research object of this study, was provided by Chengdu DESITE Biological Company, China. ALO was dissolved in fresh DMEM to prepare 0.1 mmol/L, 0.2 mmol/L, 0.4 mmol/L, 0.8 mmol/L and 1 mmol/L ALO solutions, which were separately used to treat the CCD-18Co, SW480 and HT29 cells for 24 hours (h).\",\n \"One hundred µL of SW480 or HT29 cell suspension (1×104 cells/mL) was transferred to each well of a 96-well plate. Of note, three holes were added to the same treatment group. After treatment with the ALO solutions for 24 h, SW480 or HT29 cells were added with MTT reagent (10 µL/well, PB180519, Procell, USA), which could react with mitochondria in living cells. After 4 h of binding, a HBS-1096A enzyme label analyzer (DeTie, China) was used to detect the absorbance of SW480 or HT29 cells in the mixed solutions at 490 nm, and then cell viability was calculated based on the absorbance.\",\n \"SW480 or HT29 cells in logarithmic growth phase were seeded in 96-well plates at 1×104 cells/well. EdU reagent (ST067) purchased from Beyotime was diluted to 50 μmol/L with fresh DMEM medium. The EdU dilution was then used to incubate the cells at 100 μL/well for 2 h. After removing the culture medium, the SW480 or HT29 cells were fixed with methanol at room temperature for 15 minutes (min). Positive fluorescence expression (red) in the collected SW480 or HT29 cells were observed, photographed and recorded under a Leica DMi8 inverted fluorescence microscope (magnification 100 ×). Then DAPI reagent was used to stain the nuclei, and the stains were observed under a microscope.\",\n \"Two hundred SW480 or HT29 cells were transferred to petri dishes containing complete medium (10 mL). In order to distribute the cells evenly, the petri dishes were rotated slowly for 1 min after cell inoculation. Next, the culture dishes containing cells were placed in an incubator for routine culture. During the culture, the medium was changed every 2 days (d). After 14 d, the SW480 or HT29 cells were soaked with Giemsa dye for 20 min and then the colony formation of the cells was observed under a microscope.\",\n \"An Annexin V-FITC Apoptosis Detection Kit (CA1020, Solarbio, China) and a flow cytometer (CytoFLEX, BECKMAN COULTER, USA) were used to detect changes in the apoptosis of SW480 or HT29 cells. In brief, SW480 or HT29 cells were prepared into 1×106 cells/mL cell suspension with 1 mL 1× Binding Buffer. One hundred μL of SW480 or HT29 cells and 5 μL of Annexin V-FITC reagent were added to a centrifuge tube and mixed for 10 min in the dark at room temperature. Next, 5 μL of PI reagent was added to the centrifuge tube and incubated in the dark at room temperature for 5 min. Afterwards, PBS was added to adjust the mixture to a final volume of 500 μL. After half an hour, the cells in the centrifuge tube were transferred to the detection instrument to calculate the number of apoptotic cells.\",\n \"Starbase (http://starbase.sysu.edu.cn/index.php) was used to retrieve and analyze information of differential expression of miR-296-5p in Colon adenocarcinoma (COAD) patients and healthy volunteers. TargetScan (http://www.targetscan.org) was used to predict the circRNA that contained a binding sequence for miR-296-5p and the downstream target genes of miR-296-5p. The binding sequences predicted by TargetScan were designed by COBIOER (China) and used to construct the following reporter plasmids for dual luciferase experiments: NOP2/Sun RNA methyltransferase 2 wild type (circNSUN2-WT), circNSUN2-mutant type (MUT), Signal Transducer and Activator of Transcription 3 (STAT3)-WT and STAT3-MUT. The reporter plasmids were separately co-transfected with miR-296-5p mimic or mimic control into SW480 and HT29 cells using liposome for 48 h. Luciferase activity in the transfected SW480 and HT29 cells was detected using Dual-Luciferase® Reporter Assay System (E1960, Promega, USA) with a GloMax 20/20 luminometer (Promega, USA).\",\n \"qRT-PCR is a reliable method for rapid detection of gene mRNA levels. Briefly, total RNA of SW480 and HT29 cells was fully isolated by the conventional RNA extraction reagent TRIzol (15,596,018, Invitrogen, USA), and subsequently reverse transcribed into cDNA using the RNA reverse transcription reagent (RR047A) developed by TaKaRa (Japan). The qRT-PCR reaction system which consisted of cDNA, gene primers (Sangon synthesis), SYBR® Green (S4438-20RXN, Sigma-Aldrich, Germany) and DEPC water was added to a 96-well plate and put into the detection instrument (Bio-Rad thermal cycler T100). The reaction conditions were set as follows: pre-denaturation at 95 °C for 10 min, denaturation at 95 °C for 15 seconds (s), and annealing at 58 °C for 1 min, for a total of 40 cycles. According to Anita Ciesielska’s report,19 gene mRNA levels were quantified by the 2−ΔΔCT method. GAPDH and U6 were internal references in the experiment. The specific sequences of synthetic primers were shown in Table 1.Table 1Primers for qRT-PCRGeneForward Primer (5ʹ-3ʹ)Reverse Primer (5ʹ-3ʹ)miR-296-5pCCTGTGTCGTATCCAGTGCAAGTCGTATCCAGTGCGTGTCGcircDDX17TGCCAACCACAACATCCTCCACGCTCCCCAGGATTACCAAATcircHIPK3TATGTTGGTGGATCCTGTTCGGCATGGTGGGTAGACCAAGACTTGTGAcircZFRAACCACCACAGATTCACTATAACCACCACAGATTCACTATcircRNA_100290GTCATTCCCTCTTTAATGGTGCAGAACTTCCGCTCTAACATACHas_circ_0026344CTCAGCCTCTAGCATAAGCTCAGGCAAGAGAATGATTTGAACcircVAPATGGATTCCAAATTGAGATGCGTATTCACTTTTCTATCCGATGGATTTCGCHsa_circ_0009361AGAACCAGATTCGAGACGCCGTGCTCTTCAATGCCACCTTCcircNSUN2CTTGAGAAAATCGCCACACTTGTTGAGGAGCAGTGGTGGcircACAP2TCAGAGCATCTGCCCAAAGTTAAATGAACCCCAAGGCTCCGcircITGA7GTGTGCACAGGTCCTTCCAATGGAAGTTCTGTGAGGGACGcircEIF4G3CCTACCCCATCCCCTTATTCACCGTGCTGTAGACTGCTGAGSTAT3CCCCATACCTGAAGACCAAGGGACTCAAACTGCCCTCCTU6CTCGCTTCGGCAGCACAAACGCTTCACGAATTTGCGTGAPDHTGTGGGCATCAATGGATTTGGACACCATGTATTCCGGGTCAAT\\n\\nPrimers for qRT-PCR\",\n \"Exogenous up-regulation or knockdown of test genes is a common method to observe their regulatory effects on cells or tissues. Liposome transfection with Lipofectamine 3000 (L3000008, ThermoFisher Scientific, USA) is the most common and convenient method for exogenous interference. Therefore, we commissioned Guangzhou GENESEED Biological Company to synthesize circNSUN2 overexpressed plasmid, circNSUN2 silenced plasmid (sh-circNSUN2), STAT3 overexpressed plasmid and their respective negative controls. MiR-296-5p mimic (miR10000690-1-5) and mimic control (miR1N0000001-1-5), as well as miR-296-5p inhibitor (miR20000690-1-5) and inhibitor control (miR2N0000001-1-5), were purchased directly from Guangzhou RIOBOBIO Company. The plasmids were transfected into SW480 and HT29 cells according to the grouping using Lipofectamine 3000. After 48 h, the success rate of transfection of the cells was determined by qRT-PCR.\",\n \"RIPA lysate (tissue/cell, R0010) produced by Solarbio (China) was used to separate protein from SW480 and HT29 cells, and its concentration was determined with BCA protein concentration determination reagent (PC0020, Solarbio, China). Then the protein was subjected to high temperature denaturation to maintain a relatively stable state. After being electrophoresed by SDS-PAGE, the protein was transferred to the membrane carrier (PVDF membrane). The membrane was soaked with the same BSA sealant (SW3015) produced by Solarbio for 2 h at room temperature. Next, the membrane was incubated with Abcam (USA) antibodies (anti-STAT3, ab68153, 88 kDa; Bax, ab32503, 21 kDa; Bcl-2, ab59348, 26 kDa) that could bind to the corresponding antigens in the protein on the membrane overnight (4 °C), with GAPDH (ab8245, 36KD) as an internal reference. To identify the antigen-antibody complex, a secondary antibody labeled with HRP (Goat Anti-Rabbit antibody, 1:10,000, ab6721; Goat Anti-Mouse antibody, 1:10000, ab205719) was used to treat the PVDF membrane for 1.5 h at room temperature. Fluorescent signal of the complex was enhanced using the ECL Western Blotting Substrate (PE0010, Solarbio) and detected using the Gel Doc™ XR+ imaging system (BIO-RAD, USA). The gray value of the bands was calculated by Image Lab software to determine the final gene protein level.\",\n \"SPSS 20.0 software (IBM, USA) was employed for statistical analysis of the data obtained in the experiments. Differences between two groups were compared by independent sample t-test, and those between multiple groups were compared by one-way ANOVA followed by Tukey’s test. When p<0.05, the difference was considered statistically significant.\",\n \"ALO at a Concentration Gradient Inhibited Proliferation Yet Promoted Apoptosis in CRC Cells In this study, the toxicity of ALO in normal cells (CCD-18Co) was detected, ALO showed no significant toxicity to CCD-18Co cells (Figure 1A). In order to study the effects of ALO on the growth and basic physiological functions of CRC cells, we used different concentrations of ALO to treat SW480 and HT29 cells. The results showed that 0.2 mmol/L, 0.4 mmol/L, 0.8 mmol/L and 1 mmol/L ALO solutions all inhibited the viability of SW480 and HT29 cells (p<0.05, Figure 1B and C). Moreover, the viability of SW480 and HT29 cells was close to 50% under the treatment of 0.8 mmol/L ALO solution, but was reduced to less than 50% when the ALO concentration was increased to 1 mmol/L (Figure 1B and C). In the following EdU cell proliferation experiment, the red fluorescence expression was reduced in SW480 and HT29 cells under the treatment of 0.8 mmol/L ALO (Figure 1D and E). Consistently, the clone formation experiment also showed that different concentrations of ALO (0.2 mmol/L, 0.4 mmol/L, 0.8 mmol/L) reduced the number of cell clones and inhibited the proliferation of SW480 and HT29 cells (p<0.05, Figure 1F–H). At the same time, the increase in the concentration of the ALO solution accelerated the death of CRC cells. As shown in Figure 1I–K, the number of apoptotic SW480 and HT29 cells increased significantly after treatment with different concentrations of ALO (p<0.01, Figure 1I–K).Figure 1ALO at a concentration gradient inhibited proliferation yet promoted apoptosis in CRC cells. (A) The effect of ALO on the normal colonic tissue cells (CCD-18Co) was detected by MTT experiments. (B and C) MTT experiments showed that ALO at a concentration gradient (0.1 mmol/L, 0.2 mmol/L, 0.4 mmol/L, 0.8 mmol/L and 1 mmol/L) inhibited the activity of SW480 and HT29 cells. (D and E) EdU cell proliferation experiments showed that 0.8 mmol/L ALO inhibited the proliferation of SW480 and HT29 cells. The magnification was 100×. (F–H) The clone formation experiment showed that ALO at a concentration gradient (0.2 mmol/L, 0.4 mmol/L and 0.8 mmol/L) inhibited the proliferation of SW480 and HT29 cells. (I–K) Flow cytometry experiments showed that ALO at a concentration gradient (0.2 mmol/L, 0.4 mmol/L and 0.8 mmol/L) promoted the apoptosis of SW480 and HT29 cells. All experiments were repeated three times to obtain average values. *p<0.05, **p<0.01, ***p<0.001 vs Control.Abbreviations: ALO, aloperine; CRC, colorectal cancer.\\nALO at a concentration gradient inhibited proliferation yet promoted apoptosis in CRC cells. (A) The effect of ALO on the normal colonic tissue cells (CCD-18Co) was detected by MTT experiments. (B and C) MTT experiments showed that ALO at a concentration gradient (0.1 mmol/L, 0.2 mmol/L, 0.4 mmol/L, 0.8 mmol/L and 1 mmol/L) inhibited the activity of SW480 and HT29 cells. (D and E) EdU cell proliferation experiments showed that 0.8 mmol/L ALO inhibited the proliferation of SW480 and HT29 cells. The magnification was 100×. (F–H) The clone formation experiment showed that ALO at a concentration gradient (0.2 mmol/L, 0.4 mmol/L and 0.8 mmol/L) inhibited the proliferation of SW480 and HT29 cells. (I–K) Flow cytometry experiments showed that ALO at a concentration gradient (0.2 mmol/L, 0.4 mmol/L and 0.8 mmol/L) promoted the apoptosis of SW480 and HT29 cells. All experiments were repeated three times to obtain average values. *p<0.05, **p<0.01, ***p<0.001 vs Control.\\nIn this study, the toxicity of ALO in normal cells (CCD-18Co) was detected, ALO showed no significant toxicity to CCD-18Co cells (Figure 1A). In order to study the effects of ALO on the growth and basic physiological functions of CRC cells, we used different concentrations of ALO to treat SW480 and HT29 cells. The results showed that 0.2 mmol/L, 0.4 mmol/L, 0.8 mmol/L and 1 mmol/L ALO solutions all inhibited the viability of SW480 and HT29 cells (p<0.05, Figure 1B and C). Moreover, the viability of SW480 and HT29 cells was close to 50% under the treatment of 0.8 mmol/L ALO solution, but was reduced to less than 50% when the ALO concentration was increased to 1 mmol/L (Figure 1B and C). In the following EdU cell proliferation experiment, the red fluorescence expression was reduced in SW480 and HT29 cells under the treatment of 0.8 mmol/L ALO (Figure 1D and E). Consistently, the clone formation experiment also showed that different concentrations of ALO (0.2 mmol/L, 0.4 mmol/L, 0.8 mmol/L) reduced the number of cell clones and inhibited the proliferation of SW480 and HT29 cells (p<0.05, Figure 1F–H). At the same time, the increase in the concentration of the ALO solution accelerated the death of CRC cells. As shown in Figure 1I–K, the number of apoptotic SW480 and HT29 cells increased significantly after treatment with different concentrations of ALO (p<0.01, Figure 1I–K).Figure 1ALO at a concentration gradient inhibited proliferation yet promoted apoptosis in CRC cells. (A) The effect of ALO on the normal colonic tissue cells (CCD-18Co) was detected by MTT experiments. (B and C) MTT experiments showed that ALO at a concentration gradient (0.1 mmol/L, 0.2 mmol/L, 0.4 mmol/L, 0.8 mmol/L and 1 mmol/L) inhibited the activity of SW480 and HT29 cells. (D and E) EdU cell proliferation experiments showed that 0.8 mmol/L ALO inhibited the proliferation of SW480 and HT29 cells. The magnification was 100×. (F–H) The clone formation experiment showed that ALO at a concentration gradient (0.2 mmol/L, 0.4 mmol/L and 0.8 mmol/L) inhibited the proliferation of SW480 and HT29 cells. (I–K) Flow cytometry experiments showed that ALO at a concentration gradient (0.2 mmol/L, 0.4 mmol/L and 0.8 mmol/L) promoted the apoptosis of SW480 and HT29 cells. All experiments were repeated three times to obtain average values. *p<0.05, **p<0.01, ***p<0.001 vs Control.Abbreviations: ALO, aloperine; CRC, colorectal cancer.\\nALO at a concentration gradient inhibited proliferation yet promoted apoptosis in CRC cells. (A) The effect of ALO on the normal colonic tissue cells (CCD-18Co) was detected by MTT experiments. (B and C) MTT experiments showed that ALO at a concentration gradient (0.1 mmol/L, 0.2 mmol/L, 0.4 mmol/L, 0.8 mmol/L and 1 mmol/L) inhibited the activity of SW480 and HT29 cells. (D and E) EdU cell proliferation experiments showed that 0.8 mmol/L ALO inhibited the proliferation of SW480 and HT29 cells. The magnification was 100×. (F–H) The clone formation experiment showed that ALO at a concentration gradient (0.2 mmol/L, 0.4 mmol/L and 0.8 mmol/L) inhibited the proliferation of SW480 and HT29 cells. (I–K) Flow cytometry experiments showed that ALO at a concentration gradient (0.2 mmol/L, 0.4 mmol/L and 0.8 mmol/L) promoted the apoptosis of SW480 and HT29 cells. All experiments were repeated three times to obtain average values. *p<0.05, **p<0.01, ***p<0.001 vs Control.\\nThe Abnormally Low Expression of miR-296-5p in Colon Cancer Could Be Up-Regulated by ALO Data from the Starbase database showed that the expression of miR-296-5p in colon cancer patients was significantly lower than that in healthy people (p=2.5e-6, Figure 2A). In this study, however, after treatment with different concentrations of ALO for 24 h, miR-296-5p activity in SW480 and HT29 cells was significantly increased (p<0.01, Figure 2B and C), suggesting that ALO can stimulate the up-regulation of miR-296-5p.Figure 2The abnormally low expression of miR-296-5p in colon cancer could be upregulated by ALO. (A) Starbase (http://starbase.sysu.edu.cn/index.php) was used to retrieve the information of differential expression of miR-296-5p in colon adenocarcinoma (COAD, n=450) patients and healthy volunteers (n=8) in vivo. P=2.5e-6. (B and C) The qRT-PCR experiment showed that ALO at a concentration gradient (0.1 mmol/L, 0.2 mmol/L, 0.4 mmol/L, 0.8 mmol/L and 1 mmol/L) up-regulated miR-296-5p expression in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. **p<0.01, ***p<0.001 vs Control.Abbreviation: qRT-PCR, quantitative real-time polymerase chain reaction.\\nThe abnormally low expression of miR-296-5p in colon cancer could be upregulated by ALO. (A) Starbase (http://starbase.sysu.edu.cn/index.php) was used to retrieve the information of differential expression of miR-296-5p in colon adenocarcinoma (COAD, n=450) patients and healthy volunteers (n=8) in vivo. P=2.5e-6. (B and C) The qRT-PCR experiment showed that ALO at a concentration gradient (0.1 mmol/L, 0.2 mmol/L, 0.4 mmol/L, 0.8 mmol/L and 1 mmol/L) up-regulated miR-296-5p expression in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. **p<0.01, ***p<0.001 vs Control.\\nData from the Starbase database showed that the expression of miR-296-5p in colon cancer patients was significantly lower than that in healthy people (p=2.5e-6, Figure 2A). In this study, however, after treatment with different concentrations of ALO for 24 h, miR-296-5p activity in SW480 and HT29 cells was significantly increased (p<0.01, Figure 2B and C), suggesting that ALO can stimulate the up-regulation of miR-296-5p.Figure 2The abnormally low expression of miR-296-5p in colon cancer could be upregulated by ALO. (A) Starbase (http://starbase.sysu.edu.cn/index.php) was used to retrieve the information of differential expression of miR-296-5p in colon adenocarcinoma (COAD, n=450) patients and healthy volunteers (n=8) in vivo. P=2.5e-6. (B and C) The qRT-PCR experiment showed that ALO at a concentration gradient (0.1 mmol/L, 0.2 mmol/L, 0.4 mmol/L, 0.8 mmol/L and 1 mmol/L) up-regulated miR-296-5p expression in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. **p<0.01, ***p<0.001 vs Control.Abbreviation: qRT-PCR, quantitative real-time polymerase chain reaction.\\nThe abnormally low expression of miR-296-5p in colon cancer could be upregulated by ALO. (A) Starbase (http://starbase.sysu.edu.cn/index.php) was used to retrieve the information of differential expression of miR-296-5p in colon adenocarcinoma (COAD, n=450) patients and healthy volunteers (n=8) in vivo. P=2.5e-6. (B and C) The qRT-PCR experiment showed that ALO at a concentration gradient (0.1 mmol/L, 0.2 mmol/L, 0.4 mmol/L, 0.8 mmol/L and 1 mmol/L) up-regulated miR-296-5p expression in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. **p<0.01, ***p<0.001 vs Control.\\nAmong the Differentially Expressed circRNAs in CRC, Only circNSUN2 Not Only Had a Target Site for miR-296-5p, but Also Could Be Regulated by ALO Through literature review, we screened 12 circRNAs that were reported to be associated with CRC: circDDX17, circHIPK3, circZFR, circRNA_100290, Has_circ_0026344, circVAPA, Hsa_circ_0009361, circNSUN2, circACAP2, circACVRL1, circITGA7 and circEIF4G3. We first detected the regulatory effect of ALO on these differentially expressed circRNAs by qRT-PCR to perform preliminary screening (Figure 3A). The results showed that the expressions of circDDX17, Has_circ_0026344, circVAPA, circNSUN2, circACAP2, circACVRL1 and circEIF4G3 were regulated by 0.8 mmol/L ALO (p<0.001, Figure 3A). Through subsequent screening of target genes, we found that only circNSUN2 and miR-296-5p have targeted binding sites (Figure 3B). After further verification, it was confirmed that circNSUN2-WT and miR-296-5p mimic did reduce the luciferase activity in SW480 and HT29 cells (p<0.001, Figure 3C and D). Therefore, we chose circNSUN2 as the next research object.Figure 3Among the differentially expressed circRNAs in CRC, only circNSUN2 not only had a target site for miR-296-5p, but also could be regulated by ALO. (A) qRT-PCR detected the regulation of 0.8 mmol/L ALO on the differentially expressed circRNAs in CRC. GAPDH played the role of internal reference. (B) The TargetScan database (http://www.targetscan.org) was used to predict the target sites of circNSUN2 and miR-296-5p. (C and D) The dual luciferase experiment confirmed that circNSUN2 can bind to miR-296-5p in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. ***p<0.001 vs Control; +++p<0.001 vs MC.Abbreviation: MC, miR-296-5p mimic control.\\nAmong the differentially expressed circRNAs in CRC, only circNSUN2 not only had a target site for miR-296-5p, but also could be regulated by ALO. (A) qRT-PCR detected the regulation of 0.8 mmol/L ALO on the differentially expressed circRNAs in CRC. GAPDH played the role of internal reference. (B) The TargetScan database (http://www.targetscan.org) was used to predict the target sites of circNSUN2 and miR-296-5p. (C and D) The dual luciferase experiment confirmed that circNSUN2 can bind to miR-296-5p in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. ***p<0.001 vs Control; +++p<0.001 vs MC.\\nThrough literature review, we screened 12 circRNAs that were reported to be associated with CRC: circDDX17, circHIPK3, circZFR, circRNA_100290, Has_circ_0026344, circVAPA, Hsa_circ_0009361, circNSUN2, circACAP2, circACVRL1, circITGA7 and circEIF4G3. We first detected the regulatory effect of ALO on these differentially expressed circRNAs by qRT-PCR to perform preliminary screening (Figure 3A). The results showed that the expressions of circDDX17, Has_circ_0026344, circVAPA, circNSUN2, circACAP2, circACVRL1 and circEIF4G3 were regulated by 0.8 mmol/L ALO (p<0.001, Figure 3A). Through subsequent screening of target genes, we found that only circNSUN2 and miR-296-5p have targeted binding sites (Figure 3B). After further verification, it was confirmed that circNSUN2-WT and miR-296-5p mimic did reduce the luciferase activity in SW480 and HT29 cells (p<0.001, Figure 3C and D). Therefore, we chose circNSUN2 as the next research object.Figure 3Among the differentially expressed circRNAs in CRC, only circNSUN2 not only had a target site for miR-296-5p, but also could be regulated by ALO. (A) qRT-PCR detected the regulation of 0.8 mmol/L ALO on the differentially expressed circRNAs in CRC. GAPDH played the role of internal reference. (B) The TargetScan database (http://www.targetscan.org) was used to predict the target sites of circNSUN2 and miR-296-5p. (C and D) The dual luciferase experiment confirmed that circNSUN2 can bind to miR-296-5p in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. ***p<0.001 vs Control; +++p<0.001 vs MC.Abbreviation: MC, miR-296-5p mimic control.\\nAmong the differentially expressed circRNAs in CRC, only circNSUN2 not only had a target site for miR-296-5p, but also could be regulated by ALO. (A) qRT-PCR detected the regulation of 0.8 mmol/L ALO on the differentially expressed circRNAs in CRC. GAPDH played the role of internal reference. (B) The TargetScan database (http://www.targetscan.org) was used to predict the target sites of circNSUN2 and miR-296-5p. (C and D) The dual luciferase experiment confirmed that circNSUN2 can bind to miR-296-5p in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. ***p<0.001 vs Control; +++p<0.001 vs MC.\\nOverexpression of circNSUN2 Partially Offset the Suppression of ALO on the Biological Behavior of Cancer Cells In SW480 and HT29 cells, ALO suppressed the mRNA level of circNSUN2, whereas transfection of circNSUN2 overexpression significantly increased the expression of circNSUN2 (p<0.001, Figure 4A and B). The following basic cell function experiments showed that the upregulation of circNSUN2 partially offset the inhibitory effect of ALO on CRC cell viability, proliferation and apoptosis (p<0.001, Figure 4C–L).Figure 4Overexpression of circNSUN2 partially offset the suppression of ALO on the biological behavior of cancer cells. (A and B) The qRT-PCR experiment showed that ALO (0.8 mmol/L) inhibited the expression of circNSUN2 in SW480 and HT29 cells. GAPDH played the role of internal reference. (C and D) The MTT experiment showed that overexpression of circNSUN2 partially offset the inhibition of ALO on cell viability in SW480 and HT29 cells. (E and F) EdU cell proliferation experiments showed that overexpression of circNSUN2 partially offset the inhibition of ALO on proliferation in SW480 and HT29 cells. The magnification was 100×. (G–I) The clone formation experiment showed that overexpression of circNSUN2 partially offset the inhibition of ALO on proliferation in SW480 and HT29 cells. (J–L) Flow cytometry experiments showed that overexpression of circNSUN2 partially offset the promotion of ALO on apoptosis in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. ***p<0.001 vs Control; ^p<0.05, ^^p<0.01, ^^^p<0.001 vs ALO+NC.Abbreviations: CircNSUN2, NOP2/Sun RNA methyltransferase 2; NC, negative control.\\nOverexpression of circNSUN2 partially offset the suppression of ALO on the biological behavior of cancer cells. (A and B) The qRT-PCR experiment showed that ALO (0.8 mmol/L) inhibited the expression of circNSUN2 in SW480 and HT29 cells. GAPDH played the role of internal reference. (C and D) The MTT experiment showed that overexpression of circNSUN2 partially offset the inhibition of ALO on cell viability in SW480 and HT29 cells. (E and F) EdU cell proliferation experiments showed that overexpression of circNSUN2 partially offset the inhibition of ALO on proliferation in SW480 and HT29 cells. The magnification was 100×. (G–I) The clone formation experiment showed that overexpression of circNSUN2 partially offset the inhibition of ALO on proliferation in SW480 and HT29 cells. (J–L) Flow cytometry experiments showed that overexpression of circNSUN2 partially offset the promotion of ALO on apoptosis in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. ***p<0.001 vs Control; ^p<0.05, ^^p<0.01, ^^^p<0.001 vs ALO+NC.\\nIn SW480 and HT29 cells, ALO suppressed the mRNA level of circNSUN2, whereas transfection of circNSUN2 overexpression significantly increased the expression of circNSUN2 (p<0.001, Figure 4A and B). The following basic cell function experiments showed that the upregulation of circNSUN2 partially offset the inhibitory effect of ALO on CRC cell viability, proliferation and apoptosis (p<0.001, Figure 4C–L).Figure 4Overexpression of circNSUN2 partially offset the suppression of ALO on the biological behavior of cancer cells. (A and B) The qRT-PCR experiment showed that ALO (0.8 mmol/L) inhibited the expression of circNSUN2 in SW480 and HT29 cells. GAPDH played the role of internal reference. (C and D) The MTT experiment showed that overexpression of circNSUN2 partially offset the inhibition of ALO on cell viability in SW480 and HT29 cells. (E and F) EdU cell proliferation experiments showed that overexpression of circNSUN2 partially offset the inhibition of ALO on proliferation in SW480 and HT29 cells. The magnification was 100×. (G–I) The clone formation experiment showed that overexpression of circNSUN2 partially offset the inhibition of ALO on proliferation in SW480 and HT29 cells. (J–L) Flow cytometry experiments showed that overexpression of circNSUN2 partially offset the promotion of ALO on apoptosis in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. ***p<0.001 vs Control; ^p<0.05, ^^p<0.01, ^^^p<0.001 vs ALO+NC.Abbreviations: CircNSUN2, NOP2/Sun RNA methyltransferase 2; NC, negative control.\\nOverexpression of circNSUN2 partially offset the suppression of ALO on the biological behavior of cancer cells. (A and B) The qRT-PCR experiment showed that ALO (0.8 mmol/L) inhibited the expression of circNSUN2 in SW480 and HT29 cells. GAPDH played the role of internal reference. (C and D) The MTT experiment showed that overexpression of circNSUN2 partially offset the inhibition of ALO on cell viability in SW480 and HT29 cells. (E and F) EdU cell proliferation experiments showed that overexpression of circNSUN2 partially offset the inhibition of ALO on proliferation in SW480 and HT29 cells. The magnification was 100×. (G–I) The clone formation experiment showed that overexpression of circNSUN2 partially offset the inhibition of ALO on proliferation in SW480 and HT29 cells. (J–L) Flow cytometry experiments showed that overexpression of circNSUN2 partially offset the promotion of ALO on apoptosis in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. ***p<0.001 vs Control; ^p<0.05, ^^p<0.01, ^^^p<0.001 vs ALO+NC.\\nSh-circNSUN2 Inhibited the Malignant Development of CRC Cells, Which Was Neutralized by miR-296-5p Inhibitor We also tested the mutual regulation of circNSUN2 and miR-296-5p in SW480 and HT29 cells. Sh-circNSUN2 could down-regulate the expression of circNSUN2, and besides, it inhibited the proliferation of SW480 and HT29 cells, while promoting their apoptosis and the expression of miR-296-5p (p<0.001, Figure 5A–J). However, the inhibitory effect of sh-circNSUN2 on the malignant development of the two cancer cells was neutralized by miR-296-5p inhibitor (p<0.001, Figure 5A–J).Figure 5Sh-circNSUN2 inhibited the malignant development of CRC cells, which was neutralized by miR-296-5p inhibitor. (A and B) The qRT-PCR experiment showed that miR-296-5p inhibitor neutralized the inhibitory effect of sh-circNSUN2 on circNSUN2 expression. GAPDH played the role of internal reference. (C and D) The qRT-PCR experiment showed that sh-circNSUN2 up-regulated the mRNA level of miR-296-5p. U6 played the role of internal reference. (E–G) The clone formation experiment showed that the effect of sh-circNSUN2 on the proliferation of SW480 and HT29 cells was neutralized by miR-296-5p inhibitor. (H-J) Flow cytometry experiments showed that the effect of sh-circNSUN2 on promoting the apoptosis of SW480 and HT29 cells was neutralized by miR-296-5p inhibitor. All experiments were repeated three times to obtain average values. ***p<0.001 vs sh-NC+IC; ^^^p<0.001 vs sh-circNSUN2+IC; ###p<0.001 vs sh-NC+I.Abbreviations: Sh-circNSUN2, silent circNSUN2; sh-NC, silent negative control; IC, miR-296-5p inhibitor control; I, miR-296-5p inhibitor.\\nSh-circNSUN2 inhibited the malignant development of CRC cells, which was neutralized by miR-296-5p inhibitor. (A and B) The qRT-PCR experiment showed that miR-296-5p inhibitor neutralized the inhibitory effect of sh-circNSUN2 on circNSUN2 expression. GAPDH played the role of internal reference. (C and D) The qRT-PCR experiment showed that sh-circNSUN2 up-regulated the mRNA level of miR-296-5p. U6 played the role of internal reference. (E–G) The clone formation experiment showed that the effect of sh-circNSUN2 on the proliferation of SW480 and HT29 cells was neutralized by miR-296-5p inhibitor. (H-J) Flow cytometry experiments showed that the effect of sh-circNSUN2 on promoting the apoptosis of SW480 and HT29 cells was neutralized by miR-296-5p inhibitor. All experiments were repeated three times to obtain average values. ***p<0.001 vs sh-NC+IC; ^^^p<0.001 vs sh-circNSUN2+IC; ###p<0.001 vs sh-NC+I.\\nWe also tested the mutual regulation of circNSUN2 and miR-296-5p in SW480 and HT29 cells. Sh-circNSUN2 could down-regulate the expression of circNSUN2, and besides, it inhibited the proliferation of SW480 and HT29 cells, while promoting their apoptosis and the expression of miR-296-5p (p<0.001, Figure 5A–J). However, the inhibitory effect of sh-circNSUN2 on the malignant development of the two cancer cells was neutralized by miR-296-5p inhibitor (p<0.001, Figure 5A–J).Figure 5Sh-circNSUN2 inhibited the malignant development of CRC cells, which was neutralized by miR-296-5p inhibitor. (A and B) The qRT-PCR experiment showed that miR-296-5p inhibitor neutralized the inhibitory effect of sh-circNSUN2 on circNSUN2 expression. GAPDH played the role of internal reference. (C and D) The qRT-PCR experiment showed that sh-circNSUN2 up-regulated the mRNA level of miR-296-5p. U6 played the role of internal reference. (E–G) The clone formation experiment showed that the effect of sh-circNSUN2 on the proliferation of SW480 and HT29 cells was neutralized by miR-296-5p inhibitor. (H-J) Flow cytometry experiments showed that the effect of sh-circNSUN2 on promoting the apoptosis of SW480 and HT29 cells was neutralized by miR-296-5p inhibitor. All experiments were repeated three times to obtain average values. ***p<0.001 vs sh-NC+IC; ^^^p<0.001 vs sh-circNSUN2+IC; ###p<0.001 vs sh-NC+I.Abbreviations: Sh-circNSUN2, silent circNSUN2; sh-NC, silent negative control; IC, miR-296-5p inhibitor control; I, miR-296-5p inhibitor.\\nSh-circNSUN2 inhibited the malignant development of CRC cells, which was neutralized by miR-296-5p inhibitor. (A and B) The qRT-PCR experiment showed that miR-296-5p inhibitor neutralized the inhibitory effect of sh-circNSUN2 on circNSUN2 expression. GAPDH played the role of internal reference. (C and D) The qRT-PCR experiment showed that sh-circNSUN2 up-regulated the mRNA level of miR-296-5p. U6 played the role of internal reference. (E–G) The clone formation experiment showed that the effect of sh-circNSUN2 on the proliferation of SW480 and HT29 cells was neutralized by miR-296-5p inhibitor. (H-J) Flow cytometry experiments showed that the effect of sh-circNSUN2 on promoting the apoptosis of SW480 and HT29 cells was neutralized by miR-296-5p inhibitor. All experiments were repeated three times to obtain average values. ***p<0.001 vs sh-NC+IC; ^^^p<0.001 vs sh-circNSUN2+IC; ###p<0.001 vs sh-NC+I.\\nMiR-296-5p Bound to STAT3 and Upregulation of Mir-296-5p Inhibited the Expression of STAT3 in CRC Cells We screened the target genes of miR-296-5p, and found that miR-296-5p and STAT3 have targeted binding sites (Figure 6A), and this binding relation was further confirmed by a dual-luciferase verification test (p<0.001, Figure 6B and C). Next, we used qRT-PCR and Western blot to analyze the mRNA and protein levels of miR-296-5p and STAT. It was found that miR-296-5p mimic up-regulated miR-296-5p expression while preventing the activation of STAT3 (p<0.001, Figure 7A–G). However, the overexpression of STAT3 reversed the regulation of miR-296-5p mimic on the two genes (p<0.001, Figure 7A–G).Figure 6The targeted binding of miR-296-5p to STAT3. (A) TargetScan was used to predict the binding sequence of miR-296-5p and STAT3. (B and C) Dual luciferase experiment confirmed that miR-296-5p can bind to STAT3 in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. +++p<0.001 vs MC.Figure 7Up-regulation of miR-296-5p inhibited the expression of STAT3 in CRC cells. (A and B) The qRT-PCR experiment showed that miR-296-5p mimic up-regulated the mRNA level of miR-296-5p. U6 played the role of internal reference. (C and D) The qRT-PCR experiment showed that miR-296-5p mimic suppressed the mRNA level of STAT3, while overexpression of STAT3 reversed this effect. GAPDH played the role of internal reference. (E–G) The Western blot experiment showed that miR-296-5p mimic inhibited the protein level of STAT3, while over-expression of STAT3 reversed this effect. GAPDH played the role of internal reference. All experiments were repeated three times to obtain average values. ***p<0.001 vs MC+NC; ^^^p<0.001 vs M+NC; ###p<0.001 vs MC+STAT3.Abbreviation: STAT3, signal transducer and activator of transcription 3.\\nThe targeted binding of miR-296-5p to STAT3. (A) TargetScan was used to predict the binding sequence of miR-296-5p and STAT3. (B and C) Dual luciferase experiment confirmed that miR-296-5p can bind to STAT3 in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. +++p<0.001 vs MC.\\nUp-regulation of miR-296-5p inhibited the expression of STAT3 in CRC cells. (A and B) The qRT-PCR experiment showed that miR-296-5p mimic up-regulated the mRNA level of miR-296-5p. U6 played the role of internal reference. (C and D) The qRT-PCR experiment showed that miR-296-5p mimic suppressed the mRNA level of STAT3, while overexpression of STAT3 reversed this effect. GAPDH played the role of internal reference. (E–G) The Western blot experiment showed that miR-296-5p mimic inhibited the protein level of STAT3, while over-expression of STAT3 reversed this effect. GAPDH played the role of internal reference. All experiments were repeated three times to obtain average values. ***p<0.001 vs MC+NC; ^^^p<0.001 vs M+NC; ###p<0.001 vs MC+STAT3.\\nWe screened the target genes of miR-296-5p, and found that miR-296-5p and STAT3 have targeted binding sites (Figure 6A), and this binding relation was further confirmed by a dual-luciferase verification test (p<0.001, Figure 6B and C). Next, we used qRT-PCR and Western blot to analyze the mRNA and protein levels of miR-296-5p and STAT. It was found that miR-296-5p mimic up-regulated miR-296-5p expression while preventing the activation of STAT3 (p<0.001, Figure 7A–G). However, the overexpression of STAT3 reversed the regulation of miR-296-5p mimic on the two genes (p<0.001, Figure 7A–G).Figure 6The targeted binding of miR-296-5p to STAT3. (A) TargetScan was used to predict the binding sequence of miR-296-5p and STAT3. (B and C) Dual luciferase experiment confirmed that miR-296-5p can bind to STAT3 in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. +++p<0.001 vs MC.Figure 7Up-regulation of miR-296-5p inhibited the expression of STAT3 in CRC cells. (A and B) The qRT-PCR experiment showed that miR-296-5p mimic up-regulated the mRNA level of miR-296-5p. U6 played the role of internal reference. (C and D) The qRT-PCR experiment showed that miR-296-5p mimic suppressed the mRNA level of STAT3, while overexpression of STAT3 reversed this effect. GAPDH played the role of internal reference. (E–G) The Western blot experiment showed that miR-296-5p mimic inhibited the protein level of STAT3, while over-expression of STAT3 reversed this effect. GAPDH played the role of internal reference. All experiments were repeated three times to obtain average values. ***p<0.001 vs MC+NC; ^^^p<0.001 vs M+NC; ###p<0.001 vs MC+STAT3.Abbreviation: STAT3, signal transducer and activator of transcription 3.\\nThe targeted binding of miR-296-5p to STAT3. (A) TargetScan was used to predict the binding sequence of miR-296-5p and STAT3. (B and C) Dual luciferase experiment confirmed that miR-296-5p can bind to STAT3 in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. +++p<0.001 vs MC.\\nUp-regulation of miR-296-5p inhibited the expression of STAT3 in CRC cells. (A and B) The qRT-PCR experiment showed that miR-296-5p mimic up-regulated the mRNA level of miR-296-5p. U6 played the role of internal reference. (C and D) The qRT-PCR experiment showed that miR-296-5p mimic suppressed the mRNA level of STAT3, while overexpression of STAT3 reversed this effect. GAPDH played the role of internal reference. (E–G) The Western blot experiment showed that miR-296-5p mimic inhibited the protein level of STAT3, while over-expression of STAT3 reversed this effect. GAPDH played the role of internal reference. All experiments were repeated three times to obtain average values. ***p<0.001 vs MC+NC; ^^^p<0.001 vs M+NC; ###p<0.001 vs MC+STAT3.\\nMiR-296-5p Mimic Inhibited Proliferation and Promoted Apoptosis in CRC Cells by Regulating Apoptosis-Related Genes, Which Was Partially Reversed by Overexpressed STAT3 The following cell experiments showed that by activating Bax and blocking the protein activity of Bcl-2, miR-296-5p mimic inhibited proliferation yet accelerated apoptosis in cancer cells (p<0.001, Figure 8A–I). However, up-regulation of STAT3 produced a completely opposite regulatory effect (p<0.001, Figure 8A–I). More importantly, overexpression of STAT3 neutralized the regulation of miR-296-5p mimic on CRC cells (p<0.001, Figure 8A–I).Figure 8MiR-296-5p mimic inhibited proliferation and promoted apoptosis in CRC cells by regulating apoptosis-related genes, which was partially reversed by overexpression of STAT3. (A–C) The clone formation experiment showed that the inhibitory effect of miR-296-5p mimic on the proliferation of SW480 and HT29 cells was neutralized by overexpressed STAT3. (D–F) Flow cytometry experiments showed that miR-296-5p mimic promoted the apoptosis of SW480 and HT29 cells, which was neutralized by overexpressed STAT3. (G–I) Western blot experiments showed that the regulation of apoptosis-related protein expression by miR-296-5p mimic was neutralized by overexpressed STAT3. GAPDH played the role of internal reference. All experiments were repeated three times to obtain average values. *p<0.05, **p<0.01, ***p<0.001 vs MC+NC; ^^p<0.01, ^^^p<0.001 vs M+NC; ##p<0.01, ###p<0.001 vs MC+STAT3.\\nMiR-296-5p mimic inhibited proliferation and promoted apoptosis in CRC cells by regulating apoptosis-related genes, which was partially reversed by overexpression of STAT3. (A–C) The clone formation experiment showed that the inhibitory effect of miR-296-5p mimic on the proliferation of SW480 and HT29 cells was neutralized by overexpressed STAT3. (D–F) Flow cytometry experiments showed that miR-296-5p mimic promoted the apoptosis of SW480 and HT29 cells, which was neutralized by overexpressed STAT3. (G–I) Western blot experiments showed that the regulation of apoptosis-related protein expression by miR-296-5p mimic was neutralized by overexpressed STAT3. GAPDH played the role of internal reference. All experiments were repeated three times to obtain average values. *p<0.05, **p<0.01, ***p<0.001 vs MC+NC; ^^p<0.01, ^^^p<0.001 vs M+NC; ##p<0.01, ###p<0.001 vs MC+STAT3.\\nThe following cell experiments showed that by activating Bax and blocking the protein activity of Bcl-2, miR-296-5p mimic inhibited proliferation yet accelerated apoptosis in cancer cells (p<0.001, Figure 8A–I). However, up-regulation of STAT3 produced a completely opposite regulatory effect (p<0.001, Figure 8A–I). More importantly, overexpression of STAT3 neutralized the regulation of miR-296-5p mimic on CRC cells (p<0.001, Figure 8A–I).Figure 8MiR-296-5p mimic inhibited proliferation and promoted apoptosis in CRC cells by regulating apoptosis-related genes, which was partially reversed by overexpression of STAT3. (A–C) The clone formation experiment showed that the inhibitory effect of miR-296-5p mimic on the proliferation of SW480 and HT29 cells was neutralized by overexpressed STAT3. (D–F) Flow cytometry experiments showed that miR-296-5p mimic promoted the apoptosis of SW480 and HT29 cells, which was neutralized by overexpressed STAT3. (G–I) Western blot experiments showed that the regulation of apoptosis-related protein expression by miR-296-5p mimic was neutralized by overexpressed STAT3. GAPDH played the role of internal reference. All experiments were repeated three times to obtain average values. *p<0.05, **p<0.01, ***p<0.001 vs MC+NC; ^^p<0.01, ^^^p<0.001 vs M+NC; ##p<0.01, ###p<0.001 vs MC+STAT3.\\nMiR-296-5p mimic inhibited proliferation and promoted apoptosis in CRC cells by regulating apoptosis-related genes, which was partially reversed by overexpression of STAT3. (A–C) The clone formation experiment showed that the inhibitory effect of miR-296-5p mimic on the proliferation of SW480 and HT29 cells was neutralized by overexpressed STAT3. (D–F) Flow cytometry experiments showed that miR-296-5p mimic promoted the apoptosis of SW480 and HT29 cells, which was neutralized by overexpressed STAT3. (G–I) Western blot experiments showed that the regulation of apoptosis-related protein expression by miR-296-5p mimic was neutralized by overexpressed STAT3. GAPDH played the role of internal reference. All experiments were repeated three times to obtain average values. *p<0.05, **p<0.01, ***p<0.001 vs MC+NC; ^^p<0.01, ^^^p<0.001 vs M+NC; ##p<0.01, ###p<0.001 vs MC+STAT3.\",\n \"In this study, the toxicity of ALO in normal cells (CCD-18Co) was detected, ALO showed no significant toxicity to CCD-18Co cells (Figure 1A). In order to study the effects of ALO on the growth and basic physiological functions of CRC cells, we used different concentrations of ALO to treat SW480 and HT29 cells. The results showed that 0.2 mmol/L, 0.4 mmol/L, 0.8 mmol/L and 1 mmol/L ALO solutions all inhibited the viability of SW480 and HT29 cells (p<0.05, Figure 1B and C). Moreover, the viability of SW480 and HT29 cells was close to 50% under the treatment of 0.8 mmol/L ALO solution, but was reduced to less than 50% when the ALO concentration was increased to 1 mmol/L (Figure 1B and C). In the following EdU cell proliferation experiment, the red fluorescence expression was reduced in SW480 and HT29 cells under the treatment of 0.8 mmol/L ALO (Figure 1D and E). Consistently, the clone formation experiment also showed that different concentrations of ALO (0.2 mmol/L, 0.4 mmol/L, 0.8 mmol/L) reduced the number of cell clones and inhibited the proliferation of SW480 and HT29 cells (p<0.05, Figure 1F–H). At the same time, the increase in the concentration of the ALO solution accelerated the death of CRC cells. As shown in Figure 1I–K, the number of apoptotic SW480 and HT29 cells increased significantly after treatment with different concentrations of ALO (p<0.01, Figure 1I–K).Figure 1ALO at a concentration gradient inhibited proliferation yet promoted apoptosis in CRC cells. (A) The effect of ALO on the normal colonic tissue cells (CCD-18Co) was detected by MTT experiments. (B and C) MTT experiments showed that ALO at a concentration gradient (0.1 mmol/L, 0.2 mmol/L, 0.4 mmol/L, 0.8 mmol/L and 1 mmol/L) inhibited the activity of SW480 and HT29 cells. (D and E) EdU cell proliferation experiments showed that 0.8 mmol/L ALO inhibited the proliferation of SW480 and HT29 cells. The magnification was 100×. (F–H) The clone formation experiment showed that ALO at a concentration gradient (0.2 mmol/L, 0.4 mmol/L and 0.8 mmol/L) inhibited the proliferation of SW480 and HT29 cells. (I–K) Flow cytometry experiments showed that ALO at a concentration gradient (0.2 mmol/L, 0.4 mmol/L and 0.8 mmol/L) promoted the apoptosis of SW480 and HT29 cells. All experiments were repeated three times to obtain average values. *p<0.05, **p<0.01, ***p<0.001 vs Control.Abbreviations: ALO, aloperine; CRC, colorectal cancer.\\nALO at a concentration gradient inhibited proliferation yet promoted apoptosis in CRC cells. (A) The effect of ALO on the normal colonic tissue cells (CCD-18Co) was detected by MTT experiments. (B and C) MTT experiments showed that ALO at a concentration gradient (0.1 mmol/L, 0.2 mmol/L, 0.4 mmol/L, 0.8 mmol/L and 1 mmol/L) inhibited the activity of SW480 and HT29 cells. (D and E) EdU cell proliferation experiments showed that 0.8 mmol/L ALO inhibited the proliferation of SW480 and HT29 cells. The magnification was 100×. (F–H) The clone formation experiment showed that ALO at a concentration gradient (0.2 mmol/L, 0.4 mmol/L and 0.8 mmol/L) inhibited the proliferation of SW480 and HT29 cells. (I–K) Flow cytometry experiments showed that ALO at a concentration gradient (0.2 mmol/L, 0.4 mmol/L and 0.8 mmol/L) promoted the apoptosis of SW480 and HT29 cells. All experiments were repeated three times to obtain average values. *p<0.05, **p<0.01, ***p<0.001 vs Control.\",\n \"Data from the Starbase database showed that the expression of miR-296-5p in colon cancer patients was significantly lower than that in healthy people (p=2.5e-6, Figure 2A). In this study, however, after treatment with different concentrations of ALO for 24 h, miR-296-5p activity in SW480 and HT29 cells was significantly increased (p<0.01, Figure 2B and C), suggesting that ALO can stimulate the up-regulation of miR-296-5p.Figure 2The abnormally low expression of miR-296-5p in colon cancer could be upregulated by ALO. (A) Starbase (http://starbase.sysu.edu.cn/index.php) was used to retrieve the information of differential expression of miR-296-5p in colon adenocarcinoma (COAD, n=450) patients and healthy volunteers (n=8) in vivo. P=2.5e-6. (B and C) The qRT-PCR experiment showed that ALO at a concentration gradient (0.1 mmol/L, 0.2 mmol/L, 0.4 mmol/L, 0.8 mmol/L and 1 mmol/L) up-regulated miR-296-5p expression in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. **p<0.01, ***p<0.001 vs Control.Abbreviation: qRT-PCR, quantitative real-time polymerase chain reaction.\\nThe abnormally low expression of miR-296-5p in colon cancer could be upregulated by ALO. (A) Starbase (http://starbase.sysu.edu.cn/index.php) was used to retrieve the information of differential expression of miR-296-5p in colon adenocarcinoma (COAD, n=450) patients and healthy volunteers (n=8) in vivo. P=2.5e-6. (B and C) The qRT-PCR experiment showed that ALO at a concentration gradient (0.1 mmol/L, 0.2 mmol/L, 0.4 mmol/L, 0.8 mmol/L and 1 mmol/L) up-regulated miR-296-5p expression in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. **p<0.01, ***p<0.001 vs Control.\",\n \"Through literature review, we screened 12 circRNAs that were reported to be associated with CRC: circDDX17, circHIPK3, circZFR, circRNA_100290, Has_circ_0026344, circVAPA, Hsa_circ_0009361, circNSUN2, circACAP2, circACVRL1, circITGA7 and circEIF4G3. We first detected the regulatory effect of ALO on these differentially expressed circRNAs by qRT-PCR to perform preliminary screening (Figure 3A). The results showed that the expressions of circDDX17, Has_circ_0026344, circVAPA, circNSUN2, circACAP2, circACVRL1 and circEIF4G3 were regulated by 0.8 mmol/L ALO (p<0.001, Figure 3A). Through subsequent screening of target genes, we found that only circNSUN2 and miR-296-5p have targeted binding sites (Figure 3B). After further verification, it was confirmed that circNSUN2-WT and miR-296-5p mimic did reduce the luciferase activity in SW480 and HT29 cells (p<0.001, Figure 3C and D). Therefore, we chose circNSUN2 as the next research object.Figure 3Among the differentially expressed circRNAs in CRC, only circNSUN2 not only had a target site for miR-296-5p, but also could be regulated by ALO. (A) qRT-PCR detected the regulation of 0.8 mmol/L ALO on the differentially expressed circRNAs in CRC. GAPDH played the role of internal reference. (B) The TargetScan database (http://www.targetscan.org) was used to predict the target sites of circNSUN2 and miR-296-5p. (C and D) The dual luciferase experiment confirmed that circNSUN2 can bind to miR-296-5p in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. ***p<0.001 vs Control; +++p<0.001 vs MC.Abbreviation: MC, miR-296-5p mimic control.\\nAmong the differentially expressed circRNAs in CRC, only circNSUN2 not only had a target site for miR-296-5p, but also could be regulated by ALO. (A) qRT-PCR detected the regulation of 0.8 mmol/L ALO on the differentially expressed circRNAs in CRC. GAPDH played the role of internal reference. (B) The TargetScan database (http://www.targetscan.org) was used to predict the target sites of circNSUN2 and miR-296-5p. (C and D) The dual luciferase experiment confirmed that circNSUN2 can bind to miR-296-5p in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. ***p<0.001 vs Control; +++p<0.001 vs MC.\",\n \"In SW480 and HT29 cells, ALO suppressed the mRNA level of circNSUN2, whereas transfection of circNSUN2 overexpression significantly increased the expression of circNSUN2 (p<0.001, Figure 4A and B). The following basic cell function experiments showed that the upregulation of circNSUN2 partially offset the inhibitory effect of ALO on CRC cell viability, proliferation and apoptosis (p<0.001, Figure 4C–L).Figure 4Overexpression of circNSUN2 partially offset the suppression of ALO on the biological behavior of cancer cells. (A and B) The qRT-PCR experiment showed that ALO (0.8 mmol/L) inhibited the expression of circNSUN2 in SW480 and HT29 cells. GAPDH played the role of internal reference. (C and D) The MTT experiment showed that overexpression of circNSUN2 partially offset the inhibition of ALO on cell viability in SW480 and HT29 cells. (E and F) EdU cell proliferation experiments showed that overexpression of circNSUN2 partially offset the inhibition of ALO on proliferation in SW480 and HT29 cells. The magnification was 100×. (G–I) The clone formation experiment showed that overexpression of circNSUN2 partially offset the inhibition of ALO on proliferation in SW480 and HT29 cells. (J–L) Flow cytometry experiments showed that overexpression of circNSUN2 partially offset the promotion of ALO on apoptosis in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. ***p<0.001 vs Control; ^p<0.05, ^^p<0.01, ^^^p<0.001 vs ALO+NC.Abbreviations: CircNSUN2, NOP2/Sun RNA methyltransferase 2; NC, negative control.\\nOverexpression of circNSUN2 partially offset the suppression of ALO on the biological behavior of cancer cells. (A and B) The qRT-PCR experiment showed that ALO (0.8 mmol/L) inhibited the expression of circNSUN2 in SW480 and HT29 cells. GAPDH played the role of internal reference. (C and D) The MTT experiment showed that overexpression of circNSUN2 partially offset the inhibition of ALO on cell viability in SW480 and HT29 cells. (E and F) EdU cell proliferation experiments showed that overexpression of circNSUN2 partially offset the inhibition of ALO on proliferation in SW480 and HT29 cells. The magnification was 100×. (G–I) The clone formation experiment showed that overexpression of circNSUN2 partially offset the inhibition of ALO on proliferation in SW480 and HT29 cells. (J–L) Flow cytometry experiments showed that overexpression of circNSUN2 partially offset the promotion of ALO on apoptosis in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. ***p<0.001 vs Control; ^p<0.05, ^^p<0.01, ^^^p<0.001 vs ALO+NC.\",\n \"We also tested the mutual regulation of circNSUN2 and miR-296-5p in SW480 and HT29 cells. Sh-circNSUN2 could down-regulate the expression of circNSUN2, and besides, it inhibited the proliferation of SW480 and HT29 cells, while promoting their apoptosis and the expression of miR-296-5p (p<0.001, Figure 5A–J). However, the inhibitory effect of sh-circNSUN2 on the malignant development of the two cancer cells was neutralized by miR-296-5p inhibitor (p<0.001, Figure 5A–J).Figure 5Sh-circNSUN2 inhibited the malignant development of CRC cells, which was neutralized by miR-296-5p inhibitor. (A and B) The qRT-PCR experiment showed that miR-296-5p inhibitor neutralized the inhibitory effect of sh-circNSUN2 on circNSUN2 expression. GAPDH played the role of internal reference. (C and D) The qRT-PCR experiment showed that sh-circNSUN2 up-regulated the mRNA level of miR-296-5p. U6 played the role of internal reference. (E–G) The clone formation experiment showed that the effect of sh-circNSUN2 on the proliferation of SW480 and HT29 cells was neutralized by miR-296-5p inhibitor. (H-J) Flow cytometry experiments showed that the effect of sh-circNSUN2 on promoting the apoptosis of SW480 and HT29 cells was neutralized by miR-296-5p inhibitor. All experiments were repeated three times to obtain average values. ***p<0.001 vs sh-NC+IC; ^^^p<0.001 vs sh-circNSUN2+IC; ###p<0.001 vs sh-NC+I.Abbreviations: Sh-circNSUN2, silent circNSUN2; sh-NC, silent negative control; IC, miR-296-5p inhibitor control; I, miR-296-5p inhibitor.\\nSh-circNSUN2 inhibited the malignant development of CRC cells, which was neutralized by miR-296-5p inhibitor. (A and B) The qRT-PCR experiment showed that miR-296-5p inhibitor neutralized the inhibitory effect of sh-circNSUN2 on circNSUN2 expression. GAPDH played the role of internal reference. (C and D) The qRT-PCR experiment showed that sh-circNSUN2 up-regulated the mRNA level of miR-296-5p. U6 played the role of internal reference. (E–G) The clone formation experiment showed that the effect of sh-circNSUN2 on the proliferation of SW480 and HT29 cells was neutralized by miR-296-5p inhibitor. (H-J) Flow cytometry experiments showed that the effect of sh-circNSUN2 on promoting the apoptosis of SW480 and HT29 cells was neutralized by miR-296-5p inhibitor. All experiments were repeated three times to obtain average values. ***p<0.001 vs sh-NC+IC; ^^^p<0.001 vs sh-circNSUN2+IC; ###p<0.001 vs sh-NC+I.\",\n \"We screened the target genes of miR-296-5p, and found that miR-296-5p and STAT3 have targeted binding sites (Figure 6A), and this binding relation was further confirmed by a dual-luciferase verification test (p<0.001, Figure 6B and C). Next, we used qRT-PCR and Western blot to analyze the mRNA and protein levels of miR-296-5p and STAT. It was found that miR-296-5p mimic up-regulated miR-296-5p expression while preventing the activation of STAT3 (p<0.001, Figure 7A–G). However, the overexpression of STAT3 reversed the regulation of miR-296-5p mimic on the two genes (p<0.001, Figure 7A–G).Figure 6The targeted binding of miR-296-5p to STAT3. (A) TargetScan was used to predict the binding sequence of miR-296-5p and STAT3. (B and C) Dual luciferase experiment confirmed that miR-296-5p can bind to STAT3 in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. +++p<0.001 vs MC.Figure 7Up-regulation of miR-296-5p inhibited the expression of STAT3 in CRC cells. (A and B) The qRT-PCR experiment showed that miR-296-5p mimic up-regulated the mRNA level of miR-296-5p. U6 played the role of internal reference. (C and D) The qRT-PCR experiment showed that miR-296-5p mimic suppressed the mRNA level of STAT3, while overexpression of STAT3 reversed this effect. GAPDH played the role of internal reference. (E–G) The Western blot experiment showed that miR-296-5p mimic inhibited the protein level of STAT3, while over-expression of STAT3 reversed this effect. GAPDH played the role of internal reference. All experiments were repeated three times to obtain average values. ***p<0.001 vs MC+NC; ^^^p<0.001 vs M+NC; ###p<0.001 vs MC+STAT3.Abbreviation: STAT3, signal transducer and activator of transcription 3.\\nThe targeted binding of miR-296-5p to STAT3. (A) TargetScan was used to predict the binding sequence of miR-296-5p and STAT3. (B and C) Dual luciferase experiment confirmed that miR-296-5p can bind to STAT3 in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. +++p<0.001 vs MC.\\nUp-regulation of miR-296-5p inhibited the expression of STAT3 in CRC cells. (A and B) The qRT-PCR experiment showed that miR-296-5p mimic up-regulated the mRNA level of miR-296-5p. U6 played the role of internal reference. (C and D) The qRT-PCR experiment showed that miR-296-5p mimic suppressed the mRNA level of STAT3, while overexpression of STAT3 reversed this effect. GAPDH played the role of internal reference. (E–G) The Western blot experiment showed that miR-296-5p mimic inhibited the protein level of STAT3, while over-expression of STAT3 reversed this effect. GAPDH played the role of internal reference. All experiments were repeated three times to obtain average values. ***p<0.001 vs MC+NC; ^^^p<0.001 vs M+NC; ###p<0.001 vs MC+STAT3.\",\n \"The following cell experiments showed that by activating Bax and blocking the protein activity of Bcl-2, miR-296-5p mimic inhibited proliferation yet accelerated apoptosis in cancer cells (p<0.001, Figure 8A–I). However, up-regulation of STAT3 produced a completely opposite regulatory effect (p<0.001, Figure 8A–I). More importantly, overexpression of STAT3 neutralized the regulation of miR-296-5p mimic on CRC cells (p<0.001, Figure 8A–I).Figure 8MiR-296-5p mimic inhibited proliferation and promoted apoptosis in CRC cells by regulating apoptosis-related genes, which was partially reversed by overexpression of STAT3. (A–C) The clone formation experiment showed that the inhibitory effect of miR-296-5p mimic on the proliferation of SW480 and HT29 cells was neutralized by overexpressed STAT3. (D–F) Flow cytometry experiments showed that miR-296-5p mimic promoted the apoptosis of SW480 and HT29 cells, which was neutralized by overexpressed STAT3. (G–I) Western blot experiments showed that the regulation of apoptosis-related protein expression by miR-296-5p mimic was neutralized by overexpressed STAT3. GAPDH played the role of internal reference. All experiments were repeated three times to obtain average values. *p<0.05, **p<0.01, ***p<0.001 vs MC+NC; ^^p<0.01, ^^^p<0.001 vs M+NC; ##p<0.01, ###p<0.001 vs MC+STAT3.\\nMiR-296-5p mimic inhibited proliferation and promoted apoptosis in CRC cells by regulating apoptosis-related genes, which was partially reversed by overexpression of STAT3. (A–C) The clone formation experiment showed that the inhibitory effect of miR-296-5p mimic on the proliferation of SW480 and HT29 cells was neutralized by overexpressed STAT3. (D–F) Flow cytometry experiments showed that miR-296-5p mimic promoted the apoptosis of SW480 and HT29 cells, which was neutralized by overexpressed STAT3. (G–I) Western blot experiments showed that the regulation of apoptosis-related protein expression by miR-296-5p mimic was neutralized by overexpressed STAT3. GAPDH played the role of internal reference. All experiments were repeated three times to obtain average values. *p<0.05, **p<0.01, ***p<0.001 vs MC+NC; ^^p<0.01, ^^^p<0.001 vs M+NC; ##p<0.01, ###p<0.001 vs MC+STAT3.\",\n \"CRC is a malignant lesion of the mucosal epithelium of the colon or rectum under the action of various carcinogenic factors such as environment or heredity.2 Most CRC patients suffer from adenocarcinoma, which usually develops from polyps. Tumors developed from polyps can infiltrate in a circular shape along the horizontal axis of the intestinal tube, develop into the deep layer of the intestinal wall, and finally penetrate the intestinal wall and metastasize to blood vessels or lymphatic vessels.20 This is one of the important reasons for the high recurrence and metastasis rates of CRC. The ultimate goals of CRC research are to improve the current treatment of CRC, save patients’ lives and improve their quality of life. Based on the role of traditional Chinese medicine in disease prevention and treatment, this study explored the effects of ALO, an alkaloid exhibiting strong anticancer activity, on the proliferation and apoptosis of CRC cells. The results showed that after ALO treatment, the viability and proliferation of CRC cells were significantly inhibited; on the contrary, the number of apoptotic cancer cells was significantly increased. This is consistent with the previous experimental results.21 In order to probe deeper into the upstream mechanism of ALO in alleviating CRC carcinogenesis through the miR-296-5p/STAT3 axis, we screened and verified the upstream targeting circRNA of miR-296-5p.\\nCircRNAs are a type of covalently closed circular non-coding RNA formed by back-splicing of mRNA precursors (pre-mRNA).22 CircRNA was considered to be a useless splicing by-product in early research. With the deepening of research, it is found that circRNA has a wide range of sources and plays a variety of functional roles in the growth and development of organisms, with the characteristics of conservative, stable, and tissue-specific.22,23 The most familiar and extensively studied function of circRNA is its sponging effect on miRNA.24 For example, circRNA ciRS-7 which contains more than 70 miR-7 binding sites can achieve competitive adsorption of miR-7 through the AGO2 protein.25 Besides, increasing reports on cancer indicate that circRNAs, such as circRNA-cTFRC, circPSMC3 and circSETD3, act as sponges of miRNAs and participate in transcriptional regulation.26–28 Based on these literature reports, we screened circRNAs that were reported to be abnormally expressed in CRC, and finally obtained 12 circRNAs. After detecting their expressions in ALO-treated cancer cells and predicting their binding sequence for miR-296-5p, circNSUN2 was finally identified as the research object of this study.\\nCircNSUN2, which maps to the 5p15 amplicon in CRCs, was confirmed by Chen et al to regulate cytoplasmic output and promote liver metastasis of CRC through N 6-methyladenosine modification.29 Similarly, our experimental results uncovered that knockdown of circNSUN2 inhibited proliferation and accelerated apoptosis in CRC cells. More importantly, we for the first time revealed that ALO can prevent the activation of circNSUN2 and offset the promotion effect of overexpression of circNSUN2 on esophageal cancer progression. Since circNSUN2 and miR-296-5p have targeting sequences, we verified the cellular regulatory effects of circNSUN2 and miR-296-5p/STAT3 through dual luciferase experiments and rescue experiments. The final result confirmed our conjecture that regulating the circNSUN2/miR-296-5p/STAT3 axis can reduce the proliferation rate and increase the apoptosis rate of CRC cells.\\nAt present, blocking proliferation and increasing apoptosis in cancer cells are the intensively discussed mechanisms in CRC study. Tumor cells can proliferate quickly and unrestrictedly, and thus, inhibiting their proliferation can produce an anti-tumor effect.30 Apoptosis, alternatively called programmed cell death, is an autonomous cell death process strictly controlled by multiple genes. Apoptosis is an important part of cell life cycle, and it is also an important link in regulating the development of the body and maintaining the stability of the internal environment in organisms.31,32 Our research shows that ALO inhibits the proliferation and promotes the apoptosis of CRC cells by regulating the circNSUN2/miR-296-5p/STAT3 pathway, and ultimately prevents the tumorigenesis of CRC.\\nThere are still certain limitations in our research. Although we have confirmed the effect of ALO on inducing apoptosis and reducing proliferation in CRC cells and clarified the underlying mechanism, the circNSUN2/miR-296-5p/STAT3 axis and the pathways related to proliferation and apoptosis have not been analyzed yet, which will be further explored in future research. In addition, the anticancer effect of ALO on CRC and its experimental concentration needs to be further tested in animal and clinical experiments. Whether ALO has an impact on drug resistance in CRC is also a direction of future research.\"\n]"},"all_sections_sec_types":{"kind":"list like","value":["intro",null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null],"string":"[\n \"intro\",\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null\n]"},"keywords":{"kind":"list like","value":["colorectal cancer","aloperine","NOP2/Sun RNA methyltransferase 2","miR-296-5p","signal transducer and activator of transcription 3"],"string":"[\n \"colorectal cancer\",\n \"aloperine\",\n \"NOP2/Sun RNA methyltransferase 2\",\n \"miR-296-5p\",\n \"signal transducer and activator of transcription 3\"\n]"},"whole_article_text":{"kind":"string","value":"Introduction:\nCancer has become the first killer in the world and the biggest obstacle for extending human life expectancy. It is estimated that there were 18.1 million new cancer cases and 9.6 million cancer deaths in 2018.1 Colorectal cancer (CRC) is one of the most common gastrointestinal tumors.2 According to the data from the International Agency for Research on Cancer (IARC),1 the number of new CRC cases worldwide in 2018 was approximately 1.09 million, making CRC the fourth most prevalent malignant tumor after lung, breast and prostate cancers; meantime, CRC had a high mortality rate of about 9.2%, ranking only after lung cancer. Epidemiological studies have shown that the incidence of CRC varies significantly in different countries, and is higher in developed areas.3 More importantly, as the incidence of CRC rises rapidly in people under 50, the age of onset of the disease is getting lower.4,5 Therefore, it is urgent to improve the diagnosis rate and treatment effect of CRC.\nCurrently, the main clinical treatment of CRC is surgery combined with adjuvant radiotherapy, chemotherapy or molecular targeted therapy. Surgery is generally considered as the first choice for comprehensive treatment of CRC. This method is suitable for patients whose tumors are confined to the intestinal wall while penetrating the intestinal wall and invading the serous or extraserous membrane without lymph node metastasis.6,7 However, the availability of radical resection is limited as most patients with CRC have adenocarcinoma, which generally develops from polyps and is metastatic by nature.8 As a result, CRC patients tend to undergo simple resection, which leads to a high recurrence rate. Chemotherapy, therefore, is still necessary for postoperative and late-stage CRC patients.9,10 At present, the commonly used chemotherapeutic drugs in clinic mainly include 5-fluorouracil (5-Fu), oxaliplatin and its derivatives.9 Chemotherapy is highly risky because it indiscriminately kills tumor cells and immune cells and thereby reduces the anti-tumor immune effect.11 Likewise, radiotherapy greatly weakens the body’s immunity.12 Hence, researchers have proposed to find effective treatments or drugs that cause less damage to patients.\nWith advances in the research and development of traditional Chinese medicine, more attention has been paid to the roles of traditional Chinese medicine and its active ingredients in disease prevention and treatment. At the same time, new targets for molecular targeted therapy of diseases have been discovered in the exploration of the molecular mechanisms of traditional Chinese medicine and its monomers. Aloperine (ALO) has been confirmed to have a significant anti-cancer activity. ALO is a component of the traditional Chinese medicine Sophora alopecuroides L.13 It is also one of the main alkaloids separated and extracted in the lab, with a molecular formula of C15H24N2.14 Recent studies have found that ALO has the effects of anti-inflammation, immunosuppression, redox suppression, cardiovascular protection and anti-cancer, among which its tumor-suppressing activity has been extensively reported. For example, Yu et al revealed that ALO inhibited the carcinogenesis process by regulating excessive autophagy in thyroid cancer cells;15 Liu et al demonstrated that ALO inhibited the PI3K/Akt pathway, increased apoptosis and caused cell cycle block in liver cancer cells;16 besides, ALO was also found to exert an anti-cancer effect in prostate cancer and breast cancer.17,18 In the previous study, our research group found that ALO up-regulated miR-296-5p and inhibited the activity of its target gene STAT3, thereby inhibiting the proliferation and inducing the apoptosis of CRC cells. However, the activation pathway of miR-296-5p is unclear. In order to clarify the upstream activation pathway of ALO-upregulated miR-296-5p, this study will explore the mechanism of ALO in inhibiting CRC from the perspective of the circRNA-miRNA-mRNA regulatory network.\n\nMethod:\nCell Purchase and ALO Processing CCD-18Co and CRC cell lines SW480 (CCL-228™) and HT29 (HTB-38™) used in this research were provided by the American Type Culture Collection (ATCC). According to the culture requirements, CCD-18Co, SW480 and HT29 cells were separately inoculated in DMEM medium (30–2007, ATCC) containing 10% fetal bovine serum and incubated in a D180-P cell incubator (RWD, China) containing 5% CO2 at 37 °C.\nALO (DK0052, CAS NO: 56293-29-9, HPLC≥98%), the research object of this study, was provided by Chengdu DESITE Biological Company, China. ALO was dissolved in fresh DMEM to prepare 0.1 mmol/L, 0.2 mmol/L, 0.4 mmol/L, 0.8 mmol/L and 1 mmol/L ALO solutions, which were separately used to treat the CCD-18Co, SW480 and HT29 cells for 24 hours (h).\nCCD-18Co and CRC cell lines SW480 (CCL-228™) and HT29 (HTB-38™) used in this research were provided by the American Type Culture Collection (ATCC). According to the culture requirements, CCD-18Co, SW480 and HT29 cells were separately inoculated in DMEM medium (30–2007, ATCC) containing 10% fetal bovine serum and incubated in a D180-P cell incubator (RWD, China) containing 5% CO2 at 37 °C.\nALO (DK0052, CAS NO: 56293-29-9, HPLC≥98%), the research object of this study, was provided by Chengdu DESITE Biological Company, China. ALO was dissolved in fresh DMEM to prepare 0.1 mmol/L, 0.2 mmol/L, 0.4 mmol/L, 0.8 mmol/L and 1 mmol/L ALO solutions, which were separately used to treat the CCD-18Co, SW480 and HT29 cells for 24 hours (h).\nMTT Assay One hundred µL of SW480 or HT29 cell suspension (1×104 cells/mL) was transferred to each well of a 96-well plate. Of note, three holes were added to the same treatment group. After treatment with the ALO solutions for 24 h, SW480 or HT29 cells were added with MTT reagent (10 µL/well, PB180519, Procell, USA), which could react with mitochondria in living cells. After 4 h of binding, a HBS-1096A enzyme label analyzer (DeTie, China) was used to detect the absorbance of SW480 or HT29 cells in the mixed solutions at 490 nm, and then cell viability was calculated based on the absorbance.\nOne hundred µL of SW480 or HT29 cell suspension (1×104 cells/mL) was transferred to each well of a 96-well plate. Of note, three holes were added to the same treatment group. After treatment with the ALO solutions for 24 h, SW480 or HT29 cells were added with MTT reagent (10 µL/well, PB180519, Procell, USA), which could react with mitochondria in living cells. After 4 h of binding, a HBS-1096A enzyme label analyzer (DeTie, China) was used to detect the absorbance of SW480 or HT29 cells in the mixed solutions at 490 nm, and then cell viability was calculated based on the absorbance.\nEdU (5-Ethynyl-2ʹ-Deoxyuridine) Cell Proliferation Experiment SW480 or HT29 cells in logarithmic growth phase were seeded in 96-well plates at 1×104 cells/well. EdU reagent (ST067) purchased from Beyotime was diluted to 50 μmol/L with fresh DMEM medium. The EdU dilution was then used to incubate the cells at 100 μL/well for 2 h. After removing the culture medium, the SW480 or HT29 cells were fixed with methanol at room temperature for 15 minutes (min). Positive fluorescence expression (red) in the collected SW480 or HT29 cells were observed, photographed and recorded under a Leica DMi8 inverted fluorescence microscope (magnification 100 ×). Then DAPI reagent was used to stain the nuclei, and the stains were observed under a microscope.\nSW480 or HT29 cells in logarithmic growth phase were seeded in 96-well plates at 1×104 cells/well. EdU reagent (ST067) purchased from Beyotime was diluted to 50 μmol/L with fresh DMEM medium. The EdU dilution was then used to incubate the cells at 100 μL/well for 2 h. After removing the culture medium, the SW480 or HT29 cells were fixed with methanol at room temperature for 15 minutes (min). Positive fluorescence expression (red) in the collected SW480 or HT29 cells were observed, photographed and recorded under a Leica DMi8 inverted fluorescence microscope (magnification 100 ×). Then DAPI reagent was used to stain the nuclei, and the stains were observed under a microscope.\nColony Formation Assay Two hundred SW480 or HT29 cells were transferred to petri dishes containing complete medium (10 mL). In order to distribute the cells evenly, the petri dishes were rotated slowly for 1 min after cell inoculation. Next, the culture dishes containing cells were placed in an incubator for routine culture. During the culture, the medium was changed every 2 days (d). After 14 d, the SW480 or HT29 cells were soaked with Giemsa dye for 20 min and then the colony formation of the cells was observed under a microscope.\nTwo hundred SW480 or HT29 cells were transferred to petri dishes containing complete medium (10 mL). In order to distribute the cells evenly, the petri dishes were rotated slowly for 1 min after cell inoculation. Next, the culture dishes containing cells were placed in an incubator for routine culture. During the culture, the medium was changed every 2 days (d). After 14 d, the SW480 or HT29 cells were soaked with Giemsa dye for 20 min and then the colony formation of the cells was observed under a microscope.\nApoptosis Experiment An Annexin V-FITC Apoptosis Detection Kit (CA1020, Solarbio, China) and a flow cytometer (CytoFLEX, BECKMAN COULTER, USA) were used to detect changes in the apoptosis of SW480 or HT29 cells. In brief, SW480 or HT29 cells were prepared into 1×106 cells/mL cell suspension with 1 mL 1× Binding Buffer. One hundred μL of SW480 or HT29 cells and 5 μL of Annexin V-FITC reagent were added to a centrifuge tube and mixed for 10 min in the dark at room temperature. Next, 5 μL of PI reagent was added to the centrifuge tube and incubated in the dark at room temperature for 5 min. Afterwards, PBS was added to adjust the mixture to a final volume of 500 μL. After half an hour, the cells in the centrifuge tube were transferred to the detection instrument to calculate the number of apoptotic cells.\nAn Annexin V-FITC Apoptosis Detection Kit (CA1020, Solarbio, China) and a flow cytometer (CytoFLEX, BECKMAN COULTER, USA) were used to detect changes in the apoptosis of SW480 or HT29 cells. In brief, SW480 or HT29 cells were prepared into 1×106 cells/mL cell suspension with 1 mL 1× Binding Buffer. One hundred μL of SW480 or HT29 cells and 5 μL of Annexin V-FITC reagent were added to a centrifuge tube and mixed for 10 min in the dark at room temperature. Next, 5 μL of PI reagent was added to the centrifuge tube and incubated in the dark at room temperature for 5 min. Afterwards, PBS was added to adjust the mixture to a final volume of 500 μL. After half an hour, the cells in the centrifuge tube were transferred to the detection instrument to calculate the number of apoptotic cells.\nBioinformatics Analysis and Target Gene Binding Verification Starbase (http://starbase.sysu.edu.cn/index.php) was used to retrieve and analyze information of differential expression of miR-296-5p in Colon adenocarcinoma (COAD) patients and healthy volunteers. TargetScan (http://www.targetscan.org) was used to predict the circRNA that contained a binding sequence for miR-296-5p and the downstream target genes of miR-296-5p. The binding sequences predicted by TargetScan were designed by COBIOER (China) and used to construct the following reporter plasmids for dual luciferase experiments: NOP2/Sun RNA methyltransferase 2 wild type (circNSUN2-WT), circNSUN2-mutant type (MUT), Signal Transducer and Activator of Transcription 3 (STAT3)-WT and STAT3-MUT. The reporter plasmids were separately co-transfected with miR-296-5p mimic or mimic control into SW480 and HT29 cells using liposome for 48 h. Luciferase activity in the transfected SW480 and HT29 cells was detected using Dual-Luciferase® Reporter Assay System (E1960, Promega, USA) with a GloMax 20/20 luminometer (Promega, USA).\nStarbase (http://starbase.sysu.edu.cn/index.php) was used to retrieve and analyze information of differential expression of miR-296-5p in Colon adenocarcinoma (COAD) patients and healthy volunteers. TargetScan (http://www.targetscan.org) was used to predict the circRNA that contained a binding sequence for miR-296-5p and the downstream target genes of miR-296-5p. The binding sequences predicted by TargetScan were designed by COBIOER (China) and used to construct the following reporter plasmids for dual luciferase experiments: NOP2/Sun RNA methyltransferase 2 wild type (circNSUN2-WT), circNSUN2-mutant type (MUT), Signal Transducer and Activator of Transcription 3 (STAT3)-WT and STAT3-MUT. The reporter plasmids were separately co-transfected with miR-296-5p mimic or mimic control into SW480 and HT29 cells using liposome for 48 h. Luciferase activity in the transfected SW480 and HT29 cells was detected using Dual-Luciferase® Reporter Assay System (E1960, Promega, USA) with a GloMax 20/20 luminometer (Promega, USA).\nQuantitative Real-Time Polymerase Chain Reaction (qRT-PCR) qRT-PCR is a reliable method for rapid detection of gene mRNA levels. Briefly, total RNA of SW480 and HT29 cells was fully isolated by the conventional RNA extraction reagent TRIzol (15,596,018, Invitrogen, USA), and subsequently reverse transcribed into cDNA using the RNA reverse transcription reagent (RR047A) developed by TaKaRa (Japan). The qRT-PCR reaction system which consisted of cDNA, gene primers (Sangon synthesis), SYBR® Green (S4438-20RXN, Sigma-Aldrich, Germany) and DEPC water was added to a 96-well plate and put into the detection instrument (Bio-Rad thermal cycler T100). The reaction conditions were set as follows: pre-denaturation at 95 °C for 10 min, denaturation at 95 °C for 15 seconds (s), and annealing at 58 °C for 1 min, for a total of 40 cycles. According to Anita Ciesielska’s report,19 gene mRNA levels were quantified by the 2−ΔΔCT method. GAPDH and U6 were internal references in the experiment. The specific sequences of synthetic primers were shown in Table 1.Table 1Primers for qRT-PCRGeneForward Primer (5ʹ-3ʹ)Reverse Primer (5ʹ-3ʹ)miR-296-5pCCTGTGTCGTATCCAGTGCAAGTCGTATCCAGTGCGTGTCGcircDDX17TGCCAACCACAACATCCTCCACGCTCCCCAGGATTACCAAATcircHIPK3TATGTTGGTGGATCCTGTTCGGCATGGTGGGTAGACCAAGACTTGTGAcircZFRAACCACCACAGATTCACTATAACCACCACAGATTCACTATcircRNA_100290GTCATTCCCTCTTTAATGGTGCAGAACTTCCGCTCTAACATACHas_circ_0026344CTCAGCCTCTAGCATAAGCTCAGGCAAGAGAATGATTTGAACcircVAPATGGATTCCAAATTGAGATGCGTATTCACTTTTCTATCCGATGGATTTCGCHsa_circ_0009361AGAACCAGATTCGAGACGCCGTGCTCTTCAATGCCACCTTCcircNSUN2CTTGAGAAAATCGCCACACTTGTTGAGGAGCAGTGGTGGcircACAP2TCAGAGCATCTGCCCAAAGTTAAATGAACCCCAAGGCTCCGcircITGA7GTGTGCACAGGTCCTTCCAATGGAAGTTCTGTGAGGGACGcircEIF4G3CCTACCCCATCCCCTTATTCACCGTGCTGTAGACTGCTGAGSTAT3CCCCATACCTGAAGACCAAGGGACTCAAACTGCCCTCCTU6CTCGCTTCGGCAGCACAAACGCTTCACGAATTTGCGTGAPDHTGTGGGCATCAATGGATTTGGACACCATGTATTCCGGGTCAAT\n\nPrimers for qRT-PCR\nqRT-PCR is a reliable method for rapid detection of gene mRNA levels. Briefly, total RNA of SW480 and HT29 cells was fully isolated by the conventional RNA extraction reagent TRIzol (15,596,018, Invitrogen, USA), and subsequently reverse transcribed into cDNA using the RNA reverse transcription reagent (RR047A) developed by TaKaRa (Japan). The qRT-PCR reaction system which consisted of cDNA, gene primers (Sangon synthesis), SYBR® Green (S4438-20RXN, Sigma-Aldrich, Germany) and DEPC water was added to a 96-well plate and put into the detection instrument (Bio-Rad thermal cycler T100). The reaction conditions were set as follows: pre-denaturation at 95 °C for 10 min, denaturation at 95 °C for 15 seconds (s), and annealing at 58 °C for 1 min, for a total of 40 cycles. According to Anita Ciesielska’s report,19 gene mRNA levels were quantified by the 2−ΔΔCT method. GAPDH and U6 were internal references in the experiment. The specific sequences of synthetic primers were shown in Table 1.Table 1Primers for qRT-PCRGeneForward Primer (5ʹ-3ʹ)Reverse Primer (5ʹ-3ʹ)miR-296-5pCCTGTGTCGTATCCAGTGCAAGTCGTATCCAGTGCGTGTCGcircDDX17TGCCAACCACAACATCCTCCACGCTCCCCAGGATTACCAAATcircHIPK3TATGTTGGTGGATCCTGTTCGGCATGGTGGGTAGACCAAGACTTGTGAcircZFRAACCACCACAGATTCACTATAACCACCACAGATTCACTATcircRNA_100290GTCATTCCCTCTTTAATGGTGCAGAACTTCCGCTCTAACATACHas_circ_0026344CTCAGCCTCTAGCATAAGCTCAGGCAAGAGAATGATTTGAACcircVAPATGGATTCCAAATTGAGATGCGTATTCACTTTTCTATCCGATGGATTTCGCHsa_circ_0009361AGAACCAGATTCGAGACGCCGTGCTCTTCAATGCCACCTTCcircNSUN2CTTGAGAAAATCGCCACACTTGTTGAGGAGCAGTGGTGGcircACAP2TCAGAGCATCTGCCCAAAGTTAAATGAACCCCAAGGCTCCGcircITGA7GTGTGCACAGGTCCTTCCAATGGAAGTTCTGTGAGGGACGcircEIF4G3CCTACCCCATCCCCTTATTCACCGTGCTGTAGACTGCTGAGSTAT3CCCCATACCTGAAGACCAAGGGACTCAAACTGCCCTCCTU6CTCGCTTCGGCAGCACAAACGCTTCACGAATTTGCGTGAPDHTGTGGGCATCAATGGATTTGGACACCATGTATTCCGGGTCAAT\n\nPrimers for qRT-PCR\nCell Transfection Exogenous up-regulation or knockdown of test genes is a common method to observe their regulatory effects on cells or tissues. Liposome transfection with Lipofectamine 3000 (L3000008, ThermoFisher Scientific, USA) is the most common and convenient method for exogenous interference. Therefore, we commissioned Guangzhou GENESEED Biological Company to synthesize circNSUN2 overexpressed plasmid, circNSUN2 silenced plasmid (sh-circNSUN2), STAT3 overexpressed plasmid and their respective negative controls. MiR-296-5p mimic (miR10000690-1-5) and mimic control (miR1N0000001-1-5), as well as miR-296-5p inhibitor (miR20000690-1-5) and inhibitor control (miR2N0000001-1-5), were purchased directly from Guangzhou RIOBOBIO Company. The plasmids were transfected into SW480 and HT29 cells according to the grouping using Lipofectamine 3000. After 48 h, the success rate of transfection of the cells was determined by qRT-PCR.\nExogenous up-regulation or knockdown of test genes is a common method to observe their regulatory effects on cells or tissues. Liposome transfection with Lipofectamine 3000 (L3000008, ThermoFisher Scientific, USA) is the most common and convenient method for exogenous interference. Therefore, we commissioned Guangzhou GENESEED Biological Company to synthesize circNSUN2 overexpressed plasmid, circNSUN2 silenced plasmid (sh-circNSUN2), STAT3 overexpressed plasmid and their respective negative controls. MiR-296-5p mimic (miR10000690-1-5) and mimic control (miR1N0000001-1-5), as well as miR-296-5p inhibitor (miR20000690-1-5) and inhibitor control (miR2N0000001-1-5), were purchased directly from Guangzhou RIOBOBIO Company. The plasmids were transfected into SW480 and HT29 cells according to the grouping using Lipofectamine 3000. After 48 h, the success rate of transfection of the cells was determined by qRT-PCR.\nWestern Blot RIPA lysate (tissue/cell, R0010) produced by Solarbio (China) was used to separate protein from SW480 and HT29 cells, and its concentration was determined with BCA protein concentration determination reagent (PC0020, Solarbio, China). Then the protein was subjected to high temperature denaturation to maintain a relatively stable state. After being electrophoresed by SDS-PAGE, the protein was transferred to the membrane carrier (PVDF membrane). The membrane was soaked with the same BSA sealant (SW3015) produced by Solarbio for 2 h at room temperature. Next, the membrane was incubated with Abcam (USA) antibodies (anti-STAT3, ab68153, 88 kDa; Bax, ab32503, 21 kDa; Bcl-2, ab59348, 26 kDa) that could bind to the corresponding antigens in the protein on the membrane overnight (4 °C), with GAPDH (ab8245, 36KD) as an internal reference. To identify the antigen-antibody complex, a secondary antibody labeled with HRP (Goat Anti-Rabbit antibody, 1:10,000, ab6721; Goat Anti-Mouse antibody, 1:10000, ab205719) was used to treat the PVDF membrane for 1.5 h at room temperature. Fluorescent signal of the complex was enhanced using the ECL Western Blotting Substrate (PE0010, Solarbio) and detected using the Gel Doc™ XR+ imaging system (BIO-RAD, USA). The gray value of the bands was calculated by Image Lab software to determine the final gene protein level.\nRIPA lysate (tissue/cell, R0010) produced by Solarbio (China) was used to separate protein from SW480 and HT29 cells, and its concentration was determined with BCA protein concentration determination reagent (PC0020, Solarbio, China). Then the protein was subjected to high temperature denaturation to maintain a relatively stable state. After being electrophoresed by SDS-PAGE, the protein was transferred to the membrane carrier (PVDF membrane). The membrane was soaked with the same BSA sealant (SW3015) produced by Solarbio for 2 h at room temperature. Next, the membrane was incubated with Abcam (USA) antibodies (anti-STAT3, ab68153, 88 kDa; Bax, ab32503, 21 kDa; Bcl-2, ab59348, 26 kDa) that could bind to the corresponding antigens in the protein on the membrane overnight (4 °C), with GAPDH (ab8245, 36KD) as an internal reference. To identify the antigen-antibody complex, a secondary antibody labeled with HRP (Goat Anti-Rabbit antibody, 1:10,000, ab6721; Goat Anti-Mouse antibody, 1:10000, ab205719) was used to treat the PVDF membrane for 1.5 h at room temperature. Fluorescent signal of the complex was enhanced using the ECL Western Blotting Substrate (PE0010, Solarbio) and detected using the Gel Doc™ XR+ imaging system (BIO-RAD, USA). The gray value of the bands was calculated by Image Lab software to determine the final gene protein level.\nStatistical Analysis SPSS 20.0 software (IBM, USA) was employed for statistical analysis of the data obtained in the experiments. Differences between two groups were compared by independent sample t-test, and those between multiple groups were compared by one-way ANOVA followed by Tukey’s test. When p<0.05, the difference was considered statistically significant.\nSPSS 20.0 software (IBM, USA) was employed for statistical analysis of the data obtained in the experiments. Differences between two groups were compared by independent sample t-test, and those between multiple groups were compared by one-way ANOVA followed by Tukey’s test. When p<0.05, the difference was considered statistically significant.\n\nCell Purchase and ALO Processing:\nCCD-18Co and CRC cell lines SW480 (CCL-228™) and HT29 (HTB-38™) used in this research were provided by the American Type Culture Collection (ATCC). According to the culture requirements, CCD-18Co, SW480 and HT29 cells were separately inoculated in DMEM medium (30–2007, ATCC) containing 10% fetal bovine serum and incubated in a D180-P cell incubator (RWD, China) containing 5% CO2 at 37 °C.\nALO (DK0052, CAS NO: 56293-29-9, HPLC≥98%), the research object of this study, was provided by Chengdu DESITE Biological Company, China. ALO was dissolved in fresh DMEM to prepare 0.1 mmol/L, 0.2 mmol/L, 0.4 mmol/L, 0.8 mmol/L and 1 mmol/L ALO solutions, which were separately used to treat the CCD-18Co, SW480 and HT29 cells for 24 hours (h).\n\nMTT Assay:\nOne hundred µL of SW480 or HT29 cell suspension (1×104 cells/mL) was transferred to each well of a 96-well plate. Of note, three holes were added to the same treatment group. After treatment with the ALO solutions for 24 h, SW480 or HT29 cells were added with MTT reagent (10 µL/well, PB180519, Procell, USA), which could react with mitochondria in living cells. After 4 h of binding, a HBS-1096A enzyme label analyzer (DeTie, China) was used to detect the absorbance of SW480 or HT29 cells in the mixed solutions at 490 nm, and then cell viability was calculated based on the absorbance.\n\nEdU (5-Ethynyl-2ʹ-Deoxyuridine) Cell Proliferation Experiment:\nSW480 or HT29 cells in logarithmic growth phase were seeded in 96-well plates at 1×104 cells/well. EdU reagent (ST067) purchased from Beyotime was diluted to 50 μmol/L with fresh DMEM medium. The EdU dilution was then used to incubate the cells at 100 μL/well for 2 h. After removing the culture medium, the SW480 or HT29 cells were fixed with methanol at room temperature for 15 minutes (min). Positive fluorescence expression (red) in the collected SW480 or HT29 cells were observed, photographed and recorded under a Leica DMi8 inverted fluorescence microscope (magnification 100 ×). Then DAPI reagent was used to stain the nuclei, and the stains were observed under a microscope.\n\nColony Formation Assay:\nTwo hundred SW480 or HT29 cells were transferred to petri dishes containing complete medium (10 mL). In order to distribute the cells evenly, the petri dishes were rotated slowly for 1 min after cell inoculation. Next, the culture dishes containing cells were placed in an incubator for routine culture. During the culture, the medium was changed every 2 days (d). After 14 d, the SW480 or HT29 cells were soaked with Giemsa dye for 20 min and then the colony formation of the cells was observed under a microscope.\n\nApoptosis Experiment:\nAn Annexin V-FITC Apoptosis Detection Kit (CA1020, Solarbio, China) and a flow cytometer (CytoFLEX, BECKMAN COULTER, USA) were used to detect changes in the apoptosis of SW480 or HT29 cells. In brief, SW480 or HT29 cells were prepared into 1×106 cells/mL cell suspension with 1 mL 1× Binding Buffer. One hundred μL of SW480 or HT29 cells and 5 μL of Annexin V-FITC reagent were added to a centrifuge tube and mixed for 10 min in the dark at room temperature. Next, 5 μL of PI reagent was added to the centrifuge tube and incubated in the dark at room temperature for 5 min. Afterwards, PBS was added to adjust the mixture to a final volume of 500 μL. After half an hour, the cells in the centrifuge tube were transferred to the detection instrument to calculate the number of apoptotic cells.\n\nBioinformatics Analysis and Target Gene Binding Verification:\nStarbase (http://starbase.sysu.edu.cn/index.php) was used to retrieve and analyze information of differential expression of miR-296-5p in Colon adenocarcinoma (COAD) patients and healthy volunteers. TargetScan (http://www.targetscan.org) was used to predict the circRNA that contained a binding sequence for miR-296-5p and the downstream target genes of miR-296-5p. The binding sequences predicted by TargetScan were designed by COBIOER (China) and used to construct the following reporter plasmids for dual luciferase experiments: NOP2/Sun RNA methyltransferase 2 wild type (circNSUN2-WT), circNSUN2-mutant type (MUT), Signal Transducer and Activator of Transcription 3 (STAT3)-WT and STAT3-MUT. The reporter plasmids were separately co-transfected with miR-296-5p mimic or mimic control into SW480 and HT29 cells using liposome for 48 h. Luciferase activity in the transfected SW480 and HT29 cells was detected using Dual-Luciferase® Reporter Assay System (E1960, Promega, USA) with a GloMax 20/20 luminometer (Promega, USA).\n\nQuantitative Real-Time Polymerase Chain Reaction (qRT-PCR):\nqRT-PCR is a reliable method for rapid detection of gene mRNA levels. Briefly, total RNA of SW480 and HT29 cells was fully isolated by the conventional RNA extraction reagent TRIzol (15,596,018, Invitrogen, USA), and subsequently reverse transcribed into cDNA using the RNA reverse transcription reagent (RR047A) developed by TaKaRa (Japan). The qRT-PCR reaction system which consisted of cDNA, gene primers (Sangon synthesis), SYBR® Green (S4438-20RXN, Sigma-Aldrich, Germany) and DEPC water was added to a 96-well plate and put into the detection instrument (Bio-Rad thermal cycler T100). The reaction conditions were set as follows: pre-denaturation at 95 °C for 10 min, denaturation at 95 °C for 15 seconds (s), and annealing at 58 °C for 1 min, for a total of 40 cycles. According to Anita Ciesielska’s report,19 gene mRNA levels were quantified by the 2−ΔΔCT method. GAPDH and U6 were internal references in the experiment. The specific sequences of synthetic primers were shown in Table 1.Table 1Primers for qRT-PCRGeneForward Primer (5ʹ-3ʹ)Reverse Primer (5ʹ-3ʹ)miR-296-5pCCTGTGTCGTATCCAGTGCAAGTCGTATCCAGTGCGTGTCGcircDDX17TGCCAACCACAACATCCTCCACGCTCCCCAGGATTACCAAATcircHIPK3TATGTTGGTGGATCCTGTTCGGCATGGTGGGTAGACCAAGACTTGTGAcircZFRAACCACCACAGATTCACTATAACCACCACAGATTCACTATcircRNA_100290GTCATTCCCTCTTTAATGGTGCAGAACTTCCGCTCTAACATACHas_circ_0026344CTCAGCCTCTAGCATAAGCTCAGGCAAGAGAATGATTTGAACcircVAPATGGATTCCAAATTGAGATGCGTATTCACTTTTCTATCCGATGGATTTCGCHsa_circ_0009361AGAACCAGATTCGAGACGCCGTGCTCTTCAATGCCACCTTCcircNSUN2CTTGAGAAAATCGCCACACTTGTTGAGGAGCAGTGGTGGcircACAP2TCAGAGCATCTGCCCAAAGTTAAATGAACCCCAAGGCTCCGcircITGA7GTGTGCACAGGTCCTTCCAATGGAAGTTCTGTGAGGGACGcircEIF4G3CCTACCCCATCCCCTTATTCACCGTGCTGTAGACTGCTGAGSTAT3CCCCATACCTGAAGACCAAGGGACTCAAACTGCCCTCCTU6CTCGCTTCGGCAGCACAAACGCTTCACGAATTTGCGTGAPDHTGTGGGCATCAATGGATTTGGACACCATGTATTCCGGGTCAAT\n\nPrimers for qRT-PCR\n\nCell Transfection:\nExogenous up-regulation or knockdown of test genes is a common method to observe their regulatory effects on cells or tissues. Liposome transfection with Lipofectamine 3000 (L3000008, ThermoFisher Scientific, USA) is the most common and convenient method for exogenous interference. Therefore, we commissioned Guangzhou GENESEED Biological Company to synthesize circNSUN2 overexpressed plasmid, circNSUN2 silenced plasmid (sh-circNSUN2), STAT3 overexpressed plasmid and their respective negative controls. MiR-296-5p mimic (miR10000690-1-5) and mimic control (miR1N0000001-1-5), as well as miR-296-5p inhibitor (miR20000690-1-5) and inhibitor control (miR2N0000001-1-5), were purchased directly from Guangzhou RIOBOBIO Company. The plasmids were transfected into SW480 and HT29 cells according to the grouping using Lipofectamine 3000. After 48 h, the success rate of transfection of the cells was determined by qRT-PCR.\n\nWestern Blot:\nRIPA lysate (tissue/cell, R0010) produced by Solarbio (China) was used to separate protein from SW480 and HT29 cells, and its concentration was determined with BCA protein concentration determination reagent (PC0020, Solarbio, China). Then the protein was subjected to high temperature denaturation to maintain a relatively stable state. After being electrophoresed by SDS-PAGE, the protein was transferred to the membrane carrier (PVDF membrane). The membrane was soaked with the same BSA sealant (SW3015) produced by Solarbio for 2 h at room temperature. Next, the membrane was incubated with Abcam (USA) antibodies (anti-STAT3, ab68153, 88 kDa; Bax, ab32503, 21 kDa; Bcl-2, ab59348, 26 kDa) that could bind to the corresponding antigens in the protein on the membrane overnight (4 °C), with GAPDH (ab8245, 36KD) as an internal reference. To identify the antigen-antibody complex, a secondary antibody labeled with HRP (Goat Anti-Rabbit antibody, 1:10,000, ab6721; Goat Anti-Mouse antibody, 1:10000, ab205719) was used to treat the PVDF membrane for 1.5 h at room temperature. Fluorescent signal of the complex was enhanced using the ECL Western Blotting Substrate (PE0010, Solarbio) and detected using the Gel Doc™ XR+ imaging system (BIO-RAD, USA). The gray value of the bands was calculated by Image Lab software to determine the final gene protein level.\n\nStatistical Analysis:\nSPSS 20.0 software (IBM, USA) was employed for statistical analysis of the data obtained in the experiments. Differences between two groups were compared by independent sample t-test, and those between multiple groups were compared by one-way ANOVA followed by Tukey’s test. When p<0.05, the difference was considered statistically significant.\n\nResult:\nALO at a Concentration Gradient Inhibited Proliferation Yet Promoted Apoptosis in CRC Cells In this study, the toxicity of ALO in normal cells (CCD-18Co) was detected, ALO showed no significant toxicity to CCD-18Co cells (Figure 1A). In order to study the effects of ALO on the growth and basic physiological functions of CRC cells, we used different concentrations of ALO to treat SW480 and HT29 cells. The results showed that 0.2 mmol/L, 0.4 mmol/L, 0.8 mmol/L and 1 mmol/L ALO solutions all inhibited the viability of SW480 and HT29 cells (p<0.05, Figure 1B and C). Moreover, the viability of SW480 and HT29 cells was close to 50% under the treatment of 0.8 mmol/L ALO solution, but was reduced to less than 50% when the ALO concentration was increased to 1 mmol/L (Figure 1B and C). In the following EdU cell proliferation experiment, the red fluorescence expression was reduced in SW480 and HT29 cells under the treatment of 0.8 mmol/L ALO (Figure 1D and E). Consistently, the clone formation experiment also showed that different concentrations of ALO (0.2 mmol/L, 0.4 mmol/L, 0.8 mmol/L) reduced the number of cell clones and inhibited the proliferation of SW480 and HT29 cells (p<0.05, Figure 1F–H). At the same time, the increase in the concentration of the ALO solution accelerated the death of CRC cells. As shown in Figure 1I–K, the number of apoptotic SW480 and HT29 cells increased significantly after treatment with different concentrations of ALO (p<0.01, Figure 1I–K).Figure 1ALO at a concentration gradient inhibited proliferation yet promoted apoptosis in CRC cells. (A) The effect of ALO on the normal colonic tissue cells (CCD-18Co) was detected by MTT experiments. (B and C) MTT experiments showed that ALO at a concentration gradient (0.1 mmol/L, 0.2 mmol/L, 0.4 mmol/L, 0.8 mmol/L and 1 mmol/L) inhibited the activity of SW480 and HT29 cells. (D and E) EdU cell proliferation experiments showed that 0.8 mmol/L ALO inhibited the proliferation of SW480 and HT29 cells. The magnification was 100×. (F–H) The clone formation experiment showed that ALO at a concentration gradient (0.2 mmol/L, 0.4 mmol/L and 0.8 mmol/L) inhibited the proliferation of SW480 and HT29 cells. (I–K) Flow cytometry experiments showed that ALO at a concentration gradient (0.2 mmol/L, 0.4 mmol/L and 0.8 mmol/L) promoted the apoptosis of SW480 and HT29 cells. All experiments were repeated three times to obtain average values. *p<0.05, **p<0.01, ***p<0.001 vs Control.Abbreviations: ALO, aloperine; CRC, colorectal cancer.\nALO at a concentration gradient inhibited proliferation yet promoted apoptosis in CRC cells. (A) The effect of ALO on the normal colonic tissue cells (CCD-18Co) was detected by MTT experiments. (B and C) MTT experiments showed that ALO at a concentration gradient (0.1 mmol/L, 0.2 mmol/L, 0.4 mmol/L, 0.8 mmol/L and 1 mmol/L) inhibited the activity of SW480 and HT29 cells. (D and E) EdU cell proliferation experiments showed that 0.8 mmol/L ALO inhibited the proliferation of SW480 and HT29 cells. The magnification was 100×. (F–H) The clone formation experiment showed that ALO at a concentration gradient (0.2 mmol/L, 0.4 mmol/L and 0.8 mmol/L) inhibited the proliferation of SW480 and HT29 cells. (I–K) Flow cytometry experiments showed that ALO at a concentration gradient (0.2 mmol/L, 0.4 mmol/L and 0.8 mmol/L) promoted the apoptosis of SW480 and HT29 cells. All experiments were repeated three times to obtain average values. *p<0.05, **p<0.01, ***p<0.001 vs Control.\nIn this study, the toxicity of ALO in normal cells (CCD-18Co) was detected, ALO showed no significant toxicity to CCD-18Co cells (Figure 1A). In order to study the effects of ALO on the growth and basic physiological functions of CRC cells, we used different concentrations of ALO to treat SW480 and HT29 cells. The results showed that 0.2 mmol/L, 0.4 mmol/L, 0.8 mmol/L and 1 mmol/L ALO solutions all inhibited the viability of SW480 and HT29 cells (p<0.05, Figure 1B and C). Moreover, the viability of SW480 and HT29 cells was close to 50% under the treatment of 0.8 mmol/L ALO solution, but was reduced to less than 50% when the ALO concentration was increased to 1 mmol/L (Figure 1B and C). In the following EdU cell proliferation experiment, the red fluorescence expression was reduced in SW480 and HT29 cells under the treatment of 0.8 mmol/L ALO (Figure 1D and E). Consistently, the clone formation experiment also showed that different concentrations of ALO (0.2 mmol/L, 0.4 mmol/L, 0.8 mmol/L) reduced the number of cell clones and inhibited the proliferation of SW480 and HT29 cells (p<0.05, Figure 1F–H). At the same time, the increase in the concentration of the ALO solution accelerated the death of CRC cells. As shown in Figure 1I–K, the number of apoptotic SW480 and HT29 cells increased significantly after treatment with different concentrations of ALO (p<0.01, Figure 1I–K).Figure 1ALO at a concentration gradient inhibited proliferation yet promoted apoptosis in CRC cells. (A) The effect of ALO on the normal colonic tissue cells (CCD-18Co) was detected by MTT experiments. (B and C) MTT experiments showed that ALO at a concentration gradient (0.1 mmol/L, 0.2 mmol/L, 0.4 mmol/L, 0.8 mmol/L and 1 mmol/L) inhibited the activity of SW480 and HT29 cells. (D and E) EdU cell proliferation experiments showed that 0.8 mmol/L ALO inhibited the proliferation of SW480 and HT29 cells. The magnification was 100×. (F–H) The clone formation experiment showed that ALO at a concentration gradient (0.2 mmol/L, 0.4 mmol/L and 0.8 mmol/L) inhibited the proliferation of SW480 and HT29 cells. (I–K) Flow cytometry experiments showed that ALO at a concentration gradient (0.2 mmol/L, 0.4 mmol/L and 0.8 mmol/L) promoted the apoptosis of SW480 and HT29 cells. All experiments were repeated three times to obtain average values. *p<0.05, **p<0.01, ***p<0.001 vs Control.Abbreviations: ALO, aloperine; CRC, colorectal cancer.\nALO at a concentration gradient inhibited proliferation yet promoted apoptosis in CRC cells. (A) The effect of ALO on the normal colonic tissue cells (CCD-18Co) was detected by MTT experiments. (B and C) MTT experiments showed that ALO at a concentration gradient (0.1 mmol/L, 0.2 mmol/L, 0.4 mmol/L, 0.8 mmol/L and 1 mmol/L) inhibited the activity of SW480 and HT29 cells. (D and E) EdU cell proliferation experiments showed that 0.8 mmol/L ALO inhibited the proliferation of SW480 and HT29 cells. The magnification was 100×. (F–H) The clone formation experiment showed that ALO at a concentration gradient (0.2 mmol/L, 0.4 mmol/L and 0.8 mmol/L) inhibited the proliferation of SW480 and HT29 cells. (I–K) Flow cytometry experiments showed that ALO at a concentration gradient (0.2 mmol/L, 0.4 mmol/L and 0.8 mmol/L) promoted the apoptosis of SW480 and HT29 cells. All experiments were repeated three times to obtain average values. *p<0.05, **p<0.01, ***p<0.001 vs Control.\nThe Abnormally Low Expression of miR-296-5p in Colon Cancer Could Be Up-Regulated by ALO Data from the Starbase database showed that the expression of miR-296-5p in colon cancer patients was significantly lower than that in healthy people (p=2.5e-6, Figure 2A). In this study, however, after treatment with different concentrations of ALO for 24 h, miR-296-5p activity in SW480 and HT29 cells was significantly increased (p<0.01, Figure 2B and C), suggesting that ALO can stimulate the up-regulation of miR-296-5p.Figure 2The abnormally low expression of miR-296-5p in colon cancer could be upregulated by ALO. (A) Starbase (http://starbase.sysu.edu.cn/index.php) was used to retrieve the information of differential expression of miR-296-5p in colon adenocarcinoma (COAD, n=450) patients and healthy volunteers (n=8) in vivo. P=2.5e-6. (B and C) The qRT-PCR experiment showed that ALO at a concentration gradient (0.1 mmol/L, 0.2 mmol/L, 0.4 mmol/L, 0.8 mmol/L and 1 mmol/L) up-regulated miR-296-5p expression in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. **p<0.01, ***p<0.001 vs Control.Abbreviation: qRT-PCR, quantitative real-time polymerase chain reaction.\nThe abnormally low expression of miR-296-5p in colon cancer could be upregulated by ALO. (A) Starbase (http://starbase.sysu.edu.cn/index.php) was used to retrieve the information of differential expression of miR-296-5p in colon adenocarcinoma (COAD, n=450) patients and healthy volunteers (n=8) in vivo. P=2.5e-6. (B and C) The qRT-PCR experiment showed that ALO at a concentration gradient (0.1 mmol/L, 0.2 mmol/L, 0.4 mmol/L, 0.8 mmol/L and 1 mmol/L) up-regulated miR-296-5p expression in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. **p<0.01, ***p<0.001 vs Control.\nData from the Starbase database showed that the expression of miR-296-5p in colon cancer patients was significantly lower than that in healthy people (p=2.5e-6, Figure 2A). In this study, however, after treatment with different concentrations of ALO for 24 h, miR-296-5p activity in SW480 and HT29 cells was significantly increased (p<0.01, Figure 2B and C), suggesting that ALO can stimulate the up-regulation of miR-296-5p.Figure 2The abnormally low expression of miR-296-5p in colon cancer could be upregulated by ALO. (A) Starbase (http://starbase.sysu.edu.cn/index.php) was used to retrieve the information of differential expression of miR-296-5p in colon adenocarcinoma (COAD, n=450) patients and healthy volunteers (n=8) in vivo. P=2.5e-6. (B and C) The qRT-PCR experiment showed that ALO at a concentration gradient (0.1 mmol/L, 0.2 mmol/L, 0.4 mmol/L, 0.8 mmol/L and 1 mmol/L) up-regulated miR-296-5p expression in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. **p<0.01, ***p<0.001 vs Control.Abbreviation: qRT-PCR, quantitative real-time polymerase chain reaction.\nThe abnormally low expression of miR-296-5p in colon cancer could be upregulated by ALO. (A) Starbase (http://starbase.sysu.edu.cn/index.php) was used to retrieve the information of differential expression of miR-296-5p in colon adenocarcinoma (COAD, n=450) patients and healthy volunteers (n=8) in vivo. P=2.5e-6. (B and C) The qRT-PCR experiment showed that ALO at a concentration gradient (0.1 mmol/L, 0.2 mmol/L, 0.4 mmol/L, 0.8 mmol/L and 1 mmol/L) up-regulated miR-296-5p expression in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. **p<0.01, ***p<0.001 vs Control.\nAmong the Differentially Expressed circRNAs in CRC, Only circNSUN2 Not Only Had a Target Site for miR-296-5p, but Also Could Be Regulated by ALO Through literature review, we screened 12 circRNAs that were reported to be associated with CRC: circDDX17, circHIPK3, circZFR, circRNA_100290, Has_circ_0026344, circVAPA, Hsa_circ_0009361, circNSUN2, circACAP2, circACVRL1, circITGA7 and circEIF4G3. We first detected the regulatory effect of ALO on these differentially expressed circRNAs by qRT-PCR to perform preliminary screening (Figure 3A). The results showed that the expressions of circDDX17, Has_circ_0026344, circVAPA, circNSUN2, circACAP2, circACVRL1 and circEIF4G3 were regulated by 0.8 mmol/L ALO (p<0.001, Figure 3A). Through subsequent screening of target genes, we found that only circNSUN2 and miR-296-5p have targeted binding sites (Figure 3B). After further verification, it was confirmed that circNSUN2-WT and miR-296-5p mimic did reduce the luciferase activity in SW480 and HT29 cells (p<0.001, Figure 3C and D). Therefore, we chose circNSUN2 as the next research object.Figure 3Among the differentially expressed circRNAs in CRC, only circNSUN2 not only had a target site for miR-296-5p, but also could be regulated by ALO. (A) qRT-PCR detected the regulation of 0.8 mmol/L ALO on the differentially expressed circRNAs in CRC. GAPDH played the role of internal reference. (B) The TargetScan database (http://www.targetscan.org) was used to predict the target sites of circNSUN2 and miR-296-5p. (C and D) The dual luciferase experiment confirmed that circNSUN2 can bind to miR-296-5p in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. ***p<0.001 vs Control; +++p<0.001 vs MC.Abbreviation: MC, miR-296-5p mimic control.\nAmong the differentially expressed circRNAs in CRC, only circNSUN2 not only had a target site for miR-296-5p, but also could be regulated by ALO. (A) qRT-PCR detected the regulation of 0.8 mmol/L ALO on the differentially expressed circRNAs in CRC. GAPDH played the role of internal reference. (B) The TargetScan database (http://www.targetscan.org) was used to predict the target sites of circNSUN2 and miR-296-5p. (C and D) The dual luciferase experiment confirmed that circNSUN2 can bind to miR-296-5p in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. ***p<0.001 vs Control; +++p<0.001 vs MC.\nThrough literature review, we screened 12 circRNAs that were reported to be associated with CRC: circDDX17, circHIPK3, circZFR, circRNA_100290, Has_circ_0026344, circVAPA, Hsa_circ_0009361, circNSUN2, circACAP2, circACVRL1, circITGA7 and circEIF4G3. We first detected the regulatory effect of ALO on these differentially expressed circRNAs by qRT-PCR to perform preliminary screening (Figure 3A). The results showed that the expressions of circDDX17, Has_circ_0026344, circVAPA, circNSUN2, circACAP2, circACVRL1 and circEIF4G3 were regulated by 0.8 mmol/L ALO (p<0.001, Figure 3A). Through subsequent screening of target genes, we found that only circNSUN2 and miR-296-5p have targeted binding sites (Figure 3B). After further verification, it was confirmed that circNSUN2-WT and miR-296-5p mimic did reduce the luciferase activity in SW480 and HT29 cells (p<0.001, Figure 3C and D). Therefore, we chose circNSUN2 as the next research object.Figure 3Among the differentially expressed circRNAs in CRC, only circNSUN2 not only had a target site for miR-296-5p, but also could be regulated by ALO. (A) qRT-PCR detected the regulation of 0.8 mmol/L ALO on the differentially expressed circRNAs in CRC. GAPDH played the role of internal reference. (B) The TargetScan database (http://www.targetscan.org) was used to predict the target sites of circNSUN2 and miR-296-5p. (C and D) The dual luciferase experiment confirmed that circNSUN2 can bind to miR-296-5p in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. ***p<0.001 vs Control; +++p<0.001 vs MC.Abbreviation: MC, miR-296-5p mimic control.\nAmong the differentially expressed circRNAs in CRC, only circNSUN2 not only had a target site for miR-296-5p, but also could be regulated by ALO. (A) qRT-PCR detected the regulation of 0.8 mmol/L ALO on the differentially expressed circRNAs in CRC. GAPDH played the role of internal reference. (B) The TargetScan database (http://www.targetscan.org) was used to predict the target sites of circNSUN2 and miR-296-5p. (C and D) The dual luciferase experiment confirmed that circNSUN2 can bind to miR-296-5p in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. ***p<0.001 vs Control; +++p<0.001 vs MC.\nOverexpression of circNSUN2 Partially Offset the Suppression of ALO on the Biological Behavior of Cancer Cells In SW480 and HT29 cells, ALO suppressed the mRNA level of circNSUN2, whereas transfection of circNSUN2 overexpression significantly increased the expression of circNSUN2 (p<0.001, Figure 4A and B). The following basic cell function experiments showed that the upregulation of circNSUN2 partially offset the inhibitory effect of ALO on CRC cell viability, proliferation and apoptosis (p<0.001, Figure 4C–L).Figure 4Overexpression of circNSUN2 partially offset the suppression of ALO on the biological behavior of cancer cells. (A and B) The qRT-PCR experiment showed that ALO (0.8 mmol/L) inhibited the expression of circNSUN2 in SW480 and HT29 cells. GAPDH played the role of internal reference. (C and D) The MTT experiment showed that overexpression of circNSUN2 partially offset the inhibition of ALO on cell viability in SW480 and HT29 cells. (E and F) EdU cell proliferation experiments showed that overexpression of circNSUN2 partially offset the inhibition of ALO on proliferation in SW480 and HT29 cells. The magnification was 100×. (G–I) The clone formation experiment showed that overexpression of circNSUN2 partially offset the inhibition of ALO on proliferation in SW480 and HT29 cells. (J–L) Flow cytometry experiments showed that overexpression of circNSUN2 partially offset the promotion of ALO on apoptosis in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. ***p<0.001 vs Control; ^p<0.05, ^^p<0.01, ^^^p<0.001 vs ALO+NC.Abbreviations: CircNSUN2, NOP2/Sun RNA methyltransferase 2; NC, negative control.\nOverexpression of circNSUN2 partially offset the suppression of ALO on the biological behavior of cancer cells. (A and B) The qRT-PCR experiment showed that ALO (0.8 mmol/L) inhibited the expression of circNSUN2 in SW480 and HT29 cells. GAPDH played the role of internal reference. (C and D) The MTT experiment showed that overexpression of circNSUN2 partially offset the inhibition of ALO on cell viability in SW480 and HT29 cells. (E and F) EdU cell proliferation experiments showed that overexpression of circNSUN2 partially offset the inhibition of ALO on proliferation in SW480 and HT29 cells. The magnification was 100×. (G–I) The clone formation experiment showed that overexpression of circNSUN2 partially offset the inhibition of ALO on proliferation in SW480 and HT29 cells. (J–L) Flow cytometry experiments showed that overexpression of circNSUN2 partially offset the promotion of ALO on apoptosis in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. ***p<0.001 vs Control; ^p<0.05, ^^p<0.01, ^^^p<0.001 vs ALO+NC.\nIn SW480 and HT29 cells, ALO suppressed the mRNA level of circNSUN2, whereas transfection of circNSUN2 overexpression significantly increased the expression of circNSUN2 (p<0.001, Figure 4A and B). The following basic cell function experiments showed that the upregulation of circNSUN2 partially offset the inhibitory effect of ALO on CRC cell viability, proliferation and apoptosis (p<0.001, Figure 4C–L).Figure 4Overexpression of circNSUN2 partially offset the suppression of ALO on the biological behavior of cancer cells. (A and B) The qRT-PCR experiment showed that ALO (0.8 mmol/L) inhibited the expression of circNSUN2 in SW480 and HT29 cells. GAPDH played the role of internal reference. (C and D) The MTT experiment showed that overexpression of circNSUN2 partially offset the inhibition of ALO on cell viability in SW480 and HT29 cells. (E and F) EdU cell proliferation experiments showed that overexpression of circNSUN2 partially offset the inhibition of ALO on proliferation in SW480 and HT29 cells. The magnification was 100×. (G–I) The clone formation experiment showed that overexpression of circNSUN2 partially offset the inhibition of ALO on proliferation in SW480 and HT29 cells. (J–L) Flow cytometry experiments showed that overexpression of circNSUN2 partially offset the promotion of ALO on apoptosis in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. ***p<0.001 vs Control; ^p<0.05, ^^p<0.01, ^^^p<0.001 vs ALO+NC.Abbreviations: CircNSUN2, NOP2/Sun RNA methyltransferase 2; NC, negative control.\nOverexpression of circNSUN2 partially offset the suppression of ALO on the biological behavior of cancer cells. (A and B) The qRT-PCR experiment showed that ALO (0.8 mmol/L) inhibited the expression of circNSUN2 in SW480 and HT29 cells. GAPDH played the role of internal reference. (C and D) The MTT experiment showed that overexpression of circNSUN2 partially offset the inhibition of ALO on cell viability in SW480 and HT29 cells. (E and F) EdU cell proliferation experiments showed that overexpression of circNSUN2 partially offset the inhibition of ALO on proliferation in SW480 and HT29 cells. The magnification was 100×. (G–I) The clone formation experiment showed that overexpression of circNSUN2 partially offset the inhibition of ALO on proliferation in SW480 and HT29 cells. (J–L) Flow cytometry experiments showed that overexpression of circNSUN2 partially offset the promotion of ALO on apoptosis in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. ***p<0.001 vs Control; ^p<0.05, ^^p<0.01, ^^^p<0.001 vs ALO+NC.\nSh-circNSUN2 Inhibited the Malignant Development of CRC Cells, Which Was Neutralized by miR-296-5p Inhibitor We also tested the mutual regulation of circNSUN2 and miR-296-5p in SW480 and HT29 cells. Sh-circNSUN2 could down-regulate the expression of circNSUN2, and besides, it inhibited the proliferation of SW480 and HT29 cells, while promoting their apoptosis and the expression of miR-296-5p (p<0.001, Figure 5A–J). However, the inhibitory effect of sh-circNSUN2 on the malignant development of the two cancer cells was neutralized by miR-296-5p inhibitor (p<0.001, Figure 5A–J).Figure 5Sh-circNSUN2 inhibited the malignant development of CRC cells, which was neutralized by miR-296-5p inhibitor. (A and B) The qRT-PCR experiment showed that miR-296-5p inhibitor neutralized the inhibitory effect of sh-circNSUN2 on circNSUN2 expression. GAPDH played the role of internal reference. (C and D) The qRT-PCR experiment showed that sh-circNSUN2 up-regulated the mRNA level of miR-296-5p. U6 played the role of internal reference. (E–G) The clone formation experiment showed that the effect of sh-circNSUN2 on the proliferation of SW480 and HT29 cells was neutralized by miR-296-5p inhibitor. (H-J) Flow cytometry experiments showed that the effect of sh-circNSUN2 on promoting the apoptosis of SW480 and HT29 cells was neutralized by miR-296-5p inhibitor. All experiments were repeated three times to obtain average values. ***p<0.001 vs sh-NC+IC; ^^^p<0.001 vs sh-circNSUN2+IC; ###p<0.001 vs sh-NC+I.Abbreviations: Sh-circNSUN2, silent circNSUN2; sh-NC, silent negative control; IC, miR-296-5p inhibitor control; I, miR-296-5p inhibitor.\nSh-circNSUN2 inhibited the malignant development of CRC cells, which was neutralized by miR-296-5p inhibitor. (A and B) The qRT-PCR experiment showed that miR-296-5p inhibitor neutralized the inhibitory effect of sh-circNSUN2 on circNSUN2 expression. GAPDH played the role of internal reference. (C and D) The qRT-PCR experiment showed that sh-circNSUN2 up-regulated the mRNA level of miR-296-5p. U6 played the role of internal reference. (E–G) The clone formation experiment showed that the effect of sh-circNSUN2 on the proliferation of SW480 and HT29 cells was neutralized by miR-296-5p inhibitor. (H-J) Flow cytometry experiments showed that the effect of sh-circNSUN2 on promoting the apoptosis of SW480 and HT29 cells was neutralized by miR-296-5p inhibitor. All experiments were repeated three times to obtain average values. ***p<0.001 vs sh-NC+IC; ^^^p<0.001 vs sh-circNSUN2+IC; ###p<0.001 vs sh-NC+I.\nWe also tested the mutual regulation of circNSUN2 and miR-296-5p in SW480 and HT29 cells. Sh-circNSUN2 could down-regulate the expression of circNSUN2, and besides, it inhibited the proliferation of SW480 and HT29 cells, while promoting their apoptosis and the expression of miR-296-5p (p<0.001, Figure 5A–J). However, the inhibitory effect of sh-circNSUN2 on the malignant development of the two cancer cells was neutralized by miR-296-5p inhibitor (p<0.001, Figure 5A–J).Figure 5Sh-circNSUN2 inhibited the malignant development of CRC cells, which was neutralized by miR-296-5p inhibitor. (A and B) The qRT-PCR experiment showed that miR-296-5p inhibitor neutralized the inhibitory effect of sh-circNSUN2 on circNSUN2 expression. GAPDH played the role of internal reference. (C and D) The qRT-PCR experiment showed that sh-circNSUN2 up-regulated the mRNA level of miR-296-5p. U6 played the role of internal reference. (E–G) The clone formation experiment showed that the effect of sh-circNSUN2 on the proliferation of SW480 and HT29 cells was neutralized by miR-296-5p inhibitor. (H-J) Flow cytometry experiments showed that the effect of sh-circNSUN2 on promoting the apoptosis of SW480 and HT29 cells was neutralized by miR-296-5p inhibitor. All experiments were repeated three times to obtain average values. ***p<0.001 vs sh-NC+IC; ^^^p<0.001 vs sh-circNSUN2+IC; ###p<0.001 vs sh-NC+I.Abbreviations: Sh-circNSUN2, silent circNSUN2; sh-NC, silent negative control; IC, miR-296-5p inhibitor control; I, miR-296-5p inhibitor.\nSh-circNSUN2 inhibited the malignant development of CRC cells, which was neutralized by miR-296-5p inhibitor. (A and B) The qRT-PCR experiment showed that miR-296-5p inhibitor neutralized the inhibitory effect of sh-circNSUN2 on circNSUN2 expression. GAPDH played the role of internal reference. (C and D) The qRT-PCR experiment showed that sh-circNSUN2 up-regulated the mRNA level of miR-296-5p. U6 played the role of internal reference. (E–G) The clone formation experiment showed that the effect of sh-circNSUN2 on the proliferation of SW480 and HT29 cells was neutralized by miR-296-5p inhibitor. (H-J) Flow cytometry experiments showed that the effect of sh-circNSUN2 on promoting the apoptosis of SW480 and HT29 cells was neutralized by miR-296-5p inhibitor. All experiments were repeated three times to obtain average values. ***p<0.001 vs sh-NC+IC; ^^^p<0.001 vs sh-circNSUN2+IC; ###p<0.001 vs sh-NC+I.\nMiR-296-5p Bound to STAT3 and Upregulation of Mir-296-5p Inhibited the Expression of STAT3 in CRC Cells We screened the target genes of miR-296-5p, and found that miR-296-5p and STAT3 have targeted binding sites (Figure 6A), and this binding relation was further confirmed by a dual-luciferase verification test (p<0.001, Figure 6B and C). Next, we used qRT-PCR and Western blot to analyze the mRNA and protein levels of miR-296-5p and STAT. It was found that miR-296-5p mimic up-regulated miR-296-5p expression while preventing the activation of STAT3 (p<0.001, Figure 7A–G). However, the overexpression of STAT3 reversed the regulation of miR-296-5p mimic on the two genes (p<0.001, Figure 7A–G).Figure 6The targeted binding of miR-296-5p to STAT3. (A) TargetScan was used to predict the binding sequence of miR-296-5p and STAT3. (B and C) Dual luciferase experiment confirmed that miR-296-5p can bind to STAT3 in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. +++p<0.001 vs MC.Figure 7Up-regulation of miR-296-5p inhibited the expression of STAT3 in CRC cells. (A and B) The qRT-PCR experiment showed that miR-296-5p mimic up-regulated the mRNA level of miR-296-5p. U6 played the role of internal reference. (C and D) The qRT-PCR experiment showed that miR-296-5p mimic suppressed the mRNA level of STAT3, while overexpression of STAT3 reversed this effect. GAPDH played the role of internal reference. (E–G) The Western blot experiment showed that miR-296-5p mimic inhibited the protein level of STAT3, while over-expression of STAT3 reversed this effect. GAPDH played the role of internal reference. All experiments were repeated three times to obtain average values. ***p<0.001 vs MC+NC; ^^^p<0.001 vs M+NC; ###p<0.001 vs MC+STAT3.Abbreviation: STAT3, signal transducer and activator of transcription 3.\nThe targeted binding of miR-296-5p to STAT3. (A) TargetScan was used to predict the binding sequence of miR-296-5p and STAT3. (B and C) Dual luciferase experiment confirmed that miR-296-5p can bind to STAT3 in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. +++p<0.001 vs MC.\nUp-regulation of miR-296-5p inhibited the expression of STAT3 in CRC cells. (A and B) The qRT-PCR experiment showed that miR-296-5p mimic up-regulated the mRNA level of miR-296-5p. U6 played the role of internal reference. (C and D) The qRT-PCR experiment showed that miR-296-5p mimic suppressed the mRNA level of STAT3, while overexpression of STAT3 reversed this effect. GAPDH played the role of internal reference. (E–G) The Western blot experiment showed that miR-296-5p mimic inhibited the protein level of STAT3, while over-expression of STAT3 reversed this effect. GAPDH played the role of internal reference. All experiments were repeated three times to obtain average values. ***p<0.001 vs MC+NC; ^^^p<0.001 vs M+NC; ###p<0.001 vs MC+STAT3.\nWe screened the target genes of miR-296-5p, and found that miR-296-5p and STAT3 have targeted binding sites (Figure 6A), and this binding relation was further confirmed by a dual-luciferase verification test (p<0.001, Figure 6B and C). Next, we used qRT-PCR and Western blot to analyze the mRNA and protein levels of miR-296-5p and STAT. It was found that miR-296-5p mimic up-regulated miR-296-5p expression while preventing the activation of STAT3 (p<0.001, Figure 7A–G). However, the overexpression of STAT3 reversed the regulation of miR-296-5p mimic on the two genes (p<0.001, Figure 7A–G).Figure 6The targeted binding of miR-296-5p to STAT3. (A) TargetScan was used to predict the binding sequence of miR-296-5p and STAT3. (B and C) Dual luciferase experiment confirmed that miR-296-5p can bind to STAT3 in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. +++p<0.001 vs MC.Figure 7Up-regulation of miR-296-5p inhibited the expression of STAT3 in CRC cells. (A and B) The qRT-PCR experiment showed that miR-296-5p mimic up-regulated the mRNA level of miR-296-5p. U6 played the role of internal reference. (C and D) The qRT-PCR experiment showed that miR-296-5p mimic suppressed the mRNA level of STAT3, while overexpression of STAT3 reversed this effect. GAPDH played the role of internal reference. (E–G) The Western blot experiment showed that miR-296-5p mimic inhibited the protein level of STAT3, while over-expression of STAT3 reversed this effect. GAPDH played the role of internal reference. All experiments were repeated three times to obtain average values. ***p<0.001 vs MC+NC; ^^^p<0.001 vs M+NC; ###p<0.001 vs MC+STAT3.Abbreviation: STAT3, signal transducer and activator of transcription 3.\nThe targeted binding of miR-296-5p to STAT3. (A) TargetScan was used to predict the binding sequence of miR-296-5p and STAT3. (B and C) Dual luciferase experiment confirmed that miR-296-5p can bind to STAT3 in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. +++p<0.001 vs MC.\nUp-regulation of miR-296-5p inhibited the expression of STAT3 in CRC cells. (A and B) The qRT-PCR experiment showed that miR-296-5p mimic up-regulated the mRNA level of miR-296-5p. U6 played the role of internal reference. (C and D) The qRT-PCR experiment showed that miR-296-5p mimic suppressed the mRNA level of STAT3, while overexpression of STAT3 reversed this effect. GAPDH played the role of internal reference. (E–G) The Western blot experiment showed that miR-296-5p mimic inhibited the protein level of STAT3, while over-expression of STAT3 reversed this effect. GAPDH played the role of internal reference. All experiments were repeated three times to obtain average values. ***p<0.001 vs MC+NC; ^^^p<0.001 vs M+NC; ###p<0.001 vs MC+STAT3.\nMiR-296-5p Mimic Inhibited Proliferation and Promoted Apoptosis in CRC Cells by Regulating Apoptosis-Related Genes, Which Was Partially Reversed by Overexpressed STAT3 The following cell experiments showed that by activating Bax and blocking the protein activity of Bcl-2, miR-296-5p mimic inhibited proliferation yet accelerated apoptosis in cancer cells (p<0.001, Figure 8A–I). However, up-regulation of STAT3 produced a completely opposite regulatory effect (p<0.001, Figure 8A–I). More importantly, overexpression of STAT3 neutralized the regulation of miR-296-5p mimic on CRC cells (p<0.001, Figure 8A–I).Figure 8MiR-296-5p mimic inhibited proliferation and promoted apoptosis in CRC cells by regulating apoptosis-related genes, which was partially reversed by overexpression of STAT3. (A–C) The clone formation experiment showed that the inhibitory effect of miR-296-5p mimic on the proliferation of SW480 and HT29 cells was neutralized by overexpressed STAT3. (D–F) Flow cytometry experiments showed that miR-296-5p mimic promoted the apoptosis of SW480 and HT29 cells, which was neutralized by overexpressed STAT3. (G–I) Western blot experiments showed that the regulation of apoptosis-related protein expression by miR-296-5p mimic was neutralized by overexpressed STAT3. GAPDH played the role of internal reference. All experiments were repeated three times to obtain average values. *p<0.05, **p<0.01, ***p<0.001 vs MC+NC; ^^p<0.01, ^^^p<0.001 vs M+NC; ##p<0.01, ###p<0.001 vs MC+STAT3.\nMiR-296-5p mimic inhibited proliferation and promoted apoptosis in CRC cells by regulating apoptosis-related genes, which was partially reversed by overexpression of STAT3. (A–C) The clone formation experiment showed that the inhibitory effect of miR-296-5p mimic on the proliferation of SW480 and HT29 cells was neutralized by overexpressed STAT3. (D–F) Flow cytometry experiments showed that miR-296-5p mimic promoted the apoptosis of SW480 and HT29 cells, which was neutralized by overexpressed STAT3. (G–I) Western blot experiments showed that the regulation of apoptosis-related protein expression by miR-296-5p mimic was neutralized by overexpressed STAT3. GAPDH played the role of internal reference. All experiments were repeated three times to obtain average values. *p<0.05, **p<0.01, ***p<0.001 vs MC+NC; ^^p<0.01, ^^^p<0.001 vs M+NC; ##p<0.01, ###p<0.001 vs MC+STAT3.\nThe following cell experiments showed that by activating Bax and blocking the protein activity of Bcl-2, miR-296-5p mimic inhibited proliferation yet accelerated apoptosis in cancer cells (p<0.001, Figure 8A–I). However, up-regulation of STAT3 produced a completely opposite regulatory effect (p<0.001, Figure 8A–I). More importantly, overexpression of STAT3 neutralized the regulation of miR-296-5p mimic on CRC cells (p<0.001, Figure 8A–I).Figure 8MiR-296-5p mimic inhibited proliferation and promoted apoptosis in CRC cells by regulating apoptosis-related genes, which was partially reversed by overexpression of STAT3. (A–C) The clone formation experiment showed that the inhibitory effect of miR-296-5p mimic on the proliferation of SW480 and HT29 cells was neutralized by overexpressed STAT3. (D–F) Flow cytometry experiments showed that miR-296-5p mimic promoted the apoptosis of SW480 and HT29 cells, which was neutralized by overexpressed STAT3. (G–I) Western blot experiments showed that the regulation of apoptosis-related protein expression by miR-296-5p mimic was neutralized by overexpressed STAT3. GAPDH played the role of internal reference. All experiments were repeated three times to obtain average values. *p<0.05, **p<0.01, ***p<0.001 vs MC+NC; ^^p<0.01, ^^^p<0.001 vs M+NC; ##p<0.01, ###p<0.001 vs MC+STAT3.\nMiR-296-5p mimic inhibited proliferation and promoted apoptosis in CRC cells by regulating apoptosis-related genes, which was partially reversed by overexpression of STAT3. (A–C) The clone formation experiment showed that the inhibitory effect of miR-296-5p mimic on the proliferation of SW480 and HT29 cells was neutralized by overexpressed STAT3. (D–F) Flow cytometry experiments showed that miR-296-5p mimic promoted the apoptosis of SW480 and HT29 cells, which was neutralized by overexpressed STAT3. (G–I) Western blot experiments showed that the regulation of apoptosis-related protein expression by miR-296-5p mimic was neutralized by overexpressed STAT3. GAPDH played the role of internal reference. All experiments were repeated three times to obtain average values. *p<0.05, **p<0.01, ***p<0.001 vs MC+NC; ^^p<0.01, ^^^p<0.001 vs M+NC; ##p<0.01, ###p<0.001 vs MC+STAT3.\n\nALO at a Concentration Gradient Inhibited Proliferation Yet Promoted Apoptosis in CRC Cells:\nIn this study, the toxicity of ALO in normal cells (CCD-18Co) was detected, ALO showed no significant toxicity to CCD-18Co cells (Figure 1A). In order to study the effects of ALO on the growth and basic physiological functions of CRC cells, we used different concentrations of ALO to treat SW480 and HT29 cells. The results showed that 0.2 mmol/L, 0.4 mmol/L, 0.8 mmol/L and 1 mmol/L ALO solutions all inhibited the viability of SW480 and HT29 cells (p<0.05, Figure 1B and C). Moreover, the viability of SW480 and HT29 cells was close to 50% under the treatment of 0.8 mmol/L ALO solution, but was reduced to less than 50% when the ALO concentration was increased to 1 mmol/L (Figure 1B and C). In the following EdU cell proliferation experiment, the red fluorescence expression was reduced in SW480 and HT29 cells under the treatment of 0.8 mmol/L ALO (Figure 1D and E). Consistently, the clone formation experiment also showed that different concentrations of ALO (0.2 mmol/L, 0.4 mmol/L, 0.8 mmol/L) reduced the number of cell clones and inhibited the proliferation of SW480 and HT29 cells (p<0.05, Figure 1F–H). At the same time, the increase in the concentration of the ALO solution accelerated the death of CRC cells. As shown in Figure 1I–K, the number of apoptotic SW480 and HT29 cells increased significantly after treatment with different concentrations of ALO (p<0.01, Figure 1I–K).Figure 1ALO at a concentration gradient inhibited proliferation yet promoted apoptosis in CRC cells. (A) The effect of ALO on the normal colonic tissue cells (CCD-18Co) was detected by MTT experiments. (B and C) MTT experiments showed that ALO at a concentration gradient (0.1 mmol/L, 0.2 mmol/L, 0.4 mmol/L, 0.8 mmol/L and 1 mmol/L) inhibited the activity of SW480 and HT29 cells. (D and E) EdU cell proliferation experiments showed that 0.8 mmol/L ALO inhibited the proliferation of SW480 and HT29 cells. The magnification was 100×. (F–H) The clone formation experiment showed that ALO at a concentration gradient (0.2 mmol/L, 0.4 mmol/L and 0.8 mmol/L) inhibited the proliferation of SW480 and HT29 cells. (I–K) Flow cytometry experiments showed that ALO at a concentration gradient (0.2 mmol/L, 0.4 mmol/L and 0.8 mmol/L) promoted the apoptosis of SW480 and HT29 cells. All experiments were repeated three times to obtain average values. *p<0.05, **p<0.01, ***p<0.001 vs Control.Abbreviations: ALO, aloperine; CRC, colorectal cancer.\nALO at a concentration gradient inhibited proliferation yet promoted apoptosis in CRC cells. (A) The effect of ALO on the normal colonic tissue cells (CCD-18Co) was detected by MTT experiments. (B and C) MTT experiments showed that ALO at a concentration gradient (0.1 mmol/L, 0.2 mmol/L, 0.4 mmol/L, 0.8 mmol/L and 1 mmol/L) inhibited the activity of SW480 and HT29 cells. (D and E) EdU cell proliferation experiments showed that 0.8 mmol/L ALO inhibited the proliferation of SW480 and HT29 cells. The magnification was 100×. (F–H) The clone formation experiment showed that ALO at a concentration gradient (0.2 mmol/L, 0.4 mmol/L and 0.8 mmol/L) inhibited the proliferation of SW480 and HT29 cells. (I–K) Flow cytometry experiments showed that ALO at a concentration gradient (0.2 mmol/L, 0.4 mmol/L and 0.8 mmol/L) promoted the apoptosis of SW480 and HT29 cells. All experiments were repeated three times to obtain average values. *p<0.05, **p<0.01, ***p<0.001 vs Control.\n\nThe Abnormally Low Expression of miR-296-5p in Colon Cancer Could Be Up-Regulated by ALO:\nData from the Starbase database showed that the expression of miR-296-5p in colon cancer patients was significantly lower than that in healthy people (p=2.5e-6, Figure 2A). In this study, however, after treatment with different concentrations of ALO for 24 h, miR-296-5p activity in SW480 and HT29 cells was significantly increased (p<0.01, Figure 2B and C), suggesting that ALO can stimulate the up-regulation of miR-296-5p.Figure 2The abnormally low expression of miR-296-5p in colon cancer could be upregulated by ALO. (A) Starbase (http://starbase.sysu.edu.cn/index.php) was used to retrieve the information of differential expression of miR-296-5p in colon adenocarcinoma (COAD, n=450) patients and healthy volunteers (n=8) in vivo. P=2.5e-6. (B and C) The qRT-PCR experiment showed that ALO at a concentration gradient (0.1 mmol/L, 0.2 mmol/L, 0.4 mmol/L, 0.8 mmol/L and 1 mmol/L) up-regulated miR-296-5p expression in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. **p<0.01, ***p<0.001 vs Control.Abbreviation: qRT-PCR, quantitative real-time polymerase chain reaction.\nThe abnormally low expression of miR-296-5p in colon cancer could be upregulated by ALO. (A) Starbase (http://starbase.sysu.edu.cn/index.php) was used to retrieve the information of differential expression of miR-296-5p in colon adenocarcinoma (COAD, n=450) patients and healthy volunteers (n=8) in vivo. P=2.5e-6. (B and C) The qRT-PCR experiment showed that ALO at a concentration gradient (0.1 mmol/L, 0.2 mmol/L, 0.4 mmol/L, 0.8 mmol/L and 1 mmol/L) up-regulated miR-296-5p expression in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. **p<0.01, ***p<0.001 vs Control.\n\nAmong the Differentially Expressed circRNAs in CRC, Only circNSUN2 Not Only Had a Target Site for miR-296-5p, but Also Could Be Regulated by ALO:\nThrough literature review, we screened 12 circRNAs that were reported to be associated with CRC: circDDX17, circHIPK3, circZFR, circRNA_100290, Has_circ_0026344, circVAPA, Hsa_circ_0009361, circNSUN2, circACAP2, circACVRL1, circITGA7 and circEIF4G3. We first detected the regulatory effect of ALO on these differentially expressed circRNAs by qRT-PCR to perform preliminary screening (Figure 3A). The results showed that the expressions of circDDX17, Has_circ_0026344, circVAPA, circNSUN2, circACAP2, circACVRL1 and circEIF4G3 were regulated by 0.8 mmol/L ALO (p<0.001, Figure 3A). Through subsequent screening of target genes, we found that only circNSUN2 and miR-296-5p have targeted binding sites (Figure 3B). After further verification, it was confirmed that circNSUN2-WT and miR-296-5p mimic did reduce the luciferase activity in SW480 and HT29 cells (p<0.001, Figure 3C and D). Therefore, we chose circNSUN2 as the next research object.Figure 3Among the differentially expressed circRNAs in CRC, only circNSUN2 not only had a target site for miR-296-5p, but also could be regulated by ALO. (A) qRT-PCR detected the regulation of 0.8 mmol/L ALO on the differentially expressed circRNAs in CRC. GAPDH played the role of internal reference. (B) The TargetScan database (http://www.targetscan.org) was used to predict the target sites of circNSUN2 and miR-296-5p. (C and D) The dual luciferase experiment confirmed that circNSUN2 can bind to miR-296-5p in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. ***p<0.001 vs Control; +++p<0.001 vs MC.Abbreviation: MC, miR-296-5p mimic control.\nAmong the differentially expressed circRNAs in CRC, only circNSUN2 not only had a target site for miR-296-5p, but also could be regulated by ALO. (A) qRT-PCR detected the regulation of 0.8 mmol/L ALO on the differentially expressed circRNAs in CRC. GAPDH played the role of internal reference. (B) The TargetScan database (http://www.targetscan.org) was used to predict the target sites of circNSUN2 and miR-296-5p. (C and D) The dual luciferase experiment confirmed that circNSUN2 can bind to miR-296-5p in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. ***p<0.001 vs Control; +++p<0.001 vs MC.\n\nOverexpression of circNSUN2 Partially Offset the Suppression of ALO on the Biological Behavior of Cancer Cells:\nIn SW480 and HT29 cells, ALO suppressed the mRNA level of circNSUN2, whereas transfection of circNSUN2 overexpression significantly increased the expression of circNSUN2 (p<0.001, Figure 4A and B). The following basic cell function experiments showed that the upregulation of circNSUN2 partially offset the inhibitory effect of ALO on CRC cell viability, proliferation and apoptosis (p<0.001, Figure 4C–L).Figure 4Overexpression of circNSUN2 partially offset the suppression of ALO on the biological behavior of cancer cells. (A and B) The qRT-PCR experiment showed that ALO (0.8 mmol/L) inhibited the expression of circNSUN2 in SW480 and HT29 cells. GAPDH played the role of internal reference. (C and D) The MTT experiment showed that overexpression of circNSUN2 partially offset the inhibition of ALO on cell viability in SW480 and HT29 cells. (E and F) EdU cell proliferation experiments showed that overexpression of circNSUN2 partially offset the inhibition of ALO on proliferation in SW480 and HT29 cells. The magnification was 100×. (G–I) The clone formation experiment showed that overexpression of circNSUN2 partially offset the inhibition of ALO on proliferation in SW480 and HT29 cells. (J–L) Flow cytometry experiments showed that overexpression of circNSUN2 partially offset the promotion of ALO on apoptosis in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. ***p<0.001 vs Control; ^p<0.05, ^^p<0.01, ^^^p<0.001 vs ALO+NC.Abbreviations: CircNSUN2, NOP2/Sun RNA methyltransferase 2; NC, negative control.\nOverexpression of circNSUN2 partially offset the suppression of ALO on the biological behavior of cancer cells. (A and B) The qRT-PCR experiment showed that ALO (0.8 mmol/L) inhibited the expression of circNSUN2 in SW480 and HT29 cells. GAPDH played the role of internal reference. (C and D) The MTT experiment showed that overexpression of circNSUN2 partially offset the inhibition of ALO on cell viability in SW480 and HT29 cells. (E and F) EdU cell proliferation experiments showed that overexpression of circNSUN2 partially offset the inhibition of ALO on proliferation in SW480 and HT29 cells. The magnification was 100×. (G–I) The clone formation experiment showed that overexpression of circNSUN2 partially offset the inhibition of ALO on proliferation in SW480 and HT29 cells. (J–L) Flow cytometry experiments showed that overexpression of circNSUN2 partially offset the promotion of ALO on apoptosis in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. ***p<0.001 vs Control; ^p<0.05, ^^p<0.01, ^^^p<0.001 vs ALO+NC.\n\nSh-circNSUN2 Inhibited the Malignant Development of CRC Cells, Which Was Neutralized by miR-296-5p Inhibitor:\nWe also tested the mutual regulation of circNSUN2 and miR-296-5p in SW480 and HT29 cells. Sh-circNSUN2 could down-regulate the expression of circNSUN2, and besides, it inhibited the proliferation of SW480 and HT29 cells, while promoting their apoptosis and the expression of miR-296-5p (p<0.001, Figure 5A–J). However, the inhibitory effect of sh-circNSUN2 on the malignant development of the two cancer cells was neutralized by miR-296-5p inhibitor (p<0.001, Figure 5A–J).Figure 5Sh-circNSUN2 inhibited the malignant development of CRC cells, which was neutralized by miR-296-5p inhibitor. (A and B) The qRT-PCR experiment showed that miR-296-5p inhibitor neutralized the inhibitory effect of sh-circNSUN2 on circNSUN2 expression. GAPDH played the role of internal reference. (C and D) The qRT-PCR experiment showed that sh-circNSUN2 up-regulated the mRNA level of miR-296-5p. U6 played the role of internal reference. (E–G) The clone formation experiment showed that the effect of sh-circNSUN2 on the proliferation of SW480 and HT29 cells was neutralized by miR-296-5p inhibitor. (H-J) Flow cytometry experiments showed that the effect of sh-circNSUN2 on promoting the apoptosis of SW480 and HT29 cells was neutralized by miR-296-5p inhibitor. All experiments were repeated three times to obtain average values. ***p<0.001 vs sh-NC+IC; ^^^p<0.001 vs sh-circNSUN2+IC; ###p<0.001 vs sh-NC+I.Abbreviations: Sh-circNSUN2, silent circNSUN2; sh-NC, silent negative control; IC, miR-296-5p inhibitor control; I, miR-296-5p inhibitor.\nSh-circNSUN2 inhibited the malignant development of CRC cells, which was neutralized by miR-296-5p inhibitor. (A and B) The qRT-PCR experiment showed that miR-296-5p inhibitor neutralized the inhibitory effect of sh-circNSUN2 on circNSUN2 expression. GAPDH played the role of internal reference. (C and D) The qRT-PCR experiment showed that sh-circNSUN2 up-regulated the mRNA level of miR-296-5p. U6 played the role of internal reference. (E–G) The clone formation experiment showed that the effect of sh-circNSUN2 on the proliferation of SW480 and HT29 cells was neutralized by miR-296-5p inhibitor. (H-J) Flow cytometry experiments showed that the effect of sh-circNSUN2 on promoting the apoptosis of SW480 and HT29 cells was neutralized by miR-296-5p inhibitor. All experiments were repeated three times to obtain average values. ***p<0.001 vs sh-NC+IC; ^^^p<0.001 vs sh-circNSUN2+IC; ###p<0.001 vs sh-NC+I.\n\nMiR-296-5p Bound to STAT3 and Upregulation of Mir-296-5p Inhibited the Expression of STAT3 in CRC Cells:\nWe screened the target genes of miR-296-5p, and found that miR-296-5p and STAT3 have targeted binding sites (Figure 6A), and this binding relation was further confirmed by a dual-luciferase verification test (p<0.001, Figure 6B and C). Next, we used qRT-PCR and Western blot to analyze the mRNA and protein levels of miR-296-5p and STAT. It was found that miR-296-5p mimic up-regulated miR-296-5p expression while preventing the activation of STAT3 (p<0.001, Figure 7A–G). However, the overexpression of STAT3 reversed the regulation of miR-296-5p mimic on the two genes (p<0.001, Figure 7A–G).Figure 6The targeted binding of miR-296-5p to STAT3. (A) TargetScan was used to predict the binding sequence of miR-296-5p and STAT3. (B and C) Dual luciferase experiment confirmed that miR-296-5p can bind to STAT3 in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. +++p<0.001 vs MC.Figure 7Up-regulation of miR-296-5p inhibited the expression of STAT3 in CRC cells. (A and B) The qRT-PCR experiment showed that miR-296-5p mimic up-regulated the mRNA level of miR-296-5p. U6 played the role of internal reference. (C and D) The qRT-PCR experiment showed that miR-296-5p mimic suppressed the mRNA level of STAT3, while overexpression of STAT3 reversed this effect. GAPDH played the role of internal reference. (E–G) The Western blot experiment showed that miR-296-5p mimic inhibited the protein level of STAT3, while over-expression of STAT3 reversed this effect. GAPDH played the role of internal reference. All experiments were repeated three times to obtain average values. ***p<0.001 vs MC+NC; ^^^p<0.001 vs M+NC; ###p<0.001 vs MC+STAT3.Abbreviation: STAT3, signal transducer and activator of transcription 3.\nThe targeted binding of miR-296-5p to STAT3. (A) TargetScan was used to predict the binding sequence of miR-296-5p and STAT3. (B and C) Dual luciferase experiment confirmed that miR-296-5p can bind to STAT3 in SW480 and HT29 cells. All experiments were repeated three times to obtain average values. +++p<0.001 vs MC.\nUp-regulation of miR-296-5p inhibited the expression of STAT3 in CRC cells. (A and B) The qRT-PCR experiment showed that miR-296-5p mimic up-regulated the mRNA level of miR-296-5p. U6 played the role of internal reference. (C and D) The qRT-PCR experiment showed that miR-296-5p mimic suppressed the mRNA level of STAT3, while overexpression of STAT3 reversed this effect. GAPDH played the role of internal reference. (E–G) The Western blot experiment showed that miR-296-5p mimic inhibited the protein level of STAT3, while over-expression of STAT3 reversed this effect. GAPDH played the role of internal reference. All experiments were repeated three times to obtain average values. ***p<0.001 vs MC+NC; ^^^p<0.001 vs M+NC; ###p<0.001 vs MC+STAT3.\n\nMiR-296-5p Mimic Inhibited Proliferation and Promoted Apoptosis in CRC Cells by Regulating Apoptosis-Related Genes, Which Was Partially Reversed by Overexpressed STAT3:\nThe following cell experiments showed that by activating Bax and blocking the protein activity of Bcl-2, miR-296-5p mimic inhibited proliferation yet accelerated apoptosis in cancer cells (p<0.001, Figure 8A–I). However, up-regulation of STAT3 produced a completely opposite regulatory effect (p<0.001, Figure 8A–I). More importantly, overexpression of STAT3 neutralized the regulation of miR-296-5p mimic on CRC cells (p<0.001, Figure 8A–I).Figure 8MiR-296-5p mimic inhibited proliferation and promoted apoptosis in CRC cells by regulating apoptosis-related genes, which was partially reversed by overexpression of STAT3. (A–C) The clone formation experiment showed that the inhibitory effect of miR-296-5p mimic on the proliferation of SW480 and HT29 cells was neutralized by overexpressed STAT3. (D–F) Flow cytometry experiments showed that miR-296-5p mimic promoted the apoptosis of SW480 and HT29 cells, which was neutralized by overexpressed STAT3. (G–I) Western blot experiments showed that the regulation of apoptosis-related protein expression by miR-296-5p mimic was neutralized by overexpressed STAT3. GAPDH played the role of internal reference. All experiments were repeated three times to obtain average values. *p<0.05, **p<0.01, ***p<0.001 vs MC+NC; ^^p<0.01, ^^^p<0.001 vs M+NC; ##p<0.01, ###p<0.001 vs MC+STAT3.\nMiR-296-5p mimic inhibited proliferation and promoted apoptosis in CRC cells by regulating apoptosis-related genes, which was partially reversed by overexpression of STAT3. (A–C) The clone formation experiment showed that the inhibitory effect of miR-296-5p mimic on the proliferation of SW480 and HT29 cells was neutralized by overexpressed STAT3. (D–F) Flow cytometry experiments showed that miR-296-5p mimic promoted the apoptosis of SW480 and HT29 cells, which was neutralized by overexpressed STAT3. (G–I) Western blot experiments showed that the regulation of apoptosis-related protein expression by miR-296-5p mimic was neutralized by overexpressed STAT3. GAPDH played the role of internal reference. All experiments were repeated three times to obtain average values. *p<0.05, **p<0.01, ***p<0.001 vs MC+NC; ^^p<0.01, ^^^p<0.001 vs M+NC; ##p<0.01, ###p<0.001 vs MC+STAT3.\n\nDiscussion:\nCRC is a malignant lesion of the mucosal epithelium of the colon or rectum under the action of various carcinogenic factors such as environment or heredity.2 Most CRC patients suffer from adenocarcinoma, which usually develops from polyps. Tumors developed from polyps can infiltrate in a circular shape along the horizontal axis of the intestinal tube, develop into the deep layer of the intestinal wall, and finally penetrate the intestinal wall and metastasize to blood vessels or lymphatic vessels.20 This is one of the important reasons for the high recurrence and metastasis rates of CRC. The ultimate goals of CRC research are to improve the current treatment of CRC, save patients’ lives and improve their quality of life. Based on the role of traditional Chinese medicine in disease prevention and treatment, this study explored the effects of ALO, an alkaloid exhibiting strong anticancer activity, on the proliferation and apoptosis of CRC cells. The results showed that after ALO treatment, the viability and proliferation of CRC cells were significantly inhibited; on the contrary, the number of apoptotic cancer cells was significantly increased. This is consistent with the previous experimental results.21 In order to probe deeper into the upstream mechanism of ALO in alleviating CRC carcinogenesis through the miR-296-5p/STAT3 axis, we screened and verified the upstream targeting circRNA of miR-296-5p.\nCircRNAs are a type of covalently closed circular non-coding RNA formed by back-splicing of mRNA precursors (pre-mRNA).22 CircRNA was considered to be a useless splicing by-product in early research. With the deepening of research, it is found that circRNA has a wide range of sources and plays a variety of functional roles in the growth and development of organisms, with the characteristics of conservative, stable, and tissue-specific.22,23 The most familiar and extensively studied function of circRNA is its sponging effect on miRNA.24 For example, circRNA ciRS-7 which contains more than 70 miR-7 binding sites can achieve competitive adsorption of miR-7 through the AGO2 protein.25 Besides, increasing reports on cancer indicate that circRNAs, such as circRNA-cTFRC, circPSMC3 and circSETD3, act as sponges of miRNAs and participate in transcriptional regulation.26–28 Based on these literature reports, we screened circRNAs that were reported to be abnormally expressed in CRC, and finally obtained 12 circRNAs. After detecting their expressions in ALO-treated cancer cells and predicting their binding sequence for miR-296-5p, circNSUN2 was finally identified as the research object of this study.\nCircNSUN2, which maps to the 5p15 amplicon in CRCs, was confirmed by Chen et al to regulate cytoplasmic output and promote liver metastasis of CRC through N 6-methyladenosine modification.29 Similarly, our experimental results uncovered that knockdown of circNSUN2 inhibited proliferation and accelerated apoptosis in CRC cells. More importantly, we for the first time revealed that ALO can prevent the activation of circNSUN2 and offset the promotion effect of overexpression of circNSUN2 on esophageal cancer progression. Since circNSUN2 and miR-296-5p have targeting sequences, we verified the cellular regulatory effects of circNSUN2 and miR-296-5p/STAT3 through dual luciferase experiments and rescue experiments. The final result confirmed our conjecture that regulating the circNSUN2/miR-296-5p/STAT3 axis can reduce the proliferation rate and increase the apoptosis rate of CRC cells.\nAt present, blocking proliferation and increasing apoptosis in cancer cells are the intensively discussed mechanisms in CRC study. Tumor cells can proliferate quickly and unrestrictedly, and thus, inhibiting their proliferation can produce an anti-tumor effect.30 Apoptosis, alternatively called programmed cell death, is an autonomous cell death process strictly controlled by multiple genes. Apoptosis is an important part of cell life cycle, and it is also an important link in regulating the development of the body and maintaining the stability of the internal environment in organisms.31,32 Our research shows that ALO inhibits the proliferation and promotes the apoptosis of CRC cells by regulating the circNSUN2/miR-296-5p/STAT3 pathway, and ultimately prevents the tumorigenesis of CRC.\nThere are still certain limitations in our research. Although we have confirmed the effect of ALO on inducing apoptosis and reducing proliferation in CRC cells and clarified the underlying mechanism, the circNSUN2/miR-296-5p/STAT3 axis and the pathways related to proliferation and apoptosis have not been analyzed yet, which will be further explored in future research. In addition, the anticancer effect of ALO on CRC and its experimental concentration needs to be further tested in animal and clinical experiments. Whether ALO has an impact on drug resistance in CRC is also a direction of future research.\n\n"},"whole_article_abstract":{"kind":"string","value":"Background:\nAloperine can regulate miR-296-5p/Signal Transducer and Activator of Transcription 3 (STAT3) pathway to inhibit the malignant development of colorectal cancer (CRC), but the regulatory mechanism is unclear. This study explored the upstream mechanism of Aloperine in reducing CRC damage from the perspective of the circRNA-miRNA-mRNA regulatory network.\n\nMethods:\nAfter treatment with gradient concentrations of Aloperine (0.1 mmol/L, 0.2 mmol/L, 0.4 mmol/L, 0.8 mmol/L and 1 mmol/L) for 24 hours, changes in CRC cell proliferation and apoptosis were detected by functional experiments. Data of the differential expression of miR-296-5p in CRC patients and healthy people were obtained from Starbase. The effects of Aloperine on 12 differentially expressed circRNAs were detected. The binding of miR-296-5p with NOP2/Sun RNA methyltransferase 2 (circNSUN2) and STAT3 was predicted by TargetScan and confirmed through dual-luciferase experiments. The expressions of circNSUN2, miR-296-5p and STAT3 as well as apoptosis-related genes in CRC cells were detected by qRT-PCR and Western blot as needed. Rescue experiments were conducted to test the regulatory effects of circNSUN2, miR-296-5p and STAT3 on CRC cells.\n\nResults:\nAloperine at a concentration gradient inhibited proliferation and promoted apoptosis in CRC cells. The abnormally low expression of miR-296-5p in CRC could be upregulated by Aloperine. Among the differentially expressed circRNAs in CRC, only circNSUN2 not only targets miR-296-5p, but also can be regulated by Aloperine. The up-regulation of circNSUN2 offset the inhibitory effect of Aloperine on cancer cells. The rescue experiments finally confirmed the regulation of circNSUN2/miR-296-5p/STAT3 axis in CRC cells.\n\nConclusions:\nBy regulating the circNSUN2/miR-296-5p/STAT3 pathway, Aloperine prevents the malignant development of CRC cells."},"background_conclusion_text":{"kind":"string","value":"Introduction:\nCancer has become the first killer in the world and the biggest obstacle for extending human life expectancy. It is estimated that there were 18.1 million new cancer cases and 9.6 million cancer deaths in 2018.1 Colorectal cancer (CRC) is one of the most common gastrointestinal tumors.2 According to the data from the International Agency for Research on Cancer (IARC),1 the number of new CRC cases worldwide in 2018 was approximately 1.09 million, making CRC the fourth most prevalent malignant tumor after lung, breast and prostate cancers; meantime, CRC had a high mortality rate of about 9.2%, ranking only after lung cancer. Epidemiological studies have shown that the incidence of CRC varies significantly in different countries, and is higher in developed areas.3 More importantly, as the incidence of CRC rises rapidly in people under 50, the age of onset of the disease is getting lower.4,5 Therefore, it is urgent to improve the diagnosis rate and treatment effect of CRC.\nCurrently, the main clinical treatment of CRC is surgery combined with adjuvant radiotherapy, chemotherapy or molecular targeted therapy. Surgery is generally considered as the first choice for comprehensive treatment of CRC. This method is suitable for patients whose tumors are confined to the intestinal wall while penetrating the intestinal wall and invading the serous or extraserous membrane without lymph node metastasis.6,7 However, the availability of radical resection is limited as most patients with CRC have adenocarcinoma, which generally develops from polyps and is metastatic by nature.8 As a result, CRC patients tend to undergo simple resection, which leads to a high recurrence rate. Chemotherapy, therefore, is still necessary for postoperative and late-stage CRC patients.9,10 At present, the commonly used chemotherapeutic drugs in clinic mainly include 5-fluorouracil (5-Fu), oxaliplatin and its derivatives.9 Chemotherapy is highly risky because it indiscriminately kills tumor cells and immune cells and thereby reduces the anti-tumor immune effect.11 Likewise, radiotherapy greatly weakens the body’s immunity.12 Hence, researchers have proposed to find effective treatments or drugs that cause less damage to patients.\nWith advances in the research and development of traditional Chinese medicine, more attention has been paid to the roles of traditional Chinese medicine and its active ingredients in disease prevention and treatment. At the same time, new targets for molecular targeted therapy of diseases have been discovered in the exploration of the molecular mechanisms of traditional Chinese medicine and its monomers. Aloperine (ALO) has been confirmed to have a significant anti-cancer activity. ALO is a component of the traditional Chinese medicine Sophora alopecuroides L.13 It is also one of the main alkaloids separated and extracted in the lab, with a molecular formula of C15H24N2.14 Recent studies have found that ALO has the effects of anti-inflammation, immunosuppression, redox suppression, cardiovascular protection and anti-cancer, among which its tumor-suppressing activity has been extensively reported. For example, Yu et al revealed that ALO inhibited the carcinogenesis process by regulating excessive autophagy in thyroid cancer cells;15 Liu et al demonstrated that ALO inhibited the PI3K/Akt pathway, increased apoptosis and caused cell cycle block in liver cancer cells;16 besides, ALO was also found to exert an anti-cancer effect in prostate cancer and breast cancer.17,18 In the previous study, our research group found that ALO up-regulated miR-296-5p and inhibited the activity of its target gene STAT3, thereby inhibiting the proliferation and inducing the apoptosis of CRC cells. However, the activation pathway of miR-296-5p is unclear. In order to clarify the upstream activation pathway of ALO-upregulated miR-296-5p, this study will explore the mechanism of ALO in inhibiting CRC from the perspective of the circRNA-miRNA-mRNA regulatory network.\n\nDiscussion:\nCRC is a malignant lesion of the mucosal epithelium of the colon or rectum under the action of various carcinogenic factors such as environment or heredity.2 Most CRC patients suffer from adenocarcinoma, which usually develops from polyps. Tumors developed from polyps can infiltrate in a circular shape along the horizontal axis of the intestinal tube, develop into the deep layer of the intestinal wall, and finally penetrate the intestinal wall and metastasize to blood vessels or lymphatic vessels.20 This is one of the important reasons for the high recurrence and metastasis rates of CRC. The ultimate goals of CRC research are to improve the current treatment of CRC, save patients’ lives and improve their quality of life. Based on the role of traditional Chinese medicine in disease prevention and treatment, this study explored the effects of ALO, an alkaloid exhibiting strong anticancer activity, on the proliferation and apoptosis of CRC cells. The results showed that after ALO treatment, the viability and proliferation of CRC cells were significantly inhibited; on the contrary, the number of apoptotic cancer cells was significantly increased. This is consistent with the previous experimental results.21 In order to probe deeper into the upstream mechanism of ALO in alleviating CRC carcinogenesis through the miR-296-5p/STAT3 axis, we screened and verified the upstream targeting circRNA of miR-296-5p.\nCircRNAs are a type of covalently closed circular non-coding RNA formed by back-splicing of mRNA precursors (pre-mRNA).22 CircRNA was considered to be a useless splicing by-product in early research. With the deepening of research, it is found that circRNA has a wide range of sources and plays a variety of functional roles in the growth and development of organisms, with the characteristics of conservative, stable, and tissue-specific.22,23 The most familiar and extensively studied function of circRNA is its sponging effect on miRNA.24 For example, circRNA ciRS-7 which contains more than 70 miR-7 binding sites can achieve competitive adsorption of miR-7 through the AGO2 protein.25 Besides, increasing reports on cancer indicate that circRNAs, such as circRNA-cTFRC, circPSMC3 and circSETD3, act as sponges of miRNAs and participate in transcriptional regulation.26–28 Based on these literature reports, we screened circRNAs that were reported to be abnormally expressed in CRC, and finally obtained 12 circRNAs. After detecting their expressions in ALO-treated cancer cells and predicting their binding sequence for miR-296-5p, circNSUN2 was finally identified as the research object of this study.\nCircNSUN2, which maps to the 5p15 amplicon in CRCs, was confirmed by Chen et al to regulate cytoplasmic output and promote liver metastasis of CRC through N 6-methyladenosine modification.29 Similarly, our experimental results uncovered that knockdown of circNSUN2 inhibited proliferation and accelerated apoptosis in CRC cells. More importantly, we for the first time revealed that ALO can prevent the activation of circNSUN2 and offset the promotion effect of overexpression of circNSUN2 on esophageal cancer progression. Since circNSUN2 and miR-296-5p have targeting sequences, we verified the cellular regulatory effects of circNSUN2 and miR-296-5p/STAT3 through dual luciferase experiments and rescue experiments. The final result confirmed our conjecture that regulating the circNSUN2/miR-296-5p/STAT3 axis can reduce the proliferation rate and increase the apoptosis rate of CRC cells.\nAt present, blocking proliferation and increasing apoptosis in cancer cells are the intensively discussed mechanisms in CRC study. Tumor cells can proliferate quickly and unrestrictedly, and thus, inhibiting their proliferation can produce an anti-tumor effect.30 Apoptosis, alternatively called programmed cell death, is an autonomous cell death process strictly controlled by multiple genes. Apoptosis is an important part of cell life cycle, and it is also an important link in regulating the development of the body and maintaining the stability of the internal environment in organisms.31,32 Our research shows that ALO inhibits the proliferation and promotes the apoptosis of CRC cells by regulating the circNSUN2/miR-296-5p/STAT3 pathway, and ultimately prevents the tumorigenesis of CRC.\nThere are still certain limitations in our research. Although we have confirmed the effect of ALO on inducing apoptosis and reducing proliferation in CRC cells and clarified the underlying mechanism, the circNSUN2/miR-296-5p/STAT3 axis and the pathways related to proliferation and apoptosis have not been analyzed yet, which will be further explored in future research. In addition, the anticancer effect of ALO on CRC and its experimental concentration needs to be further tested in animal and clinical experiments. Whether ALO has an impact on drug resistance in CRC is also a direction of future research."},"background_conclusion_abstract":{"kind":"string","value":"Background:\nAloperine can regulate miR-296-5p/Signal Transducer and Activator of Transcription 3 (STAT3) pathway to inhibit the malignant development of colorectal cancer (CRC), but the regulatory mechanism is unclear. This study explored the upstream mechanism of Aloperine in reducing CRC damage from the perspective of the circRNA-miRNA-mRNA regulatory network.\n\nMethods:\nAfter treatment with gradient concentrations of Aloperine (0.1 mmol/L, 0.2 mmol/L, 0.4 mmol/L, 0.8 mmol/L and 1 mmol/L) for 24 hours, changes in CRC cell proliferation and apoptosis were detected by functional experiments. Data of the differential expression of miR-296-5p in CRC patients and healthy people were obtained from Starbase. The effects of Aloperine on 12 differentially expressed circRNAs were detected. The binding of miR-296-5p with NOP2/Sun RNA methyltransferase 2 (circNSUN2) and STAT3 was predicted by TargetScan and confirmed through dual-luciferase experiments. The expressions of circNSUN2, miR-296-5p and STAT3 as well as apoptosis-related genes in CRC cells were detected by qRT-PCR and Western blot as needed. Rescue experiments were conducted to test the regulatory effects of circNSUN2, miR-296-5p and STAT3 on CRC cells.\n\nResults:\nAloperine at a concentration gradient inhibited proliferation and promoted apoptosis in CRC cells. The abnormally low expression of miR-296-5p in CRC could be upregulated by Aloperine. Among the differentially expressed circRNAs in CRC, only circNSUN2 not only targets miR-296-5p, but also can be regulated by Aloperine. The up-regulation of circNSUN2 offset the inhibitory effect of Aloperine on cancer cells. The rescue experiments finally confirmed the regulation of circNSUN2/miR-296-5p/STAT3 axis in CRC cells.\n\nConclusions:\nBy regulating the circNSUN2/miR-296-5p/STAT3 pathway, Aloperine prevents the malignant development of CRC cells."},"whole_article_text_length":{"kind":"number","value":17564,"string":"17,564"},"whole_article_abstract_length":{"kind":"number","value":354,"string":"354"},"other_sections_lengths":{"kind":"list like","value":[3307,174,127,135,102,165,183,226,170,277,62,7293,749,370,448,475,507,596,425,836],"string":"[\n 3307,\n 174,\n 127,\n 135,\n 102,\n 165,\n 183,\n 226,\n 170,\n 277,\n 62,\n 7293,\n 749,\n 370,\n 448,\n 475,\n 507,\n 596,\n 425,\n 836\n]"},"num_sections":{"kind":"number","value":21,"string":"21"},"most_frequent_words":{"kind":"list like","value":["cells","296","mir","mir 296","5p","296 5p","mir 296 5p","alo","sw480","ht29"],"string":"[\n \"cells\",\n \"296\",\n \"mir\",\n \"mir 296\",\n \"5p\",\n \"296 5p\",\n \"mir 296 5p\",\n \"alo\",\n \"sw480\",\n \"ht29\"\n]"},"keybert_topics":{"kind":"list like","value":["cancer crc common","colon cancer regulated","crc study tumor","colorectal cancer alo","2018 colorectal cancer"],"string":"[\n \"cancer crc common\",\n \"colon cancer regulated\",\n \"crc study tumor\",\n \"colorectal cancer alo\",\n \"2018 colorectal cancer\"\n]"},"annotated_base_background_abstract_prompt":{"kind":"string","value":""},"annotated_base_methods_abstract_prompt":{"kind":"null"},"annotated_base_results_abstract_prompt":{"kind":"null"},"annotated_base_conclusions_abstract_prompt":{"kind":"string","value":""},"annotated_base_whole_article_abstract_prompt":{"kind":"string","value":""},"annotated_base_background_conclusion_abstract_prompt":{"kind":"string","value":""},"annotated_keywords_background_abstract_prompt":{"kind":"string","value":"[CONTENT] colorectal cancer | aloperine | NOP2/Sun RNA methyltransferase 2 | miR-296-5p | signal transducer and activator of transcription 3 [SUMMARY]"},"annotated_keywords_methods_abstract_prompt":{"kind":"null"},"annotated_keywords_results_abstract_prompt":{"kind":"null"},"annotated_keywords_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] colorectal cancer | aloperine | NOP2/Sun RNA methyltransferase 2 | miR-296-5p | signal transducer and activator of transcription 3 [SUMMARY]"},"annotated_keywords_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] colorectal cancer | aloperine | NOP2/Sun RNA methyltransferase 2 | miR-296-5p | signal transducer and activator of transcription 3 [SUMMARY]"},"annotated_keywords_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] colorectal cancer | aloperine | NOP2/Sun RNA methyltransferase 2 | miR-296-5p | signal transducer and activator of transcription 3 [SUMMARY]"},"annotated_mesh_background_abstract_prompt":{"kind":"string","value":"[CONTENT] Antineoplastic Agents | Apoptosis | Cell Line, Tumor | Cell Proliferation | Colorectal Neoplasms | Computational Biology | Dose-Response Relationship, Drug | Drug Screening Assays, Antitumor | Humans | Methyltransferases | MicroRNAs | Molecular Structure | Quinolizidines | RNA, Circular | STAT3 Transcription Factor | Structure-Activity Relationship [SUMMARY]"},"annotated_mesh_methods_abstract_prompt":{"kind":"null"},"annotated_mesh_results_abstract_prompt":{"kind":"null"},"annotated_mesh_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] Antineoplastic Agents | Apoptosis | Cell Line, Tumor | Cell Proliferation | Colorectal Neoplasms | Computational Biology | Dose-Response Relationship, Drug | Drug Screening Assays, Antitumor | Humans | Methyltransferases | MicroRNAs | Molecular Structure | Quinolizidines | RNA, Circular | STAT3 Transcription Factor | Structure-Activity Relationship [SUMMARY]"},"annotated_mesh_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] Antineoplastic Agents | Apoptosis | Cell Line, Tumor | Cell Proliferation | Colorectal Neoplasms | Computational Biology | Dose-Response Relationship, Drug | Drug Screening Assays, Antitumor | Humans | Methyltransferases | MicroRNAs | Molecular Structure | Quinolizidines | RNA, Circular | STAT3 Transcription Factor | Structure-Activity Relationship [SUMMARY]"},"annotated_mesh_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] Antineoplastic Agents | Apoptosis | Cell Line, Tumor | Cell Proliferation | Colorectal Neoplasms | Computational Biology | Dose-Response Relationship, Drug | Drug Screening Assays, Antitumor | Humans | Methyltransferases | MicroRNAs | Molecular Structure | Quinolizidines | RNA, Circular | STAT3 Transcription Factor | Structure-Activity Relationship [SUMMARY]"},"annotated_keybert_background_abstract_prompt":{"kind":"string","value":"[CONTENT] cancer crc common | colon cancer regulated | crc study tumor | colorectal cancer alo | 2018 colorectal cancer [SUMMARY]"},"annotated_keybert_methods_abstract_prompt":{"kind":"null"},"annotated_keybert_results_abstract_prompt":{"kind":"null"},"annotated_keybert_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] cancer crc common | colon cancer regulated | crc study tumor | colorectal cancer alo | 2018 colorectal cancer [SUMMARY]"},"annotated_keybert_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] cancer crc common | colon cancer regulated | crc study tumor | colorectal cancer alo | 2018 colorectal cancer [SUMMARY]"},"annotated_keybert_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] cancer crc common | colon cancer regulated | crc study tumor | colorectal cancer alo | 2018 colorectal cancer [SUMMARY]"},"annotated_most_frequent_background_abstract_prompt":{"kind":"string","value":"[CONTENT] cells | 296 | mir | mir 296 | 5p | 296 5p | mir 296 5p | alo | sw480 | ht29 [SUMMARY]"},"annotated_most_frequent_methods_abstract_prompt":{"kind":"null"},"annotated_most_frequent_results_abstract_prompt":{"kind":"null"},"annotated_most_frequent_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] cells | 296 | mir | mir 296 | 5p | 296 5p | mir 296 5p | alo | sw480 | ht29 [SUMMARY]"},"annotated_most_frequent_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] cells | 296 | mir | mir 296 | 5p | 296 5p | mir 296 5p | alo | sw480 | ht29 [SUMMARY]"},"annotated_most_frequent_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] cells | 296 | mir | mir 296 | 5p | 296 5p | mir 296 5p | alo | sw480 | ht29 [SUMMARY]"},"annotated_tf_idf_background_abstract_prompt":{"kind":"string","value":"[CONTENT] cancer | crc | alo | molecular | anti | medicine | traditional chinese medicine | tumor | chinese | chinese medicine [SUMMARY]"},"annotated_tf_idf_methods_abstract_prompt":{"kind":"null"},"annotated_tf_idf_results_abstract_prompt":{"kind":"null"},"annotated_tf_idf_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] crc | circnsun2 | research | proliferation | apoptosis | mir | alo | circrna | axis | 5p stat3 [SUMMARY]"},"annotated_tf_idf_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] cells | 5p | 296 5p | mir 296 5p | mir | 296 | mir 296 | alo | circnsun2 | mmol [SUMMARY]"},"annotated_tf_idf_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] cells | 5p | 296 5p | mir 296 5p | mir | 296 | mir 296 | alo | circnsun2 | mmol [SUMMARY]"},"annotated_entity_plan_background_abstract_prompt":{"kind":"string","value":"[CONTENT] Activator of Transcription 3 ( | STAT3 | CRC ||| Aloperine | CRC [SUMMARY]"},"annotated_entity_plan_methods_abstract_prompt":{"kind":"null"},"annotated_entity_plan_results_abstract_prompt":{"kind":"null"},"annotated_entity_plan_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] ||| STAT3 | Aloperine | CRC [SUMMARY]"},"annotated_entity_plan_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] Activator of Transcription 3 ( | STAT3 | CRC ||| Aloperine | CRC ||| Aloperine | 0.1 mmol | 0.2 | 0.4 | 0.8 | 1 | 24 hours | CRC ||| CRC ||| Aloperine | 12 ||| NOP2 | 2 | STAT3 | TargetScan ||| STAT3 | CRC ||| STAT3 | CRC ||| ||| CRC ||| CRC | Aloperine ||| CRC | Aloperine ||| Aloperine ||| CRC ||| ||| ||| STAT3 | Aloperine | CRC [SUMMARY]"},"annotated_entity_plan_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] Activator of Transcription 3 ( | STAT3 | CRC ||| Aloperine | CRC ||| Aloperine | 0.1 mmol | 0.2 | 0.4 | 0.8 | 1 | 24 hours | CRC ||| CRC ||| Aloperine | 12 ||| NOP2 | 2 | STAT3 | TargetScan ||| STAT3 | CRC ||| STAT3 | CRC ||| ||| CRC ||| CRC | Aloperine ||| CRC | Aloperine ||| Aloperine ||| CRC ||| ||| ||| STAT3 | Aloperine | CRC [SUMMARY]"}}},{"rowIdx":274,"cells":{"title":{"kind":"string","value":"Safety and Efficacy of Drug-Coated Balloons in Patients with Acute Coronary Syndromes and Vulnerable Plaque."},"pmid":{"kind":"string","value":"36198017"},"background_abstract":{"kind":"string","value":"Percutaneous coronary intervention (PCI) is the main treatment option for acute coronary syndromes (ACS) often related to the progression and rupture of vulnerable plaques. While drug-eluting stents (DES) are now routinely used in PCI, drug-coated balloons (DCB) are a new strategy to PCI and their practice in the treatment of ACS with vulnerable plaques has not been reported. This study aimed to evaluate the safety and efficacy of DCB in ACS complicated with vulnerable plaque lesions."},"background_abstract_label":{"kind":"string","value":"BACKGROUND"},"methods_abstract":{"kind":"string","value":"123 patients were retrospectively analyzed and diagnosed with ACS and given PCI in our Cardiology Department from December 2020 to July 2022. Vulnerable plaques were confirmed by intravenous ultrasound (IVUS) in all patients. According to individual treatment plan, patients were entered into either DCB (n = 55) or DES (n = 68) groups. The results of coronary angiography and IVUS before and immediately after percutaneous coronary intervention were analyzed. The occurrence of major adverse cardiovascular events (MACE) and the results of coronary angiography were also evaluated during follow-up."},"methods_abstract_label":{"kind":"string","value":"METHODS"},"results_abstract":{"kind":"string","value":"There were no significant differences in baseline clinical characteristics, preoperative minimal luminal diameter (MLD), and preoperative diameter stenosis (DS) between the two groups. Also, there were no differences in IVUS plaque burden (PB), vessel area, and lumen area in the two groups before and immediately after PCI. The efficacy analysis showed that immediately after PCI, the DCB group had smaller MLD and higher degrees of lumen stenosis than the DES group (P < 0.05). However, during follow-up, no significant differences in MLD and DS were seen in two groups; relatively, late loss in luminal diameter（LLL）in the DCB group was smaller (P＜0.05). Safety analysis showed that during follow-up, 9 patients developed restenosis after DCB implantation while restenosis occurred in 10 patients with DES treatment, no statistical difference in the incidence of restenosis in the two groups. Besides, there was no statistical difference in the incidence of major adverse cardiac events（MACE）during hospitalization and follow-up in the DCB group (7.3% (4/55)) and the DES group (8.8% (6/68))."},"results_abstract_label":{"kind":"string","value":"RESULTS"},"conclusions_abstract":{"kind":"string","value":"DCB is safe and effective for ACS complicated with vulnerable plaque and has an advantage over DES in LLL."},"conclusions_abstract_label":{"kind":"string","value":"CONCLUSION"},"mesh_descriptor_names":{"kind":"list like","value":["Acute Coronary Syndrome","Angioplasty, Balloon, Coronary","Constriction, Pathologic","Coronary Angiography","Coronary Artery Disease","Coronary Restenosis","Drug-Eluting Stents","Humans","Percutaneous Coronary Intervention","Retrospective Studies","Treatment Outcome"],"string":"[\n \"Acute Coronary Syndrome\",\n \"Angioplasty, Balloon, Coronary\",\n \"Constriction, Pathologic\",\n \"Coronary Angiography\",\n \"Coronary Artery Disease\",\n \"Coronary Restenosis\",\n \"Drug-Eluting Stents\",\n \"Humans\",\n \"Percutaneous Coronary Intervention\",\n \"Retrospective Studies\",\n \"Treatment Outcome\"\n]"},"pmcid":{"kind":"string","value":"9537494"},"background_title":{"kind":"string","value":"Introduction"},"background_text":{"kind":"string","value":"Acute coronary syndromes (ACS) is a serious coronary heart disease that threatens\nhuman health. Coronary plaques in such patients usually have a large plaque burden\nwith lipid-rich necrotic cores, called vulnerable plaques, whose progression and\nrupture lead to unstable angina, acute non-ST elevation myocardial infarction, and\nacute ST-elevation myocardial infarction.1–4 Percutaneous coronary\nintervention is now an effective treatment approach for ACS. PCI can restore normal\nblood flow through myocardial revascularization and thus greatly improves symptoms\nand prognosis in ACS patients.5,6\nCompared with traditional two-dimensional coronary angiography (CAG), new\nintraluminal imaging techniques such as IVUS have more efficacy in reducing\ncomplications after drug-eluting stents placement, decreasing in-stent restenosis\nrate, and improving treatment outcome. Equally important, IVUS can better provide\nintravascular imaging data for coronary vascular lesions with coronary overlapping,\nanatomical abnormalities, aneurysms, myocardial bridges, and calcifications,\nespecially in the identification of coronary culprit vessels. Because most ACS\npatients present with plaque ruptures, an accurate identification of the nature,\nextent and location of plaque ruptures is particularly important for the formulation\nof diagnosis and treatment planning. In this context, IVUS is superior to CAG in\nproviding relevant imaging information and intraprocedural guidance of PCI and\nevaluating postoperative results and clinical prognosis.7,8\nCurrently, DES is the preferred strategy for PCI in patients with ACS. The use of DES\nhas the advantages of inducing intimal hyperplasia, thickening fibrous cap, and\nnormalizing wall stress to reduce plaque rupture. However, subsequent complications\nof DES may occur, such as in-stent restenosis, in-stent hyperplasia, and stent\nfracture, and require long-term dual antiplatelet therapy. In severe cases, there\nmay be no reflow phenomenon after stent placement, rapidly resulting in a larger\narea of myocardium infarction.9–11 DCB are a new\nrevascularization technique that has become the treatment of choice for in-stent\nrestenosis because it meets the new concept of intervention without implantation.\nIndications of DCB also include small vessel disease and bifurcation disease, among\nothers.12–14 However,\nthere are few reports related to the use of DCB in ACS patients with vulnerable\nplaques. Therefore, this study used DES as a parallel treatment to examine the\nsafety and efficacy of DCB in ACS complicated with vulnerable plaques lesions under\nthe guidance of IVUS."},"methods_title":{"kind":"null"},"methods_text":{"kind":"null"},"results_title":{"kind":"string","value":"Results"},"results_text":{"kind":"string","value":"Baseline Demographic and Clinical Characteristics A total of 123 patients were included in this study, of whom 55 received DCB and\n68 received DES. As shown in Table 1, there were no statistically\nsignificant differences between the DCB and the DES groups in terms of average\nage, gender distribution, ejection fraction, and other clinical characteristics\n(P > 0.05). Coronary heart disease-related risk factors\nsuch as hypertension, hyperlipidemia, diabetes, and smoking history were also\nnot significantly different between the two groups (P >\n0.05). Similarly, there were no significant differences in clinical diagnosis of\nST-elevation myocardial infarction, non-ST-elevation myocardial infarction, and\nunstable angina pectoris between the two groups\n(X2 = 0.92,P>0.05).\nBaseline Patient Characteristics of the Study Population.\nComparing two data sets using the continuity corrected chi-square\ntest; DCB: drug coated balloon, DES: drug-eluting stent; STEMI\n:ST-segment elevation myocardial infarction, NSTEMI: non-STEMI; MI\n:myocardial infarction; PCI :percutaneous coronary intervention;\nCABG: coronary artery bypass grafting; LVEF: Left Ventricle\nejection\nA total of 123 patients were included in this study, of whom 55 received DCB and\n68 received DES. As shown in Table 1, there were no statistically\nsignificant differences between the DCB and the DES groups in terms of average\nage, gender distribution, ejection fraction, and other clinical characteristics\n(P > 0.05). Coronary heart disease-related risk factors\nsuch as hypertension, hyperlipidemia, diabetes, and smoking history were also\nnot significantly different between the two groups (P >\n0.05). Similarly, there were no significant differences in clinical diagnosis of\nST-elevation myocardial infarction, non-ST-elevation myocardial infarction, and\nunstable angina pectoris between the two groups\n(X2 = 0.92,P>0.05).\nBaseline Patient Characteristics of the Study Population.\nComparing two data sets using the continuity corrected chi-square\ntest; DCB: drug coated balloon, DES: drug-eluting stent; STEMI\n:ST-segment elevation myocardial infarction, NSTEMI: non-STEMI; MI\n:myocardial infarction; PCI :percutaneous coronary intervention;\nCABG: coronary artery bypass grafting; LVEF: Left Ventricle\nejection\nProcedural Characteristics of PCI By combining preoperative ECG and echocardiography and intraoperative CAG\nresults, the culprit vessels in ACS patients were diagnosed and PCI was\nperformed. The target vessel locations included left anterior descending artery,\nleft circumflex artery, right coronary artery, and ramus coronary artery, and\nthere were no statistically significant differences in target vessel locations\nbetween the DCB and DES treatment\ngroups(X2 = 2.83,P＞0.05). According to\nintraoperative CAG, coronary artery lesions in the two groups were divided into\nsingle vessel disease, double vessel disease, and triple vessel disease, and\nthere were no significant differences in multivessel disease between the two\ngroups (X2 = 0.01,P＞0.05). Except for the\nsignificant difference in intraoperative balloon usage between the two groups\n(X2 = 7.69,P＜0.05), there were no statistical\ndifferences in other PCI procedural characteristics, including implantation\ndiameter and length, number of patients with intraoperative dissection, number\nof patients with temporary pacemakers, and number of patients using intra-aortic\nballoon pump(IABP)(P > 0.05). The procedural characteristics\nof PCI in the two groups are shown in Table 2.\nProcedural Characteristics of PCI.\nComparing two data sets using the continuity corrected chi-square\ntest #Comparing two data sets using the Fisher's exact\nprobability test; DCB: drug coated balloon, DES: drug-eluting stent;\nIABP: intra-aortic balloon pump\nBy combining preoperative ECG and echocardiography and intraoperative CAG\nresults, the culprit vessels in ACS patients were diagnosed and PCI was\nperformed. The target vessel locations included left anterior descending artery,\nleft circumflex artery, right coronary artery, and ramus coronary artery, and\nthere were no statistically significant differences in target vessel locations\nbetween the DCB and DES treatment\ngroups(X2 = 2.83,P＞0.05). According to\nintraoperative CAG, coronary artery lesions in the two groups were divided into\nsingle vessel disease, double vessel disease, and triple vessel disease, and\nthere were no significant differences in multivessel disease between the two\ngroups (X2 = 0.01,P＞0.05). Except for the\nsignificant difference in intraoperative balloon usage between the two groups\n(X2 = 7.69,P＜0.05), there were no statistical\ndifferences in other PCI procedural characteristics, including implantation\ndiameter and length, number of patients with intraoperative dissection, number\nof patients with temporary pacemakers, and number of patients using intra-aortic\nballoon pump(IABP)(P > 0.05). The procedural characteristics\nof PCI in the two groups are shown in Table 2.\nProcedural Characteristics of PCI.\nComparing two data sets using the continuity corrected chi-square\ntest #Comparing two data sets using the Fisher's exact\nprobability test; DCB: drug coated balloon, DES: drug-eluting stent;\nIABP: intra-aortic balloon pump\nQCA and IVUS Analysis The preoperative, postoperative and follow-up results of PCI are shown in Table 3. There were\nno significant differences in preoperative RFD, MLD and DS between the two\ngroups (P > 0.05). However, CAG immediately after DCB or DES\nimplantation showed significantly smaller MLD and greater degree of stenosis in\nthe DCB group than in the DES group (P < 0.05). Further\nfollow-up CAG after PCI revealed that there were no statistically significant\ndifferences in MLD, stenosis degree, and coronary restenosis between the two\ngroups (P > 0.05), except that compared to the DES group,\nthe DCB group had less LLL in lumen diameter and a greater number of lumen\nenlargements (P < 0.05).\nComparison of Coronary QCA and IVUS Results Between DCB and DES\nGroups.\nDCB: drug coated balloon, DES: drug-eluting stent; CAG: coronary\nartery angiography; IVUS: intravascular ultrasound; MLD: minimal\nlumen diameter; LLL: late luminal loss;\nBoth groups underwent IVUS before and after PCI. The results showed that there\nwere no significant differences in vessel area and lumen area between the two\ngroups (P > 0.05). In addition, no statistically significant\ndifferences were seen in preoperative plaque burden （79.05 ± 4.17 VS\n80.07 ± 4.68）and postprocedural plaque burden （61.69 ± 5.64 VS\n63.32 ± 6.07）between the two groups (P > 0.05).\nThe preoperative, postoperative and follow-up results of PCI are shown in Table 3. There were\nno significant differences in preoperative RFD, MLD and DS between the two\ngroups (P > 0.05). However, CAG immediately after DCB or DES\nimplantation showed significantly smaller MLD and greater degree of stenosis in\nthe DCB group than in the DES group (P < 0.05). Further\nfollow-up CAG after PCI revealed that there were no statistically significant\ndifferences in MLD, stenosis degree, and coronary restenosis between the two\ngroups (P > 0.05), except that compared to the DES group,\nthe DCB group had less LLL in lumen diameter and a greater number of lumen\nenlargements (P < 0.05).\nComparison of Coronary QCA and IVUS Results Between DCB and DES\nGroups.\nDCB: drug coated balloon, DES: drug-eluting stent; CAG: coronary\nartery angiography; IVUS: intravascular ultrasound; MLD: minimal\nlumen diameter; LLL: late luminal loss;\nBoth groups underwent IVUS before and after PCI. The results showed that there\nwere no significant differences in vessel area and lumen area between the two\ngroups (P > 0.05). In addition, no statistically significant\ndifferences were seen in preoperative plaque burden （79.05 ± 4.17 VS\n80.07 ± 4.68）and postprocedural plaque burden （61.69 ± 5.64 VS\n63.32 ± 6.07）between the two groups (P > 0.05).\nFollow-up MACE During the follow-up period, there were no significant differences in target\nlesion revascularization, myocardial infarction, and cardiac death between the\ntwo groups (P > 0.05). And survival analysis shows that\nthere were no significant differences in the survival rate of the two groups\n(Log-rank,X2 = 0.01,P > 0.05). The detailed data are shown in\nTable 4 and\nFigure 2.\nComparison of MACE Between DCB and DES Groups.\nComparing two data sets using the continuity corrected chi-square\ntest; #Comparing two data sets using the Fisher's exact\nprobability test; DCB: drug coated balloon, DES: drug-eluting stent;\nMACE: major adverse cardiovascular event; TLR: target lesion\nrevascularization; MI: myocardial infarction.\nDuring the follow-up period, there were no significant differences in target\nlesion revascularization, myocardial infarction, and cardiac death between the\ntwo groups (P > 0.05). And survival analysis shows that\nthere were no significant differences in the survival rate of the two groups\n(Log-rank,X2 = 0.01,P > 0.05). The detailed data are shown in\nTable 4 and\nFigure 2.\nComparison of MACE Between DCB and DES Groups.\nComparing two data sets using the continuity corrected chi-square\ntest; #Comparing two data sets using the Fisher's exact\nprobability test; DCB: drug coated balloon, DES: drug-eluting stent;\nMACE: major adverse cardiovascular event; TLR: target lesion\nrevascularization; MI: myocardial infarction."},"conclusions_title":{"kind":"string","value":"Conclusion"},"conclusions_text":{"kind":"string","value":"In summary, DCB is safe and effective in the treatment of ACS complicated with\nvulnerable plaque, and DCB has the advantage over DES in LLL. Our work provides\npractical experience in the interventional treatment of vulnerable plaques in\nACS."},"other_sections_titles":{"kind":"list like","value":["Materials and Methods","Patients","PCI Procedure","Analysis Parameters","Statistical Analysis","Baseline Demographic and Clinical Characteristics","Procedural Characteristics of PCI","QCA and IVUS Analysis","Follow-up MACE","Limitations","Conclusion"],"string":"[\n \"Materials and Methods\",\n \"Patients\",\n \"PCI Procedure\",\n \"Analysis Parameters\",\n \"Statistical Analysis\",\n \"Baseline Demographic and Clinical Characteristics\",\n \"Procedural Characteristics of PCI\",\n \"QCA and IVUS Analysis\",\n \"Follow-up MACE\",\n \"Limitations\",\n \"Conclusion\"\n]"},"other_sections_texts":{"kind":"list like","value":["Patients This retrospectively study investigated 123 patients who were diagnosed with ACS\nand underwent PCI in the Department of Cardiology, the First Affiliated Hospital\nof Zhengzhou University from December 2020 to July 2022, and all patients were\nconfirmed to have vulnerable plaques by IVUS. According to the treatment\nstrategy they received, patients were entered into either the DCB treatment\ngroup (n = 55) or the DES treatment group (n = 61). Figure 1 shows the study flowchart.\nThe flowchart of this study.\nInclusion criteria: (1) 18-85 years of age; (2) meeting the diagnostic criteria\nof the European Heart Association for ACS 15,16(3) having definite\nculprit vessel; and (4) gray-scale IVUS showing PB ≥ 70% and minimal luminal\narea (MLA) ≤ 4.0 mm2.\n17\n Exclusion criteria: (1) chronic total occlusion; (2) heavily calcified\nlesions requiring rotational atherectomy; (3) patients with thrombus aspiration;\n(4) severe valvular insufficiency or valvular stenosis; (5) definite\ncontraindications to antiplatelet drugs or anticoagulants; (6) dissection repair\nremedial stents after DCB implantation; (7) severe renal insufficiency (GFR<\n30 ml/min) or severe liver insufficiency; and (8) patients who declined DCB and\nDES implantation.\nThe survival curve of DCB and DES groups\nThis retrospectively study investigated 123 patients who were diagnosed with ACS\nand underwent PCI in the Department of Cardiology, the First Affiliated Hospital\nof Zhengzhou University from December 2020 to July 2022, and all patients were\nconfirmed to have vulnerable plaques by IVUS. According to the treatment\nstrategy they received, patients were entered into either the DCB treatment\ngroup (n = 55) or the DES treatment group (n = 61). Figure 1 shows the study flowchart.\nThe flowchart of this study.\nInclusion criteria: (1) 18-85 years of age; (2) meeting the diagnostic criteria\nof the European Heart Association for ACS 15,16(3) having definite\nculprit vessel; and (4) gray-scale IVUS showing PB ≥ 70% and minimal luminal\narea (MLA) ≤ 4.0 mm2.\n17\n Exclusion criteria: (1) chronic total occlusion; (2) heavily calcified\nlesions requiring rotational atherectomy; (3) patients with thrombus aspiration;\n(4) severe valvular insufficiency or valvular stenosis; (5) definite\ncontraindications to antiplatelet drugs or anticoagulants; (6) dissection repair\nremedial stents after DCB implantation; (7) severe renal insufficiency (GFR<\n30 ml/min) or severe liver insufficiency; and (8) patients who declined DCB and\nDES implantation.\nThe survival curve of DCB and DES groups\nPCI Procedure Patients received dual antiplatelet therapy.18–22 Scheme 1: preoperative\nloading dose of aspirin 300 mg + ticagrelor 180 mg, postoperative maintenance\ndose of aspirin 100 mg + ticagrelor 90 mg, twice a day. Scheme 2: preoperative\nloading dose of aspirin 300 mg + clopidogrel 300 mg, postoperative maintenance\ndose of aspirin 100 mg/d + clopidogrel 75 mg/d. For special ACS patients, such\nas emergency PCI patients, comatose patients, and patients with poor\ncooperation, preoperative dual antiplatelet drug therapy might not be given and\nreplaced by continuous intravenous anticoagulant drugs.\nAfter routine sterilization and draping, the radial artery or femoral artery was\nselected for arterial puncture and sheath insertion. CAG was performed according\nto established protocols. To clearly display the culprit vessels, an appropriate\npositioning was selected for imaging according to the preoperative\nelectrocardiogram, and multi-position imaging was performed if necessary.\nCulprit vessel were determined by combining the patient's electrocardiogram,\nechocardiography, and intraoperative angiography, and graded according to the\nThrombolysis in Myocardial Infarction (TIMI) criteria. For TIMI grade < 3\nculprit vessels, a compliant balloon was used to pre-dilate the culprit vessel\nlesions to restore TIMI grade 3 flow. According to the patient's condition, IVUS\nwas performed at the appropriate time. The probe was sent to the relatively\nnormal segment at the distal end of the culprit vessel, and then withdrew to the\nrelatively normal segment at the proximal end at a constant rate of 0.5 mm/s to\nobtain intravascular imaging including vessel area, lumen area and plaque\nburden.\nBefore DCB or DES implant in the culprit vessel, an appropriate compliant\nballoon, semi-compliant balloon, cutting balloon, or spinous balloon was used to\ndilate the lesion and reduce culprit stenosis.\n23\n For DCB implantation, if the degree of vascular stenosis was ≤ 30%, and\nthere was no dissection or only type A or B dissection after dilation, a\nsize-matched DCB was chosen based on IVUS imaging and placed in the lesion\nsustained release before balloon withdrawal. The length of the DCB catheter\nexceed the target lesion by at least 5 mm. and the ratio of DCB diameters with\nreference vessel diameters were 0.8–1.0. The recommended inflation time was at\nleast 40 s at >7 atm. For DES implantation, after pre-dilation, a\nsize-matched DES based on IVUS imaging was placed in the lesion and released. A\nhigh-pressure balloon with appropriate dimensions was selected for post-stenting\ndilation to ensure that the stent was fully expanded and adhered to the vessel\nwall. IVUS examination was performed after DCB and DES implantation to further\nevaluate the immediate postoperative efficacy and guide further treatment of\nstent malapposition and dissection. At the same time, postoperative IVUS imaging\nresults were recorded.\nIn this study, DCB had a paclitaxel/iohexol matrix coating on the Bingo\nDrug-Coated Balloon (Bingo™, Yinyi Biotech, Dalian, China). And the IVUS imaging\nwere done with\nthe computer program (H749A70200,Boston Scientific, Shanghai, China).\nPatients received dual antiplatelet therapy.18–22 Scheme 1: preoperative\nloading dose of aspirin 300 mg + ticagrelor 180 mg, postoperative maintenance\ndose of aspirin 100 mg + ticagrelor 90 mg, twice a day. Scheme 2: preoperative\nloading dose of aspirin 300 mg + clopidogrel 300 mg, postoperative maintenance\ndose of aspirin 100 mg/d + clopidogrel 75 mg/d. For special ACS patients, such\nas emergency PCI patients, comatose patients, and patients with poor\ncooperation, preoperative dual antiplatelet drug therapy might not be given and\nreplaced by continuous intravenous anticoagulant drugs.\nAfter routine sterilization and draping, the radial artery or femoral artery was\nselected for arterial puncture and sheath insertion. CAG was performed according\nto established protocols. To clearly display the culprit vessels, an appropriate\npositioning was selected for imaging according to the preoperative\nelectrocardiogram, and multi-position imaging was performed if necessary.\nCulprit vessel were determined by combining the patient's electrocardiogram,\nechocardiography, and intraoperative angiography, and graded according to the\nThrombolysis in Myocardial Infarction (TIMI) criteria. For TIMI grade < 3\nculprit vessels, a compliant balloon was used to pre-dilate the culprit vessel\nlesions to restore TIMI grade 3 flow. According to the patient's condition, IVUS\nwas performed at the appropriate time. The probe was sent to the relatively\nnormal segment at the distal end of the culprit vessel, and then withdrew to the\nrelatively normal segment at the proximal end at a constant rate of 0.5 mm/s to\nobtain intravascular imaging including vessel area, lumen area and plaque\nburden.\nBefore DCB or DES implant in the culprit vessel, an appropriate compliant\nballoon, semi-compliant balloon, cutting balloon, or spinous balloon was used to\ndilate the lesion and reduce culprit stenosis.\n23\n For DCB implantation, if the degree of vascular stenosis was ≤ 30%, and\nthere was no dissection or only type A or B dissection after dilation, a\nsize-matched DCB was chosen based on IVUS imaging and placed in the lesion\nsustained release before balloon withdrawal. The length of the DCB catheter\nexceed the target lesion by at least 5 mm. and the ratio of DCB diameters with\nreference vessel diameters were 0.8–1.0. The recommended inflation time was at\nleast 40 s at >7 atm. For DES implantation, after pre-dilation, a\nsize-matched DES based on IVUS imaging was placed in the lesion and released. A\nhigh-pressure balloon with appropriate dimensions was selected for post-stenting\ndilation to ensure that the stent was fully expanded and adhered to the vessel\nwall. IVUS examination was performed after DCB and DES implantation to further\nevaluate the immediate postoperative efficacy and guide further treatment of\nstent malapposition and dissection. At the same time, postoperative IVUS imaging\nresults were recorded.\nIn this study, DCB had a paclitaxel/iohexol matrix coating on the Bingo\nDrug-Coated Balloon (Bingo™, Yinyi Biotech, Dalian, China). And the IVUS imaging\nwere done with\nthe computer program (H749A70200,Boston Scientific, Shanghai, China).\nAnalysis Parameters Basic patient information was collected, including sex, age, clinical diagnosis,\nleft ventricular ejection fraction (LVEF), history of hypertension,\nhyperlipidemia, diabetes, smoking, myocardial infarction, previous PCI, previous\ncoronary artery bypass grafting (CABG), and family history of coronary heart\ndisease.\nQuantitative coronary angiography (QCA) software was used to analyze the CAG data\nof all patients, including (1) preoperative and immediate postoperative\nreference vessel diameter (RFD), minimal luminal diameter, and diameter\nstenosis; (2) MLD, DS, LLL, restenosis lesions, and lumen enlargement at\nfollow-up.\nThe IVUS analysis were also evaluated, including preoperative and immediate\npostoperative vessel area, plaque burden, and lumen area. Because the PB ≥70%\nwas the strongest independent predictor of subsequent lesion-related events in\nthe first PROSPECT study, and Gregg et al defined vulnerable plaque with this PB\nthreshold.17,24 In this study, the PB criterion was selected to define\na vulnerable plaque, and the result of CAG and IVUS were evaluated by two\nexperienced researchers.\nAll patients passed the outpatient or telephone assessment 1, 3, 6, and 12 months\nafter PCI. Major adverse cardiovascular events during hospitalization and\nfollow-up were target lesion revascularization (TLR), myocardial infarction\n(MI), and cardiac death.\nBasic patient information was collected, including sex, age, clinical diagnosis,\nleft ventricular ejection fraction (LVEF), history of hypertension,\nhyperlipidemia, diabetes, smoking, myocardial infarction, previous PCI, previous\ncoronary artery bypass grafting (CABG), and family history of coronary heart\ndisease.\nQuantitative coronary angiography (QCA) software was used to analyze the CAG data\nof all patients, including (1) preoperative and immediate postoperative\nreference vessel diameter (RFD), minimal luminal diameter, and diameter\nstenosis; (2) MLD, DS, LLL, restenosis lesions, and lumen enlargement at\nfollow-up.\nThe IVUS analysis were also evaluated, including preoperative and immediate\npostoperative vessel area, plaque burden, and lumen area. Because the PB ≥70%\nwas the strongest independent predictor of subsequent lesion-related events in\nthe first PROSPECT study, and Gregg et al defined vulnerable plaque with this PB\nthreshold.17,24 In this study, the PB criterion was selected to define\na vulnerable plaque, and the result of CAG and IVUS were evaluated by two\nexperienced researchers.\nAll patients passed the outpatient or telephone assessment 1, 3, 6, and 12 months\nafter PCI. Major adverse cardiovascular events during hospitalization and\nfollow-up were target lesion revascularization (TLR), myocardial infarction\n(MI), and cardiac death.\nStatistical Analysis SPSS version 22.0 (IBM Corporation, Armonk, NY, USA). was used for statistical\nanalysis. Quantitative data that conformed to normal distribution were analyzed\nby t-test and expressed as mean ± standard deviation (SD); those that did not\nconform to normal distribution were analyzed by Wilcoxon rank-sum test and\nexpressed as median and quartile range (M (P25, P75)). Qualitative data were\ncompared using the chi-square test. If T ≤ 5, the continuity-corrected\nchi-square test and Fisher's exact probability test were used and expressed as\nnumber of cases (%). A Kaplan-Meier curve was used to describe survival\nanalysis. Two-tailed P < 0.05 indicates a statistically significant\ndifference.\nSPSS version 22.0 (IBM Corporation, Armonk, NY, USA). was used for statistical\nanalysis. Quantitative data that conformed to normal distribution were analyzed\nby t-test and expressed as mean ± standard deviation (SD); those that did not\nconform to normal distribution were analyzed by Wilcoxon rank-sum test and\nexpressed as median and quartile range (M (P25, P75)). Qualitative data were\ncompared using the chi-square test. If T ≤ 5, the continuity-corrected\nchi-square test and Fisher's exact probability test were used and expressed as\nnumber of cases (%). A Kaplan-Meier curve was used to describe survival\nanalysis. Two-tailed P < 0.05 indicates a statistically significant\ndifference.","This retrospectively study investigated 123 patients who were diagnosed with ACS\nand underwent PCI in the Department of Cardiology, the First Affiliated Hospital\nof Zhengzhou University from December 2020 to July 2022, and all patients were\nconfirmed to have vulnerable plaques by IVUS. According to the treatment\nstrategy they received, patients were entered into either the DCB treatment\ngroup (n = 55) or the DES treatment group (n = 61). Figure 1 shows the study flowchart.\nThe flowchart of this study.\nInclusion criteria: (1) 18-85 years of age; (2) meeting the diagnostic criteria\nof the European Heart Association for ACS 15,16(3) having definite\nculprit vessel; and (4) gray-scale IVUS showing PB ≥ 70% and minimal luminal\narea (MLA) ≤ 4.0 mm2.\n17\n Exclusion criteria: (1) chronic total occlusion; (2) heavily calcified\nlesions requiring rotational atherectomy; (3) patients with thrombus aspiration;\n(4) severe valvular insufficiency or valvular stenosis; (5) definite\ncontraindications to antiplatelet drugs or anticoagulants; (6) dissection repair\nremedial stents after DCB implantation; (7) severe renal insufficiency (GFR<\n30 ml/min) or severe liver insufficiency; and (8) patients who declined DCB and\nDES implantation.\nThe survival curve of DCB and DES groups","Patients received dual antiplatelet therapy.18–22 Scheme 1: preoperative\nloading dose of aspirin 300 mg + ticagrelor 180 mg, postoperative maintenance\ndose of aspirin 100 mg + ticagrelor 90 mg, twice a day. Scheme 2: preoperative\nloading dose of aspirin 300 mg + clopidogrel 300 mg, postoperative maintenance\ndose of aspirin 100 mg/d + clopidogrel 75 mg/d. For special ACS patients, such\nas emergency PCI patients, comatose patients, and patients with poor\ncooperation, preoperative dual antiplatelet drug therapy might not be given and\nreplaced by continuous intravenous anticoagulant drugs.\nAfter routine sterilization and draping, the radial artery or femoral artery was\nselected for arterial puncture and sheath insertion. CAG was performed according\nto established protocols. To clearly display the culprit vessels, an appropriate\npositioning was selected for imaging according to the preoperative\nelectrocardiogram, and multi-position imaging was performed if necessary.\nCulprit vessel were determined by combining the patient's electrocardiogram,\nechocardiography, and intraoperative angiography, and graded according to the\nThrombolysis in Myocardial Infarction (TIMI) criteria. For TIMI grade < 3\nculprit vessels, a compliant balloon was used to pre-dilate the culprit vessel\nlesions to restore TIMI grade 3 flow. According to the patient's condition, IVUS\nwas performed at the appropriate time. The probe was sent to the relatively\nnormal segment at the distal end of the culprit vessel, and then withdrew to the\nrelatively normal segment at the proximal end at a constant rate of 0.5 mm/s to\nobtain intravascular imaging including vessel area, lumen area and plaque\nburden.\nBefore DCB or DES implant in the culprit vessel, an appropriate compliant\nballoon, semi-compliant balloon, cutting balloon, or spinous balloon was used to\ndilate the lesion and reduce culprit stenosis.\n23\n For DCB implantation, if the degree of vascular stenosis was ≤ 30%, and\nthere was no dissection or only type A or B dissection after dilation, a\nsize-matched DCB was chosen based on IVUS imaging and placed in the lesion\nsustained release before balloon withdrawal. The length of the DCB catheter\nexceed the target lesion by at least 5 mm. and the ratio of DCB diameters with\nreference vessel diameters were 0.8–1.0. The recommended inflation time was at\nleast 40 s at >7 atm. For DES implantation, after pre-dilation, a\nsize-matched DES based on IVUS imaging was placed in the lesion and released. A\nhigh-pressure balloon with appropriate dimensions was selected for post-stenting\ndilation to ensure that the stent was fully expanded and adhered to the vessel\nwall. IVUS examination was performed after DCB and DES implantation to further\nevaluate the immediate postoperative efficacy and guide further treatment of\nstent malapposition and dissection. At the same time, postoperative IVUS imaging\nresults were recorded.\nIn this study, DCB had a paclitaxel/iohexol matrix coating on the Bingo\nDrug-Coated Balloon (Bingo™, Yinyi Biotech, Dalian, China). And the IVUS imaging\nwere done with\nthe computer program (H749A70200,Boston Scientific, Shanghai, China).","Basic patient information was collected, including sex, age, clinical diagnosis,\nleft ventricular ejection fraction (LVEF), history of hypertension,\nhyperlipidemia, diabetes, smoking, myocardial infarction, previous PCI, previous\ncoronary artery bypass grafting (CABG), and family history of coronary heart\ndisease.\nQuantitative coronary angiography (QCA) software was used to analyze the CAG data\nof all patients, including (1) preoperative and immediate postoperative\nreference vessel diameter (RFD), minimal luminal diameter, and diameter\nstenosis; (2) MLD, DS, LLL, restenosis lesions, and lumen enlargement at\nfollow-up.\nThe IVUS analysis were also evaluated, including preoperative and immediate\npostoperative vessel area, plaque burden, and lumen area. Because the PB ≥70%\nwas the strongest independent predictor of subsequent lesion-related events in\nthe first PROSPECT study, and Gregg et al defined vulnerable plaque with this PB\nthreshold.17,24 In this study, the PB criterion was selected to define\na vulnerable plaque, and the result of CAG and IVUS were evaluated by two\nexperienced researchers.\nAll patients passed the outpatient or telephone assessment 1, 3, 6, and 12 months\nafter PCI. Major adverse cardiovascular events during hospitalization and\nfollow-up were target lesion revascularization (TLR), myocardial infarction\n(MI), and cardiac death.","SPSS version 22.0 (IBM Corporation, Armonk, NY, USA). was used for statistical\nanalysis. Quantitative data that conformed to normal distribution were analyzed\nby t-test and expressed as mean ± standard deviation (SD); those that did not\nconform to normal distribution were analyzed by Wilcoxon rank-sum test and\nexpressed as median and quartile range (M (P25, P75)). Qualitative data were\ncompared using the chi-square test. If T ≤ 5, the continuity-corrected\nchi-square test and Fisher's exact probability test were used and expressed as\nnumber of cases (%). A Kaplan-Meier curve was used to describe survival\nanalysis. Two-tailed P < 0.05 indicates a statistically significant\ndifference.","A total of 123 patients were included in this study, of whom 55 received DCB and\n68 received DES. As shown in Table 1, there were no statistically\nsignificant differences between the DCB and the DES groups in terms of average\nage, gender distribution, ejection fraction, and other clinical characteristics\n(P > 0.05). Coronary heart disease-related risk factors\nsuch as hypertension, hyperlipidemia, diabetes, and smoking history were also\nnot significantly different between the two groups (P >\n0.05). Similarly, there were no significant differences in clinical diagnosis of\nST-elevation myocardial infarction, non-ST-elevation myocardial infarction, and\nunstable angina pectoris between the two groups\n(X2 = 0.92,P>0.05).\nBaseline Patient Characteristics of the Study Population.\nComparing two data sets using the continuity corrected chi-square\ntest; DCB: drug coated balloon, DES: drug-eluting stent; STEMI\n:ST-segment elevation myocardial infarction, NSTEMI: non-STEMI; MI\n:myocardial infarction; PCI :percutaneous coronary intervention;\nCABG: coronary artery bypass grafting; LVEF: Left Ventricle\nejection","By combining preoperative ECG and echocardiography and intraoperative CAG\nresults, the culprit vessels in ACS patients were diagnosed and PCI was\nperformed. The target vessel locations included left anterior descending artery,\nleft circumflex artery, right coronary artery, and ramus coronary artery, and\nthere were no statistically significant differences in target vessel locations\nbetween the DCB and DES treatment\ngroups(X2 = 2.83,P＞0.05). According to\nintraoperative CAG, coronary artery lesions in the two groups were divided into\nsingle vessel disease, double vessel disease, and triple vessel disease, and\nthere were no significant differences in multivessel disease between the two\ngroups (X2 = 0.01,P＞0.05). Except for the\nsignificant difference in intraoperative balloon usage between the two groups\n(X2 = 7.69,P＜0.05), there were no statistical\ndifferences in other PCI procedural characteristics, including implantation\ndiameter and length, number of patients with intraoperative dissection, number\nof patients with temporary pacemakers, and number of patients using intra-aortic\nballoon pump(IABP)(P > 0.05). The procedural characteristics\nof PCI in the two groups are shown in Table 2.\nProcedural Characteristics of PCI.\nComparing two data sets using the continuity corrected chi-square\ntest #Comparing two data sets using the Fisher's exact\nprobability test; DCB: drug coated balloon, DES: drug-eluting stent;\nIABP: intra-aortic balloon pump","The preoperative, postoperative and follow-up results of PCI are shown in Table 3. There were\nno significant differences in preoperative RFD, MLD and DS between the two\ngroups (P > 0.05). However, CAG immediately after DCB or DES\nimplantation showed significantly smaller MLD and greater degree of stenosis in\nthe DCB group than in the DES group (P < 0.05). Further\nfollow-up CAG after PCI revealed that there were no statistically significant\ndifferences in MLD, stenosis degree, and coronary restenosis between the two\ngroups (P > 0.05), except that compared to the DES group,\nthe DCB group had less LLL in lumen diameter and a greater number of lumen\nenlargements (P < 0.05).\nComparison of Coronary QCA and IVUS Results Between DCB and DES\nGroups.\nDCB: drug coated balloon, DES: drug-eluting stent; CAG: coronary\nartery angiography; IVUS: intravascular ultrasound; MLD: minimal\nlumen diameter; LLL: late luminal loss;\nBoth groups underwent IVUS before and after PCI. The results showed that there\nwere no significant differences in vessel area and lumen area between the two\ngroups (P > 0.05). In addition, no statistically significant\ndifferences were seen in preoperative plaque burden （79.05 ± 4.17 VS\n80.07 ± 4.68）and postprocedural plaque burden （61.69 ± 5.64 VS\n63.32 ± 6.07）between the two groups (P > 0.05).","During the follow-up period, there were no significant differences in target\nlesion revascularization, myocardial infarction, and cardiac death between the\ntwo groups (P > 0.05). And survival analysis shows that\nthere were no significant differences in the survival rate of the two groups\n(Log-rank,X2 = 0.01,P > 0.05). The detailed data are shown in\nTable 4 and\nFigure 2.\nComparison of MACE Between DCB and DES Groups.\nComparing two data sets using the continuity corrected chi-square\ntest; #Comparing two data sets using the Fisher's exact\nprobability test; DCB: drug coated balloon, DES: drug-eluting stent;\nMACE: major adverse cardiovascular event; TLR: target lesion\nrevascularization; MI: myocardial infarction.","This study has certain limitations. As a retrospective, single-center, and\nsmall-sample study, there may be patient selection bias. Second, although CAG was\nperformed during follow-up in both groups, we had to assess MLD using QCA\nimmediately after PCI due to the lack of IVUS imaging during follow-up. These\nlimitations may have affected the results to some extent. Therefore, to further\nvalidate our conclusions, multiple interventional methods such as optical coherence\ntomography(OCT), virtual histology IVUS (VH-IVUS), and near-infrared spectroscopy\n(NIRS) can be used to comprehensively detect and identify vulnerable plaques to\nachieve accurate diagnosis of lesions. In addition, multicenter, large sample, and\nlonger follow-up studies are needed.","In summary, DCB is safe and effective in the treatment of ACS complicated with\nvulnerable plaque, and DCB has the advantage over DES in LLL. Our work provides\npractical experience in the interventional treatment of vulnerable plaques in\nACS."],"string":"[\n \"Patients This retrospectively study investigated 123 patients who were diagnosed with ACS\\nand underwent PCI in the Department of Cardiology, the First Affiliated Hospital\\nof Zhengzhou University from December 2020 to July 2022, and all patients were\\nconfirmed to have vulnerable plaques by IVUS. According to the treatment\\nstrategy they received, patients were entered into either the DCB treatment\\ngroup (n = 55) or the DES treatment group (n = 61). Figure 1 shows the study flowchart.\\nThe flowchart of this study.\\nInclusion criteria: (1) 18-85 years of age; (2) meeting the diagnostic criteria\\nof the European Heart Association for ACS 15,16(3) having definite\\nculprit vessel; and (4) gray-scale IVUS showing PB ≥ 70% and minimal luminal\\narea (MLA) ≤ 4.0 mm2.\\n17\\n Exclusion criteria: (1) chronic total occlusion; (2) heavily calcified\\nlesions requiring rotational atherectomy; (3) patients with thrombus aspiration;\\n(4) severe valvular insufficiency or valvular stenosis; (5) definite\\ncontraindications to antiplatelet drugs or anticoagulants; (6) dissection repair\\nremedial stents after DCB implantation; (7) severe renal insufficiency (GFR<\\n30 ml/min) or severe liver insufficiency; and (8) patients who declined DCB and\\nDES implantation.\\nThe survival curve of DCB and DES groups\\nThis retrospectively study investigated 123 patients who were diagnosed with ACS\\nand underwent PCI in the Department of Cardiology, the First Affiliated Hospital\\nof Zhengzhou University from December 2020 to July 2022, and all patients were\\nconfirmed to have vulnerable plaques by IVUS. According to the treatment\\nstrategy they received, patients were entered into either the DCB treatment\\ngroup (n = 55) or the DES treatment group (n = 61). Figure 1 shows the study flowchart.\\nThe flowchart of this study.\\nInclusion criteria: (1) 18-85 years of age; (2) meeting the diagnostic criteria\\nof the European Heart Association for ACS 15,16(3) having definite\\nculprit vessel; and (4) gray-scale IVUS showing PB ≥ 70% and minimal luminal\\narea (MLA) ≤ 4.0 mm2.\\n17\\n Exclusion criteria: (1) chronic total occlusion; (2) heavily calcified\\nlesions requiring rotational atherectomy; (3) patients with thrombus aspiration;\\n(4) severe valvular insufficiency or valvular stenosis; (5) definite\\ncontraindications to antiplatelet drugs or anticoagulants; (6) dissection repair\\nremedial stents after DCB implantation; (7) severe renal insufficiency (GFR<\\n30 ml/min) or severe liver insufficiency; and (8) patients who declined DCB and\\nDES implantation.\\nThe survival curve of DCB and DES groups\\nPCI Procedure Patients received dual antiplatelet therapy.18–22 Scheme 1: preoperative\\nloading dose of aspirin 300 mg + ticagrelor 180 mg, postoperative maintenance\\ndose of aspirin 100 mg + ticagrelor 90 mg, twice a day. Scheme 2: preoperative\\nloading dose of aspirin 300 mg + clopidogrel 300 mg, postoperative maintenance\\ndose of aspirin 100 mg/d + clopidogrel 75 mg/d. For special ACS patients, such\\nas emergency PCI patients, comatose patients, and patients with poor\\ncooperation, preoperative dual antiplatelet drug therapy might not be given and\\nreplaced by continuous intravenous anticoagulant drugs.\\nAfter routine sterilization and draping, the radial artery or femoral artery was\\nselected for arterial puncture and sheath insertion. CAG was performed according\\nto established protocols. To clearly display the culprit vessels, an appropriate\\npositioning was selected for imaging according to the preoperative\\nelectrocardiogram, and multi-position imaging was performed if necessary.\\nCulprit vessel were determined by combining the patient's electrocardiogram,\\nechocardiography, and intraoperative angiography, and graded according to the\\nThrombolysis in Myocardial Infarction (TIMI) criteria. For TIMI grade < 3\\nculprit vessels, a compliant balloon was used to pre-dilate the culprit vessel\\nlesions to restore TIMI grade 3 flow. According to the patient's condition, IVUS\\nwas performed at the appropriate time. The probe was sent to the relatively\\nnormal segment at the distal end of the culprit vessel, and then withdrew to the\\nrelatively normal segment at the proximal end at a constant rate of 0.5 mm/s to\\nobtain intravascular imaging including vessel area, lumen area and plaque\\nburden.\\nBefore DCB or DES implant in the culprit vessel, an appropriate compliant\\nballoon, semi-compliant balloon, cutting balloon, or spinous balloon was used to\\ndilate the lesion and reduce culprit stenosis.\\n23\\n For DCB implantation, if the degree of vascular stenosis was ≤ 30%, and\\nthere was no dissection or only type A or B dissection after dilation, a\\nsize-matched DCB was chosen based on IVUS imaging and placed in the lesion\\nsustained release before balloon withdrawal. The length of the DCB catheter\\nexceed the target lesion by at least 5 mm. and the ratio of DCB diameters with\\nreference vessel diameters were 0.8–1.0. The recommended inflation time was at\\nleast 40 s at >7 atm. For DES implantation, after pre-dilation, a\\nsize-matched DES based on IVUS imaging was placed in the lesion and released. A\\nhigh-pressure balloon with appropriate dimensions was selected for post-stenting\\ndilation to ensure that the stent was fully expanded and adhered to the vessel\\nwall. IVUS examination was performed after DCB and DES implantation to further\\nevaluate the immediate postoperative efficacy and guide further treatment of\\nstent malapposition and dissection. At the same time, postoperative IVUS imaging\\nresults were recorded.\\nIn this study, DCB had a paclitaxel/iohexol matrix coating on the Bingo\\nDrug-Coated Balloon (Bingo™, Yinyi Biotech, Dalian, China). And the IVUS imaging\\nwere done with\\nthe computer program (H749A70200,Boston Scientific, Shanghai, China).\\nPatients received dual antiplatelet therapy.18–22 Scheme 1: preoperative\\nloading dose of aspirin 300 mg + ticagrelor 180 mg, postoperative maintenance\\ndose of aspirin 100 mg + ticagrelor 90 mg, twice a day. Scheme 2: preoperative\\nloading dose of aspirin 300 mg + clopidogrel 300 mg, postoperative maintenance\\ndose of aspirin 100 mg/d + clopidogrel 75 mg/d. For special ACS patients, such\\nas emergency PCI patients, comatose patients, and patients with poor\\ncooperation, preoperative dual antiplatelet drug therapy might not be given and\\nreplaced by continuous intravenous anticoagulant drugs.\\nAfter routine sterilization and draping, the radial artery or femoral artery was\\nselected for arterial puncture and sheath insertion. CAG was performed according\\nto established protocols. To clearly display the culprit vessels, an appropriate\\npositioning was selected for imaging according to the preoperative\\nelectrocardiogram, and multi-position imaging was performed if necessary.\\nCulprit vessel were determined by combining the patient's electrocardiogram,\\nechocardiography, and intraoperative angiography, and graded according to the\\nThrombolysis in Myocardial Infarction (TIMI) criteria. For TIMI grade < 3\\nculprit vessels, a compliant balloon was used to pre-dilate the culprit vessel\\nlesions to restore TIMI grade 3 flow. According to the patient's condition, IVUS\\nwas performed at the appropriate time. The probe was sent to the relatively\\nnormal segment at the distal end of the culprit vessel, and then withdrew to the\\nrelatively normal segment at the proximal end at a constant rate of 0.5 mm/s to\\nobtain intravascular imaging including vessel area, lumen area and plaque\\nburden.\\nBefore DCB or DES implant in the culprit vessel, an appropriate compliant\\nballoon, semi-compliant balloon, cutting balloon, or spinous balloon was used to\\ndilate the lesion and reduce culprit stenosis.\\n23\\n For DCB implantation, if the degree of vascular stenosis was ≤ 30%, and\\nthere was no dissection or only type A or B dissection after dilation, a\\nsize-matched DCB was chosen based on IVUS imaging and placed in the lesion\\nsustained release before balloon withdrawal. The length of the DCB catheter\\nexceed the target lesion by at least 5 mm. and the ratio of DCB diameters with\\nreference vessel diameters were 0.8–1.0. The recommended inflation time was at\\nleast 40 s at >7 atm. For DES implantation, after pre-dilation, a\\nsize-matched DES based on IVUS imaging was placed in the lesion and released. A\\nhigh-pressure balloon with appropriate dimensions was selected for post-stenting\\ndilation to ensure that the stent was fully expanded and adhered to the vessel\\nwall. IVUS examination was performed after DCB and DES implantation to further\\nevaluate the immediate postoperative efficacy and guide further treatment of\\nstent malapposition and dissection. At the same time, postoperative IVUS imaging\\nresults were recorded.\\nIn this study, DCB had a paclitaxel/iohexol matrix coating on the Bingo\\nDrug-Coated Balloon (Bingo™, Yinyi Biotech, Dalian, China). And the IVUS imaging\\nwere done with\\nthe computer program (H749A70200,Boston Scientific, Shanghai, China).\\nAnalysis Parameters Basic patient information was collected, including sex, age, clinical diagnosis,\\nleft ventricular ejection fraction (LVEF), history of hypertension,\\nhyperlipidemia, diabetes, smoking, myocardial infarction, previous PCI, previous\\ncoronary artery bypass grafting (CABG), and family history of coronary heart\\ndisease.\\nQuantitative coronary angiography (QCA) software was used to analyze the CAG data\\nof all patients, including (1) preoperative and immediate postoperative\\nreference vessel diameter (RFD), minimal luminal diameter, and diameter\\nstenosis; (2) MLD, DS, LLL, restenosis lesions, and lumen enlargement at\\nfollow-up.\\nThe IVUS analysis were also evaluated, including preoperative and immediate\\npostoperative vessel area, plaque burden, and lumen area. Because the PB ≥70%\\nwas the strongest independent predictor of subsequent lesion-related events in\\nthe first PROSPECT study, and Gregg et al defined vulnerable plaque with this PB\\nthreshold.17,24 In this study, the PB criterion was selected to define\\na vulnerable plaque, and the result of CAG and IVUS were evaluated by two\\nexperienced researchers.\\nAll patients passed the outpatient or telephone assessment 1, 3, 6, and 12 months\\nafter PCI. Major adverse cardiovascular events during hospitalization and\\nfollow-up were target lesion revascularization (TLR), myocardial infarction\\n(MI), and cardiac death.\\nBasic patient information was collected, including sex, age, clinical diagnosis,\\nleft ventricular ejection fraction (LVEF), history of hypertension,\\nhyperlipidemia, diabetes, smoking, myocardial infarction, previous PCI, previous\\ncoronary artery bypass grafting (CABG), and family history of coronary heart\\ndisease.\\nQuantitative coronary angiography (QCA) software was used to analyze the CAG data\\nof all patients, including (1) preoperative and immediate postoperative\\nreference vessel diameter (RFD), minimal luminal diameter, and diameter\\nstenosis; (2) MLD, DS, LLL, restenosis lesions, and lumen enlargement at\\nfollow-up.\\nThe IVUS analysis were also evaluated, including preoperative and immediate\\npostoperative vessel area, plaque burden, and lumen area. Because the PB ≥70%\\nwas the strongest independent predictor of subsequent lesion-related events in\\nthe first PROSPECT study, and Gregg et al defined vulnerable plaque with this PB\\nthreshold.17,24 In this study, the PB criterion was selected to define\\na vulnerable plaque, and the result of CAG and IVUS were evaluated by two\\nexperienced researchers.\\nAll patients passed the outpatient or telephone assessment 1, 3, 6, and 12 months\\nafter PCI. Major adverse cardiovascular events during hospitalization and\\nfollow-up were target lesion revascularization (TLR), myocardial infarction\\n(MI), and cardiac death.\\nStatistical Analysis SPSS version 22.0 (IBM Corporation, Armonk, NY, USA). was used for statistical\\nanalysis. Quantitative data that conformed to normal distribution were analyzed\\nby t-test and expressed as mean ± standard deviation (SD); those that did not\\nconform to normal distribution were analyzed by Wilcoxon rank-sum test and\\nexpressed as median and quartile range (M (P25, P75)). Qualitative data were\\ncompared using the chi-square test. If T ≤ 5, the continuity-corrected\\nchi-square test and Fisher's exact probability test were used and expressed as\\nnumber of cases (%). A Kaplan-Meier curve was used to describe survival\\nanalysis. Two-tailed P < 0.05 indicates a statistically significant\\ndifference.\\nSPSS version 22.0 (IBM Corporation, Armonk, NY, USA). was used for statistical\\nanalysis. Quantitative data that conformed to normal distribution were analyzed\\nby t-test and expressed as mean ± standard deviation (SD); those that did not\\nconform to normal distribution were analyzed by Wilcoxon rank-sum test and\\nexpressed as median and quartile range (M (P25, P75)). Qualitative data were\\ncompared using the chi-square test. If T ≤ 5, the continuity-corrected\\nchi-square test and Fisher's exact probability test were used and expressed as\\nnumber of cases (%). A Kaplan-Meier curve was used to describe survival\\nanalysis. Two-tailed P < 0.05 indicates a statistically significant\\ndifference.\",\n \"This retrospectively study investigated 123 patients who were diagnosed with ACS\\nand underwent PCI in the Department of Cardiology, the First Affiliated Hospital\\nof Zhengzhou University from December 2020 to July 2022, and all patients were\\nconfirmed to have vulnerable plaques by IVUS. According to the treatment\\nstrategy they received, patients were entered into either the DCB treatment\\ngroup (n = 55) or the DES treatment group (n = 61). Figure 1 shows the study flowchart.\\nThe flowchart of this study.\\nInclusion criteria: (1) 18-85 years of age; (2) meeting the diagnostic criteria\\nof the European Heart Association for ACS 15,16(3) having definite\\nculprit vessel; and (4) gray-scale IVUS showing PB ≥ 70% and minimal luminal\\narea (MLA) ≤ 4.0 mm2.\\n17\\n Exclusion criteria: (1) chronic total occlusion; (2) heavily calcified\\nlesions requiring rotational atherectomy; (3) patients with thrombus aspiration;\\n(4) severe valvular insufficiency or valvular stenosis; (5) definite\\ncontraindications to antiplatelet drugs or anticoagulants; (6) dissection repair\\nremedial stents after DCB implantation; (7) severe renal insufficiency (GFR<\\n30 ml/min) or severe liver insufficiency; and (8) patients who declined DCB and\\nDES implantation.\\nThe survival curve of DCB and DES groups\",\n \"Patients received dual antiplatelet therapy.18–22 Scheme 1: preoperative\\nloading dose of aspirin 300 mg + ticagrelor 180 mg, postoperative maintenance\\ndose of aspirin 100 mg + ticagrelor 90 mg, twice a day. Scheme 2: preoperative\\nloading dose of aspirin 300 mg + clopidogrel 300 mg, postoperative maintenance\\ndose of aspirin 100 mg/d + clopidogrel 75 mg/d. For special ACS patients, such\\nas emergency PCI patients, comatose patients, and patients with poor\\ncooperation, preoperative dual antiplatelet drug therapy might not be given and\\nreplaced by continuous intravenous anticoagulant drugs.\\nAfter routine sterilization and draping, the radial artery or femoral artery was\\nselected for arterial puncture and sheath insertion. CAG was performed according\\nto established protocols. To clearly display the culprit vessels, an appropriate\\npositioning was selected for imaging according to the preoperative\\nelectrocardiogram, and multi-position imaging was performed if necessary.\\nCulprit vessel were determined by combining the patient's electrocardiogram,\\nechocardiography, and intraoperative angiography, and graded according to the\\nThrombolysis in Myocardial Infarction (TIMI) criteria. For TIMI grade < 3\\nculprit vessels, a compliant balloon was used to pre-dilate the culprit vessel\\nlesions to restore TIMI grade 3 flow. According to the patient's condition, IVUS\\nwas performed at the appropriate time. The probe was sent to the relatively\\nnormal segment at the distal end of the culprit vessel, and then withdrew to the\\nrelatively normal segment at the proximal end at a constant rate of 0.5 mm/s to\\nobtain intravascular imaging including vessel area, lumen area and plaque\\nburden.\\nBefore DCB or DES implant in the culprit vessel, an appropriate compliant\\nballoon, semi-compliant balloon, cutting balloon, or spinous balloon was used to\\ndilate the lesion and reduce culprit stenosis.\\n23\\n For DCB implantation, if the degree of vascular stenosis was ≤ 30%, and\\nthere was no dissection or only type A or B dissection after dilation, a\\nsize-matched DCB was chosen based on IVUS imaging and placed in the lesion\\nsustained release before balloon withdrawal. The length of the DCB catheter\\nexceed the target lesion by at least 5 mm. and the ratio of DCB diameters with\\nreference vessel diameters were 0.8–1.0. The recommended inflation time was at\\nleast 40 s at >7 atm. For DES implantation, after pre-dilation, a\\nsize-matched DES based on IVUS imaging was placed in the lesion and released. A\\nhigh-pressure balloon with appropriate dimensions was selected for post-stenting\\ndilation to ensure that the stent was fully expanded and adhered to the vessel\\nwall. IVUS examination was performed after DCB and DES implantation to further\\nevaluate the immediate postoperative efficacy and guide further treatment of\\nstent malapposition and dissection. At the same time, postoperative IVUS imaging\\nresults were recorded.\\nIn this study, DCB had a paclitaxel/iohexol matrix coating on the Bingo\\nDrug-Coated Balloon (Bingo™, Yinyi Biotech, Dalian, China). And the IVUS imaging\\nwere done with\\nthe computer program (H749A70200,Boston Scientific, Shanghai, China).\",\n \"Basic patient information was collected, including sex, age, clinical diagnosis,\\nleft ventricular ejection fraction (LVEF), history of hypertension,\\nhyperlipidemia, diabetes, smoking, myocardial infarction, previous PCI, previous\\ncoronary artery bypass grafting (CABG), and family history of coronary heart\\ndisease.\\nQuantitative coronary angiography (QCA) software was used to analyze the CAG data\\nof all patients, including (1) preoperative and immediate postoperative\\nreference vessel diameter (RFD), minimal luminal diameter, and diameter\\nstenosis; (2) MLD, DS, LLL, restenosis lesions, and lumen enlargement at\\nfollow-up.\\nThe IVUS analysis were also evaluated, including preoperative and immediate\\npostoperative vessel area, plaque burden, and lumen area. Because the PB ≥70%\\nwas the strongest independent predictor of subsequent lesion-related events in\\nthe first PROSPECT study, and Gregg et al defined vulnerable plaque with this PB\\nthreshold.17,24 In this study, the PB criterion was selected to define\\na vulnerable plaque, and the result of CAG and IVUS were evaluated by two\\nexperienced researchers.\\nAll patients passed the outpatient or telephone assessment 1, 3, 6, and 12 months\\nafter PCI. Major adverse cardiovascular events during hospitalization and\\nfollow-up were target lesion revascularization (TLR), myocardial infarction\\n(MI), and cardiac death.\",\n \"SPSS version 22.0 (IBM Corporation, Armonk, NY, USA). was used for statistical\\nanalysis. Quantitative data that conformed to normal distribution were analyzed\\nby t-test and expressed as mean ± standard deviation (SD); those that did not\\nconform to normal distribution were analyzed by Wilcoxon rank-sum test and\\nexpressed as median and quartile range (M (P25, P75)). Qualitative data were\\ncompared using the chi-square test. If T ≤ 5, the continuity-corrected\\nchi-square test and Fisher's exact probability test were used and expressed as\\nnumber of cases (%). A Kaplan-Meier curve was used to describe survival\\nanalysis. Two-tailed P < 0.05 indicates a statistically significant\\ndifference.\",\n \"A total of 123 patients were included in this study, of whom 55 received DCB and\\n68 received DES. As shown in Table 1, there were no statistically\\nsignificant differences between the DCB and the DES groups in terms of average\\nage, gender distribution, ejection fraction, and other clinical characteristics\\n(P > 0.05). Coronary heart disease-related risk factors\\nsuch as hypertension, hyperlipidemia, diabetes, and smoking history were also\\nnot significantly different between the two groups (P >\\n0.05). Similarly, there were no significant differences in clinical diagnosis of\\nST-elevation myocardial infarction, non-ST-elevation myocardial infarction, and\\nunstable angina pectoris between the two groups\\n(X2 = 0.92,P>0.05).\\nBaseline Patient Characteristics of the Study Population.\\nComparing two data sets using the continuity corrected chi-square\\ntest; DCB: drug coated balloon, DES: drug-eluting stent; STEMI\\n:ST-segment elevation myocardial infarction, NSTEMI: non-STEMI; MI\\n:myocardial infarction; PCI :percutaneous coronary intervention;\\nCABG: coronary artery bypass grafting; LVEF: Left Ventricle\\nejection\",\n \"By combining preoperative ECG and echocardiography and intraoperative CAG\\nresults, the culprit vessels in ACS patients were diagnosed and PCI was\\nperformed. The target vessel locations included left anterior descending artery,\\nleft circumflex artery, right coronary artery, and ramus coronary artery, and\\nthere were no statistically significant differences in target vessel locations\\nbetween the DCB and DES treatment\\ngroups(X2 = 2.83,P＞0.05). According to\\nintraoperative CAG, coronary artery lesions in the two groups were divided into\\nsingle vessel disease, double vessel disease, and triple vessel disease, and\\nthere were no significant differences in multivessel disease between the two\\ngroups (X2 = 0.01,P＞0.05). Except for the\\nsignificant difference in intraoperative balloon usage between the two groups\\n(X2 = 7.69,P＜0.05), there were no statistical\\ndifferences in other PCI procedural characteristics, including implantation\\ndiameter and length, number of patients with intraoperative dissection, number\\nof patients with temporary pacemakers, and number of patients using intra-aortic\\nballoon pump(IABP)(P > 0.05). The procedural characteristics\\nof PCI in the two groups are shown in Table 2.\\nProcedural Characteristics of PCI.\\nComparing two data sets using the continuity corrected chi-square\\ntest #Comparing two data sets using the Fisher's exact\\nprobability test; DCB: drug coated balloon, DES: drug-eluting stent;\\nIABP: intra-aortic balloon pump\",\n \"The preoperative, postoperative and follow-up results of PCI are shown in Table 3. There were\\nno significant differences in preoperative RFD, MLD and DS between the two\\ngroups (P > 0.05). However, CAG immediately after DCB or DES\\nimplantation showed significantly smaller MLD and greater degree of stenosis in\\nthe DCB group than in the DES group (P < 0.05). Further\\nfollow-up CAG after PCI revealed that there were no statistically significant\\ndifferences in MLD, stenosis degree, and coronary restenosis between the two\\ngroups (P > 0.05), except that compared to the DES group,\\nthe DCB group had less LLL in lumen diameter and a greater number of lumen\\nenlargements (P < 0.05).\\nComparison of Coronary QCA and IVUS Results Between DCB and DES\\nGroups.\\nDCB: drug coated balloon, DES: drug-eluting stent; CAG: coronary\\nartery angiography; IVUS: intravascular ultrasound; MLD: minimal\\nlumen diameter; LLL: late luminal loss;\\nBoth groups underwent IVUS before and after PCI. The results showed that there\\nwere no significant differences in vessel area and lumen area between the two\\ngroups (P > 0.05). In addition, no statistically significant\\ndifferences were seen in preoperative plaque burden （79.05 ± 4.17 VS\\n80.07 ± 4.68）and postprocedural plaque burden （61.69 ± 5.64 VS\\n63.32 ± 6.07）between the two groups (P > 0.05).\",\n \"During the follow-up period, there were no significant differences in target\\nlesion revascularization, myocardial infarction, and cardiac death between the\\ntwo groups (P > 0.05). And survival analysis shows that\\nthere were no significant differences in the survival rate of the two groups\\n(Log-rank,X2 = 0.01,P > 0.05). The detailed data are shown in\\nTable 4 and\\nFigure 2.\\nComparison of MACE Between DCB and DES Groups.\\nComparing two data sets using the continuity corrected chi-square\\ntest; #Comparing two data sets using the Fisher's exact\\nprobability test; DCB: drug coated balloon, DES: drug-eluting stent;\\nMACE: major adverse cardiovascular event; TLR: target lesion\\nrevascularization; MI: myocardial infarction.\",\n \"This study has certain limitations. As a retrospective, single-center, and\\nsmall-sample study, there may be patient selection bias. Second, although CAG was\\nperformed during follow-up in both groups, we had to assess MLD using QCA\\nimmediately after PCI due to the lack of IVUS imaging during follow-up. These\\nlimitations may have affected the results to some extent. Therefore, to further\\nvalidate our conclusions, multiple interventional methods such as optical coherence\\ntomography(OCT), virtual histology IVUS (VH-IVUS), and near-infrared spectroscopy\\n(NIRS) can be used to comprehensively detect and identify vulnerable plaques to\\nachieve accurate diagnosis of lesions. In addition, multicenter, large sample, and\\nlonger follow-up studies are needed.\",\n \"In summary, DCB is safe and effective in the treatment of ACS complicated with\\nvulnerable plaque, and DCB has the advantage over DES in LLL. Our work provides\\npractical experience in the interventional treatment of vulnerable plaques in\\nACS.\"\n]"},"other_sections_sec_types":{"kind":"list like","value":[null,null,null,null,null,null,null,null,null,null,null],"string":"[\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null\n]"},"all_sections_titles":{"kind":"list like","value":["Introduction","Materials and Methods","Patients","PCI Procedure","Analysis Parameters","Statistical Analysis","Results","Baseline Demographic and Clinical Characteristics","Procedural Characteristics of PCI","QCA and IVUS Analysis","Follow-up MACE","Discussion","Limitations","Conclusion"],"string":"[\n \"Introduction\",\n \"Materials and Methods\",\n \"Patients\",\n \"PCI Procedure\",\n \"Analysis Parameters\",\n \"Statistical Analysis\",\n \"Results\",\n \"Baseline Demographic and Clinical Characteristics\",\n \"Procedural Characteristics of PCI\",\n \"QCA and IVUS Analysis\",\n \"Follow-up MACE\",\n \"Discussion\",\n \"Limitations\",\n \"Conclusion\"\n]"},"all_sections_texts":{"kind":"list like","value":["Acute coronary syndromes (ACS) is a serious coronary heart disease that threatens\nhuman health. Coronary plaques in such patients usually have a large plaque burden\nwith lipid-rich necrotic cores, called vulnerable plaques, whose progression and\nrupture lead to unstable angina, acute non-ST elevation myocardial infarction, and\nacute ST-elevation myocardial infarction.1–4 Percutaneous coronary\nintervention is now an effective treatment approach for ACS. PCI can restore normal\nblood flow through myocardial revascularization and thus greatly improves symptoms\nand prognosis in ACS patients.5,6\nCompared with traditional two-dimensional coronary angiography (CAG), new\nintraluminal imaging techniques such as IVUS have more efficacy in reducing\ncomplications after drug-eluting stents placement, decreasing in-stent restenosis\nrate, and improving treatment outcome. Equally important, IVUS can better provide\nintravascular imaging data for coronary vascular lesions with coronary overlapping,\nanatomical abnormalities, aneurysms, myocardial bridges, and calcifications,\nespecially in the identification of coronary culprit vessels. Because most ACS\npatients present with plaque ruptures, an accurate identification of the nature,\nextent and location of plaque ruptures is particularly important for the formulation\nof diagnosis and treatment planning. In this context, IVUS is superior to CAG in\nproviding relevant imaging information and intraprocedural guidance of PCI and\nevaluating postoperative results and clinical prognosis.7,8\nCurrently, DES is the preferred strategy for PCI in patients with ACS. The use of DES\nhas the advantages of inducing intimal hyperplasia, thickening fibrous cap, and\nnormalizing wall stress to reduce plaque rupture. However, subsequent complications\nof DES may occur, such as in-stent restenosis, in-stent hyperplasia, and stent\nfracture, and require long-term dual antiplatelet therapy. In severe cases, there\nmay be no reflow phenomenon after stent placement, rapidly resulting in a larger\narea of myocardium infarction.9–11 DCB are a new\nrevascularization technique that has become the treatment of choice for in-stent\nrestenosis because it meets the new concept of intervention without implantation.\nIndications of DCB also include small vessel disease and bifurcation disease, among\nothers.12–14 However,\nthere are few reports related to the use of DCB in ACS patients with vulnerable\nplaques. Therefore, this study used DES as a parallel treatment to examine the\nsafety and efficacy of DCB in ACS complicated with vulnerable plaques lesions under\nthe guidance of IVUS.","Patients This retrospectively study investigated 123 patients who were diagnosed with ACS\nand underwent PCI in the Department of Cardiology, the First Affiliated Hospital\nof Zhengzhou University from December 2020 to July 2022, and all patients were\nconfirmed to have vulnerable plaques by IVUS. According to the treatment\nstrategy they received, patients were entered into either the DCB treatment\ngroup (n = 55) or the DES treatment group (n = 61). Figure 1 shows the study flowchart.\nThe flowchart of this study.\nInclusion criteria: (1) 18-85 years of age; (2) meeting the diagnostic criteria\nof the European Heart Association for ACS 15,16(3) having definite\nculprit vessel; and (4) gray-scale IVUS showing PB ≥ 70% and minimal luminal\narea (MLA) ≤ 4.0 mm2.\n17\n Exclusion criteria: (1) chronic total occlusion; (2) heavily calcified\nlesions requiring rotational atherectomy; (3) patients with thrombus aspiration;\n(4) severe valvular insufficiency or valvular stenosis; (5) definite\ncontraindications to antiplatelet drugs or anticoagulants; (6) dissection repair\nremedial stents after DCB implantation; (7) severe renal insufficiency (GFR<\n30 ml/min) or severe liver insufficiency; and (8) patients who declined DCB and\nDES implantation.\nThe survival curve of DCB and DES groups\nThis retrospectively study investigated 123 patients who were diagnosed with ACS\nand underwent PCI in the Department of Cardiology, the First Affiliated Hospital\nof Zhengzhou University from December 2020 to July 2022, and all patients were\nconfirmed to have vulnerable plaques by IVUS. According to the treatment\nstrategy they received, patients were entered into either the DCB treatment\ngroup (n = 55) or the DES treatment group (n = 61). Figure 1 shows the study flowchart.\nThe flowchart of this study.\nInclusion criteria: (1) 18-85 years of age; (2) meeting the diagnostic criteria\nof the European Heart Association for ACS 15,16(3) having definite\nculprit vessel; and (4) gray-scale IVUS showing PB ≥ 70% and minimal luminal\narea (MLA) ≤ 4.0 mm2.\n17\n Exclusion criteria: (1) chronic total occlusion; (2) heavily calcified\nlesions requiring rotational atherectomy; (3) patients with thrombus aspiration;\n(4) severe valvular insufficiency or valvular stenosis; (5) definite\ncontraindications to antiplatelet drugs or anticoagulants; (6) dissection repair\nremedial stents after DCB implantation; (7) severe renal insufficiency (GFR<\n30 ml/min) or severe liver insufficiency; and (8) patients who declined DCB and\nDES implantation.\nThe survival curve of DCB and DES groups\nPCI Procedure Patients received dual antiplatelet therapy.18–22 Scheme 1: preoperative\nloading dose of aspirin 300 mg + ticagrelor 180 mg, postoperative maintenance\ndose of aspirin 100 mg + ticagrelor 90 mg, twice a day. Scheme 2: preoperative\nloading dose of aspirin 300 mg + clopidogrel 300 mg, postoperative maintenance\ndose of aspirin 100 mg/d + clopidogrel 75 mg/d. For special ACS patients, such\nas emergency PCI patients, comatose patients, and patients with poor\ncooperation, preoperative dual antiplatelet drug therapy might not be given and\nreplaced by continuous intravenous anticoagulant drugs.\nAfter routine sterilization and draping, the radial artery or femoral artery was\nselected for arterial puncture and sheath insertion. CAG was performed according\nto established protocols. To clearly display the culprit vessels, an appropriate\npositioning was selected for imaging according to the preoperative\nelectrocardiogram, and multi-position imaging was performed if necessary.\nCulprit vessel were determined by combining the patient's electrocardiogram,\nechocardiography, and intraoperative angiography, and graded according to the\nThrombolysis in Myocardial Infarction (TIMI) criteria. For TIMI grade < 3\nculprit vessels, a compliant balloon was used to pre-dilate the culprit vessel\nlesions to restore TIMI grade 3 flow. According to the patient's condition, IVUS\nwas performed at the appropriate time. The probe was sent to the relatively\nnormal segment at the distal end of the culprit vessel, and then withdrew to the\nrelatively normal segment at the proximal end at a constant rate of 0.5 mm/s to\nobtain intravascular imaging including vessel area, lumen area and plaque\nburden.\nBefore DCB or DES implant in the culprit vessel, an appropriate compliant\nballoon, semi-compliant balloon, cutting balloon, or spinous balloon was used to\ndilate the lesion and reduce culprit stenosis.\n23\n For DCB implantation, if the degree of vascular stenosis was ≤ 30%, and\nthere was no dissection or only type A or B dissection after dilation, a\nsize-matched DCB was chosen based on IVUS imaging and placed in the lesion\nsustained release before balloon withdrawal. The length of the DCB catheter\nexceed the target lesion by at least 5 mm. and the ratio of DCB diameters with\nreference vessel diameters were 0.8–1.0. The recommended inflation time was at\nleast 40 s at >7 atm. For DES implantation, after pre-dilation, a\nsize-matched DES based on IVUS imaging was placed in the lesion and released. A\nhigh-pressure balloon with appropriate dimensions was selected for post-stenting\ndilation to ensure that the stent was fully expanded and adhered to the vessel\nwall. IVUS examination was performed after DCB and DES implantation to further\nevaluate the immediate postoperative efficacy and guide further treatment of\nstent malapposition and dissection. At the same time, postoperative IVUS imaging\nresults were recorded.\nIn this study, DCB had a paclitaxel/iohexol matrix coating on the Bingo\nDrug-Coated Balloon (Bingo™, Yinyi Biotech, Dalian, China). And the IVUS imaging\nwere done with\nthe computer program (H749A70200,Boston Scientific, Shanghai, China).\nPatients received dual antiplatelet therapy.18–22 Scheme 1: preoperative\nloading dose of aspirin 300 mg + ticagrelor 180 mg, postoperative maintenance\ndose of aspirin 100 mg + ticagrelor 90 mg, twice a day. Scheme 2: preoperative\nloading dose of aspirin 300 mg + clopidogrel 300 mg, postoperative maintenance\ndose of aspirin 100 mg/d + clopidogrel 75 mg/d. For special ACS patients, such\nas emergency PCI patients, comatose patients, and patients with poor\ncooperation, preoperative dual antiplatelet drug therapy might not be given and\nreplaced by continuous intravenous anticoagulant drugs.\nAfter routine sterilization and draping, the radial artery or femoral artery was\nselected for arterial puncture and sheath insertion. CAG was performed according\nto established protocols. To clearly display the culprit vessels, an appropriate\npositioning was selected for imaging according to the preoperative\nelectrocardiogram, and multi-position imaging was performed if necessary.\nCulprit vessel were determined by combining the patient's electrocardiogram,\nechocardiography, and intraoperative angiography, and graded according to the\nThrombolysis in Myocardial Infarction (TIMI) criteria. For TIMI grade < 3\nculprit vessels, a compliant balloon was used to pre-dilate the culprit vessel\nlesions to restore TIMI grade 3 flow. According to the patient's condition, IVUS\nwas performed at the appropriate time. The probe was sent to the relatively\nnormal segment at the distal end of the culprit vessel, and then withdrew to the\nrelatively normal segment at the proximal end at a constant rate of 0.5 mm/s to\nobtain intravascular imaging including vessel area, lumen area and plaque\nburden.\nBefore DCB or DES implant in the culprit vessel, an appropriate compliant\nballoon, semi-compliant balloon, cutting balloon, or spinous balloon was used to\ndilate the lesion and reduce culprit stenosis.\n23\n For DCB implantation, if the degree of vascular stenosis was ≤ 30%, and\nthere was no dissection or only type A or B dissection after dilation, a\nsize-matched DCB was chosen based on IVUS imaging and placed in the lesion\nsustained release before balloon withdrawal. The length of the DCB catheter\nexceed the target lesion by at least 5 mm. and the ratio of DCB diameters with\nreference vessel diameters were 0.8–1.0. The recommended inflation time was at\nleast 40 s at >7 atm. For DES implantation, after pre-dilation, a\nsize-matched DES based on IVUS imaging was placed in the lesion and released. A\nhigh-pressure balloon with appropriate dimensions was selected for post-stenting\ndilation to ensure that the stent was fully expanded and adhered to the vessel\nwall. IVUS examination was performed after DCB and DES implantation to further\nevaluate the immediate postoperative efficacy and guide further treatment of\nstent malapposition and dissection. At the same time, postoperative IVUS imaging\nresults were recorded.\nIn this study, DCB had a paclitaxel/iohexol matrix coating on the Bingo\nDrug-Coated Balloon (Bingo™, Yinyi Biotech, Dalian, China). And the IVUS imaging\nwere done with\nthe computer program (H749A70200,Boston Scientific, Shanghai, China).\nAnalysis Parameters Basic patient information was collected, including sex, age, clinical diagnosis,\nleft ventricular ejection fraction (LVEF), history of hypertension,\nhyperlipidemia, diabetes, smoking, myocardial infarction, previous PCI, previous\ncoronary artery bypass grafting (CABG), and family history of coronary heart\ndisease.\nQuantitative coronary angiography (QCA) software was used to analyze the CAG data\nof all patients, including (1) preoperative and immediate postoperative\nreference vessel diameter (RFD), minimal luminal diameter, and diameter\nstenosis; (2) MLD, DS, LLL, restenosis lesions, and lumen enlargement at\nfollow-up.\nThe IVUS analysis were also evaluated, including preoperative and immediate\npostoperative vessel area, plaque burden, and lumen area. Because the PB ≥70%\nwas the strongest independent predictor of subsequent lesion-related events in\nthe first PROSPECT study, and Gregg et al defined vulnerable plaque with this PB\nthreshold.17,24 In this study, the PB criterion was selected to define\na vulnerable plaque, and the result of CAG and IVUS were evaluated by two\nexperienced researchers.\nAll patients passed the outpatient or telephone assessment 1, 3, 6, and 12 months\nafter PCI. Major adverse cardiovascular events during hospitalization and\nfollow-up were target lesion revascularization (TLR), myocardial infarction\n(MI), and cardiac death.\nBasic patient information was collected, including sex, age, clinical diagnosis,\nleft ventricular ejection fraction (LVEF), history of hypertension,\nhyperlipidemia, diabetes, smoking, myocardial infarction, previous PCI, previous\ncoronary artery bypass grafting (CABG), and family history of coronary heart\ndisease.\nQuantitative coronary angiography (QCA) software was used to analyze the CAG data\nof all patients, including (1) preoperative and immediate postoperative\nreference vessel diameter (RFD), minimal luminal diameter, and diameter\nstenosis; (2) MLD, DS, LLL, restenosis lesions, and lumen enlargement at\nfollow-up.\nThe IVUS analysis were also evaluated, including preoperative and immediate\npostoperative vessel area, plaque burden, and lumen area. Because the PB ≥70%\nwas the strongest independent predictor of subsequent lesion-related events in\nthe first PROSPECT study, and Gregg et al defined vulnerable plaque with this PB\nthreshold.17,24 In this study, the PB criterion was selected to define\na vulnerable plaque, and the result of CAG and IVUS were evaluated by two\nexperienced researchers.\nAll patients passed the outpatient or telephone assessment 1, 3, 6, and 12 months\nafter PCI. Major adverse cardiovascular events during hospitalization and\nfollow-up were target lesion revascularization (TLR), myocardial infarction\n(MI), and cardiac death.\nStatistical Analysis SPSS version 22.0 (IBM Corporation, Armonk, NY, USA). was used for statistical\nanalysis. Quantitative data that conformed to normal distribution were analyzed\nby t-test and expressed as mean ± standard deviation (SD); those that did not\nconform to normal distribution were analyzed by Wilcoxon rank-sum test and\nexpressed as median and quartile range (M (P25, P75)). Qualitative data were\ncompared using the chi-square test. If T ≤ 5, the continuity-corrected\nchi-square test and Fisher's exact probability test were used and expressed as\nnumber of cases (%). A Kaplan-Meier curve was used to describe survival\nanalysis. Two-tailed P < 0.05 indicates a statistically significant\ndifference.\nSPSS version 22.0 (IBM Corporation, Armonk, NY, USA). was used for statistical\nanalysis. Quantitative data that conformed to normal distribution were analyzed\nby t-test and expressed as mean ± standard deviation (SD); those that did not\nconform to normal distribution were analyzed by Wilcoxon rank-sum test and\nexpressed as median and quartile range (M (P25, P75)). Qualitative data were\ncompared using the chi-square test. If T ≤ 5, the continuity-corrected\nchi-square test and Fisher's exact probability test were used and expressed as\nnumber of cases (%). A Kaplan-Meier curve was used to describe survival\nanalysis. Two-tailed P < 0.05 indicates a statistically significant\ndifference.","This retrospectively study investigated 123 patients who were diagnosed with ACS\nand underwent PCI in the Department of Cardiology, the First Affiliated Hospital\nof Zhengzhou University from December 2020 to July 2022, and all patients were\nconfirmed to have vulnerable plaques by IVUS. According to the treatment\nstrategy they received, patients were entered into either the DCB treatment\ngroup (n = 55) or the DES treatment group (n = 61). Figure 1 shows the study flowchart.\nThe flowchart of this study.\nInclusion criteria: (1) 18-85 years of age; (2) meeting the diagnostic criteria\nof the European Heart Association for ACS 15,16(3) having definite\nculprit vessel; and (4) gray-scale IVUS showing PB ≥ 70% and minimal luminal\narea (MLA) ≤ 4.0 mm2.\n17\n Exclusion criteria: (1) chronic total occlusion; (2) heavily calcified\nlesions requiring rotational atherectomy; (3) patients with thrombus aspiration;\n(4) severe valvular insufficiency or valvular stenosis; (5) definite\ncontraindications to antiplatelet drugs or anticoagulants; (6) dissection repair\nremedial stents after DCB implantation; (7) severe renal insufficiency (GFR<\n30 ml/min) or severe liver insufficiency; and (8) patients who declined DCB and\nDES implantation.\nThe survival curve of DCB and DES groups","Patients received dual antiplatelet therapy.18–22 Scheme 1: preoperative\nloading dose of aspirin 300 mg + ticagrelor 180 mg, postoperative maintenance\ndose of aspirin 100 mg + ticagrelor 90 mg, twice a day. Scheme 2: preoperative\nloading dose of aspirin 300 mg + clopidogrel 300 mg, postoperative maintenance\ndose of aspirin 100 mg/d + clopidogrel 75 mg/d. For special ACS patients, such\nas emergency PCI patients, comatose patients, and patients with poor\ncooperation, preoperative dual antiplatelet drug therapy might not be given and\nreplaced by continuous intravenous anticoagulant drugs.\nAfter routine sterilization and draping, the radial artery or femoral artery was\nselected for arterial puncture and sheath insertion. CAG was performed according\nto established protocols. To clearly display the culprit vessels, an appropriate\npositioning was selected for imaging according to the preoperative\nelectrocardiogram, and multi-position imaging was performed if necessary.\nCulprit vessel were determined by combining the patient's electrocardiogram,\nechocardiography, and intraoperative angiography, and graded according to the\nThrombolysis in Myocardial Infarction (TIMI) criteria. For TIMI grade < 3\nculprit vessels, a compliant balloon was used to pre-dilate the culprit vessel\nlesions to restore TIMI grade 3 flow. According to the patient's condition, IVUS\nwas performed at the appropriate time. The probe was sent to the relatively\nnormal segment at the distal end of the culprit vessel, and then withdrew to the\nrelatively normal segment at the proximal end at a constant rate of 0.5 mm/s to\nobtain intravascular imaging including vessel area, lumen area and plaque\nburden.\nBefore DCB or DES implant in the culprit vessel, an appropriate compliant\nballoon, semi-compliant balloon, cutting balloon, or spinous balloon was used to\ndilate the lesion and reduce culprit stenosis.\n23\n For DCB implantation, if the degree of vascular stenosis was ≤ 30%, and\nthere was no dissection or only type A or B dissection after dilation, a\nsize-matched DCB was chosen based on IVUS imaging and placed in the lesion\nsustained release before balloon withdrawal. The length of the DCB catheter\nexceed the target lesion by at least 5 mm. and the ratio of DCB diameters with\nreference vessel diameters were 0.8–1.0. The recommended inflation time was at\nleast 40 s at >7 atm. For DES implantation, after pre-dilation, a\nsize-matched DES based on IVUS imaging was placed in the lesion and released. A\nhigh-pressure balloon with appropriate dimensions was selected for post-stenting\ndilation to ensure that the stent was fully expanded and adhered to the vessel\nwall. IVUS examination was performed after DCB and DES implantation to further\nevaluate the immediate postoperative efficacy and guide further treatment of\nstent malapposition and dissection. At the same time, postoperative IVUS imaging\nresults were recorded.\nIn this study, DCB had a paclitaxel/iohexol matrix coating on the Bingo\nDrug-Coated Balloon (Bingo™, Yinyi Biotech, Dalian, China). And the IVUS imaging\nwere done with\nthe computer program (H749A70200,Boston Scientific, Shanghai, China).","Basic patient information was collected, including sex, age, clinical diagnosis,\nleft ventricular ejection fraction (LVEF), history of hypertension,\nhyperlipidemia, diabetes, smoking, myocardial infarction, previous PCI, previous\ncoronary artery bypass grafting (CABG), and family history of coronary heart\ndisease.\nQuantitative coronary angiography (QCA) software was used to analyze the CAG data\nof all patients, including (1) preoperative and immediate postoperative\nreference vessel diameter (RFD), minimal luminal diameter, and diameter\nstenosis; (2) MLD, DS, LLL, restenosis lesions, and lumen enlargement at\nfollow-up.\nThe IVUS analysis were also evaluated, including preoperative and immediate\npostoperative vessel area, plaque burden, and lumen area. Because the PB ≥70%\nwas the strongest independent predictor of subsequent lesion-related events in\nthe first PROSPECT study, and Gregg et al defined vulnerable plaque with this PB\nthreshold.17,24 In this study, the PB criterion was selected to define\na vulnerable plaque, and the result of CAG and IVUS were evaluated by two\nexperienced researchers.\nAll patients passed the outpatient or telephone assessment 1, 3, 6, and 12 months\nafter PCI. Major adverse cardiovascular events during hospitalization and\nfollow-up were target lesion revascularization (TLR), myocardial infarction\n(MI), and cardiac death.","SPSS version 22.0 (IBM Corporation, Armonk, NY, USA). was used for statistical\nanalysis. Quantitative data that conformed to normal distribution were analyzed\nby t-test and expressed as mean ± standard deviation (SD); those that did not\nconform to normal distribution were analyzed by Wilcoxon rank-sum test and\nexpressed as median and quartile range (M (P25, P75)). Qualitative data were\ncompared using the chi-square test. If T ≤ 5, the continuity-corrected\nchi-square test and Fisher's exact probability test were used and expressed as\nnumber of cases (%). A Kaplan-Meier curve was used to describe survival\nanalysis. Two-tailed P < 0.05 indicates a statistically significant\ndifference.","Baseline Demographic and Clinical Characteristics A total of 123 patients were included in this study, of whom 55 received DCB and\n68 received DES. As shown in Table 1, there were no statistically\nsignificant differences between the DCB and the DES groups in terms of average\nage, gender distribution, ejection fraction, and other clinical characteristics\n(P > 0.05). Coronary heart disease-related risk factors\nsuch as hypertension, hyperlipidemia, diabetes, and smoking history were also\nnot significantly different between the two groups (P >\n0.05). Similarly, there were no significant differences in clinical diagnosis of\nST-elevation myocardial infarction, non-ST-elevation myocardial infarction, and\nunstable angina pectoris between the two groups\n(X2 = 0.92,P>0.05).\nBaseline Patient Characteristics of the Study Population.\nComparing two data sets using the continuity corrected chi-square\ntest; DCB: drug coated balloon, DES: drug-eluting stent; STEMI\n:ST-segment elevation myocardial infarction, NSTEMI: non-STEMI; MI\n:myocardial infarction; PCI :percutaneous coronary intervention;\nCABG: coronary artery bypass grafting; LVEF: Left Ventricle\nejection\nA total of 123 patients were included in this study, of whom 55 received DCB and\n68 received DES. As shown in Table 1, there were no statistically\nsignificant differences between the DCB and the DES groups in terms of average\nage, gender distribution, ejection fraction, and other clinical characteristics\n(P > 0.05). Coronary heart disease-related risk factors\nsuch as hypertension, hyperlipidemia, diabetes, and smoking history were also\nnot significantly different between the two groups (P >\n0.05). Similarly, there were no significant differences in clinical diagnosis of\nST-elevation myocardial infarction, non-ST-elevation myocardial infarction, and\nunstable angina pectoris between the two groups\n(X2 = 0.92,P>0.05).\nBaseline Patient Characteristics of the Study Population.\nComparing two data sets using the continuity corrected chi-square\ntest; DCB: drug coated balloon, DES: drug-eluting stent; STEMI\n:ST-segment elevation myocardial infarction, NSTEMI: non-STEMI; MI\n:myocardial infarction; PCI :percutaneous coronary intervention;\nCABG: coronary artery bypass grafting; LVEF: Left Ventricle\nejection\nProcedural Characteristics of PCI By combining preoperative ECG and echocardiography and intraoperative CAG\nresults, the culprit vessels in ACS patients were diagnosed and PCI was\nperformed. The target vessel locations included left anterior descending artery,\nleft circumflex artery, right coronary artery, and ramus coronary artery, and\nthere were no statistically significant differences in target vessel locations\nbetween the DCB and DES treatment\ngroups(X2 = 2.83,P＞0.05). According to\nintraoperative CAG, coronary artery lesions in the two groups were divided into\nsingle vessel disease, double vessel disease, and triple vessel disease, and\nthere were no significant differences in multivessel disease between the two\ngroups (X2 = 0.01,P＞0.05). Except for the\nsignificant difference in intraoperative balloon usage between the two groups\n(X2 = 7.69,P＜0.05), there were no statistical\ndifferences in other PCI procedural characteristics, including implantation\ndiameter and length, number of patients with intraoperative dissection, number\nof patients with temporary pacemakers, and number of patients using intra-aortic\nballoon pump(IABP)(P > 0.05). The procedural characteristics\nof PCI in the two groups are shown in Table 2.\nProcedural Characteristics of PCI.\nComparing two data sets using the continuity corrected chi-square\ntest #Comparing two data sets using the Fisher's exact\nprobability test; DCB: drug coated balloon, DES: drug-eluting stent;\nIABP: intra-aortic balloon pump\nBy combining preoperative ECG and echocardiography and intraoperative CAG\nresults, the culprit vessels in ACS patients were diagnosed and PCI was\nperformed. The target vessel locations included left anterior descending artery,\nleft circumflex artery, right coronary artery, and ramus coronary artery, and\nthere were no statistically significant differences in target vessel locations\nbetween the DCB and DES treatment\ngroups(X2 = 2.83,P＞0.05). According to\nintraoperative CAG, coronary artery lesions in the two groups were divided into\nsingle vessel disease, double vessel disease, and triple vessel disease, and\nthere were no significant differences in multivessel disease between the two\ngroups (X2 = 0.01,P＞0.05). Except for the\nsignificant difference in intraoperative balloon usage between the two groups\n(X2 = 7.69,P＜0.05), there were no statistical\ndifferences in other PCI procedural characteristics, including implantation\ndiameter and length, number of patients with intraoperative dissection, number\nof patients with temporary pacemakers, and number of patients using intra-aortic\nballoon pump(IABP)(P > 0.05). The procedural characteristics\nof PCI in the two groups are shown in Table 2.\nProcedural Characteristics of PCI.\nComparing two data sets using the continuity corrected chi-square\ntest #Comparing two data sets using the Fisher's exact\nprobability test; DCB: drug coated balloon, DES: drug-eluting stent;\nIABP: intra-aortic balloon pump\nQCA and IVUS Analysis The preoperative, postoperative and follow-up results of PCI are shown in Table 3. There were\nno significant differences in preoperative RFD, MLD and DS between the two\ngroups (P > 0.05). However, CAG immediately after DCB or DES\nimplantation showed significantly smaller MLD and greater degree of stenosis in\nthe DCB group than in the DES group (P < 0.05). Further\nfollow-up CAG after PCI revealed that there were no statistically significant\ndifferences in MLD, stenosis degree, and coronary restenosis between the two\ngroups (P > 0.05), except that compared to the DES group,\nthe DCB group had less LLL in lumen diameter and a greater number of lumen\nenlargements (P < 0.05).\nComparison of Coronary QCA and IVUS Results Between DCB and DES\nGroups.\nDCB: drug coated balloon, DES: drug-eluting stent; CAG: coronary\nartery angiography; IVUS: intravascular ultrasound; MLD: minimal\nlumen diameter; LLL: late luminal loss;\nBoth groups underwent IVUS before and after PCI. The results showed that there\nwere no significant differences in vessel area and lumen area between the two\ngroups (P > 0.05). In addition, no statistically significant\ndifferences were seen in preoperative plaque burden （79.05 ± 4.17 VS\n80.07 ± 4.68）and postprocedural plaque burden （61.69 ± 5.64 VS\n63.32 ± 6.07）between the two groups (P > 0.05).\nThe preoperative, postoperative and follow-up results of PCI are shown in Table 3. There were\nno significant differences in preoperative RFD, MLD and DS between the two\ngroups (P > 0.05). However, CAG immediately after DCB or DES\nimplantation showed significantly smaller MLD and greater degree of stenosis in\nthe DCB group than in the DES group (P < 0.05). Further\nfollow-up CAG after PCI revealed that there were no statistically significant\ndifferences in MLD, stenosis degree, and coronary restenosis between the two\ngroups (P > 0.05), except that compared to the DES group,\nthe DCB group had less LLL in lumen diameter and a greater number of lumen\nenlargements (P < 0.05).\nComparison of Coronary QCA and IVUS Results Between DCB and DES\nGroups.\nDCB: drug coated balloon, DES: drug-eluting stent; CAG: coronary\nartery angiography; IVUS: intravascular ultrasound; MLD: minimal\nlumen diameter; LLL: late luminal loss;\nBoth groups underwent IVUS before and after PCI. The results showed that there\nwere no significant differences in vessel area and lumen area between the two\ngroups (P > 0.05). In addition, no statistically significant\ndifferences were seen in preoperative plaque burden （79.05 ± 4.17 VS\n80.07 ± 4.68）and postprocedural plaque burden （61.69 ± 5.64 VS\n63.32 ± 6.07）between the two groups (P > 0.05).\nFollow-up MACE During the follow-up period, there were no significant differences in target\nlesion revascularization, myocardial infarction, and cardiac death between the\ntwo groups (P > 0.05). And survival analysis shows that\nthere were no significant differences in the survival rate of the two groups\n(Log-rank,X2 = 0.01,P > 0.05). The detailed data are shown in\nTable 4 and\nFigure 2.\nComparison of MACE Between DCB and DES Groups.\nComparing two data sets using the continuity corrected chi-square\ntest; #Comparing two data sets using the Fisher's exact\nprobability test; DCB: drug coated balloon, DES: drug-eluting stent;\nMACE: major adverse cardiovascular event; TLR: target lesion\nrevascularization; MI: myocardial infarction.\nDuring the follow-up period, there were no significant differences in target\nlesion revascularization, myocardial infarction, and cardiac death between the\ntwo groups (P > 0.05). And survival analysis shows that\nthere were no significant differences in the survival rate of the two groups\n(Log-rank,X2 = 0.01,P > 0.05). The detailed data are shown in\nTable 4 and\nFigure 2.\nComparison of MACE Between DCB and DES Groups.\nComparing two data sets using the continuity corrected chi-square\ntest; #Comparing two data sets using the Fisher's exact\nprobability test; DCB: drug coated balloon, DES: drug-eluting stent;\nMACE: major adverse cardiovascular event; TLR: target lesion\nrevascularization; MI: myocardial infarction.","A total of 123 patients were included in this study, of whom 55 received DCB and\n68 received DES. As shown in Table 1, there were no statistically\nsignificant differences between the DCB and the DES groups in terms of average\nage, gender distribution, ejection fraction, and other clinical characteristics\n(P > 0.05). Coronary heart disease-related risk factors\nsuch as hypertension, hyperlipidemia, diabetes, and smoking history were also\nnot significantly different between the two groups (P >\n0.05). Similarly, there were no significant differences in clinical diagnosis of\nST-elevation myocardial infarction, non-ST-elevation myocardial infarction, and\nunstable angina pectoris between the two groups\n(X2 = 0.92,P>0.05).\nBaseline Patient Characteristics of the Study Population.\nComparing two data sets using the continuity corrected chi-square\ntest; DCB: drug coated balloon, DES: drug-eluting stent; STEMI\n:ST-segment elevation myocardial infarction, NSTEMI: non-STEMI; MI\n:myocardial infarction; PCI :percutaneous coronary intervention;\nCABG: coronary artery bypass grafting; LVEF: Left Ventricle\nejection","By combining preoperative ECG and echocardiography and intraoperative CAG\nresults, the culprit vessels in ACS patients were diagnosed and PCI was\nperformed. The target vessel locations included left anterior descending artery,\nleft circumflex artery, right coronary artery, and ramus coronary artery, and\nthere were no statistically significant differences in target vessel locations\nbetween the DCB and DES treatment\ngroups(X2 = 2.83,P＞0.05). According to\nintraoperative CAG, coronary artery lesions in the two groups were divided into\nsingle vessel disease, double vessel disease, and triple vessel disease, and\nthere were no significant differences in multivessel disease between the two\ngroups (X2 = 0.01,P＞0.05). Except for the\nsignificant difference in intraoperative balloon usage between the two groups\n(X2 = 7.69,P＜0.05), there were no statistical\ndifferences in other PCI procedural characteristics, including implantation\ndiameter and length, number of patients with intraoperative dissection, number\nof patients with temporary pacemakers, and number of patients using intra-aortic\nballoon pump(IABP)(P > 0.05). The procedural characteristics\nof PCI in the two groups are shown in Table 2.\nProcedural Characteristics of PCI.\nComparing two data sets using the continuity corrected chi-square\ntest #Comparing two data sets using the Fisher's exact\nprobability test; DCB: drug coated balloon, DES: drug-eluting stent;\nIABP: intra-aortic balloon pump","The preoperative, postoperative and follow-up results of PCI are shown in Table 3. There were\nno significant differences in preoperative RFD, MLD and DS between the two\ngroups (P > 0.05). However, CAG immediately after DCB or DES\nimplantation showed significantly smaller MLD and greater degree of stenosis in\nthe DCB group than in the DES group (P < 0.05). Further\nfollow-up CAG after PCI revealed that there were no statistically significant\ndifferences in MLD, stenosis degree, and coronary restenosis between the two\ngroups (P > 0.05), except that compared to the DES group,\nthe DCB group had less LLL in lumen diameter and a greater number of lumen\nenlargements (P < 0.05).\nComparison of Coronary QCA and IVUS Results Between DCB and DES\nGroups.\nDCB: drug coated balloon, DES: drug-eluting stent; CAG: coronary\nartery angiography; IVUS: intravascular ultrasound; MLD: minimal\nlumen diameter; LLL: late luminal loss;\nBoth groups underwent IVUS before and after PCI. The results showed that there\nwere no significant differences in vessel area and lumen area between the two\ngroups (P > 0.05). In addition, no statistically significant\ndifferences were seen in preoperative plaque burden （79.05 ± 4.17 VS\n80.07 ± 4.68）and postprocedural plaque burden （61.69 ± 5.64 VS\n63.32 ± 6.07）between the two groups (P > 0.05).","During the follow-up period, there were no significant differences in target\nlesion revascularization, myocardial infarction, and cardiac death between the\ntwo groups (P > 0.05). And survival analysis shows that\nthere were no significant differences in the survival rate of the two groups\n(Log-rank,X2 = 0.01,P > 0.05). The detailed data are shown in\nTable 4 and\nFigure 2.\nComparison of MACE Between DCB and DES Groups.\nComparing two data sets using the continuity corrected chi-square\ntest; #Comparing two data sets using the Fisher's exact\nprobability test; DCB: drug coated balloon, DES: drug-eluting stent;\nMACE: major adverse cardiovascular event; TLR: target lesion\nrevascularization; MI: myocardial infarction.","DES is the preferred strategy for reperfusion in patients with ACS. However, delayed\nhealing and vascular remodeling of the vascular endothelium often occur after DES\nimplantation due to the vulnerability of the plaque and the hypercoagulable state\nand persistent inflammation caused by residual metals, leading to a greatly\nincreased risk of late in-stent thrombosis.10,25 As a new strategy for the\ntreatment of coronary artery disease, DCB has achieved reliable efficacy in various\ntypes of coronary artery disease such as in-stent stenosis due to its unique drug\nrelease behavior and no residual metal substances. Similarly, DCB has also been used\nin ACS lesions, but little is known about the effect of plaque vulnerability on the\nefficacy of DCB in ACS patients.\nAt present, there are many ways to identify vulnerable plaques, and each imaging\nmethod, whether invasive or non-invasive, has different diagnostic criteria. Gregg\net al noted that PB ≥ 70% or MLA ≤ 4.0 mm2 on IVUS are the strongest\nindependent predictors of subsequent MACE after PCI.17,26–30 Therefore, this study\nsimilarly adopted this criterion to define vulnerable plaque. We also analyzed IVUS\nin the DCB and DES groups before and immediately after PCI. We found no significant\ndifferences in vessel area, PB, and lumen area between the two groups, indicating\nthat DCB has comparable effects on vulnerable plaques and luminal structure\nimmediately after implantation in ACS patients compared to DES. Nevertheless, the\nlong-term efficacy of DCB is unclear, as patients in both groups did not undergo\nIVUS during follow-up for economic or other reasons. It has been reported that\nduring IVUS follow-up of DCB in the treatment of native vessel lesions, DCB was\nfound to stabilize plaques by reducing PB and altering plaque composition, thereby\nreducing subsequent adverse events caused by plaque rupture. 31,32 Whether this\neffect of DCB appears in the treatment of ACS complicated with vulnerable plaques\nwarrants further IVUS follow-up analysis.\nIn this study, CAG was performed before both DCB and DES implantation, and there were\nno significant differences in preoperative MLD and DS between the two groups. For\nthe immediate postoperative CAG results showing that the DCB group had a smaller MLD\nand greater degree of stenosis than the DES group, one possible reason is that DCB,\nas a transport tool for antiproliferative drugs, lacks the effective support of\nmetal nets like DES. Secondly, in the DES group, to ensure the full contact between\nthe stent and the blood vessel, a high-pressure balloon of suitable size was used\nfor post-dilation, which can allow the DES to dilate diseased vessels more\neffectively, reduce the elastic recoil of vessels, and ultimately prevent lumen stenosis.\n33\n For DCB-PCI, in addition to adequate pretreatment prior to implantation to\navoid the occurrence of dissection, the lumen size and lesions had been evaluated by\nIVUS to minimize the difference in vasodilation with DES.\nFollow-up CAG results showed that there were no significant differences in MLD and DS\nbetween the two groups, but the DCB group had smaller LLL and greater lumen\nenlargement. There are multiple reasons for these results. First, unlike patients in\nprevious studies, patients in this study had a higher PB and a higher proportion of\npatients in the DCB group used cutting balloons or spinous balloons than in the DES\ngroup, which may allow for a more complete and uniform dispersion of the internal\nand medial plaques. Second, compared to DES, DCB has a larger contact area with the\ndiseased arterial segment, enabling the antiproliferative drugs to diffuse uniformly\nand rapidly to the diseased vessel. Further, the residual metal stent material and\nlong-term sustained release of anti-proliferative drugs in the DES group could lead\nto delayed endothelialization and inflammatory response, while in the DCB group, the\narterial vasomotor and dilatation functions were relatively stable because there was\nno metal residual problem, which may be the reason for the late lumen enlargement.\nAll these factors together resulted in better vascular remodeling and then less\nlumen loss diameter and greater lumen enlargement in the DCB group.34–36 On the other hand, there were\nno significant differences in the incidence of target lesion revascularization,\nmyocardial infarction, and cardiac death between the two groups during follow-up,\nalso suggesting that DCB is non-inferior to DES in long-term outcomes.","This study has certain limitations. As a retrospective, single-center, and\nsmall-sample study, there may be patient selection bias. Second, although CAG was\nperformed during follow-up in both groups, we had to assess MLD using QCA\nimmediately after PCI due to the lack of IVUS imaging during follow-up. These\nlimitations may have affected the results to some extent. Therefore, to further\nvalidate our conclusions, multiple interventional methods such as optical coherence\ntomography(OCT), virtual histology IVUS (VH-IVUS), and near-infrared spectroscopy\n(NIRS) can be used to comprehensively detect and identify vulnerable plaques to\nachieve accurate diagnosis of lesions. In addition, multicenter, large sample, and\nlonger follow-up studies are needed.","In summary, DCB is safe and effective in the treatment of ACS complicated with\nvulnerable plaque, and DCB has the advantage over DES in LLL. Our work provides\npractical experience in the interventional treatment of vulnerable plaques in\nACS."],"string":"[\n \"Acute coronary syndromes (ACS) is a serious coronary heart disease that threatens\\nhuman health. Coronary plaques in such patients usually have a large plaque burden\\nwith lipid-rich necrotic cores, called vulnerable plaques, whose progression and\\nrupture lead to unstable angina, acute non-ST elevation myocardial infarction, and\\nacute ST-elevation myocardial infarction.1–4 Percutaneous coronary\\nintervention is now an effective treatment approach for ACS. PCI can restore normal\\nblood flow through myocardial revascularization and thus greatly improves symptoms\\nand prognosis in ACS patients.5,6\\nCompared with traditional two-dimensional coronary angiography (CAG), new\\nintraluminal imaging techniques such as IVUS have more efficacy in reducing\\ncomplications after drug-eluting stents placement, decreasing in-stent restenosis\\nrate, and improving treatment outcome. Equally important, IVUS can better provide\\nintravascular imaging data for coronary vascular lesions with coronary overlapping,\\nanatomical abnormalities, aneurysms, myocardial bridges, and calcifications,\\nespecially in the identification of coronary culprit vessels. Because most ACS\\npatients present with plaque ruptures, an accurate identification of the nature,\\nextent and location of plaque ruptures is particularly important for the formulation\\nof diagnosis and treatment planning. In this context, IVUS is superior to CAG in\\nproviding relevant imaging information and intraprocedural guidance of PCI and\\nevaluating postoperative results and clinical prognosis.7,8\\nCurrently, DES is the preferred strategy for PCI in patients with ACS. The use of DES\\nhas the advantages of inducing intimal hyperplasia, thickening fibrous cap, and\\nnormalizing wall stress to reduce plaque rupture. However, subsequent complications\\nof DES may occur, such as in-stent restenosis, in-stent hyperplasia, and stent\\nfracture, and require long-term dual antiplatelet therapy. In severe cases, there\\nmay be no reflow phenomenon after stent placement, rapidly resulting in a larger\\narea of myocardium infarction.9–11 DCB are a new\\nrevascularization technique that has become the treatment of choice for in-stent\\nrestenosis because it meets the new concept of intervention without implantation.\\nIndications of DCB also include small vessel disease and bifurcation disease, among\\nothers.12–14 However,\\nthere are few reports related to the use of DCB in ACS patients with vulnerable\\nplaques. Therefore, this study used DES as a parallel treatment to examine the\\nsafety and efficacy of DCB in ACS complicated with vulnerable plaques lesions under\\nthe guidance of IVUS.\",\n \"Patients This retrospectively study investigated 123 patients who were diagnosed with ACS\\nand underwent PCI in the Department of Cardiology, the First Affiliated Hospital\\nof Zhengzhou University from December 2020 to July 2022, and all patients were\\nconfirmed to have vulnerable plaques by IVUS. According to the treatment\\nstrategy they received, patients were entered into either the DCB treatment\\ngroup (n = 55) or the DES treatment group (n = 61). Figure 1 shows the study flowchart.\\nThe flowchart of this study.\\nInclusion criteria: (1) 18-85 years of age; (2) meeting the diagnostic criteria\\nof the European Heart Association for ACS 15,16(3) having definite\\nculprit vessel; and (4) gray-scale IVUS showing PB ≥ 70% and minimal luminal\\narea (MLA) ≤ 4.0 mm2.\\n17\\n Exclusion criteria: (1) chronic total occlusion; (2) heavily calcified\\nlesions requiring rotational atherectomy; (3) patients with thrombus aspiration;\\n(4) severe valvular insufficiency or valvular stenosis; (5) definite\\ncontraindications to antiplatelet drugs or anticoagulants; (6) dissection repair\\nremedial stents after DCB implantation; (7) severe renal insufficiency (GFR<\\n30 ml/min) or severe liver insufficiency; and (8) patients who declined DCB and\\nDES implantation.\\nThe survival curve of DCB and DES groups\\nThis retrospectively study investigated 123 patients who were diagnosed with ACS\\nand underwent PCI in the Department of Cardiology, the First Affiliated Hospital\\nof Zhengzhou University from December 2020 to July 2022, and all patients were\\nconfirmed to have vulnerable plaques by IVUS. According to the treatment\\nstrategy they received, patients were entered into either the DCB treatment\\ngroup (n = 55) or the DES treatment group (n = 61). Figure 1 shows the study flowchart.\\nThe flowchart of this study.\\nInclusion criteria: (1) 18-85 years of age; (2) meeting the diagnostic criteria\\nof the European Heart Association for ACS 15,16(3) having definite\\nculprit vessel; and (4) gray-scale IVUS showing PB ≥ 70% and minimal luminal\\narea (MLA) ≤ 4.0 mm2.\\n17\\n Exclusion criteria: (1) chronic total occlusion; (2) heavily calcified\\nlesions requiring rotational atherectomy; (3) patients with thrombus aspiration;\\n(4) severe valvular insufficiency or valvular stenosis; (5) definite\\ncontraindications to antiplatelet drugs or anticoagulants; (6) dissection repair\\nremedial stents after DCB implantation; (7) severe renal insufficiency (GFR<\\n30 ml/min) or severe liver insufficiency; and (8) patients who declined DCB and\\nDES implantation.\\nThe survival curve of DCB and DES groups\\nPCI Procedure Patients received dual antiplatelet therapy.18–22 Scheme 1: preoperative\\nloading dose of aspirin 300 mg + ticagrelor 180 mg, postoperative maintenance\\ndose of aspirin 100 mg + ticagrelor 90 mg, twice a day. Scheme 2: preoperative\\nloading dose of aspirin 300 mg + clopidogrel 300 mg, postoperative maintenance\\ndose of aspirin 100 mg/d + clopidogrel 75 mg/d. For special ACS patients, such\\nas emergency PCI patients, comatose patients, and patients with poor\\ncooperation, preoperative dual antiplatelet drug therapy might not be given and\\nreplaced by continuous intravenous anticoagulant drugs.\\nAfter routine sterilization and draping, the radial artery or femoral artery was\\nselected for arterial puncture and sheath insertion. CAG was performed according\\nto established protocols. To clearly display the culprit vessels, an appropriate\\npositioning was selected for imaging according to the preoperative\\nelectrocardiogram, and multi-position imaging was performed if necessary.\\nCulprit vessel were determined by combining the patient's electrocardiogram,\\nechocardiography, and intraoperative angiography, and graded according to the\\nThrombolysis in Myocardial Infarction (TIMI) criteria. For TIMI grade < 3\\nculprit vessels, a compliant balloon was used to pre-dilate the culprit vessel\\nlesions to restore TIMI grade 3 flow. According to the patient's condition, IVUS\\nwas performed at the appropriate time. The probe was sent to the relatively\\nnormal segment at the distal end of the culprit vessel, and then withdrew to the\\nrelatively normal segment at the proximal end at a constant rate of 0.5 mm/s to\\nobtain intravascular imaging including vessel area, lumen area and plaque\\nburden.\\nBefore DCB or DES implant in the culprit vessel, an appropriate compliant\\nballoon, semi-compliant balloon, cutting balloon, or spinous balloon was used to\\ndilate the lesion and reduce culprit stenosis.\\n23\\n For DCB implantation, if the degree of vascular stenosis was ≤ 30%, and\\nthere was no dissection or only type A or B dissection after dilation, a\\nsize-matched DCB was chosen based on IVUS imaging and placed in the lesion\\nsustained release before balloon withdrawal. The length of the DCB catheter\\nexceed the target lesion by at least 5 mm. and the ratio of DCB diameters with\\nreference vessel diameters were 0.8–1.0. The recommended inflation time was at\\nleast 40 s at >7 atm. For DES implantation, after pre-dilation, a\\nsize-matched DES based on IVUS imaging was placed in the lesion and released. A\\nhigh-pressure balloon with appropriate dimensions was selected for post-stenting\\ndilation to ensure that the stent was fully expanded and adhered to the vessel\\nwall. IVUS examination was performed after DCB and DES implantation to further\\nevaluate the immediate postoperative efficacy and guide further treatment of\\nstent malapposition and dissection. At the same time, postoperative IVUS imaging\\nresults were recorded.\\nIn this study, DCB had a paclitaxel/iohexol matrix coating on the Bingo\\nDrug-Coated Balloon (Bingo™, Yinyi Biotech, Dalian, China). And the IVUS imaging\\nwere done with\\nthe computer program (H749A70200,Boston Scientific, Shanghai, China).\\nPatients received dual antiplatelet therapy.18–22 Scheme 1: preoperative\\nloading dose of aspirin 300 mg + ticagrelor 180 mg, postoperative maintenance\\ndose of aspirin 100 mg + ticagrelor 90 mg, twice a day. Scheme 2: preoperative\\nloading dose of aspirin 300 mg + clopidogrel 300 mg, postoperative maintenance\\ndose of aspirin 100 mg/d + clopidogrel 75 mg/d. For special ACS patients, such\\nas emergency PCI patients, comatose patients, and patients with poor\\ncooperation, preoperative dual antiplatelet drug therapy might not be given and\\nreplaced by continuous intravenous anticoagulant drugs.\\nAfter routine sterilization and draping, the radial artery or femoral artery was\\nselected for arterial puncture and sheath insertion. CAG was performed according\\nto established protocols. To clearly display the culprit vessels, an appropriate\\npositioning was selected for imaging according to the preoperative\\nelectrocardiogram, and multi-position imaging was performed if necessary.\\nCulprit vessel were determined by combining the patient's electrocardiogram,\\nechocardiography, and intraoperative angiography, and graded according to the\\nThrombolysis in Myocardial Infarction (TIMI) criteria. For TIMI grade < 3\\nculprit vessels, a compliant balloon was used to pre-dilate the culprit vessel\\nlesions to restore TIMI grade 3 flow. According to the patient's condition, IVUS\\nwas performed at the appropriate time. The probe was sent to the relatively\\nnormal segment at the distal end of the culprit vessel, and then withdrew to the\\nrelatively normal segment at the proximal end at a constant rate of 0.5 mm/s to\\nobtain intravascular imaging including vessel area, lumen area and plaque\\nburden.\\nBefore DCB or DES implant in the culprit vessel, an appropriate compliant\\nballoon, semi-compliant balloon, cutting balloon, or spinous balloon was used to\\ndilate the lesion and reduce culprit stenosis.\\n23\\n For DCB implantation, if the degree of vascular stenosis was ≤ 30%, and\\nthere was no dissection or only type A or B dissection after dilation, a\\nsize-matched DCB was chosen based on IVUS imaging and placed in the lesion\\nsustained release before balloon withdrawal. The length of the DCB catheter\\nexceed the target lesion by at least 5 mm. and the ratio of DCB diameters with\\nreference vessel diameters were 0.8–1.0. The recommended inflation time was at\\nleast 40 s at >7 atm. For DES implantation, after pre-dilation, a\\nsize-matched DES based on IVUS imaging was placed in the lesion and released. A\\nhigh-pressure balloon with appropriate dimensions was selected for post-stenting\\ndilation to ensure that the stent was fully expanded and adhered to the vessel\\nwall. IVUS examination was performed after DCB and DES implantation to further\\nevaluate the immediate postoperative efficacy and guide further treatment of\\nstent malapposition and dissection. At the same time, postoperative IVUS imaging\\nresults were recorded.\\nIn this study, DCB had a paclitaxel/iohexol matrix coating on the Bingo\\nDrug-Coated Balloon (Bingo™, Yinyi Biotech, Dalian, China). And the IVUS imaging\\nwere done with\\nthe computer program (H749A70200,Boston Scientific, Shanghai, China).\\nAnalysis Parameters Basic patient information was collected, including sex, age, clinical diagnosis,\\nleft ventricular ejection fraction (LVEF), history of hypertension,\\nhyperlipidemia, diabetes, smoking, myocardial infarction, previous PCI, previous\\ncoronary artery bypass grafting (CABG), and family history of coronary heart\\ndisease.\\nQuantitative coronary angiography (QCA) software was used to analyze the CAG data\\nof all patients, including (1) preoperative and immediate postoperative\\nreference vessel diameter (RFD), minimal luminal diameter, and diameter\\nstenosis; (2) MLD, DS, LLL, restenosis lesions, and lumen enlargement at\\nfollow-up.\\nThe IVUS analysis were also evaluated, including preoperative and immediate\\npostoperative vessel area, plaque burden, and lumen area. Because the PB ≥70%\\nwas the strongest independent predictor of subsequent lesion-related events in\\nthe first PROSPECT study, and Gregg et al defined vulnerable plaque with this PB\\nthreshold.17,24 In this study, the PB criterion was selected to define\\na vulnerable plaque, and the result of CAG and IVUS were evaluated by two\\nexperienced researchers.\\nAll patients passed the outpatient or telephone assessment 1, 3, 6, and 12 months\\nafter PCI. Major adverse cardiovascular events during hospitalization and\\nfollow-up were target lesion revascularization (TLR), myocardial infarction\\n(MI), and cardiac death.\\nBasic patient information was collected, including sex, age, clinical diagnosis,\\nleft ventricular ejection fraction (LVEF), history of hypertension,\\nhyperlipidemia, diabetes, smoking, myocardial infarction, previous PCI, previous\\ncoronary artery bypass grafting (CABG), and family history of coronary heart\\ndisease.\\nQuantitative coronary angiography (QCA) software was used to analyze the CAG data\\nof all patients, including (1) preoperative and immediate postoperative\\nreference vessel diameter (RFD), minimal luminal diameter, and diameter\\nstenosis; (2) MLD, DS, LLL, restenosis lesions, and lumen enlargement at\\nfollow-up.\\nThe IVUS analysis were also evaluated, including preoperative and immediate\\npostoperative vessel area, plaque burden, and lumen area. Because the PB ≥70%\\nwas the strongest independent predictor of subsequent lesion-related events in\\nthe first PROSPECT study, and Gregg et al defined vulnerable plaque with this PB\\nthreshold.17,24 In this study, the PB criterion was selected to define\\na vulnerable plaque, and the result of CAG and IVUS were evaluated by two\\nexperienced researchers.\\nAll patients passed the outpatient or telephone assessment 1, 3, 6, and 12 months\\nafter PCI. Major adverse cardiovascular events during hospitalization and\\nfollow-up were target lesion revascularization (TLR), myocardial infarction\\n(MI), and cardiac death.\\nStatistical Analysis SPSS version 22.0 (IBM Corporation, Armonk, NY, USA). was used for statistical\\nanalysis. Quantitative data that conformed to normal distribution were analyzed\\nby t-test and expressed as mean ± standard deviation (SD); those that did not\\nconform to normal distribution were analyzed by Wilcoxon rank-sum test and\\nexpressed as median and quartile range (M (P25, P75)). Qualitative data were\\ncompared using the chi-square test. If T ≤ 5, the continuity-corrected\\nchi-square test and Fisher's exact probability test were used and expressed as\\nnumber of cases (%). A Kaplan-Meier curve was used to describe survival\\nanalysis. Two-tailed P < 0.05 indicates a statistically significant\\ndifference.\\nSPSS version 22.0 (IBM Corporation, Armonk, NY, USA). was used for statistical\\nanalysis. Quantitative data that conformed to normal distribution were analyzed\\nby t-test and expressed as mean ± standard deviation (SD); those that did not\\nconform to normal distribution were analyzed by Wilcoxon rank-sum test and\\nexpressed as median and quartile range (M (P25, P75)). Qualitative data were\\ncompared using the chi-square test. If T ≤ 5, the continuity-corrected\\nchi-square test and Fisher's exact probability test were used and expressed as\\nnumber of cases (%). A Kaplan-Meier curve was used to describe survival\\nanalysis. Two-tailed P < 0.05 indicates a statistically significant\\ndifference.\",\n \"This retrospectively study investigated 123 patients who were diagnosed with ACS\\nand underwent PCI in the Department of Cardiology, the First Affiliated Hospital\\nof Zhengzhou University from December 2020 to July 2022, and all patients were\\nconfirmed to have vulnerable plaques by IVUS. According to the treatment\\nstrategy they received, patients were entered into either the DCB treatment\\ngroup (n = 55) or the DES treatment group (n = 61). Figure 1 shows the study flowchart.\\nThe flowchart of this study.\\nInclusion criteria: (1) 18-85 years of age; (2) meeting the diagnostic criteria\\nof the European Heart Association for ACS 15,16(3) having definite\\nculprit vessel; and (4) gray-scale IVUS showing PB ≥ 70% and minimal luminal\\narea (MLA) ≤ 4.0 mm2.\\n17\\n Exclusion criteria: (1) chronic total occlusion; (2) heavily calcified\\nlesions requiring rotational atherectomy; (3) patients with thrombus aspiration;\\n(4) severe valvular insufficiency or valvular stenosis; (5) definite\\ncontraindications to antiplatelet drugs or anticoagulants; (6) dissection repair\\nremedial stents after DCB implantation; (7) severe renal insufficiency (GFR<\\n30 ml/min) or severe liver insufficiency; and (8) patients who declined DCB and\\nDES implantation.\\nThe survival curve of DCB and DES groups\",\n \"Patients received dual antiplatelet therapy.18–22 Scheme 1: preoperative\\nloading dose of aspirin 300 mg + ticagrelor 180 mg, postoperative maintenance\\ndose of aspirin 100 mg + ticagrelor 90 mg, twice a day. Scheme 2: preoperative\\nloading dose of aspirin 300 mg + clopidogrel 300 mg, postoperative maintenance\\ndose of aspirin 100 mg/d + clopidogrel 75 mg/d. For special ACS patients, such\\nas emergency PCI patients, comatose patients, and patients with poor\\ncooperation, preoperative dual antiplatelet drug therapy might not be given and\\nreplaced by continuous intravenous anticoagulant drugs.\\nAfter routine sterilization and draping, the radial artery or femoral artery was\\nselected for arterial puncture and sheath insertion. CAG was performed according\\nto established protocols. To clearly display the culprit vessels, an appropriate\\npositioning was selected for imaging according to the preoperative\\nelectrocardiogram, and multi-position imaging was performed if necessary.\\nCulprit vessel were determined by combining the patient's electrocardiogram,\\nechocardiography, and intraoperative angiography, and graded according to the\\nThrombolysis in Myocardial Infarction (TIMI) criteria. For TIMI grade < 3\\nculprit vessels, a compliant balloon was used to pre-dilate the culprit vessel\\nlesions to restore TIMI grade 3 flow. According to the patient's condition, IVUS\\nwas performed at the appropriate time. The probe was sent to the relatively\\nnormal segment at the distal end of the culprit vessel, and then withdrew to the\\nrelatively normal segment at the proximal end at a constant rate of 0.5 mm/s to\\nobtain intravascular imaging including vessel area, lumen area and plaque\\nburden.\\nBefore DCB or DES implant in the culprit vessel, an appropriate compliant\\nballoon, semi-compliant balloon, cutting balloon, or spinous balloon was used to\\ndilate the lesion and reduce culprit stenosis.\\n23\\n For DCB implantation, if the degree of vascular stenosis was ≤ 30%, and\\nthere was no dissection or only type A or B dissection after dilation, a\\nsize-matched DCB was chosen based on IVUS imaging and placed in the lesion\\nsustained release before balloon withdrawal. The length of the DCB catheter\\nexceed the target lesion by at least 5 mm. and the ratio of DCB diameters with\\nreference vessel diameters were 0.8–1.0. The recommended inflation time was at\\nleast 40 s at >7 atm. For DES implantation, after pre-dilation, a\\nsize-matched DES based on IVUS imaging was placed in the lesion and released. A\\nhigh-pressure balloon with appropriate dimensions was selected for post-stenting\\ndilation to ensure that the stent was fully expanded and adhered to the vessel\\nwall. IVUS examination was performed after DCB and DES implantation to further\\nevaluate the immediate postoperative efficacy and guide further treatment of\\nstent malapposition and dissection. At the same time, postoperative IVUS imaging\\nresults were recorded.\\nIn this study, DCB had a paclitaxel/iohexol matrix coating on the Bingo\\nDrug-Coated Balloon (Bingo™, Yinyi Biotech, Dalian, China). And the IVUS imaging\\nwere done with\\nthe computer program (H749A70200,Boston Scientific, Shanghai, China).\",\n \"Basic patient information was collected, including sex, age, clinical diagnosis,\\nleft ventricular ejection fraction (LVEF), history of hypertension,\\nhyperlipidemia, diabetes, smoking, myocardial infarction, previous PCI, previous\\ncoronary artery bypass grafting (CABG), and family history of coronary heart\\ndisease.\\nQuantitative coronary angiography (QCA) software was used to analyze the CAG data\\nof all patients, including (1) preoperative and immediate postoperative\\nreference vessel diameter (RFD), minimal luminal diameter, and diameter\\nstenosis; (2) MLD, DS, LLL, restenosis lesions, and lumen enlargement at\\nfollow-up.\\nThe IVUS analysis were also evaluated, including preoperative and immediate\\npostoperative vessel area, plaque burden, and lumen area. Because the PB ≥70%\\nwas the strongest independent predictor of subsequent lesion-related events in\\nthe first PROSPECT study, and Gregg et al defined vulnerable plaque with this PB\\nthreshold.17,24 In this study, the PB criterion was selected to define\\na vulnerable plaque, and the result of CAG and IVUS were evaluated by two\\nexperienced researchers.\\nAll patients passed the outpatient or telephone assessment 1, 3, 6, and 12 months\\nafter PCI. Major adverse cardiovascular events during hospitalization and\\nfollow-up were target lesion revascularization (TLR), myocardial infarction\\n(MI), and cardiac death.\",\n \"SPSS version 22.0 (IBM Corporation, Armonk, NY, USA). was used for statistical\\nanalysis. Quantitative data that conformed to normal distribution were analyzed\\nby t-test and expressed as mean ± standard deviation (SD); those that did not\\nconform to normal distribution were analyzed by Wilcoxon rank-sum test and\\nexpressed as median and quartile range (M (P25, P75)). Qualitative data were\\ncompared using the chi-square test. If T ≤ 5, the continuity-corrected\\nchi-square test and Fisher's exact probability test were used and expressed as\\nnumber of cases (%). A Kaplan-Meier curve was used to describe survival\\nanalysis. Two-tailed P < 0.05 indicates a statistically significant\\ndifference.\",\n \"Baseline Demographic and Clinical Characteristics A total of 123 patients were included in this study, of whom 55 received DCB and\\n68 received DES. As shown in Table 1, there were no statistically\\nsignificant differences between the DCB and the DES groups in terms of average\\nage, gender distribution, ejection fraction, and other clinical characteristics\\n(P > 0.05). Coronary heart disease-related risk factors\\nsuch as hypertension, hyperlipidemia, diabetes, and smoking history were also\\nnot significantly different between the two groups (P >\\n0.05). Similarly, there were no significant differences in clinical diagnosis of\\nST-elevation myocardial infarction, non-ST-elevation myocardial infarction, and\\nunstable angina pectoris between the two groups\\n(X2 = 0.92,P>0.05).\\nBaseline Patient Characteristics of the Study Population.\\nComparing two data sets using the continuity corrected chi-square\\ntest; DCB: drug coated balloon, DES: drug-eluting stent; STEMI\\n:ST-segment elevation myocardial infarction, NSTEMI: non-STEMI; MI\\n:myocardial infarction; PCI :percutaneous coronary intervention;\\nCABG: coronary artery bypass grafting; LVEF: Left Ventricle\\nejection\\nA total of 123 patients were included in this study, of whom 55 received DCB and\\n68 received DES. As shown in Table 1, there were no statistically\\nsignificant differences between the DCB and the DES groups in terms of average\\nage, gender distribution, ejection fraction, and other clinical characteristics\\n(P > 0.05). Coronary heart disease-related risk factors\\nsuch as hypertension, hyperlipidemia, diabetes, and smoking history were also\\nnot significantly different between the two groups (P >\\n0.05). Similarly, there were no significant differences in clinical diagnosis of\\nST-elevation myocardial infarction, non-ST-elevation myocardial infarction, and\\nunstable angina pectoris between the two groups\\n(X2 = 0.92,P>0.05).\\nBaseline Patient Characteristics of the Study Population.\\nComparing two data sets using the continuity corrected chi-square\\ntest; DCB: drug coated balloon, DES: drug-eluting stent; STEMI\\n:ST-segment elevation myocardial infarction, NSTEMI: non-STEMI; MI\\n:myocardial infarction; PCI :percutaneous coronary intervention;\\nCABG: coronary artery bypass grafting; LVEF: Left Ventricle\\nejection\\nProcedural Characteristics of PCI By combining preoperative ECG and echocardiography and intraoperative CAG\\nresults, the culprit vessels in ACS patients were diagnosed and PCI was\\nperformed. The target vessel locations included left anterior descending artery,\\nleft circumflex artery, right coronary artery, and ramus coronary artery, and\\nthere were no statistically significant differences in target vessel locations\\nbetween the DCB and DES treatment\\ngroups(X2 = 2.83,P＞0.05). According to\\nintraoperative CAG, coronary artery lesions in the two groups were divided into\\nsingle vessel disease, double vessel disease, and triple vessel disease, and\\nthere were no significant differences in multivessel disease between the two\\ngroups (X2 = 0.01,P＞0.05). Except for the\\nsignificant difference in intraoperative balloon usage between the two groups\\n(X2 = 7.69,P＜0.05), there were no statistical\\ndifferences in other PCI procedural characteristics, including implantation\\ndiameter and length, number of patients with intraoperative dissection, number\\nof patients with temporary pacemakers, and number of patients using intra-aortic\\nballoon pump(IABP)(P > 0.05). The procedural characteristics\\nof PCI in the two groups are shown in Table 2.\\nProcedural Characteristics of PCI.\\nComparing two data sets using the continuity corrected chi-square\\ntest #Comparing two data sets using the Fisher's exact\\nprobability test; DCB: drug coated balloon, DES: drug-eluting stent;\\nIABP: intra-aortic balloon pump\\nBy combining preoperative ECG and echocardiography and intraoperative CAG\\nresults, the culprit vessels in ACS patients were diagnosed and PCI was\\nperformed. The target vessel locations included left anterior descending artery,\\nleft circumflex artery, right coronary artery, and ramus coronary artery, and\\nthere were no statistically significant differences in target vessel locations\\nbetween the DCB and DES treatment\\ngroups(X2 = 2.83,P＞0.05). According to\\nintraoperative CAG, coronary artery lesions in the two groups were divided into\\nsingle vessel disease, double vessel disease, and triple vessel disease, and\\nthere were no significant differences in multivessel disease between the two\\ngroups (X2 = 0.01,P＞0.05). Except for the\\nsignificant difference in intraoperative balloon usage between the two groups\\n(X2 = 7.69,P＜0.05), there were no statistical\\ndifferences in other PCI procedural characteristics, including implantation\\ndiameter and length, number of patients with intraoperative dissection, number\\nof patients with temporary pacemakers, and number of patients using intra-aortic\\nballoon pump(IABP)(P > 0.05). The procedural characteristics\\nof PCI in the two groups are shown in Table 2.\\nProcedural Characteristics of PCI.\\nComparing two data sets using the continuity corrected chi-square\\ntest #Comparing two data sets using the Fisher's exact\\nprobability test; DCB: drug coated balloon, DES: drug-eluting stent;\\nIABP: intra-aortic balloon pump\\nQCA and IVUS Analysis The preoperative, postoperative and follow-up results of PCI are shown in Table 3. There were\\nno significant differences in preoperative RFD, MLD and DS between the two\\ngroups (P > 0.05). However, CAG immediately after DCB or DES\\nimplantation showed significantly smaller MLD and greater degree of stenosis in\\nthe DCB group than in the DES group (P < 0.05). Further\\nfollow-up CAG after PCI revealed that there were no statistically significant\\ndifferences in MLD, stenosis degree, and coronary restenosis between the two\\ngroups (P > 0.05), except that compared to the DES group,\\nthe DCB group had less LLL in lumen diameter and a greater number of lumen\\nenlargements (P < 0.05).\\nComparison of Coronary QCA and IVUS Results Between DCB and DES\\nGroups.\\nDCB: drug coated balloon, DES: drug-eluting stent; CAG: coronary\\nartery angiography; IVUS: intravascular ultrasound; MLD: minimal\\nlumen diameter; LLL: late luminal loss;\\nBoth groups underwent IVUS before and after PCI. The results showed that there\\nwere no significant differences in vessel area and lumen area between the two\\ngroups (P > 0.05). In addition, no statistically significant\\ndifferences were seen in preoperative plaque burden （79.05 ± 4.17 VS\\n80.07 ± 4.68）and postprocedural plaque burden （61.69 ± 5.64 VS\\n63.32 ± 6.07）between the two groups (P > 0.05).\\nThe preoperative, postoperative and follow-up results of PCI are shown in Table 3. There were\\nno significant differences in preoperative RFD, MLD and DS between the two\\ngroups (P > 0.05). However, CAG immediately after DCB or DES\\nimplantation showed significantly smaller MLD and greater degree of stenosis in\\nthe DCB group than in the DES group (P < 0.05). Further\\nfollow-up CAG after PCI revealed that there were no statistically significant\\ndifferences in MLD, stenosis degree, and coronary restenosis between the two\\ngroups (P > 0.05), except that compared to the DES group,\\nthe DCB group had less LLL in lumen diameter and a greater number of lumen\\nenlargements (P < 0.05).\\nComparison of Coronary QCA and IVUS Results Between DCB and DES\\nGroups.\\nDCB: drug coated balloon, DES: drug-eluting stent; CAG: coronary\\nartery angiography; IVUS: intravascular ultrasound; MLD: minimal\\nlumen diameter; LLL: late luminal loss;\\nBoth groups underwent IVUS before and after PCI. The results showed that there\\nwere no significant differences in vessel area and lumen area between the two\\ngroups (P > 0.05). In addition, no statistically significant\\ndifferences were seen in preoperative plaque burden （79.05 ± 4.17 VS\\n80.07 ± 4.68）and postprocedural plaque burden （61.69 ± 5.64 VS\\n63.32 ± 6.07）between the two groups (P > 0.05).\\nFollow-up MACE During the follow-up period, there were no significant differences in target\\nlesion revascularization, myocardial infarction, and cardiac death between the\\ntwo groups (P > 0.05). And survival analysis shows that\\nthere were no significant differences in the survival rate of the two groups\\n(Log-rank,X2 = 0.01,P > 0.05). The detailed data are shown in\\nTable 4 and\\nFigure 2.\\nComparison of MACE Between DCB and DES Groups.\\nComparing two data sets using the continuity corrected chi-square\\ntest; #Comparing two data sets using the Fisher's exact\\nprobability test; DCB: drug coated balloon, DES: drug-eluting stent;\\nMACE: major adverse cardiovascular event; TLR: target lesion\\nrevascularization; MI: myocardial infarction.\\nDuring the follow-up period, there were no significant differences in target\\nlesion revascularization, myocardial infarction, and cardiac death between the\\ntwo groups (P > 0.05). And survival analysis shows that\\nthere were no significant differences in the survival rate of the two groups\\n(Log-rank,X2 = 0.01,P > 0.05). The detailed data are shown in\\nTable 4 and\\nFigure 2.\\nComparison of MACE Between DCB and DES Groups.\\nComparing two data sets using the continuity corrected chi-square\\ntest; #Comparing two data sets using the Fisher's exact\\nprobability test; DCB: drug coated balloon, DES: drug-eluting stent;\\nMACE: major adverse cardiovascular event; TLR: target lesion\\nrevascularization; MI: myocardial infarction.\",\n \"A total of 123 patients were included in this study, of whom 55 received DCB and\\n68 received DES. As shown in Table 1, there were no statistically\\nsignificant differences between the DCB and the DES groups in terms of average\\nage, gender distribution, ejection fraction, and other clinical characteristics\\n(P > 0.05). Coronary heart disease-related risk factors\\nsuch as hypertension, hyperlipidemia, diabetes, and smoking history were also\\nnot significantly different between the two groups (P >\\n0.05). Similarly, there were no significant differences in clinical diagnosis of\\nST-elevation myocardial infarction, non-ST-elevation myocardial infarction, and\\nunstable angina pectoris between the two groups\\n(X2 = 0.92,P>0.05).\\nBaseline Patient Characteristics of the Study Population.\\nComparing two data sets using the continuity corrected chi-square\\ntest; DCB: drug coated balloon, DES: drug-eluting stent; STEMI\\n:ST-segment elevation myocardial infarction, NSTEMI: non-STEMI; MI\\n:myocardial infarction; PCI :percutaneous coronary intervention;\\nCABG: coronary artery bypass grafting; LVEF: Left Ventricle\\nejection\",\n \"By combining preoperative ECG and echocardiography and intraoperative CAG\\nresults, the culprit vessels in ACS patients were diagnosed and PCI was\\nperformed. The target vessel locations included left anterior descending artery,\\nleft circumflex artery, right coronary artery, and ramus coronary artery, and\\nthere were no statistically significant differences in target vessel locations\\nbetween the DCB and DES treatment\\ngroups(X2 = 2.83,P＞0.05). According to\\nintraoperative CAG, coronary artery lesions in the two groups were divided into\\nsingle vessel disease, double vessel disease, and triple vessel disease, and\\nthere were no significant differences in multivessel disease between the two\\ngroups (X2 = 0.01,P＞0.05). Except for the\\nsignificant difference in intraoperative balloon usage between the two groups\\n(X2 = 7.69,P＜0.05), there were no statistical\\ndifferences in other PCI procedural characteristics, including implantation\\ndiameter and length, number of patients with intraoperative dissection, number\\nof patients with temporary pacemakers, and number of patients using intra-aortic\\nballoon pump(IABP)(P > 0.05). The procedural characteristics\\nof PCI in the two groups are shown in Table 2.\\nProcedural Characteristics of PCI.\\nComparing two data sets using the continuity corrected chi-square\\ntest #Comparing two data sets using the Fisher's exact\\nprobability test; DCB: drug coated balloon, DES: drug-eluting stent;\\nIABP: intra-aortic balloon pump\",\n \"The preoperative, postoperative and follow-up results of PCI are shown in Table 3. There were\\nno significant differences in preoperative RFD, MLD and DS between the two\\ngroups (P > 0.05). However, CAG immediately after DCB or DES\\nimplantation showed significantly smaller MLD and greater degree of stenosis in\\nthe DCB group than in the DES group (P < 0.05). Further\\nfollow-up CAG after PCI revealed that there were no statistically significant\\ndifferences in MLD, stenosis degree, and coronary restenosis between the two\\ngroups (P > 0.05), except that compared to the DES group,\\nthe DCB group had less LLL in lumen diameter and a greater number of lumen\\nenlargements (P < 0.05).\\nComparison of Coronary QCA and IVUS Results Between DCB and DES\\nGroups.\\nDCB: drug coated balloon, DES: drug-eluting stent; CAG: coronary\\nartery angiography; IVUS: intravascular ultrasound; MLD: minimal\\nlumen diameter; LLL: late luminal loss;\\nBoth groups underwent IVUS before and after PCI. The results showed that there\\nwere no significant differences in vessel area and lumen area between the two\\ngroups (P > 0.05). In addition, no statistically significant\\ndifferences were seen in preoperative plaque burden （79.05 ± 4.17 VS\\n80.07 ± 4.68）and postprocedural plaque burden （61.69 ± 5.64 VS\\n63.32 ± 6.07）between the two groups (P > 0.05).\",\n \"During the follow-up period, there were no significant differences in target\\nlesion revascularization, myocardial infarction, and cardiac death between the\\ntwo groups (P > 0.05). And survival analysis shows that\\nthere were no significant differences in the survival rate of the two groups\\n(Log-rank,X2 = 0.01,P > 0.05). The detailed data are shown in\\nTable 4 and\\nFigure 2.\\nComparison of MACE Between DCB and DES Groups.\\nComparing two data sets using the continuity corrected chi-square\\ntest; #Comparing two data sets using the Fisher's exact\\nprobability test; DCB: drug coated balloon, DES: drug-eluting stent;\\nMACE: major adverse cardiovascular event; TLR: target lesion\\nrevascularization; MI: myocardial infarction.\",\n \"DES is the preferred strategy for reperfusion in patients with ACS. However, delayed\\nhealing and vascular remodeling of the vascular endothelium often occur after DES\\nimplantation due to the vulnerability of the plaque and the hypercoagulable state\\nand persistent inflammation caused by residual metals, leading to a greatly\\nincreased risk of late in-stent thrombosis.10,25 As a new strategy for the\\ntreatment of coronary artery disease, DCB has achieved reliable efficacy in various\\ntypes of coronary artery disease such as in-stent stenosis due to its unique drug\\nrelease behavior and no residual metal substances. Similarly, DCB has also been used\\nin ACS lesions, but little is known about the effect of plaque vulnerability on the\\nefficacy of DCB in ACS patients.\\nAt present, there are many ways to identify vulnerable plaques, and each imaging\\nmethod, whether invasive or non-invasive, has different diagnostic criteria. Gregg\\net al noted that PB ≥ 70% or MLA ≤ 4.0 mm2 on IVUS are the strongest\\nindependent predictors of subsequent MACE after PCI.17,26–30 Therefore, this study\\nsimilarly adopted this criterion to define vulnerable plaque. We also analyzed IVUS\\nin the DCB and DES groups before and immediately after PCI. We found no significant\\ndifferences in vessel area, PB, and lumen area between the two groups, indicating\\nthat DCB has comparable effects on vulnerable plaques and luminal structure\\nimmediately after implantation in ACS patients compared to DES. Nevertheless, the\\nlong-term efficacy of DCB is unclear, as patients in both groups did not undergo\\nIVUS during follow-up for economic or other reasons. It has been reported that\\nduring IVUS follow-up of DCB in the treatment of native vessel lesions, DCB was\\nfound to stabilize plaques by reducing PB and altering plaque composition, thereby\\nreducing subsequent adverse events caused by plaque rupture. 31,32 Whether this\\neffect of DCB appears in the treatment of ACS complicated with vulnerable plaques\\nwarrants further IVUS follow-up analysis.\\nIn this study, CAG was performed before both DCB and DES implantation, and there were\\nno significant differences in preoperative MLD and DS between the two groups. For\\nthe immediate postoperative CAG results showing that the DCB group had a smaller MLD\\nand greater degree of stenosis than the DES group, one possible reason is that DCB,\\nas a transport tool for antiproliferative drugs, lacks the effective support of\\nmetal nets like DES. Secondly, in the DES group, to ensure the full contact between\\nthe stent and the blood vessel, a high-pressure balloon of suitable size was used\\nfor post-dilation, which can allow the DES to dilate diseased vessels more\\neffectively, reduce the elastic recoil of vessels, and ultimately prevent lumen stenosis.\\n33\\n For DCB-PCI, in addition to adequate pretreatment prior to implantation to\\navoid the occurrence of dissection, the lumen size and lesions had been evaluated by\\nIVUS to minimize the difference in vasodilation with DES.\\nFollow-up CAG results showed that there were no significant differences in MLD and DS\\nbetween the two groups, but the DCB group had smaller LLL and greater lumen\\nenlargement. There are multiple reasons for these results. First, unlike patients in\\nprevious studies, patients in this study had a higher PB and a higher proportion of\\npatients in the DCB group used cutting balloons or spinous balloons than in the DES\\ngroup, which may allow for a more complete and uniform dispersion of the internal\\nand medial plaques. Second, compared to DES, DCB has a larger contact area with the\\ndiseased arterial segment, enabling the antiproliferative drugs to diffuse uniformly\\nand rapidly to the diseased vessel. Further, the residual metal stent material and\\nlong-term sustained release of anti-proliferative drugs in the DES group could lead\\nto delayed endothelialization and inflammatory response, while in the DCB group, the\\narterial vasomotor and dilatation functions were relatively stable because there was\\nno metal residual problem, which may be the reason for the late lumen enlargement.\\nAll these factors together resulted in better vascular remodeling and then less\\nlumen loss diameter and greater lumen enlargement in the DCB group.34–36 On the other hand, there were\\nno significant differences in the incidence of target lesion revascularization,\\nmyocardial infarction, and cardiac death between the two groups during follow-up,\\nalso suggesting that DCB is non-inferior to DES in long-term outcomes.\",\n \"This study has certain limitations. As a retrospective, single-center, and\\nsmall-sample study, there may be patient selection bias. Second, although CAG was\\nperformed during follow-up in both groups, we had to assess MLD using QCA\\nimmediately after PCI due to the lack of IVUS imaging during follow-up. These\\nlimitations may have affected the results to some extent. Therefore, to further\\nvalidate our conclusions, multiple interventional methods such as optical coherence\\ntomography(OCT), virtual histology IVUS (VH-IVUS), and near-infrared spectroscopy\\n(NIRS) can be used to comprehensively detect and identify vulnerable plaques to\\nachieve accurate diagnosis of lesions. In addition, multicenter, large sample, and\\nlonger follow-up studies are needed.\",\n \"In summary, DCB is safe and effective in the treatment of ACS complicated with\\nvulnerable plaque, and DCB has the advantage over DES in LLL. Our work provides\\npractical experience in the interventional treatment of vulnerable plaques in\\nACS.\"\n]"},"all_sections_sec_types":{"kind":"list like","value":["intro",null,null,null,null,null,"results",null,null,null,null,"discussion",null,null],"string":"[\n \"intro\",\n null,\n null,\n null,\n null,\n null,\n \"results\",\n null,\n null,\n null,\n null,\n \"discussion\",\n null,\n null\n]"},"keywords":{"kind":"list like","value":["acute coronary syndromes","drug coated balloon","drug-eluting stent","vulnerable plaque"],"string":"[\n \"acute coronary syndromes\",\n \"drug coated balloon\",\n \"drug-eluting stent\",\n \"vulnerable plaque\"\n]"},"whole_article_text":{"kind":"string","value":"Introduction:\nAcute coronary syndromes (ACS) is a serious coronary heart disease that threatens\nhuman health. Coronary plaques in such patients usually have a large plaque burden\nwith lipid-rich necrotic cores, called vulnerable plaques, whose progression and\nrupture lead to unstable angina, acute non-ST elevation myocardial infarction, and\nacute ST-elevation myocardial infarction.1–4 Percutaneous coronary\nintervention is now an effective treatment approach for ACS. PCI can restore normal\nblood flow through myocardial revascularization and thus greatly improves symptoms\nand prognosis in ACS patients.5,6\nCompared with traditional two-dimensional coronary angiography (CAG), new\nintraluminal imaging techniques such as IVUS have more efficacy in reducing\ncomplications after drug-eluting stents placement, decreasing in-stent restenosis\nrate, and improving treatment outcome. Equally important, IVUS can better provide\nintravascular imaging data for coronary vascular lesions with coronary overlapping,\nanatomical abnormalities, aneurysms, myocardial bridges, and calcifications,\nespecially in the identification of coronary culprit vessels. Because most ACS\npatients present with plaque ruptures, an accurate identification of the nature,\nextent and location of plaque ruptures is particularly important for the formulation\nof diagnosis and treatment planning. In this context, IVUS is superior to CAG in\nproviding relevant imaging information and intraprocedural guidance of PCI and\nevaluating postoperative results and clinical prognosis.7,8\nCurrently, DES is the preferred strategy for PCI in patients with ACS. The use of DES\nhas the advantages of inducing intimal hyperplasia, thickening fibrous cap, and\nnormalizing wall stress to reduce plaque rupture. However, subsequent complications\nof DES may occur, such as in-stent restenosis, in-stent hyperplasia, and stent\nfracture, and require long-term dual antiplatelet therapy. In severe cases, there\nmay be no reflow phenomenon after stent placement, rapidly resulting in a larger\narea of myocardium infarction.9–11 DCB are a new\nrevascularization technique that has become the treatment of choice for in-stent\nrestenosis because it meets the new concept of intervention without implantation.\nIndications of DCB also include small vessel disease and bifurcation disease, among\nothers.12–14 However,\nthere are few reports related to the use of DCB in ACS patients with vulnerable\nplaques. Therefore, this study used DES as a parallel treatment to examine the\nsafety and efficacy of DCB in ACS complicated with vulnerable plaques lesions under\nthe guidance of IVUS.\n\nMaterials and Methods:\nPatients This retrospectively study investigated 123 patients who were diagnosed with ACS\nand underwent PCI in the Department of Cardiology, the First Affiliated Hospital\nof Zhengzhou University from December 2020 to July 2022, and all patients were\nconfirmed to have vulnerable plaques by IVUS. According to the treatment\nstrategy they received, patients were entered into either the DCB treatment\ngroup (n = 55) or the DES treatment group (n = 61). Figure 1 shows the study flowchart.\nThe flowchart of this study.\nInclusion criteria: (1) 18-85 years of age; (2) meeting the diagnostic criteria\nof the European Heart Association for ACS 15,16(3) having definite\nculprit vessel; and (4) gray-scale IVUS showing PB ≥ 70% and minimal luminal\narea (MLA) ≤ 4.0 mm2.\n17\n Exclusion criteria: (1) chronic total occlusion; (2) heavily calcified\nlesions requiring rotational atherectomy; (3) patients with thrombus aspiration;\n(4) severe valvular insufficiency or valvular stenosis; (5) definite\ncontraindications to antiplatelet drugs or anticoagulants; (6) dissection repair\nremedial stents after DCB implantation; (7) severe renal insufficiency (GFR<\n30 ml/min) or severe liver insufficiency; and (8) patients who declined DCB and\nDES implantation.\nThe survival curve of DCB and DES groups\nThis retrospectively study investigated 123 patients who were diagnosed with ACS\nand underwent PCI in the Department of Cardiology, the First Affiliated Hospital\nof Zhengzhou University from December 2020 to July 2022, and all patients were\nconfirmed to have vulnerable plaques by IVUS. According to the treatment\nstrategy they received, patients were entered into either the DCB treatment\ngroup (n = 55) or the DES treatment group (n = 61). Figure 1 shows the study flowchart.\nThe flowchart of this study.\nInclusion criteria: (1) 18-85 years of age; (2) meeting the diagnostic criteria\nof the European Heart Association for ACS 15,16(3) having definite\nculprit vessel; and (4) gray-scale IVUS showing PB ≥ 70% and minimal luminal\narea (MLA) ≤ 4.0 mm2.\n17\n Exclusion criteria: (1) chronic total occlusion; (2) heavily calcified\nlesions requiring rotational atherectomy; (3) patients with thrombus aspiration;\n(4) severe valvular insufficiency or valvular stenosis; (5) definite\ncontraindications to antiplatelet drugs or anticoagulants; (6) dissection repair\nremedial stents after DCB implantation; (7) severe renal insufficiency (GFR<\n30 ml/min) or severe liver insufficiency; and (8) patients who declined DCB and\nDES implantation.\nThe survival curve of DCB and DES groups\nPCI Procedure Patients received dual antiplatelet therapy.18–22 Scheme 1: preoperative\nloading dose of aspirin 300 mg + ticagrelor 180 mg, postoperative maintenance\ndose of aspirin 100 mg + ticagrelor 90 mg, twice a day. Scheme 2: preoperative\nloading dose of aspirin 300 mg + clopidogrel 300 mg, postoperative maintenance\ndose of aspirin 100 mg/d + clopidogrel 75 mg/d. For special ACS patients, such\nas emergency PCI patients, comatose patients, and patients with poor\ncooperation, preoperative dual antiplatelet drug therapy might not be given and\nreplaced by continuous intravenous anticoagulant drugs.\nAfter routine sterilization and draping, the radial artery or femoral artery was\nselected for arterial puncture and sheath insertion. CAG was performed according\nto established protocols. To clearly display the culprit vessels, an appropriate\npositioning was selected for imaging according to the preoperative\nelectrocardiogram, and multi-position imaging was performed if necessary.\nCulprit vessel were determined by combining the patient's electrocardiogram,\nechocardiography, and intraoperative angiography, and graded according to the\nThrombolysis in Myocardial Infarction (TIMI) criteria. For TIMI grade < 3\nculprit vessels, a compliant balloon was used to pre-dilate the culprit vessel\nlesions to restore TIMI grade 3 flow. According to the patient's condition, IVUS\nwas performed at the appropriate time. The probe was sent to the relatively\nnormal segment at the distal end of the culprit vessel, and then withdrew to the\nrelatively normal segment at the proximal end at a constant rate of 0.5 mm/s to\nobtain intravascular imaging including vessel area, lumen area and plaque\nburden.\nBefore DCB or DES implant in the culprit vessel, an appropriate compliant\nballoon, semi-compliant balloon, cutting balloon, or spinous balloon was used to\ndilate the lesion and reduce culprit stenosis.\n23\n For DCB implantation, if the degree of vascular stenosis was ≤ 30%, and\nthere was no dissection or only type A or B dissection after dilation, a\nsize-matched DCB was chosen based on IVUS imaging and placed in the lesion\nsustained release before balloon withdrawal. The length of the DCB catheter\nexceed the target lesion by at least 5 mm. and the ratio of DCB diameters with\nreference vessel diameters were 0.8–1.0. The recommended inflation time was at\nleast 40 s at >7 atm. For DES implantation, after pre-dilation, a\nsize-matched DES based on IVUS imaging was placed in the lesion and released. A\nhigh-pressure balloon with appropriate dimensions was selected for post-stenting\ndilation to ensure that the stent was fully expanded and adhered to the vessel\nwall. IVUS examination was performed after DCB and DES implantation to further\nevaluate the immediate postoperative efficacy and guide further treatment of\nstent malapposition and dissection. At the same time, postoperative IVUS imaging\nresults were recorded.\nIn this study, DCB had a paclitaxel/iohexol matrix coating on the Bingo\nDrug-Coated Balloon (Bingo™, Yinyi Biotech, Dalian, China). And the IVUS imaging\nwere done with\nthe computer program (H749A70200,Boston Scientific, Shanghai, China).\nPatients received dual antiplatelet therapy.18–22 Scheme 1: preoperative\nloading dose of aspirin 300 mg + ticagrelor 180 mg, postoperative maintenance\ndose of aspirin 100 mg + ticagrelor 90 mg, twice a day. Scheme 2: preoperative\nloading dose of aspirin 300 mg + clopidogrel 300 mg, postoperative maintenance\ndose of aspirin 100 mg/d + clopidogrel 75 mg/d. For special ACS patients, such\nas emergency PCI patients, comatose patients, and patients with poor\ncooperation, preoperative dual antiplatelet drug therapy might not be given and\nreplaced by continuous intravenous anticoagulant drugs.\nAfter routine sterilization and draping, the radial artery or femoral artery was\nselected for arterial puncture and sheath insertion. CAG was performed according\nto established protocols. To clearly display the culprit vessels, an appropriate\npositioning was selected for imaging according to the preoperative\nelectrocardiogram, and multi-position imaging was performed if necessary.\nCulprit vessel were determined by combining the patient's electrocardiogram,\nechocardiography, and intraoperative angiography, and graded according to the\nThrombolysis in Myocardial Infarction (TIMI) criteria. For TIMI grade < 3\nculprit vessels, a compliant balloon was used to pre-dilate the culprit vessel\nlesions to restore TIMI grade 3 flow. According to the patient's condition, IVUS\nwas performed at the appropriate time. The probe was sent to the relatively\nnormal segment at the distal end of the culprit vessel, and then withdrew to the\nrelatively normal segment at the proximal end at a constant rate of 0.5 mm/s to\nobtain intravascular imaging including vessel area, lumen area and plaque\nburden.\nBefore DCB or DES implant in the culprit vessel, an appropriate compliant\nballoon, semi-compliant balloon, cutting balloon, or spinous balloon was used to\ndilate the lesion and reduce culprit stenosis.\n23\n For DCB implantation, if the degree of vascular stenosis was ≤ 30%, and\nthere was no dissection or only type A or B dissection after dilation, a\nsize-matched DCB was chosen based on IVUS imaging and placed in the lesion\nsustained release before balloon withdrawal. The length of the DCB catheter\nexceed the target lesion by at least 5 mm. and the ratio of DCB diameters with\nreference vessel diameters were 0.8–1.0. The recommended inflation time was at\nleast 40 s at >7 atm. For DES implantation, after pre-dilation, a\nsize-matched DES based on IVUS imaging was placed in the lesion and released. A\nhigh-pressure balloon with appropriate dimensions was selected for post-stenting\ndilation to ensure that the stent was fully expanded and adhered to the vessel\nwall. IVUS examination was performed after DCB and DES implantation to further\nevaluate the immediate postoperative efficacy and guide further treatment of\nstent malapposition and dissection. At the same time, postoperative IVUS imaging\nresults were recorded.\nIn this study, DCB had a paclitaxel/iohexol matrix coating on the Bingo\nDrug-Coated Balloon (Bingo™, Yinyi Biotech, Dalian, China). And the IVUS imaging\nwere done with\nthe computer program (H749A70200,Boston Scientific, Shanghai, China).\nAnalysis Parameters Basic patient information was collected, including sex, age, clinical diagnosis,\nleft ventricular ejection fraction (LVEF), history of hypertension,\nhyperlipidemia, diabetes, smoking, myocardial infarction, previous PCI, previous\ncoronary artery bypass grafting (CABG), and family history of coronary heart\ndisease.\nQuantitative coronary angiography (QCA) software was used to analyze the CAG data\nof all patients, including (1) preoperative and immediate postoperative\nreference vessel diameter (RFD), minimal luminal diameter, and diameter\nstenosis; (2) MLD, DS, LLL, restenosis lesions, and lumen enlargement at\nfollow-up.\nThe IVUS analysis were also evaluated, including preoperative and immediate\npostoperative vessel area, plaque burden, and lumen area. Because the PB ≥70%\nwas the strongest independent predictor of subsequent lesion-related events in\nthe first PROSPECT study, and Gregg et al defined vulnerable plaque with this PB\nthreshold.17,24 In this study, the PB criterion was selected to define\na vulnerable plaque, and the result of CAG and IVUS were evaluated by two\nexperienced researchers.\nAll patients passed the outpatient or telephone assessment 1, 3, 6, and 12 months\nafter PCI. Major adverse cardiovascular events during hospitalization and\nfollow-up were target lesion revascularization (TLR), myocardial infarction\n(MI), and cardiac death.\nBasic patient information was collected, including sex, age, clinical diagnosis,\nleft ventricular ejection fraction (LVEF), history of hypertension,\nhyperlipidemia, diabetes, smoking, myocardial infarction, previous PCI, previous\ncoronary artery bypass grafting (CABG), and family history of coronary heart\ndisease.\nQuantitative coronary angiography (QCA) software was used to analyze the CAG data\nof all patients, including (1) preoperative and immediate postoperative\nreference vessel diameter (RFD), minimal luminal diameter, and diameter\nstenosis; (2) MLD, DS, LLL, restenosis lesions, and lumen enlargement at\nfollow-up.\nThe IVUS analysis were also evaluated, including preoperative and immediate\npostoperative vessel area, plaque burden, and lumen area. Because the PB ≥70%\nwas the strongest independent predictor of subsequent lesion-related events in\nthe first PROSPECT study, and Gregg et al defined vulnerable plaque with this PB\nthreshold.17,24 In this study, the PB criterion was selected to define\na vulnerable plaque, and the result of CAG and IVUS were evaluated by two\nexperienced researchers.\nAll patients passed the outpatient or telephone assessment 1, 3, 6, and 12 months\nafter PCI. Major adverse cardiovascular events during hospitalization and\nfollow-up were target lesion revascularization (TLR), myocardial infarction\n(MI), and cardiac death.\nStatistical Analysis SPSS version 22.0 (IBM Corporation, Armonk, NY, USA). was used for statistical\nanalysis. Quantitative data that conformed to normal distribution were analyzed\nby t-test and expressed as mean ± standard deviation (SD); those that did not\nconform to normal distribution were analyzed by Wilcoxon rank-sum test and\nexpressed as median and quartile range (M (P25, P75)). Qualitative data were\ncompared using the chi-square test. If T ≤ 5, the continuity-corrected\nchi-square test and Fisher's exact probability test were used and expressed as\nnumber of cases (%). A Kaplan-Meier curve was used to describe survival\nanalysis. Two-tailed P < 0.05 indicates a statistically significant\ndifference.\nSPSS version 22.0 (IBM Corporation, Armonk, NY, USA). was used for statistical\nanalysis. Quantitative data that conformed to normal distribution were analyzed\nby t-test and expressed as mean ± standard deviation (SD); those that did not\nconform to normal distribution were analyzed by Wilcoxon rank-sum test and\nexpressed as median and quartile range (M (P25, P75)). Qualitative data were\ncompared using the chi-square test. If T ≤ 5, the continuity-corrected\nchi-square test and Fisher's exact probability test were used and expressed as\nnumber of cases (%). A Kaplan-Meier curve was used to describe survival\nanalysis. Two-tailed P < 0.05 indicates a statistically significant\ndifference.\n\nPatients:\nThis retrospectively study investigated 123 patients who were diagnosed with ACS\nand underwent PCI in the Department of Cardiology, the First Affiliated Hospital\nof Zhengzhou University from December 2020 to July 2022, and all patients were\nconfirmed to have vulnerable plaques by IVUS. According to the treatment\nstrategy they received, patients were entered into either the DCB treatment\ngroup (n = 55) or the DES treatment group (n = 61). Figure 1 shows the study flowchart.\nThe flowchart of this study.\nInclusion criteria: (1) 18-85 years of age; (2) meeting the diagnostic criteria\nof the European Heart Association for ACS 15,16(3) having definite\nculprit vessel; and (4) gray-scale IVUS showing PB ≥ 70% and minimal luminal\narea (MLA) ≤ 4.0 mm2.\n17\n Exclusion criteria: (1) chronic total occlusion; (2) heavily calcified\nlesions requiring rotational atherectomy; (3) patients with thrombus aspiration;\n(4) severe valvular insufficiency or valvular stenosis; (5) definite\ncontraindications to antiplatelet drugs or anticoagulants; (6) dissection repair\nremedial stents after DCB implantation; (7) severe renal insufficiency (GFR<\n30 ml/min) or severe liver insufficiency; and (8) patients who declined DCB and\nDES implantation.\nThe survival curve of DCB and DES groups\n\nPCI Procedure:\nPatients received dual antiplatelet therapy.18–22 Scheme 1: preoperative\nloading dose of aspirin 300 mg + ticagrelor 180 mg, postoperative maintenance\ndose of aspirin 100 mg + ticagrelor 90 mg, twice a day. Scheme 2: preoperative\nloading dose of aspirin 300 mg + clopidogrel 300 mg, postoperative maintenance\ndose of aspirin 100 mg/d + clopidogrel 75 mg/d. For special ACS patients, such\nas emergency PCI patients, comatose patients, and patients with poor\ncooperation, preoperative dual antiplatelet drug therapy might not be given and\nreplaced by continuous intravenous anticoagulant drugs.\nAfter routine sterilization and draping, the radial artery or femoral artery was\nselected for arterial puncture and sheath insertion. CAG was performed according\nto established protocols. To clearly display the culprit vessels, an appropriate\npositioning was selected for imaging according to the preoperative\nelectrocardiogram, and multi-position imaging was performed if necessary.\nCulprit vessel were determined by combining the patient's electrocardiogram,\nechocardiography, and intraoperative angiography, and graded according to the\nThrombolysis in Myocardial Infarction (TIMI) criteria. For TIMI grade < 3\nculprit vessels, a compliant balloon was used to pre-dilate the culprit vessel\nlesions to restore TIMI grade 3 flow. According to the patient's condition, IVUS\nwas performed at the appropriate time. The probe was sent to the relatively\nnormal segment at the distal end of the culprit vessel, and then withdrew to the\nrelatively normal segment at the proximal end at a constant rate of 0.5 mm/s to\nobtain intravascular imaging including vessel area, lumen area and plaque\nburden.\nBefore DCB or DES implant in the culprit vessel, an appropriate compliant\nballoon, semi-compliant balloon, cutting balloon, or spinous balloon was used to\ndilate the lesion and reduce culprit stenosis.\n23\n For DCB implantation, if the degree of vascular stenosis was ≤ 30%, and\nthere was no dissection or only type A or B dissection after dilation, a\nsize-matched DCB was chosen based on IVUS imaging and placed in the lesion\nsustained release before balloon withdrawal. The length of the DCB catheter\nexceed the target lesion by at least 5 mm. and the ratio of DCB diameters with\nreference vessel diameters were 0.8–1.0. The recommended inflation time was at\nleast 40 s at >7 atm. For DES implantation, after pre-dilation, a\nsize-matched DES based on IVUS imaging was placed in the lesion and released. A\nhigh-pressure balloon with appropriate dimensions was selected for post-stenting\ndilation to ensure that the stent was fully expanded and adhered to the vessel\nwall. IVUS examination was performed after DCB and DES implantation to further\nevaluate the immediate postoperative efficacy and guide further treatment of\nstent malapposition and dissection. At the same time, postoperative IVUS imaging\nresults were recorded.\nIn this study, DCB had a paclitaxel/iohexol matrix coating on the Bingo\nDrug-Coated Balloon (Bingo™, Yinyi Biotech, Dalian, China). And the IVUS imaging\nwere done with\nthe computer program (H749A70200,Boston Scientific, Shanghai, China).\n\nAnalysis Parameters:\nBasic patient information was collected, including sex, age, clinical diagnosis,\nleft ventricular ejection fraction (LVEF), history of hypertension,\nhyperlipidemia, diabetes, smoking, myocardial infarction, previous PCI, previous\ncoronary artery bypass grafting (CABG), and family history of coronary heart\ndisease.\nQuantitative coronary angiography (QCA) software was used to analyze the CAG data\nof all patients, including (1) preoperative and immediate postoperative\nreference vessel diameter (RFD), minimal luminal diameter, and diameter\nstenosis; (2) MLD, DS, LLL, restenosis lesions, and lumen enlargement at\nfollow-up.\nThe IVUS analysis were also evaluated, including preoperative and immediate\npostoperative vessel area, plaque burden, and lumen area. Because the PB ≥70%\nwas the strongest independent predictor of subsequent lesion-related events in\nthe first PROSPECT study, and Gregg et al defined vulnerable plaque with this PB\nthreshold.17,24 In this study, the PB criterion was selected to define\na vulnerable plaque, and the result of CAG and IVUS were evaluated by two\nexperienced researchers.\nAll patients passed the outpatient or telephone assessment 1, 3, 6, and 12 months\nafter PCI. Major adverse cardiovascular events during hospitalization and\nfollow-up were target lesion revascularization (TLR), myocardial infarction\n(MI), and cardiac death.\n\nStatistical Analysis:\nSPSS version 22.0 (IBM Corporation, Armonk, NY, USA). was used for statistical\nanalysis. Quantitative data that conformed to normal distribution were analyzed\nby t-test and expressed as mean ± standard deviation (SD); those that did not\nconform to normal distribution were analyzed by Wilcoxon rank-sum test and\nexpressed as median and quartile range (M (P25, P75)). Qualitative data were\ncompared using the chi-square test. If T ≤ 5, the continuity-corrected\nchi-square test and Fisher's exact probability test were used and expressed as\nnumber of cases (%). A Kaplan-Meier curve was used to describe survival\nanalysis. Two-tailed P < 0.05 indicates a statistically significant\ndifference.\n\nResults:\nBaseline Demographic and Clinical Characteristics A total of 123 patients were included in this study, of whom 55 received DCB and\n68 received DES. As shown in Table 1, there were no statistically\nsignificant differences between the DCB and the DES groups in terms of average\nage, gender distribution, ejection fraction, and other clinical characteristics\n(P > 0.05). Coronary heart disease-related risk factors\nsuch as hypertension, hyperlipidemia, diabetes, and smoking history were also\nnot significantly different between the two groups (P >\n0.05). Similarly, there were no significant differences in clinical diagnosis of\nST-elevation myocardial infarction, non-ST-elevation myocardial infarction, and\nunstable angina pectoris between the two groups\n(X2 = 0.92,P>0.05).\nBaseline Patient Characteristics of the Study Population.\nComparing two data sets using the continuity corrected chi-square\ntest; DCB: drug coated balloon, DES: drug-eluting stent; STEMI\n:ST-segment elevation myocardial infarction, NSTEMI: non-STEMI; MI\n:myocardial infarction; PCI :percutaneous coronary intervention;\nCABG: coronary artery bypass grafting; LVEF: Left Ventricle\nejection\nA total of 123 patients were included in this study, of whom 55 received DCB and\n68 received DES. As shown in Table 1, there were no statistically\nsignificant differences between the DCB and the DES groups in terms of average\nage, gender distribution, ejection fraction, and other clinical characteristics\n(P > 0.05). Coronary heart disease-related risk factors\nsuch as hypertension, hyperlipidemia, diabetes, and smoking history were also\nnot significantly different between the two groups (P >\n0.05). Similarly, there were no significant differences in clinical diagnosis of\nST-elevation myocardial infarction, non-ST-elevation myocardial infarction, and\nunstable angina pectoris between the two groups\n(X2 = 0.92,P>0.05).\nBaseline Patient Characteristics of the Study Population.\nComparing two data sets using the continuity corrected chi-square\ntest; DCB: drug coated balloon, DES: drug-eluting stent; STEMI\n:ST-segment elevation myocardial infarction, NSTEMI: non-STEMI; MI\n:myocardial infarction; PCI :percutaneous coronary intervention;\nCABG: coronary artery bypass grafting; LVEF: Left Ventricle\nejection\nProcedural Characteristics of PCI By combining preoperative ECG and echocardiography and intraoperative CAG\nresults, the culprit vessels in ACS patients were diagnosed and PCI was\nperformed. The target vessel locations included left anterior descending artery,\nleft circumflex artery, right coronary artery, and ramus coronary artery, and\nthere were no statistically significant differences in target vessel locations\nbetween the DCB and DES treatment\ngroups(X2 = 2.83,P＞0.05). According to\nintraoperative CAG, coronary artery lesions in the two groups were divided into\nsingle vessel disease, double vessel disease, and triple vessel disease, and\nthere were no significant differences in multivessel disease between the two\ngroups (X2 = 0.01,P＞0.05). Except for the\nsignificant difference in intraoperative balloon usage between the two groups\n(X2 = 7.69,P＜0.05), there were no statistical\ndifferences in other PCI procedural characteristics, including implantation\ndiameter and length, number of patients with intraoperative dissection, number\nof patients with temporary pacemakers, and number of patients using intra-aortic\nballoon pump(IABP)(P > 0.05). The procedural characteristics\nof PCI in the two groups are shown in Table 2.\nProcedural Characteristics of PCI.\nComparing two data sets using the continuity corrected chi-square\ntest #Comparing two data sets using the Fisher's exact\nprobability test; DCB: drug coated balloon, DES: drug-eluting stent;\nIABP: intra-aortic balloon pump\nBy combining preoperative ECG and echocardiography and intraoperative CAG\nresults, the culprit vessels in ACS patients were diagnosed and PCI was\nperformed. The target vessel locations included left anterior descending artery,\nleft circumflex artery, right coronary artery, and ramus coronary artery, and\nthere were no statistically significant differences in target vessel locations\nbetween the DCB and DES treatment\ngroups(X2 = 2.83,P＞0.05). According to\nintraoperative CAG, coronary artery lesions in the two groups were divided into\nsingle vessel disease, double vessel disease, and triple vessel disease, and\nthere were no significant differences in multivessel disease between the two\ngroups (X2 = 0.01,P＞0.05). Except for the\nsignificant difference in intraoperative balloon usage between the two groups\n(X2 = 7.69,P＜0.05), there were no statistical\ndifferences in other PCI procedural characteristics, including implantation\ndiameter and length, number of patients with intraoperative dissection, number\nof patients with temporary pacemakers, and number of patients using intra-aortic\nballoon pump(IABP)(P > 0.05). The procedural characteristics\nof PCI in the two groups are shown in Table 2.\nProcedural Characteristics of PCI.\nComparing two data sets using the continuity corrected chi-square\ntest #Comparing two data sets using the Fisher's exact\nprobability test; DCB: drug coated balloon, DES: drug-eluting stent;\nIABP: intra-aortic balloon pump\nQCA and IVUS Analysis The preoperative, postoperative and follow-up results of PCI are shown in Table 3. There were\nno significant differences in preoperative RFD, MLD and DS between the two\ngroups (P > 0.05). However, CAG immediately after DCB or DES\nimplantation showed significantly smaller MLD and greater degree of stenosis in\nthe DCB group than in the DES group (P < 0.05). Further\nfollow-up CAG after PCI revealed that there were no statistically significant\ndifferences in MLD, stenosis degree, and coronary restenosis between the two\ngroups (P > 0.05), except that compared to the DES group,\nthe DCB group had less LLL in lumen diameter and a greater number of lumen\nenlargements (P < 0.05).\nComparison of Coronary QCA and IVUS Results Between DCB and DES\nGroups.\nDCB: drug coated balloon, DES: drug-eluting stent; CAG: coronary\nartery angiography; IVUS: intravascular ultrasound; MLD: minimal\nlumen diameter; LLL: late luminal loss;\nBoth groups underwent IVUS before and after PCI. The results showed that there\nwere no significant differences in vessel area and lumen area between the two\ngroups (P > 0.05). In addition, no statistically significant\ndifferences were seen in preoperative plaque burden （79.05 ± 4.17 VS\n80.07 ± 4.68）and postprocedural plaque burden （61.69 ± 5.64 VS\n63.32 ± 6.07）between the two groups (P > 0.05).\nThe preoperative, postoperative and follow-up results of PCI are shown in Table 3. There were\nno significant differences in preoperative RFD, MLD and DS between the two\ngroups (P > 0.05). However, CAG immediately after DCB or DES\nimplantation showed significantly smaller MLD and greater degree of stenosis in\nthe DCB group than in the DES group (P < 0.05). Further\nfollow-up CAG after PCI revealed that there were no statistically significant\ndifferences in MLD, stenosis degree, and coronary restenosis between the two\ngroups (P > 0.05), except that compared to the DES group,\nthe DCB group had less LLL in lumen diameter and a greater number of lumen\nenlargements (P < 0.05).\nComparison of Coronary QCA and IVUS Results Between DCB and DES\nGroups.\nDCB: drug coated balloon, DES: drug-eluting stent; CAG: coronary\nartery angiography; IVUS: intravascular ultrasound; MLD: minimal\nlumen diameter; LLL: late luminal loss;\nBoth groups underwent IVUS before and after PCI. The results showed that there\nwere no significant differences in vessel area and lumen area between the two\ngroups (P > 0.05). In addition, no statistically significant\ndifferences were seen in preoperative plaque burden （79.05 ± 4.17 VS\n80.07 ± 4.68）and postprocedural plaque burden （61.69 ± 5.64 VS\n63.32 ± 6.07）between the two groups (P > 0.05).\nFollow-up MACE During the follow-up period, there were no significant differences in target\nlesion revascularization, myocardial infarction, and cardiac death between the\ntwo groups (P > 0.05). And survival analysis shows that\nthere were no significant differences in the survival rate of the two groups\n(Log-rank,X2 = 0.01,P > 0.05). The detailed data are shown in\nTable 4 and\nFigure 2.\nComparison of MACE Between DCB and DES Groups.\nComparing two data sets using the continuity corrected chi-square\ntest; #Comparing two data sets using the Fisher's exact\nprobability test; DCB: drug coated balloon, DES: drug-eluting stent;\nMACE: major adverse cardiovascular event; TLR: target lesion\nrevascularization; MI: myocardial infarction.\nDuring the follow-up period, there were no significant differences in target\nlesion revascularization, myocardial infarction, and cardiac death between the\ntwo groups (P > 0.05). And survival analysis shows that\nthere were no significant differences in the survival rate of the two groups\n(Log-rank,X2 = 0.01,P > 0.05). The detailed data are shown in\nTable 4 and\nFigure 2.\nComparison of MACE Between DCB and DES Groups.\nComparing two data sets using the continuity corrected chi-square\ntest; #Comparing two data sets using the Fisher's exact\nprobability test; DCB: drug coated balloon, DES: drug-eluting stent;\nMACE: major adverse cardiovascular event; TLR: target lesion\nrevascularization; MI: myocardial infarction.\n\nBaseline Demographic and Clinical Characteristics:\nA total of 123 patients were included in this study, of whom 55 received DCB and\n68 received DES. As shown in Table 1, there were no statistically\nsignificant differences between the DCB and the DES groups in terms of average\nage, gender distribution, ejection fraction, and other clinical characteristics\n(P > 0.05). Coronary heart disease-related risk factors\nsuch as hypertension, hyperlipidemia, diabetes, and smoking history were also\nnot significantly different between the two groups (P >\n0.05). Similarly, there were no significant differences in clinical diagnosis of\nST-elevation myocardial infarction, non-ST-elevation myocardial infarction, and\nunstable angina pectoris between the two groups\n(X2 = 0.92,P>0.05).\nBaseline Patient Characteristics of the Study Population.\nComparing two data sets using the continuity corrected chi-square\ntest; DCB: drug coated balloon, DES: drug-eluting stent; STEMI\n:ST-segment elevation myocardial infarction, NSTEMI: non-STEMI; MI\n:myocardial infarction; PCI :percutaneous coronary intervention;\nCABG: coronary artery bypass grafting; LVEF: Left Ventricle\nejection\n\nProcedural Characteristics of PCI:\nBy combining preoperative ECG and echocardiography and intraoperative CAG\nresults, the culprit vessels in ACS patients were diagnosed and PCI was\nperformed. The target vessel locations included left anterior descending artery,\nleft circumflex artery, right coronary artery, and ramus coronary artery, and\nthere were no statistically significant differences in target vessel locations\nbetween the DCB and DES treatment\ngroups(X2 = 2.83,P＞0.05). According to\nintraoperative CAG, coronary artery lesions in the two groups were divided into\nsingle vessel disease, double vessel disease, and triple vessel disease, and\nthere were no significant differences in multivessel disease between the two\ngroups (X2 = 0.01,P＞0.05). Except for the\nsignificant difference in intraoperative balloon usage between the two groups\n(X2 = 7.69,P＜0.05), there were no statistical\ndifferences in other PCI procedural characteristics, including implantation\ndiameter and length, number of patients with intraoperative dissection, number\nof patients with temporary pacemakers, and number of patients using intra-aortic\nballoon pump(IABP)(P > 0.05). The procedural characteristics\nof PCI in the two groups are shown in Table 2.\nProcedural Characteristics of PCI.\nComparing two data sets using the continuity corrected chi-square\ntest #Comparing two data sets using the Fisher's exact\nprobability test; DCB: drug coated balloon, DES: drug-eluting stent;\nIABP: intra-aortic balloon pump\n\nQCA and IVUS Analysis:\nThe preoperative, postoperative and follow-up results of PCI are shown in Table 3. There were\nno significant differences in preoperative RFD, MLD and DS between the two\ngroups (P > 0.05). However, CAG immediately after DCB or DES\nimplantation showed significantly smaller MLD and greater degree of stenosis in\nthe DCB group than in the DES group (P < 0.05). Further\nfollow-up CAG after PCI revealed that there were no statistically significant\ndifferences in MLD, stenosis degree, and coronary restenosis between the two\ngroups (P > 0.05), except that compared to the DES group,\nthe DCB group had less LLL in lumen diameter and a greater number of lumen\nenlargements (P < 0.05).\nComparison of Coronary QCA and IVUS Results Between DCB and DES\nGroups.\nDCB: drug coated balloon, DES: drug-eluting stent; CAG: coronary\nartery angiography; IVUS: intravascular ultrasound; MLD: minimal\nlumen diameter; LLL: late luminal loss;\nBoth groups underwent IVUS before and after PCI. The results showed that there\nwere no significant differences in vessel area and lumen area between the two\ngroups (P > 0.05). In addition, no statistically significant\ndifferences were seen in preoperative plaque burden （79.05 ± 4.17 VS\n80.07 ± 4.68）and postprocedural plaque burden （61.69 ± 5.64 VS\n63.32 ± 6.07）between the two groups (P > 0.05).\n\nFollow-up MACE:\nDuring the follow-up period, there were no significant differences in target\nlesion revascularization, myocardial infarction, and cardiac death between the\ntwo groups (P > 0.05). And survival analysis shows that\nthere were no significant differences in the survival rate of the two groups\n(Log-rank,X2 = 0.01,P > 0.05). The detailed data are shown in\nTable 4 and\nFigure 2.\nComparison of MACE Between DCB and DES Groups.\nComparing two data sets using the continuity corrected chi-square\ntest; #Comparing two data sets using the Fisher's exact\nprobability test; DCB: drug coated balloon, DES: drug-eluting stent;\nMACE: major adverse cardiovascular event; TLR: target lesion\nrevascularization; MI: myocardial infarction.\n\nDiscussion:\nDES is the preferred strategy for reperfusion in patients with ACS. However, delayed\nhealing and vascular remodeling of the vascular endothelium often occur after DES\nimplantation due to the vulnerability of the plaque and the hypercoagulable state\nand persistent inflammation caused by residual metals, leading to a greatly\nincreased risk of late in-stent thrombosis.10,25 As a new strategy for the\ntreatment of coronary artery disease, DCB has achieved reliable efficacy in various\ntypes of coronary artery disease such as in-stent stenosis due to its unique drug\nrelease behavior and no residual metal substances. Similarly, DCB has also been used\nin ACS lesions, but little is known about the effect of plaque vulnerability on the\nefficacy of DCB in ACS patients.\nAt present, there are many ways to identify vulnerable plaques, and each imaging\nmethod, whether invasive or non-invasive, has different diagnostic criteria. Gregg\net al noted that PB ≥ 70% or MLA ≤ 4.0 mm2 on IVUS are the strongest\nindependent predictors of subsequent MACE after PCI.17,26–30 Therefore, this study\nsimilarly adopted this criterion to define vulnerable plaque. We also analyzed IVUS\nin the DCB and DES groups before and immediately after PCI. We found no significant\ndifferences in vessel area, PB, and lumen area between the two groups, indicating\nthat DCB has comparable effects on vulnerable plaques and luminal structure\nimmediately after implantation in ACS patients compared to DES. Nevertheless, the\nlong-term efficacy of DCB is unclear, as patients in both groups did not undergo\nIVUS during follow-up for economic or other reasons. It has been reported that\nduring IVUS follow-up of DCB in the treatment of native vessel lesions, DCB was\nfound to stabilize plaques by reducing PB and altering plaque composition, thereby\nreducing subsequent adverse events caused by plaque rupture. 31,32 Whether this\neffect of DCB appears in the treatment of ACS complicated with vulnerable plaques\nwarrants further IVUS follow-up analysis.\nIn this study, CAG was performed before both DCB and DES implantation, and there were\nno significant differences in preoperative MLD and DS between the two groups. For\nthe immediate postoperative CAG results showing that the DCB group had a smaller MLD\nand greater degree of stenosis than the DES group, one possible reason is that DCB,\nas a transport tool for antiproliferative drugs, lacks the effective support of\nmetal nets like DES. Secondly, in the DES group, to ensure the full contact between\nthe stent and the blood vessel, a high-pressure balloon of suitable size was used\nfor post-dilation, which can allow the DES to dilate diseased vessels more\neffectively, reduce the elastic recoil of vessels, and ultimately prevent lumen stenosis.\n33\n For DCB-PCI, in addition to adequate pretreatment prior to implantation to\navoid the occurrence of dissection, the lumen size and lesions had been evaluated by\nIVUS to minimize the difference in vasodilation with DES.\nFollow-up CAG results showed that there were no significant differences in MLD and DS\nbetween the two groups, but the DCB group had smaller LLL and greater lumen\nenlargement. There are multiple reasons for these results. First, unlike patients in\nprevious studies, patients in this study had a higher PB and a higher proportion of\npatients in the DCB group used cutting balloons or spinous balloons than in the DES\ngroup, which may allow for a more complete and uniform dispersion of the internal\nand medial plaques. Second, compared to DES, DCB has a larger contact area with the\ndiseased arterial segment, enabling the antiproliferative drugs to diffuse uniformly\nand rapidly to the diseased vessel. Further, the residual metal stent material and\nlong-term sustained release of anti-proliferative drugs in the DES group could lead\nto delayed endothelialization and inflammatory response, while in the DCB group, the\narterial vasomotor and dilatation functions were relatively stable because there was\nno metal residual problem, which may be the reason for the late lumen enlargement.\nAll these factors together resulted in better vascular remodeling and then less\nlumen loss diameter and greater lumen enlargement in the DCB group.34–36 On the other hand, there were\nno significant differences in the incidence of target lesion revascularization,\nmyocardial infarction, and cardiac death between the two groups during follow-up,\nalso suggesting that DCB is non-inferior to DES in long-term outcomes.\n\nLimitations:\nThis study has certain limitations. As a retrospective, single-center, and\nsmall-sample study, there may be patient selection bias. Second, although CAG was\nperformed during follow-up in both groups, we had to assess MLD using QCA\nimmediately after PCI due to the lack of IVUS imaging during follow-up. These\nlimitations may have affected the results to some extent. Therefore, to further\nvalidate our conclusions, multiple interventional methods such as optical coherence\ntomography(OCT), virtual histology IVUS (VH-IVUS), and near-infrared spectroscopy\n(NIRS) can be used to comprehensively detect and identify vulnerable plaques to\nachieve accurate diagnosis of lesions. In addition, multicenter, large sample, and\nlonger follow-up studies are needed.\n\nConclusion:\nIn summary, DCB is safe and effective in the treatment of ACS complicated with\nvulnerable plaque, and DCB has the advantage over DES in LLL. Our work provides\npractical experience in the interventional treatment of vulnerable plaques in\nACS.\n\n"},"whole_article_abstract":{"kind":"string","value":"Background:\nPercutaneous coronary intervention (PCI) is the main treatment option for acute coronary syndromes (ACS) often related to the progression and rupture of vulnerable plaques. While drug-eluting stents (DES) are now routinely used in PCI, drug-coated balloons (DCB) are a new strategy to PCI and their practice in the treatment of ACS with vulnerable plaques has not been reported. This study aimed to evaluate the safety and efficacy of DCB in ACS complicated with vulnerable plaque lesions.\n\nMethods:\n123 patients were retrospectively analyzed and diagnosed with ACS and given PCI in our Cardiology Department from December 2020 to July 2022. Vulnerable plaques were confirmed by intravenous ultrasound (IVUS) in all patients. According to individual treatment plan, patients were entered into either DCB (n = 55) or DES (n = 68) groups. The results of coronary angiography and IVUS before and immediately after percutaneous coronary intervention were analyzed. The occurrence of major adverse cardiovascular events (MACE) and the results of coronary angiography were also evaluated during follow-up.\n\nResults:\nThere were no significant differences in baseline clinical characteristics, preoperative minimal luminal diameter (MLD), and preoperative diameter stenosis (DS) between the two groups. Also, there were no differences in IVUS plaque burden (PB), vessel area, and lumen area in the two groups before and immediately after PCI. The efficacy analysis showed that immediately after PCI, the DCB group had smaller MLD and higher degrees of lumen stenosis than the DES group (P < 0.05). However, during follow-up, no significant differences in MLD and DS were seen in two groups; relatively, late loss in luminal diameter（LLL）in the DCB group was smaller (P＜0.05). Safety analysis showed that during follow-up, 9 patients developed restenosis after DCB implantation while restenosis occurred in 10 patients with DES treatment, no statistical difference in the incidence of restenosis in the two groups. Besides, there was no statistical difference in the incidence of major adverse cardiac events（MACE）during hospitalization and follow-up in the DCB group (7.3% (4/55)) and the DES group (8.8% (6/68)).\n\nConclusions:\nDCB is safe and effective for ACS complicated with vulnerable plaque and has an advantage over DES in LLL."},"background_conclusion_text":{"kind":"string","value":"Introduction:\nAcute coronary syndromes (ACS) is a serious coronary heart disease that threatens\nhuman health. Coronary plaques in such patients usually have a large plaque burden\nwith lipid-rich necrotic cores, called vulnerable plaques, whose progression and\nrupture lead to unstable angina, acute non-ST elevation myocardial infarction, and\nacute ST-elevation myocardial infarction.1–4 Percutaneous coronary\nintervention is now an effective treatment approach for ACS. PCI can restore normal\nblood flow through myocardial revascularization and thus greatly improves symptoms\nand prognosis in ACS patients.5,6\nCompared with traditional two-dimensional coronary angiography (CAG), new\nintraluminal imaging techniques such as IVUS have more efficacy in reducing\ncomplications after drug-eluting stents placement, decreasing in-stent restenosis\nrate, and improving treatment outcome. Equally important, IVUS can better provide\nintravascular imaging data for coronary vascular lesions with coronary overlapping,\nanatomical abnormalities, aneurysms, myocardial bridges, and calcifications,\nespecially in the identification of coronary culprit vessels. Because most ACS\npatients present with plaque ruptures, an accurate identification of the nature,\nextent and location of plaque ruptures is particularly important for the formulation\nof diagnosis and treatment planning. In this context, IVUS is superior to CAG in\nproviding relevant imaging information and intraprocedural guidance of PCI and\nevaluating postoperative results and clinical prognosis.7,8\nCurrently, DES is the preferred strategy for PCI in patients with ACS. The use of DES\nhas the advantages of inducing intimal hyperplasia, thickening fibrous cap, and\nnormalizing wall stress to reduce plaque rupture. However, subsequent complications\nof DES may occur, such as in-stent restenosis, in-stent hyperplasia, and stent\nfracture, and require long-term dual antiplatelet therapy. In severe cases, there\nmay be no reflow phenomenon after stent placement, rapidly resulting in a larger\narea of myocardium infarction.9–11 DCB are a new\nrevascularization technique that has become the treatment of choice for in-stent\nrestenosis because it meets the new concept of intervention without implantation.\nIndications of DCB also include small vessel disease and bifurcation disease, among\nothers.12–14 However,\nthere are few reports related to the use of DCB in ACS patients with vulnerable\nplaques. Therefore, this study used DES as a parallel treatment to examine the\nsafety and efficacy of DCB in ACS complicated with vulnerable plaques lesions under\nthe guidance of IVUS.\n\nConclusion:\nIn summary, DCB is safe and effective in the treatment of ACS complicated with\nvulnerable plaque, and DCB has the advantage over DES in LLL. Our work provides\npractical experience in the interventional treatment of vulnerable plaques in\nACS."},"background_conclusion_abstract":{"kind":"string","value":"Background:\nPercutaneous coronary intervention (PCI) is the main treatment option for acute coronary syndromes (ACS) often related to the progression and rupture of vulnerable plaques. While drug-eluting stents (DES) are now routinely used in PCI, drug-coated balloons (DCB) are a new strategy to PCI and their practice in the treatment of ACS with vulnerable plaques has not been reported. This study aimed to evaluate the safety and efficacy of DCB in ACS complicated with vulnerable plaque lesions.\n\nMethods:\n123 patients were retrospectively analyzed and diagnosed with ACS and given PCI in our Cardiology Department from December 2020 to July 2022. Vulnerable plaques were confirmed by intravenous ultrasound (IVUS) in all patients. According to individual treatment plan, patients were entered into either DCB (n = 55) or DES (n = 68) groups. The results of coronary angiography and IVUS before and immediately after percutaneous coronary intervention were analyzed. The occurrence of major adverse cardiovascular events (MACE) and the results of coronary angiography were also evaluated during follow-up.\n\nResults:\nThere were no significant differences in baseline clinical characteristics, preoperative minimal luminal diameter (MLD), and preoperative diameter stenosis (DS) between the two groups. Also, there were no differences in IVUS plaque burden (PB), vessel area, and lumen area in the two groups before and immediately after PCI. The efficacy analysis showed that immediately after PCI, the DCB group had smaller MLD and higher degrees of lumen stenosis than the DES group (P < 0.05). However, during follow-up, no significant differences in MLD and DS were seen in two groups; relatively, late loss in luminal diameter（LLL）in the DCB group was smaller (P＜0.05). Safety analysis showed that during follow-up, 9 patients developed restenosis after DCB implantation while restenosis occurred in 10 patients with DES treatment, no statistical difference in the incidence of restenosis in the two groups. Besides, there was no statistical difference in the incidence of major adverse cardiac events（MACE）during hospitalization and follow-up in the DCB group (7.3% (4/55)) and the DES group (8.8% (6/68)).\n\nConclusions:\nDCB is safe and effective for ACS complicated with vulnerable plaque and has an advantage over DES in LLL."},"whole_article_text_length":{"kind":"number","value":8389,"string":"8,389"},"whole_article_abstract_length":{"kind":"number","value":444,"string":"444"},"other_sections_lengths":{"kind":"list like","value":[2640,272,620,268,153,225,273,288,155,152,46],"string":"[\n 2640,\n 272,\n 620,\n 268,\n 153,\n 225,\n 273,\n 288,\n 155,\n 152,\n 46\n]"},"num_sections":{"kind":"number","value":14,"string":"14"},"most_frequent_words":{"kind":"list like","value":["dcb","des","patients","groups","ivus","vessel","05","pci","coronary","balloon"],"string":"[\n \"dcb\",\n \"des\",\n \"patients\",\n \"groups\",\n \"ivus\",\n \"vessel\",\n \"05\",\n \"pci\",\n \"coronary\",\n \"balloon\"\n]"},"keybert_topics":{"kind":"list like","value":["coronary vascular lesions","imaging data coronary","health coronary plaques","quantitative coronary angiography","acute coronary syndromes"],"string":"[\n \"coronary vascular lesions\",\n \"imaging data coronary\",\n \"health coronary plaques\",\n \"quantitative coronary angiography\",\n \"acute coronary syndromes\"\n]"},"annotated_base_background_abstract_prompt":{"kind":"string","value":""},"annotated_base_methods_abstract_prompt":{"kind":"null"},"annotated_base_results_abstract_prompt":{"kind":"string","value":""},"annotated_base_conclusions_abstract_prompt":{"kind":"string","value":""},"annotated_base_whole_article_abstract_prompt":{"kind":"string","value":""},"annotated_base_background_conclusion_abstract_prompt":{"kind":"string","value":""},"annotated_keywords_background_abstract_prompt":{"kind":"string","value":"[CONTENT] acute coronary syndromes | drug coated balloon | drug-eluting stent | vulnerable plaque [SUMMARY]"},"annotated_keywords_methods_abstract_prompt":{"kind":"null"},"annotated_keywords_results_abstract_prompt":{"kind":"string","value":"[CONTENT] acute coronary syndromes | drug coated balloon | drug-eluting stent | vulnerable plaque [SUMMARY]"},"annotated_keywords_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] acute coronary syndromes | drug coated balloon | drug-eluting stent | vulnerable plaque [SUMMARY]"},"annotated_keywords_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] acute coronary syndromes | drug coated balloon | drug-eluting stent | vulnerable plaque [SUMMARY]"},"annotated_keywords_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] acute coronary syndromes | drug coated balloon | drug-eluting stent | vulnerable plaque [SUMMARY]"},"annotated_mesh_background_abstract_prompt":{"kind":"string","value":"[CONTENT] Acute Coronary Syndrome | Angioplasty, Balloon, Coronary | Constriction, Pathologic | Coronary Angiography | Coronary Artery Disease | Coronary Restenosis | Drug-Eluting Stents | Humans | Percutaneous Coronary Intervention | Retrospective Studies | Treatment Outcome [SUMMARY]"},"annotated_mesh_methods_abstract_prompt":{"kind":"null"},"annotated_mesh_results_abstract_prompt":{"kind":"string","value":"[CONTENT] Acute Coronary Syndrome | Angioplasty, Balloon, Coronary | Constriction, Pathologic | Coronary Angiography | Coronary Artery Disease | Coronary Restenosis | Drug-Eluting Stents | Humans | Percutaneous Coronary Intervention | Retrospective Studies | Treatment Outcome [SUMMARY]"},"annotated_mesh_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] Acute Coronary Syndrome | Angioplasty, Balloon, Coronary | Constriction, Pathologic | Coronary Angiography | Coronary Artery Disease | Coronary Restenosis | Drug-Eluting Stents | Humans | Percutaneous Coronary Intervention | Retrospective Studies | Treatment Outcome [SUMMARY]"},"annotated_mesh_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] Acute Coronary Syndrome | Angioplasty, Balloon, Coronary | Constriction, Pathologic | Coronary Angiography | Coronary Artery Disease | Coronary Restenosis | Drug-Eluting Stents | Humans | Percutaneous Coronary Intervention | Retrospective Studies | Treatment Outcome [SUMMARY]"},"annotated_mesh_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] Acute Coronary Syndrome | Angioplasty, Balloon, Coronary | Constriction, Pathologic | Coronary Angiography | Coronary Artery Disease | Coronary Restenosis | Drug-Eluting Stents | Humans | Percutaneous Coronary Intervention | Retrospective Studies | Treatment Outcome [SUMMARY]"},"annotated_keybert_background_abstract_prompt":{"kind":"string","value":"[CONTENT] coronary vascular lesions | imaging data coronary | health coronary plaques | quantitative coronary angiography | acute coronary syndromes [SUMMARY]"},"annotated_keybert_methods_abstract_prompt":{"kind":"null"},"annotated_keybert_results_abstract_prompt":{"kind":"string","value":"[CONTENT] coronary vascular lesions | imaging data coronary | health coronary plaques | quantitative coronary angiography | acute coronary syndromes [SUMMARY]"},"annotated_keybert_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] coronary vascular lesions | imaging data coronary | health coronary plaques | quantitative coronary angiography | acute coronary syndromes [SUMMARY]"},"annotated_keybert_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] coronary vascular lesions | imaging data coronary | health coronary plaques | quantitative coronary angiography | acute coronary syndromes [SUMMARY]"},"annotated_keybert_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] coronary vascular lesions | imaging data coronary | health coronary plaques | quantitative coronary angiography | acute coronary syndromes [SUMMARY]"},"annotated_most_frequent_background_abstract_prompt":{"kind":"string","value":"[CONTENT] dcb | des | patients | groups | ivus | vessel | 05 | pci | coronary | balloon [SUMMARY]"},"annotated_most_frequent_methods_abstract_prompt":{"kind":"null"},"annotated_most_frequent_results_abstract_prompt":{"kind":"string","value":"[CONTENT] dcb | des | patients | groups | ivus | vessel | 05 | pci | coronary | balloon [SUMMARY]"},"annotated_most_frequent_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] dcb | des | patients | groups | ivus | vessel | 05 | pci | coronary | balloon [SUMMARY]"},"annotated_most_frequent_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] dcb | des | patients | groups | ivus | vessel | 05 | pci | coronary | balloon [SUMMARY]"},"annotated_most_frequent_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] dcb | des | patients | groups | ivus | vessel | 05 | pci | coronary | balloon [SUMMARY]"},"annotated_tf_idf_background_abstract_prompt":{"kind":"string","value":"[CONTENT] coronary | acs | stent restenosis | acute | stent | new | treatment | plaques | patients | plaque [SUMMARY]"},"annotated_tf_idf_methods_abstract_prompt":{"kind":"null"},"annotated_tf_idf_results_abstract_prompt":{"kind":"string","value":"[CONTENT] 05 | groups | differences | significant differences | significant | des | dcb | characteristics | coronary | groups 05 [SUMMARY]"},"annotated_tf_idf_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] vulnerable | acs | treatment | dcb safe | des lll work provides | summary | practical | practical experience | practical experience interventional | safe [SUMMARY]"},"annotated_tf_idf_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] dcb | des | groups | 05 | patients | ivus | coronary | differences | significant | vessel [SUMMARY]"},"annotated_tf_idf_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] dcb | des | groups | 05 | patients | ivus | coronary | differences | significant | vessel [SUMMARY]"},"annotated_entity_plan_background_abstract_prompt":{"kind":"string","value":"[CONTENT] ||| PCI | DCB | ACS ||| DCB | ACS [SUMMARY]"},"annotated_entity_plan_methods_abstract_prompt":{"kind":"null"},"annotated_entity_plan_results_abstract_prompt":{"kind":"string","value":"[CONTENT] MLD | two ||| IVUS | two | PCI ||| PCI | DCB | MLD ||| MLD | two | DCB | P＜0.05 ||| 9 | DCB | 10 | two ||| DCB | 7.3% | 8.8% | 6/68 [SUMMARY]"},"annotated_entity_plan_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] DCB | ACS | LLL [SUMMARY]"},"annotated_entity_plan_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] ||| PCI | DCB | ACS ||| DCB | ACS ||| 123 | ACS | Cardiology Department | December 2020 to July 2022 ||| ||| DCB | 55 | 68 ||| IVUS ||| ||| MLD | two ||| IVUS | two | PCI ||| PCI | DCB | MLD ||| MLD | two | DCB | P＜0.05 ||| 9 | DCB | 10 | two ||| DCB | 7.3% | 8.8% | 6/68 ||| DCB | ACS | LLL [SUMMARY]"},"annotated_entity_plan_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] ||| PCI | DCB | ACS ||| DCB | ACS ||| 123 | ACS | Cardiology Department | December 2020 to July 2022 ||| ||| DCB | 55 | 68 ||| IVUS ||| ||| MLD | two ||| IVUS | two | PCI ||| PCI | DCB | MLD ||| MLD | two | DCB | P＜0.05 ||| 9 | DCB | 10 | two ||| DCB | 7.3% | 8.8% | 6/68 ||| DCB | ACS | LLL [SUMMARY]"}}},{"rowIdx":275,"cells":{"title":{"kind":"string","value":"Effects of Developmental Failure of Swallowing Threshold on Obesity and Eating Behaviors in Children Aged 5-15 Years."},"pmid":{"kind":"string","value":"35807794"},"background_abstract":{"kind":"string","value":"The aim of the present study was to identify factors related to developmental failure of swallowing threshold in children aged 5-15 years."},"background_abstract_label":{"kind":"string","value":"BACKGROUND"},"methods_abstract":{"kind":"string","value":"A total of 83 children aged 5-15 years were included in this study. A self-administered lifestyle questionnaire was completed, along with hand grip strength and oral function tests. Swallowing threshold was determined based on the concentration of dissolved glucose obtained from gummy jellies when the participants signaled that they wanted to swallow the chewed gummy jellies. Developmental failure of swallowing threshold was defined as glucose concentrations in the lowest 20th percentile. After univariate analysis, multivariate binary logistic regression analysis was used to identify factors associated with developmental failure of swallowing threshold."},"methods_abstract_label":{"kind":"string","value":"METHODS"},"results_abstract":{"kind":"string","value":"Hand grip strength was significantly correlated with masticatory performance (r = 0.611, p < 0.01). Logistic regression analysis revealed factors related to developmental failure of swallowing threshold, i.e., overweight/obesity (Odds ratio) (OR) = 5.343, p = 0.031, 95% CI = 1.168-24.437) and eating between meals at least once a day (OR = 4.934, p = 0.049, 95% CI = 1.004-24.244)."},"results_abstract_label":{"kind":"string","value":"RESULTS"},"conclusions_abstract":{"kind":"string","value":"Developmental failure of swallowing threshold was closely associated with childhood obesity in 5- to 15-year-old children."},"conclusions_abstract_label":{"kind":"string","value":"CONCLUSIONS"},"mesh_descriptor_names":{"kind":"list like","value":["Adolescent","Child","Child, Preschool","Deglutition","Feeding Behavior","Glucose","Hand Strength","Humans","Mastication","Pediatric Obesity"],"string":"[\n \"Adolescent\",\n \"Child\",\n \"Child, Preschool\",\n \"Deglutition\",\n \"Feeding Behavior\",\n \"Glucose\",\n \"Hand Strength\",\n \"Humans\",\n \"Mastication\",\n \"Pediatric Obesity\"\n]"},"pmcid":{"kind":"string","value":"9268440"},"background_title":{"kind":"string","value":"1. Introduction"},"background_text":{"kind":"string","value":"Eating habits acquired in early childhood can change over time based on personal experience and learning [1,2]. Eating habits acquired in childhood or adolescence may persist into adulthood [3]. Poor dietary habits established during childhood that persist into adulthood increase the risk of obesity and related adverse consequences [4,5,6].\nIn previous studies, early childhood obesity was associated with a higher risk of adult metabolic syndrome [7,8]. Eating behaviors such as frequent consumption of fast food and sugar-sweetened beverages are associated with the development of childhood obesity [9]. In addition, factors such as eating frequency, amount, and occasions may interact to cause obesity [5,7]. Other studies indicated that early modification of poor eating habits could improve health and decrease the future risk of metabolic syndrome, type 2 diabetes, and atherosclerotic cardiovascular diseases [10,11].\nA relationship between obesity and eating speed was reported recently. Eating speed was associated with the incidence of metabolic syndrome in adults [12,13], while eating quickly was associated with increased body mass index (BMI) in young adults [14]. Similar results have also been reported in children [15,16,17]. In addition, chewing well before swallowing might be an effective method for reducing food intake and facilitating healthy weight management in preschool children [18] and adults [19].\nThese findings suggest that appropriate eating behaviors should be acquired within the critical early time period. However, the number of chewing cycles, chewing time, and chewing rate have not been analyzed in the context of the swallowing threshold, and it remains unclear when these functions are acquired. We hypothesized that these functions would be acquired relatively early in childhood. \nAdditionally, based on previous studies, we hypothesized that developmental failure of swallowing threshold in children would be associated with eating fast, chewing frequently, and other habits [17,20]. This developmental failure could involve physiological problems including obesity. Identifying the factors associated with developmental failure of swallowing threshold may aid the development of strategies for improving it.\nThe primary purpose of this study was to clarify differences in swallowing thresholds and other oral functions according to age, as well as when functions related to the swallowing threshold are acquired. A secondary aim was to determine factors related to the developmental failure of swallowing threshold in children aged 5–15 years. In this study, swallowing threshold was determined based on the concentration of dissolved glucose obtained from gummy jellies when the participants signaled that they wanted to swallow the chewed gummy jellies."},"methods_title":{"kind":"null"},"methods_text":{"kind":"null"},"results_title":{"kind":"string","value":"3. Results"},"results_text":{"kind":"string","value":"The ICCs for all measurement items ranged from 0.84 to 0.91, indicating a high degree of intraobserver reliability. \nThe anthropometric parameters and dental examination results are shown in Table 1 according to age and sex. The 15-year-old males were significantly taller than females of the same age (p < 0.05). The DMFT index in 11-year-old females was significantly higher than in males of the same age (p < 0.05).\nHand grip strength and oral functional parameters are shown in Table 2 according to age and sex. The hand grip strengths in 5-, 10-, 12-, 14-, and 15-year-old males were significantly greater compared to females of the same age (all p < 0.05). \nWhen considering the results of comparing the mean scores of males and females for all measurement items by age, hand grip strength in 14–15-year-old participants was significantly greater compared to 5–7-year-old participants (all p < 0.05). Hand grip strength increased with age. The maximum occlusal force was greatest in 15-year-old participants and was significantly greater than in those aged 5–7 years (all p < 0.05). Masticatory performance was lowest in 5-year-old participants and was significantly lower than in those aged 12, 14, and 15 years (all p < 0.05). The number of chewing cycles was highest in 9-year-old participants and was significantly higher than in those aged 6 and 14 years (both p < 0.05). The change in chewing time with age was similar to the change in the number of chewing cycles. The swallowing threshold was highest in 15-year-old participants, followed by the 8-year-old participants; both were significantly higher than in those aged 6 years (p < 0.05).\nThe results of Pearson’s bivariate correlation analyses are shown in Table 3. Hand grip strength was significantly and positively correlated with masticatory performance (r = 0.611, p < 0.01) and was strongly correlated with maximum occlusal force (r = 0.791, p < 0.01). Swallowing threshold was strongly correlated with masticatory performance (r = 0.727, p < 0.01) and was significantly and positively correlated with the number of erupted teeth, maximum occlusal force, and number of chewing cycles (r = 0.432, p < 0.01; r = 0.493, p < 0.01; r = 0.465, p < 0.01, respectively). The number of chewing cycles was strongly correlated with chewing time (r = 0.801, p < 0.01). The correlation between age and swallowing threshold was weak but significant (r = 0.362, p < 0.01; Figure 1).\nTable 4 summarizes the data on developmental failure of swallowing threshold based on demographic and health-related variables. Participants with developmental failure of swallowing threshold were more likely to be overweight or obese, eat between meals at least once per day, and have low or no physical activity (p = 0.014, p = 0.021, and p = 0.006, respectively).\nTable 5 summarizes the data on developmental failure of swallowing threshold based on age, height, body weight, dental status, hand grip strength, and masticatory function. Swallowing threshold, age, number of erupted teeth, maximum occlusal force, masticatory performance, number of chewing cycles, and chewing time in the develop-mental failure of swallowing threshold group were significantly lower compared to the normal swallowing threshold group (all p < 0.05). The mean DMFT index in the developmental failure swallowing threshold group was significantly higher compared to the normal swallowing threshold group (p < 0.05). The mean chewing time to swallow in the normal swallowing threshold group was 20.91 s. Masticatory performance was similar between the groups.\nTable 6 shows the predictors of developmental failure of swallowing threshold, as revealed by logistic regression. Overweight or obese individuals had higher odds of developmental failure of swallowing threshold (OR = 5.343, p = 0.031, 95% CI = 1.168–24.437). Eating between meals once or more a day was also associated with higher odds of developmental failure of swallowing threshold (OR = 4.934, p = 0.049, 95% CI = 1.004–24.244)."},"conclusions_title":{"kind":"string","value":"5. Conclusions"},"conclusions_text":{"kind":"string","value":"We found that the relationship between swallowing threshold and age was unclear, whereas the number of chewing cycles and chewing time tended to gradually decrease after reaching a peak at the age of 9 and 8 years, respectively. An appropriate number of chewing cycles and chewing time until swallowing should be established by 9 years of age. Developmental failure of swallowing threshold was closely associated with childhood obesity and eating between meals at least once per day among the 5- to 15-year-old individuals."},"other_sections_titles":{"kind":"list like","value":["2. Materials and Methods","2.2. Questionnaire","2.3. Anthropometry and Dental Examination","2.4. Hand Grip Strength","2.5. Maximum Occlusal Force","2.6. Masticatory Performance","2.7. Swallowing Threshold","2.8. Reliability of Measurements","2.9. Statistical Analysis"],"string":"[\n \"2. Materials and Methods\",\n \"2.2. Questionnaire\",\n \"2.3. Anthropometry and Dental Examination\",\n \"2.4. Hand Grip Strength\",\n \"2.5. Maximum Occlusal Force\",\n \"2.6. Masticatory Performance\",\n \"2.7. Swallowing Threshold\",\n \"2.8. Reliability of Measurements\",\n \"2.9. Statistical Analysis\"\n]"},"other_sections_texts":{"kind":"list like","value":["2.1. Participants This study was conducted in accordance with the Declaration of Helsinki and was approved by the Human Investigation Committee of Kyushu Dental University (approval number: 18–37). All participants and their parents/guardians provided written informed consent for participation in the study. The participants were recruited following an initial examination at two private dental clinics in Japan. The inclusion criteria were as follows: aged 5–15 years, normal language comprehension, cooperative behavior, and no current dental diseases or complications. The exclusion criteria included systemic disturbances causing swallowing impairment, obvious facial asymmetry that could affect the measurements, soft tissue abnormalities, temporomandibular joint dysfunction, and dental or structural irregularities.\nUsing software (PS: Power and Sample Size Calculation, available from the Vanderbilt University’s website), sample size was calculated. The study was based on a continuous response variable ranging from normal swallowing threshold to developmental failure of swallowing threshold; four normal participants were recruited for every participant with the developmental failure of swallowing threshold. In a previous study, the data for each group were normally distributed, with a standard deviation (SD) of swallowing threshold of 40 mg/dL [21]. Based on a true difference of 40 mg/dL between the developmental failure and normal groups, 14 developmental failure and 56 normal participants were required to reject the null hypothesis (i.e., the population means of the developmental failure and normal groups are equal) with a power of 0.9. The type I error probability for the null hypothesis was 0.05.\nThe participants were divided into 11 groups based on their chronological age, and each group was further divided into two subgroups based on sex. Details of the participants are shown in Table 1. \nThis study was conducted in accordance with the Declaration of Helsinki and was approved by the Human Investigation Committee of Kyushu Dental University (approval number: 18–37). All participants and their parents/guardians provided written informed consent for participation in the study. The participants were recruited following an initial examination at two private dental clinics in Japan. The inclusion criteria were as follows: aged 5–15 years, normal language comprehension, cooperative behavior, and no current dental diseases or complications. The exclusion criteria included systemic disturbances causing swallowing impairment, obvious facial asymmetry that could affect the measurements, soft tissue abnormalities, temporomandibular joint dysfunction, and dental or structural irregularities.\nUsing software (PS: Power and Sample Size Calculation, available from the Vanderbilt University’s website), sample size was calculated. The study was based on a continuous response variable ranging from normal swallowing threshold to developmental failure of swallowing threshold; four normal participants were recruited for every participant with the developmental failure of swallowing threshold. In a previous study, the data for each group were normally distributed, with a standard deviation (SD) of swallowing threshold of 40 mg/dL [21]. Based on a true difference of 40 mg/dL between the developmental failure and normal groups, 14 developmental failure and 56 normal participants were required to reject the null hypothesis (i.e., the population means of the developmental failure and normal groups are equal) with a power of 0.9. The type I error probability for the null hypothesis was 0.05.\nThe participants were divided into 11 groups based on their chronological age, and each group was further divided into two subgroups based on sex. Details of the participants are shown in Table 1. \n2.2. Questionnaire The survey solicited the following information: demographic characteristics (sex and age), eating habits, physical activity, and sleep [21].\nThe survey solicited the following information: demographic characteristics (sex and age), eating habits, physical activity, and sleep [21].\n2.3. Anthropometry and Dental Examination Height and body weight were measured in the consultation room of the clinic. Height was measured to an accuracy of ±0.1 cm using a portable digital stadiometer (AD-653; A&D, Tokyo, Japan), with the head in the Frankfort plane, whereas body weight was measured with an accuracy of 0.1 kg [22]. Rohrer and Kaup indices were calculated from the height and weight. \nDuring intraoral examination, the number of erupted teeth and the decayed, missing, and filled teeth (DMFT) index were recorded for each patient [23].\nHeight and body weight were measured in the consultation room of the clinic. Height was measured to an accuracy of ±0.1 cm using a portable digital stadiometer (AD-653; A&D, Tokyo, Japan), with the head in the Frankfort plane, whereas body weight was measured with an accuracy of 0.1 kg [22]. Rohrer and Kaup indices were calculated from the height and weight. \nDuring intraoral examination, the number of erupted teeth and the decayed, missing, and filled teeth (DMFT) index were recorded for each patient [23].\n2.4. Hand Grip Strength Hand grip strength is generally used as an index of muscle weakness in the diagnosis of sarcopenia [24]. Therefore, hand grip strength measurement was performed for evaluating systemic muscle strength in the participants. A portable grip strength meter (T-2288; Toei Light Co., Ltd., Saitama, Japan) was used to measure hand grip strength. Participants were asked to stand and hold the dynamometer in their hand with the arm parallel to the body, but without squeezing the arm against the body. Hand grip strength (kg) was measured twice for each hand (alternately) with a 30 s interval between trials. The highest value from either the left or right hand was recorded as the grip strength [20].\nHand grip strength is generally used as an index of muscle weakness in the diagnosis of sarcopenia [24]. Therefore, hand grip strength measurement was performed for evaluating systemic muscle strength in the participants. A portable grip strength meter (T-2288; Toei Light Co., Ltd., Saitama, Japan) was used to measure hand grip strength. Participants were asked to stand and hold the dynamometer in their hand with the arm parallel to the body, but without squeezing the arm against the body. Hand grip strength (kg) was measured twice for each hand (alternately) with a 30 s interval between trials. The highest value from either the left or right hand was recorded as the grip strength [20].\n2.5. Maximum Occlusal Force Maximum occlusal force was measured using a portable occlusal force meter (GM10; Nagano Keiki Co., Ltd., Tokyo, Japan). The participants were instructed to bite down with maximal voluntary muscular effort using their first molars. Maximum occlusal force was measured on each side, with a 30 s interval between bite measurements. The larger of the values from the left and right sides was recorded as the maximum bite force [21].\nMaximum occlusal force was measured using a portable occlusal force meter (GM10; Nagano Keiki Co., Ltd., Tokyo, Japan). The participants were instructed to bite down with maximal voluntary muscular effort using their first molars. Maximum occlusal force was measured on each side, with a 30 s interval between bite measurements. The larger of the values from the left and right sides was recorded as the maximum bite force [21].\n2.6. Masticatory Performance Masticatory performance was determined by measuring the concentration of dissolved glucose obtained from a cylindrical gummy jelly (GLUCOLUMN; GC Co., Ltd., Tokyo, Japan) using a glucose-measuring device (GLUCO SENSOR GS-II; GC Co., Ltd., Tokyo, Japan). Prior to the test, the participants were instructed regarding the chewing movements and mouth rinsing procedures to prevent swallowing. The participants were then instructed to chew the gummy jelly for 20 s freely. After chewing, the participants were asked to take 10 mL of distilled water into their mouth and spit out the gummy jelly and distilled water into a filter cup. The glucose concentration in the filtrate (mg/dL) was measured using a dedicated device [20].\nMasticatory performance was determined by measuring the concentration of dissolved glucose obtained from a cylindrical gummy jelly (GLUCOLUMN; GC Co., Ltd., Tokyo, Japan) using a glucose-measuring device (GLUCO SENSOR GS-II; GC Co., Ltd., Tokyo, Japan). Prior to the test, the participants were instructed regarding the chewing movements and mouth rinsing procedures to prevent swallowing. The participants were then instructed to chew the gummy jelly for 20 s freely. After chewing, the participants were asked to take 10 mL of distilled water into their mouth and spit out the gummy jelly and distilled water into a filter cup. The glucose concentration in the filtrate (mg/dL) was measured using a dedicated device [20].\n2.7. Swallowing Threshold Following evaluation of the masticatory performance, an assessment of the swallowing threshold was performed using the test gummy jellies (GLUCOLUMN; GC Co. Ltd.). Variables related to swallowing (i.e., the number of chewing cycles, chewing time, chewing rate, and glucose concentration in the filtrate (mg/dL)) were assessed for all participants. Each participant was instructed to chew a gummy jelly freely until feeling the desire to swallow, at which time they were instructed to stop chewing and signal to the examiner that they were ready to expel the gummy jelly. The examiner visually quantified the number of chewing cycles. The time from the onset of chewing to the moment when the participants raised their hands was recorded using a stopwatch [20]. The subsequent steps were the same as those used for the evaluation of masticatory performance. We determined that the glucose concentration in the filtrate was an indicator of the swallowing threshold. Participants with low swallowing threshold tend to signal that they want to swallow the gummy jelly quickly, resulting in low glucose concentrations. A glucose concentration in the lowest 20th percentile was defined as developmental failure of swallowing threshold, based on previous studies reporting the criteria for sarcopenia [21,25]. \nFollowing evaluation of the masticatory performance, an assessment of the swallowing threshold was performed using the test gummy jellies (GLUCOLUMN; GC Co. Ltd.). Variables related to swallowing (i.e., the number of chewing cycles, chewing time, chewing rate, and glucose concentration in the filtrate (mg/dL)) were assessed for all participants. Each participant was instructed to chew a gummy jelly freely until feeling the desire to swallow, at which time they were instructed to stop chewing and signal to the examiner that they were ready to expel the gummy jelly. The examiner visually quantified the number of chewing cycles. The time from the onset of chewing to the moment when the participants raised their hands was recorded using a stopwatch [20]. The subsequent steps were the same as those used for the evaluation of masticatory performance. We determined that the glucose concentration in the filtrate was an indicator of the swallowing threshold. Participants with low swallowing threshold tend to signal that they want to swallow the gummy jelly quickly, resulting in low glucose concentrations. A glucose concentration in the lowest 20th percentile was defined as developmental failure of swallowing threshold, based on previous studies reporting the criteria for sarcopenia [21,25]. \n2.8. Reliability of Measurements All measurements were performed in duplicate, separated by 30 s rest periods, and the mean values were calculated for the subsequent analyses. All examinations were performed by the same examiner. Data generated during sample collection were assessed for reliability. Random error was evaluated based on intraobserver reliability, which was quantified using the intraclass correlation coefficient (ICC; where 0.8 ≤ ICC ≤ 1.0 corresponded to high reliability) [26].\nAll measurements were performed in duplicate, separated by 30 s rest periods, and the mean values were calculated for the subsequent analyses. All examinations were performed by the same examiner. Data generated during sample collection were assessed for reliability. Random error was evaluated based on intraobserver reliability, which was quantified using the intraclass correlation coefficient (ICC; where 0.8 ≤ ICC ≤ 1.0 corresponded to high reliability) [26].\n2.9. Statistical Analysis The Shapiro–Wilk test was used to determine data normality. All continuous data were expressed as means ± SD. The means were compared between two groups using a two-tailed t-test or the Mann–Whitney U test. The Kruskal–Wallis test was used for comparisons between more than two groups. Pearson’s correlation coefficient was used to determine associations among glucose concentration before swallowing and other variables. The chi-squared test or Fisher’s exact test was used, as appropriate, to compare categorical variables between the normal swallowing threshold and developmental failure of swallowing threshold (lowest 20%) groups. Binary logistic regression analysis with the forward selection (conditional) method was used to identify factors predicting developmental failure of swallowing threshold. Independent variables that were significant in the univariate analyses were included. Categorical variables were coded appropriately before being entered into the model. Adjusted odds ratios (ORs) and 95% confidence intervals (CIs) were calculated for the low swallowing threshold groups. A p-value < 0.05 was considered statistically significant. All data were analyzed using SPSS Statistics for Windows software (version 23.0; IBM Corp., Armonk, NY, USA).\nThe Shapiro–Wilk test was used to determine data normality. All continuous data were expressed as means ± SD. The means were compared between two groups using a two-tailed t-test or the Mann–Whitney U test. The Kruskal–Wallis test was used for comparisons between more than two groups. Pearson’s correlation coefficient was used to determine associations among glucose concentration before swallowing and other variables. The chi-squared test or Fisher’s exact test was used, as appropriate, to compare categorical variables between the normal swallowing threshold and developmental failure of swallowing threshold (lowest 20%) groups. Binary logistic regression analysis with the forward selection (conditional) method was used to identify factors predicting developmental failure of swallowing threshold. Independent variables that were significant in the univariate analyses were included. Categorical variables were coded appropriately before being entered into the model. Adjusted odds ratios (ORs) and 95% confidence intervals (CIs) were calculated for the low swallowing threshold groups. A p-value < 0.05 was considered statistically significant. All data were analyzed using SPSS Statistics for Windows software (version 23.0; IBM Corp., Armonk, NY, USA).","The survey solicited the following information: demographic characteristics (sex and age), eating habits, physical activity, and sleep [21].","Height and body weight were measured in the consultation room of the clinic. Height was measured to an accuracy of ±0.1 cm using a portable digital stadiometer (AD-653; A&D, Tokyo, Japan), with the head in the Frankfort plane, whereas body weight was measured with an accuracy of 0.1 kg [22]. Rohrer and Kaup indices were calculated from the height and weight. \nDuring intraoral examination, the number of erupted teeth and the decayed, missing, and filled teeth (DMFT) index were recorded for each patient [23].","Hand grip strength is generally used as an index of muscle weakness in the diagnosis of sarcopenia [24]. Therefore, hand grip strength measurement was performed for evaluating systemic muscle strength in the participants. A portable grip strength meter (T-2288; Toei Light Co., Ltd., Saitama, Japan) was used to measure hand grip strength. Participants were asked to stand and hold the dynamometer in their hand with the arm parallel to the body, but without squeezing the arm against the body. Hand grip strength (kg) was measured twice for each hand (alternately) with a 30 s interval between trials. The highest value from either the left or right hand was recorded as the grip strength [20].","Maximum occlusal force was measured using a portable occlusal force meter (GM10; Nagano Keiki Co., Ltd., Tokyo, Japan). The participants were instructed to bite down with maximal voluntary muscular effort using their first molars. Maximum occlusal force was measured on each side, with a 30 s interval between bite measurements. The larger of the values from the left and right sides was recorded as the maximum bite force [21].","Masticatory performance was determined by measuring the concentration of dissolved glucose obtained from a cylindrical gummy jelly (GLUCOLUMN; GC Co., Ltd., Tokyo, Japan) using a glucose-measuring device (GLUCO SENSOR GS-II; GC Co., Ltd., Tokyo, Japan). Prior to the test, the participants were instructed regarding the chewing movements and mouth rinsing procedures to prevent swallowing. The participants were then instructed to chew the gummy jelly for 20 s freely. After chewing, the participants were asked to take 10 mL of distilled water into their mouth and spit out the gummy jelly and distilled water into a filter cup. The glucose concentration in the filtrate (mg/dL) was measured using a dedicated device [20].","Following evaluation of the masticatory performance, an assessment of the swallowing threshold was performed using the test gummy jellies (GLUCOLUMN; GC Co. Ltd.). Variables related to swallowing (i.e., the number of chewing cycles, chewing time, chewing rate, and glucose concentration in the filtrate (mg/dL)) were assessed for all participants. Each participant was instructed to chew a gummy jelly freely until feeling the desire to swallow, at which time they were instructed to stop chewing and signal to the examiner that they were ready to expel the gummy jelly. The examiner visually quantified the number of chewing cycles. The time from the onset of chewing to the moment when the participants raised their hands was recorded using a stopwatch [20]. The subsequent steps were the same as those used for the evaluation of masticatory performance. We determined that the glucose concentration in the filtrate was an indicator of the swallowing threshold. Participants with low swallowing threshold tend to signal that they want to swallow the gummy jelly quickly, resulting in low glucose concentrations. A glucose concentration in the lowest 20th percentile was defined as developmental failure of swallowing threshold, based on previous studies reporting the criteria for sarcopenia [21,25]. ","All measurements were performed in duplicate, separated by 30 s rest periods, and the mean values were calculated for the subsequent analyses. All examinations were performed by the same examiner. Data generated during sample collection were assessed for reliability. Random error was evaluated based on intraobserver reliability, which was quantified using the intraclass correlation coefficient (ICC; where 0.8 ≤ ICC ≤ 1.0 corresponded to high reliability) [26].","The Shapiro–Wilk test was used to determine data normality. All continuous data were expressed as means ± SD. The means were compared between two groups using a two-tailed t-test or the Mann–Whitney U test. The Kruskal–Wallis test was used for comparisons between more than two groups. Pearson’s correlation coefficient was used to determine associations among glucose concentration before swallowing and other variables. The chi-squared test or Fisher’s exact test was used, as appropriate, to compare categorical variables between the normal swallowing threshold and developmental failure of swallowing threshold (lowest 20%) groups. Binary logistic regression analysis with the forward selection (conditional) method was used to identify factors predicting developmental failure of swallowing threshold. Independent variables that were significant in the univariate analyses were included. Categorical variables were coded appropriately before being entered into the model. Adjusted odds ratios (ORs) and 95% confidence intervals (CIs) were calculated for the low swallowing threshold groups. A p-value < 0.05 was considered statistically significant. All data were analyzed using SPSS Statistics for Windows software (version 23.0; IBM Corp., Armonk, NY, USA)."],"string":"[\n \"2.1. Participants This study was conducted in accordance with the Declaration of Helsinki and was approved by the Human Investigation Committee of Kyushu Dental University (approval number: 18–37). All participants and their parents/guardians provided written informed consent for participation in the study. The participants were recruited following an initial examination at two private dental clinics in Japan. The inclusion criteria were as follows: aged 5–15 years, normal language comprehension, cooperative behavior, and no current dental diseases or complications. The exclusion criteria included systemic disturbances causing swallowing impairment, obvious facial asymmetry that could affect the measurements, soft tissue abnormalities, temporomandibular joint dysfunction, and dental or structural irregularities.\\nUsing software (PS: Power and Sample Size Calculation, available from the Vanderbilt University’s website), sample size was calculated. The study was based on a continuous response variable ranging from normal swallowing threshold to developmental failure of swallowing threshold; four normal participants were recruited for every participant with the developmental failure of swallowing threshold. In a previous study, the data for each group were normally distributed, with a standard deviation (SD) of swallowing threshold of 40 mg/dL [21]. Based on a true difference of 40 mg/dL between the developmental failure and normal groups, 14 developmental failure and 56 normal participants were required to reject the null hypothesis (i.e., the population means of the developmental failure and normal groups are equal) with a power of 0.9. The type I error probability for the null hypothesis was 0.05.\\nThe participants were divided into 11 groups based on their chronological age, and each group was further divided into two subgroups based on sex. Details of the participants are shown in Table 1. \\nThis study was conducted in accordance with the Declaration of Helsinki and was approved by the Human Investigation Committee of Kyushu Dental University (approval number: 18–37). All participants and their parents/guardians provided written informed consent for participation in the study. The participants were recruited following an initial examination at two private dental clinics in Japan. The inclusion criteria were as follows: aged 5–15 years, normal language comprehension, cooperative behavior, and no current dental diseases or complications. The exclusion criteria included systemic disturbances causing swallowing impairment, obvious facial asymmetry that could affect the measurements, soft tissue abnormalities, temporomandibular joint dysfunction, and dental or structural irregularities.\\nUsing software (PS: Power and Sample Size Calculation, available from the Vanderbilt University’s website), sample size was calculated. The study was based on a continuous response variable ranging from normal swallowing threshold to developmental failure of swallowing threshold; four normal participants were recruited for every participant with the developmental failure of swallowing threshold. In a previous study, the data for each group were normally distributed, with a standard deviation (SD) of swallowing threshold of 40 mg/dL [21]. Based on a true difference of 40 mg/dL between the developmental failure and normal groups, 14 developmental failure and 56 normal participants were required to reject the null hypothesis (i.e., the population means of the developmental failure and normal groups are equal) with a power of 0.9. The type I error probability for the null hypothesis was 0.05.\\nThe participants were divided into 11 groups based on their chronological age, and each group was further divided into two subgroups based on sex. Details of the participants are shown in Table 1. \\n2.2. Questionnaire The survey solicited the following information: demographic characteristics (sex and age), eating habits, physical activity, and sleep [21].\\nThe survey solicited the following information: demographic characteristics (sex and age), eating habits, physical activity, and sleep [21].\\n2.3. Anthropometry and Dental Examination Height and body weight were measured in the consultation room of the clinic. Height was measured to an accuracy of ±0.1 cm using a portable digital stadiometer (AD-653; A&D, Tokyo, Japan), with the head in the Frankfort plane, whereas body weight was measured with an accuracy of 0.1 kg [22]. Rohrer and Kaup indices were calculated from the height and weight. \\nDuring intraoral examination, the number of erupted teeth and the decayed, missing, and filled teeth (DMFT) index were recorded for each patient [23].\\nHeight and body weight were measured in the consultation room of the clinic. Height was measured to an accuracy of ±0.1 cm using a portable digital stadiometer (AD-653; A&D, Tokyo, Japan), with the head in the Frankfort plane, whereas body weight was measured with an accuracy of 0.1 kg [22]. Rohrer and Kaup indices were calculated from the height and weight. \\nDuring intraoral examination, the number of erupted teeth and the decayed, missing, and filled teeth (DMFT) index were recorded for each patient [23].\\n2.4. Hand Grip Strength Hand grip strength is generally used as an index of muscle weakness in the diagnosis of sarcopenia [24]. Therefore, hand grip strength measurement was performed for evaluating systemic muscle strength in the participants. A portable grip strength meter (T-2288; Toei Light Co., Ltd., Saitama, Japan) was used to measure hand grip strength. Participants were asked to stand and hold the dynamometer in their hand with the arm parallel to the body, but without squeezing the arm against the body. Hand grip strength (kg) was measured twice for each hand (alternately) with a 30 s interval between trials. The highest value from either the left or right hand was recorded as the grip strength [20].\\nHand grip strength is generally used as an index of muscle weakness in the diagnosis of sarcopenia [24]. Therefore, hand grip strength measurement was performed for evaluating systemic muscle strength in the participants. A portable grip strength meter (T-2288; Toei Light Co., Ltd., Saitama, Japan) was used to measure hand grip strength. Participants were asked to stand and hold the dynamometer in their hand with the arm parallel to the body, but without squeezing the arm against the body. Hand grip strength (kg) was measured twice for each hand (alternately) with a 30 s interval between trials. The highest value from either the left or right hand was recorded as the grip strength [20].\\n2.5. Maximum Occlusal Force Maximum occlusal force was measured using a portable occlusal force meter (GM10; Nagano Keiki Co., Ltd., Tokyo, Japan). The participants were instructed to bite down with maximal voluntary muscular effort using their first molars. Maximum occlusal force was measured on each side, with a 30 s interval between bite measurements. The larger of the values from the left and right sides was recorded as the maximum bite force [21].\\nMaximum occlusal force was measured using a portable occlusal force meter (GM10; Nagano Keiki Co., Ltd., Tokyo, Japan). The participants were instructed to bite down with maximal voluntary muscular effort using their first molars. Maximum occlusal force was measured on each side, with a 30 s interval between bite measurements. The larger of the values from the left and right sides was recorded as the maximum bite force [21].\\n2.6. Masticatory Performance Masticatory performance was determined by measuring the concentration of dissolved glucose obtained from a cylindrical gummy jelly (GLUCOLUMN; GC Co., Ltd., Tokyo, Japan) using a glucose-measuring device (GLUCO SENSOR GS-II; GC Co., Ltd., Tokyo, Japan). Prior to the test, the participants were instructed regarding the chewing movements and mouth rinsing procedures to prevent swallowing. The participants were then instructed to chew the gummy jelly for 20 s freely. After chewing, the participants were asked to take 10 mL of distilled water into their mouth and spit out the gummy jelly and distilled water into a filter cup. The glucose concentration in the filtrate (mg/dL) was measured using a dedicated device [20].\\nMasticatory performance was determined by measuring the concentration of dissolved glucose obtained from a cylindrical gummy jelly (GLUCOLUMN; GC Co., Ltd., Tokyo, Japan) using a glucose-measuring device (GLUCO SENSOR GS-II; GC Co., Ltd., Tokyo, Japan). Prior to the test, the participants were instructed regarding the chewing movements and mouth rinsing procedures to prevent swallowing. The participants were then instructed to chew the gummy jelly for 20 s freely. After chewing, the participants were asked to take 10 mL of distilled water into their mouth and spit out the gummy jelly and distilled water into a filter cup. The glucose concentration in the filtrate (mg/dL) was measured using a dedicated device [20].\\n2.7. Swallowing Threshold Following evaluation of the masticatory performance, an assessment of the swallowing threshold was performed using the test gummy jellies (GLUCOLUMN; GC Co. Ltd.). Variables related to swallowing (i.e., the number of chewing cycles, chewing time, chewing rate, and glucose concentration in the filtrate (mg/dL)) were assessed for all participants. Each participant was instructed to chew a gummy jelly freely until feeling the desire to swallow, at which time they were instructed to stop chewing and signal to the examiner that they were ready to expel the gummy jelly. The examiner visually quantified the number of chewing cycles. The time from the onset of chewing to the moment when the participants raised their hands was recorded using a stopwatch [20]. The subsequent steps were the same as those used for the evaluation of masticatory performance. We determined that the glucose concentration in the filtrate was an indicator of the swallowing threshold. Participants with low swallowing threshold tend to signal that they want to swallow the gummy jelly quickly, resulting in low glucose concentrations. A glucose concentration in the lowest 20th percentile was defined as developmental failure of swallowing threshold, based on previous studies reporting the criteria for sarcopenia [21,25]. \\nFollowing evaluation of the masticatory performance, an assessment of the swallowing threshold was performed using the test gummy jellies (GLUCOLUMN; GC Co. Ltd.). Variables related to swallowing (i.e., the number of chewing cycles, chewing time, chewing rate, and glucose concentration in the filtrate (mg/dL)) were assessed for all participants. Each participant was instructed to chew a gummy jelly freely until feeling the desire to swallow, at which time they were instructed to stop chewing and signal to the examiner that they were ready to expel the gummy jelly. The examiner visually quantified the number of chewing cycles. The time from the onset of chewing to the moment when the participants raised their hands was recorded using a stopwatch [20]. The subsequent steps were the same as those used for the evaluation of masticatory performance. We determined that the glucose concentration in the filtrate was an indicator of the swallowing threshold. Participants with low swallowing threshold tend to signal that they want to swallow the gummy jelly quickly, resulting in low glucose concentrations. A glucose concentration in the lowest 20th percentile was defined as developmental failure of swallowing threshold, based on previous studies reporting the criteria for sarcopenia [21,25]. \\n2.8. Reliability of Measurements All measurements were performed in duplicate, separated by 30 s rest periods, and the mean values were calculated for the subsequent analyses. All examinations were performed by the same examiner. Data generated during sample collection were assessed for reliability. Random error was evaluated based on intraobserver reliability, which was quantified using the intraclass correlation coefficient (ICC; where 0.8 ≤ ICC ≤ 1.0 corresponded to high reliability) [26].\\nAll measurements were performed in duplicate, separated by 30 s rest periods, and the mean values were calculated for the subsequent analyses. All examinations were performed by the same examiner. Data generated during sample collection were assessed for reliability. Random error was evaluated based on intraobserver reliability, which was quantified using the intraclass correlation coefficient (ICC; where 0.8 ≤ ICC ≤ 1.0 corresponded to high reliability) [26].\\n2.9. Statistical Analysis The Shapiro–Wilk test was used to determine data normality. All continuous data were expressed as means ± SD. The means were compared between two groups using a two-tailed t-test or the Mann–Whitney U test. The Kruskal–Wallis test was used for comparisons between more than two groups. Pearson’s correlation coefficient was used to determine associations among glucose concentration before swallowing and other variables. The chi-squared test or Fisher’s exact test was used, as appropriate, to compare categorical variables between the normal swallowing threshold and developmental failure of swallowing threshold (lowest 20%) groups. Binary logistic regression analysis with the forward selection (conditional) method was used to identify factors predicting developmental failure of swallowing threshold. Independent variables that were significant in the univariate analyses were included. Categorical variables were coded appropriately before being entered into the model. Adjusted odds ratios (ORs) and 95% confidence intervals (CIs) were calculated for the low swallowing threshold groups. A p-value < 0.05 was considered statistically significant. All data were analyzed using SPSS Statistics for Windows software (version 23.0; IBM Corp., Armonk, NY, USA).\\nThe Shapiro–Wilk test was used to determine data normality. All continuous data were expressed as means ± SD. The means were compared between two groups using a two-tailed t-test or the Mann–Whitney U test. The Kruskal–Wallis test was used for comparisons between more than two groups. Pearson’s correlation coefficient was used to determine associations among glucose concentration before swallowing and other variables. The chi-squared test or Fisher’s exact test was used, as appropriate, to compare categorical variables between the normal swallowing threshold and developmental failure of swallowing threshold (lowest 20%) groups. Binary logistic regression analysis with the forward selection (conditional) method was used to identify factors predicting developmental failure of swallowing threshold. Independent variables that were significant in the univariate analyses were included. Categorical variables were coded appropriately before being entered into the model. Adjusted odds ratios (ORs) and 95% confidence intervals (CIs) were calculated for the low swallowing threshold groups. A p-value < 0.05 was considered statistically significant. All data were analyzed using SPSS Statistics for Windows software (version 23.0; IBM Corp., Armonk, NY, USA).\",\n \"The survey solicited the following information: demographic characteristics (sex and age), eating habits, physical activity, and sleep [21].\",\n \"Height and body weight were measured in the consultation room of the clinic. Height was measured to an accuracy of ±0.1 cm using a portable digital stadiometer (AD-653; A&D, Tokyo, Japan), with the head in the Frankfort plane, whereas body weight was measured with an accuracy of 0.1 kg [22]. Rohrer and Kaup indices were calculated from the height and weight. \\nDuring intraoral examination, the number of erupted teeth and the decayed, missing, and filled teeth (DMFT) index were recorded for each patient [23].\",\n \"Hand grip strength is generally used as an index of muscle weakness in the diagnosis of sarcopenia [24]. Therefore, hand grip strength measurement was performed for evaluating systemic muscle strength in the participants. A portable grip strength meter (T-2288; Toei Light Co., Ltd., Saitama, Japan) was used to measure hand grip strength. Participants were asked to stand and hold the dynamometer in their hand with the arm parallel to the body, but without squeezing the arm against the body. Hand grip strength (kg) was measured twice for each hand (alternately) with a 30 s interval between trials. The highest value from either the left or right hand was recorded as the grip strength [20].\",\n \"Maximum occlusal force was measured using a portable occlusal force meter (GM10; Nagano Keiki Co., Ltd., Tokyo, Japan). The participants were instructed to bite down with maximal voluntary muscular effort using their first molars. Maximum occlusal force was measured on each side, with a 30 s interval between bite measurements. The larger of the values from the left and right sides was recorded as the maximum bite force [21].\",\n \"Masticatory performance was determined by measuring the concentration of dissolved glucose obtained from a cylindrical gummy jelly (GLUCOLUMN; GC Co., Ltd., Tokyo, Japan) using a glucose-measuring device (GLUCO SENSOR GS-II; GC Co., Ltd., Tokyo, Japan). Prior to the test, the participants were instructed regarding the chewing movements and mouth rinsing procedures to prevent swallowing. The participants were then instructed to chew the gummy jelly for 20 s freely. After chewing, the participants were asked to take 10 mL of distilled water into their mouth and spit out the gummy jelly and distilled water into a filter cup. The glucose concentration in the filtrate (mg/dL) was measured using a dedicated device [20].\",\n \"Following evaluation of the masticatory performance, an assessment of the swallowing threshold was performed using the test gummy jellies (GLUCOLUMN; GC Co. Ltd.). Variables related to swallowing (i.e., the number of chewing cycles, chewing time, chewing rate, and glucose concentration in the filtrate (mg/dL)) were assessed for all participants. Each participant was instructed to chew a gummy jelly freely until feeling the desire to swallow, at which time they were instructed to stop chewing and signal to the examiner that they were ready to expel the gummy jelly. The examiner visually quantified the number of chewing cycles. The time from the onset of chewing to the moment when the participants raised their hands was recorded using a stopwatch [20]. The subsequent steps were the same as those used for the evaluation of masticatory performance. We determined that the glucose concentration in the filtrate was an indicator of the swallowing threshold. Participants with low swallowing threshold tend to signal that they want to swallow the gummy jelly quickly, resulting in low glucose concentrations. A glucose concentration in the lowest 20th percentile was defined as developmental failure of swallowing threshold, based on previous studies reporting the criteria for sarcopenia [21,25]. \",\n \"All measurements were performed in duplicate, separated by 30 s rest periods, and the mean values were calculated for the subsequent analyses. All examinations were performed by the same examiner. Data generated during sample collection were assessed for reliability. Random error was evaluated based on intraobserver reliability, which was quantified using the intraclass correlation coefficient (ICC; where 0.8 ≤ ICC ≤ 1.0 corresponded to high reliability) [26].\",\n \"The Shapiro–Wilk test was used to determine data normality. All continuous data were expressed as means ± SD. The means were compared between two groups using a two-tailed t-test or the Mann–Whitney U test. The Kruskal–Wallis test was used for comparisons between more than two groups. Pearson’s correlation coefficient was used to determine associations among glucose concentration before swallowing and other variables. The chi-squared test or Fisher’s exact test was used, as appropriate, to compare categorical variables between the normal swallowing threshold and developmental failure of swallowing threshold (lowest 20%) groups. Binary logistic regression analysis with the forward selection (conditional) method was used to identify factors predicting developmental failure of swallowing threshold. Independent variables that were significant in the univariate analyses were included. Categorical variables were coded appropriately before being entered into the model. Adjusted odds ratios (ORs) and 95% confidence intervals (CIs) were calculated for the low swallowing threshold groups. A p-value < 0.05 was considered statistically significant. All data were analyzed using SPSS Statistics for Windows software (version 23.0; IBM Corp., Armonk, NY, USA).\"\n]"},"other_sections_sec_types":{"kind":"list like","value":[null,null,null,null,null,null,null,null,null],"string":"[\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null\n]"},"all_sections_titles":{"kind":"list like","value":["1. Introduction","2. Materials and Methods","2.1. Participants","2.2. Questionnaire","2.3. Anthropometry and Dental Examination","2.4. Hand Grip Strength","2.5. Maximum Occlusal Force","2.6. Masticatory Performance","2.7. Swallowing Threshold","2.8. Reliability of Measurements","2.9. Statistical Analysis","3. Results","4. Discussion","5. Conclusions"],"string":"[\n \"1. Introduction\",\n \"2. Materials and Methods\",\n \"2.1. Participants\",\n \"2.2. Questionnaire\",\n \"2.3. Anthropometry and Dental Examination\",\n \"2.4. Hand Grip Strength\",\n \"2.5. Maximum Occlusal Force\",\n \"2.6. Masticatory Performance\",\n \"2.7. Swallowing Threshold\",\n \"2.8. Reliability of Measurements\",\n \"2.9. Statistical Analysis\",\n \"3. Results\",\n \"4. Discussion\",\n \"5. Conclusions\"\n]"},"all_sections_texts":{"kind":"list like","value":["Eating habits acquired in early childhood can change over time based on personal experience and learning [1,2]. Eating habits acquired in childhood or adolescence may persist into adulthood [3]. Poor dietary habits established during childhood that persist into adulthood increase the risk of obesity and related adverse consequences [4,5,6].\nIn previous studies, early childhood obesity was associated with a higher risk of adult metabolic syndrome [7,8]. Eating behaviors such as frequent consumption of fast food and sugar-sweetened beverages are associated with the development of childhood obesity [9]. In addition, factors such as eating frequency, amount, and occasions may interact to cause obesity [5,7]. Other studies indicated that early modification of poor eating habits could improve health and decrease the future risk of metabolic syndrome, type 2 diabetes, and atherosclerotic cardiovascular diseases [10,11].\nA relationship between obesity and eating speed was reported recently. Eating speed was associated with the incidence of metabolic syndrome in adults [12,13], while eating quickly was associated with increased body mass index (BMI) in young adults [14]. Similar results have also been reported in children [15,16,17]. In addition, chewing well before swallowing might be an effective method for reducing food intake and facilitating healthy weight management in preschool children [18] and adults [19].\nThese findings suggest that appropriate eating behaviors should be acquired within the critical early time period. However, the number of chewing cycles, chewing time, and chewing rate have not been analyzed in the context of the swallowing threshold, and it remains unclear when these functions are acquired. We hypothesized that these functions would be acquired relatively early in childhood. \nAdditionally, based on previous studies, we hypothesized that developmental failure of swallowing threshold in children would be associated with eating fast, chewing frequently, and other habits [17,20]. This developmental failure could involve physiological problems including obesity. Identifying the factors associated with developmental failure of swallowing threshold may aid the development of strategies for improving it.\nThe primary purpose of this study was to clarify differences in swallowing thresholds and other oral functions according to age, as well as when functions related to the swallowing threshold are acquired. A secondary aim was to determine factors related to the developmental failure of swallowing threshold in children aged 5–15 years. In this study, swallowing threshold was determined based on the concentration of dissolved glucose obtained from gummy jellies when the participants signaled that they wanted to swallow the chewed gummy jellies.","2.1. Participants This study was conducted in accordance with the Declaration of Helsinki and was approved by the Human Investigation Committee of Kyushu Dental University (approval number: 18–37). All participants and their parents/guardians provided written informed consent for participation in the study. The participants were recruited following an initial examination at two private dental clinics in Japan. The inclusion criteria were as follows: aged 5–15 years, normal language comprehension, cooperative behavior, and no current dental diseases or complications. The exclusion criteria included systemic disturbances causing swallowing impairment, obvious facial asymmetry that could affect the measurements, soft tissue abnormalities, temporomandibular joint dysfunction, and dental or structural irregularities.\nUsing software (PS: Power and Sample Size Calculation, available from the Vanderbilt University’s website), sample size was calculated. The study was based on a continuous response variable ranging from normal swallowing threshold to developmental failure of swallowing threshold; four normal participants were recruited for every participant with the developmental failure of swallowing threshold. In a previous study, the data for each group were normally distributed, with a standard deviation (SD) of swallowing threshold of 40 mg/dL [21]. Based on a true difference of 40 mg/dL between the developmental failure and normal groups, 14 developmental failure and 56 normal participants were required to reject the null hypothesis (i.e., the population means of the developmental failure and normal groups are equal) with a power of 0.9. The type I error probability for the null hypothesis was 0.05.\nThe participants were divided into 11 groups based on their chronological age, and each group was further divided into two subgroups based on sex. Details of the participants are shown in Table 1. \nThis study was conducted in accordance with the Declaration of Helsinki and was approved by the Human Investigation Committee of Kyushu Dental University (approval number: 18–37). All participants and their parents/guardians provided written informed consent for participation in the study. The participants were recruited following an initial examination at two private dental clinics in Japan. The inclusion criteria were as follows: aged 5–15 years, normal language comprehension, cooperative behavior, and no current dental diseases or complications. The exclusion criteria included systemic disturbances causing swallowing impairment, obvious facial asymmetry that could affect the measurements, soft tissue abnormalities, temporomandibular joint dysfunction, and dental or structural irregularities.\nUsing software (PS: Power and Sample Size Calculation, available from the Vanderbilt University’s website), sample size was calculated. The study was based on a continuous response variable ranging from normal swallowing threshold to developmental failure of swallowing threshold; four normal participants were recruited for every participant with the developmental failure of swallowing threshold. In a previous study, the data for each group were normally distributed, with a standard deviation (SD) of swallowing threshold of 40 mg/dL [21]. Based on a true difference of 40 mg/dL between the developmental failure and normal groups, 14 developmental failure and 56 normal participants were required to reject the null hypothesis (i.e., the population means of the developmental failure and normal groups are equal) with a power of 0.9. The type I error probability for the null hypothesis was 0.05.\nThe participants were divided into 11 groups based on their chronological age, and each group was further divided into two subgroups based on sex. Details of the participants are shown in Table 1. \n2.2. Questionnaire The survey solicited the following information: demographic characteristics (sex and age), eating habits, physical activity, and sleep [21].\nThe survey solicited the following information: demographic characteristics (sex and age), eating habits, physical activity, and sleep [21].\n2.3. Anthropometry and Dental Examination Height and body weight were measured in the consultation room of the clinic. Height was measured to an accuracy of ±0.1 cm using a portable digital stadiometer (AD-653; A&D, Tokyo, Japan), with the head in the Frankfort plane, whereas body weight was measured with an accuracy of 0.1 kg [22]. Rohrer and Kaup indices were calculated from the height and weight. \nDuring intraoral examination, the number of erupted teeth and the decayed, missing, and filled teeth (DMFT) index were recorded for each patient [23].\nHeight and body weight were measured in the consultation room of the clinic. Height was measured to an accuracy of ±0.1 cm using a portable digital stadiometer (AD-653; A&D, Tokyo, Japan), with the head in the Frankfort plane, whereas body weight was measured with an accuracy of 0.1 kg [22]. Rohrer and Kaup indices were calculated from the height and weight. \nDuring intraoral examination, the number of erupted teeth and the decayed, missing, and filled teeth (DMFT) index were recorded for each patient [23].\n2.4. Hand Grip Strength Hand grip strength is generally used as an index of muscle weakness in the diagnosis of sarcopenia [24]. Therefore, hand grip strength measurement was performed for evaluating systemic muscle strength in the participants. A portable grip strength meter (T-2288; Toei Light Co., Ltd., Saitama, Japan) was used to measure hand grip strength. Participants were asked to stand and hold the dynamometer in their hand with the arm parallel to the body, but without squeezing the arm against the body. Hand grip strength (kg) was measured twice for each hand (alternately) with a 30 s interval between trials. The highest value from either the left or right hand was recorded as the grip strength [20].\nHand grip strength is generally used as an index of muscle weakness in the diagnosis of sarcopenia [24]. Therefore, hand grip strength measurement was performed for evaluating systemic muscle strength in the participants. A portable grip strength meter (T-2288; Toei Light Co., Ltd., Saitama, Japan) was used to measure hand grip strength. Participants were asked to stand and hold the dynamometer in their hand with the arm parallel to the body, but without squeezing the arm against the body. Hand grip strength (kg) was measured twice for each hand (alternately) with a 30 s interval between trials. The highest value from either the left or right hand was recorded as the grip strength [20].\n2.5. Maximum Occlusal Force Maximum occlusal force was measured using a portable occlusal force meter (GM10; Nagano Keiki Co., Ltd., Tokyo, Japan). The participants were instructed to bite down with maximal voluntary muscular effort using their first molars. Maximum occlusal force was measured on each side, with a 30 s interval between bite measurements. The larger of the values from the left and right sides was recorded as the maximum bite force [21].\nMaximum occlusal force was measured using a portable occlusal force meter (GM10; Nagano Keiki Co., Ltd., Tokyo, Japan). The participants were instructed to bite down with maximal voluntary muscular effort using their first molars. Maximum occlusal force was measured on each side, with a 30 s interval between bite measurements. The larger of the values from the left and right sides was recorded as the maximum bite force [21].\n2.6. Masticatory Performance Masticatory performance was determined by measuring the concentration of dissolved glucose obtained from a cylindrical gummy jelly (GLUCOLUMN; GC Co., Ltd., Tokyo, Japan) using a glucose-measuring device (GLUCO SENSOR GS-II; GC Co., Ltd., Tokyo, Japan). Prior to the test, the participants were instructed regarding the chewing movements and mouth rinsing procedures to prevent swallowing. The participants were then instructed to chew the gummy jelly for 20 s freely. After chewing, the participants were asked to take 10 mL of distilled water into their mouth and spit out the gummy jelly and distilled water into a filter cup. The glucose concentration in the filtrate (mg/dL) was measured using a dedicated device [20].\nMasticatory performance was determined by measuring the concentration of dissolved glucose obtained from a cylindrical gummy jelly (GLUCOLUMN; GC Co., Ltd., Tokyo, Japan) using a glucose-measuring device (GLUCO SENSOR GS-II; GC Co., Ltd., Tokyo, Japan). Prior to the test, the participants were instructed regarding the chewing movements and mouth rinsing procedures to prevent swallowing. The participants were then instructed to chew the gummy jelly for 20 s freely. After chewing, the participants were asked to take 10 mL of distilled water into their mouth and spit out the gummy jelly and distilled water into a filter cup. The glucose concentration in the filtrate (mg/dL) was measured using a dedicated device [20].\n2.7. Swallowing Threshold Following evaluation of the masticatory performance, an assessment of the swallowing threshold was performed using the test gummy jellies (GLUCOLUMN; GC Co. Ltd.). Variables related to swallowing (i.e., the number of chewing cycles, chewing time, chewing rate, and glucose concentration in the filtrate (mg/dL)) were assessed for all participants. Each participant was instructed to chew a gummy jelly freely until feeling the desire to swallow, at which time they were instructed to stop chewing and signal to the examiner that they were ready to expel the gummy jelly. The examiner visually quantified the number of chewing cycles. The time from the onset of chewing to the moment when the participants raised their hands was recorded using a stopwatch [20]. The subsequent steps were the same as those used for the evaluation of masticatory performance. We determined that the glucose concentration in the filtrate was an indicator of the swallowing threshold. Participants with low swallowing threshold tend to signal that they want to swallow the gummy jelly quickly, resulting in low glucose concentrations. A glucose concentration in the lowest 20th percentile was defined as developmental failure of swallowing threshold, based on previous studies reporting the criteria for sarcopenia [21,25]. \nFollowing evaluation of the masticatory performance, an assessment of the swallowing threshold was performed using the test gummy jellies (GLUCOLUMN; GC Co. Ltd.). Variables related to swallowing (i.e., the number of chewing cycles, chewing time, chewing rate, and glucose concentration in the filtrate (mg/dL)) were assessed for all participants. Each participant was instructed to chew a gummy jelly freely until feeling the desire to swallow, at which time they were instructed to stop chewing and signal to the examiner that they were ready to expel the gummy jelly. The examiner visually quantified the number of chewing cycles. The time from the onset of chewing to the moment when the participants raised their hands was recorded using a stopwatch [20]. The subsequent steps were the same as those used for the evaluation of masticatory performance. We determined that the glucose concentration in the filtrate was an indicator of the swallowing threshold. Participants with low swallowing threshold tend to signal that they want to swallow the gummy jelly quickly, resulting in low glucose concentrations. A glucose concentration in the lowest 20th percentile was defined as developmental failure of swallowing threshold, based on previous studies reporting the criteria for sarcopenia [21,25]. \n2.8. Reliability of Measurements All measurements were performed in duplicate, separated by 30 s rest periods, and the mean values were calculated for the subsequent analyses. All examinations were performed by the same examiner. Data generated during sample collection were assessed for reliability. Random error was evaluated based on intraobserver reliability, which was quantified using the intraclass correlation coefficient (ICC; where 0.8 ≤ ICC ≤ 1.0 corresponded to high reliability) [26].\nAll measurements were performed in duplicate, separated by 30 s rest periods, and the mean values were calculated for the subsequent analyses. All examinations were performed by the same examiner. Data generated during sample collection were assessed for reliability. Random error was evaluated based on intraobserver reliability, which was quantified using the intraclass correlation coefficient (ICC; where 0.8 ≤ ICC ≤ 1.0 corresponded to high reliability) [26].\n2.9. Statistical Analysis The Shapiro–Wilk test was used to determine data normality. All continuous data were expressed as means ± SD. The means were compared between two groups using a two-tailed t-test or the Mann–Whitney U test. The Kruskal–Wallis test was used for comparisons between more than two groups. Pearson’s correlation coefficient was used to determine associations among glucose concentration before swallowing and other variables. The chi-squared test or Fisher’s exact test was used, as appropriate, to compare categorical variables between the normal swallowing threshold and developmental failure of swallowing threshold (lowest 20%) groups. Binary logistic regression analysis with the forward selection (conditional) method was used to identify factors predicting developmental failure of swallowing threshold. Independent variables that were significant in the univariate analyses were included. Categorical variables were coded appropriately before being entered into the model. Adjusted odds ratios (ORs) and 95% confidence intervals (CIs) were calculated for the low swallowing threshold groups. A p-value < 0.05 was considered statistically significant. All data were analyzed using SPSS Statistics for Windows software (version 23.0; IBM Corp., Armonk, NY, USA).\nThe Shapiro–Wilk test was used to determine data normality. All continuous data were expressed as means ± SD. The means were compared between two groups using a two-tailed t-test or the Mann–Whitney U test. The Kruskal–Wallis test was used for comparisons between more than two groups. Pearson’s correlation coefficient was used to determine associations among glucose concentration before swallowing and other variables. The chi-squared test or Fisher’s exact test was used, as appropriate, to compare categorical variables between the normal swallowing threshold and developmental failure of swallowing threshold (lowest 20%) groups. Binary logistic regression analysis with the forward selection (conditional) method was used to identify factors predicting developmental failure of swallowing threshold. Independent variables that were significant in the univariate analyses were included. Categorical variables were coded appropriately before being entered into the model. Adjusted odds ratios (ORs) and 95% confidence intervals (CIs) were calculated for the low swallowing threshold groups. A p-value < 0.05 was considered statistically significant. All data were analyzed using SPSS Statistics for Windows software (version 23.0; IBM Corp., Armonk, NY, USA).","This study was conducted in accordance with the Declaration of Helsinki and was approved by the Human Investigation Committee of Kyushu Dental University (approval number: 18–37). All participants and their parents/guardians provided written informed consent for participation in the study. The participants were recruited following an initial examination at two private dental clinics in Japan. The inclusion criteria were as follows: aged 5–15 years, normal language comprehension, cooperative behavior, and no current dental diseases or complications. The exclusion criteria included systemic disturbances causing swallowing impairment, obvious facial asymmetry that could affect the measurements, soft tissue abnormalities, temporomandibular joint dysfunction, and dental or structural irregularities.\nUsing software (PS: Power and Sample Size Calculation, available from the Vanderbilt University’s website), sample size was calculated. The study was based on a continuous response variable ranging from normal swallowing threshold to developmental failure of swallowing threshold; four normal participants were recruited for every participant with the developmental failure of swallowing threshold. In a previous study, the data for each group were normally distributed, with a standard deviation (SD) of swallowing threshold of 40 mg/dL [21]. Based on a true difference of 40 mg/dL between the developmental failure and normal groups, 14 developmental failure and 56 normal participants were required to reject the null hypothesis (i.e., the population means of the developmental failure and normal groups are equal) with a power of 0.9. The type I error probability for the null hypothesis was 0.05.\nThe participants were divided into 11 groups based on their chronological age, and each group was further divided into two subgroups based on sex. Details of the participants are shown in Table 1. ","The survey solicited the following information: demographic characteristics (sex and age), eating habits, physical activity, and sleep [21].","Height and body weight were measured in the consultation room of the clinic. Height was measured to an accuracy of ±0.1 cm using a portable digital stadiometer (AD-653; A&D, Tokyo, Japan), with the head in the Frankfort plane, whereas body weight was measured with an accuracy of 0.1 kg [22]. Rohrer and Kaup indices were calculated from the height and weight. \nDuring intraoral examination, the number of erupted teeth and the decayed, missing, and filled teeth (DMFT) index were recorded for each patient [23].","Hand grip strength is generally used as an index of muscle weakness in the diagnosis of sarcopenia [24]. Therefore, hand grip strength measurement was performed for evaluating systemic muscle strength in the participants. A portable grip strength meter (T-2288; Toei Light Co., Ltd., Saitama, Japan) was used to measure hand grip strength. Participants were asked to stand and hold the dynamometer in their hand with the arm parallel to the body, but without squeezing the arm against the body. Hand grip strength (kg) was measured twice for each hand (alternately) with a 30 s interval between trials. The highest value from either the left or right hand was recorded as the grip strength [20].","Maximum occlusal force was measured using a portable occlusal force meter (GM10; Nagano Keiki Co., Ltd., Tokyo, Japan). The participants were instructed to bite down with maximal voluntary muscular effort using their first molars. Maximum occlusal force was measured on each side, with a 30 s interval between bite measurements. The larger of the values from the left and right sides was recorded as the maximum bite force [21].","Masticatory performance was determined by measuring the concentration of dissolved glucose obtained from a cylindrical gummy jelly (GLUCOLUMN; GC Co., Ltd., Tokyo, Japan) using a glucose-measuring device (GLUCO SENSOR GS-II; GC Co., Ltd., Tokyo, Japan). Prior to the test, the participants were instructed regarding the chewing movements and mouth rinsing procedures to prevent swallowing. The participants were then instructed to chew the gummy jelly for 20 s freely. After chewing, the participants were asked to take 10 mL of distilled water into their mouth and spit out the gummy jelly and distilled water into a filter cup. The glucose concentration in the filtrate (mg/dL) was measured using a dedicated device [20].","Following evaluation of the masticatory performance, an assessment of the swallowing threshold was performed using the test gummy jellies (GLUCOLUMN; GC Co. Ltd.). Variables related to swallowing (i.e., the number of chewing cycles, chewing time, chewing rate, and glucose concentration in the filtrate (mg/dL)) were assessed for all participants. Each participant was instructed to chew a gummy jelly freely until feeling the desire to swallow, at which time they were instructed to stop chewing and signal to the examiner that they were ready to expel the gummy jelly. The examiner visually quantified the number of chewing cycles. The time from the onset of chewing to the moment when the participants raised their hands was recorded using a stopwatch [20]. The subsequent steps were the same as those used for the evaluation of masticatory performance. We determined that the glucose concentration in the filtrate was an indicator of the swallowing threshold. Participants with low swallowing threshold tend to signal that they want to swallow the gummy jelly quickly, resulting in low glucose concentrations. A glucose concentration in the lowest 20th percentile was defined as developmental failure of swallowing threshold, based on previous studies reporting the criteria for sarcopenia [21,25]. ","All measurements were performed in duplicate, separated by 30 s rest periods, and the mean values were calculated for the subsequent analyses. All examinations were performed by the same examiner. Data generated during sample collection were assessed for reliability. Random error was evaluated based on intraobserver reliability, which was quantified using the intraclass correlation coefficient (ICC; where 0.8 ≤ ICC ≤ 1.0 corresponded to high reliability) [26].","The Shapiro–Wilk test was used to determine data normality. All continuous data were expressed as means ± SD. The means were compared between two groups using a two-tailed t-test or the Mann–Whitney U test. The Kruskal–Wallis test was used for comparisons between more than two groups. Pearson’s correlation coefficient was used to determine associations among glucose concentration before swallowing and other variables. The chi-squared test or Fisher’s exact test was used, as appropriate, to compare categorical variables between the normal swallowing threshold and developmental failure of swallowing threshold (lowest 20%) groups. Binary logistic regression analysis with the forward selection (conditional) method was used to identify factors predicting developmental failure of swallowing threshold. Independent variables that were significant in the univariate analyses were included. Categorical variables were coded appropriately before being entered into the model. Adjusted odds ratios (ORs) and 95% confidence intervals (CIs) were calculated for the low swallowing threshold groups. A p-value < 0.05 was considered statistically significant. All data were analyzed using SPSS Statistics for Windows software (version 23.0; IBM Corp., Armonk, NY, USA).","The ICCs for all measurement items ranged from 0.84 to 0.91, indicating a high degree of intraobserver reliability. \nThe anthropometric parameters and dental examination results are shown in Table 1 according to age and sex. The 15-year-old males were significantly taller than females of the same age (p < 0.05). The DMFT index in 11-year-old females was significantly higher than in males of the same age (p < 0.05).\nHand grip strength and oral functional parameters are shown in Table 2 according to age and sex. The hand grip strengths in 5-, 10-, 12-, 14-, and 15-year-old males were significantly greater compared to females of the same age (all p < 0.05). \nWhen considering the results of comparing the mean scores of males and females for all measurement items by age, hand grip strength in 14–15-year-old participants was significantly greater compared to 5–7-year-old participants (all p < 0.05). Hand grip strength increased with age. The maximum occlusal force was greatest in 15-year-old participants and was significantly greater than in those aged 5–7 years (all p < 0.05). Masticatory performance was lowest in 5-year-old participants and was significantly lower than in those aged 12, 14, and 15 years (all p < 0.05). The number of chewing cycles was highest in 9-year-old participants and was significantly higher than in those aged 6 and 14 years (both p < 0.05). The change in chewing time with age was similar to the change in the number of chewing cycles. The swallowing threshold was highest in 15-year-old participants, followed by the 8-year-old participants; both were significantly higher than in those aged 6 years (p < 0.05).\nThe results of Pearson’s bivariate correlation analyses are shown in Table 3. Hand grip strength was significantly and positively correlated with masticatory performance (r = 0.611, p < 0.01) and was strongly correlated with maximum occlusal force (r = 0.791, p < 0.01). Swallowing threshold was strongly correlated with masticatory performance (r = 0.727, p < 0.01) and was significantly and positively correlated with the number of erupted teeth, maximum occlusal force, and number of chewing cycles (r = 0.432, p < 0.01; r = 0.493, p < 0.01; r = 0.465, p < 0.01, respectively). The number of chewing cycles was strongly correlated with chewing time (r = 0.801, p < 0.01). The correlation between age and swallowing threshold was weak but significant (r = 0.362, p < 0.01; Figure 1).\nTable 4 summarizes the data on developmental failure of swallowing threshold based on demographic and health-related variables. Participants with developmental failure of swallowing threshold were more likely to be overweight or obese, eat between meals at least once per day, and have low or no physical activity (p = 0.014, p = 0.021, and p = 0.006, respectively).\nTable 5 summarizes the data on developmental failure of swallowing threshold based on age, height, body weight, dental status, hand grip strength, and masticatory function. Swallowing threshold, age, number of erupted teeth, maximum occlusal force, masticatory performance, number of chewing cycles, and chewing time in the develop-mental failure of swallowing threshold group were significantly lower compared to the normal swallowing threshold group (all p < 0.05). The mean DMFT index in the developmental failure swallowing threshold group was significantly higher compared to the normal swallowing threshold group (p < 0.05). The mean chewing time to swallow in the normal swallowing threshold group was 20.91 s. Masticatory performance was similar between the groups.\nTable 6 shows the predictors of developmental failure of swallowing threshold, as revealed by logistic regression. Overweight or obese individuals had higher odds of developmental failure of swallowing threshold (OR = 5.343, p = 0.031, 95% CI = 1.168–24.437). Eating between meals once or more a day was also associated with higher odds of developmental failure of swallowing threshold (OR = 4.934, p = 0.049, 95% CI = 1.004–24.244).","In this study, patterns of swallowing threshold in children according to age were clearly different from those of handgrip strength, maximum occlusal force, and masticatory performance. Maximum occlusal force and masticatory performance tended to increase with age, but the rates of increase between the ages of 12 and 15 years were not as high as those for hand grip strength. These results suggest that maximum occlusal force and masticatory performance do not grow rapidly during this period. \nWhen considering the results of univariate analysis, hand grip strength was significantly and positively correlated with masticatory performance as well as maximum occlusal force. The European Working Group on Sarcopenia in Older People 2 (EWGSOP2) reported that low activity and malnutrition cause secondary sarcopenia not only in the elderly but also in children [24]. Sarcopenia may be associated with developmental failure of masticatory function in childhood. \nWe found that age had a weak correlation with the swallowing threshold. At the age of 8 years, multiple participants had swallowing thresholds that reached or surpassed those of 15-year-old individuals (Figure 1). A recent study of individuals aged 20–79 years reported that the swallowing threshold values peaked at 40 s, which was close to the peak time for 15-year-old females in this study [21]. Therefore, the swallowing threshold is unlikely to be age-dependent, and we derived a “unified” threshold of developmental failure, i.e., a threshold applicable to all ages. The number of chewing cycles and chewing time tended to gradually decrease after peaking at the age of 9 and 8 years, respectively.\nIn the logistic regression analysis, obesity was significantly associated with developmental failure of swallowing threshold. A previous study reported that children who did not chew well had a significantly higher likelihood of overweight compared to the reference group of children aged 5–6 years. To assess the degree of chewing, the parents in that study answered the following self-administered questionnaire item: “How well does your child chew foods while eating?” [18]. Another study objectively evaluating the swallowing threshold reported that a lower threshold was associated with a higher BMI among preschool children aged 3–5 years. They suggested that a higher BMI was associated with a lower number of chewing cycles and a shorter chewing time [17]. A study of young adults (in their twenties) also reported that BMI was negatively correlated with the total number of chews and duration of chewing but did not correlate significantly with the chewing rate [14]. Consistent with these findings, our results showed that the number of chewing cycles and chewing time in participants with developmental failure of swallowing threshold were significantly lower compared to those with a normal swallowing threshold. However, chewing rate was not significantly different between the two groups. These findings suggested that overweight/obesity is more prevalent among children who chew their food less, and over a shorter period of time, prior to swallowing, regardless of the chewing rate.\nSeveral studies have reported that the causes of obesity are multi-faceted and include interactions among eating behaviors, obesogenic environments, genetics, and physical inactivity [5,7]. This may be indirectly associated with our finding that a significantly higher percentage of participants with developmental failure of swallowing threshold reported a lack of physical activity according to univariate analysis, although a causal relationship could not be determined.\nIn the logistic regression analysis, eating between meals at least once per day was also significantly associated with developmental failure of swallowing threshold. Chewing food activates the hypothalamic histamine nervous system and suppresses appetite through the satiety center in animals [27,28,29]. Children with developmental failure of swallowing threshold are less likely to feel sated, so it is more likely that they will eat between meals at least once per day. Several studies reported that consumption of low-quality snacks was associated with increased prevalence of obesity among children [30,31,32]. Our results indicate that this may be explained in terms of developmental failure of swallowing threshold. A study of Japanese elementary school children reported that their snacking habits were influenced by paternal eating habits [33]. Another study suggested that parents could play an important role in the control of school-age children’s food intake and choices [6]. National surveys of food intake in the United States reported that daily energy intake in children aged 2 to 18 increased significantly from 1839 kcal/day to 2023 kcal/day between 1977–1978 and 2003–2006 [34]. In addition, another study reported that children aged 2 to 11 consume extra energy and sugars in their diets but insufficient Vitamin D, calcium, and potassium [35]. This may be applicable to all eating behaviors, i.e., not just snacking.\nWe further considered strategies to improve developmental failure of swallowing threshold. First, the chewing rate until swallowing was not significantly different between participants with developmental failure and normal swallowing thresholds. Therefore, it may not be necessary to control the chewing rate. Second, prolonging the chewing time until swallowing may also not be helpful for preventing developmental failure of swallowing threshold; even after the participants with developmental failure of swallowing threshold chewed the gummy jelly for 20 s, masticatory performance was significantly lower (97.94 ± 24.97 mg/dL) than that of participants with normal swallowing thresholds (134.19 ± 40.75 mg/dL). The lower masticatory performance may be associated with the younger age, weaker occlusal forces, a higher DMFT index, and fewer erupted teeth in participants with developmental failure of swallowing threshold. If these problems are resolved, developmental failure of swallowing threshold may improve. However, as it is difficult to solve these problems immediately, increasing the number of chewing cycles may be the key to improving developmental failure of swallowing threshold. Considering that it is a critical period of development, the appropriate number of chewing cycles and chewing time until swallowing should be established by the age of 9 years.\nOur study had some limitations. First, The National Health Examination Survey among 12- to 17-year-old US adolescents reported that BMI in the adolescents with a concave facial profile was higher than that in the adolescents with a straight facial profile [36]. Another study reported that a higher BMI was associated with a lower bite force in children aged 8–10 years [37]. These results suggest that facial profile type in children, especially Class III malocclusion, could be a factor associated with developmental failure of swallowing threshold. However, in this study, relationships between the type of malocclusion and the characteristics of the swallowing threshold were not able to be clarified, because participants’ malocclusions were not evaluated in detail. In the future, it is necessary to clarify the relationship between malocclusion and obesity and developmental failure of swallowing threshold in children. Second, the number of participants was small for performing logistic regression analysis. Among the patients who visited two dental clinics, the number of patients who met all of the conditions we set was very limited. Therefore, this study was performed with the minimum required number of participants calculated by the power analysis. It may be that a highly accurate model will be built in studies with higher numbers of participants. Third, it used a cross-sectional design, which precluded determination of causal relationships between developmental failure of swallowing threshold and the variables included in the logistic regression analyses. Longitudinal studies are needed to investigate the relative influence of factors associated with developmental failure of swallowing threshold. Additionally, the diet- and lifestyle-related questionnaires evaluated only children’s problems; future studies may benefit from evaluating the parents’ eating habits and dietary education.","We found that the relationship between swallowing threshold and age was unclear, whereas the number of chewing cycles and chewing time tended to gradually decrease after reaching a peak at the age of 9 and 8 years, respectively. An appropriate number of chewing cycles and chewing time until swallowing should be established by 9 years of age. Developmental failure of swallowing threshold was closely associated with childhood obesity and eating between meals at least once per day among the 5- to 15-year-old individuals."],"string":"[\n \"Eating habits acquired in early childhood can change over time based on personal experience and learning [1,2]. Eating habits acquired in childhood or adolescence may persist into adulthood [3]. Poor dietary habits established during childhood that persist into adulthood increase the risk of obesity and related adverse consequences [4,5,6].\\nIn previous studies, early childhood obesity was associated with a higher risk of adult metabolic syndrome [7,8]. Eating behaviors such as frequent consumption of fast food and sugar-sweetened beverages are associated with the development of childhood obesity [9]. In addition, factors such as eating frequency, amount, and occasions may interact to cause obesity [5,7]. Other studies indicated that early modification of poor eating habits could improve health and decrease the future risk of metabolic syndrome, type 2 diabetes, and atherosclerotic cardiovascular diseases [10,11].\\nA relationship between obesity and eating speed was reported recently. Eating speed was associated with the incidence of metabolic syndrome in adults [12,13], while eating quickly was associated with increased body mass index (BMI) in young adults [14]. Similar results have also been reported in children [15,16,17]. In addition, chewing well before swallowing might be an effective method for reducing food intake and facilitating healthy weight management in preschool children [18] and adults [19].\\nThese findings suggest that appropriate eating behaviors should be acquired within the critical early time period. However, the number of chewing cycles, chewing time, and chewing rate have not been analyzed in the context of the swallowing threshold, and it remains unclear when these functions are acquired. We hypothesized that these functions would be acquired relatively early in childhood. \\nAdditionally, based on previous studies, we hypothesized that developmental failure of swallowing threshold in children would be associated with eating fast, chewing frequently, and other habits [17,20]. This developmental failure could involve physiological problems including obesity. Identifying the factors associated with developmental failure of swallowing threshold may aid the development of strategies for improving it.\\nThe primary purpose of this study was to clarify differences in swallowing thresholds and other oral functions according to age, as well as when functions related to the swallowing threshold are acquired. A secondary aim was to determine factors related to the developmental failure of swallowing threshold in children aged 5–15 years. In this study, swallowing threshold was determined based on the concentration of dissolved glucose obtained from gummy jellies when the participants signaled that they wanted to swallow the chewed gummy jellies.\",\n \"2.1. Participants This study was conducted in accordance with the Declaration of Helsinki and was approved by the Human Investigation Committee of Kyushu Dental University (approval number: 18–37). All participants and their parents/guardians provided written informed consent for participation in the study. The participants were recruited following an initial examination at two private dental clinics in Japan. The inclusion criteria were as follows: aged 5–15 years, normal language comprehension, cooperative behavior, and no current dental diseases or complications. The exclusion criteria included systemic disturbances causing swallowing impairment, obvious facial asymmetry that could affect the measurements, soft tissue abnormalities, temporomandibular joint dysfunction, and dental or structural irregularities.\\nUsing software (PS: Power and Sample Size Calculation, available from the Vanderbilt University’s website), sample size was calculated. The study was based on a continuous response variable ranging from normal swallowing threshold to developmental failure of swallowing threshold; four normal participants were recruited for every participant with the developmental failure of swallowing threshold. In a previous study, the data for each group were normally distributed, with a standard deviation (SD) of swallowing threshold of 40 mg/dL [21]. Based on a true difference of 40 mg/dL between the developmental failure and normal groups, 14 developmental failure and 56 normal participants were required to reject the null hypothesis (i.e., the population means of the developmental failure and normal groups are equal) with a power of 0.9. The type I error probability for the null hypothesis was 0.05.\\nThe participants were divided into 11 groups based on their chronological age, and each group was further divided into two subgroups based on sex. Details of the participants are shown in Table 1. \\nThis study was conducted in accordance with the Declaration of Helsinki and was approved by the Human Investigation Committee of Kyushu Dental University (approval number: 18–37). All participants and their parents/guardians provided written informed consent for participation in the study. The participants were recruited following an initial examination at two private dental clinics in Japan. The inclusion criteria were as follows: aged 5–15 years, normal language comprehension, cooperative behavior, and no current dental diseases or complications. The exclusion criteria included systemic disturbances causing swallowing impairment, obvious facial asymmetry that could affect the measurements, soft tissue abnormalities, temporomandibular joint dysfunction, and dental or structural irregularities.\\nUsing software (PS: Power and Sample Size Calculation, available from the Vanderbilt University’s website), sample size was calculated. The study was based on a continuous response variable ranging from normal swallowing threshold to developmental failure of swallowing threshold; four normal participants were recruited for every participant with the developmental failure of swallowing threshold. In a previous study, the data for each group were normally distributed, with a standard deviation (SD) of swallowing threshold of 40 mg/dL [21]. Based on a true difference of 40 mg/dL between the developmental failure and normal groups, 14 developmental failure and 56 normal participants were required to reject the null hypothesis (i.e., the population means of the developmental failure and normal groups are equal) with a power of 0.9. The type I error probability for the null hypothesis was 0.05.\\nThe participants were divided into 11 groups based on their chronological age, and each group was further divided into two subgroups based on sex. Details of the participants are shown in Table 1. \\n2.2. Questionnaire The survey solicited the following information: demographic characteristics (sex and age), eating habits, physical activity, and sleep [21].\\nThe survey solicited the following information: demographic characteristics (sex and age), eating habits, physical activity, and sleep [21].\\n2.3. Anthropometry and Dental Examination Height and body weight were measured in the consultation room of the clinic. Height was measured to an accuracy of ±0.1 cm using a portable digital stadiometer (AD-653; A&D, Tokyo, Japan), with the head in the Frankfort plane, whereas body weight was measured with an accuracy of 0.1 kg [22]. Rohrer and Kaup indices were calculated from the height and weight. \\nDuring intraoral examination, the number of erupted teeth and the decayed, missing, and filled teeth (DMFT) index were recorded for each patient [23].\\nHeight and body weight were measured in the consultation room of the clinic. Height was measured to an accuracy of ±0.1 cm using a portable digital stadiometer (AD-653; A&D, Tokyo, Japan), with the head in the Frankfort plane, whereas body weight was measured with an accuracy of 0.1 kg [22]. Rohrer and Kaup indices were calculated from the height and weight. \\nDuring intraoral examination, the number of erupted teeth and the decayed, missing, and filled teeth (DMFT) index were recorded for each patient [23].\\n2.4. Hand Grip Strength Hand grip strength is generally used as an index of muscle weakness in the diagnosis of sarcopenia [24]. Therefore, hand grip strength measurement was performed for evaluating systemic muscle strength in the participants. A portable grip strength meter (T-2288; Toei Light Co., Ltd., Saitama, Japan) was used to measure hand grip strength. Participants were asked to stand and hold the dynamometer in their hand with the arm parallel to the body, but without squeezing the arm against the body. Hand grip strength (kg) was measured twice for each hand (alternately) with a 30 s interval between trials. The highest value from either the left or right hand was recorded as the grip strength [20].\\nHand grip strength is generally used as an index of muscle weakness in the diagnosis of sarcopenia [24]. Therefore, hand grip strength measurement was performed for evaluating systemic muscle strength in the participants. A portable grip strength meter (T-2288; Toei Light Co., Ltd., Saitama, Japan) was used to measure hand grip strength. Participants were asked to stand and hold the dynamometer in their hand with the arm parallel to the body, but without squeezing the arm against the body. Hand grip strength (kg) was measured twice for each hand (alternately) with a 30 s interval between trials. The highest value from either the left or right hand was recorded as the grip strength [20].\\n2.5. Maximum Occlusal Force Maximum occlusal force was measured using a portable occlusal force meter (GM10; Nagano Keiki Co., Ltd., Tokyo, Japan). The participants were instructed to bite down with maximal voluntary muscular effort using their first molars. Maximum occlusal force was measured on each side, with a 30 s interval between bite measurements. The larger of the values from the left and right sides was recorded as the maximum bite force [21].\\nMaximum occlusal force was measured using a portable occlusal force meter (GM10; Nagano Keiki Co., Ltd., Tokyo, Japan). The participants were instructed to bite down with maximal voluntary muscular effort using their first molars. Maximum occlusal force was measured on each side, with a 30 s interval between bite measurements. The larger of the values from the left and right sides was recorded as the maximum bite force [21].\\n2.6. Masticatory Performance Masticatory performance was determined by measuring the concentration of dissolved glucose obtained from a cylindrical gummy jelly (GLUCOLUMN; GC Co., Ltd., Tokyo, Japan) using a glucose-measuring device (GLUCO SENSOR GS-II; GC Co., Ltd., Tokyo, Japan). Prior to the test, the participants were instructed regarding the chewing movements and mouth rinsing procedures to prevent swallowing. The participants were then instructed to chew the gummy jelly for 20 s freely. After chewing, the participants were asked to take 10 mL of distilled water into their mouth and spit out the gummy jelly and distilled water into a filter cup. The glucose concentration in the filtrate (mg/dL) was measured using a dedicated device [20].\\nMasticatory performance was determined by measuring the concentration of dissolved glucose obtained from a cylindrical gummy jelly (GLUCOLUMN; GC Co., Ltd., Tokyo, Japan) using a glucose-measuring device (GLUCO SENSOR GS-II; GC Co., Ltd., Tokyo, Japan). Prior to the test, the participants were instructed regarding the chewing movements and mouth rinsing procedures to prevent swallowing. The participants were then instructed to chew the gummy jelly for 20 s freely. After chewing, the participants were asked to take 10 mL of distilled water into their mouth and spit out the gummy jelly and distilled water into a filter cup. The glucose concentration in the filtrate (mg/dL) was measured using a dedicated device [20].\\n2.7. Swallowing Threshold Following evaluation of the masticatory performance, an assessment of the swallowing threshold was performed using the test gummy jellies (GLUCOLUMN; GC Co. Ltd.). Variables related to swallowing (i.e., the number of chewing cycles, chewing time, chewing rate, and glucose concentration in the filtrate (mg/dL)) were assessed for all participants. Each participant was instructed to chew a gummy jelly freely until feeling the desire to swallow, at which time they were instructed to stop chewing and signal to the examiner that they were ready to expel the gummy jelly. The examiner visually quantified the number of chewing cycles. The time from the onset of chewing to the moment when the participants raised their hands was recorded using a stopwatch [20]. The subsequent steps were the same as those used for the evaluation of masticatory performance. We determined that the glucose concentration in the filtrate was an indicator of the swallowing threshold. Participants with low swallowing threshold tend to signal that they want to swallow the gummy jelly quickly, resulting in low glucose concentrations. A glucose concentration in the lowest 20th percentile was defined as developmental failure of swallowing threshold, based on previous studies reporting the criteria for sarcopenia [21,25]. \\nFollowing evaluation of the masticatory performance, an assessment of the swallowing threshold was performed using the test gummy jellies (GLUCOLUMN; GC Co. Ltd.). Variables related to swallowing (i.e., the number of chewing cycles, chewing time, chewing rate, and glucose concentration in the filtrate (mg/dL)) were assessed for all participants. Each participant was instructed to chew a gummy jelly freely until feeling the desire to swallow, at which time they were instructed to stop chewing and signal to the examiner that they were ready to expel the gummy jelly. The examiner visually quantified the number of chewing cycles. The time from the onset of chewing to the moment when the participants raised their hands was recorded using a stopwatch [20]. The subsequent steps were the same as those used for the evaluation of masticatory performance. We determined that the glucose concentration in the filtrate was an indicator of the swallowing threshold. Participants with low swallowing threshold tend to signal that they want to swallow the gummy jelly quickly, resulting in low glucose concentrations. A glucose concentration in the lowest 20th percentile was defined as developmental failure of swallowing threshold, based on previous studies reporting the criteria for sarcopenia [21,25]. \\n2.8. Reliability of Measurements All measurements were performed in duplicate, separated by 30 s rest periods, and the mean values were calculated for the subsequent analyses. All examinations were performed by the same examiner. Data generated during sample collection were assessed for reliability. Random error was evaluated based on intraobserver reliability, which was quantified using the intraclass correlation coefficient (ICC; where 0.8 ≤ ICC ≤ 1.0 corresponded to high reliability) [26].\\nAll measurements were performed in duplicate, separated by 30 s rest periods, and the mean values were calculated for the subsequent analyses. All examinations were performed by the same examiner. Data generated during sample collection were assessed for reliability. Random error was evaluated based on intraobserver reliability, which was quantified using the intraclass correlation coefficient (ICC; where 0.8 ≤ ICC ≤ 1.0 corresponded to high reliability) [26].\\n2.9. Statistical Analysis The Shapiro–Wilk test was used to determine data normality. All continuous data were expressed as means ± SD. The means were compared between two groups using a two-tailed t-test or the Mann–Whitney U test. The Kruskal–Wallis test was used for comparisons between more than two groups. Pearson’s correlation coefficient was used to determine associations among glucose concentration before swallowing and other variables. The chi-squared test or Fisher’s exact test was used, as appropriate, to compare categorical variables between the normal swallowing threshold and developmental failure of swallowing threshold (lowest 20%) groups. Binary logistic regression analysis with the forward selection (conditional) method was used to identify factors predicting developmental failure of swallowing threshold. Independent variables that were significant in the univariate analyses were included. Categorical variables were coded appropriately before being entered into the model. Adjusted odds ratios (ORs) and 95% confidence intervals (CIs) were calculated for the low swallowing threshold groups. A p-value < 0.05 was considered statistically significant. All data were analyzed using SPSS Statistics for Windows software (version 23.0; IBM Corp., Armonk, NY, USA).\\nThe Shapiro–Wilk test was used to determine data normality. All continuous data were expressed as means ± SD. The means were compared between two groups using a two-tailed t-test or the Mann–Whitney U test. The Kruskal–Wallis test was used for comparisons between more than two groups. Pearson’s correlation coefficient was used to determine associations among glucose concentration before swallowing and other variables. The chi-squared test or Fisher’s exact test was used, as appropriate, to compare categorical variables between the normal swallowing threshold and developmental failure of swallowing threshold (lowest 20%) groups. Binary logistic regression analysis with the forward selection (conditional) method was used to identify factors predicting developmental failure of swallowing threshold. Independent variables that were significant in the univariate analyses were included. Categorical variables were coded appropriately before being entered into the model. Adjusted odds ratios (ORs) and 95% confidence intervals (CIs) were calculated for the low swallowing threshold groups. A p-value < 0.05 was considered statistically significant. All data were analyzed using SPSS Statistics for Windows software (version 23.0; IBM Corp., Armonk, NY, USA).\",\n \"This study was conducted in accordance with the Declaration of Helsinki and was approved by the Human Investigation Committee of Kyushu Dental University (approval number: 18–37). All participants and their parents/guardians provided written informed consent for participation in the study. The participants were recruited following an initial examination at two private dental clinics in Japan. The inclusion criteria were as follows: aged 5–15 years, normal language comprehension, cooperative behavior, and no current dental diseases or complications. The exclusion criteria included systemic disturbances causing swallowing impairment, obvious facial asymmetry that could affect the measurements, soft tissue abnormalities, temporomandibular joint dysfunction, and dental or structural irregularities.\\nUsing software (PS: Power and Sample Size Calculation, available from the Vanderbilt University’s website), sample size was calculated. The study was based on a continuous response variable ranging from normal swallowing threshold to developmental failure of swallowing threshold; four normal participants were recruited for every participant with the developmental failure of swallowing threshold. In a previous study, the data for each group were normally distributed, with a standard deviation (SD) of swallowing threshold of 40 mg/dL [21]. Based on a true difference of 40 mg/dL between the developmental failure and normal groups, 14 developmental failure and 56 normal participants were required to reject the null hypothesis (i.e., the population means of the developmental failure and normal groups are equal) with a power of 0.9. The type I error probability for the null hypothesis was 0.05.\\nThe participants were divided into 11 groups based on their chronological age, and each group was further divided into two subgroups based on sex. Details of the participants are shown in Table 1. \",\n \"The survey solicited the following information: demographic characteristics (sex and age), eating habits, physical activity, and sleep [21].\",\n \"Height and body weight were measured in the consultation room of the clinic. Height was measured to an accuracy of ±0.1 cm using a portable digital stadiometer (AD-653; A&D, Tokyo, Japan), with the head in the Frankfort plane, whereas body weight was measured with an accuracy of 0.1 kg [22]. Rohrer and Kaup indices were calculated from the height and weight. \\nDuring intraoral examination, the number of erupted teeth and the decayed, missing, and filled teeth (DMFT) index were recorded for each patient [23].\",\n \"Hand grip strength is generally used as an index of muscle weakness in the diagnosis of sarcopenia [24]. Therefore, hand grip strength measurement was performed for evaluating systemic muscle strength in the participants. A portable grip strength meter (T-2288; Toei Light Co., Ltd., Saitama, Japan) was used to measure hand grip strength. Participants were asked to stand and hold the dynamometer in their hand with the arm parallel to the body, but without squeezing the arm against the body. Hand grip strength (kg) was measured twice for each hand (alternately) with a 30 s interval between trials. The highest value from either the left or right hand was recorded as the grip strength [20].\",\n \"Maximum occlusal force was measured using a portable occlusal force meter (GM10; Nagano Keiki Co., Ltd., Tokyo, Japan). The participants were instructed to bite down with maximal voluntary muscular effort using their first molars. Maximum occlusal force was measured on each side, with a 30 s interval between bite measurements. The larger of the values from the left and right sides was recorded as the maximum bite force [21].\",\n \"Masticatory performance was determined by measuring the concentration of dissolved glucose obtained from a cylindrical gummy jelly (GLUCOLUMN; GC Co., Ltd., Tokyo, Japan) using a glucose-measuring device (GLUCO SENSOR GS-II; GC Co., Ltd., Tokyo, Japan). Prior to the test, the participants were instructed regarding the chewing movements and mouth rinsing procedures to prevent swallowing. The participants were then instructed to chew the gummy jelly for 20 s freely. After chewing, the participants were asked to take 10 mL of distilled water into their mouth and spit out the gummy jelly and distilled water into a filter cup. The glucose concentration in the filtrate (mg/dL) was measured using a dedicated device [20].\",\n \"Following evaluation of the masticatory performance, an assessment of the swallowing threshold was performed using the test gummy jellies (GLUCOLUMN; GC Co. Ltd.). Variables related to swallowing (i.e., the number of chewing cycles, chewing time, chewing rate, and glucose concentration in the filtrate (mg/dL)) were assessed for all participants. Each participant was instructed to chew a gummy jelly freely until feeling the desire to swallow, at which time they were instructed to stop chewing and signal to the examiner that they were ready to expel the gummy jelly. The examiner visually quantified the number of chewing cycles. The time from the onset of chewing to the moment when the participants raised their hands was recorded using a stopwatch [20]. The subsequent steps were the same as those used for the evaluation of masticatory performance. We determined that the glucose concentration in the filtrate was an indicator of the swallowing threshold. Participants with low swallowing threshold tend to signal that they want to swallow the gummy jelly quickly, resulting in low glucose concentrations. A glucose concentration in the lowest 20th percentile was defined as developmental failure of swallowing threshold, based on previous studies reporting the criteria for sarcopenia [21,25]. \",\n \"All measurements were performed in duplicate, separated by 30 s rest periods, and the mean values were calculated for the subsequent analyses. All examinations were performed by the same examiner. Data generated during sample collection were assessed for reliability. Random error was evaluated based on intraobserver reliability, which was quantified using the intraclass correlation coefficient (ICC; where 0.8 ≤ ICC ≤ 1.0 corresponded to high reliability) [26].\",\n \"The Shapiro–Wilk test was used to determine data normality. All continuous data were expressed as means ± SD. The means were compared between two groups using a two-tailed t-test or the Mann–Whitney U test. The Kruskal–Wallis test was used for comparisons between more than two groups. Pearson’s correlation coefficient was used to determine associations among glucose concentration before swallowing and other variables. The chi-squared test or Fisher’s exact test was used, as appropriate, to compare categorical variables between the normal swallowing threshold and developmental failure of swallowing threshold (lowest 20%) groups. Binary logistic regression analysis with the forward selection (conditional) method was used to identify factors predicting developmental failure of swallowing threshold. Independent variables that were significant in the univariate analyses were included. Categorical variables were coded appropriately before being entered into the model. Adjusted odds ratios (ORs) and 95% confidence intervals (CIs) were calculated for the low swallowing threshold groups. A p-value < 0.05 was considered statistically significant. All data were analyzed using SPSS Statistics for Windows software (version 23.0; IBM Corp., Armonk, NY, USA).\",\n \"The ICCs for all measurement items ranged from 0.84 to 0.91, indicating a high degree of intraobserver reliability. \\nThe anthropometric parameters and dental examination results are shown in Table 1 according to age and sex. The 15-year-old males were significantly taller than females of the same age (p < 0.05). The DMFT index in 11-year-old females was significantly higher than in males of the same age (p < 0.05).\\nHand grip strength and oral functional parameters are shown in Table 2 according to age and sex. The hand grip strengths in 5-, 10-, 12-, 14-, and 15-year-old males were significantly greater compared to females of the same age (all p < 0.05). \\nWhen considering the results of comparing the mean scores of males and females for all measurement items by age, hand grip strength in 14–15-year-old participants was significantly greater compared to 5–7-year-old participants (all p < 0.05). Hand grip strength increased with age. The maximum occlusal force was greatest in 15-year-old participants and was significantly greater than in those aged 5–7 years (all p < 0.05). Masticatory performance was lowest in 5-year-old participants and was significantly lower than in those aged 12, 14, and 15 years (all p < 0.05). The number of chewing cycles was highest in 9-year-old participants and was significantly higher than in those aged 6 and 14 years (both p < 0.05). The change in chewing time with age was similar to the change in the number of chewing cycles. The swallowing threshold was highest in 15-year-old participants, followed by the 8-year-old participants; both were significantly higher than in those aged 6 years (p < 0.05).\\nThe results of Pearson’s bivariate correlation analyses are shown in Table 3. Hand grip strength was significantly and positively correlated with masticatory performance (r = 0.611, p < 0.01) and was strongly correlated with maximum occlusal force (r = 0.791, p < 0.01). Swallowing threshold was strongly correlated with masticatory performance (r = 0.727, p < 0.01) and was significantly and positively correlated with the number of erupted teeth, maximum occlusal force, and number of chewing cycles (r = 0.432, p < 0.01; r = 0.493, p < 0.01; r = 0.465, p < 0.01, respectively). The number of chewing cycles was strongly correlated with chewing time (r = 0.801, p < 0.01). The correlation between age and swallowing threshold was weak but significant (r = 0.362, p < 0.01; Figure 1).\\nTable 4 summarizes the data on developmental failure of swallowing threshold based on demographic and health-related variables. Participants with developmental failure of swallowing threshold were more likely to be overweight or obese, eat between meals at least once per day, and have low or no physical activity (p = 0.014, p = 0.021, and p = 0.006, respectively).\\nTable 5 summarizes the data on developmental failure of swallowing threshold based on age, height, body weight, dental status, hand grip strength, and masticatory function. Swallowing threshold, age, number of erupted teeth, maximum occlusal force, masticatory performance, number of chewing cycles, and chewing time in the develop-mental failure of swallowing threshold group were significantly lower compared to the normal swallowing threshold group (all p < 0.05). The mean DMFT index in the developmental failure swallowing threshold group was significantly higher compared to the normal swallowing threshold group (p < 0.05). The mean chewing time to swallow in the normal swallowing threshold group was 20.91 s. Masticatory performance was similar between the groups.\\nTable 6 shows the predictors of developmental failure of swallowing threshold, as revealed by logistic regression. Overweight or obese individuals had higher odds of developmental failure of swallowing threshold (OR = 5.343, p = 0.031, 95% CI = 1.168–24.437). Eating between meals once or more a day was also associated with higher odds of developmental failure of swallowing threshold (OR = 4.934, p = 0.049, 95% CI = 1.004–24.244).\",\n \"In this study, patterns of swallowing threshold in children according to age were clearly different from those of handgrip strength, maximum occlusal force, and masticatory performance. Maximum occlusal force and masticatory performance tended to increase with age, but the rates of increase between the ages of 12 and 15 years were not as high as those for hand grip strength. These results suggest that maximum occlusal force and masticatory performance do not grow rapidly during this period. \\nWhen considering the results of univariate analysis, hand grip strength was significantly and positively correlated with masticatory performance as well as maximum occlusal force. The European Working Group on Sarcopenia in Older People 2 (EWGSOP2) reported that low activity and malnutrition cause secondary sarcopenia not only in the elderly but also in children [24]. Sarcopenia may be associated with developmental failure of masticatory function in childhood. \\nWe found that age had a weak correlation with the swallowing threshold. At the age of 8 years, multiple participants had swallowing thresholds that reached or surpassed those of 15-year-old individuals (Figure 1). A recent study of individuals aged 20–79 years reported that the swallowing threshold values peaked at 40 s, which was close to the peak time for 15-year-old females in this study [21]. Therefore, the swallowing threshold is unlikely to be age-dependent, and we derived a “unified” threshold of developmental failure, i.e., a threshold applicable to all ages. The number of chewing cycles and chewing time tended to gradually decrease after peaking at the age of 9 and 8 years, respectively.\\nIn the logistic regression analysis, obesity was significantly associated with developmental failure of swallowing threshold. A previous study reported that children who did not chew well had a significantly higher likelihood of overweight compared to the reference group of children aged 5–6 years. To assess the degree of chewing, the parents in that study answered the following self-administered questionnaire item: “How well does your child chew foods while eating?” [18]. Another study objectively evaluating the swallowing threshold reported that a lower threshold was associated with a higher BMI among preschool children aged 3–5 years. They suggested that a higher BMI was associated with a lower number of chewing cycles and a shorter chewing time [17]. A study of young adults (in their twenties) also reported that BMI was negatively correlated with the total number of chews and duration of chewing but did not correlate significantly with the chewing rate [14]. Consistent with these findings, our results showed that the number of chewing cycles and chewing time in participants with developmental failure of swallowing threshold were significantly lower compared to those with a normal swallowing threshold. However, chewing rate was not significantly different between the two groups. These findings suggested that overweight/obesity is more prevalent among children who chew their food less, and over a shorter period of time, prior to swallowing, regardless of the chewing rate.\\nSeveral studies have reported that the causes of obesity are multi-faceted and include interactions among eating behaviors, obesogenic environments, genetics, and physical inactivity [5,7]. This may be indirectly associated with our finding that a significantly higher percentage of participants with developmental failure of swallowing threshold reported a lack of physical activity according to univariate analysis, although a causal relationship could not be determined.\\nIn the logistic regression analysis, eating between meals at least once per day was also significantly associated with developmental failure of swallowing threshold. Chewing food activates the hypothalamic histamine nervous system and suppresses appetite through the satiety center in animals [27,28,29]. Children with developmental failure of swallowing threshold are less likely to feel sated, so it is more likely that they will eat between meals at least once per day. Several studies reported that consumption of low-quality snacks was associated with increased prevalence of obesity among children [30,31,32]. Our results indicate that this may be explained in terms of developmental failure of swallowing threshold. A study of Japanese elementary school children reported that their snacking habits were influenced by paternal eating habits [33]. Another study suggested that parents could play an important role in the control of school-age children’s food intake and choices [6]. National surveys of food intake in the United States reported that daily energy intake in children aged 2 to 18 increased significantly from 1839 kcal/day to 2023 kcal/day between 1977–1978 and 2003–2006 [34]. In addition, another study reported that children aged 2 to 11 consume extra energy and sugars in their diets but insufficient Vitamin D, calcium, and potassium [35]. This may be applicable to all eating behaviors, i.e., not just snacking.\\nWe further considered strategies to improve developmental failure of swallowing threshold. First, the chewing rate until swallowing was not significantly different between participants with developmental failure and normal swallowing thresholds. Therefore, it may not be necessary to control the chewing rate. Second, prolonging the chewing time until swallowing may also not be helpful for preventing developmental failure of swallowing threshold; even after the participants with developmental failure of swallowing threshold chewed the gummy jelly for 20 s, masticatory performance was significantly lower (97.94 ± 24.97 mg/dL) than that of participants with normal swallowing thresholds (134.19 ± 40.75 mg/dL). The lower masticatory performance may be associated with the younger age, weaker occlusal forces, a higher DMFT index, and fewer erupted teeth in participants with developmental failure of swallowing threshold. If these problems are resolved, developmental failure of swallowing threshold may improve. However, as it is difficult to solve these problems immediately, increasing the number of chewing cycles may be the key to improving developmental failure of swallowing threshold. Considering that it is a critical period of development, the appropriate number of chewing cycles and chewing time until swallowing should be established by the age of 9 years.\\nOur study had some limitations. First, The National Health Examination Survey among 12- to 17-year-old US adolescents reported that BMI in the adolescents with a concave facial profile was higher than that in the adolescents with a straight facial profile [36]. Another study reported that a higher BMI was associated with a lower bite force in children aged 8–10 years [37]. These results suggest that facial profile type in children, especially Class III malocclusion, could be a factor associated with developmental failure of swallowing threshold. However, in this study, relationships between the type of malocclusion and the characteristics of the swallowing threshold were not able to be clarified, because participants’ malocclusions were not evaluated in detail. In the future, it is necessary to clarify the relationship between malocclusion and obesity and developmental failure of swallowing threshold in children. Second, the number of participants was small for performing logistic regression analysis. Among the patients who visited two dental clinics, the number of patients who met all of the conditions we set was very limited. Therefore, this study was performed with the minimum required number of participants calculated by the power analysis. It may be that a highly accurate model will be built in studies with higher numbers of participants. Third, it used a cross-sectional design, which precluded determination of causal relationships between developmental failure of swallowing threshold and the variables included in the logistic regression analyses. Longitudinal studies are needed to investigate the relative influence of factors associated with developmental failure of swallowing threshold. Additionally, the diet- and lifestyle-related questionnaires evaluated only children’s problems; future studies may benefit from evaluating the parents’ eating habits and dietary education.\",\n \"We found that the relationship between swallowing threshold and age was unclear, whereas the number of chewing cycles and chewing time tended to gradually decrease after reaching a peak at the age of 9 and 8 years, respectively. An appropriate number of chewing cycles and chewing time until swallowing should be established by 9 years of age. Developmental failure of swallowing threshold was closely associated with childhood obesity and eating between meals at least once per day among the 5- to 15-year-old individuals.\"\n]"},"all_sections_sec_types":{"kind":"list like","value":["intro",null,"subjects",null,null,null,null,null,null,null,null,"results","discussion","conclusions"],"string":"[\n \"intro\",\n null,\n \"subjects\",\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n \"results\",\n \"discussion\",\n \"conclusions\"\n]"},"keywords":{"kind":"list like","value":["eating behaviors","obesity","swallowing threshold","number of chewing cycle","children"],"string":"[\n \"eating behaviors\",\n \"obesity\",\n \"swallowing threshold\",\n \"number of chewing cycle\",\n \"children\"\n]"},"whole_article_text":{"kind":"string","value":"1. Introduction:\nEating habits acquired in early childhood can change over time based on personal experience and learning [1,2]. Eating habits acquired in childhood or adolescence may persist into adulthood [3]. Poor dietary habits established during childhood that persist into adulthood increase the risk of obesity and related adverse consequences [4,5,6].\nIn previous studies, early childhood obesity was associated with a higher risk of adult metabolic syndrome [7,8]. Eating behaviors such as frequent consumption of fast food and sugar-sweetened beverages are associated with the development of childhood obesity [9]. In addition, factors such as eating frequency, amount, and occasions may interact to cause obesity [5,7]. Other studies indicated that early modification of poor eating habits could improve health and decrease the future risk of metabolic syndrome, type 2 diabetes, and atherosclerotic cardiovascular diseases [10,11].\nA relationship between obesity and eating speed was reported recently. Eating speed was associated with the incidence of metabolic syndrome in adults [12,13], while eating quickly was associated with increased body mass index (BMI) in young adults [14]. Similar results have also been reported in children [15,16,17]. In addition, chewing well before swallowing might be an effective method for reducing food intake and facilitating healthy weight management in preschool children [18] and adults [19].\nThese findings suggest that appropriate eating behaviors should be acquired within the critical early time period. However, the number of chewing cycles, chewing time, and chewing rate have not been analyzed in the context of the swallowing threshold, and it remains unclear when these functions are acquired. We hypothesized that these functions would be acquired relatively early in childhood. \nAdditionally, based on previous studies, we hypothesized that developmental failure of swallowing threshold in children would be associated with eating fast, chewing frequently, and other habits [17,20]. This developmental failure could involve physiological problems including obesity. Identifying the factors associated with developmental failure of swallowing threshold may aid the development of strategies for improving it.\nThe primary purpose of this study was to clarify differences in swallowing thresholds and other oral functions according to age, as well as when functions related to the swallowing threshold are acquired. A secondary aim was to determine factors related to the developmental failure of swallowing threshold in children aged 5–15 years. In this study, swallowing threshold was determined based on the concentration of dissolved glucose obtained from gummy jellies when the participants signaled that they wanted to swallow the chewed gummy jellies.\n\n2. Materials and Methods:\n2.1. Participants This study was conducted in accordance with the Declaration of Helsinki and was approved by the Human Investigation Committee of Kyushu Dental University (approval number: 18–37). All participants and their parents/guardians provided written informed consent for participation in the study. The participants were recruited following an initial examination at two private dental clinics in Japan. The inclusion criteria were as follows: aged 5–15 years, normal language comprehension, cooperative behavior, and no current dental diseases or complications. The exclusion criteria included systemic disturbances causing swallowing impairment, obvious facial asymmetry that could affect the measurements, soft tissue abnormalities, temporomandibular joint dysfunction, and dental or structural irregularities.\nUsing software (PS: Power and Sample Size Calculation, available from the Vanderbilt University’s website), sample size was calculated. The study was based on a continuous response variable ranging from normal swallowing threshold to developmental failure of swallowing threshold; four normal participants were recruited for every participant with the developmental failure of swallowing threshold. In a previous study, the data for each group were normally distributed, with a standard deviation (SD) of swallowing threshold of 40 mg/dL [21]. Based on a true difference of 40 mg/dL between the developmental failure and normal groups, 14 developmental failure and 56 normal participants were required to reject the null hypothesis (i.e., the population means of the developmental failure and normal groups are equal) with a power of 0.9. The type I error probability for the null hypothesis was 0.05.\nThe participants were divided into 11 groups based on their chronological age, and each group was further divided into two subgroups based on sex. Details of the participants are shown in Table 1. \nThis study was conducted in accordance with the Declaration of Helsinki and was approved by the Human Investigation Committee of Kyushu Dental University (approval number: 18–37). All participants and their parents/guardians provided written informed consent for participation in the study. The participants were recruited following an initial examination at two private dental clinics in Japan. The inclusion criteria were as follows: aged 5–15 years, normal language comprehension, cooperative behavior, and no current dental diseases or complications. The exclusion criteria included systemic disturbances causing swallowing impairment, obvious facial asymmetry that could affect the measurements, soft tissue abnormalities, temporomandibular joint dysfunction, and dental or structural irregularities.\nUsing software (PS: Power and Sample Size Calculation, available from the Vanderbilt University’s website), sample size was calculated. The study was based on a continuous response variable ranging from normal swallowing threshold to developmental failure of swallowing threshold; four normal participants were recruited for every participant with the developmental failure of swallowing threshold. In a previous study, the data for each group were normally distributed, with a standard deviation (SD) of swallowing threshold of 40 mg/dL [21]. Based on a true difference of 40 mg/dL between the developmental failure and normal groups, 14 developmental failure and 56 normal participants were required to reject the null hypothesis (i.e., the population means of the developmental failure and normal groups are equal) with a power of 0.9. The type I error probability for the null hypothesis was 0.05.\nThe participants were divided into 11 groups based on their chronological age, and each group was further divided into two subgroups based on sex. Details of the participants are shown in Table 1. \n2.2. Questionnaire The survey solicited the following information: demographic characteristics (sex and age), eating habits, physical activity, and sleep [21].\nThe survey solicited the following information: demographic characteristics (sex and age), eating habits, physical activity, and sleep [21].\n2.3. Anthropometry and Dental Examination Height and body weight were measured in the consultation room of the clinic. Height was measured to an accuracy of ±0.1 cm using a portable digital stadiometer (AD-653; A&D, Tokyo, Japan), with the head in the Frankfort plane, whereas body weight was measured with an accuracy of 0.1 kg [22]. Rohrer and Kaup indices were calculated from the height and weight. \nDuring intraoral examination, the number of erupted teeth and the decayed, missing, and filled teeth (DMFT) index were recorded for each patient [23].\nHeight and body weight were measured in the consultation room of the clinic. Height was measured to an accuracy of ±0.1 cm using a portable digital stadiometer (AD-653; A&D, Tokyo, Japan), with the head in the Frankfort plane, whereas body weight was measured with an accuracy of 0.1 kg [22]. Rohrer and Kaup indices were calculated from the height and weight. \nDuring intraoral examination, the number of erupted teeth and the decayed, missing, and filled teeth (DMFT) index were recorded for each patient [23].\n2.4. Hand Grip Strength Hand grip strength is generally used as an index of muscle weakness in the diagnosis of sarcopenia [24]. Therefore, hand grip strength measurement was performed for evaluating systemic muscle strength in the participants. A portable grip strength meter (T-2288; Toei Light Co., Ltd., Saitama, Japan) was used to measure hand grip strength. Participants were asked to stand and hold the dynamometer in their hand with the arm parallel to the body, but without squeezing the arm against the body. Hand grip strength (kg) was measured twice for each hand (alternately) with a 30 s interval between trials. The highest value from either the left or right hand was recorded as the grip strength [20].\nHand grip strength is generally used as an index of muscle weakness in the diagnosis of sarcopenia [24]. Therefore, hand grip strength measurement was performed for evaluating systemic muscle strength in the participants. A portable grip strength meter (T-2288; Toei Light Co., Ltd., Saitama, Japan) was used to measure hand grip strength. Participants were asked to stand and hold the dynamometer in their hand with the arm parallel to the body, but without squeezing the arm against the body. Hand grip strength (kg) was measured twice for each hand (alternately) with a 30 s interval between trials. The highest value from either the left or right hand was recorded as the grip strength [20].\n2.5. Maximum Occlusal Force Maximum occlusal force was measured using a portable occlusal force meter (GM10; Nagano Keiki Co., Ltd., Tokyo, Japan). The participants were instructed to bite down with maximal voluntary muscular effort using their first molars. Maximum occlusal force was measured on each side, with a 30 s interval between bite measurements. The larger of the values from the left and right sides was recorded as the maximum bite force [21].\nMaximum occlusal force was measured using a portable occlusal force meter (GM10; Nagano Keiki Co., Ltd., Tokyo, Japan). The participants were instructed to bite down with maximal voluntary muscular effort using their first molars. Maximum occlusal force was measured on each side, with a 30 s interval between bite measurements. The larger of the values from the left and right sides was recorded as the maximum bite force [21].\n2.6. Masticatory Performance Masticatory performance was determined by measuring the concentration of dissolved glucose obtained from a cylindrical gummy jelly (GLUCOLUMN; GC Co., Ltd., Tokyo, Japan) using a glucose-measuring device (GLUCO SENSOR GS-II; GC Co., Ltd., Tokyo, Japan). Prior to the test, the participants were instructed regarding the chewing movements and mouth rinsing procedures to prevent swallowing. The participants were then instructed to chew the gummy jelly for 20 s freely. After chewing, the participants were asked to take 10 mL of distilled water into their mouth and spit out the gummy jelly and distilled water into a filter cup. The glucose concentration in the filtrate (mg/dL) was measured using a dedicated device [20].\nMasticatory performance was determined by measuring the concentration of dissolved glucose obtained from a cylindrical gummy jelly (GLUCOLUMN; GC Co., Ltd., Tokyo, Japan) using a glucose-measuring device (GLUCO SENSOR GS-II; GC Co., Ltd., Tokyo, Japan). Prior to the test, the participants were instructed regarding the chewing movements and mouth rinsing procedures to prevent swallowing. The participants were then instructed to chew the gummy jelly for 20 s freely. After chewing, the participants were asked to take 10 mL of distilled water into their mouth and spit out the gummy jelly and distilled water into a filter cup. The glucose concentration in the filtrate (mg/dL) was measured using a dedicated device [20].\n2.7. Swallowing Threshold Following evaluation of the masticatory performance, an assessment of the swallowing threshold was performed using the test gummy jellies (GLUCOLUMN; GC Co. Ltd.). Variables related to swallowing (i.e., the number of chewing cycles, chewing time, chewing rate, and glucose concentration in the filtrate (mg/dL)) were assessed for all participants. Each participant was instructed to chew a gummy jelly freely until feeling the desire to swallow, at which time they were instructed to stop chewing and signal to the examiner that they were ready to expel the gummy jelly. The examiner visually quantified the number of chewing cycles. The time from the onset of chewing to the moment when the participants raised their hands was recorded using a stopwatch [20]. The subsequent steps were the same as those used for the evaluation of masticatory performance. We determined that the glucose concentration in the filtrate was an indicator of the swallowing threshold. Participants with low swallowing threshold tend to signal that they want to swallow the gummy jelly quickly, resulting in low glucose concentrations. A glucose concentration in the lowest 20th percentile was defined as developmental failure of swallowing threshold, based on previous studies reporting the criteria for sarcopenia [21,25]. \nFollowing evaluation of the masticatory performance, an assessment of the swallowing threshold was performed using the test gummy jellies (GLUCOLUMN; GC Co. Ltd.). Variables related to swallowing (i.e., the number of chewing cycles, chewing time, chewing rate, and glucose concentration in the filtrate (mg/dL)) were assessed for all participants. Each participant was instructed to chew a gummy jelly freely until feeling the desire to swallow, at which time they were instructed to stop chewing and signal to the examiner that they were ready to expel the gummy jelly. The examiner visually quantified the number of chewing cycles. The time from the onset of chewing to the moment when the participants raised their hands was recorded using a stopwatch [20]. The subsequent steps were the same as those used for the evaluation of masticatory performance. We determined that the glucose concentration in the filtrate was an indicator of the swallowing threshold. Participants with low swallowing threshold tend to signal that they want to swallow the gummy jelly quickly, resulting in low glucose concentrations. A glucose concentration in the lowest 20th percentile was defined as developmental failure of swallowing threshold, based on previous studies reporting the criteria for sarcopenia [21,25]. \n2.8. Reliability of Measurements All measurements were performed in duplicate, separated by 30 s rest periods, and the mean values were calculated for the subsequent analyses. All examinations were performed by the same examiner. Data generated during sample collection were assessed for reliability. Random error was evaluated based on intraobserver reliability, which was quantified using the intraclass correlation coefficient (ICC; where 0.8 ≤ ICC ≤ 1.0 corresponded to high reliability) [26].\nAll measurements were performed in duplicate, separated by 30 s rest periods, and the mean values were calculated for the subsequent analyses. All examinations were performed by the same examiner. Data generated during sample collection were assessed for reliability. Random error was evaluated based on intraobserver reliability, which was quantified using the intraclass correlation coefficient (ICC; where 0.8 ≤ ICC ≤ 1.0 corresponded to high reliability) [26].\n2.9. Statistical Analysis The Shapiro–Wilk test was used to determine data normality. All continuous data were expressed as means ± SD. The means were compared between two groups using a two-tailed t-test or the Mann–Whitney U test. The Kruskal–Wallis test was used for comparisons between more than two groups. Pearson’s correlation coefficient was used to determine associations among glucose concentration before swallowing and other variables. The chi-squared test or Fisher’s exact test was used, as appropriate, to compare categorical variables between the normal swallowing threshold and developmental failure of swallowing threshold (lowest 20%) groups. Binary logistic regression analysis with the forward selection (conditional) method was used to identify factors predicting developmental failure of swallowing threshold. Independent variables that were significant in the univariate analyses were included. Categorical variables were coded appropriately before being entered into the model. Adjusted odds ratios (ORs) and 95% confidence intervals (CIs) were calculated for the low swallowing threshold groups. A p-value < 0.05 was considered statistically significant. All data were analyzed using SPSS Statistics for Windows software (version 23.0; IBM Corp., Armonk, NY, USA).\nThe Shapiro–Wilk test was used to determine data normality. All continuous data were expressed as means ± SD. The means were compared between two groups using a two-tailed t-test or the Mann–Whitney U test. The Kruskal–Wallis test was used for comparisons between more than two groups. Pearson’s correlation coefficient was used to determine associations among glucose concentration before swallowing and other variables. The chi-squared test or Fisher’s exact test was used, as appropriate, to compare categorical variables between the normal swallowing threshold and developmental failure of swallowing threshold (lowest 20%) groups. Binary logistic regression analysis with the forward selection (conditional) method was used to identify factors predicting developmental failure of swallowing threshold. Independent variables that were significant in the univariate analyses were included. Categorical variables were coded appropriately before being entered into the model. Adjusted odds ratios (ORs) and 95% confidence intervals (CIs) were calculated for the low swallowing threshold groups. A p-value < 0.05 was considered statistically significant. All data were analyzed using SPSS Statistics for Windows software (version 23.0; IBM Corp., Armonk, NY, USA).\n\n2.1. Participants:\nThis study was conducted in accordance with the Declaration of Helsinki and was approved by the Human Investigation Committee of Kyushu Dental University (approval number: 18–37). All participants and their parents/guardians provided written informed consent for participation in the study. The participants were recruited following an initial examination at two private dental clinics in Japan. The inclusion criteria were as follows: aged 5–15 years, normal language comprehension, cooperative behavior, and no current dental diseases or complications. The exclusion criteria included systemic disturbances causing swallowing impairment, obvious facial asymmetry that could affect the measurements, soft tissue abnormalities, temporomandibular joint dysfunction, and dental or structural irregularities.\nUsing software (PS: Power and Sample Size Calculation, available from the Vanderbilt University’s website), sample size was calculated. The study was based on a continuous response variable ranging from normal swallowing threshold to developmental failure of swallowing threshold; four normal participants were recruited for every participant with the developmental failure of swallowing threshold. In a previous study, the data for each group were normally distributed, with a standard deviation (SD) of swallowing threshold of 40 mg/dL [21]. Based on a true difference of 40 mg/dL between the developmental failure and normal groups, 14 developmental failure and 56 normal participants were required to reject the null hypothesis (i.e., the population means of the developmental failure and normal groups are equal) with a power of 0.9. The type I error probability for the null hypothesis was 0.05.\nThe participants were divided into 11 groups based on their chronological age, and each group was further divided into two subgroups based on sex. Details of the participants are shown in Table 1. \n\n2.2. Questionnaire:\nThe survey solicited the following information: demographic characteristics (sex and age), eating habits, physical activity, and sleep [21].\n\n2.3. Anthropometry and Dental Examination:\nHeight and body weight were measured in the consultation room of the clinic. Height was measured to an accuracy of ±0.1 cm using a portable digital stadiometer (AD-653; A&D, Tokyo, Japan), with the head in the Frankfort plane, whereas body weight was measured with an accuracy of 0.1 kg [22]. Rohrer and Kaup indices were calculated from the height and weight. \nDuring intraoral examination, the number of erupted teeth and the decayed, missing, and filled teeth (DMFT) index were recorded for each patient [23].\n\n2.4. Hand Grip Strength:\nHand grip strength is generally used as an index of muscle weakness in the diagnosis of sarcopenia [24]. Therefore, hand grip strength measurement was performed for evaluating systemic muscle strength in the participants. A portable grip strength meter (T-2288; Toei Light Co., Ltd., Saitama, Japan) was used to measure hand grip strength. Participants were asked to stand and hold the dynamometer in their hand with the arm parallel to the body, but without squeezing the arm against the body. Hand grip strength (kg) was measured twice for each hand (alternately) with a 30 s interval between trials. The highest value from either the left or right hand was recorded as the grip strength [20].\n\n2.5. Maximum Occlusal Force:\nMaximum occlusal force was measured using a portable occlusal force meter (GM10; Nagano Keiki Co., Ltd., Tokyo, Japan). The participants were instructed to bite down with maximal voluntary muscular effort using their first molars. Maximum occlusal force was measured on each side, with a 30 s interval between bite measurements. The larger of the values from the left and right sides was recorded as the maximum bite force [21].\n\n2.6. Masticatory Performance:\nMasticatory performance was determined by measuring the concentration of dissolved glucose obtained from a cylindrical gummy jelly (GLUCOLUMN; GC Co., Ltd., Tokyo, Japan) using a glucose-measuring device (GLUCO SENSOR GS-II; GC Co., Ltd., Tokyo, Japan). Prior to the test, the participants were instructed regarding the chewing movements and mouth rinsing procedures to prevent swallowing. The participants were then instructed to chew the gummy jelly for 20 s freely. After chewing, the participants were asked to take 10 mL of distilled water into their mouth and spit out the gummy jelly and distilled water into a filter cup. The glucose concentration in the filtrate (mg/dL) was measured using a dedicated device [20].\n\n2.7. Swallowing Threshold:\nFollowing evaluation of the masticatory performance, an assessment of the swallowing threshold was performed using the test gummy jellies (GLUCOLUMN; GC Co. Ltd.). Variables related to swallowing (i.e., the number of chewing cycles, chewing time, chewing rate, and glucose concentration in the filtrate (mg/dL)) were assessed for all participants. Each participant was instructed to chew a gummy jelly freely until feeling the desire to swallow, at which time they were instructed to stop chewing and signal to the examiner that they were ready to expel the gummy jelly. The examiner visually quantified the number of chewing cycles. The time from the onset of chewing to the moment when the participants raised their hands was recorded using a stopwatch [20]. The subsequent steps were the same as those used for the evaluation of masticatory performance. We determined that the glucose concentration in the filtrate was an indicator of the swallowing threshold. Participants with low swallowing threshold tend to signal that they want to swallow the gummy jelly quickly, resulting in low glucose concentrations. A glucose concentration in the lowest 20th percentile was defined as developmental failure of swallowing threshold, based on previous studies reporting the criteria for sarcopenia [21,25]. \n\n2.8. Reliability of Measurements:\nAll measurements were performed in duplicate, separated by 30 s rest periods, and the mean values were calculated for the subsequent analyses. All examinations were performed by the same examiner. Data generated during sample collection were assessed for reliability. Random error was evaluated based on intraobserver reliability, which was quantified using the intraclass correlation coefficient (ICC; where 0.8 ≤ ICC ≤ 1.0 corresponded to high reliability) [26].\n\n2.9. Statistical Analysis:\nThe Shapiro–Wilk test was used to determine data normality. All continuous data were expressed as means ± SD. The means were compared between two groups using a two-tailed t-test or the Mann–Whitney U test. The Kruskal–Wallis test was used for comparisons between more than two groups. Pearson’s correlation coefficient was used to determine associations among glucose concentration before swallowing and other variables. The chi-squared test or Fisher’s exact test was used, as appropriate, to compare categorical variables between the normal swallowing threshold and developmental failure of swallowing threshold (lowest 20%) groups. Binary logistic regression analysis with the forward selection (conditional) method was used to identify factors predicting developmental failure of swallowing threshold. Independent variables that were significant in the univariate analyses were included. Categorical variables were coded appropriately before being entered into the model. Adjusted odds ratios (ORs) and 95% confidence intervals (CIs) were calculated for the low swallowing threshold groups. A p-value < 0.05 was considered statistically significant. All data were analyzed using SPSS Statistics for Windows software (version 23.0; IBM Corp., Armonk, NY, USA).\n\n3. Results:\nThe ICCs for all measurement items ranged from 0.84 to 0.91, indicating a high degree of intraobserver reliability. \nThe anthropometric parameters and dental examination results are shown in Table 1 according to age and sex. The 15-year-old males were significantly taller than females of the same age (p < 0.05). The DMFT index in 11-year-old females was significantly higher than in males of the same age (p < 0.05).\nHand grip strength and oral functional parameters are shown in Table 2 according to age and sex. The hand grip strengths in 5-, 10-, 12-, 14-, and 15-year-old males were significantly greater compared to females of the same age (all p < 0.05). \nWhen considering the results of comparing the mean scores of males and females for all measurement items by age, hand grip strength in 14–15-year-old participants was significantly greater compared to 5–7-year-old participants (all p < 0.05). Hand grip strength increased with age. The maximum occlusal force was greatest in 15-year-old participants and was significantly greater than in those aged 5–7 years (all p < 0.05). Masticatory performance was lowest in 5-year-old participants and was significantly lower than in those aged 12, 14, and 15 years (all p < 0.05). The number of chewing cycles was highest in 9-year-old participants and was significantly higher than in those aged 6 and 14 years (both p < 0.05). The change in chewing time with age was similar to the change in the number of chewing cycles. The swallowing threshold was highest in 15-year-old participants, followed by the 8-year-old participants; both were significantly higher than in those aged 6 years (p < 0.05).\nThe results of Pearson’s bivariate correlation analyses are shown in Table 3. Hand grip strength was significantly and positively correlated with masticatory performance (r = 0.611, p < 0.01) and was strongly correlated with maximum occlusal force (r = 0.791, p < 0.01). Swallowing threshold was strongly correlated with masticatory performance (r = 0.727, p < 0.01) and was significantly and positively correlated with the number of erupted teeth, maximum occlusal force, and number of chewing cycles (r = 0.432, p < 0.01; r = 0.493, p < 0.01; r = 0.465, p < 0.01, respectively). The number of chewing cycles was strongly correlated with chewing time (r = 0.801, p < 0.01). The correlation between age and swallowing threshold was weak but significant (r = 0.362, p < 0.01; Figure 1).\nTable 4 summarizes the data on developmental failure of swallowing threshold based on demographic and health-related variables. Participants with developmental failure of swallowing threshold were more likely to be overweight or obese, eat between meals at least once per day, and have low or no physical activity (p = 0.014, p = 0.021, and p = 0.006, respectively).\nTable 5 summarizes the data on developmental failure of swallowing threshold based on age, height, body weight, dental status, hand grip strength, and masticatory function. Swallowing threshold, age, number of erupted teeth, maximum occlusal force, masticatory performance, number of chewing cycles, and chewing time in the develop-mental failure of swallowing threshold group were significantly lower compared to the normal swallowing threshold group (all p < 0.05). The mean DMFT index in the developmental failure swallowing threshold group was significantly higher compared to the normal swallowing threshold group (p < 0.05). The mean chewing time to swallow in the normal swallowing threshold group was 20.91 s. Masticatory performance was similar between the groups.\nTable 6 shows the predictors of developmental failure of swallowing threshold, as revealed by logistic regression. Overweight or obese individuals had higher odds of developmental failure of swallowing threshold (OR = 5.343, p = 0.031, 95% CI = 1.168–24.437). Eating between meals once or more a day was also associated with higher odds of developmental failure of swallowing threshold (OR = 4.934, p = 0.049, 95% CI = 1.004–24.244).\n\n4. Discussion:\nIn this study, patterns of swallowing threshold in children according to age were clearly different from those of handgrip strength, maximum occlusal force, and masticatory performance. Maximum occlusal force and masticatory performance tended to increase with age, but the rates of increase between the ages of 12 and 15 years were not as high as those for hand grip strength. These results suggest that maximum occlusal force and masticatory performance do not grow rapidly during this period. \nWhen considering the results of univariate analysis, hand grip strength was significantly and positively correlated with masticatory performance as well as maximum occlusal force. The European Working Group on Sarcopenia in Older People 2 (EWGSOP2) reported that low activity and malnutrition cause secondary sarcopenia not only in the elderly but also in children [24]. Sarcopenia may be associated with developmental failure of masticatory function in childhood. \nWe found that age had a weak correlation with the swallowing threshold. At the age of 8 years, multiple participants had swallowing thresholds that reached or surpassed those of 15-year-old individuals (Figure 1). A recent study of individuals aged 20–79 years reported that the swallowing threshold values peaked at 40 s, which was close to the peak time for 15-year-old females in this study [21]. Therefore, the swallowing threshold is unlikely to be age-dependent, and we derived a “unified” threshold of developmental failure, i.e., a threshold applicable to all ages. The number of chewing cycles and chewing time tended to gradually decrease after peaking at the age of 9 and 8 years, respectively.\nIn the logistic regression analysis, obesity was significantly associated with developmental failure of swallowing threshold. A previous study reported that children who did not chew well had a significantly higher likelihood of overweight compared to the reference group of children aged 5–6 years. To assess the degree of chewing, the parents in that study answered the following self-administered questionnaire item: “How well does your child chew foods while eating?” [18]. Another study objectively evaluating the swallowing threshold reported that a lower threshold was associated with a higher BMI among preschool children aged 3–5 years. They suggested that a higher BMI was associated with a lower number of chewing cycles and a shorter chewing time [17]. A study of young adults (in their twenties) also reported that BMI was negatively correlated with the total number of chews and duration of chewing but did not correlate significantly with the chewing rate [14]. Consistent with these findings, our results showed that the number of chewing cycles and chewing time in participants with developmental failure of swallowing threshold were significantly lower compared to those with a normal swallowing threshold. However, chewing rate was not significantly different between the two groups. These findings suggested that overweight/obesity is more prevalent among children who chew their food less, and over a shorter period of time, prior to swallowing, regardless of the chewing rate.\nSeveral studies have reported that the causes of obesity are multi-faceted and include interactions among eating behaviors, obesogenic environments, genetics, and physical inactivity [5,7]. This may be indirectly associated with our finding that a significantly higher percentage of participants with developmental failure of swallowing threshold reported a lack of physical activity according to univariate analysis, although a causal relationship could not be determined.\nIn the logistic regression analysis, eating between meals at least once per day was also significantly associated with developmental failure of swallowing threshold. Chewing food activates the hypothalamic histamine nervous system and suppresses appetite through the satiety center in animals [27,28,29]. Children with developmental failure of swallowing threshold are less likely to feel sated, so it is more likely that they will eat between meals at least once per day. Several studies reported that consumption of low-quality snacks was associated with increased prevalence of obesity among children [30,31,32]. Our results indicate that this may be explained in terms of developmental failure of swallowing threshold. A study of Japanese elementary school children reported that their snacking habits were influenced by paternal eating habits [33]. Another study suggested that parents could play an important role in the control of school-age children’s food intake and choices [6]. National surveys of food intake in the United States reported that daily energy intake in children aged 2 to 18 increased significantly from 1839 kcal/day to 2023 kcal/day between 1977–1978 and 2003–2006 [34]. In addition, another study reported that children aged 2 to 11 consume extra energy and sugars in their diets but insufficient Vitamin D, calcium, and potassium [35]. This may be applicable to all eating behaviors, i.e., not just snacking.\nWe further considered strategies to improve developmental failure of swallowing threshold. First, the chewing rate until swallowing was not significantly different between participants with developmental failure and normal swallowing thresholds. Therefore, it may not be necessary to control the chewing rate. Second, prolonging the chewing time until swallowing may also not be helpful for preventing developmental failure of swallowing threshold; even after the participants with developmental failure of swallowing threshold chewed the gummy jelly for 20 s, masticatory performance was significantly lower (97.94 ± 24.97 mg/dL) than that of participants with normal swallowing thresholds (134.19 ± 40.75 mg/dL). The lower masticatory performance may be associated with the younger age, weaker occlusal forces, a higher DMFT index, and fewer erupted teeth in participants with developmental failure of swallowing threshold. If these problems are resolved, developmental failure of swallowing threshold may improve. However, as it is difficult to solve these problems immediately, increasing the number of chewing cycles may be the key to improving developmental failure of swallowing threshold. Considering that it is a critical period of development, the appropriate number of chewing cycles and chewing time until swallowing should be established by the age of 9 years.\nOur study had some limitations. First, The National Health Examination Survey among 12- to 17-year-old US adolescents reported that BMI in the adolescents with a concave facial profile was higher than that in the adolescents with a straight facial profile [36]. Another study reported that a higher BMI was associated with a lower bite force in children aged 8–10 years [37]. These results suggest that facial profile type in children, especially Class III malocclusion, could be a factor associated with developmental failure of swallowing threshold. However, in this study, relationships between the type of malocclusion and the characteristics of the swallowing threshold were not able to be clarified, because participants’ malocclusions were not evaluated in detail. In the future, it is necessary to clarify the relationship between malocclusion and obesity and developmental failure of swallowing threshold in children. Second, the number of participants was small for performing logistic regression analysis. Among the patients who visited two dental clinics, the number of patients who met all of the conditions we set was very limited. Therefore, this study was performed with the minimum required number of participants calculated by the power analysis. It may be that a highly accurate model will be built in studies with higher numbers of participants. Third, it used a cross-sectional design, which precluded determination of causal relationships between developmental failure of swallowing threshold and the variables included in the logistic regression analyses. Longitudinal studies are needed to investigate the relative influence of factors associated with developmental failure of swallowing threshold. Additionally, the diet- and lifestyle-related questionnaires evaluated only children’s problems; future studies may benefit from evaluating the parents’ eating habits and dietary education.\n\n5. Conclusions:\nWe found that the relationship between swallowing threshold and age was unclear, whereas the number of chewing cycles and chewing time tended to gradually decrease after reaching a peak at the age of 9 and 8 years, respectively. An appropriate number of chewing cycles and chewing time until swallowing should be established by 9 years of age. Developmental failure of swallowing threshold was closely associated with childhood obesity and eating between meals at least once per day among the 5- to 15-year-old individuals.\n\n"},"whole_article_abstract":{"kind":"string","value":"Background:\nThe aim of the present study was to identify factors related to developmental failure of swallowing threshold in children aged 5-15 years.\n\nMethods:\nA total of 83 children aged 5-15 years were included in this study. A self-administered lifestyle questionnaire was completed, along with hand grip strength and oral function tests. Swallowing threshold was determined based on the concentration of dissolved glucose obtained from gummy jellies when the participants signaled that they wanted to swallow the chewed gummy jellies. Developmental failure of swallowing threshold was defined as glucose concentrations in the lowest 20th percentile. After univariate analysis, multivariate binary logistic regression analysis was used to identify factors associated with developmental failure of swallowing threshold.\n\nResults:\nHand grip strength was significantly correlated with masticatory performance (r = 0.611, p < 0.01). Logistic regression analysis revealed factors related to developmental failure of swallowing threshold, i.e., overweight/obesity (Odds ratio) (OR) = 5.343, p = 0.031, 95% CI = 1.168-24.437) and eating between meals at least once a day (OR = 4.934, p = 0.049, 95% CI = 1.004-24.244).\n\nConclusions:\nDevelopmental failure of swallowing threshold was closely associated with childhood obesity in 5- to 15-year-old children."},"background_conclusion_text":{"kind":"string","value":"1. Introduction:\nEating habits acquired in early childhood can change over time based on personal experience and learning [1,2]. Eating habits acquired in childhood or adolescence may persist into adulthood [3]. Poor dietary habits established during childhood that persist into adulthood increase the risk of obesity and related adverse consequences [4,5,6].\nIn previous studies, early childhood obesity was associated with a higher risk of adult metabolic syndrome [7,8]. Eating behaviors such as frequent consumption of fast food and sugar-sweetened beverages are associated with the development of childhood obesity [9]. In addition, factors such as eating frequency, amount, and occasions may interact to cause obesity [5,7]. Other studies indicated that early modification of poor eating habits could improve health and decrease the future risk of metabolic syndrome, type 2 diabetes, and atherosclerotic cardiovascular diseases [10,11].\nA relationship between obesity and eating speed was reported recently. Eating speed was associated with the incidence of metabolic syndrome in adults [12,13], while eating quickly was associated with increased body mass index (BMI) in young adults [14]. Similar results have also been reported in children [15,16,17]. In addition, chewing well before swallowing might be an effective method for reducing food intake and facilitating healthy weight management in preschool children [18] and adults [19].\nThese findings suggest that appropriate eating behaviors should be acquired within the critical early time period. However, the number of chewing cycles, chewing time, and chewing rate have not been analyzed in the context of the swallowing threshold, and it remains unclear when these functions are acquired. We hypothesized that these functions would be acquired relatively early in childhood. \nAdditionally, based on previous studies, we hypothesized that developmental failure of swallowing threshold in children would be associated with eating fast, chewing frequently, and other habits [17,20]. This developmental failure could involve physiological problems including obesity. Identifying the factors associated with developmental failure of swallowing threshold may aid the development of strategies for improving it.\nThe primary purpose of this study was to clarify differences in swallowing thresholds and other oral functions according to age, as well as when functions related to the swallowing threshold are acquired. A secondary aim was to determine factors related to the developmental failure of swallowing threshold in children aged 5–15 years. In this study, swallowing threshold was determined based on the concentration of dissolved glucose obtained from gummy jellies when the participants signaled that they wanted to swallow the chewed gummy jellies.\n\n5. Conclusions:\nWe found that the relationship between swallowing threshold and age was unclear, whereas the number of chewing cycles and chewing time tended to gradually decrease after reaching a peak at the age of 9 and 8 years, respectively. An appropriate number of chewing cycles and chewing time until swallowing should be established by 9 years of age. Developmental failure of swallowing threshold was closely associated with childhood obesity and eating between meals at least once per day among the 5- to 15-year-old individuals."},"background_conclusion_abstract":{"kind":"string","value":"Background:\nThe aim of the present study was to identify factors related to developmental failure of swallowing threshold in children aged 5-15 years.\n\nMethods:\nA total of 83 children aged 5-15 years were included in this study. A self-administered lifestyle questionnaire was completed, along with hand grip strength and oral function tests. Swallowing threshold was determined based on the concentration of dissolved glucose obtained from gummy jellies when the participants signaled that they wanted to swallow the chewed gummy jellies. Developmental failure of swallowing threshold was defined as glucose concentrations in the lowest 20th percentile. After univariate analysis, multivariate binary logistic regression analysis was used to identify factors associated with developmental failure of swallowing threshold.\n\nResults:\nHand grip strength was significantly correlated with masticatory performance (r = 0.611, p < 0.01). Logistic regression analysis revealed factors related to developmental failure of swallowing threshold, i.e., overweight/obesity (Odds ratio) (OR) = 5.343, p = 0.031, 95% CI = 1.168-24.437) and eating between meals at least once a day (OR = 4.934, p = 0.049, 95% CI = 1.004-24.244).\n\nConclusions:\nDevelopmental failure of swallowing threshold was closely associated with childhood obesity in 5- to 15-year-old children."},"whole_article_text_length":{"kind":"number","value":6981,"string":"6,981"},"whole_article_abstract_length":{"kind":"number","value":251,"string":"251"},"other_sections_lengths":{"kind":"list like","value":[2736,27,105,136,82,140,228,79,222],"string":"[\n 2736,\n 27,\n 105,\n 136,\n 82,\n 140,\n 228,\n 79,\n 222\n]"},"num_sections":{"kind":"number","value":14,"string":"14"},"most_frequent_words":{"kind":"list like","value":["swallowing","threshold","swallowing threshold","participants","chewing","failure","developmental failure","developmental","failure swallowing threshold","failure swallowing"],"string":"[\n \"swallowing\",\n \"threshold\",\n \"swallowing threshold\",\n \"participants\",\n \"chewing\",\n \"failure\",\n \"developmental failure\",\n \"developmental\",\n \"failure swallowing threshold\",\n \"failure swallowing\"\n]"},"keybert_topics":{"kind":"list like","value":["age eating habits","development childhood obesity","eating habits dietary","obesity eating speed","associated childhood obesity"],"string":"[\n \"age eating habits\",\n \"development childhood obesity\",\n \"eating habits dietary\",\n \"obesity eating speed\",\n \"associated childhood obesity\"\n]"},"annotated_base_background_abstract_prompt":{"kind":"string","value":""},"annotated_base_methods_abstract_prompt":{"kind":"null"},"annotated_base_results_abstract_prompt":{"kind":"string","value":""},"annotated_base_conclusions_abstract_prompt":{"kind":"string","value":""},"annotated_base_whole_article_abstract_prompt":{"kind":"string","value":""},"annotated_base_background_conclusion_abstract_prompt":{"kind":"string","value":""},"annotated_keywords_background_abstract_prompt":{"kind":"string","value":"[CONTENT] eating behaviors | obesity | swallowing threshold | number of chewing cycle | children [SUMMARY]"},"annotated_keywords_methods_abstract_prompt":{"kind":"null"},"annotated_keywords_results_abstract_prompt":{"kind":"string","value":"[CONTENT] eating behaviors | obesity | swallowing threshold | number of chewing cycle | children [SUMMARY]"},"annotated_keywords_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] eating behaviors | obesity | swallowing threshold | number of chewing cycle | children [SUMMARY]"},"annotated_keywords_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] eating behaviors | obesity | swallowing threshold | number of chewing cycle | children [SUMMARY]"},"annotated_keywords_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] eating behaviors | obesity | swallowing threshold | number of chewing cycle | children [SUMMARY]"},"annotated_mesh_background_abstract_prompt":{"kind":"string","value":"[CONTENT] Adolescent | Child | Child, Preschool | Deglutition | Feeding Behavior | Glucose | Hand Strength | Humans | Mastication | Pediatric Obesity [SUMMARY]"},"annotated_mesh_methods_abstract_prompt":{"kind":"null"},"annotated_mesh_results_abstract_prompt":{"kind":"string","value":"[CONTENT] Adolescent | Child | Child, Preschool | Deglutition | Feeding Behavior | Glucose | Hand Strength | Humans | Mastication | Pediatric Obesity [SUMMARY]"},"annotated_mesh_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] Adolescent | Child | Child, Preschool | Deglutition | Feeding Behavior | Glucose | Hand Strength | Humans | Mastication | Pediatric Obesity [SUMMARY]"},"annotated_mesh_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] Adolescent | Child | Child, Preschool | Deglutition | Feeding Behavior | Glucose | Hand Strength | Humans | Mastication | Pediatric Obesity [SUMMARY]"},"annotated_mesh_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] Adolescent | Child | Child, Preschool | Deglutition | Feeding Behavior | Glucose | Hand Strength | Humans | Mastication | Pediatric Obesity [SUMMARY]"},"annotated_keybert_background_abstract_prompt":{"kind":"string","value":"[CONTENT] age eating habits | development childhood obesity | eating habits dietary | obesity eating speed | associated childhood obesity [SUMMARY]"},"annotated_keybert_methods_abstract_prompt":{"kind":"null"},"annotated_keybert_results_abstract_prompt":{"kind":"string","value":"[CONTENT] age eating habits | development childhood obesity | eating habits dietary | obesity eating speed | associated childhood obesity [SUMMARY]"},"annotated_keybert_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] age eating habits | development childhood obesity | eating habits dietary | obesity eating speed | associated childhood obesity [SUMMARY]"},"annotated_keybert_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] age eating habits | development childhood obesity | eating habits dietary | obesity eating speed | associated childhood obesity [SUMMARY]"},"annotated_keybert_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] age eating habits | development childhood obesity | eating habits dietary | obesity eating speed | associated childhood obesity [SUMMARY]"},"annotated_most_frequent_background_abstract_prompt":{"kind":"string","value":"[CONTENT] swallowing | threshold | swallowing threshold | participants | chewing | failure | developmental failure | developmental | failure swallowing threshold | failure swallowing [SUMMARY]"},"annotated_most_frequent_methods_abstract_prompt":{"kind":"null"},"annotated_most_frequent_results_abstract_prompt":{"kind":"string","value":"[CONTENT] swallowing | threshold | swallowing threshold | participants | chewing | failure | developmental failure | developmental | failure swallowing threshold | failure swallowing [SUMMARY]"},"annotated_most_frequent_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] swallowing | threshold | swallowing threshold | participants | chewing | failure | developmental failure | developmental | failure swallowing threshold | failure swallowing [SUMMARY]"},"annotated_most_frequent_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] swallowing | threshold | swallowing threshold | participants | chewing | failure | developmental failure | developmental | failure swallowing threshold | failure swallowing [SUMMARY]"},"annotated_most_frequent_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] swallowing | threshold | swallowing threshold | participants | chewing | failure | developmental failure | developmental | failure swallowing threshold | failure swallowing [SUMMARY]"},"annotated_tf_idf_background_abstract_prompt":{"kind":"string","value":"[CONTENT] acquired | eating | early | obesity | childhood | associated | functions | swallowing | habits | children [SUMMARY]"},"annotated_tf_idf_methods_abstract_prompt":{"kind":"null"},"annotated_tf_idf_results_abstract_prompt":{"kind":"string","value":"[CONTENT] significantly | 01 | old | year old | year | swallowing threshold | threshold | year old participants | old participants | swallowing [SUMMARY]"},"annotated_tf_idf_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] chewing | age | swallowing | cycles chewing time | chewing time | cycles chewing | time | chewing cycles | chewing cycles chewing | number chewing [SUMMARY]"},"annotated_tf_idf_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] swallowing | threshold | swallowing threshold | chewing | participants | failure | developmental | developmental failure | hand | strength [SUMMARY]"},"annotated_tf_idf_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] swallowing | threshold | swallowing threshold | chewing | participants | failure | developmental | developmental failure | hand | strength [SUMMARY]"},"annotated_entity_plan_background_abstract_prompt":{"kind":"string","value":"[CONTENT] 5-15 years [SUMMARY]"},"annotated_entity_plan_methods_abstract_prompt":{"kind":"null"},"annotated_entity_plan_results_abstract_prompt":{"kind":"string","value":"[CONTENT] 0.611 | p < | 0.01 ||| ||| 5.343 | 0.031 | 95% | CI | 1.168-24.437 | 4.934 | 0.049 | 95% | CI | 1.004-24.244 [SUMMARY]"},"annotated_entity_plan_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] 15-year-old [SUMMARY]"},"annotated_entity_plan_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] 5-15 years ||| 83 | 5-15 years ||| ||| ||| 20th ||| ||| ||| 0.611 | p < | 0.01 ||| ||| 5.343 | 0.031 | 95% | CI | 1.168-24.437 | 4.934 | 0.049 | 95% | CI | 1.004-24.244 ||| 15-year-old [SUMMARY]"},"annotated_entity_plan_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] 5-15 years ||| 83 | 5-15 years ||| ||| ||| 20th ||| ||| ||| 0.611 | p < | 0.01 ||| ||| 5.343 | 0.031 | 95% | CI | 1.168-24.437 | 4.934 | 0.049 | 95% | CI | 1.004-24.244 ||| 15-year-old [SUMMARY]"}}},{"rowIdx":276,"cells":{"title":{"kind":"string","value":"Risk and consequences of chemotherapy-induced neutropenic complications in patients receiving daily filgrastim: the importance of duration of prophylaxis."},"pmid":{"kind":"string","value":"24767095"},"background_abstract":{"kind":"string","value":"To examine duration of daily filgrastim prophylaxis, and risk and consequences of chemotherapy-induced neutropenic complications (CINC) requiring inpatient care."},"background_abstract_label":{"kind":"string","value":"BACKGROUND"},"methods_abstract":{"kind":"string","value":"Using a retrospective cohort design and US healthcare claims data (2001-2010), we identified all cancer patients who initiated ≥1 course of myelosuppressive chemotherapy and received daily filgrastim prophylactically in ≥1 cycle. Cycles with daily filgrastim prophylaxis were pooled for analyses. CINC was identified based on hospital admissions with a diagnosis of neutropenia, fever, or infection; consequences were characterized in terms of hospital mortality, hospital length of stay (LOS), and CINC-related healthcare expenditures."},"methods_abstract_label":{"kind":"string","value":"METHODS"},"results_abstract":{"kind":"string","value":"Risk of CINC requiring inpatient care-adjusted for patient characteristics-was 2.4 (95% CI: 1.6-3.4) and 1.9 (1.3-2.8) times higher with 1-3 (N = 8371) and 4-6 (N = 3691) days of filgrastim prophylaxis, respectively, versus ≥7 days (N = 2226). Among subjects who developed CINC, consequences with 1-3 and 4-6 (vs. ≥7) days of filgrastim prophylaxis were: mortality (8.4% [n/N = 10/119] and 4.0% [3/75] vs. 0% [0/34]); LOS (means: 7.4 [N = 243] and 7.1 [N = 99] vs. 6.5 [N = 40]); and expenditures (means: $18,912 [N = 225] and $14,907 [N = 94] vs. $13,165 [N = 39])."},"results_abstract_label":{"kind":"string","value":"RESULTS"},"conclusions_abstract":{"kind":"string","value":"In this retrospective evaluation, shorter courses of daily filgrastim prophylaxis were found to be associated with an increased risk of CINC as well as poorer outcomes among those developing this condition. Because of the limitations inherent in healthcare claims databases specifically and retrospective evaluations generally, additional research addressing these limitations is needed to confirm the findings of this study."},"conclusions_abstract_label":{"kind":"string","value":"CONCLUSIONS"},"mesh_descriptor_names":{"kind":"list like","value":["Aged","Febrile Neutropenia","Female","Filgrastim","Granulocyte Colony-Stimulating Factor","Hospitalization","Humans","Insurance Claim Review","Male","Middle Aged","Neoplasms","Neutropenia","Post-Exposure Prophylaxis","Recombinant Proteins","Retrospective Studies","Risk Assessment","United States"],"string":"[\n \"Aged\",\n \"Febrile Neutropenia\",\n \"Female\",\n \"Filgrastim\",\n \"Granulocyte Colony-Stimulating Factor\",\n \"Hospitalization\",\n \"Humans\",\n \"Insurance Claim Review\",\n \"Male\",\n \"Middle Aged\",\n \"Neoplasms\",\n \"Neutropenia\",\n \"Post-Exposure Prophylaxis\",\n \"Recombinant Proteins\",\n \"Retrospective Studies\",\n \"Risk Assessment\",\n \"United States\"\n]"},"pmcid":{"kind":"string","value":"4018988"},"background_title":{"kind":"string","value":"Background"},"background_text":{"kind":"string","value":"Neutropenia is a common side effect of myelosuppressive chemotherapy that both increases the risk of infection and diminishes patients’ ability to fight infection. When neutropenic patients become febrile, the high likelihood of infection and serious consequences thereof usually results in hospitalization\n[1,2]. FN, as well as severe or prolonged neutropenia, also can interfere with the planned delivery of treatment and adversely affect important patient outcomes\n[2-11].\nClinical practice guidelines recommend primary prophylactic use of a colony-stimulating factor (CSF)–which has been shown to reduce the risk of FN in clinical trials–when the risk of FN is 20% or higher\n[2]. While the American Society of Clinical Oncology (ASCO) initially recommended that CSF prophylaxis be administered only when FN risk is 40% or higher, in 2006, ASCO lowered the threshold to 20% based on data highlighting the importance of FN-related hospitalization as an outcome and evidence demonstrating the value of CSF prophylaxis in reducing the risk of FN, the risk of FN-related hospitalization, and the associated use of IV anti-infective agents\n[2,12,13]. The CSF filgrastim, which is widely used in clinical practice as prophylaxis against FN, requires daily administration during each cycle until neutrophil recovery occurs (in clinical trials, given typically for 10–11 days [and up to 14 days] until absolute neutrophil count [ANC] ≥10 × 109/L)\n[14-19].\nIn clinical practice, patients often receive shorter courses of daily filgrastim prophylaxis than those administered to subjects in the clinical trial setting\n[14-16,20-24]. While published studies suggest that shorter courses of daily filgrastim prophylaxis are associated with an increased risk of hospitalization for CINC, these studies focused on selected tumor types or employed data that now are over a decade old\n[23,24]. During the past decade, use of chemotherapy and supportive care in clinical practice–as well as recommended use of these agents in authoritative guidelines–has changed considerably\n[2,25-27], Moreover, only one study has examined whether CSFs may favorably impact clinical outcomes and economic costs when CINC develop despite CSF prophylaxis, and it was published a decade ago and focused on elderly patients with a single type of cancer (non-Hodgkin’s lymphoma)\n[28]. We therefore undertook a new study to evaluate the relationship between the duration of daily filgrastim prophylaxis, and the risk and consequences of CINC requiring inpatient care using a large healthcare claims database. While such databases often lack detailed clinical information (e.g., on absolute neutrophil counts), they offer access to information on the health profile and healthcare utilization of tens of millions of covered lives."},"methods_title":{"kind":"string","value":"Methods"},"methods_text":{"kind":"string","value":" Data source Patient-level information from two large healthcare claims databases–the Thomson Reuters MarketScan Commercial Claims and Encounters and Medicare Supplemental and Coordination of Benefits Database (MarketScan Database, 2001–2010) and the Intercontinental Marketing Services LifeLink Database (LifeLink Database, 2001–2008)–were pooled for analyses. Both databases comprise medical (i.e., facility and professional service) and outpatient pharmacy claims from a large number of participating private health plans, and each contains claims data for 15 million persons annually.\nData available for each facility and professional-service claim include date and place of service, diagnoses, procedures performed/services rendered, and quantity of services (professional-service claims). Data available for each retail pharmacy claim include the drug dispensed, dispensing date, quantity dispensed, and number of days supplied. All claims also include paid (i.e., reimbursed) amounts. Selected demographic and eligibility information is available for persons in both databases. All data can be arrayed to provide a detailed chronology of all medical and pharmacy services used by each plan member over time.\nThe study databases were de-identified prior to their release to study investigators, as set forth in the corresponding Data Use Agreements. The study databases have been evaluated and certified by independent third parties to be in compliance with the Health Insurance Portability and Accountability Act (HIPAA) of 1996 statistical de-identification standards and to satisfy the conditions set forth in Sections 164.514 (a)-(b) 1ii of the HIPAA Privacy Rule regarding the determination and documentation of statistically de-identified data. Use of the study databases for health services research was determined–via independent third parties–to be fully compliant with the HIPAA Privacy Rule and federal guidance on Public Welfare and the Protection of Human Subjects\n[29].\nPatient-level information from two large healthcare claims databases–the Thomson Reuters MarketScan Commercial Claims and Encounters and Medicare Supplemental and Coordination of Benefits Database (MarketScan Database, 2001–2010) and the Intercontinental Marketing Services LifeLink Database (LifeLink Database, 2001–2008)–were pooled for analyses. Both databases comprise medical (i.e., facility and professional service) and outpatient pharmacy claims from a large number of participating private health plans, and each contains claims data for 15 million persons annually.\nData available for each facility and professional-service claim include date and place of service, diagnoses, procedures performed/services rendered, and quantity of services (professional-service claims). Data available for each retail pharmacy claim include the drug dispensed, dispensing date, quantity dispensed, and number of days supplied. All claims also include paid (i.e., reimbursed) amounts. Selected demographic and eligibility information is available for persons in both databases. All data can be arrayed to provide a detailed chronology of all medical and pharmacy services used by each plan member over time.\nThe study databases were de-identified prior to their release to study investigators, as set forth in the corresponding Data Use Agreements. The study databases have been evaluated and certified by independent third parties to be in compliance with the Health Insurance Portability and Accountability Act (HIPAA) of 1996 statistical de-identification standards and to satisfy the conditions set forth in Sections 164.514 (a)-(b) 1ii of the HIPAA Privacy Rule regarding the determination and documentation of statistically de-identified data. Use of the study databases for health services research was determined–via independent third parties–to be fully compliant with the HIPAA Privacy Rule and federal guidance on Public Welfare and the Protection of Human Subjects\n[29].\n Study population The study population comprised all patients who initiated ≥1 course of myelosuppressive chemotherapy for a solid tumor or non-Hodgkin’s lymphoma (NHL) and who received daily filgrastim prophylaxis during ≥1 cycle. All cycles in which patients received daily filgrastim prophylaxis–irrespective of duration–were pooled for analyses.\n Cancer chemotherapy patients All patients, aged ≥18 years, who began ≥1 new course of myelosuppressive chemotherapy were identified; the enrollment window was July 1, 2001 through June 30, 2010 for patients in the MarketScan Database and July 1, 2001 through June 30, 2008 for patients in the LifeLink Database. Receipt of chemotherapy was ascertained based on the presence of ≥1 paid medical claim for a chemotherapy drug or administration thereof (identified using Healthcare Common Procedure Coding System [HCPCS], International Classification of Disease, Ninth Revision, Clinical Modification [ICD-9-CM], and Health Care Financing Administration Uniform Bill-92 [UB-92] revenue codes).\nPatients were considered to have initiated a new course of chemotherapy if there was a chemotherapy claim during the study period that was preceded by a period ≥60 days without any other claims for chemotherapy. Only patients who had evidence of a primary solid tumor or NHL (based on ≥2 medical claims [≥7 days apart] with a qualifying 3-digit ICD-9-CM diagnosis code during the period beginning 30 days prior to the index date and ending 30 days thereafter) were selected.\nAll patients, aged ≥18 years, who began ≥1 new course of myelosuppressive chemotherapy were identified; the enrollment window was July 1, 2001 through June 30, 2010 for patients in the MarketScan Database and July 1, 2001 through June 30, 2008 for patients in the LifeLink Database. Receipt of chemotherapy was ascertained based on the presence of ≥1 paid medical claim for a chemotherapy drug or administration thereof (identified using Healthcare Common Procedure Coding System [HCPCS], International Classification of Disease, Ninth Revision, Clinical Modification [ICD-9-CM], and Health Care Financing Administration Uniform Bill-92 [UB-92] revenue codes).\nPatients were considered to have initiated a new course of chemotherapy if there was a chemotherapy claim during the study period that was preceded by a period ≥60 days without any other claims for chemotherapy. Only patients who had evidence of a primary solid tumor or NHL (based on ≥2 medical claims [≥7 days apart] with a qualifying 3-digit ICD-9-CM diagnosis code during the period beginning 30 days prior to the index date and ending 30 days thereafter) were selected.\n Chemotherapy courses, cycles, and regimens For each cancer chemotherapy patient, each unique cycle within each course of chemotherapy was identified. The first chemotherapy cycle (of the first course) was defined as beginning with the date of initiation of chemotherapy and ending with the first service date for the next administration of chemotherapy administration (as evidenced by a medical claim with a corresponding HCPCS, ICD-9-CM, or UB-92 code) occurring at least 12 days-but no more than 59 days-after the date of initiation of chemotherapy. If a second chemotherapy cycle did not commence prior to day 60, or if there was evidence of receipt of radiation therapy (based on medical claims with relevant HCPCS, ICD-9-CM, or UB-92 codes) during this period, both the first cycle of chemotherapy and the course of chemotherapy were considered to have been completed 30 days following the beginning of the cycle or on the day prior to initiation of radiation therapy, whichever occurred first. The second and all subsequent cycles of chemotherapy, as well as subsequent courses of chemotherapy–if any–during the period of interest, were similarly defined. A maximum of 8 cycles per course were considered. For patients with multiple courses of chemotherapy, all qualifying courses were considered.\nChemotherapy regimens were ascertained based on a review of all HCPCS Level II codes for parenterally administered antineoplastic agents on medical claims with service dates within 6 days of the start of each cycle of chemotherapy. Regimens were categorized on a cycle-specific basis according to the number of agents administered that are considered to be myelotoxic (list available from authors upon request).\nFor each cancer chemotherapy patient, each unique cycle within each course of chemotherapy was identified. The first chemotherapy cycle (of the first course) was defined as beginning with the date of initiation of chemotherapy and ending with the first service date for the next administration of chemotherapy administration (as evidenced by a medical claim with a corresponding HCPCS, ICD-9-CM, or UB-92 code) occurring at least 12 days-but no more than 59 days-after the date of initiation of chemotherapy. If a second chemotherapy cycle did not commence prior to day 60, or if there was evidence of receipt of radiation therapy (based on medical claims with relevant HCPCS, ICD-9-CM, or UB-92 codes) during this period, both the first cycle of chemotherapy and the course of chemotherapy were considered to have been completed 30 days following the beginning of the cycle or on the day prior to initiation of radiation therapy, whichever occurred first. The second and all subsequent cycles of chemotherapy, as well as subsequent courses of chemotherapy–if any–during the period of interest, were similarly defined. A maximum of 8 cycles per course were considered. For patients with multiple courses of chemotherapy, all qualifying courses were considered.\nChemotherapy regimens were ascertained based on a review of all HCPCS Level II codes for parenterally administered antineoplastic agents on medical claims with service dates within 6 days of the start of each cycle of chemotherapy. Regimens were categorized on a cycle-specific basis according to the number of agents administered that are considered to be myelotoxic (list available from authors upon request).\n Daily filgrastim prophylaxis Cancer chemotherapy patients who received daily filgrastim prophylaxis during ≥1 cycle of chemotherapy were selected for inclusion in the study population. Administration of daily filgrastim on or before day 5 of a given cycle was considered to represent prophylaxis\n[14,22,23]. Receipt of daily filgrastim was identified based on medical claims (J1440, J1441) with relevant codes from the HCPCS system.\nDuration of daily filgrastim prophylaxis during the patient-cycle was characterized based on temporal patterns of administration; end of prophylaxis was defined as a gap ≥3 days in its use, outpatient administration of IV antimicrobial therapy (an indicator for possible CINC), or hospitalization for CINC. Duration of prophylaxis was characterized as 1–3, 4–6, or ≥7 days.\nCancer chemotherapy patients who received daily filgrastim prophylaxis during ≥1 cycle of chemotherapy were selected for inclusion in the study population. Administration of daily filgrastim on or before day 5 of a given cycle was considered to represent prophylaxis\n[14,22,23]. Receipt of daily filgrastim was identified based on medical claims (J1440, J1441) with relevant codes from the HCPCS system.\nDuration of daily filgrastim prophylaxis during the patient-cycle was characterized based on temporal patterns of administration; end of prophylaxis was defined as a gap ≥3 days in its use, outpatient administration of IV antimicrobial therapy (an indicator for possible CINC), or hospitalization for CINC. Duration of prophylaxis was characterized as 1–3, 4–6, or ≥7 days.\n Exclusion criteria Patient-cycles were excluded from the analytic file if the following occurred: (1) evidence of ≥2 primary cancers (solid or blood) within (i.e., +/-) 30 days of chemotherapy initiation; (2) any gaps in health benefits during the 6-month (\"pretreatment\") period prior to chemotherapy initiation; (3) evidence of hematopoietic stem cell or bone marrow transplantation prior to or during receipt of chemotherapy; (4) evidence of chemotherapy based only on medical claims for administration of the drugs (HCPCS codes identifying the specific chemotherapy agents were not available, and thus the regimen and level of myelosuppression could not be determined); (5) pharmacy claims for myelotoxic chemotherapy (only drug dispense dates are available on pharmacy claims, and because pharmacy/medical claims cannot be definitively linked, precise dates of chemotherapy administration–needed to characterize the course and cycles–could not be ascertained); (6) pharmacy claims for daily filgrastim (precise dates of administration could not be ascertained, for reasons stated above); (7) evidence of receipt of sargramostim (J2820) or pegfilgrastim (C9119, S0135, J2505) during the first 5 days of the cycle (i.e., as prophylaxis).\nPatient-cycles were excluded from the analytic file if the following occurred: (1) evidence of ≥2 primary cancers (solid or blood) within (i.e., +/-) 30 days of chemotherapy initiation; (2) any gaps in health benefits during the 6-month (\"pretreatment\") period prior to chemotherapy initiation; (3) evidence of hematopoietic stem cell or bone marrow transplantation prior to or during receipt of chemotherapy; (4) evidence of chemotherapy based only on medical claims for administration of the drugs (HCPCS codes identifying the specific chemotherapy agents were not available, and thus the regimen and level of myelosuppression could not be determined); (5) pharmacy claims for myelotoxic chemotherapy (only drug dispense dates are available on pharmacy claims, and because pharmacy/medical claims cannot be definitively linked, precise dates of chemotherapy administration–needed to characterize the course and cycles–could not be ascertained); (6) pharmacy claims for daily filgrastim (precise dates of administration could not be ascertained, for reasons stated above); (7) evidence of receipt of sargramostim (J2820) or pegfilgrastim (C9119, S0135, J2505) during the first 5 days of the cycle (i.e., as prophylaxis).\nThe study population comprised all patients who initiated ≥1 course of myelosuppressive chemotherapy for a solid tumor or non-Hodgkin’s lymphoma (NHL) and who received daily filgrastim prophylaxis during ≥1 cycle. All cycles in which patients received daily filgrastim prophylaxis–irrespective of duration–were pooled for analyses.\n Cancer chemotherapy patients All patients, aged ≥18 years, who began ≥1 new course of myelosuppressive chemotherapy were identified; the enrollment window was July 1, 2001 through June 30, 2010 for patients in the MarketScan Database and July 1, 2001 through June 30, 2008 for patients in the LifeLink Database. Receipt of chemotherapy was ascertained based on the presence of ≥1 paid medical claim for a chemotherapy drug or administration thereof (identified using Healthcare Common Procedure Coding System [HCPCS], International Classification of Disease, Ninth Revision, Clinical Modification [ICD-9-CM], and Health Care Financing Administration Uniform Bill-92 [UB-92] revenue codes).\nPatients were considered to have initiated a new course of chemotherapy if there was a chemotherapy claim during the study period that was preceded by a period ≥60 days without any other claims for chemotherapy. Only patients who had evidence of a primary solid tumor or NHL (based on ≥2 medical claims [≥7 days apart] with a qualifying 3-digit ICD-9-CM diagnosis code during the period beginning 30 days prior to the index date and ending 30 days thereafter) were selected.\nAll patients, aged ≥18 years, who began ≥1 new course of myelosuppressive chemotherapy were identified; the enrollment window was July 1, 2001 through June 30, 2010 for patients in the MarketScan Database and July 1, 2001 through June 30, 2008 for patients in the LifeLink Database. Receipt of chemotherapy was ascertained based on the presence of ≥1 paid medical claim for a chemotherapy drug or administration thereof (identified using Healthcare Common Procedure Coding System [HCPCS], International Classification of Disease, Ninth Revision, Clinical Modification [ICD-9-CM], and Health Care Financing Administration Uniform Bill-92 [UB-92] revenue codes).\nPatients were considered to have initiated a new course of chemotherapy if there was a chemotherapy claim during the study period that was preceded by a period ≥60 days without any other claims for chemotherapy. Only patients who had evidence of a primary solid tumor or NHL (based on ≥2 medical claims [≥7 days apart] with a qualifying 3-digit ICD-9-CM diagnosis code during the period beginning 30 days prior to the index date and ending 30 days thereafter) were selected.\n Chemotherapy courses, cycles, and regimens For each cancer chemotherapy patient, each unique cycle within each course of chemotherapy was identified. The first chemotherapy cycle (of the first course) was defined as beginning with the date of initiation of chemotherapy and ending with the first service date for the next administration of chemotherapy administration (as evidenced by a medical claim with a corresponding HCPCS, ICD-9-CM, or UB-92 code) occurring at least 12 days-but no more than 59 days-after the date of initiation of chemotherapy. If a second chemotherapy cycle did not commence prior to day 60, or if there was evidence of receipt of radiation therapy (based on medical claims with relevant HCPCS, ICD-9-CM, or UB-92 codes) during this period, both the first cycle of chemotherapy and the course of chemotherapy were considered to have been completed 30 days following the beginning of the cycle or on the day prior to initiation of radiation therapy, whichever occurred first. The second and all subsequent cycles of chemotherapy, as well as subsequent courses of chemotherapy–if any–during the period of interest, were similarly defined. A maximum of 8 cycles per course were considered. For patients with multiple courses of chemotherapy, all qualifying courses were considered.\nChemotherapy regimens were ascertained based on a review of all HCPCS Level II codes for parenterally administered antineoplastic agents on medical claims with service dates within 6 days of the start of each cycle of chemotherapy. Regimens were categorized on a cycle-specific basis according to the number of agents administered that are considered to be myelotoxic (list available from authors upon request).\nFor each cancer chemotherapy patient, each unique cycle within each course of chemotherapy was identified. The first chemotherapy cycle (of the first course) was defined as beginning with the date of initiation of chemotherapy and ending with the first service date for the next administration of chemotherapy administration (as evidenced by a medical claim with a corresponding HCPCS, ICD-9-CM, or UB-92 code) occurring at least 12 days-but no more than 59 days-after the date of initiation of chemotherapy. If a second chemotherapy cycle did not commence prior to day 60, or if there was evidence of receipt of radiation therapy (based on medical claims with relevant HCPCS, ICD-9-CM, or UB-92 codes) during this period, both the first cycle of chemotherapy and the course of chemotherapy were considered to have been completed 30 days following the beginning of the cycle or on the day prior to initiation of radiation therapy, whichever occurred first. The second and all subsequent cycles of chemotherapy, as well as subsequent courses of chemotherapy–if any–during the period of interest, were similarly defined. A maximum of 8 cycles per course were considered. For patients with multiple courses of chemotherapy, all qualifying courses were considered.\nChemotherapy regimens were ascertained based on a review of all HCPCS Level II codes for parenterally administered antineoplastic agents on medical claims with service dates within 6 days of the start of each cycle of chemotherapy. Regimens were categorized on a cycle-specific basis according to the number of agents administered that are considered to be myelotoxic (list available from authors upon request).\n Daily filgrastim prophylaxis Cancer chemotherapy patients who received daily filgrastim prophylaxis during ≥1 cycle of chemotherapy were selected for inclusion in the study population. Administration of daily filgrastim on or before day 5 of a given cycle was considered to represent prophylaxis\n[14,22,23]. Receipt of daily filgrastim was identified based on medical claims (J1440, J1441) with relevant codes from the HCPCS system.\nDuration of daily filgrastim prophylaxis during the patient-cycle was characterized based on temporal patterns of administration; end of prophylaxis was defined as a gap ≥3 days in its use, outpatient administration of IV antimicrobial therapy (an indicator for possible CINC), or hospitalization for CINC. Duration of prophylaxis was characterized as 1–3, 4–6, or ≥7 days.\nCancer chemotherapy patients who received daily filgrastim prophylaxis during ≥1 cycle of chemotherapy were selected for inclusion in the study population. Administration of daily filgrastim on or before day 5 of a given cycle was considered to represent prophylaxis\n[14,22,23]. Receipt of daily filgrastim was identified based on medical claims (J1440, J1441) with relevant codes from the HCPCS system.\nDuration of daily filgrastim prophylaxis during the patient-cycle was characterized based on temporal patterns of administration; end of prophylaxis was defined as a gap ≥3 days in its use, outpatient administration of IV antimicrobial therapy (an indicator for possible CINC), or hospitalization for CINC. Duration of prophylaxis was characterized as 1–3, 4–6, or ≥7 days.\n Exclusion criteria Patient-cycles were excluded from the analytic file if the following occurred: (1) evidence of ≥2 primary cancers (solid or blood) within (i.e., +/-) 30 days of chemotherapy initiation; (2) any gaps in health benefits during the 6-month (\"pretreatment\") period prior to chemotherapy initiation; (3) evidence of hematopoietic stem cell or bone marrow transplantation prior to or during receipt of chemotherapy; (4) evidence of chemotherapy based only on medical claims for administration of the drugs (HCPCS codes identifying the specific chemotherapy agents were not available, and thus the regimen and level of myelosuppression could not be determined); (5) pharmacy claims for myelotoxic chemotherapy (only drug dispense dates are available on pharmacy claims, and because pharmacy/medical claims cannot be definitively linked, precise dates of chemotherapy administration–needed to characterize the course and cycles–could not be ascertained); (6) pharmacy claims for daily filgrastim (precise dates of administration could not be ascertained, for reasons stated above); (7) evidence of receipt of sargramostim (J2820) or pegfilgrastim (C9119, S0135, J2505) during the first 5 days of the cycle (i.e., as prophylaxis).\nPatient-cycles were excluded from the analytic file if the following occurred: (1) evidence of ≥2 primary cancers (solid or blood) within (i.e., +/-) 30 days of chemotherapy initiation; (2) any gaps in health benefits during the 6-month (\"pretreatment\") period prior to chemotherapy initiation; (3) evidence of hematopoietic stem cell or bone marrow transplantation prior to or during receipt of chemotherapy; (4) evidence of chemotherapy based only on medical claims for administration of the drugs (HCPCS codes identifying the specific chemotherapy agents were not available, and thus the regimen and level of myelosuppression could not be determined); (5) pharmacy claims for myelotoxic chemotherapy (only drug dispense dates are available on pharmacy claims, and because pharmacy/medical claims cannot be definitively linked, precise dates of chemotherapy administration–needed to characterize the course and cycles–could not be ascertained); (6) pharmacy claims for daily filgrastim (precise dates of administration could not be ascertained, for reasons stated above); (7) evidence of receipt of sargramostim (J2820) or pegfilgrastim (C9119, S0135, J2505) during the first 5 days of the cycle (i.e., as prophylaxis).\n Neutropenic complications requiring inpatient care CINC was identified based on inpatient admissions with a diagnosis (principal or secondary) of neutropenia (ICD-9-CM 288.0), fever (780.6), or infection (list available upon request). Admissions were identified on a cycle-specific basis using acute-care facility inpatient claims with admission dates anytime between day 6 and the last day of the chemotherapy cycle. Episodes of CINC treated exclusively on an outpatient basis were not considered. Because CINC that occurred on the day of the last dose of prophylaxis or the following day could have resulted in an artificial truncation of a planned longer course of prophylaxis–and thus could exaggerate the risk of CINC with shorter courses–analyses were conducted alternatively with and without inclusion of these patient cycles.\nConsequences of CINC requiring inpatient care were characterized in terms of in-hospital mortality, total hospital LOS (during the cycle), and total CINC-related healthcare expenditures (i.e., from hospital discharge to end of cycle). Total CINC-related healthcare expenditures included those for the initial hospitalization as well as care provided post-discharge on an outpatient basis; outpatient expenditures comprised encounters with a diagnosis of neutropenia, fever, or infection, as well as use/prescriptions for CSF agents (i.e., as treatment) and antimicrobial therapy. Expenditures were estimated based on total paid amounts on corresponding claims. Mortality was characterized using data only from the MarketScan Database as such information was not available in the LifeLink Database.\nCINC was identified based on inpatient admissions with a diagnosis (principal or secondary) of neutropenia (ICD-9-CM 288.0), fever (780.6), or infection (list available upon request). Admissions were identified on a cycle-specific basis using acute-care facility inpatient claims with admission dates anytime between day 6 and the last day of the chemotherapy cycle. Episodes of CINC treated exclusively on an outpatient basis were not considered. Because CINC that occurred on the day of the last dose of prophylaxis or the following day could have resulted in an artificial truncation of a planned longer course of prophylaxis–and thus could exaggerate the risk of CINC with shorter courses–analyses were conducted alternatively with and without inclusion of these patient cycles.\nConsequences of CINC requiring inpatient care were characterized in terms of in-hospital mortality, total hospital LOS (during the cycle), and total CINC-related healthcare expenditures (i.e., from hospital discharge to end of cycle). Total CINC-related healthcare expenditures included those for the initial hospitalization as well as care provided post-discharge on an outpatient basis; outpatient expenditures comprised encounters with a diagnosis of neutropenia, fever, or infection, as well as use/prescriptions for CSF agents (i.e., as treatment) and antimicrobial therapy. Expenditures were estimated based on total paid amounts on corresponding claims. Mortality was characterized using data only from the MarketScan Database as such information was not available in the LifeLink Database.\n Patient characteristics Patient characteristics included: age; gender; presence of selected chronic comorbidities (cardiovascular disease, diabetes, liver disease, renal disease); history of blood disorders (anemia, neutropenia, other), infection, hospitalization (all-cause and CINC-related, respectively), chemotherapy, and radiation therapy; pre-chemotherapy healthcare expenditures; type of cancer, presence of metastases; cycle number and minimum length of prior cycles; chemotherapy regimen; receipt of antimicrobial prophylaxis in the cycle of interest; and year of chemotherapy initiation. Only observed data were utilized in defining patient characteristics.\nAge was assessed as of the first day of the first cycle of chemotherapy in the course. Chronic comorbidities and history of blood disorders, infections, hospitalization, chemotherapy, radiation therapy, presence of metastases, and healthcare expenditures were assessed from the beginning of the 6-month pretreatment period through the first day of the corresponding cycle of chemotherapy. Selected variables (i.e., anemia, neutropenia, other blood disorders, infections) were alternatively evaluated during the chemotherapy course (up to the beginning of the cycle of interest). Metastases (bone vs. other site) and chronic comorbidities were identified on the basis of ≥1 diagnosis codes on inpatient claims, ≥2 diagnosis codes on outpatient claims (excluding those for laboratory services) on different days, ≥1 procedure codes, and ≥1 drug codes, as appropriate. Blood disorders and infections were identified on the basis of ≥1 diagnosis codes (on inpatient and/or outpatient claims) and ≥1 drug codes, as appropriate. Prophylactic use of antimicrobial agents was ascertained based on a medical claim for administration of drug from cycle day 1 to cycle day 5, or a pharmacy claim for a filled prescription from cycle day -3 to cycle day 5, with a corresponding drug code.\nPatient characteristics included: age; gender; presence of selected chronic comorbidities (cardiovascular disease, diabetes, liver disease, renal disease); history of blood disorders (anemia, neutropenia, other), infection, hospitalization (all-cause and CINC-related, respectively), chemotherapy, and radiation therapy; pre-chemotherapy healthcare expenditures; type of cancer, presence of metastases; cycle number and minimum length of prior cycles; chemotherapy regimen; receipt of antimicrobial prophylaxis in the cycle of interest; and year of chemotherapy initiation. Only observed data were utilized in defining patient characteristics.\nAge was assessed as of the first day of the first cycle of chemotherapy in the course. Chronic comorbidities and history of blood disorders, infections, hospitalization, chemotherapy, radiation therapy, presence of metastases, and healthcare expenditures were assessed from the beginning of the 6-month pretreatment period through the first day of the corresponding cycle of chemotherapy. Selected variables (i.e., anemia, neutropenia, other blood disorders, infections) were alternatively evaluated during the chemotherapy course (up to the beginning of the cycle of interest). Metastases (bone vs. other site) and chronic comorbidities were identified on the basis of ≥1 diagnosis codes on inpatient claims, ≥2 diagnosis codes on outpatient claims (excluding those for laboratory services) on different days, ≥1 procedure codes, and ≥1 drug codes, as appropriate. Blood disorders and infections were identified on the basis of ≥1 diagnosis codes (on inpatient and/or outpatient claims) and ≥1 drug codes, as appropriate. Prophylactic use of antimicrobial agents was ascertained based on a medical claim for administration of drug from cycle day 1 to cycle day 5, or a pharmacy claim for a filled prescription from cycle day -3 to cycle day 5, with a corresponding drug code.\n Statistical analyses Incidence of CINC was evaluated for each patient-cycle in which daily filgrastim was administered prophylactically, and was estimated by same-cycle duration of prophylaxis in an unadjusted and adjusted context. For the latter, a generalized estimating equation (GEE) with a binomial distribution, logistic link function, and exchangeable correlation structure was employed. The GEE method accounts for correlation among repeated measures for the same subject (in this instance, across cycles), while controlling for both fixed characteristics (e.g., gender) and time-dependent covariates (e.g., first versus subsequent cycles). All observed patient characteristics were entered into, and retained in, the multivariate model. Subgroup and sensitivity analyses focusing on the first cycle only, employing a narrow definition of CINC (which was identified using the diagnosis code for neutropenia only but otherwise the same algorithm for the broad definition as described above), and focusing on alternative tumor types separately (i.e., breast cancer, lung cancer, colorectal cancer, and NHL) also were conducted.\nIn-hospital mortality, hospital LOS, and CINC-related healthcare expenditures among patients who developed CINC were descriptively evaluated by duration of daily filgrastim prophylaxis. Confidence intervals (95%) for in-hospital mortality were computed using the Wilson score interval; confidence intervals for LOS and economic costs were computed using nonparametric bootstrapping (percentile method) from the study population (1,000 replicates with replacement). Confidence intervals for in-hospital mortality, hospital LOS, and healthcare expenditures were estimated assuming independence between observations. Only observed data were used in defining study variables; patients who developed CINC but were missing data on study measures–because such data either were not recorded on claims or were not provided by some health plans–were excluded from corresponding analyses.\nIncidence of CINC was evaluated for each patient-cycle in which daily filgrastim was administered prophylactically, and was estimated by same-cycle duration of prophylaxis in an unadjusted and adjusted context. For the latter, a generalized estimating equation (GEE) with a binomial distribution, logistic link function, and exchangeable correlation structure was employed. The GEE method accounts for correlation among repeated measures for the same subject (in this instance, across cycles), while controlling for both fixed characteristics (e.g., gender) and time-dependent covariates (e.g., first versus subsequent cycles). All observed patient characteristics were entered into, and retained in, the multivariate model. Subgroup and sensitivity analyses focusing on the first cycle only, employing a narrow definition of CINC (which was identified using the diagnosis code for neutropenia only but otherwise the same algorithm for the broad definition as described above), and focusing on alternative tumor types separately (i.e., breast cancer, lung cancer, colorectal cancer, and NHL) also were conducted.\nIn-hospital mortality, hospital LOS, and CINC-related healthcare expenditures among patients who developed CINC were descriptively evaluated by duration of daily filgrastim prophylaxis. Confidence intervals (95%) for in-hospital mortality were computed using the Wilson score interval; confidence intervals for LOS and economic costs were computed using nonparametric bootstrapping (percentile method) from the study population (1,000 replicates with replacement). Confidence intervals for in-hospital mortality, hospital LOS, and healthcare expenditures were estimated assuming independence between observations. Only observed data were used in defining study variables; patients who developed CINC but were missing data on study measures–because such data either were not recorded on claims or were not provided by some health plans–were excluded from corresponding analyses."},"results_title":{"kind":"string","value":"Results"},"results_text":{"kind":"string","value":"A total of 135,921 adult patients initiated a new course of chemotherapy for a solid tumor or NHL during the period of interest and met all other eligibility criteria. Among these patients, a total of 5,477 received daily filgrastim as prophylaxis during ≥1 cycle of chemotherapy and thus were included in the study population; these patients contributed a total of 14,288 daily filgrastim prophylaxis patient-cycles to the analytic file. Filgrastim was administered for 1–3 days in 58% of cycles (n = 8,371), 4–6 days in 26% of cycles (n = 3,691), and ≥7days in 16% of cycles (n = 2,226) (Figure \n1).\nDays of filgrastim prophylaxis.\nMean (SD) age of patients was 54 (13) years for 1–3 days of filgrastim prophylaxis, 57 (14) years for 4–6 days of prophylaxis, and 56 (13) years for ≥7 days of prophylaxis (Table \n1). Breast cancer was the most common tumor type (48% for 1–3 days, 50% for 4–6 days, 49% for ≥7 days), followed by NHL (10%, 13%, and 13%, respectively), and colorectal cancer (16%, 10%, and 13%, respectively). Metastatic disease was present in 38% of patients receiving 1–3 days of prophylaxis, 32% receiving 4–6 days, and 35% receiving ≥7 days. Antimicrobial agents—principally oral (~90%)—were concurrently used as prophylaxis in 6.7% of patients receiving 1–3 days of filgrastim prophylaxis, 7.5% receiving 4–6 days, and 7.5% receiving ≥7 days.\nPatient, cancer, and treatment characteristics, by duration of filgrastim prophylaxis\nCrude risk of CINC during a cycle of chemotherapy was 2.9% with 1–3 days of filgrastim prophylaxis, 2.7% with 4–6 days, and 1.8% with ≥7 days. In adjusted analyses, the odds of CINC were 2.4 (95% CI: 1.6-3.4) and 1.9 (1.3-2.8) times higher with 1–3 and 4–6 days of filgrastim prophylaxis, respectively, versus ≥7 days (referent group) (Figure \n2). Among the subgroup of patients who developed CINC requiring inpatient care (n = 382), mean hospital LOS was 7.4 (6.4-8.3) days with 1–3 days of prophylaxis (n = 243), 7.1 (5.7-8.5) days with 4–6 days of prophylaxis (n = 99), and 6.5 (4.9-8.0) with ≥7 days of prophylaxis (n = 40) (Table \n2). Among the subgroup of patients who developed CINC and for whom healthcare expenditures were available (n = 358), mean total CINC-related healthcare expenditures were $18,912 (14,570-23,581) with 1–3 days of prophylaxis (n = 225), $14,907 (11,155-19,728) with 4–6 days (n = 94), and $13,165 (9,595-17,144) with ≥7 days (n = 39). Among the subgroup of patients who developed CINC and for whom discharge status was available (n = 228), in-hospital mortality was 8.4% (4.6-14.8) with 1–3 days of prophylaxis (n-119), 4.0% (1.4-11.1) with 4–6 days (n = 75), and 0% (0–10.2) with ≥7 days (n = 34).\nAdjusted odds ratios for chemotherapy-induced neutropenic complications requiring inpatient care, by duration of filgrastim prophylaxis*. *Results adjusted for patient, cancer, and chemotherapy characteristics.\nUnadjusted risk of inpatient mortality, length of stay in hospital, and healthcare expenditures for chemotherapy- induced neutropenic complications requiring inpatient care, by duration of filgrastim prophylaxis\n*n’s indicate the number of patient-cycles considered in analyses of study measures; among patients who developed CINC (n = 382), those with missing data on study measures- because such data were not recorded on claims or were not provided by some health plans- were excluded from corresponding analyses.\n**Only patients with paid amounts > $0 were considered in these analyses.\nIn subgroup analyses focusing on the first cycle only, crude risks of CINC were 7.0% with 1–3 days of filgrastim prophylaxis (n = 1054), 5.0% with 4–6 days (n = 482), and 5.2% with ≥7 days (n = 363); in adjusted analyses, odds of CINC were 1.6 (0.9-2.8) and 1.1 (0.6-2.1) times higher with 1–3 and 4–6 days (versus ≥7 days) of filgrastim prophylaxis in cycle 1 (Table \n3). In analyses employing the narrow definition for CINC, crude risks of CINC were 1.6% with 1–3 days of filgrastim prophylaxis, 1.8% with 4–6 days, and 1.3% with ≥7 days; in adjusted analyses, odds of CINC were 1.6 (1.1-2.5) and 1.7 (1.1-2.7) times higher with 1–3 and 4–6 days of filgrastim prophylaxis, respectively, versus ≥7 days. In tumor-specific analyses, adjusted odds of CINC with 1–3 and 4–6 days of filgrastim prophylaxis (vs. ≥7 days) were: 1.8 (1.0-3.0) and 1.9 (1.1-3.4) for breast cancer; 20.2 (1.9-212.4) and 14.5 (1.3-158.7) for lung cancer; 1.9 (0.2-16.7) and 2.5 (0.3-23.5) for colorectal cancer; and 2.1 (1.2-3.6) and 1.8 (1.0-3.2) for NHL. We note that not all observed differences were statistically significant in unadjusted subgroup and secondary analyses, presumably due to the lack of adjustment for systematic differences in patient characteristics between prophylaxis subgroups.\nAdjusted odds ratios for chemotherapy- induced neutropenic complications requiring inpatient care in subgroup and secondary analyses\n*Results adjusted for patient, cancer, and treatment characteristic listed in Table \n2."},"conclusions_title":{"kind":"string","value":"Conclusion"},"conclusions_text":{"kind":"string","value":"In conclusion, we found that among patients receiving myelosuppressive chemotherapy, shorter courses of daily filgrastim prophylaxis are associated with increased risk of CINC. We also found that when CINC develops despite daily filgrastim prophylaxis, the outcomes thereof may be poorer in those receiving shorter courses of prophylaxis. Additional research is needed to explore these relationships among individual tumor types and chemotherapy regimens."},"other_sections_titles":{"kind":"list like","value":["Background","Data source","Study population","Cancer chemotherapy patients","Chemotherapy courses, cycles, and regimens","Daily filgrastim prophylaxis","Exclusion criteria","Neutropenic complications requiring inpatient care","Patient characteristics","Statistical analyses","Competing interests","Authors’ contributions","Pre-publication history"],"string":"[\n \"Background\",\n \"Data source\",\n \"Study population\",\n \"Cancer chemotherapy patients\",\n \"Chemotherapy courses, cycles, and regimens\",\n \"Daily filgrastim prophylaxis\",\n \"Exclusion criteria\",\n \"Neutropenic complications requiring inpatient care\",\n \"Patient characteristics\",\n \"Statistical analyses\",\n \"Competing interests\",\n \"Authors’ contributions\",\n \"Pre-publication history\"\n]"},"other_sections_texts":{"kind":"list like","value":["Neutropenia is a common side effect of myelosuppressive chemotherapy that both increases the risk of infection and diminishes patients’ ability to fight infection. When neutropenic patients become febrile, the high likelihood of infection and serious consequences thereof usually results in hospitalization\n[1,2]. FN, as well as severe or prolonged neutropenia, also can interfere with the planned delivery of treatment and adversely affect important patient outcomes\n[2-11].\nClinical practice guidelines recommend primary prophylactic use of a colony-stimulating factor (CSF)–which has been shown to reduce the risk of FN in clinical trials–when the risk of FN is 20% or higher\n[2]. While the American Society of Clinical Oncology (ASCO) initially recommended that CSF prophylaxis be administered only when FN risk is 40% or higher, in 2006, ASCO lowered the threshold to 20% based on data highlighting the importance of FN-related hospitalization as an outcome and evidence demonstrating the value of CSF prophylaxis in reducing the risk of FN, the risk of FN-related hospitalization, and the associated use of IV anti-infective agents\n[2,12,13]. The CSF filgrastim, which is widely used in clinical practice as prophylaxis against FN, requires daily administration during each cycle until neutrophil recovery occurs (in clinical trials, given typically for 10–11 days [and up to 14 days] until absolute neutrophil count [ANC] ≥10 × 109/L)\n[14-19].\nIn clinical practice, patients often receive shorter courses of daily filgrastim prophylaxis than those administered to subjects in the clinical trial setting\n[14-16,20-24]. While published studies suggest that shorter courses of daily filgrastim prophylaxis are associated with an increased risk of hospitalization for CINC, these studies focused on selected tumor types or employed data that now are over a decade old\n[23,24]. During the past decade, use of chemotherapy and supportive care in clinical practice–as well as recommended use of these agents in authoritative guidelines–has changed considerably\n[2,25-27], Moreover, only one study has examined whether CSFs may favorably impact clinical outcomes and economic costs when CINC develop despite CSF prophylaxis, and it was published a decade ago and focused on elderly patients with a single type of cancer (non-Hodgkin’s lymphoma)\n[28]. We therefore undertook a new study to evaluate the relationship between the duration of daily filgrastim prophylaxis, and the risk and consequences of CINC requiring inpatient care using a large healthcare claims database. While such databases often lack detailed clinical information (e.g., on absolute neutrophil counts), they offer access to information on the health profile and healthcare utilization of tens of millions of covered lives.","Patient-level information from two large healthcare claims databases–the Thomson Reuters MarketScan Commercial Claims and Encounters and Medicare Supplemental and Coordination of Benefits Database (MarketScan Database, 2001–2010) and the Intercontinental Marketing Services LifeLink Database (LifeLink Database, 2001–2008)–were pooled for analyses. Both databases comprise medical (i.e., facility and professional service) and outpatient pharmacy claims from a large number of participating private health plans, and each contains claims data for 15 million persons annually.\nData available for each facility and professional-service claim include date and place of service, diagnoses, procedures performed/services rendered, and quantity of services (professional-service claims). Data available for each retail pharmacy claim include the drug dispensed, dispensing date, quantity dispensed, and number of days supplied. All claims also include paid (i.e., reimbursed) amounts. Selected demographic and eligibility information is available for persons in both databases. All data can be arrayed to provide a detailed chronology of all medical and pharmacy services used by each plan member over time.\nThe study databases were de-identified prior to their release to study investigators, as set forth in the corresponding Data Use Agreements. The study databases have been evaluated and certified by independent third parties to be in compliance with the Health Insurance Portability and Accountability Act (HIPAA) of 1996 statistical de-identification standards and to satisfy the conditions set forth in Sections 164.514 (a)-(b) 1ii of the HIPAA Privacy Rule regarding the determination and documentation of statistically de-identified data. Use of the study databases for health services research was determined–via independent third parties–to be fully compliant with the HIPAA Privacy Rule and federal guidance on Public Welfare and the Protection of Human Subjects\n[29].","The study population comprised all patients who initiated ≥1 course of myelosuppressive chemotherapy for a solid tumor or non-Hodgkin’s lymphoma (NHL) and who received daily filgrastim prophylaxis during ≥1 cycle. All cycles in which patients received daily filgrastim prophylaxis–irrespective of duration–were pooled for analyses.\n Cancer chemotherapy patients All patients, aged ≥18 years, who began ≥1 new course of myelosuppressive chemotherapy were identified; the enrollment window was July 1, 2001 through June 30, 2010 for patients in the MarketScan Database and July 1, 2001 through June 30, 2008 for patients in the LifeLink Database. Receipt of chemotherapy was ascertained based on the presence of ≥1 paid medical claim for a chemotherapy drug or administration thereof (identified using Healthcare Common Procedure Coding System [HCPCS], International Classification of Disease, Ninth Revision, Clinical Modification [ICD-9-CM], and Health Care Financing Administration Uniform Bill-92 [UB-92] revenue codes).\nPatients were considered to have initiated a new course of chemotherapy if there was a chemotherapy claim during the study period that was preceded by a period ≥60 days without any other claims for chemotherapy. Only patients who had evidence of a primary solid tumor or NHL (based on ≥2 medical claims [≥7 days apart] with a qualifying 3-digit ICD-9-CM diagnosis code during the period beginning 30 days prior to the index date and ending 30 days thereafter) were selected.\nAll patients, aged ≥18 years, who began ≥1 new course of myelosuppressive chemotherapy were identified; the enrollment window was July 1, 2001 through June 30, 2010 for patients in the MarketScan Database and July 1, 2001 through June 30, 2008 for patients in the LifeLink Database. Receipt of chemotherapy was ascertained based on the presence of ≥1 paid medical claim for a chemotherapy drug or administration thereof (identified using Healthcare Common Procedure Coding System [HCPCS], International Classification of Disease, Ninth Revision, Clinical Modification [ICD-9-CM], and Health Care Financing Administration Uniform Bill-92 [UB-92] revenue codes).\nPatients were considered to have initiated a new course of chemotherapy if there was a chemotherapy claim during the study period that was preceded by a period ≥60 days without any other claims for chemotherapy. Only patients who had evidence of a primary solid tumor or NHL (based on ≥2 medical claims [≥7 days apart] with a qualifying 3-digit ICD-9-CM diagnosis code during the period beginning 30 days prior to the index date and ending 30 days thereafter) were selected.\n Chemotherapy courses, cycles, and regimens For each cancer chemotherapy patient, each unique cycle within each course of chemotherapy was identified. The first chemotherapy cycle (of the first course) was defined as beginning with the date of initiation of chemotherapy and ending with the first service date for the next administration of chemotherapy administration (as evidenced by a medical claim with a corresponding HCPCS, ICD-9-CM, or UB-92 code) occurring at least 12 days-but no more than 59 days-after the date of initiation of chemotherapy. If a second chemotherapy cycle did not commence prior to day 60, or if there was evidence of receipt of radiation therapy (based on medical claims with relevant HCPCS, ICD-9-CM, or UB-92 codes) during this period, both the first cycle of chemotherapy and the course of chemotherapy were considered to have been completed 30 days following the beginning of the cycle or on the day prior to initiation of radiation therapy, whichever occurred first. The second and all subsequent cycles of chemotherapy, as well as subsequent courses of chemotherapy–if any–during the period of interest, were similarly defined. A maximum of 8 cycles per course were considered. For patients with multiple courses of chemotherapy, all qualifying courses were considered.\nChemotherapy regimens were ascertained based on a review of all HCPCS Level II codes for parenterally administered antineoplastic agents on medical claims with service dates within 6 days of the start of each cycle of chemotherapy. Regimens were categorized on a cycle-specific basis according to the number of agents administered that are considered to be myelotoxic (list available from authors upon request).\nFor each cancer chemotherapy patient, each unique cycle within each course of chemotherapy was identified. The first chemotherapy cycle (of the first course) was defined as beginning with the date of initiation of chemotherapy and ending with the first service date for the next administration of chemotherapy administration (as evidenced by a medical claim with a corresponding HCPCS, ICD-9-CM, or UB-92 code) occurring at least 12 days-but no more than 59 days-after the date of initiation of chemotherapy. If a second chemotherapy cycle did not commence prior to day 60, or if there was evidence of receipt of radiation therapy (based on medical claims with relevant HCPCS, ICD-9-CM, or UB-92 codes) during this period, both the first cycle of chemotherapy and the course of chemotherapy were considered to have been completed 30 days following the beginning of the cycle or on the day prior to initiation of radiation therapy, whichever occurred first. The second and all subsequent cycles of chemotherapy, as well as subsequent courses of chemotherapy–if any–during the period of interest, were similarly defined. A maximum of 8 cycles per course were considered. For patients with multiple courses of chemotherapy, all qualifying courses were considered.\nChemotherapy regimens were ascertained based on a review of all HCPCS Level II codes for parenterally administered antineoplastic agents on medical claims with service dates within 6 days of the start of each cycle of chemotherapy. Regimens were categorized on a cycle-specific basis according to the number of agents administered that are considered to be myelotoxic (list available from authors upon request).\n Daily filgrastim prophylaxis Cancer chemotherapy patients who received daily filgrastim prophylaxis during ≥1 cycle of chemotherapy were selected for inclusion in the study population. Administration of daily filgrastim on or before day 5 of a given cycle was considered to represent prophylaxis\n[14,22,23]. Receipt of daily filgrastim was identified based on medical claims (J1440, J1441) with relevant codes from the HCPCS system.\nDuration of daily filgrastim prophylaxis during the patient-cycle was characterized based on temporal patterns of administration; end of prophylaxis was defined as a gap ≥3 days in its use, outpatient administration of IV antimicrobial therapy (an indicator for possible CINC), or hospitalization for CINC. Duration of prophylaxis was characterized as 1–3, 4–6, or ≥7 days.\nCancer chemotherapy patients who received daily filgrastim prophylaxis during ≥1 cycle of chemotherapy were selected for inclusion in the study population. Administration of daily filgrastim on or before day 5 of a given cycle was considered to represent prophylaxis\n[14,22,23]. Receipt of daily filgrastim was identified based on medical claims (J1440, J1441) with relevant codes from the HCPCS system.\nDuration of daily filgrastim prophylaxis during the patient-cycle was characterized based on temporal patterns of administration; end of prophylaxis was defined as a gap ≥3 days in its use, outpatient administration of IV antimicrobial therapy (an indicator for possible CINC), or hospitalization for CINC. Duration of prophylaxis was characterized as 1–3, 4–6, or ≥7 days.\n Exclusion criteria Patient-cycles were excluded from the analytic file if the following occurred: (1) evidence of ≥2 primary cancers (solid or blood) within (i.e., +/-) 30 days of chemotherapy initiation; (2) any gaps in health benefits during the 6-month (\"pretreatment\") period prior to chemotherapy initiation; (3) evidence of hematopoietic stem cell or bone marrow transplantation prior to or during receipt of chemotherapy; (4) evidence of chemotherapy based only on medical claims for administration of the drugs (HCPCS codes identifying the specific chemotherapy agents were not available, and thus the regimen and level of myelosuppression could not be determined); (5) pharmacy claims for myelotoxic chemotherapy (only drug dispense dates are available on pharmacy claims, and because pharmacy/medical claims cannot be definitively linked, precise dates of chemotherapy administration–needed to characterize the course and cycles–could not be ascertained); (6) pharmacy claims for daily filgrastim (precise dates of administration could not be ascertained, for reasons stated above); (7) evidence of receipt of sargramostim (J2820) or pegfilgrastim (C9119, S0135, J2505) during the first 5 days of the cycle (i.e., as prophylaxis).\nPatient-cycles were excluded from the analytic file if the following occurred: (1) evidence of ≥2 primary cancers (solid or blood) within (i.e., +/-) 30 days of chemotherapy initiation; (2) any gaps in health benefits during the 6-month (\"pretreatment\") period prior to chemotherapy initiation; (3) evidence of hematopoietic stem cell or bone marrow transplantation prior to or during receipt of chemotherapy; (4) evidence of chemotherapy based only on medical claims for administration of the drugs (HCPCS codes identifying the specific chemotherapy agents were not available, and thus the regimen and level of myelosuppression could not be determined); (5) pharmacy claims for myelotoxic chemotherapy (only drug dispense dates are available on pharmacy claims, and because pharmacy/medical claims cannot be definitively linked, precise dates of chemotherapy administration–needed to characterize the course and cycles–could not be ascertained); (6) pharmacy claims for daily filgrastim (precise dates of administration could not be ascertained, for reasons stated above); (7) evidence of receipt of sargramostim (J2820) or pegfilgrastim (C9119, S0135, J2505) during the first 5 days of the cycle (i.e., as prophylaxis).","All patients, aged ≥18 years, who began ≥1 new course of myelosuppressive chemotherapy were identified; the enrollment window was July 1, 2001 through June 30, 2010 for patients in the MarketScan Database and July 1, 2001 through June 30, 2008 for patients in the LifeLink Database. Receipt of chemotherapy was ascertained based on the presence of ≥1 paid medical claim for a chemotherapy drug or administration thereof (identified using Healthcare Common Procedure Coding System [HCPCS], International Classification of Disease, Ninth Revision, Clinical Modification [ICD-9-CM], and Health Care Financing Administration Uniform Bill-92 [UB-92] revenue codes).\nPatients were considered to have initiated a new course of chemotherapy if there was a chemotherapy claim during the study period that was preceded by a period ≥60 days without any other claims for chemotherapy. Only patients who had evidence of a primary solid tumor or NHL (based on ≥2 medical claims [≥7 days apart] with a qualifying 3-digit ICD-9-CM diagnosis code during the period beginning 30 days prior to the index date and ending 30 days thereafter) were selected.","For each cancer chemotherapy patient, each unique cycle within each course of chemotherapy was identified. The first chemotherapy cycle (of the first course) was defined as beginning with the date of initiation of chemotherapy and ending with the first service date for the next administration of chemotherapy administration (as evidenced by a medical claim with a corresponding HCPCS, ICD-9-CM, or UB-92 code) occurring at least 12 days-but no more than 59 days-after the date of initiation of chemotherapy. If a second chemotherapy cycle did not commence prior to day 60, or if there was evidence of receipt of radiation therapy (based on medical claims with relevant HCPCS, ICD-9-CM, or UB-92 codes) during this period, both the first cycle of chemotherapy and the course of chemotherapy were considered to have been completed 30 days following the beginning of the cycle or on the day prior to initiation of radiation therapy, whichever occurred first. The second and all subsequent cycles of chemotherapy, as well as subsequent courses of chemotherapy–if any–during the period of interest, were similarly defined. A maximum of 8 cycles per course were considered. For patients with multiple courses of chemotherapy, all qualifying courses were considered.\nChemotherapy regimens were ascertained based on a review of all HCPCS Level II codes for parenterally administered antineoplastic agents on medical claims with service dates within 6 days of the start of each cycle of chemotherapy. Regimens were categorized on a cycle-specific basis according to the number of agents administered that are considered to be myelotoxic (list available from authors upon request).","Cancer chemotherapy patients who received daily filgrastim prophylaxis during ≥1 cycle of chemotherapy were selected for inclusion in the study population. Administration of daily filgrastim on or before day 5 of a given cycle was considered to represent prophylaxis\n[14,22,23]. Receipt of daily filgrastim was identified based on medical claims (J1440, J1441) with relevant codes from the HCPCS system.\nDuration of daily filgrastim prophylaxis during the patient-cycle was characterized based on temporal patterns of administration; end of prophylaxis was defined as a gap ≥3 days in its use, outpatient administration of IV antimicrobial therapy (an indicator for possible CINC), or hospitalization for CINC. Duration of prophylaxis was characterized as 1–3, 4–6, or ≥7 days.","Patient-cycles were excluded from the analytic file if the following occurred: (1) evidence of ≥2 primary cancers (solid or blood) within (i.e., +/-) 30 days of chemotherapy initiation; (2) any gaps in health benefits during the 6-month (\"pretreatment\") period prior to chemotherapy initiation; (3) evidence of hematopoietic stem cell or bone marrow transplantation prior to or during receipt of chemotherapy; (4) evidence of chemotherapy based only on medical claims for administration of the drugs (HCPCS codes identifying the specific chemotherapy agents were not available, and thus the regimen and level of myelosuppression could not be determined); (5) pharmacy claims for myelotoxic chemotherapy (only drug dispense dates are available on pharmacy claims, and because pharmacy/medical claims cannot be definitively linked, precise dates of chemotherapy administration–needed to characterize the course and cycles–could not be ascertained); (6) pharmacy claims for daily filgrastim (precise dates of administration could not be ascertained, for reasons stated above); (7) evidence of receipt of sargramostim (J2820) or pegfilgrastim (C9119, S0135, J2505) during the first 5 days of the cycle (i.e., as prophylaxis).","CINC was identified based on inpatient admissions with a diagnosis (principal or secondary) of neutropenia (ICD-9-CM 288.0), fever (780.6), or infection (list available upon request). Admissions were identified on a cycle-specific basis using acute-care facility inpatient claims with admission dates anytime between day 6 and the last day of the chemotherapy cycle. Episodes of CINC treated exclusively on an outpatient basis were not considered. Because CINC that occurred on the day of the last dose of prophylaxis or the following day could have resulted in an artificial truncation of a planned longer course of prophylaxis–and thus could exaggerate the risk of CINC with shorter courses–analyses were conducted alternatively with and without inclusion of these patient cycles.\nConsequences of CINC requiring inpatient care were characterized in terms of in-hospital mortality, total hospital LOS (during the cycle), and total CINC-related healthcare expenditures (i.e., from hospital discharge to end of cycle). Total CINC-related healthcare expenditures included those for the initial hospitalization as well as care provided post-discharge on an outpatient basis; outpatient expenditures comprised encounters with a diagnosis of neutropenia, fever, or infection, as well as use/prescriptions for CSF agents (i.e., as treatment) and antimicrobial therapy. Expenditures were estimated based on total paid amounts on corresponding claims. Mortality was characterized using data only from the MarketScan Database as such information was not available in the LifeLink Database.","Patient characteristics included: age; gender; presence of selected chronic comorbidities (cardiovascular disease, diabetes, liver disease, renal disease); history of blood disorders (anemia, neutropenia, other), infection, hospitalization (all-cause and CINC-related, respectively), chemotherapy, and radiation therapy; pre-chemotherapy healthcare expenditures; type of cancer, presence of metastases; cycle number and minimum length of prior cycles; chemotherapy regimen; receipt of antimicrobial prophylaxis in the cycle of interest; and year of chemotherapy initiation. Only observed data were utilized in defining patient characteristics.\nAge was assessed as of the first day of the first cycle of chemotherapy in the course. Chronic comorbidities and history of blood disorders, infections, hospitalization, chemotherapy, radiation therapy, presence of metastases, and healthcare expenditures were assessed from the beginning of the 6-month pretreatment period through the first day of the corresponding cycle of chemotherapy. Selected variables (i.e., anemia, neutropenia, other blood disorders, infections) were alternatively evaluated during the chemotherapy course (up to the beginning of the cycle of interest). Metastases (bone vs. other site) and chronic comorbidities were identified on the basis of ≥1 diagnosis codes on inpatient claims, ≥2 diagnosis codes on outpatient claims (excluding those for laboratory services) on different days, ≥1 procedure codes, and ≥1 drug codes, as appropriate. Blood disorders and infections were identified on the basis of ≥1 diagnosis codes (on inpatient and/or outpatient claims) and ≥1 drug codes, as appropriate. Prophylactic use of antimicrobial agents was ascertained based on a medical claim for administration of drug from cycle day 1 to cycle day 5, or a pharmacy claim for a filled prescription from cycle day -3 to cycle day 5, with a corresponding drug code.","Incidence of CINC was evaluated for each patient-cycle in which daily filgrastim was administered prophylactically, and was estimated by same-cycle duration of prophylaxis in an unadjusted and adjusted context. For the latter, a generalized estimating equation (GEE) with a binomial distribution, logistic link function, and exchangeable correlation structure was employed. The GEE method accounts for correlation among repeated measures for the same subject (in this instance, across cycles), while controlling for both fixed characteristics (e.g., gender) and time-dependent covariates (e.g., first versus subsequent cycles). All observed patient characteristics were entered into, and retained in, the multivariate model. Subgroup and sensitivity analyses focusing on the first cycle only, employing a narrow definition of CINC (which was identified using the diagnosis code for neutropenia only but otherwise the same algorithm for the broad definition as described above), and focusing on alternative tumor types separately (i.e., breast cancer, lung cancer, colorectal cancer, and NHL) also were conducted.\nIn-hospital mortality, hospital LOS, and CINC-related healthcare expenditures among patients who developed CINC were descriptively evaluated by duration of daily filgrastim prophylaxis. Confidence intervals (95%) for in-hospital mortality were computed using the Wilson score interval; confidence intervals for LOS and economic costs were computed using nonparametric bootstrapping (percentile method) from the study population (1,000 replicates with replacement). Confidence intervals for in-hospital mortality, hospital LOS, and healthcare expenditures were estimated assuming independence between observations. Only observed data were used in defining study variables; patients who developed CINC but were missing data on study measures–because such data either were not recorded on claims or were not provided by some health plans–were excluded from corresponding analyses.","Funding for this research was provided by Amgen Inc. to Policy Analysis Inc. (PAI). Derek Weycker, John Edelsberg, and Alex Kartashov are employed by Policy Analysis Inc. (PAI). Rich Barron and Jason Legg are employed by Amgen Inc. Andrew Glass is employed by the Center for Health Research, Kaiser Permanente Northwest, and received an honorarium for this research from Amgen Inc.","Authorship was designated based on the guidelines promulgated by the International Committee of Medical Journal Editors (2004). All persons who meet criteria for authorship are listed as authors on the title page. The contribution of each of these individuals to this study–by task–is as follows: conception and supervision (Barron, Weycker), development of design (Barron, Edelsberg, Glass, Legg, Weycker), conduct of analyses (Kartashov, Weycker), interpretation of results (all authors), preparation of manuscript (Edelsberg, Weycker), and review of manuscript (all authors). All authors have read and approved the final version of the manuscript. The study sponsor reviewed the study research plan and study manuscript; data management, processing, and analyses were conducted by Policy Analysis Inc. (PAI), and all final analytic decisions were made by study investigators.","The pre-publication history for this paper can be accessed here:\n\nhttp://www.biomedcentral.com/1472-6963/14/189/prepub\n"],"string":"[\n \"Neutropenia is a common side effect of myelosuppressive chemotherapy that both increases the risk of infection and diminishes patients’ ability to fight infection. When neutropenic patients become febrile, the high likelihood of infection and serious consequences thereof usually results in hospitalization\\n[1,2]. FN, as well as severe or prolonged neutropenia, also can interfere with the planned delivery of treatment and adversely affect important patient outcomes\\n[2-11].\\nClinical practice guidelines recommend primary prophylactic use of a colony-stimulating factor (CSF)–which has been shown to reduce the risk of FN in clinical trials–when the risk of FN is 20% or higher\\n[2]. While the American Society of Clinical Oncology (ASCO) initially recommended that CSF prophylaxis be administered only when FN risk is 40% or higher, in 2006, ASCO lowered the threshold to 20% based on data highlighting the importance of FN-related hospitalization as an outcome and evidence demonstrating the value of CSF prophylaxis in reducing the risk of FN, the risk of FN-related hospitalization, and the associated use of IV anti-infective agents\\n[2,12,13]. The CSF filgrastim, which is widely used in clinical practice as prophylaxis against FN, requires daily administration during each cycle until neutrophil recovery occurs (in clinical trials, given typically for 10–11 days [and up to 14 days] until absolute neutrophil count [ANC] ≥10 × 109/L)\\n[14-19].\\nIn clinical practice, patients often receive shorter courses of daily filgrastim prophylaxis than those administered to subjects in the clinical trial setting\\n[14-16,20-24]. While published studies suggest that shorter courses of daily filgrastim prophylaxis are associated with an increased risk of hospitalization for CINC, these studies focused on selected tumor types or employed data that now are over a decade old\\n[23,24]. During the past decade, use of chemotherapy and supportive care in clinical practice–as well as recommended use of these agents in authoritative guidelines–has changed considerably\\n[2,25-27], Moreover, only one study has examined whether CSFs may favorably impact clinical outcomes and economic costs when CINC develop despite CSF prophylaxis, and it was published a decade ago and focused on elderly patients with a single type of cancer (non-Hodgkin’s lymphoma)\\n[28]. We therefore undertook a new study to evaluate the relationship between the duration of daily filgrastim prophylaxis, and the risk and consequences of CINC requiring inpatient care using a large healthcare claims database. While such databases often lack detailed clinical information (e.g., on absolute neutrophil counts), they offer access to information on the health profile and healthcare utilization of tens of millions of covered lives.\",\n \"Patient-level information from two large healthcare claims databases–the Thomson Reuters MarketScan Commercial Claims and Encounters and Medicare Supplemental and Coordination of Benefits Database (MarketScan Database, 2001–2010) and the Intercontinental Marketing Services LifeLink Database (LifeLink Database, 2001–2008)–were pooled for analyses. Both databases comprise medical (i.e., facility and professional service) and outpatient pharmacy claims from a large number of participating private health plans, and each contains claims data for 15 million persons annually.\\nData available for each facility and professional-service claim include date and place of service, diagnoses, procedures performed/services rendered, and quantity of services (professional-service claims). Data available for each retail pharmacy claim include the drug dispensed, dispensing date, quantity dispensed, and number of days supplied. All claims also include paid (i.e., reimbursed) amounts. Selected demographic and eligibility information is available for persons in both databases. All data can be arrayed to provide a detailed chronology of all medical and pharmacy services used by each plan member over time.\\nThe study databases were de-identified prior to their release to study investigators, as set forth in the corresponding Data Use Agreements. The study databases have been evaluated and certified by independent third parties to be in compliance with the Health Insurance Portability and Accountability Act (HIPAA) of 1996 statistical de-identification standards and to satisfy the conditions set forth in Sections 164.514 (a)-(b) 1ii of the HIPAA Privacy Rule regarding the determination and documentation of statistically de-identified data. Use of the study databases for health services research was determined–via independent third parties–to be fully compliant with the HIPAA Privacy Rule and federal guidance on Public Welfare and the Protection of Human Subjects\\n[29].\",\n \"The study population comprised all patients who initiated ≥1 course of myelosuppressive chemotherapy for a solid tumor or non-Hodgkin’s lymphoma (NHL) and who received daily filgrastim prophylaxis during ≥1 cycle. All cycles in which patients received daily filgrastim prophylaxis–irrespective of duration–were pooled for analyses.\\n Cancer chemotherapy patients All patients, aged ≥18 years, who began ≥1 new course of myelosuppressive chemotherapy were identified; the enrollment window was July 1, 2001 through June 30, 2010 for patients in the MarketScan Database and July 1, 2001 through June 30, 2008 for patients in the LifeLink Database. Receipt of chemotherapy was ascertained based on the presence of ≥1 paid medical claim for a chemotherapy drug or administration thereof (identified using Healthcare Common Procedure Coding System [HCPCS], International Classification of Disease, Ninth Revision, Clinical Modification [ICD-9-CM], and Health Care Financing Administration Uniform Bill-92 [UB-92] revenue codes).\\nPatients were considered to have initiated a new course of chemotherapy if there was a chemotherapy claim during the study period that was preceded by a period ≥60 days without any other claims for chemotherapy. Only patients who had evidence of a primary solid tumor or NHL (based on ≥2 medical claims [≥7 days apart] with a qualifying 3-digit ICD-9-CM diagnosis code during the period beginning 30 days prior to the index date and ending 30 days thereafter) were selected.\\nAll patients, aged ≥18 years, who began ≥1 new course of myelosuppressive chemotherapy were identified; the enrollment window was July 1, 2001 through June 30, 2010 for patients in the MarketScan Database and July 1, 2001 through June 30, 2008 for patients in the LifeLink Database. Receipt of chemotherapy was ascertained based on the presence of ≥1 paid medical claim for a chemotherapy drug or administration thereof (identified using Healthcare Common Procedure Coding System [HCPCS], International Classification of Disease, Ninth Revision, Clinical Modification [ICD-9-CM], and Health Care Financing Administration Uniform Bill-92 [UB-92] revenue codes).\\nPatients were considered to have initiated a new course of chemotherapy if there was a chemotherapy claim during the study period that was preceded by a period ≥60 days without any other claims for chemotherapy. Only patients who had evidence of a primary solid tumor or NHL (based on ≥2 medical claims [≥7 days apart] with a qualifying 3-digit ICD-9-CM diagnosis code during the period beginning 30 days prior to the index date and ending 30 days thereafter) were selected.\\n Chemotherapy courses, cycles, and regimens For each cancer chemotherapy patient, each unique cycle within each course of chemotherapy was identified. The first chemotherapy cycle (of the first course) was defined as beginning with the date of initiation of chemotherapy and ending with the first service date for the next administration of chemotherapy administration (as evidenced by a medical claim with a corresponding HCPCS, ICD-9-CM, or UB-92 code) occurring at least 12 days-but no more than 59 days-after the date of initiation of chemotherapy. If a second chemotherapy cycle did not commence prior to day 60, or if there was evidence of receipt of radiation therapy (based on medical claims with relevant HCPCS, ICD-9-CM, or UB-92 codes) during this period, both the first cycle of chemotherapy and the course of chemotherapy were considered to have been completed 30 days following the beginning of the cycle or on the day prior to initiation of radiation therapy, whichever occurred first. The second and all subsequent cycles of chemotherapy, as well as subsequent courses of chemotherapy–if any–during the period of interest, were similarly defined. A maximum of 8 cycles per course were considered. For patients with multiple courses of chemotherapy, all qualifying courses were considered.\\nChemotherapy regimens were ascertained based on a review of all HCPCS Level II codes for parenterally administered antineoplastic agents on medical claims with service dates within 6 days of the start of each cycle of chemotherapy. Regimens were categorized on a cycle-specific basis according to the number of agents administered that are considered to be myelotoxic (list available from authors upon request).\\nFor each cancer chemotherapy patient, each unique cycle within each course of chemotherapy was identified. The first chemotherapy cycle (of the first course) was defined as beginning with the date of initiation of chemotherapy and ending with the first service date for the next administration of chemotherapy administration (as evidenced by a medical claim with a corresponding HCPCS, ICD-9-CM, or UB-92 code) occurring at least 12 days-but no more than 59 days-after the date of initiation of chemotherapy. If a second chemotherapy cycle did not commence prior to day 60, or if there was evidence of receipt of radiation therapy (based on medical claims with relevant HCPCS, ICD-9-CM, or UB-92 codes) during this period, both the first cycle of chemotherapy and the course of chemotherapy were considered to have been completed 30 days following the beginning of the cycle or on the day prior to initiation of radiation therapy, whichever occurred first. The second and all subsequent cycles of chemotherapy, as well as subsequent courses of chemotherapy–if any–during the period of interest, were similarly defined. A maximum of 8 cycles per course were considered. For patients with multiple courses of chemotherapy, all qualifying courses were considered.\\nChemotherapy regimens were ascertained based on a review of all HCPCS Level II codes for parenterally administered antineoplastic agents on medical claims with service dates within 6 days of the start of each cycle of chemotherapy. Regimens were categorized on a cycle-specific basis according to the number of agents administered that are considered to be myelotoxic (list available from authors upon request).\\n Daily filgrastim prophylaxis Cancer chemotherapy patients who received daily filgrastim prophylaxis during ≥1 cycle of chemotherapy were selected for inclusion in the study population. Administration of daily filgrastim on or before day 5 of a given cycle was considered to represent prophylaxis\\n[14,22,23]. Receipt of daily filgrastim was identified based on medical claims (J1440, J1441) with relevant codes from the HCPCS system.\\nDuration of daily filgrastim prophylaxis during the patient-cycle was characterized based on temporal patterns of administration; end of prophylaxis was defined as a gap ≥3 days in its use, outpatient administration of IV antimicrobial therapy (an indicator for possible CINC), or hospitalization for CINC. Duration of prophylaxis was characterized as 1–3, 4–6, or ≥7 days.\\nCancer chemotherapy patients who received daily filgrastim prophylaxis during ≥1 cycle of chemotherapy were selected for inclusion in the study population. Administration of daily filgrastim on or before day 5 of a given cycle was considered to represent prophylaxis\\n[14,22,23]. Receipt of daily filgrastim was identified based on medical claims (J1440, J1441) with relevant codes from the HCPCS system.\\nDuration of daily filgrastim prophylaxis during the patient-cycle was characterized based on temporal patterns of administration; end of prophylaxis was defined as a gap ≥3 days in its use, outpatient administration of IV antimicrobial therapy (an indicator for possible CINC), or hospitalization for CINC. Duration of prophylaxis was characterized as 1–3, 4–6, or ≥7 days.\\n Exclusion criteria Patient-cycles were excluded from the analytic file if the following occurred: (1) evidence of ≥2 primary cancers (solid or blood) within (i.e., +/-) 30 days of chemotherapy initiation; (2) any gaps in health benefits during the 6-month (\\\"pretreatment\\\") period prior to chemotherapy initiation; (3) evidence of hematopoietic stem cell or bone marrow transplantation prior to or during receipt of chemotherapy; (4) evidence of chemotherapy based only on medical claims for administration of the drugs (HCPCS codes identifying the specific chemotherapy agents were not available, and thus the regimen and level of myelosuppression could not be determined); (5) pharmacy claims for myelotoxic chemotherapy (only drug dispense dates are available on pharmacy claims, and because pharmacy/medical claims cannot be definitively linked, precise dates of chemotherapy administration–needed to characterize the course and cycles–could not be ascertained); (6) pharmacy claims for daily filgrastim (precise dates of administration could not be ascertained, for reasons stated above); (7) evidence of receipt of sargramostim (J2820) or pegfilgrastim (C9119, S0135, J2505) during the first 5 days of the cycle (i.e., as prophylaxis).\\nPatient-cycles were excluded from the analytic file if the following occurred: (1) evidence of ≥2 primary cancers (solid or blood) within (i.e., +/-) 30 days of chemotherapy initiation; (2) any gaps in health benefits during the 6-month (\\\"pretreatment\\\") period prior to chemotherapy initiation; (3) evidence of hematopoietic stem cell or bone marrow transplantation prior to or during receipt of chemotherapy; (4) evidence of chemotherapy based only on medical claims for administration of the drugs (HCPCS codes identifying the specific chemotherapy agents were not available, and thus the regimen and level of myelosuppression could not be determined); (5) pharmacy claims for myelotoxic chemotherapy (only drug dispense dates are available on pharmacy claims, and because pharmacy/medical claims cannot be definitively linked, precise dates of chemotherapy administration–needed to characterize the course and cycles–could not be ascertained); (6) pharmacy claims for daily filgrastim (precise dates of administration could not be ascertained, for reasons stated above); (7) evidence of receipt of sargramostim (J2820) or pegfilgrastim (C9119, S0135, J2505) during the first 5 days of the cycle (i.e., as prophylaxis).\",\n \"All patients, aged ≥18 years, who began ≥1 new course of myelosuppressive chemotherapy were identified; the enrollment window was July 1, 2001 through June 30, 2010 for patients in the MarketScan Database and July 1, 2001 through June 30, 2008 for patients in the LifeLink Database. Receipt of chemotherapy was ascertained based on the presence of ≥1 paid medical claim for a chemotherapy drug or administration thereof (identified using Healthcare Common Procedure Coding System [HCPCS], International Classification of Disease, Ninth Revision, Clinical Modification [ICD-9-CM], and Health Care Financing Administration Uniform Bill-92 [UB-92] revenue codes).\\nPatients were considered to have initiated a new course of chemotherapy if there was a chemotherapy claim during the study period that was preceded by a period ≥60 days without any other claims for chemotherapy. Only patients who had evidence of a primary solid tumor or NHL (based on ≥2 medical claims [≥7 days apart] with a qualifying 3-digit ICD-9-CM diagnosis code during the period beginning 30 days prior to the index date and ending 30 days thereafter) were selected.\",\n \"For each cancer chemotherapy patient, each unique cycle within each course of chemotherapy was identified. The first chemotherapy cycle (of the first course) was defined as beginning with the date of initiation of chemotherapy and ending with the first service date for the next administration of chemotherapy administration (as evidenced by a medical claim with a corresponding HCPCS, ICD-9-CM, or UB-92 code) occurring at least 12 days-but no more than 59 days-after the date of initiation of chemotherapy. If a second chemotherapy cycle did not commence prior to day 60, or if there was evidence of receipt of radiation therapy (based on medical claims with relevant HCPCS, ICD-9-CM, or UB-92 codes) during this period, both the first cycle of chemotherapy and the course of chemotherapy were considered to have been completed 30 days following the beginning of the cycle or on the day prior to initiation of radiation therapy, whichever occurred first. The second and all subsequent cycles of chemotherapy, as well as subsequent courses of chemotherapy–if any–during the period of interest, were similarly defined. A maximum of 8 cycles per course were considered. For patients with multiple courses of chemotherapy, all qualifying courses were considered.\\nChemotherapy regimens were ascertained based on a review of all HCPCS Level II codes for parenterally administered antineoplastic agents on medical claims with service dates within 6 days of the start of each cycle of chemotherapy. Regimens were categorized on a cycle-specific basis according to the number of agents administered that are considered to be myelotoxic (list available from authors upon request).\",\n \"Cancer chemotherapy patients who received daily filgrastim prophylaxis during ≥1 cycle of chemotherapy were selected for inclusion in the study population. Administration of daily filgrastim on or before day 5 of a given cycle was considered to represent prophylaxis\\n[14,22,23]. Receipt of daily filgrastim was identified based on medical claims (J1440, J1441) with relevant codes from the HCPCS system.\\nDuration of daily filgrastim prophylaxis during the patient-cycle was characterized based on temporal patterns of administration; end of prophylaxis was defined as a gap ≥3 days in its use, outpatient administration of IV antimicrobial therapy (an indicator for possible CINC), or hospitalization for CINC. Duration of prophylaxis was characterized as 1–3, 4–6, or ≥7 days.\",\n \"Patient-cycles were excluded from the analytic file if the following occurred: (1) evidence of ≥2 primary cancers (solid or blood) within (i.e., +/-) 30 days of chemotherapy initiation; (2) any gaps in health benefits during the 6-month (\\\"pretreatment\\\") period prior to chemotherapy initiation; (3) evidence of hematopoietic stem cell or bone marrow transplantation prior to or during receipt of chemotherapy; (4) evidence of chemotherapy based only on medical claims for administration of the drugs (HCPCS codes identifying the specific chemotherapy agents were not available, and thus the regimen and level of myelosuppression could not be determined); (5) pharmacy claims for myelotoxic chemotherapy (only drug dispense dates are available on pharmacy claims, and because pharmacy/medical claims cannot be definitively linked, precise dates of chemotherapy administration–needed to characterize the course and cycles–could not be ascertained); (6) pharmacy claims for daily filgrastim (precise dates of administration could not be ascertained, for reasons stated above); (7) evidence of receipt of sargramostim (J2820) or pegfilgrastim (C9119, S0135, J2505) during the first 5 days of the cycle (i.e., as prophylaxis).\",\n \"CINC was identified based on inpatient admissions with a diagnosis (principal or secondary) of neutropenia (ICD-9-CM 288.0), fever (780.6), or infection (list available upon request). Admissions were identified on a cycle-specific basis using acute-care facility inpatient claims with admission dates anytime between day 6 and the last day of the chemotherapy cycle. Episodes of CINC treated exclusively on an outpatient basis were not considered. Because CINC that occurred on the day of the last dose of prophylaxis or the following day could have resulted in an artificial truncation of a planned longer course of prophylaxis–and thus could exaggerate the risk of CINC with shorter courses–analyses were conducted alternatively with and without inclusion of these patient cycles.\\nConsequences of CINC requiring inpatient care were characterized in terms of in-hospital mortality, total hospital LOS (during the cycle), and total CINC-related healthcare expenditures (i.e., from hospital discharge to end of cycle). Total CINC-related healthcare expenditures included those for the initial hospitalization as well as care provided post-discharge on an outpatient basis; outpatient expenditures comprised encounters with a diagnosis of neutropenia, fever, or infection, as well as use/prescriptions for CSF agents (i.e., as treatment) and antimicrobial therapy. Expenditures were estimated based on total paid amounts on corresponding claims. Mortality was characterized using data only from the MarketScan Database as such information was not available in the LifeLink Database.\",\n \"Patient characteristics included: age; gender; presence of selected chronic comorbidities (cardiovascular disease, diabetes, liver disease, renal disease); history of blood disorders (anemia, neutropenia, other), infection, hospitalization (all-cause and CINC-related, respectively), chemotherapy, and radiation therapy; pre-chemotherapy healthcare expenditures; type of cancer, presence of metastases; cycle number and minimum length of prior cycles; chemotherapy regimen; receipt of antimicrobial prophylaxis in the cycle of interest; and year of chemotherapy initiation. Only observed data were utilized in defining patient characteristics.\\nAge was assessed as of the first day of the first cycle of chemotherapy in the course. Chronic comorbidities and history of blood disorders, infections, hospitalization, chemotherapy, radiation therapy, presence of metastases, and healthcare expenditures were assessed from the beginning of the 6-month pretreatment period through the first day of the corresponding cycle of chemotherapy. Selected variables (i.e., anemia, neutropenia, other blood disorders, infections) were alternatively evaluated during the chemotherapy course (up to the beginning of the cycle of interest). Metastases (bone vs. other site) and chronic comorbidities were identified on the basis of ≥1 diagnosis codes on inpatient claims, ≥2 diagnosis codes on outpatient claims (excluding those for laboratory services) on different days, ≥1 procedure codes, and ≥1 drug codes, as appropriate. Blood disorders and infections were identified on the basis of ≥1 diagnosis codes (on inpatient and/or outpatient claims) and ≥1 drug codes, as appropriate. Prophylactic use of antimicrobial agents was ascertained based on a medical claim for administration of drug from cycle day 1 to cycle day 5, or a pharmacy claim for a filled prescription from cycle day -3 to cycle day 5, with a corresponding drug code.\",\n \"Incidence of CINC was evaluated for each patient-cycle in which daily filgrastim was administered prophylactically, and was estimated by same-cycle duration of prophylaxis in an unadjusted and adjusted context. For the latter, a generalized estimating equation (GEE) with a binomial distribution, logistic link function, and exchangeable correlation structure was employed. The GEE method accounts for correlation among repeated measures for the same subject (in this instance, across cycles), while controlling for both fixed characteristics (e.g., gender) and time-dependent covariates (e.g., first versus subsequent cycles). All observed patient characteristics were entered into, and retained in, the multivariate model. Subgroup and sensitivity analyses focusing on the first cycle only, employing a narrow definition of CINC (which was identified using the diagnosis code for neutropenia only but otherwise the same algorithm for the broad definition as described above), and focusing on alternative tumor types separately (i.e., breast cancer, lung cancer, colorectal cancer, and NHL) also were conducted.\\nIn-hospital mortality, hospital LOS, and CINC-related healthcare expenditures among patients who developed CINC were descriptively evaluated by duration of daily filgrastim prophylaxis. Confidence intervals (95%) for in-hospital mortality were computed using the Wilson score interval; confidence intervals for LOS and economic costs were computed using nonparametric bootstrapping (percentile method) from the study population (1,000 replicates with replacement). Confidence intervals for in-hospital mortality, hospital LOS, and healthcare expenditures were estimated assuming independence between observations. Only observed data were used in defining study variables; patients who developed CINC but were missing data on study measures–because such data either were not recorded on claims or were not provided by some health plans–were excluded from corresponding analyses.\",\n \"Funding for this research was provided by Amgen Inc. to Policy Analysis Inc. (PAI). Derek Weycker, John Edelsberg, and Alex Kartashov are employed by Policy Analysis Inc. (PAI). Rich Barron and Jason Legg are employed by Amgen Inc. Andrew Glass is employed by the Center for Health Research, Kaiser Permanente Northwest, and received an honorarium for this research from Amgen Inc.\",\n \"Authorship was designated based on the guidelines promulgated by the International Committee of Medical Journal Editors (2004). All persons who meet criteria for authorship are listed as authors on the title page. The contribution of each of these individuals to this study–by task–is as follows: conception and supervision (Barron, Weycker), development of design (Barron, Edelsberg, Glass, Legg, Weycker), conduct of analyses (Kartashov, Weycker), interpretation of results (all authors), preparation of manuscript (Edelsberg, Weycker), and review of manuscript (all authors). All authors have read and approved the final version of the manuscript. The study sponsor reviewed the study research plan and study manuscript; data management, processing, and analyses were conducted by Policy Analysis Inc. (PAI), and all final analytic decisions were made by study investigators.\",\n \"The pre-publication history for this paper can be accessed here:\\n\\nhttp://www.biomedcentral.com/1472-6963/14/189/prepub\\n\"\n]"},"other_sections_sec_types":{"kind":"list like","value":[null,null,null,null,null,null,null,null,null,null,null,null,null],"string":"[\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null\n]"},"all_sections_titles":{"kind":"list like","value":["Background","Methods","Data source","Study population","Cancer chemotherapy patients","Chemotherapy courses, cycles, and regimens","Daily filgrastim prophylaxis","Exclusion criteria","Neutropenic complications requiring inpatient care","Patient characteristics","Statistical analyses","Results","Discussion","Conclusion","Competing interests","Authors’ contributions","Pre-publication history"],"string":"[\n \"Background\",\n \"Methods\",\n \"Data source\",\n \"Study population\",\n \"Cancer chemotherapy patients\",\n \"Chemotherapy courses, cycles, and regimens\",\n \"Daily filgrastim prophylaxis\",\n \"Exclusion criteria\",\n \"Neutropenic complications requiring inpatient care\",\n \"Patient characteristics\",\n \"Statistical analyses\",\n \"Results\",\n \"Discussion\",\n \"Conclusion\",\n \"Competing interests\",\n \"Authors’ contributions\",\n \"Pre-publication history\"\n]"},"all_sections_texts":{"kind":"list like","value":["Neutropenia is a common side effect of myelosuppressive chemotherapy that both increases the risk of infection and diminishes patients’ ability to fight infection. When neutropenic patients become febrile, the high likelihood of infection and serious consequences thereof usually results in hospitalization\n[1,2]. FN, as well as severe or prolonged neutropenia, also can interfere with the planned delivery of treatment and adversely affect important patient outcomes\n[2-11].\nClinical practice guidelines recommend primary prophylactic use of a colony-stimulating factor (CSF)–which has been shown to reduce the risk of FN in clinical trials–when the risk of FN is 20% or higher\n[2]. While the American Society of Clinical Oncology (ASCO) initially recommended that CSF prophylaxis be administered only when FN risk is 40% or higher, in 2006, ASCO lowered the threshold to 20% based on data highlighting the importance of FN-related hospitalization as an outcome and evidence demonstrating the value of CSF prophylaxis in reducing the risk of FN, the risk of FN-related hospitalization, and the associated use of IV anti-infective agents\n[2,12,13]. The CSF filgrastim, which is widely used in clinical practice as prophylaxis against FN, requires daily administration during each cycle until neutrophil recovery occurs (in clinical trials, given typically for 10–11 days [and up to 14 days] until absolute neutrophil count [ANC] ≥10 × 109/L)\n[14-19].\nIn clinical practice, patients often receive shorter courses of daily filgrastim prophylaxis than those administered to subjects in the clinical trial setting\n[14-16,20-24]. While published studies suggest that shorter courses of daily filgrastim prophylaxis are associated with an increased risk of hospitalization for CINC, these studies focused on selected tumor types or employed data that now are over a decade old\n[23,24]. During the past decade, use of chemotherapy and supportive care in clinical practice–as well as recommended use of these agents in authoritative guidelines–has changed considerably\n[2,25-27], Moreover, only one study has examined whether CSFs may favorably impact clinical outcomes and economic costs when CINC develop despite CSF prophylaxis, and it was published a decade ago and focused on elderly patients with a single type of cancer (non-Hodgkin’s lymphoma)\n[28]. We therefore undertook a new study to evaluate the relationship between the duration of daily filgrastim prophylaxis, and the risk and consequences of CINC requiring inpatient care using a large healthcare claims database. While such databases often lack detailed clinical information (e.g., on absolute neutrophil counts), they offer access to information on the health profile and healthcare utilization of tens of millions of covered lives."," Data source Patient-level information from two large healthcare claims databases–the Thomson Reuters MarketScan Commercial Claims and Encounters and Medicare Supplemental and Coordination of Benefits Database (MarketScan Database, 2001–2010) and the Intercontinental Marketing Services LifeLink Database (LifeLink Database, 2001–2008)–were pooled for analyses. Both databases comprise medical (i.e., facility and professional service) and outpatient pharmacy claims from a large number of participating private health plans, and each contains claims data for 15 million persons annually.\nData available for each facility and professional-service claim include date and place of service, diagnoses, procedures performed/services rendered, and quantity of services (professional-service claims). Data available for each retail pharmacy claim include the drug dispensed, dispensing date, quantity dispensed, and number of days supplied. All claims also include paid (i.e., reimbursed) amounts. Selected demographic and eligibility information is available for persons in both databases. All data can be arrayed to provide a detailed chronology of all medical and pharmacy services used by each plan member over time.\nThe study databases were de-identified prior to their release to study investigators, as set forth in the corresponding Data Use Agreements. The study databases have been evaluated and certified by independent third parties to be in compliance with the Health Insurance Portability and Accountability Act (HIPAA) of 1996 statistical de-identification standards and to satisfy the conditions set forth in Sections 164.514 (a)-(b) 1ii of the HIPAA Privacy Rule regarding the determination and documentation of statistically de-identified data. Use of the study databases for health services research was determined–via independent third parties–to be fully compliant with the HIPAA Privacy Rule and federal guidance on Public Welfare and the Protection of Human Subjects\n[29].\nPatient-level information from two large healthcare claims databases–the Thomson Reuters MarketScan Commercial Claims and Encounters and Medicare Supplemental and Coordination of Benefits Database (MarketScan Database, 2001–2010) and the Intercontinental Marketing Services LifeLink Database (LifeLink Database, 2001–2008)–were pooled for analyses. Both databases comprise medical (i.e., facility and professional service) and outpatient pharmacy claims from a large number of participating private health plans, and each contains claims data for 15 million persons annually.\nData available for each facility and professional-service claim include date and place of service, diagnoses, procedures performed/services rendered, and quantity of services (professional-service claims). Data available for each retail pharmacy claim include the drug dispensed, dispensing date, quantity dispensed, and number of days supplied. All claims also include paid (i.e., reimbursed) amounts. Selected demographic and eligibility information is available for persons in both databases. All data can be arrayed to provide a detailed chronology of all medical and pharmacy services used by each plan member over time.\nThe study databases were de-identified prior to their release to study investigators, as set forth in the corresponding Data Use Agreements. The study databases have been evaluated and certified by independent third parties to be in compliance with the Health Insurance Portability and Accountability Act (HIPAA) of 1996 statistical de-identification standards and to satisfy the conditions set forth in Sections 164.514 (a)-(b) 1ii of the HIPAA Privacy Rule regarding the determination and documentation of statistically de-identified data. Use of the study databases for health services research was determined–via independent third parties–to be fully compliant with the HIPAA Privacy Rule and federal guidance on Public Welfare and the Protection of Human Subjects\n[29].\n Study population The study population comprised all patients who initiated ≥1 course of myelosuppressive chemotherapy for a solid tumor or non-Hodgkin’s lymphoma (NHL) and who received daily filgrastim prophylaxis during ≥1 cycle. All cycles in which patients received daily filgrastim prophylaxis–irrespective of duration–were pooled for analyses.\n Cancer chemotherapy patients All patients, aged ≥18 years, who began ≥1 new course of myelosuppressive chemotherapy were identified; the enrollment window was July 1, 2001 through June 30, 2010 for patients in the MarketScan Database and July 1, 2001 through June 30, 2008 for patients in the LifeLink Database. Receipt of chemotherapy was ascertained based on the presence of ≥1 paid medical claim for a chemotherapy drug or administration thereof (identified using Healthcare Common Procedure Coding System [HCPCS], International Classification of Disease, Ninth Revision, Clinical Modification [ICD-9-CM], and Health Care Financing Administration Uniform Bill-92 [UB-92] revenue codes).\nPatients were considered to have initiated a new course of chemotherapy if there was a chemotherapy claim during the study period that was preceded by a period ≥60 days without any other claims for chemotherapy. Only patients who had evidence of a primary solid tumor or NHL (based on ≥2 medical claims [≥7 days apart] with a qualifying 3-digit ICD-9-CM diagnosis code during the period beginning 30 days prior to the index date and ending 30 days thereafter) were selected.\nAll patients, aged ≥18 years, who began ≥1 new course of myelosuppressive chemotherapy were identified; the enrollment window was July 1, 2001 through June 30, 2010 for patients in the MarketScan Database and July 1, 2001 through June 30, 2008 for patients in the LifeLink Database. Receipt of chemotherapy was ascertained based on the presence of ≥1 paid medical claim for a chemotherapy drug or administration thereof (identified using Healthcare Common Procedure Coding System [HCPCS], International Classification of Disease, Ninth Revision, Clinical Modification [ICD-9-CM], and Health Care Financing Administration Uniform Bill-92 [UB-92] revenue codes).\nPatients were considered to have initiated a new course of chemotherapy if there was a chemotherapy claim during the study period that was preceded by a period ≥60 days without any other claims for chemotherapy. Only patients who had evidence of a primary solid tumor or NHL (based on ≥2 medical claims [≥7 days apart] with a qualifying 3-digit ICD-9-CM diagnosis code during the period beginning 30 days prior to the index date and ending 30 days thereafter) were selected.\n Chemotherapy courses, cycles, and regimens For each cancer chemotherapy patient, each unique cycle within each course of chemotherapy was identified. The first chemotherapy cycle (of the first course) was defined as beginning with the date of initiation of chemotherapy and ending with the first service date for the next administration of chemotherapy administration (as evidenced by a medical claim with a corresponding HCPCS, ICD-9-CM, or UB-92 code) occurring at least 12 days-but no more than 59 days-after the date of initiation of chemotherapy. If a second chemotherapy cycle did not commence prior to day 60, or if there was evidence of receipt of radiation therapy (based on medical claims with relevant HCPCS, ICD-9-CM, or UB-92 codes) during this period, both the first cycle of chemotherapy and the course of chemotherapy were considered to have been completed 30 days following the beginning of the cycle or on the day prior to initiation of radiation therapy, whichever occurred first. The second and all subsequent cycles of chemotherapy, as well as subsequent courses of chemotherapy–if any–during the period of interest, were similarly defined. A maximum of 8 cycles per course were considered. For patients with multiple courses of chemotherapy, all qualifying courses were considered.\nChemotherapy regimens were ascertained based on a review of all HCPCS Level II codes for parenterally administered antineoplastic agents on medical claims with service dates within 6 days of the start of each cycle of chemotherapy. Regimens were categorized on a cycle-specific basis according to the number of agents administered that are considered to be myelotoxic (list available from authors upon request).\nFor each cancer chemotherapy patient, each unique cycle within each course of chemotherapy was identified. The first chemotherapy cycle (of the first course) was defined as beginning with the date of initiation of chemotherapy and ending with the first service date for the next administration of chemotherapy administration (as evidenced by a medical claim with a corresponding HCPCS, ICD-9-CM, or UB-92 code) occurring at least 12 days-but no more than 59 days-after the date of initiation of chemotherapy. If a second chemotherapy cycle did not commence prior to day 60, or if there was evidence of receipt of radiation therapy (based on medical claims with relevant HCPCS, ICD-9-CM, or UB-92 codes) during this period, both the first cycle of chemotherapy and the course of chemotherapy were considered to have been completed 30 days following the beginning of the cycle or on the day prior to initiation of radiation therapy, whichever occurred first. The second and all subsequent cycles of chemotherapy, as well as subsequent courses of chemotherapy–if any–during the period of interest, were similarly defined. A maximum of 8 cycles per course were considered. For patients with multiple courses of chemotherapy, all qualifying courses were considered.\nChemotherapy regimens were ascertained based on a review of all HCPCS Level II codes for parenterally administered antineoplastic agents on medical claims with service dates within 6 days of the start of each cycle of chemotherapy. Regimens were categorized on a cycle-specific basis according to the number of agents administered that are considered to be myelotoxic (list available from authors upon request).\n Daily filgrastim prophylaxis Cancer chemotherapy patients who received daily filgrastim prophylaxis during ≥1 cycle of chemotherapy were selected for inclusion in the study population. Administration of daily filgrastim on or before day 5 of a given cycle was considered to represent prophylaxis\n[14,22,23]. Receipt of daily filgrastim was identified based on medical claims (J1440, J1441) with relevant codes from the HCPCS system.\nDuration of daily filgrastim prophylaxis during the patient-cycle was characterized based on temporal patterns of administration; end of prophylaxis was defined as a gap ≥3 days in its use, outpatient administration of IV antimicrobial therapy (an indicator for possible CINC), or hospitalization for CINC. Duration of prophylaxis was characterized as 1–3, 4–6, or ≥7 days.\nCancer chemotherapy patients who received daily filgrastim prophylaxis during ≥1 cycle of chemotherapy were selected for inclusion in the study population. Administration of daily filgrastim on or before day 5 of a given cycle was considered to represent prophylaxis\n[14,22,23]. Receipt of daily filgrastim was identified based on medical claims (J1440, J1441) with relevant codes from the HCPCS system.\nDuration of daily filgrastim prophylaxis during the patient-cycle was characterized based on temporal patterns of administration; end of prophylaxis was defined as a gap ≥3 days in its use, outpatient administration of IV antimicrobial therapy (an indicator for possible CINC), or hospitalization for CINC. Duration of prophylaxis was characterized as 1–3, 4–6, or ≥7 days.\n Exclusion criteria Patient-cycles were excluded from the analytic file if the following occurred: (1) evidence of ≥2 primary cancers (solid or blood) within (i.e., +/-) 30 days of chemotherapy initiation; (2) any gaps in health benefits during the 6-month (\"pretreatment\") period prior to chemotherapy initiation; (3) evidence of hematopoietic stem cell or bone marrow transplantation prior to or during receipt of chemotherapy; (4) evidence of chemotherapy based only on medical claims for administration of the drugs (HCPCS codes identifying the specific chemotherapy agents were not available, and thus the regimen and level of myelosuppression could not be determined); (5) pharmacy claims for myelotoxic chemotherapy (only drug dispense dates are available on pharmacy claims, and because pharmacy/medical claims cannot be definitively linked, precise dates of chemotherapy administration–needed to characterize the course and cycles–could not be ascertained); (6) pharmacy claims for daily filgrastim (precise dates of administration could not be ascertained, for reasons stated above); (7) evidence of receipt of sargramostim (J2820) or pegfilgrastim (C9119, S0135, J2505) during the first 5 days of the cycle (i.e., as prophylaxis).\nPatient-cycles were excluded from the analytic file if the following occurred: (1) evidence of ≥2 primary cancers (solid or blood) within (i.e., +/-) 30 days of chemotherapy initiation; (2) any gaps in health benefits during the 6-month (\"pretreatment\") period prior to chemotherapy initiation; (3) evidence of hematopoietic stem cell or bone marrow transplantation prior to or during receipt of chemotherapy; (4) evidence of chemotherapy based only on medical claims for administration of the drugs (HCPCS codes identifying the specific chemotherapy agents were not available, and thus the regimen and level of myelosuppression could not be determined); (5) pharmacy claims for myelotoxic chemotherapy (only drug dispense dates are available on pharmacy claims, and because pharmacy/medical claims cannot be definitively linked, precise dates of chemotherapy administration–needed to characterize the course and cycles–could not be ascertained); (6) pharmacy claims for daily filgrastim (precise dates of administration could not be ascertained, for reasons stated above); (7) evidence of receipt of sargramostim (J2820) or pegfilgrastim (C9119, S0135, J2505) during the first 5 days of the cycle (i.e., as prophylaxis).\nThe study population comprised all patients who initiated ≥1 course of myelosuppressive chemotherapy for a solid tumor or non-Hodgkin’s lymphoma (NHL) and who received daily filgrastim prophylaxis during ≥1 cycle. All cycles in which patients received daily filgrastim prophylaxis–irrespective of duration–were pooled for analyses.\n Cancer chemotherapy patients All patients, aged ≥18 years, who began ≥1 new course of myelosuppressive chemotherapy were identified; the enrollment window was July 1, 2001 through June 30, 2010 for patients in the MarketScan Database and July 1, 2001 through June 30, 2008 for patients in the LifeLink Database. Receipt of chemotherapy was ascertained based on the presence of ≥1 paid medical claim for a chemotherapy drug or administration thereof (identified using Healthcare Common Procedure Coding System [HCPCS], International Classification of Disease, Ninth Revision, Clinical Modification [ICD-9-CM], and Health Care Financing Administration Uniform Bill-92 [UB-92] revenue codes).\nPatients were considered to have initiated a new course of chemotherapy if there was a chemotherapy claim during the study period that was preceded by a period ≥60 days without any other claims for chemotherapy. Only patients who had evidence of a primary solid tumor or NHL (based on ≥2 medical claims [≥7 days apart] with a qualifying 3-digit ICD-9-CM diagnosis code during the period beginning 30 days prior to the index date and ending 30 days thereafter) were selected.\nAll patients, aged ≥18 years, who began ≥1 new course of myelosuppressive chemotherapy were identified; the enrollment window was July 1, 2001 through June 30, 2010 for patients in the MarketScan Database and July 1, 2001 through June 30, 2008 for patients in the LifeLink Database. Receipt of chemotherapy was ascertained based on the presence of ≥1 paid medical claim for a chemotherapy drug or administration thereof (identified using Healthcare Common Procedure Coding System [HCPCS], International Classification of Disease, Ninth Revision, Clinical Modification [ICD-9-CM], and Health Care Financing Administration Uniform Bill-92 [UB-92] revenue codes).\nPatients were considered to have initiated a new course of chemotherapy if there was a chemotherapy claim during the study period that was preceded by a period ≥60 days without any other claims for chemotherapy. Only patients who had evidence of a primary solid tumor or NHL (based on ≥2 medical claims [≥7 days apart] with a qualifying 3-digit ICD-9-CM diagnosis code during the period beginning 30 days prior to the index date and ending 30 days thereafter) were selected.\n Chemotherapy courses, cycles, and regimens For each cancer chemotherapy patient, each unique cycle within each course of chemotherapy was identified. The first chemotherapy cycle (of the first course) was defined as beginning with the date of initiation of chemotherapy and ending with the first service date for the next administration of chemotherapy administration (as evidenced by a medical claim with a corresponding HCPCS, ICD-9-CM, or UB-92 code) occurring at least 12 days-but no more than 59 days-after the date of initiation of chemotherapy. If a second chemotherapy cycle did not commence prior to day 60, or if there was evidence of receipt of radiation therapy (based on medical claims with relevant HCPCS, ICD-9-CM, or UB-92 codes) during this period, both the first cycle of chemotherapy and the course of chemotherapy were considered to have been completed 30 days following the beginning of the cycle or on the day prior to initiation of radiation therapy, whichever occurred first. The second and all subsequent cycles of chemotherapy, as well as subsequent courses of chemotherapy–if any–during the period of interest, were similarly defined. A maximum of 8 cycles per course were considered. For patients with multiple courses of chemotherapy, all qualifying courses were considered.\nChemotherapy regimens were ascertained based on a review of all HCPCS Level II codes for parenterally administered antineoplastic agents on medical claims with service dates within 6 days of the start of each cycle of chemotherapy. Regimens were categorized on a cycle-specific basis according to the number of agents administered that are considered to be myelotoxic (list available from authors upon request).\nFor each cancer chemotherapy patient, each unique cycle within each course of chemotherapy was identified. The first chemotherapy cycle (of the first course) was defined as beginning with the date of initiation of chemotherapy and ending with the first service date for the next administration of chemotherapy administration (as evidenced by a medical claim with a corresponding HCPCS, ICD-9-CM, or UB-92 code) occurring at least 12 days-but no more than 59 days-after the date of initiation of chemotherapy. If a second chemotherapy cycle did not commence prior to day 60, or if there was evidence of receipt of radiation therapy (based on medical claims with relevant HCPCS, ICD-9-CM, or UB-92 codes) during this period, both the first cycle of chemotherapy and the course of chemotherapy were considered to have been completed 30 days following the beginning of the cycle or on the day prior to initiation of radiation therapy, whichever occurred first. The second and all subsequent cycles of chemotherapy, as well as subsequent courses of chemotherapy–if any–during the period of interest, were similarly defined. A maximum of 8 cycles per course were considered. For patients with multiple courses of chemotherapy, all qualifying courses were considered.\nChemotherapy regimens were ascertained based on a review of all HCPCS Level II codes for parenterally administered antineoplastic agents on medical claims with service dates within 6 days of the start of each cycle of chemotherapy. Regimens were categorized on a cycle-specific basis according to the number of agents administered that are considered to be myelotoxic (list available from authors upon request).\n Daily filgrastim prophylaxis Cancer chemotherapy patients who received daily filgrastim prophylaxis during ≥1 cycle of chemotherapy were selected for inclusion in the study population. Administration of daily filgrastim on or before day 5 of a given cycle was considered to represent prophylaxis\n[14,22,23]. Receipt of daily filgrastim was identified based on medical claims (J1440, J1441) with relevant codes from the HCPCS system.\nDuration of daily filgrastim prophylaxis during the patient-cycle was characterized based on temporal patterns of administration; end of prophylaxis was defined as a gap ≥3 days in its use, outpatient administration of IV antimicrobial therapy (an indicator for possible CINC), or hospitalization for CINC. Duration of prophylaxis was characterized as 1–3, 4–6, or ≥7 days.\nCancer chemotherapy patients who received daily filgrastim prophylaxis during ≥1 cycle of chemotherapy were selected for inclusion in the study population. Administration of daily filgrastim on or before day 5 of a given cycle was considered to represent prophylaxis\n[14,22,23]. Receipt of daily filgrastim was identified based on medical claims (J1440, J1441) with relevant codes from the HCPCS system.\nDuration of daily filgrastim prophylaxis during the patient-cycle was characterized based on temporal patterns of administration; end of prophylaxis was defined as a gap ≥3 days in its use, outpatient administration of IV antimicrobial therapy (an indicator for possible CINC), or hospitalization for CINC. Duration of prophylaxis was characterized as 1–3, 4–6, or ≥7 days.\n Exclusion criteria Patient-cycles were excluded from the analytic file if the following occurred: (1) evidence of ≥2 primary cancers (solid or blood) within (i.e., +/-) 30 days of chemotherapy initiation; (2) any gaps in health benefits during the 6-month (\"pretreatment\") period prior to chemotherapy initiation; (3) evidence of hematopoietic stem cell or bone marrow transplantation prior to or during receipt of chemotherapy; (4) evidence of chemotherapy based only on medical claims for administration of the drugs (HCPCS codes identifying the specific chemotherapy agents were not available, and thus the regimen and level of myelosuppression could not be determined); (5) pharmacy claims for myelotoxic chemotherapy (only drug dispense dates are available on pharmacy claims, and because pharmacy/medical claims cannot be definitively linked, precise dates of chemotherapy administration–needed to characterize the course and cycles–could not be ascertained); (6) pharmacy claims for daily filgrastim (precise dates of administration could not be ascertained, for reasons stated above); (7) evidence of receipt of sargramostim (J2820) or pegfilgrastim (C9119, S0135, J2505) during the first 5 days of the cycle (i.e., as prophylaxis).\nPatient-cycles were excluded from the analytic file if the following occurred: (1) evidence of ≥2 primary cancers (solid or blood) within (i.e., +/-) 30 days of chemotherapy initiation; (2) any gaps in health benefits during the 6-month (\"pretreatment\") period prior to chemotherapy initiation; (3) evidence of hematopoietic stem cell or bone marrow transplantation prior to or during receipt of chemotherapy; (4) evidence of chemotherapy based only on medical claims for administration of the drugs (HCPCS codes identifying the specific chemotherapy agents were not available, and thus the regimen and level of myelosuppression could not be determined); (5) pharmacy claims for myelotoxic chemotherapy (only drug dispense dates are available on pharmacy claims, and because pharmacy/medical claims cannot be definitively linked, precise dates of chemotherapy administration–needed to characterize the course and cycles–could not be ascertained); (6) pharmacy claims for daily filgrastim (precise dates of administration could not be ascertained, for reasons stated above); (7) evidence of receipt of sargramostim (J2820) or pegfilgrastim (C9119, S0135, J2505) during the first 5 days of the cycle (i.e., as prophylaxis).\n Neutropenic complications requiring inpatient care CINC was identified based on inpatient admissions with a diagnosis (principal or secondary) of neutropenia (ICD-9-CM 288.0), fever (780.6), or infection (list available upon request). Admissions were identified on a cycle-specific basis using acute-care facility inpatient claims with admission dates anytime between day 6 and the last day of the chemotherapy cycle. Episodes of CINC treated exclusively on an outpatient basis were not considered. Because CINC that occurred on the day of the last dose of prophylaxis or the following day could have resulted in an artificial truncation of a planned longer course of prophylaxis–and thus could exaggerate the risk of CINC with shorter courses–analyses were conducted alternatively with and without inclusion of these patient cycles.\nConsequences of CINC requiring inpatient care were characterized in terms of in-hospital mortality, total hospital LOS (during the cycle), and total CINC-related healthcare expenditures (i.e., from hospital discharge to end of cycle). Total CINC-related healthcare expenditures included those for the initial hospitalization as well as care provided post-discharge on an outpatient basis; outpatient expenditures comprised encounters with a diagnosis of neutropenia, fever, or infection, as well as use/prescriptions for CSF agents (i.e., as treatment) and antimicrobial therapy. Expenditures were estimated based on total paid amounts on corresponding claims. Mortality was characterized using data only from the MarketScan Database as such information was not available in the LifeLink Database.\nCINC was identified based on inpatient admissions with a diagnosis (principal or secondary) of neutropenia (ICD-9-CM 288.0), fever (780.6), or infection (list available upon request). Admissions were identified on a cycle-specific basis using acute-care facility inpatient claims with admission dates anytime between day 6 and the last day of the chemotherapy cycle. Episodes of CINC treated exclusively on an outpatient basis were not considered. Because CINC that occurred on the day of the last dose of prophylaxis or the following day could have resulted in an artificial truncation of a planned longer course of prophylaxis–and thus could exaggerate the risk of CINC with shorter courses–analyses were conducted alternatively with and without inclusion of these patient cycles.\nConsequences of CINC requiring inpatient care were characterized in terms of in-hospital mortality, total hospital LOS (during the cycle), and total CINC-related healthcare expenditures (i.e., from hospital discharge to end of cycle). Total CINC-related healthcare expenditures included those for the initial hospitalization as well as care provided post-discharge on an outpatient basis; outpatient expenditures comprised encounters with a diagnosis of neutropenia, fever, or infection, as well as use/prescriptions for CSF agents (i.e., as treatment) and antimicrobial therapy. Expenditures were estimated based on total paid amounts on corresponding claims. Mortality was characterized using data only from the MarketScan Database as such information was not available in the LifeLink Database.\n Patient characteristics Patient characteristics included: age; gender; presence of selected chronic comorbidities (cardiovascular disease, diabetes, liver disease, renal disease); history of blood disorders (anemia, neutropenia, other), infection, hospitalization (all-cause and CINC-related, respectively), chemotherapy, and radiation therapy; pre-chemotherapy healthcare expenditures; type of cancer, presence of metastases; cycle number and minimum length of prior cycles; chemotherapy regimen; receipt of antimicrobial prophylaxis in the cycle of interest; and year of chemotherapy initiation. Only observed data were utilized in defining patient characteristics.\nAge was assessed as of the first day of the first cycle of chemotherapy in the course. Chronic comorbidities and history of blood disorders, infections, hospitalization, chemotherapy, radiation therapy, presence of metastases, and healthcare expenditures were assessed from the beginning of the 6-month pretreatment period through the first day of the corresponding cycle of chemotherapy. Selected variables (i.e., anemia, neutropenia, other blood disorders, infections) were alternatively evaluated during the chemotherapy course (up to the beginning of the cycle of interest). Metastases (bone vs. other site) and chronic comorbidities were identified on the basis of ≥1 diagnosis codes on inpatient claims, ≥2 diagnosis codes on outpatient claims (excluding those for laboratory services) on different days, ≥1 procedure codes, and ≥1 drug codes, as appropriate. Blood disorders and infections were identified on the basis of ≥1 diagnosis codes (on inpatient and/or outpatient claims) and ≥1 drug codes, as appropriate. Prophylactic use of antimicrobial agents was ascertained based on a medical claim for administration of drug from cycle day 1 to cycle day 5, or a pharmacy claim for a filled prescription from cycle day -3 to cycle day 5, with a corresponding drug code.\nPatient characteristics included: age; gender; presence of selected chronic comorbidities (cardiovascular disease, diabetes, liver disease, renal disease); history of blood disorders (anemia, neutropenia, other), infection, hospitalization (all-cause and CINC-related, respectively), chemotherapy, and radiation therapy; pre-chemotherapy healthcare expenditures; type of cancer, presence of metastases; cycle number and minimum length of prior cycles; chemotherapy regimen; receipt of antimicrobial prophylaxis in the cycle of interest; and year of chemotherapy initiation. Only observed data were utilized in defining patient characteristics.\nAge was assessed as of the first day of the first cycle of chemotherapy in the course. Chronic comorbidities and history of blood disorders, infections, hospitalization, chemotherapy, radiation therapy, presence of metastases, and healthcare expenditures were assessed from the beginning of the 6-month pretreatment period through the first day of the corresponding cycle of chemotherapy. Selected variables (i.e., anemia, neutropenia, other blood disorders, infections) were alternatively evaluated during the chemotherapy course (up to the beginning of the cycle of interest). Metastases (bone vs. other site) and chronic comorbidities were identified on the basis of ≥1 diagnosis codes on inpatient claims, ≥2 diagnosis codes on outpatient claims (excluding those for laboratory services) on different days, ≥1 procedure codes, and ≥1 drug codes, as appropriate. Blood disorders and infections were identified on the basis of ≥1 diagnosis codes (on inpatient and/or outpatient claims) and ≥1 drug codes, as appropriate. Prophylactic use of antimicrobial agents was ascertained based on a medical claim for administration of drug from cycle day 1 to cycle day 5, or a pharmacy claim for a filled prescription from cycle day -3 to cycle day 5, with a corresponding drug code.\n Statistical analyses Incidence of CINC was evaluated for each patient-cycle in which daily filgrastim was administered prophylactically, and was estimated by same-cycle duration of prophylaxis in an unadjusted and adjusted context. For the latter, a generalized estimating equation (GEE) with a binomial distribution, logistic link function, and exchangeable correlation structure was employed. The GEE method accounts for correlation among repeated measures for the same subject (in this instance, across cycles), while controlling for both fixed characteristics (e.g., gender) and time-dependent covariates (e.g., first versus subsequent cycles). All observed patient characteristics were entered into, and retained in, the multivariate model. Subgroup and sensitivity analyses focusing on the first cycle only, employing a narrow definition of CINC (which was identified using the diagnosis code for neutropenia only but otherwise the same algorithm for the broad definition as described above), and focusing on alternative tumor types separately (i.e., breast cancer, lung cancer, colorectal cancer, and NHL) also were conducted.\nIn-hospital mortality, hospital LOS, and CINC-related healthcare expenditures among patients who developed CINC were descriptively evaluated by duration of daily filgrastim prophylaxis. Confidence intervals (95%) for in-hospital mortality were computed using the Wilson score interval; confidence intervals for LOS and economic costs were computed using nonparametric bootstrapping (percentile method) from the study population (1,000 replicates with replacement). Confidence intervals for in-hospital mortality, hospital LOS, and healthcare expenditures were estimated assuming independence between observations. Only observed data were used in defining study variables; patients who developed CINC but were missing data on study measures–because such data either were not recorded on claims or were not provided by some health plans–were excluded from corresponding analyses.\nIncidence of CINC was evaluated for each patient-cycle in which daily filgrastim was administered prophylactically, and was estimated by same-cycle duration of prophylaxis in an unadjusted and adjusted context. For the latter, a generalized estimating equation (GEE) with a binomial distribution, logistic link function, and exchangeable correlation structure was employed. The GEE method accounts for correlation among repeated measures for the same subject (in this instance, across cycles), while controlling for both fixed characteristics (e.g., gender) and time-dependent covariates (e.g., first versus subsequent cycles). All observed patient characteristics were entered into, and retained in, the multivariate model. Subgroup and sensitivity analyses focusing on the first cycle only, employing a narrow definition of CINC (which was identified using the diagnosis code for neutropenia only but otherwise the same algorithm for the broad definition as described above), and focusing on alternative tumor types separately (i.e., breast cancer, lung cancer, colorectal cancer, and NHL) also were conducted.\nIn-hospital mortality, hospital LOS, and CINC-related healthcare expenditures among patients who developed CINC were descriptively evaluated by duration of daily filgrastim prophylaxis. Confidence intervals (95%) for in-hospital mortality were computed using the Wilson score interval; confidence intervals for LOS and economic costs were computed using nonparametric bootstrapping (percentile method) from the study population (1,000 replicates with replacement). Confidence intervals for in-hospital mortality, hospital LOS, and healthcare expenditures were estimated assuming independence between observations. Only observed data were used in defining study variables; patients who developed CINC but were missing data on study measures–because such data either were not recorded on claims or were not provided by some health plans–were excluded from corresponding analyses.","Patient-level information from two large healthcare claims databases–the Thomson Reuters MarketScan Commercial Claims and Encounters and Medicare Supplemental and Coordination of Benefits Database (MarketScan Database, 2001–2010) and the Intercontinental Marketing Services LifeLink Database (LifeLink Database, 2001–2008)–were pooled for analyses. Both databases comprise medical (i.e., facility and professional service) and outpatient pharmacy claims from a large number of participating private health plans, and each contains claims data for 15 million persons annually.\nData available for each facility and professional-service claim include date and place of service, diagnoses, procedures performed/services rendered, and quantity of services (professional-service claims). Data available for each retail pharmacy claim include the drug dispensed, dispensing date, quantity dispensed, and number of days supplied. All claims also include paid (i.e., reimbursed) amounts. Selected demographic and eligibility information is available for persons in both databases. All data can be arrayed to provide a detailed chronology of all medical and pharmacy services used by each plan member over time.\nThe study databases were de-identified prior to their release to study investigators, as set forth in the corresponding Data Use Agreements. The study databases have been evaluated and certified by independent third parties to be in compliance with the Health Insurance Portability and Accountability Act (HIPAA) of 1996 statistical de-identification standards and to satisfy the conditions set forth in Sections 164.514 (a)-(b) 1ii of the HIPAA Privacy Rule regarding the determination and documentation of statistically de-identified data. Use of the study databases for health services research was determined–via independent third parties–to be fully compliant with the HIPAA Privacy Rule and federal guidance on Public Welfare and the Protection of Human Subjects\n[29].","The study population comprised all patients who initiated ≥1 course of myelosuppressive chemotherapy for a solid tumor or non-Hodgkin’s lymphoma (NHL) and who received daily filgrastim prophylaxis during ≥1 cycle. All cycles in which patients received daily filgrastim prophylaxis–irrespective of duration–were pooled for analyses.\n Cancer chemotherapy patients All patients, aged ≥18 years, who began ≥1 new course of myelosuppressive chemotherapy were identified; the enrollment window was July 1, 2001 through June 30, 2010 for patients in the MarketScan Database and July 1, 2001 through June 30, 2008 for patients in the LifeLink Database. Receipt of chemotherapy was ascertained based on the presence of ≥1 paid medical claim for a chemotherapy drug or administration thereof (identified using Healthcare Common Procedure Coding System [HCPCS], International Classification of Disease, Ninth Revision, Clinical Modification [ICD-9-CM], and Health Care Financing Administration Uniform Bill-92 [UB-92] revenue codes).\nPatients were considered to have initiated a new course of chemotherapy if there was a chemotherapy claim during the study period that was preceded by a period ≥60 days without any other claims for chemotherapy. Only patients who had evidence of a primary solid tumor or NHL (based on ≥2 medical claims [≥7 days apart] with a qualifying 3-digit ICD-9-CM diagnosis code during the period beginning 30 days prior to the index date and ending 30 days thereafter) were selected.\nAll patients, aged ≥18 years, who began ≥1 new course of myelosuppressive chemotherapy were identified; the enrollment window was July 1, 2001 through June 30, 2010 for patients in the MarketScan Database and July 1, 2001 through June 30, 2008 for patients in the LifeLink Database. Receipt of chemotherapy was ascertained based on the presence of ≥1 paid medical claim for a chemotherapy drug or administration thereof (identified using Healthcare Common Procedure Coding System [HCPCS], International Classification of Disease, Ninth Revision, Clinical Modification [ICD-9-CM], and Health Care Financing Administration Uniform Bill-92 [UB-92] revenue codes).\nPatients were considered to have initiated a new course of chemotherapy if there was a chemotherapy claim during the study period that was preceded by a period ≥60 days without any other claims for chemotherapy. Only patients who had evidence of a primary solid tumor or NHL (based on ≥2 medical claims [≥7 days apart] with a qualifying 3-digit ICD-9-CM diagnosis code during the period beginning 30 days prior to the index date and ending 30 days thereafter) were selected.\n Chemotherapy courses, cycles, and regimens For each cancer chemotherapy patient, each unique cycle within each course of chemotherapy was identified. The first chemotherapy cycle (of the first course) was defined as beginning with the date of initiation of chemotherapy and ending with the first service date for the next administration of chemotherapy administration (as evidenced by a medical claim with a corresponding HCPCS, ICD-9-CM, or UB-92 code) occurring at least 12 days-but no more than 59 days-after the date of initiation of chemotherapy. If a second chemotherapy cycle did not commence prior to day 60, or if there was evidence of receipt of radiation therapy (based on medical claims with relevant HCPCS, ICD-9-CM, or UB-92 codes) during this period, both the first cycle of chemotherapy and the course of chemotherapy were considered to have been completed 30 days following the beginning of the cycle or on the day prior to initiation of radiation therapy, whichever occurred first. The second and all subsequent cycles of chemotherapy, as well as subsequent courses of chemotherapy–if any–during the period of interest, were similarly defined. A maximum of 8 cycles per course were considered. For patients with multiple courses of chemotherapy, all qualifying courses were considered.\nChemotherapy regimens were ascertained based on a review of all HCPCS Level II codes for parenterally administered antineoplastic agents on medical claims with service dates within 6 days of the start of each cycle of chemotherapy. Regimens were categorized on a cycle-specific basis according to the number of agents administered that are considered to be myelotoxic (list available from authors upon request).\nFor each cancer chemotherapy patient, each unique cycle within each course of chemotherapy was identified. The first chemotherapy cycle (of the first course) was defined as beginning with the date of initiation of chemotherapy and ending with the first service date for the next administration of chemotherapy administration (as evidenced by a medical claim with a corresponding HCPCS, ICD-9-CM, or UB-92 code) occurring at least 12 days-but no more than 59 days-after the date of initiation of chemotherapy. If a second chemotherapy cycle did not commence prior to day 60, or if there was evidence of receipt of radiation therapy (based on medical claims with relevant HCPCS, ICD-9-CM, or UB-92 codes) during this period, both the first cycle of chemotherapy and the course of chemotherapy were considered to have been completed 30 days following the beginning of the cycle or on the day prior to initiation of radiation therapy, whichever occurred first. The second and all subsequent cycles of chemotherapy, as well as subsequent courses of chemotherapy–if any–during the period of interest, were similarly defined. A maximum of 8 cycles per course were considered. For patients with multiple courses of chemotherapy, all qualifying courses were considered.\nChemotherapy regimens were ascertained based on a review of all HCPCS Level II codes for parenterally administered antineoplastic agents on medical claims with service dates within 6 days of the start of each cycle of chemotherapy. Regimens were categorized on a cycle-specific basis according to the number of agents administered that are considered to be myelotoxic (list available from authors upon request).\n Daily filgrastim prophylaxis Cancer chemotherapy patients who received daily filgrastim prophylaxis during ≥1 cycle of chemotherapy were selected for inclusion in the study population. Administration of daily filgrastim on or before day 5 of a given cycle was considered to represent prophylaxis\n[14,22,23]. Receipt of daily filgrastim was identified based on medical claims (J1440, J1441) with relevant codes from the HCPCS system.\nDuration of daily filgrastim prophylaxis during the patient-cycle was characterized based on temporal patterns of administration; end of prophylaxis was defined as a gap ≥3 days in its use, outpatient administration of IV antimicrobial therapy (an indicator for possible CINC), or hospitalization for CINC. Duration of prophylaxis was characterized as 1–3, 4–6, or ≥7 days.\nCancer chemotherapy patients who received daily filgrastim prophylaxis during ≥1 cycle of chemotherapy were selected for inclusion in the study population. Administration of daily filgrastim on or before day 5 of a given cycle was considered to represent prophylaxis\n[14,22,23]. Receipt of daily filgrastim was identified based on medical claims (J1440, J1441) with relevant codes from the HCPCS system.\nDuration of daily filgrastim prophylaxis during the patient-cycle was characterized based on temporal patterns of administration; end of prophylaxis was defined as a gap ≥3 days in its use, outpatient administration of IV antimicrobial therapy (an indicator for possible CINC), or hospitalization for CINC. Duration of prophylaxis was characterized as 1–3, 4–6, or ≥7 days.\n Exclusion criteria Patient-cycles were excluded from the analytic file if the following occurred: (1) evidence of ≥2 primary cancers (solid or blood) within (i.e., +/-) 30 days of chemotherapy initiation; (2) any gaps in health benefits during the 6-month (\"pretreatment\") period prior to chemotherapy initiation; (3) evidence of hematopoietic stem cell or bone marrow transplantation prior to or during receipt of chemotherapy; (4) evidence of chemotherapy based only on medical claims for administration of the drugs (HCPCS codes identifying the specific chemotherapy agents were not available, and thus the regimen and level of myelosuppression could not be determined); (5) pharmacy claims for myelotoxic chemotherapy (only drug dispense dates are available on pharmacy claims, and because pharmacy/medical claims cannot be definitively linked, precise dates of chemotherapy administration–needed to characterize the course and cycles–could not be ascertained); (6) pharmacy claims for daily filgrastim (precise dates of administration could not be ascertained, for reasons stated above); (7) evidence of receipt of sargramostim (J2820) or pegfilgrastim (C9119, S0135, J2505) during the first 5 days of the cycle (i.e., as prophylaxis).\nPatient-cycles were excluded from the analytic file if the following occurred: (1) evidence of ≥2 primary cancers (solid or blood) within (i.e., +/-) 30 days of chemotherapy initiation; (2) any gaps in health benefits during the 6-month (\"pretreatment\") period prior to chemotherapy initiation; (3) evidence of hematopoietic stem cell or bone marrow transplantation prior to or during receipt of chemotherapy; (4) evidence of chemotherapy based only on medical claims for administration of the drugs (HCPCS codes identifying the specific chemotherapy agents were not available, and thus the regimen and level of myelosuppression could not be determined); (5) pharmacy claims for myelotoxic chemotherapy (only drug dispense dates are available on pharmacy claims, and because pharmacy/medical claims cannot be definitively linked, precise dates of chemotherapy administration–needed to characterize the course and cycles–could not be ascertained); (6) pharmacy claims for daily filgrastim (precise dates of administration could not be ascertained, for reasons stated above); (7) evidence of receipt of sargramostim (J2820) or pegfilgrastim (C9119, S0135, J2505) during the first 5 days of the cycle (i.e., as prophylaxis).","All patients, aged ≥18 years, who began ≥1 new course of myelosuppressive chemotherapy were identified; the enrollment window was July 1, 2001 through June 30, 2010 for patients in the MarketScan Database and July 1, 2001 through June 30, 2008 for patients in the LifeLink Database. Receipt of chemotherapy was ascertained based on the presence of ≥1 paid medical claim for a chemotherapy drug or administration thereof (identified using Healthcare Common Procedure Coding System [HCPCS], International Classification of Disease, Ninth Revision, Clinical Modification [ICD-9-CM], and Health Care Financing Administration Uniform Bill-92 [UB-92] revenue codes).\nPatients were considered to have initiated a new course of chemotherapy if there was a chemotherapy claim during the study period that was preceded by a period ≥60 days without any other claims for chemotherapy. Only patients who had evidence of a primary solid tumor or NHL (based on ≥2 medical claims [≥7 days apart] with a qualifying 3-digit ICD-9-CM diagnosis code during the period beginning 30 days prior to the index date and ending 30 days thereafter) were selected.","For each cancer chemotherapy patient, each unique cycle within each course of chemotherapy was identified. The first chemotherapy cycle (of the first course) was defined as beginning with the date of initiation of chemotherapy and ending with the first service date for the next administration of chemotherapy administration (as evidenced by a medical claim with a corresponding HCPCS, ICD-9-CM, or UB-92 code) occurring at least 12 days-but no more than 59 days-after the date of initiation of chemotherapy. If a second chemotherapy cycle did not commence prior to day 60, or if there was evidence of receipt of radiation therapy (based on medical claims with relevant HCPCS, ICD-9-CM, or UB-92 codes) during this period, both the first cycle of chemotherapy and the course of chemotherapy were considered to have been completed 30 days following the beginning of the cycle or on the day prior to initiation of radiation therapy, whichever occurred first. The second and all subsequent cycles of chemotherapy, as well as subsequent courses of chemotherapy–if any–during the period of interest, were similarly defined. A maximum of 8 cycles per course were considered. For patients with multiple courses of chemotherapy, all qualifying courses were considered.\nChemotherapy regimens were ascertained based on a review of all HCPCS Level II codes for parenterally administered antineoplastic agents on medical claims with service dates within 6 days of the start of each cycle of chemotherapy. Regimens were categorized on a cycle-specific basis according to the number of agents administered that are considered to be myelotoxic (list available from authors upon request).","Cancer chemotherapy patients who received daily filgrastim prophylaxis during ≥1 cycle of chemotherapy were selected for inclusion in the study population. Administration of daily filgrastim on or before day 5 of a given cycle was considered to represent prophylaxis\n[14,22,23]. Receipt of daily filgrastim was identified based on medical claims (J1440, J1441) with relevant codes from the HCPCS system.\nDuration of daily filgrastim prophylaxis during the patient-cycle was characterized based on temporal patterns of administration; end of prophylaxis was defined as a gap ≥3 days in its use, outpatient administration of IV antimicrobial therapy (an indicator for possible CINC), or hospitalization for CINC. Duration of prophylaxis was characterized as 1–3, 4–6, or ≥7 days.","Patient-cycles were excluded from the analytic file if the following occurred: (1) evidence of ≥2 primary cancers (solid or blood) within (i.e., +/-) 30 days of chemotherapy initiation; (2) any gaps in health benefits during the 6-month (\"pretreatment\") period prior to chemotherapy initiation; (3) evidence of hematopoietic stem cell or bone marrow transplantation prior to or during receipt of chemotherapy; (4) evidence of chemotherapy based only on medical claims for administration of the drugs (HCPCS codes identifying the specific chemotherapy agents were not available, and thus the regimen and level of myelosuppression could not be determined); (5) pharmacy claims for myelotoxic chemotherapy (only drug dispense dates are available on pharmacy claims, and because pharmacy/medical claims cannot be definitively linked, precise dates of chemotherapy administration–needed to characterize the course and cycles–could not be ascertained); (6) pharmacy claims for daily filgrastim (precise dates of administration could not be ascertained, for reasons stated above); (7) evidence of receipt of sargramostim (J2820) or pegfilgrastim (C9119, S0135, J2505) during the first 5 days of the cycle (i.e., as prophylaxis).","CINC was identified based on inpatient admissions with a diagnosis (principal or secondary) of neutropenia (ICD-9-CM 288.0), fever (780.6), or infection (list available upon request). Admissions were identified on a cycle-specific basis using acute-care facility inpatient claims with admission dates anytime between day 6 and the last day of the chemotherapy cycle. Episodes of CINC treated exclusively on an outpatient basis were not considered. Because CINC that occurred on the day of the last dose of prophylaxis or the following day could have resulted in an artificial truncation of a planned longer course of prophylaxis–and thus could exaggerate the risk of CINC with shorter courses–analyses were conducted alternatively with and without inclusion of these patient cycles.\nConsequences of CINC requiring inpatient care were characterized in terms of in-hospital mortality, total hospital LOS (during the cycle), and total CINC-related healthcare expenditures (i.e., from hospital discharge to end of cycle). Total CINC-related healthcare expenditures included those for the initial hospitalization as well as care provided post-discharge on an outpatient basis; outpatient expenditures comprised encounters with a diagnosis of neutropenia, fever, or infection, as well as use/prescriptions for CSF agents (i.e., as treatment) and antimicrobial therapy. Expenditures were estimated based on total paid amounts on corresponding claims. Mortality was characterized using data only from the MarketScan Database as such information was not available in the LifeLink Database.","Patient characteristics included: age; gender; presence of selected chronic comorbidities (cardiovascular disease, diabetes, liver disease, renal disease); history of blood disorders (anemia, neutropenia, other), infection, hospitalization (all-cause and CINC-related, respectively), chemotherapy, and radiation therapy; pre-chemotherapy healthcare expenditures; type of cancer, presence of metastases; cycle number and minimum length of prior cycles; chemotherapy regimen; receipt of antimicrobial prophylaxis in the cycle of interest; and year of chemotherapy initiation. Only observed data were utilized in defining patient characteristics.\nAge was assessed as of the first day of the first cycle of chemotherapy in the course. Chronic comorbidities and history of blood disorders, infections, hospitalization, chemotherapy, radiation therapy, presence of metastases, and healthcare expenditures were assessed from the beginning of the 6-month pretreatment period through the first day of the corresponding cycle of chemotherapy. Selected variables (i.e., anemia, neutropenia, other blood disorders, infections) were alternatively evaluated during the chemotherapy course (up to the beginning of the cycle of interest). Metastases (bone vs. other site) and chronic comorbidities were identified on the basis of ≥1 diagnosis codes on inpatient claims, ≥2 diagnosis codes on outpatient claims (excluding those for laboratory services) on different days, ≥1 procedure codes, and ≥1 drug codes, as appropriate. Blood disorders and infections were identified on the basis of ≥1 diagnosis codes (on inpatient and/or outpatient claims) and ≥1 drug codes, as appropriate. Prophylactic use of antimicrobial agents was ascertained based on a medical claim for administration of drug from cycle day 1 to cycle day 5, or a pharmacy claim for a filled prescription from cycle day -3 to cycle day 5, with a corresponding drug code.","Incidence of CINC was evaluated for each patient-cycle in which daily filgrastim was administered prophylactically, and was estimated by same-cycle duration of prophylaxis in an unadjusted and adjusted context. For the latter, a generalized estimating equation (GEE) with a binomial distribution, logistic link function, and exchangeable correlation structure was employed. The GEE method accounts for correlation among repeated measures for the same subject (in this instance, across cycles), while controlling for both fixed characteristics (e.g., gender) and time-dependent covariates (e.g., first versus subsequent cycles). All observed patient characteristics were entered into, and retained in, the multivariate model. Subgroup and sensitivity analyses focusing on the first cycle only, employing a narrow definition of CINC (which was identified using the diagnosis code for neutropenia only but otherwise the same algorithm for the broad definition as described above), and focusing on alternative tumor types separately (i.e., breast cancer, lung cancer, colorectal cancer, and NHL) also were conducted.\nIn-hospital mortality, hospital LOS, and CINC-related healthcare expenditures among patients who developed CINC were descriptively evaluated by duration of daily filgrastim prophylaxis. Confidence intervals (95%) for in-hospital mortality were computed using the Wilson score interval; confidence intervals for LOS and economic costs were computed using nonparametric bootstrapping (percentile method) from the study population (1,000 replicates with replacement). Confidence intervals for in-hospital mortality, hospital LOS, and healthcare expenditures were estimated assuming independence between observations. Only observed data were used in defining study variables; patients who developed CINC but were missing data on study measures–because such data either were not recorded on claims or were not provided by some health plans–were excluded from corresponding analyses.","A total of 135,921 adult patients initiated a new course of chemotherapy for a solid tumor or NHL during the period of interest and met all other eligibility criteria. Among these patients, a total of 5,477 received daily filgrastim as prophylaxis during ≥1 cycle of chemotherapy and thus were included in the study population; these patients contributed a total of 14,288 daily filgrastim prophylaxis patient-cycles to the analytic file. Filgrastim was administered for 1–3 days in 58% of cycles (n = 8,371), 4–6 days in 26% of cycles (n = 3,691), and ≥7days in 16% of cycles (n = 2,226) (Figure \n1).\nDays of filgrastim prophylaxis.\nMean (SD) age of patients was 54 (13) years for 1–3 days of filgrastim prophylaxis, 57 (14) years for 4–6 days of prophylaxis, and 56 (13) years for ≥7 days of prophylaxis (Table \n1). Breast cancer was the most common tumor type (48% for 1–3 days, 50% for 4–6 days, 49% for ≥7 days), followed by NHL (10%, 13%, and 13%, respectively), and colorectal cancer (16%, 10%, and 13%, respectively). Metastatic disease was present in 38% of patients receiving 1–3 days of prophylaxis, 32% receiving 4–6 days, and 35% receiving ≥7 days. Antimicrobial agents—principally oral (~90%)—were concurrently used as prophylaxis in 6.7% of patients receiving 1–3 days of filgrastim prophylaxis, 7.5% receiving 4–6 days, and 7.5% receiving ≥7 days.\nPatient, cancer, and treatment characteristics, by duration of filgrastim prophylaxis\nCrude risk of CINC during a cycle of chemotherapy was 2.9% with 1–3 days of filgrastim prophylaxis, 2.7% with 4–6 days, and 1.8% with ≥7 days. In adjusted analyses, the odds of CINC were 2.4 (95% CI: 1.6-3.4) and 1.9 (1.3-2.8) times higher with 1–3 and 4–6 days of filgrastim prophylaxis, respectively, versus ≥7 days (referent group) (Figure \n2). Among the subgroup of patients who developed CINC requiring inpatient care (n = 382), mean hospital LOS was 7.4 (6.4-8.3) days with 1–3 days of prophylaxis (n = 243), 7.1 (5.7-8.5) days with 4–6 days of prophylaxis (n = 99), and 6.5 (4.9-8.0) with ≥7 days of prophylaxis (n = 40) (Table \n2). Among the subgroup of patients who developed CINC and for whom healthcare expenditures were available (n = 358), mean total CINC-related healthcare expenditures were $18,912 (14,570-23,581) with 1–3 days of prophylaxis (n = 225), $14,907 (11,155-19,728) with 4–6 days (n = 94), and $13,165 (9,595-17,144) with ≥7 days (n = 39). Among the subgroup of patients who developed CINC and for whom discharge status was available (n = 228), in-hospital mortality was 8.4% (4.6-14.8) with 1–3 days of prophylaxis (n-119), 4.0% (1.4-11.1) with 4–6 days (n = 75), and 0% (0–10.2) with ≥7 days (n = 34).\nAdjusted odds ratios for chemotherapy-induced neutropenic complications requiring inpatient care, by duration of filgrastim prophylaxis*. *Results adjusted for patient, cancer, and chemotherapy characteristics.\nUnadjusted risk of inpatient mortality, length of stay in hospital, and healthcare expenditures for chemotherapy- induced neutropenic complications requiring inpatient care, by duration of filgrastim prophylaxis\n*n’s indicate the number of patient-cycles considered in analyses of study measures; among patients who developed CINC (n = 382), those with missing data on study measures- because such data were not recorded on claims or were not provided by some health plans- were excluded from corresponding analyses.\n**Only patients with paid amounts > $0 were considered in these analyses.\nIn subgroup analyses focusing on the first cycle only, crude risks of CINC were 7.0% with 1–3 days of filgrastim prophylaxis (n = 1054), 5.0% with 4–6 days (n = 482), and 5.2% with ≥7 days (n = 363); in adjusted analyses, odds of CINC were 1.6 (0.9-2.8) and 1.1 (0.6-2.1) times higher with 1–3 and 4–6 days (versus ≥7 days) of filgrastim prophylaxis in cycle 1 (Table \n3). In analyses employing the narrow definition for CINC, crude risks of CINC were 1.6% with 1–3 days of filgrastim prophylaxis, 1.8% with 4–6 days, and 1.3% with ≥7 days; in adjusted analyses, odds of CINC were 1.6 (1.1-2.5) and 1.7 (1.1-2.7) times higher with 1–3 and 4–6 days of filgrastim prophylaxis, respectively, versus ≥7 days. In tumor-specific analyses, adjusted odds of CINC with 1–3 and 4–6 days of filgrastim prophylaxis (vs. ≥7 days) were: 1.8 (1.0-3.0) and 1.9 (1.1-3.4) for breast cancer; 20.2 (1.9-212.4) and 14.5 (1.3-158.7) for lung cancer; 1.9 (0.2-16.7) and 2.5 (0.3-23.5) for colorectal cancer; and 2.1 (1.2-3.6) and 1.8 (1.0-3.2) for NHL. We note that not all observed differences were statistically significant in unadjusted subgroup and secondary analyses, presumably due to the lack of adjustment for systematic differences in patient characteristics between prophylaxis subgroups.\nAdjusted odds ratios for chemotherapy- induced neutropenic complications requiring inpatient care in subgroup and secondary analyses\n*Results adjusted for patient, cancer, and treatment characteristic listed in Table \n2.","In the largest retrospective study of daily filgrastim use in US clinical practice, we found that the large majority of patients undergoing cancer chemotherapy who are administered prophylaxis with daily filgrastim receive considerably fewer days of administration than subjects in clinical trials of daily filgrastim prophylaxis. In our study population, 95% of patients received fewer than 10 days of filgrastim prophylaxis and 58% received only 1–3 days, versus the typical 10–11 days required for neutrophil recovery in the pivotal clinical trials\n[17-19]. This finding is largely consistent with observations from other retrospective clinical practice studies\n[14-16,22,23]. We also found that patients who receive shorter courses of daily filgrastim prophylaxis have a substantially higher risk of CINC requiring hospitalization. Among patients in our study population who received 1–3 or 4–6 days of prophylaxis, the adjusted odds of CINC were 2.4 and 1.9 times, respectively, higher than those receiving ≥7 days. In addition, our study results suggest that, among the subgroup of patients who develop CINC despite prophylaxis, the consequences of this condition may be worse among those who receive fewer administrations of daily filgrastim prophylaxis. This last set of results should be interpreted with caution, however, as the number of patients who developed CINC despite prophylaxis was, in absolute terms, small.\nOver the past decade, the frequency of use of alternative (often, more myelosuppressive) chemotherapy regimens in US clinical practice has changed considerably, in large part due to better chemotherapy agents, combinations of agents, and dosing schedules\n[25-27]. Because these regimens are typically associated with higher levels of myelosuppression, use of supportive care–including CSF prophylaxis–also has increased\n[27]. While pegfilgrastim—a longer-acting version of filgrastim that requires only a single dose administered subcutaneously once per chemotherapy cycle—is now (by far) the most commonly used CSF prophylactic agent in US clinical practice, filgrastim accounts for a small but important segment of the prophylactic market\n[14,16]. In the present study, 60,600 (45%) of the 135,921 adult cancer chemotherapy study subjects received CSF prophylaxis in ≥1cycle during their chemotherapy course. Among the subgroup who received CSF prophylaxis, 91% received pegfilgrastim in ≥1 cycle and 9% received filgrastim. Notwithstanding these temporal changes in chemotherapy regimens and supportive care, however, our findings regarding the frequent use of shorter courses of filgrastim prophylaxis and the associated consequences–based on a study population of over 5,000 patients and nearly 15,000 patient-cycles–are largely consistent with those reported previously. In the 2006 study by Weycker and colleagues–which included 598 breast cancer, lung cancer, and NHL patients, and employed data from 1998-2002– for example, mean duration of prophylaxis ranged from 4.3 to 6.5 days across tumor types, while in the Morrison study–which included 1,451 cancer patients and employed data from 2001-2003– mean duration of prophylaxis ranged from 3.7 to 6.0 days across calendar years and cycles of use\n[15,23]. In the present study, mean (SD) duration of prophylaxis on an overall basis was 3.6 (2.9) days. Moreover, in the aforementioned 2011 study by Weycker et al., odds of CINC were reported to be 1.5 times higher for patients receiving <7 versus ≥7 days of filgrastim prophylaxis, while the corresponding odds ratio in this study was estimated to be 2.2 (95% CI 1.6-3.1)\n[14]. We note that odds ratios for CINC reported above were largely comparable when limiting attention to the most recent four-year period (2007–2010): 2.5 (1.5-4.3) with 1–3 versus ≥7 days of filgrastim prophylaxis and 2.2 (1.2-3.9) with 4–6 versus ≥7 days of filgrastim prophylaxis. Study results also were robust to alternative specifications of the multivariate model and when excluding observations for which hospitalizations occurred on the day of the last dose of daily filgrastim prophylaxis or the following day.\nWe expected that systematic differences in the prevalence of risk factors (e.g., history of neutropenia, higher doses of chemotherapy agents, metastases to bone, poor performance status) for CINC would occur according to the duration of prophylaxis (1–3, 4–6, vs. ≥7 days). We thus used techniques of multivariate regression to adjust for such risk factors–to the extent possible–in analyzing the relationship between duration of daily filgrastim prophylaxis and outcomes of interest. Given the limitations of the claims-based databases we used, however, we were forced to use proxies for certain established risk factors. For example, a proxy measure based on pre-chemotherapy healthcare expenditures was employed for performance status, which in prior research has been shown to be correlated with health status in other patient populations\n[30]. Moreover, because information was not available for some clinically important parameters (e.g., ANC), the possibility exists that the study groups differed in terms of unobserved characteristics that predispose to CINC.\nWe believe that our approach to controlling for such systematic differences was comprehensive given available data, and that the above-noted biases–if present–would confer a conservative bias to analyses. For example, if the risk of FN is higher among patients receiving higher doses of chemotherapy (which is unobservable in the study database), and these patients are more likely to receive longer durations of prophylaxis, then the estimated difference in risk between patients receiving longer versus shorter courses of prophylaxis will be smaller than the true or actual difference. That our adjustment procedures were at least to some extent successful is suggested by the greater odds ratios for CINC in the adjusted versus unadjusted analyses, but uncertainty as to the adequacy of adjustment for confounding risk factors is one of the major limitations of our study.\nIn those instances where code-based operational algorithms were used to identify risk factors of interest (e.g., using ICD-9-CM code 288.0 for neutropenia, rather than ANC) errors of omission/commission in medical coding may have impacted the accuracy of adjustment. We do not believe, however, that any such differences or limitations were for the most part systematic in nature. However, patients who have a history of CINC – and thus may be more likely to receive longer durations of prophylaxis – may be more likely to have \"neutropenia\" designated as a secondary (or even primary) diagnosis on future encounters, all else equal.\nThere is no ICD-9-CM diagnosis code for CINC (i.e., neutropenia-related fever or infection), and thus codes for neutropenia, fever, and infection were employed to identify hospitalizations assumed to be related to neutropenic complications. Patients are typically not given chemotherapy when they are neutropenic or have active infection. The timing of fever and infection after chemotherapy increases the likelihood that such outcomes are related to receipt of chemotherapy. Codes for neutropenia, fever (surrogate for active infection), and infection thus are all likely to be related to episodes if seen within a defined exposure period after receiving chemotherapy. While the sensitivity of this algorithm for identifying neutropenic complications is undoubtedly higher than that of an algorithm using only the ICD-9-CM code for neutropenia, its specificity and positive predictive value are unknown.\nOther miscellaneous limitations deserve a brief mention. CINC requiring outpatient care were not considered in our analyses due to the small total number of events (n = 60). Patients with evidence of receipt of daily filgrastim via outpatient pharmacy (3% of total) were excluded because we could not ascertain the precise dates of use. Expenditures were not adjusted to current dollars since use of a general price index may yield spurious findings when applied to specific patients in specific health plans who consumed specific healthcare services and since the distribution of study groups by calendar year was comparable. Hospital discharge disposition was available only in the MarketScan Database and information concerning LOS or paid amounts was missing for some hospitalizations. Moreover, we note that a disproportionately high percentage of patients receiving 1–3 days of filgrastim prophylaxis (vs. those receiving 4–6 or ≥7 days) who developed CINC requiring inpatient care did not have information available on discharge disposition, and thus the inpatient mortality findings must be viewed with caution. Although patients initiating \"delayed\" use of daily filgrastim (e.g., after day 5 of the chemotherapy cycle) have been included in other published evaluations, they were not considered in our analyses as such patients may have received daily filgrastim for the treatment of CINC (e.g., severe neutropenia) as opposed to prophylaxis against CINC\n[15,16]. Finally, we note that it cannot be determined from outpatient pharmacy claims data whether drugs dispensed were actually taken, when they were taken, or how much was taken. For this reason, our characterization of antimicrobial prophylaxis use (principally oral) may be upwardly biased and differences in actual use across prophylaxis subgroups may confound the results of analyses. We also note, however, that the percentage of patients with filled prescriptions for antimicrobials was relatively low (and comparable) across filgrastim prophylaxis subgroups.","In conclusion, we found that among patients receiving myelosuppressive chemotherapy, shorter courses of daily filgrastim prophylaxis are associated with increased risk of CINC. We also found that when CINC develops despite daily filgrastim prophylaxis, the outcomes thereof may be poorer in those receiving shorter courses of prophylaxis. Additional research is needed to explore these relationships among individual tumor types and chemotherapy regimens.","Funding for this research was provided by Amgen Inc. to Policy Analysis Inc. (PAI). Derek Weycker, John Edelsberg, and Alex Kartashov are employed by Policy Analysis Inc. (PAI). Rich Barron and Jason Legg are employed by Amgen Inc. Andrew Glass is employed by the Center for Health Research, Kaiser Permanente Northwest, and received an honorarium for this research from Amgen Inc.","Authorship was designated based on the guidelines promulgated by the International Committee of Medical Journal Editors (2004). All persons who meet criteria for authorship are listed as authors on the title page. The contribution of each of these individuals to this study–by task–is as follows: conception and supervision (Barron, Weycker), development of design (Barron, Edelsberg, Glass, Legg, Weycker), conduct of analyses (Kartashov, Weycker), interpretation of results (all authors), preparation of manuscript (Edelsberg, Weycker), and review of manuscript (all authors). All authors have read and approved the final version of the manuscript. The study sponsor reviewed the study research plan and study manuscript; data management, processing, and analyses were conducted by Policy Analysis Inc. (PAI), and all final analytic decisions were made by study investigators.","The pre-publication history for this paper can be accessed here:\n\nhttp://www.biomedcentral.com/1472-6963/14/189/prepub\n"],"string":"[\n \"Neutropenia is a common side effect of myelosuppressive chemotherapy that both increases the risk of infection and diminishes patients’ ability to fight infection. When neutropenic patients become febrile, the high likelihood of infection and serious consequences thereof usually results in hospitalization\\n[1,2]. FN, as well as severe or prolonged neutropenia, also can interfere with the planned delivery of treatment and adversely affect important patient outcomes\\n[2-11].\\nClinical practice guidelines recommend primary prophylactic use of a colony-stimulating factor (CSF)–which has been shown to reduce the risk of FN in clinical trials–when the risk of FN is 20% or higher\\n[2]. While the American Society of Clinical Oncology (ASCO) initially recommended that CSF prophylaxis be administered only when FN risk is 40% or higher, in 2006, ASCO lowered the threshold to 20% based on data highlighting the importance of FN-related hospitalization as an outcome and evidence demonstrating the value of CSF prophylaxis in reducing the risk of FN, the risk of FN-related hospitalization, and the associated use of IV anti-infective agents\\n[2,12,13]. The CSF filgrastim, which is widely used in clinical practice as prophylaxis against FN, requires daily administration during each cycle until neutrophil recovery occurs (in clinical trials, given typically for 10–11 days [and up to 14 days] until absolute neutrophil count [ANC] ≥10 × 109/L)\\n[14-19].\\nIn clinical practice, patients often receive shorter courses of daily filgrastim prophylaxis than those administered to subjects in the clinical trial setting\\n[14-16,20-24]. While published studies suggest that shorter courses of daily filgrastim prophylaxis are associated with an increased risk of hospitalization for CINC, these studies focused on selected tumor types or employed data that now are over a decade old\\n[23,24]. During the past decade, use of chemotherapy and supportive care in clinical practice–as well as recommended use of these agents in authoritative guidelines–has changed considerably\\n[2,25-27], Moreover, only one study has examined whether CSFs may favorably impact clinical outcomes and economic costs when CINC develop despite CSF prophylaxis, and it was published a decade ago and focused on elderly patients with a single type of cancer (non-Hodgkin’s lymphoma)\\n[28]. We therefore undertook a new study to evaluate the relationship between the duration of daily filgrastim prophylaxis, and the risk and consequences of CINC requiring inpatient care using a large healthcare claims database. While such databases often lack detailed clinical information (e.g., on absolute neutrophil counts), they offer access to information on the health profile and healthcare utilization of tens of millions of covered lives.\",\n \" Data source Patient-level information from two large healthcare claims databases–the Thomson Reuters MarketScan Commercial Claims and Encounters and Medicare Supplemental and Coordination of Benefits Database (MarketScan Database, 2001–2010) and the Intercontinental Marketing Services LifeLink Database (LifeLink Database, 2001–2008)–were pooled for analyses. Both databases comprise medical (i.e., facility and professional service) and outpatient pharmacy claims from a large number of participating private health plans, and each contains claims data for 15 million persons annually.\\nData available for each facility and professional-service claim include date and place of service, diagnoses, procedures performed/services rendered, and quantity of services (professional-service claims). Data available for each retail pharmacy claim include the drug dispensed, dispensing date, quantity dispensed, and number of days supplied. All claims also include paid (i.e., reimbursed) amounts. Selected demographic and eligibility information is available for persons in both databases. All data can be arrayed to provide a detailed chronology of all medical and pharmacy services used by each plan member over time.\\nThe study databases were de-identified prior to their release to study investigators, as set forth in the corresponding Data Use Agreements. The study databases have been evaluated and certified by independent third parties to be in compliance with the Health Insurance Portability and Accountability Act (HIPAA) of 1996 statistical de-identification standards and to satisfy the conditions set forth in Sections 164.514 (a)-(b) 1ii of the HIPAA Privacy Rule regarding the determination and documentation of statistically de-identified data. Use of the study databases for health services research was determined–via independent third parties–to be fully compliant with the HIPAA Privacy Rule and federal guidance on Public Welfare and the Protection of Human Subjects\\n[29].\\nPatient-level information from two large healthcare claims databases–the Thomson Reuters MarketScan Commercial Claims and Encounters and Medicare Supplemental and Coordination of Benefits Database (MarketScan Database, 2001–2010) and the Intercontinental Marketing Services LifeLink Database (LifeLink Database, 2001–2008)–were pooled for analyses. Both databases comprise medical (i.e., facility and professional service) and outpatient pharmacy claims from a large number of participating private health plans, and each contains claims data for 15 million persons annually.\\nData available for each facility and professional-service claim include date and place of service, diagnoses, procedures performed/services rendered, and quantity of services (professional-service claims). Data available for each retail pharmacy claim include the drug dispensed, dispensing date, quantity dispensed, and number of days supplied. All claims also include paid (i.e., reimbursed) amounts. Selected demographic and eligibility information is available for persons in both databases. All data can be arrayed to provide a detailed chronology of all medical and pharmacy services used by each plan member over time.\\nThe study databases were de-identified prior to their release to study investigators, as set forth in the corresponding Data Use Agreements. The study databases have been evaluated and certified by independent third parties to be in compliance with the Health Insurance Portability and Accountability Act (HIPAA) of 1996 statistical de-identification standards and to satisfy the conditions set forth in Sections 164.514 (a)-(b) 1ii of the HIPAA Privacy Rule regarding the determination and documentation of statistically de-identified data. Use of the study databases for health services research was determined–via independent third parties–to be fully compliant with the HIPAA Privacy Rule and federal guidance on Public Welfare and the Protection of Human Subjects\\n[29].\\n Study population The study population comprised all patients who initiated ≥1 course of myelosuppressive chemotherapy for a solid tumor or non-Hodgkin’s lymphoma (NHL) and who received daily filgrastim prophylaxis during ≥1 cycle. All cycles in which patients received daily filgrastim prophylaxis–irrespective of duration–were pooled for analyses.\\n Cancer chemotherapy patients All patients, aged ≥18 years, who began ≥1 new course of myelosuppressive chemotherapy were identified; the enrollment window was July 1, 2001 through June 30, 2010 for patients in the MarketScan Database and July 1, 2001 through June 30, 2008 for patients in the LifeLink Database. Receipt of chemotherapy was ascertained based on the presence of ≥1 paid medical claim for a chemotherapy drug or administration thereof (identified using Healthcare Common Procedure Coding System [HCPCS], International Classification of Disease, Ninth Revision, Clinical Modification [ICD-9-CM], and Health Care Financing Administration Uniform Bill-92 [UB-92] revenue codes).\\nPatients were considered to have initiated a new course of chemotherapy if there was a chemotherapy claim during the study period that was preceded by a period ≥60 days without any other claims for chemotherapy. Only patients who had evidence of a primary solid tumor or NHL (based on ≥2 medical claims [≥7 days apart] with a qualifying 3-digit ICD-9-CM diagnosis code during the period beginning 30 days prior to the index date and ending 30 days thereafter) were selected.\\nAll patients, aged ≥18 years, who began ≥1 new course of myelosuppressive chemotherapy were identified; the enrollment window was July 1, 2001 through June 30, 2010 for patients in the MarketScan Database and July 1, 2001 through June 30, 2008 for patients in the LifeLink Database. Receipt of chemotherapy was ascertained based on the presence of ≥1 paid medical claim for a chemotherapy drug or administration thereof (identified using Healthcare Common Procedure Coding System [HCPCS], International Classification of Disease, Ninth Revision, Clinical Modification [ICD-9-CM], and Health Care Financing Administration Uniform Bill-92 [UB-92] revenue codes).\\nPatients were considered to have initiated a new course of chemotherapy if there was a chemotherapy claim during the study period that was preceded by a period ≥60 days without any other claims for chemotherapy. Only patients who had evidence of a primary solid tumor or NHL (based on ≥2 medical claims [≥7 days apart] with a qualifying 3-digit ICD-9-CM diagnosis code during the period beginning 30 days prior to the index date and ending 30 days thereafter) were selected.\\n Chemotherapy courses, cycles, and regimens For each cancer chemotherapy patient, each unique cycle within each course of chemotherapy was identified. The first chemotherapy cycle (of the first course) was defined as beginning with the date of initiation of chemotherapy and ending with the first service date for the next administration of chemotherapy administration (as evidenced by a medical claim with a corresponding HCPCS, ICD-9-CM, or UB-92 code) occurring at least 12 days-but no more than 59 days-after the date of initiation of chemotherapy. If a second chemotherapy cycle did not commence prior to day 60, or if there was evidence of receipt of radiation therapy (based on medical claims with relevant HCPCS, ICD-9-CM, or UB-92 codes) during this period, both the first cycle of chemotherapy and the course of chemotherapy were considered to have been completed 30 days following the beginning of the cycle or on the day prior to initiation of radiation therapy, whichever occurred first. The second and all subsequent cycles of chemotherapy, as well as subsequent courses of chemotherapy–if any–during the period of interest, were similarly defined. A maximum of 8 cycles per course were considered. For patients with multiple courses of chemotherapy, all qualifying courses were considered.\\nChemotherapy regimens were ascertained based on a review of all HCPCS Level II codes for parenterally administered antineoplastic agents on medical claims with service dates within 6 days of the start of each cycle of chemotherapy. Regimens were categorized on a cycle-specific basis according to the number of agents administered that are considered to be myelotoxic (list available from authors upon request).\\nFor each cancer chemotherapy patient, each unique cycle within each course of chemotherapy was identified. The first chemotherapy cycle (of the first course) was defined as beginning with the date of initiation of chemotherapy and ending with the first service date for the next administration of chemotherapy administration (as evidenced by a medical claim with a corresponding HCPCS, ICD-9-CM, or UB-92 code) occurring at least 12 days-but no more than 59 days-after the date of initiation of chemotherapy. If a second chemotherapy cycle did not commence prior to day 60, or if there was evidence of receipt of radiation therapy (based on medical claims with relevant HCPCS, ICD-9-CM, or UB-92 codes) during this period, both the first cycle of chemotherapy and the course of chemotherapy were considered to have been completed 30 days following the beginning of the cycle or on the day prior to initiation of radiation therapy, whichever occurred first. The second and all subsequent cycles of chemotherapy, as well as subsequent courses of chemotherapy–if any–during the period of interest, were similarly defined. A maximum of 8 cycles per course were considered. For patients with multiple courses of chemotherapy, all qualifying courses were considered.\\nChemotherapy regimens were ascertained based on a review of all HCPCS Level II codes for parenterally administered antineoplastic agents on medical claims with service dates within 6 days of the start of each cycle of chemotherapy. Regimens were categorized on a cycle-specific basis according to the number of agents administered that are considered to be myelotoxic (list available from authors upon request).\\n Daily filgrastim prophylaxis Cancer chemotherapy patients who received daily filgrastim prophylaxis during ≥1 cycle of chemotherapy were selected for inclusion in the study population. Administration of daily filgrastim on or before day 5 of a given cycle was considered to represent prophylaxis\\n[14,22,23]. Receipt of daily filgrastim was identified based on medical claims (J1440, J1441) with relevant codes from the HCPCS system.\\nDuration of daily filgrastim prophylaxis during the patient-cycle was characterized based on temporal patterns of administration; end of prophylaxis was defined as a gap ≥3 days in its use, outpatient administration of IV antimicrobial therapy (an indicator for possible CINC), or hospitalization for CINC. Duration of prophylaxis was characterized as 1–3, 4–6, or ≥7 days.\\nCancer chemotherapy patients who received daily filgrastim prophylaxis during ≥1 cycle of chemotherapy were selected for inclusion in the study population. Administration of daily filgrastim on or before day 5 of a given cycle was considered to represent prophylaxis\\n[14,22,23]. Receipt of daily filgrastim was identified based on medical claims (J1440, J1441) with relevant codes from the HCPCS system.\\nDuration of daily filgrastim prophylaxis during the patient-cycle was characterized based on temporal patterns of administration; end of prophylaxis was defined as a gap ≥3 days in its use, outpatient administration of IV antimicrobial therapy (an indicator for possible CINC), or hospitalization for CINC. Duration of prophylaxis was characterized as 1–3, 4–6, or ≥7 days.\\n Exclusion criteria Patient-cycles were excluded from the analytic file if the following occurred: (1) evidence of ≥2 primary cancers (solid or blood) within (i.e., +/-) 30 days of chemotherapy initiation; (2) any gaps in health benefits during the 6-month (\\\"pretreatment\\\") period prior to chemotherapy initiation; (3) evidence of hematopoietic stem cell or bone marrow transplantation prior to or during receipt of chemotherapy; (4) evidence of chemotherapy based only on medical claims for administration of the drugs (HCPCS codes identifying the specific chemotherapy agents were not available, and thus the regimen and level of myelosuppression could not be determined); (5) pharmacy claims for myelotoxic chemotherapy (only drug dispense dates are available on pharmacy claims, and because pharmacy/medical claims cannot be definitively linked, precise dates of chemotherapy administration–needed to characterize the course and cycles–could not be ascertained); (6) pharmacy claims for daily filgrastim (precise dates of administration could not be ascertained, for reasons stated above); (7) evidence of receipt of sargramostim (J2820) or pegfilgrastim (C9119, S0135, J2505) during the first 5 days of the cycle (i.e., as prophylaxis).\\nPatient-cycles were excluded from the analytic file if the following occurred: (1) evidence of ≥2 primary cancers (solid or blood) within (i.e., +/-) 30 days of chemotherapy initiation; (2) any gaps in health benefits during the 6-month (\\\"pretreatment\\\") period prior to chemotherapy initiation; (3) evidence of hematopoietic stem cell or bone marrow transplantation prior to or during receipt of chemotherapy; (4) evidence of chemotherapy based only on medical claims for administration of the drugs (HCPCS codes identifying the specific chemotherapy agents were not available, and thus the regimen and level of myelosuppression could not be determined); (5) pharmacy claims for myelotoxic chemotherapy (only drug dispense dates are available on pharmacy claims, and because pharmacy/medical claims cannot be definitively linked, precise dates of chemotherapy administration–needed to characterize the course and cycles–could not be ascertained); (6) pharmacy claims for daily filgrastim (precise dates of administration could not be ascertained, for reasons stated above); (7) evidence of receipt of sargramostim (J2820) or pegfilgrastim (C9119, S0135, J2505) during the first 5 days of the cycle (i.e., as prophylaxis).\\nThe study population comprised all patients who initiated ≥1 course of myelosuppressive chemotherapy for a solid tumor or non-Hodgkin’s lymphoma (NHL) and who received daily filgrastim prophylaxis during ≥1 cycle. All cycles in which patients received daily filgrastim prophylaxis–irrespective of duration–were pooled for analyses.\\n Cancer chemotherapy patients All patients, aged ≥18 years, who began ≥1 new course of myelosuppressive chemotherapy were identified; the enrollment window was July 1, 2001 through June 30, 2010 for patients in the MarketScan Database and July 1, 2001 through June 30, 2008 for patients in the LifeLink Database. Receipt of chemotherapy was ascertained based on the presence of ≥1 paid medical claim for a chemotherapy drug or administration thereof (identified using Healthcare Common Procedure Coding System [HCPCS], International Classification of Disease, Ninth Revision, Clinical Modification [ICD-9-CM], and Health Care Financing Administration Uniform Bill-92 [UB-92] revenue codes).\\nPatients were considered to have initiated a new course of chemotherapy if there was a chemotherapy claim during the study period that was preceded by a period ≥60 days without any other claims for chemotherapy. Only patients who had evidence of a primary solid tumor or NHL (based on ≥2 medical claims [≥7 days apart] with a qualifying 3-digit ICD-9-CM diagnosis code during the period beginning 30 days prior to the index date and ending 30 days thereafter) were selected.\\nAll patients, aged ≥18 years, who began ≥1 new course of myelosuppressive chemotherapy were identified; the enrollment window was July 1, 2001 through June 30, 2010 for patients in the MarketScan Database and July 1, 2001 through June 30, 2008 for patients in the LifeLink Database. Receipt of chemotherapy was ascertained based on the presence of ≥1 paid medical claim for a chemotherapy drug or administration thereof (identified using Healthcare Common Procedure Coding System [HCPCS], International Classification of Disease, Ninth Revision, Clinical Modification [ICD-9-CM], and Health Care Financing Administration Uniform Bill-92 [UB-92] revenue codes).\\nPatients were considered to have initiated a new course of chemotherapy if there was a chemotherapy claim during the study period that was preceded by a period ≥60 days without any other claims for chemotherapy. Only patients who had evidence of a primary solid tumor or NHL (based on ≥2 medical claims [≥7 days apart] with a qualifying 3-digit ICD-9-CM diagnosis code during the period beginning 30 days prior to the index date and ending 30 days thereafter) were selected.\\n Chemotherapy courses, cycles, and regimens For each cancer chemotherapy patient, each unique cycle within each course of chemotherapy was identified. The first chemotherapy cycle (of the first course) was defined as beginning with the date of initiation of chemotherapy and ending with the first service date for the next administration of chemotherapy administration (as evidenced by a medical claim with a corresponding HCPCS, ICD-9-CM, or UB-92 code) occurring at least 12 days-but no more than 59 days-after the date of initiation of chemotherapy. If a second chemotherapy cycle did not commence prior to day 60, or if there was evidence of receipt of radiation therapy (based on medical claims with relevant HCPCS, ICD-9-CM, or UB-92 codes) during this period, both the first cycle of chemotherapy and the course of chemotherapy were considered to have been completed 30 days following the beginning of the cycle or on the day prior to initiation of radiation therapy, whichever occurred first. The second and all subsequent cycles of chemotherapy, as well as subsequent courses of chemotherapy–if any–during the period of interest, were similarly defined. A maximum of 8 cycles per course were considered. For patients with multiple courses of chemotherapy, all qualifying courses were considered.\\nChemotherapy regimens were ascertained based on a review of all HCPCS Level II codes for parenterally administered antineoplastic agents on medical claims with service dates within 6 days of the start of each cycle of chemotherapy. Regimens were categorized on a cycle-specific basis according to the number of agents administered that are considered to be myelotoxic (list available from authors upon request).\\nFor each cancer chemotherapy patient, each unique cycle within each course of chemotherapy was identified. The first chemotherapy cycle (of the first course) was defined as beginning with the date of initiation of chemotherapy and ending with the first service date for the next administration of chemotherapy administration (as evidenced by a medical claim with a corresponding HCPCS, ICD-9-CM, or UB-92 code) occurring at least 12 days-but no more than 59 days-after the date of initiation of chemotherapy. If a second chemotherapy cycle did not commence prior to day 60, or if there was evidence of receipt of radiation therapy (based on medical claims with relevant HCPCS, ICD-9-CM, or UB-92 codes) during this period, both the first cycle of chemotherapy and the course of chemotherapy were considered to have been completed 30 days following the beginning of the cycle or on the day prior to initiation of radiation therapy, whichever occurred first. The second and all subsequent cycles of chemotherapy, as well as subsequent courses of chemotherapy–if any–during the period of interest, were similarly defined. A maximum of 8 cycles per course were considered. For patients with multiple courses of chemotherapy, all qualifying courses were considered.\\nChemotherapy regimens were ascertained based on a review of all HCPCS Level II codes for parenterally administered antineoplastic agents on medical claims with service dates within 6 days of the start of each cycle of chemotherapy. Regimens were categorized on a cycle-specific basis according to the number of agents administered that are considered to be myelotoxic (list available from authors upon request).\\n Daily filgrastim prophylaxis Cancer chemotherapy patients who received daily filgrastim prophylaxis during ≥1 cycle of chemotherapy were selected for inclusion in the study population. Administration of daily filgrastim on or before day 5 of a given cycle was considered to represent prophylaxis\\n[14,22,23]. Receipt of daily filgrastim was identified based on medical claims (J1440, J1441) with relevant codes from the HCPCS system.\\nDuration of daily filgrastim prophylaxis during the patient-cycle was characterized based on temporal patterns of administration; end of prophylaxis was defined as a gap ≥3 days in its use, outpatient administration of IV antimicrobial therapy (an indicator for possible CINC), or hospitalization for CINC. Duration of prophylaxis was characterized as 1–3, 4–6, or ≥7 days.\\nCancer chemotherapy patients who received daily filgrastim prophylaxis during ≥1 cycle of chemotherapy were selected for inclusion in the study population. Administration of daily filgrastim on or before day 5 of a given cycle was considered to represent prophylaxis\\n[14,22,23]. Receipt of daily filgrastim was identified based on medical claims (J1440, J1441) with relevant codes from the HCPCS system.\\nDuration of daily filgrastim prophylaxis during the patient-cycle was characterized based on temporal patterns of administration; end of prophylaxis was defined as a gap ≥3 days in its use, outpatient administration of IV antimicrobial therapy (an indicator for possible CINC), or hospitalization for CINC. Duration of prophylaxis was characterized as 1–3, 4–6, or ≥7 days.\\n Exclusion criteria Patient-cycles were excluded from the analytic file if the following occurred: (1) evidence of ≥2 primary cancers (solid or blood) within (i.e., +/-) 30 days of chemotherapy initiation; (2) any gaps in health benefits during the 6-month (\\\"pretreatment\\\") period prior to chemotherapy initiation; (3) evidence of hematopoietic stem cell or bone marrow transplantation prior to or during receipt of chemotherapy; (4) evidence of chemotherapy based only on medical claims for administration of the drugs (HCPCS codes identifying the specific chemotherapy agents were not available, and thus the regimen and level of myelosuppression could not be determined); (5) pharmacy claims for myelotoxic chemotherapy (only drug dispense dates are available on pharmacy claims, and because pharmacy/medical claims cannot be definitively linked, precise dates of chemotherapy administration–needed to characterize the course and cycles–could not be ascertained); (6) pharmacy claims for daily filgrastim (precise dates of administration could not be ascertained, for reasons stated above); (7) evidence of receipt of sargramostim (J2820) or pegfilgrastim (C9119, S0135, J2505) during the first 5 days of the cycle (i.e., as prophylaxis).\\nPatient-cycles were excluded from the analytic file if the following occurred: (1) evidence of ≥2 primary cancers (solid or blood) within (i.e., +/-) 30 days of chemotherapy initiation; (2) any gaps in health benefits during the 6-month (\\\"pretreatment\\\") period prior to chemotherapy initiation; (3) evidence of hematopoietic stem cell or bone marrow transplantation prior to or during receipt of chemotherapy; (4) evidence of chemotherapy based only on medical claims for administration of the drugs (HCPCS codes identifying the specific chemotherapy agents were not available, and thus the regimen and level of myelosuppression could not be determined); (5) pharmacy claims for myelotoxic chemotherapy (only drug dispense dates are available on pharmacy claims, and because pharmacy/medical claims cannot be definitively linked, precise dates of chemotherapy administration–needed to characterize the course and cycles–could not be ascertained); (6) pharmacy claims for daily filgrastim (precise dates of administration could not be ascertained, for reasons stated above); (7) evidence of receipt of sargramostim (J2820) or pegfilgrastim (C9119, S0135, J2505) during the first 5 days of the cycle (i.e., as prophylaxis).\\n Neutropenic complications requiring inpatient care CINC was identified based on inpatient admissions with a diagnosis (principal or secondary) of neutropenia (ICD-9-CM 288.0), fever (780.6), or infection (list available upon request). Admissions were identified on a cycle-specific basis using acute-care facility inpatient claims with admission dates anytime between day 6 and the last day of the chemotherapy cycle. Episodes of CINC treated exclusively on an outpatient basis were not considered. Because CINC that occurred on the day of the last dose of prophylaxis or the following day could have resulted in an artificial truncation of a planned longer course of prophylaxis–and thus could exaggerate the risk of CINC with shorter courses–analyses were conducted alternatively with and without inclusion of these patient cycles.\\nConsequences of CINC requiring inpatient care were characterized in terms of in-hospital mortality, total hospital LOS (during the cycle), and total CINC-related healthcare expenditures (i.e., from hospital discharge to end of cycle). Total CINC-related healthcare expenditures included those for the initial hospitalization as well as care provided post-discharge on an outpatient basis; outpatient expenditures comprised encounters with a diagnosis of neutropenia, fever, or infection, as well as use/prescriptions for CSF agents (i.e., as treatment) and antimicrobial therapy. Expenditures were estimated based on total paid amounts on corresponding claims. Mortality was characterized using data only from the MarketScan Database as such information was not available in the LifeLink Database.\\nCINC was identified based on inpatient admissions with a diagnosis (principal or secondary) of neutropenia (ICD-9-CM 288.0), fever (780.6), or infection (list available upon request). Admissions were identified on a cycle-specific basis using acute-care facility inpatient claims with admission dates anytime between day 6 and the last day of the chemotherapy cycle. Episodes of CINC treated exclusively on an outpatient basis were not considered. Because CINC that occurred on the day of the last dose of prophylaxis or the following day could have resulted in an artificial truncation of a planned longer course of prophylaxis–and thus could exaggerate the risk of CINC with shorter courses–analyses were conducted alternatively with and without inclusion of these patient cycles.\\nConsequences of CINC requiring inpatient care were characterized in terms of in-hospital mortality, total hospital LOS (during the cycle), and total CINC-related healthcare expenditures (i.e., from hospital discharge to end of cycle). Total CINC-related healthcare expenditures included those for the initial hospitalization as well as care provided post-discharge on an outpatient basis; outpatient expenditures comprised encounters with a diagnosis of neutropenia, fever, or infection, as well as use/prescriptions for CSF agents (i.e., as treatment) and antimicrobial therapy. Expenditures were estimated based on total paid amounts on corresponding claims. Mortality was characterized using data only from the MarketScan Database as such information was not available in the LifeLink Database.\\n Patient characteristics Patient characteristics included: age; gender; presence of selected chronic comorbidities (cardiovascular disease, diabetes, liver disease, renal disease); history of blood disorders (anemia, neutropenia, other), infection, hospitalization (all-cause and CINC-related, respectively), chemotherapy, and radiation therapy; pre-chemotherapy healthcare expenditures; type of cancer, presence of metastases; cycle number and minimum length of prior cycles; chemotherapy regimen; receipt of antimicrobial prophylaxis in the cycle of interest; and year of chemotherapy initiation. Only observed data were utilized in defining patient characteristics.\\nAge was assessed as of the first day of the first cycle of chemotherapy in the course. Chronic comorbidities and history of blood disorders, infections, hospitalization, chemotherapy, radiation therapy, presence of metastases, and healthcare expenditures were assessed from the beginning of the 6-month pretreatment period through the first day of the corresponding cycle of chemotherapy. Selected variables (i.e., anemia, neutropenia, other blood disorders, infections) were alternatively evaluated during the chemotherapy course (up to the beginning of the cycle of interest). Metastases (bone vs. other site) and chronic comorbidities were identified on the basis of ≥1 diagnosis codes on inpatient claims, ≥2 diagnosis codes on outpatient claims (excluding those for laboratory services) on different days, ≥1 procedure codes, and ≥1 drug codes, as appropriate. Blood disorders and infections were identified on the basis of ≥1 diagnosis codes (on inpatient and/or outpatient claims) and ≥1 drug codes, as appropriate. Prophylactic use of antimicrobial agents was ascertained based on a medical claim for administration of drug from cycle day 1 to cycle day 5, or a pharmacy claim for a filled prescription from cycle day -3 to cycle day 5, with a corresponding drug code.\\nPatient characteristics included: age; gender; presence of selected chronic comorbidities (cardiovascular disease, diabetes, liver disease, renal disease); history of blood disorders (anemia, neutropenia, other), infection, hospitalization (all-cause and CINC-related, respectively), chemotherapy, and radiation therapy; pre-chemotherapy healthcare expenditures; type of cancer, presence of metastases; cycle number and minimum length of prior cycles; chemotherapy regimen; receipt of antimicrobial prophylaxis in the cycle of interest; and year of chemotherapy initiation. Only observed data were utilized in defining patient characteristics.\\nAge was assessed as of the first day of the first cycle of chemotherapy in the course. Chronic comorbidities and history of blood disorders, infections, hospitalization, chemotherapy, radiation therapy, presence of metastases, and healthcare expenditures were assessed from the beginning of the 6-month pretreatment period through the first day of the corresponding cycle of chemotherapy. Selected variables (i.e., anemia, neutropenia, other blood disorders, infections) were alternatively evaluated during the chemotherapy course (up to the beginning of the cycle of interest). Metastases (bone vs. other site) and chronic comorbidities were identified on the basis of ≥1 diagnosis codes on inpatient claims, ≥2 diagnosis codes on outpatient claims (excluding those for laboratory services) on different days, ≥1 procedure codes, and ≥1 drug codes, as appropriate. Blood disorders and infections were identified on the basis of ≥1 diagnosis codes (on inpatient and/or outpatient claims) and ≥1 drug codes, as appropriate. Prophylactic use of antimicrobial agents was ascertained based on a medical claim for administration of drug from cycle day 1 to cycle day 5, or a pharmacy claim for a filled prescription from cycle day -3 to cycle day 5, with a corresponding drug code.\\n Statistical analyses Incidence of CINC was evaluated for each patient-cycle in which daily filgrastim was administered prophylactically, and was estimated by same-cycle duration of prophylaxis in an unadjusted and adjusted context. For the latter, a generalized estimating equation (GEE) with a binomial distribution, logistic link function, and exchangeable correlation structure was employed. The GEE method accounts for correlation among repeated measures for the same subject (in this instance, across cycles), while controlling for both fixed characteristics (e.g., gender) and time-dependent covariates (e.g., first versus subsequent cycles). All observed patient characteristics were entered into, and retained in, the multivariate model. Subgroup and sensitivity analyses focusing on the first cycle only, employing a narrow definition of CINC (which was identified using the diagnosis code for neutropenia only but otherwise the same algorithm for the broad definition as described above), and focusing on alternative tumor types separately (i.e., breast cancer, lung cancer, colorectal cancer, and NHL) also were conducted.\\nIn-hospital mortality, hospital LOS, and CINC-related healthcare expenditures among patients who developed CINC were descriptively evaluated by duration of daily filgrastim prophylaxis. Confidence intervals (95%) for in-hospital mortality were computed using the Wilson score interval; confidence intervals for LOS and economic costs were computed using nonparametric bootstrapping (percentile method) from the study population (1,000 replicates with replacement). Confidence intervals for in-hospital mortality, hospital LOS, and healthcare expenditures were estimated assuming independence between observations. Only observed data were used in defining study variables; patients who developed CINC but were missing data on study measures–because such data either were not recorded on claims or were not provided by some health plans–were excluded from corresponding analyses.\\nIncidence of CINC was evaluated for each patient-cycle in which daily filgrastim was administered prophylactically, and was estimated by same-cycle duration of prophylaxis in an unadjusted and adjusted context. For the latter, a generalized estimating equation (GEE) with a binomial distribution, logistic link function, and exchangeable correlation structure was employed. The GEE method accounts for correlation among repeated measures for the same subject (in this instance, across cycles), while controlling for both fixed characteristics (e.g., gender) and time-dependent covariates (e.g., first versus subsequent cycles). All observed patient characteristics were entered into, and retained in, the multivariate model. Subgroup and sensitivity analyses focusing on the first cycle only, employing a narrow definition of CINC (which was identified using the diagnosis code for neutropenia only but otherwise the same algorithm for the broad definition as described above), and focusing on alternative tumor types separately (i.e., breast cancer, lung cancer, colorectal cancer, and NHL) also were conducted.\\nIn-hospital mortality, hospital LOS, and CINC-related healthcare expenditures among patients who developed CINC were descriptively evaluated by duration of daily filgrastim prophylaxis. Confidence intervals (95%) for in-hospital mortality were computed using the Wilson score interval; confidence intervals for LOS and economic costs were computed using nonparametric bootstrapping (percentile method) from the study population (1,000 replicates with replacement). Confidence intervals for in-hospital mortality, hospital LOS, and healthcare expenditures were estimated assuming independence between observations. Only observed data were used in defining study variables; patients who developed CINC but were missing data on study measures–because such data either were not recorded on claims or were not provided by some health plans–were excluded from corresponding analyses.\",\n \"Patient-level information from two large healthcare claims databases–the Thomson Reuters MarketScan Commercial Claims and Encounters and Medicare Supplemental and Coordination of Benefits Database (MarketScan Database, 2001–2010) and the Intercontinental Marketing Services LifeLink Database (LifeLink Database, 2001–2008)–were pooled for analyses. Both databases comprise medical (i.e., facility and professional service) and outpatient pharmacy claims from a large number of participating private health plans, and each contains claims data for 15 million persons annually.\\nData available for each facility and professional-service claim include date and place of service, diagnoses, procedures performed/services rendered, and quantity of services (professional-service claims). Data available for each retail pharmacy claim include the drug dispensed, dispensing date, quantity dispensed, and number of days supplied. All claims also include paid (i.e., reimbursed) amounts. Selected demographic and eligibility information is available for persons in both databases. All data can be arrayed to provide a detailed chronology of all medical and pharmacy services used by each plan member over time.\\nThe study databases were de-identified prior to their release to study investigators, as set forth in the corresponding Data Use Agreements. The study databases have been evaluated and certified by independent third parties to be in compliance with the Health Insurance Portability and Accountability Act (HIPAA) of 1996 statistical de-identification standards and to satisfy the conditions set forth in Sections 164.514 (a)-(b) 1ii of the HIPAA Privacy Rule regarding the determination and documentation of statistically de-identified data. Use of the study databases for health services research was determined–via independent third parties–to be fully compliant with the HIPAA Privacy Rule and federal guidance on Public Welfare and the Protection of Human Subjects\\n[29].\",\n \"The study population comprised all patients who initiated ≥1 course of myelosuppressive chemotherapy for a solid tumor or non-Hodgkin’s lymphoma (NHL) and who received daily filgrastim prophylaxis during ≥1 cycle. All cycles in which patients received daily filgrastim prophylaxis–irrespective of duration–were pooled for analyses.\\n Cancer chemotherapy patients All patients, aged ≥18 years, who began ≥1 new course of myelosuppressive chemotherapy were identified; the enrollment window was July 1, 2001 through June 30, 2010 for patients in the MarketScan Database and July 1, 2001 through June 30, 2008 for patients in the LifeLink Database. Receipt of chemotherapy was ascertained based on the presence of ≥1 paid medical claim for a chemotherapy drug or administration thereof (identified using Healthcare Common Procedure Coding System [HCPCS], International Classification of Disease, Ninth Revision, Clinical Modification [ICD-9-CM], and Health Care Financing Administration Uniform Bill-92 [UB-92] revenue codes).\\nPatients were considered to have initiated a new course of chemotherapy if there was a chemotherapy claim during the study period that was preceded by a period ≥60 days without any other claims for chemotherapy. Only patients who had evidence of a primary solid tumor or NHL (based on ≥2 medical claims [≥7 days apart] with a qualifying 3-digit ICD-9-CM diagnosis code during the period beginning 30 days prior to the index date and ending 30 days thereafter) were selected.\\nAll patients, aged ≥18 years, who began ≥1 new course of myelosuppressive chemotherapy were identified; the enrollment window was July 1, 2001 through June 30, 2010 for patients in the MarketScan Database and July 1, 2001 through June 30, 2008 for patients in the LifeLink Database. Receipt of chemotherapy was ascertained based on the presence of ≥1 paid medical claim for a chemotherapy drug or administration thereof (identified using Healthcare Common Procedure Coding System [HCPCS], International Classification of Disease, Ninth Revision, Clinical Modification [ICD-9-CM], and Health Care Financing Administration Uniform Bill-92 [UB-92] revenue codes).\\nPatients were considered to have initiated a new course of chemotherapy if there was a chemotherapy claim during the study period that was preceded by a period ≥60 days without any other claims for chemotherapy. Only patients who had evidence of a primary solid tumor or NHL (based on ≥2 medical claims [≥7 days apart] with a qualifying 3-digit ICD-9-CM diagnosis code during the period beginning 30 days prior to the index date and ending 30 days thereafter) were selected.\\n Chemotherapy courses, cycles, and regimens For each cancer chemotherapy patient, each unique cycle within each course of chemotherapy was identified. The first chemotherapy cycle (of the first course) was defined as beginning with the date of initiation of chemotherapy and ending with the first service date for the next administration of chemotherapy administration (as evidenced by a medical claim with a corresponding HCPCS, ICD-9-CM, or UB-92 code) occurring at least 12 days-but no more than 59 days-after the date of initiation of chemotherapy. If a second chemotherapy cycle did not commence prior to day 60, or if there was evidence of receipt of radiation therapy (based on medical claims with relevant HCPCS, ICD-9-CM, or UB-92 codes) during this period, both the first cycle of chemotherapy and the course of chemotherapy were considered to have been completed 30 days following the beginning of the cycle or on the day prior to initiation of radiation therapy, whichever occurred first. The second and all subsequent cycles of chemotherapy, as well as subsequent courses of chemotherapy–if any–during the period of interest, were similarly defined. A maximum of 8 cycles per course were considered. For patients with multiple courses of chemotherapy, all qualifying courses were considered.\\nChemotherapy regimens were ascertained based on a review of all HCPCS Level II codes for parenterally administered antineoplastic agents on medical claims with service dates within 6 days of the start of each cycle of chemotherapy. Regimens were categorized on a cycle-specific basis according to the number of agents administered that are considered to be myelotoxic (list available from authors upon request).\\nFor each cancer chemotherapy patient, each unique cycle within each course of chemotherapy was identified. The first chemotherapy cycle (of the first course) was defined as beginning with the date of initiation of chemotherapy and ending with the first service date for the next administration of chemotherapy administration (as evidenced by a medical claim with a corresponding HCPCS, ICD-9-CM, or UB-92 code) occurring at least 12 days-but no more than 59 days-after the date of initiation of chemotherapy. If a second chemotherapy cycle did not commence prior to day 60, or if there was evidence of receipt of radiation therapy (based on medical claims with relevant HCPCS, ICD-9-CM, or UB-92 codes) during this period, both the first cycle of chemotherapy and the course of chemotherapy were considered to have been completed 30 days following the beginning of the cycle or on the day prior to initiation of radiation therapy, whichever occurred first. The second and all subsequent cycles of chemotherapy, as well as subsequent courses of chemotherapy–if any–during the period of interest, were similarly defined. A maximum of 8 cycles per course were considered. For patients with multiple courses of chemotherapy, all qualifying courses were considered.\\nChemotherapy regimens were ascertained based on a review of all HCPCS Level II codes for parenterally administered antineoplastic agents on medical claims with service dates within 6 days of the start of each cycle of chemotherapy. Regimens were categorized on a cycle-specific basis according to the number of agents administered that are considered to be myelotoxic (list available from authors upon request).\\n Daily filgrastim prophylaxis Cancer chemotherapy patients who received daily filgrastim prophylaxis during ≥1 cycle of chemotherapy were selected for inclusion in the study population. Administration of daily filgrastim on or before day 5 of a given cycle was considered to represent prophylaxis\\n[14,22,23]. Receipt of daily filgrastim was identified based on medical claims (J1440, J1441) with relevant codes from the HCPCS system.\\nDuration of daily filgrastim prophylaxis during the patient-cycle was characterized based on temporal patterns of administration; end of prophylaxis was defined as a gap ≥3 days in its use, outpatient administration of IV antimicrobial therapy (an indicator for possible CINC), or hospitalization for CINC. Duration of prophylaxis was characterized as 1–3, 4–6, or ≥7 days.\\nCancer chemotherapy patients who received daily filgrastim prophylaxis during ≥1 cycle of chemotherapy were selected for inclusion in the study population. Administration of daily filgrastim on or before day 5 of a given cycle was considered to represent prophylaxis\\n[14,22,23]. Receipt of daily filgrastim was identified based on medical claims (J1440, J1441) with relevant codes from the HCPCS system.\\nDuration of daily filgrastim prophylaxis during the patient-cycle was characterized based on temporal patterns of administration; end of prophylaxis was defined as a gap ≥3 days in its use, outpatient administration of IV antimicrobial therapy (an indicator for possible CINC), or hospitalization for CINC. Duration of prophylaxis was characterized as 1–3, 4–6, or ≥7 days.\\n Exclusion criteria Patient-cycles were excluded from the analytic file if the following occurred: (1) evidence of ≥2 primary cancers (solid or blood) within (i.e., +/-) 30 days of chemotherapy initiation; (2) any gaps in health benefits during the 6-month (\\\"pretreatment\\\") period prior to chemotherapy initiation; (3) evidence of hematopoietic stem cell or bone marrow transplantation prior to or during receipt of chemotherapy; (4) evidence of chemotherapy based only on medical claims for administration of the drugs (HCPCS codes identifying the specific chemotherapy agents were not available, and thus the regimen and level of myelosuppression could not be determined); (5) pharmacy claims for myelotoxic chemotherapy (only drug dispense dates are available on pharmacy claims, and because pharmacy/medical claims cannot be definitively linked, precise dates of chemotherapy administration–needed to characterize the course and cycles–could not be ascertained); (6) pharmacy claims for daily filgrastim (precise dates of administration could not be ascertained, for reasons stated above); (7) evidence of receipt of sargramostim (J2820) or pegfilgrastim (C9119, S0135, J2505) during the first 5 days of the cycle (i.e., as prophylaxis).\\nPatient-cycles were excluded from the analytic file if the following occurred: (1) evidence of ≥2 primary cancers (solid or blood) within (i.e., +/-) 30 days of chemotherapy initiation; (2) any gaps in health benefits during the 6-month (\\\"pretreatment\\\") period prior to chemotherapy initiation; (3) evidence of hematopoietic stem cell or bone marrow transplantation prior to or during receipt of chemotherapy; (4) evidence of chemotherapy based only on medical claims for administration of the drugs (HCPCS codes identifying the specific chemotherapy agents were not available, and thus the regimen and level of myelosuppression could not be determined); (5) pharmacy claims for myelotoxic chemotherapy (only drug dispense dates are available on pharmacy claims, and because pharmacy/medical claims cannot be definitively linked, precise dates of chemotherapy administration–needed to characterize the course and cycles–could not be ascertained); (6) pharmacy claims for daily filgrastim (precise dates of administration could not be ascertained, for reasons stated above); (7) evidence of receipt of sargramostim (J2820) or pegfilgrastim (C9119, S0135, J2505) during the first 5 days of the cycle (i.e., as prophylaxis).\",\n \"All patients, aged ≥18 years, who began ≥1 new course of myelosuppressive chemotherapy were identified; the enrollment window was July 1, 2001 through June 30, 2010 for patients in the MarketScan Database and July 1, 2001 through June 30, 2008 for patients in the LifeLink Database. Receipt of chemotherapy was ascertained based on the presence of ≥1 paid medical claim for a chemotherapy drug or administration thereof (identified using Healthcare Common Procedure Coding System [HCPCS], International Classification of Disease, Ninth Revision, Clinical Modification [ICD-9-CM], and Health Care Financing Administration Uniform Bill-92 [UB-92] revenue codes).\\nPatients were considered to have initiated a new course of chemotherapy if there was a chemotherapy claim during the study period that was preceded by a period ≥60 days without any other claims for chemotherapy. Only patients who had evidence of a primary solid tumor or NHL (based on ≥2 medical claims [≥7 days apart] with a qualifying 3-digit ICD-9-CM diagnosis code during the period beginning 30 days prior to the index date and ending 30 days thereafter) were selected.\",\n \"For each cancer chemotherapy patient, each unique cycle within each course of chemotherapy was identified. The first chemotherapy cycle (of the first course) was defined as beginning with the date of initiation of chemotherapy and ending with the first service date for the next administration of chemotherapy administration (as evidenced by a medical claim with a corresponding HCPCS, ICD-9-CM, or UB-92 code) occurring at least 12 days-but no more than 59 days-after the date of initiation of chemotherapy. If a second chemotherapy cycle did not commence prior to day 60, or if there was evidence of receipt of radiation therapy (based on medical claims with relevant HCPCS, ICD-9-CM, or UB-92 codes) during this period, both the first cycle of chemotherapy and the course of chemotherapy were considered to have been completed 30 days following the beginning of the cycle or on the day prior to initiation of radiation therapy, whichever occurred first. The second and all subsequent cycles of chemotherapy, as well as subsequent courses of chemotherapy–if any–during the period of interest, were similarly defined. A maximum of 8 cycles per course were considered. For patients with multiple courses of chemotherapy, all qualifying courses were considered.\\nChemotherapy regimens were ascertained based on a review of all HCPCS Level II codes for parenterally administered antineoplastic agents on medical claims with service dates within 6 days of the start of each cycle of chemotherapy. Regimens were categorized on a cycle-specific basis according to the number of agents administered that are considered to be myelotoxic (list available from authors upon request).\",\n \"Cancer chemotherapy patients who received daily filgrastim prophylaxis during ≥1 cycle of chemotherapy were selected for inclusion in the study population. Administration of daily filgrastim on or before day 5 of a given cycle was considered to represent prophylaxis\\n[14,22,23]. Receipt of daily filgrastim was identified based on medical claims (J1440, J1441) with relevant codes from the HCPCS system.\\nDuration of daily filgrastim prophylaxis during the patient-cycle was characterized based on temporal patterns of administration; end of prophylaxis was defined as a gap ≥3 days in its use, outpatient administration of IV antimicrobial therapy (an indicator for possible CINC), or hospitalization for CINC. Duration of prophylaxis was characterized as 1–3, 4–6, or ≥7 days.\",\n \"Patient-cycles were excluded from the analytic file if the following occurred: (1) evidence of ≥2 primary cancers (solid or blood) within (i.e., +/-) 30 days of chemotherapy initiation; (2) any gaps in health benefits during the 6-month (\\\"pretreatment\\\") period prior to chemotherapy initiation; (3) evidence of hematopoietic stem cell or bone marrow transplantation prior to or during receipt of chemotherapy; (4) evidence of chemotherapy based only on medical claims for administration of the drugs (HCPCS codes identifying the specific chemotherapy agents were not available, and thus the regimen and level of myelosuppression could not be determined); (5) pharmacy claims for myelotoxic chemotherapy (only drug dispense dates are available on pharmacy claims, and because pharmacy/medical claims cannot be definitively linked, precise dates of chemotherapy administration–needed to characterize the course and cycles–could not be ascertained); (6) pharmacy claims for daily filgrastim (precise dates of administration could not be ascertained, for reasons stated above); (7) evidence of receipt of sargramostim (J2820) or pegfilgrastim (C9119, S0135, J2505) during the first 5 days of the cycle (i.e., as prophylaxis).\",\n \"CINC was identified based on inpatient admissions with a diagnosis (principal or secondary) of neutropenia (ICD-9-CM 288.0), fever (780.6), or infection (list available upon request). Admissions were identified on a cycle-specific basis using acute-care facility inpatient claims with admission dates anytime between day 6 and the last day of the chemotherapy cycle. Episodes of CINC treated exclusively on an outpatient basis were not considered. Because CINC that occurred on the day of the last dose of prophylaxis or the following day could have resulted in an artificial truncation of a planned longer course of prophylaxis–and thus could exaggerate the risk of CINC with shorter courses–analyses were conducted alternatively with and without inclusion of these patient cycles.\\nConsequences of CINC requiring inpatient care were characterized in terms of in-hospital mortality, total hospital LOS (during the cycle), and total CINC-related healthcare expenditures (i.e., from hospital discharge to end of cycle). Total CINC-related healthcare expenditures included those for the initial hospitalization as well as care provided post-discharge on an outpatient basis; outpatient expenditures comprised encounters with a diagnosis of neutropenia, fever, or infection, as well as use/prescriptions for CSF agents (i.e., as treatment) and antimicrobial therapy. Expenditures were estimated based on total paid amounts on corresponding claims. Mortality was characterized using data only from the MarketScan Database as such information was not available in the LifeLink Database.\",\n \"Patient characteristics included: age; gender; presence of selected chronic comorbidities (cardiovascular disease, diabetes, liver disease, renal disease); history of blood disorders (anemia, neutropenia, other), infection, hospitalization (all-cause and CINC-related, respectively), chemotherapy, and radiation therapy; pre-chemotherapy healthcare expenditures; type of cancer, presence of metastases; cycle number and minimum length of prior cycles; chemotherapy regimen; receipt of antimicrobial prophylaxis in the cycle of interest; and year of chemotherapy initiation. Only observed data were utilized in defining patient characteristics.\\nAge was assessed as of the first day of the first cycle of chemotherapy in the course. Chronic comorbidities and history of blood disorders, infections, hospitalization, chemotherapy, radiation therapy, presence of metastases, and healthcare expenditures were assessed from the beginning of the 6-month pretreatment period through the first day of the corresponding cycle of chemotherapy. Selected variables (i.e., anemia, neutropenia, other blood disorders, infections) were alternatively evaluated during the chemotherapy course (up to the beginning of the cycle of interest). Metastases (bone vs. other site) and chronic comorbidities were identified on the basis of ≥1 diagnosis codes on inpatient claims, ≥2 diagnosis codes on outpatient claims (excluding those for laboratory services) on different days, ≥1 procedure codes, and ≥1 drug codes, as appropriate. Blood disorders and infections were identified on the basis of ≥1 diagnosis codes (on inpatient and/or outpatient claims) and ≥1 drug codes, as appropriate. Prophylactic use of antimicrobial agents was ascertained based on a medical claim for administration of drug from cycle day 1 to cycle day 5, or a pharmacy claim for a filled prescription from cycle day -3 to cycle day 5, with a corresponding drug code.\",\n \"Incidence of CINC was evaluated for each patient-cycle in which daily filgrastim was administered prophylactically, and was estimated by same-cycle duration of prophylaxis in an unadjusted and adjusted context. For the latter, a generalized estimating equation (GEE) with a binomial distribution, logistic link function, and exchangeable correlation structure was employed. The GEE method accounts for correlation among repeated measures for the same subject (in this instance, across cycles), while controlling for both fixed characteristics (e.g., gender) and time-dependent covariates (e.g., first versus subsequent cycles). All observed patient characteristics were entered into, and retained in, the multivariate model. Subgroup and sensitivity analyses focusing on the first cycle only, employing a narrow definition of CINC (which was identified using the diagnosis code for neutropenia only but otherwise the same algorithm for the broad definition as described above), and focusing on alternative tumor types separately (i.e., breast cancer, lung cancer, colorectal cancer, and NHL) also were conducted.\\nIn-hospital mortality, hospital LOS, and CINC-related healthcare expenditures among patients who developed CINC were descriptively evaluated by duration of daily filgrastim prophylaxis. Confidence intervals (95%) for in-hospital mortality were computed using the Wilson score interval; confidence intervals for LOS and economic costs were computed using nonparametric bootstrapping (percentile method) from the study population (1,000 replicates with replacement). Confidence intervals for in-hospital mortality, hospital LOS, and healthcare expenditures were estimated assuming independence between observations. Only observed data were used in defining study variables; patients who developed CINC but were missing data on study measures–because such data either were not recorded on claims or were not provided by some health plans–were excluded from corresponding analyses.\",\n \"A total of 135,921 adult patients initiated a new course of chemotherapy for a solid tumor or NHL during the period of interest and met all other eligibility criteria. Among these patients, a total of 5,477 received daily filgrastim as prophylaxis during ≥1 cycle of chemotherapy and thus were included in the study population; these patients contributed a total of 14,288 daily filgrastim prophylaxis patient-cycles to the analytic file. Filgrastim was administered for 1–3 days in 58% of cycles (n = 8,371), 4–6 days in 26% of cycles (n = 3,691), and ≥7days in 16% of cycles (n = 2,226) (Figure \\n1).\\nDays of filgrastim prophylaxis.\\nMean (SD) age of patients was 54 (13) years for 1–3 days of filgrastim prophylaxis, 57 (14) years for 4–6 days of prophylaxis, and 56 (13) years for ≥7 days of prophylaxis (Table \\n1). Breast cancer was the most common tumor type (48% for 1–3 days, 50% for 4–6 days, 49% for ≥7 days), followed by NHL (10%, 13%, and 13%, respectively), and colorectal cancer (16%, 10%, and 13%, respectively). Metastatic disease was present in 38% of patients receiving 1–3 days of prophylaxis, 32% receiving 4–6 days, and 35% receiving ≥7 days. Antimicrobial agents—principally oral (~90%)—were concurrently used as prophylaxis in 6.7% of patients receiving 1–3 days of filgrastim prophylaxis, 7.5% receiving 4–6 days, and 7.5% receiving ≥7 days.\\nPatient, cancer, and treatment characteristics, by duration of filgrastim prophylaxis\\nCrude risk of CINC during a cycle of chemotherapy was 2.9% with 1–3 days of filgrastim prophylaxis, 2.7% with 4–6 days, and 1.8% with ≥7 days. In adjusted analyses, the odds of CINC were 2.4 (95% CI: 1.6-3.4) and 1.9 (1.3-2.8) times higher with 1–3 and 4–6 days of filgrastim prophylaxis, respectively, versus ≥7 days (referent group) (Figure \\n2). Among the subgroup of patients who developed CINC requiring inpatient care (n = 382), mean hospital LOS was 7.4 (6.4-8.3) days with 1–3 days of prophylaxis (n = 243), 7.1 (5.7-8.5) days with 4–6 days of prophylaxis (n = 99), and 6.5 (4.9-8.0) with ≥7 days of prophylaxis (n = 40) (Table \\n2). Among the subgroup of patients who developed CINC and for whom healthcare expenditures were available (n = 358), mean total CINC-related healthcare expenditures were $18,912 (14,570-23,581) with 1–3 days of prophylaxis (n = 225), $14,907 (11,155-19,728) with 4–6 days (n = 94), and $13,165 (9,595-17,144) with ≥7 days (n = 39). Among the subgroup of patients who developed CINC and for whom discharge status was available (n = 228), in-hospital mortality was 8.4% (4.6-14.8) with 1–3 days of prophylaxis (n-119), 4.0% (1.4-11.1) with 4–6 days (n = 75), and 0% (0–10.2) with ≥7 days (n = 34).\\nAdjusted odds ratios for chemotherapy-induced neutropenic complications requiring inpatient care, by duration of filgrastim prophylaxis*. *Results adjusted for patient, cancer, and chemotherapy characteristics.\\nUnadjusted risk of inpatient mortality, length of stay in hospital, and healthcare expenditures for chemotherapy- induced neutropenic complications requiring inpatient care, by duration of filgrastim prophylaxis\\n*n’s indicate the number of patient-cycles considered in analyses of study measures; among patients who developed CINC (n = 382), those with missing data on study measures- because such data were not recorded on claims or were not provided by some health plans- were excluded from corresponding analyses.\\n**Only patients with paid amounts > $0 were considered in these analyses.\\nIn subgroup analyses focusing on the first cycle only, crude risks of CINC were 7.0% with 1–3 days of filgrastim prophylaxis (n = 1054), 5.0% with 4–6 days (n = 482), and 5.2% with ≥7 days (n = 363); in adjusted analyses, odds of CINC were 1.6 (0.9-2.8) and 1.1 (0.6-2.1) times higher with 1–3 and 4–6 days (versus ≥7 days) of filgrastim prophylaxis in cycle 1 (Table \\n3). In analyses employing the narrow definition for CINC, crude risks of CINC were 1.6% with 1–3 days of filgrastim prophylaxis, 1.8% with 4–6 days, and 1.3% with ≥7 days; in adjusted analyses, odds of CINC were 1.6 (1.1-2.5) and 1.7 (1.1-2.7) times higher with 1–3 and 4–6 days of filgrastim prophylaxis, respectively, versus ≥7 days. In tumor-specific analyses, adjusted odds of CINC with 1–3 and 4–6 days of filgrastim prophylaxis (vs. ≥7 days) were: 1.8 (1.0-3.0) and 1.9 (1.1-3.4) for breast cancer; 20.2 (1.9-212.4) and 14.5 (1.3-158.7) for lung cancer; 1.9 (0.2-16.7) and 2.5 (0.3-23.5) for colorectal cancer; and 2.1 (1.2-3.6) and 1.8 (1.0-3.2) for NHL. We note that not all observed differences were statistically significant in unadjusted subgroup and secondary analyses, presumably due to the lack of adjustment for systematic differences in patient characteristics between prophylaxis subgroups.\\nAdjusted odds ratios for chemotherapy- induced neutropenic complications requiring inpatient care in subgroup and secondary analyses\\n*Results adjusted for patient, cancer, and treatment characteristic listed in Table \\n2.\",\n \"In the largest retrospective study of daily filgrastim use in US clinical practice, we found that the large majority of patients undergoing cancer chemotherapy who are administered prophylaxis with daily filgrastim receive considerably fewer days of administration than subjects in clinical trials of daily filgrastim prophylaxis. In our study population, 95% of patients received fewer than 10 days of filgrastim prophylaxis and 58% received only 1–3 days, versus the typical 10–11 days required for neutrophil recovery in the pivotal clinical trials\\n[17-19]. This finding is largely consistent with observations from other retrospective clinical practice studies\\n[14-16,22,23]. We also found that patients who receive shorter courses of daily filgrastim prophylaxis have a substantially higher risk of CINC requiring hospitalization. Among patients in our study population who received 1–3 or 4–6 days of prophylaxis, the adjusted odds of CINC were 2.4 and 1.9 times, respectively, higher than those receiving ≥7 days. In addition, our study results suggest that, among the subgroup of patients who develop CINC despite prophylaxis, the consequences of this condition may be worse among those who receive fewer administrations of daily filgrastim prophylaxis. This last set of results should be interpreted with caution, however, as the number of patients who developed CINC despite prophylaxis was, in absolute terms, small.\\nOver the past decade, the frequency of use of alternative (often, more myelosuppressive) chemotherapy regimens in US clinical practice has changed considerably, in large part due to better chemotherapy agents, combinations of agents, and dosing schedules\\n[25-27]. Because these regimens are typically associated with higher levels of myelosuppression, use of supportive care–including CSF prophylaxis–also has increased\\n[27]. While pegfilgrastim—a longer-acting version of filgrastim that requires only a single dose administered subcutaneously once per chemotherapy cycle—is now (by far) the most commonly used CSF prophylactic agent in US clinical practice, filgrastim accounts for a small but important segment of the prophylactic market\\n[14,16]. In the present study, 60,600 (45%) of the 135,921 adult cancer chemotherapy study subjects received CSF prophylaxis in ≥1cycle during their chemotherapy course. Among the subgroup who received CSF prophylaxis, 91% received pegfilgrastim in ≥1 cycle and 9% received filgrastim. Notwithstanding these temporal changes in chemotherapy regimens and supportive care, however, our findings regarding the frequent use of shorter courses of filgrastim prophylaxis and the associated consequences–based on a study population of over 5,000 patients and nearly 15,000 patient-cycles–are largely consistent with those reported previously. In the 2006 study by Weycker and colleagues–which included 598 breast cancer, lung cancer, and NHL patients, and employed data from 1998-2002– for example, mean duration of prophylaxis ranged from 4.3 to 6.5 days across tumor types, while in the Morrison study–which included 1,451 cancer patients and employed data from 2001-2003– mean duration of prophylaxis ranged from 3.7 to 6.0 days across calendar years and cycles of use\\n[15,23]. In the present study, mean (SD) duration of prophylaxis on an overall basis was 3.6 (2.9) days. Moreover, in the aforementioned 2011 study by Weycker et al., odds of CINC were reported to be 1.5 times higher for patients receiving <7 versus ≥7 days of filgrastim prophylaxis, while the corresponding odds ratio in this study was estimated to be 2.2 (95% CI 1.6-3.1)\\n[14]. We note that odds ratios for CINC reported above were largely comparable when limiting attention to the most recent four-year period (2007–2010): 2.5 (1.5-4.3) with 1–3 versus ≥7 days of filgrastim prophylaxis and 2.2 (1.2-3.9) with 4–6 versus ≥7 days of filgrastim prophylaxis. Study results also were robust to alternative specifications of the multivariate model and when excluding observations for which hospitalizations occurred on the day of the last dose of daily filgrastim prophylaxis or the following day.\\nWe expected that systematic differences in the prevalence of risk factors (e.g., history of neutropenia, higher doses of chemotherapy agents, metastases to bone, poor performance status) for CINC would occur according to the duration of prophylaxis (1–3, 4–6, vs. ≥7 days). We thus used techniques of multivariate regression to adjust for such risk factors–to the extent possible–in analyzing the relationship between duration of daily filgrastim prophylaxis and outcomes of interest. Given the limitations of the claims-based databases we used, however, we were forced to use proxies for certain established risk factors. For example, a proxy measure based on pre-chemotherapy healthcare expenditures was employed for performance status, which in prior research has been shown to be correlated with health status in other patient populations\\n[30]. Moreover, because information was not available for some clinically important parameters (e.g., ANC), the possibility exists that the study groups differed in terms of unobserved characteristics that predispose to CINC.\\nWe believe that our approach to controlling for such systematic differences was comprehensive given available data, and that the above-noted biases–if present–would confer a conservative bias to analyses. For example, if the risk of FN is higher among patients receiving higher doses of chemotherapy (which is unobservable in the study database), and these patients are more likely to receive longer durations of prophylaxis, then the estimated difference in risk between patients receiving longer versus shorter courses of prophylaxis will be smaller than the true or actual difference. That our adjustment procedures were at least to some extent successful is suggested by the greater odds ratios for CINC in the adjusted versus unadjusted analyses, but uncertainty as to the adequacy of adjustment for confounding risk factors is one of the major limitations of our study.\\nIn those instances where code-based operational algorithms were used to identify risk factors of interest (e.g., using ICD-9-CM code 288.0 for neutropenia, rather than ANC) errors of omission/commission in medical coding may have impacted the accuracy of adjustment. We do not believe, however, that any such differences or limitations were for the most part systematic in nature. However, patients who have a history of CINC – and thus may be more likely to receive longer durations of prophylaxis – may be more likely to have \\\"neutropenia\\\" designated as a secondary (or even primary) diagnosis on future encounters, all else equal.\\nThere is no ICD-9-CM diagnosis code for CINC (i.e., neutropenia-related fever or infection), and thus codes for neutropenia, fever, and infection were employed to identify hospitalizations assumed to be related to neutropenic complications. Patients are typically not given chemotherapy when they are neutropenic or have active infection. The timing of fever and infection after chemotherapy increases the likelihood that such outcomes are related to receipt of chemotherapy. Codes for neutropenia, fever (surrogate for active infection), and infection thus are all likely to be related to episodes if seen within a defined exposure period after receiving chemotherapy. While the sensitivity of this algorithm for identifying neutropenic complications is undoubtedly higher than that of an algorithm using only the ICD-9-CM code for neutropenia, its specificity and positive predictive value are unknown.\\nOther miscellaneous limitations deserve a brief mention. CINC requiring outpatient care were not considered in our analyses due to the small total number of events (n = 60). Patients with evidence of receipt of daily filgrastim via outpatient pharmacy (3% of total) were excluded because we could not ascertain the precise dates of use. Expenditures were not adjusted to current dollars since use of a general price index may yield spurious findings when applied to specific patients in specific health plans who consumed specific healthcare services and since the distribution of study groups by calendar year was comparable. Hospital discharge disposition was available only in the MarketScan Database and information concerning LOS or paid amounts was missing for some hospitalizations. Moreover, we note that a disproportionately high percentage of patients receiving 1–3 days of filgrastim prophylaxis (vs. those receiving 4–6 or ≥7 days) who developed CINC requiring inpatient care did not have information available on discharge disposition, and thus the inpatient mortality findings must be viewed with caution. Although patients initiating \\\"delayed\\\" use of daily filgrastim (e.g., after day 5 of the chemotherapy cycle) have been included in other published evaluations, they were not considered in our analyses as such patients may have received daily filgrastim for the treatment of CINC (e.g., severe neutropenia) as opposed to prophylaxis against CINC\\n[15,16]. Finally, we note that it cannot be determined from outpatient pharmacy claims data whether drugs dispensed were actually taken, when they were taken, or how much was taken. For this reason, our characterization of antimicrobial prophylaxis use (principally oral) may be upwardly biased and differences in actual use across prophylaxis subgroups may confound the results of analyses. We also note, however, that the percentage of patients with filled prescriptions for antimicrobials was relatively low (and comparable) across filgrastim prophylaxis subgroups.\",\n \"In conclusion, we found that among patients receiving myelosuppressive chemotherapy, shorter courses of daily filgrastim prophylaxis are associated with increased risk of CINC. We also found that when CINC develops despite daily filgrastim prophylaxis, the outcomes thereof may be poorer in those receiving shorter courses of prophylaxis. Additional research is needed to explore these relationships among individual tumor types and chemotherapy regimens.\",\n \"Funding for this research was provided by Amgen Inc. to Policy Analysis Inc. (PAI). Derek Weycker, John Edelsberg, and Alex Kartashov are employed by Policy Analysis Inc. (PAI). Rich Barron and Jason Legg are employed by Amgen Inc. Andrew Glass is employed by the Center for Health Research, Kaiser Permanente Northwest, and received an honorarium for this research from Amgen Inc.\",\n \"Authorship was designated based on the guidelines promulgated by the International Committee of Medical Journal Editors (2004). All persons who meet criteria for authorship are listed as authors on the title page. The contribution of each of these individuals to this study–by task–is as follows: conception and supervision (Barron, Weycker), development of design (Barron, Edelsberg, Glass, Legg, Weycker), conduct of analyses (Kartashov, Weycker), interpretation of results (all authors), preparation of manuscript (Edelsberg, Weycker), and review of manuscript (all authors). All authors have read and approved the final version of the manuscript. The study sponsor reviewed the study research plan and study manuscript; data management, processing, and analyses were conducted by Policy Analysis Inc. (PAI), and all final analytic decisions were made by study investigators.\",\n \"The pre-publication history for this paper can be accessed here:\\n\\nhttp://www.biomedcentral.com/1472-6963/14/189/prepub\\n\"\n]"},"all_sections_sec_types":{"kind":"list like","value":[null,"methods",null,null,null,null,null,null,null,null,null,"results","discussion","conclusions",null,null,null],"string":"[\n null,\n \"methods\",\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n \"results\",\n \"discussion\",\n \"conclusions\",\n null,\n null,\n null\n]"},"keywords":{"kind":"list like","value":["Filgrastim","Granulocyte colony-stimulating factor","Febrile neutropenia","Cost","Neoplasms"],"string":"[\n \"Filgrastim\",\n \"Granulocyte colony-stimulating factor\",\n \"Febrile neutropenia\",\n \"Cost\",\n \"Neoplasms\"\n]"},"whole_article_text":{"kind":"string","value":"Background:\nNeutropenia is a common side effect of myelosuppressive chemotherapy that both increases the risk of infection and diminishes patients’ ability to fight infection. When neutropenic patients become febrile, the high likelihood of infection and serious consequences thereof usually results in hospitalization\n[1,2]. FN, as well as severe or prolonged neutropenia, also can interfere with the planned delivery of treatment and adversely affect important patient outcomes\n[2-11].\nClinical practice guidelines recommend primary prophylactic use of a colony-stimulating factor (CSF)–which has been shown to reduce the risk of FN in clinical trials–when the risk of FN is 20% or higher\n[2]. While the American Society of Clinical Oncology (ASCO) initially recommended that CSF prophylaxis be administered only when FN risk is 40% or higher, in 2006, ASCO lowered the threshold to 20% based on data highlighting the importance of FN-related hospitalization as an outcome and evidence demonstrating the value of CSF prophylaxis in reducing the risk of FN, the risk of FN-related hospitalization, and the associated use of IV anti-infective agents\n[2,12,13]. The CSF filgrastim, which is widely used in clinical practice as prophylaxis against FN, requires daily administration during each cycle until neutrophil recovery occurs (in clinical trials, given typically for 10–11 days [and up to 14 days] until absolute neutrophil count [ANC] ≥10 × 109/L)\n[14-19].\nIn clinical practice, patients often receive shorter courses of daily filgrastim prophylaxis than those administered to subjects in the clinical trial setting\n[14-16,20-24]. While published studies suggest that shorter courses of daily filgrastim prophylaxis are associated with an increased risk of hospitalization for CINC, these studies focused on selected tumor types or employed data that now are over a decade old\n[23,24]. During the past decade, use of chemotherapy and supportive care in clinical practice–as well as recommended use of these agents in authoritative guidelines–has changed considerably\n[2,25-27], Moreover, only one study has examined whether CSFs may favorably impact clinical outcomes and economic costs when CINC develop despite CSF prophylaxis, and it was published a decade ago and focused on elderly patients with a single type of cancer (non-Hodgkin’s lymphoma)\n[28]. We therefore undertook a new study to evaluate the relationship between the duration of daily filgrastim prophylaxis, and the risk and consequences of CINC requiring inpatient care using a large healthcare claims database. While such databases often lack detailed clinical information (e.g., on absolute neutrophil counts), they offer access to information on the health profile and healthcare utilization of tens of millions of covered lives.\n\nMethods:\n Data source Patient-level information from two large healthcare claims databases–the Thomson Reuters MarketScan Commercial Claims and Encounters and Medicare Supplemental and Coordination of Benefits Database (MarketScan Database, 2001–2010) and the Intercontinental Marketing Services LifeLink Database (LifeLink Database, 2001–2008)–were pooled for analyses. Both databases comprise medical (i.e., facility and professional service) and outpatient pharmacy claims from a large number of participating private health plans, and each contains claims data for 15 million persons annually.\nData available for each facility and professional-service claim include date and place of service, diagnoses, procedures performed/services rendered, and quantity of services (professional-service claims). Data available for each retail pharmacy claim include the drug dispensed, dispensing date, quantity dispensed, and number of days supplied. All claims also include paid (i.e., reimbursed) amounts. Selected demographic and eligibility information is available for persons in both databases. All data can be arrayed to provide a detailed chronology of all medical and pharmacy services used by each plan member over time.\nThe study databases were de-identified prior to their release to study investigators, as set forth in the corresponding Data Use Agreements. The study databases have been evaluated and certified by independent third parties to be in compliance with the Health Insurance Portability and Accountability Act (HIPAA) of 1996 statistical de-identification standards and to satisfy the conditions set forth in Sections 164.514 (a)-(b) 1ii of the HIPAA Privacy Rule regarding the determination and documentation of statistically de-identified data. Use of the study databases for health services research was determined–via independent third parties–to be fully compliant with the HIPAA Privacy Rule and federal guidance on Public Welfare and the Protection of Human Subjects\n[29].\nPatient-level information from two large healthcare claims databases–the Thomson Reuters MarketScan Commercial Claims and Encounters and Medicare Supplemental and Coordination of Benefits Database (MarketScan Database, 2001–2010) and the Intercontinental Marketing Services LifeLink Database (LifeLink Database, 2001–2008)–were pooled for analyses. Both databases comprise medical (i.e., facility and professional service) and outpatient pharmacy claims from a large number of participating private health plans, and each contains claims data for 15 million persons annually.\nData available for each facility and professional-service claim include date and place of service, diagnoses, procedures performed/services rendered, and quantity of services (professional-service claims). Data available for each retail pharmacy claim include the drug dispensed, dispensing date, quantity dispensed, and number of days supplied. All claims also include paid (i.e., reimbursed) amounts. Selected demographic and eligibility information is available for persons in both databases. All data can be arrayed to provide a detailed chronology of all medical and pharmacy services used by each plan member over time.\nThe study databases were de-identified prior to their release to study investigators, as set forth in the corresponding Data Use Agreements. The study databases have been evaluated and certified by independent third parties to be in compliance with the Health Insurance Portability and Accountability Act (HIPAA) of 1996 statistical de-identification standards and to satisfy the conditions set forth in Sections 164.514 (a)-(b) 1ii of the HIPAA Privacy Rule regarding the determination and documentation of statistically de-identified data. Use of the study databases for health services research was determined–via independent third parties–to be fully compliant with the HIPAA Privacy Rule and federal guidance on Public Welfare and the Protection of Human Subjects\n[29].\n Study population The study population comprised all patients who initiated ≥1 course of myelosuppressive chemotherapy for a solid tumor or non-Hodgkin’s lymphoma (NHL) and who received daily filgrastim prophylaxis during ≥1 cycle. All cycles in which patients received daily filgrastim prophylaxis–irrespective of duration–were pooled for analyses.\n Cancer chemotherapy patients All patients, aged ≥18 years, who began ≥1 new course of myelosuppressive chemotherapy were identified; the enrollment window was July 1, 2001 through June 30, 2010 for patients in the MarketScan Database and July 1, 2001 through June 30, 2008 for patients in the LifeLink Database. Receipt of chemotherapy was ascertained based on the presence of ≥1 paid medical claim for a chemotherapy drug or administration thereof (identified using Healthcare Common Procedure Coding System [HCPCS], International Classification of Disease, Ninth Revision, Clinical Modification [ICD-9-CM], and Health Care Financing Administration Uniform Bill-92 [UB-92] revenue codes).\nPatients were considered to have initiated a new course of chemotherapy if there was a chemotherapy claim during the study period that was preceded by a period ≥60 days without any other claims for chemotherapy. Only patients who had evidence of a primary solid tumor or NHL (based on ≥2 medical claims [≥7 days apart] with a qualifying 3-digit ICD-9-CM diagnosis code during the period beginning 30 days prior to the index date and ending 30 days thereafter) were selected.\nAll patients, aged ≥18 years, who began ≥1 new course of myelosuppressive chemotherapy were identified; the enrollment window was July 1, 2001 through June 30, 2010 for patients in the MarketScan Database and July 1, 2001 through June 30, 2008 for patients in the LifeLink Database. Receipt of chemotherapy was ascertained based on the presence of ≥1 paid medical claim for a chemotherapy drug or administration thereof (identified using Healthcare Common Procedure Coding System [HCPCS], International Classification of Disease, Ninth Revision, Clinical Modification [ICD-9-CM], and Health Care Financing Administration Uniform Bill-92 [UB-92] revenue codes).\nPatients were considered to have initiated a new course of chemotherapy if there was a chemotherapy claim during the study period that was preceded by a period ≥60 days without any other claims for chemotherapy. Only patients who had evidence of a primary solid tumor or NHL (based on ≥2 medical claims [≥7 days apart] with a qualifying 3-digit ICD-9-CM diagnosis code during the period beginning 30 days prior to the index date and ending 30 days thereafter) were selected.\n Chemotherapy courses, cycles, and regimens For each cancer chemotherapy patient, each unique cycle within each course of chemotherapy was identified. The first chemotherapy cycle (of the first course) was defined as beginning with the date of initiation of chemotherapy and ending with the first service date for the next administration of chemotherapy administration (as evidenced by a medical claim with a corresponding HCPCS, ICD-9-CM, or UB-92 code) occurring at least 12 days-but no more than 59 days-after the date of initiation of chemotherapy. If a second chemotherapy cycle did not commence prior to day 60, or if there was evidence of receipt of radiation therapy (based on medical claims with relevant HCPCS, ICD-9-CM, or UB-92 codes) during this period, both the first cycle of chemotherapy and the course of chemotherapy were considered to have been completed 30 days following the beginning of the cycle or on the day prior to initiation of radiation therapy, whichever occurred first. The second and all subsequent cycles of chemotherapy, as well as subsequent courses of chemotherapy–if any–during the period of interest, were similarly defined. A maximum of 8 cycles per course were considered. For patients with multiple courses of chemotherapy, all qualifying courses were considered.\nChemotherapy regimens were ascertained based on a review of all HCPCS Level II codes for parenterally administered antineoplastic agents on medical claims with service dates within 6 days of the start of each cycle of chemotherapy. Regimens were categorized on a cycle-specific basis according to the number of agents administered that are considered to be myelotoxic (list available from authors upon request).\nFor each cancer chemotherapy patient, each unique cycle within each course of chemotherapy was identified. The first chemotherapy cycle (of the first course) was defined as beginning with the date of initiation of chemotherapy and ending with the first service date for the next administration of chemotherapy administration (as evidenced by a medical claim with a corresponding HCPCS, ICD-9-CM, or UB-92 code) occurring at least 12 days-but no more than 59 days-after the date of initiation of chemotherapy. If a second chemotherapy cycle did not commence prior to day 60, or if there was evidence of receipt of radiation therapy (based on medical claims with relevant HCPCS, ICD-9-CM, or UB-92 codes) during this period, both the first cycle of chemotherapy and the course of chemotherapy were considered to have been completed 30 days following the beginning of the cycle or on the day prior to initiation of radiation therapy, whichever occurred first. The second and all subsequent cycles of chemotherapy, as well as subsequent courses of chemotherapy–if any–during the period of interest, were similarly defined. A maximum of 8 cycles per course were considered. For patients with multiple courses of chemotherapy, all qualifying courses were considered.\nChemotherapy regimens were ascertained based on a review of all HCPCS Level II codes for parenterally administered antineoplastic agents on medical claims with service dates within 6 days of the start of each cycle of chemotherapy. Regimens were categorized on a cycle-specific basis according to the number of agents administered that are considered to be myelotoxic (list available from authors upon request).\n Daily filgrastim prophylaxis Cancer chemotherapy patients who received daily filgrastim prophylaxis during ≥1 cycle of chemotherapy were selected for inclusion in the study population. Administration of daily filgrastim on or before day 5 of a given cycle was considered to represent prophylaxis\n[14,22,23]. Receipt of daily filgrastim was identified based on medical claims (J1440, J1441) with relevant codes from the HCPCS system.\nDuration of daily filgrastim prophylaxis during the patient-cycle was characterized based on temporal patterns of administration; end of prophylaxis was defined as a gap ≥3 days in its use, outpatient administration of IV antimicrobial therapy (an indicator for possible CINC), or hospitalization for CINC. Duration of prophylaxis was characterized as 1–3, 4–6, or ≥7 days.\nCancer chemotherapy patients who received daily filgrastim prophylaxis during ≥1 cycle of chemotherapy were selected for inclusion in the study population. Administration of daily filgrastim on or before day 5 of a given cycle was considered to represent prophylaxis\n[14,22,23]. Receipt of daily filgrastim was identified based on medical claims (J1440, J1441) with relevant codes from the HCPCS system.\nDuration of daily filgrastim prophylaxis during the patient-cycle was characterized based on temporal patterns of administration; end of prophylaxis was defined as a gap ≥3 days in its use, outpatient administration of IV antimicrobial therapy (an indicator for possible CINC), or hospitalization for CINC. Duration of prophylaxis was characterized as 1–3, 4–6, or ≥7 days.\n Exclusion criteria Patient-cycles were excluded from the analytic file if the following occurred: (1) evidence of ≥2 primary cancers (solid or blood) within (i.e., +/-) 30 days of chemotherapy initiation; (2) any gaps in health benefits during the 6-month (\"pretreatment\") period prior to chemotherapy initiation; (3) evidence of hematopoietic stem cell or bone marrow transplantation prior to or during receipt of chemotherapy; (4) evidence of chemotherapy based only on medical claims for administration of the drugs (HCPCS codes identifying the specific chemotherapy agents were not available, and thus the regimen and level of myelosuppression could not be determined); (5) pharmacy claims for myelotoxic chemotherapy (only drug dispense dates are available on pharmacy claims, and because pharmacy/medical claims cannot be definitively linked, precise dates of chemotherapy administration–needed to characterize the course and cycles–could not be ascertained); (6) pharmacy claims for daily filgrastim (precise dates of administration could not be ascertained, for reasons stated above); (7) evidence of receipt of sargramostim (J2820) or pegfilgrastim (C9119, S0135, J2505) during the first 5 days of the cycle (i.e., as prophylaxis).\nPatient-cycles were excluded from the analytic file if the following occurred: (1) evidence of ≥2 primary cancers (solid or blood) within (i.e., +/-) 30 days of chemotherapy initiation; (2) any gaps in health benefits during the 6-month (\"pretreatment\") period prior to chemotherapy initiation; (3) evidence of hematopoietic stem cell or bone marrow transplantation prior to or during receipt of chemotherapy; (4) evidence of chemotherapy based only on medical claims for administration of the drugs (HCPCS codes identifying the specific chemotherapy agents were not available, and thus the regimen and level of myelosuppression could not be determined); (5) pharmacy claims for myelotoxic chemotherapy (only drug dispense dates are available on pharmacy claims, and because pharmacy/medical claims cannot be definitively linked, precise dates of chemotherapy administration–needed to characterize the course and cycles–could not be ascertained); (6) pharmacy claims for daily filgrastim (precise dates of administration could not be ascertained, for reasons stated above); (7) evidence of receipt of sargramostim (J2820) or pegfilgrastim (C9119, S0135, J2505) during the first 5 days of the cycle (i.e., as prophylaxis).\nThe study population comprised all patients who initiated ≥1 course of myelosuppressive chemotherapy for a solid tumor or non-Hodgkin’s lymphoma (NHL) and who received daily filgrastim prophylaxis during ≥1 cycle. All cycles in which patients received daily filgrastim prophylaxis–irrespective of duration–were pooled for analyses.\n Cancer chemotherapy patients All patients, aged ≥18 years, who began ≥1 new course of myelosuppressive chemotherapy were identified; the enrollment window was July 1, 2001 through June 30, 2010 for patients in the MarketScan Database and July 1, 2001 through June 30, 2008 for patients in the LifeLink Database. Receipt of chemotherapy was ascertained based on the presence of ≥1 paid medical claim for a chemotherapy drug or administration thereof (identified using Healthcare Common Procedure Coding System [HCPCS], International Classification of Disease, Ninth Revision, Clinical Modification [ICD-9-CM], and Health Care Financing Administration Uniform Bill-92 [UB-92] revenue codes).\nPatients were considered to have initiated a new course of chemotherapy if there was a chemotherapy claim during the study period that was preceded by a period ≥60 days without any other claims for chemotherapy. Only patients who had evidence of a primary solid tumor or NHL (based on ≥2 medical claims [≥7 days apart] with a qualifying 3-digit ICD-9-CM diagnosis code during the period beginning 30 days prior to the index date and ending 30 days thereafter) were selected.\nAll patients, aged ≥18 years, who began ≥1 new course of myelosuppressive chemotherapy were identified; the enrollment window was July 1, 2001 through June 30, 2010 for patients in the MarketScan Database and July 1, 2001 through June 30, 2008 for patients in the LifeLink Database. Receipt of chemotherapy was ascertained based on the presence of ≥1 paid medical claim for a chemotherapy drug or administration thereof (identified using Healthcare Common Procedure Coding System [HCPCS], International Classification of Disease, Ninth Revision, Clinical Modification [ICD-9-CM], and Health Care Financing Administration Uniform Bill-92 [UB-92] revenue codes).\nPatients were considered to have initiated a new course of chemotherapy if there was a chemotherapy claim during the study period that was preceded by a period ≥60 days without any other claims for chemotherapy. Only patients who had evidence of a primary solid tumor or NHL (based on ≥2 medical claims [≥7 days apart] with a qualifying 3-digit ICD-9-CM diagnosis code during the period beginning 30 days prior to the index date and ending 30 days thereafter) were selected.\n Chemotherapy courses, cycles, and regimens For each cancer chemotherapy patient, each unique cycle within each course of chemotherapy was identified. The first chemotherapy cycle (of the first course) was defined as beginning with the date of initiation of chemotherapy and ending with the first service date for the next administration of chemotherapy administration (as evidenced by a medical claim with a corresponding HCPCS, ICD-9-CM, or UB-92 code) occurring at least 12 days-but no more than 59 days-after the date of initiation of chemotherapy. If a second chemotherapy cycle did not commence prior to day 60, or if there was evidence of receipt of radiation therapy (based on medical claims with relevant HCPCS, ICD-9-CM, or UB-92 codes) during this period, both the first cycle of chemotherapy and the course of chemotherapy were considered to have been completed 30 days following the beginning of the cycle or on the day prior to initiation of radiation therapy, whichever occurred first. The second and all subsequent cycles of chemotherapy, as well as subsequent courses of chemotherapy–if any–during the period of interest, were similarly defined. A maximum of 8 cycles per course were considered. For patients with multiple courses of chemotherapy, all qualifying courses were considered.\nChemotherapy regimens were ascertained based on a review of all HCPCS Level II codes for parenterally administered antineoplastic agents on medical claims with service dates within 6 days of the start of each cycle of chemotherapy. Regimens were categorized on a cycle-specific basis according to the number of agents administered that are considered to be myelotoxic (list available from authors upon request).\nFor each cancer chemotherapy patient, each unique cycle within each course of chemotherapy was identified. The first chemotherapy cycle (of the first course) was defined as beginning with the date of initiation of chemotherapy and ending with the first service date for the next administration of chemotherapy administration (as evidenced by a medical claim with a corresponding HCPCS, ICD-9-CM, or UB-92 code) occurring at least 12 days-but no more than 59 days-after the date of initiation of chemotherapy. If a second chemotherapy cycle did not commence prior to day 60, or if there was evidence of receipt of radiation therapy (based on medical claims with relevant HCPCS, ICD-9-CM, or UB-92 codes) during this period, both the first cycle of chemotherapy and the course of chemotherapy were considered to have been completed 30 days following the beginning of the cycle or on the day prior to initiation of radiation therapy, whichever occurred first. The second and all subsequent cycles of chemotherapy, as well as subsequent courses of chemotherapy–if any–during the period of interest, were similarly defined. A maximum of 8 cycles per course were considered. For patients with multiple courses of chemotherapy, all qualifying courses were considered.\nChemotherapy regimens were ascertained based on a review of all HCPCS Level II codes for parenterally administered antineoplastic agents on medical claims with service dates within 6 days of the start of each cycle of chemotherapy. Regimens were categorized on a cycle-specific basis according to the number of agents administered that are considered to be myelotoxic (list available from authors upon request).\n Daily filgrastim prophylaxis Cancer chemotherapy patients who received daily filgrastim prophylaxis during ≥1 cycle of chemotherapy were selected for inclusion in the study population. Administration of daily filgrastim on or before day 5 of a given cycle was considered to represent prophylaxis\n[14,22,23]. Receipt of daily filgrastim was identified based on medical claims (J1440, J1441) with relevant codes from the HCPCS system.\nDuration of daily filgrastim prophylaxis during the patient-cycle was characterized based on temporal patterns of administration; end of prophylaxis was defined as a gap ≥3 days in its use, outpatient administration of IV antimicrobial therapy (an indicator for possible CINC), or hospitalization for CINC. Duration of prophylaxis was characterized as 1–3, 4–6, or ≥7 days.\nCancer chemotherapy patients who received daily filgrastim prophylaxis during ≥1 cycle of chemotherapy were selected for inclusion in the study population. Administration of daily filgrastim on or before day 5 of a given cycle was considered to represent prophylaxis\n[14,22,23]. Receipt of daily filgrastim was identified based on medical claims (J1440, J1441) with relevant codes from the HCPCS system.\nDuration of daily filgrastim prophylaxis during the patient-cycle was characterized based on temporal patterns of administration; end of prophylaxis was defined as a gap ≥3 days in its use, outpatient administration of IV antimicrobial therapy (an indicator for possible CINC), or hospitalization for CINC. Duration of prophylaxis was characterized as 1–3, 4–6, or ≥7 days.\n Exclusion criteria Patient-cycles were excluded from the analytic file if the following occurred: (1) evidence of ≥2 primary cancers (solid or blood) within (i.e., +/-) 30 days of chemotherapy initiation; (2) any gaps in health benefits during the 6-month (\"pretreatment\") period prior to chemotherapy initiation; (3) evidence of hematopoietic stem cell or bone marrow transplantation prior to or during receipt of chemotherapy; (4) evidence of chemotherapy based only on medical claims for administration of the drugs (HCPCS codes identifying the specific chemotherapy agents were not available, and thus the regimen and level of myelosuppression could not be determined); (5) pharmacy claims for myelotoxic chemotherapy (only drug dispense dates are available on pharmacy claims, and because pharmacy/medical claims cannot be definitively linked, precise dates of chemotherapy administration–needed to characterize the course and cycles–could not be ascertained); (6) pharmacy claims for daily filgrastim (precise dates of administration could not be ascertained, for reasons stated above); (7) evidence of receipt of sargramostim (J2820) or pegfilgrastim (C9119, S0135, J2505) during the first 5 days of the cycle (i.e., as prophylaxis).\nPatient-cycles were excluded from the analytic file if the following occurred: (1) evidence of ≥2 primary cancers (solid or blood) within (i.e., +/-) 30 days of chemotherapy initiation; (2) any gaps in health benefits during the 6-month (\"pretreatment\") period prior to chemotherapy initiation; (3) evidence of hematopoietic stem cell or bone marrow transplantation prior to or during receipt of chemotherapy; (4) evidence of chemotherapy based only on medical claims for administration of the drugs (HCPCS codes identifying the specific chemotherapy agents were not available, and thus the regimen and level of myelosuppression could not be determined); (5) pharmacy claims for myelotoxic chemotherapy (only drug dispense dates are available on pharmacy claims, and because pharmacy/medical claims cannot be definitively linked, precise dates of chemotherapy administration–needed to characterize the course and cycles–could not be ascertained); (6) pharmacy claims for daily filgrastim (precise dates of administration could not be ascertained, for reasons stated above); (7) evidence of receipt of sargramostim (J2820) or pegfilgrastim (C9119, S0135, J2505) during the first 5 days of the cycle (i.e., as prophylaxis).\n Neutropenic complications requiring inpatient care CINC was identified based on inpatient admissions with a diagnosis (principal or secondary) of neutropenia (ICD-9-CM 288.0), fever (780.6), or infection (list available upon request). Admissions were identified on a cycle-specific basis using acute-care facility inpatient claims with admission dates anytime between day 6 and the last day of the chemotherapy cycle. Episodes of CINC treated exclusively on an outpatient basis were not considered. Because CINC that occurred on the day of the last dose of prophylaxis or the following day could have resulted in an artificial truncation of a planned longer course of prophylaxis–and thus could exaggerate the risk of CINC with shorter courses–analyses were conducted alternatively with and without inclusion of these patient cycles.\nConsequences of CINC requiring inpatient care were characterized in terms of in-hospital mortality, total hospital LOS (during the cycle), and total CINC-related healthcare expenditures (i.e., from hospital discharge to end of cycle). Total CINC-related healthcare expenditures included those for the initial hospitalization as well as care provided post-discharge on an outpatient basis; outpatient expenditures comprised encounters with a diagnosis of neutropenia, fever, or infection, as well as use/prescriptions for CSF agents (i.e., as treatment) and antimicrobial therapy. Expenditures were estimated based on total paid amounts on corresponding claims. Mortality was characterized using data only from the MarketScan Database as such information was not available in the LifeLink Database.\nCINC was identified based on inpatient admissions with a diagnosis (principal or secondary) of neutropenia (ICD-9-CM 288.0), fever (780.6), or infection (list available upon request). Admissions were identified on a cycle-specific basis using acute-care facility inpatient claims with admission dates anytime between day 6 and the last day of the chemotherapy cycle. Episodes of CINC treated exclusively on an outpatient basis were not considered. Because CINC that occurred on the day of the last dose of prophylaxis or the following day could have resulted in an artificial truncation of a planned longer course of prophylaxis–and thus could exaggerate the risk of CINC with shorter courses–analyses were conducted alternatively with and without inclusion of these patient cycles.\nConsequences of CINC requiring inpatient care were characterized in terms of in-hospital mortality, total hospital LOS (during the cycle), and total CINC-related healthcare expenditures (i.e., from hospital discharge to end of cycle). Total CINC-related healthcare expenditures included those for the initial hospitalization as well as care provided post-discharge on an outpatient basis; outpatient expenditures comprised encounters with a diagnosis of neutropenia, fever, or infection, as well as use/prescriptions for CSF agents (i.e., as treatment) and antimicrobial therapy. Expenditures were estimated based on total paid amounts on corresponding claims. Mortality was characterized using data only from the MarketScan Database as such information was not available in the LifeLink Database.\n Patient characteristics Patient characteristics included: age; gender; presence of selected chronic comorbidities (cardiovascular disease, diabetes, liver disease, renal disease); history of blood disorders (anemia, neutropenia, other), infection, hospitalization (all-cause and CINC-related, respectively), chemotherapy, and radiation therapy; pre-chemotherapy healthcare expenditures; type of cancer, presence of metastases; cycle number and minimum length of prior cycles; chemotherapy regimen; receipt of antimicrobial prophylaxis in the cycle of interest; and year of chemotherapy initiation. Only observed data were utilized in defining patient characteristics.\nAge was assessed as of the first day of the first cycle of chemotherapy in the course. Chronic comorbidities and history of blood disorders, infections, hospitalization, chemotherapy, radiation therapy, presence of metastases, and healthcare expenditures were assessed from the beginning of the 6-month pretreatment period through the first day of the corresponding cycle of chemotherapy. Selected variables (i.e., anemia, neutropenia, other blood disorders, infections) were alternatively evaluated during the chemotherapy course (up to the beginning of the cycle of interest). Metastases (bone vs. other site) and chronic comorbidities were identified on the basis of ≥1 diagnosis codes on inpatient claims, ≥2 diagnosis codes on outpatient claims (excluding those for laboratory services) on different days, ≥1 procedure codes, and ≥1 drug codes, as appropriate. Blood disorders and infections were identified on the basis of ≥1 diagnosis codes (on inpatient and/or outpatient claims) and ≥1 drug codes, as appropriate. Prophylactic use of antimicrobial agents was ascertained based on a medical claim for administration of drug from cycle day 1 to cycle day 5, or a pharmacy claim for a filled prescription from cycle day -3 to cycle day 5, with a corresponding drug code.\nPatient characteristics included: age; gender; presence of selected chronic comorbidities (cardiovascular disease, diabetes, liver disease, renal disease); history of blood disorders (anemia, neutropenia, other), infection, hospitalization (all-cause and CINC-related, respectively), chemotherapy, and radiation therapy; pre-chemotherapy healthcare expenditures; type of cancer, presence of metastases; cycle number and minimum length of prior cycles; chemotherapy regimen; receipt of antimicrobial prophylaxis in the cycle of interest; and year of chemotherapy initiation. Only observed data were utilized in defining patient characteristics.\nAge was assessed as of the first day of the first cycle of chemotherapy in the course. Chronic comorbidities and history of blood disorders, infections, hospitalization, chemotherapy, radiation therapy, presence of metastases, and healthcare expenditures were assessed from the beginning of the 6-month pretreatment period through the first day of the corresponding cycle of chemotherapy. Selected variables (i.e., anemia, neutropenia, other blood disorders, infections) were alternatively evaluated during the chemotherapy course (up to the beginning of the cycle of interest). Metastases (bone vs. other site) and chronic comorbidities were identified on the basis of ≥1 diagnosis codes on inpatient claims, ≥2 diagnosis codes on outpatient claims (excluding those for laboratory services) on different days, ≥1 procedure codes, and ≥1 drug codes, as appropriate. Blood disorders and infections were identified on the basis of ≥1 diagnosis codes (on inpatient and/or outpatient claims) and ≥1 drug codes, as appropriate. Prophylactic use of antimicrobial agents was ascertained based on a medical claim for administration of drug from cycle day 1 to cycle day 5, or a pharmacy claim for a filled prescription from cycle day -3 to cycle day 5, with a corresponding drug code.\n Statistical analyses Incidence of CINC was evaluated for each patient-cycle in which daily filgrastim was administered prophylactically, and was estimated by same-cycle duration of prophylaxis in an unadjusted and adjusted context. For the latter, a generalized estimating equation (GEE) with a binomial distribution, logistic link function, and exchangeable correlation structure was employed. The GEE method accounts for correlation among repeated measures for the same subject (in this instance, across cycles), while controlling for both fixed characteristics (e.g., gender) and time-dependent covariates (e.g., first versus subsequent cycles). All observed patient characteristics were entered into, and retained in, the multivariate model. Subgroup and sensitivity analyses focusing on the first cycle only, employing a narrow definition of CINC (which was identified using the diagnosis code for neutropenia only but otherwise the same algorithm for the broad definition as described above), and focusing on alternative tumor types separately (i.e., breast cancer, lung cancer, colorectal cancer, and NHL) also were conducted.\nIn-hospital mortality, hospital LOS, and CINC-related healthcare expenditures among patients who developed CINC were descriptively evaluated by duration of daily filgrastim prophylaxis. Confidence intervals (95%) for in-hospital mortality were computed using the Wilson score interval; confidence intervals for LOS and economic costs were computed using nonparametric bootstrapping (percentile method) from the study population (1,000 replicates with replacement). Confidence intervals for in-hospital mortality, hospital LOS, and healthcare expenditures were estimated assuming independence between observations. Only observed data were used in defining study variables; patients who developed CINC but were missing data on study measures–because such data either were not recorded on claims or were not provided by some health plans–were excluded from corresponding analyses.\nIncidence of CINC was evaluated for each patient-cycle in which daily filgrastim was administered prophylactically, and was estimated by same-cycle duration of prophylaxis in an unadjusted and adjusted context. For the latter, a generalized estimating equation (GEE) with a binomial distribution, logistic link function, and exchangeable correlation structure was employed. The GEE method accounts for correlation among repeated measures for the same subject (in this instance, across cycles), while controlling for both fixed characteristics (e.g., gender) and time-dependent covariates (e.g., first versus subsequent cycles). All observed patient characteristics were entered into, and retained in, the multivariate model. Subgroup and sensitivity analyses focusing on the first cycle only, employing a narrow definition of CINC (which was identified using the diagnosis code for neutropenia only but otherwise the same algorithm for the broad definition as described above), and focusing on alternative tumor types separately (i.e., breast cancer, lung cancer, colorectal cancer, and NHL) also were conducted.\nIn-hospital mortality, hospital LOS, and CINC-related healthcare expenditures among patients who developed CINC were descriptively evaluated by duration of daily filgrastim prophylaxis. Confidence intervals (95%) for in-hospital mortality were computed using the Wilson score interval; confidence intervals for LOS and economic costs were computed using nonparametric bootstrapping (percentile method) from the study population (1,000 replicates with replacement). Confidence intervals for in-hospital mortality, hospital LOS, and healthcare expenditures were estimated assuming independence between observations. Only observed data were used in defining study variables; patients who developed CINC but were missing data on study measures–because such data either were not recorded on claims or were not provided by some health plans–were excluded from corresponding analyses.\n\nData source:\nPatient-level information from two large healthcare claims databases–the Thomson Reuters MarketScan Commercial Claims and Encounters and Medicare Supplemental and Coordination of Benefits Database (MarketScan Database, 2001–2010) and the Intercontinental Marketing Services LifeLink Database (LifeLink Database, 2001–2008)–were pooled for analyses. Both databases comprise medical (i.e., facility and professional service) and outpatient pharmacy claims from a large number of participating private health plans, and each contains claims data for 15 million persons annually.\nData available for each facility and professional-service claim include date and place of service, diagnoses, procedures performed/services rendered, and quantity of services (professional-service claims). Data available for each retail pharmacy claim include the drug dispensed, dispensing date, quantity dispensed, and number of days supplied. All claims also include paid (i.e., reimbursed) amounts. Selected demographic and eligibility information is available for persons in both databases. All data can be arrayed to provide a detailed chronology of all medical and pharmacy services used by each plan member over time.\nThe study databases were de-identified prior to their release to study investigators, as set forth in the corresponding Data Use Agreements. The study databases have been evaluated and certified by independent third parties to be in compliance with the Health Insurance Portability and Accountability Act (HIPAA) of 1996 statistical de-identification standards and to satisfy the conditions set forth in Sections 164.514 (a)-(b) 1ii of the HIPAA Privacy Rule regarding the determination and documentation of statistically de-identified data. Use of the study databases for health services research was determined–via independent third parties–to be fully compliant with the HIPAA Privacy Rule and federal guidance on Public Welfare and the Protection of Human Subjects\n[29].\n\nStudy population:\nThe study population comprised all patients who initiated ≥1 course of myelosuppressive chemotherapy for a solid tumor or non-Hodgkin’s lymphoma (NHL) and who received daily filgrastim prophylaxis during ≥1 cycle. All cycles in which patients received daily filgrastim prophylaxis–irrespective of duration–were pooled for analyses.\n Cancer chemotherapy patients All patients, aged ≥18 years, who began ≥1 new course of myelosuppressive chemotherapy were identified; the enrollment window was July 1, 2001 through June 30, 2010 for patients in the MarketScan Database and July 1, 2001 through June 30, 2008 for patients in the LifeLink Database. Receipt of chemotherapy was ascertained based on the presence of ≥1 paid medical claim for a chemotherapy drug or administration thereof (identified using Healthcare Common Procedure Coding System [HCPCS], International Classification of Disease, Ninth Revision, Clinical Modification [ICD-9-CM], and Health Care Financing Administration Uniform Bill-92 [UB-92] revenue codes).\nPatients were considered to have initiated a new course of chemotherapy if there was a chemotherapy claim during the study period that was preceded by a period ≥60 days without any other claims for chemotherapy. Only patients who had evidence of a primary solid tumor or NHL (based on ≥2 medical claims [≥7 days apart] with a qualifying 3-digit ICD-9-CM diagnosis code during the period beginning 30 days prior to the index date and ending 30 days thereafter) were selected.\nAll patients, aged ≥18 years, who began ≥1 new course of myelosuppressive chemotherapy were identified; the enrollment window was July 1, 2001 through June 30, 2010 for patients in the MarketScan Database and July 1, 2001 through June 30, 2008 for patients in the LifeLink Database. Receipt of chemotherapy was ascertained based on the presence of ≥1 paid medical claim for a chemotherapy drug or administration thereof (identified using Healthcare Common Procedure Coding System [HCPCS], International Classification of Disease, Ninth Revision, Clinical Modification [ICD-9-CM], and Health Care Financing Administration Uniform Bill-92 [UB-92] revenue codes).\nPatients were considered to have initiated a new course of chemotherapy if there was a chemotherapy claim during the study period that was preceded by a period ≥60 days without any other claims for chemotherapy. Only patients who had evidence of a primary solid tumor or NHL (based on ≥2 medical claims [≥7 days apart] with a qualifying 3-digit ICD-9-CM diagnosis code during the period beginning 30 days prior to the index date and ending 30 days thereafter) were selected.\n Chemotherapy courses, cycles, and regimens For each cancer chemotherapy patient, each unique cycle within each course of chemotherapy was identified. The first chemotherapy cycle (of the first course) was defined as beginning with the date of initiation of chemotherapy and ending with the first service date for the next administration of chemotherapy administration (as evidenced by a medical claim with a corresponding HCPCS, ICD-9-CM, or UB-92 code) occurring at least 12 days-but no more than 59 days-after the date of initiation of chemotherapy. If a second chemotherapy cycle did not commence prior to day 60, or if there was evidence of receipt of radiation therapy (based on medical claims with relevant HCPCS, ICD-9-CM, or UB-92 codes) during this period, both the first cycle of chemotherapy and the course of chemotherapy were considered to have been completed 30 days following the beginning of the cycle or on the day prior to initiation of radiation therapy, whichever occurred first. The second and all subsequent cycles of chemotherapy, as well as subsequent courses of chemotherapy–if any–during the period of interest, were similarly defined. A maximum of 8 cycles per course were considered. For patients with multiple courses of chemotherapy, all qualifying courses were considered.\nChemotherapy regimens were ascertained based on a review of all HCPCS Level II codes for parenterally administered antineoplastic agents on medical claims with service dates within 6 days of the start of each cycle of chemotherapy. Regimens were categorized on a cycle-specific basis according to the number of agents administered that are considered to be myelotoxic (list available from authors upon request).\nFor each cancer chemotherapy patient, each unique cycle within each course of chemotherapy was identified. The first chemotherapy cycle (of the first course) was defined as beginning with the date of initiation of chemotherapy and ending with the first service date for the next administration of chemotherapy administration (as evidenced by a medical claim with a corresponding HCPCS, ICD-9-CM, or UB-92 code) occurring at least 12 days-but no more than 59 days-after the date of initiation of chemotherapy. If a second chemotherapy cycle did not commence prior to day 60, or if there was evidence of receipt of radiation therapy (based on medical claims with relevant HCPCS, ICD-9-CM, or UB-92 codes) during this period, both the first cycle of chemotherapy and the course of chemotherapy were considered to have been completed 30 days following the beginning of the cycle or on the day prior to initiation of radiation therapy, whichever occurred first. The second and all subsequent cycles of chemotherapy, as well as subsequent courses of chemotherapy–if any–during the period of interest, were similarly defined. A maximum of 8 cycles per course were considered. For patients with multiple courses of chemotherapy, all qualifying courses were considered.\nChemotherapy regimens were ascertained based on a review of all HCPCS Level II codes for parenterally administered antineoplastic agents on medical claims with service dates within 6 days of the start of each cycle of chemotherapy. Regimens were categorized on a cycle-specific basis according to the number of agents administered that are considered to be myelotoxic (list available from authors upon request).\n Daily filgrastim prophylaxis Cancer chemotherapy patients who received daily filgrastim prophylaxis during ≥1 cycle of chemotherapy were selected for inclusion in the study population. Administration of daily filgrastim on or before day 5 of a given cycle was considered to represent prophylaxis\n[14,22,23]. Receipt of daily filgrastim was identified based on medical claims (J1440, J1441) with relevant codes from the HCPCS system.\nDuration of daily filgrastim prophylaxis during the patient-cycle was characterized based on temporal patterns of administration; end of prophylaxis was defined as a gap ≥3 days in its use, outpatient administration of IV antimicrobial therapy (an indicator for possible CINC), or hospitalization for CINC. Duration of prophylaxis was characterized as 1–3, 4–6, or ≥7 days.\nCancer chemotherapy patients who received daily filgrastim prophylaxis during ≥1 cycle of chemotherapy were selected for inclusion in the study population. Administration of daily filgrastim on or before day 5 of a given cycle was considered to represent prophylaxis\n[14,22,23]. Receipt of daily filgrastim was identified based on medical claims (J1440, J1441) with relevant codes from the HCPCS system.\nDuration of daily filgrastim prophylaxis during the patient-cycle was characterized based on temporal patterns of administration; end of prophylaxis was defined as a gap ≥3 days in its use, outpatient administration of IV antimicrobial therapy (an indicator for possible CINC), or hospitalization for CINC. Duration of prophylaxis was characterized as 1–3, 4–6, or ≥7 days.\n Exclusion criteria Patient-cycles were excluded from the analytic file if the following occurred: (1) evidence of ≥2 primary cancers (solid or blood) within (i.e., +/-) 30 days of chemotherapy initiation; (2) any gaps in health benefits during the 6-month (\"pretreatment\") period prior to chemotherapy initiation; (3) evidence of hematopoietic stem cell or bone marrow transplantation prior to or during receipt of chemotherapy; (4) evidence of chemotherapy based only on medical claims for administration of the drugs (HCPCS codes identifying the specific chemotherapy agents were not available, and thus the regimen and level of myelosuppression could not be determined); (5) pharmacy claims for myelotoxic chemotherapy (only drug dispense dates are available on pharmacy claims, and because pharmacy/medical claims cannot be definitively linked, precise dates of chemotherapy administration–needed to characterize the course and cycles–could not be ascertained); (6) pharmacy claims for daily filgrastim (precise dates of administration could not be ascertained, for reasons stated above); (7) evidence of receipt of sargramostim (J2820) or pegfilgrastim (C9119, S0135, J2505) during the first 5 days of the cycle (i.e., as prophylaxis).\nPatient-cycles were excluded from the analytic file if the following occurred: (1) evidence of ≥2 primary cancers (solid or blood) within (i.e., +/-) 30 days of chemotherapy initiation; (2) any gaps in health benefits during the 6-month (\"pretreatment\") period prior to chemotherapy initiation; (3) evidence of hematopoietic stem cell or bone marrow transplantation prior to or during receipt of chemotherapy; (4) evidence of chemotherapy based only on medical claims for administration of the drugs (HCPCS codes identifying the specific chemotherapy agents were not available, and thus the regimen and level of myelosuppression could not be determined); (5) pharmacy claims for myelotoxic chemotherapy (only drug dispense dates are available on pharmacy claims, and because pharmacy/medical claims cannot be definitively linked, precise dates of chemotherapy administration–needed to characterize the course and cycles–could not be ascertained); (6) pharmacy claims for daily filgrastim (precise dates of administration could not be ascertained, for reasons stated above); (7) evidence of receipt of sargramostim (J2820) or pegfilgrastim (C9119, S0135, J2505) during the first 5 days of the cycle (i.e., as prophylaxis).\n\nCancer chemotherapy patients:\nAll patients, aged ≥18 years, who began ≥1 new course of myelosuppressive chemotherapy were identified; the enrollment window was July 1, 2001 through June 30, 2010 for patients in the MarketScan Database and July 1, 2001 through June 30, 2008 for patients in the LifeLink Database. Receipt of chemotherapy was ascertained based on the presence of ≥1 paid medical claim for a chemotherapy drug or administration thereof (identified using Healthcare Common Procedure Coding System [HCPCS], International Classification of Disease, Ninth Revision, Clinical Modification [ICD-9-CM], and Health Care Financing Administration Uniform Bill-92 [UB-92] revenue codes).\nPatients were considered to have initiated a new course of chemotherapy if there was a chemotherapy claim during the study period that was preceded by a period ≥60 days without any other claims for chemotherapy. Only patients who had evidence of a primary solid tumor or NHL (based on ≥2 medical claims [≥7 days apart] with a qualifying 3-digit ICD-9-CM diagnosis code during the period beginning 30 days prior to the index date and ending 30 days thereafter) were selected.\n\nChemotherapy courses, cycles, and regimens:\nFor each cancer chemotherapy patient, each unique cycle within each course of chemotherapy was identified. The first chemotherapy cycle (of the first course) was defined as beginning with the date of initiation of chemotherapy and ending with the first service date for the next administration of chemotherapy administration (as evidenced by a medical claim with a corresponding HCPCS, ICD-9-CM, or UB-92 code) occurring at least 12 days-but no more than 59 days-after the date of initiation of chemotherapy. If a second chemotherapy cycle did not commence prior to day 60, or if there was evidence of receipt of radiation therapy (based on medical claims with relevant HCPCS, ICD-9-CM, or UB-92 codes) during this period, both the first cycle of chemotherapy and the course of chemotherapy were considered to have been completed 30 days following the beginning of the cycle or on the day prior to initiation of radiation therapy, whichever occurred first. The second and all subsequent cycles of chemotherapy, as well as subsequent courses of chemotherapy–if any–during the period of interest, were similarly defined. A maximum of 8 cycles per course were considered. For patients with multiple courses of chemotherapy, all qualifying courses were considered.\nChemotherapy regimens were ascertained based on a review of all HCPCS Level II codes for parenterally administered antineoplastic agents on medical claims with service dates within 6 days of the start of each cycle of chemotherapy. Regimens were categorized on a cycle-specific basis according to the number of agents administered that are considered to be myelotoxic (list available from authors upon request).\n\nDaily filgrastim prophylaxis:\nCancer chemotherapy patients who received daily filgrastim prophylaxis during ≥1 cycle of chemotherapy were selected for inclusion in the study population. Administration of daily filgrastim on or before day 5 of a given cycle was considered to represent prophylaxis\n[14,22,23]. Receipt of daily filgrastim was identified based on medical claims (J1440, J1441) with relevant codes from the HCPCS system.\nDuration of daily filgrastim prophylaxis during the patient-cycle was characterized based on temporal patterns of administration; end of prophylaxis was defined as a gap ≥3 days in its use, outpatient administration of IV antimicrobial therapy (an indicator for possible CINC), or hospitalization for CINC. Duration of prophylaxis was characterized as 1–3, 4–6, or ≥7 days.\n\nExclusion criteria:\nPatient-cycles were excluded from the analytic file if the following occurred: (1) evidence of ≥2 primary cancers (solid or blood) within (i.e., +/-) 30 days of chemotherapy initiation; (2) any gaps in health benefits during the 6-month (\"pretreatment\") period prior to chemotherapy initiation; (3) evidence of hematopoietic stem cell or bone marrow transplantation prior to or during receipt of chemotherapy; (4) evidence of chemotherapy based only on medical claims for administration of the drugs (HCPCS codes identifying the specific chemotherapy agents were not available, and thus the regimen and level of myelosuppression could not be determined); (5) pharmacy claims for myelotoxic chemotherapy (only drug dispense dates are available on pharmacy claims, and because pharmacy/medical claims cannot be definitively linked, precise dates of chemotherapy administration–needed to characterize the course and cycles–could not be ascertained); (6) pharmacy claims for daily filgrastim (precise dates of administration could not be ascertained, for reasons stated above); (7) evidence of receipt of sargramostim (J2820) or pegfilgrastim (C9119, S0135, J2505) during the first 5 days of the cycle (i.e., as prophylaxis).\n\nNeutropenic complications requiring inpatient care:\nCINC was identified based on inpatient admissions with a diagnosis (principal or secondary) of neutropenia (ICD-9-CM 288.0), fever (780.6), or infection (list available upon request). Admissions were identified on a cycle-specific basis using acute-care facility inpatient claims with admission dates anytime between day 6 and the last day of the chemotherapy cycle. Episodes of CINC treated exclusively on an outpatient basis were not considered. Because CINC that occurred on the day of the last dose of prophylaxis or the following day could have resulted in an artificial truncation of a planned longer course of prophylaxis–and thus could exaggerate the risk of CINC with shorter courses–analyses were conducted alternatively with and without inclusion of these patient cycles.\nConsequences of CINC requiring inpatient care were characterized in terms of in-hospital mortality, total hospital LOS (during the cycle), and total CINC-related healthcare expenditures (i.e., from hospital discharge to end of cycle). Total CINC-related healthcare expenditures included those for the initial hospitalization as well as care provided post-discharge on an outpatient basis; outpatient expenditures comprised encounters with a diagnosis of neutropenia, fever, or infection, as well as use/prescriptions for CSF agents (i.e., as treatment) and antimicrobial therapy. Expenditures were estimated based on total paid amounts on corresponding claims. Mortality was characterized using data only from the MarketScan Database as such information was not available in the LifeLink Database.\n\nPatient characteristics:\nPatient characteristics included: age; gender; presence of selected chronic comorbidities (cardiovascular disease, diabetes, liver disease, renal disease); history of blood disorders (anemia, neutropenia, other), infection, hospitalization (all-cause and CINC-related, respectively), chemotherapy, and radiation therapy; pre-chemotherapy healthcare expenditures; type of cancer, presence of metastases; cycle number and minimum length of prior cycles; chemotherapy regimen; receipt of antimicrobial prophylaxis in the cycle of interest; and year of chemotherapy initiation. Only observed data were utilized in defining patient characteristics.\nAge was assessed as of the first day of the first cycle of chemotherapy in the course. Chronic comorbidities and history of blood disorders, infections, hospitalization, chemotherapy, radiation therapy, presence of metastases, and healthcare expenditures were assessed from the beginning of the 6-month pretreatment period through the first day of the corresponding cycle of chemotherapy. Selected variables (i.e., anemia, neutropenia, other blood disorders, infections) were alternatively evaluated during the chemotherapy course (up to the beginning of the cycle of interest). Metastases (bone vs. other site) and chronic comorbidities were identified on the basis of ≥1 diagnosis codes on inpatient claims, ≥2 diagnosis codes on outpatient claims (excluding those for laboratory services) on different days, ≥1 procedure codes, and ≥1 drug codes, as appropriate. Blood disorders and infections were identified on the basis of ≥1 diagnosis codes (on inpatient and/or outpatient claims) and ≥1 drug codes, as appropriate. Prophylactic use of antimicrobial agents was ascertained based on a medical claim for administration of drug from cycle day 1 to cycle day 5, or a pharmacy claim for a filled prescription from cycle day -3 to cycle day 5, with a corresponding drug code.\n\nStatistical analyses:\nIncidence of CINC was evaluated for each patient-cycle in which daily filgrastim was administered prophylactically, and was estimated by same-cycle duration of prophylaxis in an unadjusted and adjusted context. For the latter, a generalized estimating equation (GEE) with a binomial distribution, logistic link function, and exchangeable correlation structure was employed. The GEE method accounts for correlation among repeated measures for the same subject (in this instance, across cycles), while controlling for both fixed characteristics (e.g., gender) and time-dependent covariates (e.g., first versus subsequent cycles). All observed patient characteristics were entered into, and retained in, the multivariate model. Subgroup and sensitivity analyses focusing on the first cycle only, employing a narrow definition of CINC (which was identified using the diagnosis code for neutropenia only but otherwise the same algorithm for the broad definition as described above), and focusing on alternative tumor types separately (i.e., breast cancer, lung cancer, colorectal cancer, and NHL) also were conducted.\nIn-hospital mortality, hospital LOS, and CINC-related healthcare expenditures among patients who developed CINC were descriptively evaluated by duration of daily filgrastim prophylaxis. Confidence intervals (95%) for in-hospital mortality were computed using the Wilson score interval; confidence intervals for LOS and economic costs were computed using nonparametric bootstrapping (percentile method) from the study population (1,000 replicates with replacement). Confidence intervals for in-hospital mortality, hospital LOS, and healthcare expenditures were estimated assuming independence between observations. Only observed data were used in defining study variables; patients who developed CINC but were missing data on study measures–because such data either were not recorded on claims or were not provided by some health plans–were excluded from corresponding analyses.\n\nResults:\nA total of 135,921 adult patients initiated a new course of chemotherapy for a solid tumor or NHL during the period of interest and met all other eligibility criteria. Among these patients, a total of 5,477 received daily filgrastim as prophylaxis during ≥1 cycle of chemotherapy and thus were included in the study population; these patients contributed a total of 14,288 daily filgrastim prophylaxis patient-cycles to the analytic file. Filgrastim was administered for 1–3 days in 58% of cycles (n = 8,371), 4–6 days in 26% of cycles (n = 3,691), and ≥7days in 16% of cycles (n = 2,226) (Figure \n1).\nDays of filgrastim prophylaxis.\nMean (SD) age of patients was 54 (13) years for 1–3 days of filgrastim prophylaxis, 57 (14) years for 4–6 days of prophylaxis, and 56 (13) years for ≥7 days of prophylaxis (Table \n1). Breast cancer was the most common tumor type (48% for 1–3 days, 50% for 4–6 days, 49% for ≥7 days), followed by NHL (10%, 13%, and 13%, respectively), and colorectal cancer (16%, 10%, and 13%, respectively). Metastatic disease was present in 38% of patients receiving 1–3 days of prophylaxis, 32% receiving 4–6 days, and 35% receiving ≥7 days. Antimicrobial agents—principally oral (~90%)—were concurrently used as prophylaxis in 6.7% of patients receiving 1–3 days of filgrastim prophylaxis, 7.5% receiving 4–6 days, and 7.5% receiving ≥7 days.\nPatient, cancer, and treatment characteristics, by duration of filgrastim prophylaxis\nCrude risk of CINC during a cycle of chemotherapy was 2.9% with 1–3 days of filgrastim prophylaxis, 2.7% with 4–6 days, and 1.8% with ≥7 days. In adjusted analyses, the odds of CINC were 2.4 (95% CI: 1.6-3.4) and 1.9 (1.3-2.8) times higher with 1–3 and 4–6 days of filgrastim prophylaxis, respectively, versus ≥7 days (referent group) (Figure \n2). Among the subgroup of patients who developed CINC requiring inpatient care (n = 382), mean hospital LOS was 7.4 (6.4-8.3) days with 1–3 days of prophylaxis (n = 243), 7.1 (5.7-8.5) days with 4–6 days of prophylaxis (n = 99), and 6.5 (4.9-8.0) with ≥7 days of prophylaxis (n = 40) (Table \n2). Among the subgroup of patients who developed CINC and for whom healthcare expenditures were available (n = 358), mean total CINC-related healthcare expenditures were $18,912 (14,570-23,581) with 1–3 days of prophylaxis (n = 225), $14,907 (11,155-19,728) with 4–6 days (n = 94), and $13,165 (9,595-17,144) with ≥7 days (n = 39). Among the subgroup of patients who developed CINC and for whom discharge status was available (n = 228), in-hospital mortality was 8.4% (4.6-14.8) with 1–3 days of prophylaxis (n-119), 4.0% (1.4-11.1) with 4–6 days (n = 75), and 0% (0–10.2) with ≥7 days (n = 34).\nAdjusted odds ratios for chemotherapy-induced neutropenic complications requiring inpatient care, by duration of filgrastim prophylaxis*. *Results adjusted for patient, cancer, and chemotherapy characteristics.\nUnadjusted risk of inpatient mortality, length of stay in hospital, and healthcare expenditures for chemotherapy- induced neutropenic complications requiring inpatient care, by duration of filgrastim prophylaxis\n*n’s indicate the number of patient-cycles considered in analyses of study measures; among patients who developed CINC (n = 382), those with missing data on study measures- because such data were not recorded on claims or were not provided by some health plans- were excluded from corresponding analyses.\n**Only patients with paid amounts > $0 were considered in these analyses.\nIn subgroup analyses focusing on the first cycle only, crude risks of CINC were 7.0% with 1–3 days of filgrastim prophylaxis (n = 1054), 5.0% with 4–6 days (n = 482), and 5.2% with ≥7 days (n = 363); in adjusted analyses, odds of CINC were 1.6 (0.9-2.8) and 1.1 (0.6-2.1) times higher with 1–3 and 4–6 days (versus ≥7 days) of filgrastim prophylaxis in cycle 1 (Table \n3). In analyses employing the narrow definition for CINC, crude risks of CINC were 1.6% with 1–3 days of filgrastim prophylaxis, 1.8% with 4–6 days, and 1.3% with ≥7 days; in adjusted analyses, odds of CINC were 1.6 (1.1-2.5) and 1.7 (1.1-2.7) times higher with 1–3 and 4–6 days of filgrastim prophylaxis, respectively, versus ≥7 days. In tumor-specific analyses, adjusted odds of CINC with 1–3 and 4–6 days of filgrastim prophylaxis (vs. ≥7 days) were: 1.8 (1.0-3.0) and 1.9 (1.1-3.4) for breast cancer; 20.2 (1.9-212.4) and 14.5 (1.3-158.7) for lung cancer; 1.9 (0.2-16.7) and 2.5 (0.3-23.5) for colorectal cancer; and 2.1 (1.2-3.6) and 1.8 (1.0-3.2) for NHL. We note that not all observed differences were statistically significant in unadjusted subgroup and secondary analyses, presumably due to the lack of adjustment for systematic differences in patient characteristics between prophylaxis subgroups.\nAdjusted odds ratios for chemotherapy- induced neutropenic complications requiring inpatient care in subgroup and secondary analyses\n*Results adjusted for patient, cancer, and treatment characteristic listed in Table \n2.\n\nDiscussion:\nIn the largest retrospective study of daily filgrastim use in US clinical practice, we found that the large majority of patients undergoing cancer chemotherapy who are administered prophylaxis with daily filgrastim receive considerably fewer days of administration than subjects in clinical trials of daily filgrastim prophylaxis. In our study population, 95% of patients received fewer than 10 days of filgrastim prophylaxis and 58% received only 1–3 days, versus the typical 10–11 days required for neutrophil recovery in the pivotal clinical trials\n[17-19]. This finding is largely consistent with observations from other retrospective clinical practice studies\n[14-16,22,23]. We also found that patients who receive shorter courses of daily filgrastim prophylaxis have a substantially higher risk of CINC requiring hospitalization. Among patients in our study population who received 1–3 or 4–6 days of prophylaxis, the adjusted odds of CINC were 2.4 and 1.9 times, respectively, higher than those receiving ≥7 days. In addition, our study results suggest that, among the subgroup of patients who develop CINC despite prophylaxis, the consequences of this condition may be worse among those who receive fewer administrations of daily filgrastim prophylaxis. This last set of results should be interpreted with caution, however, as the number of patients who developed CINC despite prophylaxis was, in absolute terms, small.\nOver the past decade, the frequency of use of alternative (often, more myelosuppressive) chemotherapy regimens in US clinical practice has changed considerably, in large part due to better chemotherapy agents, combinations of agents, and dosing schedules\n[25-27]. Because these regimens are typically associated with higher levels of myelosuppression, use of supportive care–including CSF prophylaxis–also has increased\n[27]. While pegfilgrastim—a longer-acting version of filgrastim that requires only a single dose administered subcutaneously once per chemotherapy cycle—is now (by far) the most commonly used CSF prophylactic agent in US clinical practice, filgrastim accounts for a small but important segment of the prophylactic market\n[14,16]. In the present study, 60,600 (45%) of the 135,921 adult cancer chemotherapy study subjects received CSF prophylaxis in ≥1cycle during their chemotherapy course. Among the subgroup who received CSF prophylaxis, 91% received pegfilgrastim in ≥1 cycle and 9% received filgrastim. Notwithstanding these temporal changes in chemotherapy regimens and supportive care, however, our findings regarding the frequent use of shorter courses of filgrastim prophylaxis and the associated consequences–based on a study population of over 5,000 patients and nearly 15,000 patient-cycles–are largely consistent with those reported previously. In the 2006 study by Weycker and colleagues–which included 598 breast cancer, lung cancer, and NHL patients, and employed data from 1998-2002– for example, mean duration of prophylaxis ranged from 4.3 to 6.5 days across tumor types, while in the Morrison study–which included 1,451 cancer patients and employed data from 2001-2003– mean duration of prophylaxis ranged from 3.7 to 6.0 days across calendar years and cycles of use\n[15,23]. In the present study, mean (SD) duration of prophylaxis on an overall basis was 3.6 (2.9) days. Moreover, in the aforementioned 2011 study by Weycker et al., odds of CINC were reported to be 1.5 times higher for patients receiving <7 versus ≥7 days of filgrastim prophylaxis, while the corresponding odds ratio in this study was estimated to be 2.2 (95% CI 1.6-3.1)\n[14]. We note that odds ratios for CINC reported above were largely comparable when limiting attention to the most recent four-year period (2007–2010): 2.5 (1.5-4.3) with 1–3 versus ≥7 days of filgrastim prophylaxis and 2.2 (1.2-3.9) with 4–6 versus ≥7 days of filgrastim prophylaxis. Study results also were robust to alternative specifications of the multivariate model and when excluding observations for which hospitalizations occurred on the day of the last dose of daily filgrastim prophylaxis or the following day.\nWe expected that systematic differences in the prevalence of risk factors (e.g., history of neutropenia, higher doses of chemotherapy agents, metastases to bone, poor performance status) for CINC would occur according to the duration of prophylaxis (1–3, 4–6, vs. ≥7 days). We thus used techniques of multivariate regression to adjust for such risk factors–to the extent possible–in analyzing the relationship between duration of daily filgrastim prophylaxis and outcomes of interest. Given the limitations of the claims-based databases we used, however, we were forced to use proxies for certain established risk factors. For example, a proxy measure based on pre-chemotherapy healthcare expenditures was employed for performance status, which in prior research has been shown to be correlated with health status in other patient populations\n[30]. Moreover, because information was not available for some clinically important parameters (e.g., ANC), the possibility exists that the study groups differed in terms of unobserved characteristics that predispose to CINC.\nWe believe that our approach to controlling for such systematic differences was comprehensive given available data, and that the above-noted biases–if present–would confer a conservative bias to analyses. For example, if the risk of FN is higher among patients receiving higher doses of chemotherapy (which is unobservable in the study database), and these patients are more likely to receive longer durations of prophylaxis, then the estimated difference in risk between patients receiving longer versus shorter courses of prophylaxis will be smaller than the true or actual difference. That our adjustment procedures were at least to some extent successful is suggested by the greater odds ratios for CINC in the adjusted versus unadjusted analyses, but uncertainty as to the adequacy of adjustment for confounding risk factors is one of the major limitations of our study.\nIn those instances where code-based operational algorithms were used to identify risk factors of interest (e.g., using ICD-9-CM code 288.0 for neutropenia, rather than ANC) errors of omission/commission in medical coding may have impacted the accuracy of adjustment. We do not believe, however, that any such differences or limitations were for the most part systematic in nature. However, patients who have a history of CINC – and thus may be more likely to receive longer durations of prophylaxis – may be more likely to have \"neutropenia\" designated as a secondary (or even primary) diagnosis on future encounters, all else equal.\nThere is no ICD-9-CM diagnosis code for CINC (i.e., neutropenia-related fever or infection), and thus codes for neutropenia, fever, and infection were employed to identify hospitalizations assumed to be related to neutropenic complications. Patients are typically not given chemotherapy when they are neutropenic or have active infection. The timing of fever and infection after chemotherapy increases the likelihood that such outcomes are related to receipt of chemotherapy. Codes for neutropenia, fever (surrogate for active infection), and infection thus are all likely to be related to episodes if seen within a defined exposure period after receiving chemotherapy. While the sensitivity of this algorithm for identifying neutropenic complications is undoubtedly higher than that of an algorithm using only the ICD-9-CM code for neutropenia, its specificity and positive predictive value are unknown.\nOther miscellaneous limitations deserve a brief mention. CINC requiring outpatient care were not considered in our analyses due to the small total number of events (n = 60). Patients with evidence of receipt of daily filgrastim via outpatient pharmacy (3% of total) were excluded because we could not ascertain the precise dates of use. Expenditures were not adjusted to current dollars since use of a general price index may yield spurious findings when applied to specific patients in specific health plans who consumed specific healthcare services and since the distribution of study groups by calendar year was comparable. Hospital discharge disposition was available only in the MarketScan Database and information concerning LOS or paid amounts was missing for some hospitalizations. Moreover, we note that a disproportionately high percentage of patients receiving 1–3 days of filgrastim prophylaxis (vs. those receiving 4–6 or ≥7 days) who developed CINC requiring inpatient care did not have information available on discharge disposition, and thus the inpatient mortality findings must be viewed with caution. Although patients initiating \"delayed\" use of daily filgrastim (e.g., after day 5 of the chemotherapy cycle) have been included in other published evaluations, they were not considered in our analyses as such patients may have received daily filgrastim for the treatment of CINC (e.g., severe neutropenia) as opposed to prophylaxis against CINC\n[15,16]. Finally, we note that it cannot be determined from outpatient pharmacy claims data whether drugs dispensed were actually taken, when they were taken, or how much was taken. For this reason, our characterization of antimicrobial prophylaxis use (principally oral) may be upwardly biased and differences in actual use across prophylaxis subgroups may confound the results of analyses. We also note, however, that the percentage of patients with filled prescriptions for antimicrobials was relatively low (and comparable) across filgrastim prophylaxis subgroups.\n\nConclusion:\nIn conclusion, we found that among patients receiving myelosuppressive chemotherapy, shorter courses of daily filgrastim prophylaxis are associated with increased risk of CINC. We also found that when CINC develops despite daily filgrastim prophylaxis, the outcomes thereof may be poorer in those receiving shorter courses of prophylaxis. Additional research is needed to explore these relationships among individual tumor types and chemotherapy regimens.\n\nCompeting interests:\nFunding for this research was provided by Amgen Inc. to Policy Analysis Inc. (PAI). Derek Weycker, John Edelsberg, and Alex Kartashov are employed by Policy Analysis Inc. (PAI). Rich Barron and Jason Legg are employed by Amgen Inc. Andrew Glass is employed by the Center for Health Research, Kaiser Permanente Northwest, and received an honorarium for this research from Amgen Inc.\n\nAuthors’ contributions:\nAuthorship was designated based on the guidelines promulgated by the International Committee of Medical Journal Editors (2004). All persons who meet criteria for authorship are listed as authors on the title page. The contribution of each of these individuals to this study–by task–is as follows: conception and supervision (Barron, Weycker), development of design (Barron, Edelsberg, Glass, Legg, Weycker), conduct of analyses (Kartashov, Weycker), interpretation of results (all authors), preparation of manuscript (Edelsberg, Weycker), and review of manuscript (all authors). All authors have read and approved the final version of the manuscript. The study sponsor reviewed the study research plan and study manuscript; data management, processing, and analyses were conducted by Policy Analysis Inc. (PAI), and all final analytic decisions were made by study investigators.\n\nPre-publication history:\nThe pre-publication history for this paper can be accessed here:\n\nhttp://www.biomedcentral.com/1472-6963/14/189/prepub\n\n\n"},"whole_article_abstract":{"kind":"string","value":"Background:\nTo examine duration of daily filgrastim prophylaxis, and risk and consequences of chemotherapy-induced neutropenic complications (CINC) requiring inpatient care.\n\nMethods:\nUsing a retrospective cohort design and US healthcare claims data (2001-2010), we identified all cancer patients who initiated ≥1 course of myelosuppressive chemotherapy and received daily filgrastim prophylactically in ≥1 cycle. Cycles with daily filgrastim prophylaxis were pooled for analyses. CINC was identified based on hospital admissions with a diagnosis of neutropenia, fever, or infection; consequences were characterized in terms of hospital mortality, hospital length of stay (LOS), and CINC-related healthcare expenditures.\n\nResults:\nRisk of CINC requiring inpatient care-adjusted for patient characteristics-was 2.4 (95% CI: 1.6-3.4) and 1.9 (1.3-2.8) times higher with 1-3 (N = 8371) and 4-6 (N = 3691) days of filgrastim prophylaxis, respectively, versus ≥7 days (N = 2226). Among subjects who developed CINC, consequences with 1-3 and 4-6 (vs. ≥7) days of filgrastim prophylaxis were: mortality (8.4% [n/N = 10/119] and 4.0% [3/75] vs. 0% [0/34]); LOS (means: 7.4 [N = 243] and 7.1 [N = 99] vs. 6.5 [N = 40]); and expenditures (means: $18,912 [N = 225] and $14,907 [N = 94] vs. $13,165 [N = 39]).\n\nConclusions:\nIn this retrospective evaluation, shorter courses of daily filgrastim prophylaxis were found to be associated with an increased risk of CINC as well as poorer outcomes among those developing this condition. Because of the limitations inherent in healthcare claims databases specifically and retrospective evaluations generally, additional research addressing these limitations is needed to confirm the findings of this study."},"background_conclusion_text":{"kind":"string","value":"Background:\nNeutropenia is a common side effect of myelosuppressive chemotherapy that both increases the risk of infection and diminishes patients’ ability to fight infection. When neutropenic patients become febrile, the high likelihood of infection and serious consequences thereof usually results in hospitalization\n[1,2]. FN, as well as severe or prolonged neutropenia, also can interfere with the planned delivery of treatment and adversely affect important patient outcomes\n[2-11].\nClinical practice guidelines recommend primary prophylactic use of a colony-stimulating factor (CSF)–which has been shown to reduce the risk of FN in clinical trials–when the risk of FN is 20% or higher\n[2]. While the American Society of Clinical Oncology (ASCO) initially recommended that CSF prophylaxis be administered only when FN risk is 40% or higher, in 2006, ASCO lowered the threshold to 20% based on data highlighting the importance of FN-related hospitalization as an outcome and evidence demonstrating the value of CSF prophylaxis in reducing the risk of FN, the risk of FN-related hospitalization, and the associated use of IV anti-infective agents\n[2,12,13]. The CSF filgrastim, which is widely used in clinical practice as prophylaxis against FN, requires daily administration during each cycle until neutrophil recovery occurs (in clinical trials, given typically for 10–11 days [and up to 14 days] until absolute neutrophil count [ANC] ≥10 × 109/L)\n[14-19].\nIn clinical practice, patients often receive shorter courses of daily filgrastim prophylaxis than those administered to subjects in the clinical trial setting\n[14-16,20-24]. While published studies suggest that shorter courses of daily filgrastim prophylaxis are associated with an increased risk of hospitalization for CINC, these studies focused on selected tumor types or employed data that now are over a decade old\n[23,24]. During the past decade, use of chemotherapy and supportive care in clinical practice–as well as recommended use of these agents in authoritative guidelines–has changed considerably\n[2,25-27], Moreover, only one study has examined whether CSFs may favorably impact clinical outcomes and economic costs when CINC develop despite CSF prophylaxis, and it was published a decade ago and focused on elderly patients with a single type of cancer (non-Hodgkin’s lymphoma)\n[28]. We therefore undertook a new study to evaluate the relationship between the duration of daily filgrastim prophylaxis, and the risk and consequences of CINC requiring inpatient care using a large healthcare claims database. While such databases often lack detailed clinical information (e.g., on absolute neutrophil counts), they offer access to information on the health profile and healthcare utilization of tens of millions of covered lives.\n\nConclusion:\nIn conclusion, we found that among patients receiving myelosuppressive chemotherapy, shorter courses of daily filgrastim prophylaxis are associated with increased risk of CINC. We also found that when CINC develops despite daily filgrastim prophylaxis, the outcomes thereof may be poorer in those receiving shorter courses of prophylaxis. Additional research is needed to explore these relationships among individual tumor types and chemotherapy regimens."},"background_conclusion_abstract":{"kind":"string","value":"Background:\nTo examine duration of daily filgrastim prophylaxis, and risk and consequences of chemotherapy-induced neutropenic complications (CINC) requiring inpatient care.\n\nMethods:\nUsing a retrospective cohort design and US healthcare claims data (2001-2010), we identified all cancer patients who initiated ≥1 course of myelosuppressive chemotherapy and received daily filgrastim prophylactically in ≥1 cycle. Cycles with daily filgrastim prophylaxis were pooled for analyses. CINC was identified based on hospital admissions with a diagnosis of neutropenia, fever, or infection; consequences were characterized in terms of hospital mortality, hospital length of stay (LOS), and CINC-related healthcare expenditures.\n\nResults:\nRisk of CINC requiring inpatient care-adjusted for patient characteristics-was 2.4 (95% CI: 1.6-3.4) and 1.9 (1.3-2.8) times higher with 1-3 (N = 8371) and 4-6 (N = 3691) days of filgrastim prophylaxis, respectively, versus ≥7 days (N = 2226). Among subjects who developed CINC, consequences with 1-3 and 4-6 (vs. ≥7) days of filgrastim prophylaxis were: mortality (8.4% [n/N = 10/119] and 4.0% [3/75] vs. 0% [0/34]); LOS (means: 7.4 [N = 243] and 7.1 [N = 99] vs. 6.5 [N = 40]); and expenditures (means: $18,912 [N = 225] and $14,907 [N = 94] vs. $13,165 [N = 39]).\n\nConclusions:\nIn this retrospective evaluation, shorter courses of daily filgrastim prophylaxis were found to be associated with an increased risk of CINC as well as poorer outcomes among those developing this condition. Because of the limitations inherent in healthcare claims databases specifically and retrospective evaluations generally, additional research addressing these limitations is needed to confirm the findings of this study."},"whole_article_text_length":{"kind":"number","value":14071,"string":"14,071"},"whole_article_abstract_length":{"kind":"number","value":385,"string":"385"},"other_sections_lengths":{"kind":"list like","value":[519,330,1840,208,300,135,236,277,339,336,71,166,16],"string":"[\n 519,\n 330,\n 1840,\n 208,\n 300,\n 135,\n 236,\n 277,\n 339,\n 336,\n 71,\n 166,\n 16\n]"},"num_sections":{"kind":"number","value":17,"string":"17"},"most_frequent_words":{"kind":"list like","value":["chemotherapy","days","cycle","prophylaxis","claims","patients","filgrastim","cinc","administration","daily"],"string":"[\n \"chemotherapy\",\n \"days\",\n \"cycle\",\n \"prophylaxis\",\n \"claims\",\n \"patients\",\n \"filgrastim\",\n \"cinc\",\n \"administration\",\n \"daily\"\n]"},"keybert_topics":{"kind":"list like","value":["csf prophylaxis administered","neutropenia higher doses","chemotherapy increases risk","chemotherapy codes neutropenia","recommended csf prophylaxis"],"string":"[\n \"csf prophylaxis administered\",\n \"neutropenia higher doses\",\n \"chemotherapy increases risk\",\n \"chemotherapy codes neutropenia\",\n \"recommended csf prophylaxis\"\n]"},"annotated_base_background_abstract_prompt":{"kind":"string","value":""},"annotated_base_methods_abstract_prompt":{"kind":"string","value":""},"annotated_base_results_abstract_prompt":{"kind":"string","value":""},"annotated_base_conclusions_abstract_prompt":{"kind":"string","value":""},"annotated_base_whole_article_abstract_prompt":{"kind":"string","value":""},"annotated_base_background_conclusion_abstract_prompt":{"kind":"string","value":""},"annotated_keywords_background_abstract_prompt":{"kind":"string","value":"[CONTENT] Filgrastim | Granulocyte colony-stimulating factor | Febrile neutropenia | Cost | Neoplasms [SUMMARY]"},"annotated_keywords_methods_abstract_prompt":{"kind":"string","value":"[CONTENT] Filgrastim | Granulocyte colony-stimulating factor | Febrile neutropenia | Cost | Neoplasms [SUMMARY]"},"annotated_keywords_results_abstract_prompt":{"kind":"string","value":"[CONTENT] Filgrastim | Granulocyte colony-stimulating factor | Febrile neutropenia | Cost | Neoplasms [SUMMARY]"},"annotated_keywords_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] Filgrastim | Granulocyte colony-stimulating factor | Febrile neutropenia | Cost | Neoplasms [SUMMARY]"},"annotated_keywords_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] Filgrastim | Granulocyte colony-stimulating factor | Febrile neutropenia | Cost | Neoplasms [SUMMARY]"},"annotated_keywords_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] Filgrastim | Granulocyte colony-stimulating factor | Febrile neutropenia | Cost | Neoplasms [SUMMARY]"},"annotated_mesh_background_abstract_prompt":{"kind":"string","value":"[CONTENT] Aged | Febrile Neutropenia | Female | Filgrastim | Granulocyte Colony-Stimulating Factor | Hospitalization | Humans | Insurance Claim Review | Male | Middle Aged | Neoplasms | Neutropenia | Post-Exposure Prophylaxis | Recombinant Proteins | Retrospective Studies | Risk Assessment | United States [SUMMARY]"},"annotated_mesh_methods_abstract_prompt":{"kind":"string","value":"[CONTENT] Aged | Febrile Neutropenia | Female | Filgrastim | Granulocyte Colony-Stimulating Factor | Hospitalization | Humans | Insurance Claim Review | Male | Middle Aged | Neoplasms | Neutropenia | Post-Exposure Prophylaxis | Recombinant Proteins | Retrospective Studies | Risk Assessment | United States [SUMMARY]"},"annotated_mesh_results_abstract_prompt":{"kind":"string","value":"[CONTENT] Aged | Febrile Neutropenia | Female | Filgrastim | Granulocyte Colony-Stimulating Factor | Hospitalization | Humans | Insurance Claim Review | Male | Middle Aged | Neoplasms | Neutropenia | Post-Exposure Prophylaxis | Recombinant Proteins | Retrospective Studies | Risk Assessment | United States [SUMMARY]"},"annotated_mesh_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] Aged | Febrile Neutropenia | Female | Filgrastim | Granulocyte Colony-Stimulating Factor | Hospitalization | Humans | Insurance Claim Review | Male | Middle Aged | Neoplasms | Neutropenia | Post-Exposure Prophylaxis | Recombinant Proteins | Retrospective Studies | Risk Assessment | United States [SUMMARY]"},"annotated_mesh_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] Aged | Febrile Neutropenia | Female | Filgrastim | Granulocyte Colony-Stimulating Factor | Hospitalization | Humans | Insurance Claim Review | Male | Middle Aged | Neoplasms | Neutropenia | Post-Exposure Prophylaxis | Recombinant Proteins | Retrospective Studies | Risk Assessment | United States [SUMMARY]"},"annotated_mesh_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] Aged | Febrile Neutropenia | Female | Filgrastim | Granulocyte Colony-Stimulating Factor | Hospitalization | Humans | Insurance Claim Review | Male | Middle Aged | Neoplasms | Neutropenia | Post-Exposure Prophylaxis | Recombinant Proteins | Retrospective Studies | Risk Assessment | United States [SUMMARY]"},"annotated_keybert_background_abstract_prompt":{"kind":"string","value":"[CONTENT] csf prophylaxis administered | neutropenia higher doses | chemotherapy increases risk | chemotherapy codes neutropenia | recommended csf prophylaxis [SUMMARY]"},"annotated_keybert_methods_abstract_prompt":{"kind":"string","value":"[CONTENT] csf prophylaxis administered | neutropenia higher doses | chemotherapy increases risk | chemotherapy codes neutropenia | recommended csf prophylaxis [SUMMARY]"},"annotated_keybert_results_abstract_prompt":{"kind":"string","value":"[CONTENT] csf prophylaxis administered | neutropenia higher doses | chemotherapy increases risk | chemotherapy codes neutropenia | recommended csf prophylaxis [SUMMARY]"},"annotated_keybert_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] csf prophylaxis administered | neutropenia higher doses | chemotherapy increases risk | chemotherapy codes neutropenia | recommended csf prophylaxis [SUMMARY]"},"annotated_keybert_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] csf prophylaxis administered | neutropenia higher doses | chemotherapy increases risk | chemotherapy codes neutropenia | recommended csf prophylaxis [SUMMARY]"},"annotated_keybert_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] csf prophylaxis administered | neutropenia higher doses | chemotherapy increases risk | chemotherapy codes neutropenia | recommended csf prophylaxis [SUMMARY]"},"annotated_most_frequent_background_abstract_prompt":{"kind":"string","value":"[CONTENT] chemotherapy | days | cycle | prophylaxis | claims | patients | filgrastim | cinc | administration | daily [SUMMARY]"},"annotated_most_frequent_methods_abstract_prompt":{"kind":"string","value":"[CONTENT] chemotherapy | days | cycle | prophylaxis | claims | patients | filgrastim | cinc | administration | daily [SUMMARY]"},"annotated_most_frequent_results_abstract_prompt":{"kind":"string","value":"[CONTENT] chemotherapy | days | cycle | prophylaxis | claims | patients | filgrastim | cinc | administration | daily [SUMMARY]"},"annotated_most_frequent_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] chemotherapy | days | cycle | prophylaxis | claims | patients | filgrastim | cinc | administration | daily [SUMMARY]"},"annotated_most_frequent_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] chemotherapy | days | cycle | prophylaxis | claims | patients | filgrastim | cinc | administration | daily 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shorter courses | shorter | courses | daily filgrastim prophylaxis | filgrastim prophylaxis | individual | conclusion found patients receiving [SUMMARY]"},"annotated_tf_idf_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] chemotherapy | prophylaxis | days | cycle | filgrastim | cinc | patients | claims | daily | daily filgrastim [SUMMARY]"},"annotated_tf_idf_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] chemotherapy | prophylaxis | days | cycle | filgrastim | cinc | patients | claims | daily | daily filgrastim [SUMMARY]"},"annotated_entity_plan_background_abstract_prompt":{"kind":"string","value":"[CONTENT] daily | CINC [SUMMARY]"},"annotated_entity_plan_methods_abstract_prompt":{"kind":"string","value":"[CONTENT] US | 2001-2010 | daily | ≥1 ||| daily ||| CINC | CINC [SUMMARY]"},"annotated_entity_plan_results_abstract_prompt":{"kind":"string","value":"[CONTENT] 2.4 | 95% | CI | 1.6-3.4 | 1.9 | 1.3-2.8 | 1-3 | 8371 | 4-6 | days | 2226 ||| CINC | 1-3 | days | 8.4% | n/N | 10/119 | 4.0% ||| 3/75 | 0% | 7.4 ||| 243 | 7.1 ||| 99 | 6.5 ||| 40 | 18,912 | 225 | 14,907 | 94 | 13,165 ||| 39 [SUMMARY]"},"annotated_entity_plan_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] daily | CINC ||| [SUMMARY]"},"annotated_entity_plan_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] daily | CINC ||| US | 2001-2010 | daily | ≥1 ||| daily ||| CINC | CINC ||| 2.4 | 95% | CI | 1.6-3.4 | 1.9 | 1.3-2.8 | 1-3 | 8371 | 4-6 | days | 2226 ||| CINC | 1-3 | days | 8.4% | n/N | 10/119 | 4.0% ||| 3/75 | 0% | 7.4 ||| 243 | 7.1 ||| 99 | 6.5 ||| 40 | 18,912 | 225 | 14,907 | 94 | 13,165 ||| 39 ||| daily | CINC ||| [SUMMARY]"},"annotated_entity_plan_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] daily | CINC ||| US | 2001-2010 | daily | ≥1 ||| daily ||| CINC | CINC ||| 2.4 | 95% | CI | 1.6-3.4 | 1.9 | 1.3-2.8 | 1-3 | 8371 | 4-6 | days | 2226 ||| CINC | 1-3 | days | 8.4% | n/N | 10/119 | 4.0% ||| 3/75 | 0% | 7.4 ||| 243 | 7.1 ||| 99 | 6.5 ||| 40 | 18,912 | 225 | 14,907 | 94 | 13,165 ||| 39 ||| daily | CINC ||| [SUMMARY]"}}},{"rowIdx":277,"cells":{"title":{"kind":"string","value":"Could saline irrigation clear all residual common bile duct stones after lithotripsy? A self-controlled prospective cohort study."},"pmid":{"kind":"string","value":"33584068"},"background_abstract":{"kind":"string","value":"A previous study showed that irrigation with 100 mL saline reduced residual common bile duct (CBD) stones, which potentially cause recurrent stones after endoscopic retrograde cholangiopancreatography."},"background_abstract_label":{"kind":"string","value":"BACKGROUND"},"methods_abstract":{"kind":"string","value":"This prospective self-controlled study enrolled patients receiving mechanical lithotripsy for large (> 1.2 cm) CBD stones. After occlusion cholangiography confirmed CBD stone clearance, peroral cholangioscopy (POC) was performed to determine clearance scores based on the number of residual stones. The amounts of residual stones spotted via POC were graded on a 5-point scale (score 1, worst; score 5, best). Scores were documented after only stone removal (control) and after irrigation with 50 mL and 100 mL saline, respectively. The stone composition was analyzed using infrared spectroscopy."},"methods_abstract_label":{"kind":"string","value":"METHODS"},"results_abstract":{"kind":"string","value":"Between October 2018 and January 2020, 47 patients had CBD clearance scores of 2.4 ± 1.1 without saline irrigation, 3.5 ± 0.7 with 50 mL irrigation, and 4.6 ± 0.6 with 100 mL irrigation (P < 0.001). Multivariate analysis showed that CBD diameter > 15 mm [odds ratio (OR) = 0.08, 95% confidence interval (CI): 0.01-0.49; P = 0.007] and periampullary diverticula (PAD) (OR = 6.51, 95%CI: 1.08-39.21; P = 0.041) were independent risk factors for residual stones. Bilirubin pigment stones constituted the main residual stones found in patients with PAD (P = 0.004)."},"results_abstract_label":{"kind":"string","value":"RESULTS"},"conclusions_abstract":{"kind":"string","value":"Irrigation with 100 mL of saline may not clear all residual CBD stones after lithotripsy, especially in patients with PAD and/or a dilated (> 15 mm) CBD. Pigment residual stones are soft and commonly found in patients with PAD. Additional saline irrigation may be required to remove retained stones."},"conclusions_abstract_label":{"kind":"string","value":"CONCLUSION"},"mesh_descriptor_names":{"kind":"list like","value":["Cholangiopancreatography, Endoscopic Retrograde","Common Bile Duct","Gallstones","Humans","Lithotripsy","Prospective Studies"],"string":"[\n \"Cholangiopancreatography, Endoscopic Retrograde\",\n \"Common Bile Duct\",\n \"Gallstones\",\n \"Humans\",\n \"Lithotripsy\",\n \"Prospective Studies\"\n]"},"pmcid":{"kind":"string","value":"7852583"},"background_title":{"kind":"string","value":"INTRODUCTION"},"background_text":{"kind":"string","value":"Endoscopic retrograde cholangiopancreatography (ERCP) is an effective and relatively minimally invasive technique for common bile duct (CBD) stones[1-3]. It has been reported that the recurrence rate of CBD stones after ERCP has increased from 4% to 24%[4-6]. The incidence of residual stones after mechanical lithotripsy for intractable CBD stones is 24% to 40%[7-10]. A growing number of studies suggest that an important reason for the recurrence of bile duct stones is the presence of stone debris after lithotripsy[11-13]. During ERCP, occluded cholangiography (OC) is often performed after stone removal to determine whether the stone is removed completely, but OC lacks accuracy. Even if no obvious stones are found on cholangiography, the presence of contrast can obscure small stone debris in the bile duct[8,14]. Complete bile duct clearance is necessary to decrease recurrent bile duct stones.\nSome studies[15-17] reported that irrigation of the bile duct with saline after stone extraction further improves the clearance of the bile duct and has the advantages of being a simple, low-cost procedure with rare complications. Ang et al[16] showed that a mean of 48 mL of saline solution could irrigate and flush out residual stones after the endoscopic removal of CBD stones. Ahn et al[17] found that irrigation with 100 mL of saline can flush out residual stone fragments from the bile duct into the duodenum after stone extraction. However, intraductal ultrasound (IDUS) has a high sensitivity and accuracy in diagnosing bile duct stones/debris[11,16]. This modality yields only indirect images of the debris.\nThe lack of direct evidence to support the efficacy of saline irrigation after lithotripsy prompted us to use peroral cholangioscopy (POC) to examine the bile duct and detect any residual stones/debris. The results of no irrigation after stone extraction were confirmed by OC and were compared to the effectiveness of irrigation with 50 mL or 100 mL saline. To evaluate whether irrigation with 100 mL of saline is more effective in achieving complete clearance of the bile duct after mechanical lithotripsy, we conducted this prospective self-controlled study."},"methods_title":{"kind":"string","value":"MATERIALS AND METHODS"},"methods_text":{"kind":"string","value":"Study design and participants Between October 2018 and January 2020, 47 patients with CBD stones were enrolled in a prospective clinical trial conducted in the surgical endoscopy center of The First Hospital of Lanzhou University (Figure 1). All eligible patients or their legal representatives gave informed consent before the treatment. The inclusion criteria were patients with CBD stones undergoing ERCP, able to provide informed consent, with a stone size larger than 1.2 cm and requiring mechanical lithotripsy for stone removal. The exclusion criteria included pre-ERCP acute suppurative cholangitis, acute pancreatitis, gastrointestinal (GI) tract hemorrhage and/or perforation, previous history of ERCP, prior Bilroth II gastrectomy and Roux-en-Y or cholangiojejunostomy, pregnancy or breastfeeding, coagulopathy with international normalized ratio > 1.5 and low platelet count (< 50 × 109/L) or active use of anticoagulation drugs, severe liver disease including decompensated liver cirrhosis or liver failure, septic shock, biliary-duodenal fistula confirmed before ERCP cannulation, the presence of intrahepatic duct stones and malignancy, and patient unwillingness or inability to give informed consent.\n\nFlow chart of the self-controlled study. CBD: Common bile duct; ERCP: Endoscopic retrograde cholangiopancreatography; PTCD: Percutaneous transhepatic cholangiodrainage.\nTo assess the number of residual stones, we created a CBD clearance score as follows: (1) A large number of stone fragments and biliary sludge; (2) A moderate number of stone fragments and biliary sludge; (3) A small number of stone fragments and biliary sludge; (4) Presence of biliary sludge without any stones; and (5) Completely cleared CBD without any biliary sludge. The scores were determined by two endoscopists independently (Figure 2).\n\nSpyGlass DS images and simulation graphs of the residual stone. A-E: SpyGlass DS images (A1-E1) and simulation graphs (A2-E2). Score 1: A large amount of stone fragments and biliary sludge. Score 2: A moderate amount of stone fragments and biliary sludge. Score 3: A small amount of stone fragments and biliary sludge. Score 4: Presence of biliary sludge without any stones. Score 5: Completely cleared common bile duct without any biliary sludge.\nThe study was approved by the ethics committee of The First Hospital of Lanzhou University (No. LDYYMENG2018-1028) and conducted in accordance with the ethical principles of the Declaration of Helsinki. All authors had access to the study data and reviewed and approved the final manuscript.\nBetween October 2018 and January 2020, 47 patients with CBD stones were enrolled in a prospective clinical trial conducted in the surgical endoscopy center of The First Hospital of Lanzhou University (Figure 1). All eligible patients or their legal representatives gave informed consent before the treatment. The inclusion criteria were patients with CBD stones undergoing ERCP, able to provide informed consent, with a stone size larger than 1.2 cm and requiring mechanical lithotripsy for stone removal. The exclusion criteria included pre-ERCP acute suppurative cholangitis, acute pancreatitis, gastrointestinal (GI) tract hemorrhage and/or perforation, previous history of ERCP, prior Bilroth II gastrectomy and Roux-en-Y or cholangiojejunostomy, pregnancy or breastfeeding, coagulopathy with international normalized ratio > 1.5 and low platelet count (< 50 × 109/L) or active use of anticoagulation drugs, severe liver disease including decompensated liver cirrhosis or liver failure, septic shock, biliary-duodenal fistula confirmed before ERCP cannulation, the presence of intrahepatic duct stones and malignancy, and patient unwillingness or inability to give informed consent.\n\nFlow chart of the self-controlled study. CBD: Common bile duct; ERCP: Endoscopic retrograde cholangiopancreatography; PTCD: Percutaneous transhepatic cholangiodrainage.\nTo assess the number of residual stones, we created a CBD clearance score as follows: (1) A large number of stone fragments and biliary sludge; (2) A moderate number of stone fragments and biliary sludge; (3) A small number of stone fragments and biliary sludge; (4) Presence of biliary sludge without any stones; and (5) Completely cleared CBD without any biliary sludge. The scores were determined by two endoscopists independently (Figure 2).\n\nSpyGlass DS images and simulation graphs of the residual stone. A-E: SpyGlass DS images (A1-E1) and simulation graphs (A2-E2). Score 1: A large amount of stone fragments and biliary sludge. Score 2: A moderate amount of stone fragments and biliary sludge. Score 3: A small amount of stone fragments and biliary sludge. Score 4: Presence of biliary sludge without any stones. Score 5: Completely cleared common bile duct without any biliary sludge.\nThe study was approved by the ethics committee of The First Hospital of Lanzhou University (No. LDYYMENG2018-1028) and conducted in accordance with the ethical principles of the Declaration of Helsinki. All authors had access to the study data and reviewed and approved the final manuscript.\nProcedures All endoscopic procedures were performed by two experienced endoscopists (each has performed > 1000 ERCPs). Routine prophylactic antibiotics were given in all cases. ERCP was performed with a standard duodenoscope (TJF-260V, Olympus, Tokyo, Japan). The patients received propofol anesthesia without tracheal intubation.\nDuring ERCP, cannulation was performed with a wire-guided sphincterotome. After successful cannulation, contrast was injected to determine the stone size (more or less than 12 mm) and the need for mechanical lithotripsy. All patients underwent a small sphincterotomy with an average length of 3-5 mm using the ENDO CUT mode (power setting 90-120 W; Erbe Elektromedizin, Tuebingen, Germany) followed by balloon sphincteroplasty using a controlled radial expansion (CRE) balloon (10-12 mm in diameter; Boston Scientific, Cork, Ireland). Lithotripsy was performed using an endoscopic lithotripter-compatible basket (Boston Scientific, Marlborough, MA, United States), and stones were removed using the basket and a balloon catheter. Samples of CBD stones were collected using an EndoRetrieval bag (Micro-Tech, Nanjing, China) and placed in a container for subsequent analysis. Occlusion cholangiography was performed by injecting contrast with a balloon catheter. Fluoroscopic assessment using a C-arm X-ray (OEC 9900 Elite, Salt Lake City, Utah, United States) was applied to confirm complete removal of CBD stones and the existence of stone residuals was determined by both endoscopist and radiologist. Confirmed stone residues by applying occlusion cholangiography were then followed by repeated extraction attempts. If complete stone removal was confirmed, a CBD clearance score was generated by further applying SpyGlass DS (Boston Scientific Corporation, Marlborough, Massachusetts, United States) examination. If the CBD clearance score was less than 5, the bile duct was irrigated intermittently with 50 mL of normal saline using a basket. The basket was shaken during irrigation with slight suction applied to the duodenoscope to promote drainage. The bile duct was then examined again using the SpyGlass DS to detect any residual stones/sludge and document the clearance score. If the clearance score was still less than 5, repeat irrigation of the CBD was performed using another 50 mL of normal saline. The final CBD clearance score was obtained one more time using SpyGlass DS examination. CBD stones that were not irrigated out after double 50 mL-saline irrigations continued to be irrigated with saline until they were fully cleared (Figure 3). Endoscopic nasobiliary drainage (ENBD) or biliary stenting was performed if necessary. The CBD clearance score was assessed by two blinded endoscopists who were masked to the treatment.\n\nProtocol of evaluation and irrigation procedures. CBD: Common bile duct; ERCP: Endoscopic retrograde cholangiopancreatography; POC: Peroral cholangioscopy.\nThe composition of the removed CBD stones was analyzed using infrared ray (IR) spectroscopy. One milligram (mg) of stone samples was mixed with 150 mg of potassium bromide in a mortar and ground into powder. The stone mixture was pressed into a mold and then placed into an automatic IR spectrum analyzer for automated detection to determine the IR spectrogram of the CBD stones. The IR spectrogram showed characteristic absorption peaks at a wavelength of 1460 cm-1, indicating cholesterol-based stones, and those at a wavelength of 1680 cm-1 indicated pigment-based stones.\nAll endoscopic procedures were performed by two experienced endoscopists (each has performed > 1000 ERCPs). Routine prophylactic antibiotics were given in all cases. ERCP was performed with a standard duodenoscope (TJF-260V, Olympus, Tokyo, Japan). The patients received propofol anesthesia without tracheal intubation.\nDuring ERCP, cannulation was performed with a wire-guided sphincterotome. After successful cannulation, contrast was injected to determine the stone size (more or less than 12 mm) and the need for mechanical lithotripsy. All patients underwent a small sphincterotomy with an average length of 3-5 mm using the ENDO CUT mode (power setting 90-120 W; Erbe Elektromedizin, Tuebingen, Germany) followed by balloon sphincteroplasty using a controlled radial expansion (CRE) balloon (10-12 mm in diameter; Boston Scientific, Cork, Ireland). Lithotripsy was performed using an endoscopic lithotripter-compatible basket (Boston Scientific, Marlborough, MA, United States), and stones were removed using the basket and a balloon catheter. Samples of CBD stones were collected using an EndoRetrieval bag (Micro-Tech, Nanjing, China) and placed in a container for subsequent analysis. Occlusion cholangiography was performed by injecting contrast with a balloon catheter. Fluoroscopic assessment using a C-arm X-ray (OEC 9900 Elite, Salt Lake City, Utah, United States) was applied to confirm complete removal of CBD stones and the existence of stone residuals was determined by both endoscopist and radiologist. Confirmed stone residues by applying occlusion cholangiography were then followed by repeated extraction attempts. If complete stone removal was confirmed, a CBD clearance score was generated by further applying SpyGlass DS (Boston Scientific Corporation, Marlborough, Massachusetts, United States) examination. If the CBD clearance score was less than 5, the bile duct was irrigated intermittently with 50 mL of normal saline using a basket. The basket was shaken during irrigation with slight suction applied to the duodenoscope to promote drainage. The bile duct was then examined again using the SpyGlass DS to detect any residual stones/sludge and document the clearance score. If the clearance score was still less than 5, repeat irrigation of the CBD was performed using another 50 mL of normal saline. The final CBD clearance score was obtained one more time using SpyGlass DS examination. CBD stones that were not irrigated out after double 50 mL-saline irrigations continued to be irrigated with saline until they were fully cleared (Figure 3). Endoscopic nasobiliary drainage (ENBD) or biliary stenting was performed if necessary. The CBD clearance score was assessed by two blinded endoscopists who were masked to the treatment.\n\nProtocol of evaluation and irrigation procedures. CBD: Common bile duct; ERCP: Endoscopic retrograde cholangiopancreatography; POC: Peroral cholangioscopy.\nThe composition of the removed CBD stones was analyzed using infrared ray (IR) spectroscopy. One milligram (mg) of stone samples was mixed with 150 mg of potassium bromide in a mortar and ground into powder. The stone mixture was pressed into a mold and then placed into an automatic IR spectrum analyzer for automated detection to determine the IR spectrogram of the CBD stones. The IR spectrogram showed characteristic absorption peaks at a wavelength of 1460 cm-1, indicating cholesterol-based stones, and those at a wavelength of 1680 cm-1 indicated pigment-based stones.\nDefinition for complications Most of the post-ERCP adverse events were detected within 24 h after the procedure. We routinely assessed these adverse events, including cholangitis, oozing/bleeding, pancreatitis, cholecystitis, and perforation, at 24 and 48 h after the ERCP procedure by symptoms, signs, laboratory tests, and imaging examinations if necessary[18,19]. Post-ERCP adverse events were defined as follows: (1) The diagnosis of acute cholangitis was based on Tokyo Guidelines 2018/2013 (TG18/TG13) diagnostic criteria[20]; (2) Oozing was defined as bleeding that was slight and stopped spontaneously; (3) Pancreatitis was described as new or worsening abdominal pain along with an increase in serum amylase levels (> 3 × higher than normal upper limit measured 24 h after surgery); (4) Cholecystitis was defined as pain in the epigastrium or RUQ accompanied by a positive Murphy sign, and abdominal ultrasonography showing a thickened gallbladder wall; and (5) Perforation was defined as sudden abdominal pain accompanied by retroperitoneal air and fluid.\nMechanical lithotripsy was performed by the same assistant for all patients. Stones that were fragmented successfully with only one attempt were defined as soft stones. Stones that required multiple fragmentation attempts or needed to be broken again were defined as hard stones.\nMost of the post-ERCP adverse events were detected within 24 h after the procedure. We routinely assessed these adverse events, including cholangitis, oozing/bleeding, pancreatitis, cholecystitis, and perforation, at 24 and 48 h after the ERCP procedure by symptoms, signs, laboratory tests, and imaging examinations if necessary[18,19]. Post-ERCP adverse events were defined as follows: (1) The diagnosis of acute cholangitis was based on Tokyo Guidelines 2018/2013 (TG18/TG13) diagnostic criteria[20]; (2) Oozing was defined as bleeding that was slight and stopped spontaneously; (3) Pancreatitis was described as new or worsening abdominal pain along with an increase in serum amylase levels (> 3 × higher than normal upper limit measured 24 h after surgery); (4) Cholecystitis was defined as pain in the epigastrium or RUQ accompanied by a positive Murphy sign, and abdominal ultrasonography showing a thickened gallbladder wall; and (5) Perforation was defined as sudden abdominal pain accompanied by retroperitoneal air and fluid.\nMechanical lithotripsy was performed by the same assistant for all patients. Stones that were fragmented successfully with only one attempt were defined as soft stones. Stones that required multiple fragmentation attempts or needed to be broken again were defined as hard stones.\nStatistical analysis The sample size calculation was based on the rate of residual stone clearance. According to a previous study[16] and our prior experience, we assumed a success rate of 84.5% for endoscopic extraction of CBD stones and an increase of up to 97% with saline irrigation. We estimated that 47 patients were needed in this analysis to obtain a power of 80% at the 5% level.\nCategorical variables are expressed as numbers or percentages (%). Continuous variables are presented as the mean ± SD or median and interquartile range, as appropriate. Continuous variables with a normal distribution were analyzed by paired t-test or signed-rank test. Categorical variables were analyzed using the chi-square test or Fisher’s exact test. Logistic regression was used to predict risk factors for complications, and the results are presented as odds ratios (ORs) with 95% confidence intervals (Cis). Variables with a P value of < 0.2 in the univariate analysis were included in the multivariate analysis. Linear mixed-effects models were conducted to assess the effect of saline irrigation on the clearance score with a random intercept for each patient and an unstructured covariance structure. A two-sided P value of less than 0.05 was considered statistically significant. The analyses were conducted with statistical software (SAS version 9.4, SAS Institute, Inc., NC, United States).\nThe sample size calculation was based on the rate of residual stone clearance. According to a previous study[16] and our prior experience, we assumed a success rate of 84.5% for endoscopic extraction of CBD stones and an increase of up to 97% with saline irrigation. We estimated that 47 patients were needed in this analysis to obtain a power of 80% at the 5% level.\nCategorical variables are expressed as numbers or percentages (%). Continuous variables are presented as the mean ± SD or median and interquartile range, as appropriate. Continuous variables with a normal distribution were analyzed by paired t-test or signed-rank test. Categorical variables were analyzed using the chi-square test or Fisher’s exact test. Logistic regression was used to predict risk factors for complications, and the results are presented as odds ratios (ORs) with 95% confidence intervals (Cis). Variables with a P value of < 0.2 in the univariate analysis were included in the multivariate analysis. Linear mixed-effects models were conducted to assess the effect of saline irrigation on the clearance score with a random intercept for each patient and an unstructured covariance structure. A two-sided P value of less than 0.05 was considered statistically significant. The analyses were conducted with statistical software (SAS version 9.4, SAS Institute, Inc., NC, United States)."},"results_title":{"kind":"null"},"results_text":{"kind":"null"},"conclusions_title":{"kind":"string","value":"CONCLUSION"},"conclusions_text":{"kind":"string","value":"We thank the clinical and research teams at the Department of General Surgery for providing ongoing support."},"other_sections_titles":{"kind":"list like","value":["INTRODUCTION","Study design and participants","Procedures","Definition for complications","Statistical analysis","RESULTS","DISCUSSION","CONCLUSION"],"string":"[\n \"INTRODUCTION\",\n \"Study design and participants\",\n \"Procedures\",\n \"Definition for complications\",\n \"Statistical analysis\",\n \"RESULTS\",\n \"DISCUSSION\",\n \"CONCLUSION\"\n]"},"other_sections_texts":{"kind":"list like","value":["Endoscopic retrograde cholangiopancreatography (ERCP) is an effective and relatively minimally invasive technique for common bile duct (CBD) stones[1-3]. It has been reported that the recurrence rate of CBD stones after ERCP has increased from 4% to 24%[4-6]. The incidence of residual stones after mechanical lithotripsy for intractable CBD stones is 24% to 40%[7-10]. A growing number of studies suggest that an important reason for the recurrence of bile duct stones is the presence of stone debris after lithotripsy[11-13]. During ERCP, occluded cholangiography (OC) is often performed after stone removal to determine whether the stone is removed completely, but OC lacks accuracy. Even if no obvious stones are found on cholangiography, the presence of contrast can obscure small stone debris in the bile duct[8,14]. Complete bile duct clearance is necessary to decrease recurrent bile duct stones.\nSome studies[15-17] reported that irrigation of the bile duct with saline after stone extraction further improves the clearance of the bile duct and has the advantages of being a simple, low-cost procedure with rare complications. Ang et al[16] showed that a mean of 48 mL of saline solution could irrigate and flush out residual stones after the endoscopic removal of CBD stones. Ahn et al[17] found that irrigation with 100 mL of saline can flush out residual stone fragments from the bile duct into the duodenum after stone extraction. However, intraductal ultrasound (IDUS) has a high sensitivity and accuracy in diagnosing bile duct stones/debris[11,16]. This modality yields only indirect images of the debris.\nThe lack of direct evidence to support the efficacy of saline irrigation after lithotripsy prompted us to use peroral cholangioscopy (POC) to examine the bile duct and detect any residual stones/debris. The results of no irrigation after stone extraction were confirmed by OC and were compared to the effectiveness of irrigation with 50 mL or 100 mL saline. To evaluate whether irrigation with 100 mL of saline is more effective in achieving complete clearance of the bile duct after mechanical lithotripsy, we conducted this prospective self-controlled study.","Between October 2018 and January 2020, 47 patients with CBD stones were enrolled in a prospective clinical trial conducted in the surgical endoscopy center of The First Hospital of Lanzhou University (Figure 1). All eligible patients or their legal representatives gave informed consent before the treatment. The inclusion criteria were patients with CBD stones undergoing ERCP, able to provide informed consent, with a stone size larger than 1.2 cm and requiring mechanical lithotripsy for stone removal. The exclusion criteria included pre-ERCP acute suppurative cholangitis, acute pancreatitis, gastrointestinal (GI) tract hemorrhage and/or perforation, previous history of ERCP, prior Bilroth II gastrectomy and Roux-en-Y or cholangiojejunostomy, pregnancy or breastfeeding, coagulopathy with international normalized ratio > 1.5 and low platelet count (< 50 × 109/L) or active use of anticoagulation drugs, severe liver disease including decompensated liver cirrhosis or liver failure, septic shock, biliary-duodenal fistula confirmed before ERCP cannulation, the presence of intrahepatic duct stones and malignancy, and patient unwillingness or inability to give informed consent.\n\nFlow chart of the self-controlled study. CBD: Common bile duct; ERCP: Endoscopic retrograde cholangiopancreatography; PTCD: Percutaneous transhepatic cholangiodrainage.\nTo assess the number of residual stones, we created a CBD clearance score as follows: (1) A large number of stone fragments and biliary sludge; (2) A moderate number of stone fragments and biliary sludge; (3) A small number of stone fragments and biliary sludge; (4) Presence of biliary sludge without any stones; and (5) Completely cleared CBD without any biliary sludge. The scores were determined by two endoscopists independently (Figure 2).\n\nSpyGlass DS images and simulation graphs of the residual stone. A-E: SpyGlass DS images (A1-E1) and simulation graphs (A2-E2). Score 1: A large amount of stone fragments and biliary sludge. Score 2: A moderate amount of stone fragments and biliary sludge. Score 3: A small amount of stone fragments and biliary sludge. Score 4: Presence of biliary sludge without any stones. Score 5: Completely cleared common bile duct without any biliary sludge.\nThe study was approved by the ethics committee of The First Hospital of Lanzhou University (No. LDYYMENG2018-1028) and conducted in accordance with the ethical principles of the Declaration of Helsinki. All authors had access to the study data and reviewed and approved the final manuscript.","All endoscopic procedures were performed by two experienced endoscopists (each has performed > 1000 ERCPs). Routine prophylactic antibiotics were given in all cases. ERCP was performed with a standard duodenoscope (TJF-260V, Olympus, Tokyo, Japan). The patients received propofol anesthesia without tracheal intubation.\nDuring ERCP, cannulation was performed with a wire-guided sphincterotome. After successful cannulation, contrast was injected to determine the stone size (more or less than 12 mm) and the need for mechanical lithotripsy. All patients underwent a small sphincterotomy with an average length of 3-5 mm using the ENDO CUT mode (power setting 90-120 W; Erbe Elektromedizin, Tuebingen, Germany) followed by balloon sphincteroplasty using a controlled radial expansion (CRE) balloon (10-12 mm in diameter; Boston Scientific, Cork, Ireland). Lithotripsy was performed using an endoscopic lithotripter-compatible basket (Boston Scientific, Marlborough, MA, United States), and stones were removed using the basket and a balloon catheter. Samples of CBD stones were collected using an EndoRetrieval bag (Micro-Tech, Nanjing, China) and placed in a container for subsequent analysis. Occlusion cholangiography was performed by injecting contrast with a balloon catheter. Fluoroscopic assessment using a C-arm X-ray (OEC 9900 Elite, Salt Lake City, Utah, United States) was applied to confirm complete removal of CBD stones and the existence of stone residuals was determined by both endoscopist and radiologist. Confirmed stone residues by applying occlusion cholangiography were then followed by repeated extraction attempts. If complete stone removal was confirmed, a CBD clearance score was generated by further applying SpyGlass DS (Boston Scientific Corporation, Marlborough, Massachusetts, United States) examination. If the CBD clearance score was less than 5, the bile duct was irrigated intermittently with 50 mL of normal saline using a basket. The basket was shaken during irrigation with slight suction applied to the duodenoscope to promote drainage. The bile duct was then examined again using the SpyGlass DS to detect any residual stones/sludge and document the clearance score. If the clearance score was still less than 5, repeat irrigation of the CBD was performed using another 50 mL of normal saline. The final CBD clearance score was obtained one more time using SpyGlass DS examination. CBD stones that were not irrigated out after double 50 mL-saline irrigations continued to be irrigated with saline until they were fully cleared (Figure 3). Endoscopic nasobiliary drainage (ENBD) or biliary stenting was performed if necessary. The CBD clearance score was assessed by two blinded endoscopists who were masked to the treatment.\n\nProtocol of evaluation and irrigation procedures. CBD: Common bile duct; ERCP: Endoscopic retrograde cholangiopancreatography; POC: Peroral cholangioscopy.\nThe composition of the removed CBD stones was analyzed using infrared ray (IR) spectroscopy. One milligram (mg) of stone samples was mixed with 150 mg of potassium bromide in a mortar and ground into powder. The stone mixture was pressed into a mold and then placed into an automatic IR spectrum analyzer for automated detection to determine the IR spectrogram of the CBD stones. The IR spectrogram showed characteristic absorption peaks at a wavelength of 1460 cm-1, indicating cholesterol-based stones, and those at a wavelength of 1680 cm-1 indicated pigment-based stones.","Most of the post-ERCP adverse events were detected within 24 h after the procedure. We routinely assessed these adverse events, including cholangitis, oozing/bleeding, pancreatitis, cholecystitis, and perforation, at 24 and 48 h after the ERCP procedure by symptoms, signs, laboratory tests, and imaging examinations if necessary[18,19]. Post-ERCP adverse events were defined as follows: (1) The diagnosis of acute cholangitis was based on Tokyo Guidelines 2018/2013 (TG18/TG13) diagnostic criteria[20]; (2) Oozing was defined as bleeding that was slight and stopped spontaneously; (3) Pancreatitis was described as new or worsening abdominal pain along with an increase in serum amylase levels (> 3 × higher than normal upper limit measured 24 h after surgery); (4) Cholecystitis was defined as pain in the epigastrium or RUQ accompanied by a positive Murphy sign, and abdominal ultrasonography showing a thickened gallbladder wall; and (5) Perforation was defined as sudden abdominal pain accompanied by retroperitoneal air and fluid.\nMechanical lithotripsy was performed by the same assistant for all patients. Stones that were fragmented successfully with only one attempt were defined as soft stones. Stones that required multiple fragmentation attempts or needed to be broken again were defined as hard stones.","The sample size calculation was based on the rate of residual stone clearance. According to a previous study[16] and our prior experience, we assumed a success rate of 84.5% for endoscopic extraction of CBD stones and an increase of up to 97% with saline irrigation. We estimated that 47 patients were needed in this analysis to obtain a power of 80% at the 5% level.\nCategorical variables are expressed as numbers or percentages (%). Continuous variables are presented as the mean ± SD or median and interquartile range, as appropriate. Continuous variables with a normal distribution were analyzed by paired t-test or signed-rank test. Categorical variables were analyzed using the chi-square test or Fisher’s exact test. Logistic regression was used to predict risk factors for complications, and the results are presented as odds ratios (ORs) with 95% confidence intervals (Cis). Variables with a P value of < 0.2 in the univariate analysis were included in the multivariate analysis. Linear mixed-effects models were conducted to assess the effect of saline irrigation on the clearance score with a random intercept for each patient and an unstructured covariance structure. A two-sided P value of less than 0.05 was considered statistically significant. The analyses were conducted with statistical software (SAS version 9.4, SAS Institute, Inc., NC, United States).","The patients' average age was 61 ± 16.5 years, and 23 (48.9%) were male. Comorbidities included coronary disease in three (6.4%) patients, hypertension in 14 (29.8%), diabetes in two (4.3%), liver cirrhosis in three (2.1%), and portal hypertension in one (6.4%). The stone analysis showed that 17 (36.2%) cases had cholesterol-based stones, and 30 (63.8%) had pigment-based stones.\nProcedure-related adverse events occurred in 11 (23.4%) of 47 patients, with cholangitis in four patients, bleeding in two, pancreatitis in four, and cholecystitis in one. No mortality or perforation occurred. The mean time for ERCP was 40.3 ± 15.4 min (Table 1).\nClinical and procedural characteristics of the patients\nData are expressed as the mean ± SD, median (interquartile range) or n (%). ENBD: Endoscopic nasobiliary drainage; CBD: Common bile duct.\nAfter endoscopic stone extraction, occlusion cholangiography showed that there were no residual CBD stones. Seven patients had a clearance score of 4 identified by POC with the SpyGlass DS, and no patients had a score of 5 before saline irrigation. After irrigation with 50 mL saline, CBD clearance improved to 4 in 28 patients, but none achieved a score of 5. After a total of 100 mL of saline irrigation, there was further improvement in the clearance scores: 12 (26%) patients had a score of 4, and 32 (68%) had a score of 5 identified by the SpyGlass DS. The respective CBD stone clearance rates for the control (no irrigation), 50 mL saline irrigation, and 100 mL saline irrigation were 15%, 60%, and 94%, respectively, and the difference among them was statistically significant (P < 0.001) (Table 2). The CBD clearance score (mean ± SD) was 2.4 ± 1.1 in the control, 3.5 ± 0.7 in the 50 mL saline irrigation, and 4.6 ± 0.6 in the 100 mL saline irrigation (P < 0.001) (Supplementary Figure 1).\nCommon bile duct clear score and stone clearance rate before and after saline irrigation\nAfter ERCP, 13 patients had a score of 1 (a large amount of residual stone/sludge) before saline irrigation. Only 15 patients did not reach a score of 5 (complete clearance) after 100 mL of saline irrigation. A dilated CBD diameter > 15 mm (OR = 4.93, 95%CI: 1.13-21.57; P = 0.034) was an independent risk factor for significant residual CBD stones (score = 1). Multivariate analysis revealed that CBD diameter > 15 mm (OR = 0.08, 95%CI: 0.01-0.49; P = 0.007) and the presence of periampullary diverticula (PAD) (OR = 6.51, 95%CI: 1.08-39.21; P = 0.041) were independent risk factors for failed CBD clearance despite irrigation with 100 mL saline (Table 3).\nBiliary cleanliness before and after saline irrigation, assessed by logistic regression analysis\nN: Total number; CBD: Common bile duct; PAD: Periampullary diverticula; CI: Confidence interval; OR: Odds ratio.\nFurther analysis showed that the volume of saline used for irrigation[20] (P < 0.001) was an important factor determining CBD clearance (Supplementary Table 1).\nWe used IR spectroscopy to analyze the components of stones in the CBD of all patients. IR spectroscopy analysis showed that the proportion of pigment-based stones was significantly higher in patients with PAD (93.3% vs 50.0%, P = 0.04), and these stones were mostly soft stones (77.4% vs 22.6%, P = 0.01) (Table 4).\nCorrelation between the composition of stones and variables\nCBD: Common bile duct; PAD: Periampullary diverticula.","Fluoroscopic cholangiographic imagery is currently the main method to determine the successful clearance of CBD stones[20]. However, studies involving the use of IDUS have identified residual biliary sludge within the bile duct despite the absence of filling defects on cholangiography. Mechanical lithotripsy produces a large number of stone fragments, and these minor residual CBD stones may lead to recurrent stone formation[21].\nIt has been reported that residual small CBD stone fragments can be flushed out of the bile duct using saline irrigation with a balloon catheter with a side hole[15]. A study reported that an average of 48 mL of saline could remove residual stones[16]. However, another study found that at least 100 mL of saline was needed to reduce residual stones[17]. Therefore, the effectiveness of saline irrigation on the clearance of residual bile duct stones after lithotripsy is not clear. This study's purpose differs from previous ones in that all of the patients had large stones and were post lithotripsy. In this study, we found that after ERCP and an additional round of lithotripsy were performed to remove CBD stones, POC using the SpyGlass DS showed that only 15% of the patients were relatively cleared of bile duct stones despite a negative occlusion cholangiogram. After irrigation with 50 mL of normal saline, 60% of the patients had relatively cleared bile ducts. After a total of 100 mL saline irrigation, 94% of the patients achieved complete clearance of the bile duct. The results showed that irrigation with 50 mL of saline was not enough to clear the bile duct of residual stones/sludge.\nIn this series, all patients received mechanical lithotripsy for stone fragmentation, which generates substantial stone fragments/debris, thus increasing the difficulty of clearing the bile duct. Our results showed that a good CBD clearance rate could be achieved with a larger saline irrigation volume. The clearance rate of bile duct stones could be higher for those without lithotripsy.\nResidual stones have been found in approximately 1/3 of cases after stone retrieval using endoscopic ultrasound (EUS)[22,23]. In addition to stone detection, the EUS approach might also offer an alternative for treating bile duct stones. However, the use of EUS is very challenging[24-27]. IDUS has also been used to detect small residual stones in 14/59 patients (23.7%) with residual stones[11]. However, the ultrasonic probe is costly and can be easily damaged. In addition, the method is highly operator dependent and often produces poor quality images, and the presence of air in the bile duct can affect the detection of residual stones. Many studies have shown that POC has a high sensitivity in the detection of residual CBD stones, ranging from 25.3% to 34%, where residual stones are missed by standard cholangiography[8,9,28].\nIn our study, we used the SpyGlass DS system to determine the CBD clearance score. The SpyScope has a 4-way tip deflection and is much smaller (10 French) than conventional choledochoscopes. More importantly, it has a separate and independent irrigation channel that allows intermittent or continuous irrigation of the biliary system. It also offers a direct examination of the bile duct lumen and is more accurate in detecting and diagnosing residual bile duct stones/sludge.\nResidual CBD stones can lead to recurrent stone formation. Itoi et al[7] reported that PAD and intraoperative lithotripsy were closely related to residual stones (P < 0.05), as we observed in our study. We also found that a dilated CBD with a diameter > 15 mm was an independent risk factor for failed CBD clearance and a large number of residual stones. Despite irrigation with 100 mL saline, PAD and a dilated CBD remained independent risk factors for incomplete bile duct clearance. This may be due to the presence of an air-filled duodenum/diverticulum that compresses the distal CBD, making it difficult to wash away the residual stones/sludge[29,30]. CBD clearance can be improved by increasing the volume of saline irrigation.\nOur results showed that PAD not only increased the difficulty of bile duct clearance but can also affect stone composition. We found that pigment-based stones constituted the majority of CBD stones in our patients with PAD (P = 0.004). Song et al[31] reported that recurrent bile duct stones were more likely to be brown pigmented stones than cholesterol-based stones. This may be because pigment-based stones are often related to bacterial infection, and the formation of biliary sludge will contribute to recurrent stone formation over time[32-34]. The brown pigmented stones that form as a result of bacterial infection are soft and easy to fragment and generate abundant debris. Our experience showed that soft stones/sludge require more saline irrigation to clear the bile duct. In this study, 26% of cases with biliary sludge were found even with 100 mL saline irrigation. If an effective bile duct cleaning device is used to remove biliary sludge, it may reduce the CBD stone recurrence rate after ERCP.\nWithout adequate biliary drainage, saline irrigation can increase the intraductal pressure, causing cholangitis because of contaminated bile[35]. Proper drainage methods can mitigate this stress[36,37]. No serious adverse events were reported in our study except for 10% of patients with cholangitis secondary possibly to saline irrigation. Intermittent irrigation and endoscopic suction to promote biliary drainage may lower the risk in addition to antibiotic coverage. Differences between this study and previous studies include the following: We studied the effect of flushing with saline after lithotripsy; our evaluation method was different from that of previous studies (we used SpyGlass DS examination); and we performed a component analysis related to diverticula. Our study has several methodological advantages. First, it was a prospective study using a self-controlled design, and SpyGlass DS was used to assess the presence of residual stones in the bile ducts. All of the patients showed no stones by imaging even though small residual bile duct stones were observed by SpyGlass DS, indicating that the evaluation of residual bile duct stones using SpyGlass DS is more sensitive than imaging alone. If we observe that there are still stones, it would be unethical not to continue flushing after using 50 mL of saline. Second, this study used objective definitions to assess residual stones identified by POC. Third, this study included objective stone composition analysis using IR spectroscopy.\nThe study has limitations. This study was not a randomized controlled trial. The use of direct cholangioscopy by the SpyGlass DS increases the procedure time with additional cost. Therefore, we do not recommend routine use of SpyGlass DS to rule out residual stones.","In conclusion, in patients with large CBD stones undergoing mechanical lithotripsy, routine irrigation of the bile duct with at least 100 mL of saline is recommended to improve CBD clearance, especially for patients with a dilated bile duct and/or those with PAD. Saline irrigation is simple, inexpensive, and easy to perform to improve bile duct clearance, and it may avoid recurrent stone formation."],"string":"[\n \"Endoscopic retrograde cholangiopancreatography (ERCP) is an effective and relatively minimally invasive technique for common bile duct (CBD) stones[1-3]. It has been reported that the recurrence rate of CBD stones after ERCP has increased from 4% to 24%[4-6]. The incidence of residual stones after mechanical lithotripsy for intractable CBD stones is 24% to 40%[7-10]. A growing number of studies suggest that an important reason for the recurrence of bile duct stones is the presence of stone debris after lithotripsy[11-13]. During ERCP, occluded cholangiography (OC) is often performed after stone removal to determine whether the stone is removed completely, but OC lacks accuracy. Even if no obvious stones are found on cholangiography, the presence of contrast can obscure small stone debris in the bile duct[8,14]. Complete bile duct clearance is necessary to decrease recurrent bile duct stones.\\nSome studies[15-17] reported that irrigation of the bile duct with saline after stone extraction further improves the clearance of the bile duct and has the advantages of being a simple, low-cost procedure with rare complications. Ang et al[16] showed that a mean of 48 mL of saline solution could irrigate and flush out residual stones after the endoscopic removal of CBD stones. Ahn et al[17] found that irrigation with 100 mL of saline can flush out residual stone fragments from the bile duct into the duodenum after stone extraction. However, intraductal ultrasound (IDUS) has a high sensitivity and accuracy in diagnosing bile duct stones/debris[11,16]. This modality yields only indirect images of the debris.\\nThe lack of direct evidence to support the efficacy of saline irrigation after lithotripsy prompted us to use peroral cholangioscopy (POC) to examine the bile duct and detect any residual stones/debris. The results of no irrigation after stone extraction were confirmed by OC and were compared to the effectiveness of irrigation with 50 mL or 100 mL saline. To evaluate whether irrigation with 100 mL of saline is more effective in achieving complete clearance of the bile duct after mechanical lithotripsy, we conducted this prospective self-controlled study.\",\n \"Between October 2018 and January 2020, 47 patients with CBD stones were enrolled in a prospective clinical trial conducted in the surgical endoscopy center of The First Hospital of Lanzhou University (Figure 1). All eligible patients or their legal representatives gave informed consent before the treatment. The inclusion criteria were patients with CBD stones undergoing ERCP, able to provide informed consent, with a stone size larger than 1.2 cm and requiring mechanical lithotripsy for stone removal. The exclusion criteria included pre-ERCP acute suppurative cholangitis, acute pancreatitis, gastrointestinal (GI) tract hemorrhage and/or perforation, previous history of ERCP, prior Bilroth II gastrectomy and Roux-en-Y or cholangiojejunostomy, pregnancy or breastfeeding, coagulopathy with international normalized ratio > 1.5 and low platelet count (< 50 × 109/L) or active use of anticoagulation drugs, severe liver disease including decompensated liver cirrhosis or liver failure, septic shock, biliary-duodenal fistula confirmed before ERCP cannulation, the presence of intrahepatic duct stones and malignancy, and patient unwillingness or inability to give informed consent.\\n\\nFlow chart of the self-controlled study. CBD: Common bile duct; ERCP: Endoscopic retrograde cholangiopancreatography; PTCD: Percutaneous transhepatic cholangiodrainage.\\nTo assess the number of residual stones, we created a CBD clearance score as follows: (1) A large number of stone fragments and biliary sludge; (2) A moderate number of stone fragments and biliary sludge; (3) A small number of stone fragments and biliary sludge; (4) Presence of biliary sludge without any stones; and (5) Completely cleared CBD without any biliary sludge. The scores were determined by two endoscopists independently (Figure 2).\\n\\nSpyGlass DS images and simulation graphs of the residual stone. A-E: SpyGlass DS images (A1-E1) and simulation graphs (A2-E2). Score 1: A large amount of stone fragments and biliary sludge. Score 2: A moderate amount of stone fragments and biliary sludge. Score 3: A small amount of stone fragments and biliary sludge. Score 4: Presence of biliary sludge without any stones. Score 5: Completely cleared common bile duct without any biliary sludge.\\nThe study was approved by the ethics committee of The First Hospital of Lanzhou University (No. LDYYMENG2018-1028) and conducted in accordance with the ethical principles of the Declaration of Helsinki. All authors had access to the study data and reviewed and approved the final manuscript.\",\n \"All endoscopic procedures were performed by two experienced endoscopists (each has performed > 1000 ERCPs). Routine prophylactic antibiotics were given in all cases. ERCP was performed with a standard duodenoscope (TJF-260V, Olympus, Tokyo, Japan). The patients received propofol anesthesia without tracheal intubation.\\nDuring ERCP, cannulation was performed with a wire-guided sphincterotome. After successful cannulation, contrast was injected to determine the stone size (more or less than 12 mm) and the need for mechanical lithotripsy. All patients underwent a small sphincterotomy with an average length of 3-5 mm using the ENDO CUT mode (power setting 90-120 W; Erbe Elektromedizin, Tuebingen, Germany) followed by balloon sphincteroplasty using a controlled radial expansion (CRE) balloon (10-12 mm in diameter; Boston Scientific, Cork, Ireland). Lithotripsy was performed using an endoscopic lithotripter-compatible basket (Boston Scientific, Marlborough, MA, United States), and stones were removed using the basket and a balloon catheter. Samples of CBD stones were collected using an EndoRetrieval bag (Micro-Tech, Nanjing, China) and placed in a container for subsequent analysis. Occlusion cholangiography was performed by injecting contrast with a balloon catheter. Fluoroscopic assessment using a C-arm X-ray (OEC 9900 Elite, Salt Lake City, Utah, United States) was applied to confirm complete removal of CBD stones and the existence of stone residuals was determined by both endoscopist and radiologist. Confirmed stone residues by applying occlusion cholangiography were then followed by repeated extraction attempts. If complete stone removal was confirmed, a CBD clearance score was generated by further applying SpyGlass DS (Boston Scientific Corporation, Marlborough, Massachusetts, United States) examination. If the CBD clearance score was less than 5, the bile duct was irrigated intermittently with 50 mL of normal saline using a basket. The basket was shaken during irrigation with slight suction applied to the duodenoscope to promote drainage. The bile duct was then examined again using the SpyGlass DS to detect any residual stones/sludge and document the clearance score. If the clearance score was still less than 5, repeat irrigation of the CBD was performed using another 50 mL of normal saline. The final CBD clearance score was obtained one more time using SpyGlass DS examination. CBD stones that were not irrigated out after double 50 mL-saline irrigations continued to be irrigated with saline until they were fully cleared (Figure 3). Endoscopic nasobiliary drainage (ENBD) or biliary stenting was performed if necessary. The CBD clearance score was assessed by two blinded endoscopists who were masked to the treatment.\\n\\nProtocol of evaluation and irrigation procedures. CBD: Common bile duct; ERCP: Endoscopic retrograde cholangiopancreatography; POC: Peroral cholangioscopy.\\nThe composition of the removed CBD stones was analyzed using infrared ray (IR) spectroscopy. One milligram (mg) of stone samples was mixed with 150 mg of potassium bromide in a mortar and ground into powder. The stone mixture was pressed into a mold and then placed into an automatic IR spectrum analyzer for automated detection to determine the IR spectrogram of the CBD stones. The IR spectrogram showed characteristic absorption peaks at a wavelength of 1460 cm-1, indicating cholesterol-based stones, and those at a wavelength of 1680 cm-1 indicated pigment-based stones.\",\n \"Most of the post-ERCP adverse events were detected within 24 h after the procedure. We routinely assessed these adverse events, including cholangitis, oozing/bleeding, pancreatitis, cholecystitis, and perforation, at 24 and 48 h after the ERCP procedure by symptoms, signs, laboratory tests, and imaging examinations if necessary[18,19]. Post-ERCP adverse events were defined as follows: (1) The diagnosis of acute cholangitis was based on Tokyo Guidelines 2018/2013 (TG18/TG13) diagnostic criteria[20]; (2) Oozing was defined as bleeding that was slight and stopped spontaneously; (3) Pancreatitis was described as new or worsening abdominal pain along with an increase in serum amylase levels (> 3 × higher than normal upper limit measured 24 h after surgery); (4) Cholecystitis was defined as pain in the epigastrium or RUQ accompanied by a positive Murphy sign, and abdominal ultrasonography showing a thickened gallbladder wall; and (5) Perforation was defined as sudden abdominal pain accompanied by retroperitoneal air and fluid.\\nMechanical lithotripsy was performed by the same assistant for all patients. Stones that were fragmented successfully with only one attempt were defined as soft stones. Stones that required multiple fragmentation attempts or needed to be broken again were defined as hard stones.\",\n \"The sample size calculation was based on the rate of residual stone clearance. According to a previous study[16] and our prior experience, we assumed a success rate of 84.5% for endoscopic extraction of CBD stones and an increase of up to 97% with saline irrigation. We estimated that 47 patients were needed in this analysis to obtain a power of 80% at the 5% level.\\nCategorical variables are expressed as numbers or percentages (%). Continuous variables are presented as the mean ± SD or median and interquartile range, as appropriate. Continuous variables with a normal distribution were analyzed by paired t-test or signed-rank test. Categorical variables were analyzed using the chi-square test or Fisher’s exact test. Logistic regression was used to predict risk factors for complications, and the results are presented as odds ratios (ORs) with 95% confidence intervals (Cis). Variables with a P value of < 0.2 in the univariate analysis were included in the multivariate analysis. Linear mixed-effects models were conducted to assess the effect of saline irrigation on the clearance score with a random intercept for each patient and an unstructured covariance structure. A two-sided P value of less than 0.05 was considered statistically significant. The analyses were conducted with statistical software (SAS version 9.4, SAS Institute, Inc., NC, United States).\",\n \"The patients' average age was 61 ± 16.5 years, and 23 (48.9%) were male. Comorbidities included coronary disease in three (6.4%) patients, hypertension in 14 (29.8%), diabetes in two (4.3%), liver cirrhosis in three (2.1%), and portal hypertension in one (6.4%). The stone analysis showed that 17 (36.2%) cases had cholesterol-based stones, and 30 (63.8%) had pigment-based stones.\\nProcedure-related adverse events occurred in 11 (23.4%) of 47 patients, with cholangitis in four patients, bleeding in two, pancreatitis in four, and cholecystitis in one. No mortality or perforation occurred. The mean time for ERCP was 40.3 ± 15.4 min (Table 1).\\nClinical and procedural characteristics of the patients\\nData are expressed as the mean ± SD, median (interquartile range) or n (%). ENBD: Endoscopic nasobiliary drainage; CBD: Common bile duct.\\nAfter endoscopic stone extraction, occlusion cholangiography showed that there were no residual CBD stones. Seven patients had a clearance score of 4 identified by POC with the SpyGlass DS, and no patients had a score of 5 before saline irrigation. After irrigation with 50 mL saline, CBD clearance improved to 4 in 28 patients, but none achieved a score of 5. After a total of 100 mL of saline irrigation, there was further improvement in the clearance scores: 12 (26%) patients had a score of 4, and 32 (68%) had a score of 5 identified by the SpyGlass DS. The respective CBD stone clearance rates for the control (no irrigation), 50 mL saline irrigation, and 100 mL saline irrigation were 15%, 60%, and 94%, respectively, and the difference among them was statistically significant (P < 0.001) (Table 2). The CBD clearance score (mean ± SD) was 2.4 ± 1.1 in the control, 3.5 ± 0.7 in the 50 mL saline irrigation, and 4.6 ± 0.6 in the 100 mL saline irrigation (P < 0.001) (Supplementary Figure 1).\\nCommon bile duct clear score and stone clearance rate before and after saline irrigation\\nAfter ERCP, 13 patients had a score of 1 (a large amount of residual stone/sludge) before saline irrigation. Only 15 patients did not reach a score of 5 (complete clearance) after 100 mL of saline irrigation. A dilated CBD diameter > 15 mm (OR = 4.93, 95%CI: 1.13-21.57; P = 0.034) was an independent risk factor for significant residual CBD stones (score = 1). Multivariate analysis revealed that CBD diameter > 15 mm (OR = 0.08, 95%CI: 0.01-0.49; P = 0.007) and the presence of periampullary diverticula (PAD) (OR = 6.51, 95%CI: 1.08-39.21; P = 0.041) were independent risk factors for failed CBD clearance despite irrigation with 100 mL saline (Table 3).\\nBiliary cleanliness before and after saline irrigation, assessed by logistic regression analysis\\nN: Total number; CBD: Common bile duct; PAD: Periampullary diverticula; CI: Confidence interval; OR: Odds ratio.\\nFurther analysis showed that the volume of saline used for irrigation[20] (P < 0.001) was an important factor determining CBD clearance (Supplementary Table 1).\\nWe used IR spectroscopy to analyze the components of stones in the CBD of all patients. IR spectroscopy analysis showed that the proportion of pigment-based stones was significantly higher in patients with PAD (93.3% vs 50.0%, P = 0.04), and these stones were mostly soft stones (77.4% vs 22.6%, P = 0.01) (Table 4).\\nCorrelation between the composition of stones and variables\\nCBD: Common bile duct; PAD: Periampullary diverticula.\",\n \"Fluoroscopic cholangiographic imagery is currently the main method to determine the successful clearance of CBD stones[20]. However, studies involving the use of IDUS have identified residual biliary sludge within the bile duct despite the absence of filling defects on cholangiography. Mechanical lithotripsy produces a large number of stone fragments, and these minor residual CBD stones may lead to recurrent stone formation[21].\\nIt has been reported that residual small CBD stone fragments can be flushed out of the bile duct using saline irrigation with a balloon catheter with a side hole[15]. A study reported that an average of 48 mL of saline could remove residual stones[16]. However, another study found that at least 100 mL of saline was needed to reduce residual stones[17]. Therefore, the effectiveness of saline irrigation on the clearance of residual bile duct stones after lithotripsy is not clear. This study's purpose differs from previous ones in that all of the patients had large stones and were post lithotripsy. In this study, we found that after ERCP and an additional round of lithotripsy were performed to remove CBD stones, POC using the SpyGlass DS showed that only 15% of the patients were relatively cleared of bile duct stones despite a negative occlusion cholangiogram. After irrigation with 50 mL of normal saline, 60% of the patients had relatively cleared bile ducts. After a total of 100 mL saline irrigation, 94% of the patients achieved complete clearance of the bile duct. The results showed that irrigation with 50 mL of saline was not enough to clear the bile duct of residual stones/sludge.\\nIn this series, all patients received mechanical lithotripsy for stone fragmentation, which generates substantial stone fragments/debris, thus increasing the difficulty of clearing the bile duct. Our results showed that a good CBD clearance rate could be achieved with a larger saline irrigation volume. The clearance rate of bile duct stones could be higher for those without lithotripsy.\\nResidual stones have been found in approximately 1/3 of cases after stone retrieval using endoscopic ultrasound (EUS)[22,23]. In addition to stone detection, the EUS approach might also offer an alternative for treating bile duct stones. However, the use of EUS is very challenging[24-27]. IDUS has also been used to detect small residual stones in 14/59 patients (23.7%) with residual stones[11]. However, the ultrasonic probe is costly and can be easily damaged. In addition, the method is highly operator dependent and often produces poor quality images, and the presence of air in the bile duct can affect the detection of residual stones. Many studies have shown that POC has a high sensitivity in the detection of residual CBD stones, ranging from 25.3% to 34%, where residual stones are missed by standard cholangiography[8,9,28].\\nIn our study, we used the SpyGlass DS system to determine the CBD clearance score. The SpyScope has a 4-way tip deflection and is much smaller (10 French) than conventional choledochoscopes. More importantly, it has a separate and independent irrigation channel that allows intermittent or continuous irrigation of the biliary system. It also offers a direct examination of the bile duct lumen and is more accurate in detecting and diagnosing residual bile duct stones/sludge.\\nResidual CBD stones can lead to recurrent stone formation. Itoi et al[7] reported that PAD and intraoperative lithotripsy were closely related to residual stones (P < 0.05), as we observed in our study. We also found that a dilated CBD with a diameter > 15 mm was an independent risk factor for failed CBD clearance and a large number of residual stones. Despite irrigation with 100 mL saline, PAD and a dilated CBD remained independent risk factors for incomplete bile duct clearance. This may be due to the presence of an air-filled duodenum/diverticulum that compresses the distal CBD, making it difficult to wash away the residual stones/sludge[29,30]. CBD clearance can be improved by increasing the volume of saline irrigation.\\nOur results showed that PAD not only increased the difficulty of bile duct clearance but can also affect stone composition. We found that pigment-based stones constituted the majority of CBD stones in our patients with PAD (P = 0.004). Song et al[31] reported that recurrent bile duct stones were more likely to be brown pigmented stones than cholesterol-based stones. This may be because pigment-based stones are often related to bacterial infection, and the formation of biliary sludge will contribute to recurrent stone formation over time[32-34]. The brown pigmented stones that form as a result of bacterial infection are soft and easy to fragment and generate abundant debris. Our experience showed that soft stones/sludge require more saline irrigation to clear the bile duct. In this study, 26% of cases with biliary sludge were found even with 100 mL saline irrigation. If an effective bile duct cleaning device is used to remove biliary sludge, it may reduce the CBD stone recurrence rate after ERCP.\\nWithout adequate biliary drainage, saline irrigation can increase the intraductal pressure, causing cholangitis because of contaminated bile[35]. Proper drainage methods can mitigate this stress[36,37]. No serious adverse events were reported in our study except for 10% of patients with cholangitis secondary possibly to saline irrigation. Intermittent irrigation and endoscopic suction to promote biliary drainage may lower the risk in addition to antibiotic coverage. Differences between this study and previous studies include the following: We studied the effect of flushing with saline after lithotripsy; our evaluation method was different from that of previous studies (we used SpyGlass DS examination); and we performed a component analysis related to diverticula. Our study has several methodological advantages. First, it was a prospective study using a self-controlled design, and SpyGlass DS was used to assess the presence of residual stones in the bile ducts. All of the patients showed no stones by imaging even though small residual bile duct stones were observed by SpyGlass DS, indicating that the evaluation of residual bile duct stones using SpyGlass DS is more sensitive than imaging alone. If we observe that there are still stones, it would be unethical not to continue flushing after using 50 mL of saline. Second, this study used objective definitions to assess residual stones identified by POC. Third, this study included objective stone composition analysis using IR spectroscopy.\\nThe study has limitations. This study was not a randomized controlled trial. The use of direct cholangioscopy by the SpyGlass DS increases the procedure time with additional cost. Therefore, we do not recommend routine use of SpyGlass DS to rule out residual stones.\",\n \"In conclusion, in patients with large CBD stones undergoing mechanical lithotripsy, routine irrigation of the bile duct with at least 100 mL of saline is recommended to improve CBD clearance, especially for patients with a dilated bile duct and/or those with PAD. Saline irrigation is simple, inexpensive, and easy to perform to improve bile duct clearance, and it may avoid recurrent stone formation.\"\n]"},"other_sections_sec_types":{"kind":"list like","value":[null,null,null,null,null,null,null,null],"string":"[\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null\n]"},"all_sections_titles":{"kind":"list like","value":["INTRODUCTION","MATERIALS AND METHODS","Study design and participants","Procedures","Definition for complications","Statistical analysis","RESULTS","DISCUSSION","CONCLUSION"],"string":"[\n \"INTRODUCTION\",\n \"MATERIALS AND METHODS\",\n \"Study design and participants\",\n \"Procedures\",\n \"Definition for complications\",\n \"Statistical analysis\",\n \"RESULTS\",\n \"DISCUSSION\",\n \"CONCLUSION\"\n]"},"all_sections_texts":{"kind":"list like","value":["Endoscopic retrograde cholangiopancreatography (ERCP) is an effective and relatively minimally invasive technique for common bile duct (CBD) stones[1-3]. It has been reported that the recurrence rate of CBD stones after ERCP has increased from 4% to 24%[4-6]. The incidence of residual stones after mechanical lithotripsy for intractable CBD stones is 24% to 40%[7-10]. A growing number of studies suggest that an important reason for the recurrence of bile duct stones is the presence of stone debris after lithotripsy[11-13]. During ERCP, occluded cholangiography (OC) is often performed after stone removal to determine whether the stone is removed completely, but OC lacks accuracy. Even if no obvious stones are found on cholangiography, the presence of contrast can obscure small stone debris in the bile duct[8,14]. Complete bile duct clearance is necessary to decrease recurrent bile duct stones.\nSome studies[15-17] reported that irrigation of the bile duct with saline after stone extraction further improves the clearance of the bile duct and has the advantages of being a simple, low-cost procedure with rare complications. Ang et al[16] showed that a mean of 48 mL of saline solution could irrigate and flush out residual stones after the endoscopic removal of CBD stones. Ahn et al[17] found that irrigation with 100 mL of saline can flush out residual stone fragments from the bile duct into the duodenum after stone extraction. However, intraductal ultrasound (IDUS) has a high sensitivity and accuracy in diagnosing bile duct stones/debris[11,16]. This modality yields only indirect images of the debris.\nThe lack of direct evidence to support the efficacy of saline irrigation after lithotripsy prompted us to use peroral cholangioscopy (POC) to examine the bile duct and detect any residual stones/debris. The results of no irrigation after stone extraction were confirmed by OC and were compared to the effectiveness of irrigation with 50 mL or 100 mL saline. To evaluate whether irrigation with 100 mL of saline is more effective in achieving complete clearance of the bile duct after mechanical lithotripsy, we conducted this prospective self-controlled study.","Study design and participants Between October 2018 and January 2020, 47 patients with CBD stones were enrolled in a prospective clinical trial conducted in the surgical endoscopy center of The First Hospital of Lanzhou University (Figure 1). All eligible patients or their legal representatives gave informed consent before the treatment. The inclusion criteria were patients with CBD stones undergoing ERCP, able to provide informed consent, with a stone size larger than 1.2 cm and requiring mechanical lithotripsy for stone removal. The exclusion criteria included pre-ERCP acute suppurative cholangitis, acute pancreatitis, gastrointestinal (GI) tract hemorrhage and/or perforation, previous history of ERCP, prior Bilroth II gastrectomy and Roux-en-Y or cholangiojejunostomy, pregnancy or breastfeeding, coagulopathy with international normalized ratio > 1.5 and low platelet count (< 50 × 109/L) or active use of anticoagulation drugs, severe liver disease including decompensated liver cirrhosis or liver failure, septic shock, biliary-duodenal fistula confirmed before ERCP cannulation, the presence of intrahepatic duct stones and malignancy, and patient unwillingness or inability to give informed consent.\n\nFlow chart of the self-controlled study. CBD: Common bile duct; ERCP: Endoscopic retrograde cholangiopancreatography; PTCD: Percutaneous transhepatic cholangiodrainage.\nTo assess the number of residual stones, we created a CBD clearance score as follows: (1) A large number of stone fragments and biliary sludge; (2) A moderate number of stone fragments and biliary sludge; (3) A small number of stone fragments and biliary sludge; (4) Presence of biliary sludge without any stones; and (5) Completely cleared CBD without any biliary sludge. The scores were determined by two endoscopists independently (Figure 2).\n\nSpyGlass DS images and simulation graphs of the residual stone. A-E: SpyGlass DS images (A1-E1) and simulation graphs (A2-E2). Score 1: A large amount of stone fragments and biliary sludge. Score 2: A moderate amount of stone fragments and biliary sludge. Score 3: A small amount of stone fragments and biliary sludge. Score 4: Presence of biliary sludge without any stones. Score 5: Completely cleared common bile duct without any biliary sludge.\nThe study was approved by the ethics committee of The First Hospital of Lanzhou University (No. LDYYMENG2018-1028) and conducted in accordance with the ethical principles of the Declaration of Helsinki. All authors had access to the study data and reviewed and approved the final manuscript.\nBetween October 2018 and January 2020, 47 patients with CBD stones were enrolled in a prospective clinical trial conducted in the surgical endoscopy center of The First Hospital of Lanzhou University (Figure 1). All eligible patients or their legal representatives gave informed consent before the treatment. The inclusion criteria were patients with CBD stones undergoing ERCP, able to provide informed consent, with a stone size larger than 1.2 cm and requiring mechanical lithotripsy for stone removal. The exclusion criteria included pre-ERCP acute suppurative cholangitis, acute pancreatitis, gastrointestinal (GI) tract hemorrhage and/or perforation, previous history of ERCP, prior Bilroth II gastrectomy and Roux-en-Y or cholangiojejunostomy, pregnancy or breastfeeding, coagulopathy with international normalized ratio > 1.5 and low platelet count (< 50 × 109/L) or active use of anticoagulation drugs, severe liver disease including decompensated liver cirrhosis or liver failure, septic shock, biliary-duodenal fistula confirmed before ERCP cannulation, the presence of intrahepatic duct stones and malignancy, and patient unwillingness or inability to give informed consent.\n\nFlow chart of the self-controlled study. CBD: Common bile duct; ERCP: Endoscopic retrograde cholangiopancreatography; PTCD: Percutaneous transhepatic cholangiodrainage.\nTo assess the number of residual stones, we created a CBD clearance score as follows: (1) A large number of stone fragments and biliary sludge; (2) A moderate number of stone fragments and biliary sludge; (3) A small number of stone fragments and biliary sludge; (4) Presence of biliary sludge without any stones; and (5) Completely cleared CBD without any biliary sludge. The scores were determined by two endoscopists independently (Figure 2).\n\nSpyGlass DS images and simulation graphs of the residual stone. A-E: SpyGlass DS images (A1-E1) and simulation graphs (A2-E2). Score 1: A large amount of stone fragments and biliary sludge. Score 2: A moderate amount of stone fragments and biliary sludge. Score 3: A small amount of stone fragments and biliary sludge. Score 4: Presence of biliary sludge without any stones. Score 5: Completely cleared common bile duct without any biliary sludge.\nThe study was approved by the ethics committee of The First Hospital of Lanzhou University (No. LDYYMENG2018-1028) and conducted in accordance with the ethical principles of the Declaration of Helsinki. All authors had access to the study data and reviewed and approved the final manuscript.\nProcedures All endoscopic procedures were performed by two experienced endoscopists (each has performed > 1000 ERCPs). Routine prophylactic antibiotics were given in all cases. ERCP was performed with a standard duodenoscope (TJF-260V, Olympus, Tokyo, Japan). The patients received propofol anesthesia without tracheal intubation.\nDuring ERCP, cannulation was performed with a wire-guided sphincterotome. After successful cannulation, contrast was injected to determine the stone size (more or less than 12 mm) and the need for mechanical lithotripsy. All patients underwent a small sphincterotomy with an average length of 3-5 mm using the ENDO CUT mode (power setting 90-120 W; Erbe Elektromedizin, Tuebingen, Germany) followed by balloon sphincteroplasty using a controlled radial expansion (CRE) balloon (10-12 mm in diameter; Boston Scientific, Cork, Ireland). Lithotripsy was performed using an endoscopic lithotripter-compatible basket (Boston Scientific, Marlborough, MA, United States), and stones were removed using the basket and a balloon catheter. Samples of CBD stones were collected using an EndoRetrieval bag (Micro-Tech, Nanjing, China) and placed in a container for subsequent analysis. Occlusion cholangiography was performed by injecting contrast with a balloon catheter. Fluoroscopic assessment using a C-arm X-ray (OEC 9900 Elite, Salt Lake City, Utah, United States) was applied to confirm complete removal of CBD stones and the existence of stone residuals was determined by both endoscopist and radiologist. Confirmed stone residues by applying occlusion cholangiography were then followed by repeated extraction attempts. If complete stone removal was confirmed, a CBD clearance score was generated by further applying SpyGlass DS (Boston Scientific Corporation, Marlborough, Massachusetts, United States) examination. If the CBD clearance score was less than 5, the bile duct was irrigated intermittently with 50 mL of normal saline using a basket. The basket was shaken during irrigation with slight suction applied to the duodenoscope to promote drainage. The bile duct was then examined again using the SpyGlass DS to detect any residual stones/sludge and document the clearance score. If the clearance score was still less than 5, repeat irrigation of the CBD was performed using another 50 mL of normal saline. The final CBD clearance score was obtained one more time using SpyGlass DS examination. CBD stones that were not irrigated out after double 50 mL-saline irrigations continued to be irrigated with saline until they were fully cleared (Figure 3). Endoscopic nasobiliary drainage (ENBD) or biliary stenting was performed if necessary. The CBD clearance score was assessed by two blinded endoscopists who were masked to the treatment.\n\nProtocol of evaluation and irrigation procedures. CBD: Common bile duct; ERCP: Endoscopic retrograde cholangiopancreatography; POC: Peroral cholangioscopy.\nThe composition of the removed CBD stones was analyzed using infrared ray (IR) spectroscopy. One milligram (mg) of stone samples was mixed with 150 mg of potassium bromide in a mortar and ground into powder. The stone mixture was pressed into a mold and then placed into an automatic IR spectrum analyzer for automated detection to determine the IR spectrogram of the CBD stones. The IR spectrogram showed characteristic absorption peaks at a wavelength of 1460 cm-1, indicating cholesterol-based stones, and those at a wavelength of 1680 cm-1 indicated pigment-based stones.\nAll endoscopic procedures were performed by two experienced endoscopists (each has performed > 1000 ERCPs). Routine prophylactic antibiotics were given in all cases. ERCP was performed with a standard duodenoscope (TJF-260V, Olympus, Tokyo, Japan). The patients received propofol anesthesia without tracheal intubation.\nDuring ERCP, cannulation was performed with a wire-guided sphincterotome. After successful cannulation, contrast was injected to determine the stone size (more or less than 12 mm) and the need for mechanical lithotripsy. All patients underwent a small sphincterotomy with an average length of 3-5 mm using the ENDO CUT mode (power setting 90-120 W; Erbe Elektromedizin, Tuebingen, Germany) followed by balloon sphincteroplasty using a controlled radial expansion (CRE) balloon (10-12 mm in diameter; Boston Scientific, Cork, Ireland). Lithotripsy was performed using an endoscopic lithotripter-compatible basket (Boston Scientific, Marlborough, MA, United States), and stones were removed using the basket and a balloon catheter. Samples of CBD stones were collected using an EndoRetrieval bag (Micro-Tech, Nanjing, China) and placed in a container for subsequent analysis. Occlusion cholangiography was performed by injecting contrast with a balloon catheter. Fluoroscopic assessment using a C-arm X-ray (OEC 9900 Elite, Salt Lake City, Utah, United States) was applied to confirm complete removal of CBD stones and the existence of stone residuals was determined by both endoscopist and radiologist. Confirmed stone residues by applying occlusion cholangiography were then followed by repeated extraction attempts. If complete stone removal was confirmed, a CBD clearance score was generated by further applying SpyGlass DS (Boston Scientific Corporation, Marlborough, Massachusetts, United States) examination. If the CBD clearance score was less than 5, the bile duct was irrigated intermittently with 50 mL of normal saline using a basket. The basket was shaken during irrigation with slight suction applied to the duodenoscope to promote drainage. The bile duct was then examined again using the SpyGlass DS to detect any residual stones/sludge and document the clearance score. If the clearance score was still less than 5, repeat irrigation of the CBD was performed using another 50 mL of normal saline. The final CBD clearance score was obtained one more time using SpyGlass DS examination. CBD stones that were not irrigated out after double 50 mL-saline irrigations continued to be irrigated with saline until they were fully cleared (Figure 3). Endoscopic nasobiliary drainage (ENBD) or biliary stenting was performed if necessary. The CBD clearance score was assessed by two blinded endoscopists who were masked to the treatment.\n\nProtocol of evaluation and irrigation procedures. CBD: Common bile duct; ERCP: Endoscopic retrograde cholangiopancreatography; POC: Peroral cholangioscopy.\nThe composition of the removed CBD stones was analyzed using infrared ray (IR) spectroscopy. One milligram (mg) of stone samples was mixed with 150 mg of potassium bromide in a mortar and ground into powder. The stone mixture was pressed into a mold and then placed into an automatic IR spectrum analyzer for automated detection to determine the IR spectrogram of the CBD stones. The IR spectrogram showed characteristic absorption peaks at a wavelength of 1460 cm-1, indicating cholesterol-based stones, and those at a wavelength of 1680 cm-1 indicated pigment-based stones.\nDefinition for complications Most of the post-ERCP adverse events were detected within 24 h after the procedure. We routinely assessed these adverse events, including cholangitis, oozing/bleeding, pancreatitis, cholecystitis, and perforation, at 24 and 48 h after the ERCP procedure by symptoms, signs, laboratory tests, and imaging examinations if necessary[18,19]. Post-ERCP adverse events were defined as follows: (1) The diagnosis of acute cholangitis was based on Tokyo Guidelines 2018/2013 (TG18/TG13) diagnostic criteria[20]; (2) Oozing was defined as bleeding that was slight and stopped spontaneously; (3) Pancreatitis was described as new or worsening abdominal pain along with an increase in serum amylase levels (> 3 × higher than normal upper limit measured 24 h after surgery); (4) Cholecystitis was defined as pain in the epigastrium or RUQ accompanied by a positive Murphy sign, and abdominal ultrasonography showing a thickened gallbladder wall; and (5) Perforation was defined as sudden abdominal pain accompanied by retroperitoneal air and fluid.\nMechanical lithotripsy was performed by the same assistant for all patients. Stones that were fragmented successfully with only one attempt were defined as soft stones. Stones that required multiple fragmentation attempts or needed to be broken again were defined as hard stones.\nMost of the post-ERCP adverse events were detected within 24 h after the procedure. We routinely assessed these adverse events, including cholangitis, oozing/bleeding, pancreatitis, cholecystitis, and perforation, at 24 and 48 h after the ERCP procedure by symptoms, signs, laboratory tests, and imaging examinations if necessary[18,19]. Post-ERCP adverse events were defined as follows: (1) The diagnosis of acute cholangitis was based on Tokyo Guidelines 2018/2013 (TG18/TG13) diagnostic criteria[20]; (2) Oozing was defined as bleeding that was slight and stopped spontaneously; (3) Pancreatitis was described as new or worsening abdominal pain along with an increase in serum amylase levels (> 3 × higher than normal upper limit measured 24 h after surgery); (4) Cholecystitis was defined as pain in the epigastrium or RUQ accompanied by a positive Murphy sign, and abdominal ultrasonography showing a thickened gallbladder wall; and (5) Perforation was defined as sudden abdominal pain accompanied by retroperitoneal air and fluid.\nMechanical lithotripsy was performed by the same assistant for all patients. Stones that were fragmented successfully with only one attempt were defined as soft stones. Stones that required multiple fragmentation attempts or needed to be broken again were defined as hard stones.\nStatistical analysis The sample size calculation was based on the rate of residual stone clearance. According to a previous study[16] and our prior experience, we assumed a success rate of 84.5% for endoscopic extraction of CBD stones and an increase of up to 97% with saline irrigation. We estimated that 47 patients were needed in this analysis to obtain a power of 80% at the 5% level.\nCategorical variables are expressed as numbers or percentages (%). Continuous variables are presented as the mean ± SD or median and interquartile range, as appropriate. Continuous variables with a normal distribution were analyzed by paired t-test or signed-rank test. Categorical variables were analyzed using the chi-square test or Fisher’s exact test. Logistic regression was used to predict risk factors for complications, and the results are presented as odds ratios (ORs) with 95% confidence intervals (Cis). Variables with a P value of < 0.2 in the univariate analysis were included in the multivariate analysis. Linear mixed-effects models were conducted to assess the effect of saline irrigation on the clearance score with a random intercept for each patient and an unstructured covariance structure. A two-sided P value of less than 0.05 was considered statistically significant. The analyses were conducted with statistical software (SAS version 9.4, SAS Institute, Inc., NC, United States).\nThe sample size calculation was based on the rate of residual stone clearance. According to a previous study[16] and our prior experience, we assumed a success rate of 84.5% for endoscopic extraction of CBD stones and an increase of up to 97% with saline irrigation. We estimated that 47 patients were needed in this analysis to obtain a power of 80% at the 5% level.\nCategorical variables are expressed as numbers or percentages (%). Continuous variables are presented as the mean ± SD or median and interquartile range, as appropriate. Continuous variables with a normal distribution were analyzed by paired t-test or signed-rank test. Categorical variables were analyzed using the chi-square test or Fisher’s exact test. Logistic regression was used to predict risk factors for complications, and the results are presented as odds ratios (ORs) with 95% confidence intervals (Cis). Variables with a P value of < 0.2 in the univariate analysis were included in the multivariate analysis. Linear mixed-effects models were conducted to assess the effect of saline irrigation on the clearance score with a random intercept for each patient and an unstructured covariance structure. A two-sided P value of less than 0.05 was considered statistically significant. The analyses were conducted with statistical software (SAS version 9.4, SAS Institute, Inc., NC, United States).","Between October 2018 and January 2020, 47 patients with CBD stones were enrolled in a prospective clinical trial conducted in the surgical endoscopy center of The First Hospital of Lanzhou University (Figure 1). All eligible patients or their legal representatives gave informed consent before the treatment. The inclusion criteria were patients with CBD stones undergoing ERCP, able to provide informed consent, with a stone size larger than 1.2 cm and requiring mechanical lithotripsy for stone removal. The exclusion criteria included pre-ERCP acute suppurative cholangitis, acute pancreatitis, gastrointestinal (GI) tract hemorrhage and/or perforation, previous history of ERCP, prior Bilroth II gastrectomy and Roux-en-Y or cholangiojejunostomy, pregnancy or breastfeeding, coagulopathy with international normalized ratio > 1.5 and low platelet count (< 50 × 109/L) or active use of anticoagulation drugs, severe liver disease including decompensated liver cirrhosis or liver failure, septic shock, biliary-duodenal fistula confirmed before ERCP cannulation, the presence of intrahepatic duct stones and malignancy, and patient unwillingness or inability to give informed consent.\n\nFlow chart of the self-controlled study. CBD: Common bile duct; ERCP: Endoscopic retrograde cholangiopancreatography; PTCD: Percutaneous transhepatic cholangiodrainage.\nTo assess the number of residual stones, we created a CBD clearance score as follows: (1) A large number of stone fragments and biliary sludge; (2) A moderate number of stone fragments and biliary sludge; (3) A small number of stone fragments and biliary sludge; (4) Presence of biliary sludge without any stones; and (5) Completely cleared CBD without any biliary sludge. The scores were determined by two endoscopists independently (Figure 2).\n\nSpyGlass DS images and simulation graphs of the residual stone. A-E: SpyGlass DS images (A1-E1) and simulation graphs (A2-E2). Score 1: A large amount of stone fragments and biliary sludge. Score 2: A moderate amount of stone fragments and biliary sludge. Score 3: A small amount of stone fragments and biliary sludge. Score 4: Presence of biliary sludge without any stones. Score 5: Completely cleared common bile duct without any biliary sludge.\nThe study was approved by the ethics committee of The First Hospital of Lanzhou University (No. LDYYMENG2018-1028) and conducted in accordance with the ethical principles of the Declaration of Helsinki. All authors had access to the study data and reviewed and approved the final manuscript.","All endoscopic procedures were performed by two experienced endoscopists (each has performed > 1000 ERCPs). Routine prophylactic antibiotics were given in all cases. ERCP was performed with a standard duodenoscope (TJF-260V, Olympus, Tokyo, Japan). The patients received propofol anesthesia without tracheal intubation.\nDuring ERCP, cannulation was performed with a wire-guided sphincterotome. After successful cannulation, contrast was injected to determine the stone size (more or less than 12 mm) and the need for mechanical lithotripsy. All patients underwent a small sphincterotomy with an average length of 3-5 mm using the ENDO CUT mode (power setting 90-120 W; Erbe Elektromedizin, Tuebingen, Germany) followed by balloon sphincteroplasty using a controlled radial expansion (CRE) balloon (10-12 mm in diameter; Boston Scientific, Cork, Ireland). Lithotripsy was performed using an endoscopic lithotripter-compatible basket (Boston Scientific, Marlborough, MA, United States), and stones were removed using the basket and a balloon catheter. Samples of CBD stones were collected using an EndoRetrieval bag (Micro-Tech, Nanjing, China) and placed in a container for subsequent analysis. Occlusion cholangiography was performed by injecting contrast with a balloon catheter. Fluoroscopic assessment using a C-arm X-ray (OEC 9900 Elite, Salt Lake City, Utah, United States) was applied to confirm complete removal of CBD stones and the existence of stone residuals was determined by both endoscopist and radiologist. Confirmed stone residues by applying occlusion cholangiography were then followed by repeated extraction attempts. If complete stone removal was confirmed, a CBD clearance score was generated by further applying SpyGlass DS (Boston Scientific Corporation, Marlborough, Massachusetts, United States) examination. If the CBD clearance score was less than 5, the bile duct was irrigated intermittently with 50 mL of normal saline using a basket. The basket was shaken during irrigation with slight suction applied to the duodenoscope to promote drainage. The bile duct was then examined again using the SpyGlass DS to detect any residual stones/sludge and document the clearance score. If the clearance score was still less than 5, repeat irrigation of the CBD was performed using another 50 mL of normal saline. The final CBD clearance score was obtained one more time using SpyGlass DS examination. CBD stones that were not irrigated out after double 50 mL-saline irrigations continued to be irrigated with saline until they were fully cleared (Figure 3). Endoscopic nasobiliary drainage (ENBD) or biliary stenting was performed if necessary. The CBD clearance score was assessed by two blinded endoscopists who were masked to the treatment.\n\nProtocol of evaluation and irrigation procedures. CBD: Common bile duct; ERCP: Endoscopic retrograde cholangiopancreatography; POC: Peroral cholangioscopy.\nThe composition of the removed CBD stones was analyzed using infrared ray (IR) spectroscopy. One milligram (mg) of stone samples was mixed with 150 mg of potassium bromide in a mortar and ground into powder. The stone mixture was pressed into a mold and then placed into an automatic IR spectrum analyzer for automated detection to determine the IR spectrogram of the CBD stones. The IR spectrogram showed characteristic absorption peaks at a wavelength of 1460 cm-1, indicating cholesterol-based stones, and those at a wavelength of 1680 cm-1 indicated pigment-based stones.","Most of the post-ERCP adverse events were detected within 24 h after the procedure. We routinely assessed these adverse events, including cholangitis, oozing/bleeding, pancreatitis, cholecystitis, and perforation, at 24 and 48 h after the ERCP procedure by symptoms, signs, laboratory tests, and imaging examinations if necessary[18,19]. Post-ERCP adverse events were defined as follows: (1) The diagnosis of acute cholangitis was based on Tokyo Guidelines 2018/2013 (TG18/TG13) diagnostic criteria[20]; (2) Oozing was defined as bleeding that was slight and stopped spontaneously; (3) Pancreatitis was described as new or worsening abdominal pain along with an increase in serum amylase levels (> 3 × higher than normal upper limit measured 24 h after surgery); (4) Cholecystitis was defined as pain in the epigastrium or RUQ accompanied by a positive Murphy sign, and abdominal ultrasonography showing a thickened gallbladder wall; and (5) Perforation was defined as sudden abdominal pain accompanied by retroperitoneal air and fluid.\nMechanical lithotripsy was performed by the same assistant for all patients. Stones that were fragmented successfully with only one attempt were defined as soft stones. Stones that required multiple fragmentation attempts or needed to be broken again were defined as hard stones.","The sample size calculation was based on the rate of residual stone clearance. According to a previous study[16] and our prior experience, we assumed a success rate of 84.5% for endoscopic extraction of CBD stones and an increase of up to 97% with saline irrigation. We estimated that 47 patients were needed in this analysis to obtain a power of 80% at the 5% level.\nCategorical variables are expressed as numbers or percentages (%). Continuous variables are presented as the mean ± SD or median and interquartile range, as appropriate. Continuous variables with a normal distribution were analyzed by paired t-test or signed-rank test. Categorical variables were analyzed using the chi-square test or Fisher’s exact test. Logistic regression was used to predict risk factors for complications, and the results are presented as odds ratios (ORs) with 95% confidence intervals (Cis). Variables with a P value of < 0.2 in the univariate analysis were included in the multivariate analysis. Linear mixed-effects models were conducted to assess the effect of saline irrigation on the clearance score with a random intercept for each patient and an unstructured covariance structure. A two-sided P value of less than 0.05 was considered statistically significant. The analyses were conducted with statistical software (SAS version 9.4, SAS Institute, Inc., NC, United States).","The patients' average age was 61 ± 16.5 years, and 23 (48.9%) were male. Comorbidities included coronary disease in three (6.4%) patients, hypertension in 14 (29.8%), diabetes in two (4.3%), liver cirrhosis in three (2.1%), and portal hypertension in one (6.4%). The stone analysis showed that 17 (36.2%) cases had cholesterol-based stones, and 30 (63.8%) had pigment-based stones.\nProcedure-related adverse events occurred in 11 (23.4%) of 47 patients, with cholangitis in four patients, bleeding in two, pancreatitis in four, and cholecystitis in one. No mortality or perforation occurred. The mean time for ERCP was 40.3 ± 15.4 min (Table 1).\nClinical and procedural characteristics of the patients\nData are expressed as the mean ± SD, median (interquartile range) or n (%). ENBD: Endoscopic nasobiliary drainage; CBD: Common bile duct.\nAfter endoscopic stone extraction, occlusion cholangiography showed that there were no residual CBD stones. Seven patients had a clearance score of 4 identified by POC with the SpyGlass DS, and no patients had a score of 5 before saline irrigation. After irrigation with 50 mL saline, CBD clearance improved to 4 in 28 patients, but none achieved a score of 5. After a total of 100 mL of saline irrigation, there was further improvement in the clearance scores: 12 (26%) patients had a score of 4, and 32 (68%) had a score of 5 identified by the SpyGlass DS. The respective CBD stone clearance rates for the control (no irrigation), 50 mL saline irrigation, and 100 mL saline irrigation were 15%, 60%, and 94%, respectively, and the difference among them was statistically significant (P < 0.001) (Table 2). The CBD clearance score (mean ± SD) was 2.4 ± 1.1 in the control, 3.5 ± 0.7 in the 50 mL saline irrigation, and 4.6 ± 0.6 in the 100 mL saline irrigation (P < 0.001) (Supplementary Figure 1).\nCommon bile duct clear score and stone clearance rate before and after saline irrigation\nAfter ERCP, 13 patients had a score of 1 (a large amount of residual stone/sludge) before saline irrigation. Only 15 patients did not reach a score of 5 (complete clearance) after 100 mL of saline irrigation. A dilated CBD diameter > 15 mm (OR = 4.93, 95%CI: 1.13-21.57; P = 0.034) was an independent risk factor for significant residual CBD stones (score = 1). Multivariate analysis revealed that CBD diameter > 15 mm (OR = 0.08, 95%CI: 0.01-0.49; P = 0.007) and the presence of periampullary diverticula (PAD) (OR = 6.51, 95%CI: 1.08-39.21; P = 0.041) were independent risk factors for failed CBD clearance despite irrigation with 100 mL saline (Table 3).\nBiliary cleanliness before and after saline irrigation, assessed by logistic regression analysis\nN: Total number; CBD: Common bile duct; PAD: Periampullary diverticula; CI: Confidence interval; OR: Odds ratio.\nFurther analysis showed that the volume of saline used for irrigation[20] (P < 0.001) was an important factor determining CBD clearance (Supplementary Table 1).\nWe used IR spectroscopy to analyze the components of stones in the CBD of all patients. IR spectroscopy analysis showed that the proportion of pigment-based stones was significantly higher in patients with PAD (93.3% vs 50.0%, P = 0.04), and these stones were mostly soft stones (77.4% vs 22.6%, P = 0.01) (Table 4).\nCorrelation between the composition of stones and variables\nCBD: Common bile duct; PAD: Periampullary diverticula.","Fluoroscopic cholangiographic imagery is currently the main method to determine the successful clearance of CBD stones[20]. However, studies involving the use of IDUS have identified residual biliary sludge within the bile duct despite the absence of filling defects on cholangiography. Mechanical lithotripsy produces a large number of stone fragments, and these minor residual CBD stones may lead to recurrent stone formation[21].\nIt has been reported that residual small CBD stone fragments can be flushed out of the bile duct using saline irrigation with a balloon catheter with a side hole[15]. A study reported that an average of 48 mL of saline could remove residual stones[16]. However, another study found that at least 100 mL of saline was needed to reduce residual stones[17]. Therefore, the effectiveness of saline irrigation on the clearance of residual bile duct stones after lithotripsy is not clear. This study's purpose differs from previous ones in that all of the patients had large stones and were post lithotripsy. In this study, we found that after ERCP and an additional round of lithotripsy were performed to remove CBD stones, POC using the SpyGlass DS showed that only 15% of the patients were relatively cleared of bile duct stones despite a negative occlusion cholangiogram. After irrigation with 50 mL of normal saline, 60% of the patients had relatively cleared bile ducts. After a total of 100 mL saline irrigation, 94% of the patients achieved complete clearance of the bile duct. The results showed that irrigation with 50 mL of saline was not enough to clear the bile duct of residual stones/sludge.\nIn this series, all patients received mechanical lithotripsy for stone fragmentation, which generates substantial stone fragments/debris, thus increasing the difficulty of clearing the bile duct. Our results showed that a good CBD clearance rate could be achieved with a larger saline irrigation volume. The clearance rate of bile duct stones could be higher for those without lithotripsy.\nResidual stones have been found in approximately 1/3 of cases after stone retrieval using endoscopic ultrasound (EUS)[22,23]. In addition to stone detection, the EUS approach might also offer an alternative for treating bile duct stones. However, the use of EUS is very challenging[24-27]. IDUS has also been used to detect small residual stones in 14/59 patients (23.7%) with residual stones[11]. However, the ultrasonic probe is costly and can be easily damaged. In addition, the method is highly operator dependent and often produces poor quality images, and the presence of air in the bile duct can affect the detection of residual stones. Many studies have shown that POC has a high sensitivity in the detection of residual CBD stones, ranging from 25.3% to 34%, where residual stones are missed by standard cholangiography[8,9,28].\nIn our study, we used the SpyGlass DS system to determine the CBD clearance score. The SpyScope has a 4-way tip deflection and is much smaller (10 French) than conventional choledochoscopes. More importantly, it has a separate and independent irrigation channel that allows intermittent or continuous irrigation of the biliary system. It also offers a direct examination of the bile duct lumen and is more accurate in detecting and diagnosing residual bile duct stones/sludge.\nResidual CBD stones can lead to recurrent stone formation. Itoi et al[7] reported that PAD and intraoperative lithotripsy were closely related to residual stones (P < 0.05), as we observed in our study. We also found that a dilated CBD with a diameter > 15 mm was an independent risk factor for failed CBD clearance and a large number of residual stones. Despite irrigation with 100 mL saline, PAD and a dilated CBD remained independent risk factors for incomplete bile duct clearance. This may be due to the presence of an air-filled duodenum/diverticulum that compresses the distal CBD, making it difficult to wash away the residual stones/sludge[29,30]. CBD clearance can be improved by increasing the volume of saline irrigation.\nOur results showed that PAD not only increased the difficulty of bile duct clearance but can also affect stone composition. We found that pigment-based stones constituted the majority of CBD stones in our patients with PAD (P = 0.004). Song et al[31] reported that recurrent bile duct stones were more likely to be brown pigmented stones than cholesterol-based stones. This may be because pigment-based stones are often related to bacterial infection, and the formation of biliary sludge will contribute to recurrent stone formation over time[32-34]. The brown pigmented stones that form as a result of bacterial infection are soft and easy to fragment and generate abundant debris. Our experience showed that soft stones/sludge require more saline irrigation to clear the bile duct. In this study, 26% of cases with biliary sludge were found even with 100 mL saline irrigation. If an effective bile duct cleaning device is used to remove biliary sludge, it may reduce the CBD stone recurrence rate after ERCP.\nWithout adequate biliary drainage, saline irrigation can increase the intraductal pressure, causing cholangitis because of contaminated bile[35]. Proper drainage methods can mitigate this stress[36,37]. No serious adverse events were reported in our study except for 10% of patients with cholangitis secondary possibly to saline irrigation. Intermittent irrigation and endoscopic suction to promote biliary drainage may lower the risk in addition to antibiotic coverage. Differences between this study and previous studies include the following: We studied the effect of flushing with saline after lithotripsy; our evaluation method was different from that of previous studies (we used SpyGlass DS examination); and we performed a component analysis related to diverticula. Our study has several methodological advantages. First, it was a prospective study using a self-controlled design, and SpyGlass DS was used to assess the presence of residual stones in the bile ducts. All of the patients showed no stones by imaging even though small residual bile duct stones were observed by SpyGlass DS, indicating that the evaluation of residual bile duct stones using SpyGlass DS is more sensitive than imaging alone. If we observe that there are still stones, it would be unethical not to continue flushing after using 50 mL of saline. Second, this study used objective definitions to assess residual stones identified by POC. Third, this study included objective stone composition analysis using IR spectroscopy.\nThe study has limitations. This study was not a randomized controlled trial. The use of direct cholangioscopy by the SpyGlass DS increases the procedure time with additional cost. Therefore, we do not recommend routine use of SpyGlass DS to rule out residual stones.","In conclusion, in patients with large CBD stones undergoing mechanical lithotripsy, routine irrigation of the bile duct with at least 100 mL of saline is recommended to improve CBD clearance, especially for patients with a dilated bile duct and/or those with PAD. Saline irrigation is simple, inexpensive, and easy to perform to improve bile duct clearance, and it may avoid recurrent stone formation."],"string":"[\n \"Endoscopic retrograde cholangiopancreatography (ERCP) is an effective and relatively minimally invasive technique for common bile duct (CBD) stones[1-3]. It has been reported that the recurrence rate of CBD stones after ERCP has increased from 4% to 24%[4-6]. The incidence of residual stones after mechanical lithotripsy for intractable CBD stones is 24% to 40%[7-10]. A growing number of studies suggest that an important reason for the recurrence of bile duct stones is the presence of stone debris after lithotripsy[11-13]. During ERCP, occluded cholangiography (OC) is often performed after stone removal to determine whether the stone is removed completely, but OC lacks accuracy. Even if no obvious stones are found on cholangiography, the presence of contrast can obscure small stone debris in the bile duct[8,14]. Complete bile duct clearance is necessary to decrease recurrent bile duct stones.\\nSome studies[15-17] reported that irrigation of the bile duct with saline after stone extraction further improves the clearance of the bile duct and has the advantages of being a simple, low-cost procedure with rare complications. Ang et al[16] showed that a mean of 48 mL of saline solution could irrigate and flush out residual stones after the endoscopic removal of CBD stones. Ahn et al[17] found that irrigation with 100 mL of saline can flush out residual stone fragments from the bile duct into the duodenum after stone extraction. However, intraductal ultrasound (IDUS) has a high sensitivity and accuracy in diagnosing bile duct stones/debris[11,16]. This modality yields only indirect images of the debris.\\nThe lack of direct evidence to support the efficacy of saline irrigation after lithotripsy prompted us to use peroral cholangioscopy (POC) to examine the bile duct and detect any residual stones/debris. The results of no irrigation after stone extraction were confirmed by OC and were compared to the effectiveness of irrigation with 50 mL or 100 mL saline. To evaluate whether irrigation with 100 mL of saline is more effective in achieving complete clearance of the bile duct after mechanical lithotripsy, we conducted this prospective self-controlled study.\",\n \"Study design and participants Between October 2018 and January 2020, 47 patients with CBD stones were enrolled in a prospective clinical trial conducted in the surgical endoscopy center of The First Hospital of Lanzhou University (Figure 1). All eligible patients or their legal representatives gave informed consent before the treatment. The inclusion criteria were patients with CBD stones undergoing ERCP, able to provide informed consent, with a stone size larger than 1.2 cm and requiring mechanical lithotripsy for stone removal. The exclusion criteria included pre-ERCP acute suppurative cholangitis, acute pancreatitis, gastrointestinal (GI) tract hemorrhage and/or perforation, previous history of ERCP, prior Bilroth II gastrectomy and Roux-en-Y or cholangiojejunostomy, pregnancy or breastfeeding, coagulopathy with international normalized ratio > 1.5 and low platelet count (< 50 × 109/L) or active use of anticoagulation drugs, severe liver disease including decompensated liver cirrhosis or liver failure, septic shock, biliary-duodenal fistula confirmed before ERCP cannulation, the presence of intrahepatic duct stones and malignancy, and patient unwillingness or inability to give informed consent.\\n\\nFlow chart of the self-controlled study. CBD: Common bile duct; ERCP: Endoscopic retrograde cholangiopancreatography; PTCD: Percutaneous transhepatic cholangiodrainage.\\nTo assess the number of residual stones, we created a CBD clearance score as follows: (1) A large number of stone fragments and biliary sludge; (2) A moderate number of stone fragments and biliary sludge; (3) A small number of stone fragments and biliary sludge; (4) Presence of biliary sludge without any stones; and (5) Completely cleared CBD without any biliary sludge. The scores were determined by two endoscopists independently (Figure 2).\\n\\nSpyGlass DS images and simulation graphs of the residual stone. A-E: SpyGlass DS images (A1-E1) and simulation graphs (A2-E2). Score 1: A large amount of stone fragments and biliary sludge. Score 2: A moderate amount of stone fragments and biliary sludge. Score 3: A small amount of stone fragments and biliary sludge. Score 4: Presence of biliary sludge without any stones. Score 5: Completely cleared common bile duct without any biliary sludge.\\nThe study was approved by the ethics committee of The First Hospital of Lanzhou University (No. LDYYMENG2018-1028) and conducted in accordance with the ethical principles of the Declaration of Helsinki. All authors had access to the study data and reviewed and approved the final manuscript.\\nBetween October 2018 and January 2020, 47 patients with CBD stones were enrolled in a prospective clinical trial conducted in the surgical endoscopy center of The First Hospital of Lanzhou University (Figure 1). All eligible patients or their legal representatives gave informed consent before the treatment. The inclusion criteria were patients with CBD stones undergoing ERCP, able to provide informed consent, with a stone size larger than 1.2 cm and requiring mechanical lithotripsy for stone removal. The exclusion criteria included pre-ERCP acute suppurative cholangitis, acute pancreatitis, gastrointestinal (GI) tract hemorrhage and/or perforation, previous history of ERCP, prior Bilroth II gastrectomy and Roux-en-Y or cholangiojejunostomy, pregnancy or breastfeeding, coagulopathy with international normalized ratio > 1.5 and low platelet count (< 50 × 109/L) or active use of anticoagulation drugs, severe liver disease including decompensated liver cirrhosis or liver failure, septic shock, biliary-duodenal fistula confirmed before ERCP cannulation, the presence of intrahepatic duct stones and malignancy, and patient unwillingness or inability to give informed consent.\\n\\nFlow chart of the self-controlled study. CBD: Common bile duct; ERCP: Endoscopic retrograde cholangiopancreatography; PTCD: Percutaneous transhepatic cholangiodrainage.\\nTo assess the number of residual stones, we created a CBD clearance score as follows: (1) A large number of stone fragments and biliary sludge; (2) A moderate number of stone fragments and biliary sludge; (3) A small number of stone fragments and biliary sludge; (4) Presence of biliary sludge without any stones; and (5) Completely cleared CBD without any biliary sludge. The scores were determined by two endoscopists independently (Figure 2).\\n\\nSpyGlass DS images and simulation graphs of the residual stone. A-E: SpyGlass DS images (A1-E1) and simulation graphs (A2-E2). Score 1: A large amount of stone fragments and biliary sludge. Score 2: A moderate amount of stone fragments and biliary sludge. Score 3: A small amount of stone fragments and biliary sludge. Score 4: Presence of biliary sludge without any stones. Score 5: Completely cleared common bile duct without any biliary sludge.\\nThe study was approved by the ethics committee of The First Hospital of Lanzhou University (No. LDYYMENG2018-1028) and conducted in accordance with the ethical principles of the Declaration of Helsinki. All authors had access to the study data and reviewed and approved the final manuscript.\\nProcedures All endoscopic procedures were performed by two experienced endoscopists (each has performed > 1000 ERCPs). Routine prophylactic antibiotics were given in all cases. ERCP was performed with a standard duodenoscope (TJF-260V, Olympus, Tokyo, Japan). The patients received propofol anesthesia without tracheal intubation.\\nDuring ERCP, cannulation was performed with a wire-guided sphincterotome. After successful cannulation, contrast was injected to determine the stone size (more or less than 12 mm) and the need for mechanical lithotripsy. All patients underwent a small sphincterotomy with an average length of 3-5 mm using the ENDO CUT mode (power setting 90-120 W; Erbe Elektromedizin, Tuebingen, Germany) followed by balloon sphincteroplasty using a controlled radial expansion (CRE) balloon (10-12 mm in diameter; Boston Scientific, Cork, Ireland). Lithotripsy was performed using an endoscopic lithotripter-compatible basket (Boston Scientific, Marlborough, MA, United States), and stones were removed using the basket and a balloon catheter. Samples of CBD stones were collected using an EndoRetrieval bag (Micro-Tech, Nanjing, China) and placed in a container for subsequent analysis. Occlusion cholangiography was performed by injecting contrast with a balloon catheter. Fluoroscopic assessment using a C-arm X-ray (OEC 9900 Elite, Salt Lake City, Utah, United States) was applied to confirm complete removal of CBD stones and the existence of stone residuals was determined by both endoscopist and radiologist. Confirmed stone residues by applying occlusion cholangiography were then followed by repeated extraction attempts. If complete stone removal was confirmed, a CBD clearance score was generated by further applying SpyGlass DS (Boston Scientific Corporation, Marlborough, Massachusetts, United States) examination. If the CBD clearance score was less than 5, the bile duct was irrigated intermittently with 50 mL of normal saline using a basket. The basket was shaken during irrigation with slight suction applied to the duodenoscope to promote drainage. The bile duct was then examined again using the SpyGlass DS to detect any residual stones/sludge and document the clearance score. If the clearance score was still less than 5, repeat irrigation of the CBD was performed using another 50 mL of normal saline. The final CBD clearance score was obtained one more time using SpyGlass DS examination. CBD stones that were not irrigated out after double 50 mL-saline irrigations continued to be irrigated with saline until they were fully cleared (Figure 3). Endoscopic nasobiliary drainage (ENBD) or biliary stenting was performed if necessary. The CBD clearance score was assessed by two blinded endoscopists who were masked to the treatment.\\n\\nProtocol of evaluation and irrigation procedures. CBD: Common bile duct; ERCP: Endoscopic retrograde cholangiopancreatography; POC: Peroral cholangioscopy.\\nThe composition of the removed CBD stones was analyzed using infrared ray (IR) spectroscopy. One milligram (mg) of stone samples was mixed with 150 mg of potassium bromide in a mortar and ground into powder. The stone mixture was pressed into a mold and then placed into an automatic IR spectrum analyzer for automated detection to determine the IR spectrogram of the CBD stones. The IR spectrogram showed characteristic absorption peaks at a wavelength of 1460 cm-1, indicating cholesterol-based stones, and those at a wavelength of 1680 cm-1 indicated pigment-based stones.\\nAll endoscopic procedures were performed by two experienced endoscopists (each has performed > 1000 ERCPs). Routine prophylactic antibiotics were given in all cases. ERCP was performed with a standard duodenoscope (TJF-260V, Olympus, Tokyo, Japan). The patients received propofol anesthesia without tracheal intubation.\\nDuring ERCP, cannulation was performed with a wire-guided sphincterotome. After successful cannulation, contrast was injected to determine the stone size (more or less than 12 mm) and the need for mechanical lithotripsy. All patients underwent a small sphincterotomy with an average length of 3-5 mm using the ENDO CUT mode (power setting 90-120 W; Erbe Elektromedizin, Tuebingen, Germany) followed by balloon sphincteroplasty using a controlled radial expansion (CRE) balloon (10-12 mm in diameter; Boston Scientific, Cork, Ireland). Lithotripsy was performed using an endoscopic lithotripter-compatible basket (Boston Scientific, Marlborough, MA, United States), and stones were removed using the basket and a balloon catheter. Samples of CBD stones were collected using an EndoRetrieval bag (Micro-Tech, Nanjing, China) and placed in a container for subsequent analysis. Occlusion cholangiography was performed by injecting contrast with a balloon catheter. Fluoroscopic assessment using a C-arm X-ray (OEC 9900 Elite, Salt Lake City, Utah, United States) was applied to confirm complete removal of CBD stones and the existence of stone residuals was determined by both endoscopist and radiologist. Confirmed stone residues by applying occlusion cholangiography were then followed by repeated extraction attempts. If complete stone removal was confirmed, a CBD clearance score was generated by further applying SpyGlass DS (Boston Scientific Corporation, Marlborough, Massachusetts, United States) examination. If the CBD clearance score was less than 5, the bile duct was irrigated intermittently with 50 mL of normal saline using a basket. The basket was shaken during irrigation with slight suction applied to the duodenoscope to promote drainage. The bile duct was then examined again using the SpyGlass DS to detect any residual stones/sludge and document the clearance score. If the clearance score was still less than 5, repeat irrigation of the CBD was performed using another 50 mL of normal saline. The final CBD clearance score was obtained one more time using SpyGlass DS examination. CBD stones that were not irrigated out after double 50 mL-saline irrigations continued to be irrigated with saline until they were fully cleared (Figure 3). Endoscopic nasobiliary drainage (ENBD) or biliary stenting was performed if necessary. The CBD clearance score was assessed by two blinded endoscopists who were masked to the treatment.\\n\\nProtocol of evaluation and irrigation procedures. CBD: Common bile duct; ERCP: Endoscopic retrograde cholangiopancreatography; POC: Peroral cholangioscopy.\\nThe composition of the removed CBD stones was analyzed using infrared ray (IR) spectroscopy. One milligram (mg) of stone samples was mixed with 150 mg of potassium bromide in a mortar and ground into powder. The stone mixture was pressed into a mold and then placed into an automatic IR spectrum analyzer for automated detection to determine the IR spectrogram of the CBD stones. The IR spectrogram showed characteristic absorption peaks at a wavelength of 1460 cm-1, indicating cholesterol-based stones, and those at a wavelength of 1680 cm-1 indicated pigment-based stones.\\nDefinition for complications Most of the post-ERCP adverse events were detected within 24 h after the procedure. We routinely assessed these adverse events, including cholangitis, oozing/bleeding, pancreatitis, cholecystitis, and perforation, at 24 and 48 h after the ERCP procedure by symptoms, signs, laboratory tests, and imaging examinations if necessary[18,19]. Post-ERCP adverse events were defined as follows: (1) The diagnosis of acute cholangitis was based on Tokyo Guidelines 2018/2013 (TG18/TG13) diagnostic criteria[20]; (2) Oozing was defined as bleeding that was slight and stopped spontaneously; (3) Pancreatitis was described as new or worsening abdominal pain along with an increase in serum amylase levels (> 3 × higher than normal upper limit measured 24 h after surgery); (4) Cholecystitis was defined as pain in the epigastrium or RUQ accompanied by a positive Murphy sign, and abdominal ultrasonography showing a thickened gallbladder wall; and (5) Perforation was defined as sudden abdominal pain accompanied by retroperitoneal air and fluid.\\nMechanical lithotripsy was performed by the same assistant for all patients. Stones that were fragmented successfully with only one attempt were defined as soft stones. Stones that required multiple fragmentation attempts or needed to be broken again were defined as hard stones.\\nMost of the post-ERCP adverse events were detected within 24 h after the procedure. We routinely assessed these adverse events, including cholangitis, oozing/bleeding, pancreatitis, cholecystitis, and perforation, at 24 and 48 h after the ERCP procedure by symptoms, signs, laboratory tests, and imaging examinations if necessary[18,19]. Post-ERCP adverse events were defined as follows: (1) The diagnosis of acute cholangitis was based on Tokyo Guidelines 2018/2013 (TG18/TG13) diagnostic criteria[20]; (2) Oozing was defined as bleeding that was slight and stopped spontaneously; (3) Pancreatitis was described as new or worsening abdominal pain along with an increase in serum amylase levels (> 3 × higher than normal upper limit measured 24 h after surgery); (4) Cholecystitis was defined as pain in the epigastrium or RUQ accompanied by a positive Murphy sign, and abdominal ultrasonography showing a thickened gallbladder wall; and (5) Perforation was defined as sudden abdominal pain accompanied by retroperitoneal air and fluid.\\nMechanical lithotripsy was performed by the same assistant for all patients. Stones that were fragmented successfully with only one attempt were defined as soft stones. Stones that required multiple fragmentation attempts or needed to be broken again were defined as hard stones.\\nStatistical analysis The sample size calculation was based on the rate of residual stone clearance. According to a previous study[16] and our prior experience, we assumed a success rate of 84.5% for endoscopic extraction of CBD stones and an increase of up to 97% with saline irrigation. We estimated that 47 patients were needed in this analysis to obtain a power of 80% at the 5% level.\\nCategorical variables are expressed as numbers or percentages (%). Continuous variables are presented as the mean ± SD or median and interquartile range, as appropriate. Continuous variables with a normal distribution were analyzed by paired t-test or signed-rank test. Categorical variables were analyzed using the chi-square test or Fisher’s exact test. Logistic regression was used to predict risk factors for complications, and the results are presented as odds ratios (ORs) with 95% confidence intervals (Cis). Variables with a P value of < 0.2 in the univariate analysis were included in the multivariate analysis. Linear mixed-effects models were conducted to assess the effect of saline irrigation on the clearance score with a random intercept for each patient and an unstructured covariance structure. A two-sided P value of less than 0.05 was considered statistically significant. The analyses were conducted with statistical software (SAS version 9.4, SAS Institute, Inc., NC, United States).\\nThe sample size calculation was based on the rate of residual stone clearance. According to a previous study[16] and our prior experience, we assumed a success rate of 84.5% for endoscopic extraction of CBD stones and an increase of up to 97% with saline irrigation. We estimated that 47 patients were needed in this analysis to obtain a power of 80% at the 5% level.\\nCategorical variables are expressed as numbers or percentages (%). Continuous variables are presented as the mean ± SD or median and interquartile range, as appropriate. Continuous variables with a normal distribution were analyzed by paired t-test or signed-rank test. Categorical variables were analyzed using the chi-square test or Fisher’s exact test. Logistic regression was used to predict risk factors for complications, and the results are presented as odds ratios (ORs) with 95% confidence intervals (Cis). Variables with a P value of < 0.2 in the univariate analysis were included in the multivariate analysis. Linear mixed-effects models were conducted to assess the effect of saline irrigation on the clearance score with a random intercept for each patient and an unstructured covariance structure. A two-sided P value of less than 0.05 was considered statistically significant. The analyses were conducted with statistical software (SAS version 9.4, SAS Institute, Inc., NC, United States).\",\n \"Between October 2018 and January 2020, 47 patients with CBD stones were enrolled in a prospective clinical trial conducted in the surgical endoscopy center of The First Hospital of Lanzhou University (Figure 1). All eligible patients or their legal representatives gave informed consent before the treatment. The inclusion criteria were patients with CBD stones undergoing ERCP, able to provide informed consent, with a stone size larger than 1.2 cm and requiring mechanical lithotripsy for stone removal. The exclusion criteria included pre-ERCP acute suppurative cholangitis, acute pancreatitis, gastrointestinal (GI) tract hemorrhage and/or perforation, previous history of ERCP, prior Bilroth II gastrectomy and Roux-en-Y or cholangiojejunostomy, pregnancy or breastfeeding, coagulopathy with international normalized ratio > 1.5 and low platelet count (< 50 × 109/L) or active use of anticoagulation drugs, severe liver disease including decompensated liver cirrhosis or liver failure, septic shock, biliary-duodenal fistula confirmed before ERCP cannulation, the presence of intrahepatic duct stones and malignancy, and patient unwillingness or inability to give informed consent.\\n\\nFlow chart of the self-controlled study. CBD: Common bile duct; ERCP: Endoscopic retrograde cholangiopancreatography; PTCD: Percutaneous transhepatic cholangiodrainage.\\nTo assess the number of residual stones, we created a CBD clearance score as follows: (1) A large number of stone fragments and biliary sludge; (2) A moderate number of stone fragments and biliary sludge; (3) A small number of stone fragments and biliary sludge; (4) Presence of biliary sludge without any stones; and (5) Completely cleared CBD without any biliary sludge. The scores were determined by two endoscopists independently (Figure 2).\\n\\nSpyGlass DS images and simulation graphs of the residual stone. A-E: SpyGlass DS images (A1-E1) and simulation graphs (A2-E2). Score 1: A large amount of stone fragments and biliary sludge. Score 2: A moderate amount of stone fragments and biliary sludge. Score 3: A small amount of stone fragments and biliary sludge. Score 4: Presence of biliary sludge without any stones. Score 5: Completely cleared common bile duct without any biliary sludge.\\nThe study was approved by the ethics committee of The First Hospital of Lanzhou University (No. LDYYMENG2018-1028) and conducted in accordance with the ethical principles of the Declaration of Helsinki. All authors had access to the study data and reviewed and approved the final manuscript.\",\n \"All endoscopic procedures were performed by two experienced endoscopists (each has performed > 1000 ERCPs). Routine prophylactic antibiotics were given in all cases. ERCP was performed with a standard duodenoscope (TJF-260V, Olympus, Tokyo, Japan). The patients received propofol anesthesia without tracheal intubation.\\nDuring ERCP, cannulation was performed with a wire-guided sphincterotome. After successful cannulation, contrast was injected to determine the stone size (more or less than 12 mm) and the need for mechanical lithotripsy. All patients underwent a small sphincterotomy with an average length of 3-5 mm using the ENDO CUT mode (power setting 90-120 W; Erbe Elektromedizin, Tuebingen, Germany) followed by balloon sphincteroplasty using a controlled radial expansion (CRE) balloon (10-12 mm in diameter; Boston Scientific, Cork, Ireland). Lithotripsy was performed using an endoscopic lithotripter-compatible basket (Boston Scientific, Marlborough, MA, United States), and stones were removed using the basket and a balloon catheter. Samples of CBD stones were collected using an EndoRetrieval bag (Micro-Tech, Nanjing, China) and placed in a container for subsequent analysis. Occlusion cholangiography was performed by injecting contrast with a balloon catheter. Fluoroscopic assessment using a C-arm X-ray (OEC 9900 Elite, Salt Lake City, Utah, United States) was applied to confirm complete removal of CBD stones and the existence of stone residuals was determined by both endoscopist and radiologist. Confirmed stone residues by applying occlusion cholangiography were then followed by repeated extraction attempts. If complete stone removal was confirmed, a CBD clearance score was generated by further applying SpyGlass DS (Boston Scientific Corporation, Marlborough, Massachusetts, United States) examination. If the CBD clearance score was less than 5, the bile duct was irrigated intermittently with 50 mL of normal saline using a basket. The basket was shaken during irrigation with slight suction applied to the duodenoscope to promote drainage. The bile duct was then examined again using the SpyGlass DS to detect any residual stones/sludge and document the clearance score. If the clearance score was still less than 5, repeat irrigation of the CBD was performed using another 50 mL of normal saline. The final CBD clearance score was obtained one more time using SpyGlass DS examination. CBD stones that were not irrigated out after double 50 mL-saline irrigations continued to be irrigated with saline until they were fully cleared (Figure 3). Endoscopic nasobiliary drainage (ENBD) or biliary stenting was performed if necessary. The CBD clearance score was assessed by two blinded endoscopists who were masked to the treatment.\\n\\nProtocol of evaluation and irrigation procedures. CBD: Common bile duct; ERCP: Endoscopic retrograde cholangiopancreatography; POC: Peroral cholangioscopy.\\nThe composition of the removed CBD stones was analyzed using infrared ray (IR) spectroscopy. One milligram (mg) of stone samples was mixed with 150 mg of potassium bromide in a mortar and ground into powder. The stone mixture was pressed into a mold and then placed into an automatic IR spectrum analyzer for automated detection to determine the IR spectrogram of the CBD stones. The IR spectrogram showed characteristic absorption peaks at a wavelength of 1460 cm-1, indicating cholesterol-based stones, and those at a wavelength of 1680 cm-1 indicated pigment-based stones.\",\n \"Most of the post-ERCP adverse events were detected within 24 h after the procedure. We routinely assessed these adverse events, including cholangitis, oozing/bleeding, pancreatitis, cholecystitis, and perforation, at 24 and 48 h after the ERCP procedure by symptoms, signs, laboratory tests, and imaging examinations if necessary[18,19]. Post-ERCP adverse events were defined as follows: (1) The diagnosis of acute cholangitis was based on Tokyo Guidelines 2018/2013 (TG18/TG13) diagnostic criteria[20]; (2) Oozing was defined as bleeding that was slight and stopped spontaneously; (3) Pancreatitis was described as new or worsening abdominal pain along with an increase in serum amylase levels (> 3 × higher than normal upper limit measured 24 h after surgery); (4) Cholecystitis was defined as pain in the epigastrium or RUQ accompanied by a positive Murphy sign, and abdominal ultrasonography showing a thickened gallbladder wall; and (5) Perforation was defined as sudden abdominal pain accompanied by retroperitoneal air and fluid.\\nMechanical lithotripsy was performed by the same assistant for all patients. Stones that were fragmented successfully with only one attempt were defined as soft stones. Stones that required multiple fragmentation attempts or needed to be broken again were defined as hard stones.\",\n \"The sample size calculation was based on the rate of residual stone clearance. According to a previous study[16] and our prior experience, we assumed a success rate of 84.5% for endoscopic extraction of CBD stones and an increase of up to 97% with saline irrigation. We estimated that 47 patients were needed in this analysis to obtain a power of 80% at the 5% level.\\nCategorical variables are expressed as numbers or percentages (%). Continuous variables are presented as the mean ± SD or median and interquartile range, as appropriate. Continuous variables with a normal distribution were analyzed by paired t-test or signed-rank test. Categorical variables were analyzed using the chi-square test or Fisher’s exact test. Logistic regression was used to predict risk factors for complications, and the results are presented as odds ratios (ORs) with 95% confidence intervals (Cis). Variables with a P value of < 0.2 in the univariate analysis were included in the multivariate analysis. Linear mixed-effects models were conducted to assess the effect of saline irrigation on the clearance score with a random intercept for each patient and an unstructured covariance structure. A two-sided P value of less than 0.05 was considered statistically significant. The analyses were conducted with statistical software (SAS version 9.4, SAS Institute, Inc., NC, United States).\",\n \"The patients' average age was 61 ± 16.5 years, and 23 (48.9%) were male. Comorbidities included coronary disease in three (6.4%) patients, hypertension in 14 (29.8%), diabetes in two (4.3%), liver cirrhosis in three (2.1%), and portal hypertension in one (6.4%). The stone analysis showed that 17 (36.2%) cases had cholesterol-based stones, and 30 (63.8%) had pigment-based stones.\\nProcedure-related adverse events occurred in 11 (23.4%) of 47 patients, with cholangitis in four patients, bleeding in two, pancreatitis in four, and cholecystitis in one. No mortality or perforation occurred. The mean time for ERCP was 40.3 ± 15.4 min (Table 1).\\nClinical and procedural characteristics of the patients\\nData are expressed as the mean ± SD, median (interquartile range) or n (%). ENBD: Endoscopic nasobiliary drainage; CBD: Common bile duct.\\nAfter endoscopic stone extraction, occlusion cholangiography showed that there were no residual CBD stones. Seven patients had a clearance score of 4 identified by POC with the SpyGlass DS, and no patients had a score of 5 before saline irrigation. After irrigation with 50 mL saline, CBD clearance improved to 4 in 28 patients, but none achieved a score of 5. After a total of 100 mL of saline irrigation, there was further improvement in the clearance scores: 12 (26%) patients had a score of 4, and 32 (68%) had a score of 5 identified by the SpyGlass DS. The respective CBD stone clearance rates for the control (no irrigation), 50 mL saline irrigation, and 100 mL saline irrigation were 15%, 60%, and 94%, respectively, and the difference among them was statistically significant (P < 0.001) (Table 2). The CBD clearance score (mean ± SD) was 2.4 ± 1.1 in the control, 3.5 ± 0.7 in the 50 mL saline irrigation, and 4.6 ± 0.6 in the 100 mL saline irrigation (P < 0.001) (Supplementary Figure 1).\\nCommon bile duct clear score and stone clearance rate before and after saline irrigation\\nAfter ERCP, 13 patients had a score of 1 (a large amount of residual stone/sludge) before saline irrigation. Only 15 patients did not reach a score of 5 (complete clearance) after 100 mL of saline irrigation. A dilated CBD diameter > 15 mm (OR = 4.93, 95%CI: 1.13-21.57; P = 0.034) was an independent risk factor for significant residual CBD stones (score = 1). Multivariate analysis revealed that CBD diameter > 15 mm (OR = 0.08, 95%CI: 0.01-0.49; P = 0.007) and the presence of periampullary diverticula (PAD) (OR = 6.51, 95%CI: 1.08-39.21; P = 0.041) were independent risk factors for failed CBD clearance despite irrigation with 100 mL saline (Table 3).\\nBiliary cleanliness before and after saline irrigation, assessed by logistic regression analysis\\nN: Total number; CBD: Common bile duct; PAD: Periampullary diverticula; CI: Confidence interval; OR: Odds ratio.\\nFurther analysis showed that the volume of saline used for irrigation[20] (P < 0.001) was an important factor determining CBD clearance (Supplementary Table 1).\\nWe used IR spectroscopy to analyze the components of stones in the CBD of all patients. IR spectroscopy analysis showed that the proportion of pigment-based stones was significantly higher in patients with PAD (93.3% vs 50.0%, P = 0.04), and these stones were mostly soft stones (77.4% vs 22.6%, P = 0.01) (Table 4).\\nCorrelation between the composition of stones and variables\\nCBD: Common bile duct; PAD: Periampullary diverticula.\",\n \"Fluoroscopic cholangiographic imagery is currently the main method to determine the successful clearance of CBD stones[20]. However, studies involving the use of IDUS have identified residual biliary sludge within the bile duct despite the absence of filling defects on cholangiography. Mechanical lithotripsy produces a large number of stone fragments, and these minor residual CBD stones may lead to recurrent stone formation[21].\\nIt has been reported that residual small CBD stone fragments can be flushed out of the bile duct using saline irrigation with a balloon catheter with a side hole[15]. A study reported that an average of 48 mL of saline could remove residual stones[16]. However, another study found that at least 100 mL of saline was needed to reduce residual stones[17]. Therefore, the effectiveness of saline irrigation on the clearance of residual bile duct stones after lithotripsy is not clear. This study's purpose differs from previous ones in that all of the patients had large stones and were post lithotripsy. In this study, we found that after ERCP and an additional round of lithotripsy were performed to remove CBD stones, POC using the SpyGlass DS showed that only 15% of the patients were relatively cleared of bile duct stones despite a negative occlusion cholangiogram. After irrigation with 50 mL of normal saline, 60% of the patients had relatively cleared bile ducts. After a total of 100 mL saline irrigation, 94% of the patients achieved complete clearance of the bile duct. The results showed that irrigation with 50 mL of saline was not enough to clear the bile duct of residual stones/sludge.\\nIn this series, all patients received mechanical lithotripsy for stone fragmentation, which generates substantial stone fragments/debris, thus increasing the difficulty of clearing the bile duct. Our results showed that a good CBD clearance rate could be achieved with a larger saline irrigation volume. The clearance rate of bile duct stones could be higher for those without lithotripsy.\\nResidual stones have been found in approximately 1/3 of cases after stone retrieval using endoscopic ultrasound (EUS)[22,23]. In addition to stone detection, the EUS approach might also offer an alternative for treating bile duct stones. However, the use of EUS is very challenging[24-27]. IDUS has also been used to detect small residual stones in 14/59 patients (23.7%) with residual stones[11]. However, the ultrasonic probe is costly and can be easily damaged. In addition, the method is highly operator dependent and often produces poor quality images, and the presence of air in the bile duct can affect the detection of residual stones. Many studies have shown that POC has a high sensitivity in the detection of residual CBD stones, ranging from 25.3% to 34%, where residual stones are missed by standard cholangiography[8,9,28].\\nIn our study, we used the SpyGlass DS system to determine the CBD clearance score. The SpyScope has a 4-way tip deflection and is much smaller (10 French) than conventional choledochoscopes. More importantly, it has a separate and independent irrigation channel that allows intermittent or continuous irrigation of the biliary system. It also offers a direct examination of the bile duct lumen and is more accurate in detecting and diagnosing residual bile duct stones/sludge.\\nResidual CBD stones can lead to recurrent stone formation. Itoi et al[7] reported that PAD and intraoperative lithotripsy were closely related to residual stones (P < 0.05), as we observed in our study. We also found that a dilated CBD with a diameter > 15 mm was an independent risk factor for failed CBD clearance and a large number of residual stones. Despite irrigation with 100 mL saline, PAD and a dilated CBD remained independent risk factors for incomplete bile duct clearance. This may be due to the presence of an air-filled duodenum/diverticulum that compresses the distal CBD, making it difficult to wash away the residual stones/sludge[29,30]. CBD clearance can be improved by increasing the volume of saline irrigation.\\nOur results showed that PAD not only increased the difficulty of bile duct clearance but can also affect stone composition. We found that pigment-based stones constituted the majority of CBD stones in our patients with PAD (P = 0.004). Song et al[31] reported that recurrent bile duct stones were more likely to be brown pigmented stones than cholesterol-based stones. This may be because pigment-based stones are often related to bacterial infection, and the formation of biliary sludge will contribute to recurrent stone formation over time[32-34]. The brown pigmented stones that form as a result of bacterial infection are soft and easy to fragment and generate abundant debris. Our experience showed that soft stones/sludge require more saline irrigation to clear the bile duct. In this study, 26% of cases with biliary sludge were found even with 100 mL saline irrigation. If an effective bile duct cleaning device is used to remove biliary sludge, it may reduce the CBD stone recurrence rate after ERCP.\\nWithout adequate biliary drainage, saline irrigation can increase the intraductal pressure, causing cholangitis because of contaminated bile[35]. Proper drainage methods can mitigate this stress[36,37]. No serious adverse events were reported in our study except for 10% of patients with cholangitis secondary possibly to saline irrigation. Intermittent irrigation and endoscopic suction to promote biliary drainage may lower the risk in addition to antibiotic coverage. Differences between this study and previous studies include the following: We studied the effect of flushing with saline after lithotripsy; our evaluation method was different from that of previous studies (we used SpyGlass DS examination); and we performed a component analysis related to diverticula. Our study has several methodological advantages. First, it was a prospective study using a self-controlled design, and SpyGlass DS was used to assess the presence of residual stones in the bile ducts. All of the patients showed no stones by imaging even though small residual bile duct stones were observed by SpyGlass DS, indicating that the evaluation of residual bile duct stones using SpyGlass DS is more sensitive than imaging alone. If we observe that there are still stones, it would be unethical not to continue flushing after using 50 mL of saline. Second, this study used objective definitions to assess residual stones identified by POC. Third, this study included objective stone composition analysis using IR spectroscopy.\\nThe study has limitations. This study was not a randomized controlled trial. The use of direct cholangioscopy by the SpyGlass DS increases the procedure time with additional cost. Therefore, we do not recommend routine use of SpyGlass DS to rule out residual stones.\",\n \"In conclusion, in patients with large CBD stones undergoing mechanical lithotripsy, routine irrigation of the bile duct with at least 100 mL of saline is recommended to improve CBD clearance, especially for patients with a dilated bile duct and/or those with PAD. Saline irrigation is simple, inexpensive, and easy to perform to improve bile duct clearance, and it may avoid recurrent stone formation.\"\n]"},"all_sections_sec_types":{"kind":"list like","value":[null,"methods",null,null,null,null,null,null,null],"string":"[\n null,\n \"methods\",\n null,\n null,\n null,\n null,\n null,\n null,\n null\n]"},"keywords":{"kind":"list like","value":["Endoscopic retrograde cholangiopancreatography","Common bile duct gall stones","Peroral cholangioscopy","Saline irrigation","Periampullary diverticula","Prospective cohort study"],"string":"[\n \"Endoscopic retrograde cholangiopancreatography\",\n \"Common bile duct gall stones\",\n \"Peroral cholangioscopy\",\n \"Saline irrigation\",\n \"Periampullary diverticula\",\n \"Prospective cohort study\"\n]"},"whole_article_text":{"kind":"string","value":"INTRODUCTION:\nEndoscopic retrograde cholangiopancreatography (ERCP) is an effective and relatively minimally invasive technique for common bile duct (CBD) stones[1-3]. It has been reported that the recurrence rate of CBD stones after ERCP has increased from 4% to 24%[4-6]. The incidence of residual stones after mechanical lithotripsy for intractable CBD stones is 24% to 40%[7-10]. A growing number of studies suggest that an important reason for the recurrence of bile duct stones is the presence of stone debris after lithotripsy[11-13]. During ERCP, occluded cholangiography (OC) is often performed after stone removal to determine whether the stone is removed completely, but OC lacks accuracy. Even if no obvious stones are found on cholangiography, the presence of contrast can obscure small stone debris in the bile duct[8,14]. Complete bile duct clearance is necessary to decrease recurrent bile duct stones.\nSome studies[15-17] reported that irrigation of the bile duct with saline after stone extraction further improves the clearance of the bile duct and has the advantages of being a simple, low-cost procedure with rare complications. Ang et al[16] showed that a mean of 48 mL of saline solution could irrigate and flush out residual stones after the endoscopic removal of CBD stones. Ahn et al[17] found that irrigation with 100 mL of saline can flush out residual stone fragments from the bile duct into the duodenum after stone extraction. However, intraductal ultrasound (IDUS) has a high sensitivity and accuracy in diagnosing bile duct stones/debris[11,16]. This modality yields only indirect images of the debris.\nThe lack of direct evidence to support the efficacy of saline irrigation after lithotripsy prompted us to use peroral cholangioscopy (POC) to examine the bile duct and detect any residual stones/debris. The results of no irrigation after stone extraction were confirmed by OC and were compared to the effectiveness of irrigation with 50 mL or 100 mL saline. To evaluate whether irrigation with 100 mL of saline is more effective in achieving complete clearance of the bile duct after mechanical lithotripsy, we conducted this prospective self-controlled study.\n\nMATERIALS AND METHODS:\nStudy design and participants Between October 2018 and January 2020, 47 patients with CBD stones were enrolled in a prospective clinical trial conducted in the surgical endoscopy center of The First Hospital of Lanzhou University (Figure 1). All eligible patients or their legal representatives gave informed consent before the treatment. The inclusion criteria were patients with CBD stones undergoing ERCP, able to provide informed consent, with a stone size larger than 1.2 cm and requiring mechanical lithotripsy for stone removal. The exclusion criteria included pre-ERCP acute suppurative cholangitis, acute pancreatitis, gastrointestinal (GI) tract hemorrhage and/or perforation, previous history of ERCP, prior Bilroth II gastrectomy and Roux-en-Y or cholangiojejunostomy, pregnancy or breastfeeding, coagulopathy with international normalized ratio > 1.5 and low platelet count (< 50 × 109/L) or active use of anticoagulation drugs, severe liver disease including decompensated liver cirrhosis or liver failure, septic shock, biliary-duodenal fistula confirmed before ERCP cannulation, the presence of intrahepatic duct stones and malignancy, and patient unwillingness or inability to give informed consent.\n\nFlow chart of the self-controlled study. CBD: Common bile duct; ERCP: Endoscopic retrograde cholangiopancreatography; PTCD: Percutaneous transhepatic cholangiodrainage.\nTo assess the number of residual stones, we created a CBD clearance score as follows: (1) A large number of stone fragments and biliary sludge; (2) A moderate number of stone fragments and biliary sludge; (3) A small number of stone fragments and biliary sludge; (4) Presence of biliary sludge without any stones; and (5) Completely cleared CBD without any biliary sludge. The scores were determined by two endoscopists independently (Figure 2).\n\nSpyGlass DS images and simulation graphs of the residual stone. A-E: SpyGlass DS images (A1-E1) and simulation graphs (A2-E2). Score 1: A large amount of stone fragments and biliary sludge. Score 2: A moderate amount of stone fragments and biliary sludge. Score 3: A small amount of stone fragments and biliary sludge. Score 4: Presence of biliary sludge without any stones. Score 5: Completely cleared common bile duct without any biliary sludge.\nThe study was approved by the ethics committee of The First Hospital of Lanzhou University (No. LDYYMENG2018-1028) and conducted in accordance with the ethical principles of the Declaration of Helsinki. All authors had access to the study data and reviewed and approved the final manuscript.\nBetween October 2018 and January 2020, 47 patients with CBD stones were enrolled in a prospective clinical trial conducted in the surgical endoscopy center of The First Hospital of Lanzhou University (Figure 1). All eligible patients or their legal representatives gave informed consent before the treatment. The inclusion criteria were patients with CBD stones undergoing ERCP, able to provide informed consent, with a stone size larger than 1.2 cm and requiring mechanical lithotripsy for stone removal. The exclusion criteria included pre-ERCP acute suppurative cholangitis, acute pancreatitis, gastrointestinal (GI) tract hemorrhage and/or perforation, previous history of ERCP, prior Bilroth II gastrectomy and Roux-en-Y or cholangiojejunostomy, pregnancy or breastfeeding, coagulopathy with international normalized ratio > 1.5 and low platelet count (< 50 × 109/L) or active use of anticoagulation drugs, severe liver disease including decompensated liver cirrhosis or liver failure, septic shock, biliary-duodenal fistula confirmed before ERCP cannulation, the presence of intrahepatic duct stones and malignancy, and patient unwillingness or inability to give informed consent.\n\nFlow chart of the self-controlled study. CBD: Common bile duct; ERCP: Endoscopic retrograde cholangiopancreatography; PTCD: Percutaneous transhepatic cholangiodrainage.\nTo assess the number of residual stones, we created a CBD clearance score as follows: (1) A large number of stone fragments and biliary sludge; (2) A moderate number of stone fragments and biliary sludge; (3) A small number of stone fragments and biliary sludge; (4) Presence of biliary sludge without any stones; and (5) Completely cleared CBD without any biliary sludge. The scores were determined by two endoscopists independently (Figure 2).\n\nSpyGlass DS images and simulation graphs of the residual stone. A-E: SpyGlass DS images (A1-E1) and simulation graphs (A2-E2). Score 1: A large amount of stone fragments and biliary sludge. Score 2: A moderate amount of stone fragments and biliary sludge. Score 3: A small amount of stone fragments and biliary sludge. Score 4: Presence of biliary sludge without any stones. Score 5: Completely cleared common bile duct without any biliary sludge.\nThe study was approved by the ethics committee of The First Hospital of Lanzhou University (No. LDYYMENG2018-1028) and conducted in accordance with the ethical principles of the Declaration of Helsinki. All authors had access to the study data and reviewed and approved the final manuscript.\nProcedures All endoscopic procedures were performed by two experienced endoscopists (each has performed > 1000 ERCPs). Routine prophylactic antibiotics were given in all cases. ERCP was performed with a standard duodenoscope (TJF-260V, Olympus, Tokyo, Japan). The patients received propofol anesthesia without tracheal intubation.\nDuring ERCP, cannulation was performed with a wire-guided sphincterotome. After successful cannulation, contrast was injected to determine the stone size (more or less than 12 mm) and the need for mechanical lithotripsy. All patients underwent a small sphincterotomy with an average length of 3-5 mm using the ENDO CUT mode (power setting 90-120 W; Erbe Elektromedizin, Tuebingen, Germany) followed by balloon sphincteroplasty using a controlled radial expansion (CRE) balloon (10-12 mm in diameter; Boston Scientific, Cork, Ireland). Lithotripsy was performed using an endoscopic lithotripter-compatible basket (Boston Scientific, Marlborough, MA, United States), and stones were removed using the basket and a balloon catheter. Samples of CBD stones were collected using an EndoRetrieval bag (Micro-Tech, Nanjing, China) and placed in a container for subsequent analysis. Occlusion cholangiography was performed by injecting contrast with a balloon catheter. Fluoroscopic assessment using a C-arm X-ray (OEC 9900 Elite, Salt Lake City, Utah, United States) was applied to confirm complete removal of CBD stones and the existence of stone residuals was determined by both endoscopist and radiologist. Confirmed stone residues by applying occlusion cholangiography were then followed by repeated extraction attempts. If complete stone removal was confirmed, a CBD clearance score was generated by further applying SpyGlass DS (Boston Scientific Corporation, Marlborough, Massachusetts, United States) examination. If the CBD clearance score was less than 5, the bile duct was irrigated intermittently with 50 mL of normal saline using a basket. The basket was shaken during irrigation with slight suction applied to the duodenoscope to promote drainage. The bile duct was then examined again using the SpyGlass DS to detect any residual stones/sludge and document the clearance score. If the clearance score was still less than 5, repeat irrigation of the CBD was performed using another 50 mL of normal saline. The final CBD clearance score was obtained one more time using SpyGlass DS examination. CBD stones that were not irrigated out after double 50 mL-saline irrigations continued to be irrigated with saline until they were fully cleared (Figure 3). Endoscopic nasobiliary drainage (ENBD) or biliary stenting was performed if necessary. The CBD clearance score was assessed by two blinded endoscopists who were masked to the treatment.\n\nProtocol of evaluation and irrigation procedures. CBD: Common bile duct; ERCP: Endoscopic retrograde cholangiopancreatography; POC: Peroral cholangioscopy.\nThe composition of the removed CBD stones was analyzed using infrared ray (IR) spectroscopy. One milligram (mg) of stone samples was mixed with 150 mg of potassium bromide in a mortar and ground into powder. The stone mixture was pressed into a mold and then placed into an automatic IR spectrum analyzer for automated detection to determine the IR spectrogram of the CBD stones. The IR spectrogram showed characteristic absorption peaks at a wavelength of 1460 cm-1, indicating cholesterol-based stones, and those at a wavelength of 1680 cm-1 indicated pigment-based stones.\nAll endoscopic procedures were performed by two experienced endoscopists (each has performed > 1000 ERCPs). Routine prophylactic antibiotics were given in all cases. ERCP was performed with a standard duodenoscope (TJF-260V, Olympus, Tokyo, Japan). The patients received propofol anesthesia without tracheal intubation.\nDuring ERCP, cannulation was performed with a wire-guided sphincterotome. After successful cannulation, contrast was injected to determine the stone size (more or less than 12 mm) and the need for mechanical lithotripsy. All patients underwent a small sphincterotomy with an average length of 3-5 mm using the ENDO CUT mode (power setting 90-120 W; Erbe Elektromedizin, Tuebingen, Germany) followed by balloon sphincteroplasty using a controlled radial expansion (CRE) balloon (10-12 mm in diameter; Boston Scientific, Cork, Ireland). Lithotripsy was performed using an endoscopic lithotripter-compatible basket (Boston Scientific, Marlborough, MA, United States), and stones were removed using the basket and a balloon catheter. Samples of CBD stones were collected using an EndoRetrieval bag (Micro-Tech, Nanjing, China) and placed in a container for subsequent analysis. Occlusion cholangiography was performed by injecting contrast with a balloon catheter. Fluoroscopic assessment using a C-arm X-ray (OEC 9900 Elite, Salt Lake City, Utah, United States) was applied to confirm complete removal of CBD stones and the existence of stone residuals was determined by both endoscopist and radiologist. Confirmed stone residues by applying occlusion cholangiography were then followed by repeated extraction attempts. If complete stone removal was confirmed, a CBD clearance score was generated by further applying SpyGlass DS (Boston Scientific Corporation, Marlborough, Massachusetts, United States) examination. If the CBD clearance score was less than 5, the bile duct was irrigated intermittently with 50 mL of normal saline using a basket. The basket was shaken during irrigation with slight suction applied to the duodenoscope to promote drainage. The bile duct was then examined again using the SpyGlass DS to detect any residual stones/sludge and document the clearance score. If the clearance score was still less than 5, repeat irrigation of the CBD was performed using another 50 mL of normal saline. The final CBD clearance score was obtained one more time using SpyGlass DS examination. CBD stones that were not irrigated out after double 50 mL-saline irrigations continued to be irrigated with saline until they were fully cleared (Figure 3). Endoscopic nasobiliary drainage (ENBD) or biliary stenting was performed if necessary. The CBD clearance score was assessed by two blinded endoscopists who were masked to the treatment.\n\nProtocol of evaluation and irrigation procedures. CBD: Common bile duct; ERCP: Endoscopic retrograde cholangiopancreatography; POC: Peroral cholangioscopy.\nThe composition of the removed CBD stones was analyzed using infrared ray (IR) spectroscopy. One milligram (mg) of stone samples was mixed with 150 mg of potassium bromide in a mortar and ground into powder. The stone mixture was pressed into a mold and then placed into an automatic IR spectrum analyzer for automated detection to determine the IR spectrogram of the CBD stones. The IR spectrogram showed characteristic absorption peaks at a wavelength of 1460 cm-1, indicating cholesterol-based stones, and those at a wavelength of 1680 cm-1 indicated pigment-based stones.\nDefinition for complications Most of the post-ERCP adverse events were detected within 24 h after the procedure. We routinely assessed these adverse events, including cholangitis, oozing/bleeding, pancreatitis, cholecystitis, and perforation, at 24 and 48 h after the ERCP procedure by symptoms, signs, laboratory tests, and imaging examinations if necessary[18,19]. Post-ERCP adverse events were defined as follows: (1) The diagnosis of acute cholangitis was based on Tokyo Guidelines 2018/2013 (TG18/TG13) diagnostic criteria[20]; (2) Oozing was defined as bleeding that was slight and stopped spontaneously; (3) Pancreatitis was described as new or worsening abdominal pain along with an increase in serum amylase levels (> 3 × higher than normal upper limit measured 24 h after surgery); (4) Cholecystitis was defined as pain in the epigastrium or RUQ accompanied by a positive Murphy sign, and abdominal ultrasonography showing a thickened gallbladder wall; and (5) Perforation was defined as sudden abdominal pain accompanied by retroperitoneal air and fluid.\nMechanical lithotripsy was performed by the same assistant for all patients. Stones that were fragmented successfully with only one attempt were defined as soft stones. Stones that required multiple fragmentation attempts or needed to be broken again were defined as hard stones.\nMost of the post-ERCP adverse events were detected within 24 h after the procedure. We routinely assessed these adverse events, including cholangitis, oozing/bleeding, pancreatitis, cholecystitis, and perforation, at 24 and 48 h after the ERCP procedure by symptoms, signs, laboratory tests, and imaging examinations if necessary[18,19]. Post-ERCP adverse events were defined as follows: (1) The diagnosis of acute cholangitis was based on Tokyo Guidelines 2018/2013 (TG18/TG13) diagnostic criteria[20]; (2) Oozing was defined as bleeding that was slight and stopped spontaneously; (3) Pancreatitis was described as new or worsening abdominal pain along with an increase in serum amylase levels (> 3 × higher than normal upper limit measured 24 h after surgery); (4) Cholecystitis was defined as pain in the epigastrium or RUQ accompanied by a positive Murphy sign, and abdominal ultrasonography showing a thickened gallbladder wall; and (5) Perforation was defined as sudden abdominal pain accompanied by retroperitoneal air and fluid.\nMechanical lithotripsy was performed by the same assistant for all patients. Stones that were fragmented successfully with only one attempt were defined as soft stones. Stones that required multiple fragmentation attempts or needed to be broken again were defined as hard stones.\nStatistical analysis The sample size calculation was based on the rate of residual stone clearance. According to a previous study[16] and our prior experience, we assumed a success rate of 84.5% for endoscopic extraction of CBD stones and an increase of up to 97% with saline irrigation. We estimated that 47 patients were needed in this analysis to obtain a power of 80% at the 5% level.\nCategorical variables are expressed as numbers or percentages (%). Continuous variables are presented as the mean ± SD or median and interquartile range, as appropriate. Continuous variables with a normal distribution were analyzed by paired t-test or signed-rank test. Categorical variables were analyzed using the chi-square test or Fisher’s exact test. Logistic regression was used to predict risk factors for complications, and the results are presented as odds ratios (ORs) with 95% confidence intervals (Cis). Variables with a P value of < 0.2 in the univariate analysis were included in the multivariate analysis. Linear mixed-effects models were conducted to assess the effect of saline irrigation on the clearance score with a random intercept for each patient and an unstructured covariance structure. A two-sided P value of less than 0.05 was considered statistically significant. The analyses were conducted with statistical software (SAS version 9.4, SAS Institute, Inc., NC, United States).\nThe sample size calculation was based on the rate of residual stone clearance. According to a previous study[16] and our prior experience, we assumed a success rate of 84.5% for endoscopic extraction of CBD stones and an increase of up to 97% with saline irrigation. We estimated that 47 patients were needed in this analysis to obtain a power of 80% at the 5% level.\nCategorical variables are expressed as numbers or percentages (%). Continuous variables are presented as the mean ± SD or median and interquartile range, as appropriate. Continuous variables with a normal distribution were analyzed by paired t-test or signed-rank test. Categorical variables were analyzed using the chi-square test or Fisher’s exact test. Logistic regression was used to predict risk factors for complications, and the results are presented as odds ratios (ORs) with 95% confidence intervals (Cis). Variables with a P value of < 0.2 in the univariate analysis were included in the multivariate analysis. Linear mixed-effects models were conducted to assess the effect of saline irrigation on the clearance score with a random intercept for each patient and an unstructured covariance structure. A two-sided P value of less than 0.05 was considered statistically significant. The analyses were conducted with statistical software (SAS version 9.4, SAS Institute, Inc., NC, United States).\n\nStudy design and participants:\nBetween October 2018 and January 2020, 47 patients with CBD stones were enrolled in a prospective clinical trial conducted in the surgical endoscopy center of The First Hospital of Lanzhou University (Figure 1). All eligible patients or their legal representatives gave informed consent before the treatment. The inclusion criteria were patients with CBD stones undergoing ERCP, able to provide informed consent, with a stone size larger than 1.2 cm and requiring mechanical lithotripsy for stone removal. The exclusion criteria included pre-ERCP acute suppurative cholangitis, acute pancreatitis, gastrointestinal (GI) tract hemorrhage and/or perforation, previous history of ERCP, prior Bilroth II gastrectomy and Roux-en-Y or cholangiojejunostomy, pregnancy or breastfeeding, coagulopathy with international normalized ratio > 1.5 and low platelet count (< 50 × 109/L) or active use of anticoagulation drugs, severe liver disease including decompensated liver cirrhosis or liver failure, septic shock, biliary-duodenal fistula confirmed before ERCP cannulation, the presence of intrahepatic duct stones and malignancy, and patient unwillingness or inability to give informed consent.\n\nFlow chart of the self-controlled study. CBD: Common bile duct; ERCP: Endoscopic retrograde cholangiopancreatography; PTCD: Percutaneous transhepatic cholangiodrainage.\nTo assess the number of residual stones, we created a CBD clearance score as follows: (1) A large number of stone fragments and biliary sludge; (2) A moderate number of stone fragments and biliary sludge; (3) A small number of stone fragments and biliary sludge; (4) Presence of biliary sludge without any stones; and (5) Completely cleared CBD without any biliary sludge. The scores were determined by two endoscopists independently (Figure 2).\n\nSpyGlass DS images and simulation graphs of the residual stone. A-E: SpyGlass DS images (A1-E1) and simulation graphs (A2-E2). Score 1: A large amount of stone fragments and biliary sludge. Score 2: A moderate amount of stone fragments and biliary sludge. Score 3: A small amount of stone fragments and biliary sludge. Score 4: Presence of biliary sludge without any stones. Score 5: Completely cleared common bile duct without any biliary sludge.\nThe study was approved by the ethics committee of The First Hospital of Lanzhou University (No. LDYYMENG2018-1028) and conducted in accordance with the ethical principles of the Declaration of Helsinki. All authors had access to the study data and reviewed and approved the final manuscript.\n\nProcedures:\nAll endoscopic procedures were performed by two experienced endoscopists (each has performed > 1000 ERCPs). Routine prophylactic antibiotics were given in all cases. ERCP was performed with a standard duodenoscope (TJF-260V, Olympus, Tokyo, Japan). The patients received propofol anesthesia without tracheal intubation.\nDuring ERCP, cannulation was performed with a wire-guided sphincterotome. After successful cannulation, contrast was injected to determine the stone size (more or less than 12 mm) and the need for mechanical lithotripsy. All patients underwent a small sphincterotomy with an average length of 3-5 mm using the ENDO CUT mode (power setting 90-120 W; Erbe Elektromedizin, Tuebingen, Germany) followed by balloon sphincteroplasty using a controlled radial expansion (CRE) balloon (10-12 mm in diameter; Boston Scientific, Cork, Ireland). Lithotripsy was performed using an endoscopic lithotripter-compatible basket (Boston Scientific, Marlborough, MA, United States), and stones were removed using the basket and a balloon catheter. Samples of CBD stones were collected using an EndoRetrieval bag (Micro-Tech, Nanjing, China) and placed in a container for subsequent analysis. Occlusion cholangiography was performed by injecting contrast with a balloon catheter. Fluoroscopic assessment using a C-arm X-ray (OEC 9900 Elite, Salt Lake City, Utah, United States) was applied to confirm complete removal of CBD stones and the existence of stone residuals was determined by both endoscopist and radiologist. Confirmed stone residues by applying occlusion cholangiography were then followed by repeated extraction attempts. If complete stone removal was confirmed, a CBD clearance score was generated by further applying SpyGlass DS (Boston Scientific Corporation, Marlborough, Massachusetts, United States) examination. If the CBD clearance score was less than 5, the bile duct was irrigated intermittently with 50 mL of normal saline using a basket. The basket was shaken during irrigation with slight suction applied to the duodenoscope to promote drainage. The bile duct was then examined again using the SpyGlass DS to detect any residual stones/sludge and document the clearance score. If the clearance score was still less than 5, repeat irrigation of the CBD was performed using another 50 mL of normal saline. The final CBD clearance score was obtained one more time using SpyGlass DS examination. CBD stones that were not irrigated out after double 50 mL-saline irrigations continued to be irrigated with saline until they were fully cleared (Figure 3). Endoscopic nasobiliary drainage (ENBD) or biliary stenting was performed if necessary. The CBD clearance score was assessed by two blinded endoscopists who were masked to the treatment.\n\nProtocol of evaluation and irrigation procedures. CBD: Common bile duct; ERCP: Endoscopic retrograde cholangiopancreatography; POC: Peroral cholangioscopy.\nThe composition of the removed CBD stones was analyzed using infrared ray (IR) spectroscopy. One milligram (mg) of stone samples was mixed with 150 mg of potassium bromide in a mortar and ground into powder. The stone mixture was pressed into a mold and then placed into an automatic IR spectrum analyzer for automated detection to determine the IR spectrogram of the CBD stones. The IR spectrogram showed characteristic absorption peaks at a wavelength of 1460 cm-1, indicating cholesterol-based stones, and those at a wavelength of 1680 cm-1 indicated pigment-based stones.\n\nDefinition for complications:\nMost of the post-ERCP adverse events were detected within 24 h after the procedure. We routinely assessed these adverse events, including cholangitis, oozing/bleeding, pancreatitis, cholecystitis, and perforation, at 24 and 48 h after the ERCP procedure by symptoms, signs, laboratory tests, and imaging examinations if necessary[18,19]. Post-ERCP adverse events were defined as follows: (1) The diagnosis of acute cholangitis was based on Tokyo Guidelines 2018/2013 (TG18/TG13) diagnostic criteria[20]; (2) Oozing was defined as bleeding that was slight and stopped spontaneously; (3) Pancreatitis was described as new or worsening abdominal pain along with an increase in serum amylase levels (> 3 × higher than normal upper limit measured 24 h after surgery); (4) Cholecystitis was defined as pain in the epigastrium or RUQ accompanied by a positive Murphy sign, and abdominal ultrasonography showing a thickened gallbladder wall; and (5) Perforation was defined as sudden abdominal pain accompanied by retroperitoneal air and fluid.\nMechanical lithotripsy was performed by the same assistant for all patients. Stones that were fragmented successfully with only one attempt were defined as soft stones. Stones that required multiple fragmentation attempts or needed to be broken again were defined as hard stones.\n\nStatistical analysis:\nThe sample size calculation was based on the rate of residual stone clearance. According to a previous study[16] and our prior experience, we assumed a success rate of 84.5% for endoscopic extraction of CBD stones and an increase of up to 97% with saline irrigation. We estimated that 47 patients were needed in this analysis to obtain a power of 80% at the 5% level.\nCategorical variables are expressed as numbers or percentages (%). Continuous variables are presented as the mean ± SD or median and interquartile range, as appropriate. Continuous variables with a normal distribution were analyzed by paired t-test or signed-rank test. Categorical variables were analyzed using the chi-square test or Fisher’s exact test. Logistic regression was used to predict risk factors for complications, and the results are presented as odds ratios (ORs) with 95% confidence intervals (Cis). Variables with a P value of < 0.2 in the univariate analysis were included in the multivariate analysis. Linear mixed-effects models were conducted to assess the effect of saline irrigation on the clearance score with a random intercept for each patient and an unstructured covariance structure. A two-sided P value of less than 0.05 was considered statistically significant. The analyses were conducted with statistical software (SAS version 9.4, SAS Institute, Inc., NC, United States).\n\nRESULTS:\nThe patients' average age was 61 ± 16.5 years, and 23 (48.9%) were male. Comorbidities included coronary disease in three (6.4%) patients, hypertension in 14 (29.8%), diabetes in two (4.3%), liver cirrhosis in three (2.1%), and portal hypertension in one (6.4%). The stone analysis showed that 17 (36.2%) cases had cholesterol-based stones, and 30 (63.8%) had pigment-based stones.\nProcedure-related adverse events occurred in 11 (23.4%) of 47 patients, with cholangitis in four patients, bleeding in two, pancreatitis in four, and cholecystitis in one. No mortality or perforation occurred. The mean time for ERCP was 40.3 ± 15.4 min (Table 1).\nClinical and procedural characteristics of the patients\nData are expressed as the mean ± SD, median (interquartile range) or n (%). ENBD: Endoscopic nasobiliary drainage; CBD: Common bile duct.\nAfter endoscopic stone extraction, occlusion cholangiography showed that there were no residual CBD stones. Seven patients had a clearance score of 4 identified by POC with the SpyGlass DS, and no patients had a score of 5 before saline irrigation. After irrigation with 50 mL saline, CBD clearance improved to 4 in 28 patients, but none achieved a score of 5. After a total of 100 mL of saline irrigation, there was further improvement in the clearance scores: 12 (26%) patients had a score of 4, and 32 (68%) had a score of 5 identified by the SpyGlass DS. The respective CBD stone clearance rates for the control (no irrigation), 50 mL saline irrigation, and 100 mL saline irrigation were 15%, 60%, and 94%, respectively, and the difference among them was statistically significant (P < 0.001) (Table 2). The CBD clearance score (mean ± SD) was 2.4 ± 1.1 in the control, 3.5 ± 0.7 in the 50 mL saline irrigation, and 4.6 ± 0.6 in the 100 mL saline irrigation (P < 0.001) (Supplementary Figure 1).\nCommon bile duct clear score and stone clearance rate before and after saline irrigation\nAfter ERCP, 13 patients had a score of 1 (a large amount of residual stone/sludge) before saline irrigation. Only 15 patients did not reach a score of 5 (complete clearance) after 100 mL of saline irrigation. A dilated CBD diameter > 15 mm (OR = 4.93, 95%CI: 1.13-21.57; P = 0.034) was an independent risk factor for significant residual CBD stones (score = 1). Multivariate analysis revealed that CBD diameter > 15 mm (OR = 0.08, 95%CI: 0.01-0.49; P = 0.007) and the presence of periampullary diverticula (PAD) (OR = 6.51, 95%CI: 1.08-39.21; P = 0.041) were independent risk factors for failed CBD clearance despite irrigation with 100 mL saline (Table 3).\nBiliary cleanliness before and after saline irrigation, assessed by logistic regression analysis\nN: Total number; CBD: Common bile duct; PAD: Periampullary diverticula; CI: Confidence interval; OR: Odds ratio.\nFurther analysis showed that the volume of saline used for irrigation[20] (P < 0.001) was an important factor determining CBD clearance (Supplementary Table 1).\nWe used IR spectroscopy to analyze the components of stones in the CBD of all patients. IR spectroscopy analysis showed that the proportion of pigment-based stones was significantly higher in patients with PAD (93.3% vs 50.0%, P = 0.04), and these stones were mostly soft stones (77.4% vs 22.6%, P = 0.01) (Table 4).\nCorrelation between the composition of stones and variables\nCBD: Common bile duct; PAD: Periampullary diverticula.\n\nDISCUSSION:\nFluoroscopic cholangiographic imagery is currently the main method to determine the successful clearance of CBD stones[20]. However, studies involving the use of IDUS have identified residual biliary sludge within the bile duct despite the absence of filling defects on cholangiography. Mechanical lithotripsy produces a large number of stone fragments, and these minor residual CBD stones may lead to recurrent stone formation[21].\nIt has been reported that residual small CBD stone fragments can be flushed out of the bile duct using saline irrigation with a balloon catheter with a side hole[15]. A study reported that an average of 48 mL of saline could remove residual stones[16]. However, another study found that at least 100 mL of saline was needed to reduce residual stones[17]. Therefore, the effectiveness of saline irrigation on the clearance of residual bile duct stones after lithotripsy is not clear. This study's purpose differs from previous ones in that all of the patients had large stones and were post lithotripsy. In this study, we found that after ERCP and an additional round of lithotripsy were performed to remove CBD stones, POC using the SpyGlass DS showed that only 15% of the patients were relatively cleared of bile duct stones despite a negative occlusion cholangiogram. After irrigation with 50 mL of normal saline, 60% of the patients had relatively cleared bile ducts. After a total of 100 mL saline irrigation, 94% of the patients achieved complete clearance of the bile duct. The results showed that irrigation with 50 mL of saline was not enough to clear the bile duct of residual stones/sludge.\nIn this series, all patients received mechanical lithotripsy for stone fragmentation, which generates substantial stone fragments/debris, thus increasing the difficulty of clearing the bile duct. Our results showed that a good CBD clearance rate could be achieved with a larger saline irrigation volume. The clearance rate of bile duct stones could be higher for those without lithotripsy.\nResidual stones have been found in approximately 1/3 of cases after stone retrieval using endoscopic ultrasound (EUS)[22,23]. In addition to stone detection, the EUS approach might also offer an alternative for treating bile duct stones. However, the use of EUS is very challenging[24-27]. IDUS has also been used to detect small residual stones in 14/59 patients (23.7%) with residual stones[11]. However, the ultrasonic probe is costly and can be easily damaged. In addition, the method is highly operator dependent and often produces poor quality images, and the presence of air in the bile duct can affect the detection of residual stones. Many studies have shown that POC has a high sensitivity in the detection of residual CBD stones, ranging from 25.3% to 34%, where residual stones are missed by standard cholangiography[8,9,28].\nIn our study, we used the SpyGlass DS system to determine the CBD clearance score. The SpyScope has a 4-way tip deflection and is much smaller (10 French) than conventional choledochoscopes. More importantly, it has a separate and independent irrigation channel that allows intermittent or continuous irrigation of the biliary system. It also offers a direct examination of the bile duct lumen and is more accurate in detecting and diagnosing residual bile duct stones/sludge.\nResidual CBD stones can lead to recurrent stone formation. Itoi et al[7] reported that PAD and intraoperative lithotripsy were closely related to residual stones (P < 0.05), as we observed in our study. We also found that a dilated CBD with a diameter > 15 mm was an independent risk factor for failed CBD clearance and a large number of residual stones. Despite irrigation with 100 mL saline, PAD and a dilated CBD remained independent risk factors for incomplete bile duct clearance. This may be due to the presence of an air-filled duodenum/diverticulum that compresses the distal CBD, making it difficult to wash away the residual stones/sludge[29,30]. CBD clearance can be improved by increasing the volume of saline irrigation.\nOur results showed that PAD not only increased the difficulty of bile duct clearance but can also affect stone composition. We found that pigment-based stones constituted the majority of CBD stones in our patients with PAD (P = 0.004). Song et al[31] reported that recurrent bile duct stones were more likely to be brown pigmented stones than cholesterol-based stones. This may be because pigment-based stones are often related to bacterial infection, and the formation of biliary sludge will contribute to recurrent stone formation over time[32-34]. The brown pigmented stones that form as a result of bacterial infection are soft and easy to fragment and generate abundant debris. Our experience showed that soft stones/sludge require more saline irrigation to clear the bile duct. In this study, 26% of cases with biliary sludge were found even with 100 mL saline irrigation. If an effective bile duct cleaning device is used to remove biliary sludge, it may reduce the CBD stone recurrence rate after ERCP.\nWithout adequate biliary drainage, saline irrigation can increase the intraductal pressure, causing cholangitis because of contaminated bile[35]. Proper drainage methods can mitigate this stress[36,37]. No serious adverse events were reported in our study except for 10% of patients with cholangitis secondary possibly to saline irrigation. Intermittent irrigation and endoscopic suction to promote biliary drainage may lower the risk in addition to antibiotic coverage. Differences between this study and previous studies include the following: We studied the effect of flushing with saline after lithotripsy; our evaluation method was different from that of previous studies (we used SpyGlass DS examination); and we performed a component analysis related to diverticula. Our study has several methodological advantages. First, it was a prospective study using a self-controlled design, and SpyGlass DS was used to assess the presence of residual stones in the bile ducts. All of the patients showed no stones by imaging even though small residual bile duct stones were observed by SpyGlass DS, indicating that the evaluation of residual bile duct stones using SpyGlass DS is more sensitive than imaging alone. If we observe that there are still stones, it would be unethical not to continue flushing after using 50 mL of saline. Second, this study used objective definitions to assess residual stones identified by POC. Third, this study included objective stone composition analysis using IR spectroscopy.\nThe study has limitations. This study was not a randomized controlled trial. The use of direct cholangioscopy by the SpyGlass DS increases the procedure time with additional cost. Therefore, we do not recommend routine use of SpyGlass DS to rule out residual stones.\n\nCONCLUSION:\nIn conclusion, in patients with large CBD stones undergoing mechanical lithotripsy, routine irrigation of the bile duct with at least 100 mL of saline is recommended to improve CBD clearance, especially for patients with a dilated bile duct and/or those with PAD. Saline irrigation is simple, inexpensive, and easy to perform to improve bile duct clearance, and it may avoid recurrent stone formation.\n\n"},"whole_article_abstract":{"kind":"string","value":"Background:\nA previous study showed that irrigation with 100 mL saline reduced residual common bile duct (CBD) stones, which potentially cause recurrent stones after endoscopic retrograde cholangiopancreatography.\n\nMethods:\nThis prospective self-controlled study enrolled patients receiving mechanical lithotripsy for large (> 1.2 cm) CBD stones. After occlusion cholangiography confirmed CBD stone clearance, peroral cholangioscopy (POC) was performed to determine clearance scores based on the number of residual stones. The amounts of residual stones spotted via POC were graded on a 5-point scale (score 1, worst; score 5, best). Scores were documented after only stone removal (control) and after irrigation with 50 mL and 100 mL saline, respectively. The stone composition was analyzed using infrared spectroscopy.\n\nResults:\nBetween October 2018 and January 2020, 47 patients had CBD clearance scores of 2.4 ± 1.1 without saline irrigation, 3.5 ± 0.7 with 50 mL irrigation, and 4.6 ± 0.6 with 100 mL irrigation (P < 0.001). Multivariate analysis showed that CBD diameter > 15 mm [odds ratio (OR) = 0.08, 95% confidence interval (CI): 0.01-0.49; P = 0.007] and periampullary diverticula (PAD) (OR = 6.51, 95%CI: 1.08-39.21; P = 0.041) were independent risk factors for residual stones. Bilirubin pigment stones constituted the main residual stones found in patients with PAD (P = 0.004).\n\nConclusions:\nIrrigation with 100 mL of saline may not clear all residual CBD stones after lithotripsy, especially in patients with PAD and/or a dilated (> 15 mm) CBD. Pigment residual stones are soft and commonly found in patients with PAD. Additional saline irrigation may be required to remove retained stones."},"background_conclusion_text":{"kind":"string","value":"INTRODUCTION:\nEndoscopic retrograde cholangiopancreatography (ERCP) is an effective and relatively minimally invasive technique for common bile duct (CBD) stones[1-3]. It has been reported that the recurrence rate of CBD stones after ERCP has increased from 4% to 24%[4-6]. The incidence of residual stones after mechanical lithotripsy for intractable CBD stones is 24% to 40%[7-10]. A growing number of studies suggest that an important reason for the recurrence of bile duct stones is the presence of stone debris after lithotripsy[11-13]. During ERCP, occluded cholangiography (OC) is often performed after stone removal to determine whether the stone is removed completely, but OC lacks accuracy. Even if no obvious stones are found on cholangiography, the presence of contrast can obscure small stone debris in the bile duct[8,14]. Complete bile duct clearance is necessary to decrease recurrent bile duct stones.\nSome studies[15-17] reported that irrigation of the bile duct with saline after stone extraction further improves the clearance of the bile duct and has the advantages of being a simple, low-cost procedure with rare complications. Ang et al[16] showed that a mean of 48 mL of saline solution could irrigate and flush out residual stones after the endoscopic removal of CBD stones. Ahn et al[17] found that irrigation with 100 mL of saline can flush out residual stone fragments from the bile duct into the duodenum after stone extraction. However, intraductal ultrasound (IDUS) has a high sensitivity and accuracy in diagnosing bile duct stones/debris[11,16]. This modality yields only indirect images of the debris.\nThe lack of direct evidence to support the efficacy of saline irrigation after lithotripsy prompted us to use peroral cholangioscopy (POC) to examine the bile duct and detect any residual stones/debris. The results of no irrigation after stone extraction were confirmed by OC and were compared to the effectiveness of irrigation with 50 mL or 100 mL saline. To evaluate whether irrigation with 100 mL of saline is more effective in achieving complete clearance of the bile duct after mechanical lithotripsy, we conducted this prospective self-controlled study.\n\nCONCLUSION:\nWe thank the clinical and research teams at the Department of General Surgery for providing ongoing support."},"background_conclusion_abstract":{"kind":"string","value":"Background:\nA previous study showed that irrigation with 100 mL saline reduced residual common bile duct (CBD) stones, which potentially cause recurrent stones after endoscopic retrograde cholangiopancreatography.\n\nMethods:\nThis prospective self-controlled study enrolled patients receiving mechanical lithotripsy for large (> 1.2 cm) CBD stones. After occlusion cholangiography confirmed CBD stone clearance, peroral cholangioscopy (POC) was performed to determine clearance scores based on the number of residual stones. The amounts of residual stones spotted via POC were graded on a 5-point scale (score 1, worst; score 5, best). Scores were documented after only stone removal (control) and after irrigation with 50 mL and 100 mL saline, respectively. The stone composition was analyzed using infrared spectroscopy.\n\nResults:\nBetween October 2018 and January 2020, 47 patients had CBD clearance scores of 2.4 ± 1.1 without saline irrigation, 3.5 ± 0.7 with 50 mL irrigation, and 4.6 ± 0.6 with 100 mL irrigation (P < 0.001). Multivariate analysis showed that CBD diameter > 15 mm [odds ratio (OR) = 0.08, 95% confidence interval (CI): 0.01-0.49; P = 0.007] and periampullary diverticula (PAD) (OR = 6.51, 95%CI: 1.08-39.21; P = 0.041) were independent risk factors for residual stones. Bilirubin pigment stones constituted the main residual stones found in patients with PAD (P = 0.004).\n\nConclusions:\nIrrigation with 100 mL of saline may not clear all residual CBD stones after lithotripsy, especially in patients with PAD and/or a dilated (> 15 mm) CBD. Pigment residual stones are soft and commonly found in patients with PAD. Additional saline irrigation may be required to remove retained stones."},"whole_article_text_length":{"kind":"number","value":7280,"string":"7,280"},"whole_article_abstract_length":{"kind":"number","value":332,"string":"332"},"other_sections_lengths":{"kind":"list like","value":[397,467,625,239,259,745,1236,71],"string":"[\n 397,\n 467,\n 625,\n 239,\n 259,\n 745,\n 1236,\n 71\n]"},"num_sections":{"kind":"number","value":9,"string":"9"},"most_frequent_words":{"kind":"list like","value":["stones","cbd","stone","duct","bile","saline","irrigation","bile duct","clearance","score"],"string":"[\n \"stones\",\n \"cbd\",\n \"stone\",\n \"duct\",\n \"bile\",\n \"saline\",\n \"irrigation\",\n \"bile duct\",\n \"clearance\",\n \"score\"\n]"},"keybert_topics":{"kind":"list like","value":["difficulty bile duct","improve bile duct","bile duct study","stones found cholangiography","residual stones bile"],"string":"[\n \"difficulty bile duct\",\n \"improve bile duct\",\n \"bile duct study\",\n \"stones found cholangiography\",\n \"residual stones bile\"\n]"},"annotated_base_background_abstract_prompt":{"kind":"string","value":""},"annotated_base_methods_abstract_prompt":{"kind":"string","value":""},"annotated_base_results_abstract_prompt":{"kind":"null"},"annotated_base_conclusions_abstract_prompt":{"kind":"string","value":""},"annotated_base_whole_article_abstract_prompt":{"kind":"string","value":""},"annotated_base_background_conclusion_abstract_prompt":{"kind":"string","value":""},"annotated_keywords_background_abstract_prompt":{"kind":"string","value":"[CONTENT] Endoscopic retrograde cholangiopancreatography | Common bile duct gall stones | Peroral cholangioscopy | Saline irrigation | Periampullary diverticula | Prospective cohort study [SUMMARY]"},"annotated_keywords_methods_abstract_prompt":{"kind":"string","value":"[CONTENT] Endoscopic retrograde cholangiopancreatography | Common bile duct gall stones | Peroral cholangioscopy | Saline irrigation | Periampullary diverticula | Prospective cohort study [SUMMARY]"},"annotated_keywords_results_abstract_prompt":{"kind":"null"},"annotated_keywords_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] Endoscopic retrograde cholangiopancreatography | Common bile duct gall stones | Peroral cholangioscopy | Saline irrigation | Periampullary diverticula | Prospective cohort study [SUMMARY]"},"annotated_keywords_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] Endoscopic retrograde cholangiopancreatography | Common bile duct gall stones | Peroral cholangioscopy | Saline irrigation | Periampullary diverticula | Prospective cohort study [SUMMARY]"},"annotated_keywords_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] Endoscopic retrograde cholangiopancreatography | Common bile duct gall stones | Peroral cholangioscopy | Saline irrigation | Periampullary diverticula | Prospective cohort study [SUMMARY]"},"annotated_mesh_background_abstract_prompt":{"kind":"string","value":"[CONTENT] Cholangiopancreatography, Endoscopic Retrograde | Common Bile Duct | Gallstones | Humans | Lithotripsy | Prospective Studies [SUMMARY]"},"annotated_mesh_methods_abstract_prompt":{"kind":"string","value":"[CONTENT] Cholangiopancreatography, Endoscopic Retrograde | Common Bile Duct | Gallstones | Humans | Lithotripsy | Prospective Studies [SUMMARY]"},"annotated_mesh_results_abstract_prompt":{"kind":"null"},"annotated_mesh_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] Cholangiopancreatography, Endoscopic Retrograde | Common Bile Duct | Gallstones | Humans | Lithotripsy | Prospective Studies [SUMMARY]"},"annotated_mesh_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] Cholangiopancreatography, Endoscopic Retrograde | Common Bile Duct | Gallstones | Humans | Lithotripsy | Prospective Studies [SUMMARY]"},"annotated_mesh_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] Cholangiopancreatography, Endoscopic Retrograde | Common Bile Duct | Gallstones | Humans | Lithotripsy | Prospective Studies [SUMMARY]"},"annotated_keybert_background_abstract_prompt":{"kind":"string","value":"[CONTENT] difficulty bile duct | improve bile duct | bile duct study | stones found cholangiography | residual stones bile [SUMMARY]"},"annotated_keybert_methods_abstract_prompt":{"kind":"string","value":"[CONTENT] difficulty bile duct | improve bile duct | bile duct study | stones found cholangiography | residual stones bile [SUMMARY]"},"annotated_keybert_results_abstract_prompt":{"kind":"null"},"annotated_keybert_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] difficulty bile duct | improve bile duct | bile duct study | stones found cholangiography | residual stones bile [SUMMARY]"},"annotated_keybert_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] difficulty bile duct | improve bile duct | bile duct study | stones found cholangiography | residual stones bile [SUMMARY]"},"annotated_keybert_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] difficulty bile duct | improve bile duct | bile duct study | stones found cholangiography | residual stones bile [SUMMARY]"},"annotated_most_frequent_background_abstract_prompt":{"kind":"string","value":"[CONTENT] stones | cbd | stone | duct | bile | saline | irrigation | bile duct | clearance | score [SUMMARY]"},"annotated_most_frequent_methods_abstract_prompt":{"kind":"string","value":"[CONTENT] stones | cbd | stone | duct | bile | saline | irrigation | bile duct | clearance | score [SUMMARY]"},"annotated_most_frequent_results_abstract_prompt":{"kind":"null"},"annotated_most_frequent_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] stones | cbd | stone | duct | bile | saline | irrigation | bile duct | clearance | score [SUMMARY]"},"annotated_most_frequent_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] stones | cbd | stone | duct | bile | saline | irrigation | bile duct | clearance | score [SUMMARY]"},"annotated_most_frequent_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] stones | cbd | stone | duct | bile | saline | irrigation | bile duct | clearance | score [SUMMARY]"},"annotated_tf_idf_background_abstract_prompt":{"kind":"string","value":"[CONTENT] bile duct | bile | duct | debris | stones | stone | oc | irrigation | saline | ml [SUMMARY]"},"annotated_tf_idf_methods_abstract_prompt":{"kind":"string","value":"[CONTENT] stones | biliary sludge | cbd | biliary | stone | score | sludge | performed | ercp | stone fragments biliary [SUMMARY]"},"annotated_tf_idf_results_abstract_prompt":{"kind":"null"},"annotated_tf_idf_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] improve | bile duct | duct | bile | large cbd | clearance avoid | especially patients dilated | especially patients dilated bile | improve bile | improve bile duct [SUMMARY]"},"annotated_tf_idf_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] stones | cbd | stone | duct | bile | saline | bile duct | irrigation | clearance | score [SUMMARY]"},"annotated_tf_idf_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] stones | cbd | stone | duct | bile | saline | bile duct | irrigation | clearance | score [SUMMARY]"},"annotated_entity_plan_background_abstract_prompt":{"kind":"string","value":"[CONTENT] 100 [SUMMARY]"},"annotated_entity_plan_methods_abstract_prompt":{"kind":"string","value":"[CONTENT] 1.2 cm ||| ||| 5 | 1 | 5 ||| 50 mL and | 100 mL saline ||| [SUMMARY]"},"annotated_entity_plan_results_abstract_prompt":{"kind":"null"},"annotated_entity_plan_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] 100 mL | PAD | 15 mm ||| PAD ||| [SUMMARY]"},"annotated_entity_plan_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] 100 ||| 1.2 cm ||| ||| 5 | 1 | 5 ||| 50 mL and | 100 mL saline ||| ||| Between October 2018 and January 2020 | 47 | 2.4 ± | 3.5 | 0.7 | 50 | 4.6 | 100 ||| ||| 15 mm ||| 0.08 | 95% | CI | 0.01 | 0.007 | PAD | 6.51 | 1.08-39.21 | P = | 0.041 ||| PAD | 0.004 ||| 100 mL | PAD | 15 mm ||| PAD ||| [SUMMARY]"},"annotated_entity_plan_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] 100 ||| 1.2 cm ||| ||| 5 | 1 | 5 ||| 50 mL and | 100 mL saline ||| ||| Between October 2018 and January 2020 | 47 | 2.4 ± | 3.5 | 0.7 | 50 | 4.6 | 100 ||| ||| 15 mm ||| 0.08 | 95% | CI | 0.01 | 0.007 | PAD | 6.51 | 1.08-39.21 | P = | 0.041 ||| PAD | 0.004 ||| 100 mL | PAD | 15 mm ||| PAD ||| [SUMMARY]"}}},{"rowIdx":278,"cells":{"title":{"kind":"string","value":"The association of interferon-alpha with development of collateral circulation after artery occlusion."},"pmid":{"kind":"string","value":"34599832"},"background_abstract":{"kind":"string","value":"Previous studies have demonstrated that interferon (IFN) signaling is enhanced in patients with poor collateral circulation (CC). However, the role and mechanisms of IFN-alpha in the development of CC remain unknown."},"background_abstract_label":{"kind":"string","value":"BACKGROUND"},"methods_abstract":{"kind":"string","value":"We studied the serum levels of IFN-alpha and coronary CC in a case-control study using logistics regression, including 114 coronary chronic total occlusion (CTO) patients with good coronary CC and 94 CTO patients with poor coronary CC. Restricted cubic splines was used to flexibly model the association of the levels of IFN-alpha with the incidence of good CC perfusion restoration after systemic treatment with IFN-alpha was assessed in a mice hind-limb ischemia model."},"methods_abstract_label":{"kind":"string","value":"METHODS"},"results_abstract":{"kind":"string","value":"Compared with the first IFN-alpha tertile, the risk of poor CC was higher in the third IFN-alpha tertile (OR: 4.79, 95% CI: 2.22-10.4, p < .001). A cubic spline-smoothing curve showed that the risk of poor CC increased with increasing levels of serum IFN-alpha. IFN-alpha inhibited the development of CC in a hindlimb ischemia model. Arterioles of CC in the IFN-alpha group were smaller in diameter than in the control group."},"results_abstract_label":{"kind":"string","value":"RESULTS"},"conclusions_abstract":{"kind":"string","value":"Patients with CTO and with poor CC have higher serum levels of IFN-alpha than CTO patients with good CC. IFN-alpha might impair the development of CC after artery occlusion."},"conclusions_abstract_label":{"kind":"string","value":"CONCLUSION"},"mesh_descriptor_names":{"kind":"list like","value":["Animals","Arteries","Case-Control Studies","Chronic Disease","Collateral Circulation","Coronary Angiography","Coronary Circulation","Coronary Occlusion","Humans","Interferon-alpha","Mice","Percutaneous Coronary Intervention"],"string":"[\n \"Animals\",\n \"Arteries\",\n \"Case-Control Studies\",\n \"Chronic Disease\",\n \"Collateral Circulation\",\n \"Coronary Angiography\",\n \"Coronary Circulation\",\n \"Coronary Occlusion\",\n \"Humans\",\n \"Interferon-alpha\",\n \"Mice\",\n \"Percutaneous Coronary Intervention\"\n]"},"pmcid":{"kind":"string","value":"8571556"},"background_title":{"kind":"string","value":"BACKGROUND"},"background_text":{"kind":"string","value":"Development of collateral circulation (CC), also termed as arteriogenesis, is a natural life‐preservation mechanism occurring in patients with coronary chronic total occlusion (CTO). Well‐developed CC preserves cardiac function, thus reducing cardiac mortality after coronary occlusion.\n1\n, \n2\n Patients with CTO show a great deal of heterogeneity in their arteriogenic responses to artery occlusion. This is affected by multiple factors, including inflammatory factors.\n3\n The interaction between inflammation and arteriogenesis may be worthy of study, but data on the correlation between arteriogenesis and inflammation are limited. A previous study has found that interferon (IFN) signaling in monocytes is enhanced and stimulated by lipopolysaccharide among patients with impaired coronary CC.\n4\n Furthermore, blocking the receptor of IFN have been found to be helpful in alleviating ischemia in mice.\n5\n However, whether patients with poor CC have higher levels of serum IFN or whether treatment with IFN inhibits the development of arteriogenesis remains unknown. IFN‐alpha is widely used in treating hepatitis B and C, as well as various cancers. IFN‐alpha might affect the development of CC in patients with CTO. In this study, we aimed to investigate whether patients with poor CC have higher levels of IFN‐alpha, and if so, whether IFN‐alpha inhibits the development of CC."},"methods_title":{"kind":"null"},"methods_text":{"kind":"null"},"results_title":{"kind":"string","value":"RESULTS"},"results_text":{"kind":"string","value":"Between January 2018 and June 2019, 208 patients with CTO were included in our study as required by the inclusion and exclusion criteria (Figure S1). The baseline characteristics of the included patients are presented in Table 1. No significant difference between groups was observed except ejection fraction and the levels of serum IFN‐alpha. In patients with poor CC, serum INF‐alpha levels were significantly higher (87.7 ± 42.0 vs. 59.8 ± 18.5, p < .001), and ejection fraction was significantly lower (51.2 ± 10.5 vs. 59.8 ± 18.5, p = .04) than in patients with good CC.\nBaseline characteristics of included patients\nAbbreviation: CC, collateral circulation; CRP, C‐reactive protein; LAD, left anterior descending; LCX, left circumflex; LDL, low density lipoprotein; PLT, platelet count; RCA, right coronary artery; WBC, white blood cells.\nANOVA showed that the serum levels of IFN‐alpha were significantly associated with CC, as did the Rentrop score (Figure 1). In a multivariate regression analysis, the risk of poor CC was increased with increasing IFN‐alpha tertile. Compared with the first tertile, the risk of poor CC was 3.67 (95% CI: 1.18–7.43) for model 1, 4.37 (95% CI: 2.06–9.26) for model 2, and 4.79 (95% CI: 2.22–10.4) for model 3 (Table 2). After adjusting for model 3, the cubic spline‐smoothing curve shows that the risk of poor CC increased with increasing serum IFN‐alpha levels (Figure 2). The incidence of poor CC increased with the serum levels of IFN‐alpha when the serum levels of IFN‐alpha were less than 112 pg/mL (OR: 1.0348, 95% CI: 1.0195–1.0577, p < .001, Table 3). When the levels of IFN‐alpha were larger than 112 pg/mL, the risk of poor CC did not increase with increasing levels of IFN‐alpha (OR: 1.0036, 95% CI: 0.9748–1.033, p = .807, Table 3). Our results remained robust in sensitivity analyses when we excluded patients aged >70 years or <55 years, as well as when using the fourth tertiles for the levels of serum IFN‐alpha (Tables S1–S3).\nCorrelation between the serum levels of IFN‐alpha and Rentrop scores. The levels in patients with Rentrop scores 0 and 1, or Rentrop 2 and 3, were not significantly different. The serum levels of IFN‐alpha in CTO patients with Rentrop scores of 2 and 3 were significantly different from those with Rentrop scores of 0 and 1\nOR (95% CI) of poor CC according to tertiles of the levels of serum IFN‐alpha\n\nNote: Model 1: unadjusted; model 2: adjusted age, sex, hypertension, hyperlipidemia, smoking; model 3: adjusted age, sex, hypertension, hyperlipidemia, smoking, uric acid, CRP.\nRelationship between the serum levels of IFN‐alpha and CC. A nonlinear relationship was observed between the serum levels of IFN‐alpha and CC after adjusting for model 3. Threshold effect analysis found that when serum levels of IFN‐alpha were larger than 112 pg/mL, the risks of poor CC did not keep on increasing\nThreshold effect analysis of the serum levels of IFN‐alpha on poor CC\n\nNote: When the levels of IFN‐alpha larger than 112 pg/mL, the incidence of poor CC did not increase with the increase of the levels of IFN‐alpha. Adjusted for model 3.\nAbbreviation: CC, coronary collateral circulation.\nTo assess the in vivo effect of IFN‐alpha on CC, we examined whether intraperitoneal injection of IFN‐alpha at 2000 IU/d inhibited blood perfusion recovery in a mouse hind‐limb. Laser speckle imaging showed that blood flow recovery was significantly impaired in mice treated with IFN‐alpha compared with control mice at 1 week (41.7% ± 2.66% vs. 47.3% ± 2.16%, p = 0.020), at 14 days (59.8% ± 3.31% vs. 66.5% ± 3.21%, p = .005) and at 21 days (59.8% ± 3.31% vs. 66.5% ± 3.21%, p = .005, Figure 3A,B). Although the numbers of arterioles are comparable between groups, the diameter of the arterioles in the mice treated with IFN‐alpha was smaller compared with the control group (Figure 4A). In order to study local ischemia, we examined the morphology in the gastrocnemius muscle by HE staining and Masson staining. Although we did not find obvious morphological changes by HE analysis (Figure S2), the area of interstitial fibrosis evaluated by Masson staining in the mice treated with IFN‐alpha was larger than in the control group (Figure 4B).\nINF‐alpha impairs the development of CC in a murine hindlimb ischemic model. (A) Representative laser speckle perfusion images. (B) Quantification of laser speckle perfusion (ischemic/nonischemic) in control and IFN‐alpha treated mice over time\nImmunohistochemistry of SMA in cross‐sections of the adductor muscles or Masson staining of gastrocnemius muscle collected from the control and IFN‐alpha group 3 weeks after femoral artery ligation. (A) Representative immunohistochemistry and quantification of CC numbers and diameter of adductor muscles. (B) Representative Masson staining and quantification of interstitial fibrosis of gastrocnemius muscle"},"conclusions_title":{"kind":"string","value":"CONCLUSION"},"conclusions_text":{"kind":"string","value":"CTO patients with poor CC have higher serum levels of IFN‐alpha than CTO patients with good CC. IFN‐alpha might impair the development of CC after artery occlusion."},"other_sections_titles":{"kind":"list like","value":["Study population","Ischemic hind‐limb model and laser speckle imaging","Histological analyses","Statistical analyses","Limitation","AUTHOR CONTRIBUTIONS"],"string":"[\n \"Study population\",\n \"Ischemic hind‐limb model and laser speckle imaging\",\n \"Histological analyses\",\n \"Statistical analyses\",\n \"Limitation\",\n \"AUTHOR CONTRIBUTIONS\"\n]"},"other_sections_texts":{"kind":"list like","value":["This study was conducted in accordance with the Declaration of Helsinki and with written informed consent of each participant. This study was approved by the medical ethics committee of Second Xiangya Hospital of Central South University. Patients undergoing coronary angiography at our catheter laboratory between January 2018 and June 2019 and with at least one major coronary artery total occlusion were enrolled in our study. The exclusion criteria were as follows: (1) patients with ST‐segment elevated myocardial infarction/non‐ST‐segment elevated myocardial infarction, (2) patients with severe cardiac dysfunction/cardiac shock (HYHA IV or Killip IV), (3) patients with coronary artery bypass grafting, (4) patients with malignant tumor, (5) patients with inflammatory or infectious diseases, and (6) patients with severe hepatic (Child class C) and renal dysfunction (glomerular filtration rate <15 mL/min/1.73 m2 or needing hemodialysis). Pei JY and Wang XP were blinded to the characteristics of the included patients. They reviewed the angiography results and classified the extent of coronary circulation by the Rentrop classification from 0 to 3 as follows: 0 = none, 1 = filling of side branches of the artery dilated via collateral channels without visualization of the epicardial segment, 2 = partial filling of the epicardial segment via collateral channels, and 3 = complete filling of the epicardial segment of the artery being dilated via collateral channels.\n6\n Rentrop 0–1 were classified as poor CC development; Rentrop 2–3 were classified as good CC development. Venous blood samples were collected immediately before coronary angiography. Serum was separated at 4°C, and the levels of IFN‐alpha were measured by ELISA (BMS216, Invitrogen).","The animal protocol was approved by the animal ethics committee of Second Xiangya Hospital of Central South University. All animal works were performed in Second Xiangya Hospital of Central South University. Male C57/BL mice were anesthetized with 3% isoflurane. The left femoral artery was ligated and excised as described previously.\n7\n The right leg underwent sham operation and was used as control. The mice were randomly divided into two groups: an INF‐alpha group with intraperitoneal injection of 2000 IU IFN‐alpha, and a control group with intraperitoneal injection of the same amount of saline. The IFN‐alpha concentration used is comparable to dosages used in patients. Hind‐limb prefusion was measured by laser speckle imaging under temperature‐controlled conditions, before and after left hind‐limb ligation, as well as at 3 days, 1 week, and 3 weeks following ligation. Color‐coded images of the paws representing the flux value were used to calculate the ratio of the tissue perfusion of the occluded (left) to the Sham‐operated (Sham) paw. The right hind‐limb was used as reference. All mice were put to death by cervical dislocation.","Adductor muscles from bilateral hind limbs were harvested at 3 weeks after surgery and underwent immediate tissue fixation overnight. For mouse arteriole density identification, the adductor muscles were stained with alpha‐SMA monoclonal antibody at 3 weeks after ligation.","We presented baseline characteristics of patients as frequencies and percentages for categorical variables and as means and standard deviations or interquartile range for continuous variables, depending on whether data distribution was normal (assessed by normal Q‐Q plots). We compared categorical variables using chi‐square analysis, and continuous variables were compared by analysis of variance test or Mann–Whitney U test, according to distribution type. We evaluated the relationship between serum levels of IFN‐alpha and CC development with the use of the levels of IFN‐alpha as both a continuous and a categorical variable. We constructed logistic regression model to calculate the odds ratio (OR) among tertiles. We used the first tertile as reference and used the following three models: model 1: unadjusted; model 2: adjusted age, sex, hypertension, hyperlipidemia, smoking; model 3: adjusted age, sex, hypertension, hyperlipidemia, smoking, uric acid, CRP. We also used a two‐piecewise linear regression model to examine the threshold effect of the levels of IFN‐alpha on the risk of poor CC according to the smoothing plot. The threshold value was determined using a trial method which was to move the trial turning point along the predefined interval and picked up the one which gave maximum model likelihood. A log likelihood ratio test was conducted comparing one‐line linear regression model with two‐piecewise linear model. We further used restricted cubic splines with five knots at the 5th, 35th, 50th, 65th, and 95th centiles to flexibly model the association of the levels of IFN‐alpha with the incidence of good CC adjusted for model 3 where the threshold value served as the reference.\nWe also did several sensitivity analyses by excluding patients with age <55 years or >70 years or by using the quantiles for the levels of serum IFN‐alpha. We performed all of the analyses using R version 3.4.3 (R Foundation for Statistical Computing, Vienna, Austria).","First, the main limitation of present study is the small number of patients with CTO. However, the sensitivity analysis showed the robustness of our results. Second, our study did not include all inflammatory and angiogenic factors, they may have a role in the development of CC. Third, we evaluated the extent of CC by Rentrop classification instead of CFI. Although collateral is more accurate than Rentrop classification, it is an invasive procedure needing pressure wire. Rentrop classification is more often used in routine clinical practice. Fourth, we evaluated the development of CC in a model of hind‐limb ischemia rather than heart ischemia. However, both development of lower limb CC and coronary CC share common cellular and molecular mechanism: arteriogenesis. Ischemic hind‐limb model is an ideal model for arteriogenesis including coronary circulation.\n4\n, \n5\n Ischemic hind‐limb model mimics aspects of human occlusive artery disease to investigate vascular regeneration and to test therapeutical approaches in a reproducible manner.","Xinqun Hu and Zhenhua Xing designed the study and provided methodological expertise. Zhenhua Xing drafted the manuscript. Zhenhua Xing performed the case–control study. Xiaopu Wang, Junyu Pei, Zhaowei Zhu, Shi Tai performed the animal experiments. All authors have read, provided critical feedback on, and approved the final manuscript."],"string":"[\n \"This study was conducted in accordance with the Declaration of Helsinki and with written informed consent of each participant. This study was approved by the medical ethics committee of Second Xiangya Hospital of Central South University. Patients undergoing coronary angiography at our catheter laboratory between January 2018 and June 2019 and with at least one major coronary artery total occlusion were enrolled in our study. The exclusion criteria were as follows: (1) patients with ST‐segment elevated myocardial infarction/non‐ST‐segment elevated myocardial infarction, (2) patients with severe cardiac dysfunction/cardiac shock (HYHA IV or Killip IV), (3) patients with coronary artery bypass grafting, (4) patients with malignant tumor, (5) patients with inflammatory or infectious diseases, and (6) patients with severe hepatic (Child class C) and renal dysfunction (glomerular filtration rate <15 mL/min/1.73 m2 or needing hemodialysis). Pei JY and Wang XP were blinded to the characteristics of the included patients. They reviewed the angiography results and classified the extent of coronary circulation by the Rentrop classification from 0 to 3 as follows: 0 = none, 1 = filling of side branches of the artery dilated via collateral channels without visualization of the epicardial segment, 2 = partial filling of the epicardial segment via collateral channels, and 3 = complete filling of the epicardial segment of the artery being dilated via collateral channels.\\n6\\n Rentrop 0–1 were classified as poor CC development; Rentrop 2–3 were classified as good CC development. Venous blood samples were collected immediately before coronary angiography. Serum was separated at 4°C, and the levels of IFN‐alpha were measured by ELISA (BMS216, Invitrogen).\",\n \"The animal protocol was approved by the animal ethics committee of Second Xiangya Hospital of Central South University. All animal works were performed in Second Xiangya Hospital of Central South University. Male C57/BL mice were anesthetized with 3% isoflurane. The left femoral artery was ligated and excised as described previously.\\n7\\n The right leg underwent sham operation and was used as control. The mice were randomly divided into two groups: an INF‐alpha group with intraperitoneal injection of 2000 IU IFN‐alpha, and a control group with intraperitoneal injection of the same amount of saline. The IFN‐alpha concentration used is comparable to dosages used in patients. Hind‐limb prefusion was measured by laser speckle imaging under temperature‐controlled conditions, before and after left hind‐limb ligation, as well as at 3 days, 1 week, and 3 weeks following ligation. Color‐coded images of the paws representing the flux value were used to calculate the ratio of the tissue perfusion of the occluded (left) to the Sham‐operated (Sham) paw. The right hind‐limb was used as reference. All mice were put to death by cervical dislocation.\",\n \"Adductor muscles from bilateral hind limbs were harvested at 3 weeks after surgery and underwent immediate tissue fixation overnight. For mouse arteriole density identification, the adductor muscles were stained with alpha‐SMA monoclonal antibody at 3 weeks after ligation.\",\n \"We presented baseline characteristics of patients as frequencies and percentages for categorical variables and as means and standard deviations or interquartile range for continuous variables, depending on whether data distribution was normal (assessed by normal Q‐Q plots). We compared categorical variables using chi‐square analysis, and continuous variables were compared by analysis of variance test or Mann–Whitney U test, according to distribution type. We evaluated the relationship between serum levels of IFN‐alpha and CC development with the use of the levels of IFN‐alpha as both a continuous and a categorical variable. We constructed logistic regression model to calculate the odds ratio (OR) among tertiles. We used the first tertile as reference and used the following three models: model 1: unadjusted; model 2: adjusted age, sex, hypertension, hyperlipidemia, smoking; model 3: adjusted age, sex, hypertension, hyperlipidemia, smoking, uric acid, CRP. We also used a two‐piecewise linear regression model to examine the threshold effect of the levels of IFN‐alpha on the risk of poor CC according to the smoothing plot. The threshold value was determined using a trial method which was to move the trial turning point along the predefined interval and picked up the one which gave maximum model likelihood. A log likelihood ratio test was conducted comparing one‐line linear regression model with two‐piecewise linear model. We further used restricted cubic splines with five knots at the 5th, 35th, 50th, 65th, and 95th centiles to flexibly model the association of the levels of IFN‐alpha with the incidence of good CC adjusted for model 3 where the threshold value served as the reference.\\nWe also did several sensitivity analyses by excluding patients with age <55 years or >70 years or by using the quantiles for the levels of serum IFN‐alpha. We performed all of the analyses using R version 3.4.3 (R Foundation for Statistical Computing, Vienna, Austria).\",\n \"First, the main limitation of present study is the small number of patients with CTO. However, the sensitivity analysis showed the robustness of our results. Second, our study did not include all inflammatory and angiogenic factors, they may have a role in the development of CC. Third, we evaluated the extent of CC by Rentrop classification instead of CFI. Although collateral is more accurate than Rentrop classification, it is an invasive procedure needing pressure wire. Rentrop classification is more often used in routine clinical practice. Fourth, we evaluated the development of CC in a model of hind‐limb ischemia rather than heart ischemia. However, both development of lower limb CC and coronary CC share common cellular and molecular mechanism: arteriogenesis. Ischemic hind‐limb model is an ideal model for arteriogenesis including coronary circulation.\\n4\\n, \\n5\\n Ischemic hind‐limb model mimics aspects of human occlusive artery disease to investigate vascular regeneration and to test therapeutical approaches in a reproducible manner.\",\n \"Xinqun Hu and Zhenhua Xing designed the study and provided methodological expertise. Zhenhua Xing drafted the manuscript. Zhenhua Xing performed the case–control study. Xiaopu Wang, Junyu Pei, Zhaowei Zhu, Shi Tai performed the animal experiments. All authors have read, provided critical feedback on, and approved the final manuscript.\"\n]"},"other_sections_sec_types":{"kind":"list like","value":[null,null,null,null,null,null],"string":"[\n null,\n null,\n null,\n null,\n null,\n null\n]"},"all_sections_titles":{"kind":"list like","value":["BACKGROUND","MATERIALS AND METHODS","Study population","Ischemic hind‐limb model and laser speckle imaging","Histological analyses","Statistical analyses","RESULTS","DISCUSSION","Limitation","CONCLUSION","CONFLICTS OF INTEREST","AUTHOR CONTRIBUTIONS","Supporting information"],"string":"[\n \"BACKGROUND\",\n \"MATERIALS AND METHODS\",\n \"Study population\",\n \"Ischemic hind‐limb model and laser speckle imaging\",\n \"Histological analyses\",\n \"Statistical analyses\",\n \"RESULTS\",\n \"DISCUSSION\",\n \"Limitation\",\n \"CONCLUSION\",\n \"CONFLICTS OF INTEREST\",\n \"AUTHOR CONTRIBUTIONS\",\n \"Supporting information\"\n]"},"all_sections_texts":{"kind":"list like","value":["Development of collateral circulation (CC), also termed as arteriogenesis, is a natural life‐preservation mechanism occurring in patients with coronary chronic total occlusion (CTO). Well‐developed CC preserves cardiac function, thus reducing cardiac mortality after coronary occlusion.\n1\n, \n2\n Patients with CTO show a great deal of heterogeneity in their arteriogenic responses to artery occlusion. This is affected by multiple factors, including inflammatory factors.\n3\n The interaction between inflammation and arteriogenesis may be worthy of study, but data on the correlation between arteriogenesis and inflammation are limited. A previous study has found that interferon (IFN) signaling in monocytes is enhanced and stimulated by lipopolysaccharide among patients with impaired coronary CC.\n4\n Furthermore, blocking the receptor of IFN have been found to be helpful in alleviating ischemia in mice.\n5\n However, whether patients with poor CC have higher levels of serum IFN or whether treatment with IFN inhibits the development of arteriogenesis remains unknown. IFN‐alpha is widely used in treating hepatitis B and C, as well as various cancers. IFN‐alpha might affect the development of CC in patients with CTO. In this study, we aimed to investigate whether patients with poor CC have higher levels of IFN‐alpha, and if so, whether IFN‐alpha inhibits the development of CC.","Study population This study was conducted in accordance with the Declaration of Helsinki and with written informed consent of each participant. This study was approved by the medical ethics committee of Second Xiangya Hospital of Central South University. Patients undergoing coronary angiography at our catheter laboratory between January 2018 and June 2019 and with at least one major coronary artery total occlusion were enrolled in our study. The exclusion criteria were as follows: (1) patients with ST‐segment elevated myocardial infarction/non‐ST‐segment elevated myocardial infarction, (2) patients with severe cardiac dysfunction/cardiac shock (HYHA IV or Killip IV), (3) patients with coronary artery bypass grafting, (4) patients with malignant tumor, (5) patients with inflammatory or infectious diseases, and (6) patients with severe hepatic (Child class C) and renal dysfunction (glomerular filtration rate <15 mL/min/1.73 m2 or needing hemodialysis). Pei JY and Wang XP were blinded to the characteristics of the included patients. They reviewed the angiography results and classified the extent of coronary circulation by the Rentrop classification from 0 to 3 as follows: 0 = none, 1 = filling of side branches of the artery dilated via collateral channels without visualization of the epicardial segment, 2 = partial filling of the epicardial segment via collateral channels, and 3 = complete filling of the epicardial segment of the artery being dilated via collateral channels.\n6\n Rentrop 0–1 were classified as poor CC development; Rentrop 2–3 were classified as good CC development. Venous blood samples were collected immediately before coronary angiography. Serum was separated at 4°C, and the levels of IFN‐alpha were measured by ELISA (BMS216, Invitrogen).\nThis study was conducted in accordance with the Declaration of Helsinki and with written informed consent of each participant. This study was approved by the medical ethics committee of Second Xiangya Hospital of Central South University. Patients undergoing coronary angiography at our catheter laboratory between January 2018 and June 2019 and with at least one major coronary artery total occlusion were enrolled in our study. The exclusion criteria were as follows: (1) patients with ST‐segment elevated myocardial infarction/non‐ST‐segment elevated myocardial infarction, (2) patients with severe cardiac dysfunction/cardiac shock (HYHA IV or Killip IV), (3) patients with coronary artery bypass grafting, (4) patients with malignant tumor, (5) patients with inflammatory or infectious diseases, and (6) patients with severe hepatic (Child class C) and renal dysfunction (glomerular filtration rate <15 mL/min/1.73 m2 or needing hemodialysis). Pei JY and Wang XP were blinded to the characteristics of the included patients. They reviewed the angiography results and classified the extent of coronary circulation by the Rentrop classification from 0 to 3 as follows: 0 = none, 1 = filling of side branches of the artery dilated via collateral channels without visualization of the epicardial segment, 2 = partial filling of the epicardial segment via collateral channels, and 3 = complete filling of the epicardial segment of the artery being dilated via collateral channels.\n6\n Rentrop 0–1 were classified as poor CC development; Rentrop 2–3 were classified as good CC development. Venous blood samples were collected immediately before coronary angiography. Serum was separated at 4°C, and the levels of IFN‐alpha were measured by ELISA (BMS216, Invitrogen).\nIschemic hind‐limb model and laser speckle imaging The animal protocol was approved by the animal ethics committee of Second Xiangya Hospital of Central South University. All animal works were performed in Second Xiangya Hospital of Central South University. Male C57/BL mice were anesthetized with 3% isoflurane. The left femoral artery was ligated and excised as described previously.\n7\n The right leg underwent sham operation and was used as control. The mice were randomly divided into two groups: an INF‐alpha group with intraperitoneal injection of 2000 IU IFN‐alpha, and a control group with intraperitoneal injection of the same amount of saline. The IFN‐alpha concentration used is comparable to dosages used in patients. Hind‐limb prefusion was measured by laser speckle imaging under temperature‐controlled conditions, before and after left hind‐limb ligation, as well as at 3 days, 1 week, and 3 weeks following ligation. Color‐coded images of the paws representing the flux value were used to calculate the ratio of the tissue perfusion of the occluded (left) to the Sham‐operated (Sham) paw. The right hind‐limb was used as reference. All mice were put to death by cervical dislocation.\nThe animal protocol was approved by the animal ethics committee of Second Xiangya Hospital of Central South University. All animal works were performed in Second Xiangya Hospital of Central South University. Male C57/BL mice were anesthetized with 3% isoflurane. The left femoral artery was ligated and excised as described previously.\n7\n The right leg underwent sham operation and was used as control. The mice were randomly divided into two groups: an INF‐alpha group with intraperitoneal injection of 2000 IU IFN‐alpha, and a control group with intraperitoneal injection of the same amount of saline. The IFN‐alpha concentration used is comparable to dosages used in patients. Hind‐limb prefusion was measured by laser speckle imaging under temperature‐controlled conditions, before and after left hind‐limb ligation, as well as at 3 days, 1 week, and 3 weeks following ligation. Color‐coded images of the paws representing the flux value were used to calculate the ratio of the tissue perfusion of the occluded (left) to the Sham‐operated (Sham) paw. The right hind‐limb was used as reference. All mice were put to death by cervical dislocation.\nHistological analyses Adductor muscles from bilateral hind limbs were harvested at 3 weeks after surgery and underwent immediate tissue fixation overnight. For mouse arteriole density identification, the adductor muscles were stained with alpha‐SMA monoclonal antibody at 3 weeks after ligation.\nAdductor muscles from bilateral hind limbs were harvested at 3 weeks after surgery and underwent immediate tissue fixation overnight. For mouse arteriole density identification, the adductor muscles were stained with alpha‐SMA monoclonal antibody at 3 weeks after ligation.\nStatistical analyses We presented baseline characteristics of patients as frequencies and percentages for categorical variables and as means and standard deviations or interquartile range for continuous variables, depending on whether data distribution was normal (assessed by normal Q‐Q plots). We compared categorical variables using chi‐square analysis, and continuous variables were compared by analysis of variance test or Mann–Whitney U test, according to distribution type. We evaluated the relationship between serum levels of IFN‐alpha and CC development with the use of the levels of IFN‐alpha as both a continuous and a categorical variable. We constructed logistic regression model to calculate the odds ratio (OR) among tertiles. We used the first tertile as reference and used the following three models: model 1: unadjusted; model 2: adjusted age, sex, hypertension, hyperlipidemia, smoking; model 3: adjusted age, sex, hypertension, hyperlipidemia, smoking, uric acid, CRP. We also used a two‐piecewise linear regression model to examine the threshold effect of the levels of IFN‐alpha on the risk of poor CC according to the smoothing plot. The threshold value was determined using a trial method which was to move the trial turning point along the predefined interval and picked up the one which gave maximum model likelihood. A log likelihood ratio test was conducted comparing one‐line linear regression model with two‐piecewise linear model. We further used restricted cubic splines with five knots at the 5th, 35th, 50th, 65th, and 95th centiles to flexibly model the association of the levels of IFN‐alpha with the incidence of good CC adjusted for model 3 where the threshold value served as the reference.\nWe also did several sensitivity analyses by excluding patients with age <55 years or >70 years or by using the quantiles for the levels of serum IFN‐alpha. We performed all of the analyses using R version 3.4.3 (R Foundation for Statistical Computing, Vienna, Austria).\nWe presented baseline characteristics of patients as frequencies and percentages for categorical variables and as means and standard deviations or interquartile range for continuous variables, depending on whether data distribution was normal (assessed by normal Q‐Q plots). We compared categorical variables using chi‐square analysis, and continuous variables were compared by analysis of variance test or Mann–Whitney U test, according to distribution type. We evaluated the relationship between serum levels of IFN‐alpha and CC development with the use of the levels of IFN‐alpha as both a continuous and a categorical variable. We constructed logistic regression model to calculate the odds ratio (OR) among tertiles. We used the first tertile as reference and used the following three models: model 1: unadjusted; model 2: adjusted age, sex, hypertension, hyperlipidemia, smoking; model 3: adjusted age, sex, hypertension, hyperlipidemia, smoking, uric acid, CRP. We also used a two‐piecewise linear regression model to examine the threshold effect of the levels of IFN‐alpha on the risk of poor CC according to the smoothing plot. The threshold value was determined using a trial method which was to move the trial turning point along the predefined interval and picked up the one which gave maximum model likelihood. A log likelihood ratio test was conducted comparing one‐line linear regression model with two‐piecewise linear model. We further used restricted cubic splines with five knots at the 5th, 35th, 50th, 65th, and 95th centiles to flexibly model the association of the levels of IFN‐alpha with the incidence of good CC adjusted for model 3 where the threshold value served as the reference.\nWe also did several sensitivity analyses by excluding patients with age <55 years or >70 years or by using the quantiles for the levels of serum IFN‐alpha. We performed all of the analyses using R version 3.4.3 (R Foundation for Statistical Computing, Vienna, Austria).","This study was conducted in accordance with the Declaration of Helsinki and with written informed consent of each participant. This study was approved by the medical ethics committee of Second Xiangya Hospital of Central South University. Patients undergoing coronary angiography at our catheter laboratory between January 2018 and June 2019 and with at least one major coronary artery total occlusion were enrolled in our study. The exclusion criteria were as follows: (1) patients with ST‐segment elevated myocardial infarction/non‐ST‐segment elevated myocardial infarction, (2) patients with severe cardiac dysfunction/cardiac shock (HYHA IV or Killip IV), (3) patients with coronary artery bypass grafting, (4) patients with malignant tumor, (5) patients with inflammatory or infectious diseases, and (6) patients with severe hepatic (Child class C) and renal dysfunction (glomerular filtration rate <15 mL/min/1.73 m2 or needing hemodialysis). Pei JY and Wang XP were blinded to the characteristics of the included patients. They reviewed the angiography results and classified the extent of coronary circulation by the Rentrop classification from 0 to 3 as follows: 0 = none, 1 = filling of side branches of the artery dilated via collateral channels without visualization of the epicardial segment, 2 = partial filling of the epicardial segment via collateral channels, and 3 = complete filling of the epicardial segment of the artery being dilated via collateral channels.\n6\n Rentrop 0–1 were classified as poor CC development; Rentrop 2–3 were classified as good CC development. Venous blood samples were collected immediately before coronary angiography. Serum was separated at 4°C, and the levels of IFN‐alpha were measured by ELISA (BMS216, Invitrogen).","The animal protocol was approved by the animal ethics committee of Second Xiangya Hospital of Central South University. All animal works were performed in Second Xiangya Hospital of Central South University. Male C57/BL mice were anesthetized with 3% isoflurane. The left femoral artery was ligated and excised as described previously.\n7\n The right leg underwent sham operation and was used as control. The mice were randomly divided into two groups: an INF‐alpha group with intraperitoneal injection of 2000 IU IFN‐alpha, and a control group with intraperitoneal injection of the same amount of saline. The IFN‐alpha concentration used is comparable to dosages used in patients. Hind‐limb prefusion was measured by laser speckle imaging under temperature‐controlled conditions, before and after left hind‐limb ligation, as well as at 3 days, 1 week, and 3 weeks following ligation. Color‐coded images of the paws representing the flux value were used to calculate the ratio of the tissue perfusion of the occluded (left) to the Sham‐operated (Sham) paw. The right hind‐limb was used as reference. All mice were put to death by cervical dislocation.","Adductor muscles from bilateral hind limbs were harvested at 3 weeks after surgery and underwent immediate tissue fixation overnight. For mouse arteriole density identification, the adductor muscles were stained with alpha‐SMA monoclonal antibody at 3 weeks after ligation.","We presented baseline characteristics of patients as frequencies and percentages for categorical variables and as means and standard deviations or interquartile range for continuous variables, depending on whether data distribution was normal (assessed by normal Q‐Q plots). We compared categorical variables using chi‐square analysis, and continuous variables were compared by analysis of variance test or Mann–Whitney U test, according to distribution type. We evaluated the relationship between serum levels of IFN‐alpha and CC development with the use of the levels of IFN‐alpha as both a continuous and a categorical variable. We constructed logistic regression model to calculate the odds ratio (OR) among tertiles. We used the first tertile as reference and used the following three models: model 1: unadjusted; model 2: adjusted age, sex, hypertension, hyperlipidemia, smoking; model 3: adjusted age, sex, hypertension, hyperlipidemia, smoking, uric acid, CRP. We also used a two‐piecewise linear regression model to examine the threshold effect of the levels of IFN‐alpha on the risk of poor CC according to the smoothing plot. The threshold value was determined using a trial method which was to move the trial turning point along the predefined interval and picked up the one which gave maximum model likelihood. A log likelihood ratio test was conducted comparing one‐line linear regression model with two‐piecewise linear model. We further used restricted cubic splines with five knots at the 5th, 35th, 50th, 65th, and 95th centiles to flexibly model the association of the levels of IFN‐alpha with the incidence of good CC adjusted for model 3 where the threshold value served as the reference.\nWe also did several sensitivity analyses by excluding patients with age <55 years or >70 years or by using the quantiles for the levels of serum IFN‐alpha. We performed all of the analyses using R version 3.4.3 (R Foundation for Statistical Computing, Vienna, Austria).","Between January 2018 and June 2019, 208 patients with CTO were included in our study as required by the inclusion and exclusion criteria (Figure S1). The baseline characteristics of the included patients are presented in Table 1. No significant difference between groups was observed except ejection fraction and the levels of serum IFN‐alpha. In patients with poor CC, serum INF‐alpha levels were significantly higher (87.7 ± 42.0 vs. 59.8 ± 18.5, p < .001), and ejection fraction was significantly lower (51.2 ± 10.5 vs. 59.8 ± 18.5, p = .04) than in patients with good CC.\nBaseline characteristics of included patients\nAbbreviation: CC, collateral circulation; CRP, C‐reactive protein; LAD, left anterior descending; LCX, left circumflex; LDL, low density lipoprotein; PLT, platelet count; RCA, right coronary artery; WBC, white blood cells.\nANOVA showed that the serum levels of IFN‐alpha were significantly associated with CC, as did the Rentrop score (Figure 1). In a multivariate regression analysis, the risk of poor CC was increased with increasing IFN‐alpha tertile. Compared with the first tertile, the risk of poor CC was 3.67 (95% CI: 1.18–7.43) for model 1, 4.37 (95% CI: 2.06–9.26) for model 2, and 4.79 (95% CI: 2.22–10.4) for model 3 (Table 2). After adjusting for model 3, the cubic spline‐smoothing curve shows that the risk of poor CC increased with increasing serum IFN‐alpha levels (Figure 2). The incidence of poor CC increased with the serum levels of IFN‐alpha when the serum levels of IFN‐alpha were less than 112 pg/mL (OR: 1.0348, 95% CI: 1.0195–1.0577, p < .001, Table 3). When the levels of IFN‐alpha were larger than 112 pg/mL, the risk of poor CC did not increase with increasing levels of IFN‐alpha (OR: 1.0036, 95% CI: 0.9748–1.033, p = .807, Table 3). Our results remained robust in sensitivity analyses when we excluded patients aged >70 years or <55 years, as well as when using the fourth tertiles for the levels of serum IFN‐alpha (Tables S1–S3).\nCorrelation between the serum levels of IFN‐alpha and Rentrop scores. The levels in patients with Rentrop scores 0 and 1, or Rentrop 2 and 3, were not significantly different. The serum levels of IFN‐alpha in CTO patients with Rentrop scores of 2 and 3 were significantly different from those with Rentrop scores of 0 and 1\nOR (95% CI) of poor CC according to tertiles of the levels of serum IFN‐alpha\n\nNote: Model 1: unadjusted; model 2: adjusted age, sex, hypertension, hyperlipidemia, smoking; model 3: adjusted age, sex, hypertension, hyperlipidemia, smoking, uric acid, CRP.\nRelationship between the serum levels of IFN‐alpha and CC. A nonlinear relationship was observed between the serum levels of IFN‐alpha and CC after adjusting for model 3. Threshold effect analysis found that when serum levels of IFN‐alpha were larger than 112 pg/mL, the risks of poor CC did not keep on increasing\nThreshold effect analysis of the serum levels of IFN‐alpha on poor CC\n\nNote: When the levels of IFN‐alpha larger than 112 pg/mL, the incidence of poor CC did not increase with the increase of the levels of IFN‐alpha. Adjusted for model 3.\nAbbreviation: CC, coronary collateral circulation.\nTo assess the in vivo effect of IFN‐alpha on CC, we examined whether intraperitoneal injection of IFN‐alpha at 2000 IU/d inhibited blood perfusion recovery in a mouse hind‐limb. Laser speckle imaging showed that blood flow recovery was significantly impaired in mice treated with IFN‐alpha compared with control mice at 1 week (41.7% ± 2.66% vs. 47.3% ± 2.16%, p = 0.020), at 14 days (59.8% ± 3.31% vs. 66.5% ± 3.21%, p = .005) and at 21 days (59.8% ± 3.31% vs. 66.5% ± 3.21%, p = .005, Figure 3A,B). Although the numbers of arterioles are comparable between groups, the diameter of the arterioles in the mice treated with IFN‐alpha was smaller compared with the control group (Figure 4A). In order to study local ischemia, we examined the morphology in the gastrocnemius muscle by HE staining and Masson staining. Although we did not find obvious morphological changes by HE analysis (Figure S2), the area of interstitial fibrosis evaluated by Masson staining in the mice treated with IFN‐alpha was larger than in the control group (Figure 4B).\nINF‐alpha impairs the development of CC in a murine hindlimb ischemic model. (A) Representative laser speckle perfusion images. (B) Quantification of laser speckle perfusion (ischemic/nonischemic) in control and IFN‐alpha treated mice over time\nImmunohistochemistry of SMA in cross‐sections of the adductor muscles or Masson staining of gastrocnemius muscle collected from the control and IFN‐alpha group 3 weeks after femoral artery ligation. (A) Representative immunohistochemistry and quantification of CC numbers and diameter of adductor muscles. (B) Representative Masson staining and quantification of interstitial fibrosis of gastrocnemius muscle","In this study, we first found that CTO patients with poor CC have higher serum levels of IFN‐alpha. We found that IFN‐alpha inhibits the development of CC after artery occlusion.\nThe development of CC has a close relationship with multiple factors affecting coronary occlusion, such as diabetes mellitus, exercise, age, and other factors.\n8\n, \n9\n, \n10\n Recent studies have found that inflammatory factors are involved in the progress of CC. Fan et al. found that C‐reactive protein was positively associated with poor CC in patients with coronary artery disease.\n11\n Rakhit et al.\n12\n found that tumor necrosis factor alpha (TNF‐alpha) was inversely correlated with collateral flow index (CFI), whereas IL‐6 was positively correlated with CFI. However, these case–control studies did not determine the cause and effect relationship between inflammatory factors and the development of CC. We further investigated this cause and effect relationship in animal experiments and found that IFN‐alpha inhibited the development of CC after hind‐limb ischemia.\nThe development of CC depends on local activation of endothelial cells, the eNOS signaling pathway.\n13\n Previous study has found IFN‐alpha inhabited the expression and phosphorylation of eNOS in endothelial cells.\n14\n Epstein found that aging caused collateral rarefaction by inhibiting eNOS; overexpression of eNOS restored normal CC.\n15\n Furthermore, exercise promoted CC by promoting the expression and phosphorylation of eNOS. Therefore, eNOS is an essential factor for the development of CC. IFN‐alpha inhibits the expression and phosphorylation of eNOS, which may impair the development of CC. The positive remodeling of an arteriole into an artery up to 12–20 times its original size requires the proliferation and migration of endothelial cells and VSMCs.\n16\n, \n17\n Relevant study also found inflammatory factors, such as hs‐CRP, TNF‐alpha IFN‐alpha, inhibits the proliferation and migration of endothelial cells.\n11\n, \n12\n This may be another reason for patients with high serum levels of IFN‐alpha have impaired CC.\nLimitation First, the main limitation of present study is the small number of patients with CTO. However, the sensitivity analysis showed the robustness of our results. Second, our study did not include all inflammatory and angiogenic factors, they may have a role in the development of CC. Third, we evaluated the extent of CC by Rentrop classification instead of CFI. Although collateral is more accurate than Rentrop classification, it is an invasive procedure needing pressure wire. Rentrop classification is more often used in routine clinical practice. Fourth, we evaluated the development of CC in a model of hind‐limb ischemia rather than heart ischemia. However, both development of lower limb CC and coronary CC share common cellular and molecular mechanism: arteriogenesis. Ischemic hind‐limb model is an ideal model for arteriogenesis including coronary circulation.\n4\n, \n5\n Ischemic hind‐limb model mimics aspects of human occlusive artery disease to investigate vascular regeneration and to test therapeutical approaches in a reproducible manner.\nFirst, the main limitation of present study is the small number of patients with CTO. However, the sensitivity analysis showed the robustness of our results. Second, our study did not include all inflammatory and angiogenic factors, they may have a role in the development of CC. Third, we evaluated the extent of CC by Rentrop classification instead of CFI. Although collateral is more accurate than Rentrop classification, it is an invasive procedure needing pressure wire. Rentrop classification is more often used in routine clinical practice. Fourth, we evaluated the development of CC in a model of hind‐limb ischemia rather than heart ischemia. However, both development of lower limb CC and coronary CC share common cellular and molecular mechanism: arteriogenesis. Ischemic hind‐limb model is an ideal model for arteriogenesis including coronary circulation.\n4\n, \n5\n Ischemic hind‐limb model mimics aspects of human occlusive artery disease to investigate vascular regeneration and to test therapeutical approaches in a reproducible manner.","First, the main limitation of present study is the small number of patients with CTO. However, the sensitivity analysis showed the robustness of our results. Second, our study did not include all inflammatory and angiogenic factors, they may have a role in the development of CC. Third, we evaluated the extent of CC by Rentrop classification instead of CFI. Although collateral is more accurate than Rentrop classification, it is an invasive procedure needing pressure wire. Rentrop classification is more often used in routine clinical practice. Fourth, we evaluated the development of CC in a model of hind‐limb ischemia rather than heart ischemia. However, both development of lower limb CC and coronary CC share common cellular and molecular mechanism: arteriogenesis. Ischemic hind‐limb model is an ideal model for arteriogenesis including coronary circulation.\n4\n, \n5\n Ischemic hind‐limb model mimics aspects of human occlusive artery disease to investigate vascular regeneration and to test therapeutical approaches in a reproducible manner.","CTO patients with poor CC have higher serum levels of IFN‐alpha than CTO patients with good CC. IFN‐alpha might impair the development of CC after artery occlusion.","The authors declare that they have no potential conflicts of interests.","Xinqun Hu and Zhenhua Xing designed the study and provided methodological expertise. Zhenhua Xing drafted the manuscript. Zhenhua Xing performed the case–control study. Xiaopu Wang, Junyu Pei, Zhaowei Zhu, Shi Tai performed the animal experiments. All authors have read, provided critical feedback on, and approved the final manuscript.","\nSupplementary Table S1. OR (95% CI) of poor CC according to fourths of the levels of serum IFN‐alpha\n\nSupplementary Table S2. OR (95% CI) of poor CC by excluding patients with age >70 years\n\nSupplementary Table S3. OR (95% CI) of poor CC by excluding patients with age <55 years\n\nSupplementary Figure S1. Chart of inclusion and exclusion of present study\n\nSupplementary Figure S2. HE staining of the left and right sides of gastrocnemius muscle.no obvious morphological changes were found\nClick here for additional data file."],"string":"[\n \"Development of collateral circulation (CC), also termed as arteriogenesis, is a natural life‐preservation mechanism occurring in patients with coronary chronic total occlusion (CTO). Well‐developed CC preserves cardiac function, thus reducing cardiac mortality after coronary occlusion.\\n1\\n, \\n2\\n Patients with CTO show a great deal of heterogeneity in their arteriogenic responses to artery occlusion. This is affected by multiple factors, including inflammatory factors.\\n3\\n The interaction between inflammation and arteriogenesis may be worthy of study, but data on the correlation between arteriogenesis and inflammation are limited. A previous study has found that interferon (IFN) signaling in monocytes is enhanced and stimulated by lipopolysaccharide among patients with impaired coronary CC.\\n4\\n Furthermore, blocking the receptor of IFN have been found to be helpful in alleviating ischemia in mice.\\n5\\n However, whether patients with poor CC have higher levels of serum IFN or whether treatment with IFN inhibits the development of arteriogenesis remains unknown. IFN‐alpha is widely used in treating hepatitis B and C, as well as various cancers. IFN‐alpha might affect the development of CC in patients with CTO. In this study, we aimed to investigate whether patients with poor CC have higher levels of IFN‐alpha, and if so, whether IFN‐alpha inhibits the development of CC.\",\n \"Study population This study was conducted in accordance with the Declaration of Helsinki and with written informed consent of each participant. This study was approved by the medical ethics committee of Second Xiangya Hospital of Central South University. Patients undergoing coronary angiography at our catheter laboratory between January 2018 and June 2019 and with at least one major coronary artery total occlusion were enrolled in our study. The exclusion criteria were as follows: (1) patients with ST‐segment elevated myocardial infarction/non‐ST‐segment elevated myocardial infarction, (2) patients with severe cardiac dysfunction/cardiac shock (HYHA IV or Killip IV), (3) patients with coronary artery bypass grafting, (4) patients with malignant tumor, (5) patients with inflammatory or infectious diseases, and (6) patients with severe hepatic (Child class C) and renal dysfunction (glomerular filtration rate <15 mL/min/1.73 m2 or needing hemodialysis). Pei JY and Wang XP were blinded to the characteristics of the included patients. They reviewed the angiography results and classified the extent of coronary circulation by the Rentrop classification from 0 to 3 as follows: 0 = none, 1 = filling of side branches of the artery dilated via collateral channels without visualization of the epicardial segment, 2 = partial filling of the epicardial segment via collateral channels, and 3 = complete filling of the epicardial segment of the artery being dilated via collateral channels.\\n6\\n Rentrop 0–1 were classified as poor CC development; Rentrop 2–3 were classified as good CC development. Venous blood samples were collected immediately before coronary angiography. Serum was separated at 4°C, and the levels of IFN‐alpha were measured by ELISA (BMS216, Invitrogen).\\nThis study was conducted in accordance with the Declaration of Helsinki and with written informed consent of each participant. This study was approved by the medical ethics committee of Second Xiangya Hospital of Central South University. Patients undergoing coronary angiography at our catheter laboratory between January 2018 and June 2019 and with at least one major coronary artery total occlusion were enrolled in our study. The exclusion criteria were as follows: (1) patients with ST‐segment elevated myocardial infarction/non‐ST‐segment elevated myocardial infarction, (2) patients with severe cardiac dysfunction/cardiac shock (HYHA IV or Killip IV), (3) patients with coronary artery bypass grafting, (4) patients with malignant tumor, (5) patients with inflammatory or infectious diseases, and (6) patients with severe hepatic (Child class C) and renal dysfunction (glomerular filtration rate <15 mL/min/1.73 m2 or needing hemodialysis). Pei JY and Wang XP were blinded to the characteristics of the included patients. They reviewed the angiography results and classified the extent of coronary circulation by the Rentrop classification from 0 to 3 as follows: 0 = none, 1 = filling of side branches of the artery dilated via collateral channels without visualization of the epicardial segment, 2 = partial filling of the epicardial segment via collateral channels, and 3 = complete filling of the epicardial segment of the artery being dilated via collateral channels.\\n6\\n Rentrop 0–1 were classified as poor CC development; Rentrop 2–3 were classified as good CC development. Venous blood samples were collected immediately before coronary angiography. Serum was separated at 4°C, and the levels of IFN‐alpha were measured by ELISA (BMS216, Invitrogen).\\nIschemic hind‐limb model and laser speckle imaging The animal protocol was approved by the animal ethics committee of Second Xiangya Hospital of Central South University. All animal works were performed in Second Xiangya Hospital of Central South University. Male C57/BL mice were anesthetized with 3% isoflurane. The left femoral artery was ligated and excised as described previously.\\n7\\n The right leg underwent sham operation and was used as control. The mice were randomly divided into two groups: an INF‐alpha group with intraperitoneal injection of 2000 IU IFN‐alpha, and a control group with intraperitoneal injection of the same amount of saline. The IFN‐alpha concentration used is comparable to dosages used in patients. Hind‐limb prefusion was measured by laser speckle imaging under temperature‐controlled conditions, before and after left hind‐limb ligation, as well as at 3 days, 1 week, and 3 weeks following ligation. Color‐coded images of the paws representing the flux value were used to calculate the ratio of the tissue perfusion of the occluded (left) to the Sham‐operated (Sham) paw. The right hind‐limb was used as reference. All mice were put to death by cervical dislocation.\\nThe animal protocol was approved by the animal ethics committee of Second Xiangya Hospital of Central South University. All animal works were performed in Second Xiangya Hospital of Central South University. Male C57/BL mice were anesthetized with 3% isoflurane. The left femoral artery was ligated and excised as described previously.\\n7\\n The right leg underwent sham operation and was used as control. The mice were randomly divided into two groups: an INF‐alpha group with intraperitoneal injection of 2000 IU IFN‐alpha, and a control group with intraperitoneal injection of the same amount of saline. The IFN‐alpha concentration used is comparable to dosages used in patients. Hind‐limb prefusion was measured by laser speckle imaging under temperature‐controlled conditions, before and after left hind‐limb ligation, as well as at 3 days, 1 week, and 3 weeks following ligation. Color‐coded images of the paws representing the flux value were used to calculate the ratio of the tissue perfusion of the occluded (left) to the Sham‐operated (Sham) paw. The right hind‐limb was used as reference. All mice were put to death by cervical dislocation.\\nHistological analyses Adductor muscles from bilateral hind limbs were harvested at 3 weeks after surgery and underwent immediate tissue fixation overnight. For mouse arteriole density identification, the adductor muscles were stained with alpha‐SMA monoclonal antibody at 3 weeks after ligation.\\nAdductor muscles from bilateral hind limbs were harvested at 3 weeks after surgery and underwent immediate tissue fixation overnight. For mouse arteriole density identification, the adductor muscles were stained with alpha‐SMA monoclonal antibody at 3 weeks after ligation.\\nStatistical analyses We presented baseline characteristics of patients as frequencies and percentages for categorical variables and as means and standard deviations or interquartile range for continuous variables, depending on whether data distribution was normal (assessed by normal Q‐Q plots). We compared categorical variables using chi‐square analysis, and continuous variables were compared by analysis of variance test or Mann–Whitney U test, according to distribution type. We evaluated the relationship between serum levels of IFN‐alpha and CC development with the use of the levels of IFN‐alpha as both a continuous and a categorical variable. We constructed logistic regression model to calculate the odds ratio (OR) among tertiles. We used the first tertile as reference and used the following three models: model 1: unadjusted; model 2: adjusted age, sex, hypertension, hyperlipidemia, smoking; model 3: adjusted age, sex, hypertension, hyperlipidemia, smoking, uric acid, CRP. We also used a two‐piecewise linear regression model to examine the threshold effect of the levels of IFN‐alpha on the risk of poor CC according to the smoothing plot. The threshold value was determined using a trial method which was to move the trial turning point along the predefined interval and picked up the one which gave maximum model likelihood. A log likelihood ratio test was conducted comparing one‐line linear regression model with two‐piecewise linear model. We further used restricted cubic splines with five knots at the 5th, 35th, 50th, 65th, and 95th centiles to flexibly model the association of the levels of IFN‐alpha with the incidence of good CC adjusted for model 3 where the threshold value served as the reference.\\nWe also did several sensitivity analyses by excluding patients with age <55 years or >70 years or by using the quantiles for the levels of serum IFN‐alpha. We performed all of the analyses using R version 3.4.3 (R Foundation for Statistical Computing, Vienna, Austria).\\nWe presented baseline characteristics of patients as frequencies and percentages for categorical variables and as means and standard deviations or interquartile range for continuous variables, depending on whether data distribution was normal (assessed by normal Q‐Q plots). We compared categorical variables using chi‐square analysis, and continuous variables were compared by analysis of variance test or Mann–Whitney U test, according to distribution type. We evaluated the relationship between serum levels of IFN‐alpha and CC development with the use of the levels of IFN‐alpha as both a continuous and a categorical variable. We constructed logistic regression model to calculate the odds ratio (OR) among tertiles. We used the first tertile as reference and used the following three models: model 1: unadjusted; model 2: adjusted age, sex, hypertension, hyperlipidemia, smoking; model 3: adjusted age, sex, hypertension, hyperlipidemia, smoking, uric acid, CRP. We also used a two‐piecewise linear regression model to examine the threshold effect of the levels of IFN‐alpha on the risk of poor CC according to the smoothing plot. The threshold value was determined using a trial method which was to move the trial turning point along the predefined interval and picked up the one which gave maximum model likelihood. A log likelihood ratio test was conducted comparing one‐line linear regression model with two‐piecewise linear model. We further used restricted cubic splines with five knots at the 5th, 35th, 50th, 65th, and 95th centiles to flexibly model the association of the levels of IFN‐alpha with the incidence of good CC adjusted for model 3 where the threshold value served as the reference.\\nWe also did several sensitivity analyses by excluding patients with age <55 years or >70 years or by using the quantiles for the levels of serum IFN‐alpha. We performed all of the analyses using R version 3.4.3 (R Foundation for Statistical Computing, Vienna, Austria).\",\n \"This study was conducted in accordance with the Declaration of Helsinki and with written informed consent of each participant. This study was approved by the medical ethics committee of Second Xiangya Hospital of Central South University. Patients undergoing coronary angiography at our catheter laboratory between January 2018 and June 2019 and with at least one major coronary artery total occlusion were enrolled in our study. The exclusion criteria were as follows: (1) patients with ST‐segment elevated myocardial infarction/non‐ST‐segment elevated myocardial infarction, (2) patients with severe cardiac dysfunction/cardiac shock (HYHA IV or Killip IV), (3) patients with coronary artery bypass grafting, (4) patients with malignant tumor, (5) patients with inflammatory or infectious diseases, and (6) patients with severe hepatic (Child class C) and renal dysfunction (glomerular filtration rate <15 mL/min/1.73 m2 or needing hemodialysis). Pei JY and Wang XP were blinded to the characteristics of the included patients. They reviewed the angiography results and classified the extent of coronary circulation by the Rentrop classification from 0 to 3 as follows: 0 = none, 1 = filling of side branches of the artery dilated via collateral channels without visualization of the epicardial segment, 2 = partial filling of the epicardial segment via collateral channels, and 3 = complete filling of the epicardial segment of the artery being dilated via collateral channels.\\n6\\n Rentrop 0–1 were classified as poor CC development; Rentrop 2–3 were classified as good CC development. Venous blood samples were collected immediately before coronary angiography. Serum was separated at 4°C, and the levels of IFN‐alpha were measured by ELISA (BMS216, Invitrogen).\",\n \"The animal protocol was approved by the animal ethics committee of Second Xiangya Hospital of Central South University. All animal works were performed in Second Xiangya Hospital of Central South University. Male C57/BL mice were anesthetized with 3% isoflurane. The left femoral artery was ligated and excised as described previously.\\n7\\n The right leg underwent sham operation and was used as control. The mice were randomly divided into two groups: an INF‐alpha group with intraperitoneal injection of 2000 IU IFN‐alpha, and a control group with intraperitoneal injection of the same amount of saline. The IFN‐alpha concentration used is comparable to dosages used in patients. Hind‐limb prefusion was measured by laser speckle imaging under temperature‐controlled conditions, before and after left hind‐limb ligation, as well as at 3 days, 1 week, and 3 weeks following ligation. Color‐coded images of the paws representing the flux value were used to calculate the ratio of the tissue perfusion of the occluded (left) to the Sham‐operated (Sham) paw. The right hind‐limb was used as reference. All mice were put to death by cervical dislocation.\",\n \"Adductor muscles from bilateral hind limbs were harvested at 3 weeks after surgery and underwent immediate tissue fixation overnight. For mouse arteriole density identification, the adductor muscles were stained with alpha‐SMA monoclonal antibody at 3 weeks after ligation.\",\n \"We presented baseline characteristics of patients as frequencies and percentages for categorical variables and as means and standard deviations or interquartile range for continuous variables, depending on whether data distribution was normal (assessed by normal Q‐Q plots). We compared categorical variables using chi‐square analysis, and continuous variables were compared by analysis of variance test or Mann–Whitney U test, according to distribution type. We evaluated the relationship between serum levels of IFN‐alpha and CC development with the use of the levels of IFN‐alpha as both a continuous and a categorical variable. We constructed logistic regression model to calculate the odds ratio (OR) among tertiles. We used the first tertile as reference and used the following three models: model 1: unadjusted; model 2: adjusted age, sex, hypertension, hyperlipidemia, smoking; model 3: adjusted age, sex, hypertension, hyperlipidemia, smoking, uric acid, CRP. We also used a two‐piecewise linear regression model to examine the threshold effect of the levels of IFN‐alpha on the risk of poor CC according to the smoothing plot. The threshold value was determined using a trial method which was to move the trial turning point along the predefined interval and picked up the one which gave maximum model likelihood. A log likelihood ratio test was conducted comparing one‐line linear regression model with two‐piecewise linear model. We further used restricted cubic splines with five knots at the 5th, 35th, 50th, 65th, and 95th centiles to flexibly model the association of the levels of IFN‐alpha with the incidence of good CC adjusted for model 3 where the threshold value served as the reference.\\nWe also did several sensitivity analyses by excluding patients with age <55 years or >70 years or by using the quantiles for the levels of serum IFN‐alpha. We performed all of the analyses using R version 3.4.3 (R Foundation for Statistical Computing, Vienna, Austria).\",\n \"Between January 2018 and June 2019, 208 patients with CTO were included in our study as required by the inclusion and exclusion criteria (Figure S1). The baseline characteristics of the included patients are presented in Table 1. No significant difference between groups was observed except ejection fraction and the levels of serum IFN‐alpha. In patients with poor CC, serum INF‐alpha levels were significantly higher (87.7 ± 42.0 vs. 59.8 ± 18.5, p < .001), and ejection fraction was significantly lower (51.2 ± 10.5 vs. 59.8 ± 18.5, p = .04) than in patients with good CC.\\nBaseline characteristics of included patients\\nAbbreviation: CC, collateral circulation; CRP, C‐reactive protein; LAD, left anterior descending; LCX, left circumflex; LDL, low density lipoprotein; PLT, platelet count; RCA, right coronary artery; WBC, white blood cells.\\nANOVA showed that the serum levels of IFN‐alpha were significantly associated with CC, as did the Rentrop score (Figure 1). In a multivariate regression analysis, the risk of poor CC was increased with increasing IFN‐alpha tertile. Compared with the first tertile, the risk of poor CC was 3.67 (95% CI: 1.18–7.43) for model 1, 4.37 (95% CI: 2.06–9.26) for model 2, and 4.79 (95% CI: 2.22–10.4) for model 3 (Table 2). After adjusting for model 3, the cubic spline‐smoothing curve shows that the risk of poor CC increased with increasing serum IFN‐alpha levels (Figure 2). The incidence of poor CC increased with the serum levels of IFN‐alpha when the serum levels of IFN‐alpha were less than 112 pg/mL (OR: 1.0348, 95% CI: 1.0195–1.0577, p < .001, Table 3). When the levels of IFN‐alpha were larger than 112 pg/mL, the risk of poor CC did not increase with increasing levels of IFN‐alpha (OR: 1.0036, 95% CI: 0.9748–1.033, p = .807, Table 3). Our results remained robust in sensitivity analyses when we excluded patients aged >70 years or <55 years, as well as when using the fourth tertiles for the levels of serum IFN‐alpha (Tables S1–S3).\\nCorrelation between the serum levels of IFN‐alpha and Rentrop scores. The levels in patients with Rentrop scores 0 and 1, or Rentrop 2 and 3, were not significantly different. The serum levels of IFN‐alpha in CTO patients with Rentrop scores of 2 and 3 were significantly different from those with Rentrop scores of 0 and 1\\nOR (95% CI) of poor CC according to tertiles of the levels of serum IFN‐alpha\\n\\nNote: Model 1: unadjusted; model 2: adjusted age, sex, hypertension, hyperlipidemia, smoking; model 3: adjusted age, sex, hypertension, hyperlipidemia, smoking, uric acid, CRP.\\nRelationship between the serum levels of IFN‐alpha and CC. A nonlinear relationship was observed between the serum levels of IFN‐alpha and CC after adjusting for model 3. Threshold effect analysis found that when serum levels of IFN‐alpha were larger than 112 pg/mL, the risks of poor CC did not keep on increasing\\nThreshold effect analysis of the serum levels of IFN‐alpha on poor CC\\n\\nNote: When the levels of IFN‐alpha larger than 112 pg/mL, the incidence of poor CC did not increase with the increase of the levels of IFN‐alpha. Adjusted for model 3.\\nAbbreviation: CC, coronary collateral circulation.\\nTo assess the in vivo effect of IFN‐alpha on CC, we examined whether intraperitoneal injection of IFN‐alpha at 2000 IU/d inhibited blood perfusion recovery in a mouse hind‐limb. Laser speckle imaging showed that blood flow recovery was significantly impaired in mice treated with IFN‐alpha compared with control mice at 1 week (41.7% ± 2.66% vs. 47.3% ± 2.16%, p = 0.020), at 14 days (59.8% ± 3.31% vs. 66.5% ± 3.21%, p = .005) and at 21 days (59.8% ± 3.31% vs. 66.5% ± 3.21%, p = .005, Figure 3A,B). Although the numbers of arterioles are comparable between groups, the diameter of the arterioles in the mice treated with IFN‐alpha was smaller compared with the control group (Figure 4A). In order to study local ischemia, we examined the morphology in the gastrocnemius muscle by HE staining and Masson staining. Although we did not find obvious morphological changes by HE analysis (Figure S2), the area of interstitial fibrosis evaluated by Masson staining in the mice treated with IFN‐alpha was larger than in the control group (Figure 4B).\\nINF‐alpha impairs the development of CC in a murine hindlimb ischemic model. (A) Representative laser speckle perfusion images. (B) Quantification of laser speckle perfusion (ischemic/nonischemic) in control and IFN‐alpha treated mice over time\\nImmunohistochemistry of SMA in cross‐sections of the adductor muscles or Masson staining of gastrocnemius muscle collected from the control and IFN‐alpha group 3 weeks after femoral artery ligation. (A) Representative immunohistochemistry and quantification of CC numbers and diameter of adductor muscles. (B) Representative Masson staining and quantification of interstitial fibrosis of gastrocnemius muscle\",\n \"In this study, we first found that CTO patients with poor CC have higher serum levels of IFN‐alpha. We found that IFN‐alpha inhibits the development of CC after artery occlusion.\\nThe development of CC has a close relationship with multiple factors affecting coronary occlusion, such as diabetes mellitus, exercise, age, and other factors.\\n8\\n, \\n9\\n, \\n10\\n Recent studies have found that inflammatory factors are involved in the progress of CC. Fan et al. found that C‐reactive protein was positively associated with poor CC in patients with coronary artery disease.\\n11\\n Rakhit et al.\\n12\\n found that tumor necrosis factor alpha (TNF‐alpha) was inversely correlated with collateral flow index (CFI), whereas IL‐6 was positively correlated with CFI. However, these case–control studies did not determine the cause and effect relationship between inflammatory factors and the development of CC. We further investigated this cause and effect relationship in animal experiments and found that IFN‐alpha inhibited the development of CC after hind‐limb ischemia.\\nThe development of CC depends on local activation of endothelial cells, the eNOS signaling pathway.\\n13\\n Previous study has found IFN‐alpha inhabited the expression and phosphorylation of eNOS in endothelial cells.\\n14\\n Epstein found that aging caused collateral rarefaction by inhibiting eNOS; overexpression of eNOS restored normal CC.\\n15\\n Furthermore, exercise promoted CC by promoting the expression and phosphorylation of eNOS. Therefore, eNOS is an essential factor for the development of CC. IFN‐alpha inhibits the expression and phosphorylation of eNOS, which may impair the development of CC. The positive remodeling of an arteriole into an artery up to 12–20 times its original size requires the proliferation and migration of endothelial cells and VSMCs.\\n16\\n, \\n17\\n Relevant study also found inflammatory factors, such as hs‐CRP, TNF‐alpha IFN‐alpha, inhibits the proliferation and migration of endothelial cells.\\n11\\n, \\n12\\n This may be another reason for patients with high serum levels of IFN‐alpha have impaired CC.\\nLimitation First, the main limitation of present study is the small number of patients with CTO. However, the sensitivity analysis showed the robustness of our results. Second, our study did not include all inflammatory and angiogenic factors, they may have a role in the development of CC. Third, we evaluated the extent of CC by Rentrop classification instead of CFI. Although collateral is more accurate than Rentrop classification, it is an invasive procedure needing pressure wire. Rentrop classification is more often used in routine clinical practice. Fourth, we evaluated the development of CC in a model of hind‐limb ischemia rather than heart ischemia. However, both development of lower limb CC and coronary CC share common cellular and molecular mechanism: arteriogenesis. Ischemic hind‐limb model is an ideal model for arteriogenesis including coronary circulation.\\n4\\n, \\n5\\n Ischemic hind‐limb model mimics aspects of human occlusive artery disease to investigate vascular regeneration and to test therapeutical approaches in a reproducible manner.\\nFirst, the main limitation of present study is the small number of patients with CTO. However, the sensitivity analysis showed the robustness of our results. Second, our study did not include all inflammatory and angiogenic factors, they may have a role in the development of CC. Third, we evaluated the extent of CC by Rentrop classification instead of CFI. Although collateral is more accurate than Rentrop classification, it is an invasive procedure needing pressure wire. Rentrop classification is more often used in routine clinical practice. Fourth, we evaluated the development of CC in a model of hind‐limb ischemia rather than heart ischemia. However, both development of lower limb CC and coronary CC share common cellular and molecular mechanism: arteriogenesis. Ischemic hind‐limb model is an ideal model for arteriogenesis including coronary circulation.\\n4\\n, \\n5\\n Ischemic hind‐limb model mimics aspects of human occlusive artery disease to investigate vascular regeneration and to test therapeutical approaches in a reproducible manner.\",\n \"First, the main limitation of present study is the small number of patients with CTO. However, the sensitivity analysis showed the robustness of our results. Second, our study did not include all inflammatory and angiogenic factors, they may have a role in the development of CC. Third, we evaluated the extent of CC by Rentrop classification instead of CFI. Although collateral is more accurate than Rentrop classification, it is an invasive procedure needing pressure wire. Rentrop classification is more often used in routine clinical practice. Fourth, we evaluated the development of CC in a model of hind‐limb ischemia rather than heart ischemia. However, both development of lower limb CC and coronary CC share common cellular and molecular mechanism: arteriogenesis. Ischemic hind‐limb model is an ideal model for arteriogenesis including coronary circulation.\\n4\\n, \\n5\\n Ischemic hind‐limb model mimics aspects of human occlusive artery disease to investigate vascular regeneration and to test therapeutical approaches in a reproducible manner.\",\n \"CTO patients with poor CC have higher serum levels of IFN‐alpha than CTO patients with good CC. IFN‐alpha might impair the development of CC after artery occlusion.\",\n \"The authors declare that they have no potential conflicts of interests.\",\n \"Xinqun Hu and Zhenhua Xing designed the study and provided methodological expertise. Zhenhua Xing drafted the manuscript. Zhenhua Xing performed the case–control study. Xiaopu Wang, Junyu Pei, Zhaowei Zhu, Shi Tai performed the animal experiments. All authors have read, provided critical feedback on, and approved the final manuscript.\",\n \"\\nSupplementary Table S1. OR (95% CI) of poor CC according to fourths of the levels of serum IFN‐alpha\\n\\nSupplementary Table S2. OR (95% CI) of poor CC by excluding patients with age >70 years\\n\\nSupplementary Table S3. OR (95% CI) of poor CC by excluding patients with age <55 years\\n\\nSupplementary Figure S1. Chart of inclusion and exclusion of present study\\n\\nSupplementary Figure S2. HE staining of the left and right sides of gastrocnemius muscle.no obvious morphological changes were found\\nClick here for additional data file.\"\n]"},"all_sections_sec_types":{"kind":"list like","value":["background","materials-and-methods",null,null,null,null,"results","discussion",null,"conclusions","COI-statement",null,"supplementary-material"],"string":"[\n \"background\",\n \"materials-and-methods\",\n null,\n null,\n null,\n null,\n \"results\",\n \"discussion\",\n null,\n \"conclusions\",\n \"COI-statement\",\n null,\n \"supplementary-material\"\n]"},"keywords":{"kind":"list like","value":["collateral circulation","coronary chronic total occlusion","IFN‐alpha"],"string":"[\n \"collateral circulation\",\n \"coronary chronic total occlusion\",\n \"IFN‐alpha\"\n]"},"whole_article_text":{"kind":"string","value":"BACKGROUND:\nDevelopment of collateral circulation (CC), also termed as arteriogenesis, is a natural life‐preservation mechanism occurring in patients with coronary chronic total occlusion (CTO). Well‐developed CC preserves cardiac function, thus reducing cardiac mortality after coronary occlusion.\n1\n, \n2\n Patients with CTO show a great deal of heterogeneity in their arteriogenic responses to artery occlusion. This is affected by multiple factors, including inflammatory factors.\n3\n The interaction between inflammation and arteriogenesis may be worthy of study, but data on the correlation between arteriogenesis and inflammation are limited. A previous study has found that interferon (IFN) signaling in monocytes is enhanced and stimulated by lipopolysaccharide among patients with impaired coronary CC.\n4\n Furthermore, blocking the receptor of IFN have been found to be helpful in alleviating ischemia in mice.\n5\n However, whether patients with poor CC have higher levels of serum IFN or whether treatment with IFN inhibits the development of arteriogenesis remains unknown. IFN‐alpha is widely used in treating hepatitis B and C, as well as various cancers. IFN‐alpha might affect the development of CC in patients with CTO. In this study, we aimed to investigate whether patients with poor CC have higher levels of IFN‐alpha, and if so, whether IFN‐alpha inhibits the development of CC.\n\nMATERIALS AND METHODS:\nStudy population This study was conducted in accordance with the Declaration of Helsinki and with written informed consent of each participant. This study was approved by the medical ethics committee of Second Xiangya Hospital of Central South University. Patients undergoing coronary angiography at our catheter laboratory between January 2018 and June 2019 and with at least one major coronary artery total occlusion were enrolled in our study. The exclusion criteria were as follows: (1) patients with ST‐segment elevated myocardial infarction/non‐ST‐segment elevated myocardial infarction, (2) patients with severe cardiac dysfunction/cardiac shock (HYHA IV or Killip IV), (3) patients with coronary artery bypass grafting, (4) patients with malignant tumor, (5) patients with inflammatory or infectious diseases, and (6) patients with severe hepatic (Child class C) and renal dysfunction (glomerular filtration rate <15 mL/min/1.73 m2 or needing hemodialysis). Pei JY and Wang XP were blinded to the characteristics of the included patients. They reviewed the angiography results and classified the extent of coronary circulation by the Rentrop classification from 0 to 3 as follows: 0 = none, 1 = filling of side branches of the artery dilated via collateral channels without visualization of the epicardial segment, 2 = partial filling of the epicardial segment via collateral channels, and 3 = complete filling of the epicardial segment of the artery being dilated via collateral channels.\n6\n Rentrop 0–1 were classified as poor CC development; Rentrop 2–3 were classified as good CC development. Venous blood samples were collected immediately before coronary angiography. Serum was separated at 4°C, and the levels of IFN‐alpha were measured by ELISA (BMS216, Invitrogen).\nThis study was conducted in accordance with the Declaration of Helsinki and with written informed consent of each participant. This study was approved by the medical ethics committee of Second Xiangya Hospital of Central South University. Patients undergoing coronary angiography at our catheter laboratory between January 2018 and June 2019 and with at least one major coronary artery total occlusion were enrolled in our study. The exclusion criteria were as follows: (1) patients with ST‐segment elevated myocardial infarction/non‐ST‐segment elevated myocardial infarction, (2) patients with severe cardiac dysfunction/cardiac shock (HYHA IV or Killip IV), (3) patients with coronary artery bypass grafting, (4) patients with malignant tumor, (5) patients with inflammatory or infectious diseases, and (6) patients with severe hepatic (Child class C) and renal dysfunction (glomerular filtration rate <15 mL/min/1.73 m2 or needing hemodialysis). Pei JY and Wang XP were blinded to the characteristics of the included patients. They reviewed the angiography results and classified the extent of coronary circulation by the Rentrop classification from 0 to 3 as follows: 0 = none, 1 = filling of side branches of the artery dilated via collateral channels without visualization of the epicardial segment, 2 = partial filling of the epicardial segment via collateral channels, and 3 = complete filling of the epicardial segment of the artery being dilated via collateral channels.\n6\n Rentrop 0–1 were classified as poor CC development; Rentrop 2–3 were classified as good CC development. Venous blood samples were collected immediately before coronary angiography. Serum was separated at 4°C, and the levels of IFN‐alpha were measured by ELISA (BMS216, Invitrogen).\nIschemic hind‐limb model and laser speckle imaging The animal protocol was approved by the animal ethics committee of Second Xiangya Hospital of Central South University. All animal works were performed in Second Xiangya Hospital of Central South University. Male C57/BL mice were anesthetized with 3% isoflurane. The left femoral artery was ligated and excised as described previously.\n7\n The right leg underwent sham operation and was used as control. The mice were randomly divided into two groups: an INF‐alpha group with intraperitoneal injection of 2000 IU IFN‐alpha, and a control group with intraperitoneal injection of the same amount of saline. The IFN‐alpha concentration used is comparable to dosages used in patients. Hind‐limb prefusion was measured by laser speckle imaging under temperature‐controlled conditions, before and after left hind‐limb ligation, as well as at 3 days, 1 week, and 3 weeks following ligation. Color‐coded images of the paws representing the flux value were used to calculate the ratio of the tissue perfusion of the occluded (left) to the Sham‐operated (Sham) paw. The right hind‐limb was used as reference. All mice were put to death by cervical dislocation.\nThe animal protocol was approved by the animal ethics committee of Second Xiangya Hospital of Central South University. All animal works were performed in Second Xiangya Hospital of Central South University. Male C57/BL mice were anesthetized with 3% isoflurane. The left femoral artery was ligated and excised as described previously.\n7\n The right leg underwent sham operation and was used as control. The mice were randomly divided into two groups: an INF‐alpha group with intraperitoneal injection of 2000 IU IFN‐alpha, and a control group with intraperitoneal injection of the same amount of saline. The IFN‐alpha concentration used is comparable to dosages used in patients. Hind‐limb prefusion was measured by laser speckle imaging under temperature‐controlled conditions, before and after left hind‐limb ligation, as well as at 3 days, 1 week, and 3 weeks following ligation. Color‐coded images of the paws representing the flux value were used to calculate the ratio of the tissue perfusion of the occluded (left) to the Sham‐operated (Sham) paw. The right hind‐limb was used as reference. All mice were put to death by cervical dislocation.\nHistological analyses Adductor muscles from bilateral hind limbs were harvested at 3 weeks after surgery and underwent immediate tissue fixation overnight. For mouse arteriole density identification, the adductor muscles were stained with alpha‐SMA monoclonal antibody at 3 weeks after ligation.\nAdductor muscles from bilateral hind limbs were harvested at 3 weeks after surgery and underwent immediate tissue fixation overnight. For mouse arteriole density identification, the adductor muscles were stained with alpha‐SMA monoclonal antibody at 3 weeks after ligation.\nStatistical analyses We presented baseline characteristics of patients as frequencies and percentages for categorical variables and as means and standard deviations or interquartile range for continuous variables, depending on whether data distribution was normal (assessed by normal Q‐Q plots). We compared categorical variables using chi‐square analysis, and continuous variables were compared by analysis of variance test or Mann–Whitney U test, according to distribution type. We evaluated the relationship between serum levels of IFN‐alpha and CC development with the use of the levels of IFN‐alpha as both a continuous and a categorical variable. We constructed logistic regression model to calculate the odds ratio (OR) among tertiles. We used the first tertile as reference and used the following three models: model 1: unadjusted; model 2: adjusted age, sex, hypertension, hyperlipidemia, smoking; model 3: adjusted age, sex, hypertension, hyperlipidemia, smoking, uric acid, CRP. We also used a two‐piecewise linear regression model to examine the threshold effect of the levels of IFN‐alpha on the risk of poor CC according to the smoothing plot. The threshold value was determined using a trial method which was to move the trial turning point along the predefined interval and picked up the one which gave maximum model likelihood. A log likelihood ratio test was conducted comparing one‐line linear regression model with two‐piecewise linear model. We further used restricted cubic splines with five knots at the 5th, 35th, 50th, 65th, and 95th centiles to flexibly model the association of the levels of IFN‐alpha with the incidence of good CC adjusted for model 3 where the threshold value served as the reference.\nWe also did several sensitivity analyses by excluding patients with age <55 years or >70 years or by using the quantiles for the levels of serum IFN‐alpha. We performed all of the analyses using R version 3.4.3 (R Foundation for Statistical Computing, Vienna, Austria).\nWe presented baseline characteristics of patients as frequencies and percentages for categorical variables and as means and standard deviations or interquartile range for continuous variables, depending on whether data distribution was normal (assessed by normal Q‐Q plots). We compared categorical variables using chi‐square analysis, and continuous variables were compared by analysis of variance test or Mann–Whitney U test, according to distribution type. We evaluated the relationship between serum levels of IFN‐alpha and CC development with the use of the levels of IFN‐alpha as both a continuous and a categorical variable. We constructed logistic regression model to calculate the odds ratio (OR) among tertiles. We used the first tertile as reference and used the following three models: model 1: unadjusted; model 2: adjusted age, sex, hypertension, hyperlipidemia, smoking; model 3: adjusted age, sex, hypertension, hyperlipidemia, smoking, uric acid, CRP. We also used a two‐piecewise linear regression model to examine the threshold effect of the levels of IFN‐alpha on the risk of poor CC according to the smoothing plot. The threshold value was determined using a trial method which was to move the trial turning point along the predefined interval and picked up the one which gave maximum model likelihood. A log likelihood ratio test was conducted comparing one‐line linear regression model with two‐piecewise linear model. We further used restricted cubic splines with five knots at the 5th, 35th, 50th, 65th, and 95th centiles to flexibly model the association of the levels of IFN‐alpha with the incidence of good CC adjusted for model 3 where the threshold value served as the reference.\nWe also did several sensitivity analyses by excluding patients with age <55 years or >70 years or by using the quantiles for the levels of serum IFN‐alpha. We performed all of the analyses using R version 3.4.3 (R Foundation for Statistical Computing, Vienna, Austria).\n\nStudy population:\nThis study was conducted in accordance with the Declaration of Helsinki and with written informed consent of each participant. This study was approved by the medical ethics committee of Second Xiangya Hospital of Central South University. Patients undergoing coronary angiography at our catheter laboratory between January 2018 and June 2019 and with at least one major coronary artery total occlusion were enrolled in our study. The exclusion criteria were as follows: (1) patients with ST‐segment elevated myocardial infarction/non‐ST‐segment elevated myocardial infarction, (2) patients with severe cardiac dysfunction/cardiac shock (HYHA IV or Killip IV), (3) patients with coronary artery bypass grafting, (4) patients with malignant tumor, (5) patients with inflammatory or infectious diseases, and (6) patients with severe hepatic (Child class C) and renal dysfunction (glomerular filtration rate <15 mL/min/1.73 m2 or needing hemodialysis). Pei JY and Wang XP were blinded to the characteristics of the included patients. They reviewed the angiography results and classified the extent of coronary circulation by the Rentrop classification from 0 to 3 as follows: 0 = none, 1 = filling of side branches of the artery dilated via collateral channels without visualization of the epicardial segment, 2 = partial filling of the epicardial segment via collateral channels, and 3 = complete filling of the epicardial segment of the artery being dilated via collateral channels.\n6\n Rentrop 0–1 were classified as poor CC development; Rentrop 2–3 were classified as good CC development. Venous blood samples were collected immediately before coronary angiography. Serum was separated at 4°C, and the levels of IFN‐alpha were measured by ELISA (BMS216, Invitrogen).\n\nIschemic hind‐limb model and laser speckle imaging:\nThe animal protocol was approved by the animal ethics committee of Second Xiangya Hospital of Central South University. All animal works were performed in Second Xiangya Hospital of Central South University. Male C57/BL mice were anesthetized with 3% isoflurane. The left femoral artery was ligated and excised as described previously.\n7\n The right leg underwent sham operation and was used as control. The mice were randomly divided into two groups: an INF‐alpha group with intraperitoneal injection of 2000 IU IFN‐alpha, and a control group with intraperitoneal injection of the same amount of saline. The IFN‐alpha concentration used is comparable to dosages used in patients. Hind‐limb prefusion was measured by laser speckle imaging under temperature‐controlled conditions, before and after left hind‐limb ligation, as well as at 3 days, 1 week, and 3 weeks following ligation. Color‐coded images of the paws representing the flux value were used to calculate the ratio of the tissue perfusion of the occluded (left) to the Sham‐operated (Sham) paw. The right hind‐limb was used as reference. All mice were put to death by cervical dislocation.\n\nHistological analyses:\nAdductor muscles from bilateral hind limbs were harvested at 3 weeks after surgery and underwent immediate tissue fixation overnight. For mouse arteriole density identification, the adductor muscles were stained with alpha‐SMA monoclonal antibody at 3 weeks after ligation.\n\nStatistical analyses:\nWe presented baseline characteristics of patients as frequencies and percentages for categorical variables and as means and standard deviations or interquartile range for continuous variables, depending on whether data distribution was normal (assessed by normal Q‐Q plots). We compared categorical variables using chi‐square analysis, and continuous variables were compared by analysis of variance test or Mann–Whitney U test, according to distribution type. We evaluated the relationship between serum levels of IFN‐alpha and CC development with the use of the levels of IFN‐alpha as both a continuous and a categorical variable. We constructed logistic regression model to calculate the odds ratio (OR) among tertiles. We used the first tertile as reference and used the following three models: model 1: unadjusted; model 2: adjusted age, sex, hypertension, hyperlipidemia, smoking; model 3: adjusted age, sex, hypertension, hyperlipidemia, smoking, uric acid, CRP. We also used a two‐piecewise linear regression model to examine the threshold effect of the levels of IFN‐alpha on the risk of poor CC according to the smoothing plot. The threshold value was determined using a trial method which was to move the trial turning point along the predefined interval and picked up the one which gave maximum model likelihood. A log likelihood ratio test was conducted comparing one‐line linear regression model with two‐piecewise linear model. We further used restricted cubic splines with five knots at the 5th, 35th, 50th, 65th, and 95th centiles to flexibly model the association of the levels of IFN‐alpha with the incidence of good CC adjusted for model 3 where the threshold value served as the reference.\nWe also did several sensitivity analyses by excluding patients with age <55 years or >70 years or by using the quantiles for the levels of serum IFN‐alpha. We performed all of the analyses using R version 3.4.3 (R Foundation for Statistical Computing, Vienna, Austria).\n\nRESULTS:\nBetween January 2018 and June 2019, 208 patients with CTO were included in our study as required by the inclusion and exclusion criteria (Figure S1). The baseline characteristics of the included patients are presented in Table 1. No significant difference between groups was observed except ejection fraction and the levels of serum IFN‐alpha. In patients with poor CC, serum INF‐alpha levels were significantly higher (87.7 ± 42.0 vs. 59.8 ± 18.5, p < .001), and ejection fraction was significantly lower (51.2 ± 10.5 vs. 59.8 ± 18.5, p = .04) than in patients with good CC.\nBaseline characteristics of included patients\nAbbreviation: CC, collateral circulation; CRP, C‐reactive protein; LAD, left anterior descending; LCX, left circumflex; LDL, low density lipoprotein; PLT, platelet count; RCA, right coronary artery; WBC, white blood cells.\nANOVA showed that the serum levels of IFN‐alpha were significantly associated with CC, as did the Rentrop score (Figure 1). In a multivariate regression analysis, the risk of poor CC was increased with increasing IFN‐alpha tertile. Compared with the first tertile, the risk of poor CC was 3.67 (95% CI: 1.18–7.43) for model 1, 4.37 (95% CI: 2.06–9.26) for model 2, and 4.79 (95% CI: 2.22–10.4) for model 3 (Table 2). After adjusting for model 3, the cubic spline‐smoothing curve shows that the risk of poor CC increased with increasing serum IFN‐alpha levels (Figure 2). The incidence of poor CC increased with the serum levels of IFN‐alpha when the serum levels of IFN‐alpha were less than 112 pg/mL (OR: 1.0348, 95% CI: 1.0195–1.0577, p < .001, Table 3). When the levels of IFN‐alpha were larger than 112 pg/mL, the risk of poor CC did not increase with increasing levels of IFN‐alpha (OR: 1.0036, 95% CI: 0.9748–1.033, p = .807, Table 3). Our results remained robust in sensitivity analyses when we excluded patients aged >70 years or <55 years, as well as when using the fourth tertiles for the levels of serum IFN‐alpha (Tables S1–S3).\nCorrelation between the serum levels of IFN‐alpha and Rentrop scores. The levels in patients with Rentrop scores 0 and 1, or Rentrop 2 and 3, were not significantly different. The serum levels of IFN‐alpha in CTO patients with Rentrop scores of 2 and 3 were significantly different from those with Rentrop scores of 0 and 1\nOR (95% CI) of poor CC according to tertiles of the levels of serum IFN‐alpha\n\nNote: Model 1: unadjusted; model 2: adjusted age, sex, hypertension, hyperlipidemia, smoking; model 3: adjusted age, sex, hypertension, hyperlipidemia, smoking, uric acid, CRP.\nRelationship between the serum levels of IFN‐alpha and CC. A nonlinear relationship was observed between the serum levels of IFN‐alpha and CC after adjusting for model 3. Threshold effect analysis found that when serum levels of IFN‐alpha were larger than 112 pg/mL, the risks of poor CC did not keep on increasing\nThreshold effect analysis of the serum levels of IFN‐alpha on poor CC\n\nNote: When the levels of IFN‐alpha larger than 112 pg/mL, the incidence of poor CC did not increase with the increase of the levels of IFN‐alpha. Adjusted for model 3.\nAbbreviation: CC, coronary collateral circulation.\nTo assess the in vivo effect of IFN‐alpha on CC, we examined whether intraperitoneal injection of IFN‐alpha at 2000 IU/d inhibited blood perfusion recovery in a mouse hind‐limb. Laser speckle imaging showed that blood flow recovery was significantly impaired in mice treated with IFN‐alpha compared with control mice at 1 week (41.7% ± 2.66% vs. 47.3% ± 2.16%, p = 0.020), at 14 days (59.8% ± 3.31% vs. 66.5% ± 3.21%, p = .005) and at 21 days (59.8% ± 3.31% vs. 66.5% ± 3.21%, p = .005, Figure 3A,B). Although the numbers of arterioles are comparable between groups, the diameter of the arterioles in the mice treated with IFN‐alpha was smaller compared with the control group (Figure 4A). In order to study local ischemia, we examined the morphology in the gastrocnemius muscle by HE staining and Masson staining. Although we did not find obvious morphological changes by HE analysis (Figure S2), the area of interstitial fibrosis evaluated by Masson staining in the mice treated with IFN‐alpha was larger than in the control group (Figure 4B).\nINF‐alpha impairs the development of CC in a murine hindlimb ischemic model. (A) Representative laser speckle perfusion images. (B) Quantification of laser speckle perfusion (ischemic/nonischemic) in control and IFN‐alpha treated mice over time\nImmunohistochemistry of SMA in cross‐sections of the adductor muscles or Masson staining of gastrocnemius muscle collected from the control and IFN‐alpha group 3 weeks after femoral artery ligation. (A) Representative immunohistochemistry and quantification of CC numbers and diameter of adductor muscles. (B) Representative Masson staining and quantification of interstitial fibrosis of gastrocnemius muscle\n\nDISCUSSION:\nIn this study, we first found that CTO patients with poor CC have higher serum levels of IFN‐alpha. We found that IFN‐alpha inhibits the development of CC after artery occlusion.\nThe development of CC has a close relationship with multiple factors affecting coronary occlusion, such as diabetes mellitus, exercise, age, and other factors.\n8\n, \n9\n, \n10\n Recent studies have found that inflammatory factors are involved in the progress of CC. Fan et al. found that C‐reactive protein was positively associated with poor CC in patients with coronary artery disease.\n11\n Rakhit et al.\n12\n found that tumor necrosis factor alpha (TNF‐alpha) was inversely correlated with collateral flow index (CFI), whereas IL‐6 was positively correlated with CFI. However, these case–control studies did not determine the cause and effect relationship between inflammatory factors and the development of CC. We further investigated this cause and effect relationship in animal experiments and found that IFN‐alpha inhibited the development of CC after hind‐limb ischemia.\nThe development of CC depends on local activation of endothelial cells, the eNOS signaling pathway.\n13\n Previous study has found IFN‐alpha inhabited the expression and phosphorylation of eNOS in endothelial cells.\n14\n Epstein found that aging caused collateral rarefaction by inhibiting eNOS; overexpression of eNOS restored normal CC.\n15\n Furthermore, exercise promoted CC by promoting the expression and phosphorylation of eNOS. Therefore, eNOS is an essential factor for the development of CC. IFN‐alpha inhibits the expression and phosphorylation of eNOS, which may impair the development of CC. The positive remodeling of an arteriole into an artery up to 12–20 times its original size requires the proliferation and migration of endothelial cells and VSMCs.\n16\n, \n17\n Relevant study also found inflammatory factors, such as hs‐CRP, TNF‐alpha IFN‐alpha, inhibits the proliferation and migration of endothelial cells.\n11\n, \n12\n This may be another reason for patients with high serum levels of IFN‐alpha have impaired CC.\nLimitation First, the main limitation of present study is the small number of patients with CTO. However, the sensitivity analysis showed the robustness of our results. Second, our study did not include all inflammatory and angiogenic factors, they may have a role in the development of CC. Third, we evaluated the extent of CC by Rentrop classification instead of CFI. Although collateral is more accurate than Rentrop classification, it is an invasive procedure needing pressure wire. Rentrop classification is more often used in routine clinical practice. Fourth, we evaluated the development of CC in a model of hind‐limb ischemia rather than heart ischemia. However, both development of lower limb CC and coronary CC share common cellular and molecular mechanism: arteriogenesis. Ischemic hind‐limb model is an ideal model for arteriogenesis including coronary circulation.\n4\n, \n5\n Ischemic hind‐limb model mimics aspects of human occlusive artery disease to investigate vascular regeneration and to test therapeutical approaches in a reproducible manner.\nFirst, the main limitation of present study is the small number of patients with CTO. However, the sensitivity analysis showed the robustness of our results. Second, our study did not include all inflammatory and angiogenic factors, they may have a role in the development of CC. Third, we evaluated the extent of CC by Rentrop classification instead of CFI. Although collateral is more accurate than Rentrop classification, it is an invasive procedure needing pressure wire. Rentrop classification is more often used in routine clinical practice. Fourth, we evaluated the development of CC in a model of hind‐limb ischemia rather than heart ischemia. However, both development of lower limb CC and coronary CC share common cellular and molecular mechanism: arteriogenesis. Ischemic hind‐limb model is an ideal model for arteriogenesis including coronary circulation.\n4\n, \n5\n Ischemic hind‐limb model mimics aspects of human occlusive artery disease to investigate vascular regeneration and to test therapeutical approaches in a reproducible manner.\n\nLimitation:\nFirst, the main limitation of present study is the small number of patients with CTO. However, the sensitivity analysis showed the robustness of our results. Second, our study did not include all inflammatory and angiogenic factors, they may have a role in the development of CC. Third, we evaluated the extent of CC by Rentrop classification instead of CFI. Although collateral is more accurate than Rentrop classification, it is an invasive procedure needing pressure wire. Rentrop classification is more often used in routine clinical practice. Fourth, we evaluated the development of CC in a model of hind‐limb ischemia rather than heart ischemia. However, both development of lower limb CC and coronary CC share common cellular and molecular mechanism: arteriogenesis. Ischemic hind‐limb model is an ideal model for arteriogenesis including coronary circulation.\n4\n, \n5\n Ischemic hind‐limb model mimics aspects of human occlusive artery disease to investigate vascular regeneration and to test therapeutical approaches in a reproducible manner.\n\nCONCLUSION:\nCTO patients with poor CC have higher serum levels of IFN‐alpha than CTO patients with good CC. IFN‐alpha might impair the development of CC after artery occlusion.\n\nCONFLICTS OF INTEREST:\nThe authors declare that they have no potential conflicts of interests.\n\nAUTHOR CONTRIBUTIONS:\nXinqun Hu and Zhenhua Xing designed the study and provided methodological expertise. Zhenhua Xing drafted the manuscript. Zhenhua Xing performed the case–control study. Xiaopu Wang, Junyu Pei, Zhaowei Zhu, Shi Tai performed the animal experiments. All authors have read, provided critical feedback on, and approved the final manuscript.\n\nSupporting information:\n\nSupplementary Table S1. OR (95% CI) of poor CC according to fourths of the levels of serum IFN‐alpha\n\nSupplementary Table S2. OR (95% CI) of poor CC by excluding patients with age >70 years\n\nSupplementary Table S3. OR (95% CI) of poor CC by excluding patients with age <55 years\n\nSupplementary Figure S1. Chart of inclusion and exclusion of present study\n\nSupplementary Figure S2. HE staining of the left and right sides of gastrocnemius muscle.no obvious morphological changes were found\nClick here for additional data file.\n\n"},"whole_article_abstract":{"kind":"string","value":"Background:\nPrevious studies have demonstrated that interferon (IFN) signaling is enhanced in patients with poor collateral circulation (CC). However, the role and mechanisms of IFN-alpha in the development of CC remain unknown.\n\nMethods:\nWe studied the serum levels of IFN-alpha and coronary CC in a case-control study using logistics regression, including 114 coronary chronic total occlusion (CTO) patients with good coronary CC and 94 CTO patients with poor coronary CC. Restricted cubic splines was used to flexibly model the association of the levels of IFN-alpha with the incidence of good CC perfusion restoration after systemic treatment with IFN-alpha was assessed in a mice hind-limb ischemia model.\n\nResults:\nCompared with the first IFN-alpha tertile, the risk of poor CC was higher in the third IFN-alpha tertile (OR: 4.79, 95% CI: 2.22-10.4, p < .001). A cubic spline-smoothing curve showed that the risk of poor CC increased with increasing levels of serum IFN-alpha. IFN-alpha inhibited the development of CC in a hindlimb ischemia model. Arterioles of CC in the IFN-alpha group were smaller in diameter than in the control group.\n\nConclusions:\nPatients with CTO and with poor CC have higher serum levels of IFN-alpha than CTO patients with good CC. IFN-alpha might impair the development of CC after artery occlusion."},"background_conclusion_text":{"kind":"string","value":"BACKGROUND:\nDevelopment of collateral circulation (CC), also termed as arteriogenesis, is a natural life‐preservation mechanism occurring in patients with coronary chronic total occlusion (CTO). Well‐developed CC preserves cardiac function, thus reducing cardiac mortality after coronary occlusion.\n1\n, \n2\n Patients with CTO show a great deal of heterogeneity in their arteriogenic responses to artery occlusion. This is affected by multiple factors, including inflammatory factors.\n3\n The interaction between inflammation and arteriogenesis may be worthy of study, but data on the correlation between arteriogenesis and inflammation are limited. A previous study has found that interferon (IFN) signaling in monocytes is enhanced and stimulated by lipopolysaccharide among patients with impaired coronary CC.\n4\n Furthermore, blocking the receptor of IFN have been found to be helpful in alleviating ischemia in mice.\n5\n However, whether patients with poor CC have higher levels of serum IFN or whether treatment with IFN inhibits the development of arteriogenesis remains unknown. IFN‐alpha is widely used in treating hepatitis B and C, as well as various cancers. IFN‐alpha might affect the development of CC in patients with CTO. In this study, we aimed to investigate whether patients with poor CC have higher levels of IFN‐alpha, and if so, whether IFN‐alpha inhibits the development of CC.\n\nCONCLUSION:\nCTO patients with poor CC have higher serum levels of IFN‐alpha than CTO patients with good CC. IFN‐alpha might impair the development of CC after artery occlusion."},"background_conclusion_abstract":{"kind":"string","value":"Background:\nPrevious studies have demonstrated that interferon (IFN) signaling is enhanced in patients with poor collateral circulation (CC). However, the role and mechanisms of IFN-alpha in the development of CC remain unknown.\n\nMethods:\nWe studied the serum levels of IFN-alpha and coronary CC in a case-control study using logistics regression, including 114 coronary chronic total occlusion (CTO) patients with good coronary CC and 94 CTO patients with poor coronary CC. Restricted cubic splines was used to flexibly model the association of the levels of IFN-alpha with the incidence of good CC perfusion restoration after systemic treatment with IFN-alpha was assessed in a mice hind-limb ischemia model.\n\nResults:\nCompared with the first IFN-alpha tertile, the risk of poor CC was higher in the third IFN-alpha tertile (OR: 4.79, 95% CI: 2.22-10.4, p < .001). A cubic spline-smoothing curve showed that the risk of poor CC increased with increasing levels of serum IFN-alpha. IFN-alpha inhibited the development of CC in a hindlimb ischemia model. Arterioles of CC in the IFN-alpha group were smaller in diameter than in the control group.\n\nConclusions:\nPatients with CTO and with poor CC have higher serum levels of IFN-alpha than CTO patients with good CC. IFN-alpha might impair the development of CC after artery occlusion."},"whole_article_text_length":{"kind":"number","value":5275,"string":"5,275"},"whole_article_abstract_length":{"kind":"number","value":277,"string":"277"},"other_sections_lengths":{"kind":"list like","value":[324,207,42,353,180,60],"string":"[\n 324,\n 207,\n 42,\n 353,\n 180,\n 60\n]"},"num_sections":{"kind":"number","value":13,"string":"13"},"most_frequent_words":{"kind":"list like","value":["cc","alpha","ifn","ifn alpha","patients","model","levels","levels ifn alpha","levels ifn","development"],"string":"[\n \"cc\",\n \"alpha\",\n \"ifn\",\n \"ifn alpha\",\n \"patients\",\n \"model\",\n \"levels\",\n \"levels ifn alpha\",\n \"levels ifn\",\n \"development\"\n]"},"keybert_topics":{"kind":"list like","value":["arteriogenesis including coronary","mechanism arteriogenesis ischemic","inflammatory angiogenic factors","inflammation arteriogenesis worthy","interaction inflammation arteriogenesis"],"string":"[\n \"arteriogenesis including coronary\",\n \"mechanism arteriogenesis ischemic\",\n \"inflammatory angiogenic factors\",\n \"inflammation arteriogenesis worthy\",\n \"interaction inflammation arteriogenesis\"\n]"},"annotated_base_background_abstract_prompt":{"kind":"string","value":""},"annotated_base_methods_abstract_prompt":{"kind":"null"},"annotated_base_results_abstract_prompt":{"kind":"string","value":""},"annotated_base_conclusions_abstract_prompt":{"kind":"string","value":""},"annotated_base_whole_article_abstract_prompt":{"kind":"string","value":""},"annotated_base_background_conclusion_abstract_prompt":{"kind":"string","value":""},"annotated_keywords_background_abstract_prompt":{"kind":"string","value":"[CONTENT] collateral circulation | coronary chronic total occlusion | IFN‐alpha [SUMMARY]"},"annotated_keywords_methods_abstract_prompt":{"kind":"null"},"annotated_keywords_results_abstract_prompt":{"kind":"string","value":"[CONTENT] collateral circulation | coronary chronic total occlusion | IFN‐alpha [SUMMARY]"},"annotated_keywords_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] collateral circulation | coronary chronic total occlusion | IFN‐alpha [SUMMARY]"},"annotated_keywords_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] collateral circulation | coronary chronic total occlusion | IFN‐alpha [SUMMARY]"},"annotated_keywords_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] collateral circulation | coronary chronic total occlusion | IFN‐alpha [SUMMARY]"},"annotated_mesh_background_abstract_prompt":{"kind":"string","value":"[CONTENT] Animals | Arteries | Case-Control Studies | Chronic Disease | Collateral Circulation | Coronary Angiography | Coronary Circulation | Coronary Occlusion | Humans | Interferon-alpha | Mice | Percutaneous Coronary Intervention [SUMMARY]"},"annotated_mesh_methods_abstract_prompt":{"kind":"null"},"annotated_mesh_results_abstract_prompt":{"kind":"string","value":"[CONTENT] Animals | Arteries | Case-Control Studies | Chronic Disease | Collateral Circulation | Coronary Angiography | Coronary Circulation | Coronary Occlusion | Humans | Interferon-alpha | Mice | Percutaneous Coronary Intervention [SUMMARY]"},"annotated_mesh_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] Animals | Arteries | Case-Control Studies | Chronic Disease | Collateral Circulation | Coronary Angiography | Coronary Circulation | Coronary Occlusion | Humans | Interferon-alpha | Mice | Percutaneous Coronary Intervention [SUMMARY]"},"annotated_mesh_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] Animals | Arteries | Case-Control Studies | Chronic Disease | Collateral Circulation | Coronary Angiography | Coronary Circulation | Coronary Occlusion | Humans | Interferon-alpha | Mice | Percutaneous Coronary Intervention [SUMMARY]"},"annotated_mesh_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] Animals | Arteries | Case-Control Studies | Chronic Disease | Collateral Circulation | Coronary Angiography | Coronary Circulation | Coronary Occlusion | Humans | Interferon-alpha | Mice | Percutaneous Coronary Intervention [SUMMARY]"},"annotated_keybert_background_abstract_prompt":{"kind":"string","value":"[CONTENT] arteriogenesis including coronary | mechanism arteriogenesis ischemic | inflammatory angiogenic factors | inflammation arteriogenesis worthy | interaction inflammation arteriogenesis [SUMMARY]"},"annotated_keybert_methods_abstract_prompt":{"kind":"null"},"annotated_keybert_results_abstract_prompt":{"kind":"string","value":"[CONTENT] arteriogenesis including coronary | mechanism arteriogenesis ischemic | inflammatory angiogenic factors | inflammation arteriogenesis worthy | interaction inflammation arteriogenesis [SUMMARY]"},"annotated_keybert_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] arteriogenesis including coronary | mechanism arteriogenesis ischemic | inflammatory angiogenic factors | inflammation arteriogenesis worthy | interaction inflammation arteriogenesis [SUMMARY]"},"annotated_keybert_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] arteriogenesis including coronary | mechanism arteriogenesis ischemic | inflammatory angiogenic factors | inflammation arteriogenesis worthy | interaction inflammation arteriogenesis [SUMMARY]"},"annotated_keybert_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] arteriogenesis including coronary | mechanism arteriogenesis ischemic | inflammatory angiogenic factors | inflammation arteriogenesis worthy | interaction inflammation arteriogenesis [SUMMARY]"},"annotated_most_frequent_background_abstract_prompt":{"kind":"string","value":"[CONTENT] cc | alpha | ifn | ifn alpha | patients | model | levels | levels ifn alpha | levels ifn | development [SUMMARY]"},"annotated_most_frequent_methods_abstract_prompt":{"kind":"null"},"annotated_most_frequent_results_abstract_prompt":{"kind":"string","value":"[CONTENT] cc | alpha | ifn | ifn alpha | patients | model | levels | levels ifn alpha | levels ifn | development [SUMMARY]"},"annotated_most_frequent_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] cc | alpha | ifn | ifn alpha | patients | model | levels | levels ifn alpha | levels ifn | development [SUMMARY]"},"annotated_most_frequent_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] cc | alpha | ifn | ifn alpha | patients | model | levels | levels ifn alpha | levels ifn | development [SUMMARY]"},"annotated_most_frequent_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] cc | alpha | ifn | ifn alpha | patients | model | levels | levels ifn alpha | levels ifn | development [SUMMARY]"},"annotated_tf_idf_background_abstract_prompt":{"kind":"string","value":"[CONTENT] ifn | cc | arteriogenesis | patients | cc higher levels | inflammation | poor cc higher levels | higher levels | development | occlusion [SUMMARY]"},"annotated_tf_idf_methods_abstract_prompt":{"kind":"null"},"annotated_tf_idf_results_abstract_prompt":{"kind":"string","value":"[CONTENT] alpha | ifn | ifn alpha | levels | cc | levels ifn | levels ifn alpha | serum | model | figure [SUMMARY]"},"annotated_tf_idf_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] cto patients | cc | cto | ifn alpha impair | alpha impair | ifn alpha impair development | cto patients good cc | cto patients good | patients good cc ifn | good cc ifn [SUMMARY]"},"annotated_tf_idf_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] cc | ifn | alpha | model | ifn alpha | patients | levels | development | coronary | limb [SUMMARY]"},"annotated_tf_idf_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] cc | ifn | alpha | model | ifn alpha | patients | levels | development | coronary | limb [SUMMARY]"},"annotated_entity_plan_background_abstract_prompt":{"kind":"string","value":"[CONTENT] IFN | CC ||| IFN | CC [SUMMARY]"},"annotated_entity_plan_methods_abstract_prompt":{"kind":"null"},"annotated_entity_plan_results_abstract_prompt":{"kind":"string","value":"[CONTENT] first | IFN | CC | third | IFN | 4.79 | 95% | CI | 2.22 | .001 ||| CC | IFN ||| IFN | CC ||| IFN [SUMMARY]"},"annotated_entity_plan_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] CTO | CC | IFN | CTO | CC ||| IFN | CC [SUMMARY]"},"annotated_entity_plan_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] IFN | CC ||| IFN | CC ||| IFN | CC | 114 | CTO | CC | 94 | CTO | CC ||| Restricted | IFN | CC | IFN ||| first | IFN | CC | third | IFN | 4.79 | 95% | CI | 2.22 | .001 ||| CC | IFN ||| IFN | CC ||| IFN ||| CTO | CC | IFN | CTO | CC ||| IFN | CC [SUMMARY]"},"annotated_entity_plan_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] IFN | CC ||| IFN | CC ||| IFN | CC | 114 | CTO | CC | 94 | CTO | CC ||| Restricted | IFN | CC | IFN ||| first | IFN | CC | third | IFN | 4.79 | 95% | CI | 2.22 | .001 ||| CC | IFN ||| IFN | CC ||| IFN ||| CTO | CC | IFN | CTO | CC ||| IFN | CC [SUMMARY]"}}},{"rowIdx":279,"cells":{"title":{"kind":"string","value":"Next-generation sequencing for the genetic characterization of Maedi/Visna virus isolated from the northwest of China."},"pmid":{"kind":"string","value":"34697919"},"background_abstract":{"kind":"string","value":"Maedi/Visna virus (MVV) is a contagious viral pathogen that causes considerable economic losses to the sheep industry worldwide."},"background_abstract_label":{"kind":"string","value":"BACKGROUND"},"methods_abstract":{"kind":"string","value":"Therefore, in this study, we conducted next-generation sequencing on an MVV strain obtained from northwest China to reveal its genetic evolution via phylogenetic analysis."},"methods_abstract_label":{"kind":"string","value":"METHODS"},"results_abstract":{"kind":"string","value":"A MVV strain obtained from Inner Mongolia (NM) of China was identified. Sequence analysis indicated that its whole-genome length is 9193 bp. Homology comparison of nucleotides between the NM strain and reference strains showed that the sequence homology of gag and env were 77.1%-86.8% and 67.7%-75.5%, respectively. Phylogenetic analysis revealed that the NM strain was closely related to the reference strains isolated from America, which belong to the A2 type. Notably, there were 5 amino acid insertions in variable region 4 and a highly variable motif at the C-terminal of the surface glycoprotein (SU5)."},"results_abstract_label":{"kind":"string","value":"RESULTS"},"conclusions_abstract":{"kind":"string","value":"The present study is the first to show the whole-genome sequence of an MVV obtained from China. The detailed analyses provide essential information for understanding the genetic characteristics of MVV, and the results enrich the MVV library."},"conclusions_abstract_label":{"kind":"string","value":"CONCLUSIONS"},"mesh_descriptor_names":{"kind":"list like","value":["Animals","China","High-Throughput Nucleotide Sequencing","Phylogeny","Pneumonia, Progressive Interstitial, of Sheep","Sheep","Sheep Diseases","Visna-maedi virus"],"string":"[\n \"Animals\",\n \"China\",\n \"High-Throughput Nucleotide Sequencing\",\n \"Phylogeny\",\n \"Pneumonia, Progressive Interstitial, of Sheep\",\n \"Sheep\",\n \"Sheep Diseases\",\n \"Visna-maedi virus\"\n]"},"pmcid":{"kind":"string","value":"8636652"},"background_title":{"kind":"string","value":"INTRODUCTION"},"background_text":{"kind":"string","value":"Maedi/Visna virus (MVV) belongs to the Retroviridae family and Lentivirus genus and is a monocyte/macrophage-tropic non-oncogenic virus [1]. MVV infection is persistent and chronic and is characterized by slow progressive degenerative inflammation in multiple organs, including lungs, brain, udder, and joints in sheep and goats [2]. Labored breathing associated with emaciation caused by progressive pneumonitis is the predominant feature in clinically affected sheep [3]. MMV is epidemic globally, except for Australia and New Zealand, resulting in considerable economic losses [4].\nThe MVV is a single-stranded RNA molecule with positive-sense polarity and contains 3 primary genes (gag, pol, and env) and several regulatory genes (e.g., vif, vpr-like, and rev) [5]. The gag gene encodes the internal structural proteins, such as the capsid protein (CA), which stimulates the host to produce antibodies. The pol gene encodes enzymes involved in replication and DNA integration, such as reverse transcriptase [6]. These 2 genomic regions, the gag-pol (1.8 kb) and the pol (1.2 kb), were initially proposed by Shah et al. [7] to be used in the classification of MVV subtype strains. Subsequently, many other strains were added to the phylogenetic tree, but the additions were often based on the sequence of only a small part of one of the fragments initially proposed by Olech et al. [8]. The env gene encodes surface and transmembrane glycoproteins that insert into the envelope. The surface glycoprotein (SU) contains genetically variable domains, thereby determining the antigenic variability of the different isolates.\nThe use of next-generation sequencing (NGS), also called high throughput sequencing, has rapidly expanded over recent years [91011]. Due to their high accuracy and high throughput capacity, Illumina, Inc. is the dominant player in the NGS arena. Illumina NGS supports various protocols (e.g., genomic sequencing, RNA sequencing) and provides varying levels of throughput, including the MiniSeq, MiSeq, and HiSeq models [12]. The presence of Maedi-Visna disease in China was first reported in 1966. Serological diagnosis revealed that MVV was present in several regions of China, except Inner Mongolia (NM), which is one of the major provinces of sheep production in China. However, the lack of a complete genome sequence of the Chinese MMV strains has hampered comprehension of its genetic characteristics. We have worked on ovine viral pneumonia for several years, and a case of Maedi-Visna disease was recently identified in the western portion of NM. To elucidate the molecular characteristics of our isolate, NGS was applied to obtain a complete genome sequence. Moreover, phylogenetic information on our isolate was analyzed, which may assist in elucidating the genetic evolution of MVV."},"methods_title":{"kind":"null"},"methods_text":{"kind":"null"},"results_title":{"kind":"string","value":"RESULTS"},"results_text":{"kind":"string","value":" Characterization of MVV in sheep lung tissue Gross appearance showed that the volume of the affected sheep lung was increased by 2- to 3-fold. Nodular lesions were scattered or densely distributed in local areas, and the surrounding tissue was a dark red color and provided a hard tactile sensation. Fibrotic focuses were scattered in the pleural surface (Fig. 1A). Histopathologically, lymphocytic and lymphofollicular hyperplasias were scattered or locally distributed in the pulmonary interstitium. Lymphoid follicles occurred before the peripheral bronchioles, and the germinal centers of some lymphoid follicles were obvious. There were significant proliferations of lymphocytes, macrophages, and a small number of fibroblasts in the alveolar septum. Type II epithelial cells on the inner surface of the alveolar wall had proliferated to varying degrees. Additionally, some alveolar cavities were filled with red homogeneous serous fluid as well as some macrophages and lymphocytes (Fig. 1B). A PCR procedure was performed to amplify the LTR sequence of the MVV. Detection by gel electrophoresis confirmed that the lung tissue was MVV-positive (Fig. 1C). Furthermore, electron microscope-based examination of purified MVV showed that the viral particles were approximately 80 nm (Fig. 1D).\nM, marker; MVV, Maedi/Visna virus.\nGross appearance showed that the volume of the affected sheep lung was increased by 2- to 3-fold. Nodular lesions were scattered or densely distributed in local areas, and the surrounding tissue was a dark red color and provided a hard tactile sensation. Fibrotic focuses were scattered in the pleural surface (Fig. 1A). Histopathologically, lymphocytic and lymphofollicular hyperplasias were scattered or locally distributed in the pulmonary interstitium. Lymphoid follicles occurred before the peripheral bronchioles, and the germinal centers of some lymphoid follicles were obvious. There were significant proliferations of lymphocytes, macrophages, and a small number of fibroblasts in the alveolar septum. Type II epithelial cells on the inner surface of the alveolar wall had proliferated to varying degrees. Additionally, some alveolar cavities were filled with red homogeneous serous fluid as well as some macrophages and lymphocytes (Fig. 1B). A PCR procedure was performed to amplify the LTR sequence of the MVV. Detection by gel electrophoresis confirmed that the lung tissue was MVV-positive (Fig. 1C). Furthermore, electron microscope-based examination of purified MVV showed that the viral particles were approximately 80 nm (Fig. 1D).\nM, marker; MVV, Maedi/Visna virus.\n Complete genome sequence of the NM1111 strain A total of 1.1 GB of valid data was obtained, and the data were uniformly distributed. The sequencing depth was up to 188,295 times. The values of Q20 and Q30 were 96.64% and 90.48%, respectively, and the G+C content was 40.43%. These results reveal that our data exhibited good sequencing quality. Finally, the isolate contained 9,193 nucleotides after splicing and optimization. Three primary encoding genes, the gag gene, position at 502 to 1,845, contained 1,344 nucleotides; the pol gene, position at 1716 to 5036, contained 3321 nucleotides; the env gene, position at 5,985 to 8,948, contained 2,964 nucleotides (Table 1, Fig. 2). The complete genome sequence of MVV was uploaded, named NM1111, to GenBank with the accession number MW248464 (Supplementary Data 1).\nLTR, long terminal repeat; N/A, not applicable.\nLTR, long terminal repeat.\nA total of 1.1 GB of valid data was obtained, and the data were uniformly distributed. The sequencing depth was up to 188,295 times. The values of Q20 and Q30 were 96.64% and 90.48%, respectively, and the G+C content was 40.43%. These results reveal that our data exhibited good sequencing quality. Finally, the isolate contained 9,193 nucleotides after splicing and optimization. Three primary encoding genes, the gag gene, position at 502 to 1,845, contained 1,344 nucleotides; the pol gene, position at 1716 to 5036, contained 3321 nucleotides; the env gene, position at 5,985 to 8,948, contained 2,964 nucleotides (Table 1, Fig. 2). The complete genome sequence of MVV was uploaded, named NM1111, to GenBank with the accession number MW248464 (Supplementary Data 1).\nLTR, long terminal repeat; N/A, not applicable.\nLTR, long terminal repeat.\n Phylogenetic analysis A neighbor-joining tree was constructed using the whole-genome sequence of NM1111 and the available reference strains. The phylogenetic analysis showed that the NM1111 strain was clustered in a branch of MMV type A2 (Fig. 3). Meanwhile, homology comparisons showed that the gag and pol of NM1111 had a close relationship with American strain MH916859 (Table 2). In contrast, the env had a lower sequence homology with the reference strains, suggesting the development of antigenic variability with evolution. Amino acid homology analysis indicated that the gag protein is highly conserved, especially in 2 dominant epitopes (epitope2 and epitope3). However, within the highly homologous epitope2, the Sichuan strain (KR011757) mutated from alanine (A) to valine (V) at position 221. Notably, the major homology region (MHR) region of NM1111 mutated from aspartic acid (D) to glutamic acid (E) at position 296 (Table 3). The SU region had a high degree of sequence variation, especially in variable region 4 (V4) with 5 amino acid insertions (position at 554-555, 564-566) (Table 4). Immunodominant epitope SU5 had a conserved motif (VRAYTYGV) located at the N-terminal part and a highly variable motif at the C-terminal (Table 4).\nNM, Inner Mongolia.\nN/A, not applicable.\nMHR, major homology region.\nA neighbor-joining tree was constructed using the whole-genome sequence of NM1111 and the available reference strains. The phylogenetic analysis showed that the NM1111 strain was clustered in a branch of MMV type A2 (Fig. 3). Meanwhile, homology comparisons showed that the gag and pol of NM1111 had a close relationship with American strain MH916859 (Table 2). In contrast, the env had a lower sequence homology with the reference strains, suggesting the development of antigenic variability with evolution. Amino acid homology analysis indicated that the gag protein is highly conserved, especially in 2 dominant epitopes (epitope2 and epitope3). However, within the highly homologous epitope2, the Sichuan strain (KR011757) mutated from alanine (A) to valine (V) at position 221. Notably, the major homology region (MHR) region of NM1111 mutated from aspartic acid (D) to glutamic acid (E) at position 296 (Table 3). The SU region had a high degree of sequence variation, especially in variable region 4 (V4) with 5 amino acid insertions (position at 554-555, 564-566) (Table 4). Immunodominant epitope SU5 had a conserved motif (VRAYTYGV) located at the N-terminal part and a highly variable motif at the C-terminal (Table 4).\nNM, Inner Mongolia.\nN/A, not applicable.\nMHR, major homology region."},"conclusions_title":{"kind":"null"},"conclusions_text":{"kind":"null"},"other_sections_titles":{"kind":"list like","value":["Ethics statement","Sample collection and hematoxylin and eosin (H&E) staining","Polymerase chain reaction (PCR) identification","Virus enrichment and identification","Sequencing and genome assembly","Sequence alignment and polygenetic analysis","Characterization of MVV in sheep lung tissue","Complete genome sequence of the NM1111 strain","Phylogenetic analysis"],"string":"[\n \"Ethics statement\",\n \"Sample collection and hematoxylin and eosin (H&E) staining\",\n \"Polymerase chain reaction (PCR) identification\",\n \"Virus enrichment and identification\",\n \"Sequencing and genome assembly\",\n \"Sequence alignment and polygenetic analysis\",\n \"Characterization of MVV in sheep lung tissue\",\n \"Complete genome sequence of the NM1111 strain\",\n \"Phylogenetic analysis\"\n]"},"other_sections_texts":{"kind":"list like","value":["This work does not contain any studies performed on living animals. Collection of lung tissues conformed to the experimental practices and standards approved by the animal welfare and research ethics committee of NM Agricultural University (approval ID: 2020007).","A 3-year-old Dorper ewe with serve dyspnea, cough, and wheezing was provided by a large-scale sheep farm in NM and was sacrificed for sample collection. Lung samples were kept in 10% formalin or frozen at −80°C. The lung samples were fixed in 10% formalin and processed by applying standard procedures for pathological examination. After processing, 3–5 mm-thick sections were stained with H&E for microscope examination.","MVV identification by PCR was performed as previously described [13]. Briefly, a pair of primers (F: 5′-TGACACAGCAAATGTAACCGCAAG-3′; R: 5′- CCACGTTGGGCGCCAGCTGCGAGA-3′) were used to amplify a 291 bp fragment of the long terminal repeat (LTR) region of pro-viral DNA. MVV was proliferated in choroid plexus cells and, after 3 passages, were used as positive controls. Healthy lung tissue and RNase-free and DNase-free water were used as negative and blank controls, respectively.","PCR-positive lung tissue was ground and then made into a suspension by adding a 5-fold volume of PBS. Viruses were released by performing 3 freezing and thawing cycles. The supernatants were collected after centrifugation at 4,000 r/min for 30 min, repeated 3 times, and then centrifuged at 12,000 r/min for 1 h. The obtained supernatant was centrifuged at 35,000 r/min for 3 h, and the pellet was resuspended in TNE buffer. After centrifugation of the suspension at 5,000 r/min for 10 min, the supernatant was passed through a 25% sucrose cushion and centrifuged at 35,000 r/min for 4 h. Viruses were purified by a 20%–50% gradient of sucrose and ultracentrifugation. The final acquisition was negatively stained with 2% uranyl acetate and examined by electron microscopy.","Viral RNA was extracted using the TRIzol (TAKARA, China) reagent, then reverse-transcribed into complementary DNA (cDNA) with the Prime ScriptTM RT reagent kit and g DNA Eraser (TAKARA), according to the manufacturer's recommendations. A viral cDNA library was constructed and sequenced by Shanghai Biozeron Biological Technology Co. Ltd. Briefly, 1 μg of cDNA was used with Illumina's TruSeq™ Nano DNA Sample Prep Kit for library preparation. Libraries were sequenced on the Illumina HiSeq 4000 platform at 2 × 150 bp read length. Genome assembly was performed using ABySS [14] to achieve optimal results, and corrections for single-base polymorphism and the infilling of remaining gaps were conducted in SOAPdenovo [15].","Sequence alignment was performed using DNASTAR Lasergene (version 7.1.0). To evaluate the relationship between the reference strains and the NM strain, a phylogenetic tree was constructed using the neighbor-joining method provided in molecular evolutionary genetics analysis software (version 7.0). Bootstrap values were estimated for 1,000 replicates.","Gross appearance showed that the volume of the affected sheep lung was increased by 2- to 3-fold. Nodular lesions were scattered or densely distributed in local areas, and the surrounding tissue was a dark red color and provided a hard tactile sensation. Fibrotic focuses were scattered in the pleural surface (Fig. 1A). Histopathologically, lymphocytic and lymphofollicular hyperplasias were scattered or locally distributed in the pulmonary interstitium. Lymphoid follicles occurred before the peripheral bronchioles, and the germinal centers of some lymphoid follicles were obvious. There were significant proliferations of lymphocytes, macrophages, and a small number of fibroblasts in the alveolar septum. Type II epithelial cells on the inner surface of the alveolar wall had proliferated to varying degrees. Additionally, some alveolar cavities were filled with red homogeneous serous fluid as well as some macrophages and lymphocytes (Fig. 1B). A PCR procedure was performed to amplify the LTR sequence of the MVV. Detection by gel electrophoresis confirmed that the lung tissue was MVV-positive (Fig. 1C). Furthermore, electron microscope-based examination of purified MVV showed that the viral particles were approximately 80 nm (Fig. 1D).\nM, marker; MVV, Maedi/Visna virus.","A total of 1.1 GB of valid data was obtained, and the data were uniformly distributed. The sequencing depth was up to 188,295 times. The values of Q20 and Q30 were 96.64% and 90.48%, respectively, and the G+C content was 40.43%. These results reveal that our data exhibited good sequencing quality. Finally, the isolate contained 9,193 nucleotides after splicing and optimization. Three primary encoding genes, the gag gene, position at 502 to 1,845, contained 1,344 nucleotides; the pol gene, position at 1716 to 5036, contained 3321 nucleotides; the env gene, position at 5,985 to 8,948, contained 2,964 nucleotides (Table 1, Fig. 2). The complete genome sequence of MVV was uploaded, named NM1111, to GenBank with the accession number MW248464 (Supplementary Data 1).\nLTR, long terminal repeat; N/A, not applicable.\nLTR, long terminal repeat.","A neighbor-joining tree was constructed using the whole-genome sequence of NM1111 and the available reference strains. The phylogenetic analysis showed that the NM1111 strain was clustered in a branch of MMV type A2 (Fig. 3). Meanwhile, homology comparisons showed that the gag and pol of NM1111 had a close relationship with American strain MH916859 (Table 2). In contrast, the env had a lower sequence homology with the reference strains, suggesting the development of antigenic variability with evolution. Amino acid homology analysis indicated that the gag protein is highly conserved, especially in 2 dominant epitopes (epitope2 and epitope3). However, within the highly homologous epitope2, the Sichuan strain (KR011757) mutated from alanine (A) to valine (V) at position 221. Notably, the major homology region (MHR) region of NM1111 mutated from aspartic acid (D) to glutamic acid (E) at position 296 (Table 3). The SU region had a high degree of sequence variation, especially in variable region 4 (V4) with 5 amino acid insertions (position at 554-555, 564-566) (Table 4). Immunodominant epitope SU5 had a conserved motif (VRAYTYGV) located at the N-terminal part and a highly variable motif at the C-terminal (Table 4).\nNM, Inner Mongolia.\nN/A, not applicable.\nMHR, major homology region."],"string":"[\n \"This work does not contain any studies performed on living animals. Collection of lung tissues conformed to the experimental practices and standards approved by the animal welfare and research ethics committee of NM Agricultural University (approval ID: 2020007).\",\n \"A 3-year-old Dorper ewe with serve dyspnea, cough, and wheezing was provided by a large-scale sheep farm in NM and was sacrificed for sample collection. Lung samples were kept in 10% formalin or frozen at −80°C. The lung samples were fixed in 10% formalin and processed by applying standard procedures for pathological examination. After processing, 3–5 mm-thick sections were stained with H&E for microscope examination.\",\n \"MVV identification by PCR was performed as previously described [13]. Briefly, a pair of primers (F: 5′-TGACACAGCAAATGTAACCGCAAG-3′; R: 5′- CCACGTTGGGCGCCAGCTGCGAGA-3′) were used to amplify a 291 bp fragment of the long terminal repeat (LTR) region of pro-viral DNA. MVV was proliferated in choroid plexus cells and, after 3 passages, were used as positive controls. Healthy lung tissue and RNase-free and DNase-free water were used as negative and blank controls, respectively.\",\n \"PCR-positive lung tissue was ground and then made into a suspension by adding a 5-fold volume of PBS. Viruses were released by performing 3 freezing and thawing cycles. The supernatants were collected after centrifugation at 4,000 r/min for 30 min, repeated 3 times, and then centrifuged at 12,000 r/min for 1 h. The obtained supernatant was centrifuged at 35,000 r/min for 3 h, and the pellet was resuspended in TNE buffer. After centrifugation of the suspension at 5,000 r/min for 10 min, the supernatant was passed through a 25% sucrose cushion and centrifuged at 35,000 r/min for 4 h. Viruses were purified by a 20%–50% gradient of sucrose and ultracentrifugation. The final acquisition was negatively stained with 2% uranyl acetate and examined by electron microscopy.\",\n \"Viral RNA was extracted using the TRIzol (TAKARA, China) reagent, then reverse-transcribed into complementary DNA (cDNA) with the Prime ScriptTM RT reagent kit and g DNA Eraser (TAKARA), according to the manufacturer's recommendations. A viral cDNA library was constructed and sequenced by Shanghai Biozeron Biological Technology Co. Ltd. Briefly, 1 μg of cDNA was used with Illumina's TruSeq™ Nano DNA Sample Prep Kit for library preparation. Libraries were sequenced on the Illumina HiSeq 4000 platform at 2 × 150 bp read length. Genome assembly was performed using ABySS [14] to achieve optimal results, and corrections for single-base polymorphism and the infilling of remaining gaps were conducted in SOAPdenovo [15].\",\n \"Sequence alignment was performed using DNASTAR Lasergene (version 7.1.0). To evaluate the relationship between the reference strains and the NM strain, a phylogenetic tree was constructed using the neighbor-joining method provided in molecular evolutionary genetics analysis software (version 7.0). Bootstrap values were estimated for 1,000 replicates.\",\n \"Gross appearance showed that the volume of the affected sheep lung was increased by 2- to 3-fold. Nodular lesions were scattered or densely distributed in local areas, and the surrounding tissue was a dark red color and provided a hard tactile sensation. Fibrotic focuses were scattered in the pleural surface (Fig. 1A). Histopathologically, lymphocytic and lymphofollicular hyperplasias were scattered or locally distributed in the pulmonary interstitium. Lymphoid follicles occurred before the peripheral bronchioles, and the germinal centers of some lymphoid follicles were obvious. There were significant proliferations of lymphocytes, macrophages, and a small number of fibroblasts in the alveolar septum. Type II epithelial cells on the inner surface of the alveolar wall had proliferated to varying degrees. Additionally, some alveolar cavities were filled with red homogeneous serous fluid as well as some macrophages and lymphocytes (Fig. 1B). A PCR procedure was performed to amplify the LTR sequence of the MVV. Detection by gel electrophoresis confirmed that the lung tissue was MVV-positive (Fig. 1C). Furthermore, electron microscope-based examination of purified MVV showed that the viral particles were approximately 80 nm (Fig. 1D).\\nM, marker; MVV, Maedi/Visna virus.\",\n \"A total of 1.1 GB of valid data was obtained, and the data were uniformly distributed. The sequencing depth was up to 188,295 times. The values of Q20 and Q30 were 96.64% and 90.48%, respectively, and the G+C content was 40.43%. These results reveal that our data exhibited good sequencing quality. Finally, the isolate contained 9,193 nucleotides after splicing and optimization. Three primary encoding genes, the gag gene, position at 502 to 1,845, contained 1,344 nucleotides; the pol gene, position at 1716 to 5036, contained 3321 nucleotides; the env gene, position at 5,985 to 8,948, contained 2,964 nucleotides (Table 1, Fig. 2). The complete genome sequence of MVV was uploaded, named NM1111, to GenBank with the accession number MW248464 (Supplementary Data 1).\\nLTR, long terminal repeat; N/A, not applicable.\\nLTR, long terminal repeat.\",\n \"A neighbor-joining tree was constructed using the whole-genome sequence of NM1111 and the available reference strains. The phylogenetic analysis showed that the NM1111 strain was clustered in a branch of MMV type A2 (Fig. 3). Meanwhile, homology comparisons showed that the gag and pol of NM1111 had a close relationship with American strain MH916859 (Table 2). In contrast, the env had a lower sequence homology with the reference strains, suggesting the development of antigenic variability with evolution. Amino acid homology analysis indicated that the gag protein is highly conserved, especially in 2 dominant epitopes (epitope2 and epitope3). However, within the highly homologous epitope2, the Sichuan strain (KR011757) mutated from alanine (A) to valine (V) at position 221. Notably, the major homology region (MHR) region of NM1111 mutated from aspartic acid (D) to glutamic acid (E) at position 296 (Table 3). The SU region had a high degree of sequence variation, especially in variable region 4 (V4) with 5 amino acid insertions (position at 554-555, 564-566) (Table 4). Immunodominant epitope SU5 had a conserved motif (VRAYTYGV) located at the N-terminal part and a highly variable motif at the C-terminal (Table 4).\\nNM, Inner Mongolia.\\nN/A, not applicable.\\nMHR, major homology region.\"\n]"},"other_sections_sec_types":{"kind":"list like","value":[null,null,null,null,null,null,null,null,null],"string":"[\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null\n]"},"all_sections_titles":{"kind":"list like","value":["INTRODUCTION","MATERIALS AND METHODS","Ethics statement","Sample collection and hematoxylin and eosin (H&E) staining","Polymerase chain reaction (PCR) identification","Virus enrichment and identification","Sequencing and genome assembly","Sequence alignment and polygenetic analysis","RESULTS","Characterization of MVV in sheep lung tissue","Complete genome sequence of the NM1111 strain","Phylogenetic analysis","DISCUSSION"],"string":"[\n \"INTRODUCTION\",\n \"MATERIALS AND METHODS\",\n \"Ethics statement\",\n \"Sample collection and hematoxylin and eosin (H&E) staining\",\n \"Polymerase chain reaction (PCR) identification\",\n \"Virus enrichment and identification\",\n \"Sequencing and genome assembly\",\n \"Sequence alignment and polygenetic analysis\",\n \"RESULTS\",\n \"Characterization of MVV in sheep lung tissue\",\n \"Complete genome sequence of the NM1111 strain\",\n \"Phylogenetic analysis\",\n \"DISCUSSION\"\n]"},"all_sections_texts":{"kind":"list like","value":["Maedi/Visna virus (MVV) belongs to the Retroviridae family and Lentivirus genus and is a monocyte/macrophage-tropic non-oncogenic virus [1]. MVV infection is persistent and chronic and is characterized by slow progressive degenerative inflammation in multiple organs, including lungs, brain, udder, and joints in sheep and goats [2]. Labored breathing associated with emaciation caused by progressive pneumonitis is the predominant feature in clinically affected sheep [3]. MMV is epidemic globally, except for Australia and New Zealand, resulting in considerable economic losses [4].\nThe MVV is a single-stranded RNA molecule with positive-sense polarity and contains 3 primary genes (gag, pol, and env) and several regulatory genes (e.g., vif, vpr-like, and rev) [5]. The gag gene encodes the internal structural proteins, such as the capsid protein (CA), which stimulates the host to produce antibodies. The pol gene encodes enzymes involved in replication and DNA integration, such as reverse transcriptase [6]. These 2 genomic regions, the gag-pol (1.8 kb) and the pol (1.2 kb), were initially proposed by Shah et al. [7] to be used in the classification of MVV subtype strains. Subsequently, many other strains were added to the phylogenetic tree, but the additions were often based on the sequence of only a small part of one of the fragments initially proposed by Olech et al. [8]. The env gene encodes surface and transmembrane glycoproteins that insert into the envelope. The surface glycoprotein (SU) contains genetically variable domains, thereby determining the antigenic variability of the different isolates.\nThe use of next-generation sequencing (NGS), also called high throughput sequencing, has rapidly expanded over recent years [91011]. Due to their high accuracy and high throughput capacity, Illumina, Inc. is the dominant player in the NGS arena. Illumina NGS supports various protocols (e.g., genomic sequencing, RNA sequencing) and provides varying levels of throughput, including the MiniSeq, MiSeq, and HiSeq models [12]. The presence of Maedi-Visna disease in China was first reported in 1966. Serological diagnosis revealed that MVV was present in several regions of China, except Inner Mongolia (NM), which is one of the major provinces of sheep production in China. However, the lack of a complete genome sequence of the Chinese MMV strains has hampered comprehension of its genetic characteristics. We have worked on ovine viral pneumonia for several years, and a case of Maedi-Visna disease was recently identified in the western portion of NM. To elucidate the molecular characteristics of our isolate, NGS was applied to obtain a complete genome sequence. Moreover, phylogenetic information on our isolate was analyzed, which may assist in elucidating the genetic evolution of MVV."," Ethics statement This work does not contain any studies performed on living animals. Collection of lung tissues conformed to the experimental practices and standards approved by the animal welfare and research ethics committee of NM Agricultural University (approval ID: 2020007).\nThis work does not contain any studies performed on living animals. Collection of lung tissues conformed to the experimental practices and standards approved by the animal welfare and research ethics committee of NM Agricultural University (approval ID: 2020007).\n Sample collection and hematoxylin and eosin (H&E) staining A 3-year-old Dorper ewe with serve dyspnea, cough, and wheezing was provided by a large-scale sheep farm in NM and was sacrificed for sample collection. Lung samples were kept in 10% formalin or frozen at −80°C. The lung samples were fixed in 10% formalin and processed by applying standard procedures for pathological examination. After processing, 3–5 mm-thick sections were stained with H&E for microscope examination.\nA 3-year-old Dorper ewe with serve dyspnea, cough, and wheezing was provided by a large-scale sheep farm in NM and was sacrificed for sample collection. Lung samples were kept in 10% formalin or frozen at −80°C. The lung samples were fixed in 10% formalin and processed by applying standard procedures for pathological examination. After processing, 3–5 mm-thick sections were stained with H&E for microscope examination.\n Polymerase chain reaction (PCR) identification MVV identification by PCR was performed as previously described [13]. Briefly, a pair of primers (F: 5′-TGACACAGCAAATGTAACCGCAAG-3′; R: 5′- CCACGTTGGGCGCCAGCTGCGAGA-3′) were used to amplify a 291 bp fragment of the long terminal repeat (LTR) region of pro-viral DNA. MVV was proliferated in choroid plexus cells and, after 3 passages, were used as positive controls. Healthy lung tissue and RNase-free and DNase-free water were used as negative and blank controls, respectively.\nMVV identification by PCR was performed as previously described [13]. Briefly, a pair of primers (F: 5′-TGACACAGCAAATGTAACCGCAAG-3′; R: 5′- CCACGTTGGGCGCCAGCTGCGAGA-3′) were used to amplify a 291 bp fragment of the long terminal repeat (LTR) region of pro-viral DNA. MVV was proliferated in choroid plexus cells and, after 3 passages, were used as positive controls. Healthy lung tissue and RNase-free and DNase-free water were used as negative and blank controls, respectively.\n Virus enrichment and identification PCR-positive lung tissue was ground and then made into a suspension by adding a 5-fold volume of PBS. Viruses were released by performing 3 freezing and thawing cycles. The supernatants were collected after centrifugation at 4,000 r/min for 30 min, repeated 3 times, and then centrifuged at 12,000 r/min for 1 h. The obtained supernatant was centrifuged at 35,000 r/min for 3 h, and the pellet was resuspended in TNE buffer. After centrifugation of the suspension at 5,000 r/min for 10 min, the supernatant was passed through a 25% sucrose cushion and centrifuged at 35,000 r/min for 4 h. Viruses were purified by a 20%–50% gradient of sucrose and ultracentrifugation. The final acquisition was negatively stained with 2% uranyl acetate and examined by electron microscopy.\nPCR-positive lung tissue was ground and then made into a suspension by adding a 5-fold volume of PBS. Viruses were released by performing 3 freezing and thawing cycles. The supernatants were collected after centrifugation at 4,000 r/min for 30 min, repeated 3 times, and then centrifuged at 12,000 r/min for 1 h. The obtained supernatant was centrifuged at 35,000 r/min for 3 h, and the pellet was resuspended in TNE buffer. After centrifugation of the suspension at 5,000 r/min for 10 min, the supernatant was passed through a 25% sucrose cushion and centrifuged at 35,000 r/min for 4 h. Viruses were purified by a 20%–50% gradient of sucrose and ultracentrifugation. The final acquisition was negatively stained with 2% uranyl acetate and examined by electron microscopy.\n Sequencing and genome assembly Viral RNA was extracted using the TRIzol (TAKARA, China) reagent, then reverse-transcribed into complementary DNA (cDNA) with the Prime ScriptTM RT reagent kit and g DNA Eraser (TAKARA), according to the manufacturer's recommendations. A viral cDNA library was constructed and sequenced by Shanghai Biozeron Biological Technology Co. Ltd. Briefly, 1 μg of cDNA was used with Illumina's TruSeq™ Nano DNA Sample Prep Kit for library preparation. Libraries were sequenced on the Illumina HiSeq 4000 platform at 2 × 150 bp read length. Genome assembly was performed using ABySS [14] to achieve optimal results, and corrections for single-base polymorphism and the infilling of remaining gaps were conducted in SOAPdenovo [15].\nViral RNA was extracted using the TRIzol (TAKARA, China) reagent, then reverse-transcribed into complementary DNA (cDNA) with the Prime ScriptTM RT reagent kit and g DNA Eraser (TAKARA), according to the manufacturer's recommendations. A viral cDNA library was constructed and sequenced by Shanghai Biozeron Biological Technology Co. Ltd. Briefly, 1 μg of cDNA was used with Illumina's TruSeq™ Nano DNA Sample Prep Kit for library preparation. Libraries were sequenced on the Illumina HiSeq 4000 platform at 2 × 150 bp read length. Genome assembly was performed using ABySS [14] to achieve optimal results, and corrections for single-base polymorphism and the infilling of remaining gaps were conducted in SOAPdenovo [15].\n Sequence alignment and polygenetic analysis Sequence alignment was performed using DNASTAR Lasergene (version 7.1.0). To evaluate the relationship between the reference strains and the NM strain, a phylogenetic tree was constructed using the neighbor-joining method provided in molecular evolutionary genetics analysis software (version 7.0). Bootstrap values were estimated for 1,000 replicates.\nSequence alignment was performed using DNASTAR Lasergene (version 7.1.0). To evaluate the relationship between the reference strains and the NM strain, a phylogenetic tree was constructed using the neighbor-joining method provided in molecular evolutionary genetics analysis software (version 7.0). Bootstrap values were estimated for 1,000 replicates.","This work does not contain any studies performed on living animals. Collection of lung tissues conformed to the experimental practices and standards approved by the animal welfare and research ethics committee of NM Agricultural University (approval ID: 2020007).","A 3-year-old Dorper ewe with serve dyspnea, cough, and wheezing was provided by a large-scale sheep farm in NM and was sacrificed for sample collection. Lung samples were kept in 10% formalin or frozen at −80°C. The lung samples were fixed in 10% formalin and processed by applying standard procedures for pathological examination. After processing, 3–5 mm-thick sections were stained with H&E for microscope examination.","MVV identification by PCR was performed as previously described [13]. Briefly, a pair of primers (F: 5′-TGACACAGCAAATGTAACCGCAAG-3′; R: 5′- CCACGTTGGGCGCCAGCTGCGAGA-3′) were used to amplify a 291 bp fragment of the long terminal repeat (LTR) region of pro-viral DNA. MVV was proliferated in choroid plexus cells and, after 3 passages, were used as positive controls. Healthy lung tissue and RNase-free and DNase-free water were used as negative and blank controls, respectively.","PCR-positive lung tissue was ground and then made into a suspension by adding a 5-fold volume of PBS. Viruses were released by performing 3 freezing and thawing cycles. The supernatants were collected after centrifugation at 4,000 r/min for 30 min, repeated 3 times, and then centrifuged at 12,000 r/min for 1 h. The obtained supernatant was centrifuged at 35,000 r/min for 3 h, and the pellet was resuspended in TNE buffer. After centrifugation of the suspension at 5,000 r/min for 10 min, the supernatant was passed through a 25% sucrose cushion and centrifuged at 35,000 r/min for 4 h. Viruses were purified by a 20%–50% gradient of sucrose and ultracentrifugation. The final acquisition was negatively stained with 2% uranyl acetate and examined by electron microscopy.","Viral RNA was extracted using the TRIzol (TAKARA, China) reagent, then reverse-transcribed into complementary DNA (cDNA) with the Prime ScriptTM RT reagent kit and g DNA Eraser (TAKARA), according to the manufacturer's recommendations. A viral cDNA library was constructed and sequenced by Shanghai Biozeron Biological Technology Co. Ltd. Briefly, 1 μg of cDNA was used with Illumina's TruSeq™ Nano DNA Sample Prep Kit for library preparation. Libraries were sequenced on the Illumina HiSeq 4000 platform at 2 × 150 bp read length. Genome assembly was performed using ABySS [14] to achieve optimal results, and corrections for single-base polymorphism and the infilling of remaining gaps were conducted in SOAPdenovo [15].","Sequence alignment was performed using DNASTAR Lasergene (version 7.1.0). To evaluate the relationship between the reference strains and the NM strain, a phylogenetic tree was constructed using the neighbor-joining method provided in molecular evolutionary genetics analysis software (version 7.0). Bootstrap values were estimated for 1,000 replicates."," Characterization of MVV in sheep lung tissue Gross appearance showed that the volume of the affected sheep lung was increased by 2- to 3-fold. Nodular lesions were scattered or densely distributed in local areas, and the surrounding tissue was a dark red color and provided a hard tactile sensation. Fibrotic focuses were scattered in the pleural surface (Fig. 1A). Histopathologically, lymphocytic and lymphofollicular hyperplasias were scattered or locally distributed in the pulmonary interstitium. Lymphoid follicles occurred before the peripheral bronchioles, and the germinal centers of some lymphoid follicles were obvious. There were significant proliferations of lymphocytes, macrophages, and a small number of fibroblasts in the alveolar septum. Type II epithelial cells on the inner surface of the alveolar wall had proliferated to varying degrees. Additionally, some alveolar cavities were filled with red homogeneous serous fluid as well as some macrophages and lymphocytes (Fig. 1B). A PCR procedure was performed to amplify the LTR sequence of the MVV. Detection by gel electrophoresis confirmed that the lung tissue was MVV-positive (Fig. 1C). Furthermore, electron microscope-based examination of purified MVV showed that the viral particles were approximately 80 nm (Fig. 1D).\nM, marker; MVV, Maedi/Visna virus.\nGross appearance showed that the volume of the affected sheep lung was increased by 2- to 3-fold. Nodular lesions were scattered or densely distributed in local areas, and the surrounding tissue was a dark red color and provided a hard tactile sensation. Fibrotic focuses were scattered in the pleural surface (Fig. 1A). Histopathologically, lymphocytic and lymphofollicular hyperplasias were scattered or locally distributed in the pulmonary interstitium. Lymphoid follicles occurred before the peripheral bronchioles, and the germinal centers of some lymphoid follicles were obvious. There were significant proliferations of lymphocytes, macrophages, and a small number of fibroblasts in the alveolar septum. Type II epithelial cells on the inner surface of the alveolar wall had proliferated to varying degrees. Additionally, some alveolar cavities were filled with red homogeneous serous fluid as well as some macrophages and lymphocytes (Fig. 1B). A PCR procedure was performed to amplify the LTR sequence of the MVV. Detection by gel electrophoresis confirmed that the lung tissue was MVV-positive (Fig. 1C). Furthermore, electron microscope-based examination of purified MVV showed that the viral particles were approximately 80 nm (Fig. 1D).\nM, marker; MVV, Maedi/Visna virus.\n Complete genome sequence of the NM1111 strain A total of 1.1 GB of valid data was obtained, and the data were uniformly distributed. The sequencing depth was up to 188,295 times. The values of Q20 and Q30 were 96.64% and 90.48%, respectively, and the G+C content was 40.43%. These results reveal that our data exhibited good sequencing quality. Finally, the isolate contained 9,193 nucleotides after splicing and optimization. Three primary encoding genes, the gag gene, position at 502 to 1,845, contained 1,344 nucleotides; the pol gene, position at 1716 to 5036, contained 3321 nucleotides; the env gene, position at 5,985 to 8,948, contained 2,964 nucleotides (Table 1, Fig. 2). The complete genome sequence of MVV was uploaded, named NM1111, to GenBank with the accession number MW248464 (Supplementary Data 1).\nLTR, long terminal repeat; N/A, not applicable.\nLTR, long terminal repeat.\nA total of 1.1 GB of valid data was obtained, and the data were uniformly distributed. The sequencing depth was up to 188,295 times. The values of Q20 and Q30 were 96.64% and 90.48%, respectively, and the G+C content was 40.43%. These results reveal that our data exhibited good sequencing quality. Finally, the isolate contained 9,193 nucleotides after splicing and optimization. Three primary encoding genes, the gag gene, position at 502 to 1,845, contained 1,344 nucleotides; the pol gene, position at 1716 to 5036, contained 3321 nucleotides; the env gene, position at 5,985 to 8,948, contained 2,964 nucleotides (Table 1, Fig. 2). The complete genome sequence of MVV was uploaded, named NM1111, to GenBank with the accession number MW248464 (Supplementary Data 1).\nLTR, long terminal repeat; N/A, not applicable.\nLTR, long terminal repeat.\n Phylogenetic analysis A neighbor-joining tree was constructed using the whole-genome sequence of NM1111 and the available reference strains. The phylogenetic analysis showed that the NM1111 strain was clustered in a branch of MMV type A2 (Fig. 3). Meanwhile, homology comparisons showed that the gag and pol of NM1111 had a close relationship with American strain MH916859 (Table 2). In contrast, the env had a lower sequence homology with the reference strains, suggesting the development of antigenic variability with evolution. Amino acid homology analysis indicated that the gag protein is highly conserved, especially in 2 dominant epitopes (epitope2 and epitope3). However, within the highly homologous epitope2, the Sichuan strain (KR011757) mutated from alanine (A) to valine (V) at position 221. Notably, the major homology region (MHR) region of NM1111 mutated from aspartic acid (D) to glutamic acid (E) at position 296 (Table 3). The SU region had a high degree of sequence variation, especially in variable region 4 (V4) with 5 amino acid insertions (position at 554-555, 564-566) (Table 4). Immunodominant epitope SU5 had a conserved motif (VRAYTYGV) located at the N-terminal part and a highly variable motif at the C-terminal (Table 4).\nNM, Inner Mongolia.\nN/A, not applicable.\nMHR, major homology region.\nA neighbor-joining tree was constructed using the whole-genome sequence of NM1111 and the available reference strains. The phylogenetic analysis showed that the NM1111 strain was clustered in a branch of MMV type A2 (Fig. 3). Meanwhile, homology comparisons showed that the gag and pol of NM1111 had a close relationship with American strain MH916859 (Table 2). In contrast, the env had a lower sequence homology with the reference strains, suggesting the development of antigenic variability with evolution. Amino acid homology analysis indicated that the gag protein is highly conserved, especially in 2 dominant epitopes (epitope2 and epitope3). However, within the highly homologous epitope2, the Sichuan strain (KR011757) mutated from alanine (A) to valine (V) at position 221. Notably, the major homology region (MHR) region of NM1111 mutated from aspartic acid (D) to glutamic acid (E) at position 296 (Table 3). The SU region had a high degree of sequence variation, especially in variable region 4 (V4) with 5 amino acid insertions (position at 554-555, 564-566) (Table 4). Immunodominant epitope SU5 had a conserved motif (VRAYTYGV) located at the N-terminal part and a highly variable motif at the C-terminal (Table 4).\nNM, Inner Mongolia.\nN/A, not applicable.\nMHR, major homology region.","Gross appearance showed that the volume of the affected sheep lung was increased by 2- to 3-fold. Nodular lesions were scattered or densely distributed in local areas, and the surrounding tissue was a dark red color and provided a hard tactile sensation. Fibrotic focuses were scattered in the pleural surface (Fig. 1A). Histopathologically, lymphocytic and lymphofollicular hyperplasias were scattered or locally distributed in the pulmonary interstitium. Lymphoid follicles occurred before the peripheral bronchioles, and the germinal centers of some lymphoid follicles were obvious. There were significant proliferations of lymphocytes, macrophages, and a small number of fibroblasts in the alveolar septum. Type II epithelial cells on the inner surface of the alveolar wall had proliferated to varying degrees. Additionally, some alveolar cavities were filled with red homogeneous serous fluid as well as some macrophages and lymphocytes (Fig. 1B). A PCR procedure was performed to amplify the LTR sequence of the MVV. Detection by gel electrophoresis confirmed that the lung tissue was MVV-positive (Fig. 1C). Furthermore, electron microscope-based examination of purified MVV showed that the viral particles were approximately 80 nm (Fig. 1D).\nM, marker; MVV, Maedi/Visna virus.","A total of 1.1 GB of valid data was obtained, and the data were uniformly distributed. The sequencing depth was up to 188,295 times. The values of Q20 and Q30 were 96.64% and 90.48%, respectively, and the G+C content was 40.43%. These results reveal that our data exhibited good sequencing quality. Finally, the isolate contained 9,193 nucleotides after splicing and optimization. Three primary encoding genes, the gag gene, position at 502 to 1,845, contained 1,344 nucleotides; the pol gene, position at 1716 to 5036, contained 3321 nucleotides; the env gene, position at 5,985 to 8,948, contained 2,964 nucleotides (Table 1, Fig. 2). The complete genome sequence of MVV was uploaded, named NM1111, to GenBank with the accession number MW248464 (Supplementary Data 1).\nLTR, long terminal repeat; N/A, not applicable.\nLTR, long terminal repeat.","A neighbor-joining tree was constructed using the whole-genome sequence of NM1111 and the available reference strains. The phylogenetic analysis showed that the NM1111 strain was clustered in a branch of MMV type A2 (Fig. 3). Meanwhile, homology comparisons showed that the gag and pol of NM1111 had a close relationship with American strain MH916859 (Table 2). In contrast, the env had a lower sequence homology with the reference strains, suggesting the development of antigenic variability with evolution. Amino acid homology analysis indicated that the gag protein is highly conserved, especially in 2 dominant epitopes (epitope2 and epitope3). However, within the highly homologous epitope2, the Sichuan strain (KR011757) mutated from alanine (A) to valine (V) at position 221. Notably, the major homology region (MHR) region of NM1111 mutated from aspartic acid (D) to glutamic acid (E) at position 296 (Table 3). The SU region had a high degree of sequence variation, especially in variable region 4 (V4) with 5 amino acid insertions (position at 554-555, 564-566) (Table 4). Immunodominant epitope SU5 had a conserved motif (VRAYTYGV) located at the N-terminal part and a highly variable motif at the C-terminal (Table 4).\nNM, Inner Mongolia.\nN/A, not applicable.\nMHR, major homology region.","NGS is a recent, powerful technique developed and applied for virus sequencing. In this study, the first complete genome sequence of MVV from China was obtained by NGS. Sequencing depth, high purity of the assembled data, the distribution of the GC content, and the sequencing coverage verified the reliability of the obtained results. Despite NGS being the most prevalent technology for sequencing at present, future complete genome sequencing of MVV isolates with recently developed third-generation sequencing would also be desirable.\nGag is relatively conserved in MVV and encodes a major protective antigen. CA, encoded by gag, contains 3 linear epitopes (epitope 2, MHR, and epitope 3) and can cause a strong antibody reaction during infection, thus making it of great value for serodiagnosis. These epitopes are also important for maintaining cross-reactivity in gag antigen-based serological tests [16]. The amino acid sequence analysis showed that the linear epitopes were highly conserved, but there was a mutation from aspartic acid (D) to glutamic acid (E) at position 296 in the MHR region, an observation similar to that for a type A5 strain from Poland [17]. MVV has been serologically detected in 12 regions of China (Yunnan, Guizhou, Gansu, Ningxia, Shandong, Sichuan, Hunan, Guangdong, Chongqing, Guangxi, Jilin, and Anhui), and the infection rate in those regions ranged from 4.60% to 50.00% [18]. However, when Sun et al. [19] carried out a serological investigation on MVV in China, the detection rate in NM was zero. In addition to the limited sample collection area, it was inferred that a mutation in MHR region might also have contributed. Tanaka et al. [20] reported that, in addition to the epitope2 and epitope3, MHR is usually conserved in many retroviruses. Mutations in this region may destroy capsid protein assembly and reduce human immunodeficiency virus infectivity [20]. In this study, the MHR region of the strains isolated thirty years ago, except the south Africa strain, were shown to be conserved, but other strains exhibited varying degrees of mutation. Whether the mutations in this region could reduce the virulence of MVV needs to be further elucidated.\nEnv is the most mutated gene in MVV, which can often cause antigen drift. The V4 region of the SU domain can affect cell tropism and the synthesis of neutralizing antibodies [21]. Hence, mutations in this region have an important role in interactions between the virus and the host. Based on the analysis of env protein, the mutation rate of the V4 region was high in our isolate, which is consistent with the results in the study of Hötzel and Cheevers [22]. In addition, there were 5 amino acid insertions, suggesting a high probability of antigen drift. Skraban et al. [23] reported that the SU5 epitope is responsible for a specific immune response, and it could be used to realize a specific diagnosis for a virus strain. In this study, we observed a conserved motif at the N-terminal and a highly variable motif at the C-terminal of the SU5 region, observations that are consistent with the A5 strain of MVV isolated from Poland [17]. These observations emphasize the effectiveness of identifying different MVV subtypes within local genotypes [24].\nPolygenetic analysis indicated that the NM1111 isolate was closely related to American stains. Meanwhile, based on the gag sequence construction of a neighbor-joining tree, the Sichuan strain (KR011757) was separated along the same branch from an American stain (AY101611) (Supplementary Fig. 1). The results suggest that the main epidemic MMV strain in China was probably MMV type A2. Characterizing more MVV strains isolated from China will help elucidate the genetic evolution of MMV."],"string":"[\n \"Maedi/Visna virus (MVV) belongs to the Retroviridae family and Lentivirus genus and is a monocyte/macrophage-tropic non-oncogenic virus [1]. MVV infection is persistent and chronic and is characterized by slow progressive degenerative inflammation in multiple organs, including lungs, brain, udder, and joints in sheep and goats [2]. Labored breathing associated with emaciation caused by progressive pneumonitis is the predominant feature in clinically affected sheep [3]. MMV is epidemic globally, except for Australia and New Zealand, resulting in considerable economic losses [4].\\nThe MVV is a single-stranded RNA molecule with positive-sense polarity and contains 3 primary genes (gag, pol, and env) and several regulatory genes (e.g., vif, vpr-like, and rev) [5]. The gag gene encodes the internal structural proteins, such as the capsid protein (CA), which stimulates the host to produce antibodies. The pol gene encodes enzymes involved in replication and DNA integration, such as reverse transcriptase [6]. These 2 genomic regions, the gag-pol (1.8 kb) and the pol (1.2 kb), were initially proposed by Shah et al. [7] to be used in the classification of MVV subtype strains. Subsequently, many other strains were added to the phylogenetic tree, but the additions were often based on the sequence of only a small part of one of the fragments initially proposed by Olech et al. [8]. The env gene encodes surface and transmembrane glycoproteins that insert into the envelope. The surface glycoprotein (SU) contains genetically variable domains, thereby determining the antigenic variability of the different isolates.\\nThe use of next-generation sequencing (NGS), also called high throughput sequencing, has rapidly expanded over recent years [91011]. Due to their high accuracy and high throughput capacity, Illumina, Inc. is the dominant player in the NGS arena. Illumina NGS supports various protocols (e.g., genomic sequencing, RNA sequencing) and provides varying levels of throughput, including the MiniSeq, MiSeq, and HiSeq models [12]. The presence of Maedi-Visna disease in China was first reported in 1966. Serological diagnosis revealed that MVV was present in several regions of China, except Inner Mongolia (NM), which is one of the major provinces of sheep production in China. However, the lack of a complete genome sequence of the Chinese MMV strains has hampered comprehension of its genetic characteristics. We have worked on ovine viral pneumonia for several years, and a case of Maedi-Visna disease was recently identified in the western portion of NM. To elucidate the molecular characteristics of our isolate, NGS was applied to obtain a complete genome sequence. Moreover, phylogenetic information on our isolate was analyzed, which may assist in elucidating the genetic evolution of MVV.\",\n \" Ethics statement This work does not contain any studies performed on living animals. Collection of lung tissues conformed to the experimental practices and standards approved by the animal welfare and research ethics committee of NM Agricultural University (approval ID: 2020007).\\nThis work does not contain any studies performed on living animals. Collection of lung tissues conformed to the experimental practices and standards approved by the animal welfare and research ethics committee of NM Agricultural University (approval ID: 2020007).\\n Sample collection and hematoxylin and eosin (H&E) staining A 3-year-old Dorper ewe with serve dyspnea, cough, and wheezing was provided by a large-scale sheep farm in NM and was sacrificed for sample collection. Lung samples were kept in 10% formalin or frozen at −80°C. The lung samples were fixed in 10% formalin and processed by applying standard procedures for pathological examination. After processing, 3–5 mm-thick sections were stained with H&E for microscope examination.\\nA 3-year-old Dorper ewe with serve dyspnea, cough, and wheezing was provided by a large-scale sheep farm in NM and was sacrificed for sample collection. Lung samples were kept in 10% formalin or frozen at −80°C. The lung samples were fixed in 10% formalin and processed by applying standard procedures for pathological examination. After processing, 3–5 mm-thick sections were stained with H&E for microscope examination.\\n Polymerase chain reaction (PCR) identification MVV identification by PCR was performed as previously described [13]. Briefly, a pair of primers (F: 5′-TGACACAGCAAATGTAACCGCAAG-3′; R: 5′- CCACGTTGGGCGCCAGCTGCGAGA-3′) were used to amplify a 291 bp fragment of the long terminal repeat (LTR) region of pro-viral DNA. MVV was proliferated in choroid plexus cells and, after 3 passages, were used as positive controls. Healthy lung tissue and RNase-free and DNase-free water were used as negative and blank controls, respectively.\\nMVV identification by PCR was performed as previously described [13]. Briefly, a pair of primers (F: 5′-TGACACAGCAAATGTAACCGCAAG-3′; R: 5′- CCACGTTGGGCGCCAGCTGCGAGA-3′) were used to amplify a 291 bp fragment of the long terminal repeat (LTR) region of pro-viral DNA. MVV was proliferated in choroid plexus cells and, after 3 passages, were used as positive controls. Healthy lung tissue and RNase-free and DNase-free water were used as negative and blank controls, respectively.\\n Virus enrichment and identification PCR-positive lung tissue was ground and then made into a suspension by adding a 5-fold volume of PBS. Viruses were released by performing 3 freezing and thawing cycles. The supernatants were collected after centrifugation at 4,000 r/min for 30 min, repeated 3 times, and then centrifuged at 12,000 r/min for 1 h. The obtained supernatant was centrifuged at 35,000 r/min for 3 h, and the pellet was resuspended in TNE buffer. After centrifugation of the suspension at 5,000 r/min for 10 min, the supernatant was passed through a 25% sucrose cushion and centrifuged at 35,000 r/min for 4 h. Viruses were purified by a 20%–50% gradient of sucrose and ultracentrifugation. The final acquisition was negatively stained with 2% uranyl acetate and examined by electron microscopy.\\nPCR-positive lung tissue was ground and then made into a suspension by adding a 5-fold volume of PBS. Viruses were released by performing 3 freezing and thawing cycles. The supernatants were collected after centrifugation at 4,000 r/min for 30 min, repeated 3 times, and then centrifuged at 12,000 r/min for 1 h. The obtained supernatant was centrifuged at 35,000 r/min for 3 h, and the pellet was resuspended in TNE buffer. After centrifugation of the suspension at 5,000 r/min for 10 min, the supernatant was passed through a 25% sucrose cushion and centrifuged at 35,000 r/min for 4 h. Viruses were purified by a 20%–50% gradient of sucrose and ultracentrifugation. The final acquisition was negatively stained with 2% uranyl acetate and examined by electron microscopy.\\n Sequencing and genome assembly Viral RNA was extracted using the TRIzol (TAKARA, China) reagent, then reverse-transcribed into complementary DNA (cDNA) with the Prime ScriptTM RT reagent kit and g DNA Eraser (TAKARA), according to the manufacturer's recommendations. A viral cDNA library was constructed and sequenced by Shanghai Biozeron Biological Technology Co. Ltd. Briefly, 1 μg of cDNA was used with Illumina's TruSeq™ Nano DNA Sample Prep Kit for library preparation. Libraries were sequenced on the Illumina HiSeq 4000 platform at 2 × 150 bp read length. Genome assembly was performed using ABySS [14] to achieve optimal results, and corrections for single-base polymorphism and the infilling of remaining gaps were conducted in SOAPdenovo [15].\\nViral RNA was extracted using the TRIzol (TAKARA, China) reagent, then reverse-transcribed into complementary DNA (cDNA) with the Prime ScriptTM RT reagent kit and g DNA Eraser (TAKARA), according to the manufacturer's recommendations. A viral cDNA library was constructed and sequenced by Shanghai Biozeron Biological Technology Co. Ltd. Briefly, 1 μg of cDNA was used with Illumina's TruSeq™ Nano DNA Sample Prep Kit for library preparation. Libraries were sequenced on the Illumina HiSeq 4000 platform at 2 × 150 bp read length. Genome assembly was performed using ABySS [14] to achieve optimal results, and corrections for single-base polymorphism and the infilling of remaining gaps were conducted in SOAPdenovo [15].\\n Sequence alignment and polygenetic analysis Sequence alignment was performed using DNASTAR Lasergene (version 7.1.0). To evaluate the relationship between the reference strains and the NM strain, a phylogenetic tree was constructed using the neighbor-joining method provided in molecular evolutionary genetics analysis software (version 7.0). Bootstrap values were estimated for 1,000 replicates.\\nSequence alignment was performed using DNASTAR Lasergene (version 7.1.0). To evaluate the relationship between the reference strains and the NM strain, a phylogenetic tree was constructed using the neighbor-joining method provided in molecular evolutionary genetics analysis software (version 7.0). Bootstrap values were estimated for 1,000 replicates.\",\n \"This work does not contain any studies performed on living animals. Collection of lung tissues conformed to the experimental practices and standards approved by the animal welfare and research ethics committee of NM Agricultural University (approval ID: 2020007).\",\n \"A 3-year-old Dorper ewe with serve dyspnea, cough, and wheezing was provided by a large-scale sheep farm in NM and was sacrificed for sample collection. Lung samples were kept in 10% formalin or frozen at −80°C. The lung samples were fixed in 10% formalin and processed by applying standard procedures for pathological examination. After processing, 3–5 mm-thick sections were stained with H&E for microscope examination.\",\n \"MVV identification by PCR was performed as previously described [13]. Briefly, a pair of primers (F: 5′-TGACACAGCAAATGTAACCGCAAG-3′; R: 5′- CCACGTTGGGCGCCAGCTGCGAGA-3′) were used to amplify a 291 bp fragment of the long terminal repeat (LTR) region of pro-viral DNA. MVV was proliferated in choroid plexus cells and, after 3 passages, were used as positive controls. Healthy lung tissue and RNase-free and DNase-free water were used as negative and blank controls, respectively.\",\n \"PCR-positive lung tissue was ground and then made into a suspension by adding a 5-fold volume of PBS. Viruses were released by performing 3 freezing and thawing cycles. The supernatants were collected after centrifugation at 4,000 r/min for 30 min, repeated 3 times, and then centrifuged at 12,000 r/min for 1 h. The obtained supernatant was centrifuged at 35,000 r/min for 3 h, and the pellet was resuspended in TNE buffer. After centrifugation of the suspension at 5,000 r/min for 10 min, the supernatant was passed through a 25% sucrose cushion and centrifuged at 35,000 r/min for 4 h. Viruses were purified by a 20%–50% gradient of sucrose and ultracentrifugation. The final acquisition was negatively stained with 2% uranyl acetate and examined by electron microscopy.\",\n \"Viral RNA was extracted using the TRIzol (TAKARA, China) reagent, then reverse-transcribed into complementary DNA (cDNA) with the Prime ScriptTM RT reagent kit and g DNA Eraser (TAKARA), according to the manufacturer's recommendations. A viral cDNA library was constructed and sequenced by Shanghai Biozeron Biological Technology Co. Ltd. Briefly, 1 μg of cDNA was used with Illumina's TruSeq™ Nano DNA Sample Prep Kit for library preparation. Libraries were sequenced on the Illumina HiSeq 4000 platform at 2 × 150 bp read length. Genome assembly was performed using ABySS [14] to achieve optimal results, and corrections for single-base polymorphism and the infilling of remaining gaps were conducted in SOAPdenovo [15].\",\n \"Sequence alignment was performed using DNASTAR Lasergene (version 7.1.0). To evaluate the relationship between the reference strains and the NM strain, a phylogenetic tree was constructed using the neighbor-joining method provided in molecular evolutionary genetics analysis software (version 7.0). Bootstrap values were estimated for 1,000 replicates.\",\n \" Characterization of MVV in sheep lung tissue Gross appearance showed that the volume of the affected sheep lung was increased by 2- to 3-fold. Nodular lesions were scattered or densely distributed in local areas, and the surrounding tissue was a dark red color and provided a hard tactile sensation. Fibrotic focuses were scattered in the pleural surface (Fig. 1A). Histopathologically, lymphocytic and lymphofollicular hyperplasias were scattered or locally distributed in the pulmonary interstitium. Lymphoid follicles occurred before the peripheral bronchioles, and the germinal centers of some lymphoid follicles were obvious. There were significant proliferations of lymphocytes, macrophages, and a small number of fibroblasts in the alveolar septum. Type II epithelial cells on the inner surface of the alveolar wall had proliferated to varying degrees. Additionally, some alveolar cavities were filled with red homogeneous serous fluid as well as some macrophages and lymphocytes (Fig. 1B). A PCR procedure was performed to amplify the LTR sequence of the MVV. Detection by gel electrophoresis confirmed that the lung tissue was MVV-positive (Fig. 1C). Furthermore, electron microscope-based examination of purified MVV showed that the viral particles were approximately 80 nm (Fig. 1D).\\nM, marker; MVV, Maedi/Visna virus.\\nGross appearance showed that the volume of the affected sheep lung was increased by 2- to 3-fold. Nodular lesions were scattered or densely distributed in local areas, and the surrounding tissue was a dark red color and provided a hard tactile sensation. Fibrotic focuses were scattered in the pleural surface (Fig. 1A). Histopathologically, lymphocytic and lymphofollicular hyperplasias were scattered or locally distributed in the pulmonary interstitium. Lymphoid follicles occurred before the peripheral bronchioles, and the germinal centers of some lymphoid follicles were obvious. There were significant proliferations of lymphocytes, macrophages, and a small number of fibroblasts in the alveolar septum. Type II epithelial cells on the inner surface of the alveolar wall had proliferated to varying degrees. Additionally, some alveolar cavities were filled with red homogeneous serous fluid as well as some macrophages and lymphocytes (Fig. 1B). A PCR procedure was performed to amplify the LTR sequence of the MVV. Detection by gel electrophoresis confirmed that the lung tissue was MVV-positive (Fig. 1C). Furthermore, electron microscope-based examination of purified MVV showed that the viral particles were approximately 80 nm (Fig. 1D).\\nM, marker; MVV, Maedi/Visna virus.\\n Complete genome sequence of the NM1111 strain A total of 1.1 GB of valid data was obtained, and the data were uniformly distributed. The sequencing depth was up to 188,295 times. The values of Q20 and Q30 were 96.64% and 90.48%, respectively, and the G+C content was 40.43%. These results reveal that our data exhibited good sequencing quality. Finally, the isolate contained 9,193 nucleotides after splicing and optimization. Three primary encoding genes, the gag gene, position at 502 to 1,845, contained 1,344 nucleotides; the pol gene, position at 1716 to 5036, contained 3321 nucleotides; the env gene, position at 5,985 to 8,948, contained 2,964 nucleotides (Table 1, Fig. 2). The complete genome sequence of MVV was uploaded, named NM1111, to GenBank with the accession number MW248464 (Supplementary Data 1).\\nLTR, long terminal repeat; N/A, not applicable.\\nLTR, long terminal repeat.\\nA total of 1.1 GB of valid data was obtained, and the data were uniformly distributed. The sequencing depth was up to 188,295 times. The values of Q20 and Q30 were 96.64% and 90.48%, respectively, and the G+C content was 40.43%. These results reveal that our data exhibited good sequencing quality. Finally, the isolate contained 9,193 nucleotides after splicing and optimization. Three primary encoding genes, the gag gene, position at 502 to 1,845, contained 1,344 nucleotides; the pol gene, position at 1716 to 5036, contained 3321 nucleotides; the env gene, position at 5,985 to 8,948, contained 2,964 nucleotides (Table 1, Fig. 2). The complete genome sequence of MVV was uploaded, named NM1111, to GenBank with the accession number MW248464 (Supplementary Data 1).\\nLTR, long terminal repeat; N/A, not applicable.\\nLTR, long terminal repeat.\\n Phylogenetic analysis A neighbor-joining tree was constructed using the whole-genome sequence of NM1111 and the available reference strains. The phylogenetic analysis showed that the NM1111 strain was clustered in a branch of MMV type A2 (Fig. 3). Meanwhile, homology comparisons showed that the gag and pol of NM1111 had a close relationship with American strain MH916859 (Table 2). In contrast, the env had a lower sequence homology with the reference strains, suggesting the development of antigenic variability with evolution. Amino acid homology analysis indicated that the gag protein is highly conserved, especially in 2 dominant epitopes (epitope2 and epitope3). However, within the highly homologous epitope2, the Sichuan strain (KR011757) mutated from alanine (A) to valine (V) at position 221. Notably, the major homology region (MHR) region of NM1111 mutated from aspartic acid (D) to glutamic acid (E) at position 296 (Table 3). The SU region had a high degree of sequence variation, especially in variable region 4 (V4) with 5 amino acid insertions (position at 554-555, 564-566) (Table 4). Immunodominant epitope SU5 had a conserved motif (VRAYTYGV) located at the N-terminal part and a highly variable motif at the C-terminal (Table 4).\\nNM, Inner Mongolia.\\nN/A, not applicable.\\nMHR, major homology region.\\nA neighbor-joining tree was constructed using the whole-genome sequence of NM1111 and the available reference strains. The phylogenetic analysis showed that the NM1111 strain was clustered in a branch of MMV type A2 (Fig. 3). Meanwhile, homology comparisons showed that the gag and pol of NM1111 had a close relationship with American strain MH916859 (Table 2). In contrast, the env had a lower sequence homology with the reference strains, suggesting the development of antigenic variability with evolution. Amino acid homology analysis indicated that the gag protein is highly conserved, especially in 2 dominant epitopes (epitope2 and epitope3). However, within the highly homologous epitope2, the Sichuan strain (KR011757) mutated from alanine (A) to valine (V) at position 221. Notably, the major homology region (MHR) region of NM1111 mutated from aspartic acid (D) to glutamic acid (E) at position 296 (Table 3). The SU region had a high degree of sequence variation, especially in variable region 4 (V4) with 5 amino acid insertions (position at 554-555, 564-566) (Table 4). Immunodominant epitope SU5 had a conserved motif (VRAYTYGV) located at the N-terminal part and a highly variable motif at the C-terminal (Table 4).\\nNM, Inner Mongolia.\\nN/A, not applicable.\\nMHR, major homology region.\",\n \"Gross appearance showed that the volume of the affected sheep lung was increased by 2- to 3-fold. Nodular lesions were scattered or densely distributed in local areas, and the surrounding tissue was a dark red color and provided a hard tactile sensation. Fibrotic focuses were scattered in the pleural surface (Fig. 1A). Histopathologically, lymphocytic and lymphofollicular hyperplasias were scattered or locally distributed in the pulmonary interstitium. Lymphoid follicles occurred before the peripheral bronchioles, and the germinal centers of some lymphoid follicles were obvious. There were significant proliferations of lymphocytes, macrophages, and a small number of fibroblasts in the alveolar septum. Type II epithelial cells on the inner surface of the alveolar wall had proliferated to varying degrees. Additionally, some alveolar cavities were filled with red homogeneous serous fluid as well as some macrophages and lymphocytes (Fig. 1B). A PCR procedure was performed to amplify the LTR sequence of the MVV. Detection by gel electrophoresis confirmed that the lung tissue was MVV-positive (Fig. 1C). Furthermore, electron microscope-based examination of purified MVV showed that the viral particles were approximately 80 nm (Fig. 1D).\\nM, marker; MVV, Maedi/Visna virus.\",\n \"A total of 1.1 GB of valid data was obtained, and the data were uniformly distributed. The sequencing depth was up to 188,295 times. The values of Q20 and Q30 were 96.64% and 90.48%, respectively, and the G+C content was 40.43%. These results reveal that our data exhibited good sequencing quality. Finally, the isolate contained 9,193 nucleotides after splicing and optimization. Three primary encoding genes, the gag gene, position at 502 to 1,845, contained 1,344 nucleotides; the pol gene, position at 1716 to 5036, contained 3321 nucleotides; the env gene, position at 5,985 to 8,948, contained 2,964 nucleotides (Table 1, Fig. 2). The complete genome sequence of MVV was uploaded, named NM1111, to GenBank with the accession number MW248464 (Supplementary Data 1).\\nLTR, long terminal repeat; N/A, not applicable.\\nLTR, long terminal repeat.\",\n \"A neighbor-joining tree was constructed using the whole-genome sequence of NM1111 and the available reference strains. The phylogenetic analysis showed that the NM1111 strain was clustered in a branch of MMV type A2 (Fig. 3). Meanwhile, homology comparisons showed that the gag and pol of NM1111 had a close relationship with American strain MH916859 (Table 2). In contrast, the env had a lower sequence homology with the reference strains, suggesting the development of antigenic variability with evolution. Amino acid homology analysis indicated that the gag protein is highly conserved, especially in 2 dominant epitopes (epitope2 and epitope3). However, within the highly homologous epitope2, the Sichuan strain (KR011757) mutated from alanine (A) to valine (V) at position 221. Notably, the major homology region (MHR) region of NM1111 mutated from aspartic acid (D) to glutamic acid (E) at position 296 (Table 3). The SU region had a high degree of sequence variation, especially in variable region 4 (V4) with 5 amino acid insertions (position at 554-555, 564-566) (Table 4). Immunodominant epitope SU5 had a conserved motif (VRAYTYGV) located at the N-terminal part and a highly variable motif at the C-terminal (Table 4).\\nNM, Inner Mongolia.\\nN/A, not applicable.\\nMHR, major homology region.\",\n \"NGS is a recent, powerful technique developed and applied for virus sequencing. In this study, the first complete genome sequence of MVV from China was obtained by NGS. Sequencing depth, high purity of the assembled data, the distribution of the GC content, and the sequencing coverage verified the reliability of the obtained results. Despite NGS being the most prevalent technology for sequencing at present, future complete genome sequencing of MVV isolates with recently developed third-generation sequencing would also be desirable.\\nGag is relatively conserved in MVV and encodes a major protective antigen. CA, encoded by gag, contains 3 linear epitopes (epitope 2, MHR, and epitope 3) and can cause a strong antibody reaction during infection, thus making it of great value for serodiagnosis. These epitopes are also important for maintaining cross-reactivity in gag antigen-based serological tests [16]. The amino acid sequence analysis showed that the linear epitopes were highly conserved, but there was a mutation from aspartic acid (D) to glutamic acid (E) at position 296 in the MHR region, an observation similar to that for a type A5 strain from Poland [17]. MVV has been serologically detected in 12 regions of China (Yunnan, Guizhou, Gansu, Ningxia, Shandong, Sichuan, Hunan, Guangdong, Chongqing, Guangxi, Jilin, and Anhui), and the infection rate in those regions ranged from 4.60% to 50.00% [18]. However, when Sun et al. [19] carried out a serological investigation on MVV in China, the detection rate in NM was zero. In addition to the limited sample collection area, it was inferred that a mutation in MHR region might also have contributed. Tanaka et al. [20] reported that, in addition to the epitope2 and epitope3, MHR is usually conserved in many retroviruses. Mutations in this region may destroy capsid protein assembly and reduce human immunodeficiency virus infectivity [20]. In this study, the MHR region of the strains isolated thirty years ago, except the south Africa strain, were shown to be conserved, but other strains exhibited varying degrees of mutation. Whether the mutations in this region could reduce the virulence of MVV needs to be further elucidated.\\nEnv is the most mutated gene in MVV, which can often cause antigen drift. The V4 region of the SU domain can affect cell tropism and the synthesis of neutralizing antibodies [21]. Hence, mutations in this region have an important role in interactions between the virus and the host. Based on the analysis of env protein, the mutation rate of the V4 region was high in our isolate, which is consistent with the results in the study of Hötzel and Cheevers [22]. In addition, there were 5 amino acid insertions, suggesting a high probability of antigen drift. Skraban et al. [23] reported that the SU5 epitope is responsible for a specific immune response, and it could be used to realize a specific diagnosis for a virus strain. In this study, we observed a conserved motif at the N-terminal and a highly variable motif at the C-terminal of the SU5 region, observations that are consistent with the A5 strain of MVV isolated from Poland [17]. These observations emphasize the effectiveness of identifying different MVV subtypes within local genotypes [24].\\nPolygenetic analysis indicated that the NM1111 isolate was closely related to American stains. Meanwhile, based on the gag sequence construction of a neighbor-joining tree, the Sichuan strain (KR011757) was separated along the same branch from an American stain (AY101611) (Supplementary Fig. 1). The results suggest that the main epidemic MMV strain in China was probably MMV type A2. Characterizing more MVV strains isolated from China will help elucidate the genetic evolution of MMV.\"\n]"},"all_sections_sec_types":{"kind":"list like","value":["intro","materials|methods",null,null,null,null,null,null,"results",null,null,null,"discussion"],"string":"[\n \"intro\",\n \"materials|methods\",\n null,\n null,\n null,\n null,\n null,\n null,\n \"results\",\n null,\n null,\n null,\n \"discussion\"\n]"},"keywords":{"kind":"list like","value":["Maedi/Visna virus","next-generation sequencing","phylogenetic analysis","sheep lung"],"string":"[\n \"Maedi/Visna virus\",\n \"next-generation sequencing\",\n \"phylogenetic analysis\",\n \"sheep lung\"\n]"},"whole_article_text":{"kind":"string","value":"INTRODUCTION:\nMaedi/Visna virus (MVV) belongs to the Retroviridae family and Lentivirus genus and is a monocyte/macrophage-tropic non-oncogenic virus [1]. MVV infection is persistent and chronic and is characterized by slow progressive degenerative inflammation in multiple organs, including lungs, brain, udder, and joints in sheep and goats [2]. Labored breathing associated with emaciation caused by progressive pneumonitis is the predominant feature in clinically affected sheep [3]. MMV is epidemic globally, except for Australia and New Zealand, resulting in considerable economic losses [4].\nThe MVV is a single-stranded RNA molecule with positive-sense polarity and contains 3 primary genes (gag, pol, and env) and several regulatory genes (e.g., vif, vpr-like, and rev) [5]. The gag gene encodes the internal structural proteins, such as the capsid protein (CA), which stimulates the host to produce antibodies. The pol gene encodes enzymes involved in replication and DNA integration, such as reverse transcriptase [6]. These 2 genomic regions, the gag-pol (1.8 kb) and the pol (1.2 kb), were initially proposed by Shah et al. [7] to be used in the classification of MVV subtype strains. Subsequently, many other strains were added to the phylogenetic tree, but the additions were often based on the sequence of only a small part of one of the fragments initially proposed by Olech et al. [8]. The env gene encodes surface and transmembrane glycoproteins that insert into the envelope. The surface glycoprotein (SU) contains genetically variable domains, thereby determining the antigenic variability of the different isolates.\nThe use of next-generation sequencing (NGS), also called high throughput sequencing, has rapidly expanded over recent years [91011]. Due to their high accuracy and high throughput capacity, Illumina, Inc. is the dominant player in the NGS arena. Illumina NGS supports various protocols (e.g., genomic sequencing, RNA sequencing) and provides varying levels of throughput, including the MiniSeq, MiSeq, and HiSeq models [12]. The presence of Maedi-Visna disease in China was first reported in 1966. Serological diagnosis revealed that MVV was present in several regions of China, except Inner Mongolia (NM), which is one of the major provinces of sheep production in China. However, the lack of a complete genome sequence of the Chinese MMV strains has hampered comprehension of its genetic characteristics. We have worked on ovine viral pneumonia for several years, and a case of Maedi-Visna disease was recently identified in the western portion of NM. To elucidate the molecular characteristics of our isolate, NGS was applied to obtain a complete genome sequence. Moreover, phylogenetic information on our isolate was analyzed, which may assist in elucidating the genetic evolution of MVV.\n\nMATERIALS AND METHODS:\n Ethics statement This work does not contain any studies performed on living animals. Collection of lung tissues conformed to the experimental practices and standards approved by the animal welfare and research ethics committee of NM Agricultural University (approval ID: 2020007).\nThis work does not contain any studies performed on living animals. Collection of lung tissues conformed to the experimental practices and standards approved by the animal welfare and research ethics committee of NM Agricultural University (approval ID: 2020007).\n Sample collection and hematoxylin and eosin (H&E) staining A 3-year-old Dorper ewe with serve dyspnea, cough, and wheezing was provided by a large-scale sheep farm in NM and was sacrificed for sample collection. Lung samples were kept in 10% formalin or frozen at −80°C. The lung samples were fixed in 10% formalin and processed by applying standard procedures for pathological examination. After processing, 3–5 mm-thick sections were stained with H&E for microscope examination.\nA 3-year-old Dorper ewe with serve dyspnea, cough, and wheezing was provided by a large-scale sheep farm in NM and was sacrificed for sample collection. Lung samples were kept in 10% formalin or frozen at −80°C. The lung samples were fixed in 10% formalin and processed by applying standard procedures for pathological examination. After processing, 3–5 mm-thick sections were stained with H&E for microscope examination.\n Polymerase chain reaction (PCR) identification MVV identification by PCR was performed as previously described [13]. Briefly, a pair of primers (F: 5′-TGACACAGCAAATGTAACCGCAAG-3′; R: 5′- CCACGTTGGGCGCCAGCTGCGAGA-3′) were used to amplify a 291 bp fragment of the long terminal repeat (LTR) region of pro-viral DNA. MVV was proliferated in choroid plexus cells and, after 3 passages, were used as positive controls. Healthy lung tissue and RNase-free and DNase-free water were used as negative and blank controls, respectively.\nMVV identification by PCR was performed as previously described [13]. Briefly, a pair of primers (F: 5′-TGACACAGCAAATGTAACCGCAAG-3′; R: 5′- CCACGTTGGGCGCCAGCTGCGAGA-3′) were used to amplify a 291 bp fragment of the long terminal repeat (LTR) region of pro-viral DNA. MVV was proliferated in choroid plexus cells and, after 3 passages, were used as positive controls. Healthy lung tissue and RNase-free and DNase-free water were used as negative and blank controls, respectively.\n Virus enrichment and identification PCR-positive lung tissue was ground and then made into a suspension by adding a 5-fold volume of PBS. Viruses were released by performing 3 freezing and thawing cycles. The supernatants were collected after centrifugation at 4,000 r/min for 30 min, repeated 3 times, and then centrifuged at 12,000 r/min for 1 h. The obtained supernatant was centrifuged at 35,000 r/min for 3 h, and the pellet was resuspended in TNE buffer. After centrifugation of the suspension at 5,000 r/min for 10 min, the supernatant was passed through a 25% sucrose cushion and centrifuged at 35,000 r/min for 4 h. Viruses were purified by a 20%–50% gradient of sucrose and ultracentrifugation. The final acquisition was negatively stained with 2% uranyl acetate and examined by electron microscopy.\nPCR-positive lung tissue was ground and then made into a suspension by adding a 5-fold volume of PBS. Viruses were released by performing 3 freezing and thawing cycles. The supernatants were collected after centrifugation at 4,000 r/min for 30 min, repeated 3 times, and then centrifuged at 12,000 r/min for 1 h. The obtained supernatant was centrifuged at 35,000 r/min for 3 h, and the pellet was resuspended in TNE buffer. After centrifugation of the suspension at 5,000 r/min for 10 min, the supernatant was passed through a 25% sucrose cushion and centrifuged at 35,000 r/min for 4 h. Viruses were purified by a 20%–50% gradient of sucrose and ultracentrifugation. The final acquisition was negatively stained with 2% uranyl acetate and examined by electron microscopy.\n Sequencing and genome assembly Viral RNA was extracted using the TRIzol (TAKARA, China) reagent, then reverse-transcribed into complementary DNA (cDNA) with the Prime ScriptTM RT reagent kit and g DNA Eraser (TAKARA), according to the manufacturer's recommendations. A viral cDNA library was constructed and sequenced by Shanghai Biozeron Biological Technology Co. Ltd. Briefly, 1 μg of cDNA was used with Illumina's TruSeq™ Nano DNA Sample Prep Kit for library preparation. Libraries were sequenced on the Illumina HiSeq 4000 platform at 2 × 150 bp read length. Genome assembly was performed using ABySS [14] to achieve optimal results, and corrections for single-base polymorphism and the infilling of remaining gaps were conducted in SOAPdenovo [15].\nViral RNA was extracted using the TRIzol (TAKARA, China) reagent, then reverse-transcribed into complementary DNA (cDNA) with the Prime ScriptTM RT reagent kit and g DNA Eraser (TAKARA), according to the manufacturer's recommendations. A viral cDNA library was constructed and sequenced by Shanghai Biozeron Biological Technology Co. Ltd. Briefly, 1 μg of cDNA was used with Illumina's TruSeq™ Nano DNA Sample Prep Kit for library preparation. Libraries were sequenced on the Illumina HiSeq 4000 platform at 2 × 150 bp read length. Genome assembly was performed using ABySS [14] to achieve optimal results, and corrections for single-base polymorphism and the infilling of remaining gaps were conducted in SOAPdenovo [15].\n Sequence alignment and polygenetic analysis Sequence alignment was performed using DNASTAR Lasergene (version 7.1.0). To evaluate the relationship between the reference strains and the NM strain, a phylogenetic tree was constructed using the neighbor-joining method provided in molecular evolutionary genetics analysis software (version 7.0). Bootstrap values were estimated for 1,000 replicates.\nSequence alignment was performed using DNASTAR Lasergene (version 7.1.0). To evaluate the relationship between the reference strains and the NM strain, a phylogenetic tree was constructed using the neighbor-joining method provided in molecular evolutionary genetics analysis software (version 7.0). Bootstrap values were estimated for 1,000 replicates.\n\nEthics statement:\nThis work does not contain any studies performed on living animals. Collection of lung tissues conformed to the experimental practices and standards approved by the animal welfare and research ethics committee of NM Agricultural University (approval ID: 2020007).\n\nSample collection and hematoxylin and eosin (H&E) staining:\nA 3-year-old Dorper ewe with serve dyspnea, cough, and wheezing was provided by a large-scale sheep farm in NM and was sacrificed for sample collection. Lung samples were kept in 10% formalin or frozen at −80°C. The lung samples were fixed in 10% formalin and processed by applying standard procedures for pathological examination. After processing, 3–5 mm-thick sections were stained with H&E for microscope examination.\n\nPolymerase chain reaction (PCR) identification:\nMVV identification by PCR was performed as previously described [13]. Briefly, a pair of primers (F: 5′-TGACACAGCAAATGTAACCGCAAG-3′; R: 5′- CCACGTTGGGCGCCAGCTGCGAGA-3′) were used to amplify a 291 bp fragment of the long terminal repeat (LTR) region of pro-viral DNA. MVV was proliferated in choroid plexus cells and, after 3 passages, were used as positive controls. Healthy lung tissue and RNase-free and DNase-free water were used as negative and blank controls, respectively.\n\nVirus enrichment and identification:\nPCR-positive lung tissue was ground and then made into a suspension by adding a 5-fold volume of PBS. Viruses were released by performing 3 freezing and thawing cycles. The supernatants were collected after centrifugation at 4,000 r/min for 30 min, repeated 3 times, and then centrifuged at 12,000 r/min for 1 h. The obtained supernatant was centrifuged at 35,000 r/min for 3 h, and the pellet was resuspended in TNE buffer. After centrifugation of the suspension at 5,000 r/min for 10 min, the supernatant was passed through a 25% sucrose cushion and centrifuged at 35,000 r/min for 4 h. Viruses were purified by a 20%–50% gradient of sucrose and ultracentrifugation. The final acquisition was negatively stained with 2% uranyl acetate and examined by electron microscopy.\n\nSequencing and genome assembly:\nViral RNA was extracted using the TRIzol (TAKARA, China) reagent, then reverse-transcribed into complementary DNA (cDNA) with the Prime ScriptTM RT reagent kit and g DNA Eraser (TAKARA), according to the manufacturer's recommendations. A viral cDNA library was constructed and sequenced by Shanghai Biozeron Biological Technology Co. Ltd. Briefly, 1 μg of cDNA was used with Illumina's TruSeq™ Nano DNA Sample Prep Kit for library preparation. Libraries were sequenced on the Illumina HiSeq 4000 platform at 2 × 150 bp read length. Genome assembly was performed using ABySS [14] to achieve optimal results, and corrections for single-base polymorphism and the infilling of remaining gaps were conducted in SOAPdenovo [15].\n\nSequence alignment and polygenetic analysis:\nSequence alignment was performed using DNASTAR Lasergene (version 7.1.0). To evaluate the relationship between the reference strains and the NM strain, a phylogenetic tree was constructed using the neighbor-joining method provided in molecular evolutionary genetics analysis software (version 7.0). Bootstrap values were estimated for 1,000 replicates.\n\nRESULTS:\n Characterization of MVV in sheep lung tissue Gross appearance showed that the volume of the affected sheep lung was increased by 2- to 3-fold. Nodular lesions were scattered or densely distributed in local areas, and the surrounding tissue was a dark red color and provided a hard tactile sensation. Fibrotic focuses were scattered in the pleural surface (Fig. 1A). Histopathologically, lymphocytic and lymphofollicular hyperplasias were scattered or locally distributed in the pulmonary interstitium. Lymphoid follicles occurred before the peripheral bronchioles, and the germinal centers of some lymphoid follicles were obvious. There were significant proliferations of lymphocytes, macrophages, and a small number of fibroblasts in the alveolar septum. Type II epithelial cells on the inner surface of the alveolar wall had proliferated to varying degrees. Additionally, some alveolar cavities were filled with red homogeneous serous fluid as well as some macrophages and lymphocytes (Fig. 1B). A PCR procedure was performed to amplify the LTR sequence of the MVV. Detection by gel electrophoresis confirmed that the lung tissue was MVV-positive (Fig. 1C). Furthermore, electron microscope-based examination of purified MVV showed that the viral particles were approximately 80 nm (Fig. 1D).\nM, marker; MVV, Maedi/Visna virus.\nGross appearance showed that the volume of the affected sheep lung was increased by 2- to 3-fold. Nodular lesions were scattered or densely distributed in local areas, and the surrounding tissue was a dark red color and provided a hard tactile sensation. Fibrotic focuses were scattered in the pleural surface (Fig. 1A). Histopathologically, lymphocytic and lymphofollicular hyperplasias were scattered or locally distributed in the pulmonary interstitium. Lymphoid follicles occurred before the peripheral bronchioles, and the germinal centers of some lymphoid follicles were obvious. There were significant proliferations of lymphocytes, macrophages, and a small number of fibroblasts in the alveolar septum. Type II epithelial cells on the inner surface of the alveolar wall had proliferated to varying degrees. Additionally, some alveolar cavities were filled with red homogeneous serous fluid as well as some macrophages and lymphocytes (Fig. 1B). A PCR procedure was performed to amplify the LTR sequence of the MVV. Detection by gel electrophoresis confirmed that the lung tissue was MVV-positive (Fig. 1C). Furthermore, electron microscope-based examination of purified MVV showed that the viral particles were approximately 80 nm (Fig. 1D).\nM, marker; MVV, Maedi/Visna virus.\n Complete genome sequence of the NM1111 strain A total of 1.1 GB of valid data was obtained, and the data were uniformly distributed. The sequencing depth was up to 188,295 times. The values of Q20 and Q30 were 96.64% and 90.48%, respectively, and the G+C content was 40.43%. These results reveal that our data exhibited good sequencing quality. Finally, the isolate contained 9,193 nucleotides after splicing and optimization. Three primary encoding genes, the gag gene, position at 502 to 1,845, contained 1,344 nucleotides; the pol gene, position at 1716 to 5036, contained 3321 nucleotides; the env gene, position at 5,985 to 8,948, contained 2,964 nucleotides (Table 1, Fig. 2). The complete genome sequence of MVV was uploaded, named NM1111, to GenBank with the accession number MW248464 (Supplementary Data 1).\nLTR, long terminal repeat; N/A, not applicable.\nLTR, long terminal repeat.\nA total of 1.1 GB of valid data was obtained, and the data were uniformly distributed. The sequencing depth was up to 188,295 times. The values of Q20 and Q30 were 96.64% and 90.48%, respectively, and the G+C content was 40.43%. These results reveal that our data exhibited good sequencing quality. Finally, the isolate contained 9,193 nucleotides after splicing and optimization. Three primary encoding genes, the gag gene, position at 502 to 1,845, contained 1,344 nucleotides; the pol gene, position at 1716 to 5036, contained 3321 nucleotides; the env gene, position at 5,985 to 8,948, contained 2,964 nucleotides (Table 1, Fig. 2). The complete genome sequence of MVV was uploaded, named NM1111, to GenBank with the accession number MW248464 (Supplementary Data 1).\nLTR, long terminal repeat; N/A, not applicable.\nLTR, long terminal repeat.\n Phylogenetic analysis A neighbor-joining tree was constructed using the whole-genome sequence of NM1111 and the available reference strains. The phylogenetic analysis showed that the NM1111 strain was clustered in a branch of MMV type A2 (Fig. 3). Meanwhile, homology comparisons showed that the gag and pol of NM1111 had a close relationship with American strain MH916859 (Table 2). In contrast, the env had a lower sequence homology with the reference strains, suggesting the development of antigenic variability with evolution. Amino acid homology analysis indicated that the gag protein is highly conserved, especially in 2 dominant epitopes (epitope2 and epitope3). However, within the highly homologous epitope2, the Sichuan strain (KR011757) mutated from alanine (A) to valine (V) at position 221. Notably, the major homology region (MHR) region of NM1111 mutated from aspartic acid (D) to glutamic acid (E) at position 296 (Table 3). The SU region had a high degree of sequence variation, especially in variable region 4 (V4) with 5 amino acid insertions (position at 554-555, 564-566) (Table 4). Immunodominant epitope SU5 had a conserved motif (VRAYTYGV) located at the N-terminal part and a highly variable motif at the C-terminal (Table 4).\nNM, Inner Mongolia.\nN/A, not applicable.\nMHR, major homology region.\nA neighbor-joining tree was constructed using the whole-genome sequence of NM1111 and the available reference strains. The phylogenetic analysis showed that the NM1111 strain was clustered in a branch of MMV type A2 (Fig. 3). Meanwhile, homology comparisons showed that the gag and pol of NM1111 had a close relationship with American strain MH916859 (Table 2). In contrast, the env had a lower sequence homology with the reference strains, suggesting the development of antigenic variability with evolution. Amino acid homology analysis indicated that the gag protein is highly conserved, especially in 2 dominant epitopes (epitope2 and epitope3). However, within the highly homologous epitope2, the Sichuan strain (KR011757) mutated from alanine (A) to valine (V) at position 221. Notably, the major homology region (MHR) region of NM1111 mutated from aspartic acid (D) to glutamic acid (E) at position 296 (Table 3). The SU region had a high degree of sequence variation, especially in variable region 4 (V4) with 5 amino acid insertions (position at 554-555, 564-566) (Table 4). Immunodominant epitope SU5 had a conserved motif (VRAYTYGV) located at the N-terminal part and a highly variable motif at the C-terminal (Table 4).\nNM, Inner Mongolia.\nN/A, not applicable.\nMHR, major homology region.\n\nCharacterization of MVV in sheep lung tissue:\nGross appearance showed that the volume of the affected sheep lung was increased by 2- to 3-fold. Nodular lesions were scattered or densely distributed in local areas, and the surrounding tissue was a dark red color and provided a hard tactile sensation. Fibrotic focuses were scattered in the pleural surface (Fig. 1A). Histopathologically, lymphocytic and lymphofollicular hyperplasias were scattered or locally distributed in the pulmonary interstitium. Lymphoid follicles occurred before the peripheral bronchioles, and the germinal centers of some lymphoid follicles were obvious. There were significant proliferations of lymphocytes, macrophages, and a small number of fibroblasts in the alveolar septum. Type II epithelial cells on the inner surface of the alveolar wall had proliferated to varying degrees. Additionally, some alveolar cavities were filled with red homogeneous serous fluid as well as some macrophages and lymphocytes (Fig. 1B). A PCR procedure was performed to amplify the LTR sequence of the MVV. Detection by gel electrophoresis confirmed that the lung tissue was MVV-positive (Fig. 1C). Furthermore, electron microscope-based examination of purified MVV showed that the viral particles were approximately 80 nm (Fig. 1D).\nM, marker; MVV, Maedi/Visna virus.\n\nComplete genome sequence of the NM1111 strain:\nA total of 1.1 GB of valid data was obtained, and the data were uniformly distributed. The sequencing depth was up to 188,295 times. The values of Q20 and Q30 were 96.64% and 90.48%, respectively, and the G+C content was 40.43%. These results reveal that our data exhibited good sequencing quality. Finally, the isolate contained 9,193 nucleotides after splicing and optimization. Three primary encoding genes, the gag gene, position at 502 to 1,845, contained 1,344 nucleotides; the pol gene, position at 1716 to 5036, contained 3321 nucleotides; the env gene, position at 5,985 to 8,948, contained 2,964 nucleotides (Table 1, Fig. 2). The complete genome sequence of MVV was uploaded, named NM1111, to GenBank with the accession number MW248464 (Supplementary Data 1).\nLTR, long terminal repeat; N/A, not applicable.\nLTR, long terminal repeat.\n\nPhylogenetic analysis:\nA neighbor-joining tree was constructed using the whole-genome sequence of NM1111 and the available reference strains. The phylogenetic analysis showed that the NM1111 strain was clustered in a branch of MMV type A2 (Fig. 3). Meanwhile, homology comparisons showed that the gag and pol of NM1111 had a close relationship with American strain MH916859 (Table 2). In contrast, the env had a lower sequence homology with the reference strains, suggesting the development of antigenic variability with evolution. Amino acid homology analysis indicated that the gag protein is highly conserved, especially in 2 dominant epitopes (epitope2 and epitope3). However, within the highly homologous epitope2, the Sichuan strain (KR011757) mutated from alanine (A) to valine (V) at position 221. Notably, the major homology region (MHR) region of NM1111 mutated from aspartic acid (D) to glutamic acid (E) at position 296 (Table 3). The SU region had a high degree of sequence variation, especially in variable region 4 (V4) with 5 amino acid insertions (position at 554-555, 564-566) (Table 4). Immunodominant epitope SU5 had a conserved motif (VRAYTYGV) located at the N-terminal part and a highly variable motif at the C-terminal (Table 4).\nNM, Inner Mongolia.\nN/A, not applicable.\nMHR, major homology region.\n\nDISCUSSION:\nNGS is a recent, powerful technique developed and applied for virus sequencing. In this study, the first complete genome sequence of MVV from China was obtained by NGS. Sequencing depth, high purity of the assembled data, the distribution of the GC content, and the sequencing coverage verified the reliability of the obtained results. Despite NGS being the most prevalent technology for sequencing at present, future complete genome sequencing of MVV isolates with recently developed third-generation sequencing would also be desirable.\nGag is relatively conserved in MVV and encodes a major protective antigen. CA, encoded by gag, contains 3 linear epitopes (epitope 2, MHR, and epitope 3) and can cause a strong antibody reaction during infection, thus making it of great value for serodiagnosis. These epitopes are also important for maintaining cross-reactivity in gag antigen-based serological tests [16]. The amino acid sequence analysis showed that the linear epitopes were highly conserved, but there was a mutation from aspartic acid (D) to glutamic acid (E) at position 296 in the MHR region, an observation similar to that for a type A5 strain from Poland [17]. MVV has been serologically detected in 12 regions of China (Yunnan, Guizhou, Gansu, Ningxia, Shandong, Sichuan, Hunan, Guangdong, Chongqing, Guangxi, Jilin, and Anhui), and the infection rate in those regions ranged from 4.60% to 50.00% [18]. However, when Sun et al. [19] carried out a serological investigation on MVV in China, the detection rate in NM was zero. In addition to the limited sample collection area, it was inferred that a mutation in MHR region might also have contributed. Tanaka et al. [20] reported that, in addition to the epitope2 and epitope3, MHR is usually conserved in many retroviruses. Mutations in this region may destroy capsid protein assembly and reduce human immunodeficiency virus infectivity [20]. In this study, the MHR region of the strains isolated thirty years ago, except the south Africa strain, were shown to be conserved, but other strains exhibited varying degrees of mutation. Whether the mutations in this region could reduce the virulence of MVV needs to be further elucidated.\nEnv is the most mutated gene in MVV, which can often cause antigen drift. The V4 region of the SU domain can affect cell tropism and the synthesis of neutralizing antibodies [21]. Hence, mutations in this region have an important role in interactions between the virus and the host. Based on the analysis of env protein, the mutation rate of the V4 region was high in our isolate, which is consistent with the results in the study of Hötzel and Cheevers [22]. In addition, there were 5 amino acid insertions, suggesting a high probability of antigen drift. Skraban et al. [23] reported that the SU5 epitope is responsible for a specific immune response, and it could be used to realize a specific diagnosis for a virus strain. In this study, we observed a conserved motif at the N-terminal and a highly variable motif at the C-terminal of the SU5 region, observations that are consistent with the A5 strain of MVV isolated from Poland [17]. These observations emphasize the effectiveness of identifying different MVV subtypes within local genotypes [24].\nPolygenetic analysis indicated that the NM1111 isolate was closely related to American stains. Meanwhile, based on the gag sequence construction of a neighbor-joining tree, the Sichuan strain (KR011757) was separated along the same branch from an American stain (AY101611) (Supplementary Fig. 1). The results suggest that the main epidemic MMV strain in China was probably MMV type A2. Characterizing more MVV strains isolated from China will help elucidate the genetic evolution of MMV.\n\n"},"whole_article_abstract":{"kind":"string","value":"Background:\nMaedi/Visna virus (MVV) is a contagious viral pathogen that causes considerable economic losses to the sheep industry worldwide.\n\nMethods:\nTherefore, in this study, we conducted next-generation sequencing on an MVV strain obtained from northwest China to reveal its genetic evolution via phylogenetic analysis.\n\nResults:\nA MVV strain obtained from Inner Mongolia (NM) of China was identified. Sequence analysis indicated that its whole-genome length is 9193 bp. Homology comparison of nucleotides between the NM strain and reference strains showed that the sequence homology of gag and env were 77.1%-86.8% and 67.7%-75.5%, respectively. Phylogenetic analysis revealed that the NM strain was closely related to the reference strains isolated from America, which belong to the A2 type. Notably, there were 5 amino acid insertions in variable region 4 and a highly variable motif at the C-terminal of the surface glycoprotein (SU5).\n\nConclusions:\nThe present study is the first to show the whole-genome sequence of an MVV obtained from China. The detailed analyses provide essential information for understanding the genetic characteristics of MVV, and the results enrich the MVV library."},"background_conclusion_text":{"kind":"null"},"background_conclusion_abstract":{"kind":"null"},"whole_article_text_length":{"kind":"number","value":5148,"string":"5,148"},"whole_article_abstract_length":{"kind":"number","value":221,"string":"221"},"other_sections_lengths":{"kind":"list like","value":[43,84,93,151,135,56,229,174,272],"string":"[\n 43,\n 84,\n 93,\n 151,\n 135,\n 56,\n 229,\n 174,\n 272\n]"},"num_sections":{"kind":"number","value":13,"string":"13"},"most_frequent_words":{"kind":"list like","value":["mvv","sequence","region","lung","min","strain","fig","position","000","nm"],"string":"[\n \"mvv\",\n \"sequence\",\n \"region\",\n \"lung\",\n \"min\",\n \"strain\",\n \"fig\",\n \"position\",\n \"000\",\n \"nm\"\n]"},"keybert_topics":{"kind":"list like","value":["oncogenic virus mvv","assembly viral rna","visna virus gross","viral rna","maedi visna virus"],"string":"[\n \"oncogenic virus mvv\",\n \"assembly viral rna\",\n \"visna virus gross\",\n \"viral rna\",\n \"maedi visna virus\"\n]"},"annotated_base_background_abstract_prompt":{"kind":"string","value":""},"annotated_base_methods_abstract_prompt":{"kind":"null"},"annotated_base_results_abstract_prompt":{"kind":"string","value":""},"annotated_base_conclusions_abstract_prompt":{"kind":"null"},"annotated_base_whole_article_abstract_prompt":{"kind":"string","value":""},"annotated_base_background_conclusion_abstract_prompt":{"kind":"null"},"annotated_keywords_background_abstract_prompt":{"kind":"string","value":"[CONTENT] Maedi/Visna virus | next-generation sequencing | phylogenetic analysis | sheep lung [SUMMARY]"},"annotated_keywords_methods_abstract_prompt":{"kind":"null"},"annotated_keywords_results_abstract_prompt":{"kind":"string","value":"[CONTENT] Maedi/Visna virus | next-generation sequencing | phylogenetic analysis | sheep lung [SUMMARY]"},"annotated_keywords_conclusions_abstract_prompt":{"kind":"null"},"annotated_keywords_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] Maedi/Visna virus | next-generation sequencing | phylogenetic analysis | sheep lung [SUMMARY]"},"annotated_keywords_background_conclusion_abstract_prompt":{"kind":"null"},"annotated_mesh_background_abstract_prompt":{"kind":"string","value":"[CONTENT] Animals | China | High-Throughput Nucleotide Sequencing | Phylogeny | Pneumonia, Progressive Interstitial, of Sheep | Sheep | Sheep Diseases | Visna-maedi virus [SUMMARY]"},"annotated_mesh_methods_abstract_prompt":{"kind":"null"},"annotated_mesh_results_abstract_prompt":{"kind":"string","value":"[CONTENT] Animals | China | High-Throughput Nucleotide Sequencing | Phylogeny | Pneumonia, Progressive Interstitial, of Sheep | Sheep | Sheep Diseases | Visna-maedi virus [SUMMARY]"},"annotated_mesh_conclusions_abstract_prompt":{"kind":"null"},"annotated_mesh_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] Animals | China | High-Throughput Nucleotide Sequencing | Phylogeny | Pneumonia, Progressive Interstitial, of Sheep | Sheep | Sheep Diseases | Visna-maedi virus [SUMMARY]"},"annotated_mesh_background_conclusion_abstract_prompt":{"kind":"null"},"annotated_keybert_background_abstract_prompt":{"kind":"string","value":"[CONTENT] oncogenic virus mvv | assembly viral rna | visna virus gross | viral rna | maedi visna virus [SUMMARY]"},"annotated_keybert_methods_abstract_prompt":{"kind":"null"},"annotated_keybert_results_abstract_prompt":{"kind":"string","value":"[CONTENT] oncogenic virus mvv | assembly viral rna | visna virus gross | viral rna | maedi visna virus [SUMMARY]"},"annotated_keybert_conclusions_abstract_prompt":{"kind":"null"},"annotated_keybert_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] oncogenic virus mvv | assembly viral rna | visna virus gross | viral rna | maedi visna virus [SUMMARY]"},"annotated_keybert_background_conclusion_abstract_prompt":{"kind":"null"},"annotated_most_frequent_background_abstract_prompt":{"kind":"string","value":"[CONTENT] mvv | sequence | region | lung | min | strain | fig | position | 000 | nm [SUMMARY]"},"annotated_most_frequent_methods_abstract_prompt":{"kind":"null"},"annotated_most_frequent_results_abstract_prompt":{"kind":"string","value":"[CONTENT] mvv | sequence | region | lung | min | strain | fig | position | 000 | nm [SUMMARY]"},"annotated_most_frequent_conclusions_abstract_prompt":{"kind":"null"},"annotated_most_frequent_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] mvv | sequence | region | lung | min | strain | fig | position | 000 | nm [SUMMARY]"},"annotated_most_frequent_background_conclusion_abstract_prompt":{"kind":"null"},"annotated_tf_idf_background_abstract_prompt":{"kind":"string","value":"[CONTENT] ngs | mvv | gene encodes | throughput | pol | encodes | sequencing | maedi visna | maedi | visna [SUMMARY]"},"annotated_tf_idf_methods_abstract_prompt":{"kind":"null"},"annotated_tf_idf_results_abstract_prompt":{"kind":"string","value":"[CONTENT] homology | position | table | nm1111 | fig | contained | nucleotides | region | data | acid [SUMMARY]"},"annotated_tf_idf_conclusions_abstract_prompt":{"kind":"null"},"annotated_tf_idf_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] mvv | min | region | 000 | lung | 000 min | sequence | position | strain | homology [SUMMARY]"},"annotated_tf_idf_background_conclusion_abstract_prompt":{"kind":"null"},"annotated_entity_plan_background_abstract_prompt":{"kind":"string","value":"[CONTENT] Maedi | MVV [SUMMARY]"},"annotated_entity_plan_methods_abstract_prompt":{"kind":"null"},"annotated_entity_plan_results_abstract_prompt":{"kind":"string","value":"[CONTENT] MVV | Inner Mongolia | NM | China ||| Sequence | 9193 ||| NM | 77.1%-86.8% | 67.7%-75.5% ||| NM | America | A2 ||| 5 | 4 [SUMMARY]"},"annotated_entity_plan_conclusions_abstract_prompt":{"kind":"null"},"annotated_entity_plan_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] Maedi | MVV ||| MVV | China ||| ||| MVV | Inner Mongolia | NM | China ||| Sequence | 9193 ||| NM | 77.1%-86.8% | 67.7%-75.5% ||| NM | America | A2 ||| 5 | 4 ||| first | MVV | China ||| MVV | MVV [SUMMARY]"},"annotated_entity_plan_background_conclusion_abstract_prompt":{"kind":"null"}}},{"rowIdx":280,"cells":{"title":{"kind":"string","value":"Association of Early Pubertal Onset in Female Rats With Inhalation of Lavender Oil."},"pmid":{"kind":"string","value":"35014224"},"background_abstract":{"kind":"string","value":"Central precocious puberty (CPP) is caused by early activation of the hypothalamic-pituitary-gonadal axis but its major cause remains unclear. Studies have indicated an association between chronic environmental exposure to endocrine-disrupting chemicals and pubertal onset. Essential oil is widely used in homes worldwide for relief of respiratory symptoms, stress, and/or sleep disturbance."},"background_abstract_label":{"kind":"string","value":"BACKGROUND"},"methods_abstract":{"kind":"string","value":"To evaluate this association, we compared the hormone levels and timing of vaginal opening (VO) in female rats exposed to lavender oil (LO) through different routes (study groups: control, LO nasal spray [LS], and indoor exposure to LO [LE]) during the prepubertal period. The body weights of the animals were also compared every 3 days until the day of VO, at which time gonadotropin levels and internal organ weights were assessed."},"methods_abstract_label":{"kind":"string","value":"METHODS"},"results_abstract":{"kind":"string","value":"The LS group showed early VO at 33.8 ± 1.8 days compared with the control (38.4 ± 2.9 days) and LE (36.6 ± 1.5 days) groups. Additionally, luteinizing hormone levels were significantly higher in the LE and LS groups than those in the control group. Body weights did not differ significantly among the groups."},"results_abstract_label":{"kind":"string","value":"RESULTS"},"conclusions_abstract":{"kind":"string","value":"Inhalation exposure to an exogenic simulant during the prepubertal period might trigger early pubertal onset in female rats. Further evaluation of exposure to other endocrine-disrupting chemicals capable of inducing CPP through the skin, orally, and/or nasally is warranted."},"conclusions_abstract_label":{"kind":"string","value":"CONCLUSION"},"mesh_descriptor_names":{"kind":"list like","value":["Administration, Inhalation","Animals","Female","Lavandula","Oils, Volatile","Plant Oils","Puberty, Precocious","Random Allocation","Rats"],"string":"[\n \"Administration, Inhalation\",\n \"Animals\",\n \"Female\",\n \"Lavandula\",\n \"Oils, Volatile\",\n \"Plant Oils\",\n \"Puberty, Precocious\",\n \"Random Allocation\",\n \"Rats\"\n]"},"pmcid":{"kind":"string","value":"8748666"},"background_title":{"kind":"string","value":"INTRODUCTION"},"background_text":{"kind":"string","value":"Central precocious puberty (CPP) describes the early activation of the hypothalamic–pituitary–gonadal (HPG) axis, which leads to the rapid progression of bone age, early menarche, reduction in final adult height, and the appearance of secondary sexual characteristics before 8 and 9 years of age in girls and boys, respectively.1 Traditionally, CPP is accompanied by intracranial lesions, including optic glioma, pilocytic astrocytoma, hydrocephalus, Rathke’s cleft cyst, and pituitary adenomas, in 40–90% of boys and < 10% of girls.23 The gonadotropin-releasing hormone (GnRH) stimulation test is used for diagnosing CPP, and the basal luteinizing hormone (LH) level is considered as a valuable tool to assess pubertal state.4 CPP treatment was introduced in 1980, and dosing of a recombinant GnRH agonist every 4 weeks or 3 months leads to increases in the final adult height and delayed menarche.23 Improving the final adult height of children with CPP is one of the major issues during treatment.56 However, the incidence of precocious puberty is rapidly increasing, and examination and treatment of this condition are becoming a major burden because of the associated medical expenses, although the cause of this condition remains unknown.23\nEnvironmental hormones, i.e., endocrine-disrupting chemicals, were recently suggested to contribute to the onset of puberty in childhood,78 and animal studies demonstrated that endocrine-disrupting chemicals accelerate pubertal onset.910 Additionally, previous reports showed that lavender oil (LO) and tea tree oil are associated with prepubertal gynecomastia in boys.1112 Moreover, cases of premature thelarche that resolved after cessation of exposure to lavender-containing fragrance have been reported,1314 and an in vitro study showed that components of LO, including linalool and linalyl acetate, activate estrogen-related gene expression in human cell lines.13 However, studies of the absorbance of these materials in sufficient amounts and their effect on breast growth have not been performed. A previous study suggested that smell of sense can be transmitted to the central nervous system, thereby facilitating the bypass of inhaled molecules via the nasal pathway of the blood–brain barrier.15 There are several opportunities for inhalation of numerous endocrine-disrupting chemicals from estrogenic sources in cosmetics, perfumes, air fresheners, and scented candles/diffusers using essential oils, which can directly affect olfactory stimulation of the neuroendocrine system. Since some studies suggested that essential oils may have efficacy against the coronavirus disease 2019 and its inflammatory complications,161718 the interests for home therapy using essential oils are more increasing.\nIn this study, we tested whether continuous inhalation of LO affects early gonadotropin activation and precocious puberty. However, it is difficult to limit or measure the number of EDCs to nasal exposure in human. Thus, we investigated the effects of continuous inhalation of LO on pubertal onset and gonadotropin hormone levels in an animal model and compared them to control conditions."},"methods_title":{"kind":"string","value":"METHODS"},"methods_text":{"kind":"string","value":" Animals and experimental design To obtain study animals, rats were bred using male and female Sprague–Dawley rats. From birth onward, we maintained an indoor temperature of 22°C (humidity: 30–70%) and controlled illumination (12-hour light/dark cycle) to allow breeding in a constant environment along with free access to water and food. On day 18 after birth, we identified 15 immature females and randomly divided them into three groups: olfactory stimulation groups 1 and 2 and a control group (n = 5/group). We used 100% pure LO obtained from Lavandula angustifolia (NOW Foods, Bloomingdale, IL, USA) for all experiments. Group 1 was treated by indoor exposure to LO (LE) via an LO diffuser in the cage using an LO-soaked puff (changed daily) along with daily exposure to 0.9% NaCl spray. For Group 2, LO was administered as a nasal spray of aromatic LO (LS) once daily. The control group was treated with a single exposure to a nasal spray (0.9% NaCl) daily. The dose of one spray of LO or 0.9% NaCl ranged from 72–125 µL. The body weight of the animals was measured every 3 days from postnatal day (PND) 18 to the day of vaginal opening (VO).\nTo obtain study animals, rats were bred using male and female Sprague–Dawley rats. From birth onward, we maintained an indoor temperature of 22°C (humidity: 30–70%) and controlled illumination (12-hour light/dark cycle) to allow breeding in a constant environment along with free access to water and food. On day 18 after birth, we identified 15 immature females and randomly divided them into three groups: olfactory stimulation groups 1 and 2 and a control group (n = 5/group). We used 100% pure LO obtained from Lavandula angustifolia (NOW Foods, Bloomingdale, IL, USA) for all experiments. Group 1 was treated by indoor exposure to LO (LE) via an LO diffuser in the cage using an LO-soaked puff (changed daily) along with daily exposure to 0.9% NaCl spray. For Group 2, LO was administered as a nasal spray of aromatic LO (LS) once daily. The control group was treated with a single exposure to a nasal spray (0.9% NaCl) daily. The dose of one spray of LO or 0.9% NaCl ranged from 72–125 µL. The body weight of the animals was measured every 3 days from postnatal day (PND) 18 to the day of vaginal opening (VO).\n Analysis of VO All study groups were evaluated for VO time as an indicator of pubertal initiation at a fixed time (09:00 hour) daily. The day of VO was recorded, and VO timing was compared between the three study groups.\nAll study groups were evaluated for VO time as an indicator of pubertal initiation at a fixed time (09:00 hour) daily. The day of VO was recorded, and VO timing was compared between the three study groups.\n Euthanasia and hormone assays After VO was observed in each rat, we measured serum LH, follicle-stimulating hormone (FSH), and estradiol levels to compare hormone concentrations between study groups. The endpoint of the experiment was defined as VO occurring in the last rat. For this process, truncal blood was collected into ice-cold ethylenediamine tetraacetic acid-containing tubes after decapitation, after which the tubes were centrifuged, and plasma samples were collected and stored at −20°C until analysis. The plasma levels of LH, FSH, and estradiol of each rat were measured using enzyme-linked immunosorbent assay kits (cat. No. MBS764675, MBS2502190, and MBS263850, respectively; MyBioSource, Inc., San Diego, CA, USA) according to manufacturer’s instructions.\nAfter VO was observed in each rat, we measured serum LH, follicle-stimulating hormone (FSH), and estradiol levels to compare hormone concentrations between study groups. The endpoint of the experiment was defined as VO occurring in the last rat. For this process, truncal blood was collected into ice-cold ethylenediamine tetraacetic acid-containing tubes after decapitation, after which the tubes were centrifuged, and plasma samples were collected and stored at −20°C until analysis. The plasma levels of LH, FSH, and estradiol of each rat were measured using enzyme-linked immunosorbent assay kits (cat. No. MBS764675, MBS2502190, and MBS263850, respectively; MyBioSource, Inc., San Diego, CA, USA) according to manufacturer’s instructions.\n Measurement of organ weight After euthanasia, we measured the weight of the ovaries, spleen, kidneys, and liver. The organ weight was then modified by body weight and presented as tissue weight per 150 g body weight.\nAfter euthanasia, we measured the weight of the ovaries, spleen, kidneys, and liver. The organ weight was then modified by body weight and presented as tissue weight per 150 g body weight.\n Statistical analysis Data were presented as the mean ± standard deviation, and statistical analyses were performed using SPSS software (v.26.0; SPSS, Inc., Chicago, IL, USA). Statistical significance was determined by Kruskal-Wallis test and one-way analysis of variance for multiple-group comparisons and Mann-Whitney U test for comparisons between two groups. Statistical significance was defined at P < 0.05.\nData were presented as the mean ± standard deviation, and statistical analyses were performed using SPSS software (v.26.0; SPSS, Inc., Chicago, IL, USA). Statistical significance was determined by Kruskal-Wallis test and one-way analysis of variance for multiple-group comparisons and Mann-Whitney U test for comparisons between two groups. Statistical significance was defined at P < 0.05.\n Ethics statement The procedures used and the care of animals were approved by the Institutional Animal Care and Use Committee in Southwest Medi-Chem Institute (approval No. SEMI-20-001).\nThe procedures used and the care of animals were approved by the Institutional Animal Care and Use Committee in Southwest Medi-Chem Institute (approval No. SEMI-20-001)."},"results_title":{"kind":"string","value":"RESULTS"},"results_text":{"kind":"string","value":" Effect of olfactory exposure to LO on VO and pubertal onset VO occurred earlier in the LE (33.8 ± 1.8 days) and LS (36.6 ± 1.5 days) groups than in the control group (38.4 ± 2.9 days) (Table 1), and VO in the LE group occurred significantly earlier than in the control group (P = 0.014) and LS group (P = 0.032), respectively. However, there was no significant difference between the LS and control groups with respect to VO timing (P = 0.151). Almost all rats in the LE group experienced VO at 33 days, and the control group mostly showed VO between 38 and 41 days (Fig. 1).\nLS = exposure to lavender oil as a nasal spray, LE = exposure to diffused lavender oil, VO = vaginal opening.\n*P < 0.05 vs. control; **P < 0.05 vs. LS group.\nPND = postnatal day, LE = exposure to diffused lavender oil, C = control, LS = exposure to lavender oil as a nasal spray.\nVO occurred earlier in the LE (33.8 ± 1.8 days) and LS (36.6 ± 1.5 days) groups than in the control group (38.4 ± 2.9 days) (Table 1), and VO in the LE group occurred significantly earlier than in the control group (P = 0.014) and LS group (P = 0.032), respectively. However, there was no significant difference between the LS and control groups with respect to VO timing (P = 0.151). Almost all rats in the LE group experienced VO at 33 days, and the control group mostly showed VO between 38 and 41 days (Fig. 1).\nLS = exposure to lavender oil as a nasal spray, LE = exposure to diffused lavender oil, VO = vaginal opening.\n*P < 0.05 vs. control; **P < 0.05 vs. LS group.\nPND = postnatal day, LE = exposure to diffused lavender oil, C = control, LS = exposure to lavender oil as a nasal spray.\n Measurement of gonadotropin hormone and estradiol levels LH levels were significantly higher in the LE (67.6 ± 3.0 mIU/mL) and LS (64.3 ± 7.4 mIU/mL) groups than in the control group (49.9 ± 2.7 mIU/mL; P < 0.001 for both) (Table 2). Additionally, FSH levels were significantly higher in the LE (50.9 ± 9.1 ng/mL) and LS (51.4 ± 7.1 ng/mL) groups than in the control group (35.2 ± 3.7 ng/mL; P = 0.009 and P = 0.011, respectively). Estradiol levels were elevated in both the LE (4.9 ± 1.4 ng/mL) and LS (5.3 ± 1.4 ng/mL) groups relative to the control group (3.9 ± 0.8 ng/mL), although the differences were not significant (P = 0.326 and P = 0.547, respectively).\nData are presented as the mean ± SD (n = 5).\nLH = luteinizing hormone, FSH = follicle-stimulating hormone, LS = exposure to lavender oil as a nasal spray, LE = exposure to diffused lavender oil.\naNot significant vs. control.\n*P < 0.05 vs. control; **P < 0.001 vs. control.\nLH levels were significantly higher in the LE (67.6 ± 3.0 mIU/mL) and LS (64.3 ± 7.4 mIU/mL) groups than in the control group (49.9 ± 2.7 mIU/mL; P < 0.001 for both) (Table 2). Additionally, FSH levels were significantly higher in the LE (50.9 ± 9.1 ng/mL) and LS (51.4 ± 7.1 ng/mL) groups than in the control group (35.2 ± 3.7 ng/mL; P = 0.009 and P = 0.011, respectively). Estradiol levels were elevated in both the LE (4.9 ± 1.4 ng/mL) and LS (5.3 ± 1.4 ng/mL) groups relative to the control group (3.9 ± 0.8 ng/mL), although the differences were not significant (P = 0.326 and P = 0.547, respectively).\nData are presented as the mean ± SD (n = 5).\nLH = luteinizing hormone, FSH = follicle-stimulating hormone, LS = exposure to lavender oil as a nasal spray, LE = exposure to diffused lavender oil.\naNot significant vs. control.\n*P < 0.05 vs. control; **P < 0.001 vs. control.\n Measurement of body and organ weights Measurement of the body weight of rats in the control, LE, and LS groups every 3 days from PND 18 until VO revealed no significant differences among three groups (Fig. 2 and Table 3). The weights of the ovaries, liver, and spleen after VO showed no significant differences among three groups; however, the weight of the kidneys per 150 g body weight increased significantly after VO in the LE group (1.926 ± 0.154 g) compared with that in the control (1.664 ± 0.077 g; P = 0.009) and LS (1.694 ± 0.154 g; P = 0.017) groups (Table 4).\nPND = postnatal day, LS = exposure to lavender oil as a nasal spray, LE = exposure to diffused lavender oil.\nData are presented as the mean ± SD (n = 5).\nPND = postnatal day, LS = exposure to lavender oil as a nasal spray, LE = exposure to diffused lavender oil.\naNot significant.\nData are presented as the mean ± SD (n = 5).\nLS = exposure to lavender oil as a nasal spray, LE = exposure to diffused lavender oil.\naTissue weight (g) per body weight (150 g); bNot significant.\n*P < 0.01 vs. control; **P < 0.05 vs. LS group.\nMeasurement of the body weight of rats in the control, LE, and LS groups every 3 days from PND 18 until VO revealed no significant differences among three groups (Fig. 2 and Table 3). The weights of the ovaries, liver, and spleen after VO showed no significant differences among three groups; however, the weight of the kidneys per 150 g body weight increased significantly after VO in the LE group (1.926 ± 0.154 g) compared with that in the control (1.664 ± 0.077 g; P = 0.009) and LS (1.694 ± 0.154 g; P = 0.017) groups (Table 4).\nPND = postnatal day, LS = exposure to lavender oil as a nasal spray, LE = exposure to diffused lavender oil.\nData are presented as the mean ± SD (n = 5).\nPND = postnatal day, LS = exposure to lavender oil as a nasal spray, LE = exposure to diffused lavender oil.\naNot significant.\nData are presented as the mean ± SD (n = 5).\nLS = exposure to lavender oil as a nasal spray, LE = exposure to diffused lavender oil.\naTissue weight (g) per body weight (150 g); bNot significant.\n*P < 0.01 vs. control; **P < 0.05 vs. LS group."},"conclusions_title":{"kind":"null"},"conclusions_text":{"kind":"null"},"other_sections_titles":{"kind":"list like","value":["Animals and experimental design","Analysis of VO","Euthanasia and hormone assays","Measurement of organ weight","Statistical analysis","Ethics statement","Effect of olfactory exposure to LO on VO and pubertal onset","Measurement of gonadotropin hormone and estradiol levels","Measurement of body and organ weights"],"string":"[\n \"Animals and experimental design\",\n \"Analysis of VO\",\n \"Euthanasia and hormone assays\",\n \"Measurement of organ weight\",\n \"Statistical analysis\",\n \"Ethics statement\",\n \"Effect of olfactory exposure to LO on VO and pubertal onset\",\n \"Measurement of gonadotropin hormone and estradiol levels\",\n \"Measurement of body and organ weights\"\n]"},"other_sections_texts":{"kind":"list like","value":["To obtain study animals, rats were bred using male and female Sprague–Dawley rats. From birth onward, we maintained an indoor temperature of 22°C (humidity: 30–70%) and controlled illumination (12-hour light/dark cycle) to allow breeding in a constant environment along with free access to water and food. On day 18 after birth, we identified 15 immature females and randomly divided them into three groups: olfactory stimulation groups 1 and 2 and a control group (n = 5/group). We used 100% pure LO obtained from Lavandula angustifolia (NOW Foods, Bloomingdale, IL, USA) for all experiments. Group 1 was treated by indoor exposure to LO (LE) via an LO diffuser in the cage using an LO-soaked puff (changed daily) along with daily exposure to 0.9% NaCl spray. For Group 2, LO was administered as a nasal spray of aromatic LO (LS) once daily. The control group was treated with a single exposure to a nasal spray (0.9% NaCl) daily. The dose of one spray of LO or 0.9% NaCl ranged from 72–125 µL. The body weight of the animals was measured every 3 days from postnatal day (PND) 18 to the day of vaginal opening (VO).","All study groups were evaluated for VO time as an indicator of pubertal initiation at a fixed time (09:00 hour) daily. The day of VO was recorded, and VO timing was compared between the three study groups.","After VO was observed in each rat, we measured serum LH, follicle-stimulating hormone (FSH), and estradiol levels to compare hormone concentrations between study groups. The endpoint of the experiment was defined as VO occurring in the last rat. For this process, truncal blood was collected into ice-cold ethylenediamine tetraacetic acid-containing tubes after decapitation, after which the tubes were centrifuged, and plasma samples were collected and stored at −20°C until analysis. The plasma levels of LH, FSH, and estradiol of each rat were measured using enzyme-linked immunosorbent assay kits (cat. No. MBS764675, MBS2502190, and MBS263850, respectively; MyBioSource, Inc., San Diego, CA, USA) according to manufacturer’s instructions.","After euthanasia, we measured the weight of the ovaries, spleen, kidneys, and liver. The organ weight was then modified by body weight and presented as tissue weight per 150 g body weight.","Data were presented as the mean ± standard deviation, and statistical analyses were performed using SPSS software (v.26.0; SPSS, Inc., Chicago, IL, USA). Statistical significance was determined by Kruskal-Wallis test and one-way analysis of variance for multiple-group comparisons and Mann-Whitney U test for comparisons between two groups. Statistical significance was defined at P < 0.05.","The procedures used and the care of animals were approved by the Institutional Animal Care and Use Committee in Southwest Medi-Chem Institute (approval No. SEMI-20-001).","VO occurred earlier in the LE (33.8 ± 1.8 days) and LS (36.6 ± 1.5 days) groups than in the control group (38.4 ± 2.9 days) (Table 1), and VO in the LE group occurred significantly earlier than in the control group (P = 0.014) and LS group (P = 0.032), respectively. However, there was no significant difference between the LS and control groups with respect to VO timing (P = 0.151). Almost all rats in the LE group experienced VO at 33 days, and the control group mostly showed VO between 38 and 41 days (Fig. 1).\nLS = exposure to lavender oil as a nasal spray, LE = exposure to diffused lavender oil, VO = vaginal opening.\n*P < 0.05 vs. control; **P < 0.05 vs. LS group.\nPND = postnatal day, LE = exposure to diffused lavender oil, C = control, LS = exposure to lavender oil as a nasal spray.","LH levels were significantly higher in the LE (67.6 ± 3.0 mIU/mL) and LS (64.3 ± 7.4 mIU/mL) groups than in the control group (49.9 ± 2.7 mIU/mL; P < 0.001 for both) (Table 2). Additionally, FSH levels were significantly higher in the LE (50.9 ± 9.1 ng/mL) and LS (51.4 ± 7.1 ng/mL) groups than in the control group (35.2 ± 3.7 ng/mL; P = 0.009 and P = 0.011, respectively). Estradiol levels were elevated in both the LE (4.9 ± 1.4 ng/mL) and LS (5.3 ± 1.4 ng/mL) groups relative to the control group (3.9 ± 0.8 ng/mL), although the differences were not significant (P = 0.326 and P = 0.547, respectively).\nData are presented as the mean ± SD (n = 5).\nLH = luteinizing hormone, FSH = follicle-stimulating hormone, LS = exposure to lavender oil as a nasal spray, LE = exposure to diffused lavender oil.\naNot significant vs. control.\n*P < 0.05 vs. control; **P < 0.001 vs. control.","Measurement of the body weight of rats in the control, LE, and LS groups every 3 days from PND 18 until VO revealed no significant differences among three groups (Fig. 2 and Table 3). The weights of the ovaries, liver, and spleen after VO showed no significant differences among three groups; however, the weight of the kidneys per 150 g body weight increased significantly after VO in the LE group (1.926 ± 0.154 g) compared with that in the control (1.664 ± 0.077 g; P = 0.009) and LS (1.694 ± 0.154 g; P = 0.017) groups (Table 4).\nPND = postnatal day, LS = exposure to lavender oil as a nasal spray, LE = exposure to diffused lavender oil.\nData are presented as the mean ± SD (n = 5).\nPND = postnatal day, LS = exposure to lavender oil as a nasal spray, LE = exposure to diffused lavender oil.\naNot significant.\nData are presented as the mean ± SD (n = 5).\nLS = exposure to lavender oil as a nasal spray, LE = exposure to diffused lavender oil.\naTissue weight (g) per body weight (150 g); bNot significant.\n*P < 0.01 vs. control; **P < 0.05 vs. LS group."],"string":"[\n \"To obtain study animals, rats were bred using male and female Sprague–Dawley rats. From birth onward, we maintained an indoor temperature of 22°C (humidity: 30–70%) and controlled illumination (12-hour light/dark cycle) to allow breeding in a constant environment along with free access to water and food. On day 18 after birth, we identified 15 immature females and randomly divided them into three groups: olfactory stimulation groups 1 and 2 and a control group (n = 5/group). We used 100% pure LO obtained from Lavandula angustifolia (NOW Foods, Bloomingdale, IL, USA) for all experiments. Group 1 was treated by indoor exposure to LO (LE) via an LO diffuser in the cage using an LO-soaked puff (changed daily) along with daily exposure to 0.9% NaCl spray. For Group 2, LO was administered as a nasal spray of aromatic LO (LS) once daily. The control group was treated with a single exposure to a nasal spray (0.9% NaCl) daily. The dose of one spray of LO or 0.9% NaCl ranged from 72–125 µL. The body weight of the animals was measured every 3 days from postnatal day (PND) 18 to the day of vaginal opening (VO).\",\n \"All study groups were evaluated for VO time as an indicator of pubertal initiation at a fixed time (09:00 hour) daily. The day of VO was recorded, and VO timing was compared between the three study groups.\",\n \"After VO was observed in each rat, we measured serum LH, follicle-stimulating hormone (FSH), and estradiol levels to compare hormone concentrations between study groups. The endpoint of the experiment was defined as VO occurring in the last rat. For this process, truncal blood was collected into ice-cold ethylenediamine tetraacetic acid-containing tubes after decapitation, after which the tubes were centrifuged, and plasma samples were collected and stored at −20°C until analysis. The plasma levels of LH, FSH, and estradiol of each rat were measured using enzyme-linked immunosorbent assay kits (cat. No. MBS764675, MBS2502190, and MBS263850, respectively; MyBioSource, Inc., San Diego, CA, USA) according to manufacturer’s instructions.\",\n \"After euthanasia, we measured the weight of the ovaries, spleen, kidneys, and liver. The organ weight was then modified by body weight and presented as tissue weight per 150 g body weight.\",\n \"Data were presented as the mean ± standard deviation, and statistical analyses were performed using SPSS software (v.26.0; SPSS, Inc., Chicago, IL, USA). Statistical significance was determined by Kruskal-Wallis test and one-way analysis of variance for multiple-group comparisons and Mann-Whitney U test for comparisons between two groups. Statistical significance was defined at P < 0.05.\",\n \"The procedures used and the care of animals were approved by the Institutional Animal Care and Use Committee in Southwest Medi-Chem Institute (approval No. SEMI-20-001).\",\n \"VO occurred earlier in the LE (33.8 ± 1.8 days) and LS (36.6 ± 1.5 days) groups than in the control group (38.4 ± 2.9 days) (Table 1), and VO in the LE group occurred significantly earlier than in the control group (P = 0.014) and LS group (P = 0.032), respectively. However, there was no significant difference between the LS and control groups with respect to VO timing (P = 0.151). Almost all rats in the LE group experienced VO at 33 days, and the control group mostly showed VO between 38 and 41 days (Fig. 1).\\nLS = exposure to lavender oil as a nasal spray, LE = exposure to diffused lavender oil, VO = vaginal opening.\\n*P < 0.05 vs. control; **P < 0.05 vs. LS group.\\nPND = postnatal day, LE = exposure to diffused lavender oil, C = control, LS = exposure to lavender oil as a nasal spray.\",\n \"LH levels were significantly higher in the LE (67.6 ± 3.0 mIU/mL) and LS (64.3 ± 7.4 mIU/mL) groups than in the control group (49.9 ± 2.7 mIU/mL; P < 0.001 for both) (Table 2). Additionally, FSH levels were significantly higher in the LE (50.9 ± 9.1 ng/mL) and LS (51.4 ± 7.1 ng/mL) groups than in the control group (35.2 ± 3.7 ng/mL; P = 0.009 and P = 0.011, respectively). Estradiol levels were elevated in both the LE (4.9 ± 1.4 ng/mL) and LS (5.3 ± 1.4 ng/mL) groups relative to the control group (3.9 ± 0.8 ng/mL), although the differences were not significant (P = 0.326 and P = 0.547, respectively).\\nData are presented as the mean ± SD (n = 5).\\nLH = luteinizing hormone, FSH = follicle-stimulating hormone, LS = exposure to lavender oil as a nasal spray, LE = exposure to diffused lavender oil.\\naNot significant vs. control.\\n*P < 0.05 vs. control; **P < 0.001 vs. control.\",\n \"Measurement of the body weight of rats in the control, LE, and LS groups every 3 days from PND 18 until VO revealed no significant differences among three groups (Fig. 2 and Table 3). The weights of the ovaries, liver, and spleen after VO showed no significant differences among three groups; however, the weight of the kidneys per 150 g body weight increased significantly after VO in the LE group (1.926 ± 0.154 g) compared with that in the control (1.664 ± 0.077 g; P = 0.009) and LS (1.694 ± 0.154 g; P = 0.017) groups (Table 4).\\nPND = postnatal day, LS = exposure to lavender oil as a nasal spray, LE = exposure to diffused lavender oil.\\nData are presented as the mean ± SD (n = 5).\\nPND = postnatal day, LS = exposure to lavender oil as a nasal spray, LE = exposure to diffused lavender oil.\\naNot significant.\\nData are presented as the mean ± SD (n = 5).\\nLS = exposure to lavender oil as a nasal spray, LE = exposure to diffused lavender oil.\\naTissue weight (g) per body weight (150 g); bNot significant.\\n*P < 0.01 vs. control; **P < 0.05 vs. LS group.\"\n]"},"other_sections_sec_types":{"kind":"list like","value":[null,null,null,null,null,null,null,null,null],"string":"[\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null\n]"},"all_sections_titles":{"kind":"list like","value":["INTRODUCTION","METHODS","Animals and experimental design","Analysis of VO","Euthanasia and hormone assays","Measurement of organ weight","Statistical analysis","Ethics statement","RESULTS","Effect of olfactory exposure to LO on VO and pubertal onset","Measurement of gonadotropin hormone and estradiol levels","Measurement of body and organ weights","DISCUSSION"],"string":"[\n \"INTRODUCTION\",\n \"METHODS\",\n \"Animals and experimental design\",\n \"Analysis of VO\",\n \"Euthanasia and hormone assays\",\n \"Measurement of organ weight\",\n \"Statistical analysis\",\n \"Ethics statement\",\n \"RESULTS\",\n \"Effect of olfactory exposure to LO on VO and pubertal onset\",\n \"Measurement of gonadotropin hormone and estradiol levels\",\n \"Measurement of body and organ weights\",\n \"DISCUSSION\"\n]"},"all_sections_texts":{"kind":"list like","value":["Central precocious puberty (CPP) describes the early activation of the hypothalamic–pituitary–gonadal (HPG) axis, which leads to the rapid progression of bone age, early menarche, reduction in final adult height, and the appearance of secondary sexual characteristics before 8 and 9 years of age in girls and boys, respectively.1 Traditionally, CPP is accompanied by intracranial lesions, including optic glioma, pilocytic astrocytoma, hydrocephalus, Rathke’s cleft cyst, and pituitary adenomas, in 40–90% of boys and < 10% of girls.23 The gonadotropin-releasing hormone (GnRH) stimulation test is used for diagnosing CPP, and the basal luteinizing hormone (LH) level is considered as a valuable tool to assess pubertal state.4 CPP treatment was introduced in 1980, and dosing of a recombinant GnRH agonist every 4 weeks or 3 months leads to increases in the final adult height and delayed menarche.23 Improving the final adult height of children with CPP is one of the major issues during treatment.56 However, the incidence of precocious puberty is rapidly increasing, and examination and treatment of this condition are becoming a major burden because of the associated medical expenses, although the cause of this condition remains unknown.23\nEnvironmental hormones, i.e., endocrine-disrupting chemicals, were recently suggested to contribute to the onset of puberty in childhood,78 and animal studies demonstrated that endocrine-disrupting chemicals accelerate pubertal onset.910 Additionally, previous reports showed that lavender oil (LO) and tea tree oil are associated with prepubertal gynecomastia in boys.1112 Moreover, cases of premature thelarche that resolved after cessation of exposure to lavender-containing fragrance have been reported,1314 and an in vitro study showed that components of LO, including linalool and linalyl acetate, activate estrogen-related gene expression in human cell lines.13 However, studies of the absorbance of these materials in sufficient amounts and their effect on breast growth have not been performed. A previous study suggested that smell of sense can be transmitted to the central nervous system, thereby facilitating the bypass of inhaled molecules via the nasal pathway of the blood–brain barrier.15 There are several opportunities for inhalation of numerous endocrine-disrupting chemicals from estrogenic sources in cosmetics, perfumes, air fresheners, and scented candles/diffusers using essential oils, which can directly affect olfactory stimulation of the neuroendocrine system. Since some studies suggested that essential oils may have efficacy against the coronavirus disease 2019 and its inflammatory complications,161718 the interests for home therapy using essential oils are more increasing.\nIn this study, we tested whether continuous inhalation of LO affects early gonadotropin activation and precocious puberty. However, it is difficult to limit or measure the number of EDCs to nasal exposure in human. Thus, we investigated the effects of continuous inhalation of LO on pubertal onset and gonadotropin hormone levels in an animal model and compared them to control conditions."," Animals and experimental design To obtain study animals, rats were bred using male and female Sprague–Dawley rats. From birth onward, we maintained an indoor temperature of 22°C (humidity: 30–70%) and controlled illumination (12-hour light/dark cycle) to allow breeding in a constant environment along with free access to water and food. On day 18 after birth, we identified 15 immature females and randomly divided them into three groups: olfactory stimulation groups 1 and 2 and a control group (n = 5/group). We used 100% pure LO obtained from Lavandula angustifolia (NOW Foods, Bloomingdale, IL, USA) for all experiments. Group 1 was treated by indoor exposure to LO (LE) via an LO diffuser in the cage using an LO-soaked puff (changed daily) along with daily exposure to 0.9% NaCl spray. For Group 2, LO was administered as a nasal spray of aromatic LO (LS) once daily. The control group was treated with a single exposure to a nasal spray (0.9% NaCl) daily. The dose of one spray of LO or 0.9% NaCl ranged from 72–125 µL. The body weight of the animals was measured every 3 days from postnatal day (PND) 18 to the day of vaginal opening (VO).\nTo obtain study animals, rats were bred using male and female Sprague–Dawley rats. From birth onward, we maintained an indoor temperature of 22°C (humidity: 30–70%) and controlled illumination (12-hour light/dark cycle) to allow breeding in a constant environment along with free access to water and food. On day 18 after birth, we identified 15 immature females and randomly divided them into three groups: olfactory stimulation groups 1 and 2 and a control group (n = 5/group). We used 100% pure LO obtained from Lavandula angustifolia (NOW Foods, Bloomingdale, IL, USA) for all experiments. Group 1 was treated by indoor exposure to LO (LE) via an LO diffuser in the cage using an LO-soaked puff (changed daily) along with daily exposure to 0.9% NaCl spray. For Group 2, LO was administered as a nasal spray of aromatic LO (LS) once daily. The control group was treated with a single exposure to a nasal spray (0.9% NaCl) daily. The dose of one spray of LO or 0.9% NaCl ranged from 72–125 µL. The body weight of the animals was measured every 3 days from postnatal day (PND) 18 to the day of vaginal opening (VO).\n Analysis of VO All study groups were evaluated for VO time as an indicator of pubertal initiation at a fixed time (09:00 hour) daily. The day of VO was recorded, and VO timing was compared between the three study groups.\nAll study groups were evaluated for VO time as an indicator of pubertal initiation at a fixed time (09:00 hour) daily. The day of VO was recorded, and VO timing was compared between the three study groups.\n Euthanasia and hormone assays After VO was observed in each rat, we measured serum LH, follicle-stimulating hormone (FSH), and estradiol levels to compare hormone concentrations between study groups. The endpoint of the experiment was defined as VO occurring in the last rat. For this process, truncal blood was collected into ice-cold ethylenediamine tetraacetic acid-containing tubes after decapitation, after which the tubes were centrifuged, and plasma samples were collected and stored at −20°C until analysis. The plasma levels of LH, FSH, and estradiol of each rat were measured using enzyme-linked immunosorbent assay kits (cat. No. MBS764675, MBS2502190, and MBS263850, respectively; MyBioSource, Inc., San Diego, CA, USA) according to manufacturer’s instructions.\nAfter VO was observed in each rat, we measured serum LH, follicle-stimulating hormone (FSH), and estradiol levels to compare hormone concentrations between study groups. The endpoint of the experiment was defined as VO occurring in the last rat. For this process, truncal blood was collected into ice-cold ethylenediamine tetraacetic acid-containing tubes after decapitation, after which the tubes were centrifuged, and plasma samples were collected and stored at −20°C until analysis. The plasma levels of LH, FSH, and estradiol of each rat were measured using enzyme-linked immunosorbent assay kits (cat. No. MBS764675, MBS2502190, and MBS263850, respectively; MyBioSource, Inc., San Diego, CA, USA) according to manufacturer’s instructions.\n Measurement of organ weight After euthanasia, we measured the weight of the ovaries, spleen, kidneys, and liver. The organ weight was then modified by body weight and presented as tissue weight per 150 g body weight.\nAfter euthanasia, we measured the weight of the ovaries, spleen, kidneys, and liver. The organ weight was then modified by body weight and presented as tissue weight per 150 g body weight.\n Statistical analysis Data were presented as the mean ± standard deviation, and statistical analyses were performed using SPSS software (v.26.0; SPSS, Inc., Chicago, IL, USA). Statistical significance was determined by Kruskal-Wallis test and one-way analysis of variance for multiple-group comparisons and Mann-Whitney U test for comparisons between two groups. Statistical significance was defined at P < 0.05.\nData were presented as the mean ± standard deviation, and statistical analyses were performed using SPSS software (v.26.0; SPSS, Inc., Chicago, IL, USA). Statistical significance was determined by Kruskal-Wallis test and one-way analysis of variance for multiple-group comparisons and Mann-Whitney U test for comparisons between two groups. Statistical significance was defined at P < 0.05.\n Ethics statement The procedures used and the care of animals were approved by the Institutional Animal Care and Use Committee in Southwest Medi-Chem Institute (approval No. SEMI-20-001).\nThe procedures used and the care of animals were approved by the Institutional Animal Care and Use Committee in Southwest Medi-Chem Institute (approval No. SEMI-20-001).","To obtain study animals, rats were bred using male and female Sprague–Dawley rats. From birth onward, we maintained an indoor temperature of 22°C (humidity: 30–70%) and controlled illumination (12-hour light/dark cycle) to allow breeding in a constant environment along with free access to water and food. On day 18 after birth, we identified 15 immature females and randomly divided them into three groups: olfactory stimulation groups 1 and 2 and a control group (n = 5/group). We used 100% pure LO obtained from Lavandula angustifolia (NOW Foods, Bloomingdale, IL, USA) for all experiments. Group 1 was treated by indoor exposure to LO (LE) via an LO diffuser in the cage using an LO-soaked puff (changed daily) along with daily exposure to 0.9% NaCl spray. For Group 2, LO was administered as a nasal spray of aromatic LO (LS) once daily. The control group was treated with a single exposure to a nasal spray (0.9% NaCl) daily. The dose of one spray of LO or 0.9% NaCl ranged from 72–125 µL. The body weight of the animals was measured every 3 days from postnatal day (PND) 18 to the day of vaginal opening (VO).","All study groups were evaluated for VO time as an indicator of pubertal initiation at a fixed time (09:00 hour) daily. The day of VO was recorded, and VO timing was compared between the three study groups.","After VO was observed in each rat, we measured serum LH, follicle-stimulating hormone (FSH), and estradiol levels to compare hormone concentrations between study groups. The endpoint of the experiment was defined as VO occurring in the last rat. For this process, truncal blood was collected into ice-cold ethylenediamine tetraacetic acid-containing tubes after decapitation, after which the tubes were centrifuged, and plasma samples were collected and stored at −20°C until analysis. The plasma levels of LH, FSH, and estradiol of each rat were measured using enzyme-linked immunosorbent assay kits (cat. No. MBS764675, MBS2502190, and MBS263850, respectively; MyBioSource, Inc., San Diego, CA, USA) according to manufacturer’s instructions.","After euthanasia, we measured the weight of the ovaries, spleen, kidneys, and liver. The organ weight was then modified by body weight and presented as tissue weight per 150 g body weight.","Data were presented as the mean ± standard deviation, and statistical analyses were performed using SPSS software (v.26.0; SPSS, Inc., Chicago, IL, USA). Statistical significance was determined by Kruskal-Wallis test and one-way analysis of variance for multiple-group comparisons and Mann-Whitney U test for comparisons between two groups. Statistical significance was defined at P < 0.05.","The procedures used and the care of animals were approved by the Institutional Animal Care and Use Committee in Southwest Medi-Chem Institute (approval No. SEMI-20-001)."," Effect of olfactory exposure to LO on VO and pubertal onset VO occurred earlier in the LE (33.8 ± 1.8 days) and LS (36.6 ± 1.5 days) groups than in the control group (38.4 ± 2.9 days) (Table 1), and VO in the LE group occurred significantly earlier than in the control group (P = 0.014) and LS group (P = 0.032), respectively. However, there was no significant difference between the LS and control groups with respect to VO timing (P = 0.151). Almost all rats in the LE group experienced VO at 33 days, and the control group mostly showed VO between 38 and 41 days (Fig. 1).\nLS = exposure to lavender oil as a nasal spray, LE = exposure to diffused lavender oil, VO = vaginal opening.\n*P < 0.05 vs. control; **P < 0.05 vs. LS group.\nPND = postnatal day, LE = exposure to diffused lavender oil, C = control, LS = exposure to lavender oil as a nasal spray.\nVO occurred earlier in the LE (33.8 ± 1.8 days) and LS (36.6 ± 1.5 days) groups than in the control group (38.4 ± 2.9 days) (Table 1), and VO in the LE group occurred significantly earlier than in the control group (P = 0.014) and LS group (P = 0.032), respectively. However, there was no significant difference between the LS and control groups with respect to VO timing (P = 0.151). Almost all rats in the LE group experienced VO at 33 days, and the control group mostly showed VO between 38 and 41 days (Fig. 1).\nLS = exposure to lavender oil as a nasal spray, LE = exposure to diffused lavender oil, VO = vaginal opening.\n*P < 0.05 vs. control; **P < 0.05 vs. LS group.\nPND = postnatal day, LE = exposure to diffused lavender oil, C = control, LS = exposure to lavender oil as a nasal spray.\n Measurement of gonadotropin hormone and estradiol levels LH levels were significantly higher in the LE (67.6 ± 3.0 mIU/mL) and LS (64.3 ± 7.4 mIU/mL) groups than in the control group (49.9 ± 2.7 mIU/mL; P < 0.001 for both) (Table 2). Additionally, FSH levels were significantly higher in the LE (50.9 ± 9.1 ng/mL) and LS (51.4 ± 7.1 ng/mL) groups than in the control group (35.2 ± 3.7 ng/mL; P = 0.009 and P = 0.011, respectively). Estradiol levels were elevated in both the LE (4.9 ± 1.4 ng/mL) and LS (5.3 ± 1.4 ng/mL) groups relative to the control group (3.9 ± 0.8 ng/mL), although the differences were not significant (P = 0.326 and P = 0.547, respectively).\nData are presented as the mean ± SD (n = 5).\nLH = luteinizing hormone, FSH = follicle-stimulating hormone, LS = exposure to lavender oil as a nasal spray, LE = exposure to diffused lavender oil.\naNot significant vs. control.\n*P < 0.05 vs. control; **P < 0.001 vs. control.\nLH levels were significantly higher in the LE (67.6 ± 3.0 mIU/mL) and LS (64.3 ± 7.4 mIU/mL) groups than in the control group (49.9 ± 2.7 mIU/mL; P < 0.001 for both) (Table 2). Additionally, FSH levels were significantly higher in the LE (50.9 ± 9.1 ng/mL) and LS (51.4 ± 7.1 ng/mL) groups than in the control group (35.2 ± 3.7 ng/mL; P = 0.009 and P = 0.011, respectively). Estradiol levels were elevated in both the LE (4.9 ± 1.4 ng/mL) and LS (5.3 ± 1.4 ng/mL) groups relative to the control group (3.9 ± 0.8 ng/mL), although the differences were not significant (P = 0.326 and P = 0.547, respectively).\nData are presented as the mean ± SD (n = 5).\nLH = luteinizing hormone, FSH = follicle-stimulating hormone, LS = exposure to lavender oil as a nasal spray, LE = exposure to diffused lavender oil.\naNot significant vs. control.\n*P < 0.05 vs. control; **P < 0.001 vs. control.\n Measurement of body and organ weights Measurement of the body weight of rats in the control, LE, and LS groups every 3 days from PND 18 until VO revealed no significant differences among three groups (Fig. 2 and Table 3). The weights of the ovaries, liver, and spleen after VO showed no significant differences among three groups; however, the weight of the kidneys per 150 g body weight increased significantly after VO in the LE group (1.926 ± 0.154 g) compared with that in the control (1.664 ± 0.077 g; P = 0.009) and LS (1.694 ± 0.154 g; P = 0.017) groups (Table 4).\nPND = postnatal day, LS = exposure to lavender oil as a nasal spray, LE = exposure to diffused lavender oil.\nData are presented as the mean ± SD (n = 5).\nPND = postnatal day, LS = exposure to lavender oil as a nasal spray, LE = exposure to diffused lavender oil.\naNot significant.\nData are presented as the mean ± SD (n = 5).\nLS = exposure to lavender oil as a nasal spray, LE = exposure to diffused lavender oil.\naTissue weight (g) per body weight (150 g); bNot significant.\n*P < 0.01 vs. control; **P < 0.05 vs. LS group.\nMeasurement of the body weight of rats in the control, LE, and LS groups every 3 days from PND 18 until VO revealed no significant differences among three groups (Fig. 2 and Table 3). The weights of the ovaries, liver, and spleen after VO showed no significant differences among three groups; however, the weight of the kidneys per 150 g body weight increased significantly after VO in the LE group (1.926 ± 0.154 g) compared with that in the control (1.664 ± 0.077 g; P = 0.009) and LS (1.694 ± 0.154 g; P = 0.017) groups (Table 4).\nPND = postnatal day, LS = exposure to lavender oil as a nasal spray, LE = exposure to diffused lavender oil.\nData are presented as the mean ± SD (n = 5).\nPND = postnatal day, LS = exposure to lavender oil as a nasal spray, LE = exposure to diffused lavender oil.\naNot significant.\nData are presented as the mean ± SD (n = 5).\nLS = exposure to lavender oil as a nasal spray, LE = exposure to diffused lavender oil.\naTissue weight (g) per body weight (150 g); bNot significant.\n*P < 0.01 vs. control; **P < 0.05 vs. LS group.","VO occurred earlier in the LE (33.8 ± 1.8 days) and LS (36.6 ± 1.5 days) groups than in the control group (38.4 ± 2.9 days) (Table 1), and VO in the LE group occurred significantly earlier than in the control group (P = 0.014) and LS group (P = 0.032), respectively. However, there was no significant difference between the LS and control groups with respect to VO timing (P = 0.151). Almost all rats in the LE group experienced VO at 33 days, and the control group mostly showed VO between 38 and 41 days (Fig. 1).\nLS = exposure to lavender oil as a nasal spray, LE = exposure to diffused lavender oil, VO = vaginal opening.\n*P < 0.05 vs. control; **P < 0.05 vs. LS group.\nPND = postnatal day, LE = exposure to diffused lavender oil, C = control, LS = exposure to lavender oil as a nasal spray.","LH levels were significantly higher in the LE (67.6 ± 3.0 mIU/mL) and LS (64.3 ± 7.4 mIU/mL) groups than in the control group (49.9 ± 2.7 mIU/mL; P < 0.001 for both) (Table 2). Additionally, FSH levels were significantly higher in the LE (50.9 ± 9.1 ng/mL) and LS (51.4 ± 7.1 ng/mL) groups than in the control group (35.2 ± 3.7 ng/mL; P = 0.009 and P = 0.011, respectively). Estradiol levels were elevated in both the LE (4.9 ± 1.4 ng/mL) and LS (5.3 ± 1.4 ng/mL) groups relative to the control group (3.9 ± 0.8 ng/mL), although the differences were not significant (P = 0.326 and P = 0.547, respectively).\nData are presented as the mean ± SD (n = 5).\nLH = luteinizing hormone, FSH = follicle-stimulating hormone, LS = exposure to lavender oil as a nasal spray, LE = exposure to diffused lavender oil.\naNot significant vs. control.\n*P < 0.05 vs. control; **P < 0.001 vs. control.","Measurement of the body weight of rats in the control, LE, and LS groups every 3 days from PND 18 until VO revealed no significant differences among three groups (Fig. 2 and Table 3). The weights of the ovaries, liver, and spleen after VO showed no significant differences among three groups; however, the weight of the kidneys per 150 g body weight increased significantly after VO in the LE group (1.926 ± 0.154 g) compared with that in the control (1.664 ± 0.077 g; P = 0.009) and LS (1.694 ± 0.154 g; P = 0.017) groups (Table 4).\nPND = postnatal day, LS = exposure to lavender oil as a nasal spray, LE = exposure to diffused lavender oil.\nData are presented as the mean ± SD (n = 5).\nPND = postnatal day, LS = exposure to lavender oil as a nasal spray, LE = exposure to diffused lavender oil.\naNot significant.\nData are presented as the mean ± SD (n = 5).\nLS = exposure to lavender oil as a nasal spray, LE = exposure to diffused lavender oil.\naTissue weight (g) per body weight (150 g); bNot significant.\n*P < 0.01 vs. control; **P < 0.05 vs. LS group.","In this study, we found that persistent exposure to LO is associated with the HPG axis activation and early pubertal onset. We observed early VO in rats persistently exposed to LO as compared with that in the control group.\nVO in the Sprague-Dawley rats occurs after the surge of gonadotropins ranges from PND 30.8 to 38.4, and it might be affected by the environment, nutrition, temperature, and light.192021 In a controlled environment, the mean VO of the control group in this study was 38.4 days, and VO occurred significantly earlier in the LE group (33.8 days).\nAdditionally, serum LH and FSH levels were significantly higher in the LE and LS groups than in the control group. The LH level has been considered as a golden marker of pubertal status, whereas the estradiol level showed a fluctuation during the day and could be low even in the pubertal period.22232425 Chronic and persistent exposure to estrogens could also affect gonadotropin activation. Chronic exposure to sex hormone in cases of peripheral precocious puberty, including congenital adrenal hyperplasia or McCune-Albright syndrome, could lead to the secondary CPP, and these patients require to be treated with the GnRH agonist.2627\nPrevious studies showed that estrogenic effect of LO affected the premature thelarche in girls and gynecomastia in boys.11121314 To the best of our knowledge, no previous studies have reported an association between pubertal onset and persistent olfactory exposure to LO in an animal model. Nasal inhalation is an important source of iatrogenic sex-hormone exposure, and olfactory exposure to LO may result in LO delivery to the central nervous system and bloodstream to induce an iatrogenic effect of estrogen.\nSeveral studies have shown that LO is effective at reducing menopausal symptoms and supporting healthy sleep.2829 Additionally, previous studies reported that LO does not increase estrogen levels in adults30; in contrast, in the present study, we could not conclude that LO exposure did not affect estrogen activity. Although we observed no differences in estradiol levels between the LE and control groups, the LE group showed early pubertal onset and significantly increased LH and FSH levels. Furthermore, the LS group showed no significant differences in VO compared with the control group. Therefore, the amount and persistence of LO exposure may determine pubertal onset.\nA previous animal study reported that percutaneous injection of LO when performing uterotrophic assays on immature rats results in significantly reduced body weight gain after 3 days compared with that in the control group and a group administered 17α-ethinyl estradiol.31 However, the authors only assessed weight gain and organ-weight-to-terminal-body-weight to evaluate the presence of an estrogenic effect, and did not compare hormone levels or VO timing. The different administration modalities may have been responsible for the reported differences in body weight gain between the LO injection group and the group undergoing oral estrogen administration. In the present study, we found no differences in organ weight, including the ovaries, and body weight gain between groups, despite the apparently different VO and gonadotropin levels. Interestingly, the kidney tissue weight was significantly increased only in the LE group, supporting an association between physiological LO-specific effects and endocrine effects. A recent study showed that LO exposure affects renal restoration in a dose-dependent manner by decreasing antioxidant signals and inflammatory cytokine levels, as well as by inhibiting apoptosis.32 In the present study, we measured neither renal function nor nephron numbers and used only renal tissue weight as an indicator of the positive effect of LO exposure. Therefore, increased renal tissue weight may indicate a renal burden related to LO excretion.\nFour studies have reported a total of 11 pediatric patients (seven males and four females) showing premature thelarche in females (age range: 14 months to 7 years and 9 months) and gynecomastia in males (age range: 4 years and 5 months to 10 years and 1 month) after using an LO-containing product.11121314 Ramsey et al.13 reported that patients showed symptomatic improvement after discontinuation of LO exposure accompanied by no abnormal laboratory findings. These reports suggest the estrogenic effect of topical preparation of LO. Additionally, in vitro studies showed that LO (or the LO components linalool and linalyl acetate) exerts an estrogenic effect by stimulating α-transcription of the estrogen receptor.1113 The peripheral hormones or signals transmitting to GnRH neurons may lead to GnRH secretion and stimulation of pituitary gonadotropins and gonadal sex steroids.8 Given our observation of early activation of the HPG axis after olfactory exposure to LO, further investigation of the effect of LO on gene activation related to GnRH synthesis or secretion may explain the associations between early activation of the HPG axis and LO exposure.\nWe focused on the effects of LO exposure through olfactory stimulation and not via oral ingestion or topical application. One limitation of this study is its small sample size; thus, further studies are required to validate the findings. We observed significant elevation of gonadotropin levels not only in the LE group but also in the LS group, suggesting that repetitive olfactory exposure affected the manifestation of LO-specific physiological effects. Several studies reported that essential LO affects anxiety when administered via the oral or nasal routes.333435 However, LO exposure via oral ingestion in food is not as common as skin absorption of various LO-containing cosmetic products or LO inhalation. Nevertheless, inhalation of environmental LO is difficult to quantify in humans, and epidemiological surveillance data for the sole effect of LO inhalation in children undergoing precocious puberty are unavailable. Furthermore, the frequency and duration of exposure to LO inhalation are highly variable, and the effect of LO following skin exposure on children remains inconclusive because of the variable amounts of LO to which the skin is exposed, and difficulties associated with follow-up to assess long-term effects.36\nThis study showed the effect of olfactory stimulation by LO on the early onset of puberty. These results suggest that avoidance of LO exposure to minimize unnecessary iatrogenic estrogen effects from fragrances, diffusers, and perfumes can prevent early stimulation of the HPG axis, particularly in younger children. Further in vitro evaluation of LO-related effects on central kisspeptin signaling may reveal the mechanisms associated with early activation of the HPG axis by persistent olfactory exposure to LO."],"string":"[\n \"Central precocious puberty (CPP) describes the early activation of the hypothalamic–pituitary–gonadal (HPG) axis, which leads to the rapid progression of bone age, early menarche, reduction in final adult height, and the appearance of secondary sexual characteristics before 8 and 9 years of age in girls and boys, respectively.1 Traditionally, CPP is accompanied by intracranial lesions, including optic glioma, pilocytic astrocytoma, hydrocephalus, Rathke’s cleft cyst, and pituitary adenomas, in 40–90% of boys and < 10% of girls.23 The gonadotropin-releasing hormone (GnRH) stimulation test is used for diagnosing CPP, and the basal luteinizing hormone (LH) level is considered as a valuable tool to assess pubertal state.4 CPP treatment was introduced in 1980, and dosing of a recombinant GnRH agonist every 4 weeks or 3 months leads to increases in the final adult height and delayed menarche.23 Improving the final adult height of children with CPP is one of the major issues during treatment.56 However, the incidence of precocious puberty is rapidly increasing, and examination and treatment of this condition are becoming a major burden because of the associated medical expenses, although the cause of this condition remains unknown.23\\nEnvironmental hormones, i.e., endocrine-disrupting chemicals, were recently suggested to contribute to the onset of puberty in childhood,78 and animal studies demonstrated that endocrine-disrupting chemicals accelerate pubertal onset.910 Additionally, previous reports showed that lavender oil (LO) and tea tree oil are associated with prepubertal gynecomastia in boys.1112 Moreover, cases of premature thelarche that resolved after cessation of exposure to lavender-containing fragrance have been reported,1314 and an in vitro study showed that components of LO, including linalool and linalyl acetate, activate estrogen-related gene expression in human cell lines.13 However, studies of the absorbance of these materials in sufficient amounts and their effect on breast growth have not been performed. A previous study suggested that smell of sense can be transmitted to the central nervous system, thereby facilitating the bypass of inhaled molecules via the nasal pathway of the blood–brain barrier.15 There are several opportunities for inhalation of numerous endocrine-disrupting chemicals from estrogenic sources in cosmetics, perfumes, air fresheners, and scented candles/diffusers using essential oils, which can directly affect olfactory stimulation of the neuroendocrine system. Since some studies suggested that essential oils may have efficacy against the coronavirus disease 2019 and its inflammatory complications,161718 the interests for home therapy using essential oils are more increasing.\\nIn this study, we tested whether continuous inhalation of LO affects early gonadotropin activation and precocious puberty. However, it is difficult to limit or measure the number of EDCs to nasal exposure in human. Thus, we investigated the effects of continuous inhalation of LO on pubertal onset and gonadotropin hormone levels in an animal model and compared them to control conditions.\",\n \" Animals and experimental design To obtain study animals, rats were bred using male and female Sprague–Dawley rats. From birth onward, we maintained an indoor temperature of 22°C (humidity: 30–70%) and controlled illumination (12-hour light/dark cycle) to allow breeding in a constant environment along with free access to water and food. On day 18 after birth, we identified 15 immature females and randomly divided them into three groups: olfactory stimulation groups 1 and 2 and a control group (n = 5/group). We used 100% pure LO obtained from Lavandula angustifolia (NOW Foods, Bloomingdale, IL, USA) for all experiments. Group 1 was treated by indoor exposure to LO (LE) via an LO diffuser in the cage using an LO-soaked puff (changed daily) along with daily exposure to 0.9% NaCl spray. For Group 2, LO was administered as a nasal spray of aromatic LO (LS) once daily. The control group was treated with a single exposure to a nasal spray (0.9% NaCl) daily. The dose of one spray of LO or 0.9% NaCl ranged from 72–125 µL. The body weight of the animals was measured every 3 days from postnatal day (PND) 18 to the day of vaginal opening (VO).\\nTo obtain study animals, rats were bred using male and female Sprague–Dawley rats. From birth onward, we maintained an indoor temperature of 22°C (humidity: 30–70%) and controlled illumination (12-hour light/dark cycle) to allow breeding in a constant environment along with free access to water and food. On day 18 after birth, we identified 15 immature females and randomly divided them into three groups: olfactory stimulation groups 1 and 2 and a control group (n = 5/group). We used 100% pure LO obtained from Lavandula angustifolia (NOW Foods, Bloomingdale, IL, USA) for all experiments. Group 1 was treated by indoor exposure to LO (LE) via an LO diffuser in the cage using an LO-soaked puff (changed daily) along with daily exposure to 0.9% NaCl spray. For Group 2, LO was administered as a nasal spray of aromatic LO (LS) once daily. The control group was treated with a single exposure to a nasal spray (0.9% NaCl) daily. The dose of one spray of LO or 0.9% NaCl ranged from 72–125 µL. The body weight of the animals was measured every 3 days from postnatal day (PND) 18 to the day of vaginal opening (VO).\\n Analysis of VO All study groups were evaluated for VO time as an indicator of pubertal initiation at a fixed time (09:00 hour) daily. The day of VO was recorded, and VO timing was compared between the three study groups.\\nAll study groups were evaluated for VO time as an indicator of pubertal initiation at a fixed time (09:00 hour) daily. The day of VO was recorded, and VO timing was compared between the three study groups.\\n Euthanasia and hormone assays After VO was observed in each rat, we measured serum LH, follicle-stimulating hormone (FSH), and estradiol levels to compare hormone concentrations between study groups. The endpoint of the experiment was defined as VO occurring in the last rat. For this process, truncal blood was collected into ice-cold ethylenediamine tetraacetic acid-containing tubes after decapitation, after which the tubes were centrifuged, and plasma samples were collected and stored at −20°C until analysis. The plasma levels of LH, FSH, and estradiol of each rat were measured using enzyme-linked immunosorbent assay kits (cat. No. MBS764675, MBS2502190, and MBS263850, respectively; MyBioSource, Inc., San Diego, CA, USA) according to manufacturer’s instructions.\\nAfter VO was observed in each rat, we measured serum LH, follicle-stimulating hormone (FSH), and estradiol levels to compare hormone concentrations between study groups. The endpoint of the experiment was defined as VO occurring in the last rat. For this process, truncal blood was collected into ice-cold ethylenediamine tetraacetic acid-containing tubes after decapitation, after which the tubes were centrifuged, and plasma samples were collected and stored at −20°C until analysis. The plasma levels of LH, FSH, and estradiol of each rat were measured using enzyme-linked immunosorbent assay kits (cat. No. MBS764675, MBS2502190, and MBS263850, respectively; MyBioSource, Inc., San Diego, CA, USA) according to manufacturer’s instructions.\\n Measurement of organ weight After euthanasia, we measured the weight of the ovaries, spleen, kidneys, and liver. The organ weight was then modified by body weight and presented as tissue weight per 150 g body weight.\\nAfter euthanasia, we measured the weight of the ovaries, spleen, kidneys, and liver. The organ weight was then modified by body weight and presented as tissue weight per 150 g body weight.\\n Statistical analysis Data were presented as the mean ± standard deviation, and statistical analyses were performed using SPSS software (v.26.0; SPSS, Inc., Chicago, IL, USA). Statistical significance was determined by Kruskal-Wallis test and one-way analysis of variance for multiple-group comparisons and Mann-Whitney U test for comparisons between two groups. Statistical significance was defined at P < 0.05.\\nData were presented as the mean ± standard deviation, and statistical analyses were performed using SPSS software (v.26.0; SPSS, Inc., Chicago, IL, USA). Statistical significance was determined by Kruskal-Wallis test and one-way analysis of variance for multiple-group comparisons and Mann-Whitney U test for comparisons between two groups. Statistical significance was defined at P < 0.05.\\n Ethics statement The procedures used and the care of animals were approved by the Institutional Animal Care and Use Committee in Southwest Medi-Chem Institute (approval No. SEMI-20-001).\\nThe procedures used and the care of animals were approved by the Institutional Animal Care and Use Committee in Southwest Medi-Chem Institute (approval No. SEMI-20-001).\",\n \"To obtain study animals, rats were bred using male and female Sprague–Dawley rats. From birth onward, we maintained an indoor temperature of 22°C (humidity: 30–70%) and controlled illumination (12-hour light/dark cycle) to allow breeding in a constant environment along with free access to water and food. On day 18 after birth, we identified 15 immature females and randomly divided them into three groups: olfactory stimulation groups 1 and 2 and a control group (n = 5/group). We used 100% pure LO obtained from Lavandula angustifolia (NOW Foods, Bloomingdale, IL, USA) for all experiments. Group 1 was treated by indoor exposure to LO (LE) via an LO diffuser in the cage using an LO-soaked puff (changed daily) along with daily exposure to 0.9% NaCl spray. For Group 2, LO was administered as a nasal spray of aromatic LO (LS) once daily. The control group was treated with a single exposure to a nasal spray (0.9% NaCl) daily. The dose of one spray of LO or 0.9% NaCl ranged from 72–125 µL. The body weight of the animals was measured every 3 days from postnatal day (PND) 18 to the day of vaginal opening (VO).\",\n \"All study groups were evaluated for VO time as an indicator of pubertal initiation at a fixed time (09:00 hour) daily. The day of VO was recorded, and VO timing was compared between the three study groups.\",\n \"After VO was observed in each rat, we measured serum LH, follicle-stimulating hormone (FSH), and estradiol levels to compare hormone concentrations between study groups. The endpoint of the experiment was defined as VO occurring in the last rat. For this process, truncal blood was collected into ice-cold ethylenediamine tetraacetic acid-containing tubes after decapitation, after which the tubes were centrifuged, and plasma samples were collected and stored at −20°C until analysis. The plasma levels of LH, FSH, and estradiol of each rat were measured using enzyme-linked immunosorbent assay kits (cat. No. MBS764675, MBS2502190, and MBS263850, respectively; MyBioSource, Inc., San Diego, CA, USA) according to manufacturer’s instructions.\",\n \"After euthanasia, we measured the weight of the ovaries, spleen, kidneys, and liver. The organ weight was then modified by body weight and presented as tissue weight per 150 g body weight.\",\n \"Data were presented as the mean ± standard deviation, and statistical analyses were performed using SPSS software (v.26.0; SPSS, Inc., Chicago, IL, USA). Statistical significance was determined by Kruskal-Wallis test and one-way analysis of variance for multiple-group comparisons and Mann-Whitney U test for comparisons between two groups. Statistical significance was defined at P < 0.05.\",\n \"The procedures used and the care of animals were approved by the Institutional Animal Care and Use Committee in Southwest Medi-Chem Institute (approval No. SEMI-20-001).\",\n \" Effect of olfactory exposure to LO on VO and pubertal onset VO occurred earlier in the LE (33.8 ± 1.8 days) and LS (36.6 ± 1.5 days) groups than in the control group (38.4 ± 2.9 days) (Table 1), and VO in the LE group occurred significantly earlier than in the control group (P = 0.014) and LS group (P = 0.032), respectively. However, there was no significant difference between the LS and control groups with respect to VO timing (P = 0.151). Almost all rats in the LE group experienced VO at 33 days, and the control group mostly showed VO between 38 and 41 days (Fig. 1).\\nLS = exposure to lavender oil as a nasal spray, LE = exposure to diffused lavender oil, VO = vaginal opening.\\n*P < 0.05 vs. control; **P < 0.05 vs. LS group.\\nPND = postnatal day, LE = exposure to diffused lavender oil, C = control, LS = exposure to lavender oil as a nasal spray.\\nVO occurred earlier in the LE (33.8 ± 1.8 days) and LS (36.6 ± 1.5 days) groups than in the control group (38.4 ± 2.9 days) (Table 1), and VO in the LE group occurred significantly earlier than in the control group (P = 0.014) and LS group (P = 0.032), respectively. However, there was no significant difference between the LS and control groups with respect to VO timing (P = 0.151). Almost all rats in the LE group experienced VO at 33 days, and the control group mostly showed VO between 38 and 41 days (Fig. 1).\\nLS = exposure to lavender oil as a nasal spray, LE = exposure to diffused lavender oil, VO = vaginal opening.\\n*P < 0.05 vs. control; **P < 0.05 vs. LS group.\\nPND = postnatal day, LE = exposure to diffused lavender oil, C = control, LS = exposure to lavender oil as a nasal spray.\\n Measurement of gonadotropin hormone and estradiol levels LH levels were significantly higher in the LE (67.6 ± 3.0 mIU/mL) and LS (64.3 ± 7.4 mIU/mL) groups than in the control group (49.9 ± 2.7 mIU/mL; P < 0.001 for both) (Table 2). Additionally, FSH levels were significantly higher in the LE (50.9 ± 9.1 ng/mL) and LS (51.4 ± 7.1 ng/mL) groups than in the control group (35.2 ± 3.7 ng/mL; P = 0.009 and P = 0.011, respectively). Estradiol levels were elevated in both the LE (4.9 ± 1.4 ng/mL) and LS (5.3 ± 1.4 ng/mL) groups relative to the control group (3.9 ± 0.8 ng/mL), although the differences were not significant (P = 0.326 and P = 0.547, respectively).\\nData are presented as the mean ± SD (n = 5).\\nLH = luteinizing hormone, FSH = follicle-stimulating hormone, LS = exposure to lavender oil as a nasal spray, LE = exposure to diffused lavender oil.\\naNot significant vs. control.\\n*P < 0.05 vs. control; **P < 0.001 vs. control.\\nLH levels were significantly higher in the LE (67.6 ± 3.0 mIU/mL) and LS (64.3 ± 7.4 mIU/mL) groups than in the control group (49.9 ± 2.7 mIU/mL; P < 0.001 for both) (Table 2). Additionally, FSH levels were significantly higher in the LE (50.9 ± 9.1 ng/mL) and LS (51.4 ± 7.1 ng/mL) groups than in the control group (35.2 ± 3.7 ng/mL; P = 0.009 and P = 0.011, respectively). Estradiol levels were elevated in both the LE (4.9 ± 1.4 ng/mL) and LS (5.3 ± 1.4 ng/mL) groups relative to the control group (3.9 ± 0.8 ng/mL), although the differences were not significant (P = 0.326 and P = 0.547, respectively).\\nData are presented as the mean ± SD (n = 5).\\nLH = luteinizing hormone, FSH = follicle-stimulating hormone, LS = exposure to lavender oil as a nasal spray, LE = exposure to diffused lavender oil.\\naNot significant vs. control.\\n*P < 0.05 vs. control; **P < 0.001 vs. control.\\n Measurement of body and organ weights Measurement of the body weight of rats in the control, LE, and LS groups every 3 days from PND 18 until VO revealed no significant differences among three groups (Fig. 2 and Table 3). The weights of the ovaries, liver, and spleen after VO showed no significant differences among three groups; however, the weight of the kidneys per 150 g body weight increased significantly after VO in the LE group (1.926 ± 0.154 g) compared with that in the control (1.664 ± 0.077 g; P = 0.009) and LS (1.694 ± 0.154 g; P = 0.017) groups (Table 4).\\nPND = postnatal day, LS = exposure to lavender oil as a nasal spray, LE = exposure to diffused lavender oil.\\nData are presented as the mean ± SD (n = 5).\\nPND = postnatal day, LS = exposure to lavender oil as a nasal spray, LE = exposure to diffused lavender oil.\\naNot significant.\\nData are presented as the mean ± SD (n = 5).\\nLS = exposure to lavender oil as a nasal spray, LE = exposure to diffused lavender oil.\\naTissue weight (g) per body weight (150 g); bNot significant.\\n*P < 0.01 vs. control; **P < 0.05 vs. LS group.\\nMeasurement of the body weight of rats in the control, LE, and LS groups every 3 days from PND 18 until VO revealed no significant differences among three groups (Fig. 2 and Table 3). The weights of the ovaries, liver, and spleen after VO showed no significant differences among three groups; however, the weight of the kidneys per 150 g body weight increased significantly after VO in the LE group (1.926 ± 0.154 g) compared with that in the control (1.664 ± 0.077 g; P = 0.009) and LS (1.694 ± 0.154 g; P = 0.017) groups (Table 4).\\nPND = postnatal day, LS = exposure to lavender oil as a nasal spray, LE = exposure to diffused lavender oil.\\nData are presented as the mean ± SD (n = 5).\\nPND = postnatal day, LS = exposure to lavender oil as a nasal spray, LE = exposure to diffused lavender oil.\\naNot significant.\\nData are presented as the mean ± SD (n = 5).\\nLS = exposure to lavender oil as a nasal spray, LE = exposure to diffused lavender oil.\\naTissue weight (g) per body weight (150 g); bNot significant.\\n*P < 0.01 vs. control; **P < 0.05 vs. LS group.\",\n \"VO occurred earlier in the LE (33.8 ± 1.8 days) and LS (36.6 ± 1.5 days) groups than in the control group (38.4 ± 2.9 days) (Table 1), and VO in the LE group occurred significantly earlier than in the control group (P = 0.014) and LS group (P = 0.032), respectively. However, there was no significant difference between the LS and control groups with respect to VO timing (P = 0.151). Almost all rats in the LE group experienced VO at 33 days, and the control group mostly showed VO between 38 and 41 days (Fig. 1).\\nLS = exposure to lavender oil as a nasal spray, LE = exposure to diffused lavender oil, VO = vaginal opening.\\n*P < 0.05 vs. control; **P < 0.05 vs. LS group.\\nPND = postnatal day, LE = exposure to diffused lavender oil, C = control, LS = exposure to lavender oil as a nasal spray.\",\n \"LH levels were significantly higher in the LE (67.6 ± 3.0 mIU/mL) and LS (64.3 ± 7.4 mIU/mL) groups than in the control group (49.9 ± 2.7 mIU/mL; P < 0.001 for both) (Table 2). Additionally, FSH levels were significantly higher in the LE (50.9 ± 9.1 ng/mL) and LS (51.4 ± 7.1 ng/mL) groups than in the control group (35.2 ± 3.7 ng/mL; P = 0.009 and P = 0.011, respectively). Estradiol levels were elevated in both the LE (4.9 ± 1.4 ng/mL) and LS (5.3 ± 1.4 ng/mL) groups relative to the control group (3.9 ± 0.8 ng/mL), although the differences were not significant (P = 0.326 and P = 0.547, respectively).\\nData are presented as the mean ± SD (n = 5).\\nLH = luteinizing hormone, FSH = follicle-stimulating hormone, LS = exposure to lavender oil as a nasal spray, LE = exposure to diffused lavender oil.\\naNot significant vs. control.\\n*P < 0.05 vs. control; **P < 0.001 vs. control.\",\n \"Measurement of the body weight of rats in the control, LE, and LS groups every 3 days from PND 18 until VO revealed no significant differences among three groups (Fig. 2 and Table 3). The weights of the ovaries, liver, and spleen after VO showed no significant differences among three groups; however, the weight of the kidneys per 150 g body weight increased significantly after VO in the LE group (1.926 ± 0.154 g) compared with that in the control (1.664 ± 0.077 g; P = 0.009) and LS (1.694 ± 0.154 g; P = 0.017) groups (Table 4).\\nPND = postnatal day, LS = exposure to lavender oil as a nasal spray, LE = exposure to diffused lavender oil.\\nData are presented as the mean ± SD (n = 5).\\nPND = postnatal day, LS = exposure to lavender oil as a nasal spray, LE = exposure to diffused lavender oil.\\naNot significant.\\nData are presented as the mean ± SD (n = 5).\\nLS = exposure to lavender oil as a nasal spray, LE = exposure to diffused lavender oil.\\naTissue weight (g) per body weight (150 g); bNot significant.\\n*P < 0.01 vs. control; **P < 0.05 vs. LS group.\",\n \"In this study, we found that persistent exposure to LO is associated with the HPG axis activation and early pubertal onset. We observed early VO in rats persistently exposed to LO as compared with that in the control group.\\nVO in the Sprague-Dawley rats occurs after the surge of gonadotropins ranges from PND 30.8 to 38.4, and it might be affected by the environment, nutrition, temperature, and light.192021 In a controlled environment, the mean VO of the control group in this study was 38.4 days, and VO occurred significantly earlier in the LE group (33.8 days).\\nAdditionally, serum LH and FSH levels were significantly higher in the LE and LS groups than in the control group. The LH level has been considered as a golden marker of pubertal status, whereas the estradiol level showed a fluctuation during the day and could be low even in the pubertal period.22232425 Chronic and persistent exposure to estrogens could also affect gonadotropin activation. Chronic exposure to sex hormone in cases of peripheral precocious puberty, including congenital adrenal hyperplasia or McCune-Albright syndrome, could lead to the secondary CPP, and these patients require to be treated with the GnRH agonist.2627\\nPrevious studies showed that estrogenic effect of LO affected the premature thelarche in girls and gynecomastia in boys.11121314 To the best of our knowledge, no previous studies have reported an association between pubertal onset and persistent olfactory exposure to LO in an animal model. Nasal inhalation is an important source of iatrogenic sex-hormone exposure, and olfactory exposure to LO may result in LO delivery to the central nervous system and bloodstream to induce an iatrogenic effect of estrogen.\\nSeveral studies have shown that LO is effective at reducing menopausal symptoms and supporting healthy sleep.2829 Additionally, previous studies reported that LO does not increase estrogen levels in adults30; in contrast, in the present study, we could not conclude that LO exposure did not affect estrogen activity. Although we observed no differences in estradiol levels between the LE and control groups, the LE group showed early pubertal onset and significantly increased LH and FSH levels. Furthermore, the LS group showed no significant differences in VO compared with the control group. Therefore, the amount and persistence of LO exposure may determine pubertal onset.\\nA previous animal study reported that percutaneous injection of LO when performing uterotrophic assays on immature rats results in significantly reduced body weight gain after 3 days compared with that in the control group and a group administered 17α-ethinyl estradiol.31 However, the authors only assessed weight gain and organ-weight-to-terminal-body-weight to evaluate the presence of an estrogenic effect, and did not compare hormone levels or VO timing. The different administration modalities may have been responsible for the reported differences in body weight gain between the LO injection group and the group undergoing oral estrogen administration. In the present study, we found no differences in organ weight, including the ovaries, and body weight gain between groups, despite the apparently different VO and gonadotropin levels. Interestingly, the kidney tissue weight was significantly increased only in the LE group, supporting an association between physiological LO-specific effects and endocrine effects. A recent study showed that LO exposure affects renal restoration in a dose-dependent manner by decreasing antioxidant signals and inflammatory cytokine levels, as well as by inhibiting apoptosis.32 In the present study, we measured neither renal function nor nephron numbers and used only renal tissue weight as an indicator of the positive effect of LO exposure. Therefore, increased renal tissue weight may indicate a renal burden related to LO excretion.\\nFour studies have reported a total of 11 pediatric patients (seven males and four females) showing premature thelarche in females (age range: 14 months to 7 years and 9 months) and gynecomastia in males (age range: 4 years and 5 months to 10 years and 1 month) after using an LO-containing product.11121314 Ramsey et al.13 reported that patients showed symptomatic improvement after discontinuation of LO exposure accompanied by no abnormal laboratory findings. These reports suggest the estrogenic effect of topical preparation of LO. Additionally, in vitro studies showed that LO (or the LO components linalool and linalyl acetate) exerts an estrogenic effect by stimulating α-transcription of the estrogen receptor.1113 The peripheral hormones or signals transmitting to GnRH neurons may lead to GnRH secretion and stimulation of pituitary gonadotropins and gonadal sex steroids.8 Given our observation of early activation of the HPG axis after olfactory exposure to LO, further investigation of the effect of LO on gene activation related to GnRH synthesis or secretion may explain the associations between early activation of the HPG axis and LO exposure.\\nWe focused on the effects of LO exposure through olfactory stimulation and not via oral ingestion or topical application. One limitation of this study is its small sample size; thus, further studies are required to validate the findings. We observed significant elevation of gonadotropin levels not only in the LE group but also in the LS group, suggesting that repetitive olfactory exposure affected the manifestation of LO-specific physiological effects. Several studies reported that essential LO affects anxiety when administered via the oral or nasal routes.333435 However, LO exposure via oral ingestion in food is not as common as skin absorption of various LO-containing cosmetic products or LO inhalation. Nevertheless, inhalation of environmental LO is difficult to quantify in humans, and epidemiological surveillance data for the sole effect of LO inhalation in children undergoing precocious puberty are unavailable. Furthermore, the frequency and duration of exposure to LO inhalation are highly variable, and the effect of LO following skin exposure on children remains inconclusive because of the variable amounts of LO to which the skin is exposed, and difficulties associated with follow-up to assess long-term effects.36\\nThis study showed the effect of olfactory stimulation by LO on the early onset of puberty. These results suggest that avoidance of LO exposure to minimize unnecessary iatrogenic estrogen effects from fragrances, diffusers, and perfumes can prevent early stimulation of the HPG axis, particularly in younger children. Further in vitro evaluation of LO-related effects on central kisspeptin signaling may reveal the mechanisms associated with early activation of the HPG axis by persistent olfactory exposure to LO.\"\n]"},"all_sections_sec_types":{"kind":"list like","value":["intro","methods",null,null,null,null,null,null,"results",null,null,null,"discussion"],"string":"[\n \"intro\",\n \"methods\",\n null,\n null,\n null,\n null,\n null,\n null,\n \"results\",\n null,\n null,\n null,\n \"discussion\"\n]"},"keywords":{"kind":"list like","value":["Precocious Puberty","Endocrine Disruptors","Lavender Oil","Inhalation Exposure","Vaginal Opening"],"string":"[\n \"Precocious Puberty\",\n \"Endocrine Disruptors\",\n \"Lavender Oil\",\n \"Inhalation Exposure\",\n \"Vaginal Opening\"\n]"},"whole_article_text":{"kind":"string","value":"INTRODUCTION:\nCentral precocious puberty (CPP) describes the early activation of the hypothalamic–pituitary–gonadal (HPG) axis, which leads to the rapid progression of bone age, early menarche, reduction in final adult height, and the appearance of secondary sexual characteristics before 8 and 9 years of age in girls and boys, respectively.1 Traditionally, CPP is accompanied by intracranial lesions, including optic glioma, pilocytic astrocytoma, hydrocephalus, Rathke’s cleft cyst, and pituitary adenomas, in 40–90% of boys and < 10% of girls.23 The gonadotropin-releasing hormone (GnRH) stimulation test is used for diagnosing CPP, and the basal luteinizing hormone (LH) level is considered as a valuable tool to assess pubertal state.4 CPP treatment was introduced in 1980, and dosing of a recombinant GnRH agonist every 4 weeks or 3 months leads to increases in the final adult height and delayed menarche.23 Improving the final adult height of children with CPP is one of the major issues during treatment.56 However, the incidence of precocious puberty is rapidly increasing, and examination and treatment of this condition are becoming a major burden because of the associated medical expenses, although the cause of this condition remains unknown.23\nEnvironmental hormones, i.e., endocrine-disrupting chemicals, were recently suggested to contribute to the onset of puberty in childhood,78 and animal studies demonstrated that endocrine-disrupting chemicals accelerate pubertal onset.910 Additionally, previous reports showed that lavender oil (LO) and tea tree oil are associated with prepubertal gynecomastia in boys.1112 Moreover, cases of premature thelarche that resolved after cessation of exposure to lavender-containing fragrance have been reported,1314 and an in vitro study showed that components of LO, including linalool and linalyl acetate, activate estrogen-related gene expression in human cell lines.13 However, studies of the absorbance of these materials in sufficient amounts and their effect on breast growth have not been performed. A previous study suggested that smell of sense can be transmitted to the central nervous system, thereby facilitating the bypass of inhaled molecules via the nasal pathway of the blood–brain barrier.15 There are several opportunities for inhalation of numerous endocrine-disrupting chemicals from estrogenic sources in cosmetics, perfumes, air fresheners, and scented candles/diffusers using essential oils, which can directly affect olfactory stimulation of the neuroendocrine system. Since some studies suggested that essential oils may have efficacy against the coronavirus disease 2019 and its inflammatory complications,161718 the interests for home therapy using essential oils are more increasing.\nIn this study, we tested whether continuous inhalation of LO affects early gonadotropin activation and precocious puberty. However, it is difficult to limit or measure the number of EDCs to nasal exposure in human. Thus, we investigated the effects of continuous inhalation of LO on pubertal onset and gonadotropin hormone levels in an animal model and compared them to control conditions.\n\nMETHODS:\n Animals and experimental design To obtain study animals, rats were bred using male and female Sprague–Dawley rats. From birth onward, we maintained an indoor temperature of 22°C (humidity: 30–70%) and controlled illumination (12-hour light/dark cycle) to allow breeding in a constant environment along with free access to water and food. On day 18 after birth, we identified 15 immature females and randomly divided them into three groups: olfactory stimulation groups 1 and 2 and a control group (n = 5/group). We used 100% pure LO obtained from Lavandula angustifolia (NOW Foods, Bloomingdale, IL, USA) for all experiments. Group 1 was treated by indoor exposure to LO (LE) via an LO diffuser in the cage using an LO-soaked puff (changed daily) along with daily exposure to 0.9% NaCl spray. For Group 2, LO was administered as a nasal spray of aromatic LO (LS) once daily. The control group was treated with a single exposure to a nasal spray (0.9% NaCl) daily. The dose of one spray of LO or 0.9% NaCl ranged from 72–125 µL. The body weight of the animals was measured every 3 days from postnatal day (PND) 18 to the day of vaginal opening (VO).\nTo obtain study animals, rats were bred using male and female Sprague–Dawley rats. From birth onward, we maintained an indoor temperature of 22°C (humidity: 30–70%) and controlled illumination (12-hour light/dark cycle) to allow breeding in a constant environment along with free access to water and food. On day 18 after birth, we identified 15 immature females and randomly divided them into three groups: olfactory stimulation groups 1 and 2 and a control group (n = 5/group). We used 100% pure LO obtained from Lavandula angustifolia (NOW Foods, Bloomingdale, IL, USA) for all experiments. Group 1 was treated by indoor exposure to LO (LE) via an LO diffuser in the cage using an LO-soaked puff (changed daily) along with daily exposure to 0.9% NaCl spray. For Group 2, LO was administered as a nasal spray of aromatic LO (LS) once daily. The control group was treated with a single exposure to a nasal spray (0.9% NaCl) daily. The dose of one spray of LO or 0.9% NaCl ranged from 72–125 µL. The body weight of the animals was measured every 3 days from postnatal day (PND) 18 to the day of vaginal opening (VO).\n Analysis of VO All study groups were evaluated for VO time as an indicator of pubertal initiation at a fixed time (09:00 hour) daily. The day of VO was recorded, and VO timing was compared between the three study groups.\nAll study groups were evaluated for VO time as an indicator of pubertal initiation at a fixed time (09:00 hour) daily. The day of VO was recorded, and VO timing was compared between the three study groups.\n Euthanasia and hormone assays After VO was observed in each rat, we measured serum LH, follicle-stimulating hormone (FSH), and estradiol levels to compare hormone concentrations between study groups. The endpoint of the experiment was defined as VO occurring in the last rat. For this process, truncal blood was collected into ice-cold ethylenediamine tetraacetic acid-containing tubes after decapitation, after which the tubes were centrifuged, and plasma samples were collected and stored at −20°C until analysis. The plasma levels of LH, FSH, and estradiol of each rat were measured using enzyme-linked immunosorbent assay kits (cat. No. MBS764675, MBS2502190, and MBS263850, respectively; MyBioSource, Inc., San Diego, CA, USA) according to manufacturer’s instructions.\nAfter VO was observed in each rat, we measured serum LH, follicle-stimulating hormone (FSH), and estradiol levels to compare hormone concentrations between study groups. The endpoint of the experiment was defined as VO occurring in the last rat. For this process, truncal blood was collected into ice-cold ethylenediamine tetraacetic acid-containing tubes after decapitation, after which the tubes were centrifuged, and plasma samples were collected and stored at −20°C until analysis. The plasma levels of LH, FSH, and estradiol of each rat were measured using enzyme-linked immunosorbent assay kits (cat. No. MBS764675, MBS2502190, and MBS263850, respectively; MyBioSource, Inc., San Diego, CA, USA) according to manufacturer’s instructions.\n Measurement of organ weight After euthanasia, we measured the weight of the ovaries, spleen, kidneys, and liver. The organ weight was then modified by body weight and presented as tissue weight per 150 g body weight.\nAfter euthanasia, we measured the weight of the ovaries, spleen, kidneys, and liver. The organ weight was then modified by body weight and presented as tissue weight per 150 g body weight.\n Statistical analysis Data were presented as the mean ± standard deviation, and statistical analyses were performed using SPSS software (v.26.0; SPSS, Inc., Chicago, IL, USA). Statistical significance was determined by Kruskal-Wallis test and one-way analysis of variance for multiple-group comparisons and Mann-Whitney U test for comparisons between two groups. Statistical significance was defined at P < 0.05.\nData were presented as the mean ± standard deviation, and statistical analyses were performed using SPSS software (v.26.0; SPSS, Inc., Chicago, IL, USA). Statistical significance was determined by Kruskal-Wallis test and one-way analysis of variance for multiple-group comparisons and Mann-Whitney U test for comparisons between two groups. Statistical significance was defined at P < 0.05.\n Ethics statement The procedures used and the care of animals were approved by the Institutional Animal Care and Use Committee in Southwest Medi-Chem Institute (approval No. SEMI-20-001).\nThe procedures used and the care of animals were approved by the Institutional Animal Care and Use Committee in Southwest Medi-Chem Institute (approval No. SEMI-20-001).\n\nAnimals and experimental design:\nTo obtain study animals, rats were bred using male and female Sprague–Dawley rats. From birth onward, we maintained an indoor temperature of 22°C (humidity: 30–70%) and controlled illumination (12-hour light/dark cycle) to allow breeding in a constant environment along with free access to water and food. On day 18 after birth, we identified 15 immature females and randomly divided them into three groups: olfactory stimulation groups 1 and 2 and a control group (n = 5/group). We used 100% pure LO obtained from Lavandula angustifolia (NOW Foods, Bloomingdale, IL, USA) for all experiments. Group 1 was treated by indoor exposure to LO (LE) via an LO diffuser in the cage using an LO-soaked puff (changed daily) along with daily exposure to 0.9% NaCl spray. For Group 2, LO was administered as a nasal spray of aromatic LO (LS) once daily. The control group was treated with a single exposure to a nasal spray (0.9% NaCl) daily. The dose of one spray of LO or 0.9% NaCl ranged from 72–125 µL. The body weight of the animals was measured every 3 days from postnatal day (PND) 18 to the day of vaginal opening (VO).\n\nAnalysis of VO:\nAll study groups were evaluated for VO time as an indicator of pubertal initiation at a fixed time (09:00 hour) daily. The day of VO was recorded, and VO timing was compared between the three study groups.\n\nEuthanasia and hormone assays:\nAfter VO was observed in each rat, we measured serum LH, follicle-stimulating hormone (FSH), and estradiol levels to compare hormone concentrations between study groups. The endpoint of the experiment was defined as VO occurring in the last rat. For this process, truncal blood was collected into ice-cold ethylenediamine tetraacetic acid-containing tubes after decapitation, after which the tubes were centrifuged, and plasma samples were collected and stored at −20°C until analysis. The plasma levels of LH, FSH, and estradiol of each rat were measured using enzyme-linked immunosorbent assay kits (cat. No. MBS764675, MBS2502190, and MBS263850, respectively; MyBioSource, Inc., San Diego, CA, USA) according to manufacturer’s instructions.\n\nMeasurement of organ weight:\nAfter euthanasia, we measured the weight of the ovaries, spleen, kidneys, and liver. The organ weight was then modified by body weight and presented as tissue weight per 150 g body weight.\n\nStatistical analysis:\nData were presented as the mean ± standard deviation, and statistical analyses were performed using SPSS software (v.26.0; SPSS, Inc., Chicago, IL, USA). Statistical significance was determined by Kruskal-Wallis test and one-way analysis of variance for multiple-group comparisons and Mann-Whitney U test for comparisons between two groups. Statistical significance was defined at P < 0.05.\n\nEthics statement:\nThe procedures used and the care of animals were approved by the Institutional Animal Care and Use Committee in Southwest Medi-Chem Institute (approval No. SEMI-20-001).\n\nRESULTS:\n Effect of olfactory exposure to LO on VO and pubertal onset VO occurred earlier in the LE (33.8 ± 1.8 days) and LS (36.6 ± 1.5 days) groups than in the control group (38.4 ± 2.9 days) (Table 1), and VO in the LE group occurred significantly earlier than in the control group (P = 0.014) and LS group (P = 0.032), respectively. However, there was no significant difference between the LS and control groups with respect to VO timing (P = 0.151). Almost all rats in the LE group experienced VO at 33 days, and the control group mostly showed VO between 38 and 41 days (Fig. 1).\nLS = exposure to lavender oil as a nasal spray, LE = exposure to diffused lavender oil, VO = vaginal opening.\n*P < 0.05 vs. control; **P < 0.05 vs. LS group.\nPND = postnatal day, LE = exposure to diffused lavender oil, C = control, LS = exposure to lavender oil as a nasal spray.\nVO occurred earlier in the LE (33.8 ± 1.8 days) and LS (36.6 ± 1.5 days) groups than in the control group (38.4 ± 2.9 days) (Table 1), and VO in the LE group occurred significantly earlier than in the control group (P = 0.014) and LS group (P = 0.032), respectively. However, there was no significant difference between the LS and control groups with respect to VO timing (P = 0.151). Almost all rats in the LE group experienced VO at 33 days, and the control group mostly showed VO between 38 and 41 days (Fig. 1).\nLS = exposure to lavender oil as a nasal spray, LE = exposure to diffused lavender oil, VO = vaginal opening.\n*P < 0.05 vs. control; **P < 0.05 vs. LS group.\nPND = postnatal day, LE = exposure to diffused lavender oil, C = control, LS = exposure to lavender oil as a nasal spray.\n Measurement of gonadotropin hormone and estradiol levels LH levels were significantly higher in the LE (67.6 ± 3.0 mIU/mL) and LS (64.3 ± 7.4 mIU/mL) groups than in the control group (49.9 ± 2.7 mIU/mL; P < 0.001 for both) (Table 2). Additionally, FSH levels were significantly higher in the LE (50.9 ± 9.1 ng/mL) and LS (51.4 ± 7.1 ng/mL) groups than in the control group (35.2 ± 3.7 ng/mL; P = 0.009 and P = 0.011, respectively). Estradiol levels were elevated in both the LE (4.9 ± 1.4 ng/mL) and LS (5.3 ± 1.4 ng/mL) groups relative to the control group (3.9 ± 0.8 ng/mL), although the differences were not significant (P = 0.326 and P = 0.547, respectively).\nData are presented as the mean ± SD (n = 5).\nLH = luteinizing hormone, FSH = follicle-stimulating hormone, LS = exposure to lavender oil as a nasal spray, LE = exposure to diffused lavender oil.\naNot significant vs. control.\n*P < 0.05 vs. control; **P < 0.001 vs. control.\nLH levels were significantly higher in the LE (67.6 ± 3.0 mIU/mL) and LS (64.3 ± 7.4 mIU/mL) groups than in the control group (49.9 ± 2.7 mIU/mL; P < 0.001 for both) (Table 2). Additionally, FSH levels were significantly higher in the LE (50.9 ± 9.1 ng/mL) and LS (51.4 ± 7.1 ng/mL) groups than in the control group (35.2 ± 3.7 ng/mL; P = 0.009 and P = 0.011, respectively). Estradiol levels were elevated in both the LE (4.9 ± 1.4 ng/mL) and LS (5.3 ± 1.4 ng/mL) groups relative to the control group (3.9 ± 0.8 ng/mL), although the differences were not significant (P = 0.326 and P = 0.547, respectively).\nData are presented as the mean ± SD (n = 5).\nLH = luteinizing hormone, FSH = follicle-stimulating hormone, LS = exposure to lavender oil as a nasal spray, LE = exposure to diffused lavender oil.\naNot significant vs. control.\n*P < 0.05 vs. control; **P < 0.001 vs. control.\n Measurement of body and organ weights Measurement of the body weight of rats in the control, LE, and LS groups every 3 days from PND 18 until VO revealed no significant differences among three groups (Fig. 2 and Table 3). The weights of the ovaries, liver, and spleen after VO showed no significant differences among three groups; however, the weight of the kidneys per 150 g body weight increased significantly after VO in the LE group (1.926 ± 0.154 g) compared with that in the control (1.664 ± 0.077 g; P = 0.009) and LS (1.694 ± 0.154 g; P = 0.017) groups (Table 4).\nPND = postnatal day, LS = exposure to lavender oil as a nasal spray, LE = exposure to diffused lavender oil.\nData are presented as the mean ± SD (n = 5).\nPND = postnatal day, LS = exposure to lavender oil as a nasal spray, LE = exposure to diffused lavender oil.\naNot significant.\nData are presented as the mean ± SD (n = 5).\nLS = exposure to lavender oil as a nasal spray, LE = exposure to diffused lavender oil.\naTissue weight (g) per body weight (150 g); bNot significant.\n*P < 0.01 vs. control; **P < 0.05 vs. LS group.\nMeasurement of the body weight of rats in the control, LE, and LS groups every 3 days from PND 18 until VO revealed no significant differences among three groups (Fig. 2 and Table 3). The weights of the ovaries, liver, and spleen after VO showed no significant differences among three groups; however, the weight of the kidneys per 150 g body weight increased significantly after VO in the LE group (1.926 ± 0.154 g) compared with that in the control (1.664 ± 0.077 g; P = 0.009) and LS (1.694 ± 0.154 g; P = 0.017) groups (Table 4).\nPND = postnatal day, LS = exposure to lavender oil as a nasal spray, LE = exposure to diffused lavender oil.\nData are presented as the mean ± SD (n = 5).\nPND = postnatal day, LS = exposure to lavender oil as a nasal spray, LE = exposure to diffused lavender oil.\naNot significant.\nData are presented as the mean ± SD (n = 5).\nLS = exposure to lavender oil as a nasal spray, LE = exposure to diffused lavender oil.\naTissue weight (g) per body weight (150 g); bNot significant.\n*P < 0.01 vs. control; **P < 0.05 vs. LS group.\n\nEffect of olfactory exposure to LO on VO and pubertal onset:\nVO occurred earlier in the LE (33.8 ± 1.8 days) and LS (36.6 ± 1.5 days) groups than in the control group (38.4 ± 2.9 days) (Table 1), and VO in the LE group occurred significantly earlier than in the control group (P = 0.014) and LS group (P = 0.032), respectively. However, there was no significant difference between the LS and control groups with respect to VO timing (P = 0.151). Almost all rats in the LE group experienced VO at 33 days, and the control group mostly showed VO between 38 and 41 days (Fig. 1).\nLS = exposure to lavender oil as a nasal spray, LE = exposure to diffused lavender oil, VO = vaginal opening.\n*P < 0.05 vs. control; **P < 0.05 vs. LS group.\nPND = postnatal day, LE = exposure to diffused lavender oil, C = control, LS = exposure to lavender oil as a nasal spray.\n\nMeasurement of gonadotropin hormone and estradiol levels:\nLH levels were significantly higher in the LE (67.6 ± 3.0 mIU/mL) and LS (64.3 ± 7.4 mIU/mL) groups than in the control group (49.9 ± 2.7 mIU/mL; P < 0.001 for both) (Table 2). Additionally, FSH levels were significantly higher in the LE (50.9 ± 9.1 ng/mL) and LS (51.4 ± 7.1 ng/mL) groups than in the control group (35.2 ± 3.7 ng/mL; P = 0.009 and P = 0.011, respectively). Estradiol levels were elevated in both the LE (4.9 ± 1.4 ng/mL) and LS (5.3 ± 1.4 ng/mL) groups relative to the control group (3.9 ± 0.8 ng/mL), although the differences were not significant (P = 0.326 and P = 0.547, respectively).\nData are presented as the mean ± SD (n = 5).\nLH = luteinizing hormone, FSH = follicle-stimulating hormone, LS = exposure to lavender oil as a nasal spray, LE = exposure to diffused lavender oil.\naNot significant vs. control.\n*P < 0.05 vs. control; **P < 0.001 vs. control.\n\nMeasurement of body and organ weights:\nMeasurement of the body weight of rats in the control, LE, and LS groups every 3 days from PND 18 until VO revealed no significant differences among three groups (Fig. 2 and Table 3). The weights of the ovaries, liver, and spleen after VO showed no significant differences among three groups; however, the weight of the kidneys per 150 g body weight increased significantly after VO in the LE group (1.926 ± 0.154 g) compared with that in the control (1.664 ± 0.077 g; P = 0.009) and LS (1.694 ± 0.154 g; P = 0.017) groups (Table 4).\nPND = postnatal day, LS = exposure to lavender oil as a nasal spray, LE = exposure to diffused lavender oil.\nData are presented as the mean ± SD (n = 5).\nPND = postnatal day, LS = exposure to lavender oil as a nasal spray, LE = exposure to diffused lavender oil.\naNot significant.\nData are presented as the mean ± SD (n = 5).\nLS = exposure to lavender oil as a nasal spray, LE = exposure to diffused lavender oil.\naTissue weight (g) per body weight (150 g); bNot significant.\n*P < 0.01 vs. control; **P < 0.05 vs. LS group.\n\nDISCUSSION:\nIn this study, we found that persistent exposure to LO is associated with the HPG axis activation and early pubertal onset. We observed early VO in rats persistently exposed to LO as compared with that in the control group.\nVO in the Sprague-Dawley rats occurs after the surge of gonadotropins ranges from PND 30.8 to 38.4, and it might be affected by the environment, nutrition, temperature, and light.192021 In a controlled environment, the mean VO of the control group in this study was 38.4 days, and VO occurred significantly earlier in the LE group (33.8 days).\nAdditionally, serum LH and FSH levels were significantly higher in the LE and LS groups than in the control group. The LH level has been considered as a golden marker of pubertal status, whereas the estradiol level showed a fluctuation during the day and could be low even in the pubertal period.22232425 Chronic and persistent exposure to estrogens could also affect gonadotropin activation. Chronic exposure to sex hormone in cases of peripheral precocious puberty, including congenital adrenal hyperplasia or McCune-Albright syndrome, could lead to the secondary CPP, and these patients require to be treated with the GnRH agonist.2627\nPrevious studies showed that estrogenic effect of LO affected the premature thelarche in girls and gynecomastia in boys.11121314 To the best of our knowledge, no previous studies have reported an association between pubertal onset and persistent olfactory exposure to LO in an animal model. Nasal inhalation is an important source of iatrogenic sex-hormone exposure, and olfactory exposure to LO may result in LO delivery to the central nervous system and bloodstream to induce an iatrogenic effect of estrogen.\nSeveral studies have shown that LO is effective at reducing menopausal symptoms and supporting healthy sleep.2829 Additionally, previous studies reported that LO does not increase estrogen levels in adults30; in contrast, in the present study, we could not conclude that LO exposure did not affect estrogen activity. Although we observed no differences in estradiol levels between the LE and control groups, the LE group showed early pubertal onset and significantly increased LH and FSH levels. Furthermore, the LS group showed no significant differences in VO compared with the control group. Therefore, the amount and persistence of LO exposure may determine pubertal onset.\nA previous animal study reported that percutaneous injection of LO when performing uterotrophic assays on immature rats results in significantly reduced body weight gain after 3 days compared with that in the control group and a group administered 17α-ethinyl estradiol.31 However, the authors only assessed weight gain and organ-weight-to-terminal-body-weight to evaluate the presence of an estrogenic effect, and did not compare hormone levels or VO timing. The different administration modalities may have been responsible for the reported differences in body weight gain between the LO injection group and the group undergoing oral estrogen administration. In the present study, we found no differences in organ weight, including the ovaries, and body weight gain between groups, despite the apparently different VO and gonadotropin levels. Interestingly, the kidney tissue weight was significantly increased only in the LE group, supporting an association between physiological LO-specific effects and endocrine effects. A recent study showed that LO exposure affects renal restoration in a dose-dependent manner by decreasing antioxidant signals and inflammatory cytokine levels, as well as by inhibiting apoptosis.32 In the present study, we measured neither renal function nor nephron numbers and used only renal tissue weight as an indicator of the positive effect of LO exposure. Therefore, increased renal tissue weight may indicate a renal burden related to LO excretion.\nFour studies have reported a total of 11 pediatric patients (seven males and four females) showing premature thelarche in females (age range: 14 months to 7 years and 9 months) and gynecomastia in males (age range: 4 years and 5 months to 10 years and 1 month) after using an LO-containing product.11121314 Ramsey et al.13 reported that patients showed symptomatic improvement after discontinuation of LO exposure accompanied by no abnormal laboratory findings. These reports suggest the estrogenic effect of topical preparation of LO. Additionally, in vitro studies showed that LO (or the LO components linalool and linalyl acetate) exerts an estrogenic effect by stimulating α-transcription of the estrogen receptor.1113 The peripheral hormones or signals transmitting to GnRH neurons may lead to GnRH secretion and stimulation of pituitary gonadotropins and gonadal sex steroids.8 Given our observation of early activation of the HPG axis after olfactory exposure to LO, further investigation of the effect of LO on gene activation related to GnRH synthesis or secretion may explain the associations between early activation of the HPG axis and LO exposure.\nWe focused on the effects of LO exposure through olfactory stimulation and not via oral ingestion or topical application. One limitation of this study is its small sample size; thus, further studies are required to validate the findings. We observed significant elevation of gonadotropin levels not only in the LE group but also in the LS group, suggesting that repetitive olfactory exposure affected the manifestation of LO-specific physiological effects. Several studies reported that essential LO affects anxiety when administered via the oral or nasal routes.333435 However, LO exposure via oral ingestion in food is not as common as skin absorption of various LO-containing cosmetic products or LO inhalation. Nevertheless, inhalation of environmental LO is difficult to quantify in humans, and epidemiological surveillance data for the sole effect of LO inhalation in children undergoing precocious puberty are unavailable. Furthermore, the frequency and duration of exposure to LO inhalation are highly variable, and the effect of LO following skin exposure on children remains inconclusive because of the variable amounts of LO to which the skin is exposed, and difficulties associated with follow-up to assess long-term effects.36\nThis study showed the effect of olfactory stimulation by LO on the early onset of puberty. These results suggest that avoidance of LO exposure to minimize unnecessary iatrogenic estrogen effects from fragrances, diffusers, and perfumes can prevent early stimulation of the HPG axis, particularly in younger children. Further in vitro evaluation of LO-related effects on central kisspeptin signaling may reveal the mechanisms associated with early activation of the HPG axis by persistent olfactory exposure to LO.\n\n"},"whole_article_abstract":{"kind":"string","value":"Background:\nCentral precocious puberty (CPP) is caused by early activation of the hypothalamic-pituitary-gonadal axis but its major cause remains unclear. Studies have indicated an association between chronic environmental exposure to endocrine-disrupting chemicals and pubertal onset. Essential oil is widely used in homes worldwide for relief of respiratory symptoms, stress, and/or sleep disturbance.\n\nMethods:\nTo evaluate this association, we compared the hormone levels and timing of vaginal opening (VO) in female rats exposed to lavender oil (LO) through different routes (study groups: control, LO nasal spray [LS], and indoor exposure to LO [LE]) during the prepubertal period. The body weights of the animals were also compared every 3 days until the day of VO, at which time gonadotropin levels and internal organ weights were assessed.\n\nResults:\nThe LS group showed early VO at 33.8 ± 1.8 days compared with the control (38.4 ± 2.9 days) and LE (36.6 ± 1.5 days) groups. Additionally, luteinizing hormone levels were significantly higher in the LE and LS groups than those in the control group. Body weights did not differ significantly among the groups.\n\nConclusions:\nInhalation exposure to an exogenic simulant during the prepubertal period might trigger early pubertal onset in female rats. Further evaluation of exposure to other endocrine-disrupting chemicals capable of inducing CPP through the skin, orally, and/or nasally is warranted."},"background_conclusion_text":{"kind":"null"},"background_conclusion_abstract":{"kind":"null"},"whole_article_text_length":{"kind":"number","value":5610,"string":"5,610"},"whole_article_abstract_length":{"kind":"number","value":273,"string":"273"},"other_sections_lengths":{"kind":"list like","value":[247,42,142,38,74,33,193,232,258],"string":"[\n 247,\n 42,\n 142,\n 38,\n 74,\n 33,\n 193,\n 232,\n 258\n]"},"num_sections":{"kind":"number","value":13,"string":"13"},"most_frequent_words":{"kind":"list like","value":["exposure","group","lo","control","vo","ls","le","groups","weight","oil"],"string":"[\n \"exposure\",\n \"group\",\n \"lo\",\n \"control\",\n \"vo\",\n \"ls\",\n \"le\",\n \"groups\",\n \"weight\",\n \"oil\"\n]"},"keybert_topics":{"kind":"list like","value":["puberty results suggest","early onset puberty","pituitary gonadal hpg","peripheral precocious puberty","puberty cpp describes"],"string":"[\n \"puberty results suggest\",\n \"early onset puberty\",\n \"pituitary gonadal hpg\",\n \"peripheral precocious puberty\",\n \"puberty cpp describes\"\n]"},"annotated_base_background_abstract_prompt":{"kind":"string","value":""},"annotated_base_methods_abstract_prompt":{"kind":"string","value":""},"annotated_base_results_abstract_prompt":{"kind":"string","value":""},"annotated_base_conclusions_abstract_prompt":{"kind":"null"},"annotated_base_whole_article_abstract_prompt":{"kind":"string","value":""},"annotated_base_background_conclusion_abstract_prompt":{"kind":"null"},"annotated_keywords_background_abstract_prompt":{"kind":"string","value":"[CONTENT] Precocious Puberty | Endocrine Disruptors | Lavender Oil | Inhalation Exposure | Vaginal Opening [SUMMARY]"},"annotated_keywords_methods_abstract_prompt":{"kind":"string","value":"[CONTENT] Precocious Puberty | Endocrine Disruptors | Lavender Oil | Inhalation Exposure | Vaginal Opening [SUMMARY]"},"annotated_keywords_results_abstract_prompt":{"kind":"string","value":"[CONTENT] Precocious Puberty | Endocrine Disruptors | Lavender Oil | Inhalation Exposure | Vaginal Opening [SUMMARY]"},"annotated_keywords_conclusions_abstract_prompt":{"kind":"null"},"annotated_keywords_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] Precocious Puberty | Endocrine Disruptors | Lavender Oil | Inhalation Exposure | Vaginal Opening [SUMMARY]"},"annotated_keywords_background_conclusion_abstract_prompt":{"kind":"null"},"annotated_mesh_background_abstract_prompt":{"kind":"string","value":"[CONTENT] Administration, Inhalation | Animals | Female | Lavandula | Oils, Volatile | Plant Oils | Puberty, Precocious | Random Allocation | Rats [SUMMARY]"},"annotated_mesh_methods_abstract_prompt":{"kind":"string","value":"[CONTENT] Administration, Inhalation | Animals | Female | Lavandula | Oils, Volatile | Plant Oils | Puberty, Precocious | Random Allocation | Rats [SUMMARY]"},"annotated_mesh_results_abstract_prompt":{"kind":"string","value":"[CONTENT] Administration, Inhalation | Animals | Female | Lavandula | Oils, Volatile | Plant Oils | Puberty, Precocious | Random Allocation | Rats [SUMMARY]"},"annotated_mesh_conclusions_abstract_prompt":{"kind":"null"},"annotated_mesh_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] Administration, Inhalation | Animals | Female | Lavandula | Oils, Volatile | Plant Oils | Puberty, Precocious | Random Allocation | Rats [SUMMARY]"},"annotated_mesh_background_conclusion_abstract_prompt":{"kind":"null"},"annotated_keybert_background_abstract_prompt":{"kind":"string","value":"[CONTENT] puberty results suggest | early onset puberty | pituitary gonadal hpg | peripheral precocious puberty | puberty cpp describes [SUMMARY]"},"annotated_keybert_methods_abstract_prompt":{"kind":"string","value":"[CONTENT] puberty results suggest | early onset puberty | pituitary gonadal hpg | peripheral precocious puberty | puberty cpp describes [SUMMARY]"},"annotated_keybert_results_abstract_prompt":{"kind":"string","value":"[CONTENT] puberty results suggest | early onset puberty | pituitary gonadal hpg | peripheral precocious puberty | puberty cpp describes [SUMMARY]"},"annotated_keybert_conclusions_abstract_prompt":{"kind":"null"},"annotated_keybert_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] puberty results suggest | early onset puberty | pituitary gonadal hpg | peripheral precocious puberty | puberty cpp describes [SUMMARY]"},"annotated_keybert_background_conclusion_abstract_prompt":{"kind":"null"},"annotated_most_frequent_background_abstract_prompt":{"kind":"string","value":"[CONTENT] exposure | group | lo | control | vo | ls | le | groups | weight | oil [SUMMARY]"},"annotated_most_frequent_methods_abstract_prompt":{"kind":"string","value":"[CONTENT] exposure | group | lo | control | vo | ls | le | groups | weight | oil [SUMMARY]"},"annotated_most_frequent_results_abstract_prompt":{"kind":"string","value":"[CONTENT] exposure | group | lo | control | vo | ls | le | groups | weight | oil [SUMMARY]"},"annotated_most_frequent_conclusions_abstract_prompt":{"kind":"null"},"annotated_most_frequent_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] exposure | group | lo | control | vo | ls | le | groups | weight | oil [SUMMARY]"},"annotated_most_frequent_background_conclusion_abstract_prompt":{"kind":"null"},"annotated_tf_idf_background_abstract_prompt":{"kind":"string","value":"[CONTENT] cpp | puberty | essential oils | final adult | final adult height | adult height | adult | chemicals | suggested | treatment [SUMMARY]"},"annotated_tf_idf_methods_abstract_prompt":{"kind":"string","value":"[CONTENT] lo | daily | weight | vo | statistical | group | animals | nacl | rat | groups [SUMMARY]"},"annotated_tf_idf_results_abstract_prompt":{"kind":"string","value":"[CONTENT] ls | ml | lavender | oil | lavender oil | le | control | exposure | group | ng [SUMMARY]"},"annotated_tf_idf_conclusions_abstract_prompt":{"kind":"null"},"annotated_tf_idf_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] weight | group | lo | vo | exposure | ls | control | le | groups | oil [SUMMARY]"},"annotated_tf_idf_background_conclusion_abstract_prompt":{"kind":"null"},"annotated_entity_plan_background_abstract_prompt":{"kind":"string","value":"[CONTENT] CPP ||| ||| [SUMMARY]"},"annotated_entity_plan_methods_abstract_prompt":{"kind":"string","value":"[CONTENT] LO | LO | LO ||| ||| every 3 days | the day [SUMMARY]"},"annotated_entity_plan_results_abstract_prompt":{"kind":"string","value":"[CONTENT] LS | 33.8 | 1.8 days | 38.4 | 2.9 days | LE | 36.6 ± | 1.5 days ||| LE ||| [SUMMARY]"},"annotated_entity_plan_conclusions_abstract_prompt":{"kind":"null"},"annotated_entity_plan_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] CPP ||| ||| ||| LO | LO | LO ||| ||| every 3 days | the day ||| ||| LS | 33.8 | 1.8 days | 38.4 | 2.9 days | LE | 36.6 ± | 1.5 days ||| LE ||| ||| ||| CPP [SUMMARY]"},"annotated_entity_plan_background_conclusion_abstract_prompt":{"kind":"null"}}},{"rowIdx":281,"cells":{"title":{"kind":"string","value":"Quality of life and patient-perceived symptoms in patients with psoriasis undergoing proactive or reactive management with the fixed-dose combination Cal/BD foam: A post-hoc analysis of PSO-LONG."},"pmid":{"kind":"string","value":"34543474"},"background_abstract":{"kind":"string","value":"Psoriasis has important physical and psychosocial effects that extend beyond the skin. Understanding the impact of treatment on health-related quality of life (HRQoL) and patient-perceived symptom severity in psoriasis is key to clinical decision-making."},"background_abstract_label":{"kind":"string","value":"BACKGROUND"},"methods_abstract":{"kind":"string","value":"Five hundred and twenty-one patients from the Phase 3, randomized, double-blind PSO-LONG trial were included. An initial 4-week, open-label phase of fixed-dose combination Cal/BD foam once daily (QD) was followed by a 52-week maintenance phase, at the start of which patients were randomized to a proactive management arm (Cal/BD foam twice weekly) or reactive management arm (vehicle foam twice weekly). Patient-perceived symptom severity and HRQoL were assessed using the Psoriasis Symptom Inventory (PSI), the Dermatology Life Quality Index (DLQI) and the EuroQol-5D for psoriasis (EQ-5D-5L-PSO)."},"methods_abstract_label":{"kind":"string","value":"METHODS"},"results_abstract":{"kind":"string","value":"Statistically and clinically significant improvements were observed across all PRO measures. The mean difference (standard deviation) from baseline to Week 4 was -8.97 (6.18) for PSI, -6.02 (5.46) for DLQI and 0.11 (0.15) for EQ-5D-5L-PSO scores. During maintenance, patients receiving reactive management had significantly higher DLQI (15% [p = 0.007]) and PSI (15% [p = 0.0128]) and a numerically lower EQ-5D-5L-PSO mean area under the curve score than patients receiving proactive management (1% [p = 0.0842])."},"results_abstract_label":{"kind":"string","value":"RESULTS"},"conclusions_abstract":{"kind":"string","value":"Cal/BD foam significantly improved DLQI, EQ-5D-5L-PSO and PSI scores during the open-label and maintenance phases. Patients assigned to proactive management had significantly better DLQI and PSI scores and numerically better EQ-5D-5L-PSO versus reactive management. Additionally, baseline flare was associated with worse PROs than the start of a relapse, and patients starting a relapse also had worse PROs than patients in remission."},"conclusions_abstract_label":{"kind":"string","value":"CONCLUSIONS"},"mesh_descriptor_names":{"kind":"list like","value":["Betamethasone","Dermatologic Agents","Drug Combinations","Humans","Psoriasis","Quality of Life","Treatment Outcome"],"string":"[\n \"Betamethasone\",\n \"Dermatologic Agents\",\n \"Drug Combinations\",\n \"Humans\",\n \"Psoriasis\",\n \"Quality of Life\",\n \"Treatment Outcome\"\n]"},"pmcid":{"kind":"string","value":"9298373"},"background_title":{"kind":"string","value":"Introduction"},"background_text":{"kind":"string","value":"Psoriasis is a chronic, immune‐mediated disease, with primarily skin and joint symptoms.\n1\n, \n2\n The morphology, localization and severity of lesions in psoriasis can be highly variable.\n3\n Various genetic, environmental and immunological factors have been proposed as potential contributors to the pathophysiology of this disease.\n3\n Individuals with psoriasis have also been shown to have an elevated risk of cardiovascular disease, metabolic syndrome and diabetes, compared with the general population.\n4\n\n\nGlobally, the prevalence of psoriasis has been reported to vary between 0.09% and 11.43%, which corresponds to approximately 125 million affected people.\n5\n, \n6\n Despite ongoing efforts to improve the management of this condition, the burden of disease has been increasing steadily over the past decades.\n7\n Collectively, this renders psoriasis a significant health issue worldwide.\nPsoriasis can significantly influence a person's quality of life (QoL) and cause social stigmatization, physical disability and emotional distress.\n8\n Moreover, the impact of psoriasis on QoL is similar to that in patients with other chronic conditions such as cardiovascular disease, diabetes, end‐stage renal disease, liver disease and cancer.\n9\n Skin symptoms of psoriasis including scaling, itch and pain can significantly affect physical well‐being and limit daily activities, social contacts and (skin‐exposing) activities, and work.\n10\n Psoriasis has a greater psychological burden than any other dermatological condition and has been associated with impaired emotional functioning, a negative body and self‐image, depression, anxiety and suicide risk more than any other skin condition.\n10\n, \n11\n Other factors may also be attributable to the low QoL in psoriasis patients, including the chronic and recurring nature of the disease, lack of control and fear of unexpected breakout, and feeling of hopelessness in terms of cure.\n12\n Furthermore, duration and severity of psoriasis significantly decrease the QoL.\n13\n, \n14\n\n\nFor mild to moderate psoriasis, current treatment strategies commonly involve topical agents. For moderate to severe psoriasis, topical agents are often added to phototherapy and systemic or biologic agents.\n15\n, \n16\n, \n17\n Current management strategies mainly aim to clear active disease sites and prolong symptom‐free periods.\n18\n However, long‐term disease control is challenging, and patient satisfaction with available therapies remains low.\n19\n Moreover, psoriasis is often undertreated such that patients do not achieve substantial skin clearance, symptom relief or improvements in QoL.\n20\n, \n21\n\n\nAlthough skin clearance may be achievable for most patients in the short term, long‐term strategies are important to optimize adherence and long‐term outcomes including health‐related quality of life (HRQoL).\n22\n, \n23\n However, the majority of clinical data and guidance available for topical management of psoriasis is focused on short‐term use, with limited data on long‐term use.\n23\n\n\nTherefore, understanding the impact of treatment on HRQoL and patient‐perceived symptom severity in psoriasis is key to informing clinical decision‐making, improving clinical outcomes and quality of care. Patient‐reported outcomes measures (PROs) are invaluable tools to evaluate these effects and support clinical management.\n22\n, \n24\n\n\nThis post hoc analysis of the PSO‐LONG trial captured the effect on HRQoL and patient‐perceived symptom severity of treating psoriasis with fixed‐dose calcipotriene 50 µg/g and betamethasone dipropionate 0.5 mg/g (Cal/BD) foam topical treatment through a 52‐week period. Three PRO measures were used: EuroQoL 5‐Dimensional Questionnaire for Psoriasis (EQ‐5D‐5L‐PSO), the Dermatology Life Quality Index (DLQI) and the Psoriasis Symptom Inventory (PSI). The analysis aimed to evaluate the value of Cal/BD foam for flare treatment and long‐term management (proactive vs reactive) on PROs as well as compare results at baseline flare, start of a relapse and during remission."},"methods_title":{"kind":"null"},"methods_text":{"kind":"null"},"results_title":{"kind":"string","value":"Results"},"results_text":{"kind":"string","value":"Patient demographics A total of 521 patients were included in the full analysis set used in this post hoc analysis. Patients were predominantly male (67.4%) and white (90.2%). The mean age was approximately 52.3 years. The majority of patients had moderate baseline PGA scores (85.2%). Patient characteristics are summarized in Table 1.\nDemographic and baseline characteristics of randomized patients (full analysis set; N = 521)\nDemographic and baseline characteristics\n(maintenance full analysis set; N = 521)\nA total of 521 patients were included in the full analysis set used in this post hoc analysis. Patients were predominantly male (67.4%) and white (90.2%). The mean age was approximately 52.3 years. The majority of patients had moderate baseline PGA scores (85.2%). Patient characteristics are summarized in Table 1.\nDemographic and baseline characteristics of randomized patients (full analysis set; N = 521)\nDemographic and baseline characteristics\n(maintenance full analysis set; N = 521)\nOpen‐label phase Initial flare treatment with Cal/BD foam QD during the open‐label phase led to statistically and clinically significant improvements across all PRO measures (Table 2). The mean difference from baseline to Week 4 was −8.97 (standard deviation [SD] = 6.18; P < 0.0001) for PSI scores, −6.02 (SD = 5.46; P < 0.0001) for DLQI scores and 0.11 (SD = 0.15; P < 0.0001) for EQ‐5D‐5L‐PSO scores.\nChanges in PSI, DLQI and EQ‐5D‐5L‐PSO scores in flare treatment from baseline to Week 4 (full analysis set; N = 521)\nDifference calculated from participants with both baseline and Week 4 scores.\nInitial flare treatment with Cal/BD foam QD during the open‐label phase led to statistically and clinically significant improvements across all PRO measures (Table 2). The mean difference from baseline to Week 4 was −8.97 (standard deviation [SD] = 6.18; P < 0.0001) for PSI scores, −6.02 (SD = 5.46; P < 0.0001) for DLQI scores and 0.11 (SD = 0.15; P < 0.0001) for EQ‐5D‐5L‐PSO scores.\nChanges in PSI, DLQI and EQ‐5D‐5L‐PSO scores in flare treatment from baseline to Week 4 (full analysis set; N = 521)\nDifference calculated from participants with both baseline and Week 4 scores.\nMaintenance phase The PRO improvements were maintained over the next 52 weeks for both proactive and reactive management arms, across the three PRO assessment tools. Patients receiving proactive management showed significantly greater improvements in patient‐perceived symptom severity than patients receiving reactive management: the mean PSI AUC score was 15% higher for reactive management (5.74) than for proactive management (4.99) during the maintenance phase (difference −0.75; P = 0.0128) (Table 3). It is worth noting that the levels of participant engagement with the PSI questionnaire were low in the last 2 weeks of the maintenance phase. The analysis of the PSI total score was therefore based on the first 28 weeks of the maintenance phase.\nMean PSI, DLQI and EQ‐5D‐5L‐PSO AUC scores in proactive and reactive management arms and differences during maintenance phase (full analysis set; N = 521)\nProactive management also corresponded with significantly greater improvements in DLQI AUC scores; the mean DLQI score was 15% higher for reactive management (3.40) than proactive management (2.95) (difference −0.45; P = 0.007). Although the difference between proactive and reactive management did not result in significantly greater improvement in EQ‐5D‐5L‐PSO scores, the numerical difference favoured the proactive management arm; the mean EQ‐5D‐5L‐PSO AUC score was 1% higher for proactive management (0.89) than for reactive management (0.88) (difference 0.01, P = 0.0842). The mean scores in proactive and reactive management arms for PSI, DLQI and EQ‐5D‐5L‐PSO across each visit are shown in Fig. 1.\nMean scores in proactive and reactive management arms for (a) PSI, (b) DLQI, and (c) EQ‐5D across study visits (Full Analysis Set; N = 521).\nAcross both treatment arms, patients had improvements in symptoms and HRQoL during remission compared with the baseline flare and the start of a relapse (Table 4). Additionally, the mean change (95% confidence interval [CI]; p‐value) between the start of a relapse and remission was −2.28 (95% CI: −2.64 to −1.92; <0.0001) for PSI scores, −1.32 (95% CI: −1.60 to −1.04; <0.0001) for DLQI and 0.03 (95% CI: 0.02 to 0.04; <0.0001) for EQ‐5D‐5L‐PSO (Fig. 2).\nPSI, DLQI and EQ‐5D‐5L‐PSO scores for baseline, remission and start of relapse (full analysis set; N = 521)\nDistribution of AUC scores in proactive and reactive management arms for (a) PSI, (b) DLQI, and (c) EQ‐5D (Full Analysis Set; N = 521).\nThe PRO improvements were maintained over the next 52 weeks for both proactive and reactive management arms, across the three PRO assessment tools. Patients receiving proactive management showed significantly greater improvements in patient‐perceived symptom severity than patients receiving reactive management: the mean PSI AUC score was 15% higher for reactive management (5.74) than for proactive management (4.99) during the maintenance phase (difference −0.75; P = 0.0128) (Table 3). It is worth noting that the levels of participant engagement with the PSI questionnaire were low in the last 2 weeks of the maintenance phase. The analysis of the PSI total score was therefore based on the first 28 weeks of the maintenance phase.\nMean PSI, DLQI and EQ‐5D‐5L‐PSO AUC scores in proactive and reactive management arms and differences during maintenance phase (full analysis set; N = 521)\nProactive management also corresponded with significantly greater improvements in DLQI AUC scores; the mean DLQI score was 15% higher for reactive management (3.40) than proactive management (2.95) (difference −0.45; P = 0.007). Although the difference between proactive and reactive management did not result in significantly greater improvement in EQ‐5D‐5L‐PSO scores, the numerical difference favoured the proactive management arm; the mean EQ‐5D‐5L‐PSO AUC score was 1% higher for proactive management (0.89) than for reactive management (0.88) (difference 0.01, P = 0.0842). The mean scores in proactive and reactive management arms for PSI, DLQI and EQ‐5D‐5L‐PSO across each visit are shown in Fig. 1.\nMean scores in proactive and reactive management arms for (a) PSI, (b) DLQI, and (c) EQ‐5D across study visits (Full Analysis Set; N = 521).\nAcross both treatment arms, patients had improvements in symptoms and HRQoL during remission compared with the baseline flare and the start of a relapse (Table 4). Additionally, the mean change (95% confidence interval [CI]; p‐value) between the start of a relapse and remission was −2.28 (95% CI: −2.64 to −1.92; <0.0001) for PSI scores, −1.32 (95% CI: −1.60 to −1.04; <0.0001) for DLQI and 0.03 (95% CI: 0.02 to 0.04; <0.0001) for EQ‐5D‐5L‐PSO (Fig. 2).\nPSI, DLQI and EQ‐5D‐5L‐PSO scores for baseline, remission and start of relapse (full analysis set; N = 521)\nDistribution of AUC scores in proactive and reactive management arms for (a) PSI, (b) DLQI, and (c) EQ‐5D (Full Analysis Set; N = 521)."},"conclusions_title":{"kind":"string","value":"Conclusion"},"conclusions_text":{"kind":"string","value":"In this analysis, Cal/BD foam QD flare treatment was associated with significant improvements in PROs from baseline that were maintained with a twice‐weekly application through 52 weeks. In patients undergoing proactive management, DLQI and PSI scores were significantly improved vs. patients receiving reactive management, potentially due to the reduction in the number of relapses and increased time in remission over a year of exposure.\nOverall, the results of this analysis add to the original PSO‐LONG findings, suggesting that proactive management with fixed‐dose Cal/BD foam could offer not only improved long‐term control of psoriasis but also improved HRQoL and patient‐perceived symptom severity over conventional reactive treatment with Cal/BD foam."},"other_sections_titles":{"kind":"list like","value":["Introduction","Study design","Patient‐reported outcomes","Statistical analyses","Patient demographics","Open‐label phase","Maintenance phase"],"string":"[\n \"Introduction\",\n \"Study design\",\n \"Patient‐reported outcomes\",\n \"Statistical analyses\",\n \"Patient demographics\",\n \"Open‐label phase\",\n \"Maintenance phase\"\n]"},"other_sections_texts":{"kind":"list like","value":["Psoriasis is a chronic, immune‐mediated disease, with primarily skin and joint symptoms.\n1\n, \n2\n The morphology, localization and severity of lesions in psoriasis can be highly variable.\n3\n Various genetic, environmental and immunological factors have been proposed as potential contributors to the pathophysiology of this disease.\n3\n Individuals with psoriasis have also been shown to have an elevated risk of cardiovascular disease, metabolic syndrome and diabetes, compared with the general population.\n4\n\n\nGlobally, the prevalence of psoriasis has been reported to vary between 0.09% and 11.43%, which corresponds to approximately 125 million affected people.\n5\n, \n6\n Despite ongoing efforts to improve the management of this condition, the burden of disease has been increasing steadily over the past decades.\n7\n Collectively, this renders psoriasis a significant health issue worldwide.\nPsoriasis can significantly influence a person's quality of life (QoL) and cause social stigmatization, physical disability and emotional distress.\n8\n Moreover, the impact of psoriasis on QoL is similar to that in patients with other chronic conditions such as cardiovascular disease, diabetes, end‐stage renal disease, liver disease and cancer.\n9\n Skin symptoms of psoriasis including scaling, itch and pain can significantly affect physical well‐being and limit daily activities, social contacts and (skin‐exposing) activities, and work.\n10\n Psoriasis has a greater psychological burden than any other dermatological condition and has been associated with impaired emotional functioning, a negative body and self‐image, depression, anxiety and suicide risk more than any other skin condition.\n10\n, \n11\n Other factors may also be attributable to the low QoL in psoriasis patients, including the chronic and recurring nature of the disease, lack of control and fear of unexpected breakout, and feeling of hopelessness in terms of cure.\n12\n Furthermore, duration and severity of psoriasis significantly decrease the QoL.\n13\n, \n14\n\n\nFor mild to moderate psoriasis, current treatment strategies commonly involve topical agents. For moderate to severe psoriasis, topical agents are often added to phototherapy and systemic or biologic agents.\n15\n, \n16\n, \n17\n Current management strategies mainly aim to clear active disease sites and prolong symptom‐free periods.\n18\n However, long‐term disease control is challenging, and patient satisfaction with available therapies remains low.\n19\n Moreover, psoriasis is often undertreated such that patients do not achieve substantial skin clearance, symptom relief or improvements in QoL.\n20\n, \n21\n\n\nAlthough skin clearance may be achievable for most patients in the short term, long‐term strategies are important to optimize adherence and long‐term outcomes including health‐related quality of life (HRQoL).\n22\n, \n23\n However, the majority of clinical data and guidance available for topical management of psoriasis is focused on short‐term use, with limited data on long‐term use.\n23\n\n\nTherefore, understanding the impact of treatment on HRQoL and patient‐perceived symptom severity in psoriasis is key to informing clinical decision‐making, improving clinical outcomes and quality of care. Patient‐reported outcomes measures (PROs) are invaluable tools to evaluate these effects and support clinical management.\n22\n, \n24\n\n\nThis post hoc analysis of the PSO‐LONG trial captured the effect on HRQoL and patient‐perceived symptom severity of treating psoriasis with fixed‐dose calcipotriene 50 µg/g and betamethasone dipropionate 0.5 mg/g (Cal/BD) foam topical treatment through a 52‐week period. Three PRO measures were used: EuroQoL 5‐Dimensional Questionnaire for Psoriasis (EQ‐5D‐5L‐PSO), the Dermatology Life Quality Index (DLQI) and the Psoriasis Symptom Inventory (PSI). The analysis aimed to evaluate the value of Cal/BD foam for flare treatment and long‐term management (proactive vs reactive) on PROs as well as compare results at baseline flare, start of a relapse and during remission.","This post hoc analysis included the full analysis set (n = 521) from the Phase 3, randomized, double‐blind PSO‐LONG trial (NCT02899962). The PSO‐LONG trial assessed long‐term efficacy and safety of proactive management with twice‐weekly fixed‐dose combination Cal/BD foam versus reactive management with twice‐weekly vehicle in patients with psoriasis vulgaris. Eligible patients were aged ≥18 years and had a clinical diagnosis of truncal and/or limb psoriasis for at least 6 months involving 2–30% of the body surface area (BSA), a modified Psoriasis Area and Severity Index (mPASI) score of ≥2 and a physician’s global assessment of disease severity (PGA) score of at least ‘mild’ (PGA ≥ 2).\nThe trial included an initial 4‐week, open‐label phase of fixed‐dose combination Cal/BD foam once daily, followed by a 52‐week maintenance phase for patients who achieved a PGA score of 0 or 1 and an at least 2‐grade improvement after the initial 4 weeks. At the start of the maintenance phase, patients were randomized to either proactive management (Cal/BD foam twice weekly) or reactive management (vehicle foam twice weekly). Relapses (defined as at least ‘mild’, PGA ≥ 2) were treated with fixed‐dose combination Cal/BD foam once daily (QD) for 4 weeks. Remission was defined as ‘clear’ or ‘almost clear’, PGA 0/1. The full details of the PSO‐LONG trial study design\n25\n and the efficacy and safety results\n26\n are published elsewhere.","Patients completed the EQ‐5D‐5L‐PSO, DLQI and PSI assessments. The EQ‐5D‐5L‐PSO questionnaire measures health status over five general dimensions (mobility, self‐care, usual activities, pain/discomfort and anxiety/depression) and two psoriasis‐related dimensions (skin irritation and self‐confidence).\n27\n Each dimension has five response levels, and a visual analogue scale allows patients to assess their health status with a score ranging from 0 (worst health) to 100 (best health). Responses to the questions can be converted into an index score ranging from 0.00 to 1.00, where a score of 0.00 indicates the worst health and 1.00 indicates full health.\nThe DLQI is a ten‐item questionnaire used to measure the impact of dermatological disorders on a patient’s HRQoL in the following six areas: symptoms and feelings; daily activities; leisure activities; work and school; personal relationships and treatment‐related distress.\n12\n, \n28\n Total scores range from 0–1 (‘no effect at all’) to 21–30 (‘extremely large effect’).\nThe PSI is an assessment of the severity of eight psoriasis‑related symptoms including itch, redness, scaling, burning, stinging, cracking, flaking and pain.\n29\n, \n30\n Scoring for each symptom ranges from ‘not at all severe’ (0) to ‘very severe’ (4), giving a total score range from 0 (no symptoms) to 32 (more severe symptoms).\nPatient‐reported outcomes were assessed across treatment arms at baseline (Visit 1), at the start of a relapse and during remission at all monthly scheduled and unscheduled visits. The DLQI and EQ‐5D‐5L‐PSO were completed at the trial site on an electronic slate/tablet. To ensure unbiased answers for questionnaires that were completed onsite, the PROs were collected prior to any other assessments. The PSI was completed on an eDiary device, provided for the participants for use at home. The participants were asked to complete the PSI daily during the open‐label treatment phase (starting at Visit 1), then weekly during the first 28 weeks of the maintenance phase (Weeks 4–28) and the last 2 weeks of the maintenance phase (Weeks 54–56 – only applicable for those who completed the PSI).","The statistical analyses were performed on the full analysis set (N = 521). Patient‐reported outcome results were collected within treatment arms at each visit. The integrated area under the curve (AUC) in proactive and reactive management arms during the maintenance phase was calculated for each PRO using the trapezoidal rule and subsequently normalized by the number of days in study for each patient. Additionally, PROs were assessed across treatment arms at baseline, at the start of a relapse and during remission at unscheduled and scheduled visits. Missing assessment of PRO scores in‐between non‐missing assessments in the maintenance phase was not imputed. The P‐value for treatment changes were assessed by using the Wilcoxon signed rank sum test. Differences across treatment arms were considered significant at P < 0.05.","A total of 521 patients were included in the full analysis set used in this post hoc analysis. Patients were predominantly male (67.4%) and white (90.2%). The mean age was approximately 52.3 years. The majority of patients had moderate baseline PGA scores (85.2%). Patient characteristics are summarized in Table 1.\nDemographic and baseline characteristics of randomized patients (full analysis set; N = 521)\nDemographic and baseline characteristics\n(maintenance full analysis set; N = 521)","Initial flare treatment with Cal/BD foam QD during the open‐label phase led to statistically and clinically significant improvements across all PRO measures (Table 2). The mean difference from baseline to Week 4 was −8.97 (standard deviation [SD] = 6.18; P < 0.0001) for PSI scores, −6.02 (SD = 5.46; P < 0.0001) for DLQI scores and 0.11 (SD = 0.15; P < 0.0001) for EQ‐5D‐5L‐PSO scores.\nChanges in PSI, DLQI and EQ‐5D‐5L‐PSO scores in flare treatment from baseline to Week 4 (full analysis set; N = 521)\nDifference calculated from participants with both baseline and Week 4 scores.","The PRO improvements were maintained over the next 52 weeks for both proactive and reactive management arms, across the three PRO assessment tools. Patients receiving proactive management showed significantly greater improvements in patient‐perceived symptom severity than patients receiving reactive management: the mean PSI AUC score was 15% higher for reactive management (5.74) than for proactive management (4.99) during the maintenance phase (difference −0.75; P = 0.0128) (Table 3). It is worth noting that the levels of participant engagement with the PSI questionnaire were low in the last 2 weeks of the maintenance phase. The analysis of the PSI total score was therefore based on the first 28 weeks of the maintenance phase.\nMean PSI, DLQI and EQ‐5D‐5L‐PSO AUC scores in proactive and reactive management arms and differences during maintenance phase (full analysis set; N = 521)\nProactive management also corresponded with significantly greater improvements in DLQI AUC scores; the mean DLQI score was 15% higher for reactive management (3.40) than proactive management (2.95) (difference −0.45; P = 0.007). Although the difference between proactive and reactive management did not result in significantly greater improvement in EQ‐5D‐5L‐PSO scores, the numerical difference favoured the proactive management arm; the mean EQ‐5D‐5L‐PSO AUC score was 1% higher for proactive management (0.89) than for reactive management (0.88) (difference 0.01, P = 0.0842). The mean scores in proactive and reactive management arms for PSI, DLQI and EQ‐5D‐5L‐PSO across each visit are shown in Fig. 1.\nMean scores in proactive and reactive management arms for (a) PSI, (b) DLQI, and (c) EQ‐5D across study visits (Full Analysis Set; N = 521).\nAcross both treatment arms, patients had improvements in symptoms and HRQoL during remission compared with the baseline flare and the start of a relapse (Table 4). Additionally, the mean change (95% confidence interval [CI]; p‐value) between the start of a relapse and remission was −2.28 (95% CI: −2.64 to −1.92; <0.0001) for PSI scores, −1.32 (95% CI: −1.60 to −1.04; <0.0001) for DLQI and 0.03 (95% CI: 0.02 to 0.04; <0.0001) for EQ‐5D‐5L‐PSO (Fig. 2).\nPSI, DLQI and EQ‐5D‐5L‐PSO scores for baseline, remission and start of relapse (full analysis set; N = 521)\nDistribution of AUC scores in proactive and reactive management arms for (a) PSI, (b) DLQI, and (c) EQ‐5D (Full Analysis Set; N = 521)."],"string":"[\n \"Psoriasis is a chronic, immune‐mediated disease, with primarily skin and joint symptoms.\\n1\\n, \\n2\\n The morphology, localization and severity of lesions in psoriasis can be highly variable.\\n3\\n Various genetic, environmental and immunological factors have been proposed as potential contributors to the pathophysiology of this disease.\\n3\\n Individuals with psoriasis have also been shown to have an elevated risk of cardiovascular disease, metabolic syndrome and diabetes, compared with the general population.\\n4\\n\\n\\nGlobally, the prevalence of psoriasis has been reported to vary between 0.09% and 11.43%, which corresponds to approximately 125 million affected people.\\n5\\n, \\n6\\n Despite ongoing efforts to improve the management of this condition, the burden of disease has been increasing steadily over the past decades.\\n7\\n Collectively, this renders psoriasis a significant health issue worldwide.\\nPsoriasis can significantly influence a person's quality of life (QoL) and cause social stigmatization, physical disability and emotional distress.\\n8\\n Moreover, the impact of psoriasis on QoL is similar to that in patients with other chronic conditions such as cardiovascular disease, diabetes, end‐stage renal disease, liver disease and cancer.\\n9\\n Skin symptoms of psoriasis including scaling, itch and pain can significantly affect physical well‐being and limit daily activities, social contacts and (skin‐exposing) activities, and work.\\n10\\n Psoriasis has a greater psychological burden than any other dermatological condition and has been associated with impaired emotional functioning, a negative body and self‐image, depression, anxiety and suicide risk more than any other skin condition.\\n10\\n, \\n11\\n Other factors may also be attributable to the low QoL in psoriasis patients, including the chronic and recurring nature of the disease, lack of control and fear of unexpected breakout, and feeling of hopelessness in terms of cure.\\n12\\n Furthermore, duration and severity of psoriasis significantly decrease the QoL.\\n13\\n, \\n14\\n\\n\\nFor mild to moderate psoriasis, current treatment strategies commonly involve topical agents. For moderate to severe psoriasis, topical agents are often added to phototherapy and systemic or biologic agents.\\n15\\n, \\n16\\n, \\n17\\n Current management strategies mainly aim to clear active disease sites and prolong symptom‐free periods.\\n18\\n However, long‐term disease control is challenging, and patient satisfaction with available therapies remains low.\\n19\\n Moreover, psoriasis is often undertreated such that patients do not achieve substantial skin clearance, symptom relief or improvements in QoL.\\n20\\n, \\n21\\n\\n\\nAlthough skin clearance may be achievable for most patients in the short term, long‐term strategies are important to optimize adherence and long‐term outcomes including health‐related quality of life (HRQoL).\\n22\\n, \\n23\\n However, the majority of clinical data and guidance available for topical management of psoriasis is focused on short‐term use, with limited data on long‐term use.\\n23\\n\\n\\nTherefore, understanding the impact of treatment on HRQoL and patient‐perceived symptom severity in psoriasis is key to informing clinical decision‐making, improving clinical outcomes and quality of care. Patient‐reported outcomes measures (PROs) are invaluable tools to evaluate these effects and support clinical management.\\n22\\n, \\n24\\n\\n\\nThis post hoc analysis of the PSO‐LONG trial captured the effect on HRQoL and patient‐perceived symptom severity of treating psoriasis with fixed‐dose calcipotriene 50 µg/g and betamethasone dipropionate 0.5 mg/g (Cal/BD) foam topical treatment through a 52‐week period. Three PRO measures were used: EuroQoL 5‐Dimensional Questionnaire for Psoriasis (EQ‐5D‐5L‐PSO), the Dermatology Life Quality Index (DLQI) and the Psoriasis Symptom Inventory (PSI). The analysis aimed to evaluate the value of Cal/BD foam for flare treatment and long‐term management (proactive vs reactive) on PROs as well as compare results at baseline flare, start of a relapse and during remission.\",\n \"This post hoc analysis included the full analysis set (n = 521) from the Phase 3, randomized, double‐blind PSO‐LONG trial (NCT02899962). The PSO‐LONG trial assessed long‐term efficacy and safety of proactive management with twice‐weekly fixed‐dose combination Cal/BD foam versus reactive management with twice‐weekly vehicle in patients with psoriasis vulgaris. Eligible patients were aged ≥18 years and had a clinical diagnosis of truncal and/or limb psoriasis for at least 6 months involving 2–30% of the body surface area (BSA), a modified Psoriasis Area and Severity Index (mPASI) score of ≥2 and a physician’s global assessment of disease severity (PGA) score of at least ‘mild’ (PGA ≥ 2).\\nThe trial included an initial 4‐week, open‐label phase of fixed‐dose combination Cal/BD foam once daily, followed by a 52‐week maintenance phase for patients who achieved a PGA score of 0 or 1 and an at least 2‐grade improvement after the initial 4 weeks. At the start of the maintenance phase, patients were randomized to either proactive management (Cal/BD foam twice weekly) or reactive management (vehicle foam twice weekly). Relapses (defined as at least ‘mild’, PGA ≥ 2) were treated with fixed‐dose combination Cal/BD foam once daily (QD) for 4 weeks. Remission was defined as ‘clear’ or ‘almost clear’, PGA 0/1. The full details of the PSO‐LONG trial study design\\n25\\n and the efficacy and safety results\\n26\\n are published elsewhere.\",\n \"Patients completed the EQ‐5D‐5L‐PSO, DLQI and PSI assessments. The EQ‐5D‐5L‐PSO questionnaire measures health status over five general dimensions (mobility, self‐care, usual activities, pain/discomfort and anxiety/depression) and two psoriasis‐related dimensions (skin irritation and self‐confidence).\\n27\\n Each dimension has five response levels, and a visual analogue scale allows patients to assess their health status with a score ranging from 0 (worst health) to 100 (best health). Responses to the questions can be converted into an index score ranging from 0.00 to 1.00, where a score of 0.00 indicates the worst health and 1.00 indicates full health.\\nThe DLQI is a ten‐item questionnaire used to measure the impact of dermatological disorders on a patient’s HRQoL in the following six areas: symptoms and feelings; daily activities; leisure activities; work and school; personal relationships and treatment‐related distress.\\n12\\n, \\n28\\n Total scores range from 0–1 (‘no effect at all’) to 21–30 (‘extremely large effect’).\\nThe PSI is an assessment of the severity of eight psoriasis‑related symptoms including itch, redness, scaling, burning, stinging, cracking, flaking and pain.\\n29\\n, \\n30\\n Scoring for each symptom ranges from ‘not at all severe’ (0) to ‘very severe’ (4), giving a total score range from 0 (no symptoms) to 32 (more severe symptoms).\\nPatient‐reported outcomes were assessed across treatment arms at baseline (Visit 1), at the start of a relapse and during remission at all monthly scheduled and unscheduled visits. The DLQI and EQ‐5D‐5L‐PSO were completed at the trial site on an electronic slate/tablet. To ensure unbiased answers for questionnaires that were completed onsite, the PROs were collected prior to any other assessments. The PSI was completed on an eDiary device, provided for the participants for use at home. The participants were asked to complete the PSI daily during the open‐label treatment phase (starting at Visit 1), then weekly during the first 28 weeks of the maintenance phase (Weeks 4–28) and the last 2 weeks of the maintenance phase (Weeks 54–56 – only applicable for those who completed the PSI).\",\n \"The statistical analyses were performed on the full analysis set (N = 521). Patient‐reported outcome results were collected within treatment arms at each visit. The integrated area under the curve (AUC) in proactive and reactive management arms during the maintenance phase was calculated for each PRO using the trapezoidal rule and subsequently normalized by the number of days in study for each patient. Additionally, PROs were assessed across treatment arms at baseline, at the start of a relapse and during remission at unscheduled and scheduled visits. Missing assessment of PRO scores in‐between non‐missing assessments in the maintenance phase was not imputed. The P‐value for treatment changes were assessed by using the Wilcoxon signed rank sum test. Differences across treatment arms were considered significant at P < 0.05.\",\n \"A total of 521 patients were included in the full analysis set used in this post hoc analysis. Patients were predominantly male (67.4%) and white (90.2%). The mean age was approximately 52.3 years. The majority of patients had moderate baseline PGA scores (85.2%). Patient characteristics are summarized in Table 1.\\nDemographic and baseline characteristics of randomized patients (full analysis set; N = 521)\\nDemographic and baseline characteristics\\n(maintenance full analysis set; N = 521)\",\n \"Initial flare treatment with Cal/BD foam QD during the open‐label phase led to statistically and clinically significant improvements across all PRO measures (Table 2). The mean difference from baseline to Week 4 was −8.97 (standard deviation [SD] = 6.18; P < 0.0001) for PSI scores, −6.02 (SD = 5.46; P < 0.0001) for DLQI scores and 0.11 (SD = 0.15; P < 0.0001) for EQ‐5D‐5L‐PSO scores.\\nChanges in PSI, DLQI and EQ‐5D‐5L‐PSO scores in flare treatment from baseline to Week 4 (full analysis set; N = 521)\\nDifference calculated from participants with both baseline and Week 4 scores.\",\n \"The PRO improvements were maintained over the next 52 weeks for both proactive and reactive management arms, across the three PRO assessment tools. Patients receiving proactive management showed significantly greater improvements in patient‐perceived symptom severity than patients receiving reactive management: the mean PSI AUC score was 15% higher for reactive management (5.74) than for proactive management (4.99) during the maintenance phase (difference −0.75; P = 0.0128) (Table 3). It is worth noting that the levels of participant engagement with the PSI questionnaire were low in the last 2 weeks of the maintenance phase. The analysis of the PSI total score was therefore based on the first 28 weeks of the maintenance phase.\\nMean PSI, DLQI and EQ‐5D‐5L‐PSO AUC scores in proactive and reactive management arms and differences during maintenance phase (full analysis set; N = 521)\\nProactive management also corresponded with significantly greater improvements in DLQI AUC scores; the mean DLQI score was 15% higher for reactive management (3.40) than proactive management (2.95) (difference −0.45; P = 0.007). Although the difference between proactive and reactive management did not result in significantly greater improvement in EQ‐5D‐5L‐PSO scores, the numerical difference favoured the proactive management arm; the mean EQ‐5D‐5L‐PSO AUC score was 1% higher for proactive management (0.89) than for reactive management (0.88) (difference 0.01, P = 0.0842). The mean scores in proactive and reactive management arms for PSI, DLQI and EQ‐5D‐5L‐PSO across each visit are shown in Fig. 1.\\nMean scores in proactive and reactive management arms for (a) PSI, (b) DLQI, and (c) EQ‐5D across study visits (Full Analysis Set; N = 521).\\nAcross both treatment arms, patients had improvements in symptoms and HRQoL during remission compared with the baseline flare and the start of a relapse (Table 4). Additionally, the mean change (95% confidence interval [CI]; p‐value) between the start of a relapse and remission was −2.28 (95% CI: −2.64 to −1.92; <0.0001) for PSI scores, −1.32 (95% CI: −1.60 to −1.04; <0.0001) for DLQI and 0.03 (95% CI: 0.02 to 0.04; <0.0001) for EQ‐5D‐5L‐PSO (Fig. 2).\\nPSI, DLQI and EQ‐5D‐5L‐PSO scores for baseline, remission and start of relapse (full analysis set; N = 521)\\nDistribution of AUC scores in proactive and reactive management arms for (a) PSI, (b) DLQI, and (c) EQ‐5D (Full Analysis Set; N = 521).\"\n]"},"other_sections_sec_types":{"kind":"list like","value":[null,null,null,null,null,null,null],"string":"[\n null,\n null,\n null,\n null,\n null,\n null,\n null\n]"},"all_sections_titles":{"kind":"list like","value":["Introduction","Materials and methods","Study design","Patient‐reported outcomes","Statistical analyses","Results","Patient demographics","Open‐label phase","Maintenance phase","Discussion","Conclusion"],"string":"[\n \"Introduction\",\n \"Materials and methods\",\n \"Study design\",\n \"Patient‐reported outcomes\",\n \"Statistical analyses\",\n \"Results\",\n \"Patient demographics\",\n \"Open‐label phase\",\n \"Maintenance phase\",\n \"Discussion\",\n \"Conclusion\"\n]"},"all_sections_texts":{"kind":"list like","value":["Psoriasis is a chronic, immune‐mediated disease, with primarily skin and joint symptoms.\n1\n, \n2\n The morphology, localization and severity of lesions in psoriasis can be highly variable.\n3\n Various genetic, environmental and immunological factors have been proposed as potential contributors to the pathophysiology of this disease.\n3\n Individuals with psoriasis have also been shown to have an elevated risk of cardiovascular disease, metabolic syndrome and diabetes, compared with the general population.\n4\n\n\nGlobally, the prevalence of psoriasis has been reported to vary between 0.09% and 11.43%, which corresponds to approximately 125 million affected people.\n5\n, \n6\n Despite ongoing efforts to improve the management of this condition, the burden of disease has been increasing steadily over the past decades.\n7\n Collectively, this renders psoriasis a significant health issue worldwide.\nPsoriasis can significantly influence a person's quality of life (QoL) and cause social stigmatization, physical disability and emotional distress.\n8\n Moreover, the impact of psoriasis on QoL is similar to that in patients with other chronic conditions such as cardiovascular disease, diabetes, end‐stage renal disease, liver disease and cancer.\n9\n Skin symptoms of psoriasis including scaling, itch and pain can significantly affect physical well‐being and limit daily activities, social contacts and (skin‐exposing) activities, and work.\n10\n Psoriasis has a greater psychological burden than any other dermatological condition and has been associated with impaired emotional functioning, a negative body and self‐image, depression, anxiety and suicide risk more than any other skin condition.\n10\n, \n11\n Other factors may also be attributable to the low QoL in psoriasis patients, including the chronic and recurring nature of the disease, lack of control and fear of unexpected breakout, and feeling of hopelessness in terms of cure.\n12\n Furthermore, duration and severity of psoriasis significantly decrease the QoL.\n13\n, \n14\n\n\nFor mild to moderate psoriasis, current treatment strategies commonly involve topical agents. For moderate to severe psoriasis, topical agents are often added to phototherapy and systemic or biologic agents.\n15\n, \n16\n, \n17\n Current management strategies mainly aim to clear active disease sites and prolong symptom‐free periods.\n18\n However, long‐term disease control is challenging, and patient satisfaction with available therapies remains low.\n19\n Moreover, psoriasis is often undertreated such that patients do not achieve substantial skin clearance, symptom relief or improvements in QoL.\n20\n, \n21\n\n\nAlthough skin clearance may be achievable for most patients in the short term, long‐term strategies are important to optimize adherence and long‐term outcomes including health‐related quality of life (HRQoL).\n22\n, \n23\n However, the majority of clinical data and guidance available for topical management of psoriasis is focused on short‐term use, with limited data on long‐term use.\n23\n\n\nTherefore, understanding the impact of treatment on HRQoL and patient‐perceived symptom severity in psoriasis is key to informing clinical decision‐making, improving clinical outcomes and quality of care. Patient‐reported outcomes measures (PROs) are invaluable tools to evaluate these effects and support clinical management.\n22\n, \n24\n\n\nThis post hoc analysis of the PSO‐LONG trial captured the effect on HRQoL and patient‐perceived symptom severity of treating psoriasis with fixed‐dose calcipotriene 50 µg/g and betamethasone dipropionate 0.5 mg/g (Cal/BD) foam topical treatment through a 52‐week period. Three PRO measures were used: EuroQoL 5‐Dimensional Questionnaire for Psoriasis (EQ‐5D‐5L‐PSO), the Dermatology Life Quality Index (DLQI) and the Psoriasis Symptom Inventory (PSI). The analysis aimed to evaluate the value of Cal/BD foam for flare treatment and long‐term management (proactive vs reactive) on PROs as well as compare results at baseline flare, start of a relapse and during remission.","Study design This post hoc analysis included the full analysis set (n = 521) from the Phase 3, randomized, double‐blind PSO‐LONG trial (NCT02899962). The PSO‐LONG trial assessed long‐term efficacy and safety of proactive management with twice‐weekly fixed‐dose combination Cal/BD foam versus reactive management with twice‐weekly vehicle in patients with psoriasis vulgaris. Eligible patients were aged ≥18 years and had a clinical diagnosis of truncal and/or limb psoriasis for at least 6 months involving 2–30% of the body surface area (BSA), a modified Psoriasis Area and Severity Index (mPASI) score of ≥2 and a physician’s global assessment of disease severity (PGA) score of at least ‘mild’ (PGA ≥ 2).\nThe trial included an initial 4‐week, open‐label phase of fixed‐dose combination Cal/BD foam once daily, followed by a 52‐week maintenance phase for patients who achieved a PGA score of 0 or 1 and an at least 2‐grade improvement after the initial 4 weeks. At the start of the maintenance phase, patients were randomized to either proactive management (Cal/BD foam twice weekly) or reactive management (vehicle foam twice weekly). Relapses (defined as at least ‘mild’, PGA ≥ 2) were treated with fixed‐dose combination Cal/BD foam once daily (QD) for 4 weeks. Remission was defined as ‘clear’ or ‘almost clear’, PGA 0/1. The full details of the PSO‐LONG trial study design\n25\n and the efficacy and safety results\n26\n are published elsewhere.\nThis post hoc analysis included the full analysis set (n = 521) from the Phase 3, randomized, double‐blind PSO‐LONG trial (NCT02899962). The PSO‐LONG trial assessed long‐term efficacy and safety of proactive management with twice‐weekly fixed‐dose combination Cal/BD foam versus reactive management with twice‐weekly vehicle in patients with psoriasis vulgaris. Eligible patients were aged ≥18 years and had a clinical diagnosis of truncal and/or limb psoriasis for at least 6 months involving 2–30% of the body surface area (BSA), a modified Psoriasis Area and Severity Index (mPASI) score of ≥2 and a physician’s global assessment of disease severity (PGA) score of at least ‘mild’ (PGA ≥ 2).\nThe trial included an initial 4‐week, open‐label phase of fixed‐dose combination Cal/BD foam once daily, followed by a 52‐week maintenance phase for patients who achieved a PGA score of 0 or 1 and an at least 2‐grade improvement after the initial 4 weeks. At the start of the maintenance phase, patients were randomized to either proactive management (Cal/BD foam twice weekly) or reactive management (vehicle foam twice weekly). Relapses (defined as at least ‘mild’, PGA ≥ 2) were treated with fixed‐dose combination Cal/BD foam once daily (QD) for 4 weeks. Remission was defined as ‘clear’ or ‘almost clear’, PGA 0/1. The full details of the PSO‐LONG trial study design\n25\n and the efficacy and safety results\n26\n are published elsewhere.\nPatient‐reported outcomes Patients completed the EQ‐5D‐5L‐PSO, DLQI and PSI assessments. The EQ‐5D‐5L‐PSO questionnaire measures health status over five general dimensions (mobility, self‐care, usual activities, pain/discomfort and anxiety/depression) and two psoriasis‐related dimensions (skin irritation and self‐confidence).\n27\n Each dimension has five response levels, and a visual analogue scale allows patients to assess their health status with a score ranging from 0 (worst health) to 100 (best health). Responses to the questions can be converted into an index score ranging from 0.00 to 1.00, where a score of 0.00 indicates the worst health and 1.00 indicates full health.\nThe DLQI is a ten‐item questionnaire used to measure the impact of dermatological disorders on a patient’s HRQoL in the following six areas: symptoms and feelings; daily activities; leisure activities; work and school; personal relationships and treatment‐related distress.\n12\n, \n28\n Total scores range from 0–1 (‘no effect at all’) to 21–30 (‘extremely large effect’).\nThe PSI is an assessment of the severity of eight psoriasis‑related symptoms including itch, redness, scaling, burning, stinging, cracking, flaking and pain.\n29\n, \n30\n Scoring for each symptom ranges from ‘not at all severe’ (0) to ‘very severe’ (4), giving a total score range from 0 (no symptoms) to 32 (more severe symptoms).\nPatient‐reported outcomes were assessed across treatment arms at baseline (Visit 1), at the start of a relapse and during remission at all monthly scheduled and unscheduled visits. The DLQI and EQ‐5D‐5L‐PSO were completed at the trial site on an electronic slate/tablet. To ensure unbiased answers for questionnaires that were completed onsite, the PROs were collected prior to any other assessments. The PSI was completed on an eDiary device, provided for the participants for use at home. The participants were asked to complete the PSI daily during the open‐label treatment phase (starting at Visit 1), then weekly during the first 28 weeks of the maintenance phase (Weeks 4–28) and the last 2 weeks of the maintenance phase (Weeks 54–56 – only applicable for those who completed the PSI).\nPatients completed the EQ‐5D‐5L‐PSO, DLQI and PSI assessments. The EQ‐5D‐5L‐PSO questionnaire measures health status over five general dimensions (mobility, self‐care, usual activities, pain/discomfort and anxiety/depression) and two psoriasis‐related dimensions (skin irritation and self‐confidence).\n27\n Each dimension has five response levels, and a visual analogue scale allows patients to assess their health status with a score ranging from 0 (worst health) to 100 (best health). Responses to the questions can be converted into an index score ranging from 0.00 to 1.00, where a score of 0.00 indicates the worst health and 1.00 indicates full health.\nThe DLQI is a ten‐item questionnaire used to measure the impact of dermatological disorders on a patient’s HRQoL in the following six areas: symptoms and feelings; daily activities; leisure activities; work and school; personal relationships and treatment‐related distress.\n12\n, \n28\n Total scores range from 0–1 (‘no effect at all’) to 21–30 (‘extremely large effect’).\nThe PSI is an assessment of the severity of eight psoriasis‑related symptoms including itch, redness, scaling, burning, stinging, cracking, flaking and pain.\n29\n, \n30\n Scoring for each symptom ranges from ‘not at all severe’ (0) to ‘very severe’ (4), giving a total score range from 0 (no symptoms) to 32 (more severe symptoms).\nPatient‐reported outcomes were assessed across treatment arms at baseline (Visit 1), at the start of a relapse and during remission at all monthly scheduled and unscheduled visits. The DLQI and EQ‐5D‐5L‐PSO were completed at the trial site on an electronic slate/tablet. To ensure unbiased answers for questionnaires that were completed onsite, the PROs were collected prior to any other assessments. The PSI was completed on an eDiary device, provided for the participants for use at home. The participants were asked to complete the PSI daily during the open‐label treatment phase (starting at Visit 1), then weekly during the first 28 weeks of the maintenance phase (Weeks 4–28) and the last 2 weeks of the maintenance phase (Weeks 54–56 – only applicable for those who completed the PSI).\nStatistical analyses The statistical analyses were performed on the full analysis set (N = 521). Patient‐reported outcome results were collected within treatment arms at each visit. The integrated area under the curve (AUC) in proactive and reactive management arms during the maintenance phase was calculated for each PRO using the trapezoidal rule and subsequently normalized by the number of days in study for each patient. Additionally, PROs were assessed across treatment arms at baseline, at the start of a relapse and during remission at unscheduled and scheduled visits. Missing assessment of PRO scores in‐between non‐missing assessments in the maintenance phase was not imputed. The P‐value for treatment changes were assessed by using the Wilcoxon signed rank sum test. Differences across treatment arms were considered significant at P < 0.05.\nThe statistical analyses were performed on the full analysis set (N = 521). Patient‐reported outcome results were collected within treatment arms at each visit. The integrated area under the curve (AUC) in proactive and reactive management arms during the maintenance phase was calculated for each PRO using the trapezoidal rule and subsequently normalized by the number of days in study for each patient. Additionally, PROs were assessed across treatment arms at baseline, at the start of a relapse and during remission at unscheduled and scheduled visits. Missing assessment of PRO scores in‐between non‐missing assessments in the maintenance phase was not imputed. The P‐value for treatment changes were assessed by using the Wilcoxon signed rank sum test. Differences across treatment arms were considered significant at P < 0.05.","This post hoc analysis included the full analysis set (n = 521) from the Phase 3, randomized, double‐blind PSO‐LONG trial (NCT02899962). The PSO‐LONG trial assessed long‐term efficacy and safety of proactive management with twice‐weekly fixed‐dose combination Cal/BD foam versus reactive management with twice‐weekly vehicle in patients with psoriasis vulgaris. Eligible patients were aged ≥18 years and had a clinical diagnosis of truncal and/or limb psoriasis for at least 6 months involving 2–30% of the body surface area (BSA), a modified Psoriasis Area and Severity Index (mPASI) score of ≥2 and a physician’s global assessment of disease severity (PGA) score of at least ‘mild’ (PGA ≥ 2).\nThe trial included an initial 4‐week, open‐label phase of fixed‐dose combination Cal/BD foam once daily, followed by a 52‐week maintenance phase for patients who achieved a PGA score of 0 or 1 and an at least 2‐grade improvement after the initial 4 weeks. At the start of the maintenance phase, patients were randomized to either proactive management (Cal/BD foam twice weekly) or reactive management (vehicle foam twice weekly). Relapses (defined as at least ‘mild’, PGA ≥ 2) were treated with fixed‐dose combination Cal/BD foam once daily (QD) for 4 weeks. Remission was defined as ‘clear’ or ‘almost clear’, PGA 0/1. The full details of the PSO‐LONG trial study design\n25\n and the efficacy and safety results\n26\n are published elsewhere.","Patients completed the EQ‐5D‐5L‐PSO, DLQI and PSI assessments. The EQ‐5D‐5L‐PSO questionnaire measures health status over five general dimensions (mobility, self‐care, usual activities, pain/discomfort and anxiety/depression) and two psoriasis‐related dimensions (skin irritation and self‐confidence).\n27\n Each dimension has five response levels, and a visual analogue scale allows patients to assess their health status with a score ranging from 0 (worst health) to 100 (best health). Responses to the questions can be converted into an index score ranging from 0.00 to 1.00, where a score of 0.00 indicates the worst health and 1.00 indicates full health.\nThe DLQI is a ten‐item questionnaire used to measure the impact of dermatological disorders on a patient’s HRQoL in the following six areas: symptoms and feelings; daily activities; leisure activities; work and school; personal relationships and treatment‐related distress.\n12\n, \n28\n Total scores range from 0–1 (‘no effect at all’) to 21–30 (‘extremely large effect’).\nThe PSI is an assessment of the severity of eight psoriasis‑related symptoms including itch, redness, scaling, burning, stinging, cracking, flaking and pain.\n29\n, \n30\n Scoring for each symptom ranges from ‘not at all severe’ (0) to ‘very severe’ (4), giving a total score range from 0 (no symptoms) to 32 (more severe symptoms).\nPatient‐reported outcomes were assessed across treatment arms at baseline (Visit 1), at the start of a relapse and during remission at all monthly scheduled and unscheduled visits. The DLQI and EQ‐5D‐5L‐PSO were completed at the trial site on an electronic slate/tablet. To ensure unbiased answers for questionnaires that were completed onsite, the PROs were collected prior to any other assessments. The PSI was completed on an eDiary device, provided for the participants for use at home. The participants were asked to complete the PSI daily during the open‐label treatment phase (starting at Visit 1), then weekly during the first 28 weeks of the maintenance phase (Weeks 4–28) and the last 2 weeks of the maintenance phase (Weeks 54–56 – only applicable for those who completed the PSI).","The statistical analyses were performed on the full analysis set (N = 521). Patient‐reported outcome results were collected within treatment arms at each visit. The integrated area under the curve (AUC) in proactive and reactive management arms during the maintenance phase was calculated for each PRO using the trapezoidal rule and subsequently normalized by the number of days in study for each patient. Additionally, PROs were assessed across treatment arms at baseline, at the start of a relapse and during remission at unscheduled and scheduled visits. Missing assessment of PRO scores in‐between non‐missing assessments in the maintenance phase was not imputed. The P‐value for treatment changes were assessed by using the Wilcoxon signed rank sum test. Differences across treatment arms were considered significant at P < 0.05.","Patient demographics A total of 521 patients were included in the full analysis set used in this post hoc analysis. Patients were predominantly male (67.4%) and white (90.2%). The mean age was approximately 52.3 years. The majority of patients had moderate baseline PGA scores (85.2%). Patient characteristics are summarized in Table 1.\nDemographic and baseline characteristics of randomized patients (full analysis set; N = 521)\nDemographic and baseline characteristics\n(maintenance full analysis set; N = 521)\nA total of 521 patients were included in the full analysis set used in this post hoc analysis. Patients were predominantly male (67.4%) and white (90.2%). The mean age was approximately 52.3 years. The majority of patients had moderate baseline PGA scores (85.2%). Patient characteristics are summarized in Table 1.\nDemographic and baseline characteristics of randomized patients (full analysis set; N = 521)\nDemographic and baseline characteristics\n(maintenance full analysis set; N = 521)\nOpen‐label phase Initial flare treatment with Cal/BD foam QD during the open‐label phase led to statistically and clinically significant improvements across all PRO measures (Table 2). The mean difference from baseline to Week 4 was −8.97 (standard deviation [SD] = 6.18; P < 0.0001) for PSI scores, −6.02 (SD = 5.46; P < 0.0001) for DLQI scores and 0.11 (SD = 0.15; P < 0.0001) for EQ‐5D‐5L‐PSO scores.\nChanges in PSI, DLQI and EQ‐5D‐5L‐PSO scores in flare treatment from baseline to Week 4 (full analysis set; N = 521)\nDifference calculated from participants with both baseline and Week 4 scores.\nInitial flare treatment with Cal/BD foam QD during the open‐label phase led to statistically and clinically significant improvements across all PRO measures (Table 2). The mean difference from baseline to Week 4 was −8.97 (standard deviation [SD] = 6.18; P < 0.0001) for PSI scores, −6.02 (SD = 5.46; P < 0.0001) for DLQI scores and 0.11 (SD = 0.15; P < 0.0001) for EQ‐5D‐5L‐PSO scores.\nChanges in PSI, DLQI and EQ‐5D‐5L‐PSO scores in flare treatment from baseline to Week 4 (full analysis set; N = 521)\nDifference calculated from participants with both baseline and Week 4 scores.\nMaintenance phase The PRO improvements were maintained over the next 52 weeks for both proactive and reactive management arms, across the three PRO assessment tools. Patients receiving proactive management showed significantly greater improvements in patient‐perceived symptom severity than patients receiving reactive management: the mean PSI AUC score was 15% higher for reactive management (5.74) than for proactive management (4.99) during the maintenance phase (difference −0.75; P = 0.0128) (Table 3). It is worth noting that the levels of participant engagement with the PSI questionnaire were low in the last 2 weeks of the maintenance phase. The analysis of the PSI total score was therefore based on the first 28 weeks of the maintenance phase.\nMean PSI, DLQI and EQ‐5D‐5L‐PSO AUC scores in proactive and reactive management arms and differences during maintenance phase (full analysis set; N = 521)\nProactive management also corresponded with significantly greater improvements in DLQI AUC scores; the mean DLQI score was 15% higher for reactive management (3.40) than proactive management (2.95) (difference −0.45; P = 0.007). Although the difference between proactive and reactive management did not result in significantly greater improvement in EQ‐5D‐5L‐PSO scores, the numerical difference favoured the proactive management arm; the mean EQ‐5D‐5L‐PSO AUC score was 1% higher for proactive management (0.89) than for reactive management (0.88) (difference 0.01, P = 0.0842). The mean scores in proactive and reactive management arms for PSI, DLQI and EQ‐5D‐5L‐PSO across each visit are shown in Fig. 1.\nMean scores in proactive and reactive management arms for (a) PSI, (b) DLQI, and (c) EQ‐5D across study visits (Full Analysis Set; N = 521).\nAcross both treatment arms, patients had improvements in symptoms and HRQoL during remission compared with the baseline flare and the start of a relapse (Table 4). Additionally, the mean change (95% confidence interval [CI]; p‐value) between the start of a relapse and remission was −2.28 (95% CI: −2.64 to −1.92; <0.0001) for PSI scores, −1.32 (95% CI: −1.60 to −1.04; <0.0001) for DLQI and 0.03 (95% CI: 0.02 to 0.04; <0.0001) for EQ‐5D‐5L‐PSO (Fig. 2).\nPSI, DLQI and EQ‐5D‐5L‐PSO scores for baseline, remission and start of relapse (full analysis set; N = 521)\nDistribution of AUC scores in proactive and reactive management arms for (a) PSI, (b) DLQI, and (c) EQ‐5D (Full Analysis Set; N = 521).\nThe PRO improvements were maintained over the next 52 weeks for both proactive and reactive management arms, across the three PRO assessment tools. Patients receiving proactive management showed significantly greater improvements in patient‐perceived symptom severity than patients receiving reactive management: the mean PSI AUC score was 15% higher for reactive management (5.74) than for proactive management (4.99) during the maintenance phase (difference −0.75; P = 0.0128) (Table 3). It is worth noting that the levels of participant engagement with the PSI questionnaire were low in the last 2 weeks of the maintenance phase. The analysis of the PSI total score was therefore based on the first 28 weeks of the maintenance phase.\nMean PSI, DLQI and EQ‐5D‐5L‐PSO AUC scores in proactive and reactive management arms and differences during maintenance phase (full analysis set; N = 521)\nProactive management also corresponded with significantly greater improvements in DLQI AUC scores; the mean DLQI score was 15% higher for reactive management (3.40) than proactive management (2.95) (difference −0.45; P = 0.007). Although the difference between proactive and reactive management did not result in significantly greater improvement in EQ‐5D‐5L‐PSO scores, the numerical difference favoured the proactive management arm; the mean EQ‐5D‐5L‐PSO AUC score was 1% higher for proactive management (0.89) than for reactive management (0.88) (difference 0.01, P = 0.0842). The mean scores in proactive and reactive management arms for PSI, DLQI and EQ‐5D‐5L‐PSO across each visit are shown in Fig. 1.\nMean scores in proactive and reactive management arms for (a) PSI, (b) DLQI, and (c) EQ‐5D across study visits (Full Analysis Set; N = 521).\nAcross both treatment arms, patients had improvements in symptoms and HRQoL during remission compared with the baseline flare and the start of a relapse (Table 4). Additionally, the mean change (95% confidence interval [CI]; p‐value) between the start of a relapse and remission was −2.28 (95% CI: −2.64 to −1.92; <0.0001) for PSI scores, −1.32 (95% CI: −1.60 to −1.04; <0.0001) for DLQI and 0.03 (95% CI: 0.02 to 0.04; <0.0001) for EQ‐5D‐5L‐PSO (Fig. 2).\nPSI, DLQI and EQ‐5D‐5L‐PSO scores for baseline, remission and start of relapse (full analysis set; N = 521)\nDistribution of AUC scores in proactive and reactive management arms for (a) PSI, (b) DLQI, and (c) EQ‐5D (Full Analysis Set; N = 521).","A total of 521 patients were included in the full analysis set used in this post hoc analysis. Patients were predominantly male (67.4%) and white (90.2%). The mean age was approximately 52.3 years. The majority of patients had moderate baseline PGA scores (85.2%). Patient characteristics are summarized in Table 1.\nDemographic and baseline characteristics of randomized patients (full analysis set; N = 521)\nDemographic and baseline characteristics\n(maintenance full analysis set; N = 521)","Initial flare treatment with Cal/BD foam QD during the open‐label phase led to statistically and clinically significant improvements across all PRO measures (Table 2). The mean difference from baseline to Week 4 was −8.97 (standard deviation [SD] = 6.18; P < 0.0001) for PSI scores, −6.02 (SD = 5.46; P < 0.0001) for DLQI scores and 0.11 (SD = 0.15; P < 0.0001) for EQ‐5D‐5L‐PSO scores.\nChanges in PSI, DLQI and EQ‐5D‐5L‐PSO scores in flare treatment from baseline to Week 4 (full analysis set; N = 521)\nDifference calculated from participants with both baseline and Week 4 scores.","The PRO improvements were maintained over the next 52 weeks for both proactive and reactive management arms, across the three PRO assessment tools. Patients receiving proactive management showed significantly greater improvements in patient‐perceived symptom severity than patients receiving reactive management: the mean PSI AUC score was 15% higher for reactive management (5.74) than for proactive management (4.99) during the maintenance phase (difference −0.75; P = 0.0128) (Table 3). It is worth noting that the levels of participant engagement with the PSI questionnaire were low in the last 2 weeks of the maintenance phase. The analysis of the PSI total score was therefore based on the first 28 weeks of the maintenance phase.\nMean PSI, DLQI and EQ‐5D‐5L‐PSO AUC scores in proactive and reactive management arms and differences during maintenance phase (full analysis set; N = 521)\nProactive management also corresponded with significantly greater improvements in DLQI AUC scores; the mean DLQI score was 15% higher for reactive management (3.40) than proactive management (2.95) (difference −0.45; P = 0.007). Although the difference between proactive and reactive management did not result in significantly greater improvement in EQ‐5D‐5L‐PSO scores, the numerical difference favoured the proactive management arm; the mean EQ‐5D‐5L‐PSO AUC score was 1% higher for proactive management (0.89) than for reactive management (0.88) (difference 0.01, P = 0.0842). The mean scores in proactive and reactive management arms for PSI, DLQI and EQ‐5D‐5L‐PSO across each visit are shown in Fig. 1.\nMean scores in proactive and reactive management arms for (a) PSI, (b) DLQI, and (c) EQ‐5D across study visits (Full Analysis Set; N = 521).\nAcross both treatment arms, patients had improvements in symptoms and HRQoL during remission compared with the baseline flare and the start of a relapse (Table 4). Additionally, the mean change (95% confidence interval [CI]; p‐value) between the start of a relapse and remission was −2.28 (95% CI: −2.64 to −1.92; <0.0001) for PSI scores, −1.32 (95% CI: −1.60 to −1.04; <0.0001) for DLQI and 0.03 (95% CI: 0.02 to 0.04; <0.0001) for EQ‐5D‐5L‐PSO (Fig. 2).\nPSI, DLQI and EQ‐5D‐5L‐PSO scores for baseline, remission and start of relapse (full analysis set; N = 521)\nDistribution of AUC scores in proactive and reactive management arms for (a) PSI, (b) DLQI, and (c) EQ‐5D (Full Analysis Set; N = 521).","The PSO‐LONG was the first randomized, double‐blind, 52‐week clinical trial to evaluate long‐term safety and efficacy outcomes of a proactive management strategy.\n25\n This post hoc analysis evaluated the value of Cal/BD foam for flare treatment and long‐term management (proactive vs. reactive) on PROs as well as comparing results at baseline flare, start of a relapse and during remission. To our knowledge, it is the first analysis to capture the effect on HRQoL of treating psoriasis with Cal/BD foam throughout a 52‐week period.\nIn this post hoc analysis, the patient’s HRQoL considerably improved with the Cal/BD foam QD flare treatment as demonstrated by significant changes in the DLQI, EQ‐5D‐5L‐PSO and PSI scores at randomization (end of flare) versus the baseline (start of flare). It is worth noting, however, that patients who did not achieve treatment success at the end of the open‐label phase were discontinued from the study. Therefore, those included in the maintenance phase were already shown to respond to Cal/BD foam treatment.\nFollowing resolution of the initial flare, the impact of the initial treatment on DLQI, EQ‐5D‐5L‐PSO and PSI on these patients was maintained through the 52 weeks for both proactive and reactive management. Patients assigned to proactive management had significantly better DLQI and PSI scores and numerically better EQ‐5D‐5L‐PSO scores versus reactive management. This could be attributed to dermatology‐specific and psoriasis‐specific questionnaires having a greater capacity for differentiation and sensitivity to changes on HRQoL than generic measures such as EuroQol‐5D.\n31\n\n\nThe baseline flare was associated with worse PROs than the start of a relapse. This could be due to the baseline flare representing an untreated flare, whereas the start of relapse represents flares occurring during the course of proactive or reactive management. Patients in relapse also had a poorer HRQoL and patient‐perceived symptom severity than patients in remission, which indicated that relapses had a substantial impact on the patients’ HRQoL. In the PSO‐LONG trial, the rate ratio of relapses for proactive versus reactive management was 0.54 (95% CI: 0.46–0.63; P < 0.001), and the predicted number of relapses per year of exposure was 3.1 (proactive management) versus 4.8 (reactive management), with proactive management giving 41 extra days in remission in a year.\n26\n Therefore, a reduction in the number of relapses and increased time in remission over a year of exposure in patients receiving proactive management versus reactive management can be attributed to the observed improvements in HRQoL and patient‐perceived symptom severity.\nAlthough skin clearance may be achievable for most patients in the short term, long‐term strategies are important to optimize adherence and long‐term outcomes, including HRQoL.\n22\n, \n23\n However, the majority of clinical data and guidance available for topical agents is focused on short‐term use, with limited guidance or clinical data on long‐term use.\n23\n Currently, long‐term management with topical treatment in psoriasis follows a reactive approach in response to disease relapses as opposed to a proactive approach to maintain remission. In the PSO‐LONG trial, the incidence of adverse events in the maintenance phase was similar between treatment groups and similar to the incidence reported following treatment with Cal/BD foam QD for 12 weeks, providing support for the long‐term safety and tolerability of proactive management with topical agents.\n26\n Although this analysis has inherent limitations related to its post hoc nature, the results of the PSO‐LONG trial warrant further research into the use of proactive topical treatments in the long‐term management of psoriasis, including in the real‐world clinical setting.","In this analysis, Cal/BD foam QD flare treatment was associated with significant improvements in PROs from baseline that were maintained with a twice‐weekly application through 52 weeks. In patients undergoing proactive management, DLQI and PSI scores were significantly improved vs. patients receiving reactive management, potentially due to the reduction in the number of relapses and increased time in remission over a year of exposure.\nOverall, the results of this analysis add to the original PSO‐LONG findings, suggesting that proactive management with fixed‐dose Cal/BD foam could offer not only improved long‐term control of psoriasis but also improved HRQoL and patient‐perceived symptom severity over conventional reactive treatment with Cal/BD foam."],"string":"[\n \"Psoriasis is a chronic, immune‐mediated disease, with primarily skin and joint symptoms.\\n1\\n, \\n2\\n The morphology, localization and severity of lesions in psoriasis can be highly variable.\\n3\\n Various genetic, environmental and immunological factors have been proposed as potential contributors to the pathophysiology of this disease.\\n3\\n Individuals with psoriasis have also been shown to have an elevated risk of cardiovascular disease, metabolic syndrome and diabetes, compared with the general population.\\n4\\n\\n\\nGlobally, the prevalence of psoriasis has been reported to vary between 0.09% and 11.43%, which corresponds to approximately 125 million affected people.\\n5\\n, \\n6\\n Despite ongoing efforts to improve the management of this condition, the burden of disease has been increasing steadily over the past decades.\\n7\\n Collectively, this renders psoriasis a significant health issue worldwide.\\nPsoriasis can significantly influence a person's quality of life (QoL) and cause social stigmatization, physical disability and emotional distress.\\n8\\n Moreover, the impact of psoriasis on QoL is similar to that in patients with other chronic conditions such as cardiovascular disease, diabetes, end‐stage renal disease, liver disease and cancer.\\n9\\n Skin symptoms of psoriasis including scaling, itch and pain can significantly affect physical well‐being and limit daily activities, social contacts and (skin‐exposing) activities, and work.\\n10\\n Psoriasis has a greater psychological burden than any other dermatological condition and has been associated with impaired emotional functioning, a negative body and self‐image, depression, anxiety and suicide risk more than any other skin condition.\\n10\\n, \\n11\\n Other factors may also be attributable to the low QoL in psoriasis patients, including the chronic and recurring nature of the disease, lack of control and fear of unexpected breakout, and feeling of hopelessness in terms of cure.\\n12\\n Furthermore, duration and severity of psoriasis significantly decrease the QoL.\\n13\\n, \\n14\\n\\n\\nFor mild to moderate psoriasis, current treatment strategies commonly involve topical agents. For moderate to severe psoriasis, topical agents are often added to phototherapy and systemic or biologic agents.\\n15\\n, \\n16\\n, \\n17\\n Current management strategies mainly aim to clear active disease sites and prolong symptom‐free periods.\\n18\\n However, long‐term disease control is challenging, and patient satisfaction with available therapies remains low.\\n19\\n Moreover, psoriasis is often undertreated such that patients do not achieve substantial skin clearance, symptom relief or improvements in QoL.\\n20\\n, \\n21\\n\\n\\nAlthough skin clearance may be achievable for most patients in the short term, long‐term strategies are important to optimize adherence and long‐term outcomes including health‐related quality of life (HRQoL).\\n22\\n, \\n23\\n However, the majority of clinical data and guidance available for topical management of psoriasis is focused on short‐term use, with limited data on long‐term use.\\n23\\n\\n\\nTherefore, understanding the impact of treatment on HRQoL and patient‐perceived symptom severity in psoriasis is key to informing clinical decision‐making, improving clinical outcomes and quality of care. Patient‐reported outcomes measures (PROs) are invaluable tools to evaluate these effects and support clinical management.\\n22\\n, \\n24\\n\\n\\nThis post hoc analysis of the PSO‐LONG trial captured the effect on HRQoL and patient‐perceived symptom severity of treating psoriasis with fixed‐dose calcipotriene 50 µg/g and betamethasone dipropionate 0.5 mg/g (Cal/BD) foam topical treatment through a 52‐week period. Three PRO measures were used: EuroQoL 5‐Dimensional Questionnaire for Psoriasis (EQ‐5D‐5L‐PSO), the Dermatology Life Quality Index (DLQI) and the Psoriasis Symptom Inventory (PSI). The analysis aimed to evaluate the value of Cal/BD foam for flare treatment and long‐term management (proactive vs reactive) on PROs as well as compare results at baseline flare, start of a relapse and during remission.\",\n \"Study design This post hoc analysis included the full analysis set (n = 521) from the Phase 3, randomized, double‐blind PSO‐LONG trial (NCT02899962). The PSO‐LONG trial assessed long‐term efficacy and safety of proactive management with twice‐weekly fixed‐dose combination Cal/BD foam versus reactive management with twice‐weekly vehicle in patients with psoriasis vulgaris. Eligible patients were aged ≥18 years and had a clinical diagnosis of truncal and/or limb psoriasis for at least 6 months involving 2–30% of the body surface area (BSA), a modified Psoriasis Area and Severity Index (mPASI) score of ≥2 and a physician’s global assessment of disease severity (PGA) score of at least ‘mild’ (PGA ≥ 2).\\nThe trial included an initial 4‐week, open‐label phase of fixed‐dose combination Cal/BD foam once daily, followed by a 52‐week maintenance phase for patients who achieved a PGA score of 0 or 1 and an at least 2‐grade improvement after the initial 4 weeks. At the start of the maintenance phase, patients were randomized to either proactive management (Cal/BD foam twice weekly) or reactive management (vehicle foam twice weekly). Relapses (defined as at least ‘mild’, PGA ≥ 2) were treated with fixed‐dose combination Cal/BD foam once daily (QD) for 4 weeks. Remission was defined as ‘clear’ or ‘almost clear’, PGA 0/1. The full details of the PSO‐LONG trial study design\\n25\\n and the efficacy and safety results\\n26\\n are published elsewhere.\\nThis post hoc analysis included the full analysis set (n = 521) from the Phase 3, randomized, double‐blind PSO‐LONG trial (NCT02899962). The PSO‐LONG trial assessed long‐term efficacy and safety of proactive management with twice‐weekly fixed‐dose combination Cal/BD foam versus reactive management with twice‐weekly vehicle in patients with psoriasis vulgaris. Eligible patients were aged ≥18 years and had a clinical diagnosis of truncal and/or limb psoriasis for at least 6 months involving 2–30% of the body surface area (BSA), a modified Psoriasis Area and Severity Index (mPASI) score of ≥2 and a physician’s global assessment of disease severity (PGA) score of at least ‘mild’ (PGA ≥ 2).\\nThe trial included an initial 4‐week, open‐label phase of fixed‐dose combination Cal/BD foam once daily, followed by a 52‐week maintenance phase for patients who achieved a PGA score of 0 or 1 and an at least 2‐grade improvement after the initial 4 weeks. At the start of the maintenance phase, patients were randomized to either proactive management (Cal/BD foam twice weekly) or reactive management (vehicle foam twice weekly). Relapses (defined as at least ‘mild’, PGA ≥ 2) were treated with fixed‐dose combination Cal/BD foam once daily (QD) for 4 weeks. Remission was defined as ‘clear’ or ‘almost clear’, PGA 0/1. The full details of the PSO‐LONG trial study design\\n25\\n and the efficacy and safety results\\n26\\n are published elsewhere.\\nPatient‐reported outcomes Patients completed the EQ‐5D‐5L‐PSO, DLQI and PSI assessments. The EQ‐5D‐5L‐PSO questionnaire measures health status over five general dimensions (mobility, self‐care, usual activities, pain/discomfort and anxiety/depression) and two psoriasis‐related dimensions (skin irritation and self‐confidence).\\n27\\n Each dimension has five response levels, and a visual analogue scale allows patients to assess their health status with a score ranging from 0 (worst health) to 100 (best health). Responses to the questions can be converted into an index score ranging from 0.00 to 1.00, where a score of 0.00 indicates the worst health and 1.00 indicates full health.\\nThe DLQI is a ten‐item questionnaire used to measure the impact of dermatological disorders on a patient’s HRQoL in the following six areas: symptoms and feelings; daily activities; leisure activities; work and school; personal relationships and treatment‐related distress.\\n12\\n, \\n28\\n Total scores range from 0–1 (‘no effect at all’) to 21–30 (‘extremely large effect’).\\nThe PSI is an assessment of the severity of eight psoriasis‑related symptoms including itch, redness, scaling, burning, stinging, cracking, flaking and pain.\\n29\\n, \\n30\\n Scoring for each symptom ranges from ‘not at all severe’ (0) to ‘very severe’ (4), giving a total score range from 0 (no symptoms) to 32 (more severe symptoms).\\nPatient‐reported outcomes were assessed across treatment arms at baseline (Visit 1), at the start of a relapse and during remission at all monthly scheduled and unscheduled visits. The DLQI and EQ‐5D‐5L‐PSO were completed at the trial site on an electronic slate/tablet. To ensure unbiased answers for questionnaires that were completed onsite, the PROs were collected prior to any other assessments. The PSI was completed on an eDiary device, provided for the participants for use at home. The participants were asked to complete the PSI daily during the open‐label treatment phase (starting at Visit 1), then weekly during the first 28 weeks of the maintenance phase (Weeks 4–28) and the last 2 weeks of the maintenance phase (Weeks 54–56 – only applicable for those who completed the PSI).\\nPatients completed the EQ‐5D‐5L‐PSO, DLQI and PSI assessments. The EQ‐5D‐5L‐PSO questionnaire measures health status over five general dimensions (mobility, self‐care, usual activities, pain/discomfort and anxiety/depression) and two psoriasis‐related dimensions (skin irritation and self‐confidence).\\n27\\n Each dimension has five response levels, and a visual analogue scale allows patients to assess their health status with a score ranging from 0 (worst health) to 100 (best health). Responses to the questions can be converted into an index score ranging from 0.00 to 1.00, where a score of 0.00 indicates the worst health and 1.00 indicates full health.\\nThe DLQI is a ten‐item questionnaire used to measure the impact of dermatological disorders on a patient’s HRQoL in the following six areas: symptoms and feelings; daily activities; leisure activities; work and school; personal relationships and treatment‐related distress.\\n12\\n, \\n28\\n Total scores range from 0–1 (‘no effect at all’) to 21–30 (‘extremely large effect’).\\nThe PSI is an assessment of the severity of eight psoriasis‑related symptoms including itch, redness, scaling, burning, stinging, cracking, flaking and pain.\\n29\\n, \\n30\\n Scoring for each symptom ranges from ‘not at all severe’ (0) to ‘very severe’ (4), giving a total score range from 0 (no symptoms) to 32 (more severe symptoms).\\nPatient‐reported outcomes were assessed across treatment arms at baseline (Visit 1), at the start of a relapse and during remission at all monthly scheduled and unscheduled visits. The DLQI and EQ‐5D‐5L‐PSO were completed at the trial site on an electronic slate/tablet. To ensure unbiased answers for questionnaires that were completed onsite, the PROs were collected prior to any other assessments. The PSI was completed on an eDiary device, provided for the participants for use at home. The participants were asked to complete the PSI daily during the open‐label treatment phase (starting at Visit 1), then weekly during the first 28 weeks of the maintenance phase (Weeks 4–28) and the last 2 weeks of the maintenance phase (Weeks 54–56 – only applicable for those who completed the PSI).\\nStatistical analyses The statistical analyses were performed on the full analysis set (N = 521). Patient‐reported outcome results were collected within treatment arms at each visit. The integrated area under the curve (AUC) in proactive and reactive management arms during the maintenance phase was calculated for each PRO using the trapezoidal rule and subsequently normalized by the number of days in study for each patient. Additionally, PROs were assessed across treatment arms at baseline, at the start of a relapse and during remission at unscheduled and scheduled visits. Missing assessment of PRO scores in‐between non‐missing assessments in the maintenance phase was not imputed. The P‐value for treatment changes were assessed by using the Wilcoxon signed rank sum test. Differences across treatment arms were considered significant at P < 0.05.\\nThe statistical analyses were performed on the full analysis set (N = 521). Patient‐reported outcome results were collected within treatment arms at each visit. The integrated area under the curve (AUC) in proactive and reactive management arms during the maintenance phase was calculated for each PRO using the trapezoidal rule and subsequently normalized by the number of days in study for each patient. Additionally, PROs were assessed across treatment arms at baseline, at the start of a relapse and during remission at unscheduled and scheduled visits. Missing assessment of PRO scores in‐between non‐missing assessments in the maintenance phase was not imputed. The P‐value for treatment changes were assessed by using the Wilcoxon signed rank sum test. Differences across treatment arms were considered significant at P < 0.05.\",\n \"This post hoc analysis included the full analysis set (n = 521) from the Phase 3, randomized, double‐blind PSO‐LONG trial (NCT02899962). The PSO‐LONG trial assessed long‐term efficacy and safety of proactive management with twice‐weekly fixed‐dose combination Cal/BD foam versus reactive management with twice‐weekly vehicle in patients with psoriasis vulgaris. Eligible patients were aged ≥18 years and had a clinical diagnosis of truncal and/or limb psoriasis for at least 6 months involving 2–30% of the body surface area (BSA), a modified Psoriasis Area and Severity Index (mPASI) score of ≥2 and a physician’s global assessment of disease severity (PGA) score of at least ‘mild’ (PGA ≥ 2).\\nThe trial included an initial 4‐week, open‐label phase of fixed‐dose combination Cal/BD foam once daily, followed by a 52‐week maintenance phase for patients who achieved a PGA score of 0 or 1 and an at least 2‐grade improvement after the initial 4 weeks. At the start of the maintenance phase, patients were randomized to either proactive management (Cal/BD foam twice weekly) or reactive management (vehicle foam twice weekly). Relapses (defined as at least ‘mild’, PGA ≥ 2) were treated with fixed‐dose combination Cal/BD foam once daily (QD) for 4 weeks. Remission was defined as ‘clear’ or ‘almost clear’, PGA 0/1. The full details of the PSO‐LONG trial study design\\n25\\n and the efficacy and safety results\\n26\\n are published elsewhere.\",\n \"Patients completed the EQ‐5D‐5L‐PSO, DLQI and PSI assessments. The EQ‐5D‐5L‐PSO questionnaire measures health status over five general dimensions (mobility, self‐care, usual activities, pain/discomfort and anxiety/depression) and two psoriasis‐related dimensions (skin irritation and self‐confidence).\\n27\\n Each dimension has five response levels, and a visual analogue scale allows patients to assess their health status with a score ranging from 0 (worst health) to 100 (best health). Responses to the questions can be converted into an index score ranging from 0.00 to 1.00, where a score of 0.00 indicates the worst health and 1.00 indicates full health.\\nThe DLQI is a ten‐item questionnaire used to measure the impact of dermatological disorders on a patient’s HRQoL in the following six areas: symptoms and feelings; daily activities; leisure activities; work and school; personal relationships and treatment‐related distress.\\n12\\n, \\n28\\n Total scores range from 0–1 (‘no effect at all’) to 21–30 (‘extremely large effect’).\\nThe PSI is an assessment of the severity of eight psoriasis‑related symptoms including itch, redness, scaling, burning, stinging, cracking, flaking and pain.\\n29\\n, \\n30\\n Scoring for each symptom ranges from ‘not at all severe’ (0) to ‘very severe’ (4), giving a total score range from 0 (no symptoms) to 32 (more severe symptoms).\\nPatient‐reported outcomes were assessed across treatment arms at baseline (Visit 1), at the start of a relapse and during remission at all monthly scheduled and unscheduled visits. The DLQI and EQ‐5D‐5L‐PSO were completed at the trial site on an electronic slate/tablet. To ensure unbiased answers for questionnaires that were completed onsite, the PROs were collected prior to any other assessments. The PSI was completed on an eDiary device, provided for the participants for use at home. The participants were asked to complete the PSI daily during the open‐label treatment phase (starting at Visit 1), then weekly during the first 28 weeks of the maintenance phase (Weeks 4–28) and the last 2 weeks of the maintenance phase (Weeks 54–56 – only applicable for those who completed the PSI).\",\n \"The statistical analyses were performed on the full analysis set (N = 521). Patient‐reported outcome results were collected within treatment arms at each visit. The integrated area under the curve (AUC) in proactive and reactive management arms during the maintenance phase was calculated for each PRO using the trapezoidal rule and subsequently normalized by the number of days in study for each patient. Additionally, PROs were assessed across treatment arms at baseline, at the start of a relapse and during remission at unscheduled and scheduled visits. Missing assessment of PRO scores in‐between non‐missing assessments in the maintenance phase was not imputed. The P‐value for treatment changes were assessed by using the Wilcoxon signed rank sum test. Differences across treatment arms were considered significant at P < 0.05.\",\n \"Patient demographics A total of 521 patients were included in the full analysis set used in this post hoc analysis. Patients were predominantly male (67.4%) and white (90.2%). The mean age was approximately 52.3 years. The majority of patients had moderate baseline PGA scores (85.2%). Patient characteristics are summarized in Table 1.\\nDemographic and baseline characteristics of randomized patients (full analysis set; N = 521)\\nDemographic and baseline characteristics\\n(maintenance full analysis set; N = 521)\\nA total of 521 patients were included in the full analysis set used in this post hoc analysis. Patients were predominantly male (67.4%) and white (90.2%). The mean age was approximately 52.3 years. The majority of patients had moderate baseline PGA scores (85.2%). Patient characteristics are summarized in Table 1.\\nDemographic and baseline characteristics of randomized patients (full analysis set; N = 521)\\nDemographic and baseline characteristics\\n(maintenance full analysis set; N = 521)\\nOpen‐label phase Initial flare treatment with Cal/BD foam QD during the open‐label phase led to statistically and clinically significant improvements across all PRO measures (Table 2). The mean difference from baseline to Week 4 was −8.97 (standard deviation [SD] = 6.18; P < 0.0001) for PSI scores, −6.02 (SD = 5.46; P < 0.0001) for DLQI scores and 0.11 (SD = 0.15; P < 0.0001) for EQ‐5D‐5L‐PSO scores.\\nChanges in PSI, DLQI and EQ‐5D‐5L‐PSO scores in flare treatment from baseline to Week 4 (full analysis set; N = 521)\\nDifference calculated from participants with both baseline and Week 4 scores.\\nInitial flare treatment with Cal/BD foam QD during the open‐label phase led to statistically and clinically significant improvements across all PRO measures (Table 2). The mean difference from baseline to Week 4 was −8.97 (standard deviation [SD] = 6.18; P < 0.0001) for PSI scores, −6.02 (SD = 5.46; P < 0.0001) for DLQI scores and 0.11 (SD = 0.15; P < 0.0001) for EQ‐5D‐5L‐PSO scores.\\nChanges in PSI, DLQI and EQ‐5D‐5L‐PSO scores in flare treatment from baseline to Week 4 (full analysis set; N = 521)\\nDifference calculated from participants with both baseline and Week 4 scores.\\nMaintenance phase The PRO improvements were maintained over the next 52 weeks for both proactive and reactive management arms, across the three PRO assessment tools. Patients receiving proactive management showed significantly greater improvements in patient‐perceived symptom severity than patients receiving reactive management: the mean PSI AUC score was 15% higher for reactive management (5.74) than for proactive management (4.99) during the maintenance phase (difference −0.75; P = 0.0128) (Table 3). It is worth noting that the levels of participant engagement with the PSI questionnaire were low in the last 2 weeks of the maintenance phase. The analysis of the PSI total score was therefore based on the first 28 weeks of the maintenance phase.\\nMean PSI, DLQI and EQ‐5D‐5L‐PSO AUC scores in proactive and reactive management arms and differences during maintenance phase (full analysis set; N = 521)\\nProactive management also corresponded with significantly greater improvements in DLQI AUC scores; the mean DLQI score was 15% higher for reactive management (3.40) than proactive management (2.95) (difference −0.45; P = 0.007). Although the difference between proactive and reactive management did not result in significantly greater improvement in EQ‐5D‐5L‐PSO scores, the numerical difference favoured the proactive management arm; the mean EQ‐5D‐5L‐PSO AUC score was 1% higher for proactive management (0.89) than for reactive management (0.88) (difference 0.01, P = 0.0842). The mean scores in proactive and reactive management arms for PSI, DLQI and EQ‐5D‐5L‐PSO across each visit are shown in Fig. 1.\\nMean scores in proactive and reactive management arms for (a) PSI, (b) DLQI, and (c) EQ‐5D across study visits (Full Analysis Set; N = 521).\\nAcross both treatment arms, patients had improvements in symptoms and HRQoL during remission compared with the baseline flare and the start of a relapse (Table 4). Additionally, the mean change (95% confidence interval [CI]; p‐value) between the start of a relapse and remission was −2.28 (95% CI: −2.64 to −1.92; <0.0001) for PSI scores, −1.32 (95% CI: −1.60 to −1.04; <0.0001) for DLQI and 0.03 (95% CI: 0.02 to 0.04; <0.0001) for EQ‐5D‐5L‐PSO (Fig. 2).\\nPSI, DLQI and EQ‐5D‐5L‐PSO scores for baseline, remission and start of relapse (full analysis set; N = 521)\\nDistribution of AUC scores in proactive and reactive management arms for (a) PSI, (b) DLQI, and (c) EQ‐5D (Full Analysis Set; N = 521).\\nThe PRO improvements were maintained over the next 52 weeks for both proactive and reactive management arms, across the three PRO assessment tools. Patients receiving proactive management showed significantly greater improvements in patient‐perceived symptom severity than patients receiving reactive management: the mean PSI AUC score was 15% higher for reactive management (5.74) than for proactive management (4.99) during the maintenance phase (difference −0.75; P = 0.0128) (Table 3). It is worth noting that the levels of participant engagement with the PSI questionnaire were low in the last 2 weeks of the maintenance phase. The analysis of the PSI total score was therefore based on the first 28 weeks of the maintenance phase.\\nMean PSI, DLQI and EQ‐5D‐5L‐PSO AUC scores in proactive and reactive management arms and differences during maintenance phase (full analysis set; N = 521)\\nProactive management also corresponded with significantly greater improvements in DLQI AUC scores; the mean DLQI score was 15% higher for reactive management (3.40) than proactive management (2.95) (difference −0.45; P = 0.007). Although the difference between proactive and reactive management did not result in significantly greater improvement in EQ‐5D‐5L‐PSO scores, the numerical difference favoured the proactive management arm; the mean EQ‐5D‐5L‐PSO AUC score was 1% higher for proactive management (0.89) than for reactive management (0.88) (difference 0.01, P = 0.0842). The mean scores in proactive and reactive management arms for PSI, DLQI and EQ‐5D‐5L‐PSO across each visit are shown in Fig. 1.\\nMean scores in proactive and reactive management arms for (a) PSI, (b) DLQI, and (c) EQ‐5D across study visits (Full Analysis Set; N = 521).\\nAcross both treatment arms, patients had improvements in symptoms and HRQoL during remission compared with the baseline flare and the start of a relapse (Table 4). Additionally, the mean change (95% confidence interval [CI]; p‐value) between the start of a relapse and remission was −2.28 (95% CI: −2.64 to −1.92; <0.0001) for PSI scores, −1.32 (95% CI: −1.60 to −1.04; <0.0001) for DLQI and 0.03 (95% CI: 0.02 to 0.04; <0.0001) for EQ‐5D‐5L‐PSO (Fig. 2).\\nPSI, DLQI and EQ‐5D‐5L‐PSO scores for baseline, remission and start of relapse (full analysis set; N = 521)\\nDistribution of AUC scores in proactive and reactive management arms for (a) PSI, (b) DLQI, and (c) EQ‐5D (Full Analysis Set; N = 521).\",\n \"A total of 521 patients were included in the full analysis set used in this post hoc analysis. Patients were predominantly male (67.4%) and white (90.2%). The mean age was approximately 52.3 years. The majority of patients had moderate baseline PGA scores (85.2%). Patient characteristics are summarized in Table 1.\\nDemographic and baseline characteristics of randomized patients (full analysis set; N = 521)\\nDemographic and baseline characteristics\\n(maintenance full analysis set; N = 521)\",\n \"Initial flare treatment with Cal/BD foam QD during the open‐label phase led to statistically and clinically significant improvements across all PRO measures (Table 2). The mean difference from baseline to Week 4 was −8.97 (standard deviation [SD] = 6.18; P < 0.0001) for PSI scores, −6.02 (SD = 5.46; P < 0.0001) for DLQI scores and 0.11 (SD = 0.15; P < 0.0001) for EQ‐5D‐5L‐PSO scores.\\nChanges in PSI, DLQI and EQ‐5D‐5L‐PSO scores in flare treatment from baseline to Week 4 (full analysis set; N = 521)\\nDifference calculated from participants with both baseline and Week 4 scores.\",\n \"The PRO improvements were maintained over the next 52 weeks for both proactive and reactive management arms, across the three PRO assessment tools. Patients receiving proactive management showed significantly greater improvements in patient‐perceived symptom severity than patients receiving reactive management: the mean PSI AUC score was 15% higher for reactive management (5.74) than for proactive management (4.99) during the maintenance phase (difference −0.75; P = 0.0128) (Table 3). It is worth noting that the levels of participant engagement with the PSI questionnaire were low in the last 2 weeks of the maintenance phase. The analysis of the PSI total score was therefore based on the first 28 weeks of the maintenance phase.\\nMean PSI, DLQI and EQ‐5D‐5L‐PSO AUC scores in proactive and reactive management arms and differences during maintenance phase (full analysis set; N = 521)\\nProactive management also corresponded with significantly greater improvements in DLQI AUC scores; the mean DLQI score was 15% higher for reactive management (3.40) than proactive management (2.95) (difference −0.45; P = 0.007). Although the difference between proactive and reactive management did not result in significantly greater improvement in EQ‐5D‐5L‐PSO scores, the numerical difference favoured the proactive management arm; the mean EQ‐5D‐5L‐PSO AUC score was 1% higher for proactive management (0.89) than for reactive management (0.88) (difference 0.01, P = 0.0842). The mean scores in proactive and reactive management arms for PSI, DLQI and EQ‐5D‐5L‐PSO across each visit are shown in Fig. 1.\\nMean scores in proactive and reactive management arms for (a) PSI, (b) DLQI, and (c) EQ‐5D across study visits (Full Analysis Set; N = 521).\\nAcross both treatment arms, patients had improvements in symptoms and HRQoL during remission compared with the baseline flare and the start of a relapse (Table 4). Additionally, the mean change (95% confidence interval [CI]; p‐value) between the start of a relapse and remission was −2.28 (95% CI: −2.64 to −1.92; <0.0001) for PSI scores, −1.32 (95% CI: −1.60 to −1.04; <0.0001) for DLQI and 0.03 (95% CI: 0.02 to 0.04; <0.0001) for EQ‐5D‐5L‐PSO (Fig. 2).\\nPSI, DLQI and EQ‐5D‐5L‐PSO scores for baseline, remission and start of relapse (full analysis set; N = 521)\\nDistribution of AUC scores in proactive and reactive management arms for (a) PSI, (b) DLQI, and (c) EQ‐5D (Full Analysis Set; N = 521).\",\n \"The PSO‐LONG was the first randomized, double‐blind, 52‐week clinical trial to evaluate long‐term safety and efficacy outcomes of a proactive management strategy.\\n25\\n This post hoc analysis evaluated the value of Cal/BD foam for flare treatment and long‐term management (proactive vs. reactive) on PROs as well as comparing results at baseline flare, start of a relapse and during remission. To our knowledge, it is the first analysis to capture the effect on HRQoL of treating psoriasis with Cal/BD foam throughout a 52‐week period.\\nIn this post hoc analysis, the patient’s HRQoL considerably improved with the Cal/BD foam QD flare treatment as demonstrated by significant changes in the DLQI, EQ‐5D‐5L‐PSO and PSI scores at randomization (end of flare) versus the baseline (start of flare). It is worth noting, however, that patients who did not achieve treatment success at the end of the open‐label phase were discontinued from the study. Therefore, those included in the maintenance phase were already shown to respond to Cal/BD foam treatment.\\nFollowing resolution of the initial flare, the impact of the initial treatment on DLQI, EQ‐5D‐5L‐PSO and PSI on these patients was maintained through the 52 weeks for both proactive and reactive management. Patients assigned to proactive management had significantly better DLQI and PSI scores and numerically better EQ‐5D‐5L‐PSO scores versus reactive management. This could be attributed to dermatology‐specific and psoriasis‐specific questionnaires having a greater capacity for differentiation and sensitivity to changes on HRQoL than generic measures such as EuroQol‐5D.\\n31\\n\\n\\nThe baseline flare was associated with worse PROs than the start of a relapse. This could be due to the baseline flare representing an untreated flare, whereas the start of relapse represents flares occurring during the course of proactive or reactive management. Patients in relapse also had a poorer HRQoL and patient‐perceived symptom severity than patients in remission, which indicated that relapses had a substantial impact on the patients’ HRQoL. In the PSO‐LONG trial, the rate ratio of relapses for proactive versus reactive management was 0.54 (95% CI: 0.46–0.63; P < 0.001), and the predicted number of relapses per year of exposure was 3.1 (proactive management) versus 4.8 (reactive management), with proactive management giving 41 extra days in remission in a year.\\n26\\n Therefore, a reduction in the number of relapses and increased time in remission over a year of exposure in patients receiving proactive management versus reactive management can be attributed to the observed improvements in HRQoL and patient‐perceived symptom severity.\\nAlthough skin clearance may be achievable for most patients in the short term, long‐term strategies are important to optimize adherence and long‐term outcomes, including HRQoL.\\n22\\n, \\n23\\n However, the majority of clinical data and guidance available for topical agents is focused on short‐term use, with limited guidance or clinical data on long‐term use.\\n23\\n Currently, long‐term management with topical treatment in psoriasis follows a reactive approach in response to disease relapses as opposed to a proactive approach to maintain remission. In the PSO‐LONG trial, the incidence of adverse events in the maintenance phase was similar between treatment groups and similar to the incidence reported following treatment with Cal/BD foam QD for 12 weeks, providing support for the long‐term safety and tolerability of proactive management with topical agents.\\n26\\n Although this analysis has inherent limitations related to its post hoc nature, the results of the PSO‐LONG trial warrant further research into the use of proactive topical treatments in the long‐term management of psoriasis, including in the real‐world clinical setting.\",\n \"In this analysis, Cal/BD foam QD flare treatment was associated with significant improvements in PROs from baseline that were maintained with a twice‐weekly application through 52 weeks. In patients undergoing proactive management, DLQI and PSI scores were significantly improved vs. patients receiving reactive management, potentially due to the reduction in the number of relapses and increased time in remission over a year of exposure.\\nOverall, the results of this analysis add to the original PSO‐LONG findings, suggesting that proactive management with fixed‐dose Cal/BD foam could offer not only improved long‐term control of psoriasis but also improved HRQoL and patient‐perceived symptom severity over conventional reactive treatment with Cal/BD foam.\"\n]"},"all_sections_sec_types":{"kind":"list like","value":[null,"materials-and-methods",null,null,null,"results",null,null,null,"discussion","conclusions"],"string":"[\n null,\n \"materials-and-methods\",\n null,\n null,\n null,\n \"results\",\n null,\n null,\n null,\n \"discussion\",\n \"conclusions\"\n]"},"keywords":{"kind":"list like","value":["betamethasone dipropionate","calcipotriol","psoriasis","topical administration"],"string":"[\n \"betamethasone dipropionate\",\n \"calcipotriol\",\n \"psoriasis\",\n \"topical administration\"\n]"},"whole_article_text":{"kind":"string","value":"Introduction:\nPsoriasis is a chronic, immune‐mediated disease, with primarily skin and joint symptoms.\n1\n, \n2\n The morphology, localization and severity of lesions in psoriasis can be highly variable.\n3\n Various genetic, environmental and immunological factors have been proposed as potential contributors to the pathophysiology of this disease.\n3\n Individuals with psoriasis have also been shown to have an elevated risk of cardiovascular disease, metabolic syndrome and diabetes, compared with the general population.\n4\n\n\nGlobally, the prevalence of psoriasis has been reported to vary between 0.09% and 11.43%, which corresponds to approximately 125 million affected people.\n5\n, \n6\n Despite ongoing efforts to improve the management of this condition, the burden of disease has been increasing steadily over the past decades.\n7\n Collectively, this renders psoriasis a significant health issue worldwide.\nPsoriasis can significantly influence a person's quality of life (QoL) and cause social stigmatization, physical disability and emotional distress.\n8\n Moreover, the impact of psoriasis on QoL is similar to that in patients with other chronic conditions such as cardiovascular disease, diabetes, end‐stage renal disease, liver disease and cancer.\n9\n Skin symptoms of psoriasis including scaling, itch and pain can significantly affect physical well‐being and limit daily activities, social contacts and (skin‐exposing) activities, and work.\n10\n Psoriasis has a greater psychological burden than any other dermatological condition and has been associated with impaired emotional functioning, a negative body and self‐image, depression, anxiety and suicide risk more than any other skin condition.\n10\n, \n11\n Other factors may also be attributable to the low QoL in psoriasis patients, including the chronic and recurring nature of the disease, lack of control and fear of unexpected breakout, and feeling of hopelessness in terms of cure.\n12\n Furthermore, duration and severity of psoriasis significantly decrease the QoL.\n13\n, \n14\n\n\nFor mild to moderate psoriasis, current treatment strategies commonly involve topical agents. For moderate to severe psoriasis, topical agents are often added to phototherapy and systemic or biologic agents.\n15\n, \n16\n, \n17\n Current management strategies mainly aim to clear active disease sites and prolong symptom‐free periods.\n18\n However, long‐term disease control is challenging, and patient satisfaction with available therapies remains low.\n19\n Moreover, psoriasis is often undertreated such that patients do not achieve substantial skin clearance, symptom relief or improvements in QoL.\n20\n, \n21\n\n\nAlthough skin clearance may be achievable for most patients in the short term, long‐term strategies are important to optimize adherence and long‐term outcomes including health‐related quality of life (HRQoL).\n22\n, \n23\n However, the majority of clinical data and guidance available for topical management of psoriasis is focused on short‐term use, with limited data on long‐term use.\n23\n\n\nTherefore, understanding the impact of treatment on HRQoL and patient‐perceived symptom severity in psoriasis is key to informing clinical decision‐making, improving clinical outcomes and quality of care. Patient‐reported outcomes measures (PROs) are invaluable tools to evaluate these effects and support clinical management.\n22\n, \n24\n\n\nThis post hoc analysis of the PSO‐LONG trial captured the effect on HRQoL and patient‐perceived symptom severity of treating psoriasis with fixed‐dose calcipotriene 50 µg/g and betamethasone dipropionate 0.5 mg/g (Cal/BD) foam topical treatment through a 52‐week period. Three PRO measures were used: EuroQoL 5‐Dimensional Questionnaire for Psoriasis (EQ‐5D‐5L‐PSO), the Dermatology Life Quality Index (DLQI) and the Psoriasis Symptom Inventory (PSI). The analysis aimed to evaluate the value of Cal/BD foam for flare treatment and long‐term management (proactive vs reactive) on PROs as well as compare results at baseline flare, start of a relapse and during remission.\n\nMaterials and methods:\nStudy design This post hoc analysis included the full analysis set (n = 521) from the Phase 3, randomized, double‐blind PSO‐LONG trial (NCT02899962). The PSO‐LONG trial assessed long‐term efficacy and safety of proactive management with twice‐weekly fixed‐dose combination Cal/BD foam versus reactive management with twice‐weekly vehicle in patients with psoriasis vulgaris. Eligible patients were aged ≥18 years and had a clinical diagnosis of truncal and/or limb psoriasis for at least 6 months involving 2–30% of the body surface area (BSA), a modified Psoriasis Area and Severity Index (mPASI) score of ≥2 and a physician’s global assessment of disease severity (PGA) score of at least ‘mild’ (PGA ≥ 2).\nThe trial included an initial 4‐week, open‐label phase of fixed‐dose combination Cal/BD foam once daily, followed by a 52‐week maintenance phase for patients who achieved a PGA score of 0 or 1 and an at least 2‐grade improvement after the initial 4 weeks. At the start of the maintenance phase, patients were randomized to either proactive management (Cal/BD foam twice weekly) or reactive management (vehicle foam twice weekly). Relapses (defined as at least ‘mild’, PGA ≥ 2) were treated with fixed‐dose combination Cal/BD foam once daily (QD) for 4 weeks. Remission was defined as ‘clear’ or ‘almost clear’, PGA 0/1. The full details of the PSO‐LONG trial study design\n25\n and the efficacy and safety results\n26\n are published elsewhere.\nThis post hoc analysis included the full analysis set (n = 521) from the Phase 3, randomized, double‐blind PSO‐LONG trial (NCT02899962). The PSO‐LONG trial assessed long‐term efficacy and safety of proactive management with twice‐weekly fixed‐dose combination Cal/BD foam versus reactive management with twice‐weekly vehicle in patients with psoriasis vulgaris. Eligible patients were aged ≥18 years and had a clinical diagnosis of truncal and/or limb psoriasis for at least 6 months involving 2–30% of the body surface area (BSA), a modified Psoriasis Area and Severity Index (mPASI) score of ≥2 and a physician’s global assessment of disease severity (PGA) score of at least ‘mild’ (PGA ≥ 2).\nThe trial included an initial 4‐week, open‐label phase of fixed‐dose combination Cal/BD foam once daily, followed by a 52‐week maintenance phase for patients who achieved a PGA score of 0 or 1 and an at least 2‐grade improvement after the initial 4 weeks. At the start of the maintenance phase, patients were randomized to either proactive management (Cal/BD foam twice weekly) or reactive management (vehicle foam twice weekly). Relapses (defined as at least ‘mild’, PGA ≥ 2) were treated with fixed‐dose combination Cal/BD foam once daily (QD) for 4 weeks. Remission was defined as ‘clear’ or ‘almost clear’, PGA 0/1. The full details of the PSO‐LONG trial study design\n25\n and the efficacy and safety results\n26\n are published elsewhere.\nPatient‐reported outcomes Patients completed the EQ‐5D‐5L‐PSO, DLQI and PSI assessments. The EQ‐5D‐5L‐PSO questionnaire measures health status over five general dimensions (mobility, self‐care, usual activities, pain/discomfort and anxiety/depression) and two psoriasis‐related dimensions (skin irritation and self‐confidence).\n27\n Each dimension has five response levels, and a visual analogue scale allows patients to assess their health status with a score ranging from 0 (worst health) to 100 (best health). Responses to the questions can be converted into an index score ranging from 0.00 to 1.00, where a score of 0.00 indicates the worst health and 1.00 indicates full health.\nThe DLQI is a ten‐item questionnaire used to measure the impact of dermatological disorders on a patient’s HRQoL in the following six areas: symptoms and feelings; daily activities; leisure activities; work and school; personal relationships and treatment‐related distress.\n12\n, \n28\n Total scores range from 0–1 (‘no effect at all’) to 21–30 (‘extremely large effect’).\nThe PSI is an assessment of the severity of eight psoriasis‑related symptoms including itch, redness, scaling, burning, stinging, cracking, flaking and pain.\n29\n, \n30\n Scoring for each symptom ranges from ‘not at all severe’ (0) to ‘very severe’ (4), giving a total score range from 0 (no symptoms) to 32 (more severe symptoms).\nPatient‐reported outcomes were assessed across treatment arms at baseline (Visit 1), at the start of a relapse and during remission at all monthly scheduled and unscheduled visits. The DLQI and EQ‐5D‐5L‐PSO were completed at the trial site on an electronic slate/tablet. To ensure unbiased answers for questionnaires that were completed onsite, the PROs were collected prior to any other assessments. The PSI was completed on an eDiary device, provided for the participants for use at home. The participants were asked to complete the PSI daily during the open‐label treatment phase (starting at Visit 1), then weekly during the first 28 weeks of the maintenance phase (Weeks 4–28) and the last 2 weeks of the maintenance phase (Weeks 54–56 – only applicable for those who completed the PSI).\nPatients completed the EQ‐5D‐5L‐PSO, DLQI and PSI assessments. The EQ‐5D‐5L‐PSO questionnaire measures health status over five general dimensions (mobility, self‐care, usual activities, pain/discomfort and anxiety/depression) and two psoriasis‐related dimensions (skin irritation and self‐confidence).\n27\n Each dimension has five response levels, and a visual analogue scale allows patients to assess their health status with a score ranging from 0 (worst health) to 100 (best health). Responses to the questions can be converted into an index score ranging from 0.00 to 1.00, where a score of 0.00 indicates the worst health and 1.00 indicates full health.\nThe DLQI is a ten‐item questionnaire used to measure the impact of dermatological disorders on a patient’s HRQoL in the following six areas: symptoms and feelings; daily activities; leisure activities; work and school; personal relationships and treatment‐related distress.\n12\n, \n28\n Total scores range from 0–1 (‘no effect at all’) to 21–30 (‘extremely large effect’).\nThe PSI is an assessment of the severity of eight psoriasis‑related symptoms including itch, redness, scaling, burning, stinging, cracking, flaking and pain.\n29\n, \n30\n Scoring for each symptom ranges from ‘not at all severe’ (0) to ‘very severe’ (4), giving a total score range from 0 (no symptoms) to 32 (more severe symptoms).\nPatient‐reported outcomes were assessed across treatment arms at baseline (Visit 1), at the start of a relapse and during remission at all monthly scheduled and unscheduled visits. The DLQI and EQ‐5D‐5L‐PSO were completed at the trial site on an electronic slate/tablet. To ensure unbiased answers for questionnaires that were completed onsite, the PROs were collected prior to any other assessments. The PSI was completed on an eDiary device, provided for the participants for use at home. The participants were asked to complete the PSI daily during the open‐label treatment phase (starting at Visit 1), then weekly during the first 28 weeks of the maintenance phase (Weeks 4–28) and the last 2 weeks of the maintenance phase (Weeks 54–56 – only applicable for those who completed the PSI).\nStatistical analyses The statistical analyses were performed on the full analysis set (N = 521). Patient‐reported outcome results were collected within treatment arms at each visit. The integrated area under the curve (AUC) in proactive and reactive management arms during the maintenance phase was calculated for each PRO using the trapezoidal rule and subsequently normalized by the number of days in study for each patient. Additionally, PROs were assessed across treatment arms at baseline, at the start of a relapse and during remission at unscheduled and scheduled visits. Missing assessment of PRO scores in‐between non‐missing assessments in the maintenance phase was not imputed. The P‐value for treatment changes were assessed by using the Wilcoxon signed rank sum test. Differences across treatment arms were considered significant at P < 0.05.\nThe statistical analyses were performed on the full analysis set (N = 521). Patient‐reported outcome results were collected within treatment arms at each visit. The integrated area under the curve (AUC) in proactive and reactive management arms during the maintenance phase was calculated for each PRO using the trapezoidal rule and subsequently normalized by the number of days in study for each patient. Additionally, PROs were assessed across treatment arms at baseline, at the start of a relapse and during remission at unscheduled and scheduled visits. Missing assessment of PRO scores in‐between non‐missing assessments in the maintenance phase was not imputed. The P‐value for treatment changes were assessed by using the Wilcoxon signed rank sum test. Differences across treatment arms were considered significant at P < 0.05.\n\nStudy design:\nThis post hoc analysis included the full analysis set (n = 521) from the Phase 3, randomized, double‐blind PSO‐LONG trial (NCT02899962). The PSO‐LONG trial assessed long‐term efficacy and safety of proactive management with twice‐weekly fixed‐dose combination Cal/BD foam versus reactive management with twice‐weekly vehicle in patients with psoriasis vulgaris. Eligible patients were aged ≥18 years and had a clinical diagnosis of truncal and/or limb psoriasis for at least 6 months involving 2–30% of the body surface area (BSA), a modified Psoriasis Area and Severity Index (mPASI) score of ≥2 and a physician’s global assessment of disease severity (PGA) score of at least ‘mild’ (PGA ≥ 2).\nThe trial included an initial 4‐week, open‐label phase of fixed‐dose combination Cal/BD foam once daily, followed by a 52‐week maintenance phase for patients who achieved a PGA score of 0 or 1 and an at least 2‐grade improvement after the initial 4 weeks. At the start of the maintenance phase, patients were randomized to either proactive management (Cal/BD foam twice weekly) or reactive management (vehicle foam twice weekly). Relapses (defined as at least ‘mild’, PGA ≥ 2) were treated with fixed‐dose combination Cal/BD foam once daily (QD) for 4 weeks. Remission was defined as ‘clear’ or ‘almost clear’, PGA 0/1. The full details of the PSO‐LONG trial study design\n25\n and the efficacy and safety results\n26\n are published elsewhere.\n\nPatient‐reported outcomes:\nPatients completed the EQ‐5D‐5L‐PSO, DLQI and PSI assessments. The EQ‐5D‐5L‐PSO questionnaire measures health status over five general dimensions (mobility, self‐care, usual activities, pain/discomfort and anxiety/depression) and two psoriasis‐related dimensions (skin irritation and self‐confidence).\n27\n Each dimension has five response levels, and a visual analogue scale allows patients to assess their health status with a score ranging from 0 (worst health) to 100 (best health). Responses to the questions can be converted into an index score ranging from 0.00 to 1.00, where a score of 0.00 indicates the worst health and 1.00 indicates full health.\nThe DLQI is a ten‐item questionnaire used to measure the impact of dermatological disorders on a patient’s HRQoL in the following six areas: symptoms and feelings; daily activities; leisure activities; work and school; personal relationships and treatment‐related distress.\n12\n, \n28\n Total scores range from 0–1 (‘no effect at all’) to 21–30 (‘extremely large effect’).\nThe PSI is an assessment of the severity of eight psoriasis‑related symptoms including itch, redness, scaling, burning, stinging, cracking, flaking and pain.\n29\n, \n30\n Scoring for each symptom ranges from ‘not at all severe’ (0) to ‘very severe’ (4), giving a total score range from 0 (no symptoms) to 32 (more severe symptoms).\nPatient‐reported outcomes were assessed across treatment arms at baseline (Visit 1), at the start of a relapse and during remission at all monthly scheduled and unscheduled visits. The DLQI and EQ‐5D‐5L‐PSO were completed at the trial site on an electronic slate/tablet. To ensure unbiased answers for questionnaires that were completed onsite, the PROs were collected prior to any other assessments. The PSI was completed on an eDiary device, provided for the participants for use at home. The participants were asked to complete the PSI daily during the open‐label treatment phase (starting at Visit 1), then weekly during the first 28 weeks of the maintenance phase (Weeks 4–28) and the last 2 weeks of the maintenance phase (Weeks 54–56 – only applicable for those who completed the PSI).\n\nStatistical analyses:\nThe statistical analyses were performed on the full analysis set (N = 521). Patient‐reported outcome results were collected within treatment arms at each visit. The integrated area under the curve (AUC) in proactive and reactive management arms during the maintenance phase was calculated for each PRO using the trapezoidal rule and subsequently normalized by the number of days in study for each patient. Additionally, PROs were assessed across treatment arms at baseline, at the start of a relapse and during remission at unscheduled and scheduled visits. Missing assessment of PRO scores in‐between non‐missing assessments in the maintenance phase was not imputed. The P‐value for treatment changes were assessed by using the Wilcoxon signed rank sum test. Differences across treatment arms were considered significant at P < 0.05.\n\nResults:\nPatient demographics A total of 521 patients were included in the full analysis set used in this post hoc analysis. Patients were predominantly male (67.4%) and white (90.2%). The mean age was approximately 52.3 years. The majority of patients had moderate baseline PGA scores (85.2%). Patient characteristics are summarized in Table 1.\nDemographic and baseline characteristics of randomized patients (full analysis set; N = 521)\nDemographic and baseline characteristics\n(maintenance full analysis set; N = 521)\nA total of 521 patients were included in the full analysis set used in this post hoc analysis. Patients were predominantly male (67.4%) and white (90.2%). The mean age was approximately 52.3 years. The majority of patients had moderate baseline PGA scores (85.2%). Patient characteristics are summarized in Table 1.\nDemographic and baseline characteristics of randomized patients (full analysis set; N = 521)\nDemographic and baseline characteristics\n(maintenance full analysis set; N = 521)\nOpen‐label phase Initial flare treatment with Cal/BD foam QD during the open‐label phase led to statistically and clinically significant improvements across all PRO measures (Table 2). The mean difference from baseline to Week 4 was −8.97 (standard deviation [SD] = 6.18; P < 0.0001) for PSI scores, −6.02 (SD = 5.46; P < 0.0001) for DLQI scores and 0.11 (SD = 0.15; P < 0.0001) for EQ‐5D‐5L‐PSO scores.\nChanges in PSI, DLQI and EQ‐5D‐5L‐PSO scores in flare treatment from baseline to Week 4 (full analysis set; N = 521)\nDifference calculated from participants with both baseline and Week 4 scores.\nInitial flare treatment with Cal/BD foam QD during the open‐label phase led to statistically and clinically significant improvements across all PRO measures (Table 2). The mean difference from baseline to Week 4 was −8.97 (standard deviation [SD] = 6.18; P < 0.0001) for PSI scores, −6.02 (SD = 5.46; P < 0.0001) for DLQI scores and 0.11 (SD = 0.15; P < 0.0001) for EQ‐5D‐5L‐PSO scores.\nChanges in PSI, DLQI and EQ‐5D‐5L‐PSO scores in flare treatment from baseline to Week 4 (full analysis set; N = 521)\nDifference calculated from participants with both baseline and Week 4 scores.\nMaintenance phase The PRO improvements were maintained over the next 52 weeks for both proactive and reactive management arms, across the three PRO assessment tools. Patients receiving proactive management showed significantly greater improvements in patient‐perceived symptom severity than patients receiving reactive management: the mean PSI AUC score was 15% higher for reactive management (5.74) than for proactive management (4.99) during the maintenance phase (difference −0.75; P = 0.0128) (Table 3). It is worth noting that the levels of participant engagement with the PSI questionnaire were low in the last 2 weeks of the maintenance phase. The analysis of the PSI total score was therefore based on the first 28 weeks of the maintenance phase.\nMean PSI, DLQI and EQ‐5D‐5L‐PSO AUC scores in proactive and reactive management arms and differences during maintenance phase (full analysis set; N = 521)\nProactive management also corresponded with significantly greater improvements in DLQI AUC scores; the mean DLQI score was 15% higher for reactive management (3.40) than proactive management (2.95) (difference −0.45; P = 0.007). Although the difference between proactive and reactive management did not result in significantly greater improvement in EQ‐5D‐5L‐PSO scores, the numerical difference favoured the proactive management arm; the mean EQ‐5D‐5L‐PSO AUC score was 1% higher for proactive management (0.89) than for reactive management (0.88) (difference 0.01, P = 0.0842). The mean scores in proactive and reactive management arms for PSI, DLQI and EQ‐5D‐5L‐PSO across each visit are shown in Fig. 1.\nMean scores in proactive and reactive management arms for (a) PSI, (b) DLQI, and (c) EQ‐5D across study visits (Full Analysis Set; N = 521).\nAcross both treatment arms, patients had improvements in symptoms and HRQoL during remission compared with the baseline flare and the start of a relapse (Table 4). Additionally, the mean change (95% confidence interval [CI]; p‐value) between the start of a relapse and remission was −2.28 (95% CI: −2.64 to −1.92; <0.0001) for PSI scores, −1.32 (95% CI: −1.60 to −1.04; <0.0001) for DLQI and 0.03 (95% CI: 0.02 to 0.04; <0.0001) for EQ‐5D‐5L‐PSO (Fig. 2).\nPSI, DLQI and EQ‐5D‐5L‐PSO scores for baseline, remission and start of relapse (full analysis set; N = 521)\nDistribution of AUC scores in proactive and reactive management arms for (a) PSI, (b) DLQI, and (c) EQ‐5D (Full Analysis Set; N = 521).\nThe PRO improvements were maintained over the next 52 weeks for both proactive and reactive management arms, across the three PRO assessment tools. Patients receiving proactive management showed significantly greater improvements in patient‐perceived symptom severity than patients receiving reactive management: the mean PSI AUC score was 15% higher for reactive management (5.74) than for proactive management (4.99) during the maintenance phase (difference −0.75; P = 0.0128) (Table 3). It is worth noting that the levels of participant engagement with the PSI questionnaire were low in the last 2 weeks of the maintenance phase. The analysis of the PSI total score was therefore based on the first 28 weeks of the maintenance phase.\nMean PSI, DLQI and EQ‐5D‐5L‐PSO AUC scores in proactive and reactive management arms and differences during maintenance phase (full analysis set; N = 521)\nProactive management also corresponded with significantly greater improvements in DLQI AUC scores; the mean DLQI score was 15% higher for reactive management (3.40) than proactive management (2.95) (difference −0.45; P = 0.007). Although the difference between proactive and reactive management did not result in significantly greater improvement in EQ‐5D‐5L‐PSO scores, the numerical difference favoured the proactive management arm; the mean EQ‐5D‐5L‐PSO AUC score was 1% higher for proactive management (0.89) than for reactive management (0.88) (difference 0.01, P = 0.0842). The mean scores in proactive and reactive management arms for PSI, DLQI and EQ‐5D‐5L‐PSO across each visit are shown in Fig. 1.\nMean scores in proactive and reactive management arms for (a) PSI, (b) DLQI, and (c) EQ‐5D across study visits (Full Analysis Set; N = 521).\nAcross both treatment arms, patients had improvements in symptoms and HRQoL during remission compared with the baseline flare and the start of a relapse (Table 4). Additionally, the mean change (95% confidence interval [CI]; p‐value) between the start of a relapse and remission was −2.28 (95% CI: −2.64 to −1.92; <0.0001) for PSI scores, −1.32 (95% CI: −1.60 to −1.04; <0.0001) for DLQI and 0.03 (95% CI: 0.02 to 0.04; <0.0001) for EQ‐5D‐5L‐PSO (Fig. 2).\nPSI, DLQI and EQ‐5D‐5L‐PSO scores for baseline, remission and start of relapse (full analysis set; N = 521)\nDistribution of AUC scores in proactive and reactive management arms for (a) PSI, (b) DLQI, and (c) EQ‐5D (Full Analysis Set; N = 521).\n\nPatient demographics:\nA total of 521 patients were included in the full analysis set used in this post hoc analysis. Patients were predominantly male (67.4%) and white (90.2%). The mean age was approximately 52.3 years. The majority of patients had moderate baseline PGA scores (85.2%). Patient characteristics are summarized in Table 1.\nDemographic and baseline characteristics of randomized patients (full analysis set; N = 521)\nDemographic and baseline characteristics\n(maintenance full analysis set; N = 521)\n\nOpen‐label phase:\nInitial flare treatment with Cal/BD foam QD during the open‐label phase led to statistically and clinically significant improvements across all PRO measures (Table 2). The mean difference from baseline to Week 4 was −8.97 (standard deviation [SD] = 6.18; P < 0.0001) for PSI scores, −6.02 (SD = 5.46; P < 0.0001) for DLQI scores and 0.11 (SD = 0.15; P < 0.0001) for EQ‐5D‐5L‐PSO scores.\nChanges in PSI, DLQI and EQ‐5D‐5L‐PSO scores in flare treatment from baseline to Week 4 (full analysis set; N = 521)\nDifference calculated from participants with both baseline and Week 4 scores.\n\nMaintenance phase:\nThe PRO improvements were maintained over the next 52 weeks for both proactive and reactive management arms, across the three PRO assessment tools. Patients receiving proactive management showed significantly greater improvements in patient‐perceived symptom severity than patients receiving reactive management: the mean PSI AUC score was 15% higher for reactive management (5.74) than for proactive management (4.99) during the maintenance phase (difference −0.75; P = 0.0128) (Table 3). It is worth noting that the levels of participant engagement with the PSI questionnaire were low in the last 2 weeks of the maintenance phase. The analysis of the PSI total score was therefore based on the first 28 weeks of the maintenance phase.\nMean PSI, DLQI and EQ‐5D‐5L‐PSO AUC scores in proactive and reactive management arms and differences during maintenance phase (full analysis set; N = 521)\nProactive management also corresponded with significantly greater improvements in DLQI AUC scores; the mean DLQI score was 15% higher for reactive management (3.40) than proactive management (2.95) (difference −0.45; P = 0.007). Although the difference between proactive and reactive management did not result in significantly greater improvement in EQ‐5D‐5L‐PSO scores, the numerical difference favoured the proactive management arm; the mean EQ‐5D‐5L‐PSO AUC score was 1% higher for proactive management (0.89) than for reactive management (0.88) (difference 0.01, P = 0.0842). The mean scores in proactive and reactive management arms for PSI, DLQI and EQ‐5D‐5L‐PSO across each visit are shown in Fig. 1.\nMean scores in proactive and reactive management arms for (a) PSI, (b) DLQI, and (c) EQ‐5D across study visits (Full Analysis Set; N = 521).\nAcross both treatment arms, patients had improvements in symptoms and HRQoL during remission compared with the baseline flare and the start of a relapse (Table 4). Additionally, the mean change (95% confidence interval [CI]; p‐value) between the start of a relapse and remission was −2.28 (95% CI: −2.64 to −1.92; <0.0001) for PSI scores, −1.32 (95% CI: −1.60 to −1.04; <0.0001) for DLQI and 0.03 (95% CI: 0.02 to 0.04; <0.0001) for EQ‐5D‐5L‐PSO (Fig. 2).\nPSI, DLQI and EQ‐5D‐5L‐PSO scores for baseline, remission and start of relapse (full analysis set; N = 521)\nDistribution of AUC scores in proactive and reactive management arms for (a) PSI, (b) DLQI, and (c) EQ‐5D (Full Analysis Set; N = 521).\n\nDiscussion:\nThe PSO‐LONG was the first randomized, double‐blind, 52‐week clinical trial to evaluate long‐term safety and efficacy outcomes of a proactive management strategy.\n25\n This post hoc analysis evaluated the value of Cal/BD foam for flare treatment and long‐term management (proactive vs. reactive) on PROs as well as comparing results at baseline flare, start of a relapse and during remission. To our knowledge, it is the first analysis to capture the effect on HRQoL of treating psoriasis with Cal/BD foam throughout a 52‐week period.\nIn this post hoc analysis, the patient’s HRQoL considerably improved with the Cal/BD foam QD flare treatment as demonstrated by significant changes in the DLQI, EQ‐5D‐5L‐PSO and PSI scores at randomization (end of flare) versus the baseline (start of flare). It is worth noting, however, that patients who did not achieve treatment success at the end of the open‐label phase were discontinued from the study. Therefore, those included in the maintenance phase were already shown to respond to Cal/BD foam treatment.\nFollowing resolution of the initial flare, the impact of the initial treatment on DLQI, EQ‐5D‐5L‐PSO and PSI on these patients was maintained through the 52 weeks for both proactive and reactive management. Patients assigned to proactive management had significantly better DLQI and PSI scores and numerically better EQ‐5D‐5L‐PSO scores versus reactive management. This could be attributed to dermatology‐specific and psoriasis‐specific questionnaires having a greater capacity for differentiation and sensitivity to changes on HRQoL than generic measures such as EuroQol‐5D.\n31\n\n\nThe baseline flare was associated with worse PROs than the start of a relapse. This could be due to the baseline flare representing an untreated flare, whereas the start of relapse represents flares occurring during the course of proactive or reactive management. Patients in relapse also had a poorer HRQoL and patient‐perceived symptom severity than patients in remission, which indicated that relapses had a substantial impact on the patients’ HRQoL. In the PSO‐LONG trial, the rate ratio of relapses for proactive versus reactive management was 0.54 (95% CI: 0.46–0.63; P < 0.001), and the predicted number of relapses per year of exposure was 3.1 (proactive management) versus 4.8 (reactive management), with proactive management giving 41 extra days in remission in a year.\n26\n Therefore, a reduction in the number of relapses and increased time in remission over a year of exposure in patients receiving proactive management versus reactive management can be attributed to the observed improvements in HRQoL and patient‐perceived symptom severity.\nAlthough skin clearance may be achievable for most patients in the short term, long‐term strategies are important to optimize adherence and long‐term outcomes, including HRQoL.\n22\n, \n23\n However, the majority of clinical data and guidance available for topical agents is focused on short‐term use, with limited guidance or clinical data on long‐term use.\n23\n Currently, long‐term management with topical treatment in psoriasis follows a reactive approach in response to disease relapses as opposed to a proactive approach to maintain remission. In the PSO‐LONG trial, the incidence of adverse events in the maintenance phase was similar between treatment groups and similar to the incidence reported following treatment with Cal/BD foam QD for 12 weeks, providing support for the long‐term safety and tolerability of proactive management with topical agents.\n26\n Although this analysis has inherent limitations related to its post hoc nature, the results of the PSO‐LONG trial warrant further research into the use of proactive topical treatments in the long‐term management of psoriasis, including in the real‐world clinical setting.\n\nConclusion:\nIn this analysis, Cal/BD foam QD flare treatment was associated with significant improvements in PROs from baseline that were maintained with a twice‐weekly application through 52 weeks. In patients undergoing proactive management, DLQI and PSI scores were significantly improved vs. patients receiving reactive management, potentially due to the reduction in the number of relapses and increased time in remission over a year of exposure.\nOverall, the results of this analysis add to the original PSO‐LONG findings, suggesting that proactive management with fixed‐dose Cal/BD foam could offer not only improved long‐term control of psoriasis but also improved HRQoL and patient‐perceived symptom severity over conventional reactive treatment with Cal/BD foam.\n\n"},"whole_article_abstract":{"kind":"string","value":"Background:\nPsoriasis has important physical and psychosocial effects that extend beyond the skin. Understanding the impact of treatment on health-related quality of life (HRQoL) and patient-perceived symptom severity in psoriasis is key to clinical decision-making.\n\nMethods:\nFive hundred and twenty-one patients from the Phase 3, randomized, double-blind PSO-LONG trial were included. An initial 4-week, open-label phase of fixed-dose combination Cal/BD foam once daily (QD) was followed by a 52-week maintenance phase, at the start of which patients were randomized to a proactive management arm (Cal/BD foam twice weekly) or reactive management arm (vehicle foam twice weekly). Patient-perceived symptom severity and HRQoL were assessed using the Psoriasis Symptom Inventory (PSI), the Dermatology Life Quality Index (DLQI) and the EuroQol-5D for psoriasis (EQ-5D-5L-PSO).\n\nResults:\nStatistically and clinically significant improvements were observed across all PRO measures. The mean difference (standard deviation) from baseline to Week 4 was -8.97 (6.18) for PSI, -6.02 (5.46) for DLQI and 0.11 (0.15) for EQ-5D-5L-PSO scores. During maintenance, patients receiving reactive management had significantly higher DLQI (15% [p = 0.007]) and PSI (15% [p = 0.0128]) and a numerically lower EQ-5D-5L-PSO mean area under the curve score than patients receiving proactive management (1% [p = 0.0842]).\n\nConclusions:\nCal/BD foam significantly improved DLQI, EQ-5D-5L-PSO and PSI scores during the open-label and maintenance phases. Patients assigned to proactive management had significantly better DLQI and PSI scores and numerically better EQ-5D-5L-PSO versus reactive management. Additionally, baseline flare was associated with worse PROs than the start of a relapse, and patients starting a relapse also had worse PROs than patients in remission."},"background_conclusion_text":{"kind":"string","value":"Introduction:\nPsoriasis is a chronic, immune‐mediated disease, with primarily skin and joint symptoms.\n1\n, \n2\n The morphology, localization and severity of lesions in psoriasis can be highly variable.\n3\n Various genetic, environmental and immunological factors have been proposed as potential contributors to the pathophysiology of this disease.\n3\n Individuals with psoriasis have also been shown to have an elevated risk of cardiovascular disease, metabolic syndrome and diabetes, compared with the general population.\n4\n\n\nGlobally, the prevalence of psoriasis has been reported to vary between 0.09% and 11.43%, which corresponds to approximately 125 million affected people.\n5\n, \n6\n Despite ongoing efforts to improve the management of this condition, the burden of disease has been increasing steadily over the past decades.\n7\n Collectively, this renders psoriasis a significant health issue worldwide.\nPsoriasis can significantly influence a person's quality of life (QoL) and cause social stigmatization, physical disability and emotional distress.\n8\n Moreover, the impact of psoriasis on QoL is similar to that in patients with other chronic conditions such as cardiovascular disease, diabetes, end‐stage renal disease, liver disease and cancer.\n9\n Skin symptoms of psoriasis including scaling, itch and pain can significantly affect physical well‐being and limit daily activities, social contacts and (skin‐exposing) activities, and work.\n10\n Psoriasis has a greater psychological burden than any other dermatological condition and has been associated with impaired emotional functioning, a negative body and self‐image, depression, anxiety and suicide risk more than any other skin condition.\n10\n, \n11\n Other factors may also be attributable to the low QoL in psoriasis patients, including the chronic and recurring nature of the disease, lack of control and fear of unexpected breakout, and feeling of hopelessness in terms of cure.\n12\n Furthermore, duration and severity of psoriasis significantly decrease the QoL.\n13\n, \n14\n\n\nFor mild to moderate psoriasis, current treatment strategies commonly involve topical agents. For moderate to severe psoriasis, topical agents are often added to phototherapy and systemic or biologic agents.\n15\n, \n16\n, \n17\n Current management strategies mainly aim to clear active disease sites and prolong symptom‐free periods.\n18\n However, long‐term disease control is challenging, and patient satisfaction with available therapies remains low.\n19\n Moreover, psoriasis is often undertreated such that patients do not achieve substantial skin clearance, symptom relief or improvements in QoL.\n20\n, \n21\n\n\nAlthough skin clearance may be achievable for most patients in the short term, long‐term strategies are important to optimize adherence and long‐term outcomes including health‐related quality of life (HRQoL).\n22\n, \n23\n However, the majority of clinical data and guidance available for topical management of psoriasis is focused on short‐term use, with limited data on long‐term use.\n23\n\n\nTherefore, understanding the impact of treatment on HRQoL and patient‐perceived symptom severity in psoriasis is key to informing clinical decision‐making, improving clinical outcomes and quality of care. Patient‐reported outcomes measures (PROs) are invaluable tools to evaluate these effects and support clinical management.\n22\n, \n24\n\n\nThis post hoc analysis of the PSO‐LONG trial captured the effect on HRQoL and patient‐perceived symptom severity of treating psoriasis with fixed‐dose calcipotriene 50 µg/g and betamethasone dipropionate 0.5 mg/g (Cal/BD) foam topical treatment through a 52‐week period. Three PRO measures were used: EuroQoL 5‐Dimensional Questionnaire for Psoriasis (EQ‐5D‐5L‐PSO), the Dermatology Life Quality Index (DLQI) and the Psoriasis Symptom Inventory (PSI). The analysis aimed to evaluate the value of Cal/BD foam for flare treatment and long‐term management (proactive vs reactive) on PROs as well as compare results at baseline flare, start of a relapse and during remission.\n\nConclusion:\nIn this analysis, Cal/BD foam QD flare treatment was associated with significant improvements in PROs from baseline that were maintained with a twice‐weekly application through 52 weeks. In patients undergoing proactive management, DLQI and PSI scores were significantly improved vs. patients receiving reactive management, potentially due to the reduction in the number of relapses and increased time in remission over a year of exposure.\nOverall, the results of this analysis add to the original PSO‐LONG findings, suggesting that proactive management with fixed‐dose Cal/BD foam could offer not only improved long‐term control of psoriasis but also improved HRQoL and patient‐perceived symptom severity over conventional reactive treatment with Cal/BD foam."},"background_conclusion_abstract":{"kind":"string","value":"Background:\nPsoriasis has important physical and psychosocial effects that extend beyond the skin. Understanding the impact of treatment on health-related quality of life (HRQoL) and patient-perceived symptom severity in psoriasis is key to clinical decision-making.\n\nMethods:\nFive hundred and twenty-one patients from the Phase 3, randomized, double-blind PSO-LONG trial were included. An initial 4-week, open-label phase of fixed-dose combination Cal/BD foam once daily (QD) was followed by a 52-week maintenance phase, at the start of which patients were randomized to a proactive management arm (Cal/BD foam twice weekly) or reactive management arm (vehicle foam twice weekly). Patient-perceived symptom severity and HRQoL were assessed using the Psoriasis Symptom Inventory (PSI), the Dermatology Life Quality Index (DLQI) and the EuroQol-5D for psoriasis (EQ-5D-5L-PSO).\n\nResults:\nStatistically and clinically significant improvements were observed across all PRO measures. The mean difference (standard deviation) from baseline to Week 4 was -8.97 (6.18) for PSI, -6.02 (5.46) for DLQI and 0.11 (0.15) for EQ-5D-5L-PSO scores. During maintenance, patients receiving reactive management had significantly higher DLQI (15% [p = 0.007]) and PSI (15% [p = 0.0128]) and a numerically lower EQ-5D-5L-PSO mean area under the curve score than patients receiving proactive management (1% [p = 0.0842]).\n\nConclusions:\nCal/BD foam significantly improved DLQI, EQ-5D-5L-PSO and PSI scores during the open-label and maintenance phases. Patients assigned to proactive management had significantly better DLQI and PSI scores and numerically better EQ-5D-5L-PSO versus reactive management. Additionally, baseline flare was associated with worse PROs than the start of a relapse, and patients starting a relapse also had worse PROs than patients in remission."},"whole_article_text_length":{"kind":"number","value":6447,"string":"6,447"},"whole_article_abstract_length":{"kind":"number","value":375,"string":"375"},"other_sections_lengths":{"kind":"list like","value":[733,295,423,144,103,138,514],"string":"[\n 733,\n 295,\n 423,\n 144,\n 103,\n 138,\n 514\n]"},"num_sections":{"kind":"number","value":11,"string":"11"},"most_frequent_words":{"kind":"list like","value":["management","proactive","patients","psi","scores","pso","reactive","phase","analysis","reactive management"],"string":"[\n \"management\",\n \"proactive\",\n \"patients\",\n \"psi\",\n \"scores\",\n \"pso\",\n \"reactive\",\n \"phase\",\n \"analysis\",\n \"reactive management\"\n]"},"keybert_topics":{"kind":"list like","value":["prevalence psoriasis reported","severity psoriasis significantly","disease individuals psoriasis","psoriasis highly variable","psoriasis significant health"],"string":"[\n \"prevalence psoriasis reported\",\n \"severity psoriasis significantly\",\n \"disease individuals psoriasis\",\n \"psoriasis highly variable\",\n \"psoriasis significant health\"\n]"},"annotated_base_background_abstract_prompt":{"kind":"string","value":""},"annotated_base_methods_abstract_prompt":{"kind":"null"},"annotated_base_results_abstract_prompt":{"kind":"string","value":""},"annotated_base_conclusions_abstract_prompt":{"kind":"string","value":""},"annotated_base_whole_article_abstract_prompt":{"kind":"string","value":""},"annotated_base_background_conclusion_abstract_prompt":{"kind":"string","value":""},"annotated_keywords_background_abstract_prompt":{"kind":"string","value":"[CONTENT] betamethasone dipropionate | calcipotriol | psoriasis | topical administration [SUMMARY]"},"annotated_keywords_methods_abstract_prompt":{"kind":"null"},"annotated_keywords_results_abstract_prompt":{"kind":"string","value":"[CONTENT] betamethasone dipropionate | calcipotriol | psoriasis | topical administration [SUMMARY]"},"annotated_keywords_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] betamethasone dipropionate | calcipotriol | psoriasis | topical administration [SUMMARY]"},"annotated_keywords_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] betamethasone dipropionate | calcipotriol | psoriasis | topical administration [SUMMARY]"},"annotated_keywords_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] betamethasone dipropionate | calcipotriol | psoriasis | topical administration [SUMMARY]"},"annotated_mesh_background_abstract_prompt":{"kind":"string","value":"[CONTENT] Betamethasone | Dermatologic Agents | Drug Combinations | Humans | Psoriasis | Quality of Life | Treatment Outcome [SUMMARY]"},"annotated_mesh_methods_abstract_prompt":{"kind":"null"},"annotated_mesh_results_abstract_prompt":{"kind":"string","value":"[CONTENT] Betamethasone | Dermatologic Agents | Drug Combinations | Humans | Psoriasis | Quality of Life | Treatment Outcome [SUMMARY]"},"annotated_mesh_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] Betamethasone | Dermatologic Agents | Drug Combinations | Humans | Psoriasis | Quality of Life | Treatment Outcome [SUMMARY]"},"annotated_mesh_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] Betamethasone | Dermatologic Agents | Drug Combinations | Humans | Psoriasis | Quality of Life | Treatment Outcome [SUMMARY]"},"annotated_mesh_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] Betamethasone | Dermatologic Agents | Drug Combinations | Humans | Psoriasis | Quality of Life | Treatment Outcome [SUMMARY]"},"annotated_keybert_background_abstract_prompt":{"kind":"string","value":"[CONTENT] prevalence psoriasis reported | severity psoriasis significantly | disease individuals psoriasis | psoriasis highly variable | psoriasis significant health [SUMMARY]"},"annotated_keybert_methods_abstract_prompt":{"kind":"null"},"annotated_keybert_results_abstract_prompt":{"kind":"string","value":"[CONTENT] prevalence psoriasis reported | severity psoriasis significantly | disease individuals psoriasis | psoriasis highly variable | psoriasis significant health [SUMMARY]"},"annotated_keybert_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] prevalence psoriasis reported | severity psoriasis significantly | disease individuals psoriasis | psoriasis highly variable | psoriasis significant health [SUMMARY]"},"annotated_keybert_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] prevalence psoriasis reported | severity psoriasis significantly | disease individuals psoriasis | psoriasis highly variable | psoriasis significant health [SUMMARY]"},"annotated_keybert_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] prevalence psoriasis reported | severity psoriasis significantly | disease individuals psoriasis | psoriasis highly variable | psoriasis significant health [SUMMARY]"},"annotated_most_frequent_background_abstract_prompt":{"kind":"string","value":"[CONTENT] management | proactive | patients | psi | scores | pso | reactive | phase | analysis | reactive management [SUMMARY]"},"annotated_most_frequent_methods_abstract_prompt":{"kind":"null"},"annotated_most_frequent_results_abstract_prompt":{"kind":"string","value":"[CONTENT] management | proactive | patients | psi | scores | pso | reactive | phase | analysis | reactive management [SUMMARY]"},"annotated_most_frequent_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] management | proactive | patients | psi | scores | pso | reactive | phase | analysis | reactive management [SUMMARY]"},"annotated_most_frequent_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] management | proactive | patients | psi | scores | pso | reactive | phase | analysis | reactive management [SUMMARY]"},"annotated_most_frequent_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] management | proactive | patients | psi | scores | pso | reactive | phase | analysis | reactive management [SUMMARY]"},"annotated_tf_idf_background_abstract_prompt":{"kind":"string","value":"[CONTENT] psoriasis | disease | qol | term | skin | quality | long | topical | long term | chronic [SUMMARY]"},"annotated_tf_idf_methods_abstract_prompt":{"kind":"null"},"annotated_tf_idf_results_abstract_prompt":{"kind":"string","value":"[CONTENT] management | mean | scores | proactive | difference | psi | eq 5d | eq | 5d | reactive management [SUMMARY]"},"annotated_tf_idf_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] improved | bd | bd foam | cal | cal bd | foam | cal bd foam | management | long | proactive management [SUMMARY]"},"annotated_tf_idf_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] management | proactive | patients | scores | psi | treatment | psoriasis | reactive | analysis | long [SUMMARY]"},"annotated_tf_idf_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] management | proactive | patients | scores | psi | treatment | psoriasis | reactive | analysis | long [SUMMARY]"},"annotated_entity_plan_background_abstract_prompt":{"kind":"string","value":"[CONTENT] ||| [SUMMARY]"},"annotated_entity_plan_methods_abstract_prompt":{"kind":"null"},"annotated_entity_plan_results_abstract_prompt":{"kind":"string","value":"[CONTENT] ||| Week 4 | 6.18 | PSI | 5.46 | DLQI | 0.11 | 0.15 ||| DLQI | 15% | 0.007 | PSI | 15% ||| 0.0128 | 1% | 0.0842 [SUMMARY]"},"annotated_entity_plan_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] Cal | DLQI | PSI ||| DLQI | PSI ||| [SUMMARY]"},"annotated_entity_plan_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] ||| ||| Five hundred and twenty-one | the Phase 3 ||| 4-week | Cal/BD | 52-week | weekly | weekly ||| the Psoriasis Symptom Inventory | the Dermatology Life Quality Index | DLQI ||| ||| Week 4 | 6.18 | PSI | 5.46 | DLQI | 0.11 | 0.15 ||| DLQI | 15% | 0.007 | PSI | 15% ||| 0.0128 | 1% | 0.0842 ||| DLQI | PSI ||| DLQI | PSI ||| [SUMMARY]"},"annotated_entity_plan_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] ||| ||| Five hundred and twenty-one | the Phase 3 ||| 4-week | Cal/BD | 52-week | weekly | weekly ||| the Psoriasis Symptom Inventory | the Dermatology Life Quality Index | DLQI ||| ||| Week 4 | 6.18 | PSI | 5.46 | DLQI | 0.11 | 0.15 ||| DLQI | 15% | 0.007 | PSI | 15% ||| 0.0128 | 1% | 0.0842 ||| DLQI | PSI ||| DLQI | PSI ||| [SUMMARY]"}}},{"rowIdx":282,"cells":{"title":{"kind":"string","value":"Evaluation of the potential of a simplified co-delivery system with oligodeoxynucleotides as a drug carrier for enhanced antitumor effect."},"pmid":{"kind":"string","value":"29719392"},"background_abstract":{"kind":"string","value":"We previously developed a simple effective system based on oligodeoxynucleotides with CGA repeating units (CGA-ODNs) for Dox and siRNA intracellular co-delivery."},"background_abstract_label":{"kind":"string","value":"BACKGROUND"},"methods_abstract":{"kind":"string","value":"In the present study, the in vitro cytotoxicity, gene transfection and in vivo safety of the co-delivery system were further characterized and discussed."},"methods_abstract_label":{"kind":"string","value":"METHODS"},"results_abstract":{"kind":"string","value":"Compared with poly(ethyleneimine) (PEI), both CGA-ODNs and the pH-sensitive targeted coating, o-carboxymethyl-chitosan (CMCS)-poly(ethylene glycol) (PEG)-aspargine-glycine-arginine (NGR) (CMCS-PEG-NGR, CPN) showed no obvious cytotoxicity in 72 h. The excellent transfection capability of CPN coated Dox and siRNA co-loaded nanoparticles (CPN-PDR) was confirmed by real-time PCR and Western blot analysis. It was calculated that there was no significant difference in silencing efficiency among Lipo/siRNA, CPN-modified siRNA-loaded nanoparticles (CPN-PR) and CPN-PDR. Furthermore, CPN-PDR was observed to be significantly much more toxic than Dox- and CPN-modified Dox-loaded nanoparticles (CPN-PD), implying their higher antitumor potential. Both hemolysis tests and histological assessment implied that CPN-PDR was safe for intravenous injection with nontoxicity and good biocompatibility in vitro and in vivo."},"results_abstract_label":{"kind":"string","value":"RESULTS"},"conclusions_abstract":{"kind":"string","value":"The results indicated that CPN-PDR could be a potentially promising co-delivery carrier for enhanced antitumor therapy."},"conclusions_abstract_label":{"kind":"string","value":"CONCLUSION"},"mesh_descriptor_names":{"kind":"list like","value":["Animals","Antineoplastic Agents","Chitosan","Doxorubicin","Drug Carriers","Drug Delivery Systems","Endosomes","Humans","Hydrogen-Ion Concentration","MCF-7 Cells","Mice","Nanoparticles","Oligodeoxyribonucleotides","Oligopeptides","Polyethylene Glycols","RNA, Small Interfering","Transfection"],"string":"[\n \"Animals\",\n \"Antineoplastic Agents\",\n \"Chitosan\",\n \"Doxorubicin\",\n \"Drug Carriers\",\n \"Drug Delivery Systems\",\n \"Endosomes\",\n \"Humans\",\n \"Hydrogen-Ion Concentration\",\n \"MCF-7 Cells\",\n \"Mice\",\n \"Nanoparticles\",\n \"Oligodeoxyribonucleotides\",\n \"Oligopeptides\",\n \"Polyethylene Glycols\",\n \"RNA, Small Interfering\",\n \"Transfection\"\n]"},"pmcid":{"kind":"string","value":"5916381"},"background_title":{"kind":"string","value":"Characteristics of nanoparticles"},"background_text":{"kind":"string","value":"In the optimal formulation of CPN-PDR, the weight ratio of DOX to siRNA was 1:1; therefore, the siRNA loading content was equal to the DOX loading content. Based on the facts that the molar ratio of CGA-ODNs to Dox was 1:5, the weight ratio of CPN/PEI/ODNs and siRNA was 4:1:1, and consequently, siRNA and Dox loading efficiency in CPN-PDR was calculated to be 4.93%."},"methods_title":{"kind":"string","value":"Statistical analysis"},"methods_text":{"kind":"string","value":"All studies were repeated a minimum of three times and measured at least in triplicate. The results are reported as the means ± SD. Statistical significance was analyzed using Student’s t-test. Differences between experimental groups were considered significant if the P-value was <0.05."},"results_title":{"kind":"null"},"results_text":{"kind":"null"},"conclusions_title":{"kind":"string","value":"Conclusion"},"conclusions_text":{"kind":"string","value":"In summary, the delivery system materials were demonstrated to be nontoxic and biocompatible. The nontoxic and negatively charged copolymer CPN coating could significantly decrease the cytotoxicity of the cationic core, bPDR. The obtained co-delivery system CPN-PDR was also confirmed with enhanced cytotoxicity against tumor cells. Gene transfection efficiency induced by CPN-PDR was fairly equal with that of the commercial product Lipofectamine 2000, implying the successful intracellular delivery and good transfection of siRNA. Moreover, the preliminary evaluation of safety showed CPN-PDR to have good biocompatibility, so it has great potential for further exploitation and clinical application. With the development of aptamer technology, the promising application prospects of this novel oligodeoxynucleotide-based co-loading platform will further increase."},"other_sections_titles":{"kind":"list like","value":["Introduction","Materials","Cells and animals","Preparation of nanoparticles","In vitro cytotoxicity of materials","In vitro cytotoxicity of blank nanoparticles","siRNA transfection with nanoparticles","Real-time PCR","Western blot assay","Cell proliferation assay","Hemolysis test","Histological assessment","Results and discussion","Cytotoxicity of blank nanoparticles","Expression level of VEGF mRNA","Expression level of VEGF protein","Inhibition of cell proliferation in vitro","Hemolysis test","Tissue section","Conclusion"],"string":"[\n \"Introduction\",\n \"Materials\",\n \"Cells and animals\",\n \"Preparation of nanoparticles\",\n \"In vitro cytotoxicity of materials\",\n \"In vitro cytotoxicity of blank nanoparticles\",\n \"siRNA transfection with nanoparticles\",\n \"Real-time PCR\",\n \"Western blot assay\",\n \"Cell proliferation assay\",\n \"Hemolysis test\",\n \"Histological assessment\",\n \"Results and discussion\",\n \"Cytotoxicity of blank nanoparticles\",\n \"Expression level of VEGF mRNA\",\n \"Expression level of VEGF protein\",\n \"Inhibition of cell proliferation in vitro\",\n \"Hemolysis test\",\n \"Tissue section\",\n \"Conclusion\"\n]"},"other_sections_texts":{"kind":"list like","value":["Cancer has been one of the leading causes of death worldwide, and mortality and morbidity continue to increase.1 Chemotherapy is one of the major strategies for cancer therapy. However, mono-chemotherapy may encounter problems including drug resistance and unspecific toxicity. To overcome the high mortality rate of cancer, new therapeutic strategies, such as combinational therapy, have been developed.2,3 It has been reported that a combination of chemotherapy and gene therapy could potentially achieve synergistic effects and improve target selectivity, and deter the development of cancer drug resistance.4 For example, a double-modulating strategy based on the combination of chemotherapeutic agent 5-fluorouracil (5-FU) and siRNA has been developed, in which chemotherapy enhances intratumoral siRNA delivery and the delivered siRNA enhances the chemosensitivity of tumors. Furthermore, combination therapy may compensate for the limited delivery of siRNA to tumor tissue and probably manage the 5-FU-resistant tumors.5\nRNA interference (RNAi) is a natural cellular process that regulates gene expression level. Small interfering RNAs (siRNAs), which are small double-stranded RNAs, 20–24 nucleotides (nts) in length with sequences complementary to a gene’s coding sequence, can induce degradation of corresponding messenger RNAs (mRNAs), thus blocking the translation of the mRNA into protein.6,7 The key therapeutic advantage of using RNAi lies in its ability to specifically and potently knock down the expression of disease-causing genes of known sequence. The high specificity and potency makes it widely used in treating various cancers such as breast, liver, and lung cancers.8–10 In recent years, combination of chemotherapy and siRNA-induced RNAi has become a hot topic in cancer treatment. For example, co-delivery of siRNA targeting multidrug resistance protein 1 (MDR1) or anti-survivin siRNA and chemotherapeutic drugs was demonstrated to be a promising strategy to overcome drug resistance.11–13 Anti-apoptotic gene Bcl-2 is a potential combinational therapy target owing to its important role in cell apoptosis, and combination of Bcl-2 siRNA and 5-FU could induce a remarkable increase of cell apoptosis both in vitro and in vivo.14\nTo achieve the desired combinational and synergistic effects of chemotherapy and RNAi, selection of siRNA is important, and the selection of siRNA is also the section of silencing target. The mechanism for combinational anticancer effect varies with different silencing targets. For example, siRNA targeting MDR1 or anti-apoptotic gene Bcl-2 were used to prevent drug resistance response and sensitize chemotherapeutic drugs, or enhance the apoptosis of cancer cells, both offering enhanced anticancer effect.15,16 Tumor tissues have abundant new blood vessels that pump sufficient nutrition and oxygen for tumor growth and metastasis. The dependency of tumor tissues on these new blood vessels is important in anti-angiogenesis therapy for cancer treatment.17 Recently, there have been many efforts in achieving anti-angiogenesis, including delivering siRNAs to silence specific pro-angiogenic genes, such as vascular endothelial growth factor (VEGF), fibroblast growth factor (FGF) and interleukin-8 (IL-8).18 Of these targets, VEGF has received a lot of attention owing to its key role in tumor angiogenesis. With the development of RNAi technology, siRNA with VEGF target has been widely explored and referred to as anti-VEGF treatment.19–21 In 2004, the success of bevacizumab showed VEGF to be a potential target for angiogenesis therapy. There are a number of novel anti-VEGF agents in phase III clinical trials that may come to market in the next few years.22,23 In our design, siRNA with VEGF target was selected to co-deliver with chemotherapeutic drugs, resulting in a combinational anticancer effect. The mechanism for this could be that anti-VEGF treatments sensitize the cells to chemotherapy agents by blocking blood supply, improve drug penetration by disruption or normalization of tumor vasculature and in addition directly kill cancer cells by gene therapy.24,25\nHowever, naked siRNAs may not induce efficient transfection by themselves because they are unstable and vulnerable, especially to nuclease-mediated degradation; also, the negatively charged surface blocks them from cellular endocytosis. Moreover, successful transfection of siRNA into cells is based on siRNA with complete structure, which could further initiate the RNAi process for targeted gene silencing.26,27 Thus, nanoscale delivery systems, which could co-load drug and siRNA and protect siRNA from degradation, were widely used and demonstrated to be excellent candidates. These include nanoparticles,28 liposomes,29 polyplexes30 and dendrimers.31 The basic standard involves employing cationic polymers or lipids to condense negatively charged siRNA and protect them from degradation.32,33 In our previous study,25 oligodeoxynucleotides with CGA repeating units (CGA-ODNs) were introduced to load Dox to obtain Dox-loaded CGA-ODNs (CGA-ODNs-Dox). Poly(ethyleneimine) (PEI) was then used to condense siRNA and CGA ODNs-Dox to obtain Dox and siRNA co-loaded nanoparticles (PEI/CGA-ODNs-Dox and siRNA[PDR]). Finally, the pH-sensitive targeted material, o-carboxymethyl-chitosan (CMCS)-poly(ethylene glycol) (PEG)-aspargine-glycine-arginine (NGR) (CMCS-PEG-NGR[CPN]), was used to modify PDR to obtain multifunctional CPN-PDR. CPN-PDRs were demonstrated to be multifunctional, being able to co-deliver Dox and siRNA into cells, induce pH-responsive disassembly and realize endosomal escape of gene and drug. Thus, in the present study, the cytotoxicity of the materials and the delivery system are further evaluated. Then, the transfection efficiency of siRNA is monitored by semiquantitative real-time polymerase chain reaction (real-time PCR) and Western blot techniques for mRNA level and protein level, respectively. Finally, the safety of the delivery system is evaluated by hemolysis test in vitro and histological assessment.","Dox was purchased from Dalian Meilun Biology Technology Co. Ltd (Dalian, China). Targeting human VEGF siRNA (sense: 5′-ACAUCACCAUGCAGAUUAUdTdT-3′, anti-sense: 5′-dTdTUGUAGUGGUACGUCUAAUA-3′) was obtained from Guangzhou Ribobio Co., Ltd (Guangzhou, China). CGA oligodeoxynucleotides (sequence: 5′-CGACGACGACGACGACGACGA-3′; complementary sequence: 5′-TCGTCGTCGTCGTCGTCGTCG-3′) were purchased from BGI Co. (Shenzhen, China). Fetal bovine serum (FBS) was obtained from Sijiqing Co., Ltd, (Zhejiang, China) and 3-[4,5-dimethyl-2-thiazolyl]-2,5-diphenyl-2H-tetrazolium bromide (MTT) was from Sigma-Aldrich (St Louis, MO, USA). Trizol RNA extraction was from Thermo Fisher Scientific (Waltham, MA, USA). SYBR®Green and ReverTra Ace qPCR RT Kit were purchased from Toyobo (Osaka, Japan). All solutions were made up in Millipore ultrapure water (EMD Millipore, Billerica, MA, USA) and sterilized for cell culture. All other chemicals and reagents were of analytical grade and used as received. All the primers used in real-time PCR were synthesized and purified by BGI Co. with sequences: VEGF – forward: 5′-CTGGAGTGTGTGCCCACTGA-3′; VEGF – reverse: 5′-TCCTATGTGCTGGCCTTGGT-3′; actin – forward: GAGCTACGAGCTGCCTGACG; actin – reverse: CCTAGAAGCATTTGCGGTGG.","The MCF-7 cells were purchased from the Chinese Academy of Sciences Cells Bank (Shanghai, China), and the cells were cultured in Roswell Park Memorial Institute (RPMI) 1640 medium supplemented with 10% fetal bovine serum (FBS), streptomycin at 100 µg/mL and penicillin at 100 U/mL. All cells were cultured in a 37°C incubator with 5% CO2.\nKunming mice (20±2 g) were obtained from Experimental Animal Center of Shandong University. Animal experiments were carried out according to the requirements of the Animal Management Rules of the Ministry of Health (China) and approved by the Laboratory Animal Ethics Review Committee of Qilu Medical College of Shandong University.","PDR and CPN-PDR were prepared by the method reported in our previous study.25 Briefly, CGA-ODNs-Dox was first obtained by incubating Dox with CGA-ODNs at room temperature. Then, PEI, siRNA, ODNs-Dox and CPN were dissolved and diluted to the corresponding concentrations with deionized water. After that, siRNA and CGA-ODNs-Dox were mixed by vortexing for several seconds to obtain the mixture. The mixture was added dropwise into PEI solution and mixed by vortexing and then incubated for 30 min at room temperature to form PDR. CPN-PDR was obtained by adding PDR suspension into the CPN solution under vortexing, followed by 30-min incubation at room temperature. When preparing the blank nanoparticles, the DOX and siRNA were not involved in above method. The construction of different kinds of nanoparticles is shown in Figure 1.\nThe loading content of DOX and siRNA was calculated using the following formula:34\nLoading content(%)=Weight of loading drugsWeight of nanoparticles×100%(1)","MTT assays were carried out on MCF-7 cells to evaluate the in vitro cytotoxicity of CGA-ODNs, PEI and CPN, respectively.35 MCF-7 cells were seeded with density at 7,000 cells/well in the 96-well plates and allowed to adhere overnight. CGA-ODNs, PEI and CPN solutions, which were corresponding to Dox concentrations (0.25, 0.625, 1.25, 2.5 and 5 µM), were added and incubated with the cells for 24, 48 and 72 h, respectively. Five wells were set for each concentration, and cells incubated with fresh media were taken as control. At a determined time point, 20 µL MTT solutions (5 mg/mL) were added. After 4 h incubation, the plates were centrifuged (3,000 rpm, 10 min) and the supernatant was removed. Then, 200 µL DMSO solutions were added to dissolve the formazan crystals formed by the living cells. The cell viability was calculated according to the following formula (Equation 2) after recording the absorbance at 570 nm by a microplate reader (Model 680; Bio-Rad Laboratories Inc., Hercules, CA, USA). All the assays were repeated three times.\nCell viability=Abs(sample)−Abs(blank)Abs(control)−Abs(blank)×100%(2)","The cytotoxicity of non-DOX- and siRNA-loaded nano-particles (blank PDR[bPDR]) and CPN-coated blank PDR (bCPN-PDR) were also evaluated in MCF-7 cells. bPDR and bCPN-PDR solutions, corresponding to Dox concentrations (0.25, 0.625, 1.25, 2.5 and 5 µM), were added and incubated with the cells for 24, 48 and 72 h, respectively. Five wells were set for each concentration and cells incubated with fresh media were taken as control. After the indicated time periods, cell viability was evaluated according to the procedure described above.","MCF-7 cells were seeded into 24-well plates at densities of 8×104, 6×104 and 5×104 cells/well for 24, 48 and 72 h transfection, respectively. After culturing at 37°C overnight, MCF-7 cells were treated with siRNA specific for VEGF. The siRNA was delivered either by Lipofectamine 2000 (Thermo Fisher Scientific) according to the manufacturer’s instructions or by CPN-modified siRNA-loaded nanoparticles (CPN-PR) or by CPN-modified Dox- and siRNA-loaded nanoparticles (CPN-PDR). The final concentrations of Dox and siRNA were 1 µM and 55 nM, respectively. After siRNA transfection, cells were harvested for RNA extraction and Western blot PCR analysis or subjected to Western blot assay.","The expression level of VEGF mRNA was monitored by real-time PCR technique with the standard procedure. Total RNA was extracted using Trizol agent under the standard procedure (Thermo Fisher Scientific), and cDNA synthesis was carried out using Rever Tra Ace qPCR RT Kit (Toyobo) according to the manufacturer’s instructions. The final concentration of RNA was measured by Nano Drop 2000 (Thermo Fisher Scientific), and the final product cDNA was stored at −20°C until use.\nEach real-time PCR system contained 20 µL solutions including cDNA (2 µL), SYBR®Green mix (10 µL), primer (1.6 µL) and double distilled water (6.4 µL). The reaction system was placed into PCR instrument (Roche LightCycler™; Hoffman-La Roche Ltd, Basel, Switzerland) for analysis. β-Actin served as a reference gene. Three wells were set for each group. The expression level of VEGF mRNA was normalized to the β-actin expression level and calculated by recording the Ct value at the end of the reaction.","A Western blot assay was employed to investigate the expression level of total VEGF protein after transfection. At a determined time, cells were trypsinized and pelleted by centrifugation. The protein collecting process was carried out based on the standard procedure of Total Protein Extraction Kit (BestBio, Shanghai, China). The total protein was stored at −80°C until use. Protein was quantified by BCA protein assay kit (Beyotime Biotechnology, Shanghai, China) based on the standard carve calculation (A=0.006 C+0.0919, r=0.9987). After quantification, all the samples were diluted to 2 µg/µL with sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) electrophoresis, denatured in boiling water for 5 min and stored at −80°C until use. Equal amounts of protein (50 µg) were subjected to 12% SDS-PAGE and transferred to nitrocellulose membranes using standard procedures. The membrane was blocked in 2% skimmed milk in phosphate buffered saline for 2 h and washed by Tris-buffered saline and Tween-20 (TBST) solution three times. Then the membranes were probed with primary antibodies against VEGF and glyceraldehyde 3-phosphate dehydrogenase (GAPDN) overnight at 4°C. After 3×10 min washing in TBST on shaker, membranes were incubated with horseradish peroxidase (HRP)-conjugated anti-rabbit IgG for 1 h at room temperature. With another 3×10 min washes in TBST, the membranes were developed with electrochemiluminescence (ECL) using a gel-imaging system and analyzed using Image Analysis software.","The anti-proliferation activity of Dox, CPN-PD and CPN-PDR against MCF-7 cells was tested via MTT method. MCF-7 cells were seeded into 96-well plates at a density of 7,000 cells/well. After overnight incubation, the cells were treated with various concentrations (0.002, 0.01, 0.05, 0.25 and 0.5 µM) of Dox, CPN-PD and CPN-PDR solutions and incubated for another 48 h. Five wells were set for each concentration, and cells incubated with fresh media were taken as control. After indicated time periods, cell viability was evaluated according to the procedure described in the “In vitro cytotoxicity of materials” section.","Hemolysis test was carried out to investigate the safety of CPN-PDR for intravenous injection. A 2% red blood cell suspension was collected by centrifugation and resuspension. The test was performed under the design described in Table 1. After incubated at 37°C for 3 h, all the groups were centrifuged at 3,000 rpm for 15 min and visualized by the naked eye. To measure the hemolysis ratios of each group, UV-vis spectrophotometry was carried out to record the absorbance of supernatant in each group. The quantitation of hemolysis ratios was calculated according to the following formula:\nHR(%)=Abs(sample)−Abs(−)Abs(+)−Abs(−)×100%(3)where Abs (sample), Abs (−) and Abs (+) refer to the absorbances of the samples, negative control and positive control, respectively.","In order to evaluate the compatibility and tissue toxicity of CPN-PDR in vivo, a histological observation was performed.36 Five female Kunming mice were injected with CPN-PDR at a dose of 232 µg/kg through the tail vein. In the meantime, normal saline (NS) was taken as a control reagent. After 1 week, all the mice were sacrificed, and the heart, liver, spleen, lung and kidney were separated. After washing twice with PBS, all the organs were fixed in 4% formaldehyde, dehydrated in gradient alcohol, placed in xylene, embedded in paraffin and made into sections, followed by hematoxylin-eosin (HE) staining for histological examination with microscope."," Characteristics of nanoparticles In the optimal formulation of CPN-PDR, the weight ratio of DOX to siRNA was 1:1; therefore, the siRNA loading content was equal to the DOX loading content. Based on the facts that the molar ratio of CGA-ODNs to Dox was 1:5, the weight ratio of CPN/PEI/ODNs and siRNA was 4:1:1, and consequently, siRNA and Dox loading efficiency in CPN-PDR was calculated to be 4.93%.\nIn the optimal formulation of CPN-PDR, the weight ratio of DOX to siRNA was 1:1; therefore, the siRNA loading content was equal to the DOX loading content. Based on the facts that the molar ratio of CGA-ODNs to Dox was 1:5, the weight ratio of CPN/PEI/ODNs and siRNA was 4:1:1, and consequently, siRNA and Dox loading efficiency in CPN-PDR was calculated to be 4.93%.\n Cytotoxicity of materials In the co-delivery platform, CGA-ODNs were selected as carriers of DOX, then the mixture of CGA-ODNs-Dox and siRNA (abbreviated as DR) was electrostatically interacted with PEI to obtain Dox and siRNA co-loaded nanoparticles, PEI/DR (PDR). The copolymer CPN was employed as a multifunctional material.25 In order to evaluate the safety of the carrier, MTT assays were carried out to evaluate the cytotoxicity of CGA-ODNs and CPN. In the meantime, PEI, a toxic cationic polymer, was also tested in this experiment. As shown in Figure 2A, the viability of MCF-7 cells treated with CGA-ODNs and CPN at any concentration was observed to be stable and >80% within 48 h. After 72 h incubation, the viability of MCF-7 cells treated with CPN was still >80%, but after treatment with a higher concentration of CGA-ODNs (2.5 and 5 µM), the cell viability was slightly decreased to 60%. This indicated that there is no obvious cytotoxicity of CGA-ODNs and CPN. However, the cell viability of MCF-7 cells treated with PEI was obviously dependent on the concentrations of PEI. With an increase in concentration, the MCF-7 cells treated with PEI showed lower cell viability. Moreover, this concentration-dependent trend became more obvious when incubation time increased from 24 h to 72 h.\nIn the co-delivery platform, CGA-ODNs were selected as carriers of DOX, then the mixture of CGA-ODNs-Dox and siRNA (abbreviated as DR) was electrostatically interacted with PEI to obtain Dox and siRNA co-loaded nanoparticles, PEI/DR (PDR). The copolymer CPN was employed as a multifunctional material.25 In order to evaluate the safety of the carrier, MTT assays were carried out to evaluate the cytotoxicity of CGA-ODNs and CPN. In the meantime, PEI, a toxic cationic polymer, was also tested in this experiment. As shown in Figure 2A, the viability of MCF-7 cells treated with CGA-ODNs and CPN at any concentration was observed to be stable and >80% within 48 h. After 72 h incubation, the viability of MCF-7 cells treated with CPN was still >80%, but after treatment with a higher concentration of CGA-ODNs (2.5 and 5 µM), the cell viability was slightly decreased to 60%. This indicated that there is no obvious cytotoxicity of CGA-ODNs and CPN. However, the cell viability of MCF-7 cells treated with PEI was obviously dependent on the concentrations of PEI. With an increase in concentration, the MCF-7 cells treated with PEI showed lower cell viability. Moreover, this concentration-dependent trend became more obvious when incubation time increased from 24 h to 72 h.\n Cytotoxicity of blank nanoparticles Cell viability of MCF-7 cells was also monitored to investigate the cytotoxicity of non-RNA- and non-drug-loaded carriers including bPDR and bCPN-PDR. As shown in Figure 2B, after treatment with bCPN-PDR, the cell viability was stable around 95% and shown to be concentration independent at 24 h. Meanwhile, bPDR-treated cells showed lower cell viability with time. With increased concentration, bPDR showed higher cytotoxicity, inducing 80% cell death in 72 h. Compared with the bPDR group, the cell viabilities of the bCPN-PDR group were significantly higher (P<0.05). This phenomenon could be explained by the higher positive charge of bPDR due to the positive ingredient of PEI, while the cell cytotoxicity could be significantly decreased by covering with the nontoxic negatively charged CPN (>80% cell viability in Figure 2A), which could induce the charge reversal of bPDR, implying that the negatively charged CPN coating could decrease the higher toxicity of bPDR to cells.\nCell viability of MCF-7 cells was also monitored to investigate the cytotoxicity of non-RNA- and non-drug-loaded carriers including bPDR and bCPN-PDR. As shown in Figure 2B, after treatment with bCPN-PDR, the cell viability was stable around 95% and shown to be concentration independent at 24 h. Meanwhile, bPDR-treated cells showed lower cell viability with time. With increased concentration, bPDR showed higher cytotoxicity, inducing 80% cell death in 72 h. Compared with the bPDR group, the cell viabilities of the bCPN-PDR group were significantly higher (P<0.05). This phenomenon could be explained by the higher positive charge of bPDR due to the positive ingredient of PEI, while the cell cytotoxicity could be significantly decreased by covering with the nontoxic negatively charged CPN (>80% cell viability in Figure 2A), which could induce the charge reversal of bPDR, implying that the negatively charged CPN coating could decrease the higher toxicity of bPDR to cells.\n Expression level of VEGF mRNA The transfection efficiency of CPN-PDR compared to Lipofectamine 2000 was demonstrated by delivering VEGF-siRNA into MCF-7 cells. The expression level of VEGF mRNA was measured using real-time PCR, and the results are shown in Figure 3. The expression level of VEGF mRNA was normalized to the β-actin expression and calculated based on the semiquantitative method. Untreated cells were selected as control, and the expression level was set as 100%. For lipo/siRNA group, the silencing efficiencies were 61.98%±6.96%, 31.93%±6.43% and 37.02%±3.17% at 24, 48 and 72 h, respectively. The decreased expression of VEGF mRNA reflected that gene silencing capability of lipo/siRNA. With incubation time extended from 24 to 48 h, the capability was enhanced (P<0.05). However, in 72 h, no further reduction was observed compared to that in 48 h, although the significantly downregulated expression could be observed (P<0.05 vs control, Dox and bCPN-PDR), implying that the silencing effect could reach the plateau period after 48 h and last for 72 h at least. The CPN-PR and CPN-PDR have shown similar behavior to lipo/siRNA. Compared with the control, a remarkable decrease of VEGF mRNA expression was observed (P<0.01) at 48 h. There was no significant difference in expression levels between CPN-PR and CPN-PDR (P>0.05) at any time point, implying their comparable gene delivery and transfection capability. More importantly, this capability was equivalent to the commercial Lipofectamine 2000.\nThe transfection efficiency of CPN-PDR compared to Lipofectamine 2000 was demonstrated by delivering VEGF-siRNA into MCF-7 cells. The expression level of VEGF mRNA was measured using real-time PCR, and the results are shown in Figure 3. The expression level of VEGF mRNA was normalized to the β-actin expression and calculated based on the semiquantitative method. Untreated cells were selected as control, and the expression level was set as 100%. For lipo/siRNA group, the silencing efficiencies were 61.98%±6.96%, 31.93%±6.43% and 37.02%±3.17% at 24, 48 and 72 h, respectively. The decreased expression of VEGF mRNA reflected that gene silencing capability of lipo/siRNA. With incubation time extended from 24 to 48 h, the capability was enhanced (P<0.05). However, in 72 h, no further reduction was observed compared to that in 48 h, although the significantly downregulated expression could be observed (P<0.05 vs control, Dox and bCPN-PDR), implying that the silencing effect could reach the plateau period after 48 h and last for 72 h at least. The CPN-PR and CPN-PDR have shown similar behavior to lipo/siRNA. Compared with the control, a remarkable decrease of VEGF mRNA expression was observed (P<0.01) at 48 h. There was no significant difference in expression levels between CPN-PR and CPN-PDR (P>0.05) at any time point, implying their comparable gene delivery and transfection capability. More importantly, this capability was equivalent to the commercial Lipofectamine 2000.\n Expression level of VEGF protein Based on the results of real-time PCR, the gene-silencing effect enhanced with time and reached a platform after 48 h. In this study, the Western blot technique was employed to observe VEGF protein expression at 48 h. The results are shown in Figure 4. GAPDH was selected as the inner control; all the bands were found to be equivalent in gray levels, and the amount of protein expression was positively correlated with the shade of gray in the imaging picture. From Figure 4, the gray levels of both control and bCPN-PDR, which were similar, were obvious. However, siRNA-loaded nanoparticles including lipo/siRNA, CPN-PR and CPN-PDR demonstrated a notably lower gray level. Furthermore, as marked with the red rectangle in Figure 4, expression of VEGF protein in CPN-PDR could hardly be identified. This suggested that co-delivery of anti-VEGF siRNA and Dox could downregulate the relevant VEGF protein level, which was in agreement with the results from semiquantitative real-time PCR. Taken together, these results indicate that nanoparticles loaded with anti-VEGF siRNA downregulated both the protein and the mRNA expression of VEGF.\nBased on the results of real-time PCR, the gene-silencing effect enhanced with time and reached a platform after 48 h. In this study, the Western blot technique was employed to observe VEGF protein expression at 48 h. The results are shown in Figure 4. GAPDH was selected as the inner control; all the bands were found to be equivalent in gray levels, and the amount of protein expression was positively correlated with the shade of gray in the imaging picture. From Figure 4, the gray levels of both control and bCPN-PDR, which were similar, were obvious. However, siRNA-loaded nanoparticles including lipo/siRNA, CPN-PR and CPN-PDR demonstrated a notably lower gray level. Furthermore, as marked with the red rectangle in Figure 4, expression of VEGF protein in CPN-PDR could hardly be identified. This suggested that co-delivery of anti-VEGF siRNA and Dox could downregulate the relevant VEGF protein level, which was in agreement with the results from semiquantitative real-time PCR. Taken together, these results indicate that nanoparticles loaded with anti-VEGF siRNA downregulated both the protein and the mRNA expression of VEGF.\n Inhibition of cell proliferation in vitro The ultimate goal of siRNA intracellular delivery is to restrain the proliferation of cancer cells. Different treatments against MCF-7 cells were performed to further investigate the inhibition effect. As shown in Figure 5A, an obvious concentration dependence in terms of DOX, CPN-PD and CPN-PDR could be observed after 48 h incubation. We have calculated the differences between CPN-PDR vs CPN-PD and CPN-PDR vs DOX at each dose. The results indicated that there was a significant difference between CPN-PDR vs DOX at each dose except the dose of 0.05, and there were significant differences between CPN-PDR vs CPN-PD at a dose of 0.002. It was suggested that the in vitro antitumor activity of CPN-PDR was equivalent to CPN-PD, and both of them showed a higher antitumor activity than free drug DOX, which was mainly attributed to the design of the multifunctional nano-vectors. We have also calculated the statistical differences between CPN-PDR vs CPN-PD and CPN-PDR vs DOX in the concentration range of Dox (0.002, 0.01, 0.05, 0.25 and 0.5 µM) using Student’s t-test. After statistical calculation, the significant differences were verified to exist between CPN-PDR vs CPN-PD (P<0.05) and CPN-PDR vs DOX (P<0.01). After calculation with professional software, the half maximal inhibitory concentrations (IC50) are presented in Figure 5B. They are 0.0367±0.0088 µM for Dox, 0.0266±0.0027 µM for CPN-PD and 0.0126±0.0022 µM for CPN-PDR, respectively. The IC50 of CPN-PD was 2.913-fold higher than that of CPN-PDR, and the IC50 of DOX was 2.110-fold higher than that of CPN-PDR. After statistical calculation, significant differences were found between CPN-PDR vs CPN-PD (P<0.05) and CPN-PDR vs DOX (P<0.05). With the results from real-time PCR and Western blot, the increased cytotoxicity of CPN-PDR probably resulted from siRNA delivery and successful transfection. This was because the intracellular delivery of anti-VEGF siRNA could downregulate the expression of VEGF protein. The anti-VEGF therapy may sensitize cancer cells or kill cells block the blood supply of the tumor and improve drug penetration. Taken together, the anti-VEGF siRNA and Dox co-delivery platform could induce increased cytotoxicity against cancer cells.\nThe ultimate goal of siRNA intracellular delivery is to restrain the proliferation of cancer cells. Different treatments against MCF-7 cells were performed to further investigate the inhibition effect. As shown in Figure 5A, an obvious concentration dependence in terms of DOX, CPN-PD and CPN-PDR could be observed after 48 h incubation. We have calculated the differences between CPN-PDR vs CPN-PD and CPN-PDR vs DOX at each dose. The results indicated that there was a significant difference between CPN-PDR vs DOX at each dose except the dose of 0.05, and there were significant differences between CPN-PDR vs CPN-PD at a dose of 0.002. It was suggested that the in vitro antitumor activity of CPN-PDR was equivalent to CPN-PD, and both of them showed a higher antitumor activity than free drug DOX, which was mainly attributed to the design of the multifunctional nano-vectors. We have also calculated the statistical differences between CPN-PDR vs CPN-PD and CPN-PDR vs DOX in the concentration range of Dox (0.002, 0.01, 0.05, 0.25 and 0.5 µM) using Student’s t-test. After statistical calculation, the significant differences were verified to exist between CPN-PDR vs CPN-PD (P<0.05) and CPN-PDR vs DOX (P<0.01). After calculation with professional software, the half maximal inhibitory concentrations (IC50) are presented in Figure 5B. They are 0.0367±0.0088 µM for Dox, 0.0266±0.0027 µM for CPN-PD and 0.0126±0.0022 µM for CPN-PDR, respectively. The IC50 of CPN-PD was 2.913-fold higher than that of CPN-PDR, and the IC50 of DOX was 2.110-fold higher than that of CPN-PDR. After statistical calculation, significant differences were found between CPN-PDR vs CPN-PD (P<0.05) and CPN-PDR vs DOX (P<0.05). With the results from real-time PCR and Western blot, the increased cytotoxicity of CPN-PDR probably resulted from siRNA delivery and successful transfection. This was because the intracellular delivery of anti-VEGF siRNA could downregulate the expression of VEGF protein. The anti-VEGF therapy may sensitize cancer cells or kill cells block the blood supply of the tumor and improve drug penetration. Taken together, the anti-VEGF siRNA and Dox co-delivery platform could induce increased cytotoxicity against cancer cells.\n Hemolysis test To investigate the safety of intravenous injection of CPN-PDR, a hemolysis test was performed. As shown in Table 1, different volume ratios of CPN-PDR to 2% red blood cell suspension were selected. After incubation and centrifugation, tubes were observed by the naked eye. As shown in Figure 6, no red blood cell hemolysis was observed in tube 6, in which 100% PBS was added serving as a negative control. For tube 7, 100% double distilled water was added and red cell hemolysis could be clearly observed. For tubes 1–5, in which different percentages of CPN-PDR were added, no red blood cell hemolysis occurred. Hemolysis of red blood cells can generate hemoprotein, which could be detected using UV-vis spectrophotometry. After scanning the absorbance wavelength of hemoprotein, 577 nm was selected as the detecting wavelength. According to Equation 3, hemolysis ratios were calculated based on the recorded absorbance and shown in Table 2. All the hemolysis ratios of tubes 1–5 were <5%, implying that no hemolysis was induced by addition of CPN-PDR.\nTo investigate the safety of intravenous injection of CPN-PDR, a hemolysis test was performed. As shown in Table 1, different volume ratios of CPN-PDR to 2% red blood cell suspension were selected. After incubation and centrifugation, tubes were observed by the naked eye. As shown in Figure 6, no red blood cell hemolysis was observed in tube 6, in which 100% PBS was added serving as a negative control. For tube 7, 100% double distilled water was added and red cell hemolysis could be clearly observed. For tubes 1–5, in which different percentages of CPN-PDR were added, no red blood cell hemolysis occurred. Hemolysis of red blood cells can generate hemoprotein, which could be detected using UV-vis spectrophotometry. After scanning the absorbance wavelength of hemoprotein, 577 nm was selected as the detecting wavelength. According to Equation 3, hemolysis ratios were calculated based on the recorded absorbance and shown in Table 2. All the hemolysis ratios of tubes 1–5 were <5%, implying that no hemolysis was induced by addition of CPN-PDR.\n Tissue section In this study, a histological analysis of organs (heart, liver, spleen, lung and kidney) was performed to evaluate whether CPN-PDR could cause tissue damage, inflammation and lesions. Kunming mice were injected with CPN-PDR and normal saline (NS) by tail vein, respectively. After 1 week, all the mice were sacrificed, and the heart, liver, spleen, lung and kidney were separated. After the standard procedure of HE staining for histological examination, the organs were observed under a microscope. Mice without any treatment were chosen as a control. As shown in Figure 7, compared with the control group, there was no visible histologically difference between mice administrated with CPN-PDR group and that with NS group, implying the safety of CPN-PDR in vivo.\nIn this study, a histological analysis of organs (heart, liver, spleen, lung and kidney) was performed to evaluate whether CPN-PDR could cause tissue damage, inflammation and lesions. Kunming mice were injected with CPN-PDR and normal saline (NS) by tail vein, respectively. After 1 week, all the mice were sacrificed, and the heart, liver, spleen, lung and kidney were separated. After the standard procedure of HE staining for histological examination, the organs were observed under a microscope. Mice without any treatment were chosen as a control. As shown in Figure 7, compared with the control group, there was no visible histologically difference between mice administrated with CPN-PDR group and that with NS group, implying the safety of CPN-PDR in vivo.","Cell viability of MCF-7 cells was also monitored to investigate the cytotoxicity of non-RNA- and non-drug-loaded carriers including bPDR and bCPN-PDR. As shown in Figure 2B, after treatment with bCPN-PDR, the cell viability was stable around 95% and shown to be concentration independent at 24 h. Meanwhile, bPDR-treated cells showed lower cell viability with time. With increased concentration, bPDR showed higher cytotoxicity, inducing 80% cell death in 72 h. Compared with the bPDR group, the cell viabilities of the bCPN-PDR group were significantly higher (P<0.05). This phenomenon could be explained by the higher positive charge of bPDR due to the positive ingredient of PEI, while the cell cytotoxicity could be significantly decreased by covering with the nontoxic negatively charged CPN (>80% cell viability in Figure 2A), which could induce the charge reversal of bPDR, implying that the negatively charged CPN coating could decrease the higher toxicity of bPDR to cells.","The transfection efficiency of CPN-PDR compared to Lipofectamine 2000 was demonstrated by delivering VEGF-siRNA into MCF-7 cells. The expression level of VEGF mRNA was measured using real-time PCR, and the results are shown in Figure 3. The expression level of VEGF mRNA was normalized to the β-actin expression and calculated based on the semiquantitative method. Untreated cells were selected as control, and the expression level was set as 100%. For lipo/siRNA group, the silencing efficiencies were 61.98%±6.96%, 31.93%±6.43% and 37.02%±3.17% at 24, 48 and 72 h, respectively. The decreased expression of VEGF mRNA reflected that gene silencing capability of lipo/siRNA. With incubation time extended from 24 to 48 h, the capability was enhanced (P<0.05). However, in 72 h, no further reduction was observed compared to that in 48 h, although the significantly downregulated expression could be observed (P<0.05 vs control, Dox and bCPN-PDR), implying that the silencing effect could reach the plateau period after 48 h and last for 72 h at least. The CPN-PR and CPN-PDR have shown similar behavior to lipo/siRNA. Compared with the control, a remarkable decrease of VEGF mRNA expression was observed (P<0.01) at 48 h. There was no significant difference in expression levels between CPN-PR and CPN-PDR (P>0.05) at any time point, implying their comparable gene delivery and transfection capability. More importantly, this capability was equivalent to the commercial Lipofectamine 2000.","Based on the results of real-time PCR, the gene-silencing effect enhanced with time and reached a platform after 48 h. In this study, the Western blot technique was employed to observe VEGF protein expression at 48 h. The results are shown in Figure 4. GAPDH was selected as the inner control; all the bands were found to be equivalent in gray levels, and the amount of protein expression was positively correlated with the shade of gray in the imaging picture. From Figure 4, the gray levels of both control and bCPN-PDR, which were similar, were obvious. However, siRNA-loaded nanoparticles including lipo/siRNA, CPN-PR and CPN-PDR demonstrated a notably lower gray level. Furthermore, as marked with the red rectangle in Figure 4, expression of VEGF protein in CPN-PDR could hardly be identified. This suggested that co-delivery of anti-VEGF siRNA and Dox could downregulate the relevant VEGF protein level, which was in agreement with the results from semiquantitative real-time PCR. Taken together, these results indicate that nanoparticles loaded with anti-VEGF siRNA downregulated both the protein and the mRNA expression of VEGF.","The ultimate goal of siRNA intracellular delivery is to restrain the proliferation of cancer cells. Different treatments against MCF-7 cells were performed to further investigate the inhibition effect. As shown in Figure 5A, an obvious concentration dependence in terms of DOX, CPN-PD and CPN-PDR could be observed after 48 h incubation. We have calculated the differences between CPN-PDR vs CPN-PD and CPN-PDR vs DOX at each dose. The results indicated that there was a significant difference between CPN-PDR vs DOX at each dose except the dose of 0.05, and there were significant differences between CPN-PDR vs CPN-PD at a dose of 0.002. It was suggested that the in vitro antitumor activity of CPN-PDR was equivalent to CPN-PD, and both of them showed a higher antitumor activity than free drug DOX, which was mainly attributed to the design of the multifunctional nano-vectors. We have also calculated the statistical differences between CPN-PDR vs CPN-PD and CPN-PDR vs DOX in the concentration range of Dox (0.002, 0.01, 0.05, 0.25 and 0.5 µM) using Student’s t-test. After statistical calculation, the significant differences were verified to exist between CPN-PDR vs CPN-PD (P<0.05) and CPN-PDR vs DOX (P<0.01). After calculation with professional software, the half maximal inhibitory concentrations (IC50) are presented in Figure 5B. They are 0.0367±0.0088 µM for Dox, 0.0266±0.0027 µM for CPN-PD and 0.0126±0.0022 µM for CPN-PDR, respectively. The IC50 of CPN-PD was 2.913-fold higher than that of CPN-PDR, and the IC50 of DOX was 2.110-fold higher than that of CPN-PDR. After statistical calculation, significant differences were found between CPN-PDR vs CPN-PD (P<0.05) and CPN-PDR vs DOX (P<0.05). With the results from real-time PCR and Western blot, the increased cytotoxicity of CPN-PDR probably resulted from siRNA delivery and successful transfection. This was because the intracellular delivery of anti-VEGF siRNA could downregulate the expression of VEGF protein. The anti-VEGF therapy may sensitize cancer cells or kill cells block the blood supply of the tumor and improve drug penetration. Taken together, the anti-VEGF siRNA and Dox co-delivery platform could induce increased cytotoxicity against cancer cells.","To investigate the safety of intravenous injection of CPN-PDR, a hemolysis test was performed. As shown in Table 1, different volume ratios of CPN-PDR to 2% red blood cell suspension were selected. After incubation and centrifugation, tubes were observed by the naked eye. As shown in Figure 6, no red blood cell hemolysis was observed in tube 6, in which 100% PBS was added serving as a negative control. For tube 7, 100% double distilled water was added and red cell hemolysis could be clearly observed. For tubes 1–5, in which different percentages of CPN-PDR were added, no red blood cell hemolysis occurred. Hemolysis of red blood cells can generate hemoprotein, which could be detected using UV-vis spectrophotometry. After scanning the absorbance wavelength of hemoprotein, 577 nm was selected as the detecting wavelength. According to Equation 3, hemolysis ratios were calculated based on the recorded absorbance and shown in Table 2. All the hemolysis ratios of tubes 1–5 were <5%, implying that no hemolysis was induced by addition of CPN-PDR.","In this study, a histological analysis of organs (heart, liver, spleen, lung and kidney) was performed to evaluate whether CPN-PDR could cause tissue damage, inflammation and lesions. Kunming mice were injected with CPN-PDR and normal saline (NS) by tail vein, respectively. After 1 week, all the mice were sacrificed, and the heart, liver, spleen, lung and kidney were separated. After the standard procedure of HE staining for histological examination, the organs were observed under a microscope. Mice without any treatment were chosen as a control. As shown in Figure 7, compared with the control group, there was no visible histologically difference between mice administrated with CPN-PDR group and that with NS group, implying the safety of CPN-PDR in vivo.","In summary, the delivery system materials were demonstrated to be nontoxic and biocompatible. The nontoxic and negatively charged copolymer CPN coating could significantly decrease the cytotoxicity of the cationic core, bPDR. The obtained co-delivery system CPN-PDR was also confirmed with enhanced cytotoxicity against tumor cells. Gene transfection efficiency induced by CPN-PDR was fairly equal with that of the commercial product Lipofectamine 2000, implying the successful intracellular delivery and good transfection of siRNA. Moreover, the preliminary evaluation of safety showed CPN-PDR to have good biocompatibility, so it has great potential for further exploitation and clinical application. With the development of aptamer technology, the promising application prospects of this novel oligodeoxynucleotide-based co-loading platform will further increase."],"string":"[\n \"Cancer has been one of the leading causes of death worldwide, and mortality and morbidity continue to increase.1 Chemotherapy is one of the major strategies for cancer therapy. However, mono-chemotherapy may encounter problems including drug resistance and unspecific toxicity. To overcome the high mortality rate of cancer, new therapeutic strategies, such as combinational therapy, have been developed.2,3 It has been reported that a combination of chemotherapy and gene therapy could potentially achieve synergistic effects and improve target selectivity, and deter the development of cancer drug resistance.4 For example, a double-modulating strategy based on the combination of chemotherapeutic agent 5-fluorouracil (5-FU) and siRNA has been developed, in which chemotherapy enhances intratumoral siRNA delivery and the delivered siRNA enhances the chemosensitivity of tumors. Furthermore, combination therapy may compensate for the limited delivery of siRNA to tumor tissue and probably manage the 5-FU-resistant tumors.5\\nRNA interference (RNAi) is a natural cellular process that regulates gene expression level. Small interfering RNAs (siRNAs), which are small double-stranded RNAs, 20–24 nucleotides (nts) in length with sequences complementary to a gene’s coding sequence, can induce degradation of corresponding messenger RNAs (mRNAs), thus blocking the translation of the mRNA into protein.6,7 The key therapeutic advantage of using RNAi lies in its ability to specifically and potently knock down the expression of disease-causing genes of known sequence. The high specificity and potency makes it widely used in treating various cancers such as breast, liver, and lung cancers.8–10 In recent years, combination of chemotherapy and siRNA-induced RNAi has become a hot topic in cancer treatment. For example, co-delivery of siRNA targeting multidrug resistance protein 1 (MDR1) or anti-survivin siRNA and chemotherapeutic drugs was demonstrated to be a promising strategy to overcome drug resistance.11–13 Anti-apoptotic gene Bcl-2 is a potential combinational therapy target owing to its important role in cell apoptosis, and combination of Bcl-2 siRNA and 5-FU could induce a remarkable increase of cell apoptosis both in vitro and in vivo.14\\nTo achieve the desired combinational and synergistic effects of chemotherapy and RNAi, selection of siRNA is important, and the selection of siRNA is also the section of silencing target. The mechanism for combinational anticancer effect varies with different silencing targets. For example, siRNA targeting MDR1 or anti-apoptotic gene Bcl-2 were used to prevent drug resistance response and sensitize chemotherapeutic drugs, or enhance the apoptosis of cancer cells, both offering enhanced anticancer effect.15,16 Tumor tissues have abundant new blood vessels that pump sufficient nutrition and oxygen for tumor growth and metastasis. The dependency of tumor tissues on these new blood vessels is important in anti-angiogenesis therapy for cancer treatment.17 Recently, there have been many efforts in achieving anti-angiogenesis, including delivering siRNAs to silence specific pro-angiogenic genes, such as vascular endothelial growth factor (VEGF), fibroblast growth factor (FGF) and interleukin-8 (IL-8).18 Of these targets, VEGF has received a lot of attention owing to its key role in tumor angiogenesis. With the development of RNAi technology, siRNA with VEGF target has been widely explored and referred to as anti-VEGF treatment.19–21 In 2004, the success of bevacizumab showed VEGF to be a potential target for angiogenesis therapy. There are a number of novel anti-VEGF agents in phase III clinical trials that may come to market in the next few years.22,23 In our design, siRNA with VEGF target was selected to co-deliver with chemotherapeutic drugs, resulting in a combinational anticancer effect. The mechanism for this could be that anti-VEGF treatments sensitize the cells to chemotherapy agents by blocking blood supply, improve drug penetration by disruption or normalization of tumor vasculature and in addition directly kill cancer cells by gene therapy.24,25\\nHowever, naked siRNAs may not induce efficient transfection by themselves because they are unstable and vulnerable, especially to nuclease-mediated degradation; also, the negatively charged surface blocks them from cellular endocytosis. Moreover, successful transfection of siRNA into cells is based on siRNA with complete structure, which could further initiate the RNAi process for targeted gene silencing.26,27 Thus, nanoscale delivery systems, which could co-load drug and siRNA and protect siRNA from degradation, were widely used and demonstrated to be excellent candidates. These include nanoparticles,28 liposomes,29 polyplexes30 and dendrimers.31 The basic standard involves employing cationic polymers or lipids to condense negatively charged siRNA and protect them from degradation.32,33 In our previous study,25 oligodeoxynucleotides with CGA repeating units (CGA-ODNs) were introduced to load Dox to obtain Dox-loaded CGA-ODNs (CGA-ODNs-Dox). Poly(ethyleneimine) (PEI) was then used to condense siRNA and CGA ODNs-Dox to obtain Dox and siRNA co-loaded nanoparticles (PEI/CGA-ODNs-Dox and siRNA[PDR]). Finally, the pH-sensitive targeted material, o-carboxymethyl-chitosan (CMCS)-poly(ethylene glycol) (PEG)-aspargine-glycine-arginine (NGR) (CMCS-PEG-NGR[CPN]), was used to modify PDR to obtain multifunctional CPN-PDR. CPN-PDRs were demonstrated to be multifunctional, being able to co-deliver Dox and siRNA into cells, induce pH-responsive disassembly and realize endosomal escape of gene and drug. Thus, in the present study, the cytotoxicity of the materials and the delivery system are further evaluated. Then, the transfection efficiency of siRNA is monitored by semiquantitative real-time polymerase chain reaction (real-time PCR) and Western blot techniques for mRNA level and protein level, respectively. Finally, the safety of the delivery system is evaluated by hemolysis test in vitro and histological assessment.\",\n \"Dox was purchased from Dalian Meilun Biology Technology Co. Ltd (Dalian, China). Targeting human VEGF siRNA (sense: 5′-ACAUCACCAUGCAGAUUAUdTdT-3′, anti-sense: 5′-dTdTUGUAGUGGUACGUCUAAUA-3′) was obtained from Guangzhou Ribobio Co., Ltd (Guangzhou, China). CGA oligodeoxynucleotides (sequence: 5′-CGACGACGACGACGACGACGA-3′; complementary sequence: 5′-TCGTCGTCGTCGTCGTCGTCG-3′) were purchased from BGI Co. (Shenzhen, China). Fetal bovine serum (FBS) was obtained from Sijiqing Co., Ltd, (Zhejiang, China) and 3-[4,5-dimethyl-2-thiazolyl]-2,5-diphenyl-2H-tetrazolium bromide (MTT) was from Sigma-Aldrich (St Louis, MO, USA). Trizol RNA extraction was from Thermo Fisher Scientific (Waltham, MA, USA). SYBR®Green and ReverTra Ace qPCR RT Kit were purchased from Toyobo (Osaka, Japan). All solutions were made up in Millipore ultrapure water (EMD Millipore, Billerica, MA, USA) and sterilized for cell culture. All other chemicals and reagents were of analytical grade and used as received. All the primers used in real-time PCR were synthesized and purified by BGI Co. with sequences: VEGF – forward: 5′-CTGGAGTGTGTGCCCACTGA-3′; VEGF – reverse: 5′-TCCTATGTGCTGGCCTTGGT-3′; actin – forward: GAGCTACGAGCTGCCTGACG; actin – reverse: CCTAGAAGCATTTGCGGTGG.\",\n \"The MCF-7 cells were purchased from the Chinese Academy of Sciences Cells Bank (Shanghai, China), and the cells were cultured in Roswell Park Memorial Institute (RPMI) 1640 medium supplemented with 10% fetal bovine serum (FBS), streptomycin at 100 µg/mL and penicillin at 100 U/mL. All cells were cultured in a 37°C incubator with 5% CO2.\\nKunming mice (20±2 g) were obtained from Experimental Animal Center of Shandong University. Animal experiments were carried out according to the requirements of the Animal Management Rules of the Ministry of Health (China) and approved by the Laboratory Animal Ethics Review Committee of Qilu Medical College of Shandong University.\",\n \"PDR and CPN-PDR were prepared by the method reported in our previous study.25 Briefly, CGA-ODNs-Dox was first obtained by incubating Dox with CGA-ODNs at room temperature. Then, PEI, siRNA, ODNs-Dox and CPN were dissolved and diluted to the corresponding concentrations with deionized water. After that, siRNA and CGA-ODNs-Dox were mixed by vortexing for several seconds to obtain the mixture. The mixture was added dropwise into PEI solution and mixed by vortexing and then incubated for 30 min at room temperature to form PDR. CPN-PDR was obtained by adding PDR suspension into the CPN solution under vortexing, followed by 30-min incubation at room temperature. When preparing the blank nanoparticles, the DOX and siRNA were not involved in above method. The construction of different kinds of nanoparticles is shown in Figure 1.\\nThe loading content of DOX and siRNA was calculated using the following formula:34\\nLoading content(%)=Weight of loading drugsWeight of nanoparticles×100%(1)\",\n \"MTT assays were carried out on MCF-7 cells to evaluate the in vitro cytotoxicity of CGA-ODNs, PEI and CPN, respectively.35 MCF-7 cells were seeded with density at 7,000 cells/well in the 96-well plates and allowed to adhere overnight. CGA-ODNs, PEI and CPN solutions, which were corresponding to Dox concentrations (0.25, 0.625, 1.25, 2.5 and 5 µM), were added and incubated with the cells for 24, 48 and 72 h, respectively. Five wells were set for each concentration, and cells incubated with fresh media were taken as control. At a determined time point, 20 µL MTT solutions (5 mg/mL) were added. After 4 h incubation, the plates were centrifuged (3,000 rpm, 10 min) and the supernatant was removed. Then, 200 µL DMSO solutions were added to dissolve the formazan crystals formed by the living cells. The cell viability was calculated according to the following formula (Equation 2) after recording the absorbance at 570 nm by a microplate reader (Model 680; Bio-Rad Laboratories Inc., Hercules, CA, USA). All the assays were repeated three times.\\nCell viability=Abs(sample)−Abs(blank)Abs(control)−Abs(blank)×100%(2)\",\n \"The cytotoxicity of non-DOX- and siRNA-loaded nano-particles (blank PDR[bPDR]) and CPN-coated blank PDR (bCPN-PDR) were also evaluated in MCF-7 cells. bPDR and bCPN-PDR solutions, corresponding to Dox concentrations (0.25, 0.625, 1.25, 2.5 and 5 µM), were added and incubated with the cells for 24, 48 and 72 h, respectively. Five wells were set for each concentration and cells incubated with fresh media were taken as control. After the indicated time periods, cell viability was evaluated according to the procedure described above.\",\n \"MCF-7 cells were seeded into 24-well plates at densities of 8×104, 6×104 and 5×104 cells/well for 24, 48 and 72 h transfection, respectively. After culturing at 37°C overnight, MCF-7 cells were treated with siRNA specific for VEGF. The siRNA was delivered either by Lipofectamine 2000 (Thermo Fisher Scientific) according to the manufacturer’s instructions or by CPN-modified siRNA-loaded nanoparticles (CPN-PR) or by CPN-modified Dox- and siRNA-loaded nanoparticles (CPN-PDR). The final concentrations of Dox and siRNA were 1 µM and 55 nM, respectively. After siRNA transfection, cells were harvested for RNA extraction and Western blot PCR analysis or subjected to Western blot assay.\",\n \"The expression level of VEGF mRNA was monitored by real-time PCR technique with the standard procedure. Total RNA was extracted using Trizol agent under the standard procedure (Thermo Fisher Scientific), and cDNA synthesis was carried out using Rever Tra Ace qPCR RT Kit (Toyobo) according to the manufacturer’s instructions. The final concentration of RNA was measured by Nano Drop 2000 (Thermo Fisher Scientific), and the final product cDNA was stored at −20°C until use.\\nEach real-time PCR system contained 20 µL solutions including cDNA (2 µL), SYBR®Green mix (10 µL), primer (1.6 µL) and double distilled water (6.4 µL). The reaction system was placed into PCR instrument (Roche LightCycler™; Hoffman-La Roche Ltd, Basel, Switzerland) for analysis. β-Actin served as a reference gene. Three wells were set for each group. The expression level of VEGF mRNA was normalized to the β-actin expression level and calculated by recording the Ct value at the end of the reaction.\",\n \"A Western blot assay was employed to investigate the expression level of total VEGF protein after transfection. At a determined time, cells were trypsinized and pelleted by centrifugation. The protein collecting process was carried out based on the standard procedure of Total Protein Extraction Kit (BestBio, Shanghai, China). The total protein was stored at −80°C until use. Protein was quantified by BCA protein assay kit (Beyotime Biotechnology, Shanghai, China) based on the standard carve calculation (A=0.006 C+0.0919, r=0.9987). After quantification, all the samples were diluted to 2 µg/µL with sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) electrophoresis, denatured in boiling water for 5 min and stored at −80°C until use. Equal amounts of protein (50 µg) were subjected to 12% SDS-PAGE and transferred to nitrocellulose membranes using standard procedures. The membrane was blocked in 2% skimmed milk in phosphate buffered saline for 2 h and washed by Tris-buffered saline and Tween-20 (TBST) solution three times. Then the membranes were probed with primary antibodies against VEGF and glyceraldehyde 3-phosphate dehydrogenase (GAPDN) overnight at 4°C. After 3×10 min washing in TBST on shaker, membranes were incubated with horseradish peroxidase (HRP)-conjugated anti-rabbit IgG for 1 h at room temperature. With another 3×10 min washes in TBST, the membranes were developed with electrochemiluminescence (ECL) using a gel-imaging system and analyzed using Image Analysis software.\",\n \"The anti-proliferation activity of Dox, CPN-PD and CPN-PDR against MCF-7 cells was tested via MTT method. MCF-7 cells were seeded into 96-well plates at a density of 7,000 cells/well. After overnight incubation, the cells were treated with various concentrations (0.002, 0.01, 0.05, 0.25 and 0.5 µM) of Dox, CPN-PD and CPN-PDR solutions and incubated for another 48 h. Five wells were set for each concentration, and cells incubated with fresh media were taken as control. After indicated time periods, cell viability was evaluated according to the procedure described in the “In vitro cytotoxicity of materials” section.\",\n \"Hemolysis test was carried out to investigate the safety of CPN-PDR for intravenous injection. A 2% red blood cell suspension was collected by centrifugation and resuspension. The test was performed under the design described in Table 1. After incubated at 37°C for 3 h, all the groups were centrifuged at 3,000 rpm for 15 min and visualized by the naked eye. To measure the hemolysis ratios of each group, UV-vis spectrophotometry was carried out to record the absorbance of supernatant in each group. The quantitation of hemolysis ratios was calculated according to the following formula:\\nHR(%)=Abs(sample)−Abs(−)Abs(+)−Abs(−)×100%(3)where Abs (sample), Abs (−) and Abs (+) refer to the absorbances of the samples, negative control and positive control, respectively.\",\n \"In order to evaluate the compatibility and tissue toxicity of CPN-PDR in vivo, a histological observation was performed.36 Five female Kunming mice were injected with CPN-PDR at a dose of 232 µg/kg through the tail vein. In the meantime, normal saline (NS) was taken as a control reagent. After 1 week, all the mice were sacrificed, and the heart, liver, spleen, lung and kidney were separated. After washing twice with PBS, all the organs were fixed in 4% formaldehyde, dehydrated in gradient alcohol, placed in xylene, embedded in paraffin and made into sections, followed by hematoxylin-eosin (HE) staining for histological examination with microscope.\",\n \" Characteristics of nanoparticles In the optimal formulation of CPN-PDR, the weight ratio of DOX to siRNA was 1:1; therefore, the siRNA loading content was equal to the DOX loading content. Based on the facts that the molar ratio of CGA-ODNs to Dox was 1:5, the weight ratio of CPN/PEI/ODNs and siRNA was 4:1:1, and consequently, siRNA and Dox loading efficiency in CPN-PDR was calculated to be 4.93%.\\nIn the optimal formulation of CPN-PDR, the weight ratio of DOX to siRNA was 1:1; therefore, the siRNA loading content was equal to the DOX loading content. Based on the facts that the molar ratio of CGA-ODNs to Dox was 1:5, the weight ratio of CPN/PEI/ODNs and siRNA was 4:1:1, and consequently, siRNA and Dox loading efficiency in CPN-PDR was calculated to be 4.93%.\\n Cytotoxicity of materials In the co-delivery platform, CGA-ODNs were selected as carriers of DOX, then the mixture of CGA-ODNs-Dox and siRNA (abbreviated as DR) was electrostatically interacted with PEI to obtain Dox and siRNA co-loaded nanoparticles, PEI/DR (PDR). The copolymer CPN was employed as a multifunctional material.25 In order to evaluate the safety of the carrier, MTT assays were carried out to evaluate the cytotoxicity of CGA-ODNs and CPN. In the meantime, PEI, a toxic cationic polymer, was also tested in this experiment. As shown in Figure 2A, the viability of MCF-7 cells treated with CGA-ODNs and CPN at any concentration was observed to be stable and >80% within 48 h. After 72 h incubation, the viability of MCF-7 cells treated with CPN was still >80%, but after treatment with a higher concentration of CGA-ODNs (2.5 and 5 µM), the cell viability was slightly decreased to 60%. This indicated that there is no obvious cytotoxicity of CGA-ODNs and CPN. However, the cell viability of MCF-7 cells treated with PEI was obviously dependent on the concentrations of PEI. With an increase in concentration, the MCF-7 cells treated with PEI showed lower cell viability. Moreover, this concentration-dependent trend became more obvious when incubation time increased from 24 h to 72 h.\\nIn the co-delivery platform, CGA-ODNs were selected as carriers of DOX, then the mixture of CGA-ODNs-Dox and siRNA (abbreviated as DR) was electrostatically interacted with PEI to obtain Dox and siRNA co-loaded nanoparticles, PEI/DR (PDR). The copolymer CPN was employed as a multifunctional material.25 In order to evaluate the safety of the carrier, MTT assays were carried out to evaluate the cytotoxicity of CGA-ODNs and CPN. In the meantime, PEI, a toxic cationic polymer, was also tested in this experiment. As shown in Figure 2A, the viability of MCF-7 cells treated with CGA-ODNs and CPN at any concentration was observed to be stable and >80% within 48 h. After 72 h incubation, the viability of MCF-7 cells treated with CPN was still >80%, but after treatment with a higher concentration of CGA-ODNs (2.5 and 5 µM), the cell viability was slightly decreased to 60%. This indicated that there is no obvious cytotoxicity of CGA-ODNs and CPN. However, the cell viability of MCF-7 cells treated with PEI was obviously dependent on the concentrations of PEI. With an increase in concentration, the MCF-7 cells treated with PEI showed lower cell viability. Moreover, this concentration-dependent trend became more obvious when incubation time increased from 24 h to 72 h.\\n Cytotoxicity of blank nanoparticles Cell viability of MCF-7 cells was also monitored to investigate the cytotoxicity of non-RNA- and non-drug-loaded carriers including bPDR and bCPN-PDR. As shown in Figure 2B, after treatment with bCPN-PDR, the cell viability was stable around 95% and shown to be concentration independent at 24 h. Meanwhile, bPDR-treated cells showed lower cell viability with time. With increased concentration, bPDR showed higher cytotoxicity, inducing 80% cell death in 72 h. Compared with the bPDR group, the cell viabilities of the bCPN-PDR group were significantly higher (P<0.05). This phenomenon could be explained by the higher positive charge of bPDR due to the positive ingredient of PEI, while the cell cytotoxicity could be significantly decreased by covering with the nontoxic negatively charged CPN (>80% cell viability in Figure 2A), which could induce the charge reversal of bPDR, implying that the negatively charged CPN coating could decrease the higher toxicity of bPDR to cells.\\nCell viability of MCF-7 cells was also monitored to investigate the cytotoxicity of non-RNA- and non-drug-loaded carriers including bPDR and bCPN-PDR. As shown in Figure 2B, after treatment with bCPN-PDR, the cell viability was stable around 95% and shown to be concentration independent at 24 h. Meanwhile, bPDR-treated cells showed lower cell viability with time. With increased concentration, bPDR showed higher cytotoxicity, inducing 80% cell death in 72 h. Compared with the bPDR group, the cell viabilities of the bCPN-PDR group were significantly higher (P<0.05). This phenomenon could be explained by the higher positive charge of bPDR due to the positive ingredient of PEI, while the cell cytotoxicity could be significantly decreased by covering with the nontoxic negatively charged CPN (>80% cell viability in Figure 2A), which could induce the charge reversal of bPDR, implying that the negatively charged CPN coating could decrease the higher toxicity of bPDR to cells.\\n Expression level of VEGF mRNA The transfection efficiency of CPN-PDR compared to Lipofectamine 2000 was demonstrated by delivering VEGF-siRNA into MCF-7 cells. The expression level of VEGF mRNA was measured using real-time PCR, and the results are shown in Figure 3. The expression level of VEGF mRNA was normalized to the β-actin expression and calculated based on the semiquantitative method. Untreated cells were selected as control, and the expression level was set as 100%. For lipo/siRNA group, the silencing efficiencies were 61.98%±6.96%, 31.93%±6.43% and 37.02%±3.17% at 24, 48 and 72 h, respectively. The decreased expression of VEGF mRNA reflected that gene silencing capability of lipo/siRNA. With incubation time extended from 24 to 48 h, the capability was enhanced (P<0.05). However, in 72 h, no further reduction was observed compared to that in 48 h, although the significantly downregulated expression could be observed (P<0.05 vs control, Dox and bCPN-PDR), implying that the silencing effect could reach the plateau period after 48 h and last for 72 h at least. The CPN-PR and CPN-PDR have shown similar behavior to lipo/siRNA. Compared with the control, a remarkable decrease of VEGF mRNA expression was observed (P<0.01) at 48 h. There was no significant difference in expression levels between CPN-PR and CPN-PDR (P>0.05) at any time point, implying their comparable gene delivery and transfection capability. More importantly, this capability was equivalent to the commercial Lipofectamine 2000.\\nThe transfection efficiency of CPN-PDR compared to Lipofectamine 2000 was demonstrated by delivering VEGF-siRNA into MCF-7 cells. The expression level of VEGF mRNA was measured using real-time PCR, and the results are shown in Figure 3. The expression level of VEGF mRNA was normalized to the β-actin expression and calculated based on the semiquantitative method. Untreated cells were selected as control, and the expression level was set as 100%. For lipo/siRNA group, the silencing efficiencies were 61.98%±6.96%, 31.93%±6.43% and 37.02%±3.17% at 24, 48 and 72 h, respectively. The decreased expression of VEGF mRNA reflected that gene silencing capability of lipo/siRNA. With incubation time extended from 24 to 48 h, the capability was enhanced (P<0.05). However, in 72 h, no further reduction was observed compared to that in 48 h, although the significantly downregulated expression could be observed (P<0.05 vs control, Dox and bCPN-PDR), implying that the silencing effect could reach the plateau period after 48 h and last for 72 h at least. The CPN-PR and CPN-PDR have shown similar behavior to lipo/siRNA. Compared with the control, a remarkable decrease of VEGF mRNA expression was observed (P<0.01) at 48 h. There was no significant difference in expression levels between CPN-PR and CPN-PDR (P>0.05) at any time point, implying their comparable gene delivery and transfection capability. More importantly, this capability was equivalent to the commercial Lipofectamine 2000.\\n Expression level of VEGF protein Based on the results of real-time PCR, the gene-silencing effect enhanced with time and reached a platform after 48 h. In this study, the Western blot technique was employed to observe VEGF protein expression at 48 h. The results are shown in Figure 4. GAPDH was selected as the inner control; all the bands were found to be equivalent in gray levels, and the amount of protein expression was positively correlated with the shade of gray in the imaging picture. From Figure 4, the gray levels of both control and bCPN-PDR, which were similar, were obvious. However, siRNA-loaded nanoparticles including lipo/siRNA, CPN-PR and CPN-PDR demonstrated a notably lower gray level. Furthermore, as marked with the red rectangle in Figure 4, expression of VEGF protein in CPN-PDR could hardly be identified. This suggested that co-delivery of anti-VEGF siRNA and Dox could downregulate the relevant VEGF protein level, which was in agreement with the results from semiquantitative real-time PCR. Taken together, these results indicate that nanoparticles loaded with anti-VEGF siRNA downregulated both the protein and the mRNA expression of VEGF.\\nBased on the results of real-time PCR, the gene-silencing effect enhanced with time and reached a platform after 48 h. In this study, the Western blot technique was employed to observe VEGF protein expression at 48 h. The results are shown in Figure 4. GAPDH was selected as the inner control; all the bands were found to be equivalent in gray levels, and the amount of protein expression was positively correlated with the shade of gray in the imaging picture. From Figure 4, the gray levels of both control and bCPN-PDR, which were similar, were obvious. However, siRNA-loaded nanoparticles including lipo/siRNA, CPN-PR and CPN-PDR demonstrated a notably lower gray level. Furthermore, as marked with the red rectangle in Figure 4, expression of VEGF protein in CPN-PDR could hardly be identified. This suggested that co-delivery of anti-VEGF siRNA and Dox could downregulate the relevant VEGF protein level, which was in agreement with the results from semiquantitative real-time PCR. Taken together, these results indicate that nanoparticles loaded with anti-VEGF siRNA downregulated both the protein and the mRNA expression of VEGF.\\n Inhibition of cell proliferation in vitro The ultimate goal of siRNA intracellular delivery is to restrain the proliferation of cancer cells. Different treatments against MCF-7 cells were performed to further investigate the inhibition effect. As shown in Figure 5A, an obvious concentration dependence in terms of DOX, CPN-PD and CPN-PDR could be observed after 48 h incubation. We have calculated the differences between CPN-PDR vs CPN-PD and CPN-PDR vs DOX at each dose. The results indicated that there was a significant difference between CPN-PDR vs DOX at each dose except the dose of 0.05, and there were significant differences between CPN-PDR vs CPN-PD at a dose of 0.002. It was suggested that the in vitro antitumor activity of CPN-PDR was equivalent to CPN-PD, and both of them showed a higher antitumor activity than free drug DOX, which was mainly attributed to the design of the multifunctional nano-vectors. We have also calculated the statistical differences between CPN-PDR vs CPN-PD and CPN-PDR vs DOX in the concentration range of Dox (0.002, 0.01, 0.05, 0.25 and 0.5 µM) using Student’s t-test. After statistical calculation, the significant differences were verified to exist between CPN-PDR vs CPN-PD (P<0.05) and CPN-PDR vs DOX (P<0.01). After calculation with professional software, the half maximal inhibitory concentrations (IC50) are presented in Figure 5B. They are 0.0367±0.0088 µM for Dox, 0.0266±0.0027 µM for CPN-PD and 0.0126±0.0022 µM for CPN-PDR, respectively. The IC50 of CPN-PD was 2.913-fold higher than that of CPN-PDR, and the IC50 of DOX was 2.110-fold higher than that of CPN-PDR. After statistical calculation, significant differences were found between CPN-PDR vs CPN-PD (P<0.05) and CPN-PDR vs DOX (P<0.05). With the results from real-time PCR and Western blot, the increased cytotoxicity of CPN-PDR probably resulted from siRNA delivery and successful transfection. This was because the intracellular delivery of anti-VEGF siRNA could downregulate the expression of VEGF protein. The anti-VEGF therapy may sensitize cancer cells or kill cells block the blood supply of the tumor and improve drug penetration. Taken together, the anti-VEGF siRNA and Dox co-delivery platform could induce increased cytotoxicity against cancer cells.\\nThe ultimate goal of siRNA intracellular delivery is to restrain the proliferation of cancer cells. Different treatments against MCF-7 cells were performed to further investigate the inhibition effect. As shown in Figure 5A, an obvious concentration dependence in terms of DOX, CPN-PD and CPN-PDR could be observed after 48 h incubation. We have calculated the differences between CPN-PDR vs CPN-PD and CPN-PDR vs DOX at each dose. The results indicated that there was a significant difference between CPN-PDR vs DOX at each dose except the dose of 0.05, and there were significant differences between CPN-PDR vs CPN-PD at a dose of 0.002. It was suggested that the in vitro antitumor activity of CPN-PDR was equivalent to CPN-PD, and both of them showed a higher antitumor activity than free drug DOX, which was mainly attributed to the design of the multifunctional nano-vectors. We have also calculated the statistical differences between CPN-PDR vs CPN-PD and CPN-PDR vs DOX in the concentration range of Dox (0.002, 0.01, 0.05, 0.25 and 0.5 µM) using Student’s t-test. After statistical calculation, the significant differences were verified to exist between CPN-PDR vs CPN-PD (P<0.05) and CPN-PDR vs DOX (P<0.01). After calculation with professional software, the half maximal inhibitory concentrations (IC50) are presented in Figure 5B. They are 0.0367±0.0088 µM for Dox, 0.0266±0.0027 µM for CPN-PD and 0.0126±0.0022 µM for CPN-PDR, respectively. The IC50 of CPN-PD was 2.913-fold higher than that of CPN-PDR, and the IC50 of DOX was 2.110-fold higher than that of CPN-PDR. After statistical calculation, significant differences were found between CPN-PDR vs CPN-PD (P<0.05) and CPN-PDR vs DOX (P<0.05). With the results from real-time PCR and Western blot, the increased cytotoxicity of CPN-PDR probably resulted from siRNA delivery and successful transfection. This was because the intracellular delivery of anti-VEGF siRNA could downregulate the expression of VEGF protein. The anti-VEGF therapy may sensitize cancer cells or kill cells block the blood supply of the tumor and improve drug penetration. Taken together, the anti-VEGF siRNA and Dox co-delivery platform could induce increased cytotoxicity against cancer cells.\\n Hemolysis test To investigate the safety of intravenous injection of CPN-PDR, a hemolysis test was performed. As shown in Table 1, different volume ratios of CPN-PDR to 2% red blood cell suspension were selected. After incubation and centrifugation, tubes were observed by the naked eye. As shown in Figure 6, no red blood cell hemolysis was observed in tube 6, in which 100% PBS was added serving as a negative control. For tube 7, 100% double distilled water was added and red cell hemolysis could be clearly observed. For tubes 1–5, in which different percentages of CPN-PDR were added, no red blood cell hemolysis occurred. Hemolysis of red blood cells can generate hemoprotein, which could be detected using UV-vis spectrophotometry. After scanning the absorbance wavelength of hemoprotein, 577 nm was selected as the detecting wavelength. According to Equation 3, hemolysis ratios were calculated based on the recorded absorbance and shown in Table 2. All the hemolysis ratios of tubes 1–5 were <5%, implying that no hemolysis was induced by addition of CPN-PDR.\\nTo investigate the safety of intravenous injection of CPN-PDR, a hemolysis test was performed. As shown in Table 1, different volume ratios of CPN-PDR to 2% red blood cell suspension were selected. After incubation and centrifugation, tubes were observed by the naked eye. As shown in Figure 6, no red blood cell hemolysis was observed in tube 6, in which 100% PBS was added serving as a negative control. For tube 7, 100% double distilled water was added and red cell hemolysis could be clearly observed. For tubes 1–5, in which different percentages of CPN-PDR were added, no red blood cell hemolysis occurred. Hemolysis of red blood cells can generate hemoprotein, which could be detected using UV-vis spectrophotometry. After scanning the absorbance wavelength of hemoprotein, 577 nm was selected as the detecting wavelength. According to Equation 3, hemolysis ratios were calculated based on the recorded absorbance and shown in Table 2. All the hemolysis ratios of tubes 1–5 were <5%, implying that no hemolysis was induced by addition of CPN-PDR.\\n Tissue section In this study, a histological analysis of organs (heart, liver, spleen, lung and kidney) was performed to evaluate whether CPN-PDR could cause tissue damage, inflammation and lesions. Kunming mice were injected with CPN-PDR and normal saline (NS) by tail vein, respectively. After 1 week, all the mice were sacrificed, and the heart, liver, spleen, lung and kidney were separated. After the standard procedure of HE staining for histological examination, the organs were observed under a microscope. Mice without any treatment were chosen as a control. As shown in Figure 7, compared with the control group, there was no visible histologically difference between mice administrated with CPN-PDR group and that with NS group, implying the safety of CPN-PDR in vivo.\\nIn this study, a histological analysis of organs (heart, liver, spleen, lung and kidney) was performed to evaluate whether CPN-PDR could cause tissue damage, inflammation and lesions. Kunming mice were injected with CPN-PDR and normal saline (NS) by tail vein, respectively. After 1 week, all the mice were sacrificed, and the heart, liver, spleen, lung and kidney were separated. After the standard procedure of HE staining for histological examination, the organs were observed under a microscope. Mice without any treatment were chosen as a control. As shown in Figure 7, compared with the control group, there was no visible histologically difference between mice administrated with CPN-PDR group and that with NS group, implying the safety of CPN-PDR in vivo.\",\n \"Cell viability of MCF-7 cells was also monitored to investigate the cytotoxicity of non-RNA- and non-drug-loaded carriers including bPDR and bCPN-PDR. As shown in Figure 2B, after treatment with bCPN-PDR, the cell viability was stable around 95% and shown to be concentration independent at 24 h. Meanwhile, bPDR-treated cells showed lower cell viability with time. With increased concentration, bPDR showed higher cytotoxicity, inducing 80% cell death in 72 h. Compared with the bPDR group, the cell viabilities of the bCPN-PDR group were significantly higher (P<0.05). This phenomenon could be explained by the higher positive charge of bPDR due to the positive ingredient of PEI, while the cell cytotoxicity could be significantly decreased by covering with the nontoxic negatively charged CPN (>80% cell viability in Figure 2A), which could induce the charge reversal of bPDR, implying that the negatively charged CPN coating could decrease the higher toxicity of bPDR to cells.\",\n \"The transfection efficiency of CPN-PDR compared to Lipofectamine 2000 was demonstrated by delivering VEGF-siRNA into MCF-7 cells. The expression level of VEGF mRNA was measured using real-time PCR, and the results are shown in Figure 3. The expression level of VEGF mRNA was normalized to the β-actin expression and calculated based on the semiquantitative method. Untreated cells were selected as control, and the expression level was set as 100%. For lipo/siRNA group, the silencing efficiencies were 61.98%±6.96%, 31.93%±6.43% and 37.02%±3.17% at 24, 48 and 72 h, respectively. The decreased expression of VEGF mRNA reflected that gene silencing capability of lipo/siRNA. With incubation time extended from 24 to 48 h, the capability was enhanced (P<0.05). However, in 72 h, no further reduction was observed compared to that in 48 h, although the significantly downregulated expression could be observed (P<0.05 vs control, Dox and bCPN-PDR), implying that the silencing effect could reach the plateau period after 48 h and last for 72 h at least. The CPN-PR and CPN-PDR have shown similar behavior to lipo/siRNA. Compared with the control, a remarkable decrease of VEGF mRNA expression was observed (P<0.01) at 48 h. There was no significant difference in expression levels between CPN-PR and CPN-PDR (P>0.05) at any time point, implying their comparable gene delivery and transfection capability. More importantly, this capability was equivalent to the commercial Lipofectamine 2000.\",\n \"Based on the results of real-time PCR, the gene-silencing effect enhanced with time and reached a platform after 48 h. In this study, the Western blot technique was employed to observe VEGF protein expression at 48 h. The results are shown in Figure 4. GAPDH was selected as the inner control; all the bands were found to be equivalent in gray levels, and the amount of protein expression was positively correlated with the shade of gray in the imaging picture. From Figure 4, the gray levels of both control and bCPN-PDR, which were similar, were obvious. However, siRNA-loaded nanoparticles including lipo/siRNA, CPN-PR and CPN-PDR demonstrated a notably lower gray level. Furthermore, as marked with the red rectangle in Figure 4, expression of VEGF protein in CPN-PDR could hardly be identified. This suggested that co-delivery of anti-VEGF siRNA and Dox could downregulate the relevant VEGF protein level, which was in agreement with the results from semiquantitative real-time PCR. Taken together, these results indicate that nanoparticles loaded with anti-VEGF siRNA downregulated both the protein and the mRNA expression of VEGF.\",\n \"The ultimate goal of siRNA intracellular delivery is to restrain the proliferation of cancer cells. Different treatments against MCF-7 cells were performed to further investigate the inhibition effect. As shown in Figure 5A, an obvious concentration dependence in terms of DOX, CPN-PD and CPN-PDR could be observed after 48 h incubation. We have calculated the differences between CPN-PDR vs CPN-PD and CPN-PDR vs DOX at each dose. The results indicated that there was a significant difference between CPN-PDR vs DOX at each dose except the dose of 0.05, and there were significant differences between CPN-PDR vs CPN-PD at a dose of 0.002. It was suggested that the in vitro antitumor activity of CPN-PDR was equivalent to CPN-PD, and both of them showed a higher antitumor activity than free drug DOX, which was mainly attributed to the design of the multifunctional nano-vectors. We have also calculated the statistical differences between CPN-PDR vs CPN-PD and CPN-PDR vs DOX in the concentration range of Dox (0.002, 0.01, 0.05, 0.25 and 0.5 µM) using Student’s t-test. After statistical calculation, the significant differences were verified to exist between CPN-PDR vs CPN-PD (P<0.05) and CPN-PDR vs DOX (P<0.01). After calculation with professional software, the half maximal inhibitory concentrations (IC50) are presented in Figure 5B. They are 0.0367±0.0088 µM for Dox, 0.0266±0.0027 µM for CPN-PD and 0.0126±0.0022 µM for CPN-PDR, respectively. The IC50 of CPN-PD was 2.913-fold higher than that of CPN-PDR, and the IC50 of DOX was 2.110-fold higher than that of CPN-PDR. After statistical calculation, significant differences were found between CPN-PDR vs CPN-PD (P<0.05) and CPN-PDR vs DOX (P<0.05). With the results from real-time PCR and Western blot, the increased cytotoxicity of CPN-PDR probably resulted from siRNA delivery and successful transfection. This was because the intracellular delivery of anti-VEGF siRNA could downregulate the expression of VEGF protein. The anti-VEGF therapy may sensitize cancer cells or kill cells block the blood supply of the tumor and improve drug penetration. Taken together, the anti-VEGF siRNA and Dox co-delivery platform could induce increased cytotoxicity against cancer cells.\",\n \"To investigate the safety of intravenous injection of CPN-PDR, a hemolysis test was performed. As shown in Table 1, different volume ratios of CPN-PDR to 2% red blood cell suspension were selected. After incubation and centrifugation, tubes were observed by the naked eye. As shown in Figure 6, no red blood cell hemolysis was observed in tube 6, in which 100% PBS was added serving as a negative control. For tube 7, 100% double distilled water was added and red cell hemolysis could be clearly observed. For tubes 1–5, in which different percentages of CPN-PDR were added, no red blood cell hemolysis occurred. Hemolysis of red blood cells can generate hemoprotein, which could be detected using UV-vis spectrophotometry. After scanning the absorbance wavelength of hemoprotein, 577 nm was selected as the detecting wavelength. According to Equation 3, hemolysis ratios were calculated based on the recorded absorbance and shown in Table 2. All the hemolysis ratios of tubes 1–5 were <5%, implying that no hemolysis was induced by addition of CPN-PDR.\",\n \"In this study, a histological analysis of organs (heart, liver, spleen, lung and kidney) was performed to evaluate whether CPN-PDR could cause tissue damage, inflammation and lesions. Kunming mice were injected with CPN-PDR and normal saline (NS) by tail vein, respectively. After 1 week, all the mice were sacrificed, and the heart, liver, spleen, lung and kidney were separated. After the standard procedure of HE staining for histological examination, the organs were observed under a microscope. Mice without any treatment were chosen as a control. As shown in Figure 7, compared with the control group, there was no visible histologically difference between mice administrated with CPN-PDR group and that with NS group, implying the safety of CPN-PDR in vivo.\",\n \"In summary, the delivery system materials were demonstrated to be nontoxic and biocompatible. The nontoxic and negatively charged copolymer CPN coating could significantly decrease the cytotoxicity of the cationic core, bPDR. The obtained co-delivery system CPN-PDR was also confirmed with enhanced cytotoxicity against tumor cells. Gene transfection efficiency induced by CPN-PDR was fairly equal with that of the commercial product Lipofectamine 2000, implying the successful intracellular delivery and good transfection of siRNA. Moreover, the preliminary evaluation of safety showed CPN-PDR to have good biocompatibility, so it has great potential for further exploitation and clinical application. With the development of aptamer technology, the promising application prospects of this novel oligodeoxynucleotide-based co-loading platform will further increase.\"\n]"},"other_sections_sec_types":{"kind":"list like","value":["intro","materials",null,null,"materials",null,null,null,null,null,null,null,null,null,null,null,null,null,null,null],"string":"[\n \"intro\",\n \"materials\",\n null,\n null,\n \"materials\",\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null\n]"},"all_sections_titles":{"kind":"list like","value":["Introduction","Materials and methods","Materials","Cells and animals","Preparation of nanoparticles","In vitro cytotoxicity of materials","In vitro cytotoxicity of blank nanoparticles","siRNA transfection with nanoparticles","Real-time PCR","Western blot assay","Cell proliferation assay","Hemolysis test","Histological assessment","Statistical analysis","Results and discussion","Characteristics of nanoparticles","Cytotoxicity of materials","Cytotoxicity of blank nanoparticles","Expression level of VEGF mRNA","Expression level of VEGF protein","Inhibition of cell proliferation in vitro","Hemolysis test","Tissue section","Conclusion"],"string":"[\n \"Introduction\",\n \"Materials and methods\",\n \"Materials\",\n \"Cells and animals\",\n \"Preparation of nanoparticles\",\n \"In vitro cytotoxicity of materials\",\n \"In vitro cytotoxicity of blank nanoparticles\",\n \"siRNA transfection with nanoparticles\",\n \"Real-time PCR\",\n \"Western blot assay\",\n \"Cell proliferation assay\",\n \"Hemolysis test\",\n \"Histological assessment\",\n \"Statistical analysis\",\n \"Results and discussion\",\n \"Characteristics of nanoparticles\",\n \"Cytotoxicity of materials\",\n \"Cytotoxicity of blank nanoparticles\",\n \"Expression level of VEGF mRNA\",\n \"Expression level of VEGF protein\",\n \"Inhibition of cell proliferation in vitro\",\n \"Hemolysis test\",\n \"Tissue section\",\n \"Conclusion\"\n]"},"all_sections_texts":{"kind":"list like","value":["Cancer has been one of the leading causes of death worldwide, and mortality and morbidity continue to increase.1 Chemotherapy is one of the major strategies for cancer therapy. However, mono-chemotherapy may encounter problems including drug resistance and unspecific toxicity. To overcome the high mortality rate of cancer, new therapeutic strategies, such as combinational therapy, have been developed.2,3 It has been reported that a combination of chemotherapy and gene therapy could potentially achieve synergistic effects and improve target selectivity, and deter the development of cancer drug resistance.4 For example, a double-modulating strategy based on the combination of chemotherapeutic agent 5-fluorouracil (5-FU) and siRNA has been developed, in which chemotherapy enhances intratumoral siRNA delivery and the delivered siRNA enhances the chemosensitivity of tumors. Furthermore, combination therapy may compensate for the limited delivery of siRNA to tumor tissue and probably manage the 5-FU-resistant tumors.5\nRNA interference (RNAi) is a natural cellular process that regulates gene expression level. Small interfering RNAs (siRNAs), which are small double-stranded RNAs, 20–24 nucleotides (nts) in length with sequences complementary to a gene’s coding sequence, can induce degradation of corresponding messenger RNAs (mRNAs), thus blocking the translation of the mRNA into protein.6,7 The key therapeutic advantage of using RNAi lies in its ability to specifically and potently knock down the expression of disease-causing genes of known sequence. The high specificity and potency makes it widely used in treating various cancers such as breast, liver, and lung cancers.8–10 In recent years, combination of chemotherapy and siRNA-induced RNAi has become a hot topic in cancer treatment. For example, co-delivery of siRNA targeting multidrug resistance protein 1 (MDR1) or anti-survivin siRNA and chemotherapeutic drugs was demonstrated to be a promising strategy to overcome drug resistance.11–13 Anti-apoptotic gene Bcl-2 is a potential combinational therapy target owing to its important role in cell apoptosis, and combination of Bcl-2 siRNA and 5-FU could induce a remarkable increase of cell apoptosis both in vitro and in vivo.14\nTo achieve the desired combinational and synergistic effects of chemotherapy and RNAi, selection of siRNA is important, and the selection of siRNA is also the section of silencing target. The mechanism for combinational anticancer effect varies with different silencing targets. For example, siRNA targeting MDR1 or anti-apoptotic gene Bcl-2 were used to prevent drug resistance response and sensitize chemotherapeutic drugs, or enhance the apoptosis of cancer cells, both offering enhanced anticancer effect.15,16 Tumor tissues have abundant new blood vessels that pump sufficient nutrition and oxygen for tumor growth and metastasis. The dependency of tumor tissues on these new blood vessels is important in anti-angiogenesis therapy for cancer treatment.17 Recently, there have been many efforts in achieving anti-angiogenesis, including delivering siRNAs to silence specific pro-angiogenic genes, such as vascular endothelial growth factor (VEGF), fibroblast growth factor (FGF) and interleukin-8 (IL-8).18 Of these targets, VEGF has received a lot of attention owing to its key role in tumor angiogenesis. With the development of RNAi technology, siRNA with VEGF target has been widely explored and referred to as anti-VEGF treatment.19–21 In 2004, the success of bevacizumab showed VEGF to be a potential target for angiogenesis therapy. There are a number of novel anti-VEGF agents in phase III clinical trials that may come to market in the next few years.22,23 In our design, siRNA with VEGF target was selected to co-deliver with chemotherapeutic drugs, resulting in a combinational anticancer effect. The mechanism for this could be that anti-VEGF treatments sensitize the cells to chemotherapy agents by blocking blood supply, improve drug penetration by disruption or normalization of tumor vasculature and in addition directly kill cancer cells by gene therapy.24,25\nHowever, naked siRNAs may not induce efficient transfection by themselves because they are unstable and vulnerable, especially to nuclease-mediated degradation; also, the negatively charged surface blocks them from cellular endocytosis. Moreover, successful transfection of siRNA into cells is based on siRNA with complete structure, which could further initiate the RNAi process for targeted gene silencing.26,27 Thus, nanoscale delivery systems, which could co-load drug and siRNA and protect siRNA from degradation, were widely used and demonstrated to be excellent candidates. These include nanoparticles,28 liposomes,29 polyplexes30 and dendrimers.31 The basic standard involves employing cationic polymers or lipids to condense negatively charged siRNA and protect them from degradation.32,33 In our previous study,25 oligodeoxynucleotides with CGA repeating units (CGA-ODNs) were introduced to load Dox to obtain Dox-loaded CGA-ODNs (CGA-ODNs-Dox). Poly(ethyleneimine) (PEI) was then used to condense siRNA and CGA ODNs-Dox to obtain Dox and siRNA co-loaded nanoparticles (PEI/CGA-ODNs-Dox and siRNA[PDR]). Finally, the pH-sensitive targeted material, o-carboxymethyl-chitosan (CMCS)-poly(ethylene glycol) (PEG)-aspargine-glycine-arginine (NGR) (CMCS-PEG-NGR[CPN]), was used to modify PDR to obtain multifunctional CPN-PDR. CPN-PDRs were demonstrated to be multifunctional, being able to co-deliver Dox and siRNA into cells, induce pH-responsive disassembly and realize endosomal escape of gene and drug. Thus, in the present study, the cytotoxicity of the materials and the delivery system are further evaluated. Then, the transfection efficiency of siRNA is monitored by semiquantitative real-time polymerase chain reaction (real-time PCR) and Western blot techniques for mRNA level and protein level, respectively. Finally, the safety of the delivery system is evaluated by hemolysis test in vitro and histological assessment."," Materials Dox was purchased from Dalian Meilun Biology Technology Co. Ltd (Dalian, China). Targeting human VEGF siRNA (sense: 5′-ACAUCACCAUGCAGAUUAUdTdT-3′, anti-sense: 5′-dTdTUGUAGUGGUACGUCUAAUA-3′) was obtained from Guangzhou Ribobio Co., Ltd (Guangzhou, China). CGA oligodeoxynucleotides (sequence: 5′-CGACGACGACGACGACGACGA-3′; complementary sequence: 5′-TCGTCGTCGTCGTCGTCGTCG-3′) were purchased from BGI Co. (Shenzhen, China). Fetal bovine serum (FBS) was obtained from Sijiqing Co., Ltd, (Zhejiang, China) and 3-[4,5-dimethyl-2-thiazolyl]-2,5-diphenyl-2H-tetrazolium bromide (MTT) was from Sigma-Aldrich (St Louis, MO, USA). Trizol RNA extraction was from Thermo Fisher Scientific (Waltham, MA, USA). SYBR®Green and ReverTra Ace qPCR RT Kit were purchased from Toyobo (Osaka, Japan). All solutions were made up in Millipore ultrapure water (EMD Millipore, Billerica, MA, USA) and sterilized for cell culture. All other chemicals and reagents were of analytical grade and used as received. All the primers used in real-time PCR were synthesized and purified by BGI Co. with sequences: VEGF – forward: 5′-CTGGAGTGTGTGCCCACTGA-3′; VEGF – reverse: 5′-TCCTATGTGCTGGCCTTGGT-3′; actin – forward: GAGCTACGAGCTGCCTGACG; actin – reverse: CCTAGAAGCATTTGCGGTGG.\nDox was purchased from Dalian Meilun Biology Technology Co. Ltd (Dalian, China). Targeting human VEGF siRNA (sense: 5′-ACAUCACCAUGCAGAUUAUdTdT-3′, anti-sense: 5′-dTdTUGUAGUGGUACGUCUAAUA-3′) was obtained from Guangzhou Ribobio Co., Ltd (Guangzhou, China). CGA oligodeoxynucleotides (sequence: 5′-CGACGACGACGACGACGACGA-3′; complementary sequence: 5′-TCGTCGTCGTCGTCGTCGTCG-3′) were purchased from BGI Co. (Shenzhen, China). Fetal bovine serum (FBS) was obtained from Sijiqing Co., Ltd, (Zhejiang, China) and 3-[4,5-dimethyl-2-thiazolyl]-2,5-diphenyl-2H-tetrazolium bromide (MTT) was from Sigma-Aldrich (St Louis, MO, USA). Trizol RNA extraction was from Thermo Fisher Scientific (Waltham, MA, USA). SYBR®Green and ReverTra Ace qPCR RT Kit were purchased from Toyobo (Osaka, Japan). All solutions were made up in Millipore ultrapure water (EMD Millipore, Billerica, MA, USA) and sterilized for cell culture. All other chemicals and reagents were of analytical grade and used as received. All the primers used in real-time PCR were synthesized and purified by BGI Co. with sequences: VEGF – forward: 5′-CTGGAGTGTGTGCCCACTGA-3′; VEGF – reverse: 5′-TCCTATGTGCTGGCCTTGGT-3′; actin – forward: GAGCTACGAGCTGCCTGACG; actin – reverse: CCTAGAAGCATTTGCGGTGG.\n Cells and animals The MCF-7 cells were purchased from the Chinese Academy of Sciences Cells Bank (Shanghai, China), and the cells were cultured in Roswell Park Memorial Institute (RPMI) 1640 medium supplemented with 10% fetal bovine serum (FBS), streptomycin at 100 µg/mL and penicillin at 100 U/mL. All cells were cultured in a 37°C incubator with 5% CO2.\nKunming mice (20±2 g) were obtained from Experimental Animal Center of Shandong University. Animal experiments were carried out according to the requirements of the Animal Management Rules of the Ministry of Health (China) and approved by the Laboratory Animal Ethics Review Committee of Qilu Medical College of Shandong University.\nThe MCF-7 cells were purchased from the Chinese Academy of Sciences Cells Bank (Shanghai, China), and the cells were cultured in Roswell Park Memorial Institute (RPMI) 1640 medium supplemented with 10% fetal bovine serum (FBS), streptomycin at 100 µg/mL and penicillin at 100 U/mL. All cells were cultured in a 37°C incubator with 5% CO2.\nKunming mice (20±2 g) were obtained from Experimental Animal Center of Shandong University. Animal experiments were carried out according to the requirements of the Animal Management Rules of the Ministry of Health (China) and approved by the Laboratory Animal Ethics Review Committee of Qilu Medical College of Shandong University.\n Preparation of nanoparticles PDR and CPN-PDR were prepared by the method reported in our previous study.25 Briefly, CGA-ODNs-Dox was first obtained by incubating Dox with CGA-ODNs at room temperature. Then, PEI, siRNA, ODNs-Dox and CPN were dissolved and diluted to the corresponding concentrations with deionized water. After that, siRNA and CGA-ODNs-Dox were mixed by vortexing for several seconds to obtain the mixture. The mixture was added dropwise into PEI solution and mixed by vortexing and then incubated for 30 min at room temperature to form PDR. CPN-PDR was obtained by adding PDR suspension into the CPN solution under vortexing, followed by 30-min incubation at room temperature. When preparing the blank nanoparticles, the DOX and siRNA were not involved in above method. The construction of different kinds of nanoparticles is shown in Figure 1.\nThe loading content of DOX and siRNA was calculated using the following formula:34\nLoading content(%)=Weight of loading drugsWeight of nanoparticles×100%(1)\nPDR and CPN-PDR were prepared by the method reported in our previous study.25 Briefly, CGA-ODNs-Dox was first obtained by incubating Dox with CGA-ODNs at room temperature. Then, PEI, siRNA, ODNs-Dox and CPN were dissolved and diluted to the corresponding concentrations with deionized water. After that, siRNA and CGA-ODNs-Dox were mixed by vortexing for several seconds to obtain the mixture. The mixture was added dropwise into PEI solution and mixed by vortexing and then incubated for 30 min at room temperature to form PDR. CPN-PDR was obtained by adding PDR suspension into the CPN solution under vortexing, followed by 30-min incubation at room temperature. When preparing the blank nanoparticles, the DOX and siRNA were not involved in above method. The construction of different kinds of nanoparticles is shown in Figure 1.\nThe loading content of DOX and siRNA was calculated using the following formula:34\nLoading content(%)=Weight of loading drugsWeight of nanoparticles×100%(1)\n In vitro cytotoxicity of materials MTT assays were carried out on MCF-7 cells to evaluate the in vitro cytotoxicity of CGA-ODNs, PEI and CPN, respectively.35 MCF-7 cells were seeded with density at 7,000 cells/well in the 96-well plates and allowed to adhere overnight. CGA-ODNs, PEI and CPN solutions, which were corresponding to Dox concentrations (0.25, 0.625, 1.25, 2.5 and 5 µM), were added and incubated with the cells for 24, 48 and 72 h, respectively. Five wells were set for each concentration, and cells incubated with fresh media were taken as control. At a determined time point, 20 µL MTT solutions (5 mg/mL) were added. After 4 h incubation, the plates were centrifuged (3,000 rpm, 10 min) and the supernatant was removed. Then, 200 µL DMSO solutions were added to dissolve the formazan crystals formed by the living cells. The cell viability was calculated according to the following formula (Equation 2) after recording the absorbance at 570 nm by a microplate reader (Model 680; Bio-Rad Laboratories Inc., Hercules, CA, USA). All the assays were repeated three times.\nCell viability=Abs(sample)−Abs(blank)Abs(control)−Abs(blank)×100%(2)\nMTT assays were carried out on MCF-7 cells to evaluate the in vitro cytotoxicity of CGA-ODNs, PEI and CPN, respectively.35 MCF-7 cells were seeded with density at 7,000 cells/well in the 96-well plates and allowed to adhere overnight. CGA-ODNs, PEI and CPN solutions, which were corresponding to Dox concentrations (0.25, 0.625, 1.25, 2.5 and 5 µM), were added and incubated with the cells for 24, 48 and 72 h, respectively. Five wells were set for each concentration, and cells incubated with fresh media were taken as control. At a determined time point, 20 µL MTT solutions (5 mg/mL) were added. After 4 h incubation, the plates were centrifuged (3,000 rpm, 10 min) and the supernatant was removed. Then, 200 µL DMSO solutions were added to dissolve the formazan crystals formed by the living cells. The cell viability was calculated according to the following formula (Equation 2) after recording the absorbance at 570 nm by a microplate reader (Model 680; Bio-Rad Laboratories Inc., Hercules, CA, USA). All the assays were repeated three times.\nCell viability=Abs(sample)−Abs(blank)Abs(control)−Abs(blank)×100%(2)\n In vitro cytotoxicity of blank nanoparticles The cytotoxicity of non-DOX- and siRNA-loaded nano-particles (blank PDR[bPDR]) and CPN-coated blank PDR (bCPN-PDR) were also evaluated in MCF-7 cells. bPDR and bCPN-PDR solutions, corresponding to Dox concentrations (0.25, 0.625, 1.25, 2.5 and 5 µM), were added and incubated with the cells for 24, 48 and 72 h, respectively. Five wells were set for each concentration and cells incubated with fresh media were taken as control. After the indicated time periods, cell viability was evaluated according to the procedure described above.\nThe cytotoxicity of non-DOX- and siRNA-loaded nano-particles (blank PDR[bPDR]) and CPN-coated blank PDR (bCPN-PDR) were also evaluated in MCF-7 cells. bPDR and bCPN-PDR solutions, corresponding to Dox concentrations (0.25, 0.625, 1.25, 2.5 and 5 µM), were added and incubated with the cells for 24, 48 and 72 h, respectively. Five wells were set for each concentration and cells incubated with fresh media were taken as control. After the indicated time periods, cell viability was evaluated according to the procedure described above.\n siRNA transfection with nanoparticles MCF-7 cells were seeded into 24-well plates at densities of 8×104, 6×104 and 5×104 cells/well for 24, 48 and 72 h transfection, respectively. After culturing at 37°C overnight, MCF-7 cells were treated with siRNA specific for VEGF. The siRNA was delivered either by Lipofectamine 2000 (Thermo Fisher Scientific) according to the manufacturer’s instructions or by CPN-modified siRNA-loaded nanoparticles (CPN-PR) or by CPN-modified Dox- and siRNA-loaded nanoparticles (CPN-PDR). The final concentrations of Dox and siRNA were 1 µM and 55 nM, respectively. After siRNA transfection, cells were harvested for RNA extraction and Western blot PCR analysis or subjected to Western blot assay.\nMCF-7 cells were seeded into 24-well plates at densities of 8×104, 6×104 and 5×104 cells/well for 24, 48 and 72 h transfection, respectively. After culturing at 37°C overnight, MCF-7 cells were treated with siRNA specific for VEGF. The siRNA was delivered either by Lipofectamine 2000 (Thermo Fisher Scientific) according to the manufacturer’s instructions or by CPN-modified siRNA-loaded nanoparticles (CPN-PR) or by CPN-modified Dox- and siRNA-loaded nanoparticles (CPN-PDR). The final concentrations of Dox and siRNA were 1 µM and 55 nM, respectively. After siRNA transfection, cells were harvested for RNA extraction and Western blot PCR analysis or subjected to Western blot assay.\n Real-time PCR The expression level of VEGF mRNA was monitored by real-time PCR technique with the standard procedure. Total RNA was extracted using Trizol agent under the standard procedure (Thermo Fisher Scientific), and cDNA synthesis was carried out using Rever Tra Ace qPCR RT Kit (Toyobo) according to the manufacturer’s instructions. The final concentration of RNA was measured by Nano Drop 2000 (Thermo Fisher Scientific), and the final product cDNA was stored at −20°C until use.\nEach real-time PCR system contained 20 µL solutions including cDNA (2 µL), SYBR®Green mix (10 µL), primer (1.6 µL) and double distilled water (6.4 µL). The reaction system was placed into PCR instrument (Roche LightCycler™; Hoffman-La Roche Ltd, Basel, Switzerland) for analysis. β-Actin served as a reference gene. Three wells were set for each group. The expression level of VEGF mRNA was normalized to the β-actin expression level and calculated by recording the Ct value at the end of the reaction.\nThe expression level of VEGF mRNA was monitored by real-time PCR technique with the standard procedure. Total RNA was extracted using Trizol agent under the standard procedure (Thermo Fisher Scientific), and cDNA synthesis was carried out using Rever Tra Ace qPCR RT Kit (Toyobo) according to the manufacturer’s instructions. The final concentration of RNA was measured by Nano Drop 2000 (Thermo Fisher Scientific), and the final product cDNA was stored at −20°C until use.\nEach real-time PCR system contained 20 µL solutions including cDNA (2 µL), SYBR®Green mix (10 µL), primer (1.6 µL) and double distilled water (6.4 µL). The reaction system was placed into PCR instrument (Roche LightCycler™; Hoffman-La Roche Ltd, Basel, Switzerland) for analysis. β-Actin served as a reference gene. Three wells were set for each group. The expression level of VEGF mRNA was normalized to the β-actin expression level and calculated by recording the Ct value at the end of the reaction.\n Western blot assay A Western blot assay was employed to investigate the expression level of total VEGF protein after transfection. At a determined time, cells were trypsinized and pelleted by centrifugation. The protein collecting process was carried out based on the standard procedure of Total Protein Extraction Kit (BestBio, Shanghai, China). The total protein was stored at −80°C until use. Protein was quantified by BCA protein assay kit (Beyotime Biotechnology, Shanghai, China) based on the standard carve calculation (A=0.006 C+0.0919, r=0.9987). After quantification, all the samples were diluted to 2 µg/µL with sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) electrophoresis, denatured in boiling water for 5 min and stored at −80°C until use. Equal amounts of protein (50 µg) were subjected to 12% SDS-PAGE and transferred to nitrocellulose membranes using standard procedures. The membrane was blocked in 2% skimmed milk in phosphate buffered saline for 2 h and washed by Tris-buffered saline and Tween-20 (TBST) solution three times. Then the membranes were probed with primary antibodies against VEGF and glyceraldehyde 3-phosphate dehydrogenase (GAPDN) overnight at 4°C. After 3×10 min washing in TBST on shaker, membranes were incubated with horseradish peroxidase (HRP)-conjugated anti-rabbit IgG for 1 h at room temperature. With another 3×10 min washes in TBST, the membranes were developed with electrochemiluminescence (ECL) using a gel-imaging system and analyzed using Image Analysis software.\nA Western blot assay was employed to investigate the expression level of total VEGF protein after transfection. At a determined time, cells were trypsinized and pelleted by centrifugation. The protein collecting process was carried out based on the standard procedure of Total Protein Extraction Kit (BestBio, Shanghai, China). The total protein was stored at −80°C until use. Protein was quantified by BCA protein assay kit (Beyotime Biotechnology, Shanghai, China) based on the standard carve calculation (A=0.006 C+0.0919, r=0.9987). After quantification, all the samples were diluted to 2 µg/µL with sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) electrophoresis, denatured in boiling water for 5 min and stored at −80°C until use. Equal amounts of protein (50 µg) were subjected to 12% SDS-PAGE and transferred to nitrocellulose membranes using standard procedures. The membrane was blocked in 2% skimmed milk in phosphate buffered saline for 2 h and washed by Tris-buffered saline and Tween-20 (TBST) solution three times. Then the membranes were probed with primary antibodies against VEGF and glyceraldehyde 3-phosphate dehydrogenase (GAPDN) overnight at 4°C. After 3×10 min washing in TBST on shaker, membranes were incubated with horseradish peroxidase (HRP)-conjugated anti-rabbit IgG for 1 h at room temperature. With another 3×10 min washes in TBST, the membranes were developed with electrochemiluminescence (ECL) using a gel-imaging system and analyzed using Image Analysis software.\n Cell proliferation assay The anti-proliferation activity of Dox, CPN-PD and CPN-PDR against MCF-7 cells was tested via MTT method. MCF-7 cells were seeded into 96-well plates at a density of 7,000 cells/well. After overnight incubation, the cells were treated with various concentrations (0.002, 0.01, 0.05, 0.25 and 0.5 µM) of Dox, CPN-PD and CPN-PDR solutions and incubated for another 48 h. Five wells were set for each concentration, and cells incubated with fresh media were taken as control. After indicated time periods, cell viability was evaluated according to the procedure described in the “In vitro cytotoxicity of materials” section.\nThe anti-proliferation activity of Dox, CPN-PD and CPN-PDR against MCF-7 cells was tested via MTT method. MCF-7 cells were seeded into 96-well plates at a density of 7,000 cells/well. After overnight incubation, the cells were treated with various concentrations (0.002, 0.01, 0.05, 0.25 and 0.5 µM) of Dox, CPN-PD and CPN-PDR solutions and incubated for another 48 h. Five wells were set for each concentration, and cells incubated with fresh media were taken as control. After indicated time periods, cell viability was evaluated according to the procedure described in the “In vitro cytotoxicity of materials” section.\n Hemolysis test Hemolysis test was carried out to investigate the safety of CPN-PDR for intravenous injection. A 2% red blood cell suspension was collected by centrifugation and resuspension. The test was performed under the design described in Table 1. After incubated at 37°C for 3 h, all the groups were centrifuged at 3,000 rpm for 15 min and visualized by the naked eye. To measure the hemolysis ratios of each group, UV-vis spectrophotometry was carried out to record the absorbance of supernatant in each group. The quantitation of hemolysis ratios was calculated according to the following formula:\nHR(%)=Abs(sample)−Abs(−)Abs(+)−Abs(−)×100%(3)where Abs (sample), Abs (−) and Abs (+) refer to the absorbances of the samples, negative control and positive control, respectively.\nHemolysis test was carried out to investigate the safety of CPN-PDR for intravenous injection. A 2% red blood cell suspension was collected by centrifugation and resuspension. The test was performed under the design described in Table 1. After incubated at 37°C for 3 h, all the groups were centrifuged at 3,000 rpm for 15 min and visualized by the naked eye. To measure the hemolysis ratios of each group, UV-vis spectrophotometry was carried out to record the absorbance of supernatant in each group. The quantitation of hemolysis ratios was calculated according to the following formula:\nHR(%)=Abs(sample)−Abs(−)Abs(+)−Abs(−)×100%(3)where Abs (sample), Abs (−) and Abs (+) refer to the absorbances of the samples, negative control and positive control, respectively.\n Histological assessment In order to evaluate the compatibility and tissue toxicity of CPN-PDR in vivo, a histological observation was performed.36 Five female Kunming mice were injected with CPN-PDR at a dose of 232 µg/kg through the tail vein. In the meantime, normal saline (NS) was taken as a control reagent. After 1 week, all the mice were sacrificed, and the heart, liver, spleen, lung and kidney were separated. After washing twice with PBS, all the organs were fixed in 4% formaldehyde, dehydrated in gradient alcohol, placed in xylene, embedded in paraffin and made into sections, followed by hematoxylin-eosin (HE) staining for histological examination with microscope.\nIn order to evaluate the compatibility and tissue toxicity of CPN-PDR in vivo, a histological observation was performed.36 Five female Kunming mice were injected with CPN-PDR at a dose of 232 µg/kg through the tail vein. In the meantime, normal saline (NS) was taken as a control reagent. After 1 week, all the mice were sacrificed, and the heart, liver, spleen, lung and kidney were separated. After washing twice with PBS, all the organs were fixed in 4% formaldehyde, dehydrated in gradient alcohol, placed in xylene, embedded in paraffin and made into sections, followed by hematoxylin-eosin (HE) staining for histological examination with microscope.\n Statistical analysis All studies were repeated a minimum of three times and measured at least in triplicate. The results are reported as the means ± SD. Statistical significance was analyzed using Student’s t-test. Differences between experimental groups were considered significant if the P-value was <0.05.\nAll studies were repeated a minimum of three times and measured at least in triplicate. The results are reported as the means ± SD. Statistical significance was analyzed using Student’s t-test. Differences between experimental groups were considered significant if the P-value was <0.05.","Dox was purchased from Dalian Meilun Biology Technology Co. Ltd (Dalian, China). Targeting human VEGF siRNA (sense: 5′-ACAUCACCAUGCAGAUUAUdTdT-3′, anti-sense: 5′-dTdTUGUAGUGGUACGUCUAAUA-3′) was obtained from Guangzhou Ribobio Co., Ltd (Guangzhou, China). CGA oligodeoxynucleotides (sequence: 5′-CGACGACGACGACGACGACGA-3′; complementary sequence: 5′-TCGTCGTCGTCGTCGTCGTCG-3′) were purchased from BGI Co. (Shenzhen, China). Fetal bovine serum (FBS) was obtained from Sijiqing Co., Ltd, (Zhejiang, China) and 3-[4,5-dimethyl-2-thiazolyl]-2,5-diphenyl-2H-tetrazolium bromide (MTT) was from Sigma-Aldrich (St Louis, MO, USA). Trizol RNA extraction was from Thermo Fisher Scientific (Waltham, MA, USA). SYBR®Green and ReverTra Ace qPCR RT Kit were purchased from Toyobo (Osaka, Japan). All solutions were made up in Millipore ultrapure water (EMD Millipore, Billerica, MA, USA) and sterilized for cell culture. All other chemicals and reagents were of analytical grade and used as received. All the primers used in real-time PCR were synthesized and purified by BGI Co. with sequences: VEGF – forward: 5′-CTGGAGTGTGTGCCCACTGA-3′; VEGF – reverse: 5′-TCCTATGTGCTGGCCTTGGT-3′; actin – forward: GAGCTACGAGCTGCCTGACG; actin – reverse: CCTAGAAGCATTTGCGGTGG.","The MCF-7 cells were purchased from the Chinese Academy of Sciences Cells Bank (Shanghai, China), and the cells were cultured in Roswell Park Memorial Institute (RPMI) 1640 medium supplemented with 10% fetal bovine serum (FBS), streptomycin at 100 µg/mL and penicillin at 100 U/mL. All cells were cultured in a 37°C incubator with 5% CO2.\nKunming mice (20±2 g) were obtained from Experimental Animal Center of Shandong University. Animal experiments were carried out according to the requirements of the Animal Management Rules of the Ministry of Health (China) and approved by the Laboratory Animal Ethics Review Committee of Qilu Medical College of Shandong University.","PDR and CPN-PDR were prepared by the method reported in our previous study.25 Briefly, CGA-ODNs-Dox was first obtained by incubating Dox with CGA-ODNs at room temperature. Then, PEI, siRNA, ODNs-Dox and CPN were dissolved and diluted to the corresponding concentrations with deionized water. After that, siRNA and CGA-ODNs-Dox were mixed by vortexing for several seconds to obtain the mixture. The mixture was added dropwise into PEI solution and mixed by vortexing and then incubated for 30 min at room temperature to form PDR. CPN-PDR was obtained by adding PDR suspension into the CPN solution under vortexing, followed by 30-min incubation at room temperature. When preparing the blank nanoparticles, the DOX and siRNA were not involved in above method. The construction of different kinds of nanoparticles is shown in Figure 1.\nThe loading content of DOX and siRNA was calculated using the following formula:34\nLoading content(%)=Weight of loading drugsWeight of nanoparticles×100%(1)","MTT assays were carried out on MCF-7 cells to evaluate the in vitro cytotoxicity of CGA-ODNs, PEI and CPN, respectively.35 MCF-7 cells were seeded with density at 7,000 cells/well in the 96-well plates and allowed to adhere overnight. CGA-ODNs, PEI and CPN solutions, which were corresponding to Dox concentrations (0.25, 0.625, 1.25, 2.5 and 5 µM), were added and incubated with the cells for 24, 48 and 72 h, respectively. Five wells were set for each concentration, and cells incubated with fresh media were taken as control. At a determined time point, 20 µL MTT solutions (5 mg/mL) were added. After 4 h incubation, the plates were centrifuged (3,000 rpm, 10 min) and the supernatant was removed. Then, 200 µL DMSO solutions were added to dissolve the formazan crystals formed by the living cells. The cell viability was calculated according to the following formula (Equation 2) after recording the absorbance at 570 nm by a microplate reader (Model 680; Bio-Rad Laboratories Inc., Hercules, CA, USA). All the assays were repeated three times.\nCell viability=Abs(sample)−Abs(blank)Abs(control)−Abs(blank)×100%(2)","The cytotoxicity of non-DOX- and siRNA-loaded nano-particles (blank PDR[bPDR]) and CPN-coated blank PDR (bCPN-PDR) were also evaluated in MCF-7 cells. bPDR and bCPN-PDR solutions, corresponding to Dox concentrations (0.25, 0.625, 1.25, 2.5 and 5 µM), were added and incubated with the cells for 24, 48 and 72 h, respectively. Five wells were set for each concentration and cells incubated with fresh media were taken as control. After the indicated time periods, cell viability was evaluated according to the procedure described above.","MCF-7 cells were seeded into 24-well plates at densities of 8×104, 6×104 and 5×104 cells/well for 24, 48 and 72 h transfection, respectively. After culturing at 37°C overnight, MCF-7 cells were treated with siRNA specific for VEGF. The siRNA was delivered either by Lipofectamine 2000 (Thermo Fisher Scientific) according to the manufacturer’s instructions or by CPN-modified siRNA-loaded nanoparticles (CPN-PR) or by CPN-modified Dox- and siRNA-loaded nanoparticles (CPN-PDR). The final concentrations of Dox and siRNA were 1 µM and 55 nM, respectively. After siRNA transfection, cells were harvested for RNA extraction and Western blot PCR analysis or subjected to Western blot assay.","The expression level of VEGF mRNA was monitored by real-time PCR technique with the standard procedure. Total RNA was extracted using Trizol agent under the standard procedure (Thermo Fisher Scientific), and cDNA synthesis was carried out using Rever Tra Ace qPCR RT Kit (Toyobo) according to the manufacturer’s instructions. The final concentration of RNA was measured by Nano Drop 2000 (Thermo Fisher Scientific), and the final product cDNA was stored at −20°C until use.\nEach real-time PCR system contained 20 µL solutions including cDNA (2 µL), SYBR®Green mix (10 µL), primer (1.6 µL) and double distilled water (6.4 µL). The reaction system was placed into PCR instrument (Roche LightCycler™; Hoffman-La Roche Ltd, Basel, Switzerland) for analysis. β-Actin served as a reference gene. Three wells were set for each group. The expression level of VEGF mRNA was normalized to the β-actin expression level and calculated by recording the Ct value at the end of the reaction.","A Western blot assay was employed to investigate the expression level of total VEGF protein after transfection. At a determined time, cells were trypsinized and pelleted by centrifugation. The protein collecting process was carried out based on the standard procedure of Total Protein Extraction Kit (BestBio, Shanghai, China). The total protein was stored at −80°C until use. Protein was quantified by BCA protein assay kit (Beyotime Biotechnology, Shanghai, China) based on the standard carve calculation (A=0.006 C+0.0919, r=0.9987). After quantification, all the samples were diluted to 2 µg/µL with sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) electrophoresis, denatured in boiling water for 5 min and stored at −80°C until use. Equal amounts of protein (50 µg) were subjected to 12% SDS-PAGE and transferred to nitrocellulose membranes using standard procedures. The membrane was blocked in 2% skimmed milk in phosphate buffered saline for 2 h and washed by Tris-buffered saline and Tween-20 (TBST) solution three times. Then the membranes were probed with primary antibodies against VEGF and glyceraldehyde 3-phosphate dehydrogenase (GAPDN) overnight at 4°C. After 3×10 min washing in TBST on shaker, membranes were incubated with horseradish peroxidase (HRP)-conjugated anti-rabbit IgG for 1 h at room temperature. With another 3×10 min washes in TBST, the membranes were developed with electrochemiluminescence (ECL) using a gel-imaging system and analyzed using Image Analysis software.","The anti-proliferation activity of Dox, CPN-PD and CPN-PDR against MCF-7 cells was tested via MTT method. MCF-7 cells were seeded into 96-well plates at a density of 7,000 cells/well. After overnight incubation, the cells were treated with various concentrations (0.002, 0.01, 0.05, 0.25 and 0.5 µM) of Dox, CPN-PD and CPN-PDR solutions and incubated for another 48 h. Five wells were set for each concentration, and cells incubated with fresh media were taken as control. After indicated time periods, cell viability was evaluated according to the procedure described in the “In vitro cytotoxicity of materials” section.","Hemolysis test was carried out to investigate the safety of CPN-PDR for intravenous injection. A 2% red blood cell suspension was collected by centrifugation and resuspension. The test was performed under the design described in Table 1. After incubated at 37°C for 3 h, all the groups were centrifuged at 3,000 rpm for 15 min and visualized by the naked eye. To measure the hemolysis ratios of each group, UV-vis spectrophotometry was carried out to record the absorbance of supernatant in each group. The quantitation of hemolysis ratios was calculated according to the following formula:\nHR(%)=Abs(sample)−Abs(−)Abs(+)−Abs(−)×100%(3)where Abs (sample), Abs (−) and Abs (+) refer to the absorbances of the samples, negative control and positive control, respectively.","In order to evaluate the compatibility and tissue toxicity of CPN-PDR in vivo, a histological observation was performed.36 Five female Kunming mice were injected with CPN-PDR at a dose of 232 µg/kg through the tail vein. In the meantime, normal saline (NS) was taken as a control reagent. After 1 week, all the mice were sacrificed, and the heart, liver, spleen, lung and kidney were separated. After washing twice with PBS, all the organs were fixed in 4% formaldehyde, dehydrated in gradient alcohol, placed in xylene, embedded in paraffin and made into sections, followed by hematoxylin-eosin (HE) staining for histological examination with microscope.","All studies were repeated a minimum of three times and measured at least in triplicate. The results are reported as the means ± SD. Statistical significance was analyzed using Student’s t-test. Differences between experimental groups were considered significant if the P-value was <0.05."," Characteristics of nanoparticles In the optimal formulation of CPN-PDR, the weight ratio of DOX to siRNA was 1:1; therefore, the siRNA loading content was equal to the DOX loading content. Based on the facts that the molar ratio of CGA-ODNs to Dox was 1:5, the weight ratio of CPN/PEI/ODNs and siRNA was 4:1:1, and consequently, siRNA and Dox loading efficiency in CPN-PDR was calculated to be 4.93%.\nIn the optimal formulation of CPN-PDR, the weight ratio of DOX to siRNA was 1:1; therefore, the siRNA loading content was equal to the DOX loading content. Based on the facts that the molar ratio of CGA-ODNs to Dox was 1:5, the weight ratio of CPN/PEI/ODNs and siRNA was 4:1:1, and consequently, siRNA and Dox loading efficiency in CPN-PDR was calculated to be 4.93%.\n Cytotoxicity of materials In the co-delivery platform, CGA-ODNs were selected as carriers of DOX, then the mixture of CGA-ODNs-Dox and siRNA (abbreviated as DR) was electrostatically interacted with PEI to obtain Dox and siRNA co-loaded nanoparticles, PEI/DR (PDR). The copolymer CPN was employed as a multifunctional material.25 In order to evaluate the safety of the carrier, MTT assays were carried out to evaluate the cytotoxicity of CGA-ODNs and CPN. In the meantime, PEI, a toxic cationic polymer, was also tested in this experiment. As shown in Figure 2A, the viability of MCF-7 cells treated with CGA-ODNs and CPN at any concentration was observed to be stable and >80% within 48 h. After 72 h incubation, the viability of MCF-7 cells treated with CPN was still >80%, but after treatment with a higher concentration of CGA-ODNs (2.5 and 5 µM), the cell viability was slightly decreased to 60%. This indicated that there is no obvious cytotoxicity of CGA-ODNs and CPN. However, the cell viability of MCF-7 cells treated with PEI was obviously dependent on the concentrations of PEI. With an increase in concentration, the MCF-7 cells treated with PEI showed lower cell viability. Moreover, this concentration-dependent trend became more obvious when incubation time increased from 24 h to 72 h.\nIn the co-delivery platform, CGA-ODNs were selected as carriers of DOX, then the mixture of CGA-ODNs-Dox and siRNA (abbreviated as DR) was electrostatically interacted with PEI to obtain Dox and siRNA co-loaded nanoparticles, PEI/DR (PDR). The copolymer CPN was employed as a multifunctional material.25 In order to evaluate the safety of the carrier, MTT assays were carried out to evaluate the cytotoxicity of CGA-ODNs and CPN. In the meantime, PEI, a toxic cationic polymer, was also tested in this experiment. As shown in Figure 2A, the viability of MCF-7 cells treated with CGA-ODNs and CPN at any concentration was observed to be stable and >80% within 48 h. After 72 h incubation, the viability of MCF-7 cells treated with CPN was still >80%, but after treatment with a higher concentration of CGA-ODNs (2.5 and 5 µM), the cell viability was slightly decreased to 60%. This indicated that there is no obvious cytotoxicity of CGA-ODNs and CPN. However, the cell viability of MCF-7 cells treated with PEI was obviously dependent on the concentrations of PEI. With an increase in concentration, the MCF-7 cells treated with PEI showed lower cell viability. Moreover, this concentration-dependent trend became more obvious when incubation time increased from 24 h to 72 h.\n Cytotoxicity of blank nanoparticles Cell viability of MCF-7 cells was also monitored to investigate the cytotoxicity of non-RNA- and non-drug-loaded carriers including bPDR and bCPN-PDR. As shown in Figure 2B, after treatment with bCPN-PDR, the cell viability was stable around 95% and shown to be concentration independent at 24 h. Meanwhile, bPDR-treated cells showed lower cell viability with time. With increased concentration, bPDR showed higher cytotoxicity, inducing 80% cell death in 72 h. Compared with the bPDR group, the cell viabilities of the bCPN-PDR group were significantly higher (P<0.05). This phenomenon could be explained by the higher positive charge of bPDR due to the positive ingredient of PEI, while the cell cytotoxicity could be significantly decreased by covering with the nontoxic negatively charged CPN (>80% cell viability in Figure 2A), which could induce the charge reversal of bPDR, implying that the negatively charged CPN coating could decrease the higher toxicity of bPDR to cells.\nCell viability of MCF-7 cells was also monitored to investigate the cytotoxicity of non-RNA- and non-drug-loaded carriers including bPDR and bCPN-PDR. As shown in Figure 2B, after treatment with bCPN-PDR, the cell viability was stable around 95% and shown to be concentration independent at 24 h. Meanwhile, bPDR-treated cells showed lower cell viability with time. With increased concentration, bPDR showed higher cytotoxicity, inducing 80% cell death in 72 h. Compared with the bPDR group, the cell viabilities of the bCPN-PDR group were significantly higher (P<0.05). This phenomenon could be explained by the higher positive charge of bPDR due to the positive ingredient of PEI, while the cell cytotoxicity could be significantly decreased by covering with the nontoxic negatively charged CPN (>80% cell viability in Figure 2A), which could induce the charge reversal of bPDR, implying that the negatively charged CPN coating could decrease the higher toxicity of bPDR to cells.\n Expression level of VEGF mRNA The transfection efficiency of CPN-PDR compared to Lipofectamine 2000 was demonstrated by delivering VEGF-siRNA into MCF-7 cells. The expression level of VEGF mRNA was measured using real-time PCR, and the results are shown in Figure 3. The expression level of VEGF mRNA was normalized to the β-actin expression and calculated based on the semiquantitative method. Untreated cells were selected as control, and the expression level was set as 100%. For lipo/siRNA group, the silencing efficiencies were 61.98%±6.96%, 31.93%±6.43% and 37.02%±3.17% at 24, 48 and 72 h, respectively. The decreased expression of VEGF mRNA reflected that gene silencing capability of lipo/siRNA. With incubation time extended from 24 to 48 h, the capability was enhanced (P<0.05). However, in 72 h, no further reduction was observed compared to that in 48 h, although the significantly downregulated expression could be observed (P<0.05 vs control, Dox and bCPN-PDR), implying that the silencing effect could reach the plateau period after 48 h and last for 72 h at least. The CPN-PR and CPN-PDR have shown similar behavior to lipo/siRNA. Compared with the control, a remarkable decrease of VEGF mRNA expression was observed (P<0.01) at 48 h. There was no significant difference in expression levels between CPN-PR and CPN-PDR (P>0.05) at any time point, implying their comparable gene delivery and transfection capability. More importantly, this capability was equivalent to the commercial Lipofectamine 2000.\nThe transfection efficiency of CPN-PDR compared to Lipofectamine 2000 was demonstrated by delivering VEGF-siRNA into MCF-7 cells. The expression level of VEGF mRNA was measured using real-time PCR, and the results are shown in Figure 3. The expression level of VEGF mRNA was normalized to the β-actin expression and calculated based on the semiquantitative method. Untreated cells were selected as control, and the expression level was set as 100%. For lipo/siRNA group, the silencing efficiencies were 61.98%±6.96%, 31.93%±6.43% and 37.02%±3.17% at 24, 48 and 72 h, respectively. The decreased expression of VEGF mRNA reflected that gene silencing capability of lipo/siRNA. With incubation time extended from 24 to 48 h, the capability was enhanced (P<0.05). However, in 72 h, no further reduction was observed compared to that in 48 h, although the significantly downregulated expression could be observed (P<0.05 vs control, Dox and bCPN-PDR), implying that the silencing effect could reach the plateau period after 48 h and last for 72 h at least. The CPN-PR and CPN-PDR have shown similar behavior to lipo/siRNA. Compared with the control, a remarkable decrease of VEGF mRNA expression was observed (P<0.01) at 48 h. There was no significant difference in expression levels between CPN-PR and CPN-PDR (P>0.05) at any time point, implying their comparable gene delivery and transfection capability. More importantly, this capability was equivalent to the commercial Lipofectamine 2000.\n Expression level of VEGF protein Based on the results of real-time PCR, the gene-silencing effect enhanced with time and reached a platform after 48 h. In this study, the Western blot technique was employed to observe VEGF protein expression at 48 h. The results are shown in Figure 4. GAPDH was selected as the inner control; all the bands were found to be equivalent in gray levels, and the amount of protein expression was positively correlated with the shade of gray in the imaging picture. From Figure 4, the gray levels of both control and bCPN-PDR, which were similar, were obvious. However, siRNA-loaded nanoparticles including lipo/siRNA, CPN-PR and CPN-PDR demonstrated a notably lower gray level. Furthermore, as marked with the red rectangle in Figure 4, expression of VEGF protein in CPN-PDR could hardly be identified. This suggested that co-delivery of anti-VEGF siRNA and Dox could downregulate the relevant VEGF protein level, which was in agreement with the results from semiquantitative real-time PCR. Taken together, these results indicate that nanoparticles loaded with anti-VEGF siRNA downregulated both the protein and the mRNA expression of VEGF.\nBased on the results of real-time PCR, the gene-silencing effect enhanced with time and reached a platform after 48 h. In this study, the Western blot technique was employed to observe VEGF protein expression at 48 h. The results are shown in Figure 4. GAPDH was selected as the inner control; all the bands were found to be equivalent in gray levels, and the amount of protein expression was positively correlated with the shade of gray in the imaging picture. From Figure 4, the gray levels of both control and bCPN-PDR, which were similar, were obvious. However, siRNA-loaded nanoparticles including lipo/siRNA, CPN-PR and CPN-PDR demonstrated a notably lower gray level. Furthermore, as marked with the red rectangle in Figure 4, expression of VEGF protein in CPN-PDR could hardly be identified. This suggested that co-delivery of anti-VEGF siRNA and Dox could downregulate the relevant VEGF protein level, which was in agreement with the results from semiquantitative real-time PCR. Taken together, these results indicate that nanoparticles loaded with anti-VEGF siRNA downregulated both the protein and the mRNA expression of VEGF.\n Inhibition of cell proliferation in vitro The ultimate goal of siRNA intracellular delivery is to restrain the proliferation of cancer cells. Different treatments against MCF-7 cells were performed to further investigate the inhibition effect. As shown in Figure 5A, an obvious concentration dependence in terms of DOX, CPN-PD and CPN-PDR could be observed after 48 h incubation. We have calculated the differences between CPN-PDR vs CPN-PD and CPN-PDR vs DOX at each dose. The results indicated that there was a significant difference between CPN-PDR vs DOX at each dose except the dose of 0.05, and there were significant differences between CPN-PDR vs CPN-PD at a dose of 0.002. It was suggested that the in vitro antitumor activity of CPN-PDR was equivalent to CPN-PD, and both of them showed a higher antitumor activity than free drug DOX, which was mainly attributed to the design of the multifunctional nano-vectors. We have also calculated the statistical differences between CPN-PDR vs CPN-PD and CPN-PDR vs DOX in the concentration range of Dox (0.002, 0.01, 0.05, 0.25 and 0.5 µM) using Student’s t-test. After statistical calculation, the significant differences were verified to exist between CPN-PDR vs CPN-PD (P<0.05) and CPN-PDR vs DOX (P<0.01). After calculation with professional software, the half maximal inhibitory concentrations (IC50) are presented in Figure 5B. They are 0.0367±0.0088 µM for Dox, 0.0266±0.0027 µM for CPN-PD and 0.0126±0.0022 µM for CPN-PDR, respectively. The IC50 of CPN-PD was 2.913-fold higher than that of CPN-PDR, and the IC50 of DOX was 2.110-fold higher than that of CPN-PDR. After statistical calculation, significant differences were found between CPN-PDR vs CPN-PD (P<0.05) and CPN-PDR vs DOX (P<0.05). With the results from real-time PCR and Western blot, the increased cytotoxicity of CPN-PDR probably resulted from siRNA delivery and successful transfection. This was because the intracellular delivery of anti-VEGF siRNA could downregulate the expression of VEGF protein. The anti-VEGF therapy may sensitize cancer cells or kill cells block the blood supply of the tumor and improve drug penetration. Taken together, the anti-VEGF siRNA and Dox co-delivery platform could induce increased cytotoxicity against cancer cells.\nThe ultimate goal of siRNA intracellular delivery is to restrain the proliferation of cancer cells. Different treatments against MCF-7 cells were performed to further investigate the inhibition effect. As shown in Figure 5A, an obvious concentration dependence in terms of DOX, CPN-PD and CPN-PDR could be observed after 48 h incubation. We have calculated the differences between CPN-PDR vs CPN-PD and CPN-PDR vs DOX at each dose. The results indicated that there was a significant difference between CPN-PDR vs DOX at each dose except the dose of 0.05, and there were significant differences between CPN-PDR vs CPN-PD at a dose of 0.002. It was suggested that the in vitro antitumor activity of CPN-PDR was equivalent to CPN-PD, and both of them showed a higher antitumor activity than free drug DOX, which was mainly attributed to the design of the multifunctional nano-vectors. We have also calculated the statistical differences between CPN-PDR vs CPN-PD and CPN-PDR vs DOX in the concentration range of Dox (0.002, 0.01, 0.05, 0.25 and 0.5 µM) using Student’s t-test. After statistical calculation, the significant differences were verified to exist between CPN-PDR vs CPN-PD (P<0.05) and CPN-PDR vs DOX (P<0.01). After calculation with professional software, the half maximal inhibitory concentrations (IC50) are presented in Figure 5B. They are 0.0367±0.0088 µM for Dox, 0.0266±0.0027 µM for CPN-PD and 0.0126±0.0022 µM for CPN-PDR, respectively. The IC50 of CPN-PD was 2.913-fold higher than that of CPN-PDR, and the IC50 of DOX was 2.110-fold higher than that of CPN-PDR. After statistical calculation, significant differences were found between CPN-PDR vs CPN-PD (P<0.05) and CPN-PDR vs DOX (P<0.05). With the results from real-time PCR and Western blot, the increased cytotoxicity of CPN-PDR probably resulted from siRNA delivery and successful transfection. This was because the intracellular delivery of anti-VEGF siRNA could downregulate the expression of VEGF protein. The anti-VEGF therapy may sensitize cancer cells or kill cells block the blood supply of the tumor and improve drug penetration. Taken together, the anti-VEGF siRNA and Dox co-delivery platform could induce increased cytotoxicity against cancer cells.\n Hemolysis test To investigate the safety of intravenous injection of CPN-PDR, a hemolysis test was performed. As shown in Table 1, different volume ratios of CPN-PDR to 2% red blood cell suspension were selected. After incubation and centrifugation, tubes were observed by the naked eye. As shown in Figure 6, no red blood cell hemolysis was observed in tube 6, in which 100% PBS was added serving as a negative control. For tube 7, 100% double distilled water was added and red cell hemolysis could be clearly observed. For tubes 1–5, in which different percentages of CPN-PDR were added, no red blood cell hemolysis occurred. Hemolysis of red blood cells can generate hemoprotein, which could be detected using UV-vis spectrophotometry. After scanning the absorbance wavelength of hemoprotein, 577 nm was selected as the detecting wavelength. According to Equation 3, hemolysis ratios were calculated based on the recorded absorbance and shown in Table 2. All the hemolysis ratios of tubes 1–5 were <5%, implying that no hemolysis was induced by addition of CPN-PDR.\nTo investigate the safety of intravenous injection of CPN-PDR, a hemolysis test was performed. As shown in Table 1, different volume ratios of CPN-PDR to 2% red blood cell suspension were selected. After incubation and centrifugation, tubes were observed by the naked eye. As shown in Figure 6, no red blood cell hemolysis was observed in tube 6, in which 100% PBS was added serving as a negative control. For tube 7, 100% double distilled water was added and red cell hemolysis could be clearly observed. For tubes 1–5, in which different percentages of CPN-PDR were added, no red blood cell hemolysis occurred. Hemolysis of red blood cells can generate hemoprotein, which could be detected using UV-vis spectrophotometry. After scanning the absorbance wavelength of hemoprotein, 577 nm was selected as the detecting wavelength. According to Equation 3, hemolysis ratios were calculated based on the recorded absorbance and shown in Table 2. All the hemolysis ratios of tubes 1–5 were <5%, implying that no hemolysis was induced by addition of CPN-PDR.\n Tissue section In this study, a histological analysis of organs (heart, liver, spleen, lung and kidney) was performed to evaluate whether CPN-PDR could cause tissue damage, inflammation and lesions. Kunming mice were injected with CPN-PDR and normal saline (NS) by tail vein, respectively. After 1 week, all the mice were sacrificed, and the heart, liver, spleen, lung and kidney were separated. After the standard procedure of HE staining for histological examination, the organs were observed under a microscope. Mice without any treatment were chosen as a control. As shown in Figure 7, compared with the control group, there was no visible histologically difference between mice administrated with CPN-PDR group and that with NS group, implying the safety of CPN-PDR in vivo.\nIn this study, a histological analysis of organs (heart, liver, spleen, lung and kidney) was performed to evaluate whether CPN-PDR could cause tissue damage, inflammation and lesions. Kunming mice were injected with CPN-PDR and normal saline (NS) by tail vein, respectively. After 1 week, all the mice were sacrificed, and the heart, liver, spleen, lung and kidney were separated. After the standard procedure of HE staining for histological examination, the organs were observed under a microscope. Mice without any treatment were chosen as a control. As shown in Figure 7, compared with the control group, there was no visible histologically difference between mice administrated with CPN-PDR group and that with NS group, implying the safety of CPN-PDR in vivo.","In the optimal formulation of CPN-PDR, the weight ratio of DOX to siRNA was 1:1; therefore, the siRNA loading content was equal to the DOX loading content. Based on the facts that the molar ratio of CGA-ODNs to Dox was 1:5, the weight ratio of CPN/PEI/ODNs and siRNA was 4:1:1, and consequently, siRNA and Dox loading efficiency in CPN-PDR was calculated to be 4.93%.","In the co-delivery platform, CGA-ODNs were selected as carriers of DOX, then the mixture of CGA-ODNs-Dox and siRNA (abbreviated as DR) was electrostatically interacted with PEI to obtain Dox and siRNA co-loaded nanoparticles, PEI/DR (PDR). The copolymer CPN was employed as a multifunctional material.25 In order to evaluate the safety of the carrier, MTT assays were carried out to evaluate the cytotoxicity of CGA-ODNs and CPN. In the meantime, PEI, a toxic cationic polymer, was also tested in this experiment. As shown in Figure 2A, the viability of MCF-7 cells treated with CGA-ODNs and CPN at any concentration was observed to be stable and >80% within 48 h. After 72 h incubation, the viability of MCF-7 cells treated with CPN was still >80%, but after treatment with a higher concentration of CGA-ODNs (2.5 and 5 µM), the cell viability was slightly decreased to 60%. This indicated that there is no obvious cytotoxicity of CGA-ODNs and CPN. However, the cell viability of MCF-7 cells treated with PEI was obviously dependent on the concentrations of PEI. With an increase in concentration, the MCF-7 cells treated with PEI showed lower cell viability. Moreover, this concentration-dependent trend became more obvious when incubation time increased from 24 h to 72 h.","Cell viability of MCF-7 cells was also monitored to investigate the cytotoxicity of non-RNA- and non-drug-loaded carriers including bPDR and bCPN-PDR. As shown in Figure 2B, after treatment with bCPN-PDR, the cell viability was stable around 95% and shown to be concentration independent at 24 h. Meanwhile, bPDR-treated cells showed lower cell viability with time. With increased concentration, bPDR showed higher cytotoxicity, inducing 80% cell death in 72 h. Compared with the bPDR group, the cell viabilities of the bCPN-PDR group were significantly higher (P<0.05). This phenomenon could be explained by the higher positive charge of bPDR due to the positive ingredient of PEI, while the cell cytotoxicity could be significantly decreased by covering with the nontoxic negatively charged CPN (>80% cell viability in Figure 2A), which could induce the charge reversal of bPDR, implying that the negatively charged CPN coating could decrease the higher toxicity of bPDR to cells.","The transfection efficiency of CPN-PDR compared to Lipofectamine 2000 was demonstrated by delivering VEGF-siRNA into MCF-7 cells. The expression level of VEGF mRNA was measured using real-time PCR, and the results are shown in Figure 3. The expression level of VEGF mRNA was normalized to the β-actin expression and calculated based on the semiquantitative method. Untreated cells were selected as control, and the expression level was set as 100%. For lipo/siRNA group, the silencing efficiencies were 61.98%±6.96%, 31.93%±6.43% and 37.02%±3.17% at 24, 48 and 72 h, respectively. The decreased expression of VEGF mRNA reflected that gene silencing capability of lipo/siRNA. With incubation time extended from 24 to 48 h, the capability was enhanced (P<0.05). However, in 72 h, no further reduction was observed compared to that in 48 h, although the significantly downregulated expression could be observed (P<0.05 vs control, Dox and bCPN-PDR), implying that the silencing effect could reach the plateau period after 48 h and last for 72 h at least. The CPN-PR and CPN-PDR have shown similar behavior to lipo/siRNA. Compared with the control, a remarkable decrease of VEGF mRNA expression was observed (P<0.01) at 48 h. There was no significant difference in expression levels between CPN-PR and CPN-PDR (P>0.05) at any time point, implying their comparable gene delivery and transfection capability. More importantly, this capability was equivalent to the commercial Lipofectamine 2000.","Based on the results of real-time PCR, the gene-silencing effect enhanced with time and reached a platform after 48 h. In this study, the Western blot technique was employed to observe VEGF protein expression at 48 h. The results are shown in Figure 4. GAPDH was selected as the inner control; all the bands were found to be equivalent in gray levels, and the amount of protein expression was positively correlated with the shade of gray in the imaging picture. From Figure 4, the gray levels of both control and bCPN-PDR, which were similar, were obvious. However, siRNA-loaded nanoparticles including lipo/siRNA, CPN-PR and CPN-PDR demonstrated a notably lower gray level. Furthermore, as marked with the red rectangle in Figure 4, expression of VEGF protein in CPN-PDR could hardly be identified. This suggested that co-delivery of anti-VEGF siRNA and Dox could downregulate the relevant VEGF protein level, which was in agreement with the results from semiquantitative real-time PCR. Taken together, these results indicate that nanoparticles loaded with anti-VEGF siRNA downregulated both the protein and the mRNA expression of VEGF.","The ultimate goal of siRNA intracellular delivery is to restrain the proliferation of cancer cells. Different treatments against MCF-7 cells were performed to further investigate the inhibition effect. As shown in Figure 5A, an obvious concentration dependence in terms of DOX, CPN-PD and CPN-PDR could be observed after 48 h incubation. We have calculated the differences between CPN-PDR vs CPN-PD and CPN-PDR vs DOX at each dose. The results indicated that there was a significant difference between CPN-PDR vs DOX at each dose except the dose of 0.05, and there were significant differences between CPN-PDR vs CPN-PD at a dose of 0.002. It was suggested that the in vitro antitumor activity of CPN-PDR was equivalent to CPN-PD, and both of them showed a higher antitumor activity than free drug DOX, which was mainly attributed to the design of the multifunctional nano-vectors. We have also calculated the statistical differences between CPN-PDR vs CPN-PD and CPN-PDR vs DOX in the concentration range of Dox (0.002, 0.01, 0.05, 0.25 and 0.5 µM) using Student’s t-test. After statistical calculation, the significant differences were verified to exist between CPN-PDR vs CPN-PD (P<0.05) and CPN-PDR vs DOX (P<0.01). After calculation with professional software, the half maximal inhibitory concentrations (IC50) are presented in Figure 5B. They are 0.0367±0.0088 µM for Dox, 0.0266±0.0027 µM for CPN-PD and 0.0126±0.0022 µM for CPN-PDR, respectively. The IC50 of CPN-PD was 2.913-fold higher than that of CPN-PDR, and the IC50 of DOX was 2.110-fold higher than that of CPN-PDR. After statistical calculation, significant differences were found between CPN-PDR vs CPN-PD (P<0.05) and CPN-PDR vs DOX (P<0.05). With the results from real-time PCR and Western blot, the increased cytotoxicity of CPN-PDR probably resulted from siRNA delivery and successful transfection. This was because the intracellular delivery of anti-VEGF siRNA could downregulate the expression of VEGF protein. The anti-VEGF therapy may sensitize cancer cells or kill cells block the blood supply of the tumor and improve drug penetration. Taken together, the anti-VEGF siRNA and Dox co-delivery platform could induce increased cytotoxicity against cancer cells.","To investigate the safety of intravenous injection of CPN-PDR, a hemolysis test was performed. As shown in Table 1, different volume ratios of CPN-PDR to 2% red blood cell suspension were selected. After incubation and centrifugation, tubes were observed by the naked eye. As shown in Figure 6, no red blood cell hemolysis was observed in tube 6, in which 100% PBS was added serving as a negative control. For tube 7, 100% double distilled water was added and red cell hemolysis could be clearly observed. For tubes 1–5, in which different percentages of CPN-PDR were added, no red blood cell hemolysis occurred. Hemolysis of red blood cells can generate hemoprotein, which could be detected using UV-vis spectrophotometry. After scanning the absorbance wavelength of hemoprotein, 577 nm was selected as the detecting wavelength. According to Equation 3, hemolysis ratios were calculated based on the recorded absorbance and shown in Table 2. All the hemolysis ratios of tubes 1–5 were <5%, implying that no hemolysis was induced by addition of CPN-PDR.","In this study, a histological analysis of organs (heart, liver, spleen, lung and kidney) was performed to evaluate whether CPN-PDR could cause tissue damage, inflammation and lesions. Kunming mice were injected with CPN-PDR and normal saline (NS) by tail vein, respectively. After 1 week, all the mice were sacrificed, and the heart, liver, spleen, lung and kidney were separated. After the standard procedure of HE staining for histological examination, the organs were observed under a microscope. Mice without any treatment were chosen as a control. As shown in Figure 7, compared with the control group, there was no visible histologically difference between mice administrated with CPN-PDR group and that with NS group, implying the safety of CPN-PDR in vivo.","In summary, the delivery system materials were demonstrated to be nontoxic and biocompatible. The nontoxic and negatively charged copolymer CPN coating could significantly decrease the cytotoxicity of the cationic core, bPDR. The obtained co-delivery system CPN-PDR was also confirmed with enhanced cytotoxicity against tumor cells. Gene transfection efficiency induced by CPN-PDR was fairly equal with that of the commercial product Lipofectamine 2000, implying the successful intracellular delivery and good transfection of siRNA. Moreover, the preliminary evaluation of safety showed CPN-PDR to have good biocompatibility, so it has great potential for further exploitation and clinical application. With the development of aptamer technology, the promising application prospects of this novel oligodeoxynucleotide-based co-loading platform will further increase."],"string":"[\n \"Cancer has been one of the leading causes of death worldwide, and mortality and morbidity continue to increase.1 Chemotherapy is one of the major strategies for cancer therapy. However, mono-chemotherapy may encounter problems including drug resistance and unspecific toxicity. To overcome the high mortality rate of cancer, new therapeutic strategies, such as combinational therapy, have been developed.2,3 It has been reported that a combination of chemotherapy and gene therapy could potentially achieve synergistic effects and improve target selectivity, and deter the development of cancer drug resistance.4 For example, a double-modulating strategy based on the combination of chemotherapeutic agent 5-fluorouracil (5-FU) and siRNA has been developed, in which chemotherapy enhances intratumoral siRNA delivery and the delivered siRNA enhances the chemosensitivity of tumors. Furthermore, combination therapy may compensate for the limited delivery of siRNA to tumor tissue and probably manage the 5-FU-resistant tumors.5\\nRNA interference (RNAi) is a natural cellular process that regulates gene expression level. Small interfering RNAs (siRNAs), which are small double-stranded RNAs, 20–24 nucleotides (nts) in length with sequences complementary to a gene’s coding sequence, can induce degradation of corresponding messenger RNAs (mRNAs), thus blocking the translation of the mRNA into protein.6,7 The key therapeutic advantage of using RNAi lies in its ability to specifically and potently knock down the expression of disease-causing genes of known sequence. The high specificity and potency makes it widely used in treating various cancers such as breast, liver, and lung cancers.8–10 In recent years, combination of chemotherapy and siRNA-induced RNAi has become a hot topic in cancer treatment. For example, co-delivery of siRNA targeting multidrug resistance protein 1 (MDR1) or anti-survivin siRNA and chemotherapeutic drugs was demonstrated to be a promising strategy to overcome drug resistance.11–13 Anti-apoptotic gene Bcl-2 is a potential combinational therapy target owing to its important role in cell apoptosis, and combination of Bcl-2 siRNA and 5-FU could induce a remarkable increase of cell apoptosis both in vitro and in vivo.14\\nTo achieve the desired combinational and synergistic effects of chemotherapy and RNAi, selection of siRNA is important, and the selection of siRNA is also the section of silencing target. The mechanism for combinational anticancer effect varies with different silencing targets. For example, siRNA targeting MDR1 or anti-apoptotic gene Bcl-2 were used to prevent drug resistance response and sensitize chemotherapeutic drugs, or enhance the apoptosis of cancer cells, both offering enhanced anticancer effect.15,16 Tumor tissues have abundant new blood vessels that pump sufficient nutrition and oxygen for tumor growth and metastasis. The dependency of tumor tissues on these new blood vessels is important in anti-angiogenesis therapy for cancer treatment.17 Recently, there have been many efforts in achieving anti-angiogenesis, including delivering siRNAs to silence specific pro-angiogenic genes, such as vascular endothelial growth factor (VEGF), fibroblast growth factor (FGF) and interleukin-8 (IL-8).18 Of these targets, VEGF has received a lot of attention owing to its key role in tumor angiogenesis. With the development of RNAi technology, siRNA with VEGF target has been widely explored and referred to as anti-VEGF treatment.19–21 In 2004, the success of bevacizumab showed VEGF to be a potential target for angiogenesis therapy. There are a number of novel anti-VEGF agents in phase III clinical trials that may come to market in the next few years.22,23 In our design, siRNA with VEGF target was selected to co-deliver with chemotherapeutic drugs, resulting in a combinational anticancer effect. The mechanism for this could be that anti-VEGF treatments sensitize the cells to chemotherapy agents by blocking blood supply, improve drug penetration by disruption or normalization of tumor vasculature and in addition directly kill cancer cells by gene therapy.24,25\\nHowever, naked siRNAs may not induce efficient transfection by themselves because they are unstable and vulnerable, especially to nuclease-mediated degradation; also, the negatively charged surface blocks them from cellular endocytosis. Moreover, successful transfection of siRNA into cells is based on siRNA with complete structure, which could further initiate the RNAi process for targeted gene silencing.26,27 Thus, nanoscale delivery systems, which could co-load drug and siRNA and protect siRNA from degradation, were widely used and demonstrated to be excellent candidates. These include nanoparticles,28 liposomes,29 polyplexes30 and dendrimers.31 The basic standard involves employing cationic polymers or lipids to condense negatively charged siRNA and protect them from degradation.32,33 In our previous study,25 oligodeoxynucleotides with CGA repeating units (CGA-ODNs) were introduced to load Dox to obtain Dox-loaded CGA-ODNs (CGA-ODNs-Dox). Poly(ethyleneimine) (PEI) was then used to condense siRNA and CGA ODNs-Dox to obtain Dox and siRNA co-loaded nanoparticles (PEI/CGA-ODNs-Dox and siRNA[PDR]). Finally, the pH-sensitive targeted material, o-carboxymethyl-chitosan (CMCS)-poly(ethylene glycol) (PEG)-aspargine-glycine-arginine (NGR) (CMCS-PEG-NGR[CPN]), was used to modify PDR to obtain multifunctional CPN-PDR. CPN-PDRs were demonstrated to be multifunctional, being able to co-deliver Dox and siRNA into cells, induce pH-responsive disassembly and realize endosomal escape of gene and drug. Thus, in the present study, the cytotoxicity of the materials and the delivery system are further evaluated. Then, the transfection efficiency of siRNA is monitored by semiquantitative real-time polymerase chain reaction (real-time PCR) and Western blot techniques for mRNA level and protein level, respectively. Finally, the safety of the delivery system is evaluated by hemolysis test in vitro and histological assessment.\",\n \" Materials Dox was purchased from Dalian Meilun Biology Technology Co. Ltd (Dalian, China). Targeting human VEGF siRNA (sense: 5′-ACAUCACCAUGCAGAUUAUdTdT-3′, anti-sense: 5′-dTdTUGUAGUGGUACGUCUAAUA-3′) was obtained from Guangzhou Ribobio Co., Ltd (Guangzhou, China). CGA oligodeoxynucleotides (sequence: 5′-CGACGACGACGACGACGACGA-3′; complementary sequence: 5′-TCGTCGTCGTCGTCGTCGTCG-3′) were purchased from BGI Co. (Shenzhen, China). Fetal bovine serum (FBS) was obtained from Sijiqing Co., Ltd, (Zhejiang, China) and 3-[4,5-dimethyl-2-thiazolyl]-2,5-diphenyl-2H-tetrazolium bromide (MTT) was from Sigma-Aldrich (St Louis, MO, USA). Trizol RNA extraction was from Thermo Fisher Scientific (Waltham, MA, USA). SYBR®Green and ReverTra Ace qPCR RT Kit were purchased from Toyobo (Osaka, Japan). All solutions were made up in Millipore ultrapure water (EMD Millipore, Billerica, MA, USA) and sterilized for cell culture. All other chemicals and reagents were of analytical grade and used as received. All the primers used in real-time PCR were synthesized and purified by BGI Co. with sequences: VEGF – forward: 5′-CTGGAGTGTGTGCCCACTGA-3′; VEGF – reverse: 5′-TCCTATGTGCTGGCCTTGGT-3′; actin – forward: GAGCTACGAGCTGCCTGACG; actin – reverse: CCTAGAAGCATTTGCGGTGG.\\nDox was purchased from Dalian Meilun Biology Technology Co. Ltd (Dalian, China). Targeting human VEGF siRNA (sense: 5′-ACAUCACCAUGCAGAUUAUdTdT-3′, anti-sense: 5′-dTdTUGUAGUGGUACGUCUAAUA-3′) was obtained from Guangzhou Ribobio Co., Ltd (Guangzhou, China). CGA oligodeoxynucleotides (sequence: 5′-CGACGACGACGACGACGACGA-3′; complementary sequence: 5′-TCGTCGTCGTCGTCGTCGTCG-3′) were purchased from BGI Co. (Shenzhen, China). Fetal bovine serum (FBS) was obtained from Sijiqing Co., Ltd, (Zhejiang, China) and 3-[4,5-dimethyl-2-thiazolyl]-2,5-diphenyl-2H-tetrazolium bromide (MTT) was from Sigma-Aldrich (St Louis, MO, USA). Trizol RNA extraction was from Thermo Fisher Scientific (Waltham, MA, USA). SYBR®Green and ReverTra Ace qPCR RT Kit were purchased from Toyobo (Osaka, Japan). All solutions were made up in Millipore ultrapure water (EMD Millipore, Billerica, MA, USA) and sterilized for cell culture. All other chemicals and reagents were of analytical grade and used as received. All the primers used in real-time PCR were synthesized and purified by BGI Co. with sequences: VEGF – forward: 5′-CTGGAGTGTGTGCCCACTGA-3′; VEGF – reverse: 5′-TCCTATGTGCTGGCCTTGGT-3′; actin – forward: GAGCTACGAGCTGCCTGACG; actin – reverse: CCTAGAAGCATTTGCGGTGG.\\n Cells and animals The MCF-7 cells were purchased from the Chinese Academy of Sciences Cells Bank (Shanghai, China), and the cells were cultured in Roswell Park Memorial Institute (RPMI) 1640 medium supplemented with 10% fetal bovine serum (FBS), streptomycin at 100 µg/mL and penicillin at 100 U/mL. All cells were cultured in a 37°C incubator with 5% CO2.\\nKunming mice (20±2 g) were obtained from Experimental Animal Center of Shandong University. Animal experiments were carried out according to the requirements of the Animal Management Rules of the Ministry of Health (China) and approved by the Laboratory Animal Ethics Review Committee of Qilu Medical College of Shandong University.\\nThe MCF-7 cells were purchased from the Chinese Academy of Sciences Cells Bank (Shanghai, China), and the cells were cultured in Roswell Park Memorial Institute (RPMI) 1640 medium supplemented with 10% fetal bovine serum (FBS), streptomycin at 100 µg/mL and penicillin at 100 U/mL. All cells were cultured in a 37°C incubator with 5% CO2.\\nKunming mice (20±2 g) were obtained from Experimental Animal Center of Shandong University. Animal experiments were carried out according to the requirements of the Animal Management Rules of the Ministry of Health (China) and approved by the Laboratory Animal Ethics Review Committee of Qilu Medical College of Shandong University.\\n Preparation of nanoparticles PDR and CPN-PDR were prepared by the method reported in our previous study.25 Briefly, CGA-ODNs-Dox was first obtained by incubating Dox with CGA-ODNs at room temperature. Then, PEI, siRNA, ODNs-Dox and CPN were dissolved and diluted to the corresponding concentrations with deionized water. After that, siRNA and CGA-ODNs-Dox were mixed by vortexing for several seconds to obtain the mixture. The mixture was added dropwise into PEI solution and mixed by vortexing and then incubated for 30 min at room temperature to form PDR. CPN-PDR was obtained by adding PDR suspension into the CPN solution under vortexing, followed by 30-min incubation at room temperature. When preparing the blank nanoparticles, the DOX and siRNA were not involved in above method. The construction of different kinds of nanoparticles is shown in Figure 1.\\nThe loading content of DOX and siRNA was calculated using the following formula:34\\nLoading content(%)=Weight of loading drugsWeight of nanoparticles×100%(1)\\nPDR and CPN-PDR were prepared by the method reported in our previous study.25 Briefly, CGA-ODNs-Dox was first obtained by incubating Dox with CGA-ODNs at room temperature. Then, PEI, siRNA, ODNs-Dox and CPN were dissolved and diluted to the corresponding concentrations with deionized water. After that, siRNA and CGA-ODNs-Dox were mixed by vortexing for several seconds to obtain the mixture. The mixture was added dropwise into PEI solution and mixed by vortexing and then incubated for 30 min at room temperature to form PDR. CPN-PDR was obtained by adding PDR suspension into the CPN solution under vortexing, followed by 30-min incubation at room temperature. When preparing the blank nanoparticles, the DOX and siRNA were not involved in above method. The construction of different kinds of nanoparticles is shown in Figure 1.\\nThe loading content of DOX and siRNA was calculated using the following formula:34\\nLoading content(%)=Weight of loading drugsWeight of nanoparticles×100%(1)\\n In vitro cytotoxicity of materials MTT assays were carried out on MCF-7 cells to evaluate the in vitro cytotoxicity of CGA-ODNs, PEI and CPN, respectively.35 MCF-7 cells were seeded with density at 7,000 cells/well in the 96-well plates and allowed to adhere overnight. CGA-ODNs, PEI and CPN solutions, which were corresponding to Dox concentrations (0.25, 0.625, 1.25, 2.5 and 5 µM), were added and incubated with the cells for 24, 48 and 72 h, respectively. Five wells were set for each concentration, and cells incubated with fresh media were taken as control. At a determined time point, 20 µL MTT solutions (5 mg/mL) were added. After 4 h incubation, the plates were centrifuged (3,000 rpm, 10 min) and the supernatant was removed. Then, 200 µL DMSO solutions were added to dissolve the formazan crystals formed by the living cells. The cell viability was calculated according to the following formula (Equation 2) after recording the absorbance at 570 nm by a microplate reader (Model 680; Bio-Rad Laboratories Inc., Hercules, CA, USA). All the assays were repeated three times.\\nCell viability=Abs(sample)−Abs(blank)Abs(control)−Abs(blank)×100%(2)\\nMTT assays were carried out on MCF-7 cells to evaluate the in vitro cytotoxicity of CGA-ODNs, PEI and CPN, respectively.35 MCF-7 cells were seeded with density at 7,000 cells/well in the 96-well plates and allowed to adhere overnight. CGA-ODNs, PEI and CPN solutions, which were corresponding to Dox concentrations (0.25, 0.625, 1.25, 2.5 and 5 µM), were added and incubated with the cells for 24, 48 and 72 h, respectively. Five wells were set for each concentration, and cells incubated with fresh media were taken as control. At a determined time point, 20 µL MTT solutions (5 mg/mL) were added. After 4 h incubation, the plates were centrifuged (3,000 rpm, 10 min) and the supernatant was removed. Then, 200 µL DMSO solutions were added to dissolve the formazan crystals formed by the living cells. The cell viability was calculated according to the following formula (Equation 2) after recording the absorbance at 570 nm by a microplate reader (Model 680; Bio-Rad Laboratories Inc., Hercules, CA, USA). All the assays were repeated three times.\\nCell viability=Abs(sample)−Abs(blank)Abs(control)−Abs(blank)×100%(2)\\n In vitro cytotoxicity of blank nanoparticles The cytotoxicity of non-DOX- and siRNA-loaded nano-particles (blank PDR[bPDR]) and CPN-coated blank PDR (bCPN-PDR) were also evaluated in MCF-7 cells. bPDR and bCPN-PDR solutions, corresponding to Dox concentrations (0.25, 0.625, 1.25, 2.5 and 5 µM), were added and incubated with the cells for 24, 48 and 72 h, respectively. Five wells were set for each concentration and cells incubated with fresh media were taken as control. After the indicated time periods, cell viability was evaluated according to the procedure described above.\\nThe cytotoxicity of non-DOX- and siRNA-loaded nano-particles (blank PDR[bPDR]) and CPN-coated blank PDR (bCPN-PDR) were also evaluated in MCF-7 cells. bPDR and bCPN-PDR solutions, corresponding to Dox concentrations (0.25, 0.625, 1.25, 2.5 and 5 µM), were added and incubated with the cells for 24, 48 and 72 h, respectively. Five wells were set for each concentration and cells incubated with fresh media were taken as control. After the indicated time periods, cell viability was evaluated according to the procedure described above.\\n siRNA transfection with nanoparticles MCF-7 cells were seeded into 24-well plates at densities of 8×104, 6×104 and 5×104 cells/well for 24, 48 and 72 h transfection, respectively. After culturing at 37°C overnight, MCF-7 cells were treated with siRNA specific for VEGF. The siRNA was delivered either by Lipofectamine 2000 (Thermo Fisher Scientific) according to the manufacturer’s instructions or by CPN-modified siRNA-loaded nanoparticles (CPN-PR) or by CPN-modified Dox- and siRNA-loaded nanoparticles (CPN-PDR). The final concentrations of Dox and siRNA were 1 µM and 55 nM, respectively. After siRNA transfection, cells were harvested for RNA extraction and Western blot PCR analysis or subjected to Western blot assay.\\nMCF-7 cells were seeded into 24-well plates at densities of 8×104, 6×104 and 5×104 cells/well for 24, 48 and 72 h transfection, respectively. After culturing at 37°C overnight, MCF-7 cells were treated with siRNA specific for VEGF. The siRNA was delivered either by Lipofectamine 2000 (Thermo Fisher Scientific) according to the manufacturer’s instructions or by CPN-modified siRNA-loaded nanoparticles (CPN-PR) or by CPN-modified Dox- and siRNA-loaded nanoparticles (CPN-PDR). The final concentrations of Dox and siRNA were 1 µM and 55 nM, respectively. After siRNA transfection, cells were harvested for RNA extraction and Western blot PCR analysis or subjected to Western blot assay.\\n Real-time PCR The expression level of VEGF mRNA was monitored by real-time PCR technique with the standard procedure. Total RNA was extracted using Trizol agent under the standard procedure (Thermo Fisher Scientific), and cDNA synthesis was carried out using Rever Tra Ace qPCR RT Kit (Toyobo) according to the manufacturer’s instructions. The final concentration of RNA was measured by Nano Drop 2000 (Thermo Fisher Scientific), and the final product cDNA was stored at −20°C until use.\\nEach real-time PCR system contained 20 µL solutions including cDNA (2 µL), SYBR®Green mix (10 µL), primer (1.6 µL) and double distilled water (6.4 µL). The reaction system was placed into PCR instrument (Roche LightCycler™; Hoffman-La Roche Ltd, Basel, Switzerland) for analysis. β-Actin served as a reference gene. Three wells were set for each group. The expression level of VEGF mRNA was normalized to the β-actin expression level and calculated by recording the Ct value at the end of the reaction.\\nThe expression level of VEGF mRNA was monitored by real-time PCR technique with the standard procedure. Total RNA was extracted using Trizol agent under the standard procedure (Thermo Fisher Scientific), and cDNA synthesis was carried out using Rever Tra Ace qPCR RT Kit (Toyobo) according to the manufacturer’s instructions. The final concentration of RNA was measured by Nano Drop 2000 (Thermo Fisher Scientific), and the final product cDNA was stored at −20°C until use.\\nEach real-time PCR system contained 20 µL solutions including cDNA (2 µL), SYBR®Green mix (10 µL), primer (1.6 µL) and double distilled water (6.4 µL). The reaction system was placed into PCR instrument (Roche LightCycler™; Hoffman-La Roche Ltd, Basel, Switzerland) for analysis. β-Actin served as a reference gene. Three wells were set for each group. The expression level of VEGF mRNA was normalized to the β-actin expression level and calculated by recording the Ct value at the end of the reaction.\\n Western blot assay A Western blot assay was employed to investigate the expression level of total VEGF protein after transfection. At a determined time, cells were trypsinized and pelleted by centrifugation. The protein collecting process was carried out based on the standard procedure of Total Protein Extraction Kit (BestBio, Shanghai, China). The total protein was stored at −80°C until use. Protein was quantified by BCA protein assay kit (Beyotime Biotechnology, Shanghai, China) based on the standard carve calculation (A=0.006 C+0.0919, r=0.9987). After quantification, all the samples were diluted to 2 µg/µL with sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) electrophoresis, denatured in boiling water for 5 min and stored at −80°C until use. Equal amounts of protein (50 µg) were subjected to 12% SDS-PAGE and transferred to nitrocellulose membranes using standard procedures. The membrane was blocked in 2% skimmed milk in phosphate buffered saline for 2 h and washed by Tris-buffered saline and Tween-20 (TBST) solution three times. Then the membranes were probed with primary antibodies against VEGF and glyceraldehyde 3-phosphate dehydrogenase (GAPDN) overnight at 4°C. After 3×10 min washing in TBST on shaker, membranes were incubated with horseradish peroxidase (HRP)-conjugated anti-rabbit IgG for 1 h at room temperature. With another 3×10 min washes in TBST, the membranes were developed with electrochemiluminescence (ECL) using a gel-imaging system and analyzed using Image Analysis software.\\nA Western blot assay was employed to investigate the expression level of total VEGF protein after transfection. At a determined time, cells were trypsinized and pelleted by centrifugation. The protein collecting process was carried out based on the standard procedure of Total Protein Extraction Kit (BestBio, Shanghai, China). The total protein was stored at −80°C until use. Protein was quantified by BCA protein assay kit (Beyotime Biotechnology, Shanghai, China) based on the standard carve calculation (A=0.006 C+0.0919, r=0.9987). After quantification, all the samples were diluted to 2 µg/µL with sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) electrophoresis, denatured in boiling water for 5 min and stored at −80°C until use. Equal amounts of protein (50 µg) were subjected to 12% SDS-PAGE and transferred to nitrocellulose membranes using standard procedures. The membrane was blocked in 2% skimmed milk in phosphate buffered saline for 2 h and washed by Tris-buffered saline and Tween-20 (TBST) solution three times. Then the membranes were probed with primary antibodies against VEGF and glyceraldehyde 3-phosphate dehydrogenase (GAPDN) overnight at 4°C. After 3×10 min washing in TBST on shaker, membranes were incubated with horseradish peroxidase (HRP)-conjugated anti-rabbit IgG for 1 h at room temperature. With another 3×10 min washes in TBST, the membranes were developed with electrochemiluminescence (ECL) using a gel-imaging system and analyzed using Image Analysis software.\\n Cell proliferation assay The anti-proliferation activity of Dox, CPN-PD and CPN-PDR against MCF-7 cells was tested via MTT method. MCF-7 cells were seeded into 96-well plates at a density of 7,000 cells/well. After overnight incubation, the cells were treated with various concentrations (0.002, 0.01, 0.05, 0.25 and 0.5 µM) of Dox, CPN-PD and CPN-PDR solutions and incubated for another 48 h. Five wells were set for each concentration, and cells incubated with fresh media were taken as control. After indicated time periods, cell viability was evaluated according to the procedure described in the “In vitro cytotoxicity of materials” section.\\nThe anti-proliferation activity of Dox, CPN-PD and CPN-PDR against MCF-7 cells was tested via MTT method. MCF-7 cells were seeded into 96-well plates at a density of 7,000 cells/well. After overnight incubation, the cells were treated with various concentrations (0.002, 0.01, 0.05, 0.25 and 0.5 µM) of Dox, CPN-PD and CPN-PDR solutions and incubated for another 48 h. Five wells were set for each concentration, and cells incubated with fresh media were taken as control. After indicated time periods, cell viability was evaluated according to the procedure described in the “In vitro cytotoxicity of materials” section.\\n Hemolysis test Hemolysis test was carried out to investigate the safety of CPN-PDR for intravenous injection. A 2% red blood cell suspension was collected by centrifugation and resuspension. The test was performed under the design described in Table 1. After incubated at 37°C for 3 h, all the groups were centrifuged at 3,000 rpm for 15 min and visualized by the naked eye. To measure the hemolysis ratios of each group, UV-vis spectrophotometry was carried out to record the absorbance of supernatant in each group. The quantitation of hemolysis ratios was calculated according to the following formula:\\nHR(%)=Abs(sample)−Abs(−)Abs(+)−Abs(−)×100%(3)where Abs (sample), Abs (−) and Abs (+) refer to the absorbances of the samples, negative control and positive control, respectively.\\nHemolysis test was carried out to investigate the safety of CPN-PDR for intravenous injection. A 2% red blood cell suspension was collected by centrifugation and resuspension. The test was performed under the design described in Table 1. After incubated at 37°C for 3 h, all the groups were centrifuged at 3,000 rpm for 15 min and visualized by the naked eye. To measure the hemolysis ratios of each group, UV-vis spectrophotometry was carried out to record the absorbance of supernatant in each group. The quantitation of hemolysis ratios was calculated according to the following formula:\\nHR(%)=Abs(sample)−Abs(−)Abs(+)−Abs(−)×100%(3)where Abs (sample), Abs (−) and Abs (+) refer to the absorbances of the samples, negative control and positive control, respectively.\\n Histological assessment In order to evaluate the compatibility and tissue toxicity of CPN-PDR in vivo, a histological observation was performed.36 Five female Kunming mice were injected with CPN-PDR at a dose of 232 µg/kg through the tail vein. In the meantime, normal saline (NS) was taken as a control reagent. After 1 week, all the mice were sacrificed, and the heart, liver, spleen, lung and kidney were separated. After washing twice with PBS, all the organs were fixed in 4% formaldehyde, dehydrated in gradient alcohol, placed in xylene, embedded in paraffin and made into sections, followed by hematoxylin-eosin (HE) staining for histological examination with microscope.\\nIn order to evaluate the compatibility and tissue toxicity of CPN-PDR in vivo, a histological observation was performed.36 Five female Kunming mice were injected with CPN-PDR at a dose of 232 µg/kg through the tail vein. In the meantime, normal saline (NS) was taken as a control reagent. After 1 week, all the mice were sacrificed, and the heart, liver, spleen, lung and kidney were separated. After washing twice with PBS, all the organs were fixed in 4% formaldehyde, dehydrated in gradient alcohol, placed in xylene, embedded in paraffin and made into sections, followed by hematoxylin-eosin (HE) staining for histological examination with microscope.\\n Statistical analysis All studies were repeated a minimum of three times and measured at least in triplicate. The results are reported as the means ± SD. Statistical significance was analyzed using Student’s t-test. Differences between experimental groups were considered significant if the P-value was <0.05.\\nAll studies were repeated a minimum of three times and measured at least in triplicate. The results are reported as the means ± SD. Statistical significance was analyzed using Student’s t-test. Differences between experimental groups were considered significant if the P-value was <0.05.\",\n \"Dox was purchased from Dalian Meilun Biology Technology Co. Ltd (Dalian, China). Targeting human VEGF siRNA (sense: 5′-ACAUCACCAUGCAGAUUAUdTdT-3′, anti-sense: 5′-dTdTUGUAGUGGUACGUCUAAUA-3′) was obtained from Guangzhou Ribobio Co., Ltd (Guangzhou, China). CGA oligodeoxynucleotides (sequence: 5′-CGACGACGACGACGACGACGA-3′; complementary sequence: 5′-TCGTCGTCGTCGTCGTCGTCG-3′) were purchased from BGI Co. (Shenzhen, China). Fetal bovine serum (FBS) was obtained from Sijiqing Co., Ltd, (Zhejiang, China) and 3-[4,5-dimethyl-2-thiazolyl]-2,5-diphenyl-2H-tetrazolium bromide (MTT) was from Sigma-Aldrich (St Louis, MO, USA). Trizol RNA extraction was from Thermo Fisher Scientific (Waltham, MA, USA). SYBR®Green and ReverTra Ace qPCR RT Kit were purchased from Toyobo (Osaka, Japan). All solutions were made up in Millipore ultrapure water (EMD Millipore, Billerica, MA, USA) and sterilized for cell culture. All other chemicals and reagents were of analytical grade and used as received. All the primers used in real-time PCR were synthesized and purified by BGI Co. with sequences: VEGF – forward: 5′-CTGGAGTGTGTGCCCACTGA-3′; VEGF – reverse: 5′-TCCTATGTGCTGGCCTTGGT-3′; actin – forward: GAGCTACGAGCTGCCTGACG; actin – reverse: CCTAGAAGCATTTGCGGTGG.\",\n \"The MCF-7 cells were purchased from the Chinese Academy of Sciences Cells Bank (Shanghai, China), and the cells were cultured in Roswell Park Memorial Institute (RPMI) 1640 medium supplemented with 10% fetal bovine serum (FBS), streptomycin at 100 µg/mL and penicillin at 100 U/mL. All cells were cultured in a 37°C incubator with 5% CO2.\\nKunming mice (20±2 g) were obtained from Experimental Animal Center of Shandong University. Animal experiments were carried out according to the requirements of the Animal Management Rules of the Ministry of Health (China) and approved by the Laboratory Animal Ethics Review Committee of Qilu Medical College of Shandong University.\",\n \"PDR and CPN-PDR were prepared by the method reported in our previous study.25 Briefly, CGA-ODNs-Dox was first obtained by incubating Dox with CGA-ODNs at room temperature. Then, PEI, siRNA, ODNs-Dox and CPN were dissolved and diluted to the corresponding concentrations with deionized water. After that, siRNA and CGA-ODNs-Dox were mixed by vortexing for several seconds to obtain the mixture. The mixture was added dropwise into PEI solution and mixed by vortexing and then incubated for 30 min at room temperature to form PDR. CPN-PDR was obtained by adding PDR suspension into the CPN solution under vortexing, followed by 30-min incubation at room temperature. When preparing the blank nanoparticles, the DOX and siRNA were not involved in above method. The construction of different kinds of nanoparticles is shown in Figure 1.\\nThe loading content of DOX and siRNA was calculated using the following formula:34\\nLoading content(%)=Weight of loading drugsWeight of nanoparticles×100%(1)\",\n \"MTT assays were carried out on MCF-7 cells to evaluate the in vitro cytotoxicity of CGA-ODNs, PEI and CPN, respectively.35 MCF-7 cells were seeded with density at 7,000 cells/well in the 96-well plates and allowed to adhere overnight. CGA-ODNs, PEI and CPN solutions, which were corresponding to Dox concentrations (0.25, 0.625, 1.25, 2.5 and 5 µM), were added and incubated with the cells for 24, 48 and 72 h, respectively. Five wells were set for each concentration, and cells incubated with fresh media were taken as control. At a determined time point, 20 µL MTT solutions (5 mg/mL) were added. After 4 h incubation, the plates were centrifuged (3,000 rpm, 10 min) and the supernatant was removed. Then, 200 µL DMSO solutions were added to dissolve the formazan crystals formed by the living cells. The cell viability was calculated according to the following formula (Equation 2) after recording the absorbance at 570 nm by a microplate reader (Model 680; Bio-Rad Laboratories Inc., Hercules, CA, USA). All the assays were repeated three times.\\nCell viability=Abs(sample)−Abs(blank)Abs(control)−Abs(blank)×100%(2)\",\n \"The cytotoxicity of non-DOX- and siRNA-loaded nano-particles (blank PDR[bPDR]) and CPN-coated blank PDR (bCPN-PDR) were also evaluated in MCF-7 cells. bPDR and bCPN-PDR solutions, corresponding to Dox concentrations (0.25, 0.625, 1.25, 2.5 and 5 µM), were added and incubated with the cells for 24, 48 and 72 h, respectively. Five wells were set for each concentration and cells incubated with fresh media were taken as control. After the indicated time periods, cell viability was evaluated according to the procedure described above.\",\n \"MCF-7 cells were seeded into 24-well plates at densities of 8×104, 6×104 and 5×104 cells/well for 24, 48 and 72 h transfection, respectively. After culturing at 37°C overnight, MCF-7 cells were treated with siRNA specific for VEGF. The siRNA was delivered either by Lipofectamine 2000 (Thermo Fisher Scientific) according to the manufacturer’s instructions or by CPN-modified siRNA-loaded nanoparticles (CPN-PR) or by CPN-modified Dox- and siRNA-loaded nanoparticles (CPN-PDR). The final concentrations of Dox and siRNA were 1 µM and 55 nM, respectively. After siRNA transfection, cells were harvested for RNA extraction and Western blot PCR analysis or subjected to Western blot assay.\",\n \"The expression level of VEGF mRNA was monitored by real-time PCR technique with the standard procedure. Total RNA was extracted using Trizol agent under the standard procedure (Thermo Fisher Scientific), and cDNA synthesis was carried out using Rever Tra Ace qPCR RT Kit (Toyobo) according to the manufacturer’s instructions. The final concentration of RNA was measured by Nano Drop 2000 (Thermo Fisher Scientific), and the final product cDNA was stored at −20°C until use.\\nEach real-time PCR system contained 20 µL solutions including cDNA (2 µL), SYBR®Green mix (10 µL), primer (1.6 µL) and double distilled water (6.4 µL). The reaction system was placed into PCR instrument (Roche LightCycler™; Hoffman-La Roche Ltd, Basel, Switzerland) for analysis. β-Actin served as a reference gene. Three wells were set for each group. The expression level of VEGF mRNA was normalized to the β-actin expression level and calculated by recording the Ct value at the end of the reaction.\",\n \"A Western blot assay was employed to investigate the expression level of total VEGF protein after transfection. At a determined time, cells were trypsinized and pelleted by centrifugation. The protein collecting process was carried out based on the standard procedure of Total Protein Extraction Kit (BestBio, Shanghai, China). The total protein was stored at −80°C until use. Protein was quantified by BCA protein assay kit (Beyotime Biotechnology, Shanghai, China) based on the standard carve calculation (A=0.006 C+0.0919, r=0.9987). After quantification, all the samples were diluted to 2 µg/µL with sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) electrophoresis, denatured in boiling water for 5 min and stored at −80°C until use. Equal amounts of protein (50 µg) were subjected to 12% SDS-PAGE and transferred to nitrocellulose membranes using standard procedures. The membrane was blocked in 2% skimmed milk in phosphate buffered saline for 2 h and washed by Tris-buffered saline and Tween-20 (TBST) solution three times. Then the membranes were probed with primary antibodies against VEGF and glyceraldehyde 3-phosphate dehydrogenase (GAPDN) overnight at 4°C. After 3×10 min washing in TBST on shaker, membranes were incubated with horseradish peroxidase (HRP)-conjugated anti-rabbit IgG for 1 h at room temperature. With another 3×10 min washes in TBST, the membranes were developed with electrochemiluminescence (ECL) using a gel-imaging system and analyzed using Image Analysis software.\",\n \"The anti-proliferation activity of Dox, CPN-PD and CPN-PDR against MCF-7 cells was tested via MTT method. MCF-7 cells were seeded into 96-well plates at a density of 7,000 cells/well. After overnight incubation, the cells were treated with various concentrations (0.002, 0.01, 0.05, 0.25 and 0.5 µM) of Dox, CPN-PD and CPN-PDR solutions and incubated for another 48 h. Five wells were set for each concentration, and cells incubated with fresh media were taken as control. After indicated time periods, cell viability was evaluated according to the procedure described in the “In vitro cytotoxicity of materials” section.\",\n \"Hemolysis test was carried out to investigate the safety of CPN-PDR for intravenous injection. A 2% red blood cell suspension was collected by centrifugation and resuspension. The test was performed under the design described in Table 1. After incubated at 37°C for 3 h, all the groups were centrifuged at 3,000 rpm for 15 min and visualized by the naked eye. To measure the hemolysis ratios of each group, UV-vis spectrophotometry was carried out to record the absorbance of supernatant in each group. The quantitation of hemolysis ratios was calculated according to the following formula:\\nHR(%)=Abs(sample)−Abs(−)Abs(+)−Abs(−)×100%(3)where Abs (sample), Abs (−) and Abs (+) refer to the absorbances of the samples, negative control and positive control, respectively.\",\n \"In order to evaluate the compatibility and tissue toxicity of CPN-PDR in vivo, a histological observation was performed.36 Five female Kunming mice were injected with CPN-PDR at a dose of 232 µg/kg through the tail vein. In the meantime, normal saline (NS) was taken as a control reagent. After 1 week, all the mice were sacrificed, and the heart, liver, spleen, lung and kidney were separated. After washing twice with PBS, all the organs were fixed in 4% formaldehyde, dehydrated in gradient alcohol, placed in xylene, embedded in paraffin and made into sections, followed by hematoxylin-eosin (HE) staining for histological examination with microscope.\",\n \"All studies were repeated a minimum of three times and measured at least in triplicate. The results are reported as the means ± SD. Statistical significance was analyzed using Student’s t-test. Differences between experimental groups were considered significant if the P-value was <0.05.\",\n \" Characteristics of nanoparticles In the optimal formulation of CPN-PDR, the weight ratio of DOX to siRNA was 1:1; therefore, the siRNA loading content was equal to the DOX loading content. Based on the facts that the molar ratio of CGA-ODNs to Dox was 1:5, the weight ratio of CPN/PEI/ODNs and siRNA was 4:1:1, and consequently, siRNA and Dox loading efficiency in CPN-PDR was calculated to be 4.93%.\\nIn the optimal formulation of CPN-PDR, the weight ratio of DOX to siRNA was 1:1; therefore, the siRNA loading content was equal to the DOX loading content. Based on the facts that the molar ratio of CGA-ODNs to Dox was 1:5, the weight ratio of CPN/PEI/ODNs and siRNA was 4:1:1, and consequently, siRNA and Dox loading efficiency in CPN-PDR was calculated to be 4.93%.\\n Cytotoxicity of materials In the co-delivery platform, CGA-ODNs were selected as carriers of DOX, then the mixture of CGA-ODNs-Dox and siRNA (abbreviated as DR) was electrostatically interacted with PEI to obtain Dox and siRNA co-loaded nanoparticles, PEI/DR (PDR). The copolymer CPN was employed as a multifunctional material.25 In order to evaluate the safety of the carrier, MTT assays were carried out to evaluate the cytotoxicity of CGA-ODNs and CPN. In the meantime, PEI, a toxic cationic polymer, was also tested in this experiment. As shown in Figure 2A, the viability of MCF-7 cells treated with CGA-ODNs and CPN at any concentration was observed to be stable and >80% within 48 h. After 72 h incubation, the viability of MCF-7 cells treated with CPN was still >80%, but after treatment with a higher concentration of CGA-ODNs (2.5 and 5 µM), the cell viability was slightly decreased to 60%. This indicated that there is no obvious cytotoxicity of CGA-ODNs and CPN. However, the cell viability of MCF-7 cells treated with PEI was obviously dependent on the concentrations of PEI. With an increase in concentration, the MCF-7 cells treated with PEI showed lower cell viability. Moreover, this concentration-dependent trend became more obvious when incubation time increased from 24 h to 72 h.\\nIn the co-delivery platform, CGA-ODNs were selected as carriers of DOX, then the mixture of CGA-ODNs-Dox and siRNA (abbreviated as DR) was electrostatically interacted with PEI to obtain Dox and siRNA co-loaded nanoparticles, PEI/DR (PDR). The copolymer CPN was employed as a multifunctional material.25 In order to evaluate the safety of the carrier, MTT assays were carried out to evaluate the cytotoxicity of CGA-ODNs and CPN. In the meantime, PEI, a toxic cationic polymer, was also tested in this experiment. As shown in Figure 2A, the viability of MCF-7 cells treated with CGA-ODNs and CPN at any concentration was observed to be stable and >80% within 48 h. After 72 h incubation, the viability of MCF-7 cells treated with CPN was still >80%, but after treatment with a higher concentration of CGA-ODNs (2.5 and 5 µM), the cell viability was slightly decreased to 60%. This indicated that there is no obvious cytotoxicity of CGA-ODNs and CPN. However, the cell viability of MCF-7 cells treated with PEI was obviously dependent on the concentrations of PEI. With an increase in concentration, the MCF-7 cells treated with PEI showed lower cell viability. Moreover, this concentration-dependent trend became more obvious when incubation time increased from 24 h to 72 h.\\n Cytotoxicity of blank nanoparticles Cell viability of MCF-7 cells was also monitored to investigate the cytotoxicity of non-RNA- and non-drug-loaded carriers including bPDR and bCPN-PDR. As shown in Figure 2B, after treatment with bCPN-PDR, the cell viability was stable around 95% and shown to be concentration independent at 24 h. Meanwhile, bPDR-treated cells showed lower cell viability with time. With increased concentration, bPDR showed higher cytotoxicity, inducing 80% cell death in 72 h. Compared with the bPDR group, the cell viabilities of the bCPN-PDR group were significantly higher (P<0.05). This phenomenon could be explained by the higher positive charge of bPDR due to the positive ingredient of PEI, while the cell cytotoxicity could be significantly decreased by covering with the nontoxic negatively charged CPN (>80% cell viability in Figure 2A), which could induce the charge reversal of bPDR, implying that the negatively charged CPN coating could decrease the higher toxicity of bPDR to cells.\\nCell viability of MCF-7 cells was also monitored to investigate the cytotoxicity of non-RNA- and non-drug-loaded carriers including bPDR and bCPN-PDR. As shown in Figure 2B, after treatment with bCPN-PDR, the cell viability was stable around 95% and shown to be concentration independent at 24 h. Meanwhile, bPDR-treated cells showed lower cell viability with time. With increased concentration, bPDR showed higher cytotoxicity, inducing 80% cell death in 72 h. Compared with the bPDR group, the cell viabilities of the bCPN-PDR group were significantly higher (P<0.05). This phenomenon could be explained by the higher positive charge of bPDR due to the positive ingredient of PEI, while the cell cytotoxicity could be significantly decreased by covering with the nontoxic negatively charged CPN (>80% cell viability in Figure 2A), which could induce the charge reversal of bPDR, implying that the negatively charged CPN coating could decrease the higher toxicity of bPDR to cells.\\n Expression level of VEGF mRNA The transfection efficiency of CPN-PDR compared to Lipofectamine 2000 was demonstrated by delivering VEGF-siRNA into MCF-7 cells. The expression level of VEGF mRNA was measured using real-time PCR, and the results are shown in Figure 3. The expression level of VEGF mRNA was normalized to the β-actin expression and calculated based on the semiquantitative method. Untreated cells were selected as control, and the expression level was set as 100%. For lipo/siRNA group, the silencing efficiencies were 61.98%±6.96%, 31.93%±6.43% and 37.02%±3.17% at 24, 48 and 72 h, respectively. The decreased expression of VEGF mRNA reflected that gene silencing capability of lipo/siRNA. With incubation time extended from 24 to 48 h, the capability was enhanced (P<0.05). However, in 72 h, no further reduction was observed compared to that in 48 h, although the significantly downregulated expression could be observed (P<0.05 vs control, Dox and bCPN-PDR), implying that the silencing effect could reach the plateau period after 48 h and last for 72 h at least. The CPN-PR and CPN-PDR have shown similar behavior to lipo/siRNA. Compared with the control, a remarkable decrease of VEGF mRNA expression was observed (P<0.01) at 48 h. There was no significant difference in expression levels between CPN-PR and CPN-PDR (P>0.05) at any time point, implying their comparable gene delivery and transfection capability. More importantly, this capability was equivalent to the commercial Lipofectamine 2000.\\nThe transfection efficiency of CPN-PDR compared to Lipofectamine 2000 was demonstrated by delivering VEGF-siRNA into MCF-7 cells. The expression level of VEGF mRNA was measured using real-time PCR, and the results are shown in Figure 3. The expression level of VEGF mRNA was normalized to the β-actin expression and calculated based on the semiquantitative method. Untreated cells were selected as control, and the expression level was set as 100%. For lipo/siRNA group, the silencing efficiencies were 61.98%±6.96%, 31.93%±6.43% and 37.02%±3.17% at 24, 48 and 72 h, respectively. The decreased expression of VEGF mRNA reflected that gene silencing capability of lipo/siRNA. With incubation time extended from 24 to 48 h, the capability was enhanced (P<0.05). However, in 72 h, no further reduction was observed compared to that in 48 h, although the significantly downregulated expression could be observed (P<0.05 vs control, Dox and bCPN-PDR), implying that the silencing effect could reach the plateau period after 48 h and last for 72 h at least. The CPN-PR and CPN-PDR have shown similar behavior to lipo/siRNA. Compared with the control, a remarkable decrease of VEGF mRNA expression was observed (P<0.01) at 48 h. There was no significant difference in expression levels between CPN-PR and CPN-PDR (P>0.05) at any time point, implying their comparable gene delivery and transfection capability. More importantly, this capability was equivalent to the commercial Lipofectamine 2000.\\n Expression level of VEGF protein Based on the results of real-time PCR, the gene-silencing effect enhanced with time and reached a platform after 48 h. In this study, the Western blot technique was employed to observe VEGF protein expression at 48 h. The results are shown in Figure 4. GAPDH was selected as the inner control; all the bands were found to be equivalent in gray levels, and the amount of protein expression was positively correlated with the shade of gray in the imaging picture. From Figure 4, the gray levels of both control and bCPN-PDR, which were similar, were obvious. However, siRNA-loaded nanoparticles including lipo/siRNA, CPN-PR and CPN-PDR demonstrated a notably lower gray level. Furthermore, as marked with the red rectangle in Figure 4, expression of VEGF protein in CPN-PDR could hardly be identified. This suggested that co-delivery of anti-VEGF siRNA and Dox could downregulate the relevant VEGF protein level, which was in agreement with the results from semiquantitative real-time PCR. Taken together, these results indicate that nanoparticles loaded with anti-VEGF siRNA downregulated both the protein and the mRNA expression of VEGF.\\nBased on the results of real-time PCR, the gene-silencing effect enhanced with time and reached a platform after 48 h. In this study, the Western blot technique was employed to observe VEGF protein expression at 48 h. The results are shown in Figure 4. GAPDH was selected as the inner control; all the bands were found to be equivalent in gray levels, and the amount of protein expression was positively correlated with the shade of gray in the imaging picture. From Figure 4, the gray levels of both control and bCPN-PDR, which were similar, were obvious. However, siRNA-loaded nanoparticles including lipo/siRNA, CPN-PR and CPN-PDR demonstrated a notably lower gray level. Furthermore, as marked with the red rectangle in Figure 4, expression of VEGF protein in CPN-PDR could hardly be identified. This suggested that co-delivery of anti-VEGF siRNA and Dox could downregulate the relevant VEGF protein level, which was in agreement with the results from semiquantitative real-time PCR. Taken together, these results indicate that nanoparticles loaded with anti-VEGF siRNA downregulated both the protein and the mRNA expression of VEGF.\\n Inhibition of cell proliferation in vitro The ultimate goal of siRNA intracellular delivery is to restrain the proliferation of cancer cells. Different treatments against MCF-7 cells were performed to further investigate the inhibition effect. As shown in Figure 5A, an obvious concentration dependence in terms of DOX, CPN-PD and CPN-PDR could be observed after 48 h incubation. We have calculated the differences between CPN-PDR vs CPN-PD and CPN-PDR vs DOX at each dose. The results indicated that there was a significant difference between CPN-PDR vs DOX at each dose except the dose of 0.05, and there were significant differences between CPN-PDR vs CPN-PD at a dose of 0.002. It was suggested that the in vitro antitumor activity of CPN-PDR was equivalent to CPN-PD, and both of them showed a higher antitumor activity than free drug DOX, which was mainly attributed to the design of the multifunctional nano-vectors. We have also calculated the statistical differences between CPN-PDR vs CPN-PD and CPN-PDR vs DOX in the concentration range of Dox (0.002, 0.01, 0.05, 0.25 and 0.5 µM) using Student’s t-test. After statistical calculation, the significant differences were verified to exist between CPN-PDR vs CPN-PD (P<0.05) and CPN-PDR vs DOX (P<0.01). After calculation with professional software, the half maximal inhibitory concentrations (IC50) are presented in Figure 5B. They are 0.0367±0.0088 µM for Dox, 0.0266±0.0027 µM for CPN-PD and 0.0126±0.0022 µM for CPN-PDR, respectively. The IC50 of CPN-PD was 2.913-fold higher than that of CPN-PDR, and the IC50 of DOX was 2.110-fold higher than that of CPN-PDR. After statistical calculation, significant differences were found between CPN-PDR vs CPN-PD (P<0.05) and CPN-PDR vs DOX (P<0.05). With the results from real-time PCR and Western blot, the increased cytotoxicity of CPN-PDR probably resulted from siRNA delivery and successful transfection. This was because the intracellular delivery of anti-VEGF siRNA could downregulate the expression of VEGF protein. The anti-VEGF therapy may sensitize cancer cells or kill cells block the blood supply of the tumor and improve drug penetration. Taken together, the anti-VEGF siRNA and Dox co-delivery platform could induce increased cytotoxicity against cancer cells.\\nThe ultimate goal of siRNA intracellular delivery is to restrain the proliferation of cancer cells. Different treatments against MCF-7 cells were performed to further investigate the inhibition effect. As shown in Figure 5A, an obvious concentration dependence in terms of DOX, CPN-PD and CPN-PDR could be observed after 48 h incubation. We have calculated the differences between CPN-PDR vs CPN-PD and CPN-PDR vs DOX at each dose. The results indicated that there was a significant difference between CPN-PDR vs DOX at each dose except the dose of 0.05, and there were significant differences between CPN-PDR vs CPN-PD at a dose of 0.002. It was suggested that the in vitro antitumor activity of CPN-PDR was equivalent to CPN-PD, and both of them showed a higher antitumor activity than free drug DOX, which was mainly attributed to the design of the multifunctional nano-vectors. We have also calculated the statistical differences between CPN-PDR vs CPN-PD and CPN-PDR vs DOX in the concentration range of Dox (0.002, 0.01, 0.05, 0.25 and 0.5 µM) using Student’s t-test. After statistical calculation, the significant differences were verified to exist between CPN-PDR vs CPN-PD (P<0.05) and CPN-PDR vs DOX (P<0.01). After calculation with professional software, the half maximal inhibitory concentrations (IC50) are presented in Figure 5B. They are 0.0367±0.0088 µM for Dox, 0.0266±0.0027 µM for CPN-PD and 0.0126±0.0022 µM for CPN-PDR, respectively. The IC50 of CPN-PD was 2.913-fold higher than that of CPN-PDR, and the IC50 of DOX was 2.110-fold higher than that of CPN-PDR. After statistical calculation, significant differences were found between CPN-PDR vs CPN-PD (P<0.05) and CPN-PDR vs DOX (P<0.05). With the results from real-time PCR and Western blot, the increased cytotoxicity of CPN-PDR probably resulted from siRNA delivery and successful transfection. This was because the intracellular delivery of anti-VEGF siRNA could downregulate the expression of VEGF protein. The anti-VEGF therapy may sensitize cancer cells or kill cells block the blood supply of the tumor and improve drug penetration. Taken together, the anti-VEGF siRNA and Dox co-delivery platform could induce increased cytotoxicity against cancer cells.\\n Hemolysis test To investigate the safety of intravenous injection of CPN-PDR, a hemolysis test was performed. As shown in Table 1, different volume ratios of CPN-PDR to 2% red blood cell suspension were selected. After incubation and centrifugation, tubes were observed by the naked eye. As shown in Figure 6, no red blood cell hemolysis was observed in tube 6, in which 100% PBS was added serving as a negative control. For tube 7, 100% double distilled water was added and red cell hemolysis could be clearly observed. For tubes 1–5, in which different percentages of CPN-PDR were added, no red blood cell hemolysis occurred. Hemolysis of red blood cells can generate hemoprotein, which could be detected using UV-vis spectrophotometry. After scanning the absorbance wavelength of hemoprotein, 577 nm was selected as the detecting wavelength. According to Equation 3, hemolysis ratios were calculated based on the recorded absorbance and shown in Table 2. All the hemolysis ratios of tubes 1–5 were <5%, implying that no hemolysis was induced by addition of CPN-PDR.\\nTo investigate the safety of intravenous injection of CPN-PDR, a hemolysis test was performed. As shown in Table 1, different volume ratios of CPN-PDR to 2% red blood cell suspension were selected. After incubation and centrifugation, tubes were observed by the naked eye. As shown in Figure 6, no red blood cell hemolysis was observed in tube 6, in which 100% PBS was added serving as a negative control. For tube 7, 100% double distilled water was added and red cell hemolysis could be clearly observed. For tubes 1–5, in which different percentages of CPN-PDR were added, no red blood cell hemolysis occurred. Hemolysis of red blood cells can generate hemoprotein, which could be detected using UV-vis spectrophotometry. After scanning the absorbance wavelength of hemoprotein, 577 nm was selected as the detecting wavelength. According to Equation 3, hemolysis ratios were calculated based on the recorded absorbance and shown in Table 2. All the hemolysis ratios of tubes 1–5 were <5%, implying that no hemolysis was induced by addition of CPN-PDR.\\n Tissue section In this study, a histological analysis of organs (heart, liver, spleen, lung and kidney) was performed to evaluate whether CPN-PDR could cause tissue damage, inflammation and lesions. Kunming mice were injected with CPN-PDR and normal saline (NS) by tail vein, respectively. After 1 week, all the mice were sacrificed, and the heart, liver, spleen, lung and kidney were separated. After the standard procedure of HE staining for histological examination, the organs were observed under a microscope. Mice without any treatment were chosen as a control. As shown in Figure 7, compared with the control group, there was no visible histologically difference between mice administrated with CPN-PDR group and that with NS group, implying the safety of CPN-PDR in vivo.\\nIn this study, a histological analysis of organs (heart, liver, spleen, lung and kidney) was performed to evaluate whether CPN-PDR could cause tissue damage, inflammation and lesions. Kunming mice were injected with CPN-PDR and normal saline (NS) by tail vein, respectively. After 1 week, all the mice were sacrificed, and the heart, liver, spleen, lung and kidney were separated. After the standard procedure of HE staining for histological examination, the organs were observed under a microscope. Mice without any treatment were chosen as a control. As shown in Figure 7, compared with the control group, there was no visible histologically difference between mice administrated with CPN-PDR group and that with NS group, implying the safety of CPN-PDR in vivo.\",\n \"In the optimal formulation of CPN-PDR, the weight ratio of DOX to siRNA was 1:1; therefore, the siRNA loading content was equal to the DOX loading content. Based on the facts that the molar ratio of CGA-ODNs to Dox was 1:5, the weight ratio of CPN/PEI/ODNs and siRNA was 4:1:1, and consequently, siRNA and Dox loading efficiency in CPN-PDR was calculated to be 4.93%.\",\n \"In the co-delivery platform, CGA-ODNs were selected as carriers of DOX, then the mixture of CGA-ODNs-Dox and siRNA (abbreviated as DR) was electrostatically interacted with PEI to obtain Dox and siRNA co-loaded nanoparticles, PEI/DR (PDR). The copolymer CPN was employed as a multifunctional material.25 In order to evaluate the safety of the carrier, MTT assays were carried out to evaluate the cytotoxicity of CGA-ODNs and CPN. In the meantime, PEI, a toxic cationic polymer, was also tested in this experiment. As shown in Figure 2A, the viability of MCF-7 cells treated with CGA-ODNs and CPN at any concentration was observed to be stable and >80% within 48 h. After 72 h incubation, the viability of MCF-7 cells treated with CPN was still >80%, but after treatment with a higher concentration of CGA-ODNs (2.5 and 5 µM), the cell viability was slightly decreased to 60%. This indicated that there is no obvious cytotoxicity of CGA-ODNs and CPN. However, the cell viability of MCF-7 cells treated with PEI was obviously dependent on the concentrations of PEI. With an increase in concentration, the MCF-7 cells treated with PEI showed lower cell viability. Moreover, this concentration-dependent trend became more obvious when incubation time increased from 24 h to 72 h.\",\n \"Cell viability of MCF-7 cells was also monitored to investigate the cytotoxicity of non-RNA- and non-drug-loaded carriers including bPDR and bCPN-PDR. As shown in Figure 2B, after treatment with bCPN-PDR, the cell viability was stable around 95% and shown to be concentration independent at 24 h. Meanwhile, bPDR-treated cells showed lower cell viability with time. With increased concentration, bPDR showed higher cytotoxicity, inducing 80% cell death in 72 h. Compared with the bPDR group, the cell viabilities of the bCPN-PDR group were significantly higher (P<0.05). This phenomenon could be explained by the higher positive charge of bPDR due to the positive ingredient of PEI, while the cell cytotoxicity could be significantly decreased by covering with the nontoxic negatively charged CPN (>80% cell viability in Figure 2A), which could induce the charge reversal of bPDR, implying that the negatively charged CPN coating could decrease the higher toxicity of bPDR to cells.\",\n \"The transfection efficiency of CPN-PDR compared to Lipofectamine 2000 was demonstrated by delivering VEGF-siRNA into MCF-7 cells. The expression level of VEGF mRNA was measured using real-time PCR, and the results are shown in Figure 3. The expression level of VEGF mRNA was normalized to the β-actin expression and calculated based on the semiquantitative method. Untreated cells were selected as control, and the expression level was set as 100%. For lipo/siRNA group, the silencing efficiencies were 61.98%±6.96%, 31.93%±6.43% and 37.02%±3.17% at 24, 48 and 72 h, respectively. The decreased expression of VEGF mRNA reflected that gene silencing capability of lipo/siRNA. With incubation time extended from 24 to 48 h, the capability was enhanced (P<0.05). However, in 72 h, no further reduction was observed compared to that in 48 h, although the significantly downregulated expression could be observed (P<0.05 vs control, Dox and bCPN-PDR), implying that the silencing effect could reach the plateau period after 48 h and last for 72 h at least. The CPN-PR and CPN-PDR have shown similar behavior to lipo/siRNA. Compared with the control, a remarkable decrease of VEGF mRNA expression was observed (P<0.01) at 48 h. There was no significant difference in expression levels between CPN-PR and CPN-PDR (P>0.05) at any time point, implying their comparable gene delivery and transfection capability. More importantly, this capability was equivalent to the commercial Lipofectamine 2000.\",\n \"Based on the results of real-time PCR, the gene-silencing effect enhanced with time and reached a platform after 48 h. In this study, the Western blot technique was employed to observe VEGF protein expression at 48 h. The results are shown in Figure 4. GAPDH was selected as the inner control; all the bands were found to be equivalent in gray levels, and the amount of protein expression was positively correlated with the shade of gray in the imaging picture. From Figure 4, the gray levels of both control and bCPN-PDR, which were similar, were obvious. However, siRNA-loaded nanoparticles including lipo/siRNA, CPN-PR and CPN-PDR demonstrated a notably lower gray level. Furthermore, as marked with the red rectangle in Figure 4, expression of VEGF protein in CPN-PDR could hardly be identified. This suggested that co-delivery of anti-VEGF siRNA and Dox could downregulate the relevant VEGF protein level, which was in agreement with the results from semiquantitative real-time PCR. Taken together, these results indicate that nanoparticles loaded with anti-VEGF siRNA downregulated both the protein and the mRNA expression of VEGF.\",\n \"The ultimate goal of siRNA intracellular delivery is to restrain the proliferation of cancer cells. Different treatments against MCF-7 cells were performed to further investigate the inhibition effect. As shown in Figure 5A, an obvious concentration dependence in terms of DOX, CPN-PD and CPN-PDR could be observed after 48 h incubation. We have calculated the differences between CPN-PDR vs CPN-PD and CPN-PDR vs DOX at each dose. The results indicated that there was a significant difference between CPN-PDR vs DOX at each dose except the dose of 0.05, and there were significant differences between CPN-PDR vs CPN-PD at a dose of 0.002. It was suggested that the in vitro antitumor activity of CPN-PDR was equivalent to CPN-PD, and both of them showed a higher antitumor activity than free drug DOX, which was mainly attributed to the design of the multifunctional nano-vectors. We have also calculated the statistical differences between CPN-PDR vs CPN-PD and CPN-PDR vs DOX in the concentration range of Dox (0.002, 0.01, 0.05, 0.25 and 0.5 µM) using Student’s t-test. After statistical calculation, the significant differences were verified to exist between CPN-PDR vs CPN-PD (P<0.05) and CPN-PDR vs DOX (P<0.01). After calculation with professional software, the half maximal inhibitory concentrations (IC50) are presented in Figure 5B. They are 0.0367±0.0088 µM for Dox, 0.0266±0.0027 µM for CPN-PD and 0.0126±0.0022 µM for CPN-PDR, respectively. The IC50 of CPN-PD was 2.913-fold higher than that of CPN-PDR, and the IC50 of DOX was 2.110-fold higher than that of CPN-PDR. After statistical calculation, significant differences were found between CPN-PDR vs CPN-PD (P<0.05) and CPN-PDR vs DOX (P<0.05). With the results from real-time PCR and Western blot, the increased cytotoxicity of CPN-PDR probably resulted from siRNA delivery and successful transfection. This was because the intracellular delivery of anti-VEGF siRNA could downregulate the expression of VEGF protein. The anti-VEGF therapy may sensitize cancer cells or kill cells block the blood supply of the tumor and improve drug penetration. Taken together, the anti-VEGF siRNA and Dox co-delivery platform could induce increased cytotoxicity against cancer cells.\",\n \"To investigate the safety of intravenous injection of CPN-PDR, a hemolysis test was performed. As shown in Table 1, different volume ratios of CPN-PDR to 2% red blood cell suspension were selected. After incubation and centrifugation, tubes were observed by the naked eye. As shown in Figure 6, no red blood cell hemolysis was observed in tube 6, in which 100% PBS was added serving as a negative control. For tube 7, 100% double distilled water was added and red cell hemolysis could be clearly observed. For tubes 1–5, in which different percentages of CPN-PDR were added, no red blood cell hemolysis occurred. Hemolysis of red blood cells can generate hemoprotein, which could be detected using UV-vis spectrophotometry. After scanning the absorbance wavelength of hemoprotein, 577 nm was selected as the detecting wavelength. According to Equation 3, hemolysis ratios were calculated based on the recorded absorbance and shown in Table 2. All the hemolysis ratios of tubes 1–5 were <5%, implying that no hemolysis was induced by addition of CPN-PDR.\",\n \"In this study, a histological analysis of organs (heart, liver, spleen, lung and kidney) was performed to evaluate whether CPN-PDR could cause tissue damage, inflammation and lesions. Kunming mice were injected with CPN-PDR and normal saline (NS) by tail vein, respectively. After 1 week, all the mice were sacrificed, and the heart, liver, spleen, lung and kidney were separated. After the standard procedure of HE staining for histological examination, the organs were observed under a microscope. Mice without any treatment were chosen as a control. As shown in Figure 7, compared with the control group, there was no visible histologically difference between mice administrated with CPN-PDR group and that with NS group, implying the safety of CPN-PDR in vivo.\",\n \"In summary, the delivery system materials were demonstrated to be nontoxic and biocompatible. The nontoxic and negatively charged copolymer CPN coating could significantly decrease the cytotoxicity of the cationic core, bPDR. The obtained co-delivery system CPN-PDR was also confirmed with enhanced cytotoxicity against tumor cells. Gene transfection efficiency induced by CPN-PDR was fairly equal with that of the commercial product Lipofectamine 2000, implying the successful intracellular delivery and good transfection of siRNA. Moreover, the preliminary evaluation of safety showed CPN-PDR to have good biocompatibility, so it has great potential for further exploitation and clinical application. With the development of aptamer technology, the promising application prospects of this novel oligodeoxynucleotide-based co-loading platform will further increase.\"\n]"},"all_sections_sec_types":{"kind":"list like","value":["intro","materials|methods","materials",null,null,"materials",null,null,null,null,null,null,null,"methods",null,"intro","materials",null,null,null,null,null,null,null],"string":"[\n \"intro\",\n \"materials|methods\",\n \"materials\",\n null,\n null,\n \"materials\",\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n \"methods\",\n null,\n \"intro\",\n \"materials\",\n null,\n null,\n null,\n null,\n null,\n null,\n null\n]"},"keywords":{"kind":"list like","value":["co-delivery","doxorubicin","VEGF","cytotoxicity","transfection"],"string":"[\n \"co-delivery\",\n \"doxorubicin\",\n \"VEGF\",\n \"cytotoxicity\",\n \"transfection\"\n]"},"whole_article_text":{"kind":"string","value":"Introduction:\nCancer has been one of the leading causes of death worldwide, and mortality and morbidity continue to increase.1 Chemotherapy is one of the major strategies for cancer therapy. However, mono-chemotherapy may encounter problems including drug resistance and unspecific toxicity. To overcome the high mortality rate of cancer, new therapeutic strategies, such as combinational therapy, have been developed.2,3 It has been reported that a combination of chemotherapy and gene therapy could potentially achieve synergistic effects and improve target selectivity, and deter the development of cancer drug resistance.4 For example, a double-modulating strategy based on the combination of chemotherapeutic agent 5-fluorouracil (5-FU) and siRNA has been developed, in which chemotherapy enhances intratumoral siRNA delivery and the delivered siRNA enhances the chemosensitivity of tumors. Furthermore, combination therapy may compensate for the limited delivery of siRNA to tumor tissue and probably manage the 5-FU-resistant tumors.5\nRNA interference (RNAi) is a natural cellular process that regulates gene expression level. Small interfering RNAs (siRNAs), which are small double-stranded RNAs, 20–24 nucleotides (nts) in length with sequences complementary to a gene’s coding sequence, can induce degradation of corresponding messenger RNAs (mRNAs), thus blocking the translation of the mRNA into protein.6,7 The key therapeutic advantage of using RNAi lies in its ability to specifically and potently knock down the expression of disease-causing genes of known sequence. The high specificity and potency makes it widely used in treating various cancers such as breast, liver, and lung cancers.8–10 In recent years, combination of chemotherapy and siRNA-induced RNAi has become a hot topic in cancer treatment. For example, co-delivery of siRNA targeting multidrug resistance protein 1 (MDR1) or anti-survivin siRNA and chemotherapeutic drugs was demonstrated to be a promising strategy to overcome drug resistance.11–13 Anti-apoptotic gene Bcl-2 is a potential combinational therapy target owing to its important role in cell apoptosis, and combination of Bcl-2 siRNA and 5-FU could induce a remarkable increase of cell apoptosis both in vitro and in vivo.14\nTo achieve the desired combinational and synergistic effects of chemotherapy and RNAi, selection of siRNA is important, and the selection of siRNA is also the section of silencing target. The mechanism for combinational anticancer effect varies with different silencing targets. For example, siRNA targeting MDR1 or anti-apoptotic gene Bcl-2 were used to prevent drug resistance response and sensitize chemotherapeutic drugs, or enhance the apoptosis of cancer cells, both offering enhanced anticancer effect.15,16 Tumor tissues have abundant new blood vessels that pump sufficient nutrition and oxygen for tumor growth and metastasis. The dependency of tumor tissues on these new blood vessels is important in anti-angiogenesis therapy for cancer treatment.17 Recently, there have been many efforts in achieving anti-angiogenesis, including delivering siRNAs to silence specific pro-angiogenic genes, such as vascular endothelial growth factor (VEGF), fibroblast growth factor (FGF) and interleukin-8 (IL-8).18 Of these targets, VEGF has received a lot of attention owing to its key role in tumor angiogenesis. With the development of RNAi technology, siRNA with VEGF target has been widely explored and referred to as anti-VEGF treatment.19–21 In 2004, the success of bevacizumab showed VEGF to be a potential target for angiogenesis therapy. There are a number of novel anti-VEGF agents in phase III clinical trials that may come to market in the next few years.22,23 In our design, siRNA with VEGF target was selected to co-deliver with chemotherapeutic drugs, resulting in a combinational anticancer effect. The mechanism for this could be that anti-VEGF treatments sensitize the cells to chemotherapy agents by blocking blood supply, improve drug penetration by disruption or normalization of tumor vasculature and in addition directly kill cancer cells by gene therapy.24,25\nHowever, naked siRNAs may not induce efficient transfection by themselves because they are unstable and vulnerable, especially to nuclease-mediated degradation; also, the negatively charged surface blocks them from cellular endocytosis. Moreover, successful transfection of siRNA into cells is based on siRNA with complete structure, which could further initiate the RNAi process for targeted gene silencing.26,27 Thus, nanoscale delivery systems, which could co-load drug and siRNA and protect siRNA from degradation, were widely used and demonstrated to be excellent candidates. These include nanoparticles,28 liposomes,29 polyplexes30 and dendrimers.31 The basic standard involves employing cationic polymers or lipids to condense negatively charged siRNA and protect them from degradation.32,33 In our previous study,25 oligodeoxynucleotides with CGA repeating units (CGA-ODNs) were introduced to load Dox to obtain Dox-loaded CGA-ODNs (CGA-ODNs-Dox). Poly(ethyleneimine) (PEI) was then used to condense siRNA and CGA ODNs-Dox to obtain Dox and siRNA co-loaded nanoparticles (PEI/CGA-ODNs-Dox and siRNA[PDR]). Finally, the pH-sensitive targeted material, o-carboxymethyl-chitosan (CMCS)-poly(ethylene glycol) (PEG)-aspargine-glycine-arginine (NGR) (CMCS-PEG-NGR[CPN]), was used to modify PDR to obtain multifunctional CPN-PDR. CPN-PDRs were demonstrated to be multifunctional, being able to co-deliver Dox and siRNA into cells, induce pH-responsive disassembly and realize endosomal escape of gene and drug. Thus, in the present study, the cytotoxicity of the materials and the delivery system are further evaluated. Then, the transfection efficiency of siRNA is monitored by semiquantitative real-time polymerase chain reaction (real-time PCR) and Western blot techniques for mRNA level and protein level, respectively. Finally, the safety of the delivery system is evaluated by hemolysis test in vitro and histological assessment.\n\nMaterials and methods:\n Materials Dox was purchased from Dalian Meilun Biology Technology Co. Ltd (Dalian, China). Targeting human VEGF siRNA (sense: 5′-ACAUCACCAUGCAGAUUAUdTdT-3′, anti-sense: 5′-dTdTUGUAGUGGUACGUCUAAUA-3′) was obtained from Guangzhou Ribobio Co., Ltd (Guangzhou, China). CGA oligodeoxynucleotides (sequence: 5′-CGACGACGACGACGACGACGA-3′; complementary sequence: 5′-TCGTCGTCGTCGTCGTCGTCG-3′) were purchased from BGI Co. (Shenzhen, China). Fetal bovine serum (FBS) was obtained from Sijiqing Co., Ltd, (Zhejiang, China) and 3-[4,5-dimethyl-2-thiazolyl]-2,5-diphenyl-2H-tetrazolium bromide (MTT) was from Sigma-Aldrich (St Louis, MO, USA). Trizol RNA extraction was from Thermo Fisher Scientific (Waltham, MA, USA). SYBR®Green and ReverTra Ace qPCR RT Kit were purchased from Toyobo (Osaka, Japan). All solutions were made up in Millipore ultrapure water (EMD Millipore, Billerica, MA, USA) and sterilized for cell culture. All other chemicals and reagents were of analytical grade and used as received. All the primers used in real-time PCR were synthesized and purified by BGI Co. with sequences: VEGF – forward: 5′-CTGGAGTGTGTGCCCACTGA-3′; VEGF – reverse: 5′-TCCTATGTGCTGGCCTTGGT-3′; actin – forward: GAGCTACGAGCTGCCTGACG; actin – reverse: CCTAGAAGCATTTGCGGTGG.\nDox was purchased from Dalian Meilun Biology Technology Co. Ltd (Dalian, China). Targeting human VEGF siRNA (sense: 5′-ACAUCACCAUGCAGAUUAUdTdT-3′, anti-sense: 5′-dTdTUGUAGUGGUACGUCUAAUA-3′) was obtained from Guangzhou Ribobio Co., Ltd (Guangzhou, China). CGA oligodeoxynucleotides (sequence: 5′-CGACGACGACGACGACGACGA-3′; complementary sequence: 5′-TCGTCGTCGTCGTCGTCGTCG-3′) were purchased from BGI Co. (Shenzhen, China). Fetal bovine serum (FBS) was obtained from Sijiqing Co., Ltd, (Zhejiang, China) and 3-[4,5-dimethyl-2-thiazolyl]-2,5-diphenyl-2H-tetrazolium bromide (MTT) was from Sigma-Aldrich (St Louis, MO, USA). Trizol RNA extraction was from Thermo Fisher Scientific (Waltham, MA, USA). SYBR®Green and ReverTra Ace qPCR RT Kit were purchased from Toyobo (Osaka, Japan). All solutions were made up in Millipore ultrapure water (EMD Millipore, Billerica, MA, USA) and sterilized for cell culture. All other chemicals and reagents were of analytical grade and used as received. All the primers used in real-time PCR were synthesized and purified by BGI Co. with sequences: VEGF – forward: 5′-CTGGAGTGTGTGCCCACTGA-3′; VEGF – reverse: 5′-TCCTATGTGCTGGCCTTGGT-3′; actin – forward: GAGCTACGAGCTGCCTGACG; actin – reverse: CCTAGAAGCATTTGCGGTGG.\n Cells and animals The MCF-7 cells were purchased from the Chinese Academy of Sciences Cells Bank (Shanghai, China), and the cells were cultured in Roswell Park Memorial Institute (RPMI) 1640 medium supplemented with 10% fetal bovine serum (FBS), streptomycin at 100 µg/mL and penicillin at 100 U/mL. All cells were cultured in a 37°C incubator with 5% CO2.\nKunming mice (20±2 g) were obtained from Experimental Animal Center of Shandong University. Animal experiments were carried out according to the requirements of the Animal Management Rules of the Ministry of Health (China) and approved by the Laboratory Animal Ethics Review Committee of Qilu Medical College of Shandong University.\nThe MCF-7 cells were purchased from the Chinese Academy of Sciences Cells Bank (Shanghai, China), and the cells were cultured in Roswell Park Memorial Institute (RPMI) 1640 medium supplemented with 10% fetal bovine serum (FBS), streptomycin at 100 µg/mL and penicillin at 100 U/mL. All cells were cultured in a 37°C incubator with 5% CO2.\nKunming mice (20±2 g) were obtained from Experimental Animal Center of Shandong University. Animal experiments were carried out according to the requirements of the Animal Management Rules of the Ministry of Health (China) and approved by the Laboratory Animal Ethics Review Committee of Qilu Medical College of Shandong University.\n Preparation of nanoparticles PDR and CPN-PDR were prepared by the method reported in our previous study.25 Briefly, CGA-ODNs-Dox was first obtained by incubating Dox with CGA-ODNs at room temperature. Then, PEI, siRNA, ODNs-Dox and CPN were dissolved and diluted to the corresponding concentrations with deionized water. After that, siRNA and CGA-ODNs-Dox were mixed by vortexing for several seconds to obtain the mixture. The mixture was added dropwise into PEI solution and mixed by vortexing and then incubated for 30 min at room temperature to form PDR. CPN-PDR was obtained by adding PDR suspension into the CPN solution under vortexing, followed by 30-min incubation at room temperature. When preparing the blank nanoparticles, the DOX and siRNA were not involved in above method. The construction of different kinds of nanoparticles is shown in Figure 1.\nThe loading content of DOX and siRNA was calculated using the following formula:34\nLoading content(%)=Weight of loading drugsWeight of nanoparticles×100%(1)\nPDR and CPN-PDR were prepared by the method reported in our previous study.25 Briefly, CGA-ODNs-Dox was first obtained by incubating Dox with CGA-ODNs at room temperature. Then, PEI, siRNA, ODNs-Dox and CPN were dissolved and diluted to the corresponding concentrations with deionized water. After that, siRNA and CGA-ODNs-Dox were mixed by vortexing for several seconds to obtain the mixture. The mixture was added dropwise into PEI solution and mixed by vortexing and then incubated for 30 min at room temperature to form PDR. CPN-PDR was obtained by adding PDR suspension into the CPN solution under vortexing, followed by 30-min incubation at room temperature. When preparing the blank nanoparticles, the DOX and siRNA were not involved in above method. The construction of different kinds of nanoparticles is shown in Figure 1.\nThe loading content of DOX and siRNA was calculated using the following formula:34\nLoading content(%)=Weight of loading drugsWeight of nanoparticles×100%(1)\n In vitro cytotoxicity of materials MTT assays were carried out on MCF-7 cells to evaluate the in vitro cytotoxicity of CGA-ODNs, PEI and CPN, respectively.35 MCF-7 cells were seeded with density at 7,000 cells/well in the 96-well plates and allowed to adhere overnight. CGA-ODNs, PEI and CPN solutions, which were corresponding to Dox concentrations (0.25, 0.625, 1.25, 2.5 and 5 µM), were added and incubated with the cells for 24, 48 and 72 h, respectively. Five wells were set for each concentration, and cells incubated with fresh media were taken as control. At a determined time point, 20 µL MTT solutions (5 mg/mL) were added. After 4 h incubation, the plates were centrifuged (3,000 rpm, 10 min) and the supernatant was removed. Then, 200 µL DMSO solutions were added to dissolve the formazan crystals formed by the living cells. The cell viability was calculated according to the following formula (Equation 2) after recording the absorbance at 570 nm by a microplate reader (Model 680; Bio-Rad Laboratories Inc., Hercules, CA, USA). All the assays were repeated three times.\nCell viability=Abs(sample)−Abs(blank)Abs(control)−Abs(blank)×100%(2)\nMTT assays were carried out on MCF-7 cells to evaluate the in vitro cytotoxicity of CGA-ODNs, PEI and CPN, respectively.35 MCF-7 cells were seeded with density at 7,000 cells/well in the 96-well plates and allowed to adhere overnight. CGA-ODNs, PEI and CPN solutions, which were corresponding to Dox concentrations (0.25, 0.625, 1.25, 2.5 and 5 µM), were added and incubated with the cells for 24, 48 and 72 h, respectively. Five wells were set for each concentration, and cells incubated with fresh media were taken as control. At a determined time point, 20 µL MTT solutions (5 mg/mL) were added. After 4 h incubation, the plates were centrifuged (3,000 rpm, 10 min) and the supernatant was removed. Then, 200 µL DMSO solutions were added to dissolve the formazan crystals formed by the living cells. The cell viability was calculated according to the following formula (Equation 2) after recording the absorbance at 570 nm by a microplate reader (Model 680; Bio-Rad Laboratories Inc., Hercules, CA, USA). All the assays were repeated three times.\nCell viability=Abs(sample)−Abs(blank)Abs(control)−Abs(blank)×100%(2)\n In vitro cytotoxicity of blank nanoparticles The cytotoxicity of non-DOX- and siRNA-loaded nano-particles (blank PDR[bPDR]) and CPN-coated blank PDR (bCPN-PDR) were also evaluated in MCF-7 cells. bPDR and bCPN-PDR solutions, corresponding to Dox concentrations (0.25, 0.625, 1.25, 2.5 and 5 µM), were added and incubated with the cells for 24, 48 and 72 h, respectively. Five wells were set for each concentration and cells incubated with fresh media were taken as control. After the indicated time periods, cell viability was evaluated according to the procedure described above.\nThe cytotoxicity of non-DOX- and siRNA-loaded nano-particles (blank PDR[bPDR]) and CPN-coated blank PDR (bCPN-PDR) were also evaluated in MCF-7 cells. bPDR and bCPN-PDR solutions, corresponding to Dox concentrations (0.25, 0.625, 1.25, 2.5 and 5 µM), were added and incubated with the cells for 24, 48 and 72 h, respectively. Five wells were set for each concentration and cells incubated with fresh media were taken as control. After the indicated time periods, cell viability was evaluated according to the procedure described above.\n siRNA transfection with nanoparticles MCF-7 cells were seeded into 24-well plates at densities of 8×104, 6×104 and 5×104 cells/well for 24, 48 and 72 h transfection, respectively. After culturing at 37°C overnight, MCF-7 cells were treated with siRNA specific for VEGF. The siRNA was delivered either by Lipofectamine 2000 (Thermo Fisher Scientific) according to the manufacturer’s instructions or by CPN-modified siRNA-loaded nanoparticles (CPN-PR) or by CPN-modified Dox- and siRNA-loaded nanoparticles (CPN-PDR). The final concentrations of Dox and siRNA were 1 µM and 55 nM, respectively. After siRNA transfection, cells were harvested for RNA extraction and Western blot PCR analysis or subjected to Western blot assay.\nMCF-7 cells were seeded into 24-well plates at densities of 8×104, 6×104 and 5×104 cells/well for 24, 48 and 72 h transfection, respectively. After culturing at 37°C overnight, MCF-7 cells were treated with siRNA specific for VEGF. The siRNA was delivered either by Lipofectamine 2000 (Thermo Fisher Scientific) according to the manufacturer’s instructions or by CPN-modified siRNA-loaded nanoparticles (CPN-PR) or by CPN-modified Dox- and siRNA-loaded nanoparticles (CPN-PDR). The final concentrations of Dox and siRNA were 1 µM and 55 nM, respectively. After siRNA transfection, cells were harvested for RNA extraction and Western blot PCR analysis or subjected to Western blot assay.\n Real-time PCR The expression level of VEGF mRNA was monitored by real-time PCR technique with the standard procedure. Total RNA was extracted using Trizol agent under the standard procedure (Thermo Fisher Scientific), and cDNA synthesis was carried out using Rever Tra Ace qPCR RT Kit (Toyobo) according to the manufacturer’s instructions. The final concentration of RNA was measured by Nano Drop 2000 (Thermo Fisher Scientific), and the final product cDNA was stored at −20°C until use.\nEach real-time PCR system contained 20 µL solutions including cDNA (2 µL), SYBR®Green mix (10 µL), primer (1.6 µL) and double distilled water (6.4 µL). The reaction system was placed into PCR instrument (Roche LightCycler™; Hoffman-La Roche Ltd, Basel, Switzerland) for analysis. β-Actin served as a reference gene. Three wells were set for each group. The expression level of VEGF mRNA was normalized to the β-actin expression level and calculated by recording the Ct value at the end of the reaction.\nThe expression level of VEGF mRNA was monitored by real-time PCR technique with the standard procedure. Total RNA was extracted using Trizol agent under the standard procedure (Thermo Fisher Scientific), and cDNA synthesis was carried out using Rever Tra Ace qPCR RT Kit (Toyobo) according to the manufacturer’s instructions. The final concentration of RNA was measured by Nano Drop 2000 (Thermo Fisher Scientific), and the final product cDNA was stored at −20°C until use.\nEach real-time PCR system contained 20 µL solutions including cDNA (2 µL), SYBR®Green mix (10 µL), primer (1.6 µL) and double distilled water (6.4 µL). The reaction system was placed into PCR instrument (Roche LightCycler™; Hoffman-La Roche Ltd, Basel, Switzerland) for analysis. β-Actin served as a reference gene. Three wells were set for each group. The expression level of VEGF mRNA was normalized to the β-actin expression level and calculated by recording the Ct value at the end of the reaction.\n Western blot assay A Western blot assay was employed to investigate the expression level of total VEGF protein after transfection. At a determined time, cells were trypsinized and pelleted by centrifugation. The protein collecting process was carried out based on the standard procedure of Total Protein Extraction Kit (BestBio, Shanghai, China). The total protein was stored at −80°C until use. Protein was quantified by BCA protein assay kit (Beyotime Biotechnology, Shanghai, China) based on the standard carve calculation (A=0.006 C+0.0919, r=0.9987). After quantification, all the samples were diluted to 2 µg/µL with sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) electrophoresis, denatured in boiling water for 5 min and stored at −80°C until use. Equal amounts of protein (50 µg) were subjected to 12% SDS-PAGE and transferred to nitrocellulose membranes using standard procedures. The membrane was blocked in 2% skimmed milk in phosphate buffered saline for 2 h and washed by Tris-buffered saline and Tween-20 (TBST) solution three times. Then the membranes were probed with primary antibodies against VEGF and glyceraldehyde 3-phosphate dehydrogenase (GAPDN) overnight at 4°C. After 3×10 min washing in TBST on shaker, membranes were incubated with horseradish peroxidase (HRP)-conjugated anti-rabbit IgG for 1 h at room temperature. With another 3×10 min washes in TBST, the membranes were developed with electrochemiluminescence (ECL) using a gel-imaging system and analyzed using Image Analysis software.\nA Western blot assay was employed to investigate the expression level of total VEGF protein after transfection. At a determined time, cells were trypsinized and pelleted by centrifugation. The protein collecting process was carried out based on the standard procedure of Total Protein Extraction Kit (BestBio, Shanghai, China). The total protein was stored at −80°C until use. Protein was quantified by BCA protein assay kit (Beyotime Biotechnology, Shanghai, China) based on the standard carve calculation (A=0.006 C+0.0919, r=0.9987). After quantification, all the samples were diluted to 2 µg/µL with sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) electrophoresis, denatured in boiling water for 5 min and stored at −80°C until use. Equal amounts of protein (50 µg) were subjected to 12% SDS-PAGE and transferred to nitrocellulose membranes using standard procedures. The membrane was blocked in 2% skimmed milk in phosphate buffered saline for 2 h and washed by Tris-buffered saline and Tween-20 (TBST) solution three times. Then the membranes were probed with primary antibodies against VEGF and glyceraldehyde 3-phosphate dehydrogenase (GAPDN) overnight at 4°C. After 3×10 min washing in TBST on shaker, membranes were incubated with horseradish peroxidase (HRP)-conjugated anti-rabbit IgG for 1 h at room temperature. With another 3×10 min washes in TBST, the membranes were developed with electrochemiluminescence (ECL) using a gel-imaging system and analyzed using Image Analysis software.\n Cell proliferation assay The anti-proliferation activity of Dox, CPN-PD and CPN-PDR against MCF-7 cells was tested via MTT method. MCF-7 cells were seeded into 96-well plates at a density of 7,000 cells/well. After overnight incubation, the cells were treated with various concentrations (0.002, 0.01, 0.05, 0.25 and 0.5 µM) of Dox, CPN-PD and CPN-PDR solutions and incubated for another 48 h. Five wells were set for each concentration, and cells incubated with fresh media were taken as control. After indicated time periods, cell viability was evaluated according to the procedure described in the “In vitro cytotoxicity of materials” section.\nThe anti-proliferation activity of Dox, CPN-PD and CPN-PDR against MCF-7 cells was tested via MTT method. MCF-7 cells were seeded into 96-well plates at a density of 7,000 cells/well. After overnight incubation, the cells were treated with various concentrations (0.002, 0.01, 0.05, 0.25 and 0.5 µM) of Dox, CPN-PD and CPN-PDR solutions and incubated for another 48 h. Five wells were set for each concentration, and cells incubated with fresh media were taken as control. After indicated time periods, cell viability was evaluated according to the procedure described in the “In vitro cytotoxicity of materials” section.\n Hemolysis test Hemolysis test was carried out to investigate the safety of CPN-PDR for intravenous injection. A 2% red blood cell suspension was collected by centrifugation and resuspension. The test was performed under the design described in Table 1. After incubated at 37°C for 3 h, all the groups were centrifuged at 3,000 rpm for 15 min and visualized by the naked eye. To measure the hemolysis ratios of each group, UV-vis spectrophotometry was carried out to record the absorbance of supernatant in each group. The quantitation of hemolysis ratios was calculated according to the following formula:\nHR(%)=Abs(sample)−Abs(−)Abs(+)−Abs(−)×100%(3)where Abs (sample), Abs (−) and Abs (+) refer to the absorbances of the samples, negative control and positive control, respectively.\nHemolysis test was carried out to investigate the safety of CPN-PDR for intravenous injection. A 2% red blood cell suspension was collected by centrifugation and resuspension. The test was performed under the design described in Table 1. After incubated at 37°C for 3 h, all the groups were centrifuged at 3,000 rpm for 15 min and visualized by the naked eye. To measure the hemolysis ratios of each group, UV-vis spectrophotometry was carried out to record the absorbance of supernatant in each group. The quantitation of hemolysis ratios was calculated according to the following formula:\nHR(%)=Abs(sample)−Abs(−)Abs(+)−Abs(−)×100%(3)where Abs (sample), Abs (−) and Abs (+) refer to the absorbances of the samples, negative control and positive control, respectively.\n Histological assessment In order to evaluate the compatibility and tissue toxicity of CPN-PDR in vivo, a histological observation was performed.36 Five female Kunming mice were injected with CPN-PDR at a dose of 232 µg/kg through the tail vein. In the meantime, normal saline (NS) was taken as a control reagent. After 1 week, all the mice were sacrificed, and the heart, liver, spleen, lung and kidney were separated. After washing twice with PBS, all the organs were fixed in 4% formaldehyde, dehydrated in gradient alcohol, placed in xylene, embedded in paraffin and made into sections, followed by hematoxylin-eosin (HE) staining for histological examination with microscope.\nIn order to evaluate the compatibility and tissue toxicity of CPN-PDR in vivo, a histological observation was performed.36 Five female Kunming mice were injected with CPN-PDR at a dose of 232 µg/kg through the tail vein. In the meantime, normal saline (NS) was taken as a control reagent. After 1 week, all the mice were sacrificed, and the heart, liver, spleen, lung and kidney were separated. After washing twice with PBS, all the organs were fixed in 4% formaldehyde, dehydrated in gradient alcohol, placed in xylene, embedded in paraffin and made into sections, followed by hematoxylin-eosin (HE) staining for histological examination with microscope.\n Statistical analysis All studies were repeated a minimum of three times and measured at least in triplicate. The results are reported as the means ± SD. Statistical significance was analyzed using Student’s t-test. Differences between experimental groups were considered significant if the P-value was <0.05.\nAll studies were repeated a minimum of three times and measured at least in triplicate. The results are reported as the means ± SD. Statistical significance was analyzed using Student’s t-test. Differences between experimental groups were considered significant if the P-value was <0.05.\n\nMaterials:\nDox was purchased from Dalian Meilun Biology Technology Co. Ltd (Dalian, China). Targeting human VEGF siRNA (sense: 5′-ACAUCACCAUGCAGAUUAUdTdT-3′, anti-sense: 5′-dTdTUGUAGUGGUACGUCUAAUA-3′) was obtained from Guangzhou Ribobio Co., Ltd (Guangzhou, China). CGA oligodeoxynucleotides (sequence: 5′-CGACGACGACGACGACGACGA-3′; complementary sequence: 5′-TCGTCGTCGTCGTCGTCGTCG-3′) were purchased from BGI Co. (Shenzhen, China). Fetal bovine serum (FBS) was obtained from Sijiqing Co., Ltd, (Zhejiang, China) and 3-[4,5-dimethyl-2-thiazolyl]-2,5-diphenyl-2H-tetrazolium bromide (MTT) was from Sigma-Aldrich (St Louis, MO, USA). Trizol RNA extraction was from Thermo Fisher Scientific (Waltham, MA, USA). SYBR®Green and ReverTra Ace qPCR RT Kit were purchased from Toyobo (Osaka, Japan). All solutions were made up in Millipore ultrapure water (EMD Millipore, Billerica, MA, USA) and sterilized for cell culture. All other chemicals and reagents were of analytical grade and used as received. All the primers used in real-time PCR were synthesized and purified by BGI Co. with sequences: VEGF – forward: 5′-CTGGAGTGTGTGCCCACTGA-3′; VEGF – reverse: 5′-TCCTATGTGCTGGCCTTGGT-3′; actin – forward: GAGCTACGAGCTGCCTGACG; actin – reverse: CCTAGAAGCATTTGCGGTGG.\n\nCells and animals:\nThe MCF-7 cells were purchased from the Chinese Academy of Sciences Cells Bank (Shanghai, China), and the cells were cultured in Roswell Park Memorial Institute (RPMI) 1640 medium supplemented with 10% fetal bovine serum (FBS), streptomycin at 100 µg/mL and penicillin at 100 U/mL. All cells were cultured in a 37°C incubator with 5% CO2.\nKunming mice (20±2 g) were obtained from Experimental Animal Center of Shandong University. Animal experiments were carried out according to the requirements of the Animal Management Rules of the Ministry of Health (China) and approved by the Laboratory Animal Ethics Review Committee of Qilu Medical College of Shandong University.\n\nPreparation of nanoparticles:\nPDR and CPN-PDR were prepared by the method reported in our previous study.25 Briefly, CGA-ODNs-Dox was first obtained by incubating Dox with CGA-ODNs at room temperature. Then, PEI, siRNA, ODNs-Dox and CPN were dissolved and diluted to the corresponding concentrations with deionized water. After that, siRNA and CGA-ODNs-Dox were mixed by vortexing for several seconds to obtain the mixture. The mixture was added dropwise into PEI solution and mixed by vortexing and then incubated for 30 min at room temperature to form PDR. CPN-PDR was obtained by adding PDR suspension into the CPN solution under vortexing, followed by 30-min incubation at room temperature. When preparing the blank nanoparticles, the DOX and siRNA were not involved in above method. The construction of different kinds of nanoparticles is shown in Figure 1.\nThe loading content of DOX and siRNA was calculated using the following formula:34\nLoading content(%)=Weight of loading drugsWeight of nanoparticles×100%(1)\n\nIn vitro cytotoxicity of materials:\nMTT assays were carried out on MCF-7 cells to evaluate the in vitro cytotoxicity of CGA-ODNs, PEI and CPN, respectively.35 MCF-7 cells were seeded with density at 7,000 cells/well in the 96-well plates and allowed to adhere overnight. CGA-ODNs, PEI and CPN solutions, which were corresponding to Dox concentrations (0.25, 0.625, 1.25, 2.5 and 5 µM), were added and incubated with the cells for 24, 48 and 72 h, respectively. Five wells were set for each concentration, and cells incubated with fresh media were taken as control. At a determined time point, 20 µL MTT solutions (5 mg/mL) were added. After 4 h incubation, the plates were centrifuged (3,000 rpm, 10 min) and the supernatant was removed. Then, 200 µL DMSO solutions were added to dissolve the formazan crystals formed by the living cells. The cell viability was calculated according to the following formula (Equation 2) after recording the absorbance at 570 nm by a microplate reader (Model 680; Bio-Rad Laboratories Inc., Hercules, CA, USA). All the assays were repeated three times.\nCell viability=Abs(sample)−Abs(blank)Abs(control)−Abs(blank)×100%(2)\n\nIn vitro cytotoxicity of blank nanoparticles:\nThe cytotoxicity of non-DOX- and siRNA-loaded nano-particles (blank PDR[bPDR]) and CPN-coated blank PDR (bCPN-PDR) were also evaluated in MCF-7 cells. bPDR and bCPN-PDR solutions, corresponding to Dox concentrations (0.25, 0.625, 1.25, 2.5 and 5 µM), were added and incubated with the cells for 24, 48 and 72 h, respectively. Five wells were set for each concentration and cells incubated with fresh media were taken as control. After the indicated time periods, cell viability was evaluated according to the procedure described above.\n\nsiRNA transfection with nanoparticles:\nMCF-7 cells were seeded into 24-well plates at densities of 8×104, 6×104 and 5×104 cells/well for 24, 48 and 72 h transfection, respectively. After culturing at 37°C overnight, MCF-7 cells were treated with siRNA specific for VEGF. The siRNA was delivered either by Lipofectamine 2000 (Thermo Fisher Scientific) according to the manufacturer’s instructions or by CPN-modified siRNA-loaded nanoparticles (CPN-PR) or by CPN-modified Dox- and siRNA-loaded nanoparticles (CPN-PDR). The final concentrations of Dox and siRNA were 1 µM and 55 nM, respectively. After siRNA transfection, cells were harvested for RNA extraction and Western blot PCR analysis or subjected to Western blot assay.\n\nReal-time PCR:\nThe expression level of VEGF mRNA was monitored by real-time PCR technique with the standard procedure. Total RNA was extracted using Trizol agent under the standard procedure (Thermo Fisher Scientific), and cDNA synthesis was carried out using Rever Tra Ace qPCR RT Kit (Toyobo) according to the manufacturer’s instructions. The final concentration of RNA was measured by Nano Drop 2000 (Thermo Fisher Scientific), and the final product cDNA was stored at −20°C until use.\nEach real-time PCR system contained 20 µL solutions including cDNA (2 µL), SYBR®Green mix (10 µL), primer (1.6 µL) and double distilled water (6.4 µL). The reaction system was placed into PCR instrument (Roche LightCycler™; Hoffman-La Roche Ltd, Basel, Switzerland) for analysis. β-Actin served as a reference gene. Three wells were set for each group. The expression level of VEGF mRNA was normalized to the β-actin expression level and calculated by recording the Ct value at the end of the reaction.\n\nWestern blot assay:\nA Western blot assay was employed to investigate the expression level of total VEGF protein after transfection. At a determined time, cells were trypsinized and pelleted by centrifugation. The protein collecting process was carried out based on the standard procedure of Total Protein Extraction Kit (BestBio, Shanghai, China). The total protein was stored at −80°C until use. Protein was quantified by BCA protein assay kit (Beyotime Biotechnology, Shanghai, China) based on the standard carve calculation (A=0.006 C+0.0919, r=0.9987). After quantification, all the samples were diluted to 2 µg/µL with sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) electrophoresis, denatured in boiling water for 5 min and stored at −80°C until use. Equal amounts of protein (50 µg) were subjected to 12% SDS-PAGE and transferred to nitrocellulose membranes using standard procedures. The membrane was blocked in 2% skimmed milk in phosphate buffered saline for 2 h and washed by Tris-buffered saline and Tween-20 (TBST) solution three times. Then the membranes were probed with primary antibodies against VEGF and glyceraldehyde 3-phosphate dehydrogenase (GAPDN) overnight at 4°C. After 3×10 min washing in TBST on shaker, membranes were incubated with horseradish peroxidase (HRP)-conjugated anti-rabbit IgG for 1 h at room temperature. With another 3×10 min washes in TBST, the membranes were developed with electrochemiluminescence (ECL) using a gel-imaging system and analyzed using Image Analysis software.\n\nCell proliferation assay:\nThe anti-proliferation activity of Dox, CPN-PD and CPN-PDR against MCF-7 cells was tested via MTT method. MCF-7 cells were seeded into 96-well plates at a density of 7,000 cells/well. After overnight incubation, the cells were treated with various concentrations (0.002, 0.01, 0.05, 0.25 and 0.5 µM) of Dox, CPN-PD and CPN-PDR solutions and incubated for another 48 h. Five wells were set for each concentration, and cells incubated with fresh media were taken as control. After indicated time periods, cell viability was evaluated according to the procedure described in the “In vitro cytotoxicity of materials” section.\n\nHemolysis test:\nHemolysis test was carried out to investigate the safety of CPN-PDR for intravenous injection. A 2% red blood cell suspension was collected by centrifugation and resuspension. The test was performed under the design described in Table 1. After incubated at 37°C for 3 h, all the groups were centrifuged at 3,000 rpm for 15 min and visualized by the naked eye. To measure the hemolysis ratios of each group, UV-vis spectrophotometry was carried out to record the absorbance of supernatant in each group. The quantitation of hemolysis ratios was calculated according to the following formula:\nHR(%)=Abs(sample)−Abs(−)Abs(+)−Abs(−)×100%(3)where Abs (sample), Abs (−) and Abs (+) refer to the absorbances of the samples, negative control and positive control, respectively.\n\nHistological assessment:\nIn order to evaluate the compatibility and tissue toxicity of CPN-PDR in vivo, a histological observation was performed.36 Five female Kunming mice were injected with CPN-PDR at a dose of 232 µg/kg through the tail vein. In the meantime, normal saline (NS) was taken as a control reagent. After 1 week, all the mice were sacrificed, and the heart, liver, spleen, lung and kidney were separated. After washing twice with PBS, all the organs were fixed in 4% formaldehyde, dehydrated in gradient alcohol, placed in xylene, embedded in paraffin and made into sections, followed by hematoxylin-eosin (HE) staining for histological examination with microscope.\n\nStatistical analysis:\nAll studies were repeated a minimum of three times and measured at least in triplicate. The results are reported as the means ± SD. Statistical significance was analyzed using Student’s t-test. Differences between experimental groups were considered significant if the P-value was <0.05.\n\nResults and discussion:\n Characteristics of nanoparticles In the optimal formulation of CPN-PDR, the weight ratio of DOX to siRNA was 1:1; therefore, the siRNA loading content was equal to the DOX loading content. Based on the facts that the molar ratio of CGA-ODNs to Dox was 1:5, the weight ratio of CPN/PEI/ODNs and siRNA was 4:1:1, and consequently, siRNA and Dox loading efficiency in CPN-PDR was calculated to be 4.93%.\nIn the optimal formulation of CPN-PDR, the weight ratio of DOX to siRNA was 1:1; therefore, the siRNA loading content was equal to the DOX loading content. Based on the facts that the molar ratio of CGA-ODNs to Dox was 1:5, the weight ratio of CPN/PEI/ODNs and siRNA was 4:1:1, and consequently, siRNA and Dox loading efficiency in CPN-PDR was calculated to be 4.93%.\n Cytotoxicity of materials In the co-delivery platform, CGA-ODNs were selected as carriers of DOX, then the mixture of CGA-ODNs-Dox and siRNA (abbreviated as DR) was electrostatically interacted with PEI to obtain Dox and siRNA co-loaded nanoparticles, PEI/DR (PDR). The copolymer CPN was employed as a multifunctional material.25 In order to evaluate the safety of the carrier, MTT assays were carried out to evaluate the cytotoxicity of CGA-ODNs and CPN. In the meantime, PEI, a toxic cationic polymer, was also tested in this experiment. As shown in Figure 2A, the viability of MCF-7 cells treated with CGA-ODNs and CPN at any concentration was observed to be stable and >80% within 48 h. After 72 h incubation, the viability of MCF-7 cells treated with CPN was still >80%, but after treatment with a higher concentration of CGA-ODNs (2.5 and 5 µM), the cell viability was slightly decreased to 60%. This indicated that there is no obvious cytotoxicity of CGA-ODNs and CPN. However, the cell viability of MCF-7 cells treated with PEI was obviously dependent on the concentrations of PEI. With an increase in concentration, the MCF-7 cells treated with PEI showed lower cell viability. Moreover, this concentration-dependent trend became more obvious when incubation time increased from 24 h to 72 h.\nIn the co-delivery platform, CGA-ODNs were selected as carriers of DOX, then the mixture of CGA-ODNs-Dox and siRNA (abbreviated as DR) was electrostatically interacted with PEI to obtain Dox and siRNA co-loaded nanoparticles, PEI/DR (PDR). The copolymer CPN was employed as a multifunctional material.25 In order to evaluate the safety of the carrier, MTT assays were carried out to evaluate the cytotoxicity of CGA-ODNs and CPN. In the meantime, PEI, a toxic cationic polymer, was also tested in this experiment. As shown in Figure 2A, the viability of MCF-7 cells treated with CGA-ODNs and CPN at any concentration was observed to be stable and >80% within 48 h. After 72 h incubation, the viability of MCF-7 cells treated with CPN was still >80%, but after treatment with a higher concentration of CGA-ODNs (2.5 and 5 µM), the cell viability was slightly decreased to 60%. This indicated that there is no obvious cytotoxicity of CGA-ODNs and CPN. However, the cell viability of MCF-7 cells treated with PEI was obviously dependent on the concentrations of PEI. With an increase in concentration, the MCF-7 cells treated with PEI showed lower cell viability. Moreover, this concentration-dependent trend became more obvious when incubation time increased from 24 h to 72 h.\n Cytotoxicity of blank nanoparticles Cell viability of MCF-7 cells was also monitored to investigate the cytotoxicity of non-RNA- and non-drug-loaded carriers including bPDR and bCPN-PDR. As shown in Figure 2B, after treatment with bCPN-PDR, the cell viability was stable around 95% and shown to be concentration independent at 24 h. Meanwhile, bPDR-treated cells showed lower cell viability with time. With increased concentration, bPDR showed higher cytotoxicity, inducing 80% cell death in 72 h. Compared with the bPDR group, the cell viabilities of the bCPN-PDR group were significantly higher (P<0.05). This phenomenon could be explained by the higher positive charge of bPDR due to the positive ingredient of PEI, while the cell cytotoxicity could be significantly decreased by covering with the nontoxic negatively charged CPN (>80% cell viability in Figure 2A), which could induce the charge reversal of bPDR, implying that the negatively charged CPN coating could decrease the higher toxicity of bPDR to cells.\nCell viability of MCF-7 cells was also monitored to investigate the cytotoxicity of non-RNA- and non-drug-loaded carriers including bPDR and bCPN-PDR. As shown in Figure 2B, after treatment with bCPN-PDR, the cell viability was stable around 95% and shown to be concentration independent at 24 h. Meanwhile, bPDR-treated cells showed lower cell viability with time. With increased concentration, bPDR showed higher cytotoxicity, inducing 80% cell death in 72 h. Compared with the bPDR group, the cell viabilities of the bCPN-PDR group were significantly higher (P<0.05). This phenomenon could be explained by the higher positive charge of bPDR due to the positive ingredient of PEI, while the cell cytotoxicity could be significantly decreased by covering with the nontoxic negatively charged CPN (>80% cell viability in Figure 2A), which could induce the charge reversal of bPDR, implying that the negatively charged CPN coating could decrease the higher toxicity of bPDR to cells.\n Expression level of VEGF mRNA The transfection efficiency of CPN-PDR compared to Lipofectamine 2000 was demonstrated by delivering VEGF-siRNA into MCF-7 cells. The expression level of VEGF mRNA was measured using real-time PCR, and the results are shown in Figure 3. The expression level of VEGF mRNA was normalized to the β-actin expression and calculated based on the semiquantitative method. Untreated cells were selected as control, and the expression level was set as 100%. For lipo/siRNA group, the silencing efficiencies were 61.98%±6.96%, 31.93%±6.43% and 37.02%±3.17% at 24, 48 and 72 h, respectively. The decreased expression of VEGF mRNA reflected that gene silencing capability of lipo/siRNA. With incubation time extended from 24 to 48 h, the capability was enhanced (P<0.05). However, in 72 h, no further reduction was observed compared to that in 48 h, although the significantly downregulated expression could be observed (P<0.05 vs control, Dox and bCPN-PDR), implying that the silencing effect could reach the plateau period after 48 h and last for 72 h at least. The CPN-PR and CPN-PDR have shown similar behavior to lipo/siRNA. Compared with the control, a remarkable decrease of VEGF mRNA expression was observed (P<0.01) at 48 h. There was no significant difference in expression levels between CPN-PR and CPN-PDR (P>0.05) at any time point, implying their comparable gene delivery and transfection capability. More importantly, this capability was equivalent to the commercial Lipofectamine 2000.\nThe transfection efficiency of CPN-PDR compared to Lipofectamine 2000 was demonstrated by delivering VEGF-siRNA into MCF-7 cells. The expression level of VEGF mRNA was measured using real-time PCR, and the results are shown in Figure 3. The expression level of VEGF mRNA was normalized to the β-actin expression and calculated based on the semiquantitative method. Untreated cells were selected as control, and the expression level was set as 100%. For lipo/siRNA group, the silencing efficiencies were 61.98%±6.96%, 31.93%±6.43% and 37.02%±3.17% at 24, 48 and 72 h, respectively. The decreased expression of VEGF mRNA reflected that gene silencing capability of lipo/siRNA. With incubation time extended from 24 to 48 h, the capability was enhanced (P<0.05). However, in 72 h, no further reduction was observed compared to that in 48 h, although the significantly downregulated expression could be observed (P<0.05 vs control, Dox and bCPN-PDR), implying that the silencing effect could reach the plateau period after 48 h and last for 72 h at least. The CPN-PR and CPN-PDR have shown similar behavior to lipo/siRNA. Compared with the control, a remarkable decrease of VEGF mRNA expression was observed (P<0.01) at 48 h. There was no significant difference in expression levels between CPN-PR and CPN-PDR (P>0.05) at any time point, implying their comparable gene delivery and transfection capability. More importantly, this capability was equivalent to the commercial Lipofectamine 2000.\n Expression level of VEGF protein Based on the results of real-time PCR, the gene-silencing effect enhanced with time and reached a platform after 48 h. In this study, the Western blot technique was employed to observe VEGF protein expression at 48 h. The results are shown in Figure 4. GAPDH was selected as the inner control; all the bands were found to be equivalent in gray levels, and the amount of protein expression was positively correlated with the shade of gray in the imaging picture. From Figure 4, the gray levels of both control and bCPN-PDR, which were similar, were obvious. However, siRNA-loaded nanoparticles including lipo/siRNA, CPN-PR and CPN-PDR demonstrated a notably lower gray level. Furthermore, as marked with the red rectangle in Figure 4, expression of VEGF protein in CPN-PDR could hardly be identified. This suggested that co-delivery of anti-VEGF siRNA and Dox could downregulate the relevant VEGF protein level, which was in agreement with the results from semiquantitative real-time PCR. Taken together, these results indicate that nanoparticles loaded with anti-VEGF siRNA downregulated both the protein and the mRNA expression of VEGF.\nBased on the results of real-time PCR, the gene-silencing effect enhanced with time and reached a platform after 48 h. In this study, the Western blot technique was employed to observe VEGF protein expression at 48 h. The results are shown in Figure 4. GAPDH was selected as the inner control; all the bands were found to be equivalent in gray levels, and the amount of protein expression was positively correlated with the shade of gray in the imaging picture. From Figure 4, the gray levels of both control and bCPN-PDR, which were similar, were obvious. However, siRNA-loaded nanoparticles including lipo/siRNA, CPN-PR and CPN-PDR demonstrated a notably lower gray level. Furthermore, as marked with the red rectangle in Figure 4, expression of VEGF protein in CPN-PDR could hardly be identified. This suggested that co-delivery of anti-VEGF siRNA and Dox could downregulate the relevant VEGF protein level, which was in agreement with the results from semiquantitative real-time PCR. Taken together, these results indicate that nanoparticles loaded with anti-VEGF siRNA downregulated both the protein and the mRNA expression of VEGF.\n Inhibition of cell proliferation in vitro The ultimate goal of siRNA intracellular delivery is to restrain the proliferation of cancer cells. Different treatments against MCF-7 cells were performed to further investigate the inhibition effect. As shown in Figure 5A, an obvious concentration dependence in terms of DOX, CPN-PD and CPN-PDR could be observed after 48 h incubation. We have calculated the differences between CPN-PDR vs CPN-PD and CPN-PDR vs DOX at each dose. The results indicated that there was a significant difference between CPN-PDR vs DOX at each dose except the dose of 0.05, and there were significant differences between CPN-PDR vs CPN-PD at a dose of 0.002. It was suggested that the in vitro antitumor activity of CPN-PDR was equivalent to CPN-PD, and both of them showed a higher antitumor activity than free drug DOX, which was mainly attributed to the design of the multifunctional nano-vectors. We have also calculated the statistical differences between CPN-PDR vs CPN-PD and CPN-PDR vs DOX in the concentration range of Dox (0.002, 0.01, 0.05, 0.25 and 0.5 µM) using Student’s t-test. After statistical calculation, the significant differences were verified to exist between CPN-PDR vs CPN-PD (P<0.05) and CPN-PDR vs DOX (P<0.01). After calculation with professional software, the half maximal inhibitory concentrations (IC50) are presented in Figure 5B. They are 0.0367±0.0088 µM for Dox, 0.0266±0.0027 µM for CPN-PD and 0.0126±0.0022 µM for CPN-PDR, respectively. The IC50 of CPN-PD was 2.913-fold higher than that of CPN-PDR, and the IC50 of DOX was 2.110-fold higher than that of CPN-PDR. After statistical calculation, significant differences were found between CPN-PDR vs CPN-PD (P<0.05) and CPN-PDR vs DOX (P<0.05). With the results from real-time PCR and Western blot, the increased cytotoxicity of CPN-PDR probably resulted from siRNA delivery and successful transfection. This was because the intracellular delivery of anti-VEGF siRNA could downregulate the expression of VEGF protein. The anti-VEGF therapy may sensitize cancer cells or kill cells block the blood supply of the tumor and improve drug penetration. Taken together, the anti-VEGF siRNA and Dox co-delivery platform could induce increased cytotoxicity against cancer cells.\nThe ultimate goal of siRNA intracellular delivery is to restrain the proliferation of cancer cells. Different treatments against MCF-7 cells were performed to further investigate the inhibition effect. As shown in Figure 5A, an obvious concentration dependence in terms of DOX, CPN-PD and CPN-PDR could be observed after 48 h incubation. We have calculated the differences between CPN-PDR vs CPN-PD and CPN-PDR vs DOX at each dose. The results indicated that there was a significant difference between CPN-PDR vs DOX at each dose except the dose of 0.05, and there were significant differences between CPN-PDR vs CPN-PD at a dose of 0.002. It was suggested that the in vitro antitumor activity of CPN-PDR was equivalent to CPN-PD, and both of them showed a higher antitumor activity than free drug DOX, which was mainly attributed to the design of the multifunctional nano-vectors. We have also calculated the statistical differences between CPN-PDR vs CPN-PD and CPN-PDR vs DOX in the concentration range of Dox (0.002, 0.01, 0.05, 0.25 and 0.5 µM) using Student’s t-test. After statistical calculation, the significant differences were verified to exist between CPN-PDR vs CPN-PD (P<0.05) and CPN-PDR vs DOX (P<0.01). After calculation with professional software, the half maximal inhibitory concentrations (IC50) are presented in Figure 5B. They are 0.0367±0.0088 µM for Dox, 0.0266±0.0027 µM for CPN-PD and 0.0126±0.0022 µM for CPN-PDR, respectively. The IC50 of CPN-PD was 2.913-fold higher than that of CPN-PDR, and the IC50 of DOX was 2.110-fold higher than that of CPN-PDR. After statistical calculation, significant differences were found between CPN-PDR vs CPN-PD (P<0.05) and CPN-PDR vs DOX (P<0.05). With the results from real-time PCR and Western blot, the increased cytotoxicity of CPN-PDR probably resulted from siRNA delivery and successful transfection. This was because the intracellular delivery of anti-VEGF siRNA could downregulate the expression of VEGF protein. The anti-VEGF therapy may sensitize cancer cells or kill cells block the blood supply of the tumor and improve drug penetration. Taken together, the anti-VEGF siRNA and Dox co-delivery platform could induce increased cytotoxicity against cancer cells.\n Hemolysis test To investigate the safety of intravenous injection of CPN-PDR, a hemolysis test was performed. As shown in Table 1, different volume ratios of CPN-PDR to 2% red blood cell suspension were selected. After incubation and centrifugation, tubes were observed by the naked eye. As shown in Figure 6, no red blood cell hemolysis was observed in tube 6, in which 100% PBS was added serving as a negative control. For tube 7, 100% double distilled water was added and red cell hemolysis could be clearly observed. For tubes 1–5, in which different percentages of CPN-PDR were added, no red blood cell hemolysis occurred. Hemolysis of red blood cells can generate hemoprotein, which could be detected using UV-vis spectrophotometry. After scanning the absorbance wavelength of hemoprotein, 577 nm was selected as the detecting wavelength. According to Equation 3, hemolysis ratios were calculated based on the recorded absorbance and shown in Table 2. All the hemolysis ratios of tubes 1–5 were <5%, implying that no hemolysis was induced by addition of CPN-PDR.\nTo investigate the safety of intravenous injection of CPN-PDR, a hemolysis test was performed. As shown in Table 1, different volume ratios of CPN-PDR to 2% red blood cell suspension were selected. After incubation and centrifugation, tubes were observed by the naked eye. As shown in Figure 6, no red blood cell hemolysis was observed in tube 6, in which 100% PBS was added serving as a negative control. For tube 7, 100% double distilled water was added and red cell hemolysis could be clearly observed. For tubes 1–5, in which different percentages of CPN-PDR were added, no red blood cell hemolysis occurred. Hemolysis of red blood cells can generate hemoprotein, which could be detected using UV-vis spectrophotometry. After scanning the absorbance wavelength of hemoprotein, 577 nm was selected as the detecting wavelength. According to Equation 3, hemolysis ratios were calculated based on the recorded absorbance and shown in Table 2. All the hemolysis ratios of tubes 1–5 were <5%, implying that no hemolysis was induced by addition of CPN-PDR.\n Tissue section In this study, a histological analysis of organs (heart, liver, spleen, lung and kidney) was performed to evaluate whether CPN-PDR could cause tissue damage, inflammation and lesions. Kunming mice were injected with CPN-PDR and normal saline (NS) by tail vein, respectively. After 1 week, all the mice were sacrificed, and the heart, liver, spleen, lung and kidney were separated. After the standard procedure of HE staining for histological examination, the organs were observed under a microscope. Mice without any treatment were chosen as a control. As shown in Figure 7, compared with the control group, there was no visible histologically difference between mice administrated with CPN-PDR group and that with NS group, implying the safety of CPN-PDR in vivo.\nIn this study, a histological analysis of organs (heart, liver, spleen, lung and kidney) was performed to evaluate whether CPN-PDR could cause tissue damage, inflammation and lesions. Kunming mice were injected with CPN-PDR and normal saline (NS) by tail vein, respectively. After 1 week, all the mice were sacrificed, and the heart, liver, spleen, lung and kidney were separated. After the standard procedure of HE staining for histological examination, the organs were observed under a microscope. Mice without any treatment were chosen as a control. As shown in Figure 7, compared with the control group, there was no visible histologically difference between mice administrated with CPN-PDR group and that with NS group, implying the safety of CPN-PDR in vivo.\n\nCharacteristics of nanoparticles:\nIn the optimal formulation of CPN-PDR, the weight ratio of DOX to siRNA was 1:1; therefore, the siRNA loading content was equal to the DOX loading content. Based on the facts that the molar ratio of CGA-ODNs to Dox was 1:5, the weight ratio of CPN/PEI/ODNs and siRNA was 4:1:1, and consequently, siRNA and Dox loading efficiency in CPN-PDR was calculated to be 4.93%.\n\nCytotoxicity of materials:\nIn the co-delivery platform, CGA-ODNs were selected as carriers of DOX, then the mixture of CGA-ODNs-Dox and siRNA (abbreviated as DR) was electrostatically interacted with PEI to obtain Dox and siRNA co-loaded nanoparticles, PEI/DR (PDR). The copolymer CPN was employed as a multifunctional material.25 In order to evaluate the safety of the carrier, MTT assays were carried out to evaluate the cytotoxicity of CGA-ODNs and CPN. In the meantime, PEI, a toxic cationic polymer, was also tested in this experiment. As shown in Figure 2A, the viability of MCF-7 cells treated with CGA-ODNs and CPN at any concentration was observed to be stable and >80% within 48 h. After 72 h incubation, the viability of MCF-7 cells treated with CPN was still >80%, but after treatment with a higher concentration of CGA-ODNs (2.5 and 5 µM), the cell viability was slightly decreased to 60%. This indicated that there is no obvious cytotoxicity of CGA-ODNs and CPN. However, the cell viability of MCF-7 cells treated with PEI was obviously dependent on the concentrations of PEI. With an increase in concentration, the MCF-7 cells treated with PEI showed lower cell viability. Moreover, this concentration-dependent trend became more obvious when incubation time increased from 24 h to 72 h.\n\nCytotoxicity of blank nanoparticles:\nCell viability of MCF-7 cells was also monitored to investigate the cytotoxicity of non-RNA- and non-drug-loaded carriers including bPDR and bCPN-PDR. As shown in Figure 2B, after treatment with bCPN-PDR, the cell viability was stable around 95% and shown to be concentration independent at 24 h. Meanwhile, bPDR-treated cells showed lower cell viability with time. With increased concentration, bPDR showed higher cytotoxicity, inducing 80% cell death in 72 h. Compared with the bPDR group, the cell viabilities of the bCPN-PDR group were significantly higher (P<0.05). This phenomenon could be explained by the higher positive charge of bPDR due to the positive ingredient of PEI, while the cell cytotoxicity could be significantly decreased by covering with the nontoxic negatively charged CPN (>80% cell viability in Figure 2A), which could induce the charge reversal of bPDR, implying that the negatively charged CPN coating could decrease the higher toxicity of bPDR to cells.\n\nExpression level of VEGF mRNA:\nThe transfection efficiency of CPN-PDR compared to Lipofectamine 2000 was demonstrated by delivering VEGF-siRNA into MCF-7 cells. The expression level of VEGF mRNA was measured using real-time PCR, and the results are shown in Figure 3. The expression level of VEGF mRNA was normalized to the β-actin expression and calculated based on the semiquantitative method. Untreated cells were selected as control, and the expression level was set as 100%. For lipo/siRNA group, the silencing efficiencies were 61.98%±6.96%, 31.93%±6.43% and 37.02%±3.17% at 24, 48 and 72 h, respectively. The decreased expression of VEGF mRNA reflected that gene silencing capability of lipo/siRNA. With incubation time extended from 24 to 48 h, the capability was enhanced (P<0.05). However, in 72 h, no further reduction was observed compared to that in 48 h, although the significantly downregulated expression could be observed (P<0.05 vs control, Dox and bCPN-PDR), implying that the silencing effect could reach the plateau period after 48 h and last for 72 h at least. The CPN-PR and CPN-PDR have shown similar behavior to lipo/siRNA. Compared with the control, a remarkable decrease of VEGF mRNA expression was observed (P<0.01) at 48 h. There was no significant difference in expression levels between CPN-PR and CPN-PDR (P>0.05) at any time point, implying their comparable gene delivery and transfection capability. More importantly, this capability was equivalent to the commercial Lipofectamine 2000.\n\nExpression level of VEGF protein:\nBased on the results of real-time PCR, the gene-silencing effect enhanced with time and reached a platform after 48 h. In this study, the Western blot technique was employed to observe VEGF protein expression at 48 h. The results are shown in Figure 4. GAPDH was selected as the inner control; all the bands were found to be equivalent in gray levels, and the amount of protein expression was positively correlated with the shade of gray in the imaging picture. From Figure 4, the gray levels of both control and bCPN-PDR, which were similar, were obvious. However, siRNA-loaded nanoparticles including lipo/siRNA, CPN-PR and CPN-PDR demonstrated a notably lower gray level. Furthermore, as marked with the red rectangle in Figure 4, expression of VEGF protein in CPN-PDR could hardly be identified. This suggested that co-delivery of anti-VEGF siRNA and Dox could downregulate the relevant VEGF protein level, which was in agreement with the results from semiquantitative real-time PCR. Taken together, these results indicate that nanoparticles loaded with anti-VEGF siRNA downregulated both the protein and the mRNA expression of VEGF.\n\nInhibition of cell proliferation in vitro:\nThe ultimate goal of siRNA intracellular delivery is to restrain the proliferation of cancer cells. Different treatments against MCF-7 cells were performed to further investigate the inhibition effect. As shown in Figure 5A, an obvious concentration dependence in terms of DOX, CPN-PD and CPN-PDR could be observed after 48 h incubation. We have calculated the differences between CPN-PDR vs CPN-PD and CPN-PDR vs DOX at each dose. The results indicated that there was a significant difference between CPN-PDR vs DOX at each dose except the dose of 0.05, and there were significant differences between CPN-PDR vs CPN-PD at a dose of 0.002. It was suggested that the in vitro antitumor activity of CPN-PDR was equivalent to CPN-PD, and both of them showed a higher antitumor activity than free drug DOX, which was mainly attributed to the design of the multifunctional nano-vectors. We have also calculated the statistical differences between CPN-PDR vs CPN-PD and CPN-PDR vs DOX in the concentration range of Dox (0.002, 0.01, 0.05, 0.25 and 0.5 µM) using Student’s t-test. After statistical calculation, the significant differences were verified to exist between CPN-PDR vs CPN-PD (P<0.05) and CPN-PDR vs DOX (P<0.01). After calculation with professional software, the half maximal inhibitory concentrations (IC50) are presented in Figure 5B. They are 0.0367±0.0088 µM for Dox, 0.0266±0.0027 µM for CPN-PD and 0.0126±0.0022 µM for CPN-PDR, respectively. The IC50 of CPN-PD was 2.913-fold higher than that of CPN-PDR, and the IC50 of DOX was 2.110-fold higher than that of CPN-PDR. After statistical calculation, significant differences were found between CPN-PDR vs CPN-PD (P<0.05) and CPN-PDR vs DOX (P<0.05). With the results from real-time PCR and Western blot, the increased cytotoxicity of CPN-PDR probably resulted from siRNA delivery and successful transfection. This was because the intracellular delivery of anti-VEGF siRNA could downregulate the expression of VEGF protein. The anti-VEGF therapy may sensitize cancer cells or kill cells block the blood supply of the tumor and improve drug penetration. Taken together, the anti-VEGF siRNA and Dox co-delivery platform could induce increased cytotoxicity against cancer cells.\n\nHemolysis test:\nTo investigate the safety of intravenous injection of CPN-PDR, a hemolysis test was performed. As shown in Table 1, different volume ratios of CPN-PDR to 2% red blood cell suspension were selected. After incubation and centrifugation, tubes were observed by the naked eye. As shown in Figure 6, no red blood cell hemolysis was observed in tube 6, in which 100% PBS was added serving as a negative control. For tube 7, 100% double distilled water was added and red cell hemolysis could be clearly observed. For tubes 1–5, in which different percentages of CPN-PDR were added, no red blood cell hemolysis occurred. Hemolysis of red blood cells can generate hemoprotein, which could be detected using UV-vis spectrophotometry. After scanning the absorbance wavelength of hemoprotein, 577 nm was selected as the detecting wavelength. According to Equation 3, hemolysis ratios were calculated based on the recorded absorbance and shown in Table 2. All the hemolysis ratios of tubes 1–5 were <5%, implying that no hemolysis was induced by addition of CPN-PDR.\n\nTissue section:\nIn this study, a histological analysis of organs (heart, liver, spleen, lung and kidney) was performed to evaluate whether CPN-PDR could cause tissue damage, inflammation and lesions. Kunming mice were injected with CPN-PDR and normal saline (NS) by tail vein, respectively. After 1 week, all the mice were sacrificed, and the heart, liver, spleen, lung and kidney were separated. After the standard procedure of HE staining for histological examination, the organs were observed under a microscope. Mice without any treatment were chosen as a control. As shown in Figure 7, compared with the control group, there was no visible histologically difference between mice administrated with CPN-PDR group and that with NS group, implying the safety of CPN-PDR in vivo.\n\nConclusion:\nIn summary, the delivery system materials were demonstrated to be nontoxic and biocompatible. The nontoxic and negatively charged copolymer CPN coating could significantly decrease the cytotoxicity of the cationic core, bPDR. The obtained co-delivery system CPN-PDR was also confirmed with enhanced cytotoxicity against tumor cells. Gene transfection efficiency induced by CPN-PDR was fairly equal with that of the commercial product Lipofectamine 2000, implying the successful intracellular delivery and good transfection of siRNA. Moreover, the preliminary evaluation of safety showed CPN-PDR to have good biocompatibility, so it has great potential for further exploitation and clinical application. With the development of aptamer technology, the promising application prospects of this novel oligodeoxynucleotide-based co-loading platform will further increase.\n\n"},"whole_article_abstract":{"kind":"string","value":"Background:\nWe previously developed a simple effective system based on oligodeoxynucleotides with CGA repeating units (CGA-ODNs) for Dox and siRNA intracellular co-delivery.\n\nMethods:\nIn the present study, the in vitro cytotoxicity, gene transfection and in vivo safety of the co-delivery system were further characterized and discussed.\n\nResults:\nCompared with poly(ethyleneimine) (PEI), both CGA-ODNs and the pH-sensitive targeted coating, o-carboxymethyl-chitosan (CMCS)-poly(ethylene glycol) (PEG)-aspargine-glycine-arginine (NGR) (CMCS-PEG-NGR, CPN) showed no obvious cytotoxicity in 72 h. The excellent transfection capability of CPN coated Dox and siRNA co-loaded nanoparticles (CPN-PDR) was confirmed by real-time PCR and Western blot analysis. It was calculated that there was no significant difference in silencing efficiency among Lipo/siRNA, CPN-modified siRNA-loaded nanoparticles (CPN-PR) and CPN-PDR. Furthermore, CPN-PDR was observed to be significantly much more toxic than Dox- and CPN-modified Dox-loaded nanoparticles (CPN-PD), implying their higher antitumor potential. Both hemolysis tests and histological assessment implied that CPN-PDR was safe for intravenous injection with nontoxicity and good biocompatibility in vitro and in vivo.\n\nConclusions:\nThe results indicated that CPN-PDR could be a potentially promising co-delivery carrier for enhanced antitumor therapy."},"background_conclusion_text":{"kind":"string","value":"Characteristics of nanoparticles:\nIn the optimal formulation of CPN-PDR, the weight ratio of DOX to siRNA was 1:1; therefore, the siRNA loading content was equal to the DOX loading content. Based on the facts that the molar ratio of CGA-ODNs to Dox was 1:5, the weight ratio of CPN/PEI/ODNs and siRNA was 4:1:1, and consequently, siRNA and Dox loading efficiency in CPN-PDR was calculated to be 4.93%.\n\nConclusion:\nIn summary, the delivery system materials were demonstrated to be nontoxic and biocompatible. The nontoxic and negatively charged copolymer CPN coating could significantly decrease the cytotoxicity of the cationic core, bPDR. The obtained co-delivery system CPN-PDR was also confirmed with enhanced cytotoxicity against tumor cells. Gene transfection efficiency induced by CPN-PDR was fairly equal with that of the commercial product Lipofectamine 2000, implying the successful intracellular delivery and good transfection of siRNA. Moreover, the preliminary evaluation of safety showed CPN-PDR to have good biocompatibility, so it has great potential for further exploitation and clinical application. With the development of aptamer technology, the promising application prospects of this novel oligodeoxynucleotide-based co-loading platform will further increase."},"background_conclusion_abstract":{"kind":"string","value":"Background:\nWe previously developed a simple effective system based on oligodeoxynucleotides with CGA repeating units (CGA-ODNs) for Dox and siRNA intracellular co-delivery.\n\nMethods:\nIn the present study, the in vitro cytotoxicity, gene transfection and in vivo safety of the co-delivery system were further characterized and discussed.\n\nResults:\nCompared with poly(ethyleneimine) (PEI), both CGA-ODNs and the pH-sensitive targeted coating, o-carboxymethyl-chitosan (CMCS)-poly(ethylene glycol) (PEG)-aspargine-glycine-arginine (NGR) (CMCS-PEG-NGR, CPN) showed no obvious cytotoxicity in 72 h. The excellent transfection capability of CPN coated Dox and siRNA co-loaded nanoparticles (CPN-PDR) was confirmed by real-time PCR and Western blot analysis. It was calculated that there was no significant difference in silencing efficiency among Lipo/siRNA, CPN-modified siRNA-loaded nanoparticles (CPN-PR) and CPN-PDR. Furthermore, CPN-PDR was observed to be significantly much more toxic than Dox- and CPN-modified Dox-loaded nanoparticles (CPN-PD), implying their higher antitumor potential. Both hemolysis tests and histological assessment implied that CPN-PDR was safe for intravenous injection with nontoxicity and good biocompatibility in vitro and in vivo.\n\nConclusions:\nThe results indicated that CPN-PDR could be a potentially promising co-delivery carrier for enhanced antitumor therapy."},"whole_article_text_length":{"kind":"number","value":12903,"string":"12,903"},"whole_article_abstract_length":{"kind":"number","value":273,"string":"273"},"other_sections_lengths":{"kind":"list like","value":[1055,232,129,191,228,112,137,203,279,126,142,132,3750,185,288,222,451,207,152,138],"string":"[\n 1055,\n 232,\n 129,\n 191,\n 228,\n 112,\n 137,\n 203,\n 279,\n 126,\n 142,\n 132,\n 3750,\n 185,\n 288,\n 222,\n 451,\n 207,\n 152,\n 138\n]"},"num_sections":{"kind":"number","value":24,"string":"24"},"most_frequent_words":{"kind":"list like","value":["cpn","pdr","cells","cpn pdr","sirna","dox","vegf","cell","expression","time"],"string":"[\n \"cpn\",\n \"pdr\",\n \"cells\",\n \"cpn pdr\",\n \"sirna\",\n \"dox\",\n \"vegf\",\n \"cell\",\n \"expression\",\n \"time\"\n]"},"keybert_topics":{"kind":"list like","value":["sirna targeting multidrug","interfering rnas sirnas","sirna chemotherapeutic drugs","effects chemotherapy rnai","chemotherapy rnai selection"],"string":"[\n \"sirna targeting multidrug\",\n \"interfering rnas sirnas\",\n \"sirna chemotherapeutic drugs\",\n \"effects chemotherapy rnai\",\n \"chemotherapy rnai selection\"\n]"},"annotated_base_background_abstract_prompt":{"kind":"string","value":""},"annotated_base_methods_abstract_prompt":{"kind":"string","value":""},"annotated_base_results_abstract_prompt":{"kind":"null"},"annotated_base_conclusions_abstract_prompt":{"kind":"string","value":""},"annotated_base_whole_article_abstract_prompt":{"kind":"string","value":""},"annotated_base_background_conclusion_abstract_prompt":{"kind":"string","value":""},"annotated_keywords_background_abstract_prompt":{"kind":"string","value":"[CONTENT] co-delivery | doxorubicin | VEGF | cytotoxicity | transfection [SUMMARY]"},"annotated_keywords_methods_abstract_prompt":{"kind":"string","value":"[CONTENT] co-delivery | doxorubicin | VEGF | cytotoxicity | transfection [SUMMARY]"},"annotated_keywords_results_abstract_prompt":{"kind":"null"},"annotated_keywords_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] co-delivery | doxorubicin | VEGF | cytotoxicity | transfection [SUMMARY]"},"annotated_keywords_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] co-delivery | doxorubicin | VEGF | cytotoxicity | transfection [SUMMARY]"},"annotated_keywords_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] co-delivery | doxorubicin | VEGF | cytotoxicity | transfection [SUMMARY]"},"annotated_mesh_background_abstract_prompt":{"kind":"string","value":"[CONTENT] Animals | Antineoplastic Agents | Chitosan | Doxorubicin | Drug Carriers | Drug Delivery Systems | Endosomes | Humans | Hydrogen-Ion Concentration | MCF-7 Cells | Mice | Nanoparticles | Oligodeoxyribonucleotides | Oligopeptides | Polyethylene Glycols | RNA, Small Interfering | Transfection [SUMMARY]"},"annotated_mesh_methods_abstract_prompt":{"kind":"string","value":"[CONTENT] Animals | Antineoplastic Agents | Chitosan | Doxorubicin | Drug Carriers | Drug Delivery Systems | Endosomes | Humans | Hydrogen-Ion Concentration | MCF-7 Cells | Mice | Nanoparticles | Oligodeoxyribonucleotides | Oligopeptides | Polyethylene Glycols | RNA, Small Interfering | Transfection [SUMMARY]"},"annotated_mesh_results_abstract_prompt":{"kind":"null"},"annotated_mesh_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] Animals | Antineoplastic Agents | Chitosan | Doxorubicin | Drug Carriers | Drug Delivery Systems | Endosomes | Humans | Hydrogen-Ion Concentration | MCF-7 Cells | Mice | Nanoparticles | Oligodeoxyribonucleotides | Oligopeptides | Polyethylene Glycols | RNA, Small Interfering | Transfection [SUMMARY]"},"annotated_mesh_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] Animals | Antineoplastic Agents | Chitosan | Doxorubicin | Drug Carriers | Drug Delivery Systems | Endosomes | Humans | Hydrogen-Ion Concentration | MCF-7 Cells | Mice | Nanoparticles | Oligodeoxyribonucleotides | Oligopeptides | Polyethylene Glycols | RNA, Small Interfering | Transfection [SUMMARY]"},"annotated_mesh_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] Animals | Antineoplastic Agents | Chitosan | Doxorubicin | Drug Carriers | Drug Delivery Systems | Endosomes | Humans | Hydrogen-Ion Concentration | MCF-7 Cells | Mice | Nanoparticles | Oligodeoxyribonucleotides | Oligopeptides | Polyethylene Glycols | RNA, Small Interfering | Transfection [SUMMARY]"},"annotated_keybert_background_abstract_prompt":{"kind":"string","value":"[CONTENT] sirna targeting multidrug | interfering rnas sirnas | sirna chemotherapeutic drugs | effects chemotherapy rnai | chemotherapy rnai selection [SUMMARY]"},"annotated_keybert_methods_abstract_prompt":{"kind":"string","value":"[CONTENT] sirna targeting multidrug | interfering rnas sirnas | sirna chemotherapeutic drugs | effects chemotherapy rnai | chemotherapy rnai selection [SUMMARY]"},"annotated_keybert_results_abstract_prompt":{"kind":"null"},"annotated_keybert_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] sirna targeting multidrug | interfering rnas sirnas | sirna chemotherapeutic drugs | effects chemotherapy rnai | chemotherapy rnai selection [SUMMARY]"},"annotated_keybert_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] sirna targeting multidrug | interfering rnas sirnas | sirna chemotherapeutic drugs | effects chemotherapy rnai | chemotherapy rnai selection [SUMMARY]"},"annotated_keybert_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] sirna targeting multidrug | interfering rnas sirnas | sirna chemotherapeutic drugs | effects chemotherapy rnai 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CGA | CMCS)-poly(ethylene | NGR | CMCS-PEG-NGR | CPN | 72 | CPN | Dox | CPN-PDR | PCR | Western ||| Lipo | CPN | CPN-PDR ||| CPN-PDR | CPN | CPN-PD ||| CPN-PDR ||| CPN-PDR [SUMMARY]"}}},{"rowIdx":283,"cells":{"title":{"kind":"string","value":"A Novel Signature Based on m6A RNA Methylation Regulators Reveals Distinct Prognostic Subgroups and Associates with Tumor Immunity of Patients with Pancreatic Neuroendocrine Neoplasms."},"pmid":{"kind":"string","value":"35609514"},"background_abstract":{"kind":"string","value":"The RNA N6-methyladenosine (m6A) regulators play a crucial role in tumorigenesis and could be indicators of prognosis and therapeutic targets in various cancers. However, the expression status and prognostic value of m6A regulators have not been studied in pancreatic neuroendocrine neoplasms (PanNENs). We aimed to investigate the expression patterns and prognostic value of m6A regulators and assess their correlations with immune checkpoints and infiltrates in PanNENs."},"background_abstract_label":{"kind":"string","value":"INTRODUCTION"},"methods_abstract":{"kind":"string","value":"Immunohistochemistry was performed for 15 m6A regulators and immune markers using tissue microarrays obtained from 183 patients with PanNENs. The correlation between m6A protein expression and clinicopathological parameters with recurrence-free survival (RFS) was examined using a random survival forest, Cox regression model, and survival tree analysis."},"methods_abstract_label":{"kind":"string","value":"METHODS"},"results_abstract":{"kind":"string","value":"Among the 15 m6A proteins, high expression of YTHDF2 (p < 0.001) and HNRNPC (p = 0.006) was found to be significantly associated with recurrence and served as independent risk factors in multivariate analysis. High YTHDF2 expression was associated with higher number of CD3+ T cells (p = 0.003), whereas high HNRNPC expression was significantly correlated with the expression of PD-L1 (p = 0.039). A YTHDF2-based signature was determined, including five patterns from survival tree analysis: patients with the LNnegYTHDF2high signature had a 5-year RFS rate of 92.1%, whereas patients with LNposTumorSize＜2.5 cm signature had the worst 5-year RFS rate of 0% (p < 0.001). The area under receiver operating characteristic curve was 0.870 (95% confidence interval: 0.762-0.915) for the YTHDF2-based signature. The C-index was 0.978, suggesting good discrimination ability; moreover, the risk score of recurrence served as an independent prognostic factor indicating shorter RFS."},"results_abstract_label":{"kind":"string","value":"RESULTS"},"conclusions_abstract":{"kind":"string","value":"YTHDF2 appears to serve as a promising prognostic biomarker and therapeutic target. A YTHDF2-based signature can identify distinct subgroups, which may be helpful to strategize personalized postoperative monitoring."},"conclusions_abstract_label":{"kind":"string","value":"CONCLUSIONS"},"mesh_descriptor_names":{"kind":"list like","value":["Humans","Methylation","Prognosis","Adenosine","RNA","Multivariate Analysis","Neoplasms"],"string":"[\n \"Humans\",\n \"Methylation\",\n \"Prognosis\",\n \"Adenosine\",\n \"RNA\",\n \"Multivariate Analysis\",\n \"Neoplasms\"\n]"},"pmcid":{"kind":"string","value":"9808770"},"background_title":{"kind":"string","value":"Introduction"},"background_text":{"kind":"string","value":"Pancreatic neuroendocrine neoplasms (PanNENs) are a rare, heterogeneous group of neoplasms originating from the neuroendocrine cells and classified as functional and nonfunctional depending on whether the tumor overproduces biologically active hormones [1]. However, the incidence of PanNENs has significantly increased in the last decades and has risen to 0.8 per 100,000 individuals per year [2]. This increase may be attributed, at least in part, to the improved and more frequent use of imaging in clinical diagnostic practices [3]. Moreover, both patients with nonfunctional or functional tumors are always diagnosed at a late stage and usually present locally advanced or metastatic disease [4, 5].\nCurrent clinical strategies for most patients with PanNENs primarily involve surgical resection [6]. One of the most significant causes of concern in patients with a PanNEN is cancer recurrence after curative resection. However, clinical outcomes after surgical resection vary widely, with recurrence-free survival (RFS) ranging from 0.96 to 121.9 months [7]. The most frequently used cancer staging system is based on the WHO classification which categorizes tumors into four groups based on proliferative activity and morphology: NET G1, NET G2, NET G3, and NEC [8]. However, patients with PanNEN with the same grade of tumor may demonstrate different clinical courses, whereas patients with low-grade PanNENs show unpredictable disease progression and outcomes [9]. Therefore, a clinically feasible and accurate prediction of the risk of relapse in patients with PanNENs after resection is needed.\nN6-methyladenosine (m6A) is the most common epigenetic modification of the mRNA, comprising >60% of all RNA-based modifications [10]. The m6A methylation levels are greatly associated with the immune response, stress response regulation, tumorigenesis, and miRNA processing [11, 12, 13, 14, 15, 16]. The m6A methylation plays a crucial role in disease progression of various cancers [11, 12, 16] as well as in the onset and progression of fetal growth restriction and preeclampsia [17, 18]. The modification of m6A is regulated by methyltransferases (writers) and demethylases (erasers), and m6A performs multiple functions through its interactions with specific binding proteins known as the readers [19]. The writers mainly include KIAA1429, METTL3, WTAP, METTL14, RBM15, and METTL16, which promote m6A RNA methylation. The erasers encompass fat mass and obesity-associated protein (FTO) and alkylation repair homolog protein 5 (ALKBH5). The readers comprise HNRNPC, IGF2BP2, YTHDC1, YTHDC2, YTHDF1, YTHDF2, and YTHDF3, which recognize RNA m6A binding site [19, 20, 21]. Increasing amount of evidence has emerged which shows the vital role of m6A regulators in tumorigenesis and prognosis of various cancers. Chen et al. [22] reported that METTL14 inhibited colorectal cancer (CRC) progression through primary miR-375 processing-based regulation. Cives et al. [23] revealed that METTL3 could promote the proliferation and migration of hepatocellular carcinoma. A previous study found that high YTHDF1 expression predicted a poor clinical outcome in CRC patients [24]. Additionally, the low expression of FTO was associated with a shorter RFS in patients with intrahepatic cholangiocarcinoma [25]. Overexpression of FTO suppressed acute myeloid leukemia cell differentiation [26]. Growing evidence suggests that m6A modification and its regulators play vital roles in human cancers [11, 12, 22, 23, 24, 25, 26]. m6A modification is associated with tumorigenesis, proliferation, invasion, and metastasis [22, 23, 24]. And m6A regulators were indicators of prognosis and therapeutic targets [25, 26]. Moreover, epigenetic modification and especially methylation have been already described as important events during tumorigenesis of PanNEN, and methylation modification is an epigenetic regulatory mechanism for gene expression in PanNENs, including MEN1, which involved in the chromatin remodeling in PanNENs [27]. However, whether m6A regulators could also be specific biomarkers in PanNENs is yet to be determined. And the expression status, functional roles, and clinical significance of m6A methylation regulators also remain largely unknown in PanNENs. In addition, recent studies have shown prognosis of immune infiltrates and immune checkpoints in pancreatic neuroendocrine tumors [23, 28]. However, the correlation between m6A regulators and immune infiltration and immune checkpoints is yet to be explored.\nTo better identify patients at risk and improve their follow-up management, we explored the expression profiles of m6A proteins in resected PanNENs and investigated the potential prognostic value of m6A proteins. We also analyzed the association among common immune infiltrates, immune checkpoints, and m6A regulators. Furthermore, a YTHDF2-based signature was established, which may help in postoperative monitoring and stratification of risks to guide clinical treatment options."},"methods_title":{"kind":"null"},"methods_text":{"kind":"null"},"results_title":{"kind":"string","value":"Results"},"results_text":{"kind":"string","value":"Patients' Characteristics A total of 96 patients were male (52.4%), whereas 87 patients were female (47.6%). The tumor diameter was >2.5 cm in 53.1% of the patients (97/183). The number of positive lymph nodes ranged from 1 to 29. A total of 50.7% tumors were nonfunctional, and the majority of functional tumors were classified as insulinoma (76/90). Most of the tumors (175/183) were well differentiated, and eight were poorly differentiated (Table 1). The median follow-up time was 39 months (range: 1–197 months), during which there were 43 relapses and 10 deaths, and all of the deaths were owing to disease progression. The 3- and 5-year RFS rates were 83.6% and 77.0%, respectively.\nA total of 96 patients were male (52.4%), whereas 87 patients were female (47.6%). The tumor diameter was >2.5 cm in 53.1% of the patients (97/183). The number of positive lymph nodes ranged from 1 to 29. A total of 50.7% tumors were nonfunctional, and the majority of functional tumors were classified as insulinoma (76/90). Most of the tumors (175/183) were well differentiated, and eight were poorly differentiated (Table 1). The median follow-up time was 39 months (range: 1–197 months), during which there were 43 relapses and 10 deaths, and all of the deaths were owing to disease progression. The 3- and 5-year RFS rates were 83.6% and 77.0%, respectively.\nExpression Patterns of the 15 m6A Regulators We found that the expression levels of m6A proteins in PanNENs and normal tissues were evident (Fig. 1a). The expression levels of writers (i.e., KIAA1429, METTL14, RBM15, WTAP, and METTL16), readers (i.e., IGF2BP2, HNRNPC, YTHDC1, YTHDC2, YTHDF1, YTHDF2, and YTHDF3), and erasers (ALKBH5 and FTO) were higher in PanNEN tissues than in normal tissues (p < 0.001). No statistically significant difference was evident between the normal and PanNENs tissues in the context of METTL3 expression level. Regarding METTL3, METTL14, METTL16, WTAP, RBM15, and KIAA1429 expression, a high level was observed in 26.7%, 11.5%, 35.5%, 64.4%, 67.7%, and 55.7% of tumors, respectively. Among the 183 tumors, 36.6% were deemed to have high FTO expression and 72.1% had high ALKBH5 expression. Regarding reader molecules (i.e., IGF2BP2, HNRNPC, YTHDC1, YTHDC2, YTHDF1, YTHDF2, and YTHDF3), immunostaining revealed low expression in 64.5%, 95.1%, 72.1%, 57.9%, 84.7%, 50.3%, and 96.2%, respectively, of these samples and high expression in the remaining samples (Fig. 1b). The immunohistochemical patterns of the 15 m6A proteins are shown in Figure 1c and online supplementary Figure S1. All 183 PanNENs showed nuclear immunoreactivity for METTL3, METTL14, METTL16, WTAP, RBM15, KIAA1429, FTO, ALKBH5, HNRNPC, and YTHDC1. The positive sites of IGF2BP2, YTHDC2, YTHDF1, YTHDF2, and YTHDF3 were located in the cytoplasm.\nWe found that the expression levels of m6A proteins in PanNENs and normal tissues were evident (Fig. 1a). The expression levels of writers (i.e., KIAA1429, METTL14, RBM15, WTAP, and METTL16), readers (i.e., IGF2BP2, HNRNPC, YTHDC1, YTHDC2, YTHDF1, YTHDF2, and YTHDF3), and erasers (ALKBH5 and FTO) were higher in PanNEN tissues than in normal tissues (p < 0.001). No statistically significant difference was evident between the normal and PanNENs tissues in the context of METTL3 expression level. Regarding METTL3, METTL14, METTL16, WTAP, RBM15, and KIAA1429 expression, a high level was observed in 26.7%, 11.5%, 35.5%, 64.4%, 67.7%, and 55.7% of tumors, respectively. Among the 183 tumors, 36.6% were deemed to have high FTO expression and 72.1% had high ALKBH5 expression. Regarding reader molecules (i.e., IGF2BP2, HNRNPC, YTHDC1, YTHDC2, YTHDF1, YTHDF2, and YTHDF3), immunostaining revealed low expression in 64.5%, 95.1%, 72.1%, 57.9%, 84.7%, 50.3%, and 96.2%, respectively, of these samples and high expression in the remaining samples (Fig. 1b). The immunohistochemical patterns of the 15 m6A proteins are shown in Figure 1c and online supplementary Figure S1. All 183 PanNENs showed nuclear immunoreactivity for METTL3, METTL14, METTL16, WTAP, RBM15, KIAA1429, FTO, ALKBH5, HNRNPC, and YTHDC1. The positive sites of IGF2BP2, YTHDC2, YTHDF1, YTHDF2, and YTHDF3 were located in the cytoplasm.\nAssociations among m6A Regulators, Immune Markers, and Clinicopathological Parameters in PanNENs Among the 15 proteins studied, only YTHDF2 and HNRNPC expressions had significant prognostic value. Of the 43 patients with recurrence, 12 (27.9%) showed low YTHDF2 expression, which was dramatically lower than the proportion of patients without recurrence having low YTHDF2 expression (85 of 140, 60.7%; p = 0.003; Fig. 1d). Similarly, 88.4% of the patients (38 of 43 patients) with recurrence showed low tumoral HNRNPC expression; in contrast, 97.1% of the patients without recurrence (136 of 140 patients) showed low tumoral expression of HNRNPC (p = 0.034; Fig. 1d). The log-rank test revealed that both high HNRNPC and YTHDF2 expressions predicted a significantly shortened RFS (p = 0.003 and p = 0.001, respectively; Fig. 1e, f). In the correlation analysis between YTHDF2 and HNRNPC expression and clinicopathological variables, we found that YTHDF2 expression was positively associated with tumor size (p = 0.021), nodal status (p = 0.007), functional status (p = 0.010), Ki67 index (p = 0.023), and liver metastasis (p < 0.001). There were no significant associations between YTHDF2 expression and the other clinicopathological variables, including age, sex, tumor site, lymphovascular invasion (LVI), and perineural invasion (PNI) (online suppl. Table S3).\nHigh YTHDF2 expression was associated with higher number of CD3+ T cells (p = 0.003). There were no significant associations between YTHDF2 expression and other immune markers (PD-L1, B7-H3, CD3, CD4, and CD68). However, high HNRNPC expression was significantly correlated with positive PD-L1 expression (p = 0.039) and high densities of CD3+ T cells. Moreover, there were no significant associations between HNRNPC expression and other immune markers (Table 2). Representative images of PD-L1, B7-H3 CD3, CD4, CD15, and CD68 are presented in online supplementary Figure S2.\nAmong the 15 proteins studied, only YTHDF2 and HNRNPC expressions had significant prognostic value. Of the 43 patients with recurrence, 12 (27.9%) showed low YTHDF2 expression, which was dramatically lower than the proportion of patients without recurrence having low YTHDF2 expression (85 of 140, 60.7%; p = 0.003; Fig. 1d). Similarly, 88.4% of the patients (38 of 43 patients) with recurrence showed low tumoral HNRNPC expression; in contrast, 97.1% of the patients without recurrence (136 of 140 patients) showed low tumoral expression of HNRNPC (p = 0.034; Fig. 1d). The log-rank test revealed that both high HNRNPC and YTHDF2 expressions predicted a significantly shortened RFS (p = 0.003 and p = 0.001, respectively; Fig. 1e, f). In the correlation analysis between YTHDF2 and HNRNPC expression and clinicopathological variables, we found that YTHDF2 expression was positively associated with tumor size (p = 0.021), nodal status (p = 0.007), functional status (p = 0.010), Ki67 index (p = 0.023), and liver metastasis (p < 0.001). There were no significant associations between YTHDF2 expression and the other clinicopathological variables, including age, sex, tumor site, lymphovascular invasion (LVI), and perineural invasion (PNI) (online suppl. Table S3).\nHigh YTHDF2 expression was associated with higher number of CD3+ T cells (p = 0.003). There were no significant associations between YTHDF2 expression and other immune markers (PD-L1, B7-H3, CD3, CD4, and CD68). However, high HNRNPC expression was significantly correlated with positive PD-L1 expression (p = 0.039) and high densities of CD3+ T cells. Moreover, there were no significant associations between HNRNPC expression and other immune markers (Table 2). Representative images of PD-L1, B7-H3 CD3, CD4, CD15, and CD68 are presented in online supplementary Figure S2.\nPrognostic Predictors of RFS in PanNENs A univariate Cox analysis for both the clinicopathological parameters and m6A proteins was conducted. Nine clinicopathological factors (tumor size, lymph node status, PNI, LVI, grade, functional status, liver metastasis, and Ki67 index) and five m6A proteins (METTL14, METTL16, ALKBH5, HNRNPC, YTHDF1, YTHDF2, and YTHDF3) were identified to be associated with RFS, with p values of <0.05 (Table 3). Considering analysis of collinear factors, tumor grade was found to be associated with Ki67 index and liver metastasis, and LVI was associated with lymph node status. Therefore, PNI, tumor size, lymph node status, grade, functional status, METTL14, METTL16, ALKBH5, HNRNPC, YTHDF1, YTHDF2, and YTHDF3 were included in the multivariate Cox regression model. Multivariate Cox regression analysis identified tumor size, lymph node status, tumor grade, PNI, HNRNPC expression, and YTHDF2 expression as independent risk factors for RFS after excluding collinear factors. Next, we applied an RSF model to evaluate the importance of these independent risk factors. In the analysis of variable of importance, we found that VIMP of HNRNPC was −0.0011 (Fig. 2a). In the minimal depth analysis, HNRNPC, PNI, and grade had values of 2.405, 2.482, and 2.183, respectively, which were very close to or more than the depth threshold (Fig. 2b). Therefore, they were all excluded from the RSF model. We also performed survival analyses to determine the prognostic value of YTHDF2 mRNA expression in patients with PanNET in ICGC cohort. The Kaplan-Meier plot revealed that high YTHDF2 mRNA expression was significantly associated with poor RFS (p = 0.017, Fig. 2c). Furthermore, three variables (tumor size, lymph node status, and YTHDF2 expression) significantly affected RFS. Nodal status had the largest effect on RFS, followed by YTHDF2 expression and tumor size.\nA univariate Cox analysis for both the clinicopathological parameters and m6A proteins was conducted. Nine clinicopathological factors (tumor size, lymph node status, PNI, LVI, grade, functional status, liver metastasis, and Ki67 index) and five m6A proteins (METTL14, METTL16, ALKBH5, HNRNPC, YTHDF1, YTHDF2, and YTHDF3) were identified to be associated with RFS, with p values of <0.05 (Table 3). Considering analysis of collinear factors, tumor grade was found to be associated with Ki67 index and liver metastasis, and LVI was associated with lymph node status. Therefore, PNI, tumor size, lymph node status, grade, functional status, METTL14, METTL16, ALKBH5, HNRNPC, YTHDF1, YTHDF2, and YTHDF3 were included in the multivariate Cox regression model. Multivariate Cox regression analysis identified tumor size, lymph node status, tumor grade, PNI, HNRNPC expression, and YTHDF2 expression as independent risk factors for RFS after excluding collinear factors. Next, we applied an RSF model to evaluate the importance of these independent risk factors. In the analysis of variable of importance, we found that VIMP of HNRNPC was −0.0011 (Fig. 2a). In the minimal depth analysis, HNRNPC, PNI, and grade had values of 2.405, 2.482, and 2.183, respectively, which were very close to or more than the depth threshold (Fig. 2b). Therefore, they were all excluded from the RSF model. We also performed survival analyses to determine the prognostic value of YTHDF2 mRNA expression in patients with PanNET in ICGC cohort. The Kaplan-Meier plot revealed that high YTHDF2 mRNA expression was significantly associated with poor RFS (p = 0.017, Fig. 2c). Furthermore, three variables (tumor size, lymph node status, and YTHDF2 expression) significantly affected RFS. Nodal status had the largest effect on RFS, followed by YTHDF2 expression and tumor size.\nConstruction and Evaluation of the Prognostic Model for Predicting RFS in PanNENs To construct a recurrence signature that can classify patients into subpopulations according to RFS, we further performed a recursive partitioning analysis. After pruning the decision trees using the post-pruning method, five terminal nodes representing a recurrence signature were identified (Fig. 2d). LNposTumorSize<2.5 cm (node 1; 20.8 months) and LNposTumorSize≥2.5 cmYTHDF2high (node 3; 21.1 months) patients had worse median RFS than LNnegYTHDF2high patients (node 5; 39.6 months, node 1 vs. node 5, p < 0.001), LNnegYTHDF2low patients (node 4; 43.2 months), and LNposTumorSize≥2.5 cmYTHDF2low patients (node 2; 29.3 months). Patients with the LNnegYTHDF2high (node 5) signature had a 5-year RFS rate of 92.1%, whereas patients with the LNposTumorSize<2.5 cm (node 1) signature had the worst 5-year RFS rate of 0% (Fig. 3a). To better identify risk groups for the recurrence signatures, we performed pair-wise comparisons for each recurrence signature subpopulation (on RFS) and the corresponding risk score of recurrence. The results showed that patients within the five terminal nodes can be categorized into three risk groups: low (node 4), intermediate (node 2 and node 5), and high (node 1 and node 3) with well-separated RFS curves (p < 0.001; Fig. 3b). Among the risk groups, the 5-year RFS rates were 6.0%, 70.4%, and 90.1% for the high-, intermediate-, and low-risk groups, respectively. To further test our proposed recurrence signature, we performed a subanalysis on a specific group of patients with nonsyndromic and nonfunctional PanNENs. A total of 93 PanNENs from the full cohort were eligible for this analysis (online suppl. Table S4). The Kaplan-Meier RFS curves showed clear separation among the risk groups (p = 0.018; Fig. 3c).\nFurthermore, a risk score was generated to evaluate the risk of recurrence based on variables selected from the survival tree analysis: Risk Score = (1.218 × Nodal Status) + (1.625 × Tumor Size) + (0.745 × expression value of YTHDF2) (Table 4). Multivariate analysis using a Cox proportional hazards model was performed which included PNI, tumor size, nodal status, grade, functional status, METTL14, METTL16, ALKBH5, HNRNPC, YTHDF1, YTHDF2, YTHDF3, and the risk score. The risk score was determined to be an independent prognostic factor for patients with resected PanNEN in our study based on the multivariate Cox regression, and higher risk scores were found to be associated with shorter survival (hazard ratio: 33.04, 95% CI: 4.341–251.434; p = 0.001) (Table 5). Specificity and sensitivity comparisons were performed via time-dependent ROC curve analysis of risk score. The predictive accuracy of the recurrence signature was relatively high and the area under ROC curve was 0.858 (95% CI: 0.747–0.913) at 1 year, 0.824 (95% CI: 0.754–0.912) at 3 years, and 0.870 (95% CI: 0.762–0.915) at 5 years (Fig. 3d). The C-index was 0.978 (95% CI: 0.936–1), suggesting good discrimination ability. The calibration curves also showed good agreement between the predicted and observed RFS (Fig. 3e).\nTo construct a recurrence signature that can classify patients into subpopulations according to RFS, we further performed a recursive partitioning analysis. After pruning the decision trees using the post-pruning method, five terminal nodes representing a recurrence signature were identified (Fig. 2d). LNposTumorSize<2.5 cm (node 1; 20.8 months) and LNposTumorSize≥2.5 cmYTHDF2high (node 3; 21.1 months) patients had worse median RFS than LNnegYTHDF2high patients (node 5; 39.6 months, node 1 vs. node 5, p < 0.001), LNnegYTHDF2low patients (node 4; 43.2 months), and LNposTumorSize≥2.5 cmYTHDF2low patients (node 2; 29.3 months). Patients with the LNnegYTHDF2high (node 5) signature had a 5-year RFS rate of 92.1%, whereas patients with the LNposTumorSize<2.5 cm (node 1) signature had the worst 5-year RFS rate of 0% (Fig. 3a). To better identify risk groups for the recurrence signatures, we performed pair-wise comparisons for each recurrence signature subpopulation (on RFS) and the corresponding risk score of recurrence. The results showed that patients within the five terminal nodes can be categorized into three risk groups: low (node 4), intermediate (node 2 and node 5), and high (node 1 and node 3) with well-separated RFS curves (p < 0.001; Fig. 3b). Among the risk groups, the 5-year RFS rates were 6.0%, 70.4%, and 90.1% for the high-, intermediate-, and low-risk groups, respectively. To further test our proposed recurrence signature, we performed a subanalysis on a specific group of patients with nonsyndromic and nonfunctional PanNENs. A total of 93 PanNENs from the full cohort were eligible for this analysis (online suppl. Table S4). The Kaplan-Meier RFS curves showed clear separation among the risk groups (p = 0.018; Fig. 3c).\nFurthermore, a risk score was generated to evaluate the risk of recurrence based on variables selected from the survival tree analysis: Risk Score = (1.218 × Nodal Status) + (1.625 × Tumor Size) + (0.745 × expression value of YTHDF2) (Table 4). Multivariate analysis using a Cox proportional hazards model was performed which included PNI, tumor size, nodal status, grade, functional status, METTL14, METTL16, ALKBH5, HNRNPC, YTHDF1, YTHDF2, YTHDF3, and the risk score. The risk score was determined to be an independent prognostic factor for patients with resected PanNEN in our study based on the multivariate Cox regression, and higher risk scores were found to be associated with shorter survival (hazard ratio: 33.04, 95% CI: 4.341–251.434; p = 0.001) (Table 5). Specificity and sensitivity comparisons were performed via time-dependent ROC curve analysis of risk score. The predictive accuracy of the recurrence signature was relatively high and the area under ROC curve was 0.858 (95% CI: 0.747–0.913) at 1 year, 0.824 (95% CI: 0.754–0.912) at 3 years, and 0.870 (95% CI: 0.762–0.915) at 5 years (Fig. 3d). The C-index was 0.978 (95% CI: 0.936–1), suggesting good discrimination ability. The calibration curves also showed good agreement between the predicted and observed RFS (Fig. 3e)."},"conclusions_title":{"kind":"null"},"conclusions_text":{"kind":"null"},"other_sections_titles":{"kind":"list like","value":["Patient Cohort and Follow-Up","Tissue Microarrays and Immunohistochemistry","Evaluation of Immunostaining","Risk Score Models and Random Survival Forest","Statistical Analysis","Patients' Characteristics","Expression Patterns of the 15 m6A Regulators","Associations among m6A Regulators, Immune Markers, and Clinicopathological Parameters in PanNENs","Prognostic Predictors of RFS in PanNENs","Construction and Evaluation of the Prognostic Model for Predicting RFS in PanNENs","Statement of Ethics","Funding Sources","Author Contributions"],"string":"[\n \"Patient Cohort and Follow-Up\",\n \"Tissue Microarrays and Immunohistochemistry\",\n \"Evaluation of Immunostaining\",\n \"Risk Score Models and Random Survival Forest\",\n \"Statistical Analysis\",\n \"Patients' Characteristics\",\n \"Expression Patterns of the 15 m6A Regulators\",\n \"Associations among m6A Regulators, Immune Markers, and Clinicopathological Parameters in PanNENs\",\n \"Prognostic Predictors of RFS in PanNENs\",\n \"Construction and Evaluation of the Prognostic Model for Predicting RFS in PanNENs\",\n \"Statement of Ethics\",\n \"Funding Sources\",\n \"Author Contributions\"\n]"},"other_sections_texts":{"kind":"list like","value":["A total of 183 patients with PanNENs who were admitted to the Peking Union Medical College Hospital (PUMCH) and underwent surgeries during the time period 2004–2019 with sufficient tumor tissue for evaluation were included in the current study. The FFPE specimens and matching hematoxylin and eosin slides were retrieved. Patients' clinicopathological data, including age, sex, primary tumor site, tumor size, tumor grading, functional status, and surgical procedures, were collected from the medical records. The American Joint on Cancer Committee (AJCC) 8th edition stages for each patient were defined based on the collected data. Survival and recurrence information were obtained from medical record reviews and telephone interviews. Recurrence and distant metastasis were determined based on biochemical markers, clinical multidisciplinary consultation, radiological, and/or histological examinations. The time between the surgery and tumor recurrence or last follow-up appointment was termed as RFS. Disease-specific survival was calculated from the surgery date to the time of patient death or last follow-up till time point of December 29, 2020. This retrospective study was approved by the Institutional Review Board of PUMCH (approval number: S-K1593) and conducted in accordance with the Declaration of Helsinki. Written consent for this study was not required and formally waived by the hospital's Ethics Committee.","Representative areas of tumors and adjacent normal tissue were selected from hematoxylin and eosin-stained paraffin blocks and then re-embedded into recipient tissue microarray (TMA) blocks (12 × 8 arrays). The diameter of each core was 2 mm.\nImmunohistochemical analysis was performed according to the protocol described by Zong et al. [29]. The 4-μm TMA sections were deparaffinized. Heat-mediated antigen retrieval was performed using sodium citrate buffer (pH 6.0) for 10 min. The endogenous peroxidase activity was quenched using a 3% hydrogen peroxide solution (ZSGB-BIO, Beijing, China) and then the sections were blocked with 5% normal goat serum for 30 min. TMA sections were incubated with primary antibodies against m6A methylation regulators (METTL3, METTL14, METTL16, WTAP, KIAA1429, RBM15, FTO, ALKBH5, IGF2BP2, HNRNPC, YTHDC1, YTHDC2, YTHDF1, YTHDF2, and YTHDF3) and immune markers (PD-L1, B7-H3, CD3, CD4, CD15, CD68) overnight at 4°C; the details and optimal dilutions are summarized in online supplementary Table S1 (see www.karger.com/doi/10.1159/000525228 for all online suppl. material). Stromal cells, normal acinar cells, ductal cells, and islet cells from normal tissues were incubated with primary antibodies and used as internal positive controls, whereas the same tissues without primary antibodies comprised the negative controls.","The immunostaining performed on the collected patient samples was independently assessed by two pathologists (XL Chen and SW Mo) who were blinded to the patients' clinicopathological features and clinical outcomes. Both pathologists reexamined the slides and reached a consensus in case of any discrepancy. The immunoreactivity of 15 m6A markers, PD-L1, and B7-H3 in tumor specimens was quantified using the method developed by Budwit-Novotny et al. [30]. In brief, the percentage of positively stained tumor cells was multiplied by the relative intensity of specific staining, which was assigned a value of negative (0), weak (1), distinct (2), and strong (3) [30, 31]. The density of stromal CD3, CD4, CD15, and CD68 were quantified in four ×400 high-power fields and the mean counts of four fields were used for statistical analysis as we described earlier [32]. The cutoff values of m6A markers and immune markers were determined using X-tile (Yale University, New Haven, CT, USA). These cutoff values are summarized in the online supplementary Table S2.","A random survival forest (RSF) model was constructed based on the minimal depth and variable importance (VIMP). VIMP and minimal depth were used to assess the true effect of the variable on RFS. Given that feature subsets of variables are randomly selected using RSF method, RSF can process high-dimensional (many variables) data. In our current study, we included more than ten variables. Through training of the RSF algorithm, the variables which were the most related to recurrence were selected using minimal depth and VIMP. And in the training process, the interaction between variables can be detected. Moreover, when creating random forest, unbiased estimation is used for generalization error, and the model has strong generalization ability [33]. If a large part of the features is lost, the accuracy can still be maintained [33]. Based on these strengths of RSF, this method was helpful to select relapse-related variables and construct a recurrence signature. Recurrence-specific variables defined by a VIMP of >0 and minimal depth less than the depth threshold were finally entered into a survival tree analysis [33]. The recurrence-specific variables, including nodal status, tumor size, YTHDF2, were selected by using minimal depth and VIMP. By integrating the expression level of YTHDF2, nodal status, and tumor size as well as their corresponding coefficients which were derived from the multivariate Cox model constructed only using these 3 variables, a risk score was generated, as represented below: Risk Score = (1.218 × Nodal Status) + (1.625 × Tumor Size) + (0.745 × expression value of YTHDF2).\nNodal status was divided into negative/positive and scored as 0/1; tumor size was divided into <2.5 cm/≥2.5 cm and scored as 0/1, and expression value of YTHDF2 was divided into low/high and scored as 0/1, and these scores were multiplied by the corresponding coefficients to generate a risk score. The median (median = 1.625) was used as the cutoff value for the risk score.","The continuous and categorical variables were described as median (range) and frequency (percentage), respectively. The Mann-Whitney U test was conducted to compare nonnormally distributed continuous variables, whereas a Student's t test was used to analyze normally distributed continuous variables. The χ2 test or Fisher's exact test was used to evaluate the relationship between the expression of m6A methylation regulators and categorical variables. The survival curves were plotted by Kaplan-Meier method, and the log-rank test was employed to compare the survival curves generated. The Cox proportional hazard regression model was used to estimate the hazard ratio with a 95% confidence interval (CI) for variables associated with RFS. Potential risk factors with a p value of <0.05 in the univariate Cox analysis were entered into the multivariate Cox regression model (backward Wald) after considering collinearity among the variables. We evaluated the discrimination ability of the final model with Harrell concordance index (C-index). The area under the time-dependent receiver operating characteristic (ROC) curve at different cutoff times was measured as predictive performance. Calibration was assessed by visual examination of the calibration plot. p values of <0.05 were considered statistically significant. Statistical analyses were two-sided and accomplished using SPSS software (version 21.0; IBM Corp., Armonk, NY, USA). Statistical analyses of RNA sequencing data from International Cancer Genome Consortium (ICGC) database (https://www.icgc.org/) were conducted with R version 3.5.0 (http://www.r-project.org).","A total of 96 patients were male (52.4%), whereas 87 patients were female (47.6%). The tumor diameter was >2.5 cm in 53.1% of the patients (97/183). The number of positive lymph nodes ranged from 1 to 29. A total of 50.7% tumors were nonfunctional, and the majority of functional tumors were classified as insulinoma (76/90). Most of the tumors (175/183) were well differentiated, and eight were poorly differentiated (Table 1). The median follow-up time was 39 months (range: 1–197 months), during which there were 43 relapses and 10 deaths, and all of the deaths were owing to disease progression. The 3- and 5-year RFS rates were 83.6% and 77.0%, respectively.","We found that the expression levels of m6A proteins in PanNENs and normal tissues were evident (Fig. 1a). The expression levels of writers (i.e., KIAA1429, METTL14, RBM15, WTAP, and METTL16), readers (i.e., IGF2BP2, HNRNPC, YTHDC1, YTHDC2, YTHDF1, YTHDF2, and YTHDF3), and erasers (ALKBH5 and FTO) were higher in PanNEN tissues than in normal tissues (p < 0.001). No statistically significant difference was evident between the normal and PanNENs tissues in the context of METTL3 expression level. Regarding METTL3, METTL14, METTL16, WTAP, RBM15, and KIAA1429 expression, a high level was observed in 26.7%, 11.5%, 35.5%, 64.4%, 67.7%, and 55.7% of tumors, respectively. Among the 183 tumors, 36.6% were deemed to have high FTO expression and 72.1% had high ALKBH5 expression. Regarding reader molecules (i.e., IGF2BP2, HNRNPC, YTHDC1, YTHDC2, YTHDF1, YTHDF2, and YTHDF3), immunostaining revealed low expression in 64.5%, 95.1%, 72.1%, 57.9%, 84.7%, 50.3%, and 96.2%, respectively, of these samples and high expression in the remaining samples (Fig. 1b). The immunohistochemical patterns of the 15 m6A proteins are shown in Figure 1c and online supplementary Figure S1. All 183 PanNENs showed nuclear immunoreactivity for METTL3, METTL14, METTL16, WTAP, RBM15, KIAA1429, FTO, ALKBH5, HNRNPC, and YTHDC1. The positive sites of IGF2BP2, YTHDC2, YTHDF1, YTHDF2, and YTHDF3 were located in the cytoplasm.","Among the 15 proteins studied, only YTHDF2 and HNRNPC expressions had significant prognostic value. Of the 43 patients with recurrence, 12 (27.9%) showed low YTHDF2 expression, which was dramatically lower than the proportion of patients without recurrence having low YTHDF2 expression (85 of 140, 60.7%; p = 0.003; Fig. 1d). Similarly, 88.4% of the patients (38 of 43 patients) with recurrence showed low tumoral HNRNPC expression; in contrast, 97.1% of the patients without recurrence (136 of 140 patients) showed low tumoral expression of HNRNPC (p = 0.034; Fig. 1d). The log-rank test revealed that both high HNRNPC and YTHDF2 expressions predicted a significantly shortened RFS (p = 0.003 and p = 0.001, respectively; Fig. 1e, f). In the correlation analysis between YTHDF2 and HNRNPC expression and clinicopathological variables, we found that YTHDF2 expression was positively associated with tumor size (p = 0.021), nodal status (p = 0.007), functional status (p = 0.010), Ki67 index (p = 0.023), and liver metastasis (p < 0.001). There were no significant associations between YTHDF2 expression and the other clinicopathological variables, including age, sex, tumor site, lymphovascular invasion (LVI), and perineural invasion (PNI) (online suppl. Table S3).\nHigh YTHDF2 expression was associated with higher number of CD3+ T cells (p = 0.003). There were no significant associations between YTHDF2 expression and other immune markers (PD-L1, B7-H3, CD3, CD4, and CD68). However, high HNRNPC expression was significantly correlated with positive PD-L1 expression (p = 0.039) and high densities of CD3+ T cells. Moreover, there were no significant associations between HNRNPC expression and other immune markers (Table 2). Representative images of PD-L1, B7-H3 CD3, CD4, CD15, and CD68 are presented in online supplementary Figure S2.","A univariate Cox analysis for both the clinicopathological parameters and m6A proteins was conducted. Nine clinicopathological factors (tumor size, lymph node status, PNI, LVI, grade, functional status, liver metastasis, and Ki67 index) and five m6A proteins (METTL14, METTL16, ALKBH5, HNRNPC, YTHDF1, YTHDF2, and YTHDF3) were identified to be associated with RFS, with p values of <0.05 (Table 3). Considering analysis of collinear factors, tumor grade was found to be associated with Ki67 index and liver metastasis, and LVI was associated with lymph node status. Therefore, PNI, tumor size, lymph node status, grade, functional status, METTL14, METTL16, ALKBH5, HNRNPC, YTHDF1, YTHDF2, and YTHDF3 were included in the multivariate Cox regression model. Multivariate Cox regression analysis identified tumor size, lymph node status, tumor grade, PNI, HNRNPC expression, and YTHDF2 expression as independent risk factors for RFS after excluding collinear factors. Next, we applied an RSF model to evaluate the importance of these independent risk factors. In the analysis of variable of importance, we found that VIMP of HNRNPC was −0.0011 (Fig. 2a). In the minimal depth analysis, HNRNPC, PNI, and grade had values of 2.405, 2.482, and 2.183, respectively, which were very close to or more than the depth threshold (Fig. 2b). Therefore, they were all excluded from the RSF model. We also performed survival analyses to determine the prognostic value of YTHDF2 mRNA expression in patients with PanNET in ICGC cohort. The Kaplan-Meier plot revealed that high YTHDF2 mRNA expression was significantly associated with poor RFS (p = 0.017, Fig. 2c). Furthermore, three variables (tumor size, lymph node status, and YTHDF2 expression) significantly affected RFS. Nodal status had the largest effect on RFS, followed by YTHDF2 expression and tumor size.","To construct a recurrence signature that can classify patients into subpopulations according to RFS, we further performed a recursive partitioning analysis. After pruning the decision trees using the post-pruning method, five terminal nodes representing a recurrence signature were identified (Fig. 2d). LNposTumorSize<2.5 cm (node 1; 20.8 months) and LNposTumorSize≥2.5 cmYTHDF2high (node 3; 21.1 months) patients had worse median RFS than LNnegYTHDF2high patients (node 5; 39.6 months, node 1 vs. node 5, p < 0.001), LNnegYTHDF2low patients (node 4; 43.2 months), and LNposTumorSize≥2.5 cmYTHDF2low patients (node 2; 29.3 months). Patients with the LNnegYTHDF2high (node 5) signature had a 5-year RFS rate of 92.1%, whereas patients with the LNposTumorSize<2.5 cm (node 1) signature had the worst 5-year RFS rate of 0% (Fig. 3a). To better identify risk groups for the recurrence signatures, we performed pair-wise comparisons for each recurrence signature subpopulation (on RFS) and the corresponding risk score of recurrence. The results showed that patients within the five terminal nodes can be categorized into three risk groups: low (node 4), intermediate (node 2 and node 5), and high (node 1 and node 3) with well-separated RFS curves (p < 0.001; Fig. 3b). Among the risk groups, the 5-year RFS rates were 6.0%, 70.4%, and 90.1% for the high-, intermediate-, and low-risk groups, respectively. To further test our proposed recurrence signature, we performed a subanalysis on a specific group of patients with nonsyndromic and nonfunctional PanNENs. A total of 93 PanNENs from the full cohort were eligible for this analysis (online suppl. Table S4). The Kaplan-Meier RFS curves showed clear separation among the risk groups (p = 0.018; Fig. 3c).\nFurthermore, a risk score was generated to evaluate the risk of recurrence based on variables selected from the survival tree analysis: Risk Score = (1.218 × Nodal Status) + (1.625 × Tumor Size) + (0.745 × expression value of YTHDF2) (Table 4). Multivariate analysis using a Cox proportional hazards model was performed which included PNI, tumor size, nodal status, grade, functional status, METTL14, METTL16, ALKBH5, HNRNPC, YTHDF1, YTHDF2, YTHDF3, and the risk score. The risk score was determined to be an independent prognostic factor for patients with resected PanNEN in our study based on the multivariate Cox regression, and higher risk scores were found to be associated with shorter survival (hazard ratio: 33.04, 95% CI: 4.341–251.434; p = 0.001) (Table 5). Specificity and sensitivity comparisons were performed via time-dependent ROC curve analysis of risk score. The predictive accuracy of the recurrence signature was relatively high and the area under ROC curve was 0.858 (95% CI: 0.747–0.913) at 1 year, 0.824 (95% CI: 0.754–0.912) at 3 years, and 0.870 (95% CI: 0.762–0.915) at 5 years (Fig. 3d). The C-index was 0.978 (95% CI: 0.936–1), suggesting good discrimination ability. The calibration curves also showed good agreement between the predicted and observed RFS (Fig. 3e).","The study was approved by the Institutional Review Board of Peking Union Medical College Hospital (approval number S-K1593) and conducted in accordance with the Declaration of Helsinki. Written consent for this study was not required and formally waived by the hospital's Ethics Committee.","This work was supported by grants from the Chinese Academy of Medical Sciences Initiative for Innovative Medicine (CAMS-2016-I2M-1–001), the National Scientific Data Sharing Platform for Population and Health (NCMI–YF01N–201906), and the National Natural Science Foundation of China (Nos. 81672648).","Jie Chen made substantial contributions to the conception, design, and critical revision of the manuscript. Shengwei Mo, Liju Zong, Shuangni Yu, and Zhaohui Lu made substantial contributions to tissue microarray construction. Shengwei Mo made substantial contributions to data acquisition. Xianlong Chen and Shengwei Mo performed analysis of the data and drafting of the manuscript. All the authors read and approved the final manuscript."],"string":"[\n \"A total of 183 patients with PanNENs who were admitted to the Peking Union Medical College Hospital (PUMCH) and underwent surgeries during the time period 2004–2019 with sufficient tumor tissue for evaluation were included in the current study. The FFPE specimens and matching hematoxylin and eosin slides were retrieved. Patients' clinicopathological data, including age, sex, primary tumor site, tumor size, tumor grading, functional status, and surgical procedures, were collected from the medical records. The American Joint on Cancer Committee (AJCC) 8th edition stages for each patient were defined based on the collected data. Survival and recurrence information were obtained from medical record reviews and telephone interviews. Recurrence and distant metastasis were determined based on biochemical markers, clinical multidisciplinary consultation, radiological, and/or histological examinations. The time between the surgery and tumor recurrence or last follow-up appointment was termed as RFS. Disease-specific survival was calculated from the surgery date to the time of patient death or last follow-up till time point of December 29, 2020. This retrospective study was approved by the Institutional Review Board of PUMCH (approval number: S-K1593) and conducted in accordance with the Declaration of Helsinki. Written consent for this study was not required and formally waived by the hospital's Ethics Committee.\",\n \"Representative areas of tumors and adjacent normal tissue were selected from hematoxylin and eosin-stained paraffin blocks and then re-embedded into recipient tissue microarray (TMA) blocks (12 × 8 arrays). The diameter of each core was 2 mm.\\nImmunohistochemical analysis was performed according to the protocol described by Zong et al. [29]. The 4-μm TMA sections were deparaffinized. Heat-mediated antigen retrieval was performed using sodium citrate buffer (pH 6.0) for 10 min. The endogenous peroxidase activity was quenched using a 3% hydrogen peroxide solution (ZSGB-BIO, Beijing, China) and then the sections were blocked with 5% normal goat serum for 30 min. TMA sections were incubated with primary antibodies against m6A methylation regulators (METTL3, METTL14, METTL16, WTAP, KIAA1429, RBM15, FTO, ALKBH5, IGF2BP2, HNRNPC, YTHDC1, YTHDC2, YTHDF1, YTHDF2, and YTHDF3) and immune markers (PD-L1, B7-H3, CD3, CD4, CD15, CD68) overnight at 4°C; the details and optimal dilutions are summarized in online supplementary Table S1 (see www.karger.com/doi/10.1159/000525228 for all online suppl. material). Stromal cells, normal acinar cells, ductal cells, and islet cells from normal tissues were incubated with primary antibodies and used as internal positive controls, whereas the same tissues without primary antibodies comprised the negative controls.\",\n \"The immunostaining performed on the collected patient samples was independently assessed by two pathologists (XL Chen and SW Mo) who were blinded to the patients' clinicopathological features and clinical outcomes. Both pathologists reexamined the slides and reached a consensus in case of any discrepancy. The immunoreactivity of 15 m6A markers, PD-L1, and B7-H3 in tumor specimens was quantified using the method developed by Budwit-Novotny et al. [30]. In brief, the percentage of positively stained tumor cells was multiplied by the relative intensity of specific staining, which was assigned a value of negative (0), weak (1), distinct (2), and strong (3) [30, 31]. The density of stromal CD3, CD4, CD15, and CD68 were quantified in four ×400 high-power fields and the mean counts of four fields were used for statistical analysis as we described earlier [32]. The cutoff values of m6A markers and immune markers were determined using X-tile (Yale University, New Haven, CT, USA). These cutoff values are summarized in the online supplementary Table S2.\",\n \"A random survival forest (RSF) model was constructed based on the minimal depth and variable importance (VIMP). VIMP and minimal depth were used to assess the true effect of the variable on RFS. Given that feature subsets of variables are randomly selected using RSF method, RSF can process high-dimensional (many variables) data. In our current study, we included more than ten variables. Through training of the RSF algorithm, the variables which were the most related to recurrence were selected using minimal depth and VIMP. And in the training process, the interaction between variables can be detected. Moreover, when creating random forest, unbiased estimation is used for generalization error, and the model has strong generalization ability [33]. If a large part of the features is lost, the accuracy can still be maintained [33]. Based on these strengths of RSF, this method was helpful to select relapse-related variables and construct a recurrence signature. Recurrence-specific variables defined by a VIMP of >0 and minimal depth less than the depth threshold were finally entered into a survival tree analysis [33]. The recurrence-specific variables, including nodal status, tumor size, YTHDF2, were selected by using minimal depth and VIMP. By integrating the expression level of YTHDF2, nodal status, and tumor size as well as their corresponding coefficients which were derived from the multivariate Cox model constructed only using these 3 variables, a risk score was generated, as represented below: Risk Score = (1.218 × Nodal Status) + (1.625 × Tumor Size) + (0.745 × expression value of YTHDF2).\\nNodal status was divided into negative/positive and scored as 0/1; tumor size was divided into <2.5 cm/≥2.5 cm and scored as 0/1, and expression value of YTHDF2 was divided into low/high and scored as 0/1, and these scores were multiplied by the corresponding coefficients to generate a risk score. The median (median = 1.625) was used as the cutoff value for the risk score.\",\n \"The continuous and categorical variables were described as median (range) and frequency (percentage), respectively. The Mann-Whitney U test was conducted to compare nonnormally distributed continuous variables, whereas a Student's t test was used to analyze normally distributed continuous variables. The χ2 test or Fisher's exact test was used to evaluate the relationship between the expression of m6A methylation regulators and categorical variables. The survival curves were plotted by Kaplan-Meier method, and the log-rank test was employed to compare the survival curves generated. The Cox proportional hazard regression model was used to estimate the hazard ratio with a 95% confidence interval (CI) for variables associated with RFS. Potential risk factors with a p value of <0.05 in the univariate Cox analysis were entered into the multivariate Cox regression model (backward Wald) after considering collinearity among the variables. We evaluated the discrimination ability of the final model with Harrell concordance index (C-index). The area under the time-dependent receiver operating characteristic (ROC) curve at different cutoff times was measured as predictive performance. Calibration was assessed by visual examination of the calibration plot. p values of <0.05 were considered statistically significant. Statistical analyses were two-sided and accomplished using SPSS software (version 21.0; IBM Corp., Armonk, NY, USA). Statistical analyses of RNA sequencing data from International Cancer Genome Consortium (ICGC) database (https://www.icgc.org/) were conducted with R version 3.5.0 (http://www.r-project.org).\",\n \"A total of 96 patients were male (52.4%), whereas 87 patients were female (47.6%). The tumor diameter was >2.5 cm in 53.1% of the patients (97/183). The number of positive lymph nodes ranged from 1 to 29. A total of 50.7% tumors were nonfunctional, and the majority of functional tumors were classified as insulinoma (76/90). Most of the tumors (175/183) were well differentiated, and eight were poorly differentiated (Table 1). The median follow-up time was 39 months (range: 1–197 months), during which there were 43 relapses and 10 deaths, and all of the deaths were owing to disease progression. The 3- and 5-year RFS rates were 83.6% and 77.0%, respectively.\",\n \"We found that the expression levels of m6A proteins in PanNENs and normal tissues were evident (Fig. 1a). The expression levels of writers (i.e., KIAA1429, METTL14, RBM15, WTAP, and METTL16), readers (i.e., IGF2BP2, HNRNPC, YTHDC1, YTHDC2, YTHDF1, YTHDF2, and YTHDF3), and erasers (ALKBH5 and FTO) were higher in PanNEN tissues than in normal tissues (p < 0.001). No statistically significant difference was evident between the normal and PanNENs tissues in the context of METTL3 expression level. Regarding METTL3, METTL14, METTL16, WTAP, RBM15, and KIAA1429 expression, a high level was observed in 26.7%, 11.5%, 35.5%, 64.4%, 67.7%, and 55.7% of tumors, respectively. Among the 183 tumors, 36.6% were deemed to have high FTO expression and 72.1% had high ALKBH5 expression. Regarding reader molecules (i.e., IGF2BP2, HNRNPC, YTHDC1, YTHDC2, YTHDF1, YTHDF2, and YTHDF3), immunostaining revealed low expression in 64.5%, 95.1%, 72.1%, 57.9%, 84.7%, 50.3%, and 96.2%, respectively, of these samples and high expression in the remaining samples (Fig. 1b). The immunohistochemical patterns of the 15 m6A proteins are shown in Figure 1c and online supplementary Figure S1. All 183 PanNENs showed nuclear immunoreactivity for METTL3, METTL14, METTL16, WTAP, RBM15, KIAA1429, FTO, ALKBH5, HNRNPC, and YTHDC1. The positive sites of IGF2BP2, YTHDC2, YTHDF1, YTHDF2, and YTHDF3 were located in the cytoplasm.\",\n \"Among the 15 proteins studied, only YTHDF2 and HNRNPC expressions had significant prognostic value. Of the 43 patients with recurrence, 12 (27.9%) showed low YTHDF2 expression, which was dramatically lower than the proportion of patients without recurrence having low YTHDF2 expression (85 of 140, 60.7%; p = 0.003; Fig. 1d). Similarly, 88.4% of the patients (38 of 43 patients) with recurrence showed low tumoral HNRNPC expression; in contrast, 97.1% of the patients without recurrence (136 of 140 patients) showed low tumoral expression of HNRNPC (p = 0.034; Fig. 1d). The log-rank test revealed that both high HNRNPC and YTHDF2 expressions predicted a significantly shortened RFS (p = 0.003 and p = 0.001, respectively; Fig. 1e, f). In the correlation analysis between YTHDF2 and HNRNPC expression and clinicopathological variables, we found that YTHDF2 expression was positively associated with tumor size (p = 0.021), nodal status (p = 0.007), functional status (p = 0.010), Ki67 index (p = 0.023), and liver metastasis (p < 0.001). There were no significant associations between YTHDF2 expression and the other clinicopathological variables, including age, sex, tumor site, lymphovascular invasion (LVI), and perineural invasion (PNI) (online suppl. Table S3).\\nHigh YTHDF2 expression was associated with higher number of CD3+ T cells (p = 0.003). There were no significant associations between YTHDF2 expression and other immune markers (PD-L1, B7-H3, CD3, CD4, and CD68). However, high HNRNPC expression was significantly correlated with positive PD-L1 expression (p = 0.039) and high densities of CD3+ T cells. Moreover, there were no significant associations between HNRNPC expression and other immune markers (Table 2). Representative images of PD-L1, B7-H3 CD3, CD4, CD15, and CD68 are presented in online supplementary Figure S2.\",\n \"A univariate Cox analysis for both the clinicopathological parameters and m6A proteins was conducted. Nine clinicopathological factors (tumor size, lymph node status, PNI, LVI, grade, functional status, liver metastasis, and Ki67 index) and five m6A proteins (METTL14, METTL16, ALKBH5, HNRNPC, YTHDF1, YTHDF2, and YTHDF3) were identified to be associated with RFS, with p values of <0.05 (Table 3). Considering analysis of collinear factors, tumor grade was found to be associated with Ki67 index and liver metastasis, and LVI was associated with lymph node status. Therefore, PNI, tumor size, lymph node status, grade, functional status, METTL14, METTL16, ALKBH5, HNRNPC, YTHDF1, YTHDF2, and YTHDF3 were included in the multivariate Cox regression model. Multivariate Cox regression analysis identified tumor size, lymph node status, tumor grade, PNI, HNRNPC expression, and YTHDF2 expression as independent risk factors for RFS after excluding collinear factors. Next, we applied an RSF model to evaluate the importance of these independent risk factors. In the analysis of variable of importance, we found that VIMP of HNRNPC was −0.0011 (Fig. 2a). In the minimal depth analysis, HNRNPC, PNI, and grade had values of 2.405, 2.482, and 2.183, respectively, which were very close to or more than the depth threshold (Fig. 2b). Therefore, they were all excluded from the RSF model. We also performed survival analyses to determine the prognostic value of YTHDF2 mRNA expression in patients with PanNET in ICGC cohort. The Kaplan-Meier plot revealed that high YTHDF2 mRNA expression was significantly associated with poor RFS (p = 0.017, Fig. 2c). Furthermore, three variables (tumor size, lymph node status, and YTHDF2 expression) significantly affected RFS. Nodal status had the largest effect on RFS, followed by YTHDF2 expression and tumor size.\",\n \"To construct a recurrence signature that can classify patients into subpopulations according to RFS, we further performed a recursive partitioning analysis. After pruning the decision trees using the post-pruning method, five terminal nodes representing a recurrence signature were identified (Fig. 2d). LNposTumorSize<2.5 cm (node 1; 20.8 months) and LNposTumorSize≥2.5 cmYTHDF2high (node 3; 21.1 months) patients had worse median RFS than LNnegYTHDF2high patients (node 5; 39.6 months, node 1 vs. node 5, p < 0.001), LNnegYTHDF2low patients (node 4; 43.2 months), and LNposTumorSize≥2.5 cmYTHDF2low patients (node 2; 29.3 months). Patients with the LNnegYTHDF2high (node 5) signature had a 5-year RFS rate of 92.1%, whereas patients with the LNposTumorSize<2.5 cm (node 1) signature had the worst 5-year RFS rate of 0% (Fig. 3a). To better identify risk groups for the recurrence signatures, we performed pair-wise comparisons for each recurrence signature subpopulation (on RFS) and the corresponding risk score of recurrence. The results showed that patients within the five terminal nodes can be categorized into three risk groups: low (node 4), intermediate (node 2 and node 5), and high (node 1 and node 3) with well-separated RFS curves (p < 0.001; Fig. 3b). Among the risk groups, the 5-year RFS rates were 6.0%, 70.4%, and 90.1% for the high-, intermediate-, and low-risk groups, respectively. To further test our proposed recurrence signature, we performed a subanalysis on a specific group of patients with nonsyndromic and nonfunctional PanNENs. A total of 93 PanNENs from the full cohort were eligible for this analysis (online suppl. Table S4). The Kaplan-Meier RFS curves showed clear separation among the risk groups (p = 0.018; Fig. 3c).\\nFurthermore, a risk score was generated to evaluate the risk of recurrence based on variables selected from the survival tree analysis: Risk Score = (1.218 × Nodal Status) + (1.625 × Tumor Size) + (0.745 × expression value of YTHDF2) (Table 4). Multivariate analysis using a Cox proportional hazards model was performed which included PNI, tumor size, nodal status, grade, functional status, METTL14, METTL16, ALKBH5, HNRNPC, YTHDF1, YTHDF2, YTHDF3, and the risk score. The risk score was determined to be an independent prognostic factor for patients with resected PanNEN in our study based on the multivariate Cox regression, and higher risk scores were found to be associated with shorter survival (hazard ratio: 33.04, 95% CI: 4.341–251.434; p = 0.001) (Table 5). Specificity and sensitivity comparisons were performed via time-dependent ROC curve analysis of risk score. The predictive accuracy of the recurrence signature was relatively high and the area under ROC curve was 0.858 (95% CI: 0.747–0.913) at 1 year, 0.824 (95% CI: 0.754–0.912) at 3 years, and 0.870 (95% CI: 0.762–0.915) at 5 years (Fig. 3d). The C-index was 0.978 (95% CI: 0.936–1), suggesting good discrimination ability. The calibration curves also showed good agreement between the predicted and observed RFS (Fig. 3e).\",\n \"The study was approved by the Institutional Review Board of Peking Union Medical College Hospital (approval number S-K1593) and conducted in accordance with the Declaration of Helsinki. Written consent for this study was not required and formally waived by the hospital's Ethics Committee.\",\n \"This work was supported by grants from the Chinese Academy of Medical Sciences Initiative for Innovative Medicine (CAMS-2016-I2M-1–001), the National Scientific Data Sharing Platform for Population and Health (NCMI–YF01N–201906), and the National Natural Science Foundation of China (Nos. 81672648).\",\n \"Jie Chen made substantial contributions to the conception, design, and critical revision of the manuscript. Shengwei Mo, Liju Zong, Shuangni Yu, and Zhaohui Lu made substantial contributions to tissue microarray construction. Shengwei Mo made substantial contributions to data acquisition. Xianlong Chen and Shengwei Mo performed analysis of the data and drafting of the manuscript. All the authors read and approved the final manuscript.\"\n]"},"other_sections_sec_types":{"kind":"list like","value":[null,null,null,null,null,null,null,null,null,null,null,null,null],"string":"[\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null\n]"},"all_sections_titles":{"kind":"list like","value":["Introduction","Materials and Methods","Patient Cohort and Follow-Up","Tissue Microarrays and Immunohistochemistry","Evaluation of Immunostaining","Risk Score Models and Random Survival Forest","Statistical Analysis","Results","Patients' Characteristics","Expression Patterns of the 15 m6A Regulators","Associations among m6A Regulators, Immune Markers, and Clinicopathological Parameters in PanNENs","Prognostic Predictors of RFS in PanNENs","Construction and Evaluation of the Prognostic Model for Predicting RFS in PanNENs","Discussion/Conclusion","Statement of Ethics","Conflict of Interest Statement","Funding Sources","Author Contributions","Data Availability Statement","Supplementary Material"],"string":"[\n \"Introduction\",\n \"Materials and Methods\",\n \"Patient Cohort and Follow-Up\",\n \"Tissue Microarrays and Immunohistochemistry\",\n \"Evaluation of Immunostaining\",\n \"Risk Score Models and Random Survival Forest\",\n \"Statistical Analysis\",\n \"Results\",\n \"Patients' Characteristics\",\n \"Expression Patterns of the 15 m6A Regulators\",\n \"Associations among m6A Regulators, Immune Markers, and Clinicopathological Parameters in PanNENs\",\n \"Prognostic Predictors of RFS in PanNENs\",\n \"Construction and Evaluation of the Prognostic Model for Predicting RFS in PanNENs\",\n \"Discussion/Conclusion\",\n \"Statement of Ethics\",\n \"Conflict of Interest Statement\",\n \"Funding Sources\",\n \"Author Contributions\",\n \"Data Availability Statement\",\n \"Supplementary Material\"\n]"},"all_sections_texts":{"kind":"list like","value":["Pancreatic neuroendocrine neoplasms (PanNENs) are a rare, heterogeneous group of neoplasms originating from the neuroendocrine cells and classified as functional and nonfunctional depending on whether the tumor overproduces biologically active hormones [1]. However, the incidence of PanNENs has significantly increased in the last decades and has risen to 0.8 per 100,000 individuals per year [2]. This increase may be attributed, at least in part, to the improved and more frequent use of imaging in clinical diagnostic practices [3]. Moreover, both patients with nonfunctional or functional tumors are always diagnosed at a late stage and usually present locally advanced or metastatic disease [4, 5].\nCurrent clinical strategies for most patients with PanNENs primarily involve surgical resection [6]. One of the most significant causes of concern in patients with a PanNEN is cancer recurrence after curative resection. However, clinical outcomes after surgical resection vary widely, with recurrence-free survival (RFS) ranging from 0.96 to 121.9 months [7]. The most frequently used cancer staging system is based on the WHO classification which categorizes tumors into four groups based on proliferative activity and morphology: NET G1, NET G2, NET G3, and NEC [8]. However, patients with PanNEN with the same grade of tumor may demonstrate different clinical courses, whereas patients with low-grade PanNENs show unpredictable disease progression and outcomes [9]. Therefore, a clinically feasible and accurate prediction of the risk of relapse in patients with PanNENs after resection is needed.\nN6-methyladenosine (m6A) is the most common epigenetic modification of the mRNA, comprising >60% of all RNA-based modifications [10]. The m6A methylation levels are greatly associated with the immune response, stress response regulation, tumorigenesis, and miRNA processing [11, 12, 13, 14, 15, 16]. The m6A methylation plays a crucial role in disease progression of various cancers [11, 12, 16] as well as in the onset and progression of fetal growth restriction and preeclampsia [17, 18]. The modification of m6A is regulated by methyltransferases (writers) and demethylases (erasers), and m6A performs multiple functions through its interactions with specific binding proteins known as the readers [19]. The writers mainly include KIAA1429, METTL3, WTAP, METTL14, RBM15, and METTL16, which promote m6A RNA methylation. The erasers encompass fat mass and obesity-associated protein (FTO) and alkylation repair homolog protein 5 (ALKBH5). The readers comprise HNRNPC, IGF2BP2, YTHDC1, YTHDC2, YTHDF1, YTHDF2, and YTHDF3, which recognize RNA m6A binding site [19, 20, 21]. Increasing amount of evidence has emerged which shows the vital role of m6A regulators in tumorigenesis and prognosis of various cancers. Chen et al. [22] reported that METTL14 inhibited colorectal cancer (CRC) progression through primary miR-375 processing-based regulation. Cives et al. [23] revealed that METTL3 could promote the proliferation and migration of hepatocellular carcinoma. A previous study found that high YTHDF1 expression predicted a poor clinical outcome in CRC patients [24]. Additionally, the low expression of FTO was associated with a shorter RFS in patients with intrahepatic cholangiocarcinoma [25]. Overexpression of FTO suppressed acute myeloid leukemia cell differentiation [26]. Growing evidence suggests that m6A modification and its regulators play vital roles in human cancers [11, 12, 22, 23, 24, 25, 26]. m6A modification is associated with tumorigenesis, proliferation, invasion, and metastasis [22, 23, 24]. And m6A regulators were indicators of prognosis and therapeutic targets [25, 26]. Moreover, epigenetic modification and especially methylation have been already described as important events during tumorigenesis of PanNEN, and methylation modification is an epigenetic regulatory mechanism for gene expression in PanNENs, including MEN1, which involved in the chromatin remodeling in PanNENs [27]. However, whether m6A regulators could also be specific biomarkers in PanNENs is yet to be determined. And the expression status, functional roles, and clinical significance of m6A methylation regulators also remain largely unknown in PanNENs. In addition, recent studies have shown prognosis of immune infiltrates and immune checkpoints in pancreatic neuroendocrine tumors [23, 28]. However, the correlation between m6A regulators and immune infiltration and immune checkpoints is yet to be explored.\nTo better identify patients at risk and improve their follow-up management, we explored the expression profiles of m6A proteins in resected PanNENs and investigated the potential prognostic value of m6A proteins. We also analyzed the association among common immune infiltrates, immune checkpoints, and m6A regulators. Furthermore, a YTHDF2-based signature was established, which may help in postoperative monitoring and stratification of risks to guide clinical treatment options.","Patient Cohort and Follow-Up A total of 183 patients with PanNENs who were admitted to the Peking Union Medical College Hospital (PUMCH) and underwent surgeries during the time period 2004–2019 with sufficient tumor tissue for evaluation were included in the current study. The FFPE specimens and matching hematoxylin and eosin slides were retrieved. Patients' clinicopathological data, including age, sex, primary tumor site, tumor size, tumor grading, functional status, and surgical procedures, were collected from the medical records. The American Joint on Cancer Committee (AJCC) 8th edition stages for each patient were defined based on the collected data. Survival and recurrence information were obtained from medical record reviews and telephone interviews. Recurrence and distant metastasis were determined based on biochemical markers, clinical multidisciplinary consultation, radiological, and/or histological examinations. The time between the surgery and tumor recurrence or last follow-up appointment was termed as RFS. Disease-specific survival was calculated from the surgery date to the time of patient death or last follow-up till time point of December 29, 2020. This retrospective study was approved by the Institutional Review Board of PUMCH (approval number: S-K1593) and conducted in accordance with the Declaration of Helsinki. Written consent for this study was not required and formally waived by the hospital's Ethics Committee.\nA total of 183 patients with PanNENs who were admitted to the Peking Union Medical College Hospital (PUMCH) and underwent surgeries during the time period 2004–2019 with sufficient tumor tissue for evaluation were included in the current study. The FFPE specimens and matching hematoxylin and eosin slides were retrieved. Patients' clinicopathological data, including age, sex, primary tumor site, tumor size, tumor grading, functional status, and surgical procedures, were collected from the medical records. The American Joint on Cancer Committee (AJCC) 8th edition stages for each patient were defined based on the collected data. Survival and recurrence information were obtained from medical record reviews and telephone interviews. Recurrence and distant metastasis were determined based on biochemical markers, clinical multidisciplinary consultation, radiological, and/or histological examinations. The time between the surgery and tumor recurrence or last follow-up appointment was termed as RFS. Disease-specific survival was calculated from the surgery date to the time of patient death or last follow-up till time point of December 29, 2020. This retrospective study was approved by the Institutional Review Board of PUMCH (approval number: S-K1593) and conducted in accordance with the Declaration of Helsinki. Written consent for this study was not required and formally waived by the hospital's Ethics Committee.\nTissue Microarrays and Immunohistochemistry Representative areas of tumors and adjacent normal tissue were selected from hematoxylin and eosin-stained paraffin blocks and then re-embedded into recipient tissue microarray (TMA) blocks (12 × 8 arrays). The diameter of each core was 2 mm.\nImmunohistochemical analysis was performed according to the protocol described by Zong et al. [29]. The 4-μm TMA sections were deparaffinized. Heat-mediated antigen retrieval was performed using sodium citrate buffer (pH 6.0) for 10 min. The endogenous peroxidase activity was quenched using a 3% hydrogen peroxide solution (ZSGB-BIO, Beijing, China) and then the sections were blocked with 5% normal goat serum for 30 min. TMA sections were incubated with primary antibodies against m6A methylation regulators (METTL3, METTL14, METTL16, WTAP, KIAA1429, RBM15, FTO, ALKBH5, IGF2BP2, HNRNPC, YTHDC1, YTHDC2, YTHDF1, YTHDF2, and YTHDF3) and immune markers (PD-L1, B7-H3, CD3, CD4, CD15, CD68) overnight at 4°C; the details and optimal dilutions are summarized in online supplementary Table S1 (see www.karger.com/doi/10.1159/000525228 for all online suppl. material). Stromal cells, normal acinar cells, ductal cells, and islet cells from normal tissues were incubated with primary antibodies and used as internal positive controls, whereas the same tissues without primary antibodies comprised the negative controls.\nRepresentative areas of tumors and adjacent normal tissue were selected from hematoxylin and eosin-stained paraffin blocks and then re-embedded into recipient tissue microarray (TMA) blocks (12 × 8 arrays). The diameter of each core was 2 mm.\nImmunohistochemical analysis was performed according to the protocol described by Zong et al. [29]. The 4-μm TMA sections were deparaffinized. Heat-mediated antigen retrieval was performed using sodium citrate buffer (pH 6.0) for 10 min. The endogenous peroxidase activity was quenched using a 3% hydrogen peroxide solution (ZSGB-BIO, Beijing, China) and then the sections were blocked with 5% normal goat serum for 30 min. TMA sections were incubated with primary antibodies against m6A methylation regulators (METTL3, METTL14, METTL16, WTAP, KIAA1429, RBM15, FTO, ALKBH5, IGF2BP2, HNRNPC, YTHDC1, YTHDC2, YTHDF1, YTHDF2, and YTHDF3) and immune markers (PD-L1, B7-H3, CD3, CD4, CD15, CD68) overnight at 4°C; the details and optimal dilutions are summarized in online supplementary Table S1 (see www.karger.com/doi/10.1159/000525228 for all online suppl. material). Stromal cells, normal acinar cells, ductal cells, and islet cells from normal tissues were incubated with primary antibodies and used as internal positive controls, whereas the same tissues without primary antibodies comprised the negative controls.\nEvaluation of Immunostaining The immunostaining performed on the collected patient samples was independently assessed by two pathologists (XL Chen and SW Mo) who were blinded to the patients' clinicopathological features and clinical outcomes. Both pathologists reexamined the slides and reached a consensus in case of any discrepancy. The immunoreactivity of 15 m6A markers, PD-L1, and B7-H3 in tumor specimens was quantified using the method developed by Budwit-Novotny et al. [30]. In brief, the percentage of positively stained tumor cells was multiplied by the relative intensity of specific staining, which was assigned a value of negative (0), weak (1), distinct (2), and strong (3) [30, 31]. The density of stromal CD3, CD4, CD15, and CD68 were quantified in four ×400 high-power fields and the mean counts of four fields were used for statistical analysis as we described earlier [32]. The cutoff values of m6A markers and immune markers were determined using X-tile (Yale University, New Haven, CT, USA). These cutoff values are summarized in the online supplementary Table S2.\nThe immunostaining performed on the collected patient samples was independently assessed by two pathologists (XL Chen and SW Mo) who were blinded to the patients' clinicopathological features and clinical outcomes. Both pathologists reexamined the slides and reached a consensus in case of any discrepancy. The immunoreactivity of 15 m6A markers, PD-L1, and B7-H3 in tumor specimens was quantified using the method developed by Budwit-Novotny et al. [30]. In brief, the percentage of positively stained tumor cells was multiplied by the relative intensity of specific staining, which was assigned a value of negative (0), weak (1), distinct (2), and strong (3) [30, 31]. The density of stromal CD3, CD4, CD15, and CD68 were quantified in four ×400 high-power fields and the mean counts of four fields were used for statistical analysis as we described earlier [32]. The cutoff values of m6A markers and immune markers were determined using X-tile (Yale University, New Haven, CT, USA). These cutoff values are summarized in the online supplementary Table S2.\nRisk Score Models and Random Survival Forest A random survival forest (RSF) model was constructed based on the minimal depth and variable importance (VIMP). VIMP and minimal depth were used to assess the true effect of the variable on RFS. Given that feature subsets of variables are randomly selected using RSF method, RSF can process high-dimensional (many variables) data. In our current study, we included more than ten variables. Through training of the RSF algorithm, the variables which were the most related to recurrence were selected using minimal depth and VIMP. And in the training process, the interaction between variables can be detected. Moreover, when creating random forest, unbiased estimation is used for generalization error, and the model has strong generalization ability [33]. If a large part of the features is lost, the accuracy can still be maintained [33]. Based on these strengths of RSF, this method was helpful to select relapse-related variables and construct a recurrence signature. Recurrence-specific variables defined by a VIMP of >0 and minimal depth less than the depth threshold were finally entered into a survival tree analysis [33]. The recurrence-specific variables, including nodal status, tumor size, YTHDF2, were selected by using minimal depth and VIMP. By integrating the expression level of YTHDF2, nodal status, and tumor size as well as their corresponding coefficients which were derived from the multivariate Cox model constructed only using these 3 variables, a risk score was generated, as represented below: Risk Score = (1.218 × Nodal Status) + (1.625 × Tumor Size) + (0.745 × expression value of YTHDF2).\nNodal status was divided into negative/positive and scored as 0/1; tumor size was divided into <2.5 cm/≥2.5 cm and scored as 0/1, and expression value of YTHDF2 was divided into low/high and scored as 0/1, and these scores were multiplied by the corresponding coefficients to generate a risk score. The median (median = 1.625) was used as the cutoff value for the risk score.\nA random survival forest (RSF) model was constructed based on the minimal depth and variable importance (VIMP). VIMP and minimal depth were used to assess the true effect of the variable on RFS. Given that feature subsets of variables are randomly selected using RSF method, RSF can process high-dimensional (many variables) data. In our current study, we included more than ten variables. Through training of the RSF algorithm, the variables which were the most related to recurrence were selected using minimal depth and VIMP. And in the training process, the interaction between variables can be detected. Moreover, when creating random forest, unbiased estimation is used for generalization error, and the model has strong generalization ability [33]. If a large part of the features is lost, the accuracy can still be maintained [33]. Based on these strengths of RSF, this method was helpful to select relapse-related variables and construct a recurrence signature. Recurrence-specific variables defined by a VIMP of >0 and minimal depth less than the depth threshold were finally entered into a survival tree analysis [33]. The recurrence-specific variables, including nodal status, tumor size, YTHDF2, were selected by using minimal depth and VIMP. By integrating the expression level of YTHDF2, nodal status, and tumor size as well as their corresponding coefficients which were derived from the multivariate Cox model constructed only using these 3 variables, a risk score was generated, as represented below: Risk Score = (1.218 × Nodal Status) + (1.625 × Tumor Size) + (0.745 × expression value of YTHDF2).\nNodal status was divided into negative/positive and scored as 0/1; tumor size was divided into <2.5 cm/≥2.5 cm and scored as 0/1, and expression value of YTHDF2 was divided into low/high and scored as 0/1, and these scores were multiplied by the corresponding coefficients to generate a risk score. The median (median = 1.625) was used as the cutoff value for the risk score.\nStatistical Analysis The continuous and categorical variables were described as median (range) and frequency (percentage), respectively. The Mann-Whitney U test was conducted to compare nonnormally distributed continuous variables, whereas a Student's t test was used to analyze normally distributed continuous variables. The χ2 test or Fisher's exact test was used to evaluate the relationship between the expression of m6A methylation regulators and categorical variables. The survival curves were plotted by Kaplan-Meier method, and the log-rank test was employed to compare the survival curves generated. The Cox proportional hazard regression model was used to estimate the hazard ratio with a 95% confidence interval (CI) for variables associated with RFS. Potential risk factors with a p value of <0.05 in the univariate Cox analysis were entered into the multivariate Cox regression model (backward Wald) after considering collinearity among the variables. We evaluated the discrimination ability of the final model with Harrell concordance index (C-index). The area under the time-dependent receiver operating characteristic (ROC) curve at different cutoff times was measured as predictive performance. Calibration was assessed by visual examination of the calibration plot. p values of <0.05 were considered statistically significant. Statistical analyses were two-sided and accomplished using SPSS software (version 21.0; IBM Corp., Armonk, NY, USA). Statistical analyses of RNA sequencing data from International Cancer Genome Consortium (ICGC) database (https://www.icgc.org/) were conducted with R version 3.5.0 (http://www.r-project.org).\nThe continuous and categorical variables were described as median (range) and frequency (percentage), respectively. The Mann-Whitney U test was conducted to compare nonnormally distributed continuous variables, whereas a Student's t test was used to analyze normally distributed continuous variables. The χ2 test or Fisher's exact test was used to evaluate the relationship between the expression of m6A methylation regulators and categorical variables. The survival curves were plotted by Kaplan-Meier method, and the log-rank test was employed to compare the survival curves generated. The Cox proportional hazard regression model was used to estimate the hazard ratio with a 95% confidence interval (CI) for variables associated with RFS. Potential risk factors with a p value of <0.05 in the univariate Cox analysis were entered into the multivariate Cox regression model (backward Wald) after considering collinearity among the variables. We evaluated the discrimination ability of the final model with Harrell concordance index (C-index). The area under the time-dependent receiver operating characteristic (ROC) curve at different cutoff times was measured as predictive performance. Calibration was assessed by visual examination of the calibration plot. p values of <0.05 were considered statistically significant. Statistical analyses were two-sided and accomplished using SPSS software (version 21.0; IBM Corp., Armonk, NY, USA). Statistical analyses of RNA sequencing data from International Cancer Genome Consortium (ICGC) database (https://www.icgc.org/) were conducted with R version 3.5.0 (http://www.r-project.org).","A total of 183 patients with PanNENs who were admitted to the Peking Union Medical College Hospital (PUMCH) and underwent surgeries during the time period 2004–2019 with sufficient tumor tissue for evaluation were included in the current study. The FFPE specimens and matching hematoxylin and eosin slides were retrieved. Patients' clinicopathological data, including age, sex, primary tumor site, tumor size, tumor grading, functional status, and surgical procedures, were collected from the medical records. The American Joint on Cancer Committee (AJCC) 8th edition stages for each patient were defined based on the collected data. Survival and recurrence information were obtained from medical record reviews and telephone interviews. Recurrence and distant metastasis were determined based on biochemical markers, clinical multidisciplinary consultation, radiological, and/or histological examinations. The time between the surgery and tumor recurrence or last follow-up appointment was termed as RFS. Disease-specific survival was calculated from the surgery date to the time of patient death or last follow-up till time point of December 29, 2020. This retrospective study was approved by the Institutional Review Board of PUMCH (approval number: S-K1593) and conducted in accordance with the Declaration of Helsinki. Written consent for this study was not required and formally waived by the hospital's Ethics Committee.","Representative areas of tumors and adjacent normal tissue were selected from hematoxylin and eosin-stained paraffin blocks and then re-embedded into recipient tissue microarray (TMA) blocks (12 × 8 arrays). The diameter of each core was 2 mm.\nImmunohistochemical analysis was performed according to the protocol described by Zong et al. [29]. The 4-μm TMA sections were deparaffinized. Heat-mediated antigen retrieval was performed using sodium citrate buffer (pH 6.0) for 10 min. The endogenous peroxidase activity was quenched using a 3% hydrogen peroxide solution (ZSGB-BIO, Beijing, China) and then the sections were blocked with 5% normal goat serum for 30 min. TMA sections were incubated with primary antibodies against m6A methylation regulators (METTL3, METTL14, METTL16, WTAP, KIAA1429, RBM15, FTO, ALKBH5, IGF2BP2, HNRNPC, YTHDC1, YTHDC2, YTHDF1, YTHDF2, and YTHDF3) and immune markers (PD-L1, B7-H3, CD3, CD4, CD15, CD68) overnight at 4°C; the details and optimal dilutions are summarized in online supplementary Table S1 (see www.karger.com/doi/10.1159/000525228 for all online suppl. material). Stromal cells, normal acinar cells, ductal cells, and islet cells from normal tissues were incubated with primary antibodies and used as internal positive controls, whereas the same tissues without primary antibodies comprised the negative controls.","The immunostaining performed on the collected patient samples was independently assessed by two pathologists (XL Chen and SW Mo) who were blinded to the patients' clinicopathological features and clinical outcomes. Both pathologists reexamined the slides and reached a consensus in case of any discrepancy. The immunoreactivity of 15 m6A markers, PD-L1, and B7-H3 in tumor specimens was quantified using the method developed by Budwit-Novotny et al. [30]. In brief, the percentage of positively stained tumor cells was multiplied by the relative intensity of specific staining, which was assigned a value of negative (0), weak (1), distinct (2), and strong (3) [30, 31]. The density of stromal CD3, CD4, CD15, and CD68 were quantified in four ×400 high-power fields and the mean counts of four fields were used for statistical analysis as we described earlier [32]. The cutoff values of m6A markers and immune markers were determined using X-tile (Yale University, New Haven, CT, USA). These cutoff values are summarized in the online supplementary Table S2.","A random survival forest (RSF) model was constructed based on the minimal depth and variable importance (VIMP). VIMP and minimal depth were used to assess the true effect of the variable on RFS. Given that feature subsets of variables are randomly selected using RSF method, RSF can process high-dimensional (many variables) data. In our current study, we included more than ten variables. Through training of the RSF algorithm, the variables which were the most related to recurrence were selected using minimal depth and VIMP. And in the training process, the interaction between variables can be detected. Moreover, when creating random forest, unbiased estimation is used for generalization error, and the model has strong generalization ability [33]. If a large part of the features is lost, the accuracy can still be maintained [33]. Based on these strengths of RSF, this method was helpful to select relapse-related variables and construct a recurrence signature. Recurrence-specific variables defined by a VIMP of >0 and minimal depth less than the depth threshold were finally entered into a survival tree analysis [33]. The recurrence-specific variables, including nodal status, tumor size, YTHDF2, were selected by using minimal depth and VIMP. By integrating the expression level of YTHDF2, nodal status, and tumor size as well as their corresponding coefficients which were derived from the multivariate Cox model constructed only using these 3 variables, a risk score was generated, as represented below: Risk Score = (1.218 × Nodal Status) + (1.625 × Tumor Size) + (0.745 × expression value of YTHDF2).\nNodal status was divided into negative/positive and scored as 0/1; tumor size was divided into <2.5 cm/≥2.5 cm and scored as 0/1, and expression value of YTHDF2 was divided into low/high and scored as 0/1, and these scores were multiplied by the corresponding coefficients to generate a risk score. The median (median = 1.625) was used as the cutoff value for the risk score.","The continuous and categorical variables were described as median (range) and frequency (percentage), respectively. The Mann-Whitney U test was conducted to compare nonnormally distributed continuous variables, whereas a Student's t test was used to analyze normally distributed continuous variables. The χ2 test or Fisher's exact test was used to evaluate the relationship between the expression of m6A methylation regulators and categorical variables. The survival curves were plotted by Kaplan-Meier method, and the log-rank test was employed to compare the survival curves generated. The Cox proportional hazard regression model was used to estimate the hazard ratio with a 95% confidence interval (CI) for variables associated with RFS. Potential risk factors with a p value of <0.05 in the univariate Cox analysis were entered into the multivariate Cox regression model (backward Wald) after considering collinearity among the variables. We evaluated the discrimination ability of the final model with Harrell concordance index (C-index). The area under the time-dependent receiver operating characteristic (ROC) curve at different cutoff times was measured as predictive performance. Calibration was assessed by visual examination of the calibration plot. p values of <0.05 were considered statistically significant. Statistical analyses were two-sided and accomplished using SPSS software (version 21.0; IBM Corp., Armonk, NY, USA). Statistical analyses of RNA sequencing data from International Cancer Genome Consortium (ICGC) database (https://www.icgc.org/) were conducted with R version 3.5.0 (http://www.r-project.org).","Patients' Characteristics A total of 96 patients were male (52.4%), whereas 87 patients were female (47.6%). The tumor diameter was >2.5 cm in 53.1% of the patients (97/183). The number of positive lymph nodes ranged from 1 to 29. A total of 50.7% tumors were nonfunctional, and the majority of functional tumors were classified as insulinoma (76/90). Most of the tumors (175/183) were well differentiated, and eight were poorly differentiated (Table 1). The median follow-up time was 39 months (range: 1–197 months), during which there were 43 relapses and 10 deaths, and all of the deaths were owing to disease progression. The 3- and 5-year RFS rates were 83.6% and 77.0%, respectively.\nA total of 96 patients were male (52.4%), whereas 87 patients were female (47.6%). The tumor diameter was >2.5 cm in 53.1% of the patients (97/183). The number of positive lymph nodes ranged from 1 to 29. A total of 50.7% tumors were nonfunctional, and the majority of functional tumors were classified as insulinoma (76/90). Most of the tumors (175/183) were well differentiated, and eight were poorly differentiated (Table 1). The median follow-up time was 39 months (range: 1–197 months), during which there were 43 relapses and 10 deaths, and all of the deaths were owing to disease progression. The 3- and 5-year RFS rates were 83.6% and 77.0%, respectively.\nExpression Patterns of the 15 m6A Regulators We found that the expression levels of m6A proteins in PanNENs and normal tissues were evident (Fig. 1a). The expression levels of writers (i.e., KIAA1429, METTL14, RBM15, WTAP, and METTL16), readers (i.e., IGF2BP2, HNRNPC, YTHDC1, YTHDC2, YTHDF1, YTHDF2, and YTHDF3), and erasers (ALKBH5 and FTO) were higher in PanNEN tissues than in normal tissues (p < 0.001). No statistically significant difference was evident between the normal and PanNENs tissues in the context of METTL3 expression level. Regarding METTL3, METTL14, METTL16, WTAP, RBM15, and KIAA1429 expression, a high level was observed in 26.7%, 11.5%, 35.5%, 64.4%, 67.7%, and 55.7% of tumors, respectively. Among the 183 tumors, 36.6% were deemed to have high FTO expression and 72.1% had high ALKBH5 expression. Regarding reader molecules (i.e., IGF2BP2, HNRNPC, YTHDC1, YTHDC2, YTHDF1, YTHDF2, and YTHDF3), immunostaining revealed low expression in 64.5%, 95.1%, 72.1%, 57.9%, 84.7%, 50.3%, and 96.2%, respectively, of these samples and high expression in the remaining samples (Fig. 1b). The immunohistochemical patterns of the 15 m6A proteins are shown in Figure 1c and online supplementary Figure S1. All 183 PanNENs showed nuclear immunoreactivity for METTL3, METTL14, METTL16, WTAP, RBM15, KIAA1429, FTO, ALKBH5, HNRNPC, and YTHDC1. The positive sites of IGF2BP2, YTHDC2, YTHDF1, YTHDF2, and YTHDF3 were located in the cytoplasm.\nWe found that the expression levels of m6A proteins in PanNENs and normal tissues were evident (Fig. 1a). The expression levels of writers (i.e., KIAA1429, METTL14, RBM15, WTAP, and METTL16), readers (i.e., IGF2BP2, HNRNPC, YTHDC1, YTHDC2, YTHDF1, YTHDF2, and YTHDF3), and erasers (ALKBH5 and FTO) were higher in PanNEN tissues than in normal tissues (p < 0.001). No statistically significant difference was evident between the normal and PanNENs tissues in the context of METTL3 expression level. Regarding METTL3, METTL14, METTL16, WTAP, RBM15, and KIAA1429 expression, a high level was observed in 26.7%, 11.5%, 35.5%, 64.4%, 67.7%, and 55.7% of tumors, respectively. Among the 183 tumors, 36.6% were deemed to have high FTO expression and 72.1% had high ALKBH5 expression. Regarding reader molecules (i.e., IGF2BP2, HNRNPC, YTHDC1, YTHDC2, YTHDF1, YTHDF2, and YTHDF3), immunostaining revealed low expression in 64.5%, 95.1%, 72.1%, 57.9%, 84.7%, 50.3%, and 96.2%, respectively, of these samples and high expression in the remaining samples (Fig. 1b). The immunohistochemical patterns of the 15 m6A proteins are shown in Figure 1c and online supplementary Figure S1. All 183 PanNENs showed nuclear immunoreactivity for METTL3, METTL14, METTL16, WTAP, RBM15, KIAA1429, FTO, ALKBH5, HNRNPC, and YTHDC1. The positive sites of IGF2BP2, YTHDC2, YTHDF1, YTHDF2, and YTHDF3 were located in the cytoplasm.\nAssociations among m6A Regulators, Immune Markers, and Clinicopathological Parameters in PanNENs Among the 15 proteins studied, only YTHDF2 and HNRNPC expressions had significant prognostic value. Of the 43 patients with recurrence, 12 (27.9%) showed low YTHDF2 expression, which was dramatically lower than the proportion of patients without recurrence having low YTHDF2 expression (85 of 140, 60.7%; p = 0.003; Fig. 1d). Similarly, 88.4% of the patients (38 of 43 patients) with recurrence showed low tumoral HNRNPC expression; in contrast, 97.1% of the patients without recurrence (136 of 140 patients) showed low tumoral expression of HNRNPC (p = 0.034; Fig. 1d). The log-rank test revealed that both high HNRNPC and YTHDF2 expressions predicted a significantly shortened RFS (p = 0.003 and p = 0.001, respectively; Fig. 1e, f). In the correlation analysis between YTHDF2 and HNRNPC expression and clinicopathological variables, we found that YTHDF2 expression was positively associated with tumor size (p = 0.021), nodal status (p = 0.007), functional status (p = 0.010), Ki67 index (p = 0.023), and liver metastasis (p < 0.001). There were no significant associations between YTHDF2 expression and the other clinicopathological variables, including age, sex, tumor site, lymphovascular invasion (LVI), and perineural invasion (PNI) (online suppl. Table S3).\nHigh YTHDF2 expression was associated with higher number of CD3+ T cells (p = 0.003). There were no significant associations between YTHDF2 expression and other immune markers (PD-L1, B7-H3, CD3, CD4, and CD68). However, high HNRNPC expression was significantly correlated with positive PD-L1 expression (p = 0.039) and high densities of CD3+ T cells. Moreover, there were no significant associations between HNRNPC expression and other immune markers (Table 2). Representative images of PD-L1, B7-H3 CD3, CD4, CD15, and CD68 are presented in online supplementary Figure S2.\nAmong the 15 proteins studied, only YTHDF2 and HNRNPC expressions had significant prognostic value. Of the 43 patients with recurrence, 12 (27.9%) showed low YTHDF2 expression, which was dramatically lower than the proportion of patients without recurrence having low YTHDF2 expression (85 of 140, 60.7%; p = 0.003; Fig. 1d). Similarly, 88.4% of the patients (38 of 43 patients) with recurrence showed low tumoral HNRNPC expression; in contrast, 97.1% of the patients without recurrence (136 of 140 patients) showed low tumoral expression of HNRNPC (p = 0.034; Fig. 1d). The log-rank test revealed that both high HNRNPC and YTHDF2 expressions predicted a significantly shortened RFS (p = 0.003 and p = 0.001, respectively; Fig. 1e, f). In the correlation analysis between YTHDF2 and HNRNPC expression and clinicopathological variables, we found that YTHDF2 expression was positively associated with tumor size (p = 0.021), nodal status (p = 0.007), functional status (p = 0.010), Ki67 index (p = 0.023), and liver metastasis (p < 0.001). There were no significant associations between YTHDF2 expression and the other clinicopathological variables, including age, sex, tumor site, lymphovascular invasion (LVI), and perineural invasion (PNI) (online suppl. Table S3).\nHigh YTHDF2 expression was associated with higher number of CD3+ T cells (p = 0.003). There were no significant associations between YTHDF2 expression and other immune markers (PD-L1, B7-H3, CD3, CD4, and CD68). However, high HNRNPC expression was significantly correlated with positive PD-L1 expression (p = 0.039) and high densities of CD3+ T cells. Moreover, there were no significant associations between HNRNPC expression and other immune markers (Table 2). Representative images of PD-L1, B7-H3 CD3, CD4, CD15, and CD68 are presented in online supplementary Figure S2.\nPrognostic Predictors of RFS in PanNENs A univariate Cox analysis for both the clinicopathological parameters and m6A proteins was conducted. Nine clinicopathological factors (tumor size, lymph node status, PNI, LVI, grade, functional status, liver metastasis, and Ki67 index) and five m6A proteins (METTL14, METTL16, ALKBH5, HNRNPC, YTHDF1, YTHDF2, and YTHDF3) were identified to be associated with RFS, with p values of <0.05 (Table 3). Considering analysis of collinear factors, tumor grade was found to be associated with Ki67 index and liver metastasis, and LVI was associated with lymph node status. Therefore, PNI, tumor size, lymph node status, grade, functional status, METTL14, METTL16, ALKBH5, HNRNPC, YTHDF1, YTHDF2, and YTHDF3 were included in the multivariate Cox regression model. Multivariate Cox regression analysis identified tumor size, lymph node status, tumor grade, PNI, HNRNPC expression, and YTHDF2 expression as independent risk factors for RFS after excluding collinear factors. Next, we applied an RSF model to evaluate the importance of these independent risk factors. In the analysis of variable of importance, we found that VIMP of HNRNPC was −0.0011 (Fig. 2a). In the minimal depth analysis, HNRNPC, PNI, and grade had values of 2.405, 2.482, and 2.183, respectively, which were very close to or more than the depth threshold (Fig. 2b). Therefore, they were all excluded from the RSF model. We also performed survival analyses to determine the prognostic value of YTHDF2 mRNA expression in patients with PanNET in ICGC cohort. The Kaplan-Meier plot revealed that high YTHDF2 mRNA expression was significantly associated with poor RFS (p = 0.017, Fig. 2c). Furthermore, three variables (tumor size, lymph node status, and YTHDF2 expression) significantly affected RFS. Nodal status had the largest effect on RFS, followed by YTHDF2 expression and tumor size.\nA univariate Cox analysis for both the clinicopathological parameters and m6A proteins was conducted. Nine clinicopathological factors (tumor size, lymph node status, PNI, LVI, grade, functional status, liver metastasis, and Ki67 index) and five m6A proteins (METTL14, METTL16, ALKBH5, HNRNPC, YTHDF1, YTHDF2, and YTHDF3) were identified to be associated with RFS, with p values of <0.05 (Table 3). Considering analysis of collinear factors, tumor grade was found to be associated with Ki67 index and liver metastasis, and LVI was associated with lymph node status. Therefore, PNI, tumor size, lymph node status, grade, functional status, METTL14, METTL16, ALKBH5, HNRNPC, YTHDF1, YTHDF2, and YTHDF3 were included in the multivariate Cox regression model. Multivariate Cox regression analysis identified tumor size, lymph node status, tumor grade, PNI, HNRNPC expression, and YTHDF2 expression as independent risk factors for RFS after excluding collinear factors. Next, we applied an RSF model to evaluate the importance of these independent risk factors. In the analysis of variable of importance, we found that VIMP of HNRNPC was −0.0011 (Fig. 2a). In the minimal depth analysis, HNRNPC, PNI, and grade had values of 2.405, 2.482, and 2.183, respectively, which were very close to or more than the depth threshold (Fig. 2b). Therefore, they were all excluded from the RSF model. We also performed survival analyses to determine the prognostic value of YTHDF2 mRNA expression in patients with PanNET in ICGC cohort. The Kaplan-Meier plot revealed that high YTHDF2 mRNA expression was significantly associated with poor RFS (p = 0.017, Fig. 2c). Furthermore, three variables (tumor size, lymph node status, and YTHDF2 expression) significantly affected RFS. Nodal status had the largest effect on RFS, followed by YTHDF2 expression and tumor size.\nConstruction and Evaluation of the Prognostic Model for Predicting RFS in PanNENs To construct a recurrence signature that can classify patients into subpopulations according to RFS, we further performed a recursive partitioning analysis. After pruning the decision trees using the post-pruning method, five terminal nodes representing a recurrence signature were identified (Fig. 2d). LNposTumorSize<2.5 cm (node 1; 20.8 months) and LNposTumorSize≥2.5 cmYTHDF2high (node 3; 21.1 months) patients had worse median RFS than LNnegYTHDF2high patients (node 5; 39.6 months, node 1 vs. node 5, p < 0.001), LNnegYTHDF2low patients (node 4; 43.2 months), and LNposTumorSize≥2.5 cmYTHDF2low patients (node 2; 29.3 months). Patients with the LNnegYTHDF2high (node 5) signature had a 5-year RFS rate of 92.1%, whereas patients with the LNposTumorSize<2.5 cm (node 1) signature had the worst 5-year RFS rate of 0% (Fig. 3a). To better identify risk groups for the recurrence signatures, we performed pair-wise comparisons for each recurrence signature subpopulation (on RFS) and the corresponding risk score of recurrence. The results showed that patients within the five terminal nodes can be categorized into three risk groups: low (node 4), intermediate (node 2 and node 5), and high (node 1 and node 3) with well-separated RFS curves (p < 0.001; Fig. 3b). Among the risk groups, the 5-year RFS rates were 6.0%, 70.4%, and 90.1% for the high-, intermediate-, and low-risk groups, respectively. To further test our proposed recurrence signature, we performed a subanalysis on a specific group of patients with nonsyndromic and nonfunctional PanNENs. A total of 93 PanNENs from the full cohort were eligible for this analysis (online suppl. Table S4). The Kaplan-Meier RFS curves showed clear separation among the risk groups (p = 0.018; Fig. 3c).\nFurthermore, a risk score was generated to evaluate the risk of recurrence based on variables selected from the survival tree analysis: Risk Score = (1.218 × Nodal Status) + (1.625 × Tumor Size) + (0.745 × expression value of YTHDF2) (Table 4). Multivariate analysis using a Cox proportional hazards model was performed which included PNI, tumor size, nodal status, grade, functional status, METTL14, METTL16, ALKBH5, HNRNPC, YTHDF1, YTHDF2, YTHDF3, and the risk score. The risk score was determined to be an independent prognostic factor for patients with resected PanNEN in our study based on the multivariate Cox regression, and higher risk scores were found to be associated with shorter survival (hazard ratio: 33.04, 95% CI: 4.341–251.434; p = 0.001) (Table 5). Specificity and sensitivity comparisons were performed via time-dependent ROC curve analysis of risk score. The predictive accuracy of the recurrence signature was relatively high and the area under ROC curve was 0.858 (95% CI: 0.747–0.913) at 1 year, 0.824 (95% CI: 0.754–0.912) at 3 years, and 0.870 (95% CI: 0.762–0.915) at 5 years (Fig. 3d). The C-index was 0.978 (95% CI: 0.936–1), suggesting good discrimination ability. The calibration curves also showed good agreement between the predicted and observed RFS (Fig. 3e).\nTo construct a recurrence signature that can classify patients into subpopulations according to RFS, we further performed a recursive partitioning analysis. After pruning the decision trees using the post-pruning method, five terminal nodes representing a recurrence signature were identified (Fig. 2d). LNposTumorSize<2.5 cm (node 1; 20.8 months) and LNposTumorSize≥2.5 cmYTHDF2high (node 3; 21.1 months) patients had worse median RFS than LNnegYTHDF2high patients (node 5; 39.6 months, node 1 vs. node 5, p < 0.001), LNnegYTHDF2low patients (node 4; 43.2 months), and LNposTumorSize≥2.5 cmYTHDF2low patients (node 2; 29.3 months). Patients with the LNnegYTHDF2high (node 5) signature had a 5-year RFS rate of 92.1%, whereas patients with the LNposTumorSize<2.5 cm (node 1) signature had the worst 5-year RFS rate of 0% (Fig. 3a). To better identify risk groups for the recurrence signatures, we performed pair-wise comparisons for each recurrence signature subpopulation (on RFS) and the corresponding risk score of recurrence. The results showed that patients within the five terminal nodes can be categorized into three risk groups: low (node 4), intermediate (node 2 and node 5), and high (node 1 and node 3) with well-separated RFS curves (p < 0.001; Fig. 3b). Among the risk groups, the 5-year RFS rates were 6.0%, 70.4%, and 90.1% for the high-, intermediate-, and low-risk groups, respectively. To further test our proposed recurrence signature, we performed a subanalysis on a specific group of patients with nonsyndromic and nonfunctional PanNENs. A total of 93 PanNENs from the full cohort were eligible for this analysis (online suppl. Table S4). The Kaplan-Meier RFS curves showed clear separation among the risk groups (p = 0.018; Fig. 3c).\nFurthermore, a risk score was generated to evaluate the risk of recurrence based on variables selected from the survival tree analysis: Risk Score = (1.218 × Nodal Status) + (1.625 × Tumor Size) + (0.745 × expression value of YTHDF2) (Table 4). Multivariate analysis using a Cox proportional hazards model was performed which included PNI, tumor size, nodal status, grade, functional status, METTL14, METTL16, ALKBH5, HNRNPC, YTHDF1, YTHDF2, YTHDF3, and the risk score. The risk score was determined to be an independent prognostic factor for patients with resected PanNEN in our study based on the multivariate Cox regression, and higher risk scores were found to be associated with shorter survival (hazard ratio: 33.04, 95% CI: 4.341–251.434; p = 0.001) (Table 5). Specificity and sensitivity comparisons were performed via time-dependent ROC curve analysis of risk score. The predictive accuracy of the recurrence signature was relatively high and the area under ROC curve was 0.858 (95% CI: 0.747–0.913) at 1 year, 0.824 (95% CI: 0.754–0.912) at 3 years, and 0.870 (95% CI: 0.762–0.915) at 5 years (Fig. 3d). The C-index was 0.978 (95% CI: 0.936–1), suggesting good discrimination ability. The calibration curves also showed good agreement between the predicted and observed RFS (Fig. 3e).","A total of 96 patients were male (52.4%), whereas 87 patients were female (47.6%). The tumor diameter was >2.5 cm in 53.1% of the patients (97/183). The number of positive lymph nodes ranged from 1 to 29. A total of 50.7% tumors were nonfunctional, and the majority of functional tumors were classified as insulinoma (76/90). Most of the tumors (175/183) were well differentiated, and eight were poorly differentiated (Table 1). The median follow-up time was 39 months (range: 1–197 months), during which there were 43 relapses and 10 deaths, and all of the deaths were owing to disease progression. The 3- and 5-year RFS rates were 83.6% and 77.0%, respectively.","We found that the expression levels of m6A proteins in PanNENs and normal tissues were evident (Fig. 1a). The expression levels of writers (i.e., KIAA1429, METTL14, RBM15, WTAP, and METTL16), readers (i.e., IGF2BP2, HNRNPC, YTHDC1, YTHDC2, YTHDF1, YTHDF2, and YTHDF3), and erasers (ALKBH5 and FTO) were higher in PanNEN tissues than in normal tissues (p < 0.001). No statistically significant difference was evident between the normal and PanNENs tissues in the context of METTL3 expression level. Regarding METTL3, METTL14, METTL16, WTAP, RBM15, and KIAA1429 expression, a high level was observed in 26.7%, 11.5%, 35.5%, 64.4%, 67.7%, and 55.7% of tumors, respectively. Among the 183 tumors, 36.6% were deemed to have high FTO expression and 72.1% had high ALKBH5 expression. Regarding reader molecules (i.e., IGF2BP2, HNRNPC, YTHDC1, YTHDC2, YTHDF1, YTHDF2, and YTHDF3), immunostaining revealed low expression in 64.5%, 95.1%, 72.1%, 57.9%, 84.7%, 50.3%, and 96.2%, respectively, of these samples and high expression in the remaining samples (Fig. 1b). The immunohistochemical patterns of the 15 m6A proteins are shown in Figure 1c and online supplementary Figure S1. All 183 PanNENs showed nuclear immunoreactivity for METTL3, METTL14, METTL16, WTAP, RBM15, KIAA1429, FTO, ALKBH5, HNRNPC, and YTHDC1. The positive sites of IGF2BP2, YTHDC2, YTHDF1, YTHDF2, and YTHDF3 were located in the cytoplasm.","Among the 15 proteins studied, only YTHDF2 and HNRNPC expressions had significant prognostic value. Of the 43 patients with recurrence, 12 (27.9%) showed low YTHDF2 expression, which was dramatically lower than the proportion of patients without recurrence having low YTHDF2 expression (85 of 140, 60.7%; p = 0.003; Fig. 1d). Similarly, 88.4% of the patients (38 of 43 patients) with recurrence showed low tumoral HNRNPC expression; in contrast, 97.1% of the patients without recurrence (136 of 140 patients) showed low tumoral expression of HNRNPC (p = 0.034; Fig. 1d). The log-rank test revealed that both high HNRNPC and YTHDF2 expressions predicted a significantly shortened RFS (p = 0.003 and p = 0.001, respectively; Fig. 1e, f). In the correlation analysis between YTHDF2 and HNRNPC expression and clinicopathological variables, we found that YTHDF2 expression was positively associated with tumor size (p = 0.021), nodal status (p = 0.007), functional status (p = 0.010), Ki67 index (p = 0.023), and liver metastasis (p < 0.001). There were no significant associations between YTHDF2 expression and the other clinicopathological variables, including age, sex, tumor site, lymphovascular invasion (LVI), and perineural invasion (PNI) (online suppl. Table S3).\nHigh YTHDF2 expression was associated with higher number of CD3+ T cells (p = 0.003). There were no significant associations between YTHDF2 expression and other immune markers (PD-L1, B7-H3, CD3, CD4, and CD68). However, high HNRNPC expression was significantly correlated with positive PD-L1 expression (p = 0.039) and high densities of CD3+ T cells. Moreover, there were no significant associations between HNRNPC expression and other immune markers (Table 2). Representative images of PD-L1, B7-H3 CD3, CD4, CD15, and CD68 are presented in online supplementary Figure S2.","A univariate Cox analysis for both the clinicopathological parameters and m6A proteins was conducted. Nine clinicopathological factors (tumor size, lymph node status, PNI, LVI, grade, functional status, liver metastasis, and Ki67 index) and five m6A proteins (METTL14, METTL16, ALKBH5, HNRNPC, YTHDF1, YTHDF2, and YTHDF3) were identified to be associated with RFS, with p values of <0.05 (Table 3). Considering analysis of collinear factors, tumor grade was found to be associated with Ki67 index and liver metastasis, and LVI was associated with lymph node status. Therefore, PNI, tumor size, lymph node status, grade, functional status, METTL14, METTL16, ALKBH5, HNRNPC, YTHDF1, YTHDF2, and YTHDF3 were included in the multivariate Cox regression model. Multivariate Cox regression analysis identified tumor size, lymph node status, tumor grade, PNI, HNRNPC expression, and YTHDF2 expression as independent risk factors for RFS after excluding collinear factors. Next, we applied an RSF model to evaluate the importance of these independent risk factors. In the analysis of variable of importance, we found that VIMP of HNRNPC was −0.0011 (Fig. 2a). In the minimal depth analysis, HNRNPC, PNI, and grade had values of 2.405, 2.482, and 2.183, respectively, which were very close to or more than the depth threshold (Fig. 2b). Therefore, they were all excluded from the RSF model. We also performed survival analyses to determine the prognostic value of YTHDF2 mRNA expression in patients with PanNET in ICGC cohort. The Kaplan-Meier plot revealed that high YTHDF2 mRNA expression was significantly associated with poor RFS (p = 0.017, Fig. 2c). Furthermore, three variables (tumor size, lymph node status, and YTHDF2 expression) significantly affected RFS. Nodal status had the largest effect on RFS, followed by YTHDF2 expression and tumor size.","To construct a recurrence signature that can classify patients into subpopulations according to RFS, we further performed a recursive partitioning analysis. After pruning the decision trees using the post-pruning method, five terminal nodes representing a recurrence signature were identified (Fig. 2d). LNposTumorSize<2.5 cm (node 1; 20.8 months) and LNposTumorSize≥2.5 cmYTHDF2high (node 3; 21.1 months) patients had worse median RFS than LNnegYTHDF2high patients (node 5; 39.6 months, node 1 vs. node 5, p < 0.001), LNnegYTHDF2low patients (node 4; 43.2 months), and LNposTumorSize≥2.5 cmYTHDF2low patients (node 2; 29.3 months). Patients with the LNnegYTHDF2high (node 5) signature had a 5-year RFS rate of 92.1%, whereas patients with the LNposTumorSize<2.5 cm (node 1) signature had the worst 5-year RFS rate of 0% (Fig. 3a). To better identify risk groups for the recurrence signatures, we performed pair-wise comparisons for each recurrence signature subpopulation (on RFS) and the corresponding risk score of recurrence. The results showed that patients within the five terminal nodes can be categorized into three risk groups: low (node 4), intermediate (node 2 and node 5), and high (node 1 and node 3) with well-separated RFS curves (p < 0.001; Fig. 3b). Among the risk groups, the 5-year RFS rates were 6.0%, 70.4%, and 90.1% for the high-, intermediate-, and low-risk groups, respectively. To further test our proposed recurrence signature, we performed a subanalysis on a specific group of patients with nonsyndromic and nonfunctional PanNENs. A total of 93 PanNENs from the full cohort were eligible for this analysis (online suppl. Table S4). The Kaplan-Meier RFS curves showed clear separation among the risk groups (p = 0.018; Fig. 3c).\nFurthermore, a risk score was generated to evaluate the risk of recurrence based on variables selected from the survival tree analysis: Risk Score = (1.218 × Nodal Status) + (1.625 × Tumor Size) + (0.745 × expression value of YTHDF2) (Table 4). Multivariate analysis using a Cox proportional hazards model was performed which included PNI, tumor size, nodal status, grade, functional status, METTL14, METTL16, ALKBH5, HNRNPC, YTHDF1, YTHDF2, YTHDF3, and the risk score. The risk score was determined to be an independent prognostic factor for patients with resected PanNEN in our study based on the multivariate Cox regression, and higher risk scores were found to be associated with shorter survival (hazard ratio: 33.04, 95% CI: 4.341–251.434; p = 0.001) (Table 5). Specificity and sensitivity comparisons were performed via time-dependent ROC curve analysis of risk score. The predictive accuracy of the recurrence signature was relatively high and the area under ROC curve was 0.858 (95% CI: 0.747–0.913) at 1 year, 0.824 (95% CI: 0.754–0.912) at 3 years, and 0.870 (95% CI: 0.762–0.915) at 5 years (Fig. 3d). The C-index was 0.978 (95% CI: 0.936–1), suggesting good discrimination ability. The calibration curves also showed good agreement between the predicted and observed RFS (Fig. 3e).","Identification of predictors of relapse in patients with PanNEN is important. Previous studies that investigated risk predictors identified that tumor size, lymph node status, WHO grade, Ki67 index, tertiary lymphoid structures, and tumor-infiltrating neutrophils may predict recurrence [34, 35, 36, 37]. However, a practical and effective model is urgently needed. In our study, we constructed a risk model for recurrence with a corresponding risk score to stratify patients on the basis of RFS. The recurrence signature classifies patients into three risk groups independent of grade, with each group representing a distinct RFS outcome.\nThis is the first large-scale study exploring 15 m6A proteins as potential relapse biomarkers for PanNENs, to the best of our knowledge. To date, all of these 15 proteins have not been described in PanNENs. High expression of METTL14 was observed in 11.5% of the entire cohort, and high METTL14 expression predicted poor RFS outcomes (p = 0.024), similar to pancreatic cancer [38], whereas there are opposite results in other types of cancers, such as CRC [39] and bladder cancer [40]. METTL16, another m6A methyltransferase, was also a predictor of relapse and associated with poor RFS in this cohort (p = 0.023); however, recent studies suggested that METTL16 deletion is correlated with poor disease-specific survival or RFS in hepatocellular carcinoma [41], and no difference was found between patients with METTL16-high and METTL16-low CRC [42]. We found that patients with low ALKBH5 expression had a better clinical outcome. Recent studies have also suggested that YTHDF1 and YTHDF3 play a vital role in various types of cancers [43, 44]. Liu et al. [44] showed that YTHDF1 promotes ovarian cancer progression by augmenting EIF3C translation, and its high expression indicated a poor clinical outcome. The expression of YTHDF1 and YTHDF3 was reported to be associated with poor prognosis in breast cancer patients [43]. We attempted to examine the prognostic value of this factor in patients with resected PanNENs. We found that the high expression of both YTHDF1 and YTHDF3 predicted extremely low RFS (p = 0.003 and p = 0.002).\nYTHDF2 is the most effective m6A reader that can selectively bind to the m6A site. Recent reports have described the ability of YTHDF2 to degrade both tumor promoter as well as suppressor gene mRNAs and adversely affect inflammatory reaction, vascular abnormalization, and self-renewal of leukemic stem cells [45, 46, 47, 48, 49]. Compared with normal tissues, YTHDF2 expression was upregulated in prostate cancer, pancreatic cancer, and lung adenocarcinoma; moreover, high YTHDF2 expression was associated with unfavorable clinicopathological parameters and poor survival, which was consistent with our study [50, 51, 52]. However, Shen et al. [53] found that the expression level of YTHDF2 was lower in gastric cancer than in normal gastric tissues, and low YTHDF2 expression was correlated with poor prognosis. In our PanNENs, high YTHDF2 expression was observed in 49.7% of the tumors; high YTHDF2 expression was strongly associated with poorer RFS (p = 0.0013), and in an independent ICGC cohort, the same correlation was observed (p = 0.017), which validated this finding. We also identified associations between YTHDF2 expression and clinicopathological features, including liver metastasis (p < 0.001), although we did not determine YTHDF2 expression in metastatic lesions. After adjusting for other covariables, high YTHDF2 expression was identified as an independent risk factor for recurrence in patients with PanNENs, suggesting the role of YTHDF2 in metastasis. Recent studies revealed complex biological functions of YTHDF2 in different types of cancers. YTHDF2 could mediate mRNA degradation of tumor suppressors (LHPP and NKX3-1) to promote the proliferation and migration of prostate cancer cells [50]. In pancreatic cancer, YTHDF2 knockdown resulted in epithelial-mesenchymal transition through YAP pathway to promote the invasion and migration of cancer cells [51]. Another study showed that upregulated YTHDF2 decreased FOXC2 expression level to inhibit the proliferation, invasion, and migration of gastric cancer cells [53]. However, the regulatory mechanisms of YTHDF2 require further investigations in PanNENs.\nAnother finding is the discovery that HNRNPC has prognostic significance in PanNEN recurrence. High HNRNPC expression was observed more frequently in patients with recurrence than in those without recurrence and was associated with a significantly shortened RFS. HNRNPC contributes to tumorigenesis, which may partially explain the association between high HNRNPC expression and the high recurrence rate in PanNENs. Additionally, we observed a positive correlation between HNRNPC expression and tumoral PD-L1 expression (p < 0.001). No existing literature describes the influence of HNRNPC on PD-L1 expression of PanNEN cells. Therefore, detailed mechanistic investigations are required to understand the functional link between HNRNPC and PD-L1 in PanNENs.\nThe current clinical practice of regular follow-up is the main method for postoperative monitoring of patients and allows early identification of recurrence. However, an important concern with postoperative monitoring of patients is the balance of effectiveness against cost because the radiation exposure over prolonged follow-up periods can be harmful and detrimental to the patient [54]. Current international guidelines of the European Society for Medical Oncology provide follow-up recommendations after PanNEN resection based on grade [55]. In our study, PanNENs were classified into three risk groups: low-, intermediate-, and high-risk groups. Interestingly, grade 2 tumors were distributed across different recurrence risk groups. These results imply that owing to significant heterogeneity in disease behavior, particularly with grade 2 tumors, such distinctions are insufficient and postoperative surveillance should not merely be based on grade. Considering the high accuracy of recurrence signature and its independence from grade, postoperative follow-up regimens could be customized on the basis of these alternative recurrence signatures and risk groups. In addition, adjuvant therapy for high-risk patients may be useful in improving clinical outcomes, although this strategy will need to be validated prospectively in future studies.\nThe high YTHDF2 expression (47.1%) in patients with PanNEN and its correlation with recurrence may have important therapeutic implications with potential for development and clinical use of YTHDF2 inhibitors for the treatment of high YTHDF2-expressing tumors. High HNRNPC expression was correlated with recurrence and PD-L1 expression, which may allow the development of a combinatorial therapeutic regimen using HNRNPC inhibitor and PD-L1 monoclonal antibody. Recent studies have shown that genomic mutations in PanNENs can be relevant to classify patients beyond their tumor grade and identify novel prognostic markers and therapeutic targets that could be relevant in the future for adjuvant therapy [56]. It will be interesting to investigate whether such a clinical approach is feasible in PanNENs with high HNRNPC expression in prospective clinical trials.\nSome limitations exist in the current study, despite several valuable findings. First, its retrospective nature has inherent limitations. Second, the study was performed in patients from a single institution. Although we conducted a subgroup analysis to validate the recurrence signature in a selected group, additional external validation is required to investigate whether these signatures are useful markers in other cohorts. However, the low prevalence of PanNEN in the population is an obstacle to conducting the study in a larger sample size. Therefore, clinical data from multiple centers and prospective evidence are required to validate these results.\nIn conclusion, this is the first study to explore the expression profiles of m6A proteins and association between m6A regulators and immune microenvironment in PanNENs. We identified high YTHDF2 and HNRNPC expression as independent risk factors for disease relapse after surgery. We further established a recurrence signature to identify patients with PanNEN at a higher risk of recurrence. A comprehensive understanding of m6A regulators in PanNEN may help in developing novel treatment strategies.","The study was approved by the Institutional Review Board of Peking Union Medical College Hospital (approval number S-K1593) and conducted in accordance with the Declaration of Helsinki. Written consent for this study was not required and formally waived by the hospital's Ethics Committee.","The authors declare that they have no conflict of interest.","This work was supported by grants from the Chinese Academy of Medical Sciences Initiative for Innovative Medicine (CAMS-2016-I2M-1–001), the National Scientific Data Sharing Platform for Population and Health (NCMI–YF01N–201906), and the National Natural Science Foundation of China (Nos. 81672648).","Jie Chen made substantial contributions to the conception, design, and critical revision of the manuscript. Shengwei Mo, Liju Zong, Shuangni Yu, and Zhaohui Lu made substantial contributions to tissue microarray construction. Shengwei Mo made substantial contributions to data acquisition. Xianlong Chen and Shengwei Mo performed analysis of the data and drafting of the manuscript. All the authors read and approved the final manuscript.","The data used and/or analyzed during the current study are available from the corresponding author on reasonable request.","Supplementary data\nClick here for additional data file.\nSupplementary data\nClick here for additional data file.\nSupplementary data\nClick here for additional data file.\nSupplementary data\nClick here for additional data file.\nSupplementary data\nClick here for additional data file.\nSupplementary data\nClick here for additional data file.\nSupplementary data\nClick here for additional data file."],"string":"[\n \"Pancreatic neuroendocrine neoplasms (PanNENs) are a rare, heterogeneous group of neoplasms originating from the neuroendocrine cells and classified as functional and nonfunctional depending on whether the tumor overproduces biologically active hormones [1]. However, the incidence of PanNENs has significantly increased in the last decades and has risen to 0.8 per 100,000 individuals per year [2]. This increase may be attributed, at least in part, to the improved and more frequent use of imaging in clinical diagnostic practices [3]. Moreover, both patients with nonfunctional or functional tumors are always diagnosed at a late stage and usually present locally advanced or metastatic disease [4, 5].\\nCurrent clinical strategies for most patients with PanNENs primarily involve surgical resection [6]. One of the most significant causes of concern in patients with a PanNEN is cancer recurrence after curative resection. However, clinical outcomes after surgical resection vary widely, with recurrence-free survival (RFS) ranging from 0.96 to 121.9 months [7]. The most frequently used cancer staging system is based on the WHO classification which categorizes tumors into four groups based on proliferative activity and morphology: NET G1, NET G2, NET G3, and NEC [8]. However, patients with PanNEN with the same grade of tumor may demonstrate different clinical courses, whereas patients with low-grade PanNENs show unpredictable disease progression and outcomes [9]. Therefore, a clinically feasible and accurate prediction of the risk of relapse in patients with PanNENs after resection is needed.\\nN6-methyladenosine (m6A) is the most common epigenetic modification of the mRNA, comprising >60% of all RNA-based modifications [10]. The m6A methylation levels are greatly associated with the immune response, stress response regulation, tumorigenesis, and miRNA processing [11, 12, 13, 14, 15, 16]. The m6A methylation plays a crucial role in disease progression of various cancers [11, 12, 16] as well as in the onset and progression of fetal growth restriction and preeclampsia [17, 18]. The modification of m6A is regulated by methyltransferases (writers) and demethylases (erasers), and m6A performs multiple functions through its interactions with specific binding proteins known as the readers [19]. The writers mainly include KIAA1429, METTL3, WTAP, METTL14, RBM15, and METTL16, which promote m6A RNA methylation. The erasers encompass fat mass and obesity-associated protein (FTO) and alkylation repair homolog protein 5 (ALKBH5). The readers comprise HNRNPC, IGF2BP2, YTHDC1, YTHDC2, YTHDF1, YTHDF2, and YTHDF3, which recognize RNA m6A binding site [19, 20, 21]. Increasing amount of evidence has emerged which shows the vital role of m6A regulators in tumorigenesis and prognosis of various cancers. Chen et al. [22] reported that METTL14 inhibited colorectal cancer (CRC) progression through primary miR-375 processing-based regulation. Cives et al. [23] revealed that METTL3 could promote the proliferation and migration of hepatocellular carcinoma. A previous study found that high YTHDF1 expression predicted a poor clinical outcome in CRC patients [24]. Additionally, the low expression of FTO was associated with a shorter RFS in patients with intrahepatic cholangiocarcinoma [25]. Overexpression of FTO suppressed acute myeloid leukemia cell differentiation [26]. Growing evidence suggests that m6A modification and its regulators play vital roles in human cancers [11, 12, 22, 23, 24, 25, 26]. m6A modification is associated with tumorigenesis, proliferation, invasion, and metastasis [22, 23, 24]. And m6A regulators were indicators of prognosis and therapeutic targets [25, 26]. Moreover, epigenetic modification and especially methylation have been already described as important events during tumorigenesis of PanNEN, and methylation modification is an epigenetic regulatory mechanism for gene expression in PanNENs, including MEN1, which involved in the chromatin remodeling in PanNENs [27]. However, whether m6A regulators could also be specific biomarkers in PanNENs is yet to be determined. And the expression status, functional roles, and clinical significance of m6A methylation regulators also remain largely unknown in PanNENs. In addition, recent studies have shown prognosis of immune infiltrates and immune checkpoints in pancreatic neuroendocrine tumors [23, 28]. However, the correlation between m6A regulators and immune infiltration and immune checkpoints is yet to be explored.\\nTo better identify patients at risk and improve their follow-up management, we explored the expression profiles of m6A proteins in resected PanNENs and investigated the potential prognostic value of m6A proteins. We also analyzed the association among common immune infiltrates, immune checkpoints, and m6A regulators. Furthermore, a YTHDF2-based signature was established, which may help in postoperative monitoring and stratification of risks to guide clinical treatment options.\",\n \"Patient Cohort and Follow-Up A total of 183 patients with PanNENs who were admitted to the Peking Union Medical College Hospital (PUMCH) and underwent surgeries during the time period 2004–2019 with sufficient tumor tissue for evaluation were included in the current study. The FFPE specimens and matching hematoxylin and eosin slides were retrieved. Patients' clinicopathological data, including age, sex, primary tumor site, tumor size, tumor grading, functional status, and surgical procedures, were collected from the medical records. The American Joint on Cancer Committee (AJCC) 8th edition stages for each patient were defined based on the collected data. Survival and recurrence information were obtained from medical record reviews and telephone interviews. Recurrence and distant metastasis were determined based on biochemical markers, clinical multidisciplinary consultation, radiological, and/or histological examinations. The time between the surgery and tumor recurrence or last follow-up appointment was termed as RFS. Disease-specific survival was calculated from the surgery date to the time of patient death or last follow-up till time point of December 29, 2020. This retrospective study was approved by the Institutional Review Board of PUMCH (approval number: S-K1593) and conducted in accordance with the Declaration of Helsinki. Written consent for this study was not required and formally waived by the hospital's Ethics Committee.\\nA total of 183 patients with PanNENs who were admitted to the Peking Union Medical College Hospital (PUMCH) and underwent surgeries during the time period 2004–2019 with sufficient tumor tissue for evaluation were included in the current study. The FFPE specimens and matching hematoxylin and eosin slides were retrieved. Patients' clinicopathological data, including age, sex, primary tumor site, tumor size, tumor grading, functional status, and surgical procedures, were collected from the medical records. The American Joint on Cancer Committee (AJCC) 8th edition stages for each patient were defined based on the collected data. Survival and recurrence information were obtained from medical record reviews and telephone interviews. Recurrence and distant metastasis were determined based on biochemical markers, clinical multidisciplinary consultation, radiological, and/or histological examinations. The time between the surgery and tumor recurrence or last follow-up appointment was termed as RFS. Disease-specific survival was calculated from the surgery date to the time of patient death or last follow-up till time point of December 29, 2020. This retrospective study was approved by the Institutional Review Board of PUMCH (approval number: S-K1593) and conducted in accordance with the Declaration of Helsinki. Written consent for this study was not required and formally waived by the hospital's Ethics Committee.\\nTissue Microarrays and Immunohistochemistry Representative areas of tumors and adjacent normal tissue were selected from hematoxylin and eosin-stained paraffin blocks and then re-embedded into recipient tissue microarray (TMA) blocks (12 × 8 arrays). The diameter of each core was 2 mm.\\nImmunohistochemical analysis was performed according to the protocol described by Zong et al. [29]. The 4-μm TMA sections were deparaffinized. Heat-mediated antigen retrieval was performed using sodium citrate buffer (pH 6.0) for 10 min. The endogenous peroxidase activity was quenched using a 3% hydrogen peroxide solution (ZSGB-BIO, Beijing, China) and then the sections were blocked with 5% normal goat serum for 30 min. TMA sections were incubated with primary antibodies against m6A methylation regulators (METTL3, METTL14, METTL16, WTAP, KIAA1429, RBM15, FTO, ALKBH5, IGF2BP2, HNRNPC, YTHDC1, YTHDC2, YTHDF1, YTHDF2, and YTHDF3) and immune markers (PD-L1, B7-H3, CD3, CD4, CD15, CD68) overnight at 4°C; the details and optimal dilutions are summarized in online supplementary Table S1 (see www.karger.com/doi/10.1159/000525228 for all online suppl. material). Stromal cells, normal acinar cells, ductal cells, and islet cells from normal tissues were incubated with primary antibodies and used as internal positive controls, whereas the same tissues without primary antibodies comprised the negative controls.\\nRepresentative areas of tumors and adjacent normal tissue were selected from hematoxylin and eosin-stained paraffin blocks and then re-embedded into recipient tissue microarray (TMA) blocks (12 × 8 arrays). The diameter of each core was 2 mm.\\nImmunohistochemical analysis was performed according to the protocol described by Zong et al. [29]. The 4-μm TMA sections were deparaffinized. Heat-mediated antigen retrieval was performed using sodium citrate buffer (pH 6.0) for 10 min. The endogenous peroxidase activity was quenched using a 3% hydrogen peroxide solution (ZSGB-BIO, Beijing, China) and then the sections were blocked with 5% normal goat serum for 30 min. TMA sections were incubated with primary antibodies against m6A methylation regulators (METTL3, METTL14, METTL16, WTAP, KIAA1429, RBM15, FTO, ALKBH5, IGF2BP2, HNRNPC, YTHDC1, YTHDC2, YTHDF1, YTHDF2, and YTHDF3) and immune markers (PD-L1, B7-H3, CD3, CD4, CD15, CD68) overnight at 4°C; the details and optimal dilutions are summarized in online supplementary Table S1 (see www.karger.com/doi/10.1159/000525228 for all online suppl. material). Stromal cells, normal acinar cells, ductal cells, and islet cells from normal tissues were incubated with primary antibodies and used as internal positive controls, whereas the same tissues without primary antibodies comprised the negative controls.\\nEvaluation of Immunostaining The immunostaining performed on the collected patient samples was independently assessed by two pathologists (XL Chen and SW Mo) who were blinded to the patients' clinicopathological features and clinical outcomes. Both pathologists reexamined the slides and reached a consensus in case of any discrepancy. The immunoreactivity of 15 m6A markers, PD-L1, and B7-H3 in tumor specimens was quantified using the method developed by Budwit-Novotny et al. [30]. In brief, the percentage of positively stained tumor cells was multiplied by the relative intensity of specific staining, which was assigned a value of negative (0), weak (1), distinct (2), and strong (3) [30, 31]. The density of stromal CD3, CD4, CD15, and CD68 were quantified in four ×400 high-power fields and the mean counts of four fields were used for statistical analysis as we described earlier [32]. The cutoff values of m6A markers and immune markers were determined using X-tile (Yale University, New Haven, CT, USA). These cutoff values are summarized in the online supplementary Table S2.\\nThe immunostaining performed on the collected patient samples was independently assessed by two pathologists (XL Chen and SW Mo) who were blinded to the patients' clinicopathological features and clinical outcomes. Both pathologists reexamined the slides and reached a consensus in case of any discrepancy. The immunoreactivity of 15 m6A markers, PD-L1, and B7-H3 in tumor specimens was quantified using the method developed by Budwit-Novotny et al. [30]. In brief, the percentage of positively stained tumor cells was multiplied by the relative intensity of specific staining, which was assigned a value of negative (0), weak (1), distinct (2), and strong (3) [30, 31]. The density of stromal CD3, CD4, CD15, and CD68 were quantified in four ×400 high-power fields and the mean counts of four fields were used for statistical analysis as we described earlier [32]. The cutoff values of m6A markers and immune markers were determined using X-tile (Yale University, New Haven, CT, USA). These cutoff values are summarized in the online supplementary Table S2.\\nRisk Score Models and Random Survival Forest A random survival forest (RSF) model was constructed based on the minimal depth and variable importance (VIMP). VIMP and minimal depth were used to assess the true effect of the variable on RFS. Given that feature subsets of variables are randomly selected using RSF method, RSF can process high-dimensional (many variables) data. In our current study, we included more than ten variables. Through training of the RSF algorithm, the variables which were the most related to recurrence were selected using minimal depth and VIMP. And in the training process, the interaction between variables can be detected. Moreover, when creating random forest, unbiased estimation is used for generalization error, and the model has strong generalization ability [33]. If a large part of the features is lost, the accuracy can still be maintained [33]. Based on these strengths of RSF, this method was helpful to select relapse-related variables and construct a recurrence signature. Recurrence-specific variables defined by a VIMP of >0 and minimal depth less than the depth threshold were finally entered into a survival tree analysis [33]. The recurrence-specific variables, including nodal status, tumor size, YTHDF2, were selected by using minimal depth and VIMP. By integrating the expression level of YTHDF2, nodal status, and tumor size as well as their corresponding coefficients which were derived from the multivariate Cox model constructed only using these 3 variables, a risk score was generated, as represented below: Risk Score = (1.218 × Nodal Status) + (1.625 × Tumor Size) + (0.745 × expression value of YTHDF2).\\nNodal status was divided into negative/positive and scored as 0/1; tumor size was divided into <2.5 cm/≥2.5 cm and scored as 0/1, and expression value of YTHDF2 was divided into low/high and scored as 0/1, and these scores were multiplied by the corresponding coefficients to generate a risk score. The median (median = 1.625) was used as the cutoff value for the risk score.\\nA random survival forest (RSF) model was constructed based on the minimal depth and variable importance (VIMP). VIMP and minimal depth were used to assess the true effect of the variable on RFS. Given that feature subsets of variables are randomly selected using RSF method, RSF can process high-dimensional (many variables) data. In our current study, we included more than ten variables. Through training of the RSF algorithm, the variables which were the most related to recurrence were selected using minimal depth and VIMP. And in the training process, the interaction between variables can be detected. Moreover, when creating random forest, unbiased estimation is used for generalization error, and the model has strong generalization ability [33]. If a large part of the features is lost, the accuracy can still be maintained [33]. Based on these strengths of RSF, this method was helpful to select relapse-related variables and construct a recurrence signature. Recurrence-specific variables defined by a VIMP of >0 and minimal depth less than the depth threshold were finally entered into a survival tree analysis [33]. The recurrence-specific variables, including nodal status, tumor size, YTHDF2, were selected by using minimal depth and VIMP. By integrating the expression level of YTHDF2, nodal status, and tumor size as well as their corresponding coefficients which were derived from the multivariate Cox model constructed only using these 3 variables, a risk score was generated, as represented below: Risk Score = (1.218 × Nodal Status) + (1.625 × Tumor Size) + (0.745 × expression value of YTHDF2).\\nNodal status was divided into negative/positive and scored as 0/1; tumor size was divided into <2.5 cm/≥2.5 cm and scored as 0/1, and expression value of YTHDF2 was divided into low/high and scored as 0/1, and these scores were multiplied by the corresponding coefficients to generate a risk score. The median (median = 1.625) was used as the cutoff value for the risk score.\\nStatistical Analysis The continuous and categorical variables were described as median (range) and frequency (percentage), respectively. The Mann-Whitney U test was conducted to compare nonnormally distributed continuous variables, whereas a Student's t test was used to analyze normally distributed continuous variables. The χ2 test or Fisher's exact test was used to evaluate the relationship between the expression of m6A methylation regulators and categorical variables. The survival curves were plotted by Kaplan-Meier method, and the log-rank test was employed to compare the survival curves generated. The Cox proportional hazard regression model was used to estimate the hazard ratio with a 95% confidence interval (CI) for variables associated with RFS. Potential risk factors with a p value of <0.05 in the univariate Cox analysis were entered into the multivariate Cox regression model (backward Wald) after considering collinearity among the variables. We evaluated the discrimination ability of the final model with Harrell concordance index (C-index). The area under the time-dependent receiver operating characteristic (ROC) curve at different cutoff times was measured as predictive performance. Calibration was assessed by visual examination of the calibration plot. p values of <0.05 were considered statistically significant. Statistical analyses were two-sided and accomplished using SPSS software (version 21.0; IBM Corp., Armonk, NY, USA). Statistical analyses of RNA sequencing data from International Cancer Genome Consortium (ICGC) database (https://www.icgc.org/) were conducted with R version 3.5.0 (http://www.r-project.org).\\nThe continuous and categorical variables were described as median (range) and frequency (percentage), respectively. The Mann-Whitney U test was conducted to compare nonnormally distributed continuous variables, whereas a Student's t test was used to analyze normally distributed continuous variables. The χ2 test or Fisher's exact test was used to evaluate the relationship between the expression of m6A methylation regulators and categorical variables. The survival curves were plotted by Kaplan-Meier method, and the log-rank test was employed to compare the survival curves generated. The Cox proportional hazard regression model was used to estimate the hazard ratio with a 95% confidence interval (CI) for variables associated with RFS. Potential risk factors with a p value of <0.05 in the univariate Cox analysis were entered into the multivariate Cox regression model (backward Wald) after considering collinearity among the variables. We evaluated the discrimination ability of the final model with Harrell concordance index (C-index). The area under the time-dependent receiver operating characteristic (ROC) curve at different cutoff times was measured as predictive performance. Calibration was assessed by visual examination of the calibration plot. p values of <0.05 were considered statistically significant. Statistical analyses were two-sided and accomplished using SPSS software (version 21.0; IBM Corp., Armonk, NY, USA). Statistical analyses of RNA sequencing data from International Cancer Genome Consortium (ICGC) database (https://www.icgc.org/) were conducted with R version 3.5.0 (http://www.r-project.org).\",\n \"A total of 183 patients with PanNENs who were admitted to the Peking Union Medical College Hospital (PUMCH) and underwent surgeries during the time period 2004–2019 with sufficient tumor tissue for evaluation were included in the current study. The FFPE specimens and matching hematoxylin and eosin slides were retrieved. Patients' clinicopathological data, including age, sex, primary tumor site, tumor size, tumor grading, functional status, and surgical procedures, were collected from the medical records. The American Joint on Cancer Committee (AJCC) 8th edition stages for each patient were defined based on the collected data. Survival and recurrence information were obtained from medical record reviews and telephone interviews. Recurrence and distant metastasis were determined based on biochemical markers, clinical multidisciplinary consultation, radiological, and/or histological examinations. The time between the surgery and tumor recurrence or last follow-up appointment was termed as RFS. Disease-specific survival was calculated from the surgery date to the time of patient death or last follow-up till time point of December 29, 2020. This retrospective study was approved by the Institutional Review Board of PUMCH (approval number: S-K1593) and conducted in accordance with the Declaration of Helsinki. Written consent for this study was not required and formally waived by the hospital's Ethics Committee.\",\n \"Representative areas of tumors and adjacent normal tissue were selected from hematoxylin and eosin-stained paraffin blocks and then re-embedded into recipient tissue microarray (TMA) blocks (12 × 8 arrays). The diameter of each core was 2 mm.\\nImmunohistochemical analysis was performed according to the protocol described by Zong et al. [29]. The 4-μm TMA sections were deparaffinized. Heat-mediated antigen retrieval was performed using sodium citrate buffer (pH 6.0) for 10 min. The endogenous peroxidase activity was quenched using a 3% hydrogen peroxide solution (ZSGB-BIO, Beijing, China) and then the sections were blocked with 5% normal goat serum for 30 min. TMA sections were incubated with primary antibodies against m6A methylation regulators (METTL3, METTL14, METTL16, WTAP, KIAA1429, RBM15, FTO, ALKBH5, IGF2BP2, HNRNPC, YTHDC1, YTHDC2, YTHDF1, YTHDF2, and YTHDF3) and immune markers (PD-L1, B7-H3, CD3, CD4, CD15, CD68) overnight at 4°C; the details and optimal dilutions are summarized in online supplementary Table S1 (see www.karger.com/doi/10.1159/000525228 for all online suppl. material). Stromal cells, normal acinar cells, ductal cells, and islet cells from normal tissues were incubated with primary antibodies and used as internal positive controls, whereas the same tissues without primary antibodies comprised the negative controls.\",\n \"The immunostaining performed on the collected patient samples was independently assessed by two pathologists (XL Chen and SW Mo) who were blinded to the patients' clinicopathological features and clinical outcomes. Both pathologists reexamined the slides and reached a consensus in case of any discrepancy. The immunoreactivity of 15 m6A markers, PD-L1, and B7-H3 in tumor specimens was quantified using the method developed by Budwit-Novotny et al. [30]. In brief, the percentage of positively stained tumor cells was multiplied by the relative intensity of specific staining, which was assigned a value of negative (0), weak (1), distinct (2), and strong (3) [30, 31]. The density of stromal CD3, CD4, CD15, and CD68 were quantified in four ×400 high-power fields and the mean counts of four fields were used for statistical analysis as we described earlier [32]. The cutoff values of m6A markers and immune markers were determined using X-tile (Yale University, New Haven, CT, USA). These cutoff values are summarized in the online supplementary Table S2.\",\n \"A random survival forest (RSF) model was constructed based on the minimal depth and variable importance (VIMP). VIMP and minimal depth were used to assess the true effect of the variable on RFS. Given that feature subsets of variables are randomly selected using RSF method, RSF can process high-dimensional (many variables) data. In our current study, we included more than ten variables. Through training of the RSF algorithm, the variables which were the most related to recurrence were selected using minimal depth and VIMP. And in the training process, the interaction between variables can be detected. Moreover, when creating random forest, unbiased estimation is used for generalization error, and the model has strong generalization ability [33]. If a large part of the features is lost, the accuracy can still be maintained [33]. Based on these strengths of RSF, this method was helpful to select relapse-related variables and construct a recurrence signature. Recurrence-specific variables defined by a VIMP of >0 and minimal depth less than the depth threshold were finally entered into a survival tree analysis [33]. The recurrence-specific variables, including nodal status, tumor size, YTHDF2, were selected by using minimal depth and VIMP. By integrating the expression level of YTHDF2, nodal status, and tumor size as well as their corresponding coefficients which were derived from the multivariate Cox model constructed only using these 3 variables, a risk score was generated, as represented below: Risk Score = (1.218 × Nodal Status) + (1.625 × Tumor Size) + (0.745 × expression value of YTHDF2).\\nNodal status was divided into negative/positive and scored as 0/1; tumor size was divided into <2.5 cm/≥2.5 cm and scored as 0/1, and expression value of YTHDF2 was divided into low/high and scored as 0/1, and these scores were multiplied by the corresponding coefficients to generate a risk score. The median (median = 1.625) was used as the cutoff value for the risk score.\",\n \"The continuous and categorical variables were described as median (range) and frequency (percentage), respectively. The Mann-Whitney U test was conducted to compare nonnormally distributed continuous variables, whereas a Student's t test was used to analyze normally distributed continuous variables. The χ2 test or Fisher's exact test was used to evaluate the relationship between the expression of m6A methylation regulators and categorical variables. The survival curves were plotted by Kaplan-Meier method, and the log-rank test was employed to compare the survival curves generated. The Cox proportional hazard regression model was used to estimate the hazard ratio with a 95% confidence interval (CI) for variables associated with RFS. Potential risk factors with a p value of <0.05 in the univariate Cox analysis were entered into the multivariate Cox regression model (backward Wald) after considering collinearity among the variables. We evaluated the discrimination ability of the final model with Harrell concordance index (C-index). The area under the time-dependent receiver operating characteristic (ROC) curve at different cutoff times was measured as predictive performance. Calibration was assessed by visual examination of the calibration plot. p values of <0.05 were considered statistically significant. Statistical analyses were two-sided and accomplished using SPSS software (version 21.0; IBM Corp., Armonk, NY, USA). Statistical analyses of RNA sequencing data from International Cancer Genome Consortium (ICGC) database (https://www.icgc.org/) were conducted with R version 3.5.0 (http://www.r-project.org).\",\n \"Patients' Characteristics A total of 96 patients were male (52.4%), whereas 87 patients were female (47.6%). The tumor diameter was >2.5 cm in 53.1% of the patients (97/183). The number of positive lymph nodes ranged from 1 to 29. A total of 50.7% tumors were nonfunctional, and the majority of functional tumors were classified as insulinoma (76/90). Most of the tumors (175/183) were well differentiated, and eight were poorly differentiated (Table 1). The median follow-up time was 39 months (range: 1–197 months), during which there were 43 relapses and 10 deaths, and all of the deaths were owing to disease progression. The 3- and 5-year RFS rates were 83.6% and 77.0%, respectively.\\nA total of 96 patients were male (52.4%), whereas 87 patients were female (47.6%). The tumor diameter was >2.5 cm in 53.1% of the patients (97/183). The number of positive lymph nodes ranged from 1 to 29. A total of 50.7% tumors were nonfunctional, and the majority of functional tumors were classified as insulinoma (76/90). Most of the tumors (175/183) were well differentiated, and eight were poorly differentiated (Table 1). The median follow-up time was 39 months (range: 1–197 months), during which there were 43 relapses and 10 deaths, and all of the deaths were owing to disease progression. The 3- and 5-year RFS rates were 83.6% and 77.0%, respectively.\\nExpression Patterns of the 15 m6A Regulators We found that the expression levels of m6A proteins in PanNENs and normal tissues were evident (Fig. 1a). The expression levels of writers (i.e., KIAA1429, METTL14, RBM15, WTAP, and METTL16), readers (i.e., IGF2BP2, HNRNPC, YTHDC1, YTHDC2, YTHDF1, YTHDF2, and YTHDF3), and erasers (ALKBH5 and FTO) were higher in PanNEN tissues than in normal tissues (p < 0.001). No statistically significant difference was evident between the normal and PanNENs tissues in the context of METTL3 expression level. Regarding METTL3, METTL14, METTL16, WTAP, RBM15, and KIAA1429 expression, a high level was observed in 26.7%, 11.5%, 35.5%, 64.4%, 67.7%, and 55.7% of tumors, respectively. Among the 183 tumors, 36.6% were deemed to have high FTO expression and 72.1% had high ALKBH5 expression. Regarding reader molecules (i.e., IGF2BP2, HNRNPC, YTHDC1, YTHDC2, YTHDF1, YTHDF2, and YTHDF3), immunostaining revealed low expression in 64.5%, 95.1%, 72.1%, 57.9%, 84.7%, 50.3%, and 96.2%, respectively, of these samples and high expression in the remaining samples (Fig. 1b). The immunohistochemical patterns of the 15 m6A proteins are shown in Figure 1c and online supplementary Figure S1. All 183 PanNENs showed nuclear immunoreactivity for METTL3, METTL14, METTL16, WTAP, RBM15, KIAA1429, FTO, ALKBH5, HNRNPC, and YTHDC1. The positive sites of IGF2BP2, YTHDC2, YTHDF1, YTHDF2, and YTHDF3 were located in the cytoplasm.\\nWe found that the expression levels of m6A proteins in PanNENs and normal tissues were evident (Fig. 1a). The expression levels of writers (i.e., KIAA1429, METTL14, RBM15, WTAP, and METTL16), readers (i.e., IGF2BP2, HNRNPC, YTHDC1, YTHDC2, YTHDF1, YTHDF2, and YTHDF3), and erasers (ALKBH5 and FTO) were higher in PanNEN tissues than in normal tissues (p < 0.001). No statistically significant difference was evident between the normal and PanNENs tissues in the context of METTL3 expression level. Regarding METTL3, METTL14, METTL16, WTAP, RBM15, and KIAA1429 expression, a high level was observed in 26.7%, 11.5%, 35.5%, 64.4%, 67.7%, and 55.7% of tumors, respectively. Among the 183 tumors, 36.6% were deemed to have high FTO expression and 72.1% had high ALKBH5 expression. Regarding reader molecules (i.e., IGF2BP2, HNRNPC, YTHDC1, YTHDC2, YTHDF1, YTHDF2, and YTHDF3), immunostaining revealed low expression in 64.5%, 95.1%, 72.1%, 57.9%, 84.7%, 50.3%, and 96.2%, respectively, of these samples and high expression in the remaining samples (Fig. 1b). The immunohistochemical patterns of the 15 m6A proteins are shown in Figure 1c and online supplementary Figure S1. All 183 PanNENs showed nuclear immunoreactivity for METTL3, METTL14, METTL16, WTAP, RBM15, KIAA1429, FTO, ALKBH5, HNRNPC, and YTHDC1. The positive sites of IGF2BP2, YTHDC2, YTHDF1, YTHDF2, and YTHDF3 were located in the cytoplasm.\\nAssociations among m6A Regulators, Immune Markers, and Clinicopathological Parameters in PanNENs Among the 15 proteins studied, only YTHDF2 and HNRNPC expressions had significant prognostic value. Of the 43 patients with recurrence, 12 (27.9%) showed low YTHDF2 expression, which was dramatically lower than the proportion of patients without recurrence having low YTHDF2 expression (85 of 140, 60.7%; p = 0.003; Fig. 1d). Similarly, 88.4% of the patients (38 of 43 patients) with recurrence showed low tumoral HNRNPC expression; in contrast, 97.1% of the patients without recurrence (136 of 140 patients) showed low tumoral expression of HNRNPC (p = 0.034; Fig. 1d). The log-rank test revealed that both high HNRNPC and YTHDF2 expressions predicted a significantly shortened RFS (p = 0.003 and p = 0.001, respectively; Fig. 1e, f). In the correlation analysis between YTHDF2 and HNRNPC expression and clinicopathological variables, we found that YTHDF2 expression was positively associated with tumor size (p = 0.021), nodal status (p = 0.007), functional status (p = 0.010), Ki67 index (p = 0.023), and liver metastasis (p < 0.001). There were no significant associations between YTHDF2 expression and the other clinicopathological variables, including age, sex, tumor site, lymphovascular invasion (LVI), and perineural invasion (PNI) (online suppl. Table S3).\\nHigh YTHDF2 expression was associated with higher number of CD3+ T cells (p = 0.003). There were no significant associations between YTHDF2 expression and other immune markers (PD-L1, B7-H3, CD3, CD4, and CD68). However, high HNRNPC expression was significantly correlated with positive PD-L1 expression (p = 0.039) and high densities of CD3+ T cells. Moreover, there were no significant associations between HNRNPC expression and other immune markers (Table 2). Representative images of PD-L1, B7-H3 CD3, CD4, CD15, and CD68 are presented in online supplementary Figure S2.\\nAmong the 15 proteins studied, only YTHDF2 and HNRNPC expressions had significant prognostic value. Of the 43 patients with recurrence, 12 (27.9%) showed low YTHDF2 expression, which was dramatically lower than the proportion of patients without recurrence having low YTHDF2 expression (85 of 140, 60.7%; p = 0.003; Fig. 1d). Similarly, 88.4% of the patients (38 of 43 patients) with recurrence showed low tumoral HNRNPC expression; in contrast, 97.1% of the patients without recurrence (136 of 140 patients) showed low tumoral expression of HNRNPC (p = 0.034; Fig. 1d). The log-rank test revealed that both high HNRNPC and YTHDF2 expressions predicted a significantly shortened RFS (p = 0.003 and p = 0.001, respectively; Fig. 1e, f). In the correlation analysis between YTHDF2 and HNRNPC expression and clinicopathological variables, we found that YTHDF2 expression was positively associated with tumor size (p = 0.021), nodal status (p = 0.007), functional status (p = 0.010), Ki67 index (p = 0.023), and liver metastasis (p < 0.001). There were no significant associations between YTHDF2 expression and the other clinicopathological variables, including age, sex, tumor site, lymphovascular invasion (LVI), and perineural invasion (PNI) (online suppl. Table S3).\\nHigh YTHDF2 expression was associated with higher number of CD3+ T cells (p = 0.003). There were no significant associations between YTHDF2 expression and other immune markers (PD-L1, B7-H3, CD3, CD4, and CD68). However, high HNRNPC expression was significantly correlated with positive PD-L1 expression (p = 0.039) and high densities of CD3+ T cells. Moreover, there were no significant associations between HNRNPC expression and other immune markers (Table 2). Representative images of PD-L1, B7-H3 CD3, CD4, CD15, and CD68 are presented in online supplementary Figure S2.\\nPrognostic Predictors of RFS in PanNENs A univariate Cox analysis for both the clinicopathological parameters and m6A proteins was conducted. Nine clinicopathological factors (tumor size, lymph node status, PNI, LVI, grade, functional status, liver metastasis, and Ki67 index) and five m6A proteins (METTL14, METTL16, ALKBH5, HNRNPC, YTHDF1, YTHDF2, and YTHDF3) were identified to be associated with RFS, with p values of <0.05 (Table 3). Considering analysis of collinear factors, tumor grade was found to be associated with Ki67 index and liver metastasis, and LVI was associated with lymph node status. Therefore, PNI, tumor size, lymph node status, grade, functional status, METTL14, METTL16, ALKBH5, HNRNPC, YTHDF1, YTHDF2, and YTHDF3 were included in the multivariate Cox regression model. Multivariate Cox regression analysis identified tumor size, lymph node status, tumor grade, PNI, HNRNPC expression, and YTHDF2 expression as independent risk factors for RFS after excluding collinear factors. Next, we applied an RSF model to evaluate the importance of these independent risk factors. In the analysis of variable of importance, we found that VIMP of HNRNPC was −0.0011 (Fig. 2a). In the minimal depth analysis, HNRNPC, PNI, and grade had values of 2.405, 2.482, and 2.183, respectively, which were very close to or more than the depth threshold (Fig. 2b). Therefore, they were all excluded from the RSF model. We also performed survival analyses to determine the prognostic value of YTHDF2 mRNA expression in patients with PanNET in ICGC cohort. The Kaplan-Meier plot revealed that high YTHDF2 mRNA expression was significantly associated with poor RFS (p = 0.017, Fig. 2c). Furthermore, three variables (tumor size, lymph node status, and YTHDF2 expression) significantly affected RFS. Nodal status had the largest effect on RFS, followed by YTHDF2 expression and tumor size.\\nA univariate Cox analysis for both the clinicopathological parameters and m6A proteins was conducted. Nine clinicopathological factors (tumor size, lymph node status, PNI, LVI, grade, functional status, liver metastasis, and Ki67 index) and five m6A proteins (METTL14, METTL16, ALKBH5, HNRNPC, YTHDF1, YTHDF2, and YTHDF3) were identified to be associated with RFS, with p values of <0.05 (Table 3). Considering analysis of collinear factors, tumor grade was found to be associated with Ki67 index and liver metastasis, and LVI was associated with lymph node status. Therefore, PNI, tumor size, lymph node status, grade, functional status, METTL14, METTL16, ALKBH5, HNRNPC, YTHDF1, YTHDF2, and YTHDF3 were included in the multivariate Cox regression model. Multivariate Cox regression analysis identified tumor size, lymph node status, tumor grade, PNI, HNRNPC expression, and YTHDF2 expression as independent risk factors for RFS after excluding collinear factors. Next, we applied an RSF model to evaluate the importance of these independent risk factors. In the analysis of variable of importance, we found that VIMP of HNRNPC was −0.0011 (Fig. 2a). In the minimal depth analysis, HNRNPC, PNI, and grade had values of 2.405, 2.482, and 2.183, respectively, which were very close to or more than the depth threshold (Fig. 2b). Therefore, they were all excluded from the RSF model. We also performed survival analyses to determine the prognostic value of YTHDF2 mRNA expression in patients with PanNET in ICGC cohort. The Kaplan-Meier plot revealed that high YTHDF2 mRNA expression was significantly associated with poor RFS (p = 0.017, Fig. 2c). Furthermore, three variables (tumor size, lymph node status, and YTHDF2 expression) significantly affected RFS. Nodal status had the largest effect on RFS, followed by YTHDF2 expression and tumor size.\\nConstruction and Evaluation of the Prognostic Model for Predicting RFS in PanNENs To construct a recurrence signature that can classify patients into subpopulations according to RFS, we further performed a recursive partitioning analysis. After pruning the decision trees using the post-pruning method, five terminal nodes representing a recurrence signature were identified (Fig. 2d). LNposTumorSize<2.5 cm (node 1; 20.8 months) and LNposTumorSize≥2.5 cmYTHDF2high (node 3; 21.1 months) patients had worse median RFS than LNnegYTHDF2high patients (node 5; 39.6 months, node 1 vs. node 5, p < 0.001), LNnegYTHDF2low patients (node 4; 43.2 months), and LNposTumorSize≥2.5 cmYTHDF2low patients (node 2; 29.3 months). Patients with the LNnegYTHDF2high (node 5) signature had a 5-year RFS rate of 92.1%, whereas patients with the LNposTumorSize<2.5 cm (node 1) signature had the worst 5-year RFS rate of 0% (Fig. 3a). To better identify risk groups for the recurrence signatures, we performed pair-wise comparisons for each recurrence signature subpopulation (on RFS) and the corresponding risk score of recurrence. The results showed that patients within the five terminal nodes can be categorized into three risk groups: low (node 4), intermediate (node 2 and node 5), and high (node 1 and node 3) with well-separated RFS curves (p < 0.001; Fig. 3b). Among the risk groups, the 5-year RFS rates were 6.0%, 70.4%, and 90.1% for the high-, intermediate-, and low-risk groups, respectively. To further test our proposed recurrence signature, we performed a subanalysis on a specific group of patients with nonsyndromic and nonfunctional PanNENs. A total of 93 PanNENs from the full cohort were eligible for this analysis (online suppl. Table S4). The Kaplan-Meier RFS curves showed clear separation among the risk groups (p = 0.018; Fig. 3c).\\nFurthermore, a risk score was generated to evaluate the risk of recurrence based on variables selected from the survival tree analysis: Risk Score = (1.218 × Nodal Status) + (1.625 × Tumor Size) + (0.745 × expression value of YTHDF2) (Table 4). Multivariate analysis using a Cox proportional hazards model was performed which included PNI, tumor size, nodal status, grade, functional status, METTL14, METTL16, ALKBH5, HNRNPC, YTHDF1, YTHDF2, YTHDF3, and the risk score. The risk score was determined to be an independent prognostic factor for patients with resected PanNEN in our study based on the multivariate Cox regression, and higher risk scores were found to be associated with shorter survival (hazard ratio: 33.04, 95% CI: 4.341–251.434; p = 0.001) (Table 5). Specificity and sensitivity comparisons were performed via time-dependent ROC curve analysis of risk score. The predictive accuracy of the recurrence signature was relatively high and the area under ROC curve was 0.858 (95% CI: 0.747–0.913) at 1 year, 0.824 (95% CI: 0.754–0.912) at 3 years, and 0.870 (95% CI: 0.762–0.915) at 5 years (Fig. 3d). The C-index was 0.978 (95% CI: 0.936–1), suggesting good discrimination ability. The calibration curves also showed good agreement between the predicted and observed RFS (Fig. 3e).\\nTo construct a recurrence signature that can classify patients into subpopulations according to RFS, we further performed a recursive partitioning analysis. After pruning the decision trees using the post-pruning method, five terminal nodes representing a recurrence signature were identified (Fig. 2d). LNposTumorSize<2.5 cm (node 1; 20.8 months) and LNposTumorSize≥2.5 cmYTHDF2high (node 3; 21.1 months) patients had worse median RFS than LNnegYTHDF2high patients (node 5; 39.6 months, node 1 vs. node 5, p < 0.001), LNnegYTHDF2low patients (node 4; 43.2 months), and LNposTumorSize≥2.5 cmYTHDF2low patients (node 2; 29.3 months). Patients with the LNnegYTHDF2high (node 5) signature had a 5-year RFS rate of 92.1%, whereas patients with the LNposTumorSize<2.5 cm (node 1) signature had the worst 5-year RFS rate of 0% (Fig. 3a). To better identify risk groups for the recurrence signatures, we performed pair-wise comparisons for each recurrence signature subpopulation (on RFS) and the corresponding risk score of recurrence. The results showed that patients within the five terminal nodes can be categorized into three risk groups: low (node 4), intermediate (node 2 and node 5), and high (node 1 and node 3) with well-separated RFS curves (p < 0.001; Fig. 3b). Among the risk groups, the 5-year RFS rates were 6.0%, 70.4%, and 90.1% for the high-, intermediate-, and low-risk groups, respectively. To further test our proposed recurrence signature, we performed a subanalysis on a specific group of patients with nonsyndromic and nonfunctional PanNENs. A total of 93 PanNENs from the full cohort were eligible for this analysis (online suppl. Table S4). The Kaplan-Meier RFS curves showed clear separation among the risk groups (p = 0.018; Fig. 3c).\\nFurthermore, a risk score was generated to evaluate the risk of recurrence based on variables selected from the survival tree analysis: Risk Score = (1.218 × Nodal Status) + (1.625 × Tumor Size) + (0.745 × expression value of YTHDF2) (Table 4). Multivariate analysis using a Cox proportional hazards model was performed which included PNI, tumor size, nodal status, grade, functional status, METTL14, METTL16, ALKBH5, HNRNPC, YTHDF1, YTHDF2, YTHDF3, and the risk score. The risk score was determined to be an independent prognostic factor for patients with resected PanNEN in our study based on the multivariate Cox regression, and higher risk scores were found to be associated with shorter survival (hazard ratio: 33.04, 95% CI: 4.341–251.434; p = 0.001) (Table 5). Specificity and sensitivity comparisons were performed via time-dependent ROC curve analysis of risk score. The predictive accuracy of the recurrence signature was relatively high and the area under ROC curve was 0.858 (95% CI: 0.747–0.913) at 1 year, 0.824 (95% CI: 0.754–0.912) at 3 years, and 0.870 (95% CI: 0.762–0.915) at 5 years (Fig. 3d). The C-index was 0.978 (95% CI: 0.936–1), suggesting good discrimination ability. The calibration curves also showed good agreement between the predicted and observed RFS (Fig. 3e).\",\n \"A total of 96 patients were male (52.4%), whereas 87 patients were female (47.6%). The tumor diameter was >2.5 cm in 53.1% of the patients (97/183). The number of positive lymph nodes ranged from 1 to 29. A total of 50.7% tumors were nonfunctional, and the majority of functional tumors were classified as insulinoma (76/90). Most of the tumors (175/183) were well differentiated, and eight were poorly differentiated (Table 1). The median follow-up time was 39 months (range: 1–197 months), during which there were 43 relapses and 10 deaths, and all of the deaths were owing to disease progression. The 3- and 5-year RFS rates were 83.6% and 77.0%, respectively.\",\n \"We found that the expression levels of m6A proteins in PanNENs and normal tissues were evident (Fig. 1a). The expression levels of writers (i.e., KIAA1429, METTL14, RBM15, WTAP, and METTL16), readers (i.e., IGF2BP2, HNRNPC, YTHDC1, YTHDC2, YTHDF1, YTHDF2, and YTHDF3), and erasers (ALKBH5 and FTO) were higher in PanNEN tissues than in normal tissues (p < 0.001). No statistically significant difference was evident between the normal and PanNENs tissues in the context of METTL3 expression level. Regarding METTL3, METTL14, METTL16, WTAP, RBM15, and KIAA1429 expression, a high level was observed in 26.7%, 11.5%, 35.5%, 64.4%, 67.7%, and 55.7% of tumors, respectively. Among the 183 tumors, 36.6% were deemed to have high FTO expression and 72.1% had high ALKBH5 expression. Regarding reader molecules (i.e., IGF2BP2, HNRNPC, YTHDC1, YTHDC2, YTHDF1, YTHDF2, and YTHDF3), immunostaining revealed low expression in 64.5%, 95.1%, 72.1%, 57.9%, 84.7%, 50.3%, and 96.2%, respectively, of these samples and high expression in the remaining samples (Fig. 1b). The immunohistochemical patterns of the 15 m6A proteins are shown in Figure 1c and online supplementary Figure S1. All 183 PanNENs showed nuclear immunoreactivity for METTL3, METTL14, METTL16, WTAP, RBM15, KIAA1429, FTO, ALKBH5, HNRNPC, and YTHDC1. The positive sites of IGF2BP2, YTHDC2, YTHDF1, YTHDF2, and YTHDF3 were located in the cytoplasm.\",\n \"Among the 15 proteins studied, only YTHDF2 and HNRNPC expressions had significant prognostic value. Of the 43 patients with recurrence, 12 (27.9%) showed low YTHDF2 expression, which was dramatically lower than the proportion of patients without recurrence having low YTHDF2 expression (85 of 140, 60.7%; p = 0.003; Fig. 1d). Similarly, 88.4% of the patients (38 of 43 patients) with recurrence showed low tumoral HNRNPC expression; in contrast, 97.1% of the patients without recurrence (136 of 140 patients) showed low tumoral expression of HNRNPC (p = 0.034; Fig. 1d). The log-rank test revealed that both high HNRNPC and YTHDF2 expressions predicted a significantly shortened RFS (p = 0.003 and p = 0.001, respectively; Fig. 1e, f). In the correlation analysis between YTHDF2 and HNRNPC expression and clinicopathological variables, we found that YTHDF2 expression was positively associated with tumor size (p = 0.021), nodal status (p = 0.007), functional status (p = 0.010), Ki67 index (p = 0.023), and liver metastasis (p < 0.001). There were no significant associations between YTHDF2 expression and the other clinicopathological variables, including age, sex, tumor site, lymphovascular invasion (LVI), and perineural invasion (PNI) (online suppl. Table S3).\\nHigh YTHDF2 expression was associated with higher number of CD3+ T cells (p = 0.003). There were no significant associations between YTHDF2 expression and other immune markers (PD-L1, B7-H3, CD3, CD4, and CD68). However, high HNRNPC expression was significantly correlated with positive PD-L1 expression (p = 0.039) and high densities of CD3+ T cells. Moreover, there were no significant associations between HNRNPC expression and other immune markers (Table 2). Representative images of PD-L1, B7-H3 CD3, CD4, CD15, and CD68 are presented in online supplementary Figure S2.\",\n \"A univariate Cox analysis for both the clinicopathological parameters and m6A proteins was conducted. Nine clinicopathological factors (tumor size, lymph node status, PNI, LVI, grade, functional status, liver metastasis, and Ki67 index) and five m6A proteins (METTL14, METTL16, ALKBH5, HNRNPC, YTHDF1, YTHDF2, and YTHDF3) were identified to be associated with RFS, with p values of <0.05 (Table 3). Considering analysis of collinear factors, tumor grade was found to be associated with Ki67 index and liver metastasis, and LVI was associated with lymph node status. Therefore, PNI, tumor size, lymph node status, grade, functional status, METTL14, METTL16, ALKBH5, HNRNPC, YTHDF1, YTHDF2, and YTHDF3 were included in the multivariate Cox regression model. Multivariate Cox regression analysis identified tumor size, lymph node status, tumor grade, PNI, HNRNPC expression, and YTHDF2 expression as independent risk factors for RFS after excluding collinear factors. Next, we applied an RSF model to evaluate the importance of these independent risk factors. In the analysis of variable of importance, we found that VIMP of HNRNPC was −0.0011 (Fig. 2a). In the minimal depth analysis, HNRNPC, PNI, and grade had values of 2.405, 2.482, and 2.183, respectively, which were very close to or more than the depth threshold (Fig. 2b). Therefore, they were all excluded from the RSF model. We also performed survival analyses to determine the prognostic value of YTHDF2 mRNA expression in patients with PanNET in ICGC cohort. The Kaplan-Meier plot revealed that high YTHDF2 mRNA expression was significantly associated with poor RFS (p = 0.017, Fig. 2c). Furthermore, three variables (tumor size, lymph node status, and YTHDF2 expression) significantly affected RFS. Nodal status had the largest effect on RFS, followed by YTHDF2 expression and tumor size.\",\n \"To construct a recurrence signature that can classify patients into subpopulations according to RFS, we further performed a recursive partitioning analysis. After pruning the decision trees using the post-pruning method, five terminal nodes representing a recurrence signature were identified (Fig. 2d). LNposTumorSize<2.5 cm (node 1; 20.8 months) and LNposTumorSize≥2.5 cmYTHDF2high (node 3; 21.1 months) patients had worse median RFS than LNnegYTHDF2high patients (node 5; 39.6 months, node 1 vs. node 5, p < 0.001), LNnegYTHDF2low patients (node 4; 43.2 months), and LNposTumorSize≥2.5 cmYTHDF2low patients (node 2; 29.3 months). Patients with the LNnegYTHDF2high (node 5) signature had a 5-year RFS rate of 92.1%, whereas patients with the LNposTumorSize<2.5 cm (node 1) signature had the worst 5-year RFS rate of 0% (Fig. 3a). To better identify risk groups for the recurrence signatures, we performed pair-wise comparisons for each recurrence signature subpopulation (on RFS) and the corresponding risk score of recurrence. The results showed that patients within the five terminal nodes can be categorized into three risk groups: low (node 4), intermediate (node 2 and node 5), and high (node 1 and node 3) with well-separated RFS curves (p < 0.001; Fig. 3b). Among the risk groups, the 5-year RFS rates were 6.0%, 70.4%, and 90.1% for the high-, intermediate-, and low-risk groups, respectively. To further test our proposed recurrence signature, we performed a subanalysis on a specific group of patients with nonsyndromic and nonfunctional PanNENs. A total of 93 PanNENs from the full cohort were eligible for this analysis (online suppl. Table S4). The Kaplan-Meier RFS curves showed clear separation among the risk groups (p = 0.018; Fig. 3c).\\nFurthermore, a risk score was generated to evaluate the risk of recurrence based on variables selected from the survival tree analysis: Risk Score = (1.218 × Nodal Status) + (1.625 × Tumor Size) + (0.745 × expression value of YTHDF2) (Table 4). Multivariate analysis using a Cox proportional hazards model was performed which included PNI, tumor size, nodal status, grade, functional status, METTL14, METTL16, ALKBH5, HNRNPC, YTHDF1, YTHDF2, YTHDF3, and the risk score. The risk score was determined to be an independent prognostic factor for patients with resected PanNEN in our study based on the multivariate Cox regression, and higher risk scores were found to be associated with shorter survival (hazard ratio: 33.04, 95% CI: 4.341–251.434; p = 0.001) (Table 5). Specificity and sensitivity comparisons were performed via time-dependent ROC curve analysis of risk score. The predictive accuracy of the recurrence signature was relatively high and the area under ROC curve was 0.858 (95% CI: 0.747–0.913) at 1 year, 0.824 (95% CI: 0.754–0.912) at 3 years, and 0.870 (95% CI: 0.762–0.915) at 5 years (Fig. 3d). The C-index was 0.978 (95% CI: 0.936–1), suggesting good discrimination ability. The calibration curves also showed good agreement between the predicted and observed RFS (Fig. 3e).\",\n \"Identification of predictors of relapse in patients with PanNEN is important. Previous studies that investigated risk predictors identified that tumor size, lymph node status, WHO grade, Ki67 index, tertiary lymphoid structures, and tumor-infiltrating neutrophils may predict recurrence [34, 35, 36, 37]. However, a practical and effective model is urgently needed. In our study, we constructed a risk model for recurrence with a corresponding risk score to stratify patients on the basis of RFS. The recurrence signature classifies patients into three risk groups independent of grade, with each group representing a distinct RFS outcome.\\nThis is the first large-scale study exploring 15 m6A proteins as potential relapse biomarkers for PanNENs, to the best of our knowledge. To date, all of these 15 proteins have not been described in PanNENs. High expression of METTL14 was observed in 11.5% of the entire cohort, and high METTL14 expression predicted poor RFS outcomes (p = 0.024), similar to pancreatic cancer [38], whereas there are opposite results in other types of cancers, such as CRC [39] and bladder cancer [40]. METTL16, another m6A methyltransferase, was also a predictor of relapse and associated with poor RFS in this cohort (p = 0.023); however, recent studies suggested that METTL16 deletion is correlated with poor disease-specific survival or RFS in hepatocellular carcinoma [41], and no difference was found between patients with METTL16-high and METTL16-low CRC [42]. We found that patients with low ALKBH5 expression had a better clinical outcome. Recent studies have also suggested that YTHDF1 and YTHDF3 play a vital role in various types of cancers [43, 44]. Liu et al. [44] showed that YTHDF1 promotes ovarian cancer progression by augmenting EIF3C translation, and its high expression indicated a poor clinical outcome. The expression of YTHDF1 and YTHDF3 was reported to be associated with poor prognosis in breast cancer patients [43]. We attempted to examine the prognostic value of this factor in patients with resected PanNENs. We found that the high expression of both YTHDF1 and YTHDF3 predicted extremely low RFS (p = 0.003 and p = 0.002).\\nYTHDF2 is the most effective m6A reader that can selectively bind to the m6A site. Recent reports have described the ability of YTHDF2 to degrade both tumor promoter as well as suppressor gene mRNAs and adversely affect inflammatory reaction, vascular abnormalization, and self-renewal of leukemic stem cells [45, 46, 47, 48, 49]. Compared with normal tissues, YTHDF2 expression was upregulated in prostate cancer, pancreatic cancer, and lung adenocarcinoma; moreover, high YTHDF2 expression was associated with unfavorable clinicopathological parameters and poor survival, which was consistent with our study [50, 51, 52]. However, Shen et al. [53] found that the expression level of YTHDF2 was lower in gastric cancer than in normal gastric tissues, and low YTHDF2 expression was correlated with poor prognosis. In our PanNENs, high YTHDF2 expression was observed in 49.7% of the tumors; high YTHDF2 expression was strongly associated with poorer RFS (p = 0.0013), and in an independent ICGC cohort, the same correlation was observed (p = 0.017), which validated this finding. We also identified associations between YTHDF2 expression and clinicopathological features, including liver metastasis (p < 0.001), although we did not determine YTHDF2 expression in metastatic lesions. After adjusting for other covariables, high YTHDF2 expression was identified as an independent risk factor for recurrence in patients with PanNENs, suggesting the role of YTHDF2 in metastasis. Recent studies revealed complex biological functions of YTHDF2 in different types of cancers. YTHDF2 could mediate mRNA degradation of tumor suppressors (LHPP and NKX3-1) to promote the proliferation and migration of prostate cancer cells [50]. In pancreatic cancer, YTHDF2 knockdown resulted in epithelial-mesenchymal transition through YAP pathway to promote the invasion and migration of cancer cells [51]. Another study showed that upregulated YTHDF2 decreased FOXC2 expression level to inhibit the proliferation, invasion, and migration of gastric cancer cells [53]. However, the regulatory mechanisms of YTHDF2 require further investigations in PanNENs.\\nAnother finding is the discovery that HNRNPC has prognostic significance in PanNEN recurrence. High HNRNPC expression was observed more frequently in patients with recurrence than in those without recurrence and was associated with a significantly shortened RFS. HNRNPC contributes to tumorigenesis, which may partially explain the association between high HNRNPC expression and the high recurrence rate in PanNENs. Additionally, we observed a positive correlation between HNRNPC expression and tumoral PD-L1 expression (p < 0.001). No existing literature describes the influence of HNRNPC on PD-L1 expression of PanNEN cells. Therefore, detailed mechanistic investigations are required to understand the functional link between HNRNPC and PD-L1 in PanNENs.\\nThe current clinical practice of regular follow-up is the main method for postoperative monitoring of patients and allows early identification of recurrence. However, an important concern with postoperative monitoring of patients is the balance of effectiveness against cost because the radiation exposure over prolonged follow-up periods can be harmful and detrimental to the patient [54]. Current international guidelines of the European Society for Medical Oncology provide follow-up recommendations after PanNEN resection based on grade [55]. In our study, PanNENs were classified into three risk groups: low-, intermediate-, and high-risk groups. Interestingly, grade 2 tumors were distributed across different recurrence risk groups. These results imply that owing to significant heterogeneity in disease behavior, particularly with grade 2 tumors, such distinctions are insufficient and postoperative surveillance should not merely be based on grade. Considering the high accuracy of recurrence signature and its independence from grade, postoperative follow-up regimens could be customized on the basis of these alternative recurrence signatures and risk groups. In addition, adjuvant therapy for high-risk patients may be useful in improving clinical outcomes, although this strategy will need to be validated prospectively in future studies.\\nThe high YTHDF2 expression (47.1%) in patients with PanNEN and its correlation with recurrence may have important therapeutic implications with potential for development and clinical use of YTHDF2 inhibitors for the treatment of high YTHDF2-expressing tumors. High HNRNPC expression was correlated with recurrence and PD-L1 expression, which may allow the development of a combinatorial therapeutic regimen using HNRNPC inhibitor and PD-L1 monoclonal antibody. Recent studies have shown that genomic mutations in PanNENs can be relevant to classify patients beyond their tumor grade and identify novel prognostic markers and therapeutic targets that could be relevant in the future for adjuvant therapy [56]. It will be interesting to investigate whether such a clinical approach is feasible in PanNENs with high HNRNPC expression in prospective clinical trials.\\nSome limitations exist in the current study, despite several valuable findings. First, its retrospective nature has inherent limitations. Second, the study was performed in patients from a single institution. Although we conducted a subgroup analysis to validate the recurrence signature in a selected group, additional external validation is required to investigate whether these signatures are useful markers in other cohorts. However, the low prevalence of PanNEN in the population is an obstacle to conducting the study in a larger sample size. Therefore, clinical data from multiple centers and prospective evidence are required to validate these results.\\nIn conclusion, this is the first study to explore the expression profiles of m6A proteins and association between m6A regulators and immune microenvironment in PanNENs. We identified high YTHDF2 and HNRNPC expression as independent risk factors for disease relapse after surgery. We further established a recurrence signature to identify patients with PanNEN at a higher risk of recurrence. A comprehensive understanding of m6A regulators in PanNEN may help in developing novel treatment strategies.\",\n \"The study was approved by the Institutional Review Board of Peking Union Medical College Hospital (approval number S-K1593) and conducted in accordance with the Declaration of Helsinki. Written consent for this study was not required and formally waived by the hospital's Ethics Committee.\",\n \"The authors declare that they have no conflict of interest.\",\n \"This work was supported by grants from the Chinese Academy of Medical Sciences Initiative for Innovative Medicine (CAMS-2016-I2M-1–001), the National Scientific Data Sharing Platform for Population and Health (NCMI–YF01N–201906), and the National Natural Science Foundation of China (Nos. 81672648).\",\n \"Jie Chen made substantial contributions to the conception, design, and critical revision of the manuscript. Shengwei Mo, Liju Zong, Shuangni Yu, and Zhaohui Lu made substantial contributions to tissue microarray construction. Shengwei Mo made substantial contributions to data acquisition. Xianlong Chen and Shengwei Mo performed analysis of the data and drafting of the manuscript. All the authors read and approved the final manuscript.\",\n \"The data used and/or analyzed during the current study are available from the corresponding author on reasonable request.\",\n \"Supplementary data\\nClick here for additional data file.\\nSupplementary data\\nClick here for additional data file.\\nSupplementary data\\nClick here for additional data file.\\nSupplementary data\\nClick here for additional data file.\\nSupplementary data\\nClick here for additional data file.\\nSupplementary data\\nClick here for additional data file.\\nSupplementary data\\nClick here for additional data file.\"\n]"},"all_sections_sec_types":{"kind":"list like","value":["intro","materials|methods",null,null,null,null,null,"results",null,null,null,null,null,"discussion|conclusions",null,"COI-statement",null,null,"data-availability","supplementary-material"],"string":"[\n \"intro\",\n \"materials|methods\",\n null,\n null,\n null,\n null,\n null,\n \"results\",\n null,\n null,\n null,\n null,\n null,\n \"discussion|conclusions\",\n null,\n \"COI-statement\",\n null,\n null,\n \"data-availability\",\n \"supplementary-material\"\n]"},"keywords":{"kind":"list like","value":["N6-methyladenosine methylation regulators","Pancreatic neuroendocrine neoplasms","Immune markers","Recurrence","Postoperative surveillance"],"string":"[\n \"N6-methyladenosine methylation regulators\",\n \"Pancreatic neuroendocrine neoplasms\",\n \"Immune markers\",\n \"Recurrence\",\n \"Postoperative surveillance\"\n]"},"whole_article_text":{"kind":"string","value":"Introduction:\nPancreatic neuroendocrine neoplasms (PanNENs) are a rare, heterogeneous group of neoplasms originating from the neuroendocrine cells and classified as functional and nonfunctional depending on whether the tumor overproduces biologically active hormones [1]. However, the incidence of PanNENs has significantly increased in the last decades and has risen to 0.8 per 100,000 individuals per year [2]. This increase may be attributed, at least in part, to the improved and more frequent use of imaging in clinical diagnostic practices [3]. Moreover, both patients with nonfunctional or functional tumors are always diagnosed at a late stage and usually present locally advanced or metastatic disease [4, 5].\nCurrent clinical strategies for most patients with PanNENs primarily involve surgical resection [6]. One of the most significant causes of concern in patients with a PanNEN is cancer recurrence after curative resection. However, clinical outcomes after surgical resection vary widely, with recurrence-free survival (RFS) ranging from 0.96 to 121.9 months [7]. The most frequently used cancer staging system is based on the WHO classification which categorizes tumors into four groups based on proliferative activity and morphology: NET G1, NET G2, NET G3, and NEC [8]. However, patients with PanNEN with the same grade of tumor may demonstrate different clinical courses, whereas patients with low-grade PanNENs show unpredictable disease progression and outcomes [9]. Therefore, a clinically feasible and accurate prediction of the risk of relapse in patients with PanNENs after resection is needed.\nN6-methyladenosine (m6A) is the most common epigenetic modification of the mRNA, comprising >60% of all RNA-based modifications [10]. The m6A methylation levels are greatly associated with the immune response, stress response regulation, tumorigenesis, and miRNA processing [11, 12, 13, 14, 15, 16]. The m6A methylation plays a crucial role in disease progression of various cancers [11, 12, 16] as well as in the onset and progression of fetal growth restriction and preeclampsia [17, 18]. The modification of m6A is regulated by methyltransferases (writers) and demethylases (erasers), and m6A performs multiple functions through its interactions with specific binding proteins known as the readers [19]. The writers mainly include KIAA1429, METTL3, WTAP, METTL14, RBM15, and METTL16, which promote m6A RNA methylation. The erasers encompass fat mass and obesity-associated protein (FTO) and alkylation repair homolog protein 5 (ALKBH5). The readers comprise HNRNPC, IGF2BP2, YTHDC1, YTHDC2, YTHDF1, YTHDF2, and YTHDF3, which recognize RNA m6A binding site [19, 20, 21]. Increasing amount of evidence has emerged which shows the vital role of m6A regulators in tumorigenesis and prognosis of various cancers. Chen et al. [22] reported that METTL14 inhibited colorectal cancer (CRC) progression through primary miR-375 processing-based regulation. Cives et al. [23] revealed that METTL3 could promote the proliferation and migration of hepatocellular carcinoma. A previous study found that high YTHDF1 expression predicted a poor clinical outcome in CRC patients [24]. Additionally, the low expression of FTO was associated with a shorter RFS in patients with intrahepatic cholangiocarcinoma [25]. Overexpression of FTO suppressed acute myeloid leukemia cell differentiation [26]. Growing evidence suggests that m6A modification and its regulators play vital roles in human cancers [11, 12, 22, 23, 24, 25, 26]. m6A modification is associated with tumorigenesis, proliferation, invasion, and metastasis [22, 23, 24]. And m6A regulators were indicators of prognosis and therapeutic targets [25, 26]. Moreover, epigenetic modification and especially methylation have been already described as important events during tumorigenesis of PanNEN, and methylation modification is an epigenetic regulatory mechanism for gene expression in PanNENs, including MEN1, which involved in the chromatin remodeling in PanNENs [27]. However, whether m6A regulators could also be specific biomarkers in PanNENs is yet to be determined. And the expression status, functional roles, and clinical significance of m6A methylation regulators also remain largely unknown in PanNENs. In addition, recent studies have shown prognosis of immune infiltrates and immune checkpoints in pancreatic neuroendocrine tumors [23, 28]. However, the correlation between m6A regulators and immune infiltration and immune checkpoints is yet to be explored.\nTo better identify patients at risk and improve their follow-up management, we explored the expression profiles of m6A proteins in resected PanNENs and investigated the potential prognostic value of m6A proteins. We also analyzed the association among common immune infiltrates, immune checkpoints, and m6A regulators. Furthermore, a YTHDF2-based signature was established, which may help in postoperative monitoring and stratification of risks to guide clinical treatment options.\n\nMaterials and Methods:\nPatient Cohort and Follow-Up A total of 183 patients with PanNENs who were admitted to the Peking Union Medical College Hospital (PUMCH) and underwent surgeries during the time period 2004–2019 with sufficient tumor tissue for evaluation were included in the current study. The FFPE specimens and matching hematoxylin and eosin slides were retrieved. Patients' clinicopathological data, including age, sex, primary tumor site, tumor size, tumor grading, functional status, and surgical procedures, were collected from the medical records. The American Joint on Cancer Committee (AJCC) 8th edition stages for each patient were defined based on the collected data. Survival and recurrence information were obtained from medical record reviews and telephone interviews. Recurrence and distant metastasis were determined based on biochemical markers, clinical multidisciplinary consultation, radiological, and/or histological examinations. The time between the surgery and tumor recurrence or last follow-up appointment was termed as RFS. Disease-specific survival was calculated from the surgery date to the time of patient death or last follow-up till time point of December 29, 2020. This retrospective study was approved by the Institutional Review Board of PUMCH (approval number: S-K1593) and conducted in accordance with the Declaration of Helsinki. Written consent for this study was not required and formally waived by the hospital's Ethics Committee.\nA total of 183 patients with PanNENs who were admitted to the Peking Union Medical College Hospital (PUMCH) and underwent surgeries during the time period 2004–2019 with sufficient tumor tissue for evaluation were included in the current study. The FFPE specimens and matching hematoxylin and eosin slides were retrieved. Patients' clinicopathological data, including age, sex, primary tumor site, tumor size, tumor grading, functional status, and surgical procedures, were collected from the medical records. The American Joint on Cancer Committee (AJCC) 8th edition stages for each patient were defined based on the collected data. Survival and recurrence information were obtained from medical record reviews and telephone interviews. Recurrence and distant metastasis were determined based on biochemical markers, clinical multidisciplinary consultation, radiological, and/or histological examinations. The time between the surgery and tumor recurrence or last follow-up appointment was termed as RFS. Disease-specific survival was calculated from the surgery date to the time of patient death or last follow-up till time point of December 29, 2020. This retrospective study was approved by the Institutional Review Board of PUMCH (approval number: S-K1593) and conducted in accordance with the Declaration of Helsinki. Written consent for this study was not required and formally waived by the hospital's Ethics Committee.\nTissue Microarrays and Immunohistochemistry Representative areas of tumors and adjacent normal tissue were selected from hematoxylin and eosin-stained paraffin blocks and then re-embedded into recipient tissue microarray (TMA) blocks (12 × 8 arrays). The diameter of each core was 2 mm.\nImmunohistochemical analysis was performed according to the protocol described by Zong et al. [29]. The 4-μm TMA sections were deparaffinized. Heat-mediated antigen retrieval was performed using sodium citrate buffer (pH 6.0) for 10 min. The endogenous peroxidase activity was quenched using a 3% hydrogen peroxide solution (ZSGB-BIO, Beijing, China) and then the sections were blocked with 5% normal goat serum for 30 min. TMA sections were incubated with primary antibodies against m6A methylation regulators (METTL3, METTL14, METTL16, WTAP, KIAA1429, RBM15, FTO, ALKBH5, IGF2BP2, HNRNPC, YTHDC1, YTHDC2, YTHDF1, YTHDF2, and YTHDF3) and immune markers (PD-L1, B7-H3, CD3, CD4, CD15, CD68) overnight at 4°C; the details and optimal dilutions are summarized in online supplementary Table S1 (see www.karger.com/doi/10.1159/000525228 for all online suppl. material). Stromal cells, normal acinar cells, ductal cells, and islet cells from normal tissues were incubated with primary antibodies and used as internal positive controls, whereas the same tissues without primary antibodies comprised the negative controls.\nRepresentative areas of tumors and adjacent normal tissue were selected from hematoxylin and eosin-stained paraffin blocks and then re-embedded into recipient tissue microarray (TMA) blocks (12 × 8 arrays). The diameter of each core was 2 mm.\nImmunohistochemical analysis was performed according to the protocol described by Zong et al. [29]. The 4-μm TMA sections were deparaffinized. Heat-mediated antigen retrieval was performed using sodium citrate buffer (pH 6.0) for 10 min. The endogenous peroxidase activity was quenched using a 3% hydrogen peroxide solution (ZSGB-BIO, Beijing, China) and then the sections were blocked with 5% normal goat serum for 30 min. TMA sections were incubated with primary antibodies against m6A methylation regulators (METTL3, METTL14, METTL16, WTAP, KIAA1429, RBM15, FTO, ALKBH5, IGF2BP2, HNRNPC, YTHDC1, YTHDC2, YTHDF1, YTHDF2, and YTHDF3) and immune markers (PD-L1, B7-H3, CD3, CD4, CD15, CD68) overnight at 4°C; the details and optimal dilutions are summarized in online supplementary Table S1 (see www.karger.com/doi/10.1159/000525228 for all online suppl. material). Stromal cells, normal acinar cells, ductal cells, and islet cells from normal tissues were incubated with primary antibodies and used as internal positive controls, whereas the same tissues without primary antibodies comprised the negative controls.\nEvaluation of Immunostaining The immunostaining performed on the collected patient samples was independently assessed by two pathologists (XL Chen and SW Mo) who were blinded to the patients' clinicopathological features and clinical outcomes. Both pathologists reexamined the slides and reached a consensus in case of any discrepancy. The immunoreactivity of 15 m6A markers, PD-L1, and B7-H3 in tumor specimens was quantified using the method developed by Budwit-Novotny et al. [30]. In brief, the percentage of positively stained tumor cells was multiplied by the relative intensity of specific staining, which was assigned a value of negative (0), weak (1), distinct (2), and strong (3) [30, 31]. The density of stromal CD3, CD4, CD15, and CD68 were quantified in four ×400 high-power fields and the mean counts of four fields were used for statistical analysis as we described earlier [32]. The cutoff values of m6A markers and immune markers were determined using X-tile (Yale University, New Haven, CT, USA). These cutoff values are summarized in the online supplementary Table S2.\nThe immunostaining performed on the collected patient samples was independently assessed by two pathologists (XL Chen and SW Mo) who were blinded to the patients' clinicopathological features and clinical outcomes. Both pathologists reexamined the slides and reached a consensus in case of any discrepancy. The immunoreactivity of 15 m6A markers, PD-L1, and B7-H3 in tumor specimens was quantified using the method developed by Budwit-Novotny et al. [30]. In brief, the percentage of positively stained tumor cells was multiplied by the relative intensity of specific staining, which was assigned a value of negative (0), weak (1), distinct (2), and strong (3) [30, 31]. The density of stromal CD3, CD4, CD15, and CD68 were quantified in four ×400 high-power fields and the mean counts of four fields were used for statistical analysis as we described earlier [32]. The cutoff values of m6A markers and immune markers were determined using X-tile (Yale University, New Haven, CT, USA). These cutoff values are summarized in the online supplementary Table S2.\nRisk Score Models and Random Survival Forest A random survival forest (RSF) model was constructed based on the minimal depth and variable importance (VIMP). VIMP and minimal depth were used to assess the true effect of the variable on RFS. Given that feature subsets of variables are randomly selected using RSF method, RSF can process high-dimensional (many variables) data. In our current study, we included more than ten variables. Through training of the RSF algorithm, the variables which were the most related to recurrence were selected using minimal depth and VIMP. And in the training process, the interaction between variables can be detected. Moreover, when creating random forest, unbiased estimation is used for generalization error, and the model has strong generalization ability [33]. If a large part of the features is lost, the accuracy can still be maintained [33]. Based on these strengths of RSF, this method was helpful to select relapse-related variables and construct a recurrence signature. Recurrence-specific variables defined by a VIMP of >0 and minimal depth less than the depth threshold were finally entered into a survival tree analysis [33]. The recurrence-specific variables, including nodal status, tumor size, YTHDF2, were selected by using minimal depth and VIMP. By integrating the expression level of YTHDF2, nodal status, and tumor size as well as their corresponding coefficients which were derived from the multivariate Cox model constructed only using these 3 variables, a risk score was generated, as represented below: Risk Score = (1.218 × Nodal Status) + (1.625 × Tumor Size) + (0.745 × expression value of YTHDF2).\nNodal status was divided into negative/positive and scored as 0/1; tumor size was divided into <2.5 cm/≥2.5 cm and scored as 0/1, and expression value of YTHDF2 was divided into low/high and scored as 0/1, and these scores were multiplied by the corresponding coefficients to generate a risk score. The median (median = 1.625) was used as the cutoff value for the risk score.\nA random survival forest (RSF) model was constructed based on the minimal depth and variable importance (VIMP). VIMP and minimal depth were used to assess the true effect of the variable on RFS. Given that feature subsets of variables are randomly selected using RSF method, RSF can process high-dimensional (many variables) data. In our current study, we included more than ten variables. Through training of the RSF algorithm, the variables which were the most related to recurrence were selected using minimal depth and VIMP. And in the training process, the interaction between variables can be detected. Moreover, when creating random forest, unbiased estimation is used for generalization error, and the model has strong generalization ability [33]. If a large part of the features is lost, the accuracy can still be maintained [33]. Based on these strengths of RSF, this method was helpful to select relapse-related variables and construct a recurrence signature. Recurrence-specific variables defined by a VIMP of >0 and minimal depth less than the depth threshold were finally entered into a survival tree analysis [33]. The recurrence-specific variables, including nodal status, tumor size, YTHDF2, were selected by using minimal depth and VIMP. By integrating the expression level of YTHDF2, nodal status, and tumor size as well as their corresponding coefficients which were derived from the multivariate Cox model constructed only using these 3 variables, a risk score was generated, as represented below: Risk Score = (1.218 × Nodal Status) + (1.625 × Tumor Size) + (0.745 × expression value of YTHDF2).\nNodal status was divided into negative/positive and scored as 0/1; tumor size was divided into <2.5 cm/≥2.5 cm and scored as 0/1, and expression value of YTHDF2 was divided into low/high and scored as 0/1, and these scores were multiplied by the corresponding coefficients to generate a risk score. The median (median = 1.625) was used as the cutoff value for the risk score.\nStatistical Analysis The continuous and categorical variables were described as median (range) and frequency (percentage), respectively. The Mann-Whitney U test was conducted to compare nonnormally distributed continuous variables, whereas a Student's t test was used to analyze normally distributed continuous variables. The χ2 test or Fisher's exact test was used to evaluate the relationship between the expression of m6A methylation regulators and categorical variables. The survival curves were plotted by Kaplan-Meier method, and the log-rank test was employed to compare the survival curves generated. The Cox proportional hazard regression model was used to estimate the hazard ratio with a 95% confidence interval (CI) for variables associated with RFS. Potential risk factors with a p value of <0.05 in the univariate Cox analysis were entered into the multivariate Cox regression model (backward Wald) after considering collinearity among the variables. We evaluated the discrimination ability of the final model with Harrell concordance index (C-index). The area under the time-dependent receiver operating characteristic (ROC) curve at different cutoff times was measured as predictive performance. Calibration was assessed by visual examination of the calibration plot. p values of <0.05 were considered statistically significant. Statistical analyses were two-sided and accomplished using SPSS software (version 21.0; IBM Corp., Armonk, NY, USA). Statistical analyses of RNA sequencing data from International Cancer Genome Consortium (ICGC) database (https://www.icgc.org/) were conducted with R version 3.5.0 (http://www.r-project.org).\nThe continuous and categorical variables were described as median (range) and frequency (percentage), respectively. The Mann-Whitney U test was conducted to compare nonnormally distributed continuous variables, whereas a Student's t test was used to analyze normally distributed continuous variables. The χ2 test or Fisher's exact test was used to evaluate the relationship between the expression of m6A methylation regulators and categorical variables. The survival curves were plotted by Kaplan-Meier method, and the log-rank test was employed to compare the survival curves generated. The Cox proportional hazard regression model was used to estimate the hazard ratio with a 95% confidence interval (CI) for variables associated with RFS. Potential risk factors with a p value of <0.05 in the univariate Cox analysis were entered into the multivariate Cox regression model (backward Wald) after considering collinearity among the variables. We evaluated the discrimination ability of the final model with Harrell concordance index (C-index). The area under the time-dependent receiver operating characteristic (ROC) curve at different cutoff times was measured as predictive performance. Calibration was assessed by visual examination of the calibration plot. p values of <0.05 were considered statistically significant. Statistical analyses were two-sided and accomplished using SPSS software (version 21.0; IBM Corp., Armonk, NY, USA). Statistical analyses of RNA sequencing data from International Cancer Genome Consortium (ICGC) database (https://www.icgc.org/) were conducted with R version 3.5.0 (http://www.r-project.org).\n\nPatient Cohort and Follow-Up:\nA total of 183 patients with PanNENs who were admitted to the Peking Union Medical College Hospital (PUMCH) and underwent surgeries during the time period 2004–2019 with sufficient tumor tissue for evaluation were included in the current study. The FFPE specimens and matching hematoxylin and eosin slides were retrieved. Patients' clinicopathological data, including age, sex, primary tumor site, tumor size, tumor grading, functional status, and surgical procedures, were collected from the medical records. The American Joint on Cancer Committee (AJCC) 8th edition stages for each patient were defined based on the collected data. Survival and recurrence information were obtained from medical record reviews and telephone interviews. Recurrence and distant metastasis were determined based on biochemical markers, clinical multidisciplinary consultation, radiological, and/or histological examinations. The time between the surgery and tumor recurrence or last follow-up appointment was termed as RFS. Disease-specific survival was calculated from the surgery date to the time of patient death or last follow-up till time point of December 29, 2020. This retrospective study was approved by the Institutional Review Board of PUMCH (approval number: S-K1593) and conducted in accordance with the Declaration of Helsinki. Written consent for this study was not required and formally waived by the hospital's Ethics Committee.\n\nTissue Microarrays and Immunohistochemistry:\nRepresentative areas of tumors and adjacent normal tissue were selected from hematoxylin and eosin-stained paraffin blocks and then re-embedded into recipient tissue microarray (TMA) blocks (12 × 8 arrays). The diameter of each core was 2 mm.\nImmunohistochemical analysis was performed according to the protocol described by Zong et al. [29]. The 4-μm TMA sections were deparaffinized. Heat-mediated antigen retrieval was performed using sodium citrate buffer (pH 6.0) for 10 min. The endogenous peroxidase activity was quenched using a 3% hydrogen peroxide solution (ZSGB-BIO, Beijing, China) and then the sections were blocked with 5% normal goat serum for 30 min. TMA sections were incubated with primary antibodies against m6A methylation regulators (METTL3, METTL14, METTL16, WTAP, KIAA1429, RBM15, FTO, ALKBH5, IGF2BP2, HNRNPC, YTHDC1, YTHDC2, YTHDF1, YTHDF2, and YTHDF3) and immune markers (PD-L1, B7-H3, CD3, CD4, CD15, CD68) overnight at 4°C; the details and optimal dilutions are summarized in online supplementary Table S1 (see www.karger.com/doi/10.1159/000525228 for all online suppl. material). Stromal cells, normal acinar cells, ductal cells, and islet cells from normal tissues were incubated with primary antibodies and used as internal positive controls, whereas the same tissues without primary antibodies comprised the negative controls.\n\nEvaluation of Immunostaining:\nThe immunostaining performed on the collected patient samples was independently assessed by two pathologists (XL Chen and SW Mo) who were blinded to the patients' clinicopathological features and clinical outcomes. Both pathologists reexamined the slides and reached a consensus in case of any discrepancy. The immunoreactivity of 15 m6A markers, PD-L1, and B7-H3 in tumor specimens was quantified using the method developed by Budwit-Novotny et al. [30]. In brief, the percentage of positively stained tumor cells was multiplied by the relative intensity of specific staining, which was assigned a value of negative (0), weak (1), distinct (2), and strong (3) [30, 31]. The density of stromal CD3, CD4, CD15, and CD68 were quantified in four ×400 high-power fields and the mean counts of four fields were used for statistical analysis as we described earlier [32]. The cutoff values of m6A markers and immune markers were determined using X-tile (Yale University, New Haven, CT, USA). These cutoff values are summarized in the online supplementary Table S2.\n\nRisk Score Models and Random Survival Forest:\nA random survival forest (RSF) model was constructed based on the minimal depth and variable importance (VIMP). VIMP and minimal depth were used to assess the true effect of the variable on RFS. Given that feature subsets of variables are randomly selected using RSF method, RSF can process high-dimensional (many variables) data. In our current study, we included more than ten variables. Through training of the RSF algorithm, the variables which were the most related to recurrence were selected using minimal depth and VIMP. And in the training process, the interaction between variables can be detected. Moreover, when creating random forest, unbiased estimation is used for generalization error, and the model has strong generalization ability [33]. If a large part of the features is lost, the accuracy can still be maintained [33]. Based on these strengths of RSF, this method was helpful to select relapse-related variables and construct a recurrence signature. Recurrence-specific variables defined by a VIMP of >0 and minimal depth less than the depth threshold were finally entered into a survival tree analysis [33]. The recurrence-specific variables, including nodal status, tumor size, YTHDF2, were selected by using minimal depth and VIMP. By integrating the expression level of YTHDF2, nodal status, and tumor size as well as their corresponding coefficients which were derived from the multivariate Cox model constructed only using these 3 variables, a risk score was generated, as represented below: Risk Score = (1.218 × Nodal Status) + (1.625 × Tumor Size) + (0.745 × expression value of YTHDF2).\nNodal status was divided into negative/positive and scored as 0/1; tumor size was divided into <2.5 cm/≥2.5 cm and scored as 0/1, and expression value of YTHDF2 was divided into low/high and scored as 0/1, and these scores were multiplied by the corresponding coefficients to generate a risk score. The median (median = 1.625) was used as the cutoff value for the risk score.\n\nStatistical Analysis:\nThe continuous and categorical variables were described as median (range) and frequency (percentage), respectively. The Mann-Whitney U test was conducted to compare nonnormally distributed continuous variables, whereas a Student's t test was used to analyze normally distributed continuous variables. The χ2 test or Fisher's exact test was used to evaluate the relationship between the expression of m6A methylation regulators and categorical variables. The survival curves were plotted by Kaplan-Meier method, and the log-rank test was employed to compare the survival curves generated. The Cox proportional hazard regression model was used to estimate the hazard ratio with a 95% confidence interval (CI) for variables associated with RFS. Potential risk factors with a p value of <0.05 in the univariate Cox analysis were entered into the multivariate Cox regression model (backward Wald) after considering collinearity among the variables. We evaluated the discrimination ability of the final model with Harrell concordance index (C-index). The area under the time-dependent receiver operating characteristic (ROC) curve at different cutoff times was measured as predictive performance. Calibration was assessed by visual examination of the calibration plot. p values of <0.05 were considered statistically significant. Statistical analyses were two-sided and accomplished using SPSS software (version 21.0; IBM Corp., Armonk, NY, USA). Statistical analyses of RNA sequencing data from International Cancer Genome Consortium (ICGC) database (https://www.icgc.org/) were conducted with R version 3.5.0 (http://www.r-project.org).\n\nResults:\nPatients' Characteristics A total of 96 patients were male (52.4%), whereas 87 patients were female (47.6%). The tumor diameter was >2.5 cm in 53.1% of the patients (97/183). The number of positive lymph nodes ranged from 1 to 29. A total of 50.7% tumors were nonfunctional, and the majority of functional tumors were classified as insulinoma (76/90). Most of the tumors (175/183) were well differentiated, and eight were poorly differentiated (Table 1). The median follow-up time was 39 months (range: 1–197 months), during which there were 43 relapses and 10 deaths, and all of the deaths were owing to disease progression. The 3- and 5-year RFS rates were 83.6% and 77.0%, respectively.\nA total of 96 patients were male (52.4%), whereas 87 patients were female (47.6%). The tumor diameter was >2.5 cm in 53.1% of the patients (97/183). The number of positive lymph nodes ranged from 1 to 29. A total of 50.7% tumors were nonfunctional, and the majority of functional tumors were classified as insulinoma (76/90). Most of the tumors (175/183) were well differentiated, and eight were poorly differentiated (Table 1). The median follow-up time was 39 months (range: 1–197 months), during which there were 43 relapses and 10 deaths, and all of the deaths were owing to disease progression. The 3- and 5-year RFS rates were 83.6% and 77.0%, respectively.\nExpression Patterns of the 15 m6A Regulators We found that the expression levels of m6A proteins in PanNENs and normal tissues were evident (Fig. 1a). The expression levels of writers (i.e., KIAA1429, METTL14, RBM15, WTAP, and METTL16), readers (i.e., IGF2BP2, HNRNPC, YTHDC1, YTHDC2, YTHDF1, YTHDF2, and YTHDF3), and erasers (ALKBH5 and FTO) were higher in PanNEN tissues than in normal tissues (p < 0.001). No statistically significant difference was evident between the normal and PanNENs tissues in the context of METTL3 expression level. Regarding METTL3, METTL14, METTL16, WTAP, RBM15, and KIAA1429 expression, a high level was observed in 26.7%, 11.5%, 35.5%, 64.4%, 67.7%, and 55.7% of tumors, respectively. Among the 183 tumors, 36.6% were deemed to have high FTO expression and 72.1% had high ALKBH5 expression. Regarding reader molecules (i.e., IGF2BP2, HNRNPC, YTHDC1, YTHDC2, YTHDF1, YTHDF2, and YTHDF3), immunostaining revealed low expression in 64.5%, 95.1%, 72.1%, 57.9%, 84.7%, 50.3%, and 96.2%, respectively, of these samples and high expression in the remaining samples (Fig. 1b). The immunohistochemical patterns of the 15 m6A proteins are shown in Figure 1c and online supplementary Figure S1. All 183 PanNENs showed nuclear immunoreactivity for METTL3, METTL14, METTL16, WTAP, RBM15, KIAA1429, FTO, ALKBH5, HNRNPC, and YTHDC1. The positive sites of IGF2BP2, YTHDC2, YTHDF1, YTHDF2, and YTHDF3 were located in the cytoplasm.\nWe found that the expression levels of m6A proteins in PanNENs and normal tissues were evident (Fig. 1a). The expression levels of writers (i.e., KIAA1429, METTL14, RBM15, WTAP, and METTL16), readers (i.e., IGF2BP2, HNRNPC, YTHDC1, YTHDC2, YTHDF1, YTHDF2, and YTHDF3), and erasers (ALKBH5 and FTO) were higher in PanNEN tissues than in normal tissues (p < 0.001). No statistically significant difference was evident between the normal and PanNENs tissues in the context of METTL3 expression level. Regarding METTL3, METTL14, METTL16, WTAP, RBM15, and KIAA1429 expression, a high level was observed in 26.7%, 11.5%, 35.5%, 64.4%, 67.7%, and 55.7% of tumors, respectively. Among the 183 tumors, 36.6% were deemed to have high FTO expression and 72.1% had high ALKBH5 expression. Regarding reader molecules (i.e., IGF2BP2, HNRNPC, YTHDC1, YTHDC2, YTHDF1, YTHDF2, and YTHDF3), immunostaining revealed low expression in 64.5%, 95.1%, 72.1%, 57.9%, 84.7%, 50.3%, and 96.2%, respectively, of these samples and high expression in the remaining samples (Fig. 1b). The immunohistochemical patterns of the 15 m6A proteins are shown in Figure 1c and online supplementary Figure S1. All 183 PanNENs showed nuclear immunoreactivity for METTL3, METTL14, METTL16, WTAP, RBM15, KIAA1429, FTO, ALKBH5, HNRNPC, and YTHDC1. The positive sites of IGF2BP2, YTHDC2, YTHDF1, YTHDF2, and YTHDF3 were located in the cytoplasm.\nAssociations among m6A Regulators, Immune Markers, and Clinicopathological Parameters in PanNENs Among the 15 proteins studied, only YTHDF2 and HNRNPC expressions had significant prognostic value. Of the 43 patients with recurrence, 12 (27.9%) showed low YTHDF2 expression, which was dramatically lower than the proportion of patients without recurrence having low YTHDF2 expression (85 of 140, 60.7%; p = 0.003; Fig. 1d). Similarly, 88.4% of the patients (38 of 43 patients) with recurrence showed low tumoral HNRNPC expression; in contrast, 97.1% of the patients without recurrence (136 of 140 patients) showed low tumoral expression of HNRNPC (p = 0.034; Fig. 1d). The log-rank test revealed that both high HNRNPC and YTHDF2 expressions predicted a significantly shortened RFS (p = 0.003 and p = 0.001, respectively; Fig. 1e, f). In the correlation analysis between YTHDF2 and HNRNPC expression and clinicopathological variables, we found that YTHDF2 expression was positively associated with tumor size (p = 0.021), nodal status (p = 0.007), functional status (p = 0.010), Ki67 index (p = 0.023), and liver metastasis (p < 0.001). There were no significant associations between YTHDF2 expression and the other clinicopathological variables, including age, sex, tumor site, lymphovascular invasion (LVI), and perineural invasion (PNI) (online suppl. Table S3).\nHigh YTHDF2 expression was associated with higher number of CD3+ T cells (p = 0.003). There were no significant associations between YTHDF2 expression and other immune markers (PD-L1, B7-H3, CD3, CD4, and CD68). However, high HNRNPC expression was significantly correlated with positive PD-L1 expression (p = 0.039) and high densities of CD3+ T cells. Moreover, there were no significant associations between HNRNPC expression and other immune markers (Table 2). Representative images of PD-L1, B7-H3 CD3, CD4, CD15, and CD68 are presented in online supplementary Figure S2.\nAmong the 15 proteins studied, only YTHDF2 and HNRNPC expressions had significant prognostic value. Of the 43 patients with recurrence, 12 (27.9%) showed low YTHDF2 expression, which was dramatically lower than the proportion of patients without recurrence having low YTHDF2 expression (85 of 140, 60.7%; p = 0.003; Fig. 1d). Similarly, 88.4% of the patients (38 of 43 patients) with recurrence showed low tumoral HNRNPC expression; in contrast, 97.1% of the patients without recurrence (136 of 140 patients) showed low tumoral expression of HNRNPC (p = 0.034; Fig. 1d). The log-rank test revealed that both high HNRNPC and YTHDF2 expressions predicted a significantly shortened RFS (p = 0.003 and p = 0.001, respectively; Fig. 1e, f). In the correlation analysis between YTHDF2 and HNRNPC expression and clinicopathological variables, we found that YTHDF2 expression was positively associated with tumor size (p = 0.021), nodal status (p = 0.007), functional status (p = 0.010), Ki67 index (p = 0.023), and liver metastasis (p < 0.001). There were no significant associations between YTHDF2 expression and the other clinicopathological variables, including age, sex, tumor site, lymphovascular invasion (LVI), and perineural invasion (PNI) (online suppl. Table S3).\nHigh YTHDF2 expression was associated with higher number of CD3+ T cells (p = 0.003). There were no significant associations between YTHDF2 expression and other immune markers (PD-L1, B7-H3, CD3, CD4, and CD68). However, high HNRNPC expression was significantly correlated with positive PD-L1 expression (p = 0.039) and high densities of CD3+ T cells. Moreover, there were no significant associations between HNRNPC expression and other immune markers (Table 2). Representative images of PD-L1, B7-H3 CD3, CD4, CD15, and CD68 are presented in online supplementary Figure S2.\nPrognostic Predictors of RFS in PanNENs A univariate Cox analysis for both the clinicopathological parameters and m6A proteins was conducted. Nine clinicopathological factors (tumor size, lymph node status, PNI, LVI, grade, functional status, liver metastasis, and Ki67 index) and five m6A proteins (METTL14, METTL16, ALKBH5, HNRNPC, YTHDF1, YTHDF2, and YTHDF3) were identified to be associated with RFS, with p values of <0.05 (Table 3). Considering analysis of collinear factors, tumor grade was found to be associated with Ki67 index and liver metastasis, and LVI was associated with lymph node status. Therefore, PNI, tumor size, lymph node status, grade, functional status, METTL14, METTL16, ALKBH5, HNRNPC, YTHDF1, YTHDF2, and YTHDF3 were included in the multivariate Cox regression model. Multivariate Cox regression analysis identified tumor size, lymph node status, tumor grade, PNI, HNRNPC expression, and YTHDF2 expression as independent risk factors for RFS after excluding collinear factors. Next, we applied an RSF model to evaluate the importance of these independent risk factors. In the analysis of variable of importance, we found that VIMP of HNRNPC was −0.0011 (Fig. 2a). In the minimal depth analysis, HNRNPC, PNI, and grade had values of 2.405, 2.482, and 2.183, respectively, which were very close to or more than the depth threshold (Fig. 2b). Therefore, they were all excluded from the RSF model. We also performed survival analyses to determine the prognostic value of YTHDF2 mRNA expression in patients with PanNET in ICGC cohort. The Kaplan-Meier plot revealed that high YTHDF2 mRNA expression was significantly associated with poor RFS (p = 0.017, Fig. 2c). Furthermore, three variables (tumor size, lymph node status, and YTHDF2 expression) significantly affected RFS. Nodal status had the largest effect on RFS, followed by YTHDF2 expression and tumor size.\nA univariate Cox analysis for both the clinicopathological parameters and m6A proteins was conducted. Nine clinicopathological factors (tumor size, lymph node status, PNI, LVI, grade, functional status, liver metastasis, and Ki67 index) and five m6A proteins (METTL14, METTL16, ALKBH5, HNRNPC, YTHDF1, YTHDF2, and YTHDF3) were identified to be associated with RFS, with p values of <0.05 (Table 3). Considering analysis of collinear factors, tumor grade was found to be associated with Ki67 index and liver metastasis, and LVI was associated with lymph node status. Therefore, PNI, tumor size, lymph node status, grade, functional status, METTL14, METTL16, ALKBH5, HNRNPC, YTHDF1, YTHDF2, and YTHDF3 were included in the multivariate Cox regression model. Multivariate Cox regression analysis identified tumor size, lymph node status, tumor grade, PNI, HNRNPC expression, and YTHDF2 expression as independent risk factors for RFS after excluding collinear factors. Next, we applied an RSF model to evaluate the importance of these independent risk factors. In the analysis of variable of importance, we found that VIMP of HNRNPC was −0.0011 (Fig. 2a). In the minimal depth analysis, HNRNPC, PNI, and grade had values of 2.405, 2.482, and 2.183, respectively, which were very close to or more than the depth threshold (Fig. 2b). Therefore, they were all excluded from the RSF model. We also performed survival analyses to determine the prognostic value of YTHDF2 mRNA expression in patients with PanNET in ICGC cohort. The Kaplan-Meier plot revealed that high YTHDF2 mRNA expression was significantly associated with poor RFS (p = 0.017, Fig. 2c). Furthermore, three variables (tumor size, lymph node status, and YTHDF2 expression) significantly affected RFS. Nodal status had the largest effect on RFS, followed by YTHDF2 expression and tumor size.\nConstruction and Evaluation of the Prognostic Model for Predicting RFS in PanNENs To construct a recurrence signature that can classify patients into subpopulations according to RFS, we further performed a recursive partitioning analysis. After pruning the decision trees using the post-pruning method, five terminal nodes representing a recurrence signature were identified (Fig. 2d). LNposTumorSize<2.5 cm (node 1; 20.8 months) and LNposTumorSize≥2.5 cmYTHDF2high (node 3; 21.1 months) patients had worse median RFS than LNnegYTHDF2high patients (node 5; 39.6 months, node 1 vs. node 5, p < 0.001), LNnegYTHDF2low patients (node 4; 43.2 months), and LNposTumorSize≥2.5 cmYTHDF2low patients (node 2; 29.3 months). Patients with the LNnegYTHDF2high (node 5) signature had a 5-year RFS rate of 92.1%, whereas patients with the LNposTumorSize<2.5 cm (node 1) signature had the worst 5-year RFS rate of 0% (Fig. 3a). To better identify risk groups for the recurrence signatures, we performed pair-wise comparisons for each recurrence signature subpopulation (on RFS) and the corresponding risk score of recurrence. The results showed that patients within the five terminal nodes can be categorized into three risk groups: low (node 4), intermediate (node 2 and node 5), and high (node 1 and node 3) with well-separated RFS curves (p < 0.001; Fig. 3b). Among the risk groups, the 5-year RFS rates were 6.0%, 70.4%, and 90.1% for the high-, intermediate-, and low-risk groups, respectively. To further test our proposed recurrence signature, we performed a subanalysis on a specific group of patients with nonsyndromic and nonfunctional PanNENs. A total of 93 PanNENs from the full cohort were eligible for this analysis (online suppl. Table S4). The Kaplan-Meier RFS curves showed clear separation among the risk groups (p = 0.018; Fig. 3c).\nFurthermore, a risk score was generated to evaluate the risk of recurrence based on variables selected from the survival tree analysis: Risk Score = (1.218 × Nodal Status) + (1.625 × Tumor Size) + (0.745 × expression value of YTHDF2) (Table 4). Multivariate analysis using a Cox proportional hazards model was performed which included PNI, tumor size, nodal status, grade, functional status, METTL14, METTL16, ALKBH5, HNRNPC, YTHDF1, YTHDF2, YTHDF3, and the risk score. The risk score was determined to be an independent prognostic factor for patients with resected PanNEN in our study based on the multivariate Cox regression, and higher risk scores were found to be associated with shorter survival (hazard ratio: 33.04, 95% CI: 4.341–251.434; p = 0.001) (Table 5). Specificity and sensitivity comparisons were performed via time-dependent ROC curve analysis of risk score. The predictive accuracy of the recurrence signature was relatively high and the area under ROC curve was 0.858 (95% CI: 0.747–0.913) at 1 year, 0.824 (95% CI: 0.754–0.912) at 3 years, and 0.870 (95% CI: 0.762–0.915) at 5 years (Fig. 3d). The C-index was 0.978 (95% CI: 0.936–1), suggesting good discrimination ability. The calibration curves also showed good agreement between the predicted and observed RFS (Fig. 3e).\nTo construct a recurrence signature that can classify patients into subpopulations according to RFS, we further performed a recursive partitioning analysis. After pruning the decision trees using the post-pruning method, five terminal nodes representing a recurrence signature were identified (Fig. 2d). LNposTumorSize<2.5 cm (node 1; 20.8 months) and LNposTumorSize≥2.5 cmYTHDF2high (node 3; 21.1 months) patients had worse median RFS than LNnegYTHDF2high patients (node 5; 39.6 months, node 1 vs. node 5, p < 0.001), LNnegYTHDF2low patients (node 4; 43.2 months), and LNposTumorSize≥2.5 cmYTHDF2low patients (node 2; 29.3 months). Patients with the LNnegYTHDF2high (node 5) signature had a 5-year RFS rate of 92.1%, whereas patients with the LNposTumorSize<2.5 cm (node 1) signature had the worst 5-year RFS rate of 0% (Fig. 3a). To better identify risk groups for the recurrence signatures, we performed pair-wise comparisons for each recurrence signature subpopulation (on RFS) and the corresponding risk score of recurrence. The results showed that patients within the five terminal nodes can be categorized into three risk groups: low (node 4), intermediate (node 2 and node 5), and high (node 1 and node 3) with well-separated RFS curves (p < 0.001; Fig. 3b). Among the risk groups, the 5-year RFS rates were 6.0%, 70.4%, and 90.1% for the high-, intermediate-, and low-risk groups, respectively. To further test our proposed recurrence signature, we performed a subanalysis on a specific group of patients with nonsyndromic and nonfunctional PanNENs. A total of 93 PanNENs from the full cohort were eligible for this analysis (online suppl. Table S4). The Kaplan-Meier RFS curves showed clear separation among the risk groups (p = 0.018; Fig. 3c).\nFurthermore, a risk score was generated to evaluate the risk of recurrence based on variables selected from the survival tree analysis: Risk Score = (1.218 × Nodal Status) + (1.625 × Tumor Size) + (0.745 × expression value of YTHDF2) (Table 4). Multivariate analysis using a Cox proportional hazards model was performed which included PNI, tumor size, nodal status, grade, functional status, METTL14, METTL16, ALKBH5, HNRNPC, YTHDF1, YTHDF2, YTHDF3, and the risk score. The risk score was determined to be an independent prognostic factor for patients with resected PanNEN in our study based on the multivariate Cox regression, and higher risk scores were found to be associated with shorter survival (hazard ratio: 33.04, 95% CI: 4.341–251.434; p = 0.001) (Table 5). Specificity and sensitivity comparisons were performed via time-dependent ROC curve analysis of risk score. The predictive accuracy of the recurrence signature was relatively high and the area under ROC curve was 0.858 (95% CI: 0.747–0.913) at 1 year, 0.824 (95% CI: 0.754–0.912) at 3 years, and 0.870 (95% CI: 0.762–0.915) at 5 years (Fig. 3d). The C-index was 0.978 (95% CI: 0.936–1), suggesting good discrimination ability. The calibration curves also showed good agreement between the predicted and observed RFS (Fig. 3e).\n\nPatients' Characteristics:\nA total of 96 patients were male (52.4%), whereas 87 patients were female (47.6%). The tumor diameter was >2.5 cm in 53.1% of the patients (97/183). The number of positive lymph nodes ranged from 1 to 29. A total of 50.7% tumors were nonfunctional, and the majority of functional tumors were classified as insulinoma (76/90). Most of the tumors (175/183) were well differentiated, and eight were poorly differentiated (Table 1). The median follow-up time was 39 months (range: 1–197 months), during which there were 43 relapses and 10 deaths, and all of the deaths were owing to disease progression. The 3- and 5-year RFS rates were 83.6% and 77.0%, respectively.\n\nExpression Patterns of the 15 m6A Regulators:\nWe found that the expression levels of m6A proteins in PanNENs and normal tissues were evident (Fig. 1a). The expression levels of writers (i.e., KIAA1429, METTL14, RBM15, WTAP, and METTL16), readers (i.e., IGF2BP2, HNRNPC, YTHDC1, YTHDC2, YTHDF1, YTHDF2, and YTHDF3), and erasers (ALKBH5 and FTO) were higher in PanNEN tissues than in normal tissues (p < 0.001). No statistically significant difference was evident between the normal and PanNENs tissues in the context of METTL3 expression level. Regarding METTL3, METTL14, METTL16, WTAP, RBM15, and KIAA1429 expression, a high level was observed in 26.7%, 11.5%, 35.5%, 64.4%, 67.7%, and 55.7% of tumors, respectively. Among the 183 tumors, 36.6% were deemed to have high FTO expression and 72.1% had high ALKBH5 expression. Regarding reader molecules (i.e., IGF2BP2, HNRNPC, YTHDC1, YTHDC2, YTHDF1, YTHDF2, and YTHDF3), immunostaining revealed low expression in 64.5%, 95.1%, 72.1%, 57.9%, 84.7%, 50.3%, and 96.2%, respectively, of these samples and high expression in the remaining samples (Fig. 1b). The immunohistochemical patterns of the 15 m6A proteins are shown in Figure 1c and online supplementary Figure S1. All 183 PanNENs showed nuclear immunoreactivity for METTL3, METTL14, METTL16, WTAP, RBM15, KIAA1429, FTO, ALKBH5, HNRNPC, and YTHDC1. The positive sites of IGF2BP2, YTHDC2, YTHDF1, YTHDF2, and YTHDF3 were located in the cytoplasm.\n\nAssociations among m6A Regulators, Immune Markers, and Clinicopathological Parameters in PanNENs:\nAmong the 15 proteins studied, only YTHDF2 and HNRNPC expressions had significant prognostic value. Of the 43 patients with recurrence, 12 (27.9%) showed low YTHDF2 expression, which was dramatically lower than the proportion of patients without recurrence having low YTHDF2 expression (85 of 140, 60.7%; p = 0.003; Fig. 1d). Similarly, 88.4% of the patients (38 of 43 patients) with recurrence showed low tumoral HNRNPC expression; in contrast, 97.1% of the patients without recurrence (136 of 140 patients) showed low tumoral expression of HNRNPC (p = 0.034; Fig. 1d). The log-rank test revealed that both high HNRNPC and YTHDF2 expressions predicted a significantly shortened RFS (p = 0.003 and p = 0.001, respectively; Fig. 1e, f). In the correlation analysis between YTHDF2 and HNRNPC expression and clinicopathological variables, we found that YTHDF2 expression was positively associated with tumor size (p = 0.021), nodal status (p = 0.007), functional status (p = 0.010), Ki67 index (p = 0.023), and liver metastasis (p < 0.001). There were no significant associations between YTHDF2 expression and the other clinicopathological variables, including age, sex, tumor site, lymphovascular invasion (LVI), and perineural invasion (PNI) (online suppl. Table S3).\nHigh YTHDF2 expression was associated with higher number of CD3+ T cells (p = 0.003). There were no significant associations between YTHDF2 expression and other immune markers (PD-L1, B7-H3, CD3, CD4, and CD68). However, high HNRNPC expression was significantly correlated with positive PD-L1 expression (p = 0.039) and high densities of CD3+ T cells. Moreover, there were no significant associations between HNRNPC expression and other immune markers (Table 2). Representative images of PD-L1, B7-H3 CD3, CD4, CD15, and CD68 are presented in online supplementary Figure S2.\n\nPrognostic Predictors of RFS in PanNENs:\nA univariate Cox analysis for both the clinicopathological parameters and m6A proteins was conducted. Nine clinicopathological factors (tumor size, lymph node status, PNI, LVI, grade, functional status, liver metastasis, and Ki67 index) and five m6A proteins (METTL14, METTL16, ALKBH5, HNRNPC, YTHDF1, YTHDF2, and YTHDF3) were identified to be associated with RFS, with p values of <0.05 (Table 3). Considering analysis of collinear factors, tumor grade was found to be associated with Ki67 index and liver metastasis, and LVI was associated with lymph node status. Therefore, PNI, tumor size, lymph node status, grade, functional status, METTL14, METTL16, ALKBH5, HNRNPC, YTHDF1, YTHDF2, and YTHDF3 were included in the multivariate Cox regression model. Multivariate Cox regression analysis identified tumor size, lymph node status, tumor grade, PNI, HNRNPC expression, and YTHDF2 expression as independent risk factors for RFS after excluding collinear factors. Next, we applied an RSF model to evaluate the importance of these independent risk factors. In the analysis of variable of importance, we found that VIMP of HNRNPC was −0.0011 (Fig. 2a). In the minimal depth analysis, HNRNPC, PNI, and grade had values of 2.405, 2.482, and 2.183, respectively, which were very close to or more than the depth threshold (Fig. 2b). Therefore, they were all excluded from the RSF model. We also performed survival analyses to determine the prognostic value of YTHDF2 mRNA expression in patients with PanNET in ICGC cohort. The Kaplan-Meier plot revealed that high YTHDF2 mRNA expression was significantly associated with poor RFS (p = 0.017, Fig. 2c). Furthermore, three variables (tumor size, lymph node status, and YTHDF2 expression) significantly affected RFS. Nodal status had the largest effect on RFS, followed by YTHDF2 expression and tumor size.\n\nConstruction and Evaluation of the Prognostic Model for Predicting RFS in PanNENs:\nTo construct a recurrence signature that can classify patients into subpopulations according to RFS, we further performed a recursive partitioning analysis. After pruning the decision trees using the post-pruning method, five terminal nodes representing a recurrence signature were identified (Fig. 2d). LNposTumorSize<2.5 cm (node 1; 20.8 months) and LNposTumorSize≥2.5 cmYTHDF2high (node 3; 21.1 months) patients had worse median RFS than LNnegYTHDF2high patients (node 5; 39.6 months, node 1 vs. node 5, p < 0.001), LNnegYTHDF2low patients (node 4; 43.2 months), and LNposTumorSize≥2.5 cmYTHDF2low patients (node 2; 29.3 months). Patients with the LNnegYTHDF2high (node 5) signature had a 5-year RFS rate of 92.1%, whereas patients with the LNposTumorSize<2.5 cm (node 1) signature had the worst 5-year RFS rate of 0% (Fig. 3a). To better identify risk groups for the recurrence signatures, we performed pair-wise comparisons for each recurrence signature subpopulation (on RFS) and the corresponding risk score of recurrence. The results showed that patients within the five terminal nodes can be categorized into three risk groups: low (node 4), intermediate (node 2 and node 5), and high (node 1 and node 3) with well-separated RFS curves (p < 0.001; Fig. 3b). Among the risk groups, the 5-year RFS rates were 6.0%, 70.4%, and 90.1% for the high-, intermediate-, and low-risk groups, respectively. To further test our proposed recurrence signature, we performed a subanalysis on a specific group of patients with nonsyndromic and nonfunctional PanNENs. A total of 93 PanNENs from the full cohort were eligible for this analysis (online suppl. Table S4). The Kaplan-Meier RFS curves showed clear separation among the risk groups (p = 0.018; Fig. 3c).\nFurthermore, a risk score was generated to evaluate the risk of recurrence based on variables selected from the survival tree analysis: Risk Score = (1.218 × Nodal Status) + (1.625 × Tumor Size) + (0.745 × expression value of YTHDF2) (Table 4). Multivariate analysis using a Cox proportional hazards model was performed which included PNI, tumor size, nodal status, grade, functional status, METTL14, METTL16, ALKBH5, HNRNPC, YTHDF1, YTHDF2, YTHDF3, and the risk score. The risk score was determined to be an independent prognostic factor for patients with resected PanNEN in our study based on the multivariate Cox regression, and higher risk scores were found to be associated with shorter survival (hazard ratio: 33.04, 95% CI: 4.341–251.434; p = 0.001) (Table 5). Specificity and sensitivity comparisons were performed via time-dependent ROC curve analysis of risk score. The predictive accuracy of the recurrence signature was relatively high and the area under ROC curve was 0.858 (95% CI: 0.747–0.913) at 1 year, 0.824 (95% CI: 0.754–0.912) at 3 years, and 0.870 (95% CI: 0.762–0.915) at 5 years (Fig. 3d). The C-index was 0.978 (95% CI: 0.936–1), suggesting good discrimination ability. The calibration curves also showed good agreement between the predicted and observed RFS (Fig. 3e).\n\nDiscussion/Conclusion:\nIdentification of predictors of relapse in patients with PanNEN is important. Previous studies that investigated risk predictors identified that tumor size, lymph node status, WHO grade, Ki67 index, tertiary lymphoid structures, and tumor-infiltrating neutrophils may predict recurrence [34, 35, 36, 37]. However, a practical and effective model is urgently needed. In our study, we constructed a risk model for recurrence with a corresponding risk score to stratify patients on the basis of RFS. The recurrence signature classifies patients into three risk groups independent of grade, with each group representing a distinct RFS outcome.\nThis is the first large-scale study exploring 15 m6A proteins as potential relapse biomarkers for PanNENs, to the best of our knowledge. To date, all of these 15 proteins have not been described in PanNENs. High expression of METTL14 was observed in 11.5% of the entire cohort, and high METTL14 expression predicted poor RFS outcomes (p = 0.024), similar to pancreatic cancer [38], whereas there are opposite results in other types of cancers, such as CRC [39] and bladder cancer [40]. METTL16, another m6A methyltransferase, was also a predictor of relapse and associated with poor RFS in this cohort (p = 0.023); however, recent studies suggested that METTL16 deletion is correlated with poor disease-specific survival or RFS in hepatocellular carcinoma [41], and no difference was found between patients with METTL16-high and METTL16-low CRC [42]. We found that patients with low ALKBH5 expression had a better clinical outcome. Recent studies have also suggested that YTHDF1 and YTHDF3 play a vital role in various types of cancers [43, 44]. Liu et al. [44] showed that YTHDF1 promotes ovarian cancer progression by augmenting EIF3C translation, and its high expression indicated a poor clinical outcome. The expression of YTHDF1 and YTHDF3 was reported to be associated with poor prognosis in breast cancer patients [43]. We attempted to examine the prognostic value of this factor in patients with resected PanNENs. We found that the high expression of both YTHDF1 and YTHDF3 predicted extremely low RFS (p = 0.003 and p = 0.002).\nYTHDF2 is the most effective m6A reader that can selectively bind to the m6A site. Recent reports have described the ability of YTHDF2 to degrade both tumor promoter as well as suppressor gene mRNAs and adversely affect inflammatory reaction, vascular abnormalization, and self-renewal of leukemic stem cells [45, 46, 47, 48, 49]. Compared with normal tissues, YTHDF2 expression was upregulated in prostate cancer, pancreatic cancer, and lung adenocarcinoma; moreover, high YTHDF2 expression was associated with unfavorable clinicopathological parameters and poor survival, which was consistent with our study [50, 51, 52]. However, Shen et al. [53] found that the expression level of YTHDF2 was lower in gastric cancer than in normal gastric tissues, and low YTHDF2 expression was correlated with poor prognosis. In our PanNENs, high YTHDF2 expression was observed in 49.7% of the tumors; high YTHDF2 expression was strongly associated with poorer RFS (p = 0.0013), and in an independent ICGC cohort, the same correlation was observed (p = 0.017), which validated this finding. We also identified associations between YTHDF2 expression and clinicopathological features, including liver metastasis (p < 0.001), although we did not determine YTHDF2 expression in metastatic lesions. After adjusting for other covariables, high YTHDF2 expression was identified as an independent risk factor for recurrence in patients with PanNENs, suggesting the role of YTHDF2 in metastasis. Recent studies revealed complex biological functions of YTHDF2 in different types of cancers. YTHDF2 could mediate mRNA degradation of tumor suppressors (LHPP and NKX3-1) to promote the proliferation and migration of prostate cancer cells [50]. In pancreatic cancer, YTHDF2 knockdown resulted in epithelial-mesenchymal transition through YAP pathway to promote the invasion and migration of cancer cells [51]. Another study showed that upregulated YTHDF2 decreased FOXC2 expression level to inhibit the proliferation, invasion, and migration of gastric cancer cells [53]. However, the regulatory mechanisms of YTHDF2 require further investigations in PanNENs.\nAnother finding is the discovery that HNRNPC has prognostic significance in PanNEN recurrence. High HNRNPC expression was observed more frequently in patients with recurrence than in those without recurrence and was associated with a significantly shortened RFS. HNRNPC contributes to tumorigenesis, which may partially explain the association between high HNRNPC expression and the high recurrence rate in PanNENs. Additionally, we observed a positive correlation between HNRNPC expression and tumoral PD-L1 expression (p < 0.001). No existing literature describes the influence of HNRNPC on PD-L1 expression of PanNEN cells. Therefore, detailed mechanistic investigations are required to understand the functional link between HNRNPC and PD-L1 in PanNENs.\nThe current clinical practice of regular follow-up is the main method for postoperative monitoring of patients and allows early identification of recurrence. However, an important concern with postoperative monitoring of patients is the balance of effectiveness against cost because the radiation exposure over prolonged follow-up periods can be harmful and detrimental to the patient [54]. Current international guidelines of the European Society for Medical Oncology provide follow-up recommendations after PanNEN resection based on grade [55]. In our study, PanNENs were classified into three risk groups: low-, intermediate-, and high-risk groups. Interestingly, grade 2 tumors were distributed across different recurrence risk groups. These results imply that owing to significant heterogeneity in disease behavior, particularly with grade 2 tumors, such distinctions are insufficient and postoperative surveillance should not merely be based on grade. Considering the high accuracy of recurrence signature and its independence from grade, postoperative follow-up regimens could be customized on the basis of these alternative recurrence signatures and risk groups. In addition, adjuvant therapy for high-risk patients may be useful in improving clinical outcomes, although this strategy will need to be validated prospectively in future studies.\nThe high YTHDF2 expression (47.1%) in patients with PanNEN and its correlation with recurrence may have important therapeutic implications with potential for development and clinical use of YTHDF2 inhibitors for the treatment of high YTHDF2-expressing tumors. High HNRNPC expression was correlated with recurrence and PD-L1 expression, which may allow the development of a combinatorial therapeutic regimen using HNRNPC inhibitor and PD-L1 monoclonal antibody. Recent studies have shown that genomic mutations in PanNENs can be relevant to classify patients beyond their tumor grade and identify novel prognostic markers and therapeutic targets that could be relevant in the future for adjuvant therapy [56]. It will be interesting to investigate whether such a clinical approach is feasible in PanNENs with high HNRNPC expression in prospective clinical trials.\nSome limitations exist in the current study, despite several valuable findings. First, its retrospective nature has inherent limitations. Second, the study was performed in patients from a single institution. Although we conducted a subgroup analysis to validate the recurrence signature in a selected group, additional external validation is required to investigate whether these signatures are useful markers in other cohorts. However, the low prevalence of PanNEN in the population is an obstacle to conducting the study in a larger sample size. Therefore, clinical data from multiple centers and prospective evidence are required to validate these results.\nIn conclusion, this is the first study to explore the expression profiles of m6A proteins and association between m6A regulators and immune microenvironment in PanNENs. We identified high YTHDF2 and HNRNPC expression as independent risk factors for disease relapse after surgery. We further established a recurrence signature to identify patients with PanNEN at a higher risk of recurrence. A comprehensive understanding of m6A regulators in PanNEN may help in developing novel treatment strategies.\n\nStatement of Ethics:\nThe study was approved by the Institutional Review Board of Peking Union Medical College Hospital (approval number S-K1593) and conducted in accordance with the Declaration of Helsinki. Written consent for this study was not required and formally waived by the hospital's Ethics Committee.\n\nConflict of Interest Statement:\nThe authors declare that they have no conflict of interest.\n\nFunding Sources:\nThis work was supported by grants from the Chinese Academy of Medical Sciences Initiative for Innovative Medicine (CAMS-2016-I2M-1–001), the National Scientific Data Sharing Platform for Population and Health (NCMI–YF01N–201906), and the National Natural Science Foundation of China (Nos. 81672648).\n\nAuthor Contributions:\nJie Chen made substantial contributions to the conception, design, and critical revision of the manuscript. Shengwei Mo, Liju Zong, Shuangni Yu, and Zhaohui Lu made substantial contributions to tissue microarray construction. Shengwei Mo made substantial contributions to data acquisition. Xianlong Chen and Shengwei Mo performed analysis of the data and drafting of the manuscript. All the authors read and approved the final manuscript.\n\nData Availability Statement:\nThe data used and/or analyzed during the current study are available from the corresponding author on reasonable request.\n\nSupplementary Material:\nSupplementary data\nClick here for additional data file.\nSupplementary data\nClick here for additional data file.\nSupplementary data\nClick here for additional data file.\nSupplementary data\nClick here for additional data file.\nSupplementary data\nClick here for additional data file.\nSupplementary data\nClick here for additional data file.\nSupplementary data\nClick here for additional data file.\n\n"},"whole_article_abstract":{"kind":"string","value":"Background:\nThe RNA N6-methyladenosine (m6A) regulators play a crucial role in tumorigenesis and could be indicators of prognosis and therapeutic targets in various cancers. However, the expression status and prognostic value of m6A regulators have not been studied in pancreatic neuroendocrine neoplasms (PanNENs). We aimed to investigate the expression patterns and prognostic value of m6A regulators and assess their correlations with immune checkpoints and infiltrates in PanNENs.\n\nMethods:\nImmunohistochemistry was performed for 15 m6A regulators and immune markers using tissue microarrays obtained from 183 patients with PanNENs. The correlation between m6A protein expression and clinicopathological parameters with recurrence-free survival (RFS) was examined using a random survival forest, Cox regression model, and survival tree analysis.\n\nResults:\nAmong the 15 m6A proteins, high expression of YTHDF2 (p < 0.001) and HNRNPC (p = 0.006) was found to be significantly associated with recurrence and served as independent risk factors in multivariate analysis. High YTHDF2 expression was associated with higher number of CD3+ T cells (p = 0.003), whereas high HNRNPC expression was significantly correlated with the expression of PD-L1 (p = 0.039). A YTHDF2-based signature was determined, including five patterns from survival tree analysis: patients with the LNnegYTHDF2high signature had a 5-year RFS rate of 92.1%, whereas patients with LNposTumorSize＜2.5 cm signature had the worst 5-year RFS rate of 0% (p < 0.001). The area under receiver operating characteristic curve was 0.870 (95% confidence interval: 0.762-0.915) for the YTHDF2-based signature. The C-index was 0.978, suggesting good discrimination ability; moreover, the risk score of recurrence served as an independent prognostic factor indicating shorter RFS.\n\nConclusions:\nYTHDF2 appears to serve as a promising prognostic biomarker and therapeutic target. A YTHDF2-based signature can identify distinct subgroups, which may be helpful to strategize personalized postoperative monitoring."},"background_conclusion_text":{"kind":"null"},"background_conclusion_abstract":{"kind":"null"},"whole_article_text_length":{"kind":"number","value":12523,"string":"12,523"},"whole_article_abstract_length":{"kind":"number","value":367,"string":"367"},"other_sections_lengths":{"kind":"list like","value":[242,264,216,386,280,149,304,381,360,631,50,53,73],"string":"[\n 242,\n 264,\n 216,\n 386,\n 280,\n 149,\n 304,\n 381,\n 360,\n 631,\n 50,\n 53,\n 73\n]"},"num_sections":{"kind":"number","value":20,"string":"20"},"most_frequent_words":{"kind":"list like","value":["expression","ythdf2","patients","recurrence","tumor","risk","rfs","high","hnrnpc","node"],"string":"[\n \"expression\",\n \"ythdf2\",\n \"patients\",\n \"recurrence\",\n \"tumor\",\n \"risk\",\n \"rfs\",\n \"high\",\n \"hnrnpc\",\n \"node\"\n]"},"keybert_topics":{"kind":"list like","value":["cancer pancreatic cancer","checkpoints pancreatic neuroendocrine","pannen grade tumor","pannen cancer recurrence","neuroendocrine neoplasms pannens"],"string":"[\n \"cancer pancreatic cancer\",\n \"checkpoints pancreatic neuroendocrine\",\n \"pannen grade tumor\",\n \"pannen cancer recurrence\",\n \"neuroendocrine neoplasms pannens\"\n]"},"annotated_base_background_abstract_prompt":{"kind":"string","value":""},"annotated_base_methods_abstract_prompt":{"kind":"null"},"annotated_base_results_abstract_prompt":{"kind":"string","value":""},"annotated_base_conclusions_abstract_prompt":{"kind":"null"},"annotated_base_whole_article_abstract_prompt":{"kind":"string","value":""},"annotated_base_background_conclusion_abstract_prompt":{"kind":"null"},"annotated_keywords_background_abstract_prompt":{"kind":"string","value":"[CONTENT] N6-methyladenosine methylation regulators | Pancreatic neuroendocrine neoplasms | Immune markers | Recurrence | Postoperative surveillance [SUMMARY]"},"annotated_keywords_methods_abstract_prompt":{"kind":"null"},"annotated_keywords_results_abstract_prompt":{"kind":"string","value":"[CONTENT] N6-methyladenosine methylation regulators | Pancreatic neuroendocrine neoplasms | Immune markers | Recurrence | Postoperative surveillance [SUMMARY]"},"annotated_keywords_conclusions_abstract_prompt":{"kind":"null"},"annotated_keywords_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] N6-methyladenosine methylation regulators | Pancreatic neuroendocrine neoplasms | Immune markers | Recurrence | Postoperative surveillance [SUMMARY]"},"annotated_keywords_background_conclusion_abstract_prompt":{"kind":"null"},"annotated_mesh_background_abstract_prompt":{"kind":"string","value":"[CONTENT] Humans | Methylation | Prognosis | Adenosine | RNA | Multivariate Analysis | Neoplasms [SUMMARY]"},"annotated_mesh_methods_abstract_prompt":{"kind":"null"},"annotated_mesh_results_abstract_prompt":{"kind":"string","value":"[CONTENT] Humans | Methylation | Prognosis | Adenosine | RNA | Multivariate Analysis | Neoplasms [SUMMARY]"},"annotated_mesh_conclusions_abstract_prompt":{"kind":"null"},"annotated_mesh_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] Humans | Methylation | Prognosis | Adenosine | RNA | Multivariate Analysis | Neoplasms [SUMMARY]"},"annotated_mesh_background_conclusion_abstract_prompt":{"kind":"null"},"annotated_keybert_background_abstract_prompt":{"kind":"string","value":"[CONTENT] cancer pancreatic cancer | checkpoints pancreatic neuroendocrine | pannen grade tumor | pannen cancer recurrence | neuroendocrine neoplasms pannens [SUMMARY]"},"annotated_keybert_methods_abstract_prompt":{"kind":"null"},"annotated_keybert_results_abstract_prompt":{"kind":"string","value":"[CONTENT] cancer pancreatic cancer | checkpoints pancreatic neuroendocrine | pannen grade tumor | pannen cancer recurrence | neuroendocrine neoplasms pannens [SUMMARY]"},"annotated_keybert_conclusions_abstract_prompt":{"kind":"null"},"annotated_keybert_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] cancer pancreatic cancer | checkpoints pancreatic neuroendocrine | pannen grade tumor | pannen cancer recurrence | neuroendocrine neoplasms pannens [SUMMARY]"},"annotated_keybert_background_conclusion_abstract_prompt":{"kind":"null"},"annotated_most_frequent_background_abstract_prompt":{"kind":"string","value":"[CONTENT] expression | ythdf2 | patients | recurrence | tumor | risk | rfs | high | hnrnpc | node [SUMMARY]"},"annotated_most_frequent_methods_abstract_prompt":{"kind":"null"},"annotated_most_frequent_results_abstract_prompt":{"kind":"string","value":"[CONTENT] expression | ythdf2 | patients | recurrence | tumor | risk | rfs | high | hnrnpc | node [SUMMARY]"},"annotated_most_frequent_conclusions_abstract_prompt":{"kind":"null"},"annotated_most_frequent_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] expression | ythdf2 | patients | recurrence | tumor | risk | rfs | high | hnrnpc | node [SUMMARY]"},"annotated_most_frequent_background_conclusion_abstract_prompt":{"kind":"null"},"annotated_tf_idf_background_abstract_prompt":{"kind":"string","value":"[CONTENT] m6a | modification | pannens | clinical | patients | regulators | methylation | immune | 23 | m6a regulators [SUMMARY]"},"annotated_tf_idf_methods_abstract_prompt":{"kind":"null"},"annotated_tf_idf_results_abstract_prompt":{"kind":"string","value":"[CONTENT] node | expression | ythdf2 | patients | fig | hnrnpc | risk | rfs | status | recurrence [SUMMARY]"},"annotated_tf_idf_conclusions_abstract_prompt":{"kind":"null"},"annotated_tf_idf_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] expression | ythdf2 | patients | recurrence | tumor | risk | variables | node | data | hnrnpc [SUMMARY]"},"annotated_tf_idf_background_conclusion_abstract_prompt":{"kind":"null"},"annotated_entity_plan_background_abstract_prompt":{"kind":"string","value":"[CONTENT] m6A ||| m6A ||| m6A [SUMMARY]"},"annotated_entity_plan_methods_abstract_prompt":{"kind":"null"},"annotated_entity_plan_results_abstract_prompt":{"kind":"string","value":"[CONTENT] 15 | m6A | YTHDF2 | HNRNPC | 0.006 ||| YTHDF2 | 0.003 | HNRNPC | PD-L1 | 0.039 ||| YTHDF2 | five | LNnegYTHDF2high | 5-year | RFS | 92.1% | LNposTumorSize＜2.5 cm | 5-year | RFS | 0% ||| 0.870 | 95% | 0.762-0.915 | YTHDF2 ||| 0.978 | RFS [SUMMARY]"},"annotated_entity_plan_conclusions_abstract_prompt":{"kind":"null"},"annotated_entity_plan_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] m6A ||| m6A ||| m6A | 15 | m6A | 183 ||| m6A | RFS ||| ||| 15 | m6A | YTHDF2 | HNRNPC | 0.006 ||| YTHDF2 | 0.003 | HNRNPC | PD-L1 | 0.039 ||| YTHDF2 | five | LNnegYTHDF2high | 5-year | RFS | 92.1% | LNposTumorSize＜2.5 cm | 5-year | RFS | 0% ||| 0.870 | 95% | 0.762-0.915 | YTHDF2 ||| 0.978 | RFS ||| YTHDF2 ||| YTHDF2 [SUMMARY]"},"annotated_entity_plan_background_conclusion_abstract_prompt":{"kind":"null"}}},{"rowIdx":284,"cells":{"title":{"kind":"string","value":"Brain barriers virtual: an interim solution or future opportunity?"},"pmid":{"kind":"string","value":"35232464"},"background_abstract":{"kind":"string","value":"Scientific conferences are vital communication events for scientists in academia, industry, and government agencies. In the brain barriers research field, several international conferences exist that allow researchers to present data, share knowledge, and discuss novel ideas and concepts. These meetings are critical platforms for researchers to connect and exchange breakthrough findings on a regular basis. Due to the worldwide COVID-19 pandemic, all in-person meetings were canceled in 2020. In response, we launched the Brain Barriers Virtual 2020 (BBV2020) seminar series, the first stand-in virtual event for the brain barriers field, to offer scientists a virtual platform to present their work. Here we report the aggregate attendance information on two in-person meetings compared with BBV2020 and comment on the utility of the virtual platform."},"background_abstract_label":{"kind":"string","value":"BACKGROUND"},"methods_abstract":{"kind":"string","value":"The BBV2020 seminar series was hosted on a Zoom webinar platform and was free of cost for participants. Using registration- and Zoom-based data from the BBV2020 virtual seminar series and survey data collected from BBV2020 participants, we analyzed attendance trends, global reach, participation based on career stage, and engagement of BBV2020. We compared these data with those from two previous in-person conferences, a BBB meeting held in 2018 and CVB 2019."},"methods_abstract_label":{"kind":"string","value":"METHODS"},"results_abstract":{"kind":"string","value":"We found that BBV2020 seminar participation steadily decreased over the course of the series. In contrast, live participation was consistently above 100 attendees and recording views were above 200 views per seminar. We also found that participants valued BBV2020 as a supplement during the COVID-19 pandemic in 2020. Based on one post-BBV2020 survey, the majority of participants indicated that they would prefer in-person meetings but would welcome a virtual component to future in-person meetings. Compared to in-person meetings, BBV2020 enabled participation from a broad range of career stages and was attended by scientists in academic, industry, and government agencies from a wide range of countries worldwide."},"results_abstract_label":{"kind":"string","value":"RESULTS"},"conclusions_abstract":{"kind":"string","value":"Our findings suggest that a virtual event such as the BBV2020 seminar series provides easy access to science for researchers across all career stages around the globe. However, we recognize that limitations exist. Regardless, such a virtual event could be a valuable tool for the brain barriers community to reach and engage scientists worldwide to further grow the brain barriers research field in the future."},"conclusions_abstract_label":{"kind":"string","value":"CONCLUSIONS"},"mesh_descriptor_names":{"kind":"list like","value":["COVID-19","Central Nervous System","Congresses as Topic","Humans","SARS-CoV-2","Surveys and Questionnaires","Videoconferencing"],"string":"[\n \"COVID-19\",\n \"Central Nervous System\",\n \"Congresses as Topic\",\n \"Humans\",\n \"SARS-CoV-2\",\n \"Surveys and Questionnaires\",\n \"Videoconferencing\"\n]"},"pmcid":{"kind":"string","value":"8886561"},"background_title":{"kind":"string","value":"Introduction"},"background_text":{"kind":"string","value":"On March 11, 2020, the World Health Organization (WHO) declared the coronavirus disease (COVID-19) outbreak a global pandemic [1]. Shortly after, countries worldwide issued stay-at-home orders and lockdowns: the COVID-19 pandemic had forced the world to come to an abrupt halt [2]. With global air travel restrictions and travel bans in place, enterprises including academic institutions, industry and government agencies required individuals to call off their business travel. As a result, scheduled scientific conferences were canceled throughout 2020 [3, 4]. These cancelations created a sudden void in scientific communication between researchers worldwide and forced research communities to be creative and adapt. One possibility to continue scientific communication and interaction was by switching conferences and seminars to virtual online events using novel digital platforms enabling researchers to connect and share science [3, 4].\nAs a consequence, major conferences and meetings in the brain barriers research field, including the “Barriers of the CNS Gordon Research Conference” (GRC 2020) and the international symposium on “Signal Transduction at the Blood–Brain Barriers” in Bari, Italy were first postponed until 2021 and due to the ongoing pandemic subsequently moved to 2022. The “Cerebral Vascular Biology” (CVB) conference in Uppsala, Sweden planned for 2021 was postponed until 2023. Postponing these meetings left organizers and conference chairs in a difficult position and researchers without a platform to present and share their data. Thus, these postponements created a critical need for the field to stay connected and an opportunity to establish a virtual event that could supplement the postponed conferences in 2020. The Brain Barriers Virtual 2020 Seminar Series (BBV2020) addressed this need by providing a forum for brain barrier scientists worldwide to come together online during the COVID-19 crisis in 2020. BBV2020 seminars were held weekly from May 20, 2020 to September 2, 2020 in a 1-h format featuring a live session with an invited speaker followed by time for questions and answers from the community. The seminar series was hosted on a Zoom webinar platform and was free for anyone to attend. BBV2020 served as a forum for invited speakers to present their work to the brain barriers community and enabled this community to discuss brain barriers science from the safety of their home office. Over 1300 scientists around the world were registered, each seminar was viewed live by 100–400 participants, and the video-recordings received 150–900 views each week. Participants were from academia, industry and government agencies at all career stages from around the globe.\nIn this report, we share participation data that give insight into the impact this virtual seminar series had on the brain barriers field during the COVID-19 pandemic. We compare these data with existing data from past in-person brain barriers meetings and draw conclusions from these data on the potential value of virtual meetings for the brain barriers research field in the future. While the organization of virtual events during the pandemic may seem self-evident, reflection on the impact carries utility for events in the future."},"methods_title":{"kind":"null"},"methods_text":{"kind":"null"},"results_title":{"kind":"string","value":"Results"},"results_text":{"kind":"string","value":"We organized the virtual BBV2020 seminar series for the brain barriers research field to fill the void that the COVID-19 pandemic left due to canceled and postponed meetings in 2020. The seminar series was held between May–September 2020 every Wednesday from 12:00 to 1:00 PM Eastern Daylight Time (EDT). Seminars were hosted on a Zoom webinar platform and featured a 45-min presentation from an invited speaker followed by questions and answers from the audience. In the following sections, we summarize data collected by the Zoom software, from the listserv used, and the participant survey. We compare and contrast the data from the virtual BBV2020 seminar series with data from in-person meetings from past years.\n BBV2020 attendance On May 14, 2020, we sent out the first advertisement for the BBV2020 seminar series via email using a listserv with email addresses of past conference attendees and via postings using LinkedIn, Facebook, and Twitter. By the start of the first seminar on May 20, 2020, more than 720 scientists had registered. By the end of the 16-week long BBV2020 seminar series, a total of 1302 registrants signed up on our listserv to receive the seminar link, speaker updates, and video recording links (Fig. 1A). We obtained data from two past in-person meetings from the respective conference chairs: (1) brain barriers meeting in 2018 (BBB 2018) and the (2) Cerebral Vascular Biology meeting in 2019 (CVB 2019).\n\nFig. 1\nAttendance Trends for Brain Barrier Research Conferences. A With 1,302 individuals signing up and added to the listserv, BBV2020 attracted more registrants than BBB 2018 and CVB 2019. B Number of unique viewers weekly at the live Zoom Webinar sessions of BBV2020. C Number of views (clicks) of the video recordings when available weekly for BBV2020. Black line indicates best linear fit. Gray line indicates the average (mean) for all 16 weeks\n\nAttendance Trends for Brain Barrier Research Conferences. A With 1,302 individuals signing up and added to the listserv, BBV2020 attracted more registrants than BBB 2018 and CVB 2019. B Number of unique viewers weekly at the live Zoom Webinar sessions of BBV2020. C Number of views (clicks) of the video recordings when available weekly for BBV2020. Black line indicates best linear fit. Gray line indicates the average (mean) for all 16 weeks\nStrikingly, the total number of registered applicants for the BBV2020 was more than 4-fold higher compared to the total number of registered participants for the 2018 and the CVB 2019 meetings (Fig. 1A). The large number of registrants is likely due to free registration, no associated travel expenses, a world-wide reach, and that virtual meetings come with no obligations for the attendees. In contrast, the number of attendees of the in-person meetings (BBB 2018 and CVB2019) was limited and determined by the size of the conference site.\nThe first live BBV2020 seminar was attended by 419 participants. As the seminar series progressed, the participant number during live seminars gradually decreased, the average participant number was 220, the minimum was 108 (Fig. 1B). A similar trend was observed in the numbers of total recording views: the first seminar recording had a maximum of 944 views; in average, videos had 422 views (Fig. 1C). The trendline in Fig. 1C shows a drop in recording views over the course of the seminar series. These trends held regardless of academic rank as graduate students, postdoctoral fellows, and professors all showed a similar downward trend in live attendance (Additional file 1: Fig. S1A–C). Note that we were unable to capture recording data for week 2 due to a recording error and for week 13 due to the request by the speaker to not record.\nTogether, the high number of registrants suggests that the virtual BBV2020 seminar series sparked interest. Attendance of the live seminars and the numbers of views for the recorded videos decreased over time, which may be an indicator of “virtual meeting fatigue” or attendees being on their summer vacation.\nOn May 14, 2020, we sent out the first advertisement for the BBV2020 seminar series via email using a listserv with email addresses of past conference attendees and via postings using LinkedIn, Facebook, and Twitter. By the start of the first seminar on May 20, 2020, more than 720 scientists had registered. By the end of the 16-week long BBV2020 seminar series, a total of 1302 registrants signed up on our listserv to receive the seminar link, speaker updates, and video recording links (Fig. 1A). We obtained data from two past in-person meetings from the respective conference chairs: (1) brain barriers meeting in 2018 (BBB 2018) and the (2) Cerebral Vascular Biology meeting in 2019 (CVB 2019).\n\nFig. 1\nAttendance Trends for Brain Barrier Research Conferences. A With 1,302 individuals signing up and added to the listserv, BBV2020 attracted more registrants than BBB 2018 and CVB 2019. B Number of unique viewers weekly at the live Zoom Webinar sessions of BBV2020. C Number of views (clicks) of the video recordings when available weekly for BBV2020. Black line indicates best linear fit. Gray line indicates the average (mean) for all 16 weeks\n\nAttendance Trends for Brain Barrier Research Conferences. A With 1,302 individuals signing up and added to the listserv, BBV2020 attracted more registrants than BBB 2018 and CVB 2019. B Number of unique viewers weekly at the live Zoom Webinar sessions of BBV2020. C Number of views (clicks) of the video recordings when available weekly for BBV2020. Black line indicates best linear fit. Gray line indicates the average (mean) for all 16 weeks\nStrikingly, the total number of registered applicants for the BBV2020 was more than 4-fold higher compared to the total number of registered participants for the 2018 and the CVB 2019 meetings (Fig. 1A). The large number of registrants is likely due to free registration, no associated travel expenses, a world-wide reach, and that virtual meetings come with no obligations for the attendees. In contrast, the number of attendees of the in-person meetings (BBB 2018 and CVB2019) was limited and determined by the size of the conference site.\nThe first live BBV2020 seminar was attended by 419 participants. As the seminar series progressed, the participant number during live seminars gradually decreased, the average participant number was 220, the minimum was 108 (Fig. 1B). A similar trend was observed in the numbers of total recording views: the first seminar recording had a maximum of 944 views; in average, videos had 422 views (Fig. 1C). The trendline in Fig. 1C shows a drop in recording views over the course of the seminar series. These trends held regardless of academic rank as graduate students, postdoctoral fellows, and professors all showed a similar downward trend in live attendance (Additional file 1: Fig. S1A–C). Note that we were unable to capture recording data for week 2 due to a recording error and for week 13 due to the request by the speaker to not record.\nTogether, the high number of registrants suggests that the virtual BBV2020 seminar series sparked interest. Attendance of the live seminars and the numbers of views for the recorded videos decreased over time, which may be an indicator of “virtual meeting fatigue” or attendees being on their summer vacation.\n BBV2020 survey analysis By the end of the seminar series in September, every BBV2020 registrant received an invitation to participate in an online survey created by the organizers on August 23, 2020. The survey was designed for registrants to provide feedback and consisted of 10 questions: (1) demographic question to capture career level and career type; (2) single select multiple choice question to capture attendance of past in-person brain barrier meetings, (3) single select multiple choice question on how many recorded seminars were viewed, (4) Likert scale question regarding the enrichment BBV2020 provided for the community during the COVID-19 pandemic, (5) dichotomous question asking if the registrant would still participate in BBV2020 seminars if they were continued, (6) single select multiple choice question on attendance frequency, (7) single select multiple choice question on how many times the registrant has thus far attended a brain barriers meeting previously in person, (8) matrix question to capture affective factors, (9) matrix question to capture learning and teaching, and (10) matrix question to capture combined validity and practicality of the BBV2020 series. In addition, registrants responding to the survey had the opportunity to include general comments to the organizers in a separate text box.\nOverall, 23% of registrants (total of 299 registrants) responded to the survey. Over 89% of survey respondents agreed strongly that “Attending BBV2020 was a positive experience” and 63% of responders felt that “BBV2020 is appropriate for my research”. Generally, the respondents tended to agree with the following statement: “BBV2020 has enriched the connection with the Brain Barriers community in the gap that COVID-19 has created” (n = 298 respondents to this question of a total of 299 registrants, M = 1.44, SD = 0.660, 1 = Strongly Agree, 2 = Agree, 3 = Neutral, 4 = Disagree, 5 = Strongly Disagree; Fig. 2A). However, when posed with the statements “In the future, I would prefer to attend a virtual conference over an in-person conference” or “The virtual experience is not as effective compared to the in-person conferences”, responses were neutral (M = 3.24; Fig. 2B) indicating a positive view towards a virtual conference. A difference emerged when we compared BBV2020 participants based on their career level with their preference for in-person versus virtual meeting formats: using one-way ANOVA, we found a significant difference between groups (F(9286) = 2.607, p = 0.007). Using Tukey’s HSD post hoc test indicated that Master’s students were more likely to disagree with the following statement when compared to Faculty/Professors: “I would feel more comfortable attending Brain Barrier conferences/seminars in person, but was glad to attend virtually due to COVID19.” A strong trend emerged when Master’s and Ph.D. students were grouped together and compared to Post-Docs and Faculty/Professors in an independent-samples t test. Results show graduate students in general were more favorable to a virtual conference compared to colleagues more established in their careers (t(225) = -1.972, p = 0.061). The lack of significance is in part due to the low sample size (n = 74) for graduate students.\n\nFig. 2\nPost-seminar survey results. A Volunteer respondent result when posed with the prompt “BBV2020 has enriched connection with the Brain Barriers community in the gap that COVID-19 has created”. B Volunteer respondent results when posted with the prompts “In the future, I would prefer to attend a virtual conference over an in-person conference” (Blue) and “The virtual experience is not as effective compared to the in-person conferences” (Red)\n\nPost-seminar survey results. A Volunteer respondent result when posed with the prompt “BBV2020 has enriched connection with the Brain Barriers community in the gap that COVID-19 has created”. B Volunteer respondent results when posted with the prompts “In the future, I would prefer to attend a virtual conference over an in-person conference” (Blue) and “The virtual experience is not as effective compared to the in-person conferences” (Red)\nOverall, descriptive data revealed many participants felt BBV2020 was a positive experience. In fact, 99.3% of survey respondents noted they somewhat or strongly agree that their experience was positive, with only 4.7% saying they faced technical difficulties. Additionally, most respondents agreed that virtual conferences have an important role in research (93.2%) and are more accessible than in-person conferences (90.2%). Finally, more than half of respondents (56.9%) noted virtual conferences can do things in-person conferences cannot.\nNevertheless, there continued to be a desire among many BBV2020 participants to return to conferencing in person, with 13.5% saying they found it hard to focus in the virtual format. In fact, roughly one-third (33.9%) felt the virtual conference was not as effective as an in-person conference, and only one-quarter (23.3%) said they would prefer virtual conferences to in-person conferences in the future, while 35.8% were neutral.\nTogether, our findings suggest that while BBV2020 filled a need created by a global pandemic, the enthusiasm for returning to only in-person meetings remained relatively neutral suggesting that virtual platforms may play a critical role in dissemination of research and science in the future. Moreover, the data from this study show a trend between graduate students versus professors in their response with regard to in-person meetings indicating that career stage may influence the perspective of the usefulness of in-person versus virtual platforms.\nBy the end of the seminar series in September, every BBV2020 registrant received an invitation to participate in an online survey created by the organizers on August 23, 2020. The survey was designed for registrants to provide feedback and consisted of 10 questions: (1) demographic question to capture career level and career type; (2) single select multiple choice question to capture attendance of past in-person brain barrier meetings, (3) single select multiple choice question on how many recorded seminars were viewed, (4) Likert scale question regarding the enrichment BBV2020 provided for the community during the COVID-19 pandemic, (5) dichotomous question asking if the registrant would still participate in BBV2020 seminars if they were continued, (6) single select multiple choice question on attendance frequency, (7) single select multiple choice question on how many times the registrant has thus far attended a brain barriers meeting previously in person, (8) matrix question to capture affective factors, (9) matrix question to capture learning and teaching, and (10) matrix question to capture combined validity and practicality of the BBV2020 series. In addition, registrants responding to the survey had the opportunity to include general comments to the organizers in a separate text box.\nOverall, 23% of registrants (total of 299 registrants) responded to the survey. Over 89% of survey respondents agreed strongly that “Attending BBV2020 was a positive experience” and 63% of responders felt that “BBV2020 is appropriate for my research”. Generally, the respondents tended to agree with the following statement: “BBV2020 has enriched the connection with the Brain Barriers community in the gap that COVID-19 has created” (n = 298 respondents to this question of a total of 299 registrants, M = 1.44, SD = 0.660, 1 = Strongly Agree, 2 = Agree, 3 = Neutral, 4 = Disagree, 5 = Strongly Disagree; Fig. 2A). However, when posed with the statements “In the future, I would prefer to attend a virtual conference over an in-person conference” or “The virtual experience is not as effective compared to the in-person conferences”, responses were neutral (M = 3.24; Fig. 2B) indicating a positive view towards a virtual conference. A difference emerged when we compared BBV2020 participants based on their career level with their preference for in-person versus virtual meeting formats: using one-way ANOVA, we found a significant difference between groups (F(9286) = 2.607, p = 0.007). Using Tukey’s HSD post hoc test indicated that Master’s students were more likely to disagree with the following statement when compared to Faculty/Professors: “I would feel more comfortable attending Brain Barrier conferences/seminars in person, but was glad to attend virtually due to COVID19.” A strong trend emerged when Master’s and Ph.D. students were grouped together and compared to Post-Docs and Faculty/Professors in an independent-samples t test. Results show graduate students in general were more favorable to a virtual conference compared to colleagues more established in their careers (t(225) = -1.972, p = 0.061). The lack of significance is in part due to the low sample size (n = 74) for graduate students.\n\nFig. 2\nPost-seminar survey results. A Volunteer respondent result when posed with the prompt “BBV2020 has enriched connection with the Brain Barriers community in the gap that COVID-19 has created”. B Volunteer respondent results when posted with the prompts “In the future, I would prefer to attend a virtual conference over an in-person conference” (Blue) and “The virtual experience is not as effective compared to the in-person conferences” (Red)\n\nPost-seminar survey results. A Volunteer respondent result when posed with the prompt “BBV2020 has enriched connection with the Brain Barriers community in the gap that COVID-19 has created”. B Volunteer respondent results when posted with the prompts “In the future, I would prefer to attend a virtual conference over an in-person conference” (Blue) and “The virtual experience is not as effective compared to the in-person conferences” (Red)\nOverall, descriptive data revealed many participants felt BBV2020 was a positive experience. In fact, 99.3% of survey respondents noted they somewhat or strongly agree that their experience was positive, with only 4.7% saying they faced technical difficulties. Additionally, most respondents agreed that virtual conferences have an important role in research (93.2%) and are more accessible than in-person conferences (90.2%). Finally, more than half of respondents (56.9%) noted virtual conferences can do things in-person conferences cannot.\nNevertheless, there continued to be a desire among many BBV2020 participants to return to conferencing in person, with 13.5% saying they found it hard to focus in the virtual format. In fact, roughly one-third (33.9%) felt the virtual conference was not as effective as an in-person conference, and only one-quarter (23.3%) said they would prefer virtual conferences to in-person conferences in the future, while 35.8% were neutral.\nTogether, our findings suggest that while BBV2020 filled a need created by a global pandemic, the enthusiasm for returning to only in-person meetings remained relatively neutral suggesting that virtual platforms may play a critical role in dissemination of research and science in the future. Moreover, the data from this study show a trend between graduate students versus professors in their response with regard to in-person meetings indicating that career stage may influence the perspective of the usefulness of in-person versus virtual platforms.\n Global reach of BBV2020 During the seminar registration process we collected attendants’ affiliations and based on this information we determined the global reach of the BBV2020. Registrants of the BBV2020 seminar series were from 43 countries representing six of the seven continents (Fig. 3A, B). Compared to the in-person BBB 2018 and the CVB2019, more countries were represented at the BBV2020 (Fig. 3A).\n\nFig. 3\nGlobal reach of BBV2020. A BBV2020 had more countries represented compared to BBB 2018 and CVB 2019 alone. B World map showing countries represented by BBV2020. Countries where registrants participated in BBV2020: Red and Pink. Countries not represented at BBV2020: Light Gray and Dark Gray. Countries where registrants attended BBV2020 and at least one of the in-person meetings between Meeting 2018 or CVB 2019: Red. Countries where registrants attended BBV2020 and did not attend either BBB 2018 or CVB 2019: Pink. Countries that had no participation in any of the brain barriers meetings examined in this study: Light gray. Countries that had participation in either BBB 2018 or CVB 2019 that did not participate in BBV2020: Dark Gray\n\nGlobal reach of BBV2020. A BBV2020 had more countries represented compared to BBB 2018 and CVB 2019 alone. B World map showing countries represented by BBV2020. Countries where registrants participated in BBV2020: Red and Pink. Countries not represented at BBV2020: Light Gray and Dark Gray. Countries where registrants attended BBV2020 and at least one of the in-person meetings between Meeting 2018 or CVB 2019: Red. Countries where registrants attended BBV2020 and did not attend either BBB 2018 or CVB 2019: Pink. Countries that had no participation in any of the brain barriers meetings examined in this study: Light gray. Countries that had participation in either BBB 2018 or CVB 2019 that did not participate in BBV2020: Dark Gray\nWe also compared the number of participants from each country who joined BBV2020 vs. BBB 2018 and CVB 2019. We found that the number of participants per country worldwide was also higher compared to both in person meetings (Fig. 4A–F). These data show that the virtual nature of BBV2020 enabled researchers world-wide to participate and suggest that the outreach of a virtual event can extend beyond the field.\n\nFig. 4\nGlobal view of participation over BBV2020, BBB 2018, and CVB 2019. Representation of participants for (A) BBV2020, (B) BBB 2018, and (C) CVB 2019. Ordered from top to bottom in greatest number of registrants. Number of registrants scaled by color based on number of registrants per country for (D) BBV 2020, (E) BBB 2018, and (F) CVB 2019. Light Pink: 1–10 registrants; Dark Pink: 11–50 registrants; Red: 51–100 registrants and Dark Red: > 100 registrants\n\nGlobal view of participation over BBV2020, BBB 2018, and CVB 2019. Representation of participants for (A) BBV2020, (B) BBB 2018, and (C) CVB 2019. Ordered from top to bottom in greatest number of registrants. Number of registrants scaled by color based on number of registrants per country for (D) BBV 2020, (E) BBB 2018, and (F) CVB 2019. Light Pink: 1–10 registrants; Dark Pink: 11–50 registrants; Red: 51–100 registrants and Dark Red: > 100 registrants\nDuring the seminar registration process we collected attendants’ affiliations and based on this information we determined the global reach of the BBV2020. Registrants of the BBV2020 seminar series were from 43 countries representing six of the seven continents (Fig. 3A, B). Compared to the in-person BBB 2018 and the CVB2019, more countries were represented at the BBV2020 (Fig. 3A).\n\nFig. 3\nGlobal reach of BBV2020. A BBV2020 had more countries represented compared to BBB 2018 and CVB 2019 alone. B World map showing countries represented by BBV2020. Countries where registrants participated in BBV2020: Red and Pink. Countries not represented at BBV2020: Light Gray and Dark Gray. Countries where registrants attended BBV2020 and at least one of the in-person meetings between Meeting 2018 or CVB 2019: Red. Countries where registrants attended BBV2020 and did not attend either BBB 2018 or CVB 2019: Pink. Countries that had no participation in any of the brain barriers meetings examined in this study: Light gray. Countries that had participation in either BBB 2018 or CVB 2019 that did not participate in BBV2020: Dark Gray\n\nGlobal reach of BBV2020. A BBV2020 had more countries represented compared to BBB 2018 and CVB 2019 alone. B World map showing countries represented by BBV2020. Countries where registrants participated in BBV2020: Red and Pink. Countries not represented at BBV2020: Light Gray and Dark Gray. Countries where registrants attended BBV2020 and at least one of the in-person meetings between Meeting 2018 or CVB 2019: Red. Countries where registrants attended BBV2020 and did not attend either BBB 2018 or CVB 2019: Pink. Countries that had no participation in any of the brain barriers meetings examined in this study: Light gray. Countries that had participation in either BBB 2018 or CVB 2019 that did not participate in BBV2020: Dark Gray\nWe also compared the number of participants from each country who joined BBV2020 vs. BBB 2018 and CVB 2019. We found that the number of participants per country worldwide was also higher compared to both in person meetings (Fig. 4A–F). These data show that the virtual nature of BBV2020 enabled researchers world-wide to participate and suggest that the outreach of a virtual event can extend beyond the field.\n\nFig. 4\nGlobal view of participation over BBV2020, BBB 2018, and CVB 2019. Representation of participants for (A) BBV2020, (B) BBB 2018, and (C) CVB 2019. Ordered from top to bottom in greatest number of registrants. Number of registrants scaled by color based on number of registrants per country for (D) BBV 2020, (E) BBB 2018, and (F) CVB 2019. Light Pink: 1–10 registrants; Dark Pink: 11–50 registrants; Red: 51–100 registrants and Dark Red: > 100 registrants\n\nGlobal view of participation over BBV2020, BBB 2018, and CVB 2019. Representation of participants for (A) BBV2020, (B) BBB 2018, and (C) CVB 2019. Ordered from top to bottom in greatest number of registrants. Number of registrants scaled by color based on number of registrants per country for (D) BBV 2020, (E) BBB 2018, and (F) CVB 2019. Light Pink: 1–10 registrants; Dark Pink: 11–50 registrants; Red: 51–100 registrants and Dark Red: > 100 registrants\n Career stages represented at BBV2020 We examined the self-reported career stage of BBV2020 participants to that of BBB 2018 and CVB 2019 participants. Based on the survey, more than 46% of all participants attended a brain barriers conference/seminar for the first time. We also found that in-person meetings are attended by 42.4–48.9% academicians at the professor rank, graduate students make up 22.9–25.8% and postdoctoral fellows are 6.6–17.7% of the participants (Fig. 5). In contrast, at the BBV2020 19.6% of academicians were at the professor rank, graduate students made up 30.5% and postdoctoral fellows 16.1% of attendees. We also found that BBV2020 had a larger participation from industry (18.5% vs. 5.6–6.6%), and undergraduate students (3.2% vs. 1%) compared to both in person meetings. Together, our data suggest that a virtual platform seminar like BBV2020 reaches a broader range of scientific ranks and careers.\n\nFig. 5\nProportions of various career stages participating in BBV2020, BBB 2018, and CVB 2019. Proportions of various career stages participating in (A) BBV2020, (B) BBB 2018, and (C) CVB 2019. Colors correspond to career stage: Red (professor any rank), Green (graduate student any rank), Dark Blue (research faculty or researcher), Yellow (industry scientist), Orange (postdoctoral fellow), Purple (government/regulatory), Light Blue (clinician), Green (undergraduate student)\n\nProportions of various career stages participating in BBV2020, BBB 2018, and CVB 2019. Proportions of various career stages participating in (A) BBV2020, (B) BBB 2018, and (C) CVB 2019. Colors correspond to career stage: Red (professor any rank), Green (graduate student any rank), Dark Blue (research faculty or researcher), Yellow (industry scientist), Orange (postdoctoral fellow), Purple (government/regulatory), Light Blue (clinician), Green (undergraduate student)\nWe examined the self-reported career stage of BBV2020 participants to that of BBB 2018 and CVB 2019 participants. Based on the survey, more than 46% of all participants attended a brain barriers conference/seminar for the first time. We also found that in-person meetings are attended by 42.4–48.9% academicians at the professor rank, graduate students make up 22.9–25.8% and postdoctoral fellows are 6.6–17.7% of the participants (Fig. 5). In contrast, at the BBV2020 19.6% of academicians were at the professor rank, graduate students made up 30.5% and postdoctoral fellows 16.1% of attendees. We also found that BBV2020 had a larger participation from industry (18.5% vs. 5.6–6.6%), and undergraduate students (3.2% vs. 1%) compared to both in person meetings. Together, our data suggest that a virtual platform seminar like BBV2020 reaches a broader range of scientific ranks and careers.\n\nFig. 5\nProportions of various career stages participating in BBV2020, BBB 2018, and CVB 2019. Proportions of various career stages participating in (A) BBV2020, (B) BBB 2018, and (C) CVB 2019. Colors correspond to career stage: Red (professor any rank), Green (graduate student any rank), Dark Blue (research faculty or researcher), Yellow (industry scientist), Orange (postdoctoral fellow), Purple (government/regulatory), Light Blue (clinician), Green (undergraduate student)\n\nProportions of various career stages participating in BBV2020, BBB 2018, and CVB 2019. Proportions of various career stages participating in (A) BBV2020, (B) BBB 2018, and (C) CVB 2019. Colors correspond to career stage: Red (professor any rank), Green (graduate student any rank), Dark Blue (research faculty or researcher), Yellow (industry scientist), Orange (postdoctoral fellow), Purple (government/regulatory), Light Blue (clinician), Green (undergraduate student)"},"conclusions_title":{"kind":"string","value":"Conclusion and future perspectives"},"conclusions_text":{"kind":"string","value":"\nAdditional file 1: Figure S1. Attendance Trends for BBV2020 by Academic Rank. Number of unique viewers weekly at the live Zoom Webinar sessions of BBV2020 separated by A) Graduate Student, B) Postdoctoral Fellow, and C) Professor (any rank).\nAdditional file 1: Figure S1. Attendance Trends for BBV2020 by Academic Rank. Number of unique viewers weekly at the live Zoom Webinar sessions of BBV2020 separated by A) Graduate Student, B) Postdoctoral Fellow, and C) Professor (any rank)."},"other_sections_titles":{"kind":"list like","value":["Methods","Organization","Virtual platform and technical assistance","BBV2020 schedule","Invited speakers","Advertisement and participants","BBV2020 seminar presentation","Map generation","Survey","Data collection, analysis, and statistics","BBV2020 attendance","BBV2020 survey analysis","Global reach of BBV2020","Career stages represented at BBV2020","Attendance: scientists around the globe","Survey: feedback from participants","Virtual seminars: a powerful opportunity","Conclusion and future perspectives"],"string":"[\n \"Methods\",\n \"Organization\",\n \"Virtual platform and technical assistance\",\n \"BBV2020 schedule\",\n \"Invited speakers\",\n \"Advertisement and participants\",\n \"BBV2020 seminar presentation\",\n \"Map generation\",\n \"Survey\",\n \"Data collection, analysis, and statistics\",\n \"BBV2020 attendance\",\n \"BBV2020 survey analysis\",\n \"Global reach of BBV2020\",\n \"Career stages represented at BBV2020\",\n \"Attendance: scientists around the globe\",\n \"Survey: feedback from participants\",\n \"Virtual seminars: a powerful opportunity\",\n \"Conclusion and future perspectives\"\n]"},"other_sections_texts":{"kind":"list like","value":[" Organization The BBV2020 seminar series was created by Drs. Bjoern Bauer (University of Kentucky, Lexington, KY, USA), Anika Hartz (University of Kentucky, Lexington, KY, USA), and Brandon Kim (University of Alabama, Tuscaloosa, Alabama, USA). Seminar moderators were postdoctoral researchers and scientists from the USA and Europe: Drs. Natalie Hudson (Trinity College Dublin, Dublin, Ireland), Geetika Nehra (University of Kentucky, Lexington, KY, USA), Michelle Pizzo (Denali Therapeutics Inc., San Francisco, CA, USA), and Steffen Storck (University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany).\nThe BBV2020 seminar series was created by Drs. Bjoern Bauer (University of Kentucky, Lexington, KY, USA), Anika Hartz (University of Kentucky, Lexington, KY, USA), and Brandon Kim (University of Alabama, Tuscaloosa, Alabama, USA). Seminar moderators were postdoctoral researchers and scientists from the USA and Europe: Drs. Natalie Hudson (Trinity College Dublin, Dublin, Ireland), Geetika Nehra (University of Kentucky, Lexington, KY, USA), Michelle Pizzo (Denali Therapeutics Inc., San Francisco, CA, USA), and Steffen Storck (University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany).\n Virtual platform and technical assistance BBV2020 was run on the Zoom Webinar 500 platform (Zoom Video Communications, Inc.; San Jose, CA, USA) that was funded by Dr. Bjoern Bauer using University of Kentucky funds. This particular Zoom Webinar license allowed for up to 500 live participants. The Zoom Webinar platform supports an in-session chat function, a Q&A forum, a raise hand function for live questions, and provides a panelist section with the option to share audio, video and presentation slides, a recording function and post-webinar data collection. The hosts and moderators were in control of audio and video; participants were not able to record or unmute during a session. Participants had no control function other than a “raise hand” option and the Q&A and chat function allowing to send questions or messages to the moderator. Technical assistance was provided by Todd Sizemore, College of Pharmacy, University of Kentucky.\nBBV2020 was run on the Zoom Webinar 500 platform (Zoom Video Communications, Inc.; San Jose, CA, USA) that was funded by Dr. Bjoern Bauer using University of Kentucky funds. This particular Zoom Webinar license allowed for up to 500 live participants. The Zoom Webinar platform supports an in-session chat function, a Q&A forum, a raise hand function for live questions, and provides a panelist section with the option to share audio, video and presentation slides, a recording function and post-webinar data collection. The hosts and moderators were in control of audio and video; participants were not able to record or unmute during a session. Participants had no control function other than a “raise hand” option and the Q&A and chat function allowing to send questions or messages to the moderator. Technical assistance was provided by Todd Sizemore, College of Pharmacy, University of Kentucky.\n BBV2020 schedule The BBV2020 seminar series ran from May 20 to September 2, 2020. Live seminars were held once per week from 12 to 1 PM Eastern Daylight Time (EDT) to accommodate researchers across a wide range of time zones: Central European Time (6:00 PM) to Pacific Daylight Time (9:00 AM). To accommodate colleagues with time constraints or those within incompatible time zones such as Asia or Oceania, presentations were recorded (depending on speaker permissions) and the seminar recording was made available for up to 48 h after the live seminar. The link for the recorded videos was distributed via the BBV2020 listserv that was set up through the University of Kentucky listserv.\nThe BBV2020 seminar series ran from May 20 to September 2, 2020. Live seminars were held once per week from 12 to 1 PM Eastern Daylight Time (EDT) to accommodate researchers across a wide range of time zones: Central European Time (6:00 PM) to Pacific Daylight Time (9:00 AM). To accommodate colleagues with time constraints or those within incompatible time zones such as Asia or Oceania, presentations were recorded (depending on speaker permissions) and the seminar recording was made available for up to 48 h after the live seminar. The link for the recorded videos was distributed via the BBV2020 listserv that was set up through the University of Kentucky listserv.\n Invited speakers Efforts were made to invite speakers from a variety of countries and genders. Invited speakers included organizers of postponed in-person conferences, speakers from academia and industry from different career stages. A total of 16 speakers were invited: May 20, 2020: Elga De Vries, Amsterdam UMC, Amsterdam, The Netherlands; May 27, 2020: Richard Daneman, University of California San Diego, San Diego, CA, USA; June 3, 2020: Robyn Klein, Washington University, St. Louis, MO, USA; June 10, 2020: Ayal Ben-Zvi, The Hebrew University of Jerusalem, Israel; June 17, 2020: Britta Engelhardt, University of Bern, Bern, Switzerland; June 24, 2020: Margareta Hammarlund-Udenaes, Uppsala University, Uppsala, Sweden; July 1, 2020: Daniela Virgintino, University of Bari, Bari, Italy; July 8, 2020: Eric Shusta, University of Wisconsin, Madison, WI, USA; July 15, 2020: Matthew Campbell, Trinity College Dublin, Dublin, Ireland; July 22, 2020: Krzysztof Kucharz, University of Copenhagen, Copenhagen, Denmark; July 29, 2020: William Elmquist, University of Minnesota, Minneapolis, MN, USA; August 5, 2020: Edward Neuwelt, Oregon Health & Sciences University, OR, USA; August 12, 2020: Robert Thorne, Denali Therapeutics, San Francisco, CA, USA; August 19, 2020: Teresa Sanchez, Weill Cornell Medical College, New York City, NJ, USA; August 26, 2020: Michael Taylor, University of Wisconsin, Madison, MI, USA; September 2, 2020: Joan Abbott, King’s College London, London, UK.\nSpeakers were offered a training session by the organizers to cover key aspects of a Zoom-based webinar and to test video, audio, and slide presentation prior to the live seminar. Ten out of 16 speakers took the training session. Speakers were informed by the organizers that any data presented should be considered public as security on the Zoom platform could not be ensured. Invited speakers had the opportunity to give permission to have their seminar recorded or to opt out of the recording. If a speaker gave permission to record, the entire seminar was recorded, and recordings were made available to all registered participants in a non-downloadable format through the University of Kentucky Zoom cloud for up to 48 h after the live session. Out of 16 speakers, 15 gave permission to record the live seminar.\nEfforts were made to invite speakers from a variety of countries and genders. Invited speakers included organizers of postponed in-person conferences, speakers from academia and industry from different career stages. A total of 16 speakers were invited: May 20, 2020: Elga De Vries, Amsterdam UMC, Amsterdam, The Netherlands; May 27, 2020: Richard Daneman, University of California San Diego, San Diego, CA, USA; June 3, 2020: Robyn Klein, Washington University, St. Louis, MO, USA; June 10, 2020: Ayal Ben-Zvi, The Hebrew University of Jerusalem, Israel; June 17, 2020: Britta Engelhardt, University of Bern, Bern, Switzerland; June 24, 2020: Margareta Hammarlund-Udenaes, Uppsala University, Uppsala, Sweden; July 1, 2020: Daniela Virgintino, University of Bari, Bari, Italy; July 8, 2020: Eric Shusta, University of Wisconsin, Madison, WI, USA; July 15, 2020: Matthew Campbell, Trinity College Dublin, Dublin, Ireland; July 22, 2020: Krzysztof Kucharz, University of Copenhagen, Copenhagen, Denmark; July 29, 2020: William Elmquist, University of Minnesota, Minneapolis, MN, USA; August 5, 2020: Edward Neuwelt, Oregon Health & Sciences University, OR, USA; August 12, 2020: Robert Thorne, Denali Therapeutics, San Francisco, CA, USA; August 19, 2020: Teresa Sanchez, Weill Cornell Medical College, New York City, NJ, USA; August 26, 2020: Michael Taylor, University of Wisconsin, Madison, MI, USA; September 2, 2020: Joan Abbott, King’s College London, London, UK.\nSpeakers were offered a training session by the organizers to cover key aspects of a Zoom-based webinar and to test video, audio, and slide presentation prior to the live seminar. Ten out of 16 speakers took the training session. Speakers were informed by the organizers that any data presented should be considered public as security on the Zoom platform could not be ensured. Invited speakers had the opportunity to give permission to have their seminar recorded or to opt out of the recording. If a speaker gave permission to record, the entire seminar was recorded, and recordings were made available to all registered participants in a non-downloadable format through the University of Kentucky Zoom cloud for up to 48 h after the live session. Out of 16 speakers, 15 gave permission to record the live seminar.\n Advertisement and participants To reach scientists in the brain barriers community, the seminar was advertised by email to participants of previous in-person meetings, and on social media platforms such as Facebook and LinkedIn on personal profiles. In addition, the introductory flyer was kindly distributed via email by the International Brain Barriers Society (IBBS) using their email distribution list. Interested scientists were asked to register by sending their name, affiliation, and career stage to a dedicated email address created for the organization and execution of BBV2020 (bbv2020seminar@gmail.com). Once registered, participants were added to a BBV2020 listserv provided by the University of Kentucky and received weekly updates. These weekly updates included a flyer with the information regarding the upcoming speakers and the specific zoom link for the seminar. Participants also received a follow up email after each seminar with a zoom link to the video recordings. With the permission of the speaker of the week, registered viewers had access to recordings for up to 48 h after the live seminar.\nTo reach scientists in the brain barriers community, the seminar was advertised by email to participants of previous in-person meetings, and on social media platforms such as Facebook and LinkedIn on personal profiles. In addition, the introductory flyer was kindly distributed via email by the International Brain Barriers Society (IBBS) using their email distribution list. Interested scientists were asked to register by sending their name, affiliation, and career stage to a dedicated email address created for the organization and execution of BBV2020 (bbv2020seminar@gmail.com). Once registered, participants were added to a BBV2020 listserv provided by the University of Kentucky and received weekly updates. These weekly updates included a flyer with the information regarding the upcoming speakers and the specific zoom link for the seminar. Participants also received a follow up email after each seminar with a zoom link to the video recordings. With the permission of the speaker of the week, registered viewers had access to recordings for up to 48 h after the live seminar.\n BBV2020 seminar presentation Each seminar was started with opening remarks by one of the organizers (Drs. Bauer, Hartz, or Kim) followed by an introduction of the speaker by the moderator. Moderators (Drs. Hudson, Nehra, Pizzo, Storck) rotated each week, only one moderator was assigned to each week. After the seminar presentation, the moderator facilitated the question-and-answer session. Participants were encouraged to ask a live question by raising their digital hand. When a participant raised the digital hand, the moderator would unmute the participant at which point the participant was able to ask the speaker a question directly. In addition, participants had the opportunity to ask questions via the Zoom Q&A or the chat function. Written questions were then subsequently read by the moderator. The moderator concluded each seminar by thanking the speaker and the audience for attending and by announcing the speaker for the following week.\nEach seminar was started with opening remarks by one of the organizers (Drs. Bauer, Hartz, or Kim) followed by an introduction of the speaker by the moderator. Moderators (Drs. Hudson, Nehra, Pizzo, Storck) rotated each week, only one moderator was assigned to each week. After the seminar presentation, the moderator facilitated the question-and-answer session. Participants were encouraged to ask a live question by raising their digital hand. When a participant raised the digital hand, the moderator would unmute the participant at which point the participant was able to ask the speaker a question directly. In addition, participants had the opportunity to ask questions via the Zoom Q&A or the chat function. Written questions were then subsequently read by the moderator. The moderator concluded each seminar by thanking the speaker and the audience for attending and by announcing the speaker for the following week.\n Map generation World maps highlighting countries participating in the various meetings and representation were generated using MapChart (www.mapchart.net; license: https://creativecommons.org/licenses/by-sa/4.0/).\nWorld maps highlighting countries participating in the various meetings and representation were generated using MapChart (www.mapchart.net; license: https://creativecommons.org/licenses/by-sa/4.0/).\n Survey Toward the end of the seminar series, a survey was generated using Qualtrics (Qualtrics) and run through the University of Alabama Qualtrics system. A link to the survey was sent to all registered seminar participants by email. Questions covered general audience participation, career stage, as well as affective factors. Survey results are only reported in an aggregate.\nToward the end of the seminar series, a survey was generated using Qualtrics (Qualtrics) and run through the University of Alabama Qualtrics system. A link to the survey was sent to all registered seminar participants by email. Questions covered general audience participation, career stage, as well as affective factors. Survey results are only reported in an aggregate.\n Data collection, analysis, and statistics Survey data was collected through the University of Alabama Qualtrics system and was determined by the University of Alabama IRB to be “non-human subject research”. Data from the live webinars were collected by Zoom for each seminar and reported as unique viewers to eliminate counting individuals double who potentially logged on twice. Data on video views were collected from the University of Kentucky Zoom Webinar cloud server and were reported as total accessed views. Survey data from Qualtrics was analyzed with the IBM® SPSS® software platform (IBM, Armonk, NY, USA), using one-way ANOVAs and t-tests to identify significant trends [5]. Data with a p < 0.05 are considered significant.\nSurvey data was collected through the University of Alabama Qualtrics system and was determined by the University of Alabama IRB to be “non-human subject research”. Data from the live webinars were collected by Zoom for each seminar and reported as unique viewers to eliminate counting individuals double who potentially logged on twice. Data on video views were collected from the University of Kentucky Zoom Webinar cloud server and were reported as total accessed views. Survey data from Qualtrics was analyzed with the IBM® SPSS® software platform (IBM, Armonk, NY, USA), using one-way ANOVAs and t-tests to identify significant trends [5]. Data with a p < 0.05 are considered significant.","The BBV2020 seminar series was created by Drs. Bjoern Bauer (University of Kentucky, Lexington, KY, USA), Anika Hartz (University of Kentucky, Lexington, KY, USA), and Brandon Kim (University of Alabama, Tuscaloosa, Alabama, USA). Seminar moderators were postdoctoral researchers and scientists from the USA and Europe: Drs. Natalie Hudson (Trinity College Dublin, Dublin, Ireland), Geetika Nehra (University of Kentucky, Lexington, KY, USA), Michelle Pizzo (Denali Therapeutics Inc., San Francisco, CA, USA), and Steffen Storck (University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany).","BBV2020 was run on the Zoom Webinar 500 platform (Zoom Video Communications, Inc.; San Jose, CA, USA) that was funded by Dr. Bjoern Bauer using University of Kentucky funds. This particular Zoom Webinar license allowed for up to 500 live participants. The Zoom Webinar platform supports an in-session chat function, a Q&A forum, a raise hand function for live questions, and provides a panelist section with the option to share audio, video and presentation slides, a recording function and post-webinar data collection. The hosts and moderators were in control of audio and video; participants were not able to record or unmute during a session. Participants had no control function other than a “raise hand” option and the Q&A and chat function allowing to send questions or messages to the moderator. Technical assistance was provided by Todd Sizemore, College of Pharmacy, University of Kentucky.","The BBV2020 seminar series ran from May 20 to September 2, 2020. Live seminars were held once per week from 12 to 1 PM Eastern Daylight Time (EDT) to accommodate researchers across a wide range of time zones: Central European Time (6:00 PM) to Pacific Daylight Time (9:00 AM). To accommodate colleagues with time constraints or those within incompatible time zones such as Asia or Oceania, presentations were recorded (depending on speaker permissions) and the seminar recording was made available for up to 48 h after the live seminar. The link for the recorded videos was distributed via the BBV2020 listserv that was set up through the University of Kentucky listserv.","Efforts were made to invite speakers from a variety of countries and genders. Invited speakers included organizers of postponed in-person conferences, speakers from academia and industry from different career stages. A total of 16 speakers were invited: May 20, 2020: Elga De Vries, Amsterdam UMC, Amsterdam, The Netherlands; May 27, 2020: Richard Daneman, University of California San Diego, San Diego, CA, USA; June 3, 2020: Robyn Klein, Washington University, St. Louis, MO, USA; June 10, 2020: Ayal Ben-Zvi, The Hebrew University of Jerusalem, Israel; June 17, 2020: Britta Engelhardt, University of Bern, Bern, Switzerland; June 24, 2020: Margareta Hammarlund-Udenaes, Uppsala University, Uppsala, Sweden; July 1, 2020: Daniela Virgintino, University of Bari, Bari, Italy; July 8, 2020: Eric Shusta, University of Wisconsin, Madison, WI, USA; July 15, 2020: Matthew Campbell, Trinity College Dublin, Dublin, Ireland; July 22, 2020: Krzysztof Kucharz, University of Copenhagen, Copenhagen, Denmark; July 29, 2020: William Elmquist, University of Minnesota, Minneapolis, MN, USA; August 5, 2020: Edward Neuwelt, Oregon Health & Sciences University, OR, USA; August 12, 2020: Robert Thorne, Denali Therapeutics, San Francisco, CA, USA; August 19, 2020: Teresa Sanchez, Weill Cornell Medical College, New York City, NJ, USA; August 26, 2020: Michael Taylor, University of Wisconsin, Madison, MI, USA; September 2, 2020: Joan Abbott, King’s College London, London, UK.\nSpeakers were offered a training session by the organizers to cover key aspects of a Zoom-based webinar and to test video, audio, and slide presentation prior to the live seminar. Ten out of 16 speakers took the training session. Speakers were informed by the organizers that any data presented should be considered public as security on the Zoom platform could not be ensured. Invited speakers had the opportunity to give permission to have their seminar recorded or to opt out of the recording. If a speaker gave permission to record, the entire seminar was recorded, and recordings were made available to all registered participants in a non-downloadable format through the University of Kentucky Zoom cloud for up to 48 h after the live session. Out of 16 speakers, 15 gave permission to record the live seminar.","To reach scientists in the brain barriers community, the seminar was advertised by email to participants of previous in-person meetings, and on social media platforms such as Facebook and LinkedIn on personal profiles. In addition, the introductory flyer was kindly distributed via email by the International Brain Barriers Society (IBBS) using their email distribution list. Interested scientists were asked to register by sending their name, affiliation, and career stage to a dedicated email address created for the organization and execution of BBV2020 (bbv2020seminar@gmail.com). Once registered, participants were added to a BBV2020 listserv provided by the University of Kentucky and received weekly updates. These weekly updates included a flyer with the information regarding the upcoming speakers and the specific zoom link for the seminar. Participants also received a follow up email after each seminar with a zoom link to the video recordings. With the permission of the speaker of the week, registered viewers had access to recordings for up to 48 h after the live seminar.","Each seminar was started with opening remarks by one of the organizers (Drs. Bauer, Hartz, or Kim) followed by an introduction of the speaker by the moderator. Moderators (Drs. Hudson, Nehra, Pizzo, Storck) rotated each week, only one moderator was assigned to each week. After the seminar presentation, the moderator facilitated the question-and-answer session. Participants were encouraged to ask a live question by raising their digital hand. When a participant raised the digital hand, the moderator would unmute the participant at which point the participant was able to ask the speaker a question directly. In addition, participants had the opportunity to ask questions via the Zoom Q&A or the chat function. Written questions were then subsequently read by the moderator. The moderator concluded each seminar by thanking the speaker and the audience for attending and by announcing the speaker for the following week.","World maps highlighting countries participating in the various meetings and representation were generated using MapChart (www.mapchart.net; license: https://creativecommons.org/licenses/by-sa/4.0/).","Toward the end of the seminar series, a survey was generated using Qualtrics (Qualtrics) and run through the University of Alabama Qualtrics system. A link to the survey was sent to all registered seminar participants by email. Questions covered general audience participation, career stage, as well as affective factors. Survey results are only reported in an aggregate.","Survey data was collected through the University of Alabama Qualtrics system and was determined by the University of Alabama IRB to be “non-human subject research”. Data from the live webinars were collected by Zoom for each seminar and reported as unique viewers to eliminate counting individuals double who potentially logged on twice. Data on video views were collected from the University of Kentucky Zoom Webinar cloud server and were reported as total accessed views. Survey data from Qualtrics was analyzed with the IBM® SPSS® software platform (IBM, Armonk, NY, USA), using one-way ANOVAs and t-tests to identify significant trends [5]. Data with a p < 0.05 are considered significant.","On May 14, 2020, we sent out the first advertisement for the BBV2020 seminar series via email using a listserv with email addresses of past conference attendees and via postings using LinkedIn, Facebook, and Twitter. By the start of the first seminar on May 20, 2020, more than 720 scientists had registered. By the end of the 16-week long BBV2020 seminar series, a total of 1302 registrants signed up on our listserv to receive the seminar link, speaker updates, and video recording links (Fig. 1A). We obtained data from two past in-person meetings from the respective conference chairs: (1) brain barriers meeting in 2018 (BBB 2018) and the (2) Cerebral Vascular Biology meeting in 2019 (CVB 2019).\n\nFig. 1\nAttendance Trends for Brain Barrier Research Conferences. A With 1,302 individuals signing up and added to the listserv, BBV2020 attracted more registrants than BBB 2018 and CVB 2019. B Number of unique viewers weekly at the live Zoom Webinar sessions of BBV2020. C Number of views (clicks) of the video recordings when available weekly for BBV2020. Black line indicates best linear fit. Gray line indicates the average (mean) for all 16 weeks\n\nAttendance Trends for Brain Barrier Research Conferences. A With 1,302 individuals signing up and added to the listserv, BBV2020 attracted more registrants than BBB 2018 and CVB 2019. B Number of unique viewers weekly at the live Zoom Webinar sessions of BBV2020. C Number of views (clicks) of the video recordings when available weekly for BBV2020. Black line indicates best linear fit. Gray line indicates the average (mean) for all 16 weeks\nStrikingly, the total number of registered applicants for the BBV2020 was more than 4-fold higher compared to the total number of registered participants for the 2018 and the CVB 2019 meetings (Fig. 1A). The large number of registrants is likely due to free registration, no associated travel expenses, a world-wide reach, and that virtual meetings come with no obligations for the attendees. In contrast, the number of attendees of the in-person meetings (BBB 2018 and CVB2019) was limited and determined by the size of the conference site.\nThe first live BBV2020 seminar was attended by 419 participants. As the seminar series progressed, the participant number during live seminars gradually decreased, the average participant number was 220, the minimum was 108 (Fig. 1B). A similar trend was observed in the numbers of total recording views: the first seminar recording had a maximum of 944 views; in average, videos had 422 views (Fig. 1C). The trendline in Fig. 1C shows a drop in recording views over the course of the seminar series. These trends held regardless of academic rank as graduate students, postdoctoral fellows, and professors all showed a similar downward trend in live attendance (Additional file 1: Fig. S1A–C). Note that we were unable to capture recording data for week 2 due to a recording error and for week 13 due to the request by the speaker to not record.\nTogether, the high number of registrants suggests that the virtual BBV2020 seminar series sparked interest. Attendance of the live seminars and the numbers of views for the recorded videos decreased over time, which may be an indicator of “virtual meeting fatigue” or attendees being on their summer vacation.","By the end of the seminar series in September, every BBV2020 registrant received an invitation to participate in an online survey created by the organizers on August 23, 2020. The survey was designed for registrants to provide feedback and consisted of 10 questions: (1) demographic question to capture career level and career type; (2) single select multiple choice question to capture attendance of past in-person brain barrier meetings, (3) single select multiple choice question on how many recorded seminars were viewed, (4) Likert scale question regarding the enrichment BBV2020 provided for the community during the COVID-19 pandemic, (5) dichotomous question asking if the registrant would still participate in BBV2020 seminars if they were continued, (6) single select multiple choice question on attendance frequency, (7) single select multiple choice question on how many times the registrant has thus far attended a brain barriers meeting previously in person, (8) matrix question to capture affective factors, (9) matrix question to capture learning and teaching, and (10) matrix question to capture combined validity and practicality of the BBV2020 series. In addition, registrants responding to the survey had the opportunity to include general comments to the organizers in a separate text box.\nOverall, 23% of registrants (total of 299 registrants) responded to the survey. Over 89% of survey respondents agreed strongly that “Attending BBV2020 was a positive experience” and 63% of responders felt that “BBV2020 is appropriate for my research”. Generally, the respondents tended to agree with the following statement: “BBV2020 has enriched the connection with the Brain Barriers community in the gap that COVID-19 has created” (n = 298 respondents to this question of a total of 299 registrants, M = 1.44, SD = 0.660, 1 = Strongly Agree, 2 = Agree, 3 = Neutral, 4 = Disagree, 5 = Strongly Disagree; Fig. 2A). However, when posed with the statements “In the future, I would prefer to attend a virtual conference over an in-person conference” or “The virtual experience is not as effective compared to the in-person conferences”, responses were neutral (M = 3.24; Fig. 2B) indicating a positive view towards a virtual conference. A difference emerged when we compared BBV2020 participants based on their career level with their preference for in-person versus virtual meeting formats: using one-way ANOVA, we found a significant difference between groups (F(9286) = 2.607, p = 0.007). Using Tukey’s HSD post hoc test indicated that Master’s students were more likely to disagree with the following statement when compared to Faculty/Professors: “I would feel more comfortable attending Brain Barrier conferences/seminars in person, but was glad to attend virtually due to COVID19.” A strong trend emerged when Master’s and Ph.D. students were grouped together and compared to Post-Docs and Faculty/Professors in an independent-samples t test. Results show graduate students in general were more favorable to a virtual conference compared to colleagues more established in their careers (t(225) = -1.972, p = 0.061). The lack of significance is in part due to the low sample size (n = 74) for graduate students.\n\nFig. 2\nPost-seminar survey results. A Volunteer respondent result when posed with the prompt “BBV2020 has enriched connection with the Brain Barriers community in the gap that COVID-19 has created”. B Volunteer respondent results when posted with the prompts “In the future, I would prefer to attend a virtual conference over an in-person conference” (Blue) and “The virtual experience is not as effective compared to the in-person conferences” (Red)\n\nPost-seminar survey results. A Volunteer respondent result when posed with the prompt “BBV2020 has enriched connection with the Brain Barriers community in the gap that COVID-19 has created”. B Volunteer respondent results when posted with the prompts “In the future, I would prefer to attend a virtual conference over an in-person conference” (Blue) and “The virtual experience is not as effective compared to the in-person conferences” (Red)\nOverall, descriptive data revealed many participants felt BBV2020 was a positive experience. In fact, 99.3% of survey respondents noted they somewhat or strongly agree that their experience was positive, with only 4.7% saying they faced technical difficulties. Additionally, most respondents agreed that virtual conferences have an important role in research (93.2%) and are more accessible than in-person conferences (90.2%). Finally, more than half of respondents (56.9%) noted virtual conferences can do things in-person conferences cannot.\nNevertheless, there continued to be a desire among many BBV2020 participants to return to conferencing in person, with 13.5% saying they found it hard to focus in the virtual format. In fact, roughly one-third (33.9%) felt the virtual conference was not as effective as an in-person conference, and only one-quarter (23.3%) said they would prefer virtual conferences to in-person conferences in the future, while 35.8% were neutral.\nTogether, our findings suggest that while BBV2020 filled a need created by a global pandemic, the enthusiasm for returning to only in-person meetings remained relatively neutral suggesting that virtual platforms may play a critical role in dissemination of research and science in the future. Moreover, the data from this study show a trend between graduate students versus professors in their response with regard to in-person meetings indicating that career stage may influence the perspective of the usefulness of in-person versus virtual platforms.","During the seminar registration process we collected attendants’ affiliations and based on this information we determined the global reach of the BBV2020. Registrants of the BBV2020 seminar series were from 43 countries representing six of the seven continents (Fig. 3A, B). Compared to the in-person BBB 2018 and the CVB2019, more countries were represented at the BBV2020 (Fig. 3A).\n\nFig. 3\nGlobal reach of BBV2020. A BBV2020 had more countries represented compared to BBB 2018 and CVB 2019 alone. B World map showing countries represented by BBV2020. Countries where registrants participated in BBV2020: Red and Pink. Countries not represented at BBV2020: Light Gray and Dark Gray. Countries where registrants attended BBV2020 and at least one of the in-person meetings between Meeting 2018 or CVB 2019: Red. Countries where registrants attended BBV2020 and did not attend either BBB 2018 or CVB 2019: Pink. Countries that had no participation in any of the brain barriers meetings examined in this study: Light gray. Countries that had participation in either BBB 2018 or CVB 2019 that did not participate in BBV2020: Dark Gray\n\nGlobal reach of BBV2020. A BBV2020 had more countries represented compared to BBB 2018 and CVB 2019 alone. B World map showing countries represented by BBV2020. Countries where registrants participated in BBV2020: Red and Pink. Countries not represented at BBV2020: Light Gray and Dark Gray. Countries where registrants attended BBV2020 and at least one of the in-person meetings between Meeting 2018 or CVB 2019: Red. Countries where registrants attended BBV2020 and did not attend either BBB 2018 or CVB 2019: Pink. Countries that had no participation in any of the brain barriers meetings examined in this study: Light gray. Countries that had participation in either BBB 2018 or CVB 2019 that did not participate in BBV2020: Dark Gray\nWe also compared the number of participants from each country who joined BBV2020 vs. BBB 2018 and CVB 2019. We found that the number of participants per country worldwide was also higher compared to both in person meetings (Fig. 4A–F). These data show that the virtual nature of BBV2020 enabled researchers world-wide to participate and suggest that the outreach of a virtual event can extend beyond the field.\n\nFig. 4\nGlobal view of participation over BBV2020, BBB 2018, and CVB 2019. Representation of participants for (A) BBV2020, (B) BBB 2018, and (C) CVB 2019. Ordered from top to bottom in greatest number of registrants. Number of registrants scaled by color based on number of registrants per country for (D) BBV 2020, (E) BBB 2018, and (F) CVB 2019. Light Pink: 1–10 registrants; Dark Pink: 11–50 registrants; Red: 51–100 registrants and Dark Red: > 100 registrants\n\nGlobal view of participation over BBV2020, BBB 2018, and CVB 2019. Representation of participants for (A) BBV2020, (B) BBB 2018, and (C) CVB 2019. Ordered from top to bottom in greatest number of registrants. Number of registrants scaled by color based on number of registrants per country for (D) BBV 2020, (E) BBB 2018, and (F) CVB 2019. Light Pink: 1–10 registrants; Dark Pink: 11–50 registrants; Red: 51–100 registrants and Dark Red: > 100 registrants","We examined the self-reported career stage of BBV2020 participants to that of BBB 2018 and CVB 2019 participants. Based on the survey, more than 46% of all participants attended a brain barriers conference/seminar for the first time. We also found that in-person meetings are attended by 42.4–48.9% academicians at the professor rank, graduate students make up 22.9–25.8% and postdoctoral fellows are 6.6–17.7% of the participants (Fig. 5). In contrast, at the BBV2020 19.6% of academicians were at the professor rank, graduate students made up 30.5% and postdoctoral fellows 16.1% of attendees. We also found that BBV2020 had a larger participation from industry (18.5% vs. 5.6–6.6%), and undergraduate students (3.2% vs. 1%) compared to both in person meetings. Together, our data suggest that a virtual platform seminar like BBV2020 reaches a broader range of scientific ranks and careers.\n\nFig. 5\nProportions of various career stages participating in BBV2020, BBB 2018, and CVB 2019. Proportions of various career stages participating in (A) BBV2020, (B) BBB 2018, and (C) CVB 2019. Colors correspond to career stage: Red (professor any rank), Green (graduate student any rank), Dark Blue (research faculty or researcher), Yellow (industry scientist), Orange (postdoctoral fellow), Purple (government/regulatory), Light Blue (clinician), Green (undergraduate student)\n\nProportions of various career stages participating in BBV2020, BBB 2018, and CVB 2019. Proportions of various career stages participating in (A) BBV2020, (B) BBB 2018, and (C) CVB 2019. Colors correspond to career stage: Red (professor any rank), Green (graduate student any rank), Dark Blue (research faculty or researcher), Yellow (industry scientist), Orange (postdoctoral fellow), Purple (government/regulatory), Light Blue (clinician), Green (undergraduate student)","Due to the pandemic, the idea and subsequent organization of the BBV2020 occurred in a relatively short time frame of about 4 weeks. This is in stark contrast to in-person meetings that are often scheduled and organized months and years ahead of the actual event. Advertisement of the BBV2020 encompassed personal email, postings on social media, and utilization of the International Brain Barriers Society email distribution list. Continuous advertisement resulted in the registration of over 1300 scientists. The total registrant number cannot be directly compared to that of in-person meetings since those are more complex in nature, have maximum capacity limits, and involve other factors such as travel expenses and time commitment. Similar to other meetings which transitioned to online formats during the pandemic, BBV2020 had a large increase in registrants when compared to previous in-person meetings (~7-fold and 4.5-fold higher compared to 2018 and 2019, respectively; Fig. 1; [8]. Given that previous brain barriers meetings usually have a few hundred participants, the high number of registrants could be an indicator for high interest in the field beyond traditional brain barrier research groups. In contrast, these high registrant numbers could also be due, in part, to BBV2020 being a no-cost event or could be explained by pandemic-related cancellations resulting in relatively event-free calendar of the participants. While there were more than 1,300 registrants for BBV2020, no more than 419 ever attended a live webinar. Additionally, 323 people registered for BBV2020 but never attended a live webinar. They may, however, have participated by watching the video recordings after the live webinar. A downward trend of attendance numbers for live seminars was observed throughout the course of the 16-week series (Fig. 1B). The recorded seminars drew considerable interest initially with a maximum of 944 views in week 4 of the series, but later in the series, video views dropped to about 200 per seminar (Fig. 1C). Reasons for these downward trends remain speculative, but could be due to the summer break, the selected time for the seminar making it difficult for researchers in certain time zones to attend live, zoom fatigue, or increasing responsibilities in re-opened research laboratories or at home. The downward trends we observed was in contrast to a similar virtual seminar series in another field that remained relatively flat over the course of the summer [4]. Given that average attendance of the BBV2020 was 220 per seminar and typical attendance for in-person conferences in the brain barriers field range between 150 and 300 participants (caveat: one of the in-person conferences has a strict 182-person maximum limit), we conclude that a core group of participants attended most sessions.","A survey conducted after the seminar series revealed that most participants felt that BBV2020 enhanced the connection to the brain barriers scientific community during the COVID-19 pandemic (Fig. 2A). Differences emerged between how graduate students and professors viewed BBV2020: graduate students in general were more favorable of the virtual format compared to faculty. This may be partially explained by a lack of funding for graduate student travel or political “red tape” (e.g. visas) for some researchers [9]. Additionally, students may disproportionately adapt more easily and rapidly to technology usage compared to senior colleagues [10]. Clearly, another survey would be needed to determine if a virtual component outside of a pandemic would be positively viewed by researchers in the brain barriers field.","One major advantage of a virtual seminar series such as the BBV2020 is the lack of a travel burden. Taking off the financial and time-commitment associated with in-person meetings has been shown to increase the diversity of attendees [3, 9, 11]. Based on those findings, we anticipated that BBV2020 would have representation from more countries than past in-person meetings. As shown in Fig. 3, registrants from 43 different countries were represented at BBV2020, which is 40% more compared to BBB 2018 (23 countries) and CVB 2019 (25 countries; Fig. 3). In particular, there was high participation from countries in Southeast Asia, Middle East, Africa, and South America that had not been represented at in-person conferences held in 2018 and 2019 (Figs. 3B, 4). Unsurprisingly however, considering countries represented at previous in-person meetings and due to the number of registrants for BBV2020, there was generally a greater participation from each country represented than in 2018 meeting and CVB 2019 [3, 8]. Due to the time of the BBV2020 live sessions (noon to 1 pm Eastern US Time), the majority of participants were from North America, South America, and Europe (Fig. 4). While time zone challenges are common among virtual events, switching between time zones or holding a “flipped” session (flipping evening sessions from Europe to the USA and vice versa) could increase participation from Asia and Oceania [12]. However, offering a virtual series that constantly switches the seminar time may be difficult to manage and could be confusing for participants. Another strength of virtual seminars is that they are more accessible and inclusive especially for junior researchers in the field. The in-person meetings in 2018 and 2019 were dominated by academic faculty at any professor rank and graduate students (Fig. 5). In contrast, BBV2020 saw a large reduction in the percentage of participating faculty (over 40% in person to less than 20% for BBV2020) and a larger proportion of graduate students (22–25% in person to over 30% for BBV2020), industry scientists (about 6% in person to 18.5% for BBV2020), and governmental/regulatory personnel who had signed up (Fig. 5). In addition, a weekly virtual seminar series allows scientist to pick-and-choose the sessions they are interested in, a choice that could interfere with the observed trends shown in Fig. 1. Our data further support the trends observed across a variety of disciplines who have shifted their conferences from in-person to online events, revealing that virtual conferences are more flexible, and more inclusive and accessible worldwide, especially for early-career scientists [9]. Virtual seminars also provide an opportunity for the organizers to invite prominent speakers of a field on a relatively small budget. In general, virtual conferences are known to enhance the accessibility for busy professionals. A recently published Nature poll indicates that 74% of scientists want virtual meetings to stay after the pandemic (925 poll responses) [13]. Easier accessibility, lower carbon footprint, and lower costs were the driving factors chosen in favor for virtual meetings in this poll. Moreover, virtual seminars provide an easy and affordable opportunity for researchers with disabilities, researchers with high teaching loads, or for scientists with parenting and other responsibilities to stay connected to their respective research community. During the COVID-19 pandemic, virtual seminars took center stage and raised the standard for accessibility, interactions, inclusivity, and equity in science [9]. Virtual events changed how we think about meetings and they constitute a yet underestimated, but powerful tool to share science and connect with each other. By rethinking how meetings are held, various fields have had different degrees of participation depending on the availability of the talks or content versus live sessions. These virtual platforms have the potential to create a “connectivity cascade” reaching more people than traditional meetings that are confined by a single geographical location and time [14]. BBV2020 had an impressive reach worldwide, however, there was still a notable deficit in participants from underdeveloped countries, especially Africa. These discrepancies have been noted in the context of expanding telehealth to African countries that still remain challenging due to monopolization of telecommunication infrastructure, political will, and access to adequate broadband efficiency [15]. Despite the advantages of BBV2020, we recognize that limitations remain, such as one-way delivery of material to participants. Lack of critical face-to-face interactions allowing junior scientists to have meaningful two-way communication with other participants was notably lacking in BBV2020 as seen in other virtual conferences as well [16]. The future of such virtual seminars, meetings, or conferences may lie in their inclusion as part of in-person meetings according to a recently published “best practice” guideline for virtual meetings [17]. For example, a virtual component could be included in plenary sessions, workshops, small group discussions, poster sessions, and/or social events of in-person conferences. BBV2020 represented only one aspect (a seminar held in the form of a webinar) of these common meeting events, but future events could certainly enhance the virtual experience. Regardless, virtual components could be exploited to increase diversity in the brain barriers research field which is consistent with the NINDS strategy for enhancing the diversity of neuroscience researchers [18].","Overall, the Virtual BBV2020 Seminar Series created during the COVID-19 pandemic was positively received by the brain barriers research community. Key benefits of virtual seminars like the BBV2020 are that they are convenient, affordable, eco-friendly, reach a broad audience, and are a good tool to increase diversity and equity. Thus, incorporating a virtual component during an in-person meeting or offering a virtual counterpart in addition to in-person meetings could help increase the outreach of a small research field like the brain barriers field. Nevertheless, limitations remain such as a lack of interaction between speakers and participants, which is particularly critical for junior researchers. And although BBV2020 expanded the reach beyond past in-person meetings, challenges reaching scientists in underdeveloped countries remain. Together, BBV2020 was created in a time of crisis but has the potential to thrive in the post-pandemic world. Embracing virtual scientific events may allow us to advance the brain barriers research field and have an impact on how we exchange science in the future."],"string":"[\n \" Organization The BBV2020 seminar series was created by Drs. Bjoern Bauer (University of Kentucky, Lexington, KY, USA), Anika Hartz (University of Kentucky, Lexington, KY, USA), and Brandon Kim (University of Alabama, Tuscaloosa, Alabama, USA). Seminar moderators were postdoctoral researchers and scientists from the USA and Europe: Drs. Natalie Hudson (Trinity College Dublin, Dublin, Ireland), Geetika Nehra (University of Kentucky, Lexington, KY, USA), Michelle Pizzo (Denali Therapeutics Inc., San Francisco, CA, USA), and Steffen Storck (University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany).\\nThe BBV2020 seminar series was created by Drs. Bjoern Bauer (University of Kentucky, Lexington, KY, USA), Anika Hartz (University of Kentucky, Lexington, KY, USA), and Brandon Kim (University of Alabama, Tuscaloosa, Alabama, USA). Seminar moderators were postdoctoral researchers and scientists from the USA and Europe: Drs. Natalie Hudson (Trinity College Dublin, Dublin, Ireland), Geetika Nehra (University of Kentucky, Lexington, KY, USA), Michelle Pizzo (Denali Therapeutics Inc., San Francisco, CA, USA), and Steffen Storck (University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany).\\n Virtual platform and technical assistance BBV2020 was run on the Zoom Webinar 500 platform (Zoom Video Communications, Inc.; San Jose, CA, USA) that was funded by Dr. Bjoern Bauer using University of Kentucky funds. This particular Zoom Webinar license allowed for up to 500 live participants. The Zoom Webinar platform supports an in-session chat function, a Q&A forum, a raise hand function for live questions, and provides a panelist section with the option to share audio, video and presentation slides, a recording function and post-webinar data collection. The hosts and moderators were in control of audio and video; participants were not able to record or unmute during a session. Participants had no control function other than a “raise hand” option and the Q&A and chat function allowing to send questions or messages to the moderator. Technical assistance was provided by Todd Sizemore, College of Pharmacy, University of Kentucky.\\nBBV2020 was run on the Zoom Webinar 500 platform (Zoom Video Communications, Inc.; San Jose, CA, USA) that was funded by Dr. Bjoern Bauer using University of Kentucky funds. This particular Zoom Webinar license allowed for up to 500 live participants. The Zoom Webinar platform supports an in-session chat function, a Q&A forum, a raise hand function for live questions, and provides a panelist section with the option to share audio, video and presentation slides, a recording function and post-webinar data collection. The hosts and moderators were in control of audio and video; participants were not able to record or unmute during a session. Participants had no control function other than a “raise hand” option and the Q&A and chat function allowing to send questions or messages to the moderator. Technical assistance was provided by Todd Sizemore, College of Pharmacy, University of Kentucky.\\n BBV2020 schedule The BBV2020 seminar series ran from May 20 to September 2, 2020. Live seminars were held once per week from 12 to 1 PM Eastern Daylight Time (EDT) to accommodate researchers across a wide range of time zones: Central European Time (6:00 PM) to Pacific Daylight Time (9:00 AM). To accommodate colleagues with time constraints or those within incompatible time zones such as Asia or Oceania, presentations were recorded (depending on speaker permissions) and the seminar recording was made available for up to 48 h after the live seminar. The link for the recorded videos was distributed via the BBV2020 listserv that was set up through the University of Kentucky listserv.\\nThe BBV2020 seminar series ran from May 20 to September 2, 2020. Live seminars were held once per week from 12 to 1 PM Eastern Daylight Time (EDT) to accommodate researchers across a wide range of time zones: Central European Time (6:00 PM) to Pacific Daylight Time (9:00 AM). To accommodate colleagues with time constraints or those within incompatible time zones such as Asia or Oceania, presentations were recorded (depending on speaker permissions) and the seminar recording was made available for up to 48 h after the live seminar. The link for the recorded videos was distributed via the BBV2020 listserv that was set up through the University of Kentucky listserv.\\n Invited speakers Efforts were made to invite speakers from a variety of countries and genders. Invited speakers included organizers of postponed in-person conferences, speakers from academia and industry from different career stages. A total of 16 speakers were invited: May 20, 2020: Elga De Vries, Amsterdam UMC, Amsterdam, The Netherlands; May 27, 2020: Richard Daneman, University of California San Diego, San Diego, CA, USA; June 3, 2020: Robyn Klein, Washington University, St. Louis, MO, USA; June 10, 2020: Ayal Ben-Zvi, The Hebrew University of Jerusalem, Israel; June 17, 2020: Britta Engelhardt, University of Bern, Bern, Switzerland; June 24, 2020: Margareta Hammarlund-Udenaes, Uppsala University, Uppsala, Sweden; July 1, 2020: Daniela Virgintino, University of Bari, Bari, Italy; July 8, 2020: Eric Shusta, University of Wisconsin, Madison, WI, USA; July 15, 2020: Matthew Campbell, Trinity College Dublin, Dublin, Ireland; July 22, 2020: Krzysztof Kucharz, University of Copenhagen, Copenhagen, Denmark; July 29, 2020: William Elmquist, University of Minnesota, Minneapolis, MN, USA; August 5, 2020: Edward Neuwelt, Oregon Health & Sciences University, OR, USA; August 12, 2020: Robert Thorne, Denali Therapeutics, San Francisco, CA, USA; August 19, 2020: Teresa Sanchez, Weill Cornell Medical College, New York City, NJ, USA; August 26, 2020: Michael Taylor, University of Wisconsin, Madison, MI, USA; September 2, 2020: Joan Abbott, King’s College London, London, UK.\\nSpeakers were offered a training session by the organizers to cover key aspects of a Zoom-based webinar and to test video, audio, and slide presentation prior to the live seminar. Ten out of 16 speakers took the training session. Speakers were informed by the organizers that any data presented should be considered public as security on the Zoom platform could not be ensured. Invited speakers had the opportunity to give permission to have their seminar recorded or to opt out of the recording. If a speaker gave permission to record, the entire seminar was recorded, and recordings were made available to all registered participants in a non-downloadable format through the University of Kentucky Zoom cloud for up to 48 h after the live session. Out of 16 speakers, 15 gave permission to record the live seminar.\\nEfforts were made to invite speakers from a variety of countries and genders. Invited speakers included organizers of postponed in-person conferences, speakers from academia and industry from different career stages. A total of 16 speakers were invited: May 20, 2020: Elga De Vries, Amsterdam UMC, Amsterdam, The Netherlands; May 27, 2020: Richard Daneman, University of California San Diego, San Diego, CA, USA; June 3, 2020: Robyn Klein, Washington University, St. Louis, MO, USA; June 10, 2020: Ayal Ben-Zvi, The Hebrew University of Jerusalem, Israel; June 17, 2020: Britta Engelhardt, University of Bern, Bern, Switzerland; June 24, 2020: Margareta Hammarlund-Udenaes, Uppsala University, Uppsala, Sweden; July 1, 2020: Daniela Virgintino, University of Bari, Bari, Italy; July 8, 2020: Eric Shusta, University of Wisconsin, Madison, WI, USA; July 15, 2020: Matthew Campbell, Trinity College Dublin, Dublin, Ireland; July 22, 2020: Krzysztof Kucharz, University of Copenhagen, Copenhagen, Denmark; July 29, 2020: William Elmquist, University of Minnesota, Minneapolis, MN, USA; August 5, 2020: Edward Neuwelt, Oregon Health & Sciences University, OR, USA; August 12, 2020: Robert Thorne, Denali Therapeutics, San Francisco, CA, USA; August 19, 2020: Teresa Sanchez, Weill Cornell Medical College, New York City, NJ, USA; August 26, 2020: Michael Taylor, University of Wisconsin, Madison, MI, USA; September 2, 2020: Joan Abbott, King’s College London, London, UK.\\nSpeakers were offered a training session by the organizers to cover key aspects of a Zoom-based webinar and to test video, audio, and slide presentation prior to the live seminar. Ten out of 16 speakers took the training session. Speakers were informed by the organizers that any data presented should be considered public as security on the Zoom platform could not be ensured. Invited speakers had the opportunity to give permission to have their seminar recorded or to opt out of the recording. If a speaker gave permission to record, the entire seminar was recorded, and recordings were made available to all registered participants in a non-downloadable format through the University of Kentucky Zoom cloud for up to 48 h after the live session. Out of 16 speakers, 15 gave permission to record the live seminar.\\n Advertisement and participants To reach scientists in the brain barriers community, the seminar was advertised by email to participants of previous in-person meetings, and on social media platforms such as Facebook and LinkedIn on personal profiles. In addition, the introductory flyer was kindly distributed via email by the International Brain Barriers Society (IBBS) using their email distribution list. Interested scientists were asked to register by sending their name, affiliation, and career stage to a dedicated email address created for the organization and execution of BBV2020 (bbv2020seminar@gmail.com). Once registered, participants were added to a BBV2020 listserv provided by the University of Kentucky and received weekly updates. These weekly updates included a flyer with the information regarding the upcoming speakers and the specific zoom link for the seminar. Participants also received a follow up email after each seminar with a zoom link to the video recordings. With the permission of the speaker of the week, registered viewers had access to recordings for up to 48 h after the live seminar.\\nTo reach scientists in the brain barriers community, the seminar was advertised by email to participants of previous in-person meetings, and on social media platforms such as Facebook and LinkedIn on personal profiles. In addition, the introductory flyer was kindly distributed via email by the International Brain Barriers Society (IBBS) using their email distribution list. Interested scientists were asked to register by sending their name, affiliation, and career stage to a dedicated email address created for the organization and execution of BBV2020 (bbv2020seminar@gmail.com). Once registered, participants were added to a BBV2020 listserv provided by the University of Kentucky and received weekly updates. These weekly updates included a flyer with the information regarding the upcoming speakers and the specific zoom link for the seminar. Participants also received a follow up email after each seminar with a zoom link to the video recordings. With the permission of the speaker of the week, registered viewers had access to recordings for up to 48 h after the live seminar.\\n BBV2020 seminar presentation Each seminar was started with opening remarks by one of the organizers (Drs. Bauer, Hartz, or Kim) followed by an introduction of the speaker by the moderator. Moderators (Drs. Hudson, Nehra, Pizzo, Storck) rotated each week, only one moderator was assigned to each week. After the seminar presentation, the moderator facilitated the question-and-answer session. Participants were encouraged to ask a live question by raising their digital hand. When a participant raised the digital hand, the moderator would unmute the participant at which point the participant was able to ask the speaker a question directly. In addition, participants had the opportunity to ask questions via the Zoom Q&A or the chat function. Written questions were then subsequently read by the moderator. The moderator concluded each seminar by thanking the speaker and the audience for attending and by announcing the speaker for the following week.\\nEach seminar was started with opening remarks by one of the organizers (Drs. Bauer, Hartz, or Kim) followed by an introduction of the speaker by the moderator. Moderators (Drs. Hudson, Nehra, Pizzo, Storck) rotated each week, only one moderator was assigned to each week. After the seminar presentation, the moderator facilitated the question-and-answer session. Participants were encouraged to ask a live question by raising their digital hand. When a participant raised the digital hand, the moderator would unmute the participant at which point the participant was able to ask the speaker a question directly. In addition, participants had the opportunity to ask questions via the Zoom Q&A or the chat function. Written questions were then subsequently read by the moderator. The moderator concluded each seminar by thanking the speaker and the audience for attending and by announcing the speaker for the following week.\\n Map generation World maps highlighting countries participating in the various meetings and representation were generated using MapChart (www.mapchart.net; license: https://creativecommons.org/licenses/by-sa/4.0/).\\nWorld maps highlighting countries participating in the various meetings and representation were generated using MapChart (www.mapchart.net; license: https://creativecommons.org/licenses/by-sa/4.0/).\\n Survey Toward the end of the seminar series, a survey was generated using Qualtrics (Qualtrics) and run through the University of Alabama Qualtrics system. A link to the survey was sent to all registered seminar participants by email. Questions covered general audience participation, career stage, as well as affective factors. Survey results are only reported in an aggregate.\\nToward the end of the seminar series, a survey was generated using Qualtrics (Qualtrics) and run through the University of Alabama Qualtrics system. A link to the survey was sent to all registered seminar participants by email. Questions covered general audience participation, career stage, as well as affective factors. Survey results are only reported in an aggregate.\\n Data collection, analysis, and statistics Survey data was collected through the University of Alabama Qualtrics system and was determined by the University of Alabama IRB to be “non-human subject research”. Data from the live webinars were collected by Zoom for each seminar and reported as unique viewers to eliminate counting individuals double who potentially logged on twice. Data on video views were collected from the University of Kentucky Zoom Webinar cloud server and were reported as total accessed views. Survey data from Qualtrics was analyzed with the IBM® SPSS® software platform (IBM, Armonk, NY, USA), using one-way ANOVAs and t-tests to identify significant trends [5]. Data with a p < 0.05 are considered significant.\\nSurvey data was collected through the University of Alabama Qualtrics system and was determined by the University of Alabama IRB to be “non-human subject research”. Data from the live webinars were collected by Zoom for each seminar and reported as unique viewers to eliminate counting individuals double who potentially logged on twice. Data on video views were collected from the University of Kentucky Zoom Webinar cloud server and were reported as total accessed views. Survey data from Qualtrics was analyzed with the IBM® SPSS® software platform (IBM, Armonk, NY, USA), using one-way ANOVAs and t-tests to identify significant trends [5]. Data with a p < 0.05 are considered significant.\",\n \"The BBV2020 seminar series was created by Drs. Bjoern Bauer (University of Kentucky, Lexington, KY, USA), Anika Hartz (University of Kentucky, Lexington, KY, USA), and Brandon Kim (University of Alabama, Tuscaloosa, Alabama, USA). Seminar moderators were postdoctoral researchers and scientists from the USA and Europe: Drs. Natalie Hudson (Trinity College Dublin, Dublin, Ireland), Geetika Nehra (University of Kentucky, Lexington, KY, USA), Michelle Pizzo (Denali Therapeutics Inc., San Francisco, CA, USA), and Steffen Storck (University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany).\",\n \"BBV2020 was run on the Zoom Webinar 500 platform (Zoom Video Communications, Inc.; San Jose, CA, USA) that was funded by Dr. Bjoern Bauer using University of Kentucky funds. This particular Zoom Webinar license allowed for up to 500 live participants. The Zoom Webinar platform supports an in-session chat function, a Q&A forum, a raise hand function for live questions, and provides a panelist section with the option to share audio, video and presentation slides, a recording function and post-webinar data collection. The hosts and moderators were in control of audio and video; participants were not able to record or unmute during a session. Participants had no control function other than a “raise hand” option and the Q&A and chat function allowing to send questions or messages to the moderator. Technical assistance was provided by Todd Sizemore, College of Pharmacy, University of Kentucky.\",\n \"The BBV2020 seminar series ran from May 20 to September 2, 2020. Live seminars were held once per week from 12 to 1 PM Eastern Daylight Time (EDT) to accommodate researchers across a wide range of time zones: Central European Time (6:00 PM) to Pacific Daylight Time (9:00 AM). To accommodate colleagues with time constraints or those within incompatible time zones such as Asia or Oceania, presentations were recorded (depending on speaker permissions) and the seminar recording was made available for up to 48 h after the live seminar. The link for the recorded videos was distributed via the BBV2020 listserv that was set up through the University of Kentucky listserv.\",\n \"Efforts were made to invite speakers from a variety of countries and genders. Invited speakers included organizers of postponed in-person conferences, speakers from academia and industry from different career stages. A total of 16 speakers were invited: May 20, 2020: Elga De Vries, Amsterdam UMC, Amsterdam, The Netherlands; May 27, 2020: Richard Daneman, University of California San Diego, San Diego, CA, USA; June 3, 2020: Robyn Klein, Washington University, St. Louis, MO, USA; June 10, 2020: Ayal Ben-Zvi, The Hebrew University of Jerusalem, Israel; June 17, 2020: Britta Engelhardt, University of Bern, Bern, Switzerland; June 24, 2020: Margareta Hammarlund-Udenaes, Uppsala University, Uppsala, Sweden; July 1, 2020: Daniela Virgintino, University of Bari, Bari, Italy; July 8, 2020: Eric Shusta, University of Wisconsin, Madison, WI, USA; July 15, 2020: Matthew Campbell, Trinity College Dublin, Dublin, Ireland; July 22, 2020: Krzysztof Kucharz, University of Copenhagen, Copenhagen, Denmark; July 29, 2020: William Elmquist, University of Minnesota, Minneapolis, MN, USA; August 5, 2020: Edward Neuwelt, Oregon Health & Sciences University, OR, USA; August 12, 2020: Robert Thorne, Denali Therapeutics, San Francisco, CA, USA; August 19, 2020: Teresa Sanchez, Weill Cornell Medical College, New York City, NJ, USA; August 26, 2020: Michael Taylor, University of Wisconsin, Madison, MI, USA; September 2, 2020: Joan Abbott, King’s College London, London, UK.\\nSpeakers were offered a training session by the organizers to cover key aspects of a Zoom-based webinar and to test video, audio, and slide presentation prior to the live seminar. Ten out of 16 speakers took the training session. Speakers were informed by the organizers that any data presented should be considered public as security on the Zoom platform could not be ensured. Invited speakers had the opportunity to give permission to have their seminar recorded or to opt out of the recording. If a speaker gave permission to record, the entire seminar was recorded, and recordings were made available to all registered participants in a non-downloadable format through the University of Kentucky Zoom cloud for up to 48 h after the live session. Out of 16 speakers, 15 gave permission to record the live seminar.\",\n \"To reach scientists in the brain barriers community, the seminar was advertised by email to participants of previous in-person meetings, and on social media platforms such as Facebook and LinkedIn on personal profiles. In addition, the introductory flyer was kindly distributed via email by the International Brain Barriers Society (IBBS) using their email distribution list. Interested scientists were asked to register by sending their name, affiliation, and career stage to a dedicated email address created for the organization and execution of BBV2020 (bbv2020seminar@gmail.com). Once registered, participants were added to a BBV2020 listserv provided by the University of Kentucky and received weekly updates. These weekly updates included a flyer with the information regarding the upcoming speakers and the specific zoom link for the seminar. Participants also received a follow up email after each seminar with a zoom link to the video recordings. With the permission of the speaker of the week, registered viewers had access to recordings for up to 48 h after the live seminar.\",\n \"Each seminar was started with opening remarks by one of the organizers (Drs. Bauer, Hartz, or Kim) followed by an introduction of the speaker by the moderator. Moderators (Drs. Hudson, Nehra, Pizzo, Storck) rotated each week, only one moderator was assigned to each week. After the seminar presentation, the moderator facilitated the question-and-answer session. Participants were encouraged to ask a live question by raising their digital hand. When a participant raised the digital hand, the moderator would unmute the participant at which point the participant was able to ask the speaker a question directly. In addition, participants had the opportunity to ask questions via the Zoom Q&A or the chat function. Written questions were then subsequently read by the moderator. The moderator concluded each seminar by thanking the speaker and the audience for attending and by announcing the speaker for the following week.\",\n \"World maps highlighting countries participating in the various meetings and representation were generated using MapChart (www.mapchart.net; license: https://creativecommons.org/licenses/by-sa/4.0/).\",\n \"Toward the end of the seminar series, a survey was generated using Qualtrics (Qualtrics) and run through the University of Alabama Qualtrics system. A link to the survey was sent to all registered seminar participants by email. Questions covered general audience participation, career stage, as well as affective factors. Survey results are only reported in an aggregate.\",\n \"Survey data was collected through the University of Alabama Qualtrics system and was determined by the University of Alabama IRB to be “non-human subject research”. Data from the live webinars were collected by Zoom for each seminar and reported as unique viewers to eliminate counting individuals double who potentially logged on twice. Data on video views were collected from the University of Kentucky Zoom Webinar cloud server and were reported as total accessed views. Survey data from Qualtrics was analyzed with the IBM® SPSS® software platform (IBM, Armonk, NY, USA), using one-way ANOVAs and t-tests to identify significant trends [5]. Data with a p < 0.05 are considered significant.\",\n \"On May 14, 2020, we sent out the first advertisement for the BBV2020 seminar series via email using a listserv with email addresses of past conference attendees and via postings using LinkedIn, Facebook, and Twitter. By the start of the first seminar on May 20, 2020, more than 720 scientists had registered. By the end of the 16-week long BBV2020 seminar series, a total of 1302 registrants signed up on our listserv to receive the seminar link, speaker updates, and video recording links (Fig. 1A). We obtained data from two past in-person meetings from the respective conference chairs: (1) brain barriers meeting in 2018 (BBB 2018) and the (2) Cerebral Vascular Biology meeting in 2019 (CVB 2019).\\n\\nFig. 1\\nAttendance Trends for Brain Barrier Research Conferences. A With 1,302 individuals signing up and added to the listserv, BBV2020 attracted more registrants than BBB 2018 and CVB 2019. B Number of unique viewers weekly at the live Zoom Webinar sessions of BBV2020. C Number of views (clicks) of the video recordings when available weekly for BBV2020. Black line indicates best linear fit. Gray line indicates the average (mean) for all 16 weeks\\n\\nAttendance Trends for Brain Barrier Research Conferences. A With 1,302 individuals signing up and added to the listserv, BBV2020 attracted more registrants than BBB 2018 and CVB 2019. B Number of unique viewers weekly at the live Zoom Webinar sessions of BBV2020. C Number of views (clicks) of the video recordings when available weekly for BBV2020. Black line indicates best linear fit. Gray line indicates the average (mean) for all 16 weeks\\nStrikingly, the total number of registered applicants for the BBV2020 was more than 4-fold higher compared to the total number of registered participants for the 2018 and the CVB 2019 meetings (Fig. 1A). The large number of registrants is likely due to free registration, no associated travel expenses, a world-wide reach, and that virtual meetings come with no obligations for the attendees. In contrast, the number of attendees of the in-person meetings (BBB 2018 and CVB2019) was limited and determined by the size of the conference site.\\nThe first live BBV2020 seminar was attended by 419 participants. As the seminar series progressed, the participant number during live seminars gradually decreased, the average participant number was 220, the minimum was 108 (Fig. 1B). A similar trend was observed in the numbers of total recording views: the first seminar recording had a maximum of 944 views; in average, videos had 422 views (Fig. 1C). The trendline in Fig. 1C shows a drop in recording views over the course of the seminar series. These trends held regardless of academic rank as graduate students, postdoctoral fellows, and professors all showed a similar downward trend in live attendance (Additional file 1: Fig. S1A–C). Note that we were unable to capture recording data for week 2 due to a recording error and for week 13 due to the request by the speaker to not record.\\nTogether, the high number of registrants suggests that the virtual BBV2020 seminar series sparked interest. Attendance of the live seminars and the numbers of views for the recorded videos decreased over time, which may be an indicator of “virtual meeting fatigue” or attendees being on their summer vacation.\",\n \"By the end of the seminar series in September, every BBV2020 registrant received an invitation to participate in an online survey created by the organizers on August 23, 2020. The survey was designed for registrants to provide feedback and consisted of 10 questions: (1) demographic question to capture career level and career type; (2) single select multiple choice question to capture attendance of past in-person brain barrier meetings, (3) single select multiple choice question on how many recorded seminars were viewed, (4) Likert scale question regarding the enrichment BBV2020 provided for the community during the COVID-19 pandemic, (5) dichotomous question asking if the registrant would still participate in BBV2020 seminars if they were continued, (6) single select multiple choice question on attendance frequency, (7) single select multiple choice question on how many times the registrant has thus far attended a brain barriers meeting previously in person, (8) matrix question to capture affective factors, (9) matrix question to capture learning and teaching, and (10) matrix question to capture combined validity and practicality of the BBV2020 series. In addition, registrants responding to the survey had the opportunity to include general comments to the organizers in a separate text box.\\nOverall, 23% of registrants (total of 299 registrants) responded to the survey. Over 89% of survey respondents agreed strongly that “Attending BBV2020 was a positive experience” and 63% of responders felt that “BBV2020 is appropriate for my research”. Generally, the respondents tended to agree with the following statement: “BBV2020 has enriched the connection with the Brain Barriers community in the gap that COVID-19 has created” (n = 298 respondents to this question of a total of 299 registrants, M = 1.44, SD = 0.660, 1 = Strongly Agree, 2 = Agree, 3 = Neutral, 4 = Disagree, 5 = Strongly Disagree; Fig. 2A). However, when posed with the statements “In the future, I would prefer to attend a virtual conference over an in-person conference” or “The virtual experience is not as effective compared to the in-person conferences”, responses were neutral (M = 3.24; Fig. 2B) indicating a positive view towards a virtual conference. A difference emerged when we compared BBV2020 participants based on their career level with their preference for in-person versus virtual meeting formats: using one-way ANOVA, we found a significant difference between groups (F(9286) = 2.607, p = 0.007). Using Tukey’s HSD post hoc test indicated that Master’s students were more likely to disagree with the following statement when compared to Faculty/Professors: “I would feel more comfortable attending Brain Barrier conferences/seminars in person, but was glad to attend virtually due to COVID19.” A strong trend emerged when Master’s and Ph.D. students were grouped together and compared to Post-Docs and Faculty/Professors in an independent-samples t test. Results show graduate students in general were more favorable to a virtual conference compared to colleagues more established in their careers (t(225) = -1.972, p = 0.061). The lack of significance is in part due to the low sample size (n = 74) for graduate students.\\n\\nFig. 2\\nPost-seminar survey results. A Volunteer respondent result when posed with the prompt “BBV2020 has enriched connection with the Brain Barriers community in the gap that COVID-19 has created”. B Volunteer respondent results when posted with the prompts “In the future, I would prefer to attend a virtual conference over an in-person conference” (Blue) and “The virtual experience is not as effective compared to the in-person conferences” (Red)\\n\\nPost-seminar survey results. A Volunteer respondent result when posed with the prompt “BBV2020 has enriched connection with the Brain Barriers community in the gap that COVID-19 has created”. B Volunteer respondent results when posted with the prompts “In the future, I would prefer to attend a virtual conference over an in-person conference” (Blue) and “The virtual experience is not as effective compared to the in-person conferences” (Red)\\nOverall, descriptive data revealed many participants felt BBV2020 was a positive experience. In fact, 99.3% of survey respondents noted they somewhat or strongly agree that their experience was positive, with only 4.7% saying they faced technical difficulties. Additionally, most respondents agreed that virtual conferences have an important role in research (93.2%) and are more accessible than in-person conferences (90.2%). Finally, more than half of respondents (56.9%) noted virtual conferences can do things in-person conferences cannot.\\nNevertheless, there continued to be a desire among many BBV2020 participants to return to conferencing in person, with 13.5% saying they found it hard to focus in the virtual format. In fact, roughly one-third (33.9%) felt the virtual conference was not as effective as an in-person conference, and only one-quarter (23.3%) said they would prefer virtual conferences to in-person conferences in the future, while 35.8% were neutral.\\nTogether, our findings suggest that while BBV2020 filled a need created by a global pandemic, the enthusiasm for returning to only in-person meetings remained relatively neutral suggesting that virtual platforms may play a critical role in dissemination of research and science in the future. Moreover, the data from this study show a trend between graduate students versus professors in their response with regard to in-person meetings indicating that career stage may influence the perspective of the usefulness of in-person versus virtual platforms.\",\n \"During the seminar registration process we collected attendants’ affiliations and based on this information we determined the global reach of the BBV2020. Registrants of the BBV2020 seminar series were from 43 countries representing six of the seven continents (Fig. 3A, B). Compared to the in-person BBB 2018 and the CVB2019, more countries were represented at the BBV2020 (Fig. 3A).\\n\\nFig. 3\\nGlobal reach of BBV2020. A BBV2020 had more countries represented compared to BBB 2018 and CVB 2019 alone. B World map showing countries represented by BBV2020. Countries where registrants participated in BBV2020: Red and Pink. Countries not represented at BBV2020: Light Gray and Dark Gray. Countries where registrants attended BBV2020 and at least one of the in-person meetings between Meeting 2018 or CVB 2019: Red. Countries where registrants attended BBV2020 and did not attend either BBB 2018 or CVB 2019: Pink. Countries that had no participation in any of the brain barriers meetings examined in this study: Light gray. Countries that had participation in either BBB 2018 or CVB 2019 that did not participate in BBV2020: Dark Gray\\n\\nGlobal reach of BBV2020. A BBV2020 had more countries represented compared to BBB 2018 and CVB 2019 alone. B World map showing countries represented by BBV2020. Countries where registrants participated in BBV2020: Red and Pink. Countries not represented at BBV2020: Light Gray and Dark Gray. Countries where registrants attended BBV2020 and at least one of the in-person meetings between Meeting 2018 or CVB 2019: Red. Countries where registrants attended BBV2020 and did not attend either BBB 2018 or CVB 2019: Pink. Countries that had no participation in any of the brain barriers meetings examined in this study: Light gray. Countries that had participation in either BBB 2018 or CVB 2019 that did not participate in BBV2020: Dark Gray\\nWe also compared the number of participants from each country who joined BBV2020 vs. BBB 2018 and CVB 2019. We found that the number of participants per country worldwide was also higher compared to both in person meetings (Fig. 4A–F). These data show that the virtual nature of BBV2020 enabled researchers world-wide to participate and suggest that the outreach of a virtual event can extend beyond the field.\\n\\nFig. 4\\nGlobal view of participation over BBV2020, BBB 2018, and CVB 2019. Representation of participants for (A) BBV2020, (B) BBB 2018, and (C) CVB 2019. Ordered from top to bottom in greatest number of registrants. Number of registrants scaled by color based on number of registrants per country for (D) BBV 2020, (E) BBB 2018, and (F) CVB 2019. Light Pink: 1–10 registrants; Dark Pink: 11–50 registrants; Red: 51–100 registrants and Dark Red: > 100 registrants\\n\\nGlobal view of participation over BBV2020, BBB 2018, and CVB 2019. Representation of participants for (A) BBV2020, (B) BBB 2018, and (C) CVB 2019. Ordered from top to bottom in greatest number of registrants. Number of registrants scaled by color based on number of registrants per country for (D) BBV 2020, (E) BBB 2018, and (F) CVB 2019. Light Pink: 1–10 registrants; Dark Pink: 11–50 registrants; Red: 51–100 registrants and Dark Red: > 100 registrants\",\n \"We examined the self-reported career stage of BBV2020 participants to that of BBB 2018 and CVB 2019 participants. Based on the survey, more than 46% of all participants attended a brain barriers conference/seminar for the first time. We also found that in-person meetings are attended by 42.4–48.9% academicians at the professor rank, graduate students make up 22.9–25.8% and postdoctoral fellows are 6.6–17.7% of the participants (Fig. 5). In contrast, at the BBV2020 19.6% of academicians were at the professor rank, graduate students made up 30.5% and postdoctoral fellows 16.1% of attendees. We also found that BBV2020 had a larger participation from industry (18.5% vs. 5.6–6.6%), and undergraduate students (3.2% vs. 1%) compared to both in person meetings. Together, our data suggest that a virtual platform seminar like BBV2020 reaches a broader range of scientific ranks and careers.\\n\\nFig. 5\\nProportions of various career stages participating in BBV2020, BBB 2018, and CVB 2019. Proportions of various career stages participating in (A) BBV2020, (B) BBB 2018, and (C) CVB 2019. Colors correspond to career stage: Red (professor any rank), Green (graduate student any rank), Dark Blue (research faculty or researcher), Yellow (industry scientist), Orange (postdoctoral fellow), Purple (government/regulatory), Light Blue (clinician), Green (undergraduate student)\\n\\nProportions of various career stages participating in BBV2020, BBB 2018, and CVB 2019. Proportions of various career stages participating in (A) BBV2020, (B) BBB 2018, and (C) CVB 2019. Colors correspond to career stage: Red (professor any rank), Green (graduate student any rank), Dark Blue (research faculty or researcher), Yellow (industry scientist), Orange (postdoctoral fellow), Purple (government/regulatory), Light Blue (clinician), Green (undergraduate student)\",\n \"Due to the pandemic, the idea and subsequent organization of the BBV2020 occurred in a relatively short time frame of about 4 weeks. This is in stark contrast to in-person meetings that are often scheduled and organized months and years ahead of the actual event. Advertisement of the BBV2020 encompassed personal email, postings on social media, and utilization of the International Brain Barriers Society email distribution list. Continuous advertisement resulted in the registration of over 1300 scientists. The total registrant number cannot be directly compared to that of in-person meetings since those are more complex in nature, have maximum capacity limits, and involve other factors such as travel expenses and time commitment. Similar to other meetings which transitioned to online formats during the pandemic, BBV2020 had a large increase in registrants when compared to previous in-person meetings (~7-fold and 4.5-fold higher compared to 2018 and 2019, respectively; Fig. 1; [8]. Given that previous brain barriers meetings usually have a few hundred participants, the high number of registrants could be an indicator for high interest in the field beyond traditional brain barrier research groups. In contrast, these high registrant numbers could also be due, in part, to BBV2020 being a no-cost event or could be explained by pandemic-related cancellations resulting in relatively event-free calendar of the participants. While there were more than 1,300 registrants for BBV2020, no more than 419 ever attended a live webinar. Additionally, 323 people registered for BBV2020 but never attended a live webinar. They may, however, have participated by watching the video recordings after the live webinar. A downward trend of attendance numbers for live seminars was observed throughout the course of the 16-week series (Fig. 1B). The recorded seminars drew considerable interest initially with a maximum of 944 views in week 4 of the series, but later in the series, video views dropped to about 200 per seminar (Fig. 1C). Reasons for these downward trends remain speculative, but could be due to the summer break, the selected time for the seminar making it difficult for researchers in certain time zones to attend live, zoom fatigue, or increasing responsibilities in re-opened research laboratories or at home. The downward trends we observed was in contrast to a similar virtual seminar series in another field that remained relatively flat over the course of the summer [4]. Given that average attendance of the BBV2020 was 220 per seminar and typical attendance for in-person conferences in the brain barriers field range between 150 and 300 participants (caveat: one of the in-person conferences has a strict 182-person maximum limit), we conclude that a core group of participants attended most sessions.\",\n \"A survey conducted after the seminar series revealed that most participants felt that BBV2020 enhanced the connection to the brain barriers scientific community during the COVID-19 pandemic (Fig. 2A). Differences emerged between how graduate students and professors viewed BBV2020: graduate students in general were more favorable of the virtual format compared to faculty. This may be partially explained by a lack of funding for graduate student travel or political “red tape” (e.g. visas) for some researchers [9]. Additionally, students may disproportionately adapt more easily and rapidly to technology usage compared to senior colleagues [10]. Clearly, another survey would be needed to determine if a virtual component outside of a pandemic would be positively viewed by researchers in the brain barriers field.\",\n \"One major advantage of a virtual seminar series such as the BBV2020 is the lack of a travel burden. Taking off the financial and time-commitment associated with in-person meetings has been shown to increase the diversity of attendees [3, 9, 11]. Based on those findings, we anticipated that BBV2020 would have representation from more countries than past in-person meetings. As shown in Fig. 3, registrants from 43 different countries were represented at BBV2020, which is 40% more compared to BBB 2018 (23 countries) and CVB 2019 (25 countries; Fig. 3). In particular, there was high participation from countries in Southeast Asia, Middle East, Africa, and South America that had not been represented at in-person conferences held in 2018 and 2019 (Figs. 3B, 4). Unsurprisingly however, considering countries represented at previous in-person meetings and due to the number of registrants for BBV2020, there was generally a greater participation from each country represented than in 2018 meeting and CVB 2019 [3, 8]. Due to the time of the BBV2020 live sessions (noon to 1 pm Eastern US Time), the majority of participants were from North America, South America, and Europe (Fig. 4). While time zone challenges are common among virtual events, switching between time zones or holding a “flipped” session (flipping evening sessions from Europe to the USA and vice versa) could increase participation from Asia and Oceania [12]. However, offering a virtual series that constantly switches the seminar time may be difficult to manage and could be confusing for participants. Another strength of virtual seminars is that they are more accessible and inclusive especially for junior researchers in the field. The in-person meetings in 2018 and 2019 were dominated by academic faculty at any professor rank and graduate students (Fig. 5). In contrast, BBV2020 saw a large reduction in the percentage of participating faculty (over 40% in person to less than 20% for BBV2020) and a larger proportion of graduate students (22–25% in person to over 30% for BBV2020), industry scientists (about 6% in person to 18.5% for BBV2020), and governmental/regulatory personnel who had signed up (Fig. 5). In addition, a weekly virtual seminar series allows scientist to pick-and-choose the sessions they are interested in, a choice that could interfere with the observed trends shown in Fig. 1. Our data further support the trends observed across a variety of disciplines who have shifted their conferences from in-person to online events, revealing that virtual conferences are more flexible, and more inclusive and accessible worldwide, especially for early-career scientists [9]. Virtual seminars also provide an opportunity for the organizers to invite prominent speakers of a field on a relatively small budget. In general, virtual conferences are known to enhance the accessibility for busy professionals. A recently published Nature poll indicates that 74% of scientists want virtual meetings to stay after the pandemic (925 poll responses) [13]. Easier accessibility, lower carbon footprint, and lower costs were the driving factors chosen in favor for virtual meetings in this poll. Moreover, virtual seminars provide an easy and affordable opportunity for researchers with disabilities, researchers with high teaching loads, or for scientists with parenting and other responsibilities to stay connected to their respective research community. During the COVID-19 pandemic, virtual seminars took center stage and raised the standard for accessibility, interactions, inclusivity, and equity in science [9]. Virtual events changed how we think about meetings and they constitute a yet underestimated, but powerful tool to share science and connect with each other. By rethinking how meetings are held, various fields have had different degrees of participation depending on the availability of the talks or content versus live sessions. These virtual platforms have the potential to create a “connectivity cascade” reaching more people than traditional meetings that are confined by a single geographical location and time [14]. BBV2020 had an impressive reach worldwide, however, there was still a notable deficit in participants from underdeveloped countries, especially Africa. These discrepancies have been noted in the context of expanding telehealth to African countries that still remain challenging due to monopolization of telecommunication infrastructure, political will, and access to adequate broadband efficiency [15]. Despite the advantages of BBV2020, we recognize that limitations remain, such as one-way delivery of material to participants. Lack of critical face-to-face interactions allowing junior scientists to have meaningful two-way communication with other participants was notably lacking in BBV2020 as seen in other virtual conferences as well [16]. The future of such virtual seminars, meetings, or conferences may lie in their inclusion as part of in-person meetings according to a recently published “best practice” guideline for virtual meetings [17]. For example, a virtual component could be included in plenary sessions, workshops, small group discussions, poster sessions, and/or social events of in-person conferences. BBV2020 represented only one aspect (a seminar held in the form of a webinar) of these common meeting events, but future events could certainly enhance the virtual experience. Regardless, virtual components could be exploited to increase diversity in the brain barriers research field which is consistent with the NINDS strategy for enhancing the diversity of neuroscience researchers [18].\",\n \"Overall, the Virtual BBV2020 Seminar Series created during the COVID-19 pandemic was positively received by the brain barriers research community. Key benefits of virtual seminars like the BBV2020 are that they are convenient, affordable, eco-friendly, reach a broad audience, and are a good tool to increase diversity and equity. Thus, incorporating a virtual component during an in-person meeting or offering a virtual counterpart in addition to in-person meetings could help increase the outreach of a small research field like the brain barriers field. Nevertheless, limitations remain such as a lack of interaction between speakers and participants, which is particularly critical for junior researchers. And although BBV2020 expanded the reach beyond past in-person meetings, challenges reaching scientists in underdeveloped countries remain. Together, BBV2020 was created in a time of crisis but has the potential to thrive in the post-pandemic world. Embracing virtual scientific events may allow us to advance the brain barriers research field and have an impact on how we exchange science in the future.\"\n]"},"other_sections_sec_types":{"kind":"list like","value":[null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null],"string":"[\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null\n]"},"all_sections_titles":{"kind":"list like","value":["Introduction","Methods","Organization","Virtual platform and technical assistance","BBV2020 schedule","Invited speakers","Advertisement and participants","BBV2020 seminar presentation","Map generation","Survey","Data collection, analysis, and statistics","Results","BBV2020 attendance","BBV2020 survey analysis","Global reach of BBV2020","Career stages represented at BBV2020","Discussion","Attendance: scientists around the globe","Survey: feedback from participants","Virtual seminars: a powerful opportunity","Conclusion and future perspectives","Supplementary Information"],"string":"[\n \"Introduction\",\n \"Methods\",\n \"Organization\",\n \"Virtual platform and technical assistance\",\n \"BBV2020 schedule\",\n \"Invited speakers\",\n \"Advertisement and participants\",\n \"BBV2020 seminar presentation\",\n \"Map generation\",\n \"Survey\",\n \"Data collection, analysis, and statistics\",\n \"Results\",\n \"BBV2020 attendance\",\n \"BBV2020 survey analysis\",\n \"Global reach of BBV2020\",\n \"Career stages represented at BBV2020\",\n \"Discussion\",\n \"Attendance: scientists around the globe\",\n \"Survey: feedback from participants\",\n \"Virtual seminars: a powerful opportunity\",\n \"Conclusion and future perspectives\",\n \"Supplementary Information\"\n]"},"all_sections_texts":{"kind":"list like","value":["On March 11, 2020, the World Health Organization (WHO) declared the coronavirus disease (COVID-19) outbreak a global pandemic [1]. Shortly after, countries worldwide issued stay-at-home orders and lockdowns: the COVID-19 pandemic had forced the world to come to an abrupt halt [2]. With global air travel restrictions and travel bans in place, enterprises including academic institutions, industry and government agencies required individuals to call off their business travel. As a result, scheduled scientific conferences were canceled throughout 2020 [3, 4]. These cancelations created a sudden void in scientific communication between researchers worldwide and forced research communities to be creative and adapt. One possibility to continue scientific communication and interaction was by switching conferences and seminars to virtual online events using novel digital platforms enabling researchers to connect and share science [3, 4].\nAs a consequence, major conferences and meetings in the brain barriers research field, including the “Barriers of the CNS Gordon Research Conference” (GRC 2020) and the international symposium on “Signal Transduction at the Blood–Brain Barriers” in Bari, Italy were first postponed until 2021 and due to the ongoing pandemic subsequently moved to 2022. The “Cerebral Vascular Biology” (CVB) conference in Uppsala, Sweden planned for 2021 was postponed until 2023. Postponing these meetings left organizers and conference chairs in a difficult position and researchers without a platform to present and share their data. Thus, these postponements created a critical need for the field to stay connected and an opportunity to establish a virtual event that could supplement the postponed conferences in 2020. The Brain Barriers Virtual 2020 Seminar Series (BBV2020) addressed this need by providing a forum for brain barrier scientists worldwide to come together online during the COVID-19 crisis in 2020. BBV2020 seminars were held weekly from May 20, 2020 to September 2, 2020 in a 1-h format featuring a live session with an invited speaker followed by time for questions and answers from the community. The seminar series was hosted on a Zoom webinar platform and was free for anyone to attend. BBV2020 served as a forum for invited speakers to present their work to the brain barriers community and enabled this community to discuss brain barriers science from the safety of their home office. Over 1300 scientists around the world were registered, each seminar was viewed live by 100–400 participants, and the video-recordings received 150–900 views each week. Participants were from academia, industry and government agencies at all career stages from around the globe.\nIn this report, we share participation data that give insight into the impact this virtual seminar series had on the brain barriers field during the COVID-19 pandemic. We compare these data with existing data from past in-person brain barriers meetings and draw conclusions from these data on the potential value of virtual meetings for the brain barriers research field in the future. While the organization of virtual events during the pandemic may seem self-evident, reflection on the impact carries utility for events in the future."," Organization The BBV2020 seminar series was created by Drs. Bjoern Bauer (University of Kentucky, Lexington, KY, USA), Anika Hartz (University of Kentucky, Lexington, KY, USA), and Brandon Kim (University of Alabama, Tuscaloosa, Alabama, USA). Seminar moderators were postdoctoral researchers and scientists from the USA and Europe: Drs. Natalie Hudson (Trinity College Dublin, Dublin, Ireland), Geetika Nehra (University of Kentucky, Lexington, KY, USA), Michelle Pizzo (Denali Therapeutics Inc., San Francisco, CA, USA), and Steffen Storck (University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany).\nThe BBV2020 seminar series was created by Drs. Bjoern Bauer (University of Kentucky, Lexington, KY, USA), Anika Hartz (University of Kentucky, Lexington, KY, USA), and Brandon Kim (University of Alabama, Tuscaloosa, Alabama, USA). Seminar moderators were postdoctoral researchers and scientists from the USA and Europe: Drs. Natalie Hudson (Trinity College Dublin, Dublin, Ireland), Geetika Nehra (University of Kentucky, Lexington, KY, USA), Michelle Pizzo (Denali Therapeutics Inc., San Francisco, CA, USA), and Steffen Storck (University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany).\n Virtual platform and technical assistance BBV2020 was run on the Zoom Webinar 500 platform (Zoom Video Communications, Inc.; San Jose, CA, USA) that was funded by Dr. Bjoern Bauer using University of Kentucky funds. This particular Zoom Webinar license allowed for up to 500 live participants. The Zoom Webinar platform supports an in-session chat function, a Q&A forum, a raise hand function for live questions, and provides a panelist section with the option to share audio, video and presentation slides, a recording function and post-webinar data collection. The hosts and moderators were in control of audio and video; participants were not able to record or unmute during a session. Participants had no control function other than a “raise hand” option and the Q&A and chat function allowing to send questions or messages to the moderator. Technical assistance was provided by Todd Sizemore, College of Pharmacy, University of Kentucky.\nBBV2020 was run on the Zoom Webinar 500 platform (Zoom Video Communications, Inc.; San Jose, CA, USA) that was funded by Dr. Bjoern Bauer using University of Kentucky funds. This particular Zoom Webinar license allowed for up to 500 live participants. The Zoom Webinar platform supports an in-session chat function, a Q&A forum, a raise hand function for live questions, and provides a panelist section with the option to share audio, video and presentation slides, a recording function and post-webinar data collection. The hosts and moderators were in control of audio and video; participants were not able to record or unmute during a session. Participants had no control function other than a “raise hand” option and the Q&A and chat function allowing to send questions or messages to the moderator. Technical assistance was provided by Todd Sizemore, College of Pharmacy, University of Kentucky.\n BBV2020 schedule The BBV2020 seminar series ran from May 20 to September 2, 2020. Live seminars were held once per week from 12 to 1 PM Eastern Daylight Time (EDT) to accommodate researchers across a wide range of time zones: Central European Time (6:00 PM) to Pacific Daylight Time (9:00 AM). To accommodate colleagues with time constraints or those within incompatible time zones such as Asia or Oceania, presentations were recorded (depending on speaker permissions) and the seminar recording was made available for up to 48 h after the live seminar. The link for the recorded videos was distributed via the BBV2020 listserv that was set up through the University of Kentucky listserv.\nThe BBV2020 seminar series ran from May 20 to September 2, 2020. Live seminars were held once per week from 12 to 1 PM Eastern Daylight Time (EDT) to accommodate researchers across a wide range of time zones: Central European Time (6:00 PM) to Pacific Daylight Time (9:00 AM). To accommodate colleagues with time constraints or those within incompatible time zones such as Asia or Oceania, presentations were recorded (depending on speaker permissions) and the seminar recording was made available for up to 48 h after the live seminar. The link for the recorded videos was distributed via the BBV2020 listserv that was set up through the University of Kentucky listserv.\n Invited speakers Efforts were made to invite speakers from a variety of countries and genders. Invited speakers included organizers of postponed in-person conferences, speakers from academia and industry from different career stages. A total of 16 speakers were invited: May 20, 2020: Elga De Vries, Amsterdam UMC, Amsterdam, The Netherlands; May 27, 2020: Richard Daneman, University of California San Diego, San Diego, CA, USA; June 3, 2020: Robyn Klein, Washington University, St. Louis, MO, USA; June 10, 2020: Ayal Ben-Zvi, The Hebrew University of Jerusalem, Israel; June 17, 2020: Britta Engelhardt, University of Bern, Bern, Switzerland; June 24, 2020: Margareta Hammarlund-Udenaes, Uppsala University, Uppsala, Sweden; July 1, 2020: Daniela Virgintino, University of Bari, Bari, Italy; July 8, 2020: Eric Shusta, University of Wisconsin, Madison, WI, USA; July 15, 2020: Matthew Campbell, Trinity College Dublin, Dublin, Ireland; July 22, 2020: Krzysztof Kucharz, University of Copenhagen, Copenhagen, Denmark; July 29, 2020: William Elmquist, University of Minnesota, Minneapolis, MN, USA; August 5, 2020: Edward Neuwelt, Oregon Health & Sciences University, OR, USA; August 12, 2020: Robert Thorne, Denali Therapeutics, San Francisco, CA, USA; August 19, 2020: Teresa Sanchez, Weill Cornell Medical College, New York City, NJ, USA; August 26, 2020: Michael Taylor, University of Wisconsin, Madison, MI, USA; September 2, 2020: Joan Abbott, King’s College London, London, UK.\nSpeakers were offered a training session by the organizers to cover key aspects of a Zoom-based webinar and to test video, audio, and slide presentation prior to the live seminar. Ten out of 16 speakers took the training session. Speakers were informed by the organizers that any data presented should be considered public as security on the Zoom platform could not be ensured. Invited speakers had the opportunity to give permission to have their seminar recorded or to opt out of the recording. If a speaker gave permission to record, the entire seminar was recorded, and recordings were made available to all registered participants in a non-downloadable format through the University of Kentucky Zoom cloud for up to 48 h after the live session. Out of 16 speakers, 15 gave permission to record the live seminar.\nEfforts were made to invite speakers from a variety of countries and genders. Invited speakers included organizers of postponed in-person conferences, speakers from academia and industry from different career stages. A total of 16 speakers were invited: May 20, 2020: Elga De Vries, Amsterdam UMC, Amsterdam, The Netherlands; May 27, 2020: Richard Daneman, University of California San Diego, San Diego, CA, USA; June 3, 2020: Robyn Klein, Washington University, St. Louis, MO, USA; June 10, 2020: Ayal Ben-Zvi, The Hebrew University of Jerusalem, Israel; June 17, 2020: Britta Engelhardt, University of Bern, Bern, Switzerland; June 24, 2020: Margareta Hammarlund-Udenaes, Uppsala University, Uppsala, Sweden; July 1, 2020: Daniela Virgintino, University of Bari, Bari, Italy; July 8, 2020: Eric Shusta, University of Wisconsin, Madison, WI, USA; July 15, 2020: Matthew Campbell, Trinity College Dublin, Dublin, Ireland; July 22, 2020: Krzysztof Kucharz, University of Copenhagen, Copenhagen, Denmark; July 29, 2020: William Elmquist, University of Minnesota, Minneapolis, MN, USA; August 5, 2020: Edward Neuwelt, Oregon Health & Sciences University, OR, USA; August 12, 2020: Robert Thorne, Denali Therapeutics, San Francisco, CA, USA; August 19, 2020: Teresa Sanchez, Weill Cornell Medical College, New York City, NJ, USA; August 26, 2020: Michael Taylor, University of Wisconsin, Madison, MI, USA; September 2, 2020: Joan Abbott, King’s College London, London, UK.\nSpeakers were offered a training session by the organizers to cover key aspects of a Zoom-based webinar and to test video, audio, and slide presentation prior to the live seminar. Ten out of 16 speakers took the training session. Speakers were informed by the organizers that any data presented should be considered public as security on the Zoom platform could not be ensured. Invited speakers had the opportunity to give permission to have their seminar recorded or to opt out of the recording. If a speaker gave permission to record, the entire seminar was recorded, and recordings were made available to all registered participants in a non-downloadable format through the University of Kentucky Zoom cloud for up to 48 h after the live session. Out of 16 speakers, 15 gave permission to record the live seminar.\n Advertisement and participants To reach scientists in the brain barriers community, the seminar was advertised by email to participants of previous in-person meetings, and on social media platforms such as Facebook and LinkedIn on personal profiles. In addition, the introductory flyer was kindly distributed via email by the International Brain Barriers Society (IBBS) using their email distribution list. Interested scientists were asked to register by sending their name, affiliation, and career stage to a dedicated email address created for the organization and execution of BBV2020 (bbv2020seminar@gmail.com). Once registered, participants were added to a BBV2020 listserv provided by the University of Kentucky and received weekly updates. These weekly updates included a flyer with the information regarding the upcoming speakers and the specific zoom link for the seminar. Participants also received a follow up email after each seminar with a zoom link to the video recordings. With the permission of the speaker of the week, registered viewers had access to recordings for up to 48 h after the live seminar.\nTo reach scientists in the brain barriers community, the seminar was advertised by email to participants of previous in-person meetings, and on social media platforms such as Facebook and LinkedIn on personal profiles. In addition, the introductory flyer was kindly distributed via email by the International Brain Barriers Society (IBBS) using their email distribution list. Interested scientists were asked to register by sending their name, affiliation, and career stage to a dedicated email address created for the organization and execution of BBV2020 (bbv2020seminar@gmail.com). Once registered, participants were added to a BBV2020 listserv provided by the University of Kentucky and received weekly updates. These weekly updates included a flyer with the information regarding the upcoming speakers and the specific zoom link for the seminar. Participants also received a follow up email after each seminar with a zoom link to the video recordings. With the permission of the speaker of the week, registered viewers had access to recordings for up to 48 h after the live seminar.\n BBV2020 seminar presentation Each seminar was started with opening remarks by one of the organizers (Drs. Bauer, Hartz, or Kim) followed by an introduction of the speaker by the moderator. Moderators (Drs. Hudson, Nehra, Pizzo, Storck) rotated each week, only one moderator was assigned to each week. After the seminar presentation, the moderator facilitated the question-and-answer session. Participants were encouraged to ask a live question by raising their digital hand. When a participant raised the digital hand, the moderator would unmute the participant at which point the participant was able to ask the speaker a question directly. In addition, participants had the opportunity to ask questions via the Zoom Q&A or the chat function. Written questions were then subsequently read by the moderator. The moderator concluded each seminar by thanking the speaker and the audience for attending and by announcing the speaker for the following week.\nEach seminar was started with opening remarks by one of the organizers (Drs. Bauer, Hartz, or Kim) followed by an introduction of the speaker by the moderator. Moderators (Drs. Hudson, Nehra, Pizzo, Storck) rotated each week, only one moderator was assigned to each week. After the seminar presentation, the moderator facilitated the question-and-answer session. Participants were encouraged to ask a live question by raising their digital hand. When a participant raised the digital hand, the moderator would unmute the participant at which point the participant was able to ask the speaker a question directly. In addition, participants had the opportunity to ask questions via the Zoom Q&A or the chat function. Written questions were then subsequently read by the moderator. The moderator concluded each seminar by thanking the speaker and the audience for attending and by announcing the speaker for the following week.\n Map generation World maps highlighting countries participating in the various meetings and representation were generated using MapChart (www.mapchart.net; license: https://creativecommons.org/licenses/by-sa/4.0/).\nWorld maps highlighting countries participating in the various meetings and representation were generated using MapChart (www.mapchart.net; license: https://creativecommons.org/licenses/by-sa/4.0/).\n Survey Toward the end of the seminar series, a survey was generated using Qualtrics (Qualtrics) and run through the University of Alabama Qualtrics system. A link to the survey was sent to all registered seminar participants by email. Questions covered general audience participation, career stage, as well as affective factors. Survey results are only reported in an aggregate.\nToward the end of the seminar series, a survey was generated using Qualtrics (Qualtrics) and run through the University of Alabama Qualtrics system. A link to the survey was sent to all registered seminar participants by email. Questions covered general audience participation, career stage, as well as affective factors. Survey results are only reported in an aggregate.\n Data collection, analysis, and statistics Survey data was collected through the University of Alabama Qualtrics system and was determined by the University of Alabama IRB to be “non-human subject research”. Data from the live webinars were collected by Zoom for each seminar and reported as unique viewers to eliminate counting individuals double who potentially logged on twice. Data on video views were collected from the University of Kentucky Zoom Webinar cloud server and were reported as total accessed views. Survey data from Qualtrics was analyzed with the IBM® SPSS® software platform (IBM, Armonk, NY, USA), using one-way ANOVAs and t-tests to identify significant trends [5]. Data with a p < 0.05 are considered significant.\nSurvey data was collected through the University of Alabama Qualtrics system and was determined by the University of Alabama IRB to be “non-human subject research”. Data from the live webinars were collected by Zoom for each seminar and reported as unique viewers to eliminate counting individuals double who potentially logged on twice. Data on video views were collected from the University of Kentucky Zoom Webinar cloud server and were reported as total accessed views. Survey data from Qualtrics was analyzed with the IBM® SPSS® software platform (IBM, Armonk, NY, USA), using one-way ANOVAs and t-tests to identify significant trends [5]. Data with a p < 0.05 are considered significant.","The BBV2020 seminar series was created by Drs. Bjoern Bauer (University of Kentucky, Lexington, KY, USA), Anika Hartz (University of Kentucky, Lexington, KY, USA), and Brandon Kim (University of Alabama, Tuscaloosa, Alabama, USA). Seminar moderators were postdoctoral researchers and scientists from the USA and Europe: Drs. Natalie Hudson (Trinity College Dublin, Dublin, Ireland), Geetika Nehra (University of Kentucky, Lexington, KY, USA), Michelle Pizzo (Denali Therapeutics Inc., San Francisco, CA, USA), and Steffen Storck (University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany).","BBV2020 was run on the Zoom Webinar 500 platform (Zoom Video Communications, Inc.; San Jose, CA, USA) that was funded by Dr. Bjoern Bauer using University of Kentucky funds. This particular Zoom Webinar license allowed for up to 500 live participants. The Zoom Webinar platform supports an in-session chat function, a Q&A forum, a raise hand function for live questions, and provides a panelist section with the option to share audio, video and presentation slides, a recording function and post-webinar data collection. The hosts and moderators were in control of audio and video; participants were not able to record or unmute during a session. Participants had no control function other than a “raise hand” option and the Q&A and chat function allowing to send questions or messages to the moderator. Technical assistance was provided by Todd Sizemore, College of Pharmacy, University of Kentucky.","The BBV2020 seminar series ran from May 20 to September 2, 2020. Live seminars were held once per week from 12 to 1 PM Eastern Daylight Time (EDT) to accommodate researchers across a wide range of time zones: Central European Time (6:00 PM) to Pacific Daylight Time (9:00 AM). To accommodate colleagues with time constraints or those within incompatible time zones such as Asia or Oceania, presentations were recorded (depending on speaker permissions) and the seminar recording was made available for up to 48 h after the live seminar. The link for the recorded videos was distributed via the BBV2020 listserv that was set up through the University of Kentucky listserv.","Efforts were made to invite speakers from a variety of countries and genders. Invited speakers included organizers of postponed in-person conferences, speakers from academia and industry from different career stages. A total of 16 speakers were invited: May 20, 2020: Elga De Vries, Amsterdam UMC, Amsterdam, The Netherlands; May 27, 2020: Richard Daneman, University of California San Diego, San Diego, CA, USA; June 3, 2020: Robyn Klein, Washington University, St. Louis, MO, USA; June 10, 2020: Ayal Ben-Zvi, The Hebrew University of Jerusalem, Israel; June 17, 2020: Britta Engelhardt, University of Bern, Bern, Switzerland; June 24, 2020: Margareta Hammarlund-Udenaes, Uppsala University, Uppsala, Sweden; July 1, 2020: Daniela Virgintino, University of Bari, Bari, Italy; July 8, 2020: Eric Shusta, University of Wisconsin, Madison, WI, USA; July 15, 2020: Matthew Campbell, Trinity College Dublin, Dublin, Ireland; July 22, 2020: Krzysztof Kucharz, University of Copenhagen, Copenhagen, Denmark; July 29, 2020: William Elmquist, University of Minnesota, Minneapolis, MN, USA; August 5, 2020: Edward Neuwelt, Oregon Health & Sciences University, OR, USA; August 12, 2020: Robert Thorne, Denali Therapeutics, San Francisco, CA, USA; August 19, 2020: Teresa Sanchez, Weill Cornell Medical College, New York City, NJ, USA; August 26, 2020: Michael Taylor, University of Wisconsin, Madison, MI, USA; September 2, 2020: Joan Abbott, King’s College London, London, UK.\nSpeakers were offered a training session by the organizers to cover key aspects of a Zoom-based webinar and to test video, audio, and slide presentation prior to the live seminar. Ten out of 16 speakers took the training session. Speakers were informed by the organizers that any data presented should be considered public as security on the Zoom platform could not be ensured. Invited speakers had the opportunity to give permission to have their seminar recorded or to opt out of the recording. If a speaker gave permission to record, the entire seminar was recorded, and recordings were made available to all registered participants in a non-downloadable format through the University of Kentucky Zoom cloud for up to 48 h after the live session. Out of 16 speakers, 15 gave permission to record the live seminar.","To reach scientists in the brain barriers community, the seminar was advertised by email to participants of previous in-person meetings, and on social media platforms such as Facebook and LinkedIn on personal profiles. In addition, the introductory flyer was kindly distributed via email by the International Brain Barriers Society (IBBS) using their email distribution list. Interested scientists were asked to register by sending their name, affiliation, and career stage to a dedicated email address created for the organization and execution of BBV2020 (bbv2020seminar@gmail.com). Once registered, participants were added to a BBV2020 listserv provided by the University of Kentucky and received weekly updates. These weekly updates included a flyer with the information regarding the upcoming speakers and the specific zoom link for the seminar. Participants also received a follow up email after each seminar with a zoom link to the video recordings. With the permission of the speaker of the week, registered viewers had access to recordings for up to 48 h after the live seminar.","Each seminar was started with opening remarks by one of the organizers (Drs. Bauer, Hartz, or Kim) followed by an introduction of the speaker by the moderator. Moderators (Drs. Hudson, Nehra, Pizzo, Storck) rotated each week, only one moderator was assigned to each week. After the seminar presentation, the moderator facilitated the question-and-answer session. Participants were encouraged to ask a live question by raising their digital hand. When a participant raised the digital hand, the moderator would unmute the participant at which point the participant was able to ask the speaker a question directly. In addition, participants had the opportunity to ask questions via the Zoom Q&A or the chat function. Written questions were then subsequently read by the moderator. The moderator concluded each seminar by thanking the speaker and the audience for attending and by announcing the speaker for the following week.","World maps highlighting countries participating in the various meetings and representation were generated using MapChart (www.mapchart.net; license: https://creativecommons.org/licenses/by-sa/4.0/).","Toward the end of the seminar series, a survey was generated using Qualtrics (Qualtrics) and run through the University of Alabama Qualtrics system. A link to the survey was sent to all registered seminar participants by email. Questions covered general audience participation, career stage, as well as affective factors. Survey results are only reported in an aggregate.","Survey data was collected through the University of Alabama Qualtrics system and was determined by the University of Alabama IRB to be “non-human subject research”. Data from the live webinars were collected by Zoom for each seminar and reported as unique viewers to eliminate counting individuals double who potentially logged on twice. Data on video views were collected from the University of Kentucky Zoom Webinar cloud server and were reported as total accessed views. Survey data from Qualtrics was analyzed with the IBM® SPSS® software platform (IBM, Armonk, NY, USA), using one-way ANOVAs and t-tests to identify significant trends [5]. Data with a p < 0.05 are considered significant.","We organized the virtual BBV2020 seminar series for the brain barriers research field to fill the void that the COVID-19 pandemic left due to canceled and postponed meetings in 2020. The seminar series was held between May–September 2020 every Wednesday from 12:00 to 1:00 PM Eastern Daylight Time (EDT). Seminars were hosted on a Zoom webinar platform and featured a 45-min presentation from an invited speaker followed by questions and answers from the audience. In the following sections, we summarize data collected by the Zoom software, from the listserv used, and the participant survey. We compare and contrast the data from the virtual BBV2020 seminar series with data from in-person meetings from past years.\n BBV2020 attendance On May 14, 2020, we sent out the first advertisement for the BBV2020 seminar series via email using a listserv with email addresses of past conference attendees and via postings using LinkedIn, Facebook, and Twitter. By the start of the first seminar on May 20, 2020, more than 720 scientists had registered. By the end of the 16-week long BBV2020 seminar series, a total of 1302 registrants signed up on our listserv to receive the seminar link, speaker updates, and video recording links (Fig. 1A). We obtained data from two past in-person meetings from the respective conference chairs: (1) brain barriers meeting in 2018 (BBB 2018) and the (2) Cerebral Vascular Biology meeting in 2019 (CVB 2019).\n\nFig. 1\nAttendance Trends for Brain Barrier Research Conferences. A With 1,302 individuals signing up and added to the listserv, BBV2020 attracted more registrants than BBB 2018 and CVB 2019. B Number of unique viewers weekly at the live Zoom Webinar sessions of BBV2020. C Number of views (clicks) of the video recordings when available weekly for BBV2020. Black line indicates best linear fit. Gray line indicates the average (mean) for all 16 weeks\n\nAttendance Trends for Brain Barrier Research Conferences. A With 1,302 individuals signing up and added to the listserv, BBV2020 attracted more registrants than BBB 2018 and CVB 2019. B Number of unique viewers weekly at the live Zoom Webinar sessions of BBV2020. C Number of views (clicks) of the video recordings when available weekly for BBV2020. Black line indicates best linear fit. Gray line indicates the average (mean) for all 16 weeks\nStrikingly, the total number of registered applicants for the BBV2020 was more than 4-fold higher compared to the total number of registered participants for the 2018 and the CVB 2019 meetings (Fig. 1A). The large number of registrants is likely due to free registration, no associated travel expenses, a world-wide reach, and that virtual meetings come with no obligations for the attendees. In contrast, the number of attendees of the in-person meetings (BBB 2018 and CVB2019) was limited and determined by the size of the conference site.\nThe first live BBV2020 seminar was attended by 419 participants. As the seminar series progressed, the participant number during live seminars gradually decreased, the average participant number was 220, the minimum was 108 (Fig. 1B). A similar trend was observed in the numbers of total recording views: the first seminar recording had a maximum of 944 views; in average, videos had 422 views (Fig. 1C). The trendline in Fig. 1C shows a drop in recording views over the course of the seminar series. These trends held regardless of academic rank as graduate students, postdoctoral fellows, and professors all showed a similar downward trend in live attendance (Additional file 1: Fig. S1A–C). Note that we were unable to capture recording data for week 2 due to a recording error and for week 13 due to the request by the speaker to not record.\nTogether, the high number of registrants suggests that the virtual BBV2020 seminar series sparked interest. Attendance of the live seminars and the numbers of views for the recorded videos decreased over time, which may be an indicator of “virtual meeting fatigue” or attendees being on their summer vacation.\nOn May 14, 2020, we sent out the first advertisement for the BBV2020 seminar series via email using a listserv with email addresses of past conference attendees and via postings using LinkedIn, Facebook, and Twitter. By the start of the first seminar on May 20, 2020, more than 720 scientists had registered. By the end of the 16-week long BBV2020 seminar series, a total of 1302 registrants signed up on our listserv to receive the seminar link, speaker updates, and video recording links (Fig. 1A). We obtained data from two past in-person meetings from the respective conference chairs: (1) brain barriers meeting in 2018 (BBB 2018) and the (2) Cerebral Vascular Biology meeting in 2019 (CVB 2019).\n\nFig. 1\nAttendance Trends for Brain Barrier Research Conferences. A With 1,302 individuals signing up and added to the listserv, BBV2020 attracted more registrants than BBB 2018 and CVB 2019. B Number of unique viewers weekly at the live Zoom Webinar sessions of BBV2020. C Number of views (clicks) of the video recordings when available weekly for BBV2020. Black line indicates best linear fit. Gray line indicates the average (mean) for all 16 weeks\n\nAttendance Trends for Brain Barrier Research Conferences. A With 1,302 individuals signing up and added to the listserv, BBV2020 attracted more registrants than BBB 2018 and CVB 2019. B Number of unique viewers weekly at the live Zoom Webinar sessions of BBV2020. C Number of views (clicks) of the video recordings when available weekly for BBV2020. Black line indicates best linear fit. Gray line indicates the average (mean) for all 16 weeks\nStrikingly, the total number of registered applicants for the BBV2020 was more than 4-fold higher compared to the total number of registered participants for the 2018 and the CVB 2019 meetings (Fig. 1A). The large number of registrants is likely due to free registration, no associated travel expenses, a world-wide reach, and that virtual meetings come with no obligations for the attendees. In contrast, the number of attendees of the in-person meetings (BBB 2018 and CVB2019) was limited and determined by the size of the conference site.\nThe first live BBV2020 seminar was attended by 419 participants. As the seminar series progressed, the participant number during live seminars gradually decreased, the average participant number was 220, the minimum was 108 (Fig. 1B). A similar trend was observed in the numbers of total recording views: the first seminar recording had a maximum of 944 views; in average, videos had 422 views (Fig. 1C). The trendline in Fig. 1C shows a drop in recording views over the course of the seminar series. These trends held regardless of academic rank as graduate students, postdoctoral fellows, and professors all showed a similar downward trend in live attendance (Additional file 1: Fig. S1A–C). Note that we were unable to capture recording data for week 2 due to a recording error and for week 13 due to the request by the speaker to not record.\nTogether, the high number of registrants suggests that the virtual BBV2020 seminar series sparked interest. Attendance of the live seminars and the numbers of views for the recorded videos decreased over time, which may be an indicator of “virtual meeting fatigue” or attendees being on their summer vacation.\n BBV2020 survey analysis By the end of the seminar series in September, every BBV2020 registrant received an invitation to participate in an online survey created by the organizers on August 23, 2020. The survey was designed for registrants to provide feedback and consisted of 10 questions: (1) demographic question to capture career level and career type; (2) single select multiple choice question to capture attendance of past in-person brain barrier meetings, (3) single select multiple choice question on how many recorded seminars were viewed, (4) Likert scale question regarding the enrichment BBV2020 provided for the community during the COVID-19 pandemic, (5) dichotomous question asking if the registrant would still participate in BBV2020 seminars if they were continued, (6) single select multiple choice question on attendance frequency, (7) single select multiple choice question on how many times the registrant has thus far attended a brain barriers meeting previously in person, (8) matrix question to capture affective factors, (9) matrix question to capture learning and teaching, and (10) matrix question to capture combined validity and practicality of the BBV2020 series. In addition, registrants responding to the survey had the opportunity to include general comments to the organizers in a separate text box.\nOverall, 23% of registrants (total of 299 registrants) responded to the survey. Over 89% of survey respondents agreed strongly that “Attending BBV2020 was a positive experience” and 63% of responders felt that “BBV2020 is appropriate for my research”. Generally, the respondents tended to agree with the following statement: “BBV2020 has enriched the connection with the Brain Barriers community in the gap that COVID-19 has created” (n = 298 respondents to this question of a total of 299 registrants, M = 1.44, SD = 0.660, 1 = Strongly Agree, 2 = Agree, 3 = Neutral, 4 = Disagree, 5 = Strongly Disagree; Fig. 2A). However, when posed with the statements “In the future, I would prefer to attend a virtual conference over an in-person conference” or “The virtual experience is not as effective compared to the in-person conferences”, responses were neutral (M = 3.24; Fig. 2B) indicating a positive view towards a virtual conference. A difference emerged when we compared BBV2020 participants based on their career level with their preference for in-person versus virtual meeting formats: using one-way ANOVA, we found a significant difference between groups (F(9286) = 2.607, p = 0.007). Using Tukey’s HSD post hoc test indicated that Master’s students were more likely to disagree with the following statement when compared to Faculty/Professors: “I would feel more comfortable attending Brain Barrier conferences/seminars in person, but was glad to attend virtually due to COVID19.” A strong trend emerged when Master’s and Ph.D. students were grouped together and compared to Post-Docs and Faculty/Professors in an independent-samples t test. Results show graduate students in general were more favorable to a virtual conference compared to colleagues more established in their careers (t(225) = -1.972, p = 0.061). The lack of significance is in part due to the low sample size (n = 74) for graduate students.\n\nFig. 2\nPost-seminar survey results. A Volunteer respondent result when posed with the prompt “BBV2020 has enriched connection with the Brain Barriers community in the gap that COVID-19 has created”. B Volunteer respondent results when posted with the prompts “In the future, I would prefer to attend a virtual conference over an in-person conference” (Blue) and “The virtual experience is not as effective compared to the in-person conferences” (Red)\n\nPost-seminar survey results. A Volunteer respondent result when posed with the prompt “BBV2020 has enriched connection with the Brain Barriers community in the gap that COVID-19 has created”. B Volunteer respondent results when posted with the prompts “In the future, I would prefer to attend a virtual conference over an in-person conference” (Blue) and “The virtual experience is not as effective compared to the in-person conferences” (Red)\nOverall, descriptive data revealed many participants felt BBV2020 was a positive experience. In fact, 99.3% of survey respondents noted they somewhat or strongly agree that their experience was positive, with only 4.7% saying they faced technical difficulties. Additionally, most respondents agreed that virtual conferences have an important role in research (93.2%) and are more accessible than in-person conferences (90.2%). Finally, more than half of respondents (56.9%) noted virtual conferences can do things in-person conferences cannot.\nNevertheless, there continued to be a desire among many BBV2020 participants to return to conferencing in person, with 13.5% saying they found it hard to focus in the virtual format. In fact, roughly one-third (33.9%) felt the virtual conference was not as effective as an in-person conference, and only one-quarter (23.3%) said they would prefer virtual conferences to in-person conferences in the future, while 35.8% were neutral.\nTogether, our findings suggest that while BBV2020 filled a need created by a global pandemic, the enthusiasm for returning to only in-person meetings remained relatively neutral suggesting that virtual platforms may play a critical role in dissemination of research and science in the future. Moreover, the data from this study show a trend between graduate students versus professors in their response with regard to in-person meetings indicating that career stage may influence the perspective of the usefulness of in-person versus virtual platforms.\nBy the end of the seminar series in September, every BBV2020 registrant received an invitation to participate in an online survey created by the organizers on August 23, 2020. The survey was designed for registrants to provide feedback and consisted of 10 questions: (1) demographic question to capture career level and career type; (2) single select multiple choice question to capture attendance of past in-person brain barrier meetings, (3) single select multiple choice question on how many recorded seminars were viewed, (4) Likert scale question regarding the enrichment BBV2020 provided for the community during the COVID-19 pandemic, (5) dichotomous question asking if the registrant would still participate in BBV2020 seminars if they were continued, (6) single select multiple choice question on attendance frequency, (7) single select multiple choice question on how many times the registrant has thus far attended a brain barriers meeting previously in person, (8) matrix question to capture affective factors, (9) matrix question to capture learning and teaching, and (10) matrix question to capture combined validity and practicality of the BBV2020 series. In addition, registrants responding to the survey had the opportunity to include general comments to the organizers in a separate text box.\nOverall, 23% of registrants (total of 299 registrants) responded to the survey. Over 89% of survey respondents agreed strongly that “Attending BBV2020 was a positive experience” and 63% of responders felt that “BBV2020 is appropriate for my research”. Generally, the respondents tended to agree with the following statement: “BBV2020 has enriched the connection with the Brain Barriers community in the gap that COVID-19 has created” (n = 298 respondents to this question of a total of 299 registrants, M = 1.44, SD = 0.660, 1 = Strongly Agree, 2 = Agree, 3 = Neutral, 4 = Disagree, 5 = Strongly Disagree; Fig. 2A). However, when posed with the statements “In the future, I would prefer to attend a virtual conference over an in-person conference” or “The virtual experience is not as effective compared to the in-person conferences”, responses were neutral (M = 3.24; Fig. 2B) indicating a positive view towards a virtual conference. A difference emerged when we compared BBV2020 participants based on their career level with their preference for in-person versus virtual meeting formats: using one-way ANOVA, we found a significant difference between groups (F(9286) = 2.607, p = 0.007). Using Tukey’s HSD post hoc test indicated that Master’s students were more likely to disagree with the following statement when compared to Faculty/Professors: “I would feel more comfortable attending Brain Barrier conferences/seminars in person, but was glad to attend virtually due to COVID19.” A strong trend emerged when Master’s and Ph.D. students were grouped together and compared to Post-Docs and Faculty/Professors in an independent-samples t test. Results show graduate students in general were more favorable to a virtual conference compared to colleagues more established in their careers (t(225) = -1.972, p = 0.061). The lack of significance is in part due to the low sample size (n = 74) for graduate students.\n\nFig. 2\nPost-seminar survey results. A Volunteer respondent result when posed with the prompt “BBV2020 has enriched connection with the Brain Barriers community in the gap that COVID-19 has created”. B Volunteer respondent results when posted with the prompts “In the future, I would prefer to attend a virtual conference over an in-person conference” (Blue) and “The virtual experience is not as effective compared to the in-person conferences” (Red)\n\nPost-seminar survey results. A Volunteer respondent result when posed with the prompt “BBV2020 has enriched connection with the Brain Barriers community in the gap that COVID-19 has created”. B Volunteer respondent results when posted with the prompts “In the future, I would prefer to attend a virtual conference over an in-person conference” (Blue) and “The virtual experience is not as effective compared to the in-person conferences” (Red)\nOverall, descriptive data revealed many participants felt BBV2020 was a positive experience. In fact, 99.3% of survey respondents noted they somewhat or strongly agree that their experience was positive, with only 4.7% saying they faced technical difficulties. Additionally, most respondents agreed that virtual conferences have an important role in research (93.2%) and are more accessible than in-person conferences (90.2%). Finally, more than half of respondents (56.9%) noted virtual conferences can do things in-person conferences cannot.\nNevertheless, there continued to be a desire among many BBV2020 participants to return to conferencing in person, with 13.5% saying they found it hard to focus in the virtual format. In fact, roughly one-third (33.9%) felt the virtual conference was not as effective as an in-person conference, and only one-quarter (23.3%) said they would prefer virtual conferences to in-person conferences in the future, while 35.8% were neutral.\nTogether, our findings suggest that while BBV2020 filled a need created by a global pandemic, the enthusiasm for returning to only in-person meetings remained relatively neutral suggesting that virtual platforms may play a critical role in dissemination of research and science in the future. Moreover, the data from this study show a trend between graduate students versus professors in their response with regard to in-person meetings indicating that career stage may influence the perspective of the usefulness of in-person versus virtual platforms.\n Global reach of BBV2020 During the seminar registration process we collected attendants’ affiliations and based on this information we determined the global reach of the BBV2020. Registrants of the BBV2020 seminar series were from 43 countries representing six of the seven continents (Fig. 3A, B). Compared to the in-person BBB 2018 and the CVB2019, more countries were represented at the BBV2020 (Fig. 3A).\n\nFig. 3\nGlobal reach of BBV2020. A BBV2020 had more countries represented compared to BBB 2018 and CVB 2019 alone. B World map showing countries represented by BBV2020. Countries where registrants participated in BBV2020: Red and Pink. Countries not represented at BBV2020: Light Gray and Dark Gray. Countries where registrants attended BBV2020 and at least one of the in-person meetings between Meeting 2018 or CVB 2019: Red. Countries where registrants attended BBV2020 and did not attend either BBB 2018 or CVB 2019: Pink. Countries that had no participation in any of the brain barriers meetings examined in this study: Light gray. Countries that had participation in either BBB 2018 or CVB 2019 that did not participate in BBV2020: Dark Gray\n\nGlobal reach of BBV2020. A BBV2020 had more countries represented compared to BBB 2018 and CVB 2019 alone. B World map showing countries represented by BBV2020. Countries where registrants participated in BBV2020: Red and Pink. Countries not represented at BBV2020: Light Gray and Dark Gray. Countries where registrants attended BBV2020 and at least one of the in-person meetings between Meeting 2018 or CVB 2019: Red. Countries where registrants attended BBV2020 and did not attend either BBB 2018 or CVB 2019: Pink. Countries that had no participation in any of the brain barriers meetings examined in this study: Light gray. Countries that had participation in either BBB 2018 or CVB 2019 that did not participate in BBV2020: Dark Gray\nWe also compared the number of participants from each country who joined BBV2020 vs. BBB 2018 and CVB 2019. We found that the number of participants per country worldwide was also higher compared to both in person meetings (Fig. 4A–F). These data show that the virtual nature of BBV2020 enabled researchers world-wide to participate and suggest that the outreach of a virtual event can extend beyond the field.\n\nFig. 4\nGlobal view of participation over BBV2020, BBB 2018, and CVB 2019. Representation of participants for (A) BBV2020, (B) BBB 2018, and (C) CVB 2019. Ordered from top to bottom in greatest number of registrants. Number of registrants scaled by color based on number of registrants per country for (D) BBV 2020, (E) BBB 2018, and (F) CVB 2019. Light Pink: 1–10 registrants; Dark Pink: 11–50 registrants; Red: 51–100 registrants and Dark Red: > 100 registrants\n\nGlobal view of participation over BBV2020, BBB 2018, and CVB 2019. Representation of participants for (A) BBV2020, (B) BBB 2018, and (C) CVB 2019. Ordered from top to bottom in greatest number of registrants. Number of registrants scaled by color based on number of registrants per country for (D) BBV 2020, (E) BBB 2018, and (F) CVB 2019. Light Pink: 1–10 registrants; Dark Pink: 11–50 registrants; Red: 51–100 registrants and Dark Red: > 100 registrants\nDuring the seminar registration process we collected attendants’ affiliations and based on this information we determined the global reach of the BBV2020. Registrants of the BBV2020 seminar series were from 43 countries representing six of the seven continents (Fig. 3A, B). Compared to the in-person BBB 2018 and the CVB2019, more countries were represented at the BBV2020 (Fig. 3A).\n\nFig. 3\nGlobal reach of BBV2020. A BBV2020 had more countries represented compared to BBB 2018 and CVB 2019 alone. B World map showing countries represented by BBV2020. Countries where registrants participated in BBV2020: Red and Pink. Countries not represented at BBV2020: Light Gray and Dark Gray. Countries where registrants attended BBV2020 and at least one of the in-person meetings between Meeting 2018 or CVB 2019: Red. Countries where registrants attended BBV2020 and did not attend either BBB 2018 or CVB 2019: Pink. Countries that had no participation in any of the brain barriers meetings examined in this study: Light gray. Countries that had participation in either BBB 2018 or CVB 2019 that did not participate in BBV2020: Dark Gray\n\nGlobal reach of BBV2020. A BBV2020 had more countries represented compared to BBB 2018 and CVB 2019 alone. B World map showing countries represented by BBV2020. Countries where registrants participated in BBV2020: Red and Pink. Countries not represented at BBV2020: Light Gray and Dark Gray. Countries where registrants attended BBV2020 and at least one of the in-person meetings between Meeting 2018 or CVB 2019: Red. Countries where registrants attended BBV2020 and did not attend either BBB 2018 or CVB 2019: Pink. Countries that had no participation in any of the brain barriers meetings examined in this study: Light gray. Countries that had participation in either BBB 2018 or CVB 2019 that did not participate in BBV2020: Dark Gray\nWe also compared the number of participants from each country who joined BBV2020 vs. BBB 2018 and CVB 2019. We found that the number of participants per country worldwide was also higher compared to both in person meetings (Fig. 4A–F). These data show that the virtual nature of BBV2020 enabled researchers world-wide to participate and suggest that the outreach of a virtual event can extend beyond the field.\n\nFig. 4\nGlobal view of participation over BBV2020, BBB 2018, and CVB 2019. Representation of participants for (A) BBV2020, (B) BBB 2018, and (C) CVB 2019. Ordered from top to bottom in greatest number of registrants. Number of registrants scaled by color based on number of registrants per country for (D) BBV 2020, (E) BBB 2018, and (F) CVB 2019. Light Pink: 1–10 registrants; Dark Pink: 11–50 registrants; Red: 51–100 registrants and Dark Red: > 100 registrants\n\nGlobal view of participation over BBV2020, BBB 2018, and CVB 2019. Representation of participants for (A) BBV2020, (B) BBB 2018, and (C) CVB 2019. Ordered from top to bottom in greatest number of registrants. Number of registrants scaled by color based on number of registrants per country for (D) BBV 2020, (E) BBB 2018, and (F) CVB 2019. Light Pink: 1–10 registrants; Dark Pink: 11–50 registrants; Red: 51–100 registrants and Dark Red: > 100 registrants\n Career stages represented at BBV2020 We examined the self-reported career stage of BBV2020 participants to that of BBB 2018 and CVB 2019 participants. Based on the survey, more than 46% of all participants attended a brain barriers conference/seminar for the first time. We also found that in-person meetings are attended by 42.4–48.9% academicians at the professor rank, graduate students make up 22.9–25.8% and postdoctoral fellows are 6.6–17.7% of the participants (Fig. 5). In contrast, at the BBV2020 19.6% of academicians were at the professor rank, graduate students made up 30.5% and postdoctoral fellows 16.1% of attendees. We also found that BBV2020 had a larger participation from industry (18.5% vs. 5.6–6.6%), and undergraduate students (3.2% vs. 1%) compared to both in person meetings. Together, our data suggest that a virtual platform seminar like BBV2020 reaches a broader range of scientific ranks and careers.\n\nFig. 5\nProportions of various career stages participating in BBV2020, BBB 2018, and CVB 2019. Proportions of various career stages participating in (A) BBV2020, (B) BBB 2018, and (C) CVB 2019. Colors correspond to career stage: Red (professor any rank), Green (graduate student any rank), Dark Blue (research faculty or researcher), Yellow (industry scientist), Orange (postdoctoral fellow), Purple (government/regulatory), Light Blue (clinician), Green (undergraduate student)\n\nProportions of various career stages participating in BBV2020, BBB 2018, and CVB 2019. Proportions of various career stages participating in (A) BBV2020, (B) BBB 2018, and (C) CVB 2019. Colors correspond to career stage: Red (professor any rank), Green (graduate student any rank), Dark Blue (research faculty or researcher), Yellow (industry scientist), Orange (postdoctoral fellow), Purple (government/regulatory), Light Blue (clinician), Green (undergraduate student)\nWe examined the self-reported career stage of BBV2020 participants to that of BBB 2018 and CVB 2019 participants. Based on the survey, more than 46% of all participants attended a brain barriers conference/seminar for the first time. We also found that in-person meetings are attended by 42.4–48.9% academicians at the professor rank, graduate students make up 22.9–25.8% and postdoctoral fellows are 6.6–17.7% of the participants (Fig. 5). In contrast, at the BBV2020 19.6% of academicians were at the professor rank, graduate students made up 30.5% and postdoctoral fellows 16.1% of attendees. We also found that BBV2020 had a larger participation from industry (18.5% vs. 5.6–6.6%), and undergraduate students (3.2% vs. 1%) compared to both in person meetings. Together, our data suggest that a virtual platform seminar like BBV2020 reaches a broader range of scientific ranks and careers.\n\nFig. 5\nProportions of various career stages participating in BBV2020, BBB 2018, and CVB 2019. Proportions of various career stages participating in (A) BBV2020, (B) BBB 2018, and (C) CVB 2019. Colors correspond to career stage: Red (professor any rank), Green (graduate student any rank), Dark Blue (research faculty or researcher), Yellow (industry scientist), Orange (postdoctoral fellow), Purple (government/regulatory), Light Blue (clinician), Green (undergraduate student)\n\nProportions of various career stages participating in BBV2020, BBB 2018, and CVB 2019. Proportions of various career stages participating in (A) BBV2020, (B) BBB 2018, and (C) CVB 2019. Colors correspond to career stage: Red (professor any rank), Green (graduate student any rank), Dark Blue (research faculty or researcher), Yellow (industry scientist), Orange (postdoctoral fellow), Purple (government/regulatory), Light Blue (clinician), Green (undergraduate student)","On May 14, 2020, we sent out the first advertisement for the BBV2020 seminar series via email using a listserv with email addresses of past conference attendees and via postings using LinkedIn, Facebook, and Twitter. By the start of the first seminar on May 20, 2020, more than 720 scientists had registered. By the end of the 16-week long BBV2020 seminar series, a total of 1302 registrants signed up on our listserv to receive the seminar link, speaker updates, and video recording links (Fig. 1A). We obtained data from two past in-person meetings from the respective conference chairs: (1) brain barriers meeting in 2018 (BBB 2018) and the (2) Cerebral Vascular Biology meeting in 2019 (CVB 2019).\n\nFig. 1\nAttendance Trends for Brain Barrier Research Conferences. A With 1,302 individuals signing up and added to the listserv, BBV2020 attracted more registrants than BBB 2018 and CVB 2019. B Number of unique viewers weekly at the live Zoom Webinar sessions of BBV2020. C Number of views (clicks) of the video recordings when available weekly for BBV2020. Black line indicates best linear fit. Gray line indicates the average (mean) for all 16 weeks\n\nAttendance Trends for Brain Barrier Research Conferences. A With 1,302 individuals signing up and added to the listserv, BBV2020 attracted more registrants than BBB 2018 and CVB 2019. B Number of unique viewers weekly at the live Zoom Webinar sessions of BBV2020. C Number of views (clicks) of the video recordings when available weekly for BBV2020. Black line indicates best linear fit. Gray line indicates the average (mean) for all 16 weeks\nStrikingly, the total number of registered applicants for the BBV2020 was more than 4-fold higher compared to the total number of registered participants for the 2018 and the CVB 2019 meetings (Fig. 1A). The large number of registrants is likely due to free registration, no associated travel expenses, a world-wide reach, and that virtual meetings come with no obligations for the attendees. In contrast, the number of attendees of the in-person meetings (BBB 2018 and CVB2019) was limited and determined by the size of the conference site.\nThe first live BBV2020 seminar was attended by 419 participants. As the seminar series progressed, the participant number during live seminars gradually decreased, the average participant number was 220, the minimum was 108 (Fig. 1B). A similar trend was observed in the numbers of total recording views: the first seminar recording had a maximum of 944 views; in average, videos had 422 views (Fig. 1C). The trendline in Fig. 1C shows a drop in recording views over the course of the seminar series. These trends held regardless of academic rank as graduate students, postdoctoral fellows, and professors all showed a similar downward trend in live attendance (Additional file 1: Fig. S1A–C). Note that we were unable to capture recording data for week 2 due to a recording error and for week 13 due to the request by the speaker to not record.\nTogether, the high number of registrants suggests that the virtual BBV2020 seminar series sparked interest. Attendance of the live seminars and the numbers of views for the recorded videos decreased over time, which may be an indicator of “virtual meeting fatigue” or attendees being on their summer vacation.","By the end of the seminar series in September, every BBV2020 registrant received an invitation to participate in an online survey created by the organizers on August 23, 2020. The survey was designed for registrants to provide feedback and consisted of 10 questions: (1) demographic question to capture career level and career type; (2) single select multiple choice question to capture attendance of past in-person brain barrier meetings, (3) single select multiple choice question on how many recorded seminars were viewed, (4) Likert scale question regarding the enrichment BBV2020 provided for the community during the COVID-19 pandemic, (5) dichotomous question asking if the registrant would still participate in BBV2020 seminars if they were continued, (6) single select multiple choice question on attendance frequency, (7) single select multiple choice question on how many times the registrant has thus far attended a brain barriers meeting previously in person, (8) matrix question to capture affective factors, (9) matrix question to capture learning and teaching, and (10) matrix question to capture combined validity and practicality of the BBV2020 series. In addition, registrants responding to the survey had the opportunity to include general comments to the organizers in a separate text box.\nOverall, 23% of registrants (total of 299 registrants) responded to the survey. Over 89% of survey respondents agreed strongly that “Attending BBV2020 was a positive experience” and 63% of responders felt that “BBV2020 is appropriate for my research”. Generally, the respondents tended to agree with the following statement: “BBV2020 has enriched the connection with the Brain Barriers community in the gap that COVID-19 has created” (n = 298 respondents to this question of a total of 299 registrants, M = 1.44, SD = 0.660, 1 = Strongly Agree, 2 = Agree, 3 = Neutral, 4 = Disagree, 5 = Strongly Disagree; Fig. 2A). However, when posed with the statements “In the future, I would prefer to attend a virtual conference over an in-person conference” or “The virtual experience is not as effective compared to the in-person conferences”, responses were neutral (M = 3.24; Fig. 2B) indicating a positive view towards a virtual conference. A difference emerged when we compared BBV2020 participants based on their career level with their preference for in-person versus virtual meeting formats: using one-way ANOVA, we found a significant difference between groups (F(9286) = 2.607, p = 0.007). Using Tukey’s HSD post hoc test indicated that Master’s students were more likely to disagree with the following statement when compared to Faculty/Professors: “I would feel more comfortable attending Brain Barrier conferences/seminars in person, but was glad to attend virtually due to COVID19.” A strong trend emerged when Master’s and Ph.D. students were grouped together and compared to Post-Docs and Faculty/Professors in an independent-samples t test. Results show graduate students in general were more favorable to a virtual conference compared to colleagues more established in their careers (t(225) = -1.972, p = 0.061). The lack of significance is in part due to the low sample size (n = 74) for graduate students.\n\nFig. 2\nPost-seminar survey results. A Volunteer respondent result when posed with the prompt “BBV2020 has enriched connection with the Brain Barriers community in the gap that COVID-19 has created”. B Volunteer respondent results when posted with the prompts “In the future, I would prefer to attend a virtual conference over an in-person conference” (Blue) and “The virtual experience is not as effective compared to the in-person conferences” (Red)\n\nPost-seminar survey results. A Volunteer respondent result when posed with the prompt “BBV2020 has enriched connection with the Brain Barriers community in the gap that COVID-19 has created”. B Volunteer respondent results when posted with the prompts “In the future, I would prefer to attend a virtual conference over an in-person conference” (Blue) and “The virtual experience is not as effective compared to the in-person conferences” (Red)\nOverall, descriptive data revealed many participants felt BBV2020 was a positive experience. In fact, 99.3% of survey respondents noted they somewhat or strongly agree that their experience was positive, with only 4.7% saying they faced technical difficulties. Additionally, most respondents agreed that virtual conferences have an important role in research (93.2%) and are more accessible than in-person conferences (90.2%). Finally, more than half of respondents (56.9%) noted virtual conferences can do things in-person conferences cannot.\nNevertheless, there continued to be a desire among many BBV2020 participants to return to conferencing in person, with 13.5% saying they found it hard to focus in the virtual format. In fact, roughly one-third (33.9%) felt the virtual conference was not as effective as an in-person conference, and only one-quarter (23.3%) said they would prefer virtual conferences to in-person conferences in the future, while 35.8% were neutral.\nTogether, our findings suggest that while BBV2020 filled a need created by a global pandemic, the enthusiasm for returning to only in-person meetings remained relatively neutral suggesting that virtual platforms may play a critical role in dissemination of research and science in the future. Moreover, the data from this study show a trend between graduate students versus professors in their response with regard to in-person meetings indicating that career stage may influence the perspective of the usefulness of in-person versus virtual platforms.","During the seminar registration process we collected attendants’ affiliations and based on this information we determined the global reach of the BBV2020. Registrants of the BBV2020 seminar series were from 43 countries representing six of the seven continents (Fig. 3A, B). Compared to the in-person BBB 2018 and the CVB2019, more countries were represented at the BBV2020 (Fig. 3A).\n\nFig. 3\nGlobal reach of BBV2020. A BBV2020 had more countries represented compared to BBB 2018 and CVB 2019 alone. B World map showing countries represented by BBV2020. Countries where registrants participated in BBV2020: Red and Pink. Countries not represented at BBV2020: Light Gray and Dark Gray. Countries where registrants attended BBV2020 and at least one of the in-person meetings between Meeting 2018 or CVB 2019: Red. Countries where registrants attended BBV2020 and did not attend either BBB 2018 or CVB 2019: Pink. Countries that had no participation in any of the brain barriers meetings examined in this study: Light gray. Countries that had participation in either BBB 2018 or CVB 2019 that did not participate in BBV2020: Dark Gray\n\nGlobal reach of BBV2020. A BBV2020 had more countries represented compared to BBB 2018 and CVB 2019 alone. B World map showing countries represented by BBV2020. Countries where registrants participated in BBV2020: Red and Pink. Countries not represented at BBV2020: Light Gray and Dark Gray. Countries where registrants attended BBV2020 and at least one of the in-person meetings between Meeting 2018 or CVB 2019: Red. Countries where registrants attended BBV2020 and did not attend either BBB 2018 or CVB 2019: Pink. Countries that had no participation in any of the brain barriers meetings examined in this study: Light gray. Countries that had participation in either BBB 2018 or CVB 2019 that did not participate in BBV2020: Dark Gray\nWe also compared the number of participants from each country who joined BBV2020 vs. BBB 2018 and CVB 2019. We found that the number of participants per country worldwide was also higher compared to both in person meetings (Fig. 4A–F). These data show that the virtual nature of BBV2020 enabled researchers world-wide to participate and suggest that the outreach of a virtual event can extend beyond the field.\n\nFig. 4\nGlobal view of participation over BBV2020, BBB 2018, and CVB 2019. Representation of participants for (A) BBV2020, (B) BBB 2018, and (C) CVB 2019. Ordered from top to bottom in greatest number of registrants. Number of registrants scaled by color based on number of registrants per country for (D) BBV 2020, (E) BBB 2018, and (F) CVB 2019. Light Pink: 1–10 registrants; Dark Pink: 11–50 registrants; Red: 51–100 registrants and Dark Red: > 100 registrants\n\nGlobal view of participation over BBV2020, BBB 2018, and CVB 2019. Representation of participants for (A) BBV2020, (B) BBB 2018, and (C) CVB 2019. Ordered from top to bottom in greatest number of registrants. Number of registrants scaled by color based on number of registrants per country for (D) BBV 2020, (E) BBB 2018, and (F) CVB 2019. Light Pink: 1–10 registrants; Dark Pink: 11–50 registrants; Red: 51–100 registrants and Dark Red: > 100 registrants","We examined the self-reported career stage of BBV2020 participants to that of BBB 2018 and CVB 2019 participants. Based on the survey, more than 46% of all participants attended a brain barriers conference/seminar for the first time. We also found that in-person meetings are attended by 42.4–48.9% academicians at the professor rank, graduate students make up 22.9–25.8% and postdoctoral fellows are 6.6–17.7% of the participants (Fig. 5). In contrast, at the BBV2020 19.6% of academicians were at the professor rank, graduate students made up 30.5% and postdoctoral fellows 16.1% of attendees. We also found that BBV2020 had a larger participation from industry (18.5% vs. 5.6–6.6%), and undergraduate students (3.2% vs. 1%) compared to both in person meetings. Together, our data suggest that a virtual platform seminar like BBV2020 reaches a broader range of scientific ranks and careers.\n\nFig. 5\nProportions of various career stages participating in BBV2020, BBB 2018, and CVB 2019. Proportions of various career stages participating in (A) BBV2020, (B) BBB 2018, and (C) CVB 2019. Colors correspond to career stage: Red (professor any rank), Green (graduate student any rank), Dark Blue (research faculty or researcher), Yellow (industry scientist), Orange (postdoctoral fellow), Purple (government/regulatory), Light Blue (clinician), Green (undergraduate student)\n\nProportions of various career stages participating in BBV2020, BBB 2018, and CVB 2019. Proportions of various career stages participating in (A) BBV2020, (B) BBB 2018, and (C) CVB 2019. Colors correspond to career stage: Red (professor any rank), Green (graduate student any rank), Dark Blue (research faculty or researcher), Yellow (industry scientist), Orange (postdoctoral fellow), Purple (government/regulatory), Light Blue (clinician), Green (undergraduate student)","Seemingly overnight the worldwide COVID-19 pandemic moved virtual meetings, seminars and conferences from the sidelines to center stage. For scientists, connecting on a virtual platform to share data and discuss ideas became the new norm. For many professional societies and research networks a virtual format was a viable option to offset the burden of the pandemic [6, 7]. We organized the BBV2020 seminar series for researchers in the brain barriers field to fill the vacuum canceled in-person conferences left in 2020. Using data collected throughout the BBV2020 seminar series from Zoom Webinar live attendance, number of views from posted videos, and a post-seminar survey, we captured the impact of BBV2020 on the brain barriers field. We compared these data with those from past in-person conferences and assessed the reach of the BBV2020 virtual seminar series with in-person meetings. While we acknowledge that comparisons between a seminar series to a conference may have its limitations, this was the first stand-in virtual event for the brain barriers field that may provide insight for future planning. In the following sections we discuss our findings in the context of the existing literature and provide a future perspective on how a virtual platform could impact the brain barriers field beyond the pandemic.\n Attendance: scientists around the globe Due to the pandemic, the idea and subsequent organization of the BBV2020 occurred in a relatively short time frame of about 4 weeks. This is in stark contrast to in-person meetings that are often scheduled and organized months and years ahead of the actual event. Advertisement of the BBV2020 encompassed personal email, postings on social media, and utilization of the International Brain Barriers Society email distribution list. Continuous advertisement resulted in the registration of over 1300 scientists. The total registrant number cannot be directly compared to that of in-person meetings since those are more complex in nature, have maximum capacity limits, and involve other factors such as travel expenses and time commitment. Similar to other meetings which transitioned to online formats during the pandemic, BBV2020 had a large increase in registrants when compared to previous in-person meetings (~7-fold and 4.5-fold higher compared to 2018 and 2019, respectively; Fig. 1; [8]. Given that previous brain barriers meetings usually have a few hundred participants, the high number of registrants could be an indicator for high interest in the field beyond traditional brain barrier research groups. In contrast, these high registrant numbers could also be due, in part, to BBV2020 being a no-cost event or could be explained by pandemic-related cancellations resulting in relatively event-free calendar of the participants. While there were more than 1,300 registrants for BBV2020, no more than 419 ever attended a live webinar. Additionally, 323 people registered for BBV2020 but never attended a live webinar. They may, however, have participated by watching the video recordings after the live webinar. A downward trend of attendance numbers for live seminars was observed throughout the course of the 16-week series (Fig. 1B). The recorded seminars drew considerable interest initially with a maximum of 944 views in week 4 of the series, but later in the series, video views dropped to about 200 per seminar (Fig. 1C). Reasons for these downward trends remain speculative, but could be due to the summer break, the selected time for the seminar making it difficult for researchers in certain time zones to attend live, zoom fatigue, or increasing responsibilities in re-opened research laboratories or at home. The downward trends we observed was in contrast to a similar virtual seminar series in another field that remained relatively flat over the course of the summer [4]. Given that average attendance of the BBV2020 was 220 per seminar and typical attendance for in-person conferences in the brain barriers field range between 150 and 300 participants (caveat: one of the in-person conferences has a strict 182-person maximum limit), we conclude that a core group of participants attended most sessions.\nDue to the pandemic, the idea and subsequent organization of the BBV2020 occurred in a relatively short time frame of about 4 weeks. This is in stark contrast to in-person meetings that are often scheduled and organized months and years ahead of the actual event. Advertisement of the BBV2020 encompassed personal email, postings on social media, and utilization of the International Brain Barriers Society email distribution list. Continuous advertisement resulted in the registration of over 1300 scientists. The total registrant number cannot be directly compared to that of in-person meetings since those are more complex in nature, have maximum capacity limits, and involve other factors such as travel expenses and time commitment. Similar to other meetings which transitioned to online formats during the pandemic, BBV2020 had a large increase in registrants when compared to previous in-person meetings (~7-fold and 4.5-fold higher compared to 2018 and 2019, respectively; Fig. 1; [8]. Given that previous brain barriers meetings usually have a few hundred participants, the high number of registrants could be an indicator for high interest in the field beyond traditional brain barrier research groups. In contrast, these high registrant numbers could also be due, in part, to BBV2020 being a no-cost event or could be explained by pandemic-related cancellations resulting in relatively event-free calendar of the participants. While there were more than 1,300 registrants for BBV2020, no more than 419 ever attended a live webinar. Additionally, 323 people registered for BBV2020 but never attended a live webinar. They may, however, have participated by watching the video recordings after the live webinar. A downward trend of attendance numbers for live seminars was observed throughout the course of the 16-week series (Fig. 1B). The recorded seminars drew considerable interest initially with a maximum of 944 views in week 4 of the series, but later in the series, video views dropped to about 200 per seminar (Fig. 1C). Reasons for these downward trends remain speculative, but could be due to the summer break, the selected time for the seminar making it difficult for researchers in certain time zones to attend live, zoom fatigue, or increasing responsibilities in re-opened research laboratories or at home. The downward trends we observed was in contrast to a similar virtual seminar series in another field that remained relatively flat over the course of the summer [4]. Given that average attendance of the BBV2020 was 220 per seminar and typical attendance for in-person conferences in the brain barriers field range between 150 and 300 participants (caveat: one of the in-person conferences has a strict 182-person maximum limit), we conclude that a core group of participants attended most sessions.\n Survey: feedback from participants A survey conducted after the seminar series revealed that most participants felt that BBV2020 enhanced the connection to the brain barriers scientific community during the COVID-19 pandemic (Fig. 2A). Differences emerged between how graduate students and professors viewed BBV2020: graduate students in general were more favorable of the virtual format compared to faculty. This may be partially explained by a lack of funding for graduate student travel or political “red tape” (e.g. visas) for some researchers [9]. Additionally, students may disproportionately adapt more easily and rapidly to technology usage compared to senior colleagues [10]. Clearly, another survey would be needed to determine if a virtual component outside of a pandemic would be positively viewed by researchers in the brain barriers field.\nA survey conducted after the seminar series revealed that most participants felt that BBV2020 enhanced the connection to the brain barriers scientific community during the COVID-19 pandemic (Fig. 2A). Differences emerged between how graduate students and professors viewed BBV2020: graduate students in general were more favorable of the virtual format compared to faculty. This may be partially explained by a lack of funding for graduate student travel or political “red tape” (e.g. visas) for some researchers [9]. Additionally, students may disproportionately adapt more easily and rapidly to technology usage compared to senior colleagues [10]. Clearly, another survey would be needed to determine if a virtual component outside of a pandemic would be positively viewed by researchers in the brain barriers field.\n Virtual seminars: a powerful opportunity One major advantage of a virtual seminar series such as the BBV2020 is the lack of a travel burden. Taking off the financial and time-commitment associated with in-person meetings has been shown to increase the diversity of attendees [3, 9, 11]. Based on those findings, we anticipated that BBV2020 would have representation from more countries than past in-person meetings. As shown in Fig. 3, registrants from 43 different countries were represented at BBV2020, which is 40% more compared to BBB 2018 (23 countries) and CVB 2019 (25 countries; Fig. 3). In particular, there was high participation from countries in Southeast Asia, Middle East, Africa, and South America that had not been represented at in-person conferences held in 2018 and 2019 (Figs. 3B, 4). Unsurprisingly however, considering countries represented at previous in-person meetings and due to the number of registrants for BBV2020, there was generally a greater participation from each country represented than in 2018 meeting and CVB 2019 [3, 8]. Due to the time of the BBV2020 live sessions (noon to 1 pm Eastern US Time), the majority of participants were from North America, South America, and Europe (Fig. 4). While time zone challenges are common among virtual events, switching between time zones or holding a “flipped” session (flipping evening sessions from Europe to the USA and vice versa) could increase participation from Asia and Oceania [12]. However, offering a virtual series that constantly switches the seminar time may be difficult to manage and could be confusing for participants. Another strength of virtual seminars is that they are more accessible and inclusive especially for junior researchers in the field. The in-person meetings in 2018 and 2019 were dominated by academic faculty at any professor rank and graduate students (Fig. 5). In contrast, BBV2020 saw a large reduction in the percentage of participating faculty (over 40% in person to less than 20% for BBV2020) and a larger proportion of graduate students (22–25% in person to over 30% for BBV2020), industry scientists (about 6% in person to 18.5% for BBV2020), and governmental/regulatory personnel who had signed up (Fig. 5). In addition, a weekly virtual seminar series allows scientist to pick-and-choose the sessions they are interested in, a choice that could interfere with the observed trends shown in Fig. 1. Our data further support the trends observed across a variety of disciplines who have shifted their conferences from in-person to online events, revealing that virtual conferences are more flexible, and more inclusive and accessible worldwide, especially for early-career scientists [9]. Virtual seminars also provide an opportunity for the organizers to invite prominent speakers of a field on a relatively small budget. In general, virtual conferences are known to enhance the accessibility for busy professionals. A recently published Nature poll indicates that 74% of scientists want virtual meetings to stay after the pandemic (925 poll responses) [13]. Easier accessibility, lower carbon footprint, and lower costs were the driving factors chosen in favor for virtual meetings in this poll. Moreover, virtual seminars provide an easy and affordable opportunity for researchers with disabilities, researchers with high teaching loads, or for scientists with parenting and other responsibilities to stay connected to their respective research community. During the COVID-19 pandemic, virtual seminars took center stage and raised the standard for accessibility, interactions, inclusivity, and equity in science [9]. Virtual events changed how we think about meetings and they constitute a yet underestimated, but powerful tool to share science and connect with each other. By rethinking how meetings are held, various fields have had different degrees of participation depending on the availability of the talks or content versus live sessions. These virtual platforms have the potential to create a “connectivity cascade” reaching more people than traditional meetings that are confined by a single geographical location and time [14]. BBV2020 had an impressive reach worldwide, however, there was still a notable deficit in participants from underdeveloped countries, especially Africa. These discrepancies have been noted in the context of expanding telehealth to African countries that still remain challenging due to monopolization of telecommunication infrastructure, political will, and access to adequate broadband efficiency [15]. Despite the advantages of BBV2020, we recognize that limitations remain, such as one-way delivery of material to participants. Lack of critical face-to-face interactions allowing junior scientists to have meaningful two-way communication with other participants was notably lacking in BBV2020 as seen in other virtual conferences as well [16]. The future of such virtual seminars, meetings, or conferences may lie in their inclusion as part of in-person meetings according to a recently published “best practice” guideline for virtual meetings [17]. For example, a virtual component could be included in plenary sessions, workshops, small group discussions, poster sessions, and/or social events of in-person conferences. BBV2020 represented only one aspect (a seminar held in the form of a webinar) of these common meeting events, but future events could certainly enhance the virtual experience. Regardless, virtual components could be exploited to increase diversity in the brain barriers research field which is consistent with the NINDS strategy for enhancing the diversity of neuroscience researchers [18].\nOne major advantage of a virtual seminar series such as the BBV2020 is the lack of a travel burden. Taking off the financial and time-commitment associated with in-person meetings has been shown to increase the diversity of attendees [3, 9, 11]. Based on those findings, we anticipated that BBV2020 would have representation from more countries than past in-person meetings. As shown in Fig. 3, registrants from 43 different countries were represented at BBV2020, which is 40% more compared to BBB 2018 (23 countries) and CVB 2019 (25 countries; Fig. 3). In particular, there was high participation from countries in Southeast Asia, Middle East, Africa, and South America that had not been represented at in-person conferences held in 2018 and 2019 (Figs. 3B, 4). Unsurprisingly however, considering countries represented at previous in-person meetings and due to the number of registrants for BBV2020, there was generally a greater participation from each country represented than in 2018 meeting and CVB 2019 [3, 8]. Due to the time of the BBV2020 live sessions (noon to 1 pm Eastern US Time), the majority of participants were from North America, South America, and Europe (Fig. 4). While time zone challenges are common among virtual events, switching between time zones or holding a “flipped” session (flipping evening sessions from Europe to the USA and vice versa) could increase participation from Asia and Oceania [12]. However, offering a virtual series that constantly switches the seminar time may be difficult to manage and could be confusing for participants. Another strength of virtual seminars is that they are more accessible and inclusive especially for junior researchers in the field. The in-person meetings in 2018 and 2019 were dominated by academic faculty at any professor rank and graduate students (Fig. 5). In contrast, BBV2020 saw a large reduction in the percentage of participating faculty (over 40% in person to less than 20% for BBV2020) and a larger proportion of graduate students (22–25% in person to over 30% for BBV2020), industry scientists (about 6% in person to 18.5% for BBV2020), and governmental/regulatory personnel who had signed up (Fig. 5). In addition, a weekly virtual seminar series allows scientist to pick-and-choose the sessions they are interested in, a choice that could interfere with the observed trends shown in Fig. 1. Our data further support the trends observed across a variety of disciplines who have shifted their conferences from in-person to online events, revealing that virtual conferences are more flexible, and more inclusive and accessible worldwide, especially for early-career scientists [9]. Virtual seminars also provide an opportunity for the organizers to invite prominent speakers of a field on a relatively small budget. In general, virtual conferences are known to enhance the accessibility for busy professionals. A recently published Nature poll indicates that 74% of scientists want virtual meetings to stay after the pandemic (925 poll responses) [13]. Easier accessibility, lower carbon footprint, and lower costs were the driving factors chosen in favor for virtual meetings in this poll. Moreover, virtual seminars provide an easy and affordable opportunity for researchers with disabilities, researchers with high teaching loads, or for scientists with parenting and other responsibilities to stay connected to their respective research community. During the COVID-19 pandemic, virtual seminars took center stage and raised the standard for accessibility, interactions, inclusivity, and equity in science [9]. Virtual events changed how we think about meetings and they constitute a yet underestimated, but powerful tool to share science and connect with each other. By rethinking how meetings are held, various fields have had different degrees of participation depending on the availability of the talks or content versus live sessions. These virtual platforms have the potential to create a “connectivity cascade” reaching more people than traditional meetings that are confined by a single geographical location and time [14]. BBV2020 had an impressive reach worldwide, however, there was still a notable deficit in participants from underdeveloped countries, especially Africa. These discrepancies have been noted in the context of expanding telehealth to African countries that still remain challenging due to monopolization of telecommunication infrastructure, political will, and access to adequate broadband efficiency [15]. Despite the advantages of BBV2020, we recognize that limitations remain, such as one-way delivery of material to participants. Lack of critical face-to-face interactions allowing junior scientists to have meaningful two-way communication with other participants was notably lacking in BBV2020 as seen in other virtual conferences as well [16]. The future of such virtual seminars, meetings, or conferences may lie in their inclusion as part of in-person meetings according to a recently published “best practice” guideline for virtual meetings [17]. For example, a virtual component could be included in plenary sessions, workshops, small group discussions, poster sessions, and/or social events of in-person conferences. BBV2020 represented only one aspect (a seminar held in the form of a webinar) of these common meeting events, but future events could certainly enhance the virtual experience. Regardless, virtual components could be exploited to increase diversity in the brain barriers research field which is consistent with the NINDS strategy for enhancing the diversity of neuroscience researchers [18].","Due to the pandemic, the idea and subsequent organization of the BBV2020 occurred in a relatively short time frame of about 4 weeks. This is in stark contrast to in-person meetings that are often scheduled and organized months and years ahead of the actual event. Advertisement of the BBV2020 encompassed personal email, postings on social media, and utilization of the International Brain Barriers Society email distribution list. Continuous advertisement resulted in the registration of over 1300 scientists. The total registrant number cannot be directly compared to that of in-person meetings since those are more complex in nature, have maximum capacity limits, and involve other factors such as travel expenses and time commitment. Similar to other meetings which transitioned to online formats during the pandemic, BBV2020 had a large increase in registrants when compared to previous in-person meetings (~7-fold and 4.5-fold higher compared to 2018 and 2019, respectively; Fig. 1; [8]. Given that previous brain barriers meetings usually have a few hundred participants, the high number of registrants could be an indicator for high interest in the field beyond traditional brain barrier research groups. In contrast, these high registrant numbers could also be due, in part, to BBV2020 being a no-cost event or could be explained by pandemic-related cancellations resulting in relatively event-free calendar of the participants. While there were more than 1,300 registrants for BBV2020, no more than 419 ever attended a live webinar. Additionally, 323 people registered for BBV2020 but never attended a live webinar. They may, however, have participated by watching the video recordings after the live webinar. A downward trend of attendance numbers for live seminars was observed throughout the course of the 16-week series (Fig. 1B). The recorded seminars drew considerable interest initially with a maximum of 944 views in week 4 of the series, but later in the series, video views dropped to about 200 per seminar (Fig. 1C). Reasons for these downward trends remain speculative, but could be due to the summer break, the selected time for the seminar making it difficult for researchers in certain time zones to attend live, zoom fatigue, or increasing responsibilities in re-opened research laboratories or at home. The downward trends we observed was in contrast to a similar virtual seminar series in another field that remained relatively flat over the course of the summer [4]. Given that average attendance of the BBV2020 was 220 per seminar and typical attendance for in-person conferences in the brain barriers field range between 150 and 300 participants (caveat: one of the in-person conferences has a strict 182-person maximum limit), we conclude that a core group of participants attended most sessions.","A survey conducted after the seminar series revealed that most participants felt that BBV2020 enhanced the connection to the brain barriers scientific community during the COVID-19 pandemic (Fig. 2A). Differences emerged between how graduate students and professors viewed BBV2020: graduate students in general were more favorable of the virtual format compared to faculty. This may be partially explained by a lack of funding for graduate student travel or political “red tape” (e.g. visas) for some researchers [9]. Additionally, students may disproportionately adapt more easily and rapidly to technology usage compared to senior colleagues [10]. Clearly, another survey would be needed to determine if a virtual component outside of a pandemic would be positively viewed by researchers in the brain barriers field.","One major advantage of a virtual seminar series such as the BBV2020 is the lack of a travel burden. Taking off the financial and time-commitment associated with in-person meetings has been shown to increase the diversity of attendees [3, 9, 11]. Based on those findings, we anticipated that BBV2020 would have representation from more countries than past in-person meetings. As shown in Fig. 3, registrants from 43 different countries were represented at BBV2020, which is 40% more compared to BBB 2018 (23 countries) and CVB 2019 (25 countries; Fig. 3). In particular, there was high participation from countries in Southeast Asia, Middle East, Africa, and South America that had not been represented at in-person conferences held in 2018 and 2019 (Figs. 3B, 4). Unsurprisingly however, considering countries represented at previous in-person meetings and due to the number of registrants for BBV2020, there was generally a greater participation from each country represented than in 2018 meeting and CVB 2019 [3, 8]. Due to the time of the BBV2020 live sessions (noon to 1 pm Eastern US Time), the majority of participants were from North America, South America, and Europe (Fig. 4). While time zone challenges are common among virtual events, switching between time zones or holding a “flipped” session (flipping evening sessions from Europe to the USA and vice versa) could increase participation from Asia and Oceania [12]. However, offering a virtual series that constantly switches the seminar time may be difficult to manage and could be confusing for participants. Another strength of virtual seminars is that they are more accessible and inclusive especially for junior researchers in the field. The in-person meetings in 2018 and 2019 were dominated by academic faculty at any professor rank and graduate students (Fig. 5). In contrast, BBV2020 saw a large reduction in the percentage of participating faculty (over 40% in person to less than 20% for BBV2020) and a larger proportion of graduate students (22–25% in person to over 30% for BBV2020), industry scientists (about 6% in person to 18.5% for BBV2020), and governmental/regulatory personnel who had signed up (Fig. 5). In addition, a weekly virtual seminar series allows scientist to pick-and-choose the sessions they are interested in, a choice that could interfere with the observed trends shown in Fig. 1. Our data further support the trends observed across a variety of disciplines who have shifted their conferences from in-person to online events, revealing that virtual conferences are more flexible, and more inclusive and accessible worldwide, especially for early-career scientists [9]. Virtual seminars also provide an opportunity for the organizers to invite prominent speakers of a field on a relatively small budget. In general, virtual conferences are known to enhance the accessibility for busy professionals. A recently published Nature poll indicates that 74% of scientists want virtual meetings to stay after the pandemic (925 poll responses) [13]. Easier accessibility, lower carbon footprint, and lower costs were the driving factors chosen in favor for virtual meetings in this poll. Moreover, virtual seminars provide an easy and affordable opportunity for researchers with disabilities, researchers with high teaching loads, or for scientists with parenting and other responsibilities to stay connected to their respective research community. During the COVID-19 pandemic, virtual seminars took center stage and raised the standard for accessibility, interactions, inclusivity, and equity in science [9]. Virtual events changed how we think about meetings and they constitute a yet underestimated, but powerful tool to share science and connect with each other. By rethinking how meetings are held, various fields have had different degrees of participation depending on the availability of the talks or content versus live sessions. These virtual platforms have the potential to create a “connectivity cascade” reaching more people than traditional meetings that are confined by a single geographical location and time [14]. BBV2020 had an impressive reach worldwide, however, there was still a notable deficit in participants from underdeveloped countries, especially Africa. These discrepancies have been noted in the context of expanding telehealth to African countries that still remain challenging due to monopolization of telecommunication infrastructure, political will, and access to adequate broadband efficiency [15]. Despite the advantages of BBV2020, we recognize that limitations remain, such as one-way delivery of material to participants. Lack of critical face-to-face interactions allowing junior scientists to have meaningful two-way communication with other participants was notably lacking in BBV2020 as seen in other virtual conferences as well [16]. The future of such virtual seminars, meetings, or conferences may lie in their inclusion as part of in-person meetings according to a recently published “best practice” guideline for virtual meetings [17]. For example, a virtual component could be included in plenary sessions, workshops, small group discussions, poster sessions, and/or social events of in-person conferences. BBV2020 represented only one aspect (a seminar held in the form of a webinar) of these common meeting events, but future events could certainly enhance the virtual experience. Regardless, virtual components could be exploited to increase diversity in the brain barriers research field which is consistent with the NINDS strategy for enhancing the diversity of neuroscience researchers [18].","Overall, the Virtual BBV2020 Seminar Series created during the COVID-19 pandemic was positively received by the brain barriers research community. Key benefits of virtual seminars like the BBV2020 are that they are convenient, affordable, eco-friendly, reach a broad audience, and are a good tool to increase diversity and equity. Thus, incorporating a virtual component during an in-person meeting or offering a virtual counterpart in addition to in-person meetings could help increase the outreach of a small research field like the brain barriers field. Nevertheless, limitations remain such as a lack of interaction between speakers and participants, which is particularly critical for junior researchers. And although BBV2020 expanded the reach beyond past in-person meetings, challenges reaching scientists in underdeveloped countries remain. Together, BBV2020 was created in a time of crisis but has the potential to thrive in the post-pandemic world. Embracing virtual scientific events may allow us to advance the brain barriers research field and have an impact on how we exchange science in the future."," \nAdditional file 1: Figure S1. Attendance Trends for BBV2020 by Academic Rank. Number of unique viewers weekly at the live Zoom Webinar sessions of BBV2020 separated by A) Graduate Student, B) Postdoctoral Fellow, and C) Professor (any rank).\nAdditional file 1: Figure S1. Attendance Trends for BBV2020 by Academic Rank. Number of unique viewers weekly at the live Zoom Webinar sessions of BBV2020 separated by A) Graduate Student, B) Postdoctoral Fellow, and C) Professor (any rank).\n\nAdditional file 1: Figure S1. Attendance Trends for BBV2020 by Academic Rank. Number of unique viewers weekly at the live Zoom Webinar sessions of BBV2020 separated by A) Graduate Student, B) Postdoctoral Fellow, and C) Professor (any rank).\nAdditional file 1: Figure S1. Attendance Trends for BBV2020 by Academic Rank. Number of unique viewers weekly at the live Zoom Webinar sessions of BBV2020 separated by A) Graduate Student, B) Postdoctoral Fellow, and C) Professor (any rank)."],"string":"[\n \"On March 11, 2020, the World Health Organization (WHO) declared the coronavirus disease (COVID-19) outbreak a global pandemic [1]. Shortly after, countries worldwide issued stay-at-home orders and lockdowns: the COVID-19 pandemic had forced the world to come to an abrupt halt [2]. With global air travel restrictions and travel bans in place, enterprises including academic institutions, industry and government agencies required individuals to call off their business travel. As a result, scheduled scientific conferences were canceled throughout 2020 [3, 4]. These cancelations created a sudden void in scientific communication between researchers worldwide and forced research communities to be creative and adapt. One possibility to continue scientific communication and interaction was by switching conferences and seminars to virtual online events using novel digital platforms enabling researchers to connect and share science [3, 4].\\nAs a consequence, major conferences and meetings in the brain barriers research field, including the “Barriers of the CNS Gordon Research Conference” (GRC 2020) and the international symposium on “Signal Transduction at the Blood–Brain Barriers” in Bari, Italy were first postponed until 2021 and due to the ongoing pandemic subsequently moved to 2022. The “Cerebral Vascular Biology” (CVB) conference in Uppsala, Sweden planned for 2021 was postponed until 2023. Postponing these meetings left organizers and conference chairs in a difficult position and researchers without a platform to present and share their data. Thus, these postponements created a critical need for the field to stay connected and an opportunity to establish a virtual event that could supplement the postponed conferences in 2020. The Brain Barriers Virtual 2020 Seminar Series (BBV2020) addressed this need by providing a forum for brain barrier scientists worldwide to come together online during the COVID-19 crisis in 2020. BBV2020 seminars were held weekly from May 20, 2020 to September 2, 2020 in a 1-h format featuring a live session with an invited speaker followed by time for questions and answers from the community. The seminar series was hosted on a Zoom webinar platform and was free for anyone to attend. BBV2020 served as a forum for invited speakers to present their work to the brain barriers community and enabled this community to discuss brain barriers science from the safety of their home office. Over 1300 scientists around the world were registered, each seminar was viewed live by 100–400 participants, and the video-recordings received 150–900 views each week. Participants were from academia, industry and government agencies at all career stages from around the globe.\\nIn this report, we share participation data that give insight into the impact this virtual seminar series had on the brain barriers field during the COVID-19 pandemic. We compare these data with existing data from past in-person brain barriers meetings and draw conclusions from these data on the potential value of virtual meetings for the brain barriers research field in the future. While the organization of virtual events during the pandemic may seem self-evident, reflection on the impact carries utility for events in the future.\",\n \" Organization The BBV2020 seminar series was created by Drs. Bjoern Bauer (University of Kentucky, Lexington, KY, USA), Anika Hartz (University of Kentucky, Lexington, KY, USA), and Brandon Kim (University of Alabama, Tuscaloosa, Alabama, USA). Seminar moderators were postdoctoral researchers and scientists from the USA and Europe: Drs. Natalie Hudson (Trinity College Dublin, Dublin, Ireland), Geetika Nehra (University of Kentucky, Lexington, KY, USA), Michelle Pizzo (Denali Therapeutics Inc., San Francisco, CA, USA), and Steffen Storck (University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany).\\nThe BBV2020 seminar series was created by Drs. Bjoern Bauer (University of Kentucky, Lexington, KY, USA), Anika Hartz (University of Kentucky, Lexington, KY, USA), and Brandon Kim (University of Alabama, Tuscaloosa, Alabama, USA). Seminar moderators were postdoctoral researchers and scientists from the USA and Europe: Drs. Natalie Hudson (Trinity College Dublin, Dublin, Ireland), Geetika Nehra (University of Kentucky, Lexington, KY, USA), Michelle Pizzo (Denali Therapeutics Inc., San Francisco, CA, USA), and Steffen Storck (University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany).\\n Virtual platform and technical assistance BBV2020 was run on the Zoom Webinar 500 platform (Zoom Video Communications, Inc.; San Jose, CA, USA) that was funded by Dr. Bjoern Bauer using University of Kentucky funds. This particular Zoom Webinar license allowed for up to 500 live participants. The Zoom Webinar platform supports an in-session chat function, a Q&A forum, a raise hand function for live questions, and provides a panelist section with the option to share audio, video and presentation slides, a recording function and post-webinar data collection. The hosts and moderators were in control of audio and video; participants were not able to record or unmute during a session. Participants had no control function other than a “raise hand” option and the Q&A and chat function allowing to send questions or messages to the moderator. Technical assistance was provided by Todd Sizemore, College of Pharmacy, University of Kentucky.\\nBBV2020 was run on the Zoom Webinar 500 platform (Zoom Video Communications, Inc.; San Jose, CA, USA) that was funded by Dr. Bjoern Bauer using University of Kentucky funds. This particular Zoom Webinar license allowed for up to 500 live participants. The Zoom Webinar platform supports an in-session chat function, a Q&A forum, a raise hand function for live questions, and provides a panelist section with the option to share audio, video and presentation slides, a recording function and post-webinar data collection. The hosts and moderators were in control of audio and video; participants were not able to record or unmute during a session. Participants had no control function other than a “raise hand” option and the Q&A and chat function allowing to send questions or messages to the moderator. Technical assistance was provided by Todd Sizemore, College of Pharmacy, University of Kentucky.\\n BBV2020 schedule The BBV2020 seminar series ran from May 20 to September 2, 2020. Live seminars were held once per week from 12 to 1 PM Eastern Daylight Time (EDT) to accommodate researchers across a wide range of time zones: Central European Time (6:00 PM) to Pacific Daylight Time (9:00 AM). To accommodate colleagues with time constraints or those within incompatible time zones such as Asia or Oceania, presentations were recorded (depending on speaker permissions) and the seminar recording was made available for up to 48 h after the live seminar. The link for the recorded videos was distributed via the BBV2020 listserv that was set up through the University of Kentucky listserv.\\nThe BBV2020 seminar series ran from May 20 to September 2, 2020. Live seminars were held once per week from 12 to 1 PM Eastern Daylight Time (EDT) to accommodate researchers across a wide range of time zones: Central European Time (6:00 PM) to Pacific Daylight Time (9:00 AM). To accommodate colleagues with time constraints or those within incompatible time zones such as Asia or Oceania, presentations were recorded (depending on speaker permissions) and the seminar recording was made available for up to 48 h after the live seminar. The link for the recorded videos was distributed via the BBV2020 listserv that was set up through the University of Kentucky listserv.\\n Invited speakers Efforts were made to invite speakers from a variety of countries and genders. Invited speakers included organizers of postponed in-person conferences, speakers from academia and industry from different career stages. A total of 16 speakers were invited: May 20, 2020: Elga De Vries, Amsterdam UMC, Amsterdam, The Netherlands; May 27, 2020: Richard Daneman, University of California San Diego, San Diego, CA, USA; June 3, 2020: Robyn Klein, Washington University, St. Louis, MO, USA; June 10, 2020: Ayal Ben-Zvi, The Hebrew University of Jerusalem, Israel; June 17, 2020: Britta Engelhardt, University of Bern, Bern, Switzerland; June 24, 2020: Margareta Hammarlund-Udenaes, Uppsala University, Uppsala, Sweden; July 1, 2020: Daniela Virgintino, University of Bari, Bari, Italy; July 8, 2020: Eric Shusta, University of Wisconsin, Madison, WI, USA; July 15, 2020: Matthew Campbell, Trinity College Dublin, Dublin, Ireland; July 22, 2020: Krzysztof Kucharz, University of Copenhagen, Copenhagen, Denmark; July 29, 2020: William Elmquist, University of Minnesota, Minneapolis, MN, USA; August 5, 2020: Edward Neuwelt, Oregon Health & Sciences University, OR, USA; August 12, 2020: Robert Thorne, Denali Therapeutics, San Francisco, CA, USA; August 19, 2020: Teresa Sanchez, Weill Cornell Medical College, New York City, NJ, USA; August 26, 2020: Michael Taylor, University of Wisconsin, Madison, MI, USA; September 2, 2020: Joan Abbott, King’s College London, London, UK.\\nSpeakers were offered a training session by the organizers to cover key aspects of a Zoom-based webinar and to test video, audio, and slide presentation prior to the live seminar. Ten out of 16 speakers took the training session. Speakers were informed by the organizers that any data presented should be considered public as security on the Zoom platform could not be ensured. Invited speakers had the opportunity to give permission to have their seminar recorded or to opt out of the recording. If a speaker gave permission to record, the entire seminar was recorded, and recordings were made available to all registered participants in a non-downloadable format through the University of Kentucky Zoom cloud for up to 48 h after the live session. Out of 16 speakers, 15 gave permission to record the live seminar.\\nEfforts were made to invite speakers from a variety of countries and genders. Invited speakers included organizers of postponed in-person conferences, speakers from academia and industry from different career stages. A total of 16 speakers were invited: May 20, 2020: Elga De Vries, Amsterdam UMC, Amsterdam, The Netherlands; May 27, 2020: Richard Daneman, University of California San Diego, San Diego, CA, USA; June 3, 2020: Robyn Klein, Washington University, St. Louis, MO, USA; June 10, 2020: Ayal Ben-Zvi, The Hebrew University of Jerusalem, Israel; June 17, 2020: Britta Engelhardt, University of Bern, Bern, Switzerland; June 24, 2020: Margareta Hammarlund-Udenaes, Uppsala University, Uppsala, Sweden; July 1, 2020: Daniela Virgintino, University of Bari, Bari, Italy; July 8, 2020: Eric Shusta, University of Wisconsin, Madison, WI, USA; July 15, 2020: Matthew Campbell, Trinity College Dublin, Dublin, Ireland; July 22, 2020: Krzysztof Kucharz, University of Copenhagen, Copenhagen, Denmark; July 29, 2020: William Elmquist, University of Minnesota, Minneapolis, MN, USA; August 5, 2020: Edward Neuwelt, Oregon Health & Sciences University, OR, USA; August 12, 2020: Robert Thorne, Denali Therapeutics, San Francisco, CA, USA; August 19, 2020: Teresa Sanchez, Weill Cornell Medical College, New York City, NJ, USA; August 26, 2020: Michael Taylor, University of Wisconsin, Madison, MI, USA; September 2, 2020: Joan Abbott, King’s College London, London, UK.\\nSpeakers were offered a training session by the organizers to cover key aspects of a Zoom-based webinar and to test video, audio, and slide presentation prior to the live seminar. Ten out of 16 speakers took the training session. Speakers were informed by the organizers that any data presented should be considered public as security on the Zoom platform could not be ensured. Invited speakers had the opportunity to give permission to have their seminar recorded or to opt out of the recording. If a speaker gave permission to record, the entire seminar was recorded, and recordings were made available to all registered participants in a non-downloadable format through the University of Kentucky Zoom cloud for up to 48 h after the live session. Out of 16 speakers, 15 gave permission to record the live seminar.\\n Advertisement and participants To reach scientists in the brain barriers community, the seminar was advertised by email to participants of previous in-person meetings, and on social media platforms such as Facebook and LinkedIn on personal profiles. In addition, the introductory flyer was kindly distributed via email by the International Brain Barriers Society (IBBS) using their email distribution list. Interested scientists were asked to register by sending their name, affiliation, and career stage to a dedicated email address created for the organization and execution of BBV2020 (bbv2020seminar@gmail.com). Once registered, participants were added to a BBV2020 listserv provided by the University of Kentucky and received weekly updates. These weekly updates included a flyer with the information regarding the upcoming speakers and the specific zoom link for the seminar. Participants also received a follow up email after each seminar with a zoom link to the video recordings. With the permission of the speaker of the week, registered viewers had access to recordings for up to 48 h after the live seminar.\\nTo reach scientists in the brain barriers community, the seminar was advertised by email to participants of previous in-person meetings, and on social media platforms such as Facebook and LinkedIn on personal profiles. In addition, the introductory flyer was kindly distributed via email by the International Brain Barriers Society (IBBS) using their email distribution list. Interested scientists were asked to register by sending their name, affiliation, and career stage to a dedicated email address created for the organization and execution of BBV2020 (bbv2020seminar@gmail.com). Once registered, participants were added to a BBV2020 listserv provided by the University of Kentucky and received weekly updates. These weekly updates included a flyer with the information regarding the upcoming speakers and the specific zoom link for the seminar. Participants also received a follow up email after each seminar with a zoom link to the video recordings. With the permission of the speaker of the week, registered viewers had access to recordings for up to 48 h after the live seminar.\\n BBV2020 seminar presentation Each seminar was started with opening remarks by one of the organizers (Drs. Bauer, Hartz, or Kim) followed by an introduction of the speaker by the moderator. Moderators (Drs. Hudson, Nehra, Pizzo, Storck) rotated each week, only one moderator was assigned to each week. After the seminar presentation, the moderator facilitated the question-and-answer session. Participants were encouraged to ask a live question by raising their digital hand. When a participant raised the digital hand, the moderator would unmute the participant at which point the participant was able to ask the speaker a question directly. In addition, participants had the opportunity to ask questions via the Zoom Q&A or the chat function. Written questions were then subsequently read by the moderator. The moderator concluded each seminar by thanking the speaker and the audience for attending and by announcing the speaker for the following week.\\nEach seminar was started with opening remarks by one of the organizers (Drs. Bauer, Hartz, or Kim) followed by an introduction of the speaker by the moderator. Moderators (Drs. Hudson, Nehra, Pizzo, Storck) rotated each week, only one moderator was assigned to each week. After the seminar presentation, the moderator facilitated the question-and-answer session. Participants were encouraged to ask a live question by raising their digital hand. When a participant raised the digital hand, the moderator would unmute the participant at which point the participant was able to ask the speaker a question directly. In addition, participants had the opportunity to ask questions via the Zoom Q&A or the chat function. Written questions were then subsequently read by the moderator. The moderator concluded each seminar by thanking the speaker and the audience for attending and by announcing the speaker for the following week.\\n Map generation World maps highlighting countries participating in the various meetings and representation were generated using MapChart (www.mapchart.net; license: https://creativecommons.org/licenses/by-sa/4.0/).\\nWorld maps highlighting countries participating in the various meetings and representation were generated using MapChart (www.mapchart.net; license: https://creativecommons.org/licenses/by-sa/4.0/).\\n Survey Toward the end of the seminar series, a survey was generated using Qualtrics (Qualtrics) and run through the University of Alabama Qualtrics system. A link to the survey was sent to all registered seminar participants by email. Questions covered general audience participation, career stage, as well as affective factors. Survey results are only reported in an aggregate.\\nToward the end of the seminar series, a survey was generated using Qualtrics (Qualtrics) and run through the University of Alabama Qualtrics system. A link to the survey was sent to all registered seminar participants by email. Questions covered general audience participation, career stage, as well as affective factors. Survey results are only reported in an aggregate.\\n Data collection, analysis, and statistics Survey data was collected through the University of Alabama Qualtrics system and was determined by the University of Alabama IRB to be “non-human subject research”. Data from the live webinars were collected by Zoom for each seminar and reported as unique viewers to eliminate counting individuals double who potentially logged on twice. Data on video views were collected from the University of Kentucky Zoom Webinar cloud server and were reported as total accessed views. Survey data from Qualtrics was analyzed with the IBM® SPSS® software platform (IBM, Armonk, NY, USA), using one-way ANOVAs and t-tests to identify significant trends [5]. Data with a p < 0.05 are considered significant.\\nSurvey data was collected through the University of Alabama Qualtrics system and was determined by the University of Alabama IRB to be “non-human subject research”. Data from the live webinars were collected by Zoom for each seminar and reported as unique viewers to eliminate counting individuals double who potentially logged on twice. Data on video views were collected from the University of Kentucky Zoom Webinar cloud server and were reported as total accessed views. Survey data from Qualtrics was analyzed with the IBM® SPSS® software platform (IBM, Armonk, NY, USA), using one-way ANOVAs and t-tests to identify significant trends [5]. Data with a p < 0.05 are considered significant.\",\n \"The BBV2020 seminar series was created by Drs. Bjoern Bauer (University of Kentucky, Lexington, KY, USA), Anika Hartz (University of Kentucky, Lexington, KY, USA), and Brandon Kim (University of Alabama, Tuscaloosa, Alabama, USA). Seminar moderators were postdoctoral researchers and scientists from the USA and Europe: Drs. Natalie Hudson (Trinity College Dublin, Dublin, Ireland), Geetika Nehra (University of Kentucky, Lexington, KY, USA), Michelle Pizzo (Denali Therapeutics Inc., San Francisco, CA, USA), and Steffen Storck (University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany).\",\n \"BBV2020 was run on the Zoom Webinar 500 platform (Zoom Video Communications, Inc.; San Jose, CA, USA) that was funded by Dr. Bjoern Bauer using University of Kentucky funds. This particular Zoom Webinar license allowed for up to 500 live participants. The Zoom Webinar platform supports an in-session chat function, a Q&A forum, a raise hand function for live questions, and provides a panelist section with the option to share audio, video and presentation slides, a recording function and post-webinar data collection. The hosts and moderators were in control of audio and video; participants were not able to record or unmute during a session. Participants had no control function other than a “raise hand” option and the Q&A and chat function allowing to send questions or messages to the moderator. Technical assistance was provided by Todd Sizemore, College of Pharmacy, University of Kentucky.\",\n \"The BBV2020 seminar series ran from May 20 to September 2, 2020. Live seminars were held once per week from 12 to 1 PM Eastern Daylight Time (EDT) to accommodate researchers across a wide range of time zones: Central European Time (6:00 PM) to Pacific Daylight Time (9:00 AM). To accommodate colleagues with time constraints or those within incompatible time zones such as Asia or Oceania, presentations were recorded (depending on speaker permissions) and the seminar recording was made available for up to 48 h after the live seminar. The link for the recorded videos was distributed via the BBV2020 listserv that was set up through the University of Kentucky listserv.\",\n \"Efforts were made to invite speakers from a variety of countries and genders. Invited speakers included organizers of postponed in-person conferences, speakers from academia and industry from different career stages. A total of 16 speakers were invited: May 20, 2020: Elga De Vries, Amsterdam UMC, Amsterdam, The Netherlands; May 27, 2020: Richard Daneman, University of California San Diego, San Diego, CA, USA; June 3, 2020: Robyn Klein, Washington University, St. Louis, MO, USA; June 10, 2020: Ayal Ben-Zvi, The Hebrew University of Jerusalem, Israel; June 17, 2020: Britta Engelhardt, University of Bern, Bern, Switzerland; June 24, 2020: Margareta Hammarlund-Udenaes, Uppsala University, Uppsala, Sweden; July 1, 2020: Daniela Virgintino, University of Bari, Bari, Italy; July 8, 2020: Eric Shusta, University of Wisconsin, Madison, WI, USA; July 15, 2020: Matthew Campbell, Trinity College Dublin, Dublin, Ireland; July 22, 2020: Krzysztof Kucharz, University of Copenhagen, Copenhagen, Denmark; July 29, 2020: William Elmquist, University of Minnesota, Minneapolis, MN, USA; August 5, 2020: Edward Neuwelt, Oregon Health & Sciences University, OR, USA; August 12, 2020: Robert Thorne, Denali Therapeutics, San Francisco, CA, USA; August 19, 2020: Teresa Sanchez, Weill Cornell Medical College, New York City, NJ, USA; August 26, 2020: Michael Taylor, University of Wisconsin, Madison, MI, USA; September 2, 2020: Joan Abbott, King’s College London, London, UK.\\nSpeakers were offered a training session by the organizers to cover key aspects of a Zoom-based webinar and to test video, audio, and slide presentation prior to the live seminar. Ten out of 16 speakers took the training session. Speakers were informed by the organizers that any data presented should be considered public as security on the Zoom platform could not be ensured. Invited speakers had the opportunity to give permission to have their seminar recorded or to opt out of the recording. If a speaker gave permission to record, the entire seminar was recorded, and recordings were made available to all registered participants in a non-downloadable format through the University of Kentucky Zoom cloud for up to 48 h after the live session. Out of 16 speakers, 15 gave permission to record the live seminar.\",\n \"To reach scientists in the brain barriers community, the seminar was advertised by email to participants of previous in-person meetings, and on social media platforms such as Facebook and LinkedIn on personal profiles. In addition, the introductory flyer was kindly distributed via email by the International Brain Barriers Society (IBBS) using their email distribution list. Interested scientists were asked to register by sending their name, affiliation, and career stage to a dedicated email address created for the organization and execution of BBV2020 (bbv2020seminar@gmail.com). Once registered, participants were added to a BBV2020 listserv provided by the University of Kentucky and received weekly updates. These weekly updates included a flyer with the information regarding the upcoming speakers and the specific zoom link for the seminar. Participants also received a follow up email after each seminar with a zoom link to the video recordings. With the permission of the speaker of the week, registered viewers had access to recordings for up to 48 h after the live seminar.\",\n \"Each seminar was started with opening remarks by one of the organizers (Drs. Bauer, Hartz, or Kim) followed by an introduction of the speaker by the moderator. Moderators (Drs. Hudson, Nehra, Pizzo, Storck) rotated each week, only one moderator was assigned to each week. After the seminar presentation, the moderator facilitated the question-and-answer session. Participants were encouraged to ask a live question by raising their digital hand. When a participant raised the digital hand, the moderator would unmute the participant at which point the participant was able to ask the speaker a question directly. In addition, participants had the opportunity to ask questions via the Zoom Q&A or the chat function. Written questions were then subsequently read by the moderator. The moderator concluded each seminar by thanking the speaker and the audience for attending and by announcing the speaker for the following week.\",\n \"World maps highlighting countries participating in the various meetings and representation were generated using MapChart (www.mapchart.net; license: https://creativecommons.org/licenses/by-sa/4.0/).\",\n \"Toward the end of the seminar series, a survey was generated using Qualtrics (Qualtrics) and run through the University of Alabama Qualtrics system. A link to the survey was sent to all registered seminar participants by email. Questions covered general audience participation, career stage, as well as affective factors. Survey results are only reported in an aggregate.\",\n \"Survey data was collected through the University of Alabama Qualtrics system and was determined by the University of Alabama IRB to be “non-human subject research”. Data from the live webinars were collected by Zoom for each seminar and reported as unique viewers to eliminate counting individuals double who potentially logged on twice. Data on video views were collected from the University of Kentucky Zoom Webinar cloud server and were reported as total accessed views. Survey data from Qualtrics was analyzed with the IBM® SPSS® software platform (IBM, Armonk, NY, USA), using one-way ANOVAs and t-tests to identify significant trends [5]. Data with a p < 0.05 are considered significant.\",\n \"We organized the virtual BBV2020 seminar series for the brain barriers research field to fill the void that the COVID-19 pandemic left due to canceled and postponed meetings in 2020. The seminar series was held between May–September 2020 every Wednesday from 12:00 to 1:00 PM Eastern Daylight Time (EDT). Seminars were hosted on a Zoom webinar platform and featured a 45-min presentation from an invited speaker followed by questions and answers from the audience. In the following sections, we summarize data collected by the Zoom software, from the listserv used, and the participant survey. We compare and contrast the data from the virtual BBV2020 seminar series with data from in-person meetings from past years.\\n BBV2020 attendance On May 14, 2020, we sent out the first advertisement for the BBV2020 seminar series via email using a listserv with email addresses of past conference attendees and via postings using LinkedIn, Facebook, and Twitter. By the start of the first seminar on May 20, 2020, more than 720 scientists had registered. By the end of the 16-week long BBV2020 seminar series, a total of 1302 registrants signed up on our listserv to receive the seminar link, speaker updates, and video recording links (Fig. 1A). We obtained data from two past in-person meetings from the respective conference chairs: (1) brain barriers meeting in 2018 (BBB 2018) and the (2) Cerebral Vascular Biology meeting in 2019 (CVB 2019).\\n\\nFig. 1\\nAttendance Trends for Brain Barrier Research Conferences. A With 1,302 individuals signing up and added to the listserv, BBV2020 attracted more registrants than BBB 2018 and CVB 2019. B Number of unique viewers weekly at the live Zoom Webinar sessions of BBV2020. C Number of views (clicks) of the video recordings when available weekly for BBV2020. Black line indicates best linear fit. Gray line indicates the average (mean) for all 16 weeks\\n\\nAttendance Trends for Brain Barrier Research Conferences. A With 1,302 individuals signing up and added to the listserv, BBV2020 attracted more registrants than BBB 2018 and CVB 2019. B Number of unique viewers weekly at the live Zoom Webinar sessions of BBV2020. C Number of views (clicks) of the video recordings when available weekly for BBV2020. Black line indicates best linear fit. Gray line indicates the average (mean) for all 16 weeks\\nStrikingly, the total number of registered applicants for the BBV2020 was more than 4-fold higher compared to the total number of registered participants for the 2018 and the CVB 2019 meetings (Fig. 1A). The large number of registrants is likely due to free registration, no associated travel expenses, a world-wide reach, and that virtual meetings come with no obligations for the attendees. In contrast, the number of attendees of the in-person meetings (BBB 2018 and CVB2019) was limited and determined by the size of the conference site.\\nThe first live BBV2020 seminar was attended by 419 participants. As the seminar series progressed, the participant number during live seminars gradually decreased, the average participant number was 220, the minimum was 108 (Fig. 1B). A similar trend was observed in the numbers of total recording views: the first seminar recording had a maximum of 944 views; in average, videos had 422 views (Fig. 1C). The trendline in Fig. 1C shows a drop in recording views over the course of the seminar series. These trends held regardless of academic rank as graduate students, postdoctoral fellows, and professors all showed a similar downward trend in live attendance (Additional file 1: Fig. S1A–C). Note that we were unable to capture recording data for week 2 due to a recording error and for week 13 due to the request by the speaker to not record.\\nTogether, the high number of registrants suggests that the virtual BBV2020 seminar series sparked interest. Attendance of the live seminars and the numbers of views for the recorded videos decreased over time, which may be an indicator of “virtual meeting fatigue” or attendees being on their summer vacation.\\nOn May 14, 2020, we sent out the first advertisement for the BBV2020 seminar series via email using a listserv with email addresses of past conference attendees and via postings using LinkedIn, Facebook, and Twitter. By the start of the first seminar on May 20, 2020, more than 720 scientists had registered. By the end of the 16-week long BBV2020 seminar series, a total of 1302 registrants signed up on our listserv to receive the seminar link, speaker updates, and video recording links (Fig. 1A). We obtained data from two past in-person meetings from the respective conference chairs: (1) brain barriers meeting in 2018 (BBB 2018) and the (2) Cerebral Vascular Biology meeting in 2019 (CVB 2019).\\n\\nFig. 1\\nAttendance Trends for Brain Barrier Research Conferences. A With 1,302 individuals signing up and added to the listserv, BBV2020 attracted more registrants than BBB 2018 and CVB 2019. B Number of unique viewers weekly at the live Zoom Webinar sessions of BBV2020. C Number of views (clicks) of the video recordings when available weekly for BBV2020. Black line indicates best linear fit. Gray line indicates the average (mean) for all 16 weeks\\n\\nAttendance Trends for Brain Barrier Research Conferences. A With 1,302 individuals signing up and added to the listserv, BBV2020 attracted more registrants than BBB 2018 and CVB 2019. B Number of unique viewers weekly at the live Zoom Webinar sessions of BBV2020. C Number of views (clicks) of the video recordings when available weekly for BBV2020. Black line indicates best linear fit. Gray line indicates the average (mean) for all 16 weeks\\nStrikingly, the total number of registered applicants for the BBV2020 was more than 4-fold higher compared to the total number of registered participants for the 2018 and the CVB 2019 meetings (Fig. 1A). The large number of registrants is likely due to free registration, no associated travel expenses, a world-wide reach, and that virtual meetings come with no obligations for the attendees. In contrast, the number of attendees of the in-person meetings (BBB 2018 and CVB2019) was limited and determined by the size of the conference site.\\nThe first live BBV2020 seminar was attended by 419 participants. As the seminar series progressed, the participant number during live seminars gradually decreased, the average participant number was 220, the minimum was 108 (Fig. 1B). A similar trend was observed in the numbers of total recording views: the first seminar recording had a maximum of 944 views; in average, videos had 422 views (Fig. 1C). The trendline in Fig. 1C shows a drop in recording views over the course of the seminar series. These trends held regardless of academic rank as graduate students, postdoctoral fellows, and professors all showed a similar downward trend in live attendance (Additional file 1: Fig. S1A–C). Note that we were unable to capture recording data for week 2 due to a recording error and for week 13 due to the request by the speaker to not record.\\nTogether, the high number of registrants suggests that the virtual BBV2020 seminar series sparked interest. Attendance of the live seminars and the numbers of views for the recorded videos decreased over time, which may be an indicator of “virtual meeting fatigue” or attendees being on their summer vacation.\\n BBV2020 survey analysis By the end of the seminar series in September, every BBV2020 registrant received an invitation to participate in an online survey created by the organizers on August 23, 2020. The survey was designed for registrants to provide feedback and consisted of 10 questions: (1) demographic question to capture career level and career type; (2) single select multiple choice question to capture attendance of past in-person brain barrier meetings, (3) single select multiple choice question on how many recorded seminars were viewed, (4) Likert scale question regarding the enrichment BBV2020 provided for the community during the COVID-19 pandemic, (5) dichotomous question asking if the registrant would still participate in BBV2020 seminars if they were continued, (6) single select multiple choice question on attendance frequency, (7) single select multiple choice question on how many times the registrant has thus far attended a brain barriers meeting previously in person, (8) matrix question to capture affective factors, (9) matrix question to capture learning and teaching, and (10) matrix question to capture combined validity and practicality of the BBV2020 series. In addition, registrants responding to the survey had the opportunity to include general comments to the organizers in a separate text box.\\nOverall, 23% of registrants (total of 299 registrants) responded to the survey. Over 89% of survey respondents agreed strongly that “Attending BBV2020 was a positive experience” and 63% of responders felt that “BBV2020 is appropriate for my research”. Generally, the respondents tended to agree with the following statement: “BBV2020 has enriched the connection with the Brain Barriers community in the gap that COVID-19 has created” (n = 298 respondents to this question of a total of 299 registrants, M = 1.44, SD = 0.660, 1 = Strongly Agree, 2 = Agree, 3 = Neutral, 4 = Disagree, 5 = Strongly Disagree; Fig. 2A). However, when posed with the statements “In the future, I would prefer to attend a virtual conference over an in-person conference” or “The virtual experience is not as effective compared to the in-person conferences”, responses were neutral (M = 3.24; Fig. 2B) indicating a positive view towards a virtual conference. A difference emerged when we compared BBV2020 participants based on their career level with their preference for in-person versus virtual meeting formats: using one-way ANOVA, we found a significant difference between groups (F(9286) = 2.607, p = 0.007). Using Tukey’s HSD post hoc test indicated that Master’s students were more likely to disagree with the following statement when compared to Faculty/Professors: “I would feel more comfortable attending Brain Barrier conferences/seminars in person, but was glad to attend virtually due to COVID19.” A strong trend emerged when Master’s and Ph.D. students were grouped together and compared to Post-Docs and Faculty/Professors in an independent-samples t test. Results show graduate students in general were more favorable to a virtual conference compared to colleagues more established in their careers (t(225) = -1.972, p = 0.061). The lack of significance is in part due to the low sample size (n = 74) for graduate students.\\n\\nFig. 2\\nPost-seminar survey results. A Volunteer respondent result when posed with the prompt “BBV2020 has enriched connection with the Brain Barriers community in the gap that COVID-19 has created”. B Volunteer respondent results when posted with the prompts “In the future, I would prefer to attend a virtual conference over an in-person conference” (Blue) and “The virtual experience is not as effective compared to the in-person conferences” (Red)\\n\\nPost-seminar survey results. A Volunteer respondent result when posed with the prompt “BBV2020 has enriched connection with the Brain Barriers community in the gap that COVID-19 has created”. B Volunteer respondent results when posted with the prompts “In the future, I would prefer to attend a virtual conference over an in-person conference” (Blue) and “The virtual experience is not as effective compared to the in-person conferences” (Red)\\nOverall, descriptive data revealed many participants felt BBV2020 was a positive experience. In fact, 99.3% of survey respondents noted they somewhat or strongly agree that their experience was positive, with only 4.7% saying they faced technical difficulties. Additionally, most respondents agreed that virtual conferences have an important role in research (93.2%) and are more accessible than in-person conferences (90.2%). Finally, more than half of respondents (56.9%) noted virtual conferences can do things in-person conferences cannot.\\nNevertheless, there continued to be a desire among many BBV2020 participants to return to conferencing in person, with 13.5% saying they found it hard to focus in the virtual format. In fact, roughly one-third (33.9%) felt the virtual conference was not as effective as an in-person conference, and only one-quarter (23.3%) said they would prefer virtual conferences to in-person conferences in the future, while 35.8% were neutral.\\nTogether, our findings suggest that while BBV2020 filled a need created by a global pandemic, the enthusiasm for returning to only in-person meetings remained relatively neutral suggesting that virtual platforms may play a critical role in dissemination of research and science in the future. Moreover, the data from this study show a trend between graduate students versus professors in their response with regard to in-person meetings indicating that career stage may influence the perspective of the usefulness of in-person versus virtual platforms.\\nBy the end of the seminar series in September, every BBV2020 registrant received an invitation to participate in an online survey created by the organizers on August 23, 2020. The survey was designed for registrants to provide feedback and consisted of 10 questions: (1) demographic question to capture career level and career type; (2) single select multiple choice question to capture attendance of past in-person brain barrier meetings, (3) single select multiple choice question on how many recorded seminars were viewed, (4) Likert scale question regarding the enrichment BBV2020 provided for the community during the COVID-19 pandemic, (5) dichotomous question asking if the registrant would still participate in BBV2020 seminars if they were continued, (6) single select multiple choice question on attendance frequency, (7) single select multiple choice question on how many times the registrant has thus far attended a brain barriers meeting previously in person, (8) matrix question to capture affective factors, (9) matrix question to capture learning and teaching, and (10) matrix question to capture combined validity and practicality of the BBV2020 series. In addition, registrants responding to the survey had the opportunity to include general comments to the organizers in a separate text box.\\nOverall, 23% of registrants (total of 299 registrants) responded to the survey. Over 89% of survey respondents agreed strongly that “Attending BBV2020 was a positive experience” and 63% of responders felt that “BBV2020 is appropriate for my research”. Generally, the respondents tended to agree with the following statement: “BBV2020 has enriched the connection with the Brain Barriers community in the gap that COVID-19 has created” (n = 298 respondents to this question of a total of 299 registrants, M = 1.44, SD = 0.660, 1 = Strongly Agree, 2 = Agree, 3 = Neutral, 4 = Disagree, 5 = Strongly Disagree; Fig. 2A). However, when posed with the statements “In the future, I would prefer to attend a virtual conference over an in-person conference” or “The virtual experience is not as effective compared to the in-person conferences”, responses were neutral (M = 3.24; Fig. 2B) indicating a positive view towards a virtual conference. A difference emerged when we compared BBV2020 participants based on their career level with their preference for in-person versus virtual meeting formats: using one-way ANOVA, we found a significant difference between groups (F(9286) = 2.607, p = 0.007). Using Tukey’s HSD post hoc test indicated that Master’s students were more likely to disagree with the following statement when compared to Faculty/Professors: “I would feel more comfortable attending Brain Barrier conferences/seminars in person, but was glad to attend virtually due to COVID19.” A strong trend emerged when Master’s and Ph.D. students were grouped together and compared to Post-Docs and Faculty/Professors in an independent-samples t test. Results show graduate students in general were more favorable to a virtual conference compared to colleagues more established in their careers (t(225) = -1.972, p = 0.061). The lack of significance is in part due to the low sample size (n = 74) for graduate students.\\n\\nFig. 2\\nPost-seminar survey results. A Volunteer respondent result when posed with the prompt “BBV2020 has enriched connection with the Brain Barriers community in the gap that COVID-19 has created”. B Volunteer respondent results when posted with the prompts “In the future, I would prefer to attend a virtual conference over an in-person conference” (Blue) and “The virtual experience is not as effective compared to the in-person conferences” (Red)\\n\\nPost-seminar survey results. A Volunteer respondent result when posed with the prompt “BBV2020 has enriched connection with the Brain Barriers community in the gap that COVID-19 has created”. B Volunteer respondent results when posted with the prompts “In the future, I would prefer to attend a virtual conference over an in-person conference” (Blue) and “The virtual experience is not as effective compared to the in-person conferences” (Red)\\nOverall, descriptive data revealed many participants felt BBV2020 was a positive experience. In fact, 99.3% of survey respondents noted they somewhat or strongly agree that their experience was positive, with only 4.7% saying they faced technical difficulties. Additionally, most respondents agreed that virtual conferences have an important role in research (93.2%) and are more accessible than in-person conferences (90.2%). Finally, more than half of respondents (56.9%) noted virtual conferences can do things in-person conferences cannot.\\nNevertheless, there continued to be a desire among many BBV2020 participants to return to conferencing in person, with 13.5% saying they found it hard to focus in the virtual format. In fact, roughly one-third (33.9%) felt the virtual conference was not as effective as an in-person conference, and only one-quarter (23.3%) said they would prefer virtual conferences to in-person conferences in the future, while 35.8% were neutral.\\nTogether, our findings suggest that while BBV2020 filled a need created by a global pandemic, the enthusiasm for returning to only in-person meetings remained relatively neutral suggesting that virtual platforms may play a critical role in dissemination of research and science in the future. Moreover, the data from this study show a trend between graduate students versus professors in their response with regard to in-person meetings indicating that career stage may influence the perspective of the usefulness of in-person versus virtual platforms.\\n Global reach of BBV2020 During the seminar registration process we collected attendants’ affiliations and based on this information we determined the global reach of the BBV2020. Registrants of the BBV2020 seminar series were from 43 countries representing six of the seven continents (Fig. 3A, B). Compared to the in-person BBB 2018 and the CVB2019, more countries were represented at the BBV2020 (Fig. 3A).\\n\\nFig. 3\\nGlobal reach of BBV2020. A BBV2020 had more countries represented compared to BBB 2018 and CVB 2019 alone. B World map showing countries represented by BBV2020. Countries where registrants participated in BBV2020: Red and Pink. Countries not represented at BBV2020: Light Gray and Dark Gray. Countries where registrants attended BBV2020 and at least one of the in-person meetings between Meeting 2018 or CVB 2019: Red. Countries where registrants attended BBV2020 and did not attend either BBB 2018 or CVB 2019: Pink. Countries that had no participation in any of the brain barriers meetings examined in this study: Light gray. Countries that had participation in either BBB 2018 or CVB 2019 that did not participate in BBV2020: Dark Gray\\n\\nGlobal reach of BBV2020. A BBV2020 had more countries represented compared to BBB 2018 and CVB 2019 alone. B World map showing countries represented by BBV2020. Countries where registrants participated in BBV2020: Red and Pink. Countries not represented at BBV2020: Light Gray and Dark Gray. Countries where registrants attended BBV2020 and at least one of the in-person meetings between Meeting 2018 or CVB 2019: Red. Countries where registrants attended BBV2020 and did not attend either BBB 2018 or CVB 2019: Pink. Countries that had no participation in any of the brain barriers meetings examined in this study: Light gray. Countries that had participation in either BBB 2018 or CVB 2019 that did not participate in BBV2020: Dark Gray\\nWe also compared the number of participants from each country who joined BBV2020 vs. BBB 2018 and CVB 2019. We found that the number of participants per country worldwide was also higher compared to both in person meetings (Fig. 4A–F). These data show that the virtual nature of BBV2020 enabled researchers world-wide to participate and suggest that the outreach of a virtual event can extend beyond the field.\\n\\nFig. 4\\nGlobal view of participation over BBV2020, BBB 2018, and CVB 2019. Representation of participants for (A) BBV2020, (B) BBB 2018, and (C) CVB 2019. Ordered from top to bottom in greatest number of registrants. Number of registrants scaled by color based on number of registrants per country for (D) BBV 2020, (E) BBB 2018, and (F) CVB 2019. Light Pink: 1–10 registrants; Dark Pink: 11–50 registrants; Red: 51–100 registrants and Dark Red: > 100 registrants\\n\\nGlobal view of participation over BBV2020, BBB 2018, and CVB 2019. Representation of participants for (A) BBV2020, (B) BBB 2018, and (C) CVB 2019. Ordered from top to bottom in greatest number of registrants. Number of registrants scaled by color based on number of registrants per country for (D) BBV 2020, (E) BBB 2018, and (F) CVB 2019. Light Pink: 1–10 registrants; Dark Pink: 11–50 registrants; Red: 51–100 registrants and Dark Red: > 100 registrants\\nDuring the seminar registration process we collected attendants’ affiliations and based on this information we determined the global reach of the BBV2020. Registrants of the BBV2020 seminar series were from 43 countries representing six of the seven continents (Fig. 3A, B). Compared to the in-person BBB 2018 and the CVB2019, more countries were represented at the BBV2020 (Fig. 3A).\\n\\nFig. 3\\nGlobal reach of BBV2020. A BBV2020 had more countries represented compared to BBB 2018 and CVB 2019 alone. B World map showing countries represented by BBV2020. Countries where registrants participated in BBV2020: Red and Pink. Countries not represented at BBV2020: Light Gray and Dark Gray. Countries where registrants attended BBV2020 and at least one of the in-person meetings between Meeting 2018 or CVB 2019: Red. Countries where registrants attended BBV2020 and did not attend either BBB 2018 or CVB 2019: Pink. Countries that had no participation in any of the brain barriers meetings examined in this study: Light gray. Countries that had participation in either BBB 2018 or CVB 2019 that did not participate in BBV2020: Dark Gray\\n\\nGlobal reach of BBV2020. A BBV2020 had more countries represented compared to BBB 2018 and CVB 2019 alone. B World map showing countries represented by BBV2020. Countries where registrants participated in BBV2020: Red and Pink. Countries not represented at BBV2020: Light Gray and Dark Gray. Countries where registrants attended BBV2020 and at least one of the in-person meetings between Meeting 2018 or CVB 2019: Red. Countries where registrants attended BBV2020 and did not attend either BBB 2018 or CVB 2019: Pink. Countries that had no participation in any of the brain barriers meetings examined in this study: Light gray. Countries that had participation in either BBB 2018 or CVB 2019 that did not participate in BBV2020: Dark Gray\\nWe also compared the number of participants from each country who joined BBV2020 vs. BBB 2018 and CVB 2019. We found that the number of participants per country worldwide was also higher compared to both in person meetings (Fig. 4A–F). These data show that the virtual nature of BBV2020 enabled researchers world-wide to participate and suggest that the outreach of a virtual event can extend beyond the field.\\n\\nFig. 4\\nGlobal view of participation over BBV2020, BBB 2018, and CVB 2019. Representation of participants for (A) BBV2020, (B) BBB 2018, and (C) CVB 2019. Ordered from top to bottom in greatest number of registrants. Number of registrants scaled by color based on number of registrants per country for (D) BBV 2020, (E) BBB 2018, and (F) CVB 2019. Light Pink: 1–10 registrants; Dark Pink: 11–50 registrants; Red: 51–100 registrants and Dark Red: > 100 registrants\\n\\nGlobal view of participation over BBV2020, BBB 2018, and CVB 2019. Representation of participants for (A) BBV2020, (B) BBB 2018, and (C) CVB 2019. Ordered from top to bottom in greatest number of registrants. Number of registrants scaled by color based on number of registrants per country for (D) BBV 2020, (E) BBB 2018, and (F) CVB 2019. Light Pink: 1–10 registrants; Dark Pink: 11–50 registrants; Red: 51–100 registrants and Dark Red: > 100 registrants\\n Career stages represented at BBV2020 We examined the self-reported career stage of BBV2020 participants to that of BBB 2018 and CVB 2019 participants. Based on the survey, more than 46% of all participants attended a brain barriers conference/seminar for the first time. We also found that in-person meetings are attended by 42.4–48.9% academicians at the professor rank, graduate students make up 22.9–25.8% and postdoctoral fellows are 6.6–17.7% of the participants (Fig. 5). In contrast, at the BBV2020 19.6% of academicians were at the professor rank, graduate students made up 30.5% and postdoctoral fellows 16.1% of attendees. We also found that BBV2020 had a larger participation from industry (18.5% vs. 5.6–6.6%), and undergraduate students (3.2% vs. 1%) compared to both in person meetings. Together, our data suggest that a virtual platform seminar like BBV2020 reaches a broader range of scientific ranks and careers.\\n\\nFig. 5\\nProportions of various career stages participating in BBV2020, BBB 2018, and CVB 2019. Proportions of various career stages participating in (A) BBV2020, (B) BBB 2018, and (C) CVB 2019. Colors correspond to career stage: Red (professor any rank), Green (graduate student any rank), Dark Blue (research faculty or researcher), Yellow (industry scientist), Orange (postdoctoral fellow), Purple (government/regulatory), Light Blue (clinician), Green (undergraduate student)\\n\\nProportions of various career stages participating in BBV2020, BBB 2018, and CVB 2019. Proportions of various career stages participating in (A) BBV2020, (B) BBB 2018, and (C) CVB 2019. Colors correspond to career stage: Red (professor any rank), Green (graduate student any rank), Dark Blue (research faculty or researcher), Yellow (industry scientist), Orange (postdoctoral fellow), Purple (government/regulatory), Light Blue (clinician), Green (undergraduate student)\\nWe examined the self-reported career stage of BBV2020 participants to that of BBB 2018 and CVB 2019 participants. Based on the survey, more than 46% of all participants attended a brain barriers conference/seminar for the first time. We also found that in-person meetings are attended by 42.4–48.9% academicians at the professor rank, graduate students make up 22.9–25.8% and postdoctoral fellows are 6.6–17.7% of the participants (Fig. 5). In contrast, at the BBV2020 19.6% of academicians were at the professor rank, graduate students made up 30.5% and postdoctoral fellows 16.1% of attendees. We also found that BBV2020 had a larger participation from industry (18.5% vs. 5.6–6.6%), and undergraduate students (3.2% vs. 1%) compared to both in person meetings. Together, our data suggest that a virtual platform seminar like BBV2020 reaches a broader range of scientific ranks and careers.\\n\\nFig. 5\\nProportions of various career stages participating in BBV2020, BBB 2018, and CVB 2019. Proportions of various career stages participating in (A) BBV2020, (B) BBB 2018, and (C) CVB 2019. Colors correspond to career stage: Red (professor any rank), Green (graduate student any rank), Dark Blue (research faculty or researcher), Yellow (industry scientist), Orange (postdoctoral fellow), Purple (government/regulatory), Light Blue (clinician), Green (undergraduate student)\\n\\nProportions of various career stages participating in BBV2020, BBB 2018, and CVB 2019. Proportions of various career stages participating in (A) BBV2020, (B) BBB 2018, and (C) CVB 2019. Colors correspond to career stage: Red (professor any rank), Green (graduate student any rank), Dark Blue (research faculty or researcher), Yellow (industry scientist), Orange (postdoctoral fellow), Purple (government/regulatory), Light Blue (clinician), Green (undergraduate student)\",\n \"On May 14, 2020, we sent out the first advertisement for the BBV2020 seminar series via email using a listserv with email addresses of past conference attendees and via postings using LinkedIn, Facebook, and Twitter. By the start of the first seminar on May 20, 2020, more than 720 scientists had registered. By the end of the 16-week long BBV2020 seminar series, a total of 1302 registrants signed up on our listserv to receive the seminar link, speaker updates, and video recording links (Fig. 1A). We obtained data from two past in-person meetings from the respective conference chairs: (1) brain barriers meeting in 2018 (BBB 2018) and the (2) Cerebral Vascular Biology meeting in 2019 (CVB 2019).\\n\\nFig. 1\\nAttendance Trends for Brain Barrier Research Conferences. A With 1,302 individuals signing up and added to the listserv, BBV2020 attracted more registrants than BBB 2018 and CVB 2019. B Number of unique viewers weekly at the live Zoom Webinar sessions of BBV2020. C Number of views (clicks) of the video recordings when available weekly for BBV2020. Black line indicates best linear fit. Gray line indicates the average (mean) for all 16 weeks\\n\\nAttendance Trends for Brain Barrier Research Conferences. A With 1,302 individuals signing up and added to the listserv, BBV2020 attracted more registrants than BBB 2018 and CVB 2019. B Number of unique viewers weekly at the live Zoom Webinar sessions of BBV2020. C Number of views (clicks) of the video recordings when available weekly for BBV2020. Black line indicates best linear fit. Gray line indicates the average (mean) for all 16 weeks\\nStrikingly, the total number of registered applicants for the BBV2020 was more than 4-fold higher compared to the total number of registered participants for the 2018 and the CVB 2019 meetings (Fig. 1A). The large number of registrants is likely due to free registration, no associated travel expenses, a world-wide reach, and that virtual meetings come with no obligations for the attendees. In contrast, the number of attendees of the in-person meetings (BBB 2018 and CVB2019) was limited and determined by the size of the conference site.\\nThe first live BBV2020 seminar was attended by 419 participants. As the seminar series progressed, the participant number during live seminars gradually decreased, the average participant number was 220, the minimum was 108 (Fig. 1B). A similar trend was observed in the numbers of total recording views: the first seminar recording had a maximum of 944 views; in average, videos had 422 views (Fig. 1C). The trendline in Fig. 1C shows a drop in recording views over the course of the seminar series. These trends held regardless of academic rank as graduate students, postdoctoral fellows, and professors all showed a similar downward trend in live attendance (Additional file 1: Fig. S1A–C). Note that we were unable to capture recording data for week 2 due to a recording error and for week 13 due to the request by the speaker to not record.\\nTogether, the high number of registrants suggests that the virtual BBV2020 seminar series sparked interest. Attendance of the live seminars and the numbers of views for the recorded videos decreased over time, which may be an indicator of “virtual meeting fatigue” or attendees being on their summer vacation.\",\n \"By the end of the seminar series in September, every BBV2020 registrant received an invitation to participate in an online survey created by the organizers on August 23, 2020. The survey was designed for registrants to provide feedback and consisted of 10 questions: (1) demographic question to capture career level and career type; (2) single select multiple choice question to capture attendance of past in-person brain barrier meetings, (3) single select multiple choice question on how many recorded seminars were viewed, (4) Likert scale question regarding the enrichment BBV2020 provided for the community during the COVID-19 pandemic, (5) dichotomous question asking if the registrant would still participate in BBV2020 seminars if they were continued, (6) single select multiple choice question on attendance frequency, (7) single select multiple choice question on how many times the registrant has thus far attended a brain barriers meeting previously in person, (8) matrix question to capture affective factors, (9) matrix question to capture learning and teaching, and (10) matrix question to capture combined validity and practicality of the BBV2020 series. In addition, registrants responding to the survey had the opportunity to include general comments to the organizers in a separate text box.\\nOverall, 23% of registrants (total of 299 registrants) responded to the survey. Over 89% of survey respondents agreed strongly that “Attending BBV2020 was a positive experience” and 63% of responders felt that “BBV2020 is appropriate for my research”. Generally, the respondents tended to agree with the following statement: “BBV2020 has enriched the connection with the Brain Barriers community in the gap that COVID-19 has created” (n = 298 respondents to this question of a total of 299 registrants, M = 1.44, SD = 0.660, 1 = Strongly Agree, 2 = Agree, 3 = Neutral, 4 = Disagree, 5 = Strongly Disagree; Fig. 2A). However, when posed with the statements “In the future, I would prefer to attend a virtual conference over an in-person conference” or “The virtual experience is not as effective compared to the in-person conferences”, responses were neutral (M = 3.24; Fig. 2B) indicating a positive view towards a virtual conference. A difference emerged when we compared BBV2020 participants based on their career level with their preference for in-person versus virtual meeting formats: using one-way ANOVA, we found a significant difference between groups (F(9286) = 2.607, p = 0.007). Using Tukey’s HSD post hoc test indicated that Master’s students were more likely to disagree with the following statement when compared to Faculty/Professors: “I would feel more comfortable attending Brain Barrier conferences/seminars in person, but was glad to attend virtually due to COVID19.” A strong trend emerged when Master’s and Ph.D. students were grouped together and compared to Post-Docs and Faculty/Professors in an independent-samples t test. Results show graduate students in general were more favorable to a virtual conference compared to colleagues more established in their careers (t(225) = -1.972, p = 0.061). The lack of significance is in part due to the low sample size (n = 74) for graduate students.\\n\\nFig. 2\\nPost-seminar survey results. A Volunteer respondent result when posed with the prompt “BBV2020 has enriched connection with the Brain Barriers community in the gap that COVID-19 has created”. B Volunteer respondent results when posted with the prompts “In the future, I would prefer to attend a virtual conference over an in-person conference” (Blue) and “The virtual experience is not as effective compared to the in-person conferences” (Red)\\n\\nPost-seminar survey results. A Volunteer respondent result when posed with the prompt “BBV2020 has enriched connection with the Brain Barriers community in the gap that COVID-19 has created”. B Volunteer respondent results when posted with the prompts “In the future, I would prefer to attend a virtual conference over an in-person conference” (Blue) and “The virtual experience is not as effective compared to the in-person conferences” (Red)\\nOverall, descriptive data revealed many participants felt BBV2020 was a positive experience. In fact, 99.3% of survey respondents noted they somewhat or strongly agree that their experience was positive, with only 4.7% saying they faced technical difficulties. Additionally, most respondents agreed that virtual conferences have an important role in research (93.2%) and are more accessible than in-person conferences (90.2%). Finally, more than half of respondents (56.9%) noted virtual conferences can do things in-person conferences cannot.\\nNevertheless, there continued to be a desire among many BBV2020 participants to return to conferencing in person, with 13.5% saying they found it hard to focus in the virtual format. In fact, roughly one-third (33.9%) felt the virtual conference was not as effective as an in-person conference, and only one-quarter (23.3%) said they would prefer virtual conferences to in-person conferences in the future, while 35.8% were neutral.\\nTogether, our findings suggest that while BBV2020 filled a need created by a global pandemic, the enthusiasm for returning to only in-person meetings remained relatively neutral suggesting that virtual platforms may play a critical role in dissemination of research and science in the future. Moreover, the data from this study show a trend between graduate students versus professors in their response with regard to in-person meetings indicating that career stage may influence the perspective of the usefulness of in-person versus virtual platforms.\",\n \"During the seminar registration process we collected attendants’ affiliations and based on this information we determined the global reach of the BBV2020. Registrants of the BBV2020 seminar series were from 43 countries representing six of the seven continents (Fig. 3A, B). Compared to the in-person BBB 2018 and the CVB2019, more countries were represented at the BBV2020 (Fig. 3A).\\n\\nFig. 3\\nGlobal reach of BBV2020. A BBV2020 had more countries represented compared to BBB 2018 and CVB 2019 alone. B World map showing countries represented by BBV2020. Countries where registrants participated in BBV2020: Red and Pink. Countries not represented at BBV2020: Light Gray and Dark Gray. Countries where registrants attended BBV2020 and at least one of the in-person meetings between Meeting 2018 or CVB 2019: Red. Countries where registrants attended BBV2020 and did not attend either BBB 2018 or CVB 2019: Pink. Countries that had no participation in any of the brain barriers meetings examined in this study: Light gray. Countries that had participation in either BBB 2018 or CVB 2019 that did not participate in BBV2020: Dark Gray\\n\\nGlobal reach of BBV2020. A BBV2020 had more countries represented compared to BBB 2018 and CVB 2019 alone. B World map showing countries represented by BBV2020. Countries where registrants participated in BBV2020: Red and Pink. Countries not represented at BBV2020: Light Gray and Dark Gray. Countries where registrants attended BBV2020 and at least one of the in-person meetings between Meeting 2018 or CVB 2019: Red. Countries where registrants attended BBV2020 and did not attend either BBB 2018 or CVB 2019: Pink. Countries that had no participation in any of the brain barriers meetings examined in this study: Light gray. Countries that had participation in either BBB 2018 or CVB 2019 that did not participate in BBV2020: Dark Gray\\nWe also compared the number of participants from each country who joined BBV2020 vs. BBB 2018 and CVB 2019. We found that the number of participants per country worldwide was also higher compared to both in person meetings (Fig. 4A–F). These data show that the virtual nature of BBV2020 enabled researchers world-wide to participate and suggest that the outreach of a virtual event can extend beyond the field.\\n\\nFig. 4\\nGlobal view of participation over BBV2020, BBB 2018, and CVB 2019. Representation of participants for (A) BBV2020, (B) BBB 2018, and (C) CVB 2019. Ordered from top to bottom in greatest number of registrants. Number of registrants scaled by color based on number of registrants per country for (D) BBV 2020, (E) BBB 2018, and (F) CVB 2019. Light Pink: 1–10 registrants; Dark Pink: 11–50 registrants; Red: 51–100 registrants and Dark Red: > 100 registrants\\n\\nGlobal view of participation over BBV2020, BBB 2018, and CVB 2019. Representation of participants for (A) BBV2020, (B) BBB 2018, and (C) CVB 2019. Ordered from top to bottom in greatest number of registrants. Number of registrants scaled by color based on number of registrants per country for (D) BBV 2020, (E) BBB 2018, and (F) CVB 2019. Light Pink: 1–10 registrants; Dark Pink: 11–50 registrants; Red: 51–100 registrants and Dark Red: > 100 registrants\",\n \"We examined the self-reported career stage of BBV2020 participants to that of BBB 2018 and CVB 2019 participants. Based on the survey, more than 46% of all participants attended a brain barriers conference/seminar for the first time. We also found that in-person meetings are attended by 42.4–48.9% academicians at the professor rank, graduate students make up 22.9–25.8% and postdoctoral fellows are 6.6–17.7% of the participants (Fig. 5). In contrast, at the BBV2020 19.6% of academicians were at the professor rank, graduate students made up 30.5% and postdoctoral fellows 16.1% of attendees. We also found that BBV2020 had a larger participation from industry (18.5% vs. 5.6–6.6%), and undergraduate students (3.2% vs. 1%) compared to both in person meetings. Together, our data suggest that a virtual platform seminar like BBV2020 reaches a broader range of scientific ranks and careers.\\n\\nFig. 5\\nProportions of various career stages participating in BBV2020, BBB 2018, and CVB 2019. Proportions of various career stages participating in (A) BBV2020, (B) BBB 2018, and (C) CVB 2019. Colors correspond to career stage: Red (professor any rank), Green (graduate student any rank), Dark Blue (research faculty or researcher), Yellow (industry scientist), Orange (postdoctoral fellow), Purple (government/regulatory), Light Blue (clinician), Green (undergraduate student)\\n\\nProportions of various career stages participating in BBV2020, BBB 2018, and CVB 2019. Proportions of various career stages participating in (A) BBV2020, (B) BBB 2018, and (C) CVB 2019. Colors correspond to career stage: Red (professor any rank), Green (graduate student any rank), Dark Blue (research faculty or researcher), Yellow (industry scientist), Orange (postdoctoral fellow), Purple (government/regulatory), Light Blue (clinician), Green (undergraduate student)\",\n \"Seemingly overnight the worldwide COVID-19 pandemic moved virtual meetings, seminars and conferences from the sidelines to center stage. For scientists, connecting on a virtual platform to share data and discuss ideas became the new norm. For many professional societies and research networks a virtual format was a viable option to offset the burden of the pandemic [6, 7]. We organized the BBV2020 seminar series for researchers in the brain barriers field to fill the vacuum canceled in-person conferences left in 2020. Using data collected throughout the BBV2020 seminar series from Zoom Webinar live attendance, number of views from posted videos, and a post-seminar survey, we captured the impact of BBV2020 on the brain barriers field. We compared these data with those from past in-person conferences and assessed the reach of the BBV2020 virtual seminar series with in-person meetings. While we acknowledge that comparisons between a seminar series to a conference may have its limitations, this was the first stand-in virtual event for the brain barriers field that may provide insight for future planning. In the following sections we discuss our findings in the context of the existing literature and provide a future perspective on how a virtual platform could impact the brain barriers field beyond the pandemic.\\n Attendance: scientists around the globe Due to the pandemic, the idea and subsequent organization of the BBV2020 occurred in a relatively short time frame of about 4 weeks. This is in stark contrast to in-person meetings that are often scheduled and organized months and years ahead of the actual event. Advertisement of the BBV2020 encompassed personal email, postings on social media, and utilization of the International Brain Barriers Society email distribution list. Continuous advertisement resulted in the registration of over 1300 scientists. The total registrant number cannot be directly compared to that of in-person meetings since those are more complex in nature, have maximum capacity limits, and involve other factors such as travel expenses and time commitment. Similar to other meetings which transitioned to online formats during the pandemic, BBV2020 had a large increase in registrants when compared to previous in-person meetings (~7-fold and 4.5-fold higher compared to 2018 and 2019, respectively; Fig. 1; [8]. Given that previous brain barriers meetings usually have a few hundred participants, the high number of registrants could be an indicator for high interest in the field beyond traditional brain barrier research groups. In contrast, these high registrant numbers could also be due, in part, to BBV2020 being a no-cost event or could be explained by pandemic-related cancellations resulting in relatively event-free calendar of the participants. While there were more than 1,300 registrants for BBV2020, no more than 419 ever attended a live webinar. Additionally, 323 people registered for BBV2020 but never attended a live webinar. They may, however, have participated by watching the video recordings after the live webinar. A downward trend of attendance numbers for live seminars was observed throughout the course of the 16-week series (Fig. 1B). The recorded seminars drew considerable interest initially with a maximum of 944 views in week 4 of the series, but later in the series, video views dropped to about 200 per seminar (Fig. 1C). Reasons for these downward trends remain speculative, but could be due to the summer break, the selected time for the seminar making it difficult for researchers in certain time zones to attend live, zoom fatigue, or increasing responsibilities in re-opened research laboratories or at home. The downward trends we observed was in contrast to a similar virtual seminar series in another field that remained relatively flat over the course of the summer [4]. Given that average attendance of the BBV2020 was 220 per seminar and typical attendance for in-person conferences in the brain barriers field range between 150 and 300 participants (caveat: one of the in-person conferences has a strict 182-person maximum limit), we conclude that a core group of participants attended most sessions.\\nDue to the pandemic, the idea and subsequent organization of the BBV2020 occurred in a relatively short time frame of about 4 weeks. This is in stark contrast to in-person meetings that are often scheduled and organized months and years ahead of the actual event. Advertisement of the BBV2020 encompassed personal email, postings on social media, and utilization of the International Brain Barriers Society email distribution list. Continuous advertisement resulted in the registration of over 1300 scientists. The total registrant number cannot be directly compared to that of in-person meetings since those are more complex in nature, have maximum capacity limits, and involve other factors such as travel expenses and time commitment. Similar to other meetings which transitioned to online formats during the pandemic, BBV2020 had a large increase in registrants when compared to previous in-person meetings (~7-fold and 4.5-fold higher compared to 2018 and 2019, respectively; Fig. 1; [8]. Given that previous brain barriers meetings usually have a few hundred participants, the high number of registrants could be an indicator for high interest in the field beyond traditional brain barrier research groups. In contrast, these high registrant numbers could also be due, in part, to BBV2020 being a no-cost event or could be explained by pandemic-related cancellations resulting in relatively event-free calendar of the participants. While there were more than 1,300 registrants for BBV2020, no more than 419 ever attended a live webinar. Additionally, 323 people registered for BBV2020 but never attended a live webinar. They may, however, have participated by watching the video recordings after the live webinar. A downward trend of attendance numbers for live seminars was observed throughout the course of the 16-week series (Fig. 1B). The recorded seminars drew considerable interest initially with a maximum of 944 views in week 4 of the series, but later in the series, video views dropped to about 200 per seminar (Fig. 1C). Reasons for these downward trends remain speculative, but could be due to the summer break, the selected time for the seminar making it difficult for researchers in certain time zones to attend live, zoom fatigue, or increasing responsibilities in re-opened research laboratories or at home. The downward trends we observed was in contrast to a similar virtual seminar series in another field that remained relatively flat over the course of the summer [4]. Given that average attendance of the BBV2020 was 220 per seminar and typical attendance for in-person conferences in the brain barriers field range between 150 and 300 participants (caveat: one of the in-person conferences has a strict 182-person maximum limit), we conclude that a core group of participants attended most sessions.\\n Survey: feedback from participants A survey conducted after the seminar series revealed that most participants felt that BBV2020 enhanced the connection to the brain barriers scientific community during the COVID-19 pandemic (Fig. 2A). Differences emerged between how graduate students and professors viewed BBV2020: graduate students in general were more favorable of the virtual format compared to faculty. This may be partially explained by a lack of funding for graduate student travel or political “red tape” (e.g. visas) for some researchers [9]. Additionally, students may disproportionately adapt more easily and rapidly to technology usage compared to senior colleagues [10]. Clearly, another survey would be needed to determine if a virtual component outside of a pandemic would be positively viewed by researchers in the brain barriers field.\\nA survey conducted after the seminar series revealed that most participants felt that BBV2020 enhanced the connection to the brain barriers scientific community during the COVID-19 pandemic (Fig. 2A). Differences emerged between how graduate students and professors viewed BBV2020: graduate students in general were more favorable of the virtual format compared to faculty. This may be partially explained by a lack of funding for graduate student travel or political “red tape” (e.g. visas) for some researchers [9]. Additionally, students may disproportionately adapt more easily and rapidly to technology usage compared to senior colleagues [10]. Clearly, another survey would be needed to determine if a virtual component outside of a pandemic would be positively viewed by researchers in the brain barriers field.\\n Virtual seminars: a powerful opportunity One major advantage of a virtual seminar series such as the BBV2020 is the lack of a travel burden. Taking off the financial and time-commitment associated with in-person meetings has been shown to increase the diversity of attendees [3, 9, 11]. Based on those findings, we anticipated that BBV2020 would have representation from more countries than past in-person meetings. As shown in Fig. 3, registrants from 43 different countries were represented at BBV2020, which is 40% more compared to BBB 2018 (23 countries) and CVB 2019 (25 countries; Fig. 3). In particular, there was high participation from countries in Southeast Asia, Middle East, Africa, and South America that had not been represented at in-person conferences held in 2018 and 2019 (Figs. 3B, 4). Unsurprisingly however, considering countries represented at previous in-person meetings and due to the number of registrants for BBV2020, there was generally a greater participation from each country represented than in 2018 meeting and CVB 2019 [3, 8]. Due to the time of the BBV2020 live sessions (noon to 1 pm Eastern US Time), the majority of participants were from North America, South America, and Europe (Fig. 4). While time zone challenges are common among virtual events, switching between time zones or holding a “flipped” session (flipping evening sessions from Europe to the USA and vice versa) could increase participation from Asia and Oceania [12]. However, offering a virtual series that constantly switches the seminar time may be difficult to manage and could be confusing for participants. Another strength of virtual seminars is that they are more accessible and inclusive especially for junior researchers in the field. The in-person meetings in 2018 and 2019 were dominated by academic faculty at any professor rank and graduate students (Fig. 5). In contrast, BBV2020 saw a large reduction in the percentage of participating faculty (over 40% in person to less than 20% for BBV2020) and a larger proportion of graduate students (22–25% in person to over 30% for BBV2020), industry scientists (about 6% in person to 18.5% for BBV2020), and governmental/regulatory personnel who had signed up (Fig. 5). In addition, a weekly virtual seminar series allows scientist to pick-and-choose the sessions they are interested in, a choice that could interfere with the observed trends shown in Fig. 1. Our data further support the trends observed across a variety of disciplines who have shifted their conferences from in-person to online events, revealing that virtual conferences are more flexible, and more inclusive and accessible worldwide, especially for early-career scientists [9]. Virtual seminars also provide an opportunity for the organizers to invite prominent speakers of a field on a relatively small budget. In general, virtual conferences are known to enhance the accessibility for busy professionals. A recently published Nature poll indicates that 74% of scientists want virtual meetings to stay after the pandemic (925 poll responses) [13]. Easier accessibility, lower carbon footprint, and lower costs were the driving factors chosen in favor for virtual meetings in this poll. Moreover, virtual seminars provide an easy and affordable opportunity for researchers with disabilities, researchers with high teaching loads, or for scientists with parenting and other responsibilities to stay connected to their respective research community. During the COVID-19 pandemic, virtual seminars took center stage and raised the standard for accessibility, interactions, inclusivity, and equity in science [9]. Virtual events changed how we think about meetings and they constitute a yet underestimated, but powerful tool to share science and connect with each other. By rethinking how meetings are held, various fields have had different degrees of participation depending on the availability of the talks or content versus live sessions. These virtual platforms have the potential to create a “connectivity cascade” reaching more people than traditional meetings that are confined by a single geographical location and time [14]. BBV2020 had an impressive reach worldwide, however, there was still a notable deficit in participants from underdeveloped countries, especially Africa. These discrepancies have been noted in the context of expanding telehealth to African countries that still remain challenging due to monopolization of telecommunication infrastructure, political will, and access to adequate broadband efficiency [15]. Despite the advantages of BBV2020, we recognize that limitations remain, such as one-way delivery of material to participants. Lack of critical face-to-face interactions allowing junior scientists to have meaningful two-way communication with other participants was notably lacking in BBV2020 as seen in other virtual conferences as well [16]. The future of such virtual seminars, meetings, or conferences may lie in their inclusion as part of in-person meetings according to a recently published “best practice” guideline for virtual meetings [17]. For example, a virtual component could be included in plenary sessions, workshops, small group discussions, poster sessions, and/or social events of in-person conferences. BBV2020 represented only one aspect (a seminar held in the form of a webinar) of these common meeting events, but future events could certainly enhance the virtual experience. Regardless, virtual components could be exploited to increase diversity in the brain barriers research field which is consistent with the NINDS strategy for enhancing the diversity of neuroscience researchers [18].\\nOne major advantage of a virtual seminar series such as the BBV2020 is the lack of a travel burden. Taking off the financial and time-commitment associated with in-person meetings has been shown to increase the diversity of attendees [3, 9, 11]. Based on those findings, we anticipated that BBV2020 would have representation from more countries than past in-person meetings. As shown in Fig. 3, registrants from 43 different countries were represented at BBV2020, which is 40% more compared to BBB 2018 (23 countries) and CVB 2019 (25 countries; Fig. 3). In particular, there was high participation from countries in Southeast Asia, Middle East, Africa, and South America that had not been represented at in-person conferences held in 2018 and 2019 (Figs. 3B, 4). Unsurprisingly however, considering countries represented at previous in-person meetings and due to the number of registrants for BBV2020, there was generally a greater participation from each country represented than in 2018 meeting and CVB 2019 [3, 8]. Due to the time of the BBV2020 live sessions (noon to 1 pm Eastern US Time), the majority of participants were from North America, South America, and Europe (Fig. 4). While time zone challenges are common among virtual events, switching between time zones or holding a “flipped” session (flipping evening sessions from Europe to the USA and vice versa) could increase participation from Asia and Oceania [12]. However, offering a virtual series that constantly switches the seminar time may be difficult to manage and could be confusing for participants. Another strength of virtual seminars is that they are more accessible and inclusive especially for junior researchers in the field. The in-person meetings in 2018 and 2019 were dominated by academic faculty at any professor rank and graduate students (Fig. 5). In contrast, BBV2020 saw a large reduction in the percentage of participating faculty (over 40% in person to less than 20% for BBV2020) and a larger proportion of graduate students (22–25% in person to over 30% for BBV2020), industry scientists (about 6% in person to 18.5% for BBV2020), and governmental/regulatory personnel who had signed up (Fig. 5). In addition, a weekly virtual seminar series allows scientist to pick-and-choose the sessions they are interested in, a choice that could interfere with the observed trends shown in Fig. 1. Our data further support the trends observed across a variety of disciplines who have shifted their conferences from in-person to online events, revealing that virtual conferences are more flexible, and more inclusive and accessible worldwide, especially for early-career scientists [9]. Virtual seminars also provide an opportunity for the organizers to invite prominent speakers of a field on a relatively small budget. In general, virtual conferences are known to enhance the accessibility for busy professionals. A recently published Nature poll indicates that 74% of scientists want virtual meetings to stay after the pandemic (925 poll responses) [13]. Easier accessibility, lower carbon footprint, and lower costs were the driving factors chosen in favor for virtual meetings in this poll. Moreover, virtual seminars provide an easy and affordable opportunity for researchers with disabilities, researchers with high teaching loads, or for scientists with parenting and other responsibilities to stay connected to their respective research community. During the COVID-19 pandemic, virtual seminars took center stage and raised the standard for accessibility, interactions, inclusivity, and equity in science [9]. Virtual events changed how we think about meetings and they constitute a yet underestimated, but powerful tool to share science and connect with each other. By rethinking how meetings are held, various fields have had different degrees of participation depending on the availability of the talks or content versus live sessions. These virtual platforms have the potential to create a “connectivity cascade” reaching more people than traditional meetings that are confined by a single geographical location and time [14]. BBV2020 had an impressive reach worldwide, however, there was still a notable deficit in participants from underdeveloped countries, especially Africa. These discrepancies have been noted in the context of expanding telehealth to African countries that still remain challenging due to monopolization of telecommunication infrastructure, political will, and access to adequate broadband efficiency [15]. Despite the advantages of BBV2020, we recognize that limitations remain, such as one-way delivery of material to participants. Lack of critical face-to-face interactions allowing junior scientists to have meaningful two-way communication with other participants was notably lacking in BBV2020 as seen in other virtual conferences as well [16]. The future of such virtual seminars, meetings, or conferences may lie in their inclusion as part of in-person meetings according to a recently published “best practice” guideline for virtual meetings [17]. For example, a virtual component could be included in plenary sessions, workshops, small group discussions, poster sessions, and/or social events of in-person conferences. BBV2020 represented only one aspect (a seminar held in the form of a webinar) of these common meeting events, but future events could certainly enhance the virtual experience. Regardless, virtual components could be exploited to increase diversity in the brain barriers research field which is consistent with the NINDS strategy for enhancing the diversity of neuroscience researchers [18].\",\n \"Due to the pandemic, the idea and subsequent organization of the BBV2020 occurred in a relatively short time frame of about 4 weeks. This is in stark contrast to in-person meetings that are often scheduled and organized months and years ahead of the actual event. Advertisement of the BBV2020 encompassed personal email, postings on social media, and utilization of the International Brain Barriers Society email distribution list. Continuous advertisement resulted in the registration of over 1300 scientists. The total registrant number cannot be directly compared to that of in-person meetings since those are more complex in nature, have maximum capacity limits, and involve other factors such as travel expenses and time commitment. Similar to other meetings which transitioned to online formats during the pandemic, BBV2020 had a large increase in registrants when compared to previous in-person meetings (~7-fold and 4.5-fold higher compared to 2018 and 2019, respectively; Fig. 1; [8]. Given that previous brain barriers meetings usually have a few hundred participants, the high number of registrants could be an indicator for high interest in the field beyond traditional brain barrier research groups. In contrast, these high registrant numbers could also be due, in part, to BBV2020 being a no-cost event or could be explained by pandemic-related cancellations resulting in relatively event-free calendar of the participants. While there were more than 1,300 registrants for BBV2020, no more than 419 ever attended a live webinar. Additionally, 323 people registered for BBV2020 but never attended a live webinar. They may, however, have participated by watching the video recordings after the live webinar. A downward trend of attendance numbers for live seminars was observed throughout the course of the 16-week series (Fig. 1B). The recorded seminars drew considerable interest initially with a maximum of 944 views in week 4 of the series, but later in the series, video views dropped to about 200 per seminar (Fig. 1C). Reasons for these downward trends remain speculative, but could be due to the summer break, the selected time for the seminar making it difficult for researchers in certain time zones to attend live, zoom fatigue, or increasing responsibilities in re-opened research laboratories or at home. The downward trends we observed was in contrast to a similar virtual seminar series in another field that remained relatively flat over the course of the summer [4]. Given that average attendance of the BBV2020 was 220 per seminar and typical attendance for in-person conferences in the brain barriers field range between 150 and 300 participants (caveat: one of the in-person conferences has a strict 182-person maximum limit), we conclude that a core group of participants attended most sessions.\",\n \"A survey conducted after the seminar series revealed that most participants felt that BBV2020 enhanced the connection to the brain barriers scientific community during the COVID-19 pandemic (Fig. 2A). Differences emerged between how graduate students and professors viewed BBV2020: graduate students in general were more favorable of the virtual format compared to faculty. This may be partially explained by a lack of funding for graduate student travel or political “red tape” (e.g. visas) for some researchers [9]. Additionally, students may disproportionately adapt more easily and rapidly to technology usage compared to senior colleagues [10]. Clearly, another survey would be needed to determine if a virtual component outside of a pandemic would be positively viewed by researchers in the brain barriers field.\",\n \"One major advantage of a virtual seminar series such as the BBV2020 is the lack of a travel burden. Taking off the financial and time-commitment associated with in-person meetings has been shown to increase the diversity of attendees [3, 9, 11]. Based on those findings, we anticipated that BBV2020 would have representation from more countries than past in-person meetings. As shown in Fig. 3, registrants from 43 different countries were represented at BBV2020, which is 40% more compared to BBB 2018 (23 countries) and CVB 2019 (25 countries; Fig. 3). In particular, there was high participation from countries in Southeast Asia, Middle East, Africa, and South America that had not been represented at in-person conferences held in 2018 and 2019 (Figs. 3B, 4). Unsurprisingly however, considering countries represented at previous in-person meetings and due to the number of registrants for BBV2020, there was generally a greater participation from each country represented than in 2018 meeting and CVB 2019 [3, 8]. Due to the time of the BBV2020 live sessions (noon to 1 pm Eastern US Time), the majority of participants were from North America, South America, and Europe (Fig. 4). While time zone challenges are common among virtual events, switching between time zones or holding a “flipped” session (flipping evening sessions from Europe to the USA and vice versa) could increase participation from Asia and Oceania [12]. However, offering a virtual series that constantly switches the seminar time may be difficult to manage and could be confusing for participants. Another strength of virtual seminars is that they are more accessible and inclusive especially for junior researchers in the field. The in-person meetings in 2018 and 2019 were dominated by academic faculty at any professor rank and graduate students (Fig. 5). In contrast, BBV2020 saw a large reduction in the percentage of participating faculty (over 40% in person to less than 20% for BBV2020) and a larger proportion of graduate students (22–25% in person to over 30% for BBV2020), industry scientists (about 6% in person to 18.5% for BBV2020), and governmental/regulatory personnel who had signed up (Fig. 5). In addition, a weekly virtual seminar series allows scientist to pick-and-choose the sessions they are interested in, a choice that could interfere with the observed trends shown in Fig. 1. Our data further support the trends observed across a variety of disciplines who have shifted their conferences from in-person to online events, revealing that virtual conferences are more flexible, and more inclusive and accessible worldwide, especially for early-career scientists [9]. Virtual seminars also provide an opportunity for the organizers to invite prominent speakers of a field on a relatively small budget. In general, virtual conferences are known to enhance the accessibility for busy professionals. A recently published Nature poll indicates that 74% of scientists want virtual meetings to stay after the pandemic (925 poll responses) [13]. Easier accessibility, lower carbon footprint, and lower costs were the driving factors chosen in favor for virtual meetings in this poll. Moreover, virtual seminars provide an easy and affordable opportunity for researchers with disabilities, researchers with high teaching loads, or for scientists with parenting and other responsibilities to stay connected to their respective research community. During the COVID-19 pandemic, virtual seminars took center stage and raised the standard for accessibility, interactions, inclusivity, and equity in science [9]. Virtual events changed how we think about meetings and they constitute a yet underestimated, but powerful tool to share science and connect with each other. By rethinking how meetings are held, various fields have had different degrees of participation depending on the availability of the talks or content versus live sessions. These virtual platforms have the potential to create a “connectivity cascade” reaching more people than traditional meetings that are confined by a single geographical location and time [14]. BBV2020 had an impressive reach worldwide, however, there was still a notable deficit in participants from underdeveloped countries, especially Africa. These discrepancies have been noted in the context of expanding telehealth to African countries that still remain challenging due to monopolization of telecommunication infrastructure, political will, and access to adequate broadband efficiency [15]. Despite the advantages of BBV2020, we recognize that limitations remain, such as one-way delivery of material to participants. Lack of critical face-to-face interactions allowing junior scientists to have meaningful two-way communication with other participants was notably lacking in BBV2020 as seen in other virtual conferences as well [16]. The future of such virtual seminars, meetings, or conferences may lie in their inclusion as part of in-person meetings according to a recently published “best practice” guideline for virtual meetings [17]. For example, a virtual component could be included in plenary sessions, workshops, small group discussions, poster sessions, and/or social events of in-person conferences. BBV2020 represented only one aspect (a seminar held in the form of a webinar) of these common meeting events, but future events could certainly enhance the virtual experience. Regardless, virtual components could be exploited to increase diversity in the brain barriers research field which is consistent with the NINDS strategy for enhancing the diversity of neuroscience researchers [18].\",\n \"Overall, the Virtual BBV2020 Seminar Series created during the COVID-19 pandemic was positively received by the brain barriers research community. Key benefits of virtual seminars like the BBV2020 are that they are convenient, affordable, eco-friendly, reach a broad audience, and are a good tool to increase diversity and equity. Thus, incorporating a virtual component during an in-person meeting or offering a virtual counterpart in addition to in-person meetings could help increase the outreach of a small research field like the brain barriers field. Nevertheless, limitations remain such as a lack of interaction between speakers and participants, which is particularly critical for junior researchers. And although BBV2020 expanded the reach beyond past in-person meetings, challenges reaching scientists in underdeveloped countries remain. Together, BBV2020 was created in a time of crisis but has the potential to thrive in the post-pandemic world. Embracing virtual scientific events may allow us to advance the brain barriers research field and have an impact on how we exchange science in the future.\",\n \" \\nAdditional file 1: Figure S1. Attendance Trends for BBV2020 by Academic Rank. Number of unique viewers weekly at the live Zoom Webinar sessions of BBV2020 separated by A) Graduate Student, B) Postdoctoral Fellow, and C) Professor (any rank).\\nAdditional file 1: Figure S1. Attendance Trends for BBV2020 by Academic Rank. Number of unique viewers weekly at the live Zoom Webinar sessions of BBV2020 separated by A) Graduate Student, B) Postdoctoral Fellow, and C) Professor (any rank).\\n\\nAdditional file 1: Figure S1. Attendance Trends for BBV2020 by Academic Rank. Number of unique viewers weekly at the live Zoom Webinar sessions of BBV2020 separated by A) Graduate Student, B) Postdoctoral Fellow, and C) Professor (any rank).\\nAdditional file 1: Figure S1. Attendance Trends for BBV2020 by Academic Rank. Number of unique viewers weekly at the live Zoom Webinar sessions of BBV2020 separated by A) Graduate Student, B) Postdoctoral Fellow, and C) Professor (any rank).\"\n]"},"all_sections_sec_types":{"kind":"list like","value":["introduction",null,null,null,null,null,null,null,null,null,null,"results",null,null,null,null,"discussion",null,null,null,null,"supplementary-material"],"string":"[\n \"introduction\",\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n \"results\",\n null,\n null,\n null,\n null,\n \"discussion\",\n null,\n null,\n null,\n null,\n \"supplementary-material\"\n]"},"keywords":{"kind":"list like","value":["Blood–brain barrier","BBV2020","Brain barriers virtual","Virtual seminar series","COVID-19","Education"],"string":"[\n \"Blood–brain barrier\",\n \"BBV2020\",\n \"Brain barriers virtual\",\n \"Virtual seminar series\",\n \"COVID-19\",\n \"Education\"\n]"},"whole_article_text":{"kind":"string","value":"Introduction:\nOn March 11, 2020, the World Health Organization (WHO) declared the coronavirus disease (COVID-19) outbreak a global pandemic [1]. Shortly after, countries worldwide issued stay-at-home orders and lockdowns: the COVID-19 pandemic had forced the world to come to an abrupt halt [2]. With global air travel restrictions and travel bans in place, enterprises including academic institutions, industry and government agencies required individuals to call off their business travel. As a result, scheduled scientific conferences were canceled throughout 2020 [3, 4]. These cancelations created a sudden void in scientific communication between researchers worldwide and forced research communities to be creative and adapt. One possibility to continue scientific communication and interaction was by switching conferences and seminars to virtual online events using novel digital platforms enabling researchers to connect and share science [3, 4].\nAs a consequence, major conferences and meetings in the brain barriers research field, including the “Barriers of the CNS Gordon Research Conference” (GRC 2020) and the international symposium on “Signal Transduction at the Blood–Brain Barriers” in Bari, Italy were first postponed until 2021 and due to the ongoing pandemic subsequently moved to 2022. The “Cerebral Vascular Biology” (CVB) conference in Uppsala, Sweden planned for 2021 was postponed until 2023. Postponing these meetings left organizers and conference chairs in a difficult position and researchers without a platform to present and share their data. Thus, these postponements created a critical need for the field to stay connected and an opportunity to establish a virtual event that could supplement the postponed conferences in 2020. The Brain Barriers Virtual 2020 Seminar Series (BBV2020) addressed this need by providing a forum for brain barrier scientists worldwide to come together online during the COVID-19 crisis in 2020. BBV2020 seminars were held weekly from May 20, 2020 to September 2, 2020 in a 1-h format featuring a live session with an invited speaker followed by time for questions and answers from the community. The seminar series was hosted on a Zoom webinar platform and was free for anyone to attend. BBV2020 served as a forum for invited speakers to present their work to the brain barriers community and enabled this community to discuss brain barriers science from the safety of their home office. Over 1300 scientists around the world were registered, each seminar was viewed live by 100–400 participants, and the video-recordings received 150–900 views each week. Participants were from academia, industry and government agencies at all career stages from around the globe.\nIn this report, we share participation data that give insight into the impact this virtual seminar series had on the brain barriers field during the COVID-19 pandemic. We compare these data with existing data from past in-person brain barriers meetings and draw conclusions from these data on the potential value of virtual meetings for the brain barriers research field in the future. While the organization of virtual events during the pandemic may seem self-evident, reflection on the impact carries utility for events in the future.\n\nMethods:\n Organization The BBV2020 seminar series was created by Drs. Bjoern Bauer (University of Kentucky, Lexington, KY, USA), Anika Hartz (University of Kentucky, Lexington, KY, USA), and Brandon Kim (University of Alabama, Tuscaloosa, Alabama, USA). Seminar moderators were postdoctoral researchers and scientists from the USA and Europe: Drs. Natalie Hudson (Trinity College Dublin, Dublin, Ireland), Geetika Nehra (University of Kentucky, Lexington, KY, USA), Michelle Pizzo (Denali Therapeutics Inc., San Francisco, CA, USA), and Steffen Storck (University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany).\nThe BBV2020 seminar series was created by Drs. Bjoern Bauer (University of Kentucky, Lexington, KY, USA), Anika Hartz (University of Kentucky, Lexington, KY, USA), and Brandon Kim (University of Alabama, Tuscaloosa, Alabama, USA). Seminar moderators were postdoctoral researchers and scientists from the USA and Europe: Drs. Natalie Hudson (Trinity College Dublin, Dublin, Ireland), Geetika Nehra (University of Kentucky, Lexington, KY, USA), Michelle Pizzo (Denali Therapeutics Inc., San Francisco, CA, USA), and Steffen Storck (University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany).\n Virtual platform and technical assistance BBV2020 was run on the Zoom Webinar 500 platform (Zoom Video Communications, Inc.; San Jose, CA, USA) that was funded by Dr. Bjoern Bauer using University of Kentucky funds. This particular Zoom Webinar license allowed for up to 500 live participants. The Zoom Webinar platform supports an in-session chat function, a Q&A forum, a raise hand function for live questions, and provides a panelist section with the option to share audio, video and presentation slides, a recording function and post-webinar data collection. The hosts and moderators were in control of audio and video; participants were not able to record or unmute during a session. Participants had no control function other than a “raise hand” option and the Q&A and chat function allowing to send questions or messages to the moderator. Technical assistance was provided by Todd Sizemore, College of Pharmacy, University of Kentucky.\nBBV2020 was run on the Zoom Webinar 500 platform (Zoom Video Communications, Inc.; San Jose, CA, USA) that was funded by Dr. Bjoern Bauer using University of Kentucky funds. This particular Zoom Webinar license allowed for up to 500 live participants. The Zoom Webinar platform supports an in-session chat function, a Q&A forum, a raise hand function for live questions, and provides a panelist section with the option to share audio, video and presentation slides, a recording function and post-webinar data collection. The hosts and moderators were in control of audio and video; participants were not able to record or unmute during a session. Participants had no control function other than a “raise hand” option and the Q&A and chat function allowing to send questions or messages to the moderator. Technical assistance was provided by Todd Sizemore, College of Pharmacy, University of Kentucky.\n BBV2020 schedule The BBV2020 seminar series ran from May 20 to September 2, 2020. Live seminars were held once per week from 12 to 1 PM Eastern Daylight Time (EDT) to accommodate researchers across a wide range of time zones: Central European Time (6:00 PM) to Pacific Daylight Time (9:00 AM). To accommodate colleagues with time constraints or those within incompatible time zones such as Asia or Oceania, presentations were recorded (depending on speaker permissions) and the seminar recording was made available for up to 48 h after the live seminar. The link for the recorded videos was distributed via the BBV2020 listserv that was set up through the University of Kentucky listserv.\nThe BBV2020 seminar series ran from May 20 to September 2, 2020. Live seminars were held once per week from 12 to 1 PM Eastern Daylight Time (EDT) to accommodate researchers across a wide range of time zones: Central European Time (6:00 PM) to Pacific Daylight Time (9:00 AM). To accommodate colleagues with time constraints or those within incompatible time zones such as Asia or Oceania, presentations were recorded (depending on speaker permissions) and the seminar recording was made available for up to 48 h after the live seminar. The link for the recorded videos was distributed via the BBV2020 listserv that was set up through the University of Kentucky listserv.\n Invited speakers Efforts were made to invite speakers from a variety of countries and genders. Invited speakers included organizers of postponed in-person conferences, speakers from academia and industry from different career stages. A total of 16 speakers were invited: May 20, 2020: Elga De Vries, Amsterdam UMC, Amsterdam, The Netherlands; May 27, 2020: Richard Daneman, University of California San Diego, San Diego, CA, USA; June 3, 2020: Robyn Klein, Washington University, St. Louis, MO, USA; June 10, 2020: Ayal Ben-Zvi, The Hebrew University of Jerusalem, Israel; June 17, 2020: Britta Engelhardt, University of Bern, Bern, Switzerland; June 24, 2020: Margareta Hammarlund-Udenaes, Uppsala University, Uppsala, Sweden; July 1, 2020: Daniela Virgintino, University of Bari, Bari, Italy; July 8, 2020: Eric Shusta, University of Wisconsin, Madison, WI, USA; July 15, 2020: Matthew Campbell, Trinity College Dublin, Dublin, Ireland; July 22, 2020: Krzysztof Kucharz, University of Copenhagen, Copenhagen, Denmark; July 29, 2020: William Elmquist, University of Minnesota, Minneapolis, MN, USA; August 5, 2020: Edward Neuwelt, Oregon Health & Sciences University, OR, USA; August 12, 2020: Robert Thorne, Denali Therapeutics, San Francisco, CA, USA; August 19, 2020: Teresa Sanchez, Weill Cornell Medical College, New York City, NJ, USA; August 26, 2020: Michael Taylor, University of Wisconsin, Madison, MI, USA; September 2, 2020: Joan Abbott, King’s College London, London, UK.\nSpeakers were offered a training session by the organizers to cover key aspects of a Zoom-based webinar and to test video, audio, and slide presentation prior to the live seminar. Ten out of 16 speakers took the training session. Speakers were informed by the organizers that any data presented should be considered public as security on the Zoom platform could not be ensured. Invited speakers had the opportunity to give permission to have their seminar recorded or to opt out of the recording. If a speaker gave permission to record, the entire seminar was recorded, and recordings were made available to all registered participants in a non-downloadable format through the University of Kentucky Zoom cloud for up to 48 h after the live session. Out of 16 speakers, 15 gave permission to record the live seminar.\nEfforts were made to invite speakers from a variety of countries and genders. Invited speakers included organizers of postponed in-person conferences, speakers from academia and industry from different career stages. A total of 16 speakers were invited: May 20, 2020: Elga De Vries, Amsterdam UMC, Amsterdam, The Netherlands; May 27, 2020: Richard Daneman, University of California San Diego, San Diego, CA, USA; June 3, 2020: Robyn Klein, Washington University, St. Louis, MO, USA; June 10, 2020: Ayal Ben-Zvi, The Hebrew University of Jerusalem, Israel; June 17, 2020: Britta Engelhardt, University of Bern, Bern, Switzerland; June 24, 2020: Margareta Hammarlund-Udenaes, Uppsala University, Uppsala, Sweden; July 1, 2020: Daniela Virgintino, University of Bari, Bari, Italy; July 8, 2020: Eric Shusta, University of Wisconsin, Madison, WI, USA; July 15, 2020: Matthew Campbell, Trinity College Dublin, Dublin, Ireland; July 22, 2020: Krzysztof Kucharz, University of Copenhagen, Copenhagen, Denmark; July 29, 2020: William Elmquist, University of Minnesota, Minneapolis, MN, USA; August 5, 2020: Edward Neuwelt, Oregon Health & Sciences University, OR, USA; August 12, 2020: Robert Thorne, Denali Therapeutics, San Francisco, CA, USA; August 19, 2020: Teresa Sanchez, Weill Cornell Medical College, New York City, NJ, USA; August 26, 2020: Michael Taylor, University of Wisconsin, Madison, MI, USA; September 2, 2020: Joan Abbott, King’s College London, London, UK.\nSpeakers were offered a training session by the organizers to cover key aspects of a Zoom-based webinar and to test video, audio, and slide presentation prior to the live seminar. Ten out of 16 speakers took the training session. Speakers were informed by the organizers that any data presented should be considered public as security on the Zoom platform could not be ensured. Invited speakers had the opportunity to give permission to have their seminar recorded or to opt out of the recording. If a speaker gave permission to record, the entire seminar was recorded, and recordings were made available to all registered participants in a non-downloadable format through the University of Kentucky Zoom cloud for up to 48 h after the live session. Out of 16 speakers, 15 gave permission to record the live seminar.\n Advertisement and participants To reach scientists in the brain barriers community, the seminar was advertised by email to participants of previous in-person meetings, and on social media platforms such as Facebook and LinkedIn on personal profiles. In addition, the introductory flyer was kindly distributed via email by the International Brain Barriers Society (IBBS) using their email distribution list. Interested scientists were asked to register by sending their name, affiliation, and career stage to a dedicated email address created for the organization and execution of BBV2020 (bbv2020seminar@gmail.com). Once registered, participants were added to a BBV2020 listserv provided by the University of Kentucky and received weekly updates. These weekly updates included a flyer with the information regarding the upcoming speakers and the specific zoom link for the seminar. Participants also received a follow up email after each seminar with a zoom link to the video recordings. With the permission of the speaker of the week, registered viewers had access to recordings for up to 48 h after the live seminar.\nTo reach scientists in the brain barriers community, the seminar was advertised by email to participants of previous in-person meetings, and on social media platforms such as Facebook and LinkedIn on personal profiles. In addition, the introductory flyer was kindly distributed via email by the International Brain Barriers Society (IBBS) using their email distribution list. Interested scientists were asked to register by sending their name, affiliation, and career stage to a dedicated email address created for the organization and execution of BBV2020 (bbv2020seminar@gmail.com). Once registered, participants were added to a BBV2020 listserv provided by the University of Kentucky and received weekly updates. These weekly updates included a flyer with the information regarding the upcoming speakers and the specific zoom link for the seminar. Participants also received a follow up email after each seminar with a zoom link to the video recordings. With the permission of the speaker of the week, registered viewers had access to recordings for up to 48 h after the live seminar.\n BBV2020 seminar presentation Each seminar was started with opening remarks by one of the organizers (Drs. Bauer, Hartz, or Kim) followed by an introduction of the speaker by the moderator. Moderators (Drs. Hudson, Nehra, Pizzo, Storck) rotated each week, only one moderator was assigned to each week. After the seminar presentation, the moderator facilitated the question-and-answer session. Participants were encouraged to ask a live question by raising their digital hand. When a participant raised the digital hand, the moderator would unmute the participant at which point the participant was able to ask the speaker a question directly. In addition, participants had the opportunity to ask questions via the Zoom Q&A or the chat function. Written questions were then subsequently read by the moderator. The moderator concluded each seminar by thanking the speaker and the audience for attending and by announcing the speaker for the following week.\nEach seminar was started with opening remarks by one of the organizers (Drs. Bauer, Hartz, or Kim) followed by an introduction of the speaker by the moderator. Moderators (Drs. Hudson, Nehra, Pizzo, Storck) rotated each week, only one moderator was assigned to each week. After the seminar presentation, the moderator facilitated the question-and-answer session. Participants were encouraged to ask a live question by raising their digital hand. When a participant raised the digital hand, the moderator would unmute the participant at which point the participant was able to ask the speaker a question directly. In addition, participants had the opportunity to ask questions via the Zoom Q&A or the chat function. Written questions were then subsequently read by the moderator. The moderator concluded each seminar by thanking the speaker and the audience for attending and by announcing the speaker for the following week.\n Map generation World maps highlighting countries participating in the various meetings and representation were generated using MapChart (www.mapchart.net; license: https://creativecommons.org/licenses/by-sa/4.0/).\nWorld maps highlighting countries participating in the various meetings and representation were generated using MapChart (www.mapchart.net; license: https://creativecommons.org/licenses/by-sa/4.0/).\n Survey Toward the end of the seminar series, a survey was generated using Qualtrics (Qualtrics) and run through the University of Alabama Qualtrics system. A link to the survey was sent to all registered seminar participants by email. Questions covered general audience participation, career stage, as well as affective factors. Survey results are only reported in an aggregate.\nToward the end of the seminar series, a survey was generated using Qualtrics (Qualtrics) and run through the University of Alabama Qualtrics system. A link to the survey was sent to all registered seminar participants by email. Questions covered general audience participation, career stage, as well as affective factors. Survey results are only reported in an aggregate.\n Data collection, analysis, and statistics Survey data was collected through the University of Alabama Qualtrics system and was determined by the University of Alabama IRB to be “non-human subject research”. Data from the live webinars were collected by Zoom for each seminar and reported as unique viewers to eliminate counting individuals double who potentially logged on twice. Data on video views were collected from the University of Kentucky Zoom Webinar cloud server and were reported as total accessed views. Survey data from Qualtrics was analyzed with the IBM® SPSS® software platform (IBM, Armonk, NY, USA), using one-way ANOVAs and t-tests to identify significant trends [5]. Data with a p < 0.05 are considered significant.\nSurvey data was collected through the University of Alabama Qualtrics system and was determined by the University of Alabama IRB to be “non-human subject research”. Data from the live webinars were collected by Zoom for each seminar and reported as unique viewers to eliminate counting individuals double who potentially logged on twice. Data on video views were collected from the University of Kentucky Zoom Webinar cloud server and were reported as total accessed views. Survey data from Qualtrics was analyzed with the IBM® SPSS® software platform (IBM, Armonk, NY, USA), using one-way ANOVAs and t-tests to identify significant trends [5]. Data with a p < 0.05 are considered significant.\n\nOrganization:\nThe BBV2020 seminar series was created by Drs. Bjoern Bauer (University of Kentucky, Lexington, KY, USA), Anika Hartz (University of Kentucky, Lexington, KY, USA), and Brandon Kim (University of Alabama, Tuscaloosa, Alabama, USA). Seminar moderators were postdoctoral researchers and scientists from the USA and Europe: Drs. Natalie Hudson (Trinity College Dublin, Dublin, Ireland), Geetika Nehra (University of Kentucky, Lexington, KY, USA), Michelle Pizzo (Denali Therapeutics Inc., San Francisco, CA, USA), and Steffen Storck (University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany).\n\nVirtual platform and technical assistance:\nBBV2020 was run on the Zoom Webinar 500 platform (Zoom Video Communications, Inc.; San Jose, CA, USA) that was funded by Dr. Bjoern Bauer using University of Kentucky funds. This particular Zoom Webinar license allowed for up to 500 live participants. The Zoom Webinar platform supports an in-session chat function, a Q&A forum, a raise hand function for live questions, and provides a panelist section with the option to share audio, video and presentation slides, a recording function and post-webinar data collection. The hosts and moderators were in control of audio and video; participants were not able to record or unmute during a session. Participants had no control function other than a “raise hand” option and the Q&A and chat function allowing to send questions or messages to the moderator. Technical assistance was provided by Todd Sizemore, College of Pharmacy, University of Kentucky.\n\nBBV2020 schedule:\nThe BBV2020 seminar series ran from May 20 to September 2, 2020. Live seminars were held once per week from 12 to 1 PM Eastern Daylight Time (EDT) to accommodate researchers across a wide range of time zones: Central European Time (6:00 PM) to Pacific Daylight Time (9:00 AM). To accommodate colleagues with time constraints or those within incompatible time zones such as Asia or Oceania, presentations were recorded (depending on speaker permissions) and the seminar recording was made available for up to 48 h after the live seminar. The link for the recorded videos was distributed via the BBV2020 listserv that was set up through the University of Kentucky listserv.\n\nInvited speakers:\nEfforts were made to invite speakers from a variety of countries and genders. Invited speakers included organizers of postponed in-person conferences, speakers from academia and industry from different career stages. A total of 16 speakers were invited: May 20, 2020: Elga De Vries, Amsterdam UMC, Amsterdam, The Netherlands; May 27, 2020: Richard Daneman, University of California San Diego, San Diego, CA, USA; June 3, 2020: Robyn Klein, Washington University, St. Louis, MO, USA; June 10, 2020: Ayal Ben-Zvi, The Hebrew University of Jerusalem, Israel; June 17, 2020: Britta Engelhardt, University of Bern, Bern, Switzerland; June 24, 2020: Margareta Hammarlund-Udenaes, Uppsala University, Uppsala, Sweden; July 1, 2020: Daniela Virgintino, University of Bari, Bari, Italy; July 8, 2020: Eric Shusta, University of Wisconsin, Madison, WI, USA; July 15, 2020: Matthew Campbell, Trinity College Dublin, Dublin, Ireland; July 22, 2020: Krzysztof Kucharz, University of Copenhagen, Copenhagen, Denmark; July 29, 2020: William Elmquist, University of Minnesota, Minneapolis, MN, USA; August 5, 2020: Edward Neuwelt, Oregon Health & Sciences University, OR, USA; August 12, 2020: Robert Thorne, Denali Therapeutics, San Francisco, CA, USA; August 19, 2020: Teresa Sanchez, Weill Cornell Medical College, New York City, NJ, USA; August 26, 2020: Michael Taylor, University of Wisconsin, Madison, MI, USA; September 2, 2020: Joan Abbott, King’s College London, London, UK.\nSpeakers were offered a training session by the organizers to cover key aspects of a Zoom-based webinar and to test video, audio, and slide presentation prior to the live seminar. Ten out of 16 speakers took the training session. Speakers were informed by the organizers that any data presented should be considered public as security on the Zoom platform could not be ensured. Invited speakers had the opportunity to give permission to have their seminar recorded or to opt out of the recording. If a speaker gave permission to record, the entire seminar was recorded, and recordings were made available to all registered participants in a non-downloadable format through the University of Kentucky Zoom cloud for up to 48 h after the live session. Out of 16 speakers, 15 gave permission to record the live seminar.\n\nAdvertisement and participants:\nTo reach scientists in the brain barriers community, the seminar was advertised by email to participants of previous in-person meetings, and on social media platforms such as Facebook and LinkedIn on personal profiles. In addition, the introductory flyer was kindly distributed via email by the International Brain Barriers Society (IBBS) using their email distribution list. Interested scientists were asked to register by sending their name, affiliation, and career stage to a dedicated email address created for the organization and execution of BBV2020 (bbv2020seminar@gmail.com). Once registered, participants were added to a BBV2020 listserv provided by the University of Kentucky and received weekly updates. These weekly updates included a flyer with the information regarding the upcoming speakers and the specific zoom link for the seminar. Participants also received a follow up email after each seminar with a zoom link to the video recordings. With the permission of the speaker of the week, registered viewers had access to recordings for up to 48 h after the live seminar.\n\nBBV2020 seminar presentation:\nEach seminar was started with opening remarks by one of the organizers (Drs. Bauer, Hartz, or Kim) followed by an introduction of the speaker by the moderator. Moderators (Drs. Hudson, Nehra, Pizzo, Storck) rotated each week, only one moderator was assigned to each week. After the seminar presentation, the moderator facilitated the question-and-answer session. Participants were encouraged to ask a live question by raising their digital hand. When a participant raised the digital hand, the moderator would unmute the participant at which point the participant was able to ask the speaker a question directly. In addition, participants had the opportunity to ask questions via the Zoom Q&A or the chat function. Written questions were then subsequently read by the moderator. The moderator concluded each seminar by thanking the speaker and the audience for attending and by announcing the speaker for the following week.\n\nMap generation:\nWorld maps highlighting countries participating in the various meetings and representation were generated using MapChart (www.mapchart.net; license: https://creativecommons.org/licenses/by-sa/4.0/).\n\nSurvey:\nToward the end of the seminar series, a survey was generated using Qualtrics (Qualtrics) and run through the University of Alabama Qualtrics system. A link to the survey was sent to all registered seminar participants by email. Questions covered general audience participation, career stage, as well as affective factors. Survey results are only reported in an aggregate.\n\nData collection, analysis, and statistics:\nSurvey data was collected through the University of Alabama Qualtrics system and was determined by the University of Alabama IRB to be “non-human subject research”. Data from the live webinars were collected by Zoom for each seminar and reported as unique viewers to eliminate counting individuals double who potentially logged on twice. Data on video views were collected from the University of Kentucky Zoom Webinar cloud server and were reported as total accessed views. Survey data from Qualtrics was analyzed with the IBM® SPSS® software platform (IBM, Armonk, NY, USA), using one-way ANOVAs and t-tests to identify significant trends [5]. Data with a p < 0.05 are considered significant.\n\nResults:\nWe organized the virtual BBV2020 seminar series for the brain barriers research field to fill the void that the COVID-19 pandemic left due to canceled and postponed meetings in 2020. The seminar series was held between May–September 2020 every Wednesday from 12:00 to 1:00 PM Eastern Daylight Time (EDT). Seminars were hosted on a Zoom webinar platform and featured a 45-min presentation from an invited speaker followed by questions and answers from the audience. In the following sections, we summarize data collected by the Zoom software, from the listserv used, and the participant survey. We compare and contrast the data from the virtual BBV2020 seminar series with data from in-person meetings from past years.\n BBV2020 attendance On May 14, 2020, we sent out the first advertisement for the BBV2020 seminar series via email using a listserv with email addresses of past conference attendees and via postings using LinkedIn, Facebook, and Twitter. By the start of the first seminar on May 20, 2020, more than 720 scientists had registered. By the end of the 16-week long BBV2020 seminar series, a total of 1302 registrants signed up on our listserv to receive the seminar link, speaker updates, and video recording links (Fig. 1A). We obtained data from two past in-person meetings from the respective conference chairs: (1) brain barriers meeting in 2018 (BBB 2018) and the (2) Cerebral Vascular Biology meeting in 2019 (CVB 2019).\n\nFig. 1\nAttendance Trends for Brain Barrier Research Conferences. A With 1,302 individuals signing up and added to the listserv, BBV2020 attracted more registrants than BBB 2018 and CVB 2019. B Number of unique viewers weekly at the live Zoom Webinar sessions of BBV2020. C Number of views (clicks) of the video recordings when available weekly for BBV2020. Black line indicates best linear fit. Gray line indicates the average (mean) for all 16 weeks\n\nAttendance Trends for Brain Barrier Research Conferences. A With 1,302 individuals signing up and added to the listserv, BBV2020 attracted more registrants than BBB 2018 and CVB 2019. B Number of unique viewers weekly at the live Zoom Webinar sessions of BBV2020. C Number of views (clicks) of the video recordings when available weekly for BBV2020. Black line indicates best linear fit. Gray line indicates the average (mean) for all 16 weeks\nStrikingly, the total number of registered applicants for the BBV2020 was more than 4-fold higher compared to the total number of registered participants for the 2018 and the CVB 2019 meetings (Fig. 1A). The large number of registrants is likely due to free registration, no associated travel expenses, a world-wide reach, and that virtual meetings come with no obligations for the attendees. In contrast, the number of attendees of the in-person meetings (BBB 2018 and CVB2019) was limited and determined by the size of the conference site.\nThe first live BBV2020 seminar was attended by 419 participants. As the seminar series progressed, the participant number during live seminars gradually decreased, the average participant number was 220, the minimum was 108 (Fig. 1B). A similar trend was observed in the numbers of total recording views: the first seminar recording had a maximum of 944 views; in average, videos had 422 views (Fig. 1C). The trendline in Fig. 1C shows a drop in recording views over the course of the seminar series. These trends held regardless of academic rank as graduate students, postdoctoral fellows, and professors all showed a similar downward trend in live attendance (Additional file 1: Fig. S1A–C). Note that we were unable to capture recording data for week 2 due to a recording error and for week 13 due to the request by the speaker to not record.\nTogether, the high number of registrants suggests that the virtual BBV2020 seminar series sparked interest. Attendance of the live seminars and the numbers of views for the recorded videos decreased over time, which may be an indicator of “virtual meeting fatigue” or attendees being on their summer vacation.\nOn May 14, 2020, we sent out the first advertisement for the BBV2020 seminar series via email using a listserv with email addresses of past conference attendees and via postings using LinkedIn, Facebook, and Twitter. By the start of the first seminar on May 20, 2020, more than 720 scientists had registered. By the end of the 16-week long BBV2020 seminar series, a total of 1302 registrants signed up on our listserv to receive the seminar link, speaker updates, and video recording links (Fig. 1A). We obtained data from two past in-person meetings from the respective conference chairs: (1) brain barriers meeting in 2018 (BBB 2018) and the (2) Cerebral Vascular Biology meeting in 2019 (CVB 2019).\n\nFig. 1\nAttendance Trends for Brain Barrier Research Conferences. A With 1,302 individuals signing up and added to the listserv, BBV2020 attracted more registrants than BBB 2018 and CVB 2019. B Number of unique viewers weekly at the live Zoom Webinar sessions of BBV2020. C Number of views (clicks) of the video recordings when available weekly for BBV2020. Black line indicates best linear fit. Gray line indicates the average (mean) for all 16 weeks\n\nAttendance Trends for Brain Barrier Research Conferences. A With 1,302 individuals signing up and added to the listserv, BBV2020 attracted more registrants than BBB 2018 and CVB 2019. B Number of unique viewers weekly at the live Zoom Webinar sessions of BBV2020. C Number of views (clicks) of the video recordings when available weekly for BBV2020. Black line indicates best linear fit. Gray line indicates the average (mean) for all 16 weeks\nStrikingly, the total number of registered applicants for the BBV2020 was more than 4-fold higher compared to the total number of registered participants for the 2018 and the CVB 2019 meetings (Fig. 1A). The large number of registrants is likely due to free registration, no associated travel expenses, a world-wide reach, and that virtual meetings come with no obligations for the attendees. In contrast, the number of attendees of the in-person meetings (BBB 2018 and CVB2019) was limited and determined by the size of the conference site.\nThe first live BBV2020 seminar was attended by 419 participants. As the seminar series progressed, the participant number during live seminars gradually decreased, the average participant number was 220, the minimum was 108 (Fig. 1B). A similar trend was observed in the numbers of total recording views: the first seminar recording had a maximum of 944 views; in average, videos had 422 views (Fig. 1C). The trendline in Fig. 1C shows a drop in recording views over the course of the seminar series. These trends held regardless of academic rank as graduate students, postdoctoral fellows, and professors all showed a similar downward trend in live attendance (Additional file 1: Fig. S1A–C). Note that we were unable to capture recording data for week 2 due to a recording error and for week 13 due to the request by the speaker to not record.\nTogether, the high number of registrants suggests that the virtual BBV2020 seminar series sparked interest. Attendance of the live seminars and the numbers of views for the recorded videos decreased over time, which may be an indicator of “virtual meeting fatigue” or attendees being on their summer vacation.\n BBV2020 survey analysis By the end of the seminar series in September, every BBV2020 registrant received an invitation to participate in an online survey created by the organizers on August 23, 2020. The survey was designed for registrants to provide feedback and consisted of 10 questions: (1) demographic question to capture career level and career type; (2) single select multiple choice question to capture attendance of past in-person brain barrier meetings, (3) single select multiple choice question on how many recorded seminars were viewed, (4) Likert scale question regarding the enrichment BBV2020 provided for the community during the COVID-19 pandemic, (5) dichotomous question asking if the registrant would still participate in BBV2020 seminars if they were continued, (6) single select multiple choice question on attendance frequency, (7) single select multiple choice question on how many times the registrant has thus far attended a brain barriers meeting previously in person, (8) matrix question to capture affective factors, (9) matrix question to capture learning and teaching, and (10) matrix question to capture combined validity and practicality of the BBV2020 series. In addition, registrants responding to the survey had the opportunity to include general comments to the organizers in a separate text box.\nOverall, 23% of registrants (total of 299 registrants) responded to the survey. Over 89% of survey respondents agreed strongly that “Attending BBV2020 was a positive experience” and 63% of responders felt that “BBV2020 is appropriate for my research”. Generally, the respondents tended to agree with the following statement: “BBV2020 has enriched the connection with the Brain Barriers community in the gap that COVID-19 has created” (n = 298 respondents to this question of a total of 299 registrants, M = 1.44, SD = 0.660, 1 = Strongly Agree, 2 = Agree, 3 = Neutral, 4 = Disagree, 5 = Strongly Disagree; Fig. 2A). However, when posed with the statements “In the future, I would prefer to attend a virtual conference over an in-person conference” or “The virtual experience is not as effective compared to the in-person conferences”, responses were neutral (M = 3.24; Fig. 2B) indicating a positive view towards a virtual conference. A difference emerged when we compared BBV2020 participants based on their career level with their preference for in-person versus virtual meeting formats: using one-way ANOVA, we found a significant difference between groups (F(9286) = 2.607, p = 0.007). Using Tukey’s HSD post hoc test indicated that Master’s students were more likely to disagree with the following statement when compared to Faculty/Professors: “I would feel more comfortable attending Brain Barrier conferences/seminars in person, but was glad to attend virtually due to COVID19.” A strong trend emerged when Master’s and Ph.D. students were grouped together and compared to Post-Docs and Faculty/Professors in an independent-samples t test. Results show graduate students in general were more favorable to a virtual conference compared to colleagues more established in their careers (t(225) = -1.972, p = 0.061). The lack of significance is in part due to the low sample size (n = 74) for graduate students.\n\nFig. 2\nPost-seminar survey results. A Volunteer respondent result when posed with the prompt “BBV2020 has enriched connection with the Brain Barriers community in the gap that COVID-19 has created”. B Volunteer respondent results when posted with the prompts “In the future, I would prefer to attend a virtual conference over an in-person conference” (Blue) and “The virtual experience is not as effective compared to the in-person conferences” (Red)\n\nPost-seminar survey results. A Volunteer respondent result when posed with the prompt “BBV2020 has enriched connection with the Brain Barriers community in the gap that COVID-19 has created”. B Volunteer respondent results when posted with the prompts “In the future, I would prefer to attend a virtual conference over an in-person conference” (Blue) and “The virtual experience is not as effective compared to the in-person conferences” (Red)\nOverall, descriptive data revealed many participants felt BBV2020 was a positive experience. In fact, 99.3% of survey respondents noted they somewhat or strongly agree that their experience was positive, with only 4.7% saying they faced technical difficulties. Additionally, most respondents agreed that virtual conferences have an important role in research (93.2%) and are more accessible than in-person conferences (90.2%). Finally, more than half of respondents (56.9%) noted virtual conferences can do things in-person conferences cannot.\nNevertheless, there continued to be a desire among many BBV2020 participants to return to conferencing in person, with 13.5% saying they found it hard to focus in the virtual format. In fact, roughly one-third (33.9%) felt the virtual conference was not as effective as an in-person conference, and only one-quarter (23.3%) said they would prefer virtual conferences to in-person conferences in the future, while 35.8% were neutral.\nTogether, our findings suggest that while BBV2020 filled a need created by a global pandemic, the enthusiasm for returning to only in-person meetings remained relatively neutral suggesting that virtual platforms may play a critical role in dissemination of research and science in the future. Moreover, the data from this study show a trend between graduate students versus professors in their response with regard to in-person meetings indicating that career stage may influence the perspective of the usefulness of in-person versus virtual platforms.\nBy the end of the seminar series in September, every BBV2020 registrant received an invitation to participate in an online survey created by the organizers on August 23, 2020. The survey was designed for registrants to provide feedback and consisted of 10 questions: (1) demographic question to capture career level and career type; (2) single select multiple choice question to capture attendance of past in-person brain barrier meetings, (3) single select multiple choice question on how many recorded seminars were viewed, (4) Likert scale question regarding the enrichment BBV2020 provided for the community during the COVID-19 pandemic, (5) dichotomous question asking if the registrant would still participate in BBV2020 seminars if they were continued, (6) single select multiple choice question on attendance frequency, (7) single select multiple choice question on how many times the registrant has thus far attended a brain barriers meeting previously in person, (8) matrix question to capture affective factors, (9) matrix question to capture learning and teaching, and (10) matrix question to capture combined validity and practicality of the BBV2020 series. In addition, registrants responding to the survey had the opportunity to include general comments to the organizers in a separate text box.\nOverall, 23% of registrants (total of 299 registrants) responded to the survey. Over 89% of survey respondents agreed strongly that “Attending BBV2020 was a positive experience” and 63% of responders felt that “BBV2020 is appropriate for my research”. Generally, the respondents tended to agree with the following statement: “BBV2020 has enriched the connection with the Brain Barriers community in the gap that COVID-19 has created” (n = 298 respondents to this question of a total of 299 registrants, M = 1.44, SD = 0.660, 1 = Strongly Agree, 2 = Agree, 3 = Neutral, 4 = Disagree, 5 = Strongly Disagree; Fig. 2A). However, when posed with the statements “In the future, I would prefer to attend a virtual conference over an in-person conference” or “The virtual experience is not as effective compared to the in-person conferences”, responses were neutral (M = 3.24; Fig. 2B) indicating a positive view towards a virtual conference. A difference emerged when we compared BBV2020 participants based on their career level with their preference for in-person versus virtual meeting formats: using one-way ANOVA, we found a significant difference between groups (F(9286) = 2.607, p = 0.007). Using Tukey’s HSD post hoc test indicated that Master’s students were more likely to disagree with the following statement when compared to Faculty/Professors: “I would feel more comfortable attending Brain Barrier conferences/seminars in person, but was glad to attend virtually due to COVID19.” A strong trend emerged when Master’s and Ph.D. students were grouped together and compared to Post-Docs and Faculty/Professors in an independent-samples t test. Results show graduate students in general were more favorable to a virtual conference compared to colleagues more established in their careers (t(225) = -1.972, p = 0.061). The lack of significance is in part due to the low sample size (n = 74) for graduate students.\n\nFig. 2\nPost-seminar survey results. A Volunteer respondent result when posed with the prompt “BBV2020 has enriched connection with the Brain Barriers community in the gap that COVID-19 has created”. B Volunteer respondent results when posted with the prompts “In the future, I would prefer to attend a virtual conference over an in-person conference” (Blue) and “The virtual experience is not as effective compared to the in-person conferences” (Red)\n\nPost-seminar survey results. A Volunteer respondent result when posed with the prompt “BBV2020 has enriched connection with the Brain Barriers community in the gap that COVID-19 has created”. B Volunteer respondent results when posted with the prompts “In the future, I would prefer to attend a virtual conference over an in-person conference” (Blue) and “The virtual experience is not as effective compared to the in-person conferences” (Red)\nOverall, descriptive data revealed many participants felt BBV2020 was a positive experience. In fact, 99.3% of survey respondents noted they somewhat or strongly agree that their experience was positive, with only 4.7% saying they faced technical difficulties. Additionally, most respondents agreed that virtual conferences have an important role in research (93.2%) and are more accessible than in-person conferences (90.2%). Finally, more than half of respondents (56.9%) noted virtual conferences can do things in-person conferences cannot.\nNevertheless, there continued to be a desire among many BBV2020 participants to return to conferencing in person, with 13.5% saying they found it hard to focus in the virtual format. In fact, roughly one-third (33.9%) felt the virtual conference was not as effective as an in-person conference, and only one-quarter (23.3%) said they would prefer virtual conferences to in-person conferences in the future, while 35.8% were neutral.\nTogether, our findings suggest that while BBV2020 filled a need created by a global pandemic, the enthusiasm for returning to only in-person meetings remained relatively neutral suggesting that virtual platforms may play a critical role in dissemination of research and science in the future. Moreover, the data from this study show a trend between graduate students versus professors in their response with regard to in-person meetings indicating that career stage may influence the perspective of the usefulness of in-person versus virtual platforms.\n Global reach of BBV2020 During the seminar registration process we collected attendants’ affiliations and based on this information we determined the global reach of the BBV2020. Registrants of the BBV2020 seminar series were from 43 countries representing six of the seven continents (Fig. 3A, B). Compared to the in-person BBB 2018 and the CVB2019, more countries were represented at the BBV2020 (Fig. 3A).\n\nFig. 3\nGlobal reach of BBV2020. A BBV2020 had more countries represented compared to BBB 2018 and CVB 2019 alone. B World map showing countries represented by BBV2020. Countries where registrants participated in BBV2020: Red and Pink. Countries not represented at BBV2020: Light Gray and Dark Gray. Countries where registrants attended BBV2020 and at least one of the in-person meetings between Meeting 2018 or CVB 2019: Red. Countries where registrants attended BBV2020 and did not attend either BBB 2018 or CVB 2019: Pink. Countries that had no participation in any of the brain barriers meetings examined in this study: Light gray. Countries that had participation in either BBB 2018 or CVB 2019 that did not participate in BBV2020: Dark Gray\n\nGlobal reach of BBV2020. A BBV2020 had more countries represented compared to BBB 2018 and CVB 2019 alone. B World map showing countries represented by BBV2020. Countries where registrants participated in BBV2020: Red and Pink. Countries not represented at BBV2020: Light Gray and Dark Gray. Countries where registrants attended BBV2020 and at least one of the in-person meetings between Meeting 2018 or CVB 2019: Red. Countries where registrants attended BBV2020 and did not attend either BBB 2018 or CVB 2019: Pink. Countries that had no participation in any of the brain barriers meetings examined in this study: Light gray. Countries that had participation in either BBB 2018 or CVB 2019 that did not participate in BBV2020: Dark Gray\nWe also compared the number of participants from each country who joined BBV2020 vs. BBB 2018 and CVB 2019. We found that the number of participants per country worldwide was also higher compared to both in person meetings (Fig. 4A–F). These data show that the virtual nature of BBV2020 enabled researchers world-wide to participate and suggest that the outreach of a virtual event can extend beyond the field.\n\nFig. 4\nGlobal view of participation over BBV2020, BBB 2018, and CVB 2019. Representation of participants for (A) BBV2020, (B) BBB 2018, and (C) CVB 2019. Ordered from top to bottom in greatest number of registrants. Number of registrants scaled by color based on number of registrants per country for (D) BBV 2020, (E) BBB 2018, and (F) CVB 2019. Light Pink: 1–10 registrants; Dark Pink: 11–50 registrants; Red: 51–100 registrants and Dark Red: > 100 registrants\n\nGlobal view of participation over BBV2020, BBB 2018, and CVB 2019. Representation of participants for (A) BBV2020, (B) BBB 2018, and (C) CVB 2019. Ordered from top to bottom in greatest number of registrants. Number of registrants scaled by color based on number of registrants per country for (D) BBV 2020, (E) BBB 2018, and (F) CVB 2019. Light Pink: 1–10 registrants; Dark Pink: 11–50 registrants; Red: 51–100 registrants and Dark Red: > 100 registrants\nDuring the seminar registration process we collected attendants’ affiliations and based on this information we determined the global reach of the BBV2020. Registrants of the BBV2020 seminar series were from 43 countries representing six of the seven continents (Fig. 3A, B). Compared to the in-person BBB 2018 and the CVB2019, more countries were represented at the BBV2020 (Fig. 3A).\n\nFig. 3\nGlobal reach of BBV2020. A BBV2020 had more countries represented compared to BBB 2018 and CVB 2019 alone. B World map showing countries represented by BBV2020. Countries where registrants participated in BBV2020: Red and Pink. Countries not represented at BBV2020: Light Gray and Dark Gray. Countries where registrants attended BBV2020 and at least one of the in-person meetings between Meeting 2018 or CVB 2019: Red. Countries where registrants attended BBV2020 and did not attend either BBB 2018 or CVB 2019: Pink. Countries that had no participation in any of the brain barriers meetings examined in this study: Light gray. Countries that had participation in either BBB 2018 or CVB 2019 that did not participate in BBV2020: Dark Gray\n\nGlobal reach of BBV2020. A BBV2020 had more countries represented compared to BBB 2018 and CVB 2019 alone. B World map showing countries represented by BBV2020. Countries where registrants participated in BBV2020: Red and Pink. Countries not represented at BBV2020: Light Gray and Dark Gray. Countries where registrants attended BBV2020 and at least one of the in-person meetings between Meeting 2018 or CVB 2019: Red. Countries where registrants attended BBV2020 and did not attend either BBB 2018 or CVB 2019: Pink. Countries that had no participation in any of the brain barriers meetings examined in this study: Light gray. Countries that had participation in either BBB 2018 or CVB 2019 that did not participate in BBV2020: Dark Gray\nWe also compared the number of participants from each country who joined BBV2020 vs. BBB 2018 and CVB 2019. We found that the number of participants per country worldwide was also higher compared to both in person meetings (Fig. 4A–F). These data show that the virtual nature of BBV2020 enabled researchers world-wide to participate and suggest that the outreach of a virtual event can extend beyond the field.\n\nFig. 4\nGlobal view of participation over BBV2020, BBB 2018, and CVB 2019. Representation of participants for (A) BBV2020, (B) BBB 2018, and (C) CVB 2019. Ordered from top to bottom in greatest number of registrants. Number of registrants scaled by color based on number of registrants per country for (D) BBV 2020, (E) BBB 2018, and (F) CVB 2019. Light Pink: 1–10 registrants; Dark Pink: 11–50 registrants; Red: 51–100 registrants and Dark Red: > 100 registrants\n\nGlobal view of participation over BBV2020, BBB 2018, and CVB 2019. Representation of participants for (A) BBV2020, (B) BBB 2018, and (C) CVB 2019. Ordered from top to bottom in greatest number of registrants. Number of registrants scaled by color based on number of registrants per country for (D) BBV 2020, (E) BBB 2018, and (F) CVB 2019. Light Pink: 1–10 registrants; Dark Pink: 11–50 registrants; Red: 51–100 registrants and Dark Red: > 100 registrants\n Career stages represented at BBV2020 We examined the self-reported career stage of BBV2020 participants to that of BBB 2018 and CVB 2019 participants. Based on the survey, more than 46% of all participants attended a brain barriers conference/seminar for the first time. We also found that in-person meetings are attended by 42.4–48.9% academicians at the professor rank, graduate students make up 22.9–25.8% and postdoctoral fellows are 6.6–17.7% of the participants (Fig. 5). In contrast, at the BBV2020 19.6% of academicians were at the professor rank, graduate students made up 30.5% and postdoctoral fellows 16.1% of attendees. We also found that BBV2020 had a larger participation from industry (18.5% vs. 5.6–6.6%), and undergraduate students (3.2% vs. 1%) compared to both in person meetings. Together, our data suggest that a virtual platform seminar like BBV2020 reaches a broader range of scientific ranks and careers.\n\nFig. 5\nProportions of various career stages participating in BBV2020, BBB 2018, and CVB 2019. Proportions of various career stages participating in (A) BBV2020, (B) BBB 2018, and (C) CVB 2019. Colors correspond to career stage: Red (professor any rank), Green (graduate student any rank), Dark Blue (research faculty or researcher), Yellow (industry scientist), Orange (postdoctoral fellow), Purple (government/regulatory), Light Blue (clinician), Green (undergraduate student)\n\nProportions of various career stages participating in BBV2020, BBB 2018, and CVB 2019. Proportions of various career stages participating in (A) BBV2020, (B) BBB 2018, and (C) CVB 2019. Colors correspond to career stage: Red (professor any rank), Green (graduate student any rank), Dark Blue (research faculty or researcher), Yellow (industry scientist), Orange (postdoctoral fellow), Purple (government/regulatory), Light Blue (clinician), Green (undergraduate student)\nWe examined the self-reported career stage of BBV2020 participants to that of BBB 2018 and CVB 2019 participants. Based on the survey, more than 46% of all participants attended a brain barriers conference/seminar for the first time. We also found that in-person meetings are attended by 42.4–48.9% academicians at the professor rank, graduate students make up 22.9–25.8% and postdoctoral fellows are 6.6–17.7% of the participants (Fig. 5). In contrast, at the BBV2020 19.6% of academicians were at the professor rank, graduate students made up 30.5% and postdoctoral fellows 16.1% of attendees. We also found that BBV2020 had a larger participation from industry (18.5% vs. 5.6–6.6%), and undergraduate students (3.2% vs. 1%) compared to both in person meetings. Together, our data suggest that a virtual platform seminar like BBV2020 reaches a broader range of scientific ranks and careers.\n\nFig. 5\nProportions of various career stages participating in BBV2020, BBB 2018, and CVB 2019. Proportions of various career stages participating in (A) BBV2020, (B) BBB 2018, and (C) CVB 2019. Colors correspond to career stage: Red (professor any rank), Green (graduate student any rank), Dark Blue (research faculty or researcher), Yellow (industry scientist), Orange (postdoctoral fellow), Purple (government/regulatory), Light Blue (clinician), Green (undergraduate student)\n\nProportions of various career stages participating in BBV2020, BBB 2018, and CVB 2019. Proportions of various career stages participating in (A) BBV2020, (B) BBB 2018, and (C) CVB 2019. Colors correspond to career stage: Red (professor any rank), Green (graduate student any rank), Dark Blue (research faculty or researcher), Yellow (industry scientist), Orange (postdoctoral fellow), Purple (government/regulatory), Light Blue (clinician), Green (undergraduate student)\n\nBBV2020 attendance:\nOn May 14, 2020, we sent out the first advertisement for the BBV2020 seminar series via email using a listserv with email addresses of past conference attendees and via postings using LinkedIn, Facebook, and Twitter. By the start of the first seminar on May 20, 2020, more than 720 scientists had registered. By the end of the 16-week long BBV2020 seminar series, a total of 1302 registrants signed up on our listserv to receive the seminar link, speaker updates, and video recording links (Fig. 1A). We obtained data from two past in-person meetings from the respective conference chairs: (1) brain barriers meeting in 2018 (BBB 2018) and the (2) Cerebral Vascular Biology meeting in 2019 (CVB 2019).\n\nFig. 1\nAttendance Trends for Brain Barrier Research Conferences. A With 1,302 individuals signing up and added to the listserv, BBV2020 attracted more registrants than BBB 2018 and CVB 2019. B Number of unique viewers weekly at the live Zoom Webinar sessions of BBV2020. C Number of views (clicks) of the video recordings when available weekly for BBV2020. Black line indicates best linear fit. Gray line indicates the average (mean) for all 16 weeks\n\nAttendance Trends for Brain Barrier Research Conferences. A With 1,302 individuals signing up and added to the listserv, BBV2020 attracted more registrants than BBB 2018 and CVB 2019. B Number of unique viewers weekly at the live Zoom Webinar sessions of BBV2020. C Number of views (clicks) of the video recordings when available weekly for BBV2020. Black line indicates best linear fit. Gray line indicates the average (mean) for all 16 weeks\nStrikingly, the total number of registered applicants for the BBV2020 was more than 4-fold higher compared to the total number of registered participants for the 2018 and the CVB 2019 meetings (Fig. 1A). The large number of registrants is likely due to free registration, no associated travel expenses, a world-wide reach, and that virtual meetings come with no obligations for the attendees. In contrast, the number of attendees of the in-person meetings (BBB 2018 and CVB2019) was limited and determined by the size of the conference site.\nThe first live BBV2020 seminar was attended by 419 participants. As the seminar series progressed, the participant number during live seminars gradually decreased, the average participant number was 220, the minimum was 108 (Fig. 1B). A similar trend was observed in the numbers of total recording views: the first seminar recording had a maximum of 944 views; in average, videos had 422 views (Fig. 1C). The trendline in Fig. 1C shows a drop in recording views over the course of the seminar series. These trends held regardless of academic rank as graduate students, postdoctoral fellows, and professors all showed a similar downward trend in live attendance (Additional file 1: Fig. S1A–C). Note that we were unable to capture recording data for week 2 due to a recording error and for week 13 due to the request by the speaker to not record.\nTogether, the high number of registrants suggests that the virtual BBV2020 seminar series sparked interest. Attendance of the live seminars and the numbers of views for the recorded videos decreased over time, which may be an indicator of “virtual meeting fatigue” or attendees being on their summer vacation.\n\nBBV2020 survey analysis:\nBy the end of the seminar series in September, every BBV2020 registrant received an invitation to participate in an online survey created by the organizers on August 23, 2020. The survey was designed for registrants to provide feedback and consisted of 10 questions: (1) demographic question to capture career level and career type; (2) single select multiple choice question to capture attendance of past in-person brain barrier meetings, (3) single select multiple choice question on how many recorded seminars were viewed, (4) Likert scale question regarding the enrichment BBV2020 provided for the community during the COVID-19 pandemic, (5) dichotomous question asking if the registrant would still participate in BBV2020 seminars if they were continued, (6) single select multiple choice question on attendance frequency, (7) single select multiple choice question on how many times the registrant has thus far attended a brain barriers meeting previously in person, (8) matrix question to capture affective factors, (9) matrix question to capture learning and teaching, and (10) matrix question to capture combined validity and practicality of the BBV2020 series. In addition, registrants responding to the survey had the opportunity to include general comments to the organizers in a separate text box.\nOverall, 23% of registrants (total of 299 registrants) responded to the survey. Over 89% of survey respondents agreed strongly that “Attending BBV2020 was a positive experience” and 63% of responders felt that “BBV2020 is appropriate for my research”. Generally, the respondents tended to agree with the following statement: “BBV2020 has enriched the connection with the Brain Barriers community in the gap that COVID-19 has created” (n = 298 respondents to this question of a total of 299 registrants, M = 1.44, SD = 0.660, 1 = Strongly Agree, 2 = Agree, 3 = Neutral, 4 = Disagree, 5 = Strongly Disagree; Fig. 2A). However, when posed with the statements “In the future, I would prefer to attend a virtual conference over an in-person conference” or “The virtual experience is not as effective compared to the in-person conferences”, responses were neutral (M = 3.24; Fig. 2B) indicating a positive view towards a virtual conference. A difference emerged when we compared BBV2020 participants based on their career level with their preference for in-person versus virtual meeting formats: using one-way ANOVA, we found a significant difference between groups (F(9286) = 2.607, p = 0.007). Using Tukey’s HSD post hoc test indicated that Master’s students were more likely to disagree with the following statement when compared to Faculty/Professors: “I would feel more comfortable attending Brain Barrier conferences/seminars in person, but was glad to attend virtually due to COVID19.” A strong trend emerged when Master’s and Ph.D. students were grouped together and compared to Post-Docs and Faculty/Professors in an independent-samples t test. Results show graduate students in general were more favorable to a virtual conference compared to colleagues more established in their careers (t(225) = -1.972, p = 0.061). The lack of significance is in part due to the low sample size (n = 74) for graduate students.\n\nFig. 2\nPost-seminar survey results. A Volunteer respondent result when posed with the prompt “BBV2020 has enriched connection with the Brain Barriers community in the gap that COVID-19 has created”. B Volunteer respondent results when posted with the prompts “In the future, I would prefer to attend a virtual conference over an in-person conference” (Blue) and “The virtual experience is not as effective compared to the in-person conferences” (Red)\n\nPost-seminar survey results. A Volunteer respondent result when posed with the prompt “BBV2020 has enriched connection with the Brain Barriers community in the gap that COVID-19 has created”. B Volunteer respondent results when posted with the prompts “In the future, I would prefer to attend a virtual conference over an in-person conference” (Blue) and “The virtual experience is not as effective compared to the in-person conferences” (Red)\nOverall, descriptive data revealed many participants felt BBV2020 was a positive experience. In fact, 99.3% of survey respondents noted they somewhat or strongly agree that their experience was positive, with only 4.7% saying they faced technical difficulties. Additionally, most respondents agreed that virtual conferences have an important role in research (93.2%) and are more accessible than in-person conferences (90.2%). Finally, more than half of respondents (56.9%) noted virtual conferences can do things in-person conferences cannot.\nNevertheless, there continued to be a desire among many BBV2020 participants to return to conferencing in person, with 13.5% saying they found it hard to focus in the virtual format. In fact, roughly one-third (33.9%) felt the virtual conference was not as effective as an in-person conference, and only one-quarter (23.3%) said they would prefer virtual conferences to in-person conferences in the future, while 35.8% were neutral.\nTogether, our findings suggest that while BBV2020 filled a need created by a global pandemic, the enthusiasm for returning to only in-person meetings remained relatively neutral suggesting that virtual platforms may play a critical role in dissemination of research and science in the future. Moreover, the data from this study show a trend between graduate students versus professors in their response with regard to in-person meetings indicating that career stage may influence the perspective of the usefulness of in-person versus virtual platforms.\n\nGlobal reach of BBV2020:\nDuring the seminar registration process we collected attendants’ affiliations and based on this information we determined the global reach of the BBV2020. Registrants of the BBV2020 seminar series were from 43 countries representing six of the seven continents (Fig. 3A, B). Compared to the in-person BBB 2018 and the CVB2019, more countries were represented at the BBV2020 (Fig. 3A).\n\nFig. 3\nGlobal reach of BBV2020. A BBV2020 had more countries represented compared to BBB 2018 and CVB 2019 alone. B World map showing countries represented by BBV2020. Countries where registrants participated in BBV2020: Red and Pink. Countries not represented at BBV2020: Light Gray and Dark Gray. Countries where registrants attended BBV2020 and at least one of the in-person meetings between Meeting 2018 or CVB 2019: Red. Countries where registrants attended BBV2020 and did not attend either BBB 2018 or CVB 2019: Pink. Countries that had no participation in any of the brain barriers meetings examined in this study: Light gray. Countries that had participation in either BBB 2018 or CVB 2019 that did not participate in BBV2020: Dark Gray\n\nGlobal reach of BBV2020. A BBV2020 had more countries represented compared to BBB 2018 and CVB 2019 alone. B World map showing countries represented by BBV2020. Countries where registrants participated in BBV2020: Red and Pink. Countries not represented at BBV2020: Light Gray and Dark Gray. Countries where registrants attended BBV2020 and at least one of the in-person meetings between Meeting 2018 or CVB 2019: Red. Countries where registrants attended BBV2020 and did not attend either BBB 2018 or CVB 2019: Pink. Countries that had no participation in any of the brain barriers meetings examined in this study: Light gray. Countries that had participation in either BBB 2018 or CVB 2019 that did not participate in BBV2020: Dark Gray\nWe also compared the number of participants from each country who joined BBV2020 vs. BBB 2018 and CVB 2019. We found that the number of participants per country worldwide was also higher compared to both in person meetings (Fig. 4A–F). These data show that the virtual nature of BBV2020 enabled researchers world-wide to participate and suggest that the outreach of a virtual event can extend beyond the field.\n\nFig. 4\nGlobal view of participation over BBV2020, BBB 2018, and CVB 2019. Representation of participants for (A) BBV2020, (B) BBB 2018, and (C) CVB 2019. Ordered from top to bottom in greatest number of registrants. Number of registrants scaled by color based on number of registrants per country for (D) BBV 2020, (E) BBB 2018, and (F) CVB 2019. Light Pink: 1–10 registrants; Dark Pink: 11–50 registrants; Red: 51–100 registrants and Dark Red: > 100 registrants\n\nGlobal view of participation over BBV2020, BBB 2018, and CVB 2019. Representation of participants for (A) BBV2020, (B) BBB 2018, and (C) CVB 2019. Ordered from top to bottom in greatest number of registrants. Number of registrants scaled by color based on number of registrants per country for (D) BBV 2020, (E) BBB 2018, and (F) CVB 2019. Light Pink: 1–10 registrants; Dark Pink: 11–50 registrants; Red: 51–100 registrants and Dark Red: > 100 registrants\n\nCareer stages represented at BBV2020:\nWe examined the self-reported career stage of BBV2020 participants to that of BBB 2018 and CVB 2019 participants. Based on the survey, more than 46% of all participants attended a brain barriers conference/seminar for the first time. We also found that in-person meetings are attended by 42.4–48.9% academicians at the professor rank, graduate students make up 22.9–25.8% and postdoctoral fellows are 6.6–17.7% of the participants (Fig. 5). In contrast, at the BBV2020 19.6% of academicians were at the professor rank, graduate students made up 30.5% and postdoctoral fellows 16.1% of attendees. We also found that BBV2020 had a larger participation from industry (18.5% vs. 5.6–6.6%), and undergraduate students (3.2% vs. 1%) compared to both in person meetings. Together, our data suggest that a virtual platform seminar like BBV2020 reaches a broader range of scientific ranks and careers.\n\nFig. 5\nProportions of various career stages participating in BBV2020, BBB 2018, and CVB 2019. Proportions of various career stages participating in (A) BBV2020, (B) BBB 2018, and (C) CVB 2019. Colors correspond to career stage: Red (professor any rank), Green (graduate student any rank), Dark Blue (research faculty or researcher), Yellow (industry scientist), Orange (postdoctoral fellow), Purple (government/regulatory), Light Blue (clinician), Green (undergraduate student)\n\nProportions of various career stages participating in BBV2020, BBB 2018, and CVB 2019. Proportions of various career stages participating in (A) BBV2020, (B) BBB 2018, and (C) CVB 2019. Colors correspond to career stage: Red (professor any rank), Green (graduate student any rank), Dark Blue (research faculty or researcher), Yellow (industry scientist), Orange (postdoctoral fellow), Purple (government/regulatory), Light Blue (clinician), Green (undergraduate student)\n\nDiscussion:\nSeemingly overnight the worldwide COVID-19 pandemic moved virtual meetings, seminars and conferences from the sidelines to center stage. For scientists, connecting on a virtual platform to share data and discuss ideas became the new norm. For many professional societies and research networks a virtual format was a viable option to offset the burden of the pandemic [6, 7]. We organized the BBV2020 seminar series for researchers in the brain barriers field to fill the vacuum canceled in-person conferences left in 2020. Using data collected throughout the BBV2020 seminar series from Zoom Webinar live attendance, number of views from posted videos, and a post-seminar survey, we captured the impact of BBV2020 on the brain barriers field. We compared these data with those from past in-person conferences and assessed the reach of the BBV2020 virtual seminar series with in-person meetings. While we acknowledge that comparisons between a seminar series to a conference may have its limitations, this was the first stand-in virtual event for the brain barriers field that may provide insight for future planning. In the following sections we discuss our findings in the context of the existing literature and provide a future perspective on how a virtual platform could impact the brain barriers field beyond the pandemic.\n Attendance: scientists around the globe Due to the pandemic, the idea and subsequent organization of the BBV2020 occurred in a relatively short time frame of about 4 weeks. This is in stark contrast to in-person meetings that are often scheduled and organized months and years ahead of the actual event. Advertisement of the BBV2020 encompassed personal email, postings on social media, and utilization of the International Brain Barriers Society email distribution list. Continuous advertisement resulted in the registration of over 1300 scientists. The total registrant number cannot be directly compared to that of in-person meetings since those are more complex in nature, have maximum capacity limits, and involve other factors such as travel expenses and time commitment. Similar to other meetings which transitioned to online formats during the pandemic, BBV2020 had a large increase in registrants when compared to previous in-person meetings (~7-fold and 4.5-fold higher compared to 2018 and 2019, respectively; Fig. 1; [8]. Given that previous brain barriers meetings usually have a few hundred participants, the high number of registrants could be an indicator for high interest in the field beyond traditional brain barrier research groups. In contrast, these high registrant numbers could also be due, in part, to BBV2020 being a no-cost event or could be explained by pandemic-related cancellations resulting in relatively event-free calendar of the participants. While there were more than 1,300 registrants for BBV2020, no more than 419 ever attended a live webinar. Additionally, 323 people registered for BBV2020 but never attended a live webinar. They may, however, have participated by watching the video recordings after the live webinar. A downward trend of attendance numbers for live seminars was observed throughout the course of the 16-week series (Fig. 1B). The recorded seminars drew considerable interest initially with a maximum of 944 views in week 4 of the series, but later in the series, video views dropped to about 200 per seminar (Fig. 1C). Reasons for these downward trends remain speculative, but could be due to the summer break, the selected time for the seminar making it difficult for researchers in certain time zones to attend live, zoom fatigue, or increasing responsibilities in re-opened research laboratories or at home. The downward trends we observed was in contrast to a similar virtual seminar series in another field that remained relatively flat over the course of the summer [4]. Given that average attendance of the BBV2020 was 220 per seminar and typical attendance for in-person conferences in the brain barriers field range between 150 and 300 participants (caveat: one of the in-person conferences has a strict 182-person maximum limit), we conclude that a core group of participants attended most sessions.\nDue to the pandemic, the idea and subsequent organization of the BBV2020 occurred in a relatively short time frame of about 4 weeks. This is in stark contrast to in-person meetings that are often scheduled and organized months and years ahead of the actual event. Advertisement of the BBV2020 encompassed personal email, postings on social media, and utilization of the International Brain Barriers Society email distribution list. Continuous advertisement resulted in the registration of over 1300 scientists. The total registrant number cannot be directly compared to that of in-person meetings since those are more complex in nature, have maximum capacity limits, and involve other factors such as travel expenses and time commitment. Similar to other meetings which transitioned to online formats during the pandemic, BBV2020 had a large increase in registrants when compared to previous in-person meetings (~7-fold and 4.5-fold higher compared to 2018 and 2019, respectively; Fig. 1; [8]. Given that previous brain barriers meetings usually have a few hundred participants, the high number of registrants could be an indicator for high interest in the field beyond traditional brain barrier research groups. In contrast, these high registrant numbers could also be due, in part, to BBV2020 being a no-cost event or could be explained by pandemic-related cancellations resulting in relatively event-free calendar of the participants. While there were more than 1,300 registrants for BBV2020, no more than 419 ever attended a live webinar. Additionally, 323 people registered for BBV2020 but never attended a live webinar. They may, however, have participated by watching the video recordings after the live webinar. A downward trend of attendance numbers for live seminars was observed throughout the course of the 16-week series (Fig. 1B). The recorded seminars drew considerable interest initially with a maximum of 944 views in week 4 of the series, but later in the series, video views dropped to about 200 per seminar (Fig. 1C). Reasons for these downward trends remain speculative, but could be due to the summer break, the selected time for the seminar making it difficult for researchers in certain time zones to attend live, zoom fatigue, or increasing responsibilities in re-opened research laboratories or at home. The downward trends we observed was in contrast to a similar virtual seminar series in another field that remained relatively flat over the course of the summer [4]. Given that average attendance of the BBV2020 was 220 per seminar and typical attendance for in-person conferences in the brain barriers field range between 150 and 300 participants (caveat: one of the in-person conferences has a strict 182-person maximum limit), we conclude that a core group of participants attended most sessions.\n Survey: feedback from participants A survey conducted after the seminar series revealed that most participants felt that BBV2020 enhanced the connection to the brain barriers scientific community during the COVID-19 pandemic (Fig. 2A). Differences emerged between how graduate students and professors viewed BBV2020: graduate students in general were more favorable of the virtual format compared to faculty. This may be partially explained by a lack of funding for graduate student travel or political “red tape” (e.g. visas) for some researchers [9]. Additionally, students may disproportionately adapt more easily and rapidly to technology usage compared to senior colleagues [10]. Clearly, another survey would be needed to determine if a virtual component outside of a pandemic would be positively viewed by researchers in the brain barriers field.\nA survey conducted after the seminar series revealed that most participants felt that BBV2020 enhanced the connection to the brain barriers scientific community during the COVID-19 pandemic (Fig. 2A). Differences emerged between how graduate students and professors viewed BBV2020: graduate students in general were more favorable of the virtual format compared to faculty. This may be partially explained by a lack of funding for graduate student travel or political “red tape” (e.g. visas) for some researchers [9]. Additionally, students may disproportionately adapt more easily and rapidly to technology usage compared to senior colleagues [10]. Clearly, another survey would be needed to determine if a virtual component outside of a pandemic would be positively viewed by researchers in the brain barriers field.\n Virtual seminars: a powerful opportunity One major advantage of a virtual seminar series such as the BBV2020 is the lack of a travel burden. Taking off the financial and time-commitment associated with in-person meetings has been shown to increase the diversity of attendees [3, 9, 11]. Based on those findings, we anticipated that BBV2020 would have representation from more countries than past in-person meetings. As shown in Fig. 3, registrants from 43 different countries were represented at BBV2020, which is 40% more compared to BBB 2018 (23 countries) and CVB 2019 (25 countries; Fig. 3). In particular, there was high participation from countries in Southeast Asia, Middle East, Africa, and South America that had not been represented at in-person conferences held in 2018 and 2019 (Figs. 3B, 4). Unsurprisingly however, considering countries represented at previous in-person meetings and due to the number of registrants for BBV2020, there was generally a greater participation from each country represented than in 2018 meeting and CVB 2019 [3, 8]. Due to the time of the BBV2020 live sessions (noon to 1 pm Eastern US Time), the majority of participants were from North America, South America, and Europe (Fig. 4). While time zone challenges are common among virtual events, switching between time zones or holding a “flipped” session (flipping evening sessions from Europe to the USA and vice versa) could increase participation from Asia and Oceania [12]. However, offering a virtual series that constantly switches the seminar time may be difficult to manage and could be confusing for participants. Another strength of virtual seminars is that they are more accessible and inclusive especially for junior researchers in the field. The in-person meetings in 2018 and 2019 were dominated by academic faculty at any professor rank and graduate students (Fig. 5). In contrast, BBV2020 saw a large reduction in the percentage of participating faculty (over 40% in person to less than 20% for BBV2020) and a larger proportion of graduate students (22–25% in person to over 30% for BBV2020), industry scientists (about 6% in person to 18.5% for BBV2020), and governmental/regulatory personnel who had signed up (Fig. 5). In addition, a weekly virtual seminar series allows scientist to pick-and-choose the sessions they are interested in, a choice that could interfere with the observed trends shown in Fig. 1. Our data further support the trends observed across a variety of disciplines who have shifted their conferences from in-person to online events, revealing that virtual conferences are more flexible, and more inclusive and accessible worldwide, especially for early-career scientists [9]. Virtual seminars also provide an opportunity for the organizers to invite prominent speakers of a field on a relatively small budget. In general, virtual conferences are known to enhance the accessibility for busy professionals. A recently published Nature poll indicates that 74% of scientists want virtual meetings to stay after the pandemic (925 poll responses) [13]. Easier accessibility, lower carbon footprint, and lower costs were the driving factors chosen in favor for virtual meetings in this poll. Moreover, virtual seminars provide an easy and affordable opportunity for researchers with disabilities, researchers with high teaching loads, or for scientists with parenting and other responsibilities to stay connected to their respective research community. During the COVID-19 pandemic, virtual seminars took center stage and raised the standard for accessibility, interactions, inclusivity, and equity in science [9]. Virtual events changed how we think about meetings and they constitute a yet underestimated, but powerful tool to share science and connect with each other. By rethinking how meetings are held, various fields have had different degrees of participation depending on the availability of the talks or content versus live sessions. These virtual platforms have the potential to create a “connectivity cascade” reaching more people than traditional meetings that are confined by a single geographical location and time [14]. BBV2020 had an impressive reach worldwide, however, there was still a notable deficit in participants from underdeveloped countries, especially Africa. These discrepancies have been noted in the context of expanding telehealth to African countries that still remain challenging due to monopolization of telecommunication infrastructure, political will, and access to adequate broadband efficiency [15]. Despite the advantages of BBV2020, we recognize that limitations remain, such as one-way delivery of material to participants. Lack of critical face-to-face interactions allowing junior scientists to have meaningful two-way communication with other participants was notably lacking in BBV2020 as seen in other virtual conferences as well [16]. The future of such virtual seminars, meetings, or conferences may lie in their inclusion as part of in-person meetings according to a recently published “best practice” guideline for virtual meetings [17]. For example, a virtual component could be included in plenary sessions, workshops, small group discussions, poster sessions, and/or social events of in-person conferences. BBV2020 represented only one aspect (a seminar held in the form of a webinar) of these common meeting events, but future events could certainly enhance the virtual experience. Regardless, virtual components could be exploited to increase diversity in the brain barriers research field which is consistent with the NINDS strategy for enhancing the diversity of neuroscience researchers [18].\nOne major advantage of a virtual seminar series such as the BBV2020 is the lack of a travel burden. Taking off the financial and time-commitment associated with in-person meetings has been shown to increase the diversity of attendees [3, 9, 11]. Based on those findings, we anticipated that BBV2020 would have representation from more countries than past in-person meetings. As shown in Fig. 3, registrants from 43 different countries were represented at BBV2020, which is 40% more compared to BBB 2018 (23 countries) and CVB 2019 (25 countries; Fig. 3). In particular, there was high participation from countries in Southeast Asia, Middle East, Africa, and South America that had not been represented at in-person conferences held in 2018 and 2019 (Figs. 3B, 4). Unsurprisingly however, considering countries represented at previous in-person meetings and due to the number of registrants for BBV2020, there was generally a greater participation from each country represented than in 2018 meeting and CVB 2019 [3, 8]. Due to the time of the BBV2020 live sessions (noon to 1 pm Eastern US Time), the majority of participants were from North America, South America, and Europe (Fig. 4). While time zone challenges are common among virtual events, switching between time zones or holding a “flipped” session (flipping evening sessions from Europe to the USA and vice versa) could increase participation from Asia and Oceania [12]. However, offering a virtual series that constantly switches the seminar time may be difficult to manage and could be confusing for participants. Another strength of virtual seminars is that they are more accessible and inclusive especially for junior researchers in the field. The in-person meetings in 2018 and 2019 were dominated by academic faculty at any professor rank and graduate students (Fig. 5). In contrast, BBV2020 saw a large reduction in the percentage of participating faculty (over 40% in person to less than 20% for BBV2020) and a larger proportion of graduate students (22–25% in person to over 30% for BBV2020), industry scientists (about 6% in person to 18.5% for BBV2020), and governmental/regulatory personnel who had signed up (Fig. 5). In addition, a weekly virtual seminar series allows scientist to pick-and-choose the sessions they are interested in, a choice that could interfere with the observed trends shown in Fig. 1. Our data further support the trends observed across a variety of disciplines who have shifted their conferences from in-person to online events, revealing that virtual conferences are more flexible, and more inclusive and accessible worldwide, especially for early-career scientists [9]. Virtual seminars also provide an opportunity for the organizers to invite prominent speakers of a field on a relatively small budget. In general, virtual conferences are known to enhance the accessibility for busy professionals. A recently published Nature poll indicates that 74% of scientists want virtual meetings to stay after the pandemic (925 poll responses) [13]. Easier accessibility, lower carbon footprint, and lower costs were the driving factors chosen in favor for virtual meetings in this poll. Moreover, virtual seminars provide an easy and affordable opportunity for researchers with disabilities, researchers with high teaching loads, or for scientists with parenting and other responsibilities to stay connected to their respective research community. During the COVID-19 pandemic, virtual seminars took center stage and raised the standard for accessibility, interactions, inclusivity, and equity in science [9]. Virtual events changed how we think about meetings and they constitute a yet underestimated, but powerful tool to share science and connect with each other. By rethinking how meetings are held, various fields have had different degrees of participation depending on the availability of the talks or content versus live sessions. These virtual platforms have the potential to create a “connectivity cascade” reaching more people than traditional meetings that are confined by a single geographical location and time [14]. BBV2020 had an impressive reach worldwide, however, there was still a notable deficit in participants from underdeveloped countries, especially Africa. These discrepancies have been noted in the context of expanding telehealth to African countries that still remain challenging due to monopolization of telecommunication infrastructure, political will, and access to adequate broadband efficiency [15]. Despite the advantages of BBV2020, we recognize that limitations remain, such as one-way delivery of material to participants. Lack of critical face-to-face interactions allowing junior scientists to have meaningful two-way communication with other participants was notably lacking in BBV2020 as seen in other virtual conferences as well [16]. The future of such virtual seminars, meetings, or conferences may lie in their inclusion as part of in-person meetings according to a recently published “best practice” guideline for virtual meetings [17]. For example, a virtual component could be included in plenary sessions, workshops, small group discussions, poster sessions, and/or social events of in-person conferences. BBV2020 represented only one aspect (a seminar held in the form of a webinar) of these common meeting events, but future events could certainly enhance the virtual experience. Regardless, virtual components could be exploited to increase diversity in the brain barriers research field which is consistent with the NINDS strategy for enhancing the diversity of neuroscience researchers [18].\n\nAttendance: scientists around the globe:\nDue to the pandemic, the idea and subsequent organization of the BBV2020 occurred in a relatively short time frame of about 4 weeks. This is in stark contrast to in-person meetings that are often scheduled and organized months and years ahead of the actual event. Advertisement of the BBV2020 encompassed personal email, postings on social media, and utilization of the International Brain Barriers Society email distribution list. Continuous advertisement resulted in the registration of over 1300 scientists. The total registrant number cannot be directly compared to that of in-person meetings since those are more complex in nature, have maximum capacity limits, and involve other factors such as travel expenses and time commitment. Similar to other meetings which transitioned to online formats during the pandemic, BBV2020 had a large increase in registrants when compared to previous in-person meetings (~7-fold and 4.5-fold higher compared to 2018 and 2019, respectively; Fig. 1; [8]. Given that previous brain barriers meetings usually have a few hundred participants, the high number of registrants could be an indicator for high interest in the field beyond traditional brain barrier research groups. In contrast, these high registrant numbers could also be due, in part, to BBV2020 being a no-cost event or could be explained by pandemic-related cancellations resulting in relatively event-free calendar of the participants. While there were more than 1,300 registrants for BBV2020, no more than 419 ever attended a live webinar. Additionally, 323 people registered for BBV2020 but never attended a live webinar. They may, however, have participated by watching the video recordings after the live webinar. A downward trend of attendance numbers for live seminars was observed throughout the course of the 16-week series (Fig. 1B). The recorded seminars drew considerable interest initially with a maximum of 944 views in week 4 of the series, but later in the series, video views dropped to about 200 per seminar (Fig. 1C). Reasons for these downward trends remain speculative, but could be due to the summer break, the selected time for the seminar making it difficult for researchers in certain time zones to attend live, zoom fatigue, or increasing responsibilities in re-opened research laboratories or at home. The downward trends we observed was in contrast to a similar virtual seminar series in another field that remained relatively flat over the course of the summer [4]. Given that average attendance of the BBV2020 was 220 per seminar and typical attendance for in-person conferences in the brain barriers field range between 150 and 300 participants (caveat: one of the in-person conferences has a strict 182-person maximum limit), we conclude that a core group of participants attended most sessions.\n\nSurvey: feedback from participants:\nA survey conducted after the seminar series revealed that most participants felt that BBV2020 enhanced the connection to the brain barriers scientific community during the COVID-19 pandemic (Fig. 2A). Differences emerged between how graduate students and professors viewed BBV2020: graduate students in general were more favorable of the virtual format compared to faculty. This may be partially explained by a lack of funding for graduate student travel or political “red tape” (e.g. visas) for some researchers [9]. Additionally, students may disproportionately adapt more easily and rapidly to technology usage compared to senior colleagues [10]. Clearly, another survey would be needed to determine if a virtual component outside of a pandemic would be positively viewed by researchers in the brain barriers field.\n\nVirtual seminars: a powerful opportunity:\nOne major advantage of a virtual seminar series such as the BBV2020 is the lack of a travel burden. Taking off the financial and time-commitment associated with in-person meetings has been shown to increase the diversity of attendees [3, 9, 11]. Based on those findings, we anticipated that BBV2020 would have representation from more countries than past in-person meetings. As shown in Fig. 3, registrants from 43 different countries were represented at BBV2020, which is 40% more compared to BBB 2018 (23 countries) and CVB 2019 (25 countries; Fig. 3). In particular, there was high participation from countries in Southeast Asia, Middle East, Africa, and South America that had not been represented at in-person conferences held in 2018 and 2019 (Figs. 3B, 4). Unsurprisingly however, considering countries represented at previous in-person meetings and due to the number of registrants for BBV2020, there was generally a greater participation from each country represented than in 2018 meeting and CVB 2019 [3, 8]. Due to the time of the BBV2020 live sessions (noon to 1 pm Eastern US Time), the majority of participants were from North America, South America, and Europe (Fig. 4). While time zone challenges are common among virtual events, switching between time zones or holding a “flipped” session (flipping evening sessions from Europe to the USA and vice versa) could increase participation from Asia and Oceania [12]. However, offering a virtual series that constantly switches the seminar time may be difficult to manage and could be confusing for participants. Another strength of virtual seminars is that they are more accessible and inclusive especially for junior researchers in the field. The in-person meetings in 2018 and 2019 were dominated by academic faculty at any professor rank and graduate students (Fig. 5). In contrast, BBV2020 saw a large reduction in the percentage of participating faculty (over 40% in person to less than 20% for BBV2020) and a larger proportion of graduate students (22–25% in person to over 30% for BBV2020), industry scientists (about 6% in person to 18.5% for BBV2020), and governmental/regulatory personnel who had signed up (Fig. 5). In addition, a weekly virtual seminar series allows scientist to pick-and-choose the sessions they are interested in, a choice that could interfere with the observed trends shown in Fig. 1. Our data further support the trends observed across a variety of disciplines who have shifted their conferences from in-person to online events, revealing that virtual conferences are more flexible, and more inclusive and accessible worldwide, especially for early-career scientists [9]. Virtual seminars also provide an opportunity for the organizers to invite prominent speakers of a field on a relatively small budget. In general, virtual conferences are known to enhance the accessibility for busy professionals. A recently published Nature poll indicates that 74% of scientists want virtual meetings to stay after the pandemic (925 poll responses) [13]. Easier accessibility, lower carbon footprint, and lower costs were the driving factors chosen in favor for virtual meetings in this poll. Moreover, virtual seminars provide an easy and affordable opportunity for researchers with disabilities, researchers with high teaching loads, or for scientists with parenting and other responsibilities to stay connected to their respective research community. During the COVID-19 pandemic, virtual seminars took center stage and raised the standard for accessibility, interactions, inclusivity, and equity in science [9]. Virtual events changed how we think about meetings and they constitute a yet underestimated, but powerful tool to share science and connect with each other. By rethinking how meetings are held, various fields have had different degrees of participation depending on the availability of the talks or content versus live sessions. These virtual platforms have the potential to create a “connectivity cascade” reaching more people than traditional meetings that are confined by a single geographical location and time [14]. BBV2020 had an impressive reach worldwide, however, there was still a notable deficit in participants from underdeveloped countries, especially Africa. These discrepancies have been noted in the context of expanding telehealth to African countries that still remain challenging due to monopolization of telecommunication infrastructure, political will, and access to adequate broadband efficiency [15]. Despite the advantages of BBV2020, we recognize that limitations remain, such as one-way delivery of material to participants. Lack of critical face-to-face interactions allowing junior scientists to have meaningful two-way communication with other participants was notably lacking in BBV2020 as seen in other virtual conferences as well [16]. The future of such virtual seminars, meetings, or conferences may lie in their inclusion as part of in-person meetings according to a recently published “best practice” guideline for virtual meetings [17]. For example, a virtual component could be included in plenary sessions, workshops, small group discussions, poster sessions, and/or social events of in-person conferences. BBV2020 represented only one aspect (a seminar held in the form of a webinar) of these common meeting events, but future events could certainly enhance the virtual experience. Regardless, virtual components could be exploited to increase diversity in the brain barriers research field which is consistent with the NINDS strategy for enhancing the diversity of neuroscience researchers [18].\n\nConclusion and future perspectives:\nOverall, the Virtual BBV2020 Seminar Series created during the COVID-19 pandemic was positively received by the brain barriers research community. Key benefits of virtual seminars like the BBV2020 are that they are convenient, affordable, eco-friendly, reach a broad audience, and are a good tool to increase diversity and equity. Thus, incorporating a virtual component during an in-person meeting or offering a virtual counterpart in addition to in-person meetings could help increase the outreach of a small research field like the brain barriers field. Nevertheless, limitations remain such as a lack of interaction between speakers and participants, which is particularly critical for junior researchers. And although BBV2020 expanded the reach beyond past in-person meetings, challenges reaching scientists in underdeveloped countries remain. Together, BBV2020 was created in a time of crisis but has the potential to thrive in the post-pandemic world. Embracing virtual scientific events may allow us to advance the brain barriers research field and have an impact on how we exchange science in the future.\n\nSupplementary Information:\n \nAdditional file 1: Figure S1. Attendance Trends for BBV2020 by Academic Rank. Number of unique viewers weekly at the live Zoom Webinar sessions of BBV2020 separated by A) Graduate Student, B) Postdoctoral Fellow, and C) Professor (any rank).\nAdditional file 1: Figure S1. Attendance Trends for BBV2020 by Academic Rank. Number of unique viewers weekly at the live Zoom Webinar sessions of BBV2020 separated by A) Graduate Student, B) Postdoctoral Fellow, and C) Professor (any rank).\n\nAdditional file 1: Figure S1. Attendance Trends for BBV2020 by Academic Rank. Number of unique viewers weekly at the live Zoom Webinar sessions of BBV2020 separated by A) Graduate Student, B) Postdoctoral Fellow, and C) Professor (any rank).\nAdditional file 1: Figure S1. Attendance Trends for BBV2020 by Academic Rank. Number of unique viewers weekly at the live Zoom Webinar sessions of BBV2020 separated by A) Graduate Student, B) Postdoctoral Fellow, and C) Professor (any rank).\n\n"},"whole_article_abstract":{"kind":"string","value":"Background:\nScientific conferences are vital communication events for scientists in academia, industry, and government agencies. In the brain barriers research field, several international conferences exist that allow researchers to present data, share knowledge, and discuss novel ideas and concepts. These meetings are critical platforms for researchers to connect and exchange breakthrough findings on a regular basis. Due to the worldwide COVID-19 pandemic, all in-person meetings were canceled in 2020. In response, we launched the Brain Barriers Virtual 2020 (BBV2020) seminar series, the first stand-in virtual event for the brain barriers field, to offer scientists a virtual platform to present their work. Here we report the aggregate attendance information on two in-person meetings compared with BBV2020 and comment on the utility of the virtual platform.\n\nMethods:\nThe BBV2020 seminar series was hosted on a Zoom webinar platform and was free of cost for participants. Using registration- and Zoom-based data from the BBV2020 virtual seminar series and survey data collected from BBV2020 participants, we analyzed attendance trends, global reach, participation based on career stage, and engagement of BBV2020. We compared these data with those from two previous in-person conferences, a BBB meeting held in 2018 and CVB 2019.\n\nResults:\nWe found that BBV2020 seminar participation steadily decreased over the course of the series. In contrast, live participation was consistently above 100 attendees and recording views were above 200 views per seminar. We also found that participants valued BBV2020 as a supplement during the COVID-19 pandemic in 2020. Based on one post-BBV2020 survey, the majority of participants indicated that they would prefer in-person meetings but would welcome a virtual component to future in-person meetings. Compared to in-person meetings, BBV2020 enabled participation from a broad range of career stages and was attended by scientists in academic, industry, and government agencies from a wide range of countries worldwide.\n\nConclusions:\nOur findings suggest that a virtual event such as the BBV2020 seminar series provides easy access to science for researchers across all career stages around the globe. However, we recognize that limitations exist. Regardless, such a virtual event could be a valuable tool for the brain barriers community to reach and engage scientists worldwide to further grow the brain barriers research field in the future."},"background_conclusion_text":{"kind":"string","value":"Introduction:\nOn March 11, 2020, the World Health Organization (WHO) declared the coronavirus disease (COVID-19) outbreak a global pandemic [1]. Shortly after, countries worldwide issued stay-at-home orders and lockdowns: the COVID-19 pandemic had forced the world to come to an abrupt halt [2]. With global air travel restrictions and travel bans in place, enterprises including academic institutions, industry and government agencies required individuals to call off their business travel. As a result, scheduled scientific conferences were canceled throughout 2020 [3, 4]. These cancelations created a sudden void in scientific communication between researchers worldwide and forced research communities to be creative and adapt. One possibility to continue scientific communication and interaction was by switching conferences and seminars to virtual online events using novel digital platforms enabling researchers to connect and share science [3, 4].\nAs a consequence, major conferences and meetings in the brain barriers research field, including the “Barriers of the CNS Gordon Research Conference” (GRC 2020) and the international symposium on “Signal Transduction at the Blood–Brain Barriers” in Bari, Italy were first postponed until 2021 and due to the ongoing pandemic subsequently moved to 2022. The “Cerebral Vascular Biology” (CVB) conference in Uppsala, Sweden planned for 2021 was postponed until 2023. Postponing these meetings left organizers and conference chairs in a difficult position and researchers without a platform to present and share their data. Thus, these postponements created a critical need for the field to stay connected and an opportunity to establish a virtual event that could supplement the postponed conferences in 2020. The Brain Barriers Virtual 2020 Seminar Series (BBV2020) addressed this need by providing a forum for brain barrier scientists worldwide to come together online during the COVID-19 crisis in 2020. BBV2020 seminars were held weekly from May 20, 2020 to September 2, 2020 in a 1-h format featuring a live session with an invited speaker followed by time for questions and answers from the community. The seminar series was hosted on a Zoom webinar platform and was free for anyone to attend. BBV2020 served as a forum for invited speakers to present their work to the brain barriers community and enabled this community to discuss brain barriers science from the safety of their home office. Over 1300 scientists around the world were registered, each seminar was viewed live by 100–400 participants, and the video-recordings received 150–900 views each week. Participants were from academia, industry and government agencies at all career stages from around the globe.\nIn this report, we share participation data that give insight into the impact this virtual seminar series had on the brain barriers field during the COVID-19 pandemic. We compare these data with existing data from past in-person brain barriers meetings and draw conclusions from these data on the potential value of virtual meetings for the brain barriers research field in the future. While the organization of virtual events during the pandemic may seem self-evident, reflection on the impact carries utility for events in the future.\n\nConclusion and future perspectives:\n\nAdditional file 1: Figure S1. Attendance Trends for BBV2020 by Academic Rank. Number of unique viewers weekly at the live Zoom Webinar sessions of BBV2020 separated by A) Graduate Student, B) Postdoctoral Fellow, and C) Professor (any rank).\nAdditional file 1: Figure S1. Attendance Trends for BBV2020 by Academic Rank. Number of unique viewers weekly at the live Zoom Webinar sessions of BBV2020 separated by A) Graduate Student, B) Postdoctoral Fellow, and C) Professor (any rank)."},"background_conclusion_abstract":{"kind":"string","value":"Background:\nScientific conferences are vital communication events for scientists in academia, industry, and government agencies. In the brain barriers research field, several international conferences exist that allow researchers to present data, share knowledge, and discuss novel ideas and concepts. These meetings are critical platforms for researchers to connect and exchange breakthrough findings on a regular basis. Due to the worldwide COVID-19 pandemic, all in-person meetings were canceled in 2020. In response, we launched the Brain Barriers Virtual 2020 (BBV2020) seminar series, the first stand-in virtual event for the brain barriers field, to offer scientists a virtual platform to present their work. Here we report the aggregate attendance information on two in-person meetings compared with BBV2020 and comment on the utility of the virtual platform.\n\nMethods:\nThe BBV2020 seminar series was hosted on a Zoom webinar platform and was free of cost for participants. Using registration- and Zoom-based data from the BBV2020 virtual seminar series and survey data collected from BBV2020 participants, we analyzed attendance trends, global reach, participation based on career stage, and engagement of BBV2020. We compared these data with those from two previous in-person conferences, a BBB meeting held in 2018 and CVB 2019.\n\nResults:\nWe found that BBV2020 seminar participation steadily decreased over the course of the series. In contrast, live participation was consistently above 100 attendees and recording views were above 200 views per seminar. We also found that participants valued BBV2020 as a supplement during the COVID-19 pandemic in 2020. Based on one post-BBV2020 survey, the majority of participants indicated that they would prefer in-person meetings but would welcome a virtual component to future in-person meetings. Compared to in-person meetings, BBV2020 enabled participation from a broad range of career stages and was attended by scientists in academic, industry, and government agencies from a wide range of countries worldwide.\n\nConclusions:\nOur findings suggest that a virtual event such as the BBV2020 seminar series provides easy access to science for researchers across all career stages around the globe. However, we recognize that limitations exist. Regardless, such a virtual event could be a valuable tool for the brain barriers community to reach and engage scientists worldwide to further grow the brain barriers research field in the future."},"whole_article_text_length":{"kind":"number","value":19478,"string":"19,478"},"whole_article_abstract_length":{"kind":"number","value":438,"string":"438"},"other_sections_lengths":{"kind":"list like","value":[3011,127,169,127,479,186,170,23,66,132,658,1106,641,382,524,141,1040,192],"string":"[\n 3011,\n 127,\n 169,\n 127,\n 479,\n 186,\n 170,\n 23,\n 66,\n 132,\n 658,\n 1106,\n 641,\n 382,\n 524,\n 141,\n 1040,\n 192\n]"},"num_sections":{"kind":"number","value":22,"string":"22"},"most_frequent_words":{"kind":"list like","value":["bbv2020","virtual","seminar","person","meetings","registrants","participants","2018","2019","countries"],"string":"[\n \"bbv2020\",\n \"virtual\",\n \"seminar\",\n \"person\",\n \"meetings\",\n \"registrants\",\n \"participants\",\n \"2018\",\n \"2019\",\n 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Pharmacology."},"pmid":{"kind":"string","value":"34707348"},"background_abstract":{"kind":"string","value":"Yinqin oral liquid (YOL) has curative effect for upper respiratory tract infections, especially for chronic pharyngitis (CP). Since the traditional Chinese herbal formulae are complicated, the pharmacological mechanism of YOL remains unclear. The aim of this work was to explore the active ingredients and mechanisms of YOL against CP."},"background_abstract_label":{"kind":"string","value":"BACKGROUND"},"methods_abstract":{"kind":"string","value":"First, the profile of putative target of YOL was predicted based on structural and functional similarities of all available YOL components, which were obtained from the Drug Bank database, to the known drugs using TCMSP. The chemical constituents and targets of honeysuckle, scutellaria, bupleurum and cicada were searched by TCMSP, CTD, GeneCards and other databases were used to query the CP-related genes, which were searched by UniProt database. Thereafter, the interactions network between compounds and overlapping genes was constructed, visualized, and analyzed by Cytoscape software. Finally, pathway enrichment analysis of overlapping genes was carried out on Database for Annotation, Visualization, and Integrated Discovery (DAVID) platform."},"methods_abstract_label":{"kind":"string","value":"METHODS"},"results_abstract":{"kind":"string","value":"The pathway enrichment analysis showed 55 compounds and 113 corresponding targets in the compound-target network, and the key targets involved PTGS1, ESR2, GSK3β, NCOA2, ESR1. The PPI core network contained 30 proteins, including VEGFA, IL6, ESR1, RELA and HIF1A. A total of 148 GO items were obtained (p<0.05), 102 entries on biological process (BP), 34 entries on biological process (BP) and 12 entries on cell composition (CC) were included. A total of 46 signaling pathways were obtained by KEGG pathway enrichment screening (p<0.05), involving cancer, PI3K-AKT, hepatitis, proteoglycans, p53, HIF-1 signaling pathways."},"results_abstract_label":{"kind":"string","value":"RESULTS"},"conclusions_abstract":{"kind":"string","value":"These results collectively indicate YOL (including the main ingredients luteolin and baicalein) as a highly effective therapeutic agent for anti-inflammation, through the NF-kB pathway."},"conclusions_abstract_label":{"kind":"string","value":"CONCLUSION"},"mesh_descriptor_names":{"kind":"list like","value":["Administration, Oral","Animals","Anti-Inflammatory Agents","Chronic Disease","Databases, Factual","Drugs, Chinese Herbal","Flavanones","Luteolin","Mice","NF-kappa B","Network Pharmacology","Pharyngitis","RAW 264.7 Cells"],"string":"[\n \"Administration, Oral\",\n \"Animals\",\n \"Anti-Inflammatory Agents\",\n \"Chronic Disease\",\n \"Databases, Factual\",\n \"Drugs, Chinese Herbal\",\n \"Flavanones\",\n \"Luteolin\",\n \"Mice\",\n \"NF-kappa B\",\n \"Network Pharmacology\",\n \"Pharyngitis\",\n \"RAW 264.7 Cells\"\n]"},"pmcid":{"kind":"string","value":"8542895"},"background_title":{"kind":"string","value":"Introduction"},"background_text":{"kind":"string","value":"Chronic pharyngitis (CP) is a very common disease associated with chronic inflammation involving pharyngeal mucosa, submucosal and lymphatic tissues. Clinically, CP is mainly manifested as pharyngeal discomfort (foreign body sensation, burning sensation, irritation, etc.), with occasional pharyngeal itch, cough, etc., which belongs to the category of “slow throat arthralgia” in the traditional Chinese medicine (TCM).1 CP has a high incidence and accounts for 10–12% of all pharyngeal diseases, with a long disease course, recurrence and is difficult to cure. At present, western medicine uses antibiotics supplemented by hormone preparations, such as dexamethasone and antiviral drugs, for the treatment of pharyngitis. These treatment methods have drawbacks such as narrow therapeutic spectrum, recurrence, poor tolerance and major toxic and side effects. Hence, developing more effective therapeutic strategies against CP, and reducing the occurrence of side effects from the current therapeutic options is of great clinical importance.\nTCM is a major component of complementary and alternative medicine. Owing to its excellent clinical efficacy, TCM is a research hotspot worldwide. Dialectical theory of TCM is used to treat acute and chronic pharyngitis and has unique advantages such as minor toxic side effects, obvious curative effect and treatment of both symptoms and root causes.2–4 The pharmacological effects of TCM herbal formulae play an important role in their appropriate application. However, the complex nature of herbal formulae has impeded this understanding. Numerous chemical ingredients involving multiple potential targets are present in an herbal formula. It is not adequate to explain the effects produced by a whole herbal formula if we individually consider every single ingredient in it. Yinqin oral liquid (YOL) is composed of extracts of honeysuckle, scutellaria, bupleurum and cicada. YOL has the effects of relieving wind, muscle and clearing heat, which is mainly used for cold, fever, aversion to cold, pharyngeal pain and other upper respiratory tract infections caused by wind fever and wind-cold. Long-term clinical observation has proven its effect in relieving pharyngeal pain and treating hand-foot-mouth disease. YOL contains several chemical compounds, which regulate diverse targets, and thus precisely determine the pharmacological mechanisms involved in its therapeutic actions. In addition, there are several challenges in the relationships between the herbs and diseases.5,6\nNetwork pharmacology is based on the theory of system biology. As a new subject, it selects specific signal nodes to design multi-target drug molecules through the network analysis of system biology. It is possible to study both the active components and the potential gene targets from TCM because of the establishment of network pharmacology and bioinformatics. This study predicted and analyzed the active components and target of YOL using network pharmacology, to explore the rationality of its formula and the scientific nature of treating CP, and further explore the pharmacological mechanisms of YOL on CP."},"methods_title":{"kind":"null"},"methods_text":{"kind":"null"},"results_title":{"kind":"null"},"results_text":{"kind":"null"},"conclusions_title":{"kind":"string","value":"Conclusion"},"conclusions_text":{"kind":"string","value":"This study used the network pharmacology platform to probe the YOL treatment of CP by multi-target analysis. The results showed that YOL could treat CP by acting on related targets, which were consistent with the reported literature. In addition, we have verified that YOL and its two monomer compounds, Luteolin and Baicalein inhibited activation of the NF-kB, Stat3 and PI3K-Akt pathways. Although the potential advantages of the “network target, multi-component” strategy of network pharmacology are obvious, the content of TCM ingredients is usually ignored in the research of network pharmacology of Chinese medicine, and the influence of content and concentration on the efficacy cannot be ignored. The predicted targets, which have not been discussed, may provide clues for further research on the mechanism of YOL."},"other_sections_titles":{"kind":"list like","value":["Materials and Methods","Chemicals and Reagents","Cell Culture","Collection of Drug Molecular Information and Screening of Active Ingredients","Known Therapeutic Targets of Chronic Pharyngitis (CP)","Network Construction and Analysis","Protein-Protein Interaction (PPI) Data","Pathway Enrichment Analysis","Statistical Analysis","Results","Screening and Analysis of Active Compounds","Network Analysis of “Active Component-Target” Interaction in YOL","The Construction of Key Protein Network of YOL and CP","Pharmacological Mechanisms of YOL Acting on CP","Experimental Verification of Predicted Results","Discussion","Conclusion"],"string":"[\n \"Materials and Methods\",\n \"Chemicals and Reagents\",\n \"Cell Culture\",\n \"Collection of Drug Molecular Information and Screening of Active Ingredients\",\n \"Known Therapeutic Targets of Chronic Pharyngitis (CP)\",\n \"Network Construction and Analysis\",\n \"Protein-Protein Interaction (PPI) Data\",\n \"Pathway Enrichment Analysis\",\n \"Statistical Analysis\",\n \"Results\",\n \"Screening and Analysis of Active Compounds\",\n \"Network Analysis of “Active Component-Target” Interaction in YOL\",\n \"The Construction of Key Protein Network of YOL and CP\",\n \"Pharmacological Mechanisms of YOL Acting on CP\",\n \"Experimental Verification of Predicted Results\",\n \"Discussion\",\n \"Conclusion\"\n]"},"other_sections_texts":{"kind":"list like","value":["Chemicals and Reagents Yinqin Oral Liquid (YOL) was provided by su zhou si yuan natural products research and development Co. Ltd. YOL is composed of honeysuckle, scutellaria, bupleurum and cicada, decompressed and concentrated to 2 g/mL through boiling and alcohol sinking, and stored at 4°C for later use. Primary antibodies: COX-2 and iNOS antibodies were purchased from Abcam (Cambridge, UK); glyceraldehyde-3-phosphate dehydrogenase (GAPDH) antibody was obtained from Millipore (Billerica, MA, USA); PI3K, p-AKT, AKT, p-Stats, Stat3 and p-p65 (S536) antibodies were purchased from Cell Signaling Technology (Danvers, MA, USA); and p65 antibodies was obtained from Santa Cruz (CA, USA). The horseradish peroxidase (HRP)-conjugated sheep anti-mouse or anti-rabbit secondary antibodies were purchased from Thermo Fisher (Waltham, MA, USA). The proteins were visualized using an ECL detection kit (Thermo Fisher).\nYinqin Oral Liquid (YOL) was provided by su zhou si yuan natural products research and development Co. Ltd. YOL is composed of honeysuckle, scutellaria, bupleurum and cicada, decompressed and concentrated to 2 g/mL through boiling and alcohol sinking, and stored at 4°C for later use. Primary antibodies: COX-2 and iNOS antibodies were purchased from Abcam (Cambridge, UK); glyceraldehyde-3-phosphate dehydrogenase (GAPDH) antibody was obtained from Millipore (Billerica, MA, USA); PI3K, p-AKT, AKT, p-Stats, Stat3 and p-p65 (S536) antibodies were purchased from Cell Signaling Technology (Danvers, MA, USA); and p65 antibodies was obtained from Santa Cruz (CA, USA). The horseradish peroxidase (HRP)-conjugated sheep anti-mouse or anti-rabbit secondary antibodies were purchased from Thermo Fisher (Waltham, MA, USA). The proteins were visualized using an ECL detection kit (Thermo Fisher).\nCell Culture RAW264.7 cells, a mouse macrophage cell line (Cell Bank of Chinese Academy of Sciences, Shanghai, China) were cultured using DMEM supplemented with FBS (10%) and penicillin-streptomycin (1%). The cells were cultured at 37°C in a humidified environment of 5% carbon dioxide. The medium was changed every other day and the cells were passaged at a dilution of 1:3.\nRAW264.7 cells, a mouse macrophage cell line (Cell Bank of Chinese Academy of Sciences, Shanghai, China) were cultured using DMEM supplemented with FBS (10%) and penicillin-streptomycin (1%). The cells were cultured at 37°C in a humidified environment of 5% carbon dioxide. The medium was changed every other day and the cells were passaged at a dilution of 1:3.\nCollection of Drug Molecular Information and Screening of Active Ingredients In the TCMSP database (https://tcmsp-e.com/),7 the “Herb name” was selected to retrieve the molecular ADME parameter information of honeysuckle, scutellaria, bupleurum and cicada slough, and then the screen conformed to the oral bioavailability (OB) ADME parameter information of honey (Drug-likeness, DL) of ≥0.18 or more active ingredients (Table 1). YOL ingredients were supplemented through literature review. Potential targets related to active ingredients were searched through the TCMSP database and Cytoscape 3.6.1 software.Table 155 Active Compounds Predicted by OB and DL Among 4 Herbs in YOLDrugMol IDMolecule NameMWOB%DLHoneysuckleMOL001494Mandenol308.56420.19MOL001495Ethyl linolenate306.5446.10.2MOL002914Eriodyctiol (flavanone)288.2741.350.24MOL003006(-)-(3R,8S,9R,9aS,10aS)-9-ethenyl-8-(beta-D-glucopyranosyloxy)-2,3,9,9a,10,10a-hexahydro-5-oxo-5H,8H-pyrano[4,3-d]oxazolo[3,2-a]pyridine-3-carboxylic acid_qt281.2987.470.23MOL003014Secologanic dibutylacetal_qt384.5753.650.29MOL002773Beta-carotene536.9637.180.58MOL003036ZINC03978781412.7743.830.76MOL003044Chryseriol300.2835.850.27MOL0030955-hydroxy-7-methoxy-2-(3,4,5-trimethoxyphenyl)chromone358.3751.960.41MOL003111Centauroside_qt434.4855.790.5MOL003117Ioniceracetalides B_qt314.3761.190.19MOL003128Dinethylsecologanoside434.4448.460.48MOL000358Beta-sitosterol414.7936.910.75MOL000422Kaempferol286.2541.880.24MOL000449Stigmasterol412.7743.830.76MOL000006Luteolin286.2536.160.25MOL000098Quercetin302.2546.430.28ScutellariaMOL000073Ent-Epicatechin290.2948.960.24MOL000173Wogonin284.2830.680.23MOL000228(2R)-7-hydroxy-5-methoxy-2-phenylchroman-4-one270.355.230.2MOL000359Sitosterol414.7936.910.75MOL000525Norwogonin270.2539.40.21MOL0005525,2ʹ-Dihydroxy-6,7,8-trimethoxyflavone344.3431.710.35MOL001458Coptisine320.3430.670.86MOL001490Bis[(2S)-2-ethylhexyl] benzene-1,2-dicarboxylate390.6243.590.35MOL001689Acacetin284.2834.970.24MOL002714Baicalein270.2533.520.21MOL002879Diop390.6243.590.39MOL002897Epiberberine336.3943.090.78MOL0029095,7,2,5-tetrahydroxy-8,6-dimethoxyflavone376.3433.820.45MOL002910Carthamidin288.2741.150.24MOL002913Dihydrobaicalin_qt272.2740.040.21MOL002914Eriodyctiol (flavanone)288.2741.350.24MOL002915Salvigenin328.3449.070.33MOL0029175,2ʹ,6ʹ-Trihydroxy-7,8-dimethoxyflavone330.3145.050.33MOL0029255,7,2ʹ,6ʹ-Tetrahydroxyflavone286.2537.010.24MOL002927Skullcapflavone II374.3769.510.44MOL002928Oroxylin a284.2841.370.23MOL002932Panicolin314.3176.260.29MOL0029335,7,4ʹ-Trihydroxy-8-methoxyflavone300.2836.560.27MOL002934Neobaicalein374.37104.30.44MOL002937Dihydrooroxylin A286.366.060.23MOL008206Moslosooflavone298.3144.090.25MOL01041511,13-Eicosadienoic acid, methyl ester322.5939.280.23MOL012266Rivularin344.3437.940.37MOL000490Petunidin317.2930.050.31MOL0045983,5,6,7-tetramethoxy-2-(3,4,5-trimethoxyphenyl)chromone432.4631.970.59BupleurumMOL002776Baicalin446.3940.120.75MOL001645Kaempferol308.5642.10.2MOL004718Linoleyl acetate412.7742.980.76MOL004653α-spinasterol426.546.060.66MOL004624Stigmasterol348.4847.720.53MOL004609Areapillin360.3448.960.41MOL000354Isorhamnetin316.2849.60.31MOL013187Cubebin356.457.130.64\n\n55 Active Compounds Predicted by OB and DL Among 4 Herbs in YOL\nIn the TCMSP database (https://tcmsp-e.com/),7 the “Herb name” was selected to retrieve the molecular ADME parameter information of honeysuckle, scutellaria, bupleurum and cicada slough, and then the screen conformed to the oral bioavailability (OB) ADME parameter information of honey (Drug-likeness, DL) of ≥0.18 or more active ingredients (Table 1). YOL ingredients were supplemented through literature review. Potential targets related to active ingredients were searched through the TCMSP database and Cytoscape 3.6.1 software.Table 155 Active Compounds Predicted by OB and DL Among 4 Herbs in YOLDrugMol IDMolecule NameMWOB%DLHoneysuckleMOL001494Mandenol308.56420.19MOL001495Ethyl linolenate306.5446.10.2MOL002914Eriodyctiol (flavanone)288.2741.350.24MOL003006(-)-(3R,8S,9R,9aS,10aS)-9-ethenyl-8-(beta-D-glucopyranosyloxy)-2,3,9,9a,10,10a-hexahydro-5-oxo-5H,8H-pyrano[4,3-d]oxazolo[3,2-a]pyridine-3-carboxylic acid_qt281.2987.470.23MOL003014Secologanic dibutylacetal_qt384.5753.650.29MOL002773Beta-carotene536.9637.180.58MOL003036ZINC03978781412.7743.830.76MOL003044Chryseriol300.2835.850.27MOL0030955-hydroxy-7-methoxy-2-(3,4,5-trimethoxyphenyl)chromone358.3751.960.41MOL003111Centauroside_qt434.4855.790.5MOL003117Ioniceracetalides B_qt314.3761.190.19MOL003128Dinethylsecologanoside434.4448.460.48MOL000358Beta-sitosterol414.7936.910.75MOL000422Kaempferol286.2541.880.24MOL000449Stigmasterol412.7743.830.76MOL000006Luteolin286.2536.160.25MOL000098Quercetin302.2546.430.28ScutellariaMOL000073Ent-Epicatechin290.2948.960.24MOL000173Wogonin284.2830.680.23MOL000228(2R)-7-hydroxy-5-methoxy-2-phenylchroman-4-one270.355.230.2MOL000359Sitosterol414.7936.910.75MOL000525Norwogonin270.2539.40.21MOL0005525,2ʹ-Dihydroxy-6,7,8-trimethoxyflavone344.3431.710.35MOL001458Coptisine320.3430.670.86MOL001490Bis[(2S)-2-ethylhexyl] benzene-1,2-dicarboxylate390.6243.590.35MOL001689Acacetin284.2834.970.24MOL002714Baicalein270.2533.520.21MOL002879Diop390.6243.590.39MOL002897Epiberberine336.3943.090.78MOL0029095,7,2,5-tetrahydroxy-8,6-dimethoxyflavone376.3433.820.45MOL002910Carthamidin288.2741.150.24MOL002913Dihydrobaicalin_qt272.2740.040.21MOL002914Eriodyctiol (flavanone)288.2741.350.24MOL002915Salvigenin328.3449.070.33MOL0029175,2ʹ,6ʹ-Trihydroxy-7,8-dimethoxyflavone330.3145.050.33MOL0029255,7,2ʹ,6ʹ-Tetrahydroxyflavone286.2537.010.24MOL002927Skullcapflavone II374.3769.510.44MOL002928Oroxylin a284.2841.370.23MOL002932Panicolin314.3176.260.29MOL0029335,7,4ʹ-Trihydroxy-8-methoxyflavone300.2836.560.27MOL002934Neobaicalein374.37104.30.44MOL002937Dihydrooroxylin A286.366.060.23MOL008206Moslosooflavone298.3144.090.25MOL01041511,13-Eicosadienoic acid, methyl ester322.5939.280.23MOL012266Rivularin344.3437.940.37MOL000490Petunidin317.2930.050.31MOL0045983,5,6,7-tetramethoxy-2-(3,4,5-trimethoxyphenyl)chromone432.4631.970.59BupleurumMOL002776Baicalin446.3940.120.75MOL001645Kaempferol308.5642.10.2MOL004718Linoleyl acetate412.7742.980.76MOL004653α-spinasterol426.546.060.66MOL004624Stigmasterol348.4847.720.53MOL004609Areapillin360.3448.960.41MOL000354Isorhamnetin316.2849.60.31MOL013187Cubebin356.457.130.64\n\n55 Active Compounds Predicted by OB and DL Among 4 Herbs in YOL\nKnown Therapeutic Targets of Chronic Pharyngitis (CP) In the drug treatment of CP, the known therapeutic targets were mainly obtained from two sources: GeneCards database (https://www.genecards.org/) and CTD database (http://ctd.mdibl.org/). The data analysis was conducted in 5878 therapeutic targets for the treatment of CP, after the redundant entries were removed. Figure S1 shows the specific information about these known therapeutic targets.\nIn the drug treatment of CP, the known therapeutic targets were mainly obtained from two sources: GeneCards database (https://www.genecards.org/) and CTD database (http://ctd.mdibl.org/). The data analysis was conducted in 5878 therapeutic targets for the treatment of CP, after the redundant entries were removed. Figure S1 shows the specific information about these known therapeutic targets.\nNetwork Construction and Analysis To understand the relationship between the herbs and chemical compounds that consist of YOL and its putative targets, and the therapeutic targets known for CP, Network Visualization was conducted using Cytoscape 3.6.1 software,8 and the degree between compounds and targets was analyzed.\nTo understand the relationship between the herbs and chemical compounds that consist of YOL and its putative targets, and the therapeutic targets known for CP, Network Visualization was conducted using Cytoscape 3.6.1 software,8 and the degree between compounds and targets was analyzed.\nProtein-Protein Interaction (PPI) Data PPI core network (PPICN) is used to study the relationship between chemical compounds and disease-associated protein molecules based on biochemistry, signal transduction and genetic networks.9 The differing ID types of the proteins were converted to UniProt IDs. In order to further understand how YOL and CP interact at the protein level, the selected targets in this study were uploaded on the online Venn diagram (http://bioinfogp.cnb.csic.es/tools/venny/index.html). Moreover, the genes at the intersection of the active compound and CP were selected and uploaded on STRING 10.5 (https://string-db.org) to obtain the PPI relationship.\nPPI core network (PPICN) is used to study the relationship between chemical compounds and disease-associated protein molecules based on biochemistry, signal transduction and genetic networks.9 The differing ID types of the proteins were converted to UniProt IDs. In order to further understand how YOL and CP interact at the protein level, the selected targets in this study were uploaded on the online Venn diagram (http://bioinfogp.cnb.csic.es/tools/venny/index.html). Moreover, the genes at the intersection of the active compound and CP were selected and uploaded on STRING 10.5 (https://string-db.org) to obtain the PPI relationship.\nPathway Enrichment Analysis The data was imported in the format Gene Symbol into David 6.8 database (https://david.ncifcrf.gov/). Molecular function (CC), biological process (BP), and cellular function (MF) were selected, respectively. A pathway enrichment analysis was prospectively performed using biological process enrichment GO analysis and KEGG pathway analysis (http://www.genome.jp/kegg/) to clarify the pathways involving putative CP targets, and visualized by online mapping website Omicshare Tools (http://www.omicshare.com/tools/index.php/).\nThe data was imported in the format Gene Symbol into David 6.8 database (https://david.ncifcrf.gov/). Molecular function (CC), biological process (BP), and cellular function (MF) were selected, respectively. A pathway enrichment analysis was prospectively performed using biological process enrichment GO analysis and KEGG pathway analysis (http://www.genome.jp/kegg/) to clarify the pathways involving putative CP targets, and visualized by online mapping website Omicshare Tools (http://www.omicshare.com/tools/index.php/).\nStatistical Analysis Each experiment was repeated at least in triplicate and data were shown as mean ± SD. Statistical difference in GO and KEGG analysis was performed using hypergeometric and Fisher's exact tests. P<0.05 was considered statistically significant.\nEach experiment was repeated at least in triplicate and data were shown as mean ± SD. Statistical difference in GO and KEGG analysis was performed using hypergeometric and Fisher's exact tests. P<0.05 was considered statistically significant.","Yinqin Oral Liquid (YOL) was provided by su zhou si yuan natural products research and development Co. Ltd. YOL is composed of honeysuckle, scutellaria, bupleurum and cicada, decompressed and concentrated to 2 g/mL through boiling and alcohol sinking, and stored at 4°C for later use. Primary antibodies: COX-2 and iNOS antibodies were purchased from Abcam (Cambridge, UK); glyceraldehyde-3-phosphate dehydrogenase (GAPDH) antibody was obtained from Millipore (Billerica, MA, USA); PI3K, p-AKT, AKT, p-Stats, Stat3 and p-p65 (S536) antibodies were purchased from Cell Signaling Technology (Danvers, MA, USA); and p65 antibodies was obtained from Santa Cruz (CA, USA). The horseradish peroxidase (HRP)-conjugated sheep anti-mouse or anti-rabbit secondary antibodies were purchased from Thermo Fisher (Waltham, MA, USA). The proteins were visualized using an ECL detection kit (Thermo Fisher).","RAW264.7 cells, a mouse macrophage cell line (Cell Bank of Chinese Academy of Sciences, Shanghai, China) were cultured using DMEM supplemented with FBS (10%) and penicillin-streptomycin (1%). The cells were cultured at 37°C in a humidified environment of 5% carbon dioxide. The medium was changed every other day and the cells were passaged at a dilution of 1:3.","In the TCMSP database (https://tcmsp-e.com/),7 the “Herb name” was selected to retrieve the molecular ADME parameter information of honeysuckle, scutellaria, bupleurum and cicada slough, and then the screen conformed to the oral bioavailability (OB) ADME parameter information of honey (Drug-likeness, DL) of ≥0.18 or more active ingredients (Table 1). YOL ingredients were supplemented through literature review. Potential targets related to active ingredients were searched through the TCMSP database and Cytoscape 3.6.1 software.Table 155 Active Compounds Predicted by OB and DL Among 4 Herbs in YOLDrugMol IDMolecule NameMWOB%DLHoneysuckleMOL001494Mandenol308.56420.19MOL001495Ethyl linolenate306.5446.10.2MOL002914Eriodyctiol (flavanone)288.2741.350.24MOL003006(-)-(3R,8S,9R,9aS,10aS)-9-ethenyl-8-(beta-D-glucopyranosyloxy)-2,3,9,9a,10,10a-hexahydro-5-oxo-5H,8H-pyrano[4,3-d]oxazolo[3,2-a]pyridine-3-carboxylic acid_qt281.2987.470.23MOL003014Secologanic dibutylacetal_qt384.5753.650.29MOL002773Beta-carotene536.9637.180.58MOL003036ZINC03978781412.7743.830.76MOL003044Chryseriol300.2835.850.27MOL0030955-hydroxy-7-methoxy-2-(3,4,5-trimethoxyphenyl)chromone358.3751.960.41MOL003111Centauroside_qt434.4855.790.5MOL003117Ioniceracetalides B_qt314.3761.190.19MOL003128Dinethylsecologanoside434.4448.460.48MOL000358Beta-sitosterol414.7936.910.75MOL000422Kaempferol286.2541.880.24MOL000449Stigmasterol412.7743.830.76MOL000006Luteolin286.2536.160.25MOL000098Quercetin302.2546.430.28ScutellariaMOL000073Ent-Epicatechin290.2948.960.24MOL000173Wogonin284.2830.680.23MOL000228(2R)-7-hydroxy-5-methoxy-2-phenylchroman-4-one270.355.230.2MOL000359Sitosterol414.7936.910.75MOL000525Norwogonin270.2539.40.21MOL0005525,2ʹ-Dihydroxy-6,7,8-trimethoxyflavone344.3431.710.35MOL001458Coptisine320.3430.670.86MOL001490Bis[(2S)-2-ethylhexyl] benzene-1,2-dicarboxylate390.6243.590.35MOL001689Acacetin284.2834.970.24MOL002714Baicalein270.2533.520.21MOL002879Diop390.6243.590.39MOL002897Epiberberine336.3943.090.78MOL0029095,7,2,5-tetrahydroxy-8,6-dimethoxyflavone376.3433.820.45MOL002910Carthamidin288.2741.150.24MOL002913Dihydrobaicalin_qt272.2740.040.21MOL002914Eriodyctiol (flavanone)288.2741.350.24MOL002915Salvigenin328.3449.070.33MOL0029175,2ʹ,6ʹ-Trihydroxy-7,8-dimethoxyflavone330.3145.050.33MOL0029255,7,2ʹ,6ʹ-Tetrahydroxyflavone286.2537.010.24MOL002927Skullcapflavone II374.3769.510.44MOL002928Oroxylin a284.2841.370.23MOL002932Panicolin314.3176.260.29MOL0029335,7,4ʹ-Trihydroxy-8-methoxyflavone300.2836.560.27MOL002934Neobaicalein374.37104.30.44MOL002937Dihydrooroxylin A286.366.060.23MOL008206Moslosooflavone298.3144.090.25MOL01041511,13-Eicosadienoic acid, methyl ester322.5939.280.23MOL012266Rivularin344.3437.940.37MOL000490Petunidin317.2930.050.31MOL0045983,5,6,7-tetramethoxy-2-(3,4,5-trimethoxyphenyl)chromone432.4631.970.59BupleurumMOL002776Baicalin446.3940.120.75MOL001645Kaempferol308.5642.10.2MOL004718Linoleyl acetate412.7742.980.76MOL004653α-spinasterol426.546.060.66MOL004624Stigmasterol348.4847.720.53MOL004609Areapillin360.3448.960.41MOL000354Isorhamnetin316.2849.60.31MOL013187Cubebin356.457.130.64\n\n55 Active Compounds Predicted by OB and DL Among 4 Herbs in YOL","In the drug treatment of CP, the known therapeutic targets were mainly obtained from two sources: GeneCards database (https://www.genecards.org/) and CTD database (http://ctd.mdibl.org/). The data analysis was conducted in 5878 therapeutic targets for the treatment of CP, after the redundant entries were removed. Figure S1 shows the specific information about these known therapeutic targets.","To understand the relationship between the herbs and chemical compounds that consist of YOL and its putative targets, and the therapeutic targets known for CP, Network Visualization was conducted using Cytoscape 3.6.1 software,8 and the degree between compounds and targets was analyzed.","PPI core network (PPICN) is used to study the relationship between chemical compounds and disease-associated protein molecules based on biochemistry, signal transduction and genetic networks.9 The differing ID types of the proteins were converted to UniProt IDs. In order to further understand how YOL and CP interact at the protein level, the selected targets in this study were uploaded on the online Venn diagram (http://bioinfogp.cnb.csic.es/tools/venny/index.html). Moreover, the genes at the intersection of the active compound and CP were selected and uploaded on STRING 10.5 (https://string-db.org) to obtain the PPI relationship.","The data was imported in the format Gene Symbol into David 6.8 database (https://david.ncifcrf.gov/). Molecular function (CC), biological process (BP), and cellular function (MF) were selected, respectively. A pathway enrichment analysis was prospectively performed using biological process enrichment GO analysis and KEGG pathway analysis (http://www.genome.jp/kegg/) to clarify the pathways involving putative CP targets, and visualized by online mapping website Omicshare Tools (http://www.omicshare.com/tools/index.php/).","Each experiment was repeated at least in triplicate and data were shown as mean ± SD. Statistical difference in GO and KEGG analysis was performed using hypergeometric and Fisher's exact tests. P<0.05 was considered statistically significant.","Screening and Analysis of Active Compounds First, all the compounds related to four TCMs were retrieved by TCMSP. Second, according to the screening conditions of OB ≥30% and DL ≥0.18,10 23 active constituents of honeysuckle (17 predicted relevant targets), 36 of baicalensis (30 predicted relevant targets), 17 of bupleurum (13 predicted relevant targets), and no suitable compounds of cicada slough were obtained. Among them, baicalensis and honeysuckle contained two identical ingredients, while bupleurum and honeysuckle contained three identical components. Finally, a total of 55 active components were obtained for the data analysis after removing redundant entries (Table 1).\nFirst, all the compounds related to four TCMs were retrieved by TCMSP. Second, according to the screening conditions of OB ≥30% and DL ≥0.18,10 23 active constituents of honeysuckle (17 predicted relevant targets), 36 of baicalensis (30 predicted relevant targets), 17 of bupleurum (13 predicted relevant targets), and no suitable compounds of cicada slough were obtained. Among them, baicalensis and honeysuckle contained two identical ingredients, while bupleurum and honeysuckle contained three identical components. Finally, a total of 55 active components were obtained for the data analysis after removing redundant entries (Table 1).\nNetwork Analysis of “Active Component-Target” Interaction in YOL The compound-target network contained 175 nodes including 55 compound nodes and 113 target nodes, of which two had no corresponding target and 577 edges. As shown in Figure 1, blue represents compounds in bupleurum, red represents compounds in scutellaria, purple represents compounds in honeysuckle, green represents drug targets, each edge indicates the interaction between compounds and their targets, and two of the 55 compounds were excluded from the network construction. The degree value of a node represents the amount of connected routes in the network, and the larger the shape, the higher the degree value. The network in line with its topological properties showed that the nodes with higher degree of screening were analyzed. These nodes that connect compounds or targets act as hubs in the whole network and may predict key compounds or targets. The top five compounds ranked by degree were MOL000098-quercetin, MOL000422-Kaempferol, MOL000449-stigmasterol, MOL000358-quebrachol and MOL000006-luteolin, which could interact with 154, 68, 39, 32 and 28 target proteins, respectively. PTGS1, NCOA2, AR, PRSS1 and PPARG were the top five targets with the highest degree, which could interact with 42, 38, 29, 28 and 17 compounds, respectively.Figure 1Compounds-target network diagram. Blue represents compounds in bupleurum, Red represents compounds in scutellaria, Purple represents compounds in honeysuckle, Green represents drug targets. The size represents the degree value. The larger the shape, the larger the degree value.\nCompounds-target network diagram. Blue represents compounds in bupleurum, Red represents compounds in scutellaria, Purple represents compounds in honeysuckle, Green represents drug targets. The size represents the degree value. The larger the shape, the larger the degree value.\nThe compound-target network contained 175 nodes including 55 compound nodes and 113 target nodes, of which two had no corresponding target and 577 edges. As shown in Figure 1, blue represents compounds in bupleurum, red represents compounds in scutellaria, purple represents compounds in honeysuckle, green represents drug targets, each edge indicates the interaction between compounds and their targets, and two of the 55 compounds were excluded from the network construction. The degree value of a node represents the amount of connected routes in the network, and the larger the shape, the higher the degree value. The network in line with its topological properties showed that the nodes with higher degree of screening were analyzed. These nodes that connect compounds or targets act as hubs in the whole network and may predict key compounds or targets. The top five compounds ranked by degree were MOL000098-quercetin, MOL000422-Kaempferol, MOL000449-stigmasterol, MOL000358-quebrachol and MOL000006-luteolin, which could interact with 154, 68, 39, 32 and 28 target proteins, respectively. PTGS1, NCOA2, AR, PRSS1 and PPARG were the top five targets with the highest degree, which could interact with 42, 38, 29, 28 and 17 compounds, respectively.Figure 1Compounds-target network diagram. Blue represents compounds in bupleurum, Red represents compounds in scutellaria, Purple represents compounds in honeysuckle, Green represents drug targets. The size represents the degree value. The larger the shape, the larger the degree value.\nCompounds-target network diagram. Blue represents compounds in bupleurum, Red represents compounds in scutellaria, Purple represents compounds in honeysuckle, Green represents drug targets. The size represents the degree value. The larger the shape, the larger the degree value.\nThe Construction of Key Protein Network of YOL and CP Through the internationally recognized CTD and GeneCards disease databases, 5150 and 1247 pharyngitis-related targets were obtained, respectively. Through the Venny online tool, 30 targets of honeysuckle, scutellaria, bupleuri and pharyngitis were obtained, including VEGFA, IL6, ESR1, RELA, HIF1A, etc. (Figure 2A). Finally, STRING online tool was used to construct the PPI network interaction map of drug and disease. It contained 30 nodes representing proteins and 48 edges representing the interaction between proteins. The thicker the line, the higher is the correlation degree. Through the protein interaction network, we could further explore the therapeutic target and mechanism of YOL for CP (Figure 2B).Figure 2(A) Venn diagram of compounds-target network diagram; (B) Core network diagram of protein interaction between YOL and CP.\n(A) Venn diagram of compounds-target network diagram; (B) Core network diagram of protein interaction between YOL and CP.\nThrough the internationally recognized CTD and GeneCards disease databases, 5150 and 1247 pharyngitis-related targets were obtained, respectively. Through the Venny online tool, 30 targets of honeysuckle, scutellaria, bupleuri and pharyngitis were obtained, including VEGFA, IL6, ESR1, RELA, HIF1A, etc. (Figure 2A). Finally, STRING online tool was used to construct the PPI network interaction map of drug and disease. It contained 30 nodes representing proteins and 48 edges representing the interaction between proteins. The thicker the line, the higher is the correlation degree. Through the protein interaction network, we could further explore the therapeutic target and mechanism of YOL for CP (Figure 2B).Figure 2(A) Venn diagram of compounds-target network diagram; (B) Core network diagram of protein interaction between YOL and CP.\n(A) Venn diagram of compounds-target network diagram; (B) Core network diagram of protein interaction between YOL and CP.\nPharmacological Mechanisms of YOL Acting on CP A total of 30 common targets were obtained by DAVID online tool and 148 GO items were obtained (p < 0.05) by performing the functional enrichment analysis. There were 102 entries on biological process (BP), 34 entries on Molecular Function (MF), and 12 entries on cell composition (CC) (Figure 3A). A total of 46 signaling pathways were obtained by KEGG pathway enrichment screening (p<0.05), involving cancer, PI3K-AKT, hepatitis, proteoglycans, p53, HIF-1 signaling pathways, etc. (Figure 3B and Table S1).Figure 3(A) GO analysis function annotation diagram. Biological process (BP), molecular function (MF), and cell composition (CC). (B) Enriched KEGG pathways of YOL selected targets for CP.\n(A) GO analysis function annotation diagram. Biological process (BP), molecular function (MF), and cell composition (CC). (B) Enriched KEGG pathways of YOL selected targets for CP.\nA total of 30 common targets were obtained by DAVID online tool and 148 GO items were obtained (p < 0.05) by performing the functional enrichment analysis. There were 102 entries on biological process (BP), 34 entries on Molecular Function (MF), and 12 entries on cell composition (CC) (Figure 3A). A total of 46 signaling pathways were obtained by KEGG pathway enrichment screening (p<0.05), involving cancer, PI3K-AKT, hepatitis, proteoglycans, p53, HIF-1 signaling pathways, etc. (Figure 3B and Table S1).Figure 3(A) GO analysis function annotation diagram. Biological process (BP), molecular function (MF), and cell composition (CC). (B) Enriched KEGG pathways of YOL selected targets for CP.\n(A) GO analysis function annotation diagram. Biological process (BP), molecular function (MF), and cell composition (CC). (B) Enriched KEGG pathways of YOL selected targets for CP.\nExperimental Verification of Predicted Results In order to experimentally demonstrate the predictions about the molecular mechanism of YOL, we chose two ingredients for testing (Figure 4), based on their composition score, the group to which they belong, and their herbal origin to examine how ingredients from different herbs affect the same proteins. The effects of these compounds on the expression levels of related proteins were analyzed by Western blot. Macrophages secrete several inflammatory cytokines, which play significant roles in inflammation. Signal transduction and transcriptional activator 3 (STAT3), nuclear factor kappa B (NF-kB), and phosphatidylinositol 3-kinase (PI3K)/Akt pathways are critical inflammatory pathways. LPS can significantly increase the production of many proinflammatory factors, including inducible NO synthase (iNOS) and cyclooxygenase-2 (COX-2) in macrophages. After pretreatment of macrophages with YOL, luteolin and baicalein, the protein expression levels of iNOS and COX-2 induced by LPS were significantly inhibited in a dose-dependent manner (Figure 4A). The related protein changes were consistent with iNOS and COX-2 inflammation protein expression (Figure 4B–D).Figure 4The YOL inhibits PI3K/p-AKT/p-Stats pathway in RAW 264.7 cells. Representative Western blot images show the relative expressions of iNOS, COX-2 (A), p-p65, p65 (B), PI3K, p-AKT, AKT (C), p-Stats, Stat3 (D) in the groups. The protein levels (normalized) of iNOS, COX2 (E), and p-p65, p65, PI3K, p-AKT, AKT, p-Stats, Stat3 (F) in cells. All the values are presented as mean ± SD. #P < 0.05 and ##P < 0.01 vs control group. *P < 0.05 and **P < 0.01 vs LPS group.\nThe YOL inhibits PI3K/p-AKT/p-Stats pathway in RAW 264.7 cells. Representative Western blot images show the relative expressions of iNOS, COX-2 (A), p-p65, p65 (B), PI3K, p-AKT, AKT (C), p-Stats, Stat3 (D) in the groups. The protein levels (normalized) of iNOS, COX2 (E), and p-p65, p65, PI3K, p-AKT, AKT, p-Stats, Stat3 (F) in cells. All the values are presented as mean ± SD. #P < 0.05 and ##P < 0.01 vs control group. *P < 0.05 and **P < 0.01 vs LPS group.\nIn order to experimentally demonstrate the predictions about the molecular mechanism of YOL, we chose two ingredients for testing (Figure 4), based on their composition score, the group to which they belong, and their herbal origin to examine how ingredients from different herbs affect the same proteins. The effects of these compounds on the expression levels of related proteins were analyzed by Western blot. Macrophages secrete several inflammatory cytokines, which play significant roles in inflammation. Signal transduction and transcriptional activator 3 (STAT3), nuclear factor kappa B (NF-kB), and phosphatidylinositol 3-kinase (PI3K)/Akt pathways are critical inflammatory pathways. LPS can significantly increase the production of many proinflammatory factors, including inducible NO synthase (iNOS) and cyclooxygenase-2 (COX-2) in macrophages. After pretreatment of macrophages with YOL, luteolin and baicalein, the protein expression levels of iNOS and COX-2 induced by LPS were significantly inhibited in a dose-dependent manner (Figure 4A). The related protein changes were consistent with iNOS and COX-2 inflammation protein expression (Figure 4B–D).Figure 4The YOL inhibits PI3K/p-AKT/p-Stats pathway in RAW 264.7 cells. Representative Western blot images show the relative expressions of iNOS, COX-2 (A), p-p65, p65 (B), PI3K, p-AKT, AKT (C), p-Stats, Stat3 (D) in the groups. The protein levels (normalized) of iNOS, COX2 (E), and p-p65, p65, PI3K, p-AKT, AKT, p-Stats, Stat3 (F) in cells. All the values are presented as mean ± SD. #P < 0.05 and ##P < 0.01 vs control group. *P < 0.05 and **P < 0.01 vs LPS group.\nThe YOL inhibits PI3K/p-AKT/p-Stats pathway in RAW 264.7 cells. Representative Western blot images show the relative expressions of iNOS, COX-2 (A), p-p65, p65 (B), PI3K, p-AKT, AKT (C), p-Stats, Stat3 (D) in the groups. The protein levels (normalized) of iNOS, COX2 (E), and p-p65, p65, PI3K, p-AKT, AKT, p-Stats, Stat3 (F) in cells. All the values are presented as mean ± SD. #P < 0.05 and ##P < 0.01 vs control group. *P < 0.05 and **P < 0.01 vs LPS group.","First, all the compounds related to four TCMs were retrieved by TCMSP. Second, according to the screening conditions of OB ≥30% and DL ≥0.18,10 23 active constituents of honeysuckle (17 predicted relevant targets), 36 of baicalensis (30 predicted relevant targets), 17 of bupleurum (13 predicted relevant targets), and no suitable compounds of cicada slough were obtained. Among them, baicalensis and honeysuckle contained two identical ingredients, while bupleurum and honeysuckle contained three identical components. Finally, a total of 55 active components were obtained for the data analysis after removing redundant entries (Table 1).","The compound-target network contained 175 nodes including 55 compound nodes and 113 target nodes, of which two had no corresponding target and 577 edges. As shown in Figure 1, blue represents compounds in bupleurum, red represents compounds in scutellaria, purple represents compounds in honeysuckle, green represents drug targets, each edge indicates the interaction between compounds and their targets, and two of the 55 compounds were excluded from the network construction. The degree value of a node represents the amount of connected routes in the network, and the larger the shape, the higher the degree value. The network in line with its topological properties showed that the nodes with higher degree of screening were analyzed. These nodes that connect compounds or targets act as hubs in the whole network and may predict key compounds or targets. The top five compounds ranked by degree were MOL000098-quercetin, MOL000422-Kaempferol, MOL000449-stigmasterol, MOL000358-quebrachol and MOL000006-luteolin, which could interact with 154, 68, 39, 32 and 28 target proteins, respectively. PTGS1, NCOA2, AR, PRSS1 and PPARG were the top five targets with the highest degree, which could interact with 42, 38, 29, 28 and 17 compounds, respectively.Figure 1Compounds-target network diagram. Blue represents compounds in bupleurum, Red represents compounds in scutellaria, Purple represents compounds in honeysuckle, Green represents drug targets. The size represents the degree value. The larger the shape, the larger the degree value.\nCompounds-target network diagram. Blue represents compounds in bupleurum, Red represents compounds in scutellaria, Purple represents compounds in honeysuckle, Green represents drug targets. The size represents the degree value. The larger the shape, the larger the degree value.","Through the internationally recognized CTD and GeneCards disease databases, 5150 and 1247 pharyngitis-related targets were obtained, respectively. Through the Venny online tool, 30 targets of honeysuckle, scutellaria, bupleuri and pharyngitis were obtained, including VEGFA, IL6, ESR1, RELA, HIF1A, etc. (Figure 2A). Finally, STRING online tool was used to construct the PPI network interaction map of drug and disease. It contained 30 nodes representing proteins and 48 edges representing the interaction between proteins. The thicker the line, the higher is the correlation degree. Through the protein interaction network, we could further explore the therapeutic target and mechanism of YOL for CP (Figure 2B).Figure 2(A) Venn diagram of compounds-target network diagram; (B) Core network diagram of protein interaction between YOL and CP.\n(A) Venn diagram of compounds-target network diagram; (B) Core network diagram of protein interaction between YOL and CP.","A total of 30 common targets were obtained by DAVID online tool and 148 GO items were obtained (p < 0.05) by performing the functional enrichment analysis. There were 102 entries on biological process (BP), 34 entries on Molecular Function (MF), and 12 entries on cell composition (CC) (Figure 3A). A total of 46 signaling pathways were obtained by KEGG pathway enrichment screening (p<0.05), involving cancer, PI3K-AKT, hepatitis, proteoglycans, p53, HIF-1 signaling pathways, etc. (Figure 3B and Table S1).Figure 3(A) GO analysis function annotation diagram. Biological process (BP), molecular function (MF), and cell composition (CC). (B) Enriched KEGG pathways of YOL selected targets for CP.\n(A) GO analysis function annotation diagram. Biological process (BP), molecular function (MF), and cell composition (CC). (B) Enriched KEGG pathways of YOL selected targets for CP.","In order to experimentally demonstrate the predictions about the molecular mechanism of YOL, we chose two ingredients for testing (Figure 4), based on their composition score, the group to which they belong, and their herbal origin to examine how ingredients from different herbs affect the same proteins. The effects of these compounds on the expression levels of related proteins were analyzed by Western blot. Macrophages secrete several inflammatory cytokines, which play significant roles in inflammation. Signal transduction and transcriptional activator 3 (STAT3), nuclear factor kappa B (NF-kB), and phosphatidylinositol 3-kinase (PI3K)/Akt pathways are critical inflammatory pathways. LPS can significantly increase the production of many proinflammatory factors, including inducible NO synthase (iNOS) and cyclooxygenase-2 (COX-2) in macrophages. After pretreatment of macrophages with YOL, luteolin and baicalein, the protein expression levels of iNOS and COX-2 induced by LPS were significantly inhibited in a dose-dependent manner (Figure 4A). The related protein changes were consistent with iNOS and COX-2 inflammation protein expression (Figure 4B–D).Figure 4The YOL inhibits PI3K/p-AKT/p-Stats pathway in RAW 264.7 cells. Representative Western blot images show the relative expressions of iNOS, COX-2 (A), p-p65, p65 (B), PI3K, p-AKT, AKT (C), p-Stats, Stat3 (D) in the groups. The protein levels (normalized) of iNOS, COX2 (E), and p-p65, p65, PI3K, p-AKT, AKT, p-Stats, Stat3 (F) in cells. All the values are presented as mean ± SD. #P < 0.05 and ##P < 0.01 vs control group. *P < 0.05 and **P < 0.01 vs LPS group.\nThe YOL inhibits PI3K/p-AKT/p-Stats pathway in RAW 264.7 cells. Representative Western blot images show the relative expressions of iNOS, COX-2 (A), p-p65, p65 (B), PI3K, p-AKT, AKT (C), p-Stats, Stat3 (D) in the groups. The protein levels (normalized) of iNOS, COX2 (E), and p-p65, p65, PI3K, p-AKT, AKT, p-Stats, Stat3 (F) in cells. All the values are presented as mean ± SD. #P < 0.05 and ##P < 0.01 vs control group. *P < 0.05 and **P < 0.01 vs LPS group.","Many diseases, including cancer and chronic inflammation, are known to be regulated by multiple signaling pathways. TCMs are considered as multi-component and multi-target therapeutic drugs, which is in accordance with the methodologies of network pharmacology. The holistic view of TCM considers the human body as a complex biological network system, which is connected with network pharmacology. By combining the network pharmacology with the holistic view of TCM, the disease-target-drug network is obtained. The network shows that different components of TCM may act on one target, and different targets may act on the same pathway. It is also possible that the same component of TCM acts on different targets, and the same target acts on different pathways. The multi-component, multi-target and multi-pathway of TCM can be clearly visualized through network pharmacology, which resolves many difficulties encountered in the study of TCM.13–15 CP is the diffuse inflammation of pharyngeal mucosa, submucosa and lymph tissues. Clinically, CP is mainly manifested as pharyngeal discomfort (foreign body sensation, burning sensation, stimulation sensation, etc.), pharyngeal itching, cough, etc. Pharyngitis belongs to the category of “Fenghou Bi” in TCM. Pharyngitis pertains to the category of “wind throat arthralgia” in Chinese medicine, and is often caused by evil heat entering the body, and heat in the lungs and stomach. YOL is an oral TCM preparation, which is further optimized on the basis of Jinchan oral liquid developed by Children’s Hospital of Suzhou University.5,6 However, there is a lack of research on its active ingredients and its mechanism of action.\nIn this study, 30 potential targets, 102 biological processes, 12 molecular functions, 34 cell components, and 46 KEGG pathways were obtained. The active ingredients in YOL can treat CP through multiple targets and pathways. To elucidate the biological pathways that may be affected by YOL, a significantly overexpressed KEGG pathway was identified. The results showed VEGFA, IL6, ESR1 and RELA as the key targets and the involvement of PI3K-Akt, p53 and HIF-1 signaling pathways. TCM aims to restore a patient’s health through the use of a TCM prescription, which is usually composed of two or more TCMs in optimal proportions.\nYOL, for example, consists of four TCMs. As expected, most of the selected active ingredients are directly or indirectly associated with inflammation. In a previous study, neochlorogenic acid was demonstrated to inhibit LPS-activated inflammatory responses through up-regulation of Nrf2/HO-1 and AMPK pathways.16 The decoction extract from six natural herbs, which contain honeysuckle, exhibits anti-inflammatory and immunomodulatory effects by targeting NF-κB/IL-6/STAT3 signaling.17 Luteolin is abundant in honeysuckle, which alleviates CP by inhibiting the NF-κB pathway and anti-inflammatory polarization of M1 macrophages.11 Protective effects of baicalein on liver injury in mice induced by multi-microbial septicemia were found to be based on inhibiting inflammation.12 Network pharmacological studies have shown that the main active components of honeysuckle may play a role through inflammation-related proteins such as HSP90AA1, HSP90AB1, ESR1, PTGS2, TERT and PPARG, and especially heat shock protein HSP90A (HSP90AA1).18 Flavonoids such as baicalin and wogonin alone and in combination have anti-inflammatory activity in Scutellaria.19 Network pharmacological studies have also shown that alkaloids such as coptisine and epiphone in Scutellaria and components such as dihydrokaempferol, and rographolide flavonoids also show anti-inflammatory activity. MAPK14, TNFRSF1A, EGFR and SELE are the primary anti-inflammatory components of Scutellaria. The anti-inflammatory active components in Scutellaria can directly affect MAPK14 and EGFR to produce anti-inflammatory effects, and can also indirectly affect other targets to exert anti-inflammatory effects. As one of the four p38 MAPKs, MAPK14 plays a vital role in the cellular cascade induced by extracellular stimuli such as proinflammatory cytokines or physical stimuli.20 Studies predicted that the pharmacodynamic basis of Bupleurum is mainly saponins, flavonoids, volatile oils, fatty acids and other components. Flavonoids mainly act on PI3K-Akt, NF-kB and other pathways, participate in regulating inflammatory factors, estrogen signal transduction and other physiological processes. Bupleurum-Scutellaria drug pair, first used in small bupleurum decoction in Treatise on Febrile Diseases, is an important part of Bupleurum prescription. Bupleurum is bitter, flat and clear, and can disperse the stagnation of bile fire. Scutellaria has a bitter and cold taste, and can clear internal heat. Both of them must be used together to regulate the liver and gallbladder, and clear the dampness and heat of internal accumulation, which are commonly used to treat fever, pharyngitis, and respiratory diseases.21 Cicada slough can evacuate wind and heat, and clear the throat. Clinically, it is common in classical prescriptions such as Sheng jiang San and Pharyngitis San. However, the active components of Cicada slough did not satisfy the prediction requirements in this research.\nYOL is composed of honeysuckle, scutellaria, bupleurum and cicada slough. According to TCM theory, bupleurum is bitter and flat, enters the liver and gallbladder meridian, penetrates and clears Shaoyang, and can drain the stagnation of gas. Scutellaria is bitter and cold, and clears the heat of Shaoyang. The actions of Bupleurum facilitate the clear discharge of Scutellaria, the two are compatible, and achieve the goal of reconciliation and Shaoyang. Honeysuckle cools through the surface, has heat-clearing and detoxifying effects, and also has the effect of fragrant obscenity. Cicada slough is light floating, clears the coke gas, shows honeysuckle compatibility, understanding the table evil, and is combined with the “treatment of the coke such as feather.” The combination of the four drugs, Xuan and descent together, clear the evil, thereby clearing heat and detoxification, warming the evil without hiding, scattered outside and recovered. Therefore, the treatment of CP with YOL can control the pharyngitis, regulate the balance of Yin and Yang, strengthen the foundation and improve the immunity of the body.","This study used the network pharmacology platform to probe the YOL treatment of CP by multi-target analysis. The results showed that YOL could treat CP by acting on related targets, which were consistent with the reported literature. In addition, we have verified that YOL and its two monomer compounds, Luteolin and Baicalein inhibited activation of the NF-kB, Stat3 and PI3K-Akt pathways. Although the potential advantages of the “network target, multi-component” strategy of network pharmacology are obvious, the content of TCM ingredients is usually ignored in the research of network pharmacology of Chinese medicine, and the influence of content and concentration on the efficacy cannot be ignored. The predicted targets, which have not been discussed, may provide clues for further research on the mechanism of YOL."],"string":"[\n \"Chemicals and Reagents Yinqin Oral Liquid (YOL) was provided by su zhou si yuan natural products research and development Co. Ltd. YOL is composed of honeysuckle, scutellaria, bupleurum and cicada, decompressed and concentrated to 2 g/mL through boiling and alcohol sinking, and stored at 4°C for later use. Primary antibodies: COX-2 and iNOS antibodies were purchased from Abcam (Cambridge, UK); glyceraldehyde-3-phosphate dehydrogenase (GAPDH) antibody was obtained from Millipore (Billerica, MA, USA); PI3K, p-AKT, AKT, p-Stats, Stat3 and p-p65 (S536) antibodies were purchased from Cell Signaling Technology (Danvers, MA, USA); and p65 antibodies was obtained from Santa Cruz (CA, USA). The horseradish peroxidase (HRP)-conjugated sheep anti-mouse or anti-rabbit secondary antibodies were purchased from Thermo Fisher (Waltham, MA, USA). The proteins were visualized using an ECL detection kit (Thermo Fisher).\\nYinqin Oral Liquid (YOL) was provided by su zhou si yuan natural products research and development Co. Ltd. YOL is composed of honeysuckle, scutellaria, bupleurum and cicada, decompressed and concentrated to 2 g/mL through boiling and alcohol sinking, and stored at 4°C for later use. Primary antibodies: COX-2 and iNOS antibodies were purchased from Abcam (Cambridge, UK); glyceraldehyde-3-phosphate dehydrogenase (GAPDH) antibody was obtained from Millipore (Billerica, MA, USA); PI3K, p-AKT, AKT, p-Stats, Stat3 and p-p65 (S536) antibodies were purchased from Cell Signaling Technology (Danvers, MA, USA); and p65 antibodies was obtained from Santa Cruz (CA, USA). The horseradish peroxidase (HRP)-conjugated sheep anti-mouse or anti-rabbit secondary antibodies were purchased from Thermo Fisher (Waltham, MA, USA). The proteins were visualized using an ECL detection kit (Thermo Fisher).\\nCell Culture RAW264.7 cells, a mouse macrophage cell line (Cell Bank of Chinese Academy of Sciences, Shanghai, China) were cultured using DMEM supplemented with FBS (10%) and penicillin-streptomycin (1%). The cells were cultured at 37°C in a humidified environment of 5% carbon dioxide. The medium was changed every other day and the cells were passaged at a dilution of 1:3.\\nRAW264.7 cells, a mouse macrophage cell line (Cell Bank of Chinese Academy of Sciences, Shanghai, China) were cultured using DMEM supplemented with FBS (10%) and penicillin-streptomycin (1%). The cells were cultured at 37°C in a humidified environment of 5% carbon dioxide. The medium was changed every other day and the cells were passaged at a dilution of 1:3.\\nCollection of Drug Molecular Information and Screening of Active Ingredients In the TCMSP database (https://tcmsp-e.com/),7 the “Herb name” was selected to retrieve the molecular ADME parameter information of honeysuckle, scutellaria, bupleurum and cicada slough, and then the screen conformed to the oral bioavailability (OB) ADME parameter information of honey (Drug-likeness, DL) of ≥0.18 or more active ingredients (Table 1). YOL ingredients were supplemented through literature review. Potential targets related to active ingredients were searched through the TCMSP database and Cytoscape 3.6.1 software.Table 155 Active Compounds Predicted by OB and DL Among 4 Herbs in YOLDrugMol IDMolecule NameMWOB%DLHoneysuckleMOL001494Mandenol308.56420.19MOL001495Ethyl linolenate306.5446.10.2MOL002914Eriodyctiol (flavanone)288.2741.350.24MOL003006(-)-(3R,8S,9R,9aS,10aS)-9-ethenyl-8-(beta-D-glucopyranosyloxy)-2,3,9,9a,10,10a-hexahydro-5-oxo-5H,8H-pyrano[4,3-d]oxazolo[3,2-a]pyridine-3-carboxylic acid_qt281.2987.470.23MOL003014Secologanic dibutylacetal_qt384.5753.650.29MOL002773Beta-carotene536.9637.180.58MOL003036ZINC03978781412.7743.830.76MOL003044Chryseriol300.2835.850.27MOL0030955-hydroxy-7-methoxy-2-(3,4,5-trimethoxyphenyl)chromone358.3751.960.41MOL003111Centauroside_qt434.4855.790.5MOL003117Ioniceracetalides B_qt314.3761.190.19MOL003128Dinethylsecologanoside434.4448.460.48MOL000358Beta-sitosterol414.7936.910.75MOL000422Kaempferol286.2541.880.24MOL000449Stigmasterol412.7743.830.76MOL000006Luteolin286.2536.160.25MOL000098Quercetin302.2546.430.28ScutellariaMOL000073Ent-Epicatechin290.2948.960.24MOL000173Wogonin284.2830.680.23MOL000228(2R)-7-hydroxy-5-methoxy-2-phenylchroman-4-one270.355.230.2MOL000359Sitosterol414.7936.910.75MOL000525Norwogonin270.2539.40.21MOL0005525,2ʹ-Dihydroxy-6,7,8-trimethoxyflavone344.3431.710.35MOL001458Coptisine320.3430.670.86MOL001490Bis[(2S)-2-ethylhexyl] benzene-1,2-dicarboxylate390.6243.590.35MOL001689Acacetin284.2834.970.24MOL002714Baicalein270.2533.520.21MOL002879Diop390.6243.590.39MOL002897Epiberberine336.3943.090.78MOL0029095,7,2,5-tetrahydroxy-8,6-dimethoxyflavone376.3433.820.45MOL002910Carthamidin288.2741.150.24MOL002913Dihydrobaicalin_qt272.2740.040.21MOL002914Eriodyctiol (flavanone)288.2741.350.24MOL002915Salvigenin328.3449.070.33MOL0029175,2ʹ,6ʹ-Trihydroxy-7,8-dimethoxyflavone330.3145.050.33MOL0029255,7,2ʹ,6ʹ-Tetrahydroxyflavone286.2537.010.24MOL002927Skullcapflavone II374.3769.510.44MOL002928Oroxylin a284.2841.370.23MOL002932Panicolin314.3176.260.29MOL0029335,7,4ʹ-Trihydroxy-8-methoxyflavone300.2836.560.27MOL002934Neobaicalein374.37104.30.44MOL002937Dihydrooroxylin A286.366.060.23MOL008206Moslosooflavone298.3144.090.25MOL01041511,13-Eicosadienoic acid, methyl ester322.5939.280.23MOL012266Rivularin344.3437.940.37MOL000490Petunidin317.2930.050.31MOL0045983,5,6,7-tetramethoxy-2-(3,4,5-trimethoxyphenyl)chromone432.4631.970.59BupleurumMOL002776Baicalin446.3940.120.75MOL001645Kaempferol308.5642.10.2MOL004718Linoleyl acetate412.7742.980.76MOL004653α-spinasterol426.546.060.66MOL004624Stigmasterol348.4847.720.53MOL004609Areapillin360.3448.960.41MOL000354Isorhamnetin316.2849.60.31MOL013187Cubebin356.457.130.64\\n\\n55 Active Compounds Predicted by OB and DL Among 4 Herbs in YOL\\nIn the TCMSP database (https://tcmsp-e.com/),7 the “Herb name” was selected to retrieve the molecular ADME parameter information of honeysuckle, scutellaria, bupleurum and cicada slough, and then the screen conformed to the oral bioavailability (OB) ADME parameter information of honey (Drug-likeness, DL) of ≥0.18 or more active ingredients (Table 1). YOL ingredients were supplemented through literature review. Potential targets related to active ingredients were searched through the TCMSP database and Cytoscape 3.6.1 software.Table 155 Active Compounds Predicted by OB and DL Among 4 Herbs in YOLDrugMol IDMolecule NameMWOB%DLHoneysuckleMOL001494Mandenol308.56420.19MOL001495Ethyl linolenate306.5446.10.2MOL002914Eriodyctiol (flavanone)288.2741.350.24MOL003006(-)-(3R,8S,9R,9aS,10aS)-9-ethenyl-8-(beta-D-glucopyranosyloxy)-2,3,9,9a,10,10a-hexahydro-5-oxo-5H,8H-pyrano[4,3-d]oxazolo[3,2-a]pyridine-3-carboxylic acid_qt281.2987.470.23MOL003014Secologanic dibutylacetal_qt384.5753.650.29MOL002773Beta-carotene536.9637.180.58MOL003036ZINC03978781412.7743.830.76MOL003044Chryseriol300.2835.850.27MOL0030955-hydroxy-7-methoxy-2-(3,4,5-trimethoxyphenyl)chromone358.3751.960.41MOL003111Centauroside_qt434.4855.790.5MOL003117Ioniceracetalides B_qt314.3761.190.19MOL003128Dinethylsecologanoside434.4448.460.48MOL000358Beta-sitosterol414.7936.910.75MOL000422Kaempferol286.2541.880.24MOL000449Stigmasterol412.7743.830.76MOL000006Luteolin286.2536.160.25MOL000098Quercetin302.2546.430.28ScutellariaMOL000073Ent-Epicatechin290.2948.960.24MOL000173Wogonin284.2830.680.23MOL000228(2R)-7-hydroxy-5-methoxy-2-phenylchroman-4-one270.355.230.2MOL000359Sitosterol414.7936.910.75MOL000525Norwogonin270.2539.40.21MOL0005525,2ʹ-Dihydroxy-6,7,8-trimethoxyflavone344.3431.710.35MOL001458Coptisine320.3430.670.86MOL001490Bis[(2S)-2-ethylhexyl] benzene-1,2-dicarboxylate390.6243.590.35MOL001689Acacetin284.2834.970.24MOL002714Baicalein270.2533.520.21MOL002879Diop390.6243.590.39MOL002897Epiberberine336.3943.090.78MOL0029095,7,2,5-tetrahydroxy-8,6-dimethoxyflavone376.3433.820.45MOL002910Carthamidin288.2741.150.24MOL002913Dihydrobaicalin_qt272.2740.040.21MOL002914Eriodyctiol (flavanone)288.2741.350.24MOL002915Salvigenin328.3449.070.33MOL0029175,2ʹ,6ʹ-Trihydroxy-7,8-dimethoxyflavone330.3145.050.33MOL0029255,7,2ʹ,6ʹ-Tetrahydroxyflavone286.2537.010.24MOL002927Skullcapflavone II374.3769.510.44MOL002928Oroxylin a284.2841.370.23MOL002932Panicolin314.3176.260.29MOL0029335,7,4ʹ-Trihydroxy-8-methoxyflavone300.2836.560.27MOL002934Neobaicalein374.37104.30.44MOL002937Dihydrooroxylin A286.366.060.23MOL008206Moslosooflavone298.3144.090.25MOL01041511,13-Eicosadienoic acid, methyl ester322.5939.280.23MOL012266Rivularin344.3437.940.37MOL000490Petunidin317.2930.050.31MOL0045983,5,6,7-tetramethoxy-2-(3,4,5-trimethoxyphenyl)chromone432.4631.970.59BupleurumMOL002776Baicalin446.3940.120.75MOL001645Kaempferol308.5642.10.2MOL004718Linoleyl acetate412.7742.980.76MOL004653α-spinasterol426.546.060.66MOL004624Stigmasterol348.4847.720.53MOL004609Areapillin360.3448.960.41MOL000354Isorhamnetin316.2849.60.31MOL013187Cubebin356.457.130.64\\n\\n55 Active Compounds Predicted by OB and DL Among 4 Herbs in YOL\\nKnown Therapeutic Targets of Chronic Pharyngitis (CP) In the drug treatment of CP, the known therapeutic targets were mainly obtained from two sources: GeneCards database (https://www.genecards.org/) and CTD database (http://ctd.mdibl.org/). The data analysis was conducted in 5878 therapeutic targets for the treatment of CP, after the redundant entries were removed. Figure S1 shows the specific information about these known therapeutic targets.\\nIn the drug treatment of CP, the known therapeutic targets were mainly obtained from two sources: GeneCards database (https://www.genecards.org/) and CTD database (http://ctd.mdibl.org/). The data analysis was conducted in 5878 therapeutic targets for the treatment of CP, after the redundant entries were removed. Figure S1 shows the specific information about these known therapeutic targets.\\nNetwork Construction and Analysis To understand the relationship between the herbs and chemical compounds that consist of YOL and its putative targets, and the therapeutic targets known for CP, Network Visualization was conducted using Cytoscape 3.6.1 software,8 and the degree between compounds and targets was analyzed.\\nTo understand the relationship between the herbs and chemical compounds that consist of YOL and its putative targets, and the therapeutic targets known for CP, Network Visualization was conducted using Cytoscape 3.6.1 software,8 and the degree between compounds and targets was analyzed.\\nProtein-Protein Interaction (PPI) Data PPI core network (PPICN) is used to study the relationship between chemical compounds and disease-associated protein molecules based on biochemistry, signal transduction and genetic networks.9 The differing ID types of the proteins were converted to UniProt IDs. In order to further understand how YOL and CP interact at the protein level, the selected targets in this study were uploaded on the online Venn diagram (http://bioinfogp.cnb.csic.es/tools/venny/index.html). Moreover, the genes at the intersection of the active compound and CP were selected and uploaded on STRING 10.5 (https://string-db.org) to obtain the PPI relationship.\\nPPI core network (PPICN) is used to study the relationship between chemical compounds and disease-associated protein molecules based on biochemistry, signal transduction and genetic networks.9 The differing ID types of the proteins were converted to UniProt IDs. In order to further understand how YOL and CP interact at the protein level, the selected targets in this study were uploaded on the online Venn diagram (http://bioinfogp.cnb.csic.es/tools/venny/index.html). Moreover, the genes at the intersection of the active compound and CP were selected and uploaded on STRING 10.5 (https://string-db.org) to obtain the PPI relationship.\\nPathway Enrichment Analysis The data was imported in the format Gene Symbol into David 6.8 database (https://david.ncifcrf.gov/). Molecular function (CC), biological process (BP), and cellular function (MF) were selected, respectively. A pathway enrichment analysis was prospectively performed using biological process enrichment GO analysis and KEGG pathway analysis (http://www.genome.jp/kegg/) to clarify the pathways involving putative CP targets, and visualized by online mapping website Omicshare Tools (http://www.omicshare.com/tools/index.php/).\\nThe data was imported in the format Gene Symbol into David 6.8 database (https://david.ncifcrf.gov/). Molecular function (CC), biological process (BP), and cellular function (MF) were selected, respectively. A pathway enrichment analysis was prospectively performed using biological process enrichment GO analysis and KEGG pathway analysis (http://www.genome.jp/kegg/) to clarify the pathways involving putative CP targets, and visualized by online mapping website Omicshare Tools (http://www.omicshare.com/tools/index.php/).\\nStatistical Analysis Each experiment was repeated at least in triplicate and data were shown as mean ± SD. Statistical difference in GO and KEGG analysis was performed using hypergeometric and Fisher's exact tests. P<0.05 was considered statistically significant.\\nEach experiment was repeated at least in triplicate and data were shown as mean ± SD. Statistical difference in GO and KEGG analysis was performed using hypergeometric and Fisher's exact tests. P<0.05 was considered statistically significant.\",\n \"Yinqin Oral Liquid (YOL) was provided by su zhou si yuan natural products research and development Co. Ltd. YOL is composed of honeysuckle, scutellaria, bupleurum and cicada, decompressed and concentrated to 2 g/mL through boiling and alcohol sinking, and stored at 4°C for later use. Primary antibodies: COX-2 and iNOS antibodies were purchased from Abcam (Cambridge, UK); glyceraldehyde-3-phosphate dehydrogenase (GAPDH) antibody was obtained from Millipore (Billerica, MA, USA); PI3K, p-AKT, AKT, p-Stats, Stat3 and p-p65 (S536) antibodies were purchased from Cell Signaling Technology (Danvers, MA, USA); and p65 antibodies was obtained from Santa Cruz (CA, USA). The horseradish peroxidase (HRP)-conjugated sheep anti-mouse or anti-rabbit secondary antibodies were purchased from Thermo Fisher (Waltham, MA, USA). The proteins were visualized using an ECL detection kit (Thermo Fisher).\",\n \"RAW264.7 cells, a mouse macrophage cell line (Cell Bank of Chinese Academy of Sciences, Shanghai, China) were cultured using DMEM supplemented with FBS (10%) and penicillin-streptomycin (1%). The cells were cultured at 37°C in a humidified environment of 5% carbon dioxide. The medium was changed every other day and the cells were passaged at a dilution of 1:3.\",\n \"In the TCMSP database (https://tcmsp-e.com/),7 the “Herb name” was selected to retrieve the molecular ADME parameter information of honeysuckle, scutellaria, bupleurum and cicada slough, and then the screen conformed to the oral bioavailability (OB) ADME parameter information of honey (Drug-likeness, DL) of ≥0.18 or more active ingredients (Table 1). YOL ingredients were supplemented through literature review. Potential targets related to active ingredients were searched through the TCMSP database and Cytoscape 3.6.1 software.Table 155 Active Compounds Predicted by OB and DL Among 4 Herbs in YOLDrugMol IDMolecule NameMWOB%DLHoneysuckleMOL001494Mandenol308.56420.19MOL001495Ethyl linolenate306.5446.10.2MOL002914Eriodyctiol (flavanone)288.2741.350.24MOL003006(-)-(3R,8S,9R,9aS,10aS)-9-ethenyl-8-(beta-D-glucopyranosyloxy)-2,3,9,9a,10,10a-hexahydro-5-oxo-5H,8H-pyrano[4,3-d]oxazolo[3,2-a]pyridine-3-carboxylic acid_qt281.2987.470.23MOL003014Secologanic dibutylacetal_qt384.5753.650.29MOL002773Beta-carotene536.9637.180.58MOL003036ZINC03978781412.7743.830.76MOL003044Chryseriol300.2835.850.27MOL0030955-hydroxy-7-methoxy-2-(3,4,5-trimethoxyphenyl)chromone358.3751.960.41MOL003111Centauroside_qt434.4855.790.5MOL003117Ioniceracetalides B_qt314.3761.190.19MOL003128Dinethylsecologanoside434.4448.460.48MOL000358Beta-sitosterol414.7936.910.75MOL000422Kaempferol286.2541.880.24MOL000449Stigmasterol412.7743.830.76MOL000006Luteolin286.2536.160.25MOL000098Quercetin302.2546.430.28ScutellariaMOL000073Ent-Epicatechin290.2948.960.24MOL000173Wogonin284.2830.680.23MOL000228(2R)-7-hydroxy-5-methoxy-2-phenylchroman-4-one270.355.230.2MOL000359Sitosterol414.7936.910.75MOL000525Norwogonin270.2539.40.21MOL0005525,2ʹ-Dihydroxy-6,7,8-trimethoxyflavone344.3431.710.35MOL001458Coptisine320.3430.670.86MOL001490Bis[(2S)-2-ethylhexyl] benzene-1,2-dicarboxylate390.6243.590.35MOL001689Acacetin284.2834.970.24MOL002714Baicalein270.2533.520.21MOL002879Diop390.6243.590.39MOL002897Epiberberine336.3943.090.78MOL0029095,7,2,5-tetrahydroxy-8,6-dimethoxyflavone376.3433.820.45MOL002910Carthamidin288.2741.150.24MOL002913Dihydrobaicalin_qt272.2740.040.21MOL002914Eriodyctiol (flavanone)288.2741.350.24MOL002915Salvigenin328.3449.070.33MOL0029175,2ʹ,6ʹ-Trihydroxy-7,8-dimethoxyflavone330.3145.050.33MOL0029255,7,2ʹ,6ʹ-Tetrahydroxyflavone286.2537.010.24MOL002927Skullcapflavone II374.3769.510.44MOL002928Oroxylin a284.2841.370.23MOL002932Panicolin314.3176.260.29MOL0029335,7,4ʹ-Trihydroxy-8-methoxyflavone300.2836.560.27MOL002934Neobaicalein374.37104.30.44MOL002937Dihydrooroxylin A286.366.060.23MOL008206Moslosooflavone298.3144.090.25MOL01041511,13-Eicosadienoic acid, methyl ester322.5939.280.23MOL012266Rivularin344.3437.940.37MOL000490Petunidin317.2930.050.31MOL0045983,5,6,7-tetramethoxy-2-(3,4,5-trimethoxyphenyl)chromone432.4631.970.59BupleurumMOL002776Baicalin446.3940.120.75MOL001645Kaempferol308.5642.10.2MOL004718Linoleyl acetate412.7742.980.76MOL004653α-spinasterol426.546.060.66MOL004624Stigmasterol348.4847.720.53MOL004609Areapillin360.3448.960.41MOL000354Isorhamnetin316.2849.60.31MOL013187Cubebin356.457.130.64\\n\\n55 Active Compounds Predicted by OB and DL Among 4 Herbs in YOL\",\n \"In the drug treatment of CP, the known therapeutic targets were mainly obtained from two sources: GeneCards database (https://www.genecards.org/) and CTD database (http://ctd.mdibl.org/). The data analysis was conducted in 5878 therapeutic targets for the treatment of CP, after the redundant entries were removed. Figure S1 shows the specific information about these known therapeutic targets.\",\n \"To understand the relationship between the herbs and chemical compounds that consist of YOL and its putative targets, and the therapeutic targets known for CP, Network Visualization was conducted using Cytoscape 3.6.1 software,8 and the degree between compounds and targets was analyzed.\",\n \"PPI core network (PPICN) is used to study the relationship between chemical compounds and disease-associated protein molecules based on biochemistry, signal transduction and genetic networks.9 The differing ID types of the proteins were converted to UniProt IDs. In order to further understand how YOL and CP interact at the protein level, the selected targets in this study were uploaded on the online Venn diagram (http://bioinfogp.cnb.csic.es/tools/venny/index.html). Moreover, the genes at the intersection of the active compound and CP were selected and uploaded on STRING 10.5 (https://string-db.org) to obtain the PPI relationship.\",\n \"The data was imported in the format Gene Symbol into David 6.8 database (https://david.ncifcrf.gov/). Molecular function (CC), biological process (BP), and cellular function (MF) were selected, respectively. A pathway enrichment analysis was prospectively performed using biological process enrichment GO analysis and KEGG pathway analysis (http://www.genome.jp/kegg/) to clarify the pathways involving putative CP targets, and visualized by online mapping website Omicshare Tools (http://www.omicshare.com/tools/index.php/).\",\n \"Each experiment was repeated at least in triplicate and data were shown as mean ± SD. Statistical difference in GO and KEGG analysis was performed using hypergeometric and Fisher's exact tests. P<0.05 was considered statistically significant.\",\n \"Screening and Analysis of Active Compounds First, all the compounds related to four TCMs were retrieved by TCMSP. Second, according to the screening conditions of OB ≥30% and DL ≥0.18,10 23 active constituents of honeysuckle (17 predicted relevant targets), 36 of baicalensis (30 predicted relevant targets), 17 of bupleurum (13 predicted relevant targets), and no suitable compounds of cicada slough were obtained. Among them, baicalensis and honeysuckle contained two identical ingredients, while bupleurum and honeysuckle contained three identical components. Finally, a total of 55 active components were obtained for the data analysis after removing redundant entries (Table 1).\\nFirst, all the compounds related to four TCMs were retrieved by TCMSP. Second, according to the screening conditions of OB ≥30% and DL ≥0.18,10 23 active constituents of honeysuckle (17 predicted relevant targets), 36 of baicalensis (30 predicted relevant targets), 17 of bupleurum (13 predicted relevant targets), and no suitable compounds of cicada slough were obtained. Among them, baicalensis and honeysuckle contained two identical ingredients, while bupleurum and honeysuckle contained three identical components. Finally, a total of 55 active components were obtained for the data analysis after removing redundant entries (Table 1).\\nNetwork Analysis of “Active Component-Target” Interaction in YOL The compound-target network contained 175 nodes including 55 compound nodes and 113 target nodes, of which two had no corresponding target and 577 edges. As shown in Figure 1, blue represents compounds in bupleurum, red represents compounds in scutellaria, purple represents compounds in honeysuckle, green represents drug targets, each edge indicates the interaction between compounds and their targets, and two of the 55 compounds were excluded from the network construction. The degree value of a node represents the amount of connected routes in the network, and the larger the shape, the higher the degree value. The network in line with its topological properties showed that the nodes with higher degree of screening were analyzed. These nodes that connect compounds or targets act as hubs in the whole network and may predict key compounds or targets. The top five compounds ranked by degree were MOL000098-quercetin, MOL000422-Kaempferol, MOL000449-stigmasterol, MOL000358-quebrachol and MOL000006-luteolin, which could interact with 154, 68, 39, 32 and 28 target proteins, respectively. PTGS1, NCOA2, AR, PRSS1 and PPARG were the top five targets with the highest degree, which could interact with 42, 38, 29, 28 and 17 compounds, respectively.Figure 1Compounds-target network diagram. Blue represents compounds in bupleurum, Red represents compounds in scutellaria, Purple represents compounds in honeysuckle, Green represents drug targets. The size represents the degree value. The larger the shape, the larger the degree value.\\nCompounds-target network diagram. Blue represents compounds in bupleurum, Red represents compounds in scutellaria, Purple represents compounds in honeysuckle, Green represents drug targets. The size represents the degree value. The larger the shape, the larger the degree value.\\nThe compound-target network contained 175 nodes including 55 compound nodes and 113 target nodes, of which two had no corresponding target and 577 edges. As shown in Figure 1, blue represents compounds in bupleurum, red represents compounds in scutellaria, purple represents compounds in honeysuckle, green represents drug targets, each edge indicates the interaction between compounds and their targets, and two of the 55 compounds were excluded from the network construction. The degree value of a node represents the amount of connected routes in the network, and the larger the shape, the higher the degree value. The network in line with its topological properties showed that the nodes with higher degree of screening were analyzed. These nodes that connect compounds or targets act as hubs in the whole network and may predict key compounds or targets. The top five compounds ranked by degree were MOL000098-quercetin, MOL000422-Kaempferol, MOL000449-stigmasterol, MOL000358-quebrachol and MOL000006-luteolin, which could interact with 154, 68, 39, 32 and 28 target proteins, respectively. PTGS1, NCOA2, AR, PRSS1 and PPARG were the top five targets with the highest degree, which could interact with 42, 38, 29, 28 and 17 compounds, respectively.Figure 1Compounds-target network diagram. Blue represents compounds in bupleurum, Red represents compounds in scutellaria, Purple represents compounds in honeysuckle, Green represents drug targets. The size represents the degree value. The larger the shape, the larger the degree value.\\nCompounds-target network diagram. Blue represents compounds in bupleurum, Red represents compounds in scutellaria, Purple represents compounds in honeysuckle, Green represents drug targets. The size represents the degree value. The larger the shape, the larger the degree value.\\nThe Construction of Key Protein Network of YOL and CP Through the internationally recognized CTD and GeneCards disease databases, 5150 and 1247 pharyngitis-related targets were obtained, respectively. Through the Venny online tool, 30 targets of honeysuckle, scutellaria, bupleuri and pharyngitis were obtained, including VEGFA, IL6, ESR1, RELA, HIF1A, etc. (Figure 2A). Finally, STRING online tool was used to construct the PPI network interaction map of drug and disease. It contained 30 nodes representing proteins and 48 edges representing the interaction between proteins. The thicker the line, the higher is the correlation degree. Through the protein interaction network, we could further explore the therapeutic target and mechanism of YOL for CP (Figure 2B).Figure 2(A) Venn diagram of compounds-target network diagram; (B) Core network diagram of protein interaction between YOL and CP.\\n(A) Venn diagram of compounds-target network diagram; (B) Core network diagram of protein interaction between YOL and CP.\\nThrough the internationally recognized CTD and GeneCards disease databases, 5150 and 1247 pharyngitis-related targets were obtained, respectively. Through the Venny online tool, 30 targets of honeysuckle, scutellaria, bupleuri and pharyngitis were obtained, including VEGFA, IL6, ESR1, RELA, HIF1A, etc. (Figure 2A). Finally, STRING online tool was used to construct the PPI network interaction map of drug and disease. It contained 30 nodes representing proteins and 48 edges representing the interaction between proteins. The thicker the line, the higher is the correlation degree. Through the protein interaction network, we could further explore the therapeutic target and mechanism of YOL for CP (Figure 2B).Figure 2(A) Venn diagram of compounds-target network diagram; (B) Core network diagram of protein interaction between YOL and CP.\\n(A) Venn diagram of compounds-target network diagram; (B) Core network diagram of protein interaction between YOL and CP.\\nPharmacological Mechanisms of YOL Acting on CP A total of 30 common targets were obtained by DAVID online tool and 148 GO items were obtained (p < 0.05) by performing the functional enrichment analysis. There were 102 entries on biological process (BP), 34 entries on Molecular Function (MF), and 12 entries on cell composition (CC) (Figure 3A). A total of 46 signaling pathways were obtained by KEGG pathway enrichment screening (p<0.05), involving cancer, PI3K-AKT, hepatitis, proteoglycans, p53, HIF-1 signaling pathways, etc. (Figure 3B and Table S1).Figure 3(A) GO analysis function annotation diagram. Biological process (BP), molecular function (MF), and cell composition (CC). (B) Enriched KEGG pathways of YOL selected targets for CP.\\n(A) GO analysis function annotation diagram. Biological process (BP), molecular function (MF), and cell composition (CC). (B) Enriched KEGG pathways of YOL selected targets for CP.\\nA total of 30 common targets were obtained by DAVID online tool and 148 GO items were obtained (p < 0.05) by performing the functional enrichment analysis. There were 102 entries on biological process (BP), 34 entries on Molecular Function (MF), and 12 entries on cell composition (CC) (Figure 3A). A total of 46 signaling pathways were obtained by KEGG pathway enrichment screening (p<0.05), involving cancer, PI3K-AKT, hepatitis, proteoglycans, p53, HIF-1 signaling pathways, etc. (Figure 3B and Table S1).Figure 3(A) GO analysis function annotation diagram. Biological process (BP), molecular function (MF), and cell composition (CC). (B) Enriched KEGG pathways of YOL selected targets for CP.\\n(A) GO analysis function annotation diagram. Biological process (BP), molecular function (MF), and cell composition (CC). (B) Enriched KEGG pathways of YOL selected targets for CP.\\nExperimental Verification of Predicted Results In order to experimentally demonstrate the predictions about the molecular mechanism of YOL, we chose two ingredients for testing (Figure 4), based on their composition score, the group to which they belong, and their herbal origin to examine how ingredients from different herbs affect the same proteins. The effects of these compounds on the expression levels of related proteins were analyzed by Western blot. Macrophages secrete several inflammatory cytokines, which play significant roles in inflammation. Signal transduction and transcriptional activator 3 (STAT3), nuclear factor kappa B (NF-kB), and phosphatidylinositol 3-kinase (PI3K)/Akt pathways are critical inflammatory pathways. LPS can significantly increase the production of many proinflammatory factors, including inducible NO synthase (iNOS) and cyclooxygenase-2 (COX-2) in macrophages. After pretreatment of macrophages with YOL, luteolin and baicalein, the protein expression levels of iNOS and COX-2 induced by LPS were significantly inhibited in a dose-dependent manner (Figure 4A). The related protein changes were consistent with iNOS and COX-2 inflammation protein expression (Figure 4B–D).Figure 4The YOL inhibits PI3K/p-AKT/p-Stats pathway in RAW 264.7 cells. Representative Western blot images show the relative expressions of iNOS, COX-2 (A), p-p65, p65 (B), PI3K, p-AKT, AKT (C), p-Stats, Stat3 (D) in the groups. The protein levels (normalized) of iNOS, COX2 (E), and p-p65, p65, PI3K, p-AKT, AKT, p-Stats, Stat3 (F) in cells. All the values are presented as mean ± SD. #P < 0.05 and ##P < 0.01 vs control group. *P < 0.05 and **P < 0.01 vs LPS group.\\nThe YOL inhibits PI3K/p-AKT/p-Stats pathway in RAW 264.7 cells. Representative Western blot images show the relative expressions of iNOS, COX-2 (A), p-p65, p65 (B), PI3K, p-AKT, AKT (C), p-Stats, Stat3 (D) in the groups. The protein levels (normalized) of iNOS, COX2 (E), and p-p65, p65, PI3K, p-AKT, AKT, p-Stats, Stat3 (F) in cells. All the values are presented as mean ± SD. #P < 0.05 and ##P < 0.01 vs control group. *P < 0.05 and **P < 0.01 vs LPS group.\\nIn order to experimentally demonstrate the predictions about the molecular mechanism of YOL, we chose two ingredients for testing (Figure 4), based on their composition score, the group to which they belong, and their herbal origin to examine how ingredients from different herbs affect the same proteins. The effects of these compounds on the expression levels of related proteins were analyzed by Western blot. Macrophages secrete several inflammatory cytokines, which play significant roles in inflammation. Signal transduction and transcriptional activator 3 (STAT3), nuclear factor kappa B (NF-kB), and phosphatidylinositol 3-kinase (PI3K)/Akt pathways are critical inflammatory pathways. LPS can significantly increase the production of many proinflammatory factors, including inducible NO synthase (iNOS) and cyclooxygenase-2 (COX-2) in macrophages. After pretreatment of macrophages with YOL, luteolin and baicalein, the protein expression levels of iNOS and COX-2 induced by LPS were significantly inhibited in a dose-dependent manner (Figure 4A). The related protein changes were consistent with iNOS and COX-2 inflammation protein expression (Figure 4B–D).Figure 4The YOL inhibits PI3K/p-AKT/p-Stats pathway in RAW 264.7 cells. Representative Western blot images show the relative expressions of iNOS, COX-2 (A), p-p65, p65 (B), PI3K, p-AKT, AKT (C), p-Stats, Stat3 (D) in the groups. The protein levels (normalized) of iNOS, COX2 (E), and p-p65, p65, PI3K, p-AKT, AKT, p-Stats, Stat3 (F) in cells. All the values are presented as mean ± SD. #P < 0.05 and ##P < 0.01 vs control group. *P < 0.05 and **P < 0.01 vs LPS group.\\nThe YOL inhibits PI3K/p-AKT/p-Stats pathway in RAW 264.7 cells. Representative Western blot images show the relative expressions of iNOS, COX-2 (A), p-p65, p65 (B), PI3K, p-AKT, AKT (C), p-Stats, Stat3 (D) in the groups. The protein levels (normalized) of iNOS, COX2 (E), and p-p65, p65, PI3K, p-AKT, AKT, p-Stats, Stat3 (F) in cells. All the values are presented as mean ± SD. #P < 0.05 and ##P < 0.01 vs control group. *P < 0.05 and **P < 0.01 vs LPS group.\",\n \"First, all the compounds related to four TCMs were retrieved by TCMSP. Second, according to the screening conditions of OB ≥30% and DL ≥0.18,10 23 active constituents of honeysuckle (17 predicted relevant targets), 36 of baicalensis (30 predicted relevant targets), 17 of bupleurum (13 predicted relevant targets), and no suitable compounds of cicada slough were obtained. Among them, baicalensis and honeysuckle contained two identical ingredients, while bupleurum and honeysuckle contained three identical components. Finally, a total of 55 active components were obtained for the data analysis after removing redundant entries (Table 1).\",\n \"The compound-target network contained 175 nodes including 55 compound nodes and 113 target nodes, of which two had no corresponding target and 577 edges. As shown in Figure 1, blue represents compounds in bupleurum, red represents compounds in scutellaria, purple represents compounds in honeysuckle, green represents drug targets, each edge indicates the interaction between compounds and their targets, and two of the 55 compounds were excluded from the network construction. The degree value of a node represents the amount of connected routes in the network, and the larger the shape, the higher the degree value. The network in line with its topological properties showed that the nodes with higher degree of screening were analyzed. These nodes that connect compounds or targets act as hubs in the whole network and may predict key compounds or targets. The top five compounds ranked by degree were MOL000098-quercetin, MOL000422-Kaempferol, MOL000449-stigmasterol, MOL000358-quebrachol and MOL000006-luteolin, which could interact with 154, 68, 39, 32 and 28 target proteins, respectively. PTGS1, NCOA2, AR, PRSS1 and PPARG were the top five targets with the highest degree, which could interact with 42, 38, 29, 28 and 17 compounds, respectively.Figure 1Compounds-target network diagram. Blue represents compounds in bupleurum, Red represents compounds in scutellaria, Purple represents compounds in honeysuckle, Green represents drug targets. The size represents the degree value. The larger the shape, the larger the degree value.\\nCompounds-target network diagram. Blue represents compounds in bupleurum, Red represents compounds in scutellaria, Purple represents compounds in honeysuckle, Green represents drug targets. The size represents the degree value. The larger the shape, the larger the degree value.\",\n \"Through the internationally recognized CTD and GeneCards disease databases, 5150 and 1247 pharyngitis-related targets were obtained, respectively. Through the Venny online tool, 30 targets of honeysuckle, scutellaria, bupleuri and pharyngitis were obtained, including VEGFA, IL6, ESR1, RELA, HIF1A, etc. (Figure 2A). Finally, STRING online tool was used to construct the PPI network interaction map of drug and disease. It contained 30 nodes representing proteins and 48 edges representing the interaction between proteins. The thicker the line, the higher is the correlation degree. Through the protein interaction network, we could further explore the therapeutic target and mechanism of YOL for CP (Figure 2B).Figure 2(A) Venn diagram of compounds-target network diagram; (B) Core network diagram of protein interaction between YOL and CP.\\n(A) Venn diagram of compounds-target network diagram; (B) Core network diagram of protein interaction between YOL and CP.\",\n \"A total of 30 common targets were obtained by DAVID online tool and 148 GO items were obtained (p < 0.05) by performing the functional enrichment analysis. There were 102 entries on biological process (BP), 34 entries on Molecular Function (MF), and 12 entries on cell composition (CC) (Figure 3A). A total of 46 signaling pathways were obtained by KEGG pathway enrichment screening (p<0.05), involving cancer, PI3K-AKT, hepatitis, proteoglycans, p53, HIF-1 signaling pathways, etc. (Figure 3B and Table S1).Figure 3(A) GO analysis function annotation diagram. Biological process (BP), molecular function (MF), and cell composition (CC). (B) Enriched KEGG pathways of YOL selected targets for CP.\\n(A) GO analysis function annotation diagram. Biological process (BP), molecular function (MF), and cell composition (CC). (B) Enriched KEGG pathways of YOL selected targets for CP.\",\n \"In order to experimentally demonstrate the predictions about the molecular mechanism of YOL, we chose two ingredients for testing (Figure 4), based on their composition score, the group to which they belong, and their herbal origin to examine how ingredients from different herbs affect the same proteins. The effects of these compounds on the expression levels of related proteins were analyzed by Western blot. Macrophages secrete several inflammatory cytokines, which play significant roles in inflammation. Signal transduction and transcriptional activator 3 (STAT3), nuclear factor kappa B (NF-kB), and phosphatidylinositol 3-kinase (PI3K)/Akt pathways are critical inflammatory pathways. LPS can significantly increase the production of many proinflammatory factors, including inducible NO synthase (iNOS) and cyclooxygenase-2 (COX-2) in macrophages. After pretreatment of macrophages with YOL, luteolin and baicalein, the protein expression levels of iNOS and COX-2 induced by LPS were significantly inhibited in a dose-dependent manner (Figure 4A). The related protein changes were consistent with iNOS and COX-2 inflammation protein expression (Figure 4B–D).Figure 4The YOL inhibits PI3K/p-AKT/p-Stats pathway in RAW 264.7 cells. Representative Western blot images show the relative expressions of iNOS, COX-2 (A), p-p65, p65 (B), PI3K, p-AKT, AKT (C), p-Stats, Stat3 (D) in the groups. The protein levels (normalized) of iNOS, COX2 (E), and p-p65, p65, PI3K, p-AKT, AKT, p-Stats, Stat3 (F) in cells. All the values are presented as mean ± SD. #P < 0.05 and ##P < 0.01 vs control group. *P < 0.05 and **P < 0.01 vs LPS group.\\nThe YOL inhibits PI3K/p-AKT/p-Stats pathway in RAW 264.7 cells. Representative Western blot images show the relative expressions of iNOS, COX-2 (A), p-p65, p65 (B), PI3K, p-AKT, AKT (C), p-Stats, Stat3 (D) in the groups. The protein levels (normalized) of iNOS, COX2 (E), and p-p65, p65, PI3K, p-AKT, AKT, p-Stats, Stat3 (F) in cells. All the values are presented as mean ± SD. #P < 0.05 and ##P < 0.01 vs control group. *P < 0.05 and **P < 0.01 vs LPS group.\",\n \"Many diseases, including cancer and chronic inflammation, are known to be regulated by multiple signaling pathways. TCMs are considered as multi-component and multi-target therapeutic drugs, which is in accordance with the methodologies of network pharmacology. The holistic view of TCM considers the human body as a complex biological network system, which is connected with network pharmacology. By combining the network pharmacology with the holistic view of TCM, the disease-target-drug network is obtained. The network shows that different components of TCM may act on one target, and different targets may act on the same pathway. It is also possible that the same component of TCM acts on different targets, and the same target acts on different pathways. The multi-component, multi-target and multi-pathway of TCM can be clearly visualized through network pharmacology, which resolves many difficulties encountered in the study of TCM.13–15 CP is the diffuse inflammation of pharyngeal mucosa, submucosa and lymph tissues. Clinically, CP is mainly manifested as pharyngeal discomfort (foreign body sensation, burning sensation, stimulation sensation, etc.), pharyngeal itching, cough, etc. Pharyngitis belongs to the category of “Fenghou Bi” in TCM. Pharyngitis pertains to the category of “wind throat arthralgia” in Chinese medicine, and is often caused by evil heat entering the body, and heat in the lungs and stomach. YOL is an oral TCM preparation, which is further optimized on the basis of Jinchan oral liquid developed by Children’s Hospital of Suzhou University.5,6 However, there is a lack of research on its active ingredients and its mechanism of action.\\nIn this study, 30 potential targets, 102 biological processes, 12 molecular functions, 34 cell components, and 46 KEGG pathways were obtained. The active ingredients in YOL can treat CP through multiple targets and pathways. To elucidate the biological pathways that may be affected by YOL, a significantly overexpressed KEGG pathway was identified. The results showed VEGFA, IL6, ESR1 and RELA as the key targets and the involvement of PI3K-Akt, p53 and HIF-1 signaling pathways. TCM aims to restore a patient’s health through the use of a TCM prescription, which is usually composed of two or more TCMs in optimal proportions.\\nYOL, for example, consists of four TCMs. As expected, most of the selected active ingredients are directly or indirectly associated with inflammation. In a previous study, neochlorogenic acid was demonstrated to inhibit LPS-activated inflammatory responses through up-regulation of Nrf2/HO-1 and AMPK pathways.16 The decoction extract from six natural herbs, which contain honeysuckle, exhibits anti-inflammatory and immunomodulatory effects by targeting NF-κB/IL-6/STAT3 signaling.17 Luteolin is abundant in honeysuckle, which alleviates CP by inhibiting the NF-κB pathway and anti-inflammatory polarization of M1 macrophages.11 Protective effects of baicalein on liver injury in mice induced by multi-microbial septicemia were found to be based on inhibiting inflammation.12 Network pharmacological studies have shown that the main active components of honeysuckle may play a role through inflammation-related proteins such as HSP90AA1, HSP90AB1, ESR1, PTGS2, TERT and PPARG, and especially heat shock protein HSP90A (HSP90AA1).18 Flavonoids such as baicalin and wogonin alone and in combination have anti-inflammatory activity in Scutellaria.19 Network pharmacological studies have also shown that alkaloids such as coptisine and epiphone in Scutellaria and components such as dihydrokaempferol, and rographolide flavonoids also show anti-inflammatory activity. MAPK14, TNFRSF1A, EGFR and SELE are the primary anti-inflammatory components of Scutellaria. The anti-inflammatory active components in Scutellaria can directly affect MAPK14 and EGFR to produce anti-inflammatory effects, and can also indirectly affect other targets to exert anti-inflammatory effects. As one of the four p38 MAPKs, MAPK14 plays a vital role in the cellular cascade induced by extracellular stimuli such as proinflammatory cytokines or physical stimuli.20 Studies predicted that the pharmacodynamic basis of Bupleurum is mainly saponins, flavonoids, volatile oils, fatty acids and other components. Flavonoids mainly act on PI3K-Akt, NF-kB and other pathways, participate in regulating inflammatory factors, estrogen signal transduction and other physiological processes. Bupleurum-Scutellaria drug pair, first used in small bupleurum decoction in Treatise on Febrile Diseases, is an important part of Bupleurum prescription. Bupleurum is bitter, flat and clear, and can disperse the stagnation of bile fire. Scutellaria has a bitter and cold taste, and can clear internal heat. Both of them must be used together to regulate the liver and gallbladder, and clear the dampness and heat of internal accumulation, which are commonly used to treat fever, pharyngitis, and respiratory diseases.21 Cicada slough can evacuate wind and heat, and clear the throat. Clinically, it is common in classical prescriptions such as Sheng jiang San and Pharyngitis San. However, the active components of Cicada slough did not satisfy the prediction requirements in this research.\\nYOL is composed of honeysuckle, scutellaria, bupleurum and cicada slough. According to TCM theory, bupleurum is bitter and flat, enters the liver and gallbladder meridian, penetrates and clears Shaoyang, and can drain the stagnation of gas. Scutellaria is bitter and cold, and clears the heat of Shaoyang. The actions of Bupleurum facilitate the clear discharge of Scutellaria, the two are compatible, and achieve the goal of reconciliation and Shaoyang. Honeysuckle cools through the surface, has heat-clearing and detoxifying effects, and also has the effect of fragrant obscenity. Cicada slough is light floating, clears the coke gas, shows honeysuckle compatibility, understanding the table evil, and is combined with the “treatment of the coke such as feather.” The combination of the four drugs, Xuan and descent together, clear the evil, thereby clearing heat and detoxification, warming the evil without hiding, scattered outside and recovered. Therefore, the treatment of CP with YOL can control the pharyngitis, regulate the balance of Yin and Yang, strengthen the foundation and improve the immunity of the body.\",\n \"This study used the network pharmacology platform to probe the YOL treatment of CP by multi-target analysis. The results showed that YOL could treat CP by acting on related targets, which were consistent with the reported literature. In addition, we have verified that YOL and its two monomer compounds, Luteolin and Baicalein inhibited activation of the NF-kB, Stat3 and PI3K-Akt pathways. Although the potential advantages of the “network target, multi-component” strategy of network pharmacology are obvious, the content of TCM ingredients is usually ignored in the research of network pharmacology of Chinese medicine, and the influence of content and concentration on the efficacy cannot be ignored. The predicted targets, which have not been discussed, may provide clues for further research on the mechanism of YOL.\"\n]"},"other_sections_sec_types":{"kind":"list like","value":[null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null],"string":"[\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null\n]"},"all_sections_titles":{"kind":"list like","value":["Introduction","Materials and Methods","Chemicals and Reagents","Cell Culture","Collection of Drug Molecular Information and Screening of Active Ingredients","Known Therapeutic Targets of Chronic Pharyngitis (CP)","Network Construction and Analysis","Protein-Protein Interaction (PPI) Data","Pathway Enrichment Analysis","Statistical Analysis","Results","Screening and Analysis of Active Compounds","Network Analysis of “Active Component-Target” Interaction in YOL","The Construction of Key Protein Network of YOL and CP","Pharmacological Mechanisms of YOL Acting on CP","Experimental Verification of Predicted Results","Discussion","Conclusion"],"string":"[\n \"Introduction\",\n \"Materials and Methods\",\n \"Chemicals and Reagents\",\n \"Cell Culture\",\n \"Collection of Drug Molecular Information and Screening of Active Ingredients\",\n \"Known Therapeutic Targets of Chronic Pharyngitis (CP)\",\n \"Network Construction and Analysis\",\n \"Protein-Protein Interaction (PPI) Data\",\n \"Pathway Enrichment Analysis\",\n \"Statistical Analysis\",\n \"Results\",\n \"Screening and Analysis of Active Compounds\",\n \"Network Analysis of “Active Component-Target” Interaction in YOL\",\n \"The Construction of Key Protein Network of YOL and CP\",\n \"Pharmacological Mechanisms of YOL Acting on CP\",\n \"Experimental Verification of Predicted Results\",\n \"Discussion\",\n \"Conclusion\"\n]"},"all_sections_texts":{"kind":"list like","value":["Chronic pharyngitis (CP) is a very common disease associated with chronic inflammation involving pharyngeal mucosa, submucosal and lymphatic tissues. Clinically, CP is mainly manifested as pharyngeal discomfort (foreign body sensation, burning sensation, irritation, etc.), with occasional pharyngeal itch, cough, etc., which belongs to the category of “slow throat arthralgia” in the traditional Chinese medicine (TCM).1 CP has a high incidence and accounts for 10–12% of all pharyngeal diseases, with a long disease course, recurrence and is difficult to cure. At present, western medicine uses antibiotics supplemented by hormone preparations, such as dexamethasone and antiviral drugs, for the treatment of pharyngitis. These treatment methods have drawbacks such as narrow therapeutic spectrum, recurrence, poor tolerance and major toxic and side effects. Hence, developing more effective therapeutic strategies against CP, and reducing the occurrence of side effects from the current therapeutic options is of great clinical importance.\nTCM is a major component of complementary and alternative medicine. Owing to its excellent clinical efficacy, TCM is a research hotspot worldwide. Dialectical theory of TCM is used to treat acute and chronic pharyngitis and has unique advantages such as minor toxic side effects, obvious curative effect and treatment of both symptoms and root causes.2–4 The pharmacological effects of TCM herbal formulae play an important role in their appropriate application. However, the complex nature of herbal formulae has impeded this understanding. Numerous chemical ingredients involving multiple potential targets are present in an herbal formula. It is not adequate to explain the effects produced by a whole herbal formula if we individually consider every single ingredient in it. Yinqin oral liquid (YOL) is composed of extracts of honeysuckle, scutellaria, bupleurum and cicada. YOL has the effects of relieving wind, muscle and clearing heat, which is mainly used for cold, fever, aversion to cold, pharyngeal pain and other upper respiratory tract infections caused by wind fever and wind-cold. Long-term clinical observation has proven its effect in relieving pharyngeal pain and treating hand-foot-mouth disease. YOL contains several chemical compounds, which regulate diverse targets, and thus precisely determine the pharmacological mechanisms involved in its therapeutic actions. In addition, there are several challenges in the relationships between the herbs and diseases.5,6\nNetwork pharmacology is based on the theory of system biology. As a new subject, it selects specific signal nodes to design multi-target drug molecules through the network analysis of system biology. It is possible to study both the active components and the potential gene targets from TCM because of the establishment of network pharmacology and bioinformatics. This study predicted and analyzed the active components and target of YOL using network pharmacology, to explore the rationality of its formula and the scientific nature of treating CP, and further explore the pharmacological mechanisms of YOL on CP.","Chemicals and Reagents Yinqin Oral Liquid (YOL) was provided by su zhou si yuan natural products research and development Co. Ltd. YOL is composed of honeysuckle, scutellaria, bupleurum and cicada, decompressed and concentrated to 2 g/mL through boiling and alcohol sinking, and stored at 4°C for later use. Primary antibodies: COX-2 and iNOS antibodies were purchased from Abcam (Cambridge, UK); glyceraldehyde-3-phosphate dehydrogenase (GAPDH) antibody was obtained from Millipore (Billerica, MA, USA); PI3K, p-AKT, AKT, p-Stats, Stat3 and p-p65 (S536) antibodies were purchased from Cell Signaling Technology (Danvers, MA, USA); and p65 antibodies was obtained from Santa Cruz (CA, USA). The horseradish peroxidase (HRP)-conjugated sheep anti-mouse or anti-rabbit secondary antibodies were purchased from Thermo Fisher (Waltham, MA, USA). The proteins were visualized using an ECL detection kit (Thermo Fisher).\nYinqin Oral Liquid (YOL) was provided by su zhou si yuan natural products research and development Co. Ltd. YOL is composed of honeysuckle, scutellaria, bupleurum and cicada, decompressed and concentrated to 2 g/mL through boiling and alcohol sinking, and stored at 4°C for later use. Primary antibodies: COX-2 and iNOS antibodies were purchased from Abcam (Cambridge, UK); glyceraldehyde-3-phosphate dehydrogenase (GAPDH) antibody was obtained from Millipore (Billerica, MA, USA); PI3K, p-AKT, AKT, p-Stats, Stat3 and p-p65 (S536) antibodies were purchased from Cell Signaling Technology (Danvers, MA, USA); and p65 antibodies was obtained from Santa Cruz (CA, USA). The horseradish peroxidase (HRP)-conjugated sheep anti-mouse or anti-rabbit secondary antibodies were purchased from Thermo Fisher (Waltham, MA, USA). The proteins were visualized using an ECL detection kit (Thermo Fisher).\nCell Culture RAW264.7 cells, a mouse macrophage cell line (Cell Bank of Chinese Academy of Sciences, Shanghai, China) were cultured using DMEM supplemented with FBS (10%) and penicillin-streptomycin (1%). The cells were cultured at 37°C in a humidified environment of 5% carbon dioxide. The medium was changed every other day and the cells were passaged at a dilution of 1:3.\nRAW264.7 cells, a mouse macrophage cell line (Cell Bank of Chinese Academy of Sciences, Shanghai, China) were cultured using DMEM supplemented with FBS (10%) and penicillin-streptomycin (1%). The cells were cultured at 37°C in a humidified environment of 5% carbon dioxide. The medium was changed every other day and the cells were passaged at a dilution of 1:3.\nCollection of Drug Molecular Information and Screening of Active Ingredients In the TCMSP database (https://tcmsp-e.com/),7 the “Herb name” was selected to retrieve the molecular ADME parameter information of honeysuckle, scutellaria, bupleurum and cicada slough, and then the screen conformed to the oral bioavailability (OB) ADME parameter information of honey (Drug-likeness, DL) of ≥0.18 or more active ingredients (Table 1). YOL ingredients were supplemented through literature review. Potential targets related to active ingredients were searched through the TCMSP database and Cytoscape 3.6.1 software.Table 155 Active Compounds Predicted by OB and DL Among 4 Herbs in YOLDrugMol IDMolecule NameMWOB%DLHoneysuckleMOL001494Mandenol308.56420.19MOL001495Ethyl linolenate306.5446.10.2MOL002914Eriodyctiol (flavanone)288.2741.350.24MOL003006(-)-(3R,8S,9R,9aS,10aS)-9-ethenyl-8-(beta-D-glucopyranosyloxy)-2,3,9,9a,10,10a-hexahydro-5-oxo-5H,8H-pyrano[4,3-d]oxazolo[3,2-a]pyridine-3-carboxylic acid_qt281.2987.470.23MOL003014Secologanic dibutylacetal_qt384.5753.650.29MOL002773Beta-carotene536.9637.180.58MOL003036ZINC03978781412.7743.830.76MOL003044Chryseriol300.2835.850.27MOL0030955-hydroxy-7-methoxy-2-(3,4,5-trimethoxyphenyl)chromone358.3751.960.41MOL003111Centauroside_qt434.4855.790.5MOL003117Ioniceracetalides B_qt314.3761.190.19MOL003128Dinethylsecologanoside434.4448.460.48MOL000358Beta-sitosterol414.7936.910.75MOL000422Kaempferol286.2541.880.24MOL000449Stigmasterol412.7743.830.76MOL000006Luteolin286.2536.160.25MOL000098Quercetin302.2546.430.28ScutellariaMOL000073Ent-Epicatechin290.2948.960.24MOL000173Wogonin284.2830.680.23MOL000228(2R)-7-hydroxy-5-methoxy-2-phenylchroman-4-one270.355.230.2MOL000359Sitosterol414.7936.910.75MOL000525Norwogonin270.2539.40.21MOL0005525,2ʹ-Dihydroxy-6,7,8-trimethoxyflavone344.3431.710.35MOL001458Coptisine320.3430.670.86MOL001490Bis[(2S)-2-ethylhexyl] benzene-1,2-dicarboxylate390.6243.590.35MOL001689Acacetin284.2834.970.24MOL002714Baicalein270.2533.520.21MOL002879Diop390.6243.590.39MOL002897Epiberberine336.3943.090.78MOL0029095,7,2,5-tetrahydroxy-8,6-dimethoxyflavone376.3433.820.45MOL002910Carthamidin288.2741.150.24MOL002913Dihydrobaicalin_qt272.2740.040.21MOL002914Eriodyctiol (flavanone)288.2741.350.24MOL002915Salvigenin328.3449.070.33MOL0029175,2ʹ,6ʹ-Trihydroxy-7,8-dimethoxyflavone330.3145.050.33MOL0029255,7,2ʹ,6ʹ-Tetrahydroxyflavone286.2537.010.24MOL002927Skullcapflavone II374.3769.510.44MOL002928Oroxylin a284.2841.370.23MOL002932Panicolin314.3176.260.29MOL0029335,7,4ʹ-Trihydroxy-8-methoxyflavone300.2836.560.27MOL002934Neobaicalein374.37104.30.44MOL002937Dihydrooroxylin A286.366.060.23MOL008206Moslosooflavone298.3144.090.25MOL01041511,13-Eicosadienoic acid, methyl ester322.5939.280.23MOL012266Rivularin344.3437.940.37MOL000490Petunidin317.2930.050.31MOL0045983,5,6,7-tetramethoxy-2-(3,4,5-trimethoxyphenyl)chromone432.4631.970.59BupleurumMOL002776Baicalin446.3940.120.75MOL001645Kaempferol308.5642.10.2MOL004718Linoleyl acetate412.7742.980.76MOL004653α-spinasterol426.546.060.66MOL004624Stigmasterol348.4847.720.53MOL004609Areapillin360.3448.960.41MOL000354Isorhamnetin316.2849.60.31MOL013187Cubebin356.457.130.64\n\n55 Active Compounds Predicted by OB and DL Among 4 Herbs in YOL\nIn the TCMSP database (https://tcmsp-e.com/),7 the “Herb name” was selected to retrieve the molecular ADME parameter information of honeysuckle, scutellaria, bupleurum and cicada slough, and then the screen conformed to the oral bioavailability (OB) ADME parameter information of honey (Drug-likeness, DL) of ≥0.18 or more active ingredients (Table 1). YOL ingredients were supplemented through literature review. Potential targets related to active ingredients were searched through the TCMSP database and Cytoscape 3.6.1 software.Table 155 Active Compounds Predicted by OB and DL Among 4 Herbs in YOLDrugMol IDMolecule NameMWOB%DLHoneysuckleMOL001494Mandenol308.56420.19MOL001495Ethyl linolenate306.5446.10.2MOL002914Eriodyctiol (flavanone)288.2741.350.24MOL003006(-)-(3R,8S,9R,9aS,10aS)-9-ethenyl-8-(beta-D-glucopyranosyloxy)-2,3,9,9a,10,10a-hexahydro-5-oxo-5H,8H-pyrano[4,3-d]oxazolo[3,2-a]pyridine-3-carboxylic acid_qt281.2987.470.23MOL003014Secologanic dibutylacetal_qt384.5753.650.29MOL002773Beta-carotene536.9637.180.58MOL003036ZINC03978781412.7743.830.76MOL003044Chryseriol300.2835.850.27MOL0030955-hydroxy-7-methoxy-2-(3,4,5-trimethoxyphenyl)chromone358.3751.960.41MOL003111Centauroside_qt434.4855.790.5MOL003117Ioniceracetalides B_qt314.3761.190.19MOL003128Dinethylsecologanoside434.4448.460.48MOL000358Beta-sitosterol414.7936.910.75MOL000422Kaempferol286.2541.880.24MOL000449Stigmasterol412.7743.830.76MOL000006Luteolin286.2536.160.25MOL000098Quercetin302.2546.430.28ScutellariaMOL000073Ent-Epicatechin290.2948.960.24MOL000173Wogonin284.2830.680.23MOL000228(2R)-7-hydroxy-5-methoxy-2-phenylchroman-4-one270.355.230.2MOL000359Sitosterol414.7936.910.75MOL000525Norwogonin270.2539.40.21MOL0005525,2ʹ-Dihydroxy-6,7,8-trimethoxyflavone344.3431.710.35MOL001458Coptisine320.3430.670.86MOL001490Bis[(2S)-2-ethylhexyl] benzene-1,2-dicarboxylate390.6243.590.35MOL001689Acacetin284.2834.970.24MOL002714Baicalein270.2533.520.21MOL002879Diop390.6243.590.39MOL002897Epiberberine336.3943.090.78MOL0029095,7,2,5-tetrahydroxy-8,6-dimethoxyflavone376.3433.820.45MOL002910Carthamidin288.2741.150.24MOL002913Dihydrobaicalin_qt272.2740.040.21MOL002914Eriodyctiol (flavanone)288.2741.350.24MOL002915Salvigenin328.3449.070.33MOL0029175,2ʹ,6ʹ-Trihydroxy-7,8-dimethoxyflavone330.3145.050.33MOL0029255,7,2ʹ,6ʹ-Tetrahydroxyflavone286.2537.010.24MOL002927Skullcapflavone II374.3769.510.44MOL002928Oroxylin a284.2841.370.23MOL002932Panicolin314.3176.260.29MOL0029335,7,4ʹ-Trihydroxy-8-methoxyflavone300.2836.560.27MOL002934Neobaicalein374.37104.30.44MOL002937Dihydrooroxylin A286.366.060.23MOL008206Moslosooflavone298.3144.090.25MOL01041511,13-Eicosadienoic acid, methyl ester322.5939.280.23MOL012266Rivularin344.3437.940.37MOL000490Petunidin317.2930.050.31MOL0045983,5,6,7-tetramethoxy-2-(3,4,5-trimethoxyphenyl)chromone432.4631.970.59BupleurumMOL002776Baicalin446.3940.120.75MOL001645Kaempferol308.5642.10.2MOL004718Linoleyl acetate412.7742.980.76MOL004653α-spinasterol426.546.060.66MOL004624Stigmasterol348.4847.720.53MOL004609Areapillin360.3448.960.41MOL000354Isorhamnetin316.2849.60.31MOL013187Cubebin356.457.130.64\n\n55 Active Compounds Predicted by OB and DL Among 4 Herbs in YOL\nKnown Therapeutic Targets of Chronic Pharyngitis (CP) In the drug treatment of CP, the known therapeutic targets were mainly obtained from two sources: GeneCards database (https://www.genecards.org/) and CTD database (http://ctd.mdibl.org/). The data analysis was conducted in 5878 therapeutic targets for the treatment of CP, after the redundant entries were removed. Figure S1 shows the specific information about these known therapeutic targets.\nIn the drug treatment of CP, the known therapeutic targets were mainly obtained from two sources: GeneCards database (https://www.genecards.org/) and CTD database (http://ctd.mdibl.org/). The data analysis was conducted in 5878 therapeutic targets for the treatment of CP, after the redundant entries were removed. Figure S1 shows the specific information about these known therapeutic targets.\nNetwork Construction and Analysis To understand the relationship between the herbs and chemical compounds that consist of YOL and its putative targets, and the therapeutic targets known for CP, Network Visualization was conducted using Cytoscape 3.6.1 software,8 and the degree between compounds and targets was analyzed.\nTo understand the relationship between the herbs and chemical compounds that consist of YOL and its putative targets, and the therapeutic targets known for CP, Network Visualization was conducted using Cytoscape 3.6.1 software,8 and the degree between compounds and targets was analyzed.\nProtein-Protein Interaction (PPI) Data PPI core network (PPICN) is used to study the relationship between chemical compounds and disease-associated protein molecules based on biochemistry, signal transduction and genetic networks.9 The differing ID types of the proteins were converted to UniProt IDs. In order to further understand how YOL and CP interact at the protein level, the selected targets in this study were uploaded on the online Venn diagram (http://bioinfogp.cnb.csic.es/tools/venny/index.html). Moreover, the genes at the intersection of the active compound and CP were selected and uploaded on STRING 10.5 (https://string-db.org) to obtain the PPI relationship.\nPPI core network (PPICN) is used to study the relationship between chemical compounds and disease-associated protein molecules based on biochemistry, signal transduction and genetic networks.9 The differing ID types of the proteins were converted to UniProt IDs. In order to further understand how YOL and CP interact at the protein level, the selected targets in this study were uploaded on the online Venn diagram (http://bioinfogp.cnb.csic.es/tools/venny/index.html). Moreover, the genes at the intersection of the active compound and CP were selected and uploaded on STRING 10.5 (https://string-db.org) to obtain the PPI relationship.\nPathway Enrichment Analysis The data was imported in the format Gene Symbol into David 6.8 database (https://david.ncifcrf.gov/). Molecular function (CC), biological process (BP), and cellular function (MF) were selected, respectively. A pathway enrichment analysis was prospectively performed using biological process enrichment GO analysis and KEGG pathway analysis (http://www.genome.jp/kegg/) to clarify the pathways involving putative CP targets, and visualized by online mapping website Omicshare Tools (http://www.omicshare.com/tools/index.php/).\nThe data was imported in the format Gene Symbol into David 6.8 database (https://david.ncifcrf.gov/). Molecular function (CC), biological process (BP), and cellular function (MF) were selected, respectively. A pathway enrichment analysis was prospectively performed using biological process enrichment GO analysis and KEGG pathway analysis (http://www.genome.jp/kegg/) to clarify the pathways involving putative CP targets, and visualized by online mapping website Omicshare Tools (http://www.omicshare.com/tools/index.php/).\nStatistical Analysis Each experiment was repeated at least in triplicate and data were shown as mean ± SD. Statistical difference in GO and KEGG analysis was performed using hypergeometric and Fisher's exact tests. P<0.05 was considered statistically significant.\nEach experiment was repeated at least in triplicate and data were shown as mean ± SD. Statistical difference in GO and KEGG analysis was performed using hypergeometric and Fisher's exact tests. P<0.05 was considered statistically significant.","Yinqin Oral Liquid (YOL) was provided by su zhou si yuan natural products research and development Co. Ltd. YOL is composed of honeysuckle, scutellaria, bupleurum and cicada, decompressed and concentrated to 2 g/mL through boiling and alcohol sinking, and stored at 4°C for later use. Primary antibodies: COX-2 and iNOS antibodies were purchased from Abcam (Cambridge, UK); glyceraldehyde-3-phosphate dehydrogenase (GAPDH) antibody was obtained from Millipore (Billerica, MA, USA); PI3K, p-AKT, AKT, p-Stats, Stat3 and p-p65 (S536) antibodies were purchased from Cell Signaling Technology (Danvers, MA, USA); and p65 antibodies was obtained from Santa Cruz (CA, USA). The horseradish peroxidase (HRP)-conjugated sheep anti-mouse or anti-rabbit secondary antibodies were purchased from Thermo Fisher (Waltham, MA, USA). The proteins were visualized using an ECL detection kit (Thermo Fisher).","RAW264.7 cells, a mouse macrophage cell line (Cell Bank of Chinese Academy of Sciences, Shanghai, China) were cultured using DMEM supplemented with FBS (10%) and penicillin-streptomycin (1%). The cells were cultured at 37°C in a humidified environment of 5% carbon dioxide. The medium was changed every other day and the cells were passaged at a dilution of 1:3.","In the TCMSP database (https://tcmsp-e.com/),7 the “Herb name” was selected to retrieve the molecular ADME parameter information of honeysuckle, scutellaria, bupleurum and cicada slough, and then the screen conformed to the oral bioavailability (OB) ADME parameter information of honey (Drug-likeness, DL) of ≥0.18 or more active ingredients (Table 1). YOL ingredients were supplemented through literature review. Potential targets related to active ingredients were searched through the TCMSP database and Cytoscape 3.6.1 software.Table 155 Active Compounds Predicted by OB and DL Among 4 Herbs in YOLDrugMol IDMolecule NameMWOB%DLHoneysuckleMOL001494Mandenol308.56420.19MOL001495Ethyl linolenate306.5446.10.2MOL002914Eriodyctiol (flavanone)288.2741.350.24MOL003006(-)-(3R,8S,9R,9aS,10aS)-9-ethenyl-8-(beta-D-glucopyranosyloxy)-2,3,9,9a,10,10a-hexahydro-5-oxo-5H,8H-pyrano[4,3-d]oxazolo[3,2-a]pyridine-3-carboxylic acid_qt281.2987.470.23MOL003014Secologanic dibutylacetal_qt384.5753.650.29MOL002773Beta-carotene536.9637.180.58MOL003036ZINC03978781412.7743.830.76MOL003044Chryseriol300.2835.850.27MOL0030955-hydroxy-7-methoxy-2-(3,4,5-trimethoxyphenyl)chromone358.3751.960.41MOL003111Centauroside_qt434.4855.790.5MOL003117Ioniceracetalides B_qt314.3761.190.19MOL003128Dinethylsecologanoside434.4448.460.48MOL000358Beta-sitosterol414.7936.910.75MOL000422Kaempferol286.2541.880.24MOL000449Stigmasterol412.7743.830.76MOL000006Luteolin286.2536.160.25MOL000098Quercetin302.2546.430.28ScutellariaMOL000073Ent-Epicatechin290.2948.960.24MOL000173Wogonin284.2830.680.23MOL000228(2R)-7-hydroxy-5-methoxy-2-phenylchroman-4-one270.355.230.2MOL000359Sitosterol414.7936.910.75MOL000525Norwogonin270.2539.40.21MOL0005525,2ʹ-Dihydroxy-6,7,8-trimethoxyflavone344.3431.710.35MOL001458Coptisine320.3430.670.86MOL001490Bis[(2S)-2-ethylhexyl] benzene-1,2-dicarboxylate390.6243.590.35MOL001689Acacetin284.2834.970.24MOL002714Baicalein270.2533.520.21MOL002879Diop390.6243.590.39MOL002897Epiberberine336.3943.090.78MOL0029095,7,2,5-tetrahydroxy-8,6-dimethoxyflavone376.3433.820.45MOL002910Carthamidin288.2741.150.24MOL002913Dihydrobaicalin_qt272.2740.040.21MOL002914Eriodyctiol (flavanone)288.2741.350.24MOL002915Salvigenin328.3449.070.33MOL0029175,2ʹ,6ʹ-Trihydroxy-7,8-dimethoxyflavone330.3145.050.33MOL0029255,7,2ʹ,6ʹ-Tetrahydroxyflavone286.2537.010.24MOL002927Skullcapflavone II374.3769.510.44MOL002928Oroxylin a284.2841.370.23MOL002932Panicolin314.3176.260.29MOL0029335,7,4ʹ-Trihydroxy-8-methoxyflavone300.2836.560.27MOL002934Neobaicalein374.37104.30.44MOL002937Dihydrooroxylin A286.366.060.23MOL008206Moslosooflavone298.3144.090.25MOL01041511,13-Eicosadienoic acid, methyl ester322.5939.280.23MOL012266Rivularin344.3437.940.37MOL000490Petunidin317.2930.050.31MOL0045983,5,6,7-tetramethoxy-2-(3,4,5-trimethoxyphenyl)chromone432.4631.970.59BupleurumMOL002776Baicalin446.3940.120.75MOL001645Kaempferol308.5642.10.2MOL004718Linoleyl acetate412.7742.980.76MOL004653α-spinasterol426.546.060.66MOL004624Stigmasterol348.4847.720.53MOL004609Areapillin360.3448.960.41MOL000354Isorhamnetin316.2849.60.31MOL013187Cubebin356.457.130.64\n\n55 Active Compounds Predicted by OB and DL Among 4 Herbs in YOL","In the drug treatment of CP, the known therapeutic targets were mainly obtained from two sources: GeneCards database (https://www.genecards.org/) and CTD database (http://ctd.mdibl.org/). The data analysis was conducted in 5878 therapeutic targets for the treatment of CP, after the redundant entries were removed. Figure S1 shows the specific information about these known therapeutic targets.","To understand the relationship between the herbs and chemical compounds that consist of YOL and its putative targets, and the therapeutic targets known for CP, Network Visualization was conducted using Cytoscape 3.6.1 software,8 and the degree between compounds and targets was analyzed.","PPI core network (PPICN) is used to study the relationship between chemical compounds and disease-associated protein molecules based on biochemistry, signal transduction and genetic networks.9 The differing ID types of the proteins were converted to UniProt IDs. In order to further understand how YOL and CP interact at the protein level, the selected targets in this study were uploaded on the online Venn diagram (http://bioinfogp.cnb.csic.es/tools/venny/index.html). Moreover, the genes at the intersection of the active compound and CP were selected and uploaded on STRING 10.5 (https://string-db.org) to obtain the PPI relationship.","The data was imported in the format Gene Symbol into David 6.8 database (https://david.ncifcrf.gov/). Molecular function (CC), biological process (BP), and cellular function (MF) were selected, respectively. A pathway enrichment analysis was prospectively performed using biological process enrichment GO analysis and KEGG pathway analysis (http://www.genome.jp/kegg/) to clarify the pathways involving putative CP targets, and visualized by online mapping website Omicshare Tools (http://www.omicshare.com/tools/index.php/).","Each experiment was repeated at least in triplicate and data were shown as mean ± SD. Statistical difference in GO and KEGG analysis was performed using hypergeometric and Fisher's exact tests. P<0.05 was considered statistically significant.","Screening and Analysis of Active Compounds First, all the compounds related to four TCMs were retrieved by TCMSP. Second, according to the screening conditions of OB ≥30% and DL ≥0.18,10 23 active constituents of honeysuckle (17 predicted relevant targets), 36 of baicalensis (30 predicted relevant targets), 17 of bupleurum (13 predicted relevant targets), and no suitable compounds of cicada slough were obtained. Among them, baicalensis and honeysuckle contained two identical ingredients, while bupleurum and honeysuckle contained three identical components. Finally, a total of 55 active components were obtained for the data analysis after removing redundant entries (Table 1).\nFirst, all the compounds related to four TCMs were retrieved by TCMSP. Second, according to the screening conditions of OB ≥30% and DL ≥0.18,10 23 active constituents of honeysuckle (17 predicted relevant targets), 36 of baicalensis (30 predicted relevant targets), 17 of bupleurum (13 predicted relevant targets), and no suitable compounds of cicada slough were obtained. Among them, baicalensis and honeysuckle contained two identical ingredients, while bupleurum and honeysuckle contained three identical components. Finally, a total of 55 active components were obtained for the data analysis after removing redundant entries (Table 1).\nNetwork Analysis of “Active Component-Target” Interaction in YOL The compound-target network contained 175 nodes including 55 compound nodes and 113 target nodes, of which two had no corresponding target and 577 edges. As shown in Figure 1, blue represents compounds in bupleurum, red represents compounds in scutellaria, purple represents compounds in honeysuckle, green represents drug targets, each edge indicates the interaction between compounds and their targets, and two of the 55 compounds were excluded from the network construction. The degree value of a node represents the amount of connected routes in the network, and the larger the shape, the higher the degree value. The network in line with its topological properties showed that the nodes with higher degree of screening were analyzed. These nodes that connect compounds or targets act as hubs in the whole network and may predict key compounds or targets. The top five compounds ranked by degree were MOL000098-quercetin, MOL000422-Kaempferol, MOL000449-stigmasterol, MOL000358-quebrachol and MOL000006-luteolin, which could interact with 154, 68, 39, 32 and 28 target proteins, respectively. PTGS1, NCOA2, AR, PRSS1 and PPARG were the top five targets with the highest degree, which could interact with 42, 38, 29, 28 and 17 compounds, respectively.Figure 1Compounds-target network diagram. Blue represents compounds in bupleurum, Red represents compounds in scutellaria, Purple represents compounds in honeysuckle, Green represents drug targets. The size represents the degree value. The larger the shape, the larger the degree value.\nCompounds-target network diagram. Blue represents compounds in bupleurum, Red represents compounds in scutellaria, Purple represents compounds in honeysuckle, Green represents drug targets. The size represents the degree value. The larger the shape, the larger the degree value.\nThe compound-target network contained 175 nodes including 55 compound nodes and 113 target nodes, of which two had no corresponding target and 577 edges. As shown in Figure 1, blue represents compounds in bupleurum, red represents compounds in scutellaria, purple represents compounds in honeysuckle, green represents drug targets, each edge indicates the interaction between compounds and their targets, and two of the 55 compounds were excluded from the network construction. The degree value of a node represents the amount of connected routes in the network, and the larger the shape, the higher the degree value. The network in line with its topological properties showed that the nodes with higher degree of screening were analyzed. These nodes that connect compounds or targets act as hubs in the whole network and may predict key compounds or targets. The top five compounds ranked by degree were MOL000098-quercetin, MOL000422-Kaempferol, MOL000449-stigmasterol, MOL000358-quebrachol and MOL000006-luteolin, which could interact with 154, 68, 39, 32 and 28 target proteins, respectively. PTGS1, NCOA2, AR, PRSS1 and PPARG were the top five targets with the highest degree, which could interact with 42, 38, 29, 28 and 17 compounds, respectively.Figure 1Compounds-target network diagram. Blue represents compounds in bupleurum, Red represents compounds in scutellaria, Purple represents compounds in honeysuckle, Green represents drug targets. The size represents the degree value. The larger the shape, the larger the degree value.\nCompounds-target network diagram. Blue represents compounds in bupleurum, Red represents compounds in scutellaria, Purple represents compounds in honeysuckle, Green represents drug targets. The size represents the degree value. The larger the shape, the larger the degree value.\nThe Construction of Key Protein Network of YOL and CP Through the internationally recognized CTD and GeneCards disease databases, 5150 and 1247 pharyngitis-related targets were obtained, respectively. Through the Venny online tool, 30 targets of honeysuckle, scutellaria, bupleuri and pharyngitis were obtained, including VEGFA, IL6, ESR1, RELA, HIF1A, etc. (Figure 2A). Finally, STRING online tool was used to construct the PPI network interaction map of drug and disease. It contained 30 nodes representing proteins and 48 edges representing the interaction between proteins. The thicker the line, the higher is the correlation degree. Through the protein interaction network, we could further explore the therapeutic target and mechanism of YOL for CP (Figure 2B).Figure 2(A) Venn diagram of compounds-target network diagram; (B) Core network diagram of protein interaction between YOL and CP.\n(A) Venn diagram of compounds-target network diagram; (B) Core network diagram of protein interaction between YOL and CP.\nThrough the internationally recognized CTD and GeneCards disease databases, 5150 and 1247 pharyngitis-related targets were obtained, respectively. Through the Venny online tool, 30 targets of honeysuckle, scutellaria, bupleuri and pharyngitis were obtained, including VEGFA, IL6, ESR1, RELA, HIF1A, etc. (Figure 2A). Finally, STRING online tool was used to construct the PPI network interaction map of drug and disease. It contained 30 nodes representing proteins and 48 edges representing the interaction between proteins. The thicker the line, the higher is the correlation degree. Through the protein interaction network, we could further explore the therapeutic target and mechanism of YOL for CP (Figure 2B).Figure 2(A) Venn diagram of compounds-target network diagram; (B) Core network diagram of protein interaction between YOL and CP.\n(A) Venn diagram of compounds-target network diagram; (B) Core network diagram of protein interaction between YOL and CP.\nPharmacological Mechanisms of YOL Acting on CP A total of 30 common targets were obtained by DAVID online tool and 148 GO items were obtained (p < 0.05) by performing the functional enrichment analysis. There were 102 entries on biological process (BP), 34 entries on Molecular Function (MF), and 12 entries on cell composition (CC) (Figure 3A). A total of 46 signaling pathways were obtained by KEGG pathway enrichment screening (p<0.05), involving cancer, PI3K-AKT, hepatitis, proteoglycans, p53, HIF-1 signaling pathways, etc. (Figure 3B and Table S1).Figure 3(A) GO analysis function annotation diagram. Biological process (BP), molecular function (MF), and cell composition (CC). (B) Enriched KEGG pathways of YOL selected targets for CP.\n(A) GO analysis function annotation diagram. Biological process (BP), molecular function (MF), and cell composition (CC). (B) Enriched KEGG pathways of YOL selected targets for CP.\nA total of 30 common targets were obtained by DAVID online tool and 148 GO items were obtained (p < 0.05) by performing the functional enrichment analysis. There were 102 entries on biological process (BP), 34 entries on Molecular Function (MF), and 12 entries on cell composition (CC) (Figure 3A). A total of 46 signaling pathways were obtained by KEGG pathway enrichment screening (p<0.05), involving cancer, PI3K-AKT, hepatitis, proteoglycans, p53, HIF-1 signaling pathways, etc. (Figure 3B and Table S1).Figure 3(A) GO analysis function annotation diagram. Biological process (BP), molecular function (MF), and cell composition (CC). (B) Enriched KEGG pathways of YOL selected targets for CP.\n(A) GO analysis function annotation diagram. Biological process (BP), molecular function (MF), and cell composition (CC). (B) Enriched KEGG pathways of YOL selected targets for CP.\nExperimental Verification of Predicted Results In order to experimentally demonstrate the predictions about the molecular mechanism of YOL, we chose two ingredients for testing (Figure 4), based on their composition score, the group to which they belong, and their herbal origin to examine how ingredients from different herbs affect the same proteins. The effects of these compounds on the expression levels of related proteins were analyzed by Western blot. Macrophages secrete several inflammatory cytokines, which play significant roles in inflammation. Signal transduction and transcriptional activator 3 (STAT3), nuclear factor kappa B (NF-kB), and phosphatidylinositol 3-kinase (PI3K)/Akt pathways are critical inflammatory pathways. LPS can significantly increase the production of many proinflammatory factors, including inducible NO synthase (iNOS) and cyclooxygenase-2 (COX-2) in macrophages. After pretreatment of macrophages with YOL, luteolin and baicalein, the protein expression levels of iNOS and COX-2 induced by LPS were significantly inhibited in a dose-dependent manner (Figure 4A). The related protein changes were consistent with iNOS and COX-2 inflammation protein expression (Figure 4B–D).Figure 4The YOL inhibits PI3K/p-AKT/p-Stats pathway in RAW 264.7 cells. Representative Western blot images show the relative expressions of iNOS, COX-2 (A), p-p65, p65 (B), PI3K, p-AKT, AKT (C), p-Stats, Stat3 (D) in the groups. The protein levels (normalized) of iNOS, COX2 (E), and p-p65, p65, PI3K, p-AKT, AKT, p-Stats, Stat3 (F) in cells. All the values are presented as mean ± SD. #P < 0.05 and ##P < 0.01 vs control group. *P < 0.05 and **P < 0.01 vs LPS group.\nThe YOL inhibits PI3K/p-AKT/p-Stats pathway in RAW 264.7 cells. Representative Western blot images show the relative expressions of iNOS, COX-2 (A), p-p65, p65 (B), PI3K, p-AKT, AKT (C), p-Stats, Stat3 (D) in the groups. The protein levels (normalized) of iNOS, COX2 (E), and p-p65, p65, PI3K, p-AKT, AKT, p-Stats, Stat3 (F) in cells. All the values are presented as mean ± SD. #P < 0.05 and ##P < 0.01 vs control group. *P < 0.05 and **P < 0.01 vs LPS group.\nIn order to experimentally demonstrate the predictions about the molecular mechanism of YOL, we chose two ingredients for testing (Figure 4), based on their composition score, the group to which they belong, and their herbal origin to examine how ingredients from different herbs affect the same proteins. The effects of these compounds on the expression levels of related proteins were analyzed by Western blot. Macrophages secrete several inflammatory cytokines, which play significant roles in inflammation. Signal transduction and transcriptional activator 3 (STAT3), nuclear factor kappa B (NF-kB), and phosphatidylinositol 3-kinase (PI3K)/Akt pathways are critical inflammatory pathways. LPS can significantly increase the production of many proinflammatory factors, including inducible NO synthase (iNOS) and cyclooxygenase-2 (COX-2) in macrophages. After pretreatment of macrophages with YOL, luteolin and baicalein, the protein expression levels of iNOS and COX-2 induced by LPS were significantly inhibited in a dose-dependent manner (Figure 4A). The related protein changes were consistent with iNOS and COX-2 inflammation protein expression (Figure 4B–D).Figure 4The YOL inhibits PI3K/p-AKT/p-Stats pathway in RAW 264.7 cells. Representative Western blot images show the relative expressions of iNOS, COX-2 (A), p-p65, p65 (B), PI3K, p-AKT, AKT (C), p-Stats, Stat3 (D) in the groups. The protein levels (normalized) of iNOS, COX2 (E), and p-p65, p65, PI3K, p-AKT, AKT, p-Stats, Stat3 (F) in cells. All the values are presented as mean ± SD. #P < 0.05 and ##P < 0.01 vs control group. *P < 0.05 and **P < 0.01 vs LPS group.\nThe YOL inhibits PI3K/p-AKT/p-Stats pathway in RAW 264.7 cells. Representative Western blot images show the relative expressions of iNOS, COX-2 (A), p-p65, p65 (B), PI3K, p-AKT, AKT (C), p-Stats, Stat3 (D) in the groups. The protein levels (normalized) of iNOS, COX2 (E), and p-p65, p65, PI3K, p-AKT, AKT, p-Stats, Stat3 (F) in cells. All the values are presented as mean ± SD. #P < 0.05 and ##P < 0.01 vs control group. *P < 0.05 and **P < 0.01 vs LPS group.","First, all the compounds related to four TCMs were retrieved by TCMSP. Second, according to the screening conditions of OB ≥30% and DL ≥0.18,10 23 active constituents of honeysuckle (17 predicted relevant targets), 36 of baicalensis (30 predicted relevant targets), 17 of bupleurum (13 predicted relevant targets), and no suitable compounds of cicada slough were obtained. Among them, baicalensis and honeysuckle contained two identical ingredients, while bupleurum and honeysuckle contained three identical components. Finally, a total of 55 active components were obtained for the data analysis after removing redundant entries (Table 1).","The compound-target network contained 175 nodes including 55 compound nodes and 113 target nodes, of which two had no corresponding target and 577 edges. As shown in Figure 1, blue represents compounds in bupleurum, red represents compounds in scutellaria, purple represents compounds in honeysuckle, green represents drug targets, each edge indicates the interaction between compounds and their targets, and two of the 55 compounds were excluded from the network construction. The degree value of a node represents the amount of connected routes in the network, and the larger the shape, the higher the degree value. The network in line with its topological properties showed that the nodes with higher degree of screening were analyzed. These nodes that connect compounds or targets act as hubs in the whole network and may predict key compounds or targets. The top five compounds ranked by degree were MOL000098-quercetin, MOL000422-Kaempferol, MOL000449-stigmasterol, MOL000358-quebrachol and MOL000006-luteolin, which could interact with 154, 68, 39, 32 and 28 target proteins, respectively. PTGS1, NCOA2, AR, PRSS1 and PPARG were the top five targets with the highest degree, which could interact with 42, 38, 29, 28 and 17 compounds, respectively.Figure 1Compounds-target network diagram. Blue represents compounds in bupleurum, Red represents compounds in scutellaria, Purple represents compounds in honeysuckle, Green represents drug targets. The size represents the degree value. The larger the shape, the larger the degree value.\nCompounds-target network diagram. Blue represents compounds in bupleurum, Red represents compounds in scutellaria, Purple represents compounds in honeysuckle, Green represents drug targets. The size represents the degree value. The larger the shape, the larger the degree value.","Through the internationally recognized CTD and GeneCards disease databases, 5150 and 1247 pharyngitis-related targets were obtained, respectively. Through the Venny online tool, 30 targets of honeysuckle, scutellaria, bupleuri and pharyngitis were obtained, including VEGFA, IL6, ESR1, RELA, HIF1A, etc. (Figure 2A). Finally, STRING online tool was used to construct the PPI network interaction map of drug and disease. It contained 30 nodes representing proteins and 48 edges representing the interaction between proteins. The thicker the line, the higher is the correlation degree. Through the protein interaction network, we could further explore the therapeutic target and mechanism of YOL for CP (Figure 2B).Figure 2(A) Venn diagram of compounds-target network diagram; (B) Core network diagram of protein interaction between YOL and CP.\n(A) Venn diagram of compounds-target network diagram; (B) Core network diagram of protein interaction between YOL and CP.","A total of 30 common targets were obtained by DAVID online tool and 148 GO items were obtained (p < 0.05) by performing the functional enrichment analysis. There were 102 entries on biological process (BP), 34 entries on Molecular Function (MF), and 12 entries on cell composition (CC) (Figure 3A). A total of 46 signaling pathways were obtained by KEGG pathway enrichment screening (p<0.05), involving cancer, PI3K-AKT, hepatitis, proteoglycans, p53, HIF-1 signaling pathways, etc. (Figure 3B and Table S1).Figure 3(A) GO analysis function annotation diagram. Biological process (BP), molecular function (MF), and cell composition (CC). (B) Enriched KEGG pathways of YOL selected targets for CP.\n(A) GO analysis function annotation diagram. Biological process (BP), molecular function (MF), and cell composition (CC). (B) Enriched KEGG pathways of YOL selected targets for CP.","In order to experimentally demonstrate the predictions about the molecular mechanism of YOL, we chose two ingredients for testing (Figure 4), based on their composition score, the group to which they belong, and their herbal origin to examine how ingredients from different herbs affect the same proteins. The effects of these compounds on the expression levels of related proteins were analyzed by Western blot. Macrophages secrete several inflammatory cytokines, which play significant roles in inflammation. Signal transduction and transcriptional activator 3 (STAT3), nuclear factor kappa B (NF-kB), and phosphatidylinositol 3-kinase (PI3K)/Akt pathways are critical inflammatory pathways. LPS can significantly increase the production of many proinflammatory factors, including inducible NO synthase (iNOS) and cyclooxygenase-2 (COX-2) in macrophages. After pretreatment of macrophages with YOL, luteolin and baicalein, the protein expression levels of iNOS and COX-2 induced by LPS were significantly inhibited in a dose-dependent manner (Figure 4A). The related protein changes were consistent with iNOS and COX-2 inflammation protein expression (Figure 4B–D).Figure 4The YOL inhibits PI3K/p-AKT/p-Stats pathway in RAW 264.7 cells. Representative Western blot images show the relative expressions of iNOS, COX-2 (A), p-p65, p65 (B), PI3K, p-AKT, AKT (C), p-Stats, Stat3 (D) in the groups. The protein levels (normalized) of iNOS, COX2 (E), and p-p65, p65, PI3K, p-AKT, AKT, p-Stats, Stat3 (F) in cells. All the values are presented as mean ± SD. #P < 0.05 and ##P < 0.01 vs control group. *P < 0.05 and **P < 0.01 vs LPS group.\nThe YOL inhibits PI3K/p-AKT/p-Stats pathway in RAW 264.7 cells. Representative Western blot images show the relative expressions of iNOS, COX-2 (A), p-p65, p65 (B), PI3K, p-AKT, AKT (C), p-Stats, Stat3 (D) in the groups. The protein levels (normalized) of iNOS, COX2 (E), and p-p65, p65, PI3K, p-AKT, AKT, p-Stats, Stat3 (F) in cells. All the values are presented as mean ± SD. #P < 0.05 and ##P < 0.01 vs control group. *P < 0.05 and **P < 0.01 vs LPS group.","Many diseases, including cancer and chronic inflammation, are known to be regulated by multiple signaling pathways. TCMs are considered as multi-component and multi-target therapeutic drugs, which is in accordance with the methodologies of network pharmacology. The holistic view of TCM considers the human body as a complex biological network system, which is connected with network pharmacology. By combining the network pharmacology with the holistic view of TCM, the disease-target-drug network is obtained. The network shows that different components of TCM may act on one target, and different targets may act on the same pathway. It is also possible that the same component of TCM acts on different targets, and the same target acts on different pathways. The multi-component, multi-target and multi-pathway of TCM can be clearly visualized through network pharmacology, which resolves many difficulties encountered in the study of TCM.13–15 CP is the diffuse inflammation of pharyngeal mucosa, submucosa and lymph tissues. Clinically, CP is mainly manifested as pharyngeal discomfort (foreign body sensation, burning sensation, stimulation sensation, etc.), pharyngeal itching, cough, etc. Pharyngitis belongs to the category of “Fenghou Bi” in TCM. Pharyngitis pertains to the category of “wind throat arthralgia” in Chinese medicine, and is often caused by evil heat entering the body, and heat in the lungs and stomach. YOL is an oral TCM preparation, which is further optimized on the basis of Jinchan oral liquid developed by Children’s Hospital of Suzhou University.5,6 However, there is a lack of research on its active ingredients and its mechanism of action.\nIn this study, 30 potential targets, 102 biological processes, 12 molecular functions, 34 cell components, and 46 KEGG pathways were obtained. The active ingredients in YOL can treat CP through multiple targets and pathways. To elucidate the biological pathways that may be affected by YOL, a significantly overexpressed KEGG pathway was identified. The results showed VEGFA, IL6, ESR1 and RELA as the key targets and the involvement of PI3K-Akt, p53 and HIF-1 signaling pathways. TCM aims to restore a patient’s health through the use of a TCM prescription, which is usually composed of two or more TCMs in optimal proportions.\nYOL, for example, consists of four TCMs. As expected, most of the selected active ingredients are directly or indirectly associated with inflammation. In a previous study, neochlorogenic acid was demonstrated to inhibit LPS-activated inflammatory responses through up-regulation of Nrf2/HO-1 and AMPK pathways.16 The decoction extract from six natural herbs, which contain honeysuckle, exhibits anti-inflammatory and immunomodulatory effects by targeting NF-κB/IL-6/STAT3 signaling.17 Luteolin is abundant in honeysuckle, which alleviates CP by inhibiting the NF-κB pathway and anti-inflammatory polarization of M1 macrophages.11 Protective effects of baicalein on liver injury in mice induced by multi-microbial septicemia were found to be based on inhibiting inflammation.12 Network pharmacological studies have shown that the main active components of honeysuckle may play a role through inflammation-related proteins such as HSP90AA1, HSP90AB1, ESR1, PTGS2, TERT and PPARG, and especially heat shock protein HSP90A (HSP90AA1).18 Flavonoids such as baicalin and wogonin alone and in combination have anti-inflammatory activity in Scutellaria.19 Network pharmacological studies have also shown that alkaloids such as coptisine and epiphone in Scutellaria and components such as dihydrokaempferol, and rographolide flavonoids also show anti-inflammatory activity. MAPK14, TNFRSF1A, EGFR and SELE are the primary anti-inflammatory components of Scutellaria. The anti-inflammatory active components in Scutellaria can directly affect MAPK14 and EGFR to produce anti-inflammatory effects, and can also indirectly affect other targets to exert anti-inflammatory effects. As one of the four p38 MAPKs, MAPK14 plays a vital role in the cellular cascade induced by extracellular stimuli such as proinflammatory cytokines or physical stimuli.20 Studies predicted that the pharmacodynamic basis of Bupleurum is mainly saponins, flavonoids, volatile oils, fatty acids and other components. Flavonoids mainly act on PI3K-Akt, NF-kB and other pathways, participate in regulating inflammatory factors, estrogen signal transduction and other physiological processes. Bupleurum-Scutellaria drug pair, first used in small bupleurum decoction in Treatise on Febrile Diseases, is an important part of Bupleurum prescription. Bupleurum is bitter, flat and clear, and can disperse the stagnation of bile fire. Scutellaria has a bitter and cold taste, and can clear internal heat. Both of them must be used together to regulate the liver and gallbladder, and clear the dampness and heat of internal accumulation, which are commonly used to treat fever, pharyngitis, and respiratory diseases.21 Cicada slough can evacuate wind and heat, and clear the throat. Clinically, it is common in classical prescriptions such as Sheng jiang San and Pharyngitis San. However, the active components of Cicada slough did not satisfy the prediction requirements in this research.\nYOL is composed of honeysuckle, scutellaria, bupleurum and cicada slough. According to TCM theory, bupleurum is bitter and flat, enters the liver and gallbladder meridian, penetrates and clears Shaoyang, and can drain the stagnation of gas. Scutellaria is bitter and cold, and clears the heat of Shaoyang. The actions of Bupleurum facilitate the clear discharge of Scutellaria, the two are compatible, and achieve the goal of reconciliation and Shaoyang. Honeysuckle cools through the surface, has heat-clearing and detoxifying effects, and also has the effect of fragrant obscenity. Cicada slough is light floating, clears the coke gas, shows honeysuckle compatibility, understanding the table evil, and is combined with the “treatment of the coke such as feather.” The combination of the four drugs, Xuan and descent together, clear the evil, thereby clearing heat and detoxification, warming the evil without hiding, scattered outside and recovered. Therefore, the treatment of CP with YOL can control the pharyngitis, regulate the balance of Yin and Yang, strengthen the foundation and improve the immunity of the body.","This study used the network pharmacology platform to probe the YOL treatment of CP by multi-target analysis. The results showed that YOL could treat CP by acting on related targets, which were consistent with the reported literature. In addition, we have verified that YOL and its two monomer compounds, Luteolin and Baicalein inhibited activation of the NF-kB, Stat3 and PI3K-Akt pathways. Although the potential advantages of the “network target, multi-component” strategy of network pharmacology are obvious, the content of TCM ingredients is usually ignored in the research of network pharmacology of Chinese medicine, and the influence of content and concentration on the efficacy cannot be ignored. The predicted targets, which have not been discussed, may provide clues for further research on the mechanism of YOL."],"string":"[\n \"Chronic pharyngitis (CP) is a very common disease associated with chronic inflammation involving pharyngeal mucosa, submucosal and lymphatic tissues. Clinically, CP is mainly manifested as pharyngeal discomfort (foreign body sensation, burning sensation, irritation, etc.), with occasional pharyngeal itch, cough, etc., which belongs to the category of “slow throat arthralgia” in the traditional Chinese medicine (TCM).1 CP has a high incidence and accounts for 10–12% of all pharyngeal diseases, with a long disease course, recurrence and is difficult to cure. At present, western medicine uses antibiotics supplemented by hormone preparations, such as dexamethasone and antiviral drugs, for the treatment of pharyngitis. These treatment methods have drawbacks such as narrow therapeutic spectrum, recurrence, poor tolerance and major toxic and side effects. Hence, developing more effective therapeutic strategies against CP, and reducing the occurrence of side effects from the current therapeutic options is of great clinical importance.\\nTCM is a major component of complementary and alternative medicine. Owing to its excellent clinical efficacy, TCM is a research hotspot worldwide. Dialectical theory of TCM is used to treat acute and chronic pharyngitis and has unique advantages such as minor toxic side effects, obvious curative effect and treatment of both symptoms and root causes.2–4 The pharmacological effects of TCM herbal formulae play an important role in their appropriate application. However, the complex nature of herbal formulae has impeded this understanding. Numerous chemical ingredients involving multiple potential targets are present in an herbal formula. It is not adequate to explain the effects produced by a whole herbal formula if we individually consider every single ingredient in it. Yinqin oral liquid (YOL) is composed of extracts of honeysuckle, scutellaria, bupleurum and cicada. YOL has the effects of relieving wind, muscle and clearing heat, which is mainly used for cold, fever, aversion to cold, pharyngeal pain and other upper respiratory tract infections caused by wind fever and wind-cold. Long-term clinical observation has proven its effect in relieving pharyngeal pain and treating hand-foot-mouth disease. YOL contains several chemical compounds, which regulate diverse targets, and thus precisely determine the pharmacological mechanisms involved in its therapeutic actions. In addition, there are several challenges in the relationships between the herbs and diseases.5,6\\nNetwork pharmacology is based on the theory of system biology. As a new subject, it selects specific signal nodes to design multi-target drug molecules through the network analysis of system biology. It is possible to study both the active components and the potential gene targets from TCM because of the establishment of network pharmacology and bioinformatics. This study predicted and analyzed the active components and target of YOL using network pharmacology, to explore the rationality of its formula and the scientific nature of treating CP, and further explore the pharmacological mechanisms of YOL on CP.\",\n \"Chemicals and Reagents Yinqin Oral Liquid (YOL) was provided by su zhou si yuan natural products research and development Co. Ltd. YOL is composed of honeysuckle, scutellaria, bupleurum and cicada, decompressed and concentrated to 2 g/mL through boiling and alcohol sinking, and stored at 4°C for later use. Primary antibodies: COX-2 and iNOS antibodies were purchased from Abcam (Cambridge, UK); glyceraldehyde-3-phosphate dehydrogenase (GAPDH) antibody was obtained from Millipore (Billerica, MA, USA); PI3K, p-AKT, AKT, p-Stats, Stat3 and p-p65 (S536) antibodies were purchased from Cell Signaling Technology (Danvers, MA, USA); and p65 antibodies was obtained from Santa Cruz (CA, USA). The horseradish peroxidase (HRP)-conjugated sheep anti-mouse or anti-rabbit secondary antibodies were purchased from Thermo Fisher (Waltham, MA, USA). The proteins were visualized using an ECL detection kit (Thermo Fisher).\\nYinqin Oral Liquid (YOL) was provided by su zhou si yuan natural products research and development Co. Ltd. YOL is composed of honeysuckle, scutellaria, bupleurum and cicada, decompressed and concentrated to 2 g/mL through boiling and alcohol sinking, and stored at 4°C for later use. Primary antibodies: COX-2 and iNOS antibodies were purchased from Abcam (Cambridge, UK); glyceraldehyde-3-phosphate dehydrogenase (GAPDH) antibody was obtained from Millipore (Billerica, MA, USA); PI3K, p-AKT, AKT, p-Stats, Stat3 and p-p65 (S536) antibodies were purchased from Cell Signaling Technology (Danvers, MA, USA); and p65 antibodies was obtained from Santa Cruz (CA, USA). The horseradish peroxidase (HRP)-conjugated sheep anti-mouse or anti-rabbit secondary antibodies were purchased from Thermo Fisher (Waltham, MA, USA). The proteins were visualized using an ECL detection kit (Thermo Fisher).\\nCell Culture RAW264.7 cells, a mouse macrophage cell line (Cell Bank of Chinese Academy of Sciences, Shanghai, China) were cultured using DMEM supplemented with FBS (10%) and penicillin-streptomycin (1%). The cells were cultured at 37°C in a humidified environment of 5% carbon dioxide. The medium was changed every other day and the cells were passaged at a dilution of 1:3.\\nRAW264.7 cells, a mouse macrophage cell line (Cell Bank of Chinese Academy of Sciences, Shanghai, China) were cultured using DMEM supplemented with FBS (10%) and penicillin-streptomycin (1%). The cells were cultured at 37°C in a humidified environment of 5% carbon dioxide. The medium was changed every other day and the cells were passaged at a dilution of 1:3.\\nCollection of Drug Molecular Information and Screening of Active Ingredients In the TCMSP database (https://tcmsp-e.com/),7 the “Herb name” was selected to retrieve the molecular ADME parameter information of honeysuckle, scutellaria, bupleurum and cicada slough, and then the screen conformed to the oral bioavailability (OB) ADME parameter information of honey (Drug-likeness, DL) of ≥0.18 or more active ingredients (Table 1). YOL ingredients were supplemented through literature review. Potential targets related to active ingredients were searched through the TCMSP database and Cytoscape 3.6.1 software.Table 155 Active Compounds Predicted by OB and DL Among 4 Herbs in YOLDrugMol IDMolecule NameMWOB%DLHoneysuckleMOL001494Mandenol308.56420.19MOL001495Ethyl linolenate306.5446.10.2MOL002914Eriodyctiol (flavanone)288.2741.350.24MOL003006(-)-(3R,8S,9R,9aS,10aS)-9-ethenyl-8-(beta-D-glucopyranosyloxy)-2,3,9,9a,10,10a-hexahydro-5-oxo-5H,8H-pyrano[4,3-d]oxazolo[3,2-a]pyridine-3-carboxylic acid_qt281.2987.470.23MOL003014Secologanic dibutylacetal_qt384.5753.650.29MOL002773Beta-carotene536.9637.180.58MOL003036ZINC03978781412.7743.830.76MOL003044Chryseriol300.2835.850.27MOL0030955-hydroxy-7-methoxy-2-(3,4,5-trimethoxyphenyl)chromone358.3751.960.41MOL003111Centauroside_qt434.4855.790.5MOL003117Ioniceracetalides B_qt314.3761.190.19MOL003128Dinethylsecologanoside434.4448.460.48MOL000358Beta-sitosterol414.7936.910.75MOL000422Kaempferol286.2541.880.24MOL000449Stigmasterol412.7743.830.76MOL000006Luteolin286.2536.160.25MOL000098Quercetin302.2546.430.28ScutellariaMOL000073Ent-Epicatechin290.2948.960.24MOL000173Wogonin284.2830.680.23MOL000228(2R)-7-hydroxy-5-methoxy-2-phenylchroman-4-one270.355.230.2MOL000359Sitosterol414.7936.910.75MOL000525Norwogonin270.2539.40.21MOL0005525,2ʹ-Dihydroxy-6,7,8-trimethoxyflavone344.3431.710.35MOL001458Coptisine320.3430.670.86MOL001490Bis[(2S)-2-ethylhexyl] benzene-1,2-dicarboxylate390.6243.590.35MOL001689Acacetin284.2834.970.24MOL002714Baicalein270.2533.520.21MOL002879Diop390.6243.590.39MOL002897Epiberberine336.3943.090.78MOL0029095,7,2,5-tetrahydroxy-8,6-dimethoxyflavone376.3433.820.45MOL002910Carthamidin288.2741.150.24MOL002913Dihydrobaicalin_qt272.2740.040.21MOL002914Eriodyctiol (flavanone)288.2741.350.24MOL002915Salvigenin328.3449.070.33MOL0029175,2ʹ,6ʹ-Trihydroxy-7,8-dimethoxyflavone330.3145.050.33MOL0029255,7,2ʹ,6ʹ-Tetrahydroxyflavone286.2537.010.24MOL002927Skullcapflavone II374.3769.510.44MOL002928Oroxylin a284.2841.370.23MOL002932Panicolin314.3176.260.29MOL0029335,7,4ʹ-Trihydroxy-8-methoxyflavone300.2836.560.27MOL002934Neobaicalein374.37104.30.44MOL002937Dihydrooroxylin A286.366.060.23MOL008206Moslosooflavone298.3144.090.25MOL01041511,13-Eicosadienoic acid, methyl ester322.5939.280.23MOL012266Rivularin344.3437.940.37MOL000490Petunidin317.2930.050.31MOL0045983,5,6,7-tetramethoxy-2-(3,4,5-trimethoxyphenyl)chromone432.4631.970.59BupleurumMOL002776Baicalin446.3940.120.75MOL001645Kaempferol308.5642.10.2MOL004718Linoleyl acetate412.7742.980.76MOL004653α-spinasterol426.546.060.66MOL004624Stigmasterol348.4847.720.53MOL004609Areapillin360.3448.960.41MOL000354Isorhamnetin316.2849.60.31MOL013187Cubebin356.457.130.64\\n\\n55 Active Compounds Predicted by OB and DL Among 4 Herbs in YOL\\nIn the TCMSP database (https://tcmsp-e.com/),7 the “Herb name” was selected to retrieve the molecular ADME parameter information of honeysuckle, scutellaria, bupleurum and cicada slough, and then the screen conformed to the oral bioavailability (OB) ADME parameter information of honey (Drug-likeness, DL) of ≥0.18 or more active ingredients (Table 1). YOL ingredients were supplemented through literature review. Potential targets related to active ingredients were searched through the TCMSP database and Cytoscape 3.6.1 software.Table 155 Active Compounds Predicted by OB and DL Among 4 Herbs in YOLDrugMol IDMolecule NameMWOB%DLHoneysuckleMOL001494Mandenol308.56420.19MOL001495Ethyl linolenate306.5446.10.2MOL002914Eriodyctiol (flavanone)288.2741.350.24MOL003006(-)-(3R,8S,9R,9aS,10aS)-9-ethenyl-8-(beta-D-glucopyranosyloxy)-2,3,9,9a,10,10a-hexahydro-5-oxo-5H,8H-pyrano[4,3-d]oxazolo[3,2-a]pyridine-3-carboxylic acid_qt281.2987.470.23MOL003014Secologanic dibutylacetal_qt384.5753.650.29MOL002773Beta-carotene536.9637.180.58MOL003036ZINC03978781412.7743.830.76MOL003044Chryseriol300.2835.850.27MOL0030955-hydroxy-7-methoxy-2-(3,4,5-trimethoxyphenyl)chromone358.3751.960.41MOL003111Centauroside_qt434.4855.790.5MOL003117Ioniceracetalides B_qt314.3761.190.19MOL003128Dinethylsecologanoside434.4448.460.48MOL000358Beta-sitosterol414.7936.910.75MOL000422Kaempferol286.2541.880.24MOL000449Stigmasterol412.7743.830.76MOL000006Luteolin286.2536.160.25MOL000098Quercetin302.2546.430.28ScutellariaMOL000073Ent-Epicatechin290.2948.960.24MOL000173Wogonin284.2830.680.23MOL000228(2R)-7-hydroxy-5-methoxy-2-phenylchroman-4-one270.355.230.2MOL000359Sitosterol414.7936.910.75MOL000525Norwogonin270.2539.40.21MOL0005525,2ʹ-Dihydroxy-6,7,8-trimethoxyflavone344.3431.710.35MOL001458Coptisine320.3430.670.86MOL001490Bis[(2S)-2-ethylhexyl] benzene-1,2-dicarboxylate390.6243.590.35MOL001689Acacetin284.2834.970.24MOL002714Baicalein270.2533.520.21MOL002879Diop390.6243.590.39MOL002897Epiberberine336.3943.090.78MOL0029095,7,2,5-tetrahydroxy-8,6-dimethoxyflavone376.3433.820.45MOL002910Carthamidin288.2741.150.24MOL002913Dihydrobaicalin_qt272.2740.040.21MOL002914Eriodyctiol (flavanone)288.2741.350.24MOL002915Salvigenin328.3449.070.33MOL0029175,2ʹ,6ʹ-Trihydroxy-7,8-dimethoxyflavone330.3145.050.33MOL0029255,7,2ʹ,6ʹ-Tetrahydroxyflavone286.2537.010.24MOL002927Skullcapflavone II374.3769.510.44MOL002928Oroxylin a284.2841.370.23MOL002932Panicolin314.3176.260.29MOL0029335,7,4ʹ-Trihydroxy-8-methoxyflavone300.2836.560.27MOL002934Neobaicalein374.37104.30.44MOL002937Dihydrooroxylin A286.366.060.23MOL008206Moslosooflavone298.3144.090.25MOL01041511,13-Eicosadienoic acid, methyl ester322.5939.280.23MOL012266Rivularin344.3437.940.37MOL000490Petunidin317.2930.050.31MOL0045983,5,6,7-tetramethoxy-2-(3,4,5-trimethoxyphenyl)chromone432.4631.970.59BupleurumMOL002776Baicalin446.3940.120.75MOL001645Kaempferol308.5642.10.2MOL004718Linoleyl acetate412.7742.980.76MOL004653α-spinasterol426.546.060.66MOL004624Stigmasterol348.4847.720.53MOL004609Areapillin360.3448.960.41MOL000354Isorhamnetin316.2849.60.31MOL013187Cubebin356.457.130.64\\n\\n55 Active Compounds Predicted by OB and DL Among 4 Herbs in YOL\\nKnown Therapeutic Targets of Chronic Pharyngitis (CP) In the drug treatment of CP, the known therapeutic targets were mainly obtained from two sources: GeneCards database (https://www.genecards.org/) and CTD database (http://ctd.mdibl.org/). The data analysis was conducted in 5878 therapeutic targets for the treatment of CP, after the redundant entries were removed. Figure S1 shows the specific information about these known therapeutic targets.\\nIn the drug treatment of CP, the known therapeutic targets were mainly obtained from two sources: GeneCards database (https://www.genecards.org/) and CTD database (http://ctd.mdibl.org/). The data analysis was conducted in 5878 therapeutic targets for the treatment of CP, after the redundant entries were removed. Figure S1 shows the specific information about these known therapeutic targets.\\nNetwork Construction and Analysis To understand the relationship between the herbs and chemical compounds that consist of YOL and its putative targets, and the therapeutic targets known for CP, Network Visualization was conducted using Cytoscape 3.6.1 software,8 and the degree between compounds and targets was analyzed.\\nTo understand the relationship between the herbs and chemical compounds that consist of YOL and its putative targets, and the therapeutic targets known for CP, Network Visualization was conducted using Cytoscape 3.6.1 software,8 and the degree between compounds and targets was analyzed.\\nProtein-Protein Interaction (PPI) Data PPI core network (PPICN) is used to study the relationship between chemical compounds and disease-associated protein molecules based on biochemistry, signal transduction and genetic networks.9 The differing ID types of the proteins were converted to UniProt IDs. In order to further understand how YOL and CP interact at the protein level, the selected targets in this study were uploaded on the online Venn diagram (http://bioinfogp.cnb.csic.es/tools/venny/index.html). Moreover, the genes at the intersection of the active compound and CP were selected and uploaded on STRING 10.5 (https://string-db.org) to obtain the PPI relationship.\\nPPI core network (PPICN) is used to study the relationship between chemical compounds and disease-associated protein molecules based on biochemistry, signal transduction and genetic networks.9 The differing ID types of the proteins were converted to UniProt IDs. In order to further understand how YOL and CP interact at the protein level, the selected targets in this study were uploaded on the online Venn diagram (http://bioinfogp.cnb.csic.es/tools/venny/index.html). Moreover, the genes at the intersection of the active compound and CP were selected and uploaded on STRING 10.5 (https://string-db.org) to obtain the PPI relationship.\\nPathway Enrichment Analysis The data was imported in the format Gene Symbol into David 6.8 database (https://david.ncifcrf.gov/). Molecular function (CC), biological process (BP), and cellular function (MF) were selected, respectively. A pathway enrichment analysis was prospectively performed using biological process enrichment GO analysis and KEGG pathway analysis (http://www.genome.jp/kegg/) to clarify the pathways involving putative CP targets, and visualized by online mapping website Omicshare Tools (http://www.omicshare.com/tools/index.php/).\\nThe data was imported in the format Gene Symbol into David 6.8 database (https://david.ncifcrf.gov/). Molecular function (CC), biological process (BP), and cellular function (MF) were selected, respectively. A pathway enrichment analysis was prospectively performed using biological process enrichment GO analysis and KEGG pathway analysis (http://www.genome.jp/kegg/) to clarify the pathways involving putative CP targets, and visualized by online mapping website Omicshare Tools (http://www.omicshare.com/tools/index.php/).\\nStatistical Analysis Each experiment was repeated at least in triplicate and data were shown as mean ± SD. Statistical difference in GO and KEGG analysis was performed using hypergeometric and Fisher's exact tests. P<0.05 was considered statistically significant.\\nEach experiment was repeated at least in triplicate and data were shown as mean ± SD. Statistical difference in GO and KEGG analysis was performed using hypergeometric and Fisher's exact tests. P<0.05 was considered statistically significant.\",\n \"Yinqin Oral Liquid (YOL) was provided by su zhou si yuan natural products research and development Co. Ltd. YOL is composed of honeysuckle, scutellaria, bupleurum and cicada, decompressed and concentrated to 2 g/mL through boiling and alcohol sinking, and stored at 4°C for later use. Primary antibodies: COX-2 and iNOS antibodies were purchased from Abcam (Cambridge, UK); glyceraldehyde-3-phosphate dehydrogenase (GAPDH) antibody was obtained from Millipore (Billerica, MA, USA); PI3K, p-AKT, AKT, p-Stats, Stat3 and p-p65 (S536) antibodies were purchased from Cell Signaling Technology (Danvers, MA, USA); and p65 antibodies was obtained from Santa Cruz (CA, USA). The horseradish peroxidase (HRP)-conjugated sheep anti-mouse or anti-rabbit secondary antibodies were purchased from Thermo Fisher (Waltham, MA, USA). The proteins were visualized using an ECL detection kit (Thermo Fisher).\",\n \"RAW264.7 cells, a mouse macrophage cell line (Cell Bank of Chinese Academy of Sciences, Shanghai, China) were cultured using DMEM supplemented with FBS (10%) and penicillin-streptomycin (1%). The cells were cultured at 37°C in a humidified environment of 5% carbon dioxide. The medium was changed every other day and the cells were passaged at a dilution of 1:3.\",\n \"In the TCMSP database (https://tcmsp-e.com/),7 the “Herb name” was selected to retrieve the molecular ADME parameter information of honeysuckle, scutellaria, bupleurum and cicada slough, and then the screen conformed to the oral bioavailability (OB) ADME parameter information of honey (Drug-likeness, DL) of ≥0.18 or more active ingredients (Table 1). YOL ingredients were supplemented through literature review. Potential targets related to active ingredients were searched through the TCMSP database and Cytoscape 3.6.1 software.Table 155 Active Compounds Predicted by OB and DL Among 4 Herbs in YOLDrugMol IDMolecule NameMWOB%DLHoneysuckleMOL001494Mandenol308.56420.19MOL001495Ethyl linolenate306.5446.10.2MOL002914Eriodyctiol (flavanone)288.2741.350.24MOL003006(-)-(3R,8S,9R,9aS,10aS)-9-ethenyl-8-(beta-D-glucopyranosyloxy)-2,3,9,9a,10,10a-hexahydro-5-oxo-5H,8H-pyrano[4,3-d]oxazolo[3,2-a]pyridine-3-carboxylic acid_qt281.2987.470.23MOL003014Secologanic dibutylacetal_qt384.5753.650.29MOL002773Beta-carotene536.9637.180.58MOL003036ZINC03978781412.7743.830.76MOL003044Chryseriol300.2835.850.27MOL0030955-hydroxy-7-methoxy-2-(3,4,5-trimethoxyphenyl)chromone358.3751.960.41MOL003111Centauroside_qt434.4855.790.5MOL003117Ioniceracetalides B_qt314.3761.190.19MOL003128Dinethylsecologanoside434.4448.460.48MOL000358Beta-sitosterol414.7936.910.75MOL000422Kaempferol286.2541.880.24MOL000449Stigmasterol412.7743.830.76MOL000006Luteolin286.2536.160.25MOL000098Quercetin302.2546.430.28ScutellariaMOL000073Ent-Epicatechin290.2948.960.24MOL000173Wogonin284.2830.680.23MOL000228(2R)-7-hydroxy-5-methoxy-2-phenylchroman-4-one270.355.230.2MOL000359Sitosterol414.7936.910.75MOL000525Norwogonin270.2539.40.21MOL0005525,2ʹ-Dihydroxy-6,7,8-trimethoxyflavone344.3431.710.35MOL001458Coptisine320.3430.670.86MOL001490Bis[(2S)-2-ethylhexyl] benzene-1,2-dicarboxylate390.6243.590.35MOL001689Acacetin284.2834.970.24MOL002714Baicalein270.2533.520.21MOL002879Diop390.6243.590.39MOL002897Epiberberine336.3943.090.78MOL0029095,7,2,5-tetrahydroxy-8,6-dimethoxyflavone376.3433.820.45MOL002910Carthamidin288.2741.150.24MOL002913Dihydrobaicalin_qt272.2740.040.21MOL002914Eriodyctiol (flavanone)288.2741.350.24MOL002915Salvigenin328.3449.070.33MOL0029175,2ʹ,6ʹ-Trihydroxy-7,8-dimethoxyflavone330.3145.050.33MOL0029255,7,2ʹ,6ʹ-Tetrahydroxyflavone286.2537.010.24MOL002927Skullcapflavone II374.3769.510.44MOL002928Oroxylin a284.2841.370.23MOL002932Panicolin314.3176.260.29MOL0029335,7,4ʹ-Trihydroxy-8-methoxyflavone300.2836.560.27MOL002934Neobaicalein374.37104.30.44MOL002937Dihydrooroxylin A286.366.060.23MOL008206Moslosooflavone298.3144.090.25MOL01041511,13-Eicosadienoic acid, methyl ester322.5939.280.23MOL012266Rivularin344.3437.940.37MOL000490Petunidin317.2930.050.31MOL0045983,5,6,7-tetramethoxy-2-(3,4,5-trimethoxyphenyl)chromone432.4631.970.59BupleurumMOL002776Baicalin446.3940.120.75MOL001645Kaempferol308.5642.10.2MOL004718Linoleyl acetate412.7742.980.76MOL004653α-spinasterol426.546.060.66MOL004624Stigmasterol348.4847.720.53MOL004609Areapillin360.3448.960.41MOL000354Isorhamnetin316.2849.60.31MOL013187Cubebin356.457.130.64\\n\\n55 Active Compounds Predicted by OB and DL Among 4 Herbs in YOL\",\n \"In the drug treatment of CP, the known therapeutic targets were mainly obtained from two sources: GeneCards database (https://www.genecards.org/) and CTD database (http://ctd.mdibl.org/). The data analysis was conducted in 5878 therapeutic targets for the treatment of CP, after the redundant entries were removed. Figure S1 shows the specific information about these known therapeutic targets.\",\n \"To understand the relationship between the herbs and chemical compounds that consist of YOL and its putative targets, and the therapeutic targets known for CP, Network Visualization was conducted using Cytoscape 3.6.1 software,8 and the degree between compounds and targets was analyzed.\",\n \"PPI core network (PPICN) is used to study the relationship between chemical compounds and disease-associated protein molecules based on biochemistry, signal transduction and genetic networks.9 The differing ID types of the proteins were converted to UniProt IDs. In order to further understand how YOL and CP interact at the protein level, the selected targets in this study were uploaded on the online Venn diagram (http://bioinfogp.cnb.csic.es/tools/venny/index.html). Moreover, the genes at the intersection of the active compound and CP were selected and uploaded on STRING 10.5 (https://string-db.org) to obtain the PPI relationship.\",\n \"The data was imported in the format Gene Symbol into David 6.8 database (https://david.ncifcrf.gov/). Molecular function (CC), biological process (BP), and cellular function (MF) were selected, respectively. A pathway enrichment analysis was prospectively performed using biological process enrichment GO analysis and KEGG pathway analysis (http://www.genome.jp/kegg/) to clarify the pathways involving putative CP targets, and visualized by online mapping website Omicshare Tools (http://www.omicshare.com/tools/index.php/).\",\n \"Each experiment was repeated at least in triplicate and data were shown as mean ± SD. Statistical difference in GO and KEGG analysis was performed using hypergeometric and Fisher's exact tests. P<0.05 was considered statistically significant.\",\n \"Screening and Analysis of Active Compounds First, all the compounds related to four TCMs were retrieved by TCMSP. Second, according to the screening conditions of OB ≥30% and DL ≥0.18,10 23 active constituents of honeysuckle (17 predicted relevant targets), 36 of baicalensis (30 predicted relevant targets), 17 of bupleurum (13 predicted relevant targets), and no suitable compounds of cicada slough were obtained. Among them, baicalensis and honeysuckle contained two identical ingredients, while bupleurum and honeysuckle contained three identical components. Finally, a total of 55 active components were obtained for the data analysis after removing redundant entries (Table 1).\\nFirst, all the compounds related to four TCMs were retrieved by TCMSP. Second, according to the screening conditions of OB ≥30% and DL ≥0.18,10 23 active constituents of honeysuckle (17 predicted relevant targets), 36 of baicalensis (30 predicted relevant targets), 17 of bupleurum (13 predicted relevant targets), and no suitable compounds of cicada slough were obtained. Among them, baicalensis and honeysuckle contained two identical ingredients, while bupleurum and honeysuckle contained three identical components. Finally, a total of 55 active components were obtained for the data analysis after removing redundant entries (Table 1).\\nNetwork Analysis of “Active Component-Target” Interaction in YOL The compound-target network contained 175 nodes including 55 compound nodes and 113 target nodes, of which two had no corresponding target and 577 edges. As shown in Figure 1, blue represents compounds in bupleurum, red represents compounds in scutellaria, purple represents compounds in honeysuckle, green represents drug targets, each edge indicates the interaction between compounds and their targets, and two of the 55 compounds were excluded from the network construction. The degree value of a node represents the amount of connected routes in the network, and the larger the shape, the higher the degree value. The network in line with its topological properties showed that the nodes with higher degree of screening were analyzed. These nodes that connect compounds or targets act as hubs in the whole network and may predict key compounds or targets. The top five compounds ranked by degree were MOL000098-quercetin, MOL000422-Kaempferol, MOL000449-stigmasterol, MOL000358-quebrachol and MOL000006-luteolin, which could interact with 154, 68, 39, 32 and 28 target proteins, respectively. PTGS1, NCOA2, AR, PRSS1 and PPARG were the top five targets with the highest degree, which could interact with 42, 38, 29, 28 and 17 compounds, respectively.Figure 1Compounds-target network diagram. Blue represents compounds in bupleurum, Red represents compounds in scutellaria, Purple represents compounds in honeysuckle, Green represents drug targets. The size represents the degree value. The larger the shape, the larger the degree value.\\nCompounds-target network diagram. Blue represents compounds in bupleurum, Red represents compounds in scutellaria, Purple represents compounds in honeysuckle, Green represents drug targets. The size represents the degree value. The larger the shape, the larger the degree value.\\nThe compound-target network contained 175 nodes including 55 compound nodes and 113 target nodes, of which two had no corresponding target and 577 edges. As shown in Figure 1, blue represents compounds in bupleurum, red represents compounds in scutellaria, purple represents compounds in honeysuckle, green represents drug targets, each edge indicates the interaction between compounds and their targets, and two of the 55 compounds were excluded from the network construction. The degree value of a node represents the amount of connected routes in the network, and the larger the shape, the higher the degree value. The network in line with its topological properties showed that the nodes with higher degree of screening were analyzed. These nodes that connect compounds or targets act as hubs in the whole network and may predict key compounds or targets. The top five compounds ranked by degree were MOL000098-quercetin, MOL000422-Kaempferol, MOL000449-stigmasterol, MOL000358-quebrachol and MOL000006-luteolin, which could interact with 154, 68, 39, 32 and 28 target proteins, respectively. PTGS1, NCOA2, AR, PRSS1 and PPARG were the top five targets with the highest degree, which could interact with 42, 38, 29, 28 and 17 compounds, respectively.Figure 1Compounds-target network diagram. Blue represents compounds in bupleurum, Red represents compounds in scutellaria, Purple represents compounds in honeysuckle, Green represents drug targets. The size represents the degree value. The larger the shape, the larger the degree value.\\nCompounds-target network diagram. Blue represents compounds in bupleurum, Red represents compounds in scutellaria, Purple represents compounds in honeysuckle, Green represents drug targets. The size represents the degree value. The larger the shape, the larger the degree value.\\nThe Construction of Key Protein Network of YOL and CP Through the internationally recognized CTD and GeneCards disease databases, 5150 and 1247 pharyngitis-related targets were obtained, respectively. Through the Venny online tool, 30 targets of honeysuckle, scutellaria, bupleuri and pharyngitis were obtained, including VEGFA, IL6, ESR1, RELA, HIF1A, etc. (Figure 2A). Finally, STRING online tool was used to construct the PPI network interaction map of drug and disease. It contained 30 nodes representing proteins and 48 edges representing the interaction between proteins. The thicker the line, the higher is the correlation degree. Through the protein interaction network, we could further explore the therapeutic target and mechanism of YOL for CP (Figure 2B).Figure 2(A) Venn diagram of compounds-target network diagram; (B) Core network diagram of protein interaction between YOL and CP.\\n(A) Venn diagram of compounds-target network diagram; (B) Core network diagram of protein interaction between YOL and CP.\\nThrough the internationally recognized CTD and GeneCards disease databases, 5150 and 1247 pharyngitis-related targets were obtained, respectively. Through the Venny online tool, 30 targets of honeysuckle, scutellaria, bupleuri and pharyngitis were obtained, including VEGFA, IL6, ESR1, RELA, HIF1A, etc. (Figure 2A). Finally, STRING online tool was used to construct the PPI network interaction map of drug and disease. It contained 30 nodes representing proteins and 48 edges representing the interaction between proteins. The thicker the line, the higher is the correlation degree. Through the protein interaction network, we could further explore the therapeutic target and mechanism of YOL for CP (Figure 2B).Figure 2(A) Venn diagram of compounds-target network diagram; (B) Core network diagram of protein interaction between YOL and CP.\\n(A) Venn diagram of compounds-target network diagram; (B) Core network diagram of protein interaction between YOL and CP.\\nPharmacological Mechanisms of YOL Acting on CP A total of 30 common targets were obtained by DAVID online tool and 148 GO items were obtained (p < 0.05) by performing the functional enrichment analysis. There were 102 entries on biological process (BP), 34 entries on Molecular Function (MF), and 12 entries on cell composition (CC) (Figure 3A). A total of 46 signaling pathways were obtained by KEGG pathway enrichment screening (p<0.05), involving cancer, PI3K-AKT, hepatitis, proteoglycans, p53, HIF-1 signaling pathways, etc. (Figure 3B and Table S1).Figure 3(A) GO analysis function annotation diagram. Biological process (BP), molecular function (MF), and cell composition (CC). (B) Enriched KEGG pathways of YOL selected targets for CP.\\n(A) GO analysis function annotation diagram. Biological process (BP), molecular function (MF), and cell composition (CC). (B) Enriched KEGG pathways of YOL selected targets for CP.\\nA total of 30 common targets were obtained by DAVID online tool and 148 GO items were obtained (p < 0.05) by performing the functional enrichment analysis. There were 102 entries on biological process (BP), 34 entries on Molecular Function (MF), and 12 entries on cell composition (CC) (Figure 3A). A total of 46 signaling pathways were obtained by KEGG pathway enrichment screening (p<0.05), involving cancer, PI3K-AKT, hepatitis, proteoglycans, p53, HIF-1 signaling pathways, etc. (Figure 3B and Table S1).Figure 3(A) GO analysis function annotation diagram. Biological process (BP), molecular function (MF), and cell composition (CC). (B) Enriched KEGG pathways of YOL selected targets for CP.\\n(A) GO analysis function annotation diagram. Biological process (BP), molecular function (MF), and cell composition (CC). (B) Enriched KEGG pathways of YOL selected targets for CP.\\nExperimental Verification of Predicted Results In order to experimentally demonstrate the predictions about the molecular mechanism of YOL, we chose two ingredients for testing (Figure 4), based on their composition score, the group to which they belong, and their herbal origin to examine how ingredients from different herbs affect the same proteins. The effects of these compounds on the expression levels of related proteins were analyzed by Western blot. Macrophages secrete several inflammatory cytokines, which play significant roles in inflammation. Signal transduction and transcriptional activator 3 (STAT3), nuclear factor kappa B (NF-kB), and phosphatidylinositol 3-kinase (PI3K)/Akt pathways are critical inflammatory pathways. LPS can significantly increase the production of many proinflammatory factors, including inducible NO synthase (iNOS) and cyclooxygenase-2 (COX-2) in macrophages. After pretreatment of macrophages with YOL, luteolin and baicalein, the protein expression levels of iNOS and COX-2 induced by LPS were significantly inhibited in a dose-dependent manner (Figure 4A). The related protein changes were consistent with iNOS and COX-2 inflammation protein expression (Figure 4B–D).Figure 4The YOL inhibits PI3K/p-AKT/p-Stats pathway in RAW 264.7 cells. Representative Western blot images show the relative expressions of iNOS, COX-2 (A), p-p65, p65 (B), PI3K, p-AKT, AKT (C), p-Stats, Stat3 (D) in the groups. The protein levels (normalized) of iNOS, COX2 (E), and p-p65, p65, PI3K, p-AKT, AKT, p-Stats, Stat3 (F) in cells. All the values are presented as mean ± SD. #P < 0.05 and ##P < 0.01 vs control group. *P < 0.05 and **P < 0.01 vs LPS group.\\nThe YOL inhibits PI3K/p-AKT/p-Stats pathway in RAW 264.7 cells. Representative Western blot images show the relative expressions of iNOS, COX-2 (A), p-p65, p65 (B), PI3K, p-AKT, AKT (C), p-Stats, Stat3 (D) in the groups. The protein levels (normalized) of iNOS, COX2 (E), and p-p65, p65, PI3K, p-AKT, AKT, p-Stats, Stat3 (F) in cells. All the values are presented as mean ± SD. #P < 0.05 and ##P < 0.01 vs control group. *P < 0.05 and **P < 0.01 vs LPS group.\\nIn order to experimentally demonstrate the predictions about the molecular mechanism of YOL, we chose two ingredients for testing (Figure 4), based on their composition score, the group to which they belong, and their herbal origin to examine how ingredients from different herbs affect the same proteins. The effects of these compounds on the expression levels of related proteins were analyzed by Western blot. Macrophages secrete several inflammatory cytokines, which play significant roles in inflammation. Signal transduction and transcriptional activator 3 (STAT3), nuclear factor kappa B (NF-kB), and phosphatidylinositol 3-kinase (PI3K)/Akt pathways are critical inflammatory pathways. LPS can significantly increase the production of many proinflammatory factors, including inducible NO synthase (iNOS) and cyclooxygenase-2 (COX-2) in macrophages. After pretreatment of macrophages with YOL, luteolin and baicalein, the protein expression levels of iNOS and COX-2 induced by LPS were significantly inhibited in a dose-dependent manner (Figure 4A). The related protein changes were consistent with iNOS and COX-2 inflammation protein expression (Figure 4B–D).Figure 4The YOL inhibits PI3K/p-AKT/p-Stats pathway in RAW 264.7 cells. Representative Western blot images show the relative expressions of iNOS, COX-2 (A), p-p65, p65 (B), PI3K, p-AKT, AKT (C), p-Stats, Stat3 (D) in the groups. The protein levels (normalized) of iNOS, COX2 (E), and p-p65, p65, PI3K, p-AKT, AKT, p-Stats, Stat3 (F) in cells. All the values are presented as mean ± SD. #P < 0.05 and ##P < 0.01 vs control group. *P < 0.05 and **P < 0.01 vs LPS group.\\nThe YOL inhibits PI3K/p-AKT/p-Stats pathway in RAW 264.7 cells. Representative Western blot images show the relative expressions of iNOS, COX-2 (A), p-p65, p65 (B), PI3K, p-AKT, AKT (C), p-Stats, Stat3 (D) in the groups. The protein levels (normalized) of iNOS, COX2 (E), and p-p65, p65, PI3K, p-AKT, AKT, p-Stats, Stat3 (F) in cells. All the values are presented as mean ± SD. #P < 0.05 and ##P < 0.01 vs control group. *P < 0.05 and **P < 0.01 vs LPS group.\",\n \"First, all the compounds related to four TCMs were retrieved by TCMSP. Second, according to the screening conditions of OB ≥30% and DL ≥0.18,10 23 active constituents of honeysuckle (17 predicted relevant targets), 36 of baicalensis (30 predicted relevant targets), 17 of bupleurum (13 predicted relevant targets), and no suitable compounds of cicada slough were obtained. Among them, baicalensis and honeysuckle contained two identical ingredients, while bupleurum and honeysuckle contained three identical components. Finally, a total of 55 active components were obtained for the data analysis after removing redundant entries (Table 1).\",\n \"The compound-target network contained 175 nodes including 55 compound nodes and 113 target nodes, of which two had no corresponding target and 577 edges. As shown in Figure 1, blue represents compounds in bupleurum, red represents compounds in scutellaria, purple represents compounds in honeysuckle, green represents drug targets, each edge indicates the interaction between compounds and their targets, and two of the 55 compounds were excluded from the network construction. The degree value of a node represents the amount of connected routes in the network, and the larger the shape, the higher the degree value. The network in line with its topological properties showed that the nodes with higher degree of screening were analyzed. These nodes that connect compounds or targets act as hubs in the whole network and may predict key compounds or targets. The top five compounds ranked by degree were MOL000098-quercetin, MOL000422-Kaempferol, MOL000449-stigmasterol, MOL000358-quebrachol and MOL000006-luteolin, which could interact with 154, 68, 39, 32 and 28 target proteins, respectively. PTGS1, NCOA2, AR, PRSS1 and PPARG were the top five targets with the highest degree, which could interact with 42, 38, 29, 28 and 17 compounds, respectively.Figure 1Compounds-target network diagram. Blue represents compounds in bupleurum, Red represents compounds in scutellaria, Purple represents compounds in honeysuckle, Green represents drug targets. The size represents the degree value. The larger the shape, the larger the degree value.\\nCompounds-target network diagram. Blue represents compounds in bupleurum, Red represents compounds in scutellaria, Purple represents compounds in honeysuckle, Green represents drug targets. The size represents the degree value. The larger the shape, the larger the degree value.\",\n \"Through the internationally recognized CTD and GeneCards disease databases, 5150 and 1247 pharyngitis-related targets were obtained, respectively. Through the Venny online tool, 30 targets of honeysuckle, scutellaria, bupleuri and pharyngitis were obtained, including VEGFA, IL6, ESR1, RELA, HIF1A, etc. (Figure 2A). Finally, STRING online tool was used to construct the PPI network interaction map of drug and disease. It contained 30 nodes representing proteins and 48 edges representing the interaction between proteins. The thicker the line, the higher is the correlation degree. Through the protein interaction network, we could further explore the therapeutic target and mechanism of YOL for CP (Figure 2B).Figure 2(A) Venn diagram of compounds-target network diagram; (B) Core network diagram of protein interaction between YOL and CP.\\n(A) Venn diagram of compounds-target network diagram; (B) Core network diagram of protein interaction between YOL and CP.\",\n \"A total of 30 common targets were obtained by DAVID online tool and 148 GO items were obtained (p < 0.05) by performing the functional enrichment analysis. There were 102 entries on biological process (BP), 34 entries on Molecular Function (MF), and 12 entries on cell composition (CC) (Figure 3A). A total of 46 signaling pathways were obtained by KEGG pathway enrichment screening (p<0.05), involving cancer, PI3K-AKT, hepatitis, proteoglycans, p53, HIF-1 signaling pathways, etc. (Figure 3B and Table S1).Figure 3(A) GO analysis function annotation diagram. Biological process (BP), molecular function (MF), and cell composition (CC). (B) Enriched KEGG pathways of YOL selected targets for CP.\\n(A) GO analysis function annotation diagram. Biological process (BP), molecular function (MF), and cell composition (CC). (B) Enriched KEGG pathways of YOL selected targets for CP.\",\n \"In order to experimentally demonstrate the predictions about the molecular mechanism of YOL, we chose two ingredients for testing (Figure 4), based on their composition score, the group to which they belong, and their herbal origin to examine how ingredients from different herbs affect the same proteins. The effects of these compounds on the expression levels of related proteins were analyzed by Western blot. Macrophages secrete several inflammatory cytokines, which play significant roles in inflammation. Signal transduction and transcriptional activator 3 (STAT3), nuclear factor kappa B (NF-kB), and phosphatidylinositol 3-kinase (PI3K)/Akt pathways are critical inflammatory pathways. LPS can significantly increase the production of many proinflammatory factors, including inducible NO synthase (iNOS) and cyclooxygenase-2 (COX-2) in macrophages. After pretreatment of macrophages with YOL, luteolin and baicalein, the protein expression levels of iNOS and COX-2 induced by LPS were significantly inhibited in a dose-dependent manner (Figure 4A). The related protein changes were consistent with iNOS and COX-2 inflammation protein expression (Figure 4B–D).Figure 4The YOL inhibits PI3K/p-AKT/p-Stats pathway in RAW 264.7 cells. Representative Western blot images show the relative expressions of iNOS, COX-2 (A), p-p65, p65 (B), PI3K, p-AKT, AKT (C), p-Stats, Stat3 (D) in the groups. The protein levels (normalized) of iNOS, COX2 (E), and p-p65, p65, PI3K, p-AKT, AKT, p-Stats, Stat3 (F) in cells. All the values are presented as mean ± SD. #P < 0.05 and ##P < 0.01 vs control group. *P < 0.05 and **P < 0.01 vs LPS group.\\nThe YOL inhibits PI3K/p-AKT/p-Stats pathway in RAW 264.7 cells. Representative Western blot images show the relative expressions of iNOS, COX-2 (A), p-p65, p65 (B), PI3K, p-AKT, AKT (C), p-Stats, Stat3 (D) in the groups. The protein levels (normalized) of iNOS, COX2 (E), and p-p65, p65, PI3K, p-AKT, AKT, p-Stats, Stat3 (F) in cells. All the values are presented as mean ± SD. #P < 0.05 and ##P < 0.01 vs control group. *P < 0.05 and **P < 0.01 vs LPS group.\",\n \"Many diseases, including cancer and chronic inflammation, are known to be regulated by multiple signaling pathways. TCMs are considered as multi-component and multi-target therapeutic drugs, which is in accordance with the methodologies of network pharmacology. The holistic view of TCM considers the human body as a complex biological network system, which is connected with network pharmacology. By combining the network pharmacology with the holistic view of TCM, the disease-target-drug network is obtained. The network shows that different components of TCM may act on one target, and different targets may act on the same pathway. It is also possible that the same component of TCM acts on different targets, and the same target acts on different pathways. The multi-component, multi-target and multi-pathway of TCM can be clearly visualized through network pharmacology, which resolves many difficulties encountered in the study of TCM.13–15 CP is the diffuse inflammation of pharyngeal mucosa, submucosa and lymph tissues. Clinically, CP is mainly manifested as pharyngeal discomfort (foreign body sensation, burning sensation, stimulation sensation, etc.), pharyngeal itching, cough, etc. Pharyngitis belongs to the category of “Fenghou Bi” in TCM. Pharyngitis pertains to the category of “wind throat arthralgia” in Chinese medicine, and is often caused by evil heat entering the body, and heat in the lungs and stomach. YOL is an oral TCM preparation, which is further optimized on the basis of Jinchan oral liquid developed by Children’s Hospital of Suzhou University.5,6 However, there is a lack of research on its active ingredients and its mechanism of action.\\nIn this study, 30 potential targets, 102 biological processes, 12 molecular functions, 34 cell components, and 46 KEGG pathways were obtained. The active ingredients in YOL can treat CP through multiple targets and pathways. To elucidate the biological pathways that may be affected by YOL, a significantly overexpressed KEGG pathway was identified. The results showed VEGFA, IL6, ESR1 and RELA as the key targets and the involvement of PI3K-Akt, p53 and HIF-1 signaling pathways. TCM aims to restore a patient’s health through the use of a TCM prescription, which is usually composed of two or more TCMs in optimal proportions.\\nYOL, for example, consists of four TCMs. As expected, most of the selected active ingredients are directly or indirectly associated with inflammation. In a previous study, neochlorogenic acid was demonstrated to inhibit LPS-activated inflammatory responses through up-regulation of Nrf2/HO-1 and AMPK pathways.16 The decoction extract from six natural herbs, which contain honeysuckle, exhibits anti-inflammatory and immunomodulatory effects by targeting NF-κB/IL-6/STAT3 signaling.17 Luteolin is abundant in honeysuckle, which alleviates CP by inhibiting the NF-κB pathway and anti-inflammatory polarization of M1 macrophages.11 Protective effects of baicalein on liver injury in mice induced by multi-microbial septicemia were found to be based on inhibiting inflammation.12 Network pharmacological studies have shown that the main active components of honeysuckle may play a role through inflammation-related proteins such as HSP90AA1, HSP90AB1, ESR1, PTGS2, TERT and PPARG, and especially heat shock protein HSP90A (HSP90AA1).18 Flavonoids such as baicalin and wogonin alone and in combination have anti-inflammatory activity in Scutellaria.19 Network pharmacological studies have also shown that alkaloids such as coptisine and epiphone in Scutellaria and components such as dihydrokaempferol, and rographolide flavonoids also show anti-inflammatory activity. MAPK14, TNFRSF1A, EGFR and SELE are the primary anti-inflammatory components of Scutellaria. The anti-inflammatory active components in Scutellaria can directly affect MAPK14 and EGFR to produce anti-inflammatory effects, and can also indirectly affect other targets to exert anti-inflammatory effects. As one of the four p38 MAPKs, MAPK14 plays a vital role in the cellular cascade induced by extracellular stimuli such as proinflammatory cytokines or physical stimuli.20 Studies predicted that the pharmacodynamic basis of Bupleurum is mainly saponins, flavonoids, volatile oils, fatty acids and other components. Flavonoids mainly act on PI3K-Akt, NF-kB and other pathways, participate in regulating inflammatory factors, estrogen signal transduction and other physiological processes. Bupleurum-Scutellaria drug pair, first used in small bupleurum decoction in Treatise on Febrile Diseases, is an important part of Bupleurum prescription. Bupleurum is bitter, flat and clear, and can disperse the stagnation of bile fire. Scutellaria has a bitter and cold taste, and can clear internal heat. Both of them must be used together to regulate the liver and gallbladder, and clear the dampness and heat of internal accumulation, which are commonly used to treat fever, pharyngitis, and respiratory diseases.21 Cicada slough can evacuate wind and heat, and clear the throat. Clinically, it is common in classical prescriptions such as Sheng jiang San and Pharyngitis San. However, the active components of Cicada slough did not satisfy the prediction requirements in this research.\\nYOL is composed of honeysuckle, scutellaria, bupleurum and cicada slough. According to TCM theory, bupleurum is bitter and flat, enters the liver and gallbladder meridian, penetrates and clears Shaoyang, and can drain the stagnation of gas. Scutellaria is bitter and cold, and clears the heat of Shaoyang. The actions of Bupleurum facilitate the clear discharge of Scutellaria, the two are compatible, and achieve the goal of reconciliation and Shaoyang. Honeysuckle cools through the surface, has heat-clearing and detoxifying effects, and also has the effect of fragrant obscenity. Cicada slough is light floating, clears the coke gas, shows honeysuckle compatibility, understanding the table evil, and is combined with the “treatment of the coke such as feather.” The combination of the four drugs, Xuan and descent together, clear the evil, thereby clearing heat and detoxification, warming the evil without hiding, scattered outside and recovered. Therefore, the treatment of CP with YOL can control the pharyngitis, regulate the balance of Yin and Yang, strengthen the foundation and improve the immunity of the body.\",\n \"This study used the network pharmacology platform to probe the YOL treatment of CP by multi-target analysis. The results showed that YOL could treat CP by acting on related targets, which were consistent with the reported literature. In addition, we have verified that YOL and its two monomer compounds, Luteolin and Baicalein inhibited activation of the NF-kB, Stat3 and PI3K-Akt pathways. Although the potential advantages of the “network target, multi-component” strategy of network pharmacology are obvious, the content of TCM ingredients is usually ignored in the research of network pharmacology of Chinese medicine, and the influence of content and concentration on the efficacy cannot be ignored. The predicted targets, which have not been discussed, may provide clues for further research on the mechanism of YOL.\"\n]"},"all_sections_sec_types":{"kind":"list like","value":["intro",null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null],"string":"[\n \"intro\",\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null\n]"},"keywords":{"kind":"list like","value":["Yinqin oral liquid","chronic pharyngitis","network pharmacology","NF-kB","multi-target analysis"],"string":"[\n \"Yinqin oral liquid\",\n \"chronic pharyngitis\",\n \"network pharmacology\",\n \"NF-kB\",\n \"multi-target analysis\"\n]"},"whole_article_text":{"kind":"string","value":"Introduction:\nChronic pharyngitis (CP) is a very common disease associated with chronic inflammation involving pharyngeal mucosa, submucosal and lymphatic tissues. Clinically, CP is mainly manifested as pharyngeal discomfort (foreign body sensation, burning sensation, irritation, etc.), with occasional pharyngeal itch, cough, etc., which belongs to the category of “slow throat arthralgia” in the traditional Chinese medicine (TCM).1 CP has a high incidence and accounts for 10–12% of all pharyngeal diseases, with a long disease course, recurrence and is difficult to cure. At present, western medicine uses antibiotics supplemented by hormone preparations, such as dexamethasone and antiviral drugs, for the treatment of pharyngitis. These treatment methods have drawbacks such as narrow therapeutic spectrum, recurrence, poor tolerance and major toxic and side effects. Hence, developing more effective therapeutic strategies against CP, and reducing the occurrence of side effects from the current therapeutic options is of great clinical importance.\nTCM is a major component of complementary and alternative medicine. Owing to its excellent clinical efficacy, TCM is a research hotspot worldwide. Dialectical theory of TCM is used to treat acute and chronic pharyngitis and has unique advantages such as minor toxic side effects, obvious curative effect and treatment of both symptoms and root causes.2–4 The pharmacological effects of TCM herbal formulae play an important role in their appropriate application. However, the complex nature of herbal formulae has impeded this understanding. Numerous chemical ingredients involving multiple potential targets are present in an herbal formula. It is not adequate to explain the effects produced by a whole herbal formula if we individually consider every single ingredient in it. Yinqin oral liquid (YOL) is composed of extracts of honeysuckle, scutellaria, bupleurum and cicada. YOL has the effects of relieving wind, muscle and clearing heat, which is mainly used for cold, fever, aversion to cold, pharyngeal pain and other upper respiratory tract infections caused by wind fever and wind-cold. Long-term clinical observation has proven its effect in relieving pharyngeal pain and treating hand-foot-mouth disease. YOL contains several chemical compounds, which regulate diverse targets, and thus precisely determine the pharmacological mechanisms involved in its therapeutic actions. In addition, there are several challenges in the relationships between the herbs and diseases.5,6\nNetwork pharmacology is based on the theory of system biology. As a new subject, it selects specific signal nodes to design multi-target drug molecules through the network analysis of system biology. It is possible to study both the active components and the potential gene targets from TCM because of the establishment of network pharmacology and bioinformatics. This study predicted and analyzed the active components and target of YOL using network pharmacology, to explore the rationality of its formula and the scientific nature of treating CP, and further explore the pharmacological mechanisms of YOL on CP.\n\nMaterials and Methods:\nChemicals and Reagents Yinqin Oral Liquid (YOL) was provided by su zhou si yuan natural products research and development Co. Ltd. YOL is composed of honeysuckle, scutellaria, bupleurum and cicada, decompressed and concentrated to 2 g/mL through boiling and alcohol sinking, and stored at 4°C for later use. Primary antibodies: COX-2 and iNOS antibodies were purchased from Abcam (Cambridge, UK); glyceraldehyde-3-phosphate dehydrogenase (GAPDH) antibody was obtained from Millipore (Billerica, MA, USA); PI3K, p-AKT, AKT, p-Stats, Stat3 and p-p65 (S536) antibodies were purchased from Cell Signaling Technology (Danvers, MA, USA); and p65 antibodies was obtained from Santa Cruz (CA, USA). The horseradish peroxidase (HRP)-conjugated sheep anti-mouse or anti-rabbit secondary antibodies were purchased from Thermo Fisher (Waltham, MA, USA). The proteins were visualized using an ECL detection kit (Thermo Fisher).\nYinqin Oral Liquid (YOL) was provided by su zhou si yuan natural products research and development Co. Ltd. YOL is composed of honeysuckle, scutellaria, bupleurum and cicada, decompressed and concentrated to 2 g/mL through boiling and alcohol sinking, and stored at 4°C for later use. Primary antibodies: COX-2 and iNOS antibodies were purchased from Abcam (Cambridge, UK); glyceraldehyde-3-phosphate dehydrogenase (GAPDH) antibody was obtained from Millipore (Billerica, MA, USA); PI3K, p-AKT, AKT, p-Stats, Stat3 and p-p65 (S536) antibodies were purchased from Cell Signaling Technology (Danvers, MA, USA); and p65 antibodies was obtained from Santa Cruz (CA, USA). The horseradish peroxidase (HRP)-conjugated sheep anti-mouse or anti-rabbit secondary antibodies were purchased from Thermo Fisher (Waltham, MA, USA). The proteins were visualized using an ECL detection kit (Thermo Fisher).\nCell Culture RAW264.7 cells, a mouse macrophage cell line (Cell Bank of Chinese Academy of Sciences, Shanghai, China) were cultured using DMEM supplemented with FBS (10%) and penicillin-streptomycin (1%). The cells were cultured at 37°C in a humidified environment of 5% carbon dioxide. The medium was changed every other day and the cells were passaged at a dilution of 1:3.\nRAW264.7 cells, a mouse macrophage cell line (Cell Bank of Chinese Academy of Sciences, Shanghai, China) were cultured using DMEM supplemented with FBS (10%) and penicillin-streptomycin (1%). The cells were cultured at 37°C in a humidified environment of 5% carbon dioxide. The medium was changed every other day and the cells were passaged at a dilution of 1:3.\nCollection of Drug Molecular Information and Screening of Active Ingredients In the TCMSP database (https://tcmsp-e.com/),7 the “Herb name” was selected to retrieve the molecular ADME parameter information of honeysuckle, scutellaria, bupleurum and cicada slough, and then the screen conformed to the oral bioavailability (OB) ADME parameter information of honey (Drug-likeness, DL) of ≥0.18 or more active ingredients (Table 1). YOL ingredients were supplemented through literature review. Potential targets related to active ingredients were searched through the TCMSP database and Cytoscape 3.6.1 software.Table 155 Active Compounds Predicted by OB and DL Among 4 Herbs in YOLDrugMol IDMolecule NameMWOB%DLHoneysuckleMOL001494Mandenol308.56420.19MOL001495Ethyl linolenate306.5446.10.2MOL002914Eriodyctiol (flavanone)288.2741.350.24MOL003006(-)-(3R,8S,9R,9aS,10aS)-9-ethenyl-8-(beta-D-glucopyranosyloxy)-2,3,9,9a,10,10a-hexahydro-5-oxo-5H,8H-pyrano[4,3-d]oxazolo[3,2-a]pyridine-3-carboxylic acid_qt281.2987.470.23MOL003014Secologanic dibutylacetal_qt384.5753.650.29MOL002773Beta-carotene536.9637.180.58MOL003036ZINC03978781412.7743.830.76MOL003044Chryseriol300.2835.850.27MOL0030955-hydroxy-7-methoxy-2-(3,4,5-trimethoxyphenyl)chromone358.3751.960.41MOL003111Centauroside_qt434.4855.790.5MOL003117Ioniceracetalides B_qt314.3761.190.19MOL003128Dinethylsecologanoside434.4448.460.48MOL000358Beta-sitosterol414.7936.910.75MOL000422Kaempferol286.2541.880.24MOL000449Stigmasterol412.7743.830.76MOL000006Luteolin286.2536.160.25MOL000098Quercetin302.2546.430.28ScutellariaMOL000073Ent-Epicatechin290.2948.960.24MOL000173Wogonin284.2830.680.23MOL000228(2R)-7-hydroxy-5-methoxy-2-phenylchroman-4-one270.355.230.2MOL000359Sitosterol414.7936.910.75MOL000525Norwogonin270.2539.40.21MOL0005525,2ʹ-Dihydroxy-6,7,8-trimethoxyflavone344.3431.710.35MOL001458Coptisine320.3430.670.86MOL001490Bis[(2S)-2-ethylhexyl] benzene-1,2-dicarboxylate390.6243.590.35MOL001689Acacetin284.2834.970.24MOL002714Baicalein270.2533.520.21MOL002879Diop390.6243.590.39MOL002897Epiberberine336.3943.090.78MOL0029095,7,2,5-tetrahydroxy-8,6-dimethoxyflavone376.3433.820.45MOL002910Carthamidin288.2741.150.24MOL002913Dihydrobaicalin_qt272.2740.040.21MOL002914Eriodyctiol (flavanone)288.2741.350.24MOL002915Salvigenin328.3449.070.33MOL0029175,2ʹ,6ʹ-Trihydroxy-7,8-dimethoxyflavone330.3145.050.33MOL0029255,7,2ʹ,6ʹ-Tetrahydroxyflavone286.2537.010.24MOL002927Skullcapflavone II374.3769.510.44MOL002928Oroxylin a284.2841.370.23MOL002932Panicolin314.3176.260.29MOL0029335,7,4ʹ-Trihydroxy-8-methoxyflavone300.2836.560.27MOL002934Neobaicalein374.37104.30.44MOL002937Dihydrooroxylin A286.366.060.23MOL008206Moslosooflavone298.3144.090.25MOL01041511,13-Eicosadienoic acid, methyl ester322.5939.280.23MOL012266Rivularin344.3437.940.37MOL000490Petunidin317.2930.050.31MOL0045983,5,6,7-tetramethoxy-2-(3,4,5-trimethoxyphenyl)chromone432.4631.970.59BupleurumMOL002776Baicalin446.3940.120.75MOL001645Kaempferol308.5642.10.2MOL004718Linoleyl acetate412.7742.980.76MOL004653α-spinasterol426.546.060.66MOL004624Stigmasterol348.4847.720.53MOL004609Areapillin360.3448.960.41MOL000354Isorhamnetin316.2849.60.31MOL013187Cubebin356.457.130.64\n\n55 Active Compounds Predicted by OB and DL Among 4 Herbs in YOL\nIn the TCMSP database (https://tcmsp-e.com/),7 the “Herb name” was selected to retrieve the molecular ADME parameter information of honeysuckle, scutellaria, bupleurum and cicada slough, and then the screen conformed to the oral bioavailability (OB) ADME parameter information of honey (Drug-likeness, DL) of ≥0.18 or more active ingredients (Table 1). YOL ingredients were supplemented through literature review. Potential targets related to active ingredients were searched through the TCMSP database and Cytoscape 3.6.1 software.Table 155 Active Compounds Predicted by OB and DL Among 4 Herbs in YOLDrugMol IDMolecule NameMWOB%DLHoneysuckleMOL001494Mandenol308.56420.19MOL001495Ethyl linolenate306.5446.10.2MOL002914Eriodyctiol (flavanone)288.2741.350.24MOL003006(-)-(3R,8S,9R,9aS,10aS)-9-ethenyl-8-(beta-D-glucopyranosyloxy)-2,3,9,9a,10,10a-hexahydro-5-oxo-5H,8H-pyrano[4,3-d]oxazolo[3,2-a]pyridine-3-carboxylic acid_qt281.2987.470.23MOL003014Secologanic dibutylacetal_qt384.5753.650.29MOL002773Beta-carotene536.9637.180.58MOL003036ZINC03978781412.7743.830.76MOL003044Chryseriol300.2835.850.27MOL0030955-hydroxy-7-methoxy-2-(3,4,5-trimethoxyphenyl)chromone358.3751.960.41MOL003111Centauroside_qt434.4855.790.5MOL003117Ioniceracetalides B_qt314.3761.190.19MOL003128Dinethylsecologanoside434.4448.460.48MOL000358Beta-sitosterol414.7936.910.75MOL000422Kaempferol286.2541.880.24MOL000449Stigmasterol412.7743.830.76MOL000006Luteolin286.2536.160.25MOL000098Quercetin302.2546.430.28ScutellariaMOL000073Ent-Epicatechin290.2948.960.24MOL000173Wogonin284.2830.680.23MOL000228(2R)-7-hydroxy-5-methoxy-2-phenylchroman-4-one270.355.230.2MOL000359Sitosterol414.7936.910.75MOL000525Norwogonin270.2539.40.21MOL0005525,2ʹ-Dihydroxy-6,7,8-trimethoxyflavone344.3431.710.35MOL001458Coptisine320.3430.670.86MOL001490Bis[(2S)-2-ethylhexyl] benzene-1,2-dicarboxylate390.6243.590.35MOL001689Acacetin284.2834.970.24MOL002714Baicalein270.2533.520.21MOL002879Diop390.6243.590.39MOL002897Epiberberine336.3943.090.78MOL0029095,7,2,5-tetrahydroxy-8,6-dimethoxyflavone376.3433.820.45MOL002910Carthamidin288.2741.150.24MOL002913Dihydrobaicalin_qt272.2740.040.21MOL002914Eriodyctiol (flavanone)288.2741.350.24MOL002915Salvigenin328.3449.070.33MOL0029175,2ʹ,6ʹ-Trihydroxy-7,8-dimethoxyflavone330.3145.050.33MOL0029255,7,2ʹ,6ʹ-Tetrahydroxyflavone286.2537.010.24MOL002927Skullcapflavone II374.3769.510.44MOL002928Oroxylin a284.2841.370.23MOL002932Panicolin314.3176.260.29MOL0029335,7,4ʹ-Trihydroxy-8-methoxyflavone300.2836.560.27MOL002934Neobaicalein374.37104.30.44MOL002937Dihydrooroxylin A286.366.060.23MOL008206Moslosooflavone298.3144.090.25MOL01041511,13-Eicosadienoic acid, methyl ester322.5939.280.23MOL012266Rivularin344.3437.940.37MOL000490Petunidin317.2930.050.31MOL0045983,5,6,7-tetramethoxy-2-(3,4,5-trimethoxyphenyl)chromone432.4631.970.59BupleurumMOL002776Baicalin446.3940.120.75MOL001645Kaempferol308.5642.10.2MOL004718Linoleyl acetate412.7742.980.76MOL004653α-spinasterol426.546.060.66MOL004624Stigmasterol348.4847.720.53MOL004609Areapillin360.3448.960.41MOL000354Isorhamnetin316.2849.60.31MOL013187Cubebin356.457.130.64\n\n55 Active Compounds Predicted by OB and DL Among 4 Herbs in YOL\nKnown Therapeutic Targets of Chronic Pharyngitis (CP) In the drug treatment of CP, the known therapeutic targets were mainly obtained from two sources: GeneCards database (https://www.genecards.org/) and CTD database (http://ctd.mdibl.org/). The data analysis was conducted in 5878 therapeutic targets for the treatment of CP, after the redundant entries were removed. Figure S1 shows the specific information about these known therapeutic targets.\nIn the drug treatment of CP, the known therapeutic targets were mainly obtained from two sources: GeneCards database (https://www.genecards.org/) and CTD database (http://ctd.mdibl.org/). The data analysis was conducted in 5878 therapeutic targets for the treatment of CP, after the redundant entries were removed. Figure S1 shows the specific information about these known therapeutic targets.\nNetwork Construction and Analysis To understand the relationship between the herbs and chemical compounds that consist of YOL and its putative targets, and the therapeutic targets known for CP, Network Visualization was conducted using Cytoscape 3.6.1 software,8 and the degree between compounds and targets was analyzed.\nTo understand the relationship between the herbs and chemical compounds that consist of YOL and its putative targets, and the therapeutic targets known for CP, Network Visualization was conducted using Cytoscape 3.6.1 software,8 and the degree between compounds and targets was analyzed.\nProtein-Protein Interaction (PPI) Data PPI core network (PPICN) is used to study the relationship between chemical compounds and disease-associated protein molecules based on biochemistry, signal transduction and genetic networks.9 The differing ID types of the proteins were converted to UniProt IDs. In order to further understand how YOL and CP interact at the protein level, the selected targets in this study were uploaded on the online Venn diagram (http://bioinfogp.cnb.csic.es/tools/venny/index.html). Moreover, the genes at the intersection of the active compound and CP were selected and uploaded on STRING 10.5 (https://string-db.org) to obtain the PPI relationship.\nPPI core network (PPICN) is used to study the relationship between chemical compounds and disease-associated protein molecules based on biochemistry, signal transduction and genetic networks.9 The differing ID types of the proteins were converted to UniProt IDs. In order to further understand how YOL and CP interact at the protein level, the selected targets in this study were uploaded on the online Venn diagram (http://bioinfogp.cnb.csic.es/tools/venny/index.html). Moreover, the genes at the intersection of the active compound and CP were selected and uploaded on STRING 10.5 (https://string-db.org) to obtain the PPI relationship.\nPathway Enrichment Analysis The data was imported in the format Gene Symbol into David 6.8 database (https://david.ncifcrf.gov/). Molecular function (CC), biological process (BP), and cellular function (MF) were selected, respectively. A pathway enrichment analysis was prospectively performed using biological process enrichment GO analysis and KEGG pathway analysis (http://www.genome.jp/kegg/) to clarify the pathways involving putative CP targets, and visualized by online mapping website Omicshare Tools (http://www.omicshare.com/tools/index.php/).\nThe data was imported in the format Gene Symbol into David 6.8 database (https://david.ncifcrf.gov/). Molecular function (CC), biological process (BP), and cellular function (MF) were selected, respectively. A pathway enrichment analysis was prospectively performed using biological process enrichment GO analysis and KEGG pathway analysis (http://www.genome.jp/kegg/) to clarify the pathways involving putative CP targets, and visualized by online mapping website Omicshare Tools (http://www.omicshare.com/tools/index.php/).\nStatistical Analysis Each experiment was repeated at least in triplicate and data were shown as mean ± SD. Statistical difference in GO and KEGG analysis was performed using hypergeometric and Fisher's exact tests. P<0.05 was considered statistically significant.\nEach experiment was repeated at least in triplicate and data were shown as mean ± SD. Statistical difference in GO and KEGG analysis was performed using hypergeometric and Fisher's exact tests. P<0.05 was considered statistically significant.\n\nChemicals and Reagents:\nYinqin Oral Liquid (YOL) was provided by su zhou si yuan natural products research and development Co. Ltd. YOL is composed of honeysuckle, scutellaria, bupleurum and cicada, decompressed and concentrated to 2 g/mL through boiling and alcohol sinking, and stored at 4°C for later use. Primary antibodies: COX-2 and iNOS antibodies were purchased from Abcam (Cambridge, UK); glyceraldehyde-3-phosphate dehydrogenase (GAPDH) antibody was obtained from Millipore (Billerica, MA, USA); PI3K, p-AKT, AKT, p-Stats, Stat3 and p-p65 (S536) antibodies were purchased from Cell Signaling Technology (Danvers, MA, USA); and p65 antibodies was obtained from Santa Cruz (CA, USA). The horseradish peroxidase (HRP)-conjugated sheep anti-mouse or anti-rabbit secondary antibodies were purchased from Thermo Fisher (Waltham, MA, USA). The proteins were visualized using an ECL detection kit (Thermo Fisher).\n\nCell Culture:\nRAW264.7 cells, a mouse macrophage cell line (Cell Bank of Chinese Academy of Sciences, Shanghai, China) were cultured using DMEM supplemented with FBS (10%) and penicillin-streptomycin (1%). The cells were cultured at 37°C in a humidified environment of 5% carbon dioxide. The medium was changed every other day and the cells were passaged at a dilution of 1:3.\n\nCollection of Drug Molecular Information and Screening of Active Ingredients:\nIn the TCMSP database (https://tcmsp-e.com/),7 the “Herb name” was selected to retrieve the molecular ADME parameter information of honeysuckle, scutellaria, bupleurum and cicada slough, and then the screen conformed to the oral bioavailability (OB) ADME parameter information of honey (Drug-likeness, DL) of ≥0.18 or more active ingredients (Table 1). YOL ingredients were supplemented through literature review. Potential targets related to active ingredients were searched through the TCMSP database and Cytoscape 3.6.1 software.Table 155 Active Compounds Predicted by OB and DL Among 4 Herbs in YOLDrugMol IDMolecule NameMWOB%DLHoneysuckleMOL001494Mandenol308.56420.19MOL001495Ethyl linolenate306.5446.10.2MOL002914Eriodyctiol (flavanone)288.2741.350.24MOL003006(-)-(3R,8S,9R,9aS,10aS)-9-ethenyl-8-(beta-D-glucopyranosyloxy)-2,3,9,9a,10,10a-hexahydro-5-oxo-5H,8H-pyrano[4,3-d]oxazolo[3,2-a]pyridine-3-carboxylic acid_qt281.2987.470.23MOL003014Secologanic dibutylacetal_qt384.5753.650.29MOL002773Beta-carotene536.9637.180.58MOL003036ZINC03978781412.7743.830.76MOL003044Chryseriol300.2835.850.27MOL0030955-hydroxy-7-methoxy-2-(3,4,5-trimethoxyphenyl)chromone358.3751.960.41MOL003111Centauroside_qt434.4855.790.5MOL003117Ioniceracetalides B_qt314.3761.190.19MOL003128Dinethylsecologanoside434.4448.460.48MOL000358Beta-sitosterol414.7936.910.75MOL000422Kaempferol286.2541.880.24MOL000449Stigmasterol412.7743.830.76MOL000006Luteolin286.2536.160.25MOL000098Quercetin302.2546.430.28ScutellariaMOL000073Ent-Epicatechin290.2948.960.24MOL000173Wogonin284.2830.680.23MOL000228(2R)-7-hydroxy-5-methoxy-2-phenylchroman-4-one270.355.230.2MOL000359Sitosterol414.7936.910.75MOL000525Norwogonin270.2539.40.21MOL0005525,2ʹ-Dihydroxy-6,7,8-trimethoxyflavone344.3431.710.35MOL001458Coptisine320.3430.670.86MOL001490Bis[(2S)-2-ethylhexyl] benzene-1,2-dicarboxylate390.6243.590.35MOL001689Acacetin284.2834.970.24MOL002714Baicalein270.2533.520.21MOL002879Diop390.6243.590.39MOL002897Epiberberine336.3943.090.78MOL0029095,7,2,5-tetrahydroxy-8,6-dimethoxyflavone376.3433.820.45MOL002910Carthamidin288.2741.150.24MOL002913Dihydrobaicalin_qt272.2740.040.21MOL002914Eriodyctiol (flavanone)288.2741.350.24MOL002915Salvigenin328.3449.070.33MOL0029175,2ʹ,6ʹ-Trihydroxy-7,8-dimethoxyflavone330.3145.050.33MOL0029255,7,2ʹ,6ʹ-Tetrahydroxyflavone286.2537.010.24MOL002927Skullcapflavone II374.3769.510.44MOL002928Oroxylin a284.2841.370.23MOL002932Panicolin314.3176.260.29MOL0029335,7,4ʹ-Trihydroxy-8-methoxyflavone300.2836.560.27MOL002934Neobaicalein374.37104.30.44MOL002937Dihydrooroxylin A286.366.060.23MOL008206Moslosooflavone298.3144.090.25MOL01041511,13-Eicosadienoic acid, methyl ester322.5939.280.23MOL012266Rivularin344.3437.940.37MOL000490Petunidin317.2930.050.31MOL0045983,5,6,7-tetramethoxy-2-(3,4,5-trimethoxyphenyl)chromone432.4631.970.59BupleurumMOL002776Baicalin446.3940.120.75MOL001645Kaempferol308.5642.10.2MOL004718Linoleyl acetate412.7742.980.76MOL004653α-spinasterol426.546.060.66MOL004624Stigmasterol348.4847.720.53MOL004609Areapillin360.3448.960.41MOL000354Isorhamnetin316.2849.60.31MOL013187Cubebin356.457.130.64\n\n55 Active Compounds Predicted by OB and DL Among 4 Herbs in YOL\n\nKnown Therapeutic Targets of Chronic Pharyngitis (CP):\nIn the drug treatment of CP, the known therapeutic targets were mainly obtained from two sources: GeneCards database (https://www.genecards.org/) and CTD database (http://ctd.mdibl.org/). The data analysis was conducted in 5878 therapeutic targets for the treatment of CP, after the redundant entries were removed. Figure S1 shows the specific information about these known therapeutic targets.\n\nNetwork Construction and Analysis:\nTo understand the relationship between the herbs and chemical compounds that consist of YOL and its putative targets, and the therapeutic targets known for CP, Network Visualization was conducted using Cytoscape 3.6.1 software,8 and the degree between compounds and targets was analyzed.\n\nProtein-Protein Interaction (PPI) Data:\nPPI core network (PPICN) is used to study the relationship between chemical compounds and disease-associated protein molecules based on biochemistry, signal transduction and genetic networks.9 The differing ID types of the proteins were converted to UniProt IDs. In order to further understand how YOL and CP interact at the protein level, the selected targets in this study were uploaded on the online Venn diagram (http://bioinfogp.cnb.csic.es/tools/venny/index.html). Moreover, the genes at the intersection of the active compound and CP were selected and uploaded on STRING 10.5 (https://string-db.org) to obtain the PPI relationship.\n\nPathway Enrichment Analysis:\nThe data was imported in the format Gene Symbol into David 6.8 database (https://david.ncifcrf.gov/). Molecular function (CC), biological process (BP), and cellular function (MF) were selected, respectively. A pathway enrichment analysis was prospectively performed using biological process enrichment GO analysis and KEGG pathway analysis (http://www.genome.jp/kegg/) to clarify the pathways involving putative CP targets, and visualized by online mapping website Omicshare Tools (http://www.omicshare.com/tools/index.php/).\n\nStatistical Analysis:\nEach experiment was repeated at least in triplicate and data were shown as mean ± SD. Statistical difference in GO and KEGG analysis was performed using hypergeometric and Fisher's exact tests. P<0.05 was considered statistically significant.\n\nResults:\nScreening and Analysis of Active Compounds First, all the compounds related to four TCMs were retrieved by TCMSP. Second, according to the screening conditions of OB ≥30% and DL ≥0.18,10 23 active constituents of honeysuckle (17 predicted relevant targets), 36 of baicalensis (30 predicted relevant targets), 17 of bupleurum (13 predicted relevant targets), and no suitable compounds of cicada slough were obtained. Among them, baicalensis and honeysuckle contained two identical ingredients, while bupleurum and honeysuckle contained three identical components. Finally, a total of 55 active components were obtained for the data analysis after removing redundant entries (Table 1).\nFirst, all the compounds related to four TCMs were retrieved by TCMSP. Second, according to the screening conditions of OB ≥30% and DL ≥0.18,10 23 active constituents of honeysuckle (17 predicted relevant targets), 36 of baicalensis (30 predicted relevant targets), 17 of bupleurum (13 predicted relevant targets), and no suitable compounds of cicada slough were obtained. Among them, baicalensis and honeysuckle contained two identical ingredients, while bupleurum and honeysuckle contained three identical components. Finally, a total of 55 active components were obtained for the data analysis after removing redundant entries (Table 1).\nNetwork Analysis of “Active Component-Target” Interaction in YOL The compound-target network contained 175 nodes including 55 compound nodes and 113 target nodes, of which two had no corresponding target and 577 edges. As shown in Figure 1, blue represents compounds in bupleurum, red represents compounds in scutellaria, purple represents compounds in honeysuckle, green represents drug targets, each edge indicates the interaction between compounds and their targets, and two of the 55 compounds were excluded from the network construction. The degree value of a node represents the amount of connected routes in the network, and the larger the shape, the higher the degree value. The network in line with its topological properties showed that the nodes with higher degree of screening were analyzed. These nodes that connect compounds or targets act as hubs in the whole network and may predict key compounds or targets. The top five compounds ranked by degree were MOL000098-quercetin, MOL000422-Kaempferol, MOL000449-stigmasterol, MOL000358-quebrachol and MOL000006-luteolin, which could interact with 154, 68, 39, 32 and 28 target proteins, respectively. PTGS1, NCOA2, AR, PRSS1 and PPARG were the top five targets with the highest degree, which could interact with 42, 38, 29, 28 and 17 compounds, respectively.Figure 1Compounds-target network diagram. Blue represents compounds in bupleurum, Red represents compounds in scutellaria, Purple represents compounds in honeysuckle, Green represents drug targets. The size represents the degree value. The larger the shape, the larger the degree value.\nCompounds-target network diagram. Blue represents compounds in bupleurum, Red represents compounds in scutellaria, Purple represents compounds in honeysuckle, Green represents drug targets. The size represents the degree value. The larger the shape, the larger the degree value.\nThe compound-target network contained 175 nodes including 55 compound nodes and 113 target nodes, of which two had no corresponding target and 577 edges. As shown in Figure 1, blue represents compounds in bupleurum, red represents compounds in scutellaria, purple represents compounds in honeysuckle, green represents drug targets, each edge indicates the interaction between compounds and their targets, and two of the 55 compounds were excluded from the network construction. The degree value of a node represents the amount of connected routes in the network, and the larger the shape, the higher the degree value. The network in line with its topological properties showed that the nodes with higher degree of screening were analyzed. These nodes that connect compounds or targets act as hubs in the whole network and may predict key compounds or targets. The top five compounds ranked by degree were MOL000098-quercetin, MOL000422-Kaempferol, MOL000449-stigmasterol, MOL000358-quebrachol and MOL000006-luteolin, which could interact with 154, 68, 39, 32 and 28 target proteins, respectively. PTGS1, NCOA2, AR, PRSS1 and PPARG were the top five targets with the highest degree, which could interact with 42, 38, 29, 28 and 17 compounds, respectively.Figure 1Compounds-target network diagram. Blue represents compounds in bupleurum, Red represents compounds in scutellaria, Purple represents compounds in honeysuckle, Green represents drug targets. The size represents the degree value. The larger the shape, the larger the degree value.\nCompounds-target network diagram. Blue represents compounds in bupleurum, Red represents compounds in scutellaria, Purple represents compounds in honeysuckle, Green represents drug targets. The size represents the degree value. The larger the shape, the larger the degree value.\nThe Construction of Key Protein Network of YOL and CP Through the internationally recognized CTD and GeneCards disease databases, 5150 and 1247 pharyngitis-related targets were obtained, respectively. Through the Venny online tool, 30 targets of honeysuckle, scutellaria, bupleuri and pharyngitis were obtained, including VEGFA, IL6, ESR1, RELA, HIF1A, etc. (Figure 2A). Finally, STRING online tool was used to construct the PPI network interaction map of drug and disease. It contained 30 nodes representing proteins and 48 edges representing the interaction between proteins. The thicker the line, the higher is the correlation degree. Through the protein interaction network, we could further explore the therapeutic target and mechanism of YOL for CP (Figure 2B).Figure 2(A) Venn diagram of compounds-target network diagram; (B) Core network diagram of protein interaction between YOL and CP.\n(A) Venn diagram of compounds-target network diagram; (B) Core network diagram of protein interaction between YOL and CP.\nThrough the internationally recognized CTD and GeneCards disease databases, 5150 and 1247 pharyngitis-related targets were obtained, respectively. Through the Venny online tool, 30 targets of honeysuckle, scutellaria, bupleuri and pharyngitis were obtained, including VEGFA, IL6, ESR1, RELA, HIF1A, etc. (Figure 2A). Finally, STRING online tool was used to construct the PPI network interaction map of drug and disease. It contained 30 nodes representing proteins and 48 edges representing the interaction between proteins. The thicker the line, the higher is the correlation degree. Through the protein interaction network, we could further explore the therapeutic target and mechanism of YOL for CP (Figure 2B).Figure 2(A) Venn diagram of compounds-target network diagram; (B) Core network diagram of protein interaction between YOL and CP.\n(A) Venn diagram of compounds-target network diagram; (B) Core network diagram of protein interaction between YOL and CP.\nPharmacological Mechanisms of YOL Acting on CP A total of 30 common targets were obtained by DAVID online tool and 148 GO items were obtained (p < 0.05) by performing the functional enrichment analysis. There were 102 entries on biological process (BP), 34 entries on Molecular Function (MF), and 12 entries on cell composition (CC) (Figure 3A). A total of 46 signaling pathways were obtained by KEGG pathway enrichment screening (p<0.05), involving cancer, PI3K-AKT, hepatitis, proteoglycans, p53, HIF-1 signaling pathways, etc. (Figure 3B and Table S1).Figure 3(A) GO analysis function annotation diagram. Biological process (BP), molecular function (MF), and cell composition (CC). (B) Enriched KEGG pathways of YOL selected targets for CP.\n(A) GO analysis function annotation diagram. Biological process (BP), molecular function (MF), and cell composition (CC). (B) Enriched KEGG pathways of YOL selected targets for CP.\nA total of 30 common targets were obtained by DAVID online tool and 148 GO items were obtained (p < 0.05) by performing the functional enrichment analysis. There were 102 entries on biological process (BP), 34 entries on Molecular Function (MF), and 12 entries on cell composition (CC) (Figure 3A). A total of 46 signaling pathways were obtained by KEGG pathway enrichment screening (p<0.05), involving cancer, PI3K-AKT, hepatitis, proteoglycans, p53, HIF-1 signaling pathways, etc. (Figure 3B and Table S1).Figure 3(A) GO analysis function annotation diagram. Biological process (BP), molecular function (MF), and cell composition (CC). (B) Enriched KEGG pathways of YOL selected targets for CP.\n(A) GO analysis function annotation diagram. Biological process (BP), molecular function (MF), and cell composition (CC). (B) Enriched KEGG pathways of YOL selected targets for CP.\nExperimental Verification of Predicted Results In order to experimentally demonstrate the predictions about the molecular mechanism of YOL, we chose two ingredients for testing (Figure 4), based on their composition score, the group to which they belong, and their herbal origin to examine how ingredients from different herbs affect the same proteins. The effects of these compounds on the expression levels of related proteins were analyzed by Western blot. Macrophages secrete several inflammatory cytokines, which play significant roles in inflammation. Signal transduction and transcriptional activator 3 (STAT3), nuclear factor kappa B (NF-kB), and phosphatidylinositol 3-kinase (PI3K)/Akt pathways are critical inflammatory pathways. LPS can significantly increase the production of many proinflammatory factors, including inducible NO synthase (iNOS) and cyclooxygenase-2 (COX-2) in macrophages. After pretreatment of macrophages with YOL, luteolin and baicalein, the protein expression levels of iNOS and COX-2 induced by LPS were significantly inhibited in a dose-dependent manner (Figure 4A). The related protein changes were consistent with iNOS and COX-2 inflammation protein expression (Figure 4B–D).Figure 4The YOL inhibits PI3K/p-AKT/p-Stats pathway in RAW 264.7 cells. Representative Western blot images show the relative expressions of iNOS, COX-2 (A), p-p65, p65 (B), PI3K, p-AKT, AKT (C), p-Stats, Stat3 (D) in the groups. The protein levels (normalized) of iNOS, COX2 (E), and p-p65, p65, PI3K, p-AKT, AKT, p-Stats, Stat3 (F) in cells. All the values are presented as mean ± SD. #P < 0.05 and ##P < 0.01 vs control group. *P < 0.05 and **P < 0.01 vs LPS group.\nThe YOL inhibits PI3K/p-AKT/p-Stats pathway in RAW 264.7 cells. Representative Western blot images show the relative expressions of iNOS, COX-2 (A), p-p65, p65 (B), PI3K, p-AKT, AKT (C), p-Stats, Stat3 (D) in the groups. The protein levels (normalized) of iNOS, COX2 (E), and p-p65, p65, PI3K, p-AKT, AKT, p-Stats, Stat3 (F) in cells. All the values are presented as mean ± SD. #P < 0.05 and ##P < 0.01 vs control group. *P < 0.05 and **P < 0.01 vs LPS group.\nIn order to experimentally demonstrate the predictions about the molecular mechanism of YOL, we chose two ingredients for testing (Figure 4), based on their composition score, the group to which they belong, and their herbal origin to examine how ingredients from different herbs affect the same proteins. The effects of these compounds on the expression levels of related proteins were analyzed by Western blot. Macrophages secrete several inflammatory cytokines, which play significant roles in inflammation. Signal transduction and transcriptional activator 3 (STAT3), nuclear factor kappa B (NF-kB), and phosphatidylinositol 3-kinase (PI3K)/Akt pathways are critical inflammatory pathways. LPS can significantly increase the production of many proinflammatory factors, including inducible NO synthase (iNOS) and cyclooxygenase-2 (COX-2) in macrophages. After pretreatment of macrophages with YOL, luteolin and baicalein, the protein expression levels of iNOS and COX-2 induced by LPS were significantly inhibited in a dose-dependent manner (Figure 4A). The related protein changes were consistent with iNOS and COX-2 inflammation protein expression (Figure 4B–D).Figure 4The YOL inhibits PI3K/p-AKT/p-Stats pathway in RAW 264.7 cells. Representative Western blot images show the relative expressions of iNOS, COX-2 (A), p-p65, p65 (B), PI3K, p-AKT, AKT (C), p-Stats, Stat3 (D) in the groups. The protein levels (normalized) of iNOS, COX2 (E), and p-p65, p65, PI3K, p-AKT, AKT, p-Stats, Stat3 (F) in cells. All the values are presented as mean ± SD. #P < 0.05 and ##P < 0.01 vs control group. *P < 0.05 and **P < 0.01 vs LPS group.\nThe YOL inhibits PI3K/p-AKT/p-Stats pathway in RAW 264.7 cells. Representative Western blot images show the relative expressions of iNOS, COX-2 (A), p-p65, p65 (B), PI3K, p-AKT, AKT (C), p-Stats, Stat3 (D) in the groups. The protein levels (normalized) of iNOS, COX2 (E), and p-p65, p65, PI3K, p-AKT, AKT, p-Stats, Stat3 (F) in cells. All the values are presented as mean ± SD. #P < 0.05 and ##P < 0.01 vs control group. *P < 0.05 and **P < 0.01 vs LPS group.\n\nScreening and Analysis of Active Compounds:\nFirst, all the compounds related to four TCMs were retrieved by TCMSP. Second, according to the screening conditions of OB ≥30% and DL ≥0.18,10 23 active constituents of honeysuckle (17 predicted relevant targets), 36 of baicalensis (30 predicted relevant targets), 17 of bupleurum (13 predicted relevant targets), and no suitable compounds of cicada slough were obtained. Among them, baicalensis and honeysuckle contained two identical ingredients, while bupleurum and honeysuckle contained three identical components. Finally, a total of 55 active components were obtained for the data analysis after removing redundant entries (Table 1).\n\nNetwork Analysis of “Active Component-Target” Interaction in YOL:\nThe compound-target network contained 175 nodes including 55 compound nodes and 113 target nodes, of which two had no corresponding target and 577 edges. As shown in Figure 1, blue represents compounds in bupleurum, red represents compounds in scutellaria, purple represents compounds in honeysuckle, green represents drug targets, each edge indicates the interaction between compounds and their targets, and two of the 55 compounds were excluded from the network construction. The degree value of a node represents the amount of connected routes in the network, and the larger the shape, the higher the degree value. The network in line with its topological properties showed that the nodes with higher degree of screening were analyzed. These nodes that connect compounds or targets act as hubs in the whole network and may predict key compounds or targets. The top five compounds ranked by degree were MOL000098-quercetin, MOL000422-Kaempferol, MOL000449-stigmasterol, MOL000358-quebrachol and MOL000006-luteolin, which could interact with 154, 68, 39, 32 and 28 target proteins, respectively. PTGS1, NCOA2, AR, PRSS1 and PPARG were the top five targets with the highest degree, which could interact with 42, 38, 29, 28 and 17 compounds, respectively.Figure 1Compounds-target network diagram. Blue represents compounds in bupleurum, Red represents compounds in scutellaria, Purple represents compounds in honeysuckle, Green represents drug targets. The size represents the degree value. The larger the shape, the larger the degree value.\nCompounds-target network diagram. Blue represents compounds in bupleurum, Red represents compounds in scutellaria, Purple represents compounds in honeysuckle, Green represents drug targets. The size represents the degree value. The larger the shape, the larger the degree value.\n\nThe Construction of Key Protein Network of YOL and CP:\nThrough the internationally recognized CTD and GeneCards disease databases, 5150 and 1247 pharyngitis-related targets were obtained, respectively. Through the Venny online tool, 30 targets of honeysuckle, scutellaria, bupleuri and pharyngitis were obtained, including VEGFA, IL6, ESR1, RELA, HIF1A, etc. (Figure 2A). Finally, STRING online tool was used to construct the PPI network interaction map of drug and disease. It contained 30 nodes representing proteins and 48 edges representing the interaction between proteins. The thicker the line, the higher is the correlation degree. Through the protein interaction network, we could further explore the therapeutic target and mechanism of YOL for CP (Figure 2B).Figure 2(A) Venn diagram of compounds-target network diagram; (B) Core network diagram of protein interaction between YOL and CP.\n(A) Venn diagram of compounds-target network diagram; (B) Core network diagram of protein interaction between YOL and CP.\n\nPharmacological Mechanisms of YOL Acting on CP:\nA total of 30 common targets were obtained by DAVID online tool and 148 GO items were obtained (p < 0.05) by performing the functional enrichment analysis. There were 102 entries on biological process (BP), 34 entries on Molecular Function (MF), and 12 entries on cell composition (CC) (Figure 3A). A total of 46 signaling pathways were obtained by KEGG pathway enrichment screening (p<0.05), involving cancer, PI3K-AKT, hepatitis, proteoglycans, p53, HIF-1 signaling pathways, etc. (Figure 3B and Table S1).Figure 3(A) GO analysis function annotation diagram. Biological process (BP), molecular function (MF), and cell composition (CC). (B) Enriched KEGG pathways of YOL selected targets for CP.\n(A) GO analysis function annotation diagram. Biological process (BP), molecular function (MF), and cell composition (CC). (B) Enriched KEGG pathways of YOL selected targets for CP.\n\nExperimental Verification of Predicted Results:\nIn order to experimentally demonstrate the predictions about the molecular mechanism of YOL, we chose two ingredients for testing (Figure 4), based on their composition score, the group to which they belong, and their herbal origin to examine how ingredients from different herbs affect the same proteins. The effects of these compounds on the expression levels of related proteins were analyzed by Western blot. Macrophages secrete several inflammatory cytokines, which play significant roles in inflammation. Signal transduction and transcriptional activator 3 (STAT3), nuclear factor kappa B (NF-kB), and phosphatidylinositol 3-kinase (PI3K)/Akt pathways are critical inflammatory pathways. LPS can significantly increase the production of many proinflammatory factors, including inducible NO synthase (iNOS) and cyclooxygenase-2 (COX-2) in macrophages. After pretreatment of macrophages with YOL, luteolin and baicalein, the protein expression levels of iNOS and COX-2 induced by LPS were significantly inhibited in a dose-dependent manner (Figure 4A). The related protein changes were consistent with iNOS and COX-2 inflammation protein expression (Figure 4B–D).Figure 4The YOL inhibits PI3K/p-AKT/p-Stats pathway in RAW 264.7 cells. Representative Western blot images show the relative expressions of iNOS, COX-2 (A), p-p65, p65 (B), PI3K, p-AKT, AKT (C), p-Stats, Stat3 (D) in the groups. The protein levels (normalized) of iNOS, COX2 (E), and p-p65, p65, PI3K, p-AKT, AKT, p-Stats, Stat3 (F) in cells. All the values are presented as mean ± SD. #P < 0.05 and ##P < 0.01 vs control group. *P < 0.05 and **P < 0.01 vs LPS group.\nThe YOL inhibits PI3K/p-AKT/p-Stats pathway in RAW 264.7 cells. Representative Western blot images show the relative expressions of iNOS, COX-2 (A), p-p65, p65 (B), PI3K, p-AKT, AKT (C), p-Stats, Stat3 (D) in the groups. The protein levels (normalized) of iNOS, COX2 (E), and p-p65, p65, PI3K, p-AKT, AKT, p-Stats, Stat3 (F) in cells. All the values are presented as mean ± SD. #P < 0.05 and ##P < 0.01 vs control group. *P < 0.05 and **P < 0.01 vs LPS group.\n\nDiscussion:\nMany diseases, including cancer and chronic inflammation, are known to be regulated by multiple signaling pathways. TCMs are considered as multi-component and multi-target therapeutic drugs, which is in accordance with the methodologies of network pharmacology. The holistic view of TCM considers the human body as a complex biological network system, which is connected with network pharmacology. By combining the network pharmacology with the holistic view of TCM, the disease-target-drug network is obtained. The network shows that different components of TCM may act on one target, and different targets may act on the same pathway. It is also possible that the same component of TCM acts on different targets, and the same target acts on different pathways. The multi-component, multi-target and multi-pathway of TCM can be clearly visualized through network pharmacology, which resolves many difficulties encountered in the study of TCM.13–15 CP is the diffuse inflammation of pharyngeal mucosa, submucosa and lymph tissues. Clinically, CP is mainly manifested as pharyngeal discomfort (foreign body sensation, burning sensation, stimulation sensation, etc.), pharyngeal itching, cough, etc. Pharyngitis belongs to the category of “Fenghou Bi” in TCM. Pharyngitis pertains to the category of “wind throat arthralgia” in Chinese medicine, and is often caused by evil heat entering the body, and heat in the lungs and stomach. YOL is an oral TCM preparation, which is further optimized on the basis of Jinchan oral liquid developed by Children’s Hospital of Suzhou University.5,6 However, there is a lack of research on its active ingredients and its mechanism of action.\nIn this study, 30 potential targets, 102 biological processes, 12 molecular functions, 34 cell components, and 46 KEGG pathways were obtained. The active ingredients in YOL can treat CP through multiple targets and pathways. To elucidate the biological pathways that may be affected by YOL, a significantly overexpressed KEGG pathway was identified. The results showed VEGFA, IL6, ESR1 and RELA as the key targets and the involvement of PI3K-Akt, p53 and HIF-1 signaling pathways. TCM aims to restore a patient’s health through the use of a TCM prescription, which is usually composed of two or more TCMs in optimal proportions.\nYOL, for example, consists of four TCMs. As expected, most of the selected active ingredients are directly or indirectly associated with inflammation. In a previous study, neochlorogenic acid was demonstrated to inhibit LPS-activated inflammatory responses through up-regulation of Nrf2/HO-1 and AMPK pathways.16 The decoction extract from six natural herbs, which contain honeysuckle, exhibits anti-inflammatory and immunomodulatory effects by targeting NF-κB/IL-6/STAT3 signaling.17 Luteolin is abundant in honeysuckle, which alleviates CP by inhibiting the NF-κB pathway and anti-inflammatory polarization of M1 macrophages.11 Protective effects of baicalein on liver injury in mice induced by multi-microbial septicemia were found to be based on inhibiting inflammation.12 Network pharmacological studies have shown that the main active components of honeysuckle may play a role through inflammation-related proteins such as HSP90AA1, HSP90AB1, ESR1, PTGS2, TERT and PPARG, and especially heat shock protein HSP90A (HSP90AA1).18 Flavonoids such as baicalin and wogonin alone and in combination have anti-inflammatory activity in Scutellaria.19 Network pharmacological studies have also shown that alkaloids such as coptisine and epiphone in Scutellaria and components such as dihydrokaempferol, and rographolide flavonoids also show anti-inflammatory activity. MAPK14, TNFRSF1A, EGFR and SELE are the primary anti-inflammatory components of Scutellaria. The anti-inflammatory active components in Scutellaria can directly affect MAPK14 and EGFR to produce anti-inflammatory effects, and can also indirectly affect other targets to exert anti-inflammatory effects. As one of the four p38 MAPKs, MAPK14 plays a vital role in the cellular cascade induced by extracellular stimuli such as proinflammatory cytokines or physical stimuli.20 Studies predicted that the pharmacodynamic basis of Bupleurum is mainly saponins, flavonoids, volatile oils, fatty acids and other components. Flavonoids mainly act on PI3K-Akt, NF-kB and other pathways, participate in regulating inflammatory factors, estrogen signal transduction and other physiological processes. Bupleurum-Scutellaria drug pair, first used in small bupleurum decoction in Treatise on Febrile Diseases, is an important part of Bupleurum prescription. Bupleurum is bitter, flat and clear, and can disperse the stagnation of bile fire. Scutellaria has a bitter and cold taste, and can clear internal heat. Both of them must be used together to regulate the liver and gallbladder, and clear the dampness and heat of internal accumulation, which are commonly used to treat fever, pharyngitis, and respiratory diseases.21 Cicada slough can evacuate wind and heat, and clear the throat. Clinically, it is common in classical prescriptions such as Sheng jiang San and Pharyngitis San. However, the active components of Cicada slough did not satisfy the prediction requirements in this research.\nYOL is composed of honeysuckle, scutellaria, bupleurum and cicada slough. According to TCM theory, bupleurum is bitter and flat, enters the liver and gallbladder meridian, penetrates and clears Shaoyang, and can drain the stagnation of gas. Scutellaria is bitter and cold, and clears the heat of Shaoyang. The actions of Bupleurum facilitate the clear discharge of Scutellaria, the two are compatible, and achieve the goal of reconciliation and Shaoyang. Honeysuckle cools through the surface, has heat-clearing and detoxifying effects, and also has the effect of fragrant obscenity. Cicada slough is light floating, clears the coke gas, shows honeysuckle compatibility, understanding the table evil, and is combined with the “treatment of the coke such as feather.” The combination of the four drugs, Xuan and descent together, clear the evil, thereby clearing heat and detoxification, warming the evil without hiding, scattered outside and recovered. Therefore, the treatment of CP with YOL can control the pharyngitis, regulate the balance of Yin and Yang, strengthen the foundation and improve the immunity of the body.\n\nConclusion:\nThis study used the network pharmacology platform to probe the YOL treatment of CP by multi-target analysis. The results showed that YOL could treat CP by acting on related targets, which were consistent with the reported literature. In addition, we have verified that YOL and its two monomer compounds, Luteolin and Baicalein inhibited activation of the NF-kB, Stat3 and PI3K-Akt pathways. Although the potential advantages of the “network target, multi-component” strategy of network pharmacology are obvious, the content of TCM ingredients is usually ignored in the research of network pharmacology of Chinese medicine, and the influence of content and concentration on the efficacy cannot be ignored. The predicted targets, which have not been discussed, may provide clues for further research on the mechanism of YOL.\n\n"},"whole_article_abstract":{"kind":"string","value":"Background:\nYinqin oral liquid (YOL) has curative effect for upper respiratory tract infections, especially for chronic pharyngitis (CP). Since the traditional Chinese herbal formulae are complicated, the pharmacological mechanism of YOL remains unclear. The aim of this work was to explore the active ingredients and mechanisms of YOL against CP.\n\nMethods:\nFirst, the profile of putative target of YOL was predicted based on structural and functional similarities of all available YOL components, which were obtained from the Drug Bank database, to the known drugs using TCMSP. The chemical constituents and targets of honeysuckle, scutellaria, bupleurum and cicada were searched by TCMSP, CTD, GeneCards and other databases were used to query the CP-related genes, which were searched by UniProt database. Thereafter, the interactions network between compounds and overlapping genes was constructed, visualized, and analyzed by Cytoscape software. Finally, pathway enrichment analysis of overlapping genes was carried out on Database for Annotation, Visualization, and Integrated Discovery (DAVID) platform.\n\nResults:\nThe pathway enrichment analysis showed 55 compounds and 113 corresponding targets in the compound-target network, and the key targets involved PTGS1, ESR2, GSK3β, NCOA2, ESR1. The PPI core network contained 30 proteins, including VEGFA, IL6, ESR1, RELA and HIF1A. A total of 148 GO items were obtained (p<0.05), 102 entries on biological process (BP), 34 entries on biological process (BP) and 12 entries on cell composition (CC) were included. A total of 46 signaling pathways were obtained by KEGG pathway enrichment screening (p<0.05), involving cancer, PI3K-AKT, hepatitis, proteoglycans, p53, HIF-1 signaling pathways.\n\nConclusions:\nThese results collectively indicate YOL (including the main ingredients luteolin and baicalein) as a highly effective therapeutic agent for anti-inflammation, through the NF-kB pathway."},"background_conclusion_text":{"kind":"string","value":"Introduction:\nChronic pharyngitis (CP) is a very common disease associated with chronic inflammation involving pharyngeal mucosa, submucosal and lymphatic tissues. Clinically, CP is mainly manifested as pharyngeal discomfort (foreign body sensation, burning sensation, irritation, etc.), with occasional pharyngeal itch, cough, etc., which belongs to the category of “slow throat arthralgia” in the traditional Chinese medicine (TCM).1 CP has a high incidence and accounts for 10–12% of all pharyngeal diseases, with a long disease course, recurrence and is difficult to cure. At present, western medicine uses antibiotics supplemented by hormone preparations, such as dexamethasone and antiviral drugs, for the treatment of pharyngitis. These treatment methods have drawbacks such as narrow therapeutic spectrum, recurrence, poor tolerance and major toxic and side effects. Hence, developing more effective therapeutic strategies against CP, and reducing the occurrence of side effects from the current therapeutic options is of great clinical importance.\nTCM is a major component of complementary and alternative medicine. Owing to its excellent clinical efficacy, TCM is a research hotspot worldwide. Dialectical theory of TCM is used to treat acute and chronic pharyngitis and has unique advantages such as minor toxic side effects, obvious curative effect and treatment of both symptoms and root causes.2–4 The pharmacological effects of TCM herbal formulae play an important role in their appropriate application. However, the complex nature of herbal formulae has impeded this understanding. Numerous chemical ingredients involving multiple potential targets are present in an herbal formula. It is not adequate to explain the effects produced by a whole herbal formula if we individually consider every single ingredient in it. Yinqin oral liquid (YOL) is composed of extracts of honeysuckle, scutellaria, bupleurum and cicada. YOL has the effects of relieving wind, muscle and clearing heat, which is mainly used for cold, fever, aversion to cold, pharyngeal pain and other upper respiratory tract infections caused by wind fever and wind-cold. Long-term clinical observation has proven its effect in relieving pharyngeal pain and treating hand-foot-mouth disease. YOL contains several chemical compounds, which regulate diverse targets, and thus precisely determine the pharmacological mechanisms involved in its therapeutic actions. In addition, there are several challenges in the relationships between the herbs and diseases.5,6\nNetwork pharmacology is based on the theory of system biology. As a new subject, it selects specific signal nodes to design multi-target drug molecules through the network analysis of system biology. It is possible to study both the active components and the potential gene targets from TCM because of the establishment of network pharmacology and bioinformatics. This study predicted and analyzed the active components and target of YOL using network pharmacology, to explore the rationality of its formula and the scientific nature of treating CP, and further explore the pharmacological mechanisms of YOL on CP.\n\nConclusion:\nThis study used the network pharmacology platform to probe the YOL treatment of CP by multi-target analysis. The results showed that YOL could treat CP by acting on related targets, which were consistent with the reported literature. In addition, we have verified that YOL and its two monomer compounds, Luteolin and Baicalein inhibited activation of the NF-kB, Stat3 and PI3K-Akt pathways. Although the potential advantages of the “network target, multi-component” strategy of network pharmacology are obvious, the content of TCM ingredients is usually ignored in the research of network pharmacology of Chinese medicine, and the influence of content and concentration on the efficacy cannot be ignored. The predicted targets, which have not been discussed, may provide clues for further research on the mechanism of YOL."},"background_conclusion_abstract":{"kind":"string","value":"Background:\nYinqin oral liquid (YOL) has curative effect for upper respiratory tract infections, especially for chronic pharyngitis (CP). Since the traditional Chinese herbal formulae are complicated, the pharmacological mechanism of YOL remains unclear. The aim of this work was to explore the active ingredients and mechanisms of YOL against CP.\n\nMethods:\nFirst, the profile of putative target of YOL was predicted based on structural and functional similarities of all available YOL components, which were obtained from the Drug Bank database, to the known drugs using TCMSP. The chemical constituents and targets of honeysuckle, scutellaria, bupleurum and cicada were searched by TCMSP, CTD, GeneCards and other databases were used to query the CP-related genes, which were searched by UniProt database. Thereafter, the interactions network between compounds and overlapping genes was constructed, visualized, and analyzed by Cytoscape software. Finally, pathway enrichment analysis of overlapping genes was carried out on Database for Annotation, Visualization, and Integrated Discovery (DAVID) platform.\n\nResults:\nThe pathway enrichment analysis showed 55 compounds and 113 corresponding targets in the compound-target network, and the key targets involved PTGS1, ESR2, GSK3β, NCOA2, ESR1. The PPI core network contained 30 proteins, including VEGFA, IL6, ESR1, RELA and HIF1A. A total of 148 GO items were obtained (p<0.05), 102 entries on biological process (BP), 34 entries on biological process (BP) and 12 entries on cell composition (CC) were included. A total of 46 signaling pathways were obtained by KEGG pathway enrichment screening (p<0.05), involving cancer, PI3K-AKT, hepatitis, proteoglycans, p53, HIF-1 signaling pathways.\n\nConclusions:\nThese results collectively indicate YOL (including the main ingredients luteolin and baicalein) as a highly effective therapeutic agent for anti-inflammation, through the NF-kB pathway."},"whole_article_text_length":{"kind":"number","value":8336,"string":"8,336"},"whole_article_abstract_length":{"kind":"number","value":358,"string":"358"},"other_sections_lengths":{"kind":"list like","value":[1644,185,77,198,64,45,104,81,40,2649,113,329,179,188,491,1126,150],"string":"[\n 1644,\n 185,\n 77,\n 198,\n 64,\n 45,\n 104,\n 81,\n 40,\n 2649,\n 113,\n 329,\n 179,\n 188,\n 491,\n 1126,\n 150\n]"},"num_sections":{"kind":"number","value":18,"string":"18"},"most_frequent_words":{"kind":"list like","value":["targets","compounds","network","yol","cp","represents","akt","figure","analysis","target"],"string":"[\n \"targets\",\n \"compounds\",\n \"network\",\n \"yol\",\n \"cp\",\n \"represents\",\n \"akt\",\n \"figure\",\n \"analysis\",\n \"target\"\n]"},"keybert_topics":{"kind":"list like","value":["tcm pharyngitis","pharyngitis cp drug","chronic pharyngitis","pharyngitis treatment methods","targets chronic pharyngitis"],"string":"[\n \"tcm pharyngitis\",\n \"pharyngitis cp drug\",\n \"chronic pharyngitis\",\n \"pharyngitis treatment methods\",\n \"targets chronic pharyngitis\"\n]"},"annotated_base_background_abstract_prompt":{"kind":"string","value":""},"annotated_base_methods_abstract_prompt":{"kind":"null"},"annotated_base_results_abstract_prompt":{"kind":"null"},"annotated_base_conclusions_abstract_prompt":{"kind":"string","value":""},"annotated_base_whole_article_abstract_prompt":{"kind":"string","value":""},"annotated_base_background_conclusion_abstract_prompt":{"kind":"string","value":""},"annotated_keywords_background_abstract_prompt":{"kind":"string","value":"[CONTENT] Yinqin oral liquid | chronic pharyngitis | network pharmacology | NF-kB | multi-target analysis [SUMMARY]"},"annotated_keywords_methods_abstract_prompt":{"kind":"null"},"annotated_keywords_results_abstract_prompt":{"kind":"null"},"annotated_keywords_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] Yinqin oral liquid | chronic pharyngitis | network pharmacology | NF-kB | multi-target analysis [SUMMARY]"},"annotated_keywords_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] Yinqin oral liquid | chronic pharyngitis | network pharmacology | NF-kB | multi-target analysis [SUMMARY]"},"annotated_keywords_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] Yinqin oral liquid | chronic pharyngitis | network pharmacology | NF-kB | multi-target analysis [SUMMARY]"},"annotated_mesh_background_abstract_prompt":{"kind":"string","value":"[CONTENT] Administration, Oral | Animals | Anti-Inflammatory Agents | Chronic Disease | Databases, Factual | Drugs, Chinese Herbal | Flavanones | Luteolin | Mice | NF-kappa B | Network Pharmacology | Pharyngitis | RAW 264.7 Cells [SUMMARY]"},"annotated_mesh_methods_abstract_prompt":{"kind":"null"},"annotated_mesh_results_abstract_prompt":{"kind":"null"},"annotated_mesh_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] Administration, Oral | Animals | Anti-Inflammatory Agents | Chronic Disease | Databases, Factual | Drugs, Chinese Herbal | Flavanones | Luteolin | Mice | NF-kappa B | Network Pharmacology | Pharyngitis | RAW 264.7 Cells [SUMMARY]"},"annotated_mesh_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] Administration, Oral | Animals | Anti-Inflammatory Agents | Chronic Disease | Databases, Factual | Drugs, Chinese Herbal | Flavanones | Luteolin | Mice | NF-kappa B | Network Pharmacology | Pharyngitis | RAW 264.7 Cells [SUMMARY]"},"annotated_mesh_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] Administration, Oral | Animals | Anti-Inflammatory Agents | Chronic Disease | Databases, Factual | Drugs, Chinese Herbal | Flavanones | Luteolin | Mice | NF-kappa B | Network Pharmacology | Pharyngitis | RAW 264.7 Cells [SUMMARY]"},"annotated_keybert_background_abstract_prompt":{"kind":"string","value":"[CONTENT] tcm pharyngitis | pharyngitis cp drug | chronic pharyngitis | pharyngitis treatment methods | targets chronic pharyngitis [SUMMARY]"},"annotated_keybert_methods_abstract_prompt":{"kind":"null"},"annotated_keybert_results_abstract_prompt":{"kind":"null"},"annotated_keybert_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] tcm pharyngitis | pharyngitis cp drug | chronic pharyngitis | pharyngitis treatment methods | targets chronic pharyngitis [SUMMARY]"},"annotated_keybert_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] tcm pharyngitis | pharyngitis cp drug | chronic pharyngitis | pharyngitis treatment methods | targets chronic pharyngitis [SUMMARY]"},"annotated_keybert_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] tcm pharyngitis | pharyngitis cp drug | chronic pharyngitis | pharyngitis treatment methods | targets chronic pharyngitis [SUMMARY]"},"annotated_most_frequent_background_abstract_prompt":{"kind":"string","value":"[CONTENT] targets | compounds | network | yol | cp | represents | akt | figure | analysis | target [SUMMARY]"},"annotated_most_frequent_methods_abstract_prompt":{"kind":"null"},"annotated_most_frequent_results_abstract_prompt":{"kind":"null"},"annotated_most_frequent_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] targets | compounds | network | yol | cp | represents | akt | figure | analysis | target [SUMMARY]"},"annotated_most_frequent_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] targets | compounds | network | yol | cp | represents | akt | figure | analysis | target [SUMMARY]"},"annotated_most_frequent_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] targets | compounds | network | yol | cp | represents | akt | figure | analysis | target [SUMMARY]"},"annotated_tf_idf_background_abstract_prompt":{"kind":"string","value":"[CONTENT] pharyngeal | tcm | effects | herbal | clinical | formula | cp | cold | wind | pharmacological [SUMMARY]"},"annotated_tf_idf_methods_abstract_prompt":{"kind":"null"},"annotated_tf_idf_results_abstract_prompt":{"kind":"null"},"annotated_tf_idf_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] pharmacology | network pharmacology | network | content | ignored | yol | multi | research | target | ingredients usually ignored research [SUMMARY]"},"annotated_tf_idf_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] targets | network | compounds | yol | cp | represents | analysis | akt | target | figure [SUMMARY]"},"annotated_tf_idf_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] targets | network | compounds | yol | cp | represents | analysis | akt | target | figure [SUMMARY]"},"annotated_entity_plan_background_abstract_prompt":{"kind":"string","value":"[CONTENT] Yinqin | YOL ||| Chinese | YOL ||| YOL [SUMMARY]"},"annotated_entity_plan_methods_abstract_prompt":{"kind":"null"},"annotated_entity_plan_results_abstract_prompt":{"kind":"null"},"annotated_entity_plan_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] YOL | luteolin | baicalein [SUMMARY]"},"annotated_entity_plan_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] YOL ||| Chinese | YOL ||| YOL | CP. ||| First | YOL | YOL | the Drug Bank | TCMSP ||| cicada | TCMSP | CTD | GeneCards | UniProt ||| Cytoscape ||| Database for Annotation | Integrated Discovery ||| ||| 55 | 113 | PTGS1 | ESR2 | NCOA2 ||| 30 | VEGFA | 148 | p<0.05 | 102 | 34 | 12 | CC ||| 46 | KEGG | p<0.05 | proteoglycans ||| YOL | luteolin | baicalein [SUMMARY]"},"annotated_entity_plan_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] YOL ||| Chinese | YOL ||| YOL | CP. ||| First | YOL | YOL | the Drug Bank | TCMSP ||| cicada | TCMSP | CTD | GeneCards | UniProt ||| Cytoscape ||| Database for Annotation | Integrated Discovery ||| ||| 55 | 113 | PTGS1 | ESR2 | NCOA2 ||| 30 | VEGFA | 148 | p<0.05 | 102 | 34 | 12 | CC ||| 46 | KEGG | p<0.05 | proteoglycans ||| YOL | luteolin | baicalein [SUMMARY]"}}},{"rowIdx":286,"cells":{"title":{"kind":"string","value":"Work ability and associated factors in people living with human T-cell leukemia virus type 1."},"pmid":{"kind":"string","value":"35946625"},"background_abstract":{"kind":"string","value":"Infection with the human T-lymphotropic virus type 1 (HTLV-1) affects an estimated 10-15 million people worldwide. However, knowledge of the impact of HTLV-1 infection on work ability is lacking. This study aimed to measure the frequency and identify factors associated with poor work ability in patients living with HTLV-1."},"background_abstract_label":{"kind":"string","value":"BACKGROUND"},"methods_abstract":{"kind":"string","value":"This cross-sectional study included 207 individuals infected with HTLV-1 who attended the University Hospital in Salvador, Bahia, Brazil. HTLV-1 antibodies were detected in the participants' blood by enzyme-linked immunosorbent assay (ELISA) and confirmed by western blotting. Participants answered a questionnaire on sociodemographic data, personal habits, clinical data, health-related quality of life, and work ability, evaluated using the work ability index questionnaire. A Poisson regression model with a robust variance estimate was used to identify the factors associated with the prevalence of poor work ability."},"methods_abstract_label":{"kind":"string","value":"METHODS"},"results_abstract":{"kind":"string","value":"Patients mean age was 55.2, ranging from 19 to 84 years, 73.0% were females, 100% had monthly family income less than US$ 394, and 33.8% presented HTLV-1 associated myelopathy/tropical spastic paraparesis (HAM/TSP). No individual was classified as having excellent work ability. Poor work ability prevalence was strongly associated (prevalence ratio; 95% confidence interval [CI]) with sedentarism (1.30; 1.03-1.65), neurological symptoms (1.25; 1.02-1.52), and low physical (0.95; 0.94-0.96) and mental (0.98; 0.97-0.99) component summaries of health-related quality of life."},"results_abstract_label":{"kind":"string","value":"RESULTS"},"conclusions_abstract":{"kind":"string","value":"Poor work ability among people living with HTLV-1 is associated with sedentarism, neurologic symptoms, and low health-related quality of life."},"conclusions_abstract_label":{"kind":"string","value":"CONCLUSIONS"},"mesh_descriptor_names":{"kind":"list like","value":["Cross-Sectional Studies","Female","HTLV-I Infections","Human T-lymphotropic virus 1","Humans","Leukemia, T-Cell","Male","Middle Aged","Paraparesis, Tropical Spastic","Quality of Life","Work Capacity Evaluation"],"string":"[\n \"Cross-Sectional Studies\",\n \"Female\",\n \"HTLV-I Infections\",\n \"Human T-lymphotropic virus 1\",\n \"Humans\",\n \"Leukemia, T-Cell\",\n \"Male\",\n \"Middle Aged\",\n \"Paraparesis, Tropical Spastic\",\n \"Quality of Life\",\n \"Work Capacity Evaluation\"\n]"},"pmcid":{"kind":"string","value":"9344948"},"background_title":{"kind":"string","value":"INTRODUCTION"},"background_text":{"kind":"string","value":"Human T-lymphotropic virus type 1 (HTLV-1) is a type C retrovirus that was first\nisolated and identified from a patient with cutaneous T-cell malignancy in 1980\n1\n. It is transmitted through breastfeeding, sexual contact, blood transfusion,\nand sharing syringes and needles\n2\n. \nThe prevalence of HTLV-1 infection is poorly known; however, it is estimated that it\naffects 10-15 million people worldwide\n3\n. Clusters of high prevalence were found in nearby areas with negligible\nprevalence. HTLV-1 infection is endemic in southwestern Japan, sub-Saharan Africa,\nSouth America, and the Caribbean area, with foci in the Middle East and\nAustralo-Melanesia\n4\n. HTLV-1 infection is frequent in Brazil\n5\n, in the State of Bahia\n6\n, particularly in its capital, Salvador city, with an estimated prevalence of\n1.8%\n7\n. \nMost people (approximately 95%) infected with HTLV-1 remain asymptomatic\n8\n. Individuals with HTLV-1 have a 57% greater risk of death due to any cause\nthan those HTLV-1-negative individuals. HTLV-1 is associated with increased odds of\nseborrheic dermatitis and Sjogren’s syndrome and a lower relative risk of gastric\ncancer\n9\n. HTLV-1 can cause two severe diseases, adult T-cell leukemia/lymphoma (ATLL)\nand HTLV-1-associated myelopathy/tropical spastic paraparesis (HAM/TSP). It is\nestimated that 0.25-3% of people infected with HTLV-1 will develop HAM/TSP during\ntheir lifetime. HAM/TSP mainly occurs in adulthood. HAM/TSP has an insidious onset\nthat progressively evolves to neurological features, such as spasticity or\nhyperreflexia of the lower extremities, lower extremity muscle weakness, and urinary\nbladder disturbances. Approximately 50% of cases present with sensory disturbances\nand low back pain. HAM/TSP can be associated with other HTLV-1 associated symptoms\nlike uveitis, myositis, and infective dermatitis\n10\n. Patients with HAM/TSP have difficulty performing daily routine activities,\nparticularly because of a disturbed gait that compromises physical, emotional, and\nsocial aspects, impairing their quality of life\n11\n. Patients with HIV-HTLV-1 coinfection or HTLV-1 infection report more\ndifficulty performing daily activities than those with exclusive infection with\nhuman immunodeficiency virus (HIV)\n12\n. \nThe work ability index (WAI) questionnaire is an instrument that evaluates workers’\nperception of work demands and the environment, work organization, work community,\npromotion of workers’ health and functional capacity, and promotion of professional\ncompetence. Good work ability means high-quality work, enjoyment of staying in one’s\njob, and the expectation of a meaningful retirement\n13\n. \nTherefore, this study aimed to measure the frequency and identify factors associated\nwith work ability in patients living with HTLV-1. "},"methods_title":{"kind":"string","value":"METHODS"},"methods_text":{"kind":"string","value":"Study design and study population This cross-sectional study was conducted from February 2018 to December 2019 at\nthe University Hospital, Federal University of Bahia, Brazil. This study is part\nof broader research that investigates other health aspects of people with\nHTLV-1\n14\n. The target population comprised 209 individuals aged 18 years or older\nwho were invited to participate in the study. Severe cognitive deficits that\nprevented the elicitation of reliable information in the interview were an\nexclusion criterion. There were only two refusals, resulting in a final study\npopulation of 207 individuals.\nThis cross-sectional study was conducted from February 2018 to December 2019 at\nthe University Hospital, Federal University of Bahia, Brazil. This study is part\nof broader research that investigates other health aspects of people with\nHTLV-1\n14\n. The target population comprised 209 individuals aged 18 years or older\nwho were invited to participate in the study. Severe cognitive deficits that\nprevented the elicitation of reliable information in the interview were an\nexclusion criterion. There were only two refusals, resulting in a final study\npopulation of 207 individuals.\nData collection instruments and procedures Participants were interviewed by a member of the research team after the medical\nconsultation in a quiet room, keeping the patient’s privacy. Information about\nsociodemographic characteristics (age, race, schooling, civil status, number of\nchildren, and monthly family income - coded as < 1 Brazilian minimum wage and\n1-2 Brazilian minimal wage. One Brazilian minimal wage was equivalent to US$ 197\nby the time of the study) personal habits (smoking, drinking, and sedentarism),\nhealth-related quality of life, clinical data (comorbidities and HTLV-1\nsymptoms), and work ability were collected using structured questionnaires. \nWork ability was used as the dependent variable. Work ability was evaluated using\nthe WAI questionnaire. WAI is a summary measure of seven dimensions (range\n7-49): 1 -current work ability compared with the lifetime best, 2 - work ability\nin relation to the demands of the job, 3 - number of current diseases diagnosed\nby a physician, 4 - estimated work impairment due to diseases, 5 - sick leave, 6\n- self-prognosis of work ability 2 years from now, and 7 - mental resources. The\ntotal score was classified into four work ability categories: poor (7-27\npoints), moderate (28-36 points), good (37-43 points), and excellent (44-49\npoints). For the purposes of this study, the four possible subgroups of WAI were\ncategorized as poor versus others\n15\n. The WAI questionnaire was validated in a Brazilian population and\nshowed satisfactory psychometric properties\n16\n.\nNeurologic evaluation was performed on all 207 individuals at the University\nHospital, according to the World Health Organization criteria\n17\n. Seventy (33.8%) of the 207 individuals in the study presented\nneurological symptoms; all of them presented weakness and spasticity of one or\nboth legs, compatible with HAM/TSP. Other diagnostics were 44 lumbar pain, 33\nneurogenic bladder, 28 hyperreflexia, 11 polyneuropathy, and 5 erectile\ndysfunction (Figure 1).\n\nFIGURE 1:Study population flowchart.\n\nHealth-related quality of life was evaluated using the 36-Item Short-Form Health\nSurvey version 2 (SF-36v2) questionnaire. This instrument comprises 36 items\nthat generate eight domains: physical functioning, physical role, bodily pain,\ngeneral health, vitality, social functioning, emotional role, and mental health.\nTwo summary measures can be calculated from these domains: physical and mental\ncomponent summaries. The psychometric properties of the SF-36v2 have been\nvalidated in a Brazilian population\n18\n. PRO CoRE software, version 1.4 (Optum Inc., Johnston, RI, USA), was\nused to score the survey. The normalized scores have a mean of 50 and a standard\ndeviation of 10, a transformation that enables better comparisons among domains.\nA commercial license (license number QM025905) granted permission for using the\nSF-36v2. \nParticipants were interviewed by a member of the research team after the medical\nconsultation in a quiet room, keeping the patient’s privacy. Information about\nsociodemographic characteristics (age, race, schooling, civil status, number of\nchildren, and monthly family income - coded as < 1 Brazilian minimum wage and\n1-2 Brazilian minimal wage. One Brazilian minimal wage was equivalent to US$ 197\nby the time of the study) personal habits (smoking, drinking, and sedentarism),\nhealth-related quality of life, clinical data (comorbidities and HTLV-1\nsymptoms), and work ability were collected using structured questionnaires. \nWork ability was used as the dependent variable. Work ability was evaluated using\nthe WAI questionnaire. WAI is a summary measure of seven dimensions (range\n7-49): 1 -current work ability compared with the lifetime best, 2 - work ability\nin relation to the demands of the job, 3 - number of current diseases diagnosed\nby a physician, 4 - estimated work impairment due to diseases, 5 - sick leave, 6\n- self-prognosis of work ability 2 years from now, and 7 - mental resources. The\ntotal score was classified into four work ability categories: poor (7-27\npoints), moderate (28-36 points), good (37-43 points), and excellent (44-49\npoints). For the purposes of this study, the four possible subgroups of WAI were\ncategorized as poor versus others\n15\n. The WAI questionnaire was validated in a Brazilian population and\nshowed satisfactory psychometric properties\n16\n.\nNeurologic evaluation was performed on all 207 individuals at the University\nHospital, according to the World Health Organization criteria\n17\n. Seventy (33.8%) of the 207 individuals in the study presented\nneurological symptoms; all of them presented weakness and spasticity of one or\nboth legs, compatible with HAM/TSP. Other diagnostics were 44 lumbar pain, 33\nneurogenic bladder, 28 hyperreflexia, 11 polyneuropathy, and 5 erectile\ndysfunction (Figure 1).\n\nFIGURE 1:Study population flowchart.\n\nHealth-related quality of life was evaluated using the 36-Item Short-Form Health\nSurvey version 2 (SF-36v2) questionnaire. This instrument comprises 36 items\nthat generate eight domains: physical functioning, physical role, bodily pain,\ngeneral health, vitality, social functioning, emotional role, and mental health.\nTwo summary measures can be calculated from these domains: physical and mental\ncomponent summaries. The psychometric properties of the SF-36v2 have been\nvalidated in a Brazilian population\n18\n. PRO CoRE software, version 1.4 (Optum Inc., Johnston, RI, USA), was\nused to score the survey. The normalized scores have a mean of 50 and a standard\ndeviation of 10, a transformation that enables better comparisons among domains.\nA commercial license (license number QM025905) granted permission for using the\nSF-36v2. \nLaboratory examinations HTLV-1 antibodies were detected in the blood of the participants by enzyme-linked\nimmunosorbent assay (ELISA) and confirmed by western blotting at the Infectology\nResearch Laboratory, University Hospital, Federal University of Bahia.\nHTLV-1 antibodies were detected in the blood of the participants by enzyme-linked\nimmunosorbent assay (ELISA) and confirmed by western blotting at the Infectology\nResearch Laboratory, University Hospital, Federal University of Bahia.\nStatistical data analysis Differences between subgroups of continuous variables were compared using\nStudent's t-test. Differences between subgroups of categorical variables were\ncompared using Pearson's chi-square test. Variables with a\nP-value < 0.20 in bivariate analysis were selected for\ncomposing a Poisson regression model with robust variance estimators that had\nwork ability as the dependent variable\n19\n\n-\n\n21\n. Cronbach’s alpha coefficient was used to evaluate the internal\nconsistency of the SF-36v2 and WAI instruments; values above 0.70 were\nconsidered acceptable\n22\n\n-\n\n23\n. \nDifferences between subgroups of continuous variables were compared using\nStudent's t-test. Differences between subgroups of categorical variables were\ncompared using Pearson's chi-square test. Variables with a\nP-value < 0.20 in bivariate analysis were selected for\ncomposing a Poisson regression model with robust variance estimators that had\nwork ability as the dependent variable\n19\n\n-\n\n21\n. Cronbach’s alpha coefficient was used to evaluate the internal\nconsistency of the SF-36v2 and WAI instruments; values above 0.70 were\nconsidered acceptable\n22\n\n-\n\n23\n. \nEthical aspects The research protocol was approved by the research ethics committee of the\nFederal University of Bahia (opinion number:30762714.4.0000.5577). All the\nparticipants provided written informed consent. \nThe research protocol was approved by the research ethics committee of the\nFederal University of Bahia (opinion number:30762714.4.0000.5577). All the\nparticipants provided written informed consent. "},"results_title":{"kind":"string","value":"RESULTS"},"results_text":{"kind":"string","value":"The mean age was 55.2, ranging from 19 to 84 years, 73.0% were females, 100% had a\nmonthly family income less than US$ 394, and 33.8% presented HAM/TSP. The work\nability of 207 individuals with HTLV-1 was poor in 54.1% (n = 112), moderate in\n37.7% (n = 78), and good in 8.2% (n = 17). No individual was classified as having\nexcellent work ability. The alpha coefficient of the WAI questionnaire was 0.84,\nindicating a high internal consistency.\nThe poor work ability prevalence rate was significantly higher (P\n< 0.20) among individuals who had children, had schooling < 8 years, civil\nstatus other than stable relation, who did not referred alcohol consumption,\nsedentary status, comorbidities, and neurological symptoms (Table 1).\n\nTABLE 1:Work ability according to characteristics of 207 individuals with\nHTLV-1, Salvador, Brazil, 2018-2019.\nWork ability \n\n\n\nPoor Moderate/good \n\n\n\n(n = 112) (n = 95) \n\n\nCharacteristicn%n%PR95% CIP-valueSex\n\n\n\n\n\n\nMale3155.42544.61.030.78-1.360.826Female8153.47046.6\n\n\nRace\n\n\n\n\n\n\nWhite 1058.8741.21.100.72-1.670.683Other10253.78846.3\n\n\nChildren\n\n\n\n\n\n\nNo1139.31760.70.700.43-1.120.090Yes10156.47843.6\n\n\nSchooling\n\n\n\n\n\n\n< 8 years8061.15138.91.451.08-1.950.008≥ 8 years3242.14457.9\n\n\nCivil status\n\n\n\n\n\n\nOther6658.94641.11.220.94-1.580.131Stable relation4648.44951.6\n\n\nMonthly family income\n\n\n\n\n\n\n< 1 MW3655.42944.61.040.79-1.350.8031 to 2 MW7653.56646.5\n\n\nSmoking\n\n\n\n\n\n\nYes1168.8531.21.300.91-1.860.222No10152.99047.1\n\n\nDrinking\n\n\n\n\n\n\nYes2242.33057.70.730.52-1.030.049No9058.16541.9\n\n\nComorbidities\n\n\n\n\n\n\nYes9759.16740.91.701.11-2.600.005No1534.92865.1\n\n\nSedentary\n\n\n\n\n\n\nYes7957.75842.31.220.92-1.630.151No3347.13752.9\n\n\nNeurological symptoms\n\n\n\n\n\n\nYes5578.61521.41.891.50-2.38< 0.001No5741.68058.4\n\n\n\n*Fisher test; MW: Brazilian minimal wage\n(approx. 197.39 US$/month).\n\n\n*Fisher test; MW: Brazilian minimal wage\n(approx. 197.39 US$/month).\nBivariate analyses showed that individuals with poor work ability presented\nsystematically lower (P < 0.001) SF-36 domain scores and\nphysical and mental component summaries of health-related quality of life and were\nsignificantly older (P < 0.042) than those with moderate or good\nwork ability (Table 2).\n\nTABLE 2:Work ability according to SF-36 health-related quality of life\ndomains (mean [SD], in %) and age (mean [SD], in years) of 207\nindividuals with HTLV-1, Salvador, Brazil, 2018-2019. \n\nWork ability \n\n\nPoorModerate/good\nVariableCronbach alpha(n = 112)(n = 95)\n\nP-value\nPhysical Functioning0.9533.1 (10.9)50.9 (8.9)< 0.001Role Physical0.9529.1 (10.4)48.4 (12.9)< 0.001Bodily Pain0.7834.6 (11.2)47.5 (11.1)< 0.001General Health0.7736.2 (10.0)50.2 (8.9)< 0.001Vitality0.8241.6 (12.7)56.2 (10.0)< 0.001Social Functioning0.7538.3 (14.3)52.9 (8.9)< 0.001Role Emotional0.9432.8 (16.6)49.0 (13.6)< 0.001Mental Health0.8439.1 (15.5)51.6 (10.3)< 0.001Physical Component Summary-32.7 (9.7)49.1 (9.8)< 0.001Mental Component Summary-40.3 (16.6)52.5 (10.7)< 0.001Age-56.8 (12.4)53.3 (12.4)0.042\n\nThe Poisson regression model estimated that adjusted prevalence rates (PR) of poor\nwork ability were 30% higher among sedentary individuals (PR = 1.30; 95% confidence\ninterval [CI]: 1.03-1.65) and 25% higher among those with neurological symptoms (PR\n= 1.25; 95% CI: 1.02-1.52). The mean level of the physical component summary of the\nhealth-related quality of life was 5% lower (PR = 0.95; 95% CI: 0.94-0.96), and the\nmean level of the mental component summary was 2% lower (PR = 0.98; 95% CI:\n0.97-0.99) among individuals with poor work ability compared with those with\nmoderate or good work ability. The alpha coefficients of the eight domains of the\nSF36v2 questionnaire varied from 0.75 to 0.95, revealing high internal consistency\n(Table 3).\n\nTABLE 3:Results of Poisson regression having the prevalence ratio of low work\nability as the dependent variable among 207 individuals with HTLV-1,\nSalvador, Brazil, 2018-2019.Predictors (referent)PR95% CI\n\nP-value\nChildren (Yes)0.910.66-1.260.571Schooling (≥ 8 years)1.050.84--1.310.696Civil status (Stable relation)1.070.87-1.310.502Drinking (No)1.120.86-1.460.392Comorbidities (No)0.980.69-1.350.845Age (Years)1.011.00-1.020.061Sedentary (No)1.301.03-1.650.030Neurological symptoms (No)1.251.02-1.520.028Physical Component Summary (%)0.950.94--0.96< 0.001Mental Component Summary (%)0.980.97-0.99< 0.001\nPR: adjusted prevalence ratio.\n\n\nPR: adjusted prevalence ratio."},"conclusions_title":{"kind":"null"},"conclusions_text":{"kind":"null"},"other_sections_titles":{"kind":"list like","value":["Study design and study population","Data collection instruments and procedures","Laboratory examinations","Statistical data analysis","Ethical aspects"],"string":"[\n \"Study design and study population\",\n \"Data collection instruments and procedures\",\n \"Laboratory examinations\",\n \"Statistical data analysis\",\n \"Ethical aspects\"\n]"},"other_sections_texts":{"kind":"list like","value":["This cross-sectional study was conducted from February 2018 to December 2019 at\nthe University Hospital, Federal University of Bahia, Brazil. This study is part\nof broader research that investigates other health aspects of people with\nHTLV-1\n14\n. The target population comprised 209 individuals aged 18 years or older\nwho were invited to participate in the study. Severe cognitive deficits that\nprevented the elicitation of reliable information in the interview were an\nexclusion criterion. There were only two refusals, resulting in a final study\npopulation of 207 individuals.","Participants were interviewed by a member of the research team after the medical\nconsultation in a quiet room, keeping the patient’s privacy. Information about\nsociodemographic characteristics (age, race, schooling, civil status, number of\nchildren, and monthly family income - coded as < 1 Brazilian minimum wage and\n1-2 Brazilian minimal wage. One Brazilian minimal wage was equivalent to US$ 197\nby the time of the study) personal habits (smoking, drinking, and sedentarism),\nhealth-related quality of life, clinical data (comorbidities and HTLV-1\nsymptoms), and work ability were collected using structured questionnaires. \nWork ability was used as the dependent variable. Work ability was evaluated using\nthe WAI questionnaire. WAI is a summary measure of seven dimensions (range\n7-49): 1 -current work ability compared with the lifetime best, 2 - work ability\nin relation to the demands of the job, 3 - number of current diseases diagnosed\nby a physician, 4 - estimated work impairment due to diseases, 5 - sick leave, 6\n- self-prognosis of work ability 2 years from now, and 7 - mental resources. The\ntotal score was classified into four work ability categories: poor (7-27\npoints), moderate (28-36 points), good (37-43 points), and excellent (44-49\npoints). For the purposes of this study, the four possible subgroups of WAI were\ncategorized as poor versus others\n15\n. The WAI questionnaire was validated in a Brazilian population and\nshowed satisfactory psychometric properties\n16\n.\nNeurologic evaluation was performed on all 207 individuals at the University\nHospital, according to the World Health Organization criteria\n17\n. Seventy (33.8%) of the 207 individuals in the study presented\nneurological symptoms; all of them presented weakness and spasticity of one or\nboth legs, compatible with HAM/TSP. Other diagnostics were 44 lumbar pain, 33\nneurogenic bladder, 28 hyperreflexia, 11 polyneuropathy, and 5 erectile\ndysfunction (Figure 1).\n\nFIGURE 1:Study population flowchart.\n\nHealth-related quality of life was evaluated using the 36-Item Short-Form Health\nSurvey version 2 (SF-36v2) questionnaire. This instrument comprises 36 items\nthat generate eight domains: physical functioning, physical role, bodily pain,\ngeneral health, vitality, social functioning, emotional role, and mental health.\nTwo summary measures can be calculated from these domains: physical and mental\ncomponent summaries. The psychometric properties of the SF-36v2 have been\nvalidated in a Brazilian population\n18\n. PRO CoRE software, version 1.4 (Optum Inc., Johnston, RI, USA), was\nused to score the survey. The normalized scores have a mean of 50 and a standard\ndeviation of 10, a transformation that enables better comparisons among domains.\nA commercial license (license number QM025905) granted permission for using the\nSF-36v2. ","HTLV-1 antibodies were detected in the blood of the participants by enzyme-linked\nimmunosorbent assay (ELISA) and confirmed by western blotting at the Infectology\nResearch Laboratory, University Hospital, Federal University of Bahia.","Differences between subgroups of continuous variables were compared using\nStudent's t-test. Differences between subgroups of categorical variables were\ncompared using Pearson's chi-square test. Variables with a\nP-value < 0.20 in bivariate analysis were selected for\ncomposing a Poisson regression model with robust variance estimators that had\nwork ability as the dependent variable\n19\n\n-\n\n21\n. Cronbach’s alpha coefficient was used to evaluate the internal\nconsistency of the SF-36v2 and WAI instruments; values above 0.70 were\nconsidered acceptable\n22\n\n-\n\n23\n. ","The research protocol was approved by the research ethics committee of the\nFederal University of Bahia (opinion number:30762714.4.0000.5577). All the\nparticipants provided written informed consent. "],"string":"[\n \"This cross-sectional study was conducted from February 2018 to December 2019 at\\nthe University Hospital, Federal University of Bahia, Brazil. This study is part\\nof broader research that investigates other health aspects of people with\\nHTLV-1\\n14\\n. The target population comprised 209 individuals aged 18 years or older\\nwho were invited to participate in the study. Severe cognitive deficits that\\nprevented the elicitation of reliable information in the interview were an\\nexclusion criterion. There were only two refusals, resulting in a final study\\npopulation of 207 individuals.\",\n \"Participants were interviewed by a member of the research team after the medical\\nconsultation in a quiet room, keeping the patient’s privacy. Information about\\nsociodemographic characteristics (age, race, schooling, civil status, number of\\nchildren, and monthly family income - coded as < 1 Brazilian minimum wage and\\n1-2 Brazilian minimal wage. One Brazilian minimal wage was equivalent to US$ 197\\nby the time of the study) personal habits (smoking, drinking, and sedentarism),\\nhealth-related quality of life, clinical data (comorbidities and HTLV-1\\nsymptoms), and work ability were collected using structured questionnaires. \\nWork ability was used as the dependent variable. Work ability was evaluated using\\nthe WAI questionnaire. WAI is a summary measure of seven dimensions (range\\n7-49): 1 -current work ability compared with the lifetime best, 2 - work ability\\nin relation to the demands of the job, 3 - number of current diseases diagnosed\\nby a physician, 4 - estimated work impairment due to diseases, 5 - sick leave, 6\\n- self-prognosis of work ability 2 years from now, and 7 - mental resources. The\\ntotal score was classified into four work ability categories: poor (7-27\\npoints), moderate (28-36 points), good (37-43 points), and excellent (44-49\\npoints). For the purposes of this study, the four possible subgroups of WAI were\\ncategorized as poor versus others\\n15\\n. The WAI questionnaire was validated in a Brazilian population and\\nshowed satisfactory psychometric properties\\n16\\n.\\nNeurologic evaluation was performed on all 207 individuals at the University\\nHospital, according to the World Health Organization criteria\\n17\\n. Seventy (33.8%) of the 207 individuals in the study presented\\nneurological symptoms; all of them presented weakness and spasticity of one or\\nboth legs, compatible with HAM/TSP. Other diagnostics were 44 lumbar pain, 33\\nneurogenic bladder, 28 hyperreflexia, 11 polyneuropathy, and 5 erectile\\ndysfunction (Figure 1).\\n\\nFIGURE 1:Study population flowchart.\\n\\nHealth-related quality of life was evaluated using the 36-Item Short-Form Health\\nSurvey version 2 (SF-36v2) questionnaire. This instrument comprises 36 items\\nthat generate eight domains: physical functioning, physical role, bodily pain,\\ngeneral health, vitality, social functioning, emotional role, and mental health.\\nTwo summary measures can be calculated from these domains: physical and mental\\ncomponent summaries. The psychometric properties of the SF-36v2 have been\\nvalidated in a Brazilian population\\n18\\n. PRO CoRE software, version 1.4 (Optum Inc., Johnston, RI, USA), was\\nused to score the survey. The normalized scores have a mean of 50 and a standard\\ndeviation of 10, a transformation that enables better comparisons among domains.\\nA commercial license (license number QM025905) granted permission for using the\\nSF-36v2. \",\n \"HTLV-1 antibodies were detected in the blood of the participants by enzyme-linked\\nimmunosorbent assay (ELISA) and confirmed by western blotting at the Infectology\\nResearch Laboratory, University Hospital, Federal University of Bahia.\",\n \"Differences between subgroups of continuous variables were compared using\\nStudent's t-test. Differences between subgroups of categorical variables were\\ncompared using Pearson's chi-square test. Variables with a\\nP-value < 0.20 in bivariate analysis were selected for\\ncomposing a Poisson regression model with robust variance estimators that had\\nwork ability as the dependent variable\\n19\\n\\n-\\n\\n21\\n. Cronbach’s alpha coefficient was used to evaluate the internal\\nconsistency of the SF-36v2 and WAI instruments; values above 0.70 were\\nconsidered acceptable\\n22\\n\\n-\\n\\n23\\n. \",\n \"The research protocol was approved by the research ethics committee of the\\nFederal University of Bahia (opinion number:30762714.4.0000.5577). All the\\nparticipants provided written informed consent. \"\n]"},"other_sections_sec_types":{"kind":"list like","value":[null,null,null,null,null],"string":"[\n null,\n null,\n null,\n null,\n null\n]"},"all_sections_titles":{"kind":"list like","value":["INTRODUCTION","METHODS","Study design and study population","Data collection instruments and procedures","Laboratory examinations","Statistical data analysis","Ethical aspects","RESULTS","DISCUSSION"],"string":"[\n \"INTRODUCTION\",\n \"METHODS\",\n \"Study design and study population\",\n \"Data collection instruments and procedures\",\n \"Laboratory examinations\",\n \"Statistical data analysis\",\n \"Ethical aspects\",\n \"RESULTS\",\n \"DISCUSSION\"\n]"},"all_sections_texts":{"kind":"list like","value":["Human T-lymphotropic virus type 1 (HTLV-1) is a type C retrovirus that was first\nisolated and identified from a patient with cutaneous T-cell malignancy in 1980\n1\n. It is transmitted through breastfeeding, sexual contact, blood transfusion,\nand sharing syringes and needles\n2\n. \nThe prevalence of HTLV-1 infection is poorly known; however, it is estimated that it\naffects 10-15 million people worldwide\n3\n. Clusters of high prevalence were found in nearby areas with negligible\nprevalence. HTLV-1 infection is endemic in southwestern Japan, sub-Saharan Africa,\nSouth America, and the Caribbean area, with foci in the Middle East and\nAustralo-Melanesia\n4\n. HTLV-1 infection is frequent in Brazil\n5\n, in the State of Bahia\n6\n, particularly in its capital, Salvador city, with an estimated prevalence of\n1.8%\n7\n. \nMost people (approximately 95%) infected with HTLV-1 remain asymptomatic\n8\n. Individuals with HTLV-1 have a 57% greater risk of death due to any cause\nthan those HTLV-1-negative individuals. HTLV-1 is associated with increased odds of\nseborrheic dermatitis and Sjogren’s syndrome and a lower relative risk of gastric\ncancer\n9\n. HTLV-1 can cause two severe diseases, adult T-cell leukemia/lymphoma (ATLL)\nand HTLV-1-associated myelopathy/tropical spastic paraparesis (HAM/TSP). It is\nestimated that 0.25-3% of people infected with HTLV-1 will develop HAM/TSP during\ntheir lifetime. HAM/TSP mainly occurs in adulthood. HAM/TSP has an insidious onset\nthat progressively evolves to neurological features, such as spasticity or\nhyperreflexia of the lower extremities, lower extremity muscle weakness, and urinary\nbladder disturbances. Approximately 50% of cases present with sensory disturbances\nand low back pain. HAM/TSP can be associated with other HTLV-1 associated symptoms\nlike uveitis, myositis, and infective dermatitis\n10\n. Patients with HAM/TSP have difficulty performing daily routine activities,\nparticularly because of a disturbed gait that compromises physical, emotional, and\nsocial aspects, impairing their quality of life\n11\n. Patients with HIV-HTLV-1 coinfection or HTLV-1 infection report more\ndifficulty performing daily activities than those with exclusive infection with\nhuman immunodeficiency virus (HIV)\n12\n. \nThe work ability index (WAI) questionnaire is an instrument that evaluates workers’\nperception of work demands and the environment, work organization, work community,\npromotion of workers’ health and functional capacity, and promotion of professional\ncompetence. Good work ability means high-quality work, enjoyment of staying in one’s\njob, and the expectation of a meaningful retirement\n13\n. \nTherefore, this study aimed to measure the frequency and identify factors associated\nwith work ability in patients living with HTLV-1. ","Study design and study population This cross-sectional study was conducted from February 2018 to December 2019 at\nthe University Hospital, Federal University of Bahia, Brazil. This study is part\nof broader research that investigates other health aspects of people with\nHTLV-1\n14\n. The target population comprised 209 individuals aged 18 years or older\nwho were invited to participate in the study. Severe cognitive deficits that\nprevented the elicitation of reliable information in the interview were an\nexclusion criterion. There were only two refusals, resulting in a final study\npopulation of 207 individuals.\nThis cross-sectional study was conducted from February 2018 to December 2019 at\nthe University Hospital, Federal University of Bahia, Brazil. This study is part\nof broader research that investigates other health aspects of people with\nHTLV-1\n14\n. The target population comprised 209 individuals aged 18 years or older\nwho were invited to participate in the study. Severe cognitive deficits that\nprevented the elicitation of reliable information in the interview were an\nexclusion criterion. There were only two refusals, resulting in a final study\npopulation of 207 individuals.\nData collection instruments and procedures Participants were interviewed by a member of the research team after the medical\nconsultation in a quiet room, keeping the patient’s privacy. Information about\nsociodemographic characteristics (age, race, schooling, civil status, number of\nchildren, and monthly family income - coded as < 1 Brazilian minimum wage and\n1-2 Brazilian minimal wage. One Brazilian minimal wage was equivalent to US$ 197\nby the time of the study) personal habits (smoking, drinking, and sedentarism),\nhealth-related quality of life, clinical data (comorbidities and HTLV-1\nsymptoms), and work ability were collected using structured questionnaires. \nWork ability was used as the dependent variable. Work ability was evaluated using\nthe WAI questionnaire. WAI is a summary measure of seven dimensions (range\n7-49): 1 -current work ability compared with the lifetime best, 2 - work ability\nin relation to the demands of the job, 3 - number of current diseases diagnosed\nby a physician, 4 - estimated work impairment due to diseases, 5 - sick leave, 6\n- self-prognosis of work ability 2 years from now, and 7 - mental resources. The\ntotal score was classified into four work ability categories: poor (7-27\npoints), moderate (28-36 points), good (37-43 points), and excellent (44-49\npoints). For the purposes of this study, the four possible subgroups of WAI were\ncategorized as poor versus others\n15\n. The WAI questionnaire was validated in a Brazilian population and\nshowed satisfactory psychometric properties\n16\n.\nNeurologic evaluation was performed on all 207 individuals at the University\nHospital, according to the World Health Organization criteria\n17\n. Seventy (33.8%) of the 207 individuals in the study presented\nneurological symptoms; all of them presented weakness and spasticity of one or\nboth legs, compatible with HAM/TSP. Other diagnostics were 44 lumbar pain, 33\nneurogenic bladder, 28 hyperreflexia, 11 polyneuropathy, and 5 erectile\ndysfunction (Figure 1).\n\nFIGURE 1:Study population flowchart.\n\nHealth-related quality of life was evaluated using the 36-Item Short-Form Health\nSurvey version 2 (SF-36v2) questionnaire. This instrument comprises 36 items\nthat generate eight domains: physical functioning, physical role, bodily pain,\ngeneral health, vitality, social functioning, emotional role, and mental health.\nTwo summary measures can be calculated from these domains: physical and mental\ncomponent summaries. The psychometric properties of the SF-36v2 have been\nvalidated in a Brazilian population\n18\n. PRO CoRE software, version 1.4 (Optum Inc., Johnston, RI, USA), was\nused to score the survey. The normalized scores have a mean of 50 and a standard\ndeviation of 10, a transformation that enables better comparisons among domains.\nA commercial license (license number QM025905) granted permission for using the\nSF-36v2. \nParticipants were interviewed by a member of the research team after the medical\nconsultation in a quiet room, keeping the patient’s privacy. Information about\nsociodemographic characteristics (age, race, schooling, civil status, number of\nchildren, and monthly family income - coded as < 1 Brazilian minimum wage and\n1-2 Brazilian minimal wage. One Brazilian minimal wage was equivalent to US$ 197\nby the time of the study) personal habits (smoking, drinking, and sedentarism),\nhealth-related quality of life, clinical data (comorbidities and HTLV-1\nsymptoms), and work ability were collected using structured questionnaires. \nWork ability was used as the dependent variable. Work ability was evaluated using\nthe WAI questionnaire. WAI is a summary measure of seven dimensions (range\n7-49): 1 -current work ability compared with the lifetime best, 2 - work ability\nin relation to the demands of the job, 3 - number of current diseases diagnosed\nby a physician, 4 - estimated work impairment due to diseases, 5 - sick leave, 6\n- self-prognosis of work ability 2 years from now, and 7 - mental resources. The\ntotal score was classified into four work ability categories: poor (7-27\npoints), moderate (28-36 points), good (37-43 points), and excellent (44-49\npoints). For the purposes of this study, the four possible subgroups of WAI were\ncategorized as poor versus others\n15\n. The WAI questionnaire was validated in a Brazilian population and\nshowed satisfactory psychometric properties\n16\n.\nNeurologic evaluation was performed on all 207 individuals at the University\nHospital, according to the World Health Organization criteria\n17\n. Seventy (33.8%) of the 207 individuals in the study presented\nneurological symptoms; all of them presented weakness and spasticity of one or\nboth legs, compatible with HAM/TSP. Other diagnostics were 44 lumbar pain, 33\nneurogenic bladder, 28 hyperreflexia, 11 polyneuropathy, and 5 erectile\ndysfunction (Figure 1).\n\nFIGURE 1:Study population flowchart.\n\nHealth-related quality of life was evaluated using the 36-Item Short-Form Health\nSurvey version 2 (SF-36v2) questionnaire. This instrument comprises 36 items\nthat generate eight domains: physical functioning, physical role, bodily pain,\ngeneral health, vitality, social functioning, emotional role, and mental health.\nTwo summary measures can be calculated from these domains: physical and mental\ncomponent summaries. The psychometric properties of the SF-36v2 have been\nvalidated in a Brazilian population\n18\n. PRO CoRE software, version 1.4 (Optum Inc., Johnston, RI, USA), was\nused to score the survey. The normalized scores have a mean of 50 and a standard\ndeviation of 10, a transformation that enables better comparisons among domains.\nA commercial license (license number QM025905) granted permission for using the\nSF-36v2. \nLaboratory examinations HTLV-1 antibodies were detected in the blood of the participants by enzyme-linked\nimmunosorbent assay (ELISA) and confirmed by western blotting at the Infectology\nResearch Laboratory, University Hospital, Federal University of Bahia.\nHTLV-1 antibodies were detected in the blood of the participants by enzyme-linked\nimmunosorbent assay (ELISA) and confirmed by western blotting at the Infectology\nResearch Laboratory, University Hospital, Federal University of Bahia.\nStatistical data analysis Differences between subgroups of continuous variables were compared using\nStudent's t-test. Differences between subgroups of categorical variables were\ncompared using Pearson's chi-square test. Variables with a\nP-value < 0.20 in bivariate analysis were selected for\ncomposing a Poisson regression model with robust variance estimators that had\nwork ability as the dependent variable\n19\n\n-\n\n21\n. Cronbach’s alpha coefficient was used to evaluate the internal\nconsistency of the SF-36v2 and WAI instruments; values above 0.70 were\nconsidered acceptable\n22\n\n-\n\n23\n. \nDifferences between subgroups of continuous variables were compared using\nStudent's t-test. Differences between subgroups of categorical variables were\ncompared using Pearson's chi-square test. Variables with a\nP-value < 0.20 in bivariate analysis were selected for\ncomposing a Poisson regression model with robust variance estimators that had\nwork ability as the dependent variable\n19\n\n-\n\n21\n. Cronbach’s alpha coefficient was used to evaluate the internal\nconsistency of the SF-36v2 and WAI instruments; values above 0.70 were\nconsidered acceptable\n22\n\n-\n\n23\n. \nEthical aspects The research protocol was approved by the research ethics committee of the\nFederal University of Bahia (opinion number:30762714.4.0000.5577). All the\nparticipants provided written informed consent. \nThe research protocol was approved by the research ethics committee of the\nFederal University of Bahia (opinion number:30762714.4.0000.5577). All the\nparticipants provided written informed consent. ","This cross-sectional study was conducted from February 2018 to December 2019 at\nthe University Hospital, Federal University of Bahia, Brazil. This study is part\nof broader research that investigates other health aspects of people with\nHTLV-1\n14\n. The target population comprised 209 individuals aged 18 years or older\nwho were invited to participate in the study. Severe cognitive deficits that\nprevented the elicitation of reliable information in the interview were an\nexclusion criterion. There were only two refusals, resulting in a final study\npopulation of 207 individuals.","Participants were interviewed by a member of the research team after the medical\nconsultation in a quiet room, keeping the patient’s privacy. Information about\nsociodemographic characteristics (age, race, schooling, civil status, number of\nchildren, and monthly family income - coded as < 1 Brazilian minimum wage and\n1-2 Brazilian minimal wage. One Brazilian minimal wage was equivalent to US$ 197\nby the time of the study) personal habits (smoking, drinking, and sedentarism),\nhealth-related quality of life, clinical data (comorbidities and HTLV-1\nsymptoms), and work ability were collected using structured questionnaires. \nWork ability was used as the dependent variable. Work ability was evaluated using\nthe WAI questionnaire. WAI is a summary measure of seven dimensions (range\n7-49): 1 -current work ability compared with the lifetime best, 2 - work ability\nin relation to the demands of the job, 3 - number of current diseases diagnosed\nby a physician, 4 - estimated work impairment due to diseases, 5 - sick leave, 6\n- self-prognosis of work ability 2 years from now, and 7 - mental resources. The\ntotal score was classified into four work ability categories: poor (7-27\npoints), moderate (28-36 points), good (37-43 points), and excellent (44-49\npoints). For the purposes of this study, the four possible subgroups of WAI were\ncategorized as poor versus others\n15\n. The WAI questionnaire was validated in a Brazilian population and\nshowed satisfactory psychometric properties\n16\n.\nNeurologic evaluation was performed on all 207 individuals at the University\nHospital, according to the World Health Organization criteria\n17\n. Seventy (33.8%) of the 207 individuals in the study presented\nneurological symptoms; all of them presented weakness and spasticity of one or\nboth legs, compatible with HAM/TSP. Other diagnostics were 44 lumbar pain, 33\nneurogenic bladder, 28 hyperreflexia, 11 polyneuropathy, and 5 erectile\ndysfunction (Figure 1).\n\nFIGURE 1:Study population flowchart.\n\nHealth-related quality of life was evaluated using the 36-Item Short-Form Health\nSurvey version 2 (SF-36v2) questionnaire. This instrument comprises 36 items\nthat generate eight domains: physical functioning, physical role, bodily pain,\ngeneral health, vitality, social functioning, emotional role, and mental health.\nTwo summary measures can be calculated from these domains: physical and mental\ncomponent summaries. The psychometric properties of the SF-36v2 have been\nvalidated in a Brazilian population\n18\n. PRO CoRE software, version 1.4 (Optum Inc., Johnston, RI, USA), was\nused to score the survey. The normalized scores have a mean of 50 and a standard\ndeviation of 10, a transformation that enables better comparisons among domains.\nA commercial license (license number QM025905) granted permission for using the\nSF-36v2. ","HTLV-1 antibodies were detected in the blood of the participants by enzyme-linked\nimmunosorbent assay (ELISA) and confirmed by western blotting at the Infectology\nResearch Laboratory, University Hospital, Federal University of Bahia.","Differences between subgroups of continuous variables were compared using\nStudent's t-test. Differences between subgroups of categorical variables were\ncompared using Pearson's chi-square test. Variables with a\nP-value < 0.20 in bivariate analysis were selected for\ncomposing a Poisson regression model with robust variance estimators that had\nwork ability as the dependent variable\n19\n\n-\n\n21\n. Cronbach’s alpha coefficient was used to evaluate the internal\nconsistency of the SF-36v2 and WAI instruments; values above 0.70 were\nconsidered acceptable\n22\n\n-\n\n23\n. ","The research protocol was approved by the research ethics committee of the\nFederal University of Bahia (opinion number:30762714.4.0000.5577). All the\nparticipants provided written informed consent. ","The mean age was 55.2, ranging from 19 to 84 years, 73.0% were females, 100% had a\nmonthly family income less than US$ 394, and 33.8% presented HAM/TSP. The work\nability of 207 individuals with HTLV-1 was poor in 54.1% (n = 112), moderate in\n37.7% (n = 78), and good in 8.2% (n = 17). No individual was classified as having\nexcellent work ability. The alpha coefficient of the WAI questionnaire was 0.84,\nindicating a high internal consistency.\nThe poor work ability prevalence rate was significantly higher (P\n< 0.20) among individuals who had children, had schooling < 8 years, civil\nstatus other than stable relation, who did not referred alcohol consumption,\nsedentary status, comorbidities, and neurological symptoms (Table 1).\n\nTABLE 1:Work ability according to characteristics of 207 individuals with\nHTLV-1, Salvador, Brazil, 2018-2019.\nWork ability \n\n\n\nPoor Moderate/good \n\n\n\n(n = 112) (n = 95) \n\n\nCharacteristicn%n%PR95% CIP-valueSex\n\n\n\n\n\n\nMale3155.42544.61.030.78-1.360.826Female8153.47046.6\n\n\nRace\n\n\n\n\n\n\nWhite 1058.8741.21.100.72-1.670.683Other10253.78846.3\n\n\nChildren\n\n\n\n\n\n\nNo1139.31760.70.700.43-1.120.090Yes10156.47843.6\n\n\nSchooling\n\n\n\n\n\n\n< 8 years8061.15138.91.451.08-1.950.008≥ 8 years3242.14457.9\n\n\nCivil status\n\n\n\n\n\n\nOther6658.94641.11.220.94-1.580.131Stable relation4648.44951.6\n\n\nMonthly family income\n\n\n\n\n\n\n< 1 MW3655.42944.61.040.79-1.350.8031 to 2 MW7653.56646.5\n\n\nSmoking\n\n\n\n\n\n\nYes1168.8531.21.300.91-1.860.222No10152.99047.1\n\n\nDrinking\n\n\n\n\n\n\nYes2242.33057.70.730.52-1.030.049No9058.16541.9\n\n\nComorbidities\n\n\n\n\n\n\nYes9759.16740.91.701.11-2.600.005No1534.92865.1\n\n\nSedentary\n\n\n\n\n\n\nYes7957.75842.31.220.92-1.630.151No3347.13752.9\n\n\nNeurological symptoms\n\n\n\n\n\n\nYes5578.61521.41.891.50-2.38< 0.001No5741.68058.4\n\n\n\n*Fisher test; MW: Brazilian minimal wage\n(approx. 197.39 US$/month).\n\n\n*Fisher test; MW: Brazilian minimal wage\n(approx. 197.39 US$/month).\nBivariate analyses showed that individuals with poor work ability presented\nsystematically lower (P < 0.001) SF-36 domain scores and\nphysical and mental component summaries of health-related quality of life and were\nsignificantly older (P < 0.042) than those with moderate or good\nwork ability (Table 2).\n\nTABLE 2:Work ability according to SF-36 health-related quality of life\ndomains (mean [SD], in %) and age (mean [SD], in years) of 207\nindividuals with HTLV-1, Salvador, Brazil, 2018-2019. \n\nWork ability \n\n\nPoorModerate/good\nVariableCronbach alpha(n = 112)(n = 95)\n\nP-value\nPhysical Functioning0.9533.1 (10.9)50.9 (8.9)< 0.001Role Physical0.9529.1 (10.4)48.4 (12.9)< 0.001Bodily Pain0.7834.6 (11.2)47.5 (11.1)< 0.001General Health0.7736.2 (10.0)50.2 (8.9)< 0.001Vitality0.8241.6 (12.7)56.2 (10.0)< 0.001Social Functioning0.7538.3 (14.3)52.9 (8.9)< 0.001Role Emotional0.9432.8 (16.6)49.0 (13.6)< 0.001Mental Health0.8439.1 (15.5)51.6 (10.3)< 0.001Physical Component Summary-32.7 (9.7)49.1 (9.8)< 0.001Mental Component Summary-40.3 (16.6)52.5 (10.7)< 0.001Age-56.8 (12.4)53.3 (12.4)0.042\n\nThe Poisson regression model estimated that adjusted prevalence rates (PR) of poor\nwork ability were 30% higher among sedentary individuals (PR = 1.30; 95% confidence\ninterval [CI]: 1.03-1.65) and 25% higher among those with neurological symptoms (PR\n= 1.25; 95% CI: 1.02-1.52). The mean level of the physical component summary of the\nhealth-related quality of life was 5% lower (PR = 0.95; 95% CI: 0.94-0.96), and the\nmean level of the mental component summary was 2% lower (PR = 0.98; 95% CI:\n0.97-0.99) among individuals with poor work ability compared with those with\nmoderate or good work ability. The alpha coefficients of the eight domains of the\nSF36v2 questionnaire varied from 0.75 to 0.95, revealing high internal consistency\n(Table 3).\n\nTABLE 3:Results of Poisson regression having the prevalence ratio of low work\nability as the dependent variable among 207 individuals with HTLV-1,\nSalvador, Brazil, 2018-2019.Predictors (referent)PR95% CI\n\nP-value\nChildren (Yes)0.910.66-1.260.571Schooling (≥ 8 years)1.050.84--1.310.696Civil status (Stable relation)1.070.87-1.310.502Drinking (No)1.120.86-1.460.392Comorbidities (No)0.980.69-1.350.845Age (Years)1.011.00-1.020.061Sedentary (No)1.301.03-1.650.030Neurological symptoms (No)1.251.02-1.520.028Physical Component Summary (%)0.950.94--0.96< 0.001Mental Component Summary (%)0.980.97-0.99< 0.001\nPR: adjusted prevalence ratio.\n\n\nPR: adjusted prevalence ratio.","Poor work ability was common in the study population (54.1%). In addition, poor work\nability is associated with an increased risk of sickness absence\n24\n, early retirement\n25\n, and higher mortality in older age\n26\n.\nThis study among people living with HTLV-1 found that poor work ability was\nassociated with sedentarism, neurologic symptoms, and low health-related quality of\nlife in both the physical and mental components.\nMultivariate analysis estimated that the adjusted prevalence of poor work ability was\n30% higher among individuals with a sedentary lifestyle and 25% higher among those\npresenting with neurologic symptoms. People infected with HTLV-1, who already\npresent with neurological symptoms, are expected to have impaired work ability.\nPatients with HAM/TSP usually have impaired gait, dependence on daily activities,\nand a poor quality of life due to intense muscle weakness\n27\n. The same reasoning applies to the relationship between sedentarism and poor\nwork ability\n28\n. Unfortunately, this study did not collect information on the temporal\nsequence of the relationship between these independent variables (sedentarism and\nneurologic symptoms) and outcomes (work ability).\nHTLV-1 infection has been associated with several diseases. Fortunately, only a few\nof these are fatal, such as leukemia. However, this disease is rare and has a\nrelatively low impact on the community mortality rates\n9\n. The results of this study raise awareness of the poorly recognized burden\nof HTLV-1 infection on the morbidity caused by neurologic symptoms and its impact on\nwork ability.\nIndividuals with poor work ability had a lower health-related quality of life than\nthose with moderate or good work ability. The differences found in the bivariate\nanalyses were confirmed in the multivariate analyses, which estimated a 5% lower\nphysical and a 2% lower mental component summary for patients with poor work ability\nafter adjusting for relevant variables. The complex construct of the WAI\nquestionnaire has many points of convergence with that of the SF36v2\n29\n since both deal with physical and mental demands. Therefore, the WAI score\nis expected to be strongly associated with SF36v2 dimensions and component\nsummaries\n30\n\n,\n\n31\n. For example, the progressive and disabling gait disturbances of patients\nwith HTLV-1 may impair physical, emotional, social, and mental aspects that, in\nturn, may modify the health-related quality of life perception\n11\n.\nThe magnitude of the differences in physical and mental component summaries according\nto work ability can be analyzed from the perspective of the minimal clinically\nimportant difference (MCID)\n32\n. The concept of MCID evolved to minimal important difference, defined as\n“the smallest difference in score in the domain of interest that patients perceive\nas important, either beneficial or harmful, and that would lead the clinician to\nconsider a change in the patient’s management\n33\n.”\nThe MCID for physical component summary varied in studies, including patients with\nmoderate to severe psoriasis (2.5 points)\n34\n, undergoing lumbar spine surgeries (4.11-5.21\n35\n; and 4.93)\n36\n, and surgical (7.83) and non-surgical (2.15) patients with spinal\ndeformities\n37\n. The MDIC for mental component summary was 2.5 points in a study of patients\nwith moderate to severe psoriasis\n34\n. Roughly half of all patients treated for hepatitis C fail to achieve\nclinically important improvements in physical and mental component summaries\n38\n. However, the MCID is not an immutable characteristic and may vary by\npopulation and context\n39\n. Concerning patients’ quality of life scores, the MCID for group-level is\nnecessarily smaller than those for individual patient-level\n40\n. We are unaware of a study that determined the MCID for the work ability\nindex among patients with non-alcoholic fatty liver disease (NAFLD), similar to our\nstudy population. \nThe high frequency (54.1%) of poor work ability among patients with NAFLD and the\nnature of the factors associated with poor work ability suggest the need to\nimplement strategies to provide adequate health care among people living with\nHTLV-1.\nOne important limitation of this preliminary cross-sectional study is the lack of\ninformation about the temporal sequence of neurological symptoms and sedentarism\nrelated to the investigated outcomes and poor work ability. However, to the best of\nour knowledge, this is the first study to evaluate work ability and associated\nfactors among people living with HTLV-1. \nThe frequency of poor work ability among people living with HTLV-1 was high and was\nassociated with sedentarism, neurologic symptoms, and low health-related quality of\nlife."],"string":"[\n \"Human T-lymphotropic virus type 1 (HTLV-1) is a type C retrovirus that was first\\nisolated and identified from a patient with cutaneous T-cell malignancy in 1980\\n1\\n. It is transmitted through breastfeeding, sexual contact, blood transfusion,\\nand sharing syringes and needles\\n2\\n. \\nThe prevalence of HTLV-1 infection is poorly known; however, it is estimated that it\\naffects 10-15 million people worldwide\\n3\\n. Clusters of high prevalence were found in nearby areas with negligible\\nprevalence. HTLV-1 infection is endemic in southwestern Japan, sub-Saharan Africa,\\nSouth America, and the Caribbean area, with foci in the Middle East and\\nAustralo-Melanesia\\n4\\n. HTLV-1 infection is frequent in Brazil\\n5\\n, in the State of Bahia\\n6\\n, particularly in its capital, Salvador city, with an estimated prevalence of\\n1.8%\\n7\\n. \\nMost people (approximately 95%) infected with HTLV-1 remain asymptomatic\\n8\\n. Individuals with HTLV-1 have a 57% greater risk of death due to any cause\\nthan those HTLV-1-negative individuals. HTLV-1 is associated with increased odds of\\nseborrheic dermatitis and Sjogren’s syndrome and a lower relative risk of gastric\\ncancer\\n9\\n. HTLV-1 can cause two severe diseases, adult T-cell leukemia/lymphoma (ATLL)\\nand HTLV-1-associated myelopathy/tropical spastic paraparesis (HAM/TSP). It is\\nestimated that 0.25-3% of people infected with HTLV-1 will develop HAM/TSP during\\ntheir lifetime. HAM/TSP mainly occurs in adulthood. HAM/TSP has an insidious onset\\nthat progressively evolves to neurological features, such as spasticity or\\nhyperreflexia of the lower extremities, lower extremity muscle weakness, and urinary\\nbladder disturbances. Approximately 50% of cases present with sensory disturbances\\nand low back pain. HAM/TSP can be associated with other HTLV-1 associated symptoms\\nlike uveitis, myositis, and infective dermatitis\\n10\\n. Patients with HAM/TSP have difficulty performing daily routine activities,\\nparticularly because of a disturbed gait that compromises physical, emotional, and\\nsocial aspects, impairing their quality of life\\n11\\n. Patients with HIV-HTLV-1 coinfection or HTLV-1 infection report more\\ndifficulty performing daily activities than those with exclusive infection with\\nhuman immunodeficiency virus (HIV)\\n12\\n. \\nThe work ability index (WAI) questionnaire is an instrument that evaluates workers’\\nperception of work demands and the environment, work organization, work community,\\npromotion of workers’ health and functional capacity, and promotion of professional\\ncompetence. Good work ability means high-quality work, enjoyment of staying in one’s\\njob, and the expectation of a meaningful retirement\\n13\\n. \\nTherefore, this study aimed to measure the frequency and identify factors associated\\nwith work ability in patients living with HTLV-1. \",\n \"Study design and study population This cross-sectional study was conducted from February 2018 to December 2019 at\\nthe University Hospital, Federal University of Bahia, Brazil. This study is part\\nof broader research that investigates other health aspects of people with\\nHTLV-1\\n14\\n. The target population comprised 209 individuals aged 18 years or older\\nwho were invited to participate in the study. Severe cognitive deficits that\\nprevented the elicitation of reliable information in the interview were an\\nexclusion criterion. There were only two refusals, resulting in a final study\\npopulation of 207 individuals.\\nThis cross-sectional study was conducted from February 2018 to December 2019 at\\nthe University Hospital, Federal University of Bahia, Brazil. This study is part\\nof broader research that investigates other health aspects of people with\\nHTLV-1\\n14\\n. The target population comprised 209 individuals aged 18 years or older\\nwho were invited to participate in the study. Severe cognitive deficits that\\nprevented the elicitation of reliable information in the interview were an\\nexclusion criterion. There were only two refusals, resulting in a final study\\npopulation of 207 individuals.\\nData collection instruments and procedures Participants were interviewed by a member of the research team after the medical\\nconsultation in a quiet room, keeping the patient’s privacy. Information about\\nsociodemographic characteristics (age, race, schooling, civil status, number of\\nchildren, and monthly family income - coded as < 1 Brazilian minimum wage and\\n1-2 Brazilian minimal wage. One Brazilian minimal wage was equivalent to US$ 197\\nby the time of the study) personal habits (smoking, drinking, and sedentarism),\\nhealth-related quality of life, clinical data (comorbidities and HTLV-1\\nsymptoms), and work ability were collected using structured questionnaires. \\nWork ability was used as the dependent variable. Work ability was evaluated using\\nthe WAI questionnaire. WAI is a summary measure of seven dimensions (range\\n7-49): 1 -current work ability compared with the lifetime best, 2 - work ability\\nin relation to the demands of the job, 3 - number of current diseases diagnosed\\nby a physician, 4 - estimated work impairment due to diseases, 5 - sick leave, 6\\n- self-prognosis of work ability 2 years from now, and 7 - mental resources. The\\ntotal score was classified into four work ability categories: poor (7-27\\npoints), moderate (28-36 points), good (37-43 points), and excellent (44-49\\npoints). For the purposes of this study, the four possible subgroups of WAI were\\ncategorized as poor versus others\\n15\\n. The WAI questionnaire was validated in a Brazilian population and\\nshowed satisfactory psychometric properties\\n16\\n.\\nNeurologic evaluation was performed on all 207 individuals at the University\\nHospital, according to the World Health Organization criteria\\n17\\n. Seventy (33.8%) of the 207 individuals in the study presented\\nneurological symptoms; all of them presented weakness and spasticity of one or\\nboth legs, compatible with HAM/TSP. Other diagnostics were 44 lumbar pain, 33\\nneurogenic bladder, 28 hyperreflexia, 11 polyneuropathy, and 5 erectile\\ndysfunction (Figure 1).\\n\\nFIGURE 1:Study population flowchart.\\n\\nHealth-related quality of life was evaluated using the 36-Item Short-Form Health\\nSurvey version 2 (SF-36v2) questionnaire. This instrument comprises 36 items\\nthat generate eight domains: physical functioning, physical role, bodily pain,\\ngeneral health, vitality, social functioning, emotional role, and mental health.\\nTwo summary measures can be calculated from these domains: physical and mental\\ncomponent summaries. The psychometric properties of the SF-36v2 have been\\nvalidated in a Brazilian population\\n18\\n. PRO CoRE software, version 1.4 (Optum Inc., Johnston, RI, USA), was\\nused to score the survey. The normalized scores have a mean of 50 and a standard\\ndeviation of 10, a transformation that enables better comparisons among domains.\\nA commercial license (license number QM025905) granted permission for using the\\nSF-36v2. \\nParticipants were interviewed by a member of the research team after the medical\\nconsultation in a quiet room, keeping the patient’s privacy. Information about\\nsociodemographic characteristics (age, race, schooling, civil status, number of\\nchildren, and monthly family income - coded as < 1 Brazilian minimum wage and\\n1-2 Brazilian minimal wage. One Brazilian minimal wage was equivalent to US$ 197\\nby the time of the study) personal habits (smoking, drinking, and sedentarism),\\nhealth-related quality of life, clinical data (comorbidities and HTLV-1\\nsymptoms), and work ability were collected using structured questionnaires. \\nWork ability was used as the dependent variable. Work ability was evaluated using\\nthe WAI questionnaire. WAI is a summary measure of seven dimensions (range\\n7-49): 1 -current work ability compared with the lifetime best, 2 - work ability\\nin relation to the demands of the job, 3 - number of current diseases diagnosed\\nby a physician, 4 - estimated work impairment due to diseases, 5 - sick leave, 6\\n- self-prognosis of work ability 2 years from now, and 7 - mental resources. The\\ntotal score was classified into four work ability categories: poor (7-27\\npoints), moderate (28-36 points), good (37-43 points), and excellent (44-49\\npoints). For the purposes of this study, the four possible subgroups of WAI were\\ncategorized as poor versus others\\n15\\n. The WAI questionnaire was validated in a Brazilian population and\\nshowed satisfactory psychometric properties\\n16\\n.\\nNeurologic evaluation was performed on all 207 individuals at the University\\nHospital, according to the World Health Organization criteria\\n17\\n. Seventy (33.8%) of the 207 individuals in the study presented\\nneurological symptoms; all of them presented weakness and spasticity of one or\\nboth legs, compatible with HAM/TSP. Other diagnostics were 44 lumbar pain, 33\\nneurogenic bladder, 28 hyperreflexia, 11 polyneuropathy, and 5 erectile\\ndysfunction (Figure 1).\\n\\nFIGURE 1:Study population flowchart.\\n\\nHealth-related quality of life was evaluated using the 36-Item Short-Form Health\\nSurvey version 2 (SF-36v2) questionnaire. This instrument comprises 36 items\\nthat generate eight domains: physical functioning, physical role, bodily pain,\\ngeneral health, vitality, social functioning, emotional role, and mental health.\\nTwo summary measures can be calculated from these domains: physical and mental\\ncomponent summaries. The psychometric properties of the SF-36v2 have been\\nvalidated in a Brazilian population\\n18\\n. PRO CoRE software, version 1.4 (Optum Inc., Johnston, RI, USA), was\\nused to score the survey. The normalized scores have a mean of 50 and a standard\\ndeviation of 10, a transformation that enables better comparisons among domains.\\nA commercial license (license number QM025905) granted permission for using the\\nSF-36v2. \\nLaboratory examinations HTLV-1 antibodies were detected in the blood of the participants by enzyme-linked\\nimmunosorbent assay (ELISA) and confirmed by western blotting at the Infectology\\nResearch Laboratory, University Hospital, Federal University of Bahia.\\nHTLV-1 antibodies were detected in the blood of the participants by enzyme-linked\\nimmunosorbent assay (ELISA) and confirmed by western blotting at the Infectology\\nResearch Laboratory, University Hospital, Federal University of Bahia.\\nStatistical data analysis Differences between subgroups of continuous variables were compared using\\nStudent's t-test. Differences between subgroups of categorical variables were\\ncompared using Pearson's chi-square test. Variables with a\\nP-value < 0.20 in bivariate analysis were selected for\\ncomposing a Poisson regression model with robust variance estimators that had\\nwork ability as the dependent variable\\n19\\n\\n-\\n\\n21\\n. Cronbach’s alpha coefficient was used to evaluate the internal\\nconsistency of the SF-36v2 and WAI instruments; values above 0.70 were\\nconsidered acceptable\\n22\\n\\n-\\n\\n23\\n. \\nDifferences between subgroups of continuous variables were compared using\\nStudent's t-test. Differences between subgroups of categorical variables were\\ncompared using Pearson's chi-square test. Variables with a\\nP-value < 0.20 in bivariate analysis were selected for\\ncomposing a Poisson regression model with robust variance estimators that had\\nwork ability as the dependent variable\\n19\\n\\n-\\n\\n21\\n. Cronbach’s alpha coefficient was used to evaluate the internal\\nconsistency of the SF-36v2 and WAI instruments; values above 0.70 were\\nconsidered acceptable\\n22\\n\\n-\\n\\n23\\n. \\nEthical aspects The research protocol was approved by the research ethics committee of the\\nFederal University of Bahia (opinion number:30762714.4.0000.5577). All the\\nparticipants provided written informed consent. \\nThe research protocol was approved by the research ethics committee of the\\nFederal University of Bahia (opinion number:30762714.4.0000.5577). All the\\nparticipants provided written informed consent. \",\n \"This cross-sectional study was conducted from February 2018 to December 2019 at\\nthe University Hospital, Federal University of Bahia, Brazil. This study is part\\nof broader research that investigates other health aspects of people with\\nHTLV-1\\n14\\n. The target population comprised 209 individuals aged 18 years or older\\nwho were invited to participate in the study. Severe cognitive deficits that\\nprevented the elicitation of reliable information in the interview were an\\nexclusion criterion. There were only two refusals, resulting in a final study\\npopulation of 207 individuals.\",\n \"Participants were interviewed by a member of the research team after the medical\\nconsultation in a quiet room, keeping the patient’s privacy. Information about\\nsociodemographic characteristics (age, race, schooling, civil status, number of\\nchildren, and monthly family income - coded as < 1 Brazilian minimum wage and\\n1-2 Brazilian minimal wage. One Brazilian minimal wage was equivalent to US$ 197\\nby the time of the study) personal habits (smoking, drinking, and sedentarism),\\nhealth-related quality of life, clinical data (comorbidities and HTLV-1\\nsymptoms), and work ability were collected using structured questionnaires. \\nWork ability was used as the dependent variable. Work ability was evaluated using\\nthe WAI questionnaire. WAI is a summary measure of seven dimensions (range\\n7-49): 1 -current work ability compared with the lifetime best, 2 - work ability\\nin relation to the demands of the job, 3 - number of current diseases diagnosed\\nby a physician, 4 - estimated work impairment due to diseases, 5 - sick leave, 6\\n- self-prognosis of work ability 2 years from now, and 7 - mental resources. The\\ntotal score was classified into four work ability categories: poor (7-27\\npoints), moderate (28-36 points), good (37-43 points), and excellent (44-49\\npoints). For the purposes of this study, the four possible subgroups of WAI were\\ncategorized as poor versus others\\n15\\n. The WAI questionnaire was validated in a Brazilian population and\\nshowed satisfactory psychometric properties\\n16\\n.\\nNeurologic evaluation was performed on all 207 individuals at the University\\nHospital, according to the World Health Organization criteria\\n17\\n. Seventy (33.8%) of the 207 individuals in the study presented\\nneurological symptoms; all of them presented weakness and spasticity of one or\\nboth legs, compatible with HAM/TSP. Other diagnostics were 44 lumbar pain, 33\\nneurogenic bladder, 28 hyperreflexia, 11 polyneuropathy, and 5 erectile\\ndysfunction (Figure 1).\\n\\nFIGURE 1:Study population flowchart.\\n\\nHealth-related quality of life was evaluated using the 36-Item Short-Form Health\\nSurvey version 2 (SF-36v2) questionnaire. This instrument comprises 36 items\\nthat generate eight domains: physical functioning, physical role, bodily pain,\\ngeneral health, vitality, social functioning, emotional role, and mental health.\\nTwo summary measures can be calculated from these domains: physical and mental\\ncomponent summaries. The psychometric properties of the SF-36v2 have been\\nvalidated in a Brazilian population\\n18\\n. PRO CoRE software, version 1.4 (Optum Inc., Johnston, RI, USA), was\\nused to score the survey. The normalized scores have a mean of 50 and a standard\\ndeviation of 10, a transformation that enables better comparisons among domains.\\nA commercial license (license number QM025905) granted permission for using the\\nSF-36v2. \",\n \"HTLV-1 antibodies were detected in the blood of the participants by enzyme-linked\\nimmunosorbent assay (ELISA) and confirmed by western blotting at the Infectology\\nResearch Laboratory, University Hospital, Federal University of Bahia.\",\n \"Differences between subgroups of continuous variables were compared using\\nStudent's t-test. Differences between subgroups of categorical variables were\\ncompared using Pearson's chi-square test. Variables with a\\nP-value < 0.20 in bivariate analysis were selected for\\ncomposing a Poisson regression model with robust variance estimators that had\\nwork ability as the dependent variable\\n19\\n\\n-\\n\\n21\\n. Cronbach’s alpha coefficient was used to evaluate the internal\\nconsistency of the SF-36v2 and WAI instruments; values above 0.70 were\\nconsidered acceptable\\n22\\n\\n-\\n\\n23\\n. \",\n \"The research protocol was approved by the research ethics committee of the\\nFederal University of Bahia (opinion number:30762714.4.0000.5577). All the\\nparticipants provided written informed consent. \",\n \"The mean age was 55.2, ranging from 19 to 84 years, 73.0% were females, 100% had a\\nmonthly family income less than US$ 394, and 33.8% presented HAM/TSP. The work\\nability of 207 individuals with HTLV-1 was poor in 54.1% (n = 112), moderate in\\n37.7% (n = 78), and good in 8.2% (n = 17). No individual was classified as having\\nexcellent work ability. The alpha coefficient of the WAI questionnaire was 0.84,\\nindicating a high internal consistency.\\nThe poor work ability prevalence rate was significantly higher (P\\n< 0.20) among individuals who had children, had schooling < 8 years, civil\\nstatus other than stable relation, who did not referred alcohol consumption,\\nsedentary status, comorbidities, and neurological symptoms (Table 1).\\n\\nTABLE 1:Work ability according to characteristics of 207 individuals with\\nHTLV-1, Salvador, Brazil, 2018-2019.\\nWork ability \\n\\n\\n\\nPoor Moderate/good \\n\\n\\n\\n(n = 112) (n = 95) \\n\\n\\nCharacteristicn%n%PR95% CIP-valueSex\\n\\n\\n\\n\\n\\n\\nMale3155.42544.61.030.78-1.360.826Female8153.47046.6\\n\\n\\nRace\\n\\n\\n\\n\\n\\n\\nWhite 1058.8741.21.100.72-1.670.683Other10253.78846.3\\n\\n\\nChildren\\n\\n\\n\\n\\n\\n\\nNo1139.31760.70.700.43-1.120.090Yes10156.47843.6\\n\\n\\nSchooling\\n\\n\\n\\n\\n\\n\\n< 8 years8061.15138.91.451.08-1.950.008≥ 8 years3242.14457.9\\n\\n\\nCivil status\\n\\n\\n\\n\\n\\n\\nOther6658.94641.11.220.94-1.580.131Stable relation4648.44951.6\\n\\n\\nMonthly family income\\n\\n\\n\\n\\n\\n\\n< 1 MW3655.42944.61.040.79-1.350.8031 to 2 MW7653.56646.5\\n\\n\\nSmoking\\n\\n\\n\\n\\n\\n\\nYes1168.8531.21.300.91-1.860.222No10152.99047.1\\n\\n\\nDrinking\\n\\n\\n\\n\\n\\n\\nYes2242.33057.70.730.52-1.030.049No9058.16541.9\\n\\n\\nComorbidities\\n\\n\\n\\n\\n\\n\\nYes9759.16740.91.701.11-2.600.005No1534.92865.1\\n\\n\\nSedentary\\n\\n\\n\\n\\n\\n\\nYes7957.75842.31.220.92-1.630.151No3347.13752.9\\n\\n\\nNeurological symptoms\\n\\n\\n\\n\\n\\n\\nYes5578.61521.41.891.50-2.38< 0.001No5741.68058.4\\n\\n\\n\\n*Fisher test; MW: Brazilian minimal wage\\n(approx. 197.39 US$/month).\\n\\n\\n*Fisher test; MW: Brazilian minimal wage\\n(approx. 197.39 US$/month).\\nBivariate analyses showed that individuals with poor work ability presented\\nsystematically lower (P < 0.001) SF-36 domain scores and\\nphysical and mental component summaries of health-related quality of life and were\\nsignificantly older (P < 0.042) than those with moderate or good\\nwork ability (Table 2).\\n\\nTABLE 2:Work ability according to SF-36 health-related quality of life\\ndomains (mean [SD], in %) and age (mean [SD], in years) of 207\\nindividuals with HTLV-1, Salvador, Brazil, 2018-2019. \\n\\nWork ability \\n\\n\\nPoorModerate/good\\nVariableCronbach alpha(n = 112)(n = 95)\\n\\nP-value\\nPhysical Functioning0.9533.1 (10.9)50.9 (8.9)< 0.001Role Physical0.9529.1 (10.4)48.4 (12.9)< 0.001Bodily Pain0.7834.6 (11.2)47.5 (11.1)< 0.001General Health0.7736.2 (10.0)50.2 (8.9)< 0.001Vitality0.8241.6 (12.7)56.2 (10.0)< 0.001Social Functioning0.7538.3 (14.3)52.9 (8.9)< 0.001Role Emotional0.9432.8 (16.6)49.0 (13.6)< 0.001Mental Health0.8439.1 (15.5)51.6 (10.3)< 0.001Physical Component Summary-32.7 (9.7)49.1 (9.8)< 0.001Mental Component Summary-40.3 (16.6)52.5 (10.7)< 0.001Age-56.8 (12.4)53.3 (12.4)0.042\\n\\nThe Poisson regression model estimated that adjusted prevalence rates (PR) of poor\\nwork ability were 30% higher among sedentary individuals (PR = 1.30; 95% confidence\\ninterval [CI]: 1.03-1.65) and 25% higher among those with neurological symptoms (PR\\n= 1.25; 95% CI: 1.02-1.52). The mean level of the physical component summary of the\\nhealth-related quality of life was 5% lower (PR = 0.95; 95% CI: 0.94-0.96), and the\\nmean level of the mental component summary was 2% lower (PR = 0.98; 95% CI:\\n0.97-0.99) among individuals with poor work ability compared with those with\\nmoderate or good work ability. The alpha coefficients of the eight domains of the\\nSF36v2 questionnaire varied from 0.75 to 0.95, revealing high internal consistency\\n(Table 3).\\n\\nTABLE 3:Results of Poisson regression having the prevalence ratio of low work\\nability as the dependent variable among 207 individuals with HTLV-1,\\nSalvador, Brazil, 2018-2019.Predictors (referent)PR95% CI\\n\\nP-value\\nChildren (Yes)0.910.66-1.260.571Schooling (≥ 8 years)1.050.84--1.310.696Civil status (Stable relation)1.070.87-1.310.502Drinking (No)1.120.86-1.460.392Comorbidities (No)0.980.69-1.350.845Age (Years)1.011.00-1.020.061Sedentary (No)1.301.03-1.650.030Neurological symptoms (No)1.251.02-1.520.028Physical Component Summary (%)0.950.94--0.96< 0.001Mental Component Summary (%)0.980.97-0.99< 0.001\\nPR: adjusted prevalence ratio.\\n\\n\\nPR: adjusted prevalence ratio.\",\n \"Poor work ability was common in the study population (54.1%). In addition, poor work\\nability is associated with an increased risk of sickness absence\\n24\\n, early retirement\\n25\\n, and higher mortality in older age\\n26\\n.\\nThis study among people living with HTLV-1 found that poor work ability was\\nassociated with sedentarism, neurologic symptoms, and low health-related quality of\\nlife in both the physical and mental components.\\nMultivariate analysis estimated that the adjusted prevalence of poor work ability was\\n30% higher among individuals with a sedentary lifestyle and 25% higher among those\\npresenting with neurologic symptoms. People infected with HTLV-1, who already\\npresent with neurological symptoms, are expected to have impaired work ability.\\nPatients with HAM/TSP usually have impaired gait, dependence on daily activities,\\nand a poor quality of life due to intense muscle weakness\\n27\\n. The same reasoning applies to the relationship between sedentarism and poor\\nwork ability\\n28\\n. Unfortunately, this study did not collect information on the temporal\\nsequence of the relationship between these independent variables (sedentarism and\\nneurologic symptoms) and outcomes (work ability).\\nHTLV-1 infection has been associated with several diseases. Fortunately, only a few\\nof these are fatal, such as leukemia. However, this disease is rare and has a\\nrelatively low impact on the community mortality rates\\n9\\n. The results of this study raise awareness of the poorly recognized burden\\nof HTLV-1 infection on the morbidity caused by neurologic symptoms and its impact on\\nwork ability.\\nIndividuals with poor work ability had a lower health-related quality of life than\\nthose with moderate or good work ability. The differences found in the bivariate\\nanalyses were confirmed in the multivariate analyses, which estimated a 5% lower\\nphysical and a 2% lower mental component summary for patients with poor work ability\\nafter adjusting for relevant variables. The complex construct of the WAI\\nquestionnaire has many points of convergence with that of the SF36v2\\n29\\n since both deal with physical and mental demands. Therefore, the WAI score\\nis expected to be strongly associated with SF36v2 dimensions and component\\nsummaries\\n30\\n\\n,\\n\\n31\\n. For example, the progressive and disabling gait disturbances of patients\\nwith HTLV-1 may impair physical, emotional, social, and mental aspects that, in\\nturn, may modify the health-related quality of life perception\\n11\\n.\\nThe magnitude of the differences in physical and mental component summaries according\\nto work ability can be analyzed from the perspective of the minimal clinically\\nimportant difference (MCID)\\n32\\n. The concept of MCID evolved to minimal important difference, defined as\\n“the smallest difference in score in the domain of interest that patients perceive\\nas important, either beneficial or harmful, and that would lead the clinician to\\nconsider a change in the patient’s management\\n33\\n.”\\nThe MCID for physical component summary varied in studies, including patients with\\nmoderate to severe psoriasis (2.5 points)\\n34\\n, undergoing lumbar spine surgeries (4.11-5.21\\n35\\n; and 4.93)\\n36\\n, and surgical (7.83) and non-surgical (2.15) patients with spinal\\ndeformities\\n37\\n. The MDIC for mental component summary was 2.5 points in a study of patients\\nwith moderate to severe psoriasis\\n34\\n. Roughly half of all patients treated for hepatitis C fail to achieve\\nclinically important improvements in physical and mental component summaries\\n38\\n. However, the MCID is not an immutable characteristic and may vary by\\npopulation and context\\n39\\n. Concerning patients’ quality of life scores, the MCID for group-level is\\nnecessarily smaller than those for individual patient-level\\n40\\n. We are unaware of a study that determined the MCID for the work ability\\nindex among patients with non-alcoholic fatty liver disease (NAFLD), similar to our\\nstudy population. \\nThe high frequency (54.1%) of poor work ability among patients with NAFLD and the\\nnature of the factors associated with poor work ability suggest the need to\\nimplement strategies to provide adequate health care among people living with\\nHTLV-1.\\nOne important limitation of this preliminary cross-sectional study is the lack of\\ninformation about the temporal sequence of neurological symptoms and sedentarism\\nrelated to the investigated outcomes and poor work ability. However, to the best of\\nour knowledge, this is the first study to evaluate work ability and associated\\nfactors among people living with HTLV-1. \\nThe frequency of poor work ability among people living with HTLV-1 was high and was\\nassociated with sedentarism, neurologic symptoms, and low health-related quality of\\nlife.\"\n]"},"all_sections_sec_types":{"kind":"list like","value":["intro","methods",null,null,null,null,null,"results","discussion"],"string":"[\n \"intro\",\n \"methods\",\n null,\n null,\n null,\n null,\n null,\n \"results\",\n \"discussion\"\n]"},"keywords":{"kind":"list like","value":["human T-lymphotropic virus 1","Work capacity evaluation","Paraparesis","Tropical spastic","Quality of life"],"string":"[\n \"human T-lymphotropic virus 1\",\n \"Work capacity evaluation\",\n \"Paraparesis\",\n \"Tropical spastic\",\n \"Quality of life\"\n]"},"whole_article_text":{"kind":"string","value":"INTRODUCTION:\nHuman T-lymphotropic virus type 1 (HTLV-1) is a type C retrovirus that was first\nisolated and identified from a patient with cutaneous T-cell malignancy in 1980\n1\n. It is transmitted through breastfeeding, sexual contact, blood transfusion,\nand sharing syringes and needles\n2\n. \nThe prevalence of HTLV-1 infection is poorly known; however, it is estimated that it\naffects 10-15 million people worldwide\n3\n. Clusters of high prevalence were found in nearby areas with negligible\nprevalence. HTLV-1 infection is endemic in southwestern Japan, sub-Saharan Africa,\nSouth America, and the Caribbean area, with foci in the Middle East and\nAustralo-Melanesia\n4\n. HTLV-1 infection is frequent in Brazil\n5\n, in the State of Bahia\n6\n, particularly in its capital, Salvador city, with an estimated prevalence of\n1.8%\n7\n. \nMost people (approximately 95%) infected with HTLV-1 remain asymptomatic\n8\n. Individuals with HTLV-1 have a 57% greater risk of death due to any cause\nthan those HTLV-1-negative individuals. HTLV-1 is associated with increased odds of\nseborrheic dermatitis and Sjogren’s syndrome and a lower relative risk of gastric\ncancer\n9\n. HTLV-1 can cause two severe diseases, adult T-cell leukemia/lymphoma (ATLL)\nand HTLV-1-associated myelopathy/tropical spastic paraparesis (HAM/TSP). It is\nestimated that 0.25-3% of people infected with HTLV-1 will develop HAM/TSP during\ntheir lifetime. HAM/TSP mainly occurs in adulthood. HAM/TSP has an insidious onset\nthat progressively evolves to neurological features, such as spasticity or\nhyperreflexia of the lower extremities, lower extremity muscle weakness, and urinary\nbladder disturbances. Approximately 50% of cases present with sensory disturbances\nand low back pain. HAM/TSP can be associated with other HTLV-1 associated symptoms\nlike uveitis, myositis, and infective dermatitis\n10\n. Patients with HAM/TSP have difficulty performing daily routine activities,\nparticularly because of a disturbed gait that compromises physical, emotional, and\nsocial aspects, impairing their quality of life\n11\n. Patients with HIV-HTLV-1 coinfection or HTLV-1 infection report more\ndifficulty performing daily activities than those with exclusive infection with\nhuman immunodeficiency virus (HIV)\n12\n. \nThe work ability index (WAI) questionnaire is an instrument that evaluates workers’\nperception of work demands and the environment, work organization, work community,\npromotion of workers’ health and functional capacity, and promotion of professional\ncompetence. Good work ability means high-quality work, enjoyment of staying in one’s\njob, and the expectation of a meaningful retirement\n13\n. \nTherefore, this study aimed to measure the frequency and identify factors associated\nwith work ability in patients living with HTLV-1. \n\nMETHODS:\nStudy design and study population This cross-sectional study was conducted from February 2018 to December 2019 at\nthe University Hospital, Federal University of Bahia, Brazil. This study is part\nof broader research that investigates other health aspects of people with\nHTLV-1\n14\n. The target population comprised 209 individuals aged 18 years or older\nwho were invited to participate in the study. Severe cognitive deficits that\nprevented the elicitation of reliable information in the interview were an\nexclusion criterion. There were only two refusals, resulting in a final study\npopulation of 207 individuals.\nThis cross-sectional study was conducted from February 2018 to December 2019 at\nthe University Hospital, Federal University of Bahia, Brazil. This study is part\nof broader research that investigates other health aspects of people with\nHTLV-1\n14\n. The target population comprised 209 individuals aged 18 years or older\nwho were invited to participate in the study. Severe cognitive deficits that\nprevented the elicitation of reliable information in the interview were an\nexclusion criterion. There were only two refusals, resulting in a final study\npopulation of 207 individuals.\nData collection instruments and procedures Participants were interviewed by a member of the research team after the medical\nconsultation in a quiet room, keeping the patient’s privacy. Information about\nsociodemographic characteristics (age, race, schooling, civil status, number of\nchildren, and monthly family income - coded as < 1 Brazilian minimum wage and\n1-2 Brazilian minimal wage. One Brazilian minimal wage was equivalent to US$ 197\nby the time of the study) personal habits (smoking, drinking, and sedentarism),\nhealth-related quality of life, clinical data (comorbidities and HTLV-1\nsymptoms), and work ability were collected using structured questionnaires. \nWork ability was used as the dependent variable. Work ability was evaluated using\nthe WAI questionnaire. WAI is a summary measure of seven dimensions (range\n7-49): 1 -current work ability compared with the lifetime best, 2 - work ability\nin relation to the demands of the job, 3 - number of current diseases diagnosed\nby a physician, 4 - estimated work impairment due to diseases, 5 - sick leave, 6\n- self-prognosis of work ability 2 years from now, and 7 - mental resources. The\ntotal score was classified into four work ability categories: poor (7-27\npoints), moderate (28-36 points), good (37-43 points), and excellent (44-49\npoints). For the purposes of this study, the four possible subgroups of WAI were\ncategorized as poor versus others\n15\n. The WAI questionnaire was validated in a Brazilian population and\nshowed satisfactory psychometric properties\n16\n.\nNeurologic evaluation was performed on all 207 individuals at the University\nHospital, according to the World Health Organization criteria\n17\n. Seventy (33.8%) of the 207 individuals in the study presented\nneurological symptoms; all of them presented weakness and spasticity of one or\nboth legs, compatible with HAM/TSP. Other diagnostics were 44 lumbar pain, 33\nneurogenic bladder, 28 hyperreflexia, 11 polyneuropathy, and 5 erectile\ndysfunction (Figure 1).\n\nFIGURE 1:Study population flowchart.\n\nHealth-related quality of life was evaluated using the 36-Item Short-Form Health\nSurvey version 2 (SF-36v2) questionnaire. This instrument comprises 36 items\nthat generate eight domains: physical functioning, physical role, bodily pain,\ngeneral health, vitality, social functioning, emotional role, and mental health.\nTwo summary measures can be calculated from these domains: physical and mental\ncomponent summaries. The psychometric properties of the SF-36v2 have been\nvalidated in a Brazilian population\n18\n. PRO CoRE software, version 1.4 (Optum Inc., Johnston, RI, USA), was\nused to score the survey. The normalized scores have a mean of 50 and a standard\ndeviation of 10, a transformation that enables better comparisons among domains.\nA commercial license (license number QM025905) granted permission for using the\nSF-36v2. \nParticipants were interviewed by a member of the research team after the medical\nconsultation in a quiet room, keeping the patient’s privacy. Information about\nsociodemographic characteristics (age, race, schooling, civil status, number of\nchildren, and monthly family income - coded as < 1 Brazilian minimum wage and\n1-2 Brazilian minimal wage. One Brazilian minimal wage was equivalent to US$ 197\nby the time of the study) personal habits (smoking, drinking, and sedentarism),\nhealth-related quality of life, clinical data (comorbidities and HTLV-1\nsymptoms), and work ability were collected using structured questionnaires. \nWork ability was used as the dependent variable. Work ability was evaluated using\nthe WAI questionnaire. WAI is a summary measure of seven dimensions (range\n7-49): 1 -current work ability compared with the lifetime best, 2 - work ability\nin relation to the demands of the job, 3 - number of current diseases diagnosed\nby a physician, 4 - estimated work impairment due to diseases, 5 - sick leave, 6\n- self-prognosis of work ability 2 years from now, and 7 - mental resources. The\ntotal score was classified into four work ability categories: poor (7-27\npoints), moderate (28-36 points), good (37-43 points), and excellent (44-49\npoints). For the purposes of this study, the four possible subgroups of WAI were\ncategorized as poor versus others\n15\n. The WAI questionnaire was validated in a Brazilian population and\nshowed satisfactory psychometric properties\n16\n.\nNeurologic evaluation was performed on all 207 individuals at the University\nHospital, according to the World Health Organization criteria\n17\n. Seventy (33.8%) of the 207 individuals in the study presented\nneurological symptoms; all of them presented weakness and spasticity of one or\nboth legs, compatible with HAM/TSP. Other diagnostics were 44 lumbar pain, 33\nneurogenic bladder, 28 hyperreflexia, 11 polyneuropathy, and 5 erectile\ndysfunction (Figure 1).\n\nFIGURE 1:Study population flowchart.\n\nHealth-related quality of life was evaluated using the 36-Item Short-Form Health\nSurvey version 2 (SF-36v2) questionnaire. This instrument comprises 36 items\nthat generate eight domains: physical functioning, physical role, bodily pain,\ngeneral health, vitality, social functioning, emotional role, and mental health.\nTwo summary measures can be calculated from these domains: physical and mental\ncomponent summaries. The psychometric properties of the SF-36v2 have been\nvalidated in a Brazilian population\n18\n. PRO CoRE software, version 1.4 (Optum Inc., Johnston, RI, USA), was\nused to score the survey. The normalized scores have a mean of 50 and a standard\ndeviation of 10, a transformation that enables better comparisons among domains.\nA commercial license (license number QM025905) granted permission for using the\nSF-36v2. \nLaboratory examinations HTLV-1 antibodies were detected in the blood of the participants by enzyme-linked\nimmunosorbent assay (ELISA) and confirmed by western blotting at the Infectology\nResearch Laboratory, University Hospital, Federal University of Bahia.\nHTLV-1 antibodies were detected in the blood of the participants by enzyme-linked\nimmunosorbent assay (ELISA) and confirmed by western blotting at the Infectology\nResearch Laboratory, University Hospital, Federal University of Bahia.\nStatistical data analysis Differences between subgroups of continuous variables were compared using\nStudent's t-test. Differences between subgroups of categorical variables were\ncompared using Pearson's chi-square test. Variables with a\nP-value < 0.20 in bivariate analysis were selected for\ncomposing a Poisson regression model with robust variance estimators that had\nwork ability as the dependent variable\n19\n\n-\n\n21\n. Cronbach’s alpha coefficient was used to evaluate the internal\nconsistency of the SF-36v2 and WAI instruments; values above 0.70 were\nconsidered acceptable\n22\n\n-\n\n23\n. \nDifferences between subgroups of continuous variables were compared using\nStudent's t-test. Differences between subgroups of categorical variables were\ncompared using Pearson's chi-square test. Variables with a\nP-value < 0.20 in bivariate analysis were selected for\ncomposing a Poisson regression model with robust variance estimators that had\nwork ability as the dependent variable\n19\n\n-\n\n21\n. Cronbach’s alpha coefficient was used to evaluate the internal\nconsistency of the SF-36v2 and WAI instruments; values above 0.70 were\nconsidered acceptable\n22\n\n-\n\n23\n. \nEthical aspects The research protocol was approved by the research ethics committee of the\nFederal University of Bahia (opinion number:30762714.4.0000.5577). All the\nparticipants provided written informed consent. \nThe research protocol was approved by the research ethics committee of the\nFederal University of Bahia (opinion number:30762714.4.0000.5577). All the\nparticipants provided written informed consent. \n\nStudy design and study population:\nThis cross-sectional study was conducted from February 2018 to December 2019 at\nthe University Hospital, Federal University of Bahia, Brazil. This study is part\nof broader research that investigates other health aspects of people with\nHTLV-1\n14\n. The target population comprised 209 individuals aged 18 years or older\nwho were invited to participate in the study. Severe cognitive deficits that\nprevented the elicitation of reliable information in the interview were an\nexclusion criterion. There were only two refusals, resulting in a final study\npopulation of 207 individuals.\n\nData collection instruments and procedures:\nParticipants were interviewed by a member of the research team after the medical\nconsultation in a quiet room, keeping the patient’s privacy. Information about\nsociodemographic characteristics (age, race, schooling, civil status, number of\nchildren, and monthly family income - coded as < 1 Brazilian minimum wage and\n1-2 Brazilian minimal wage. One Brazilian minimal wage was equivalent to US$ 197\nby the time of the study) personal habits (smoking, drinking, and sedentarism),\nhealth-related quality of life, clinical data (comorbidities and HTLV-1\nsymptoms), and work ability were collected using structured questionnaires. \nWork ability was used as the dependent variable. Work ability was evaluated using\nthe WAI questionnaire. WAI is a summary measure of seven dimensions (range\n7-49): 1 -current work ability compared with the lifetime best, 2 - work ability\nin relation to the demands of the job, 3 - number of current diseases diagnosed\nby a physician, 4 - estimated work impairment due to diseases, 5 - sick leave, 6\n- self-prognosis of work ability 2 years from now, and 7 - mental resources. The\ntotal score was classified into four work ability categories: poor (7-27\npoints), moderate (28-36 points), good (37-43 points), and excellent (44-49\npoints). For the purposes of this study, the four possible subgroups of WAI were\ncategorized as poor versus others\n15\n. The WAI questionnaire was validated in a Brazilian population and\nshowed satisfactory psychometric properties\n16\n.\nNeurologic evaluation was performed on all 207 individuals at the University\nHospital, according to the World Health Organization criteria\n17\n. Seventy (33.8%) of the 207 individuals in the study presented\nneurological symptoms; all of them presented weakness and spasticity of one or\nboth legs, compatible with HAM/TSP. Other diagnostics were 44 lumbar pain, 33\nneurogenic bladder, 28 hyperreflexia, 11 polyneuropathy, and 5 erectile\ndysfunction (Figure 1).\n\nFIGURE 1:Study population flowchart.\n\nHealth-related quality of life was evaluated using the 36-Item Short-Form Health\nSurvey version 2 (SF-36v2) questionnaire. This instrument comprises 36 items\nthat generate eight domains: physical functioning, physical role, bodily pain,\ngeneral health, vitality, social functioning, emotional role, and mental health.\nTwo summary measures can be calculated from these domains: physical and mental\ncomponent summaries. The psychometric properties of the SF-36v2 have been\nvalidated in a Brazilian population\n18\n. PRO CoRE software, version 1.4 (Optum Inc., Johnston, RI, USA), was\nused to score the survey. The normalized scores have a mean of 50 and a standard\ndeviation of 10, a transformation that enables better comparisons among domains.\nA commercial license (license number QM025905) granted permission for using the\nSF-36v2. \n\nLaboratory examinations:\nHTLV-1 antibodies were detected in the blood of the participants by enzyme-linked\nimmunosorbent assay (ELISA) and confirmed by western blotting at the Infectology\nResearch Laboratory, University Hospital, Federal University of Bahia.\n\nStatistical data analysis:\nDifferences between subgroups of continuous variables were compared using\nStudent's t-test. Differences between subgroups of categorical variables were\ncompared using Pearson's chi-square test. Variables with a\nP-value < 0.20 in bivariate analysis were selected for\ncomposing a Poisson regression model with robust variance estimators that had\nwork ability as the dependent variable\n19\n\n-\n\n21\n. Cronbach’s alpha coefficient was used to evaluate the internal\nconsistency of the SF-36v2 and WAI instruments; values above 0.70 were\nconsidered acceptable\n22\n\n-\n\n23\n. \n\nEthical aspects:\nThe research protocol was approved by the research ethics committee of the\nFederal University of Bahia (opinion number:30762714.4.0000.5577). All the\nparticipants provided written informed consent. \n\nRESULTS:\nThe mean age was 55.2, ranging from 19 to 84 years, 73.0% were females, 100% had a\nmonthly family income less than US$ 394, and 33.8% presented HAM/TSP. The work\nability of 207 individuals with HTLV-1 was poor in 54.1% (n = 112), moderate in\n37.7% (n = 78), and good in 8.2% (n = 17). No individual was classified as having\nexcellent work ability. The alpha coefficient of the WAI questionnaire was 0.84,\nindicating a high internal consistency.\nThe poor work ability prevalence rate was significantly higher (P\n< 0.20) among individuals who had children, had schooling < 8 years, civil\nstatus other than stable relation, who did not referred alcohol consumption,\nsedentary status, comorbidities, and neurological symptoms (Table 1).\n\nTABLE 1:Work ability according to characteristics of 207 individuals with\nHTLV-1, Salvador, Brazil, 2018-2019.\nWork ability \n\n\n\nPoor Moderate/good \n\n\n\n(n = 112) (n = 95) \n\n\nCharacteristicn%n%PR95% CIP-valueSex\n\n\n\n\n\n\nMale3155.42544.61.030.78-1.360.826Female8153.47046.6\n\n\nRace\n\n\n\n\n\n\nWhite 1058.8741.21.100.72-1.670.683Other10253.78846.3\n\n\nChildren\n\n\n\n\n\n\nNo1139.31760.70.700.43-1.120.090Yes10156.47843.6\n\n\nSchooling\n\n\n\n\n\n\n< 8 years8061.15138.91.451.08-1.950.008≥ 8 years3242.14457.9\n\n\nCivil status\n\n\n\n\n\n\nOther6658.94641.11.220.94-1.580.131Stable relation4648.44951.6\n\n\nMonthly family income\n\n\n\n\n\n\n< 1 MW3655.42944.61.040.79-1.350.8031 to 2 MW7653.56646.5\n\n\nSmoking\n\n\n\n\n\n\nYes1168.8531.21.300.91-1.860.222No10152.99047.1\n\n\nDrinking\n\n\n\n\n\n\nYes2242.33057.70.730.52-1.030.049No9058.16541.9\n\n\nComorbidities\n\n\n\n\n\n\nYes9759.16740.91.701.11-2.600.005No1534.92865.1\n\n\nSedentary\n\n\n\n\n\n\nYes7957.75842.31.220.92-1.630.151No3347.13752.9\n\n\nNeurological symptoms\n\n\n\n\n\n\nYes5578.61521.41.891.50-2.38< 0.001No5741.68058.4\n\n\n\n*Fisher test; MW: Brazilian minimal wage\n(approx. 197.39 US$/month).\n\n\n*Fisher test; MW: Brazilian minimal wage\n(approx. 197.39 US$/month).\nBivariate analyses showed that individuals with poor work ability presented\nsystematically lower (P < 0.001) SF-36 domain scores and\nphysical and mental component summaries of health-related quality of life and were\nsignificantly older (P < 0.042) than those with moderate or good\nwork ability (Table 2).\n\nTABLE 2:Work ability according to SF-36 health-related quality of life\ndomains (mean [SD], in %) and age (mean [SD], in years) of 207\nindividuals with HTLV-1, Salvador, Brazil, 2018-2019. \n\nWork ability \n\n\nPoorModerate/good\nVariableCronbach alpha(n = 112)(n = 95)\n\nP-value\nPhysical Functioning0.9533.1 (10.9)50.9 (8.9)< 0.001Role Physical0.9529.1 (10.4)48.4 (12.9)< 0.001Bodily Pain0.7834.6 (11.2)47.5 (11.1)< 0.001General Health0.7736.2 (10.0)50.2 (8.9)< 0.001Vitality0.8241.6 (12.7)56.2 (10.0)< 0.001Social Functioning0.7538.3 (14.3)52.9 (8.9)< 0.001Role Emotional0.9432.8 (16.6)49.0 (13.6)< 0.001Mental Health0.8439.1 (15.5)51.6 (10.3)< 0.001Physical Component Summary-32.7 (9.7)49.1 (9.8)< 0.001Mental Component Summary-40.3 (16.6)52.5 (10.7)< 0.001Age-56.8 (12.4)53.3 (12.4)0.042\n\nThe Poisson regression model estimated that adjusted prevalence rates (PR) of poor\nwork ability were 30% higher among sedentary individuals (PR = 1.30; 95% confidence\ninterval [CI]: 1.03-1.65) and 25% higher among those with neurological symptoms (PR\n= 1.25; 95% CI: 1.02-1.52). The mean level of the physical component summary of the\nhealth-related quality of life was 5% lower (PR = 0.95; 95% CI: 0.94-0.96), and the\nmean level of the mental component summary was 2% lower (PR = 0.98; 95% CI:\n0.97-0.99) among individuals with poor work ability compared with those with\nmoderate or good work ability. The alpha coefficients of the eight domains of the\nSF36v2 questionnaire varied from 0.75 to 0.95, revealing high internal consistency\n(Table 3).\n\nTABLE 3:Results of Poisson regression having the prevalence ratio of low work\nability as the dependent variable among 207 individuals with HTLV-1,\nSalvador, Brazil, 2018-2019.Predictors (referent)PR95% CI\n\nP-value\nChildren (Yes)0.910.66-1.260.571Schooling (≥ 8 years)1.050.84--1.310.696Civil status (Stable relation)1.070.87-1.310.502Drinking (No)1.120.86-1.460.392Comorbidities (No)0.980.69-1.350.845Age (Years)1.011.00-1.020.061Sedentary (No)1.301.03-1.650.030Neurological symptoms (No)1.251.02-1.520.028Physical Component Summary (%)0.950.94--0.96< 0.001Mental Component Summary (%)0.980.97-0.99< 0.001\nPR: adjusted prevalence ratio.\n\n\nPR: adjusted prevalence ratio.\n\nDISCUSSION:\nPoor work ability was common in the study population (54.1%). In addition, poor work\nability is associated with an increased risk of sickness absence\n24\n, early retirement\n25\n, and higher mortality in older age\n26\n.\nThis study among people living with HTLV-1 found that poor work ability was\nassociated with sedentarism, neurologic symptoms, and low health-related quality of\nlife in both the physical and mental components.\nMultivariate analysis estimated that the adjusted prevalence of poor work ability was\n30% higher among individuals with a sedentary lifestyle and 25% higher among those\npresenting with neurologic symptoms. People infected with HTLV-1, who already\npresent with neurological symptoms, are expected to have impaired work ability.\nPatients with HAM/TSP usually have impaired gait, dependence on daily activities,\nand a poor quality of life due to intense muscle weakness\n27\n. The same reasoning applies to the relationship between sedentarism and poor\nwork ability\n28\n. Unfortunately, this study did not collect information on the temporal\nsequence of the relationship between these independent variables (sedentarism and\nneurologic symptoms) and outcomes (work ability).\nHTLV-1 infection has been associated with several diseases. Fortunately, only a few\nof these are fatal, such as leukemia. However, this disease is rare and has a\nrelatively low impact on the community mortality rates\n9\n. The results of this study raise awareness of the poorly recognized burden\nof HTLV-1 infection on the morbidity caused by neurologic symptoms and its impact on\nwork ability.\nIndividuals with poor work ability had a lower health-related quality of life than\nthose with moderate or good work ability. The differences found in the bivariate\nanalyses were confirmed in the multivariate analyses, which estimated a 5% lower\nphysical and a 2% lower mental component summary for patients with poor work ability\nafter adjusting for relevant variables. The complex construct of the WAI\nquestionnaire has many points of convergence with that of the SF36v2\n29\n since both deal with physical and mental demands. Therefore, the WAI score\nis expected to be strongly associated with SF36v2 dimensions and component\nsummaries\n30\n\n,\n\n31\n. For example, the progressive and disabling gait disturbances of patients\nwith HTLV-1 may impair physical, emotional, social, and mental aspects that, in\nturn, may modify the health-related quality of life perception\n11\n.\nThe magnitude of the differences in physical and mental component summaries according\nto work ability can be analyzed from the perspective of the minimal clinically\nimportant difference (MCID)\n32\n. The concept of MCID evolved to minimal important difference, defined as\n“the smallest difference in score in the domain of interest that patients perceive\nas important, either beneficial or harmful, and that would lead the clinician to\nconsider a change in the patient’s management\n33\n.”\nThe MCID for physical component summary varied in studies, including patients with\nmoderate to severe psoriasis (2.5 points)\n34\n, undergoing lumbar spine surgeries (4.11-5.21\n35\n; and 4.93)\n36\n, and surgical (7.83) and non-surgical (2.15) patients with spinal\ndeformities\n37\n. The MDIC for mental component summary was 2.5 points in a study of patients\nwith moderate to severe psoriasis\n34\n. Roughly half of all patients treated for hepatitis C fail to achieve\nclinically important improvements in physical and mental component summaries\n38\n. However, the MCID is not an immutable characteristic and may vary by\npopulation and context\n39\n. Concerning patients’ quality of life scores, the MCID for group-level is\nnecessarily smaller than those for individual patient-level\n40\n. We are unaware of a study that determined the MCID for the work ability\nindex among patients with non-alcoholic fatty liver disease (NAFLD), similar to our\nstudy population. \nThe high frequency (54.1%) of poor work ability among patients with NAFLD and the\nnature of the factors associated with poor work ability suggest the need to\nimplement strategies to provide adequate health care among people living with\nHTLV-1.\nOne important limitation of this preliminary cross-sectional study is the lack of\ninformation about the temporal sequence of neurological symptoms and sedentarism\nrelated to the investigated outcomes and poor work ability. However, to the best of\nour knowledge, this is the first study to evaluate work ability and associated\nfactors among people living with HTLV-1. \nThe frequency of poor work ability among people living with HTLV-1 was high and was\nassociated with sedentarism, neurologic symptoms, and low health-related quality of\nlife.\n\n"},"whole_article_abstract":{"kind":"string","value":"Background:\nInfection with the human T-lymphotropic virus type 1 (HTLV-1) affects an estimated 10-15 million people worldwide. However, knowledge of the impact of HTLV-1 infection on work ability is lacking. This study aimed to measure the frequency and identify factors associated with poor work ability in patients living with HTLV-1.\n\nMethods:\nThis cross-sectional study included 207 individuals infected with HTLV-1 who attended the University Hospital in Salvador, Bahia, Brazil. HTLV-1 antibodies were detected in the participants' blood by enzyme-linked immunosorbent assay (ELISA) and confirmed by western blotting. Participants answered a questionnaire on sociodemographic data, personal habits, clinical data, health-related quality of life, and work ability, evaluated using the work ability index questionnaire. A Poisson regression model with a robust variance estimate was used to identify the factors associated with the prevalence of poor work ability.\n\nResults:\nPatients mean age was 55.2, ranging from 19 to 84 years, 73.0% were females, 100% had monthly family income less than US$ 394, and 33.8% presented HTLV-1 associated myelopathy/tropical spastic paraparesis (HAM/TSP). No individual was classified as having excellent work ability. Poor work ability prevalence was strongly associated (prevalence ratio; 95% confidence interval [CI]) with sedentarism (1.30; 1.03-1.65), neurological symptoms (1.25; 1.02-1.52), and low physical (0.95; 0.94-0.96) and mental (0.98; 0.97-0.99) component summaries of health-related quality of life.\n\nConclusions:\nPoor work ability among people living with HTLV-1 is associated with sedentarism, neurologic symptoms, and low health-related quality of life."},"background_conclusion_text":{"kind":"null"},"background_conclusion_abstract":{"kind":"null"},"whole_article_text_length":{"kind":"number","value":5002,"string":"5,002"},"whole_article_abstract_length":{"kind":"number","value":327,"string":"327"},"other_sections_lengths":{"kind":"list like","value":[106,582,40,109,31],"string":"[\n 106,\n 582,\n 40,\n 109,\n 31\n]"},"num_sections":{"kind":"number","value":9,"string":"9"},"most_frequent_words":{"kind":"list like","value":["work","work ability","ability","study","htlv","health","individuals","poor","physical","population"],"string":"[\n \"work\",\n \"work ability\",\n \"ability\",\n \"study\",\n \"htlv\",\n \"health\",\n \"individuals\",\n \"poor\",\n \"physical\",\n \"population\"\n]"},"keybert_topics":{"kind":"list like","value":["negligible prevalence htlv","infected htlv develop","people infected htlv","htlv infection endemic","prevalence htlv infection"],"string":"[\n \"negligible prevalence htlv\",\n \"infected htlv develop\",\n \"people infected htlv\",\n \"htlv infection endemic\",\n \"prevalence htlv infection\"\n]"},"annotated_base_background_abstract_prompt":{"kind":"string","value":""},"annotated_base_methods_abstract_prompt":{"kind":"string","value":""},"annotated_base_results_abstract_prompt":{"kind":"string","value":""},"annotated_base_conclusions_abstract_prompt":{"kind":"null"},"annotated_base_whole_article_abstract_prompt":{"kind":"string","value":""},"annotated_base_background_conclusion_abstract_prompt":{"kind":"null"},"annotated_keywords_background_abstract_prompt":{"kind":"string","value":"[CONTENT] human T-lymphotropic virus 1 | Work capacity evaluation | Paraparesis | Tropical spastic | Quality of life [SUMMARY]"},"annotated_keywords_methods_abstract_prompt":{"kind":"string","value":"[CONTENT] human T-lymphotropic virus 1 | Work capacity evaluation | Paraparesis | Tropical spastic | Quality of life [SUMMARY]"},"annotated_keywords_results_abstract_prompt":{"kind":"string","value":"[CONTENT] human T-lymphotropic virus 1 | Work capacity evaluation | Paraparesis | Tropical spastic | Quality of life [SUMMARY]"},"annotated_keywords_conclusions_abstract_prompt":{"kind":"null"},"annotated_keywords_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] human T-lymphotropic virus 1 | Work capacity evaluation | Paraparesis | Tropical spastic | Quality of life [SUMMARY]"},"annotated_keywords_background_conclusion_abstract_prompt":{"kind":"null"},"annotated_mesh_background_abstract_prompt":{"kind":"string","value":"[CONTENT] Cross-Sectional Studies | Female | HTLV-I Infections | Human T-lymphotropic virus 1 | Humans | Leukemia, T-Cell | Male | Middle Aged | Paraparesis, Tropical Spastic | Quality of Life | Work Capacity Evaluation [SUMMARY]"},"annotated_mesh_methods_abstract_prompt":{"kind":"string","value":"[CONTENT] Cross-Sectional Studies | Female | HTLV-I Infections | Human T-lymphotropic virus 1 | Humans | Leukemia, T-Cell | Male | Middle Aged | Paraparesis, Tropical Spastic | Quality of Life | Work Capacity Evaluation [SUMMARY]"},"annotated_mesh_results_abstract_prompt":{"kind":"string","value":"[CONTENT] Cross-Sectional Studies | Female | HTLV-I Infections | Human T-lymphotropic virus 1 | Humans | Leukemia, T-Cell | Male | Middle Aged | Paraparesis, Tropical Spastic | Quality of Life | Work Capacity Evaluation [SUMMARY]"},"annotated_mesh_conclusions_abstract_prompt":{"kind":"null"},"annotated_mesh_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] Cross-Sectional Studies | Female | HTLV-I Infections | Human T-lymphotropic virus 1 | Humans | Leukemia, T-Cell | Male | Middle Aged | Paraparesis, Tropical Spastic | Quality of Life | Work Capacity Evaluation [SUMMARY]"},"annotated_mesh_background_conclusion_abstract_prompt":{"kind":"null"},"annotated_keybert_background_abstract_prompt":{"kind":"string","value":"[CONTENT] negligible prevalence htlv | infected htlv develop | people infected htlv | htlv infection endemic | prevalence htlv infection [SUMMARY]"},"annotated_keybert_methods_abstract_prompt":{"kind":"string","value":"[CONTENT] negligible prevalence htlv | infected htlv develop | people infected htlv | htlv infection endemic | prevalence htlv infection [SUMMARY]"},"annotated_keybert_results_abstract_prompt":{"kind":"string","value":"[CONTENT] negligible prevalence htlv | infected htlv develop | people infected htlv | htlv infection endemic | prevalence htlv infection [SUMMARY]"},"annotated_keybert_conclusions_abstract_prompt":{"kind":"null"},"annotated_keybert_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] negligible prevalence htlv | infected htlv develop | people infected htlv | htlv infection endemic | prevalence htlv infection [SUMMARY]"},"annotated_keybert_background_conclusion_abstract_prompt":{"kind":"null"},"annotated_most_frequent_background_abstract_prompt":{"kind":"string","value":"[CONTENT] work | work ability | ability | study | htlv | health | individuals | poor | physical | population [SUMMARY]"},"annotated_most_frequent_methods_abstract_prompt":{"kind":"string","value":"[CONTENT] work | work ability | ability | study | htlv | health | individuals | poor | physical | population [SUMMARY]"},"annotated_most_frequent_results_abstract_prompt":{"kind":"string","value":"[CONTENT] work | work ability | ability | study | htlv | health | individuals | poor | physical | population [SUMMARY]"},"annotated_most_frequent_conclusions_abstract_prompt":{"kind":"null"},"annotated_most_frequent_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] work | work ability | ability | study | htlv | health | individuals | poor | physical | population [SUMMARY]"},"annotated_most_frequent_background_conclusion_abstract_prompt":{"kind":"null"},"annotated_tf_idf_background_abstract_prompt":{"kind":"string","value":"[CONTENT] htlv | infection | associated | work | ham | tsp | ham tsp | htlv infection | htlv associated | prevalence [SUMMARY]"},"annotated_tf_idf_methods_abstract_prompt":{"kind":"string","value":"[CONTENT] study | work | ability | work ability | brazilian | health | population | university | number | points [SUMMARY]"},"annotated_tf_idf_results_abstract_prompt":{"kind":"string","value":"[CONTENT] pr | work ability | work | ability | 95 | table | component summary | ci | component | individuals [SUMMARY]"},"annotated_tf_idf_conclusions_abstract_prompt":{"kind":"null"},"annotated_tf_idf_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] work | ability | work ability | study | htlv | university | research | health | population | individuals [SUMMARY]"},"annotated_tf_idf_background_conclusion_abstract_prompt":{"kind":"null"},"annotated_entity_plan_background_abstract_prompt":{"kind":"string","value":"[CONTENT] 1 | an estimated 10-15 million ||| ||| [SUMMARY]"},"annotated_entity_plan_methods_abstract_prompt":{"kind":"string","value":"[CONTENT] 207 | the University Hospital | Salvador | Bahia | Brazil ||| ELISA ||| ||| Poisson [SUMMARY]"},"annotated_entity_plan_results_abstract_prompt":{"kind":"string","value":"[CONTENT] 55.2 | 19 to 84 years | 73.0% | 100% | monthly | less than US$ 394 | 33.8% ||| ||| 95% ||| CI | 1.30 | 1.03-1.65 | 1.25 | 1.02 | 0.95 | 0.94 | 0.98 | 0.97 [SUMMARY]"},"annotated_entity_plan_conclusions_abstract_prompt":{"kind":"null"},"annotated_entity_plan_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] 1 | an estimated 10-15 million ||| ||| ||| 207 | the University Hospital | Salvador | Bahia | Brazil ||| ELISA ||| ||| Poisson ||| ||| 55.2 | 19 to 84 years | 73.0% | 100% | monthly | less than US$ 394 | 33.8% ||| ||| 95% ||| CI | 1.30 | 1.03-1.65 | 1.25 | 1.02 | 0.95 | 0.94 | 0.98 | 0.97 ||| [SUMMARY]"},"annotated_entity_plan_background_conclusion_abstract_prompt":{"kind":"null"}}},{"rowIdx":287,"cells":{"title":{"kind":"string","value":"Body mass index and survival in women with breast cancer-systematic literature review and meta-analysis of 82 follow-up studies."},"pmid":{"kind":"string","value":"24769692"},"background_abstract":{"kind":"string","value":"Positive association between obesity and survival after breast cancer was demonstrated in previous meta-analyses of published data, but only the results for the comparison of obese versus non-obese was summarised."},"background_abstract_label":{"kind":"string","value":"BACKGROUND"},"methods_abstract":{"kind":"string","value":"We systematically searched in MEDLINE and EMBASE for follow-up studies of breast cancer survivors with body mass index (BMI) before and after diagnosis, and total and cause-specific mortality until June 2013, as part of the World Cancer Research Fund Continuous Update Project. Random-effects meta-analyses were conducted to explore the magnitude and the shape of the associations."},"methods_abstract_label":{"kind":"string","value":"METHODS"},"results_abstract":{"kind":"string","value":"Eighty-two studies, including 213 075 breast cancer survivors with 41 477 deaths (23 182 from breast cancer) were identified. For BMI before diagnosis, compared with normal weight women, the summary relative risks (RRs) of total mortality were 1.41 [95% confidence interval (CI) 1.29-1.53] for obese (BMI >30.0), 1.07 (95 CI 1.02-1.12) for overweight (BMI 25.0-<30.0) and 1.10 (95% CI 0.92-1.31) for underweight (BMI <18.5) women. For obese women, the summary RRs were 1.75 (95% CI 1.26-2.41) for pre-menopausal and 1.34 (95% CI 1.18-1.53) for post-menopausal breast cancer. For each 5 kg/m(2) increment of BMI before, <12 months after, and ≥12 months after diagnosis, increased risks of 17%, 11%, and 8% for total mortality, and 18%, 14%, and 29% for breast cancer mortality were observed, respectively."},"results_abstract_label":{"kind":"string","value":"RESULTS"},"conclusions_abstract":{"kind":"string","value":"Obesity is associated with poorer overall and breast cancer survival in pre- and post-menopausal breast cancer, regardless of when BMI is ascertained. Being overweight is also related to a higher risk of mortality. Randomised clinical trials are needed to test interventions for weight loss and maintenance on survival in women with breast cancer."},"conclusions_abstract_label":{"kind":"string","value":"CONCLUSIONS"},"mesh_descriptor_names":{"kind":"list like","value":["Body Mass Index","Breast Neoplasms","Female","Follow-Up Studies","Humans","MEDLINE","Obesity","Prognosis","Randomized Controlled Trials as Topic","Risk Factors","Survivors"],"string":"[\n \"Body Mass Index\",\n \"Breast Neoplasms\",\n \"Female\",\n \"Follow-Up Studies\",\n \"Humans\",\n \"MEDLINE\",\n \"Obesity\",\n \"Prognosis\",\n \"Randomized Controlled Trials as Topic\",\n \"Risk Factors\",\n \"Survivors\"\n]"},"pmcid":{"kind":"string","value":"4176449"},"background_title":{"kind":"string","value":"introduction"},"background_text":{"kind":"string","value":"The number of female breast cancer survivors is growing because of longer survival as a consequence of advances in treatment and early diagnosis. There were ∼2.6 million female breast cancer survivors in US in 2008 [1], and in the UK, breast cancer accounted for ∼28% of the 2 million cancer survivors in 2008 [2].\nObesity is a pandemic health concern, with over 500 million adults worldwide estimated to be obese and 958 million were overweight in 2008 [3]. One of the established risk factors for breast cancer development in post-menopausal women is obesity [4], which has further been linked to breast cancer recurrence [5] and poorer survival in pre- and post-menopausal breast cancer [6, 7]. Preliminary findings from randomised, controlled trials suggest that lifestyle modifications improved biomarkers associated with breast cancer progression and overall survival [8].\nThe biological mechanisms underlying the association between obesity and breast cancer survival are not established, and could involve interacting mediators of hormones, adipocytokines, and inflammatory cytokines which link to cell survival or apoptosis, migration, and proliferation [9]. Higher level of oestradiol produced in postmenopausal women through aromatisation of androgens in the adipose tissues [10], and higher level of insulin [11], a condition common in obese women, are linked to poorer prognosis in breast cancer. A possible interaction between leptin and insulin [12], and obesity-related markers of inflammation [13] have also been linked to breast cancer outcomes. Non-biological mechanisms could include chemotherapy under-dosing in obese women, suboptimal treatment, and obesity-related complications [14].\nNumerous studies have examined the relationship between obesity and breast cancer outcomes, and past reviews have concluded that obesity is linked to a lower survival; however, when investigated in a meta-analysis of published data, only the results of obese compared with non-obese or lighter women were summarised [6, 7, 15].\nWe carried out a systematic literature review and meta-analysis of published studies to explore the magnitude and the shape of the association between body fatness, as measured by body mass index (BMI), and the risk of total and cause-specific mortality, overall and in women with pre- and post-menopausal breast cancer. As body weight may change close to diagnosis and during primary treatment of breast cancer [16], we examined BMI in three periods: before diagnosis, <12 months after diagnosis, and ≥12 months after breast cancer diagnosis."},"methods_title":{"kind":"string","value":"materials and methods"},"methods_text":{"kind":"string","value":" data sources and search We carried out a systematic literature search, limited to publications in English, for articles on BMI and survival in women with breast cancer in OVID MEDLINE and EMBASE from inception to 30 June 2013 using the search strategy implemented for the WCRF/AICR Continuous Update Project on breast cancer survival. The search strategy contained medical subject headings and text words that covered a broad range of factors on diet, physical activity, and anthropometry. The protocol for the review is available at http://www.dietandcancerreport.org/index.php [17]. In addition, we hand-searched the reference lists of relevant articles, reviews, and meta-analysis papers.\nWe carried out a systematic literature search, limited to publications in English, for articles on BMI and survival in women with breast cancer in OVID MEDLINE and EMBASE from inception to 30 June 2013 using the search strategy implemented for the WCRF/AICR Continuous Update Project on breast cancer survival. The search strategy contained medical subject headings and text words that covered a broad range of factors on diet, physical activity, and anthropometry. The protocol for the review is available at http://www.dietandcancerreport.org/index.php [17]. In addition, we hand-searched the reference lists of relevant articles, reviews, and meta-analysis papers.\n study selection Included were follow-up studies of breast cancer survivors, which reported estimates of the associations of BMI ascertained before and after breast cancer diagnosis with total or cause-specific mortality risks. Studies that investigated BMI after diagnosis were divided into two groups: BMI <12 months after diagnosis (BMI <12 months) and BMI 12 months or more after diagnosis (BMI ≥12 months). Outcomes included total mortality, breast cancer mortality, death from cardiovascular disease, and death from causes other than breast cancer. When multiple publications on the same study population were found, results based on longer follow-up and more outcomes were selected for the meta-analysis.\nIncluded were follow-up studies of breast cancer survivors, which reported estimates of the associations of BMI ascertained before and after breast cancer diagnosis with total or cause-specific mortality risks. Studies that investigated BMI after diagnosis were divided into two groups: BMI <12 months after diagnosis (BMI <12 months) and BMI 12 months or more after diagnosis (BMI ≥12 months). Outcomes included total mortality, breast cancer mortality, death from cardiovascular disease, and death from causes other than breast cancer. When multiple publications on the same study population were found, results based on longer follow-up and more outcomes were selected for the meta-analysis.\n data extraction DSMC, TN, and DA conducted the search. DSMC, ARV, and DNR extracted the study characteristics, tumour-related information, cancer treatment, timing and method of weight and height assessment, BMI levels, number of outcomes and population at-risk, outcome type, estimates of association and their measure of variance [95% confidence interval (CI) or P value], and adjustment factors in the analysis.\nDSMC, TN, and DA conducted the search. DSMC, ARV, and DNR extracted the study characteristics, tumour-related information, cancer treatment, timing and method of weight and height assessment, BMI levels, number of outcomes and population at-risk, outcome type, estimates of association and their measure of variance [95% confidence interval (CI) or P value], and adjustment factors in the analysis.\n statistical analysis Categorical and dose–response meta-analyses were conducted using random-effects models to account for between-study heterogeneity [18]. Summary relative risks (RRs) were estimated using the average of the natural logarithm of the RRs of each study weighted by the inverse of the variance and then unweighted by applying a random-effects variance component which is derived from the extent of variability of the effect sizes of the studies. The maximally adjusted RR estimates were used for the meta-analysis except for the follow-up of randomised, controlled trials [19, 20] where unadjusted results were also included, as these studies mostly involved a more homogeneous study population. BMI or Quetelet's Index (QI) measured in units of kg/m2 was used.\nWe conducted categorical meta-analyses by pooling the categorical results reported in the studies. The studies used different BMI categories. In some studies, underweight (BMI <18.5 kg/m2 according to WHO international classification) and normal weight women (BMI 18.5–<25.0 kg/m2) were classified together but, in some studies, they were classified separately. Similarly, most studies classified overweight (BMI 25.0–<30.0 kg/m2) and obese (BMI ≥30.0 kg/m2) women separately but, in some studies, overweight and obese women were combined. The reference category was normal weight or underweight together with normal weight, depending on the studies. For convenience, the BMI categories are referred to as underweight, normal weight, overweight, and obese in the present review. We derived the RRs for overweight and obese women compared with normal weight women in two studies [19, 21] that had more than four BMI categories using the method of Hamling et al. [22]. Studies that reported results for obese compared with non-obese women were analysed separately.\nThe non-linear dose–response relationship between BMI and mortality was examined using the best-fitting second-order fractional polynomial regression model [23], defined as the one with the lowest deviance. Non-linearity was tested using the likelihood ratio test [24]. In the non-linear meta-analysis, the reference category was the lowest BMI category in each study and RRs were recalculated using the method of Hamling et al. [22] when the reference category was not the lowest BMI category in the study.\nWe also conducted linear dose–response meta-analyses, excluding the category underweight when reported separately in the studies, by pooling estimates of RR per unit increase (with its standard error) provided by the studies, or derived by us from categorical data using generalised least-squares for trend estimation [25]. To estimate the trend, the numbers of outcomes and population at-risk for at least three BMI categories, or the information required to derive them using standard methods [26], and means or medians of the BMI categories, or if not reported in the studies, the estimated midpoints of the categories had to be available. When the extreme BMI categories were open-ended, we used the width of the adjacent close-ended category to estimate the midpoints. Where the RRs were presented by subgroups (age group [27], menopausal status [28, 29], stage [30] or subtype [31] of breast cancer, or others [32–34]), an overall estimate for the study was obtained by a fixed-effect model before pooling in the meta-analysis. We estimated the risk increase of death for an increment of 5 kg/m2 of BMI.\nTo assess heterogeneity, we computed the Cochran Q test and I2 statistic [35]. The cut points of 30% and 50% were used for low, moderate, and substantial level of heterogeneity. Sources of heterogeneity were explored by meta-regression and subgroup analyses using pre-defined factors, including indicators of study quality (menopausal status, hormone receptor status, number of outcomes, length of follow-up, study design, geographic location, BMI assessment, adjustment for confounders, and others). Small study or publication bias was examined by Egger's test [36] and visual inspection of the funnel plots. The influence of each individual study on the summary RR was examined by excluding the study in turn [37]. A P value of <0.05 was considered statistically significant. All analyses were conducted using Stata version 12.1 (Stata Statistical Software: Release 12, StataCorp LP, College Station, TX).\nCategorical and dose–response meta-analyses were conducted using random-effects models to account for between-study heterogeneity [18]. Summary relative risks (RRs) were estimated using the average of the natural logarithm of the RRs of each study weighted by the inverse of the variance and then unweighted by applying a random-effects variance component which is derived from the extent of variability of the effect sizes of the studies. The maximally adjusted RR estimates were used for the meta-analysis except for the follow-up of randomised, controlled trials [19, 20] where unadjusted results were also included, as these studies mostly involved a more homogeneous study population. BMI or Quetelet's Index (QI) measured in units of kg/m2 was used.\nWe conducted categorical meta-analyses by pooling the categorical results reported in the studies. The studies used different BMI categories. In some studies, underweight (BMI <18.5 kg/m2 according to WHO international classification) and normal weight women (BMI 18.5–<25.0 kg/m2) were classified together but, in some studies, they were classified separately. Similarly, most studies classified overweight (BMI 25.0–<30.0 kg/m2) and obese (BMI ≥30.0 kg/m2) women separately but, in some studies, overweight and obese women were combined. The reference category was normal weight or underweight together with normal weight, depending on the studies. For convenience, the BMI categories are referred to as underweight, normal weight, overweight, and obese in the present review. We derived the RRs for overweight and obese women compared with normal weight women in two studies [19, 21] that had more than four BMI categories using the method of Hamling et al. [22]. Studies that reported results for obese compared with non-obese women were analysed separately.\nThe non-linear dose–response relationship between BMI and mortality was examined using the best-fitting second-order fractional polynomial regression model [23], defined as the one with the lowest deviance. Non-linearity was tested using the likelihood ratio test [24]. In the non-linear meta-analysis, the reference category was the lowest BMI category in each study and RRs were recalculated using the method of Hamling et al. [22] when the reference category was not the lowest BMI category in the study.\nWe also conducted linear dose–response meta-analyses, excluding the category underweight when reported separately in the studies, by pooling estimates of RR per unit increase (with its standard error) provided by the studies, or derived by us from categorical data using generalised least-squares for trend estimation [25]. To estimate the trend, the numbers of outcomes and population at-risk for at least three BMI categories, or the information required to derive them using standard methods [26], and means or medians of the BMI categories, or if not reported in the studies, the estimated midpoints of the categories had to be available. When the extreme BMI categories were open-ended, we used the width of the adjacent close-ended category to estimate the midpoints. Where the RRs were presented by subgroups (age group [27], menopausal status [28, 29], stage [30] or subtype [31] of breast cancer, or others [32–34]), an overall estimate for the study was obtained by a fixed-effect model before pooling in the meta-analysis. We estimated the risk increase of death for an increment of 5 kg/m2 of BMI.\nTo assess heterogeneity, we computed the Cochran Q test and I2 statistic [35]. The cut points of 30% and 50% were used for low, moderate, and substantial level of heterogeneity. Sources of heterogeneity were explored by meta-regression and subgroup analyses using pre-defined factors, including indicators of study quality (menopausal status, hormone receptor status, number of outcomes, length of follow-up, study design, geographic location, BMI assessment, adjustment for confounders, and others). Small study or publication bias was examined by Egger's test [36] and visual inspection of the funnel plots. The influence of each individual study on the summary RR was examined by excluding the study in turn [37]. A P value of <0.05 was considered statistically significant. All analyses were conducted using Stata version 12.1 (Stata Statistical Software: Release 12, StataCorp LP, College Station, TX)."},"results_title":{"kind":"string","value":"results"},"results_text":{"kind":"string","value":"A total of 124 publications investigating the relationship of body fatness and mortality in women with breast cancer were identified. We excluded 31 publications, including four publications on other obesity indices [38–41], 12 publications without a measure of association [42–53], and 15 publications superseded by publications of the same study with more outcomes [54–68]. A further 14 publications were excluded because of insufficient data for the meta-analysis (five publications [69–73]) or unadjusted results (nine publications [74–82]), from which nine publications reported statistically significant increased risk of total, breast cancer or non-breast cancer mortality in obese women (before or <12 months after diagnosis) compared with the reference BMI [69, 71–74, 76, 77, 79, 82], two publications reported non-significant inverse associations [75, 80] and three publications reported no association [70, 78, 81] of BMI with survival after breast cancer. Hence, 79 publications from 82 follow-up studies with 41 477 deaths (23 182 from breast cancer) in 213 075 breast cancer survivors were included in the meta-analyses (Figure 1). Supplementary Table S1, available at Annals of Oncology online shows the characteristics of the studies included in the meta-analyses and details of the excluded studies are in supplementary Table S2, available at Annals of Oncology online. Results of the meta-analyses are summarised in Table 1.Table 1.Summary of meta-analyses of BMI and survival in women with breast canceraBMI before diagnosisBMI <12 months after diagnosisBMI ≥12 months after diagnosisNRR (95% CI)I2 (%)PhNRR (95% CI)I2 (%)PhNRR (95% CI)I2 (%)PhTotal mortality Under versus normal weight101.10 (0.92–1.31)48%0.04111.25 (0.99–0.57)63%<0.0131.29 (1.02–1.63)0%0.39 Over versus normal weight191.07 (1.02–1.12)0%0.88221.07 (1.02–1.12)21%0.1840.98 (0.86–1.11)0%0.72 Obese versus normal weight211.41 (1.29–1.53)38%0.04241.23 (1.12–1.33)69%<0.0151.21 (1.06–1.38)0%0.70 Obese versus non-obese–––121.26 (1.07–1.47)80%<0.01––– Per 5 kg/m2 increase151.17 (1.13–1.21)7%0.38121.11 (1.06–1.16)55%0.0141.08 (1.01–1.15)0%0.52Breast cancer mortality Under versus normal weight81.02 (0.85–1.21)31%0.1851.53 (1.27–1.83)0%0.5911.10 (0.15–8.08)– Over versus normal weight211.11 (1.06–1.17)0%0.66121.11 (1.03–1.20)14%0.3121.37 (0.96–1.95)0%0.90 Obese versus normal weight221.35 (1.24–1.47)36%0.05121.25 (1.10–1.42)53%0.0221.68 (0.90–3.15)67%0.08 Obese versus non-obese–––61.26 (1.05–1.51)64%0.02––– Per 5 kg/m2 increase181.18 (1.12–1.25)47%0.0181.14 (1.05–1.24)66%0.0121.29 (0.97–1.72)64%0.10Cardiovascular disease related mortality Over versus normal weight21.01 (0.80–1.29)0%0.87–––––– Obese versus normal weight21.60 (0.66–3.87)78%0.03–––––– Per 5 kg/m2 increase21.21 (0.83–1.77)80%0.03––––––Non-breast cancer mortality Over versus normal weight–––50.96 (0.83–1.11)26%0.25––– Obese versus normal weight–––51.29 (0.99–1.68)72%0.01–––aBMI before and after diagnosis (<12 months after, or ≥12 months after diagnosis) was classified according to the exposure period which the studies referred to in the BMI assessment; the BMI categories were included in the categorical meta-analyses as defined by the studies.Ph, P for heterogeneity between studies.Figure 1.Flowchart of search.\nSummary of meta-analyses of BMI and survival in women with breast cancera\naBMI before and after diagnosis (<12 months after, or ≥12 months after diagnosis) was classified according to the exposure period which the studies referred to in the BMI assessment; the BMI categories were included in the categorical meta-analyses as defined by the studies.\nPh, P for heterogeneity between studies.\nFlowchart of search.\nStudies were follow-up of women with breast cancer identified in prospective aetiologic cohort studies (women were free of cancer at enrolment), or cohorts of breast cancer survivors whose participants were identified in hospitals or through cancer registries, or follow-up of breast cancer patients enrolled in case–control studies or randomised clinical trials.\nSome studies included only premenopausal women [83–85] or postmenopausal women [21, 27, 86–94], but most studies included both. Menopausal status was usually determined at time of diagnosis. Year of diagnosis was from 1957–1965 [70] to 2002–2009 [74]. Patient tumour characteristics and stage of disease at diagnosis varied across studies, and some studies included carcinoma in situ. No all studies provided clinical information on the tumour, treatment, and co-morbidities.\nMost of the studies were based in North America or Europe. There were three studies from each of Australia [79, 95, 96], Korea [97, 98] and China [99–101]; two studies from Japan [71, 102]; one study from Tunisia [103] and four international studies [19, 104–106]. Study size ranged from 96 [107] to 24 698 patients [97]. Total number of deaths ranged from 56 [93] to 7397 [108], and the proportion of deaths from breast cancer ranged from 22% [27] to 98% [84] when reported. All but eight studies [30, 93, 94, 98, 99, 109–111] had an average follow-up of more than 5 years.\n BMI and total mortality categorical meta-analysis For BMI before diagnosis, compared with normal weight women, the summary RRs were 1.41 (95% CI 1.29–1.53, 21 studies) for obese women, 1.07 (95% CI 1.02–1.12, 19 studies) for overweight women, and 1.10 (95% CI 0.92–1.31, 10 studies) for underweight women (Figure 2). For BMI <12 months after diagnosis and the same comparisons, the summary RRs were 1.23 (95% CI 1.12–1.33, 24 studies) for obese women, 1.07 (95% CI 1.02–1.12, 22 studies) for overweight women, and 1.25 (95% CI 0.99–1.57, 11 studies) for underweight women (supplementary Figure S1, available at Annals of Oncology online). Substantial heterogeneities were observed between studies of obese women and underweight women (I2 = 69%, P < 0.01; I2 = 63%, P < 0.01, respectively). For BMI ≥12 months after diagnosis, the summary RRs were 1.21 (95% CI 1.06–1.38, 5 studies) for obese women, 0.98 (95% CI 0.86–1.11, 4 studies) for overweight women, and 1.29 (95% CI 1.02–1.63, 3 studies) for underweight women (supplementary Figure S2, available at Annals of Oncology online). Twelve additional studies reported results for obese versus non-obese women <12 months after diagnosis, and the summary RR was 1.26 (95% CI 1.07–1.47, I2 = 80%, P < 0.01).Figure 2.Categorical meta-analysis of pre-diagnosis BMI and total mortality.\nCategorical meta-analysis of pre-diagnosis BMI and total mortality.\nFor BMI before diagnosis, compared with normal weight women, the summary RRs were 1.41 (95% CI 1.29–1.53, 21 studies) for obese women, 1.07 (95% CI 1.02–1.12, 19 studies) for overweight women, and 1.10 (95% CI 0.92–1.31, 10 studies) for underweight women (Figure 2). For BMI <12 months after diagnosis and the same comparisons, the summary RRs were 1.23 (95% CI 1.12–1.33, 24 studies) for obese women, 1.07 (95% CI 1.02–1.12, 22 studies) for overweight women, and 1.25 (95% CI 0.99–1.57, 11 studies) for underweight women (supplementary Figure S1, available at Annals of Oncology online). Substantial heterogeneities were observed between studies of obese women and underweight women (I2 = 69%, P < 0.01; I2 = 63%, P < 0.01, respectively). For BMI ≥12 months after diagnosis, the summary RRs were 1.21 (95% CI 1.06–1.38, 5 studies) for obese women, 0.98 (95% CI 0.86–1.11, 4 studies) for overweight women, and 1.29 (95% CI 1.02–1.63, 3 studies) for underweight women (supplementary Figure S2, available at Annals of Oncology online). Twelve additional studies reported results for obese versus non-obese women <12 months after diagnosis, and the summary RR was 1.26 (95% CI 1.07–1.47, I2 = 80%, P < 0.01).Figure 2.Categorical meta-analysis of pre-diagnosis BMI and total mortality.\nCategorical meta-analysis of pre-diagnosis BMI and total mortality.\n dose–response meta-analysis There was evidence of a J-shaped association in the non-linear dose–response meta-analyses of BMI before and after diagnosis with total mortality (all P < 0.01; Figure 3), suggesting that underweight women may be at slightly increased risk compared with normal weight women. The curves show linear increasing trends from 20 kg/m2 for BMI before diagnosis and <12 months after diagnosis, and from 25 kg/m2 for BMI ≥12 months after diagnosis. When linear models were fitted excluding the underweight category, the summary RRs of total mortality for each 5 kg/m2 increase in BMI were 1.17 (95% CI 1.13–1.21, 15 studies, 6358 deaths), 1.11 (95% CI 1.06–1.16, 12 studies, 6020 deaths), and 1.08 (95% CI 1.01–1.15, 4 studies, 1703 deaths) for BMI before, <12 months after, and ≥12 months after diagnosis, respectively (Figure 4). Substantial heterogeneity was observed between studies on BMI <12 months after diagnosis (I2 = 55%, P = 0.01).Figure 3.Non-linear dose–response curves of BMI and mortality.Figure 4.Linear dose–response meta-analysis of BMI and total mortality.\nNon-linear dose–response curves of BMI and mortality.\nLinear dose–response meta-analysis of BMI and total mortality.\nThere was evidence of a J-shaped association in the non-linear dose–response meta-analyses of BMI before and after diagnosis with total mortality (all P < 0.01; Figure 3), suggesting that underweight women may be at slightly increased risk compared with normal weight women. The curves show linear increasing trends from 20 kg/m2 for BMI before diagnosis and <12 months after diagnosis, and from 25 kg/m2 for BMI ≥12 months after diagnosis. When linear models were fitted excluding the underweight category, the summary RRs of total mortality for each 5 kg/m2 increase in BMI were 1.17 (95% CI 1.13–1.21, 15 studies, 6358 deaths), 1.11 (95% CI 1.06–1.16, 12 studies, 6020 deaths), and 1.08 (95% CI 1.01–1.15, 4 studies, 1703 deaths) for BMI before, <12 months after, and ≥12 months after diagnosis, respectively (Figure 4). Substantial heterogeneity was observed between studies on BMI <12 months after diagnosis (I2 = 55%, P = 0.01).Figure 3.Non-linear dose–response curves of BMI and mortality.Figure 4.Linear dose–response meta-analysis of BMI and total mortality.\nNon-linear dose–response curves of BMI and mortality.\nLinear dose–response meta-analysis of BMI and total mortality.\n categorical meta-analysis For BMI before diagnosis, compared with normal weight women, the summary RRs were 1.41 (95% CI 1.29–1.53, 21 studies) for obese women, 1.07 (95% CI 1.02–1.12, 19 studies) for overweight women, and 1.10 (95% CI 0.92–1.31, 10 studies) for underweight women (Figure 2). For BMI <12 months after diagnosis and the same comparisons, the summary RRs were 1.23 (95% CI 1.12–1.33, 24 studies) for obese women, 1.07 (95% CI 1.02–1.12, 22 studies) for overweight women, and 1.25 (95% CI 0.99–1.57, 11 studies) for underweight women (supplementary Figure S1, available at Annals of Oncology online). Substantial heterogeneities were observed between studies of obese women and underweight women (I2 = 69%, P < 0.01; I2 = 63%, P < 0.01, respectively). For BMI ≥12 months after diagnosis, the summary RRs were 1.21 (95% CI 1.06–1.38, 5 studies) for obese women, 0.98 (95% CI 0.86–1.11, 4 studies) for overweight women, and 1.29 (95% CI 1.02–1.63, 3 studies) for underweight women (supplementary Figure S2, available at Annals of Oncology online). Twelve additional studies reported results for obese versus non-obese women <12 months after diagnosis, and the summary RR was 1.26 (95% CI 1.07–1.47, I2 = 80%, P < 0.01).Figure 2.Categorical meta-analysis of pre-diagnosis BMI and total mortality.\nCategorical meta-analysis of pre-diagnosis BMI and total mortality.\nFor BMI before diagnosis, compared with normal weight women, the summary RRs were 1.41 (95% CI 1.29–1.53, 21 studies) for obese women, 1.07 (95% CI 1.02–1.12, 19 studies) for overweight women, and 1.10 (95% CI 0.92–1.31, 10 studies) for underweight women (Figure 2). For BMI <12 months after diagnosis and the same comparisons, the summary RRs were 1.23 (95% CI 1.12–1.33, 24 studies) for obese women, 1.07 (95% CI 1.02–1.12, 22 studies) for overweight women, and 1.25 (95% CI 0.99–1.57, 11 studies) for underweight women (supplementary Figure S1, available at Annals of Oncology online). Substantial heterogeneities were observed between studies of obese women and underweight women (I2 = 69%, P < 0.01; I2 = 63%, P < 0.01, respectively). For BMI ≥12 months after diagnosis, the summary RRs were 1.21 (95% CI 1.06–1.38, 5 studies) for obese women, 0.98 (95% CI 0.86–1.11, 4 studies) for overweight women, and 1.29 (95% CI 1.02–1.63, 3 studies) for underweight women (supplementary Figure S2, available at Annals of Oncology online). Twelve additional studies reported results for obese versus non-obese women <12 months after diagnosis, and the summary RR was 1.26 (95% CI 1.07–1.47, I2 = 80%, P < 0.01).Figure 2.Categorical meta-analysis of pre-diagnosis BMI and total mortality.\nCategorical meta-analysis of pre-diagnosis BMI and total mortality.\n dose–response meta-analysis There was evidence of a J-shaped association in the non-linear dose–response meta-analyses of BMI before and after diagnosis with total mortality (all P < 0.01; Figure 3), suggesting that underweight women may be at slightly increased risk compared with normal weight women. The curves show linear increasing trends from 20 kg/m2 for BMI before diagnosis and <12 months after diagnosis, and from 25 kg/m2 for BMI ≥12 months after diagnosis. When linear models were fitted excluding the underweight category, the summary RRs of total mortality for each 5 kg/m2 increase in BMI were 1.17 (95% CI 1.13–1.21, 15 studies, 6358 deaths), 1.11 (95% CI 1.06–1.16, 12 studies, 6020 deaths), and 1.08 (95% CI 1.01–1.15, 4 studies, 1703 deaths) for BMI before, <12 months after, and ≥12 months after diagnosis, respectively (Figure 4). Substantial heterogeneity was observed between studies on BMI <12 months after diagnosis (I2 = 55%, P = 0.01).Figure 3.Non-linear dose–response curves of BMI and mortality.Figure 4.Linear dose–response meta-analysis of BMI and total mortality.\nNon-linear dose–response curves of BMI and mortality.\nLinear dose–response meta-analysis of BMI and total mortality.\nThere was evidence of a J-shaped association in the non-linear dose–response meta-analyses of BMI before and after diagnosis with total mortality (all P < 0.01; Figure 3), suggesting that underweight women may be at slightly increased risk compared with normal weight women. The curves show linear increasing trends from 20 kg/m2 for BMI before diagnosis and <12 months after diagnosis, and from 25 kg/m2 for BMI ≥12 months after diagnosis. When linear models were fitted excluding the underweight category, the summary RRs of total mortality for each 5 kg/m2 increase in BMI were 1.17 (95% CI 1.13–1.21, 15 studies, 6358 deaths), 1.11 (95% CI 1.06–1.16, 12 studies, 6020 deaths), and 1.08 (95% CI 1.01–1.15, 4 studies, 1703 deaths) for BMI before, <12 months after, and ≥12 months after diagnosis, respectively (Figure 4). Substantial heterogeneity was observed between studies on BMI <12 months after diagnosis (I2 = 55%, P = 0.01).Figure 3.Non-linear dose–response curves of BMI and mortality.Figure 4.Linear dose–response meta-analysis of BMI and total mortality.\nNon-linear dose–response curves of BMI and mortality.\nLinear dose–response meta-analysis of BMI and total mortality.\n BMI and breast cancer mortality categorical meta-analysis BMI was significantly associated with breast cancer mortality. Compared with normal weight women, for BMI before diagnosis, the summary RRs were 1.35 (95% CI 1.24–1.47, 22 studies) for obese women, 1.11 (95% CI 1.06–1.17, 21 studies) for overweight women, and 1.02 (95% CI 0.85–1.21, 8 studies) for underweight women (Figure 5). For BMI <12 months after diagnosis, the summary RRs were 1.25 (95% CI 1.10–1.42, 12 studies) for obese women, 1.11 (95% CI 1.03–1.20, 12 studies) for overweight women, and 1.53 (95% CI 1.27–1.83, 5 studies) for underweight women (supplementary Figure S3, available at Annals of Oncology online). Substantial heterogeneity was observed between studies of obese women (I2 = 53%, P = 0.02). For BMI ≥12 months after diagnosis, the summary RRs of the two studies identified were 1.68 (95% CI 0.90–3.15) for obese women and 1.37 (95% CI 0.96–1.95) for overweight women (supplementary Figure S4, available at Annals of Oncology online). The summary of another six studies that reported RRs for obese versus non-obese <12 months after diagnosis was 1.26 (95% CI 1.05–1.51, I2 = 64%, P = 0.02).Figure 5.Categorical meta-analysis of pre-diagnosis BMI and breast cancer mortality.\nCategorical meta-analysis of pre-diagnosis BMI and breast cancer mortality.\nBMI was significantly associated with breast cancer mortality. Compared with normal weight women, for BMI before diagnosis, the summary RRs were 1.35 (95% CI 1.24–1.47, 22 studies) for obese women, 1.11 (95% CI 1.06–1.17, 21 studies) for overweight women, and 1.02 (95% CI 0.85–1.21, 8 studies) for underweight women (Figure 5). For BMI <12 months after diagnosis, the summary RRs were 1.25 (95% CI 1.10–1.42, 12 studies) for obese women, 1.11 (95% CI 1.03–1.20, 12 studies) for overweight women, and 1.53 (95% CI 1.27–1.83, 5 studies) for underweight women (supplementary Figure S3, available at Annals of Oncology online). Substantial heterogeneity was observed between studies of obese women (I2 = 53%, P = 0.02). For BMI ≥12 months after diagnosis, the summary RRs of the two studies identified were 1.68 (95% CI 0.90–3.15) for obese women and 1.37 (95% CI 0.96–1.95) for overweight women (supplementary Figure S4, available at Annals of Oncology online). The summary of another six studies that reported RRs for obese versus non-obese <12 months after diagnosis was 1.26 (95% CI 1.05–1.51, I2 = 64%, P = 0.02).Figure 5.Categorical meta-analysis of pre-diagnosis BMI and breast cancer mortality.\nCategorical meta-analysis of pre-diagnosis BMI and breast cancer mortality.\n dose–response meta-analysis There was no significant evidence of a non-linear relationship between BMI before, <12 months after, and ≥12 months after diagnosis and breast cancer mortality (P = 0.21, P = 1.00, P = 0.86, respectively) (Figure 3). When linear models were fitted excluding data from the underweight category, statistically significant increased risks of breast cancer mortality with BMI before and <12 months after diagnosis were observed (Figure 6). The summary RRs for each 5 kg/m2 increase were 1.18 (95% CI 1.12–1.25, 18 studies, 5262 breast cancer deaths) for BMI before diagnosis and 1.14 (95% CI 1.05–1.24, 8 studies, 3857 breast cancer deaths) for BMI <12 months after diagnosis, with moderate (I2 = 47%, P = 0.01) and substantial (I2 = 66%, P = 0.01) heterogeneities between studies, respectively. Only two studies on BMI ≥12 months after diagnosis and breast cancer mortality (N = 220 deaths) were identified. The summary RR was 1.29 (95% CI 0.97–1.72).Figure 6.Linear dose–response meta-analysis of BMI and breast cancer mortality.\nLinear dose–response meta-analysis of BMI and breast cancer mortality.\nThere was no significant evidence of a non-linear relationship between BMI before, <12 months after, and ≥12 months after diagnosis and breast cancer mortality (P = 0.21, P = 1.00, P = 0.86, respectively) (Figure 3). When linear models were fitted excluding data from the underweight category, statistically significant increased risks of breast cancer mortality with BMI before and <12 months after diagnosis were observed (Figure 6). The summary RRs for each 5 kg/m2 increase were 1.18 (95% CI 1.12–1.25, 18 studies, 5262 breast cancer deaths) for BMI before diagnosis and 1.14 (95% CI 1.05–1.24, 8 studies, 3857 breast cancer deaths) for BMI <12 months after diagnosis, with moderate (I2 = 47%, P = 0.01) and substantial (I2 = 66%, P = 0.01) heterogeneities between studies, respectively. Only two studies on BMI ≥12 months after diagnosis and breast cancer mortality (N = 220 deaths) were identified. The summary RR was 1.29 (95% CI 0.97–1.72).Figure 6.Linear dose–response meta-analysis of BMI and breast cancer mortality.\nLinear dose–response meta-analysis of BMI and breast cancer mortality.\n categorical meta-analysis BMI was significantly associated with breast cancer mortality. Compared with normal weight women, for BMI before diagnosis, the summary RRs were 1.35 (95% CI 1.24–1.47, 22 studies) for obese women, 1.11 (95% CI 1.06–1.17, 21 studies) for overweight women, and 1.02 (95% CI 0.85–1.21, 8 studies) for underweight women (Figure 5). For BMI <12 months after diagnosis, the summary RRs were 1.25 (95% CI 1.10–1.42, 12 studies) for obese women, 1.11 (95% CI 1.03–1.20, 12 studies) for overweight women, and 1.53 (95% CI 1.27–1.83, 5 studies) for underweight women (supplementary Figure S3, available at Annals of Oncology online). Substantial heterogeneity was observed between studies of obese women (I2 = 53%, P = 0.02). For BMI ≥12 months after diagnosis, the summary RRs of the two studies identified were 1.68 (95% CI 0.90–3.15) for obese women and 1.37 (95% CI 0.96–1.95) for overweight women (supplementary Figure S4, available at Annals of Oncology online). The summary of another six studies that reported RRs for obese versus non-obese <12 months after diagnosis was 1.26 (95% CI 1.05–1.51, I2 = 64%, P = 0.02).Figure 5.Categorical meta-analysis of pre-diagnosis BMI and breast cancer mortality.\nCategorical meta-analysis of pre-diagnosis BMI and breast cancer mortality.\nBMI was significantly associated with breast cancer mortality. Compared with normal weight women, for BMI before diagnosis, the summary RRs were 1.35 (95% CI 1.24–1.47, 22 studies) for obese women, 1.11 (95% CI 1.06–1.17, 21 studies) for overweight women, and 1.02 (95% CI 0.85–1.21, 8 studies) for underweight women (Figure 5). For BMI <12 months after diagnosis, the summary RRs were 1.25 (95% CI 1.10–1.42, 12 studies) for obese women, 1.11 (95% CI 1.03–1.20, 12 studies) for overweight women, and 1.53 (95% CI 1.27–1.83, 5 studies) for underweight women (supplementary Figure S3, available at Annals of Oncology online). Substantial heterogeneity was observed between studies of obese women (I2 = 53%, P = 0.02). For BMI ≥12 months after diagnosis, the summary RRs of the two studies identified were 1.68 (95% CI 0.90–3.15) for obese women and 1.37 (95% CI 0.96–1.95) for overweight women (supplementary Figure S4, available at Annals of Oncology online). The summary of another six studies that reported RRs for obese versus non-obese <12 months after diagnosis was 1.26 (95% CI 1.05–1.51, I2 = 64%, P = 0.02).Figure 5.Categorical meta-analysis of pre-diagnosis BMI and breast cancer mortality.\nCategorical meta-analysis of pre-diagnosis BMI and breast cancer mortality.\n dose–response meta-analysis There was no significant evidence of a non-linear relationship between BMI before, <12 months after, and ≥12 months after diagnosis and breast cancer mortality (P = 0.21, P = 1.00, P = 0.86, respectively) (Figure 3). When linear models were fitted excluding data from the underweight category, statistically significant increased risks of breast cancer mortality with BMI before and <12 months after diagnosis were observed (Figure 6). The summary RRs for each 5 kg/m2 increase were 1.18 (95% CI 1.12–1.25, 18 studies, 5262 breast cancer deaths) for BMI before diagnosis and 1.14 (95% CI 1.05–1.24, 8 studies, 3857 breast cancer deaths) for BMI <12 months after diagnosis, with moderate (I2 = 47%, P = 0.01) and substantial (I2 = 66%, P = 0.01) heterogeneities between studies, respectively. Only two studies on BMI ≥12 months after diagnosis and breast cancer mortality (N = 220 deaths) were identified. The summary RR was 1.29 (95% CI 0.97–1.72).Figure 6.Linear dose–response meta-analysis of BMI and breast cancer mortality.\nLinear dose–response meta-analysis of BMI and breast cancer mortality.\nThere was no significant evidence of a non-linear relationship between BMI before, <12 months after, and ≥12 months after diagnosis and breast cancer mortality (P = 0.21, P = 1.00, P = 0.86, respectively) (Figure 3). When linear models were fitted excluding data from the underweight category, statistically significant increased risks of breast cancer mortality with BMI before and <12 months after diagnosis were observed (Figure 6). The summary RRs for each 5 kg/m2 increase were 1.18 (95% CI 1.12–1.25, 18 studies, 5262 breast cancer deaths) for BMI before diagnosis and 1.14 (95% CI 1.05–1.24, 8 studies, 3857 breast cancer deaths) for BMI <12 months after diagnosis, with moderate (I2 = 47%, P = 0.01) and substantial (I2 = 66%, P = 0.01) heterogeneities between studies, respectively. Only two studies on BMI ≥12 months after diagnosis and breast cancer mortality (N = 220 deaths) were identified. The summary RR was 1.29 (95% CI 0.97–1.72).Figure 6.Linear dose–response meta-analysis of BMI and breast cancer mortality.\nLinear dose–response meta-analysis of BMI and breast cancer mortality.\n BMI and other mortality outcomes Only two studies reported results for death from cardiovascular disease (N = 151 deaths) [27, 112]. The summary RR for obese versus normal weight before diagnosis was 1.60 (95% CI 0.66–3.87). No association was observed for overweight versus normal weight (summary RR = 1.01, 95% CI 0.80–1.29). For each 5 kg/m2 increase in BMI, the summary RR was 1.21 (95% CI 0.83–1.77). Five studies reported results for deaths from any cause other than breast cancer (N = 2704 deaths) [21, 34, 108, 113, 114]. The summary RRs were 1.29 (95% CI 0.99–1.68, I2 = 72%, P = 0.01) for obese women, and 0.96 (95% CI 0.83–1.11, I2 = 26%, P = 0.25) for overweight women compared with normal weight women.\nOnly two studies reported results for death from cardiovascular disease (N = 151 deaths) [27, 112]. The summary RR for obese versus normal weight before diagnosis was 1.60 (95% CI 0.66–3.87). No association was observed for overweight versus normal weight (summary RR = 1.01, 95% CI 0.80–1.29). For each 5 kg/m2 increase in BMI, the summary RR was 1.21 (95% CI 0.83–1.77). Five studies reported results for deaths from any cause other than breast cancer (N = 2704 deaths) [21, 34, 108, 113, 114]. The summary RRs were 1.29 (95% CI 0.99–1.68, I2 = 72%, P = 0.01) for obese women, and 0.96 (95% CI 0.83–1.11, I2 = 26%, P = 0.25) for overweight women compared with normal weight women.\n subgroup, meta-regression, and sensitivity analyses The results of the subgroup and meta-regression analyses are in supplementary Tables S3 and S4, available at Annals of Oncology online. Subgroup analysis was not carried out for BMI ≥12 months after diagnosis as the limited number of studies would hinder any meaningful comparisons.\nIncreased risks of mortality were observed in the meta-analyses by menopausal status. While the summary risk estimates seem stronger with premenopausal breast cancer, there was no significant heterogeneity between pre- and post-menopausal breast cancer as shown in the meta-regression analyses (P = 0.28–0.89) (supplementary Tables S3 and S4, available at Annals of Oncology online). For BMI before diagnosis and total mortality, the summary RRs for obese versus normal weight were 1.75 (95% CI 1.26–2.41, I2 = 70%, P < 0.01, 7 studies) in women with pre-menopausal breast cancer and 1.34 (95% CI 1.18–1.53, I2 = 27%, P = 0.20, 9 studies) in women with post-menopausal breast cancer.\nStudies with larger number of deaths [105, 115], conducted in Europe [28, 115], or with weight and height assessed through medical records [28, 104, 115, 116] tended to report weaker associations for BMI <12 months after diagnosis and total mortality compared with other studies (meta-regression P = 0.01, 0.02, 0.01, respectively) (supplementary Table S3, available at Annals of Oncology online); while studies with larger number of deaths [101], conducted in Asia [101, 102], or adjusted for co-morbidity [101, 102] reported weaker associations for BMI <12 months after diagnosis and breast cancer mortality (meta-regression P = 0.01, 0.02, 0.01, respectively) (supplementary Table S4, available at Annals of Oncology online).\nAnalyses stratified by study designs, or restricted to studies with invasive cases only, early-stage non-metastatic cases only, or mammography screening detected cases only, or controlled for previous diseases did not produce results that were materially different from those obtained in the overall analyses (results not shown). Summary risk estimates remained statistically significant when each study was omitted in turn, except for BMI ≥12 months after diagnosis and total mortality. The summary RR was 1.06 (95% CI 0.98–1.15) per 5 kg/m2 increase when Flatt et al. [117] which contributed 315 deaths was omitted.\nThe results of the subgroup and meta-regression analyses are in supplementary Tables S3 and S4, available at Annals of Oncology online. Subgroup analysis was not carried out for BMI ≥12 months after diagnosis as the limited number of studies would hinder any meaningful comparisons.\nIncreased risks of mortality were observed in the meta-analyses by menopausal status. While the summary risk estimates seem stronger with premenopausal breast cancer, there was no significant heterogeneity between pre- and post-menopausal breast cancer as shown in the meta-regression analyses (P = 0.28–0.89) (supplementary Tables S3 and S4, available at Annals of Oncology online). For BMI before diagnosis and total mortality, the summary RRs for obese versus normal weight were 1.75 (95% CI 1.26–2.41, I2 = 70%, P < 0.01, 7 studies) in women with pre-menopausal breast cancer and 1.34 (95% CI 1.18–1.53, I2 = 27%, P = 0.20, 9 studies) in women with post-menopausal breast cancer.\nStudies with larger number of deaths [105, 115], conducted in Europe [28, 115], or with weight and height assessed through medical records [28, 104, 115, 116] tended to report weaker associations for BMI <12 months after diagnosis and total mortality compared with other studies (meta-regression P = 0.01, 0.02, 0.01, respectively) (supplementary Table S3, available at Annals of Oncology online); while studies with larger number of deaths [101], conducted in Asia [101, 102], or adjusted for co-morbidity [101, 102] reported weaker associations for BMI <12 months after diagnosis and breast cancer mortality (meta-regression P = 0.01, 0.02, 0.01, respectively) (supplementary Table S4, available at Annals of Oncology online).\nAnalyses stratified by study designs, or restricted to studies with invasive cases only, early-stage non-metastatic cases only, or mammography screening detected cases only, or controlled for previous diseases did not produce results that were materially different from those obtained in the overall analyses (results not shown). Summary risk estimates remained statistically significant when each study was omitted in turn, except for BMI ≥12 months after diagnosis and total mortality. The summary RR was 1.06 (95% CI 0.98–1.15) per 5 kg/m2 increase when Flatt et al. [117] which contributed 315 deaths was omitted.\n small studies or publication bias Asymmetry was only detected in the funnel plots of BMI <12 months after diagnosis and total mortality, and breast cancer mortality, which suggests that small studies with an inverse association are missing (plots not shown). Egger's tests were borderline significant (P = 0.05) or statistically significant (P = 0.03), respectively.\nAsymmetry was only detected in the funnel plots of BMI <12 months after diagnosis and total mortality, and breast cancer mortality, which suggests that small studies with an inverse association are missing (plots not shown). Egger's tests were borderline significant (P = 0.05) or statistically significant (P = 0.03), respectively."},"conclusions_title":{"kind":"null"},"conclusions_text":{"kind":"null"},"other_sections_titles":{"kind":"list like","value":["data sources and search","study selection","data extraction","statistical analysis","BMI and total mortality","categorical meta-analysis","dose–response meta-analysis","BMI and breast cancer mortality","categorical meta-analysis","dose–response meta-analysis","BMI and other mortality outcomes","subgroup, meta-regression, and sensitivity analyses","small studies or publication bias","funding","disclosure"],"string":"[\n \"data sources and search\",\n \"study selection\",\n \"data extraction\",\n \"statistical analysis\",\n \"BMI and total mortality\",\n \"categorical meta-analysis\",\n \"dose–response meta-analysis\",\n \"BMI and breast cancer mortality\",\n \"categorical meta-analysis\",\n \"dose–response meta-analysis\",\n \"BMI and other mortality outcomes\",\n \"subgroup, meta-regression, and sensitivity analyses\",\n \"small studies or publication bias\",\n \"funding\",\n \"disclosure\"\n]"},"other_sections_texts":{"kind":"list like","value":["We carried out a systematic literature search, limited to publications in English, for articles on BMI and survival in women with breast cancer in OVID MEDLINE and EMBASE from inception to 30 June 2013 using the search strategy implemented for the WCRF/AICR Continuous Update Project on breast cancer survival. The search strategy contained medical subject headings and text words that covered a broad range of factors on diet, physical activity, and anthropometry. The protocol for the review is available at http://www.dietandcancerreport.org/index.php [17]. In addition, we hand-searched the reference lists of relevant articles, reviews, and meta-analysis papers.","Included were follow-up studies of breast cancer survivors, which reported estimates of the associations of BMI ascertained before and after breast cancer diagnosis with total or cause-specific mortality risks. Studies that investigated BMI after diagnosis were divided into two groups: BMI <12 months after diagnosis (BMI <12 months) and BMI 12 months or more after diagnosis (BMI ≥12 months). Outcomes included total mortality, breast cancer mortality, death from cardiovascular disease, and death from causes other than breast cancer. When multiple publications on the same study population were found, results based on longer follow-up and more outcomes were selected for the meta-analysis.","DSMC, TN, and DA conducted the search. DSMC, ARV, and DNR extracted the study characteristics, tumour-related information, cancer treatment, timing and method of weight and height assessment, BMI levels, number of outcomes and population at-risk, outcome type, estimates of association and their measure of variance [95% confidence interval (CI) or P value], and adjustment factors in the analysis.","Categorical and dose–response meta-analyses were conducted using random-effects models to account for between-study heterogeneity [18]. Summary relative risks (RRs) were estimated using the average of the natural logarithm of the RRs of each study weighted by the inverse of the variance and then unweighted by applying a random-effects variance component which is derived from the extent of variability of the effect sizes of the studies. The maximally adjusted RR estimates were used for the meta-analysis except for the follow-up of randomised, controlled trials [19, 20] where unadjusted results were also included, as these studies mostly involved a more homogeneous study population. BMI or Quetelet's Index (QI) measured in units of kg/m2 was used.\nWe conducted categorical meta-analyses by pooling the categorical results reported in the studies. The studies used different BMI categories. In some studies, underweight (BMI <18.5 kg/m2 according to WHO international classification) and normal weight women (BMI 18.5–<25.0 kg/m2) were classified together but, in some studies, they were classified separately. Similarly, most studies classified overweight (BMI 25.0–<30.0 kg/m2) and obese (BMI ≥30.0 kg/m2) women separately but, in some studies, overweight and obese women were combined. The reference category was normal weight or underweight together with normal weight, depending on the studies. For convenience, the BMI categories are referred to as underweight, normal weight, overweight, and obese in the present review. We derived the RRs for overweight and obese women compared with normal weight women in two studies [19, 21] that had more than four BMI categories using the method of Hamling et al. [22]. Studies that reported results for obese compared with non-obese women were analysed separately.\nThe non-linear dose–response relationship between BMI and mortality was examined using the best-fitting second-order fractional polynomial regression model [23], defined as the one with the lowest deviance. Non-linearity was tested using the likelihood ratio test [24]. In the non-linear meta-analysis, the reference category was the lowest BMI category in each study and RRs were recalculated using the method of Hamling et al. [22] when the reference category was not the lowest BMI category in the study.\nWe also conducted linear dose–response meta-analyses, excluding the category underweight when reported separately in the studies, by pooling estimates of RR per unit increase (with its standard error) provided by the studies, or derived by us from categorical data using generalised least-squares for trend estimation [25]. To estimate the trend, the numbers of outcomes and population at-risk for at least three BMI categories, or the information required to derive them using standard methods [26], and means or medians of the BMI categories, or if not reported in the studies, the estimated midpoints of the categories had to be available. When the extreme BMI categories were open-ended, we used the width of the adjacent close-ended category to estimate the midpoints. Where the RRs were presented by subgroups (age group [27], menopausal status [28, 29], stage [30] or subtype [31] of breast cancer, or others [32–34]), an overall estimate for the study was obtained by a fixed-effect model before pooling in the meta-analysis. We estimated the risk increase of death for an increment of 5 kg/m2 of BMI.\nTo assess heterogeneity, we computed the Cochran Q test and I2 statistic [35]. The cut points of 30% and 50% were used for low, moderate, and substantial level of heterogeneity. Sources of heterogeneity were explored by meta-regression and subgroup analyses using pre-defined factors, including indicators of study quality (menopausal status, hormone receptor status, number of outcomes, length of follow-up, study design, geographic location, BMI assessment, adjustment for confounders, and others). Small study or publication bias was examined by Egger's test [36] and visual inspection of the funnel plots. The influence of each individual study on the summary RR was examined by excluding the study in turn [37]. A P value of <0.05 was considered statistically significant. All analyses were conducted using Stata version 12.1 (Stata Statistical Software: Release 12, StataCorp LP, College Station, TX)."," categorical meta-analysis For BMI before diagnosis, compared with normal weight women, the summary RRs were 1.41 (95% CI 1.29–1.53, 21 studies) for obese women, 1.07 (95% CI 1.02–1.12, 19 studies) for overweight women, and 1.10 (95% CI 0.92–1.31, 10 studies) for underweight women (Figure 2). For BMI <12 months after diagnosis and the same comparisons, the summary RRs were 1.23 (95% CI 1.12–1.33, 24 studies) for obese women, 1.07 (95% CI 1.02–1.12, 22 studies) for overweight women, and 1.25 (95% CI 0.99–1.57, 11 studies) for underweight women (supplementary Figure S1, available at Annals of Oncology online). Substantial heterogeneities were observed between studies of obese women and underweight women (I2 = 69%, P < 0.01; I2 = 63%, P < 0.01, respectively). For BMI ≥12 months after diagnosis, the summary RRs were 1.21 (95% CI 1.06–1.38, 5 studies) for obese women, 0.98 (95% CI 0.86–1.11, 4 studies) for overweight women, and 1.29 (95% CI 1.02–1.63, 3 studies) for underweight women (supplementary Figure S2, available at Annals of Oncology online). Twelve additional studies reported results for obese versus non-obese women <12 months after diagnosis, and the summary RR was 1.26 (95% CI 1.07–1.47, I2 = 80%, P < 0.01).Figure 2.Categorical meta-analysis of pre-diagnosis BMI and total mortality.\nCategorical meta-analysis of pre-diagnosis BMI and total mortality.\nFor BMI before diagnosis, compared with normal weight women, the summary RRs were 1.41 (95% CI 1.29–1.53, 21 studies) for obese women, 1.07 (95% CI 1.02–1.12, 19 studies) for overweight women, and 1.10 (95% CI 0.92–1.31, 10 studies) for underweight women (Figure 2). For BMI <12 months after diagnosis and the same comparisons, the summary RRs were 1.23 (95% CI 1.12–1.33, 24 studies) for obese women, 1.07 (95% CI 1.02–1.12, 22 studies) for overweight women, and 1.25 (95% CI 0.99–1.57, 11 studies) for underweight women (supplementary Figure S1, available at Annals of Oncology online). Substantial heterogeneities were observed between studies of obese women and underweight women (I2 = 69%, P < 0.01; I2 = 63%, P < 0.01, respectively). For BMI ≥12 months after diagnosis, the summary RRs were 1.21 (95% CI 1.06–1.38, 5 studies) for obese women, 0.98 (95% CI 0.86–1.11, 4 studies) for overweight women, and 1.29 (95% CI 1.02–1.63, 3 studies) for underweight women (supplementary Figure S2, available at Annals of Oncology online). Twelve additional studies reported results for obese versus non-obese women <12 months after diagnosis, and the summary RR was 1.26 (95% CI 1.07–1.47, I2 = 80%, P < 0.01).Figure 2.Categorical meta-analysis of pre-diagnosis BMI and total mortality.\nCategorical meta-analysis of pre-diagnosis BMI and total mortality.\n dose–response meta-analysis There was evidence of a J-shaped association in the non-linear dose–response meta-analyses of BMI before and after diagnosis with total mortality (all P < 0.01; Figure 3), suggesting that underweight women may be at slightly increased risk compared with normal weight women. The curves show linear increasing trends from 20 kg/m2 for BMI before diagnosis and <12 months after diagnosis, and from 25 kg/m2 for BMI ≥12 months after diagnosis. When linear models were fitted excluding the underweight category, the summary RRs of total mortality for each 5 kg/m2 increase in BMI were 1.17 (95% CI 1.13–1.21, 15 studies, 6358 deaths), 1.11 (95% CI 1.06–1.16, 12 studies, 6020 deaths), and 1.08 (95% CI 1.01–1.15, 4 studies, 1703 deaths) for BMI before, <12 months after, and ≥12 months after diagnosis, respectively (Figure 4). Substantial heterogeneity was observed between studies on BMI <12 months after diagnosis (I2 = 55%, P = 0.01).Figure 3.Non-linear dose–response curves of BMI and mortality.Figure 4.Linear dose–response meta-analysis of BMI and total mortality.\nNon-linear dose–response curves of BMI and mortality.\nLinear dose–response meta-analysis of BMI and total mortality.\nThere was evidence of a J-shaped association in the non-linear dose–response meta-analyses of BMI before and after diagnosis with total mortality (all P < 0.01; Figure 3), suggesting that underweight women may be at slightly increased risk compared with normal weight women. The curves show linear increasing trends from 20 kg/m2 for BMI before diagnosis and <12 months after diagnosis, and from 25 kg/m2 for BMI ≥12 months after diagnosis. When linear models were fitted excluding the underweight category, the summary RRs of total mortality for each 5 kg/m2 increase in BMI were 1.17 (95% CI 1.13–1.21, 15 studies, 6358 deaths), 1.11 (95% CI 1.06–1.16, 12 studies, 6020 deaths), and 1.08 (95% CI 1.01–1.15, 4 studies, 1703 deaths) for BMI before, <12 months after, and ≥12 months after diagnosis, respectively (Figure 4). Substantial heterogeneity was observed between studies on BMI <12 months after diagnosis (I2 = 55%, P = 0.01).Figure 3.Non-linear dose–response curves of BMI and mortality.Figure 4.Linear dose–response meta-analysis of BMI and total mortality.\nNon-linear dose–response curves of BMI and mortality.\nLinear dose–response meta-analysis of BMI and total mortality.","For BMI before diagnosis, compared with normal weight women, the summary RRs were 1.41 (95% CI 1.29–1.53, 21 studies) for obese women, 1.07 (95% CI 1.02–1.12, 19 studies) for overweight women, and 1.10 (95% CI 0.92–1.31, 10 studies) for underweight women (Figure 2). For BMI <12 months after diagnosis and the same comparisons, the summary RRs were 1.23 (95% CI 1.12–1.33, 24 studies) for obese women, 1.07 (95% CI 1.02–1.12, 22 studies) for overweight women, and 1.25 (95% CI 0.99–1.57, 11 studies) for underweight women (supplementary Figure S1, available at Annals of Oncology online). Substantial heterogeneities were observed between studies of obese women and underweight women (I2 = 69%, P < 0.01; I2 = 63%, P < 0.01, respectively). For BMI ≥12 months after diagnosis, the summary RRs were 1.21 (95% CI 1.06–1.38, 5 studies) for obese women, 0.98 (95% CI 0.86–1.11, 4 studies) for overweight women, and 1.29 (95% CI 1.02–1.63, 3 studies) for underweight women (supplementary Figure S2, available at Annals of Oncology online). Twelve additional studies reported results for obese versus non-obese women <12 months after diagnosis, and the summary RR was 1.26 (95% CI 1.07–1.47, I2 = 80%, P < 0.01).Figure 2.Categorical meta-analysis of pre-diagnosis BMI and total mortality.\nCategorical meta-analysis of pre-diagnosis BMI and total mortality.","There was evidence of a J-shaped association in the non-linear dose–response meta-analyses of BMI before and after diagnosis with total mortality (all P < 0.01; Figure 3), suggesting that underweight women may be at slightly increased risk compared with normal weight women. The curves show linear increasing trends from 20 kg/m2 for BMI before diagnosis and <12 months after diagnosis, and from 25 kg/m2 for BMI ≥12 months after diagnosis. When linear models were fitted excluding the underweight category, the summary RRs of total mortality for each 5 kg/m2 increase in BMI were 1.17 (95% CI 1.13–1.21, 15 studies, 6358 deaths), 1.11 (95% CI 1.06–1.16, 12 studies, 6020 deaths), and 1.08 (95% CI 1.01–1.15, 4 studies, 1703 deaths) for BMI before, <12 months after, and ≥12 months after diagnosis, respectively (Figure 4). Substantial heterogeneity was observed between studies on BMI <12 months after diagnosis (I2 = 55%, P = 0.01).Figure 3.Non-linear dose–response curves of BMI and mortality.Figure 4.Linear dose–response meta-analysis of BMI and total mortality.\nNon-linear dose–response curves of BMI and mortality.\nLinear dose–response meta-analysis of BMI and total mortality."," categorical meta-analysis BMI was significantly associated with breast cancer mortality. Compared with normal weight women, for BMI before diagnosis, the summary RRs were 1.35 (95% CI 1.24–1.47, 22 studies) for obese women, 1.11 (95% CI 1.06–1.17, 21 studies) for overweight women, and 1.02 (95% CI 0.85–1.21, 8 studies) for underweight women (Figure 5). For BMI <12 months after diagnosis, the summary RRs were 1.25 (95% CI 1.10–1.42, 12 studies) for obese women, 1.11 (95% CI 1.03–1.20, 12 studies) for overweight women, and 1.53 (95% CI 1.27–1.83, 5 studies) for underweight women (supplementary Figure S3, available at Annals of Oncology online). Substantial heterogeneity was observed between studies of obese women (I2 = 53%, P = 0.02). For BMI ≥12 months after diagnosis, the summary RRs of the two studies identified were 1.68 (95% CI 0.90–3.15) for obese women and 1.37 (95% CI 0.96–1.95) for overweight women (supplementary Figure S4, available at Annals of Oncology online). The summary of another six studies that reported RRs for obese versus non-obese <12 months after diagnosis was 1.26 (95% CI 1.05–1.51, I2 = 64%, P = 0.02).Figure 5.Categorical meta-analysis of pre-diagnosis BMI and breast cancer mortality.\nCategorical meta-analysis of pre-diagnosis BMI and breast cancer mortality.\nBMI was significantly associated with breast cancer mortality. Compared with normal weight women, for BMI before diagnosis, the summary RRs were 1.35 (95% CI 1.24–1.47, 22 studies) for obese women, 1.11 (95% CI 1.06–1.17, 21 studies) for overweight women, and 1.02 (95% CI 0.85–1.21, 8 studies) for underweight women (Figure 5). For BMI <12 months after diagnosis, the summary RRs were 1.25 (95% CI 1.10–1.42, 12 studies) for obese women, 1.11 (95% CI 1.03–1.20, 12 studies) for overweight women, and 1.53 (95% CI 1.27–1.83, 5 studies) for underweight women (supplementary Figure S3, available at Annals of Oncology online). Substantial heterogeneity was observed between studies of obese women (I2 = 53%, P = 0.02). For BMI ≥12 months after diagnosis, the summary RRs of the two studies identified were 1.68 (95% CI 0.90–3.15) for obese women and 1.37 (95% CI 0.96–1.95) for overweight women (supplementary Figure S4, available at Annals of Oncology online). The summary of another six studies that reported RRs for obese versus non-obese <12 months after diagnosis was 1.26 (95% CI 1.05–1.51, I2 = 64%, P = 0.02).Figure 5.Categorical meta-analysis of pre-diagnosis BMI and breast cancer mortality.\nCategorical meta-analysis of pre-diagnosis BMI and breast cancer mortality.\n dose–response meta-analysis There was no significant evidence of a non-linear relationship between BMI before, <12 months after, and ≥12 months after diagnosis and breast cancer mortality (P = 0.21, P = 1.00, P = 0.86, respectively) (Figure 3). When linear models were fitted excluding data from the underweight category, statistically significant increased risks of breast cancer mortality with BMI before and <12 months after diagnosis were observed (Figure 6). The summary RRs for each 5 kg/m2 increase were 1.18 (95% CI 1.12–1.25, 18 studies, 5262 breast cancer deaths) for BMI before diagnosis and 1.14 (95% CI 1.05–1.24, 8 studies, 3857 breast cancer deaths) for BMI <12 months after diagnosis, with moderate (I2 = 47%, P = 0.01) and substantial (I2 = 66%, P = 0.01) heterogeneities between studies, respectively. Only two studies on BMI ≥12 months after diagnosis and breast cancer mortality (N = 220 deaths) were identified. The summary RR was 1.29 (95% CI 0.97–1.72).Figure 6.Linear dose–response meta-analysis of BMI and breast cancer mortality.\nLinear dose–response meta-analysis of BMI and breast cancer mortality.\nThere was no significant evidence of a non-linear relationship between BMI before, <12 months after, and ≥12 months after diagnosis and breast cancer mortality (P = 0.21, P = 1.00, P = 0.86, respectively) (Figure 3). When linear models were fitted excluding data from the underweight category, statistically significant increased risks of breast cancer mortality with BMI before and <12 months after diagnosis were observed (Figure 6). The summary RRs for each 5 kg/m2 increase were 1.18 (95% CI 1.12–1.25, 18 studies, 5262 breast cancer deaths) for BMI before diagnosis and 1.14 (95% CI 1.05–1.24, 8 studies, 3857 breast cancer deaths) for BMI <12 months after diagnosis, with moderate (I2 = 47%, P = 0.01) and substantial (I2 = 66%, P = 0.01) heterogeneities between studies, respectively. Only two studies on BMI ≥12 months after diagnosis and breast cancer mortality (N = 220 deaths) were identified. The summary RR was 1.29 (95% CI 0.97–1.72).Figure 6.Linear dose–response meta-analysis of BMI and breast cancer mortality.\nLinear dose–response meta-analysis of BMI and breast cancer mortality.","BMI was significantly associated with breast cancer mortality. Compared with normal weight women, for BMI before diagnosis, the summary RRs were 1.35 (95% CI 1.24–1.47, 22 studies) for obese women, 1.11 (95% CI 1.06–1.17, 21 studies) for overweight women, and 1.02 (95% CI 0.85–1.21, 8 studies) for underweight women (Figure 5). For BMI <12 months after diagnosis, the summary RRs were 1.25 (95% CI 1.10–1.42, 12 studies) for obese women, 1.11 (95% CI 1.03–1.20, 12 studies) for overweight women, and 1.53 (95% CI 1.27–1.83, 5 studies) for underweight women (supplementary Figure S3, available at Annals of Oncology online). Substantial heterogeneity was observed between studies of obese women (I2 = 53%, P = 0.02). For BMI ≥12 months after diagnosis, the summary RRs of the two studies identified were 1.68 (95% CI 0.90–3.15) for obese women and 1.37 (95% CI 0.96–1.95) for overweight women (supplementary Figure S4, available at Annals of Oncology online). The summary of another six studies that reported RRs for obese versus non-obese <12 months after diagnosis was 1.26 (95% CI 1.05–1.51, I2 = 64%, P = 0.02).Figure 5.Categorical meta-analysis of pre-diagnosis BMI and breast cancer mortality.\nCategorical meta-analysis of pre-diagnosis BMI and breast cancer mortality.","There was no significant evidence of a non-linear relationship between BMI before, <12 months after, and ≥12 months after diagnosis and breast cancer mortality (P = 0.21, P = 1.00, P = 0.86, respectively) (Figure 3). When linear models were fitted excluding data from the underweight category, statistically significant increased risks of breast cancer mortality with BMI before and <12 months after diagnosis were observed (Figure 6). The summary RRs for each 5 kg/m2 increase were 1.18 (95% CI 1.12–1.25, 18 studies, 5262 breast cancer deaths) for BMI before diagnosis and 1.14 (95% CI 1.05–1.24, 8 studies, 3857 breast cancer deaths) for BMI <12 months after diagnosis, with moderate (I2 = 47%, P = 0.01) and substantial (I2 = 66%, P = 0.01) heterogeneities between studies, respectively. Only two studies on BMI ≥12 months after diagnosis and breast cancer mortality (N = 220 deaths) were identified. The summary RR was 1.29 (95% CI 0.97–1.72).Figure 6.Linear dose–response meta-analysis of BMI and breast cancer mortality.\nLinear dose–response meta-analysis of BMI and breast cancer mortality.","Only two studies reported results for death from cardiovascular disease (N = 151 deaths) [27, 112]. The summary RR for obese versus normal weight before diagnosis was 1.60 (95% CI 0.66–3.87). No association was observed for overweight versus normal weight (summary RR = 1.01, 95% CI 0.80–1.29). For each 5 kg/m2 increase in BMI, the summary RR was 1.21 (95% CI 0.83–1.77). Five studies reported results for deaths from any cause other than breast cancer (N = 2704 deaths) [21, 34, 108, 113, 114]. The summary RRs were 1.29 (95% CI 0.99–1.68, I2 = 72%, P = 0.01) for obese women, and 0.96 (95% CI 0.83–1.11, I2 = 26%, P = 0.25) for overweight women compared with normal weight women.","The results of the subgroup and meta-regression analyses are in supplementary Tables S3 and S4, available at Annals of Oncology online. Subgroup analysis was not carried out for BMI ≥12 months after diagnosis as the limited number of studies would hinder any meaningful comparisons.\nIncreased risks of mortality were observed in the meta-analyses by menopausal status. While the summary risk estimates seem stronger with premenopausal breast cancer, there was no significant heterogeneity between pre- and post-menopausal breast cancer as shown in the meta-regression analyses (P = 0.28–0.89) (supplementary Tables S3 and S4, available at Annals of Oncology online). For BMI before diagnosis and total mortality, the summary RRs for obese versus normal weight were 1.75 (95% CI 1.26–2.41, I2 = 70%, P < 0.01, 7 studies) in women with pre-menopausal breast cancer and 1.34 (95% CI 1.18–1.53, I2 = 27%, P = 0.20, 9 studies) in women with post-menopausal breast cancer.\nStudies with larger number of deaths [105, 115], conducted in Europe [28, 115], or with weight and height assessed through medical records [28, 104, 115, 116] tended to report weaker associations for BMI <12 months after diagnosis and total mortality compared with other studies (meta-regression P = 0.01, 0.02, 0.01, respectively) (supplementary Table S3, available at Annals of Oncology online); while studies with larger number of deaths [101], conducted in Asia [101, 102], or adjusted for co-morbidity [101, 102] reported weaker associations for BMI <12 months after diagnosis and breast cancer mortality (meta-regression P = 0.01, 0.02, 0.01, respectively) (supplementary Table S4, available at Annals of Oncology online).\nAnalyses stratified by study designs, or restricted to studies with invasive cases only, early-stage non-metastatic cases only, or mammography screening detected cases only, or controlled for previous diseases did not produce results that were materially different from those obtained in the overall analyses (results not shown). Summary risk estimates remained statistically significant when each study was omitted in turn, except for BMI ≥12 months after diagnosis and total mortality. The summary RR was 1.06 (95% CI 0.98–1.15) per 5 kg/m2 increase when Flatt et al. [117] which contributed 315 deaths was omitted.","Asymmetry was only detected in the funnel plots of BMI <12 months after diagnosis and total mortality, and breast cancer mortality, which suggests that small studies with an inverse association are missing (plots not shown). Egger's tests were borderline significant (P = 0.05) or statistically significant (P = 0.03), respectively.","This work was supported by the World Cancer Research Fund International (grant number: 2007/SP01) (http://www.wcrf-uk.org/). The funder of this study had no role in the decisions about the design and conduct of the study; collection, management, analysis, or interpretation of the data; or the preparation, review, or approval of the manuscript. The views expressed in this review are the opinions of the authors. They may not represent the views of the World Cancer Research Fund International/American Institute for Cancer Research and may differ from those in future updates of the evidence related to food, nutrition, physical activity, and cancer survival.","DCG reports personal fees from World Cancer Research Fund/American Institute for Cancer Research, during the conduct of the study; grants from Danone, and grants from Kelloggs, outside the submitted work. AM reports personal fees from Metagenics/Metaproteomics, personal fees from Pfizer, outside the submitted work. All remaining authors have declared no conflicts of interest."],"string":"[\n \"We carried out a systematic literature search, limited to publications in English, for articles on BMI and survival in women with breast cancer in OVID MEDLINE and EMBASE from inception to 30 June 2013 using the search strategy implemented for the WCRF/AICR Continuous Update Project on breast cancer survival. The search strategy contained medical subject headings and text words that covered a broad range of factors on diet, physical activity, and anthropometry. The protocol for the review is available at http://www.dietandcancerreport.org/index.php [17]. In addition, we hand-searched the reference lists of relevant articles, reviews, and meta-analysis papers.\",\n \"Included were follow-up studies of breast cancer survivors, which reported estimates of the associations of BMI ascertained before and after breast cancer diagnosis with total or cause-specific mortality risks. Studies that investigated BMI after diagnosis were divided into two groups: BMI <12 months after diagnosis (BMI <12 months) and BMI 12 months or more after diagnosis (BMI ≥12 months). Outcomes included total mortality, breast cancer mortality, death from cardiovascular disease, and death from causes other than breast cancer. When multiple publications on the same study population were found, results based on longer follow-up and more outcomes were selected for the meta-analysis.\",\n \"DSMC, TN, and DA conducted the search. DSMC, ARV, and DNR extracted the study characteristics, tumour-related information, cancer treatment, timing and method of weight and height assessment, BMI levels, number of outcomes and population at-risk, outcome type, estimates of association and their measure of variance [95% confidence interval (CI) or P value], and adjustment factors in the analysis.\",\n \"Categorical and dose–response meta-analyses were conducted using random-effects models to account for between-study heterogeneity [18]. Summary relative risks (RRs) were estimated using the average of the natural logarithm of the RRs of each study weighted by the inverse of the variance and then unweighted by applying a random-effects variance component which is derived from the extent of variability of the effect sizes of the studies. The maximally adjusted RR estimates were used for the meta-analysis except for the follow-up of randomised, controlled trials [19, 20] where unadjusted results were also included, as these studies mostly involved a more homogeneous study population. BMI or Quetelet's Index (QI) measured in units of kg/m2 was used.\\nWe conducted categorical meta-analyses by pooling the categorical results reported in the studies. The studies used different BMI categories. In some studies, underweight (BMI <18.5 kg/m2 according to WHO international classification) and normal weight women (BMI 18.5–<25.0 kg/m2) were classified together but, in some studies, they were classified separately. Similarly, most studies classified overweight (BMI 25.0–<30.0 kg/m2) and obese (BMI ≥30.0 kg/m2) women separately but, in some studies, overweight and obese women were combined. The reference category was normal weight or underweight together with normal weight, depending on the studies. For convenience, the BMI categories are referred to as underweight, normal weight, overweight, and obese in the present review. We derived the RRs for overweight and obese women compared with normal weight women in two studies [19, 21] that had more than four BMI categories using the method of Hamling et al. [22]. Studies that reported results for obese compared with non-obese women were analysed separately.\\nThe non-linear dose–response relationship between BMI and mortality was examined using the best-fitting second-order fractional polynomial regression model [23], defined as the one with the lowest deviance. Non-linearity was tested using the likelihood ratio test [24]. In the non-linear meta-analysis, the reference category was the lowest BMI category in each study and RRs were recalculated using the method of Hamling et al. [22] when the reference category was not the lowest BMI category in the study.\\nWe also conducted linear dose–response meta-analyses, excluding the category underweight when reported separately in the studies, by pooling estimates of RR per unit increase (with its standard error) provided by the studies, or derived by us from categorical data using generalised least-squares for trend estimation [25]. To estimate the trend, the numbers of outcomes and population at-risk for at least three BMI categories, or the information required to derive them using standard methods [26], and means or medians of the BMI categories, or if not reported in the studies, the estimated midpoints of the categories had to be available. When the extreme BMI categories were open-ended, we used the width of the adjacent close-ended category to estimate the midpoints. Where the RRs were presented by subgroups (age group [27], menopausal status [28, 29], stage [30] or subtype [31] of breast cancer, or others [32–34]), an overall estimate for the study was obtained by a fixed-effect model before pooling in the meta-analysis. We estimated the risk increase of death for an increment of 5 kg/m2 of BMI.\\nTo assess heterogeneity, we computed the Cochran Q test and I2 statistic [35]. The cut points of 30% and 50% were used for low, moderate, and substantial level of heterogeneity. Sources of heterogeneity were explored by meta-regression and subgroup analyses using pre-defined factors, including indicators of study quality (menopausal status, hormone receptor status, number of outcomes, length of follow-up, study design, geographic location, BMI assessment, adjustment for confounders, and others). Small study or publication bias was examined by Egger's test [36] and visual inspection of the funnel plots. The influence of each individual study on the summary RR was examined by excluding the study in turn [37]. A P value of <0.05 was considered statistically significant. All analyses were conducted using Stata version 12.1 (Stata Statistical Software: Release 12, StataCorp LP, College Station, TX).\",\n \" categorical meta-analysis For BMI before diagnosis, compared with normal weight women, the summary RRs were 1.41 (95% CI 1.29–1.53, 21 studies) for obese women, 1.07 (95% CI 1.02–1.12, 19 studies) for overweight women, and 1.10 (95% CI 0.92–1.31, 10 studies) for underweight women (Figure 2). For BMI <12 months after diagnosis and the same comparisons, the summary RRs were 1.23 (95% CI 1.12–1.33, 24 studies) for obese women, 1.07 (95% CI 1.02–1.12, 22 studies) for overweight women, and 1.25 (95% CI 0.99–1.57, 11 studies) for underweight women (supplementary Figure S1, available at Annals of Oncology online). Substantial heterogeneities were observed between studies of obese women and underweight women (I2 = 69%, P < 0.01; I2 = 63%, P < 0.01, respectively). For BMI ≥12 months after diagnosis, the summary RRs were 1.21 (95% CI 1.06–1.38, 5 studies) for obese women, 0.98 (95% CI 0.86–1.11, 4 studies) for overweight women, and 1.29 (95% CI 1.02–1.63, 3 studies) for underweight women (supplementary Figure S2, available at Annals of Oncology online). Twelve additional studies reported results for obese versus non-obese women <12 months after diagnosis, and the summary RR was 1.26 (95% CI 1.07–1.47, I2 = 80%, P < 0.01).Figure 2.Categorical meta-analysis of pre-diagnosis BMI and total mortality.\\nCategorical meta-analysis of pre-diagnosis BMI and total mortality.\\nFor BMI before diagnosis, compared with normal weight women, the summary RRs were 1.41 (95% CI 1.29–1.53, 21 studies) for obese women, 1.07 (95% CI 1.02–1.12, 19 studies) for overweight women, and 1.10 (95% CI 0.92–1.31, 10 studies) for underweight women (Figure 2). For BMI <12 months after diagnosis and the same comparisons, the summary RRs were 1.23 (95% CI 1.12–1.33, 24 studies) for obese women, 1.07 (95% CI 1.02–1.12, 22 studies) for overweight women, and 1.25 (95% CI 0.99–1.57, 11 studies) for underweight women (supplementary Figure S1, available at Annals of Oncology online). Substantial heterogeneities were observed between studies of obese women and underweight women (I2 = 69%, P < 0.01; I2 = 63%, P < 0.01, respectively). For BMI ≥12 months after diagnosis, the summary RRs were 1.21 (95% CI 1.06–1.38, 5 studies) for obese women, 0.98 (95% CI 0.86–1.11, 4 studies) for overweight women, and 1.29 (95% CI 1.02–1.63, 3 studies) for underweight women (supplementary Figure S2, available at Annals of Oncology online). Twelve additional studies reported results for obese versus non-obese women <12 months after diagnosis, and the summary RR was 1.26 (95% CI 1.07–1.47, I2 = 80%, P < 0.01).Figure 2.Categorical meta-analysis of pre-diagnosis BMI and total mortality.\\nCategorical meta-analysis of pre-diagnosis BMI and total mortality.\\n dose–response meta-analysis There was evidence of a J-shaped association in the non-linear dose–response meta-analyses of BMI before and after diagnosis with total mortality (all P < 0.01; Figure 3), suggesting that underweight women may be at slightly increased risk compared with normal weight women. The curves show linear increasing trends from 20 kg/m2 for BMI before diagnosis and <12 months after diagnosis, and from 25 kg/m2 for BMI ≥12 months after diagnosis. When linear models were fitted excluding the underweight category, the summary RRs of total mortality for each 5 kg/m2 increase in BMI were 1.17 (95% CI 1.13–1.21, 15 studies, 6358 deaths), 1.11 (95% CI 1.06–1.16, 12 studies, 6020 deaths), and 1.08 (95% CI 1.01–1.15, 4 studies, 1703 deaths) for BMI before, <12 months after, and ≥12 months after diagnosis, respectively (Figure 4). Substantial heterogeneity was observed between studies on BMI <12 months after diagnosis (I2 = 55%, P = 0.01).Figure 3.Non-linear dose–response curves of BMI and mortality.Figure 4.Linear dose–response meta-analysis of BMI and total mortality.\\nNon-linear dose–response curves of BMI and mortality.\\nLinear dose–response meta-analysis of BMI and total mortality.\\nThere was evidence of a J-shaped association in the non-linear dose–response meta-analyses of BMI before and after diagnosis with total mortality (all P < 0.01; Figure 3), suggesting that underweight women may be at slightly increased risk compared with normal weight women. The curves show linear increasing trends from 20 kg/m2 for BMI before diagnosis and <12 months after diagnosis, and from 25 kg/m2 for BMI ≥12 months after diagnosis. When linear models were fitted excluding the underweight category, the summary RRs of total mortality for each 5 kg/m2 increase in BMI were 1.17 (95% CI 1.13–1.21, 15 studies, 6358 deaths), 1.11 (95% CI 1.06–1.16, 12 studies, 6020 deaths), and 1.08 (95% CI 1.01–1.15, 4 studies, 1703 deaths) for BMI before, <12 months after, and ≥12 months after diagnosis, respectively (Figure 4). Substantial heterogeneity was observed between studies on BMI <12 months after diagnosis (I2 = 55%, P = 0.01).Figure 3.Non-linear dose–response curves of BMI and mortality.Figure 4.Linear dose–response meta-analysis of BMI and total mortality.\\nNon-linear dose–response curves of BMI and mortality.\\nLinear dose–response meta-analysis of BMI and total mortality.\",\n \"For BMI before diagnosis, compared with normal weight women, the summary RRs were 1.41 (95% CI 1.29–1.53, 21 studies) for obese women, 1.07 (95% CI 1.02–1.12, 19 studies) for overweight women, and 1.10 (95% CI 0.92–1.31, 10 studies) for underweight women (Figure 2). For BMI <12 months after diagnosis and the same comparisons, the summary RRs were 1.23 (95% CI 1.12–1.33, 24 studies) for obese women, 1.07 (95% CI 1.02–1.12, 22 studies) for overweight women, and 1.25 (95% CI 0.99–1.57, 11 studies) for underweight women (supplementary Figure S1, available at Annals of Oncology online). Substantial heterogeneities were observed between studies of obese women and underweight women (I2 = 69%, P < 0.01; I2 = 63%, P < 0.01, respectively). For BMI ≥12 months after diagnosis, the summary RRs were 1.21 (95% CI 1.06–1.38, 5 studies) for obese women, 0.98 (95% CI 0.86–1.11, 4 studies) for overweight women, and 1.29 (95% CI 1.02–1.63, 3 studies) for underweight women (supplementary Figure S2, available at Annals of Oncology online). Twelve additional studies reported results for obese versus non-obese women <12 months after diagnosis, and the summary RR was 1.26 (95% CI 1.07–1.47, I2 = 80%, P < 0.01).Figure 2.Categorical meta-analysis of pre-diagnosis BMI and total mortality.\\nCategorical meta-analysis of pre-diagnosis BMI and total mortality.\",\n \"There was evidence of a J-shaped association in the non-linear dose–response meta-analyses of BMI before and after diagnosis with total mortality (all P < 0.01; Figure 3), suggesting that underweight women may be at slightly increased risk compared with normal weight women. The curves show linear increasing trends from 20 kg/m2 for BMI before diagnosis and <12 months after diagnosis, and from 25 kg/m2 for BMI ≥12 months after diagnosis. When linear models were fitted excluding the underweight category, the summary RRs of total mortality for each 5 kg/m2 increase in BMI were 1.17 (95% CI 1.13–1.21, 15 studies, 6358 deaths), 1.11 (95% CI 1.06–1.16, 12 studies, 6020 deaths), and 1.08 (95% CI 1.01–1.15, 4 studies, 1703 deaths) for BMI before, <12 months after, and ≥12 months after diagnosis, respectively (Figure 4). Substantial heterogeneity was observed between studies on BMI <12 months after diagnosis (I2 = 55%, P = 0.01).Figure 3.Non-linear dose–response curves of BMI and mortality.Figure 4.Linear dose–response meta-analysis of BMI and total mortality.\\nNon-linear dose–response curves of BMI and mortality.\\nLinear dose–response meta-analysis of BMI and total mortality.\",\n \" categorical meta-analysis BMI was significantly associated with breast cancer mortality. Compared with normal weight women, for BMI before diagnosis, the summary RRs were 1.35 (95% CI 1.24–1.47, 22 studies) for obese women, 1.11 (95% CI 1.06–1.17, 21 studies) for overweight women, and 1.02 (95% CI 0.85–1.21, 8 studies) for underweight women (Figure 5). For BMI <12 months after diagnosis, the summary RRs were 1.25 (95% CI 1.10–1.42, 12 studies) for obese women, 1.11 (95% CI 1.03–1.20, 12 studies) for overweight women, and 1.53 (95% CI 1.27–1.83, 5 studies) for underweight women (supplementary Figure S3, available at Annals of Oncology online). Substantial heterogeneity was observed between studies of obese women (I2 = 53%, P = 0.02). For BMI ≥12 months after diagnosis, the summary RRs of the two studies identified were 1.68 (95% CI 0.90–3.15) for obese women and 1.37 (95% CI 0.96–1.95) for overweight women (supplementary Figure S4, available at Annals of Oncology online). The summary of another six studies that reported RRs for obese versus non-obese <12 months after diagnosis was 1.26 (95% CI 1.05–1.51, I2 = 64%, P = 0.02).Figure 5.Categorical meta-analysis of pre-diagnosis BMI and breast cancer mortality.\\nCategorical meta-analysis of pre-diagnosis BMI and breast cancer mortality.\\nBMI was significantly associated with breast cancer mortality. Compared with normal weight women, for BMI before diagnosis, the summary RRs were 1.35 (95% CI 1.24–1.47, 22 studies) for obese women, 1.11 (95% CI 1.06–1.17, 21 studies) for overweight women, and 1.02 (95% CI 0.85–1.21, 8 studies) for underweight women (Figure 5). For BMI <12 months after diagnosis, the summary RRs were 1.25 (95% CI 1.10–1.42, 12 studies) for obese women, 1.11 (95% CI 1.03–1.20, 12 studies) for overweight women, and 1.53 (95% CI 1.27–1.83, 5 studies) for underweight women (supplementary Figure S3, available at Annals of Oncology online). Substantial heterogeneity was observed between studies of obese women (I2 = 53%, P = 0.02). For BMI ≥12 months after diagnosis, the summary RRs of the two studies identified were 1.68 (95% CI 0.90–3.15) for obese women and 1.37 (95% CI 0.96–1.95) for overweight women (supplementary Figure S4, available at Annals of Oncology online). The summary of another six studies that reported RRs for obese versus non-obese <12 months after diagnosis was 1.26 (95% CI 1.05–1.51, I2 = 64%, P = 0.02).Figure 5.Categorical meta-analysis of pre-diagnosis BMI and breast cancer mortality.\\nCategorical meta-analysis of pre-diagnosis BMI and breast cancer mortality.\\n dose–response meta-analysis There was no significant evidence of a non-linear relationship between BMI before, <12 months after, and ≥12 months after diagnosis and breast cancer mortality (P = 0.21, P = 1.00, P = 0.86, respectively) (Figure 3). When linear models were fitted excluding data from the underweight category, statistically significant increased risks of breast cancer mortality with BMI before and <12 months after diagnosis were observed (Figure 6). The summary RRs for each 5 kg/m2 increase were 1.18 (95% CI 1.12–1.25, 18 studies, 5262 breast cancer deaths) for BMI before diagnosis and 1.14 (95% CI 1.05–1.24, 8 studies, 3857 breast cancer deaths) for BMI <12 months after diagnosis, with moderate (I2 = 47%, P = 0.01) and substantial (I2 = 66%, P = 0.01) heterogeneities between studies, respectively. Only two studies on BMI ≥12 months after diagnosis and breast cancer mortality (N = 220 deaths) were identified. The summary RR was 1.29 (95% CI 0.97–1.72).Figure 6.Linear dose–response meta-analysis of BMI and breast cancer mortality.\\nLinear dose–response meta-analysis of BMI and breast cancer mortality.\\nThere was no significant evidence of a non-linear relationship between BMI before, <12 months after, and ≥12 months after diagnosis and breast cancer mortality (P = 0.21, P = 1.00, P = 0.86, respectively) (Figure 3). When linear models were fitted excluding data from the underweight category, statistically significant increased risks of breast cancer mortality with BMI before and <12 months after diagnosis were observed (Figure 6). The summary RRs for each 5 kg/m2 increase were 1.18 (95% CI 1.12–1.25, 18 studies, 5262 breast cancer deaths) for BMI before diagnosis and 1.14 (95% CI 1.05–1.24, 8 studies, 3857 breast cancer deaths) for BMI <12 months after diagnosis, with moderate (I2 = 47%, P = 0.01) and substantial (I2 = 66%, P = 0.01) heterogeneities between studies, respectively. Only two studies on BMI ≥12 months after diagnosis and breast cancer mortality (N = 220 deaths) were identified. The summary RR was 1.29 (95% CI 0.97–1.72).Figure 6.Linear dose–response meta-analysis of BMI and breast cancer mortality.\\nLinear dose–response meta-analysis of BMI and breast cancer mortality.\",\n \"BMI was significantly associated with breast cancer mortality. Compared with normal weight women, for BMI before diagnosis, the summary RRs were 1.35 (95% CI 1.24–1.47, 22 studies) for obese women, 1.11 (95% CI 1.06–1.17, 21 studies) for overweight women, and 1.02 (95% CI 0.85–1.21, 8 studies) for underweight women (Figure 5). For BMI <12 months after diagnosis, the summary RRs were 1.25 (95% CI 1.10–1.42, 12 studies) for obese women, 1.11 (95% CI 1.03–1.20, 12 studies) for overweight women, and 1.53 (95% CI 1.27–1.83, 5 studies) for underweight women (supplementary Figure S3, available at Annals of Oncology online). Substantial heterogeneity was observed between studies of obese women (I2 = 53%, P = 0.02). For BMI ≥12 months after diagnosis, the summary RRs of the two studies identified were 1.68 (95% CI 0.90–3.15) for obese women and 1.37 (95% CI 0.96–1.95) for overweight women (supplementary Figure S4, available at Annals of Oncology online). The summary of another six studies that reported RRs for obese versus non-obese <12 months after diagnosis was 1.26 (95% CI 1.05–1.51, I2 = 64%, P = 0.02).Figure 5.Categorical meta-analysis of pre-diagnosis BMI and breast cancer mortality.\\nCategorical meta-analysis of pre-diagnosis BMI and breast cancer mortality.\",\n \"There was no significant evidence of a non-linear relationship between BMI before, <12 months after, and ≥12 months after diagnosis and breast cancer mortality (P = 0.21, P = 1.00, P = 0.86, respectively) (Figure 3). When linear models were fitted excluding data from the underweight category, statistically significant increased risks of breast cancer mortality with BMI before and <12 months after diagnosis were observed (Figure 6). The summary RRs for each 5 kg/m2 increase were 1.18 (95% CI 1.12–1.25, 18 studies, 5262 breast cancer deaths) for BMI before diagnosis and 1.14 (95% CI 1.05–1.24, 8 studies, 3857 breast cancer deaths) for BMI <12 months after diagnosis, with moderate (I2 = 47%, P = 0.01) and substantial (I2 = 66%, P = 0.01) heterogeneities between studies, respectively. Only two studies on BMI ≥12 months after diagnosis and breast cancer mortality (N = 220 deaths) were identified. The summary RR was 1.29 (95% CI 0.97–1.72).Figure 6.Linear dose–response meta-analysis of BMI and breast cancer mortality.\\nLinear dose–response meta-analysis of BMI and breast cancer mortality.\",\n \"Only two studies reported results for death from cardiovascular disease (N = 151 deaths) [27, 112]. The summary RR for obese versus normal weight before diagnosis was 1.60 (95% CI 0.66–3.87). No association was observed for overweight versus normal weight (summary RR = 1.01, 95% CI 0.80–1.29). For each 5 kg/m2 increase in BMI, the summary RR was 1.21 (95% CI 0.83–1.77). Five studies reported results for deaths from any cause other than breast cancer (N = 2704 deaths) [21, 34, 108, 113, 114]. The summary RRs were 1.29 (95% CI 0.99–1.68, I2 = 72%, P = 0.01) for obese women, and 0.96 (95% CI 0.83–1.11, I2 = 26%, P = 0.25) for overweight women compared with normal weight women.\",\n \"The results of the subgroup and meta-regression analyses are in supplementary Tables S3 and S4, available at Annals of Oncology online. Subgroup analysis was not carried out for BMI ≥12 months after diagnosis as the limited number of studies would hinder any meaningful comparisons.\\nIncreased risks of mortality were observed in the meta-analyses by menopausal status. While the summary risk estimates seem stronger with premenopausal breast cancer, there was no significant heterogeneity between pre- and post-menopausal breast cancer as shown in the meta-regression analyses (P = 0.28–0.89) (supplementary Tables S3 and S4, available at Annals of Oncology online). For BMI before diagnosis and total mortality, the summary RRs for obese versus normal weight were 1.75 (95% CI 1.26–2.41, I2 = 70%, P < 0.01, 7 studies) in women with pre-menopausal breast cancer and 1.34 (95% CI 1.18–1.53, I2 = 27%, P = 0.20, 9 studies) in women with post-menopausal breast cancer.\\nStudies with larger number of deaths [105, 115], conducted in Europe [28, 115], or with weight and height assessed through medical records [28, 104, 115, 116] tended to report weaker associations for BMI <12 months after diagnosis and total mortality compared with other studies (meta-regression P = 0.01, 0.02, 0.01, respectively) (supplementary Table S3, available at Annals of Oncology online); while studies with larger number of deaths [101], conducted in Asia [101, 102], or adjusted for co-morbidity [101, 102] reported weaker associations for BMI <12 months after diagnosis and breast cancer mortality (meta-regression P = 0.01, 0.02, 0.01, respectively) (supplementary Table S4, available at Annals of Oncology online).\\nAnalyses stratified by study designs, or restricted to studies with invasive cases only, early-stage non-metastatic cases only, or mammography screening detected cases only, or controlled for previous diseases did not produce results that were materially different from those obtained in the overall analyses (results not shown). Summary risk estimates remained statistically significant when each study was omitted in turn, except for BMI ≥12 months after diagnosis and total mortality. The summary RR was 1.06 (95% CI 0.98–1.15) per 5 kg/m2 increase when Flatt et al. [117] which contributed 315 deaths was omitted.\",\n \"Asymmetry was only detected in the funnel plots of BMI <12 months after diagnosis and total mortality, and breast cancer mortality, which suggests that small studies with an inverse association are missing (plots not shown). Egger's tests were borderline significant (P = 0.05) or statistically significant (P = 0.03), respectively.\",\n \"This work was supported by the World Cancer Research Fund International (grant number: 2007/SP01) (http://www.wcrf-uk.org/). The funder of this study had no role in the decisions about the design and conduct of the study; collection, management, analysis, or interpretation of the data; or the preparation, review, or approval of the manuscript. The views expressed in this review are the opinions of the authors. They may not represent the views of the World Cancer Research Fund International/American Institute for Cancer Research and may differ from those in future updates of the evidence related to food, nutrition, physical activity, and cancer survival.\",\n \"DCG reports personal fees from World Cancer Research Fund/American Institute for Cancer Research, during the conduct of the study; grants from Danone, and grants from Kelloggs, outside the submitted work. AM reports personal fees from Metagenics/Metaproteomics, personal fees from Pfizer, outside the submitted work. All remaining authors have declared no conflicts of interest.\"\n]"},"other_sections_sec_types":{"kind":"list like","value":[null,null,null,null,null,null,null,null,null,null,null,null,null,null,null],"string":"[\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null\n]"},"all_sections_titles":{"kind":"list like","value":["introduction","materials and methods","data sources and search","study selection","data extraction","statistical analysis","results","BMI and total mortality","categorical meta-analysis","dose–response meta-analysis","BMI and breast cancer mortality","categorical meta-analysis","dose–response meta-analysis","BMI and other mortality outcomes","subgroup, meta-regression, and sensitivity analyses","small studies or publication bias","discussion","funding","disclosure","Supplementary Material"],"string":"[\n \"introduction\",\n \"materials and methods\",\n \"data sources and search\",\n \"study selection\",\n \"data extraction\",\n \"statistical analysis\",\n \"results\",\n \"BMI and total mortality\",\n \"categorical meta-analysis\",\n \"dose–response meta-analysis\",\n \"BMI and breast cancer mortality\",\n \"categorical meta-analysis\",\n \"dose–response meta-analysis\",\n \"BMI and other mortality outcomes\",\n \"subgroup, meta-regression, and sensitivity analyses\",\n \"small studies or publication bias\",\n \"discussion\",\n \"funding\",\n \"disclosure\",\n \"Supplementary Material\"\n]"},"all_sections_texts":{"kind":"list like","value":["The number of female breast cancer survivors is growing because of longer survival as a consequence of advances in treatment and early diagnosis. There were ∼2.6 million female breast cancer survivors in US in 2008 [1], and in the UK, breast cancer accounted for ∼28% of the 2 million cancer survivors in 2008 [2].\nObesity is a pandemic health concern, with over 500 million adults worldwide estimated to be obese and 958 million were overweight in 2008 [3]. One of the established risk factors for breast cancer development in post-menopausal women is obesity [4], which has further been linked to breast cancer recurrence [5] and poorer survival in pre- and post-menopausal breast cancer [6, 7]. Preliminary findings from randomised, controlled trials suggest that lifestyle modifications improved biomarkers associated with breast cancer progression and overall survival [8].\nThe biological mechanisms underlying the association between obesity and breast cancer survival are not established, and could involve interacting mediators of hormones, adipocytokines, and inflammatory cytokines which link to cell survival or apoptosis, migration, and proliferation [9]. Higher level of oestradiol produced in postmenopausal women through aromatisation of androgens in the adipose tissues [10], and higher level of insulin [11], a condition common in obese women, are linked to poorer prognosis in breast cancer. A possible interaction between leptin and insulin [12], and obesity-related markers of inflammation [13] have also been linked to breast cancer outcomes. Non-biological mechanisms could include chemotherapy under-dosing in obese women, suboptimal treatment, and obesity-related complications [14].\nNumerous studies have examined the relationship between obesity and breast cancer outcomes, and past reviews have concluded that obesity is linked to a lower survival; however, when investigated in a meta-analysis of published data, only the results of obese compared with non-obese or lighter women were summarised [6, 7, 15].\nWe carried out a systematic literature review and meta-analysis of published studies to explore the magnitude and the shape of the association between body fatness, as measured by body mass index (BMI), and the risk of total and cause-specific mortality, overall and in women with pre- and post-menopausal breast cancer. As body weight may change close to diagnosis and during primary treatment of breast cancer [16], we examined BMI in three periods: before diagnosis, <12 months after diagnosis, and ≥12 months after breast cancer diagnosis."," data sources and search We carried out a systematic literature search, limited to publications in English, for articles on BMI and survival in women with breast cancer in OVID MEDLINE and EMBASE from inception to 30 June 2013 using the search strategy implemented for the WCRF/AICR Continuous Update Project on breast cancer survival. The search strategy contained medical subject headings and text words that covered a broad range of factors on diet, physical activity, and anthropometry. The protocol for the review is available at http://www.dietandcancerreport.org/index.php [17]. In addition, we hand-searched the reference lists of relevant articles, reviews, and meta-analysis papers.\nWe carried out a systematic literature search, limited to publications in English, for articles on BMI and survival in women with breast cancer in OVID MEDLINE and EMBASE from inception to 30 June 2013 using the search strategy implemented for the WCRF/AICR Continuous Update Project on breast cancer survival. The search strategy contained medical subject headings and text words that covered a broad range of factors on diet, physical activity, and anthropometry. The protocol for the review is available at http://www.dietandcancerreport.org/index.php [17]. In addition, we hand-searched the reference lists of relevant articles, reviews, and meta-analysis papers.\n study selection Included were follow-up studies of breast cancer survivors, which reported estimates of the associations of BMI ascertained before and after breast cancer diagnosis with total or cause-specific mortality risks. Studies that investigated BMI after diagnosis were divided into two groups: BMI <12 months after diagnosis (BMI <12 months) and BMI 12 months or more after diagnosis (BMI ≥12 months). Outcomes included total mortality, breast cancer mortality, death from cardiovascular disease, and death from causes other than breast cancer. When multiple publications on the same study population were found, results based on longer follow-up and more outcomes were selected for the meta-analysis.\nIncluded were follow-up studies of breast cancer survivors, which reported estimates of the associations of BMI ascertained before and after breast cancer diagnosis with total or cause-specific mortality risks. Studies that investigated BMI after diagnosis were divided into two groups: BMI <12 months after diagnosis (BMI <12 months) and BMI 12 months or more after diagnosis (BMI ≥12 months). Outcomes included total mortality, breast cancer mortality, death from cardiovascular disease, and death from causes other than breast cancer. When multiple publications on the same study population were found, results based on longer follow-up and more outcomes were selected for the meta-analysis.\n data extraction DSMC, TN, and DA conducted the search. DSMC, ARV, and DNR extracted the study characteristics, tumour-related information, cancer treatment, timing and method of weight and height assessment, BMI levels, number of outcomes and population at-risk, outcome type, estimates of association and their measure of variance [95% confidence interval (CI) or P value], and adjustment factors in the analysis.\nDSMC, TN, and DA conducted the search. DSMC, ARV, and DNR extracted the study characteristics, tumour-related information, cancer treatment, timing and method of weight and height assessment, BMI levels, number of outcomes and population at-risk, outcome type, estimates of association and their measure of variance [95% confidence interval (CI) or P value], and adjustment factors in the analysis.\n statistical analysis Categorical and dose–response meta-analyses were conducted using random-effects models to account for between-study heterogeneity [18]. Summary relative risks (RRs) were estimated using the average of the natural logarithm of the RRs of each study weighted by the inverse of the variance and then unweighted by applying a random-effects variance component which is derived from the extent of variability of the effect sizes of the studies. The maximally adjusted RR estimates were used for the meta-analysis except for the follow-up of randomised, controlled trials [19, 20] where unadjusted results were also included, as these studies mostly involved a more homogeneous study population. BMI or Quetelet's Index (QI) measured in units of kg/m2 was used.\nWe conducted categorical meta-analyses by pooling the categorical results reported in the studies. The studies used different BMI categories. In some studies, underweight (BMI <18.5 kg/m2 according to WHO international classification) and normal weight women (BMI 18.5–<25.0 kg/m2) were classified together but, in some studies, they were classified separately. Similarly, most studies classified overweight (BMI 25.0–<30.0 kg/m2) and obese (BMI ≥30.0 kg/m2) women separately but, in some studies, overweight and obese women were combined. The reference category was normal weight or underweight together with normal weight, depending on the studies. For convenience, the BMI categories are referred to as underweight, normal weight, overweight, and obese in the present review. We derived the RRs for overweight and obese women compared with normal weight women in two studies [19, 21] that had more than four BMI categories using the method of Hamling et al. [22]. Studies that reported results for obese compared with non-obese women were analysed separately.\nThe non-linear dose–response relationship between BMI and mortality was examined using the best-fitting second-order fractional polynomial regression model [23], defined as the one with the lowest deviance. Non-linearity was tested using the likelihood ratio test [24]. In the non-linear meta-analysis, the reference category was the lowest BMI category in each study and RRs were recalculated using the method of Hamling et al. [22] when the reference category was not the lowest BMI category in the study.\nWe also conducted linear dose–response meta-analyses, excluding the category underweight when reported separately in the studies, by pooling estimates of RR per unit increase (with its standard error) provided by the studies, or derived by us from categorical data using generalised least-squares for trend estimation [25]. To estimate the trend, the numbers of outcomes and population at-risk for at least three BMI categories, or the information required to derive them using standard methods [26], and means or medians of the BMI categories, or if not reported in the studies, the estimated midpoints of the categories had to be available. When the extreme BMI categories were open-ended, we used the width of the adjacent close-ended category to estimate the midpoints. Where the RRs were presented by subgroups (age group [27], menopausal status [28, 29], stage [30] or subtype [31] of breast cancer, or others [32–34]), an overall estimate for the study was obtained by a fixed-effect model before pooling in the meta-analysis. We estimated the risk increase of death for an increment of 5 kg/m2 of BMI.\nTo assess heterogeneity, we computed the Cochran Q test and I2 statistic [35]. The cut points of 30% and 50% were used for low, moderate, and substantial level of heterogeneity. Sources of heterogeneity were explored by meta-regression and subgroup analyses using pre-defined factors, including indicators of study quality (menopausal status, hormone receptor status, number of outcomes, length of follow-up, study design, geographic location, BMI assessment, adjustment for confounders, and others). Small study or publication bias was examined by Egger's test [36] and visual inspection of the funnel plots. The influence of each individual study on the summary RR was examined by excluding the study in turn [37]. A P value of <0.05 was considered statistically significant. All analyses were conducted using Stata version 12.1 (Stata Statistical Software: Release 12, StataCorp LP, College Station, TX).\nCategorical and dose–response meta-analyses were conducted using random-effects models to account for between-study heterogeneity [18]. Summary relative risks (RRs) were estimated using the average of the natural logarithm of the RRs of each study weighted by the inverse of the variance and then unweighted by applying a random-effects variance component which is derived from the extent of variability of the effect sizes of the studies. The maximally adjusted RR estimates were used for the meta-analysis except for the follow-up of randomised, controlled trials [19, 20] where unadjusted results were also included, as these studies mostly involved a more homogeneous study population. BMI or Quetelet's Index (QI) measured in units of kg/m2 was used.\nWe conducted categorical meta-analyses by pooling the categorical results reported in the studies. The studies used different BMI categories. In some studies, underweight (BMI <18.5 kg/m2 according to WHO international classification) and normal weight women (BMI 18.5–<25.0 kg/m2) were classified together but, in some studies, they were classified separately. Similarly, most studies classified overweight (BMI 25.0–<30.0 kg/m2) and obese (BMI ≥30.0 kg/m2) women separately but, in some studies, overweight and obese women were combined. The reference category was normal weight or underweight together with normal weight, depending on the studies. For convenience, the BMI categories are referred to as underweight, normal weight, overweight, and obese in the present review. We derived the RRs for overweight and obese women compared with normal weight women in two studies [19, 21] that had more than four BMI categories using the method of Hamling et al. [22]. Studies that reported results for obese compared with non-obese women were analysed separately.\nThe non-linear dose–response relationship between BMI and mortality was examined using the best-fitting second-order fractional polynomial regression model [23], defined as the one with the lowest deviance. Non-linearity was tested using the likelihood ratio test [24]. In the non-linear meta-analysis, the reference category was the lowest BMI category in each study and RRs were recalculated using the method of Hamling et al. [22] when the reference category was not the lowest BMI category in the study.\nWe also conducted linear dose–response meta-analyses, excluding the category underweight when reported separately in the studies, by pooling estimates of RR per unit increase (with its standard error) provided by the studies, or derived by us from categorical data using generalised least-squares for trend estimation [25]. To estimate the trend, the numbers of outcomes and population at-risk for at least three BMI categories, or the information required to derive them using standard methods [26], and means or medians of the BMI categories, or if not reported in the studies, the estimated midpoints of the categories had to be available. When the extreme BMI categories were open-ended, we used the width of the adjacent close-ended category to estimate the midpoints. Where the RRs were presented by subgroups (age group [27], menopausal status [28, 29], stage [30] or subtype [31] of breast cancer, or others [32–34]), an overall estimate for the study was obtained by a fixed-effect model before pooling in the meta-analysis. We estimated the risk increase of death for an increment of 5 kg/m2 of BMI.\nTo assess heterogeneity, we computed the Cochran Q test and I2 statistic [35]. The cut points of 30% and 50% were used for low, moderate, and substantial level of heterogeneity. Sources of heterogeneity were explored by meta-regression and subgroup analyses using pre-defined factors, including indicators of study quality (menopausal status, hormone receptor status, number of outcomes, length of follow-up, study design, geographic location, BMI assessment, adjustment for confounders, and others). Small study or publication bias was examined by Egger's test [36] and visual inspection of the funnel plots. The influence of each individual study on the summary RR was examined by excluding the study in turn [37]. A P value of <0.05 was considered statistically significant. All analyses were conducted using Stata version 12.1 (Stata Statistical Software: Release 12, StataCorp LP, College Station, TX).","We carried out a systematic literature search, limited to publications in English, for articles on BMI and survival in women with breast cancer in OVID MEDLINE and EMBASE from inception to 30 June 2013 using the search strategy implemented for the WCRF/AICR Continuous Update Project on breast cancer survival. The search strategy contained medical subject headings and text words that covered a broad range of factors on diet, physical activity, and anthropometry. The protocol for the review is available at http://www.dietandcancerreport.org/index.php [17]. In addition, we hand-searched the reference lists of relevant articles, reviews, and meta-analysis papers.","Included were follow-up studies of breast cancer survivors, which reported estimates of the associations of BMI ascertained before and after breast cancer diagnosis with total or cause-specific mortality risks. Studies that investigated BMI after diagnosis were divided into two groups: BMI <12 months after diagnosis (BMI <12 months) and BMI 12 months or more after diagnosis (BMI ≥12 months). Outcomes included total mortality, breast cancer mortality, death from cardiovascular disease, and death from causes other than breast cancer. When multiple publications on the same study population were found, results based on longer follow-up and more outcomes were selected for the meta-analysis.","DSMC, TN, and DA conducted the search. DSMC, ARV, and DNR extracted the study characteristics, tumour-related information, cancer treatment, timing and method of weight and height assessment, BMI levels, number of outcomes and population at-risk, outcome type, estimates of association and their measure of variance [95% confidence interval (CI) or P value], and adjustment factors in the analysis.","Categorical and dose–response meta-analyses were conducted using random-effects models to account for between-study heterogeneity [18]. Summary relative risks (RRs) were estimated using the average of the natural logarithm of the RRs of each study weighted by the inverse of the variance and then unweighted by applying a random-effects variance component which is derived from the extent of variability of the effect sizes of the studies. The maximally adjusted RR estimates were used for the meta-analysis except for the follow-up of randomised, controlled trials [19, 20] where unadjusted results were also included, as these studies mostly involved a more homogeneous study population. BMI or Quetelet's Index (QI) measured in units of kg/m2 was used.\nWe conducted categorical meta-analyses by pooling the categorical results reported in the studies. The studies used different BMI categories. In some studies, underweight (BMI <18.5 kg/m2 according to WHO international classification) and normal weight women (BMI 18.5–<25.0 kg/m2) were classified together but, in some studies, they were classified separately. Similarly, most studies classified overweight (BMI 25.0–<30.0 kg/m2) and obese (BMI ≥30.0 kg/m2) women separately but, in some studies, overweight and obese women were combined. The reference category was normal weight or underweight together with normal weight, depending on the studies. For convenience, the BMI categories are referred to as underweight, normal weight, overweight, and obese in the present review. We derived the RRs for overweight and obese women compared with normal weight women in two studies [19, 21] that had more than four BMI categories using the method of Hamling et al. [22]. Studies that reported results for obese compared with non-obese women were analysed separately.\nThe non-linear dose–response relationship between BMI and mortality was examined using the best-fitting second-order fractional polynomial regression model [23], defined as the one with the lowest deviance. Non-linearity was tested using the likelihood ratio test [24]. In the non-linear meta-analysis, the reference category was the lowest BMI category in each study and RRs were recalculated using the method of Hamling et al. [22] when the reference category was not the lowest BMI category in the study.\nWe also conducted linear dose–response meta-analyses, excluding the category underweight when reported separately in the studies, by pooling estimates of RR per unit increase (with its standard error) provided by the studies, or derived by us from categorical data using generalised least-squares for trend estimation [25]. To estimate the trend, the numbers of outcomes and population at-risk for at least three BMI categories, or the information required to derive them using standard methods [26], and means or medians of the BMI categories, or if not reported in the studies, the estimated midpoints of the categories had to be available. When the extreme BMI categories were open-ended, we used the width of the adjacent close-ended category to estimate the midpoints. Where the RRs were presented by subgroups (age group [27], menopausal status [28, 29], stage [30] or subtype [31] of breast cancer, or others [32–34]), an overall estimate for the study was obtained by a fixed-effect model before pooling in the meta-analysis. We estimated the risk increase of death for an increment of 5 kg/m2 of BMI.\nTo assess heterogeneity, we computed the Cochran Q test and I2 statistic [35]. The cut points of 30% and 50% were used for low, moderate, and substantial level of heterogeneity. Sources of heterogeneity were explored by meta-regression and subgroup analyses using pre-defined factors, including indicators of study quality (menopausal status, hormone receptor status, number of outcomes, length of follow-up, study design, geographic location, BMI assessment, adjustment for confounders, and others). Small study or publication bias was examined by Egger's test [36] and visual inspection of the funnel plots. The influence of each individual study on the summary RR was examined by excluding the study in turn [37]. A P value of <0.05 was considered statistically significant. All analyses were conducted using Stata version 12.1 (Stata Statistical Software: Release 12, StataCorp LP, College Station, TX).","A total of 124 publications investigating the relationship of body fatness and mortality in women with breast cancer were identified. We excluded 31 publications, including four publications on other obesity indices [38–41], 12 publications without a measure of association [42–53], and 15 publications superseded by publications of the same study with more outcomes [54–68]. A further 14 publications were excluded because of insufficient data for the meta-analysis (five publications [69–73]) or unadjusted results (nine publications [74–82]), from which nine publications reported statistically significant increased risk of total, breast cancer or non-breast cancer mortality in obese women (before or <12 months after diagnosis) compared with the reference BMI [69, 71–74, 76, 77, 79, 82], two publications reported non-significant inverse associations [75, 80] and three publications reported no association [70, 78, 81] of BMI with survival after breast cancer. Hence, 79 publications from 82 follow-up studies with 41 477 deaths (23 182 from breast cancer) in 213 075 breast cancer survivors were included in the meta-analyses (Figure 1). Supplementary Table S1, available at Annals of Oncology online shows the characteristics of the studies included in the meta-analyses and details of the excluded studies are in supplementary Table S2, available at Annals of Oncology online. Results of the meta-analyses are summarised in Table 1.Table 1.Summary of meta-analyses of BMI and survival in women with breast canceraBMI before diagnosisBMI <12 months after diagnosisBMI ≥12 months after diagnosisNRR (95% CI)I2 (%)PhNRR (95% CI)I2 (%)PhNRR (95% CI)I2 (%)PhTotal mortality Under versus normal weight101.10 (0.92–1.31)48%0.04111.25 (0.99–0.57)63%<0.0131.29 (1.02–1.63)0%0.39 Over versus normal weight191.07 (1.02–1.12)0%0.88221.07 (1.02–1.12)21%0.1840.98 (0.86–1.11)0%0.72 Obese versus normal weight211.41 (1.29–1.53)38%0.04241.23 (1.12–1.33)69%<0.0151.21 (1.06–1.38)0%0.70 Obese versus non-obese–––121.26 (1.07–1.47)80%<0.01––– Per 5 kg/m2 increase151.17 (1.13–1.21)7%0.38121.11 (1.06–1.16)55%0.0141.08 (1.01–1.15)0%0.52Breast cancer mortality Under versus normal weight81.02 (0.85–1.21)31%0.1851.53 (1.27–1.83)0%0.5911.10 (0.15–8.08)– Over versus normal weight211.11 (1.06–1.17)0%0.66121.11 (1.03–1.20)14%0.3121.37 (0.96–1.95)0%0.90 Obese versus normal weight221.35 (1.24–1.47)36%0.05121.25 (1.10–1.42)53%0.0221.68 (0.90–3.15)67%0.08 Obese versus non-obese–––61.26 (1.05–1.51)64%0.02––– Per 5 kg/m2 increase181.18 (1.12–1.25)47%0.0181.14 (1.05–1.24)66%0.0121.29 (0.97–1.72)64%0.10Cardiovascular disease related mortality Over versus normal weight21.01 (0.80–1.29)0%0.87–––––– Obese versus normal weight21.60 (0.66–3.87)78%0.03–––––– Per 5 kg/m2 increase21.21 (0.83–1.77)80%0.03––––––Non-breast cancer mortality Over versus normal weight–––50.96 (0.83–1.11)26%0.25––– Obese versus normal weight–––51.29 (0.99–1.68)72%0.01–––aBMI before and after diagnosis (<12 months after, or ≥12 months after diagnosis) was classified according to the exposure period which the studies referred to in the BMI assessment; the BMI categories were included in the categorical meta-analyses as defined by the studies.Ph, P for heterogeneity between studies.Figure 1.Flowchart of search.\nSummary of meta-analyses of BMI and survival in women with breast cancera\naBMI before and after diagnosis (<12 months after, or ≥12 months after diagnosis) was classified according to the exposure period which the studies referred to in the BMI assessment; the BMI categories were included in the categorical meta-analyses as defined by the studies.\nPh, P for heterogeneity between studies.\nFlowchart of search.\nStudies were follow-up of women with breast cancer identified in prospective aetiologic cohort studies (women were free of cancer at enrolment), or cohorts of breast cancer survivors whose participants were identified in hospitals or through cancer registries, or follow-up of breast cancer patients enrolled in case–control studies or randomised clinical trials.\nSome studies included only premenopausal women [83–85] or postmenopausal women [21, 27, 86–94], but most studies included both. Menopausal status was usually determined at time of diagnosis. Year of diagnosis was from 1957–1965 [70] to 2002–2009 [74]. Patient tumour characteristics and stage of disease at diagnosis varied across studies, and some studies included carcinoma in situ. No all studies provided clinical information on the tumour, treatment, and co-morbidities.\nMost of the studies were based in North America or Europe. There were three studies from each of Australia [79, 95, 96], Korea [97, 98] and China [99–101]; two studies from Japan [71, 102]; one study from Tunisia [103] and four international studies [19, 104–106]. Study size ranged from 96 [107] to 24 698 patients [97]. Total number of deaths ranged from 56 [93] to 7397 [108], and the proportion of deaths from breast cancer ranged from 22% [27] to 98% [84] when reported. All but eight studies [30, 93, 94, 98, 99, 109–111] had an average follow-up of more than 5 years.\n BMI and total mortality categorical meta-analysis For BMI before diagnosis, compared with normal weight women, the summary RRs were 1.41 (95% CI 1.29–1.53, 21 studies) for obese women, 1.07 (95% CI 1.02–1.12, 19 studies) for overweight women, and 1.10 (95% CI 0.92–1.31, 10 studies) for underweight women (Figure 2). For BMI <12 months after diagnosis and the same comparisons, the summary RRs were 1.23 (95% CI 1.12–1.33, 24 studies) for obese women, 1.07 (95% CI 1.02–1.12, 22 studies) for overweight women, and 1.25 (95% CI 0.99–1.57, 11 studies) for underweight women (supplementary Figure S1, available at Annals of Oncology online). Substantial heterogeneities were observed between studies of obese women and underweight women (I2 = 69%, P < 0.01; I2 = 63%, P < 0.01, respectively). For BMI ≥12 months after diagnosis, the summary RRs were 1.21 (95% CI 1.06–1.38, 5 studies) for obese women, 0.98 (95% CI 0.86–1.11, 4 studies) for overweight women, and 1.29 (95% CI 1.02–1.63, 3 studies) for underweight women (supplementary Figure S2, available at Annals of Oncology online). Twelve additional studies reported results for obese versus non-obese women <12 months after diagnosis, and the summary RR was 1.26 (95% CI 1.07–1.47, I2 = 80%, P < 0.01).Figure 2.Categorical meta-analysis of pre-diagnosis BMI and total mortality.\nCategorical meta-analysis of pre-diagnosis BMI and total mortality.\nFor BMI before diagnosis, compared with normal weight women, the summary RRs were 1.41 (95% CI 1.29–1.53, 21 studies) for obese women, 1.07 (95% CI 1.02–1.12, 19 studies) for overweight women, and 1.10 (95% CI 0.92–1.31, 10 studies) for underweight women (Figure 2). For BMI <12 months after diagnosis and the same comparisons, the summary RRs were 1.23 (95% CI 1.12–1.33, 24 studies) for obese women, 1.07 (95% CI 1.02–1.12, 22 studies) for overweight women, and 1.25 (95% CI 0.99–1.57, 11 studies) for underweight women (supplementary Figure S1, available at Annals of Oncology online). Substantial heterogeneities were observed between studies of obese women and underweight women (I2 = 69%, P < 0.01; I2 = 63%, P < 0.01, respectively). For BMI ≥12 months after diagnosis, the summary RRs were 1.21 (95% CI 1.06–1.38, 5 studies) for obese women, 0.98 (95% CI 0.86–1.11, 4 studies) for overweight women, and 1.29 (95% CI 1.02–1.63, 3 studies) for underweight women (supplementary Figure S2, available at Annals of Oncology online). Twelve additional studies reported results for obese versus non-obese women <12 months after diagnosis, and the summary RR was 1.26 (95% CI 1.07–1.47, I2 = 80%, P < 0.01).Figure 2.Categorical meta-analysis of pre-diagnosis BMI and total mortality.\nCategorical meta-analysis of pre-diagnosis BMI and total mortality.\n dose–response meta-analysis There was evidence of a J-shaped association in the non-linear dose–response meta-analyses of BMI before and after diagnosis with total mortality (all P < 0.01; Figure 3), suggesting that underweight women may be at slightly increased risk compared with normal weight women. The curves show linear increasing trends from 20 kg/m2 for BMI before diagnosis and <12 months after diagnosis, and from 25 kg/m2 for BMI ≥12 months after diagnosis. When linear models were fitted excluding the underweight category, the summary RRs of total mortality for each 5 kg/m2 increase in BMI were 1.17 (95% CI 1.13–1.21, 15 studies, 6358 deaths), 1.11 (95% CI 1.06–1.16, 12 studies, 6020 deaths), and 1.08 (95% CI 1.01–1.15, 4 studies, 1703 deaths) for BMI before, <12 months after, and ≥12 months after diagnosis, respectively (Figure 4). Substantial heterogeneity was observed between studies on BMI <12 months after diagnosis (I2 = 55%, P = 0.01).Figure 3.Non-linear dose–response curves of BMI and mortality.Figure 4.Linear dose–response meta-analysis of BMI and total mortality.\nNon-linear dose–response curves of BMI and mortality.\nLinear dose–response meta-analysis of BMI and total mortality.\nThere was evidence of a J-shaped association in the non-linear dose–response meta-analyses of BMI before and after diagnosis with total mortality (all P < 0.01; Figure 3), suggesting that underweight women may be at slightly increased risk compared with normal weight women. The curves show linear increasing trends from 20 kg/m2 for BMI before diagnosis and <12 months after diagnosis, and from 25 kg/m2 for BMI ≥12 months after diagnosis. When linear models were fitted excluding the underweight category, the summary RRs of total mortality for each 5 kg/m2 increase in BMI were 1.17 (95% CI 1.13–1.21, 15 studies, 6358 deaths), 1.11 (95% CI 1.06–1.16, 12 studies, 6020 deaths), and 1.08 (95% CI 1.01–1.15, 4 studies, 1703 deaths) for BMI before, <12 months after, and ≥12 months after diagnosis, respectively (Figure 4). Substantial heterogeneity was observed between studies on BMI <12 months after diagnosis (I2 = 55%, P = 0.01).Figure 3.Non-linear dose–response curves of BMI and mortality.Figure 4.Linear dose–response meta-analysis of BMI and total mortality.\nNon-linear dose–response curves of BMI and mortality.\nLinear dose–response meta-analysis of BMI and total mortality.\n categorical meta-analysis For BMI before diagnosis, compared with normal weight women, the summary RRs were 1.41 (95% CI 1.29–1.53, 21 studies) for obese women, 1.07 (95% CI 1.02–1.12, 19 studies) for overweight women, and 1.10 (95% CI 0.92–1.31, 10 studies) for underweight women (Figure 2). For BMI <12 months after diagnosis and the same comparisons, the summary RRs were 1.23 (95% CI 1.12–1.33, 24 studies) for obese women, 1.07 (95% CI 1.02–1.12, 22 studies) for overweight women, and 1.25 (95% CI 0.99–1.57, 11 studies) for underweight women (supplementary Figure S1, available at Annals of Oncology online). Substantial heterogeneities were observed between studies of obese women and underweight women (I2 = 69%, P < 0.01; I2 = 63%, P < 0.01, respectively). For BMI ≥12 months after diagnosis, the summary RRs were 1.21 (95% CI 1.06–1.38, 5 studies) for obese women, 0.98 (95% CI 0.86–1.11, 4 studies) for overweight women, and 1.29 (95% CI 1.02–1.63, 3 studies) for underweight women (supplementary Figure S2, available at Annals of Oncology online). Twelve additional studies reported results for obese versus non-obese women <12 months after diagnosis, and the summary RR was 1.26 (95% CI 1.07–1.47, I2 = 80%, P < 0.01).Figure 2.Categorical meta-analysis of pre-diagnosis BMI and total mortality.\nCategorical meta-analysis of pre-diagnosis BMI and total mortality.\nFor BMI before diagnosis, compared with normal weight women, the summary RRs were 1.41 (95% CI 1.29–1.53, 21 studies) for obese women, 1.07 (95% CI 1.02–1.12, 19 studies) for overweight women, and 1.10 (95% CI 0.92–1.31, 10 studies) for underweight women (Figure 2). For BMI <12 months after diagnosis and the same comparisons, the summary RRs were 1.23 (95% CI 1.12–1.33, 24 studies) for obese women, 1.07 (95% CI 1.02–1.12, 22 studies) for overweight women, and 1.25 (95% CI 0.99–1.57, 11 studies) for underweight women (supplementary Figure S1, available at Annals of Oncology online). Substantial heterogeneities were observed between studies of obese women and underweight women (I2 = 69%, P < 0.01; I2 = 63%, P < 0.01, respectively). For BMI ≥12 months after diagnosis, the summary RRs were 1.21 (95% CI 1.06–1.38, 5 studies) for obese women, 0.98 (95% CI 0.86–1.11, 4 studies) for overweight women, and 1.29 (95% CI 1.02–1.63, 3 studies) for underweight women (supplementary Figure S2, available at Annals of Oncology online). Twelve additional studies reported results for obese versus non-obese women <12 months after diagnosis, and the summary RR was 1.26 (95% CI 1.07–1.47, I2 = 80%, P < 0.01).Figure 2.Categorical meta-analysis of pre-diagnosis BMI and total mortality.\nCategorical meta-analysis of pre-diagnosis BMI and total mortality.\n dose–response meta-analysis There was evidence of a J-shaped association in the non-linear dose–response meta-analyses of BMI before and after diagnosis with total mortality (all P < 0.01; Figure 3), suggesting that underweight women may be at slightly increased risk compared with normal weight women. The curves show linear increasing trends from 20 kg/m2 for BMI before diagnosis and <12 months after diagnosis, and from 25 kg/m2 for BMI ≥12 months after diagnosis. When linear models were fitted excluding the underweight category, the summary RRs of total mortality for each 5 kg/m2 increase in BMI were 1.17 (95% CI 1.13–1.21, 15 studies, 6358 deaths), 1.11 (95% CI 1.06–1.16, 12 studies, 6020 deaths), and 1.08 (95% CI 1.01–1.15, 4 studies, 1703 deaths) for BMI before, <12 months after, and ≥12 months after diagnosis, respectively (Figure 4). Substantial heterogeneity was observed between studies on BMI <12 months after diagnosis (I2 = 55%, P = 0.01).Figure 3.Non-linear dose–response curves of BMI and mortality.Figure 4.Linear dose–response meta-analysis of BMI and total mortality.\nNon-linear dose–response curves of BMI and mortality.\nLinear dose–response meta-analysis of BMI and total mortality.\nThere was evidence of a J-shaped association in the non-linear dose–response meta-analyses of BMI before and after diagnosis with total mortality (all P < 0.01; Figure 3), suggesting that underweight women may be at slightly increased risk compared with normal weight women. The curves show linear increasing trends from 20 kg/m2 for BMI before diagnosis and <12 months after diagnosis, and from 25 kg/m2 for BMI ≥12 months after diagnosis. When linear models were fitted excluding the underweight category, the summary RRs of total mortality for each 5 kg/m2 increase in BMI were 1.17 (95% CI 1.13–1.21, 15 studies, 6358 deaths), 1.11 (95% CI 1.06–1.16, 12 studies, 6020 deaths), and 1.08 (95% CI 1.01–1.15, 4 studies, 1703 deaths) for BMI before, <12 months after, and ≥12 months after diagnosis, respectively (Figure 4). Substantial heterogeneity was observed between studies on BMI <12 months after diagnosis (I2 = 55%, P = 0.01).Figure 3.Non-linear dose–response curves of BMI and mortality.Figure 4.Linear dose–response meta-analysis of BMI and total mortality.\nNon-linear dose–response curves of BMI and mortality.\nLinear dose–response meta-analysis of BMI and total mortality.\n BMI and breast cancer mortality categorical meta-analysis BMI was significantly associated with breast cancer mortality. Compared with normal weight women, for BMI before diagnosis, the summary RRs were 1.35 (95% CI 1.24–1.47, 22 studies) for obese women, 1.11 (95% CI 1.06–1.17, 21 studies) for overweight women, and 1.02 (95% CI 0.85–1.21, 8 studies) for underweight women (Figure 5). For BMI <12 months after diagnosis, the summary RRs were 1.25 (95% CI 1.10–1.42, 12 studies) for obese women, 1.11 (95% CI 1.03–1.20, 12 studies) for overweight women, and 1.53 (95% CI 1.27–1.83, 5 studies) for underweight women (supplementary Figure S3, available at Annals of Oncology online). Substantial heterogeneity was observed between studies of obese women (I2 = 53%, P = 0.02). For BMI ≥12 months after diagnosis, the summary RRs of the two studies identified were 1.68 (95% CI 0.90–3.15) for obese women and 1.37 (95% CI 0.96–1.95) for overweight women (supplementary Figure S4, available at Annals of Oncology online). The summary of another six studies that reported RRs for obese versus non-obese <12 months after diagnosis was 1.26 (95% CI 1.05–1.51, I2 = 64%, P = 0.02).Figure 5.Categorical meta-analysis of pre-diagnosis BMI and breast cancer mortality.\nCategorical meta-analysis of pre-diagnosis BMI and breast cancer mortality.\nBMI was significantly associated with breast cancer mortality. Compared with normal weight women, for BMI before diagnosis, the summary RRs were 1.35 (95% CI 1.24–1.47, 22 studies) for obese women, 1.11 (95% CI 1.06–1.17, 21 studies) for overweight women, and 1.02 (95% CI 0.85–1.21, 8 studies) for underweight women (Figure 5). For BMI <12 months after diagnosis, the summary RRs were 1.25 (95% CI 1.10–1.42, 12 studies) for obese women, 1.11 (95% CI 1.03–1.20, 12 studies) for overweight women, and 1.53 (95% CI 1.27–1.83, 5 studies) for underweight women (supplementary Figure S3, available at Annals of Oncology online). Substantial heterogeneity was observed between studies of obese women (I2 = 53%, P = 0.02). For BMI ≥12 months after diagnosis, the summary RRs of the two studies identified were 1.68 (95% CI 0.90–3.15) for obese women and 1.37 (95% CI 0.96–1.95) for overweight women (supplementary Figure S4, available at Annals of Oncology online). The summary of another six studies that reported RRs for obese versus non-obese <12 months after diagnosis was 1.26 (95% CI 1.05–1.51, I2 = 64%, P = 0.02).Figure 5.Categorical meta-analysis of pre-diagnosis BMI and breast cancer mortality.\nCategorical meta-analysis of pre-diagnosis BMI and breast cancer mortality.\n dose–response meta-analysis There was no significant evidence of a non-linear relationship between BMI before, <12 months after, and ≥12 months after diagnosis and breast cancer mortality (P = 0.21, P = 1.00, P = 0.86, respectively) (Figure 3). When linear models were fitted excluding data from the underweight category, statistically significant increased risks of breast cancer mortality with BMI before and <12 months after diagnosis were observed (Figure 6). The summary RRs for each 5 kg/m2 increase were 1.18 (95% CI 1.12–1.25, 18 studies, 5262 breast cancer deaths) for BMI before diagnosis and 1.14 (95% CI 1.05–1.24, 8 studies, 3857 breast cancer deaths) for BMI <12 months after diagnosis, with moderate (I2 = 47%, P = 0.01) and substantial (I2 = 66%, P = 0.01) heterogeneities between studies, respectively. Only two studies on BMI ≥12 months after diagnosis and breast cancer mortality (N = 220 deaths) were identified. The summary RR was 1.29 (95% CI 0.97–1.72).Figure 6.Linear dose–response meta-analysis of BMI and breast cancer mortality.\nLinear dose–response meta-analysis of BMI and breast cancer mortality.\nThere was no significant evidence of a non-linear relationship between BMI before, <12 months after, and ≥12 months after diagnosis and breast cancer mortality (P = 0.21, P = 1.00, P = 0.86, respectively) (Figure 3). When linear models were fitted excluding data from the underweight category, statistically significant increased risks of breast cancer mortality with BMI before and <12 months after diagnosis were observed (Figure 6). The summary RRs for each 5 kg/m2 increase were 1.18 (95% CI 1.12–1.25, 18 studies, 5262 breast cancer deaths) for BMI before diagnosis and 1.14 (95% CI 1.05–1.24, 8 studies, 3857 breast cancer deaths) for BMI <12 months after diagnosis, with moderate (I2 = 47%, P = 0.01) and substantial (I2 = 66%, P = 0.01) heterogeneities between studies, respectively. Only two studies on BMI ≥12 months after diagnosis and breast cancer mortality (N = 220 deaths) were identified. The summary RR was 1.29 (95% CI 0.97–1.72).Figure 6.Linear dose–response meta-analysis of BMI and breast cancer mortality.\nLinear dose–response meta-analysis of BMI and breast cancer mortality.\n categorical meta-analysis BMI was significantly associated with breast cancer mortality. Compared with normal weight women, for BMI before diagnosis, the summary RRs were 1.35 (95% CI 1.24–1.47, 22 studies) for obese women, 1.11 (95% CI 1.06–1.17, 21 studies) for overweight women, and 1.02 (95% CI 0.85–1.21, 8 studies) for underweight women (Figure 5). For BMI <12 months after diagnosis, the summary RRs were 1.25 (95% CI 1.10–1.42, 12 studies) for obese women, 1.11 (95% CI 1.03–1.20, 12 studies) for overweight women, and 1.53 (95% CI 1.27–1.83, 5 studies) for underweight women (supplementary Figure S3, available at Annals of Oncology online). Substantial heterogeneity was observed between studies of obese women (I2 = 53%, P = 0.02). For BMI ≥12 months after diagnosis, the summary RRs of the two studies identified were 1.68 (95% CI 0.90–3.15) for obese women and 1.37 (95% CI 0.96–1.95) for overweight women (supplementary Figure S4, available at Annals of Oncology online). The summary of another six studies that reported RRs for obese versus non-obese <12 months after diagnosis was 1.26 (95% CI 1.05–1.51, I2 = 64%, P = 0.02).Figure 5.Categorical meta-analysis of pre-diagnosis BMI and breast cancer mortality.\nCategorical meta-analysis of pre-diagnosis BMI and breast cancer mortality.\nBMI was significantly associated with breast cancer mortality. Compared with normal weight women, for BMI before diagnosis, the summary RRs were 1.35 (95% CI 1.24–1.47, 22 studies) for obese women, 1.11 (95% CI 1.06–1.17, 21 studies) for overweight women, and 1.02 (95% CI 0.85–1.21, 8 studies) for underweight women (Figure 5). For BMI <12 months after diagnosis, the summary RRs were 1.25 (95% CI 1.10–1.42, 12 studies) for obese women, 1.11 (95% CI 1.03–1.20, 12 studies) for overweight women, and 1.53 (95% CI 1.27–1.83, 5 studies) for underweight women (supplementary Figure S3, available at Annals of Oncology online). Substantial heterogeneity was observed between studies of obese women (I2 = 53%, P = 0.02). For BMI ≥12 months after diagnosis, the summary RRs of the two studies identified were 1.68 (95% CI 0.90–3.15) for obese women and 1.37 (95% CI 0.96–1.95) for overweight women (supplementary Figure S4, available at Annals of Oncology online). The summary of another six studies that reported RRs for obese versus non-obese <12 months after diagnosis was 1.26 (95% CI 1.05–1.51, I2 = 64%, P = 0.02).Figure 5.Categorical meta-analysis of pre-diagnosis BMI and breast cancer mortality.\nCategorical meta-analysis of pre-diagnosis BMI and breast cancer mortality.\n dose–response meta-analysis There was no significant evidence of a non-linear relationship between BMI before, <12 months after, and ≥12 months after diagnosis and breast cancer mortality (P = 0.21, P = 1.00, P = 0.86, respectively) (Figure 3). When linear models were fitted excluding data from the underweight category, statistically significant increased risks of breast cancer mortality with BMI before and <12 months after diagnosis were observed (Figure 6). The summary RRs for each 5 kg/m2 increase were 1.18 (95% CI 1.12–1.25, 18 studies, 5262 breast cancer deaths) for BMI before diagnosis and 1.14 (95% CI 1.05–1.24, 8 studies, 3857 breast cancer deaths) for BMI <12 months after diagnosis, with moderate (I2 = 47%, P = 0.01) and substantial (I2 = 66%, P = 0.01) heterogeneities between studies, respectively. Only two studies on BMI ≥12 months after diagnosis and breast cancer mortality (N = 220 deaths) were identified. The summary RR was 1.29 (95% CI 0.97–1.72).Figure 6.Linear dose–response meta-analysis of BMI and breast cancer mortality.\nLinear dose–response meta-analysis of BMI and breast cancer mortality.\nThere was no significant evidence of a non-linear relationship between BMI before, <12 months after, and ≥12 months after diagnosis and breast cancer mortality (P = 0.21, P = 1.00, P = 0.86, respectively) (Figure 3). When linear models were fitted excluding data from the underweight category, statistically significant increased risks of breast cancer mortality with BMI before and <12 months after diagnosis were observed (Figure 6). The summary RRs for each 5 kg/m2 increase were 1.18 (95% CI 1.12–1.25, 18 studies, 5262 breast cancer deaths) for BMI before diagnosis and 1.14 (95% CI 1.05–1.24, 8 studies, 3857 breast cancer deaths) for BMI <12 months after diagnosis, with moderate (I2 = 47%, P = 0.01) and substantial (I2 = 66%, P = 0.01) heterogeneities between studies, respectively. Only two studies on BMI ≥12 months after diagnosis and breast cancer mortality (N = 220 deaths) were identified. The summary RR was 1.29 (95% CI 0.97–1.72).Figure 6.Linear dose–response meta-analysis of BMI and breast cancer mortality.\nLinear dose–response meta-analysis of BMI and breast cancer mortality.\n BMI and other mortality outcomes Only two studies reported results for death from cardiovascular disease (N = 151 deaths) [27, 112]. The summary RR for obese versus normal weight before diagnosis was 1.60 (95% CI 0.66–3.87). No association was observed for overweight versus normal weight (summary RR = 1.01, 95% CI 0.80–1.29). For each 5 kg/m2 increase in BMI, the summary RR was 1.21 (95% CI 0.83–1.77). Five studies reported results for deaths from any cause other than breast cancer (N = 2704 deaths) [21, 34, 108, 113, 114]. The summary RRs were 1.29 (95% CI 0.99–1.68, I2 = 72%, P = 0.01) for obese women, and 0.96 (95% CI 0.83–1.11, I2 = 26%, P = 0.25) for overweight women compared with normal weight women.\nOnly two studies reported results for death from cardiovascular disease (N = 151 deaths) [27, 112]. The summary RR for obese versus normal weight before diagnosis was 1.60 (95% CI 0.66–3.87). No association was observed for overweight versus normal weight (summary RR = 1.01, 95% CI 0.80–1.29). For each 5 kg/m2 increase in BMI, the summary RR was 1.21 (95% CI 0.83–1.77). Five studies reported results for deaths from any cause other than breast cancer (N = 2704 deaths) [21, 34, 108, 113, 114]. The summary RRs were 1.29 (95% CI 0.99–1.68, I2 = 72%, P = 0.01) for obese women, and 0.96 (95% CI 0.83–1.11, I2 = 26%, P = 0.25) for overweight women compared with normal weight women.\n subgroup, meta-regression, and sensitivity analyses The results of the subgroup and meta-regression analyses are in supplementary Tables S3 and S4, available at Annals of Oncology online. Subgroup analysis was not carried out for BMI ≥12 months after diagnosis as the limited number of studies would hinder any meaningful comparisons.\nIncreased risks of mortality were observed in the meta-analyses by menopausal status. While the summary risk estimates seem stronger with premenopausal breast cancer, there was no significant heterogeneity between pre- and post-menopausal breast cancer as shown in the meta-regression analyses (P = 0.28–0.89) (supplementary Tables S3 and S4, available at Annals of Oncology online). For BMI before diagnosis and total mortality, the summary RRs for obese versus normal weight were 1.75 (95% CI 1.26–2.41, I2 = 70%, P < 0.01, 7 studies) in women with pre-menopausal breast cancer and 1.34 (95% CI 1.18–1.53, I2 = 27%, P = 0.20, 9 studies) in women with post-menopausal breast cancer.\nStudies with larger number of deaths [105, 115], conducted in Europe [28, 115], or with weight and height assessed through medical records [28, 104, 115, 116] tended to report weaker associations for BMI <12 months after diagnosis and total mortality compared with other studies (meta-regression P = 0.01, 0.02, 0.01, respectively) (supplementary Table S3, available at Annals of Oncology online); while studies with larger number of deaths [101], conducted in Asia [101, 102], or adjusted for co-morbidity [101, 102] reported weaker associations for BMI <12 months after diagnosis and breast cancer mortality (meta-regression P = 0.01, 0.02, 0.01, respectively) (supplementary Table S4, available at Annals of Oncology online).\nAnalyses stratified by study designs, or restricted to studies with invasive cases only, early-stage non-metastatic cases only, or mammography screening detected cases only, or controlled for previous diseases did not produce results that were materially different from those obtained in the overall analyses (results not shown). Summary risk estimates remained statistically significant when each study was omitted in turn, except for BMI ≥12 months after diagnosis and total mortality. The summary RR was 1.06 (95% CI 0.98–1.15) per 5 kg/m2 increase when Flatt et al. [117] which contributed 315 deaths was omitted.\nThe results of the subgroup and meta-regression analyses are in supplementary Tables S3 and S4, available at Annals of Oncology online. Subgroup analysis was not carried out for BMI ≥12 months after diagnosis as the limited number of studies would hinder any meaningful comparisons.\nIncreased risks of mortality were observed in the meta-analyses by menopausal status. While the summary risk estimates seem stronger with premenopausal breast cancer, there was no significant heterogeneity between pre- and post-menopausal breast cancer as shown in the meta-regression analyses (P = 0.28–0.89) (supplementary Tables S3 and S4, available at Annals of Oncology online). For BMI before diagnosis and total mortality, the summary RRs for obese versus normal weight were 1.75 (95% CI 1.26–2.41, I2 = 70%, P < 0.01, 7 studies) in women with pre-menopausal breast cancer and 1.34 (95% CI 1.18–1.53, I2 = 27%, P = 0.20, 9 studies) in women with post-menopausal breast cancer.\nStudies with larger number of deaths [105, 115], conducted in Europe [28, 115], or with weight and height assessed through medical records [28, 104, 115, 116] tended to report weaker associations for BMI <12 months after diagnosis and total mortality compared with other studies (meta-regression P = 0.01, 0.02, 0.01, respectively) (supplementary Table S3, available at Annals of Oncology online); while studies with larger number of deaths [101], conducted in Asia [101, 102], or adjusted for co-morbidity [101, 102] reported weaker associations for BMI <12 months after diagnosis and breast cancer mortality (meta-regression P = 0.01, 0.02, 0.01, respectively) (supplementary Table S4, available at Annals of Oncology online).\nAnalyses stratified by study designs, or restricted to studies with invasive cases only, early-stage non-metastatic cases only, or mammography screening detected cases only, or controlled for previous diseases did not produce results that were materially different from those obtained in the overall analyses (results not shown). Summary risk estimates remained statistically significant when each study was omitted in turn, except for BMI ≥12 months after diagnosis and total mortality. The summary RR was 1.06 (95% CI 0.98–1.15) per 5 kg/m2 increase when Flatt et al. [117] which contributed 315 deaths was omitted.\n small studies or publication bias Asymmetry was only detected in the funnel plots of BMI <12 months after diagnosis and total mortality, and breast cancer mortality, which suggests that small studies with an inverse association are missing (plots not shown). Egger's tests were borderline significant (P = 0.05) or statistically significant (P = 0.03), respectively.\nAsymmetry was only detected in the funnel plots of BMI <12 months after diagnosis and total mortality, and breast cancer mortality, which suggests that small studies with an inverse association are missing (plots not shown). Egger's tests were borderline significant (P = 0.05) or statistically significant (P = 0.03), respectively."," categorical meta-analysis For BMI before diagnosis, compared with normal weight women, the summary RRs were 1.41 (95% CI 1.29–1.53, 21 studies) for obese women, 1.07 (95% CI 1.02–1.12, 19 studies) for overweight women, and 1.10 (95% CI 0.92–1.31, 10 studies) for underweight women (Figure 2). For BMI <12 months after diagnosis and the same comparisons, the summary RRs were 1.23 (95% CI 1.12–1.33, 24 studies) for obese women, 1.07 (95% CI 1.02–1.12, 22 studies) for overweight women, and 1.25 (95% CI 0.99–1.57, 11 studies) for underweight women (supplementary Figure S1, available at Annals of Oncology online). Substantial heterogeneities were observed between studies of obese women and underweight women (I2 = 69%, P < 0.01; I2 = 63%, P < 0.01, respectively). For BMI ≥12 months after diagnosis, the summary RRs were 1.21 (95% CI 1.06–1.38, 5 studies) for obese women, 0.98 (95% CI 0.86–1.11, 4 studies) for overweight women, and 1.29 (95% CI 1.02–1.63, 3 studies) for underweight women (supplementary Figure S2, available at Annals of Oncology online). Twelve additional studies reported results for obese versus non-obese women <12 months after diagnosis, and the summary RR was 1.26 (95% CI 1.07–1.47, I2 = 80%, P < 0.01).Figure 2.Categorical meta-analysis of pre-diagnosis BMI and total mortality.\nCategorical meta-analysis of pre-diagnosis BMI and total mortality.\nFor BMI before diagnosis, compared with normal weight women, the summary RRs were 1.41 (95% CI 1.29–1.53, 21 studies) for obese women, 1.07 (95% CI 1.02–1.12, 19 studies) for overweight women, and 1.10 (95% CI 0.92–1.31, 10 studies) for underweight women (Figure 2). For BMI <12 months after diagnosis and the same comparisons, the summary RRs were 1.23 (95% CI 1.12–1.33, 24 studies) for obese women, 1.07 (95% CI 1.02–1.12, 22 studies) for overweight women, and 1.25 (95% CI 0.99–1.57, 11 studies) for underweight women (supplementary Figure S1, available at Annals of Oncology online). Substantial heterogeneities were observed between studies of obese women and underweight women (I2 = 69%, P < 0.01; I2 = 63%, P < 0.01, respectively). For BMI ≥12 months after diagnosis, the summary RRs were 1.21 (95% CI 1.06–1.38, 5 studies) for obese women, 0.98 (95% CI 0.86–1.11, 4 studies) for overweight women, and 1.29 (95% CI 1.02–1.63, 3 studies) for underweight women (supplementary Figure S2, available at Annals of Oncology online). Twelve additional studies reported results for obese versus non-obese women <12 months after diagnosis, and the summary RR was 1.26 (95% CI 1.07–1.47, I2 = 80%, P < 0.01).Figure 2.Categorical meta-analysis of pre-diagnosis BMI and total mortality.\nCategorical meta-analysis of pre-diagnosis BMI and total mortality.\n dose–response meta-analysis There was evidence of a J-shaped association in the non-linear dose–response meta-analyses of BMI before and after diagnosis with total mortality (all P < 0.01; Figure 3), suggesting that underweight women may be at slightly increased risk compared with normal weight women. The curves show linear increasing trends from 20 kg/m2 for BMI before diagnosis and <12 months after diagnosis, and from 25 kg/m2 for BMI ≥12 months after diagnosis. When linear models were fitted excluding the underweight category, the summary RRs of total mortality for each 5 kg/m2 increase in BMI were 1.17 (95% CI 1.13–1.21, 15 studies, 6358 deaths), 1.11 (95% CI 1.06–1.16, 12 studies, 6020 deaths), and 1.08 (95% CI 1.01–1.15, 4 studies, 1703 deaths) for BMI before, <12 months after, and ≥12 months after diagnosis, respectively (Figure 4). Substantial heterogeneity was observed between studies on BMI <12 months after diagnosis (I2 = 55%, P = 0.01).Figure 3.Non-linear dose–response curves of BMI and mortality.Figure 4.Linear dose–response meta-analysis of BMI and total mortality.\nNon-linear dose–response curves of BMI and mortality.\nLinear dose–response meta-analysis of BMI and total mortality.\nThere was evidence of a J-shaped association in the non-linear dose–response meta-analyses of BMI before and after diagnosis with total mortality (all P < 0.01; Figure 3), suggesting that underweight women may be at slightly increased risk compared with normal weight women. The curves show linear increasing trends from 20 kg/m2 for BMI before diagnosis and <12 months after diagnosis, and from 25 kg/m2 for BMI ≥12 months after diagnosis. When linear models were fitted excluding the underweight category, the summary RRs of total mortality for each 5 kg/m2 increase in BMI were 1.17 (95% CI 1.13–1.21, 15 studies, 6358 deaths), 1.11 (95% CI 1.06–1.16, 12 studies, 6020 deaths), and 1.08 (95% CI 1.01–1.15, 4 studies, 1703 deaths) for BMI before, <12 months after, and ≥12 months after diagnosis, respectively (Figure 4). Substantial heterogeneity was observed between studies on BMI <12 months after diagnosis (I2 = 55%, P = 0.01).Figure 3.Non-linear dose–response curves of BMI and mortality.Figure 4.Linear dose–response meta-analysis of BMI and total mortality.\nNon-linear dose–response curves of BMI and mortality.\nLinear dose–response meta-analysis of BMI and total mortality.","For BMI before diagnosis, compared with normal weight women, the summary RRs were 1.41 (95% CI 1.29–1.53, 21 studies) for obese women, 1.07 (95% CI 1.02–1.12, 19 studies) for overweight women, and 1.10 (95% CI 0.92–1.31, 10 studies) for underweight women (Figure 2). For BMI <12 months after diagnosis and the same comparisons, the summary RRs were 1.23 (95% CI 1.12–1.33, 24 studies) for obese women, 1.07 (95% CI 1.02–1.12, 22 studies) for overweight women, and 1.25 (95% CI 0.99–1.57, 11 studies) for underweight women (supplementary Figure S1, available at Annals of Oncology online). Substantial heterogeneities were observed between studies of obese women and underweight women (I2 = 69%, P < 0.01; I2 = 63%, P < 0.01, respectively). For BMI ≥12 months after diagnosis, the summary RRs were 1.21 (95% CI 1.06–1.38, 5 studies) for obese women, 0.98 (95% CI 0.86–1.11, 4 studies) for overweight women, and 1.29 (95% CI 1.02–1.63, 3 studies) for underweight women (supplementary Figure S2, available at Annals of Oncology online). Twelve additional studies reported results for obese versus non-obese women <12 months after diagnosis, and the summary RR was 1.26 (95% CI 1.07–1.47, I2 = 80%, P < 0.01).Figure 2.Categorical meta-analysis of pre-diagnosis BMI and total mortality.\nCategorical meta-analysis of pre-diagnosis BMI and total mortality.","There was evidence of a J-shaped association in the non-linear dose–response meta-analyses of BMI before and after diagnosis with total mortality (all P < 0.01; Figure 3), suggesting that underweight women may be at slightly increased risk compared with normal weight women. The curves show linear increasing trends from 20 kg/m2 for BMI before diagnosis and <12 months after diagnosis, and from 25 kg/m2 for BMI ≥12 months after diagnosis. When linear models were fitted excluding the underweight category, the summary RRs of total mortality for each 5 kg/m2 increase in BMI were 1.17 (95% CI 1.13–1.21, 15 studies, 6358 deaths), 1.11 (95% CI 1.06–1.16, 12 studies, 6020 deaths), and 1.08 (95% CI 1.01–1.15, 4 studies, 1703 deaths) for BMI before, <12 months after, and ≥12 months after diagnosis, respectively (Figure 4). Substantial heterogeneity was observed between studies on BMI <12 months after diagnosis (I2 = 55%, P = 0.01).Figure 3.Non-linear dose–response curves of BMI and mortality.Figure 4.Linear dose–response meta-analysis of BMI and total mortality.\nNon-linear dose–response curves of BMI and mortality.\nLinear dose–response meta-analysis of BMI and total mortality."," categorical meta-analysis BMI was significantly associated with breast cancer mortality. Compared with normal weight women, for BMI before diagnosis, the summary RRs were 1.35 (95% CI 1.24–1.47, 22 studies) for obese women, 1.11 (95% CI 1.06–1.17, 21 studies) for overweight women, and 1.02 (95% CI 0.85–1.21, 8 studies) for underweight women (Figure 5). For BMI <12 months after diagnosis, the summary RRs were 1.25 (95% CI 1.10–1.42, 12 studies) for obese women, 1.11 (95% CI 1.03–1.20, 12 studies) for overweight women, and 1.53 (95% CI 1.27–1.83, 5 studies) for underweight women (supplementary Figure S3, available at Annals of Oncology online). Substantial heterogeneity was observed between studies of obese women (I2 = 53%, P = 0.02). For BMI ≥12 months after diagnosis, the summary RRs of the two studies identified were 1.68 (95% CI 0.90–3.15) for obese women and 1.37 (95% CI 0.96–1.95) for overweight women (supplementary Figure S4, available at Annals of Oncology online). The summary of another six studies that reported RRs for obese versus non-obese <12 months after diagnosis was 1.26 (95% CI 1.05–1.51, I2 = 64%, P = 0.02).Figure 5.Categorical meta-analysis of pre-diagnosis BMI and breast cancer mortality.\nCategorical meta-analysis of pre-diagnosis BMI and breast cancer mortality.\nBMI was significantly associated with breast cancer mortality. Compared with normal weight women, for BMI before diagnosis, the summary RRs were 1.35 (95% CI 1.24–1.47, 22 studies) for obese women, 1.11 (95% CI 1.06–1.17, 21 studies) for overweight women, and 1.02 (95% CI 0.85–1.21, 8 studies) for underweight women (Figure 5). For BMI <12 months after diagnosis, the summary RRs were 1.25 (95% CI 1.10–1.42, 12 studies) for obese women, 1.11 (95% CI 1.03–1.20, 12 studies) for overweight women, and 1.53 (95% CI 1.27–1.83, 5 studies) for underweight women (supplementary Figure S3, available at Annals of Oncology online). Substantial heterogeneity was observed between studies of obese women (I2 = 53%, P = 0.02). For BMI ≥12 months after diagnosis, the summary RRs of the two studies identified were 1.68 (95% CI 0.90–3.15) for obese women and 1.37 (95% CI 0.96–1.95) for overweight women (supplementary Figure S4, available at Annals of Oncology online). The summary of another six studies that reported RRs for obese versus non-obese <12 months after diagnosis was 1.26 (95% CI 1.05–1.51, I2 = 64%, P = 0.02).Figure 5.Categorical meta-analysis of pre-diagnosis BMI and breast cancer mortality.\nCategorical meta-analysis of pre-diagnosis BMI and breast cancer mortality.\n dose–response meta-analysis There was no significant evidence of a non-linear relationship between BMI before, <12 months after, and ≥12 months after diagnosis and breast cancer mortality (P = 0.21, P = 1.00, P = 0.86, respectively) (Figure 3). When linear models were fitted excluding data from the underweight category, statistically significant increased risks of breast cancer mortality with BMI before and <12 months after diagnosis were observed (Figure 6). The summary RRs for each 5 kg/m2 increase were 1.18 (95% CI 1.12–1.25, 18 studies, 5262 breast cancer deaths) for BMI before diagnosis and 1.14 (95% CI 1.05–1.24, 8 studies, 3857 breast cancer deaths) for BMI <12 months after diagnosis, with moderate (I2 = 47%, P = 0.01) and substantial (I2 = 66%, P = 0.01) heterogeneities between studies, respectively. Only two studies on BMI ≥12 months after diagnosis and breast cancer mortality (N = 220 deaths) were identified. The summary RR was 1.29 (95% CI 0.97–1.72).Figure 6.Linear dose–response meta-analysis of BMI and breast cancer mortality.\nLinear dose–response meta-analysis of BMI and breast cancer mortality.\nThere was no significant evidence of a non-linear relationship between BMI before, <12 months after, and ≥12 months after diagnosis and breast cancer mortality (P = 0.21, P = 1.00, P = 0.86, respectively) (Figure 3). When linear models were fitted excluding data from the underweight category, statistically significant increased risks of breast cancer mortality with BMI before and <12 months after diagnosis were observed (Figure 6). The summary RRs for each 5 kg/m2 increase were 1.18 (95% CI 1.12–1.25, 18 studies, 5262 breast cancer deaths) for BMI before diagnosis and 1.14 (95% CI 1.05–1.24, 8 studies, 3857 breast cancer deaths) for BMI <12 months after diagnosis, with moderate (I2 = 47%, P = 0.01) and substantial (I2 = 66%, P = 0.01) heterogeneities between studies, respectively. Only two studies on BMI ≥12 months after diagnosis and breast cancer mortality (N = 220 deaths) were identified. The summary RR was 1.29 (95% CI 0.97–1.72).Figure 6.Linear dose–response meta-analysis of BMI and breast cancer mortality.\nLinear dose–response meta-analysis of BMI and breast cancer mortality.","BMI was significantly associated with breast cancer mortality. Compared with normal weight women, for BMI before diagnosis, the summary RRs were 1.35 (95% CI 1.24–1.47, 22 studies) for obese women, 1.11 (95% CI 1.06–1.17, 21 studies) for overweight women, and 1.02 (95% CI 0.85–1.21, 8 studies) for underweight women (Figure 5). For BMI <12 months after diagnosis, the summary RRs were 1.25 (95% CI 1.10–1.42, 12 studies) for obese women, 1.11 (95% CI 1.03–1.20, 12 studies) for overweight women, and 1.53 (95% CI 1.27–1.83, 5 studies) for underweight women (supplementary Figure S3, available at Annals of Oncology online). Substantial heterogeneity was observed between studies of obese women (I2 = 53%, P = 0.02). For BMI ≥12 months after diagnosis, the summary RRs of the two studies identified were 1.68 (95% CI 0.90–3.15) for obese women and 1.37 (95% CI 0.96–1.95) for overweight women (supplementary Figure S4, available at Annals of Oncology online). The summary of another six studies that reported RRs for obese versus non-obese <12 months after diagnosis was 1.26 (95% CI 1.05–1.51, I2 = 64%, P = 0.02).Figure 5.Categorical meta-analysis of pre-diagnosis BMI and breast cancer mortality.\nCategorical meta-analysis of pre-diagnosis BMI and breast cancer mortality.","There was no significant evidence of a non-linear relationship between BMI before, <12 months after, and ≥12 months after diagnosis and breast cancer mortality (P = 0.21, P = 1.00, P = 0.86, respectively) (Figure 3). When linear models were fitted excluding data from the underweight category, statistically significant increased risks of breast cancer mortality with BMI before and <12 months after diagnosis were observed (Figure 6). The summary RRs for each 5 kg/m2 increase were 1.18 (95% CI 1.12–1.25, 18 studies, 5262 breast cancer deaths) for BMI before diagnosis and 1.14 (95% CI 1.05–1.24, 8 studies, 3857 breast cancer deaths) for BMI <12 months after diagnosis, with moderate (I2 = 47%, P = 0.01) and substantial (I2 = 66%, P = 0.01) heterogeneities between studies, respectively. Only two studies on BMI ≥12 months after diagnosis and breast cancer mortality (N = 220 deaths) were identified. The summary RR was 1.29 (95% CI 0.97–1.72).Figure 6.Linear dose–response meta-analysis of BMI and breast cancer mortality.\nLinear dose–response meta-analysis of BMI and breast cancer mortality.","Only two studies reported results for death from cardiovascular disease (N = 151 deaths) [27, 112]. The summary RR for obese versus normal weight before diagnosis was 1.60 (95% CI 0.66–3.87). No association was observed for overweight versus normal weight (summary RR = 1.01, 95% CI 0.80–1.29). For each 5 kg/m2 increase in BMI, the summary RR was 1.21 (95% CI 0.83–1.77). Five studies reported results for deaths from any cause other than breast cancer (N = 2704 deaths) [21, 34, 108, 113, 114]. The summary RRs were 1.29 (95% CI 0.99–1.68, I2 = 72%, P = 0.01) for obese women, and 0.96 (95% CI 0.83–1.11, I2 = 26%, P = 0.25) for overweight women compared with normal weight women.","The results of the subgroup and meta-regression analyses are in supplementary Tables S3 and S4, available at Annals of Oncology online. Subgroup analysis was not carried out for BMI ≥12 months after diagnosis as the limited number of studies would hinder any meaningful comparisons.\nIncreased risks of mortality were observed in the meta-analyses by menopausal status. While the summary risk estimates seem stronger with premenopausal breast cancer, there was no significant heterogeneity between pre- and post-menopausal breast cancer as shown in the meta-regression analyses (P = 0.28–0.89) (supplementary Tables S3 and S4, available at Annals of Oncology online). For BMI before diagnosis and total mortality, the summary RRs for obese versus normal weight were 1.75 (95% CI 1.26–2.41, I2 = 70%, P < 0.01, 7 studies) in women with pre-menopausal breast cancer and 1.34 (95% CI 1.18–1.53, I2 = 27%, P = 0.20, 9 studies) in women with post-menopausal breast cancer.\nStudies with larger number of deaths [105, 115], conducted in Europe [28, 115], or with weight and height assessed through medical records [28, 104, 115, 116] tended to report weaker associations for BMI <12 months after diagnosis and total mortality compared with other studies (meta-regression P = 0.01, 0.02, 0.01, respectively) (supplementary Table S3, available at Annals of Oncology online); while studies with larger number of deaths [101], conducted in Asia [101, 102], or adjusted for co-morbidity [101, 102] reported weaker associations for BMI <12 months after diagnosis and breast cancer mortality (meta-regression P = 0.01, 0.02, 0.01, respectively) (supplementary Table S4, available at Annals of Oncology online).\nAnalyses stratified by study designs, or restricted to studies with invasive cases only, early-stage non-metastatic cases only, or mammography screening detected cases only, or controlled for previous diseases did not produce results that were materially different from those obtained in the overall analyses (results not shown). Summary risk estimates remained statistically significant when each study was omitted in turn, except for BMI ≥12 months after diagnosis and total mortality. The summary RR was 1.06 (95% CI 0.98–1.15) per 5 kg/m2 increase when Flatt et al. [117] which contributed 315 deaths was omitted.","Asymmetry was only detected in the funnel plots of BMI <12 months after diagnosis and total mortality, and breast cancer mortality, which suggests that small studies with an inverse association are missing (plots not shown). Egger's tests were borderline significant (P = 0.05) or statistically significant (P = 0.03), respectively.","The present systematic literature review and meta-analysis of follow-up studies clearly supports that, in breast cancer survivors, higher BMI is consistently associated with lower overall and breast cancer survival, regardless of when BMI is ascertained. The limited number of studies on death from cardiovascular disease is also consistent with a positive association. For before, <12 months after, and 12 months or more after breast cancer diagnosis, compared with normal weight women, obese women had 41%, 23%, and 21% higher risk for total mortality, and 35%, 25%, and 68% increased risk for breast cancer mortality, respectively. The findings were supported by the positive associations observed in the linear dose–response meta-analysis. All associations were statistically significant, apart from the relationship between BMI ≥12 months after diagnosis and breast cancer mortality. This may be due to limited statistical power, with only 220 breast cancer deaths from two follow-up studies.\nPositive associations, in some cases statistically significant, were also observed in overweight, and underweight women compared with normal weight women. Women with BMI of 20 kg/m2 before, or <12 months after diagnosis, and of 25 kg/m2 12 months or more after diagnosis appeared to have the lowest mortality risk in the non-linear dose–response analysis. Co-morbid conditions may cause the observed increased risk in underweight women. Thorough investigation within the group and on their contribution to the shape of the association is hindered, as not all studies in this review reported results for this group. The increased risk associated with obesity was similar in pre- or post-menopausal breast cancer. We did not find any evidence of a protective effect of obesity on survival after pre-menopausal breast cancer, contrary to what has been observed for the development of breast cancer in pre-menopausal women [4].\nA large body of evidence with 41 477 deaths (23 182 from breast cancer) in over 210 000 breast cancer survivors was systematically reviewed in the present study. We carried out categorical, linear, and non-linear dose–response meta-analyses to examine the magnitude and the shape of the associations for total and cause-specific mortality in underweight, overweight, and obese women by time periods before and after diagnosis that is important in relation to the population-at-risk and breast cancer survivors. Our findings agree with and further extend the results from previous meta-analyses. A review published in 2010 reported statistically significant increased risks of 33% of both total and breast cancer mortality for obesity versus non-obesity around diagnosis [7]. These estimates are slightly higher than ours, which may be explained by the different search periods and inclusion criteria for the articles (33 studies and 15 studies included in the analyses, respectively). Another review published in 2012 further reported consistent positive associations of total and breast cancer mortality with higher versus lower BMI around diagnosis [6]. No significant differences were observed by menopausal status or hormone receptor status. The After Breast Cancer Pooling Project of four prospective cohort studies found differential effects of levels of pre-diagnosis obesity on survival [118]. Compared with normal weight women, significant or borderline significant increased risks of 81% of total and 40% of breast cancer mortality were only observed for morbidly obese (≥40 kg/m2) women and not for women in other obesity categories. We observed statistically significant increased risks also for overweight women, probably because of a larger number of studies. We were unable to investigate the associations with severely and morbidly obese women because only two studies included in this review reported such results [19, 113]. Overall, our findings are consistent with previous meta-analyses in showing elevated total and breast cancer mortality associated with higher BMI and support the current guidelines for breast cancer survivors to stay as lean as possible within the normal range of body weight [4], for overweight women to avoid weight gain during treatment and for obese women to lose weight after treatment [119].\nThe present review is limited by the challenges and flaws encountered by the individual epidemiological studies evaluating the body fatness–mortality relationship in breast cancer survivors. Most studies did not adjust for co-morbidities and assess intentional weight loss. Women with more serious health issues, and especially smokers, may lose weight but are at an increased risk of mortality, and this might cause an apparent increased risk in underweight women. Body weight information through the natural history of the disease and treatment information were usually not complete or available. Increase of body weight post-diagnosis is common in women with breast cancer, particularly during chemotherapy [16]. Chemotherapy under-dosing is a common problem in obese women and may contribute to their increased mortality [120]. Although several studies with pre-diagnosis BMI adjusted for underlying illnesses or excluded the first few years of follow-up, reverse causation may have affected the results in studies that assessed BMI in women with cancer and other illnesses. However, in these studies, the associations were similar to other studies. Possible survival benefit (subjects with better prognostic factors survive) may be present in the survival cohorts, in which the range of BMI could be narrower, and may cause an underestimation of the association.\nFollow-up studies with variable characteristics were pooled in the meta-analysis. Women identified in clinical trials may have had specific tumour subtypes, with fewer co-morbidities, and were more likely to receive protocol treatments with high treatment completion rates. Women who were recruited through mammography screening programmes may have had healthier lifestyles or access to medical facilities, and more likely to be diagnosed with in situ or early-stage breast cancer. Cancer detection methods, tumour classifications and treatment regimens change over time, and may vary within (if follow-up is long) and between studies, and could not be simply examined by using the diagnosis or treatment date. We cannot rule out the effect of unmeasured or residual confounding in our analysis. Nevertheless, most results were adjusted for multiple confounding factors, including tumour stage or other-related variables and stratified analyses by several key factors showed similar summary risk estimates. Small study or publication bias was observed in the analyses of BMI <12 months after diagnosis. However, the overall evidence is supported by large, well-designed studies and is unlikely to be changed. We did not conduct analyses by race/ethnicity and treatment types as only limited studies had published results.\nFuture studies of body fatness and breast cancer outcomes should aim to account for co-morbidities, separate intended and unintended changes of body weight, and collect complete treatment information during study follow-up. Randomised clinical trials are needed to test interventions for weight loss and maintenance on survival in women with breast cancer.\nIn conclusion, the present systematic literature review and meta-analysis extends and confirms the associations of obesity with an unfavourable overall and breast cancer survival in pre- and post-menopausal breast cancer, regardless of when BMI is ascertained. Increased risks of mortality in underweight and overweight women were also observed. Given the comparable elevated risks with obesity in the development (for post-menopausal women) and prognosis of breast cancer, and the complications with cancer treatment and other obesity-related co-morbidities, it is prudent to maintain a healthy body weight (BMI 18.5–<25.0 kg/m2) throughout life.","This work was supported by the World Cancer Research Fund International (grant number: 2007/SP01) (http://www.wcrf-uk.org/). The funder of this study had no role in the decisions about the design and conduct of the study; collection, management, analysis, or interpretation of the data; or the preparation, review, or approval of the manuscript. The views expressed in this review are the opinions of the authors. They may not represent the views of the World Cancer Research Fund International/American Institute for Cancer Research and may differ from those in future updates of the evidence related to food, nutrition, physical activity, and cancer survival.","DCG reports personal fees from World Cancer Research Fund/American Institute for Cancer Research, during the conduct of the study; grants from Danone, and grants from Kelloggs, outside the submitted work. AM reports personal fees from Metagenics/Metaproteomics, personal fees from Pfizer, outside the submitted work. All remaining authors have declared no conflicts of interest.",""],"string":"[\n \"The number of female breast cancer survivors is growing because of longer survival as a consequence of advances in treatment and early diagnosis. There were ∼2.6 million female breast cancer survivors in US in 2008 [1], and in the UK, breast cancer accounted for ∼28% of the 2 million cancer survivors in 2008 [2].\\nObesity is a pandemic health concern, with over 500 million adults worldwide estimated to be obese and 958 million were overweight in 2008 [3]. One of the established risk factors for breast cancer development in post-menopausal women is obesity [4], which has further been linked to breast cancer recurrence [5] and poorer survival in pre- and post-menopausal breast cancer [6, 7]. Preliminary findings from randomised, controlled trials suggest that lifestyle modifications improved biomarkers associated with breast cancer progression and overall survival [8].\\nThe biological mechanisms underlying the association between obesity and breast cancer survival are not established, and could involve interacting mediators of hormones, adipocytokines, and inflammatory cytokines which link to cell survival or apoptosis, migration, and proliferation [9]. Higher level of oestradiol produced in postmenopausal women through aromatisation of androgens in the adipose tissues [10], and higher level of insulin [11], a condition common in obese women, are linked to poorer prognosis in breast cancer. A possible interaction between leptin and insulin [12], and obesity-related markers of inflammation [13] have also been linked to breast cancer outcomes. Non-biological mechanisms could include chemotherapy under-dosing in obese women, suboptimal treatment, and obesity-related complications [14].\\nNumerous studies have examined the relationship between obesity and breast cancer outcomes, and past reviews have concluded that obesity is linked to a lower survival; however, when investigated in a meta-analysis of published data, only the results of obese compared with non-obese or lighter women were summarised [6, 7, 15].\\nWe carried out a systematic literature review and meta-analysis of published studies to explore the magnitude and the shape of the association between body fatness, as measured by body mass index (BMI), and the risk of total and cause-specific mortality, overall and in women with pre- and post-menopausal breast cancer. As body weight may change close to diagnosis and during primary treatment of breast cancer [16], we examined BMI in three periods: before diagnosis, <12 months after diagnosis, and ≥12 months after breast cancer diagnosis.\",\n \" data sources and search We carried out a systematic literature search, limited to publications in English, for articles on BMI and survival in women with breast cancer in OVID MEDLINE and EMBASE from inception to 30 June 2013 using the search strategy implemented for the WCRF/AICR Continuous Update Project on breast cancer survival. The search strategy contained medical subject headings and text words that covered a broad range of factors on diet, physical activity, and anthropometry. The protocol for the review is available at http://www.dietandcancerreport.org/index.php [17]. In addition, we hand-searched the reference lists of relevant articles, reviews, and meta-analysis papers.\\nWe carried out a systematic literature search, limited to publications in English, for articles on BMI and survival in women with breast cancer in OVID MEDLINE and EMBASE from inception to 30 June 2013 using the search strategy implemented for the WCRF/AICR Continuous Update Project on breast cancer survival. The search strategy contained medical subject headings and text words that covered a broad range of factors on diet, physical activity, and anthropometry. The protocol for the review is available at http://www.dietandcancerreport.org/index.php [17]. In addition, we hand-searched the reference lists of relevant articles, reviews, and meta-analysis papers.\\n study selection Included were follow-up studies of breast cancer survivors, which reported estimates of the associations of BMI ascertained before and after breast cancer diagnosis with total or cause-specific mortality risks. Studies that investigated BMI after diagnosis were divided into two groups: BMI <12 months after diagnosis (BMI <12 months) and BMI 12 months or more after diagnosis (BMI ≥12 months). Outcomes included total mortality, breast cancer mortality, death from cardiovascular disease, and death from causes other than breast cancer. When multiple publications on the same study population were found, results based on longer follow-up and more outcomes were selected for the meta-analysis.\\nIncluded were follow-up studies of breast cancer survivors, which reported estimates of the associations of BMI ascertained before and after breast cancer diagnosis with total or cause-specific mortality risks. Studies that investigated BMI after diagnosis were divided into two groups: BMI <12 months after diagnosis (BMI <12 months) and BMI 12 months or more after diagnosis (BMI ≥12 months). Outcomes included total mortality, breast cancer mortality, death from cardiovascular disease, and death from causes other than breast cancer. When multiple publications on the same study population were found, results based on longer follow-up and more outcomes were selected for the meta-analysis.\\n data extraction DSMC, TN, and DA conducted the search. DSMC, ARV, and DNR extracted the study characteristics, tumour-related information, cancer treatment, timing and method of weight and height assessment, BMI levels, number of outcomes and population at-risk, outcome type, estimates of association and their measure of variance [95% confidence interval (CI) or P value], and adjustment factors in the analysis.\\nDSMC, TN, and DA conducted the search. DSMC, ARV, and DNR extracted the study characteristics, tumour-related information, cancer treatment, timing and method of weight and height assessment, BMI levels, number of outcomes and population at-risk, outcome type, estimates of association and their measure of variance [95% confidence interval (CI) or P value], and adjustment factors in the analysis.\\n statistical analysis Categorical and dose–response meta-analyses were conducted using random-effects models to account for between-study heterogeneity [18]. Summary relative risks (RRs) were estimated using the average of the natural logarithm of the RRs of each study weighted by the inverse of the variance and then unweighted by applying a random-effects variance component which is derived from the extent of variability of the effect sizes of the studies. The maximally adjusted RR estimates were used for the meta-analysis except for the follow-up of randomised, controlled trials [19, 20] where unadjusted results were also included, as these studies mostly involved a more homogeneous study population. BMI or Quetelet's Index (QI) measured in units of kg/m2 was used.\\nWe conducted categorical meta-analyses by pooling the categorical results reported in the studies. The studies used different BMI categories. In some studies, underweight (BMI <18.5 kg/m2 according to WHO international classification) and normal weight women (BMI 18.5–<25.0 kg/m2) were classified together but, in some studies, they were classified separately. Similarly, most studies classified overweight (BMI 25.0–<30.0 kg/m2) and obese (BMI ≥30.0 kg/m2) women separately but, in some studies, overweight and obese women were combined. The reference category was normal weight or underweight together with normal weight, depending on the studies. For convenience, the BMI categories are referred to as underweight, normal weight, overweight, and obese in the present review. We derived the RRs for overweight and obese women compared with normal weight women in two studies [19, 21] that had more than four BMI categories using the method of Hamling et al. [22]. Studies that reported results for obese compared with non-obese women were analysed separately.\\nThe non-linear dose–response relationship between BMI and mortality was examined using the best-fitting second-order fractional polynomial regression model [23], defined as the one with the lowest deviance. Non-linearity was tested using the likelihood ratio test [24]. In the non-linear meta-analysis, the reference category was the lowest BMI category in each study and RRs were recalculated using the method of Hamling et al. [22] when the reference category was not the lowest BMI category in the study.\\nWe also conducted linear dose–response meta-analyses, excluding the category underweight when reported separately in the studies, by pooling estimates of RR per unit increase (with its standard error) provided by the studies, or derived by us from categorical data using generalised least-squares for trend estimation [25]. To estimate the trend, the numbers of outcomes and population at-risk for at least three BMI categories, or the information required to derive them using standard methods [26], and means or medians of the BMI categories, or if not reported in the studies, the estimated midpoints of the categories had to be available. When the extreme BMI categories were open-ended, we used the width of the adjacent close-ended category to estimate the midpoints. Where the RRs were presented by subgroups (age group [27], menopausal status [28, 29], stage [30] or subtype [31] of breast cancer, or others [32–34]), an overall estimate for the study was obtained by a fixed-effect model before pooling in the meta-analysis. We estimated the risk increase of death for an increment of 5 kg/m2 of BMI.\\nTo assess heterogeneity, we computed the Cochran Q test and I2 statistic [35]. The cut points of 30% and 50% were used for low, moderate, and substantial level of heterogeneity. Sources of heterogeneity were explored by meta-regression and subgroup analyses using pre-defined factors, including indicators of study quality (menopausal status, hormone receptor status, number of outcomes, length of follow-up, study design, geographic location, BMI assessment, adjustment for confounders, and others). Small study or publication bias was examined by Egger's test [36] and visual inspection of the funnel plots. The influence of each individual study on the summary RR was examined by excluding the study in turn [37]. A P value of <0.05 was considered statistically significant. All analyses were conducted using Stata version 12.1 (Stata Statistical Software: Release 12, StataCorp LP, College Station, TX).\\nCategorical and dose–response meta-analyses were conducted using random-effects models to account for between-study heterogeneity [18]. Summary relative risks (RRs) were estimated using the average of the natural logarithm of the RRs of each study weighted by the inverse of the variance and then unweighted by applying a random-effects variance component which is derived from the extent of variability of the effect sizes of the studies. The maximally adjusted RR estimates were used for the meta-analysis except for the follow-up of randomised, controlled trials [19, 20] where unadjusted results were also included, as these studies mostly involved a more homogeneous study population. BMI or Quetelet's Index (QI) measured in units of kg/m2 was used.\\nWe conducted categorical meta-analyses by pooling the categorical results reported in the studies. The studies used different BMI categories. In some studies, underweight (BMI <18.5 kg/m2 according to WHO international classification) and normal weight women (BMI 18.5–<25.0 kg/m2) were classified together but, in some studies, they were classified separately. Similarly, most studies classified overweight (BMI 25.0–<30.0 kg/m2) and obese (BMI ≥30.0 kg/m2) women separately but, in some studies, overweight and obese women were combined. The reference category was normal weight or underweight together with normal weight, depending on the studies. For convenience, the BMI categories are referred to as underweight, normal weight, overweight, and obese in the present review. We derived the RRs for overweight and obese women compared with normal weight women in two studies [19, 21] that had more than four BMI categories using the method of Hamling et al. [22]. Studies that reported results for obese compared with non-obese women were analysed separately.\\nThe non-linear dose–response relationship between BMI and mortality was examined using the best-fitting second-order fractional polynomial regression model [23], defined as the one with the lowest deviance. Non-linearity was tested using the likelihood ratio test [24]. In the non-linear meta-analysis, the reference category was the lowest BMI category in each study and RRs were recalculated using the method of Hamling et al. [22] when the reference category was not the lowest BMI category in the study.\\nWe also conducted linear dose–response meta-analyses, excluding the category underweight when reported separately in the studies, by pooling estimates of RR per unit increase (with its standard error) provided by the studies, or derived by us from categorical data using generalised least-squares for trend estimation [25]. To estimate the trend, the numbers of outcomes and population at-risk for at least three BMI categories, or the information required to derive them using standard methods [26], and means or medians of the BMI categories, or if not reported in the studies, the estimated midpoints of the categories had to be available. When the extreme BMI categories were open-ended, we used the width of the adjacent close-ended category to estimate the midpoints. Where the RRs were presented by subgroups (age group [27], menopausal status [28, 29], stage [30] or subtype [31] of breast cancer, or others [32–34]), an overall estimate for the study was obtained by a fixed-effect model before pooling in the meta-analysis. We estimated the risk increase of death for an increment of 5 kg/m2 of BMI.\\nTo assess heterogeneity, we computed the Cochran Q test and I2 statistic [35]. The cut points of 30% and 50% were used for low, moderate, and substantial level of heterogeneity. Sources of heterogeneity were explored by meta-regression and subgroup analyses using pre-defined factors, including indicators of study quality (menopausal status, hormone receptor status, number of outcomes, length of follow-up, study design, geographic location, BMI assessment, adjustment for confounders, and others). Small study or publication bias was examined by Egger's test [36] and visual inspection of the funnel plots. The influence of each individual study on the summary RR was examined by excluding the study in turn [37]. A P value of <0.05 was considered statistically significant. All analyses were conducted using Stata version 12.1 (Stata Statistical Software: Release 12, StataCorp LP, College Station, TX).\",\n \"We carried out a systematic literature search, limited to publications in English, for articles on BMI and survival in women with breast cancer in OVID MEDLINE and EMBASE from inception to 30 June 2013 using the search strategy implemented for the WCRF/AICR Continuous Update Project on breast cancer survival. The search strategy contained medical subject headings and text words that covered a broad range of factors on diet, physical activity, and anthropometry. The protocol for the review is available at http://www.dietandcancerreport.org/index.php [17]. In addition, we hand-searched the reference lists of relevant articles, reviews, and meta-analysis papers.\",\n \"Included were follow-up studies of breast cancer survivors, which reported estimates of the associations of BMI ascertained before and after breast cancer diagnosis with total or cause-specific mortality risks. Studies that investigated BMI after diagnosis were divided into two groups: BMI <12 months after diagnosis (BMI <12 months) and BMI 12 months or more after diagnosis (BMI ≥12 months). Outcomes included total mortality, breast cancer mortality, death from cardiovascular disease, and death from causes other than breast cancer. When multiple publications on the same study population were found, results based on longer follow-up and more outcomes were selected for the meta-analysis.\",\n \"DSMC, TN, and DA conducted the search. DSMC, ARV, and DNR extracted the study characteristics, tumour-related information, cancer treatment, timing and method of weight and height assessment, BMI levels, number of outcomes and population at-risk, outcome type, estimates of association and their measure of variance [95% confidence interval (CI) or P value], and adjustment factors in the analysis.\",\n \"Categorical and dose–response meta-analyses were conducted using random-effects models to account for between-study heterogeneity [18]. Summary relative risks (RRs) were estimated using the average of the natural logarithm of the RRs of each study weighted by the inverse of the variance and then unweighted by applying a random-effects variance component which is derived from the extent of variability of the effect sizes of the studies. The maximally adjusted RR estimates were used for the meta-analysis except for the follow-up of randomised, controlled trials [19, 20] where unadjusted results were also included, as these studies mostly involved a more homogeneous study population. BMI or Quetelet's Index (QI) measured in units of kg/m2 was used.\\nWe conducted categorical meta-analyses by pooling the categorical results reported in the studies. The studies used different BMI categories. In some studies, underweight (BMI <18.5 kg/m2 according to WHO international classification) and normal weight women (BMI 18.5–<25.0 kg/m2) were classified together but, in some studies, they were classified separately. Similarly, most studies classified overweight (BMI 25.0–<30.0 kg/m2) and obese (BMI ≥30.0 kg/m2) women separately but, in some studies, overweight and obese women were combined. The reference category was normal weight or underweight together with normal weight, depending on the studies. For convenience, the BMI categories are referred to as underweight, normal weight, overweight, and obese in the present review. We derived the RRs for overweight and obese women compared with normal weight women in two studies [19, 21] that had more than four BMI categories using the method of Hamling et al. [22]. Studies that reported results for obese compared with non-obese women were analysed separately.\\nThe non-linear dose–response relationship between BMI and mortality was examined using the best-fitting second-order fractional polynomial regression model [23], defined as the one with the lowest deviance. Non-linearity was tested using the likelihood ratio test [24]. In the non-linear meta-analysis, the reference category was the lowest BMI category in each study and RRs were recalculated using the method of Hamling et al. [22] when the reference category was not the lowest BMI category in the study.\\nWe also conducted linear dose–response meta-analyses, excluding the category underweight when reported separately in the studies, by pooling estimates of RR per unit increase (with its standard error) provided by the studies, or derived by us from categorical data using generalised least-squares for trend estimation [25]. To estimate the trend, the numbers of outcomes and population at-risk for at least three BMI categories, or the information required to derive them using standard methods [26], and means or medians of the BMI categories, or if not reported in the studies, the estimated midpoints of the categories had to be available. When the extreme BMI categories were open-ended, we used the width of the adjacent close-ended category to estimate the midpoints. Where the RRs were presented by subgroups (age group [27], menopausal status [28, 29], stage [30] or subtype [31] of breast cancer, or others [32–34]), an overall estimate for the study was obtained by a fixed-effect model before pooling in the meta-analysis. We estimated the risk increase of death for an increment of 5 kg/m2 of BMI.\\nTo assess heterogeneity, we computed the Cochran Q test and I2 statistic [35]. The cut points of 30% and 50% were used for low, moderate, and substantial level of heterogeneity. Sources of heterogeneity were explored by meta-regression and subgroup analyses using pre-defined factors, including indicators of study quality (menopausal status, hormone receptor status, number of outcomes, length of follow-up, study design, geographic location, BMI assessment, adjustment for confounders, and others). Small study or publication bias was examined by Egger's test [36] and visual inspection of the funnel plots. The influence of each individual study on the summary RR was examined by excluding the study in turn [37]. A P value of <0.05 was considered statistically significant. All analyses were conducted using Stata version 12.1 (Stata Statistical Software: Release 12, StataCorp LP, College Station, TX).\",\n \"A total of 124 publications investigating the relationship of body fatness and mortality in women with breast cancer were identified. We excluded 31 publications, including four publications on other obesity indices [38–41], 12 publications without a measure of association [42–53], and 15 publications superseded by publications of the same study with more outcomes [54–68]. A further 14 publications were excluded because of insufficient data for the meta-analysis (five publications [69–73]) or unadjusted results (nine publications [74–82]), from which nine publications reported statistically significant increased risk of total, breast cancer or non-breast cancer mortality in obese women (before or <12 months after diagnosis) compared with the reference BMI [69, 71–74, 76, 77, 79, 82], two publications reported non-significant inverse associations [75, 80] and three publications reported no association [70, 78, 81] of BMI with survival after breast cancer. Hence, 79 publications from 82 follow-up studies with 41 477 deaths (23 182 from breast cancer) in 213 075 breast cancer survivors were included in the meta-analyses (Figure 1). Supplementary Table S1, available at Annals of Oncology online shows the characteristics of the studies included in the meta-analyses and details of the excluded studies are in supplementary Table S2, available at Annals of Oncology online. Results of the meta-analyses are summarised in Table 1.Table 1.Summary of meta-analyses of BMI and survival in women with breast canceraBMI before diagnosisBMI <12 months after diagnosisBMI ≥12 months after diagnosisNRR (95% CI)I2 (%)PhNRR (95% CI)I2 (%)PhNRR (95% CI)I2 (%)PhTotal mortality Under versus normal weight101.10 (0.92–1.31)48%0.04111.25 (0.99–0.57)63%<0.0131.29 (1.02–1.63)0%0.39 Over versus normal weight191.07 (1.02–1.12)0%0.88221.07 (1.02–1.12)21%0.1840.98 (0.86–1.11)0%0.72 Obese versus normal weight211.41 (1.29–1.53)38%0.04241.23 (1.12–1.33)69%<0.0151.21 (1.06–1.38)0%0.70 Obese versus non-obese–––121.26 (1.07–1.47)80%<0.01––– Per 5 kg/m2 increase151.17 (1.13–1.21)7%0.38121.11 (1.06–1.16)55%0.0141.08 (1.01–1.15)0%0.52Breast cancer mortality Under versus normal weight81.02 (0.85–1.21)31%0.1851.53 (1.27–1.83)0%0.5911.10 (0.15–8.08)– Over versus normal weight211.11 (1.06–1.17)0%0.66121.11 (1.03–1.20)14%0.3121.37 (0.96–1.95)0%0.90 Obese versus normal weight221.35 (1.24–1.47)36%0.05121.25 (1.10–1.42)53%0.0221.68 (0.90–3.15)67%0.08 Obese versus non-obese–––61.26 (1.05–1.51)64%0.02––– Per 5 kg/m2 increase181.18 (1.12–1.25)47%0.0181.14 (1.05–1.24)66%0.0121.29 (0.97–1.72)64%0.10Cardiovascular disease related mortality Over versus normal weight21.01 (0.80–1.29)0%0.87–––––– Obese versus normal weight21.60 (0.66–3.87)78%0.03–––––– Per 5 kg/m2 increase21.21 (0.83–1.77)80%0.03––––––Non-breast cancer mortality Over versus normal weight–––50.96 (0.83–1.11)26%0.25––– Obese versus normal weight–––51.29 (0.99–1.68)72%0.01–––aBMI before and after diagnosis (<12 months after, or ≥12 months after diagnosis) was classified according to the exposure period which the studies referred to in the BMI assessment; the BMI categories were included in the categorical meta-analyses as defined by the studies.Ph, P for heterogeneity between studies.Figure 1.Flowchart of search.\\nSummary of meta-analyses of BMI and survival in women with breast cancera\\naBMI before and after diagnosis (<12 months after, or ≥12 months after diagnosis) was classified according to the exposure period which the studies referred to in the BMI assessment; the BMI categories were included in the categorical meta-analyses as defined by the studies.\\nPh, P for heterogeneity between studies.\\nFlowchart of search.\\nStudies were follow-up of women with breast cancer identified in prospective aetiologic cohort studies (women were free of cancer at enrolment), or cohorts of breast cancer survivors whose participants were identified in hospitals or through cancer registries, or follow-up of breast cancer patients enrolled in case–control studies or randomised clinical trials.\\nSome studies included only premenopausal women [83–85] or postmenopausal women [21, 27, 86–94], but most studies included both. Menopausal status was usually determined at time of diagnosis. Year of diagnosis was from 1957–1965 [70] to 2002–2009 [74]. Patient tumour characteristics and stage of disease at diagnosis varied across studies, and some studies included carcinoma in situ. No all studies provided clinical information on the tumour, treatment, and co-morbidities.\\nMost of the studies were based in North America or Europe. There were three studies from each of Australia [79, 95, 96], Korea [97, 98] and China [99–101]; two studies from Japan [71, 102]; one study from Tunisia [103] and four international studies [19, 104–106]. Study size ranged from 96 [107] to 24 698 patients [97]. Total number of deaths ranged from 56 [93] to 7397 [108], and the proportion of deaths from breast cancer ranged from 22% [27] to 98% [84] when reported. All but eight studies [30, 93, 94, 98, 99, 109–111] had an average follow-up of more than 5 years.\\n BMI and total mortality categorical meta-analysis For BMI before diagnosis, compared with normal weight women, the summary RRs were 1.41 (95% CI 1.29–1.53, 21 studies) for obese women, 1.07 (95% CI 1.02–1.12, 19 studies) for overweight women, and 1.10 (95% CI 0.92–1.31, 10 studies) for underweight women (Figure 2). For BMI <12 months after diagnosis and the same comparisons, the summary RRs were 1.23 (95% CI 1.12–1.33, 24 studies) for obese women, 1.07 (95% CI 1.02–1.12, 22 studies) for overweight women, and 1.25 (95% CI 0.99–1.57, 11 studies) for underweight women (supplementary Figure S1, available at Annals of Oncology online). Substantial heterogeneities were observed between studies of obese women and underweight women (I2 = 69%, P < 0.01; I2 = 63%, P < 0.01, respectively). For BMI ≥12 months after diagnosis, the summary RRs were 1.21 (95% CI 1.06–1.38, 5 studies) for obese women, 0.98 (95% CI 0.86–1.11, 4 studies) for overweight women, and 1.29 (95% CI 1.02–1.63, 3 studies) for underweight women (supplementary Figure S2, available at Annals of Oncology online). Twelve additional studies reported results for obese versus non-obese women <12 months after diagnosis, and the summary RR was 1.26 (95% CI 1.07–1.47, I2 = 80%, P < 0.01).Figure 2.Categorical meta-analysis of pre-diagnosis BMI and total mortality.\\nCategorical meta-analysis of pre-diagnosis BMI and total mortality.\\nFor BMI before diagnosis, compared with normal weight women, the summary RRs were 1.41 (95% CI 1.29–1.53, 21 studies) for obese women, 1.07 (95% CI 1.02–1.12, 19 studies) for overweight women, and 1.10 (95% CI 0.92–1.31, 10 studies) for underweight women (Figure 2). For BMI <12 months after diagnosis and the same comparisons, the summary RRs were 1.23 (95% CI 1.12–1.33, 24 studies) for obese women, 1.07 (95% CI 1.02–1.12, 22 studies) for overweight women, and 1.25 (95% CI 0.99–1.57, 11 studies) for underweight women (supplementary Figure S1, available at Annals of Oncology online). Substantial heterogeneities were observed between studies of obese women and underweight women (I2 = 69%, P < 0.01; I2 = 63%, P < 0.01, respectively). For BMI ≥12 months after diagnosis, the summary RRs were 1.21 (95% CI 1.06–1.38, 5 studies) for obese women, 0.98 (95% CI 0.86–1.11, 4 studies) for overweight women, and 1.29 (95% CI 1.02–1.63, 3 studies) for underweight women (supplementary Figure S2, available at Annals of Oncology online). Twelve additional studies reported results for obese versus non-obese women <12 months after diagnosis, and the summary RR was 1.26 (95% CI 1.07–1.47, I2 = 80%, P < 0.01).Figure 2.Categorical meta-analysis of pre-diagnosis BMI and total mortality.\\nCategorical meta-analysis of pre-diagnosis BMI and total mortality.\\n dose–response meta-analysis There was evidence of a J-shaped association in the non-linear dose–response meta-analyses of BMI before and after diagnosis with total mortality (all P < 0.01; Figure 3), suggesting that underweight women may be at slightly increased risk compared with normal weight women. The curves show linear increasing trends from 20 kg/m2 for BMI before diagnosis and <12 months after diagnosis, and from 25 kg/m2 for BMI ≥12 months after diagnosis. When linear models were fitted excluding the underweight category, the summary RRs of total mortality for each 5 kg/m2 increase in BMI were 1.17 (95% CI 1.13–1.21, 15 studies, 6358 deaths), 1.11 (95% CI 1.06–1.16, 12 studies, 6020 deaths), and 1.08 (95% CI 1.01–1.15, 4 studies, 1703 deaths) for BMI before, <12 months after, and ≥12 months after diagnosis, respectively (Figure 4). Substantial heterogeneity was observed between studies on BMI <12 months after diagnosis (I2 = 55%, P = 0.01).Figure 3.Non-linear dose–response curves of BMI and mortality.Figure 4.Linear dose–response meta-analysis of BMI and total mortality.\\nNon-linear dose–response curves of BMI and mortality.\\nLinear dose–response meta-analysis of BMI and total mortality.\\nThere was evidence of a J-shaped association in the non-linear dose–response meta-analyses of BMI before and after diagnosis with total mortality (all P < 0.01; Figure 3), suggesting that underweight women may be at slightly increased risk compared with normal weight women. The curves show linear increasing trends from 20 kg/m2 for BMI before diagnosis and <12 months after diagnosis, and from 25 kg/m2 for BMI ≥12 months after diagnosis. When linear models were fitted excluding the underweight category, the summary RRs of total mortality for each 5 kg/m2 increase in BMI were 1.17 (95% CI 1.13–1.21, 15 studies, 6358 deaths), 1.11 (95% CI 1.06–1.16, 12 studies, 6020 deaths), and 1.08 (95% CI 1.01–1.15, 4 studies, 1703 deaths) for BMI before, <12 months after, and ≥12 months after diagnosis, respectively (Figure 4). Substantial heterogeneity was observed between studies on BMI <12 months after diagnosis (I2 = 55%, P = 0.01).Figure 3.Non-linear dose–response curves of BMI and mortality.Figure 4.Linear dose–response meta-analysis of BMI and total mortality.\\nNon-linear dose–response curves of BMI and mortality.\\nLinear dose–response meta-analysis of BMI and total mortality.\\n categorical meta-analysis For BMI before diagnosis, compared with normal weight women, the summary RRs were 1.41 (95% CI 1.29–1.53, 21 studies) for obese women, 1.07 (95% CI 1.02–1.12, 19 studies) for overweight women, and 1.10 (95% CI 0.92–1.31, 10 studies) for underweight women (Figure 2). For BMI <12 months after diagnosis and the same comparisons, the summary RRs were 1.23 (95% CI 1.12–1.33, 24 studies) for obese women, 1.07 (95% CI 1.02–1.12, 22 studies) for overweight women, and 1.25 (95% CI 0.99–1.57, 11 studies) for underweight women (supplementary Figure S1, available at Annals of Oncology online). Substantial heterogeneities were observed between studies of obese women and underweight women (I2 = 69%, P < 0.01; I2 = 63%, P < 0.01, respectively). For BMI ≥12 months after diagnosis, the summary RRs were 1.21 (95% CI 1.06–1.38, 5 studies) for obese women, 0.98 (95% CI 0.86–1.11, 4 studies) for overweight women, and 1.29 (95% CI 1.02–1.63, 3 studies) for underweight women (supplementary Figure S2, available at Annals of Oncology online). Twelve additional studies reported results for obese versus non-obese women <12 months after diagnosis, and the summary RR was 1.26 (95% CI 1.07–1.47, I2 = 80%, P < 0.01).Figure 2.Categorical meta-analysis of pre-diagnosis BMI and total mortality.\\nCategorical meta-analysis of pre-diagnosis BMI and total mortality.\\nFor BMI before diagnosis, compared with normal weight women, the summary RRs were 1.41 (95% CI 1.29–1.53, 21 studies) for obese women, 1.07 (95% CI 1.02–1.12, 19 studies) for overweight women, and 1.10 (95% CI 0.92–1.31, 10 studies) for underweight women (Figure 2). For BMI <12 months after diagnosis and the same comparisons, the summary RRs were 1.23 (95% CI 1.12–1.33, 24 studies) for obese women, 1.07 (95% CI 1.02–1.12, 22 studies) for overweight women, and 1.25 (95% CI 0.99–1.57, 11 studies) for underweight women (supplementary Figure S1, available at Annals of Oncology online). Substantial heterogeneities were observed between studies of obese women and underweight women (I2 = 69%, P < 0.01; I2 = 63%, P < 0.01, respectively). For BMI ≥12 months after diagnosis, the summary RRs were 1.21 (95% CI 1.06–1.38, 5 studies) for obese women, 0.98 (95% CI 0.86–1.11, 4 studies) for overweight women, and 1.29 (95% CI 1.02–1.63, 3 studies) for underweight women (supplementary Figure S2, available at Annals of Oncology online). Twelve additional studies reported results for obese versus non-obese women <12 months after diagnosis, and the summary RR was 1.26 (95% CI 1.07–1.47, I2 = 80%, P < 0.01).Figure 2.Categorical meta-analysis of pre-diagnosis BMI and total mortality.\\nCategorical meta-analysis of pre-diagnosis BMI and total mortality.\\n dose–response meta-analysis There was evidence of a J-shaped association in the non-linear dose–response meta-analyses of BMI before and after diagnosis with total mortality (all P < 0.01; Figure 3), suggesting that underweight women may be at slightly increased risk compared with normal weight women. The curves show linear increasing trends from 20 kg/m2 for BMI before diagnosis and <12 months after diagnosis, and from 25 kg/m2 for BMI ≥12 months after diagnosis. When linear models were fitted excluding the underweight category, the summary RRs of total mortality for each 5 kg/m2 increase in BMI were 1.17 (95% CI 1.13–1.21, 15 studies, 6358 deaths), 1.11 (95% CI 1.06–1.16, 12 studies, 6020 deaths), and 1.08 (95% CI 1.01–1.15, 4 studies, 1703 deaths) for BMI before, <12 months after, and ≥12 months after diagnosis, respectively (Figure 4). Substantial heterogeneity was observed between studies on BMI <12 months after diagnosis (I2 = 55%, P = 0.01).Figure 3.Non-linear dose–response curves of BMI and mortality.Figure 4.Linear dose–response meta-analysis of BMI and total mortality.\\nNon-linear dose–response curves of BMI and mortality.\\nLinear dose–response meta-analysis of BMI and total mortality.\\nThere was evidence of a J-shaped association in the non-linear dose–response meta-analyses of BMI before and after diagnosis with total mortality (all P < 0.01; Figure 3), suggesting that underweight women may be at slightly increased risk compared with normal weight women. The curves show linear increasing trends from 20 kg/m2 for BMI before diagnosis and <12 months after diagnosis, and from 25 kg/m2 for BMI ≥12 months after diagnosis. When linear models were fitted excluding the underweight category, the summary RRs of total mortality for each 5 kg/m2 increase in BMI were 1.17 (95% CI 1.13–1.21, 15 studies, 6358 deaths), 1.11 (95% CI 1.06–1.16, 12 studies, 6020 deaths), and 1.08 (95% CI 1.01–1.15, 4 studies, 1703 deaths) for BMI before, <12 months after, and ≥12 months after diagnosis, respectively (Figure 4). Substantial heterogeneity was observed between studies on BMI <12 months after diagnosis (I2 = 55%, P = 0.01).Figure 3.Non-linear dose–response curves of BMI and mortality.Figure 4.Linear dose–response meta-analysis of BMI and total mortality.\\nNon-linear dose–response curves of BMI and mortality.\\nLinear dose–response meta-analysis of BMI and total mortality.\\n BMI and breast cancer mortality categorical meta-analysis BMI was significantly associated with breast cancer mortality. Compared with normal weight women, for BMI before diagnosis, the summary RRs were 1.35 (95% CI 1.24–1.47, 22 studies) for obese women, 1.11 (95% CI 1.06–1.17, 21 studies) for overweight women, and 1.02 (95% CI 0.85–1.21, 8 studies) for underweight women (Figure 5). For BMI <12 months after diagnosis, the summary RRs were 1.25 (95% CI 1.10–1.42, 12 studies) for obese women, 1.11 (95% CI 1.03–1.20, 12 studies) for overweight women, and 1.53 (95% CI 1.27–1.83, 5 studies) for underweight women (supplementary Figure S3, available at Annals of Oncology online). Substantial heterogeneity was observed between studies of obese women (I2 = 53%, P = 0.02). For BMI ≥12 months after diagnosis, the summary RRs of the two studies identified were 1.68 (95% CI 0.90–3.15) for obese women and 1.37 (95% CI 0.96–1.95) for overweight women (supplementary Figure S4, available at Annals of Oncology online). The summary of another six studies that reported RRs for obese versus non-obese <12 months after diagnosis was 1.26 (95% CI 1.05–1.51, I2 = 64%, P = 0.02).Figure 5.Categorical meta-analysis of pre-diagnosis BMI and breast cancer mortality.\\nCategorical meta-analysis of pre-diagnosis BMI and breast cancer mortality.\\nBMI was significantly associated with breast cancer mortality. Compared with normal weight women, for BMI before diagnosis, the summary RRs were 1.35 (95% CI 1.24–1.47, 22 studies) for obese women, 1.11 (95% CI 1.06–1.17, 21 studies) for overweight women, and 1.02 (95% CI 0.85–1.21, 8 studies) for underweight women (Figure 5). For BMI <12 months after diagnosis, the summary RRs were 1.25 (95% CI 1.10–1.42, 12 studies) for obese women, 1.11 (95% CI 1.03–1.20, 12 studies) for overweight women, and 1.53 (95% CI 1.27–1.83, 5 studies) for underweight women (supplementary Figure S3, available at Annals of Oncology online). Substantial heterogeneity was observed between studies of obese women (I2 = 53%, P = 0.02). For BMI ≥12 months after diagnosis, the summary RRs of the two studies identified were 1.68 (95% CI 0.90–3.15) for obese women and 1.37 (95% CI 0.96–1.95) for overweight women (supplementary Figure S4, available at Annals of Oncology online). The summary of another six studies that reported RRs for obese versus non-obese <12 months after diagnosis was 1.26 (95% CI 1.05–1.51, I2 = 64%, P = 0.02).Figure 5.Categorical meta-analysis of pre-diagnosis BMI and breast cancer mortality.\\nCategorical meta-analysis of pre-diagnosis BMI and breast cancer mortality.\\n dose–response meta-analysis There was no significant evidence of a non-linear relationship between BMI before, <12 months after, and ≥12 months after diagnosis and breast cancer mortality (P = 0.21, P = 1.00, P = 0.86, respectively) (Figure 3). When linear models were fitted excluding data from the underweight category, statistically significant increased risks of breast cancer mortality with BMI before and <12 months after diagnosis were observed (Figure 6). The summary RRs for each 5 kg/m2 increase were 1.18 (95% CI 1.12–1.25, 18 studies, 5262 breast cancer deaths) for BMI before diagnosis and 1.14 (95% CI 1.05–1.24, 8 studies, 3857 breast cancer deaths) for BMI <12 months after diagnosis, with moderate (I2 = 47%, P = 0.01) and substantial (I2 = 66%, P = 0.01) heterogeneities between studies, respectively. Only two studies on BMI ≥12 months after diagnosis and breast cancer mortality (N = 220 deaths) were identified. The summary RR was 1.29 (95% CI 0.97–1.72).Figure 6.Linear dose–response meta-analysis of BMI and breast cancer mortality.\\nLinear dose–response meta-analysis of BMI and breast cancer mortality.\\nThere was no significant evidence of a non-linear relationship between BMI before, <12 months after, and ≥12 months after diagnosis and breast cancer mortality (P = 0.21, P = 1.00, P = 0.86, respectively) (Figure 3). When linear models were fitted excluding data from the underweight category, statistically significant increased risks of breast cancer mortality with BMI before and <12 months after diagnosis were observed (Figure 6). The summary RRs for each 5 kg/m2 increase were 1.18 (95% CI 1.12–1.25, 18 studies, 5262 breast cancer deaths) for BMI before diagnosis and 1.14 (95% CI 1.05–1.24, 8 studies, 3857 breast cancer deaths) for BMI <12 months after diagnosis, with moderate (I2 = 47%, P = 0.01) and substantial (I2 = 66%, P = 0.01) heterogeneities between studies, respectively. Only two studies on BMI ≥12 months after diagnosis and breast cancer mortality (N = 220 deaths) were identified. The summary RR was 1.29 (95% CI 0.97–1.72).Figure 6.Linear dose–response meta-analysis of BMI and breast cancer mortality.\\nLinear dose–response meta-analysis of BMI and breast cancer mortality.\\n categorical meta-analysis BMI was significantly associated with breast cancer mortality. Compared with normal weight women, for BMI before diagnosis, the summary RRs were 1.35 (95% CI 1.24–1.47, 22 studies) for obese women, 1.11 (95% CI 1.06–1.17, 21 studies) for overweight women, and 1.02 (95% CI 0.85–1.21, 8 studies) for underweight women (Figure 5). For BMI <12 months after diagnosis, the summary RRs were 1.25 (95% CI 1.10–1.42, 12 studies) for obese women, 1.11 (95% CI 1.03–1.20, 12 studies) for overweight women, and 1.53 (95% CI 1.27–1.83, 5 studies) for underweight women (supplementary Figure S3, available at Annals of Oncology online). Substantial heterogeneity was observed between studies of obese women (I2 = 53%, P = 0.02). For BMI ≥12 months after diagnosis, the summary RRs of the two studies identified were 1.68 (95% CI 0.90–3.15) for obese women and 1.37 (95% CI 0.96–1.95) for overweight women (supplementary Figure S4, available at Annals of Oncology online). The summary of another six studies that reported RRs for obese versus non-obese <12 months after diagnosis was 1.26 (95% CI 1.05–1.51, I2 = 64%, P = 0.02).Figure 5.Categorical meta-analysis of pre-diagnosis BMI and breast cancer mortality.\\nCategorical meta-analysis of pre-diagnosis BMI and breast cancer mortality.\\nBMI was significantly associated with breast cancer mortality. Compared with normal weight women, for BMI before diagnosis, the summary RRs were 1.35 (95% CI 1.24–1.47, 22 studies) for obese women, 1.11 (95% CI 1.06–1.17, 21 studies) for overweight women, and 1.02 (95% CI 0.85–1.21, 8 studies) for underweight women (Figure 5). For BMI <12 months after diagnosis, the summary RRs were 1.25 (95% CI 1.10–1.42, 12 studies) for obese women, 1.11 (95% CI 1.03–1.20, 12 studies) for overweight women, and 1.53 (95% CI 1.27–1.83, 5 studies) for underweight women (supplementary Figure S3, available at Annals of Oncology online). Substantial heterogeneity was observed between studies of obese women (I2 = 53%, P = 0.02). For BMI ≥12 months after diagnosis, the summary RRs of the two studies identified were 1.68 (95% CI 0.90–3.15) for obese women and 1.37 (95% CI 0.96–1.95) for overweight women (supplementary Figure S4, available at Annals of Oncology online). The summary of another six studies that reported RRs for obese versus non-obese <12 months after diagnosis was 1.26 (95% CI 1.05–1.51, I2 = 64%, P = 0.02).Figure 5.Categorical meta-analysis of pre-diagnosis BMI and breast cancer mortality.\\nCategorical meta-analysis of pre-diagnosis BMI and breast cancer mortality.\\n dose–response meta-analysis There was no significant evidence of a non-linear relationship between BMI before, <12 months after, and ≥12 months after diagnosis and breast cancer mortality (P = 0.21, P = 1.00, P = 0.86, respectively) (Figure 3). When linear models were fitted excluding data from the underweight category, statistically significant increased risks of breast cancer mortality with BMI before and <12 months after diagnosis were observed (Figure 6). The summary RRs for each 5 kg/m2 increase were 1.18 (95% CI 1.12–1.25, 18 studies, 5262 breast cancer deaths) for BMI before diagnosis and 1.14 (95% CI 1.05–1.24, 8 studies, 3857 breast cancer deaths) for BMI <12 months after diagnosis, with moderate (I2 = 47%, P = 0.01) and substantial (I2 = 66%, P = 0.01) heterogeneities between studies, respectively. Only two studies on BMI ≥12 months after diagnosis and breast cancer mortality (N = 220 deaths) were identified. The summary RR was 1.29 (95% CI 0.97–1.72).Figure 6.Linear dose–response meta-analysis of BMI and breast cancer mortality.\\nLinear dose–response meta-analysis of BMI and breast cancer mortality.\\nThere was no significant evidence of a non-linear relationship between BMI before, <12 months after, and ≥12 months after diagnosis and breast cancer mortality (P = 0.21, P = 1.00, P = 0.86, respectively) (Figure 3). When linear models were fitted excluding data from the underweight category, statistically significant increased risks of breast cancer mortality with BMI before and <12 months after diagnosis were observed (Figure 6). The summary RRs for each 5 kg/m2 increase were 1.18 (95% CI 1.12–1.25, 18 studies, 5262 breast cancer deaths) for BMI before diagnosis and 1.14 (95% CI 1.05–1.24, 8 studies, 3857 breast cancer deaths) for BMI <12 months after diagnosis, with moderate (I2 = 47%, P = 0.01) and substantial (I2 = 66%, P = 0.01) heterogeneities between studies, respectively. Only two studies on BMI ≥12 months after diagnosis and breast cancer mortality (N = 220 deaths) were identified. The summary RR was 1.29 (95% CI 0.97–1.72).Figure 6.Linear dose–response meta-analysis of BMI and breast cancer mortality.\\nLinear dose–response meta-analysis of BMI and breast cancer mortality.\\n BMI and other mortality outcomes Only two studies reported results for death from cardiovascular disease (N = 151 deaths) [27, 112]. The summary RR for obese versus normal weight before diagnosis was 1.60 (95% CI 0.66–3.87). No association was observed for overweight versus normal weight (summary RR = 1.01, 95% CI 0.80–1.29). For each 5 kg/m2 increase in BMI, the summary RR was 1.21 (95% CI 0.83–1.77). Five studies reported results for deaths from any cause other than breast cancer (N = 2704 deaths) [21, 34, 108, 113, 114]. The summary RRs were 1.29 (95% CI 0.99–1.68, I2 = 72%, P = 0.01) for obese women, and 0.96 (95% CI 0.83–1.11, I2 = 26%, P = 0.25) for overweight women compared with normal weight women.\\nOnly two studies reported results for death from cardiovascular disease (N = 151 deaths) [27, 112]. The summary RR for obese versus normal weight before diagnosis was 1.60 (95% CI 0.66–3.87). No association was observed for overweight versus normal weight (summary RR = 1.01, 95% CI 0.80–1.29). For each 5 kg/m2 increase in BMI, the summary RR was 1.21 (95% CI 0.83–1.77). Five studies reported results for deaths from any cause other than breast cancer (N = 2704 deaths) [21, 34, 108, 113, 114]. The summary RRs were 1.29 (95% CI 0.99–1.68, I2 = 72%, P = 0.01) for obese women, and 0.96 (95% CI 0.83–1.11, I2 = 26%, P = 0.25) for overweight women compared with normal weight women.\\n subgroup, meta-regression, and sensitivity analyses The results of the subgroup and meta-regression analyses are in supplementary Tables S3 and S4, available at Annals of Oncology online. Subgroup analysis was not carried out for BMI ≥12 months after diagnosis as the limited number of studies would hinder any meaningful comparisons.\\nIncreased risks of mortality were observed in the meta-analyses by menopausal status. While the summary risk estimates seem stronger with premenopausal breast cancer, there was no significant heterogeneity between pre- and post-menopausal breast cancer as shown in the meta-regression analyses (P = 0.28–0.89) (supplementary Tables S3 and S4, available at Annals of Oncology online). For BMI before diagnosis and total mortality, the summary RRs for obese versus normal weight were 1.75 (95% CI 1.26–2.41, I2 = 70%, P < 0.01, 7 studies) in women with pre-menopausal breast cancer and 1.34 (95% CI 1.18–1.53, I2 = 27%, P = 0.20, 9 studies) in women with post-menopausal breast cancer.\\nStudies with larger number of deaths [105, 115], conducted in Europe [28, 115], or with weight and height assessed through medical records [28, 104, 115, 116] tended to report weaker associations for BMI <12 months after diagnosis and total mortality compared with other studies (meta-regression P = 0.01, 0.02, 0.01, respectively) (supplementary Table S3, available at Annals of Oncology online); while studies with larger number of deaths [101], conducted in Asia [101, 102], or adjusted for co-morbidity [101, 102] reported weaker associations for BMI <12 months after diagnosis and breast cancer mortality (meta-regression P = 0.01, 0.02, 0.01, respectively) (supplementary Table S4, available at Annals of Oncology online).\\nAnalyses stratified by study designs, or restricted to studies with invasive cases only, early-stage non-metastatic cases only, or mammography screening detected cases only, or controlled for previous diseases did not produce results that were materially different from those obtained in the overall analyses (results not shown). Summary risk estimates remained statistically significant when each study was omitted in turn, except for BMI ≥12 months after diagnosis and total mortality. The summary RR was 1.06 (95% CI 0.98–1.15) per 5 kg/m2 increase when Flatt et al. [117] which contributed 315 deaths was omitted.\\nThe results of the subgroup and meta-regression analyses are in supplementary Tables S3 and S4, available at Annals of Oncology online. Subgroup analysis was not carried out for BMI ≥12 months after diagnosis as the limited number of studies would hinder any meaningful comparisons.\\nIncreased risks of mortality were observed in the meta-analyses by menopausal status. While the summary risk estimates seem stronger with premenopausal breast cancer, there was no significant heterogeneity between pre- and post-menopausal breast cancer as shown in the meta-regression analyses (P = 0.28–0.89) (supplementary Tables S3 and S4, available at Annals of Oncology online). For BMI before diagnosis and total mortality, the summary RRs for obese versus normal weight were 1.75 (95% CI 1.26–2.41, I2 = 70%, P < 0.01, 7 studies) in women with pre-menopausal breast cancer and 1.34 (95% CI 1.18–1.53, I2 = 27%, P = 0.20, 9 studies) in women with post-menopausal breast cancer.\\nStudies with larger number of deaths [105, 115], conducted in Europe [28, 115], or with weight and height assessed through medical records [28, 104, 115, 116] tended to report weaker associations for BMI <12 months after diagnosis and total mortality compared with other studies (meta-regression P = 0.01, 0.02, 0.01, respectively) (supplementary Table S3, available at Annals of Oncology online); while studies with larger number of deaths [101], conducted in Asia [101, 102], or adjusted for co-morbidity [101, 102] reported weaker associations for BMI <12 months after diagnosis and breast cancer mortality (meta-regression P = 0.01, 0.02, 0.01, respectively) (supplementary Table S4, available at Annals of Oncology online).\\nAnalyses stratified by study designs, or restricted to studies with invasive cases only, early-stage non-metastatic cases only, or mammography screening detected cases only, or controlled for previous diseases did not produce results that were materially different from those obtained in the overall analyses (results not shown). Summary risk estimates remained statistically significant when each study was omitted in turn, except for BMI ≥12 months after diagnosis and total mortality. The summary RR was 1.06 (95% CI 0.98–1.15) per 5 kg/m2 increase when Flatt et al. [117] which contributed 315 deaths was omitted.\\n small studies or publication bias Asymmetry was only detected in the funnel plots of BMI <12 months after diagnosis and total mortality, and breast cancer mortality, which suggests that small studies with an inverse association are missing (plots not shown). Egger's tests were borderline significant (P = 0.05) or statistically significant (P = 0.03), respectively.\\nAsymmetry was only detected in the funnel plots of BMI <12 months after diagnosis and total mortality, and breast cancer mortality, which suggests that small studies with an inverse association are missing (plots not shown). Egger's tests were borderline significant (P = 0.05) or statistically significant (P = 0.03), respectively.\",\n \" categorical meta-analysis For BMI before diagnosis, compared with normal weight women, the summary RRs were 1.41 (95% CI 1.29–1.53, 21 studies) for obese women, 1.07 (95% CI 1.02–1.12, 19 studies) for overweight women, and 1.10 (95% CI 0.92–1.31, 10 studies) for underweight women (Figure 2). For BMI <12 months after diagnosis and the same comparisons, the summary RRs were 1.23 (95% CI 1.12–1.33, 24 studies) for obese women, 1.07 (95% CI 1.02–1.12, 22 studies) for overweight women, and 1.25 (95% CI 0.99–1.57, 11 studies) for underweight women (supplementary Figure S1, available at Annals of Oncology online). Substantial heterogeneities were observed between studies of obese women and underweight women (I2 = 69%, P < 0.01; I2 = 63%, P < 0.01, respectively). For BMI ≥12 months after diagnosis, the summary RRs were 1.21 (95% CI 1.06–1.38, 5 studies) for obese women, 0.98 (95% CI 0.86–1.11, 4 studies) for overweight women, and 1.29 (95% CI 1.02–1.63, 3 studies) for underweight women (supplementary Figure S2, available at Annals of Oncology online). Twelve additional studies reported results for obese versus non-obese women <12 months after diagnosis, and the summary RR was 1.26 (95% CI 1.07–1.47, I2 = 80%, P < 0.01).Figure 2.Categorical meta-analysis of pre-diagnosis BMI and total mortality.\\nCategorical meta-analysis of pre-diagnosis BMI and total mortality.\\nFor BMI before diagnosis, compared with normal weight women, the summary RRs were 1.41 (95% CI 1.29–1.53, 21 studies) for obese women, 1.07 (95% CI 1.02–1.12, 19 studies) for overweight women, and 1.10 (95% CI 0.92–1.31, 10 studies) for underweight women (Figure 2). For BMI <12 months after diagnosis and the same comparisons, the summary RRs were 1.23 (95% CI 1.12–1.33, 24 studies) for obese women, 1.07 (95% CI 1.02–1.12, 22 studies) for overweight women, and 1.25 (95% CI 0.99–1.57, 11 studies) for underweight women (supplementary Figure S1, available at Annals of Oncology online). Substantial heterogeneities were observed between studies of obese women and underweight women (I2 = 69%, P < 0.01; I2 = 63%, P < 0.01, respectively). For BMI ≥12 months after diagnosis, the summary RRs were 1.21 (95% CI 1.06–1.38, 5 studies) for obese women, 0.98 (95% CI 0.86–1.11, 4 studies) for overweight women, and 1.29 (95% CI 1.02–1.63, 3 studies) for underweight women (supplementary Figure S2, available at Annals of Oncology online). Twelve additional studies reported results for obese versus non-obese women <12 months after diagnosis, and the summary RR was 1.26 (95% CI 1.07–1.47, I2 = 80%, P < 0.01).Figure 2.Categorical meta-analysis of pre-diagnosis BMI and total mortality.\\nCategorical meta-analysis of pre-diagnosis BMI and total mortality.\\n dose–response meta-analysis There was evidence of a J-shaped association in the non-linear dose–response meta-analyses of BMI before and after diagnosis with total mortality (all P < 0.01; Figure 3), suggesting that underweight women may be at slightly increased risk compared with normal weight women. The curves show linear increasing trends from 20 kg/m2 for BMI before diagnosis and <12 months after diagnosis, and from 25 kg/m2 for BMI ≥12 months after diagnosis. When linear models were fitted excluding the underweight category, the summary RRs of total mortality for each 5 kg/m2 increase in BMI were 1.17 (95% CI 1.13–1.21, 15 studies, 6358 deaths), 1.11 (95% CI 1.06–1.16, 12 studies, 6020 deaths), and 1.08 (95% CI 1.01–1.15, 4 studies, 1703 deaths) for BMI before, <12 months after, and ≥12 months after diagnosis, respectively (Figure 4). Substantial heterogeneity was observed between studies on BMI <12 months after diagnosis (I2 = 55%, P = 0.01).Figure 3.Non-linear dose–response curves of BMI and mortality.Figure 4.Linear dose–response meta-analysis of BMI and total mortality.\\nNon-linear dose–response curves of BMI and mortality.\\nLinear dose–response meta-analysis of BMI and total mortality.\\nThere was evidence of a J-shaped association in the non-linear dose–response meta-analyses of BMI before and after diagnosis with total mortality (all P < 0.01; Figure 3), suggesting that underweight women may be at slightly increased risk compared with normal weight women. The curves show linear increasing trends from 20 kg/m2 for BMI before diagnosis and <12 months after diagnosis, and from 25 kg/m2 for BMI ≥12 months after diagnosis. When linear models were fitted excluding the underweight category, the summary RRs of total mortality for each 5 kg/m2 increase in BMI were 1.17 (95% CI 1.13–1.21, 15 studies, 6358 deaths), 1.11 (95% CI 1.06–1.16, 12 studies, 6020 deaths), and 1.08 (95% CI 1.01–1.15, 4 studies, 1703 deaths) for BMI before, <12 months after, and ≥12 months after diagnosis, respectively (Figure 4). Substantial heterogeneity was observed between studies on BMI <12 months after diagnosis (I2 = 55%, P = 0.01).Figure 3.Non-linear dose–response curves of BMI and mortality.Figure 4.Linear dose–response meta-analysis of BMI and total mortality.\\nNon-linear dose–response curves of BMI and mortality.\\nLinear dose–response meta-analysis of BMI and total mortality.\",\n \"For BMI before diagnosis, compared with normal weight women, the summary RRs were 1.41 (95% CI 1.29–1.53, 21 studies) for obese women, 1.07 (95% CI 1.02–1.12, 19 studies) for overweight women, and 1.10 (95% CI 0.92–1.31, 10 studies) for underweight women (Figure 2). For BMI <12 months after diagnosis and the same comparisons, the summary RRs were 1.23 (95% CI 1.12–1.33, 24 studies) for obese women, 1.07 (95% CI 1.02–1.12, 22 studies) for overweight women, and 1.25 (95% CI 0.99–1.57, 11 studies) for underweight women (supplementary Figure S1, available at Annals of Oncology online). Substantial heterogeneities were observed between studies of obese women and underweight women (I2 = 69%, P < 0.01; I2 = 63%, P < 0.01, respectively). For BMI ≥12 months after diagnosis, the summary RRs were 1.21 (95% CI 1.06–1.38, 5 studies) for obese women, 0.98 (95% CI 0.86–1.11, 4 studies) for overweight women, and 1.29 (95% CI 1.02–1.63, 3 studies) for underweight women (supplementary Figure S2, available at Annals of Oncology online). Twelve additional studies reported results for obese versus non-obese women <12 months after diagnosis, and the summary RR was 1.26 (95% CI 1.07–1.47, I2 = 80%, P < 0.01).Figure 2.Categorical meta-analysis of pre-diagnosis BMI and total mortality.\\nCategorical meta-analysis of pre-diagnosis BMI and total mortality.\",\n \"There was evidence of a J-shaped association in the non-linear dose–response meta-analyses of BMI before and after diagnosis with total mortality (all P < 0.01; Figure 3), suggesting that underweight women may be at slightly increased risk compared with normal weight women. The curves show linear increasing trends from 20 kg/m2 for BMI before diagnosis and <12 months after diagnosis, and from 25 kg/m2 for BMI ≥12 months after diagnosis. When linear models were fitted excluding the underweight category, the summary RRs of total mortality for each 5 kg/m2 increase in BMI were 1.17 (95% CI 1.13–1.21, 15 studies, 6358 deaths), 1.11 (95% CI 1.06–1.16, 12 studies, 6020 deaths), and 1.08 (95% CI 1.01–1.15, 4 studies, 1703 deaths) for BMI before, <12 months after, and ≥12 months after diagnosis, respectively (Figure 4). Substantial heterogeneity was observed between studies on BMI <12 months after diagnosis (I2 = 55%, P = 0.01).Figure 3.Non-linear dose–response curves of BMI and mortality.Figure 4.Linear dose–response meta-analysis of BMI and total mortality.\\nNon-linear dose–response curves of BMI and mortality.\\nLinear dose–response meta-analysis of BMI and total mortality.\",\n \" categorical meta-analysis BMI was significantly associated with breast cancer mortality. Compared with normal weight women, for BMI before diagnosis, the summary RRs were 1.35 (95% CI 1.24–1.47, 22 studies) for obese women, 1.11 (95% CI 1.06–1.17, 21 studies) for overweight women, and 1.02 (95% CI 0.85–1.21, 8 studies) for underweight women (Figure 5). For BMI <12 months after diagnosis, the summary RRs were 1.25 (95% CI 1.10–1.42, 12 studies) for obese women, 1.11 (95% CI 1.03–1.20, 12 studies) for overweight women, and 1.53 (95% CI 1.27–1.83, 5 studies) for underweight women (supplementary Figure S3, available at Annals of Oncology online). Substantial heterogeneity was observed between studies of obese women (I2 = 53%, P = 0.02). For BMI ≥12 months after diagnosis, the summary RRs of the two studies identified were 1.68 (95% CI 0.90–3.15) for obese women and 1.37 (95% CI 0.96–1.95) for overweight women (supplementary Figure S4, available at Annals of Oncology online). The summary of another six studies that reported RRs for obese versus non-obese <12 months after diagnosis was 1.26 (95% CI 1.05–1.51, I2 = 64%, P = 0.02).Figure 5.Categorical meta-analysis of pre-diagnosis BMI and breast cancer mortality.\\nCategorical meta-analysis of pre-diagnosis BMI and breast cancer mortality.\\nBMI was significantly associated with breast cancer mortality. Compared with normal weight women, for BMI before diagnosis, the summary RRs were 1.35 (95% CI 1.24–1.47, 22 studies) for obese women, 1.11 (95% CI 1.06–1.17, 21 studies) for overweight women, and 1.02 (95% CI 0.85–1.21, 8 studies) for underweight women (Figure 5). For BMI <12 months after diagnosis, the summary RRs were 1.25 (95% CI 1.10–1.42, 12 studies) for obese women, 1.11 (95% CI 1.03–1.20, 12 studies) for overweight women, and 1.53 (95% CI 1.27–1.83, 5 studies) for underweight women (supplementary Figure S3, available at Annals of Oncology online). Substantial heterogeneity was observed between studies of obese women (I2 = 53%, P = 0.02). For BMI ≥12 months after diagnosis, the summary RRs of the two studies identified were 1.68 (95% CI 0.90–3.15) for obese women and 1.37 (95% CI 0.96–1.95) for overweight women (supplementary Figure S4, available at Annals of Oncology online). The summary of another six studies that reported RRs for obese versus non-obese <12 months after diagnosis was 1.26 (95% CI 1.05–1.51, I2 = 64%, P = 0.02).Figure 5.Categorical meta-analysis of pre-diagnosis BMI and breast cancer mortality.\\nCategorical meta-analysis of pre-diagnosis BMI and breast cancer mortality.\\n dose–response meta-analysis There was no significant evidence of a non-linear relationship between BMI before, <12 months after, and ≥12 months after diagnosis and breast cancer mortality (P = 0.21, P = 1.00, P = 0.86, respectively) (Figure 3). When linear models were fitted excluding data from the underweight category, statistically significant increased risks of breast cancer mortality with BMI before and <12 months after diagnosis were observed (Figure 6). The summary RRs for each 5 kg/m2 increase were 1.18 (95% CI 1.12–1.25, 18 studies, 5262 breast cancer deaths) for BMI before diagnosis and 1.14 (95% CI 1.05–1.24, 8 studies, 3857 breast cancer deaths) for BMI <12 months after diagnosis, with moderate (I2 = 47%, P = 0.01) and substantial (I2 = 66%, P = 0.01) heterogeneities between studies, respectively. Only two studies on BMI ≥12 months after diagnosis and breast cancer mortality (N = 220 deaths) were identified. The summary RR was 1.29 (95% CI 0.97–1.72).Figure 6.Linear dose–response meta-analysis of BMI and breast cancer mortality.\\nLinear dose–response meta-analysis of BMI and breast cancer mortality.\\nThere was no significant evidence of a non-linear relationship between BMI before, <12 months after, and ≥12 months after diagnosis and breast cancer mortality (P = 0.21, P = 1.00, P = 0.86, respectively) (Figure 3). When linear models were fitted excluding data from the underweight category, statistically significant increased risks of breast cancer mortality with BMI before and <12 months after diagnosis were observed (Figure 6). The summary RRs for each 5 kg/m2 increase were 1.18 (95% CI 1.12–1.25, 18 studies, 5262 breast cancer deaths) for BMI before diagnosis and 1.14 (95% CI 1.05–1.24, 8 studies, 3857 breast cancer deaths) for BMI <12 months after diagnosis, with moderate (I2 = 47%, P = 0.01) and substantial (I2 = 66%, P = 0.01) heterogeneities between studies, respectively. Only two studies on BMI ≥12 months after diagnosis and breast cancer mortality (N = 220 deaths) were identified. The summary RR was 1.29 (95% CI 0.97–1.72).Figure 6.Linear dose–response meta-analysis of BMI and breast cancer mortality.\\nLinear dose–response meta-analysis of BMI and breast cancer mortality.\",\n \"BMI was significantly associated with breast cancer mortality. Compared with normal weight women, for BMI before diagnosis, the summary RRs were 1.35 (95% CI 1.24–1.47, 22 studies) for obese women, 1.11 (95% CI 1.06–1.17, 21 studies) for overweight women, and 1.02 (95% CI 0.85–1.21, 8 studies) for underweight women (Figure 5). For BMI <12 months after diagnosis, the summary RRs were 1.25 (95% CI 1.10–1.42, 12 studies) for obese women, 1.11 (95% CI 1.03–1.20, 12 studies) for overweight women, and 1.53 (95% CI 1.27–1.83, 5 studies) for underweight women (supplementary Figure S3, available at Annals of Oncology online). Substantial heterogeneity was observed between studies of obese women (I2 = 53%, P = 0.02). For BMI ≥12 months after diagnosis, the summary RRs of the two studies identified were 1.68 (95% CI 0.90–3.15) for obese women and 1.37 (95% CI 0.96–1.95) for overweight women (supplementary Figure S4, available at Annals of Oncology online). The summary of another six studies that reported RRs for obese versus non-obese <12 months after diagnosis was 1.26 (95% CI 1.05–1.51, I2 = 64%, P = 0.02).Figure 5.Categorical meta-analysis of pre-diagnosis BMI and breast cancer mortality.\\nCategorical meta-analysis of pre-diagnosis BMI and breast cancer mortality.\",\n \"There was no significant evidence of a non-linear relationship between BMI before, <12 months after, and ≥12 months after diagnosis and breast cancer mortality (P = 0.21, P = 1.00, P = 0.86, respectively) (Figure 3). When linear models were fitted excluding data from the underweight category, statistically significant increased risks of breast cancer mortality with BMI before and <12 months after diagnosis were observed (Figure 6). The summary RRs for each 5 kg/m2 increase were 1.18 (95% CI 1.12–1.25, 18 studies, 5262 breast cancer deaths) for BMI before diagnosis and 1.14 (95% CI 1.05–1.24, 8 studies, 3857 breast cancer deaths) for BMI <12 months after diagnosis, with moderate (I2 = 47%, P = 0.01) and substantial (I2 = 66%, P = 0.01) heterogeneities between studies, respectively. Only two studies on BMI ≥12 months after diagnosis and breast cancer mortality (N = 220 deaths) were identified. The summary RR was 1.29 (95% CI 0.97–1.72).Figure 6.Linear dose–response meta-analysis of BMI and breast cancer mortality.\\nLinear dose–response meta-analysis of BMI and breast cancer mortality.\",\n \"Only two studies reported results for death from cardiovascular disease (N = 151 deaths) [27, 112]. The summary RR for obese versus normal weight before diagnosis was 1.60 (95% CI 0.66–3.87). No association was observed for overweight versus normal weight (summary RR = 1.01, 95% CI 0.80–1.29). For each 5 kg/m2 increase in BMI, the summary RR was 1.21 (95% CI 0.83–1.77). Five studies reported results for deaths from any cause other than breast cancer (N = 2704 deaths) [21, 34, 108, 113, 114]. The summary RRs were 1.29 (95% CI 0.99–1.68, I2 = 72%, P = 0.01) for obese women, and 0.96 (95% CI 0.83–1.11, I2 = 26%, P = 0.25) for overweight women compared with normal weight women.\",\n \"The results of the subgroup and meta-regression analyses are in supplementary Tables S3 and S4, available at Annals of Oncology online. Subgroup analysis was not carried out for BMI ≥12 months after diagnosis as the limited number of studies would hinder any meaningful comparisons.\\nIncreased risks of mortality were observed in the meta-analyses by menopausal status. While the summary risk estimates seem stronger with premenopausal breast cancer, there was no significant heterogeneity between pre- and post-menopausal breast cancer as shown in the meta-regression analyses (P = 0.28–0.89) (supplementary Tables S3 and S4, available at Annals of Oncology online). For BMI before diagnosis and total mortality, the summary RRs for obese versus normal weight were 1.75 (95% CI 1.26–2.41, I2 = 70%, P < 0.01, 7 studies) in women with pre-menopausal breast cancer and 1.34 (95% CI 1.18–1.53, I2 = 27%, P = 0.20, 9 studies) in women with post-menopausal breast cancer.\\nStudies with larger number of deaths [105, 115], conducted in Europe [28, 115], or with weight and height assessed through medical records [28, 104, 115, 116] tended to report weaker associations for BMI <12 months after diagnosis and total mortality compared with other studies (meta-regression P = 0.01, 0.02, 0.01, respectively) (supplementary Table S3, available at Annals of Oncology online); while studies with larger number of deaths [101], conducted in Asia [101, 102], or adjusted for co-morbidity [101, 102] reported weaker associations for BMI <12 months after diagnosis and breast cancer mortality (meta-regression P = 0.01, 0.02, 0.01, respectively) (supplementary Table S4, available at Annals of Oncology online).\\nAnalyses stratified by study designs, or restricted to studies with invasive cases only, early-stage non-metastatic cases only, or mammography screening detected cases only, or controlled for previous diseases did not produce results that were materially different from those obtained in the overall analyses (results not shown). Summary risk estimates remained statistically significant when each study was omitted in turn, except for BMI ≥12 months after diagnosis and total mortality. The summary RR was 1.06 (95% CI 0.98–1.15) per 5 kg/m2 increase when Flatt et al. [117] which contributed 315 deaths was omitted.\",\n \"Asymmetry was only detected in the funnel plots of BMI <12 months after diagnosis and total mortality, and breast cancer mortality, which suggests that small studies with an inverse association are missing (plots not shown). Egger's tests were borderline significant (P = 0.05) or statistically significant (P = 0.03), respectively.\",\n \"The present systematic literature review and meta-analysis of follow-up studies clearly supports that, in breast cancer survivors, higher BMI is consistently associated with lower overall and breast cancer survival, regardless of when BMI is ascertained. The limited number of studies on death from cardiovascular disease is also consistent with a positive association. For before, <12 months after, and 12 months or more after breast cancer diagnosis, compared with normal weight women, obese women had 41%, 23%, and 21% higher risk for total mortality, and 35%, 25%, and 68% increased risk for breast cancer mortality, respectively. The findings were supported by the positive associations observed in the linear dose–response meta-analysis. All associations were statistically significant, apart from the relationship between BMI ≥12 months after diagnosis and breast cancer mortality. This may be due to limited statistical power, with only 220 breast cancer deaths from two follow-up studies.\\nPositive associations, in some cases statistically significant, were also observed in overweight, and underweight women compared with normal weight women. Women with BMI of 20 kg/m2 before, or <12 months after diagnosis, and of 25 kg/m2 12 months or more after diagnosis appeared to have the lowest mortality risk in the non-linear dose–response analysis. Co-morbid conditions may cause the observed increased risk in underweight women. Thorough investigation within the group and on their contribution to the shape of the association is hindered, as not all studies in this review reported results for this group. The increased risk associated with obesity was similar in pre- or post-menopausal breast cancer. We did not find any evidence of a protective effect of obesity on survival after pre-menopausal breast cancer, contrary to what has been observed for the development of breast cancer in pre-menopausal women [4].\\nA large body of evidence with 41 477 deaths (23 182 from breast cancer) in over 210 000 breast cancer survivors was systematically reviewed in the present study. We carried out categorical, linear, and non-linear dose–response meta-analyses to examine the magnitude and the shape of the associations for total and cause-specific mortality in underweight, overweight, and obese women by time periods before and after diagnosis that is important in relation to the population-at-risk and breast cancer survivors. Our findings agree with and further extend the results from previous meta-analyses. A review published in 2010 reported statistically significant increased risks of 33% of both total and breast cancer mortality for obesity versus non-obesity around diagnosis [7]. These estimates are slightly higher than ours, which may be explained by the different search periods and inclusion criteria for the articles (33 studies and 15 studies included in the analyses, respectively). Another review published in 2012 further reported consistent positive associations of total and breast cancer mortality with higher versus lower BMI around diagnosis [6]. No significant differences were observed by menopausal status or hormone receptor status. The After Breast Cancer Pooling Project of four prospective cohort studies found differential effects of levels of pre-diagnosis obesity on survival [118]. Compared with normal weight women, significant or borderline significant increased risks of 81% of total and 40% of breast cancer mortality were only observed for morbidly obese (≥40 kg/m2) women and not for women in other obesity categories. We observed statistically significant increased risks also for overweight women, probably because of a larger number of studies. We were unable to investigate the associations with severely and morbidly obese women because only two studies included in this review reported such results [19, 113]. Overall, our findings are consistent with previous meta-analyses in showing elevated total and breast cancer mortality associated with higher BMI and support the current guidelines for breast cancer survivors to stay as lean as possible within the normal range of body weight [4], for overweight women to avoid weight gain during treatment and for obese women to lose weight after treatment [119].\\nThe present review is limited by the challenges and flaws encountered by the individual epidemiological studies evaluating the body fatness–mortality relationship in breast cancer survivors. Most studies did not adjust for co-morbidities and assess intentional weight loss. Women with more serious health issues, and especially smokers, may lose weight but are at an increased risk of mortality, and this might cause an apparent increased risk in underweight women. Body weight information through the natural history of the disease and treatment information were usually not complete or available. Increase of body weight post-diagnosis is common in women with breast cancer, particularly during chemotherapy [16]. Chemotherapy under-dosing is a common problem in obese women and may contribute to their increased mortality [120]. Although several studies with pre-diagnosis BMI adjusted for underlying illnesses or excluded the first few years of follow-up, reverse causation may have affected the results in studies that assessed BMI in women with cancer and other illnesses. However, in these studies, the associations were similar to other studies. Possible survival benefit (subjects with better prognostic factors survive) may be present in the survival cohorts, in which the range of BMI could be narrower, and may cause an underestimation of the association.\\nFollow-up studies with variable characteristics were pooled in the meta-analysis. Women identified in clinical trials may have had specific tumour subtypes, with fewer co-morbidities, and were more likely to receive protocol treatments with high treatment completion rates. Women who were recruited through mammography screening programmes may have had healthier lifestyles or access to medical facilities, and more likely to be diagnosed with in situ or early-stage breast cancer. Cancer detection methods, tumour classifications and treatment regimens change over time, and may vary within (if follow-up is long) and between studies, and could not be simply examined by using the diagnosis or treatment date. We cannot rule out the effect of unmeasured or residual confounding in our analysis. Nevertheless, most results were adjusted for multiple confounding factors, including tumour stage or other-related variables and stratified analyses by several key factors showed similar summary risk estimates. Small study or publication bias was observed in the analyses of BMI <12 months after diagnosis. However, the overall evidence is supported by large, well-designed studies and is unlikely to be changed. We did not conduct analyses by race/ethnicity and treatment types as only limited studies had published results.\\nFuture studies of body fatness and breast cancer outcomes should aim to account for co-morbidities, separate intended and unintended changes of body weight, and collect complete treatment information during study follow-up. Randomised clinical trials are needed to test interventions for weight loss and maintenance on survival in women with breast cancer.\\nIn conclusion, the present systematic literature review and meta-analysis extends and confirms the associations of obesity with an unfavourable overall and breast cancer survival in pre- and post-menopausal breast cancer, regardless of when BMI is ascertained. Increased risks of mortality in underweight and overweight women were also observed. Given the comparable elevated risks with obesity in the development (for post-menopausal women) and prognosis of breast cancer, and the complications with cancer treatment and other obesity-related co-morbidities, it is prudent to maintain a healthy body weight (BMI 18.5–<25.0 kg/m2) throughout life.\",\n \"This work was supported by the World Cancer Research Fund International (grant number: 2007/SP01) (http://www.wcrf-uk.org/). The funder of this study had no role in the decisions about the design and conduct of the study; collection, management, analysis, or interpretation of the data; or the preparation, review, or approval of the manuscript. The views expressed in this review are the opinions of the authors. They may not represent the views of the World Cancer Research Fund International/American Institute for Cancer Research and may differ from those in future updates of the evidence related to food, nutrition, physical activity, and cancer survival.\",\n \"DCG reports personal fees from World Cancer Research Fund/American Institute for Cancer Research, during the conduct of the study; grants from Danone, and grants from Kelloggs, outside the submitted work. AM reports personal fees from Metagenics/Metaproteomics, personal fees from Pfizer, outside the submitted work. All remaining authors have declared no conflicts of interest.\",\n \"\"\n]"},"all_sections_sec_types":{"kind":"list like","value":["intro","methods",null,null,null,null,"results",null,null,null,null,null,null,null,null,null,"discussion",null,null,"supplementary-material"],"string":"[\n \"intro\",\n \"methods\",\n null,\n null,\n null,\n null,\n \"results\",\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n \"discussion\",\n null,\n null,\n \"supplementary-material\"\n]"},"keywords":{"kind":"list like","value":["body mass index","meta-analysis","survival after breast cancer","systematic literature review"],"string":"[\n \"body mass index\",\n \"meta-analysis\",\n \"survival after breast cancer\",\n \"systematic literature review\"\n]"},"whole_article_text":{"kind":"string","value":"introduction:\nThe number of female breast cancer survivors is growing because of longer survival as a consequence of advances in treatment and early diagnosis. There were ∼2.6 million female breast cancer survivors in US in 2008 [1], and in the UK, breast cancer accounted for ∼28% of the 2 million cancer survivors in 2008 [2].\nObesity is a pandemic health concern, with over 500 million adults worldwide estimated to be obese and 958 million were overweight in 2008 [3]. One of the established risk factors for breast cancer development in post-menopausal women is obesity [4], which has further been linked to breast cancer recurrence [5] and poorer survival in pre- and post-menopausal breast cancer [6, 7]. Preliminary findings from randomised, controlled trials suggest that lifestyle modifications improved biomarkers associated with breast cancer progression and overall survival [8].\nThe biological mechanisms underlying the association between obesity and breast cancer survival are not established, and could involve interacting mediators of hormones, adipocytokines, and inflammatory cytokines which link to cell survival or apoptosis, migration, and proliferation [9]. Higher level of oestradiol produced in postmenopausal women through aromatisation of androgens in the adipose tissues [10], and higher level of insulin [11], a condition common in obese women, are linked to poorer prognosis in breast cancer. A possible interaction between leptin and insulin [12], and obesity-related markers of inflammation [13] have also been linked to breast cancer outcomes. Non-biological mechanisms could include chemotherapy under-dosing in obese women, suboptimal treatment, and obesity-related complications [14].\nNumerous studies have examined the relationship between obesity and breast cancer outcomes, and past reviews have concluded that obesity is linked to a lower survival; however, when investigated in a meta-analysis of published data, only the results of obese compared with non-obese or lighter women were summarised [6, 7, 15].\nWe carried out a systematic literature review and meta-analysis of published studies to explore the magnitude and the shape of the association between body fatness, as measured by body mass index (BMI), and the risk of total and cause-specific mortality, overall and in women with pre- and post-menopausal breast cancer. As body weight may change close to diagnosis and during primary treatment of breast cancer [16], we examined BMI in three periods: before diagnosis, <12 months after diagnosis, and ≥12 months after breast cancer diagnosis.\n\nmaterials and methods:\n data sources and search We carried out a systematic literature search, limited to publications in English, for articles on BMI and survival in women with breast cancer in OVID MEDLINE and EMBASE from inception to 30 June 2013 using the search strategy implemented for the WCRF/AICR Continuous Update Project on breast cancer survival. The search strategy contained medical subject headings and text words that covered a broad range of factors on diet, physical activity, and anthropometry. The protocol for the review is available at http://www.dietandcancerreport.org/index.php [17]. In addition, we hand-searched the reference lists of relevant articles, reviews, and meta-analysis papers.\nWe carried out a systematic literature search, limited to publications in English, for articles on BMI and survival in women with breast cancer in OVID MEDLINE and EMBASE from inception to 30 June 2013 using the search strategy implemented for the WCRF/AICR Continuous Update Project on breast cancer survival. The search strategy contained medical subject headings and text words that covered a broad range of factors on diet, physical activity, and anthropometry. The protocol for the review is available at http://www.dietandcancerreport.org/index.php [17]. In addition, we hand-searched the reference lists of relevant articles, reviews, and meta-analysis papers.\n study selection Included were follow-up studies of breast cancer survivors, which reported estimates of the associations of BMI ascertained before and after breast cancer diagnosis with total or cause-specific mortality risks. Studies that investigated BMI after diagnosis were divided into two groups: BMI <12 months after diagnosis (BMI <12 months) and BMI 12 months or more after diagnosis (BMI ≥12 months). Outcomes included total mortality, breast cancer mortality, death from cardiovascular disease, and death from causes other than breast cancer. When multiple publications on the same study population were found, results based on longer follow-up and more outcomes were selected for the meta-analysis.\nIncluded were follow-up studies of breast cancer survivors, which reported estimates of the associations of BMI ascertained before and after breast cancer diagnosis with total or cause-specific mortality risks. Studies that investigated BMI after diagnosis were divided into two groups: BMI <12 months after diagnosis (BMI <12 months) and BMI 12 months or more after diagnosis (BMI ≥12 months). Outcomes included total mortality, breast cancer mortality, death from cardiovascular disease, and death from causes other than breast cancer. When multiple publications on the same study population were found, results based on longer follow-up and more outcomes were selected for the meta-analysis.\n data extraction DSMC, TN, and DA conducted the search. DSMC, ARV, and DNR extracted the study characteristics, tumour-related information, cancer treatment, timing and method of weight and height assessment, BMI levels, number of outcomes and population at-risk, outcome type, estimates of association and their measure of variance [95% confidence interval (CI) or P value], and adjustment factors in the analysis.\nDSMC, TN, and DA conducted the search. DSMC, ARV, and DNR extracted the study characteristics, tumour-related information, cancer treatment, timing and method of weight and height assessment, BMI levels, number of outcomes and population at-risk, outcome type, estimates of association and their measure of variance [95% confidence interval (CI) or P value], and adjustment factors in the analysis.\n statistical analysis Categorical and dose–response meta-analyses were conducted using random-effects models to account for between-study heterogeneity [18]. Summary relative risks (RRs) were estimated using the average of the natural logarithm of the RRs of each study weighted by the inverse of the variance and then unweighted by applying a random-effects variance component which is derived from the extent of variability of the effect sizes of the studies. The maximally adjusted RR estimates were used for the meta-analysis except for the follow-up of randomised, controlled trials [19, 20] where unadjusted results were also included, as these studies mostly involved a more homogeneous study population. BMI or Quetelet's Index (QI) measured in units of kg/m2 was used.\nWe conducted categorical meta-analyses by pooling the categorical results reported in the studies. The studies used different BMI categories. In some studies, underweight (BMI <18.5 kg/m2 according to WHO international classification) and normal weight women (BMI 18.5–<25.0 kg/m2) were classified together but, in some studies, they were classified separately. Similarly, most studies classified overweight (BMI 25.0–<30.0 kg/m2) and obese (BMI ≥30.0 kg/m2) women separately but, in some studies, overweight and obese women were combined. The reference category was normal weight or underweight together with normal weight, depending on the studies. For convenience, the BMI categories are referred to as underweight, normal weight, overweight, and obese in the present review. We derived the RRs for overweight and obese women compared with normal weight women in two studies [19, 21] that had more than four BMI categories using the method of Hamling et al. [22]. Studies that reported results for obese compared with non-obese women were analysed separately.\nThe non-linear dose–response relationship between BMI and mortality was examined using the best-fitting second-order fractional polynomial regression model [23], defined as the one with the lowest deviance. Non-linearity was tested using the likelihood ratio test [24]. In the non-linear meta-analysis, the reference category was the lowest BMI category in each study and RRs were recalculated using the method of Hamling et al. [22] when the reference category was not the lowest BMI category in the study.\nWe also conducted linear dose–response meta-analyses, excluding the category underweight when reported separately in the studies, by pooling estimates of RR per unit increase (with its standard error) provided by the studies, or derived by us from categorical data using generalised least-squares for trend estimation [25]. To estimate the trend, the numbers of outcomes and population at-risk for at least three BMI categories, or the information required to derive them using standard methods [26], and means or medians of the BMI categories, or if not reported in the studies, the estimated midpoints of the categories had to be available. When the extreme BMI categories were open-ended, we used the width of the adjacent close-ended category to estimate the midpoints. Where the RRs were presented by subgroups (age group [27], menopausal status [28, 29], stage [30] or subtype [31] of breast cancer, or others [32–34]), an overall estimate for the study was obtained by a fixed-effect model before pooling in the meta-analysis. We estimated the risk increase of death for an increment of 5 kg/m2 of BMI.\nTo assess heterogeneity, we computed the Cochran Q test and I2 statistic [35]. The cut points of 30% and 50% were used for low, moderate, and substantial level of heterogeneity. Sources of heterogeneity were explored by meta-regression and subgroup analyses using pre-defined factors, including indicators of study quality (menopausal status, hormone receptor status, number of outcomes, length of follow-up, study design, geographic location, BMI assessment, adjustment for confounders, and others). Small study or publication bias was examined by Egger's test [36] and visual inspection of the funnel plots. The influence of each individual study on the summary RR was examined by excluding the study in turn [37]. A P value of <0.05 was considered statistically significant. All analyses were conducted using Stata version 12.1 (Stata Statistical Software: Release 12, StataCorp LP, College Station, TX).\nCategorical and dose–response meta-analyses were conducted using random-effects models to account for between-study heterogeneity [18]. Summary relative risks (RRs) were estimated using the average of the natural logarithm of the RRs of each study weighted by the inverse of the variance and then unweighted by applying a random-effects variance component which is derived from the extent of variability of the effect sizes of the studies. The maximally adjusted RR estimates were used for the meta-analysis except for the follow-up of randomised, controlled trials [19, 20] where unadjusted results were also included, as these studies mostly involved a more homogeneous study population. BMI or Quetelet's Index (QI) measured in units of kg/m2 was used.\nWe conducted categorical meta-analyses by pooling the categorical results reported in the studies. The studies used different BMI categories. In some studies, underweight (BMI <18.5 kg/m2 according to WHO international classification) and normal weight women (BMI 18.5–<25.0 kg/m2) were classified together but, in some studies, they were classified separately. Similarly, most studies classified overweight (BMI 25.0–<30.0 kg/m2) and obese (BMI ≥30.0 kg/m2) women separately but, in some studies, overweight and obese women were combined. The reference category was normal weight or underweight together with normal weight, depending on the studies. For convenience, the BMI categories are referred to as underweight, normal weight, overweight, and obese in the present review. We derived the RRs for overweight and obese women compared with normal weight women in two studies [19, 21] that had more than four BMI categories using the method of Hamling et al. [22]. Studies that reported results for obese compared with non-obese women were analysed separately.\nThe non-linear dose–response relationship between BMI and mortality was examined using the best-fitting second-order fractional polynomial regression model [23], defined as the one with the lowest deviance. Non-linearity was tested using the likelihood ratio test [24]. In the non-linear meta-analysis, the reference category was the lowest BMI category in each study and RRs were recalculated using the method of Hamling et al. [22] when the reference category was not the lowest BMI category in the study.\nWe also conducted linear dose–response meta-analyses, excluding the category underweight when reported separately in the studies, by pooling estimates of RR per unit increase (with its standard error) provided by the studies, or derived by us from categorical data using generalised least-squares for trend estimation [25]. To estimate the trend, the numbers of outcomes and population at-risk for at least three BMI categories, or the information required to derive them using standard methods [26], and means or medians of the BMI categories, or if not reported in the studies, the estimated midpoints of the categories had to be available. When the extreme BMI categories were open-ended, we used the width of the adjacent close-ended category to estimate the midpoints. Where the RRs were presented by subgroups (age group [27], menopausal status [28, 29], stage [30] or subtype [31] of breast cancer, or others [32–34]), an overall estimate for the study was obtained by a fixed-effect model before pooling in the meta-analysis. We estimated the risk increase of death for an increment of 5 kg/m2 of BMI.\nTo assess heterogeneity, we computed the Cochran Q test and I2 statistic [35]. The cut points of 30% and 50% were used for low, moderate, and substantial level of heterogeneity. Sources of heterogeneity were explored by meta-regression and subgroup analyses using pre-defined factors, including indicators of study quality (menopausal status, hormone receptor status, number of outcomes, length of follow-up, study design, geographic location, BMI assessment, adjustment for confounders, and others). Small study or publication bias was examined by Egger's test [36] and visual inspection of the funnel plots. The influence of each individual study on the summary RR was examined by excluding the study in turn [37]. A P value of <0.05 was considered statistically significant. All analyses were conducted using Stata version 12.1 (Stata Statistical Software: Release 12, StataCorp LP, College Station, TX).\n\ndata sources and search:\nWe carried out a systematic literature search, limited to publications in English, for articles on BMI and survival in women with breast cancer in OVID MEDLINE and EMBASE from inception to 30 June 2013 using the search strategy implemented for the WCRF/AICR Continuous Update Project on breast cancer survival. The search strategy contained medical subject headings and text words that covered a broad range of factors on diet, physical activity, and anthropometry. The protocol for the review is available at http://www.dietandcancerreport.org/index.php [17]. In addition, we hand-searched the reference lists of relevant articles, reviews, and meta-analysis papers.\n\nstudy selection:\nIncluded were follow-up studies of breast cancer survivors, which reported estimates of the associations of BMI ascertained before and after breast cancer diagnosis with total or cause-specific mortality risks. Studies that investigated BMI after diagnosis were divided into two groups: BMI <12 months after diagnosis (BMI <12 months) and BMI 12 months or more after diagnosis (BMI ≥12 months). Outcomes included total mortality, breast cancer mortality, death from cardiovascular disease, and death from causes other than breast cancer. When multiple publications on the same study population were found, results based on longer follow-up and more outcomes were selected for the meta-analysis.\n\ndata extraction:\nDSMC, TN, and DA conducted the search. DSMC, ARV, and DNR extracted the study characteristics, tumour-related information, cancer treatment, timing and method of weight and height assessment, BMI levels, number of outcomes and population at-risk, outcome type, estimates of association and their measure of variance [95% confidence interval (CI) or P value], and adjustment factors in the analysis.\n\nstatistical analysis:\nCategorical and dose–response meta-analyses were conducted using random-effects models to account for between-study heterogeneity [18]. Summary relative risks (RRs) were estimated using the average of the natural logarithm of the RRs of each study weighted by the inverse of the variance and then unweighted by applying a random-effects variance component which is derived from the extent of variability of the effect sizes of the studies. The maximally adjusted RR estimates were used for the meta-analysis except for the follow-up of randomised, controlled trials [19, 20] where unadjusted results were also included, as these studies mostly involved a more homogeneous study population. BMI or Quetelet's Index (QI) measured in units of kg/m2 was used.\nWe conducted categorical meta-analyses by pooling the categorical results reported in the studies. The studies used different BMI categories. In some studies, underweight (BMI <18.5 kg/m2 according to WHO international classification) and normal weight women (BMI 18.5–<25.0 kg/m2) were classified together but, in some studies, they were classified separately. Similarly, most studies classified overweight (BMI 25.0–<30.0 kg/m2) and obese (BMI ≥30.0 kg/m2) women separately but, in some studies, overweight and obese women were combined. The reference category was normal weight or underweight together with normal weight, depending on the studies. For convenience, the BMI categories are referred to as underweight, normal weight, overweight, and obese in the present review. We derived the RRs for overweight and obese women compared with normal weight women in two studies [19, 21] that had more than four BMI categories using the method of Hamling et al. [22]. Studies that reported results for obese compared with non-obese women were analysed separately.\nThe non-linear dose–response relationship between BMI and mortality was examined using the best-fitting second-order fractional polynomial regression model [23], defined as the one with the lowest deviance. Non-linearity was tested using the likelihood ratio test [24]. In the non-linear meta-analysis, the reference category was the lowest BMI category in each study and RRs were recalculated using the method of Hamling et al. [22] when the reference category was not the lowest BMI category in the study.\nWe also conducted linear dose–response meta-analyses, excluding the category underweight when reported separately in the studies, by pooling estimates of RR per unit increase (with its standard error) provided by the studies, or derived by us from categorical data using generalised least-squares for trend estimation [25]. To estimate the trend, the numbers of outcomes and population at-risk for at least three BMI categories, or the information required to derive them using standard methods [26], and means or medians of the BMI categories, or if not reported in the studies, the estimated midpoints of the categories had to be available. When the extreme BMI categories were open-ended, we used the width of the adjacent close-ended category to estimate the midpoints. Where the RRs were presented by subgroups (age group [27], menopausal status [28, 29], stage [30] or subtype [31] of breast cancer, or others [32–34]), an overall estimate for the study was obtained by a fixed-effect model before pooling in the meta-analysis. We estimated the risk increase of death for an increment of 5 kg/m2 of BMI.\nTo assess heterogeneity, we computed the Cochran Q test and I2 statistic [35]. The cut points of 30% and 50% were used for low, moderate, and substantial level of heterogeneity. Sources of heterogeneity were explored by meta-regression and subgroup analyses using pre-defined factors, including indicators of study quality (menopausal status, hormone receptor status, number of outcomes, length of follow-up, study design, geographic location, BMI assessment, adjustment for confounders, and others). Small study or publication bias was examined by Egger's test [36] and visual inspection of the funnel plots. The influence of each individual study on the summary RR was examined by excluding the study in turn [37]. A P value of <0.05 was considered statistically significant. All analyses were conducted using Stata version 12.1 (Stata Statistical Software: Release 12, StataCorp LP, College Station, TX).\n\nresults:\nA total of 124 publications investigating the relationship of body fatness and mortality in women with breast cancer were identified. We excluded 31 publications, including four publications on other obesity indices [38–41], 12 publications without a measure of association [42–53], and 15 publications superseded by publications of the same study with more outcomes [54–68]. A further 14 publications were excluded because of insufficient data for the meta-analysis (five publications [69–73]) or unadjusted results (nine publications [74–82]), from which nine publications reported statistically significant increased risk of total, breast cancer or non-breast cancer mortality in obese women (before or <12 months after diagnosis) compared with the reference BMI [69, 71–74, 76, 77, 79, 82], two publications reported non-significant inverse associations [75, 80] and three publications reported no association [70, 78, 81] of BMI with survival after breast cancer. Hence, 79 publications from 82 follow-up studies with 41 477 deaths (23 182 from breast cancer) in 213 075 breast cancer survivors were included in the meta-analyses (Figure 1). Supplementary Table S1, available at Annals of Oncology online shows the characteristics of the studies included in the meta-analyses and details of the excluded studies are in supplementary Table S2, available at Annals of Oncology online. Results of the meta-analyses are summarised in Table 1.Table 1.Summary of meta-analyses of BMI and survival in women with breast canceraBMI before diagnosisBMI <12 months after diagnosisBMI ≥12 months after diagnosisNRR (95% CI)I2 (%)PhNRR (95% CI)I2 (%)PhNRR (95% CI)I2 (%)PhTotal mortality Under versus normal weight101.10 (0.92–1.31)48%0.04111.25 (0.99–0.57)63%<0.0131.29 (1.02–1.63)0%0.39 Over versus normal weight191.07 (1.02–1.12)0%0.88221.07 (1.02–1.12)21%0.1840.98 (0.86–1.11)0%0.72 Obese versus normal weight211.41 (1.29–1.53)38%0.04241.23 (1.12–1.33)69%<0.0151.21 (1.06–1.38)0%0.70 Obese versus non-obese–––121.26 (1.07–1.47)80%<0.01––– Per 5 kg/m2 increase151.17 (1.13–1.21)7%0.38121.11 (1.06–1.16)55%0.0141.08 (1.01–1.15)0%0.52Breast cancer mortality Under versus normal weight81.02 (0.85–1.21)31%0.1851.53 (1.27–1.83)0%0.5911.10 (0.15–8.08)– Over versus normal weight211.11 (1.06–1.17)0%0.66121.11 (1.03–1.20)14%0.3121.37 (0.96–1.95)0%0.90 Obese versus normal weight221.35 (1.24–1.47)36%0.05121.25 (1.10–1.42)53%0.0221.68 (0.90–3.15)67%0.08 Obese versus non-obese–––61.26 (1.05–1.51)64%0.02––– Per 5 kg/m2 increase181.18 (1.12–1.25)47%0.0181.14 (1.05–1.24)66%0.0121.29 (0.97–1.72)64%0.10Cardiovascular disease related mortality Over versus normal weight21.01 (0.80–1.29)0%0.87–––––– Obese versus normal weight21.60 (0.66–3.87)78%0.03–––––– Per 5 kg/m2 increase21.21 (0.83–1.77)80%0.03––––––Non-breast cancer mortality Over versus normal weight–––50.96 (0.83–1.11)26%0.25––– Obese versus normal weight–––51.29 (0.99–1.68)72%0.01–––aBMI before and after diagnosis (<12 months after, or ≥12 months after diagnosis) was classified according to the exposure period which the studies referred to in the BMI assessment; the BMI categories were included in the categorical meta-analyses as defined by the studies.Ph, P for heterogeneity between studies.Figure 1.Flowchart of search.\nSummary of meta-analyses of BMI and survival in women with breast cancera\naBMI before and after diagnosis (<12 months after, or ≥12 months after diagnosis) was classified according to the exposure period which the studies referred to in the BMI assessment; the BMI categories were included in the categorical meta-analyses as defined by the studies.\nPh, P for heterogeneity between studies.\nFlowchart of search.\nStudies were follow-up of women with breast cancer identified in prospective aetiologic cohort studies (women were free of cancer at enrolment), or cohorts of breast cancer survivors whose participants were identified in hospitals or through cancer registries, or follow-up of breast cancer patients enrolled in case–control studies or randomised clinical trials.\nSome studies included only premenopausal women [83–85] or postmenopausal women [21, 27, 86–94], but most studies included both. Menopausal status was usually determined at time of diagnosis. Year of diagnosis was from 1957–1965 [70] to 2002–2009 [74]. Patient tumour characteristics and stage of disease at diagnosis varied across studies, and some studies included carcinoma in situ. No all studies provided clinical information on the tumour, treatment, and co-morbidities.\nMost of the studies were based in North America or Europe. There were three studies from each of Australia [79, 95, 96], Korea [97, 98] and China [99–101]; two studies from Japan [71, 102]; one study from Tunisia [103] and four international studies [19, 104–106]. Study size ranged from 96 [107] to 24 698 patients [97]. Total number of deaths ranged from 56 [93] to 7397 [108], and the proportion of deaths from breast cancer ranged from 22% [27] to 98% [84] when reported. All but eight studies [30, 93, 94, 98, 99, 109–111] had an average follow-up of more than 5 years.\n BMI and total mortality categorical meta-analysis For BMI before diagnosis, compared with normal weight women, the summary RRs were 1.41 (95% CI 1.29–1.53, 21 studies) for obese women, 1.07 (95% CI 1.02–1.12, 19 studies) for overweight women, and 1.10 (95% CI 0.92–1.31, 10 studies) for underweight women (Figure 2). For BMI <12 months after diagnosis and the same comparisons, the summary RRs were 1.23 (95% CI 1.12–1.33, 24 studies) for obese women, 1.07 (95% CI 1.02–1.12, 22 studies) for overweight women, and 1.25 (95% CI 0.99–1.57, 11 studies) for underweight women (supplementary Figure S1, available at Annals of Oncology online). Substantial heterogeneities were observed between studies of obese women and underweight women (I2 = 69%, P < 0.01; I2 = 63%, P < 0.01, respectively). For BMI ≥12 months after diagnosis, the summary RRs were 1.21 (95% CI 1.06–1.38, 5 studies) for obese women, 0.98 (95% CI 0.86–1.11, 4 studies) for overweight women, and 1.29 (95% CI 1.02–1.63, 3 studies) for underweight women (supplementary Figure S2, available at Annals of Oncology online). Twelve additional studies reported results for obese versus non-obese women <12 months after diagnosis, and the summary RR was 1.26 (95% CI 1.07–1.47, I2 = 80%, P < 0.01).Figure 2.Categorical meta-analysis of pre-diagnosis BMI and total mortality.\nCategorical meta-analysis of pre-diagnosis BMI and total mortality.\nFor BMI before diagnosis, compared with normal weight women, the summary RRs were 1.41 (95% CI 1.29–1.53, 21 studies) for obese women, 1.07 (95% CI 1.02–1.12, 19 studies) for overweight women, and 1.10 (95% CI 0.92–1.31, 10 studies) for underweight women (Figure 2). For BMI <12 months after diagnosis and the same comparisons, the summary RRs were 1.23 (95% CI 1.12–1.33, 24 studies) for obese women, 1.07 (95% CI 1.02–1.12, 22 studies) for overweight women, and 1.25 (95% CI 0.99–1.57, 11 studies) for underweight women (supplementary Figure S1, available at Annals of Oncology online). Substantial heterogeneities were observed between studies of obese women and underweight women (I2 = 69%, P < 0.01; I2 = 63%, P < 0.01, respectively). For BMI ≥12 months after diagnosis, the summary RRs were 1.21 (95% CI 1.06–1.38, 5 studies) for obese women, 0.98 (95% CI 0.86–1.11, 4 studies) for overweight women, and 1.29 (95% CI 1.02–1.63, 3 studies) for underweight women (supplementary Figure S2, available at Annals of Oncology online). Twelve additional studies reported results for obese versus non-obese women <12 months after diagnosis, and the summary RR was 1.26 (95% CI 1.07–1.47, I2 = 80%, P < 0.01).Figure 2.Categorical meta-analysis of pre-diagnosis BMI and total mortality.\nCategorical meta-analysis of pre-diagnosis BMI and total mortality.\n dose–response meta-analysis There was evidence of a J-shaped association in the non-linear dose–response meta-analyses of BMI before and after diagnosis with total mortality (all P < 0.01; Figure 3), suggesting that underweight women may be at slightly increased risk compared with normal weight women. The curves show linear increasing trends from 20 kg/m2 for BMI before diagnosis and <12 months after diagnosis, and from 25 kg/m2 for BMI ≥12 months after diagnosis. When linear models were fitted excluding the underweight category, the summary RRs of total mortality for each 5 kg/m2 increase in BMI were 1.17 (95% CI 1.13–1.21, 15 studies, 6358 deaths), 1.11 (95% CI 1.06–1.16, 12 studies, 6020 deaths), and 1.08 (95% CI 1.01–1.15, 4 studies, 1703 deaths) for BMI before, <12 months after, and ≥12 months after diagnosis, respectively (Figure 4). Substantial heterogeneity was observed between studies on BMI <12 months after diagnosis (I2 = 55%, P = 0.01).Figure 3.Non-linear dose–response curves of BMI and mortality.Figure 4.Linear dose–response meta-analysis of BMI and total mortality.\nNon-linear dose–response curves of BMI and mortality.\nLinear dose–response meta-analysis of BMI and total mortality.\nThere was evidence of a J-shaped association in the non-linear dose–response meta-analyses of BMI before and after diagnosis with total mortality (all P < 0.01; Figure 3), suggesting that underweight women may be at slightly increased risk compared with normal weight women. The curves show linear increasing trends from 20 kg/m2 for BMI before diagnosis and <12 months after diagnosis, and from 25 kg/m2 for BMI ≥12 months after diagnosis. When linear models were fitted excluding the underweight category, the summary RRs of total mortality for each 5 kg/m2 increase in BMI were 1.17 (95% CI 1.13–1.21, 15 studies, 6358 deaths), 1.11 (95% CI 1.06–1.16, 12 studies, 6020 deaths), and 1.08 (95% CI 1.01–1.15, 4 studies, 1703 deaths) for BMI before, <12 months after, and ≥12 months after diagnosis, respectively (Figure 4). Substantial heterogeneity was observed between studies on BMI <12 months after diagnosis (I2 = 55%, P = 0.01).Figure 3.Non-linear dose–response curves of BMI and mortality.Figure 4.Linear dose–response meta-analysis of BMI and total mortality.\nNon-linear dose–response curves of BMI and mortality.\nLinear dose–response meta-analysis of BMI and total mortality.\n categorical meta-analysis For BMI before diagnosis, compared with normal weight women, the summary RRs were 1.41 (95% CI 1.29–1.53, 21 studies) for obese women, 1.07 (95% CI 1.02–1.12, 19 studies) for overweight women, and 1.10 (95% CI 0.92–1.31, 10 studies) for underweight women (Figure 2). For BMI <12 months after diagnosis and the same comparisons, the summary RRs were 1.23 (95% CI 1.12–1.33, 24 studies) for obese women, 1.07 (95% CI 1.02–1.12, 22 studies) for overweight women, and 1.25 (95% CI 0.99–1.57, 11 studies) for underweight women (supplementary Figure S1, available at Annals of Oncology online). Substantial heterogeneities were observed between studies of obese women and underweight women (I2 = 69%, P < 0.01; I2 = 63%, P < 0.01, respectively). For BMI ≥12 months after diagnosis, the summary RRs were 1.21 (95% CI 1.06–1.38, 5 studies) for obese women, 0.98 (95% CI 0.86–1.11, 4 studies) for overweight women, and 1.29 (95% CI 1.02–1.63, 3 studies) for underweight women (supplementary Figure S2, available at Annals of Oncology online). Twelve additional studies reported results for obese versus non-obese women <12 months after diagnosis, and the summary RR was 1.26 (95% CI 1.07–1.47, I2 = 80%, P < 0.01).Figure 2.Categorical meta-analysis of pre-diagnosis BMI and total mortality.\nCategorical meta-analysis of pre-diagnosis BMI and total mortality.\nFor BMI before diagnosis, compared with normal weight women, the summary RRs were 1.41 (95% CI 1.29–1.53, 21 studies) for obese women, 1.07 (95% CI 1.02–1.12, 19 studies) for overweight women, and 1.10 (95% CI 0.92–1.31, 10 studies) for underweight women (Figure 2). For BMI <12 months after diagnosis and the same comparisons, the summary RRs were 1.23 (95% CI 1.12–1.33, 24 studies) for obese women, 1.07 (95% CI 1.02–1.12, 22 studies) for overweight women, and 1.25 (95% CI 0.99–1.57, 11 studies) for underweight women (supplementary Figure S1, available at Annals of Oncology online). Substantial heterogeneities were observed between studies of obese women and underweight women (I2 = 69%, P < 0.01; I2 = 63%, P < 0.01, respectively). For BMI ≥12 months after diagnosis, the summary RRs were 1.21 (95% CI 1.06–1.38, 5 studies) for obese women, 0.98 (95% CI 0.86–1.11, 4 studies) for overweight women, and 1.29 (95% CI 1.02–1.63, 3 studies) for underweight women (supplementary Figure S2, available at Annals of Oncology online). Twelve additional studies reported results for obese versus non-obese women <12 months after diagnosis, and the summary RR was 1.26 (95% CI 1.07–1.47, I2 = 80%, P < 0.01).Figure 2.Categorical meta-analysis of pre-diagnosis BMI and total mortality.\nCategorical meta-analysis of pre-diagnosis BMI and total mortality.\n dose–response meta-analysis There was evidence of a J-shaped association in the non-linear dose–response meta-analyses of BMI before and after diagnosis with total mortality (all P < 0.01; Figure 3), suggesting that underweight women may be at slightly increased risk compared with normal weight women. The curves show linear increasing trends from 20 kg/m2 for BMI before diagnosis and <12 months after diagnosis, and from 25 kg/m2 for BMI ≥12 months after diagnosis. When linear models were fitted excluding the underweight category, the summary RRs of total mortality for each 5 kg/m2 increase in BMI were 1.17 (95% CI 1.13–1.21, 15 studies, 6358 deaths), 1.11 (95% CI 1.06–1.16, 12 studies, 6020 deaths), and 1.08 (95% CI 1.01–1.15, 4 studies, 1703 deaths) for BMI before, <12 months after, and ≥12 months after diagnosis, respectively (Figure 4). Substantial heterogeneity was observed between studies on BMI <12 months after diagnosis (I2 = 55%, P = 0.01).Figure 3.Non-linear dose–response curves of BMI and mortality.Figure 4.Linear dose–response meta-analysis of BMI and total mortality.\nNon-linear dose–response curves of BMI and mortality.\nLinear dose–response meta-analysis of BMI and total mortality.\nThere was evidence of a J-shaped association in the non-linear dose–response meta-analyses of BMI before and after diagnosis with total mortality (all P < 0.01; Figure 3), suggesting that underweight women may be at slightly increased risk compared with normal weight women. The curves show linear increasing trends from 20 kg/m2 for BMI before diagnosis and <12 months after diagnosis, and from 25 kg/m2 for BMI ≥12 months after diagnosis. When linear models were fitted excluding the underweight category, the summary RRs of total mortality for each 5 kg/m2 increase in BMI were 1.17 (95% CI 1.13–1.21, 15 studies, 6358 deaths), 1.11 (95% CI 1.06–1.16, 12 studies, 6020 deaths), and 1.08 (95% CI 1.01–1.15, 4 studies, 1703 deaths) for BMI before, <12 months after, and ≥12 months after diagnosis, respectively (Figure 4). Substantial heterogeneity was observed between studies on BMI <12 months after diagnosis (I2 = 55%, P = 0.01).Figure 3.Non-linear dose–response curves of BMI and mortality.Figure 4.Linear dose–response meta-analysis of BMI and total mortality.\nNon-linear dose–response curves of BMI and mortality.\nLinear dose–response meta-analysis of BMI and total mortality.\n BMI and breast cancer mortality categorical meta-analysis BMI was significantly associated with breast cancer mortality. Compared with normal weight women, for BMI before diagnosis, the summary RRs were 1.35 (95% CI 1.24–1.47, 22 studies) for obese women, 1.11 (95% CI 1.06–1.17, 21 studies) for overweight women, and 1.02 (95% CI 0.85–1.21, 8 studies) for underweight women (Figure 5). For BMI <12 months after diagnosis, the summary RRs were 1.25 (95% CI 1.10–1.42, 12 studies) for obese women, 1.11 (95% CI 1.03–1.20, 12 studies) for overweight women, and 1.53 (95% CI 1.27–1.83, 5 studies) for underweight women (supplementary Figure S3, available at Annals of Oncology online). Substantial heterogeneity was observed between studies of obese women (I2 = 53%, P = 0.02). For BMI ≥12 months after diagnosis, the summary RRs of the two studies identified were 1.68 (95% CI 0.90–3.15) for obese women and 1.37 (95% CI 0.96–1.95) for overweight women (supplementary Figure S4, available at Annals of Oncology online). The summary of another six studies that reported RRs for obese versus non-obese <12 months after diagnosis was 1.26 (95% CI 1.05–1.51, I2 = 64%, P = 0.02).Figure 5.Categorical meta-analysis of pre-diagnosis BMI and breast cancer mortality.\nCategorical meta-analysis of pre-diagnosis BMI and breast cancer mortality.\nBMI was significantly associated with breast cancer mortality. Compared with normal weight women, for BMI before diagnosis, the summary RRs were 1.35 (95% CI 1.24–1.47, 22 studies) for obese women, 1.11 (95% CI 1.06–1.17, 21 studies) for overweight women, and 1.02 (95% CI 0.85–1.21, 8 studies) for underweight women (Figure 5). For BMI <12 months after diagnosis, the summary RRs were 1.25 (95% CI 1.10–1.42, 12 studies) for obese women, 1.11 (95% CI 1.03–1.20, 12 studies) for overweight women, and 1.53 (95% CI 1.27–1.83, 5 studies) for underweight women (supplementary Figure S3, available at Annals of Oncology online). Substantial heterogeneity was observed between studies of obese women (I2 = 53%, P = 0.02). For BMI ≥12 months after diagnosis, the summary RRs of the two studies identified were 1.68 (95% CI 0.90–3.15) for obese women and 1.37 (95% CI 0.96–1.95) for overweight women (supplementary Figure S4, available at Annals of Oncology online). The summary of another six studies that reported RRs for obese versus non-obese <12 months after diagnosis was 1.26 (95% CI 1.05–1.51, I2 = 64%, P = 0.02).Figure 5.Categorical meta-analysis of pre-diagnosis BMI and breast cancer mortality.\nCategorical meta-analysis of pre-diagnosis BMI and breast cancer mortality.\n dose–response meta-analysis There was no significant evidence of a non-linear relationship between BMI before, <12 months after, and ≥12 months after diagnosis and breast cancer mortality (P = 0.21, P = 1.00, P = 0.86, respectively) (Figure 3). When linear models were fitted excluding data from the underweight category, statistically significant increased risks of breast cancer mortality with BMI before and <12 months after diagnosis were observed (Figure 6). The summary RRs for each 5 kg/m2 increase were 1.18 (95% CI 1.12–1.25, 18 studies, 5262 breast cancer deaths) for BMI before diagnosis and 1.14 (95% CI 1.05–1.24, 8 studies, 3857 breast cancer deaths) for BMI <12 months after diagnosis, with moderate (I2 = 47%, P = 0.01) and substantial (I2 = 66%, P = 0.01) heterogeneities between studies, respectively. Only two studies on BMI ≥12 months after diagnosis and breast cancer mortality (N = 220 deaths) were identified. The summary RR was 1.29 (95% CI 0.97–1.72).Figure 6.Linear dose–response meta-analysis of BMI and breast cancer mortality.\nLinear dose–response meta-analysis of BMI and breast cancer mortality.\nThere was no significant evidence of a non-linear relationship between BMI before, <12 months after, and ≥12 months after diagnosis and breast cancer mortality (P = 0.21, P = 1.00, P = 0.86, respectively) (Figure 3). When linear models were fitted excluding data from the underweight category, statistically significant increased risks of breast cancer mortality with BMI before and <12 months after diagnosis were observed (Figure 6). The summary RRs for each 5 kg/m2 increase were 1.18 (95% CI 1.12–1.25, 18 studies, 5262 breast cancer deaths) for BMI before diagnosis and 1.14 (95% CI 1.05–1.24, 8 studies, 3857 breast cancer deaths) for BMI <12 months after diagnosis, with moderate (I2 = 47%, P = 0.01) and substantial (I2 = 66%, P = 0.01) heterogeneities between studies, respectively. Only two studies on BMI ≥12 months after diagnosis and breast cancer mortality (N = 220 deaths) were identified. The summary RR was 1.29 (95% CI 0.97–1.72).Figure 6.Linear dose–response meta-analysis of BMI and breast cancer mortality.\nLinear dose–response meta-analysis of BMI and breast cancer mortality.\n categorical meta-analysis BMI was significantly associated with breast cancer mortality. Compared with normal weight women, for BMI before diagnosis, the summary RRs were 1.35 (95% CI 1.24–1.47, 22 studies) for obese women, 1.11 (95% CI 1.06–1.17, 21 studies) for overweight women, and 1.02 (95% CI 0.85–1.21, 8 studies) for underweight women (Figure 5). For BMI <12 months after diagnosis, the summary RRs were 1.25 (95% CI 1.10–1.42, 12 studies) for obese women, 1.11 (95% CI 1.03–1.20, 12 studies) for overweight women, and 1.53 (95% CI 1.27–1.83, 5 studies) for underweight women (supplementary Figure S3, available at Annals of Oncology online). Substantial heterogeneity was observed between studies of obese women (I2 = 53%, P = 0.02). For BMI ≥12 months after diagnosis, the summary RRs of the two studies identified were 1.68 (95% CI 0.90–3.15) for obese women and 1.37 (95% CI 0.96–1.95) for overweight women (supplementary Figure S4, available at Annals of Oncology online). The summary of another six studies that reported RRs for obese versus non-obese <12 months after diagnosis was 1.26 (95% CI 1.05–1.51, I2 = 64%, P = 0.02).Figure 5.Categorical meta-analysis of pre-diagnosis BMI and breast cancer mortality.\nCategorical meta-analysis of pre-diagnosis BMI and breast cancer mortality.\nBMI was significantly associated with breast cancer mortality. Compared with normal weight women, for BMI before diagnosis, the summary RRs were 1.35 (95% CI 1.24–1.47, 22 studies) for obese women, 1.11 (95% CI 1.06–1.17, 21 studies) for overweight women, and 1.02 (95% CI 0.85–1.21, 8 studies) for underweight women (Figure 5). For BMI <12 months after diagnosis, the summary RRs were 1.25 (95% CI 1.10–1.42, 12 studies) for obese women, 1.11 (95% CI 1.03–1.20, 12 studies) for overweight women, and 1.53 (95% CI 1.27–1.83, 5 studies) for underweight women (supplementary Figure S3, available at Annals of Oncology online). Substantial heterogeneity was observed between studies of obese women (I2 = 53%, P = 0.02). For BMI ≥12 months after diagnosis, the summary RRs of the two studies identified were 1.68 (95% CI 0.90–3.15) for obese women and 1.37 (95% CI 0.96–1.95) for overweight women (supplementary Figure S4, available at Annals of Oncology online). The summary of another six studies that reported RRs for obese versus non-obese <12 months after diagnosis was 1.26 (95% CI 1.05–1.51, I2 = 64%, P = 0.02).Figure 5.Categorical meta-analysis of pre-diagnosis BMI and breast cancer mortality.\nCategorical meta-analysis of pre-diagnosis BMI and breast cancer mortality.\n dose–response meta-analysis There was no significant evidence of a non-linear relationship between BMI before, <12 months after, and ≥12 months after diagnosis and breast cancer mortality (P = 0.21, P = 1.00, P = 0.86, respectively) (Figure 3). When linear models were fitted excluding data from the underweight category, statistically significant increased risks of breast cancer mortality with BMI before and <12 months after diagnosis were observed (Figure 6). The summary RRs for each 5 kg/m2 increase were 1.18 (95% CI 1.12–1.25, 18 studies, 5262 breast cancer deaths) for BMI before diagnosis and 1.14 (95% CI 1.05–1.24, 8 studies, 3857 breast cancer deaths) for BMI <12 months after diagnosis, with moderate (I2 = 47%, P = 0.01) and substantial (I2 = 66%, P = 0.01) heterogeneities between studies, respectively. Only two studies on BMI ≥12 months after diagnosis and breast cancer mortality (N = 220 deaths) were identified. The summary RR was 1.29 (95% CI 0.97–1.72).Figure 6.Linear dose–response meta-analysis of BMI and breast cancer mortality.\nLinear dose–response meta-analysis of BMI and breast cancer mortality.\nThere was no significant evidence of a non-linear relationship between BMI before, <12 months after, and ≥12 months after diagnosis and breast cancer mortality (P = 0.21, P = 1.00, P = 0.86, respectively) (Figure 3). When linear models were fitted excluding data from the underweight category, statistically significant increased risks of breast cancer mortality with BMI before and <12 months after diagnosis were observed (Figure 6). The summary RRs for each 5 kg/m2 increase were 1.18 (95% CI 1.12–1.25, 18 studies, 5262 breast cancer deaths) for BMI before diagnosis and 1.14 (95% CI 1.05–1.24, 8 studies, 3857 breast cancer deaths) for BMI <12 months after diagnosis, with moderate (I2 = 47%, P = 0.01) and substantial (I2 = 66%, P = 0.01) heterogeneities between studies, respectively. Only two studies on BMI ≥12 months after diagnosis and breast cancer mortality (N = 220 deaths) were identified. The summary RR was 1.29 (95% CI 0.97–1.72).Figure 6.Linear dose–response meta-analysis of BMI and breast cancer mortality.\nLinear dose–response meta-analysis of BMI and breast cancer mortality.\n BMI and other mortality outcomes Only two studies reported results for death from cardiovascular disease (N = 151 deaths) [27, 112]. The summary RR for obese versus normal weight before diagnosis was 1.60 (95% CI 0.66–3.87). No association was observed for overweight versus normal weight (summary RR = 1.01, 95% CI 0.80–1.29). For each 5 kg/m2 increase in BMI, the summary RR was 1.21 (95% CI 0.83–1.77). Five studies reported results for deaths from any cause other than breast cancer (N = 2704 deaths) [21, 34, 108, 113, 114]. The summary RRs were 1.29 (95% CI 0.99–1.68, I2 = 72%, P = 0.01) for obese women, and 0.96 (95% CI 0.83–1.11, I2 = 26%, P = 0.25) for overweight women compared with normal weight women.\nOnly two studies reported results for death from cardiovascular disease (N = 151 deaths) [27, 112]. The summary RR for obese versus normal weight before diagnosis was 1.60 (95% CI 0.66–3.87). No association was observed for overweight versus normal weight (summary RR = 1.01, 95% CI 0.80–1.29). For each 5 kg/m2 increase in BMI, the summary RR was 1.21 (95% CI 0.83–1.77). Five studies reported results for deaths from any cause other than breast cancer (N = 2704 deaths) [21, 34, 108, 113, 114]. The summary RRs were 1.29 (95% CI 0.99–1.68, I2 = 72%, P = 0.01) for obese women, and 0.96 (95% CI 0.83–1.11, I2 = 26%, P = 0.25) for overweight women compared with normal weight women.\n subgroup, meta-regression, and sensitivity analyses The results of the subgroup and meta-regression analyses are in supplementary Tables S3 and S4, available at Annals of Oncology online. Subgroup analysis was not carried out for BMI ≥12 months after diagnosis as the limited number of studies would hinder any meaningful comparisons.\nIncreased risks of mortality were observed in the meta-analyses by menopausal status. While the summary risk estimates seem stronger with premenopausal breast cancer, there was no significant heterogeneity between pre- and post-menopausal breast cancer as shown in the meta-regression analyses (P = 0.28–0.89) (supplementary Tables S3 and S4, available at Annals of Oncology online). For BMI before diagnosis and total mortality, the summary RRs for obese versus normal weight were 1.75 (95% CI 1.26–2.41, I2 = 70%, P < 0.01, 7 studies) in women with pre-menopausal breast cancer and 1.34 (95% CI 1.18–1.53, I2 = 27%, P = 0.20, 9 studies) in women with post-menopausal breast cancer.\nStudies with larger number of deaths [105, 115], conducted in Europe [28, 115], or with weight and height assessed through medical records [28, 104, 115, 116] tended to report weaker associations for BMI <12 months after diagnosis and total mortality compared with other studies (meta-regression P = 0.01, 0.02, 0.01, respectively) (supplementary Table S3, available at Annals of Oncology online); while studies with larger number of deaths [101], conducted in Asia [101, 102], or adjusted for co-morbidity [101, 102] reported weaker associations for BMI <12 months after diagnosis and breast cancer mortality (meta-regression P = 0.01, 0.02, 0.01, respectively) (supplementary Table S4, available at Annals of Oncology online).\nAnalyses stratified by study designs, or restricted to studies with invasive cases only, early-stage non-metastatic cases only, or mammography screening detected cases only, or controlled for previous diseases did not produce results that were materially different from those obtained in the overall analyses (results not shown). Summary risk estimates remained statistically significant when each study was omitted in turn, except for BMI ≥12 months after diagnosis and total mortality. The summary RR was 1.06 (95% CI 0.98–1.15) per 5 kg/m2 increase when Flatt et al. [117] which contributed 315 deaths was omitted.\nThe results of the subgroup and meta-regression analyses are in supplementary Tables S3 and S4, available at Annals of Oncology online. Subgroup analysis was not carried out for BMI ≥12 months after diagnosis as the limited number of studies would hinder any meaningful comparisons.\nIncreased risks of mortality were observed in the meta-analyses by menopausal status. While the summary risk estimates seem stronger with premenopausal breast cancer, there was no significant heterogeneity between pre- and post-menopausal breast cancer as shown in the meta-regression analyses (P = 0.28–0.89) (supplementary Tables S3 and S4, available at Annals of Oncology online). For BMI before diagnosis and total mortality, the summary RRs for obese versus normal weight were 1.75 (95% CI 1.26–2.41, I2 = 70%, P < 0.01, 7 studies) in women with pre-menopausal breast cancer and 1.34 (95% CI 1.18–1.53, I2 = 27%, P = 0.20, 9 studies) in women with post-menopausal breast cancer.\nStudies with larger number of deaths [105, 115], conducted in Europe [28, 115], or with weight and height assessed through medical records [28, 104, 115, 116] tended to report weaker associations for BMI <12 months after diagnosis and total mortality compared with other studies (meta-regression P = 0.01, 0.02, 0.01, respectively) (supplementary Table S3, available at Annals of Oncology online); while studies with larger number of deaths [101], conducted in Asia [101, 102], or adjusted for co-morbidity [101, 102] reported weaker associations for BMI <12 months after diagnosis and breast cancer mortality (meta-regression P = 0.01, 0.02, 0.01, respectively) (supplementary Table S4, available at Annals of Oncology online).\nAnalyses stratified by study designs, or restricted to studies with invasive cases only, early-stage non-metastatic cases only, or mammography screening detected cases only, or controlled for previous diseases did not produce results that were materially different from those obtained in the overall analyses (results not shown). Summary risk estimates remained statistically significant when each study was omitted in turn, except for BMI ≥12 months after diagnosis and total mortality. The summary RR was 1.06 (95% CI 0.98–1.15) per 5 kg/m2 increase when Flatt et al. [117] which contributed 315 deaths was omitted.\n small studies or publication bias Asymmetry was only detected in the funnel plots of BMI <12 months after diagnosis and total mortality, and breast cancer mortality, which suggests that small studies with an inverse association are missing (plots not shown). Egger's tests were borderline significant (P = 0.05) or statistically significant (P = 0.03), respectively.\nAsymmetry was only detected in the funnel plots of BMI <12 months after diagnosis and total mortality, and breast cancer mortality, which suggests that small studies with an inverse association are missing (plots not shown). Egger's tests were borderline significant (P = 0.05) or statistically significant (P = 0.03), respectively.\n\nBMI and total mortality:\n categorical meta-analysis For BMI before diagnosis, compared with normal weight women, the summary RRs were 1.41 (95% CI 1.29–1.53, 21 studies) for obese women, 1.07 (95% CI 1.02–1.12, 19 studies) for overweight women, and 1.10 (95% CI 0.92–1.31, 10 studies) for underweight women (Figure 2). For BMI <12 months after diagnosis and the same comparisons, the summary RRs were 1.23 (95% CI 1.12–1.33, 24 studies) for obese women, 1.07 (95% CI 1.02–1.12, 22 studies) for overweight women, and 1.25 (95% CI 0.99–1.57, 11 studies) for underweight women (supplementary Figure S1, available at Annals of Oncology online). Substantial heterogeneities were observed between studies of obese women and underweight women (I2 = 69%, P < 0.01; I2 = 63%, P < 0.01, respectively). For BMI ≥12 months after diagnosis, the summary RRs were 1.21 (95% CI 1.06–1.38, 5 studies) for obese women, 0.98 (95% CI 0.86–1.11, 4 studies) for overweight women, and 1.29 (95% CI 1.02–1.63, 3 studies) for underweight women (supplementary Figure S2, available at Annals of Oncology online). Twelve additional studies reported results for obese versus non-obese women <12 months after diagnosis, and the summary RR was 1.26 (95% CI 1.07–1.47, I2 = 80%, P < 0.01).Figure 2.Categorical meta-analysis of pre-diagnosis BMI and total mortality.\nCategorical meta-analysis of pre-diagnosis BMI and total mortality.\nFor BMI before diagnosis, compared with normal weight women, the summary RRs were 1.41 (95% CI 1.29–1.53, 21 studies) for obese women, 1.07 (95% CI 1.02–1.12, 19 studies) for overweight women, and 1.10 (95% CI 0.92–1.31, 10 studies) for underweight women (Figure 2). For BMI <12 months after diagnosis and the same comparisons, the summary RRs were 1.23 (95% CI 1.12–1.33, 24 studies) for obese women, 1.07 (95% CI 1.02–1.12, 22 studies) for overweight women, and 1.25 (95% CI 0.99–1.57, 11 studies) for underweight women (supplementary Figure S1, available at Annals of Oncology online). Substantial heterogeneities were observed between studies of obese women and underweight women (I2 = 69%, P < 0.01; I2 = 63%, P < 0.01, respectively). For BMI ≥12 months after diagnosis, the summary RRs were 1.21 (95% CI 1.06–1.38, 5 studies) for obese women, 0.98 (95% CI 0.86–1.11, 4 studies) for overweight women, and 1.29 (95% CI 1.02–1.63, 3 studies) for underweight women (supplementary Figure S2, available at Annals of Oncology online). Twelve additional studies reported results for obese versus non-obese women <12 months after diagnosis, and the summary RR was 1.26 (95% CI 1.07–1.47, I2 = 80%, P < 0.01).Figure 2.Categorical meta-analysis of pre-diagnosis BMI and total mortality.\nCategorical meta-analysis of pre-diagnosis BMI and total mortality.\n dose–response meta-analysis There was evidence of a J-shaped association in the non-linear dose–response meta-analyses of BMI before and after diagnosis with total mortality (all P < 0.01; Figure 3), suggesting that underweight women may be at slightly increased risk compared with normal weight women. The curves show linear increasing trends from 20 kg/m2 for BMI before diagnosis and <12 months after diagnosis, and from 25 kg/m2 for BMI ≥12 months after diagnosis. When linear models were fitted excluding the underweight category, the summary RRs of total mortality for each 5 kg/m2 increase in BMI were 1.17 (95% CI 1.13–1.21, 15 studies, 6358 deaths), 1.11 (95% CI 1.06–1.16, 12 studies, 6020 deaths), and 1.08 (95% CI 1.01–1.15, 4 studies, 1703 deaths) for BMI before, <12 months after, and ≥12 months after diagnosis, respectively (Figure 4). Substantial heterogeneity was observed between studies on BMI <12 months after diagnosis (I2 = 55%, P = 0.01).Figure 3.Non-linear dose–response curves of BMI and mortality.Figure 4.Linear dose–response meta-analysis of BMI and total mortality.\nNon-linear dose–response curves of BMI and mortality.\nLinear dose–response meta-analysis of BMI and total mortality.\nThere was evidence of a J-shaped association in the non-linear dose–response meta-analyses of BMI before and after diagnosis with total mortality (all P < 0.01; Figure 3), suggesting that underweight women may be at slightly increased risk compared with normal weight women. The curves show linear increasing trends from 20 kg/m2 for BMI before diagnosis and <12 months after diagnosis, and from 25 kg/m2 for BMI ≥12 months after diagnosis. When linear models were fitted excluding the underweight category, the summary RRs of total mortality for each 5 kg/m2 increase in BMI were 1.17 (95% CI 1.13–1.21, 15 studies, 6358 deaths), 1.11 (95% CI 1.06–1.16, 12 studies, 6020 deaths), and 1.08 (95% CI 1.01–1.15, 4 studies, 1703 deaths) for BMI before, <12 months after, and ≥12 months after diagnosis, respectively (Figure 4). Substantial heterogeneity was observed between studies on BMI <12 months after diagnosis (I2 = 55%, P = 0.01).Figure 3.Non-linear dose–response curves of BMI and mortality.Figure 4.Linear dose–response meta-analysis of BMI and total mortality.\nNon-linear dose–response curves of BMI and mortality.\nLinear dose–response meta-analysis of BMI and total mortality.\n\ncategorical meta-analysis:\nFor BMI before diagnosis, compared with normal weight women, the summary RRs were 1.41 (95% CI 1.29–1.53, 21 studies) for obese women, 1.07 (95% CI 1.02–1.12, 19 studies) for overweight women, and 1.10 (95% CI 0.92–1.31, 10 studies) for underweight women (Figure 2). For BMI <12 months after diagnosis and the same comparisons, the summary RRs were 1.23 (95% CI 1.12–1.33, 24 studies) for obese women, 1.07 (95% CI 1.02–1.12, 22 studies) for overweight women, and 1.25 (95% CI 0.99–1.57, 11 studies) for underweight women (supplementary Figure S1, available at Annals of Oncology online). Substantial heterogeneities were observed between studies of obese women and underweight women (I2 = 69%, P < 0.01; I2 = 63%, P < 0.01, respectively). For BMI ≥12 months after diagnosis, the summary RRs were 1.21 (95% CI 1.06–1.38, 5 studies) for obese women, 0.98 (95% CI 0.86–1.11, 4 studies) for overweight women, and 1.29 (95% CI 1.02–1.63, 3 studies) for underweight women (supplementary Figure S2, available at Annals of Oncology online). Twelve additional studies reported results for obese versus non-obese women <12 months after diagnosis, and the summary RR was 1.26 (95% CI 1.07–1.47, I2 = 80%, P < 0.01).Figure 2.Categorical meta-analysis of pre-diagnosis BMI and total mortality.\nCategorical meta-analysis of pre-diagnosis BMI and total mortality.\n\ndose–response meta-analysis:\nThere was evidence of a J-shaped association in the non-linear dose–response meta-analyses of BMI before and after diagnosis with total mortality (all P < 0.01; Figure 3), suggesting that underweight women may be at slightly increased risk compared with normal weight women. The curves show linear increasing trends from 20 kg/m2 for BMI before diagnosis and <12 months after diagnosis, and from 25 kg/m2 for BMI ≥12 months after diagnosis. When linear models were fitted excluding the underweight category, the summary RRs of total mortality for each 5 kg/m2 increase in BMI were 1.17 (95% CI 1.13–1.21, 15 studies, 6358 deaths), 1.11 (95% CI 1.06–1.16, 12 studies, 6020 deaths), and 1.08 (95% CI 1.01–1.15, 4 studies, 1703 deaths) for BMI before, <12 months after, and ≥12 months after diagnosis, respectively (Figure 4). Substantial heterogeneity was observed between studies on BMI <12 months after diagnosis (I2 = 55%, P = 0.01).Figure 3.Non-linear dose–response curves of BMI and mortality.Figure 4.Linear dose–response meta-analysis of BMI and total mortality.\nNon-linear dose–response curves of BMI and mortality.\nLinear dose–response meta-analysis of BMI and total mortality.\n\nBMI and breast cancer mortality:\n categorical meta-analysis BMI was significantly associated with breast cancer mortality. Compared with normal weight women, for BMI before diagnosis, the summary RRs were 1.35 (95% CI 1.24–1.47, 22 studies) for obese women, 1.11 (95% CI 1.06–1.17, 21 studies) for overweight women, and 1.02 (95% CI 0.85–1.21, 8 studies) for underweight women (Figure 5). For BMI <12 months after diagnosis, the summary RRs were 1.25 (95% CI 1.10–1.42, 12 studies) for obese women, 1.11 (95% CI 1.03–1.20, 12 studies) for overweight women, and 1.53 (95% CI 1.27–1.83, 5 studies) for underweight women (supplementary Figure S3, available at Annals of Oncology online). Substantial heterogeneity was observed between studies of obese women (I2 = 53%, P = 0.02). For BMI ≥12 months after diagnosis, the summary RRs of the two studies identified were 1.68 (95% CI 0.90–3.15) for obese women and 1.37 (95% CI 0.96–1.95) for overweight women (supplementary Figure S4, available at Annals of Oncology online). The summary of another six studies that reported RRs for obese versus non-obese <12 months after diagnosis was 1.26 (95% CI 1.05–1.51, I2 = 64%, P = 0.02).Figure 5.Categorical meta-analysis of pre-diagnosis BMI and breast cancer mortality.\nCategorical meta-analysis of pre-diagnosis BMI and breast cancer mortality.\nBMI was significantly associated with breast cancer mortality. Compared with normal weight women, for BMI before diagnosis, the summary RRs were 1.35 (95% CI 1.24–1.47, 22 studies) for obese women, 1.11 (95% CI 1.06–1.17, 21 studies) for overweight women, and 1.02 (95% CI 0.85–1.21, 8 studies) for underweight women (Figure 5). For BMI <12 months after diagnosis, the summary RRs were 1.25 (95% CI 1.10–1.42, 12 studies) for obese women, 1.11 (95% CI 1.03–1.20, 12 studies) for overweight women, and 1.53 (95% CI 1.27–1.83, 5 studies) for underweight women (supplementary Figure S3, available at Annals of Oncology online). Substantial heterogeneity was observed between studies of obese women (I2 = 53%, P = 0.02). For BMI ≥12 months after diagnosis, the summary RRs of the two studies identified were 1.68 (95% CI 0.90–3.15) for obese women and 1.37 (95% CI 0.96–1.95) for overweight women (supplementary Figure S4, available at Annals of Oncology online). The summary of another six studies that reported RRs for obese versus non-obese <12 months after diagnosis was 1.26 (95% CI 1.05–1.51, I2 = 64%, P = 0.02).Figure 5.Categorical meta-analysis of pre-diagnosis BMI and breast cancer mortality.\nCategorical meta-analysis of pre-diagnosis BMI and breast cancer mortality.\n dose–response meta-analysis There was no significant evidence of a non-linear relationship between BMI before, <12 months after, and ≥12 months after diagnosis and breast cancer mortality (P = 0.21, P = 1.00, P = 0.86, respectively) (Figure 3). When linear models were fitted excluding data from the underweight category, statistically significant increased risks of breast cancer mortality with BMI before and <12 months after diagnosis were observed (Figure 6). The summary RRs for each 5 kg/m2 increase were 1.18 (95% CI 1.12–1.25, 18 studies, 5262 breast cancer deaths) for BMI before diagnosis and 1.14 (95% CI 1.05–1.24, 8 studies, 3857 breast cancer deaths) for BMI <12 months after diagnosis, with moderate (I2 = 47%, P = 0.01) and substantial (I2 = 66%, P = 0.01) heterogeneities between studies, respectively. Only two studies on BMI ≥12 months after diagnosis and breast cancer mortality (N = 220 deaths) were identified. The summary RR was 1.29 (95% CI 0.97–1.72).Figure 6.Linear dose–response meta-analysis of BMI and breast cancer mortality.\nLinear dose–response meta-analysis of BMI and breast cancer mortality.\nThere was no significant evidence of a non-linear relationship between BMI before, <12 months after, and ≥12 months after diagnosis and breast cancer mortality (P = 0.21, P = 1.00, P = 0.86, respectively) (Figure 3). When linear models were fitted excluding data from the underweight category, statistically significant increased risks of breast cancer mortality with BMI before and <12 months after diagnosis were observed (Figure 6). The summary RRs for each 5 kg/m2 increase were 1.18 (95% CI 1.12–1.25, 18 studies, 5262 breast cancer deaths) for BMI before diagnosis and 1.14 (95% CI 1.05–1.24, 8 studies, 3857 breast cancer deaths) for BMI <12 months after diagnosis, with moderate (I2 = 47%, P = 0.01) and substantial (I2 = 66%, P = 0.01) heterogeneities between studies, respectively. Only two studies on BMI ≥12 months after diagnosis and breast cancer mortality (N = 220 deaths) were identified. The summary RR was 1.29 (95% CI 0.97–1.72).Figure 6.Linear dose–response meta-analysis of BMI and breast cancer mortality.\nLinear dose–response meta-analysis of BMI and breast cancer mortality.\n\ncategorical meta-analysis:\nBMI was significantly associated with breast cancer mortality. Compared with normal weight women, for BMI before diagnosis, the summary RRs were 1.35 (95% CI 1.24–1.47, 22 studies) for obese women, 1.11 (95% CI 1.06–1.17, 21 studies) for overweight women, and 1.02 (95% CI 0.85–1.21, 8 studies) for underweight women (Figure 5). For BMI <12 months after diagnosis, the summary RRs were 1.25 (95% CI 1.10–1.42, 12 studies) for obese women, 1.11 (95% CI 1.03–1.20, 12 studies) for overweight women, and 1.53 (95% CI 1.27–1.83, 5 studies) for underweight women (supplementary Figure S3, available at Annals of Oncology online). Substantial heterogeneity was observed between studies of obese women (I2 = 53%, P = 0.02). For BMI ≥12 months after diagnosis, the summary RRs of the two studies identified were 1.68 (95% CI 0.90–3.15) for obese women and 1.37 (95% CI 0.96–1.95) for overweight women (supplementary Figure S4, available at Annals of Oncology online). The summary of another six studies that reported RRs for obese versus non-obese <12 months after diagnosis was 1.26 (95% CI 1.05–1.51, I2 = 64%, P = 0.02).Figure 5.Categorical meta-analysis of pre-diagnosis BMI and breast cancer mortality.\nCategorical meta-analysis of pre-diagnosis BMI and breast cancer mortality.\n\ndose–response meta-analysis:\nThere was no significant evidence of a non-linear relationship between BMI before, <12 months after, and ≥12 months after diagnosis and breast cancer mortality (P = 0.21, P = 1.00, P = 0.86, respectively) (Figure 3). When linear models were fitted excluding data from the underweight category, statistically significant increased risks of breast cancer mortality with BMI before and <12 months after diagnosis were observed (Figure 6). The summary RRs for each 5 kg/m2 increase were 1.18 (95% CI 1.12–1.25, 18 studies, 5262 breast cancer deaths) for BMI before diagnosis and 1.14 (95% CI 1.05–1.24, 8 studies, 3857 breast cancer deaths) for BMI <12 months after diagnosis, with moderate (I2 = 47%, P = 0.01) and substantial (I2 = 66%, P = 0.01) heterogeneities between studies, respectively. Only two studies on BMI ≥12 months after diagnosis and breast cancer mortality (N = 220 deaths) were identified. The summary RR was 1.29 (95% CI 0.97–1.72).Figure 6.Linear dose–response meta-analysis of BMI and breast cancer mortality.\nLinear dose–response meta-analysis of BMI and breast cancer mortality.\n\nBMI and other mortality outcomes:\nOnly two studies reported results for death from cardiovascular disease (N = 151 deaths) [27, 112]. The summary RR for obese versus normal weight before diagnosis was 1.60 (95% CI 0.66–3.87). No association was observed for overweight versus normal weight (summary RR = 1.01, 95% CI 0.80–1.29). For each 5 kg/m2 increase in BMI, the summary RR was 1.21 (95% CI 0.83–1.77). Five studies reported results for deaths from any cause other than breast cancer (N = 2704 deaths) [21, 34, 108, 113, 114]. The summary RRs were 1.29 (95% CI 0.99–1.68, I2 = 72%, P = 0.01) for obese women, and 0.96 (95% CI 0.83–1.11, I2 = 26%, P = 0.25) for overweight women compared with normal weight women.\n\nsubgroup, meta-regression, and sensitivity analyses:\nThe results of the subgroup and meta-regression analyses are in supplementary Tables S3 and S4, available at Annals of Oncology online. Subgroup analysis was not carried out for BMI ≥12 months after diagnosis as the limited number of studies would hinder any meaningful comparisons.\nIncreased risks of mortality were observed in the meta-analyses by menopausal status. While the summary risk estimates seem stronger with premenopausal breast cancer, there was no significant heterogeneity between pre- and post-menopausal breast cancer as shown in the meta-regression analyses (P = 0.28–0.89) (supplementary Tables S3 and S4, available at Annals of Oncology online). For BMI before diagnosis and total mortality, the summary RRs for obese versus normal weight were 1.75 (95% CI 1.26–2.41, I2 = 70%, P < 0.01, 7 studies) in women with pre-menopausal breast cancer and 1.34 (95% CI 1.18–1.53, I2 = 27%, P = 0.20, 9 studies) in women with post-menopausal breast cancer.\nStudies with larger number of deaths [105, 115], conducted in Europe [28, 115], or with weight and height assessed through medical records [28, 104, 115, 116] tended to report weaker associations for BMI <12 months after diagnosis and total mortality compared with other studies (meta-regression P = 0.01, 0.02, 0.01, respectively) (supplementary Table S3, available at Annals of Oncology online); while studies with larger number of deaths [101], conducted in Asia [101, 102], or adjusted for co-morbidity [101, 102] reported weaker associations for BMI <12 months after diagnosis and breast cancer mortality (meta-regression P = 0.01, 0.02, 0.01, respectively) (supplementary Table S4, available at Annals of Oncology online).\nAnalyses stratified by study designs, or restricted to studies with invasive cases only, early-stage non-metastatic cases only, or mammography screening detected cases only, or controlled for previous diseases did not produce results that were materially different from those obtained in the overall analyses (results not shown). Summary risk estimates remained statistically significant when each study was omitted in turn, except for BMI ≥12 months after diagnosis and total mortality. The summary RR was 1.06 (95% CI 0.98–1.15) per 5 kg/m2 increase when Flatt et al. [117] which contributed 315 deaths was omitted.\n\nsmall studies or publication bias:\nAsymmetry was only detected in the funnel plots of BMI <12 months after diagnosis and total mortality, and breast cancer mortality, which suggests that small studies with an inverse association are missing (plots not shown). Egger's tests were borderline significant (P = 0.05) or statistically significant (P = 0.03), respectively.\n\ndiscussion:\nThe present systematic literature review and meta-analysis of follow-up studies clearly supports that, in breast cancer survivors, higher BMI is consistently associated with lower overall and breast cancer survival, regardless of when BMI is ascertained. The limited number of studies on death from cardiovascular disease is also consistent with a positive association. For before, <12 months after, and 12 months or more after breast cancer diagnosis, compared with normal weight women, obese women had 41%, 23%, and 21% higher risk for total mortality, and 35%, 25%, and 68% increased risk for breast cancer mortality, respectively. The findings were supported by the positive associations observed in the linear dose–response meta-analysis. All associations were statistically significant, apart from the relationship between BMI ≥12 months after diagnosis and breast cancer mortality. This may be due to limited statistical power, with only 220 breast cancer deaths from two follow-up studies.\nPositive associations, in some cases statistically significant, were also observed in overweight, and underweight women compared with normal weight women. Women with BMI of 20 kg/m2 before, or <12 months after diagnosis, and of 25 kg/m2 12 months or more after diagnosis appeared to have the lowest mortality risk in the non-linear dose–response analysis. Co-morbid conditions may cause the observed increased risk in underweight women. Thorough investigation within the group and on their contribution to the shape of the association is hindered, as not all studies in this review reported results for this group. The increased risk associated with obesity was similar in pre- or post-menopausal breast cancer. We did not find any evidence of a protective effect of obesity on survival after pre-menopausal breast cancer, contrary to what has been observed for the development of breast cancer in pre-menopausal women [4].\nA large body of evidence with 41 477 deaths (23 182 from breast cancer) in over 210 000 breast cancer survivors was systematically reviewed in the present study. We carried out categorical, linear, and non-linear dose–response meta-analyses to examine the magnitude and the shape of the associations for total and cause-specific mortality in underweight, overweight, and obese women by time periods before and after diagnosis that is important in relation to the population-at-risk and breast cancer survivors. Our findings agree with and further extend the results from previous meta-analyses. A review published in 2010 reported statistically significant increased risks of 33% of both total and breast cancer mortality for obesity versus non-obesity around diagnosis [7]. These estimates are slightly higher than ours, which may be explained by the different search periods and inclusion criteria for the articles (33 studies and 15 studies included in the analyses, respectively). Another review published in 2012 further reported consistent positive associations of total and breast cancer mortality with higher versus lower BMI around diagnosis [6]. No significant differences were observed by menopausal status or hormone receptor status. The After Breast Cancer Pooling Project of four prospective cohort studies found differential effects of levels of pre-diagnosis obesity on survival [118]. Compared with normal weight women, significant or borderline significant increased risks of 81% of total and 40% of breast cancer mortality were only observed for morbidly obese (≥40 kg/m2) women and not for women in other obesity categories. We observed statistically significant increased risks also for overweight women, probably because of a larger number of studies. We were unable to investigate the associations with severely and morbidly obese women because only two studies included in this review reported such results [19, 113]. Overall, our findings are consistent with previous meta-analyses in showing elevated total and breast cancer mortality associated with higher BMI and support the current guidelines for breast cancer survivors to stay as lean as possible within the normal range of body weight [4], for overweight women to avoid weight gain during treatment and for obese women to lose weight after treatment [119].\nThe present review is limited by the challenges and flaws encountered by the individual epidemiological studies evaluating the body fatness–mortality relationship in breast cancer survivors. Most studies did not adjust for co-morbidities and assess intentional weight loss. Women with more serious health issues, and especially smokers, may lose weight but are at an increased risk of mortality, and this might cause an apparent increased risk in underweight women. Body weight information through the natural history of the disease and treatment information were usually not complete or available. Increase of body weight post-diagnosis is common in women with breast cancer, particularly during chemotherapy [16]. Chemotherapy under-dosing is a common problem in obese women and may contribute to their increased mortality [120]. Although several studies with pre-diagnosis BMI adjusted for underlying illnesses or excluded the first few years of follow-up, reverse causation may have affected the results in studies that assessed BMI in women with cancer and other illnesses. However, in these studies, the associations were similar to other studies. Possible survival benefit (subjects with better prognostic factors survive) may be present in the survival cohorts, in which the range of BMI could be narrower, and may cause an underestimation of the association.\nFollow-up studies with variable characteristics were pooled in the meta-analysis. Women identified in clinical trials may have had specific tumour subtypes, with fewer co-morbidities, and were more likely to receive protocol treatments with high treatment completion rates. Women who were recruited through mammography screening programmes may have had healthier lifestyles or access to medical facilities, and more likely to be diagnosed with in situ or early-stage breast cancer. Cancer detection methods, tumour classifications and treatment regimens change over time, and may vary within (if follow-up is long) and between studies, and could not be simply examined by using the diagnosis or treatment date. We cannot rule out the effect of unmeasured or residual confounding in our analysis. Nevertheless, most results were adjusted for multiple confounding factors, including tumour stage or other-related variables and stratified analyses by several key factors showed similar summary risk estimates. Small study or publication bias was observed in the analyses of BMI <12 months after diagnosis. However, the overall evidence is supported by large, well-designed studies and is unlikely to be changed. We did not conduct analyses by race/ethnicity and treatment types as only limited studies had published results.\nFuture studies of body fatness and breast cancer outcomes should aim to account for co-morbidities, separate intended and unintended changes of body weight, and collect complete treatment information during study follow-up. Randomised clinical trials are needed to test interventions for weight loss and maintenance on survival in women with breast cancer.\nIn conclusion, the present systematic literature review and meta-analysis extends and confirms the associations of obesity with an unfavourable overall and breast cancer survival in pre- and post-menopausal breast cancer, regardless of when BMI is ascertained. Increased risks of mortality in underweight and overweight women were also observed. Given the comparable elevated risks with obesity in the development (for post-menopausal women) and prognosis of breast cancer, and the complications with cancer treatment and other obesity-related co-morbidities, it is prudent to maintain a healthy body weight (BMI 18.5–<25.0 kg/m2) throughout life.\n\nfunding:\nThis work was supported by the World Cancer Research Fund International (grant number: 2007/SP01) (http://www.wcrf-uk.org/). The funder of this study had no role in the decisions about the design and conduct of the study; collection, management, analysis, or interpretation of the data; or the preparation, review, or approval of the manuscript. The views expressed in this review are the opinions of the authors. They may not represent the views of the World Cancer Research Fund International/American Institute for Cancer Research and may differ from those in future updates of the evidence related to food, nutrition, physical activity, and cancer survival.\n\ndisclosure:\nDCG reports personal fees from World Cancer Research Fund/American Institute for Cancer Research, during the conduct of the study; grants from Danone, and grants from Kelloggs, outside the submitted work. AM reports personal fees from Metagenics/Metaproteomics, personal fees from Pfizer, outside the submitted work. All remaining authors have declared no conflicts of interest.\n\nSupplementary Material:\n\n\n"},"whole_article_abstract":{"kind":"string","value":"Background:\nPositive association between obesity and survival after breast cancer was demonstrated in previous meta-analyses of published data, but only the results for the comparison of obese versus non-obese was summarised.\n\nMethods:\nWe systematically searched in MEDLINE and EMBASE for follow-up studies of breast cancer survivors with body mass index (BMI) before and after diagnosis, and total and cause-specific mortality until June 2013, as part of the World Cancer Research Fund Continuous Update Project. Random-effects meta-analyses were conducted to explore the magnitude and the shape of the associations.\n\nResults:\nEighty-two studies, including 213 075 breast cancer survivors with 41 477 deaths (23 182 from breast cancer) were identified. For BMI before diagnosis, compared with normal weight women, the summary relative risks (RRs) of total mortality were 1.41 [95% confidence interval (CI) 1.29-1.53] for obese (BMI >30.0), 1.07 (95 CI 1.02-1.12) for overweight (BMI 25.0-<30.0) and 1.10 (95% CI 0.92-1.31) for underweight (BMI <18.5) women. For obese women, the summary RRs were 1.75 (95% CI 1.26-2.41) for pre-menopausal and 1.34 (95% CI 1.18-1.53) for post-menopausal breast cancer. For each 5 kg/m(2) increment of BMI before, <12 months after, and ≥12 months after diagnosis, increased risks of 17%, 11%, and 8% for total mortality, and 18%, 14%, and 29% for breast cancer mortality were observed, respectively.\n\nConclusions:\nObesity is associated with poorer overall and breast cancer survival in pre- and post-menopausal breast cancer, regardless of when BMI is ascertained. Being overweight is also related to a higher risk of mortality. Randomised clinical trials are needed to test interventions for weight loss and maintenance on survival in women with breast cancer."},"background_conclusion_text":{"kind":"null"},"background_conclusion_abstract":{"kind":"null"},"whole_article_text_length":{"kind":"number","value":16332,"string":"16,332"},"whole_article_abstract_length":{"kind":"number","value":377,"string":"377"},"other_sections_lengths":{"kind":"list like","value":[115,125,81,861,1120,298,254,1022,273,230,165,464,63,123,66],"string":"[\n 115,\n 125,\n 81,\n 861,\n 1120,\n 298,\n 254,\n 1022,\n 273,\n 230,\n 165,\n 464,\n 63,\n 123,\n 66\n]"},"num_sections":{"kind":"number","value":20,"string":"20"},"most_frequent_words":{"kind":"list like","value":["studies","bmi","women","diagnosis","12","95","ci","95 ci","cancer","mortality"],"string":"[\n \"studies\",\n \"bmi\",\n \"women\",\n \"diagnosis\",\n \"12\",\n \"95\",\n \"ci\",\n \"95 ci\",\n \"cancer\",\n \"mortality\"\n]"},"keybert_topics":{"kind":"list like","value":["cancer mortality obese","breast cancer survivors","menopausal breast cancer","obesity linked breast","menopausal women obesity"],"string":"[\n \"cancer mortality obese\",\n \"breast cancer survivors\",\n \"menopausal breast cancer\",\n \"obesity linked breast\",\n \"menopausal women obesity\"\n]"},"annotated_base_background_abstract_prompt":{"kind":"string","value":""},"annotated_base_methods_abstract_prompt":{"kind":"string","value":""},"annotated_base_results_abstract_prompt":{"kind":"string","value":""},"annotated_base_conclusions_abstract_prompt":{"kind":"null"},"annotated_base_whole_article_abstract_prompt":{"kind":"string","value":""},"annotated_base_background_conclusion_abstract_prompt":{"kind":"null"},"annotated_keywords_background_abstract_prompt":{"kind":"string","value":"[CONTENT] body mass index | meta-analysis | survival after breast cancer | systematic literature review [SUMMARY]"},"annotated_keywords_methods_abstract_prompt":{"kind":"string","value":"[CONTENT] body mass index | meta-analysis | survival after breast cancer | systematic literature review [SUMMARY]"},"annotated_keywords_results_abstract_prompt":{"kind":"string","value":"[CONTENT] body mass index | meta-analysis | survival after breast cancer | systematic literature review [SUMMARY]"},"annotated_keywords_conclusions_abstract_prompt":{"kind":"null"},"annotated_keywords_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] body mass index | meta-analysis | survival after breast cancer | systematic literature review [SUMMARY]"},"annotated_keywords_background_conclusion_abstract_prompt":{"kind":"null"},"annotated_mesh_background_abstract_prompt":{"kind":"string","value":"[CONTENT] Body Mass Index | Breast Neoplasms | Female | Follow-Up Studies | Humans | MEDLINE | Obesity | Prognosis | Randomized Controlled Trials as Topic | Risk Factors | Survivors [SUMMARY]"},"annotated_mesh_methods_abstract_prompt":{"kind":"string","value":"[CONTENT] Body Mass Index | Breast Neoplasms | Female | Follow-Up Studies | Humans | MEDLINE | Obesity | Prognosis | Randomized Controlled Trials as Topic | Risk Factors | Survivors [SUMMARY]"},"annotated_mesh_results_abstract_prompt":{"kind":"string","value":"[CONTENT] Body Mass Index | Breast Neoplasms | Female | Follow-Up Studies | Humans | MEDLINE | Obesity | Prognosis | Randomized Controlled Trials as Topic | Risk Factors | Survivors [SUMMARY]"},"annotated_mesh_conclusions_abstract_prompt":{"kind":"null"},"annotated_mesh_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] Body Mass Index | Breast Neoplasms | Female | Follow-Up Studies | Humans | MEDLINE | Obesity | Prognosis | Randomized Controlled Trials as Topic | Risk Factors | Survivors [SUMMARY]"},"annotated_mesh_background_conclusion_abstract_prompt":{"kind":"null"},"annotated_keybert_background_abstract_prompt":{"kind":"string","value":"[CONTENT] cancer mortality obese | breast cancer survivors | menopausal breast cancer | obesity linked breast | menopausal women obesity [SUMMARY]"},"annotated_keybert_methods_abstract_prompt":{"kind":"string","value":"[CONTENT] cancer mortality obese | breast cancer survivors | menopausal breast cancer | obesity linked breast | menopausal women obesity [SUMMARY]"},"annotated_keybert_results_abstract_prompt":{"kind":"string","value":"[CONTENT] cancer mortality obese | breast cancer survivors | menopausal breast cancer | obesity linked breast | menopausal women obesity [SUMMARY]"},"annotated_keybert_conclusions_abstract_prompt":{"kind":"null"},"annotated_keybert_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] cancer mortality obese | breast cancer survivors | menopausal breast cancer | obesity linked breast | menopausal women obesity [SUMMARY]"},"annotated_keybert_background_conclusion_abstract_prompt":{"kind":"null"},"annotated_most_frequent_background_abstract_prompt":{"kind":"string","value":"[CONTENT] studies | bmi | women | diagnosis | 12 | 95 | ci | 95 ci | cancer | mortality [SUMMARY]"},"annotated_most_frequent_methods_abstract_prompt":{"kind":"string","value":"[CONTENT] studies | bmi | women | diagnosis | 12 | 95 | ci | 95 ci | cancer | mortality [SUMMARY]"},"annotated_most_frequent_results_abstract_prompt":{"kind":"string","value":"[CONTENT] studies | bmi | women | diagnosis | 12 | 95 | ci | 95 ci | cancer | mortality [SUMMARY]"},"annotated_most_frequent_conclusions_abstract_prompt":{"kind":"null"},"annotated_most_frequent_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] studies | bmi | women | diagnosis | 12 | 95 | ci | 95 ci | cancer | mortality [SUMMARY]"},"annotated_most_frequent_background_conclusion_abstract_prompt":{"kind":"null"},"annotated_tf_idf_background_abstract_prompt":{"kind":"string","value":"[CONTENT] breast cancer | breast | obesity | cancer | million | linked | survival | 2008 | women | body [SUMMARY]"},"annotated_tf_idf_methods_abstract_prompt":{"kind":"string","value":"[CONTENT] bmi | study | studies | categories | bmi categories | category | 30 | meta | separately | conducted [SUMMARY]"},"annotated_tf_idf_results_abstract_prompt":{"kind":"string","value":"[CONTENT] 95 ci | studies | 95 | ci | women | bmi | diagnosis | 12 | figure | mortality [SUMMARY]"},"annotated_tf_idf_conclusions_abstract_prompt":{"kind":"null"},"annotated_tf_idf_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] studies | bmi | women | 95 ci | 95 | ci | diagnosis | cancer | 12 | breast [SUMMARY]"},"annotated_tf_idf_background_conclusion_abstract_prompt":{"kind":"null"},"annotated_entity_plan_background_abstract_prompt":{"kind":"string","value":"[CONTENT] obese [SUMMARY]"},"annotated_entity_plan_methods_abstract_prompt":{"kind":"string","value":"[CONTENT] MEDLINE | BMI | June 2013 | the World Cancer Research Fund ||| [SUMMARY]"},"annotated_entity_plan_results_abstract_prompt":{"kind":"string","value":"[CONTENT] Eighty-two | 213 075 | 41 477 | 23 182 ||| BMI | 1.41 | 95% | CI | 1.29-1.53 | BMI | 30.0 | 1.07 | 95 | CI | 1.02-1.12 | BMI | 1.10 | 95% | CI | 0.92-1.31 ||| obese | 1.75 | 95% | CI | 1.26-2.41 | 1.34 | 95% | CI | 1.18-1.53 ||| 5 kg/m(2 | BMI | 12 months | months | 17% | 11% | 8% | 18% | 14% | 29% [SUMMARY]"},"annotated_entity_plan_conclusions_abstract_prompt":{"kind":"null"},"annotated_entity_plan_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] obese ||| MEDLINE | BMI | June 2013 | the World Cancer Research Fund ||| ||| Eighty-two | 213 075 | 41 477 | 23 182 ||| BMI | 1.41 | 95% | CI | 1.29-1.53 | BMI | 30.0 | 1.07 | 95 | CI | 1.02-1.12 | BMI | 1.10 | 95% | CI | 0.92-1.31 ||| obese | 1.75 | 95% | CI | 1.26-2.41 | 1.34 | 95% | CI | 1.18-1.53 ||| 5 kg/m(2 | BMI | 12 months | months | 17% | 11% | 8% | 18% | 14% | 29% ||| BMI ||| ||| [SUMMARY]"},"annotated_entity_plan_background_conclusion_abstract_prompt":{"kind":"null"}}},{"rowIdx":288,"cells":{"title":{"kind":"string","value":"Is application of salt for 3 days locally is sufficient to treat umbilical granuloma?"},"pmid":{"kind":"string","value":"34341201"},"background_abstract":{"kind":"string","value":"The falling of Umbilical stump occurs by 7-15 days of age. The healing of umbilical stump may be complicated by Umbilical Granuloma. It is often treated by chemical cauterisation which require repeated applications and may lead to local or systemic complications. Common salt by way of its dessicative property may help in treatment of Umbilical Granuloma."},"background_abstract_label":{"kind":"string","value":"BACKGROUND"},"methods_abstract":{"kind":"string","value":"This is retrospective study over 3 years from a pediatric surgery unit in Northern India. The study subjects were infants less than 10 weeks of age who presented with umbilical granuloma. The method of salt application was 1 pinch of common salt for 1 hour twice a day for 3 consecutive days. The babies were assessed at day 5th for resolution. The success was defined as thrice resolution after 3 cycles. The baseline demographic details were taken and the association of success of treatment was analyzed."},"methods_abstract_label":{"kind":"string","value":"MATERIALS AND METHODS"},"results_abstract":{"kind":"string","value":"A total of 36 infants were given treatment in form of common salt application for treatment of umbilical granuloma. The success of around 96% and the cases which presented early responded well. Most of the cases resolved after 3 cycles of treatment."},"results_abstract_label":{"kind":"string","value":"RESULTS"},"conclusions_abstract":{"kind":"string","value":"The common salt application is effective in treatment of granuloma without any side effects."},"conclusions_abstract_label":{"kind":"string","value":"CONCLUSION"},"mesh_descriptor_names":{"kind":"list like","value":["Digestive System Abnormalities","Female","Granuloma","Humans","Infant","Infant, Newborn","Male","Retrospective Studies","Treatment Outcome","Wound Healing"],"string":"[\n \"Digestive System Abnormalities\",\n \"Female\",\n \"Granuloma\",\n \"Humans\",\n \"Infant\",\n \"Infant, Newborn\",\n \"Male\",\n \"Retrospective Studies\",\n \"Treatment Outcome\",\n \"Wound Healing\"\n]"},"pmcid":{"kind":"string","value":"8362916"},"background_title":{"kind":"string","value":"INTRODUCTION"},"background_text":{"kind":"string","value":"Umbilical stump separates by 7–15 days postpartum.[1] Umbilical ring is closed by epithelisation after the stump falls from the umbilicus.[2] In few infants, the umbilical ring is not closed properly and develop small velvety, pink to bright red coloured outgrowth at the umbilicus called as umbilical granuloma (UG). It needs attention because of serous discharge and if not treated may lead to infection or mild oozing of blood.[3] Umbilical granuloma is not a congenital abnormality, but it represents granulation tissue which is not properly epithelialised. Histologically, it is composed of fibroblasts and capillaries with no nerves.[1] The spontaneous resolution of umbilical granuloma is not known, hence the early and proper treatment is necessary to avoid potential infective complications.[3] Umbilical Granuloma (UG) can be treated by various modalities and most common modality is chemical cauterisation. The drugs used for chemical cauterisation are silver nitrate sticks,[45] copper sulphate granules,[67] ethanol wipes,[8] doxycycline,[9] clobetasol propionate[1011] and more recently, common salt is used for chemical cauterisation. All chemical agents used for the treatment of UG have shown complete resolution by different authors though each chemical has its own merits and demerits.\nMajor drawback of chemical cauterisation is need of repeated applications and local side effect. Silver nitrate sticks and copper sulphate granules may lead to burning of local skin if not applied properly and require expertise for proper application to get the desired result, hence require a frequent visit to the hospital. Clobetasol propionate does not require any expertise for application and can be safely applied by parents at home. Disadvantage of clobestsol propionate is a long time frame of treatment which is usually of 30 days and being a steroid, it has a potential risk of local and systemic side effect[11] Ethanol wipes and doxycycline are comparatively safe and can be used by parents at home, but their results were not convincing, hence not widely used.\nIn search of safer and effective management of umbilical granuloma, common salt was used by few researchers[1213] based on its desiccative property. Application of salt leads to a higher concentration of sodium ion around UG and draws water out of it, resulting in shrinkage of the granulation tissue. This study was carried out to know the effect of salt on UG and duration of salt (rock salt) treatment to achieve the optimal result."},"methods_title":{"kind":"null"},"methods_text":{"kind":"null"},"results_title":{"kind":"string","value":"RESULTS"},"results_text":{"kind":"string","value":"A total of 36 patients with umbilical granuloma aged between 4 weeks and 20 weeks (except one) were managed during July 2018–December 2019 [Table 1]: In 6 cases, granuloma separated after one complete session of salt application, whereas in 11 cases, it took two complete sessions for the resolution of UG. In most cases, UG was resolved with three cycles of salt application. Two infants required four complete sessions for the resolution of UG [Table 2]. The cases presented early required relatively less number of salt applications than who presented late and this difference was found out to be statistically significant. One case lost to follow-up after second session of salt application, whereas one case not responded to salt application and managed with suture ligation. None of patients had a local complication in terms of skin colour change, skin maceration or excessive cry during salt application. No parent complained for any difficulty or irritability of their infant while applying salt. All parents completed the treatment without difficulty and had satisfactory result except in two cases, one of which lost to follow-up and another which was termed as nonresponder and latter treated by suture ligation technique.\nDemographic profile of study infants. n=36\nChi-square test applied\nResponse in study infants in different age groups. n=36\nFisher’s exact test applied. *Patient lost to follow-up after second cycle and considered as non-responder, #Patient was 18 month old, not responded to the cauterisation with salt and managed with suture ligation"},"conclusions_title":{"kind":"string","value":"CONCLUSION"},"conclusions_text":{"kind":"string","value":"Umbilical granuloma can be effectively managed with common salt, and the best result is obtained if application salt is done at least for 9–12 days.\nFinancial support and sponsorship Nil.\nNil.\nConflicts of interest There are no conflicts of interest.\nThere are no conflicts of interest."},"other_sections_titles":{"kind":"list like","value":["Statistical analysis","Limitation(s)","Financial support and sponsorship"],"string":"[\n \"Statistical analysis\",\n \"Limitation(s)\",\n \"Financial support and sponsorship\"\n]"},"other_sections_texts":{"kind":"list like","value":["Data were entered into Excel and scrutinised for error. Further, categorical variables were expressed in frequency and percentage. Association of categorical variable was assessed by Chi-square/Fisher's exact test. P < 0.05 was considered as statistically significant and analysed by Stata 14 (StataCorp, College Station, Texas, USA).","It was a retrospective study and there was no comparison with any other mode of treatment. The prospective study with three or four arms would have been better because, in the present series, the same patients were subjected to the next cycle once if not resolved with one cycle till completion of the fourth cycle. The contact time of the salt to the UG was subjective because we have to rely on the parents. This retrospective observational study was carried to know the efficacy of common salt/table salt (rock salt) in the management of umbilical granuloma.","Nil."],"string":"[\n \"Data were entered into Excel and scrutinised for error. Further, categorical variables were expressed in frequency and percentage. Association of categorical variable was assessed by Chi-square/Fisher's exact test. P < 0.05 was considered as statistically significant and analysed by Stata 14 (StataCorp, College Station, Texas, USA).\",\n \"It was a retrospective study and there was no comparison with any other mode of treatment. The prospective study with three or four arms would have been better because, in the present series, the same patients were subjected to the next cycle once if not resolved with one cycle till completion of the fourth cycle. The contact time of the salt to the UG was subjective because we have to rely on the parents. This retrospective observational study was carried to know the efficacy of common salt/table salt (rock salt) in the management of umbilical granuloma.\",\n \"Nil.\"\n]"},"other_sections_sec_types":{"kind":"list like","value":[null,null,null],"string":"[\n null,\n null,\n null\n]"},"all_sections_titles":{"kind":"list like","value":["INTRODUCTION","MATERIALS AND METHODS","Statistical analysis","RESULTS","DISCUSSION","Limitation(s)","CONCLUSION","Financial support and sponsorship","Conflicts of interest"],"string":"[\n \"INTRODUCTION\",\n \"MATERIALS AND METHODS\",\n \"Statistical analysis\",\n \"RESULTS\",\n \"DISCUSSION\",\n \"Limitation(s)\",\n \"CONCLUSION\",\n \"Financial support and sponsorship\",\n \"Conflicts of interest\"\n]"},"all_sections_texts":{"kind":"list like","value":["Umbilical stump separates by 7–15 days postpartum.[1] Umbilical ring is closed by epithelisation after the stump falls from the umbilicus.[2] In few infants, the umbilical ring is not closed properly and develop small velvety, pink to bright red coloured outgrowth at the umbilicus called as umbilical granuloma (UG). It needs attention because of serous discharge and if not treated may lead to infection or mild oozing of blood.[3] Umbilical granuloma is not a congenital abnormality, but it represents granulation tissue which is not properly epithelialised. Histologically, it is composed of fibroblasts and capillaries with no nerves.[1] The spontaneous resolution of umbilical granuloma is not known, hence the early and proper treatment is necessary to avoid potential infective complications.[3] Umbilical Granuloma (UG) can be treated by various modalities and most common modality is chemical cauterisation. The drugs used for chemical cauterisation are silver nitrate sticks,[45] copper sulphate granules,[67] ethanol wipes,[8] doxycycline,[9] clobetasol propionate[1011] and more recently, common salt is used for chemical cauterisation. All chemical agents used for the treatment of UG have shown complete resolution by different authors though each chemical has its own merits and demerits.\nMajor drawback of chemical cauterisation is need of repeated applications and local side effect. Silver nitrate sticks and copper sulphate granules may lead to burning of local skin if not applied properly and require expertise for proper application to get the desired result, hence require a frequent visit to the hospital. Clobetasol propionate does not require any expertise for application and can be safely applied by parents at home. Disadvantage of clobestsol propionate is a long time frame of treatment which is usually of 30 days and being a steroid, it has a potential risk of local and systemic side effect[11] Ethanol wipes and doxycycline are comparatively safe and can be used by parents at home, but their results were not convincing, hence not widely used.\nIn search of safer and effective management of umbilical granuloma, common salt was used by few researchers[1213] based on its desiccative property. Application of salt leads to a higher concentration of sodium ion around UG and draws water out of it, resulting in shrinkage of the granulation tissue. This study was carried out to know the effect of salt on UG and duration of salt (rock salt) treatment to achieve the optimal result.","This was a retrospective study performed at paediatric surgery outpatient clinic, tertiary centre in North India, from January 2015 to December 2018.\nAs per the record, a total of 40 infants of UG presented to the paediatric surgery outpatient department, of which only 36 were managed with salt application. Four patients already received some treatment from the general practitioner, hence were not included in the study.\nAs per the record, the procedure followed for salt application was (i) lesion cleaned with lukewarm water soaked cotton ball [Figure 1], (ii) pinch of rock salt sprinkled over lesion and removed after 1 h and (iii) procedure properly taught and explained to parent of the infant to repeat at home twice daily for 3 days and review on 5th day. This is one complete session of salt application. At the first review that is on 5th day if UG resolved, it was considered as cured and the patient was reviewed after 1 month. If UG was not resolved at the first visit, parents are advised to repeat the same procedure. As per the record, application of salt twice daily for 1 h for 3 consecutive days was considered as one complete cycle.\nShowing the effect of slat application\nAs per the records, the patients were levelled nonresponsive if they do not respond to four cycles of salt application and were managed with suture ligation technique. All patients were followed for 12 weeks.\n\nCured: If granuloma was separated completely displaying normal skin cover and without any serous discharge,Nonresponsive: No change in the nature of the disease\n\nCured: If granuloma was separated completely displaying normal skin cover and without any serous discharge,\nNonresponsive: No change in the nature of the disease\nThe following parameters were retrieved from the record at each visit: (1) change in colour of lesion, (2) change in size of the lesion, (3) soakage if any from the lesion, (4) excessive crying during salt application and (7) any skin change. Parents did not report any behavioural changes, sleep disturbances or irritability in the infants after the application (though the first application is done by the treating clinician itself.)\nStatistical analysis Data were entered into Excel and scrutinised for error. Further, categorical variables were expressed in frequency and percentage. Association of categorical variable was assessed by Chi-square/Fisher's exact test. P < 0.05 was considered as statistically significant and analysed by Stata 14 (StataCorp, College Station, Texas, USA).\nData were entered into Excel and scrutinised for error. Further, categorical variables were expressed in frequency and percentage. Association of categorical variable was assessed by Chi-square/Fisher's exact test. P < 0.05 was considered as statistically significant and analysed by Stata 14 (StataCorp, College Station, Texas, USA).","Data were entered into Excel and scrutinised for error. Further, categorical variables were expressed in frequency and percentage. Association of categorical variable was assessed by Chi-square/Fisher's exact test. P < 0.05 was considered as statistically significant and analysed by Stata 14 (StataCorp, College Station, Texas, USA).","A total of 36 patients with umbilical granuloma aged between 4 weeks and 20 weeks (except one) were managed during July 2018–December 2019 [Table 1]: In 6 cases, granuloma separated after one complete session of salt application, whereas in 11 cases, it took two complete sessions for the resolution of UG. In most cases, UG was resolved with three cycles of salt application. Two infants required four complete sessions for the resolution of UG [Table 2]. The cases presented early required relatively less number of salt applications than who presented late and this difference was found out to be statistically significant. One case lost to follow-up after second session of salt application, whereas one case not responded to salt application and managed with suture ligation. None of patients had a local complication in terms of skin colour change, skin maceration or excessive cry during salt application. No parent complained for any difficulty or irritability of their infant while applying salt. All parents completed the treatment without difficulty and had satisfactory result except in two cases, one of which lost to follow-up and another which was termed as nonresponder and latter treated by suture ligation technique.\nDemographic profile of study infants. n=36\nChi-square test applied\nResponse in study infants in different age groups. n=36\nFisher’s exact test applied. *Patient lost to follow-up after second cycle and considered as non-responder, #Patient was 18 month old, not responded to the cauterisation with salt and managed with suture ligation","UG is relatively a common condition with no recognised associated anomalies. Chemical cauterisation is the most common method to treat UG. Few other umbilical conditions may present in a similar manner which are difficult to distinguish from UG like patent urachus and patent vitello-intestinal duct and should not be treated with chemical cauterisation. Therefore adequate assessment of the discharge and swelling of the umbilicus should be done to minimise diagnostic errors and delay in proper management.\nApplication of common salt for treating umbilical granuloma has been tried in the past and used at some centres in India[1314] for the management of UG. A review of the English literature revealed the following steps for salt application: (i) cleaning of the umbilical area with a wet cotton pad, (ii) pinch of salt crystals is sprinkled on the granuloma, (iii) granuloma is closed with an adhesive drape, (iv) drape is opened 30 min after the procedure and the application procedure is terminated and (v) this process was repeated two times a day for 3 days and the patient was reviewed on the 6th day.[15]\nThe procedure of salt application was modification which was done by either increasing the days of application or increase the time of contact of salt to the granuloma for better results. It ranged from single application where salt remained for 24 h[16] to 12 application of salt over 7 consecutive days where salt was removed after 1 h of each application.[14] Few studies adopted the covering of the granuloma site after application of salt using adhesive tape for ensuring proper contact of the salt to the granuloma,[161718] whereas others left the site open to prevent possible sogginess around the granuloma.[141719]\nMost of the published series on chemical cauterisation of UG using common salt had excellent results[141718] and only in few results were not convincing.[67] The overall efficacy of salt to treat UG ranged from 53% to 100%, as reported in different studies.[13141516171819] It was not clear how many days of treatment is required for optimum result; current studies used salt in a phased manner and determined the time and number of salt application for the resolution of UG.\nIn the present study, salt was used as the primary agent for the cauterisation of UG with slight modification of standard technique based on the fact that local and systemic side effects of salt are negligible, its application can be done by the parents easily at their home if properly demonstrated to them, duration of treatment is short and last but not the least was the low cost and easy availability of the salt. Modification of the standard procedure (vide above 16) done in the present study was (a) contact time of salt was made to 1 h instead of 30 min at each application and (b) patients were not marked as nonresponsive if UG was not resolved after application of salt for 3 consecutive days (one cycle). The patients were asked to apply at least for three more cycles of salt application before marking them as no responder. With this modification, complete resolution of UG was achieved in 96% cases and only one case who was nonresponsive to cauterisation with salt was 18 months.\nLimitation(s) It was a retrospective study and there was no comparison with any other mode of treatment. The prospective study with three or four arms would have been better because, in the present series, the same patients were subjected to the next cycle once if not resolved with one cycle till completion of the fourth cycle. The contact time of the salt to the UG was subjective because we have to rely on the parents. This retrospective observational study was carried to know the efficacy of common salt/table salt (rock salt) in the management of umbilical granuloma.\nIt was a retrospective study and there was no comparison with any other mode of treatment. The prospective study with three or four arms would have been better because, in the present series, the same patients were subjected to the next cycle once if not resolved with one cycle till completion of the fourth cycle. The contact time of the salt to the UG was subjective because we have to rely on the parents. This retrospective observational study was carried to know the efficacy of common salt/table salt (rock salt) in the management of umbilical granuloma.","It was a retrospective study and there was no comparison with any other mode of treatment. The prospective study with three or four arms would have been better because, in the present series, the same patients were subjected to the next cycle once if not resolved with one cycle till completion of the fourth cycle. The contact time of the salt to the UG was subjective because we have to rely on the parents. This retrospective observational study was carried to know the efficacy of common salt/table salt (rock salt) in the management of umbilical granuloma.","Umbilical granuloma can be effectively managed with common salt, and the best result is obtained if application salt is done at least for 9–12 days.\nFinancial support and sponsorship Nil.\nNil.\nConflicts of interest There are no conflicts of interest.\nThere are no conflicts of interest.","Nil.","There are no conflicts of interest."],"string":"[\n \"Umbilical stump separates by 7–15 days postpartum.[1] Umbilical ring is closed by epithelisation after the stump falls from the umbilicus.[2] In few infants, the umbilical ring is not closed properly and develop small velvety, pink to bright red coloured outgrowth at the umbilicus called as umbilical granuloma (UG). It needs attention because of serous discharge and if not treated may lead to infection or mild oozing of blood.[3] Umbilical granuloma is not a congenital abnormality, but it represents granulation tissue which is not properly epithelialised. Histologically, it is composed of fibroblasts and capillaries with no nerves.[1] The spontaneous resolution of umbilical granuloma is not known, hence the early and proper treatment is necessary to avoid potential infective complications.[3] Umbilical Granuloma (UG) can be treated by various modalities and most common modality is chemical cauterisation. The drugs used for chemical cauterisation are silver nitrate sticks,[45] copper sulphate granules,[67] ethanol wipes,[8] doxycycline,[9] clobetasol propionate[1011] and more recently, common salt is used for chemical cauterisation. All chemical agents used for the treatment of UG have shown complete resolution by different authors though each chemical has its own merits and demerits.\\nMajor drawback of chemical cauterisation is need of repeated applications and local side effect. Silver nitrate sticks and copper sulphate granules may lead to burning of local skin if not applied properly and require expertise for proper application to get the desired result, hence require a frequent visit to the hospital. Clobetasol propionate does not require any expertise for application and can be safely applied by parents at home. Disadvantage of clobestsol propionate is a long time frame of treatment which is usually of 30 days and being a steroid, it has a potential risk of local and systemic side effect[11] Ethanol wipes and doxycycline are comparatively safe and can be used by parents at home, but their results were not convincing, hence not widely used.\\nIn search of safer and effective management of umbilical granuloma, common salt was used by few researchers[1213] based on its desiccative property. Application of salt leads to a higher concentration of sodium ion around UG and draws water out of it, resulting in shrinkage of the granulation tissue. This study was carried out to know the effect of salt on UG and duration of salt (rock salt) treatment to achieve the optimal result.\",\n \"This was a retrospective study performed at paediatric surgery outpatient clinic, tertiary centre in North India, from January 2015 to December 2018.\\nAs per the record, a total of 40 infants of UG presented to the paediatric surgery outpatient department, of which only 36 were managed with salt application. Four patients already received some treatment from the general practitioner, hence were not included in the study.\\nAs per the record, the procedure followed for salt application was (i) lesion cleaned with lukewarm water soaked cotton ball [Figure 1], (ii) pinch of rock salt sprinkled over lesion and removed after 1 h and (iii) procedure properly taught and explained to parent of the infant to repeat at home twice daily for 3 days and review on 5th day. This is one complete session of salt application. At the first review that is on 5th day if UG resolved, it was considered as cured and the patient was reviewed after 1 month. If UG was not resolved at the first visit, parents are advised to repeat the same procedure. As per the record, application of salt twice daily for 1 h for 3 consecutive days was considered as one complete cycle.\\nShowing the effect of slat application\\nAs per the records, the patients were levelled nonresponsive if they do not respond to four cycles of salt application and were managed with suture ligation technique. All patients were followed for 12 weeks.\\n\\nCured: If granuloma was separated completely displaying normal skin cover and without any serous discharge,Nonresponsive: No change in the nature of the disease\\n\\nCured: If granuloma was separated completely displaying normal skin cover and without any serous discharge,\\nNonresponsive: No change in the nature of the disease\\nThe following parameters were retrieved from the record at each visit: (1) change in colour of lesion, (2) change in size of the lesion, (3) soakage if any from the lesion, (4) excessive crying during salt application and (7) any skin change. Parents did not report any behavioural changes, sleep disturbances or irritability in the infants after the application (though the first application is done by the treating clinician itself.)\\nStatistical analysis Data were entered into Excel and scrutinised for error. Further, categorical variables were expressed in frequency and percentage. Association of categorical variable was assessed by Chi-square/Fisher's exact test. P < 0.05 was considered as statistically significant and analysed by Stata 14 (StataCorp, College Station, Texas, USA).\\nData were entered into Excel and scrutinised for error. Further, categorical variables were expressed in frequency and percentage. Association of categorical variable was assessed by Chi-square/Fisher's exact test. P < 0.05 was considered as statistically significant and analysed by Stata 14 (StataCorp, College Station, Texas, USA).\",\n \"Data were entered into Excel and scrutinised for error. Further, categorical variables were expressed in frequency and percentage. Association of categorical variable was assessed by Chi-square/Fisher's exact test. P < 0.05 was considered as statistically significant and analysed by Stata 14 (StataCorp, College Station, Texas, USA).\",\n \"A total of 36 patients with umbilical granuloma aged between 4 weeks and 20 weeks (except one) were managed during July 2018–December 2019 [Table 1]: In 6 cases, granuloma separated after one complete session of salt application, whereas in 11 cases, it took two complete sessions for the resolution of UG. In most cases, UG was resolved with three cycles of salt application. Two infants required four complete sessions for the resolution of UG [Table 2]. The cases presented early required relatively less number of salt applications than who presented late and this difference was found out to be statistically significant. One case lost to follow-up after second session of salt application, whereas one case not responded to salt application and managed with suture ligation. None of patients had a local complication in terms of skin colour change, skin maceration or excessive cry during salt application. No parent complained for any difficulty or irritability of their infant while applying salt. All parents completed the treatment without difficulty and had satisfactory result except in two cases, one of which lost to follow-up and another which was termed as nonresponder and latter treated by suture ligation technique.\\nDemographic profile of study infants. n=36\\nChi-square test applied\\nResponse in study infants in different age groups. n=36\\nFisher’s exact test applied. *Patient lost to follow-up after second cycle and considered as non-responder, #Patient was 18 month old, not responded to the cauterisation with salt and managed with suture ligation\",\n \"UG is relatively a common condition with no recognised associated anomalies. Chemical cauterisation is the most common method to treat UG. Few other umbilical conditions may present in a similar manner which are difficult to distinguish from UG like patent urachus and patent vitello-intestinal duct and should not be treated with chemical cauterisation. Therefore adequate assessment of the discharge and swelling of the umbilicus should be done to minimise diagnostic errors and delay in proper management.\\nApplication of common salt for treating umbilical granuloma has been tried in the past and used at some centres in India[1314] for the management of UG. A review of the English literature revealed the following steps for salt application: (i) cleaning of the umbilical area with a wet cotton pad, (ii) pinch of salt crystals is sprinkled on the granuloma, (iii) granuloma is closed with an adhesive drape, (iv) drape is opened 30 min after the procedure and the application procedure is terminated and (v) this process was repeated two times a day for 3 days and the patient was reviewed on the 6th day.[15]\\nThe procedure of salt application was modification which was done by either increasing the days of application or increase the time of contact of salt to the granuloma for better results. It ranged from single application where salt remained for 24 h[16] to 12 application of salt over 7 consecutive days where salt was removed after 1 h of each application.[14] Few studies adopted the covering of the granuloma site after application of salt using adhesive tape for ensuring proper contact of the salt to the granuloma,[161718] whereas others left the site open to prevent possible sogginess around the granuloma.[141719]\\nMost of the published series on chemical cauterisation of UG using common salt had excellent results[141718] and only in few results were not convincing.[67] The overall efficacy of salt to treat UG ranged from 53% to 100%, as reported in different studies.[13141516171819] It was not clear how many days of treatment is required for optimum result; current studies used salt in a phased manner and determined the time and number of salt application for the resolution of UG.\\nIn the present study, salt was used as the primary agent for the cauterisation of UG with slight modification of standard technique based on the fact that local and systemic side effects of salt are negligible, its application can be done by the parents easily at their home if properly demonstrated to them, duration of treatment is short and last but not the least was the low cost and easy availability of the salt. Modification of the standard procedure (vide above 16) done in the present study was (a) contact time of salt was made to 1 h instead of 30 min at each application and (b) patients were not marked as nonresponsive if UG was not resolved after application of salt for 3 consecutive days (one cycle). The patients were asked to apply at least for three more cycles of salt application before marking them as no responder. With this modification, complete resolution of UG was achieved in 96% cases and only one case who was nonresponsive to cauterisation with salt was 18 months.\\nLimitation(s) It was a retrospective study and there was no comparison with any other mode of treatment. The prospective study with three or four arms would have been better because, in the present series, the same patients were subjected to the next cycle once if not resolved with one cycle till completion of the fourth cycle. The contact time of the salt to the UG was subjective because we have to rely on the parents. This retrospective observational study was carried to know the efficacy of common salt/table salt (rock salt) in the management of umbilical granuloma.\\nIt was a retrospective study and there was no comparison with any other mode of treatment. The prospective study with three or four arms would have been better because, in the present series, the same patients were subjected to the next cycle once if not resolved with one cycle till completion of the fourth cycle. The contact time of the salt to the UG was subjective because we have to rely on the parents. This retrospective observational study was carried to know the efficacy of common salt/table salt (rock salt) in the management of umbilical granuloma.\",\n \"It was a retrospective study and there was no comparison with any other mode of treatment. The prospective study with three or four arms would have been better because, in the present series, the same patients were subjected to the next cycle once if not resolved with one cycle till completion of the fourth cycle. The contact time of the salt to the UG was subjective because we have to rely on the parents. This retrospective observational study was carried to know the efficacy of common salt/table salt (rock salt) in the management of umbilical granuloma.\",\n \"Umbilical granuloma can be effectively managed with common salt, and the best result is obtained if application salt is done at least for 9–12 days.\\nFinancial support and sponsorship Nil.\\nNil.\\nConflicts of interest There are no conflicts of interest.\\nThere are no conflicts of interest.\",\n \"Nil.\",\n \"There are no conflicts of interest.\"\n]"},"all_sections_sec_types":{"kind":"list like","value":["intro","materials|methods",null,"results","discussion",null,"conclusion",null,"COI-statement"],"string":"[\n \"intro\",\n \"materials|methods\",\n null,\n \"results\",\n \"discussion\",\n null,\n \"conclusion\",\n null,\n \"COI-statement\"\n]"},"keywords":{"kind":"list like","value":["Chemical cauterisation","persistent discharge","umbilical polyp"],"string":"[\n \"Chemical cauterisation\",\n \"persistent discharge\",\n \"umbilical polyp\"\n]"},"whole_article_text":{"kind":"string","value":"INTRODUCTION:\nUmbilical stump separates by 7–15 days postpartum.[1] Umbilical ring is closed by epithelisation after the stump falls from the umbilicus.[2] In few infants, the umbilical ring is not closed properly and develop small velvety, pink to bright red coloured outgrowth at the umbilicus called as umbilical granuloma (UG). It needs attention because of serous discharge and if not treated may lead to infection or mild oozing of blood.[3] Umbilical granuloma is not a congenital abnormality, but it represents granulation tissue which is not properly epithelialised. Histologically, it is composed of fibroblasts and capillaries with no nerves.[1] The spontaneous resolution of umbilical granuloma is not known, hence the early and proper treatment is necessary to avoid potential infective complications.[3] Umbilical Granuloma (UG) can be treated by various modalities and most common modality is chemical cauterisation. The drugs used for chemical cauterisation are silver nitrate sticks,[45] copper sulphate granules,[67] ethanol wipes,[8] doxycycline,[9] clobetasol propionate[1011] and more recently, common salt is used for chemical cauterisation. All chemical agents used for the treatment of UG have shown complete resolution by different authors though each chemical has its own merits and demerits.\nMajor drawback of chemical cauterisation is need of repeated applications and local side effect. Silver nitrate sticks and copper sulphate granules may lead to burning of local skin if not applied properly and require expertise for proper application to get the desired result, hence require a frequent visit to the hospital. Clobetasol propionate does not require any expertise for application and can be safely applied by parents at home. Disadvantage of clobestsol propionate is a long time frame of treatment which is usually of 30 days and being a steroid, it has a potential risk of local and systemic side effect[11] Ethanol wipes and doxycycline are comparatively safe and can be used by parents at home, but their results were not convincing, hence not widely used.\nIn search of safer and effective management of umbilical granuloma, common salt was used by few researchers[1213] based on its desiccative property. Application of salt leads to a higher concentration of sodium ion around UG and draws water out of it, resulting in shrinkage of the granulation tissue. This study was carried out to know the effect of salt on UG and duration of salt (rock salt) treatment to achieve the optimal result.\n\nMATERIALS AND METHODS:\nThis was a retrospective study performed at paediatric surgery outpatient clinic, tertiary centre in North India, from January 2015 to December 2018.\nAs per the record, a total of 40 infants of UG presented to the paediatric surgery outpatient department, of which only 36 were managed with salt application. Four patients already received some treatment from the general practitioner, hence were not included in the study.\nAs per the record, the procedure followed for salt application was (i) lesion cleaned with lukewarm water soaked cotton ball [Figure 1], (ii) pinch of rock salt sprinkled over lesion and removed after 1 h and (iii) procedure properly taught and explained to parent of the infant to repeat at home twice daily for 3 days and review on 5th day. This is one complete session of salt application. At the first review that is on 5th day if UG resolved, it was considered as cured and the patient was reviewed after 1 month. If UG was not resolved at the first visit, parents are advised to repeat the same procedure. As per the record, application of salt twice daily for 1 h for 3 consecutive days was considered as one complete cycle.\nShowing the effect of slat application\nAs per the records, the patients were levelled nonresponsive if they do not respond to four cycles of salt application and were managed with suture ligation technique. All patients were followed for 12 weeks.\n\nCured: If granuloma was separated completely displaying normal skin cover and without any serous discharge,Nonresponsive: No change in the nature of the disease\n\nCured: If granuloma was separated completely displaying normal skin cover and without any serous discharge,\nNonresponsive: No change in the nature of the disease\nThe following parameters were retrieved from the record at each visit: (1) change in colour of lesion, (2) change in size of the lesion, (3) soakage if any from the lesion, (4) excessive crying during salt application and (7) any skin change. Parents did not report any behavioural changes, sleep disturbances or irritability in the infants after the application (though the first application is done by the treating clinician itself.)\nStatistical analysis Data were entered into Excel and scrutinised for error. Further, categorical variables were expressed in frequency and percentage. Association of categorical variable was assessed by Chi-square/Fisher's exact test. P < 0.05 was considered as statistically significant and analysed by Stata 14 (StataCorp, College Station, Texas, USA).\nData were entered into Excel and scrutinised for error. Further, categorical variables were expressed in frequency and percentage. Association of categorical variable was assessed by Chi-square/Fisher's exact test. P < 0.05 was considered as statistically significant and analysed by Stata 14 (StataCorp, College Station, Texas, USA).\n\nStatistical analysis:\nData were entered into Excel and scrutinised for error. Further, categorical variables were expressed in frequency and percentage. Association of categorical variable was assessed by Chi-square/Fisher's exact test. P < 0.05 was considered as statistically significant and analysed by Stata 14 (StataCorp, College Station, Texas, USA).\n\nRESULTS:\nA total of 36 patients with umbilical granuloma aged between 4 weeks and 20 weeks (except one) were managed during July 2018–December 2019 [Table 1]: In 6 cases, granuloma separated after one complete session of salt application, whereas in 11 cases, it took two complete sessions for the resolution of UG. In most cases, UG was resolved with three cycles of salt application. Two infants required four complete sessions for the resolution of UG [Table 2]. The cases presented early required relatively less number of salt applications than who presented late and this difference was found out to be statistically significant. One case lost to follow-up after second session of salt application, whereas one case not responded to salt application and managed with suture ligation. None of patients had a local complication in terms of skin colour change, skin maceration or excessive cry during salt application. No parent complained for any difficulty or irritability of their infant while applying salt. All parents completed the treatment without difficulty and had satisfactory result except in two cases, one of which lost to follow-up and another which was termed as nonresponder and latter treated by suture ligation technique.\nDemographic profile of study infants. n=36\nChi-square test applied\nResponse in study infants in different age groups. n=36\nFisher’s exact test applied. *Patient lost to follow-up after second cycle and considered as non-responder, #Patient was 18 month old, not responded to the cauterisation with salt and managed with suture ligation\n\nDISCUSSION:\nUG is relatively a common condition with no recognised associated anomalies. Chemical cauterisation is the most common method to treat UG. Few other umbilical conditions may present in a similar manner which are difficult to distinguish from UG like patent urachus and patent vitello-intestinal duct and should not be treated with chemical cauterisation. Therefore adequate assessment of the discharge and swelling of the umbilicus should be done to minimise diagnostic errors and delay in proper management.\nApplication of common salt for treating umbilical granuloma has been tried in the past and used at some centres in India[1314] for the management of UG. A review of the English literature revealed the following steps for salt application: (i) cleaning of the umbilical area with a wet cotton pad, (ii) pinch of salt crystals is sprinkled on the granuloma, (iii) granuloma is closed with an adhesive drape, (iv) drape is opened 30 min after the procedure and the application procedure is terminated and (v) this process was repeated two times a day for 3 days and the patient was reviewed on the 6th day.[15]\nThe procedure of salt application was modification which was done by either increasing the days of application or increase the time of contact of salt to the granuloma for better results. It ranged from single application where salt remained for 24 h[16] to 12 application of salt over 7 consecutive days where salt was removed after 1 h of each application.[14] Few studies adopted the covering of the granuloma site after application of salt using adhesive tape for ensuring proper contact of the salt to the granuloma,[161718] whereas others left the site open to prevent possible sogginess around the granuloma.[141719]\nMost of the published series on chemical cauterisation of UG using common salt had excellent results[141718] and only in few results were not convincing.[67] The overall efficacy of salt to treat UG ranged from 53% to 100%, as reported in different studies.[13141516171819] It was not clear how many days of treatment is required for optimum result; current studies used salt in a phased manner and determined the time and number of salt application for the resolution of UG.\nIn the present study, salt was used as the primary agent for the cauterisation of UG with slight modification of standard technique based on the fact that local and systemic side effects of salt are negligible, its application can be done by the parents easily at their home if properly demonstrated to them, duration of treatment is short and last but not the least was the low cost and easy availability of the salt. Modification of the standard procedure (vide above 16) done in the present study was (a) contact time of salt was made to 1 h instead of 30 min at each application and (b) patients were not marked as nonresponsive if UG was not resolved after application of salt for 3 consecutive days (one cycle). The patients were asked to apply at least for three more cycles of salt application before marking them as no responder. With this modification, complete resolution of UG was achieved in 96% cases and only one case who was nonresponsive to cauterisation with salt was 18 months.\nLimitation(s) It was a retrospective study and there was no comparison with any other mode of treatment. The prospective study with three or four arms would have been better because, in the present series, the same patients were subjected to the next cycle once if not resolved with one cycle till completion of the fourth cycle. The contact time of the salt to the UG was subjective because we have to rely on the parents. This retrospective observational study was carried to know the efficacy of common salt/table salt (rock salt) in the management of umbilical granuloma.\nIt was a retrospective study and there was no comparison with any other mode of treatment. The prospective study with three or four arms would have been better because, in the present series, the same patients were subjected to the next cycle once if not resolved with one cycle till completion of the fourth cycle. The contact time of the salt to the UG was subjective because we have to rely on the parents. This retrospective observational study was carried to know the efficacy of common salt/table salt (rock salt) in the management of umbilical granuloma.\n\nLimitation(s):\nIt was a retrospective study and there was no comparison with any other mode of treatment. The prospective study with three or four arms would have been better because, in the present series, the same patients were subjected to the next cycle once if not resolved with one cycle till completion of the fourth cycle. The contact time of the salt to the UG was subjective because we have to rely on the parents. This retrospective observational study was carried to know the efficacy of common salt/table salt (rock salt) in the management of umbilical granuloma.\n\nCONCLUSION:\nUmbilical granuloma can be effectively managed with common salt, and the best result is obtained if application salt is done at least for 9–12 days.\nFinancial support and sponsorship Nil.\nNil.\nConflicts of interest There are no conflicts of interest.\nThere are no conflicts of interest.\n\nFinancial support and sponsorship:\nNil.\n\nConflicts of interest:\nThere are no conflicts of interest.\n\n"},"whole_article_abstract":{"kind":"string","value":"Background:\nThe falling of Umbilical stump occurs by 7-15 days of age. The healing of umbilical stump may be complicated by Umbilical Granuloma. It is often treated by chemical cauterisation which require repeated applications and may lead to local or systemic complications. Common salt by way of its dessicative property may help in treatment of Umbilical Granuloma.\n\nMethods:\nThis is retrospective study over 3 years from a pediatric surgery unit in Northern India. The study subjects were infants less than 10 weeks of age who presented with umbilical granuloma. The method of salt application was 1 pinch of common salt for 1 hour twice a day for 3 consecutive days. The babies were assessed at day 5th for resolution. The success was defined as thrice resolution after 3 cycles. The baseline demographic details were taken and the association of success of treatment was analyzed.\n\nResults:\nA total of 36 infants were given treatment in form of common salt application for treatment of umbilical granuloma. The success of around 96% and the cases which presented early responded well. Most of the cases resolved after 3 cycles of treatment.\n\nConclusions:\nThe common salt application is effective in treatment of granuloma without any side effects."},"background_conclusion_text":{"kind":"string","value":"INTRODUCTION:\nUmbilical stump separates by 7–15 days postpartum.[1] Umbilical ring is closed by epithelisation after the stump falls from the umbilicus.[2] In few infants, the umbilical ring is not closed properly and develop small velvety, pink to bright red coloured outgrowth at the umbilicus called as umbilical granuloma (UG). It needs attention because of serous discharge and if not treated may lead to infection or mild oozing of blood.[3] Umbilical granuloma is not a congenital abnormality, but it represents granulation tissue which is not properly epithelialised. Histologically, it is composed of fibroblasts and capillaries with no nerves.[1] The spontaneous resolution of umbilical granuloma is not known, hence the early and proper treatment is necessary to avoid potential infective complications.[3] Umbilical Granuloma (UG) can be treated by various modalities and most common modality is chemical cauterisation. The drugs used for chemical cauterisation are silver nitrate sticks,[45] copper sulphate granules,[67] ethanol wipes,[8] doxycycline,[9] clobetasol propionate[1011] and more recently, common salt is used for chemical cauterisation. All chemical agents used for the treatment of UG have shown complete resolution by different authors though each chemical has its own merits and demerits.\nMajor drawback of chemical cauterisation is need of repeated applications and local side effect. Silver nitrate sticks and copper sulphate granules may lead to burning of local skin if not applied properly and require expertise for proper application to get the desired result, hence require a frequent visit to the hospital. Clobetasol propionate does not require any expertise for application and can be safely applied by parents at home. Disadvantage of clobestsol propionate is a long time frame of treatment which is usually of 30 days and being a steroid, it has a potential risk of local and systemic side effect[11] Ethanol wipes and doxycycline are comparatively safe and can be used by parents at home, but their results were not convincing, hence not widely used.\nIn search of safer and effective management of umbilical granuloma, common salt was used by few researchers[1213] based on its desiccative property. Application of salt leads to a higher concentration of sodium ion around UG and draws water out of it, resulting in shrinkage of the granulation tissue. This study was carried out to know the effect of salt on UG and duration of salt (rock salt) treatment to achieve the optimal result.\n\nCONCLUSION:\nUmbilical granuloma can be effectively managed with common salt, and the best result is obtained if application salt is done at least for 9–12 days.\nFinancial support and sponsorship Nil.\nNil.\nConflicts of interest There are no conflicts of interest.\nThere are no conflicts of interest."},"background_conclusion_abstract":{"kind":"string","value":"Background:\nThe falling of Umbilical stump occurs by 7-15 days of age. The healing of umbilical stump may be complicated by Umbilical Granuloma. It is often treated by chemical cauterisation which require repeated applications and may lead to local or systemic complications. Common salt by way of its dessicative property may help in treatment of Umbilical Granuloma.\n\nMethods:\nThis is retrospective study over 3 years from a pediatric surgery unit in Northern India. The study subjects were infants less than 10 weeks of age who presented with umbilical granuloma. The method of salt application was 1 pinch of common salt for 1 hour twice a day for 3 consecutive days. The babies were assessed at day 5th for resolution. The success was defined as thrice resolution after 3 cycles. The baseline demographic details were taken and the association of success of treatment was analyzed.\n\nResults:\nA total of 36 infants were given treatment in form of common salt application for treatment of umbilical granuloma. The success of around 96% and the cases which presented early responded well. Most of the cases resolved after 3 cycles of treatment.\n\nConclusions:\nThe common salt application is effective in treatment of granuloma without any side effects."},"whole_article_text_length":{"kind":"number","value":2349,"string":"2,349"},"whole_article_abstract_length":{"kind":"number","value":230,"string":"230"},"other_sections_lengths":{"kind":"list like","value":[61,106,2],"string":"[\n 61,\n 106,\n 2\n]"},"num_sections":{"kind":"number","value":9,"string":"9"},"most_frequent_words":{"kind":"list like","value":["salt","application","ug","granuloma","umbilical","study","salt application","cycle","umbilical granuloma","treatment"],"string":"[\n \"salt\",\n \"application\",\n \"ug\",\n \"granuloma\",\n \"umbilical\",\n \"study\",\n \"salt application\",\n \"cycle\",\n \"umbilical granuloma\",\n \"treatment\"\n]"},"keybert_topics":{"kind":"list like","value":["umbilical granuloma tried","umbilical granuloma known","umbilical granuloma congenital","treating umbilical 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Newborn | Male | Retrospective Studies | Treatment Outcome | Wound Healing [SUMMARY]"},"annotated_mesh_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] Digestive System Abnormalities | Female | Granuloma | Humans | Infant | Infant, Newborn | Male | Retrospective Studies | Treatment Outcome | Wound Healing [SUMMARY]"},"annotated_mesh_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] Digestive System Abnormalities | Female | Granuloma | Humans | Infant | Infant, Newborn | Male | Retrospective Studies | Treatment Outcome | Wound Healing [SUMMARY]"},"annotated_mesh_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] Digestive System Abnormalities | Female | Granuloma | Humans | Infant | Infant, Newborn | Male | Retrospective Studies | Treatment Outcome | Wound Healing [SUMMARY]"},"annotated_keybert_background_abstract_prompt":{"kind":"string","value":"[CONTENT] umbilical granuloma tried | umbilical granuloma known | umbilical 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granuloma known | umbilical granuloma congenital | treating umbilical granuloma | umbilical granuloma ug [SUMMARY]"},"annotated_most_frequent_background_abstract_prompt":{"kind":"string","value":"[CONTENT] salt | application | ug | granuloma | umbilical | study | salt application | cycle | umbilical granuloma | treatment [SUMMARY]"},"annotated_most_frequent_methods_abstract_prompt":{"kind":"null"},"annotated_most_frequent_results_abstract_prompt":{"kind":"string","value":"[CONTENT] salt | application | ug | granuloma | umbilical | study | salt application | cycle | umbilical granuloma | treatment [SUMMARY]"},"annotated_most_frequent_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] salt | application | ug | granuloma | umbilical | study | salt application | cycle | umbilical granuloma | treatment [SUMMARY]"},"annotated_most_frequent_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] salt | application | ug | granuloma | umbilical | study | salt application | cycle | umbilical granuloma | treatment [SUMMARY]"},"annotated_most_frequent_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] salt | application | ug | granuloma | umbilical | study | salt application | cycle | umbilical granuloma | treatment [SUMMARY]"},"annotated_tf_idf_background_abstract_prompt":{"kind":"string","value":"[CONTENT] chemical | umbilical | chemical cauterisation | salt | require | propionate | cauterisation | umbilical granuloma | ug | granuloma [SUMMARY]"},"annotated_tf_idf_methods_abstract_prompt":{"kind":"null"},"annotated_tf_idf_results_abstract_prompt":{"kind":"string","value":"[CONTENT] cases | salt | salt application | follow | lost | lost follow | application | suture ligation | suture | ligation [SUMMARY]"},"annotated_tf_idf_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] conflicts interest | conflicts | interest | conflicts interest conflicts interest | conflicts interest conflicts | interest conflicts interest | interest conflicts | nil | salt | nil conflicts interest conflicts [SUMMARY]"},"annotated_tf_idf_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] nil | salt | conflicts interest | interest | conflicts | application | ug | study | granuloma | umbilical [SUMMARY]"},"annotated_tf_idf_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] nil | salt | conflicts interest | interest | conflicts | application | ug | study | granuloma | umbilical [SUMMARY]"},"annotated_entity_plan_background_abstract_prompt":{"kind":"string","value":"[CONTENT] 7-15 days of age ||| Umbilical Granuloma ||| ||| Umbilical Granuloma [SUMMARY]"},"annotated_entity_plan_methods_abstract_prompt":{"kind":"null"},"annotated_entity_plan_results_abstract_prompt":{"kind":"string","value":"[CONTENT] 36 ||| around 96% ||| 3 [SUMMARY]"},"annotated_entity_plan_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] [SUMMARY]"},"annotated_entity_plan_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] 7-15 days of age ||| Umbilical Granuloma ||| ||| Umbilical Granuloma ||| 3 years | Northern India ||| less than 10 weeks of age ||| 1 | 1 hour | 3 consecutive days ||| day 5th ||| 3 ||| ||| 36 ||| around 96% ||| 3 ||| [SUMMARY]"},"annotated_entity_plan_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] 7-15 days of age ||| Umbilical Granuloma ||| ||| Umbilical Granuloma ||| 3 years | Northern India ||| less than 10 weeks of age ||| 1 | 1 hour | 3 consecutive days ||| day 5th ||| 3 ||| ||| 36 ||| around 96% ||| 3 ||| [SUMMARY]"}}},{"rowIdx":289,"cells":{"title":{"kind":"string","value":"Cannabidiol (CBD) Use among children with juvenile idiopathic arthritis."},"pmid":{"kind":"string","value":"34903213"},"background_abstract":{"kind":"string","value":"Juvenile idiopathic arthritis (JIA) is common and difficult to treat. Cannabidiol (CBD) is now widely available, but no studies to date have investigated the use of CBD for JIA."},"background_abstract_label":{"kind":"string","value":"BACKGROUND"},"methods_abstract":{"kind":"string","value":"We performed a chart review to identify patients with JIA at a Midwestern medical institution between 2017 and 2019. We surveyed primary caregivers of JIA patients using an anonymous, online survey with questions on caregiver knowledge and attitudes towards CBD. We compared respondents with no interest in CBD use vs. those contemplating or currently using CBD using descriptive statistics."},"methods_abstract_label":{"kind":"string","value":"METHODS"},"results_abstract":{"kind":"string","value":"Of 900 reviewed charts, 422 met inclusion criteria. Of these, 236 consented to be sent a survey link, and n=136 (58%) completed surveys. Overall, 34.5% (n=47) of respondents reported no interest in using a CBD product for their child's JIA, while 54% (n=79) reported contemplating using CBD and 7% (n=10) reported currently giving their child CBD. Only 2% of respondents contemplating or actively using a CBD product learned about CBD from their child's rheumatologist, compared with television (70%) or a friend (50%). Most respondents had not talked to their child's rheumatologist about using CBD. Of those currently using CBD, most used oral or topical products, and only 10% of respondents (n=1) knew what dose they were giving their child."},"results_abstract_label":{"kind":"string","value":"RESULTS"},"conclusions_abstract":{"kind":"string","value":"Our results show infrequent use but a large interest in CBD among caregivers of children with JIA. Given CBD's unknown safety profile in children with JIA, this study highlights a need for better studies and education around CBD for pediatric rheumatologists."},"conclusions_abstract_label":{"kind":"string","value":"CONCLUSIONS"},"mesh_descriptor_names":{"kind":"list like","value":["Adolescent","Arthritis, Juvenile","Cannabidiol","Child","Female","Health Knowledge, Attitudes, Practice","Humans","Male","Parents","Surveys and Questionnaires"],"string":"[\n \"Adolescent\",\n \"Arthritis, Juvenile\",\n \"Cannabidiol\",\n \"Child\",\n \"Female\",\n \"Health Knowledge, Attitudes, Practice\",\n \"Humans\",\n \"Male\",\n \"Parents\",\n \"Surveys and Questionnaires\"\n]"},"pmcid":{"kind":"string","value":"8670290"},"background_title":{"kind":"string","value":"Background"},"background_text":{"kind":"string","value":"Juvenile idiopathic arthritis (JIA) is the most common type of chronic arthritis in children, affecting 1 in 1000 children. It is an important cause of short and long-term disability and causes significant financial burden with annual direct medical costs ranging $400-$7,000 [1]. Effective treatments for JIA include non-steroidal anti-inflammatory drugs (NSAIDs), corticosteroids, disease modifying anti-rheumatic drugs (DMARDs), and biologic agents, but each carries potential adverse effects [2]. Indeed, parents and children frequently worry about side effects and the long-term safety of medications prescribed for JIA [3, 4]. As a result, many parents and children (34-92%) use complementary and integrative medicine (CIM) separately or in conjunction with standard treatment of JIA [5–10].\nOne such CIM treatment gaining popularity in the past years is cannabidiol (CBD), which is derived from Cannabis sativa. Since the removal of some CBD products from the Controlled Substances Act in 2018, a vast number of products made from hemp (Cannabis sativa with <0.3% Δ-9-tetrahydrocannabinol [THC]) have become available in brick and mortar retailers in topical, oral or inhaled forms [11]. CBD is non-intoxicating and has been widely advertised as a safe and natural therapy for many ailments including chronic pain, arthritis, other inflammatory diseases, and mental health conditions, resulting in frequent use for these conditions [12, 13]. In non-human animal studies, CBD reduces pain and inflammation due to arthritis, but these findings have not been validated in human studies [14, 15]. With the exception of Epidiolex, which is approved for the treatment of the rare seizure disorders Lennox Gastaut and Dravet Syndrome, CBD is minimally regulated by the Food and Drug Administration (FDA) [16, 17]. With the exception of these rare seizure disorders, evidence of a therapeutic benefit of CBD for pediatric conditions is lacking [18].\nCBD’s safety profile has only been characterized among individuals with Dravet and Lennox Gastaut syndrome, so whether CBD is safe for use in healthy children or other pediatric populations remains unknown. Complicating matters, CBD is a promiscuous molecule that interacts with numerous systems in the body (e.g., serotonergic 5HT1A, endocannabinoid system as cannabinoid receptor 1 antagonist) [19, 20], and may interact with the metabolism of drugs commonly taken by children with JIA including prednisone and naproxen [21]. Further, testing of safety and potency of CBD products is not governed by a strong regulatory apparatus, [22, 23] and a recent JAMA study revealed that only 31% of CBD products sold online are accurately labeled for potency with 21% of samples containing THC [24]. As such, there are safety concerns about use in children, especially those with JIA.\nAs pediatric rheumatologists, the authors (C.F., M. R.) have been frequently asked about using CBD products to treat JIA symptoms, but to date, there is no literature available regarding the use of CBD in children with JIA. The objective of this study was to determine the frequency of CBD use among children with JIA and investigate caregiver knowledge and opinions about CBD use for their children."},"methods_title":{"kind":"null"},"methods_text":{"kind":"null"},"results_title":{"kind":"string","value":"Results"},"results_text":{"kind":"string","value":"Participation Overall, 422 JIA patients met inclusion criteria. Of those, 236 parent/guardians agreed to be sent the survey link and 136 participants completed the survey (58% response rate, Fig. 1). 10 respondents (7%) reported using a CBD product to treat their child’s JIA, 79 respondents (58%) reported contemplating use of a CBD product to treat their child’s JIA, and 47 respondents (34.5%) reported no interest in starting a CBD product. Demographic characteristics of the survey respondents are shown in Table 1. The study population was largely white, had a bachelor’s degree or higher, and had an annual income of more than 50,000 dollars per year. A large majority of respondents in both groups reported using one or more CIM therapies in their lifetime. There was no significant difference in the specific types or number of CIM therapies used across groups. Report of high disease activity was more frequent among those currently using or contemplating CBD use than those not contemplating use.\nFig. 1Flow diagram of studyTable 1Correlation of demographics and disease characteristicsParent/guardian and child demographics and disease characteristicsTotal (N=136)Not contemplating starting a CBD product for child (N=47)Contemplating starting a CBD product for child (N=79) and using CBD product for child (N=10)P-valueRespondent parent/guardian age in years (mean +/- SD)29.7 (8)26.5 (7)Respondent Parent/guardian Gender- Female N (%)119 (87)42 (89)77 (86)Race/ethnicity- N (%)White/Caucasian132 (97)44 (94)88 (98.9)Black/African American2 (1)1 (2)1 (1.1)Asian American2 (1)2 (4)0Parent/guardian education levelHigh School or GED20 (16)2 (4)18 (21)χ2= 8.03p=0.045*Some college but no degree26 (19)7 (15)19 (22)Associate degree26 (19)12 (26)14 (16)Bachelor’s degree or higher62 (46)26 (55)36 (41)Income level- US dollars per yearLess than 19,0003 (2)1 (2)2 (2)χ2= 1.34p= 0.7220,000 to 49,00027 (20)7 (15)20 (22)50,000 to 99,00041 (30)13 (28)28 (32)100,000 or more65 (48)26 (55)39 (44)Child’s age in years- (mean +/- SD)11 (4)11.9 (4)Child gender: Female- N (%)95 (70)31 (66)64 (72)JIA duration N (%)< 6 months2 (1)2 (4)0χ2= 1.45p= 0.696-12 months9 (7)3 (6)6 (6)> 12- 24 months20 (15)7 (15)13 (15)> 24 months105 (77)35 (75)70 (79)JIA Subtype N (%)Oligoarticular47 (35)19 (47)28 (32)χ2= 7.93p= 0.13Polyarticular53 (39)12 (26)41 (46)Psoriatic arthritis6 (4)3 (2)3 (3)Systemic14 (10)8 (17)6 (7)Ankylosing spondylitis2 (1)02 (2)Enthesitis related arthritis1 (0.7)1 (2)0Unsure12 (9)3 (6)9 (10)Current disease activity N (%)Active59 (44)15 (29)44 (49)χ2= 9.56p= 0.022*Inactive on medication49 (36)16 (34)33 (37)Inactive off medication for < 1 year18 (13)11 (23)7 (7.8)Clinical remission10 (7)5 (10)5 (5.6)Current medications reported using N (%)None22 (16)10 (21)12 (13)χ2= 2.47p= 0.48NSAID69 (51)23 (49)46 (51)Non-biologic DMARD51 (38)18 (38)33 (37)Biologic DMARD65 (48)18 (38)47 (52)aColumn percentages are displayedbP-values derived from the chi-squared (χ 2 ) or Wilcoxon testscDMARDs disease-modifying antirheumatic drugs, NSAIDs nonsteroidal anti-inflammatory drugsdMedication categories are not mutually exclusive, therefore, medications do not sum to 100%\nFlow diagram of study\nCorrelation of demographics and disease characteristics\nχ2= 8.03\np=0.045*\nχ2= 1.34\np= 0.72\nχ2= 1.45\np= 0.69\nχ2= 7.93\np= 0.13\nχ2= 9.56\np= 0.022*\nχ2= 2.47\np= 0.48\naColumn percentages are displayed\nbP-values derived from the chi-squared (χ 2 ) or Wilcoxon tests\ncDMARDs disease-modifying antirheumatic drugs, NSAIDs nonsteroidal anti-inflammatory drugs\ndMedication categories are not mutually exclusive, therefore, medications do not sum to 100%\nA majority of those using CBD or contemplating using CBD for their child learned about it from TV (66%), a friend/relative (34%) or JIA online blog/support group (35%). Very few obtained information from a scientific journal article (17%) or their child’s rheumatologist (2%). Around half (52%) used 2 or more sources to learn about CBD. A majority of parents/guardians (75%) reported believing that CBD would reduce their child’s joint pain (Fig. 2 A), while only 15% of respondents reported believing that CBD has side effects. More than half of respondents reported thinking that CBD is safe because it is a natural product (Fig. 2 C). Nearly two-thirds (63%, n = 56) of respondents had not discussed using CBD with their child’s rheumatologist and over half (61%) of those did not plan on discussing with their child’s rheumatologist for the following reasons: scared of what provider may think (35%), felt they wouldn’t be taken seriously (29%), and believed rheumatologist would have no knowledge about CBD (18%).\nFig. 2Parent/gaurdian perceptions of those using CBD for their child’s arthritis and those contemplating use of CBD (n=89) on how they percieve CBD will help their child’s arthritis (A), how they learned about CBD (B), perception of safety fo CBD (C). 56 respondents haven’t told their child’s rheumatologist for the following reasons (D)\nParent/gaurdian perceptions of those using CBD for their child’s arthritis and those contemplating use of CBD (n=89) on how they percieve CBD will help their child’s arthritis (A), how they learned about CBD (B), perception of safety fo CBD (C). 56 respondents haven’t told their child’s rheumatologist for the following reasons (D)\nOverall, 422 JIA patients met inclusion criteria. Of those, 236 parent/guardians agreed to be sent the survey link and 136 participants completed the survey (58% response rate, Fig. 1). 10 respondents (7%) reported using a CBD product to treat their child’s JIA, 79 respondents (58%) reported contemplating use of a CBD product to treat their child’s JIA, and 47 respondents (34.5%) reported no interest in starting a CBD product. Demographic characteristics of the survey respondents are shown in Table 1. The study population was largely white, had a bachelor’s degree or higher, and had an annual income of more than 50,000 dollars per year. A large majority of respondents in both groups reported using one or more CIM therapies in their lifetime. There was no significant difference in the specific types or number of CIM therapies used across groups. Report of high disease activity was more frequent among those currently using or contemplating CBD use than those not contemplating use.\nFig. 1Flow diagram of studyTable 1Correlation of demographics and disease characteristicsParent/guardian and child demographics and disease characteristicsTotal (N=136)Not contemplating starting a CBD product for child (N=47)Contemplating starting a CBD product for child (N=79) and using CBD product for child (N=10)P-valueRespondent parent/guardian age in years (mean +/- SD)29.7 (8)26.5 (7)Respondent Parent/guardian Gender- Female N (%)119 (87)42 (89)77 (86)Race/ethnicity- N (%)White/Caucasian132 (97)44 (94)88 (98.9)Black/African American2 (1)1 (2)1 (1.1)Asian American2 (1)2 (4)0Parent/guardian education levelHigh School or GED20 (16)2 (4)18 (21)χ2= 8.03p=0.045*Some college but no degree26 (19)7 (15)19 (22)Associate degree26 (19)12 (26)14 (16)Bachelor’s degree or higher62 (46)26 (55)36 (41)Income level- US dollars per yearLess than 19,0003 (2)1 (2)2 (2)χ2= 1.34p= 0.7220,000 to 49,00027 (20)7 (15)20 (22)50,000 to 99,00041 (30)13 (28)28 (32)100,000 or more65 (48)26 (55)39 (44)Child’s age in years- (mean +/- SD)11 (4)11.9 (4)Child gender: Female- N (%)95 (70)31 (66)64 (72)JIA duration N (%)< 6 months2 (1)2 (4)0χ2= 1.45p= 0.696-12 months9 (7)3 (6)6 (6)> 12- 24 months20 (15)7 (15)13 (15)> 24 months105 (77)35 (75)70 (79)JIA Subtype N (%)Oligoarticular47 (35)19 (47)28 (32)χ2= 7.93p= 0.13Polyarticular53 (39)12 (26)41 (46)Psoriatic arthritis6 (4)3 (2)3 (3)Systemic14 (10)8 (17)6 (7)Ankylosing spondylitis2 (1)02 (2)Enthesitis related arthritis1 (0.7)1 (2)0Unsure12 (9)3 (6)9 (10)Current disease activity N (%)Active59 (44)15 (29)44 (49)χ2= 9.56p= 0.022*Inactive on medication49 (36)16 (34)33 (37)Inactive off medication for < 1 year18 (13)11 (23)7 (7.8)Clinical remission10 (7)5 (10)5 (5.6)Current medications reported using N (%)None22 (16)10 (21)12 (13)χ2= 2.47p= 0.48NSAID69 (51)23 (49)46 (51)Non-biologic DMARD51 (38)18 (38)33 (37)Biologic DMARD65 (48)18 (38)47 (52)aColumn percentages are displayedbP-values derived from the chi-squared (χ 2 ) or Wilcoxon testscDMARDs disease-modifying antirheumatic drugs, NSAIDs nonsteroidal anti-inflammatory drugsdMedication categories are not mutually exclusive, therefore, medications do not sum to 100%\nFlow diagram of study\nCorrelation of demographics and disease characteristics\nχ2= 8.03\np=0.045*\nχ2= 1.34\np= 0.72\nχ2= 1.45\np= 0.69\nχ2= 7.93\np= 0.13\nχ2= 9.56\np= 0.022*\nχ2= 2.47\np= 0.48\naColumn percentages are displayed\nbP-values derived from the chi-squared (χ 2 ) or Wilcoxon tests\ncDMARDs disease-modifying antirheumatic drugs, NSAIDs nonsteroidal anti-inflammatory drugs\ndMedication categories are not mutually exclusive, therefore, medications do not sum to 100%\nA majority of those using CBD or contemplating using CBD for their child learned about it from TV (66%), a friend/relative (34%) or JIA online blog/support group (35%). Very few obtained information from a scientific journal article (17%) or their child’s rheumatologist (2%). Around half (52%) used 2 or more sources to learn about CBD. A majority of parents/guardians (75%) reported believing that CBD would reduce their child’s joint pain (Fig. 2 A), while only 15% of respondents reported believing that CBD has side effects. More than half of respondents reported thinking that CBD is safe because it is a natural product (Fig. 2 C). Nearly two-thirds (63%, n = 56) of respondents had not discussed using CBD with their child’s rheumatologist and over half (61%) of those did not plan on discussing with their child’s rheumatologist for the following reasons: scared of what provider may think (35%), felt they wouldn’t be taken seriously (29%), and believed rheumatologist would have no knowledge about CBD (18%).\nFig. 2Parent/gaurdian perceptions of those using CBD for their child’s arthritis and those contemplating use of CBD (n=89) on how they percieve CBD will help their child’s arthritis (A), how they learned about CBD (B), perception of safety fo CBD (C). 56 respondents haven’t told their child’s rheumatologist for the following reasons (D)\nParent/gaurdian perceptions of those using CBD for their child’s arthritis and those contemplating use of CBD (n=89) on how they percieve CBD will help their child’s arthritis (A), how they learned about CBD (B), perception of safety fo CBD (C). 56 respondents haven’t told their child’s rheumatologist for the following reasons (D)\nContemplating CBD use Respondents contemplating starting a CBD product for their child’s JIA (n=79) were interested in the following CBD products: CBD oil balm (30%), oil drops (25%), gummies (15%), soft gels/capsules (6.5%), and oil roll on (23%). Around a third (32%) of respondents were unsure what products they were interested in. Of those respondents (n=52) who were interested in starting a CBD product, 32.6% were interested in only oral CBD, 36.5% in a combination of oral and topical CBD, and 30.7% were interested only in topical CBD.\nRespondents contemplating starting a CBD product for their child’s JIA (n=79) were interested in the following CBD products: CBD oil balm (30%), oil drops (25%), gummies (15%), soft gels/capsules (6.5%), and oil roll on (23%). Around a third (32%) of respondents were unsure what products they were interested in. Of those respondents (n=52) who were interested in starting a CBD product, 32.6% were interested in only oral CBD, 36.5% in a combination of oral and topical CBD, and 30.7% were interested only in topical CBD.\nCurrent CBD use Respondents using CBD products for their child’s JIA (n=10) reported administering CBD orally (50%) or topically (50%). The majority (60%) reported using CBD on an as needed basis, while 40% reported using CBD on a scheduled basis. Overall, 40% reported administering CBD once per day, 20% twice per day and 40% at least three times per day. Respondents who reported administering CBD as needed (n=6) gave it for joint pain (66%), joint swelling (50%), joint stiffness (66%), and/or when their child requested it (33%). Respondents reported their child’s overall wellbeing to be an average 3.6 prior to starting CBD (0 = very poor, 10 = very good) and 5.3 after taking a CBD product. Half (50%, n=5) of parents reported improvement of their child’s wellbeing after they started CBD while 30% reported no change in their child’s wellbeing and 20% reported decreased well-being. Respondents used the following CBD products: oil drops (40%), lotion (10%), soft gels (10%) and oil balm (40%). Only one respondent knew the total dose of CBD administered per day (20 mg daily) while 70% (n=7) were unsure and 20% (n=2) reported they believed that the dose of CBD was irrelevant.\nRespondents using CBD products for their child’s JIA (n=10) reported administering CBD orally (50%) or topically (50%). The majority (60%) reported using CBD on an as needed basis, while 40% reported using CBD on a scheduled basis. Overall, 40% reported administering CBD once per day, 20% twice per day and 40% at least three times per day. Respondents who reported administering CBD as needed (n=6) gave it for joint pain (66%), joint swelling (50%), joint stiffness (66%), and/or when their child requested it (33%). Respondents reported their child’s overall wellbeing to be an average 3.6 prior to starting CBD (0 = very poor, 10 = very good) and 5.3 after taking a CBD product. Half (50%, n=5) of parents reported improvement of their child’s wellbeing after they started CBD while 30% reported no change in their child’s wellbeing and 20% reported decreased well-being. Respondents used the following CBD products: oil drops (40%), lotion (10%), soft gels (10%) and oil balm (40%). Only one respondent knew the total dose of CBD administered per day (20 mg daily) while 70% (n=7) were unsure and 20% (n=2) reported they believed that the dose of CBD was irrelevant."},"conclusions_title":{"kind":"string","value":"Conclusions"},"conclusions_text":{"kind":"string","value":"As CBD continues to gain popularity, parental interest in CBD for treating their child’s health condition(s) will likely increase. In this study, we show that while CBD use is currently infrequent for JIA, many parents/guardians are interested in using CBD to help with JIA symptoms. As such, is important that pediatric rheumatologists and other pediatric providers educate themselves about CBD to increase their comfort in discussing CBD and its potential safety issues with their patients and/or parents. Such efforts should focus on harm-reduction, communicating uncertainty without harming the patient-physician relationship, and guiding interested parties to reliable sources on CBD (e.g. the Arthritis Foundation)[35] to ensure that they are obtaining information based on scientific evidence. In addition, rigorous clinical studies are warranted to investigate both safety and efficacy of CBD in JIA to bridge the gap in knowledge."},"other_sections_titles":{"kind":"list like","value":["Background","Methods","Participant eligibility","Survey","Statistical analysis","Participation","Contemplating CBD use","Current CBD use","Limitations",""],"string":"[\n \"Background\",\n \"Methods\",\n \"Participant eligibility\",\n \"Survey\",\n \"Statistical analysis\",\n \"Participation\",\n \"Contemplating CBD use\",\n \"Current CBD use\",\n \"Limitations\",\n \"\"\n]"},"other_sections_texts":{"kind":"list like","value":["Juvenile idiopathic arthritis (JIA) is the most common type of chronic arthritis in children, affecting 1 in 1000 children. It is an important cause of short and long-term disability and causes significant financial burden with annual direct medical costs ranging $400-$7,000 [1]. Effective treatments for JIA include non-steroidal anti-inflammatory drugs (NSAIDs), corticosteroids, disease modifying anti-rheumatic drugs (DMARDs), and biologic agents, but each carries potential adverse effects [2]. Indeed, parents and children frequently worry about side effects and the long-term safety of medications prescribed for JIA [3, 4]. As a result, many parents and children (34-92%) use complementary and integrative medicine (CIM) separately or in conjunction with standard treatment of JIA [5–10].\nOne such CIM treatment gaining popularity in the past years is cannabidiol (CBD), which is derived from Cannabis sativa. Since the removal of some CBD products from the Controlled Substances Act in 2018, a vast number of products made from hemp (Cannabis sativa with <0.3% Δ-9-tetrahydrocannabinol [THC]) have become available in brick and mortar retailers in topical, oral or inhaled forms [11]. CBD is non-intoxicating and has been widely advertised as a safe and natural therapy for many ailments including chronic pain, arthritis, other inflammatory diseases, and mental health conditions, resulting in frequent use for these conditions [12, 13]. In non-human animal studies, CBD reduces pain and inflammation due to arthritis, but these findings have not been validated in human studies [14, 15]. With the exception of Epidiolex, which is approved for the treatment of the rare seizure disorders Lennox Gastaut and Dravet Syndrome, CBD is minimally regulated by the Food and Drug Administration (FDA) [16, 17]. With the exception of these rare seizure disorders, evidence of a therapeutic benefit of CBD for pediatric conditions is lacking [18].\nCBD’s safety profile has only been characterized among individuals with Dravet and Lennox Gastaut syndrome, so whether CBD is safe for use in healthy children or other pediatric populations remains unknown. Complicating matters, CBD is a promiscuous molecule that interacts with numerous systems in the body (e.g., serotonergic 5HT1A, endocannabinoid system as cannabinoid receptor 1 antagonist) [19, 20], and may interact with the metabolism of drugs commonly taken by children with JIA including prednisone and naproxen [21]. Further, testing of safety and potency of CBD products is not governed by a strong regulatory apparatus, [22, 23] and a recent JAMA study revealed that only 31% of CBD products sold online are accurately labeled for potency with 21% of samples containing THC [24]. As such, there are safety concerns about use in children, especially those with JIA.\nAs pediatric rheumatologists, the authors (C.F., M. R.) have been frequently asked about using CBD products to treat JIA symptoms, but to date, there is no literature available regarding the use of CBD in children with JIA. The objective of this study was to determine the frequency of CBD use among children with JIA and investigate caregiver knowledge and opinions about CBD use for their children.","All study procedures and protocols were approved by the Institutional Review Board (IRB) at the University of Michigan (HUM00169198). We first conducted an administrative data query at the University of Michigan to identify all children ages 0-17 years of age at the time of a visit associated with the ICD-10 code for JIA between 1/1/2017 and 12/31/2019. That administrative data query identified 900 patients with ICD-10 codes for JIA.\nParticipant eligibility The charts of those 900 patients were then reviewed by C.F. Parents or guardians of patients were invited to participate in the study if the patient was younger than 18 years of age at the time of survey, had a diagnosis of JIA, had more than 1 visit to Pediatric Rheumatology clinic, and had been evaluated by a Pediatric Rheumatologist within the last 18 months. N=422 eligible participants were contacted by phone and invited to take an anonymous online survey created by the authors using a unique link through Qualtrics between December of 2019 and February of 2020. Only respondents interested in the survey were sent the unique link. Respondents were not compensated for completing the survey.\nThe charts of those 900 patients were then reviewed by C.F. Parents or guardians of patients were invited to participate in the study if the patient was younger than 18 years of age at the time of survey, had a diagnosis of JIA, had more than 1 visit to Pediatric Rheumatology clinic, and had been evaluated by a Pediatric Rheumatologist within the last 18 months. N=422 eligible participants were contacted by phone and invited to take an anonymous online survey created by the authors using a unique link through Qualtrics between December of 2019 and February of 2020. Only respondents interested in the survey were sent the unique link. Respondents were not compensated for completing the survey.\nSurvey The survey consisted of 83 items, some of which were variably displayed depending on participant’s responses. Questions addressed parent/guardian demographics (age, gender, ethnicity, education level, annual household income), use of complementary and integrative medicine (CIM) over lifetime (no use, 1 CIM, 2-4 CIMs, > 4 CIM), history of parent/guardian CBD product and cannabis use, child demographics and disease characteristics (age, gender, subtype of JIA, disease duration, parent/guardian report of disease activity at last rheumatology appointment, current rheumatologic medications used, co-morbid health conditions), and total number of CIM therapies used over child’s lifetime.\nRespondents using CBD or contemplating CBD use for treatment of their child’s arthritis answered questions about sources of CBD information, perceptions of how CBD might improve their child’s arthritis, perceptions of the safety of CBD, and whether they had discussed CBD with their child’s provider. If respondents had not discussed CBD with their child’s healthcare provider, they were asked for the reasons why.\nIf parent/guardian reported using CBD product for child’s arthritis, they were asked questions about their CBD product(s), route of CBD administration, frequency of CBD use, parental perception of child’s disease activity pre and post-CBD use (using a 0-10 visual analogue scale), and total daily dosage of CBD (if known).\nThe survey consisted of 83 items, some of which were variably displayed depending on participant’s responses. Questions addressed parent/guardian demographics (age, gender, ethnicity, education level, annual household income), use of complementary and integrative medicine (CIM) over lifetime (no use, 1 CIM, 2-4 CIMs, > 4 CIM), history of parent/guardian CBD product and cannabis use, child demographics and disease characteristics (age, gender, subtype of JIA, disease duration, parent/guardian report of disease activity at last rheumatology appointment, current rheumatologic medications used, co-morbid health conditions), and total number of CIM therapies used over child’s lifetime.\nRespondents using CBD or contemplating CBD use for treatment of their child’s arthritis answered questions about sources of CBD information, perceptions of how CBD might improve their child’s arthritis, perceptions of the safety of CBD, and whether they had discussed CBD with their child’s provider. If respondents had not discussed CBD with their child’s healthcare provider, they were asked for the reasons why.\nIf parent/guardian reported using CBD product for child’s arthritis, they were asked questions about their CBD product(s), route of CBD administration, frequency of CBD use, parental perception of child’s disease activity pre and post-CBD use (using a 0-10 visual analogue scale), and total daily dosage of CBD (if known).\nStatistical analysis We performed descriptive analyses, and present results as frequency, n (%) and mean +/- standard deviation for categorical and continuous variables, respectively. We used Fisher’s chi-square test to assess differences in categorical variables. Participants were divided into 2 comparison groups for analyses: currently using or contemplating starting a CBD product for their child and no interest in starting a CBD product. Participants using CBD for treatment of their child’s arthritis were not used for standalone comparison due to small sample size (n=10). All statistical analysis was performed using Microsoft Excel (2016, Microsoft Corporation).\nWe performed descriptive analyses, and present results as frequency, n (%) and mean +/- standard deviation for categorical and continuous variables, respectively. We used Fisher’s chi-square test to assess differences in categorical variables. Participants were divided into 2 comparison groups for analyses: currently using or contemplating starting a CBD product for their child and no interest in starting a CBD product. Participants using CBD for treatment of their child’s arthritis were not used for standalone comparison due to small sample size (n=10). All statistical analysis was performed using Microsoft Excel (2016, Microsoft Corporation).","The charts of those 900 patients were then reviewed by C.F. Parents or guardians of patients were invited to participate in the study if the patient was younger than 18 years of age at the time of survey, had a diagnosis of JIA, had more than 1 visit to Pediatric Rheumatology clinic, and had been evaluated by a Pediatric Rheumatologist within the last 18 months. N=422 eligible participants were contacted by phone and invited to take an anonymous online survey created by the authors using a unique link through Qualtrics between December of 2019 and February of 2020. Only respondents interested in the survey were sent the unique link. Respondents were not compensated for completing the survey.","The survey consisted of 83 items, some of which were variably displayed depending on participant’s responses. Questions addressed parent/guardian demographics (age, gender, ethnicity, education level, annual household income), use of complementary and integrative medicine (CIM) over lifetime (no use, 1 CIM, 2-4 CIMs, > 4 CIM), history of parent/guardian CBD product and cannabis use, child demographics and disease characteristics (age, gender, subtype of JIA, disease duration, parent/guardian report of disease activity at last rheumatology appointment, current rheumatologic medications used, co-morbid health conditions), and total number of CIM therapies used over child’s lifetime.\nRespondents using CBD or contemplating CBD use for treatment of their child’s arthritis answered questions about sources of CBD information, perceptions of how CBD might improve their child’s arthritis, perceptions of the safety of CBD, and whether they had discussed CBD with their child’s provider. If respondents had not discussed CBD with their child’s healthcare provider, they were asked for the reasons why.\nIf parent/guardian reported using CBD product for child’s arthritis, they were asked questions about their CBD product(s), route of CBD administration, frequency of CBD use, parental perception of child’s disease activity pre and post-CBD use (using a 0-10 visual analogue scale), and total daily dosage of CBD (if known).","We performed descriptive analyses, and present results as frequency, n (%) and mean +/- standard deviation for categorical and continuous variables, respectively. We used Fisher’s chi-square test to assess differences in categorical variables. Participants were divided into 2 comparison groups for analyses: currently using or contemplating starting a CBD product for their child and no interest in starting a CBD product. Participants using CBD for treatment of their child’s arthritis were not used for standalone comparison due to small sample size (n=10). All statistical analysis was performed using Microsoft Excel (2016, Microsoft Corporation).","Overall, 422 JIA patients met inclusion criteria. Of those, 236 parent/guardians agreed to be sent the survey link and 136 participants completed the survey (58% response rate, Fig. 1). 10 respondents (7%) reported using a CBD product to treat their child’s JIA, 79 respondents (58%) reported contemplating use of a CBD product to treat their child’s JIA, and 47 respondents (34.5%) reported no interest in starting a CBD product. Demographic characteristics of the survey respondents are shown in Table 1. The study population was largely white, had a bachelor’s degree or higher, and had an annual income of more than 50,000 dollars per year. A large majority of respondents in both groups reported using one or more CIM therapies in their lifetime. There was no significant difference in the specific types or number of CIM therapies used across groups. Report of high disease activity was more frequent among those currently using or contemplating CBD use than those not contemplating use.\nFig. 1Flow diagram of studyTable 1Correlation of demographics and disease characteristicsParent/guardian and child demographics and disease characteristicsTotal (N=136)Not contemplating starting a CBD product for child (N=47)Contemplating starting a CBD product for child (N=79) and using CBD product for child (N=10)P-valueRespondent parent/guardian age in years (mean +/- SD)29.7 (8)26.5 (7)Respondent Parent/guardian Gender- Female N (%)119 (87)42 (89)77 (86)Race/ethnicity- N (%)White/Caucasian132 (97)44 (94)88 (98.9)Black/African American2 (1)1 (2)1 (1.1)Asian American2 (1)2 (4)0Parent/guardian education levelHigh School or GED20 (16)2 (4)18 (21)χ2= 8.03p=0.045*Some college but no degree26 (19)7 (15)19 (22)Associate degree26 (19)12 (26)14 (16)Bachelor’s degree or higher62 (46)26 (55)36 (41)Income level- US dollars per yearLess than 19,0003 (2)1 (2)2 (2)χ2= 1.34p= 0.7220,000 to 49,00027 (20)7 (15)20 (22)50,000 to 99,00041 (30)13 (28)28 (32)100,000 or more65 (48)26 (55)39 (44)Child’s age in years- (mean +/- SD)11 (4)11.9 (4)Child gender: Female- N (%)95 (70)31 (66)64 (72)JIA duration N (%)< 6 months2 (1)2 (4)0χ2= 1.45p= 0.696-12 months9 (7)3 (6)6 (6)> 12- 24 months20 (15)7 (15)13 (15)> 24 months105 (77)35 (75)70 (79)JIA Subtype N (%)Oligoarticular47 (35)19 (47)28 (32)χ2= 7.93p= 0.13Polyarticular53 (39)12 (26)41 (46)Psoriatic arthritis6 (4)3 (2)3 (3)Systemic14 (10)8 (17)6 (7)Ankylosing spondylitis2 (1)02 (2)Enthesitis related arthritis1 (0.7)1 (2)0Unsure12 (9)3 (6)9 (10)Current disease activity N (%)Active59 (44)15 (29)44 (49)χ2= 9.56p= 0.022*Inactive on medication49 (36)16 (34)33 (37)Inactive off medication for < 1 year18 (13)11 (23)7 (7.8)Clinical remission10 (7)5 (10)5 (5.6)Current medications reported using N (%)None22 (16)10 (21)12 (13)χ2= 2.47p= 0.48NSAID69 (51)23 (49)46 (51)Non-biologic DMARD51 (38)18 (38)33 (37)Biologic DMARD65 (48)18 (38)47 (52)aColumn percentages are displayedbP-values derived from the chi-squared (χ 2 ) or Wilcoxon testscDMARDs disease-modifying antirheumatic drugs, NSAIDs nonsteroidal anti-inflammatory drugsdMedication categories are not mutually exclusive, therefore, medications do not sum to 100%\nFlow diagram of study\nCorrelation of demographics and disease characteristics\nχ2= 8.03\np=0.045*\nχ2= 1.34\np= 0.72\nχ2= 1.45\np= 0.69\nχ2= 7.93\np= 0.13\nχ2= 9.56\np= 0.022*\nχ2= 2.47\np= 0.48\naColumn percentages are displayed\nbP-values derived from the chi-squared (χ 2 ) or Wilcoxon tests\ncDMARDs disease-modifying antirheumatic drugs, NSAIDs nonsteroidal anti-inflammatory drugs\ndMedication categories are not mutually exclusive, therefore, medications do not sum to 100%\nA majority of those using CBD or contemplating using CBD for their child learned about it from TV (66%), a friend/relative (34%) or JIA online blog/support group (35%). Very few obtained information from a scientific journal article (17%) or their child’s rheumatologist (2%). Around half (52%) used 2 or more sources to learn about CBD. A majority of parents/guardians (75%) reported believing that CBD would reduce their child’s joint pain (Fig. 2 A), while only 15% of respondents reported believing that CBD has side effects. More than half of respondents reported thinking that CBD is safe because it is a natural product (Fig. 2 C). Nearly two-thirds (63%, n = 56) of respondents had not discussed using CBD with their child’s rheumatologist and over half (61%) of those did not plan on discussing with their child’s rheumatologist for the following reasons: scared of what provider may think (35%), felt they wouldn’t be taken seriously (29%), and believed rheumatologist would have no knowledge about CBD (18%).\nFig. 2Parent/gaurdian perceptions of those using CBD for their child’s arthritis and those contemplating use of CBD (n=89) on how they percieve CBD will help their child’s arthritis (A), how they learned about CBD (B), perception of safety fo CBD (C). 56 respondents haven’t told their child’s rheumatologist for the following reasons (D)\nParent/gaurdian perceptions of those using CBD for their child’s arthritis and those contemplating use of CBD (n=89) on how they percieve CBD will help their child’s arthritis (A), how they learned about CBD (B), perception of safety fo CBD (C). 56 respondents haven’t told their child’s rheumatologist for the following reasons (D)","Respondents contemplating starting a CBD product for their child’s JIA (n=79) were interested in the following CBD products: CBD oil balm (30%), oil drops (25%), gummies (15%), soft gels/capsules (6.5%), and oil roll on (23%). Around a third (32%) of respondents were unsure what products they were interested in. Of those respondents (n=52) who were interested in starting a CBD product, 32.6% were interested in only oral CBD, 36.5% in a combination of oral and topical CBD, and 30.7% were interested only in topical CBD.","Respondents using CBD products for their child’s JIA (n=10) reported administering CBD orally (50%) or topically (50%). The majority (60%) reported using CBD on an as needed basis, while 40% reported using CBD on a scheduled basis. Overall, 40% reported administering CBD once per day, 20% twice per day and 40% at least three times per day. Respondents who reported administering CBD as needed (n=6) gave it for joint pain (66%), joint swelling (50%), joint stiffness (66%), and/or when their child requested it (33%). Respondents reported their child’s overall wellbeing to be an average 3.6 prior to starting CBD (0 = very poor, 10 = very good) and 5.3 after taking a CBD product. Half (50%, n=5) of parents reported improvement of their child’s wellbeing after they started CBD while 30% reported no change in their child’s wellbeing and 20% reported decreased well-being. Respondents used the following CBD products: oil drops (40%), lotion (10%), soft gels (10%) and oil balm (40%). Only one respondent knew the total dose of CBD administered per day (20 mg daily) while 70% (n=7) were unsure and 20% (n=2) reported they believed that the dose of CBD was irrelevant.","Respondents of both panels were similar in terms of race/ethnicity; education, age, and gender, however, > 95% of respondents were white/Caucasian which is not representative of the JIA patient population at our institution or in the US. Survey links were only generated for parents or guardians who expressed interest in participating in the study, so selection bias was likely present. In addition, respondents may have interpreted survey questions differently than we intended and wording of questions may have introduced bias. Finally, we only queried the parents/guardians of individuals with JIA rather than directly asking individuals with JIA about their experiences with or interest in CBD.","\nAdditional file 1\nAdditional file 1"],"string":"[\n \"Juvenile idiopathic arthritis (JIA) is the most common type of chronic arthritis in children, affecting 1 in 1000 children. It is an important cause of short and long-term disability and causes significant financial burden with annual direct medical costs ranging $400-$7,000 [1]. Effective treatments for JIA include non-steroidal anti-inflammatory drugs (NSAIDs), corticosteroids, disease modifying anti-rheumatic drugs (DMARDs), and biologic agents, but each carries potential adverse effects [2]. Indeed, parents and children frequently worry about side effects and the long-term safety of medications prescribed for JIA [3, 4]. As a result, many parents and children (34-92%) use complementary and integrative medicine (CIM) separately or in conjunction with standard treatment of JIA [5–10].\\nOne such CIM treatment gaining popularity in the past years is cannabidiol (CBD), which is derived from Cannabis sativa. Since the removal of some CBD products from the Controlled Substances Act in 2018, a vast number of products made from hemp (Cannabis sativa with <0.3% Δ-9-tetrahydrocannabinol [THC]) have become available in brick and mortar retailers in topical, oral or inhaled forms [11]. CBD is non-intoxicating and has been widely advertised as a safe and natural therapy for many ailments including chronic pain, arthritis, other inflammatory diseases, and mental health conditions, resulting in frequent use for these conditions [12, 13]. In non-human animal studies, CBD reduces pain and inflammation due to arthritis, but these findings have not been validated in human studies [14, 15]. With the exception of Epidiolex, which is approved for the treatment of the rare seizure disorders Lennox Gastaut and Dravet Syndrome, CBD is minimally regulated by the Food and Drug Administration (FDA) [16, 17]. With the exception of these rare seizure disorders, evidence of a therapeutic benefit of CBD for pediatric conditions is lacking [18].\\nCBD’s safety profile has only been characterized among individuals with Dravet and Lennox Gastaut syndrome, so whether CBD is safe for use in healthy children or other pediatric populations remains unknown. Complicating matters, CBD is a promiscuous molecule that interacts with numerous systems in the body (e.g., serotonergic 5HT1A, endocannabinoid system as cannabinoid receptor 1 antagonist) [19, 20], and may interact with the metabolism of drugs commonly taken by children with JIA including prednisone and naproxen [21]. Further, testing of safety and potency of CBD products is not governed by a strong regulatory apparatus, [22, 23] and a recent JAMA study revealed that only 31% of CBD products sold online are accurately labeled for potency with 21% of samples containing THC [24]. As such, there are safety concerns about use in children, especially those with JIA.\\nAs pediatric rheumatologists, the authors (C.F., M. R.) have been frequently asked about using CBD products to treat JIA symptoms, but to date, there is no literature available regarding the use of CBD in children with JIA. The objective of this study was to determine the frequency of CBD use among children with JIA and investigate caregiver knowledge and opinions about CBD use for their children.\",\n \"All study procedures and protocols were approved by the Institutional Review Board (IRB) at the University of Michigan (HUM00169198). We first conducted an administrative data query at the University of Michigan to identify all children ages 0-17 years of age at the time of a visit associated with the ICD-10 code for JIA between 1/1/2017 and 12/31/2019. That administrative data query identified 900 patients with ICD-10 codes for JIA.\\nParticipant eligibility The charts of those 900 patients were then reviewed by C.F. Parents or guardians of patients were invited to participate in the study if the patient was younger than 18 years of age at the time of survey, had a diagnosis of JIA, had more than 1 visit to Pediatric Rheumatology clinic, and had been evaluated by a Pediatric Rheumatologist within the last 18 months. N=422 eligible participants were contacted by phone and invited to take an anonymous online survey created by the authors using a unique link through Qualtrics between December of 2019 and February of 2020. Only respondents interested in the survey were sent the unique link. Respondents were not compensated for completing the survey.\\nThe charts of those 900 patients were then reviewed by C.F. Parents or guardians of patients were invited to participate in the study if the patient was younger than 18 years of age at the time of survey, had a diagnosis of JIA, had more than 1 visit to Pediatric Rheumatology clinic, and had been evaluated by a Pediatric Rheumatologist within the last 18 months. N=422 eligible participants were contacted by phone and invited to take an anonymous online survey created by the authors using a unique link through Qualtrics between December of 2019 and February of 2020. Only respondents interested in the survey were sent the unique link. Respondents were not compensated for completing the survey.\\nSurvey The survey consisted of 83 items, some of which were variably displayed depending on participant’s responses. Questions addressed parent/guardian demographics (age, gender, ethnicity, education level, annual household income), use of complementary and integrative medicine (CIM) over lifetime (no use, 1 CIM, 2-4 CIMs, > 4 CIM), history of parent/guardian CBD product and cannabis use, child demographics and disease characteristics (age, gender, subtype of JIA, disease duration, parent/guardian report of disease activity at last rheumatology appointment, current rheumatologic medications used, co-morbid health conditions), and total number of CIM therapies used over child’s lifetime.\\nRespondents using CBD or contemplating CBD use for treatment of their child’s arthritis answered questions about sources of CBD information, perceptions of how CBD might improve their child’s arthritis, perceptions of the safety of CBD, and whether they had discussed CBD with their child’s provider. If respondents had not discussed CBD with their child’s healthcare provider, they were asked for the reasons why.\\nIf parent/guardian reported using CBD product for child’s arthritis, they were asked questions about their CBD product(s), route of CBD administration, frequency of CBD use, parental perception of child’s disease activity pre and post-CBD use (using a 0-10 visual analogue scale), and total daily dosage of CBD (if known).\\nThe survey consisted of 83 items, some of which were variably displayed depending on participant’s responses. Questions addressed parent/guardian demographics (age, gender, ethnicity, education level, annual household income), use of complementary and integrative medicine (CIM) over lifetime (no use, 1 CIM, 2-4 CIMs, > 4 CIM), history of parent/guardian CBD product and cannabis use, child demographics and disease characteristics (age, gender, subtype of JIA, disease duration, parent/guardian report of disease activity at last rheumatology appointment, current rheumatologic medications used, co-morbid health conditions), and total number of CIM therapies used over child’s lifetime.\\nRespondents using CBD or contemplating CBD use for treatment of their child’s arthritis answered questions about sources of CBD information, perceptions of how CBD might improve their child’s arthritis, perceptions of the safety of CBD, and whether they had discussed CBD with their child’s provider. If respondents had not discussed CBD with their child’s healthcare provider, they were asked for the reasons why.\\nIf parent/guardian reported using CBD product for child’s arthritis, they were asked questions about their CBD product(s), route of CBD administration, frequency of CBD use, parental perception of child’s disease activity pre and post-CBD use (using a 0-10 visual analogue scale), and total daily dosage of CBD (if known).\\nStatistical analysis We performed descriptive analyses, and present results as frequency, n (%) and mean +/- standard deviation for categorical and continuous variables, respectively. We used Fisher’s chi-square test to assess differences in categorical variables. Participants were divided into 2 comparison groups for analyses: currently using or contemplating starting a CBD product for their child and no interest in starting a CBD product. Participants using CBD for treatment of their child’s arthritis were not used for standalone comparison due to small sample size (n=10). All statistical analysis was performed using Microsoft Excel (2016, Microsoft Corporation).\\nWe performed descriptive analyses, and present results as frequency, n (%) and mean +/- standard deviation for categorical and continuous variables, respectively. We used Fisher’s chi-square test to assess differences in categorical variables. Participants were divided into 2 comparison groups for analyses: currently using or contemplating starting a CBD product for their child and no interest in starting a CBD product. Participants using CBD for treatment of their child’s arthritis were not used for standalone comparison due to small sample size (n=10). All statistical analysis was performed using Microsoft Excel (2016, Microsoft Corporation).\",\n \"The charts of those 900 patients were then reviewed by C.F. Parents or guardians of patients were invited to participate in the study if the patient was younger than 18 years of age at the time of survey, had a diagnosis of JIA, had more than 1 visit to Pediatric Rheumatology clinic, and had been evaluated by a Pediatric Rheumatologist within the last 18 months. N=422 eligible participants were contacted by phone and invited to take an anonymous online survey created by the authors using a unique link through Qualtrics between December of 2019 and February of 2020. Only respondents interested in the survey were sent the unique link. Respondents were not compensated for completing the survey.\",\n \"The survey consisted of 83 items, some of which were variably displayed depending on participant’s responses. Questions addressed parent/guardian demographics (age, gender, ethnicity, education level, annual household income), use of complementary and integrative medicine (CIM) over lifetime (no use, 1 CIM, 2-4 CIMs, > 4 CIM), history of parent/guardian CBD product and cannabis use, child demographics and disease characteristics (age, gender, subtype of JIA, disease duration, parent/guardian report of disease activity at last rheumatology appointment, current rheumatologic medications used, co-morbid health conditions), and total number of CIM therapies used over child’s lifetime.\\nRespondents using CBD or contemplating CBD use for treatment of their child’s arthritis answered questions about sources of CBD information, perceptions of how CBD might improve their child’s arthritis, perceptions of the safety of CBD, and whether they had discussed CBD with their child’s provider. If respondents had not discussed CBD with their child’s healthcare provider, they were asked for the reasons why.\\nIf parent/guardian reported using CBD product for child’s arthritis, they were asked questions about their CBD product(s), route of CBD administration, frequency of CBD use, parental perception of child’s disease activity pre and post-CBD use (using a 0-10 visual analogue scale), and total daily dosage of CBD (if known).\",\n \"We performed descriptive analyses, and present results as frequency, n (%) and mean +/- standard deviation for categorical and continuous variables, respectively. We used Fisher’s chi-square test to assess differences in categorical variables. Participants were divided into 2 comparison groups for analyses: currently using or contemplating starting a CBD product for their child and no interest in starting a CBD product. Participants using CBD for treatment of their child’s arthritis were not used for standalone comparison due to small sample size (n=10). All statistical analysis was performed using Microsoft Excel (2016, Microsoft Corporation).\",\n \"Overall, 422 JIA patients met inclusion criteria. Of those, 236 parent/guardians agreed to be sent the survey link and 136 participants completed the survey (58% response rate, Fig. 1). 10 respondents (7%) reported using a CBD product to treat their child’s JIA, 79 respondents (58%) reported contemplating use of a CBD product to treat their child’s JIA, and 47 respondents (34.5%) reported no interest in starting a CBD product. Demographic characteristics of the survey respondents are shown in Table 1. The study population was largely white, had a bachelor’s degree or higher, and had an annual income of more than 50,000 dollars per year. A large majority of respondents in both groups reported using one or more CIM therapies in their lifetime. There was no significant difference in the specific types or number of CIM therapies used across groups. Report of high disease activity was more frequent among those currently using or contemplating CBD use than those not contemplating use.\\nFig. 1Flow diagram of studyTable 1Correlation of demographics and disease characteristicsParent/guardian and child demographics and disease characteristicsTotal (N=136)Not contemplating starting a CBD product for child (N=47)Contemplating starting a CBD product for child (N=79) and using CBD product for child (N=10)P-valueRespondent parent/guardian age in years (mean +/- SD)29.7 (8)26.5 (7)Respondent Parent/guardian Gender- Female N (%)119 (87)42 (89)77 (86)Race/ethnicity- N (%)White/Caucasian132 (97)44 (94)88 (98.9)Black/African American2 (1)1 (2)1 (1.1)Asian American2 (1)2 (4)0Parent/guardian education levelHigh School or GED20 (16)2 (4)18 (21)χ2= 8.03p=0.045*Some college but no degree26 (19)7 (15)19 (22)Associate degree26 (19)12 (26)14 (16)Bachelor’s degree or higher62 (46)26 (55)36 (41)Income level- US dollars per yearLess than 19,0003 (2)1 (2)2 (2)χ2= 1.34p= 0.7220,000 to 49,00027 (20)7 (15)20 (22)50,000 to 99,00041 (30)13 (28)28 (32)100,000 or more65 (48)26 (55)39 (44)Child’s age in years- (mean +/- SD)11 (4)11.9 (4)Child gender: Female- N (%)95 (70)31 (66)64 (72)JIA duration N (%)< 6 months2 (1)2 (4)0χ2= 1.45p= 0.696-12 months9 (7)3 (6)6 (6)> 12- 24 months20 (15)7 (15)13 (15)> 24 months105 (77)35 (75)70 (79)JIA Subtype N (%)Oligoarticular47 (35)19 (47)28 (32)χ2= 7.93p= 0.13Polyarticular53 (39)12 (26)41 (46)Psoriatic arthritis6 (4)3 (2)3 (3)Systemic14 (10)8 (17)6 (7)Ankylosing spondylitis2 (1)02 (2)Enthesitis related arthritis1 (0.7)1 (2)0Unsure12 (9)3 (6)9 (10)Current disease activity N (%)Active59 (44)15 (29)44 (49)χ2= 9.56p= 0.022*Inactive on medication49 (36)16 (34)33 (37)Inactive off medication for < 1 year18 (13)11 (23)7 (7.8)Clinical remission10 (7)5 (10)5 (5.6)Current medications reported using N (%)None22 (16)10 (21)12 (13)χ2= 2.47p= 0.48NSAID69 (51)23 (49)46 (51)Non-biologic DMARD51 (38)18 (38)33 (37)Biologic DMARD65 (48)18 (38)47 (52)aColumn percentages are displayedbP-values derived from the chi-squared (χ 2 ) or Wilcoxon testscDMARDs disease-modifying antirheumatic drugs, NSAIDs nonsteroidal anti-inflammatory drugsdMedication categories are not mutually exclusive, therefore, medications do not sum to 100%\\nFlow diagram of study\\nCorrelation of demographics and disease characteristics\\nχ2= 8.03\\np=0.045*\\nχ2= 1.34\\np= 0.72\\nχ2= 1.45\\np= 0.69\\nχ2= 7.93\\np= 0.13\\nχ2= 9.56\\np= 0.022*\\nχ2= 2.47\\np= 0.48\\naColumn percentages are displayed\\nbP-values derived from the chi-squared (χ 2 ) or Wilcoxon tests\\ncDMARDs disease-modifying antirheumatic drugs, NSAIDs nonsteroidal anti-inflammatory drugs\\ndMedication categories are not mutually exclusive, therefore, medications do not sum to 100%\\nA majority of those using CBD or contemplating using CBD for their child learned about it from TV (66%), a friend/relative (34%) or JIA online blog/support group (35%). Very few obtained information from a scientific journal article (17%) or their child’s rheumatologist (2%). Around half (52%) used 2 or more sources to learn about CBD. A majority of parents/guardians (75%) reported believing that CBD would reduce their child’s joint pain (Fig. 2 A), while only 15% of respondents reported believing that CBD has side effects. More than half of respondents reported thinking that CBD is safe because it is a natural product (Fig. 2 C). Nearly two-thirds (63%, n = 56) of respondents had not discussed using CBD with their child’s rheumatologist and over half (61%) of those did not plan on discussing with their child’s rheumatologist for the following reasons: scared of what provider may think (35%), felt they wouldn’t be taken seriously (29%), and believed rheumatologist would have no knowledge about CBD (18%).\\nFig. 2Parent/gaurdian perceptions of those using CBD for their child’s arthritis and those contemplating use of CBD (n=89) on how they percieve CBD will help their child’s arthritis (A), how they learned about CBD (B), perception of safety fo CBD (C). 56 respondents haven’t told their child’s rheumatologist for the following reasons (D)\\nParent/gaurdian perceptions of those using CBD for their child’s arthritis and those contemplating use of CBD (n=89) on how they percieve CBD will help their child’s arthritis (A), how they learned about CBD (B), perception of safety fo CBD (C). 56 respondents haven’t told their child’s rheumatologist for the following reasons (D)\",\n \"Respondents contemplating starting a CBD product for their child’s JIA (n=79) were interested in the following CBD products: CBD oil balm (30%), oil drops (25%), gummies (15%), soft gels/capsules (6.5%), and oil roll on (23%). Around a third (32%) of respondents were unsure what products they were interested in. Of those respondents (n=52) who were interested in starting a CBD product, 32.6% were interested in only oral CBD, 36.5% in a combination of oral and topical CBD, and 30.7% were interested only in topical CBD.\",\n \"Respondents using CBD products for their child’s JIA (n=10) reported administering CBD orally (50%) or topically (50%). The majority (60%) reported using CBD on an as needed basis, while 40% reported using CBD on a scheduled basis. Overall, 40% reported administering CBD once per day, 20% twice per day and 40% at least three times per day. Respondents who reported administering CBD as needed (n=6) gave it for joint pain (66%), joint swelling (50%), joint stiffness (66%), and/or when their child requested it (33%). Respondents reported their child’s overall wellbeing to be an average 3.6 prior to starting CBD (0 = very poor, 10 = very good) and 5.3 after taking a CBD product. Half (50%, n=5) of parents reported improvement of their child’s wellbeing after they started CBD while 30% reported no change in their child’s wellbeing and 20% reported decreased well-being. Respondents used the following CBD products: oil drops (40%), lotion (10%), soft gels (10%) and oil balm (40%). Only one respondent knew the total dose of CBD administered per day (20 mg daily) while 70% (n=7) were unsure and 20% (n=2) reported they believed that the dose of CBD was irrelevant.\",\n \"Respondents of both panels were similar in terms of race/ethnicity; education, age, and gender, however, > 95% of respondents were white/Caucasian which is not representative of the JIA patient population at our institution or in the US. Survey links were only generated for parents or guardians who expressed interest in participating in the study, so selection bias was likely present. In addition, respondents may have interpreted survey questions differently than we intended and wording of questions may have introduced bias. Finally, we only queried the parents/guardians of individuals with JIA rather than directly asking individuals with JIA about their experiences with or interest in CBD.\",\n \"\\nAdditional file 1\\nAdditional file 1\"\n]"},"other_sections_sec_types":{"kind":"list like","value":[null,null,null,null,null,null,null,null,null,null],"string":"[\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null\n]"},"all_sections_titles":{"kind":"list like","value":["Background","Methods","Participant eligibility","Survey","Statistical analysis","Results","Participation","Contemplating CBD use","Current CBD use","Discussion","Limitations","Conclusions","Supplementary information",""],"string":"[\n \"Background\",\n \"Methods\",\n \"Participant eligibility\",\n \"Survey\",\n \"Statistical analysis\",\n \"Results\",\n \"Participation\",\n \"Contemplating CBD use\",\n \"Current CBD use\",\n \"Discussion\",\n \"Limitations\",\n \"Conclusions\",\n \"Supplementary information\",\n \"\"\n]"},"all_sections_texts":{"kind":"list like","value":["Juvenile idiopathic arthritis (JIA) is the most common type of chronic arthritis in children, affecting 1 in 1000 children. It is an important cause of short and long-term disability and causes significant financial burden with annual direct medical costs ranging $400-$7,000 [1]. Effective treatments for JIA include non-steroidal anti-inflammatory drugs (NSAIDs), corticosteroids, disease modifying anti-rheumatic drugs (DMARDs), and biologic agents, but each carries potential adverse effects [2]. Indeed, parents and children frequently worry about side effects and the long-term safety of medications prescribed for JIA [3, 4]. As a result, many parents and children (34-92%) use complementary and integrative medicine (CIM) separately or in conjunction with standard treatment of JIA [5–10].\nOne such CIM treatment gaining popularity in the past years is cannabidiol (CBD), which is derived from Cannabis sativa. Since the removal of some CBD products from the Controlled Substances Act in 2018, a vast number of products made from hemp (Cannabis sativa with <0.3% Δ-9-tetrahydrocannabinol [THC]) have become available in brick and mortar retailers in topical, oral or inhaled forms [11]. CBD is non-intoxicating and has been widely advertised as a safe and natural therapy for many ailments including chronic pain, arthritis, other inflammatory diseases, and mental health conditions, resulting in frequent use for these conditions [12, 13]. In non-human animal studies, CBD reduces pain and inflammation due to arthritis, but these findings have not been validated in human studies [14, 15]. With the exception of Epidiolex, which is approved for the treatment of the rare seizure disorders Lennox Gastaut and Dravet Syndrome, CBD is minimally regulated by the Food and Drug Administration (FDA) [16, 17]. With the exception of these rare seizure disorders, evidence of a therapeutic benefit of CBD for pediatric conditions is lacking [18].\nCBD’s safety profile has only been characterized among individuals with Dravet and Lennox Gastaut syndrome, so whether CBD is safe for use in healthy children or other pediatric populations remains unknown. Complicating matters, CBD is a promiscuous molecule that interacts with numerous systems in the body (e.g., serotonergic 5HT1A, endocannabinoid system as cannabinoid receptor 1 antagonist) [19, 20], and may interact with the metabolism of drugs commonly taken by children with JIA including prednisone and naproxen [21]. Further, testing of safety and potency of CBD products is not governed by a strong regulatory apparatus, [22, 23] and a recent JAMA study revealed that only 31% of CBD products sold online are accurately labeled for potency with 21% of samples containing THC [24]. As such, there are safety concerns about use in children, especially those with JIA.\nAs pediatric rheumatologists, the authors (C.F., M. R.) have been frequently asked about using CBD products to treat JIA symptoms, but to date, there is no literature available regarding the use of CBD in children with JIA. The objective of this study was to determine the frequency of CBD use among children with JIA and investigate caregiver knowledge and opinions about CBD use for their children.","All study procedures and protocols were approved by the Institutional Review Board (IRB) at the University of Michigan (HUM00169198). We first conducted an administrative data query at the University of Michigan to identify all children ages 0-17 years of age at the time of a visit associated with the ICD-10 code for JIA between 1/1/2017 and 12/31/2019. That administrative data query identified 900 patients with ICD-10 codes for JIA.\nParticipant eligibility The charts of those 900 patients were then reviewed by C.F. Parents or guardians of patients were invited to participate in the study if the patient was younger than 18 years of age at the time of survey, had a diagnosis of JIA, had more than 1 visit to Pediatric Rheumatology clinic, and had been evaluated by a Pediatric Rheumatologist within the last 18 months. N=422 eligible participants were contacted by phone and invited to take an anonymous online survey created by the authors using a unique link through Qualtrics between December of 2019 and February of 2020. Only respondents interested in the survey were sent the unique link. Respondents were not compensated for completing the survey.\nThe charts of those 900 patients were then reviewed by C.F. Parents or guardians of patients were invited to participate in the study if the patient was younger than 18 years of age at the time of survey, had a diagnosis of JIA, had more than 1 visit to Pediatric Rheumatology clinic, and had been evaluated by a Pediatric Rheumatologist within the last 18 months. N=422 eligible participants were contacted by phone and invited to take an anonymous online survey created by the authors using a unique link through Qualtrics between December of 2019 and February of 2020. Only respondents interested in the survey were sent the unique link. Respondents were not compensated for completing the survey.\nSurvey The survey consisted of 83 items, some of which were variably displayed depending on participant’s responses. Questions addressed parent/guardian demographics (age, gender, ethnicity, education level, annual household income), use of complementary and integrative medicine (CIM) over lifetime (no use, 1 CIM, 2-4 CIMs, > 4 CIM), history of parent/guardian CBD product and cannabis use, child demographics and disease characteristics (age, gender, subtype of JIA, disease duration, parent/guardian report of disease activity at last rheumatology appointment, current rheumatologic medications used, co-morbid health conditions), and total number of CIM therapies used over child’s lifetime.\nRespondents using CBD or contemplating CBD use for treatment of their child’s arthritis answered questions about sources of CBD information, perceptions of how CBD might improve their child’s arthritis, perceptions of the safety of CBD, and whether they had discussed CBD with their child’s provider. If respondents had not discussed CBD with their child’s healthcare provider, they were asked for the reasons why.\nIf parent/guardian reported using CBD product for child’s arthritis, they were asked questions about their CBD product(s), route of CBD administration, frequency of CBD use, parental perception of child’s disease activity pre and post-CBD use (using a 0-10 visual analogue scale), and total daily dosage of CBD (if known).\nThe survey consisted of 83 items, some of which were variably displayed depending on participant’s responses. Questions addressed parent/guardian demographics (age, gender, ethnicity, education level, annual household income), use of complementary and integrative medicine (CIM) over lifetime (no use, 1 CIM, 2-4 CIMs, > 4 CIM), history of parent/guardian CBD product and cannabis use, child demographics and disease characteristics (age, gender, subtype of JIA, disease duration, parent/guardian report of disease activity at last rheumatology appointment, current rheumatologic medications used, co-morbid health conditions), and total number of CIM therapies used over child’s lifetime.\nRespondents using CBD or contemplating CBD use for treatment of their child’s arthritis answered questions about sources of CBD information, perceptions of how CBD might improve their child’s arthritis, perceptions of the safety of CBD, and whether they had discussed CBD with their child’s provider. If respondents had not discussed CBD with their child’s healthcare provider, they were asked for the reasons why.\nIf parent/guardian reported using CBD product for child’s arthritis, they were asked questions about their CBD product(s), route of CBD administration, frequency of CBD use, parental perception of child’s disease activity pre and post-CBD use (using a 0-10 visual analogue scale), and total daily dosage of CBD (if known).\nStatistical analysis We performed descriptive analyses, and present results as frequency, n (%) and mean +/- standard deviation for categorical and continuous variables, respectively. We used Fisher’s chi-square test to assess differences in categorical variables. Participants were divided into 2 comparison groups for analyses: currently using or contemplating starting a CBD product for their child and no interest in starting a CBD product. Participants using CBD for treatment of their child’s arthritis were not used for standalone comparison due to small sample size (n=10). All statistical analysis was performed using Microsoft Excel (2016, Microsoft Corporation).\nWe performed descriptive analyses, and present results as frequency, n (%) and mean +/- standard deviation for categorical and continuous variables, respectively. We used Fisher’s chi-square test to assess differences in categorical variables. Participants were divided into 2 comparison groups for analyses: currently using or contemplating starting a CBD product for their child and no interest in starting a CBD product. Participants using CBD for treatment of their child’s arthritis were not used for standalone comparison due to small sample size (n=10). All statistical analysis was performed using Microsoft Excel (2016, Microsoft Corporation).","The charts of those 900 patients were then reviewed by C.F. Parents or guardians of patients were invited to participate in the study if the patient was younger than 18 years of age at the time of survey, had a diagnosis of JIA, had more than 1 visit to Pediatric Rheumatology clinic, and had been evaluated by a Pediatric Rheumatologist within the last 18 months. N=422 eligible participants were contacted by phone and invited to take an anonymous online survey created by the authors using a unique link through Qualtrics between December of 2019 and February of 2020. Only respondents interested in the survey were sent the unique link. Respondents were not compensated for completing the survey.","The survey consisted of 83 items, some of which were variably displayed depending on participant’s responses. Questions addressed parent/guardian demographics (age, gender, ethnicity, education level, annual household income), use of complementary and integrative medicine (CIM) over lifetime (no use, 1 CIM, 2-4 CIMs, > 4 CIM), history of parent/guardian CBD product and cannabis use, child demographics and disease characteristics (age, gender, subtype of JIA, disease duration, parent/guardian report of disease activity at last rheumatology appointment, current rheumatologic medications used, co-morbid health conditions), and total number of CIM therapies used over child’s lifetime.\nRespondents using CBD or contemplating CBD use for treatment of their child’s arthritis answered questions about sources of CBD information, perceptions of how CBD might improve their child’s arthritis, perceptions of the safety of CBD, and whether they had discussed CBD with their child’s provider. If respondents had not discussed CBD with their child’s healthcare provider, they were asked for the reasons why.\nIf parent/guardian reported using CBD product for child’s arthritis, they were asked questions about their CBD product(s), route of CBD administration, frequency of CBD use, parental perception of child’s disease activity pre and post-CBD use (using a 0-10 visual analogue scale), and total daily dosage of CBD (if known).","We performed descriptive analyses, and present results as frequency, n (%) and mean +/- standard deviation for categorical and continuous variables, respectively. We used Fisher’s chi-square test to assess differences in categorical variables. Participants were divided into 2 comparison groups for analyses: currently using or contemplating starting a CBD product for their child and no interest in starting a CBD product. Participants using CBD for treatment of their child’s arthritis were not used for standalone comparison due to small sample size (n=10). All statistical analysis was performed using Microsoft Excel (2016, Microsoft Corporation).","Participation Overall, 422 JIA patients met inclusion criteria. Of those, 236 parent/guardians agreed to be sent the survey link and 136 participants completed the survey (58% response rate, Fig. 1). 10 respondents (7%) reported using a CBD product to treat their child’s JIA, 79 respondents (58%) reported contemplating use of a CBD product to treat their child’s JIA, and 47 respondents (34.5%) reported no interest in starting a CBD product. Demographic characteristics of the survey respondents are shown in Table 1. The study population was largely white, had a bachelor’s degree or higher, and had an annual income of more than 50,000 dollars per year. A large majority of respondents in both groups reported using one or more CIM therapies in their lifetime. There was no significant difference in the specific types or number of CIM therapies used across groups. Report of high disease activity was more frequent among those currently using or contemplating CBD use than those not contemplating use.\nFig. 1Flow diagram of studyTable 1Correlation of demographics and disease characteristicsParent/guardian and child demographics and disease characteristicsTotal (N=136)Not contemplating starting a CBD product for child (N=47)Contemplating starting a CBD product for child (N=79) and using CBD product for child (N=10)P-valueRespondent parent/guardian age in years (mean +/- SD)29.7 (8)26.5 (7)Respondent Parent/guardian Gender- Female N (%)119 (87)42 (89)77 (86)Race/ethnicity- N (%)White/Caucasian132 (97)44 (94)88 (98.9)Black/African American2 (1)1 (2)1 (1.1)Asian American2 (1)2 (4)0Parent/guardian education levelHigh School or GED20 (16)2 (4)18 (21)χ2= 8.03p=0.045*Some college but no degree26 (19)7 (15)19 (22)Associate degree26 (19)12 (26)14 (16)Bachelor’s degree or higher62 (46)26 (55)36 (41)Income level- US dollars per yearLess than 19,0003 (2)1 (2)2 (2)χ2= 1.34p= 0.7220,000 to 49,00027 (20)7 (15)20 (22)50,000 to 99,00041 (30)13 (28)28 (32)100,000 or more65 (48)26 (55)39 (44)Child’s age in years- (mean +/- SD)11 (4)11.9 (4)Child gender: Female- N (%)95 (70)31 (66)64 (72)JIA duration N (%)< 6 months2 (1)2 (4)0χ2= 1.45p= 0.696-12 months9 (7)3 (6)6 (6)> 12- 24 months20 (15)7 (15)13 (15)> 24 months105 (77)35 (75)70 (79)JIA Subtype N (%)Oligoarticular47 (35)19 (47)28 (32)χ2= 7.93p= 0.13Polyarticular53 (39)12 (26)41 (46)Psoriatic arthritis6 (4)3 (2)3 (3)Systemic14 (10)8 (17)6 (7)Ankylosing spondylitis2 (1)02 (2)Enthesitis related arthritis1 (0.7)1 (2)0Unsure12 (9)3 (6)9 (10)Current disease activity N (%)Active59 (44)15 (29)44 (49)χ2= 9.56p= 0.022*Inactive on medication49 (36)16 (34)33 (37)Inactive off medication for < 1 year18 (13)11 (23)7 (7.8)Clinical remission10 (7)5 (10)5 (5.6)Current medications reported using N (%)None22 (16)10 (21)12 (13)χ2= 2.47p= 0.48NSAID69 (51)23 (49)46 (51)Non-biologic DMARD51 (38)18 (38)33 (37)Biologic DMARD65 (48)18 (38)47 (52)aColumn percentages are displayedbP-values derived from the chi-squared (χ 2 ) or Wilcoxon testscDMARDs disease-modifying antirheumatic drugs, NSAIDs nonsteroidal anti-inflammatory drugsdMedication categories are not mutually exclusive, therefore, medications do not sum to 100%\nFlow diagram of study\nCorrelation of demographics and disease characteristics\nχ2= 8.03\np=0.045*\nχ2= 1.34\np= 0.72\nχ2= 1.45\np= 0.69\nχ2= 7.93\np= 0.13\nχ2= 9.56\np= 0.022*\nχ2= 2.47\np= 0.48\naColumn percentages are displayed\nbP-values derived from the chi-squared (χ 2 ) or Wilcoxon tests\ncDMARDs disease-modifying antirheumatic drugs, NSAIDs nonsteroidal anti-inflammatory drugs\ndMedication categories are not mutually exclusive, therefore, medications do not sum to 100%\nA majority of those using CBD or contemplating using CBD for their child learned about it from TV (66%), a friend/relative (34%) or JIA online blog/support group (35%). Very few obtained information from a scientific journal article (17%) or their child’s rheumatologist (2%). Around half (52%) used 2 or more sources to learn about CBD. A majority of parents/guardians (75%) reported believing that CBD would reduce their child’s joint pain (Fig. 2 A), while only 15% of respondents reported believing that CBD has side effects. More than half of respondents reported thinking that CBD is safe because it is a natural product (Fig. 2 C). Nearly two-thirds (63%, n = 56) of respondents had not discussed using CBD with their child’s rheumatologist and over half (61%) of those did not plan on discussing with their child’s rheumatologist for the following reasons: scared of what provider may think (35%), felt they wouldn’t be taken seriously (29%), and believed rheumatologist would have no knowledge about CBD (18%).\nFig. 2Parent/gaurdian perceptions of those using CBD for their child’s arthritis and those contemplating use of CBD (n=89) on how they percieve CBD will help their child’s arthritis (A), how they learned about CBD (B), perception of safety fo CBD (C). 56 respondents haven’t told their child’s rheumatologist for the following reasons (D)\nParent/gaurdian perceptions of those using CBD for their child’s arthritis and those contemplating use of CBD (n=89) on how they percieve CBD will help their child’s arthritis (A), how they learned about CBD (B), perception of safety fo CBD (C). 56 respondents haven’t told their child’s rheumatologist for the following reasons (D)\nOverall, 422 JIA patients met inclusion criteria. Of those, 236 parent/guardians agreed to be sent the survey link and 136 participants completed the survey (58% response rate, Fig. 1). 10 respondents (7%) reported using a CBD product to treat their child’s JIA, 79 respondents (58%) reported contemplating use of a CBD product to treat their child’s JIA, and 47 respondents (34.5%) reported no interest in starting a CBD product. Demographic characteristics of the survey respondents are shown in Table 1. The study population was largely white, had a bachelor’s degree or higher, and had an annual income of more than 50,000 dollars per year. A large majority of respondents in both groups reported using one or more CIM therapies in their lifetime. There was no significant difference in the specific types or number of CIM therapies used across groups. Report of high disease activity was more frequent among those currently using or contemplating CBD use than those not contemplating use.\nFig. 1Flow diagram of studyTable 1Correlation of demographics and disease characteristicsParent/guardian and child demographics and disease characteristicsTotal (N=136)Not contemplating starting a CBD product for child (N=47)Contemplating starting a CBD product for child (N=79) and using CBD product for child (N=10)P-valueRespondent parent/guardian age in years (mean +/- SD)29.7 (8)26.5 (7)Respondent Parent/guardian Gender- Female N (%)119 (87)42 (89)77 (86)Race/ethnicity- N (%)White/Caucasian132 (97)44 (94)88 (98.9)Black/African American2 (1)1 (2)1 (1.1)Asian American2 (1)2 (4)0Parent/guardian education levelHigh School or GED20 (16)2 (4)18 (21)χ2= 8.03p=0.045*Some college but no degree26 (19)7 (15)19 (22)Associate degree26 (19)12 (26)14 (16)Bachelor’s degree or higher62 (46)26 (55)36 (41)Income level- US dollars per yearLess than 19,0003 (2)1 (2)2 (2)χ2= 1.34p= 0.7220,000 to 49,00027 (20)7 (15)20 (22)50,000 to 99,00041 (30)13 (28)28 (32)100,000 or more65 (48)26 (55)39 (44)Child’s age in years- (mean +/- SD)11 (4)11.9 (4)Child gender: Female- N (%)95 (70)31 (66)64 (72)JIA duration N (%)< 6 months2 (1)2 (4)0χ2= 1.45p= 0.696-12 months9 (7)3 (6)6 (6)> 12- 24 months20 (15)7 (15)13 (15)> 24 months105 (77)35 (75)70 (79)JIA Subtype N (%)Oligoarticular47 (35)19 (47)28 (32)χ2= 7.93p= 0.13Polyarticular53 (39)12 (26)41 (46)Psoriatic arthritis6 (4)3 (2)3 (3)Systemic14 (10)8 (17)6 (7)Ankylosing spondylitis2 (1)02 (2)Enthesitis related arthritis1 (0.7)1 (2)0Unsure12 (9)3 (6)9 (10)Current disease activity N (%)Active59 (44)15 (29)44 (49)χ2= 9.56p= 0.022*Inactive on medication49 (36)16 (34)33 (37)Inactive off medication for < 1 year18 (13)11 (23)7 (7.8)Clinical remission10 (7)5 (10)5 (5.6)Current medications reported using N (%)None22 (16)10 (21)12 (13)χ2= 2.47p= 0.48NSAID69 (51)23 (49)46 (51)Non-biologic DMARD51 (38)18 (38)33 (37)Biologic DMARD65 (48)18 (38)47 (52)aColumn percentages are displayedbP-values derived from the chi-squared (χ 2 ) or Wilcoxon testscDMARDs disease-modifying antirheumatic drugs, NSAIDs nonsteroidal anti-inflammatory drugsdMedication categories are not mutually exclusive, therefore, medications do not sum to 100%\nFlow diagram of study\nCorrelation of demographics and disease characteristics\nχ2= 8.03\np=0.045*\nχ2= 1.34\np= 0.72\nχ2= 1.45\np= 0.69\nχ2= 7.93\np= 0.13\nχ2= 9.56\np= 0.022*\nχ2= 2.47\np= 0.48\naColumn percentages are displayed\nbP-values derived from the chi-squared (χ 2 ) or Wilcoxon tests\ncDMARDs disease-modifying antirheumatic drugs, NSAIDs nonsteroidal anti-inflammatory drugs\ndMedication categories are not mutually exclusive, therefore, medications do not sum to 100%\nA majority of those using CBD or contemplating using CBD for their child learned about it from TV (66%), a friend/relative (34%) or JIA online blog/support group (35%). Very few obtained information from a scientific journal article (17%) or their child’s rheumatologist (2%). Around half (52%) used 2 or more sources to learn about CBD. A majority of parents/guardians (75%) reported believing that CBD would reduce their child’s joint pain (Fig. 2 A), while only 15% of respondents reported believing that CBD has side effects. More than half of respondents reported thinking that CBD is safe because it is a natural product (Fig. 2 C). Nearly two-thirds (63%, n = 56) of respondents had not discussed using CBD with their child’s rheumatologist and over half (61%) of those did not plan on discussing with their child’s rheumatologist for the following reasons: scared of what provider may think (35%), felt they wouldn’t be taken seriously (29%), and believed rheumatologist would have no knowledge about CBD (18%).\nFig. 2Parent/gaurdian perceptions of those using CBD for their child’s arthritis and those contemplating use of CBD (n=89) on how they percieve CBD will help their child’s arthritis (A), how they learned about CBD (B), perception of safety fo CBD (C). 56 respondents haven’t told their child’s rheumatologist for the following reasons (D)\nParent/gaurdian perceptions of those using CBD for their child’s arthritis and those contemplating use of CBD (n=89) on how they percieve CBD will help their child’s arthritis (A), how they learned about CBD (B), perception of safety fo CBD (C). 56 respondents haven’t told their child’s rheumatologist for the following reasons (D)\nContemplating CBD use Respondents contemplating starting a CBD product for their child’s JIA (n=79) were interested in the following CBD products: CBD oil balm (30%), oil drops (25%), gummies (15%), soft gels/capsules (6.5%), and oil roll on (23%). Around a third (32%) of respondents were unsure what products they were interested in. Of those respondents (n=52) who were interested in starting a CBD product, 32.6% were interested in only oral CBD, 36.5% in a combination of oral and topical CBD, and 30.7% were interested only in topical CBD.\nRespondents contemplating starting a CBD product for their child’s JIA (n=79) were interested in the following CBD products: CBD oil balm (30%), oil drops (25%), gummies (15%), soft gels/capsules (6.5%), and oil roll on (23%). Around a third (32%) of respondents were unsure what products they were interested in. Of those respondents (n=52) who were interested in starting a CBD product, 32.6% were interested in only oral CBD, 36.5% in a combination of oral and topical CBD, and 30.7% were interested only in topical CBD.\nCurrent CBD use Respondents using CBD products for their child’s JIA (n=10) reported administering CBD orally (50%) or topically (50%). The majority (60%) reported using CBD on an as needed basis, while 40% reported using CBD on a scheduled basis. Overall, 40% reported administering CBD once per day, 20% twice per day and 40% at least three times per day. Respondents who reported administering CBD as needed (n=6) gave it for joint pain (66%), joint swelling (50%), joint stiffness (66%), and/or when their child requested it (33%). Respondents reported their child’s overall wellbeing to be an average 3.6 prior to starting CBD (0 = very poor, 10 = very good) and 5.3 after taking a CBD product. Half (50%, n=5) of parents reported improvement of their child’s wellbeing after they started CBD while 30% reported no change in their child’s wellbeing and 20% reported decreased well-being. Respondents used the following CBD products: oil drops (40%), lotion (10%), soft gels (10%) and oil balm (40%). Only one respondent knew the total dose of CBD administered per day (20 mg daily) while 70% (n=7) were unsure and 20% (n=2) reported they believed that the dose of CBD was irrelevant.\nRespondents using CBD products for their child’s JIA (n=10) reported administering CBD orally (50%) or topically (50%). The majority (60%) reported using CBD on an as needed basis, while 40% reported using CBD on a scheduled basis. Overall, 40% reported administering CBD once per day, 20% twice per day and 40% at least three times per day. Respondents who reported administering CBD as needed (n=6) gave it for joint pain (66%), joint swelling (50%), joint stiffness (66%), and/or when their child requested it (33%). Respondents reported their child’s overall wellbeing to be an average 3.6 prior to starting CBD (0 = very poor, 10 = very good) and 5.3 after taking a CBD product. Half (50%, n=5) of parents reported improvement of their child’s wellbeing after they started CBD while 30% reported no change in their child’s wellbeing and 20% reported decreased well-being. Respondents used the following CBD products: oil drops (40%), lotion (10%), soft gels (10%) and oil balm (40%). Only one respondent knew the total dose of CBD administered per day (20 mg daily) while 70% (n=7) were unsure and 20% (n=2) reported they believed that the dose of CBD was irrelevant.","Overall, 422 JIA patients met inclusion criteria. Of those, 236 parent/guardians agreed to be sent the survey link and 136 participants completed the survey (58% response rate, Fig. 1). 10 respondents (7%) reported using a CBD product to treat their child’s JIA, 79 respondents (58%) reported contemplating use of a CBD product to treat their child’s JIA, and 47 respondents (34.5%) reported no interest in starting a CBD product. Demographic characteristics of the survey respondents are shown in Table 1. The study population was largely white, had a bachelor’s degree or higher, and had an annual income of more than 50,000 dollars per year. A large majority of respondents in both groups reported using one or more CIM therapies in their lifetime. There was no significant difference in the specific types or number of CIM therapies used across groups. Report of high disease activity was more frequent among those currently using or contemplating CBD use than those not contemplating use.\nFig. 1Flow diagram of studyTable 1Correlation of demographics and disease characteristicsParent/guardian and child demographics and disease characteristicsTotal (N=136)Not contemplating starting a CBD product for child (N=47)Contemplating starting a CBD product for child (N=79) and using CBD product for child (N=10)P-valueRespondent parent/guardian age in years (mean +/- SD)29.7 (8)26.5 (7)Respondent Parent/guardian Gender- Female N (%)119 (87)42 (89)77 (86)Race/ethnicity- N (%)White/Caucasian132 (97)44 (94)88 (98.9)Black/African American2 (1)1 (2)1 (1.1)Asian American2 (1)2 (4)0Parent/guardian education levelHigh School or GED20 (16)2 (4)18 (21)χ2= 8.03p=0.045*Some college but no degree26 (19)7 (15)19 (22)Associate degree26 (19)12 (26)14 (16)Bachelor’s degree or higher62 (46)26 (55)36 (41)Income level- US dollars per yearLess than 19,0003 (2)1 (2)2 (2)χ2= 1.34p= 0.7220,000 to 49,00027 (20)7 (15)20 (22)50,000 to 99,00041 (30)13 (28)28 (32)100,000 or more65 (48)26 (55)39 (44)Child’s age in years- (mean +/- SD)11 (4)11.9 (4)Child gender: Female- N (%)95 (70)31 (66)64 (72)JIA duration N (%)< 6 months2 (1)2 (4)0χ2= 1.45p= 0.696-12 months9 (7)3 (6)6 (6)> 12- 24 months20 (15)7 (15)13 (15)> 24 months105 (77)35 (75)70 (79)JIA Subtype N (%)Oligoarticular47 (35)19 (47)28 (32)χ2= 7.93p= 0.13Polyarticular53 (39)12 (26)41 (46)Psoriatic arthritis6 (4)3 (2)3 (3)Systemic14 (10)8 (17)6 (7)Ankylosing spondylitis2 (1)02 (2)Enthesitis related arthritis1 (0.7)1 (2)0Unsure12 (9)3 (6)9 (10)Current disease activity N (%)Active59 (44)15 (29)44 (49)χ2= 9.56p= 0.022*Inactive on medication49 (36)16 (34)33 (37)Inactive off medication for < 1 year18 (13)11 (23)7 (7.8)Clinical remission10 (7)5 (10)5 (5.6)Current medications reported using N (%)None22 (16)10 (21)12 (13)χ2= 2.47p= 0.48NSAID69 (51)23 (49)46 (51)Non-biologic DMARD51 (38)18 (38)33 (37)Biologic DMARD65 (48)18 (38)47 (52)aColumn percentages are displayedbP-values derived from the chi-squared (χ 2 ) or Wilcoxon testscDMARDs disease-modifying antirheumatic drugs, NSAIDs nonsteroidal anti-inflammatory drugsdMedication categories are not mutually exclusive, therefore, medications do not sum to 100%\nFlow diagram of study\nCorrelation of demographics and disease characteristics\nχ2= 8.03\np=0.045*\nχ2= 1.34\np= 0.72\nχ2= 1.45\np= 0.69\nχ2= 7.93\np= 0.13\nχ2= 9.56\np= 0.022*\nχ2= 2.47\np= 0.48\naColumn percentages are displayed\nbP-values derived from the chi-squared (χ 2 ) or Wilcoxon tests\ncDMARDs disease-modifying antirheumatic drugs, NSAIDs nonsteroidal anti-inflammatory drugs\ndMedication categories are not mutually exclusive, therefore, medications do not sum to 100%\nA majority of those using CBD or contemplating using CBD for their child learned about it from TV (66%), a friend/relative (34%) or JIA online blog/support group (35%). Very few obtained information from a scientific journal article (17%) or their child’s rheumatologist (2%). Around half (52%) used 2 or more sources to learn about CBD. A majority of parents/guardians (75%) reported believing that CBD would reduce their child’s joint pain (Fig. 2 A), while only 15% of respondents reported believing that CBD has side effects. More than half of respondents reported thinking that CBD is safe because it is a natural product (Fig. 2 C). Nearly two-thirds (63%, n = 56) of respondents had not discussed using CBD with their child’s rheumatologist and over half (61%) of those did not plan on discussing with their child’s rheumatologist for the following reasons: scared of what provider may think (35%), felt they wouldn’t be taken seriously (29%), and believed rheumatologist would have no knowledge about CBD (18%).\nFig. 2Parent/gaurdian perceptions of those using CBD for their child’s arthritis and those contemplating use of CBD (n=89) on how they percieve CBD will help their child’s arthritis (A), how they learned about CBD (B), perception of safety fo CBD (C). 56 respondents haven’t told their child’s rheumatologist for the following reasons (D)\nParent/gaurdian perceptions of those using CBD for their child’s arthritis and those contemplating use of CBD (n=89) on how they percieve CBD will help their child’s arthritis (A), how they learned about CBD (B), perception of safety fo CBD (C). 56 respondents haven’t told their child’s rheumatologist for the following reasons (D)","Respondents contemplating starting a CBD product for their child’s JIA (n=79) were interested in the following CBD products: CBD oil balm (30%), oil drops (25%), gummies (15%), soft gels/capsules (6.5%), and oil roll on (23%). Around a third (32%) of respondents were unsure what products they were interested in. Of those respondents (n=52) who were interested in starting a CBD product, 32.6% were interested in only oral CBD, 36.5% in a combination of oral and topical CBD, and 30.7% were interested only in topical CBD.","Respondents using CBD products for their child’s JIA (n=10) reported administering CBD orally (50%) or topically (50%). The majority (60%) reported using CBD on an as needed basis, while 40% reported using CBD on a scheduled basis. Overall, 40% reported administering CBD once per day, 20% twice per day and 40% at least three times per day. Respondents who reported administering CBD as needed (n=6) gave it for joint pain (66%), joint swelling (50%), joint stiffness (66%), and/or when their child requested it (33%). Respondents reported their child’s overall wellbeing to be an average 3.6 prior to starting CBD (0 = very poor, 10 = very good) and 5.3 after taking a CBD product. Half (50%, n=5) of parents reported improvement of their child’s wellbeing after they started CBD while 30% reported no change in their child’s wellbeing and 20% reported decreased well-being. Respondents used the following CBD products: oil drops (40%), lotion (10%), soft gels (10%) and oil balm (40%). Only one respondent knew the total dose of CBD administered per day (20 mg daily) while 70% (n=7) were unsure and 20% (n=2) reported they believed that the dose of CBD was irrelevant.","To our knowledge, this is the first study exploring parent/guardian knowledge and opinions regarding CBD use for their children with JIA. We found that while CBD use is infrequent, there is a strong parent/guardian interest in using CBD for treating JIA, especially among respondents reporting more active disease and a longer disease course. Use of stronger medications such as biologics, on the other hand, was not associated with a significant difference in CBD interest. These findings are consistent with other studies showing that children with JIA use CIM more frequently if they have more active disease and longer disease duration, and that use of immunosuppressive or biologic medications is not a factor related to CIM use among children with JIA [6, 25].\nThe majority of the survey respondents learned about CBD from television, the internet (JIA online blog/support group), or friend or family member with only a small percentage of respondents learned from a health care provider or scientific study, mirroring results from other studies of adults using CBD oil or cannabis [26, 27]. Our study further showed that many parent/guardians are not discussing CBD with their child’s rheumatologist. This is because they expressed worry that their child’s rheumatologist would negatively judge them and or not take them seriously if they discussed their experience with or interest in CBD. This finding is similar to a recent study in which only 9.6% of young adults reported discussing CBD usage with their healthcare provider [27]. Previous studies evaluating CIM use in adolescents with JIA have demonstrated similarly low rates of discussions with their health care provider, [10] and parents of children with other chronic health conditions have reported similar reasons for not discussing CIM with their child’s health provider. These results suggest that providers need CBD and CIM-related education to better serve individuals with JIA, and also that providers need to specifically ask about use of CBD and other CIM modalities.\nAs CBD becomes increasingly more popular, parental interest in using CBD to treat their child’s health conditions continues to grow. The use of the search terms for “CBD for children” and “CBD for kids” have increased since 2018, [22] and numerous blog posts and other forms of media report positive results from giving CBD to children [22]. These forms of media do often mention preclinical CBD research conducted in mice, which demonstrate that CBD has potent anti-inflammatory and analgesic effects in induced inflammatory arthritis [14, 15]. Further, some small clinical trials of CBD in adults do show that CBD may have analgesic activity (in neuropathy and temporomandibular joint disorder [28, 29]) and short-term anxiolytic effects, [30–32] and several clinical trials of CBD in arthritis are ongoing (for example, in rheumatoid arthritis) [33]. However, what is often not communicated is that studies on safety and efficacy of CBD among children with those symptoms (e.g., pain, inflammation) have not yet been conducted. As such, additional rigorous research is needed to investigate whether these preliminary therapeutic findings translate to the JIA context.\nConsistent with prior reports about CBD administration among young adults, [12, 27] a majority of those using CBD for their child’s arthritis are administering CBD orally (60%) on an as needed basis as often as several times per day for joint pain and/or stiffness. In addition, 69% of those contemplating CBD expressed interest in an oral CBD product (alone or in combination with topical CBD). This strong interest in oral CBD is important to note, as CBD has been suggested to interact with the liver enzyme cytochrome P450 and could interfere with the metabolism of several commonly prescribed rheumatologic medications, including prednisone, naproxen, and tofacitinib, potentially leading to increased drug levels and increased risk of toxicity.\nThe large majority of respondents believed CBD is safe because it is a natural product and did not believe there were adverse effects of CBD. Surprisingly, only 1 of 10 participants currently giving their child CBD knew what dose they were administering. The overall safety of CBD for healthy children or other clinical populations remains unknown but the Epidiolex trials, [16, 34] which used high doses of CBD, reported non-serious adverse effects in children including dry mouth, sedation, and/or decreased appetite. Other studies have reported similar adverse effects in young adults or adults taking CBD [13, 27].","Respondents of both panels were similar in terms of race/ethnicity; education, age, and gender, however, > 95% of respondents were white/Caucasian which is not representative of the JIA patient population at our institution or in the US. Survey links were only generated for parents or guardians who expressed interest in participating in the study, so selection bias was likely present. In addition, respondents may have interpreted survey questions differently than we intended and wording of questions may have introduced bias. Finally, we only queried the parents/guardians of individuals with JIA rather than directly asking individuals with JIA about their experiences with or interest in CBD.","As CBD continues to gain popularity, parental interest in CBD for treating their child’s health condition(s) will likely increase. In this study, we show that while CBD use is currently infrequent for JIA, many parents/guardians are interested in using CBD to help with JIA symptoms. As such, is important that pediatric rheumatologists and other pediatric providers educate themselves about CBD to increase their comfort in discussing CBD and its potential safety issues with their patients and/or parents. Such efforts should focus on harm-reduction, communicating uncertainty without harming the patient-physician relationship, and guiding interested parties to reliable sources on CBD (e.g. the Arthritis Foundation)[35] to ensure that they are obtaining information based on scientific evidence. In addition, rigorous clinical studies are warranted to investigate both safety and efficacy of CBD in JIA to bridge the gap in knowledge."," \nAdditional file 1\nAdditional file 1\n\nAdditional file 1\nAdditional file 1","\nAdditional file 1\nAdditional file 1"],"string":"[\n \"Juvenile idiopathic arthritis (JIA) is the most common type of chronic arthritis in children, affecting 1 in 1000 children. It is an important cause of short and long-term disability and causes significant financial burden with annual direct medical costs ranging $400-$7,000 [1]. Effective treatments for JIA include non-steroidal anti-inflammatory drugs (NSAIDs), corticosteroids, disease modifying anti-rheumatic drugs (DMARDs), and biologic agents, but each carries potential adverse effects [2]. Indeed, parents and children frequently worry about side effects and the long-term safety of medications prescribed for JIA [3, 4]. As a result, many parents and children (34-92%) use complementary and integrative medicine (CIM) separately or in conjunction with standard treatment of JIA [5–10].\\nOne such CIM treatment gaining popularity in the past years is cannabidiol (CBD), which is derived from Cannabis sativa. Since the removal of some CBD products from the Controlled Substances Act in 2018, a vast number of products made from hemp (Cannabis sativa with <0.3% Δ-9-tetrahydrocannabinol [THC]) have become available in brick and mortar retailers in topical, oral or inhaled forms [11]. CBD is non-intoxicating and has been widely advertised as a safe and natural therapy for many ailments including chronic pain, arthritis, other inflammatory diseases, and mental health conditions, resulting in frequent use for these conditions [12, 13]. In non-human animal studies, CBD reduces pain and inflammation due to arthritis, but these findings have not been validated in human studies [14, 15]. With the exception of Epidiolex, which is approved for the treatment of the rare seizure disorders Lennox Gastaut and Dravet Syndrome, CBD is minimally regulated by the Food and Drug Administration (FDA) [16, 17]. With the exception of these rare seizure disorders, evidence of a therapeutic benefit of CBD for pediatric conditions is lacking [18].\\nCBD’s safety profile has only been characterized among individuals with Dravet and Lennox Gastaut syndrome, so whether CBD is safe for use in healthy children or other pediatric populations remains unknown. Complicating matters, CBD is a promiscuous molecule that interacts with numerous systems in the body (e.g., serotonergic 5HT1A, endocannabinoid system as cannabinoid receptor 1 antagonist) [19, 20], and may interact with the metabolism of drugs commonly taken by children with JIA including prednisone and naproxen [21]. Further, testing of safety and potency of CBD products is not governed by a strong regulatory apparatus, [22, 23] and a recent JAMA study revealed that only 31% of CBD products sold online are accurately labeled for potency with 21% of samples containing THC [24]. As such, there are safety concerns about use in children, especially those with JIA.\\nAs pediatric rheumatologists, the authors (C.F., M. R.) have been frequently asked about using CBD products to treat JIA symptoms, but to date, there is no literature available regarding the use of CBD in children with JIA. The objective of this study was to determine the frequency of CBD use among children with JIA and investigate caregiver knowledge and opinions about CBD use for their children.\",\n \"All study procedures and protocols were approved by the Institutional Review Board (IRB) at the University of Michigan (HUM00169198). We first conducted an administrative data query at the University of Michigan to identify all children ages 0-17 years of age at the time of a visit associated with the ICD-10 code for JIA between 1/1/2017 and 12/31/2019. That administrative data query identified 900 patients with ICD-10 codes for JIA.\\nParticipant eligibility The charts of those 900 patients were then reviewed by C.F. Parents or guardians of patients were invited to participate in the study if the patient was younger than 18 years of age at the time of survey, had a diagnosis of JIA, had more than 1 visit to Pediatric Rheumatology clinic, and had been evaluated by a Pediatric Rheumatologist within the last 18 months. N=422 eligible participants were contacted by phone and invited to take an anonymous online survey created by the authors using a unique link through Qualtrics between December of 2019 and February of 2020. Only respondents interested in the survey were sent the unique link. Respondents were not compensated for completing the survey.\\nThe charts of those 900 patients were then reviewed by C.F. Parents or guardians of patients were invited to participate in the study if the patient was younger than 18 years of age at the time of survey, had a diagnosis of JIA, had more than 1 visit to Pediatric Rheumatology clinic, and had been evaluated by a Pediatric Rheumatologist within the last 18 months. N=422 eligible participants were contacted by phone and invited to take an anonymous online survey created by the authors using a unique link through Qualtrics between December of 2019 and February of 2020. Only respondents interested in the survey were sent the unique link. Respondents were not compensated for completing the survey.\\nSurvey The survey consisted of 83 items, some of which were variably displayed depending on participant’s responses. Questions addressed parent/guardian demographics (age, gender, ethnicity, education level, annual household income), use of complementary and integrative medicine (CIM) over lifetime (no use, 1 CIM, 2-4 CIMs, > 4 CIM), history of parent/guardian CBD product and cannabis use, child demographics and disease characteristics (age, gender, subtype of JIA, disease duration, parent/guardian report of disease activity at last rheumatology appointment, current rheumatologic medications used, co-morbid health conditions), and total number of CIM therapies used over child’s lifetime.\\nRespondents using CBD or contemplating CBD use for treatment of their child’s arthritis answered questions about sources of CBD information, perceptions of how CBD might improve their child’s arthritis, perceptions of the safety of CBD, and whether they had discussed CBD with their child’s provider. If respondents had not discussed CBD with their child’s healthcare provider, they were asked for the reasons why.\\nIf parent/guardian reported using CBD product for child’s arthritis, they were asked questions about their CBD product(s), route of CBD administration, frequency of CBD use, parental perception of child’s disease activity pre and post-CBD use (using a 0-10 visual analogue scale), and total daily dosage of CBD (if known).\\nThe survey consisted of 83 items, some of which were variably displayed depending on participant’s responses. Questions addressed parent/guardian demographics (age, gender, ethnicity, education level, annual household income), use of complementary and integrative medicine (CIM) over lifetime (no use, 1 CIM, 2-4 CIMs, > 4 CIM), history of parent/guardian CBD product and cannabis use, child demographics and disease characteristics (age, gender, subtype of JIA, disease duration, parent/guardian report of disease activity at last rheumatology appointment, current rheumatologic medications used, co-morbid health conditions), and total number of CIM therapies used over child’s lifetime.\\nRespondents using CBD or contemplating CBD use for treatment of their child’s arthritis answered questions about sources of CBD information, perceptions of how CBD might improve their child’s arthritis, perceptions of the safety of CBD, and whether they had discussed CBD with their child’s provider. If respondents had not discussed CBD with their child’s healthcare provider, they were asked for the reasons why.\\nIf parent/guardian reported using CBD product for child’s arthritis, they were asked questions about their CBD product(s), route of CBD administration, frequency of CBD use, parental perception of child’s disease activity pre and post-CBD use (using a 0-10 visual analogue scale), and total daily dosage of CBD (if known).\\nStatistical analysis We performed descriptive analyses, and present results as frequency, n (%) and mean +/- standard deviation for categorical and continuous variables, respectively. We used Fisher’s chi-square test to assess differences in categorical variables. Participants were divided into 2 comparison groups for analyses: currently using or contemplating starting a CBD product for their child and no interest in starting a CBD product. Participants using CBD for treatment of their child’s arthritis were not used for standalone comparison due to small sample size (n=10). All statistical analysis was performed using Microsoft Excel (2016, Microsoft Corporation).\\nWe performed descriptive analyses, and present results as frequency, n (%) and mean +/- standard deviation for categorical and continuous variables, respectively. We used Fisher’s chi-square test to assess differences in categorical variables. Participants were divided into 2 comparison groups for analyses: currently using or contemplating starting a CBD product for their child and no interest in starting a CBD product. Participants using CBD for treatment of their child’s arthritis were not used for standalone comparison due to small sample size (n=10). All statistical analysis was performed using Microsoft Excel (2016, Microsoft Corporation).\",\n \"The charts of those 900 patients were then reviewed by C.F. Parents or guardians of patients were invited to participate in the study if the patient was younger than 18 years of age at the time of survey, had a diagnosis of JIA, had more than 1 visit to Pediatric Rheumatology clinic, and had been evaluated by a Pediatric Rheumatologist within the last 18 months. N=422 eligible participants were contacted by phone and invited to take an anonymous online survey created by the authors using a unique link through Qualtrics between December of 2019 and February of 2020. Only respondents interested in the survey were sent the unique link. Respondents were not compensated for completing the survey.\",\n \"The survey consisted of 83 items, some of which were variably displayed depending on participant’s responses. Questions addressed parent/guardian demographics (age, gender, ethnicity, education level, annual household income), use of complementary and integrative medicine (CIM) over lifetime (no use, 1 CIM, 2-4 CIMs, > 4 CIM), history of parent/guardian CBD product and cannabis use, child demographics and disease characteristics (age, gender, subtype of JIA, disease duration, parent/guardian report of disease activity at last rheumatology appointment, current rheumatologic medications used, co-morbid health conditions), and total number of CIM therapies used over child’s lifetime.\\nRespondents using CBD or contemplating CBD use for treatment of their child’s arthritis answered questions about sources of CBD information, perceptions of how CBD might improve their child’s arthritis, perceptions of the safety of CBD, and whether they had discussed CBD with their child’s provider. If respondents had not discussed CBD with their child’s healthcare provider, they were asked for the reasons why.\\nIf parent/guardian reported using CBD product for child’s arthritis, they were asked questions about their CBD product(s), route of CBD administration, frequency of CBD use, parental perception of child’s disease activity pre and post-CBD use (using a 0-10 visual analogue scale), and total daily dosage of CBD (if known).\",\n \"We performed descriptive analyses, and present results as frequency, n (%) and mean +/- standard deviation for categorical and continuous variables, respectively. We used Fisher’s chi-square test to assess differences in categorical variables. Participants were divided into 2 comparison groups for analyses: currently using or contemplating starting a CBD product for their child and no interest in starting a CBD product. Participants using CBD for treatment of their child’s arthritis were not used for standalone comparison due to small sample size (n=10). All statistical analysis was performed using Microsoft Excel (2016, Microsoft Corporation).\",\n \"Participation Overall, 422 JIA patients met inclusion criteria. Of those, 236 parent/guardians agreed to be sent the survey link and 136 participants completed the survey (58% response rate, Fig. 1). 10 respondents (7%) reported using a CBD product to treat their child’s JIA, 79 respondents (58%) reported contemplating use of a CBD product to treat their child’s JIA, and 47 respondents (34.5%) reported no interest in starting a CBD product. Demographic characteristics of the survey respondents are shown in Table 1. The study population was largely white, had a bachelor’s degree or higher, and had an annual income of more than 50,000 dollars per year. A large majority of respondents in both groups reported using one or more CIM therapies in their lifetime. There was no significant difference in the specific types or number of CIM therapies used across groups. Report of high disease activity was more frequent among those currently using or contemplating CBD use than those not contemplating use.\\nFig. 1Flow diagram of studyTable 1Correlation of demographics and disease characteristicsParent/guardian and child demographics and disease characteristicsTotal (N=136)Not contemplating starting a CBD product for child (N=47)Contemplating starting a CBD product for child (N=79) and using CBD product for child (N=10)P-valueRespondent parent/guardian age in years (mean +/- SD)29.7 (8)26.5 (7)Respondent Parent/guardian Gender- Female N (%)119 (87)42 (89)77 (86)Race/ethnicity- N (%)White/Caucasian132 (97)44 (94)88 (98.9)Black/African American2 (1)1 (2)1 (1.1)Asian American2 (1)2 (4)0Parent/guardian education levelHigh School or GED20 (16)2 (4)18 (21)χ2= 8.03p=0.045*Some college but no degree26 (19)7 (15)19 (22)Associate degree26 (19)12 (26)14 (16)Bachelor’s degree or higher62 (46)26 (55)36 (41)Income level- US dollars per yearLess than 19,0003 (2)1 (2)2 (2)χ2= 1.34p= 0.7220,000 to 49,00027 (20)7 (15)20 (22)50,000 to 99,00041 (30)13 (28)28 (32)100,000 or more65 (48)26 (55)39 (44)Child’s age in years- (mean +/- SD)11 (4)11.9 (4)Child gender: Female- N (%)95 (70)31 (66)64 (72)JIA duration N (%)< 6 months2 (1)2 (4)0χ2= 1.45p= 0.696-12 months9 (7)3 (6)6 (6)> 12- 24 months20 (15)7 (15)13 (15)> 24 months105 (77)35 (75)70 (79)JIA Subtype N (%)Oligoarticular47 (35)19 (47)28 (32)χ2= 7.93p= 0.13Polyarticular53 (39)12 (26)41 (46)Psoriatic arthritis6 (4)3 (2)3 (3)Systemic14 (10)8 (17)6 (7)Ankylosing spondylitis2 (1)02 (2)Enthesitis related arthritis1 (0.7)1 (2)0Unsure12 (9)3 (6)9 (10)Current disease activity N (%)Active59 (44)15 (29)44 (49)χ2= 9.56p= 0.022*Inactive on medication49 (36)16 (34)33 (37)Inactive off medication for < 1 year18 (13)11 (23)7 (7.8)Clinical remission10 (7)5 (10)5 (5.6)Current medications reported using N (%)None22 (16)10 (21)12 (13)χ2= 2.47p= 0.48NSAID69 (51)23 (49)46 (51)Non-biologic DMARD51 (38)18 (38)33 (37)Biologic DMARD65 (48)18 (38)47 (52)aColumn percentages are displayedbP-values derived from the chi-squared (χ 2 ) or Wilcoxon testscDMARDs disease-modifying antirheumatic drugs, NSAIDs nonsteroidal anti-inflammatory drugsdMedication categories are not mutually exclusive, therefore, medications do not sum to 100%\\nFlow diagram of study\\nCorrelation of demographics and disease characteristics\\nχ2= 8.03\\np=0.045*\\nχ2= 1.34\\np= 0.72\\nχ2= 1.45\\np= 0.69\\nχ2= 7.93\\np= 0.13\\nχ2= 9.56\\np= 0.022*\\nχ2= 2.47\\np= 0.48\\naColumn percentages are displayed\\nbP-values derived from the chi-squared (χ 2 ) or Wilcoxon tests\\ncDMARDs disease-modifying antirheumatic drugs, NSAIDs nonsteroidal anti-inflammatory drugs\\ndMedication categories are not mutually exclusive, therefore, medications do not sum to 100%\\nA majority of those using CBD or contemplating using CBD for their child learned about it from TV (66%), a friend/relative (34%) or JIA online blog/support group (35%). Very few obtained information from a scientific journal article (17%) or their child’s rheumatologist (2%). Around half (52%) used 2 or more sources to learn about CBD. A majority of parents/guardians (75%) reported believing that CBD would reduce their child’s joint pain (Fig. 2 A), while only 15% of respondents reported believing that CBD has side effects. More than half of respondents reported thinking that CBD is safe because it is a natural product (Fig. 2 C). Nearly two-thirds (63%, n = 56) of respondents had not discussed using CBD with their child’s rheumatologist and over half (61%) of those did not plan on discussing with their child’s rheumatologist for the following reasons: scared of what provider may think (35%), felt they wouldn’t be taken seriously (29%), and believed rheumatologist would have no knowledge about CBD (18%).\\nFig. 2Parent/gaurdian perceptions of those using CBD for their child’s arthritis and those contemplating use of CBD (n=89) on how they percieve CBD will help their child’s arthritis (A), how they learned about CBD (B), perception of safety fo CBD (C). 56 respondents haven’t told their child’s rheumatologist for the following reasons (D)\\nParent/gaurdian perceptions of those using CBD for their child’s arthritis and those contemplating use of CBD (n=89) on how they percieve CBD will help their child’s arthritis (A), how they learned about CBD (B), perception of safety fo CBD (C). 56 respondents haven’t told their child’s rheumatologist for the following reasons (D)\\nOverall, 422 JIA patients met inclusion criteria. Of those, 236 parent/guardians agreed to be sent the survey link and 136 participants completed the survey (58% response rate, Fig. 1). 10 respondents (7%) reported using a CBD product to treat their child’s JIA, 79 respondents (58%) reported contemplating use of a CBD product to treat their child’s JIA, and 47 respondents (34.5%) reported no interest in starting a CBD product. Demographic characteristics of the survey respondents are shown in Table 1. The study population was largely white, had a bachelor’s degree or higher, and had an annual income of more than 50,000 dollars per year. A large majority of respondents in both groups reported using one or more CIM therapies in their lifetime. There was no significant difference in the specific types or number of CIM therapies used across groups. Report of high disease activity was more frequent among those currently using or contemplating CBD use than those not contemplating use.\\nFig. 1Flow diagram of studyTable 1Correlation of demographics and disease characteristicsParent/guardian and child demographics and disease characteristicsTotal (N=136)Not contemplating starting a CBD product for child (N=47)Contemplating starting a CBD product for child (N=79) and using CBD product for child (N=10)P-valueRespondent parent/guardian age in years (mean +/- SD)29.7 (8)26.5 (7)Respondent Parent/guardian Gender- Female N (%)119 (87)42 (89)77 (86)Race/ethnicity- N (%)White/Caucasian132 (97)44 (94)88 (98.9)Black/African American2 (1)1 (2)1 (1.1)Asian American2 (1)2 (4)0Parent/guardian education levelHigh School or GED20 (16)2 (4)18 (21)χ2= 8.03p=0.045*Some college but no degree26 (19)7 (15)19 (22)Associate degree26 (19)12 (26)14 (16)Bachelor’s degree or higher62 (46)26 (55)36 (41)Income level- US dollars per yearLess than 19,0003 (2)1 (2)2 (2)χ2= 1.34p= 0.7220,000 to 49,00027 (20)7 (15)20 (22)50,000 to 99,00041 (30)13 (28)28 (32)100,000 or more65 (48)26 (55)39 (44)Child’s age in years- (mean +/- SD)11 (4)11.9 (4)Child gender: Female- N (%)95 (70)31 (66)64 (72)JIA duration N (%)< 6 months2 (1)2 (4)0χ2= 1.45p= 0.696-12 months9 (7)3 (6)6 (6)> 12- 24 months20 (15)7 (15)13 (15)> 24 months105 (77)35 (75)70 (79)JIA Subtype N (%)Oligoarticular47 (35)19 (47)28 (32)χ2= 7.93p= 0.13Polyarticular53 (39)12 (26)41 (46)Psoriatic arthritis6 (4)3 (2)3 (3)Systemic14 (10)8 (17)6 (7)Ankylosing spondylitis2 (1)02 (2)Enthesitis related arthritis1 (0.7)1 (2)0Unsure12 (9)3 (6)9 (10)Current disease activity N (%)Active59 (44)15 (29)44 (49)χ2= 9.56p= 0.022*Inactive on medication49 (36)16 (34)33 (37)Inactive off medication for < 1 year18 (13)11 (23)7 (7.8)Clinical remission10 (7)5 (10)5 (5.6)Current medications reported using N (%)None22 (16)10 (21)12 (13)χ2= 2.47p= 0.48NSAID69 (51)23 (49)46 (51)Non-biologic DMARD51 (38)18 (38)33 (37)Biologic DMARD65 (48)18 (38)47 (52)aColumn percentages are displayedbP-values derived from the chi-squared (χ 2 ) or Wilcoxon testscDMARDs disease-modifying antirheumatic drugs, NSAIDs nonsteroidal anti-inflammatory drugsdMedication categories are not mutually exclusive, therefore, medications do not sum to 100%\\nFlow diagram of study\\nCorrelation of demographics and disease characteristics\\nχ2= 8.03\\np=0.045*\\nχ2= 1.34\\np= 0.72\\nχ2= 1.45\\np= 0.69\\nχ2= 7.93\\np= 0.13\\nχ2= 9.56\\np= 0.022*\\nχ2= 2.47\\np= 0.48\\naColumn percentages are displayed\\nbP-values derived from the chi-squared (χ 2 ) or Wilcoxon tests\\ncDMARDs disease-modifying antirheumatic drugs, NSAIDs nonsteroidal anti-inflammatory drugs\\ndMedication categories are not mutually exclusive, therefore, medications do not sum to 100%\\nA majority of those using CBD or contemplating using CBD for their child learned about it from TV (66%), a friend/relative (34%) or JIA online blog/support group (35%). Very few obtained information from a scientific journal article (17%) or their child’s rheumatologist (2%). Around half (52%) used 2 or more sources to learn about CBD. A majority of parents/guardians (75%) reported believing that CBD would reduce their child’s joint pain (Fig. 2 A), while only 15% of respondents reported believing that CBD has side effects. More than half of respondents reported thinking that CBD is safe because it is a natural product (Fig. 2 C). Nearly two-thirds (63%, n = 56) of respondents had not discussed using CBD with their child’s rheumatologist and over half (61%) of those did not plan on discussing with their child’s rheumatologist for the following reasons: scared of what provider may think (35%), felt they wouldn’t be taken seriously (29%), and believed rheumatologist would have no knowledge about CBD (18%).\\nFig. 2Parent/gaurdian perceptions of those using CBD for their child’s arthritis and those contemplating use of CBD (n=89) on how they percieve CBD will help their child’s arthritis (A), how they learned about CBD (B), perception of safety fo CBD (C). 56 respondents haven’t told their child’s rheumatologist for the following reasons (D)\\nParent/gaurdian perceptions of those using CBD for their child’s arthritis and those contemplating use of CBD (n=89) on how they percieve CBD will help their child’s arthritis (A), how they learned about CBD (B), perception of safety fo CBD (C). 56 respondents haven’t told their child’s rheumatologist for the following reasons (D)\\nContemplating CBD use Respondents contemplating starting a CBD product for their child’s JIA (n=79) were interested in the following CBD products: CBD oil balm (30%), oil drops (25%), gummies (15%), soft gels/capsules (6.5%), and oil roll on (23%). Around a third (32%) of respondents were unsure what products they were interested in. Of those respondents (n=52) who were interested in starting a CBD product, 32.6% were interested in only oral CBD, 36.5% in a combination of oral and topical CBD, and 30.7% were interested only in topical CBD.\\nRespondents contemplating starting a CBD product for their child’s JIA (n=79) were interested in the following CBD products: CBD oil balm (30%), oil drops (25%), gummies (15%), soft gels/capsules (6.5%), and oil roll on (23%). Around a third (32%) of respondents were unsure what products they were interested in. Of those respondents (n=52) who were interested in starting a CBD product, 32.6% were interested in only oral CBD, 36.5% in a combination of oral and topical CBD, and 30.7% were interested only in topical CBD.\\nCurrent CBD use Respondents using CBD products for their child’s JIA (n=10) reported administering CBD orally (50%) or topically (50%). The majority (60%) reported using CBD on an as needed basis, while 40% reported using CBD on a scheduled basis. Overall, 40% reported administering CBD once per day, 20% twice per day and 40% at least three times per day. Respondents who reported administering CBD as needed (n=6) gave it for joint pain (66%), joint swelling (50%), joint stiffness (66%), and/or when their child requested it (33%). Respondents reported their child’s overall wellbeing to be an average 3.6 prior to starting CBD (0 = very poor, 10 = very good) and 5.3 after taking a CBD product. Half (50%, n=5) of parents reported improvement of their child’s wellbeing after they started CBD while 30% reported no change in their child’s wellbeing and 20% reported decreased well-being. Respondents used the following CBD products: oil drops (40%), lotion (10%), soft gels (10%) and oil balm (40%). Only one respondent knew the total dose of CBD administered per day (20 mg daily) while 70% (n=7) were unsure and 20% (n=2) reported they believed that the dose of CBD was irrelevant.\\nRespondents using CBD products for their child’s JIA (n=10) reported administering CBD orally (50%) or topically (50%). The majority (60%) reported using CBD on an as needed basis, while 40% reported using CBD on a scheduled basis. Overall, 40% reported administering CBD once per day, 20% twice per day and 40% at least three times per day. Respondents who reported administering CBD as needed (n=6) gave it for joint pain (66%), joint swelling (50%), joint stiffness (66%), and/or when their child requested it (33%). Respondents reported their child’s overall wellbeing to be an average 3.6 prior to starting CBD (0 = very poor, 10 = very good) and 5.3 after taking a CBD product. Half (50%, n=5) of parents reported improvement of their child’s wellbeing after they started CBD while 30% reported no change in their child’s wellbeing and 20% reported decreased well-being. Respondents used the following CBD products: oil drops (40%), lotion (10%), soft gels (10%) and oil balm (40%). Only one respondent knew the total dose of CBD administered per day (20 mg daily) while 70% (n=7) were unsure and 20% (n=2) reported they believed that the dose of CBD was irrelevant.\",\n \"Overall, 422 JIA patients met inclusion criteria. Of those, 236 parent/guardians agreed to be sent the survey link and 136 participants completed the survey (58% response rate, Fig. 1). 10 respondents (7%) reported using a CBD product to treat their child’s JIA, 79 respondents (58%) reported contemplating use of a CBD product to treat their child’s JIA, and 47 respondents (34.5%) reported no interest in starting a CBD product. Demographic characteristics of the survey respondents are shown in Table 1. The study population was largely white, had a bachelor’s degree or higher, and had an annual income of more than 50,000 dollars per year. A large majority of respondents in both groups reported using one or more CIM therapies in their lifetime. There was no significant difference in the specific types or number of CIM therapies used across groups. Report of high disease activity was more frequent among those currently using or contemplating CBD use than those not contemplating use.\\nFig. 1Flow diagram of studyTable 1Correlation of demographics and disease characteristicsParent/guardian and child demographics and disease characteristicsTotal (N=136)Not contemplating starting a CBD product for child (N=47)Contemplating starting a CBD product for child (N=79) and using CBD product for child (N=10)P-valueRespondent parent/guardian age in years (mean +/- SD)29.7 (8)26.5 (7)Respondent Parent/guardian Gender- Female N (%)119 (87)42 (89)77 (86)Race/ethnicity- N (%)White/Caucasian132 (97)44 (94)88 (98.9)Black/African American2 (1)1 (2)1 (1.1)Asian American2 (1)2 (4)0Parent/guardian education levelHigh School or GED20 (16)2 (4)18 (21)χ2= 8.03p=0.045*Some college but no degree26 (19)7 (15)19 (22)Associate degree26 (19)12 (26)14 (16)Bachelor’s degree or higher62 (46)26 (55)36 (41)Income level- US dollars per yearLess than 19,0003 (2)1 (2)2 (2)χ2= 1.34p= 0.7220,000 to 49,00027 (20)7 (15)20 (22)50,000 to 99,00041 (30)13 (28)28 (32)100,000 or more65 (48)26 (55)39 (44)Child’s age in years- (mean +/- SD)11 (4)11.9 (4)Child gender: Female- N (%)95 (70)31 (66)64 (72)JIA duration N (%)< 6 months2 (1)2 (4)0χ2= 1.45p= 0.696-12 months9 (7)3 (6)6 (6)> 12- 24 months20 (15)7 (15)13 (15)> 24 months105 (77)35 (75)70 (79)JIA Subtype N (%)Oligoarticular47 (35)19 (47)28 (32)χ2= 7.93p= 0.13Polyarticular53 (39)12 (26)41 (46)Psoriatic arthritis6 (4)3 (2)3 (3)Systemic14 (10)8 (17)6 (7)Ankylosing spondylitis2 (1)02 (2)Enthesitis related arthritis1 (0.7)1 (2)0Unsure12 (9)3 (6)9 (10)Current disease activity N (%)Active59 (44)15 (29)44 (49)χ2= 9.56p= 0.022*Inactive on medication49 (36)16 (34)33 (37)Inactive off medication for < 1 year18 (13)11 (23)7 (7.8)Clinical remission10 (7)5 (10)5 (5.6)Current medications reported using N (%)None22 (16)10 (21)12 (13)χ2= 2.47p= 0.48NSAID69 (51)23 (49)46 (51)Non-biologic DMARD51 (38)18 (38)33 (37)Biologic DMARD65 (48)18 (38)47 (52)aColumn percentages are displayedbP-values derived from the chi-squared (χ 2 ) or Wilcoxon testscDMARDs disease-modifying antirheumatic drugs, NSAIDs nonsteroidal anti-inflammatory drugsdMedication categories are not mutually exclusive, therefore, medications do not sum to 100%\\nFlow diagram of study\\nCorrelation of demographics and disease characteristics\\nχ2= 8.03\\np=0.045*\\nχ2= 1.34\\np= 0.72\\nχ2= 1.45\\np= 0.69\\nχ2= 7.93\\np= 0.13\\nχ2= 9.56\\np= 0.022*\\nχ2= 2.47\\np= 0.48\\naColumn percentages are displayed\\nbP-values derived from the chi-squared (χ 2 ) or Wilcoxon tests\\ncDMARDs disease-modifying antirheumatic drugs, NSAIDs nonsteroidal anti-inflammatory drugs\\ndMedication categories are not mutually exclusive, therefore, medications do not sum to 100%\\nA majority of those using CBD or contemplating using CBD for their child learned about it from TV (66%), a friend/relative (34%) or JIA online blog/support group (35%). Very few obtained information from a scientific journal article (17%) or their child’s rheumatologist (2%). Around half (52%) used 2 or more sources to learn about CBD. A majority of parents/guardians (75%) reported believing that CBD would reduce their child’s joint pain (Fig. 2 A), while only 15% of respondents reported believing that CBD has side effects. More than half of respondents reported thinking that CBD is safe because it is a natural product (Fig. 2 C). Nearly two-thirds (63%, n = 56) of respondents had not discussed using CBD with their child’s rheumatologist and over half (61%) of those did not plan on discussing with their child’s rheumatologist for the following reasons: scared of what provider may think (35%), felt they wouldn’t be taken seriously (29%), and believed rheumatologist would have no knowledge about CBD (18%).\\nFig. 2Parent/gaurdian perceptions of those using CBD for their child’s arthritis and those contemplating use of CBD (n=89) on how they percieve CBD will help their child’s arthritis (A), how they learned about CBD (B), perception of safety fo CBD (C). 56 respondents haven’t told their child’s rheumatologist for the following reasons (D)\\nParent/gaurdian perceptions of those using CBD for their child’s arthritis and those contemplating use of CBD (n=89) on how they percieve CBD will help their child’s arthritis (A), how they learned about CBD (B), perception of safety fo CBD (C). 56 respondents haven’t told their child’s rheumatologist for the following reasons (D)\",\n \"Respondents contemplating starting a CBD product for their child’s JIA (n=79) were interested in the following CBD products: CBD oil balm (30%), oil drops (25%), gummies (15%), soft gels/capsules (6.5%), and oil roll on (23%). Around a third (32%) of respondents were unsure what products they were interested in. Of those respondents (n=52) who were interested in starting a CBD product, 32.6% were interested in only oral CBD, 36.5% in a combination of oral and topical CBD, and 30.7% were interested only in topical CBD.\",\n \"Respondents using CBD products for their child’s JIA (n=10) reported administering CBD orally (50%) or topically (50%). The majority (60%) reported using CBD on an as needed basis, while 40% reported using CBD on a scheduled basis. Overall, 40% reported administering CBD once per day, 20% twice per day and 40% at least three times per day. Respondents who reported administering CBD as needed (n=6) gave it for joint pain (66%), joint swelling (50%), joint stiffness (66%), and/or when their child requested it (33%). Respondents reported their child’s overall wellbeing to be an average 3.6 prior to starting CBD (0 = very poor, 10 = very good) and 5.3 after taking a CBD product. Half (50%, n=5) of parents reported improvement of their child’s wellbeing after they started CBD while 30% reported no change in their child’s wellbeing and 20% reported decreased well-being. Respondents used the following CBD products: oil drops (40%), lotion (10%), soft gels (10%) and oil balm (40%). Only one respondent knew the total dose of CBD administered per day (20 mg daily) while 70% (n=7) were unsure and 20% (n=2) reported they believed that the dose of CBD was irrelevant.\",\n \"To our knowledge, this is the first study exploring parent/guardian knowledge and opinions regarding CBD use for their children with JIA. We found that while CBD use is infrequent, there is a strong parent/guardian interest in using CBD for treating JIA, especially among respondents reporting more active disease and a longer disease course. Use of stronger medications such as biologics, on the other hand, was not associated with a significant difference in CBD interest. These findings are consistent with other studies showing that children with JIA use CIM more frequently if they have more active disease and longer disease duration, and that use of immunosuppressive or biologic medications is not a factor related to CIM use among children with JIA [6, 25].\\nThe majority of the survey respondents learned about CBD from television, the internet (JIA online blog/support group), or friend or family member with only a small percentage of respondents learned from a health care provider or scientific study, mirroring results from other studies of adults using CBD oil or cannabis [26, 27]. Our study further showed that many parent/guardians are not discussing CBD with their child’s rheumatologist. This is because they expressed worry that their child’s rheumatologist would negatively judge them and or not take them seriously if they discussed their experience with or interest in CBD. This finding is similar to a recent study in which only 9.6% of young adults reported discussing CBD usage with their healthcare provider [27]. Previous studies evaluating CIM use in adolescents with JIA have demonstrated similarly low rates of discussions with their health care provider, [10] and parents of children with other chronic health conditions have reported similar reasons for not discussing CIM with their child’s health provider. These results suggest that providers need CBD and CIM-related education to better serve individuals with JIA, and also that providers need to specifically ask about use of CBD and other CIM modalities.\\nAs CBD becomes increasingly more popular, parental interest in using CBD to treat their child’s health conditions continues to grow. The use of the search terms for “CBD for children” and “CBD for kids” have increased since 2018, [22] and numerous blog posts and other forms of media report positive results from giving CBD to children [22]. These forms of media do often mention preclinical CBD research conducted in mice, which demonstrate that CBD has potent anti-inflammatory and analgesic effects in induced inflammatory arthritis [14, 15]. Further, some small clinical trials of CBD in adults do show that CBD may have analgesic activity (in neuropathy and temporomandibular joint disorder [28, 29]) and short-term anxiolytic effects, [30–32] and several clinical trials of CBD in arthritis are ongoing (for example, in rheumatoid arthritis) [33]. However, what is often not communicated is that studies on safety and efficacy of CBD among children with those symptoms (e.g., pain, inflammation) have not yet been conducted. As such, additional rigorous research is needed to investigate whether these preliminary therapeutic findings translate to the JIA context.\\nConsistent with prior reports about CBD administration among young adults, [12, 27] a majority of those using CBD for their child’s arthritis are administering CBD orally (60%) on an as needed basis as often as several times per day for joint pain and/or stiffness. In addition, 69% of those contemplating CBD expressed interest in an oral CBD product (alone or in combination with topical CBD). This strong interest in oral CBD is important to note, as CBD has been suggested to interact with the liver enzyme cytochrome P450 and could interfere with the metabolism of several commonly prescribed rheumatologic medications, including prednisone, naproxen, and tofacitinib, potentially leading to increased drug levels and increased risk of toxicity.\\nThe large majority of respondents believed CBD is safe because it is a natural product and did not believe there were adverse effects of CBD. Surprisingly, only 1 of 10 participants currently giving their child CBD knew what dose they were administering. The overall safety of CBD for healthy children or other clinical populations remains unknown but the Epidiolex trials, [16, 34] which used high doses of CBD, reported non-serious adverse effects in children including dry mouth, sedation, and/or decreased appetite. Other studies have reported similar adverse effects in young adults or adults taking CBD [13, 27].\",\n \"Respondents of both panels were similar in terms of race/ethnicity; education, age, and gender, however, > 95% of respondents were white/Caucasian which is not representative of the JIA patient population at our institution or in the US. Survey links were only generated for parents or guardians who expressed interest in participating in the study, so selection bias was likely present. In addition, respondents may have interpreted survey questions differently than we intended and wording of questions may have introduced bias. Finally, we only queried the parents/guardians of individuals with JIA rather than directly asking individuals with JIA about their experiences with or interest in CBD.\",\n \"As CBD continues to gain popularity, parental interest in CBD for treating their child’s health condition(s) will likely increase. In this study, we show that while CBD use is currently infrequent for JIA, many parents/guardians are interested in using CBD to help with JIA symptoms. As such, is important that pediatric rheumatologists and other pediatric providers educate themselves about CBD to increase their comfort in discussing CBD and its potential safety issues with their patients and/or parents. Such efforts should focus on harm-reduction, communicating uncertainty without harming the patient-physician relationship, and guiding interested parties to reliable sources on CBD (e.g. the Arthritis Foundation)[35] to ensure that they are obtaining information based on scientific evidence. In addition, rigorous clinical studies are warranted to investigate both safety and efficacy of CBD in JIA to bridge the gap in knowledge.\",\n \" \\nAdditional file 1\\nAdditional file 1\\n\\nAdditional file 1\\nAdditional file 1\",\n \"\\nAdditional file 1\\nAdditional file 1\"\n]"},"all_sections_sec_types":{"kind":"list like","value":[null,null,null,null,null,"results",null,null,null,"discussion",null,"conclusion","supplementary-material",null],"string":"[\n null,\n null,\n null,\n null,\n null,\n \"results\",\n null,\n null,\n null,\n \"discussion\",\n null,\n \"conclusion\",\n \"supplementary-material\",\n null\n]"},"keywords":{"kind":"list like","value":["Juvenile idiopathic arthritis","Cannabidiol","Pediatric rheumatology","Complementary and integrative medicine"],"string":"[\n \"Juvenile idiopathic arthritis\",\n \"Cannabidiol\",\n \"Pediatric rheumatology\",\n \"Complementary and integrative medicine\"\n]"},"whole_article_text":{"kind":"string","value":"Background:\nJuvenile idiopathic arthritis (JIA) is the most common type of chronic arthritis in children, affecting 1 in 1000 children. It is an important cause of short and long-term disability and causes significant financial burden with annual direct medical costs ranging $400-$7,000 [1]. Effective treatments for JIA include non-steroidal anti-inflammatory drugs (NSAIDs), corticosteroids, disease modifying anti-rheumatic drugs (DMARDs), and biologic agents, but each carries potential adverse effects [2]. Indeed, parents and children frequently worry about side effects and the long-term safety of medications prescribed for JIA [3, 4]. As a result, many parents and children (34-92%) use complementary and integrative medicine (CIM) separately or in conjunction with standard treatment of JIA [5–10].\nOne such CIM treatment gaining popularity in the past years is cannabidiol (CBD), which is derived from Cannabis sativa. Since the removal of some CBD products from the Controlled Substances Act in 2018, a vast number of products made from hemp (Cannabis sativa with <0.3% Δ-9-tetrahydrocannabinol [THC]) have become available in brick and mortar retailers in topical, oral or inhaled forms [11]. CBD is non-intoxicating and has been widely advertised as a safe and natural therapy for many ailments including chronic pain, arthritis, other inflammatory diseases, and mental health conditions, resulting in frequent use for these conditions [12, 13]. In non-human animal studies, CBD reduces pain and inflammation due to arthritis, but these findings have not been validated in human studies [14, 15]. With the exception of Epidiolex, which is approved for the treatment of the rare seizure disorders Lennox Gastaut and Dravet Syndrome, CBD is minimally regulated by the Food and Drug Administration (FDA) [16, 17]. With the exception of these rare seizure disorders, evidence of a therapeutic benefit of CBD for pediatric conditions is lacking [18].\nCBD’s safety profile has only been characterized among individuals with Dravet and Lennox Gastaut syndrome, so whether CBD is safe for use in healthy children or other pediatric populations remains unknown. Complicating matters, CBD is a promiscuous molecule that interacts with numerous systems in the body (e.g., serotonergic 5HT1A, endocannabinoid system as cannabinoid receptor 1 antagonist) [19, 20], and may interact with the metabolism of drugs commonly taken by children with JIA including prednisone and naproxen [21]. Further, testing of safety and potency of CBD products is not governed by a strong regulatory apparatus, [22, 23] and a recent JAMA study revealed that only 31% of CBD products sold online are accurately labeled for potency with 21% of samples containing THC [24]. As such, there are safety concerns about use in children, especially those with JIA.\nAs pediatric rheumatologists, the authors (C.F., M. R.) have been frequently asked about using CBD products to treat JIA symptoms, but to date, there is no literature available regarding the use of CBD in children with JIA. The objective of this study was to determine the frequency of CBD use among children with JIA and investigate caregiver knowledge and opinions about CBD use for their children.\n\nMethods:\nAll study procedures and protocols were approved by the Institutional Review Board (IRB) at the University of Michigan (HUM00169198). We first conducted an administrative data query at the University of Michigan to identify all children ages 0-17 years of age at the time of a visit associated with the ICD-10 code for JIA between 1/1/2017 and 12/31/2019. That administrative data query identified 900 patients with ICD-10 codes for JIA.\nParticipant eligibility The charts of those 900 patients were then reviewed by C.F. Parents or guardians of patients were invited to participate in the study if the patient was younger than 18 years of age at the time of survey, had a diagnosis of JIA, had more than 1 visit to Pediatric Rheumatology clinic, and had been evaluated by a Pediatric Rheumatologist within the last 18 months. N=422 eligible participants were contacted by phone and invited to take an anonymous online survey created by the authors using a unique link through Qualtrics between December of 2019 and February of 2020. Only respondents interested in the survey were sent the unique link. Respondents were not compensated for completing the survey.\nThe charts of those 900 patients were then reviewed by C.F. Parents or guardians of patients were invited to participate in the study if the patient was younger than 18 years of age at the time of survey, had a diagnosis of JIA, had more than 1 visit to Pediatric Rheumatology clinic, and had been evaluated by a Pediatric Rheumatologist within the last 18 months. N=422 eligible participants were contacted by phone and invited to take an anonymous online survey created by the authors using a unique link through Qualtrics between December of 2019 and February of 2020. Only respondents interested in the survey were sent the unique link. Respondents were not compensated for completing the survey.\nSurvey The survey consisted of 83 items, some of which were variably displayed depending on participant’s responses. Questions addressed parent/guardian demographics (age, gender, ethnicity, education level, annual household income), use of complementary and integrative medicine (CIM) over lifetime (no use, 1 CIM, 2-4 CIMs, > 4 CIM), history of parent/guardian CBD product and cannabis use, child demographics and disease characteristics (age, gender, subtype of JIA, disease duration, parent/guardian report of disease activity at last rheumatology appointment, current rheumatologic medications used, co-morbid health conditions), and total number of CIM therapies used over child’s lifetime.\nRespondents using CBD or contemplating CBD use for treatment of their child’s arthritis answered questions about sources of CBD information, perceptions of how CBD might improve their child’s arthritis, perceptions of the safety of CBD, and whether they had discussed CBD with their child’s provider. If respondents had not discussed CBD with their child’s healthcare provider, they were asked for the reasons why.\nIf parent/guardian reported using CBD product for child’s arthritis, they were asked questions about their CBD product(s), route of CBD administration, frequency of CBD use, parental perception of child’s disease activity pre and post-CBD use (using a 0-10 visual analogue scale), and total daily dosage of CBD (if known).\nThe survey consisted of 83 items, some of which were variably displayed depending on participant’s responses. Questions addressed parent/guardian demographics (age, gender, ethnicity, education level, annual household income), use of complementary and integrative medicine (CIM) over lifetime (no use, 1 CIM, 2-4 CIMs, > 4 CIM), history of parent/guardian CBD product and cannabis use, child demographics and disease characteristics (age, gender, subtype of JIA, disease duration, parent/guardian report of disease activity at last rheumatology appointment, current rheumatologic medications used, co-morbid health conditions), and total number of CIM therapies used over child’s lifetime.\nRespondents using CBD or contemplating CBD use for treatment of their child’s arthritis answered questions about sources of CBD information, perceptions of how CBD might improve their child’s arthritis, perceptions of the safety of CBD, and whether they had discussed CBD with their child’s provider. If respondents had not discussed CBD with their child’s healthcare provider, they were asked for the reasons why.\nIf parent/guardian reported using CBD product for child’s arthritis, they were asked questions about their CBD product(s), route of CBD administration, frequency of CBD use, parental perception of child’s disease activity pre and post-CBD use (using a 0-10 visual analogue scale), and total daily dosage of CBD (if known).\nStatistical analysis We performed descriptive analyses, and present results as frequency, n (%) and mean +/- standard deviation for categorical and continuous variables, respectively. We used Fisher’s chi-square test to assess differences in categorical variables. Participants were divided into 2 comparison groups for analyses: currently using or contemplating starting a CBD product for their child and no interest in starting a CBD product. Participants using CBD for treatment of their child’s arthritis were not used for standalone comparison due to small sample size (n=10). All statistical analysis was performed using Microsoft Excel (2016, Microsoft Corporation).\nWe performed descriptive analyses, and present results as frequency, n (%) and mean +/- standard deviation for categorical and continuous variables, respectively. We used Fisher’s chi-square test to assess differences in categorical variables. Participants were divided into 2 comparison groups for analyses: currently using or contemplating starting a CBD product for their child and no interest in starting a CBD product. Participants using CBD for treatment of their child’s arthritis were not used for standalone comparison due to small sample size (n=10). All statistical analysis was performed using Microsoft Excel (2016, Microsoft Corporation).\n\nParticipant eligibility:\nThe charts of those 900 patients were then reviewed by C.F. Parents or guardians of patients were invited to participate in the study if the patient was younger than 18 years of age at the time of survey, had a diagnosis of JIA, had more than 1 visit to Pediatric Rheumatology clinic, and had been evaluated by a Pediatric Rheumatologist within the last 18 months. N=422 eligible participants were contacted by phone and invited to take an anonymous online survey created by the authors using a unique link through Qualtrics between December of 2019 and February of 2020. Only respondents interested in the survey were sent the unique link. Respondents were not compensated for completing the survey.\n\nSurvey:\nThe survey consisted of 83 items, some of which were variably displayed depending on participant’s responses. Questions addressed parent/guardian demographics (age, gender, ethnicity, education level, annual household income), use of complementary and integrative medicine (CIM) over lifetime (no use, 1 CIM, 2-4 CIMs, > 4 CIM), history of parent/guardian CBD product and cannabis use, child demographics and disease characteristics (age, gender, subtype of JIA, disease duration, parent/guardian report of disease activity at last rheumatology appointment, current rheumatologic medications used, co-morbid health conditions), and total number of CIM therapies used over child’s lifetime.\nRespondents using CBD or contemplating CBD use for treatment of their child’s arthritis answered questions about sources of CBD information, perceptions of how CBD might improve their child’s arthritis, perceptions of the safety of CBD, and whether they had discussed CBD with their child’s provider. If respondents had not discussed CBD with their child’s healthcare provider, they were asked for the reasons why.\nIf parent/guardian reported using CBD product for child’s arthritis, they were asked questions about their CBD product(s), route of CBD administration, frequency of CBD use, parental perception of child’s disease activity pre and post-CBD use (using a 0-10 visual analogue scale), and total daily dosage of CBD (if known).\n\nStatistical analysis:\nWe performed descriptive analyses, and present results as frequency, n (%) and mean +/- standard deviation for categorical and continuous variables, respectively. We used Fisher’s chi-square test to assess differences in categorical variables. Participants were divided into 2 comparison groups for analyses: currently using or contemplating starting a CBD product for their child and no interest in starting a CBD product. Participants using CBD for treatment of their child’s arthritis were not used for standalone comparison due to small sample size (n=10). All statistical analysis was performed using Microsoft Excel (2016, Microsoft Corporation).\n\nResults:\nParticipation Overall, 422 JIA patients met inclusion criteria. Of those, 236 parent/guardians agreed to be sent the survey link and 136 participants completed the survey (58% response rate, Fig. 1). 10 respondents (7%) reported using a CBD product to treat their child’s JIA, 79 respondents (58%) reported contemplating use of a CBD product to treat their child’s JIA, and 47 respondents (34.5%) reported no interest in starting a CBD product. Demographic characteristics of the survey respondents are shown in Table 1. The study population was largely white, had a bachelor’s degree or higher, and had an annual income of more than 50,000 dollars per year. A large majority of respondents in both groups reported using one or more CIM therapies in their lifetime. There was no significant difference in the specific types or number of CIM therapies used across groups. Report of high disease activity was more frequent among those currently using or contemplating CBD use than those not contemplating use.\nFig. 1Flow diagram of studyTable 1Correlation of demographics and disease characteristicsParent/guardian and child demographics and disease characteristicsTotal (N=136)Not contemplating starting a CBD product for child (N=47)Contemplating starting a CBD product for child (N=79) and using CBD product for child (N=10)P-valueRespondent parent/guardian age in years (mean +/- SD)29.7 (8)26.5 (7)Respondent Parent/guardian Gender- Female N (%)119 (87)42 (89)77 (86)Race/ethnicity- N (%)White/Caucasian132 (97)44 (94)88 (98.9)Black/African American2 (1)1 (2)1 (1.1)Asian American2 (1)2 (4)0Parent/guardian education levelHigh School or GED20 (16)2 (4)18 (21)χ2= 8.03p=0.045*Some college but no degree26 (19)7 (15)19 (22)Associate degree26 (19)12 (26)14 (16)Bachelor’s degree or higher62 (46)26 (55)36 (41)Income level- US dollars per yearLess than 19,0003 (2)1 (2)2 (2)χ2= 1.34p= 0.7220,000 to 49,00027 (20)7 (15)20 (22)50,000 to 99,00041 (30)13 (28)28 (32)100,000 or more65 (48)26 (55)39 (44)Child’s age in years- (mean +/- SD)11 (4)11.9 (4)Child gender: Female- N (%)95 (70)31 (66)64 (72)JIA duration N (%)< 6 months2 (1)2 (4)0χ2= 1.45p= 0.696-12 months9 (7)3 (6)6 (6)> 12- 24 months20 (15)7 (15)13 (15)> 24 months105 (77)35 (75)70 (79)JIA Subtype N (%)Oligoarticular47 (35)19 (47)28 (32)χ2= 7.93p= 0.13Polyarticular53 (39)12 (26)41 (46)Psoriatic arthritis6 (4)3 (2)3 (3)Systemic14 (10)8 (17)6 (7)Ankylosing spondylitis2 (1)02 (2)Enthesitis related arthritis1 (0.7)1 (2)0Unsure12 (9)3 (6)9 (10)Current disease activity N (%)Active59 (44)15 (29)44 (49)χ2= 9.56p= 0.022*Inactive on medication49 (36)16 (34)33 (37)Inactive off medication for < 1 year18 (13)11 (23)7 (7.8)Clinical remission10 (7)5 (10)5 (5.6)Current medications reported using N (%)None22 (16)10 (21)12 (13)χ2= 2.47p= 0.48NSAID69 (51)23 (49)46 (51)Non-biologic DMARD51 (38)18 (38)33 (37)Biologic DMARD65 (48)18 (38)47 (52)aColumn percentages are displayedbP-values derived from the chi-squared (χ 2 ) or Wilcoxon testscDMARDs disease-modifying antirheumatic drugs, NSAIDs nonsteroidal anti-inflammatory drugsdMedication categories are not mutually exclusive, therefore, medications do not sum to 100%\nFlow diagram of study\nCorrelation of demographics and disease characteristics\nχ2= 8.03\np=0.045*\nχ2= 1.34\np= 0.72\nχ2= 1.45\np= 0.69\nχ2= 7.93\np= 0.13\nχ2= 9.56\np= 0.022*\nχ2= 2.47\np= 0.48\naColumn percentages are displayed\nbP-values derived from the chi-squared (χ 2 ) or Wilcoxon tests\ncDMARDs disease-modifying antirheumatic drugs, NSAIDs nonsteroidal anti-inflammatory drugs\ndMedication categories are not mutually exclusive, therefore, medications do not sum to 100%\nA majority of those using CBD or contemplating using CBD for their child learned about it from TV (66%), a friend/relative (34%) or JIA online blog/support group (35%). Very few obtained information from a scientific journal article (17%) or their child’s rheumatologist (2%). Around half (52%) used 2 or more sources to learn about CBD. A majority of parents/guardians (75%) reported believing that CBD would reduce their child’s joint pain (Fig. 2 A), while only 15% of respondents reported believing that CBD has side effects. More than half of respondents reported thinking that CBD is safe because it is a natural product (Fig. 2 C). Nearly two-thirds (63%, n = 56) of respondents had not discussed using CBD with their child’s rheumatologist and over half (61%) of those did not plan on discussing with their child’s rheumatologist for the following reasons: scared of what provider may think (35%), felt they wouldn’t be taken seriously (29%), and believed rheumatologist would have no knowledge about CBD (18%).\nFig. 2Parent/gaurdian perceptions of those using CBD for their child’s arthritis and those contemplating use of CBD (n=89) on how they percieve CBD will help their child’s arthritis (A), how they learned about CBD (B), perception of safety fo CBD (C). 56 respondents haven’t told their child’s rheumatologist for the following reasons (D)\nParent/gaurdian perceptions of those using CBD for their child’s arthritis and those contemplating use of CBD (n=89) on how they percieve CBD will help their child’s arthritis (A), how they learned about CBD (B), perception of safety fo CBD (C). 56 respondents haven’t told their child’s rheumatologist for the following reasons (D)\nOverall, 422 JIA patients met inclusion criteria. Of those, 236 parent/guardians agreed to be sent the survey link and 136 participants completed the survey (58% response rate, Fig. 1). 10 respondents (7%) reported using a CBD product to treat their child’s JIA, 79 respondents (58%) reported contemplating use of a CBD product to treat their child’s JIA, and 47 respondents (34.5%) reported no interest in starting a CBD product. Demographic characteristics of the survey respondents are shown in Table 1. The study population was largely white, had a bachelor’s degree or higher, and had an annual income of more than 50,000 dollars per year. A large majority of respondents in both groups reported using one or more CIM therapies in their lifetime. There was no significant difference in the specific types or number of CIM therapies used across groups. Report of high disease activity was more frequent among those currently using or contemplating CBD use than those not contemplating use.\nFig. 1Flow diagram of studyTable 1Correlation of demographics and disease characteristicsParent/guardian and child demographics and disease characteristicsTotal (N=136)Not contemplating starting a CBD product for child (N=47)Contemplating starting a CBD product for child (N=79) and using CBD product for child (N=10)P-valueRespondent parent/guardian age in years (mean +/- SD)29.7 (8)26.5 (7)Respondent Parent/guardian Gender- Female N (%)119 (87)42 (89)77 (86)Race/ethnicity- N (%)White/Caucasian132 (97)44 (94)88 (98.9)Black/African American2 (1)1 (2)1 (1.1)Asian American2 (1)2 (4)0Parent/guardian education levelHigh School or GED20 (16)2 (4)18 (21)χ2= 8.03p=0.045*Some college but no degree26 (19)7 (15)19 (22)Associate degree26 (19)12 (26)14 (16)Bachelor’s degree or higher62 (46)26 (55)36 (41)Income level- US dollars per yearLess than 19,0003 (2)1 (2)2 (2)χ2= 1.34p= 0.7220,000 to 49,00027 (20)7 (15)20 (22)50,000 to 99,00041 (30)13 (28)28 (32)100,000 or more65 (48)26 (55)39 (44)Child’s age in years- (mean +/- SD)11 (4)11.9 (4)Child gender: Female- N (%)95 (70)31 (66)64 (72)JIA duration N (%)< 6 months2 (1)2 (4)0χ2= 1.45p= 0.696-12 months9 (7)3 (6)6 (6)> 12- 24 months20 (15)7 (15)13 (15)> 24 months105 (77)35 (75)70 (79)JIA Subtype N (%)Oligoarticular47 (35)19 (47)28 (32)χ2= 7.93p= 0.13Polyarticular53 (39)12 (26)41 (46)Psoriatic arthritis6 (4)3 (2)3 (3)Systemic14 (10)8 (17)6 (7)Ankylosing spondylitis2 (1)02 (2)Enthesitis related arthritis1 (0.7)1 (2)0Unsure12 (9)3 (6)9 (10)Current disease activity N (%)Active59 (44)15 (29)44 (49)χ2= 9.56p= 0.022*Inactive on medication49 (36)16 (34)33 (37)Inactive off medication for < 1 year18 (13)11 (23)7 (7.8)Clinical remission10 (7)5 (10)5 (5.6)Current medications reported using N (%)None22 (16)10 (21)12 (13)χ2= 2.47p= 0.48NSAID69 (51)23 (49)46 (51)Non-biologic DMARD51 (38)18 (38)33 (37)Biologic DMARD65 (48)18 (38)47 (52)aColumn percentages are displayedbP-values derived from the chi-squared (χ 2 ) or Wilcoxon testscDMARDs disease-modifying antirheumatic drugs, NSAIDs nonsteroidal anti-inflammatory drugsdMedication categories are not mutually exclusive, therefore, medications do not sum to 100%\nFlow diagram of study\nCorrelation of demographics and disease characteristics\nχ2= 8.03\np=0.045*\nχ2= 1.34\np= 0.72\nχ2= 1.45\np= 0.69\nχ2= 7.93\np= 0.13\nχ2= 9.56\np= 0.022*\nχ2= 2.47\np= 0.48\naColumn percentages are displayed\nbP-values derived from the chi-squared (χ 2 ) or Wilcoxon tests\ncDMARDs disease-modifying antirheumatic drugs, NSAIDs nonsteroidal anti-inflammatory drugs\ndMedication categories are not mutually exclusive, therefore, medications do not sum to 100%\nA majority of those using CBD or contemplating using CBD for their child learned about it from TV (66%), a friend/relative (34%) or JIA online blog/support group (35%). Very few obtained information from a scientific journal article (17%) or their child’s rheumatologist (2%). Around half (52%) used 2 or more sources to learn about CBD. A majority of parents/guardians (75%) reported believing that CBD would reduce their child’s joint pain (Fig. 2 A), while only 15% of respondents reported believing that CBD has side effects. More than half of respondents reported thinking that CBD is safe because it is a natural product (Fig. 2 C). Nearly two-thirds (63%, n = 56) of respondents had not discussed using CBD with their child’s rheumatologist and over half (61%) of those did not plan on discussing with their child’s rheumatologist for the following reasons: scared of what provider may think (35%), felt they wouldn’t be taken seriously (29%), and believed rheumatologist would have no knowledge about CBD (18%).\nFig. 2Parent/gaurdian perceptions of those using CBD for their child’s arthritis and those contemplating use of CBD (n=89) on how they percieve CBD will help their child’s arthritis (A), how they learned about CBD (B), perception of safety fo CBD (C). 56 respondents haven’t told their child’s rheumatologist for the following reasons (D)\nParent/gaurdian perceptions of those using CBD for their child’s arthritis and those contemplating use of CBD (n=89) on how they percieve CBD will help their child’s arthritis (A), how they learned about CBD (B), perception of safety fo CBD (C). 56 respondents haven’t told their child’s rheumatologist for the following reasons (D)\nContemplating CBD use Respondents contemplating starting a CBD product for their child’s JIA (n=79) were interested in the following CBD products: CBD oil balm (30%), oil drops (25%), gummies (15%), soft gels/capsules (6.5%), and oil roll on (23%). Around a third (32%) of respondents were unsure what products they were interested in. Of those respondents (n=52) who were interested in starting a CBD product, 32.6% were interested in only oral CBD, 36.5% in a combination of oral and topical CBD, and 30.7% were interested only in topical CBD.\nRespondents contemplating starting a CBD product for their child’s JIA (n=79) were interested in the following CBD products: CBD oil balm (30%), oil drops (25%), gummies (15%), soft gels/capsules (6.5%), and oil roll on (23%). Around a third (32%) of respondents were unsure what products they were interested in. Of those respondents (n=52) who were interested in starting a CBD product, 32.6% were interested in only oral CBD, 36.5% in a combination of oral and topical CBD, and 30.7% were interested only in topical CBD.\nCurrent CBD use Respondents using CBD products for their child’s JIA (n=10) reported administering CBD orally (50%) or topically (50%). The majority (60%) reported using CBD on an as needed basis, while 40% reported using CBD on a scheduled basis. Overall, 40% reported administering CBD once per day, 20% twice per day and 40% at least three times per day. Respondents who reported administering CBD as needed (n=6) gave it for joint pain (66%), joint swelling (50%), joint stiffness (66%), and/or when their child requested it (33%). Respondents reported their child’s overall wellbeing to be an average 3.6 prior to starting CBD (0 = very poor, 10 = very good) and 5.3 after taking a CBD product. Half (50%, n=5) of parents reported improvement of their child’s wellbeing after they started CBD while 30% reported no change in their child’s wellbeing and 20% reported decreased well-being. Respondents used the following CBD products: oil drops (40%), lotion (10%), soft gels (10%) and oil balm (40%). Only one respondent knew the total dose of CBD administered per day (20 mg daily) while 70% (n=7) were unsure and 20% (n=2) reported they believed that the dose of CBD was irrelevant.\nRespondents using CBD products for their child’s JIA (n=10) reported administering CBD orally (50%) or topically (50%). The majority (60%) reported using CBD on an as needed basis, while 40% reported using CBD on a scheduled basis. Overall, 40% reported administering CBD once per day, 20% twice per day and 40% at least three times per day. Respondents who reported administering CBD as needed (n=6) gave it for joint pain (66%), joint swelling (50%), joint stiffness (66%), and/or when their child requested it (33%). Respondents reported their child’s overall wellbeing to be an average 3.6 prior to starting CBD (0 = very poor, 10 = very good) and 5.3 after taking a CBD product. Half (50%, n=5) of parents reported improvement of their child’s wellbeing after they started CBD while 30% reported no change in their child’s wellbeing and 20% reported decreased well-being. Respondents used the following CBD products: oil drops (40%), lotion (10%), soft gels (10%) and oil balm (40%). Only one respondent knew the total dose of CBD administered per day (20 mg daily) while 70% (n=7) were unsure and 20% (n=2) reported they believed that the dose of CBD was irrelevant.\n\nParticipation:\nOverall, 422 JIA patients met inclusion criteria. Of those, 236 parent/guardians agreed to be sent the survey link and 136 participants completed the survey (58% response rate, Fig. 1). 10 respondents (7%) reported using a CBD product to treat their child’s JIA, 79 respondents (58%) reported contemplating use of a CBD product to treat their child’s JIA, and 47 respondents (34.5%) reported no interest in starting a CBD product. Demographic characteristics of the survey respondents are shown in Table 1. The study population was largely white, had a bachelor’s degree or higher, and had an annual income of more than 50,000 dollars per year. A large majority of respondents in both groups reported using one or more CIM therapies in their lifetime. There was no significant difference in the specific types or number of CIM therapies used across groups. Report of high disease activity was more frequent among those currently using or contemplating CBD use than those not contemplating use.\nFig. 1Flow diagram of studyTable 1Correlation of demographics and disease characteristicsParent/guardian and child demographics and disease characteristicsTotal (N=136)Not contemplating starting a CBD product for child (N=47)Contemplating starting a CBD product for child (N=79) and using CBD product for child (N=10)P-valueRespondent parent/guardian age in years (mean +/- SD)29.7 (8)26.5 (7)Respondent Parent/guardian Gender- Female N (%)119 (87)42 (89)77 (86)Race/ethnicity- N (%)White/Caucasian132 (97)44 (94)88 (98.9)Black/African American2 (1)1 (2)1 (1.1)Asian American2 (1)2 (4)0Parent/guardian education levelHigh School or GED20 (16)2 (4)18 (21)χ2= 8.03p=0.045*Some college but no degree26 (19)7 (15)19 (22)Associate degree26 (19)12 (26)14 (16)Bachelor’s degree or higher62 (46)26 (55)36 (41)Income level- US dollars per yearLess than 19,0003 (2)1 (2)2 (2)χ2= 1.34p= 0.7220,000 to 49,00027 (20)7 (15)20 (22)50,000 to 99,00041 (30)13 (28)28 (32)100,000 or more65 (48)26 (55)39 (44)Child’s age in years- (mean +/- SD)11 (4)11.9 (4)Child gender: Female- N (%)95 (70)31 (66)64 (72)JIA duration N (%)< 6 months2 (1)2 (4)0χ2= 1.45p= 0.696-12 months9 (7)3 (6)6 (6)> 12- 24 months20 (15)7 (15)13 (15)> 24 months105 (77)35 (75)70 (79)JIA Subtype N (%)Oligoarticular47 (35)19 (47)28 (32)χ2= 7.93p= 0.13Polyarticular53 (39)12 (26)41 (46)Psoriatic arthritis6 (4)3 (2)3 (3)Systemic14 (10)8 (17)6 (7)Ankylosing spondylitis2 (1)02 (2)Enthesitis related arthritis1 (0.7)1 (2)0Unsure12 (9)3 (6)9 (10)Current disease activity N (%)Active59 (44)15 (29)44 (49)χ2= 9.56p= 0.022*Inactive on medication49 (36)16 (34)33 (37)Inactive off medication for < 1 year18 (13)11 (23)7 (7.8)Clinical remission10 (7)5 (10)5 (5.6)Current medications reported using N (%)None22 (16)10 (21)12 (13)χ2= 2.47p= 0.48NSAID69 (51)23 (49)46 (51)Non-biologic DMARD51 (38)18 (38)33 (37)Biologic DMARD65 (48)18 (38)47 (52)aColumn percentages are displayedbP-values derived from the chi-squared (χ 2 ) or Wilcoxon testscDMARDs disease-modifying antirheumatic drugs, NSAIDs nonsteroidal anti-inflammatory drugsdMedication categories are not mutually exclusive, therefore, medications do not sum to 100%\nFlow diagram of study\nCorrelation of demographics and disease characteristics\nχ2= 8.03\np=0.045*\nχ2= 1.34\np= 0.72\nχ2= 1.45\np= 0.69\nχ2= 7.93\np= 0.13\nχ2= 9.56\np= 0.022*\nχ2= 2.47\np= 0.48\naColumn percentages are displayed\nbP-values derived from the chi-squared (χ 2 ) or Wilcoxon tests\ncDMARDs disease-modifying antirheumatic drugs, NSAIDs nonsteroidal anti-inflammatory drugs\ndMedication categories are not mutually exclusive, therefore, medications do not sum to 100%\nA majority of those using CBD or contemplating using CBD for their child learned about it from TV (66%), a friend/relative (34%) or JIA online blog/support group (35%). Very few obtained information from a scientific journal article (17%) or their child’s rheumatologist (2%). Around half (52%) used 2 or more sources to learn about CBD. A majority of parents/guardians (75%) reported believing that CBD would reduce their child’s joint pain (Fig. 2 A), while only 15% of respondents reported believing that CBD has side effects. More than half of respondents reported thinking that CBD is safe because it is a natural product (Fig. 2 C). Nearly two-thirds (63%, n = 56) of respondents had not discussed using CBD with their child’s rheumatologist and over half (61%) of those did not plan on discussing with their child’s rheumatologist for the following reasons: scared of what provider may think (35%), felt they wouldn’t be taken seriously (29%), and believed rheumatologist would have no knowledge about CBD (18%).\nFig. 2Parent/gaurdian perceptions of those using CBD for their child’s arthritis and those contemplating use of CBD (n=89) on how they percieve CBD will help their child’s arthritis (A), how they learned about CBD (B), perception of safety fo CBD (C). 56 respondents haven’t told their child’s rheumatologist for the following reasons (D)\nParent/gaurdian perceptions of those using CBD for their child’s arthritis and those contemplating use of CBD (n=89) on how they percieve CBD will help their child’s arthritis (A), how they learned about CBD (B), perception of safety fo CBD (C). 56 respondents haven’t told their child’s rheumatologist for the following reasons (D)\n\nContemplating CBD use:\nRespondents contemplating starting a CBD product for their child’s JIA (n=79) were interested in the following CBD products: CBD oil balm (30%), oil drops (25%), gummies (15%), soft gels/capsules (6.5%), and oil roll on (23%). Around a third (32%) of respondents were unsure what products they were interested in. Of those respondents (n=52) who were interested in starting a CBD product, 32.6% were interested in only oral CBD, 36.5% in a combination of oral and topical CBD, and 30.7% were interested only in topical CBD.\n\nCurrent CBD use:\nRespondents using CBD products for their child’s JIA (n=10) reported administering CBD orally (50%) or topically (50%). The majority (60%) reported using CBD on an as needed basis, while 40% reported using CBD on a scheduled basis. Overall, 40% reported administering CBD once per day, 20% twice per day and 40% at least three times per day. Respondents who reported administering CBD as needed (n=6) gave it for joint pain (66%), joint swelling (50%), joint stiffness (66%), and/or when their child requested it (33%). Respondents reported their child’s overall wellbeing to be an average 3.6 prior to starting CBD (0 = very poor, 10 = very good) and 5.3 after taking a CBD product. Half (50%, n=5) of parents reported improvement of their child’s wellbeing after they started CBD while 30% reported no change in their child’s wellbeing and 20% reported decreased well-being. Respondents used the following CBD products: oil drops (40%), lotion (10%), soft gels (10%) and oil balm (40%). Only one respondent knew the total dose of CBD administered per day (20 mg daily) while 70% (n=7) were unsure and 20% (n=2) reported they believed that the dose of CBD was irrelevant.\n\nDiscussion:\nTo our knowledge, this is the first study exploring parent/guardian knowledge and opinions regarding CBD use for their children with JIA. We found that while CBD use is infrequent, there is a strong parent/guardian interest in using CBD for treating JIA, especially among respondents reporting more active disease and a longer disease course. Use of stronger medications such as biologics, on the other hand, was not associated with a significant difference in CBD interest. These findings are consistent with other studies showing that children with JIA use CIM more frequently if they have more active disease and longer disease duration, and that use of immunosuppressive or biologic medications is not a factor related to CIM use among children with JIA [6, 25].\nThe majority of the survey respondents learned about CBD from television, the internet (JIA online blog/support group), or friend or family member with only a small percentage of respondents learned from a health care provider or scientific study, mirroring results from other studies of adults using CBD oil or cannabis [26, 27]. Our study further showed that many parent/guardians are not discussing CBD with their child’s rheumatologist. This is because they expressed worry that their child’s rheumatologist would negatively judge them and or not take them seriously if they discussed their experience with or interest in CBD. This finding is similar to a recent study in which only 9.6% of young adults reported discussing CBD usage with their healthcare provider [27]. Previous studies evaluating CIM use in adolescents with JIA have demonstrated similarly low rates of discussions with their health care provider, [10] and parents of children with other chronic health conditions have reported similar reasons for not discussing CIM with their child’s health provider. These results suggest that providers need CBD and CIM-related education to better serve individuals with JIA, and also that providers need to specifically ask about use of CBD and other CIM modalities.\nAs CBD becomes increasingly more popular, parental interest in using CBD to treat their child’s health conditions continues to grow. The use of the search terms for “CBD for children” and “CBD for kids” have increased since 2018, [22] and numerous blog posts and other forms of media report positive results from giving CBD to children [22]. These forms of media do often mention preclinical CBD research conducted in mice, which demonstrate that CBD has potent anti-inflammatory and analgesic effects in induced inflammatory arthritis [14, 15]. Further, some small clinical trials of CBD in adults do show that CBD may have analgesic activity (in neuropathy and temporomandibular joint disorder [28, 29]) and short-term anxiolytic effects, [30–32] and several clinical trials of CBD in arthritis are ongoing (for example, in rheumatoid arthritis) [33]. However, what is often not communicated is that studies on safety and efficacy of CBD among children with those symptoms (e.g., pain, inflammation) have not yet been conducted. As such, additional rigorous research is needed to investigate whether these preliminary therapeutic findings translate to the JIA context.\nConsistent with prior reports about CBD administration among young adults, [12, 27] a majority of those using CBD for their child’s arthritis are administering CBD orally (60%) on an as needed basis as often as several times per day for joint pain and/or stiffness. In addition, 69% of those contemplating CBD expressed interest in an oral CBD product (alone or in combination with topical CBD). This strong interest in oral CBD is important to note, as CBD has been suggested to interact with the liver enzyme cytochrome P450 and could interfere with the metabolism of several commonly prescribed rheumatologic medications, including prednisone, naproxen, and tofacitinib, potentially leading to increased drug levels and increased risk of toxicity.\nThe large majority of respondents believed CBD is safe because it is a natural product and did not believe there were adverse effects of CBD. Surprisingly, only 1 of 10 participants currently giving their child CBD knew what dose they were administering. The overall safety of CBD for healthy children or other clinical populations remains unknown but the Epidiolex trials, [16, 34] which used high doses of CBD, reported non-serious adverse effects in children including dry mouth, sedation, and/or decreased appetite. Other studies have reported similar adverse effects in young adults or adults taking CBD [13, 27].\n\nLimitations:\nRespondents of both panels were similar in terms of race/ethnicity; education, age, and gender, however, > 95% of respondents were white/Caucasian which is not representative of the JIA patient population at our institution or in the US. Survey links were only generated for parents or guardians who expressed interest in participating in the study, so selection bias was likely present. In addition, respondents may have interpreted survey questions differently than we intended and wording of questions may have introduced bias. Finally, we only queried the parents/guardians of individuals with JIA rather than directly asking individuals with JIA about their experiences with or interest in CBD.\n\nConclusions:\nAs CBD continues to gain popularity, parental interest in CBD for treating their child’s health condition(s) will likely increase. In this study, we show that while CBD use is currently infrequent for JIA, many parents/guardians are interested in using CBD to help with JIA symptoms. As such, is important that pediatric rheumatologists and other pediatric providers educate themselves about CBD to increase their comfort in discussing CBD and its potential safety issues with their patients and/or parents. Such efforts should focus on harm-reduction, communicating uncertainty without harming the patient-physician relationship, and guiding interested parties to reliable sources on CBD (e.g. the Arthritis Foundation)[35] to ensure that they are obtaining information based on scientific evidence. In addition, rigorous clinical studies are warranted to investigate both safety and efficacy of CBD in JIA to bridge the gap in knowledge.\n\nSupplementary information:\n \nAdditional file 1\nAdditional file 1\n\nAdditional file 1\nAdditional file 1\n\n:\n\nAdditional file 1\nAdditional file 1\n\n"},"whole_article_abstract":{"kind":"string","value":"Background:\nJuvenile idiopathic arthritis (JIA) is common and difficult to treat. Cannabidiol (CBD) is now widely available, but no studies to date have investigated the use of CBD for JIA.\n\nMethods:\nWe performed a chart review to identify patients with JIA at a Midwestern medical institution between 2017 and 2019. We surveyed primary caregivers of JIA patients using an anonymous, online survey with questions on caregiver knowledge and attitudes towards CBD. We compared respondents with no interest in CBD use vs. those contemplating or currently using CBD using descriptive statistics.\n\nResults:\nOf 900 reviewed charts, 422 met inclusion criteria. Of these, 236 consented to be sent a survey link, and n=136 (58%) completed surveys. Overall, 34.5% (n=47) of respondents reported no interest in using a CBD product for their child's JIA, while 54% (n=79) reported contemplating using CBD and 7% (n=10) reported currently giving their child CBD. Only 2% of respondents contemplating or actively using a CBD product learned about CBD from their child's rheumatologist, compared with television (70%) or a friend (50%). Most respondents had not talked to their child's rheumatologist about using CBD. Of those currently using CBD, most used oral or topical products, and only 10% of respondents (n=1) knew what dose they were giving their child.\n\nConclusions:\nOur results show infrequent use but a large interest in CBD among caregivers of children with JIA. Given CBD's unknown safety profile in children with JIA, this study highlights a need for better studies and education around CBD for pediatric rheumatologists."},"background_conclusion_text":{"kind":"string","value":"Background:\nJuvenile idiopathic arthritis (JIA) is the most common type of chronic arthritis in children, affecting 1 in 1000 children. It is an important cause of short and long-term disability and causes significant financial burden with annual direct medical costs ranging $400-$7,000 [1]. Effective treatments for JIA include non-steroidal anti-inflammatory drugs (NSAIDs), corticosteroids, disease modifying anti-rheumatic drugs (DMARDs), and biologic agents, but each carries potential adverse effects [2]. Indeed, parents and children frequently worry about side effects and the long-term safety of medications prescribed for JIA [3, 4]. As a result, many parents and children (34-92%) use complementary and integrative medicine (CIM) separately or in conjunction with standard treatment of JIA [5–10].\nOne such CIM treatment gaining popularity in the past years is cannabidiol (CBD), which is derived from Cannabis sativa. Since the removal of some CBD products from the Controlled Substances Act in 2018, a vast number of products made from hemp (Cannabis sativa with <0.3% Δ-9-tetrahydrocannabinol [THC]) have become available in brick and mortar retailers in topical, oral or inhaled forms [11]. CBD is non-intoxicating and has been widely advertised as a safe and natural therapy for many ailments including chronic pain, arthritis, other inflammatory diseases, and mental health conditions, resulting in frequent use for these conditions [12, 13]. In non-human animal studies, CBD reduces pain and inflammation due to arthritis, but these findings have not been validated in human studies [14, 15]. With the exception of Epidiolex, which is approved for the treatment of the rare seizure disorders Lennox Gastaut and Dravet Syndrome, CBD is minimally regulated by the Food and Drug Administration (FDA) [16, 17]. With the exception of these rare seizure disorders, evidence of a therapeutic benefit of CBD for pediatric conditions is lacking [18].\nCBD’s safety profile has only been characterized among individuals with Dravet and Lennox Gastaut syndrome, so whether CBD is safe for use in healthy children or other pediatric populations remains unknown. Complicating matters, CBD is a promiscuous molecule that interacts with numerous systems in the body (e.g., serotonergic 5HT1A, endocannabinoid system as cannabinoid receptor 1 antagonist) [19, 20], and may interact with the metabolism of drugs commonly taken by children with JIA including prednisone and naproxen [21]. Further, testing of safety and potency of CBD products is not governed by a strong regulatory apparatus, [22, 23] and a recent JAMA study revealed that only 31% of CBD products sold online are accurately labeled for potency with 21% of samples containing THC [24]. As such, there are safety concerns about use in children, especially those with JIA.\nAs pediatric rheumatologists, the authors (C.F., M. R.) have been frequently asked about using CBD products to treat JIA symptoms, but to date, there is no literature available regarding the use of CBD in children with JIA. The objective of this study was to determine the frequency of CBD use among children with JIA and investigate caregiver knowledge and opinions about CBD use for their children.\n\nConclusions:\nAs CBD continues to gain popularity, parental interest in CBD for treating their child’s health condition(s) will likely increase. In this study, we show that while CBD use is currently infrequent for JIA, many parents/guardians are interested in using CBD to help with JIA symptoms. As such, is important that pediatric rheumatologists and other pediatric providers educate themselves about CBD to increase their comfort in discussing CBD and its potential safety issues with their patients and/or parents. Such efforts should focus on harm-reduction, communicating uncertainty without harming the patient-physician relationship, and guiding interested parties to reliable sources on CBD (e.g. the Arthritis Foundation)[35] to ensure that they are obtaining information based on scientific evidence. In addition, rigorous clinical studies are warranted to investigate both safety and efficacy of CBD in JIA to bridge the gap in knowledge."},"background_conclusion_abstract":{"kind":"string","value":"Background:\nJuvenile idiopathic arthritis (JIA) is common and difficult to treat. Cannabidiol (CBD) is now widely available, but no studies to date have investigated the use of CBD for JIA.\n\nMethods:\nWe performed a chart review to identify patients with JIA at a Midwestern medical institution between 2017 and 2019. We surveyed primary caregivers of JIA patients using an anonymous, online survey with questions on caregiver knowledge and attitudes towards CBD. We compared respondents with no interest in CBD use vs. those contemplating or currently using CBD using descriptive statistics.\n\nResults:\nOf 900 reviewed charts, 422 met inclusion criteria. Of these, 236 consented to be sent a survey link, and n=136 (58%) completed surveys. Overall, 34.5% (n=47) of respondents reported no interest in using a CBD product for their child's JIA, while 54% (n=79) reported contemplating using CBD and 7% (n=10) reported currently giving their child CBD. Only 2% of respondents contemplating or actively using a CBD product learned about CBD from their child's rheumatologist, compared with television (70%) or a friend (50%). Most respondents had not talked to their child's rheumatologist about using CBD. Of those currently using CBD, most used oral or topical products, and only 10% of respondents (n=1) knew what dose they were giving their child.\n\nConclusions:\nOur results show infrequent use but a large interest in CBD among caregivers of children with JIA. Given CBD's unknown safety profile in children with JIA, this study highlights a need for better studies and education around CBD for pediatric rheumatologists."},"whole_article_text_length":{"kind":"number","value":8021,"string":"8,021"},"whole_article_abstract_length":{"kind":"number","value":317,"string":"317"},"other_sections_lengths":{"kind":"list like","value":[622,1115,124,275,114,1116,124,276,124,8],"string":"[\n 622,\n 1115,\n 124,\n 275,\n 114,\n 1116,\n 124,\n 276,\n 124,\n 8\n]"},"num_sections":{"kind":"number","value":14,"string":"14"},"most_frequent_words":{"kind":"list like","value":["cbd","child","respondents","reported","jia","use","product","cbd product","10","disease"],"string":"[\n \"cbd\",\n \"child\",\n \"respondents\",\n \"reported\",\n \"jia\",\n \"use\",\n \"product\",\n \"cbd product\",\n \"10\",\n \"disease\"\n]"},"keybert_topics":{"kind":"list like","value":["cbd product treat","cbd products treat","cbd arthritis foundation","arthritis learned cbd","cbd 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[SUMMARY]"},"annotated_tf_idf_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] cbd | child | file | additional file | additional | respondents | reported | use | additional file additional file | file additional file [SUMMARY]"},"annotated_tf_idf_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] cbd | child | file | additional file | additional | respondents | reported | use | additional file additional file | file additional file [SUMMARY]"},"annotated_entity_plan_background_abstract_prompt":{"kind":"string","value":"[CONTENT] Juvenile | JIA ||| Cannabidiol (CBD | JIA [SUMMARY]"},"annotated_entity_plan_methods_abstract_prompt":{"kind":"null"},"annotated_entity_plan_results_abstract_prompt":{"kind":"string","value":"[CONTENT] 422 ||| 236 | 58% ||| 34.5% | JIA | 54% | n=79 | 7% ||| Only 2% | 70% | 50% ||| CBD ||| only 10% [SUMMARY]"},"annotated_entity_plan_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] JIA ||| JIA 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be enhanced by loss of teeth and the use of poorly fitting prostheses."},"background_abstract_label":{"kind":"string","value":"BACKGROUND"},"methods_abstract":{"kind":"string","value":"Thirty elderly individuals affected by stroke in chronic phase participated. All subjects underwent assessment of their oral condition, with classification from the Functional Oral Intake Scale (FOIS) and nasoendoscopic swallowing assessment to classify the degree of dysphagia. The statistical analysis examined a heterogeneous group (HG, n=30) and two groups designated by the affected body part, right (RHG, n=8) and left (LHG, n=11), excluding totally dentate or edentulous individuals without rehabilitation with more than one episode of stroke."},"methods_abstract_label":{"kind":"string","value":"METHODS"},"results_abstract":{"kind":"string","value":"There was a negative correlation between the need for replacement prostheses and the FOIS scale for the HG (P=0.02) and RHG (P=0.01). Differences in FOIS between types of prostheses of the upper dental arch in the LHG (P=0.01) and lower dental arch in the RHG (P=0.04). A negative correlation was found between the number of teeth present and the degree of dysfunction in swallowing liquid in the LHG (P=0.05). There were differences in the performance in swallowing solids between individuals without prosthesis and those with partial prosthesis in the inferior dental arch (P=0.04) for the HG."},"results_abstract_label":{"kind":"string","value":"RESULTS"},"conclusions_abstract":{"kind":"string","value":"The need for replacement prostheses, type of prostheses, and the number of teeth of elderly patients poststroke in chronic phase showed an association with the level of oral intake and the degree of oropharyngeal dysphagia."},"conclusions_abstract_label":{"kind":"string","value":"CONCLUSION"},"mesh_descriptor_names":{"kind":"list like","value":["Aged","Aged, 80 and over","Brazil","Chronic Disease","Deglutition","Deglutition Disorders","Dental Prosthesis","Diagnosis, Oral","Endoscopy, Gastrointestinal","Female","Humans","Male","Mouth Rehabilitation","Oral Health","Outcome Assessment, Health Care","Risk Factors","Severity of Illness Index","Stroke","Tooth Loss"],"string":"[\n \"Aged\",\n \"Aged, 80 and over\",\n \"Brazil\",\n \"Chronic Disease\",\n \"Deglutition\",\n \"Deglutition Disorders\",\n \"Dental Prosthesis\",\n \"Diagnosis, Oral\",\n \"Endoscopy, Gastrointestinal\",\n \"Female\",\n \"Humans\",\n \"Male\",\n \"Mouth Rehabilitation\",\n \"Oral Health\",\n \"Outcome Assessment, Health Care\",\n \"Risk Factors\",\n \"Severity of Illness Index\",\n \"Stroke\",\n \"Tooth Loss\"\n]"},"pmcid":{"kind":"string","value":"4279671"},"background_title":{"kind":"string","value":"Introduction"},"background_text":{"kind":"string","value":"The process of swallowing physiologically changes with aging. The loss of teeth, very common in this population, is related to the reduction of bone tissue, receptors (proprioceptors and periodontal ligaments), and muscle atrophy. Consequently, orofacial functions are impaired in toothless individuals.1 In addition, prostheses are functionally less efficient when compared to individuals with natural dentition.1,2\nThe stability of the jaw occlusion of the posterior teeth or prostheses is important for the swallowing function.2 The dental condition and the use of poorly fitting prostheses may contribute to the difficulties arising from old age, and the oral diet and nutritional status of the elderly may be affected by changes related to aging3 and changes in skill and desire to feel, bite, chew4 and swallow food,3,5 significantly compromising and further aggravating the quality of life of these individuals.6\nThe literature has shown the influence of oral health condition on swallowing and nutrition, indirectly influencing the daily life activities of the elderly.7 Furthermore, it is clear that maintenance of oral health is essential for the general health, quality of life, chewing ability8 and, mainly, the reduction of pneumonia risk in fragile elderly people,8,9 for it is already known that bacterial colonies in oropharyngeal tissues and in dental plaque are the greatest precursors for the development of aspiration respiratory infections.10\nBesides the influence of the loss of dentition, the functional impairments resulting from aging may be potentiated by neurological changes resulting in dysphagia in patients, an important cause of morbidity and mortality in this population.11\nIn the adult and elderly population, dysphagia is most commonly associated with stroke.12 More than half of patients who have suffered stroke present between six and ten types of disability, muscle weakness being the most prevalent one, followed by communication, speech, and swallowing disorders.13 Specifically regarding swallowing, the stroke entails increased oropharyngeal transit time,14,15 changes in motor control of tongue,16 as well as the presence of laryngotracheal aspiration of food.16,17\nOnce the physiological changes resulting from aging in swallowing may be aggravated by loss of teeth and the use of poorly fitting prostheses, the severity of dysphagia could be influenced by the oral health condition and use of dental prostheses in elderly patients with neurological diseases. However, according to the literature studied, no research has made the correlation between the findings of the swallowing performance with oral health in elderly patients after stroke. Therefore, the aim of this study was to relate the condition of oral health to the level of oral intake and the degree of swallowing dysfunction in elderly patients with stroke in chronic phase."},"methods_title":{"kind":"string","value":"Statistical analysis"},"methods_text":{"kind":"string","value":"Spearman’s test was used to verify the correlation between the oral health condition (DMFT, number of teeth present, and the need for prostheses replacement) and the performance in the swallowing function (classification in the FOIS scale and dysphagia rating in each consistency). For this analysis to be performed, the FOIS scale and the degree of dysphagia were numerically classified from 1 to 7 and from 1 to 4, respectively.\nFor comparison of FOIS results and dysphagia classification, among the different types of prostheses used in the upper and lower dental arches (complete dental prosthesis, partial dental prosthesis, denture or none), the Kruskal–Wallis test was used, followed by the Dunn’s test."},"results_title":{"kind":"string","value":"Results"},"results_text":{"kind":"string","value":"According to the evaluation of oral condition, the mean of DMFT of elderly individuals affected by stroke in this study was 28.7, while the mean of remaining teeth was 5.6, showing that most individuals were edentulous. Table 2 shows the types of prostheses used by the elderly individuals affected by stroke in each dental arch and the need for prostheses replacement.\nAccording to the results of the functional oral intake scale, no individuals were found to be on a single consistency diet or tube dependent. Most individuals (53%) presented as level V on the FOIS, followed by level IV (34%) and level VII (13%).\nWith the severity rating of the swallowing disorder, no occurrence of severe dysphagia was verified. The distribution of the elderly affected by stroke according to severity rating of the swallowing disorder is shown in Table 3, as well.\nAccording to the results of the statistical analysis, correlations between the oral health condition and the results of the FOIS scale in elderly individuals affected by stroke in this study were found. There was a statistically significant negative correlation between the need for replacement of prostheses and the rating scale, and also in the HG and in RHG, showing that the greater the need for replacement of the prosthesis, the worse the rating of the FOIS scale.\nThere were differences in the classification of the FOIS scale, for the LHG, in the different types of prostheses used in the upper dental arch, whereas for the RHG the difference in the classification of FOIS occurred between groups of individuals with different types of prostheses used in the lower dental arch. After completion of the Dunn’s post-test, the difference was confirmed only in the LHG, demonstrating that individuals of this group with partial prostheses in the upper arch (ie, that still had natural elements and whose missed teeth had been rehabilitated) had better ratings in the FOIS scale compared to individuals with total prostheses.\nThe results of the correlation between the FOIS scale and the oral health condition are shown in Table 4.\nIn the statistical analysis between the degree of swallowing dysfunction and oral health condition, a significant negative correlation between the number of teeth and the swallowing performance of liquid in the LHG was found, demonstrating that the greater the number of teeth, the lower the degree of swallowing dysfunction.\nAccording to the Kruskal–Wallis test, there was no difference in the performance of swallowing solid boluses depending on the type of prosthesis used in the lower dental arch; in the HG, however, when applying the post-Dunn’s test, this difference was not confirmed (P>0.05). The comparison of the FOIS scale between the types of dental prostheses is shown in Table 5.\nThe results of the correlation between the degree of dysphagia and the oral health condition are shown in Table 6, and the comparison of the degree of swallowing dysfunction between types of dental prostheses is described in Table 7."},"conclusions_title":{"kind":"string","value":"Conclusion"},"conclusions_text":{"kind":"string","value":"The findings of this study indicate that the oral health condition of elderly individuals after stroke in chronic phase showed an association with the level of oral intake and the degree of oropharyngeal dysphagia. The limitations of the study should be considered, one being the lack of access to the imaging examinations to characterize the individuals according to the type of stroke, damaged areas, and extent of injury, and heterogeneity of the time of brain involvement, in addition to the effects of medications and other comorbidities, which were not taken into account. Also, owing to the small sample, strong correlations could not be observed, and a cause/effect relationship cannot be determined.\nDespite the limitations of this study, and the fact that hemiplegia does not necessarily represent a direct relationship with the injured brain hemisphere contralaterally, since cerebellum lesions may cause ipsilateral motor damages,31 the findings indicated that the level of oral intake was influenced by the condition or type of dental prostheses, regardless of the side of the body affected. On the other hand, the number of teeth were demonstrated to be related to the level of dysphagia in individuals with right hemiplegia, which shows that the oral health condition differentially affects the physiological responses of swallowing, influenced by the brain damage.\nConsidering the present sample, further research is necessary pertaining to the oral condition and the neurological involvement of individuals. In addition, these issues demonstrate the need for an interdisciplinary research team comprising dentists, physicians, speech pathologists, and other health professionals, so as to study and define the treatment and guidelines to patients and caretakers, in order to maintain the best oral health condition in the elderly presented with oropharyngeal dysphagia."},"other_sections_titles":{"kind":"list like","value":["Methods","Procedures","Oral condition evaluation","Functional Oral Intake Scale","Fiberoptic endoscopic evaluation of swallowing","Conclusion"],"string":"[\n \"Methods\",\n \"Procedures\",\n \"Oral condition evaluation\",\n \"Functional Oral Intake Scale\",\n \"Fiberoptic endoscopic evaluation of swallowing\",\n \"Conclusion\"\n]"},"other_sections_texts":{"kind":"list like","value":[" Study design and subjects Thirty poststroke individuals, aged 61 to 90 years, whose injury happened 6 months to 9 years previously to their participation in this prospective study. Table 1 shows the characterization data of the sample.\nThe inclusion criteria considered the following cases: affected by stroke as attested by a medical report, through clinical diagnosis and imaging, with a minimum time of 6 months; aged over 60 years, being in regular clinical neurological monitoring, not having undergone dysphagia rehabilitation; presenting general stable health that would enable the realization of the proposed tests.\nIn order to achieve a greater delineation of the group, in addition to considering the group of 30 individuals (heterogeneous group [HG]), the total dentate, the total edentulous without dental prostheses, and individuals with more than one episode of stroke were excluded and the participants were divided into two groups, according to the affected body side: right hemiplegic group (RHG, n=8) and left hemiplegic group (LHG, n=11). A distribution and recruitment fluxogram is shown in Figure 1.\nThirty poststroke individuals, aged 61 to 90 years, whose injury happened 6 months to 9 years previously to their participation in this prospective study. Table 1 shows the characterization data of the sample.\nThe inclusion criteria considered the following cases: affected by stroke as attested by a medical report, through clinical diagnosis and imaging, with a minimum time of 6 months; aged over 60 years, being in regular clinical neurological monitoring, not having undergone dysphagia rehabilitation; presenting general stable health that would enable the realization of the proposed tests.\nIn order to achieve a greater delineation of the group, in addition to considering the group of 30 individuals (heterogeneous group [HG]), the total dentate, the total edentulous without dental prostheses, and individuals with more than one episode of stroke were excluded and the participants were divided into two groups, according to the affected body side: right hemiplegic group (RHG, n=8) and left hemiplegic group (LHG, n=11). A distribution and recruitment fluxogram is shown in Figure 1.\n Procedures Oral condition evaluation The participants underwent assessment of various components of oral health through traditional health indicators, based on the presence or absence of disease. The data were collected from the clinical examination by a dentist as instructed by the WHO – Oral Health Surveys: Basic Methods,18 including the assessment of dental status by the number of decayed, missing, and filled teeth (DMFT) and evaluation of dental prostheses.\nThe prostheses were evaluated for retention, functional stability, smile aesthetics, degree of bone resorption, and quality of the mucosa.19\nFrom the evaluation, the dental arches were classified as without prosthesis or with the presence of partial or complete dental prosthesis; later, the use or need for replacement of dental prostheses was indicated. The need for prostheses replacement was as follows: 0, no need to change; 1, partial dental prosthesis was necessary in one of the dental arches; 2, need for partial dental prosthesis in both dental arches; 3, need for complete dental prosthesis in one of the dental arches; 4, need for dental prosthesis in both dental arches.\nThe participants underwent assessment of various components of oral health through traditional health indicators, based on the presence or absence of disease. The data were collected from the clinical examination by a dentist as instructed by the WHO – Oral Health Surveys: Basic Methods,18 including the assessment of dental status by the number of decayed, missing, and filled teeth (DMFT) and evaluation of dental prostheses.\nThe prostheses were evaluated for retention, functional stability, smile aesthetics, degree of bone resorption, and quality of the mucosa.19\nFrom the evaluation, the dental arches were classified as without prosthesis or with the presence of partial or complete dental prosthesis; later, the use or need for replacement of dental prostheses was indicated. The need for prostheses replacement was as follows: 0, no need to change; 1, partial dental prosthesis was necessary in one of the dental arches; 2, need for partial dental prosthesis in both dental arches; 3, need for complete dental prosthesis in one of the dental arches; 4, need for dental prosthesis in both dental arches.\n Functional Oral Intake Scale Research of oral ingestion was performed by reviewing the usual food consumption patterns referred to in the 24-hour dietary recall. From the data obtained by the research of oral ingestion, patients were classified according to the levels of the Functional Oral Intake Scale (FOIS),20 on a scale from I to VII, considering the diet characteristics based on the properties and texture of the food.21\nResearch of oral ingestion was performed by reviewing the usual food consumption patterns referred to in the 24-hour dietary recall. From the data obtained by the research of oral ingestion, patients were classified according to the levels of the Functional Oral Intake Scale (FOIS),20 on a scale from I to VII, considering the diet characteristics based on the properties and texture of the food.21\n Fiberoptic endoscopic evaluation of swallowing The fiberoptic endoscopic evaluation of swallowing (FEES) was performed by an otorhinolaryngologist physician collaboratively with the speech therapist. Subjects were asked to remain seated with their heads arranged in the direction of the body axis, without bending or rotation, and the examination was performed using a flexible endoscopic equipment of fiberoptic bronchoscopic type (model Olympus CLV-U20) and an Olympus OTV–SC nasopharyngoscope (Olympus Corporation, Tokyo, Japan).\nThree consistent standardized foods were evaluated: liquid (10 mL of filtered water), thick pudding (10 mL of thickened dietary grape juice, reaching a final consistency similar to that of pudding), and solid (half a slice of bread, 1 cm thick). From the data obtained by the FEES, the severity rating of the swallowing disorder was determined in accordance with the scale of functional impairment of swallowing of Macedo Filho,22,23 which subdivides the dysphagia severity levels (normal, mild, moderate, and severe).\nThe fiberoptic endoscopic evaluation of swallowing (FEES) was performed by an otorhinolaryngologist physician collaboratively with the speech therapist. Subjects were asked to remain seated with their heads arranged in the direction of the body axis, without bending or rotation, and the examination was performed using a flexible endoscopic equipment of fiberoptic bronchoscopic type (model Olympus CLV-U20) and an Olympus OTV–SC nasopharyngoscope (Olympus Corporation, Tokyo, Japan).\nThree consistent standardized foods were evaluated: liquid (10 mL of filtered water), thick pudding (10 mL of thickened dietary grape juice, reaching a final consistency similar to that of pudding), and solid (half a slice of bread, 1 cm thick). From the data obtained by the FEES, the severity rating of the swallowing disorder was determined in accordance with the scale of functional impairment of swallowing of Macedo Filho,22,23 which subdivides the dysphagia severity levels (normal, mild, moderate, and severe).\n Statistical analysis Spearman’s test was used to verify the correlation between the oral health condition (DMFT, number of teeth present, and the need for prostheses replacement) and the performance in the swallowing function (classification in the FOIS scale and dysphagia rating in each consistency). For this analysis to be performed, the FOIS scale and the degree of dysphagia were numerically classified from 1 to 7 and from 1 to 4, respectively.\nFor comparison of FOIS results and dysphagia classification, among the different types of prostheses used in the upper and lower dental arches (complete dental prosthesis, partial dental prosthesis, denture or none), the Kruskal–Wallis test was used, followed by the Dunn’s test.\nSpearman’s test was used to verify the correlation between the oral health condition (DMFT, number of teeth present, and the need for prostheses replacement) and the performance in the swallowing function (classification in the FOIS scale and dysphagia rating in each consistency). For this analysis to be performed, the FOIS scale and the degree of dysphagia were numerically classified from 1 to 7 and from 1 to 4, respectively.\nFor comparison of FOIS results and dysphagia classification, among the different types of prostheses used in the upper and lower dental arches (complete dental prosthesis, partial dental prosthesis, denture or none), the Kruskal–Wallis test was used, followed by the Dunn’s test.\n Oral condition evaluation The participants underwent assessment of various components of oral health through traditional health indicators, based on the presence or absence of disease. The data were collected from the clinical examination by a dentist as instructed by the WHO – Oral Health Surveys: Basic Methods,18 including the assessment of dental status by the number of decayed, missing, and filled teeth (DMFT) and evaluation of dental prostheses.\nThe prostheses were evaluated for retention, functional stability, smile aesthetics, degree of bone resorption, and quality of the mucosa.19\nFrom the evaluation, the dental arches were classified as without prosthesis or with the presence of partial or complete dental prosthesis; later, the use or need for replacement of dental prostheses was indicated. The need for prostheses replacement was as follows: 0, no need to change; 1, partial dental prosthesis was necessary in one of the dental arches; 2, need for partial dental prosthesis in both dental arches; 3, need for complete dental prosthesis in one of the dental arches; 4, need for dental prosthesis in both dental arches.\nThe participants underwent assessment of various components of oral health through traditional health indicators, based on the presence or absence of disease. The data were collected from the clinical examination by a dentist as instructed by the WHO – Oral Health Surveys: Basic Methods,18 including the assessment of dental status by the number of decayed, missing, and filled teeth (DMFT) and evaluation of dental prostheses.\nThe prostheses were evaluated for retention, functional stability, smile aesthetics, degree of bone resorption, and quality of the mucosa.19\nFrom the evaluation, the dental arches were classified as without prosthesis or with the presence of partial or complete dental prosthesis; later, the use or need for replacement of dental prostheses was indicated. The need for prostheses replacement was as follows: 0, no need to change; 1, partial dental prosthesis was necessary in one of the dental arches; 2, need for partial dental prosthesis in both dental arches; 3, need for complete dental prosthesis in one of the dental arches; 4, need for dental prosthesis in both dental arches.\n Functional Oral Intake Scale Research of oral ingestion was performed by reviewing the usual food consumption patterns referred to in the 24-hour dietary recall. From the data obtained by the research of oral ingestion, patients were classified according to the levels of the Functional Oral Intake Scale (FOIS),20 on a scale from I to VII, considering the diet characteristics based on the properties and texture of the food.21\nResearch of oral ingestion was performed by reviewing the usual food consumption patterns referred to in the 24-hour dietary recall. From the data obtained by the research of oral ingestion, patients were classified according to the levels of the Functional Oral Intake Scale (FOIS),20 on a scale from I to VII, considering the diet characteristics based on the properties and texture of the food.21\n Fiberoptic endoscopic evaluation of swallowing The fiberoptic endoscopic evaluation of swallowing (FEES) was performed by an otorhinolaryngologist physician collaboratively with the speech therapist. Subjects were asked to remain seated with their heads arranged in the direction of the body axis, without bending or rotation, and the examination was performed using a flexible endoscopic equipment of fiberoptic bronchoscopic type (model Olympus CLV-U20) and an Olympus OTV–SC nasopharyngoscope (Olympus Corporation, Tokyo, Japan).\nThree consistent standardized foods were evaluated: liquid (10 mL of filtered water), thick pudding (10 mL of thickened dietary grape juice, reaching a final consistency similar to that of pudding), and solid (half a slice of bread, 1 cm thick). From the data obtained by the FEES, the severity rating of the swallowing disorder was determined in accordance with the scale of functional impairment of swallowing of Macedo Filho,22,23 which subdivides the dysphagia severity levels (normal, mild, moderate, and severe).\nThe fiberoptic endoscopic evaluation of swallowing (FEES) was performed by an otorhinolaryngologist physician collaboratively with the speech therapist. Subjects were asked to remain seated with their heads arranged in the direction of the body axis, without bending or rotation, and the examination was performed using a flexible endoscopic equipment of fiberoptic bronchoscopic type (model Olympus CLV-U20) and an Olympus OTV–SC nasopharyngoscope (Olympus Corporation, Tokyo, Japan).\nThree consistent standardized foods were evaluated: liquid (10 mL of filtered water), thick pudding (10 mL of thickened dietary grape juice, reaching a final consistency similar to that of pudding), and solid (half a slice of bread, 1 cm thick). From the data obtained by the FEES, the severity rating of the swallowing disorder was determined in accordance with the scale of functional impairment of swallowing of Macedo Filho,22,23 which subdivides the dysphagia severity levels (normal, mild, moderate, and severe).\n Statistical analysis Spearman’s test was used to verify the correlation between the oral health condition (DMFT, number of teeth present, and the need for prostheses replacement) and the performance in the swallowing function (classification in the FOIS scale and dysphagia rating in each consistency). For this analysis to be performed, the FOIS scale and the degree of dysphagia were numerically classified from 1 to 7 and from 1 to 4, respectively.\nFor comparison of FOIS results and dysphagia classification, among the different types of prostheses used in the upper and lower dental arches (complete dental prosthesis, partial dental prosthesis, denture or none), the Kruskal–Wallis test was used, followed by the Dunn’s test.\nSpearman’s test was used to verify the correlation between the oral health condition (DMFT, number of teeth present, and the need for prostheses replacement) and the performance in the swallowing function (classification in the FOIS scale and dysphagia rating in each consistency). For this analysis to be performed, the FOIS scale and the degree of dysphagia were numerically classified from 1 to 7 and from 1 to 4, respectively.\nFor comparison of FOIS results and dysphagia classification, among the different types of prostheses used in the upper and lower dental arches (complete dental prosthesis, partial dental prosthesis, denture or none), the Kruskal–Wallis test was used, followed by the Dunn’s test."," Oral condition evaluation The participants underwent assessment of various components of oral health through traditional health indicators, based on the presence or absence of disease. The data were collected from the clinical examination by a dentist as instructed by the WHO – Oral Health Surveys: Basic Methods,18 including the assessment of dental status by the number of decayed, missing, and filled teeth (DMFT) and evaluation of dental prostheses.\nThe prostheses were evaluated for retention, functional stability, smile aesthetics, degree of bone resorption, and quality of the mucosa.19\nFrom the evaluation, the dental arches were classified as without prosthesis or with the presence of partial or complete dental prosthesis; later, the use or need for replacement of dental prostheses was indicated. The need for prostheses replacement was as follows: 0, no need to change; 1, partial dental prosthesis was necessary in one of the dental arches; 2, need for partial dental prosthesis in both dental arches; 3, need for complete dental prosthesis in one of the dental arches; 4, need for dental prosthesis in both dental arches.\nThe participants underwent assessment of various components of oral health through traditional health indicators, based on the presence or absence of disease. The data were collected from the clinical examination by a dentist as instructed by the WHO – Oral Health Surveys: Basic Methods,18 including the assessment of dental status by the number of decayed, missing, and filled teeth (DMFT) and evaluation of dental prostheses.\nThe prostheses were evaluated for retention, functional stability, smile aesthetics, degree of bone resorption, and quality of the mucosa.19\nFrom the evaluation, the dental arches were classified as without prosthesis or with the presence of partial or complete dental prosthesis; later, the use or need for replacement of dental prostheses was indicated. The need for prostheses replacement was as follows: 0, no need to change; 1, partial dental prosthesis was necessary in one of the dental arches; 2, need for partial dental prosthesis in both dental arches; 3, need for complete dental prosthesis in one of the dental arches; 4, need for dental prosthesis in both dental arches.\n Functional Oral Intake Scale Research of oral ingestion was performed by reviewing the usual food consumption patterns referred to in the 24-hour dietary recall. From the data obtained by the research of oral ingestion, patients were classified according to the levels of the Functional Oral Intake Scale (FOIS),20 on a scale from I to VII, considering the diet characteristics based on the properties and texture of the food.21\nResearch of oral ingestion was performed by reviewing the usual food consumption patterns referred to in the 24-hour dietary recall. From the data obtained by the research of oral ingestion, patients were classified according to the levels of the Functional Oral Intake Scale (FOIS),20 on a scale from I to VII, considering the diet characteristics based on the properties and texture of the food.21\n Fiberoptic endoscopic evaluation of swallowing The fiberoptic endoscopic evaluation of swallowing (FEES) was performed by an otorhinolaryngologist physician collaboratively with the speech therapist. Subjects were asked to remain seated with their heads arranged in the direction of the body axis, without bending or rotation, and the examination was performed using a flexible endoscopic equipment of fiberoptic bronchoscopic type (model Olympus CLV-U20) and an Olympus OTV–SC nasopharyngoscope (Olympus Corporation, Tokyo, Japan).\nThree consistent standardized foods were evaluated: liquid (10 mL of filtered water), thick pudding (10 mL of thickened dietary grape juice, reaching a final consistency similar to that of pudding), and solid (half a slice of bread, 1 cm thick). From the data obtained by the FEES, the severity rating of the swallowing disorder was determined in accordance with the scale of functional impairment of swallowing of Macedo Filho,22,23 which subdivides the dysphagia severity levels (normal, mild, moderate, and severe).\nThe fiberoptic endoscopic evaluation of swallowing (FEES) was performed by an otorhinolaryngologist physician collaboratively with the speech therapist. Subjects were asked to remain seated with their heads arranged in the direction of the body axis, without bending or rotation, and the examination was performed using a flexible endoscopic equipment of fiberoptic bronchoscopic type (model Olympus CLV-U20) and an Olympus OTV–SC nasopharyngoscope (Olympus Corporation, Tokyo, Japan).\nThree consistent standardized foods were evaluated: liquid (10 mL of filtered water), thick pudding (10 mL of thickened dietary grape juice, reaching a final consistency similar to that of pudding), and solid (half a slice of bread, 1 cm thick). From the data obtained by the FEES, the severity rating of the swallowing disorder was determined in accordance with the scale of functional impairment of swallowing of Macedo Filho,22,23 which subdivides the dysphagia severity levels (normal, mild, moderate, and severe).\n Statistical analysis Spearman’s test was used to verify the correlation between the oral health condition (DMFT, number of teeth present, and the need for prostheses replacement) and the performance in the swallowing function (classification in the FOIS scale and dysphagia rating in each consistency). For this analysis to be performed, the FOIS scale and the degree of dysphagia were numerically classified from 1 to 7 and from 1 to 4, respectively.\nFor comparison of FOIS results and dysphagia classification, among the different types of prostheses used in the upper and lower dental arches (complete dental prosthesis, partial dental prosthesis, denture or none), the Kruskal–Wallis test was used, followed by the Dunn’s test.\nSpearman’s test was used to verify the correlation between the oral health condition (DMFT, number of teeth present, and the need for prostheses replacement) and the performance in the swallowing function (classification in the FOIS scale and dysphagia rating in each consistency). For this analysis to be performed, the FOIS scale and the degree of dysphagia were numerically classified from 1 to 7 and from 1 to 4, respectively.\nFor comparison of FOIS results and dysphagia classification, among the different types of prostheses used in the upper and lower dental arches (complete dental prosthesis, partial dental prosthesis, denture or none), the Kruskal–Wallis test was used, followed by the Dunn’s test.","The participants underwent assessment of various components of oral health through traditional health indicators, based on the presence or absence of disease. The data were collected from the clinical examination by a dentist as instructed by the WHO – Oral Health Surveys: Basic Methods,18 including the assessment of dental status by the number of decayed, missing, and filled teeth (DMFT) and evaluation of dental prostheses.\nThe prostheses were evaluated for retention, functional stability, smile aesthetics, degree of bone resorption, and quality of the mucosa.19\nFrom the evaluation, the dental arches were classified as without prosthesis or with the presence of partial or complete dental prosthesis; later, the use or need for replacement of dental prostheses was indicated. The need for prostheses replacement was as follows: 0, no need to change; 1, partial dental prosthesis was necessary in one of the dental arches; 2, need for partial dental prosthesis in both dental arches; 3, need for complete dental prosthesis in one of the dental arches; 4, need for dental prosthesis in both dental arches.","Research of oral ingestion was performed by reviewing the usual food consumption patterns referred to in the 24-hour dietary recall. From the data obtained by the research of oral ingestion, patients were classified according to the levels of the Functional Oral Intake Scale (FOIS),20 on a scale from I to VII, considering the diet characteristics based on the properties and texture of the food.21","The fiberoptic endoscopic evaluation of swallowing (FEES) was performed by an otorhinolaryngologist physician collaboratively with the speech therapist. Subjects were asked to remain seated with their heads arranged in the direction of the body axis, without bending or rotation, and the examination was performed using a flexible endoscopic equipment of fiberoptic bronchoscopic type (model Olympus CLV-U20) and an Olympus OTV–SC nasopharyngoscope (Olympus Corporation, Tokyo, Japan).\nThree consistent standardized foods were evaluated: liquid (10 mL of filtered water), thick pudding (10 mL of thickened dietary grape juice, reaching a final consistency similar to that of pudding), and solid (half a slice of bread, 1 cm thick). From the data obtained by the FEES, the severity rating of the swallowing disorder was determined in accordance with the scale of functional impairment of swallowing of Macedo Filho,22,23 which subdivides the dysphagia severity levels (normal, mild, moderate, and severe).","The findings of this study indicate that the oral health condition of elderly individuals after stroke in chronic phase showed an association with the level of oral intake and the degree of oropharyngeal dysphagia. The limitations of the study should be considered, one being the lack of access to the imaging examinations to characterize the individuals according to the type of stroke, damaged areas, and extent of injury, and heterogeneity of the time of brain involvement, in addition to the effects of medications and other comorbidities, which were not taken into account. Also, owing to the small sample, strong correlations could not be observed, and a cause/effect relationship cannot be determined.\nDespite the limitations of this study, and the fact that hemiplegia does not necessarily represent a direct relationship with the injured brain hemisphere contralaterally, since cerebellum lesions may cause ipsilateral motor damages,31 the findings indicated that the level of oral intake was influenced by the condition or type of dental prostheses, regardless of the side of the body affected. On the other hand, the number of teeth were demonstrated to be related to the level of dysphagia in individuals with right hemiplegia, which shows that the oral health condition differentially affects the physiological responses of swallowing, influenced by the brain damage.\nConsidering the present sample, further research is necessary pertaining to the oral condition and the neurological involvement of individuals. In addition, these issues demonstrate the need for an interdisciplinary research team comprising dentists, physicians, speech pathologists, and other health professionals, so as to study and define the treatment and guidelines to patients and caretakers, in order to maintain the best oral health condition in the elderly presented with oropharyngeal dysphagia."],"string":"[\n \" Study design and subjects Thirty poststroke individuals, aged 61 to 90 years, whose injury happened 6 months to 9 years previously to their participation in this prospective study. Table 1 shows the characterization data of the sample.\\nThe inclusion criteria considered the following cases: affected by stroke as attested by a medical report, through clinical diagnosis and imaging, with a minimum time of 6 months; aged over 60 years, being in regular clinical neurological monitoring, not having undergone dysphagia rehabilitation; presenting general stable health that would enable the realization of the proposed tests.\\nIn order to achieve a greater delineation of the group, in addition to considering the group of 30 individuals (heterogeneous group [HG]), the total dentate, the total edentulous without dental prostheses, and individuals with more than one episode of stroke were excluded and the participants were divided into two groups, according to the affected body side: right hemiplegic group (RHG, n=8) and left hemiplegic group (LHG, n=11). A distribution and recruitment fluxogram is shown in Figure 1.\\nThirty poststroke individuals, aged 61 to 90 years, whose injury happened 6 months to 9 years previously to their participation in this prospective study. Table 1 shows the characterization data of the sample.\\nThe inclusion criteria considered the following cases: affected by stroke as attested by a medical report, through clinical diagnosis and imaging, with a minimum time of 6 months; aged over 60 years, being in regular clinical neurological monitoring, not having undergone dysphagia rehabilitation; presenting general stable health that would enable the realization of the proposed tests.\\nIn order to achieve a greater delineation of the group, in addition to considering the group of 30 individuals (heterogeneous group [HG]), the total dentate, the total edentulous without dental prostheses, and individuals with more than one episode of stroke were excluded and the participants were divided into two groups, according to the affected body side: right hemiplegic group (RHG, n=8) and left hemiplegic group (LHG, n=11). A distribution and recruitment fluxogram is shown in Figure 1.\\n Procedures Oral condition evaluation The participants underwent assessment of various components of oral health through traditional health indicators, based on the presence or absence of disease. The data were collected from the clinical examination by a dentist as instructed by the WHO – Oral Health Surveys: Basic Methods,18 including the assessment of dental status by the number of decayed, missing, and filled teeth (DMFT) and evaluation of dental prostheses.\\nThe prostheses were evaluated for retention, functional stability, smile aesthetics, degree of bone resorption, and quality of the mucosa.19\\nFrom the evaluation, the dental arches were classified as without prosthesis or with the presence of partial or complete dental prosthesis; later, the use or need for replacement of dental prostheses was indicated. The need for prostheses replacement was as follows: 0, no need to change; 1, partial dental prosthesis was necessary in one of the dental arches; 2, need for partial dental prosthesis in both dental arches; 3, need for complete dental prosthesis in one of the dental arches; 4, need for dental prosthesis in both dental arches.\\nThe participants underwent assessment of various components of oral health through traditional health indicators, based on the presence or absence of disease. The data were collected from the clinical examination by a dentist as instructed by the WHO – Oral Health Surveys: Basic Methods,18 including the assessment of dental status by the number of decayed, missing, and filled teeth (DMFT) and evaluation of dental prostheses.\\nThe prostheses were evaluated for retention, functional stability, smile aesthetics, degree of bone resorption, and quality of the mucosa.19\\nFrom the evaluation, the dental arches were classified as without prosthesis or with the presence of partial or complete dental prosthesis; later, the use or need for replacement of dental prostheses was indicated. The need for prostheses replacement was as follows: 0, no need to change; 1, partial dental prosthesis was necessary in one of the dental arches; 2, need for partial dental prosthesis in both dental arches; 3, need for complete dental prosthesis in one of the dental arches; 4, need for dental prosthesis in both dental arches.\\n Functional Oral Intake Scale Research of oral ingestion was performed by reviewing the usual food consumption patterns referred to in the 24-hour dietary recall. From the data obtained by the research of oral ingestion, patients were classified according to the levels of the Functional Oral Intake Scale (FOIS),20 on a scale from I to VII, considering the diet characteristics based on the properties and texture of the food.21\\nResearch of oral ingestion was performed by reviewing the usual food consumption patterns referred to in the 24-hour dietary recall. From the data obtained by the research of oral ingestion, patients were classified according to the levels of the Functional Oral Intake Scale (FOIS),20 on a scale from I to VII, considering the diet characteristics based on the properties and texture of the food.21\\n Fiberoptic endoscopic evaluation of swallowing The fiberoptic endoscopic evaluation of swallowing (FEES) was performed by an otorhinolaryngologist physician collaboratively with the speech therapist. Subjects were asked to remain seated with their heads arranged in the direction of the body axis, without bending or rotation, and the examination was performed using a flexible endoscopic equipment of fiberoptic bronchoscopic type (model Olympus CLV-U20) and an Olympus OTV–SC nasopharyngoscope (Olympus Corporation, Tokyo, Japan).\\nThree consistent standardized foods were evaluated: liquid (10 mL of filtered water), thick pudding (10 mL of thickened dietary grape juice, reaching a final consistency similar to that of pudding), and solid (half a slice of bread, 1 cm thick). From the data obtained by the FEES, the severity rating of the swallowing disorder was determined in accordance with the scale of functional impairment of swallowing of Macedo Filho,22,23 which subdivides the dysphagia severity levels (normal, mild, moderate, and severe).\\nThe fiberoptic endoscopic evaluation of swallowing (FEES) was performed by an otorhinolaryngologist physician collaboratively with the speech therapist. Subjects were asked to remain seated with their heads arranged in the direction of the body axis, without bending or rotation, and the examination was performed using a flexible endoscopic equipment of fiberoptic bronchoscopic type (model Olympus CLV-U20) and an Olympus OTV–SC nasopharyngoscope (Olympus Corporation, Tokyo, Japan).\\nThree consistent standardized foods were evaluated: liquid (10 mL of filtered water), thick pudding (10 mL of thickened dietary grape juice, reaching a final consistency similar to that of pudding), and solid (half a slice of bread, 1 cm thick). From the data obtained by the FEES, the severity rating of the swallowing disorder was determined in accordance with the scale of functional impairment of swallowing of Macedo Filho,22,23 which subdivides the dysphagia severity levels (normal, mild, moderate, and severe).\\n Statistical analysis Spearman’s test was used to verify the correlation between the oral health condition (DMFT, number of teeth present, and the need for prostheses replacement) and the performance in the swallowing function (classification in the FOIS scale and dysphagia rating in each consistency). For this analysis to be performed, the FOIS scale and the degree of dysphagia were numerically classified from 1 to 7 and from 1 to 4, respectively.\\nFor comparison of FOIS results and dysphagia classification, among the different types of prostheses used in the upper and lower dental arches (complete dental prosthesis, partial dental prosthesis, denture or none), the Kruskal–Wallis test was used, followed by the Dunn’s test.\\nSpearman’s test was used to verify the correlation between the oral health condition (DMFT, number of teeth present, and the need for prostheses replacement) and the performance in the swallowing function (classification in the FOIS scale and dysphagia rating in each consistency). For this analysis to be performed, the FOIS scale and the degree of dysphagia were numerically classified from 1 to 7 and from 1 to 4, respectively.\\nFor comparison of FOIS results and dysphagia classification, among the different types of prostheses used in the upper and lower dental arches (complete dental prosthesis, partial dental prosthesis, denture or none), the Kruskal–Wallis test was used, followed by the Dunn’s test.\\n Oral condition evaluation The participants underwent assessment of various components of oral health through traditional health indicators, based on the presence or absence of disease. The data were collected from the clinical examination by a dentist as instructed by the WHO – Oral Health Surveys: Basic Methods,18 including the assessment of dental status by the number of decayed, missing, and filled teeth (DMFT) and evaluation of dental prostheses.\\nThe prostheses were evaluated for retention, functional stability, smile aesthetics, degree of bone resorption, and quality of the mucosa.19\\nFrom the evaluation, the dental arches were classified as without prosthesis or with the presence of partial or complete dental prosthesis; later, the use or need for replacement of dental prostheses was indicated. The need for prostheses replacement was as follows: 0, no need to change; 1, partial dental prosthesis was necessary in one of the dental arches; 2, need for partial dental prosthesis in both dental arches; 3, need for complete dental prosthesis in one of the dental arches; 4, need for dental prosthesis in both dental arches.\\nThe participants underwent assessment of various components of oral health through traditional health indicators, based on the presence or absence of disease. The data were collected from the clinical examination by a dentist as instructed by the WHO – Oral Health Surveys: Basic Methods,18 including the assessment of dental status by the number of decayed, missing, and filled teeth (DMFT) and evaluation of dental prostheses.\\nThe prostheses were evaluated for retention, functional stability, smile aesthetics, degree of bone resorption, and quality of the mucosa.19\\nFrom the evaluation, the dental arches were classified as without prosthesis or with the presence of partial or complete dental prosthesis; later, the use or need for replacement of dental prostheses was indicated. The need for prostheses replacement was as follows: 0, no need to change; 1, partial dental prosthesis was necessary in one of the dental arches; 2, need for partial dental prosthesis in both dental arches; 3, need for complete dental prosthesis in one of the dental arches; 4, need for dental prosthesis in both dental arches.\\n Functional Oral Intake Scale Research of oral ingestion was performed by reviewing the usual food consumption patterns referred to in the 24-hour dietary recall. From the data obtained by the research of oral ingestion, patients were classified according to the levels of the Functional Oral Intake Scale (FOIS),20 on a scale from I to VII, considering the diet characteristics based on the properties and texture of the food.21\\nResearch of oral ingestion was performed by reviewing the usual food consumption patterns referred to in the 24-hour dietary recall. From the data obtained by the research of oral ingestion, patients were classified according to the levels of the Functional Oral Intake Scale (FOIS),20 on a scale from I to VII, considering the diet characteristics based on the properties and texture of the food.21\\n Fiberoptic endoscopic evaluation of swallowing The fiberoptic endoscopic evaluation of swallowing (FEES) was performed by an otorhinolaryngologist physician collaboratively with the speech therapist. Subjects were asked to remain seated with their heads arranged in the direction of the body axis, without bending or rotation, and the examination was performed using a flexible endoscopic equipment of fiberoptic bronchoscopic type (model Olympus CLV-U20) and an Olympus OTV–SC nasopharyngoscope (Olympus Corporation, Tokyo, Japan).\\nThree consistent standardized foods were evaluated: liquid (10 mL of filtered water), thick pudding (10 mL of thickened dietary grape juice, reaching a final consistency similar to that of pudding), and solid (half a slice of bread, 1 cm thick). From the data obtained by the FEES, the severity rating of the swallowing disorder was determined in accordance with the scale of functional impairment of swallowing of Macedo Filho,22,23 which subdivides the dysphagia severity levels (normal, mild, moderate, and severe).\\nThe fiberoptic endoscopic evaluation of swallowing (FEES) was performed by an otorhinolaryngologist physician collaboratively with the speech therapist. Subjects were asked to remain seated with their heads arranged in the direction of the body axis, without bending or rotation, and the examination was performed using a flexible endoscopic equipment of fiberoptic bronchoscopic type (model Olympus CLV-U20) and an Olympus OTV–SC nasopharyngoscope (Olympus Corporation, Tokyo, Japan).\\nThree consistent standardized foods were evaluated: liquid (10 mL of filtered water), thick pudding (10 mL of thickened dietary grape juice, reaching a final consistency similar to that of pudding), and solid (half a slice of bread, 1 cm thick). From the data obtained by the FEES, the severity rating of the swallowing disorder was determined in accordance with the scale of functional impairment of swallowing of Macedo Filho,22,23 which subdivides the dysphagia severity levels (normal, mild, moderate, and severe).\\n Statistical analysis Spearman’s test was used to verify the correlation between the oral health condition (DMFT, number of teeth present, and the need for prostheses replacement) and the performance in the swallowing function (classification in the FOIS scale and dysphagia rating in each consistency). For this analysis to be performed, the FOIS scale and the degree of dysphagia were numerically classified from 1 to 7 and from 1 to 4, respectively.\\nFor comparison of FOIS results and dysphagia classification, among the different types of prostheses used in the upper and lower dental arches (complete dental prosthesis, partial dental prosthesis, denture or none), the Kruskal–Wallis test was used, followed by the Dunn’s test.\\nSpearman’s test was used to verify the correlation between the oral health condition (DMFT, number of teeth present, and the need for prostheses replacement) and the performance in the swallowing function (classification in the FOIS scale and dysphagia rating in each consistency). For this analysis to be performed, the FOIS scale and the degree of dysphagia were numerically classified from 1 to 7 and from 1 to 4, respectively.\\nFor comparison of FOIS results and dysphagia classification, among the different types of prostheses used in the upper and lower dental arches (complete dental prosthesis, partial dental prosthesis, denture or none), the Kruskal–Wallis test was used, followed by the Dunn’s test.\",\n \" Oral condition evaluation The participants underwent assessment of various components of oral health through traditional health indicators, based on the presence or absence of disease. The data were collected from the clinical examination by a dentist as instructed by the WHO – Oral Health Surveys: Basic Methods,18 including the assessment of dental status by the number of decayed, missing, and filled teeth (DMFT) and evaluation of dental prostheses.\\nThe prostheses were evaluated for retention, functional stability, smile aesthetics, degree of bone resorption, and quality of the mucosa.19\\nFrom the evaluation, the dental arches were classified as without prosthesis or with the presence of partial or complete dental prosthesis; later, the use or need for replacement of dental prostheses was indicated. The need for prostheses replacement was as follows: 0, no need to change; 1, partial dental prosthesis was necessary in one of the dental arches; 2, need for partial dental prosthesis in both dental arches; 3, need for complete dental prosthesis in one of the dental arches; 4, need for dental prosthesis in both dental arches.\\nThe participants underwent assessment of various components of oral health through traditional health indicators, based on the presence or absence of disease. The data were collected from the clinical examination by a dentist as instructed by the WHO – Oral Health Surveys: Basic Methods,18 including the assessment of dental status by the number of decayed, missing, and filled teeth (DMFT) and evaluation of dental prostheses.\\nThe prostheses were evaluated for retention, functional stability, smile aesthetics, degree of bone resorption, and quality of the mucosa.19\\nFrom the evaluation, the dental arches were classified as without prosthesis or with the presence of partial or complete dental prosthesis; later, the use or need for replacement of dental prostheses was indicated. The need for prostheses replacement was as follows: 0, no need to change; 1, partial dental prosthesis was necessary in one of the dental arches; 2, need for partial dental prosthesis in both dental arches; 3, need for complete dental prosthesis in one of the dental arches; 4, need for dental prosthesis in both dental arches.\\n Functional Oral Intake Scale Research of oral ingestion was performed by reviewing the usual food consumption patterns referred to in the 24-hour dietary recall. From the data obtained by the research of oral ingestion, patients were classified according to the levels of the Functional Oral Intake Scale (FOIS),20 on a scale from I to VII, considering the diet characteristics based on the properties and texture of the food.21\\nResearch of oral ingestion was performed by reviewing the usual food consumption patterns referred to in the 24-hour dietary recall. From the data obtained by the research of oral ingestion, patients were classified according to the levels of the Functional Oral Intake Scale (FOIS),20 on a scale from I to VII, considering the diet characteristics based on the properties and texture of the food.21\\n Fiberoptic endoscopic evaluation of swallowing The fiberoptic endoscopic evaluation of swallowing (FEES) was performed by an otorhinolaryngologist physician collaboratively with the speech therapist. Subjects were asked to remain seated with their heads arranged in the direction of the body axis, without bending or rotation, and the examination was performed using a flexible endoscopic equipment of fiberoptic bronchoscopic type (model Olympus CLV-U20) and an Olympus OTV–SC nasopharyngoscope (Olympus Corporation, Tokyo, Japan).\\nThree consistent standardized foods were evaluated: liquid (10 mL of filtered water), thick pudding (10 mL of thickened dietary grape juice, reaching a final consistency similar to that of pudding), and solid (half a slice of bread, 1 cm thick). From the data obtained by the FEES, the severity rating of the swallowing disorder was determined in accordance with the scale of functional impairment of swallowing of Macedo Filho,22,23 which subdivides the dysphagia severity levels (normal, mild, moderate, and severe).\\nThe fiberoptic endoscopic evaluation of swallowing (FEES) was performed by an otorhinolaryngologist physician collaboratively with the speech therapist. Subjects were asked to remain seated with their heads arranged in the direction of the body axis, without bending or rotation, and the examination was performed using a flexible endoscopic equipment of fiberoptic bronchoscopic type (model Olympus CLV-U20) and an Olympus OTV–SC nasopharyngoscope (Olympus Corporation, Tokyo, Japan).\\nThree consistent standardized foods were evaluated: liquid (10 mL of filtered water), thick pudding (10 mL of thickened dietary grape juice, reaching a final consistency similar to that of pudding), and solid (half a slice of bread, 1 cm thick). From the data obtained by the FEES, the severity rating of the swallowing disorder was determined in accordance with the scale of functional impairment of swallowing of Macedo Filho,22,23 which subdivides the dysphagia severity levels (normal, mild, moderate, and severe).\\n Statistical analysis Spearman’s test was used to verify the correlation between the oral health condition (DMFT, number of teeth present, and the need for prostheses replacement) and the performance in the swallowing function (classification in the FOIS scale and dysphagia rating in each consistency). For this analysis to be performed, the FOIS scale and the degree of dysphagia were numerically classified from 1 to 7 and from 1 to 4, respectively.\\nFor comparison of FOIS results and dysphagia classification, among the different types of prostheses used in the upper and lower dental arches (complete dental prosthesis, partial dental prosthesis, denture or none), the Kruskal–Wallis test was used, followed by the Dunn’s test.\\nSpearman’s test was used to verify the correlation between the oral health condition (DMFT, number of teeth present, and the need for prostheses replacement) and the performance in the swallowing function (classification in the FOIS scale and dysphagia rating in each consistency). For this analysis to be performed, the FOIS scale and the degree of dysphagia were numerically classified from 1 to 7 and from 1 to 4, respectively.\\nFor comparison of FOIS results and dysphagia classification, among the different types of prostheses used in the upper and lower dental arches (complete dental prosthesis, partial dental prosthesis, denture or none), the Kruskal–Wallis test was used, followed by the Dunn’s test.\",\n \"The participants underwent assessment of various components of oral health through traditional health indicators, based on the presence or absence of disease. The data were collected from the clinical examination by a dentist as instructed by the WHO – Oral Health Surveys: Basic Methods,18 including the assessment of dental status by the number of decayed, missing, and filled teeth (DMFT) and evaluation of dental prostheses.\\nThe prostheses were evaluated for retention, functional stability, smile aesthetics, degree of bone resorption, and quality of the mucosa.19\\nFrom the evaluation, the dental arches were classified as without prosthesis or with the presence of partial or complete dental prosthesis; later, the use or need for replacement of dental prostheses was indicated. The need for prostheses replacement was as follows: 0, no need to change; 1, partial dental prosthesis was necessary in one of the dental arches; 2, need for partial dental prosthesis in both dental arches; 3, need for complete dental prosthesis in one of the dental arches; 4, need for dental prosthesis in both dental arches.\",\n \"Research of oral ingestion was performed by reviewing the usual food consumption patterns referred to in the 24-hour dietary recall. From the data obtained by the research of oral ingestion, patients were classified according to the levels of the Functional Oral Intake Scale (FOIS),20 on a scale from I to VII, considering the diet characteristics based on the properties and texture of the food.21\",\n \"The fiberoptic endoscopic evaluation of swallowing (FEES) was performed by an otorhinolaryngologist physician collaboratively with the speech therapist. Subjects were asked to remain seated with their heads arranged in the direction of the body axis, without bending or rotation, and the examination was performed using a flexible endoscopic equipment of fiberoptic bronchoscopic type (model Olympus CLV-U20) and an Olympus OTV–SC nasopharyngoscope (Olympus Corporation, Tokyo, Japan).\\nThree consistent standardized foods were evaluated: liquid (10 mL of filtered water), thick pudding (10 mL of thickened dietary grape juice, reaching a final consistency similar to that of pudding), and solid (half a slice of bread, 1 cm thick). From the data obtained by the FEES, the severity rating of the swallowing disorder was determined in accordance with the scale of functional impairment of swallowing of Macedo Filho,22,23 which subdivides the dysphagia severity levels (normal, mild, moderate, and severe).\",\n \"The findings of this study indicate that the oral health condition of elderly individuals after stroke in chronic phase showed an association with the level of oral intake and the degree of oropharyngeal dysphagia. The limitations of the study should be considered, one being the lack of access to the imaging examinations to characterize the individuals according to the type of stroke, damaged areas, and extent of injury, and heterogeneity of the time of brain involvement, in addition to the effects of medications and other comorbidities, which were not taken into account. Also, owing to the small sample, strong correlations could not be observed, and a cause/effect relationship cannot be determined.\\nDespite the limitations of this study, and the fact that hemiplegia does not necessarily represent a direct relationship with the injured brain hemisphere contralaterally, since cerebellum lesions may cause ipsilateral motor damages,31 the findings indicated that the level of oral intake was influenced by the condition or type of dental prostheses, regardless of the side of the body affected. On the other hand, the number of teeth were demonstrated to be related to the level of dysphagia in individuals with right hemiplegia, which shows that the oral health condition differentially affects the physiological responses of swallowing, influenced by the brain damage.\\nConsidering the present sample, further research is necessary pertaining to the oral condition and the neurological involvement of individuals. In addition, these issues demonstrate the need for an interdisciplinary research team comprising dentists, physicians, speech pathologists, and other health professionals, so as to study and define the treatment and guidelines to patients and caretakers, in order to maintain the best oral health condition in the elderly presented with oropharyngeal dysphagia.\"\n]"},"other_sections_sec_types":{"kind":"list like","value":["methods","methods",null,null,null,null],"string":"[\n \"methods\",\n \"methods\",\n null,\n null,\n null,\n null\n]"},"all_sections_titles":{"kind":"list like","value":["Introduction","Methods","Study design and subjects","Procedures","Oral condition evaluation","Functional Oral Intake Scale","Fiberoptic endoscopic evaluation of swallowing","Statistical analysis","Results","Discussion","Conclusion"],"string":"[\n \"Introduction\",\n \"Methods\",\n \"Study design and subjects\",\n \"Procedures\",\n \"Oral condition evaluation\",\n \"Functional Oral Intake Scale\",\n \"Fiberoptic endoscopic evaluation of swallowing\",\n \"Statistical analysis\",\n \"Results\",\n \"Discussion\",\n \"Conclusion\"\n]"},"all_sections_texts":{"kind":"list like","value":["The process of swallowing physiologically changes with aging. The loss of teeth, very common in this population, is related to the reduction of bone tissue, receptors (proprioceptors and periodontal ligaments), and muscle atrophy. Consequently, orofacial functions are impaired in toothless individuals.1 In addition, prostheses are functionally less efficient when compared to individuals with natural dentition.1,2\nThe stability of the jaw occlusion of the posterior teeth or prostheses is important for the swallowing function.2 The dental condition and the use of poorly fitting prostheses may contribute to the difficulties arising from old age, and the oral diet and nutritional status of the elderly may be affected by changes related to aging3 and changes in skill and desire to feel, bite, chew4 and swallow food,3,5 significantly compromising and further aggravating the quality of life of these individuals.6\nThe literature has shown the influence of oral health condition on swallowing and nutrition, indirectly influencing the daily life activities of the elderly.7 Furthermore, it is clear that maintenance of oral health is essential for the general health, quality of life, chewing ability8 and, mainly, the reduction of pneumonia risk in fragile elderly people,8,9 for it is already known that bacterial colonies in oropharyngeal tissues and in dental plaque are the greatest precursors for the development of aspiration respiratory infections.10\nBesides the influence of the loss of dentition, the functional impairments resulting from aging may be potentiated by neurological changes resulting in dysphagia in patients, an important cause of morbidity and mortality in this population.11\nIn the adult and elderly population, dysphagia is most commonly associated with stroke.12 More than half of patients who have suffered stroke present between six and ten types of disability, muscle weakness being the most prevalent one, followed by communication, speech, and swallowing disorders.13 Specifically regarding swallowing, the stroke entails increased oropharyngeal transit time,14,15 changes in motor control of tongue,16 as well as the presence of laryngotracheal aspiration of food.16,17\nOnce the physiological changes resulting from aging in swallowing may be aggravated by loss of teeth and the use of poorly fitting prostheses, the severity of dysphagia could be influenced by the oral health condition and use of dental prostheses in elderly patients with neurological diseases. However, according to the literature studied, no research has made the correlation between the findings of the swallowing performance with oral health in elderly patients after stroke. Therefore, the aim of this study was to relate the condition of oral health to the level of oral intake and the degree of swallowing dysfunction in elderly patients with stroke in chronic phase."," Study design and subjects Thirty poststroke individuals, aged 61 to 90 years, whose injury happened 6 months to 9 years previously to their participation in this prospective study. Table 1 shows the characterization data of the sample.\nThe inclusion criteria considered the following cases: affected by stroke as attested by a medical report, through clinical diagnosis and imaging, with a minimum time of 6 months; aged over 60 years, being in regular clinical neurological monitoring, not having undergone dysphagia rehabilitation; presenting general stable health that would enable the realization of the proposed tests.\nIn order to achieve a greater delineation of the group, in addition to considering the group of 30 individuals (heterogeneous group [HG]), the total dentate, the total edentulous without dental prostheses, and individuals with more than one episode of stroke were excluded and the participants were divided into two groups, according to the affected body side: right hemiplegic group (RHG, n=8) and left hemiplegic group (LHG, n=11). A distribution and recruitment fluxogram is shown in Figure 1.\nThirty poststroke individuals, aged 61 to 90 years, whose injury happened 6 months to 9 years previously to their participation in this prospective study. Table 1 shows the characterization data of the sample.\nThe inclusion criteria considered the following cases: affected by stroke as attested by a medical report, through clinical diagnosis and imaging, with a minimum time of 6 months; aged over 60 years, being in regular clinical neurological monitoring, not having undergone dysphagia rehabilitation; presenting general stable health that would enable the realization of the proposed tests.\nIn order to achieve a greater delineation of the group, in addition to considering the group of 30 individuals (heterogeneous group [HG]), the total dentate, the total edentulous without dental prostheses, and individuals with more than one episode of stroke were excluded and the participants were divided into two groups, according to the affected body side: right hemiplegic group (RHG, n=8) and left hemiplegic group (LHG, n=11). A distribution and recruitment fluxogram is shown in Figure 1.\n Procedures Oral condition evaluation The participants underwent assessment of various components of oral health through traditional health indicators, based on the presence or absence of disease. The data were collected from the clinical examination by a dentist as instructed by the WHO – Oral Health Surveys: Basic Methods,18 including the assessment of dental status by the number of decayed, missing, and filled teeth (DMFT) and evaluation of dental prostheses.\nThe prostheses were evaluated for retention, functional stability, smile aesthetics, degree of bone resorption, and quality of the mucosa.19\nFrom the evaluation, the dental arches were classified as without prosthesis or with the presence of partial or complete dental prosthesis; later, the use or need for replacement of dental prostheses was indicated. The need for prostheses replacement was as follows: 0, no need to change; 1, partial dental prosthesis was necessary in one of the dental arches; 2, need for partial dental prosthesis in both dental arches; 3, need for complete dental prosthesis in one of the dental arches; 4, need for dental prosthesis in both dental arches.\nThe participants underwent assessment of various components of oral health through traditional health indicators, based on the presence or absence of disease. The data were collected from the clinical examination by a dentist as instructed by the WHO – Oral Health Surveys: Basic Methods,18 including the assessment of dental status by the number of decayed, missing, and filled teeth (DMFT) and evaluation of dental prostheses.\nThe prostheses were evaluated for retention, functional stability, smile aesthetics, degree of bone resorption, and quality of the mucosa.19\nFrom the evaluation, the dental arches were classified as without prosthesis or with the presence of partial or complete dental prosthesis; later, the use or need for replacement of dental prostheses was indicated. The need for prostheses replacement was as follows: 0, no need to change; 1, partial dental prosthesis was necessary in one of the dental arches; 2, need for partial dental prosthesis in both dental arches; 3, need for complete dental prosthesis in one of the dental arches; 4, need for dental prosthesis in both dental arches.\n Functional Oral Intake Scale Research of oral ingestion was performed by reviewing the usual food consumption patterns referred to in the 24-hour dietary recall. From the data obtained by the research of oral ingestion, patients were classified according to the levels of the Functional Oral Intake Scale (FOIS),20 on a scale from I to VII, considering the diet characteristics based on the properties and texture of the food.21\nResearch of oral ingestion was performed by reviewing the usual food consumption patterns referred to in the 24-hour dietary recall. From the data obtained by the research of oral ingestion, patients were classified according to the levels of the Functional Oral Intake Scale (FOIS),20 on a scale from I to VII, considering the diet characteristics based on the properties and texture of the food.21\n Fiberoptic endoscopic evaluation of swallowing The fiberoptic endoscopic evaluation of swallowing (FEES) was performed by an otorhinolaryngologist physician collaboratively with the speech therapist. Subjects were asked to remain seated with their heads arranged in the direction of the body axis, without bending or rotation, and the examination was performed using a flexible endoscopic equipment of fiberoptic bronchoscopic type (model Olympus CLV-U20) and an Olympus OTV–SC nasopharyngoscope (Olympus Corporation, Tokyo, Japan).\nThree consistent standardized foods were evaluated: liquid (10 mL of filtered water), thick pudding (10 mL of thickened dietary grape juice, reaching a final consistency similar to that of pudding), and solid (half a slice of bread, 1 cm thick). From the data obtained by the FEES, the severity rating of the swallowing disorder was determined in accordance with the scale of functional impairment of swallowing of Macedo Filho,22,23 which subdivides the dysphagia severity levels (normal, mild, moderate, and severe).\nThe fiberoptic endoscopic evaluation of swallowing (FEES) was performed by an otorhinolaryngologist physician collaboratively with the speech therapist. Subjects were asked to remain seated with their heads arranged in the direction of the body axis, without bending or rotation, and the examination was performed using a flexible endoscopic equipment of fiberoptic bronchoscopic type (model Olympus CLV-U20) and an Olympus OTV–SC nasopharyngoscope (Olympus Corporation, Tokyo, Japan).\nThree consistent standardized foods were evaluated: liquid (10 mL of filtered water), thick pudding (10 mL of thickened dietary grape juice, reaching a final consistency similar to that of pudding), and solid (half a slice of bread, 1 cm thick). From the data obtained by the FEES, the severity rating of the swallowing disorder was determined in accordance with the scale of functional impairment of swallowing of Macedo Filho,22,23 which subdivides the dysphagia severity levels (normal, mild, moderate, and severe).\n Statistical analysis Spearman’s test was used to verify the correlation between the oral health condition (DMFT, number of teeth present, and the need for prostheses replacement) and the performance in the swallowing function (classification in the FOIS scale and dysphagia rating in each consistency). For this analysis to be performed, the FOIS scale and the degree of dysphagia were numerically classified from 1 to 7 and from 1 to 4, respectively.\nFor comparison of FOIS results and dysphagia classification, among the different types of prostheses used in the upper and lower dental arches (complete dental prosthesis, partial dental prosthesis, denture or none), the Kruskal–Wallis test was used, followed by the Dunn’s test.\nSpearman’s test was used to verify the correlation between the oral health condition (DMFT, number of teeth present, and the need for prostheses replacement) and the performance in the swallowing function (classification in the FOIS scale and dysphagia rating in each consistency). For this analysis to be performed, the FOIS scale and the degree of dysphagia were numerically classified from 1 to 7 and from 1 to 4, respectively.\nFor comparison of FOIS results and dysphagia classification, among the different types of prostheses used in the upper and lower dental arches (complete dental prosthesis, partial dental prosthesis, denture or none), the Kruskal–Wallis test was used, followed by the Dunn’s test.\n Oral condition evaluation The participants underwent assessment of various components of oral health through traditional health indicators, based on the presence or absence of disease. The data were collected from the clinical examination by a dentist as instructed by the WHO – Oral Health Surveys: Basic Methods,18 including the assessment of dental status by the number of decayed, missing, and filled teeth (DMFT) and evaluation of dental prostheses.\nThe prostheses were evaluated for retention, functional stability, smile aesthetics, degree of bone resorption, and quality of the mucosa.19\nFrom the evaluation, the dental arches were classified as without prosthesis or with the presence of partial or complete dental prosthesis; later, the use or need for replacement of dental prostheses was indicated. The need for prostheses replacement was as follows: 0, no need to change; 1, partial dental prosthesis was necessary in one of the dental arches; 2, need for partial dental prosthesis in both dental arches; 3, need for complete dental prosthesis in one of the dental arches; 4, need for dental prosthesis in both dental arches.\nThe participants underwent assessment of various components of oral health through traditional health indicators, based on the presence or absence of disease. The data were collected from the clinical examination by a dentist as instructed by the WHO – Oral Health Surveys: Basic Methods,18 including the assessment of dental status by the number of decayed, missing, and filled teeth (DMFT) and evaluation of dental prostheses.\nThe prostheses were evaluated for retention, functional stability, smile aesthetics, degree of bone resorption, and quality of the mucosa.19\nFrom the evaluation, the dental arches were classified as without prosthesis or with the presence of partial or complete dental prosthesis; later, the use or need for replacement of dental prostheses was indicated. The need for prostheses replacement was as follows: 0, no need to change; 1, partial dental prosthesis was necessary in one of the dental arches; 2, need for partial dental prosthesis in both dental arches; 3, need for complete dental prosthesis in one of the dental arches; 4, need for dental prosthesis in both dental arches.\n Functional Oral Intake Scale Research of oral ingestion was performed by reviewing the usual food consumption patterns referred to in the 24-hour dietary recall. From the data obtained by the research of oral ingestion, patients were classified according to the levels of the Functional Oral Intake Scale (FOIS),20 on a scale from I to VII, considering the diet characteristics based on the properties and texture of the food.21\nResearch of oral ingestion was performed by reviewing the usual food consumption patterns referred to in the 24-hour dietary recall. From the data obtained by the research of oral ingestion, patients were classified according to the levels of the Functional Oral Intake Scale (FOIS),20 on a scale from I to VII, considering the diet characteristics based on the properties and texture of the food.21\n Fiberoptic endoscopic evaluation of swallowing The fiberoptic endoscopic evaluation of swallowing (FEES) was performed by an otorhinolaryngologist physician collaboratively with the speech therapist. Subjects were asked to remain seated with their heads arranged in the direction of the body axis, without bending or rotation, and the examination was performed using a flexible endoscopic equipment of fiberoptic bronchoscopic type (model Olympus CLV-U20) and an Olympus OTV–SC nasopharyngoscope (Olympus Corporation, Tokyo, Japan).\nThree consistent standardized foods were evaluated: liquid (10 mL of filtered water), thick pudding (10 mL of thickened dietary grape juice, reaching a final consistency similar to that of pudding), and solid (half a slice of bread, 1 cm thick). From the data obtained by the FEES, the severity rating of the swallowing disorder was determined in accordance with the scale of functional impairment of swallowing of Macedo Filho,22,23 which subdivides the dysphagia severity levels (normal, mild, moderate, and severe).\nThe fiberoptic endoscopic evaluation of swallowing (FEES) was performed by an otorhinolaryngologist physician collaboratively with the speech therapist. Subjects were asked to remain seated with their heads arranged in the direction of the body axis, without bending or rotation, and the examination was performed using a flexible endoscopic equipment of fiberoptic bronchoscopic type (model Olympus CLV-U20) and an Olympus OTV–SC nasopharyngoscope (Olympus Corporation, Tokyo, Japan).\nThree consistent standardized foods were evaluated: liquid (10 mL of filtered water), thick pudding (10 mL of thickened dietary grape juice, reaching a final consistency similar to that of pudding), and solid (half a slice of bread, 1 cm thick). From the data obtained by the FEES, the severity rating of the swallowing disorder was determined in accordance with the scale of functional impairment of swallowing of Macedo Filho,22,23 which subdivides the dysphagia severity levels (normal, mild, moderate, and severe).\n Statistical analysis Spearman’s test was used to verify the correlation between the oral health condition (DMFT, number of teeth present, and the need for prostheses replacement) and the performance in the swallowing function (classification in the FOIS scale and dysphagia rating in each consistency). For this analysis to be performed, the FOIS scale and the degree of dysphagia were numerically classified from 1 to 7 and from 1 to 4, respectively.\nFor comparison of FOIS results and dysphagia classification, among the different types of prostheses used in the upper and lower dental arches (complete dental prosthesis, partial dental prosthesis, denture or none), the Kruskal–Wallis test was used, followed by the Dunn’s test.\nSpearman’s test was used to verify the correlation between the oral health condition (DMFT, number of teeth present, and the need for prostheses replacement) and the performance in the swallowing function (classification in the FOIS scale and dysphagia rating in each consistency). For this analysis to be performed, the FOIS scale and the degree of dysphagia were numerically classified from 1 to 7 and from 1 to 4, respectively.\nFor comparison of FOIS results and dysphagia classification, among the different types of prostheses used in the upper and lower dental arches (complete dental prosthesis, partial dental prosthesis, denture or none), the Kruskal–Wallis test was used, followed by the Dunn’s test.","Thirty poststroke individuals, aged 61 to 90 years, whose injury happened 6 months to 9 years previously to their participation in this prospective study. Table 1 shows the characterization data of the sample.\nThe inclusion criteria considered the following cases: affected by stroke as attested by a medical report, through clinical diagnosis and imaging, with a minimum time of 6 months; aged over 60 years, being in regular clinical neurological monitoring, not having undergone dysphagia rehabilitation; presenting general stable health that would enable the realization of the proposed tests.\nIn order to achieve a greater delineation of the group, in addition to considering the group of 30 individuals (heterogeneous group [HG]), the total dentate, the total edentulous without dental prostheses, and individuals with more than one episode of stroke were excluded and the participants were divided into two groups, according to the affected body side: right hemiplegic group (RHG, n=8) and left hemiplegic group (LHG, n=11). A distribution and recruitment fluxogram is shown in Figure 1."," Oral condition evaluation The participants underwent assessment of various components of oral health through traditional health indicators, based on the presence or absence of disease. The data were collected from the clinical examination by a dentist as instructed by the WHO – Oral Health Surveys: Basic Methods,18 including the assessment of dental status by the number of decayed, missing, and filled teeth (DMFT) and evaluation of dental prostheses.\nThe prostheses were evaluated for retention, functional stability, smile aesthetics, degree of bone resorption, and quality of the mucosa.19\nFrom the evaluation, the dental arches were classified as without prosthesis or with the presence of partial or complete dental prosthesis; later, the use or need for replacement of dental prostheses was indicated. The need for prostheses replacement was as follows: 0, no need to change; 1, partial dental prosthesis was necessary in one of the dental arches; 2, need for partial dental prosthesis in both dental arches; 3, need for complete dental prosthesis in one of the dental arches; 4, need for dental prosthesis in both dental arches.\nThe participants underwent assessment of various components of oral health through traditional health indicators, based on the presence or absence of disease. The data were collected from the clinical examination by a dentist as instructed by the WHO – Oral Health Surveys: Basic Methods,18 including the assessment of dental status by the number of decayed, missing, and filled teeth (DMFT) and evaluation of dental prostheses.\nThe prostheses were evaluated for retention, functional stability, smile aesthetics, degree of bone resorption, and quality of the mucosa.19\nFrom the evaluation, the dental arches were classified as without prosthesis or with the presence of partial or complete dental prosthesis; later, the use or need for replacement of dental prostheses was indicated. The need for prostheses replacement was as follows: 0, no need to change; 1, partial dental prosthesis was necessary in one of the dental arches; 2, need for partial dental prosthesis in both dental arches; 3, need for complete dental prosthesis in one of the dental arches; 4, need for dental prosthesis in both dental arches.\n Functional Oral Intake Scale Research of oral ingestion was performed by reviewing the usual food consumption patterns referred to in the 24-hour dietary recall. From the data obtained by the research of oral ingestion, patients were classified according to the levels of the Functional Oral Intake Scale (FOIS),20 on a scale from I to VII, considering the diet characteristics based on the properties and texture of the food.21\nResearch of oral ingestion was performed by reviewing the usual food consumption patterns referred to in the 24-hour dietary recall. From the data obtained by the research of oral ingestion, patients were classified according to the levels of the Functional Oral Intake Scale (FOIS),20 on a scale from I to VII, considering the diet characteristics based on the properties and texture of the food.21\n Fiberoptic endoscopic evaluation of swallowing The fiberoptic endoscopic evaluation of swallowing (FEES) was performed by an otorhinolaryngologist physician collaboratively with the speech therapist. Subjects were asked to remain seated with their heads arranged in the direction of the body axis, without bending or rotation, and the examination was performed using a flexible endoscopic equipment of fiberoptic bronchoscopic type (model Olympus CLV-U20) and an Olympus OTV–SC nasopharyngoscope (Olympus Corporation, Tokyo, Japan).\nThree consistent standardized foods were evaluated: liquid (10 mL of filtered water), thick pudding (10 mL of thickened dietary grape juice, reaching a final consistency similar to that of pudding), and solid (half a slice of bread, 1 cm thick). From the data obtained by the FEES, the severity rating of the swallowing disorder was determined in accordance with the scale of functional impairment of swallowing of Macedo Filho,22,23 which subdivides the dysphagia severity levels (normal, mild, moderate, and severe).\nThe fiberoptic endoscopic evaluation of swallowing (FEES) was performed by an otorhinolaryngologist physician collaboratively with the speech therapist. Subjects were asked to remain seated with their heads arranged in the direction of the body axis, without bending or rotation, and the examination was performed using a flexible endoscopic equipment of fiberoptic bronchoscopic type (model Olympus CLV-U20) and an Olympus OTV–SC nasopharyngoscope (Olympus Corporation, Tokyo, Japan).\nThree consistent standardized foods were evaluated: liquid (10 mL of filtered water), thick pudding (10 mL of thickened dietary grape juice, reaching a final consistency similar to that of pudding), and solid (half a slice of bread, 1 cm thick). From the data obtained by the FEES, the severity rating of the swallowing disorder was determined in accordance with the scale of functional impairment of swallowing of Macedo Filho,22,23 which subdivides the dysphagia severity levels (normal, mild, moderate, and severe).\n Statistical analysis Spearman’s test was used to verify the correlation between the oral health condition (DMFT, number of teeth present, and the need for prostheses replacement) and the performance in the swallowing function (classification in the FOIS scale and dysphagia rating in each consistency). For this analysis to be performed, the FOIS scale and the degree of dysphagia were numerically classified from 1 to 7 and from 1 to 4, respectively.\nFor comparison of FOIS results and dysphagia classification, among the different types of prostheses used in the upper and lower dental arches (complete dental prosthesis, partial dental prosthesis, denture or none), the Kruskal–Wallis test was used, followed by the Dunn’s test.\nSpearman’s test was used to verify the correlation between the oral health condition (DMFT, number of teeth present, and the need for prostheses replacement) and the performance in the swallowing function (classification in the FOIS scale and dysphagia rating in each consistency). For this analysis to be performed, the FOIS scale and the degree of dysphagia were numerically classified from 1 to 7 and from 1 to 4, respectively.\nFor comparison of FOIS results and dysphagia classification, among the different types of prostheses used in the upper and lower dental arches (complete dental prosthesis, partial dental prosthesis, denture or none), the Kruskal–Wallis test was used, followed by the Dunn’s test.","The participants underwent assessment of various components of oral health through traditional health indicators, based on the presence or absence of disease. The data were collected from the clinical examination by a dentist as instructed by the WHO – Oral Health Surveys: Basic Methods,18 including the assessment of dental status by the number of decayed, missing, and filled teeth (DMFT) and evaluation of dental prostheses.\nThe prostheses were evaluated for retention, functional stability, smile aesthetics, degree of bone resorption, and quality of the mucosa.19\nFrom the evaluation, the dental arches were classified as without prosthesis or with the presence of partial or complete dental prosthesis; later, the use or need for replacement of dental prostheses was indicated. The need for prostheses replacement was as follows: 0, no need to change; 1, partial dental prosthesis was necessary in one of the dental arches; 2, need for partial dental prosthesis in both dental arches; 3, need for complete dental prosthesis in one of the dental arches; 4, need for dental prosthesis in both dental arches.","Research of oral ingestion was performed by reviewing the usual food consumption patterns referred to in the 24-hour dietary recall. From the data obtained by the research of oral ingestion, patients were classified according to the levels of the Functional Oral Intake Scale (FOIS),20 on a scale from I to VII, considering the diet characteristics based on the properties and texture of the food.21","The fiberoptic endoscopic evaluation of swallowing (FEES) was performed by an otorhinolaryngologist physician collaboratively with the speech therapist. Subjects were asked to remain seated with their heads arranged in the direction of the body axis, without bending or rotation, and the examination was performed using a flexible endoscopic equipment of fiberoptic bronchoscopic type (model Olympus CLV-U20) and an Olympus OTV–SC nasopharyngoscope (Olympus Corporation, Tokyo, Japan).\nThree consistent standardized foods were evaluated: liquid (10 mL of filtered water), thick pudding (10 mL of thickened dietary grape juice, reaching a final consistency similar to that of pudding), and solid (half a slice of bread, 1 cm thick). From the data obtained by the FEES, the severity rating of the swallowing disorder was determined in accordance with the scale of functional impairment of swallowing of Macedo Filho,22,23 which subdivides the dysphagia severity levels (normal, mild, moderate, and severe).","Spearman’s test was used to verify the correlation between the oral health condition (DMFT, number of teeth present, and the need for prostheses replacement) and the performance in the swallowing function (classification in the FOIS scale and dysphagia rating in each consistency). For this analysis to be performed, the FOIS scale and the degree of dysphagia were numerically classified from 1 to 7 and from 1 to 4, respectively.\nFor comparison of FOIS results and dysphagia classification, among the different types of prostheses used in the upper and lower dental arches (complete dental prosthesis, partial dental prosthesis, denture or none), the Kruskal–Wallis test was used, followed by the Dunn’s test.","According to the evaluation of oral condition, the mean of DMFT of elderly individuals affected by stroke in this study was 28.7, while the mean of remaining teeth was 5.6, showing that most individuals were edentulous. Table 2 shows the types of prostheses used by the elderly individuals affected by stroke in each dental arch and the need for prostheses replacement.\nAccording to the results of the functional oral intake scale, no individuals were found to be on a single consistency diet or tube dependent. Most individuals (53%) presented as level V on the FOIS, followed by level IV (34%) and level VII (13%).\nWith the severity rating of the swallowing disorder, no occurrence of severe dysphagia was verified. The distribution of the elderly affected by stroke according to severity rating of the swallowing disorder is shown in Table 3, as well.\nAccording to the results of the statistical analysis, correlations between the oral health condition and the results of the FOIS scale in elderly individuals affected by stroke in this study were found. There was a statistically significant negative correlation between the need for replacement of prostheses and the rating scale, and also in the HG and in RHG, showing that the greater the need for replacement of the prosthesis, the worse the rating of the FOIS scale.\nThere were differences in the classification of the FOIS scale, for the LHG, in the different types of prostheses used in the upper dental arch, whereas for the RHG the difference in the classification of FOIS occurred between groups of individuals with different types of prostheses used in the lower dental arch. After completion of the Dunn’s post-test, the difference was confirmed only in the LHG, demonstrating that individuals of this group with partial prostheses in the upper arch (ie, that still had natural elements and whose missed teeth had been rehabilitated) had better ratings in the FOIS scale compared to individuals with total prostheses.\nThe results of the correlation between the FOIS scale and the oral health condition are shown in Table 4.\nIn the statistical analysis between the degree of swallowing dysfunction and oral health condition, a significant negative correlation between the number of teeth and the swallowing performance of liquid in the LHG was found, demonstrating that the greater the number of teeth, the lower the degree of swallowing dysfunction.\nAccording to the Kruskal–Wallis test, there was no difference in the performance of swallowing solid boluses depending on the type of prosthesis used in the lower dental arch; in the HG, however, when applying the post-Dunn’s test, this difference was not confirmed (P>0.05). The comparison of the FOIS scale between the types of dental prostheses is shown in Table 5.\nThe results of the correlation between the degree of dysphagia and the oral health condition are shown in Table 6, and the comparison of the degree of swallowing dysfunction between types of dental prostheses is described in Table 7.","According to the literature, individuals affected by stroke often exhibit decreased level of consciousness, paralysis of the muscles involved in swallowing, sensory deficits of the pharynx and oral cavity, as well as loss of appetite.24 With these disorders related to swallowing, dysphagia becomes an important symptom in this population, and may be aggravated by teeth loss and the use of poorly fitting prosthesis, common during aging. Furthermore, the hypothesis that oropharyngeal colonization is primarily responsible for the subsequent respiratory infection, owing to aspiration, is strongly supported by the literature.10\nNo literature studies have made the correlation between the findings of the swallowing performance and the condition of the oral health in elderly patients after stroke.\nThe results of this study showed some correlations between the oral condition and the data of the swallowing performance even when elderly individuals affected by stroke were part of the heterogeneous group.\nBesides the statistical analysis for the study group, in an attempt to reduce its heterogeneity, the subjects were divided according to the side of the motor impairment after stroke. Former studies have suggested that dysphagia maybe associated with diffuse lesions in one or both hemispheres, anterior fossa, or brainstem, as swallowing has a bilateral cortical representation. It has been well established that the protection of the airways during swallowing requires a precise coordination with accurate movements of the structures involved and adequate propulsion of the bolus to the oropharynx and esophagus;25 thus, the motor ability for swallowing seems to be essential for this protection.\nIn the HG and RHG, it was found that the higher the need for replacement of the prosthesis, the worse the rating in the FOIS scale; ie, individuals with greater need for replacement of the prostheses had a greater perception in making more compensation and changes in their diet, which reflected the classification level of oral intake.\nThe FOIS scale was developed to describe the functional level of the daily oral intake of food and liquid of a patient, considering the changes in diet and need for compensation during swallowing.20 These limitations and changes in oral intake result from patient perception and self-perception of swallowing ability that is impaired. Therefore, from the results, it can be inferred that when there was a greater need for replacement of the prosthesis, the patient’s perception of doing more compensation and dietary modifications is reflected in the classification of the FOIS scale.\nSignificant changes in oral condition can have a major effect on the functions of eating and drinking and on the nutritional status of elderly individuals and those with neurological diseases.26 Thus, the selection of food is determined by the difficulty in chewing and swallowing of the individuals. Missing teeth and the use of prosthesis cause changes in the chewing ability of the elderly according to the type of food, and influences the swallowing function;27 thus, soft or mashed foods are preferentially ingested by the elderly, due to the difficulty in mastication,28 showing that there is an influence of oral health condition on the selection of foods.\nBesides the influence of the oral condition on swallowing, there was a difference between the types of prostheses and classification of the FOIS scale when the results of the evaluation of individuals with impaired right and left hemiplegia were analyzed separately. The difference between the types of prostheses could be confirmed by post-Dunn’s test in the LHG, and demonstrated that individuals with partial dentures had better ratings in the FOIS scale than individuals with complete prostheses. No studies were found relating the oral condition and the rating scale. Thus, we can infer that when there was partial prosthesis rehabilitation along with the presence of some dental elements, subjects needed less compensation and changes in the oral diet. In this sense, studies show that the use of prostheses in the edentulous contributes to the maintenance of the physiological process of swallowing in the elderly,29 the fixation of the prosthesis in the mandible,30 and the presence of natural elements enables greater preservation of functional swallowing.\nWhen comparing the swallowing performance according to the type of prosthesis used, there was no difference between the types of prostheses fitted in the lower dental arch in swallowing solids in the HG. Studies in healthy elderly subjects demonstrate the importance of the oral condition in the performance of the oral phase of swallowing. Changes in masticatory function related to the oral condition, such as difficulties biting and chewing food27 demonstrated an influence on the pharyngeal phase of swallowing, such as premature escape of food, waste retention in the vallecula and pyriform sinuses, and the presence of coughing and gagging.27\nMoreover, the absence of dental prosthesis can result in a longer duration of swallowing,29 and the chewing and swallowing difficulties in healthy older adults decreased after the replacement of removable total prostheses by mandibular implant-supported prostheses.30 The literature has shown positive effects in the swallowing function with the use of prostheses and the differences in swallowing performance with prostheses in the upper or inferior arches.8 A study with healthy elderly individuals showed the differences between the total duration of swallowing, the latency period before pharynx elevation, duration of preparatory and oral phase, when using or not using the dental prostheses.29\nFinally, in the LHG, it was observed that the smaller the number of teeth present, the worse the rating of dysphagia for liquid. Although this study aimed at finding an influence of the presence and number of teeth on chewing and consequently on swallowing solids,1,27,28 it also demonstrates the influence of the oral condition on liquid swallowing for this population. According to Tamura et al,2 especially for liquid food, which demands more coordination during swallowing, stability in the jaw is also required, so that there is greater stability of the hyolaryngeal complex. To start the pharyngeal phase, the rise of the hyoid bone by the suprahyoid muscles and laryngeal elevation should occur. To complement these events, the jaw must be stabilized by natural or rehabilitated teeth into the proper position, because when the elevation of the hyoid bone and larynx is insufficient, there is an increased risk of aspiration during swallowing.","The findings of this study indicate that the oral health condition of elderly individuals after stroke in chronic phase showed an association with the level of oral intake and the degree of oropharyngeal dysphagia. The limitations of the study should be considered, one being the lack of access to the imaging examinations to characterize the individuals according to the type of stroke, damaged areas, and extent of injury, and heterogeneity of the time of brain involvement, in addition to the effects of medications and other comorbidities, which were not taken into account. Also, owing to the small sample, strong correlations could not be observed, and a cause/effect relationship cannot be determined.\nDespite the limitations of this study, and the fact that hemiplegia does not necessarily represent a direct relationship with the injured brain hemisphere contralaterally, since cerebellum lesions may cause ipsilateral motor damages,31 the findings indicated that the level of oral intake was influenced by the condition or type of dental prostheses, regardless of the side of the body affected. On the other hand, the number of teeth were demonstrated to be related to the level of dysphagia in individuals with right hemiplegia, which shows that the oral health condition differentially affects the physiological responses of swallowing, influenced by the brain damage.\nConsidering the present sample, further research is necessary pertaining to the oral condition and the neurological involvement of individuals. In addition, these issues demonstrate the need for an interdisciplinary research team comprising dentists, physicians, speech pathologists, and other health professionals, so as to study and define the treatment and guidelines to patients and caretakers, in order to maintain the best oral health condition in the elderly presented with oropharyngeal dysphagia."],"string":"[\n \"The process of swallowing physiologically changes with aging. The loss of teeth, very common in this population, is related to the reduction of bone tissue, receptors (proprioceptors and periodontal ligaments), and muscle atrophy. Consequently, orofacial functions are impaired in toothless individuals.1 In addition, prostheses are functionally less efficient when compared to individuals with natural dentition.1,2\\nThe stability of the jaw occlusion of the posterior teeth or prostheses is important for the swallowing function.2 The dental condition and the use of poorly fitting prostheses may contribute to the difficulties arising from old age, and the oral diet and nutritional status of the elderly may be affected by changes related to aging3 and changes in skill and desire to feel, bite, chew4 and swallow food,3,5 significantly compromising and further aggravating the quality of life of these individuals.6\\nThe literature has shown the influence of oral health condition on swallowing and nutrition, indirectly influencing the daily life activities of the elderly.7 Furthermore, it is clear that maintenance of oral health is essential for the general health, quality of life, chewing ability8 and, mainly, the reduction of pneumonia risk in fragile elderly people,8,9 for it is already known that bacterial colonies in oropharyngeal tissues and in dental plaque are the greatest precursors for the development of aspiration respiratory infections.10\\nBesides the influence of the loss of dentition, the functional impairments resulting from aging may be potentiated by neurological changes resulting in dysphagia in patients, an important cause of morbidity and mortality in this population.11\\nIn the adult and elderly population, dysphagia is most commonly associated with stroke.12 More than half of patients who have suffered stroke present between six and ten types of disability, muscle weakness being the most prevalent one, followed by communication, speech, and swallowing disorders.13 Specifically regarding swallowing, the stroke entails increased oropharyngeal transit time,14,15 changes in motor control of tongue,16 as well as the presence of laryngotracheal aspiration of food.16,17\\nOnce the physiological changes resulting from aging in swallowing may be aggravated by loss of teeth and the use of poorly fitting prostheses, the severity of dysphagia could be influenced by the oral health condition and use of dental prostheses in elderly patients with neurological diseases. However, according to the literature studied, no research has made the correlation between the findings of the swallowing performance with oral health in elderly patients after stroke. Therefore, the aim of this study was to relate the condition of oral health to the level of oral intake and the degree of swallowing dysfunction in elderly patients with stroke in chronic phase.\",\n \" Study design and subjects Thirty poststroke individuals, aged 61 to 90 years, whose injury happened 6 months to 9 years previously to their participation in this prospective study. Table 1 shows the characterization data of the sample.\\nThe inclusion criteria considered the following cases: affected by stroke as attested by a medical report, through clinical diagnosis and imaging, with a minimum time of 6 months; aged over 60 years, being in regular clinical neurological monitoring, not having undergone dysphagia rehabilitation; presenting general stable health that would enable the realization of the proposed tests.\\nIn order to achieve a greater delineation of the group, in addition to considering the group of 30 individuals (heterogeneous group [HG]), the total dentate, the total edentulous without dental prostheses, and individuals with more than one episode of stroke were excluded and the participants were divided into two groups, according to the affected body side: right hemiplegic group (RHG, n=8) and left hemiplegic group (LHG, n=11). A distribution and recruitment fluxogram is shown in Figure 1.\\nThirty poststroke individuals, aged 61 to 90 years, whose injury happened 6 months to 9 years previously to their participation in this prospective study. Table 1 shows the characterization data of the sample.\\nThe inclusion criteria considered the following cases: affected by stroke as attested by a medical report, through clinical diagnosis and imaging, with a minimum time of 6 months; aged over 60 years, being in regular clinical neurological monitoring, not having undergone dysphagia rehabilitation; presenting general stable health that would enable the realization of the proposed tests.\\nIn order to achieve a greater delineation of the group, in addition to considering the group of 30 individuals (heterogeneous group [HG]), the total dentate, the total edentulous without dental prostheses, and individuals with more than one episode of stroke were excluded and the participants were divided into two groups, according to the affected body side: right hemiplegic group (RHG, n=8) and left hemiplegic group (LHG, n=11). A distribution and recruitment fluxogram is shown in Figure 1.\\n Procedures Oral condition evaluation The participants underwent assessment of various components of oral health through traditional health indicators, based on the presence or absence of disease. The data were collected from the clinical examination by a dentist as instructed by the WHO – Oral Health Surveys: Basic Methods,18 including the assessment of dental status by the number of decayed, missing, and filled teeth (DMFT) and evaluation of dental prostheses.\\nThe prostheses were evaluated for retention, functional stability, smile aesthetics, degree of bone resorption, and quality of the mucosa.19\\nFrom the evaluation, the dental arches were classified as without prosthesis or with the presence of partial or complete dental prosthesis; later, the use or need for replacement of dental prostheses was indicated. The need for prostheses replacement was as follows: 0, no need to change; 1, partial dental prosthesis was necessary in one of the dental arches; 2, need for partial dental prosthesis in both dental arches; 3, need for complete dental prosthesis in one of the dental arches; 4, need for dental prosthesis in both dental arches.\\nThe participants underwent assessment of various components of oral health through traditional health indicators, based on the presence or absence of disease. The data were collected from the clinical examination by a dentist as instructed by the WHO – Oral Health Surveys: Basic Methods,18 including the assessment of dental status by the number of decayed, missing, and filled teeth (DMFT) and evaluation of dental prostheses.\\nThe prostheses were evaluated for retention, functional stability, smile aesthetics, degree of bone resorption, and quality of the mucosa.19\\nFrom the evaluation, the dental arches were classified as without prosthesis or with the presence of partial or complete dental prosthesis; later, the use or need for replacement of dental prostheses was indicated. The need for prostheses replacement was as follows: 0, no need to change; 1, partial dental prosthesis was necessary in one of the dental arches; 2, need for partial dental prosthesis in both dental arches; 3, need for complete dental prosthesis in one of the dental arches; 4, need for dental prosthesis in both dental arches.\\n Functional Oral Intake Scale Research of oral ingestion was performed by reviewing the usual food consumption patterns referred to in the 24-hour dietary recall. From the data obtained by the research of oral ingestion, patients were classified according to the levels of the Functional Oral Intake Scale (FOIS),20 on a scale from I to VII, considering the diet characteristics based on the properties and texture of the food.21\\nResearch of oral ingestion was performed by reviewing the usual food consumption patterns referred to in the 24-hour dietary recall. From the data obtained by the research of oral ingestion, patients were classified according to the levels of the Functional Oral Intake Scale (FOIS),20 on a scale from I to VII, considering the diet characteristics based on the properties and texture of the food.21\\n Fiberoptic endoscopic evaluation of swallowing The fiberoptic endoscopic evaluation of swallowing (FEES) was performed by an otorhinolaryngologist physician collaboratively with the speech therapist. Subjects were asked to remain seated with their heads arranged in the direction of the body axis, without bending or rotation, and the examination was performed using a flexible endoscopic equipment of fiberoptic bronchoscopic type (model Olympus CLV-U20) and an Olympus OTV–SC nasopharyngoscope (Olympus Corporation, Tokyo, Japan).\\nThree consistent standardized foods were evaluated: liquid (10 mL of filtered water), thick pudding (10 mL of thickened dietary grape juice, reaching a final consistency similar to that of pudding), and solid (half a slice of bread, 1 cm thick). From the data obtained by the FEES, the severity rating of the swallowing disorder was determined in accordance with the scale of functional impairment of swallowing of Macedo Filho,22,23 which subdivides the dysphagia severity levels (normal, mild, moderate, and severe).\\nThe fiberoptic endoscopic evaluation of swallowing (FEES) was performed by an otorhinolaryngologist physician collaboratively with the speech therapist. Subjects were asked to remain seated with their heads arranged in the direction of the body axis, without bending or rotation, and the examination was performed using a flexible endoscopic equipment of fiberoptic bronchoscopic type (model Olympus CLV-U20) and an Olympus OTV–SC nasopharyngoscope (Olympus Corporation, Tokyo, Japan).\\nThree consistent standardized foods were evaluated: liquid (10 mL of filtered water), thick pudding (10 mL of thickened dietary grape juice, reaching a final consistency similar to that of pudding), and solid (half a slice of bread, 1 cm thick). From the data obtained by the FEES, the severity rating of the swallowing disorder was determined in accordance with the scale of functional impairment of swallowing of Macedo Filho,22,23 which subdivides the dysphagia severity levels (normal, mild, moderate, and severe).\\n Statistical analysis Spearman’s test was used to verify the correlation between the oral health condition (DMFT, number of teeth present, and the need for prostheses replacement) and the performance in the swallowing function (classification in the FOIS scale and dysphagia rating in each consistency). For this analysis to be performed, the FOIS scale and the degree of dysphagia were numerically classified from 1 to 7 and from 1 to 4, respectively.\\nFor comparison of FOIS results and dysphagia classification, among the different types of prostheses used in the upper and lower dental arches (complete dental prosthesis, partial dental prosthesis, denture or none), the Kruskal–Wallis test was used, followed by the Dunn’s test.\\nSpearman’s test was used to verify the correlation between the oral health condition (DMFT, number of teeth present, and the need for prostheses replacement) and the performance in the swallowing function (classification in the FOIS scale and dysphagia rating in each consistency). For this analysis to be performed, the FOIS scale and the degree of dysphagia were numerically classified from 1 to 7 and from 1 to 4, respectively.\\nFor comparison of FOIS results and dysphagia classification, among the different types of prostheses used in the upper and lower dental arches (complete dental prosthesis, partial dental prosthesis, denture or none), the Kruskal–Wallis test was used, followed by the Dunn’s test.\\n Oral condition evaluation The participants underwent assessment of various components of oral health through traditional health indicators, based on the presence or absence of disease. The data were collected from the clinical examination by a dentist as instructed by the WHO – Oral Health Surveys: Basic Methods,18 including the assessment of dental status by the number of decayed, missing, and filled teeth (DMFT) and evaluation of dental prostheses.\\nThe prostheses were evaluated for retention, functional stability, smile aesthetics, degree of bone resorption, and quality of the mucosa.19\\nFrom the evaluation, the dental arches were classified as without prosthesis or with the presence of partial or complete dental prosthesis; later, the use or need for replacement of dental prostheses was indicated. The need for prostheses replacement was as follows: 0, no need to change; 1, partial dental prosthesis was necessary in one of the dental arches; 2, need for partial dental prosthesis in both dental arches; 3, need for complete dental prosthesis in one of the dental arches; 4, need for dental prosthesis in both dental arches.\\nThe participants underwent assessment of various components of oral health through traditional health indicators, based on the presence or absence of disease. The data were collected from the clinical examination by a dentist as instructed by the WHO – Oral Health Surveys: Basic Methods,18 including the assessment of dental status by the number of decayed, missing, and filled teeth (DMFT) and evaluation of dental prostheses.\\nThe prostheses were evaluated for retention, functional stability, smile aesthetics, degree of bone resorption, and quality of the mucosa.19\\nFrom the evaluation, the dental arches were classified as without prosthesis or with the presence of partial or complete dental prosthesis; later, the use or need for replacement of dental prostheses was indicated. The need for prostheses replacement was as follows: 0, no need to change; 1, partial dental prosthesis was necessary in one of the dental arches; 2, need for partial dental prosthesis in both dental arches; 3, need for complete dental prosthesis in one of the dental arches; 4, need for dental prosthesis in both dental arches.\\n Functional Oral Intake Scale Research of oral ingestion was performed by reviewing the usual food consumption patterns referred to in the 24-hour dietary recall. From the data obtained by the research of oral ingestion, patients were classified according to the levels of the Functional Oral Intake Scale (FOIS),20 on a scale from I to VII, considering the diet characteristics based on the properties and texture of the food.21\\nResearch of oral ingestion was performed by reviewing the usual food consumption patterns referred to in the 24-hour dietary recall. From the data obtained by the research of oral ingestion, patients were classified according to the levels of the Functional Oral Intake Scale (FOIS),20 on a scale from I to VII, considering the diet characteristics based on the properties and texture of the food.21\\n Fiberoptic endoscopic evaluation of swallowing The fiberoptic endoscopic evaluation of swallowing (FEES) was performed by an otorhinolaryngologist physician collaboratively with the speech therapist. Subjects were asked to remain seated with their heads arranged in the direction of the body axis, without bending or rotation, and the examination was performed using a flexible endoscopic equipment of fiberoptic bronchoscopic type (model Olympus CLV-U20) and an Olympus OTV–SC nasopharyngoscope (Olympus Corporation, Tokyo, Japan).\\nThree consistent standardized foods were evaluated: liquid (10 mL of filtered water), thick pudding (10 mL of thickened dietary grape juice, reaching a final consistency similar to that of pudding), and solid (half a slice of bread, 1 cm thick). From the data obtained by the FEES, the severity rating of the swallowing disorder was determined in accordance with the scale of functional impairment of swallowing of Macedo Filho,22,23 which subdivides the dysphagia severity levels (normal, mild, moderate, and severe).\\nThe fiberoptic endoscopic evaluation of swallowing (FEES) was performed by an otorhinolaryngologist physician collaboratively with the speech therapist. Subjects were asked to remain seated with their heads arranged in the direction of the body axis, without bending or rotation, and the examination was performed using a flexible endoscopic equipment of fiberoptic bronchoscopic type (model Olympus CLV-U20) and an Olympus OTV–SC nasopharyngoscope (Olympus Corporation, Tokyo, Japan).\\nThree consistent standardized foods were evaluated: liquid (10 mL of filtered water), thick pudding (10 mL of thickened dietary grape juice, reaching a final consistency similar to that of pudding), and solid (half a slice of bread, 1 cm thick). From the data obtained by the FEES, the severity rating of the swallowing disorder was determined in accordance with the scale of functional impairment of swallowing of Macedo Filho,22,23 which subdivides the dysphagia severity levels (normal, mild, moderate, and severe).\\n Statistical analysis Spearman’s test was used to verify the correlation between the oral health condition (DMFT, number of teeth present, and the need for prostheses replacement) and the performance in the swallowing function (classification in the FOIS scale and dysphagia rating in each consistency). For this analysis to be performed, the FOIS scale and the degree of dysphagia were numerically classified from 1 to 7 and from 1 to 4, respectively.\\nFor comparison of FOIS results and dysphagia classification, among the different types of prostheses used in the upper and lower dental arches (complete dental prosthesis, partial dental prosthesis, denture or none), the Kruskal–Wallis test was used, followed by the Dunn’s test.\\nSpearman’s test was used to verify the correlation between the oral health condition (DMFT, number of teeth present, and the need for prostheses replacement) and the performance in the swallowing function (classification in the FOIS scale and dysphagia rating in each consistency). For this analysis to be performed, the FOIS scale and the degree of dysphagia were numerically classified from 1 to 7 and from 1 to 4, respectively.\\nFor comparison of FOIS results and dysphagia classification, among the different types of prostheses used in the upper and lower dental arches (complete dental prosthesis, partial dental prosthesis, denture or none), the Kruskal–Wallis test was used, followed by the Dunn’s test.\",\n \"Thirty poststroke individuals, aged 61 to 90 years, whose injury happened 6 months to 9 years previously to their participation in this prospective study. Table 1 shows the characterization data of the sample.\\nThe inclusion criteria considered the following cases: affected by stroke as attested by a medical report, through clinical diagnosis and imaging, with a minimum time of 6 months; aged over 60 years, being in regular clinical neurological monitoring, not having undergone dysphagia rehabilitation; presenting general stable health that would enable the realization of the proposed tests.\\nIn order to achieve a greater delineation of the group, in addition to considering the group of 30 individuals (heterogeneous group [HG]), the total dentate, the total edentulous without dental prostheses, and individuals with more than one episode of stroke were excluded and the participants were divided into two groups, according to the affected body side: right hemiplegic group (RHG, n=8) and left hemiplegic group (LHG, n=11). A distribution and recruitment fluxogram is shown in Figure 1.\",\n \" Oral condition evaluation The participants underwent assessment of various components of oral health through traditional health indicators, based on the presence or absence of disease. The data were collected from the clinical examination by a dentist as instructed by the WHO – Oral Health Surveys: Basic Methods,18 including the assessment of dental status by the number of decayed, missing, and filled teeth (DMFT) and evaluation of dental prostheses.\\nThe prostheses were evaluated for retention, functional stability, smile aesthetics, degree of bone resorption, and quality of the mucosa.19\\nFrom the evaluation, the dental arches were classified as without prosthesis or with the presence of partial or complete dental prosthesis; later, the use or need for replacement of dental prostheses was indicated. The need for prostheses replacement was as follows: 0, no need to change; 1, partial dental prosthesis was necessary in one of the dental arches; 2, need for partial dental prosthesis in both dental arches; 3, need for complete dental prosthesis in one of the dental arches; 4, need for dental prosthesis in both dental arches.\\nThe participants underwent assessment of various components of oral health through traditional health indicators, based on the presence or absence of disease. The data were collected from the clinical examination by a dentist as instructed by the WHO – Oral Health Surveys: Basic Methods,18 including the assessment of dental status by the number of decayed, missing, and filled teeth (DMFT) and evaluation of dental prostheses.\\nThe prostheses were evaluated for retention, functional stability, smile aesthetics, degree of bone resorption, and quality of the mucosa.19\\nFrom the evaluation, the dental arches were classified as without prosthesis or with the presence of partial or complete dental prosthesis; later, the use or need for replacement of dental prostheses was indicated. The need for prostheses replacement was as follows: 0, no need to change; 1, partial dental prosthesis was necessary in one of the dental arches; 2, need for partial dental prosthesis in both dental arches; 3, need for complete dental prosthesis in one of the dental arches; 4, need for dental prosthesis in both dental arches.\\n Functional Oral Intake Scale Research of oral ingestion was performed by reviewing the usual food consumption patterns referred to in the 24-hour dietary recall. From the data obtained by the research of oral ingestion, patients were classified according to the levels of the Functional Oral Intake Scale (FOIS),20 on a scale from I to VII, considering the diet characteristics based on the properties and texture of the food.21\\nResearch of oral ingestion was performed by reviewing the usual food consumption patterns referred to in the 24-hour dietary recall. From the data obtained by the research of oral ingestion, patients were classified according to the levels of the Functional Oral Intake Scale (FOIS),20 on a scale from I to VII, considering the diet characteristics based on the properties and texture of the food.21\\n Fiberoptic endoscopic evaluation of swallowing The fiberoptic endoscopic evaluation of swallowing (FEES) was performed by an otorhinolaryngologist physician collaboratively with the speech therapist. Subjects were asked to remain seated with their heads arranged in the direction of the body axis, without bending or rotation, and the examination was performed using a flexible endoscopic equipment of fiberoptic bronchoscopic type (model Olympus CLV-U20) and an Olympus OTV–SC nasopharyngoscope (Olympus Corporation, Tokyo, Japan).\\nThree consistent standardized foods were evaluated: liquid (10 mL of filtered water), thick pudding (10 mL of thickened dietary grape juice, reaching a final consistency similar to that of pudding), and solid (half a slice of bread, 1 cm thick). From the data obtained by the FEES, the severity rating of the swallowing disorder was determined in accordance with the scale of functional impairment of swallowing of Macedo Filho,22,23 which subdivides the dysphagia severity levels (normal, mild, moderate, and severe).\\nThe fiberoptic endoscopic evaluation of swallowing (FEES) was performed by an otorhinolaryngologist physician collaboratively with the speech therapist. Subjects were asked to remain seated with their heads arranged in the direction of the body axis, without bending or rotation, and the examination was performed using a flexible endoscopic equipment of fiberoptic bronchoscopic type (model Olympus CLV-U20) and an Olympus OTV–SC nasopharyngoscope (Olympus Corporation, Tokyo, Japan).\\nThree consistent standardized foods were evaluated: liquid (10 mL of filtered water), thick pudding (10 mL of thickened dietary grape juice, reaching a final consistency similar to that of pudding), and solid (half a slice of bread, 1 cm thick). From the data obtained by the FEES, the severity rating of the swallowing disorder was determined in accordance with the scale of functional impairment of swallowing of Macedo Filho,22,23 which subdivides the dysphagia severity levels (normal, mild, moderate, and severe).\\n Statistical analysis Spearman’s test was used to verify the correlation between the oral health condition (DMFT, number of teeth present, and the need for prostheses replacement) and the performance in the swallowing function (classification in the FOIS scale and dysphagia rating in each consistency). For this analysis to be performed, the FOIS scale and the degree of dysphagia were numerically classified from 1 to 7 and from 1 to 4, respectively.\\nFor comparison of FOIS results and dysphagia classification, among the different types of prostheses used in the upper and lower dental arches (complete dental prosthesis, partial dental prosthesis, denture or none), the Kruskal–Wallis test was used, followed by the Dunn’s test.\\nSpearman’s test was used to verify the correlation between the oral health condition (DMFT, number of teeth present, and the need for prostheses replacement) and the performance in the swallowing function (classification in the FOIS scale and dysphagia rating in each consistency). For this analysis to be performed, the FOIS scale and the degree of dysphagia were numerically classified from 1 to 7 and from 1 to 4, respectively.\\nFor comparison of FOIS results and dysphagia classification, among the different types of prostheses used in the upper and lower dental arches (complete dental prosthesis, partial dental prosthesis, denture or none), the Kruskal–Wallis test was used, followed by the Dunn’s test.\",\n \"The participants underwent assessment of various components of oral health through traditional health indicators, based on the presence or absence of disease. The data were collected from the clinical examination by a dentist as instructed by the WHO – Oral Health Surveys: Basic Methods,18 including the assessment of dental status by the number of decayed, missing, and filled teeth (DMFT) and evaluation of dental prostheses.\\nThe prostheses were evaluated for retention, functional stability, smile aesthetics, degree of bone resorption, and quality of the mucosa.19\\nFrom the evaluation, the dental arches were classified as without prosthesis or with the presence of partial or complete dental prosthesis; later, the use or need for replacement of dental prostheses was indicated. The need for prostheses replacement was as follows: 0, no need to change; 1, partial dental prosthesis was necessary in one of the dental arches; 2, need for partial dental prosthesis in both dental arches; 3, need for complete dental prosthesis in one of the dental arches; 4, need for dental prosthesis in both dental arches.\",\n \"Research of oral ingestion was performed by reviewing the usual food consumption patterns referred to in the 24-hour dietary recall. From the data obtained by the research of oral ingestion, patients were classified according to the levels of the Functional Oral Intake Scale (FOIS),20 on a scale from I to VII, considering the diet characteristics based on the properties and texture of the food.21\",\n \"The fiberoptic endoscopic evaluation of swallowing (FEES) was performed by an otorhinolaryngologist physician collaboratively with the speech therapist. Subjects were asked to remain seated with their heads arranged in the direction of the body axis, without bending or rotation, and the examination was performed using a flexible endoscopic equipment of fiberoptic bronchoscopic type (model Olympus CLV-U20) and an Olympus OTV–SC nasopharyngoscope (Olympus Corporation, Tokyo, Japan).\\nThree consistent standardized foods were evaluated: liquid (10 mL of filtered water), thick pudding (10 mL of thickened dietary grape juice, reaching a final consistency similar to that of pudding), and solid (half a slice of bread, 1 cm thick). From the data obtained by the FEES, the severity rating of the swallowing disorder was determined in accordance with the scale of functional impairment of swallowing of Macedo Filho,22,23 which subdivides the dysphagia severity levels (normal, mild, moderate, and severe).\",\n \"Spearman’s test was used to verify the correlation between the oral health condition (DMFT, number of teeth present, and the need for prostheses replacement) and the performance in the swallowing function (classification in the FOIS scale and dysphagia rating in each consistency). For this analysis to be performed, the FOIS scale and the degree of dysphagia were numerically classified from 1 to 7 and from 1 to 4, respectively.\\nFor comparison of FOIS results and dysphagia classification, among the different types of prostheses used in the upper and lower dental arches (complete dental prosthesis, partial dental prosthesis, denture or none), the Kruskal–Wallis test was used, followed by the Dunn’s test.\",\n \"According to the evaluation of oral condition, the mean of DMFT of elderly individuals affected by stroke in this study was 28.7, while the mean of remaining teeth was 5.6, showing that most individuals were edentulous. Table 2 shows the types of prostheses used by the elderly individuals affected by stroke in each dental arch and the need for prostheses replacement.\\nAccording to the results of the functional oral intake scale, no individuals were found to be on a single consistency diet or tube dependent. Most individuals (53%) presented as level V on the FOIS, followed by level IV (34%) and level VII (13%).\\nWith the severity rating of the swallowing disorder, no occurrence of severe dysphagia was verified. The distribution of the elderly affected by stroke according to severity rating of the swallowing disorder is shown in Table 3, as well.\\nAccording to the results of the statistical analysis, correlations between the oral health condition and the results of the FOIS scale in elderly individuals affected by stroke in this study were found. There was a statistically significant negative correlation between the need for replacement of prostheses and the rating scale, and also in the HG and in RHG, showing that the greater the need for replacement of the prosthesis, the worse the rating of the FOIS scale.\\nThere were differences in the classification of the FOIS scale, for the LHG, in the different types of prostheses used in the upper dental arch, whereas for the RHG the difference in the classification of FOIS occurred between groups of individuals with different types of prostheses used in the lower dental arch. After completion of the Dunn’s post-test, the difference was confirmed only in the LHG, demonstrating that individuals of this group with partial prostheses in the upper arch (ie, that still had natural elements and whose missed teeth had been rehabilitated) had better ratings in the FOIS scale compared to individuals with total prostheses.\\nThe results of the correlation between the FOIS scale and the oral health condition are shown in Table 4.\\nIn the statistical analysis between the degree of swallowing dysfunction and oral health condition, a significant negative correlation between the number of teeth and the swallowing performance of liquid in the LHG was found, demonstrating that the greater the number of teeth, the lower the degree of swallowing dysfunction.\\nAccording to the Kruskal–Wallis test, there was no difference in the performance of swallowing solid boluses depending on the type of prosthesis used in the lower dental arch; in the HG, however, when applying the post-Dunn’s test, this difference was not confirmed (P>0.05). The comparison of the FOIS scale between the types of dental prostheses is shown in Table 5.\\nThe results of the correlation between the degree of dysphagia and the oral health condition are shown in Table 6, and the comparison of the degree of swallowing dysfunction between types of dental prostheses is described in Table 7.\",\n \"According to the literature, individuals affected by stroke often exhibit decreased level of consciousness, paralysis of the muscles involved in swallowing, sensory deficits of the pharynx and oral cavity, as well as loss of appetite.24 With these disorders related to swallowing, dysphagia becomes an important symptom in this population, and may be aggravated by teeth loss and the use of poorly fitting prosthesis, common during aging. Furthermore, the hypothesis that oropharyngeal colonization is primarily responsible for the subsequent respiratory infection, owing to aspiration, is strongly supported by the literature.10\\nNo literature studies have made the correlation between the findings of the swallowing performance and the condition of the oral health in elderly patients after stroke.\\nThe results of this study showed some correlations between the oral condition and the data of the swallowing performance even when elderly individuals affected by stroke were part of the heterogeneous group.\\nBesides the statistical analysis for the study group, in an attempt to reduce its heterogeneity, the subjects were divided according to the side of the motor impairment after stroke. Former studies have suggested that dysphagia maybe associated with diffuse lesions in one or both hemispheres, anterior fossa, or brainstem, as swallowing has a bilateral cortical representation. It has been well established that the protection of the airways during swallowing requires a precise coordination with accurate movements of the structures involved and adequate propulsion of the bolus to the oropharynx and esophagus;25 thus, the motor ability for swallowing seems to be essential for this protection.\\nIn the HG and RHG, it was found that the higher the need for replacement of the prosthesis, the worse the rating in the FOIS scale; ie, individuals with greater need for replacement of the prostheses had a greater perception in making more compensation and changes in their diet, which reflected the classification level of oral intake.\\nThe FOIS scale was developed to describe the functional level of the daily oral intake of food and liquid of a patient, considering the changes in diet and need for compensation during swallowing.20 These limitations and changes in oral intake result from patient perception and self-perception of swallowing ability that is impaired. Therefore, from the results, it can be inferred that when there was a greater need for replacement of the prosthesis, the patient’s perception of doing more compensation and dietary modifications is reflected in the classification of the FOIS scale.\\nSignificant changes in oral condition can have a major effect on the functions of eating and drinking and on the nutritional status of elderly individuals and those with neurological diseases.26 Thus, the selection of food is determined by the difficulty in chewing and swallowing of the individuals. Missing teeth and the use of prosthesis cause changes in the chewing ability of the elderly according to the type of food, and influences the swallowing function;27 thus, soft or mashed foods are preferentially ingested by the elderly, due to the difficulty in mastication,28 showing that there is an influence of oral health condition on the selection of foods.\\nBesides the influence of the oral condition on swallowing, there was a difference between the types of prostheses and classification of the FOIS scale when the results of the evaluation of individuals with impaired right and left hemiplegia were analyzed separately. The difference between the types of prostheses could be confirmed by post-Dunn’s test in the LHG, and demonstrated that individuals with partial dentures had better ratings in the FOIS scale than individuals with complete prostheses. No studies were found relating the oral condition and the rating scale. Thus, we can infer that when there was partial prosthesis rehabilitation along with the presence of some dental elements, subjects needed less compensation and changes in the oral diet. In this sense, studies show that the use of prostheses in the edentulous contributes to the maintenance of the physiological process of swallowing in the elderly,29 the fixation of the prosthesis in the mandible,30 and the presence of natural elements enables greater preservation of functional swallowing.\\nWhen comparing the swallowing performance according to the type of prosthesis used, there was no difference between the types of prostheses fitted in the lower dental arch in swallowing solids in the HG. Studies in healthy elderly subjects demonstrate the importance of the oral condition in the performance of the oral phase of swallowing. Changes in masticatory function related to the oral condition, such as difficulties biting and chewing food27 demonstrated an influence on the pharyngeal phase of swallowing, such as premature escape of food, waste retention in the vallecula and pyriform sinuses, and the presence of coughing and gagging.27\\nMoreover, the absence of dental prosthesis can result in a longer duration of swallowing,29 and the chewing and swallowing difficulties in healthy older adults decreased after the replacement of removable total prostheses by mandibular implant-supported prostheses.30 The literature has shown positive effects in the swallowing function with the use of prostheses and the differences in swallowing performance with prostheses in the upper or inferior arches.8 A study with healthy elderly individuals showed the differences between the total duration of swallowing, the latency period before pharynx elevation, duration of preparatory and oral phase, when using or not using the dental prostheses.29\\nFinally, in the LHG, it was observed that the smaller the number of teeth present, the worse the rating of dysphagia for liquid. Although this study aimed at finding an influence of the presence and number of teeth on chewing and consequently on swallowing solids,1,27,28 it also demonstrates the influence of the oral condition on liquid swallowing for this population. According to Tamura et al,2 especially for liquid food, which demands more coordination during swallowing, stability in the jaw is also required, so that there is greater stability of the hyolaryngeal complex. To start the pharyngeal phase, the rise of the hyoid bone by the suprahyoid muscles and laryngeal elevation should occur. To complement these events, the jaw must be stabilized by natural or rehabilitated teeth into the proper position, because when the elevation of the hyoid bone and larynx is insufficient, there is an increased risk of aspiration during swallowing.\",\n \"The findings of this study indicate that the oral health condition of elderly individuals after stroke in chronic phase showed an association with the level of oral intake and the degree of oropharyngeal dysphagia. The limitations of the study should be considered, one being the lack of access to the imaging examinations to characterize the individuals according to the type of stroke, damaged areas, and extent of injury, and heterogeneity of the time of brain involvement, in addition to the effects of medications and other comorbidities, which were not taken into account. Also, owing to the small sample, strong correlations could not be observed, and a cause/effect relationship cannot be determined.\\nDespite the limitations of this study, and the fact that hemiplegia does not necessarily represent a direct relationship with the injured brain hemisphere contralaterally, since cerebellum lesions may cause ipsilateral motor damages,31 the findings indicated that the level of oral intake was influenced by the condition or type of dental prostheses, regardless of the side of the body affected. On the other hand, the number of teeth were demonstrated to be related to the level of dysphagia in individuals with right hemiplegia, which shows that the oral health condition differentially affects the physiological responses of swallowing, influenced by the brain damage.\\nConsidering the present sample, further research is necessary pertaining to the oral condition and the neurological involvement of individuals. In addition, these issues demonstrate the need for an interdisciplinary research team comprising dentists, physicians, speech pathologists, and other health professionals, so as to study and define the treatment and guidelines to patients and caretakers, in order to maintain the best oral health condition in the elderly presented with oropharyngeal dysphagia.\"\n]"},"all_sections_sec_types":{"kind":"list like","value":["intro","methods","methods|subjects","methods",null,null,null,"methods","results","discussion",null],"string":"[\n \"intro\",\n \"methods\",\n \"methods|subjects\",\n \"methods\",\n null,\n null,\n null,\n \"methods\",\n \"results\",\n \"discussion\",\n null\n]"},"keywords":{"kind":"list like","value":["deglutition","mouth rehabilitation","aged","prosthodontics","dysphagia","cerebrovascular disorders"],"string":"[\n \"deglutition\",\n \"mouth rehabilitation\",\n \"aged\",\n \"prosthodontics\",\n \"dysphagia\",\n \"cerebrovascular disorders\"\n]"},"whole_article_text":{"kind":"string","value":"Introduction:\nThe process of swallowing physiologically changes with aging. The loss of teeth, very common in this population, is related to the reduction of bone tissue, receptors (proprioceptors and periodontal ligaments), and muscle atrophy. Consequently, orofacial functions are impaired in toothless individuals.1 In addition, prostheses are functionally less efficient when compared to individuals with natural dentition.1,2\nThe stability of the jaw occlusion of the posterior teeth or prostheses is important for the swallowing function.2 The dental condition and the use of poorly fitting prostheses may contribute to the difficulties arising from old age, and the oral diet and nutritional status of the elderly may be affected by changes related to aging3 and changes in skill and desire to feel, bite, chew4 and swallow food,3,5 significantly compromising and further aggravating the quality of life of these individuals.6\nThe literature has shown the influence of oral health condition on swallowing and nutrition, indirectly influencing the daily life activities of the elderly.7 Furthermore, it is clear that maintenance of oral health is essential for the general health, quality of life, chewing ability8 and, mainly, the reduction of pneumonia risk in fragile elderly people,8,9 for it is already known that bacterial colonies in oropharyngeal tissues and in dental plaque are the greatest precursors for the development of aspiration respiratory infections.10\nBesides the influence of the loss of dentition, the functional impairments resulting from aging may be potentiated by neurological changes resulting in dysphagia in patients, an important cause of morbidity and mortality in this population.11\nIn the adult and elderly population, dysphagia is most commonly associated with stroke.12 More than half of patients who have suffered stroke present between six and ten types of disability, muscle weakness being the most prevalent one, followed by communication, speech, and swallowing disorders.13 Specifically regarding swallowing, the stroke entails increased oropharyngeal transit time,14,15 changes in motor control of tongue,16 as well as the presence of laryngotracheal aspiration of food.16,17\nOnce the physiological changes resulting from aging in swallowing may be aggravated by loss of teeth and the use of poorly fitting prostheses, the severity of dysphagia could be influenced by the oral health condition and use of dental prostheses in elderly patients with neurological diseases. However, according to the literature studied, no research has made the correlation between the findings of the swallowing performance with oral health in elderly patients after stroke. Therefore, the aim of this study was to relate the condition of oral health to the level of oral intake and the degree of swallowing dysfunction in elderly patients with stroke in chronic phase.\n\nMethods:\n Study design and subjects Thirty poststroke individuals, aged 61 to 90 years, whose injury happened 6 months to 9 years previously to their participation in this prospective study. Table 1 shows the characterization data of the sample.\nThe inclusion criteria considered the following cases: affected by stroke as attested by a medical report, through clinical diagnosis and imaging, with a minimum time of 6 months; aged over 60 years, being in regular clinical neurological monitoring, not having undergone dysphagia rehabilitation; presenting general stable health that would enable the realization of the proposed tests.\nIn order to achieve a greater delineation of the group, in addition to considering the group of 30 individuals (heterogeneous group [HG]), the total dentate, the total edentulous without dental prostheses, and individuals with more than one episode of stroke were excluded and the participants were divided into two groups, according to the affected body side: right hemiplegic group (RHG, n=8) and left hemiplegic group (LHG, n=11). A distribution and recruitment fluxogram is shown in Figure 1.\nThirty poststroke individuals, aged 61 to 90 years, whose injury happened 6 months to 9 years previously to their participation in this prospective study. Table 1 shows the characterization data of the sample.\nThe inclusion criteria considered the following cases: affected by stroke as attested by a medical report, through clinical diagnosis and imaging, with a minimum time of 6 months; aged over 60 years, being in regular clinical neurological monitoring, not having undergone dysphagia rehabilitation; presenting general stable health that would enable the realization of the proposed tests.\nIn order to achieve a greater delineation of the group, in addition to considering the group of 30 individuals (heterogeneous group [HG]), the total dentate, the total edentulous without dental prostheses, and individuals with more than one episode of stroke were excluded and the participants were divided into two groups, according to the affected body side: right hemiplegic group (RHG, n=8) and left hemiplegic group (LHG, n=11). A distribution and recruitment fluxogram is shown in Figure 1.\n Procedures Oral condition evaluation The participants underwent assessment of various components of oral health through traditional health indicators, based on the presence or absence of disease. The data were collected from the clinical examination by a dentist as instructed by the WHO – Oral Health Surveys: Basic Methods,18 including the assessment of dental status by the number of decayed, missing, and filled teeth (DMFT) and evaluation of dental prostheses.\nThe prostheses were evaluated for retention, functional stability, smile aesthetics, degree of bone resorption, and quality of the mucosa.19\nFrom the evaluation, the dental arches were classified as without prosthesis or with the presence of partial or complete dental prosthesis; later, the use or need for replacement of dental prostheses was indicated. The need for prostheses replacement was as follows: 0, no need to change; 1, partial dental prosthesis was necessary in one of the dental arches; 2, need for partial dental prosthesis in both dental arches; 3, need for complete dental prosthesis in one of the dental arches; 4, need for dental prosthesis in both dental arches.\nThe participants underwent assessment of various components of oral health through traditional health indicators, based on the presence or absence of disease. The data were collected from the clinical examination by a dentist as instructed by the WHO – Oral Health Surveys: Basic Methods,18 including the assessment of dental status by the number of decayed, missing, and filled teeth (DMFT) and evaluation of dental prostheses.\nThe prostheses were evaluated for retention, functional stability, smile aesthetics, degree of bone resorption, and quality of the mucosa.19\nFrom the evaluation, the dental arches were classified as without prosthesis or with the presence of partial or complete dental prosthesis; later, the use or need for replacement of dental prostheses was indicated. The need for prostheses replacement was as follows: 0, no need to change; 1, partial dental prosthesis was necessary in one of the dental arches; 2, need for partial dental prosthesis in both dental arches; 3, need for complete dental prosthesis in one of the dental arches; 4, need for dental prosthesis in both dental arches.\n Functional Oral Intake Scale Research of oral ingestion was performed by reviewing the usual food consumption patterns referred to in the 24-hour dietary recall. From the data obtained by the research of oral ingestion, patients were classified according to the levels of the Functional Oral Intake Scale (FOIS),20 on a scale from I to VII, considering the diet characteristics based on the properties and texture of the food.21\nResearch of oral ingestion was performed by reviewing the usual food consumption patterns referred to in the 24-hour dietary recall. From the data obtained by the research of oral ingestion, patients were classified according to the levels of the Functional Oral Intake Scale (FOIS),20 on a scale from I to VII, considering the diet characteristics based on the properties and texture of the food.21\n Fiberoptic endoscopic evaluation of swallowing The fiberoptic endoscopic evaluation of swallowing (FEES) was performed by an otorhinolaryngologist physician collaboratively with the speech therapist. Subjects were asked to remain seated with their heads arranged in the direction of the body axis, without bending or rotation, and the examination was performed using a flexible endoscopic equipment of fiberoptic bronchoscopic type (model Olympus CLV-U20) and an Olympus OTV–SC nasopharyngoscope (Olympus Corporation, Tokyo, Japan).\nThree consistent standardized foods were evaluated: liquid (10 mL of filtered water), thick pudding (10 mL of thickened dietary grape juice, reaching a final consistency similar to that of pudding), and solid (half a slice of bread, 1 cm thick). From the data obtained by the FEES, the severity rating of the swallowing disorder was determined in accordance with the scale of functional impairment of swallowing of Macedo Filho,22,23 which subdivides the dysphagia severity levels (normal, mild, moderate, and severe).\nThe fiberoptic endoscopic evaluation of swallowing (FEES) was performed by an otorhinolaryngologist physician collaboratively with the speech therapist. Subjects were asked to remain seated with their heads arranged in the direction of the body axis, without bending or rotation, and the examination was performed using a flexible endoscopic equipment of fiberoptic bronchoscopic type (model Olympus CLV-U20) and an Olympus OTV–SC nasopharyngoscope (Olympus Corporation, Tokyo, Japan).\nThree consistent standardized foods were evaluated: liquid (10 mL of filtered water), thick pudding (10 mL of thickened dietary grape juice, reaching a final consistency similar to that of pudding), and solid (half a slice of bread, 1 cm thick). From the data obtained by the FEES, the severity rating of the swallowing disorder was determined in accordance with the scale of functional impairment of swallowing of Macedo Filho,22,23 which subdivides the dysphagia severity levels (normal, mild, moderate, and severe).\n Statistical analysis Spearman’s test was used to verify the correlation between the oral health condition (DMFT, number of teeth present, and the need for prostheses replacement) and the performance in the swallowing function (classification in the FOIS scale and dysphagia rating in each consistency). For this analysis to be performed, the FOIS scale and the degree of dysphagia were numerically classified from 1 to 7 and from 1 to 4, respectively.\nFor comparison of FOIS results and dysphagia classification, among the different types of prostheses used in the upper and lower dental arches (complete dental prosthesis, partial dental prosthesis, denture or none), the Kruskal–Wallis test was used, followed by the Dunn’s test.\nSpearman’s test was used to verify the correlation between the oral health condition (DMFT, number of teeth present, and the need for prostheses replacement) and the performance in the swallowing function (classification in the FOIS scale and dysphagia rating in each consistency). For this analysis to be performed, the FOIS scale and the degree of dysphagia were numerically classified from 1 to 7 and from 1 to 4, respectively.\nFor comparison of FOIS results and dysphagia classification, among the different types of prostheses used in the upper and lower dental arches (complete dental prosthesis, partial dental prosthesis, denture or none), the Kruskal–Wallis test was used, followed by the Dunn’s test.\n Oral condition evaluation The participants underwent assessment of various components of oral health through traditional health indicators, based on the presence or absence of disease. The data were collected from the clinical examination by a dentist as instructed by the WHO – Oral Health Surveys: Basic Methods,18 including the assessment of dental status by the number of decayed, missing, and filled teeth (DMFT) and evaluation of dental prostheses.\nThe prostheses were evaluated for retention, functional stability, smile aesthetics, degree of bone resorption, and quality of the mucosa.19\nFrom the evaluation, the dental arches were classified as without prosthesis or with the presence of partial or complete dental prosthesis; later, the use or need for replacement of dental prostheses was indicated. The need for prostheses replacement was as follows: 0, no need to change; 1, partial dental prosthesis was necessary in one of the dental arches; 2, need for partial dental prosthesis in both dental arches; 3, need for complete dental prosthesis in one of the dental arches; 4, need for dental prosthesis in both dental arches.\nThe participants underwent assessment of various components of oral health through traditional health indicators, based on the presence or absence of disease. The data were collected from the clinical examination by a dentist as instructed by the WHO – Oral Health Surveys: Basic Methods,18 including the assessment of dental status by the number of decayed, missing, and filled teeth (DMFT) and evaluation of dental prostheses.\nThe prostheses were evaluated for retention, functional stability, smile aesthetics, degree of bone resorption, and quality of the mucosa.19\nFrom the evaluation, the dental arches were classified as without prosthesis or with the presence of partial or complete dental prosthesis; later, the use or need for replacement of dental prostheses was indicated. The need for prostheses replacement was as follows: 0, no need to change; 1, partial dental prosthesis was necessary in one of the dental arches; 2, need for partial dental prosthesis in both dental arches; 3, need for complete dental prosthesis in one of the dental arches; 4, need for dental prosthesis in both dental arches.\n Functional Oral Intake Scale Research of oral ingestion was performed by reviewing the usual food consumption patterns referred to in the 24-hour dietary recall. From the data obtained by the research of oral ingestion, patients were classified according to the levels of the Functional Oral Intake Scale (FOIS),20 on a scale from I to VII, considering the diet characteristics based on the properties and texture of the food.21\nResearch of oral ingestion was performed by reviewing the usual food consumption patterns referred to in the 24-hour dietary recall. From the data obtained by the research of oral ingestion, patients were classified according to the levels of the Functional Oral Intake Scale (FOIS),20 on a scale from I to VII, considering the diet characteristics based on the properties and texture of the food.21\n Fiberoptic endoscopic evaluation of swallowing The fiberoptic endoscopic evaluation of swallowing (FEES) was performed by an otorhinolaryngologist physician collaboratively with the speech therapist. Subjects were asked to remain seated with their heads arranged in the direction of the body axis, without bending or rotation, and the examination was performed using a flexible endoscopic equipment of fiberoptic bronchoscopic type (model Olympus CLV-U20) and an Olympus OTV–SC nasopharyngoscope (Olympus Corporation, Tokyo, Japan).\nThree consistent standardized foods were evaluated: liquid (10 mL of filtered water), thick pudding (10 mL of thickened dietary grape juice, reaching a final consistency similar to that of pudding), and solid (half a slice of bread, 1 cm thick). From the data obtained by the FEES, the severity rating of the swallowing disorder was determined in accordance with the scale of functional impairment of swallowing of Macedo Filho,22,23 which subdivides the dysphagia severity levels (normal, mild, moderate, and severe).\nThe fiberoptic endoscopic evaluation of swallowing (FEES) was performed by an otorhinolaryngologist physician collaboratively with the speech therapist. Subjects were asked to remain seated with their heads arranged in the direction of the body axis, without bending or rotation, and the examination was performed using a flexible endoscopic equipment of fiberoptic bronchoscopic type (model Olympus CLV-U20) and an Olympus OTV–SC nasopharyngoscope (Olympus Corporation, Tokyo, Japan).\nThree consistent standardized foods were evaluated: liquid (10 mL of filtered water), thick pudding (10 mL of thickened dietary grape juice, reaching a final consistency similar to that of pudding), and solid (half a slice of bread, 1 cm thick). From the data obtained by the FEES, the severity rating of the swallowing disorder was determined in accordance with the scale of functional impairment of swallowing of Macedo Filho,22,23 which subdivides the dysphagia severity levels (normal, mild, moderate, and severe).\n Statistical analysis Spearman’s test was used to verify the correlation between the oral health condition (DMFT, number of teeth present, and the need for prostheses replacement) and the performance in the swallowing function (classification in the FOIS scale and dysphagia rating in each consistency). For this analysis to be performed, the FOIS scale and the degree of dysphagia were numerically classified from 1 to 7 and from 1 to 4, respectively.\nFor comparison of FOIS results and dysphagia classification, among the different types of prostheses used in the upper and lower dental arches (complete dental prosthesis, partial dental prosthesis, denture or none), the Kruskal–Wallis test was used, followed by the Dunn’s test.\nSpearman’s test was used to verify the correlation between the oral health condition (DMFT, number of teeth present, and the need for prostheses replacement) and the performance in the swallowing function (classification in the FOIS scale and dysphagia rating in each consistency). For this analysis to be performed, the FOIS scale and the degree of dysphagia were numerically classified from 1 to 7 and from 1 to 4, respectively.\nFor comparison of FOIS results and dysphagia classification, among the different types of prostheses used in the upper and lower dental arches (complete dental prosthesis, partial dental prosthesis, denture or none), the Kruskal–Wallis test was used, followed by the Dunn’s test.\n\nStudy design and subjects:\nThirty poststroke individuals, aged 61 to 90 years, whose injury happened 6 months to 9 years previously to their participation in this prospective study. Table 1 shows the characterization data of the sample.\nThe inclusion criteria considered the following cases: affected by stroke as attested by a medical report, through clinical diagnosis and imaging, with a minimum time of 6 months; aged over 60 years, being in regular clinical neurological monitoring, not having undergone dysphagia rehabilitation; presenting general stable health that would enable the realization of the proposed tests.\nIn order to achieve a greater delineation of the group, in addition to considering the group of 30 individuals (heterogeneous group [HG]), the total dentate, the total edentulous without dental prostheses, and individuals with more than one episode of stroke were excluded and the participants were divided into two groups, according to the affected body side: right hemiplegic group (RHG, n=8) and left hemiplegic group (LHG, n=11). A distribution and recruitment fluxogram is shown in Figure 1.\n\nProcedures:\n Oral condition evaluation The participants underwent assessment of various components of oral health through traditional health indicators, based on the presence or absence of disease. The data were collected from the clinical examination by a dentist as instructed by the WHO – Oral Health Surveys: Basic Methods,18 including the assessment of dental status by the number of decayed, missing, and filled teeth (DMFT) and evaluation of dental prostheses.\nThe prostheses were evaluated for retention, functional stability, smile aesthetics, degree of bone resorption, and quality of the mucosa.19\nFrom the evaluation, the dental arches were classified as without prosthesis or with the presence of partial or complete dental prosthesis; later, the use or need for replacement of dental prostheses was indicated. The need for prostheses replacement was as follows: 0, no need to change; 1, partial dental prosthesis was necessary in one of the dental arches; 2, need for partial dental prosthesis in both dental arches; 3, need for complete dental prosthesis in one of the dental arches; 4, need for dental prosthesis in both dental arches.\nThe participants underwent assessment of various components of oral health through traditional health indicators, based on the presence or absence of disease. The data were collected from the clinical examination by a dentist as instructed by the WHO – Oral Health Surveys: Basic Methods,18 including the assessment of dental status by the number of decayed, missing, and filled teeth (DMFT) and evaluation of dental prostheses.\nThe prostheses were evaluated for retention, functional stability, smile aesthetics, degree of bone resorption, and quality of the mucosa.19\nFrom the evaluation, the dental arches were classified as without prosthesis or with the presence of partial or complete dental prosthesis; later, the use or need for replacement of dental prostheses was indicated. The need for prostheses replacement was as follows: 0, no need to change; 1, partial dental prosthesis was necessary in one of the dental arches; 2, need for partial dental prosthesis in both dental arches; 3, need for complete dental prosthesis in one of the dental arches; 4, need for dental prosthesis in both dental arches.\n Functional Oral Intake Scale Research of oral ingestion was performed by reviewing the usual food consumption patterns referred to in the 24-hour dietary recall. From the data obtained by the research of oral ingestion, patients were classified according to the levels of the Functional Oral Intake Scale (FOIS),20 on a scale from I to VII, considering the diet characteristics based on the properties and texture of the food.21\nResearch of oral ingestion was performed by reviewing the usual food consumption patterns referred to in the 24-hour dietary recall. From the data obtained by the research of oral ingestion, patients were classified according to the levels of the Functional Oral Intake Scale (FOIS),20 on a scale from I to VII, considering the diet characteristics based on the properties and texture of the food.21\n Fiberoptic endoscopic evaluation of swallowing The fiberoptic endoscopic evaluation of swallowing (FEES) was performed by an otorhinolaryngologist physician collaboratively with the speech therapist. Subjects were asked to remain seated with their heads arranged in the direction of the body axis, without bending or rotation, and the examination was performed using a flexible endoscopic equipment of fiberoptic bronchoscopic type (model Olympus CLV-U20) and an Olympus OTV–SC nasopharyngoscope (Olympus Corporation, Tokyo, Japan).\nThree consistent standardized foods were evaluated: liquid (10 mL of filtered water), thick pudding (10 mL of thickened dietary grape juice, reaching a final consistency similar to that of pudding), and solid (half a slice of bread, 1 cm thick). From the data obtained by the FEES, the severity rating of the swallowing disorder was determined in accordance with the scale of functional impairment of swallowing of Macedo Filho,22,23 which subdivides the dysphagia severity levels (normal, mild, moderate, and severe).\nThe fiberoptic endoscopic evaluation of swallowing (FEES) was performed by an otorhinolaryngologist physician collaboratively with the speech therapist. Subjects were asked to remain seated with their heads arranged in the direction of the body axis, without bending or rotation, and the examination was performed using a flexible endoscopic equipment of fiberoptic bronchoscopic type (model Olympus CLV-U20) and an Olympus OTV–SC nasopharyngoscope (Olympus Corporation, Tokyo, Japan).\nThree consistent standardized foods were evaluated: liquid (10 mL of filtered water), thick pudding (10 mL of thickened dietary grape juice, reaching a final consistency similar to that of pudding), and solid (half a slice of bread, 1 cm thick). From the data obtained by the FEES, the severity rating of the swallowing disorder was determined in accordance with the scale of functional impairment of swallowing of Macedo Filho,22,23 which subdivides the dysphagia severity levels (normal, mild, moderate, and severe).\n Statistical analysis Spearman’s test was used to verify the correlation between the oral health condition (DMFT, number of teeth present, and the need for prostheses replacement) and the performance in the swallowing function (classification in the FOIS scale and dysphagia rating in each consistency). For this analysis to be performed, the FOIS scale and the degree of dysphagia were numerically classified from 1 to 7 and from 1 to 4, respectively.\nFor comparison of FOIS results and dysphagia classification, among the different types of prostheses used in the upper and lower dental arches (complete dental prosthesis, partial dental prosthesis, denture or none), the Kruskal–Wallis test was used, followed by the Dunn’s test.\nSpearman’s test was used to verify the correlation between the oral health condition (DMFT, number of teeth present, and the need for prostheses replacement) and the performance in the swallowing function (classification in the FOIS scale and dysphagia rating in each consistency). For this analysis to be performed, the FOIS scale and the degree of dysphagia were numerically classified from 1 to 7 and from 1 to 4, respectively.\nFor comparison of FOIS results and dysphagia classification, among the different types of prostheses used in the upper and lower dental arches (complete dental prosthesis, partial dental prosthesis, denture or none), the Kruskal–Wallis test was used, followed by the Dunn’s test.\n\nOral condition evaluation:\nThe participants underwent assessment of various components of oral health through traditional health indicators, based on the presence or absence of disease. The data were collected from the clinical examination by a dentist as instructed by the WHO – Oral Health Surveys: Basic Methods,18 including the assessment of dental status by the number of decayed, missing, and filled teeth (DMFT) and evaluation of dental prostheses.\nThe prostheses were evaluated for retention, functional stability, smile aesthetics, degree of bone resorption, and quality of the mucosa.19\nFrom the evaluation, the dental arches were classified as without prosthesis or with the presence of partial or complete dental prosthesis; later, the use or need for replacement of dental prostheses was indicated. The need for prostheses replacement was as follows: 0, no need to change; 1, partial dental prosthesis was necessary in one of the dental arches; 2, need for partial dental prosthesis in both dental arches; 3, need for complete dental prosthesis in one of the dental arches; 4, need for dental prosthesis in both dental arches.\n\nFunctional Oral Intake Scale:\nResearch of oral ingestion was performed by reviewing the usual food consumption patterns referred to in the 24-hour dietary recall. From the data obtained by the research of oral ingestion, patients were classified according to the levels of the Functional Oral Intake Scale (FOIS),20 on a scale from I to VII, considering the diet characteristics based on the properties and texture of the food.21\n\nFiberoptic endoscopic evaluation of swallowing:\nThe fiberoptic endoscopic evaluation of swallowing (FEES) was performed by an otorhinolaryngologist physician collaboratively with the speech therapist. Subjects were asked to remain seated with their heads arranged in the direction of the body axis, without bending or rotation, and the examination was performed using a flexible endoscopic equipment of fiberoptic bronchoscopic type (model Olympus CLV-U20) and an Olympus OTV–SC nasopharyngoscope (Olympus Corporation, Tokyo, Japan).\nThree consistent standardized foods were evaluated: liquid (10 mL of filtered water), thick pudding (10 mL of thickened dietary grape juice, reaching a final consistency similar to that of pudding), and solid (half a slice of bread, 1 cm thick). From the data obtained by the FEES, the severity rating of the swallowing disorder was determined in accordance with the scale of functional impairment of swallowing of Macedo Filho,22,23 which subdivides the dysphagia severity levels (normal, mild, moderate, and severe).\n\nStatistical analysis:\nSpearman’s test was used to verify the correlation between the oral health condition (DMFT, number of teeth present, and the need for prostheses replacement) and the performance in the swallowing function (classification in the FOIS scale and dysphagia rating in each consistency). For this analysis to be performed, the FOIS scale and the degree of dysphagia were numerically classified from 1 to 7 and from 1 to 4, respectively.\nFor comparison of FOIS results and dysphagia classification, among the different types of prostheses used in the upper and lower dental arches (complete dental prosthesis, partial dental prosthesis, denture or none), the Kruskal–Wallis test was used, followed by the Dunn’s test.\n\nResults:\nAccording to the evaluation of oral condition, the mean of DMFT of elderly individuals affected by stroke in this study was 28.7, while the mean of remaining teeth was 5.6, showing that most individuals were edentulous. Table 2 shows the types of prostheses used by the elderly individuals affected by stroke in each dental arch and the need for prostheses replacement.\nAccording to the results of the functional oral intake scale, no individuals were found to be on a single consistency diet or tube dependent. Most individuals (53%) presented as level V on the FOIS, followed by level IV (34%) and level VII (13%).\nWith the severity rating of the swallowing disorder, no occurrence of severe dysphagia was verified. The distribution of the elderly affected by stroke according to severity rating of the swallowing disorder is shown in Table 3, as well.\nAccording to the results of the statistical analysis, correlations between the oral health condition and the results of the FOIS scale in elderly individuals affected by stroke in this study were found. There was a statistically significant negative correlation between the need for replacement of prostheses and the rating scale, and also in the HG and in RHG, showing that the greater the need for replacement of the prosthesis, the worse the rating of the FOIS scale.\nThere were differences in the classification of the FOIS scale, for the LHG, in the different types of prostheses used in the upper dental arch, whereas for the RHG the difference in the classification of FOIS occurred between groups of individuals with different types of prostheses used in the lower dental arch. After completion of the Dunn’s post-test, the difference was confirmed only in the LHG, demonstrating that individuals of this group with partial prostheses in the upper arch (ie, that still had natural elements and whose missed teeth had been rehabilitated) had better ratings in the FOIS scale compared to individuals with total prostheses.\nThe results of the correlation between the FOIS scale and the oral health condition are shown in Table 4.\nIn the statistical analysis between the degree of swallowing dysfunction and oral health condition, a significant negative correlation between the number of teeth and the swallowing performance of liquid in the LHG was found, demonstrating that the greater the number of teeth, the lower the degree of swallowing dysfunction.\nAccording to the Kruskal–Wallis test, there was no difference in the performance of swallowing solid boluses depending on the type of prosthesis used in the lower dental arch; in the HG, however, when applying the post-Dunn’s test, this difference was not confirmed (P>0.05). The comparison of the FOIS scale between the types of dental prostheses is shown in Table 5.\nThe results of the correlation between the degree of dysphagia and the oral health condition are shown in Table 6, and the comparison of the degree of swallowing dysfunction between types of dental prostheses is described in Table 7.\n\nDiscussion:\nAccording to the literature, individuals affected by stroke often exhibit decreased level of consciousness, paralysis of the muscles involved in swallowing, sensory deficits of the pharynx and oral cavity, as well as loss of appetite.24 With these disorders related to swallowing, dysphagia becomes an important symptom in this population, and may be aggravated by teeth loss and the use of poorly fitting prosthesis, common during aging. Furthermore, the hypothesis that oropharyngeal colonization is primarily responsible for the subsequent respiratory infection, owing to aspiration, is strongly supported by the literature.10\nNo literature studies have made the correlation between the findings of the swallowing performance and the condition of the oral health in elderly patients after stroke.\nThe results of this study showed some correlations between the oral condition and the data of the swallowing performance even when elderly individuals affected by stroke were part of the heterogeneous group.\nBesides the statistical analysis for the study group, in an attempt to reduce its heterogeneity, the subjects were divided according to the side of the motor impairment after stroke. Former studies have suggested that dysphagia maybe associated with diffuse lesions in one or both hemispheres, anterior fossa, or brainstem, as swallowing has a bilateral cortical representation. It has been well established that the protection of the airways during swallowing requires a precise coordination with accurate movements of the structures involved and adequate propulsion of the bolus to the oropharynx and esophagus;25 thus, the motor ability for swallowing seems to be essential for this protection.\nIn the HG and RHG, it was found that the higher the need for replacement of the prosthesis, the worse the rating in the FOIS scale; ie, individuals with greater need for replacement of the prostheses had a greater perception in making more compensation and changes in their diet, which reflected the classification level of oral intake.\nThe FOIS scale was developed to describe the functional level of the daily oral intake of food and liquid of a patient, considering the changes in diet and need for compensation during swallowing.20 These limitations and changes in oral intake result from patient perception and self-perception of swallowing ability that is impaired. Therefore, from the results, it can be inferred that when there was a greater need for replacement of the prosthesis, the patient’s perception of doing more compensation and dietary modifications is reflected in the classification of the FOIS scale.\nSignificant changes in oral condition can have a major effect on the functions of eating and drinking and on the nutritional status of elderly individuals and those with neurological diseases.26 Thus, the selection of food is determined by the difficulty in chewing and swallowing of the individuals. Missing teeth and the use of prosthesis cause changes in the chewing ability of the elderly according to the type of food, and influences the swallowing function;27 thus, soft or mashed foods are preferentially ingested by the elderly, due to the difficulty in mastication,28 showing that there is an influence of oral health condition on the selection of foods.\nBesides the influence of the oral condition on swallowing, there was a difference between the types of prostheses and classification of the FOIS scale when the results of the evaluation of individuals with impaired right and left hemiplegia were analyzed separately. The difference between the types of prostheses could be confirmed by post-Dunn’s test in the LHG, and demonstrated that individuals with partial dentures had better ratings in the FOIS scale than individuals with complete prostheses. No studies were found relating the oral condition and the rating scale. Thus, we can infer that when there was partial prosthesis rehabilitation along with the presence of some dental elements, subjects needed less compensation and changes in the oral diet. In this sense, studies show that the use of prostheses in the edentulous contributes to the maintenance of the physiological process of swallowing in the elderly,29 the fixation of the prosthesis in the mandible,30 and the presence of natural elements enables greater preservation of functional swallowing.\nWhen comparing the swallowing performance according to the type of prosthesis used, there was no difference between the types of prostheses fitted in the lower dental arch in swallowing solids in the HG. Studies in healthy elderly subjects demonstrate the importance of the oral condition in the performance of the oral phase of swallowing. Changes in masticatory function related to the oral condition, such as difficulties biting and chewing food27 demonstrated an influence on the pharyngeal phase of swallowing, such as premature escape of food, waste retention in the vallecula and pyriform sinuses, and the presence of coughing and gagging.27\nMoreover, the absence of dental prosthesis can result in a longer duration of swallowing,29 and the chewing and swallowing difficulties in healthy older adults decreased after the replacement of removable total prostheses by mandibular implant-supported prostheses.30 The literature has shown positive effects in the swallowing function with the use of prostheses and the differences in swallowing performance with prostheses in the upper or inferior arches.8 A study with healthy elderly individuals showed the differences between the total duration of swallowing, the latency period before pharynx elevation, duration of preparatory and oral phase, when using or not using the dental prostheses.29\nFinally, in the LHG, it was observed that the smaller the number of teeth present, the worse the rating of dysphagia for liquid. Although this study aimed at finding an influence of the presence and number of teeth on chewing and consequently on swallowing solids,1,27,28 it also demonstrates the influence of the oral condition on liquid swallowing for this population. According to Tamura et al,2 especially for liquid food, which demands more coordination during swallowing, stability in the jaw is also required, so that there is greater stability of the hyolaryngeal complex. To start the pharyngeal phase, the rise of the hyoid bone by the suprahyoid muscles and laryngeal elevation should occur. To complement these events, the jaw must be stabilized by natural or rehabilitated teeth into the proper position, because when the elevation of the hyoid bone and larynx is insufficient, there is an increased risk of aspiration during swallowing.\n\nConclusion:\nThe findings of this study indicate that the oral health condition of elderly individuals after stroke in chronic phase showed an association with the level of oral intake and the degree of oropharyngeal dysphagia. The limitations of the study should be considered, one being the lack of access to the imaging examinations to characterize the individuals according to the type of stroke, damaged areas, and extent of injury, and heterogeneity of the time of brain involvement, in addition to the effects of medications and other comorbidities, which were not taken into account. Also, owing to the small sample, strong correlations could not be observed, and a cause/effect relationship cannot be determined.\nDespite the limitations of this study, and the fact that hemiplegia does not necessarily represent a direct relationship with the injured brain hemisphere contralaterally, since cerebellum lesions may cause ipsilateral motor damages,31 the findings indicated that the level of oral intake was influenced by the condition or type of dental prostheses, regardless of the side of the body affected. On the other hand, the number of teeth were demonstrated to be related to the level of dysphagia in individuals with right hemiplegia, which shows that the oral health condition differentially affects the physiological responses of swallowing, influenced by the brain damage.\nConsidering the present sample, further research is necessary pertaining to the oral condition and the neurological involvement of individuals. In addition, these issues demonstrate the need for an interdisciplinary research team comprising dentists, physicians, speech pathologists, and other health professionals, so as to study and define the treatment and guidelines to patients and caretakers, in order to maintain the best oral health condition in the elderly presented with oropharyngeal dysphagia.\n\n"},"whole_article_abstract":{"kind":"string","value":"Background:\nAccording to the literature, the occurrence of dysphagia is high in cases of stroke, and its severity can be enhanced by loss of teeth and the use of poorly fitting prostheses.\n\nMethods:\nThirty elderly individuals affected by stroke in chronic phase participated. All subjects underwent assessment of their oral condition, with classification from the Functional Oral Intake Scale (FOIS) and nasoendoscopic swallowing assessment to classify the degree of dysphagia. The statistical analysis examined a heterogeneous group (HG, n=30) and two groups designated by the affected body part, right (RHG, n=8) and left (LHG, n=11), excluding totally dentate or edentulous individuals without rehabilitation with more than one episode of stroke.\n\nResults:\nThere was a negative correlation between the need for replacement prostheses and the FOIS scale for the HG (P=0.02) and RHG (P=0.01). Differences in FOIS between types of prostheses of the upper dental arch in the LHG (P=0.01) and lower dental arch in the RHG (P=0.04). A negative correlation was found between the number of teeth present and the degree of dysfunction in swallowing liquid in the LHG (P=0.05). There were differences in the performance in swallowing solids between individuals without prosthesis and those with partial prosthesis in the inferior dental arch (P=0.04) for the HG.\n\nConclusions:\nThe need for replacement prostheses, type of prostheses, and the number of teeth of elderly patients poststroke in chronic phase showed an association with the level of oral intake and the degree of oropharyngeal dysphagia."},"background_conclusion_text":{"kind":"string","value":"Introduction:\nThe process of swallowing physiologically changes with aging. The loss of teeth, very common in this population, is related to the reduction of bone tissue, receptors (proprioceptors and periodontal ligaments), and muscle atrophy. Consequently, orofacial functions are impaired in toothless individuals.1 In addition, prostheses are functionally less efficient when compared to individuals with natural dentition.1,2\nThe stability of the jaw occlusion of the posterior teeth or prostheses is important for the swallowing function.2 The dental condition and the use of poorly fitting prostheses may contribute to the difficulties arising from old age, and the oral diet and nutritional status of the elderly may be affected by changes related to aging3 and changes in skill and desire to feel, bite, chew4 and swallow food,3,5 significantly compromising and further aggravating the quality of life of these individuals.6\nThe literature has shown the influence of oral health condition on swallowing and nutrition, indirectly influencing the daily life activities of the elderly.7 Furthermore, it is clear that maintenance of oral health is essential for the general health, quality of life, chewing ability8 and, mainly, the reduction of pneumonia risk in fragile elderly people,8,9 for it is already known that bacterial colonies in oropharyngeal tissues and in dental plaque are the greatest precursors for the development of aspiration respiratory infections.10\nBesides the influence of the loss of dentition, the functional impairments resulting from aging may be potentiated by neurological changes resulting in dysphagia in patients, an important cause of morbidity and mortality in this population.11\nIn the adult and elderly population, dysphagia is most commonly associated with stroke.12 More than half of patients who have suffered stroke present between six and ten types of disability, muscle weakness being the most prevalent one, followed by communication, speech, and swallowing disorders.13 Specifically regarding swallowing, the stroke entails increased oropharyngeal transit time,14,15 changes in motor control of tongue,16 as well as the presence of laryngotracheal aspiration of food.16,17\nOnce the physiological changes resulting from aging in swallowing may be aggravated by loss of teeth and the use of poorly fitting prostheses, the severity of dysphagia could be influenced by the oral health condition and use of dental prostheses in elderly patients with neurological diseases. However, according to the literature studied, no research has made the correlation between the findings of the swallowing performance with oral health in elderly patients after stroke. Therefore, the aim of this study was to relate the condition of oral health to the level of oral intake and the degree of swallowing dysfunction in elderly patients with stroke in chronic phase.\n\nConclusion:\nThe findings of this study indicate that the oral health condition of elderly individuals after stroke in chronic phase showed an association with the level of oral intake and the degree of oropharyngeal dysphagia. The limitations of the study should be considered, one being the lack of access to the imaging examinations to characterize the individuals according to the type of stroke, damaged areas, and extent of injury, and heterogeneity of the time of brain involvement, in addition to the effects of medications and other comorbidities, which were not taken into account. Also, owing to the small sample, strong correlations could not be observed, and a cause/effect relationship cannot be determined.\nDespite the limitations of this study, and the fact that hemiplegia does not necessarily represent a direct relationship with the injured brain hemisphere contralaterally, since cerebellum lesions may cause ipsilateral motor damages,31 the findings indicated that the level of oral intake was influenced by the condition or type of dental prostheses, regardless of the side of the body affected. On the other hand, the number of teeth were demonstrated to be related to the level of dysphagia in individuals with right hemiplegia, which shows that the oral health condition differentially affects the physiological responses of swallowing, influenced by the brain damage.\nConsidering the present sample, further research is necessary pertaining to the oral condition and the neurological involvement of individuals. In addition, these issues demonstrate the need for an interdisciplinary research team comprising dentists, physicians, speech pathologists, and other health professionals, so as to study and define the treatment and guidelines to patients and caretakers, in order to maintain the best oral health condition in the elderly presented with oropharyngeal dysphagia."},"background_conclusion_abstract":{"kind":"string","value":"Background:\nAccording to the literature, the occurrence of dysphagia is high in cases of stroke, and its severity can be enhanced by loss of teeth and the use of poorly fitting prostheses.\n\nMethods:\nThirty elderly individuals affected by stroke in chronic phase participated. All subjects underwent assessment of their oral condition, with classification from the Functional Oral Intake Scale (FOIS) and nasoendoscopic swallowing assessment to classify the degree of dysphagia. The statistical analysis examined a heterogeneous group (HG, n=30) and two groups designated by the affected body part, right (RHG, n=8) and left (LHG, n=11), excluding totally dentate or edentulous individuals without rehabilitation with more than one episode of stroke.\n\nResults:\nThere was a negative correlation between the need for replacement prostheses and the FOIS scale for the HG (P=0.02) and RHG (P=0.01). Differences in FOIS between types of prostheses of the upper dental arch in the LHG (P=0.01) and lower dental arch in the RHG (P=0.04). A negative correlation was found between the number of teeth present and the degree of dysfunction in swallowing liquid in the LHG (P=0.05). There were differences in the performance in swallowing solids between individuals without prosthesis and those with partial prosthesis in the inferior dental arch (P=0.04) for the HG.\n\nConclusions:\nThe need for replacement prostheses, type of prostheses, and the number of teeth of elderly patients poststroke in chronic phase showed an association with the level of oral intake and the degree of oropharyngeal dysphagia."},"whole_article_text_length":{"kind":"number","value":7294,"string":"7,294"},"whole_article_abstract_length":{"kind":"number","value":293,"string":"293"},"other_sections_lengths":{"kind":"list like","value":[2801,1198,202,70,181,316],"string":"[\n 2801,\n 1198,\n 202,\n 70,\n 181,\n 316\n]"},"num_sections":{"kind":"number","value":11,"string":"11"},"most_frequent_words":{"kind":"list like","value":["dental","oral","swallowing","prostheses","prosthesis","need","scale","dental prosthesis","health","arches"],"string":"[\n \"dental\",\n \"oral\",\n \"swallowing\",\n \"prostheses\",\n \"prosthesis\",\n \"need\",\n \"scale\",\n \"dental prosthesis\",\n \"health\",\n \"arches\"\n]"},"keybert_topics":{"kind":"list like","value":["chewing ability elderly","teeth swallowing performance","age oral diet","swallowing function dental","oral health elderly"],"string":"[\n \"chewing ability elderly\",\n \"teeth swallowing performance\",\n \"age oral diet\",\n \"swallowing function dental\",\n \"oral health elderly\"\n]"},"annotated_base_background_abstract_prompt":{"kind":"string","value":""},"annotated_base_methods_abstract_prompt":{"kind":"string","value":""},"annotated_base_results_abstract_prompt":{"kind":"string","value":""},"annotated_base_conclusions_abstract_prompt":{"kind":"string","value":""},"annotated_base_whole_article_abstract_prompt":{"kind":"string","value":""},"annotated_base_background_conclusion_abstract_prompt":{"kind":"string","value":""},"annotated_keywords_background_abstract_prompt":{"kind":"string","value":"[CONTENT] deglutition | mouth rehabilitation | aged | prosthodontics | dysphagia | cerebrovascular disorders [SUMMARY]"},"annotated_keywords_methods_abstract_prompt":{"kind":"string","value":"[CONTENT] deglutition | mouth rehabilitation | aged | prosthodontics | dysphagia | cerebrovascular disorders [SUMMARY]"},"annotated_keywords_results_abstract_prompt":{"kind":"string","value":"[CONTENT] deglutition | mouth rehabilitation | aged | prosthodontics | dysphagia | cerebrovascular disorders [SUMMARY]"},"annotated_keywords_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] deglutition | mouth rehabilitation | aged | prosthodontics | dysphagia | cerebrovascular disorders [SUMMARY]"},"annotated_keywords_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] deglutition | mouth rehabilitation | aged | prosthodontics | dysphagia | cerebrovascular disorders [SUMMARY]"},"annotated_keywords_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] deglutition | mouth rehabilitation | aged | prosthodontics | dysphagia | cerebrovascular disorders [SUMMARY]"},"annotated_mesh_background_abstract_prompt":{"kind":"string","value":"[CONTENT] Aged | Aged, 80 and over | Brazil | Chronic Disease | Deglutition | Deglutition Disorders | Dental Prosthesis | Diagnosis, Oral | Endoscopy, Gastrointestinal | Female | Humans | Male | Mouth Rehabilitation | Oral Health | Outcome Assessment, Health Care | Risk Factors | Severity of Illness Index | Stroke | Tooth Loss [SUMMARY]"},"annotated_mesh_methods_abstract_prompt":{"kind":"string","value":"[CONTENT] Aged | Aged, 80 and over | Brazil | Chronic Disease | Deglutition | Deglutition Disorders | Dental Prosthesis | Diagnosis, Oral | Endoscopy, Gastrointestinal | Female | Humans | Male | Mouth Rehabilitation | Oral Health | Outcome Assessment, Health Care | Risk Factors | Severity of Illness Index | Stroke | Tooth Loss [SUMMARY]"},"annotated_mesh_results_abstract_prompt":{"kind":"string","value":"[CONTENT] Aged | Aged, 80 and over | Brazil | Chronic Disease | Deglutition | Deglutition Disorders | Dental Prosthesis | Diagnosis, Oral | Endoscopy, Gastrointestinal | Female | Humans | Male | Mouth Rehabilitation | Oral Health | Outcome Assessment, Health Care | Risk Factors | Severity of Illness Index | Stroke | Tooth Loss [SUMMARY]"},"annotated_mesh_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] Aged | Aged, 80 and over | Brazil | Chronic Disease | Deglutition | Deglutition Disorders | Dental Prosthesis | Diagnosis, Oral | Endoscopy, Gastrointestinal | Female | Humans | Male | Mouth Rehabilitation | Oral Health | Outcome Assessment, Health Care | Risk Factors | Severity of Illness Index | Stroke | Tooth Loss [SUMMARY]"},"annotated_mesh_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] Aged | Aged, 80 and over | Brazil | Chronic Disease | Deglutition | Deglutition Disorders | Dental Prosthesis | Diagnosis, Oral | Endoscopy, Gastrointestinal | Female | Humans | Male | Mouth Rehabilitation | Oral Health | Outcome Assessment, Health Care | Risk Factors | Severity of Illness Index | Stroke | Tooth Loss [SUMMARY]"},"annotated_mesh_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] Aged | Aged, 80 and over | Brazil | Chronic Disease | Deglutition | Deglutition Disorders | Dental Prosthesis | Diagnosis, Oral | Endoscopy, Gastrointestinal | Female | Humans | Male | Mouth Rehabilitation | Oral Health | Outcome Assessment, Health Care | Risk Factors | Severity of Illness Index | Stroke | Tooth Loss [SUMMARY]"},"annotated_keybert_background_abstract_prompt":{"kind":"string","value":"[CONTENT] chewing ability elderly | teeth swallowing performance | age oral diet | swallowing function dental | oral health elderly [SUMMARY]"},"annotated_keybert_methods_abstract_prompt":{"kind":"string","value":"[CONTENT] chewing ability elderly | teeth swallowing performance | age oral diet | swallowing function dental | oral health elderly [SUMMARY]"},"annotated_keybert_results_abstract_prompt":{"kind":"string","value":"[CONTENT] chewing ability elderly | teeth swallowing performance | age oral diet | swallowing function dental | oral health elderly [SUMMARY]"},"annotated_keybert_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] chewing ability elderly | teeth swallowing performance | age oral diet | swallowing function dental | oral health elderly [SUMMARY]"},"annotated_keybert_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] chewing ability elderly | teeth swallowing performance | age oral diet | swallowing function dental | oral health elderly [SUMMARY]"},"annotated_keybert_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] chewing ability elderly | teeth swallowing performance | age oral diet | swallowing function dental | oral health elderly [SUMMARY]"},"annotated_most_frequent_background_abstract_prompt":{"kind":"string","value":"[CONTENT] dental | oral | swallowing | prostheses | prosthesis | need | scale | dental prosthesis | health | arches [SUMMARY]"},"annotated_most_frequent_methods_abstract_prompt":{"kind":"string","value":"[CONTENT] dental | oral | swallowing | prostheses | prosthesis | need | scale | dental prosthesis | health | arches [SUMMARY]"},"annotated_most_frequent_results_abstract_prompt":{"kind":"string","value":"[CONTENT] dental | oral | swallowing | prostheses | prosthesis | need | scale | dental prosthesis | health | arches [SUMMARY]"},"annotated_most_frequent_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] dental | oral | swallowing | prostheses | prosthesis | need | scale | dental prosthesis | health | arches [SUMMARY]"},"annotated_most_frequent_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] dental | oral | swallowing | prostheses | prosthesis | need | scale | dental prosthesis | health | arches [SUMMARY]"},"annotated_most_frequent_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] dental | oral | swallowing | prostheses | prosthesis | need | scale | dental prosthesis | health | arches [SUMMARY]"},"annotated_tf_idf_background_abstract_prompt":{"kind":"string","value":"[CONTENT] changes | elderly | swallowing | life | resulting | oral | stroke | patients | loss | population [SUMMARY]"},"annotated_tf_idf_methods_abstract_prompt":{"kind":"string","value":"[CONTENT] test | fois | dental | dysphagia | fois scale | classification | dental prosthesis | prosthesis | scale | prostheses [SUMMARY]"},"annotated_tf_idf_results_abstract_prompt":{"kind":"string","value":"[CONTENT] individuals | table | fois | arch | scale | shown table | prostheses | fois scale | dental arch | difference [SUMMARY]"},"annotated_tf_idf_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] brain | oral | condition | individuals | study | level | condition elderly | oropharyngeal dysphagia | relationship | involvement [SUMMARY]"},"annotated_tf_idf_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] dental | oral | swallowing | prosthesis | prostheses | scale | dental prosthesis | need | fois | dental arches [SUMMARY]"},"annotated_tf_idf_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] dental | oral | swallowing | prosthesis | prostheses | scale | dental prosthesis | need | fois | dental arches [SUMMARY]"},"annotated_entity_plan_background_abstract_prompt":{"kind":"string","value":"[CONTENT] dysphagia [SUMMARY]"},"annotated_entity_plan_methods_abstract_prompt":{"kind":"string","value":"[CONTENT] ||| the Functional Oral Intake Scale | FOIS | dysphagia ||| HG | two | RHG | LHG | n=11 | more than one [SUMMARY]"},"annotated_entity_plan_results_abstract_prompt":{"kind":"string","value":"[CONTENT] FOIS | HG | RHG ||| FOIS | LHG | RHG ||| LHG ||| HG [SUMMARY]"},"annotated_entity_plan_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] dysphagia [SUMMARY]"},"annotated_entity_plan_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] ||| dysphagia ||| ||| the Functional Oral Intake Scale | FOIS | dysphagia ||| HG | two | RHG | LHG | n=11 | more than one ||| FOIS | HG | RHG ||| FOIS | LHG | RHG ||| LHG ||| HG ||| dysphagia [SUMMARY]"},"annotated_entity_plan_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] ||| dysphagia ||| ||| the Functional Oral Intake Scale | FOIS | dysphagia ||| HG | two | RHG | LHG | n=11 | more than one ||| FOIS | HG | RHG ||| FOIS | LHG | RHG ||| LHG ||| HG ||| dysphagia [SUMMARY]"}}},{"rowIdx":291,"cells":{"title":{"kind":"string","value":"Longitudinal Relationship between Cognitive Function and Health-Related Quality of Life among Middle-Aged and Older Patients with Diabetes in China: Digital Usage Behavior Differences."},"pmid":{"kind":"string","value":"36231699"},"background_abstract":{"kind":"string","value":"Cognitive function and health-related quality of life (HRQoL) are important issues in diabetes care. According to the China Association for Aging, it is estimated that by 2030, the number of elderly people with dementia in China will reach 22 million. The World Health Organization reports that by 2044, the number of people with diabetes in China is expected to reach 175 million."},"background_abstract_label":{"kind":"string","value":"BACKGROUND"},"methods_abstract":{"kind":"string","value":"Cohort analyses were conducted based on 854 diabetic patients aged ≥45 years from the third (2015) and fourth (2018) survey of the China Health and Retirement Longitudinal Study (CHARLS). Correlation analysis, repeated-measures variance analysis, and cross-lagged panel models were used to measure the difference in digital usage behavior in the established relationship."},"methods_abstract_label":{"kind":"string","value":"METHODS"},"results_abstract":{"kind":"string","value":"The results show that the cognitive function of middle-aged and older diabetic patients is positively correlated with HRQoL. HRQoL at T1 could significantly predict cognitive function at T2 (PCS: B = 0.12, p < 0.01; MCS: B = 0.14, p < 0.01). This relationship is more associated with individual performance than digital usage behavior."},"results_abstract_label":{"kind":"string","value":"RESULTS"},"conclusions_abstract":{"kind":"string","value":"Unidirectional associations may exist between cognitive function and HRQoL among middle-aged and older Chinese diabetes patients. In the future, doctors and nurses can recognize the lowering of self-perceived HRQoL of middle-aged and older diabetic patients, and thus draw more attention to their cognitive function, in turn strengthening the evaluation, detection, and intervention of their cognitive function."},"conclusions_abstract_label":{"kind":"string","value":"CONCLUSIONS"},"mesh_descriptor_names":{"kind":"list like","value":["Aged","China","Cognition","Diabetes Mellitus","Humans","Longitudinal Studies","Middle Aged","Quality of Life"],"string":"[\n \"Aged\",\n \"China\",\n \"Cognition\",\n \"Diabetes Mellitus\",\n \"Humans\",\n \"Longitudinal Studies\",\n \"Middle Aged\",\n \"Quality of Life\"\n]"},"pmcid":{"kind":"string","value":"9566018"},"background_title":{"kind":"string","value":"1. Introduction"},"background_text":{"kind":"string","value":"Cognitive impairment is one of the common complications in elderly patients [1]. For example, in China, the prevalence of mild cognitive impairments in the aging population (aged 60 and above) is 14.71% [2], and as age increases, its annual rate of progression to dementia is between 8% and 15% [2]. At the same time, cognitive impairment is also one of the common complications of diabetic patients. It is estimated that by 2045, about 170 million elderly people in China will be diabetic patients [3]. Therefore, the academic community has paid great attention to the cognitive function of the diabetic population, and found that the cognitive function of the diabetic population is closely related to the quality of life (QoL) of the elderly [4].\nHRQoL is the perceived physical and mental health of an individual or group over time, including both physical component summary (PCS) and mental component summary (MCS) [5]. In contrast with the QoL, HRQoL pays special attention to the impact of disease and treatment process on the life of a person or a group [6]. Existing studies have found that changes in cognitive function are positively correlated with changes in HRQoL, and play a predictive role in future changes in HRQoL [7,8]. For example, cognitive decline was found to be a predictor of HRQoL decline in studies on multiple sclerosis patients, AIDS patients, and older women [4,9,10]. At the same time, some scholars have found that changes in HRQoL—whether it is the PCS or the MCS of HRQoL—can also predict cognitive changes in individuals in the future [11]. For example, Ezzati’s 2019 study demonstrated that changes in HRQoL preceded changes in cognition and predicted the occurrence of dementia [12]. Thus, cognitive function may be bi-directionally associated with HRQoL.\nThe bidirectional relationship between cognitive function and HRQoL may be more pronounced in terms of diabetic patients. This is because not only are people with diabetes 1–2 times more likely to develop cognitive risks than the general population [13,14], but this risk will increase over time [15]. At the same time, with the deepening of the research on HRQoL, the medical community generally believes that the core of diabetes management should include HRQoL maintenance in addition to prevention and delay of its complications [16]. In summary, this research aims to focus on middle-aged and older diabetic patients (over 45 years old) in China—not only because China has one of the largest numbers of diabetic patients in the world, but also because the age of the population affected with diabetes is showing a downward trend [3,17]. Thus, we propose the first hypothesis: in middle-aged and older Chinese patients with diabetes, the relationship between cognitive function and HRQoL may be bidirectional.\nTo our knowledge, previous studies on cognitive function and HRQoL have focused on unmodifiable clinical factors, such as age, gender, etc., with a lack of studies on modifiable factors [4]. Digital technologies such as the Internet and smartphones have attracted the attention of psychologists because of their portability, rapidity, and immediacy. In clinical medicine, digital usage behaviors are often used for managing and intervening with patients with diabetes and cognitive dysfunction [18]. Thus, digital technology may be seen as a modifiable factor in the relationship between cognitive function and HRQoL. We also found that with the continuous development and improvement of digital technology, digital usage behavior can have a profound impact on the cognitive function of patients with chronic diseases [18,19,20]. In addition to this, it is important that digital usage behavior, such as Internet use, can also have a certain impact on HRQoL [21]. Previous research in other populations found that using the Internet resulted in significantly higher PCS and physical pain scores [22]. According to self-determination theory, we believe that when individuals use the internet for leisure or work, their needs are met, which in turn produces positive long-term psychological outcomes such as quality of life [23]. So far, existing research is leaning towards the belief that an increase in digital usage behavior can improve the health of individuals [24]. However, there are also studies holding the opposite view; they believe that the long-term digital usage behavior reduces patients’ time for outdoor activities, and that a large amount of negative information received due to digital usage behavior is more likely to cause mood swings, which may have side effects on patients’ recovery [25]. Therefore, it is easy to find that digital usage behavior is likely to have various effects, so we propose another hypothesis: in middle-aged and older Chinese patients with diabetes, the relationship between cognitive function and HRQoL may differ considering digital usage behavior.\nTo date, most studies on the association of cognitive function with HRQoL have used cross-sectional designs, and have failed to elucidate the direction of influence between cognitive function and HRQoL due to the limitations of cross-sectional studies in terms of making causal inferences and explaining the direction of association. Therefore, it is necessary to further explore, especially for diabetic patients, the longitudinal association between the cognitive function and HRQoL, and to clarify the direction of their effects. As a longitudinal study, this study attempts to use a cross-lag model to explore the relationship between cognitive function and HRQoL in Chinese middle-aged and older diabetic patients, as well as the direction of this relationship and whether there were differences in digital usage behavior. This research is attempting to explore the longitudinal relationship between cognition and HRQoL in middle-aged and older diabetic patients, and to provide evidence which can promote their health.\nIt should be noted that, due to the existence of multiple databases in China, in addition to the China Health and Retirement Longitudinal Survey (CHARLS), most scholars use databases such as the China Household Finance Survey when researching topics related to public health [26]. There is more content regarding the physical and mental health of middle-aged and elderly people, so we chose CHARLS as our data source."},"methods_title":{"kind":"null"},"methods_text":{"kind":"null"},"results_title":{"kind":"string","value":"3. Results"},"results_text":{"kind":"string","value":"3.1. Descriptive Statistics The variable descriptive statistics and correlation analysis results are shown in Table 3 and Table 4. The correlation analysis shows that there is a significant correlation between cognition at T1 and T2 and HRQoL levels at T1 and T2, indicating that cognitive function and HRQoL levels in middle-aged and older diabetic patients have a certain relationship, which is showing stability. Meanwhile, simultaneous and sequential correlations between cognitive function and HRQoL levels are also significant. The correlation coefficients between cognitive function and HRQoL at T1 are 0.28 (p < 0.01) and 0.37 (p < 0.01); the correlation coefficients between cognitive function at T2 and HRQoL at T2 are 0.32 (p < 0.01) and 0.30 (p < 0.01); the correlation coefficients between cognitive function at T1 and HRQoL at T2 are 0.25 (p < 0.01) and 0.22 (p < 0.01); and the correlation coefficients between HRQoL at T1 and cognitive function at T2 are 0.32 (p < 0.01) and 0.36 (p < 0.01). This indicates that cognitive function is basically consistent with the synchronous and stable correlations of HRQoL levels, which is in line with the basic assumptions of the cross-lag design.\nThe variable descriptive statistics and correlation analysis results are shown in Table 3 and Table 4. The correlation analysis shows that there is a significant correlation between cognition at T1 and T2 and HRQoL levels at T1 and T2, indicating that cognitive function and HRQoL levels in middle-aged and older diabetic patients have a certain relationship, which is showing stability. Meanwhile, simultaneous and sequential correlations between cognitive function and HRQoL levels are also significant. The correlation coefficients between cognitive function and HRQoL at T1 are 0.28 (p < 0.01) and 0.37 (p < 0.01); the correlation coefficients between cognitive function at T2 and HRQoL at T2 are 0.32 (p < 0.01) and 0.30 (p < 0.01); the correlation coefficients between cognitive function at T1 and HRQoL at T2 are 0.25 (p < 0.01) and 0.22 (p < 0.01); and the correlation coefficients between HRQoL at T1 and cognitive function at T2 are 0.32 (p < 0.01) and 0.36 (p < 0.01). This indicates that cognitive function is basically consistent with the synchronous and stable correlations of HRQoL levels, which is in line with the basic assumptions of the cross-lag design.\n3.2. Stability Analysis of Cognitive Function and HRQoL With cognitive function as the dependent variable, a 2 (test time: T1/T2) × 2 (digital usage behavior: use/non-use) repeated measures square analysis was performed. The results show that the testing time is the main and most significant effect (F = 11.21, p < 0.001, η2 = 0.01). The cognitive function level at T2 is significantly lower than at T1, and there are developmental differences. The main effect of digital usage behavior is significant (F = 36.325, p < 0.001, η2 = 0.06), and the cognitive function of patients using digital technology is significantly better than those patients without digital technology. The interaction between the two is not significant (F = 2.22, p > 0.05, η2 = 0.04).\nWith PCS as the dependent variable, a 2 (test time: T1/T2) × 2 (digital usage behavior: use/non-use) repeated measures square analysis was performed. The results show that the testing time is the main and most significant effect (F =16.25, p < 0.001, η2 = 0.03), the PCS level of the post-test is significantly lower than that of the pre-test, and there is a developmental difference. The main effect of digital usage behavior is significant (F = 36.54, p < 0.001, η2 = 0.06), and the PCS of patients using digital technology is significantly better than those not using digital technology. The interaction between the two is not significant (F = 0.18, p > 0.05, η2 = 0.00).\nWith MCS as the dependent variable, a 2 (test time: T1/T2) × 2 (digital usage behavior: use/non-use) repeated measures square analysis was performed. The results showed that the testing time is the main and most significant effect (F = 44.93, p < 0.001, η2 = 0.07), the MCS level of the post-test is significantly lower than the pre-test, and there is a developmental difference. The main effect of digital usage behavior is significant (F = 50.97, p < 0.001, η2 = 0.08), and the MCS of patients using digital technology is significantly better than those not using digital technology. The interaction between the two is significant (F = 4.43, p < 0.05, η2 = 0.08).\nWith cognitive function as the dependent variable, a 2 (test time: T1/T2) × 2 (digital usage behavior: use/non-use) repeated measures square analysis was performed. The results show that the testing time is the main and most significant effect (F = 11.21, p < 0.001, η2 = 0.01). The cognitive function level at T2 is significantly lower than at T1, and there are developmental differences. The main effect of digital usage behavior is significant (F = 36.325, p < 0.001, η2 = 0.06), and the cognitive function of patients using digital technology is significantly better than those patients without digital technology. The interaction between the two is not significant (F = 2.22, p > 0.05, η2 = 0.04).\nWith PCS as the dependent variable, a 2 (test time: T1/T2) × 2 (digital usage behavior: use/non-use) repeated measures square analysis was performed. The results show that the testing time is the main and most significant effect (F =16.25, p < 0.001, η2 = 0.03), the PCS level of the post-test is significantly lower than that of the pre-test, and there is a developmental difference. The main effect of digital usage behavior is significant (F = 36.54, p < 0.001, η2 = 0.06), and the PCS of patients using digital technology is significantly better than those not using digital technology. The interaction between the two is not significant (F = 0.18, p > 0.05, η2 = 0.00).\nWith MCS as the dependent variable, a 2 (test time: T1/T2) × 2 (digital usage behavior: use/non-use) repeated measures square analysis was performed. The results showed that the testing time is the main and most significant effect (F = 44.93, p < 0.001, η2 = 0.07), the MCS level of the post-test is significantly lower than the pre-test, and there is a developmental difference. The main effect of digital usage behavior is significant (F = 50.97, p < 0.001, η2 = 0.08), and the MCS of patients using digital technology is significantly better than those not using digital technology. The interaction between the two is significant (F = 4.43, p < 0.05, η2 = 0.08).\n3.3. Cross-Lagged Analysis of Cognitive Function and HRQoL In the cross-lag model which this study applied, HRQoL at T2 was predicted by cognitive function at T1, and cognitive function at T2 was predicted by HRQoL at T1. After the corresponding control variables were added to the models, both models achieved acceptable fitness criteria (Model 1: RMSEA = 0.093; CFI = 0.994, TCL = 0.912; Model 2: RMSEA = 0.098; CFI = 0.984, TCL = 0.901).\nThe cross-lag relationship between cognitive function and PCS scores at two points in time is shown from the test of Model 1 in Figure 2. First, in the same time period, the baseline association between cognitive function and PCS is significantly positive (B = 0.19, p < 0.01), implying better cognitive performance in middle-aged and older diabetic patients with higher PCS scores at T1, and vice versa. Second, there is no significant correlation between cognitive function at T1 and PCS at T2 (B = 0.05, p > 0.01), but PCS at T1 is positively correlated with cognitive function at T2 (B = 0.12, p < 0.01). This suggests that there is a positive and significant relationship between PCS and cognitive function in middle-aged and older diabetic patients over time, but early cognitive function in middle-aged and older diabetic patients does not affect later PSC.\nThe cross-lag relationship between cognitive function and MCS scores at the two points in time is shown in Model 2 of Figure 2. First, in the same time period, the baseline association between cognitive function and MCS is significantly positively correlated (B = 0.31, p < 0.01), which implies a better cognitive performance in middle-aged and older diabetic patients with higher MCS scores at T1, and vice versa. Second, there is no significant correlation between cognitive function at T1 and MCS at T2 (B = 0.05, p > 0.01), but there is a significant positive correlation between MCS at T1 and cognitive function at T2 (B = 0.14, p < 0.01). This suggests that there is a positive and significant relationship between MCS and cognitive function in middle-aged and older diabetic patients over time, but early cognitive function in middle-aged and older diabetic patients does not affect later MCS. In conclusion, there may be a one-way causal relationship between cognitive function and HRQoL, and the causal direction is from HRQoL to cognition.\nIn the cross-lag model which this study applied, HRQoL at T2 was predicted by cognitive function at T1, and cognitive function at T2 was predicted by HRQoL at T1. After the corresponding control variables were added to the models, both models achieved acceptable fitness criteria (Model 1: RMSEA = 0.093; CFI = 0.994, TCL = 0.912; Model 2: RMSEA = 0.098; CFI = 0.984, TCL = 0.901).\nThe cross-lag relationship between cognitive function and PCS scores at two points in time is shown from the test of Model 1 in Figure 2. First, in the same time period, the baseline association between cognitive function and PCS is significantly positive (B = 0.19, p < 0.01), implying better cognitive performance in middle-aged and older diabetic patients with higher PCS scores at T1, and vice versa. Second, there is no significant correlation between cognitive function at T1 and PCS at T2 (B = 0.05, p > 0.01), but PCS at T1 is positively correlated with cognitive function at T2 (B = 0.12, p < 0.01). This suggests that there is a positive and significant relationship between PCS and cognitive function in middle-aged and older diabetic patients over time, but early cognitive function in middle-aged and older diabetic patients does not affect later PSC.\nThe cross-lag relationship between cognitive function and MCS scores at the two points in time is shown in Model 2 of Figure 2. First, in the same time period, the baseline association between cognitive function and MCS is significantly positively correlated (B = 0.31, p < 0.01), which implies a better cognitive performance in middle-aged and older diabetic patients with higher MCS scores at T1, and vice versa. Second, there is no significant correlation between cognitive function at T1 and MCS at T2 (B = 0.05, p > 0.01), but there is a significant positive correlation between MCS at T1 and cognitive function at T2 (B = 0.14, p < 0.01). This suggests that there is a positive and significant relationship between MCS and cognitive function in middle-aged and older diabetic patients over time, but early cognitive function in middle-aged and older diabetic patients does not affect later MCS. In conclusion, there may be a one-way causal relationship between cognitive function and HRQoL, and the causal direction is from HRQoL to cognition.\n3.4. Heterogeneity Analysis In order to investigate whether the cross-lag relationship between cognitive function and HRQoL differs in digital usage behavior, the study performed a multi-group analysis. All diabetic patients were divided into two groups, one representing non-digital usage behavior (Figure 3), and the other representing digital usage behavior (Figure 4). In the grouped cross-lag model, the corresponding control variables were also added to the model. The models in Figure 3 and Figure 4 met fitness criteria (Figure 3: Model 3: RMSEA = 0.073, CFI = 0.996, TCL = 0.901; Model 4: RMSEA = 0.100, CFI = 0.990, TCL = 0.936. Figure 4: Model 5: RMSEA = 0.073, CFI = 0.996, TCL = 0.941; Model 6: RMSEA = 0.079, CFI = 0.970, TCL = 0.936). In both sets of models, the study focused on the relationship between cognitive function and HRQoL at a point in time, as well as their relationship at the time of follow-up research.\nFor middle-aged and older diabetic patients using digital technology, although the model could be fitted, cognitive function at T1 did not significantly predict HRQoL at T2 (PCS: B = 0.30, p > 0.01; MCS: B = 0.14, p > 0.01), and vice versa (PCS: B = 0.06, p > 0.01; MCS: B = 0.05, p > 0.01) (see Figure 3). For diabetic patients who did not use digital technology, although T1 cognitive function did not significantly predict T2 HRQoL (PCS: B = 0.04, p > 0.01; MCS: B = 0.05, p > 0.01), T1 HRQoL significantly predicted T2 cognitive function (PCS: B = 0.10, p < 0.01; MCS: B = 0.11, p < 0.01), and the results were consistent across all diabetic patients (see Figure 4). This shows that the cross-lag model of cognitive function and HRQoL in middle-aged and older diabetic patients shows variance in the use of digital technology, that is, this relationship will be affected by digital usage behavior.\nIn order to investigate whether the cross-lag relationship between cognitive function and HRQoL differs in digital usage behavior, the study performed a multi-group analysis. All diabetic patients were divided into two groups, one representing non-digital usage behavior (Figure 3), and the other representing digital usage behavior (Figure 4). In the grouped cross-lag model, the corresponding control variables were also added to the model. The models in Figure 3 and Figure 4 met fitness criteria (Figure 3: Model 3: RMSEA = 0.073, CFI = 0.996, TCL = 0.901; Model 4: RMSEA = 0.100, CFI = 0.990, TCL = 0.936. Figure 4: Model 5: RMSEA = 0.073, CFI = 0.996, TCL = 0.941; Model 6: RMSEA = 0.079, CFI = 0.970, TCL = 0.936). In both sets of models, the study focused on the relationship between cognitive function and HRQoL at a point in time, as well as their relationship at the time of follow-up research.\nFor middle-aged and older diabetic patients using digital technology, although the model could be fitted, cognitive function at T1 did not significantly predict HRQoL at T2 (PCS: B = 0.30, p > 0.01; MCS: B = 0.14, p > 0.01), and vice versa (PCS: B = 0.06, p > 0.01; MCS: B = 0.05, p > 0.01) (see Figure 3). For diabetic patients who did not use digital technology, although T1 cognitive function did not significantly predict T2 HRQoL (PCS: B = 0.04, p > 0.01; MCS: B = 0.05, p > 0.01), T1 HRQoL significantly predicted T2 cognitive function (PCS: B = 0.10, p < 0.01; MCS: B = 0.11, p < 0.01), and the results were consistent across all diabetic patients (see Figure 4). This shows that the cross-lag model of cognitive function and HRQoL in middle-aged and older diabetic patients shows variance in the use of digital technology, that is, this relationship will be affected by digital usage behavior."},"conclusions_title":{"kind":"string","value":"5. Conclusions"},"conclusions_text":{"kind":"string","value":"The cognitive function and HRQoL (PCS and MCS) in middle-aged and older Chinese diabetic patients have a certain degree of lateral stability, and both tend to increase over time. More importantly, this study found that HRQoL at T1 can significantly predict cognitive function at T2, but cognitive function at T1 cannot significantly predict HRQoL at T2. HRQoL may be an antecedent for middle-aged and older diabetic patients’ cognitive function, and this predictive relationship can be different depending on digital usage behavior. In the future, effective intervention on HRQoL of middle-aged and older diabetic patients can be considered to improve their cognitive function, thereby promoting the healthy development of middle-aged and older diabetic patients."},"other_sections_titles":{"kind":"list like","value":["2. Materials and Methods","2.2. Measurements","2.2.1. Cognitive Functioning","2.2.2. HRQoL","2.2.3. Socio-Demographic Variables","2.3. Statistical Methods","3.1. Descriptive Statistics","3.2. Stability Analysis of Cognitive Function and HRQoL","3.3. Cross-Lagged Analysis of Cognitive Function and HRQoL","3.4. Heterogeneity Analysis"],"string":"[\n \"2. Materials and Methods\",\n \"2.2. Measurements\",\n \"2.2.1. Cognitive Functioning\",\n \"2.2.2. HRQoL\",\n \"2.2.3. Socio-Demographic Variables\",\n \"2.3. Statistical Methods\",\n \"3.1. Descriptive Statistics\",\n \"3.2. Stability Analysis of Cognitive Function and HRQoL\",\n \"3.3. Cross-Lagged Analysis of Cognitive Function and HRQoL\",\n \"3.4. Heterogeneity Analysis\"\n]"},"other_sections_texts":{"kind":"list like","value":["2.1. Participants The China Health and Retirement Longitudinal Survey (CHARLS) is a representative follow-up survey of middle-aged and older people in China, chaired by the National Research Institute of Development at Peking University. The study protocol was approved by the Institutional Review Board of Peking University (approval number: IRB00001052-11015), and the study protocol complies with the ethical guidelines of the 1975 Declaration of Helsinki. In order to obtain the relevant data, we applied for the CHARLS database online on 31 August 2022, and approval was quickly obtained. This study used two waves of CHARLS data, from 2015 (T1) and 2018 (T2). CHARLS data can be accessed through its official website (https://charls.pku.edu.cn (accessed on 31 August 2022)).\nFigure 1 shows the detailed process for including and excluding study participants. Wave 3 was used as the baseline data set for this study, and individuals who lacked information on HRQoL, cognitive function, digital usage behavior, and covariates were excluded from this study. Subsequently, new participants in 2018 were further excluded. Individuals lacking information on digital usage behavior, cognitive function, and covariates were also excluded from this study. In addition, in the sample selection, the research team excluded individuals with other diseases (such as cognitive diseases that may affect the detection process, mental diseases, and other diseases related to aging), and included individuals with diabetes only. In addition, the abbreviations of the main variables involved in the study are organized in Table 1.\nThe China Health and Retirement Longitudinal Survey (CHARLS) is a representative follow-up survey of middle-aged and older people in China, chaired by the National Research Institute of Development at Peking University. The study protocol was approved by the Institutional Review Board of Peking University (approval number: IRB00001052-11015), and the study protocol complies with the ethical guidelines of the 1975 Declaration of Helsinki. In order to obtain the relevant data, we applied for the CHARLS database online on 31 August 2022, and approval was quickly obtained. This study used two waves of CHARLS data, from 2015 (T1) and 2018 (T2). CHARLS data can be accessed through its official website (https://charls.pku.edu.cn (accessed on 31 August 2022)).\nFigure 1 shows the detailed process for including and excluding study participants. Wave 3 was used as the baseline data set for this study, and individuals who lacked information on HRQoL, cognitive function, digital usage behavior, and covariates were excluded from this study. Subsequently, new participants in 2018 were further excluded. Individuals lacking information on digital usage behavior, cognitive function, and covariates were also excluded from this study. In addition, in the sample selection, the research team excluded individuals with other diseases (such as cognitive diseases that may affect the detection process, mental diseases, and other diseases related to aging), and included individuals with diabetes only. In addition, the abbreviations of the main variables involved in the study are organized in Table 1.\n2.2. Measurements 2.2.1. Cognitive Functioning This study obtained data on cognitive function from baseline data in 2015 and follow-up data in 2018. The baseline data stipulated by CHARLS is from 2011, but we include CHARLS 2015 (T1) as the baseline data in our study. CHARLS is similar to the cognitive assessment used in the American Health and Retirement Study, and has constructed cognitive function evaluation criteria from the same two aspects of memory and mental state [27,28]. Furthermore, previous studies have also used the CHARLS cognitive function assessment criteria for class-correspondence studies. Firstly, memory evaluation includes immediate word recall (0–10 points) and delayed word recall (0–10 points). Mental state is measured from three dimensions: orientation, visual construction, and mathematical performance. Orientation (0–5 points) is measured by asking respondents to name the date, day of the week, and season; visual construction is assessed by drawing a previously displayed picture (0–1 points); and mathematical performance (0–5 points) is measured by asking respondents to subtract 7 consecutive times from 100. The scores of the participants’ cognitive function are equal to the sum of the scores of memory and mental state. Cognitive function scores range from 0 to 31 points, higher cognitive function scores indicating better cognitive function. Finally, in order to obtain a composite measure of cognitive function, this study normalized and averaged the total cognitive function score by adding up memory and mental state scores [29].\nThis study obtained data on cognitive function from baseline data in 2015 and follow-up data in 2018. The baseline data stipulated by CHARLS is from 2011, but we include CHARLS 2015 (T1) as the baseline data in our study. CHARLS is similar to the cognitive assessment used in the American Health and Retirement Study, and has constructed cognitive function evaluation criteria from the same two aspects of memory and mental state [27,28]. Furthermore, previous studies have also used the CHARLS cognitive function assessment criteria for class-correspondence studies. Firstly, memory evaluation includes immediate word recall (0–10 points) and delayed word recall (0–10 points). Mental state is measured from three dimensions: orientation, visual construction, and mathematical performance. Orientation (0–5 points) is measured by asking respondents to name the date, day of the week, and season; visual construction is assessed by drawing a previously displayed picture (0–1 points); and mathematical performance (0–5 points) is measured by asking respondents to subtract 7 consecutive times from 100. The scores of the participants’ cognitive function are equal to the sum of the scores of memory and mental state. Cognitive function scores range from 0 to 31 points, higher cognitive function scores indicating better cognitive function. Finally, in order to obtain a composite measure of cognitive function, this study normalized and averaged the total cognitive function score by adding up memory and mental state scores [29].\n2.2.2. HRQoL This study used a new scale construction based on the variables of the Short Form 36 (SF-36) and the CHARLS questionnaire to measure HRQoL in diabetic patients. The construction of the new scale was derived from the eight dimensions of SF-36, and the corresponding variables of CHARLS were selected to assess the following eight dimensions (Table 2): physical function (PF), role–body (RP), body pain (BP), general health (GH), vitality (VT), social functioning (SF), role–emotion (RE), and mental health (MH). The scores for the above eight dimensions were calculated by adding up the category scores and then converting the raw scores to a 0 to 100 scale. Scores from the eight subscales were aggregated into two overall scores according to the conceptual model of the SF-36 [30]. Physical function, body roles, body pain, and general perceptions of health were calculated as PCS, and mental health, vitality, emotional role, and social functioning were calculated as MCS. Although the HRQoL questionnaire based on CHARLS is slightly different from other HRQoL questionnaires, they all have similar focuses, including physical, emotional, and social diversity. The questionnaire has been determined to be effective in the Chinese population [31], and has already been used in related research [32].\nThis study used a new scale construction based on the variables of the Short Form 36 (SF-36) and the CHARLS questionnaire to measure HRQoL in diabetic patients. The construction of the new scale was derived from the eight dimensions of SF-36, and the corresponding variables of CHARLS were selected to assess the following eight dimensions (Table 2): physical function (PF), role–body (RP), body pain (BP), general health (GH), vitality (VT), social functioning (SF), role–emotion (RE), and mental health (MH). The scores for the above eight dimensions were calculated by adding up the category scores and then converting the raw scores to a 0 to 100 scale. Scores from the eight subscales were aggregated into two overall scores according to the conceptual model of the SF-36 [30]. Physical function, body roles, body pain, and general perceptions of health were calculated as PCS, and mental health, vitality, emotional role, and social functioning were calculated as MCS. Although the HRQoL questionnaire based on CHARLS is slightly different from other HRQoL questionnaires, they all have similar focuses, including physical, emotional, and social diversity. The questionnaire has been determined to be effective in the Chinese population [31], and has already been used in related research [32].\n2.2.3. Socio-Demographic Variables In order to minimize the possibility of other variables influencing the cognitive function–HRQoL relationship study, and to simplify the model, this research controlled for several specific covariates associated with cognitive function and HRQoL. According to previous studies, all covariates were based on baseline data (CHARLS 2015) [32]. Firstly, population control variables include age, gender, and education status. Secondly, since HRQoL can be divided into two parts, PCS and MCS, this study used different control variables in the models for cognitive function, PCS, and MCS. This research included depression as well as current smoking and drinking habits as control variables for PCS scores. In this research, PCS scores are also considered with marital status, depression, physical activity, and current smoking and drinking habits as control variables.\nIn order to minimize the possibility of other variables influencing the cognitive function–HRQoL relationship study, and to simplify the model, this research controlled for several specific covariates associated with cognitive function and HRQoL. According to previous studies, all covariates were based on baseline data (CHARLS 2015) [32]. Firstly, population control variables include age, gender, and education status. Secondly, since HRQoL can be divided into two parts, PCS and MCS, this study used different control variables in the models for cognitive function, PCS, and MCS. This research included depression as well as current smoking and drinking habits as control variables for PCS scores. In this research, PCS scores are also considered with marital status, depression, physical activity, and current smoking and drinking habits as control variables.\n2.2.1. Cognitive Functioning This study obtained data on cognitive function from baseline data in 2015 and follow-up data in 2018. The baseline data stipulated by CHARLS is from 2011, but we include CHARLS 2015 (T1) as the baseline data in our study. CHARLS is similar to the cognitive assessment used in the American Health and Retirement Study, and has constructed cognitive function evaluation criteria from the same two aspects of memory and mental state [27,28]. Furthermore, previous studies have also used the CHARLS cognitive function assessment criteria for class-correspondence studies. Firstly, memory evaluation includes immediate word recall (0–10 points) and delayed word recall (0–10 points). Mental state is measured from three dimensions: orientation, visual construction, and mathematical performance. Orientation (0–5 points) is measured by asking respondents to name the date, day of the week, and season; visual construction is assessed by drawing a previously displayed picture (0–1 points); and mathematical performance (0–5 points) is measured by asking respondents to subtract 7 consecutive times from 100. The scores of the participants’ cognitive function are equal to the sum of the scores of memory and mental state. Cognitive function scores range from 0 to 31 points, higher cognitive function scores indicating better cognitive function. Finally, in order to obtain a composite measure of cognitive function, this study normalized and averaged the total cognitive function score by adding up memory and mental state scores [29].\nThis study obtained data on cognitive function from baseline data in 2015 and follow-up data in 2018. The baseline data stipulated by CHARLS is from 2011, but we include CHARLS 2015 (T1) as the baseline data in our study. CHARLS is similar to the cognitive assessment used in the American Health and Retirement Study, and has constructed cognitive function evaluation criteria from the same two aspects of memory and mental state [27,28]. Furthermore, previous studies have also used the CHARLS cognitive function assessment criteria for class-correspondence studies. Firstly, memory evaluation includes immediate word recall (0–10 points) and delayed word recall (0–10 points). Mental state is measured from three dimensions: orientation, visual construction, and mathematical performance. Orientation (0–5 points) is measured by asking respondents to name the date, day of the week, and season; visual construction is assessed by drawing a previously displayed picture (0–1 points); and mathematical performance (0–5 points) is measured by asking respondents to subtract 7 consecutive times from 100. The scores of the participants’ cognitive function are equal to the sum of the scores of memory and mental state. Cognitive function scores range from 0 to 31 points, higher cognitive function scores indicating better cognitive function. Finally, in order to obtain a composite measure of cognitive function, this study normalized and averaged the total cognitive function score by adding up memory and mental state scores [29].\n2.2.2. HRQoL This study used a new scale construction based on the variables of the Short Form 36 (SF-36) and the CHARLS questionnaire to measure HRQoL in diabetic patients. The construction of the new scale was derived from the eight dimensions of SF-36, and the corresponding variables of CHARLS were selected to assess the following eight dimensions (Table 2): physical function (PF), role–body (RP), body pain (BP), general health (GH), vitality (VT), social functioning (SF), role–emotion (RE), and mental health (MH). The scores for the above eight dimensions were calculated by adding up the category scores and then converting the raw scores to a 0 to 100 scale. Scores from the eight subscales were aggregated into two overall scores according to the conceptual model of the SF-36 [30]. Physical function, body roles, body pain, and general perceptions of health were calculated as PCS, and mental health, vitality, emotional role, and social functioning were calculated as MCS. Although the HRQoL questionnaire based on CHARLS is slightly different from other HRQoL questionnaires, they all have similar focuses, including physical, emotional, and social diversity. The questionnaire has been determined to be effective in the Chinese population [31], and has already been used in related research [32].\nThis study used a new scale construction based on the variables of the Short Form 36 (SF-36) and the CHARLS questionnaire to measure HRQoL in diabetic patients. The construction of the new scale was derived from the eight dimensions of SF-36, and the corresponding variables of CHARLS were selected to assess the following eight dimensions (Table 2): physical function (PF), role–body (RP), body pain (BP), general health (GH), vitality (VT), social functioning (SF), role–emotion (RE), and mental health (MH). The scores for the above eight dimensions were calculated by adding up the category scores and then converting the raw scores to a 0 to 100 scale. Scores from the eight subscales were aggregated into two overall scores according to the conceptual model of the SF-36 [30]. Physical function, body roles, body pain, and general perceptions of health were calculated as PCS, and mental health, vitality, emotional role, and social functioning were calculated as MCS. Although the HRQoL questionnaire based on CHARLS is slightly different from other HRQoL questionnaires, they all have similar focuses, including physical, emotional, and social diversity. The questionnaire has been determined to be effective in the Chinese population [31], and has already been used in related research [32].\n2.2.3. Socio-Demographic Variables In order to minimize the possibility of other variables influencing the cognitive function–HRQoL relationship study, and to simplify the model, this research controlled for several specific covariates associated with cognitive function and HRQoL. According to previous studies, all covariates were based on baseline data (CHARLS 2015) [32]. Firstly, population control variables include age, gender, and education status. Secondly, since HRQoL can be divided into two parts, PCS and MCS, this study used different control variables in the models for cognitive function, PCS, and MCS. This research included depression as well as current smoking and drinking habits as control variables for PCS scores. In this research, PCS scores are also considered with marital status, depression, physical activity, and current smoking and drinking habits as control variables.\nIn order to minimize the possibility of other variables influencing the cognitive function–HRQoL relationship study, and to simplify the model, this research controlled for several specific covariates associated with cognitive function and HRQoL. According to previous studies, all covariates were based on baseline data (CHARLS 2015) [32]. Firstly, population control variables include age, gender, and education status. Secondly, since HRQoL can be divided into two parts, PCS and MCS, this study used different control variables in the models for cognitive function, PCS, and MCS. This research included depression as well as current smoking and drinking habits as control variables for PCS scores. In this research, PCS scores are also considered with marital status, depression, physical activity, and current smoking and drinking habits as control variables.\n2.3. Statistical Methods IBM SPSS Statistics, version 23 (IBM SPSS Statistics, Armonk, NY, USA) and Mplus version 8.0 are used for data analysis. Descriptive analyses of diabetic patient characteristics, cognitive function, and PCM and MCS of HRQoL were performed. The relationship between cognitive function and HRQoL in diabetic patients at T1 and T2 was assessed using the Pearson correlation test. A repeated-measures analysis of variance was performed to assess the differences between these variables with and without the digital usage behavior. Furthermore, cross-lagged panel structural equation modeling (SEM) was used to assess the longitudinal association between cognition and HRQoL at T1 and T2. Finally, a multi-group test was performed to assess whether digital usage behavior was making a difference in this relationship. Finally, regarding the grouping of digital technologies as well, previous studies were referred to [24]. Digital technology usage was obtained from 2015 baseline data. In the CHARLS 2015 questionnaire, internet usage was measured using the following question whether respondents have used the internet in the last month (0 for no, 1 for yes).\nIBM SPSS Statistics, version 23 (IBM SPSS Statistics, Armonk, NY, USA) and Mplus version 8.0 are used for data analysis. Descriptive analyses of diabetic patient characteristics, cognitive function, and PCM and MCS of HRQoL were performed. The relationship between cognitive function and HRQoL in diabetic patients at T1 and T2 was assessed using the Pearson correlation test. A repeated-measures analysis of variance was performed to assess the differences between these variables with and without the digital usage behavior. Furthermore, cross-lagged panel structural equation modeling (SEM) was used to assess the longitudinal association between cognition and HRQoL at T1 and T2. Finally, a multi-group test was performed to assess whether digital usage behavior was making a difference in this relationship. Finally, regarding the grouping of digital technologies as well, previous studies were referred to [24]. Digital technology usage was obtained from 2015 baseline data. In the CHARLS 2015 questionnaire, internet usage was measured using the following question whether respondents have used the internet in the last month (0 for no, 1 for yes).","2.2.1. Cognitive Functioning This study obtained data on cognitive function from baseline data in 2015 and follow-up data in 2018. The baseline data stipulated by CHARLS is from 2011, but we include CHARLS 2015 (T1) as the baseline data in our study. CHARLS is similar to the cognitive assessment used in the American Health and Retirement Study, and has constructed cognitive function evaluation criteria from the same two aspects of memory and mental state [27,28]. Furthermore, previous studies have also used the CHARLS cognitive function assessment criteria for class-correspondence studies. Firstly, memory evaluation includes immediate word recall (0–10 points) and delayed word recall (0–10 points). Mental state is measured from three dimensions: orientation, visual construction, and mathematical performance. Orientation (0–5 points) is measured by asking respondents to name the date, day of the week, and season; visual construction is assessed by drawing a previously displayed picture (0–1 points); and mathematical performance (0–5 points) is measured by asking respondents to subtract 7 consecutive times from 100. The scores of the participants’ cognitive function are equal to the sum of the scores of memory and mental state. Cognitive function scores range from 0 to 31 points, higher cognitive function scores indicating better cognitive function. Finally, in order to obtain a composite measure of cognitive function, this study normalized and averaged the total cognitive function score by adding up memory and mental state scores [29].\nThis study obtained data on cognitive function from baseline data in 2015 and follow-up data in 2018. The baseline data stipulated by CHARLS is from 2011, but we include CHARLS 2015 (T1) as the baseline data in our study. CHARLS is similar to the cognitive assessment used in the American Health and Retirement Study, and has constructed cognitive function evaluation criteria from the same two aspects of memory and mental state [27,28]. Furthermore, previous studies have also used the CHARLS cognitive function assessment criteria for class-correspondence studies. Firstly, memory evaluation includes immediate word recall (0–10 points) and delayed word recall (0–10 points). Mental state is measured from three dimensions: orientation, visual construction, and mathematical performance. Orientation (0–5 points) is measured by asking respondents to name the date, day of the week, and season; visual construction is assessed by drawing a previously displayed picture (0–1 points); and mathematical performance (0–5 points) is measured by asking respondents to subtract 7 consecutive times from 100. The scores of the participants’ cognitive function are equal to the sum of the scores of memory and mental state. Cognitive function scores range from 0 to 31 points, higher cognitive function scores indicating better cognitive function. Finally, in order to obtain a composite measure of cognitive function, this study normalized and averaged the total cognitive function score by adding up memory and mental state scores [29].\n2.2.2. HRQoL This study used a new scale construction based on the variables of the Short Form 36 (SF-36) and the CHARLS questionnaire to measure HRQoL in diabetic patients. The construction of the new scale was derived from the eight dimensions of SF-36, and the corresponding variables of CHARLS were selected to assess the following eight dimensions (Table 2): physical function (PF), role–body (RP), body pain (BP), general health (GH), vitality (VT), social functioning (SF), role–emotion (RE), and mental health (MH). The scores for the above eight dimensions were calculated by adding up the category scores and then converting the raw scores to a 0 to 100 scale. Scores from the eight subscales were aggregated into two overall scores according to the conceptual model of the SF-36 [30]. Physical function, body roles, body pain, and general perceptions of health were calculated as PCS, and mental health, vitality, emotional role, and social functioning were calculated as MCS. Although the HRQoL questionnaire based on CHARLS is slightly different from other HRQoL questionnaires, they all have similar focuses, including physical, emotional, and social diversity. The questionnaire has been determined to be effective in the Chinese population [31], and has already been used in related research [32].\nThis study used a new scale construction based on the variables of the Short Form 36 (SF-36) and the CHARLS questionnaire to measure HRQoL in diabetic patients. The construction of the new scale was derived from the eight dimensions of SF-36, and the corresponding variables of CHARLS were selected to assess the following eight dimensions (Table 2): physical function (PF), role–body (RP), body pain (BP), general health (GH), vitality (VT), social functioning (SF), role–emotion (RE), and mental health (MH). The scores for the above eight dimensions were calculated by adding up the category scores and then converting the raw scores to a 0 to 100 scale. Scores from the eight subscales were aggregated into two overall scores according to the conceptual model of the SF-36 [30]. Physical function, body roles, body pain, and general perceptions of health were calculated as PCS, and mental health, vitality, emotional role, and social functioning were calculated as MCS. Although the HRQoL questionnaire based on CHARLS is slightly different from other HRQoL questionnaires, they all have similar focuses, including physical, emotional, and social diversity. The questionnaire has been determined to be effective in the Chinese population [31], and has already been used in related research [32].\n2.2.3. Socio-Demographic Variables In order to minimize the possibility of other variables influencing the cognitive function–HRQoL relationship study, and to simplify the model, this research controlled for several specific covariates associated with cognitive function and HRQoL. According to previous studies, all covariates were based on baseline data (CHARLS 2015) [32]. Firstly, population control variables include age, gender, and education status. Secondly, since HRQoL can be divided into two parts, PCS and MCS, this study used different control variables in the models for cognitive function, PCS, and MCS. This research included depression as well as current smoking and drinking habits as control variables for PCS scores. In this research, PCS scores are also considered with marital status, depression, physical activity, and current smoking and drinking habits as control variables.\nIn order to minimize the possibility of other variables influencing the cognitive function–HRQoL relationship study, and to simplify the model, this research controlled for several specific covariates associated with cognitive function and HRQoL. According to previous studies, all covariates were based on baseline data (CHARLS 2015) [32]. Firstly, population control variables include age, gender, and education status. Secondly, since HRQoL can be divided into two parts, PCS and MCS, this study used different control variables in the models for cognitive function, PCS, and MCS. This research included depression as well as current smoking and drinking habits as control variables for PCS scores. In this research, PCS scores are also considered with marital status, depression, physical activity, and current smoking and drinking habits as control variables.","This study obtained data on cognitive function from baseline data in 2015 and follow-up data in 2018. The baseline data stipulated by CHARLS is from 2011, but we include CHARLS 2015 (T1) as the baseline data in our study. CHARLS is similar to the cognitive assessment used in the American Health and Retirement Study, and has constructed cognitive function evaluation criteria from the same two aspects of memory and mental state [27,28]. Furthermore, previous studies have also used the CHARLS cognitive function assessment criteria for class-correspondence studies. Firstly, memory evaluation includes immediate word recall (0–10 points) and delayed word recall (0–10 points). Mental state is measured from three dimensions: orientation, visual construction, and mathematical performance. Orientation (0–5 points) is measured by asking respondents to name the date, day of the week, and season; visual construction is assessed by drawing a previously displayed picture (0–1 points); and mathematical performance (0–5 points) is measured by asking respondents to subtract 7 consecutive times from 100. The scores of the participants’ cognitive function are equal to the sum of the scores of memory and mental state. Cognitive function scores range from 0 to 31 points, higher cognitive function scores indicating better cognitive function. Finally, in order to obtain a composite measure of cognitive function, this study normalized and averaged the total cognitive function score by adding up memory and mental state scores [29].","This study used a new scale construction based on the variables of the Short Form 36 (SF-36) and the CHARLS questionnaire to measure HRQoL in diabetic patients. The construction of the new scale was derived from the eight dimensions of SF-36, and the corresponding variables of CHARLS were selected to assess the following eight dimensions (Table 2): physical function (PF), role–body (RP), body pain (BP), general health (GH), vitality (VT), social functioning (SF), role–emotion (RE), and mental health (MH). The scores for the above eight dimensions were calculated by adding up the category scores and then converting the raw scores to a 0 to 100 scale. Scores from the eight subscales were aggregated into two overall scores according to the conceptual model of the SF-36 [30]. Physical function, body roles, body pain, and general perceptions of health were calculated as PCS, and mental health, vitality, emotional role, and social functioning were calculated as MCS. Although the HRQoL questionnaire based on CHARLS is slightly different from other HRQoL questionnaires, they all have similar focuses, including physical, emotional, and social diversity. The questionnaire has been determined to be effective in the Chinese population [31], and has already been used in related research [32].","In order to minimize the possibility of other variables influencing the cognitive function–HRQoL relationship study, and to simplify the model, this research controlled for several specific covariates associated with cognitive function and HRQoL. According to previous studies, all covariates were based on baseline data (CHARLS 2015) [32]. Firstly, population control variables include age, gender, and education status. Secondly, since HRQoL can be divided into two parts, PCS and MCS, this study used different control variables in the models for cognitive function, PCS, and MCS. This research included depression as well as current smoking and drinking habits as control variables for PCS scores. In this research, PCS scores are also considered with marital status, depression, physical activity, and current smoking and drinking habits as control variables.","IBM SPSS Statistics, version 23 (IBM SPSS Statistics, Armonk, NY, USA) and Mplus version 8.0 are used for data analysis. Descriptive analyses of diabetic patient characteristics, cognitive function, and PCM and MCS of HRQoL were performed. The relationship between cognitive function and HRQoL in diabetic patients at T1 and T2 was assessed using the Pearson correlation test. A repeated-measures analysis of variance was performed to assess the differences between these variables with and without the digital usage behavior. Furthermore, cross-lagged panel structural equation modeling (SEM) was used to assess the longitudinal association between cognition and HRQoL at T1 and T2. Finally, a multi-group test was performed to assess whether digital usage behavior was making a difference in this relationship. Finally, regarding the grouping of digital technologies as well, previous studies were referred to [24]. Digital technology usage was obtained from 2015 baseline data. In the CHARLS 2015 questionnaire, internet usage was measured using the following question whether respondents have used the internet in the last month (0 for no, 1 for yes).","The variable descriptive statistics and correlation analysis results are shown in Table 3 and Table 4. The correlation analysis shows that there is a significant correlation between cognition at T1 and T2 and HRQoL levels at T1 and T2, indicating that cognitive function and HRQoL levels in middle-aged and older diabetic patients have a certain relationship, which is showing stability. Meanwhile, simultaneous and sequential correlations between cognitive function and HRQoL levels are also significant. The correlation coefficients between cognitive function and HRQoL at T1 are 0.28 (p < 0.01) and 0.37 (p < 0.01); the correlation coefficients between cognitive function at T2 and HRQoL at T2 are 0.32 (p < 0.01) and 0.30 (p < 0.01); the correlation coefficients between cognitive function at T1 and HRQoL at T2 are 0.25 (p < 0.01) and 0.22 (p < 0.01); and the correlation coefficients between HRQoL at T1 and cognitive function at T2 are 0.32 (p < 0.01) and 0.36 (p < 0.01). This indicates that cognitive function is basically consistent with the synchronous and stable correlations of HRQoL levels, which is in line with the basic assumptions of the cross-lag design.","With cognitive function as the dependent variable, a 2 (test time: T1/T2) × 2 (digital usage behavior: use/non-use) repeated measures square analysis was performed. The results show that the testing time is the main and most significant effect (F = 11.21, p < 0.001, η2 = 0.01). The cognitive function level at T2 is significantly lower than at T1, and there are developmental differences. The main effect of digital usage behavior is significant (F = 36.325, p < 0.001, η2 = 0.06), and the cognitive function of patients using digital technology is significantly better than those patients without digital technology. The interaction between the two is not significant (F = 2.22, p > 0.05, η2 = 0.04).\nWith PCS as the dependent variable, a 2 (test time: T1/T2) × 2 (digital usage behavior: use/non-use) repeated measures square analysis was performed. The results show that the testing time is the main and most significant effect (F =16.25, p < 0.001, η2 = 0.03), the PCS level of the post-test is significantly lower than that of the pre-test, and there is a developmental difference. The main effect of digital usage behavior is significant (F = 36.54, p < 0.001, η2 = 0.06), and the PCS of patients using digital technology is significantly better than those not using digital technology. The interaction between the two is not significant (F = 0.18, p > 0.05, η2 = 0.00).\nWith MCS as the dependent variable, a 2 (test time: T1/T2) × 2 (digital usage behavior: use/non-use) repeated measures square analysis was performed. The results showed that the testing time is the main and most significant effect (F = 44.93, p < 0.001, η2 = 0.07), the MCS level of the post-test is significantly lower than the pre-test, and there is a developmental difference. The main effect of digital usage behavior is significant (F = 50.97, p < 0.001, η2 = 0.08), and the MCS of patients using digital technology is significantly better than those not using digital technology. The interaction between the two is significant (F = 4.43, p < 0.05, η2 = 0.08).","In the cross-lag model which this study applied, HRQoL at T2 was predicted by cognitive function at T1, and cognitive function at T2 was predicted by HRQoL at T1. After the corresponding control variables were added to the models, both models achieved acceptable fitness criteria (Model 1: RMSEA = 0.093; CFI = 0.994, TCL = 0.912; Model 2: RMSEA = 0.098; CFI = 0.984, TCL = 0.901).\nThe cross-lag relationship between cognitive function and PCS scores at two points in time is shown from the test of Model 1 in Figure 2. First, in the same time period, the baseline association between cognitive function and PCS is significantly positive (B = 0.19, p < 0.01), implying better cognitive performance in middle-aged and older diabetic patients with higher PCS scores at T1, and vice versa. Second, there is no significant correlation between cognitive function at T1 and PCS at T2 (B = 0.05, p > 0.01), but PCS at T1 is positively correlated with cognitive function at T2 (B = 0.12, p < 0.01). This suggests that there is a positive and significant relationship between PCS and cognitive function in middle-aged and older diabetic patients over time, but early cognitive function in middle-aged and older diabetic patients does not affect later PSC.\nThe cross-lag relationship between cognitive function and MCS scores at the two points in time is shown in Model 2 of Figure 2. First, in the same time period, the baseline association between cognitive function and MCS is significantly positively correlated (B = 0.31, p < 0.01), which implies a better cognitive performance in middle-aged and older diabetic patients with higher MCS scores at T1, and vice versa. Second, there is no significant correlation between cognitive function at T1 and MCS at T2 (B = 0.05, p > 0.01), but there is a significant positive correlation between MCS at T1 and cognitive function at T2 (B = 0.14, p < 0.01). This suggests that there is a positive and significant relationship between MCS and cognitive function in middle-aged and older diabetic patients over time, but early cognitive function in middle-aged and older diabetic patients does not affect later MCS. In conclusion, there may be a one-way causal relationship between cognitive function and HRQoL, and the causal direction is from HRQoL to cognition.","In order to investigate whether the cross-lag relationship between cognitive function and HRQoL differs in digital usage behavior, the study performed a multi-group analysis. All diabetic patients were divided into two groups, one representing non-digital usage behavior (Figure 3), and the other representing digital usage behavior (Figure 4). In the grouped cross-lag model, the corresponding control variables were also added to the model. The models in Figure 3 and Figure 4 met fitness criteria (Figure 3: Model 3: RMSEA = 0.073, CFI = 0.996, TCL = 0.901; Model 4: RMSEA = 0.100, CFI = 0.990, TCL = 0.936. Figure 4: Model 5: RMSEA = 0.073, CFI = 0.996, TCL = 0.941; Model 6: RMSEA = 0.079, CFI = 0.970, TCL = 0.936). In both sets of models, the study focused on the relationship between cognitive function and HRQoL at a point in time, as well as their relationship at the time of follow-up research.\nFor middle-aged and older diabetic patients using digital technology, although the model could be fitted, cognitive function at T1 did not significantly predict HRQoL at T2 (PCS: B = 0.30, p > 0.01; MCS: B = 0.14, p > 0.01), and vice versa (PCS: B = 0.06, p > 0.01; MCS: B = 0.05, p > 0.01) (see Figure 3). For diabetic patients who did not use digital technology, although T1 cognitive function did not significantly predict T2 HRQoL (PCS: B = 0.04, p > 0.01; MCS: B = 0.05, p > 0.01), T1 HRQoL significantly predicted T2 cognitive function (PCS: B = 0.10, p < 0.01; MCS: B = 0.11, p < 0.01), and the results were consistent across all diabetic patients (see Figure 4). This shows that the cross-lag model of cognitive function and HRQoL in middle-aged and older diabetic patients shows variance in the use of digital technology, that is, this relationship will be affected by digital usage behavior."],"string":"[\n \"2.1. Participants The China Health and Retirement Longitudinal Survey (CHARLS) is a representative follow-up survey of middle-aged and older people in China, chaired by the National Research Institute of Development at Peking University. The study protocol was approved by the Institutional Review Board of Peking University (approval number: IRB00001052-11015), and the study protocol complies with the ethical guidelines of the 1975 Declaration of Helsinki. In order to obtain the relevant data, we applied for the CHARLS database online on 31 August 2022, and approval was quickly obtained. This study used two waves of CHARLS data, from 2015 (T1) and 2018 (T2). CHARLS data can be accessed through its official website (https://charls.pku.edu.cn (accessed on 31 August 2022)).\\nFigure 1 shows the detailed process for including and excluding study participants. Wave 3 was used as the baseline data set for this study, and individuals who lacked information on HRQoL, cognitive function, digital usage behavior, and covariates were excluded from this study. Subsequently, new participants in 2018 were further excluded. Individuals lacking information on digital usage behavior, cognitive function, and covariates were also excluded from this study. In addition, in the sample selection, the research team excluded individuals with other diseases (such as cognitive diseases that may affect the detection process, mental diseases, and other diseases related to aging), and included individuals with diabetes only. In addition, the abbreviations of the main variables involved in the study are organized in Table 1.\\nThe China Health and Retirement Longitudinal Survey (CHARLS) is a representative follow-up survey of middle-aged and older people in China, chaired by the National Research Institute of Development at Peking University. The study protocol was approved by the Institutional Review Board of Peking University (approval number: IRB00001052-11015), and the study protocol complies with the ethical guidelines of the 1975 Declaration of Helsinki. In order to obtain the relevant data, we applied for the CHARLS database online on 31 August 2022, and approval was quickly obtained. This study used two waves of CHARLS data, from 2015 (T1) and 2018 (T2). CHARLS data can be accessed through its official website (https://charls.pku.edu.cn (accessed on 31 August 2022)).\\nFigure 1 shows the detailed process for including and excluding study participants. Wave 3 was used as the baseline data set for this study, and individuals who lacked information on HRQoL, cognitive function, digital usage behavior, and covariates were excluded from this study. Subsequently, new participants in 2018 were further excluded. Individuals lacking information on digital usage behavior, cognitive function, and covariates were also excluded from this study. In addition, in the sample selection, the research team excluded individuals with other diseases (such as cognitive diseases that may affect the detection process, mental diseases, and other diseases related to aging), and included individuals with diabetes only. In addition, the abbreviations of the main variables involved in the study are organized in Table 1.\\n2.2. Measurements 2.2.1. Cognitive Functioning This study obtained data on cognitive function from baseline data in 2015 and follow-up data in 2018. The baseline data stipulated by CHARLS is from 2011, but we include CHARLS 2015 (T1) as the baseline data in our study. CHARLS is similar to the cognitive assessment used in the American Health and Retirement Study, and has constructed cognitive function evaluation criteria from the same two aspects of memory and mental state [27,28]. Furthermore, previous studies have also used the CHARLS cognitive function assessment criteria for class-correspondence studies. Firstly, memory evaluation includes immediate word recall (0–10 points) and delayed word recall (0–10 points). Mental state is measured from three dimensions: orientation, visual construction, and mathematical performance. Orientation (0–5 points) is measured by asking respondents to name the date, day of the week, and season; visual construction is assessed by drawing a previously displayed picture (0–1 points); and mathematical performance (0–5 points) is measured by asking respondents to subtract 7 consecutive times from 100. The scores of the participants’ cognitive function are equal to the sum of the scores of memory and mental state. Cognitive function scores range from 0 to 31 points, higher cognitive function scores indicating better cognitive function. Finally, in order to obtain a composite measure of cognitive function, this study normalized and averaged the total cognitive function score by adding up memory and mental state scores [29].\\nThis study obtained data on cognitive function from baseline data in 2015 and follow-up data in 2018. The baseline data stipulated by CHARLS is from 2011, but we include CHARLS 2015 (T1) as the baseline data in our study. CHARLS is similar to the cognitive assessment used in the American Health and Retirement Study, and has constructed cognitive function evaluation criteria from the same two aspects of memory and mental state [27,28]. Furthermore, previous studies have also used the CHARLS cognitive function assessment criteria for class-correspondence studies. Firstly, memory evaluation includes immediate word recall (0–10 points) and delayed word recall (0–10 points). Mental state is measured from three dimensions: orientation, visual construction, and mathematical performance. Orientation (0–5 points) is measured by asking respondents to name the date, day of the week, and season; visual construction is assessed by drawing a previously displayed picture (0–1 points); and mathematical performance (0–5 points) is measured by asking respondents to subtract 7 consecutive times from 100. The scores of the participants’ cognitive function are equal to the sum of the scores of memory and mental state. Cognitive function scores range from 0 to 31 points, higher cognitive function scores indicating better cognitive function. Finally, in order to obtain a composite measure of cognitive function, this study normalized and averaged the total cognitive function score by adding up memory and mental state scores [29].\\n2.2.2. HRQoL This study used a new scale construction based on the variables of the Short Form 36 (SF-36) and the CHARLS questionnaire to measure HRQoL in diabetic patients. The construction of the new scale was derived from the eight dimensions of SF-36, and the corresponding variables of CHARLS were selected to assess the following eight dimensions (Table 2): physical function (PF), role–body (RP), body pain (BP), general health (GH), vitality (VT), social functioning (SF), role–emotion (RE), and mental health (MH). The scores for the above eight dimensions were calculated by adding up the category scores and then converting the raw scores to a 0 to 100 scale. Scores from the eight subscales were aggregated into two overall scores according to the conceptual model of the SF-36 [30]. Physical function, body roles, body pain, and general perceptions of health were calculated as PCS, and mental health, vitality, emotional role, and social functioning were calculated as MCS. Although the HRQoL questionnaire based on CHARLS is slightly different from other HRQoL questionnaires, they all have similar focuses, including physical, emotional, and social diversity. The questionnaire has been determined to be effective in the Chinese population [31], and has already been used in related research [32].\\nThis study used a new scale construction based on the variables of the Short Form 36 (SF-36) and the CHARLS questionnaire to measure HRQoL in diabetic patients. The construction of the new scale was derived from the eight dimensions of SF-36, and the corresponding variables of CHARLS were selected to assess the following eight dimensions (Table 2): physical function (PF), role–body (RP), body pain (BP), general health (GH), vitality (VT), social functioning (SF), role–emotion (RE), and mental health (MH). The scores for the above eight dimensions were calculated by adding up the category scores and then converting the raw scores to a 0 to 100 scale. Scores from the eight subscales were aggregated into two overall scores according to the conceptual model of the SF-36 [30]. Physical function, body roles, body pain, and general perceptions of health were calculated as PCS, and mental health, vitality, emotional role, and social functioning were calculated as MCS. Although the HRQoL questionnaire based on CHARLS is slightly different from other HRQoL questionnaires, they all have similar focuses, including physical, emotional, and social diversity. The questionnaire has been determined to be effective in the Chinese population [31], and has already been used in related research [32].\\n2.2.3. Socio-Demographic Variables In order to minimize the possibility of other variables influencing the cognitive function–HRQoL relationship study, and to simplify the model, this research controlled for several specific covariates associated with cognitive function and HRQoL. According to previous studies, all covariates were based on baseline data (CHARLS 2015) [32]. Firstly, population control variables include age, gender, and education status. Secondly, since HRQoL can be divided into two parts, PCS and MCS, this study used different control variables in the models for cognitive function, PCS, and MCS. This research included depression as well as current smoking and drinking habits as control variables for PCS scores. In this research, PCS scores are also considered with marital status, depression, physical activity, and current smoking and drinking habits as control variables.\\nIn order to minimize the possibility of other variables influencing the cognitive function–HRQoL relationship study, and to simplify the model, this research controlled for several specific covariates associated with cognitive function and HRQoL. According to previous studies, all covariates were based on baseline data (CHARLS 2015) [32]. Firstly, population control variables include age, gender, and education status. Secondly, since HRQoL can be divided into two parts, PCS and MCS, this study used different control variables in the models for cognitive function, PCS, and MCS. This research included depression as well as current smoking and drinking habits as control variables for PCS scores. In this research, PCS scores are also considered with marital status, depression, physical activity, and current smoking and drinking habits as control variables.\\n2.2.1. Cognitive Functioning This study obtained data on cognitive function from baseline data in 2015 and follow-up data in 2018. The baseline data stipulated by CHARLS is from 2011, but we include CHARLS 2015 (T1) as the baseline data in our study. CHARLS is similar to the cognitive assessment used in the American Health and Retirement Study, and has constructed cognitive function evaluation criteria from the same two aspects of memory and mental state [27,28]. Furthermore, previous studies have also used the CHARLS cognitive function assessment criteria for class-correspondence studies. Firstly, memory evaluation includes immediate word recall (0–10 points) and delayed word recall (0–10 points). Mental state is measured from three dimensions: orientation, visual construction, and mathematical performance. Orientation (0–5 points) is measured by asking respondents to name the date, day of the week, and season; visual construction is assessed by drawing a previously displayed picture (0–1 points); and mathematical performance (0–5 points) is measured by asking respondents to subtract 7 consecutive times from 100. The scores of the participants’ cognitive function are equal to the sum of the scores of memory and mental state. Cognitive function scores range from 0 to 31 points, higher cognitive function scores indicating better cognitive function. Finally, in order to obtain a composite measure of cognitive function, this study normalized and averaged the total cognitive function score by adding up memory and mental state scores [29].\\nThis study obtained data on cognitive function from baseline data in 2015 and follow-up data in 2018. The baseline data stipulated by CHARLS is from 2011, but we include CHARLS 2015 (T1) as the baseline data in our study. CHARLS is similar to the cognitive assessment used in the American Health and Retirement Study, and has constructed cognitive function evaluation criteria from the same two aspects of memory and mental state [27,28]. Furthermore, previous studies have also used the CHARLS cognitive function assessment criteria for class-correspondence studies. Firstly, memory evaluation includes immediate word recall (0–10 points) and delayed word recall (0–10 points). Mental state is measured from three dimensions: orientation, visual construction, and mathematical performance. Orientation (0–5 points) is measured by asking respondents to name the date, day of the week, and season; visual construction is assessed by drawing a previously displayed picture (0–1 points); and mathematical performance (0–5 points) is measured by asking respondents to subtract 7 consecutive times from 100. The scores of the participants’ cognitive function are equal to the sum of the scores of memory and mental state. Cognitive function scores range from 0 to 31 points, higher cognitive function scores indicating better cognitive function. Finally, in order to obtain a composite measure of cognitive function, this study normalized and averaged the total cognitive function score by adding up memory and mental state scores [29].\\n2.2.2. HRQoL This study used a new scale construction based on the variables of the Short Form 36 (SF-36) and the CHARLS questionnaire to measure HRQoL in diabetic patients. The construction of the new scale was derived from the eight dimensions of SF-36, and the corresponding variables of CHARLS were selected to assess the following eight dimensions (Table 2): physical function (PF), role–body (RP), body pain (BP), general health (GH), vitality (VT), social functioning (SF), role–emotion (RE), and mental health (MH). The scores for the above eight dimensions were calculated by adding up the category scores and then converting the raw scores to a 0 to 100 scale. Scores from the eight subscales were aggregated into two overall scores according to the conceptual model of the SF-36 [30]. Physical function, body roles, body pain, and general perceptions of health were calculated as PCS, and mental health, vitality, emotional role, and social functioning were calculated as MCS. Although the HRQoL questionnaire based on CHARLS is slightly different from other HRQoL questionnaires, they all have similar focuses, including physical, emotional, and social diversity. The questionnaire has been determined to be effective in the Chinese population [31], and has already been used in related research [32].\\nThis study used a new scale construction based on the variables of the Short Form 36 (SF-36) and the CHARLS questionnaire to measure HRQoL in diabetic patients. The construction of the new scale was derived from the eight dimensions of SF-36, and the corresponding variables of CHARLS were selected to assess the following eight dimensions (Table 2): physical function (PF), role–body (RP), body pain (BP), general health (GH), vitality (VT), social functioning (SF), role–emotion (RE), and mental health (MH). The scores for the above eight dimensions were calculated by adding up the category scores and then converting the raw scores to a 0 to 100 scale. Scores from the eight subscales were aggregated into two overall scores according to the conceptual model of the SF-36 [30]. Physical function, body roles, body pain, and general perceptions of health were calculated as PCS, and mental health, vitality, emotional role, and social functioning were calculated as MCS. Although the HRQoL questionnaire based on CHARLS is slightly different from other HRQoL questionnaires, they all have similar focuses, including physical, emotional, and social diversity. The questionnaire has been determined to be effective in the Chinese population [31], and has already been used in related research [32].\\n2.2.3. Socio-Demographic Variables In order to minimize the possibility of other variables influencing the cognitive function–HRQoL relationship study, and to simplify the model, this research controlled for several specific covariates associated with cognitive function and HRQoL. According to previous studies, all covariates were based on baseline data (CHARLS 2015) [32]. Firstly, population control variables include age, gender, and education status. Secondly, since HRQoL can be divided into two parts, PCS and MCS, this study used different control variables in the models for cognitive function, PCS, and MCS. This research included depression as well as current smoking and drinking habits as control variables for PCS scores. In this research, PCS scores are also considered with marital status, depression, physical activity, and current smoking and drinking habits as control variables.\\nIn order to minimize the possibility of other variables influencing the cognitive function–HRQoL relationship study, and to simplify the model, this research controlled for several specific covariates associated with cognitive function and HRQoL. According to previous studies, all covariates were based on baseline data (CHARLS 2015) [32]. Firstly, population control variables include age, gender, and education status. Secondly, since HRQoL can be divided into two parts, PCS and MCS, this study used different control variables in the models for cognitive function, PCS, and MCS. This research included depression as well as current smoking and drinking habits as control variables for PCS scores. In this research, PCS scores are also considered with marital status, depression, physical activity, and current smoking and drinking habits as control variables.\\n2.3. Statistical Methods IBM SPSS Statistics, version 23 (IBM SPSS Statistics, Armonk, NY, USA) and Mplus version 8.0 are used for data analysis. Descriptive analyses of diabetic patient characteristics, cognitive function, and PCM and MCS of HRQoL were performed. The relationship between cognitive function and HRQoL in diabetic patients at T1 and T2 was assessed using the Pearson correlation test. A repeated-measures analysis of variance was performed to assess the differences between these variables with and without the digital usage behavior. Furthermore, cross-lagged panel structural equation modeling (SEM) was used to assess the longitudinal association between cognition and HRQoL at T1 and T2. Finally, a multi-group test was performed to assess whether digital usage behavior was making a difference in this relationship. Finally, regarding the grouping of digital technologies as well, previous studies were referred to [24]. Digital technology usage was obtained from 2015 baseline data. In the CHARLS 2015 questionnaire, internet usage was measured using the following question whether respondents have used the internet in the last month (0 for no, 1 for yes).\\nIBM SPSS Statistics, version 23 (IBM SPSS Statistics, Armonk, NY, USA) and Mplus version 8.0 are used for data analysis. Descriptive analyses of diabetic patient characteristics, cognitive function, and PCM and MCS of HRQoL were performed. The relationship between cognitive function and HRQoL in diabetic patients at T1 and T2 was assessed using the Pearson correlation test. A repeated-measures analysis of variance was performed to assess the differences between these variables with and without the digital usage behavior. Furthermore, cross-lagged panel structural equation modeling (SEM) was used to assess the longitudinal association between cognition and HRQoL at T1 and T2. Finally, a multi-group test was performed to assess whether digital usage behavior was making a difference in this relationship. Finally, regarding the grouping of digital technologies as well, previous studies were referred to [24]. Digital technology usage was obtained from 2015 baseline data. In the CHARLS 2015 questionnaire, internet usage was measured using the following question whether respondents have used the internet in the last month (0 for no, 1 for yes).\",\n \"2.2.1. Cognitive Functioning This study obtained data on cognitive function from baseline data in 2015 and follow-up data in 2018. The baseline data stipulated by CHARLS is from 2011, but we include CHARLS 2015 (T1) as the baseline data in our study. CHARLS is similar to the cognitive assessment used in the American Health and Retirement Study, and has constructed cognitive function evaluation criteria from the same two aspects of memory and mental state [27,28]. Furthermore, previous studies have also used the CHARLS cognitive function assessment criteria for class-correspondence studies. Firstly, memory evaluation includes immediate word recall (0–10 points) and delayed word recall (0–10 points). Mental state is measured from three dimensions: orientation, visual construction, and mathematical performance. Orientation (0–5 points) is measured by asking respondents to name the date, day of the week, and season; visual construction is assessed by drawing a previously displayed picture (0–1 points); and mathematical performance (0–5 points) is measured by asking respondents to subtract 7 consecutive times from 100. The scores of the participants’ cognitive function are equal to the sum of the scores of memory and mental state. Cognitive function scores range from 0 to 31 points, higher cognitive function scores indicating better cognitive function. Finally, in order to obtain a composite measure of cognitive function, this study normalized and averaged the total cognitive function score by adding up memory and mental state scores [29].\\nThis study obtained data on cognitive function from baseline data in 2015 and follow-up data in 2018. The baseline data stipulated by CHARLS is from 2011, but we include CHARLS 2015 (T1) as the baseline data in our study. CHARLS is similar to the cognitive assessment used in the American Health and Retirement Study, and has constructed cognitive function evaluation criteria from the same two aspects of memory and mental state [27,28]. Furthermore, previous studies have also used the CHARLS cognitive function assessment criteria for class-correspondence studies. Firstly, memory evaluation includes immediate word recall (0–10 points) and delayed word recall (0–10 points). Mental state is measured from three dimensions: orientation, visual construction, and mathematical performance. Orientation (0–5 points) is measured by asking respondents to name the date, day of the week, and season; visual construction is assessed by drawing a previously displayed picture (0–1 points); and mathematical performance (0–5 points) is measured by asking respondents to subtract 7 consecutive times from 100. The scores of the participants’ cognitive function are equal to the sum of the scores of memory and mental state. Cognitive function scores range from 0 to 31 points, higher cognitive function scores indicating better cognitive function. Finally, in order to obtain a composite measure of cognitive function, this study normalized and averaged the total cognitive function score by adding up memory and mental state scores [29].\\n2.2.2. HRQoL This study used a new scale construction based on the variables of the Short Form 36 (SF-36) and the CHARLS questionnaire to measure HRQoL in diabetic patients. The construction of the new scale was derived from the eight dimensions of SF-36, and the corresponding variables of CHARLS were selected to assess the following eight dimensions (Table 2): physical function (PF), role–body (RP), body pain (BP), general health (GH), vitality (VT), social functioning (SF), role–emotion (RE), and mental health (MH). The scores for the above eight dimensions were calculated by adding up the category scores and then converting the raw scores to a 0 to 100 scale. Scores from the eight subscales were aggregated into two overall scores according to the conceptual model of the SF-36 [30]. Physical function, body roles, body pain, and general perceptions of health were calculated as PCS, and mental health, vitality, emotional role, and social functioning were calculated as MCS. Although the HRQoL questionnaire based on CHARLS is slightly different from other HRQoL questionnaires, they all have similar focuses, including physical, emotional, and social diversity. The questionnaire has been determined to be effective in the Chinese population [31], and has already been used in related research [32].\\nThis study used a new scale construction based on the variables of the Short Form 36 (SF-36) and the CHARLS questionnaire to measure HRQoL in diabetic patients. The construction of the new scale was derived from the eight dimensions of SF-36, and the corresponding variables of CHARLS were selected to assess the following eight dimensions (Table 2): physical function (PF), role–body (RP), body pain (BP), general health (GH), vitality (VT), social functioning (SF), role–emotion (RE), and mental health (MH). The scores for the above eight dimensions were calculated by adding up the category scores and then converting the raw scores to a 0 to 100 scale. Scores from the eight subscales were aggregated into two overall scores according to the conceptual model of the SF-36 [30]. Physical function, body roles, body pain, and general perceptions of health were calculated as PCS, and mental health, vitality, emotional role, and social functioning were calculated as MCS. Although the HRQoL questionnaire based on CHARLS is slightly different from other HRQoL questionnaires, they all have similar focuses, including physical, emotional, and social diversity. The questionnaire has been determined to be effective in the Chinese population [31], and has already been used in related research [32].\\n2.2.3. Socio-Demographic Variables In order to minimize the possibility of other variables influencing the cognitive function–HRQoL relationship study, and to simplify the model, this research controlled for several specific covariates associated with cognitive function and HRQoL. According to previous studies, all covariates were based on baseline data (CHARLS 2015) [32]. Firstly, population control variables include age, gender, and education status. Secondly, since HRQoL can be divided into two parts, PCS and MCS, this study used different control variables in the models for cognitive function, PCS, and MCS. This research included depression as well as current smoking and drinking habits as control variables for PCS scores. In this research, PCS scores are also considered with marital status, depression, physical activity, and current smoking and drinking habits as control variables.\\nIn order to minimize the possibility of other variables influencing the cognitive function–HRQoL relationship study, and to simplify the model, this research controlled for several specific covariates associated with cognitive function and HRQoL. According to previous studies, all covariates were based on baseline data (CHARLS 2015) [32]. Firstly, population control variables include age, gender, and education status. Secondly, since HRQoL can be divided into two parts, PCS and MCS, this study used different control variables in the models for cognitive function, PCS, and MCS. This research included depression as well as current smoking and drinking habits as control variables for PCS scores. In this research, PCS scores are also considered with marital status, depression, physical activity, and current smoking and drinking habits as control variables.\",\n \"This study obtained data on cognitive function from baseline data in 2015 and follow-up data in 2018. The baseline data stipulated by CHARLS is from 2011, but we include CHARLS 2015 (T1) as the baseline data in our study. CHARLS is similar to the cognitive assessment used in the American Health and Retirement Study, and has constructed cognitive function evaluation criteria from the same two aspects of memory and mental state [27,28]. Furthermore, previous studies have also used the CHARLS cognitive function assessment criteria for class-correspondence studies. Firstly, memory evaluation includes immediate word recall (0–10 points) and delayed word recall (0–10 points). Mental state is measured from three dimensions: orientation, visual construction, and mathematical performance. Orientation (0–5 points) is measured by asking respondents to name the date, day of the week, and season; visual construction is assessed by drawing a previously displayed picture (0–1 points); and mathematical performance (0–5 points) is measured by asking respondents to subtract 7 consecutive times from 100. The scores of the participants’ cognitive function are equal to the sum of the scores of memory and mental state. Cognitive function scores range from 0 to 31 points, higher cognitive function scores indicating better cognitive function. Finally, in order to obtain a composite measure of cognitive function, this study normalized and averaged the total cognitive function score by adding up memory and mental state scores [29].\",\n \"This study used a new scale construction based on the variables of the Short Form 36 (SF-36) and the CHARLS questionnaire to measure HRQoL in diabetic patients. The construction of the new scale was derived from the eight dimensions of SF-36, and the corresponding variables of CHARLS were selected to assess the following eight dimensions (Table 2): physical function (PF), role–body (RP), body pain (BP), general health (GH), vitality (VT), social functioning (SF), role–emotion (RE), and mental health (MH). The scores for the above eight dimensions were calculated by adding up the category scores and then converting the raw scores to a 0 to 100 scale. Scores from the eight subscales were aggregated into two overall scores according to the conceptual model of the SF-36 [30]. Physical function, body roles, body pain, and general perceptions of health were calculated as PCS, and mental health, vitality, emotional role, and social functioning were calculated as MCS. Although the HRQoL questionnaire based on CHARLS is slightly different from other HRQoL questionnaires, they all have similar focuses, including physical, emotional, and social diversity. The questionnaire has been determined to be effective in the Chinese population [31], and has already been used in related research [32].\",\n \"In order to minimize the possibility of other variables influencing the cognitive function–HRQoL relationship study, and to simplify the model, this research controlled for several specific covariates associated with cognitive function and HRQoL. According to previous studies, all covariates were based on baseline data (CHARLS 2015) [32]. Firstly, population control variables include age, gender, and education status. Secondly, since HRQoL can be divided into two parts, PCS and MCS, this study used different control variables in the models for cognitive function, PCS, and MCS. This research included depression as well as current smoking and drinking habits as control variables for PCS scores. In this research, PCS scores are also considered with marital status, depression, physical activity, and current smoking and drinking habits as control variables.\",\n \"IBM SPSS Statistics, version 23 (IBM SPSS Statistics, Armonk, NY, USA) and Mplus version 8.0 are used for data analysis. Descriptive analyses of diabetic patient characteristics, cognitive function, and PCM and MCS of HRQoL were performed. The relationship between cognitive function and HRQoL in diabetic patients at T1 and T2 was assessed using the Pearson correlation test. A repeated-measures analysis of variance was performed to assess the differences between these variables with and without the digital usage behavior. Furthermore, cross-lagged panel structural equation modeling (SEM) was used to assess the longitudinal association between cognition and HRQoL at T1 and T2. Finally, a multi-group test was performed to assess whether digital usage behavior was making a difference in this relationship. Finally, regarding the grouping of digital technologies as well, previous studies were referred to [24]. Digital technology usage was obtained from 2015 baseline data. In the CHARLS 2015 questionnaire, internet usage was measured using the following question whether respondents have used the internet in the last month (0 for no, 1 for yes).\",\n \"The variable descriptive statistics and correlation analysis results are shown in Table 3 and Table 4. The correlation analysis shows that there is a significant correlation between cognition at T1 and T2 and HRQoL levels at T1 and T2, indicating that cognitive function and HRQoL levels in middle-aged and older diabetic patients have a certain relationship, which is showing stability. Meanwhile, simultaneous and sequential correlations between cognitive function and HRQoL levels are also significant. The correlation coefficients between cognitive function and HRQoL at T1 are 0.28 (p < 0.01) and 0.37 (p < 0.01); the correlation coefficients between cognitive function at T2 and HRQoL at T2 are 0.32 (p < 0.01) and 0.30 (p < 0.01); the correlation coefficients between cognitive function at T1 and HRQoL at T2 are 0.25 (p < 0.01) and 0.22 (p < 0.01); and the correlation coefficients between HRQoL at T1 and cognitive function at T2 are 0.32 (p < 0.01) and 0.36 (p < 0.01). This indicates that cognitive function is basically consistent with the synchronous and stable correlations of HRQoL levels, which is in line with the basic assumptions of the cross-lag design.\",\n \"With cognitive function as the dependent variable, a 2 (test time: T1/T2) × 2 (digital usage behavior: use/non-use) repeated measures square analysis was performed. The results show that the testing time is the main and most significant effect (F = 11.21, p < 0.001, η2 = 0.01). The cognitive function level at T2 is significantly lower than at T1, and there are developmental differences. The main effect of digital usage behavior is significant (F = 36.325, p < 0.001, η2 = 0.06), and the cognitive function of patients using digital technology is significantly better than those patients without digital technology. The interaction between the two is not significant (F = 2.22, p > 0.05, η2 = 0.04).\\nWith PCS as the dependent variable, a 2 (test time: T1/T2) × 2 (digital usage behavior: use/non-use) repeated measures square analysis was performed. The results show that the testing time is the main and most significant effect (F =16.25, p < 0.001, η2 = 0.03), the PCS level of the post-test is significantly lower than that of the pre-test, and there is a developmental difference. The main effect of digital usage behavior is significant (F = 36.54, p < 0.001, η2 = 0.06), and the PCS of patients using digital technology is significantly better than those not using digital technology. The interaction between the two is not significant (F = 0.18, p > 0.05, η2 = 0.00).\\nWith MCS as the dependent variable, a 2 (test time: T1/T2) × 2 (digital usage behavior: use/non-use) repeated measures square analysis was performed. The results showed that the testing time is the main and most significant effect (F = 44.93, p < 0.001, η2 = 0.07), the MCS level of the post-test is significantly lower than the pre-test, and there is a developmental difference. The main effect of digital usage behavior is significant (F = 50.97, p < 0.001, η2 = 0.08), and the MCS of patients using digital technology is significantly better than those not using digital technology. The interaction between the two is significant (F = 4.43, p < 0.05, η2 = 0.08).\",\n \"In the cross-lag model which this study applied, HRQoL at T2 was predicted by cognitive function at T1, and cognitive function at T2 was predicted by HRQoL at T1. After the corresponding control variables were added to the models, both models achieved acceptable fitness criteria (Model 1: RMSEA = 0.093; CFI = 0.994, TCL = 0.912; Model 2: RMSEA = 0.098; CFI = 0.984, TCL = 0.901).\\nThe cross-lag relationship between cognitive function and PCS scores at two points in time is shown from the test of Model 1 in Figure 2. First, in the same time period, the baseline association between cognitive function and PCS is significantly positive (B = 0.19, p < 0.01), implying better cognitive performance in middle-aged and older diabetic patients with higher PCS scores at T1, and vice versa. Second, there is no significant correlation between cognitive function at T1 and PCS at T2 (B = 0.05, p > 0.01), but PCS at T1 is positively correlated with cognitive function at T2 (B = 0.12, p < 0.01). This suggests that there is a positive and significant relationship between PCS and cognitive function in middle-aged and older diabetic patients over time, but early cognitive function in middle-aged and older diabetic patients does not affect later PSC.\\nThe cross-lag relationship between cognitive function and MCS scores at the two points in time is shown in Model 2 of Figure 2. First, in the same time period, the baseline association between cognitive function and MCS is significantly positively correlated (B = 0.31, p < 0.01), which implies a better cognitive performance in middle-aged and older diabetic patients with higher MCS scores at T1, and vice versa. Second, there is no significant correlation between cognitive function at T1 and MCS at T2 (B = 0.05, p > 0.01), but there is a significant positive correlation between MCS at T1 and cognitive function at T2 (B = 0.14, p < 0.01). This suggests that there is a positive and significant relationship between MCS and cognitive function in middle-aged and older diabetic patients over time, but early cognitive function in middle-aged and older diabetic patients does not affect later MCS. In conclusion, there may be a one-way causal relationship between cognitive function and HRQoL, and the causal direction is from HRQoL to cognition.\",\n \"In order to investigate whether the cross-lag relationship between cognitive function and HRQoL differs in digital usage behavior, the study performed a multi-group analysis. All diabetic patients were divided into two groups, one representing non-digital usage behavior (Figure 3), and the other representing digital usage behavior (Figure 4). In the grouped cross-lag model, the corresponding control variables were also added to the model. The models in Figure 3 and Figure 4 met fitness criteria (Figure 3: Model 3: RMSEA = 0.073, CFI = 0.996, TCL = 0.901; Model 4: RMSEA = 0.100, CFI = 0.990, TCL = 0.936. Figure 4: Model 5: RMSEA = 0.073, CFI = 0.996, TCL = 0.941; Model 6: RMSEA = 0.079, CFI = 0.970, TCL = 0.936). In both sets of models, the study focused on the relationship between cognitive function and HRQoL at a point in time, as well as their relationship at the time of follow-up research.\\nFor middle-aged and older diabetic patients using digital technology, although the model could be fitted, cognitive function at T1 did not significantly predict HRQoL at T2 (PCS: B = 0.30, p > 0.01; MCS: B = 0.14, p > 0.01), and vice versa (PCS: B = 0.06, p > 0.01; MCS: B = 0.05, p > 0.01) (see Figure 3). For diabetic patients who did not use digital technology, although T1 cognitive function did not significantly predict T2 HRQoL (PCS: B = 0.04, p > 0.01; MCS: B = 0.05, p > 0.01), T1 HRQoL significantly predicted T2 cognitive function (PCS: B = 0.10, p < 0.01; MCS: B = 0.11, p < 0.01), and the results were consistent across all diabetic patients (see Figure 4). This shows that the cross-lag model of cognitive function and HRQoL in middle-aged and older diabetic patients shows variance in the use of digital technology, that is, this relationship will be affected by digital usage behavior.\"\n]"},"other_sections_sec_types":{"kind":"list like","value":[null,null,null,null,null,null,null,null,null,null],"string":"[\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null\n]"},"all_sections_titles":{"kind":"list like","value":["1. Introduction","2. Materials and Methods","2.1. Participants","2.2. Measurements","2.2.1. Cognitive Functioning","2.2.2. HRQoL","2.2.3. Socio-Demographic Variables","2.3. Statistical Methods","3. Results","3.1. Descriptive Statistics","3.2. Stability Analysis of Cognitive Function and HRQoL","3.3. Cross-Lagged Analysis of Cognitive Function and HRQoL","3.4. Heterogeneity Analysis","4. Discussion","5. Conclusions"],"string":"[\n \"1. Introduction\",\n \"2. Materials and Methods\",\n \"2.1. Participants\",\n \"2.2. Measurements\",\n \"2.2.1. Cognitive Functioning\",\n \"2.2.2. HRQoL\",\n \"2.2.3. Socio-Demographic Variables\",\n \"2.3. Statistical Methods\",\n \"3. Results\",\n \"3.1. Descriptive Statistics\",\n \"3.2. Stability Analysis of Cognitive Function and HRQoL\",\n \"3.3. Cross-Lagged Analysis of Cognitive Function and HRQoL\",\n \"3.4. Heterogeneity Analysis\",\n \"4. Discussion\",\n \"5. Conclusions\"\n]"},"all_sections_texts":{"kind":"list like","value":["Cognitive impairment is one of the common complications in elderly patients [1]. For example, in China, the prevalence of mild cognitive impairments in the aging population (aged 60 and above) is 14.71% [2], and as age increases, its annual rate of progression to dementia is between 8% and 15% [2]. At the same time, cognitive impairment is also one of the common complications of diabetic patients. It is estimated that by 2045, about 170 million elderly people in China will be diabetic patients [3]. Therefore, the academic community has paid great attention to the cognitive function of the diabetic population, and found that the cognitive function of the diabetic population is closely related to the quality of life (QoL) of the elderly [4].\nHRQoL is the perceived physical and mental health of an individual or group over time, including both physical component summary (PCS) and mental component summary (MCS) [5]. In contrast with the QoL, HRQoL pays special attention to the impact of disease and treatment process on the life of a person or a group [6]. Existing studies have found that changes in cognitive function are positively correlated with changes in HRQoL, and play a predictive role in future changes in HRQoL [7,8]. For example, cognitive decline was found to be a predictor of HRQoL decline in studies on multiple sclerosis patients, AIDS patients, and older women [4,9,10]. At the same time, some scholars have found that changes in HRQoL—whether it is the PCS or the MCS of HRQoL—can also predict cognitive changes in individuals in the future [11]. For example, Ezzati’s 2019 study demonstrated that changes in HRQoL preceded changes in cognition and predicted the occurrence of dementia [12]. Thus, cognitive function may be bi-directionally associated with HRQoL.\nThe bidirectional relationship between cognitive function and HRQoL may be more pronounced in terms of diabetic patients. This is because not only are people with diabetes 1–2 times more likely to develop cognitive risks than the general population [13,14], but this risk will increase over time [15]. At the same time, with the deepening of the research on HRQoL, the medical community generally believes that the core of diabetes management should include HRQoL maintenance in addition to prevention and delay of its complications [16]. In summary, this research aims to focus on middle-aged and older diabetic patients (over 45 years old) in China—not only because China has one of the largest numbers of diabetic patients in the world, but also because the age of the population affected with diabetes is showing a downward trend [3,17]. Thus, we propose the first hypothesis: in middle-aged and older Chinese patients with diabetes, the relationship between cognitive function and HRQoL may be bidirectional.\nTo our knowledge, previous studies on cognitive function and HRQoL have focused on unmodifiable clinical factors, such as age, gender, etc., with a lack of studies on modifiable factors [4]. Digital technologies such as the Internet and smartphones have attracted the attention of psychologists because of their portability, rapidity, and immediacy. In clinical medicine, digital usage behaviors are often used for managing and intervening with patients with diabetes and cognitive dysfunction [18]. Thus, digital technology may be seen as a modifiable factor in the relationship between cognitive function and HRQoL. We also found that with the continuous development and improvement of digital technology, digital usage behavior can have a profound impact on the cognitive function of patients with chronic diseases [18,19,20]. In addition to this, it is important that digital usage behavior, such as Internet use, can also have a certain impact on HRQoL [21]. Previous research in other populations found that using the Internet resulted in significantly higher PCS and physical pain scores [22]. According to self-determination theory, we believe that when individuals use the internet for leisure or work, their needs are met, which in turn produces positive long-term psychological outcomes such as quality of life [23]. So far, existing research is leaning towards the belief that an increase in digital usage behavior can improve the health of individuals [24]. However, there are also studies holding the opposite view; they believe that the long-term digital usage behavior reduces patients’ time for outdoor activities, and that a large amount of negative information received due to digital usage behavior is more likely to cause mood swings, which may have side effects on patients’ recovery [25]. Therefore, it is easy to find that digital usage behavior is likely to have various effects, so we propose another hypothesis: in middle-aged and older Chinese patients with diabetes, the relationship between cognitive function and HRQoL may differ considering digital usage behavior.\nTo date, most studies on the association of cognitive function with HRQoL have used cross-sectional designs, and have failed to elucidate the direction of influence between cognitive function and HRQoL due to the limitations of cross-sectional studies in terms of making causal inferences and explaining the direction of association. Therefore, it is necessary to further explore, especially for diabetic patients, the longitudinal association between the cognitive function and HRQoL, and to clarify the direction of their effects. As a longitudinal study, this study attempts to use a cross-lag model to explore the relationship between cognitive function and HRQoL in Chinese middle-aged and older diabetic patients, as well as the direction of this relationship and whether there were differences in digital usage behavior. This research is attempting to explore the longitudinal relationship between cognition and HRQoL in middle-aged and older diabetic patients, and to provide evidence which can promote their health.\nIt should be noted that, due to the existence of multiple databases in China, in addition to the China Health and Retirement Longitudinal Survey (CHARLS), most scholars use databases such as the China Household Finance Survey when researching topics related to public health [26]. There is more content regarding the physical and mental health of middle-aged and elderly people, so we chose CHARLS as our data source.","2.1. Participants The China Health and Retirement Longitudinal Survey (CHARLS) is a representative follow-up survey of middle-aged and older people in China, chaired by the National Research Institute of Development at Peking University. The study protocol was approved by the Institutional Review Board of Peking University (approval number: IRB00001052-11015), and the study protocol complies with the ethical guidelines of the 1975 Declaration of Helsinki. In order to obtain the relevant data, we applied for the CHARLS database online on 31 August 2022, and approval was quickly obtained. This study used two waves of CHARLS data, from 2015 (T1) and 2018 (T2). CHARLS data can be accessed through its official website (https://charls.pku.edu.cn (accessed on 31 August 2022)).\nFigure 1 shows the detailed process for including and excluding study participants. Wave 3 was used as the baseline data set for this study, and individuals who lacked information on HRQoL, cognitive function, digital usage behavior, and covariates were excluded from this study. Subsequently, new participants in 2018 were further excluded. Individuals lacking information on digital usage behavior, cognitive function, and covariates were also excluded from this study. In addition, in the sample selection, the research team excluded individuals with other diseases (such as cognitive diseases that may affect the detection process, mental diseases, and other diseases related to aging), and included individuals with diabetes only. In addition, the abbreviations of the main variables involved in the study are organized in Table 1.\nThe China Health and Retirement Longitudinal Survey (CHARLS) is a representative follow-up survey of middle-aged and older people in China, chaired by the National Research Institute of Development at Peking University. The study protocol was approved by the Institutional Review Board of Peking University (approval number: IRB00001052-11015), and the study protocol complies with the ethical guidelines of the 1975 Declaration of Helsinki. In order to obtain the relevant data, we applied for the CHARLS database online on 31 August 2022, and approval was quickly obtained. This study used two waves of CHARLS data, from 2015 (T1) and 2018 (T2). CHARLS data can be accessed through its official website (https://charls.pku.edu.cn (accessed on 31 August 2022)).\nFigure 1 shows the detailed process for including and excluding study participants. Wave 3 was used as the baseline data set for this study, and individuals who lacked information on HRQoL, cognitive function, digital usage behavior, and covariates were excluded from this study. Subsequently, new participants in 2018 were further excluded. Individuals lacking information on digital usage behavior, cognitive function, and covariates were also excluded from this study. In addition, in the sample selection, the research team excluded individuals with other diseases (such as cognitive diseases that may affect the detection process, mental diseases, and other diseases related to aging), and included individuals with diabetes only. In addition, the abbreviations of the main variables involved in the study are organized in Table 1.\n2.2. Measurements 2.2.1. Cognitive Functioning This study obtained data on cognitive function from baseline data in 2015 and follow-up data in 2018. The baseline data stipulated by CHARLS is from 2011, but we include CHARLS 2015 (T1) as the baseline data in our study. CHARLS is similar to the cognitive assessment used in the American Health and Retirement Study, and has constructed cognitive function evaluation criteria from the same two aspects of memory and mental state [27,28]. Furthermore, previous studies have also used the CHARLS cognitive function assessment criteria for class-correspondence studies. Firstly, memory evaluation includes immediate word recall (0–10 points) and delayed word recall (0–10 points). Mental state is measured from three dimensions: orientation, visual construction, and mathematical performance. Orientation (0–5 points) is measured by asking respondents to name the date, day of the week, and season; visual construction is assessed by drawing a previously displayed picture (0–1 points); and mathematical performance (0–5 points) is measured by asking respondents to subtract 7 consecutive times from 100. The scores of the participants’ cognitive function are equal to the sum of the scores of memory and mental state. Cognitive function scores range from 0 to 31 points, higher cognitive function scores indicating better cognitive function. Finally, in order to obtain a composite measure of cognitive function, this study normalized and averaged the total cognitive function score by adding up memory and mental state scores [29].\nThis study obtained data on cognitive function from baseline data in 2015 and follow-up data in 2018. The baseline data stipulated by CHARLS is from 2011, but we include CHARLS 2015 (T1) as the baseline data in our study. CHARLS is similar to the cognitive assessment used in the American Health and Retirement Study, and has constructed cognitive function evaluation criteria from the same two aspects of memory and mental state [27,28]. Furthermore, previous studies have also used the CHARLS cognitive function assessment criteria for class-correspondence studies. Firstly, memory evaluation includes immediate word recall (0–10 points) and delayed word recall (0–10 points). Mental state is measured from three dimensions: orientation, visual construction, and mathematical performance. Orientation (0–5 points) is measured by asking respondents to name the date, day of the week, and season; visual construction is assessed by drawing a previously displayed picture (0–1 points); and mathematical performance (0–5 points) is measured by asking respondents to subtract 7 consecutive times from 100. The scores of the participants’ cognitive function are equal to the sum of the scores of memory and mental state. Cognitive function scores range from 0 to 31 points, higher cognitive function scores indicating better cognitive function. Finally, in order to obtain a composite measure of cognitive function, this study normalized and averaged the total cognitive function score by adding up memory and mental state scores [29].\n2.2.2. HRQoL This study used a new scale construction based on the variables of the Short Form 36 (SF-36) and the CHARLS questionnaire to measure HRQoL in diabetic patients. The construction of the new scale was derived from the eight dimensions of SF-36, and the corresponding variables of CHARLS were selected to assess the following eight dimensions (Table 2): physical function (PF), role–body (RP), body pain (BP), general health (GH), vitality (VT), social functioning (SF), role–emotion (RE), and mental health (MH). The scores for the above eight dimensions were calculated by adding up the category scores and then converting the raw scores to a 0 to 100 scale. Scores from the eight subscales were aggregated into two overall scores according to the conceptual model of the SF-36 [30]. Physical function, body roles, body pain, and general perceptions of health were calculated as PCS, and mental health, vitality, emotional role, and social functioning were calculated as MCS. Although the HRQoL questionnaire based on CHARLS is slightly different from other HRQoL questionnaires, they all have similar focuses, including physical, emotional, and social diversity. The questionnaire has been determined to be effective in the Chinese population [31], and has already been used in related research [32].\nThis study used a new scale construction based on the variables of the Short Form 36 (SF-36) and the CHARLS questionnaire to measure HRQoL in diabetic patients. The construction of the new scale was derived from the eight dimensions of SF-36, and the corresponding variables of CHARLS were selected to assess the following eight dimensions (Table 2): physical function (PF), role–body (RP), body pain (BP), general health (GH), vitality (VT), social functioning (SF), role–emotion (RE), and mental health (MH). The scores for the above eight dimensions were calculated by adding up the category scores and then converting the raw scores to a 0 to 100 scale. Scores from the eight subscales were aggregated into two overall scores according to the conceptual model of the SF-36 [30]. Physical function, body roles, body pain, and general perceptions of health were calculated as PCS, and mental health, vitality, emotional role, and social functioning were calculated as MCS. Although the HRQoL questionnaire based on CHARLS is slightly different from other HRQoL questionnaires, they all have similar focuses, including physical, emotional, and social diversity. The questionnaire has been determined to be effective in the Chinese population [31], and has already been used in related research [32].\n2.2.3. Socio-Demographic Variables In order to minimize the possibility of other variables influencing the cognitive function–HRQoL relationship study, and to simplify the model, this research controlled for several specific covariates associated with cognitive function and HRQoL. According to previous studies, all covariates were based on baseline data (CHARLS 2015) [32]. Firstly, population control variables include age, gender, and education status. Secondly, since HRQoL can be divided into two parts, PCS and MCS, this study used different control variables in the models for cognitive function, PCS, and MCS. This research included depression as well as current smoking and drinking habits as control variables for PCS scores. In this research, PCS scores are also considered with marital status, depression, physical activity, and current smoking and drinking habits as control variables.\nIn order to minimize the possibility of other variables influencing the cognitive function–HRQoL relationship study, and to simplify the model, this research controlled for several specific covariates associated with cognitive function and HRQoL. According to previous studies, all covariates were based on baseline data (CHARLS 2015) [32]. Firstly, population control variables include age, gender, and education status. Secondly, since HRQoL can be divided into two parts, PCS and MCS, this study used different control variables in the models for cognitive function, PCS, and MCS. This research included depression as well as current smoking and drinking habits as control variables for PCS scores. In this research, PCS scores are also considered with marital status, depression, physical activity, and current smoking and drinking habits as control variables.\n2.2.1. Cognitive Functioning This study obtained data on cognitive function from baseline data in 2015 and follow-up data in 2018. The baseline data stipulated by CHARLS is from 2011, but we include CHARLS 2015 (T1) as the baseline data in our study. CHARLS is similar to the cognitive assessment used in the American Health and Retirement Study, and has constructed cognitive function evaluation criteria from the same two aspects of memory and mental state [27,28]. Furthermore, previous studies have also used the CHARLS cognitive function assessment criteria for class-correspondence studies. Firstly, memory evaluation includes immediate word recall (0–10 points) and delayed word recall (0–10 points). Mental state is measured from three dimensions: orientation, visual construction, and mathematical performance. Orientation (0–5 points) is measured by asking respondents to name the date, day of the week, and season; visual construction is assessed by drawing a previously displayed picture (0–1 points); and mathematical performance (0–5 points) is measured by asking respondents to subtract 7 consecutive times from 100. The scores of the participants’ cognitive function are equal to the sum of the scores of memory and mental state. Cognitive function scores range from 0 to 31 points, higher cognitive function scores indicating better cognitive function. Finally, in order to obtain a composite measure of cognitive function, this study normalized and averaged the total cognitive function score by adding up memory and mental state scores [29].\nThis study obtained data on cognitive function from baseline data in 2015 and follow-up data in 2018. The baseline data stipulated by CHARLS is from 2011, but we include CHARLS 2015 (T1) as the baseline data in our study. CHARLS is similar to the cognitive assessment used in the American Health and Retirement Study, and has constructed cognitive function evaluation criteria from the same two aspects of memory and mental state [27,28]. Furthermore, previous studies have also used the CHARLS cognitive function assessment criteria for class-correspondence studies. Firstly, memory evaluation includes immediate word recall (0–10 points) and delayed word recall (0–10 points). Mental state is measured from three dimensions: orientation, visual construction, and mathematical performance. Orientation (0–5 points) is measured by asking respondents to name the date, day of the week, and season; visual construction is assessed by drawing a previously displayed picture (0–1 points); and mathematical performance (0–5 points) is measured by asking respondents to subtract 7 consecutive times from 100. The scores of the participants’ cognitive function are equal to the sum of the scores of memory and mental state. Cognitive function scores range from 0 to 31 points, higher cognitive function scores indicating better cognitive function. Finally, in order to obtain a composite measure of cognitive function, this study normalized and averaged the total cognitive function score by adding up memory and mental state scores [29].\n2.2.2. HRQoL This study used a new scale construction based on the variables of the Short Form 36 (SF-36) and the CHARLS questionnaire to measure HRQoL in diabetic patients. The construction of the new scale was derived from the eight dimensions of SF-36, and the corresponding variables of CHARLS were selected to assess the following eight dimensions (Table 2): physical function (PF), role–body (RP), body pain (BP), general health (GH), vitality (VT), social functioning (SF), role–emotion (RE), and mental health (MH). The scores for the above eight dimensions were calculated by adding up the category scores and then converting the raw scores to a 0 to 100 scale. Scores from the eight subscales were aggregated into two overall scores according to the conceptual model of the SF-36 [30]. Physical function, body roles, body pain, and general perceptions of health were calculated as PCS, and mental health, vitality, emotional role, and social functioning were calculated as MCS. Although the HRQoL questionnaire based on CHARLS is slightly different from other HRQoL questionnaires, they all have similar focuses, including physical, emotional, and social diversity. The questionnaire has been determined to be effective in the Chinese population [31], and has already been used in related research [32].\nThis study used a new scale construction based on the variables of the Short Form 36 (SF-36) and the CHARLS questionnaire to measure HRQoL in diabetic patients. The construction of the new scale was derived from the eight dimensions of SF-36, and the corresponding variables of CHARLS were selected to assess the following eight dimensions (Table 2): physical function (PF), role–body (RP), body pain (BP), general health (GH), vitality (VT), social functioning (SF), role–emotion (RE), and mental health (MH). The scores for the above eight dimensions were calculated by adding up the category scores and then converting the raw scores to a 0 to 100 scale. Scores from the eight subscales were aggregated into two overall scores according to the conceptual model of the SF-36 [30]. Physical function, body roles, body pain, and general perceptions of health were calculated as PCS, and mental health, vitality, emotional role, and social functioning were calculated as MCS. Although the HRQoL questionnaire based on CHARLS is slightly different from other HRQoL questionnaires, they all have similar focuses, including physical, emotional, and social diversity. The questionnaire has been determined to be effective in the Chinese population [31], and has already been used in related research [32].\n2.2.3. Socio-Demographic Variables In order to minimize the possibility of other variables influencing the cognitive function–HRQoL relationship study, and to simplify the model, this research controlled for several specific covariates associated with cognitive function and HRQoL. According to previous studies, all covariates were based on baseline data (CHARLS 2015) [32]. Firstly, population control variables include age, gender, and education status. Secondly, since HRQoL can be divided into two parts, PCS and MCS, this study used different control variables in the models for cognitive function, PCS, and MCS. This research included depression as well as current smoking and drinking habits as control variables for PCS scores. In this research, PCS scores are also considered with marital status, depression, physical activity, and current smoking and drinking habits as control variables.\nIn order to minimize the possibility of other variables influencing the cognitive function–HRQoL relationship study, and to simplify the model, this research controlled for several specific covariates associated with cognitive function and HRQoL. According to previous studies, all covariates were based on baseline data (CHARLS 2015) [32]. Firstly, population control variables include age, gender, and education status. Secondly, since HRQoL can be divided into two parts, PCS and MCS, this study used different control variables in the models for cognitive function, PCS, and MCS. This research included depression as well as current smoking and drinking habits as control variables for PCS scores. In this research, PCS scores are also considered with marital status, depression, physical activity, and current smoking and drinking habits as control variables.\n2.3. Statistical Methods IBM SPSS Statistics, version 23 (IBM SPSS Statistics, Armonk, NY, USA) and Mplus version 8.0 are used for data analysis. Descriptive analyses of diabetic patient characteristics, cognitive function, and PCM and MCS of HRQoL were performed. The relationship between cognitive function and HRQoL in diabetic patients at T1 and T2 was assessed using the Pearson correlation test. A repeated-measures analysis of variance was performed to assess the differences between these variables with and without the digital usage behavior. Furthermore, cross-lagged panel structural equation modeling (SEM) was used to assess the longitudinal association between cognition and HRQoL at T1 and T2. Finally, a multi-group test was performed to assess whether digital usage behavior was making a difference in this relationship. Finally, regarding the grouping of digital technologies as well, previous studies were referred to [24]. Digital technology usage was obtained from 2015 baseline data. In the CHARLS 2015 questionnaire, internet usage was measured using the following question whether respondents have used the internet in the last month (0 for no, 1 for yes).\nIBM SPSS Statistics, version 23 (IBM SPSS Statistics, Armonk, NY, USA) and Mplus version 8.0 are used for data analysis. Descriptive analyses of diabetic patient characteristics, cognitive function, and PCM and MCS of HRQoL were performed. The relationship between cognitive function and HRQoL in diabetic patients at T1 and T2 was assessed using the Pearson correlation test. A repeated-measures analysis of variance was performed to assess the differences between these variables with and without the digital usage behavior. Furthermore, cross-lagged panel structural equation modeling (SEM) was used to assess the longitudinal association between cognition and HRQoL at T1 and T2. Finally, a multi-group test was performed to assess whether digital usage behavior was making a difference in this relationship. Finally, regarding the grouping of digital technologies as well, previous studies were referred to [24]. Digital technology usage was obtained from 2015 baseline data. In the CHARLS 2015 questionnaire, internet usage was measured using the following question whether respondents have used the internet in the last month (0 for no, 1 for yes).","The China Health and Retirement Longitudinal Survey (CHARLS) is a representative follow-up survey of middle-aged and older people in China, chaired by the National Research Institute of Development at Peking University. The study protocol was approved by the Institutional Review Board of Peking University (approval number: IRB00001052-11015), and the study protocol complies with the ethical guidelines of the 1975 Declaration of Helsinki. In order to obtain the relevant data, we applied for the CHARLS database online on 31 August 2022, and approval was quickly obtained. This study used two waves of CHARLS data, from 2015 (T1) and 2018 (T2). CHARLS data can be accessed through its official website (https://charls.pku.edu.cn (accessed on 31 August 2022)).\nFigure 1 shows the detailed process for including and excluding study participants. Wave 3 was used as the baseline data set for this study, and individuals who lacked information on HRQoL, cognitive function, digital usage behavior, and covariates were excluded from this study. Subsequently, new participants in 2018 were further excluded. Individuals lacking information on digital usage behavior, cognitive function, and covariates were also excluded from this study. In addition, in the sample selection, the research team excluded individuals with other diseases (such as cognitive diseases that may affect the detection process, mental diseases, and other diseases related to aging), and included individuals with diabetes only. In addition, the abbreviations of the main variables involved in the study are organized in Table 1.","2.2.1. Cognitive Functioning This study obtained data on cognitive function from baseline data in 2015 and follow-up data in 2018. The baseline data stipulated by CHARLS is from 2011, but we include CHARLS 2015 (T1) as the baseline data in our study. CHARLS is similar to the cognitive assessment used in the American Health and Retirement Study, and has constructed cognitive function evaluation criteria from the same two aspects of memory and mental state [27,28]. Furthermore, previous studies have also used the CHARLS cognitive function assessment criteria for class-correspondence studies. Firstly, memory evaluation includes immediate word recall (0–10 points) and delayed word recall (0–10 points). Mental state is measured from three dimensions: orientation, visual construction, and mathematical performance. Orientation (0–5 points) is measured by asking respondents to name the date, day of the week, and season; visual construction is assessed by drawing a previously displayed picture (0–1 points); and mathematical performance (0–5 points) is measured by asking respondents to subtract 7 consecutive times from 100. The scores of the participants’ cognitive function are equal to the sum of the scores of memory and mental state. Cognitive function scores range from 0 to 31 points, higher cognitive function scores indicating better cognitive function. Finally, in order to obtain a composite measure of cognitive function, this study normalized and averaged the total cognitive function score by adding up memory and mental state scores [29].\nThis study obtained data on cognitive function from baseline data in 2015 and follow-up data in 2018. The baseline data stipulated by CHARLS is from 2011, but we include CHARLS 2015 (T1) as the baseline data in our study. CHARLS is similar to the cognitive assessment used in the American Health and Retirement Study, and has constructed cognitive function evaluation criteria from the same two aspects of memory and mental state [27,28]. Furthermore, previous studies have also used the CHARLS cognitive function assessment criteria for class-correspondence studies. Firstly, memory evaluation includes immediate word recall (0–10 points) and delayed word recall (0–10 points). Mental state is measured from three dimensions: orientation, visual construction, and mathematical performance. Orientation (0–5 points) is measured by asking respondents to name the date, day of the week, and season; visual construction is assessed by drawing a previously displayed picture (0–1 points); and mathematical performance (0–5 points) is measured by asking respondents to subtract 7 consecutive times from 100. The scores of the participants’ cognitive function are equal to the sum of the scores of memory and mental state. Cognitive function scores range from 0 to 31 points, higher cognitive function scores indicating better cognitive function. Finally, in order to obtain a composite measure of cognitive function, this study normalized and averaged the total cognitive function score by adding up memory and mental state scores [29].\n2.2.2. HRQoL This study used a new scale construction based on the variables of the Short Form 36 (SF-36) and the CHARLS questionnaire to measure HRQoL in diabetic patients. The construction of the new scale was derived from the eight dimensions of SF-36, and the corresponding variables of CHARLS were selected to assess the following eight dimensions (Table 2): physical function (PF), role–body (RP), body pain (BP), general health (GH), vitality (VT), social functioning (SF), role–emotion (RE), and mental health (MH). The scores for the above eight dimensions were calculated by adding up the category scores and then converting the raw scores to a 0 to 100 scale. Scores from the eight subscales were aggregated into two overall scores according to the conceptual model of the SF-36 [30]. Physical function, body roles, body pain, and general perceptions of health were calculated as PCS, and mental health, vitality, emotional role, and social functioning were calculated as MCS. Although the HRQoL questionnaire based on CHARLS is slightly different from other HRQoL questionnaires, they all have similar focuses, including physical, emotional, and social diversity. The questionnaire has been determined to be effective in the Chinese population [31], and has already been used in related research [32].\nThis study used a new scale construction based on the variables of the Short Form 36 (SF-36) and the CHARLS questionnaire to measure HRQoL in diabetic patients. The construction of the new scale was derived from the eight dimensions of SF-36, and the corresponding variables of CHARLS were selected to assess the following eight dimensions (Table 2): physical function (PF), role–body (RP), body pain (BP), general health (GH), vitality (VT), social functioning (SF), role–emotion (RE), and mental health (MH). The scores for the above eight dimensions were calculated by adding up the category scores and then converting the raw scores to a 0 to 100 scale. Scores from the eight subscales were aggregated into two overall scores according to the conceptual model of the SF-36 [30]. Physical function, body roles, body pain, and general perceptions of health were calculated as PCS, and mental health, vitality, emotional role, and social functioning were calculated as MCS. Although the HRQoL questionnaire based on CHARLS is slightly different from other HRQoL questionnaires, they all have similar focuses, including physical, emotional, and social diversity. The questionnaire has been determined to be effective in the Chinese population [31], and has already been used in related research [32].\n2.2.3. Socio-Demographic Variables In order to minimize the possibility of other variables influencing the cognitive function–HRQoL relationship study, and to simplify the model, this research controlled for several specific covariates associated with cognitive function and HRQoL. According to previous studies, all covariates were based on baseline data (CHARLS 2015) [32]. Firstly, population control variables include age, gender, and education status. Secondly, since HRQoL can be divided into two parts, PCS and MCS, this study used different control variables in the models for cognitive function, PCS, and MCS. This research included depression as well as current smoking and drinking habits as control variables for PCS scores. In this research, PCS scores are also considered with marital status, depression, physical activity, and current smoking and drinking habits as control variables.\nIn order to minimize the possibility of other variables influencing the cognitive function–HRQoL relationship study, and to simplify the model, this research controlled for several specific covariates associated with cognitive function and HRQoL. According to previous studies, all covariates were based on baseline data (CHARLS 2015) [32]. Firstly, population control variables include age, gender, and education status. Secondly, since HRQoL can be divided into two parts, PCS and MCS, this study used different control variables in the models for cognitive function, PCS, and MCS. This research included depression as well as current smoking and drinking habits as control variables for PCS scores. In this research, PCS scores are also considered with marital status, depression, physical activity, and current smoking and drinking habits as control variables.","This study obtained data on cognitive function from baseline data in 2015 and follow-up data in 2018. The baseline data stipulated by CHARLS is from 2011, but we include CHARLS 2015 (T1) as the baseline data in our study. CHARLS is similar to the cognitive assessment used in the American Health and Retirement Study, and has constructed cognitive function evaluation criteria from the same two aspects of memory and mental state [27,28]. Furthermore, previous studies have also used the CHARLS cognitive function assessment criteria for class-correspondence studies. Firstly, memory evaluation includes immediate word recall (0–10 points) and delayed word recall (0–10 points). Mental state is measured from three dimensions: orientation, visual construction, and mathematical performance. Orientation (0–5 points) is measured by asking respondents to name the date, day of the week, and season; visual construction is assessed by drawing a previously displayed picture (0–1 points); and mathematical performance (0–5 points) is measured by asking respondents to subtract 7 consecutive times from 100. The scores of the participants’ cognitive function are equal to the sum of the scores of memory and mental state. Cognitive function scores range from 0 to 31 points, higher cognitive function scores indicating better cognitive function. Finally, in order to obtain a composite measure of cognitive function, this study normalized and averaged the total cognitive function score by adding up memory and mental state scores [29].","This study used a new scale construction based on the variables of the Short Form 36 (SF-36) and the CHARLS questionnaire to measure HRQoL in diabetic patients. The construction of the new scale was derived from the eight dimensions of SF-36, and the corresponding variables of CHARLS were selected to assess the following eight dimensions (Table 2): physical function (PF), role–body (RP), body pain (BP), general health (GH), vitality (VT), social functioning (SF), role–emotion (RE), and mental health (MH). The scores for the above eight dimensions were calculated by adding up the category scores and then converting the raw scores to a 0 to 100 scale. Scores from the eight subscales were aggregated into two overall scores according to the conceptual model of the SF-36 [30]. Physical function, body roles, body pain, and general perceptions of health were calculated as PCS, and mental health, vitality, emotional role, and social functioning were calculated as MCS. Although the HRQoL questionnaire based on CHARLS is slightly different from other HRQoL questionnaires, they all have similar focuses, including physical, emotional, and social diversity. The questionnaire has been determined to be effective in the Chinese population [31], and has already been used in related research [32].","In order to minimize the possibility of other variables influencing the cognitive function–HRQoL relationship study, and to simplify the model, this research controlled for several specific covariates associated with cognitive function and HRQoL. According to previous studies, all covariates were based on baseline data (CHARLS 2015) [32]. Firstly, population control variables include age, gender, and education status. Secondly, since HRQoL can be divided into two parts, PCS and MCS, this study used different control variables in the models for cognitive function, PCS, and MCS. This research included depression as well as current smoking and drinking habits as control variables for PCS scores. In this research, PCS scores are also considered with marital status, depression, physical activity, and current smoking and drinking habits as control variables.","IBM SPSS Statistics, version 23 (IBM SPSS Statistics, Armonk, NY, USA) and Mplus version 8.0 are used for data analysis. Descriptive analyses of diabetic patient characteristics, cognitive function, and PCM and MCS of HRQoL were performed. The relationship between cognitive function and HRQoL in diabetic patients at T1 and T2 was assessed using the Pearson correlation test. A repeated-measures analysis of variance was performed to assess the differences between these variables with and without the digital usage behavior. Furthermore, cross-lagged panel structural equation modeling (SEM) was used to assess the longitudinal association between cognition and HRQoL at T1 and T2. Finally, a multi-group test was performed to assess whether digital usage behavior was making a difference in this relationship. Finally, regarding the grouping of digital technologies as well, previous studies were referred to [24]. Digital technology usage was obtained from 2015 baseline data. In the CHARLS 2015 questionnaire, internet usage was measured using the following question whether respondents have used the internet in the last month (0 for no, 1 for yes).","3.1. Descriptive Statistics The variable descriptive statistics and correlation analysis results are shown in Table 3 and Table 4. The correlation analysis shows that there is a significant correlation between cognition at T1 and T2 and HRQoL levels at T1 and T2, indicating that cognitive function and HRQoL levels in middle-aged and older diabetic patients have a certain relationship, which is showing stability. Meanwhile, simultaneous and sequential correlations between cognitive function and HRQoL levels are also significant. The correlation coefficients between cognitive function and HRQoL at T1 are 0.28 (p < 0.01) and 0.37 (p < 0.01); the correlation coefficients between cognitive function at T2 and HRQoL at T2 are 0.32 (p < 0.01) and 0.30 (p < 0.01); the correlation coefficients between cognitive function at T1 and HRQoL at T2 are 0.25 (p < 0.01) and 0.22 (p < 0.01); and the correlation coefficients between HRQoL at T1 and cognitive function at T2 are 0.32 (p < 0.01) and 0.36 (p < 0.01). This indicates that cognitive function is basically consistent with the synchronous and stable correlations of HRQoL levels, which is in line with the basic assumptions of the cross-lag design.\nThe variable descriptive statistics and correlation analysis results are shown in Table 3 and Table 4. The correlation analysis shows that there is a significant correlation between cognition at T1 and T2 and HRQoL levels at T1 and T2, indicating that cognitive function and HRQoL levels in middle-aged and older diabetic patients have a certain relationship, which is showing stability. Meanwhile, simultaneous and sequential correlations between cognitive function and HRQoL levels are also significant. The correlation coefficients between cognitive function and HRQoL at T1 are 0.28 (p < 0.01) and 0.37 (p < 0.01); the correlation coefficients between cognitive function at T2 and HRQoL at T2 are 0.32 (p < 0.01) and 0.30 (p < 0.01); the correlation coefficients between cognitive function at T1 and HRQoL at T2 are 0.25 (p < 0.01) and 0.22 (p < 0.01); and the correlation coefficients between HRQoL at T1 and cognitive function at T2 are 0.32 (p < 0.01) and 0.36 (p < 0.01). This indicates that cognitive function is basically consistent with the synchronous and stable correlations of HRQoL levels, which is in line with the basic assumptions of the cross-lag design.\n3.2. Stability Analysis of Cognitive Function and HRQoL With cognitive function as the dependent variable, a 2 (test time: T1/T2) × 2 (digital usage behavior: use/non-use) repeated measures square analysis was performed. The results show that the testing time is the main and most significant effect (F = 11.21, p < 0.001, η2 = 0.01). The cognitive function level at T2 is significantly lower than at T1, and there are developmental differences. The main effect of digital usage behavior is significant (F = 36.325, p < 0.001, η2 = 0.06), and the cognitive function of patients using digital technology is significantly better than those patients without digital technology. The interaction between the two is not significant (F = 2.22, p > 0.05, η2 = 0.04).\nWith PCS as the dependent variable, a 2 (test time: T1/T2) × 2 (digital usage behavior: use/non-use) repeated measures square analysis was performed. The results show that the testing time is the main and most significant effect (F =16.25, p < 0.001, η2 = 0.03), the PCS level of the post-test is significantly lower than that of the pre-test, and there is a developmental difference. The main effect of digital usage behavior is significant (F = 36.54, p < 0.001, η2 = 0.06), and the PCS of patients using digital technology is significantly better than those not using digital technology. The interaction between the two is not significant (F = 0.18, p > 0.05, η2 = 0.00).\nWith MCS as the dependent variable, a 2 (test time: T1/T2) × 2 (digital usage behavior: use/non-use) repeated measures square analysis was performed. The results showed that the testing time is the main and most significant effect (F = 44.93, p < 0.001, η2 = 0.07), the MCS level of the post-test is significantly lower than the pre-test, and there is a developmental difference. The main effect of digital usage behavior is significant (F = 50.97, p < 0.001, η2 = 0.08), and the MCS of patients using digital technology is significantly better than those not using digital technology. The interaction between the two is significant (F = 4.43, p < 0.05, η2 = 0.08).\nWith cognitive function as the dependent variable, a 2 (test time: T1/T2) × 2 (digital usage behavior: use/non-use) repeated measures square analysis was performed. The results show that the testing time is the main and most significant effect (F = 11.21, p < 0.001, η2 = 0.01). The cognitive function level at T2 is significantly lower than at T1, and there are developmental differences. The main effect of digital usage behavior is significant (F = 36.325, p < 0.001, η2 = 0.06), and the cognitive function of patients using digital technology is significantly better than those patients without digital technology. The interaction between the two is not significant (F = 2.22, p > 0.05, η2 = 0.04).\nWith PCS as the dependent variable, a 2 (test time: T1/T2) × 2 (digital usage behavior: use/non-use) repeated measures square analysis was performed. The results show that the testing time is the main and most significant effect (F =16.25, p < 0.001, η2 = 0.03), the PCS level of the post-test is significantly lower than that of the pre-test, and there is a developmental difference. The main effect of digital usage behavior is significant (F = 36.54, p < 0.001, η2 = 0.06), and the PCS of patients using digital technology is significantly better than those not using digital technology. The interaction between the two is not significant (F = 0.18, p > 0.05, η2 = 0.00).\nWith MCS as the dependent variable, a 2 (test time: T1/T2) × 2 (digital usage behavior: use/non-use) repeated measures square analysis was performed. The results showed that the testing time is the main and most significant effect (F = 44.93, p < 0.001, η2 = 0.07), the MCS level of the post-test is significantly lower than the pre-test, and there is a developmental difference. The main effect of digital usage behavior is significant (F = 50.97, p < 0.001, η2 = 0.08), and the MCS of patients using digital technology is significantly better than those not using digital technology. The interaction between the two is significant (F = 4.43, p < 0.05, η2 = 0.08).\n3.3. Cross-Lagged Analysis of Cognitive Function and HRQoL In the cross-lag model which this study applied, HRQoL at T2 was predicted by cognitive function at T1, and cognitive function at T2 was predicted by HRQoL at T1. After the corresponding control variables were added to the models, both models achieved acceptable fitness criteria (Model 1: RMSEA = 0.093; CFI = 0.994, TCL = 0.912; Model 2: RMSEA = 0.098; CFI = 0.984, TCL = 0.901).\nThe cross-lag relationship between cognitive function and PCS scores at two points in time is shown from the test of Model 1 in Figure 2. First, in the same time period, the baseline association between cognitive function and PCS is significantly positive (B = 0.19, p < 0.01), implying better cognitive performance in middle-aged and older diabetic patients with higher PCS scores at T1, and vice versa. Second, there is no significant correlation between cognitive function at T1 and PCS at T2 (B = 0.05, p > 0.01), but PCS at T1 is positively correlated with cognitive function at T2 (B = 0.12, p < 0.01). This suggests that there is a positive and significant relationship between PCS and cognitive function in middle-aged and older diabetic patients over time, but early cognitive function in middle-aged and older diabetic patients does not affect later PSC.\nThe cross-lag relationship between cognitive function and MCS scores at the two points in time is shown in Model 2 of Figure 2. First, in the same time period, the baseline association between cognitive function and MCS is significantly positively correlated (B = 0.31, p < 0.01), which implies a better cognitive performance in middle-aged and older diabetic patients with higher MCS scores at T1, and vice versa. Second, there is no significant correlation between cognitive function at T1 and MCS at T2 (B = 0.05, p > 0.01), but there is a significant positive correlation between MCS at T1 and cognitive function at T2 (B = 0.14, p < 0.01). This suggests that there is a positive and significant relationship between MCS and cognitive function in middle-aged and older diabetic patients over time, but early cognitive function in middle-aged and older diabetic patients does not affect later MCS. In conclusion, there may be a one-way causal relationship between cognitive function and HRQoL, and the causal direction is from HRQoL to cognition.\nIn the cross-lag model which this study applied, HRQoL at T2 was predicted by cognitive function at T1, and cognitive function at T2 was predicted by HRQoL at T1. After the corresponding control variables were added to the models, both models achieved acceptable fitness criteria (Model 1: RMSEA = 0.093; CFI = 0.994, TCL = 0.912; Model 2: RMSEA = 0.098; CFI = 0.984, TCL = 0.901).\nThe cross-lag relationship between cognitive function and PCS scores at two points in time is shown from the test of Model 1 in Figure 2. First, in the same time period, the baseline association between cognitive function and PCS is significantly positive (B = 0.19, p < 0.01), implying better cognitive performance in middle-aged and older diabetic patients with higher PCS scores at T1, and vice versa. Second, there is no significant correlation between cognitive function at T1 and PCS at T2 (B = 0.05, p > 0.01), but PCS at T1 is positively correlated with cognitive function at T2 (B = 0.12, p < 0.01). This suggests that there is a positive and significant relationship between PCS and cognitive function in middle-aged and older diabetic patients over time, but early cognitive function in middle-aged and older diabetic patients does not affect later PSC.\nThe cross-lag relationship between cognitive function and MCS scores at the two points in time is shown in Model 2 of Figure 2. First, in the same time period, the baseline association between cognitive function and MCS is significantly positively correlated (B = 0.31, p < 0.01), which implies a better cognitive performance in middle-aged and older diabetic patients with higher MCS scores at T1, and vice versa. Second, there is no significant correlation between cognitive function at T1 and MCS at T2 (B = 0.05, p > 0.01), but there is a significant positive correlation between MCS at T1 and cognitive function at T2 (B = 0.14, p < 0.01). This suggests that there is a positive and significant relationship between MCS and cognitive function in middle-aged and older diabetic patients over time, but early cognitive function in middle-aged and older diabetic patients does not affect later MCS. In conclusion, there may be a one-way causal relationship between cognitive function and HRQoL, and the causal direction is from HRQoL to cognition.\n3.4. Heterogeneity Analysis In order to investigate whether the cross-lag relationship between cognitive function and HRQoL differs in digital usage behavior, the study performed a multi-group analysis. All diabetic patients were divided into two groups, one representing non-digital usage behavior (Figure 3), and the other representing digital usage behavior (Figure 4). In the grouped cross-lag model, the corresponding control variables were also added to the model. The models in Figure 3 and Figure 4 met fitness criteria (Figure 3: Model 3: RMSEA = 0.073, CFI = 0.996, TCL = 0.901; Model 4: RMSEA = 0.100, CFI = 0.990, TCL = 0.936. Figure 4: Model 5: RMSEA = 0.073, CFI = 0.996, TCL = 0.941; Model 6: RMSEA = 0.079, CFI = 0.970, TCL = 0.936). In both sets of models, the study focused on the relationship between cognitive function and HRQoL at a point in time, as well as their relationship at the time of follow-up research.\nFor middle-aged and older diabetic patients using digital technology, although the model could be fitted, cognitive function at T1 did not significantly predict HRQoL at T2 (PCS: B = 0.30, p > 0.01; MCS: B = 0.14, p > 0.01), and vice versa (PCS: B = 0.06, p > 0.01; MCS: B = 0.05, p > 0.01) (see Figure 3). For diabetic patients who did not use digital technology, although T1 cognitive function did not significantly predict T2 HRQoL (PCS: B = 0.04, p > 0.01; MCS: B = 0.05, p > 0.01), T1 HRQoL significantly predicted T2 cognitive function (PCS: B = 0.10, p < 0.01; MCS: B = 0.11, p < 0.01), and the results were consistent across all diabetic patients (see Figure 4). This shows that the cross-lag model of cognitive function and HRQoL in middle-aged and older diabetic patients shows variance in the use of digital technology, that is, this relationship will be affected by digital usage behavior.\nIn order to investigate whether the cross-lag relationship between cognitive function and HRQoL differs in digital usage behavior, the study performed a multi-group analysis. All diabetic patients were divided into two groups, one representing non-digital usage behavior (Figure 3), and the other representing digital usage behavior (Figure 4). In the grouped cross-lag model, the corresponding control variables were also added to the model. The models in Figure 3 and Figure 4 met fitness criteria (Figure 3: Model 3: RMSEA = 0.073, CFI = 0.996, TCL = 0.901; Model 4: RMSEA = 0.100, CFI = 0.990, TCL = 0.936. Figure 4: Model 5: RMSEA = 0.073, CFI = 0.996, TCL = 0.941; Model 6: RMSEA = 0.079, CFI = 0.970, TCL = 0.936). In both sets of models, the study focused on the relationship between cognitive function and HRQoL at a point in time, as well as their relationship at the time of follow-up research.\nFor middle-aged and older diabetic patients using digital technology, although the model could be fitted, cognitive function at T1 did not significantly predict HRQoL at T2 (PCS: B = 0.30, p > 0.01; MCS: B = 0.14, p > 0.01), and vice versa (PCS: B = 0.06, p > 0.01; MCS: B = 0.05, p > 0.01) (see Figure 3). For diabetic patients who did not use digital technology, although T1 cognitive function did not significantly predict T2 HRQoL (PCS: B = 0.04, p > 0.01; MCS: B = 0.05, p > 0.01), T1 HRQoL significantly predicted T2 cognitive function (PCS: B = 0.10, p < 0.01; MCS: B = 0.11, p < 0.01), and the results were consistent across all diabetic patients (see Figure 4). This shows that the cross-lag model of cognitive function and HRQoL in middle-aged and older diabetic patients shows variance in the use of digital technology, that is, this relationship will be affected by digital usage behavior.","The variable descriptive statistics and correlation analysis results are shown in Table 3 and Table 4. The correlation analysis shows that there is a significant correlation between cognition at T1 and T2 and HRQoL levels at T1 and T2, indicating that cognitive function and HRQoL levels in middle-aged and older diabetic patients have a certain relationship, which is showing stability. Meanwhile, simultaneous and sequential correlations between cognitive function and HRQoL levels are also significant. The correlation coefficients between cognitive function and HRQoL at T1 are 0.28 (p < 0.01) and 0.37 (p < 0.01); the correlation coefficients between cognitive function at T2 and HRQoL at T2 are 0.32 (p < 0.01) and 0.30 (p < 0.01); the correlation coefficients between cognitive function at T1 and HRQoL at T2 are 0.25 (p < 0.01) and 0.22 (p < 0.01); and the correlation coefficients between HRQoL at T1 and cognitive function at T2 are 0.32 (p < 0.01) and 0.36 (p < 0.01). This indicates that cognitive function is basically consistent with the synchronous and stable correlations of HRQoL levels, which is in line with the basic assumptions of the cross-lag design.","With cognitive function as the dependent variable, a 2 (test time: T1/T2) × 2 (digital usage behavior: use/non-use) repeated measures square analysis was performed. The results show that the testing time is the main and most significant effect (F = 11.21, p < 0.001, η2 = 0.01). The cognitive function level at T2 is significantly lower than at T1, and there are developmental differences. The main effect of digital usage behavior is significant (F = 36.325, p < 0.001, η2 = 0.06), and the cognitive function of patients using digital technology is significantly better than those patients without digital technology. The interaction between the two is not significant (F = 2.22, p > 0.05, η2 = 0.04).\nWith PCS as the dependent variable, a 2 (test time: T1/T2) × 2 (digital usage behavior: use/non-use) repeated measures square analysis was performed. The results show that the testing time is the main and most significant effect (F =16.25, p < 0.001, η2 = 0.03), the PCS level of the post-test is significantly lower than that of the pre-test, and there is a developmental difference. The main effect of digital usage behavior is significant (F = 36.54, p < 0.001, η2 = 0.06), and the PCS of patients using digital technology is significantly better than those not using digital technology. The interaction between the two is not significant (F = 0.18, p > 0.05, η2 = 0.00).\nWith MCS as the dependent variable, a 2 (test time: T1/T2) × 2 (digital usage behavior: use/non-use) repeated measures square analysis was performed. The results showed that the testing time is the main and most significant effect (F = 44.93, p < 0.001, η2 = 0.07), the MCS level of the post-test is significantly lower than the pre-test, and there is a developmental difference. The main effect of digital usage behavior is significant (F = 50.97, p < 0.001, η2 = 0.08), and the MCS of patients using digital technology is significantly better than those not using digital technology. The interaction between the two is significant (F = 4.43, p < 0.05, η2 = 0.08).","In the cross-lag model which this study applied, HRQoL at T2 was predicted by cognitive function at T1, and cognitive function at T2 was predicted by HRQoL at T1. After the corresponding control variables were added to the models, both models achieved acceptable fitness criteria (Model 1: RMSEA = 0.093; CFI = 0.994, TCL = 0.912; Model 2: RMSEA = 0.098; CFI = 0.984, TCL = 0.901).\nThe cross-lag relationship between cognitive function and PCS scores at two points in time is shown from the test of Model 1 in Figure 2. First, in the same time period, the baseline association between cognitive function and PCS is significantly positive (B = 0.19, p < 0.01), implying better cognitive performance in middle-aged and older diabetic patients with higher PCS scores at T1, and vice versa. Second, there is no significant correlation between cognitive function at T1 and PCS at T2 (B = 0.05, p > 0.01), but PCS at T1 is positively correlated with cognitive function at T2 (B = 0.12, p < 0.01). This suggests that there is a positive and significant relationship between PCS and cognitive function in middle-aged and older diabetic patients over time, but early cognitive function in middle-aged and older diabetic patients does not affect later PSC.\nThe cross-lag relationship between cognitive function and MCS scores at the two points in time is shown in Model 2 of Figure 2. First, in the same time period, the baseline association between cognitive function and MCS is significantly positively correlated (B = 0.31, p < 0.01), which implies a better cognitive performance in middle-aged and older diabetic patients with higher MCS scores at T1, and vice versa. Second, there is no significant correlation between cognitive function at T1 and MCS at T2 (B = 0.05, p > 0.01), but there is a significant positive correlation between MCS at T1 and cognitive function at T2 (B = 0.14, p < 0.01). This suggests that there is a positive and significant relationship between MCS and cognitive function in middle-aged and older diabetic patients over time, but early cognitive function in middle-aged and older diabetic patients does not affect later MCS. In conclusion, there may be a one-way causal relationship between cognitive function and HRQoL, and the causal direction is from HRQoL to cognition.","In order to investigate whether the cross-lag relationship between cognitive function and HRQoL differs in digital usage behavior, the study performed a multi-group analysis. All diabetic patients were divided into two groups, one representing non-digital usage behavior (Figure 3), and the other representing digital usage behavior (Figure 4). In the grouped cross-lag model, the corresponding control variables were also added to the model. The models in Figure 3 and Figure 4 met fitness criteria (Figure 3: Model 3: RMSEA = 0.073, CFI = 0.996, TCL = 0.901; Model 4: RMSEA = 0.100, CFI = 0.990, TCL = 0.936. Figure 4: Model 5: RMSEA = 0.073, CFI = 0.996, TCL = 0.941; Model 6: RMSEA = 0.079, CFI = 0.970, TCL = 0.936). In both sets of models, the study focused on the relationship between cognitive function and HRQoL at a point in time, as well as their relationship at the time of follow-up research.\nFor middle-aged and older diabetic patients using digital technology, although the model could be fitted, cognitive function at T1 did not significantly predict HRQoL at T2 (PCS: B = 0.30, p > 0.01; MCS: B = 0.14, p > 0.01), and vice versa (PCS: B = 0.06, p > 0.01; MCS: B = 0.05, p > 0.01) (see Figure 3). For diabetic patients who did not use digital technology, although T1 cognitive function did not significantly predict T2 HRQoL (PCS: B = 0.04, p > 0.01; MCS: B = 0.05, p > 0.01), T1 HRQoL significantly predicted T2 cognitive function (PCS: B = 0.10, p < 0.01; MCS: B = 0.11, p < 0.01), and the results were consistent across all diabetic patients (see Figure 4). This shows that the cross-lag model of cognitive function and HRQoL in middle-aged and older diabetic patients shows variance in the use of digital technology, that is, this relationship will be affected by digital usage behavior.","The present study aimed to further deepen our understanding of the association between cognition and HRQoL. This study used a cross-lagged model and controlled for the corresponding covariates in order to verify the longitudinal relationship between cognition and HRQoL in middle-aged and older diabetic patients. The corresponding findings suggest that this relationship is unidirectional in the cross-lag model, that is, only HRQoL at T1 can predict cognitive function at T2, and cognitive function at T1 does not predict HRQoL at T2. This study further explored whether the use of digital technology would lead to different manifestations of this relationship, and divided the study population into two categories: those who used digital technology and those who did not. The results found that this one-way lag relationship still existed in middle-aged and older diabetic patients who did not use digital technology, but was not significant in patients who used digital technology.\nFirst of all, the study found that there are certain developmental differences in the cognitive function, PCS, and MCS levels of middle-aged and older diabetic patients. There is an increasing trend over time which is consistent with previous research results [33,34]. From an age perspective, not only are changes in cognitive function closely related to age [17,35], but HRQoL also has a distinct age-specific trajectory [36]. Therefore, as patients with diabetes age, the risk of cognitive decline and reduced PCS and MCS levels increases [15,37]. Second, diabetes is considered a risk factor for developing cognitive impairments [38]. Although some studies suggest that it may not accelerate the cognitive deterioration process, there is a general consensus in the academic community that diabetes increases the risk of abnormal cognition by increasing the incidence of cognition-related diseases or affecting blood sugar levels [37,38]. Furthermore, previous cross-sectional and longitudinal studies have identified that chronic diseases such as diabetes, as well as BMI, can affect HRQoL in a negative way [39,40]. Thus, this trend may be more obvious in the diabetic population.\nIt was found that the cognitive function of T1 was highly correlated with the PCS and MCS at T1 and T2, and the cognitive function of T2 was highly correlated with the PCS and MCS at T1 and T2, which was in line with the basic assumption of a cross-lag design. Our results are consistent with previous studies on other populations [9]. In the subsequent cross-lag analysis, the results were found to be consistent with previous studies [12]. The cognitive function, PCS, and MCS levels of middle-aged and elderly diabetic patients have a certain degree of lateral stability between T1 and T2, and HRQoL can predict cognitive function. However, there is no research to explain its internal mechanism. Verghese, J. et al., and Daviglus, M.L. et al., suggest that interventions to improve physical and general mental health can prevent or delay the onset of dementia [41,42]. Since HRQoL scores from CHARLS in this study include evaluations of physical activity and general mental health, it is believed that physical activity may be a plausible mechanism by which HRQoL can predict changes in cognitive function, based on previous studies. The mediating role of physical activity in this relationship should be further investigated in the future. In general, there are few studies focusing on the impact of HRQoL on cognitive function. In the future, more in-depth research is needed to explore the internal mechanism of how HRQoL affects cognitive function [43,44].\nThird, although cognitive function at T1 was found to be significantly associated with HRQoL at T2 (PCS and MCS) in the initial correlation analysis, the predictive role of cognitive function at T1 on HRQoL at T2 in the cross-lag model was not significant, suggesting that cognitive function at T1 does not predict HRQoL at T2. This is consistent with previous studies [45]. One possible explanation is that the differences in cognitive function between the participants included are relatively small at baseline and at follow-up, as shown by a mean baseline cognitive function score of 15.73 and follow-up of 15.03. Therefore, there may not be enough individuals with the degree of cognitive function variation that would interfere with the study results. On the other hand, it may be that clinical health status alone does not determine a better quality of life [32], and for older adults, perceived life satisfaction appears to be more affected by ability to perform daily tasks [46]. Therefore, the influence of cognitive function on HRQoL may not be significant.\nFinally, results show that there is a significant difference in the use of digital technology in the one-way predictive role of cognitive function and HRQoL, that is, the causal relationship between cognitive function and HRQoL is more applicable to diabetic patients who do not use digital technology. This finding is consistent with the study hypothesis. In terms of cognitive function, previous studies believe that the use of digital technology can slow the decline of cognitive function by increasing the cognitive reserve of individuals [18,47,48,49,50]. It has also been confirmed that in the diabetic population, digital usage behavior can improve the cognitive function of patients [51]; therefore, individuals who do not use digital technology tend to have greater changes in cognitive function, which are easier to detect than changes in individuals who do use digital technology. In terms of HRQoL, the use of digital technology has a direct positive impact on HRQoL [52], and for people with diabetes, digital usage behavior can have an indirect impact on HRQoL by stimulating positive lifestyle changes such as physical activity [53,54,55,56]. Therefore, it is believed that due to direct or indirect impact of digital usage behavior on cognitive function and HRQoL, digital usage behavior may have weakened the longitudinal association between cognitive function and HRQoL in middle-aged and older diabetic patients to some extent.\nThis study can help us to make some policy recommendations. First, the aging population has greatly increased the public health burden in China. Cognitive function and HRQoL in diabetic patients were affected to varying degrees. Therefore, it is necessary to improve the relevant medical service system for diabetics in China as soon as possible to ensure that the medical needs of the elderly are met. In addition, attention will need to be out into improving the Internet and medical service systems. In order to reduce the digital technology gap, promotion of internet use and healthy aging is encouraged.\nThis study did not focus on describing intra-individual variation due to the inherent limitations of the cross-lag model [57]. However, the cross-lag model used in this study still provides new evidence on the longitudinal relationship between cognitive function and HRQoL in middle-aged and older Chinese diabetic patients, and may provide new insights into the underlying mechanisms behind this relationship. Aside from the limitations of the model itself, there are several potential limitations to consider. First, there was a 3-year lag between baseline and follow-up, which may be considered too long to assess the cross-lag relationship between cognitive function and HRQoL. Future studies will need to test whether shorter and longer temporal associations differ in terms of gaining insight into the exact relationship between cognitive function and HRQoL in different conditions. Second, this study only selected the data of wave 3 and wave 4 of the CHARLS database, and did not include wave 1 or wave 2. This study used three questions that were only included after wave 3 in the evaluation criteria for the digital usage behavior. If there were enough data, it would obviously be better to include waves 1 and 2 in the analysis. In addition, a large portion of participants were excluded due to lack of necessary data. Although we controlled for demographic variables, this may still lead to selective bias in the data. Third, in the group analysis, due to the significant sample size difference between those who used digital technology and those who did not, results may have been inaccurate. However, the study results still show that the longitudinal relationship between cognitive function and HRQoL differs in terms of digital usage behavior. Finally, because patients with diabetes often have other chronic diseases of the elderly, future research is needed to further distinguish and identify the differences between diabetes and other chronic diseases of the elderly.","The cognitive function and HRQoL (PCS and MCS) in middle-aged and older Chinese diabetic patients have a certain degree of lateral stability, and both tend to increase over time. More importantly, this study found that HRQoL at T1 can significantly predict cognitive function at T2, but cognitive function at T1 cannot significantly predict HRQoL at T2. HRQoL may be an antecedent for middle-aged and older diabetic patients’ cognitive function, and this predictive relationship can be different depending on digital usage behavior. In the future, effective intervention on HRQoL of middle-aged and older diabetic patients can be considered to improve their cognitive function, thereby promoting the healthy development of middle-aged and older diabetic patients."],"string":"[\n \"Cognitive impairment is one of the common complications in elderly patients [1]. For example, in China, the prevalence of mild cognitive impairments in the aging population (aged 60 and above) is 14.71% [2], and as age increases, its annual rate of progression to dementia is between 8% and 15% [2]. At the same time, cognitive impairment is also one of the common complications of diabetic patients. It is estimated that by 2045, about 170 million elderly people in China will be diabetic patients [3]. Therefore, the academic community has paid great attention to the cognitive function of the diabetic population, and found that the cognitive function of the diabetic population is closely related to the quality of life (QoL) of the elderly [4].\\nHRQoL is the perceived physical and mental health of an individual or group over time, including both physical component summary (PCS) and mental component summary (MCS) [5]. In contrast with the QoL, HRQoL pays special attention to the impact of disease and treatment process on the life of a person or a group [6]. Existing studies have found that changes in cognitive function are positively correlated with changes in HRQoL, and play a predictive role in future changes in HRQoL [7,8]. For example, cognitive decline was found to be a predictor of HRQoL decline in studies on multiple sclerosis patients, AIDS patients, and older women [4,9,10]. At the same time, some scholars have found that changes in HRQoL—whether it is the PCS or the MCS of HRQoL—can also predict cognitive changes in individuals in the future [11]. For example, Ezzati’s 2019 study demonstrated that changes in HRQoL preceded changes in cognition and predicted the occurrence of dementia [12]. Thus, cognitive function may be bi-directionally associated with HRQoL.\\nThe bidirectional relationship between cognitive function and HRQoL may be more pronounced in terms of diabetic patients. This is because not only are people with diabetes 1–2 times more likely to develop cognitive risks than the general population [13,14], but this risk will increase over time [15]. At the same time, with the deepening of the research on HRQoL, the medical community generally believes that the core of diabetes management should include HRQoL maintenance in addition to prevention and delay of its complications [16]. In summary, this research aims to focus on middle-aged and older diabetic patients (over 45 years old) in China—not only because China has one of the largest numbers of diabetic patients in the world, but also because the age of the population affected with diabetes is showing a downward trend [3,17]. Thus, we propose the first hypothesis: in middle-aged and older Chinese patients with diabetes, the relationship between cognitive function and HRQoL may be bidirectional.\\nTo our knowledge, previous studies on cognitive function and HRQoL have focused on unmodifiable clinical factors, such as age, gender, etc., with a lack of studies on modifiable factors [4]. Digital technologies such as the Internet and smartphones have attracted the attention of psychologists because of their portability, rapidity, and immediacy. In clinical medicine, digital usage behaviors are often used for managing and intervening with patients with diabetes and cognitive dysfunction [18]. Thus, digital technology may be seen as a modifiable factor in the relationship between cognitive function and HRQoL. We also found that with the continuous development and improvement of digital technology, digital usage behavior can have a profound impact on the cognitive function of patients with chronic diseases [18,19,20]. In addition to this, it is important that digital usage behavior, such as Internet use, can also have a certain impact on HRQoL [21]. Previous research in other populations found that using the Internet resulted in significantly higher PCS and physical pain scores [22]. According to self-determination theory, we believe that when individuals use the internet for leisure or work, their needs are met, which in turn produces positive long-term psychological outcomes such as quality of life [23]. So far, existing research is leaning towards the belief that an increase in digital usage behavior can improve the health of individuals [24]. However, there are also studies holding the opposite view; they believe that the long-term digital usage behavior reduces patients’ time for outdoor activities, and that a large amount of negative information received due to digital usage behavior is more likely to cause mood swings, which may have side effects on patients’ recovery [25]. Therefore, it is easy to find that digital usage behavior is likely to have various effects, so we propose another hypothesis: in middle-aged and older Chinese patients with diabetes, the relationship between cognitive function and HRQoL may differ considering digital usage behavior.\\nTo date, most studies on the association of cognitive function with HRQoL have used cross-sectional designs, and have failed to elucidate the direction of influence between cognitive function and HRQoL due to the limitations of cross-sectional studies in terms of making causal inferences and explaining the direction of association. Therefore, it is necessary to further explore, especially for diabetic patients, the longitudinal association between the cognitive function and HRQoL, and to clarify the direction of their effects. As a longitudinal study, this study attempts to use a cross-lag model to explore the relationship between cognitive function and HRQoL in Chinese middle-aged and older diabetic patients, as well as the direction of this relationship and whether there were differences in digital usage behavior. This research is attempting to explore the longitudinal relationship between cognition and HRQoL in middle-aged and older diabetic patients, and to provide evidence which can promote their health.\\nIt should be noted that, due to the existence of multiple databases in China, in addition to the China Health and Retirement Longitudinal Survey (CHARLS), most scholars use databases such as the China Household Finance Survey when researching topics related to public health [26]. There is more content regarding the physical and mental health of middle-aged and elderly people, so we chose CHARLS as our data source.\",\n \"2.1. Participants The China Health and Retirement Longitudinal Survey (CHARLS) is a representative follow-up survey of middle-aged and older people in China, chaired by the National Research Institute of Development at Peking University. The study protocol was approved by the Institutional Review Board of Peking University (approval number: IRB00001052-11015), and the study protocol complies with the ethical guidelines of the 1975 Declaration of Helsinki. In order to obtain the relevant data, we applied for the CHARLS database online on 31 August 2022, and approval was quickly obtained. This study used two waves of CHARLS data, from 2015 (T1) and 2018 (T2). CHARLS data can be accessed through its official website (https://charls.pku.edu.cn (accessed on 31 August 2022)).\\nFigure 1 shows the detailed process for including and excluding study participants. Wave 3 was used as the baseline data set for this study, and individuals who lacked information on HRQoL, cognitive function, digital usage behavior, and covariates were excluded from this study. Subsequently, new participants in 2018 were further excluded. Individuals lacking information on digital usage behavior, cognitive function, and covariates were also excluded from this study. In addition, in the sample selection, the research team excluded individuals with other diseases (such as cognitive diseases that may affect the detection process, mental diseases, and other diseases related to aging), and included individuals with diabetes only. In addition, the abbreviations of the main variables involved in the study are organized in Table 1.\\nThe China Health and Retirement Longitudinal Survey (CHARLS) is a representative follow-up survey of middle-aged and older people in China, chaired by the National Research Institute of Development at Peking University. The study protocol was approved by the Institutional Review Board of Peking University (approval number: IRB00001052-11015), and the study protocol complies with the ethical guidelines of the 1975 Declaration of Helsinki. In order to obtain the relevant data, we applied for the CHARLS database online on 31 August 2022, and approval was quickly obtained. This study used two waves of CHARLS data, from 2015 (T1) and 2018 (T2). CHARLS data can be accessed through its official website (https://charls.pku.edu.cn (accessed on 31 August 2022)).\\nFigure 1 shows the detailed process for including and excluding study participants. Wave 3 was used as the baseline data set for this study, and individuals who lacked information on HRQoL, cognitive function, digital usage behavior, and covariates were excluded from this study. Subsequently, new participants in 2018 were further excluded. Individuals lacking information on digital usage behavior, cognitive function, and covariates were also excluded from this study. In addition, in the sample selection, the research team excluded individuals with other diseases (such as cognitive diseases that may affect the detection process, mental diseases, and other diseases related to aging), and included individuals with diabetes only. In addition, the abbreviations of the main variables involved in the study are organized in Table 1.\\n2.2. Measurements 2.2.1. Cognitive Functioning This study obtained data on cognitive function from baseline data in 2015 and follow-up data in 2018. The baseline data stipulated by CHARLS is from 2011, but we include CHARLS 2015 (T1) as the baseline data in our study. CHARLS is similar to the cognitive assessment used in the American Health and Retirement Study, and has constructed cognitive function evaluation criteria from the same two aspects of memory and mental state [27,28]. Furthermore, previous studies have also used the CHARLS cognitive function assessment criteria for class-correspondence studies. Firstly, memory evaluation includes immediate word recall (0–10 points) and delayed word recall (0–10 points). Mental state is measured from three dimensions: orientation, visual construction, and mathematical performance. Orientation (0–5 points) is measured by asking respondents to name the date, day of the week, and season; visual construction is assessed by drawing a previously displayed picture (0–1 points); and mathematical performance (0–5 points) is measured by asking respondents to subtract 7 consecutive times from 100. The scores of the participants’ cognitive function are equal to the sum of the scores of memory and mental state. Cognitive function scores range from 0 to 31 points, higher cognitive function scores indicating better cognitive function. Finally, in order to obtain a composite measure of cognitive function, this study normalized and averaged the total cognitive function score by adding up memory and mental state scores [29].\\nThis study obtained data on cognitive function from baseline data in 2015 and follow-up data in 2018. The baseline data stipulated by CHARLS is from 2011, but we include CHARLS 2015 (T1) as the baseline data in our study. CHARLS is similar to the cognitive assessment used in the American Health and Retirement Study, and has constructed cognitive function evaluation criteria from the same two aspects of memory and mental state [27,28]. Furthermore, previous studies have also used the CHARLS cognitive function assessment criteria for class-correspondence studies. Firstly, memory evaluation includes immediate word recall (0–10 points) and delayed word recall (0–10 points). Mental state is measured from three dimensions: orientation, visual construction, and mathematical performance. Orientation (0–5 points) is measured by asking respondents to name the date, day of the week, and season; visual construction is assessed by drawing a previously displayed picture (0–1 points); and mathematical performance (0–5 points) is measured by asking respondents to subtract 7 consecutive times from 100. The scores of the participants’ cognitive function are equal to the sum of the scores of memory and mental state. Cognitive function scores range from 0 to 31 points, higher cognitive function scores indicating better cognitive function. Finally, in order to obtain a composite measure of cognitive function, this study normalized and averaged the total cognitive function score by adding up memory and mental state scores [29].\\n2.2.2. HRQoL This study used a new scale construction based on the variables of the Short Form 36 (SF-36) and the CHARLS questionnaire to measure HRQoL in diabetic patients. The construction of the new scale was derived from the eight dimensions of SF-36, and the corresponding variables of CHARLS were selected to assess the following eight dimensions (Table 2): physical function (PF), role–body (RP), body pain (BP), general health (GH), vitality (VT), social functioning (SF), role–emotion (RE), and mental health (MH). The scores for the above eight dimensions were calculated by adding up the category scores and then converting the raw scores to a 0 to 100 scale. Scores from the eight subscales were aggregated into two overall scores according to the conceptual model of the SF-36 [30]. Physical function, body roles, body pain, and general perceptions of health were calculated as PCS, and mental health, vitality, emotional role, and social functioning were calculated as MCS. Although the HRQoL questionnaire based on CHARLS is slightly different from other HRQoL questionnaires, they all have similar focuses, including physical, emotional, and social diversity. The questionnaire has been determined to be effective in the Chinese population [31], and has already been used in related research [32].\\nThis study used a new scale construction based on the variables of the Short Form 36 (SF-36) and the CHARLS questionnaire to measure HRQoL in diabetic patients. The construction of the new scale was derived from the eight dimensions of SF-36, and the corresponding variables of CHARLS were selected to assess the following eight dimensions (Table 2): physical function (PF), role–body (RP), body pain (BP), general health (GH), vitality (VT), social functioning (SF), role–emotion (RE), and mental health (MH). The scores for the above eight dimensions were calculated by adding up the category scores and then converting the raw scores to a 0 to 100 scale. Scores from the eight subscales were aggregated into two overall scores according to the conceptual model of the SF-36 [30]. Physical function, body roles, body pain, and general perceptions of health were calculated as PCS, and mental health, vitality, emotional role, and social functioning were calculated as MCS. Although the HRQoL questionnaire based on CHARLS is slightly different from other HRQoL questionnaires, they all have similar focuses, including physical, emotional, and social diversity. The questionnaire has been determined to be effective in the Chinese population [31], and has already been used in related research [32].\\n2.2.3. Socio-Demographic Variables In order to minimize the possibility of other variables influencing the cognitive function–HRQoL relationship study, and to simplify the model, this research controlled for several specific covariates associated with cognitive function and HRQoL. According to previous studies, all covariates were based on baseline data (CHARLS 2015) [32]. Firstly, population control variables include age, gender, and education status. Secondly, since HRQoL can be divided into two parts, PCS and MCS, this study used different control variables in the models for cognitive function, PCS, and MCS. This research included depression as well as current smoking and drinking habits as control variables for PCS scores. In this research, PCS scores are also considered with marital status, depression, physical activity, and current smoking and drinking habits as control variables.\\nIn order to minimize the possibility of other variables influencing the cognitive function–HRQoL relationship study, and to simplify the model, this research controlled for several specific covariates associated with cognitive function and HRQoL. According to previous studies, all covariates were based on baseline data (CHARLS 2015) [32]. Firstly, population control variables include age, gender, and education status. Secondly, since HRQoL can be divided into two parts, PCS and MCS, this study used different control variables in the models for cognitive function, PCS, and MCS. This research included depression as well as current smoking and drinking habits as control variables for PCS scores. In this research, PCS scores are also considered with marital status, depression, physical activity, and current smoking and drinking habits as control variables.\\n2.2.1. Cognitive Functioning This study obtained data on cognitive function from baseline data in 2015 and follow-up data in 2018. The baseline data stipulated by CHARLS is from 2011, but we include CHARLS 2015 (T1) as the baseline data in our study. CHARLS is similar to the cognitive assessment used in the American Health and Retirement Study, and has constructed cognitive function evaluation criteria from the same two aspects of memory and mental state [27,28]. Furthermore, previous studies have also used the CHARLS cognitive function assessment criteria for class-correspondence studies. Firstly, memory evaluation includes immediate word recall (0–10 points) and delayed word recall (0–10 points). Mental state is measured from three dimensions: orientation, visual construction, and mathematical performance. Orientation (0–5 points) is measured by asking respondents to name the date, day of the week, and season; visual construction is assessed by drawing a previously displayed picture (0–1 points); and mathematical performance (0–5 points) is measured by asking respondents to subtract 7 consecutive times from 100. The scores of the participants’ cognitive function are equal to the sum of the scores of memory and mental state. Cognitive function scores range from 0 to 31 points, higher cognitive function scores indicating better cognitive function. Finally, in order to obtain a composite measure of cognitive function, this study normalized and averaged the total cognitive function score by adding up memory and mental state scores [29].\\nThis study obtained data on cognitive function from baseline data in 2015 and follow-up data in 2018. The baseline data stipulated by CHARLS is from 2011, but we include CHARLS 2015 (T1) as the baseline data in our study. CHARLS is similar to the cognitive assessment used in the American Health and Retirement Study, and has constructed cognitive function evaluation criteria from the same two aspects of memory and mental state [27,28]. Furthermore, previous studies have also used the CHARLS cognitive function assessment criteria for class-correspondence studies. Firstly, memory evaluation includes immediate word recall (0–10 points) and delayed word recall (0–10 points). Mental state is measured from three dimensions: orientation, visual construction, and mathematical performance. Orientation (0–5 points) is measured by asking respondents to name the date, day of the week, and season; visual construction is assessed by drawing a previously displayed picture (0–1 points); and mathematical performance (0–5 points) is measured by asking respondents to subtract 7 consecutive times from 100. The scores of the participants’ cognitive function are equal to the sum of the scores of memory and mental state. Cognitive function scores range from 0 to 31 points, higher cognitive function scores indicating better cognitive function. Finally, in order to obtain a composite measure of cognitive function, this study normalized and averaged the total cognitive function score by adding up memory and mental state scores [29].\\n2.2.2. HRQoL This study used a new scale construction based on the variables of the Short Form 36 (SF-36) and the CHARLS questionnaire to measure HRQoL in diabetic patients. The construction of the new scale was derived from the eight dimensions of SF-36, and the corresponding variables of CHARLS were selected to assess the following eight dimensions (Table 2): physical function (PF), role–body (RP), body pain (BP), general health (GH), vitality (VT), social functioning (SF), role–emotion (RE), and mental health (MH). The scores for the above eight dimensions were calculated by adding up the category scores and then converting the raw scores to a 0 to 100 scale. Scores from the eight subscales were aggregated into two overall scores according to the conceptual model of the SF-36 [30]. Physical function, body roles, body pain, and general perceptions of health were calculated as PCS, and mental health, vitality, emotional role, and social functioning were calculated as MCS. Although the HRQoL questionnaire based on CHARLS is slightly different from other HRQoL questionnaires, they all have similar focuses, including physical, emotional, and social diversity. The questionnaire has been determined to be effective in the Chinese population [31], and has already been used in related research [32].\\nThis study used a new scale construction based on the variables of the Short Form 36 (SF-36) and the CHARLS questionnaire to measure HRQoL in diabetic patients. The construction of the new scale was derived from the eight dimensions of SF-36, and the corresponding variables of CHARLS were selected to assess the following eight dimensions (Table 2): physical function (PF), role–body (RP), body pain (BP), general health (GH), vitality (VT), social functioning (SF), role–emotion (RE), and mental health (MH). The scores for the above eight dimensions were calculated by adding up the category scores and then converting the raw scores to a 0 to 100 scale. Scores from the eight subscales were aggregated into two overall scores according to the conceptual model of the SF-36 [30]. Physical function, body roles, body pain, and general perceptions of health were calculated as PCS, and mental health, vitality, emotional role, and social functioning were calculated as MCS. Although the HRQoL questionnaire based on CHARLS is slightly different from other HRQoL questionnaires, they all have similar focuses, including physical, emotional, and social diversity. The questionnaire has been determined to be effective in the Chinese population [31], and has already been used in related research [32].\\n2.2.3. Socio-Demographic Variables In order to minimize the possibility of other variables influencing the cognitive function–HRQoL relationship study, and to simplify the model, this research controlled for several specific covariates associated with cognitive function and HRQoL. According to previous studies, all covariates were based on baseline data (CHARLS 2015) [32]. Firstly, population control variables include age, gender, and education status. Secondly, since HRQoL can be divided into two parts, PCS and MCS, this study used different control variables in the models for cognitive function, PCS, and MCS. This research included depression as well as current smoking and drinking habits as control variables for PCS scores. In this research, PCS scores are also considered with marital status, depression, physical activity, and current smoking and drinking habits as control variables.\\nIn order to minimize the possibility of other variables influencing the cognitive function–HRQoL relationship study, and to simplify the model, this research controlled for several specific covariates associated with cognitive function and HRQoL. According to previous studies, all covariates were based on baseline data (CHARLS 2015) [32]. Firstly, population control variables include age, gender, and education status. Secondly, since HRQoL can be divided into two parts, PCS and MCS, this study used different control variables in the models for cognitive function, PCS, and MCS. This research included depression as well as current smoking and drinking habits as control variables for PCS scores. In this research, PCS scores are also considered with marital status, depression, physical activity, and current smoking and drinking habits as control variables.\\n2.3. Statistical Methods IBM SPSS Statistics, version 23 (IBM SPSS Statistics, Armonk, NY, USA) and Mplus version 8.0 are used for data analysis. Descriptive analyses of diabetic patient characteristics, cognitive function, and PCM and MCS of HRQoL were performed. The relationship between cognitive function and HRQoL in diabetic patients at T1 and T2 was assessed using the Pearson correlation test. A repeated-measures analysis of variance was performed to assess the differences between these variables with and without the digital usage behavior. Furthermore, cross-lagged panel structural equation modeling (SEM) was used to assess the longitudinal association between cognition and HRQoL at T1 and T2. Finally, a multi-group test was performed to assess whether digital usage behavior was making a difference in this relationship. Finally, regarding the grouping of digital technologies as well, previous studies were referred to [24]. Digital technology usage was obtained from 2015 baseline data. In the CHARLS 2015 questionnaire, internet usage was measured using the following question whether respondents have used the internet in the last month (0 for no, 1 for yes).\\nIBM SPSS Statistics, version 23 (IBM SPSS Statistics, Armonk, NY, USA) and Mplus version 8.0 are used for data analysis. Descriptive analyses of diabetic patient characteristics, cognitive function, and PCM and MCS of HRQoL were performed. The relationship between cognitive function and HRQoL in diabetic patients at T1 and T2 was assessed using the Pearson correlation test. A repeated-measures analysis of variance was performed to assess the differences between these variables with and without the digital usage behavior. Furthermore, cross-lagged panel structural equation modeling (SEM) was used to assess the longitudinal association between cognition and HRQoL at T1 and T2. Finally, a multi-group test was performed to assess whether digital usage behavior was making a difference in this relationship. Finally, regarding the grouping of digital technologies as well, previous studies were referred to [24]. Digital technology usage was obtained from 2015 baseline data. In the CHARLS 2015 questionnaire, internet usage was measured using the following question whether respondents have used the internet in the last month (0 for no, 1 for yes).\",\n \"The China Health and Retirement Longitudinal Survey (CHARLS) is a representative follow-up survey of middle-aged and older people in China, chaired by the National Research Institute of Development at Peking University. The study protocol was approved by the Institutional Review Board of Peking University (approval number: IRB00001052-11015), and the study protocol complies with the ethical guidelines of the 1975 Declaration of Helsinki. In order to obtain the relevant data, we applied for the CHARLS database online on 31 August 2022, and approval was quickly obtained. This study used two waves of CHARLS data, from 2015 (T1) and 2018 (T2). CHARLS data can be accessed through its official website (https://charls.pku.edu.cn (accessed on 31 August 2022)).\\nFigure 1 shows the detailed process for including and excluding study participants. Wave 3 was used as the baseline data set for this study, and individuals who lacked information on HRQoL, cognitive function, digital usage behavior, and covariates were excluded from this study. Subsequently, new participants in 2018 were further excluded. Individuals lacking information on digital usage behavior, cognitive function, and covariates were also excluded from this study. In addition, in the sample selection, the research team excluded individuals with other diseases (such as cognitive diseases that may affect the detection process, mental diseases, and other diseases related to aging), and included individuals with diabetes only. In addition, the abbreviations of the main variables involved in the study are organized in Table 1.\",\n \"2.2.1. Cognitive Functioning This study obtained data on cognitive function from baseline data in 2015 and follow-up data in 2018. The baseline data stipulated by CHARLS is from 2011, but we include CHARLS 2015 (T1) as the baseline data in our study. CHARLS is similar to the cognitive assessment used in the American Health and Retirement Study, and has constructed cognitive function evaluation criteria from the same two aspects of memory and mental state [27,28]. Furthermore, previous studies have also used the CHARLS cognitive function assessment criteria for class-correspondence studies. Firstly, memory evaluation includes immediate word recall (0–10 points) and delayed word recall (0–10 points). Mental state is measured from three dimensions: orientation, visual construction, and mathematical performance. Orientation (0–5 points) is measured by asking respondents to name the date, day of the week, and season; visual construction is assessed by drawing a previously displayed picture (0–1 points); and mathematical performance (0–5 points) is measured by asking respondents to subtract 7 consecutive times from 100. The scores of the participants’ cognitive function are equal to the sum of the scores of memory and mental state. Cognitive function scores range from 0 to 31 points, higher cognitive function scores indicating better cognitive function. Finally, in order to obtain a composite measure of cognitive function, this study normalized and averaged the total cognitive function score by adding up memory and mental state scores [29].\\nThis study obtained data on cognitive function from baseline data in 2015 and follow-up data in 2018. The baseline data stipulated by CHARLS is from 2011, but we include CHARLS 2015 (T1) as the baseline data in our study. CHARLS is similar to the cognitive assessment used in the American Health and Retirement Study, and has constructed cognitive function evaluation criteria from the same two aspects of memory and mental state [27,28]. Furthermore, previous studies have also used the CHARLS cognitive function assessment criteria for class-correspondence studies. Firstly, memory evaluation includes immediate word recall (0–10 points) and delayed word recall (0–10 points). Mental state is measured from three dimensions: orientation, visual construction, and mathematical performance. Orientation (0–5 points) is measured by asking respondents to name the date, day of the week, and season; visual construction is assessed by drawing a previously displayed picture (0–1 points); and mathematical performance (0–5 points) is measured by asking respondents to subtract 7 consecutive times from 100. The scores of the participants’ cognitive function are equal to the sum of the scores of memory and mental state. Cognitive function scores range from 0 to 31 points, higher cognitive function scores indicating better cognitive function. Finally, in order to obtain a composite measure of cognitive function, this study normalized and averaged the total cognitive function score by adding up memory and mental state scores [29].\\n2.2.2. HRQoL This study used a new scale construction based on the variables of the Short Form 36 (SF-36) and the CHARLS questionnaire to measure HRQoL in diabetic patients. The construction of the new scale was derived from the eight dimensions of SF-36, and the corresponding variables of CHARLS were selected to assess the following eight dimensions (Table 2): physical function (PF), role–body (RP), body pain (BP), general health (GH), vitality (VT), social functioning (SF), role–emotion (RE), and mental health (MH). The scores for the above eight dimensions were calculated by adding up the category scores and then converting the raw scores to a 0 to 100 scale. Scores from the eight subscales were aggregated into two overall scores according to the conceptual model of the SF-36 [30]. Physical function, body roles, body pain, and general perceptions of health were calculated as PCS, and mental health, vitality, emotional role, and social functioning were calculated as MCS. Although the HRQoL questionnaire based on CHARLS is slightly different from other HRQoL questionnaires, they all have similar focuses, including physical, emotional, and social diversity. The questionnaire has been determined to be effective in the Chinese population [31], and has already been used in related research [32].\\nThis study used a new scale construction based on the variables of the Short Form 36 (SF-36) and the CHARLS questionnaire to measure HRQoL in diabetic patients. The construction of the new scale was derived from the eight dimensions of SF-36, and the corresponding variables of CHARLS were selected to assess the following eight dimensions (Table 2): physical function (PF), role–body (RP), body pain (BP), general health (GH), vitality (VT), social functioning (SF), role–emotion (RE), and mental health (MH). The scores for the above eight dimensions were calculated by adding up the category scores and then converting the raw scores to a 0 to 100 scale. Scores from the eight subscales were aggregated into two overall scores according to the conceptual model of the SF-36 [30]. Physical function, body roles, body pain, and general perceptions of health were calculated as PCS, and mental health, vitality, emotional role, and social functioning were calculated as MCS. Although the HRQoL questionnaire based on CHARLS is slightly different from other HRQoL questionnaires, they all have similar focuses, including physical, emotional, and social diversity. The questionnaire has been determined to be effective in the Chinese population [31], and has already been used in related research [32].\\n2.2.3. Socio-Demographic Variables In order to minimize the possibility of other variables influencing the cognitive function–HRQoL relationship study, and to simplify the model, this research controlled for several specific covariates associated with cognitive function and HRQoL. According to previous studies, all covariates were based on baseline data (CHARLS 2015) [32]. Firstly, population control variables include age, gender, and education status. Secondly, since HRQoL can be divided into two parts, PCS and MCS, this study used different control variables in the models for cognitive function, PCS, and MCS. This research included depression as well as current smoking and drinking habits as control variables for PCS scores. In this research, PCS scores are also considered with marital status, depression, physical activity, and current smoking and drinking habits as control variables.\\nIn order to minimize the possibility of other variables influencing the cognitive function–HRQoL relationship study, and to simplify the model, this research controlled for several specific covariates associated with cognitive function and HRQoL. According to previous studies, all covariates were based on baseline data (CHARLS 2015) [32]. Firstly, population control variables include age, gender, and education status. Secondly, since HRQoL can be divided into two parts, PCS and MCS, this study used different control variables in the models for cognitive function, PCS, and MCS. This research included depression as well as current smoking and drinking habits as control variables for PCS scores. In this research, PCS scores are also considered with marital status, depression, physical activity, and current smoking and drinking habits as control variables.\",\n \"This study obtained data on cognitive function from baseline data in 2015 and follow-up data in 2018. The baseline data stipulated by CHARLS is from 2011, but we include CHARLS 2015 (T1) as the baseline data in our study. CHARLS is similar to the cognitive assessment used in the American Health and Retirement Study, and has constructed cognitive function evaluation criteria from the same two aspects of memory and mental state [27,28]. Furthermore, previous studies have also used the CHARLS cognitive function assessment criteria for class-correspondence studies. Firstly, memory evaluation includes immediate word recall (0–10 points) and delayed word recall (0–10 points). Mental state is measured from three dimensions: orientation, visual construction, and mathematical performance. Orientation (0–5 points) is measured by asking respondents to name the date, day of the week, and season; visual construction is assessed by drawing a previously displayed picture (0–1 points); and mathematical performance (0–5 points) is measured by asking respondents to subtract 7 consecutive times from 100. The scores of the participants’ cognitive function are equal to the sum of the scores of memory and mental state. Cognitive function scores range from 0 to 31 points, higher cognitive function scores indicating better cognitive function. Finally, in order to obtain a composite measure of cognitive function, this study normalized and averaged the total cognitive function score by adding up memory and mental state scores [29].\",\n \"This study used a new scale construction based on the variables of the Short Form 36 (SF-36) and the CHARLS questionnaire to measure HRQoL in diabetic patients. The construction of the new scale was derived from the eight dimensions of SF-36, and the corresponding variables of CHARLS were selected to assess the following eight dimensions (Table 2): physical function (PF), role–body (RP), body pain (BP), general health (GH), vitality (VT), social functioning (SF), role–emotion (RE), and mental health (MH). The scores for the above eight dimensions were calculated by adding up the category scores and then converting the raw scores to a 0 to 100 scale. Scores from the eight subscales were aggregated into two overall scores according to the conceptual model of the SF-36 [30]. Physical function, body roles, body pain, and general perceptions of health were calculated as PCS, and mental health, vitality, emotional role, and social functioning were calculated as MCS. Although the HRQoL questionnaire based on CHARLS is slightly different from other HRQoL questionnaires, they all have similar focuses, including physical, emotional, and social diversity. The questionnaire has been determined to be effective in the Chinese population [31], and has already been used in related research [32].\",\n \"In order to minimize the possibility of other variables influencing the cognitive function–HRQoL relationship study, and to simplify the model, this research controlled for several specific covariates associated with cognitive function and HRQoL. According to previous studies, all covariates were based on baseline data (CHARLS 2015) [32]. Firstly, population control variables include age, gender, and education status. Secondly, since HRQoL can be divided into two parts, PCS and MCS, this study used different control variables in the models for cognitive function, PCS, and MCS. This research included depression as well as current smoking and drinking habits as control variables for PCS scores. In this research, PCS scores are also considered with marital status, depression, physical activity, and current smoking and drinking habits as control variables.\",\n \"IBM SPSS Statistics, version 23 (IBM SPSS Statistics, Armonk, NY, USA) and Mplus version 8.0 are used for data analysis. Descriptive analyses of diabetic patient characteristics, cognitive function, and PCM and MCS of HRQoL were performed. The relationship between cognitive function and HRQoL in diabetic patients at T1 and T2 was assessed using the Pearson correlation test. A repeated-measures analysis of variance was performed to assess the differences between these variables with and without the digital usage behavior. Furthermore, cross-lagged panel structural equation modeling (SEM) was used to assess the longitudinal association between cognition and HRQoL at T1 and T2. Finally, a multi-group test was performed to assess whether digital usage behavior was making a difference in this relationship. Finally, regarding the grouping of digital technologies as well, previous studies were referred to [24]. Digital technology usage was obtained from 2015 baseline data. In the CHARLS 2015 questionnaire, internet usage was measured using the following question whether respondents have used the internet in the last month (0 for no, 1 for yes).\",\n \"3.1. Descriptive Statistics The variable descriptive statistics and correlation analysis results are shown in Table 3 and Table 4. The correlation analysis shows that there is a significant correlation between cognition at T1 and T2 and HRQoL levels at T1 and T2, indicating that cognitive function and HRQoL levels in middle-aged and older diabetic patients have a certain relationship, which is showing stability. Meanwhile, simultaneous and sequential correlations between cognitive function and HRQoL levels are also significant. The correlation coefficients between cognitive function and HRQoL at T1 are 0.28 (p < 0.01) and 0.37 (p < 0.01); the correlation coefficients between cognitive function at T2 and HRQoL at T2 are 0.32 (p < 0.01) and 0.30 (p < 0.01); the correlation coefficients between cognitive function at T1 and HRQoL at T2 are 0.25 (p < 0.01) and 0.22 (p < 0.01); and the correlation coefficients between HRQoL at T1 and cognitive function at T2 are 0.32 (p < 0.01) and 0.36 (p < 0.01). This indicates that cognitive function is basically consistent with the synchronous and stable correlations of HRQoL levels, which is in line with the basic assumptions of the cross-lag design.\\nThe variable descriptive statistics and correlation analysis results are shown in Table 3 and Table 4. The correlation analysis shows that there is a significant correlation between cognition at T1 and T2 and HRQoL levels at T1 and T2, indicating that cognitive function and HRQoL levels in middle-aged and older diabetic patients have a certain relationship, which is showing stability. Meanwhile, simultaneous and sequential correlations between cognitive function and HRQoL levels are also significant. The correlation coefficients between cognitive function and HRQoL at T1 are 0.28 (p < 0.01) and 0.37 (p < 0.01); the correlation coefficients between cognitive function at T2 and HRQoL at T2 are 0.32 (p < 0.01) and 0.30 (p < 0.01); the correlation coefficients between cognitive function at T1 and HRQoL at T2 are 0.25 (p < 0.01) and 0.22 (p < 0.01); and the correlation coefficients between HRQoL at T1 and cognitive function at T2 are 0.32 (p < 0.01) and 0.36 (p < 0.01). This indicates that cognitive function is basically consistent with the synchronous and stable correlations of HRQoL levels, which is in line with the basic assumptions of the cross-lag design.\\n3.2. Stability Analysis of Cognitive Function and HRQoL With cognitive function as the dependent variable, a 2 (test time: T1/T2) × 2 (digital usage behavior: use/non-use) repeated measures square analysis was performed. The results show that the testing time is the main and most significant effect (F = 11.21, p < 0.001, η2 = 0.01). The cognitive function level at T2 is significantly lower than at T1, and there are developmental differences. The main effect of digital usage behavior is significant (F = 36.325, p < 0.001, η2 = 0.06), and the cognitive function of patients using digital technology is significantly better than those patients without digital technology. The interaction between the two is not significant (F = 2.22, p > 0.05, η2 = 0.04).\\nWith PCS as the dependent variable, a 2 (test time: T1/T2) × 2 (digital usage behavior: use/non-use) repeated measures square analysis was performed. The results show that the testing time is the main and most significant effect (F =16.25, p < 0.001, η2 = 0.03), the PCS level of the post-test is significantly lower than that of the pre-test, and there is a developmental difference. The main effect of digital usage behavior is significant (F = 36.54, p < 0.001, η2 = 0.06), and the PCS of patients using digital technology is significantly better than those not using digital technology. The interaction between the two is not significant (F = 0.18, p > 0.05, η2 = 0.00).\\nWith MCS as the dependent variable, a 2 (test time: T1/T2) × 2 (digital usage behavior: use/non-use) repeated measures square analysis was performed. The results showed that the testing time is the main and most significant effect (F = 44.93, p < 0.001, η2 = 0.07), the MCS level of the post-test is significantly lower than the pre-test, and there is a developmental difference. The main effect of digital usage behavior is significant (F = 50.97, p < 0.001, η2 = 0.08), and the MCS of patients using digital technology is significantly better than those not using digital technology. The interaction between the two is significant (F = 4.43, p < 0.05, η2 = 0.08).\\nWith cognitive function as the dependent variable, a 2 (test time: T1/T2) × 2 (digital usage behavior: use/non-use) repeated measures square analysis was performed. The results show that the testing time is the main and most significant effect (F = 11.21, p < 0.001, η2 = 0.01). The cognitive function level at T2 is significantly lower than at T1, and there are developmental differences. The main effect of digital usage behavior is significant (F = 36.325, p < 0.001, η2 = 0.06), and the cognitive function of patients using digital technology is significantly better than those patients without digital technology. The interaction between the two is not significant (F = 2.22, p > 0.05, η2 = 0.04).\\nWith PCS as the dependent variable, a 2 (test time: T1/T2) × 2 (digital usage behavior: use/non-use) repeated measures square analysis was performed. The results show that the testing time is the main and most significant effect (F =16.25, p < 0.001, η2 = 0.03), the PCS level of the post-test is significantly lower than that of the pre-test, and there is a developmental difference. The main effect of digital usage behavior is significant (F = 36.54, p < 0.001, η2 = 0.06), and the PCS of patients using digital technology is significantly better than those not using digital technology. The interaction between the two is not significant (F = 0.18, p > 0.05, η2 = 0.00).\\nWith MCS as the dependent variable, a 2 (test time: T1/T2) × 2 (digital usage behavior: use/non-use) repeated measures square analysis was performed. The results showed that the testing time is the main and most significant effect (F = 44.93, p < 0.001, η2 = 0.07), the MCS level of the post-test is significantly lower than the pre-test, and there is a developmental difference. The main effect of digital usage behavior is significant (F = 50.97, p < 0.001, η2 = 0.08), and the MCS of patients using digital technology is significantly better than those not using digital technology. The interaction between the two is significant (F = 4.43, p < 0.05, η2 = 0.08).\\n3.3. Cross-Lagged Analysis of Cognitive Function and HRQoL In the cross-lag model which this study applied, HRQoL at T2 was predicted by cognitive function at T1, and cognitive function at T2 was predicted by HRQoL at T1. After the corresponding control variables were added to the models, both models achieved acceptable fitness criteria (Model 1: RMSEA = 0.093; CFI = 0.994, TCL = 0.912; Model 2: RMSEA = 0.098; CFI = 0.984, TCL = 0.901).\\nThe cross-lag relationship between cognitive function and PCS scores at two points in time is shown from the test of Model 1 in Figure 2. First, in the same time period, the baseline association between cognitive function and PCS is significantly positive (B = 0.19, p < 0.01), implying better cognitive performance in middle-aged and older diabetic patients with higher PCS scores at T1, and vice versa. Second, there is no significant correlation between cognitive function at T1 and PCS at T2 (B = 0.05, p > 0.01), but PCS at T1 is positively correlated with cognitive function at T2 (B = 0.12, p < 0.01). This suggests that there is a positive and significant relationship between PCS and cognitive function in middle-aged and older diabetic patients over time, but early cognitive function in middle-aged and older diabetic patients does not affect later PSC.\\nThe cross-lag relationship between cognitive function and MCS scores at the two points in time is shown in Model 2 of Figure 2. First, in the same time period, the baseline association between cognitive function and MCS is significantly positively correlated (B = 0.31, p < 0.01), which implies a better cognitive performance in middle-aged and older diabetic patients with higher MCS scores at T1, and vice versa. Second, there is no significant correlation between cognitive function at T1 and MCS at T2 (B = 0.05, p > 0.01), but there is a significant positive correlation between MCS at T1 and cognitive function at T2 (B = 0.14, p < 0.01). This suggests that there is a positive and significant relationship between MCS and cognitive function in middle-aged and older diabetic patients over time, but early cognitive function in middle-aged and older diabetic patients does not affect later MCS. In conclusion, there may be a one-way causal relationship between cognitive function and HRQoL, and the causal direction is from HRQoL to cognition.\\nIn the cross-lag model which this study applied, HRQoL at T2 was predicted by cognitive function at T1, and cognitive function at T2 was predicted by HRQoL at T1. After the corresponding control variables were added to the models, both models achieved acceptable fitness criteria (Model 1: RMSEA = 0.093; CFI = 0.994, TCL = 0.912; Model 2: RMSEA = 0.098; CFI = 0.984, TCL = 0.901).\\nThe cross-lag relationship between cognitive function and PCS scores at two points in time is shown from the test of Model 1 in Figure 2. First, in the same time period, the baseline association between cognitive function and PCS is significantly positive (B = 0.19, p < 0.01), implying better cognitive performance in middle-aged and older diabetic patients with higher PCS scores at T1, and vice versa. Second, there is no significant correlation between cognitive function at T1 and PCS at T2 (B = 0.05, p > 0.01), but PCS at T1 is positively correlated with cognitive function at T2 (B = 0.12, p < 0.01). This suggests that there is a positive and significant relationship between PCS and cognitive function in middle-aged and older diabetic patients over time, but early cognitive function in middle-aged and older diabetic patients does not affect later PSC.\\nThe cross-lag relationship between cognitive function and MCS scores at the two points in time is shown in Model 2 of Figure 2. First, in the same time period, the baseline association between cognitive function and MCS is significantly positively correlated (B = 0.31, p < 0.01), which implies a better cognitive performance in middle-aged and older diabetic patients with higher MCS scores at T1, and vice versa. Second, there is no significant correlation between cognitive function at T1 and MCS at T2 (B = 0.05, p > 0.01), but there is a significant positive correlation between MCS at T1 and cognitive function at T2 (B = 0.14, p < 0.01). This suggests that there is a positive and significant relationship between MCS and cognitive function in middle-aged and older diabetic patients over time, but early cognitive function in middle-aged and older diabetic patients does not affect later MCS. In conclusion, there may be a one-way causal relationship between cognitive function and HRQoL, and the causal direction is from HRQoL to cognition.\\n3.4. Heterogeneity Analysis In order to investigate whether the cross-lag relationship between cognitive function and HRQoL differs in digital usage behavior, the study performed a multi-group analysis. All diabetic patients were divided into two groups, one representing non-digital usage behavior (Figure 3), and the other representing digital usage behavior (Figure 4). In the grouped cross-lag model, the corresponding control variables were also added to the model. The models in Figure 3 and Figure 4 met fitness criteria (Figure 3: Model 3: RMSEA = 0.073, CFI = 0.996, TCL = 0.901; Model 4: RMSEA = 0.100, CFI = 0.990, TCL = 0.936. Figure 4: Model 5: RMSEA = 0.073, CFI = 0.996, TCL = 0.941; Model 6: RMSEA = 0.079, CFI = 0.970, TCL = 0.936). In both sets of models, the study focused on the relationship between cognitive function and HRQoL at a point in time, as well as their relationship at the time of follow-up research.\\nFor middle-aged and older diabetic patients using digital technology, although the model could be fitted, cognitive function at T1 did not significantly predict HRQoL at T2 (PCS: B = 0.30, p > 0.01; MCS: B = 0.14, p > 0.01), and vice versa (PCS: B = 0.06, p > 0.01; MCS: B = 0.05, p > 0.01) (see Figure 3). For diabetic patients who did not use digital technology, although T1 cognitive function did not significantly predict T2 HRQoL (PCS: B = 0.04, p > 0.01; MCS: B = 0.05, p > 0.01), T1 HRQoL significantly predicted T2 cognitive function (PCS: B = 0.10, p < 0.01; MCS: B = 0.11, p < 0.01), and the results were consistent across all diabetic patients (see Figure 4). This shows that the cross-lag model of cognitive function and HRQoL in middle-aged and older diabetic patients shows variance in the use of digital technology, that is, this relationship will be affected by digital usage behavior.\\nIn order to investigate whether the cross-lag relationship between cognitive function and HRQoL differs in digital usage behavior, the study performed a multi-group analysis. All diabetic patients were divided into two groups, one representing non-digital usage behavior (Figure 3), and the other representing digital usage behavior (Figure 4). In the grouped cross-lag model, the corresponding control variables were also added to the model. The models in Figure 3 and Figure 4 met fitness criteria (Figure 3: Model 3: RMSEA = 0.073, CFI = 0.996, TCL = 0.901; Model 4: RMSEA = 0.100, CFI = 0.990, TCL = 0.936. Figure 4: Model 5: RMSEA = 0.073, CFI = 0.996, TCL = 0.941; Model 6: RMSEA = 0.079, CFI = 0.970, TCL = 0.936). In both sets of models, the study focused on the relationship between cognitive function and HRQoL at a point in time, as well as their relationship at the time of follow-up research.\\nFor middle-aged and older diabetic patients using digital technology, although the model could be fitted, cognitive function at T1 did not significantly predict HRQoL at T2 (PCS: B = 0.30, p > 0.01; MCS: B = 0.14, p > 0.01), and vice versa (PCS: B = 0.06, p > 0.01; MCS: B = 0.05, p > 0.01) (see Figure 3). For diabetic patients who did not use digital technology, although T1 cognitive function did not significantly predict T2 HRQoL (PCS: B = 0.04, p > 0.01; MCS: B = 0.05, p > 0.01), T1 HRQoL significantly predicted T2 cognitive function (PCS: B = 0.10, p < 0.01; MCS: B = 0.11, p < 0.01), and the results were consistent across all diabetic patients (see Figure 4). This shows that the cross-lag model of cognitive function and HRQoL in middle-aged and older diabetic patients shows variance in the use of digital technology, that is, this relationship will be affected by digital usage behavior.\",\n \"The variable descriptive statistics and correlation analysis results are shown in Table 3 and Table 4. The correlation analysis shows that there is a significant correlation between cognition at T1 and T2 and HRQoL levels at T1 and T2, indicating that cognitive function and HRQoL levels in middle-aged and older diabetic patients have a certain relationship, which is showing stability. Meanwhile, simultaneous and sequential correlations between cognitive function and HRQoL levels are also significant. The correlation coefficients between cognitive function and HRQoL at T1 are 0.28 (p < 0.01) and 0.37 (p < 0.01); the correlation coefficients between cognitive function at T2 and HRQoL at T2 are 0.32 (p < 0.01) and 0.30 (p < 0.01); the correlation coefficients between cognitive function at T1 and HRQoL at T2 are 0.25 (p < 0.01) and 0.22 (p < 0.01); and the correlation coefficients between HRQoL at T1 and cognitive function at T2 are 0.32 (p < 0.01) and 0.36 (p < 0.01). This indicates that cognitive function is basically consistent with the synchronous and stable correlations of HRQoL levels, which is in line with the basic assumptions of the cross-lag design.\",\n \"With cognitive function as the dependent variable, a 2 (test time: T1/T2) × 2 (digital usage behavior: use/non-use) repeated measures square analysis was performed. The results show that the testing time is the main and most significant effect (F = 11.21, p < 0.001, η2 = 0.01). The cognitive function level at T2 is significantly lower than at T1, and there are developmental differences. The main effect of digital usage behavior is significant (F = 36.325, p < 0.001, η2 = 0.06), and the cognitive function of patients using digital technology is significantly better than those patients without digital technology. The interaction between the two is not significant (F = 2.22, p > 0.05, η2 = 0.04).\\nWith PCS as the dependent variable, a 2 (test time: T1/T2) × 2 (digital usage behavior: use/non-use) repeated measures square analysis was performed. The results show that the testing time is the main and most significant effect (F =16.25, p < 0.001, η2 = 0.03), the PCS level of the post-test is significantly lower than that of the pre-test, and there is a developmental difference. The main effect of digital usage behavior is significant (F = 36.54, p < 0.001, η2 = 0.06), and the PCS of patients using digital technology is significantly better than those not using digital technology. The interaction between the two is not significant (F = 0.18, p > 0.05, η2 = 0.00).\\nWith MCS as the dependent variable, a 2 (test time: T1/T2) × 2 (digital usage behavior: use/non-use) repeated measures square analysis was performed. The results showed that the testing time is the main and most significant effect (F = 44.93, p < 0.001, η2 = 0.07), the MCS level of the post-test is significantly lower than the pre-test, and there is a developmental difference. The main effect of digital usage behavior is significant (F = 50.97, p < 0.001, η2 = 0.08), and the MCS of patients using digital technology is significantly better than those not using digital technology. The interaction between the two is significant (F = 4.43, p < 0.05, η2 = 0.08).\",\n \"In the cross-lag model which this study applied, HRQoL at T2 was predicted by cognitive function at T1, and cognitive function at T2 was predicted by HRQoL at T1. After the corresponding control variables were added to the models, both models achieved acceptable fitness criteria (Model 1: RMSEA = 0.093; CFI = 0.994, TCL = 0.912; Model 2: RMSEA = 0.098; CFI = 0.984, TCL = 0.901).\\nThe cross-lag relationship between cognitive function and PCS scores at two points in time is shown from the test of Model 1 in Figure 2. First, in the same time period, the baseline association between cognitive function and PCS is significantly positive (B = 0.19, p < 0.01), implying better cognitive performance in middle-aged and older diabetic patients with higher PCS scores at T1, and vice versa. Second, there is no significant correlation between cognitive function at T1 and PCS at T2 (B = 0.05, p > 0.01), but PCS at T1 is positively correlated with cognitive function at T2 (B = 0.12, p < 0.01). This suggests that there is a positive and significant relationship between PCS and cognitive function in middle-aged and older diabetic patients over time, but early cognitive function in middle-aged and older diabetic patients does not affect later PSC.\\nThe cross-lag relationship between cognitive function and MCS scores at the two points in time is shown in Model 2 of Figure 2. First, in the same time period, the baseline association between cognitive function and MCS is significantly positively correlated (B = 0.31, p < 0.01), which implies a better cognitive performance in middle-aged and older diabetic patients with higher MCS scores at T1, and vice versa. Second, there is no significant correlation between cognitive function at T1 and MCS at T2 (B = 0.05, p > 0.01), but there is a significant positive correlation between MCS at T1 and cognitive function at T2 (B = 0.14, p < 0.01). This suggests that there is a positive and significant relationship between MCS and cognitive function in middle-aged and older diabetic patients over time, but early cognitive function in middle-aged and older diabetic patients does not affect later MCS. In conclusion, there may be a one-way causal relationship between cognitive function and HRQoL, and the causal direction is from HRQoL to cognition.\",\n \"In order to investigate whether the cross-lag relationship between cognitive function and HRQoL differs in digital usage behavior, the study performed a multi-group analysis. All diabetic patients were divided into two groups, one representing non-digital usage behavior (Figure 3), and the other representing digital usage behavior (Figure 4). In the grouped cross-lag model, the corresponding control variables were also added to the model. The models in Figure 3 and Figure 4 met fitness criteria (Figure 3: Model 3: RMSEA = 0.073, CFI = 0.996, TCL = 0.901; Model 4: RMSEA = 0.100, CFI = 0.990, TCL = 0.936. Figure 4: Model 5: RMSEA = 0.073, CFI = 0.996, TCL = 0.941; Model 6: RMSEA = 0.079, CFI = 0.970, TCL = 0.936). In both sets of models, the study focused on the relationship between cognitive function and HRQoL at a point in time, as well as their relationship at the time of follow-up research.\\nFor middle-aged and older diabetic patients using digital technology, although the model could be fitted, cognitive function at T1 did not significantly predict HRQoL at T2 (PCS: B = 0.30, p > 0.01; MCS: B = 0.14, p > 0.01), and vice versa (PCS: B = 0.06, p > 0.01; MCS: B = 0.05, p > 0.01) (see Figure 3). For diabetic patients who did not use digital technology, although T1 cognitive function did not significantly predict T2 HRQoL (PCS: B = 0.04, p > 0.01; MCS: B = 0.05, p > 0.01), T1 HRQoL significantly predicted T2 cognitive function (PCS: B = 0.10, p < 0.01; MCS: B = 0.11, p < 0.01), and the results were consistent across all diabetic patients (see Figure 4). This shows that the cross-lag model of cognitive function and HRQoL in middle-aged and older diabetic patients shows variance in the use of digital technology, that is, this relationship will be affected by digital usage behavior.\",\n \"The present study aimed to further deepen our understanding of the association between cognition and HRQoL. This study used a cross-lagged model and controlled for the corresponding covariates in order to verify the longitudinal relationship between cognition and HRQoL in middle-aged and older diabetic patients. The corresponding findings suggest that this relationship is unidirectional in the cross-lag model, that is, only HRQoL at T1 can predict cognitive function at T2, and cognitive function at T1 does not predict HRQoL at T2. This study further explored whether the use of digital technology would lead to different manifestations of this relationship, and divided the study population into two categories: those who used digital technology and those who did not. The results found that this one-way lag relationship still existed in middle-aged and older diabetic patients who did not use digital technology, but was not significant in patients who used digital technology.\\nFirst of all, the study found that there are certain developmental differences in the cognitive function, PCS, and MCS levels of middle-aged and older diabetic patients. There is an increasing trend over time which is consistent with previous research results [33,34]. From an age perspective, not only are changes in cognitive function closely related to age [17,35], but HRQoL also has a distinct age-specific trajectory [36]. Therefore, as patients with diabetes age, the risk of cognitive decline and reduced PCS and MCS levels increases [15,37]. Second, diabetes is considered a risk factor for developing cognitive impairments [38]. Although some studies suggest that it may not accelerate the cognitive deterioration process, there is a general consensus in the academic community that diabetes increases the risk of abnormal cognition by increasing the incidence of cognition-related diseases or affecting blood sugar levels [37,38]. Furthermore, previous cross-sectional and longitudinal studies have identified that chronic diseases such as diabetes, as well as BMI, can affect HRQoL in a negative way [39,40]. Thus, this trend may be more obvious in the diabetic population.\\nIt was found that the cognitive function of T1 was highly correlated with the PCS and MCS at T1 and T2, and the cognitive function of T2 was highly correlated with the PCS and MCS at T1 and T2, which was in line with the basic assumption of a cross-lag design. Our results are consistent with previous studies on other populations [9]. In the subsequent cross-lag analysis, the results were found to be consistent with previous studies [12]. The cognitive function, PCS, and MCS levels of middle-aged and elderly diabetic patients have a certain degree of lateral stability between T1 and T2, and HRQoL can predict cognitive function. However, there is no research to explain its internal mechanism. Verghese, J. et al., and Daviglus, M.L. et al., suggest that interventions to improve physical and general mental health can prevent or delay the onset of dementia [41,42]. Since HRQoL scores from CHARLS in this study include evaluations of physical activity and general mental health, it is believed that physical activity may be a plausible mechanism by which HRQoL can predict changes in cognitive function, based on previous studies. The mediating role of physical activity in this relationship should be further investigated in the future. In general, there are few studies focusing on the impact of HRQoL on cognitive function. In the future, more in-depth research is needed to explore the internal mechanism of how HRQoL affects cognitive function [43,44].\\nThird, although cognitive function at T1 was found to be significantly associated with HRQoL at T2 (PCS and MCS) in the initial correlation analysis, the predictive role of cognitive function at T1 on HRQoL at T2 in the cross-lag model was not significant, suggesting that cognitive function at T1 does not predict HRQoL at T2. This is consistent with previous studies [45]. One possible explanation is that the differences in cognitive function between the participants included are relatively small at baseline and at follow-up, as shown by a mean baseline cognitive function score of 15.73 and follow-up of 15.03. Therefore, there may not be enough individuals with the degree of cognitive function variation that would interfere with the study results. On the other hand, it may be that clinical health status alone does not determine a better quality of life [32], and for older adults, perceived life satisfaction appears to be more affected by ability to perform daily tasks [46]. Therefore, the influence of cognitive function on HRQoL may not be significant.\\nFinally, results show that there is a significant difference in the use of digital technology in the one-way predictive role of cognitive function and HRQoL, that is, the causal relationship between cognitive function and HRQoL is more applicable to diabetic patients who do not use digital technology. This finding is consistent with the study hypothesis. In terms of cognitive function, previous studies believe that the use of digital technology can slow the decline of cognitive function by increasing the cognitive reserve of individuals [18,47,48,49,50]. It has also been confirmed that in the diabetic population, digital usage behavior can improve the cognitive function of patients [51]; therefore, individuals who do not use digital technology tend to have greater changes in cognitive function, which are easier to detect than changes in individuals who do use digital technology. In terms of HRQoL, the use of digital technology has a direct positive impact on HRQoL [52], and for people with diabetes, digital usage behavior can have an indirect impact on HRQoL by stimulating positive lifestyle changes such as physical activity [53,54,55,56]. Therefore, it is believed that due to direct or indirect impact of digital usage behavior on cognitive function and HRQoL, digital usage behavior may have weakened the longitudinal association between cognitive function and HRQoL in middle-aged and older diabetic patients to some extent.\\nThis study can help us to make some policy recommendations. First, the aging population has greatly increased the public health burden in China. Cognitive function and HRQoL in diabetic patients were affected to varying degrees. Therefore, it is necessary to improve the relevant medical service system for diabetics in China as soon as possible to ensure that the medical needs of the elderly are met. In addition, attention will need to be out into improving the Internet and medical service systems. In order to reduce the digital technology gap, promotion of internet use and healthy aging is encouraged.\\nThis study did not focus on describing intra-individual variation due to the inherent limitations of the cross-lag model [57]. However, the cross-lag model used in this study still provides new evidence on the longitudinal relationship between cognitive function and HRQoL in middle-aged and older Chinese diabetic patients, and may provide new insights into the underlying mechanisms behind this relationship. Aside from the limitations of the model itself, there are several potential limitations to consider. First, there was a 3-year lag between baseline and follow-up, which may be considered too long to assess the cross-lag relationship between cognitive function and HRQoL. Future studies will need to test whether shorter and longer temporal associations differ in terms of gaining insight into the exact relationship between cognitive function and HRQoL in different conditions. Second, this study only selected the data of wave 3 and wave 4 of the CHARLS database, and did not include wave 1 or wave 2. This study used three questions that were only included after wave 3 in the evaluation criteria for the digital usage behavior. If there were enough data, it would obviously be better to include waves 1 and 2 in the analysis. In addition, a large portion of participants were excluded due to lack of necessary data. Although we controlled for demographic variables, this may still lead to selective bias in the data. Third, in the group analysis, due to the significant sample size difference between those who used digital technology and those who did not, results may have been inaccurate. However, the study results still show that the longitudinal relationship between cognitive function and HRQoL differs in terms of digital usage behavior. Finally, because patients with diabetes often have other chronic diseases of the elderly, future research is needed to further distinguish and identify the differences between diabetes and other chronic diseases of the elderly.\",\n \"The cognitive function and HRQoL (PCS and MCS) in middle-aged and older Chinese diabetic patients have a certain degree of lateral stability, and both tend to increase over time. More importantly, this study found that HRQoL at T1 can significantly predict cognitive function at T2, but cognitive function at T1 cannot significantly predict HRQoL at T2. HRQoL may be an antecedent for middle-aged and older diabetic patients’ cognitive function, and this predictive relationship can be different depending on digital usage behavior. In the future, effective intervention on HRQoL of middle-aged and older diabetic patients can be considered to improve their cognitive function, thereby promoting the healthy development of middle-aged and older diabetic patients.\"\n]"},"all_sections_sec_types":{"kind":"list like","value":["intro",null,"subjects",null,null,null,null,null,"results",null,null,null,null,"discussion","conclusions"],"string":"[\n \"intro\",\n null,\n \"subjects\",\n null,\n null,\n null,\n null,\n null,\n \"results\",\n null,\n null,\n null,\n null,\n \"discussion\",\n \"conclusions\"\n]"},"keywords":{"kind":"list like","value":["cognitive function","HRQoL","digital usage behavior","middle-aged and older people","diabetes"],"string":"[\n \"cognitive function\",\n \"HRQoL\",\n \"digital usage behavior\",\n \"middle-aged and older people\",\n \"diabetes\"\n]"},"whole_article_text":{"kind":"string","value":"1. Introduction:\nCognitive impairment is one of the common complications in elderly patients [1]. For example, in China, the prevalence of mild cognitive impairments in the aging population (aged 60 and above) is 14.71% [2], and as age increases, its annual rate of progression to dementia is between 8% and 15% [2]. At the same time, cognitive impairment is also one of the common complications of diabetic patients. It is estimated that by 2045, about 170 million elderly people in China will be diabetic patients [3]. Therefore, the academic community has paid great attention to the cognitive function of the diabetic population, and found that the cognitive function of the diabetic population is closely related to the quality of life (QoL) of the elderly [4].\nHRQoL is the perceived physical and mental health of an individual or group over time, including both physical component summary (PCS) and mental component summary (MCS) [5]. In contrast with the QoL, HRQoL pays special attention to the impact of disease and treatment process on the life of a person or a group [6]. Existing studies have found that changes in cognitive function are positively correlated with changes in HRQoL, and play a predictive role in future changes in HRQoL [7,8]. For example, cognitive decline was found to be a predictor of HRQoL decline in studies on multiple sclerosis patients, AIDS patients, and older women [4,9,10]. At the same time, some scholars have found that changes in HRQoL—whether it is the PCS or the MCS of HRQoL—can also predict cognitive changes in individuals in the future [11]. For example, Ezzati’s 2019 study demonstrated that changes in HRQoL preceded changes in cognition and predicted the occurrence of dementia [12]. Thus, cognitive function may be bi-directionally associated with HRQoL.\nThe bidirectional relationship between cognitive function and HRQoL may be more pronounced in terms of diabetic patients. This is because not only are people with diabetes 1–2 times more likely to develop cognitive risks than the general population [13,14], but this risk will increase over time [15]. At the same time, with the deepening of the research on HRQoL, the medical community generally believes that the core of diabetes management should include HRQoL maintenance in addition to prevention and delay of its complications [16]. In summary, this research aims to focus on middle-aged and older diabetic patients (over 45 years old) in China—not only because China has one of the largest numbers of diabetic patients in the world, but also because the age of the population affected with diabetes is showing a downward trend [3,17]. Thus, we propose the first hypothesis: in middle-aged and older Chinese patients with diabetes, the relationship between cognitive function and HRQoL may be bidirectional.\nTo our knowledge, previous studies on cognitive function and HRQoL have focused on unmodifiable clinical factors, such as age, gender, etc., with a lack of studies on modifiable factors [4]. Digital technologies such as the Internet and smartphones have attracted the attention of psychologists because of their portability, rapidity, and immediacy. In clinical medicine, digital usage behaviors are often used for managing and intervening with patients with diabetes and cognitive dysfunction [18]. Thus, digital technology may be seen as a modifiable factor in the relationship between cognitive function and HRQoL. We also found that with the continuous development and improvement of digital technology, digital usage behavior can have a profound impact on the cognitive function of patients with chronic diseases [18,19,20]. In addition to this, it is important that digital usage behavior, such as Internet use, can also have a certain impact on HRQoL [21]. Previous research in other populations found that using the Internet resulted in significantly higher PCS and physical pain scores [22]. According to self-determination theory, we believe that when individuals use the internet for leisure or work, their needs are met, which in turn produces positive long-term psychological outcomes such as quality of life [23]. So far, existing research is leaning towards the belief that an increase in digital usage behavior can improve the health of individuals [24]. However, there are also studies holding the opposite view; they believe that the long-term digital usage behavior reduces patients’ time for outdoor activities, and that a large amount of negative information received due to digital usage behavior is more likely to cause mood swings, which may have side effects on patients’ recovery [25]. Therefore, it is easy to find that digital usage behavior is likely to have various effects, so we propose another hypothesis: in middle-aged and older Chinese patients with diabetes, the relationship between cognitive function and HRQoL may differ considering digital usage behavior.\nTo date, most studies on the association of cognitive function with HRQoL have used cross-sectional designs, and have failed to elucidate the direction of influence between cognitive function and HRQoL due to the limitations of cross-sectional studies in terms of making causal inferences and explaining the direction of association. Therefore, it is necessary to further explore, especially for diabetic patients, the longitudinal association between the cognitive function and HRQoL, and to clarify the direction of their effects. As a longitudinal study, this study attempts to use a cross-lag model to explore the relationship between cognitive function and HRQoL in Chinese middle-aged and older diabetic patients, as well as the direction of this relationship and whether there were differences in digital usage behavior. This research is attempting to explore the longitudinal relationship between cognition and HRQoL in middle-aged and older diabetic patients, and to provide evidence which can promote their health.\nIt should be noted that, due to the existence of multiple databases in China, in addition to the China Health and Retirement Longitudinal Survey (CHARLS), most scholars use databases such as the China Household Finance Survey when researching topics related to public health [26]. There is more content regarding the physical and mental health of middle-aged and elderly people, so we chose CHARLS as our data source.\n\n2. Materials and Methods:\n2.1. Participants The China Health and Retirement Longitudinal Survey (CHARLS) is a representative follow-up survey of middle-aged and older people in China, chaired by the National Research Institute of Development at Peking University. The study protocol was approved by the Institutional Review Board of Peking University (approval number: IRB00001052-11015), and the study protocol complies with the ethical guidelines of the 1975 Declaration of Helsinki. In order to obtain the relevant data, we applied for the CHARLS database online on 31 August 2022, and approval was quickly obtained. This study used two waves of CHARLS data, from 2015 (T1) and 2018 (T2). CHARLS data can be accessed through its official website (https://charls.pku.edu.cn (accessed on 31 August 2022)).\nFigure 1 shows the detailed process for including and excluding study participants. Wave 3 was used as the baseline data set for this study, and individuals who lacked information on HRQoL, cognitive function, digital usage behavior, and covariates were excluded from this study. Subsequently, new participants in 2018 were further excluded. Individuals lacking information on digital usage behavior, cognitive function, and covariates were also excluded from this study. In addition, in the sample selection, the research team excluded individuals with other diseases (such as cognitive diseases that may affect the detection process, mental diseases, and other diseases related to aging), and included individuals with diabetes only. In addition, the abbreviations of the main variables involved in the study are organized in Table 1.\nThe China Health and Retirement Longitudinal Survey (CHARLS) is a representative follow-up survey of middle-aged and older people in China, chaired by the National Research Institute of Development at Peking University. The study protocol was approved by the Institutional Review Board of Peking University (approval number: IRB00001052-11015), and the study protocol complies with the ethical guidelines of the 1975 Declaration of Helsinki. In order to obtain the relevant data, we applied for the CHARLS database online on 31 August 2022, and approval was quickly obtained. This study used two waves of CHARLS data, from 2015 (T1) and 2018 (T2). CHARLS data can be accessed through its official website (https://charls.pku.edu.cn (accessed on 31 August 2022)).\nFigure 1 shows the detailed process for including and excluding study participants. Wave 3 was used as the baseline data set for this study, and individuals who lacked information on HRQoL, cognitive function, digital usage behavior, and covariates were excluded from this study. Subsequently, new participants in 2018 were further excluded. Individuals lacking information on digital usage behavior, cognitive function, and covariates were also excluded from this study. In addition, in the sample selection, the research team excluded individuals with other diseases (such as cognitive diseases that may affect the detection process, mental diseases, and other diseases related to aging), and included individuals with diabetes only. In addition, the abbreviations of the main variables involved in the study are organized in Table 1.\n2.2. Measurements 2.2.1. Cognitive Functioning This study obtained data on cognitive function from baseline data in 2015 and follow-up data in 2018. The baseline data stipulated by CHARLS is from 2011, but we include CHARLS 2015 (T1) as the baseline data in our study. CHARLS is similar to the cognitive assessment used in the American Health and Retirement Study, and has constructed cognitive function evaluation criteria from the same two aspects of memory and mental state [27,28]. Furthermore, previous studies have also used the CHARLS cognitive function assessment criteria for class-correspondence studies. Firstly, memory evaluation includes immediate word recall (0–10 points) and delayed word recall (0–10 points). Mental state is measured from three dimensions: orientation, visual construction, and mathematical performance. Orientation (0–5 points) is measured by asking respondents to name the date, day of the week, and season; visual construction is assessed by drawing a previously displayed picture (0–1 points); and mathematical performance (0–5 points) is measured by asking respondents to subtract 7 consecutive times from 100. The scores of the participants’ cognitive function are equal to the sum of the scores of memory and mental state. Cognitive function scores range from 0 to 31 points, higher cognitive function scores indicating better cognitive function. Finally, in order to obtain a composite measure of cognitive function, this study normalized and averaged the total cognitive function score by adding up memory and mental state scores [29].\nThis study obtained data on cognitive function from baseline data in 2015 and follow-up data in 2018. The baseline data stipulated by CHARLS is from 2011, but we include CHARLS 2015 (T1) as the baseline data in our study. CHARLS is similar to the cognitive assessment used in the American Health and Retirement Study, and has constructed cognitive function evaluation criteria from the same two aspects of memory and mental state [27,28]. Furthermore, previous studies have also used the CHARLS cognitive function assessment criteria for class-correspondence studies. Firstly, memory evaluation includes immediate word recall (0–10 points) and delayed word recall (0–10 points). Mental state is measured from three dimensions: orientation, visual construction, and mathematical performance. Orientation (0–5 points) is measured by asking respondents to name the date, day of the week, and season; visual construction is assessed by drawing a previously displayed picture (0–1 points); and mathematical performance (0–5 points) is measured by asking respondents to subtract 7 consecutive times from 100. The scores of the participants’ cognitive function are equal to the sum of the scores of memory and mental state. Cognitive function scores range from 0 to 31 points, higher cognitive function scores indicating better cognitive function. Finally, in order to obtain a composite measure of cognitive function, this study normalized and averaged the total cognitive function score by adding up memory and mental state scores [29].\n2.2.2. HRQoL This study used a new scale construction based on the variables of the Short Form 36 (SF-36) and the CHARLS questionnaire to measure HRQoL in diabetic patients. The construction of the new scale was derived from the eight dimensions of SF-36, and the corresponding variables of CHARLS were selected to assess the following eight dimensions (Table 2): physical function (PF), role–body (RP), body pain (BP), general health (GH), vitality (VT), social functioning (SF), role–emotion (RE), and mental health (MH). The scores for the above eight dimensions were calculated by adding up the category scores and then converting the raw scores to a 0 to 100 scale. Scores from the eight subscales were aggregated into two overall scores according to the conceptual model of the SF-36 [30]. Physical function, body roles, body pain, and general perceptions of health were calculated as PCS, and mental health, vitality, emotional role, and social functioning were calculated as MCS. Although the HRQoL questionnaire based on CHARLS is slightly different from other HRQoL questionnaires, they all have similar focuses, including physical, emotional, and social diversity. The questionnaire has been determined to be effective in the Chinese population [31], and has already been used in related research [32].\nThis study used a new scale construction based on the variables of the Short Form 36 (SF-36) and the CHARLS questionnaire to measure HRQoL in diabetic patients. The construction of the new scale was derived from the eight dimensions of SF-36, and the corresponding variables of CHARLS were selected to assess the following eight dimensions (Table 2): physical function (PF), role–body (RP), body pain (BP), general health (GH), vitality (VT), social functioning (SF), role–emotion (RE), and mental health (MH). The scores for the above eight dimensions were calculated by adding up the category scores and then converting the raw scores to a 0 to 100 scale. Scores from the eight subscales were aggregated into two overall scores according to the conceptual model of the SF-36 [30]. Physical function, body roles, body pain, and general perceptions of health were calculated as PCS, and mental health, vitality, emotional role, and social functioning were calculated as MCS. Although the HRQoL questionnaire based on CHARLS is slightly different from other HRQoL questionnaires, they all have similar focuses, including physical, emotional, and social diversity. The questionnaire has been determined to be effective in the Chinese population [31], and has already been used in related research [32].\n2.2.3. Socio-Demographic Variables In order to minimize the possibility of other variables influencing the cognitive function–HRQoL relationship study, and to simplify the model, this research controlled for several specific covariates associated with cognitive function and HRQoL. According to previous studies, all covariates were based on baseline data (CHARLS 2015) [32]. Firstly, population control variables include age, gender, and education status. Secondly, since HRQoL can be divided into two parts, PCS and MCS, this study used different control variables in the models for cognitive function, PCS, and MCS. This research included depression as well as current smoking and drinking habits as control variables for PCS scores. In this research, PCS scores are also considered with marital status, depression, physical activity, and current smoking and drinking habits as control variables.\nIn order to minimize the possibility of other variables influencing the cognitive function–HRQoL relationship study, and to simplify the model, this research controlled for several specific covariates associated with cognitive function and HRQoL. According to previous studies, all covariates were based on baseline data (CHARLS 2015) [32]. Firstly, population control variables include age, gender, and education status. Secondly, since HRQoL can be divided into two parts, PCS and MCS, this study used different control variables in the models for cognitive function, PCS, and MCS. This research included depression as well as current smoking and drinking habits as control variables for PCS scores. In this research, PCS scores are also considered with marital status, depression, physical activity, and current smoking and drinking habits as control variables.\n2.2.1. Cognitive Functioning This study obtained data on cognitive function from baseline data in 2015 and follow-up data in 2018. The baseline data stipulated by CHARLS is from 2011, but we include CHARLS 2015 (T1) as the baseline data in our study. CHARLS is similar to the cognitive assessment used in the American Health and Retirement Study, and has constructed cognitive function evaluation criteria from the same two aspects of memory and mental state [27,28]. Furthermore, previous studies have also used the CHARLS cognitive function assessment criteria for class-correspondence studies. Firstly, memory evaluation includes immediate word recall (0–10 points) and delayed word recall (0–10 points). Mental state is measured from three dimensions: orientation, visual construction, and mathematical performance. Orientation (0–5 points) is measured by asking respondents to name the date, day of the week, and season; visual construction is assessed by drawing a previously displayed picture (0–1 points); and mathematical performance (0–5 points) is measured by asking respondents to subtract 7 consecutive times from 100. The scores of the participants’ cognitive function are equal to the sum of the scores of memory and mental state. Cognitive function scores range from 0 to 31 points, higher cognitive function scores indicating better cognitive function. Finally, in order to obtain a composite measure of cognitive function, this study normalized and averaged the total cognitive function score by adding up memory and mental state scores [29].\nThis study obtained data on cognitive function from baseline data in 2015 and follow-up data in 2018. The baseline data stipulated by CHARLS is from 2011, but we include CHARLS 2015 (T1) as the baseline data in our study. CHARLS is similar to the cognitive assessment used in the American Health and Retirement Study, and has constructed cognitive function evaluation criteria from the same two aspects of memory and mental state [27,28]. Furthermore, previous studies have also used the CHARLS cognitive function assessment criteria for class-correspondence studies. Firstly, memory evaluation includes immediate word recall (0–10 points) and delayed word recall (0–10 points). Mental state is measured from three dimensions: orientation, visual construction, and mathematical performance. Orientation (0–5 points) is measured by asking respondents to name the date, day of the week, and season; visual construction is assessed by drawing a previously displayed picture (0–1 points); and mathematical performance (0–5 points) is measured by asking respondents to subtract 7 consecutive times from 100. The scores of the participants’ cognitive function are equal to the sum of the scores of memory and mental state. Cognitive function scores range from 0 to 31 points, higher cognitive function scores indicating better cognitive function. Finally, in order to obtain a composite measure of cognitive function, this study normalized and averaged the total cognitive function score by adding up memory and mental state scores [29].\n2.2.2. HRQoL This study used a new scale construction based on the variables of the Short Form 36 (SF-36) and the CHARLS questionnaire to measure HRQoL in diabetic patients. The construction of the new scale was derived from the eight dimensions of SF-36, and the corresponding variables of CHARLS were selected to assess the following eight dimensions (Table 2): physical function (PF), role–body (RP), body pain (BP), general health (GH), vitality (VT), social functioning (SF), role–emotion (RE), and mental health (MH). The scores for the above eight dimensions were calculated by adding up the category scores and then converting the raw scores to a 0 to 100 scale. Scores from the eight subscales were aggregated into two overall scores according to the conceptual model of the SF-36 [30]. Physical function, body roles, body pain, and general perceptions of health were calculated as PCS, and mental health, vitality, emotional role, and social functioning were calculated as MCS. Although the HRQoL questionnaire based on CHARLS is slightly different from other HRQoL questionnaires, they all have similar focuses, including physical, emotional, and social diversity. The questionnaire has been determined to be effective in the Chinese population [31], and has already been used in related research [32].\nThis study used a new scale construction based on the variables of the Short Form 36 (SF-36) and the CHARLS questionnaire to measure HRQoL in diabetic patients. The construction of the new scale was derived from the eight dimensions of SF-36, and the corresponding variables of CHARLS were selected to assess the following eight dimensions (Table 2): physical function (PF), role–body (RP), body pain (BP), general health (GH), vitality (VT), social functioning (SF), role–emotion (RE), and mental health (MH). The scores for the above eight dimensions were calculated by adding up the category scores and then converting the raw scores to a 0 to 100 scale. Scores from the eight subscales were aggregated into two overall scores according to the conceptual model of the SF-36 [30]. Physical function, body roles, body pain, and general perceptions of health were calculated as PCS, and mental health, vitality, emotional role, and social functioning were calculated as MCS. Although the HRQoL questionnaire based on CHARLS is slightly different from other HRQoL questionnaires, they all have similar focuses, including physical, emotional, and social diversity. The questionnaire has been determined to be effective in the Chinese population [31], and has already been used in related research [32].\n2.2.3. Socio-Demographic Variables In order to minimize the possibility of other variables influencing the cognitive function–HRQoL relationship study, and to simplify the model, this research controlled for several specific covariates associated with cognitive function and HRQoL. According to previous studies, all covariates were based on baseline data (CHARLS 2015) [32]. Firstly, population control variables include age, gender, and education status. Secondly, since HRQoL can be divided into two parts, PCS and MCS, this study used different control variables in the models for cognitive function, PCS, and MCS. This research included depression as well as current smoking and drinking habits as control variables for PCS scores. In this research, PCS scores are also considered with marital status, depression, physical activity, and current smoking and drinking habits as control variables.\nIn order to minimize the possibility of other variables influencing the cognitive function–HRQoL relationship study, and to simplify the model, this research controlled for several specific covariates associated with cognitive function and HRQoL. According to previous studies, all covariates were based on baseline data (CHARLS 2015) [32]. Firstly, population control variables include age, gender, and education status. Secondly, since HRQoL can be divided into two parts, PCS and MCS, this study used different control variables in the models for cognitive function, PCS, and MCS. This research included depression as well as current smoking and drinking habits as control variables for PCS scores. In this research, PCS scores are also considered with marital status, depression, physical activity, and current smoking and drinking habits as control variables.\n2.3. Statistical Methods IBM SPSS Statistics, version 23 (IBM SPSS Statistics, Armonk, NY, USA) and Mplus version 8.0 are used for data analysis. Descriptive analyses of diabetic patient characteristics, cognitive function, and PCM and MCS of HRQoL were performed. The relationship between cognitive function and HRQoL in diabetic patients at T1 and T2 was assessed using the Pearson correlation test. A repeated-measures analysis of variance was performed to assess the differences between these variables with and without the digital usage behavior. Furthermore, cross-lagged panel structural equation modeling (SEM) was used to assess the longitudinal association between cognition and HRQoL at T1 and T2. Finally, a multi-group test was performed to assess whether digital usage behavior was making a difference in this relationship. Finally, regarding the grouping of digital technologies as well, previous studies were referred to [24]. Digital technology usage was obtained from 2015 baseline data. In the CHARLS 2015 questionnaire, internet usage was measured using the following question whether respondents have used the internet in the last month (0 for no, 1 for yes).\nIBM SPSS Statistics, version 23 (IBM SPSS Statistics, Armonk, NY, USA) and Mplus version 8.0 are used for data analysis. Descriptive analyses of diabetic patient characteristics, cognitive function, and PCM and MCS of HRQoL were performed. The relationship between cognitive function and HRQoL in diabetic patients at T1 and T2 was assessed using the Pearson correlation test. A repeated-measures analysis of variance was performed to assess the differences between these variables with and without the digital usage behavior. Furthermore, cross-lagged panel structural equation modeling (SEM) was used to assess the longitudinal association between cognition and HRQoL at T1 and T2. Finally, a multi-group test was performed to assess whether digital usage behavior was making a difference in this relationship. Finally, regarding the grouping of digital technologies as well, previous studies were referred to [24]. Digital technology usage was obtained from 2015 baseline data. In the CHARLS 2015 questionnaire, internet usage was measured using the following question whether respondents have used the internet in the last month (0 for no, 1 for yes).\n\n2.1. Participants:\nThe China Health and Retirement Longitudinal Survey (CHARLS) is a representative follow-up survey of middle-aged and older people in China, chaired by the National Research Institute of Development at Peking University. The study protocol was approved by the Institutional Review Board of Peking University (approval number: IRB00001052-11015), and the study protocol complies with the ethical guidelines of the 1975 Declaration of Helsinki. In order to obtain the relevant data, we applied for the CHARLS database online on 31 August 2022, and approval was quickly obtained. This study used two waves of CHARLS data, from 2015 (T1) and 2018 (T2). CHARLS data can be accessed through its official website (https://charls.pku.edu.cn (accessed on 31 August 2022)).\nFigure 1 shows the detailed process for including and excluding study participants. Wave 3 was used as the baseline data set for this study, and individuals who lacked information on HRQoL, cognitive function, digital usage behavior, and covariates were excluded from this study. Subsequently, new participants in 2018 were further excluded. Individuals lacking information on digital usage behavior, cognitive function, and covariates were also excluded from this study. In addition, in the sample selection, the research team excluded individuals with other diseases (such as cognitive diseases that may affect the detection process, mental diseases, and other diseases related to aging), and included individuals with diabetes only. In addition, the abbreviations of the main variables involved in the study are organized in Table 1.\n\n2.2. Measurements:\n2.2.1. Cognitive Functioning This study obtained data on cognitive function from baseline data in 2015 and follow-up data in 2018. The baseline data stipulated by CHARLS is from 2011, but we include CHARLS 2015 (T1) as the baseline data in our study. CHARLS is similar to the cognitive assessment used in the American Health and Retirement Study, and has constructed cognitive function evaluation criteria from the same two aspects of memory and mental state [27,28]. Furthermore, previous studies have also used the CHARLS cognitive function assessment criteria for class-correspondence studies. Firstly, memory evaluation includes immediate word recall (0–10 points) and delayed word recall (0–10 points). Mental state is measured from three dimensions: orientation, visual construction, and mathematical performance. Orientation (0–5 points) is measured by asking respondents to name the date, day of the week, and season; visual construction is assessed by drawing a previously displayed picture (0–1 points); and mathematical performance (0–5 points) is measured by asking respondents to subtract 7 consecutive times from 100. The scores of the participants’ cognitive function are equal to the sum of the scores of memory and mental state. Cognitive function scores range from 0 to 31 points, higher cognitive function scores indicating better cognitive function. Finally, in order to obtain a composite measure of cognitive function, this study normalized and averaged the total cognitive function score by adding up memory and mental state scores [29].\nThis study obtained data on cognitive function from baseline data in 2015 and follow-up data in 2018. The baseline data stipulated by CHARLS is from 2011, but we include CHARLS 2015 (T1) as the baseline data in our study. CHARLS is similar to the cognitive assessment used in the American Health and Retirement Study, and has constructed cognitive function evaluation criteria from the same two aspects of memory and mental state [27,28]. Furthermore, previous studies have also used the CHARLS cognitive function assessment criteria for class-correspondence studies. Firstly, memory evaluation includes immediate word recall (0–10 points) and delayed word recall (0–10 points). Mental state is measured from three dimensions: orientation, visual construction, and mathematical performance. Orientation (0–5 points) is measured by asking respondents to name the date, day of the week, and season; visual construction is assessed by drawing a previously displayed picture (0–1 points); and mathematical performance (0–5 points) is measured by asking respondents to subtract 7 consecutive times from 100. The scores of the participants’ cognitive function are equal to the sum of the scores of memory and mental state. Cognitive function scores range from 0 to 31 points, higher cognitive function scores indicating better cognitive function. Finally, in order to obtain a composite measure of cognitive function, this study normalized and averaged the total cognitive function score by adding up memory and mental state scores [29].\n2.2.2. HRQoL This study used a new scale construction based on the variables of the Short Form 36 (SF-36) and the CHARLS questionnaire to measure HRQoL in diabetic patients. The construction of the new scale was derived from the eight dimensions of SF-36, and the corresponding variables of CHARLS were selected to assess the following eight dimensions (Table 2): physical function (PF), role–body (RP), body pain (BP), general health (GH), vitality (VT), social functioning (SF), role–emotion (RE), and mental health (MH). The scores for the above eight dimensions were calculated by adding up the category scores and then converting the raw scores to a 0 to 100 scale. Scores from the eight subscales were aggregated into two overall scores according to the conceptual model of the SF-36 [30]. Physical function, body roles, body pain, and general perceptions of health were calculated as PCS, and mental health, vitality, emotional role, and social functioning were calculated as MCS. Although the HRQoL questionnaire based on CHARLS is slightly different from other HRQoL questionnaires, they all have similar focuses, including physical, emotional, and social diversity. The questionnaire has been determined to be effective in the Chinese population [31], and has already been used in related research [32].\nThis study used a new scale construction based on the variables of the Short Form 36 (SF-36) and the CHARLS questionnaire to measure HRQoL in diabetic patients. The construction of the new scale was derived from the eight dimensions of SF-36, and the corresponding variables of CHARLS were selected to assess the following eight dimensions (Table 2): physical function (PF), role–body (RP), body pain (BP), general health (GH), vitality (VT), social functioning (SF), role–emotion (RE), and mental health (MH). The scores for the above eight dimensions were calculated by adding up the category scores and then converting the raw scores to a 0 to 100 scale. Scores from the eight subscales were aggregated into two overall scores according to the conceptual model of the SF-36 [30]. Physical function, body roles, body pain, and general perceptions of health were calculated as PCS, and mental health, vitality, emotional role, and social functioning were calculated as MCS. Although the HRQoL questionnaire based on CHARLS is slightly different from other HRQoL questionnaires, they all have similar focuses, including physical, emotional, and social diversity. The questionnaire has been determined to be effective in the Chinese population [31], and has already been used in related research [32].\n2.2.3. Socio-Demographic Variables In order to minimize the possibility of other variables influencing the cognitive function–HRQoL relationship study, and to simplify the model, this research controlled for several specific covariates associated with cognitive function and HRQoL. According to previous studies, all covariates were based on baseline data (CHARLS 2015) [32]. Firstly, population control variables include age, gender, and education status. Secondly, since HRQoL can be divided into two parts, PCS and MCS, this study used different control variables in the models for cognitive function, PCS, and MCS. This research included depression as well as current smoking and drinking habits as control variables for PCS scores. In this research, PCS scores are also considered with marital status, depression, physical activity, and current smoking and drinking habits as control variables.\nIn order to minimize the possibility of other variables influencing the cognitive function–HRQoL relationship study, and to simplify the model, this research controlled for several specific covariates associated with cognitive function and HRQoL. According to previous studies, all covariates were based on baseline data (CHARLS 2015) [32]. Firstly, population control variables include age, gender, and education status. Secondly, since HRQoL can be divided into two parts, PCS and MCS, this study used different control variables in the models for cognitive function, PCS, and MCS. This research included depression as well as current smoking and drinking habits as control variables for PCS scores. In this research, PCS scores are also considered with marital status, depression, physical activity, and current smoking and drinking habits as control variables.\n\n2.2.1. Cognitive Functioning:\nThis study obtained data on cognitive function from baseline data in 2015 and follow-up data in 2018. The baseline data stipulated by CHARLS is from 2011, but we include CHARLS 2015 (T1) as the baseline data in our study. CHARLS is similar to the cognitive assessment used in the American Health and Retirement Study, and has constructed cognitive function evaluation criteria from the same two aspects of memory and mental state [27,28]. Furthermore, previous studies have also used the CHARLS cognitive function assessment criteria for class-correspondence studies. Firstly, memory evaluation includes immediate word recall (0–10 points) and delayed word recall (0–10 points). Mental state is measured from three dimensions: orientation, visual construction, and mathematical performance. Orientation (0–5 points) is measured by asking respondents to name the date, day of the week, and season; visual construction is assessed by drawing a previously displayed picture (0–1 points); and mathematical performance (0–5 points) is measured by asking respondents to subtract 7 consecutive times from 100. The scores of the participants’ cognitive function are equal to the sum of the scores of memory and mental state. Cognitive function scores range from 0 to 31 points, higher cognitive function scores indicating better cognitive function. Finally, in order to obtain a composite measure of cognitive function, this study normalized and averaged the total cognitive function score by adding up memory and mental state scores [29].\n\n2.2.2. HRQoL:\nThis study used a new scale construction based on the variables of the Short Form 36 (SF-36) and the CHARLS questionnaire to measure HRQoL in diabetic patients. The construction of the new scale was derived from the eight dimensions of SF-36, and the corresponding variables of CHARLS were selected to assess the following eight dimensions (Table 2): physical function (PF), role–body (RP), body pain (BP), general health (GH), vitality (VT), social functioning (SF), role–emotion (RE), and mental health (MH). The scores for the above eight dimensions were calculated by adding up the category scores and then converting the raw scores to a 0 to 100 scale. Scores from the eight subscales were aggregated into two overall scores according to the conceptual model of the SF-36 [30]. Physical function, body roles, body pain, and general perceptions of health were calculated as PCS, and mental health, vitality, emotional role, and social functioning were calculated as MCS. Although the HRQoL questionnaire based on CHARLS is slightly different from other HRQoL questionnaires, they all have similar focuses, including physical, emotional, and social diversity. The questionnaire has been determined to be effective in the Chinese population [31], and has already been used in related research [32].\n\n2.2.3. Socio-Demographic Variables:\nIn order to minimize the possibility of other variables influencing the cognitive function–HRQoL relationship study, and to simplify the model, this research controlled for several specific covariates associated with cognitive function and HRQoL. According to previous studies, all covariates were based on baseline data (CHARLS 2015) [32]. Firstly, population control variables include age, gender, and education status. Secondly, since HRQoL can be divided into two parts, PCS and MCS, this study used different control variables in the models for cognitive function, PCS, and MCS. This research included depression as well as current smoking and drinking habits as control variables for PCS scores. In this research, PCS scores are also considered with marital status, depression, physical activity, and current smoking and drinking habits as control variables.\n\n2.3. Statistical Methods:\nIBM SPSS Statistics, version 23 (IBM SPSS Statistics, Armonk, NY, USA) and Mplus version 8.0 are used for data analysis. Descriptive analyses of diabetic patient characteristics, cognitive function, and PCM and MCS of HRQoL were performed. The relationship between cognitive function and HRQoL in diabetic patients at T1 and T2 was assessed using the Pearson correlation test. A repeated-measures analysis of variance was performed to assess the differences between these variables with and without the digital usage behavior. Furthermore, cross-lagged panel structural equation modeling (SEM) was used to assess the longitudinal association between cognition and HRQoL at T1 and T2. Finally, a multi-group test was performed to assess whether digital usage behavior was making a difference in this relationship. Finally, regarding the grouping of digital technologies as well, previous studies were referred to [24]. Digital technology usage was obtained from 2015 baseline data. In the CHARLS 2015 questionnaire, internet usage was measured using the following question whether respondents have used the internet in the last month (0 for no, 1 for yes).\n\n3. Results:\n3.1. Descriptive Statistics The variable descriptive statistics and correlation analysis results are shown in Table 3 and Table 4. The correlation analysis shows that there is a significant correlation between cognition at T1 and T2 and HRQoL levels at T1 and T2, indicating that cognitive function and HRQoL levels in middle-aged and older diabetic patients have a certain relationship, which is showing stability. Meanwhile, simultaneous and sequential correlations between cognitive function and HRQoL levels are also significant. The correlation coefficients between cognitive function and HRQoL at T1 are 0.28 (p < 0.01) and 0.37 (p < 0.01); the correlation coefficients between cognitive function at T2 and HRQoL at T2 are 0.32 (p < 0.01) and 0.30 (p < 0.01); the correlation coefficients between cognitive function at T1 and HRQoL at T2 are 0.25 (p < 0.01) and 0.22 (p < 0.01); and the correlation coefficients between HRQoL at T1 and cognitive function at T2 are 0.32 (p < 0.01) and 0.36 (p < 0.01). This indicates that cognitive function is basically consistent with the synchronous and stable correlations of HRQoL levels, which is in line with the basic assumptions of the cross-lag design.\nThe variable descriptive statistics and correlation analysis results are shown in Table 3 and Table 4. The correlation analysis shows that there is a significant correlation between cognition at T1 and T2 and HRQoL levels at T1 and T2, indicating that cognitive function and HRQoL levels in middle-aged and older diabetic patients have a certain relationship, which is showing stability. Meanwhile, simultaneous and sequential correlations between cognitive function and HRQoL levels are also significant. The correlation coefficients between cognitive function and HRQoL at T1 are 0.28 (p < 0.01) and 0.37 (p < 0.01); the correlation coefficients between cognitive function at T2 and HRQoL at T2 are 0.32 (p < 0.01) and 0.30 (p < 0.01); the correlation coefficients between cognitive function at T1 and HRQoL at T2 are 0.25 (p < 0.01) and 0.22 (p < 0.01); and the correlation coefficients between HRQoL at T1 and cognitive function at T2 are 0.32 (p < 0.01) and 0.36 (p < 0.01). This indicates that cognitive function is basically consistent with the synchronous and stable correlations of HRQoL levels, which is in line with the basic assumptions of the cross-lag design.\n3.2. Stability Analysis of Cognitive Function and HRQoL With cognitive function as the dependent variable, a 2 (test time: T1/T2) × 2 (digital usage behavior: use/non-use) repeated measures square analysis was performed. The results show that the testing time is the main and most significant effect (F = 11.21, p < 0.001, η2 = 0.01). The cognitive function level at T2 is significantly lower than at T1, and there are developmental differences. The main effect of digital usage behavior is significant (F = 36.325, p < 0.001, η2 = 0.06), and the cognitive function of patients using digital technology is significantly better than those patients without digital technology. The interaction between the two is not significant (F = 2.22, p > 0.05, η2 = 0.04).\nWith PCS as the dependent variable, a 2 (test time: T1/T2) × 2 (digital usage behavior: use/non-use) repeated measures square analysis was performed. The results show that the testing time is the main and most significant effect (F =16.25, p < 0.001, η2 = 0.03), the PCS level of the post-test is significantly lower than that of the pre-test, and there is a developmental difference. The main effect of digital usage behavior is significant (F = 36.54, p < 0.001, η2 = 0.06), and the PCS of patients using digital technology is significantly better than those not using digital technology. The interaction between the two is not significant (F = 0.18, p > 0.05, η2 = 0.00).\nWith MCS as the dependent variable, a 2 (test time: T1/T2) × 2 (digital usage behavior: use/non-use) repeated measures square analysis was performed. The results showed that the testing time is the main and most significant effect (F = 44.93, p < 0.001, η2 = 0.07), the MCS level of the post-test is significantly lower than the pre-test, and there is a developmental difference. The main effect of digital usage behavior is significant (F = 50.97, p < 0.001, η2 = 0.08), and the MCS of patients using digital technology is significantly better than those not using digital technology. The interaction between the two is significant (F = 4.43, p < 0.05, η2 = 0.08).\nWith cognitive function as the dependent variable, a 2 (test time: T1/T2) × 2 (digital usage behavior: use/non-use) repeated measures square analysis was performed. The results show that the testing time is the main and most significant effect (F = 11.21, p < 0.001, η2 = 0.01). The cognitive function level at T2 is significantly lower than at T1, and there are developmental differences. The main effect of digital usage behavior is significant (F = 36.325, p < 0.001, η2 = 0.06), and the cognitive function of patients using digital technology is significantly better than those patients without digital technology. The interaction between the two is not significant (F = 2.22, p > 0.05, η2 = 0.04).\nWith PCS as the dependent variable, a 2 (test time: T1/T2) × 2 (digital usage behavior: use/non-use) repeated measures square analysis was performed. The results show that the testing time is the main and most significant effect (F =16.25, p < 0.001, η2 = 0.03), the PCS level of the post-test is significantly lower than that of the pre-test, and there is a developmental difference. The main effect of digital usage behavior is significant (F = 36.54, p < 0.001, η2 = 0.06), and the PCS of patients using digital technology is significantly better than those not using digital technology. The interaction between the two is not significant (F = 0.18, p > 0.05, η2 = 0.00).\nWith MCS as the dependent variable, a 2 (test time: T1/T2) × 2 (digital usage behavior: use/non-use) repeated measures square analysis was performed. The results showed that the testing time is the main and most significant effect (F = 44.93, p < 0.001, η2 = 0.07), the MCS level of the post-test is significantly lower than the pre-test, and there is a developmental difference. The main effect of digital usage behavior is significant (F = 50.97, p < 0.001, η2 = 0.08), and the MCS of patients using digital technology is significantly better than those not using digital technology. The interaction between the two is significant (F = 4.43, p < 0.05, η2 = 0.08).\n3.3. Cross-Lagged Analysis of Cognitive Function and HRQoL In the cross-lag model which this study applied, HRQoL at T2 was predicted by cognitive function at T1, and cognitive function at T2 was predicted by HRQoL at T1. After the corresponding control variables were added to the models, both models achieved acceptable fitness criteria (Model 1: RMSEA = 0.093; CFI = 0.994, TCL = 0.912; Model 2: RMSEA = 0.098; CFI = 0.984, TCL = 0.901).\nThe cross-lag relationship between cognitive function and PCS scores at two points in time is shown from the test of Model 1 in Figure 2. First, in the same time period, the baseline association between cognitive function and PCS is significantly positive (B = 0.19, p < 0.01), implying better cognitive performance in middle-aged and older diabetic patients with higher PCS scores at T1, and vice versa. Second, there is no significant correlation between cognitive function at T1 and PCS at T2 (B = 0.05, p > 0.01), but PCS at T1 is positively correlated with cognitive function at T2 (B = 0.12, p < 0.01). This suggests that there is a positive and significant relationship between PCS and cognitive function in middle-aged and older diabetic patients over time, but early cognitive function in middle-aged and older diabetic patients does not affect later PSC.\nThe cross-lag relationship between cognitive function and MCS scores at the two points in time is shown in Model 2 of Figure 2. First, in the same time period, the baseline association between cognitive function and MCS is significantly positively correlated (B = 0.31, p < 0.01), which implies a better cognitive performance in middle-aged and older diabetic patients with higher MCS scores at T1, and vice versa. Second, there is no significant correlation between cognitive function at T1 and MCS at T2 (B = 0.05, p > 0.01), but there is a significant positive correlation between MCS at T1 and cognitive function at T2 (B = 0.14, p < 0.01). This suggests that there is a positive and significant relationship between MCS and cognitive function in middle-aged and older diabetic patients over time, but early cognitive function in middle-aged and older diabetic patients does not affect later MCS. In conclusion, there may be a one-way causal relationship between cognitive function and HRQoL, and the causal direction is from HRQoL to cognition.\nIn the cross-lag model which this study applied, HRQoL at T2 was predicted by cognitive function at T1, and cognitive function at T2 was predicted by HRQoL at T1. After the corresponding control variables were added to the models, both models achieved acceptable fitness criteria (Model 1: RMSEA = 0.093; CFI = 0.994, TCL = 0.912; Model 2: RMSEA = 0.098; CFI = 0.984, TCL = 0.901).\nThe cross-lag relationship between cognitive function and PCS scores at two points in time is shown from the test of Model 1 in Figure 2. First, in the same time period, the baseline association between cognitive function and PCS is significantly positive (B = 0.19, p < 0.01), implying better cognitive performance in middle-aged and older diabetic patients with higher PCS scores at T1, and vice versa. Second, there is no significant correlation between cognitive function at T1 and PCS at T2 (B = 0.05, p > 0.01), but PCS at T1 is positively correlated with cognitive function at T2 (B = 0.12, p < 0.01). This suggests that there is a positive and significant relationship between PCS and cognitive function in middle-aged and older diabetic patients over time, but early cognitive function in middle-aged and older diabetic patients does not affect later PSC.\nThe cross-lag relationship between cognitive function and MCS scores at the two points in time is shown in Model 2 of Figure 2. First, in the same time period, the baseline association between cognitive function and MCS is significantly positively correlated (B = 0.31, p < 0.01), which implies a better cognitive performance in middle-aged and older diabetic patients with higher MCS scores at T1, and vice versa. Second, there is no significant correlation between cognitive function at T1 and MCS at T2 (B = 0.05, p > 0.01), but there is a significant positive correlation between MCS at T1 and cognitive function at T2 (B = 0.14, p < 0.01). This suggests that there is a positive and significant relationship between MCS and cognitive function in middle-aged and older diabetic patients over time, but early cognitive function in middle-aged and older diabetic patients does not affect later MCS. In conclusion, there may be a one-way causal relationship between cognitive function and HRQoL, and the causal direction is from HRQoL to cognition.\n3.4. Heterogeneity Analysis In order to investigate whether the cross-lag relationship between cognitive function and HRQoL differs in digital usage behavior, the study performed a multi-group analysis. All diabetic patients were divided into two groups, one representing non-digital usage behavior (Figure 3), and the other representing digital usage behavior (Figure 4). In the grouped cross-lag model, the corresponding control variables were also added to the model. The models in Figure 3 and Figure 4 met fitness criteria (Figure 3: Model 3: RMSEA = 0.073, CFI = 0.996, TCL = 0.901; Model 4: RMSEA = 0.100, CFI = 0.990, TCL = 0.936. Figure 4: Model 5: RMSEA = 0.073, CFI = 0.996, TCL = 0.941; Model 6: RMSEA = 0.079, CFI = 0.970, TCL = 0.936). In both sets of models, the study focused on the relationship between cognitive function and HRQoL at a point in time, as well as their relationship at the time of follow-up research.\nFor middle-aged and older diabetic patients using digital technology, although the model could be fitted, cognitive function at T1 did not significantly predict HRQoL at T2 (PCS: B = 0.30, p > 0.01; MCS: B = 0.14, p > 0.01), and vice versa (PCS: B = 0.06, p > 0.01; MCS: B = 0.05, p > 0.01) (see Figure 3). For diabetic patients who did not use digital technology, although T1 cognitive function did not significantly predict T2 HRQoL (PCS: B = 0.04, p > 0.01; MCS: B = 0.05, p > 0.01), T1 HRQoL significantly predicted T2 cognitive function (PCS: B = 0.10, p < 0.01; MCS: B = 0.11, p < 0.01), and the results were consistent across all diabetic patients (see Figure 4). This shows that the cross-lag model of cognitive function and HRQoL in middle-aged and older diabetic patients shows variance in the use of digital technology, that is, this relationship will be affected by digital usage behavior.\nIn order to investigate whether the cross-lag relationship between cognitive function and HRQoL differs in digital usage behavior, the study performed a multi-group analysis. All diabetic patients were divided into two groups, one representing non-digital usage behavior (Figure 3), and the other representing digital usage behavior (Figure 4). In the grouped cross-lag model, the corresponding control variables were also added to the model. The models in Figure 3 and Figure 4 met fitness criteria (Figure 3: Model 3: RMSEA = 0.073, CFI = 0.996, TCL = 0.901; Model 4: RMSEA = 0.100, CFI = 0.990, TCL = 0.936. Figure 4: Model 5: RMSEA = 0.073, CFI = 0.996, TCL = 0.941; Model 6: RMSEA = 0.079, CFI = 0.970, TCL = 0.936). In both sets of models, the study focused on the relationship between cognitive function and HRQoL at a point in time, as well as their relationship at the time of follow-up research.\nFor middle-aged and older diabetic patients using digital technology, although the model could be fitted, cognitive function at T1 did not significantly predict HRQoL at T2 (PCS: B = 0.30, p > 0.01; MCS: B = 0.14, p > 0.01), and vice versa (PCS: B = 0.06, p > 0.01; MCS: B = 0.05, p > 0.01) (see Figure 3). For diabetic patients who did not use digital technology, although T1 cognitive function did not significantly predict T2 HRQoL (PCS: B = 0.04, p > 0.01; MCS: B = 0.05, p > 0.01), T1 HRQoL significantly predicted T2 cognitive function (PCS: B = 0.10, p < 0.01; MCS: B = 0.11, p < 0.01), and the results were consistent across all diabetic patients (see Figure 4). This shows that the cross-lag model of cognitive function and HRQoL in middle-aged and older diabetic patients shows variance in the use of digital technology, that is, this relationship will be affected by digital usage behavior.\n\n3.1. Descriptive Statistics:\nThe variable descriptive statistics and correlation analysis results are shown in Table 3 and Table 4. The correlation analysis shows that there is a significant correlation between cognition at T1 and T2 and HRQoL levels at T1 and T2, indicating that cognitive function and HRQoL levels in middle-aged and older diabetic patients have a certain relationship, which is showing stability. Meanwhile, simultaneous and sequential correlations between cognitive function and HRQoL levels are also significant. The correlation coefficients between cognitive function and HRQoL at T1 are 0.28 (p < 0.01) and 0.37 (p < 0.01); the correlation coefficients between cognitive function at T2 and HRQoL at T2 are 0.32 (p < 0.01) and 0.30 (p < 0.01); the correlation coefficients between cognitive function at T1 and HRQoL at T2 are 0.25 (p < 0.01) and 0.22 (p < 0.01); and the correlation coefficients between HRQoL at T1 and cognitive function at T2 are 0.32 (p < 0.01) and 0.36 (p < 0.01). This indicates that cognitive function is basically consistent with the synchronous and stable correlations of HRQoL levels, which is in line with the basic assumptions of the cross-lag design.\n\n3.2. Stability Analysis of Cognitive Function and HRQoL:\nWith cognitive function as the dependent variable, a 2 (test time: T1/T2) × 2 (digital usage behavior: use/non-use) repeated measures square analysis was performed. The results show that the testing time is the main and most significant effect (F = 11.21, p < 0.001, η2 = 0.01). The cognitive function level at T2 is significantly lower than at T1, and there are developmental differences. The main effect of digital usage behavior is significant (F = 36.325, p < 0.001, η2 = 0.06), and the cognitive function of patients using digital technology is significantly better than those patients without digital technology. The interaction between the two is not significant (F = 2.22, p > 0.05, η2 = 0.04).\nWith PCS as the dependent variable, a 2 (test time: T1/T2) × 2 (digital usage behavior: use/non-use) repeated measures square analysis was performed. The results show that the testing time is the main and most significant effect (F =16.25, p < 0.001, η2 = 0.03), the PCS level of the post-test is significantly lower than that of the pre-test, and there is a developmental difference. The main effect of digital usage behavior is significant (F = 36.54, p < 0.001, η2 = 0.06), and the PCS of patients using digital technology is significantly better than those not using digital technology. The interaction between the two is not significant (F = 0.18, p > 0.05, η2 = 0.00).\nWith MCS as the dependent variable, a 2 (test time: T1/T2) × 2 (digital usage behavior: use/non-use) repeated measures square analysis was performed. The results showed that the testing time is the main and most significant effect (F = 44.93, p < 0.001, η2 = 0.07), the MCS level of the post-test is significantly lower than the pre-test, and there is a developmental difference. The main effect of digital usage behavior is significant (F = 50.97, p < 0.001, η2 = 0.08), and the MCS of patients using digital technology is significantly better than those not using digital technology. The interaction between the two is significant (F = 4.43, p < 0.05, η2 = 0.08).\n\n3.3. Cross-Lagged Analysis of Cognitive Function and HRQoL:\nIn the cross-lag model which this study applied, HRQoL at T2 was predicted by cognitive function at T1, and cognitive function at T2 was predicted by HRQoL at T1. After the corresponding control variables were added to the models, both models achieved acceptable fitness criteria (Model 1: RMSEA = 0.093; CFI = 0.994, TCL = 0.912; Model 2: RMSEA = 0.098; CFI = 0.984, TCL = 0.901).\nThe cross-lag relationship between cognitive function and PCS scores at two points in time is shown from the test of Model 1 in Figure 2. First, in the same time period, the baseline association between cognitive function and PCS is significantly positive (B = 0.19, p < 0.01), implying better cognitive performance in middle-aged and older diabetic patients with higher PCS scores at T1, and vice versa. Second, there is no significant correlation between cognitive function at T1 and PCS at T2 (B = 0.05, p > 0.01), but PCS at T1 is positively correlated with cognitive function at T2 (B = 0.12, p < 0.01). This suggests that there is a positive and significant relationship between PCS and cognitive function in middle-aged and older diabetic patients over time, but early cognitive function in middle-aged and older diabetic patients does not affect later PSC.\nThe cross-lag relationship between cognitive function and MCS scores at the two points in time is shown in Model 2 of Figure 2. First, in the same time period, the baseline association between cognitive function and MCS is significantly positively correlated (B = 0.31, p < 0.01), which implies a better cognitive performance in middle-aged and older diabetic patients with higher MCS scores at T1, and vice versa. Second, there is no significant correlation between cognitive function at T1 and MCS at T2 (B = 0.05, p > 0.01), but there is a significant positive correlation between MCS at T1 and cognitive function at T2 (B = 0.14, p < 0.01). This suggests that there is a positive and significant relationship between MCS and cognitive function in middle-aged and older diabetic patients over time, but early cognitive function in middle-aged and older diabetic patients does not affect later MCS. In conclusion, there may be a one-way causal relationship between cognitive function and HRQoL, and the causal direction is from HRQoL to cognition.\n\n3.4. Heterogeneity Analysis:\nIn order to investigate whether the cross-lag relationship between cognitive function and HRQoL differs in digital usage behavior, the study performed a multi-group analysis. All diabetic patients were divided into two groups, one representing non-digital usage behavior (Figure 3), and the other representing digital usage behavior (Figure 4). In the grouped cross-lag model, the corresponding control variables were also added to the model. The models in Figure 3 and Figure 4 met fitness criteria (Figure 3: Model 3: RMSEA = 0.073, CFI = 0.996, TCL = 0.901; Model 4: RMSEA = 0.100, CFI = 0.990, TCL = 0.936. Figure 4: Model 5: RMSEA = 0.073, CFI = 0.996, TCL = 0.941; Model 6: RMSEA = 0.079, CFI = 0.970, TCL = 0.936). In both sets of models, the study focused on the relationship between cognitive function and HRQoL at a point in time, as well as their relationship at the time of follow-up research.\nFor middle-aged and older diabetic patients using digital technology, although the model could be fitted, cognitive function at T1 did not significantly predict HRQoL at T2 (PCS: B = 0.30, p > 0.01; MCS: B = 0.14, p > 0.01), and vice versa (PCS: B = 0.06, p > 0.01; MCS: B = 0.05, p > 0.01) (see Figure 3). For diabetic patients who did not use digital technology, although T1 cognitive function did not significantly predict T2 HRQoL (PCS: B = 0.04, p > 0.01; MCS: B = 0.05, p > 0.01), T1 HRQoL significantly predicted T2 cognitive function (PCS: B = 0.10, p < 0.01; MCS: B = 0.11, p < 0.01), and the results were consistent across all diabetic patients (see Figure 4). This shows that the cross-lag model of cognitive function and HRQoL in middle-aged and older diabetic patients shows variance in the use of digital technology, that is, this relationship will be affected by digital usage behavior.\n\n4. Discussion:\nThe present study aimed to further deepen our understanding of the association between cognition and HRQoL. This study used a cross-lagged model and controlled for the corresponding covariates in order to verify the longitudinal relationship between cognition and HRQoL in middle-aged and older diabetic patients. The corresponding findings suggest that this relationship is unidirectional in the cross-lag model, that is, only HRQoL at T1 can predict cognitive function at T2, and cognitive function at T1 does not predict HRQoL at T2. This study further explored whether the use of digital technology would lead to different manifestations of this relationship, and divided the study population into two categories: those who used digital technology and those who did not. The results found that this one-way lag relationship still existed in middle-aged and older diabetic patients who did not use digital technology, but was not significant in patients who used digital technology.\nFirst of all, the study found that there are certain developmental differences in the cognitive function, PCS, and MCS levels of middle-aged and older diabetic patients. There is an increasing trend over time which is consistent with previous research results [33,34]. From an age perspective, not only are changes in cognitive function closely related to age [17,35], but HRQoL also has a distinct age-specific trajectory [36]. Therefore, as patients with diabetes age, the risk of cognitive decline and reduced PCS and MCS levels increases [15,37]. Second, diabetes is considered a risk factor for developing cognitive impairments [38]. Although some studies suggest that it may not accelerate the cognitive deterioration process, there is a general consensus in the academic community that diabetes increases the risk of abnormal cognition by increasing the incidence of cognition-related diseases or affecting blood sugar levels [37,38]. Furthermore, previous cross-sectional and longitudinal studies have identified that chronic diseases such as diabetes, as well as BMI, can affect HRQoL in a negative way [39,40]. Thus, this trend may be more obvious in the diabetic population.\nIt was found that the cognitive function of T1 was highly correlated with the PCS and MCS at T1 and T2, and the cognitive function of T2 was highly correlated with the PCS and MCS at T1 and T2, which was in line with the basic assumption of a cross-lag design. Our results are consistent with previous studies on other populations [9]. In the subsequent cross-lag analysis, the results were found to be consistent with previous studies [12]. The cognitive function, PCS, and MCS levels of middle-aged and elderly diabetic patients have a certain degree of lateral stability between T1 and T2, and HRQoL can predict cognitive function. However, there is no research to explain its internal mechanism. Verghese, J. et al., and Daviglus, M.L. et al., suggest that interventions to improve physical and general mental health can prevent or delay the onset of dementia [41,42]. Since HRQoL scores from CHARLS in this study include evaluations of physical activity and general mental health, it is believed that physical activity may be a plausible mechanism by which HRQoL can predict changes in cognitive function, based on previous studies. The mediating role of physical activity in this relationship should be further investigated in the future. In general, there are few studies focusing on the impact of HRQoL on cognitive function. In the future, more in-depth research is needed to explore the internal mechanism of how HRQoL affects cognitive function [43,44].\nThird, although cognitive function at T1 was found to be significantly associated with HRQoL at T2 (PCS and MCS) in the initial correlation analysis, the predictive role of cognitive function at T1 on HRQoL at T2 in the cross-lag model was not significant, suggesting that cognitive function at T1 does not predict HRQoL at T2. This is consistent with previous studies [45]. One possible explanation is that the differences in cognitive function between the participants included are relatively small at baseline and at follow-up, as shown by a mean baseline cognitive function score of 15.73 and follow-up of 15.03. Therefore, there may not be enough individuals with the degree of cognitive function variation that would interfere with the study results. On the other hand, it may be that clinical health status alone does not determine a better quality of life [32], and for older adults, perceived life satisfaction appears to be more affected by ability to perform daily tasks [46]. Therefore, the influence of cognitive function on HRQoL may not be significant.\nFinally, results show that there is a significant difference in the use of digital technology in the one-way predictive role of cognitive function and HRQoL, that is, the causal relationship between cognitive function and HRQoL is more applicable to diabetic patients who do not use digital technology. This finding is consistent with the study hypothesis. In terms of cognitive function, previous studies believe that the use of digital technology can slow the decline of cognitive function by increasing the cognitive reserve of individuals [18,47,48,49,50]. It has also been confirmed that in the diabetic population, digital usage behavior can improve the cognitive function of patients [51]; therefore, individuals who do not use digital technology tend to have greater changes in cognitive function, which are easier to detect than changes in individuals who do use digital technology. In terms of HRQoL, the use of digital technology has a direct positive impact on HRQoL [52], and for people with diabetes, digital usage behavior can have an indirect impact on HRQoL by stimulating positive lifestyle changes such as physical activity [53,54,55,56]. Therefore, it is believed that due to direct or indirect impact of digital usage behavior on cognitive function and HRQoL, digital usage behavior may have weakened the longitudinal association between cognitive function and HRQoL in middle-aged and older diabetic patients to some extent.\nThis study can help us to make some policy recommendations. First, the aging population has greatly increased the public health burden in China. Cognitive function and HRQoL in diabetic patients were affected to varying degrees. Therefore, it is necessary to improve the relevant medical service system for diabetics in China as soon as possible to ensure that the medical needs of the elderly are met. In addition, attention will need to be out into improving the Internet and medical service systems. In order to reduce the digital technology gap, promotion of internet use and healthy aging is encouraged.\nThis study did not focus on describing intra-individual variation due to the inherent limitations of the cross-lag model [57]. However, the cross-lag model used in this study still provides new evidence on the longitudinal relationship between cognitive function and HRQoL in middle-aged and older Chinese diabetic patients, and may provide new insights into the underlying mechanisms behind this relationship. Aside from the limitations of the model itself, there are several potential limitations to consider. First, there was a 3-year lag between baseline and follow-up, which may be considered too long to assess the cross-lag relationship between cognitive function and HRQoL. Future studies will need to test whether shorter and longer temporal associations differ in terms of gaining insight into the exact relationship between cognitive function and HRQoL in different conditions. Second, this study only selected the data of wave 3 and wave 4 of the CHARLS database, and did not include wave 1 or wave 2. This study used three questions that were only included after wave 3 in the evaluation criteria for the digital usage behavior. If there were enough data, it would obviously be better to include waves 1 and 2 in the analysis. In addition, a large portion of participants were excluded due to lack of necessary data. Although we controlled for demographic variables, this may still lead to selective bias in the data. Third, in the group analysis, due to the significant sample size difference between those who used digital technology and those who did not, results may have been inaccurate. However, the study results still show that the longitudinal relationship between cognitive function and HRQoL differs in terms of digital usage behavior. Finally, because patients with diabetes often have other chronic diseases of the elderly, future research is needed to further distinguish and identify the differences between diabetes and other chronic diseases of the elderly.\n\n5. Conclusions:\nThe cognitive function and HRQoL (PCS and MCS) in middle-aged and older Chinese diabetic patients have a certain degree of lateral stability, and both tend to increase over time. More importantly, this study found that HRQoL at T1 can significantly predict cognitive function at T2, but cognitive function at T1 cannot significantly predict HRQoL at T2. HRQoL may be an antecedent for middle-aged and older diabetic patients’ cognitive function, and this predictive relationship can be different depending on digital usage behavior. In the future, effective intervention on HRQoL of middle-aged and older diabetic patients can be considered to improve their cognitive function, thereby promoting the healthy development of middle-aged and older diabetic patients.\n\n"},"whole_article_abstract":{"kind":"string","value":"Background:\nCognitive function and health-related quality of life (HRQoL) are important issues in diabetes care. According to the China Association for Aging, it is estimated that by 2030, the number of elderly people with dementia in China will reach 22 million. The World Health Organization reports that by 2044, the number of people with diabetes in China is expected to reach 175 million.\n\nMethods:\nCohort analyses were conducted based on 854 diabetic patients aged ≥45 years from the third (2015) and fourth (2018) survey of the China Health and Retirement Longitudinal Study (CHARLS). Correlation analysis, repeated-measures variance analysis, and cross-lagged panel models were used to measure the difference in digital usage behavior in the established relationship.\n\nResults:\nThe results show that the cognitive function of middle-aged and older diabetic patients is positively correlated with HRQoL. HRQoL at T1 could significantly predict cognitive function at T2 (PCS: B = 0.12, p < 0.01; MCS: B = 0.14, p < 0.01). This relationship is more associated with individual performance than digital usage behavior.\n\nConclusions:\nUnidirectional associations may exist between cognitive function and HRQoL among middle-aged and older Chinese diabetes patients. In the future, doctors and nurses can recognize the lowering of self-perceived HRQoL of middle-aged and older diabetic patients, and thus draw more attention to their cognitive function, in turn strengthening the evaluation, detection, and intervention of their cognitive function."},"background_conclusion_text":{"kind":"string","value":"1. Introduction:\nCognitive impairment is one of the common complications in elderly patients [1]. For example, in China, the prevalence of mild cognitive impairments in the aging population (aged 60 and above) is 14.71% [2], and as age increases, its annual rate of progression to dementia is between 8% and 15% [2]. At the same time, cognitive impairment is also one of the common complications of diabetic patients. It is estimated that by 2045, about 170 million elderly people in China will be diabetic patients [3]. Therefore, the academic community has paid great attention to the cognitive function of the diabetic population, and found that the cognitive function of the diabetic population is closely related to the quality of life (QoL) of the elderly [4].\nHRQoL is the perceived physical and mental health of an individual or group over time, including both physical component summary (PCS) and mental component summary (MCS) [5]. In contrast with the QoL, HRQoL pays special attention to the impact of disease and treatment process on the life of a person or a group [6]. Existing studies have found that changes in cognitive function are positively correlated with changes in HRQoL, and play a predictive role in future changes in HRQoL [7,8]. For example, cognitive decline was found to be a predictor of HRQoL decline in studies on multiple sclerosis patients, AIDS patients, and older women [4,9,10]. At the same time, some scholars have found that changes in HRQoL—whether it is the PCS or the MCS of HRQoL—can also predict cognitive changes in individuals in the future [11]. For example, Ezzati’s 2019 study demonstrated that changes in HRQoL preceded changes in cognition and predicted the occurrence of dementia [12]. Thus, cognitive function may be bi-directionally associated with HRQoL.\nThe bidirectional relationship between cognitive function and HRQoL may be more pronounced in terms of diabetic patients. This is because not only are people with diabetes 1–2 times more likely to develop cognitive risks than the general population [13,14], but this risk will increase over time [15]. At the same time, with the deepening of the research on HRQoL, the medical community generally believes that the core of diabetes management should include HRQoL maintenance in addition to prevention and delay of its complications [16]. In summary, this research aims to focus on middle-aged and older diabetic patients (over 45 years old) in China—not only because China has one of the largest numbers of diabetic patients in the world, but also because the age of the population affected with diabetes is showing a downward trend [3,17]. Thus, we propose the first hypothesis: in middle-aged and older Chinese patients with diabetes, the relationship between cognitive function and HRQoL may be bidirectional.\nTo our knowledge, previous studies on cognitive function and HRQoL have focused on unmodifiable clinical factors, such as age, gender, etc., with a lack of studies on modifiable factors [4]. Digital technologies such as the Internet and smartphones have attracted the attention of psychologists because of their portability, rapidity, and immediacy. In clinical medicine, digital usage behaviors are often used for managing and intervening with patients with diabetes and cognitive dysfunction [18]. Thus, digital technology may be seen as a modifiable factor in the relationship between cognitive function and HRQoL. We also found that with the continuous development and improvement of digital technology, digital usage behavior can have a profound impact on the cognitive function of patients with chronic diseases [18,19,20]. In addition to this, it is important that digital usage behavior, such as Internet use, can also have a certain impact on HRQoL [21]. Previous research in other populations found that using the Internet resulted in significantly higher PCS and physical pain scores [22]. According to self-determination theory, we believe that when individuals use the internet for leisure or work, their needs are met, which in turn produces positive long-term psychological outcomes such as quality of life [23]. So far, existing research is leaning towards the belief that an increase in digital usage behavior can improve the health of individuals [24]. However, there are also studies holding the opposite view; they believe that the long-term digital usage behavior reduces patients’ time for outdoor activities, and that a large amount of negative information received due to digital usage behavior is more likely to cause mood swings, which may have side effects on patients’ recovery [25]. Therefore, it is easy to find that digital usage behavior is likely to have various effects, so we propose another hypothesis: in middle-aged and older Chinese patients with diabetes, the relationship between cognitive function and HRQoL may differ considering digital usage behavior.\nTo date, most studies on the association of cognitive function with HRQoL have used cross-sectional designs, and have failed to elucidate the direction of influence between cognitive function and HRQoL due to the limitations of cross-sectional studies in terms of making causal inferences and explaining the direction of association. Therefore, it is necessary to further explore, especially for diabetic patients, the longitudinal association between the cognitive function and HRQoL, and to clarify the direction of their effects. As a longitudinal study, this study attempts to use a cross-lag model to explore the relationship between cognitive function and HRQoL in Chinese middle-aged and older diabetic patients, as well as the direction of this relationship and whether there were differences in digital usage behavior. This research is attempting to explore the longitudinal relationship between cognition and HRQoL in middle-aged and older diabetic patients, and to provide evidence which can promote their health.\nIt should be noted that, due to the existence of multiple databases in China, in addition to the China Health and Retirement Longitudinal Survey (CHARLS), most scholars use databases such as the China Household Finance Survey when researching topics related to public health [26]. There is more content regarding the physical and mental health of middle-aged and elderly people, so we chose CHARLS as our data source.\n\n5. Conclusions:\nThe cognitive function and HRQoL (PCS and MCS) in middle-aged and older Chinese diabetic patients have a certain degree of lateral stability, and both tend to increase over time. More importantly, this study found that HRQoL at T1 can significantly predict cognitive function at T2, but cognitive function at T1 cannot significantly predict HRQoL at T2. HRQoL may be an antecedent for middle-aged and older diabetic patients’ cognitive function, and this predictive relationship can be different depending on digital usage behavior. In the future, effective intervention on HRQoL of middle-aged and older diabetic patients can be considered to improve their cognitive function, thereby promoting the healthy development of middle-aged and older diabetic patients."},"background_conclusion_abstract":{"kind":"string","value":"Background:\nCognitive function and health-related quality of life (HRQoL) are important issues in diabetes care. According to the China Association for Aging, it is estimated that by 2030, the number of elderly people with dementia in China will reach 22 million. The World Health Organization reports that by 2044, the number of people with diabetes in China is expected to reach 175 million.\n\nMethods:\nCohort analyses were conducted based on 854 diabetic patients aged ≥45 years from the third (2015) and fourth (2018) survey of the China Health and Retirement Longitudinal Study (CHARLS). Correlation analysis, repeated-measures variance analysis, and cross-lagged panel models were used to measure the difference in digital usage behavior in the established relationship.\n\nResults:\nThe results show that the cognitive function of middle-aged and older diabetic patients is positively correlated with HRQoL. HRQoL at T1 could significantly predict cognitive function at T2 (PCS: B = 0.12, p < 0.01; MCS: B = 0.14, p < 0.01). This relationship is more associated with individual performance than digital usage behavior.\n\nConclusions:\nUnidirectional associations may exist between cognitive function and HRQoL among middle-aged and older Chinese diabetes patients. In the future, doctors and nurses can recognize the lowering of self-perceived HRQoL of middle-aged and older diabetic patients, and thus draw more attention to their cognitive function, in turn strengthening the evaluation, detection, and intervention of their cognitive function."},"whole_article_text_length":{"kind":"number","value":14040,"string":"14,040"},"whole_article_abstract_length":{"kind":"number","value":292,"string":"292"},"other_sections_lengths":{"kind":"list like","value":[3773,1384,274,258,151,207,222,456,462,410],"string":"[\n 3773,\n 1384,\n 274,\n 258,\n 151,\n 207,\n 222,\n 456,\n 462,\n 410\n]"},"num_sections":{"kind":"number","value":15,"string":"15"},"most_frequent_words":{"kind":"list like","value":["cognitive","function","cognitive function","hrqol","digital","study","scores","patients","t1","pcs"],"string":"[\n \"cognitive\",\n \"function\",\n \"cognitive function\",\n \"hrqol\",\n \"digital\",\n \"study\",\n \"scores\",\n \"patients\",\n \"t1\",\n 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[SUMMARY]"}}},{"rowIdx":292,"cells":{"title":{"kind":"string","value":"Repair or replace ischemic mitral regurgitation during coronary artery bypass grafting? A meta-analysis."},"pmid":{"kind":"string","value":"27585461"},"background_abstract":{"kind":"string","value":"No agreement has been reached for the best surgical treatment for patients with chronic ischemic mitral regurgitation (IMR) undergoing coronary artery bypass grafting (CABG). Our objective was to meta-analyze the clinical outcomes of repair and replacement."},"background_abstract_label":{"kind":"string","value":"BACKGROUND"},"methods_abstract":{"kind":"string","value":"A computerized search was performed using Pubmed, Embase, Ovid medline and Cochrane Library. The search terms \"ischemic or ischaemic\" and \"mitral valve\" and \"repair or replacement or annuloplasty\" and \"coronary artery bypass grafting\" were entered as MeSH terms and keywords. The primary outcomes were operative mortality and late mortality. Secondary outcomes were 2+ or greater recurrence of mitral regurgitation and reoperation rate."},"methods_abstract_label":{"kind":"string","value":"METHODS"},"results_abstract":{"kind":"string","value":"Eleven studies were eligible for the final meta-analysis. These studies included a total of 1750 patients, 60.4 % of whom received mitral valve repair. All patients underwent concomitant coronary artery bypass graft. No differences were found in operative mortality (summary odds ratio [OR] 0.65; 95 % confidence interval [CI] 0.43-1.00; p = 0.05), late mortality (summary hazard ratio [HR] 0.87; 95 % confidence interval [CI] 0.67-1.14; p = 0.31) and reoperation (summary odds ratio [OR] 1.47; 95 % confidence interval [CI] 0.90-2.38; p = 0.12). Regurgitation recurrence was lower in the replacement group (summary odds ratio [OR] 5.41; 95 % confidence interval [CI] 3.12-9.38; p < 0.001)."},"results_abstract_label":{"kind":"string","value":"RESULTS"},"conclusions_abstract":{"kind":"string","value":"In patients with chronic ischemic mitral regurgitation during CABG, mitral valve replacement is associated with lower recurrence of regurgitation. No differences were found regarding survival and reoperation rates."},"conclusions_abstract_label":{"kind":"string","value":"CONCLUSION"},"mesh_descriptor_names":{"kind":"list like","value":["Aged","Coronary Artery Bypass","Female","Heart Valve Prosthesis Implantation","Humans","Male","Middle Aged","Mitral Valve","Mitral Valve Insufficiency","Myocardial Ischemia","Reoperation","Survival Rate","Treatment Outcome"],"string":"[\n \"Aged\",\n \"Coronary Artery Bypass\",\n \"Female\",\n \"Heart Valve Prosthesis Implantation\",\n \"Humans\",\n \"Male\",\n \"Middle Aged\",\n \"Mitral Valve\",\n \"Mitral Valve Insufficiency\",\n \"Myocardial Ischemia\",\n \"Reoperation\",\n \"Survival Rate\",\n \"Treatment Outcome\"\n]"},"pmcid":{"kind":"string","value":"5008002"},"background_title":{"kind":"string","value":"Background"},"background_text":{"kind":"string","value":"Chronic ischemic mitral regurgitation (IMR) is a frequent and important complication after myocardial infarction. Its pathophysiologic mechanisms account for remodeling of segmental/global left ventricle (LV) inducing papillary muscle displacement and leaflet tethering [1]. The presence of IMR is independently associated with mortality and morbidity after myocardial infarction [2].\nGiven the severity of IMR, surgery performed for IMR ranges from coronary artery bypass grafting (CABG) alone to both CABG and mitral valve surgery [3, 4]. Two randomized trials indicated that repair was associated with a reduced prevalence of mitral regurgitation but did not show a clinically meaningful advantage of adding mitral valve repair to CABG [5, 6]. In addition, when compared with replacement, previous meta-analyses concluded that repair is associated with lower operative mortality but higher recurrence of regurgitation in patients with ischemic mitral regurgitation, with or without CABG [7, 8]. For patients with chronic IMR undergoing combined CABG, the best surgical treatment is still controversial. Some studies support replacement [9, 10], others support repair [11, 12], and others showed similar survival for the two procedures [13]. Current guidelines recommend mitral valve surgery for severe IMR, but do not demonstrate a specific type of procedure [14, 15]. Numerous non-randomized studies have been published comparing the clinical outcomes between MVP + CABG and MVR + CABG for IMR. However, there is still no systematic and quantitative assessment of accumulated literature on this topic. Meta-analysis is a powerful tool to provide meaningful comparison of short and long-term outcomes of these procedures. The present meta-analysis aimed to assess the clinical outcomes of patients who underwent mitral valve surgery and CABG for chronic IMR."},"methods_title":{"kind":"null"},"methods_text":{"kind":"null"},"results_title":{"kind":"string","value":"Results"},"results_text":{"kind":"string","value":" Search results and study quality The literature search identified a total of 545 studies, which were published between 1965 and 2015. On the basis of title and abstracts, 34 articles were selected and reviewed in full. Eleven articles met the inclusion and exclusion criteria [9–13, 23–28] (Fig. 1). Of the included studies, there were ten retrospective observational studies [9–13, 23–27] and one prospective observational study [28]. All were nonrandomized studies. These studies included a total of 1807 patients, 1091 (60.4 %) of whom underwent repair and 716 (39.6 %) of whom underwent replacement. All patients had CABG. Patient characteristics and a summary of operative details are summarized in Tables 1 and 2, respectively. With the exception of the replacement patients being older in 2 of the studies, the two groups were similar in terms of hypertension (HTN), diabetes, atrial fibrillation (AF), left ventricular ejection fraction (LVEF) and the New York Heart Association (NYHA) class. Eight of the studies reported data on the type of prosthesis used for mitral valve replacement and preservation of the subvalvular apparatus. In half of the studies, the majority of patients received a bioprothesis valve. In addition, preservation of the subvalvular (either total or partial) apparatus were performed in the vast majority of mitral valve replacements.Fig. 1Flow chart of study selectionTable 1Key Features of Included StudiesStudySubjectsMean AgeMale (%)HTN (%)Diabetes (%)AF (%)NYHA III-IV (%)Mean LVEF (%)MR gradeFollow-up periodMVP + CABGMVR + CABGMVP + CABGMVR + CABGMVP + CABGMVR + CABGMVP + CABGMVR + CABGMVP + CABGMVR + CABGMVP + CABGMVR + CABGMVP + CABGMVR + CABGMVP + CABGMVR + CABGMVP + CABGMVR + CABGLorusso et al.24424466667369414136351213NRNR35352.8 ± 0.52.8 ± 0.546.5bmoLio et al.98286570d\n746181893532NRNR61713234NRNR45bmoLjubacev et al.3441NRNRNRNR85803256d\n2617NRNRNRNRNRNRIn-hospitalRoshanali et al.263157578377NRNRNRNRNRNRNRNR38403.6 ± 0.53.5 ± 0.540.2a moMaltais et al.302857070686371683426NRNR85913434NRNR4.2ayrsQiu et al.1121067172645672753032282653493535NRNR48.1amoMicovic et al.865261b\n62b\n7273746521152729645029362.7 ± 0.62.5 ± 0.732a moBonacchi et al.3618NRNRNRNRNRNRNRNRNRNRNRNR2727NRNR32a moSilberman et al.38146267d\n749350574557NRNR49cd\n32cd\n<25 %NRNR38amoMantovani et al.61416868675454512615NRNRNRNR45453.1 ± 0.83.3 ± 0.736.8a moReece et al.5456676941d\n68d\nNRNR2221NRNRNRNR4440NRNRIn-hospital\na = mean; b = median; c Percentage class IV; d\np < 0.05 between MVr and MVR\nAbbreviations: AF atrial fibrillation, LVEF left ventricular ejection fraction, CABG coronary artery bypass grafting, MR mitral regurgitation, HTN hypertension, MVP mitral valve repair, MVR mitral valve replacement, NR not reportedTable 2Operative characteristicsCPB time (min)ACC time (min)MVR prosthesis typeSubvalvular apparatus preservationMVP partial/suture annuloplasty (%)MVP ring annuloplasty (%)MVP undersizingMVPMVRMVPMVRMechanical %Bioprothesis %Anterior + Posterior (%)Posterior (%)None (%)Lorusso et al. [9]14514594944753482443010027 (26 mm) 52 (28 mm) 13 (30 mm) 6 (32 mm) 1 (34 mm) 1 (36 mm)Lio et al. [10] 156180107132366410000010037 % open ring 63 % closed ring 37 % rigid ring 63 % semi-rigid ringLjubacev et al. [24]1451529699NRNRNRNRNRNRNRNRRoshanali et al. [28]NRNRNRNR100010000NRNRNRMaltais et al. [13]NRNRNRNR4654NRNRNR89242 (24–28 mm) 36 (30–34 mm)Qiu et al. [26]13612910598386211890010030 mmMicovic et al. [11]NRNRNRNR100001000595Median 28 mm (range, 26–34 mm)Bonacchi et al. [12]NRNRNRNRNRNR010001783NRSilberman et al. [23]154184991111000NRNRNR010026 ± 1.2 mmMantovani et al. [25]1791731311227624010000100ModerateReece et al. [27]112132152171NRNRNRNRNR010028 mm males 26 mm females\nAbbreviations: MVP mitral valve repair, MVR mitral valve replacement, ACC time aortic cross-clamping time, CPB time, cardiopulmonary bypass time, NR not reported\nFlow chart of study selection\nKey Features of Included Studies\n\na = mean; b = median; c Percentage class IV; d\np < 0.05 between MVr and MVR\n\nAbbreviations: AF atrial fibrillation, LVEF left ventricular ejection fraction, CABG coronary artery bypass grafting, MR mitral regurgitation, HTN hypertension, MVP mitral valve repair, MVR mitral valve replacement, NR not reported\nOperative characteristics\n\nAbbreviations: MVP mitral valve repair, MVR mitral valve replacement, ACC time aortic cross-clamping time, CPB time, cardiopulmonary bypass time, NR not reported\nAll the eleven trials were assessed by the Newcastle-Ottawa Scale for quality assessment risk evaluation of adequacy of selection, comparability, and outcomes assessment for individual trials (Table 3). All studies included in our meta-analysis were of high-quality (had ≥ 6 scores).Table 3Study quality assessment using the Newcastle-Ottawa Scale for nonrandomized studiesSelectionOutcomeFirst author, year of publication (reference)Representativeness of exposed cohortSelection of nonexposed cohortAscertainment of exposureOutcome of interest absent at start of studyComparability (Based on design and analysis)Assessment of outcomeFollow-up long enough for outcomesto occurAdequacy of follow-upTotal scoreLorusso et al.111101117Lio et al.111111118Ljubacev et al.111101016Roshanali et al.111121119Maltais et al.111121119Qiu et al.111121119Micovic et al.111121119Bonacchi et al.111121119Silberman et al.111111118Mantovani et al.111121119Reece et al.111111017\nStudy quality assessment using the Newcastle-Ottawa Scale for nonrandomized studies\nThe literature search identified a total of 545 studies, which were published between 1965 and 2015. On the basis of title and abstracts, 34 articles were selected and reviewed in full. Eleven articles met the inclusion and exclusion criteria [9–13, 23–28] (Fig. 1). Of the included studies, there were ten retrospective observational studies [9–13, 23–27] and one prospective observational study [28]. All were nonrandomized studies. These studies included a total of 1807 patients, 1091 (60.4 %) of whom underwent repair and 716 (39.6 %) of whom underwent replacement. All patients had CABG. Patient characteristics and a summary of operative details are summarized in Tables 1 and 2, respectively. With the exception of the replacement patients being older in 2 of the studies, the two groups were similar in terms of hypertension (HTN), diabetes, atrial fibrillation (AF), left ventricular ejection fraction (LVEF) and the New York Heart Association (NYHA) class. Eight of the studies reported data on the type of prosthesis used for mitral valve replacement and preservation of the subvalvular apparatus. In half of the studies, the majority of patients received a bioprothesis valve. In addition, preservation of the subvalvular (either total or partial) apparatus were performed in the vast majority of mitral valve replacements.Fig. 1Flow chart of study selectionTable 1Key Features of Included StudiesStudySubjectsMean AgeMale (%)HTN (%)Diabetes (%)AF (%)NYHA III-IV (%)Mean LVEF (%)MR gradeFollow-up periodMVP + CABGMVR + CABGMVP + CABGMVR + CABGMVP + CABGMVR + CABGMVP + CABGMVR + CABGMVP + CABGMVR + CABGMVP + CABGMVR + CABGMVP + CABGMVR + CABGMVP + CABGMVR + CABGMVP + CABGMVR + CABGLorusso et al.24424466667369414136351213NRNR35352.8 ± 0.52.8 ± 0.546.5bmoLio et al.98286570d\n746181893532NRNR61713234NRNR45bmoLjubacev et al.3441NRNRNRNR85803256d\n2617NRNRNRNRNRNRIn-hospitalRoshanali et al.263157578377NRNRNRNRNRNRNRNR38403.6 ± 0.53.5 ± 0.540.2a moMaltais et al.302857070686371683426NRNR85913434NRNR4.2ayrsQiu et al.1121067172645672753032282653493535NRNR48.1amoMicovic et al.865261b\n62b\n7273746521152729645029362.7 ± 0.62.5 ± 0.732a moBonacchi et al.3618NRNRNRNRNRNRNRNRNRNRNRNR2727NRNR32a moSilberman et al.38146267d\n749350574557NRNR49cd\n32cd\n<25 %NRNR38amoMantovani et al.61416868675454512615NRNRNRNR45453.1 ± 0.83.3 ± 0.736.8a moReece et al.5456676941d\n68d\nNRNR2221NRNRNRNR4440NRNRIn-hospital\na = mean; b = median; c Percentage class IV; d\np < 0.05 between MVr and MVR\nAbbreviations: AF atrial fibrillation, LVEF left ventricular ejection fraction, CABG coronary artery bypass grafting, MR mitral regurgitation, HTN hypertension, MVP mitral valve repair, MVR mitral valve replacement, NR not reportedTable 2Operative characteristicsCPB time (min)ACC time (min)MVR prosthesis typeSubvalvular apparatus preservationMVP partial/suture annuloplasty (%)MVP ring annuloplasty (%)MVP undersizingMVPMVRMVPMVRMechanical %Bioprothesis %Anterior + Posterior (%)Posterior (%)None (%)Lorusso et al. [9]14514594944753482443010027 (26 mm) 52 (28 mm) 13 (30 mm) 6 (32 mm) 1 (34 mm) 1 (36 mm)Lio et al. [10] 156180107132366410000010037 % open ring 63 % closed ring 37 % rigid ring 63 % semi-rigid ringLjubacev et al. [24]1451529699NRNRNRNRNRNRNRNRRoshanali et al. [28]NRNRNRNR100010000NRNRNRMaltais et al. [13]NRNRNRNR4654NRNRNR89242 (24–28 mm) 36 (30–34 mm)Qiu et al. [26]13612910598386211890010030 mmMicovic et al. [11]NRNRNRNR100001000595Median 28 mm (range, 26–34 mm)Bonacchi et al. [12]NRNRNRNRNRNR010001783NRSilberman et al. [23]154184991111000NRNRNR010026 ± 1.2 mmMantovani et al. [25]1791731311227624010000100ModerateReece et al. [27]112132152171NRNRNRNRNR010028 mm males 26 mm females\nAbbreviations: MVP mitral valve repair, MVR mitral valve replacement, ACC time aortic cross-clamping time, CPB time, cardiopulmonary bypass time, NR not reported\nFlow chart of study selection\nKey Features of Included Studies\n\na = mean; b = median; c Percentage class IV; d\np < 0.05 between MVr and MVR\n\nAbbreviations: AF atrial fibrillation, LVEF left ventricular ejection fraction, CABG coronary artery bypass grafting, MR mitral regurgitation, HTN hypertension, MVP mitral valve repair, MVR mitral valve replacement, NR not reported\nOperative characteristics\n\nAbbreviations: MVP mitral valve repair, MVR mitral valve replacement, ACC time aortic cross-clamping time, CPB time, cardiopulmonary bypass time, NR not reported\nAll the eleven trials were assessed by the Newcastle-Ottawa Scale for quality assessment risk evaluation of adequacy of selection, comparability, and outcomes assessment for individual trials (Table 3). All studies included in our meta-analysis were of high-quality (had ≥ 6 scores).Table 3Study quality assessment using the Newcastle-Ottawa Scale for nonrandomized studiesSelectionOutcomeFirst author, year of publication (reference)Representativeness of exposed cohortSelection of nonexposed cohortAscertainment of exposureOutcome of interest absent at start of studyComparability (Based on design and analysis)Assessment of outcomeFollow-up long enough for outcomesto occurAdequacy of follow-upTotal scoreLorusso et al.111101117Lio et al.111111118Ljubacev et al.111101016Roshanali et al.111121119Maltais et al.111121119Qiu et al.111121119Micovic et al.111121119Bonacchi et al.111121119Silberman et al.111111118Mantovani et al.111121119Reece et al.111111017\nStudy quality assessment using the Newcastle-Ottawa Scale for nonrandomized studies\n Peri-operative mortality Ten observational studies involving a total of 1750 patients reported operative mortality. The odds ratios in the study ranged from 0.16 to 2.32 (Fig. 2). The summary odds ratio was 0.65 (95 % CI, 0.43-1.00), P = 0.05, indicating there was a reduced peri-operative mortality trend towards repair, but no statistical significance reached. In assessing potential heterogeneity across the studies, I2 = 0 %, and no publication bias was found either from the Egger’s test (P = 0.83) or the Begg’s test (P = 0.68).Fig. 2Mitral valve repair versus mitral valve replacement on peri-operative mortality\nMitral valve repair versus mitral valve replacement on peri-operative mortality\nTen observational studies involving a total of 1750 patients reported operative mortality. The odds ratios in the study ranged from 0.16 to 2.32 (Fig. 2). The summary odds ratio was 0.65 (95 % CI, 0.43-1.00), P = 0.05, indicating there was a reduced peri-operative mortality trend towards repair, but no statistical significance reached. In assessing potential heterogeneity across the studies, I2 = 0 %, and no publication bias was found either from the Egger’s test (P = 0.83) or the Begg’s test (P = 0.68).Fig. 2Mitral valve repair versus mitral valve replacement on peri-operative mortality\nMitral valve repair versus mitral valve replacement on peri-operative mortality\n Late mortality A total of nine studies (1622 Patients) reported late mortality (Fig. 3). The overall hazard ratio was 0.87 (95 % CI, 0.67-1.14; P = 0.31), suggesting late mortality was not significantly reduced following repair. Further, heterogeneity was moderate (I2 = 30 %). It was noted that ten of the studies included patients with different degrees of regurgitation and left ventricular dysfunction, with exception of one study [23], all of the patients included in this study had severely impaired LV function (ejection fraction <25 %) and severe ischemic MR undergoing CABG. Severely decreased left ventricular function and severe IMR could have the potential pathophysiological effect on the mortality rates of those patients. Hence, sensitivity analysis was conducted to only include studies in which not all of the patients had severe ischemic MR and severely impaired LV function undergoing CABG. Restricting analysis to these studies had no significant impact on the reduction of late mortality following repair (the summary hazard ratio, 1.03; 95 % CI, 0.90-1.17; P = 0.66). Whereas, heterogeneity suggested by I2 was significantly reduced to 0 %, indicating no variability exists among the rest studies. Further exclusion of any single study did not significantly reduce the heterogeneity. In addition, our study included 10 retrospective studies and 1 prospective study. The different study designs may influence outcomes of meta-analysis. Therefore, sensitivity analysis was performed to only include retrospective studies. Restricting analysis to these studies did not significantly impact on the result for late mortality (HR, 0.86; 95 % CI, 0.64-1.14; P = 0.30; I2 = 38 %).Fig. 3Mitral valve repair versus mitral valve replacement on late mortality\nMitral valve repair versus mitral valve replacement on late mortality\nA total of nine studies (1622 Patients) reported late mortality (Fig. 3). The overall hazard ratio was 0.87 (95 % CI, 0.67-1.14; P = 0.31), suggesting late mortality was not significantly reduced following repair. Further, heterogeneity was moderate (I2 = 30 %). It was noted that ten of the studies included patients with different degrees of regurgitation and left ventricular dysfunction, with exception of one study [23], all of the patients included in this study had severely impaired LV function (ejection fraction <25 %) and severe ischemic MR undergoing CABG. Severely decreased left ventricular function and severe IMR could have the potential pathophysiological effect on the mortality rates of those patients. Hence, sensitivity analysis was conducted to only include studies in which not all of the patients had severe ischemic MR and severely impaired LV function undergoing CABG. Restricting analysis to these studies had no significant impact on the reduction of late mortality following repair (the summary hazard ratio, 1.03; 95 % CI, 0.90-1.17; P = 0.66). Whereas, heterogeneity suggested by I2 was significantly reduced to 0 %, indicating no variability exists among the rest studies. Further exclusion of any single study did not significantly reduce the heterogeneity. In addition, our study included 10 retrospective studies and 1 prospective study. The different study designs may influence outcomes of meta-analysis. Therefore, sensitivity analysis was performed to only include retrospective studies. Restricting analysis to these studies did not significantly impact on the result for late mortality (HR, 0.86; 95 % CI, 0.64-1.14; P = 0.30; I2 = 38 %).Fig. 3Mitral valve repair versus mitral valve replacement on late mortality\nMitral valve repair versus mitral valve replacement on late mortality\n Mitral valve reoperation Reoperation due to such as MV regurgitation, thromboembolism and prosthetic endocarditis was reported in five studies involving a total of 845 patients. The combined odds ratio was 1.47, suggesting the trend went towards the preference of replacement. Nevertheless, no significant differences were reached between the two surgical approaches (95 % CI, 0.90-2.38; I2 = 0 %; P = 0.12) (Fig. 4).Fig. 4Mitral valve repair versus mitral valve replacement on reoperation\nMitral valve repair versus mitral valve replacement on reoperation\nReoperation due to such as MV regurgitation, thromboembolism and prosthetic endocarditis was reported in five studies involving a total of 845 patients. The combined odds ratio was 1.47, suggesting the trend went towards the preference of replacement. Nevertheless, no significant differences were reached between the two surgical approaches (95 % CI, 0.90-2.38; I2 = 0 %; P = 0.12) (Fig. 4).Fig. 4Mitral valve repair versus mitral valve replacement on reoperation\nMitral valve repair versus mitral valve replacement on reoperation\n Recurrence of MR Five studies involving a total of 837 patients provided data regarding recurrence of MR during the follow-up. The MVP + CABG group was associated with a significantly increased recurrence rate of MR (OR, 5.41; 95 % CI: 3.12–9.38; P < 0.001) with low heterogeneity among those studies (I2 = 10 %) (Fig. 5). Sensitivity analysis was also performed to only include retrospective studies. Restricting analysis to these studies did not significantly impact on the result for recurrence of MR (OR, 5.97; 95 % CI, 3.36-10.58; P < 0.001; I2 = 0 %).Fig. 5Mitral valve repair versus mitral valve replacement on recurrence of mitral valve regurgitation\nMitral valve repair versus mitral valve replacement on recurrence of mitral valve regurgitation\nFive studies involving a total of 837 patients provided data regarding recurrence of MR during the follow-up. The MVP + CABG group was associated with a significantly increased recurrence rate of MR (OR, 5.41; 95 % CI: 3.12–9.38; P < 0.001) with low heterogeneity among those studies (I2 = 10 %) (Fig. 5). Sensitivity analysis was also performed to only include retrospective studies. Restricting analysis to these studies did not significantly impact on the result for recurrence of MR (OR, 5.97; 95 % CI, 3.36-10.58; P < 0.001; I2 = 0 %).Fig. 5Mitral valve repair versus mitral valve replacement on recurrence of mitral valve regurgitation\nMitral valve repair versus mitral valve replacement on recurrence of mitral valve regurgitation"},"conclusions_title":{"kind":"string","value":"Conclusions"},"conclusions_text":{"kind":"string","value":"In patients with chronic ischemic mitral regurgitation during CABG, mitral valve replacement is associated with lower recurrence of regurgitation. No differences were found regarding survival and reoperation rates."},"other_sections_titles":{"kind":"list like","value":["Search strategy","Study selection","Data extraction and quality assessment","Statistical analysis","Search results and study quality","Peri-operative mortality","Late mortality","Mitral valve reoperation","Recurrence of MR","Limitations"],"string":"[\n \"Search strategy\",\n \"Study selection\",\n \"Data extraction and quality assessment\",\n \"Statistical analysis\",\n \"Search results and study quality\",\n \"Peri-operative mortality\",\n \"Late mortality\",\n \"Mitral valve reoperation\",\n \"Recurrence of MR\",\n \"Limitations\"\n]"},"other_sections_texts":{"kind":"list like","value":["This meta-analysis was conducted according to the recommendations of the Meta-Analysis of Observational Studies in Epidemiology (MOOSE) [16]. A computerized search was performed using Pubmed, Embase, Ovid medline and Cochrane Library from their dates of inception to December 2015 without language restriction. The search terms “ischemic or ischaemic” and “mitral valve” and “repair or replacement or annuloplasty” and “coronary artery bypass grafting” were entered as MeSH terms and keywords. The language of publication was restricted to English. We also reviewed the full text and references lists of all relevant review articles in detail. YW and XS independently undertook the literature search, screening of titles and abstracts. Any disagreement was resolved by consensus.","Articles were included if there is a direct comparison of repair versus replacement and all patients with IMR had CABG. The exclusion criteria were applied to select the final articles for the meta-analysis: (1) ischemic etiology in only a subset of the patients with outcomes not specifically provided (2) nonischemic dilated cardiomyopathy (3) beating heart procedures (4) concomitant surgical ventricular restoration (5) preoperative hemodynamic instability (6) lack of annuloplasty in > 20 % of the patients in the repair group (7) acute IMR.","All data were extracted independently by 2 investigators (Y.W., X.S.) according to the prespecified selection criteria, with disagreement resolved by consensus among all authors. The following data from each study were extracted: the last name of the first author, year of publication, study population, patients’ age and gender, comorbidities, cardiac function, severity of mitral regurgitation at baseline and follow-up period. Any disagreement was resolved by consensus.\nBased on the extracted data, the quality of the included studies was evaluated using the nine-item Newcastle-Ottawa Quality scale [17], a widely used tool for the quality assessment of non-randomized trials. The high-quality study was defined as a study with ≥6 scores.","The primary end points were operative mortality and late mortality (considered to be year after operation). Operative mortality was defined as death within 30 days after operation or in-hospital death. Secondary end points were MR recurrence 2+ or greater and reoperation at follow-up. The meta-analysis was performed using Review Manager (Revman, version 5.3 for windows, Oxford, England, Cochrane Collaboration) and Stata (version 11.0; StataCorp, College Station, TX). Hazard ratio (HR) with a 95 % confidence intervals (CIs), directly extracted from these included studies or indirectly calculated using the method of Tierney and colleagues [18] to assess the efficacy of the surgical intervention in each study. A summary of odds ratio (OR) and their corresponding 95 % CI were computed for each dichotomous outcome using either fixed-effects models or, in the presence of substantial heterogeneity (I2 > 50 %), random-effects models [19]. Statistical heterogeneity across studies was examined with Cochran’s Q test as well as the I2 statistics. Studies with an I2 statistics of <25 % were considered to have low heterogeneity, those with an I2 statistics of 25–50 % were considered to have moderate heterogeneity, and those with an I2 statistics of >50 % were considered to have a high degree of heterogeneity [20]. If there was high heterogeneity, the possible clinical and methodological factors for this were further explored. Potential sources of heterogeneity were investigated using sensitivity analyses and each study involved in the meta-analysis was excluded each time to reflect the influence of the individual data set on the pooled RRs.\nPublication bias was assessed using the Egger regression asymmetry test [21] and Begg adjusted rank correlation test [22]; a P value of less than 0.05 was considered representative of statistically significant publication bias. Meta-analysis results are displayed in forest plots. A p value < 0.05 was considered statistically significant.","The literature search identified a total of 545 studies, which were published between 1965 and 2015. On the basis of title and abstracts, 34 articles were selected and reviewed in full. Eleven articles met the inclusion and exclusion criteria [9–13, 23–28] (Fig. 1). Of the included studies, there were ten retrospective observational studies [9–13, 23–27] and one prospective observational study [28]. All were nonrandomized studies. These studies included a total of 1807 patients, 1091 (60.4 %) of whom underwent repair and 716 (39.6 %) of whom underwent replacement. All patients had CABG. Patient characteristics and a summary of operative details are summarized in Tables 1 and 2, respectively. With the exception of the replacement patients being older in 2 of the studies, the two groups were similar in terms of hypertension (HTN), diabetes, atrial fibrillation (AF), left ventricular ejection fraction (LVEF) and the New York Heart Association (NYHA) class. Eight of the studies reported data on the type of prosthesis used for mitral valve replacement and preservation of the subvalvular apparatus. In half of the studies, the majority of patients received a bioprothesis valve. In addition, preservation of the subvalvular (either total or partial) apparatus were performed in the vast majority of mitral valve replacements.Fig. 1Flow chart of study selectionTable 1Key Features of Included StudiesStudySubjectsMean AgeMale (%)HTN (%)Diabetes (%)AF (%)NYHA III-IV (%)Mean LVEF (%)MR gradeFollow-up periodMVP + CABGMVR + CABGMVP + CABGMVR + CABGMVP + CABGMVR + CABGMVP + CABGMVR + CABGMVP + CABGMVR + CABGMVP + CABGMVR + CABGMVP + CABGMVR + CABGMVP + CABGMVR + CABGMVP + CABGMVR + CABGLorusso et al.24424466667369414136351213NRNR35352.8 ± 0.52.8 ± 0.546.5bmoLio et al.98286570d\n746181893532NRNR61713234NRNR45bmoLjubacev et al.3441NRNRNRNR85803256d\n2617NRNRNRNRNRNRIn-hospitalRoshanali et al.263157578377NRNRNRNRNRNRNRNR38403.6 ± 0.53.5 ± 0.540.2a moMaltais et al.302857070686371683426NRNR85913434NRNR4.2ayrsQiu et al.1121067172645672753032282653493535NRNR48.1amoMicovic et al.865261b\n62b\n7273746521152729645029362.7 ± 0.62.5 ± 0.732a moBonacchi et al.3618NRNRNRNRNRNRNRNRNRNRNRNR2727NRNR32a moSilberman et al.38146267d\n749350574557NRNR49cd\n32cd\n<25 %NRNR38amoMantovani et al.61416868675454512615NRNRNRNR45453.1 ± 0.83.3 ± 0.736.8a moReece et al.5456676941d\n68d\nNRNR2221NRNRNRNR4440NRNRIn-hospital\na = mean; b = median; c Percentage class IV; d\np < 0.05 between MVr and MVR\nAbbreviations: AF atrial fibrillation, LVEF left ventricular ejection fraction, CABG coronary artery bypass grafting, MR mitral regurgitation, HTN hypertension, MVP mitral valve repair, MVR mitral valve replacement, NR not reportedTable 2Operative characteristicsCPB time (min)ACC time (min)MVR prosthesis typeSubvalvular apparatus preservationMVP partial/suture annuloplasty (%)MVP ring annuloplasty (%)MVP undersizingMVPMVRMVPMVRMechanical %Bioprothesis %Anterior + Posterior (%)Posterior (%)None (%)Lorusso et al. [9]14514594944753482443010027 (26 mm) 52 (28 mm) 13 (30 mm) 6 (32 mm) 1 (34 mm) 1 (36 mm)Lio et al. [10] 156180107132366410000010037 % open ring 63 % closed ring 37 % rigid ring 63 % semi-rigid ringLjubacev et al. [24]1451529699NRNRNRNRNRNRNRNRRoshanali et al. [28]NRNRNRNR100010000NRNRNRMaltais et al. [13]NRNRNRNR4654NRNRNR89242 (24–28 mm) 36 (30–34 mm)Qiu et al. [26]13612910598386211890010030 mmMicovic et al. [11]NRNRNRNR100001000595Median 28 mm (range, 26–34 mm)Bonacchi et al. [12]NRNRNRNRNRNR010001783NRSilberman et al. [23]154184991111000NRNRNR010026 ± 1.2 mmMantovani et al. [25]1791731311227624010000100ModerateReece et al. [27]112132152171NRNRNRNRNR010028 mm males 26 mm females\nAbbreviations: MVP mitral valve repair, MVR mitral valve replacement, ACC time aortic cross-clamping time, CPB time, cardiopulmonary bypass time, NR not reported\nFlow chart of study selection\nKey Features of Included Studies\n\na = mean; b = median; c Percentage class IV; d\np < 0.05 between MVr and MVR\n\nAbbreviations: AF atrial fibrillation, LVEF left ventricular ejection fraction, CABG coronary artery bypass grafting, MR mitral regurgitation, HTN hypertension, MVP mitral valve repair, MVR mitral valve replacement, NR not reported\nOperative characteristics\n\nAbbreviations: MVP mitral valve repair, MVR mitral valve replacement, ACC time aortic cross-clamping time, CPB time, cardiopulmonary bypass time, NR not reported\nAll the eleven trials were assessed by the Newcastle-Ottawa Scale for quality assessment risk evaluation of adequacy of selection, comparability, and outcomes assessment for individual trials (Table 3). All studies included in our meta-analysis were of high-quality (had ≥ 6 scores).Table 3Study quality assessment using the Newcastle-Ottawa Scale for nonrandomized studiesSelectionOutcomeFirst author, year of publication (reference)Representativeness of exposed cohortSelection of nonexposed cohortAscertainment of exposureOutcome of interest absent at start of studyComparability (Based on design and analysis)Assessment of outcomeFollow-up long enough for outcomesto occurAdequacy of follow-upTotal scoreLorusso et al.111101117Lio et al.111111118Ljubacev et al.111101016Roshanali et al.111121119Maltais et al.111121119Qiu et al.111121119Micovic et al.111121119Bonacchi et al.111121119Silberman et al.111111118Mantovani et al.111121119Reece et al.111111017\nStudy quality assessment using the Newcastle-Ottawa Scale for nonrandomized studies","Ten observational studies involving a total of 1750 patients reported operative mortality. The odds ratios in the study ranged from 0.16 to 2.32 (Fig. 2). The summary odds ratio was 0.65 (95 % CI, 0.43-1.00), P = 0.05, indicating there was a reduced peri-operative mortality trend towards repair, but no statistical significance reached. In assessing potential heterogeneity across the studies, I2 = 0 %, and no publication bias was found either from the Egger’s test (P = 0.83) or the Begg’s test (P = 0.68).Fig. 2Mitral valve repair versus mitral valve replacement on peri-operative mortality\nMitral valve repair versus mitral valve replacement on peri-operative mortality","A total of nine studies (1622 Patients) reported late mortality (Fig. 3). The overall hazard ratio was 0.87 (95 % CI, 0.67-1.14; P = 0.31), suggesting late mortality was not significantly reduced following repair. Further, heterogeneity was moderate (I2 = 30 %). It was noted that ten of the studies included patients with different degrees of regurgitation and left ventricular dysfunction, with exception of one study [23], all of the patients included in this study had severely impaired LV function (ejection fraction <25 %) and severe ischemic MR undergoing CABG. Severely decreased left ventricular function and severe IMR could have the potential pathophysiological effect on the mortality rates of those patients. Hence, sensitivity analysis was conducted to only include studies in which not all of the patients had severe ischemic MR and severely impaired LV function undergoing CABG. Restricting analysis to these studies had no significant impact on the reduction of late mortality following repair (the summary hazard ratio, 1.03; 95 % CI, 0.90-1.17; P = 0.66). Whereas, heterogeneity suggested by I2 was significantly reduced to 0 %, indicating no variability exists among the rest studies. Further exclusion of any single study did not significantly reduce the heterogeneity. In addition, our study included 10 retrospective studies and 1 prospective study. The different study designs may influence outcomes of meta-analysis. Therefore, sensitivity analysis was performed to only include retrospective studies. Restricting analysis to these studies did not significantly impact on the result for late mortality (HR, 0.86; 95 % CI, 0.64-1.14; P = 0.30; I2 = 38 %).Fig. 3Mitral valve repair versus mitral valve replacement on late mortality\nMitral valve repair versus mitral valve replacement on late mortality","Reoperation due to such as MV regurgitation, thromboembolism and prosthetic endocarditis was reported in five studies involving a total of 845 patients. The combined odds ratio was 1.47, suggesting the trend went towards the preference of replacement. Nevertheless, no significant differences were reached between the two surgical approaches (95 % CI, 0.90-2.38; I2 = 0 %; P = 0.12) (Fig. 4).Fig. 4Mitral valve repair versus mitral valve replacement on reoperation\nMitral valve repair versus mitral valve replacement on reoperation","Five studies involving a total of 837 patients provided data regarding recurrence of MR during the follow-up. The MVP + CABG group was associated with a significantly increased recurrence rate of MR (OR, 5.41; 95 % CI: 3.12–9.38; P < 0.001) with low heterogeneity among those studies (I2 = 10 %) (Fig. 5). Sensitivity analysis was also performed to only include retrospective studies. Restricting analysis to these studies did not significantly impact on the result for recurrence of MR (OR, 5.97; 95 % CI, 3.36-10.58; P < 0.001; I2 = 0 %).Fig. 5Mitral valve repair versus mitral valve replacement on recurrence of mitral valve regurgitation\nMitral valve repair versus mitral valve replacement on recurrence of mitral valve regurgitation","Our meta-analysis has several limitations. Firstly, this study was based on observational, retrospective studies with inherent bias of such study designs. The publications included in this meta-analysis were relatively small and nonrandomized studies. Secondly, changes in NYHA class, LVEF and left ventricular reversal remodeling were too scarcely reported in the included studies to enable meta-analysis. Eight out of eleven studies included in our meta-analysis reported data on the subvalvular apparatus preservation in mitral valve replacement yet with lack of uniform preservation of both the anterior and posterior leaflets. The other three studies had no description regarding subvalvular apparatus preservation. Thirdly, potential confounding factors such as preoperative risk evaluation (STS score i.e.), mitral valve more suitable for repair, age, cause of mitral regurgitation (ischemia, fibrosis, ventricular remodeling), EF and complexity of revascularization were not considered or adjusted in some of the studies included in our meta-analysis. Therefore, the superiority of repair over replacement may be affected by this and other factors that are not possible to be revealed with meta-analysis of observational trials. Well-designed RCTs are required to further verify the conclusion. Another limitation of our report is the fact that follow-up periods were heterogeneous between some studies with different use of mean and median durations of follow-up. Therefore, subgroup analysis could not be performed statistically."],"string":"[\n \"This meta-analysis was conducted according to the recommendations of the Meta-Analysis of Observational Studies in Epidemiology (MOOSE) [16]. A computerized search was performed using Pubmed, Embase, Ovid medline and Cochrane Library from their dates of inception to December 2015 without language restriction. The search terms “ischemic or ischaemic” and “mitral valve” and “repair or replacement or annuloplasty” and “coronary artery bypass grafting” were entered as MeSH terms and keywords. The language of publication was restricted to English. We also reviewed the full text and references lists of all relevant review articles in detail. YW and XS independently undertook the literature search, screening of titles and abstracts. Any disagreement was resolved by consensus.\",\n \"Articles were included if there is a direct comparison of repair versus replacement and all patients with IMR had CABG. The exclusion criteria were applied to select the final articles for the meta-analysis: (1) ischemic etiology in only a subset of the patients with outcomes not specifically provided (2) nonischemic dilated cardiomyopathy (3) beating heart procedures (4) concomitant surgical ventricular restoration (5) preoperative hemodynamic instability (6) lack of annuloplasty in > 20 % of the patients in the repair group (7) acute IMR.\",\n \"All data were extracted independently by 2 investigators (Y.W., X.S.) according to the prespecified selection criteria, with disagreement resolved by consensus among all authors. The following data from each study were extracted: the last name of the first author, year of publication, study population, patients’ age and gender, comorbidities, cardiac function, severity of mitral regurgitation at baseline and follow-up period. Any disagreement was resolved by consensus.\\nBased on the extracted data, the quality of the included studies was evaluated using the nine-item Newcastle-Ottawa Quality scale [17], a widely used tool for the quality assessment of non-randomized trials. The high-quality study was defined as a study with ≥6 scores.\",\n \"The primary end points were operative mortality and late mortality (considered to be year after operation). Operative mortality was defined as death within 30 days after operation or in-hospital death. Secondary end points were MR recurrence 2+ or greater and reoperation at follow-up. The meta-analysis was performed using Review Manager (Revman, version 5.3 for windows, Oxford, England, Cochrane Collaboration) and Stata (version 11.0; StataCorp, College Station, TX). Hazard ratio (HR) with a 95 % confidence intervals (CIs), directly extracted from these included studies or indirectly calculated using the method of Tierney and colleagues [18] to assess the efficacy of the surgical intervention in each study. A summary of odds ratio (OR) and their corresponding 95 % CI were computed for each dichotomous outcome using either fixed-effects models or, in the presence of substantial heterogeneity (I2 > 50 %), random-effects models [19]. Statistical heterogeneity across studies was examined with Cochran’s Q test as well as the I2 statistics. Studies with an I2 statistics of <25 % were considered to have low heterogeneity, those with an I2 statistics of 25–50 % were considered to have moderate heterogeneity, and those with an I2 statistics of >50 % were considered to have a high degree of heterogeneity [20]. If there was high heterogeneity, the possible clinical and methodological factors for this were further explored. Potential sources of heterogeneity were investigated using sensitivity analyses and each study involved in the meta-analysis was excluded each time to reflect the influence of the individual data set on the pooled RRs.\\nPublication bias was assessed using the Egger regression asymmetry test [21] and Begg adjusted rank correlation test [22]; a P value of less than 0.05 was considered representative of statistically significant publication bias. Meta-analysis results are displayed in forest plots. A p value < 0.05 was considered statistically significant.\",\n \"The literature search identified a total of 545 studies, which were published between 1965 and 2015. On the basis of title and abstracts, 34 articles were selected and reviewed in full. Eleven articles met the inclusion and exclusion criteria [9–13, 23–28] (Fig. 1). Of the included studies, there were ten retrospective observational studies [9–13, 23–27] and one prospective observational study [28]. All were nonrandomized studies. These studies included a total of 1807 patients, 1091 (60.4 %) of whom underwent repair and 716 (39.6 %) of whom underwent replacement. All patients had CABG. Patient characteristics and a summary of operative details are summarized in Tables 1 and 2, respectively. With the exception of the replacement patients being older in 2 of the studies, the two groups were similar in terms of hypertension (HTN), diabetes, atrial fibrillation (AF), left ventricular ejection fraction (LVEF) and the New York Heart Association (NYHA) class. Eight of the studies reported data on the type of prosthesis used for mitral valve replacement and preservation of the subvalvular apparatus. In half of the studies, the majority of patients received a bioprothesis valve. In addition, preservation of the subvalvular (either total or partial) apparatus were performed in the vast majority of mitral valve replacements.Fig. 1Flow chart of study selectionTable 1Key Features of Included StudiesStudySubjectsMean AgeMale (%)HTN (%)Diabetes (%)AF (%)NYHA III-IV (%)Mean LVEF (%)MR gradeFollow-up periodMVP + CABGMVR + CABGMVP + CABGMVR + CABGMVP + CABGMVR + CABGMVP + CABGMVR + CABGMVP + CABGMVR + CABGMVP + CABGMVR + CABGMVP + CABGMVR + CABGMVP + CABGMVR + CABGMVP + CABGMVR + CABGLorusso et al.24424466667369414136351213NRNR35352.8 ± 0.52.8 ± 0.546.5bmoLio et al.98286570d\\n746181893532NRNR61713234NRNR45bmoLjubacev et al.3441NRNRNRNR85803256d\\n2617NRNRNRNRNRNRIn-hospitalRoshanali et al.263157578377NRNRNRNRNRNRNRNR38403.6 ± 0.53.5 ± 0.540.2a moMaltais et al.302857070686371683426NRNR85913434NRNR4.2ayrsQiu et al.1121067172645672753032282653493535NRNR48.1amoMicovic et al.865261b\\n62b\\n7273746521152729645029362.7 ± 0.62.5 ± 0.732a moBonacchi et al.3618NRNRNRNRNRNRNRNRNRNRNRNR2727NRNR32a moSilberman et al.38146267d\\n749350574557NRNR49cd\\n32cd\\n<25 %NRNR38amoMantovani et al.61416868675454512615NRNRNRNR45453.1 ± 0.83.3 ± 0.736.8a moReece et al.5456676941d\\n68d\\nNRNR2221NRNRNRNR4440NRNRIn-hospital\\na = mean; b = median; c Percentage class IV; d\\np < 0.05 between MVr and MVR\\nAbbreviations: AF atrial fibrillation, LVEF left ventricular ejection fraction, CABG coronary artery bypass grafting, MR mitral regurgitation, HTN hypertension, MVP mitral valve repair, MVR mitral valve replacement, NR not reportedTable 2Operative characteristicsCPB time (min)ACC time (min)MVR prosthesis typeSubvalvular apparatus preservationMVP partial/suture annuloplasty (%)MVP ring annuloplasty (%)MVP undersizingMVPMVRMVPMVRMechanical %Bioprothesis %Anterior + Posterior (%)Posterior (%)None (%)Lorusso et al. [9]14514594944753482443010027 (26 mm) 52 (28 mm) 13 (30 mm) 6 (32 mm) 1 (34 mm) 1 (36 mm)Lio et al. [10] 156180107132366410000010037 % open ring 63 % closed ring 37 % rigid ring 63 % semi-rigid ringLjubacev et al. [24]1451529699NRNRNRNRNRNRNRNRRoshanali et al. [28]NRNRNRNR100010000NRNRNRMaltais et al. [13]NRNRNRNR4654NRNRNR89242 (24–28 mm) 36 (30–34 mm)Qiu et al. [26]13612910598386211890010030 mmMicovic et al. [11]NRNRNRNR100001000595Median 28 mm (range, 26–34 mm)Bonacchi et al. [12]NRNRNRNRNRNR010001783NRSilberman et al. [23]154184991111000NRNRNR010026 ± 1.2 mmMantovani et al. [25]1791731311227624010000100ModerateReece et al. [27]112132152171NRNRNRNRNR010028 mm males 26 mm females\\nAbbreviations: MVP mitral valve repair, MVR mitral valve replacement, ACC time aortic cross-clamping time, CPB time, cardiopulmonary bypass time, NR not reported\\nFlow chart of study selection\\nKey Features of Included Studies\\n\\na = mean; b = median; c Percentage class IV; d\\np < 0.05 between MVr and MVR\\n\\nAbbreviations: AF atrial fibrillation, LVEF left ventricular ejection fraction, CABG coronary artery bypass grafting, MR mitral regurgitation, HTN hypertension, MVP mitral valve repair, MVR mitral valve replacement, NR not reported\\nOperative characteristics\\n\\nAbbreviations: MVP mitral valve repair, MVR mitral valve replacement, ACC time aortic cross-clamping time, CPB time, cardiopulmonary bypass time, NR not reported\\nAll the eleven trials were assessed by the Newcastle-Ottawa Scale for quality assessment risk evaluation of adequacy of selection, comparability, and outcomes assessment for individual trials (Table 3). All studies included in our meta-analysis were of high-quality (had ≥ 6 scores).Table 3Study quality assessment using the Newcastle-Ottawa Scale for nonrandomized studiesSelectionOutcomeFirst author, year of publication (reference)Representativeness of exposed cohortSelection of nonexposed cohortAscertainment of exposureOutcome of interest absent at start of studyComparability (Based on design and analysis)Assessment of outcomeFollow-up long enough for outcomesto occurAdequacy of follow-upTotal scoreLorusso et al.111101117Lio et al.111111118Ljubacev et al.111101016Roshanali et al.111121119Maltais et al.111121119Qiu et al.111121119Micovic et al.111121119Bonacchi et al.111121119Silberman et al.111111118Mantovani et al.111121119Reece et al.111111017\\nStudy quality assessment using the Newcastle-Ottawa Scale for nonrandomized studies\",\n \"Ten observational studies involving a total of 1750 patients reported operative mortality. The odds ratios in the study ranged from 0.16 to 2.32 (Fig. 2). The summary odds ratio was 0.65 (95 % CI, 0.43-1.00), P = 0.05, indicating there was a reduced peri-operative mortality trend towards repair, but no statistical significance reached. In assessing potential heterogeneity across the studies, I2 = 0 %, and no publication bias was found either from the Egger’s test (P = 0.83) or the Begg’s test (P = 0.68).Fig. 2Mitral valve repair versus mitral valve replacement on peri-operative mortality\\nMitral valve repair versus mitral valve replacement on peri-operative mortality\",\n \"A total of nine studies (1622 Patients) reported late mortality (Fig. 3). The overall hazard ratio was 0.87 (95 % CI, 0.67-1.14; P = 0.31), suggesting late mortality was not significantly reduced following repair. Further, heterogeneity was moderate (I2 = 30 %). It was noted that ten of the studies included patients with different degrees of regurgitation and left ventricular dysfunction, with exception of one study [23], all of the patients included in this study had severely impaired LV function (ejection fraction <25 %) and severe ischemic MR undergoing CABG. Severely decreased left ventricular function and severe IMR could have the potential pathophysiological effect on the mortality rates of those patients. Hence, sensitivity analysis was conducted to only include studies in which not all of the patients had severe ischemic MR and severely impaired LV function undergoing CABG. Restricting analysis to these studies had no significant impact on the reduction of late mortality following repair (the summary hazard ratio, 1.03; 95 % CI, 0.90-1.17; P = 0.66). Whereas, heterogeneity suggested by I2 was significantly reduced to 0 %, indicating no variability exists among the rest studies. Further exclusion of any single study did not significantly reduce the heterogeneity. In addition, our study included 10 retrospective studies and 1 prospective study. The different study designs may influence outcomes of meta-analysis. Therefore, sensitivity analysis was performed to only include retrospective studies. Restricting analysis to these studies did not significantly impact on the result for late mortality (HR, 0.86; 95 % CI, 0.64-1.14; P = 0.30; I2 = 38 %).Fig. 3Mitral valve repair versus mitral valve replacement on late mortality\\nMitral valve repair versus mitral valve replacement on late mortality\",\n \"Reoperation due to such as MV regurgitation, thromboembolism and prosthetic endocarditis was reported in five studies involving a total of 845 patients. The combined odds ratio was 1.47, suggesting the trend went towards the preference of replacement. Nevertheless, no significant differences were reached between the two surgical approaches (95 % CI, 0.90-2.38; I2 = 0 %; P = 0.12) (Fig. 4).Fig. 4Mitral valve repair versus mitral valve replacement on reoperation\\nMitral valve repair versus mitral valve replacement on reoperation\",\n \"Five studies involving a total of 837 patients provided data regarding recurrence of MR during the follow-up. The MVP + CABG group was associated with a significantly increased recurrence rate of MR (OR, 5.41; 95 % CI: 3.12–9.38; P < 0.001) with low heterogeneity among those studies (I2 = 10 %) (Fig. 5). Sensitivity analysis was also performed to only include retrospective studies. Restricting analysis to these studies did not significantly impact on the result for recurrence of MR (OR, 5.97; 95 % CI, 3.36-10.58; P < 0.001; I2 = 0 %).Fig. 5Mitral valve repair versus mitral valve replacement on recurrence of mitral valve regurgitation\\nMitral valve repair versus mitral valve replacement on recurrence of mitral valve regurgitation\",\n \"Our meta-analysis has several limitations. Firstly, this study was based on observational, retrospective studies with inherent bias of such study designs. The publications included in this meta-analysis were relatively small and nonrandomized studies. Secondly, changes in NYHA class, LVEF and left ventricular reversal remodeling were too scarcely reported in the included studies to enable meta-analysis. Eight out of eleven studies included in our meta-analysis reported data on the subvalvular apparatus preservation in mitral valve replacement yet with lack of uniform preservation of both the anterior and posterior leaflets. The other three studies had no description regarding subvalvular apparatus preservation. Thirdly, potential confounding factors such as preoperative risk evaluation (STS score i.e.), mitral valve more suitable for repair, age, cause of mitral regurgitation (ischemia, fibrosis, ventricular remodeling), EF and complexity of revascularization were not considered or adjusted in some of the studies included in our meta-analysis. Therefore, the superiority of repair over replacement may be affected by this and other factors that are not possible to be revealed with meta-analysis of observational trials. Well-designed RCTs are required to further verify the conclusion. Another limitation of our report is the fact that follow-up periods were heterogeneous between some studies with different use of mean and median durations of follow-up. Therefore, subgroup analysis could not be performed statistically.\"\n]"},"other_sections_sec_types":{"kind":"list like","value":[null,null,null,null,null,null,null,null,null,null],"string":"[\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null\n]"},"all_sections_titles":{"kind":"list like","value":["Background","Methods","Search strategy","Study selection","Data extraction and quality assessment","Statistical analysis","Results","Search results and study quality","Peri-operative mortality","Late mortality","Mitral valve reoperation","Recurrence of MR","Discussion","Limitations","Conclusions"],"string":"[\n \"Background\",\n \"Methods\",\n \"Search strategy\",\n \"Study selection\",\n \"Data extraction and quality assessment\",\n \"Statistical analysis\",\n \"Results\",\n \"Search results and study quality\",\n \"Peri-operative mortality\",\n \"Late mortality\",\n \"Mitral valve reoperation\",\n \"Recurrence of MR\",\n \"Discussion\",\n \"Limitations\",\n \"Conclusions\"\n]"},"all_sections_texts":{"kind":"list like","value":["Chronic ischemic mitral regurgitation (IMR) is a frequent and important complication after myocardial infarction. Its pathophysiologic mechanisms account for remodeling of segmental/global left ventricle (LV) inducing papillary muscle displacement and leaflet tethering [1]. The presence of IMR is independently associated with mortality and morbidity after myocardial infarction [2].\nGiven the severity of IMR, surgery performed for IMR ranges from coronary artery bypass grafting (CABG) alone to both CABG and mitral valve surgery [3, 4]. Two randomized trials indicated that repair was associated with a reduced prevalence of mitral regurgitation but did not show a clinically meaningful advantage of adding mitral valve repair to CABG [5, 6]. In addition, when compared with replacement, previous meta-analyses concluded that repair is associated with lower operative mortality but higher recurrence of regurgitation in patients with ischemic mitral regurgitation, with or without CABG [7, 8]. For patients with chronic IMR undergoing combined CABG, the best surgical treatment is still controversial. Some studies support replacement [9, 10], others support repair [11, 12], and others showed similar survival for the two procedures [13]. Current guidelines recommend mitral valve surgery for severe IMR, but do not demonstrate a specific type of procedure [14, 15]. Numerous non-randomized studies have been published comparing the clinical outcomes between MVP + CABG and MVR + CABG for IMR. However, there is still no systematic and quantitative assessment of accumulated literature on this topic. Meta-analysis is a powerful tool to provide meaningful comparison of short and long-term outcomes of these procedures. The present meta-analysis aimed to assess the clinical outcomes of patients who underwent mitral valve surgery and CABG for chronic IMR."," Search strategy This meta-analysis was conducted according to the recommendations of the Meta-Analysis of Observational Studies in Epidemiology (MOOSE) [16]. A computerized search was performed using Pubmed, Embase, Ovid medline and Cochrane Library from their dates of inception to December 2015 without language restriction. The search terms “ischemic or ischaemic” and “mitral valve” and “repair or replacement or annuloplasty” and “coronary artery bypass grafting” were entered as MeSH terms and keywords. The language of publication was restricted to English. We also reviewed the full text and references lists of all relevant review articles in detail. YW and XS independently undertook the literature search, screening of titles and abstracts. Any disagreement was resolved by consensus.\nThis meta-analysis was conducted according to the recommendations of the Meta-Analysis of Observational Studies in Epidemiology (MOOSE) [16]. A computerized search was performed using Pubmed, Embase, Ovid medline and Cochrane Library from their dates of inception to December 2015 without language restriction. The search terms “ischemic or ischaemic” and “mitral valve” and “repair or replacement or annuloplasty” and “coronary artery bypass grafting” were entered as MeSH terms and keywords. The language of publication was restricted to English. We also reviewed the full text and references lists of all relevant review articles in detail. YW and XS independently undertook the literature search, screening of titles and abstracts. Any disagreement was resolved by consensus.\n Study selection Articles were included if there is a direct comparison of repair versus replacement and all patients with IMR had CABG. The exclusion criteria were applied to select the final articles for the meta-analysis: (1) ischemic etiology in only a subset of the patients with outcomes not specifically provided (2) nonischemic dilated cardiomyopathy (3) beating heart procedures (4) concomitant surgical ventricular restoration (5) preoperative hemodynamic instability (6) lack of annuloplasty in > 20 % of the patients in the repair group (7) acute IMR.\nArticles were included if there is a direct comparison of repair versus replacement and all patients with IMR had CABG. The exclusion criteria were applied to select the final articles for the meta-analysis: (1) ischemic etiology in only a subset of the patients with outcomes not specifically provided (2) nonischemic dilated cardiomyopathy (3) beating heart procedures (4) concomitant surgical ventricular restoration (5) preoperative hemodynamic instability (6) lack of annuloplasty in > 20 % of the patients in the repair group (7) acute IMR.\n Data extraction and quality assessment All data were extracted independently by 2 investigators (Y.W., X.S.) according to the prespecified selection criteria, with disagreement resolved by consensus among all authors. The following data from each study were extracted: the last name of the first author, year of publication, study population, patients’ age and gender, comorbidities, cardiac function, severity of mitral regurgitation at baseline and follow-up period. Any disagreement was resolved by consensus.\nBased on the extracted data, the quality of the included studies was evaluated using the nine-item Newcastle-Ottawa Quality scale [17], a widely used tool for the quality assessment of non-randomized trials. The high-quality study was defined as a study with ≥6 scores.\nAll data were extracted independently by 2 investigators (Y.W., X.S.) according to the prespecified selection criteria, with disagreement resolved by consensus among all authors. The following data from each study were extracted: the last name of the first author, year of publication, study population, patients’ age and gender, comorbidities, cardiac function, severity of mitral regurgitation at baseline and follow-up period. Any disagreement was resolved by consensus.\nBased on the extracted data, the quality of the included studies was evaluated using the nine-item Newcastle-Ottawa Quality scale [17], a widely used tool for the quality assessment of non-randomized trials. The high-quality study was defined as a study with ≥6 scores.\n Statistical analysis The primary end points were operative mortality and late mortality (considered to be year after operation). Operative mortality was defined as death within 30 days after operation or in-hospital death. Secondary end points were MR recurrence 2+ or greater and reoperation at follow-up. The meta-analysis was performed using Review Manager (Revman, version 5.3 for windows, Oxford, England, Cochrane Collaboration) and Stata (version 11.0; StataCorp, College Station, TX). Hazard ratio (HR) with a 95 % confidence intervals (CIs), directly extracted from these included studies or indirectly calculated using the method of Tierney and colleagues [18] to assess the efficacy of the surgical intervention in each study. A summary of odds ratio (OR) and their corresponding 95 % CI were computed for each dichotomous outcome using either fixed-effects models or, in the presence of substantial heterogeneity (I2 > 50 %), random-effects models [19]. Statistical heterogeneity across studies was examined with Cochran’s Q test as well as the I2 statistics. Studies with an I2 statistics of <25 % were considered to have low heterogeneity, those with an I2 statistics of 25–50 % were considered to have moderate heterogeneity, and those with an I2 statistics of >50 % were considered to have a high degree of heterogeneity [20]. If there was high heterogeneity, the possible clinical and methodological factors for this were further explored. Potential sources of heterogeneity were investigated using sensitivity analyses and each study involved in the meta-analysis was excluded each time to reflect the influence of the individual data set on the pooled RRs.\nPublication bias was assessed using the Egger regression asymmetry test [21] and Begg adjusted rank correlation test [22]; a P value of less than 0.05 was considered representative of statistically significant publication bias. Meta-analysis results are displayed in forest plots. A p value < 0.05 was considered statistically significant.\nThe primary end points were operative mortality and late mortality (considered to be year after operation). Operative mortality was defined as death within 30 days after operation or in-hospital death. Secondary end points were MR recurrence 2+ or greater and reoperation at follow-up. The meta-analysis was performed using Review Manager (Revman, version 5.3 for windows, Oxford, England, Cochrane Collaboration) and Stata (version 11.0; StataCorp, College Station, TX). Hazard ratio (HR) with a 95 % confidence intervals (CIs), directly extracted from these included studies or indirectly calculated using the method of Tierney and colleagues [18] to assess the efficacy of the surgical intervention in each study. A summary of odds ratio (OR) and their corresponding 95 % CI were computed for each dichotomous outcome using either fixed-effects models or, in the presence of substantial heterogeneity (I2 > 50 %), random-effects models [19]. Statistical heterogeneity across studies was examined with Cochran’s Q test as well as the I2 statistics. Studies with an I2 statistics of <25 % were considered to have low heterogeneity, those with an I2 statistics of 25–50 % were considered to have moderate heterogeneity, and those with an I2 statistics of >50 % were considered to have a high degree of heterogeneity [20]. If there was high heterogeneity, the possible clinical and methodological factors for this were further explored. Potential sources of heterogeneity were investigated using sensitivity analyses and each study involved in the meta-analysis was excluded each time to reflect the influence of the individual data set on the pooled RRs.\nPublication bias was assessed using the Egger regression asymmetry test [21] and Begg adjusted rank correlation test [22]; a P value of less than 0.05 was considered representative of statistically significant publication bias. Meta-analysis results are displayed in forest plots. A p value < 0.05 was considered statistically significant.","This meta-analysis was conducted according to the recommendations of the Meta-Analysis of Observational Studies in Epidemiology (MOOSE) [16]. A computerized search was performed using Pubmed, Embase, Ovid medline and Cochrane Library from their dates of inception to December 2015 without language restriction. The search terms “ischemic or ischaemic” and “mitral valve” and “repair or replacement or annuloplasty” and “coronary artery bypass grafting” were entered as MeSH terms and keywords. The language of publication was restricted to English. We also reviewed the full text and references lists of all relevant review articles in detail. YW and XS independently undertook the literature search, screening of titles and abstracts. Any disagreement was resolved by consensus.","Articles were included if there is a direct comparison of repair versus replacement and all patients with IMR had CABG. The exclusion criteria were applied to select the final articles for the meta-analysis: (1) ischemic etiology in only a subset of the patients with outcomes not specifically provided (2) nonischemic dilated cardiomyopathy (3) beating heart procedures (4) concomitant surgical ventricular restoration (5) preoperative hemodynamic instability (6) lack of annuloplasty in > 20 % of the patients in the repair group (7) acute IMR.","All data were extracted independently by 2 investigators (Y.W., X.S.) according to the prespecified selection criteria, with disagreement resolved by consensus among all authors. The following data from each study were extracted: the last name of the first author, year of publication, study population, patients’ age and gender, comorbidities, cardiac function, severity of mitral regurgitation at baseline and follow-up period. Any disagreement was resolved by consensus.\nBased on the extracted data, the quality of the included studies was evaluated using the nine-item Newcastle-Ottawa Quality scale [17], a widely used tool for the quality assessment of non-randomized trials. The high-quality study was defined as a study with ≥6 scores.","The primary end points were operative mortality and late mortality (considered to be year after operation). Operative mortality was defined as death within 30 days after operation or in-hospital death. Secondary end points were MR recurrence 2+ or greater and reoperation at follow-up. The meta-analysis was performed using Review Manager (Revman, version 5.3 for windows, Oxford, England, Cochrane Collaboration) and Stata (version 11.0; StataCorp, College Station, TX). Hazard ratio (HR) with a 95 % confidence intervals (CIs), directly extracted from these included studies or indirectly calculated using the method of Tierney and colleagues [18] to assess the efficacy of the surgical intervention in each study. A summary of odds ratio (OR) and their corresponding 95 % CI were computed for each dichotomous outcome using either fixed-effects models or, in the presence of substantial heterogeneity (I2 > 50 %), random-effects models [19]. Statistical heterogeneity across studies was examined with Cochran’s Q test as well as the I2 statistics. Studies with an I2 statistics of <25 % were considered to have low heterogeneity, those with an I2 statistics of 25–50 % were considered to have moderate heterogeneity, and those with an I2 statistics of >50 % were considered to have a high degree of heterogeneity [20]. If there was high heterogeneity, the possible clinical and methodological factors for this were further explored. Potential sources of heterogeneity were investigated using sensitivity analyses and each study involved in the meta-analysis was excluded each time to reflect the influence of the individual data set on the pooled RRs.\nPublication bias was assessed using the Egger regression asymmetry test [21] and Begg adjusted rank correlation test [22]; a P value of less than 0.05 was considered representative of statistically significant publication bias. Meta-analysis results are displayed in forest plots. A p value < 0.05 was considered statistically significant."," Search results and study quality The literature search identified a total of 545 studies, which were published between 1965 and 2015. On the basis of title and abstracts, 34 articles were selected and reviewed in full. Eleven articles met the inclusion and exclusion criteria [9–13, 23–28] (Fig. 1). Of the included studies, there were ten retrospective observational studies [9–13, 23–27] and one prospective observational study [28]. All were nonrandomized studies. These studies included a total of 1807 patients, 1091 (60.4 %) of whom underwent repair and 716 (39.6 %) of whom underwent replacement. All patients had CABG. Patient characteristics and a summary of operative details are summarized in Tables 1 and 2, respectively. With the exception of the replacement patients being older in 2 of the studies, the two groups were similar in terms of hypertension (HTN), diabetes, atrial fibrillation (AF), left ventricular ejection fraction (LVEF) and the New York Heart Association (NYHA) class. Eight of the studies reported data on the type of prosthesis used for mitral valve replacement and preservation of the subvalvular apparatus. In half of the studies, the majority of patients received a bioprothesis valve. In addition, preservation of the subvalvular (either total or partial) apparatus were performed in the vast majority of mitral valve replacements.Fig. 1Flow chart of study selectionTable 1Key Features of Included StudiesStudySubjectsMean AgeMale (%)HTN (%)Diabetes (%)AF (%)NYHA III-IV (%)Mean LVEF (%)MR gradeFollow-up periodMVP + CABGMVR + CABGMVP + CABGMVR + CABGMVP + CABGMVR + CABGMVP + CABGMVR + CABGMVP + CABGMVR + CABGMVP + CABGMVR + CABGMVP + CABGMVR + CABGMVP + CABGMVR + CABGMVP + CABGMVR + CABGLorusso et al.24424466667369414136351213NRNR35352.8 ± 0.52.8 ± 0.546.5bmoLio et al.98286570d\n746181893532NRNR61713234NRNR45bmoLjubacev et al.3441NRNRNRNR85803256d\n2617NRNRNRNRNRNRIn-hospitalRoshanali et al.263157578377NRNRNRNRNRNRNRNR38403.6 ± 0.53.5 ± 0.540.2a moMaltais et al.302857070686371683426NRNR85913434NRNR4.2ayrsQiu et al.1121067172645672753032282653493535NRNR48.1amoMicovic et al.865261b\n62b\n7273746521152729645029362.7 ± 0.62.5 ± 0.732a moBonacchi et al.3618NRNRNRNRNRNRNRNRNRNRNRNR2727NRNR32a moSilberman et al.38146267d\n749350574557NRNR49cd\n32cd\n<25 %NRNR38amoMantovani et al.61416868675454512615NRNRNRNR45453.1 ± 0.83.3 ± 0.736.8a moReece et al.5456676941d\n68d\nNRNR2221NRNRNRNR4440NRNRIn-hospital\na = mean; b = median; c Percentage class IV; d\np < 0.05 between MVr and MVR\nAbbreviations: AF atrial fibrillation, LVEF left ventricular ejection fraction, CABG coronary artery bypass grafting, MR mitral regurgitation, HTN hypertension, MVP mitral valve repair, MVR mitral valve replacement, NR not reportedTable 2Operative characteristicsCPB time (min)ACC time (min)MVR prosthesis typeSubvalvular apparatus preservationMVP partial/suture annuloplasty (%)MVP ring annuloplasty (%)MVP undersizingMVPMVRMVPMVRMechanical %Bioprothesis %Anterior + Posterior (%)Posterior (%)None (%)Lorusso et al. [9]14514594944753482443010027 (26 mm) 52 (28 mm) 13 (30 mm) 6 (32 mm) 1 (34 mm) 1 (36 mm)Lio et al. [10] 156180107132366410000010037 % open ring 63 % closed ring 37 % rigid ring 63 % semi-rigid ringLjubacev et al. [24]1451529699NRNRNRNRNRNRNRNRRoshanali et al. [28]NRNRNRNR100010000NRNRNRMaltais et al. [13]NRNRNRNR4654NRNRNR89242 (24–28 mm) 36 (30–34 mm)Qiu et al. [26]13612910598386211890010030 mmMicovic et al. [11]NRNRNRNR100001000595Median 28 mm (range, 26–34 mm)Bonacchi et al. [12]NRNRNRNRNRNR010001783NRSilberman et al. [23]154184991111000NRNRNR010026 ± 1.2 mmMantovani et al. [25]1791731311227624010000100ModerateReece et al. [27]112132152171NRNRNRNRNR010028 mm males 26 mm females\nAbbreviations: MVP mitral valve repair, MVR mitral valve replacement, ACC time aortic cross-clamping time, CPB time, cardiopulmonary bypass time, NR not reported\nFlow chart of study selection\nKey Features of Included Studies\n\na = mean; b = median; c Percentage class IV; d\np < 0.05 between MVr and MVR\n\nAbbreviations: AF atrial fibrillation, LVEF left ventricular ejection fraction, CABG coronary artery bypass grafting, MR mitral regurgitation, HTN hypertension, MVP mitral valve repair, MVR mitral valve replacement, NR not reported\nOperative characteristics\n\nAbbreviations: MVP mitral valve repair, MVR mitral valve replacement, ACC time aortic cross-clamping time, CPB time, cardiopulmonary bypass time, NR not reported\nAll the eleven trials were assessed by the Newcastle-Ottawa Scale for quality assessment risk evaluation of adequacy of selection, comparability, and outcomes assessment for individual trials (Table 3). All studies included in our meta-analysis were of high-quality (had ≥ 6 scores).Table 3Study quality assessment using the Newcastle-Ottawa Scale for nonrandomized studiesSelectionOutcomeFirst author, year of publication (reference)Representativeness of exposed cohortSelection of nonexposed cohortAscertainment of exposureOutcome of interest absent at start of studyComparability (Based on design and analysis)Assessment of outcomeFollow-up long enough for outcomesto occurAdequacy of follow-upTotal scoreLorusso et al.111101117Lio et al.111111118Ljubacev et al.111101016Roshanali et al.111121119Maltais et al.111121119Qiu et al.111121119Micovic et al.111121119Bonacchi et al.111121119Silberman et al.111111118Mantovani et al.111121119Reece et al.111111017\nStudy quality assessment using the Newcastle-Ottawa Scale for nonrandomized studies\nThe literature search identified a total of 545 studies, which were published between 1965 and 2015. On the basis of title and abstracts, 34 articles were selected and reviewed in full. Eleven articles met the inclusion and exclusion criteria [9–13, 23–28] (Fig. 1). Of the included studies, there were ten retrospective observational studies [9–13, 23–27] and one prospective observational study [28]. All were nonrandomized studies. These studies included a total of 1807 patients, 1091 (60.4 %) of whom underwent repair and 716 (39.6 %) of whom underwent replacement. All patients had CABG. Patient characteristics and a summary of operative details are summarized in Tables 1 and 2, respectively. With the exception of the replacement patients being older in 2 of the studies, the two groups were similar in terms of hypertension (HTN), diabetes, atrial fibrillation (AF), left ventricular ejection fraction (LVEF) and the New York Heart Association (NYHA) class. Eight of the studies reported data on the type of prosthesis used for mitral valve replacement and preservation of the subvalvular apparatus. In half of the studies, the majority of patients received a bioprothesis valve. In addition, preservation of the subvalvular (either total or partial) apparatus were performed in the vast majority of mitral valve replacements.Fig. 1Flow chart of study selectionTable 1Key Features of Included StudiesStudySubjectsMean AgeMale (%)HTN (%)Diabetes (%)AF (%)NYHA III-IV (%)Mean LVEF (%)MR gradeFollow-up periodMVP + CABGMVR + CABGMVP + CABGMVR + CABGMVP + CABGMVR + CABGMVP + CABGMVR + CABGMVP + CABGMVR + CABGMVP + CABGMVR + CABGMVP + CABGMVR + CABGMVP + CABGMVR + CABGMVP + CABGMVR + CABGLorusso et al.24424466667369414136351213NRNR35352.8 ± 0.52.8 ± 0.546.5bmoLio et al.98286570d\n746181893532NRNR61713234NRNR45bmoLjubacev et al.3441NRNRNRNR85803256d\n2617NRNRNRNRNRNRIn-hospitalRoshanali et al.263157578377NRNRNRNRNRNRNRNR38403.6 ± 0.53.5 ± 0.540.2a moMaltais et al.302857070686371683426NRNR85913434NRNR4.2ayrsQiu et al.1121067172645672753032282653493535NRNR48.1amoMicovic et al.865261b\n62b\n7273746521152729645029362.7 ± 0.62.5 ± 0.732a moBonacchi et al.3618NRNRNRNRNRNRNRNRNRNRNRNR2727NRNR32a moSilberman et al.38146267d\n749350574557NRNR49cd\n32cd\n<25 %NRNR38amoMantovani et al.61416868675454512615NRNRNRNR45453.1 ± 0.83.3 ± 0.736.8a moReece et al.5456676941d\n68d\nNRNR2221NRNRNRNR4440NRNRIn-hospital\na = mean; b = median; c Percentage class IV; d\np < 0.05 between MVr and MVR\nAbbreviations: AF atrial fibrillation, LVEF left ventricular ejection fraction, CABG coronary artery bypass grafting, MR mitral regurgitation, HTN hypertension, MVP mitral valve repair, MVR mitral valve replacement, NR not reportedTable 2Operative characteristicsCPB time (min)ACC time (min)MVR prosthesis typeSubvalvular apparatus preservationMVP partial/suture annuloplasty (%)MVP ring annuloplasty (%)MVP undersizingMVPMVRMVPMVRMechanical %Bioprothesis %Anterior + Posterior (%)Posterior (%)None (%)Lorusso et al. [9]14514594944753482443010027 (26 mm) 52 (28 mm) 13 (30 mm) 6 (32 mm) 1 (34 mm) 1 (36 mm)Lio et al. [10] 156180107132366410000010037 % open ring 63 % closed ring 37 % rigid ring 63 % semi-rigid ringLjubacev et al. [24]1451529699NRNRNRNRNRNRNRNRRoshanali et al. [28]NRNRNRNR100010000NRNRNRMaltais et al. [13]NRNRNRNR4654NRNRNR89242 (24–28 mm) 36 (30–34 mm)Qiu et al. [26]13612910598386211890010030 mmMicovic et al. [11]NRNRNRNR100001000595Median 28 mm (range, 26–34 mm)Bonacchi et al. [12]NRNRNRNRNRNR010001783NRSilberman et al. [23]154184991111000NRNRNR010026 ± 1.2 mmMantovani et al. [25]1791731311227624010000100ModerateReece et al. [27]112132152171NRNRNRNRNR010028 mm males 26 mm females\nAbbreviations: MVP mitral valve repair, MVR mitral valve replacement, ACC time aortic cross-clamping time, CPB time, cardiopulmonary bypass time, NR not reported\nFlow chart of study selection\nKey Features of Included Studies\n\na = mean; b = median; c Percentage class IV; d\np < 0.05 between MVr and MVR\n\nAbbreviations: AF atrial fibrillation, LVEF left ventricular ejection fraction, CABG coronary artery bypass grafting, MR mitral regurgitation, HTN hypertension, MVP mitral valve repair, MVR mitral valve replacement, NR not reported\nOperative characteristics\n\nAbbreviations: MVP mitral valve repair, MVR mitral valve replacement, ACC time aortic cross-clamping time, CPB time, cardiopulmonary bypass time, NR not reported\nAll the eleven trials were assessed by the Newcastle-Ottawa Scale for quality assessment risk evaluation of adequacy of selection, comparability, and outcomes assessment for individual trials (Table 3). All studies included in our meta-analysis were of high-quality (had ≥ 6 scores).Table 3Study quality assessment using the Newcastle-Ottawa Scale for nonrandomized studiesSelectionOutcomeFirst author, year of publication (reference)Representativeness of exposed cohortSelection of nonexposed cohortAscertainment of exposureOutcome of interest absent at start of studyComparability (Based on design and analysis)Assessment of outcomeFollow-up long enough for outcomesto occurAdequacy of follow-upTotal scoreLorusso et al.111101117Lio et al.111111118Ljubacev et al.111101016Roshanali et al.111121119Maltais et al.111121119Qiu et al.111121119Micovic et al.111121119Bonacchi et al.111121119Silberman et al.111111118Mantovani et al.111121119Reece et al.111111017\nStudy quality assessment using the Newcastle-Ottawa Scale for nonrandomized studies\n Peri-operative mortality Ten observational studies involving a total of 1750 patients reported operative mortality. The odds ratios in the study ranged from 0.16 to 2.32 (Fig. 2). The summary odds ratio was 0.65 (95 % CI, 0.43-1.00), P = 0.05, indicating there was a reduced peri-operative mortality trend towards repair, but no statistical significance reached. In assessing potential heterogeneity across the studies, I2 = 0 %, and no publication bias was found either from the Egger’s test (P = 0.83) or the Begg’s test (P = 0.68).Fig. 2Mitral valve repair versus mitral valve replacement on peri-operative mortality\nMitral valve repair versus mitral valve replacement on peri-operative mortality\nTen observational studies involving a total of 1750 patients reported operative mortality. The odds ratios in the study ranged from 0.16 to 2.32 (Fig. 2). The summary odds ratio was 0.65 (95 % CI, 0.43-1.00), P = 0.05, indicating there was a reduced peri-operative mortality trend towards repair, but no statistical significance reached. In assessing potential heterogeneity across the studies, I2 = 0 %, and no publication bias was found either from the Egger’s test (P = 0.83) or the Begg’s test (P = 0.68).Fig. 2Mitral valve repair versus mitral valve replacement on peri-operative mortality\nMitral valve repair versus mitral valve replacement on peri-operative mortality\n Late mortality A total of nine studies (1622 Patients) reported late mortality (Fig. 3). The overall hazard ratio was 0.87 (95 % CI, 0.67-1.14; P = 0.31), suggesting late mortality was not significantly reduced following repair. Further, heterogeneity was moderate (I2 = 30 %). It was noted that ten of the studies included patients with different degrees of regurgitation and left ventricular dysfunction, with exception of one study [23], all of the patients included in this study had severely impaired LV function (ejection fraction <25 %) and severe ischemic MR undergoing CABG. Severely decreased left ventricular function and severe IMR could have the potential pathophysiological effect on the mortality rates of those patients. Hence, sensitivity analysis was conducted to only include studies in which not all of the patients had severe ischemic MR and severely impaired LV function undergoing CABG. Restricting analysis to these studies had no significant impact on the reduction of late mortality following repair (the summary hazard ratio, 1.03; 95 % CI, 0.90-1.17; P = 0.66). Whereas, heterogeneity suggested by I2 was significantly reduced to 0 %, indicating no variability exists among the rest studies. Further exclusion of any single study did not significantly reduce the heterogeneity. In addition, our study included 10 retrospective studies and 1 prospective study. The different study designs may influence outcomes of meta-analysis. Therefore, sensitivity analysis was performed to only include retrospective studies. Restricting analysis to these studies did not significantly impact on the result for late mortality (HR, 0.86; 95 % CI, 0.64-1.14; P = 0.30; I2 = 38 %).Fig. 3Mitral valve repair versus mitral valve replacement on late mortality\nMitral valve repair versus mitral valve replacement on late mortality\nA total of nine studies (1622 Patients) reported late mortality (Fig. 3). The overall hazard ratio was 0.87 (95 % CI, 0.67-1.14; P = 0.31), suggesting late mortality was not significantly reduced following repair. Further, heterogeneity was moderate (I2 = 30 %). It was noted that ten of the studies included patients with different degrees of regurgitation and left ventricular dysfunction, with exception of one study [23], all of the patients included in this study had severely impaired LV function (ejection fraction <25 %) and severe ischemic MR undergoing CABG. Severely decreased left ventricular function and severe IMR could have the potential pathophysiological effect on the mortality rates of those patients. Hence, sensitivity analysis was conducted to only include studies in which not all of the patients had severe ischemic MR and severely impaired LV function undergoing CABG. Restricting analysis to these studies had no significant impact on the reduction of late mortality following repair (the summary hazard ratio, 1.03; 95 % CI, 0.90-1.17; P = 0.66). Whereas, heterogeneity suggested by I2 was significantly reduced to 0 %, indicating no variability exists among the rest studies. Further exclusion of any single study did not significantly reduce the heterogeneity. In addition, our study included 10 retrospective studies and 1 prospective study. The different study designs may influence outcomes of meta-analysis. Therefore, sensitivity analysis was performed to only include retrospective studies. Restricting analysis to these studies did not significantly impact on the result for late mortality (HR, 0.86; 95 % CI, 0.64-1.14; P = 0.30; I2 = 38 %).Fig. 3Mitral valve repair versus mitral valve replacement on late mortality\nMitral valve repair versus mitral valve replacement on late mortality\n Mitral valve reoperation Reoperation due to such as MV regurgitation, thromboembolism and prosthetic endocarditis was reported in five studies involving a total of 845 patients. The combined odds ratio was 1.47, suggesting the trend went towards the preference of replacement. Nevertheless, no significant differences were reached between the two surgical approaches (95 % CI, 0.90-2.38; I2 = 0 %; P = 0.12) (Fig. 4).Fig. 4Mitral valve repair versus mitral valve replacement on reoperation\nMitral valve repair versus mitral valve replacement on reoperation\nReoperation due to such as MV regurgitation, thromboembolism and prosthetic endocarditis was reported in five studies involving a total of 845 patients. The combined odds ratio was 1.47, suggesting the trend went towards the preference of replacement. Nevertheless, no significant differences were reached between the two surgical approaches (95 % CI, 0.90-2.38; I2 = 0 %; P = 0.12) (Fig. 4).Fig. 4Mitral valve repair versus mitral valve replacement on reoperation\nMitral valve repair versus mitral valve replacement on reoperation\n Recurrence of MR Five studies involving a total of 837 patients provided data regarding recurrence of MR during the follow-up. The MVP + CABG group was associated with a significantly increased recurrence rate of MR (OR, 5.41; 95 % CI: 3.12–9.38; P < 0.001) with low heterogeneity among those studies (I2 = 10 %) (Fig. 5). Sensitivity analysis was also performed to only include retrospective studies. Restricting analysis to these studies did not significantly impact on the result for recurrence of MR (OR, 5.97; 95 % CI, 3.36-10.58; P < 0.001; I2 = 0 %).Fig. 5Mitral valve repair versus mitral valve replacement on recurrence of mitral valve regurgitation\nMitral valve repair versus mitral valve replacement on recurrence of mitral valve regurgitation\nFive studies involving a total of 837 patients provided data regarding recurrence of MR during the follow-up. The MVP + CABG group was associated with a significantly increased recurrence rate of MR (OR, 5.41; 95 % CI: 3.12–9.38; P < 0.001) with low heterogeneity among those studies (I2 = 10 %) (Fig. 5). Sensitivity analysis was also performed to only include retrospective studies. Restricting analysis to these studies did not significantly impact on the result for recurrence of MR (OR, 5.97; 95 % CI, 3.36-10.58; P < 0.001; I2 = 0 %).Fig. 5Mitral valve repair versus mitral valve replacement on recurrence of mitral valve regurgitation\nMitral valve repair versus mitral valve replacement on recurrence of mitral valve regurgitation","The literature search identified a total of 545 studies, which were published between 1965 and 2015. On the basis of title and abstracts, 34 articles were selected and reviewed in full. Eleven articles met the inclusion and exclusion criteria [9–13, 23–28] (Fig. 1). Of the included studies, there were ten retrospective observational studies [9–13, 23–27] and one prospective observational study [28]. All were nonrandomized studies. These studies included a total of 1807 patients, 1091 (60.4 %) of whom underwent repair and 716 (39.6 %) of whom underwent replacement. All patients had CABG. Patient characteristics and a summary of operative details are summarized in Tables 1 and 2, respectively. With the exception of the replacement patients being older in 2 of the studies, the two groups were similar in terms of hypertension (HTN), diabetes, atrial fibrillation (AF), left ventricular ejection fraction (LVEF) and the New York Heart Association (NYHA) class. Eight of the studies reported data on the type of prosthesis used for mitral valve replacement and preservation of the subvalvular apparatus. In half of the studies, the majority of patients received a bioprothesis valve. In addition, preservation of the subvalvular (either total or partial) apparatus were performed in the vast majority of mitral valve replacements.Fig. 1Flow chart of study selectionTable 1Key Features of Included StudiesStudySubjectsMean AgeMale (%)HTN (%)Diabetes (%)AF (%)NYHA III-IV (%)Mean LVEF (%)MR gradeFollow-up periodMVP + CABGMVR + CABGMVP + CABGMVR + CABGMVP + CABGMVR + CABGMVP + CABGMVR + CABGMVP + CABGMVR + CABGMVP + CABGMVR + CABGMVP + CABGMVR + CABGMVP + CABGMVR + CABGMVP + CABGMVR + CABGLorusso et al.24424466667369414136351213NRNR35352.8 ± 0.52.8 ± 0.546.5bmoLio et al.98286570d\n746181893532NRNR61713234NRNR45bmoLjubacev et al.3441NRNRNRNR85803256d\n2617NRNRNRNRNRNRIn-hospitalRoshanali et al.263157578377NRNRNRNRNRNRNRNR38403.6 ± 0.53.5 ± 0.540.2a moMaltais et al.302857070686371683426NRNR85913434NRNR4.2ayrsQiu et al.1121067172645672753032282653493535NRNR48.1amoMicovic et al.865261b\n62b\n7273746521152729645029362.7 ± 0.62.5 ± 0.732a moBonacchi et al.3618NRNRNRNRNRNRNRNRNRNRNRNR2727NRNR32a moSilberman et al.38146267d\n749350574557NRNR49cd\n32cd\n<25 %NRNR38amoMantovani et al.61416868675454512615NRNRNRNR45453.1 ± 0.83.3 ± 0.736.8a moReece et al.5456676941d\n68d\nNRNR2221NRNRNRNR4440NRNRIn-hospital\na = mean; b = median; c Percentage class IV; d\np < 0.05 between MVr and MVR\nAbbreviations: AF atrial fibrillation, LVEF left ventricular ejection fraction, CABG coronary artery bypass grafting, MR mitral regurgitation, HTN hypertension, MVP mitral valve repair, MVR mitral valve replacement, NR not reportedTable 2Operative characteristicsCPB time (min)ACC time (min)MVR prosthesis typeSubvalvular apparatus preservationMVP partial/suture annuloplasty (%)MVP ring annuloplasty (%)MVP undersizingMVPMVRMVPMVRMechanical %Bioprothesis %Anterior + Posterior (%)Posterior (%)None (%)Lorusso et al. [9]14514594944753482443010027 (26 mm) 52 (28 mm) 13 (30 mm) 6 (32 mm) 1 (34 mm) 1 (36 mm)Lio et al. [10] 156180107132366410000010037 % open ring 63 % closed ring 37 % rigid ring 63 % semi-rigid ringLjubacev et al. [24]1451529699NRNRNRNRNRNRNRNRRoshanali et al. [28]NRNRNRNR100010000NRNRNRMaltais et al. [13]NRNRNRNR4654NRNRNR89242 (24–28 mm) 36 (30–34 mm)Qiu et al. [26]13612910598386211890010030 mmMicovic et al. [11]NRNRNRNR100001000595Median 28 mm (range, 26–34 mm)Bonacchi et al. [12]NRNRNRNRNRNR010001783NRSilberman et al. [23]154184991111000NRNRNR010026 ± 1.2 mmMantovani et al. [25]1791731311227624010000100ModerateReece et al. [27]112132152171NRNRNRNRNR010028 mm males 26 mm females\nAbbreviations: MVP mitral valve repair, MVR mitral valve replacement, ACC time aortic cross-clamping time, CPB time, cardiopulmonary bypass time, NR not reported\nFlow chart of study selection\nKey Features of Included Studies\n\na = mean; b = median; c Percentage class IV; d\np < 0.05 between MVr and MVR\n\nAbbreviations: AF atrial fibrillation, LVEF left ventricular ejection fraction, CABG coronary artery bypass grafting, MR mitral regurgitation, HTN hypertension, MVP mitral valve repair, MVR mitral valve replacement, NR not reported\nOperative characteristics\n\nAbbreviations: MVP mitral valve repair, MVR mitral valve replacement, ACC time aortic cross-clamping time, CPB time, cardiopulmonary bypass time, NR not reported\nAll the eleven trials were assessed by the Newcastle-Ottawa Scale for quality assessment risk evaluation of adequacy of selection, comparability, and outcomes assessment for individual trials (Table 3). All studies included in our meta-analysis were of high-quality (had ≥ 6 scores).Table 3Study quality assessment using the Newcastle-Ottawa Scale for nonrandomized studiesSelectionOutcomeFirst author, year of publication (reference)Representativeness of exposed cohortSelection of nonexposed cohortAscertainment of exposureOutcome of interest absent at start of studyComparability (Based on design and analysis)Assessment of outcomeFollow-up long enough for outcomesto occurAdequacy of follow-upTotal scoreLorusso et al.111101117Lio et al.111111118Ljubacev et al.111101016Roshanali et al.111121119Maltais et al.111121119Qiu et al.111121119Micovic et al.111121119Bonacchi et al.111121119Silberman et al.111111118Mantovani et al.111121119Reece et al.111111017\nStudy quality assessment using the Newcastle-Ottawa Scale for nonrandomized studies","Ten observational studies involving a total of 1750 patients reported operative mortality. The odds ratios in the study ranged from 0.16 to 2.32 (Fig. 2). The summary odds ratio was 0.65 (95 % CI, 0.43-1.00), P = 0.05, indicating there was a reduced peri-operative mortality trend towards repair, but no statistical significance reached. In assessing potential heterogeneity across the studies, I2 = 0 %, and no publication bias was found either from the Egger’s test (P = 0.83) or the Begg’s test (P = 0.68).Fig. 2Mitral valve repair versus mitral valve replacement on peri-operative mortality\nMitral valve repair versus mitral valve replacement on peri-operative mortality","A total of nine studies (1622 Patients) reported late mortality (Fig. 3). The overall hazard ratio was 0.87 (95 % CI, 0.67-1.14; P = 0.31), suggesting late mortality was not significantly reduced following repair. Further, heterogeneity was moderate (I2 = 30 %). It was noted that ten of the studies included patients with different degrees of regurgitation and left ventricular dysfunction, with exception of one study [23], all of the patients included in this study had severely impaired LV function (ejection fraction <25 %) and severe ischemic MR undergoing CABG. Severely decreased left ventricular function and severe IMR could have the potential pathophysiological effect on the mortality rates of those patients. Hence, sensitivity analysis was conducted to only include studies in which not all of the patients had severe ischemic MR and severely impaired LV function undergoing CABG. Restricting analysis to these studies had no significant impact on the reduction of late mortality following repair (the summary hazard ratio, 1.03; 95 % CI, 0.90-1.17; P = 0.66). Whereas, heterogeneity suggested by I2 was significantly reduced to 0 %, indicating no variability exists among the rest studies. Further exclusion of any single study did not significantly reduce the heterogeneity. In addition, our study included 10 retrospective studies and 1 prospective study. The different study designs may influence outcomes of meta-analysis. Therefore, sensitivity analysis was performed to only include retrospective studies. Restricting analysis to these studies did not significantly impact on the result for late mortality (HR, 0.86; 95 % CI, 0.64-1.14; P = 0.30; I2 = 38 %).Fig. 3Mitral valve repair versus mitral valve replacement on late mortality\nMitral valve repair versus mitral valve replacement on late mortality","Reoperation due to such as MV regurgitation, thromboembolism and prosthetic endocarditis was reported in five studies involving a total of 845 patients. The combined odds ratio was 1.47, suggesting the trend went towards the preference of replacement. Nevertheless, no significant differences were reached between the two surgical approaches (95 % CI, 0.90-2.38; I2 = 0 %; P = 0.12) (Fig. 4).Fig. 4Mitral valve repair versus mitral valve replacement on reoperation\nMitral valve repair versus mitral valve replacement on reoperation","Five studies involving a total of 837 patients provided data regarding recurrence of MR during the follow-up. The MVP + CABG group was associated with a significantly increased recurrence rate of MR (OR, 5.41; 95 % CI: 3.12–9.38; P < 0.001) with low heterogeneity among those studies (I2 = 10 %) (Fig. 5). Sensitivity analysis was also performed to only include retrospective studies. Restricting analysis to these studies did not significantly impact on the result for recurrence of MR (OR, 5.97; 95 % CI, 3.36-10.58; P < 0.001; I2 = 0 %).Fig. 5Mitral valve repair versus mitral valve replacement on recurrence of mitral valve regurgitation\nMitral valve repair versus mitral valve replacement on recurrence of mitral valve regurgitation","In our meta-analysis of eleven studies, which included patients undergoing repair or replacement electively with CABG surgery, no differences were found regarding peri-operative mortality and long-term survival. Mitral valve replacement was associated with lower incidence of mitral regurgitation in patients with IMR during CABG. The Society of Thoracic Surgeons reports MVP + CABG group had approximately 5 % (4.8 % in-hospital mortality and 5.3 % operative mortality) nationwide mortality rates in contrast with 8 % (7.8 % in-hospital mortality and 8.5 % operative mortality) for MVR + CABG group [29].\nModerate and severe recurrent MR after a restrictive annuloplasty ring, occurred early and affected a substantial proportion of patients by 2 years [30]. In the present study, the main disadvantage of MVP + CABG group is recurrence of MR compared to MVR + CABG group. Although MR recurrence was common, mitral valve reoperation was not. Similar reoperation rate was found between both groups, which suggested that not all patients with MR recurrence 2+ or greater needed reoperation. There were several possible explanations. IMR after annuloplasty might be considered inconsequential compared with the underlying myocardial disease, or surgeons might be hesitant to perform a second or third cardiac operation in these elderly, high-risk patients [31]. Many patients with recurrent MR were just too sick or too old or both to even consider reoperating on them.\nA case-matched study found that replacement was associated with lower incidence of valve-related complications than was repair and both mitral valve procedures showed no significant difference in LV function at follow-up [32]. However, replacement had greater thromboembolic and ischemic stroke rates than repair despite anticoagulant therapy [33]. Although mitral valve replacement can sufficiently correct regurgitation, the structural integrity of the mitral valve is usually compromised after replacement, leading to a continuous damage on the left ventricular tethered loop, which results in adverse effects on left ventricular contraction and poor prognosis [8]. Therefore, individualized consideration should be given to the two surgical procedures.\nTo date, there have been no RCTs that compared the clinical outcomes of the two surgical management particularly in patients with chronic IMR during CABG. To our knowledge, our report is the first meta-analysis comparing short-term and long-term outcomes of two mitral valve procedures specifically on patients with chronic IMR undergoing concomitant CABG. We selected studies for this meta-analysis with rigorous inclusion and exclusion criteria. All the patients in the studies underwent concomitant CABG, which ensures homogeneity of IMR patients and facilitates comparisons between trials. In addition, patients with acute IMR due to ruptured papillary muscles were excluded in our study, thus the outcomes of this meta-analysis not only truly reflect the surgical intervention of chronic IMR but also avoid biasing the results toward worsening the replacement group. By excluding articles that had > 20 % lack of annuloplasty ring, we have made the comparison between the two mitral valves surgeries more powerful. Therefore, the results of our study truly reflect the surgical management of patients with IMR simultaneous to CABG.\n Limitations Our meta-analysis has several limitations. Firstly, this study was based on observational, retrospective studies with inherent bias of such study designs. The publications included in this meta-analysis were relatively small and nonrandomized studies. Secondly, changes in NYHA class, LVEF and left ventricular reversal remodeling were too scarcely reported in the included studies to enable meta-analysis. Eight out of eleven studies included in our meta-analysis reported data on the subvalvular apparatus preservation in mitral valve replacement yet with lack of uniform preservation of both the anterior and posterior leaflets. The other three studies had no description regarding subvalvular apparatus preservation. Thirdly, potential confounding factors such as preoperative risk evaluation (STS score i.e.), mitral valve more suitable for repair, age, cause of mitral regurgitation (ischemia, fibrosis, ventricular remodeling), EF and complexity of revascularization were not considered or adjusted in some of the studies included in our meta-analysis. Therefore, the superiority of repair over replacement may be affected by this and other factors that are not possible to be revealed with meta-analysis of observational trials. Well-designed RCTs are required to further verify the conclusion. Another limitation of our report is the fact that follow-up periods were heterogeneous between some studies with different use of mean and median durations of follow-up. Therefore, subgroup analysis could not be performed statistically.\nOur meta-analysis has several limitations. Firstly, this study was based on observational, retrospective studies with inherent bias of such study designs. The publications included in this meta-analysis were relatively small and nonrandomized studies. Secondly, changes in NYHA class, LVEF and left ventricular reversal remodeling were too scarcely reported in the included studies to enable meta-analysis. Eight out of eleven studies included in our meta-analysis reported data on the subvalvular apparatus preservation in mitral valve replacement yet with lack of uniform preservation of both the anterior and posterior leaflets. The other three studies had no description regarding subvalvular apparatus preservation. Thirdly, potential confounding factors such as preoperative risk evaluation (STS score i.e.), mitral valve more suitable for repair, age, cause of mitral regurgitation (ischemia, fibrosis, ventricular remodeling), EF and complexity of revascularization were not considered or adjusted in some of the studies included in our meta-analysis. Therefore, the superiority of repair over replacement may be affected by this and other factors that are not possible to be revealed with meta-analysis of observational trials. Well-designed RCTs are required to further verify the conclusion. Another limitation of our report is the fact that follow-up periods were heterogeneous between some studies with different use of mean and median durations of follow-up. Therefore, subgroup analysis could not be performed statistically.","Our meta-analysis has several limitations. Firstly, this study was based on observational, retrospective studies with inherent bias of such study designs. The publications included in this meta-analysis were relatively small and nonrandomized studies. Secondly, changes in NYHA class, LVEF and left ventricular reversal remodeling were too scarcely reported in the included studies to enable meta-analysis. Eight out of eleven studies included in our meta-analysis reported data on the subvalvular apparatus preservation in mitral valve replacement yet with lack of uniform preservation of both the anterior and posterior leaflets. The other three studies had no description regarding subvalvular apparatus preservation. Thirdly, potential confounding factors such as preoperative risk evaluation (STS score i.e.), mitral valve more suitable for repair, age, cause of mitral regurgitation (ischemia, fibrosis, ventricular remodeling), EF and complexity of revascularization were not considered or adjusted in some of the studies included in our meta-analysis. Therefore, the superiority of repair over replacement may be affected by this and other factors that are not possible to be revealed with meta-analysis of observational trials. Well-designed RCTs are required to further verify the conclusion. Another limitation of our report is the fact that follow-up periods were heterogeneous between some studies with different use of mean and median durations of follow-up. Therefore, subgroup analysis could not be performed statistically.","In patients with chronic ischemic mitral regurgitation during CABG, mitral valve replacement is associated with lower recurrence of regurgitation. No differences were found regarding survival and reoperation rates."],"string":"[\n \"Chronic ischemic mitral regurgitation (IMR) is a frequent and important complication after myocardial infarction. Its pathophysiologic mechanisms account for remodeling of segmental/global left ventricle (LV) inducing papillary muscle displacement and leaflet tethering [1]. The presence of IMR is independently associated with mortality and morbidity after myocardial infarction [2].\\nGiven the severity of IMR, surgery performed for IMR ranges from coronary artery bypass grafting (CABG) alone to both CABG and mitral valve surgery [3, 4]. Two randomized trials indicated that repair was associated with a reduced prevalence of mitral regurgitation but did not show a clinically meaningful advantage of adding mitral valve repair to CABG [5, 6]. In addition, when compared with replacement, previous meta-analyses concluded that repair is associated with lower operative mortality but higher recurrence of regurgitation in patients with ischemic mitral regurgitation, with or without CABG [7, 8]. For patients with chronic IMR undergoing combined CABG, the best surgical treatment is still controversial. Some studies support replacement [9, 10], others support repair [11, 12], and others showed similar survival for the two procedures [13]. Current guidelines recommend mitral valve surgery for severe IMR, but do not demonstrate a specific type of procedure [14, 15]. Numerous non-randomized studies have been published comparing the clinical outcomes between MVP + CABG and MVR + CABG for IMR. However, there is still no systematic and quantitative assessment of accumulated literature on this topic. Meta-analysis is a powerful tool to provide meaningful comparison of short and long-term outcomes of these procedures. The present meta-analysis aimed to assess the clinical outcomes of patients who underwent mitral valve surgery and CABG for chronic IMR.\",\n \" Search strategy This meta-analysis was conducted according to the recommendations of the Meta-Analysis of Observational Studies in Epidemiology (MOOSE) [16]. A computerized search was performed using Pubmed, Embase, Ovid medline and Cochrane Library from their dates of inception to December 2015 without language restriction. The search terms “ischemic or ischaemic” and “mitral valve” and “repair or replacement or annuloplasty” and “coronary artery bypass grafting” were entered as MeSH terms and keywords. The language of publication was restricted to English. We also reviewed the full text and references lists of all relevant review articles in detail. YW and XS independently undertook the literature search, screening of titles and abstracts. Any disagreement was resolved by consensus.\\nThis meta-analysis was conducted according to the recommendations of the Meta-Analysis of Observational Studies in Epidemiology (MOOSE) [16]. A computerized search was performed using Pubmed, Embase, Ovid medline and Cochrane Library from their dates of inception to December 2015 without language restriction. The search terms “ischemic or ischaemic” and “mitral valve” and “repair or replacement or annuloplasty” and “coronary artery bypass grafting” were entered as MeSH terms and keywords. The language of publication was restricted to English. We also reviewed the full text and references lists of all relevant review articles in detail. YW and XS independently undertook the literature search, screening of titles and abstracts. Any disagreement was resolved by consensus.\\n Study selection Articles were included if there is a direct comparison of repair versus replacement and all patients with IMR had CABG. The exclusion criteria were applied to select the final articles for the meta-analysis: (1) ischemic etiology in only a subset of the patients with outcomes not specifically provided (2) nonischemic dilated cardiomyopathy (3) beating heart procedures (4) concomitant surgical ventricular restoration (5) preoperative hemodynamic instability (6) lack of annuloplasty in > 20 % of the patients in the repair group (7) acute IMR.\\nArticles were included if there is a direct comparison of repair versus replacement and all patients with IMR had CABG. The exclusion criteria were applied to select the final articles for the meta-analysis: (1) ischemic etiology in only a subset of the patients with outcomes not specifically provided (2) nonischemic dilated cardiomyopathy (3) beating heart procedures (4) concomitant surgical ventricular restoration (5) preoperative hemodynamic instability (6) lack of annuloplasty in > 20 % of the patients in the repair group (7) acute IMR.\\n Data extraction and quality assessment All data were extracted independently by 2 investigators (Y.W., X.S.) according to the prespecified selection criteria, with disagreement resolved by consensus among all authors. The following data from each study were extracted: the last name of the first author, year of publication, study population, patients’ age and gender, comorbidities, cardiac function, severity of mitral regurgitation at baseline and follow-up period. Any disagreement was resolved by consensus.\\nBased on the extracted data, the quality of the included studies was evaluated using the nine-item Newcastle-Ottawa Quality scale [17], a widely used tool for the quality assessment of non-randomized trials. The high-quality study was defined as a study with ≥6 scores.\\nAll data were extracted independently by 2 investigators (Y.W., X.S.) according to the prespecified selection criteria, with disagreement resolved by consensus among all authors. The following data from each study were extracted: the last name of the first author, year of publication, study population, patients’ age and gender, comorbidities, cardiac function, severity of mitral regurgitation at baseline and follow-up period. Any disagreement was resolved by consensus.\\nBased on the extracted data, the quality of the included studies was evaluated using the nine-item Newcastle-Ottawa Quality scale [17], a widely used tool for the quality assessment of non-randomized trials. The high-quality study was defined as a study with ≥6 scores.\\n Statistical analysis The primary end points were operative mortality and late mortality (considered to be year after operation). Operative mortality was defined as death within 30 days after operation or in-hospital death. Secondary end points were MR recurrence 2+ or greater and reoperation at follow-up. The meta-analysis was performed using Review Manager (Revman, version 5.3 for windows, Oxford, England, Cochrane Collaboration) and Stata (version 11.0; StataCorp, College Station, TX). Hazard ratio (HR) with a 95 % confidence intervals (CIs), directly extracted from these included studies or indirectly calculated using the method of Tierney and colleagues [18] to assess the efficacy of the surgical intervention in each study. A summary of odds ratio (OR) and their corresponding 95 % CI were computed for each dichotomous outcome using either fixed-effects models or, in the presence of substantial heterogeneity (I2 > 50 %), random-effects models [19]. Statistical heterogeneity across studies was examined with Cochran’s Q test as well as the I2 statistics. Studies with an I2 statistics of <25 % were considered to have low heterogeneity, those with an I2 statistics of 25–50 % were considered to have moderate heterogeneity, and those with an I2 statistics of >50 % were considered to have a high degree of heterogeneity [20]. If there was high heterogeneity, the possible clinical and methodological factors for this were further explored. Potential sources of heterogeneity were investigated using sensitivity analyses and each study involved in the meta-analysis was excluded each time to reflect the influence of the individual data set on the pooled RRs.\\nPublication bias was assessed using the Egger regression asymmetry test [21] and Begg adjusted rank correlation test [22]; a P value of less than 0.05 was considered representative of statistically significant publication bias. Meta-analysis results are displayed in forest plots. A p value < 0.05 was considered statistically significant.\\nThe primary end points were operative mortality and late mortality (considered to be year after operation). Operative mortality was defined as death within 30 days after operation or in-hospital death. Secondary end points were MR recurrence 2+ or greater and reoperation at follow-up. The meta-analysis was performed using Review Manager (Revman, version 5.3 for windows, Oxford, England, Cochrane Collaboration) and Stata (version 11.0; StataCorp, College Station, TX). Hazard ratio (HR) with a 95 % confidence intervals (CIs), directly extracted from these included studies or indirectly calculated using the method of Tierney and colleagues [18] to assess the efficacy of the surgical intervention in each study. A summary of odds ratio (OR) and their corresponding 95 % CI were computed for each dichotomous outcome using either fixed-effects models or, in the presence of substantial heterogeneity (I2 > 50 %), random-effects models [19]. Statistical heterogeneity across studies was examined with Cochran’s Q test as well as the I2 statistics. Studies with an I2 statistics of <25 % were considered to have low heterogeneity, those with an I2 statistics of 25–50 % were considered to have moderate heterogeneity, and those with an I2 statistics of >50 % were considered to have a high degree of heterogeneity [20]. If there was high heterogeneity, the possible clinical and methodological factors for this were further explored. Potential sources of heterogeneity were investigated using sensitivity analyses and each study involved in the meta-analysis was excluded each time to reflect the influence of the individual data set on the pooled RRs.\\nPublication bias was assessed using the Egger regression asymmetry test [21] and Begg adjusted rank correlation test [22]; a P value of less than 0.05 was considered representative of statistically significant publication bias. Meta-analysis results are displayed in forest plots. A p value < 0.05 was considered statistically significant.\",\n \"This meta-analysis was conducted according to the recommendations of the Meta-Analysis of Observational Studies in Epidemiology (MOOSE) [16]. A computerized search was performed using Pubmed, Embase, Ovid medline and Cochrane Library from their dates of inception to December 2015 without language restriction. The search terms “ischemic or ischaemic” and “mitral valve” and “repair or replacement or annuloplasty” and “coronary artery bypass grafting” were entered as MeSH terms and keywords. The language of publication was restricted to English. We also reviewed the full text and references lists of all relevant review articles in detail. YW and XS independently undertook the literature search, screening of titles and abstracts. Any disagreement was resolved by consensus.\",\n \"Articles were included if there is a direct comparison of repair versus replacement and all patients with IMR had CABG. The exclusion criteria were applied to select the final articles for the meta-analysis: (1) ischemic etiology in only a subset of the patients with outcomes not specifically provided (2) nonischemic dilated cardiomyopathy (3) beating heart procedures (4) concomitant surgical ventricular restoration (5) preoperative hemodynamic instability (6) lack of annuloplasty in > 20 % of the patients in the repair group (7) acute IMR.\",\n \"All data were extracted independently by 2 investigators (Y.W., X.S.) according to the prespecified selection criteria, with disagreement resolved by consensus among all authors. The following data from each study were extracted: the last name of the first author, year of publication, study population, patients’ age and gender, comorbidities, cardiac function, severity of mitral regurgitation at baseline and follow-up period. Any disagreement was resolved by consensus.\\nBased on the extracted data, the quality of the included studies was evaluated using the nine-item Newcastle-Ottawa Quality scale [17], a widely used tool for the quality assessment of non-randomized trials. The high-quality study was defined as a study with ≥6 scores.\",\n \"The primary end points were operative mortality and late mortality (considered to be year after operation). Operative mortality was defined as death within 30 days after operation or in-hospital death. Secondary end points were MR recurrence 2+ or greater and reoperation at follow-up. The meta-analysis was performed using Review Manager (Revman, version 5.3 for windows, Oxford, England, Cochrane Collaboration) and Stata (version 11.0; StataCorp, College Station, TX). Hazard ratio (HR) with a 95 % confidence intervals (CIs), directly extracted from these included studies or indirectly calculated using the method of Tierney and colleagues [18] to assess the efficacy of the surgical intervention in each study. A summary of odds ratio (OR) and their corresponding 95 % CI were computed for each dichotomous outcome using either fixed-effects models or, in the presence of substantial heterogeneity (I2 > 50 %), random-effects models [19]. Statistical heterogeneity across studies was examined with Cochran’s Q test as well as the I2 statistics. Studies with an I2 statistics of <25 % were considered to have low heterogeneity, those with an I2 statistics of 25–50 % were considered to have moderate heterogeneity, and those with an I2 statistics of >50 % were considered to have a high degree of heterogeneity [20]. If there was high heterogeneity, the possible clinical and methodological factors for this were further explored. Potential sources of heterogeneity were investigated using sensitivity analyses and each study involved in the meta-analysis was excluded each time to reflect the influence of the individual data set on the pooled RRs.\\nPublication bias was assessed using the Egger regression asymmetry test [21] and Begg adjusted rank correlation test [22]; a P value of less than 0.05 was considered representative of statistically significant publication bias. Meta-analysis results are displayed in forest plots. A p value < 0.05 was considered statistically significant.\",\n \" Search results and study quality The literature search identified a total of 545 studies, which were published between 1965 and 2015. On the basis of title and abstracts, 34 articles were selected and reviewed in full. Eleven articles met the inclusion and exclusion criteria [9–13, 23–28] (Fig. 1). Of the included studies, there were ten retrospective observational studies [9–13, 23–27] and one prospective observational study [28]. All were nonrandomized studies. These studies included a total of 1807 patients, 1091 (60.4 %) of whom underwent repair and 716 (39.6 %) of whom underwent replacement. All patients had CABG. Patient characteristics and a summary of operative details are summarized in Tables 1 and 2, respectively. With the exception of the replacement patients being older in 2 of the studies, the two groups were similar in terms of hypertension (HTN), diabetes, atrial fibrillation (AF), left ventricular ejection fraction (LVEF) and the New York Heart Association (NYHA) class. Eight of the studies reported data on the type of prosthesis used for mitral valve replacement and preservation of the subvalvular apparatus. In half of the studies, the majority of patients received a bioprothesis valve. In addition, preservation of the subvalvular (either total or partial) apparatus were performed in the vast majority of mitral valve replacements.Fig. 1Flow chart of study selectionTable 1Key Features of Included StudiesStudySubjectsMean AgeMale (%)HTN (%)Diabetes (%)AF (%)NYHA III-IV (%)Mean LVEF (%)MR gradeFollow-up periodMVP + CABGMVR + CABGMVP + CABGMVR + CABGMVP + CABGMVR + CABGMVP + CABGMVR + CABGMVP + CABGMVR + CABGMVP + CABGMVR + CABGMVP + CABGMVR + CABGMVP + CABGMVR + CABGMVP + CABGMVR + CABGLorusso et al.24424466667369414136351213NRNR35352.8 ± 0.52.8 ± 0.546.5bmoLio et al.98286570d\\n746181893532NRNR61713234NRNR45bmoLjubacev et al.3441NRNRNRNR85803256d\\n2617NRNRNRNRNRNRIn-hospitalRoshanali et al.263157578377NRNRNRNRNRNRNRNR38403.6 ± 0.53.5 ± 0.540.2a moMaltais et al.302857070686371683426NRNR85913434NRNR4.2ayrsQiu et al.1121067172645672753032282653493535NRNR48.1amoMicovic et al.865261b\\n62b\\n7273746521152729645029362.7 ± 0.62.5 ± 0.732a moBonacchi et al.3618NRNRNRNRNRNRNRNRNRNRNRNR2727NRNR32a moSilberman et al.38146267d\\n749350574557NRNR49cd\\n32cd\\n<25 %NRNR38amoMantovani et al.61416868675454512615NRNRNRNR45453.1 ± 0.83.3 ± 0.736.8a moReece et al.5456676941d\\n68d\\nNRNR2221NRNRNRNR4440NRNRIn-hospital\\na = mean; b = median; c Percentage class IV; d\\np < 0.05 between MVr and MVR\\nAbbreviations: AF atrial fibrillation, LVEF left ventricular ejection fraction, CABG coronary artery bypass grafting, MR mitral regurgitation, HTN hypertension, MVP mitral valve repair, MVR mitral valve replacement, NR not reportedTable 2Operative characteristicsCPB time (min)ACC time (min)MVR prosthesis typeSubvalvular apparatus preservationMVP partial/suture annuloplasty (%)MVP ring annuloplasty (%)MVP undersizingMVPMVRMVPMVRMechanical %Bioprothesis %Anterior + Posterior (%)Posterior (%)None (%)Lorusso et al. [9]14514594944753482443010027 (26 mm) 52 (28 mm) 13 (30 mm) 6 (32 mm) 1 (34 mm) 1 (36 mm)Lio et al. [10] 156180107132366410000010037 % open ring 63 % closed ring 37 % rigid ring 63 % semi-rigid ringLjubacev et al. [24]1451529699NRNRNRNRNRNRNRNRRoshanali et al. [28]NRNRNRNR100010000NRNRNRMaltais et al. [13]NRNRNRNR4654NRNRNR89242 (24–28 mm) 36 (30–34 mm)Qiu et al. [26]13612910598386211890010030 mmMicovic et al. [11]NRNRNRNR100001000595Median 28 mm (range, 26–34 mm)Bonacchi et al. [12]NRNRNRNRNRNR010001783NRSilberman et al. [23]154184991111000NRNRNR010026 ± 1.2 mmMantovani et al. [25]1791731311227624010000100ModerateReece et al. [27]112132152171NRNRNRNRNR010028 mm males 26 mm females\\nAbbreviations: MVP mitral valve repair, MVR mitral valve replacement, ACC time aortic cross-clamping time, CPB time, cardiopulmonary bypass time, NR not reported\\nFlow chart of study selection\\nKey Features of Included Studies\\n\\na = mean; b = median; c Percentage class IV; d\\np < 0.05 between MVr and MVR\\n\\nAbbreviations: AF atrial fibrillation, LVEF left ventricular ejection fraction, CABG coronary artery bypass grafting, MR mitral regurgitation, HTN hypertension, MVP mitral valve repair, MVR mitral valve replacement, NR not reported\\nOperative characteristics\\n\\nAbbreviations: MVP mitral valve repair, MVR mitral valve replacement, ACC time aortic cross-clamping time, CPB time, cardiopulmonary bypass time, NR not reported\\nAll the eleven trials were assessed by the Newcastle-Ottawa Scale for quality assessment risk evaluation of adequacy of selection, comparability, and outcomes assessment for individual trials (Table 3). All studies included in our meta-analysis were of high-quality (had ≥ 6 scores).Table 3Study quality assessment using the Newcastle-Ottawa Scale for nonrandomized studiesSelectionOutcomeFirst author, year of publication (reference)Representativeness of exposed cohortSelection of nonexposed cohortAscertainment of exposureOutcome of interest absent at start of studyComparability (Based on design and analysis)Assessment of outcomeFollow-up long enough for outcomesto occurAdequacy of follow-upTotal scoreLorusso et al.111101117Lio et al.111111118Ljubacev et al.111101016Roshanali et al.111121119Maltais et al.111121119Qiu et al.111121119Micovic et al.111121119Bonacchi et al.111121119Silberman et al.111111118Mantovani et al.111121119Reece et al.111111017\\nStudy quality assessment using the Newcastle-Ottawa Scale for nonrandomized studies\\nThe literature search identified a total of 545 studies, which were published between 1965 and 2015. On the basis of title and abstracts, 34 articles were selected and reviewed in full. Eleven articles met the inclusion and exclusion criteria [9–13, 23–28] (Fig. 1). Of the included studies, there were ten retrospective observational studies [9–13, 23–27] and one prospective observational study [28]. All were nonrandomized studies. These studies included a total of 1807 patients, 1091 (60.4 %) of whom underwent repair and 716 (39.6 %) of whom underwent replacement. All patients had CABG. Patient characteristics and a summary of operative details are summarized in Tables 1 and 2, respectively. With the exception of the replacement patients being older in 2 of the studies, the two groups were similar in terms of hypertension (HTN), diabetes, atrial fibrillation (AF), left ventricular ejection fraction (LVEF) and the New York Heart Association (NYHA) class. Eight of the studies reported data on the type of prosthesis used for mitral valve replacement and preservation of the subvalvular apparatus. In half of the studies, the majority of patients received a bioprothesis valve. In addition, preservation of the subvalvular (either total or partial) apparatus were performed in the vast majority of mitral valve replacements.Fig. 1Flow chart of study selectionTable 1Key Features of Included StudiesStudySubjectsMean AgeMale (%)HTN (%)Diabetes (%)AF (%)NYHA III-IV (%)Mean LVEF (%)MR gradeFollow-up periodMVP + CABGMVR + CABGMVP + CABGMVR + CABGMVP + CABGMVR + CABGMVP + CABGMVR + CABGMVP + CABGMVR + CABGMVP + CABGMVR + CABGMVP + CABGMVR + CABGMVP + CABGMVR + CABGMVP + CABGMVR + CABGLorusso et al.24424466667369414136351213NRNR35352.8 ± 0.52.8 ± 0.546.5bmoLio et al.98286570d\\n746181893532NRNR61713234NRNR45bmoLjubacev et al.3441NRNRNRNR85803256d\\n2617NRNRNRNRNRNRIn-hospitalRoshanali et al.263157578377NRNRNRNRNRNRNRNR38403.6 ± 0.53.5 ± 0.540.2a moMaltais et al.302857070686371683426NRNR85913434NRNR4.2ayrsQiu et al.1121067172645672753032282653493535NRNR48.1amoMicovic et al.865261b\\n62b\\n7273746521152729645029362.7 ± 0.62.5 ± 0.732a moBonacchi et al.3618NRNRNRNRNRNRNRNRNRNRNRNR2727NRNR32a moSilberman et al.38146267d\\n749350574557NRNR49cd\\n32cd\\n<25 %NRNR38amoMantovani et al.61416868675454512615NRNRNRNR45453.1 ± 0.83.3 ± 0.736.8a moReece et al.5456676941d\\n68d\\nNRNR2221NRNRNRNR4440NRNRIn-hospital\\na = mean; b = median; c Percentage class IV; d\\np < 0.05 between MVr and MVR\\nAbbreviations: AF atrial fibrillation, LVEF left ventricular ejection fraction, CABG coronary artery bypass grafting, MR mitral regurgitation, HTN hypertension, MVP mitral valve repair, MVR mitral valve replacement, NR not reportedTable 2Operative characteristicsCPB time (min)ACC time (min)MVR prosthesis typeSubvalvular apparatus preservationMVP partial/suture annuloplasty (%)MVP ring annuloplasty (%)MVP undersizingMVPMVRMVPMVRMechanical %Bioprothesis %Anterior + Posterior (%)Posterior (%)None (%)Lorusso et al. [9]14514594944753482443010027 (26 mm) 52 (28 mm) 13 (30 mm) 6 (32 mm) 1 (34 mm) 1 (36 mm)Lio et al. [10] 156180107132366410000010037 % open ring 63 % closed ring 37 % rigid ring 63 % semi-rigid ringLjubacev et al. [24]1451529699NRNRNRNRNRNRNRNRRoshanali et al. [28]NRNRNRNR100010000NRNRNRMaltais et al. [13]NRNRNRNR4654NRNRNR89242 (24–28 mm) 36 (30–34 mm)Qiu et al. [26]13612910598386211890010030 mmMicovic et al. [11]NRNRNRNR100001000595Median 28 mm (range, 26–34 mm)Bonacchi et al. [12]NRNRNRNRNRNR010001783NRSilberman et al. [23]154184991111000NRNRNR010026 ± 1.2 mmMantovani et al. [25]1791731311227624010000100ModerateReece et al. [27]112132152171NRNRNRNRNR010028 mm males 26 mm females\\nAbbreviations: MVP mitral valve repair, MVR mitral valve replacement, ACC time aortic cross-clamping time, CPB time, cardiopulmonary bypass time, NR not reported\\nFlow chart of study selection\\nKey Features of Included Studies\\n\\na = mean; b = median; c Percentage class IV; d\\np < 0.05 between MVr and MVR\\n\\nAbbreviations: AF atrial fibrillation, LVEF left ventricular ejection fraction, CABG coronary artery bypass grafting, MR mitral regurgitation, HTN hypertension, MVP mitral valve repair, MVR mitral valve replacement, NR not reported\\nOperative characteristics\\n\\nAbbreviations: MVP mitral valve repair, MVR mitral valve replacement, ACC time aortic cross-clamping time, CPB time, cardiopulmonary bypass time, NR not reported\\nAll the eleven trials were assessed by the Newcastle-Ottawa Scale for quality assessment risk evaluation of adequacy of selection, comparability, and outcomes assessment for individual trials (Table 3). All studies included in our meta-analysis were of high-quality (had ≥ 6 scores).Table 3Study quality assessment using the Newcastle-Ottawa Scale for nonrandomized studiesSelectionOutcomeFirst author, year of publication (reference)Representativeness of exposed cohortSelection of nonexposed cohortAscertainment of exposureOutcome of interest absent at start of studyComparability (Based on design and analysis)Assessment of outcomeFollow-up long enough for outcomesto occurAdequacy of follow-upTotal scoreLorusso et al.111101117Lio et al.111111118Ljubacev et al.111101016Roshanali et al.111121119Maltais et al.111121119Qiu et al.111121119Micovic et al.111121119Bonacchi et al.111121119Silberman et al.111111118Mantovani et al.111121119Reece et al.111111017\\nStudy quality assessment using the Newcastle-Ottawa Scale for nonrandomized studies\\n Peri-operative mortality Ten observational studies involving a total of 1750 patients reported operative mortality. The odds ratios in the study ranged from 0.16 to 2.32 (Fig. 2). The summary odds ratio was 0.65 (95 % CI, 0.43-1.00), P = 0.05, indicating there was a reduced peri-operative mortality trend towards repair, but no statistical significance reached. In assessing potential heterogeneity across the studies, I2 = 0 %, and no publication bias was found either from the Egger’s test (P = 0.83) or the Begg’s test (P = 0.68).Fig. 2Mitral valve repair versus mitral valve replacement on peri-operative mortality\\nMitral valve repair versus mitral valve replacement on peri-operative mortality\\nTen observational studies involving a total of 1750 patients reported operative mortality. The odds ratios in the study ranged from 0.16 to 2.32 (Fig. 2). The summary odds ratio was 0.65 (95 % CI, 0.43-1.00), P = 0.05, indicating there was a reduced peri-operative mortality trend towards repair, but no statistical significance reached. In assessing potential heterogeneity across the studies, I2 = 0 %, and no publication bias was found either from the Egger’s test (P = 0.83) or the Begg’s test (P = 0.68).Fig. 2Mitral valve repair versus mitral valve replacement on peri-operative mortality\\nMitral valve repair versus mitral valve replacement on peri-operative mortality\\n Late mortality A total of nine studies (1622 Patients) reported late mortality (Fig. 3). The overall hazard ratio was 0.87 (95 % CI, 0.67-1.14; P = 0.31), suggesting late mortality was not significantly reduced following repair. Further, heterogeneity was moderate (I2 = 30 %). It was noted that ten of the studies included patients with different degrees of regurgitation and left ventricular dysfunction, with exception of one study [23], all of the patients included in this study had severely impaired LV function (ejection fraction <25 %) and severe ischemic MR undergoing CABG. Severely decreased left ventricular function and severe IMR could have the potential pathophysiological effect on the mortality rates of those patients. Hence, sensitivity analysis was conducted to only include studies in which not all of the patients had severe ischemic MR and severely impaired LV function undergoing CABG. Restricting analysis to these studies had no significant impact on the reduction of late mortality following repair (the summary hazard ratio, 1.03; 95 % CI, 0.90-1.17; P = 0.66). Whereas, heterogeneity suggested by I2 was significantly reduced to 0 %, indicating no variability exists among the rest studies. Further exclusion of any single study did not significantly reduce the heterogeneity. In addition, our study included 10 retrospective studies and 1 prospective study. The different study designs may influence outcomes of meta-analysis. Therefore, sensitivity analysis was performed to only include retrospective studies. Restricting analysis to these studies did not significantly impact on the result for late mortality (HR, 0.86; 95 % CI, 0.64-1.14; P = 0.30; I2 = 38 %).Fig. 3Mitral valve repair versus mitral valve replacement on late mortality\\nMitral valve repair versus mitral valve replacement on late mortality\\nA total of nine studies (1622 Patients) reported late mortality (Fig. 3). The overall hazard ratio was 0.87 (95 % CI, 0.67-1.14; P = 0.31), suggesting late mortality was not significantly reduced following repair. Further, heterogeneity was moderate (I2 = 30 %). It was noted that ten of the studies included patients with different degrees of regurgitation and left ventricular dysfunction, with exception of one study [23], all of the patients included in this study had severely impaired LV function (ejection fraction <25 %) and severe ischemic MR undergoing CABG. Severely decreased left ventricular function and severe IMR could have the potential pathophysiological effect on the mortality rates of those patients. Hence, sensitivity analysis was conducted to only include studies in which not all of the patients had severe ischemic MR and severely impaired LV function undergoing CABG. Restricting analysis to these studies had no significant impact on the reduction of late mortality following repair (the summary hazard ratio, 1.03; 95 % CI, 0.90-1.17; P = 0.66). Whereas, heterogeneity suggested by I2 was significantly reduced to 0 %, indicating no variability exists among the rest studies. Further exclusion of any single study did not significantly reduce the heterogeneity. In addition, our study included 10 retrospective studies and 1 prospective study. The different study designs may influence outcomes of meta-analysis. Therefore, sensitivity analysis was performed to only include retrospective studies. Restricting analysis to these studies did not significantly impact on the result for late mortality (HR, 0.86; 95 % CI, 0.64-1.14; P = 0.30; I2 = 38 %).Fig. 3Mitral valve repair versus mitral valve replacement on late mortality\\nMitral valve repair versus mitral valve replacement on late mortality\\n Mitral valve reoperation Reoperation due to such as MV regurgitation, thromboembolism and prosthetic endocarditis was reported in five studies involving a total of 845 patients. The combined odds ratio was 1.47, suggesting the trend went towards the preference of replacement. Nevertheless, no significant differences were reached between the two surgical approaches (95 % CI, 0.90-2.38; I2 = 0 %; P = 0.12) (Fig. 4).Fig. 4Mitral valve repair versus mitral valve replacement on reoperation\\nMitral valve repair versus mitral valve replacement on reoperation\\nReoperation due to such as MV regurgitation, thromboembolism and prosthetic endocarditis was reported in five studies involving a total of 845 patients. The combined odds ratio was 1.47, suggesting the trend went towards the preference of replacement. Nevertheless, no significant differences were reached between the two surgical approaches (95 % CI, 0.90-2.38; I2 = 0 %; P = 0.12) (Fig. 4).Fig. 4Mitral valve repair versus mitral valve replacement on reoperation\\nMitral valve repair versus mitral valve replacement on reoperation\\n Recurrence of MR Five studies involving a total of 837 patients provided data regarding recurrence of MR during the follow-up. The MVP + CABG group was associated with a significantly increased recurrence rate of MR (OR, 5.41; 95 % CI: 3.12–9.38; P < 0.001) with low heterogeneity among those studies (I2 = 10 %) (Fig. 5). Sensitivity analysis was also performed to only include retrospective studies. Restricting analysis to these studies did not significantly impact on the result for recurrence of MR (OR, 5.97; 95 % CI, 3.36-10.58; P < 0.001; I2 = 0 %).Fig. 5Mitral valve repair versus mitral valve replacement on recurrence of mitral valve regurgitation\\nMitral valve repair versus mitral valve replacement on recurrence of mitral valve regurgitation\\nFive studies involving a total of 837 patients provided data regarding recurrence of MR during the follow-up. The MVP + CABG group was associated with a significantly increased recurrence rate of MR (OR, 5.41; 95 % CI: 3.12–9.38; P < 0.001) with low heterogeneity among those studies (I2 = 10 %) (Fig. 5). Sensitivity analysis was also performed to only include retrospective studies. Restricting analysis to these studies did not significantly impact on the result for recurrence of MR (OR, 5.97; 95 % CI, 3.36-10.58; P < 0.001; I2 = 0 %).Fig. 5Mitral valve repair versus mitral valve replacement on recurrence of mitral valve regurgitation\\nMitral valve repair versus mitral valve replacement on recurrence of mitral valve regurgitation\",\n \"The literature search identified a total of 545 studies, which were published between 1965 and 2015. On the basis of title and abstracts, 34 articles were selected and reviewed in full. Eleven articles met the inclusion and exclusion criteria [9–13, 23–28] (Fig. 1). Of the included studies, there were ten retrospective observational studies [9–13, 23–27] and one prospective observational study [28]. All were nonrandomized studies. These studies included a total of 1807 patients, 1091 (60.4 %) of whom underwent repair and 716 (39.6 %) of whom underwent replacement. All patients had CABG. Patient characteristics and a summary of operative details are summarized in Tables 1 and 2, respectively. With the exception of the replacement patients being older in 2 of the studies, the two groups were similar in terms of hypertension (HTN), diabetes, atrial fibrillation (AF), left ventricular ejection fraction (LVEF) and the New York Heart Association (NYHA) class. Eight of the studies reported data on the type of prosthesis used for mitral valve replacement and preservation of the subvalvular apparatus. In half of the studies, the majority of patients received a bioprothesis valve. In addition, preservation of the subvalvular (either total or partial) apparatus were performed in the vast majority of mitral valve replacements.Fig. 1Flow chart of study selectionTable 1Key Features of Included StudiesStudySubjectsMean AgeMale (%)HTN (%)Diabetes (%)AF (%)NYHA III-IV (%)Mean LVEF (%)MR gradeFollow-up periodMVP + CABGMVR + CABGMVP + CABGMVR + CABGMVP + CABGMVR + CABGMVP + CABGMVR + CABGMVP + CABGMVR + CABGMVP + CABGMVR + CABGMVP + CABGMVR + CABGMVP + CABGMVR + CABGMVP + CABGMVR + CABGLorusso et al.24424466667369414136351213NRNR35352.8 ± 0.52.8 ± 0.546.5bmoLio et al.98286570d\\n746181893532NRNR61713234NRNR45bmoLjubacev et al.3441NRNRNRNR85803256d\\n2617NRNRNRNRNRNRIn-hospitalRoshanali et al.263157578377NRNRNRNRNRNRNRNR38403.6 ± 0.53.5 ± 0.540.2a moMaltais et al.302857070686371683426NRNR85913434NRNR4.2ayrsQiu et al.1121067172645672753032282653493535NRNR48.1amoMicovic et al.865261b\\n62b\\n7273746521152729645029362.7 ± 0.62.5 ± 0.732a moBonacchi et al.3618NRNRNRNRNRNRNRNRNRNRNRNR2727NRNR32a moSilberman et al.38146267d\\n749350574557NRNR49cd\\n32cd\\n<25 %NRNR38amoMantovani et al.61416868675454512615NRNRNRNR45453.1 ± 0.83.3 ± 0.736.8a moReece et al.5456676941d\\n68d\\nNRNR2221NRNRNRNR4440NRNRIn-hospital\\na = mean; b = median; c Percentage class IV; d\\np < 0.05 between MVr and MVR\\nAbbreviations: AF atrial fibrillation, LVEF left ventricular ejection fraction, CABG coronary artery bypass grafting, MR mitral regurgitation, HTN hypertension, MVP mitral valve repair, MVR mitral valve replacement, NR not reportedTable 2Operative characteristicsCPB time (min)ACC time (min)MVR prosthesis typeSubvalvular apparatus preservationMVP partial/suture annuloplasty (%)MVP ring annuloplasty (%)MVP undersizingMVPMVRMVPMVRMechanical %Bioprothesis %Anterior + Posterior (%)Posterior (%)None (%)Lorusso et al. [9]14514594944753482443010027 (26 mm) 52 (28 mm) 13 (30 mm) 6 (32 mm) 1 (34 mm) 1 (36 mm)Lio et al. [10] 156180107132366410000010037 % open ring 63 % closed ring 37 % rigid ring 63 % semi-rigid ringLjubacev et al. [24]1451529699NRNRNRNRNRNRNRNRRoshanali et al. [28]NRNRNRNR100010000NRNRNRMaltais et al. [13]NRNRNRNR4654NRNRNR89242 (24–28 mm) 36 (30–34 mm)Qiu et al. [26]13612910598386211890010030 mmMicovic et al. [11]NRNRNRNR100001000595Median 28 mm (range, 26–34 mm)Bonacchi et al. [12]NRNRNRNRNRNR010001783NRSilberman et al. [23]154184991111000NRNRNR010026 ± 1.2 mmMantovani et al. [25]1791731311227624010000100ModerateReece et al. [27]112132152171NRNRNRNRNR010028 mm males 26 mm females\\nAbbreviations: MVP mitral valve repair, MVR mitral valve replacement, ACC time aortic cross-clamping time, CPB time, cardiopulmonary bypass time, NR not reported\\nFlow chart of study selection\\nKey Features of Included Studies\\n\\na = mean; b = median; c Percentage class IV; d\\np < 0.05 between MVr and MVR\\n\\nAbbreviations: AF atrial fibrillation, LVEF left ventricular ejection fraction, CABG coronary artery bypass grafting, MR mitral regurgitation, HTN hypertension, MVP mitral valve repair, MVR mitral valve replacement, NR not reported\\nOperative characteristics\\n\\nAbbreviations: MVP mitral valve repair, MVR mitral valve replacement, ACC time aortic cross-clamping time, CPB time, cardiopulmonary bypass time, NR not reported\\nAll the eleven trials were assessed by the Newcastle-Ottawa Scale for quality assessment risk evaluation of adequacy of selection, comparability, and outcomes assessment for individual trials (Table 3). All studies included in our meta-analysis were of high-quality (had ≥ 6 scores).Table 3Study quality assessment using the Newcastle-Ottawa Scale for nonrandomized studiesSelectionOutcomeFirst author, year of publication (reference)Representativeness of exposed cohortSelection of nonexposed cohortAscertainment of exposureOutcome of interest absent at start of studyComparability (Based on design and analysis)Assessment of outcomeFollow-up long enough for outcomesto occurAdequacy of follow-upTotal scoreLorusso et al.111101117Lio et al.111111118Ljubacev et al.111101016Roshanali et al.111121119Maltais et al.111121119Qiu et al.111121119Micovic et al.111121119Bonacchi et al.111121119Silberman et al.111111118Mantovani et al.111121119Reece et al.111111017\\nStudy quality assessment using the Newcastle-Ottawa Scale for nonrandomized studies\",\n \"Ten observational studies involving a total of 1750 patients reported operative mortality. The odds ratios in the study ranged from 0.16 to 2.32 (Fig. 2). The summary odds ratio was 0.65 (95 % CI, 0.43-1.00), P = 0.05, indicating there was a reduced peri-operative mortality trend towards repair, but no statistical significance reached. In assessing potential heterogeneity across the studies, I2 = 0 %, and no publication bias was found either from the Egger’s test (P = 0.83) or the Begg’s test (P = 0.68).Fig. 2Mitral valve repair versus mitral valve replacement on peri-operative mortality\\nMitral valve repair versus mitral valve replacement on peri-operative mortality\",\n \"A total of nine studies (1622 Patients) reported late mortality (Fig. 3). The overall hazard ratio was 0.87 (95 % CI, 0.67-1.14; P = 0.31), suggesting late mortality was not significantly reduced following repair. Further, heterogeneity was moderate (I2 = 30 %). It was noted that ten of the studies included patients with different degrees of regurgitation and left ventricular dysfunction, with exception of one study [23], all of the patients included in this study had severely impaired LV function (ejection fraction <25 %) and severe ischemic MR undergoing CABG. Severely decreased left ventricular function and severe IMR could have the potential pathophysiological effect on the mortality rates of those patients. Hence, sensitivity analysis was conducted to only include studies in which not all of the patients had severe ischemic MR and severely impaired LV function undergoing CABG. Restricting analysis to these studies had no significant impact on the reduction of late mortality following repair (the summary hazard ratio, 1.03; 95 % CI, 0.90-1.17; P = 0.66). Whereas, heterogeneity suggested by I2 was significantly reduced to 0 %, indicating no variability exists among the rest studies. Further exclusion of any single study did not significantly reduce the heterogeneity. In addition, our study included 10 retrospective studies and 1 prospective study. The different study designs may influence outcomes of meta-analysis. Therefore, sensitivity analysis was performed to only include retrospective studies. Restricting analysis to these studies did not significantly impact on the result for late mortality (HR, 0.86; 95 % CI, 0.64-1.14; P = 0.30; I2 = 38 %).Fig. 3Mitral valve repair versus mitral valve replacement on late mortality\\nMitral valve repair versus mitral valve replacement on late mortality\",\n \"Reoperation due to such as MV regurgitation, thromboembolism and prosthetic endocarditis was reported in five studies involving a total of 845 patients. The combined odds ratio was 1.47, suggesting the trend went towards the preference of replacement. Nevertheless, no significant differences were reached between the two surgical approaches (95 % CI, 0.90-2.38; I2 = 0 %; P = 0.12) (Fig. 4).Fig. 4Mitral valve repair versus mitral valve replacement on reoperation\\nMitral valve repair versus mitral valve replacement on reoperation\",\n \"Five studies involving a total of 837 patients provided data regarding recurrence of MR during the follow-up. The MVP + CABG group was associated with a significantly increased recurrence rate of MR (OR, 5.41; 95 % CI: 3.12–9.38; P < 0.001) with low heterogeneity among those studies (I2 = 10 %) (Fig. 5). Sensitivity analysis was also performed to only include retrospective studies. Restricting analysis to these studies did not significantly impact on the result for recurrence of MR (OR, 5.97; 95 % CI, 3.36-10.58; P < 0.001; I2 = 0 %).Fig. 5Mitral valve repair versus mitral valve replacement on recurrence of mitral valve regurgitation\\nMitral valve repair versus mitral valve replacement on recurrence of mitral valve regurgitation\",\n \"In our meta-analysis of eleven studies, which included patients undergoing repair or replacement electively with CABG surgery, no differences were found regarding peri-operative mortality and long-term survival. Mitral valve replacement was associated with lower incidence of mitral regurgitation in patients with IMR during CABG. The Society of Thoracic Surgeons reports MVP + CABG group had approximately 5 % (4.8 % in-hospital mortality and 5.3 % operative mortality) nationwide mortality rates in contrast with 8 % (7.8 % in-hospital mortality and 8.5 % operative mortality) for MVR + CABG group [29].\\nModerate and severe recurrent MR after a restrictive annuloplasty ring, occurred early and affected a substantial proportion of patients by 2 years [30]. In the present study, the main disadvantage of MVP + CABG group is recurrence of MR compared to MVR + CABG group. Although MR recurrence was common, mitral valve reoperation was not. Similar reoperation rate was found between both groups, which suggested that not all patients with MR recurrence 2+ or greater needed reoperation. There were several possible explanations. IMR after annuloplasty might be considered inconsequential compared with the underlying myocardial disease, or surgeons might be hesitant to perform a second or third cardiac operation in these elderly, high-risk patients [31]. Many patients with recurrent MR were just too sick or too old or both to even consider reoperating on them.\\nA case-matched study found that replacement was associated with lower incidence of valve-related complications than was repair and both mitral valve procedures showed no significant difference in LV function at follow-up [32]. However, replacement had greater thromboembolic and ischemic stroke rates than repair despite anticoagulant therapy [33]. Although mitral valve replacement can sufficiently correct regurgitation, the structural integrity of the mitral valve is usually compromised after replacement, leading to a continuous damage on the left ventricular tethered loop, which results in adverse effects on left ventricular contraction and poor prognosis [8]. Therefore, individualized consideration should be given to the two surgical procedures.\\nTo date, there have been no RCTs that compared the clinical outcomes of the two surgical management particularly in patients with chronic IMR during CABG. To our knowledge, our report is the first meta-analysis comparing short-term and long-term outcomes of two mitral valve procedures specifically on patients with chronic IMR undergoing concomitant CABG. We selected studies for this meta-analysis with rigorous inclusion and exclusion criteria. All the patients in the studies underwent concomitant CABG, which ensures homogeneity of IMR patients and facilitates comparisons between trials. In addition, patients with acute IMR due to ruptured papillary muscles were excluded in our study, thus the outcomes of this meta-analysis not only truly reflect the surgical intervention of chronic IMR but also avoid biasing the results toward worsening the replacement group. By excluding articles that had > 20 % lack of annuloplasty ring, we have made the comparison between the two mitral valves surgeries more powerful. Therefore, the results of our study truly reflect the surgical management of patients with IMR simultaneous to CABG.\\n Limitations Our meta-analysis has several limitations. Firstly, this study was based on observational, retrospective studies with inherent bias of such study designs. The publications included in this meta-analysis were relatively small and nonrandomized studies. Secondly, changes in NYHA class, LVEF and left ventricular reversal remodeling were too scarcely reported in the included studies to enable meta-analysis. Eight out of eleven studies included in our meta-analysis reported data on the subvalvular apparatus preservation in mitral valve replacement yet with lack of uniform preservation of both the anterior and posterior leaflets. The other three studies had no description regarding subvalvular apparatus preservation. Thirdly, potential confounding factors such as preoperative risk evaluation (STS score i.e.), mitral valve more suitable for repair, age, cause of mitral regurgitation (ischemia, fibrosis, ventricular remodeling), EF and complexity of revascularization were not considered or adjusted in some of the studies included in our meta-analysis. Therefore, the superiority of repair over replacement may be affected by this and other factors that are not possible to be revealed with meta-analysis of observational trials. Well-designed RCTs are required to further verify the conclusion. Another limitation of our report is the fact that follow-up periods were heterogeneous between some studies with different use of mean and median durations of follow-up. Therefore, subgroup analysis could not be performed statistically.\\nOur meta-analysis has several limitations. Firstly, this study was based on observational, retrospective studies with inherent bias of such study designs. The publications included in this meta-analysis were relatively small and nonrandomized studies. Secondly, changes in NYHA class, LVEF and left ventricular reversal remodeling were too scarcely reported in the included studies to enable meta-analysis. Eight out of eleven studies included in our meta-analysis reported data on the subvalvular apparatus preservation in mitral valve replacement yet with lack of uniform preservation of both the anterior and posterior leaflets. The other three studies had no description regarding subvalvular apparatus preservation. Thirdly, potential confounding factors such as preoperative risk evaluation (STS score i.e.), mitral valve more suitable for repair, age, cause of mitral regurgitation (ischemia, fibrosis, ventricular remodeling), EF and complexity of revascularization were not considered or adjusted in some of the studies included in our meta-analysis. Therefore, the superiority of repair over replacement may be affected by this and other factors that are not possible to be revealed with meta-analysis of observational trials. Well-designed RCTs are required to further verify the conclusion. Another limitation of our report is the fact that follow-up periods were heterogeneous between some studies with different use of mean and median durations of follow-up. Therefore, subgroup analysis could not be performed statistically.\",\n \"Our meta-analysis has several limitations. Firstly, this study was based on observational, retrospective studies with inherent bias of such study designs. The publications included in this meta-analysis were relatively small and nonrandomized studies. Secondly, changes in NYHA class, LVEF and left ventricular reversal remodeling were too scarcely reported in the included studies to enable meta-analysis. Eight out of eleven studies included in our meta-analysis reported data on the subvalvular apparatus preservation in mitral valve replacement yet with lack of uniform preservation of both the anterior and posterior leaflets. The other three studies had no description regarding subvalvular apparatus preservation. Thirdly, potential confounding factors such as preoperative risk evaluation (STS score i.e.), mitral valve more suitable for repair, age, cause of mitral regurgitation (ischemia, fibrosis, ventricular remodeling), EF and complexity of revascularization were not considered or adjusted in some of the studies included in our meta-analysis. Therefore, the superiority of repair over replacement may be affected by this and other factors that are not possible to be revealed with meta-analysis of observational trials. Well-designed RCTs are required to further verify the conclusion. Another limitation of our report is the fact that follow-up periods were heterogeneous between some studies with different use of mean and median durations of follow-up. Therefore, subgroup analysis could not be performed statistically.\",\n \"In patients with chronic ischemic mitral regurgitation during CABG, mitral valve replacement is associated with lower recurrence of regurgitation. No differences were found regarding survival and reoperation rates.\"\n]"},"all_sections_sec_types":{"kind":"list like","value":["introduction","materials|methods",null,null,null,null,"results",null,null,null,null,null,"discussion",null,"conclusion"],"string":"[\n \"introduction\",\n \"materials|methods\",\n null,\n null,\n null,\n null,\n \"results\",\n null,\n null,\n null,\n null,\n null,\n \"discussion\",\n null,\n \"conclusion\"\n]"},"keywords":{"kind":"list like","value":["Ischemic mitral regurgitation","Mitral valve repair","Mitral valve replacement","Coronary artery bypass grafting","Meta-analysis"],"string":"[\n \"Ischemic mitral regurgitation\",\n \"Mitral valve repair\",\n \"Mitral valve replacement\",\n \"Coronary artery bypass grafting\",\n \"Meta-analysis\"\n]"},"whole_article_text":{"kind":"string","value":"Background:\nChronic ischemic mitral regurgitation (IMR) is a frequent and important complication after myocardial infarction. Its pathophysiologic mechanisms account for remodeling of segmental/global left ventricle (LV) inducing papillary muscle displacement and leaflet tethering [1]. The presence of IMR is independently associated with mortality and morbidity after myocardial infarction [2].\nGiven the severity of IMR, surgery performed for IMR ranges from coronary artery bypass grafting (CABG) alone to both CABG and mitral valve surgery [3, 4]. Two randomized trials indicated that repair was associated with a reduced prevalence of mitral regurgitation but did not show a clinically meaningful advantage of adding mitral valve repair to CABG [5, 6]. In addition, when compared with replacement, previous meta-analyses concluded that repair is associated with lower operative mortality but higher recurrence of regurgitation in patients with ischemic mitral regurgitation, with or without CABG [7, 8]. For patients with chronic IMR undergoing combined CABG, the best surgical treatment is still controversial. Some studies support replacement [9, 10], others support repair [11, 12], and others showed similar survival for the two procedures [13]. Current guidelines recommend mitral valve surgery for severe IMR, but do not demonstrate a specific type of procedure [14, 15]. Numerous non-randomized studies have been published comparing the clinical outcomes between MVP + CABG and MVR + CABG for IMR. However, there is still no systematic and quantitative assessment of accumulated literature on this topic. Meta-analysis is a powerful tool to provide meaningful comparison of short and long-term outcomes of these procedures. The present meta-analysis aimed to assess the clinical outcomes of patients who underwent mitral valve surgery and CABG for chronic IMR.\n\nMethods:\n Search strategy This meta-analysis was conducted according to the recommendations of the Meta-Analysis of Observational Studies in Epidemiology (MOOSE) [16]. A computerized search was performed using Pubmed, Embase, Ovid medline and Cochrane Library from their dates of inception to December 2015 without language restriction. The search terms “ischemic or ischaemic” and “mitral valve” and “repair or replacement or annuloplasty” and “coronary artery bypass grafting” were entered as MeSH terms and keywords. The language of publication was restricted to English. We also reviewed the full text and references lists of all relevant review articles in detail. YW and XS independently undertook the literature search, screening of titles and abstracts. Any disagreement was resolved by consensus.\nThis meta-analysis was conducted according to the recommendations of the Meta-Analysis of Observational Studies in Epidemiology (MOOSE) [16]. A computerized search was performed using Pubmed, Embase, Ovid medline and Cochrane Library from their dates of inception to December 2015 without language restriction. The search terms “ischemic or ischaemic” and “mitral valve” and “repair or replacement or annuloplasty” and “coronary artery bypass grafting” were entered as MeSH terms and keywords. The language of publication was restricted to English. We also reviewed the full text and references lists of all relevant review articles in detail. YW and XS independently undertook the literature search, screening of titles and abstracts. Any disagreement was resolved by consensus.\n Study selection Articles were included if there is a direct comparison of repair versus replacement and all patients with IMR had CABG. The exclusion criteria were applied to select the final articles for the meta-analysis: (1) ischemic etiology in only a subset of the patients with outcomes not specifically provided (2) nonischemic dilated cardiomyopathy (3) beating heart procedures (4) concomitant surgical ventricular restoration (5) preoperative hemodynamic instability (6) lack of annuloplasty in > 20 % of the patients in the repair group (7) acute IMR.\nArticles were included if there is a direct comparison of repair versus replacement and all patients with IMR had CABG. The exclusion criteria were applied to select the final articles for the meta-analysis: (1) ischemic etiology in only a subset of the patients with outcomes not specifically provided (2) nonischemic dilated cardiomyopathy (3) beating heart procedures (4) concomitant surgical ventricular restoration (5) preoperative hemodynamic instability (6) lack of annuloplasty in > 20 % of the patients in the repair group (7) acute IMR.\n Data extraction and quality assessment All data were extracted independently by 2 investigators (Y.W., X.S.) according to the prespecified selection criteria, with disagreement resolved by consensus among all authors. The following data from each study were extracted: the last name of the first author, year of publication, study population, patients’ age and gender, comorbidities, cardiac function, severity of mitral regurgitation at baseline and follow-up period. Any disagreement was resolved by consensus.\nBased on the extracted data, the quality of the included studies was evaluated using the nine-item Newcastle-Ottawa Quality scale [17], a widely used tool for the quality assessment of non-randomized trials. The high-quality study was defined as a study with ≥6 scores.\nAll data were extracted independently by 2 investigators (Y.W., X.S.) according to the prespecified selection criteria, with disagreement resolved by consensus among all authors. The following data from each study were extracted: the last name of the first author, year of publication, study population, patients’ age and gender, comorbidities, cardiac function, severity of mitral regurgitation at baseline and follow-up period. Any disagreement was resolved by consensus.\nBased on the extracted data, the quality of the included studies was evaluated using the nine-item Newcastle-Ottawa Quality scale [17], a widely used tool for the quality assessment of non-randomized trials. The high-quality study was defined as a study with ≥6 scores.\n Statistical analysis The primary end points were operative mortality and late mortality (considered to be year after operation). Operative mortality was defined as death within 30 days after operation or in-hospital death. Secondary end points were MR recurrence 2+ or greater and reoperation at follow-up. The meta-analysis was performed using Review Manager (Revman, version 5.3 for windows, Oxford, England, Cochrane Collaboration) and Stata (version 11.0; StataCorp, College Station, TX). Hazard ratio (HR) with a 95 % confidence intervals (CIs), directly extracted from these included studies or indirectly calculated using the method of Tierney and colleagues [18] to assess the efficacy of the surgical intervention in each study. A summary of odds ratio (OR) and their corresponding 95 % CI were computed for each dichotomous outcome using either fixed-effects models or, in the presence of substantial heterogeneity (I2 > 50 %), random-effects models [19]. Statistical heterogeneity across studies was examined with Cochran’s Q test as well as the I2 statistics. Studies with an I2 statistics of <25 % were considered to have low heterogeneity, those with an I2 statistics of 25–50 % were considered to have moderate heterogeneity, and those with an I2 statistics of >50 % were considered to have a high degree of heterogeneity [20]. If there was high heterogeneity, the possible clinical and methodological factors for this were further explored. Potential sources of heterogeneity were investigated using sensitivity analyses and each study involved in the meta-analysis was excluded each time to reflect the influence of the individual data set on the pooled RRs.\nPublication bias was assessed using the Egger regression asymmetry test [21] and Begg adjusted rank correlation test [22]; a P value of less than 0.05 was considered representative of statistically significant publication bias. Meta-analysis results are displayed in forest plots. A p value < 0.05 was considered statistically significant.\nThe primary end points were operative mortality and late mortality (considered to be year after operation). Operative mortality was defined as death within 30 days after operation or in-hospital death. Secondary end points were MR recurrence 2+ or greater and reoperation at follow-up. The meta-analysis was performed using Review Manager (Revman, version 5.3 for windows, Oxford, England, Cochrane Collaboration) and Stata (version 11.0; StataCorp, College Station, TX). Hazard ratio (HR) with a 95 % confidence intervals (CIs), directly extracted from these included studies or indirectly calculated using the method of Tierney and colleagues [18] to assess the efficacy of the surgical intervention in each study. A summary of odds ratio (OR) and their corresponding 95 % CI were computed for each dichotomous outcome using either fixed-effects models or, in the presence of substantial heterogeneity (I2 > 50 %), random-effects models [19]. Statistical heterogeneity across studies was examined with Cochran’s Q test as well as the I2 statistics. Studies with an I2 statistics of <25 % were considered to have low heterogeneity, those with an I2 statistics of 25–50 % were considered to have moderate heterogeneity, and those with an I2 statistics of >50 % were considered to have a high degree of heterogeneity [20]. If there was high heterogeneity, the possible clinical and methodological factors for this were further explored. Potential sources of heterogeneity were investigated using sensitivity analyses and each study involved in the meta-analysis was excluded each time to reflect the influence of the individual data set on the pooled RRs.\nPublication bias was assessed using the Egger regression asymmetry test [21] and Begg adjusted rank correlation test [22]; a P value of less than 0.05 was considered representative of statistically significant publication bias. Meta-analysis results are displayed in forest plots. A p value < 0.05 was considered statistically significant.\n\nSearch strategy:\nThis meta-analysis was conducted according to the recommendations of the Meta-Analysis of Observational Studies in Epidemiology (MOOSE) [16]. A computerized search was performed using Pubmed, Embase, Ovid medline and Cochrane Library from their dates of inception to December 2015 without language restriction. The search terms “ischemic or ischaemic” and “mitral valve” and “repair or replacement or annuloplasty” and “coronary artery bypass grafting” were entered as MeSH terms and keywords. The language of publication was restricted to English. We also reviewed the full text and references lists of all relevant review articles in detail. YW and XS independently undertook the literature search, screening of titles and abstracts. Any disagreement was resolved by consensus.\n\nStudy selection:\nArticles were included if there is a direct comparison of repair versus replacement and all patients with IMR had CABG. The exclusion criteria were applied to select the final articles for the meta-analysis: (1) ischemic etiology in only a subset of the patients with outcomes not specifically provided (2) nonischemic dilated cardiomyopathy (3) beating heart procedures (4) concomitant surgical ventricular restoration (5) preoperative hemodynamic instability (6) lack of annuloplasty in > 20 % of the patients in the repair group (7) acute IMR.\n\nData extraction and quality assessment:\nAll data were extracted independently by 2 investigators (Y.W., X.S.) according to the prespecified selection criteria, with disagreement resolved by consensus among all authors. The following data from each study were extracted: the last name of the first author, year of publication, study population, patients’ age and gender, comorbidities, cardiac function, severity of mitral regurgitation at baseline and follow-up period. Any disagreement was resolved by consensus.\nBased on the extracted data, the quality of the included studies was evaluated using the nine-item Newcastle-Ottawa Quality scale [17], a widely used tool for the quality assessment of non-randomized trials. The high-quality study was defined as a study with ≥6 scores.\n\nStatistical analysis:\nThe primary end points were operative mortality and late mortality (considered to be year after operation). Operative mortality was defined as death within 30 days after operation or in-hospital death. Secondary end points were MR recurrence 2+ or greater and reoperation at follow-up. The meta-analysis was performed using Review Manager (Revman, version 5.3 for windows, Oxford, England, Cochrane Collaboration) and Stata (version 11.0; StataCorp, College Station, TX). Hazard ratio (HR) with a 95 % confidence intervals (CIs), directly extracted from these included studies or indirectly calculated using the method of Tierney and colleagues [18] to assess the efficacy of the surgical intervention in each study. A summary of odds ratio (OR) and their corresponding 95 % CI were computed for each dichotomous outcome using either fixed-effects models or, in the presence of substantial heterogeneity (I2 > 50 %), random-effects models [19]. Statistical heterogeneity across studies was examined with Cochran’s Q test as well as the I2 statistics. Studies with an I2 statistics of <25 % were considered to have low heterogeneity, those with an I2 statistics of 25–50 % were considered to have moderate heterogeneity, and those with an I2 statistics of >50 % were considered to have a high degree of heterogeneity [20]. If there was high heterogeneity, the possible clinical and methodological factors for this were further explored. Potential sources of heterogeneity were investigated using sensitivity analyses and each study involved in the meta-analysis was excluded each time to reflect the influence of the individual data set on the pooled RRs.\nPublication bias was assessed using the Egger regression asymmetry test [21] and Begg adjusted rank correlation test [22]; a P value of less than 0.05 was considered representative of statistically significant publication bias. Meta-analysis results are displayed in forest plots. A p value < 0.05 was considered statistically significant.\n\nResults:\n Search results and study quality The literature search identified a total of 545 studies, which were published between 1965 and 2015. On the basis of title and abstracts, 34 articles were selected and reviewed in full. Eleven articles met the inclusion and exclusion criteria [9–13, 23–28] (Fig. 1). Of the included studies, there were ten retrospective observational studies [9–13, 23–27] and one prospective observational study [28]. All were nonrandomized studies. These studies included a total of 1807 patients, 1091 (60.4 %) of whom underwent repair and 716 (39.6 %) of whom underwent replacement. All patients had CABG. Patient characteristics and a summary of operative details are summarized in Tables 1 and 2, respectively. With the exception of the replacement patients being older in 2 of the studies, the two groups were similar in terms of hypertension (HTN), diabetes, atrial fibrillation (AF), left ventricular ejection fraction (LVEF) and the New York Heart Association (NYHA) class. Eight of the studies reported data on the type of prosthesis used for mitral valve replacement and preservation of the subvalvular apparatus. In half of the studies, the majority of patients received a bioprothesis valve. In addition, preservation of the subvalvular (either total or partial) apparatus were performed in the vast majority of mitral valve replacements.Fig. 1Flow chart of study selectionTable 1Key Features of Included StudiesStudySubjectsMean AgeMale (%)HTN (%)Diabetes (%)AF (%)NYHA III-IV (%)Mean LVEF (%)MR gradeFollow-up periodMVP + CABGMVR + CABGMVP + CABGMVR + CABGMVP + CABGMVR + CABGMVP + CABGMVR + CABGMVP + CABGMVR + CABGMVP + CABGMVR + CABGMVP + CABGMVR + CABGMVP + CABGMVR + CABGMVP + CABGMVR + CABGLorusso et al.24424466667369414136351213NRNR35352.8 ± 0.52.8 ± 0.546.5bmoLio et al.98286570d\n746181893532NRNR61713234NRNR45bmoLjubacev et al.3441NRNRNRNR85803256d\n2617NRNRNRNRNRNRIn-hospitalRoshanali et al.263157578377NRNRNRNRNRNRNRNR38403.6 ± 0.53.5 ± 0.540.2a moMaltais et al.302857070686371683426NRNR85913434NRNR4.2ayrsQiu et al.1121067172645672753032282653493535NRNR48.1amoMicovic et al.865261b\n62b\n7273746521152729645029362.7 ± 0.62.5 ± 0.732a moBonacchi et al.3618NRNRNRNRNRNRNRNRNRNRNRNR2727NRNR32a moSilberman et al.38146267d\n749350574557NRNR49cd\n32cd\n<25 %NRNR38amoMantovani et al.61416868675454512615NRNRNRNR45453.1 ± 0.83.3 ± 0.736.8a moReece et al.5456676941d\n68d\nNRNR2221NRNRNRNR4440NRNRIn-hospital\na = mean; b = median; c Percentage class IV; d\np < 0.05 between MVr and MVR\nAbbreviations: AF atrial fibrillation, LVEF left ventricular ejection fraction, CABG coronary artery bypass grafting, MR mitral regurgitation, HTN hypertension, MVP mitral valve repair, MVR mitral valve replacement, NR not reportedTable 2Operative characteristicsCPB time (min)ACC time (min)MVR prosthesis typeSubvalvular apparatus preservationMVP partial/suture annuloplasty (%)MVP ring annuloplasty (%)MVP undersizingMVPMVRMVPMVRMechanical %Bioprothesis %Anterior + Posterior (%)Posterior (%)None (%)Lorusso et al. [9]14514594944753482443010027 (26 mm) 52 (28 mm) 13 (30 mm) 6 (32 mm) 1 (34 mm) 1 (36 mm)Lio et al. [10] 156180107132366410000010037 % open ring 63 % closed ring 37 % rigid ring 63 % semi-rigid ringLjubacev et al. [24]1451529699NRNRNRNRNRNRNRNRRoshanali et al. [28]NRNRNRNR100010000NRNRNRMaltais et al. [13]NRNRNRNR4654NRNRNR89242 (24–28 mm) 36 (30–34 mm)Qiu et al. [26]13612910598386211890010030 mmMicovic et al. [11]NRNRNRNR100001000595Median 28 mm (range, 26–34 mm)Bonacchi et al. [12]NRNRNRNRNRNR010001783NRSilberman et al. [23]154184991111000NRNRNR010026 ± 1.2 mmMantovani et al. [25]1791731311227624010000100ModerateReece et al. [27]112132152171NRNRNRNRNR010028 mm males 26 mm females\nAbbreviations: MVP mitral valve repair, MVR mitral valve replacement, ACC time aortic cross-clamping time, CPB time, cardiopulmonary bypass time, NR not reported\nFlow chart of study selection\nKey Features of Included Studies\n\na = mean; b = median; c Percentage class IV; d\np < 0.05 between MVr and MVR\n\nAbbreviations: AF atrial fibrillation, LVEF left ventricular ejection fraction, CABG coronary artery bypass grafting, MR mitral regurgitation, HTN hypertension, MVP mitral valve repair, MVR mitral valve replacement, NR not reported\nOperative characteristics\n\nAbbreviations: MVP mitral valve repair, MVR mitral valve replacement, ACC time aortic cross-clamping time, CPB time, cardiopulmonary bypass time, NR not reported\nAll the eleven trials were assessed by the Newcastle-Ottawa Scale for quality assessment risk evaluation of adequacy of selection, comparability, and outcomes assessment for individual trials (Table 3). All studies included in our meta-analysis were of high-quality (had ≥ 6 scores).Table 3Study quality assessment using the Newcastle-Ottawa Scale for nonrandomized studiesSelectionOutcomeFirst author, year of publication (reference)Representativeness of exposed cohortSelection of nonexposed cohortAscertainment of exposureOutcome of interest absent at start of studyComparability (Based on design and analysis)Assessment of outcomeFollow-up long enough for outcomesto occurAdequacy of follow-upTotal scoreLorusso et al.111101117Lio et al.111111118Ljubacev et al.111101016Roshanali et al.111121119Maltais et al.111121119Qiu et al.111121119Micovic et al.111121119Bonacchi et al.111121119Silberman et al.111111118Mantovani et al.111121119Reece et al.111111017\nStudy quality assessment using the Newcastle-Ottawa Scale for nonrandomized studies\nThe literature search identified a total of 545 studies, which were published between 1965 and 2015. On the basis of title and abstracts, 34 articles were selected and reviewed in full. Eleven articles met the inclusion and exclusion criteria [9–13, 23–28] (Fig. 1). Of the included studies, there were ten retrospective observational studies [9–13, 23–27] and one prospective observational study [28]. All were nonrandomized studies. These studies included a total of 1807 patients, 1091 (60.4 %) of whom underwent repair and 716 (39.6 %) of whom underwent replacement. All patients had CABG. Patient characteristics and a summary of operative details are summarized in Tables 1 and 2, respectively. With the exception of the replacement patients being older in 2 of the studies, the two groups were similar in terms of hypertension (HTN), diabetes, atrial fibrillation (AF), left ventricular ejection fraction (LVEF) and the New York Heart Association (NYHA) class. Eight of the studies reported data on the type of prosthesis used for mitral valve replacement and preservation of the subvalvular apparatus. In half of the studies, the majority of patients received a bioprothesis valve. In addition, preservation of the subvalvular (either total or partial) apparatus were performed in the vast majority of mitral valve replacements.Fig. 1Flow chart of study selectionTable 1Key Features of Included StudiesStudySubjectsMean AgeMale (%)HTN (%)Diabetes (%)AF (%)NYHA III-IV (%)Mean LVEF (%)MR gradeFollow-up periodMVP + CABGMVR + CABGMVP + CABGMVR + CABGMVP + CABGMVR + CABGMVP + CABGMVR + CABGMVP + CABGMVR + CABGMVP + CABGMVR + CABGMVP + CABGMVR + CABGMVP + CABGMVR + CABGMVP + CABGMVR + CABGLorusso et al.24424466667369414136351213NRNR35352.8 ± 0.52.8 ± 0.546.5bmoLio et al.98286570d\n746181893532NRNR61713234NRNR45bmoLjubacev et al.3441NRNRNRNR85803256d\n2617NRNRNRNRNRNRIn-hospitalRoshanali et al.263157578377NRNRNRNRNRNRNRNR38403.6 ± 0.53.5 ± 0.540.2a moMaltais et al.302857070686371683426NRNR85913434NRNR4.2ayrsQiu et al.1121067172645672753032282653493535NRNR48.1amoMicovic et al.865261b\n62b\n7273746521152729645029362.7 ± 0.62.5 ± 0.732a moBonacchi et al.3618NRNRNRNRNRNRNRNRNRNRNRNR2727NRNR32a moSilberman et al.38146267d\n749350574557NRNR49cd\n32cd\n<25 %NRNR38amoMantovani et al.61416868675454512615NRNRNRNR45453.1 ± 0.83.3 ± 0.736.8a moReece et al.5456676941d\n68d\nNRNR2221NRNRNRNR4440NRNRIn-hospital\na = mean; b = median; c Percentage class IV; d\np < 0.05 between MVr and MVR\nAbbreviations: AF atrial fibrillation, LVEF left ventricular ejection fraction, CABG coronary artery bypass grafting, MR mitral regurgitation, HTN hypertension, MVP mitral valve repair, MVR mitral valve replacement, NR not reportedTable 2Operative characteristicsCPB time (min)ACC time (min)MVR prosthesis typeSubvalvular apparatus preservationMVP partial/suture annuloplasty (%)MVP ring annuloplasty (%)MVP undersizingMVPMVRMVPMVRMechanical %Bioprothesis %Anterior + Posterior (%)Posterior (%)None (%)Lorusso et al. [9]14514594944753482443010027 (26 mm) 52 (28 mm) 13 (30 mm) 6 (32 mm) 1 (34 mm) 1 (36 mm)Lio et al. [10] 156180107132366410000010037 % open ring 63 % closed ring 37 % rigid ring 63 % semi-rigid ringLjubacev et al. [24]1451529699NRNRNRNRNRNRNRNRRoshanali et al. [28]NRNRNRNR100010000NRNRNRMaltais et al. [13]NRNRNRNR4654NRNRNR89242 (24–28 mm) 36 (30–34 mm)Qiu et al. [26]13612910598386211890010030 mmMicovic et al. [11]NRNRNRNR100001000595Median 28 mm (range, 26–34 mm)Bonacchi et al. [12]NRNRNRNRNRNR010001783NRSilberman et al. [23]154184991111000NRNRNR010026 ± 1.2 mmMantovani et al. [25]1791731311227624010000100ModerateReece et al. [27]112132152171NRNRNRNRNR010028 mm males 26 mm females\nAbbreviations: MVP mitral valve repair, MVR mitral valve replacement, ACC time aortic cross-clamping time, CPB time, cardiopulmonary bypass time, NR not reported\nFlow chart of study selection\nKey Features of Included Studies\n\na = mean; b = median; c Percentage class IV; d\np < 0.05 between MVr and MVR\n\nAbbreviations: AF atrial fibrillation, LVEF left ventricular ejection fraction, CABG coronary artery bypass grafting, MR mitral regurgitation, HTN hypertension, MVP mitral valve repair, MVR mitral valve replacement, NR not reported\nOperative characteristics\n\nAbbreviations: MVP mitral valve repair, MVR mitral valve replacement, ACC time aortic cross-clamping time, CPB time, cardiopulmonary bypass time, NR not reported\nAll the eleven trials were assessed by the Newcastle-Ottawa Scale for quality assessment risk evaluation of adequacy of selection, comparability, and outcomes assessment for individual trials (Table 3). All studies included in our meta-analysis were of high-quality (had ≥ 6 scores).Table 3Study quality assessment using the Newcastle-Ottawa Scale for nonrandomized studiesSelectionOutcomeFirst author, year of publication (reference)Representativeness of exposed cohortSelection of nonexposed cohortAscertainment of exposureOutcome of interest absent at start of studyComparability (Based on design and analysis)Assessment of outcomeFollow-up long enough for outcomesto occurAdequacy of follow-upTotal scoreLorusso et al.111101117Lio et al.111111118Ljubacev et al.111101016Roshanali et al.111121119Maltais et al.111121119Qiu et al.111121119Micovic et al.111121119Bonacchi et al.111121119Silberman et al.111111118Mantovani et al.111121119Reece et al.111111017\nStudy quality assessment using the Newcastle-Ottawa Scale for nonrandomized studies\n Peri-operative mortality Ten observational studies involving a total of 1750 patients reported operative mortality. The odds ratios in the study ranged from 0.16 to 2.32 (Fig. 2). The summary odds ratio was 0.65 (95 % CI, 0.43-1.00), P = 0.05, indicating there was a reduced peri-operative mortality trend towards repair, but no statistical significance reached. In assessing potential heterogeneity across the studies, I2 = 0 %, and no publication bias was found either from the Egger’s test (P = 0.83) or the Begg’s test (P = 0.68).Fig. 2Mitral valve repair versus mitral valve replacement on peri-operative mortality\nMitral valve repair versus mitral valve replacement on peri-operative mortality\nTen observational studies involving a total of 1750 patients reported operative mortality. The odds ratios in the study ranged from 0.16 to 2.32 (Fig. 2). The summary odds ratio was 0.65 (95 % CI, 0.43-1.00), P = 0.05, indicating there was a reduced peri-operative mortality trend towards repair, but no statistical significance reached. In assessing potential heterogeneity across the studies, I2 = 0 %, and no publication bias was found either from the Egger’s test (P = 0.83) or the Begg’s test (P = 0.68).Fig. 2Mitral valve repair versus mitral valve replacement on peri-operative mortality\nMitral valve repair versus mitral valve replacement on peri-operative mortality\n Late mortality A total of nine studies (1622 Patients) reported late mortality (Fig. 3). The overall hazard ratio was 0.87 (95 % CI, 0.67-1.14; P = 0.31), suggesting late mortality was not significantly reduced following repair. Further, heterogeneity was moderate (I2 = 30 %). It was noted that ten of the studies included patients with different degrees of regurgitation and left ventricular dysfunction, with exception of one study [23], all of the patients included in this study had severely impaired LV function (ejection fraction <25 %) and severe ischemic MR undergoing CABG. Severely decreased left ventricular function and severe IMR could have the potential pathophysiological effect on the mortality rates of those patients. Hence, sensitivity analysis was conducted to only include studies in which not all of the patients had severe ischemic MR and severely impaired LV function undergoing CABG. Restricting analysis to these studies had no significant impact on the reduction of late mortality following repair (the summary hazard ratio, 1.03; 95 % CI, 0.90-1.17; P = 0.66). Whereas, heterogeneity suggested by I2 was significantly reduced to 0 %, indicating no variability exists among the rest studies. Further exclusion of any single study did not significantly reduce the heterogeneity. In addition, our study included 10 retrospective studies and 1 prospective study. The different study designs may influence outcomes of meta-analysis. Therefore, sensitivity analysis was performed to only include retrospective studies. Restricting analysis to these studies did not significantly impact on the result for late mortality (HR, 0.86; 95 % CI, 0.64-1.14; P = 0.30; I2 = 38 %).Fig. 3Mitral valve repair versus mitral valve replacement on late mortality\nMitral valve repair versus mitral valve replacement on late mortality\nA total of nine studies (1622 Patients) reported late mortality (Fig. 3). The overall hazard ratio was 0.87 (95 % CI, 0.67-1.14; P = 0.31), suggesting late mortality was not significantly reduced following repair. Further, heterogeneity was moderate (I2 = 30 %). It was noted that ten of the studies included patients with different degrees of regurgitation and left ventricular dysfunction, with exception of one study [23], all of the patients included in this study had severely impaired LV function (ejection fraction <25 %) and severe ischemic MR undergoing CABG. Severely decreased left ventricular function and severe IMR could have the potential pathophysiological effect on the mortality rates of those patients. Hence, sensitivity analysis was conducted to only include studies in which not all of the patients had severe ischemic MR and severely impaired LV function undergoing CABG. Restricting analysis to these studies had no significant impact on the reduction of late mortality following repair (the summary hazard ratio, 1.03; 95 % CI, 0.90-1.17; P = 0.66). Whereas, heterogeneity suggested by I2 was significantly reduced to 0 %, indicating no variability exists among the rest studies. Further exclusion of any single study did not significantly reduce the heterogeneity. In addition, our study included 10 retrospective studies and 1 prospective study. The different study designs may influence outcomes of meta-analysis. Therefore, sensitivity analysis was performed to only include retrospective studies. Restricting analysis to these studies did not significantly impact on the result for late mortality (HR, 0.86; 95 % CI, 0.64-1.14; P = 0.30; I2 = 38 %).Fig. 3Mitral valve repair versus mitral valve replacement on late mortality\nMitral valve repair versus mitral valve replacement on late mortality\n Mitral valve reoperation Reoperation due to such as MV regurgitation, thromboembolism and prosthetic endocarditis was reported in five studies involving a total of 845 patients. The combined odds ratio was 1.47, suggesting the trend went towards the preference of replacement. Nevertheless, no significant differences were reached between the two surgical approaches (95 % CI, 0.90-2.38; I2 = 0 %; P = 0.12) (Fig. 4).Fig. 4Mitral valve repair versus mitral valve replacement on reoperation\nMitral valve repair versus mitral valve replacement on reoperation\nReoperation due to such as MV regurgitation, thromboembolism and prosthetic endocarditis was reported in five studies involving a total of 845 patients. The combined odds ratio was 1.47, suggesting the trend went towards the preference of replacement. Nevertheless, no significant differences were reached between the two surgical approaches (95 % CI, 0.90-2.38; I2 = 0 %; P = 0.12) (Fig. 4).Fig. 4Mitral valve repair versus mitral valve replacement on reoperation\nMitral valve repair versus mitral valve replacement on reoperation\n Recurrence of MR Five studies involving a total of 837 patients provided data regarding recurrence of MR during the follow-up. The MVP + CABG group was associated with a significantly increased recurrence rate of MR (OR, 5.41; 95 % CI: 3.12–9.38; P < 0.001) with low heterogeneity among those studies (I2 = 10 %) (Fig. 5). Sensitivity analysis was also performed to only include retrospective studies. Restricting analysis to these studies did not significantly impact on the result for recurrence of MR (OR, 5.97; 95 % CI, 3.36-10.58; P < 0.001; I2 = 0 %).Fig. 5Mitral valve repair versus mitral valve replacement on recurrence of mitral valve regurgitation\nMitral valve repair versus mitral valve replacement on recurrence of mitral valve regurgitation\nFive studies involving a total of 837 patients provided data regarding recurrence of MR during the follow-up. The MVP + CABG group was associated with a significantly increased recurrence rate of MR (OR, 5.41; 95 % CI: 3.12–9.38; P < 0.001) with low heterogeneity among those studies (I2 = 10 %) (Fig. 5). Sensitivity analysis was also performed to only include retrospective studies. Restricting analysis to these studies did not significantly impact on the result for recurrence of MR (OR, 5.97; 95 % CI, 3.36-10.58; P < 0.001; I2 = 0 %).Fig. 5Mitral valve repair versus mitral valve replacement on recurrence of mitral valve regurgitation\nMitral valve repair versus mitral valve replacement on recurrence of mitral valve regurgitation\n\nSearch results and study quality:\nThe literature search identified a total of 545 studies, which were published between 1965 and 2015. On the basis of title and abstracts, 34 articles were selected and reviewed in full. Eleven articles met the inclusion and exclusion criteria [9–13, 23–28] (Fig. 1). Of the included studies, there were ten retrospective observational studies [9–13, 23–27] and one prospective observational study [28]. All were nonrandomized studies. These studies included a total of 1807 patients, 1091 (60.4 %) of whom underwent repair and 716 (39.6 %) of whom underwent replacement. All patients had CABG. Patient characteristics and a summary of operative details are summarized in Tables 1 and 2, respectively. With the exception of the replacement patients being older in 2 of the studies, the two groups were similar in terms of hypertension (HTN), diabetes, atrial fibrillation (AF), left ventricular ejection fraction (LVEF) and the New York Heart Association (NYHA) class. Eight of the studies reported data on the type of prosthesis used for mitral valve replacement and preservation of the subvalvular apparatus. In half of the studies, the majority of patients received a bioprothesis valve. In addition, preservation of the subvalvular (either total or partial) apparatus were performed in the vast majority of mitral valve replacements.Fig. 1Flow chart of study selectionTable 1Key Features of Included StudiesStudySubjectsMean AgeMale (%)HTN (%)Diabetes (%)AF (%)NYHA III-IV (%)Mean LVEF (%)MR gradeFollow-up periodMVP + CABGMVR + CABGMVP + CABGMVR + CABGMVP + CABGMVR + CABGMVP + CABGMVR + CABGMVP + CABGMVR + CABGMVP + CABGMVR + CABGMVP + CABGMVR + CABGMVP + CABGMVR + CABGMVP + CABGMVR + CABGLorusso et al.24424466667369414136351213NRNR35352.8 ± 0.52.8 ± 0.546.5bmoLio et al.98286570d\n746181893532NRNR61713234NRNR45bmoLjubacev et al.3441NRNRNRNR85803256d\n2617NRNRNRNRNRNRIn-hospitalRoshanali et al.263157578377NRNRNRNRNRNRNRNR38403.6 ± 0.53.5 ± 0.540.2a moMaltais et al.302857070686371683426NRNR85913434NRNR4.2ayrsQiu et al.1121067172645672753032282653493535NRNR48.1amoMicovic et al.865261b\n62b\n7273746521152729645029362.7 ± 0.62.5 ± 0.732a moBonacchi et al.3618NRNRNRNRNRNRNRNRNRNRNRNR2727NRNR32a moSilberman et al.38146267d\n749350574557NRNR49cd\n32cd\n<25 %NRNR38amoMantovani et al.61416868675454512615NRNRNRNR45453.1 ± 0.83.3 ± 0.736.8a moReece et al.5456676941d\n68d\nNRNR2221NRNRNRNR4440NRNRIn-hospital\na = mean; b = median; c Percentage class IV; d\np < 0.05 between MVr and MVR\nAbbreviations: AF atrial fibrillation, LVEF left ventricular ejection fraction, CABG coronary artery bypass grafting, MR mitral regurgitation, HTN hypertension, MVP mitral valve repair, MVR mitral valve replacement, NR not reportedTable 2Operative characteristicsCPB time (min)ACC time (min)MVR prosthesis typeSubvalvular apparatus preservationMVP partial/suture annuloplasty (%)MVP ring annuloplasty (%)MVP undersizingMVPMVRMVPMVRMechanical %Bioprothesis %Anterior + Posterior (%)Posterior (%)None (%)Lorusso et al. [9]14514594944753482443010027 (26 mm) 52 (28 mm) 13 (30 mm) 6 (32 mm) 1 (34 mm) 1 (36 mm)Lio et al. [10] 156180107132366410000010037 % open ring 63 % closed ring 37 % rigid ring 63 % semi-rigid ringLjubacev et al. [24]1451529699NRNRNRNRNRNRNRNRRoshanali et al. [28]NRNRNRNR100010000NRNRNRMaltais et al. [13]NRNRNRNR4654NRNRNR89242 (24–28 mm) 36 (30–34 mm)Qiu et al. [26]13612910598386211890010030 mmMicovic et al. [11]NRNRNRNR100001000595Median 28 mm (range, 26–34 mm)Bonacchi et al. [12]NRNRNRNRNRNR010001783NRSilberman et al. [23]154184991111000NRNRNR010026 ± 1.2 mmMantovani et al. [25]1791731311227624010000100ModerateReece et al. [27]112132152171NRNRNRNRNR010028 mm males 26 mm females\nAbbreviations: MVP mitral valve repair, MVR mitral valve replacement, ACC time aortic cross-clamping time, CPB time, cardiopulmonary bypass time, NR not reported\nFlow chart of study selection\nKey Features of Included Studies\n\na = mean; b = median; c Percentage class IV; d\np < 0.05 between MVr and MVR\n\nAbbreviations: AF atrial fibrillation, LVEF left ventricular ejection fraction, CABG coronary artery bypass grafting, MR mitral regurgitation, HTN hypertension, MVP mitral valve repair, MVR mitral valve replacement, NR not reported\nOperative characteristics\n\nAbbreviations: MVP mitral valve repair, MVR mitral valve replacement, ACC time aortic cross-clamping time, CPB time, cardiopulmonary bypass time, NR not reported\nAll the eleven trials were assessed by the Newcastle-Ottawa Scale for quality assessment risk evaluation of adequacy of selection, comparability, and outcomes assessment for individual trials (Table 3). All studies included in our meta-analysis were of high-quality (had ≥ 6 scores).Table 3Study quality assessment using the Newcastle-Ottawa Scale for nonrandomized studiesSelectionOutcomeFirst author, year of publication (reference)Representativeness of exposed cohortSelection of nonexposed cohortAscertainment of exposureOutcome of interest absent at start of studyComparability (Based on design and analysis)Assessment of outcomeFollow-up long enough for outcomesto occurAdequacy of follow-upTotal scoreLorusso et al.111101117Lio et al.111111118Ljubacev et al.111101016Roshanali et al.111121119Maltais et al.111121119Qiu et al.111121119Micovic et al.111121119Bonacchi et al.111121119Silberman et al.111111118Mantovani et al.111121119Reece et al.111111017\nStudy quality assessment using the Newcastle-Ottawa Scale for nonrandomized studies\n\nPeri-operative mortality:\nTen observational studies involving a total of 1750 patients reported operative mortality. The odds ratios in the study ranged from 0.16 to 2.32 (Fig. 2). The summary odds ratio was 0.65 (95 % CI, 0.43-1.00), P = 0.05, indicating there was a reduced peri-operative mortality trend towards repair, but no statistical significance reached. In assessing potential heterogeneity across the studies, I2 = 0 %, and no publication bias was found either from the Egger’s test (P = 0.83) or the Begg’s test (P = 0.68).Fig. 2Mitral valve repair versus mitral valve replacement on peri-operative mortality\nMitral valve repair versus mitral valve replacement on peri-operative mortality\n\nLate mortality:\nA total of nine studies (1622 Patients) reported late mortality (Fig. 3). The overall hazard ratio was 0.87 (95 % CI, 0.67-1.14; P = 0.31), suggesting late mortality was not significantly reduced following repair. Further, heterogeneity was moderate (I2 = 30 %). It was noted that ten of the studies included patients with different degrees of regurgitation and left ventricular dysfunction, with exception of one study [23], all of the patients included in this study had severely impaired LV function (ejection fraction <25 %) and severe ischemic MR undergoing CABG. Severely decreased left ventricular function and severe IMR could have the potential pathophysiological effect on the mortality rates of those patients. Hence, sensitivity analysis was conducted to only include studies in which not all of the patients had severe ischemic MR and severely impaired LV function undergoing CABG. Restricting analysis to these studies had no significant impact on the reduction of late mortality following repair (the summary hazard ratio, 1.03; 95 % CI, 0.90-1.17; P = 0.66). Whereas, heterogeneity suggested by I2 was significantly reduced to 0 %, indicating no variability exists among the rest studies. Further exclusion of any single study did not significantly reduce the heterogeneity. In addition, our study included 10 retrospective studies and 1 prospective study. The different study designs may influence outcomes of meta-analysis. Therefore, sensitivity analysis was performed to only include retrospective studies. Restricting analysis to these studies did not significantly impact on the result for late mortality (HR, 0.86; 95 % CI, 0.64-1.14; P = 0.30; I2 = 38 %).Fig. 3Mitral valve repair versus mitral valve replacement on late mortality\nMitral valve repair versus mitral valve replacement on late mortality\n\nMitral valve reoperation:\nReoperation due to such as MV regurgitation, thromboembolism and prosthetic endocarditis was reported in five studies involving a total of 845 patients. The combined odds ratio was 1.47, suggesting the trend went towards the preference of replacement. Nevertheless, no significant differences were reached between the two surgical approaches (95 % CI, 0.90-2.38; I2 = 0 %; P = 0.12) (Fig. 4).Fig. 4Mitral valve repair versus mitral valve replacement on reoperation\nMitral valve repair versus mitral valve replacement on reoperation\n\nRecurrence of MR:\nFive studies involving a total of 837 patients provided data regarding recurrence of MR during the follow-up. The MVP + CABG group was associated with a significantly increased recurrence rate of MR (OR, 5.41; 95 % CI: 3.12–9.38; P < 0.001) with low heterogeneity among those studies (I2 = 10 %) (Fig. 5). Sensitivity analysis was also performed to only include retrospective studies. Restricting analysis to these studies did not significantly impact on the result for recurrence of MR (OR, 5.97; 95 % CI, 3.36-10.58; P < 0.001; I2 = 0 %).Fig. 5Mitral valve repair versus mitral valve replacement on recurrence of mitral valve regurgitation\nMitral valve repair versus mitral valve replacement on recurrence of mitral valve regurgitation\n\nDiscussion:\nIn our meta-analysis of eleven studies, which included patients undergoing repair or replacement electively with CABG surgery, no differences were found regarding peri-operative mortality and long-term survival. Mitral valve replacement was associated with lower incidence of mitral regurgitation in patients with IMR during CABG. The Society of Thoracic Surgeons reports MVP + CABG group had approximately 5 % (4.8 % in-hospital mortality and 5.3 % operative mortality) nationwide mortality rates in contrast with 8 % (7.8 % in-hospital mortality and 8.5 % operative mortality) for MVR + CABG group [29].\nModerate and severe recurrent MR after a restrictive annuloplasty ring, occurred early and affected a substantial proportion of patients by 2 years [30]. In the present study, the main disadvantage of MVP + CABG group is recurrence of MR compared to MVR + CABG group. Although MR recurrence was common, mitral valve reoperation was not. Similar reoperation rate was found between both groups, which suggested that not all patients with MR recurrence 2+ or greater needed reoperation. There were several possible explanations. IMR after annuloplasty might be considered inconsequential compared with the underlying myocardial disease, or surgeons might be hesitant to perform a second or third cardiac operation in these elderly, high-risk patients [31]. Many patients with recurrent MR were just too sick or too old or both to even consider reoperating on them.\nA case-matched study found that replacement was associated with lower incidence of valve-related complications than was repair and both mitral valve procedures showed no significant difference in LV function at follow-up [32]. However, replacement had greater thromboembolic and ischemic stroke rates than repair despite anticoagulant therapy [33]. Although mitral valve replacement can sufficiently correct regurgitation, the structural integrity of the mitral valve is usually compromised after replacement, leading to a continuous damage on the left ventricular tethered loop, which results in adverse effects on left ventricular contraction and poor prognosis [8]. Therefore, individualized consideration should be given to the two surgical procedures.\nTo date, there have been no RCTs that compared the clinical outcomes of the two surgical management particularly in patients with chronic IMR during CABG. To our knowledge, our report is the first meta-analysis comparing short-term and long-term outcomes of two mitral valve procedures specifically on patients with chronic IMR undergoing concomitant CABG. We selected studies for this meta-analysis with rigorous inclusion and exclusion criteria. All the patients in the studies underwent concomitant CABG, which ensures homogeneity of IMR patients and facilitates comparisons between trials. In addition, patients with acute IMR due to ruptured papillary muscles were excluded in our study, thus the outcomes of this meta-analysis not only truly reflect the surgical intervention of chronic IMR but also avoid biasing the results toward worsening the replacement group. By excluding articles that had > 20 % lack of annuloplasty ring, we have made the comparison between the two mitral valves surgeries more powerful. Therefore, the results of our study truly reflect the surgical management of patients with IMR simultaneous to CABG.\n Limitations Our meta-analysis has several limitations. Firstly, this study was based on observational, retrospective studies with inherent bias of such study designs. The publications included in this meta-analysis were relatively small and nonrandomized studies. Secondly, changes in NYHA class, LVEF and left ventricular reversal remodeling were too scarcely reported in the included studies to enable meta-analysis. Eight out of eleven studies included in our meta-analysis reported data on the subvalvular apparatus preservation in mitral valve replacement yet with lack of uniform preservation of both the anterior and posterior leaflets. The other three studies had no description regarding subvalvular apparatus preservation. Thirdly, potential confounding factors such as preoperative risk evaluation (STS score i.e.), mitral valve more suitable for repair, age, cause of mitral regurgitation (ischemia, fibrosis, ventricular remodeling), EF and complexity of revascularization were not considered or adjusted in some of the studies included in our meta-analysis. Therefore, the superiority of repair over replacement may be affected by this and other factors that are not possible to be revealed with meta-analysis of observational trials. Well-designed RCTs are required to further verify the conclusion. Another limitation of our report is the fact that follow-up periods were heterogeneous between some studies with different use of mean and median durations of follow-up. Therefore, subgroup analysis could not be performed statistically.\nOur meta-analysis has several limitations. Firstly, this study was based on observational, retrospective studies with inherent bias of such study designs. The publications included in this meta-analysis were relatively small and nonrandomized studies. Secondly, changes in NYHA class, LVEF and left ventricular reversal remodeling were too scarcely reported in the included studies to enable meta-analysis. Eight out of eleven studies included in our meta-analysis reported data on the subvalvular apparatus preservation in mitral valve replacement yet with lack of uniform preservation of both the anterior and posterior leaflets. The other three studies had no description regarding subvalvular apparatus preservation. Thirdly, potential confounding factors such as preoperative risk evaluation (STS score i.e.), mitral valve more suitable for repair, age, cause of mitral regurgitation (ischemia, fibrosis, ventricular remodeling), EF and complexity of revascularization were not considered or adjusted in some of the studies included in our meta-analysis. Therefore, the superiority of repair over replacement may be affected by this and other factors that are not possible to be revealed with meta-analysis of observational trials. Well-designed RCTs are required to further verify the conclusion. Another limitation of our report is the fact that follow-up periods were heterogeneous between some studies with different use of mean and median durations of follow-up. Therefore, subgroup analysis could not be performed statistically.\n\nLimitations:\nOur meta-analysis has several limitations. Firstly, this study was based on observational, retrospective studies with inherent bias of such study designs. The publications included in this meta-analysis were relatively small and nonrandomized studies. Secondly, changes in NYHA class, LVEF and left ventricular reversal remodeling were too scarcely reported in the included studies to enable meta-analysis. Eight out of eleven studies included in our meta-analysis reported data on the subvalvular apparatus preservation in mitral valve replacement yet with lack of uniform preservation of both the anterior and posterior leaflets. The other three studies had no description regarding subvalvular apparatus preservation. Thirdly, potential confounding factors such as preoperative risk evaluation (STS score i.e.), mitral valve more suitable for repair, age, cause of mitral regurgitation (ischemia, fibrosis, ventricular remodeling), EF and complexity of revascularization were not considered or adjusted in some of the studies included in our meta-analysis. Therefore, the superiority of repair over replacement may be affected by this and other factors that are not possible to be revealed with meta-analysis of observational trials. Well-designed RCTs are required to further verify the conclusion. Another limitation of our report is the fact that follow-up periods were heterogeneous between some studies with different use of mean and median durations of follow-up. Therefore, subgroup analysis could not be performed statistically.\n\nConclusions:\nIn patients with chronic ischemic mitral regurgitation during CABG, mitral valve replacement is associated with lower recurrence of regurgitation. No differences were found regarding survival and reoperation rates.\n\n"},"whole_article_abstract":{"kind":"string","value":"Background:\nNo agreement has been reached for the best surgical treatment for patients with chronic ischemic mitral regurgitation (IMR) undergoing coronary artery bypass grafting (CABG). Our objective was to meta-analyze the clinical outcomes of repair and replacement.\n\nMethods:\nA computerized search was performed using Pubmed, Embase, Ovid medline and Cochrane Library. The search terms \"ischemic or ischaemic\" and \"mitral valve\" and \"repair or replacement or annuloplasty\" and \"coronary artery bypass grafting\" were entered as MeSH terms and keywords. The primary outcomes were operative mortality and late mortality. Secondary outcomes were 2+ or greater recurrence of mitral regurgitation and reoperation rate.\n\nResults:\nEleven studies were eligible for the final meta-analysis. These studies included a total of 1750 patients, 60.4 % of whom received mitral valve repair. All patients underwent concomitant coronary artery bypass graft. No differences were found in operative mortality (summary odds ratio [OR] 0.65; 95 % confidence interval [CI] 0.43-1.00; p = 0.05), late mortality (summary hazard ratio [HR] 0.87; 95 % confidence interval [CI] 0.67-1.14; p = 0.31) and reoperation (summary odds ratio [OR] 1.47; 95 % confidence interval [CI] 0.90-2.38; p = 0.12). Regurgitation recurrence was lower in the replacement group (summary odds ratio [OR] 5.41; 95 % confidence interval [CI] 3.12-9.38; p < 0.001).\n\nConclusions:\nIn patients with chronic ischemic mitral regurgitation during CABG, mitral valve replacement is associated with lower recurrence of regurgitation. No differences were found regarding survival and reoperation rates."},"background_conclusion_text":{"kind":"string","value":"Background:\nChronic ischemic mitral regurgitation (IMR) is a frequent and important complication after myocardial infarction. Its pathophysiologic mechanisms account for remodeling of segmental/global left ventricle (LV) inducing papillary muscle displacement and leaflet tethering [1]. The presence of IMR is independently associated with mortality and morbidity after myocardial infarction [2].\nGiven the severity of IMR, surgery performed for IMR ranges from coronary artery bypass grafting (CABG) alone to both CABG and mitral valve surgery [3, 4]. Two randomized trials indicated that repair was associated with a reduced prevalence of mitral regurgitation but did not show a clinically meaningful advantage of adding mitral valve repair to CABG [5, 6]. In addition, when compared with replacement, previous meta-analyses concluded that repair is associated with lower operative mortality but higher recurrence of regurgitation in patients with ischemic mitral regurgitation, with or without CABG [7, 8]. For patients with chronic IMR undergoing combined CABG, the best surgical treatment is still controversial. Some studies support replacement [9, 10], others support repair [11, 12], and others showed similar survival for the two procedures [13]. Current guidelines recommend mitral valve surgery for severe IMR, but do not demonstrate a specific type of procedure [14, 15]. Numerous non-randomized studies have been published comparing the clinical outcomes between MVP + CABG and MVR + CABG for IMR. However, there is still no systematic and quantitative assessment of accumulated literature on this topic. Meta-analysis is a powerful tool to provide meaningful comparison of short and long-term outcomes of these procedures. The present meta-analysis aimed to assess the clinical outcomes of patients who underwent mitral valve surgery and CABG for chronic IMR.\n\nConclusions:\nIn patients with chronic ischemic mitral regurgitation during CABG, mitral valve replacement is associated with lower recurrence of regurgitation. No differences were found regarding survival and reoperation rates."},"background_conclusion_abstract":{"kind":"string","value":"Background:\nNo agreement has been reached for the best surgical treatment for patients with chronic ischemic mitral regurgitation (IMR) undergoing coronary artery bypass grafting (CABG). Our objective was to meta-analyze the clinical outcomes of repair and replacement.\n\nMethods:\nA computerized search was performed using Pubmed, Embase, Ovid medline and Cochrane Library. The search terms \"ischemic or ischaemic\" and \"mitral valve\" and \"repair or replacement or annuloplasty\" and \"coronary artery bypass grafting\" were entered as MeSH terms and keywords. The primary outcomes were operative mortality and late mortality. Secondary outcomes were 2+ or greater recurrence of mitral regurgitation and reoperation rate.\n\nResults:\nEleven studies were eligible for the final meta-analysis. These studies included a total of 1750 patients, 60.4 % of whom received mitral valve repair. All patients underwent concomitant coronary artery bypass graft. No differences were found in operative mortality (summary odds ratio [OR] 0.65; 95 % confidence interval [CI] 0.43-1.00; p = 0.05), late mortality (summary hazard ratio [HR] 0.87; 95 % confidence interval [CI] 0.67-1.14; p = 0.31) and reoperation (summary odds ratio [OR] 1.47; 95 % confidence interval [CI] 0.90-2.38; p = 0.12). Regurgitation recurrence was lower in the replacement group (summary odds ratio [OR] 5.41; 95 % confidence interval [CI] 3.12-9.38; p < 0.001).\n\nConclusions:\nIn patients with chronic ischemic mitral regurgitation during CABG, mitral valve replacement is associated with lower recurrence of regurgitation. No differences were found regarding survival and reoperation rates."},"whole_article_text_length":{"kind":"number","value":9512,"string":"9,512"},"whole_article_abstract_length":{"kind":"number","value":332,"string":"332"},"other_sections_lengths":{"kind":"list like","value":[138,105,140,383,1010,145,356,102,161,261],"string":"[\n 138,\n 105,\n 140,\n 383,\n 1010,\n 145,\n 356,\n 102,\n 161,\n 261\n]"},"num_sections":{"kind":"number","value":15,"string":"15"},"most_frequent_words":{"kind":"list like","value":["studies","mitral","valve","mitral valve","analysis","repair","replacement","study","patients","mortality"],"string":"[\n \"studies\",\n \"mitral\",\n \"valve\",\n \"mitral valve\",\n \"analysis\",\n \"repair\",\n \"replacement\",\n \"study\",\n \"patients\",\n \"mortality\"\n]"},"keybert_topics":{"kind":"list like","value":["cabg mitral valve","mitral valve reoperation","severity mitral regurgitation","mitral regurgitation imr","ischemic mitral regurgitation"],"string":"[\n \"cabg mitral valve\",\n \"mitral valve reoperation\",\n \"severity mitral regurgitation\",\n \"mitral regurgitation imr\",\n \"ischemic mitral regurgitation\"\n]"},"annotated_base_background_abstract_prompt":{"kind":"string","value":""},"annotated_base_methods_abstract_prompt":{"kind":"null"},"annotated_base_results_abstract_prompt":{"kind":"string","value":""},"annotated_base_conclusions_abstract_prompt":{"kind":"string","value":""},"annotated_base_whole_article_abstract_prompt":{"kind":"string","value":""},"annotated_base_background_conclusion_abstract_prompt":{"kind":"string","value":""},"annotated_keywords_background_abstract_prompt":{"kind":"string","value":"[CONTENT] Ischemic mitral regurgitation | Mitral valve repair | Mitral valve replacement | Coronary artery bypass grafting | Meta-analysis [SUMMARY]"},"annotated_keywords_methods_abstract_prompt":{"kind":"null"},"annotated_keywords_results_abstract_prompt":{"kind":"string","value":"[CONTENT] Ischemic mitral regurgitation | Mitral valve repair | Mitral valve replacement | Coronary artery bypass grafting | Meta-analysis [SUMMARY]"},"annotated_keywords_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] Ischemic mitral regurgitation | Mitral valve repair | Mitral valve replacement | Coronary artery bypass grafting | Meta-analysis [SUMMARY]"},"annotated_keywords_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] Ischemic mitral regurgitation | Mitral valve repair | Mitral valve replacement | Coronary artery bypass grafting | Meta-analysis [SUMMARY]"},"annotated_keywords_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] Ischemic mitral regurgitation | Mitral valve repair | Mitral valve replacement | Coronary artery bypass grafting | Meta-analysis [SUMMARY]"},"annotated_mesh_background_abstract_prompt":{"kind":"string","value":"[CONTENT] Aged | Coronary Artery Bypass | Female | Heart Valve Prosthesis Implantation | Humans | Male | Middle Aged | Mitral Valve | Mitral Valve Insufficiency | Myocardial Ischemia | Reoperation | Survival Rate | Treatment Outcome [SUMMARY]"},"annotated_mesh_methods_abstract_prompt":{"kind":"null"},"annotated_mesh_results_abstract_prompt":{"kind":"string","value":"[CONTENT] Aged | Coronary Artery Bypass | Female | Heart Valve Prosthesis Implantation | Humans | Male | Middle Aged | Mitral Valve | Mitral Valve Insufficiency | Myocardial Ischemia | Reoperation | Survival Rate | Treatment Outcome [SUMMARY]"},"annotated_mesh_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] Aged | Coronary Artery Bypass | Female | Heart Valve Prosthesis Implantation | Humans | Male | Middle Aged | Mitral Valve | Mitral Valve Insufficiency | Myocardial Ischemia | Reoperation | Survival Rate | Treatment Outcome [SUMMARY]"},"annotated_mesh_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] Aged | Coronary Artery Bypass | Female | Heart Valve Prosthesis Implantation | Humans | Male | Middle Aged | Mitral Valve | Mitral Valve Insufficiency | Myocardial Ischemia | Reoperation | Survival Rate | Treatment Outcome [SUMMARY]"},"annotated_mesh_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] Aged | Coronary Artery Bypass | Female | Heart Valve Prosthesis Implantation | Humans | Male | Middle Aged | Mitral Valve | Mitral Valve Insufficiency | Myocardial Ischemia | Reoperation | Survival Rate | Treatment Outcome [SUMMARY]"},"annotated_keybert_background_abstract_prompt":{"kind":"string","value":"[CONTENT] cabg mitral valve | mitral valve reoperation | severity mitral regurgitation | mitral regurgitation imr | ischemic mitral regurgitation [SUMMARY]"},"annotated_keybert_methods_abstract_prompt":{"kind":"null"},"annotated_keybert_results_abstract_prompt":{"kind":"string","value":"[CONTENT] cabg mitral valve | mitral valve reoperation | severity mitral regurgitation | mitral regurgitation imr | ischemic mitral regurgitation [SUMMARY]"},"annotated_keybert_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] cabg mitral valve | mitral valve reoperation | severity mitral regurgitation | mitral regurgitation imr | ischemic mitral regurgitation [SUMMARY]"},"annotated_keybert_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] cabg mitral valve | mitral valve reoperation | severity mitral regurgitation | mitral regurgitation imr | ischemic mitral regurgitation [SUMMARY]"},"annotated_keybert_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] cabg mitral valve | mitral valve reoperation | severity mitral regurgitation | mitral regurgitation imr | ischemic mitral regurgitation [SUMMARY]"},"annotated_most_frequent_background_abstract_prompt":{"kind":"string","value":"[CONTENT] studies | mitral | valve | mitral valve | analysis | repair | replacement | study | patients | mortality [SUMMARY]"},"annotated_most_frequent_methods_abstract_prompt":{"kind":"null"},"annotated_most_frequent_results_abstract_prompt":{"kind":"string","value":"[CONTENT] studies | mitral | valve | mitral valve | analysis | repair | replacement | study | patients | mortality [SUMMARY]"},"annotated_most_frequent_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] studies | mitral | valve | mitral valve | analysis | repair | replacement | study | patients | mortality [SUMMARY]"},"annotated_most_frequent_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] studies | mitral | valve | mitral valve | analysis | repair | replacement | study | patients | mortality [SUMMARY]"},"annotated_most_frequent_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] studies | mitral | valve | mitral valve | analysis | repair | replacement | study | patients | mortality [SUMMARY]"},"annotated_tf_idf_background_abstract_prompt":{"kind":"string","value":"[CONTENT] imr | cabg | surgery | valve surgery | mitral valve surgery | mitral | chronic | meaningful | infarction | myocardial infarction [SUMMARY]"},"annotated_tf_idf_methods_abstract_prompt":{"kind":"null"},"annotated_tf_idf_results_abstract_prompt":{"kind":"string","value":"[CONTENT] valve | mm | mitral | mitral valve | studies | cabgmvr | time | cabgmvp cabgmvr | cabgmvr cabgmvp | cabgmvr cabgmvp cabgmvr [SUMMARY]"},"annotated_tf_idf_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] regurgitation differences | reoperation rates | regurgitation cabg mitral | regurgitation cabg mitral valve | survival reoperation | regurgitation differences found | patients chronic ischemic | patients chronic ischemic mitral | regurgitation differences found survival | recurrence regurgitation differences [SUMMARY]"},"annotated_tf_idf_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] mitral | valve | studies | mitral valve | analysis | repair | study | replacement | patients | mortality [SUMMARY]"},"annotated_tf_idf_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] mitral | valve | studies | mitral valve | analysis | repair | study | replacement | patients | mortality [SUMMARY]"},"annotated_entity_plan_background_abstract_prompt":{"kind":"string","value":"[CONTENT] ||| [SUMMARY]"},"annotated_entity_plan_methods_abstract_prompt":{"kind":"null"},"annotated_entity_plan_results_abstract_prompt":{"kind":"string","value":"[CONTENT] ||| 1750 | 60.4 % ||| ||| ||| 0.65 | 95 % | CI | 0.43 | 0.05 ||| 0.87 | 95 % | CI] 0.67-1.14 | 0.31 ||| 1.47 | 95 % | CI] 0.90-2.38 | 0.12 ||| 5.41 | 95 % | CI | 3.12-9.38 | 0.001 [SUMMARY]"},"annotated_entity_plan_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] CABG ||| [SUMMARY]"},"annotated_entity_plan_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] ||| ||| Pubmed, Embase | Ovid | Cochrane Library ||| ||| ||| 2+ ||| ||| ||| 1750 | 60.4 % ||| ||| ||| 0.65 | 95 % | CI | 0.43 | 0.05 ||| 0.87 | 95 % | CI] 0.67-1.14 | 0.31 ||| 1.47 | 95 % | CI] 0.90-2.38 | 0.12 ||| 5.41 | 95 % | CI | 3.12-9.38 | 0.001 ||| CABG ||| [SUMMARY]"},"annotated_entity_plan_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] ||| ||| Pubmed, Embase | Ovid | Cochrane Library ||| ||| ||| 2+ ||| ||| ||| 1750 | 60.4 % ||| ||| ||| 0.65 | 95 % | CI | 0.43 | 0.05 ||| 0.87 | 95 % | CI] 0.67-1.14 | 0.31 ||| 1.47 | 95 % | CI] 0.90-2.38 | 0.12 ||| 5.41 | 95 % | CI | 3.12-9.38 | 0.001 ||| CABG ||| [SUMMARY]"}}},{"rowIdx":293,"cells":{"title":{"kind":"string","value":"Novel reassortant 2.3.4.4B H5N6 highly pathogenic avian influenza viruses circulating among wild, domestic birds in Xinjiang, Northwest China."},"pmid":{"kind":"string","value":"34170087"},"background_abstract":{"kind":"string","value":"The H5 avian influenza viruses (AIVs) of clade 2.3.4.4 circulate in wild and domestic birds worldwide. In 2017, nine strains of H5N6 AIVs were isolated from aquatic poultry in Xinjiang, Northwest China."},"background_abstract_label":{"kind":"string","value":"BACKGROUND"},"methods_abstract":{"kind":"string","value":"AIVs were isolated from oropharyngeal and cloacal swabs of poultry. Identification was accomplished by inoculating isolates into embryonated chicken eggs and performing hemagglutination tests and reverse transcription polymerase chain reaction (RT-PCR). The viral genomes were amplified with RT-PCR and then sequenced. The sequence alignment, phylogenetic, and molecular characteristic analyses were performed by using bioinformatic software."},"methods_abstract_label":{"kind":"string","value":"METHODS"},"results_abstract":{"kind":"string","value":"Nine isolates originated from the same ancestor. The viral HA gene belonged to clade 2.3.4.4B, while the NA gene had a close phylogenetic relationship with the 2.3.4.4C H5N6 highly pathogenic avian influenza viruses (HPAIVs) isolated from shoveler ducks in Ningxia in 2015. The NP gene was grouped into an independent subcluster within the 2.3.4.4B H5N8 AIVs, and the remaining six genes all had close phylogenetic relationships with the 2.3.4.4B H5N8 HPAIVs isolated from the wild birds in China, Egypt, Uganda, Cameroon, and India in 2016-2017, Multiple basic amino acid residues associated with HPAIVs were located adjacent to the cleavage site of the HA protein. The nine isolates comprised reassortant 2.3.4.4B HPAIVs originating from 2.3.4.4B H5N8 and 2.3.4.4C H5N6 viruses in wild birds."},"results_abstract_label":{"kind":"string","value":"RESULTS"},"conclusions_abstract":{"kind":"string","value":"These results suggest that the Northern Tianshan Mountain wetlands in Xinjiang may have a key role in AIVs disseminating from Central China to the Eurasian continent and East African."},"conclusions_abstract_label":{"kind":"string","value":"CONCLUSIONS"},"mesh_descriptor_names":{"kind":"list like","value":["Animals","Animals, Domestic","Animals, Wild","Birds","China","Influenza A virus","Influenza in Birds","Phylogeny","Reassortant Viruses","Virulence","Whole Genome Sequencing"],"string":"[\n \"Animals\",\n \"Animals, Domestic\",\n \"Animals, Wild\",\n \"Birds\",\n \"China\",\n \"Influenza A virus\",\n \"Influenza in Birds\",\n \"Phylogeny\",\n \"Reassortant Viruses\",\n \"Virulence\",\n \"Whole Genome Sequencing\"\n]"},"pmcid":{"kind":"string","value":"8318794"},"background_title":{"kind":"string","value":"INTRODUCTION"},"background_text":{"kind":"string","value":"Since 2014, highly pathogenic avian influenza viruses (HPAIVs) of clade 2.3.4.4 (H5) have been monitored in poultry and wild birds in European and Asian countries, and the viruses of that clade have evolved into eight groups (A-H) [12]. Group A includes H5N8 avian influenza viruses (AIVs) that were identified in Asia, Europe, and North America [1]. In 2016, a novel H5N8 AIV lineage initially emerged in the wild birds in Qinghai Lake, spread to Mongolia, Siberia, and Europe, and was subsequently identified in China and Korea; this new lineage was classified into clade 2.3.4.4B [3]. On the other hand, group C H5N6 AIVs were first reported in Laos in 2013 and gradually became the main source of sporadic AIV infections in poultry in Southern China [4]. Group D comprises H5N6 viruses mainly identified in China and Vietnam. Subsequent studies have reported that H5N6 viruses of groups 2.3.4.4E, 2.3.4.4F, 2.3.4.4G, and 2.3.4.4H have been isolated from poultry and wild birds [2].\nOutbreaks of 2.3.4.4B H5N8 AIVs were reported in South Korea in 2014 [5]. The viruses were subsequently disseminated via migratory birds and have since been detected in wild and domestic birds worldwide [12678]. Almost simultaneously, 2.3.4.4B H5N6 HPAIVs were undergoing global transmission and have been detected in wild and domestic birds [8910111213]. The cocirculation of H5N6 and H5N8 viruses among wild and domestic birds could increase the probability of reassorting novel viruses [91011]; indeed, reassortants derived from the H5N6 and H5N8 HPAIVs have been detected in various wild birds and poultry [891415]. Moreover, it has been reported that 46% of all 2.3.4.4B H5N6 viruses in domestic poultry were derived from wild birds [9]. Thus, reassortant viruses could pose potential threats to poultry farming and human public health. In this study, nine H5N6 AIVs were isolated from aquatic poultry in Xinjiang Uyghur Autonomous Region, China, and all were grouped into clade 2.3.4.4B. This study aimed to analyze the origin of the isolates."},"methods_title":{"kind":"null"},"methods_text":{"kind":"null"},"results_title":{"kind":"string","value":"RESULTS"},"results_text":{"kind":"string","value":"Virus isolation Nine strains of H5N6 AIV were isolated between July and November 2017, one from a duck swab and the rest from goose swabs. The eight gene segments of the 9 isolates shared very high nucleotide homology (> 99.9%); thus, the isolates originated from the same ancestor. The strains have been designated as A/goose/Xinjiang/011-015/2017(H5N6), A/duck/Xinjiang/016/2017 (H5N6), and A/goose/Xinjiang/017-019/2017(H5N6), or XJ-H5N6/2017 for short. The complete sequences of the isolates have been submitted to NCBI (accession numbers: MW109029-MW109036, MW110101-MW110108, and MW110205-MW110260).\nNine strains of H5N6 AIV were isolated between July and November 2017, one from a duck swab and the rest from goose swabs. The eight gene segments of the 9 isolates shared very high nucleotide homology (> 99.9%); thus, the isolates originated from the same ancestor. The strains have been designated as A/goose/Xinjiang/011-015/2017(H5N6), A/duck/Xinjiang/016/2017 (H5N6), and A/goose/Xinjiang/017-019/2017(H5N6), or XJ-H5N6/2017 for short. The complete sequences of the isolates have been submitted to NCBI (accession numbers: MW109029-MW109036, MW110101-MW110108, and MW110205-MW110260).\nSequence and phylogenetic analysis Homology BLAST and phylogenetic analyses were performed on eight genes of the viral isolates (Table 1 and Fig. 1). The viral NA gene exhibited the highest sequence homology (99.1%), and the sequence clustered together with those of H5N6 AIVs isolated from shoveler ducks sampled in Ningxia (NX488-53) and from the environment of Chongqing in 2015; those sequences were designated as 2.3.4.4C H5N6 AIVs. The viral NP gene segments shared the highest sequence homology (99.4%) with 2.3.4.4B H5N8 AIVs isolated from the green-winged teal in Egypt in 2016 and were grouped into an independent subcluster within the phylogenetic tree. The remaining six genes had the highest sequence homologies (99.1%–99.6%) and relatively close genetic relationships in the phylogenetic tree with the 2.3.4.4B H5N8 AIVs. Five of those genes, HA, PB2, NS, PA, and MP, clustered together with 2.3.4.4B H5N8 AIVs isolated from migratory swans in Sanmenxia, Hubei, and Shanxi in 2016, and this sixth gene, PB1, clustered together with H5N8 AIVs isolated from grey-headed gulls sampled in Uganda in 2017. The above results suggest that XJ-H5N6/2017 is a novel reassortant 2.3.4.4B H5N6 AIV derived from 2.3.4.4B H5N8 and 2.3.4.4C H5N6 viruses present in wild birds.\nHomology BLAST and phylogenetic analyses were performed on eight genes of the viral isolates (Table 1 and Fig. 1). The viral NA gene exhibited the highest sequence homology (99.1%), and the sequence clustered together with those of H5N6 AIVs isolated from shoveler ducks sampled in Ningxia (NX488-53) and from the environment of Chongqing in 2015; those sequences were designated as 2.3.4.4C H5N6 AIVs. The viral NP gene segments shared the highest sequence homology (99.4%) with 2.3.4.4B H5N8 AIVs isolated from the green-winged teal in Egypt in 2016 and were grouped into an independent subcluster within the phylogenetic tree. The remaining six genes had the highest sequence homologies (99.1%–99.6%) and relatively close genetic relationships in the phylogenetic tree with the 2.3.4.4B H5N8 AIVs. Five of those genes, HA, PB2, NS, PA, and MP, clustered together with 2.3.4.4B H5N8 AIVs isolated from migratory swans in Sanmenxia, Hubei, and Shanxi in 2016, and this sixth gene, PB1, clustered together with H5N8 AIVs isolated from grey-headed gulls sampled in Uganda in 2017. The above results suggest that XJ-H5N6/2017 is a novel reassortant 2.3.4.4B H5N6 AIV derived from 2.3.4.4B H5N8 and 2.3.4.4C H5N6 viruses present in wild birds.\nMolecular characteristics of the H5N6 virus isolates Assessment of the multiple amino acid mutations present in the nine isolates could help elucidate H5N6 virulence. Multiple basic amino acids (KEKRRKR↓GLF) were observed at cleavage sites in the HA protein of the H5N6 isolates (Table 2), suggesting that the isolates were HPAIVs. The viral receptor-binding sites all contained Q226 and G228 (H3 numbering), indicative of an avian-like α2,3-sialic acid (α2,3-SA) receptor-binding preference; however, the S128P, S137A, and T160A mutations in HA could enhance the capacity to bind to a human-like α-2,6-SA receptor [17]. In all nine isolates, 11 amino acid deletions (59–69) in the NA stalk were observed, suggesting the isolates could have different adaptive and virulence characteristics in poultry and mammals [18]. In addition, the L89V, G309D, R477G, I495V [19], and I504V [20] mutations in the PB2 protein and the P42S and D92E mutations in the NS1 were retained in all nine isolates; notably, those mutations can enhance viral virulence and replication activity in mammals [21]. Moreover, the M2-F38L mutation has been associated with antiviral resistance (amantadine and rimantadine) [22].\nAssessment of the multiple amino acid mutations present in the nine isolates could help elucidate H5N6 virulence. Multiple basic amino acids (KEKRRKR↓GLF) were observed at cleavage sites in the HA protein of the H5N6 isolates (Table 2), suggesting that the isolates were HPAIVs. The viral receptor-binding sites all contained Q226 and G228 (H3 numbering), indicative of an avian-like α2,3-sialic acid (α2,3-SA) receptor-binding preference; however, the S128P, S137A, and T160A mutations in HA could enhance the capacity to bind to a human-like α-2,6-SA receptor [17]. In all nine isolates, 11 amino acid deletions (59–69) in the NA stalk were observed, suggesting the isolates could have different adaptive and virulence characteristics in poultry and mammals [18]. In addition, the L89V, G309D, R477G, I495V [19], and I504V [20] mutations in the PB2 protein and the P42S and D92E mutations in the NS1 were retained in all nine isolates; notably, those mutations can enhance viral virulence and replication activity in mammals [21]. Moreover, the M2-F38L mutation has been associated with antiviral resistance (amantadine and rimantadine) [22]."},"conclusions_title":{"kind":"null"},"conclusions_text":{"kind":"null"},"other_sections_titles":{"kind":"list like","value":["Sample collection","Virus isolation and identification","Whole-genome sequencing","Phylogenetic analysis","Virus isolation","Sequence and phylogenetic analysis","Molecular characteristics of the H5N6 virus isolates"],"string":"[\n \"Sample collection\",\n \"Virus isolation and identification\",\n \"Whole-genome sequencing\",\n \"Phylogenetic analysis\",\n \"Virus isolation\",\n \"Sequence and phylogenetic analysis\",\n \"Molecular characteristics of the H5N6 virus isolates\"\n]"},"other_sections_texts":{"kind":"list like","value":["From March 2017 to December 2018, a total of 354 oropharyngeal and cloacal swabs from apparently healthy poultry (chickens, ducks, and geese) in live poultry markets (LPMs) in Urumqi, China were collected and placed in tubes containing viral transport medium DMEM (1,000 u/L penicillin, 500 ug/L streptomycin, 100 mg/L Nystatin, 100 mg/L Polymyxin B sulfate salt, 1000 mg/L Sulfamethoxazole, 0.05 g/L Ofloxacin). These tubes were kept in an icebox at −20°C before transport to the laboratory and then immediately stored at −80°C.\nThe animal experiment portion of this study was approved by the Committee on the Ethics of Animal Experiments of Xinjiang Key Laboratory of Biological Resources and Genetic Engineering (BRGE-AE001) and carried out under the guidelines of the Animal Care and Use Committee of the College of Life Science and Technology, Xinjiang University.","The swab samples were vortexed and centrifuged, and the supernatants inoculated into 10-day-old specific-pathogen-free embryonated chicken eggs. Seventy-two hours after inoculation, allantois fluid was harvested, and hemagglutinin (HA) activity was assayed using 1% chicken red blood cells [14]. All HA-positive samples underwent reverse transcription polymerase chain reaction (RT-PCR) using universal primers targeting the PB1 gene [15]. Viral RNA extraction (Bioer) and RT-PCR (TOYOBO) were performed according to the manufacturers' instructions.\nRoutine surveillance samples were processed in a biosafety level 2 (BSL-2) laboratory of the Center for Influenza Research and Early-Warning (CASCIRE), Chinese Academy of Science, while experiments with live H5N6 viruses were conducted in a CASCIRE biosafety level 3 (BSL-3) laboratory. Coveralls, gloves, and N95 masks were used during the work at the BSL-2 and BSL-3 laboratories, and all wastes were autoclaved.","Next-generation sequencing was used to determine the whole-genome sequences of the AIV isolates. The viral RNA samples were quantified using the 2100 Bioanalyzer System (Agilent Technologies). The RT-PCR and cDNA synthesis procedures were conducted using PrimeScript One-Step RT-PCR Kit Ver.2 (RR055A Takara) and influenza A-specific primers MBTuni-12 and MBTuni-13. Sequencing libraries with an insert size of 200bp were prepared by end-repairing, dA-tailing, adaptor ligation, and PCR amplification, all performed according to the standard manufacturer's instructions (Illumina, USA). The libraries were sequenced on an Illumina HiSeq4000 platform (Illumina) [416].","Phylogenetic trees were constructed for each gene segment of the nine AIV isolates to investigate their evolutionary relationships. The AIV reference sequences were obtained from GenBank (http://www.ncbi.nlm.nih.gov/genbank) and GISAID (http://www.gisaid.org) via the online Basic Local Alignment Search Tool (BLAST). The datasets for phylogenetic analysis were generated by aligning with reference sequences closely related to the viral sequences of the isolates and removing sequences with the same strain name and those without a clear subtype or collection date. The final sequence numbers of each gene segment are PB2 37, PB1 28, PA 32, HA 35, NP 33, NA 24, MP 27, and NS 33 (Supplementary Table 1). The sequences were aligned with ClustalW using MEGA 7.0. The general time-reversible nucleotide substitution model with invariant sites (I) and the gamma rate of heterogeneity (GTR +Γ) results were selected as providing the best fit for all datasets. Maximum clade credibility (MCC) phylogenetic trees were generated by applying maximum likelihood analysis with 1000 bootstrap replicates in the MEGA-X program and visualized/annotated using Fig tree 1.4.3 software [4].","Nine strains of H5N6 AIV were isolated between July and November 2017, one from a duck swab and the rest from goose swabs. The eight gene segments of the 9 isolates shared very high nucleotide homology (> 99.9%); thus, the isolates originated from the same ancestor. The strains have been designated as A/goose/Xinjiang/011-015/2017(H5N6), A/duck/Xinjiang/016/2017 (H5N6), and A/goose/Xinjiang/017-019/2017(H5N6), or XJ-H5N6/2017 for short. The complete sequences of the isolates have been submitted to NCBI (accession numbers: MW109029-MW109036, MW110101-MW110108, and MW110205-MW110260).","Homology BLAST and phylogenetic analyses were performed on eight genes of the viral isolates (Table 1 and Fig. 1). The viral NA gene exhibited the highest sequence homology (99.1%), and the sequence clustered together with those of H5N6 AIVs isolated from shoveler ducks sampled in Ningxia (NX488-53) and from the environment of Chongqing in 2015; those sequences were designated as 2.3.4.4C H5N6 AIVs. The viral NP gene segments shared the highest sequence homology (99.4%) with 2.3.4.4B H5N8 AIVs isolated from the green-winged teal in Egypt in 2016 and were grouped into an independent subcluster within the phylogenetic tree. The remaining six genes had the highest sequence homologies (99.1%–99.6%) and relatively close genetic relationships in the phylogenetic tree with the 2.3.4.4B H5N8 AIVs. Five of those genes, HA, PB2, NS, PA, and MP, clustered together with 2.3.4.4B H5N8 AIVs isolated from migratory swans in Sanmenxia, Hubei, and Shanxi in 2016, and this sixth gene, PB1, clustered together with H5N8 AIVs isolated from grey-headed gulls sampled in Uganda in 2017. The above results suggest that XJ-H5N6/2017 is a novel reassortant 2.3.4.4B H5N6 AIV derived from 2.3.4.4B H5N8 and 2.3.4.4C H5N6 viruses present in wild birds.","Assessment of the multiple amino acid mutations present in the nine isolates could help elucidate H5N6 virulence. Multiple basic amino acids (KEKRRKR↓GLF) were observed at cleavage sites in the HA protein of the H5N6 isolates (Table 2), suggesting that the isolates were HPAIVs. The viral receptor-binding sites all contained Q226 and G228 (H3 numbering), indicative of an avian-like α2,3-sialic acid (α2,3-SA) receptor-binding preference; however, the S128P, S137A, and T160A mutations in HA could enhance the capacity to bind to a human-like α-2,6-SA receptor [17]. In all nine isolates, 11 amino acid deletions (59–69) in the NA stalk were observed, suggesting the isolates could have different adaptive and virulence characteristics in poultry and mammals [18]. In addition, the L89V, G309D, R477G, I495V [19], and I504V [20] mutations in the PB2 protein and the P42S and D92E mutations in the NS1 were retained in all nine isolates; notably, those mutations can enhance viral virulence and replication activity in mammals [21]. Moreover, the M2-F38L mutation has been associated with antiviral resistance (amantadine and rimantadine) [22]."],"string":"[\n \"From March 2017 to December 2018, a total of 354 oropharyngeal and cloacal swabs from apparently healthy poultry (chickens, ducks, and geese) in live poultry markets (LPMs) in Urumqi, China were collected and placed in tubes containing viral transport medium DMEM (1,000 u/L penicillin, 500 ug/L streptomycin, 100 mg/L Nystatin, 100 mg/L Polymyxin B sulfate salt, 1000 mg/L Sulfamethoxazole, 0.05 g/L Ofloxacin). These tubes were kept in an icebox at −20°C before transport to the laboratory and then immediately stored at −80°C.\\nThe animal experiment portion of this study was approved by the Committee on the Ethics of Animal Experiments of Xinjiang Key Laboratory of Biological Resources and Genetic Engineering (BRGE-AE001) and carried out under the guidelines of the Animal Care and Use Committee of the College of Life Science and Technology, Xinjiang University.\",\n \"The swab samples were vortexed and centrifuged, and the supernatants inoculated into 10-day-old specific-pathogen-free embryonated chicken eggs. Seventy-two hours after inoculation, allantois fluid was harvested, and hemagglutinin (HA) activity was assayed using 1% chicken red blood cells [14]. All HA-positive samples underwent reverse transcription polymerase chain reaction (RT-PCR) using universal primers targeting the PB1 gene [15]. Viral RNA extraction (Bioer) and RT-PCR (TOYOBO) were performed according to the manufacturers' instructions.\\nRoutine surveillance samples were processed in a biosafety level 2 (BSL-2) laboratory of the Center for Influenza Research and Early-Warning (CASCIRE), Chinese Academy of Science, while experiments with live H5N6 viruses were conducted in a CASCIRE biosafety level 3 (BSL-3) laboratory. Coveralls, gloves, and N95 masks were used during the work at the BSL-2 and BSL-3 laboratories, and all wastes were autoclaved.\",\n \"Next-generation sequencing was used to determine the whole-genome sequences of the AIV isolates. The viral RNA samples were quantified using the 2100 Bioanalyzer System (Agilent Technologies). The RT-PCR and cDNA synthesis procedures were conducted using PrimeScript One-Step RT-PCR Kit Ver.2 (RR055A Takara) and influenza A-specific primers MBTuni-12 and MBTuni-13. Sequencing libraries with an insert size of 200bp were prepared by end-repairing, dA-tailing, adaptor ligation, and PCR amplification, all performed according to the standard manufacturer's instructions (Illumina, USA). The libraries were sequenced on an Illumina HiSeq4000 platform (Illumina) [416].\",\n \"Phylogenetic trees were constructed for each gene segment of the nine AIV isolates to investigate their evolutionary relationships. The AIV reference sequences were obtained from GenBank (http://www.ncbi.nlm.nih.gov/genbank) and GISAID (http://www.gisaid.org) via the online Basic Local Alignment Search Tool (BLAST). The datasets for phylogenetic analysis were generated by aligning with reference sequences closely related to the viral sequences of the isolates and removing sequences with the same strain name and those without a clear subtype or collection date. The final sequence numbers of each gene segment are PB2 37, PB1 28, PA 32, HA 35, NP 33, NA 24, MP 27, and NS 33 (Supplementary Table 1). The sequences were aligned with ClustalW using MEGA 7.0. The general time-reversible nucleotide substitution model with invariant sites (I) and the gamma rate of heterogeneity (GTR +Γ) results were selected as providing the best fit for all datasets. Maximum clade credibility (MCC) phylogenetic trees were generated by applying maximum likelihood analysis with 1000 bootstrap replicates in the MEGA-X program and visualized/annotated using Fig tree 1.4.3 software [4].\",\n \"Nine strains of H5N6 AIV were isolated between July and November 2017, one from a duck swab and the rest from goose swabs. The eight gene segments of the 9 isolates shared very high nucleotide homology (> 99.9%); thus, the isolates originated from the same ancestor. The strains have been designated as A/goose/Xinjiang/011-015/2017(H5N6), A/duck/Xinjiang/016/2017 (H5N6), and A/goose/Xinjiang/017-019/2017(H5N6), or XJ-H5N6/2017 for short. The complete sequences of the isolates have been submitted to NCBI (accession numbers: MW109029-MW109036, MW110101-MW110108, and MW110205-MW110260).\",\n \"Homology BLAST and phylogenetic analyses were performed on eight genes of the viral isolates (Table 1 and Fig. 1). The viral NA gene exhibited the highest sequence homology (99.1%), and the sequence clustered together with those of H5N6 AIVs isolated from shoveler ducks sampled in Ningxia (NX488-53) and from the environment of Chongqing in 2015; those sequences were designated as 2.3.4.4C H5N6 AIVs. The viral NP gene segments shared the highest sequence homology (99.4%) with 2.3.4.4B H5N8 AIVs isolated from the green-winged teal in Egypt in 2016 and were grouped into an independent subcluster within the phylogenetic tree. The remaining six genes had the highest sequence homologies (99.1%–99.6%) and relatively close genetic relationships in the phylogenetic tree with the 2.3.4.4B H5N8 AIVs. Five of those genes, HA, PB2, NS, PA, and MP, clustered together with 2.3.4.4B H5N8 AIVs isolated from migratory swans in Sanmenxia, Hubei, and Shanxi in 2016, and this sixth gene, PB1, clustered together with H5N8 AIVs isolated from grey-headed gulls sampled in Uganda in 2017. The above results suggest that XJ-H5N6/2017 is a novel reassortant 2.3.4.4B H5N6 AIV derived from 2.3.4.4B H5N8 and 2.3.4.4C H5N6 viruses present in wild birds.\",\n \"Assessment of the multiple amino acid mutations present in the nine isolates could help elucidate H5N6 virulence. Multiple basic amino acids (KEKRRKR↓GLF) were observed at cleavage sites in the HA protein of the H5N6 isolates (Table 2), suggesting that the isolates were HPAIVs. The viral receptor-binding sites all contained Q226 and G228 (H3 numbering), indicative of an avian-like α2,3-sialic acid (α2,3-SA) receptor-binding preference; however, the S128P, S137A, and T160A mutations in HA could enhance the capacity to bind to a human-like α-2,6-SA receptor [17]. In all nine isolates, 11 amino acid deletions (59–69) in the NA stalk were observed, suggesting the isolates could have different adaptive and virulence characteristics in poultry and mammals [18]. In addition, the L89V, G309D, R477G, I495V [19], and I504V [20] mutations in the PB2 protein and the P42S and D92E mutations in the NS1 were retained in all nine isolates; notably, those mutations can enhance viral virulence and replication activity in mammals [21]. Moreover, the M2-F38L mutation has been associated with antiviral resistance (amantadine and rimantadine) [22].\"\n]"},"other_sections_sec_types":{"kind":"list like","value":[null,null,null,null,null,null,null],"string":"[\n null,\n null,\n null,\n null,\n null,\n null,\n null\n]"},"all_sections_titles":{"kind":"list like","value":["INTRODUCTION","MATERIALS AND METHODS","Sample collection","Virus isolation and identification","Whole-genome sequencing","Phylogenetic analysis","RESULTS","Virus isolation","Sequence and phylogenetic analysis","Molecular characteristics of the H5N6 virus isolates","DISCUSSION"],"string":"[\n \"INTRODUCTION\",\n \"MATERIALS AND METHODS\",\n \"Sample collection\",\n \"Virus isolation and identification\",\n \"Whole-genome sequencing\",\n \"Phylogenetic analysis\",\n \"RESULTS\",\n \"Virus isolation\",\n \"Sequence and phylogenetic analysis\",\n \"Molecular characteristics of the H5N6 virus isolates\",\n \"DISCUSSION\"\n]"},"all_sections_texts":{"kind":"list like","value":["Since 2014, highly pathogenic avian influenza viruses (HPAIVs) of clade 2.3.4.4 (H5) have been monitored in poultry and wild birds in European and Asian countries, and the viruses of that clade have evolved into eight groups (A-H) [12]. Group A includes H5N8 avian influenza viruses (AIVs) that were identified in Asia, Europe, and North America [1]. In 2016, a novel H5N8 AIV lineage initially emerged in the wild birds in Qinghai Lake, spread to Mongolia, Siberia, and Europe, and was subsequently identified in China and Korea; this new lineage was classified into clade 2.3.4.4B [3]. On the other hand, group C H5N6 AIVs were first reported in Laos in 2013 and gradually became the main source of sporadic AIV infections in poultry in Southern China [4]. Group D comprises H5N6 viruses mainly identified in China and Vietnam. Subsequent studies have reported that H5N6 viruses of groups 2.3.4.4E, 2.3.4.4F, 2.3.4.4G, and 2.3.4.4H have been isolated from poultry and wild birds [2].\nOutbreaks of 2.3.4.4B H5N8 AIVs were reported in South Korea in 2014 [5]. The viruses were subsequently disseminated via migratory birds and have since been detected in wild and domestic birds worldwide [12678]. Almost simultaneously, 2.3.4.4B H5N6 HPAIVs were undergoing global transmission and have been detected in wild and domestic birds [8910111213]. The cocirculation of H5N6 and H5N8 viruses among wild and domestic birds could increase the probability of reassorting novel viruses [91011]; indeed, reassortants derived from the H5N6 and H5N8 HPAIVs have been detected in various wild birds and poultry [891415]. Moreover, it has been reported that 46% of all 2.3.4.4B H5N6 viruses in domestic poultry were derived from wild birds [9]. Thus, reassortant viruses could pose potential threats to poultry farming and human public health. In this study, nine H5N6 AIVs were isolated from aquatic poultry in Xinjiang Uyghur Autonomous Region, China, and all were grouped into clade 2.3.4.4B. This study aimed to analyze the origin of the isolates.","Sample collection From March 2017 to December 2018, a total of 354 oropharyngeal and cloacal swabs from apparently healthy poultry (chickens, ducks, and geese) in live poultry markets (LPMs) in Urumqi, China were collected and placed in tubes containing viral transport medium DMEM (1,000 u/L penicillin, 500 ug/L streptomycin, 100 mg/L Nystatin, 100 mg/L Polymyxin B sulfate salt, 1000 mg/L Sulfamethoxazole, 0.05 g/L Ofloxacin). These tubes were kept in an icebox at −20°C before transport to the laboratory and then immediately stored at −80°C.\nThe animal experiment portion of this study was approved by the Committee on the Ethics of Animal Experiments of Xinjiang Key Laboratory of Biological Resources and Genetic Engineering (BRGE-AE001) and carried out under the guidelines of the Animal Care and Use Committee of the College of Life Science and Technology, Xinjiang University.\nFrom March 2017 to December 2018, a total of 354 oropharyngeal and cloacal swabs from apparently healthy poultry (chickens, ducks, and geese) in live poultry markets (LPMs) in Urumqi, China were collected and placed in tubes containing viral transport medium DMEM (1,000 u/L penicillin, 500 ug/L streptomycin, 100 mg/L Nystatin, 100 mg/L Polymyxin B sulfate salt, 1000 mg/L Sulfamethoxazole, 0.05 g/L Ofloxacin). These tubes were kept in an icebox at −20°C before transport to the laboratory and then immediately stored at −80°C.\nThe animal experiment portion of this study was approved by the Committee on the Ethics of Animal Experiments of Xinjiang Key Laboratory of Biological Resources and Genetic Engineering (BRGE-AE001) and carried out under the guidelines of the Animal Care and Use Committee of the College of Life Science and Technology, Xinjiang University.\nVirus isolation and identification The swab samples were vortexed and centrifuged, and the supernatants inoculated into 10-day-old specific-pathogen-free embryonated chicken eggs. Seventy-two hours after inoculation, allantois fluid was harvested, and hemagglutinin (HA) activity was assayed using 1% chicken red blood cells [14]. All HA-positive samples underwent reverse transcription polymerase chain reaction (RT-PCR) using universal primers targeting the PB1 gene [15]. Viral RNA extraction (Bioer) and RT-PCR (TOYOBO) were performed according to the manufacturers' instructions.\nRoutine surveillance samples were processed in a biosafety level 2 (BSL-2) laboratory of the Center for Influenza Research and Early-Warning (CASCIRE), Chinese Academy of Science, while experiments with live H5N6 viruses were conducted in a CASCIRE biosafety level 3 (BSL-3) laboratory. Coveralls, gloves, and N95 masks were used during the work at the BSL-2 and BSL-3 laboratories, and all wastes were autoclaved.\nThe swab samples were vortexed and centrifuged, and the supernatants inoculated into 10-day-old specific-pathogen-free embryonated chicken eggs. Seventy-two hours after inoculation, allantois fluid was harvested, and hemagglutinin (HA) activity was assayed using 1% chicken red blood cells [14]. All HA-positive samples underwent reverse transcription polymerase chain reaction (RT-PCR) using universal primers targeting the PB1 gene [15]. Viral RNA extraction (Bioer) and RT-PCR (TOYOBO) were performed according to the manufacturers' instructions.\nRoutine surveillance samples were processed in a biosafety level 2 (BSL-2) laboratory of the Center for Influenza Research and Early-Warning (CASCIRE), Chinese Academy of Science, while experiments with live H5N6 viruses were conducted in a CASCIRE biosafety level 3 (BSL-3) laboratory. Coveralls, gloves, and N95 masks were used during the work at the BSL-2 and BSL-3 laboratories, and all wastes were autoclaved.\nWhole-genome sequencing Next-generation sequencing was used to determine the whole-genome sequences of the AIV isolates. The viral RNA samples were quantified using the 2100 Bioanalyzer System (Agilent Technologies). The RT-PCR and cDNA synthesis procedures were conducted using PrimeScript One-Step RT-PCR Kit Ver.2 (RR055A Takara) and influenza A-specific primers MBTuni-12 and MBTuni-13. Sequencing libraries with an insert size of 200bp were prepared by end-repairing, dA-tailing, adaptor ligation, and PCR amplification, all performed according to the standard manufacturer's instructions (Illumina, USA). The libraries were sequenced on an Illumina HiSeq4000 platform (Illumina) [416].\nNext-generation sequencing was used to determine the whole-genome sequences of the AIV isolates. The viral RNA samples were quantified using the 2100 Bioanalyzer System (Agilent Technologies). The RT-PCR and cDNA synthesis procedures were conducted using PrimeScript One-Step RT-PCR Kit Ver.2 (RR055A Takara) and influenza A-specific primers MBTuni-12 and MBTuni-13. Sequencing libraries with an insert size of 200bp were prepared by end-repairing, dA-tailing, adaptor ligation, and PCR amplification, all performed according to the standard manufacturer's instructions (Illumina, USA). The libraries were sequenced on an Illumina HiSeq4000 platform (Illumina) [416].\nPhylogenetic analysis Phylogenetic trees were constructed for each gene segment of the nine AIV isolates to investigate their evolutionary relationships. The AIV reference sequences were obtained from GenBank (http://www.ncbi.nlm.nih.gov/genbank) and GISAID (http://www.gisaid.org) via the online Basic Local Alignment Search Tool (BLAST). The datasets for phylogenetic analysis were generated by aligning with reference sequences closely related to the viral sequences of the isolates and removing sequences with the same strain name and those without a clear subtype or collection date. The final sequence numbers of each gene segment are PB2 37, PB1 28, PA 32, HA 35, NP 33, NA 24, MP 27, and NS 33 (Supplementary Table 1). The sequences were aligned with ClustalW using MEGA 7.0. The general time-reversible nucleotide substitution model with invariant sites (I) and the gamma rate of heterogeneity (GTR +Γ) results were selected as providing the best fit for all datasets. Maximum clade credibility (MCC) phylogenetic trees were generated by applying maximum likelihood analysis with 1000 bootstrap replicates in the MEGA-X program and visualized/annotated using Fig tree 1.4.3 software [4].\nPhylogenetic trees were constructed for each gene segment of the nine AIV isolates to investigate their evolutionary relationships. The AIV reference sequences were obtained from GenBank (http://www.ncbi.nlm.nih.gov/genbank) and GISAID (http://www.gisaid.org) via the online Basic Local Alignment Search Tool (BLAST). The datasets for phylogenetic analysis were generated by aligning with reference sequences closely related to the viral sequences of the isolates and removing sequences with the same strain name and those without a clear subtype or collection date. The final sequence numbers of each gene segment are PB2 37, PB1 28, PA 32, HA 35, NP 33, NA 24, MP 27, and NS 33 (Supplementary Table 1). The sequences were aligned with ClustalW using MEGA 7.0. The general time-reversible nucleotide substitution model with invariant sites (I) and the gamma rate of heterogeneity (GTR +Γ) results were selected as providing the best fit for all datasets. Maximum clade credibility (MCC) phylogenetic trees were generated by applying maximum likelihood analysis with 1000 bootstrap replicates in the MEGA-X program and visualized/annotated using Fig tree 1.4.3 software [4].","From March 2017 to December 2018, a total of 354 oropharyngeal and cloacal swabs from apparently healthy poultry (chickens, ducks, and geese) in live poultry markets (LPMs) in Urumqi, China were collected and placed in tubes containing viral transport medium DMEM (1,000 u/L penicillin, 500 ug/L streptomycin, 100 mg/L Nystatin, 100 mg/L Polymyxin B sulfate salt, 1000 mg/L Sulfamethoxazole, 0.05 g/L Ofloxacin). These tubes were kept in an icebox at −20°C before transport to the laboratory and then immediately stored at −80°C.\nThe animal experiment portion of this study was approved by the Committee on the Ethics of Animal Experiments of Xinjiang Key Laboratory of Biological Resources and Genetic Engineering (BRGE-AE001) and carried out under the guidelines of the Animal Care and Use Committee of the College of Life Science and Technology, Xinjiang University.","The swab samples were vortexed and centrifuged, and the supernatants inoculated into 10-day-old specific-pathogen-free embryonated chicken eggs. Seventy-two hours after inoculation, allantois fluid was harvested, and hemagglutinin (HA) activity was assayed using 1% chicken red blood cells [14]. All HA-positive samples underwent reverse transcription polymerase chain reaction (RT-PCR) using universal primers targeting the PB1 gene [15]. Viral RNA extraction (Bioer) and RT-PCR (TOYOBO) were performed according to the manufacturers' instructions.\nRoutine surveillance samples were processed in a biosafety level 2 (BSL-2) laboratory of the Center for Influenza Research and Early-Warning (CASCIRE), Chinese Academy of Science, while experiments with live H5N6 viruses were conducted in a CASCIRE biosafety level 3 (BSL-3) laboratory. Coveralls, gloves, and N95 masks were used during the work at the BSL-2 and BSL-3 laboratories, and all wastes were autoclaved.","Next-generation sequencing was used to determine the whole-genome sequences of the AIV isolates. The viral RNA samples were quantified using the 2100 Bioanalyzer System (Agilent Technologies). The RT-PCR and cDNA synthesis procedures were conducted using PrimeScript One-Step RT-PCR Kit Ver.2 (RR055A Takara) and influenza A-specific primers MBTuni-12 and MBTuni-13. Sequencing libraries with an insert size of 200bp were prepared by end-repairing, dA-tailing, adaptor ligation, and PCR amplification, all performed according to the standard manufacturer's instructions (Illumina, USA). The libraries were sequenced on an Illumina HiSeq4000 platform (Illumina) [416].","Phylogenetic trees were constructed for each gene segment of the nine AIV isolates to investigate their evolutionary relationships. The AIV reference sequences were obtained from GenBank (http://www.ncbi.nlm.nih.gov/genbank) and GISAID (http://www.gisaid.org) via the online Basic Local Alignment Search Tool (BLAST). The datasets for phylogenetic analysis were generated by aligning with reference sequences closely related to the viral sequences of the isolates and removing sequences with the same strain name and those without a clear subtype or collection date. The final sequence numbers of each gene segment are PB2 37, PB1 28, PA 32, HA 35, NP 33, NA 24, MP 27, and NS 33 (Supplementary Table 1). The sequences were aligned with ClustalW using MEGA 7.0. The general time-reversible nucleotide substitution model with invariant sites (I) and the gamma rate of heterogeneity (GTR +Γ) results were selected as providing the best fit for all datasets. Maximum clade credibility (MCC) phylogenetic trees were generated by applying maximum likelihood analysis with 1000 bootstrap replicates in the MEGA-X program and visualized/annotated using Fig tree 1.4.3 software [4].","Virus isolation Nine strains of H5N6 AIV were isolated between July and November 2017, one from a duck swab and the rest from goose swabs. The eight gene segments of the 9 isolates shared very high nucleotide homology (> 99.9%); thus, the isolates originated from the same ancestor. The strains have been designated as A/goose/Xinjiang/011-015/2017(H5N6), A/duck/Xinjiang/016/2017 (H5N6), and A/goose/Xinjiang/017-019/2017(H5N6), or XJ-H5N6/2017 for short. The complete sequences of the isolates have been submitted to NCBI (accession numbers: MW109029-MW109036, MW110101-MW110108, and MW110205-MW110260).\nNine strains of H5N6 AIV were isolated between July and November 2017, one from a duck swab and the rest from goose swabs. The eight gene segments of the 9 isolates shared very high nucleotide homology (> 99.9%); thus, the isolates originated from the same ancestor. The strains have been designated as A/goose/Xinjiang/011-015/2017(H5N6), A/duck/Xinjiang/016/2017 (H5N6), and A/goose/Xinjiang/017-019/2017(H5N6), or XJ-H5N6/2017 for short. The complete sequences of the isolates have been submitted to NCBI (accession numbers: MW109029-MW109036, MW110101-MW110108, and MW110205-MW110260).\nSequence and phylogenetic analysis Homology BLAST and phylogenetic analyses were performed on eight genes of the viral isolates (Table 1 and Fig. 1). The viral NA gene exhibited the highest sequence homology (99.1%), and the sequence clustered together with those of H5N6 AIVs isolated from shoveler ducks sampled in Ningxia (NX488-53) and from the environment of Chongqing in 2015; those sequences were designated as 2.3.4.4C H5N6 AIVs. The viral NP gene segments shared the highest sequence homology (99.4%) with 2.3.4.4B H5N8 AIVs isolated from the green-winged teal in Egypt in 2016 and were grouped into an independent subcluster within the phylogenetic tree. The remaining six genes had the highest sequence homologies (99.1%–99.6%) and relatively close genetic relationships in the phylogenetic tree with the 2.3.4.4B H5N8 AIVs. Five of those genes, HA, PB2, NS, PA, and MP, clustered together with 2.3.4.4B H5N8 AIVs isolated from migratory swans in Sanmenxia, Hubei, and Shanxi in 2016, and this sixth gene, PB1, clustered together with H5N8 AIVs isolated from grey-headed gulls sampled in Uganda in 2017. The above results suggest that XJ-H5N6/2017 is a novel reassortant 2.3.4.4B H5N6 AIV derived from 2.3.4.4B H5N8 and 2.3.4.4C H5N6 viruses present in wild birds.\nHomology BLAST and phylogenetic analyses were performed on eight genes of the viral isolates (Table 1 and Fig. 1). The viral NA gene exhibited the highest sequence homology (99.1%), and the sequence clustered together with those of H5N6 AIVs isolated from shoveler ducks sampled in Ningxia (NX488-53) and from the environment of Chongqing in 2015; those sequences were designated as 2.3.4.4C H5N6 AIVs. The viral NP gene segments shared the highest sequence homology (99.4%) with 2.3.4.4B H5N8 AIVs isolated from the green-winged teal in Egypt in 2016 and were grouped into an independent subcluster within the phylogenetic tree. The remaining six genes had the highest sequence homologies (99.1%–99.6%) and relatively close genetic relationships in the phylogenetic tree with the 2.3.4.4B H5N8 AIVs. Five of those genes, HA, PB2, NS, PA, and MP, clustered together with 2.3.4.4B H5N8 AIVs isolated from migratory swans in Sanmenxia, Hubei, and Shanxi in 2016, and this sixth gene, PB1, clustered together with H5N8 AIVs isolated from grey-headed gulls sampled in Uganda in 2017. The above results suggest that XJ-H5N6/2017 is a novel reassortant 2.3.4.4B H5N6 AIV derived from 2.3.4.4B H5N8 and 2.3.4.4C H5N6 viruses present in wild birds.\nMolecular characteristics of the H5N6 virus isolates Assessment of the multiple amino acid mutations present in the nine isolates could help elucidate H5N6 virulence. Multiple basic amino acids (KEKRRKR↓GLF) were observed at cleavage sites in the HA protein of the H5N6 isolates (Table 2), suggesting that the isolates were HPAIVs. The viral receptor-binding sites all contained Q226 and G228 (H3 numbering), indicative of an avian-like α2,3-sialic acid (α2,3-SA) receptor-binding preference; however, the S128P, S137A, and T160A mutations in HA could enhance the capacity to bind to a human-like α-2,6-SA receptor [17]. In all nine isolates, 11 amino acid deletions (59–69) in the NA stalk were observed, suggesting the isolates could have different adaptive and virulence characteristics in poultry and mammals [18]. In addition, the L89V, G309D, R477G, I495V [19], and I504V [20] mutations in the PB2 protein and the P42S and D92E mutations in the NS1 were retained in all nine isolates; notably, those mutations can enhance viral virulence and replication activity in mammals [21]. Moreover, the M2-F38L mutation has been associated with antiviral resistance (amantadine and rimantadine) [22].\nAssessment of the multiple amino acid mutations present in the nine isolates could help elucidate H5N6 virulence. Multiple basic amino acids (KEKRRKR↓GLF) were observed at cleavage sites in the HA protein of the H5N6 isolates (Table 2), suggesting that the isolates were HPAIVs. The viral receptor-binding sites all contained Q226 and G228 (H3 numbering), indicative of an avian-like α2,3-sialic acid (α2,3-SA) receptor-binding preference; however, the S128P, S137A, and T160A mutations in HA could enhance the capacity to bind to a human-like α-2,6-SA receptor [17]. In all nine isolates, 11 amino acid deletions (59–69) in the NA stalk were observed, suggesting the isolates could have different adaptive and virulence characteristics in poultry and mammals [18]. In addition, the L89V, G309D, R477G, I495V [19], and I504V [20] mutations in the PB2 protein and the P42S and D92E mutations in the NS1 were retained in all nine isolates; notably, those mutations can enhance viral virulence and replication activity in mammals [21]. Moreover, the M2-F38L mutation has been associated with antiviral resistance (amantadine and rimantadine) [22].","Nine strains of H5N6 AIV were isolated between July and November 2017, one from a duck swab and the rest from goose swabs. The eight gene segments of the 9 isolates shared very high nucleotide homology (> 99.9%); thus, the isolates originated from the same ancestor. The strains have been designated as A/goose/Xinjiang/011-015/2017(H5N6), A/duck/Xinjiang/016/2017 (H5N6), and A/goose/Xinjiang/017-019/2017(H5N6), or XJ-H5N6/2017 for short. The complete sequences of the isolates have been submitted to NCBI (accession numbers: MW109029-MW109036, MW110101-MW110108, and MW110205-MW110260).","Homology BLAST and phylogenetic analyses were performed on eight genes of the viral isolates (Table 1 and Fig. 1). The viral NA gene exhibited the highest sequence homology (99.1%), and the sequence clustered together with those of H5N6 AIVs isolated from shoveler ducks sampled in Ningxia (NX488-53) and from the environment of Chongqing in 2015; those sequences were designated as 2.3.4.4C H5N6 AIVs. The viral NP gene segments shared the highest sequence homology (99.4%) with 2.3.4.4B H5N8 AIVs isolated from the green-winged teal in Egypt in 2016 and were grouped into an independent subcluster within the phylogenetic tree. The remaining six genes had the highest sequence homologies (99.1%–99.6%) and relatively close genetic relationships in the phylogenetic tree with the 2.3.4.4B H5N8 AIVs. Five of those genes, HA, PB2, NS, PA, and MP, clustered together with 2.3.4.4B H5N8 AIVs isolated from migratory swans in Sanmenxia, Hubei, and Shanxi in 2016, and this sixth gene, PB1, clustered together with H5N8 AIVs isolated from grey-headed gulls sampled in Uganda in 2017. The above results suggest that XJ-H5N6/2017 is a novel reassortant 2.3.4.4B H5N6 AIV derived from 2.3.4.4B H5N8 and 2.3.4.4C H5N6 viruses present in wild birds.","Assessment of the multiple amino acid mutations present in the nine isolates could help elucidate H5N6 virulence. Multiple basic amino acids (KEKRRKR↓GLF) were observed at cleavage sites in the HA protein of the H5N6 isolates (Table 2), suggesting that the isolates were HPAIVs. The viral receptor-binding sites all contained Q226 and G228 (H3 numbering), indicative of an avian-like α2,3-sialic acid (α2,3-SA) receptor-binding preference; however, the S128P, S137A, and T160A mutations in HA could enhance the capacity to bind to a human-like α-2,6-SA receptor [17]. In all nine isolates, 11 amino acid deletions (59–69) in the NA stalk were observed, suggesting the isolates could have different adaptive and virulence characteristics in poultry and mammals [18]. In addition, the L89V, G309D, R477G, I495V [19], and I504V [20] mutations in the PB2 protein and the P42S and D92E mutations in the NS1 were retained in all nine isolates; notably, those mutations can enhance viral virulence and replication activity in mammals [21]. Moreover, the M2-F38L mutation has been associated with antiviral resistance (amantadine and rimantadine) [22].","The Xinjiang region is located in northwest China within the interior of the Eurasian Continent. It comprises an overlapping bird migration region between the Central Asian Flyway and the West Asian–East African Flyway. The Northern Tianshan Mountain (NTM) region in Xinjiang includes many complex mountain-oasis-desert systems, and several water reservoirs have been formed by dam construction in narrow mouths of ravines or rivers, with much of the water coming from snowmelt [23]. Many wild birds migrate along the NTM region every year, and the reservoirs in the region have become key stopover areas for migratory birds from Eurasia and Africa. Moreover, these reservoirs are also used during aquatic poultry farming, thereby increasing the odds of contact between aquatic poultry and wild birds, including direct contact with infected wild birds or indirect contact with related materials (e.g., wild-bird feces), which could result in the introduction of wild-bird origin AIVs into aquatic poultry [824]. The majority of H5 HPAIVs detections in wild and domestic birds have been associated with migratory flyways and wild-bird aggregation areas [825]. This wild-domestic bird interface has had an important role in the spread, reassortment, evolution, and epidemiology of AIVs [8]; in particular, 2.3.4.4 H5 HPAIVs that have emerged since 2013. Such HPAIVs are continuing to reassort, evolve, and spread and have been detected in wild and domestic birds around the world [26]. Since 2016, HPAIVs outbreaks in Xinjiang have occurred repeatedly, and there have been several outbreaks of H5N6 HPAIVs (including 2.3.4.4B and 2.3.4.4H) in poultry and migratory birds sampled in Xinjiang [2728]. In this study, the 2.3.4.4B HPAIV isolates from the aquatic poultry in the LPMs of Urumqi were shown to be novel reassortant viruses derived from 2.3.4.4B H5N8 and 2.3.4.4C H5N6 AIVs of wild birds, suggesting that 2.3.4.4 clade AIVs are circulating in both wild and domestic birds in Xinjiang.\nIn this study, the NA gene of XJ-H5N6/2017 had a close relationship with the wild-bird origin 2.3.4.4C H5N6 AIVs identified from Chongqing and Ningxia, China. Our previous study reported a reassortant 2.3.4.4C H5N6 AIV in Xinjiang (GISAID accession nos.: EPI1548859-1548874), which originated from the NX488-53 virus isolated in December 2016 [29]. The NP gene sequences of the isolates in this study were grouped into an independent subcluster within the 2.3.4.4B H5N8 AIVs, indicating that NP has continued to evolve in Xinjiang. The PB1 gene was most closely related to the 2.3.4.4B H5N8 AIVs from the migratory wild birds sampled in Africa in 2017. In addition, it had a close relationship with 2.3.4.4B H5N8 AIVs from the migratory swans sampled in Central China in 2016. The remaining five genes had close relationships with 2.3.4.4B H5N8 AIVs identified from the migratory swans sampled in Central China in 2016 and bar-headed geese sampled in Qinghai in 2017. The Sanmenxia wetland of the Yellow River is an important wintering ground for migratory swans, with the swans arriving at the wetland in late October each year, departing the wetland during the late February to late March period of the following year, and migrating northwest directly to Siberia and northwest China (e.g., Qinghai wetlands) for breeding and molting [30]. An outbreak of H5N1 HPAIV has occurred at the Sanmenxia wetland, and migrating swans carrying the H5N1 HPAIVs have spread it over long distances [3132]. Also, H5N8 HPAIVs from wild birds in Hubei, China have been reported to cause the death of migratory swans, and the H5N8 viruses have been isolated in samples from Qinghai Lake and Europe. Thus, the wetlands and lakes in Central China may have a key role in spreading H5N8 viruses in the East Asian-Australasian and Central Asian flyways [12]. These observations suggest that the isolated 2.3.4.4B H5N8 AIVs could have spread into Xinjiang by infected migratory wild birds present in Central China in 2016 and 2017 and spreading into Africa in 2017. Furthermore, these 2.3.4.4B H5N8 AIVs might have reassorted with the NX488-53 virus to generate the novel 2.3.4.4B virus of XJ-H5N6/2017, which could then circulate within aquatic poultry in the water reservoirs of NTM, subsequently spreading into the LPMs of Urumqi via the sale of live poultry. In January 2020, 2.3.4.4H H5N6 HPAIVs [SW/XJ/1/2020(H5N6)] were identified from migratory whooper and mute swans in Xinjiang [28], and the low level of similarity between the isolates in this study and those of SW/XJ/1/2020(H5N6) suggests that these viruses spread into Xinjiang independently. Based on the above observations, the wetlands in Xinjiang may have a key role in the AIVs circulating among the migratory birds and aquatic poultry in Xinjiang and in disseminating the viruses from Central China to the Eurasian continent and East African via the Central Asian Flyway and the West Asian–East African Flyway.\nIn our study, the multiple mutations detected in the isolates may be associated with viral virulence, adaptation, and transmission (Table 2). The viral HA protein included cleavage sites containing multiple basic amino acids associated with HPAIVs; moreover, the S128P, S137A, and T160A mutations can enhance binding capacity to a human-like α-2,6-SA receptor [17]. These three mutations were also present in the NX488-53 virus, which can infect BALB/c mice and transmit among guinea pigs via direct contact or aerosol routes [3334]. The 11 amino acid deletion in the stalk region of the NA protein has been associated with the adaptation of wild-bird origin AIVs to gallinaceous poultry [35], increasing viral virulence [36], and enhancing viral transmission in ducks [37]. It has also been reported that the NA short-stalk region in H7N9 [36] and H5N1 [38] AIVs can increase virulence in mice, and it is characteristic of the viral adaptation for chickens [35]. Five amino acid substitutions in the PB2 protein (L89V, G309D, R477G, I495V, and I504V) may increase viral replication activity in mammalian cells and viral virulence in mice [1920]. Two amino acid substitutions in the NS1 protein (D92E and P42S) could be associated with high viral fatality rates and replication efficiency [213940].\nIn summary, our data indicate that XJ-H5N6/2017 is a novel reassortant 2.3.4.4B HPAIV originating from the 2.3.4.4B H5N8 and 2.3.4.4C H5N6 viruses present in wild birds. The multiple mutations in the amino acids of the novel reassortant are associated with its pathogenicity. To detect novel reassortant HPAIVs in a timely manner, long-term, risk-based surveillance and analysis of AIVs in poultry and wild birds is essential in Xinjiang, China."],"string":"[\n \"Since 2014, highly pathogenic avian influenza viruses (HPAIVs) of clade 2.3.4.4 (H5) have been monitored in poultry and wild birds in European and Asian countries, and the viruses of that clade have evolved into eight groups (A-H) [12]. Group A includes H5N8 avian influenza viruses (AIVs) that were identified in Asia, Europe, and North America [1]. In 2016, a novel H5N8 AIV lineage initially emerged in the wild birds in Qinghai Lake, spread to Mongolia, Siberia, and Europe, and was subsequently identified in China and Korea; this new lineage was classified into clade 2.3.4.4B [3]. On the other hand, group C H5N6 AIVs were first reported in Laos in 2013 and gradually became the main source of sporadic AIV infections in poultry in Southern China [4]. Group D comprises H5N6 viruses mainly identified in China and Vietnam. Subsequent studies have reported that H5N6 viruses of groups 2.3.4.4E, 2.3.4.4F, 2.3.4.4G, and 2.3.4.4H have been isolated from poultry and wild birds [2].\\nOutbreaks of 2.3.4.4B H5N8 AIVs were reported in South Korea in 2014 [5]. The viruses were subsequently disseminated via migratory birds and have since been detected in wild and domestic birds worldwide [12678]. Almost simultaneously, 2.3.4.4B H5N6 HPAIVs were undergoing global transmission and have been detected in wild and domestic birds [8910111213]. The cocirculation of H5N6 and H5N8 viruses among wild and domestic birds could increase the probability of reassorting novel viruses [91011]; indeed, reassortants derived from the H5N6 and H5N8 HPAIVs have been detected in various wild birds and poultry [891415]. Moreover, it has been reported that 46% of all 2.3.4.4B H5N6 viruses in domestic poultry were derived from wild birds [9]. Thus, reassortant viruses could pose potential threats to poultry farming and human public health. In this study, nine H5N6 AIVs were isolated from aquatic poultry in Xinjiang Uyghur Autonomous Region, China, and all were grouped into clade 2.3.4.4B. This study aimed to analyze the origin of the isolates.\",\n \"Sample collection From March 2017 to December 2018, a total of 354 oropharyngeal and cloacal swabs from apparently healthy poultry (chickens, ducks, and geese) in live poultry markets (LPMs) in Urumqi, China were collected and placed in tubes containing viral transport medium DMEM (1,000 u/L penicillin, 500 ug/L streptomycin, 100 mg/L Nystatin, 100 mg/L Polymyxin B sulfate salt, 1000 mg/L Sulfamethoxazole, 0.05 g/L Ofloxacin). These tubes were kept in an icebox at −20°C before transport to the laboratory and then immediately stored at −80°C.\\nThe animal experiment portion of this study was approved by the Committee on the Ethics of Animal Experiments of Xinjiang Key Laboratory of Biological Resources and Genetic Engineering (BRGE-AE001) and carried out under the guidelines of the Animal Care and Use Committee of the College of Life Science and Technology, Xinjiang University.\\nFrom March 2017 to December 2018, a total of 354 oropharyngeal and cloacal swabs from apparently healthy poultry (chickens, ducks, and geese) in live poultry markets (LPMs) in Urumqi, China were collected and placed in tubes containing viral transport medium DMEM (1,000 u/L penicillin, 500 ug/L streptomycin, 100 mg/L Nystatin, 100 mg/L Polymyxin B sulfate salt, 1000 mg/L Sulfamethoxazole, 0.05 g/L Ofloxacin). These tubes were kept in an icebox at −20°C before transport to the laboratory and then immediately stored at −80°C.\\nThe animal experiment portion of this study was approved by the Committee on the Ethics of Animal Experiments of Xinjiang Key Laboratory of Biological Resources and Genetic Engineering (BRGE-AE001) and carried out under the guidelines of the Animal Care and Use Committee of the College of Life Science and Technology, Xinjiang University.\\nVirus isolation and identification The swab samples were vortexed and centrifuged, and the supernatants inoculated into 10-day-old specific-pathogen-free embryonated chicken eggs. Seventy-two hours after inoculation, allantois fluid was harvested, and hemagglutinin (HA) activity was assayed using 1% chicken red blood cells [14]. All HA-positive samples underwent reverse transcription polymerase chain reaction (RT-PCR) using universal primers targeting the PB1 gene [15]. Viral RNA extraction (Bioer) and RT-PCR (TOYOBO) were performed according to the manufacturers' instructions.\\nRoutine surveillance samples were processed in a biosafety level 2 (BSL-2) laboratory of the Center for Influenza Research and Early-Warning (CASCIRE), Chinese Academy of Science, while experiments with live H5N6 viruses were conducted in a CASCIRE biosafety level 3 (BSL-3) laboratory. Coveralls, gloves, and N95 masks were used during the work at the BSL-2 and BSL-3 laboratories, and all wastes were autoclaved.\\nThe swab samples were vortexed and centrifuged, and the supernatants inoculated into 10-day-old specific-pathogen-free embryonated chicken eggs. Seventy-two hours after inoculation, allantois fluid was harvested, and hemagglutinin (HA) activity was assayed using 1% chicken red blood cells [14]. All HA-positive samples underwent reverse transcription polymerase chain reaction (RT-PCR) using universal primers targeting the PB1 gene [15]. Viral RNA extraction (Bioer) and RT-PCR (TOYOBO) were performed according to the manufacturers' instructions.\\nRoutine surveillance samples were processed in a biosafety level 2 (BSL-2) laboratory of the Center for Influenza Research and Early-Warning (CASCIRE), Chinese Academy of Science, while experiments with live H5N6 viruses were conducted in a CASCIRE biosafety level 3 (BSL-3) laboratory. Coveralls, gloves, and N95 masks were used during the work at the BSL-2 and BSL-3 laboratories, and all wastes were autoclaved.\\nWhole-genome sequencing Next-generation sequencing was used to determine the whole-genome sequences of the AIV isolates. The viral RNA samples were quantified using the 2100 Bioanalyzer System (Agilent Technologies). The RT-PCR and cDNA synthesis procedures were conducted using PrimeScript One-Step RT-PCR Kit Ver.2 (RR055A Takara) and influenza A-specific primers MBTuni-12 and MBTuni-13. Sequencing libraries with an insert size of 200bp were prepared by end-repairing, dA-tailing, adaptor ligation, and PCR amplification, all performed according to the standard manufacturer's instructions (Illumina, USA). The libraries were sequenced on an Illumina HiSeq4000 platform (Illumina) [416].\\nNext-generation sequencing was used to determine the whole-genome sequences of the AIV isolates. The viral RNA samples were quantified using the 2100 Bioanalyzer System (Agilent Technologies). The RT-PCR and cDNA synthesis procedures were conducted using PrimeScript One-Step RT-PCR Kit Ver.2 (RR055A Takara) and influenza A-specific primers MBTuni-12 and MBTuni-13. Sequencing libraries with an insert size of 200bp were prepared by end-repairing, dA-tailing, adaptor ligation, and PCR amplification, all performed according to the standard manufacturer's instructions (Illumina, USA). The libraries were sequenced on an Illumina HiSeq4000 platform (Illumina) [416].\\nPhylogenetic analysis Phylogenetic trees were constructed for each gene segment of the nine AIV isolates to investigate their evolutionary relationships. The AIV reference sequences were obtained from GenBank (http://www.ncbi.nlm.nih.gov/genbank) and GISAID (http://www.gisaid.org) via the online Basic Local Alignment Search Tool (BLAST). The datasets for phylogenetic analysis were generated by aligning with reference sequences closely related to the viral sequences of the isolates and removing sequences with the same strain name and those without a clear subtype or collection date. The final sequence numbers of each gene segment are PB2 37, PB1 28, PA 32, HA 35, NP 33, NA 24, MP 27, and NS 33 (Supplementary Table 1). The sequences were aligned with ClustalW using MEGA 7.0. The general time-reversible nucleotide substitution model with invariant sites (I) and the gamma rate of heterogeneity (GTR +Γ) results were selected as providing the best fit for all datasets. Maximum clade credibility (MCC) phylogenetic trees were generated by applying maximum likelihood analysis with 1000 bootstrap replicates in the MEGA-X program and visualized/annotated using Fig tree 1.4.3 software [4].\\nPhylogenetic trees were constructed for each gene segment of the nine AIV isolates to investigate their evolutionary relationships. The AIV reference sequences were obtained from GenBank (http://www.ncbi.nlm.nih.gov/genbank) and GISAID (http://www.gisaid.org) via the online Basic Local Alignment Search Tool (BLAST). The datasets for phylogenetic analysis were generated by aligning with reference sequences closely related to the viral sequences of the isolates and removing sequences with the same strain name and those without a clear subtype or collection date. The final sequence numbers of each gene segment are PB2 37, PB1 28, PA 32, HA 35, NP 33, NA 24, MP 27, and NS 33 (Supplementary Table 1). The sequences were aligned with ClustalW using MEGA 7.0. The general time-reversible nucleotide substitution model with invariant sites (I) and the gamma rate of heterogeneity (GTR +Γ) results were selected as providing the best fit for all datasets. Maximum clade credibility (MCC) phylogenetic trees were generated by applying maximum likelihood analysis with 1000 bootstrap replicates in the MEGA-X program and visualized/annotated using Fig tree 1.4.3 software [4].\",\n \"From March 2017 to December 2018, a total of 354 oropharyngeal and cloacal swabs from apparently healthy poultry (chickens, ducks, and geese) in live poultry markets (LPMs) in Urumqi, China were collected and placed in tubes containing viral transport medium DMEM (1,000 u/L penicillin, 500 ug/L streptomycin, 100 mg/L Nystatin, 100 mg/L Polymyxin B sulfate salt, 1000 mg/L Sulfamethoxazole, 0.05 g/L Ofloxacin). These tubes were kept in an icebox at −20°C before transport to the laboratory and then immediately stored at −80°C.\\nThe animal experiment portion of this study was approved by the Committee on the Ethics of Animal Experiments of Xinjiang Key Laboratory of Biological Resources and Genetic Engineering (BRGE-AE001) and carried out under the guidelines of the Animal Care and Use Committee of the College of Life Science and Technology, Xinjiang University.\",\n \"The swab samples were vortexed and centrifuged, and the supernatants inoculated into 10-day-old specific-pathogen-free embryonated chicken eggs. Seventy-two hours after inoculation, allantois fluid was harvested, and hemagglutinin (HA) activity was assayed using 1% chicken red blood cells [14]. All HA-positive samples underwent reverse transcription polymerase chain reaction (RT-PCR) using universal primers targeting the PB1 gene [15]. Viral RNA extraction (Bioer) and RT-PCR (TOYOBO) were performed according to the manufacturers' instructions.\\nRoutine surveillance samples were processed in a biosafety level 2 (BSL-2) laboratory of the Center for Influenza Research and Early-Warning (CASCIRE), Chinese Academy of Science, while experiments with live H5N6 viruses were conducted in a CASCIRE biosafety level 3 (BSL-3) laboratory. Coveralls, gloves, and N95 masks were used during the work at the BSL-2 and BSL-3 laboratories, and all wastes were autoclaved.\",\n \"Next-generation sequencing was used to determine the whole-genome sequences of the AIV isolates. The viral RNA samples were quantified using the 2100 Bioanalyzer System (Agilent Technologies). The RT-PCR and cDNA synthesis procedures were conducted using PrimeScript One-Step RT-PCR Kit Ver.2 (RR055A Takara) and influenza A-specific primers MBTuni-12 and MBTuni-13. Sequencing libraries with an insert size of 200bp were prepared by end-repairing, dA-tailing, adaptor ligation, and PCR amplification, all performed according to the standard manufacturer's instructions (Illumina, USA). The libraries were sequenced on an Illumina HiSeq4000 platform (Illumina) [416].\",\n \"Phylogenetic trees were constructed for each gene segment of the nine AIV isolates to investigate their evolutionary relationships. The AIV reference sequences were obtained from GenBank (http://www.ncbi.nlm.nih.gov/genbank) and GISAID (http://www.gisaid.org) via the online Basic Local Alignment Search Tool (BLAST). The datasets for phylogenetic analysis were generated by aligning with reference sequences closely related to the viral sequences of the isolates and removing sequences with the same strain name and those without a clear subtype or collection date. The final sequence numbers of each gene segment are PB2 37, PB1 28, PA 32, HA 35, NP 33, NA 24, MP 27, and NS 33 (Supplementary Table 1). The sequences were aligned with ClustalW using MEGA 7.0. The general time-reversible nucleotide substitution model with invariant sites (I) and the gamma rate of heterogeneity (GTR +Γ) results were selected as providing the best fit for all datasets. Maximum clade credibility (MCC) phylogenetic trees were generated by applying maximum likelihood analysis with 1000 bootstrap replicates in the MEGA-X program and visualized/annotated using Fig tree 1.4.3 software [4].\",\n \"Virus isolation Nine strains of H5N6 AIV were isolated between July and November 2017, one from a duck swab and the rest from goose swabs. The eight gene segments of the 9 isolates shared very high nucleotide homology (> 99.9%); thus, the isolates originated from the same ancestor. The strains have been designated as A/goose/Xinjiang/011-015/2017(H5N6), A/duck/Xinjiang/016/2017 (H5N6), and A/goose/Xinjiang/017-019/2017(H5N6), or XJ-H5N6/2017 for short. The complete sequences of the isolates have been submitted to NCBI (accession numbers: MW109029-MW109036, MW110101-MW110108, and MW110205-MW110260).\\nNine strains of H5N6 AIV were isolated between July and November 2017, one from a duck swab and the rest from goose swabs. The eight gene segments of the 9 isolates shared very high nucleotide homology (> 99.9%); thus, the isolates originated from the same ancestor. The strains have been designated as A/goose/Xinjiang/011-015/2017(H5N6), A/duck/Xinjiang/016/2017 (H5N6), and A/goose/Xinjiang/017-019/2017(H5N6), or XJ-H5N6/2017 for short. The complete sequences of the isolates have been submitted to NCBI (accession numbers: MW109029-MW109036, MW110101-MW110108, and MW110205-MW110260).\\nSequence and phylogenetic analysis Homology BLAST and phylogenetic analyses were performed on eight genes of the viral isolates (Table 1 and Fig. 1). The viral NA gene exhibited the highest sequence homology (99.1%), and the sequence clustered together with those of H5N6 AIVs isolated from shoveler ducks sampled in Ningxia (NX488-53) and from the environment of Chongqing in 2015; those sequences were designated as 2.3.4.4C H5N6 AIVs. The viral NP gene segments shared the highest sequence homology (99.4%) with 2.3.4.4B H5N8 AIVs isolated from the green-winged teal in Egypt in 2016 and were grouped into an independent subcluster within the phylogenetic tree. The remaining six genes had the highest sequence homologies (99.1%–99.6%) and relatively close genetic relationships in the phylogenetic tree with the 2.3.4.4B H5N8 AIVs. Five of those genes, HA, PB2, NS, PA, and MP, clustered together with 2.3.4.4B H5N8 AIVs isolated from migratory swans in Sanmenxia, Hubei, and Shanxi in 2016, and this sixth gene, PB1, clustered together with H5N8 AIVs isolated from grey-headed gulls sampled in Uganda in 2017. The above results suggest that XJ-H5N6/2017 is a novel reassortant 2.3.4.4B H5N6 AIV derived from 2.3.4.4B H5N8 and 2.3.4.4C H5N6 viruses present in wild birds.\\nHomology BLAST and phylogenetic analyses were performed on eight genes of the viral isolates (Table 1 and Fig. 1). The viral NA gene exhibited the highest sequence homology (99.1%), and the sequence clustered together with those of H5N6 AIVs isolated from shoveler ducks sampled in Ningxia (NX488-53) and from the environment of Chongqing in 2015; those sequences were designated as 2.3.4.4C H5N6 AIVs. The viral NP gene segments shared the highest sequence homology (99.4%) with 2.3.4.4B H5N8 AIVs isolated from the green-winged teal in Egypt in 2016 and were grouped into an independent subcluster within the phylogenetic tree. The remaining six genes had the highest sequence homologies (99.1%–99.6%) and relatively close genetic relationships in the phylogenetic tree with the 2.3.4.4B H5N8 AIVs. Five of those genes, HA, PB2, NS, PA, and MP, clustered together with 2.3.4.4B H5N8 AIVs isolated from migratory swans in Sanmenxia, Hubei, and Shanxi in 2016, and this sixth gene, PB1, clustered together with H5N8 AIVs isolated from grey-headed gulls sampled in Uganda in 2017. The above results suggest that XJ-H5N6/2017 is a novel reassortant 2.3.4.4B H5N6 AIV derived from 2.3.4.4B H5N8 and 2.3.4.4C H5N6 viruses present in wild birds.\\nMolecular characteristics of the H5N6 virus isolates Assessment of the multiple amino acid mutations present in the nine isolates could help elucidate H5N6 virulence. Multiple basic amino acids (KEKRRKR↓GLF) were observed at cleavage sites in the HA protein of the H5N6 isolates (Table 2), suggesting that the isolates were HPAIVs. The viral receptor-binding sites all contained Q226 and G228 (H3 numbering), indicative of an avian-like α2,3-sialic acid (α2,3-SA) receptor-binding preference; however, the S128P, S137A, and T160A mutations in HA could enhance the capacity to bind to a human-like α-2,6-SA receptor [17]. In all nine isolates, 11 amino acid deletions (59–69) in the NA stalk were observed, suggesting the isolates could have different adaptive and virulence characteristics in poultry and mammals [18]. In addition, the L89V, G309D, R477G, I495V [19], and I504V [20] mutations in the PB2 protein and the P42S and D92E mutations in the NS1 were retained in all nine isolates; notably, those mutations can enhance viral virulence and replication activity in mammals [21]. Moreover, the M2-F38L mutation has been associated with antiviral resistance (amantadine and rimantadine) [22].\\nAssessment of the multiple amino acid mutations present in the nine isolates could help elucidate H5N6 virulence. Multiple basic amino acids (KEKRRKR↓GLF) were observed at cleavage sites in the HA protein of the H5N6 isolates (Table 2), suggesting that the isolates were HPAIVs. The viral receptor-binding sites all contained Q226 and G228 (H3 numbering), indicative of an avian-like α2,3-sialic acid (α2,3-SA) receptor-binding preference; however, the S128P, S137A, and T160A mutations in HA could enhance the capacity to bind to a human-like α-2,6-SA receptor [17]. In all nine isolates, 11 amino acid deletions (59–69) in the NA stalk were observed, suggesting the isolates could have different adaptive and virulence characteristics in poultry and mammals [18]. In addition, the L89V, G309D, R477G, I495V [19], and I504V [20] mutations in the PB2 protein and the P42S and D92E mutations in the NS1 were retained in all nine isolates; notably, those mutations can enhance viral virulence and replication activity in mammals [21]. Moreover, the M2-F38L mutation has been associated with antiviral resistance (amantadine and rimantadine) [22].\",\n \"Nine strains of H5N6 AIV were isolated between July and November 2017, one from a duck swab and the rest from goose swabs. The eight gene segments of the 9 isolates shared very high nucleotide homology (> 99.9%); thus, the isolates originated from the same ancestor. The strains have been designated as A/goose/Xinjiang/011-015/2017(H5N6), A/duck/Xinjiang/016/2017 (H5N6), and A/goose/Xinjiang/017-019/2017(H5N6), or XJ-H5N6/2017 for short. The complete sequences of the isolates have been submitted to NCBI (accession numbers: MW109029-MW109036, MW110101-MW110108, and MW110205-MW110260).\",\n \"Homology BLAST and phylogenetic analyses were performed on eight genes of the viral isolates (Table 1 and Fig. 1). The viral NA gene exhibited the highest sequence homology (99.1%), and the sequence clustered together with those of H5N6 AIVs isolated from shoveler ducks sampled in Ningxia (NX488-53) and from the environment of Chongqing in 2015; those sequences were designated as 2.3.4.4C H5N6 AIVs. The viral NP gene segments shared the highest sequence homology (99.4%) with 2.3.4.4B H5N8 AIVs isolated from the green-winged teal in Egypt in 2016 and were grouped into an independent subcluster within the phylogenetic tree. The remaining six genes had the highest sequence homologies (99.1%–99.6%) and relatively close genetic relationships in the phylogenetic tree with the 2.3.4.4B H5N8 AIVs. Five of those genes, HA, PB2, NS, PA, and MP, clustered together with 2.3.4.4B H5N8 AIVs isolated from migratory swans in Sanmenxia, Hubei, and Shanxi in 2016, and this sixth gene, PB1, clustered together with H5N8 AIVs isolated from grey-headed gulls sampled in Uganda in 2017. The above results suggest that XJ-H5N6/2017 is a novel reassortant 2.3.4.4B H5N6 AIV derived from 2.3.4.4B H5N8 and 2.3.4.4C H5N6 viruses present in wild birds.\",\n \"Assessment of the multiple amino acid mutations present in the nine isolates could help elucidate H5N6 virulence. Multiple basic amino acids (KEKRRKR↓GLF) were observed at cleavage sites in the HA protein of the H5N6 isolates (Table 2), suggesting that the isolates were HPAIVs. The viral receptor-binding sites all contained Q226 and G228 (H3 numbering), indicative of an avian-like α2,3-sialic acid (α2,3-SA) receptor-binding preference; however, the S128P, S137A, and T160A mutations in HA could enhance the capacity to bind to a human-like α-2,6-SA receptor [17]. In all nine isolates, 11 amino acid deletions (59–69) in the NA stalk were observed, suggesting the isolates could have different adaptive and virulence characteristics in poultry and mammals [18]. In addition, the L89V, G309D, R477G, I495V [19], and I504V [20] mutations in the PB2 protein and the P42S and D92E mutations in the NS1 were retained in all nine isolates; notably, those mutations can enhance viral virulence and replication activity in mammals [21]. Moreover, the M2-F38L mutation has been associated with antiviral resistance (amantadine and rimantadine) [22].\",\n \"The Xinjiang region is located in northwest China within the interior of the Eurasian Continent. It comprises an overlapping bird migration region between the Central Asian Flyway and the West Asian–East African Flyway. The Northern Tianshan Mountain (NTM) region in Xinjiang includes many complex mountain-oasis-desert systems, and several water reservoirs have been formed by dam construction in narrow mouths of ravines or rivers, with much of the water coming from snowmelt [23]. Many wild birds migrate along the NTM region every year, and the reservoirs in the region have become key stopover areas for migratory birds from Eurasia and Africa. Moreover, these reservoirs are also used during aquatic poultry farming, thereby increasing the odds of contact between aquatic poultry and wild birds, including direct contact with infected wild birds or indirect contact with related materials (e.g., wild-bird feces), which could result in the introduction of wild-bird origin AIVs into aquatic poultry [824]. The majority of H5 HPAIVs detections in wild and domestic birds have been associated with migratory flyways and wild-bird aggregation areas [825]. This wild-domestic bird interface has had an important role in the spread, reassortment, evolution, and epidemiology of AIVs [8]; in particular, 2.3.4.4 H5 HPAIVs that have emerged since 2013. Such HPAIVs are continuing to reassort, evolve, and spread and have been detected in wild and domestic birds around the world [26]. Since 2016, HPAIVs outbreaks in Xinjiang have occurred repeatedly, and there have been several outbreaks of H5N6 HPAIVs (including 2.3.4.4B and 2.3.4.4H) in poultry and migratory birds sampled in Xinjiang [2728]. In this study, the 2.3.4.4B HPAIV isolates from the aquatic poultry in the LPMs of Urumqi were shown to be novel reassortant viruses derived from 2.3.4.4B H5N8 and 2.3.4.4C H5N6 AIVs of wild birds, suggesting that 2.3.4.4 clade AIVs are circulating in both wild and domestic birds in Xinjiang.\\nIn this study, the NA gene of XJ-H5N6/2017 had a close relationship with the wild-bird origin 2.3.4.4C H5N6 AIVs identified from Chongqing and Ningxia, China. Our previous study reported a reassortant 2.3.4.4C H5N6 AIV in Xinjiang (GISAID accession nos.: EPI1548859-1548874), which originated from the NX488-53 virus isolated in December 2016 [29]. The NP gene sequences of the isolates in this study were grouped into an independent subcluster within the 2.3.4.4B H5N8 AIVs, indicating that NP has continued to evolve in Xinjiang. The PB1 gene was most closely related to the 2.3.4.4B H5N8 AIVs from the migratory wild birds sampled in Africa in 2017. In addition, it had a close relationship with 2.3.4.4B H5N8 AIVs from the migratory swans sampled in Central China in 2016. The remaining five genes had close relationships with 2.3.4.4B H5N8 AIVs identified from the migratory swans sampled in Central China in 2016 and bar-headed geese sampled in Qinghai in 2017. The Sanmenxia wetland of the Yellow River is an important wintering ground for migratory swans, with the swans arriving at the wetland in late October each year, departing the wetland during the late February to late March period of the following year, and migrating northwest directly to Siberia and northwest China (e.g., Qinghai wetlands) for breeding and molting [30]. An outbreak of H5N1 HPAIV has occurred at the Sanmenxia wetland, and migrating swans carrying the H5N1 HPAIVs have spread it over long distances [3132]. Also, H5N8 HPAIVs from wild birds in Hubei, China have been reported to cause the death of migratory swans, and the H5N8 viruses have been isolated in samples from Qinghai Lake and Europe. Thus, the wetlands and lakes in Central China may have a key role in spreading H5N8 viruses in the East Asian-Australasian and Central Asian flyways [12]. These observations suggest that the isolated 2.3.4.4B H5N8 AIVs could have spread into Xinjiang by infected migratory wild birds present in Central China in 2016 and 2017 and spreading into Africa in 2017. Furthermore, these 2.3.4.4B H5N8 AIVs might have reassorted with the NX488-53 virus to generate the novel 2.3.4.4B virus of XJ-H5N6/2017, which could then circulate within aquatic poultry in the water reservoirs of NTM, subsequently spreading into the LPMs of Urumqi via the sale of live poultry. In January 2020, 2.3.4.4H H5N6 HPAIVs [SW/XJ/1/2020(H5N6)] were identified from migratory whooper and mute swans in Xinjiang [28], and the low level of similarity between the isolates in this study and those of SW/XJ/1/2020(H5N6) suggests that these viruses spread into Xinjiang independently. Based on the above observations, the wetlands in Xinjiang may have a key role in the AIVs circulating among the migratory birds and aquatic poultry in Xinjiang and in disseminating the viruses from Central China to the Eurasian continent and East African via the Central Asian Flyway and the West Asian–East African Flyway.\\nIn our study, the multiple mutations detected in the isolates may be associated with viral virulence, adaptation, and transmission (Table 2). The viral HA protein included cleavage sites containing multiple basic amino acids associated with HPAIVs; moreover, the S128P, S137A, and T160A mutations can enhance binding capacity to a human-like α-2,6-SA receptor [17]. These three mutations were also present in the NX488-53 virus, which can infect BALB/c mice and transmit among guinea pigs via direct contact or aerosol routes [3334]. The 11 amino acid deletion in the stalk region of the NA protein has been associated with the adaptation of wild-bird origin AIVs to gallinaceous poultry [35], increasing viral virulence [36], and enhancing viral transmission in ducks [37]. It has also been reported that the NA short-stalk region in H7N9 [36] and H5N1 [38] AIVs can increase virulence in mice, and it is characteristic of the viral adaptation for chickens [35]. Five amino acid substitutions in the PB2 protein (L89V, G309D, R477G, I495V, and I504V) may increase viral replication activity in mammalian cells and viral virulence in mice [1920]. Two amino acid substitutions in the NS1 protein (D92E and P42S) could be associated with high viral fatality rates and replication efficiency [213940].\\nIn summary, our data indicate that XJ-H5N6/2017 is a novel reassortant 2.3.4.4B HPAIV originating from the 2.3.4.4B H5N8 and 2.3.4.4C H5N6 viruses present in wild birds. The multiple mutations in the amino acids of the novel reassortant are associated with its pathogenicity. To detect novel reassortant HPAIVs in a timely manner, long-term, risk-based surveillance and analysis of AIVs in poultry and wild birds is essential in Xinjiang, China.\"\n]"},"all_sections_sec_types":{"kind":"list like","value":["intro","materials|methods",null,null,null,null,"results",null,null,null,"discussion"],"string":"[\n \"intro\",\n \"materials|methods\",\n null,\n null,\n null,\n null,\n \"results\",\n null,\n null,\n null,\n \"discussion\"\n]"},"keywords":{"kind":"list like","value":["Highly pathogenic avian influenza virus","H5N6","reassortant","poultry","wild bird"],"string":"[\n \"Highly pathogenic avian influenza virus\",\n \"H5N6\",\n \"reassortant\",\n \"poultry\",\n \"wild bird\"\n]"},"whole_article_text":{"kind":"string","value":"INTRODUCTION:\nSince 2014, highly pathogenic avian influenza viruses (HPAIVs) of clade 2.3.4.4 (H5) have been monitored in poultry and wild birds in European and Asian countries, and the viruses of that clade have evolved into eight groups (A-H) [12]. Group A includes H5N8 avian influenza viruses (AIVs) that were identified in Asia, Europe, and North America [1]. In 2016, a novel H5N8 AIV lineage initially emerged in the wild birds in Qinghai Lake, spread to Mongolia, Siberia, and Europe, and was subsequently identified in China and Korea; this new lineage was classified into clade 2.3.4.4B [3]. On the other hand, group C H5N6 AIVs were first reported in Laos in 2013 and gradually became the main source of sporadic AIV infections in poultry in Southern China [4]. Group D comprises H5N6 viruses mainly identified in China and Vietnam. Subsequent studies have reported that H5N6 viruses of groups 2.3.4.4E, 2.3.4.4F, 2.3.4.4G, and 2.3.4.4H have been isolated from poultry and wild birds [2].\nOutbreaks of 2.3.4.4B H5N8 AIVs were reported in South Korea in 2014 [5]. The viruses were subsequently disseminated via migratory birds and have since been detected in wild and domestic birds worldwide [12678]. Almost simultaneously, 2.3.4.4B H5N6 HPAIVs were undergoing global transmission and have been detected in wild and domestic birds [8910111213]. The cocirculation of H5N6 and H5N8 viruses among wild and domestic birds could increase the probability of reassorting novel viruses [91011]; indeed, reassortants derived from the H5N6 and H5N8 HPAIVs have been detected in various wild birds and poultry [891415]. Moreover, it has been reported that 46% of all 2.3.4.4B H5N6 viruses in domestic poultry were derived from wild birds [9]. Thus, reassortant viruses could pose potential threats to poultry farming and human public health. In this study, nine H5N6 AIVs were isolated from aquatic poultry in Xinjiang Uyghur Autonomous Region, China, and all were grouped into clade 2.3.4.4B. This study aimed to analyze the origin of the isolates.\n\nMATERIALS AND METHODS:\nSample collection From March 2017 to December 2018, a total of 354 oropharyngeal and cloacal swabs from apparently healthy poultry (chickens, ducks, and geese) in live poultry markets (LPMs) in Urumqi, China were collected and placed in tubes containing viral transport medium DMEM (1,000 u/L penicillin, 500 ug/L streptomycin, 100 mg/L Nystatin, 100 mg/L Polymyxin B sulfate salt, 1000 mg/L Sulfamethoxazole, 0.05 g/L Ofloxacin). These tubes were kept in an icebox at −20°C before transport to the laboratory and then immediately stored at −80°C.\nThe animal experiment portion of this study was approved by the Committee on the Ethics of Animal Experiments of Xinjiang Key Laboratory of Biological Resources and Genetic Engineering (BRGE-AE001) and carried out under the guidelines of the Animal Care and Use Committee of the College of Life Science and Technology, Xinjiang University.\nFrom March 2017 to December 2018, a total of 354 oropharyngeal and cloacal swabs from apparently healthy poultry (chickens, ducks, and geese) in live poultry markets (LPMs) in Urumqi, China were collected and placed in tubes containing viral transport medium DMEM (1,000 u/L penicillin, 500 ug/L streptomycin, 100 mg/L Nystatin, 100 mg/L Polymyxin B sulfate salt, 1000 mg/L Sulfamethoxazole, 0.05 g/L Ofloxacin). These tubes were kept in an icebox at −20°C before transport to the laboratory and then immediately stored at −80°C.\nThe animal experiment portion of this study was approved by the Committee on the Ethics of Animal Experiments of Xinjiang Key Laboratory of Biological Resources and Genetic Engineering (BRGE-AE001) and carried out under the guidelines of the Animal Care and Use Committee of the College of Life Science and Technology, Xinjiang University.\nVirus isolation and identification The swab samples were vortexed and centrifuged, and the supernatants inoculated into 10-day-old specific-pathogen-free embryonated chicken eggs. Seventy-two hours after inoculation, allantois fluid was harvested, and hemagglutinin (HA) activity was assayed using 1% chicken red blood cells [14]. All HA-positive samples underwent reverse transcription polymerase chain reaction (RT-PCR) using universal primers targeting the PB1 gene [15]. Viral RNA extraction (Bioer) and RT-PCR (TOYOBO) were performed according to the manufacturers' instructions.\nRoutine surveillance samples were processed in a biosafety level 2 (BSL-2) laboratory of the Center for Influenza Research and Early-Warning (CASCIRE), Chinese Academy of Science, while experiments with live H5N6 viruses were conducted in a CASCIRE biosafety level 3 (BSL-3) laboratory. Coveralls, gloves, and N95 masks were used during the work at the BSL-2 and BSL-3 laboratories, and all wastes were autoclaved.\nThe swab samples were vortexed and centrifuged, and the supernatants inoculated into 10-day-old specific-pathogen-free embryonated chicken eggs. Seventy-two hours after inoculation, allantois fluid was harvested, and hemagglutinin (HA) activity was assayed using 1% chicken red blood cells [14]. All HA-positive samples underwent reverse transcription polymerase chain reaction (RT-PCR) using universal primers targeting the PB1 gene [15]. Viral RNA extraction (Bioer) and RT-PCR (TOYOBO) were performed according to the manufacturers' instructions.\nRoutine surveillance samples were processed in a biosafety level 2 (BSL-2) laboratory of the Center for Influenza Research and Early-Warning (CASCIRE), Chinese Academy of Science, while experiments with live H5N6 viruses were conducted in a CASCIRE biosafety level 3 (BSL-3) laboratory. Coveralls, gloves, and N95 masks were used during the work at the BSL-2 and BSL-3 laboratories, and all wastes were autoclaved.\nWhole-genome sequencing Next-generation sequencing was used to determine the whole-genome sequences of the AIV isolates. The viral RNA samples were quantified using the 2100 Bioanalyzer System (Agilent Technologies). The RT-PCR and cDNA synthesis procedures were conducted using PrimeScript One-Step RT-PCR Kit Ver.2 (RR055A Takara) and influenza A-specific primers MBTuni-12 and MBTuni-13. Sequencing libraries with an insert size of 200bp were prepared by end-repairing, dA-tailing, adaptor ligation, and PCR amplification, all performed according to the standard manufacturer's instructions (Illumina, USA). The libraries were sequenced on an Illumina HiSeq4000 platform (Illumina) [416].\nNext-generation sequencing was used to determine the whole-genome sequences of the AIV isolates. The viral RNA samples were quantified using the 2100 Bioanalyzer System (Agilent Technologies). The RT-PCR and cDNA synthesis procedures were conducted using PrimeScript One-Step RT-PCR Kit Ver.2 (RR055A Takara) and influenza A-specific primers MBTuni-12 and MBTuni-13. Sequencing libraries with an insert size of 200bp were prepared by end-repairing, dA-tailing, adaptor ligation, and PCR amplification, all performed according to the standard manufacturer's instructions (Illumina, USA). The libraries were sequenced on an Illumina HiSeq4000 platform (Illumina) [416].\nPhylogenetic analysis Phylogenetic trees were constructed for each gene segment of the nine AIV isolates to investigate their evolutionary relationships. The AIV reference sequences were obtained from GenBank (http://www.ncbi.nlm.nih.gov/genbank) and GISAID (http://www.gisaid.org) via the online Basic Local Alignment Search Tool (BLAST). The datasets for phylogenetic analysis were generated by aligning with reference sequences closely related to the viral sequences of the isolates and removing sequences with the same strain name and those without a clear subtype or collection date. The final sequence numbers of each gene segment are PB2 37, PB1 28, PA 32, HA 35, NP 33, NA 24, MP 27, and NS 33 (Supplementary Table 1). The sequences were aligned with ClustalW using MEGA 7.0. The general time-reversible nucleotide substitution model with invariant sites (I) and the gamma rate of heterogeneity (GTR +Γ) results were selected as providing the best fit for all datasets. Maximum clade credibility (MCC) phylogenetic trees were generated by applying maximum likelihood analysis with 1000 bootstrap replicates in the MEGA-X program and visualized/annotated using Fig tree 1.4.3 software [4].\nPhylogenetic trees were constructed for each gene segment of the nine AIV isolates to investigate their evolutionary relationships. The AIV reference sequences were obtained from GenBank (http://www.ncbi.nlm.nih.gov/genbank) and GISAID (http://www.gisaid.org) via the online Basic Local Alignment Search Tool (BLAST). The datasets for phylogenetic analysis were generated by aligning with reference sequences closely related to the viral sequences of the isolates and removing sequences with the same strain name and those without a clear subtype or collection date. The final sequence numbers of each gene segment are PB2 37, PB1 28, PA 32, HA 35, NP 33, NA 24, MP 27, and NS 33 (Supplementary Table 1). The sequences were aligned with ClustalW using MEGA 7.0. The general time-reversible nucleotide substitution model with invariant sites (I) and the gamma rate of heterogeneity (GTR +Γ) results were selected as providing the best fit for all datasets. Maximum clade credibility (MCC) phylogenetic trees were generated by applying maximum likelihood analysis with 1000 bootstrap replicates in the MEGA-X program and visualized/annotated using Fig tree 1.4.3 software [4].\n\nSample collection:\nFrom March 2017 to December 2018, a total of 354 oropharyngeal and cloacal swabs from apparently healthy poultry (chickens, ducks, and geese) in live poultry markets (LPMs) in Urumqi, China were collected and placed in tubes containing viral transport medium DMEM (1,000 u/L penicillin, 500 ug/L streptomycin, 100 mg/L Nystatin, 100 mg/L Polymyxin B sulfate salt, 1000 mg/L Sulfamethoxazole, 0.05 g/L Ofloxacin). These tubes were kept in an icebox at −20°C before transport to the laboratory and then immediately stored at −80°C.\nThe animal experiment portion of this study was approved by the Committee on the Ethics of Animal Experiments of Xinjiang Key Laboratory of Biological Resources and Genetic Engineering (BRGE-AE001) and carried out under the guidelines of the Animal Care and Use Committee of the College of Life Science and Technology, Xinjiang University.\n\nVirus isolation and identification:\nThe swab samples were vortexed and centrifuged, and the supernatants inoculated into 10-day-old specific-pathogen-free embryonated chicken eggs. Seventy-two hours after inoculation, allantois fluid was harvested, and hemagglutinin (HA) activity was assayed using 1% chicken red blood cells [14]. All HA-positive samples underwent reverse transcription polymerase chain reaction (RT-PCR) using universal primers targeting the PB1 gene [15]. Viral RNA extraction (Bioer) and RT-PCR (TOYOBO) were performed according to the manufacturers' instructions.\nRoutine surveillance samples were processed in a biosafety level 2 (BSL-2) laboratory of the Center for Influenza Research and Early-Warning (CASCIRE), Chinese Academy of Science, while experiments with live H5N6 viruses were conducted in a CASCIRE biosafety level 3 (BSL-3) laboratory. Coveralls, gloves, and N95 masks were used during the work at the BSL-2 and BSL-3 laboratories, and all wastes were autoclaved.\n\nWhole-genome sequencing:\nNext-generation sequencing was used to determine the whole-genome sequences of the AIV isolates. The viral RNA samples were quantified using the 2100 Bioanalyzer System (Agilent Technologies). The RT-PCR and cDNA synthesis procedures were conducted using PrimeScript One-Step RT-PCR Kit Ver.2 (RR055A Takara) and influenza A-specific primers MBTuni-12 and MBTuni-13. Sequencing libraries with an insert size of 200bp were prepared by end-repairing, dA-tailing, adaptor ligation, and PCR amplification, all performed according to the standard manufacturer's instructions (Illumina, USA). The libraries were sequenced on an Illumina HiSeq4000 platform (Illumina) [416].\n\nPhylogenetic analysis:\nPhylogenetic trees were constructed for each gene segment of the nine AIV isolates to investigate their evolutionary relationships. The AIV reference sequences were obtained from GenBank (http://www.ncbi.nlm.nih.gov/genbank) and GISAID (http://www.gisaid.org) via the online Basic Local Alignment Search Tool (BLAST). The datasets for phylogenetic analysis were generated by aligning with reference sequences closely related to the viral sequences of the isolates and removing sequences with the same strain name and those without a clear subtype or collection date. The final sequence numbers of each gene segment are PB2 37, PB1 28, PA 32, HA 35, NP 33, NA 24, MP 27, and NS 33 (Supplementary Table 1). The sequences were aligned with ClustalW using MEGA 7.0. The general time-reversible nucleotide substitution model with invariant sites (I) and the gamma rate of heterogeneity (GTR +Γ) results were selected as providing the best fit for all datasets. Maximum clade credibility (MCC) phylogenetic trees were generated by applying maximum likelihood analysis with 1000 bootstrap replicates in the MEGA-X program and visualized/annotated using Fig tree 1.4.3 software [4].\n\nRESULTS:\nVirus isolation Nine strains of H5N6 AIV were isolated between July and November 2017, one from a duck swab and the rest from goose swabs. The eight gene segments of the 9 isolates shared very high nucleotide homology (> 99.9%); thus, the isolates originated from the same ancestor. The strains have been designated as A/goose/Xinjiang/011-015/2017(H5N6), A/duck/Xinjiang/016/2017 (H5N6), and A/goose/Xinjiang/017-019/2017(H5N6), or XJ-H5N6/2017 for short. The complete sequences of the isolates have been submitted to NCBI (accession numbers: MW109029-MW109036, MW110101-MW110108, and MW110205-MW110260).\nNine strains of H5N6 AIV were isolated between July and November 2017, one from a duck swab and the rest from goose swabs. The eight gene segments of the 9 isolates shared very high nucleotide homology (> 99.9%); thus, the isolates originated from the same ancestor. The strains have been designated as A/goose/Xinjiang/011-015/2017(H5N6), A/duck/Xinjiang/016/2017 (H5N6), and A/goose/Xinjiang/017-019/2017(H5N6), or XJ-H5N6/2017 for short. The complete sequences of the isolates have been submitted to NCBI (accession numbers: MW109029-MW109036, MW110101-MW110108, and MW110205-MW110260).\nSequence and phylogenetic analysis Homology BLAST and phylogenetic analyses were performed on eight genes of the viral isolates (Table 1 and Fig. 1). The viral NA gene exhibited the highest sequence homology (99.1%), and the sequence clustered together with those of H5N6 AIVs isolated from shoveler ducks sampled in Ningxia (NX488-53) and from the environment of Chongqing in 2015; those sequences were designated as 2.3.4.4C H5N6 AIVs. The viral NP gene segments shared the highest sequence homology (99.4%) with 2.3.4.4B H5N8 AIVs isolated from the green-winged teal in Egypt in 2016 and were grouped into an independent subcluster within the phylogenetic tree. The remaining six genes had the highest sequence homologies (99.1%–99.6%) and relatively close genetic relationships in the phylogenetic tree with the 2.3.4.4B H5N8 AIVs. Five of those genes, HA, PB2, NS, PA, and MP, clustered together with 2.3.4.4B H5N8 AIVs isolated from migratory swans in Sanmenxia, Hubei, and Shanxi in 2016, and this sixth gene, PB1, clustered together with H5N8 AIVs isolated from grey-headed gulls sampled in Uganda in 2017. The above results suggest that XJ-H5N6/2017 is a novel reassortant 2.3.4.4B H5N6 AIV derived from 2.3.4.4B H5N8 and 2.3.4.4C H5N6 viruses present in wild birds.\nHomology BLAST and phylogenetic analyses were performed on eight genes of the viral isolates (Table 1 and Fig. 1). The viral NA gene exhibited the highest sequence homology (99.1%), and the sequence clustered together with those of H5N6 AIVs isolated from shoveler ducks sampled in Ningxia (NX488-53) and from the environment of Chongqing in 2015; those sequences were designated as 2.3.4.4C H5N6 AIVs. The viral NP gene segments shared the highest sequence homology (99.4%) with 2.3.4.4B H5N8 AIVs isolated from the green-winged teal in Egypt in 2016 and were grouped into an independent subcluster within the phylogenetic tree. The remaining six genes had the highest sequence homologies (99.1%–99.6%) and relatively close genetic relationships in the phylogenetic tree with the 2.3.4.4B H5N8 AIVs. Five of those genes, HA, PB2, NS, PA, and MP, clustered together with 2.3.4.4B H5N8 AIVs isolated from migratory swans in Sanmenxia, Hubei, and Shanxi in 2016, and this sixth gene, PB1, clustered together with H5N8 AIVs isolated from grey-headed gulls sampled in Uganda in 2017. The above results suggest that XJ-H5N6/2017 is a novel reassortant 2.3.4.4B H5N6 AIV derived from 2.3.4.4B H5N8 and 2.3.4.4C H5N6 viruses present in wild birds.\nMolecular characteristics of the H5N6 virus isolates Assessment of the multiple amino acid mutations present in the nine isolates could help elucidate H5N6 virulence. Multiple basic amino acids (KEKRRKR↓GLF) were observed at cleavage sites in the HA protein of the H5N6 isolates (Table 2), suggesting that the isolates were HPAIVs. The viral receptor-binding sites all contained Q226 and G228 (H3 numbering), indicative of an avian-like α2,3-sialic acid (α2,3-SA) receptor-binding preference; however, the S128P, S137A, and T160A mutations in HA could enhance the capacity to bind to a human-like α-2,6-SA receptor [17]. In all nine isolates, 11 amino acid deletions (59–69) in the NA stalk were observed, suggesting the isolates could have different adaptive and virulence characteristics in poultry and mammals [18]. In addition, the L89V, G309D, R477G, I495V [19], and I504V [20] mutations in the PB2 protein and the P42S and D92E mutations in the NS1 were retained in all nine isolates; notably, those mutations can enhance viral virulence and replication activity in mammals [21]. Moreover, the M2-F38L mutation has been associated with antiviral resistance (amantadine and rimantadine) [22].\nAssessment of the multiple amino acid mutations present in the nine isolates could help elucidate H5N6 virulence. Multiple basic amino acids (KEKRRKR↓GLF) were observed at cleavage sites in the HA protein of the H5N6 isolates (Table 2), suggesting that the isolates were HPAIVs. The viral receptor-binding sites all contained Q226 and G228 (H3 numbering), indicative of an avian-like α2,3-sialic acid (α2,3-SA) receptor-binding preference; however, the S128P, S137A, and T160A mutations in HA could enhance the capacity to bind to a human-like α-2,6-SA receptor [17]. In all nine isolates, 11 amino acid deletions (59–69) in the NA stalk were observed, suggesting the isolates could have different adaptive and virulence characteristics in poultry and mammals [18]. In addition, the L89V, G309D, R477G, I495V [19], and I504V [20] mutations in the PB2 protein and the P42S and D92E mutations in the NS1 were retained in all nine isolates; notably, those mutations can enhance viral virulence and replication activity in mammals [21]. Moreover, the M2-F38L mutation has been associated with antiviral resistance (amantadine and rimantadine) [22].\n\nVirus isolation:\nNine strains of H5N6 AIV were isolated between July and November 2017, one from a duck swab and the rest from goose swabs. The eight gene segments of the 9 isolates shared very high nucleotide homology (> 99.9%); thus, the isolates originated from the same ancestor. The strains have been designated as A/goose/Xinjiang/011-015/2017(H5N6), A/duck/Xinjiang/016/2017 (H5N6), and A/goose/Xinjiang/017-019/2017(H5N6), or XJ-H5N6/2017 for short. The complete sequences of the isolates have been submitted to NCBI (accession numbers: MW109029-MW109036, MW110101-MW110108, and MW110205-MW110260).\n\nSequence and phylogenetic analysis:\nHomology BLAST and phylogenetic analyses were performed on eight genes of the viral isolates (Table 1 and Fig. 1). The viral NA gene exhibited the highest sequence homology (99.1%), and the sequence clustered together with those of H5N6 AIVs isolated from shoveler ducks sampled in Ningxia (NX488-53) and from the environment of Chongqing in 2015; those sequences were designated as 2.3.4.4C H5N6 AIVs. The viral NP gene segments shared the highest sequence homology (99.4%) with 2.3.4.4B H5N8 AIVs isolated from the green-winged teal in Egypt in 2016 and were grouped into an independent subcluster within the phylogenetic tree. The remaining six genes had the highest sequence homologies (99.1%–99.6%) and relatively close genetic relationships in the phylogenetic tree with the 2.3.4.4B H5N8 AIVs. Five of those genes, HA, PB2, NS, PA, and MP, clustered together with 2.3.4.4B H5N8 AIVs isolated from migratory swans in Sanmenxia, Hubei, and Shanxi in 2016, and this sixth gene, PB1, clustered together with H5N8 AIVs isolated from grey-headed gulls sampled in Uganda in 2017. The above results suggest that XJ-H5N6/2017 is a novel reassortant 2.3.4.4B H5N6 AIV derived from 2.3.4.4B H5N8 and 2.3.4.4C H5N6 viruses present in wild birds.\n\nMolecular characteristics of the H5N6 virus isolates:\nAssessment of the multiple amino acid mutations present in the nine isolates could help elucidate H5N6 virulence. Multiple basic amino acids (KEKRRKR↓GLF) were observed at cleavage sites in the HA protein of the H5N6 isolates (Table 2), suggesting that the isolates were HPAIVs. The viral receptor-binding sites all contained Q226 and G228 (H3 numbering), indicative of an avian-like α2,3-sialic acid (α2,3-SA) receptor-binding preference; however, the S128P, S137A, and T160A mutations in HA could enhance the capacity to bind to a human-like α-2,6-SA receptor [17]. In all nine isolates, 11 amino acid deletions (59–69) in the NA stalk were observed, suggesting the isolates could have different adaptive and virulence characteristics in poultry and mammals [18]. In addition, the L89V, G309D, R477G, I495V [19], and I504V [20] mutations in the PB2 protein and the P42S and D92E mutations in the NS1 were retained in all nine isolates; notably, those mutations can enhance viral virulence and replication activity in mammals [21]. Moreover, the M2-F38L mutation has been associated with antiviral resistance (amantadine and rimantadine) [22].\n\nDISCUSSION:\nThe Xinjiang region is located in northwest China within the interior of the Eurasian Continent. It comprises an overlapping bird migration region between the Central Asian Flyway and the West Asian–East African Flyway. The Northern Tianshan Mountain (NTM) region in Xinjiang includes many complex mountain-oasis-desert systems, and several water reservoirs have been formed by dam construction in narrow mouths of ravines or rivers, with much of the water coming from snowmelt [23]. Many wild birds migrate along the NTM region every year, and the reservoirs in the region have become key stopover areas for migratory birds from Eurasia and Africa. Moreover, these reservoirs are also used during aquatic poultry farming, thereby increasing the odds of contact between aquatic poultry and wild birds, including direct contact with infected wild birds or indirect contact with related materials (e.g., wild-bird feces), which could result in the introduction of wild-bird origin AIVs into aquatic poultry [824]. The majority of H5 HPAIVs detections in wild and domestic birds have been associated with migratory flyways and wild-bird aggregation areas [825]. This wild-domestic bird interface has had an important role in the spread, reassortment, evolution, and epidemiology of AIVs [8]; in particular, 2.3.4.4 H5 HPAIVs that have emerged since 2013. Such HPAIVs are continuing to reassort, evolve, and spread and have been detected in wild and domestic birds around the world [26]. Since 2016, HPAIVs outbreaks in Xinjiang have occurred repeatedly, and there have been several outbreaks of H5N6 HPAIVs (including 2.3.4.4B and 2.3.4.4H) in poultry and migratory birds sampled in Xinjiang [2728]. In this study, the 2.3.4.4B HPAIV isolates from the aquatic poultry in the LPMs of Urumqi were shown to be novel reassortant viruses derived from 2.3.4.4B H5N8 and 2.3.4.4C H5N6 AIVs of wild birds, suggesting that 2.3.4.4 clade AIVs are circulating in both wild and domestic birds in Xinjiang.\nIn this study, the NA gene of XJ-H5N6/2017 had a close relationship with the wild-bird origin 2.3.4.4C H5N6 AIVs identified from Chongqing and Ningxia, China. Our previous study reported a reassortant 2.3.4.4C H5N6 AIV in Xinjiang (GISAID accession nos.: EPI1548859-1548874), which originated from the NX488-53 virus isolated in December 2016 [29]. The NP gene sequences of the isolates in this study were grouped into an independent subcluster within the 2.3.4.4B H5N8 AIVs, indicating that NP has continued to evolve in Xinjiang. The PB1 gene was most closely related to the 2.3.4.4B H5N8 AIVs from the migratory wild birds sampled in Africa in 2017. In addition, it had a close relationship with 2.3.4.4B H5N8 AIVs from the migratory swans sampled in Central China in 2016. The remaining five genes had close relationships with 2.3.4.4B H5N8 AIVs identified from the migratory swans sampled in Central China in 2016 and bar-headed geese sampled in Qinghai in 2017. The Sanmenxia wetland of the Yellow River is an important wintering ground for migratory swans, with the swans arriving at the wetland in late October each year, departing the wetland during the late February to late March period of the following year, and migrating northwest directly to Siberia and northwest China (e.g., Qinghai wetlands) for breeding and molting [30]. An outbreak of H5N1 HPAIV has occurred at the Sanmenxia wetland, and migrating swans carrying the H5N1 HPAIVs have spread it over long distances [3132]. Also, H5N8 HPAIVs from wild birds in Hubei, China have been reported to cause the death of migratory swans, and the H5N8 viruses have been isolated in samples from Qinghai Lake and Europe. Thus, the wetlands and lakes in Central China may have a key role in spreading H5N8 viruses in the East Asian-Australasian and Central Asian flyways [12]. These observations suggest that the isolated 2.3.4.4B H5N8 AIVs could have spread into Xinjiang by infected migratory wild birds present in Central China in 2016 and 2017 and spreading into Africa in 2017. Furthermore, these 2.3.4.4B H5N8 AIVs might have reassorted with the NX488-53 virus to generate the novel 2.3.4.4B virus of XJ-H5N6/2017, which could then circulate within aquatic poultry in the water reservoirs of NTM, subsequently spreading into the LPMs of Urumqi via the sale of live poultry. In January 2020, 2.3.4.4H H5N6 HPAIVs [SW/XJ/1/2020(H5N6)] were identified from migratory whooper and mute swans in Xinjiang [28], and the low level of similarity between the isolates in this study and those of SW/XJ/1/2020(H5N6) suggests that these viruses spread into Xinjiang independently. Based on the above observations, the wetlands in Xinjiang may have a key role in the AIVs circulating among the migratory birds and aquatic poultry in Xinjiang and in disseminating the viruses from Central China to the Eurasian continent and East African via the Central Asian Flyway and the West Asian–East African Flyway.\nIn our study, the multiple mutations detected in the isolates may be associated with viral virulence, adaptation, and transmission (Table 2). The viral HA protein included cleavage sites containing multiple basic amino acids associated with HPAIVs; moreover, the S128P, S137A, and T160A mutations can enhance binding capacity to a human-like α-2,6-SA receptor [17]. These three mutations were also present in the NX488-53 virus, which can infect BALB/c mice and transmit among guinea pigs via direct contact or aerosol routes [3334]. The 11 amino acid deletion in the stalk region of the NA protein has been associated with the adaptation of wild-bird origin AIVs to gallinaceous poultry [35], increasing viral virulence [36], and enhancing viral transmission in ducks [37]. It has also been reported that the NA short-stalk region in H7N9 [36] and H5N1 [38] AIVs can increase virulence in mice, and it is characteristic of the viral adaptation for chickens [35]. Five amino acid substitutions in the PB2 protein (L89V, G309D, R477G, I495V, and I504V) may increase viral replication activity in mammalian cells and viral virulence in mice [1920]. Two amino acid substitutions in the NS1 protein (D92E and P42S) could be associated with high viral fatality rates and replication efficiency [213940].\nIn summary, our data indicate that XJ-H5N6/2017 is a novel reassortant 2.3.4.4B HPAIV originating from the 2.3.4.4B H5N8 and 2.3.4.4C H5N6 viruses present in wild birds. The multiple mutations in the amino acids of the novel reassortant are associated with its pathogenicity. To detect novel reassortant HPAIVs in a timely manner, long-term, risk-based surveillance and analysis of AIVs in poultry and wild birds is essential in Xinjiang, China.\n\n"},"whole_article_abstract":{"kind":"string","value":"Background:\nThe H5 avian influenza viruses (AIVs) of clade 2.3.4.4 circulate in wild and domestic birds worldwide. In 2017, nine strains of H5N6 AIVs were isolated from aquatic poultry in Xinjiang, Northwest China.\n\nMethods:\nAIVs were isolated from oropharyngeal and cloacal swabs of poultry. Identification was accomplished by inoculating isolates into embryonated chicken eggs and performing hemagglutination tests and reverse transcription polymerase chain reaction (RT-PCR). The viral genomes were amplified with RT-PCR and then sequenced. The sequence alignment, phylogenetic, and molecular characteristic analyses were performed by using bioinformatic software.\n\nResults:\nNine isolates originated from the same ancestor. The viral HA gene belonged to clade 2.3.4.4B, while the NA gene had a close phylogenetic relationship with the 2.3.4.4C H5N6 highly pathogenic avian influenza viruses (HPAIVs) isolated from shoveler ducks in Ningxia in 2015. The NP gene was grouped into an independent subcluster within the 2.3.4.4B H5N8 AIVs, and the remaining six genes all had close phylogenetic relationships with the 2.3.4.4B H5N8 HPAIVs isolated from the wild birds in China, Egypt, Uganda, Cameroon, and India in 2016-2017, Multiple basic amino acid residues associated with HPAIVs were located adjacent to the cleavage site of the HA protein. The nine isolates comprised reassortant 2.3.4.4B HPAIVs originating from 2.3.4.4B H5N8 and 2.3.4.4C H5N6 viruses in wild birds.\n\nConclusions:\nThese results suggest that the Northern Tianshan Mountain wetlands in Xinjiang may have a key role in AIVs disseminating from Central China to the Eurasian continent and East African."},"background_conclusion_text":{"kind":"null"},"background_conclusion_abstract":{"kind":"null"},"whole_article_text_length":{"kind":"number","value":5613,"string":"5,613"},"whole_article_abstract_length":{"kind":"number","value":287,"string":"287"},"other_sections_lengths":{"kind":"list like","value":[174,185,125,210,124,234,235],"string":"[\n 174,\n 185,\n 125,\n 210,\n 124,\n 234,\n 235\n]"},"num_sections":{"kind":"number","value":11,"string":"11"},"most_frequent_words":{"kind":"list like","value":["h5n6","isolates","aivs","viral","4b","h5n8","2017","xinjiang","wild","birds"],"string":"[\n \"h5n6\",\n \"isolates\",\n \"aivs\",\n \"viral\",\n \"4b\",\n \"h5n8\",\n \"2017\",\n \"xinjiang\",\n \"wild\",\n \"birds\"\n]"},"keybert_topics":{"kind":"list like","value":["h5n6 viruses groups","characteristics h5n6 virus","viruses domestic poultry","pathogenic avian influenza","avian influenza viruses"],"string":"[\n \"h5n6 viruses groups\",\n \"characteristics h5n6 virus\",\n \"viruses domestic poultry\",\n \"pathogenic avian influenza\",\n \"avian influenza viruses\"\n]"},"annotated_base_background_abstract_prompt":{"kind":"string","value":""},"annotated_base_methods_abstract_prompt":{"kind":"null"},"annotated_base_results_abstract_prompt":{"kind":"string","value":""},"annotated_base_conclusions_abstract_prompt":{"kind":"null"},"annotated_base_whole_article_abstract_prompt":{"kind":"string","value":""},"annotated_base_background_conclusion_abstract_prompt":{"kind":"null"},"annotated_keywords_background_abstract_prompt":{"kind":"string","value":"[CONTENT] Highly pathogenic avian influenza virus | H5N6 | reassortant | poultry | wild bird [SUMMARY]"},"annotated_keywords_methods_abstract_prompt":{"kind":"null"},"annotated_keywords_results_abstract_prompt":{"kind":"string","value":"[CONTENT] Highly pathogenic avian influenza virus | H5N6 | reassortant | poultry | wild bird [SUMMARY]"},"annotated_keywords_conclusions_abstract_prompt":{"kind":"null"},"annotated_keywords_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] Highly pathogenic avian influenza virus | H5N6 | reassortant | poultry | wild bird [SUMMARY]"},"annotated_keywords_background_conclusion_abstract_prompt":{"kind":"null"},"annotated_mesh_background_abstract_prompt":{"kind":"string","value":"[CONTENT] Animals | Animals, Domestic | Animals, Wild | Birds | China | Influenza A virus | Influenza in Birds | Phylogeny | Reassortant Viruses | Virulence | Whole Genome Sequencing [SUMMARY]"},"annotated_mesh_methods_abstract_prompt":{"kind":"null"},"annotated_mesh_results_abstract_prompt":{"kind":"string","value":"[CONTENT] Animals | Animals, Domestic | Animals, Wild | Birds | China | Influenza A virus | Influenza in Birds | Phylogeny | Reassortant Viruses | Virulence | Whole Genome Sequencing [SUMMARY]"},"annotated_mesh_conclusions_abstract_prompt":{"kind":"null"},"annotated_mesh_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] Animals | Animals, Domestic | Animals, Wild | Birds | China | Influenza A virus | Influenza in Birds | Phylogeny | Reassortant Viruses | Virulence | Whole Genome Sequencing [SUMMARY]"},"annotated_mesh_background_conclusion_abstract_prompt":{"kind":"null"},"annotated_keybert_background_abstract_prompt":{"kind":"string","value":"[CONTENT] h5n6 viruses groups | characteristics h5n6 virus | viruses domestic poultry | pathogenic avian influenza | avian influenza viruses [SUMMARY]"},"annotated_keybert_methods_abstract_prompt":{"kind":"null"},"annotated_keybert_results_abstract_prompt":{"kind":"string","value":"[CONTENT] h5n6 viruses groups | characteristics h5n6 virus | viruses domestic poultry | pathogenic avian influenza | avian influenza viruses [SUMMARY]"},"annotated_keybert_conclusions_abstract_prompt":{"kind":"null"},"annotated_keybert_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] h5n6 viruses groups | characteristics h5n6 virus | viruses domestic poultry | pathogenic avian influenza | avian influenza viruses [SUMMARY]"},"annotated_keybert_background_conclusion_abstract_prompt":{"kind":"null"},"annotated_most_frequent_background_abstract_prompt":{"kind":"string","value":"[CONTENT] h5n6 | isolates | aivs | viral | 4b | h5n8 | 2017 | xinjiang | wild | birds [SUMMARY]"},"annotated_most_frequent_methods_abstract_prompt":{"kind":"null"},"annotated_most_frequent_results_abstract_prompt":{"kind":"string","value":"[CONTENT] h5n6 | isolates | aivs | viral | 4b | h5n8 | 2017 | xinjiang | wild | birds 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Egypt | Uganda | Cameroon | India | 2016-2017 | Multiple | HA ||| nine | 2.3.4.4B | 2.3.4.4B | 2.3.4.4C H5N6 ||| the Northern Tianshan Mountain | Xinjiang | Central China | Eurasian | East African [SUMMARY]"},"annotated_entity_plan_background_conclusion_abstract_prompt":{"kind":"null"}}},{"rowIdx":294,"cells":{"title":{"kind":"string","value":"Trends in conventional cardiovascular risk factors and myocardial infarction subtypes among young Chinese men with a first acute myocardial infarction."},"pmid":{"kind":"string","value":"34964143"},"background_abstract":{"kind":"string","value":"There is limited data on the characteristics of conventional risk factors (RFs) in young Chinese men hospitalized with a first acute myocardial infarction (AMI)."},"background_abstract_label":{"kind":"string","value":"BACKGROUND"},"methods_abstract":{"kind":"string","value":"A total of 2739 men aged 18-44 years hospitalized for a first AMI were identified from 2007 to 2017. The overall prevalence of RFs and their respective temporal trends and subtypes of AMI were evaluated."},"methods_abstract_label":{"kind":"string","value":"METHODS"},"results_abstract":{"kind":"string","value":"The most prevalent conditions were smoking, followed by hypertension and then obesity. Patients aged <35 years had a much higher prevalence of hypercholesterolemia and obesity. Compared with a similar reference population in the United States, young Chinese men had a higher prevalence of smoking and dyslipidemia, but a lower prevalence of obesity, hypertension, and diabetes. The prevalence of hypertension increased from 2007 through 2017 (p trend <.001), whereas smoking decreased gradually. AMI frequently presented as ST-segment elevation MI (STEMI) (77.5%). Cluster of conventional RFs (3 RFs, odds ratio [OR]: 1.69, 95% confidence interval [CI]: 1.11-2.57; ≥4 RFs, OR: 2.50, 95% CI: 1.55-4.03] and multivessel disease (OR = 1.32, 95% CI: 1.08-1.60) increased the risk of non-STEMI (NSTEMI)."},"results_abstract_label":{"kind":"string","value":"RESULTS"},"conclusions_abstract":{"kind":"string","value":"Conventional RFs were highly prevalent in young Chinese men who were hospitalized for first AMI events, and the temporal trends varied different between China and US populations. Multivessel disease and cluster of conventional RFs are closely related to NSTEMI. Optimized preventive strategies among young adults are warranted."},"conclusions_abstract_label":{"kind":"string","value":"CONCLUSIONS"},"mesh_descriptor_names":{"kind":"list like","value":["Cardiovascular Diseases","China","Heart Disease Risk Factors","Humans","Male","Myocardial Infarction","Non-ST Elevated Myocardial Infarction","Risk Factors","ST Elevation Myocardial Infarction","United States","Young Adult"],"string":"[\n \"Cardiovascular Diseases\",\n \"China\",\n \"Heart Disease Risk Factors\",\n \"Humans\",\n \"Male\",\n \"Myocardial Infarction\",\n \"Non-ST Elevated Myocardial Infarction\",\n \"Risk Factors\",\n \"ST Elevation Myocardial Infarction\",\n \"United States\",\n \"Young Adult\"\n]"},"pmcid":{"kind":"string","value":"8799041"},"background_title":{"kind":"string","value":"INTRODUCTION"},"background_text":{"kind":"string","value":"The primary and secondary prevention of coronary heart disease (CHD) in young adults has garnered tremendous attention given the rapid increase in the incidence of acute coronary events and hospitalization rates, especially in young men. Evidence from observational epidemiological studies showed the incidence of acute coronary events increased by 37.4% in the year 2009 compared to 2007 in young adults aged 35–39 years, making it the largest increase for this age group.\n1\n Hospitalization rates for acute myocardial infarction (AMI) per 100 000 population experienced the most significant increase in young men (<55 years), by 45.8% from 2007 to 2012 in Beijing;\n2\n the proportion of young adults hospitalized for CHD was nearly 90% in men from 2013 to 2014 in Beijing.\n3\n This trend parallels an increase in cardiovascular risk factors (RFs) including smoking, hypertension, diabetes, obesity, and dyslipidemia in the general Chinese population as well as an increase in hospitalizations for AMI.\n2\n, \n4\n, \n5\n, \n6\n\n\nThe Prospective Urban Rural Epidemiology (PURE) study showed that approximately 70% of cardiovascular disease (CVD) cases were attributed to modifiable RFs.\n7\n Though several studies have evaluated the prevalence of these RFs during a first or any episode of AMI and have found a high prevalence of at least 1 RF (approximately 90%),\n8\n, \n9\n, \n10\n most patients were classified as being at low or intermediate risk by traditional CHD risk prediction scores.\n11\n, \n12\n Unfortunately, this does not aid in the development of appropriate primary preventive strategies to decrease the risk of CHD. The prevalence of conventional RFs, other clinical characteristics, and their trends need to be clarified, which can be used in formulating preventive strategies.\nFew studies have evaluated recent long‐term trends and prevalence of modifiable RFs during a first AMI in young adults in China. In a retrospective analysis of patients with coronary artery disease aged ≤45 years conducted from 2010 to 2014,\n6\n the prevalence figures varied for the United States and German populations.\n8\n, \n10\n Contemporary data about trends in and prevalence of modifiable RFs, and subtypes of AMI are lacking in young patients. Using a retrospective analysis of hospital data from 2007 to 2017, we aimed to evaluate the trends in and prevalence of modifiable RFs and subtypes of MI during the first AMI in young Chinese men. This evidence will provide a reference point for the development of preventive strategies in this population."},"methods_title":{"kind":"string","value":"METHODS"},"methods_text":{"kind":"string","value":"Participants This study was based on a retrospective, single‐center analysis of young men hospitalized for a first AMI. Clinical and demographic data were collected from Beijing Anzhen Hospital by trained abstractors, using physician notes, laboratory reports, patient histories, and discharge summaries from January 2007 through December 2017. Young men aged 18–44 years hospitalized for a first AMI were identified from 2007 to 2017; 136 patients were sampled at the end of 2007 and a total of 2739 patients were recruited at the end of 2017 (Figure S1). The reference population for this analysis has previously been reported by Yandrapalli et al. Briefly, hospitalizations for a first AMI in young adults aged 18–44 years were identified from the US Healthcare Cost and Utilization Project (HCUP) National Inpatient Sample (NIS) from January 2005 through September 2015.\n10\n Hospitalizations for AMI were determined according to the fourth universal definition of MI.\n13\n AMI cases were first identified by excluding cases with secondary diagnoses of prior MI, prior percutaneous coronary intervention, prior coronary artery bypass grafting, post‐AMI syndrome, chronic ischemic heart disease, heart transplant recipient, and coronary arterial disease of bypass grafts or in transplanted hearts. Cases with a history of heart failure (HF), arteritis, congenital heart disease, and cancer were excluded. Those without coronary angiography and those with missing values for laboratory reports were excluded.\nThis study was based on a retrospective, single‐center analysis of young men hospitalized for a first AMI. Clinical and demographic data were collected from Beijing Anzhen Hospital by trained abstractors, using physician notes, laboratory reports, patient histories, and discharge summaries from January 2007 through December 2017. Young men aged 18–44 years hospitalized for a first AMI were identified from 2007 to 2017; 136 patients were sampled at the end of 2007 and a total of 2739 patients were recruited at the end of 2017 (Figure S1). The reference population for this analysis has previously been reported by Yandrapalli et al. Briefly, hospitalizations for a first AMI in young adults aged 18–44 years were identified from the US Healthcare Cost and Utilization Project (HCUP) National Inpatient Sample (NIS) from January 2005 through September 2015.\n10\n Hospitalizations for AMI were determined according to the fourth universal definition of MI.\n13\n AMI cases were first identified by excluding cases with secondary diagnoses of prior MI, prior percutaneous coronary intervention, prior coronary artery bypass grafting, post‐AMI syndrome, chronic ischemic heart disease, heart transplant recipient, and coronary arterial disease of bypass grafts or in transplanted hearts. Cases with a history of heart failure (HF), arteritis, congenital heart disease, and cancer were excluded. Those without coronary angiography and those with missing values for laboratory reports were excluded.\nMeasurements and diagnostic criteria Primary outcomes of interest were the overall prevalence of the RFs and their respective temporal trends and subtypes of AMI. AMI was defined based on established criteria, which included\n13\n: detection of a rise and/or fall of cardiac biomarker values (preferably cardiac troponin [cTn]) with at least one value above the 99th percentile upper reference limit (URL) and with at least one of the following: (1) ischemia; (2) new or presumed new significant ST‐segment–T wave (ST–T) changes or new left bundle branch block; (3) development of pathological Q waves in the ECG; (4) imaging evidence of new loss of viable myocardium or new regional wall motion abnormality; and (5) detection of an intracoronary thrombus by angiography or autopsy. AMI was classified as ST‐segment elevation myocardial infarction (STEMI) and non‐STEMI (NSTEMI). Prevalent hypertension and diabetes were defined based on a documented history of hypertension and diabetes, respectively, in medical records. Hypercholesterolemia was defined as total cholesterol (TC) ≥ 5.2 mmol/L (200 mg/dl) or low‐density lipoprotein cholesterol (LDL‐C) ≥ 3.4 mmol/L (130 mg/dl). Obesity was defined as a body mass index (BMI) ≥ 28 (kg/m2). Smokers were defined as those who reported smoking cigarettes for >6 months. When the conventional RFs were compared with that of the US population, dyslipidemia and obesity were defined according to criteria in the report by Yandrapalli' et al.\n10\n\n\nPrimary outcomes of interest were the overall prevalence of the RFs and their respective temporal trends and subtypes of AMI. AMI was defined based on established criteria, which included\n13\n: detection of a rise and/or fall of cardiac biomarker values (preferably cardiac troponin [cTn]) with at least one value above the 99th percentile upper reference limit (URL) and with at least one of the following: (1) ischemia; (2) new or presumed new significant ST‐segment–T wave (ST–T) changes or new left bundle branch block; (3) development of pathological Q waves in the ECG; (4) imaging evidence of new loss of viable myocardium or new regional wall motion abnormality; and (5) detection of an intracoronary thrombus by angiography or autopsy. AMI was classified as ST‐segment elevation myocardial infarction (STEMI) and non‐STEMI (NSTEMI). Prevalent hypertension and diabetes were defined based on a documented history of hypertension and diabetes, respectively, in medical records. Hypercholesterolemia was defined as total cholesterol (TC) ≥ 5.2 mmol/L (200 mg/dl) or low‐density lipoprotein cholesterol (LDL‐C) ≥ 3.4 mmol/L (130 mg/dl). Obesity was defined as a body mass index (BMI) ≥ 28 (kg/m2). Smokers were defined as those who reported smoking cigarettes for >6 months. When the conventional RFs were compared with that of the US population, dyslipidemia and obesity were defined according to criteria in the report by Yandrapalli' et al.\n10\n\n\nStatistical analysis Categorical variables were expressed as total numbers (proportions), differences in RF prevalence across age groups were compared using χ\n2 tests for categorical variables and trends in the prevalence of RFs were analyzed using linear‐by‐linear association. Normally distributed continuous variables were presented as means ± standard deviations. A student's t test was used to compare two independent samples for normally distributed continuous variables and a Mann–Whitney U test for continuous variables with skewed distributions. Odds ratios (ORs) with 95% confidence intervals (CIs) for associations were derived from multivariable logistic regression. All reported p values were two sided. Statistical analysis was performed using IBM SPSS Statistics version 25.0 (IBM).\nCategorical variables were expressed as total numbers (proportions), differences in RF prevalence across age groups were compared using χ\n2 tests for categorical variables and trends in the prevalence of RFs were analyzed using linear‐by‐linear association. Normally distributed continuous variables were presented as means ± standard deviations. A student's t test was used to compare two independent samples for normally distributed continuous variables and a Mann–Whitney U test for continuous variables with skewed distributions. Odds ratios (ORs) with 95% confidence intervals (CIs) for associations were derived from multivariable logistic regression. All reported p values were two sided. Statistical analysis was performed using IBM SPSS Statistics version 25.0 (IBM)."},"results_title":{"kind":"string","value":"RESULTS"},"results_text":{"kind":"string","value":"Prevalence of conventional cardiovascular RFs and MI subtypes across different groups In this study, 2739 cases of first AMI hospitalizations in young men were enrolled from January 1, 2007, to December 31, 2017. Of the total, 462 (16.9%) patients were <35 years, 761 (27.8%) were 35–39 years and 1516 (55.3%) were 40–44 years. The most frequent presentation of AMI was STEMI (77.5%) and over 80% underwent revascularization. Baseline characteristics of the overall sample and by age subgroups are presented in Table 1. The most prevalent RFs were smoking (75.5%), followed by hypertension (40.6%) and then obesity (38.3%). The proportion of patients without any of the five conventional CVD RFs was only 5.8% and 71.7% of patients had at least 2 RFs. Significant differences were noted for the prevalence of obesity, hypertension, diabetes, and hypercholesterolemia across age groups. Patients aged <35 years had a higher prevalence of hypercholesterolemia and obesity, with lower values for hypertension; the proportion of patients with 2 or 3 RFs in this age group was also higher than that for the other age groups.\nBaseline characteristics of the study population\n\nNote: Data are presented as mean ± standard deviation or n (%).\nAbbreviations: AMI, acute myocardial infarction; NSTEMI, non‐ST‐segment elevation myocardial infarction; STEMI, ST‐segment elevation myocardial infarction.\nIn this study, 2739 cases of first AMI hospitalizations in young men were enrolled from January 1, 2007, to December 31, 2017. Of the total, 462 (16.9%) patients were <35 years, 761 (27.8%) were 35–39 years and 1516 (55.3%) were 40–44 years. The most frequent presentation of AMI was STEMI (77.5%) and over 80% underwent revascularization. Baseline characteristics of the overall sample and by age subgroups are presented in Table 1. The most prevalent RFs were smoking (75.5%), followed by hypertension (40.6%) and then obesity (38.3%). The proportion of patients without any of the five conventional CVD RFs was only 5.8% and 71.7% of patients had at least 2 RFs. Significant differences were noted for the prevalence of obesity, hypertension, diabetes, and hypercholesterolemia across age groups. Patients aged <35 years had a higher prevalence of hypercholesterolemia and obesity, with lower values for hypertension; the proportion of patients with 2 or 3 RFs in this age group was also higher than that for the other age groups.\nBaseline characteristics of the study population\n\nNote: Data are presented as mean ± standard deviation or n (%).\nAbbreviations: AMI, acute myocardial infarction; NSTEMI, non‐ST‐segment elevation myocardial infarction; STEMI, ST‐segment elevation myocardial infarction.\nPrevalence of conventional CVD RFs compared with a reference population We compared the prevalence of classic modifiable cardiovascular RFs in the study population with that of the reference population. Compared with the reference population, young Chinese men had a higher prevalence of smoking (75.5% vs. 58.1%; rate difference 17.4%) and dyslipidemia (65.0% vs. 54.6%; rate difference 10.4%), with lower prevalence of obesity (13.1% vs. 18.6%; rate difference 19.7%), hypertension (40.6% vs. 49.3%; rate difference 8.7%), and diabetes (14.9% vs. 19.9%; rate difference 5.0%). The three leading conventional RFs in the reference population were smoking, dyslipidemia, and hypertension, and this was similar for the target population (Table 2).\nPrevalence of conventional cardiovascular risk factors in the study population compared with the reference population\n\nNote: Data are presented as mean ± standard deviation or n (%).\nAbbreviation: BMI, body mass index.\nWe compared the prevalence of classic modifiable cardiovascular RFs in the study population with that of the reference population. Compared with the reference population, young Chinese men had a higher prevalence of smoking (75.5% vs. 58.1%; rate difference 17.4%) and dyslipidemia (65.0% vs. 54.6%; rate difference 10.4%), with lower prevalence of obesity (13.1% vs. 18.6%; rate difference 19.7%), hypertension (40.6% vs. 49.3%; rate difference 8.7%), and diabetes (14.9% vs. 19.9%; rate difference 5.0%). The three leading conventional RFs in the reference population were smoking, dyslipidemia, and hypertension, and this was similar for the target population (Table 2).\nPrevalence of conventional cardiovascular risk factors in the study population compared with the reference population\n\nNote: Data are presented as mean ± standard deviation or n (%).\nAbbreviation: BMI, body mass index.\nConventional cardiovascular RFs and other clinical characteristics across AMI subtypes Compared to STEMI patients, NSTEMI patients had a high prevalence of all individual conventional RFs except for smoking, which was similar in prevalence. The proportion of patients who had at least 3 RFs was 39.6% in NSTEMI patients, which was higher than that in STEMI patients (27.5%). The coronary artery involved most frequently was left anterior descending coronary artery (LAD) in both groups, but this was higher in STEMI patients. Patients with multivessel coronary disease and those without significant coronary stenosis were higher in the NSTEMI group (Table 3).\nConventional risk factors and clinic characteristics according to AMI subtype\n\nNote: Data are presented as n (%).\nAbbreviations: EF, ejection fraction; LAD, left anterior descending coronary artery; LCX, left circumflex coronary artery; NSTEMI, non‐ST elevation acute coronary syndrome; RCA, right coronary artery; STEMI, ST‐segment elevation myocardial infarction.\nWe also conducted logistic regression analyses to evaluate the relationships between AMI subtype and conventional RFs. Multiple conventional RFs significantly increased the risk of NSTEMI; the OR was 1.69 (95% confident interval [CI]: 1.11–2.57) for patients with three RFs and 2.50 (95% CI: 1.55–4.03) for patients with at least 4 RFs. Patients with multivessel coronary disease (OR = 1.32, 95% CI: 1.08–1.60) and patients without significant coronary stenosis or normal patients (OR = 2.01, 95% CI: 1.47–2.76) had an increased risk of NSTEMI compared with those with the single vessel disease after adjusting for the other covariates (Table 4).\nRelationship between AMI subtype and conventional RFs and coronary artery disease\nCompared to STEMI patients, NSTEMI patients had a high prevalence of all individual conventional RFs except for smoking, which was similar in prevalence. The proportion of patients who had at least 3 RFs was 39.6% in NSTEMI patients, which was higher than that in STEMI patients (27.5%). The coronary artery involved most frequently was left anterior descending coronary artery (LAD) in both groups, but this was higher in STEMI patients. Patients with multivessel coronary disease and those without significant coronary stenosis were higher in the NSTEMI group (Table 3).\nConventional risk factors and clinic characteristics according to AMI subtype\n\nNote: Data are presented as n (%).\nAbbreviations: EF, ejection fraction; LAD, left anterior descending coronary artery; LCX, left circumflex coronary artery; NSTEMI, non‐ST elevation acute coronary syndrome; RCA, right coronary artery; STEMI, ST‐segment elevation myocardial infarction.\nWe also conducted logistic regression analyses to evaluate the relationships between AMI subtype and conventional RFs. Multiple conventional RFs significantly increased the risk of NSTEMI; the OR was 1.69 (95% confident interval [CI]: 1.11–2.57) for patients with three RFs and 2.50 (95% CI: 1.55–4.03) for patients with at least 4 RFs. Patients with multivessel coronary disease (OR = 1.32, 95% CI: 1.08–1.60) and patients without significant coronary stenosis or normal patients (OR = 2.01, 95% CI: 1.47–2.76) had an increased risk of NSTEMI compared with those with the single vessel disease after adjusting for the other covariates (Table 4).\nRelationship between AMI subtype and conventional RFs and coronary artery disease\nTrends of conventional RFs and AMI subtypes The trends in the prevalence of conventional RFs are shown in Figure 1A. Compared with 2007, the prevalence of hypertension increased in 2017 (p trend = 0.004) that for smoking decreased gradually (p trend <0.05) and those for diabetes, hypercholesterolemia, and obesity remained unchanged (p trend >0.05). Between 2007 and 2017, the prevalence of hypertension increased from 26.5% to 44.2% (rate difference 17.7%) and that for smoking decreased from 77.9% to 72.4% (rate difference 5.5%). The prevalence of hypertension increased through 2007 and 2011, and then maintained a little shift. The greatest relative increase in prevalence between 2007 and 2009 was observed for hypercholesterolemia (from 28.7% to 35.5%), decreased in 2010 (from 35.5% to 20.5%), and then maintained a little shift. There were little shifts for diabetes and obesity (diabetes, 9.6% in 2007 and 14.8% in 2017, p = .283, rate difference 5.2%; obesity, 36.8% in 2007, and 40.7% in 2017, p = .105, rate difference 3.9%).\nTrends in the percentage of five conventional risk factors and acute myocardial infarction (AMI) subtype during a first acute myocardial infarction in young men 18–44 years old between 2007 and 2017. (A) Trends in the percentage of conventional risk factors, the increasing prevalence was noted for hypertension (p trend = 0.004). Decline in the rates of current smoking and drinking are appreciated. There was a similar prevalence for diabetes and hypercholesterolemia and obesity over time. (B) Trends in the percentage of AMI subtybe, the Increasing percentage was noted for NSTEMI(p trend <0.001)\nThe most frequent presentation of AMI was STEMI, but the proportion decreased gradually from 90.4% to 63.2% (p trend <0.001), then stabilized at around 65% during the current years (Figure 1B).\nThe mean SBP and BMI increased over the three periods; the mean SBP increased from 119.4 mmHg to 123.3 mmHg (mean difference 3.9 mmHg), with BMI increasing from 26.5 kg/m2 to 27.6 kg/m2 (mean difference 1.2 kg/m2). The mean TC decreased from 4.62 mmol/L to 4.49 mmol/L and LDL‐C decreased from 2.97 mmol/L to 2.80 mmol/L. No differences were seen in mean DBP and FPG over the three periods (Table S1).\nThe trends in the prevalence of conventional RFs are shown in Figure 1A. Compared with 2007, the prevalence of hypertension increased in 2017 (p trend = 0.004) that for smoking decreased gradually (p trend <0.05) and those for diabetes, hypercholesterolemia, and obesity remained unchanged (p trend >0.05). Between 2007 and 2017, the prevalence of hypertension increased from 26.5% to 44.2% (rate difference 17.7%) and that for smoking decreased from 77.9% to 72.4% (rate difference 5.5%). The prevalence of hypertension increased through 2007 and 2011, and then maintained a little shift. The greatest relative increase in prevalence between 2007 and 2009 was observed for hypercholesterolemia (from 28.7% to 35.5%), decreased in 2010 (from 35.5% to 20.5%), and then maintained a little shift. There were little shifts for diabetes and obesity (diabetes, 9.6% in 2007 and 14.8% in 2017, p = .283, rate difference 5.2%; obesity, 36.8% in 2007, and 40.7% in 2017, p = .105, rate difference 3.9%).\nTrends in the percentage of five conventional risk factors and acute myocardial infarction (AMI) subtype during a first acute myocardial infarction in young men 18–44 years old between 2007 and 2017. (A) Trends in the percentage of conventional risk factors, the increasing prevalence was noted for hypertension (p trend = 0.004). Decline in the rates of current smoking and drinking are appreciated. There was a similar prevalence for diabetes and hypercholesterolemia and obesity over time. (B) Trends in the percentage of AMI subtybe, the Increasing percentage was noted for NSTEMI(p trend <0.001)\nThe most frequent presentation of AMI was STEMI, but the proportion decreased gradually from 90.4% to 63.2% (p trend <0.001), then stabilized at around 65% during the current years (Figure 1B).\nThe mean SBP and BMI increased over the three periods; the mean SBP increased from 119.4 mmHg to 123.3 mmHg (mean difference 3.9 mmHg), with BMI increasing from 26.5 kg/m2 to 27.6 kg/m2 (mean difference 1.2 kg/m2). The mean TC decreased from 4.62 mmol/L to 4.49 mmol/L and LDL‐C decreased from 2.97 mmol/L to 2.80 mmol/L. No differences were seen in mean DBP and FPG over the three periods (Table S1)."},"conclusions_title":{"kind":"string","value":"CONCLUSIONS"},"conclusions_text":{"kind":"string","value":"In conclusion, the characteristics of conventional RFs among young Chinese men hospitalized with a first MI have important healthcare implications with regard to the planning of appropriate primary preventative strategies. The three leading cardiovascular RFs (smoking, hypertension, and obesity) should be the target of intervention and treatment strategies aimed at reducing the incidence of MI in young adults. Furthermore, preventing hypercholesterolemia in patients aged 34 years or younger. should also be a primary focus. Young adults with two or more conventional RFs should be given considerable attention even if they were classified as being at low or intermediate risk by traditional CHD risk prediction scores."},"other_sections_titles":{"kind":"list like","value":["INTRODUCTION","Participants","Measurements and diagnostic criteria","Statistical analysis","Prevalence of conventional cardiovascular RFs and MI subtypes across different groups","Prevalence of conventional CVD RFs compared with a reference population","Conventional cardiovascular RFs and other clinical characteristics across AMI subtypes","Trends of conventional RFs and AMI subtypes","CONTROL OF CONVENTIONAL RFS","LIMITATIONS"],"string":"[\n \"INTRODUCTION\",\n \"Participants\",\n \"Measurements and diagnostic criteria\",\n \"Statistical analysis\",\n \"Prevalence of conventional cardiovascular RFs and MI subtypes across different groups\",\n \"Prevalence of conventional CVD RFs compared with a reference population\",\n \"Conventional cardiovascular RFs and other clinical characteristics across AMI subtypes\",\n \"Trends of conventional RFs and AMI subtypes\",\n \"CONTROL OF CONVENTIONAL RFS\",\n \"LIMITATIONS\"\n]"},"other_sections_texts":{"kind":"list like","value":["The primary and secondary prevention of coronary heart disease (CHD) in young adults has garnered tremendous attention given the rapid increase in the incidence of acute coronary events and hospitalization rates, especially in young men. Evidence from observational epidemiological studies showed the incidence of acute coronary events increased by 37.4% in the year 2009 compared to 2007 in young adults aged 35–39 years, making it the largest increase for this age group.\n1\n Hospitalization rates for acute myocardial infarction (AMI) per 100 000 population experienced the most significant increase in young men (<55 years), by 45.8% from 2007 to 2012 in Beijing;\n2\n the proportion of young adults hospitalized for CHD was nearly 90% in men from 2013 to 2014 in Beijing.\n3\n This trend parallels an increase in cardiovascular risk factors (RFs) including smoking, hypertension, diabetes, obesity, and dyslipidemia in the general Chinese population as well as an increase in hospitalizations for AMI.\n2\n, \n4\n, \n5\n, \n6\n\n\nThe Prospective Urban Rural Epidemiology (PURE) study showed that approximately 70% of cardiovascular disease (CVD) cases were attributed to modifiable RFs.\n7\n Though several studies have evaluated the prevalence of these RFs during a first or any episode of AMI and have found a high prevalence of at least 1 RF (approximately 90%),\n8\n, \n9\n, \n10\n most patients were classified as being at low or intermediate risk by traditional CHD risk prediction scores.\n11\n, \n12\n Unfortunately, this does not aid in the development of appropriate primary preventive strategies to decrease the risk of CHD. The prevalence of conventional RFs, other clinical characteristics, and their trends need to be clarified, which can be used in formulating preventive strategies.\nFew studies have evaluated recent long‐term trends and prevalence of modifiable RFs during a first AMI in young adults in China. In a retrospective analysis of patients with coronary artery disease aged ≤45 years conducted from 2010 to 2014,\n6\n the prevalence figures varied for the United States and German populations.\n8\n, \n10\n Contemporary data about trends in and prevalence of modifiable RFs, and subtypes of AMI are lacking in young patients. Using a retrospective analysis of hospital data from 2007 to 2017, we aimed to evaluate the trends in and prevalence of modifiable RFs and subtypes of MI during the first AMI in young Chinese men. This evidence will provide a reference point for the development of preventive strategies in this population.","This study was based on a retrospective, single‐center analysis of young men hospitalized for a first AMI. Clinical and demographic data were collected from Beijing Anzhen Hospital by trained abstractors, using physician notes, laboratory reports, patient histories, and discharge summaries from January 2007 through December 2017. Young men aged 18–44 years hospitalized for a first AMI were identified from 2007 to 2017; 136 patients were sampled at the end of 2007 and a total of 2739 patients were recruited at the end of 2017 (Figure S1). The reference population for this analysis has previously been reported by Yandrapalli et al. Briefly, hospitalizations for a first AMI in young adults aged 18–44 years were identified from the US Healthcare Cost and Utilization Project (HCUP) National Inpatient Sample (NIS) from January 2005 through September 2015.\n10\n Hospitalizations for AMI were determined according to the fourth universal definition of MI.\n13\n AMI cases were first identified by excluding cases with secondary diagnoses of prior MI, prior percutaneous coronary intervention, prior coronary artery bypass grafting, post‐AMI syndrome, chronic ischemic heart disease, heart transplant recipient, and coronary arterial disease of bypass grafts or in transplanted hearts. Cases with a history of heart failure (HF), arteritis, congenital heart disease, and cancer were excluded. Those without coronary angiography and those with missing values for laboratory reports were excluded.","Primary outcomes of interest were the overall prevalence of the RFs and their respective temporal trends and subtypes of AMI. AMI was defined based on established criteria, which included\n13\n: detection of a rise and/or fall of cardiac biomarker values (preferably cardiac troponin [cTn]) with at least one value above the 99th percentile upper reference limit (URL) and with at least one of the following: (1) ischemia; (2) new or presumed new significant ST‐segment–T wave (ST–T) changes or new left bundle branch block; (3) development of pathological Q waves in the ECG; (4) imaging evidence of new loss of viable myocardium or new regional wall motion abnormality; and (5) detection of an intracoronary thrombus by angiography or autopsy. AMI was classified as ST‐segment elevation myocardial infarction (STEMI) and non‐STEMI (NSTEMI). Prevalent hypertension and diabetes were defined based on a documented history of hypertension and diabetes, respectively, in medical records. Hypercholesterolemia was defined as total cholesterol (TC) ≥ 5.2 mmol/L (200 mg/dl) or low‐density lipoprotein cholesterol (LDL‐C) ≥ 3.4 mmol/L (130 mg/dl). Obesity was defined as a body mass index (BMI) ≥ 28 (kg/m2). Smokers were defined as those who reported smoking cigarettes for >6 months. When the conventional RFs were compared with that of the US population, dyslipidemia and obesity were defined according to criteria in the report by Yandrapalli' et al.\n10\n\n","Categorical variables were expressed as total numbers (proportions), differences in RF prevalence across age groups were compared using χ\n2 tests for categorical variables and trends in the prevalence of RFs were analyzed using linear‐by‐linear association. Normally distributed continuous variables were presented as means ± standard deviations. A student's t test was used to compare two independent samples for normally distributed continuous variables and a Mann–Whitney U test for continuous variables with skewed distributions. Odds ratios (ORs) with 95% confidence intervals (CIs) for associations were derived from multivariable logistic regression. All reported p values were two sided. Statistical analysis was performed using IBM SPSS Statistics version 25.0 (IBM).","In this study, 2739 cases of first AMI hospitalizations in young men were enrolled from January 1, 2007, to December 31, 2017. Of the total, 462 (16.9%) patients were <35 years, 761 (27.8%) were 35–39 years and 1516 (55.3%) were 40–44 years. The most frequent presentation of AMI was STEMI (77.5%) and over 80% underwent revascularization. Baseline characteristics of the overall sample and by age subgroups are presented in Table 1. The most prevalent RFs were smoking (75.5%), followed by hypertension (40.6%) and then obesity (38.3%). The proportion of patients without any of the five conventional CVD RFs was only 5.8% and 71.7% of patients had at least 2 RFs. Significant differences were noted for the prevalence of obesity, hypertension, diabetes, and hypercholesterolemia across age groups. Patients aged <35 years had a higher prevalence of hypercholesterolemia and obesity, with lower values for hypertension; the proportion of patients with 2 or 3 RFs in this age group was also higher than that for the other age groups.\nBaseline characteristics of the study population\n\nNote: Data are presented as mean ± standard deviation or n (%).\nAbbreviations: AMI, acute myocardial infarction; NSTEMI, non‐ST‐segment elevation myocardial infarction; STEMI, ST‐segment elevation myocardial infarction.","We compared the prevalence of classic modifiable cardiovascular RFs in the study population with that of the reference population. Compared with the reference population, young Chinese men had a higher prevalence of smoking (75.5% vs. 58.1%; rate difference 17.4%) and dyslipidemia (65.0% vs. 54.6%; rate difference 10.4%), with lower prevalence of obesity (13.1% vs. 18.6%; rate difference 19.7%), hypertension (40.6% vs. 49.3%; rate difference 8.7%), and diabetes (14.9% vs. 19.9%; rate difference 5.0%). The three leading conventional RFs in the reference population were smoking, dyslipidemia, and hypertension, and this was similar for the target population (Table 2).\nPrevalence of conventional cardiovascular risk factors in the study population compared with the reference population\n\nNote: Data are presented as mean ± standard deviation or n (%).\nAbbreviation: BMI, body mass index.","Compared to STEMI patients, NSTEMI patients had a high prevalence of all individual conventional RFs except for smoking, which was similar in prevalence. The proportion of patients who had at least 3 RFs was 39.6% in NSTEMI patients, which was higher than that in STEMI patients (27.5%). The coronary artery involved most frequently was left anterior descending coronary artery (LAD) in both groups, but this was higher in STEMI patients. Patients with multivessel coronary disease and those without significant coronary stenosis were higher in the NSTEMI group (Table 3).\nConventional risk factors and clinic characteristics according to AMI subtype\n\nNote: Data are presented as n (%).\nAbbreviations: EF, ejection fraction; LAD, left anterior descending coronary artery; LCX, left circumflex coronary artery; NSTEMI, non‐ST elevation acute coronary syndrome; RCA, right coronary artery; STEMI, ST‐segment elevation myocardial infarction.\nWe also conducted logistic regression analyses to evaluate the relationships between AMI subtype and conventional RFs. Multiple conventional RFs significantly increased the risk of NSTEMI; the OR was 1.69 (95% confident interval [CI]: 1.11–2.57) for patients with three RFs and 2.50 (95% CI: 1.55–4.03) for patients with at least 4 RFs. Patients with multivessel coronary disease (OR = 1.32, 95% CI: 1.08–1.60) and patients without significant coronary stenosis or normal patients (OR = 2.01, 95% CI: 1.47–2.76) had an increased risk of NSTEMI compared with those with the single vessel disease after adjusting for the other covariates (Table 4).\nRelationship between AMI subtype and conventional RFs and coronary artery disease","The trends in the prevalence of conventional RFs are shown in Figure 1A. Compared with 2007, the prevalence of hypertension increased in 2017 (p trend = 0.004) that for smoking decreased gradually (p trend <0.05) and those for diabetes, hypercholesterolemia, and obesity remained unchanged (p trend >0.05). Between 2007 and 2017, the prevalence of hypertension increased from 26.5% to 44.2% (rate difference 17.7%) and that for smoking decreased from 77.9% to 72.4% (rate difference 5.5%). The prevalence of hypertension increased through 2007 and 2011, and then maintained a little shift. The greatest relative increase in prevalence between 2007 and 2009 was observed for hypercholesterolemia (from 28.7% to 35.5%), decreased in 2010 (from 35.5% to 20.5%), and then maintained a little shift. There were little shifts for diabetes and obesity (diabetes, 9.6% in 2007 and 14.8% in 2017, p = .283, rate difference 5.2%; obesity, 36.8% in 2007, and 40.7% in 2017, p = .105, rate difference 3.9%).\nTrends in the percentage of five conventional risk factors and acute myocardial infarction (AMI) subtype during a first acute myocardial infarction in young men 18–44 years old between 2007 and 2017. (A) Trends in the percentage of conventional risk factors, the increasing prevalence was noted for hypertension (p trend = 0.004). Decline in the rates of current smoking and drinking are appreciated. There was a similar prevalence for diabetes and hypercholesterolemia and obesity over time. (B) Trends in the percentage of AMI subtybe, the Increasing percentage was noted for NSTEMI(p trend <0.001)\nThe most frequent presentation of AMI was STEMI, but the proportion decreased gradually from 90.4% to 63.2% (p trend <0.001), then stabilized at around 65% during the current years (Figure 1B).\nThe mean SBP and BMI increased over the three periods; the mean SBP increased from 119.4 mmHg to 123.3 mmHg (mean difference 3.9 mmHg), with BMI increasing from 26.5 kg/m2 to 27.6 kg/m2 (mean difference 1.2 kg/m2). The mean TC decreased from 4.62 mmol/L to 4.49 mmol/L and LDL‐C decreased from 2.97 mmol/L to 2.80 mmol/L. No differences were seen in mean DBP and FPG over the three periods (Table S1).","The control rate of hypertension and diabetes was 62.8% (699/1113) and 17.7% (72/407), respectively; smoking cessation was 4.8% (105/2173).","There are some limitations that deserve consideration. This was a single‐center study and the data were collected from Beijing Anzhen hospital, which is well known for the management of CHD. Furthermore, some patients may have more severe symptoms. Hence, the results may not be extrapolated to the entire nation. Data on conventional RFs were collected from medical records; therefore, health behaviors such as physical activity, sleep duration, emotion, and stress could not be included. The sample for the annual number of AMI hospitalizations was not large enough, which may have influenced the comparisons of conventional RFs and trends in RFs over time with the reference US population."],"string":"[\n \"The primary and secondary prevention of coronary heart disease (CHD) in young adults has garnered tremendous attention given the rapid increase in the incidence of acute coronary events and hospitalization rates, especially in young men. Evidence from observational epidemiological studies showed the incidence of acute coronary events increased by 37.4% in the year 2009 compared to 2007 in young adults aged 35–39 years, making it the largest increase for this age group.\\n1\\n Hospitalization rates for acute myocardial infarction (AMI) per 100 000 population experienced the most significant increase in young men (<55 years), by 45.8% from 2007 to 2012 in Beijing;\\n2\\n the proportion of young adults hospitalized for CHD was nearly 90% in men from 2013 to 2014 in Beijing.\\n3\\n This trend parallels an increase in cardiovascular risk factors (RFs) including smoking, hypertension, diabetes, obesity, and dyslipidemia in the general Chinese population as well as an increase in hospitalizations for AMI.\\n2\\n, \\n4\\n, \\n5\\n, \\n6\\n\\n\\nThe Prospective Urban Rural Epidemiology (PURE) study showed that approximately 70% of cardiovascular disease (CVD) cases were attributed to modifiable RFs.\\n7\\n Though several studies have evaluated the prevalence of these RFs during a first or any episode of AMI and have found a high prevalence of at least 1 RF (approximately 90%),\\n8\\n, \\n9\\n, \\n10\\n most patients were classified as being at low or intermediate risk by traditional CHD risk prediction scores.\\n11\\n, \\n12\\n Unfortunately, this does not aid in the development of appropriate primary preventive strategies to decrease the risk of CHD. The prevalence of conventional RFs, other clinical characteristics, and their trends need to be clarified, which can be used in formulating preventive strategies.\\nFew studies have evaluated recent long‐term trends and prevalence of modifiable RFs during a first AMI in young adults in China. In a retrospective analysis of patients with coronary artery disease aged ≤45 years conducted from 2010 to 2014,\\n6\\n the prevalence figures varied for the United States and German populations.\\n8\\n, \\n10\\n Contemporary data about trends in and prevalence of modifiable RFs, and subtypes of AMI are lacking in young patients. Using a retrospective analysis of hospital data from 2007 to 2017, we aimed to evaluate the trends in and prevalence of modifiable RFs and subtypes of MI during the first AMI in young Chinese men. This evidence will provide a reference point for the development of preventive strategies in this population.\",\n \"This study was based on a retrospective, single‐center analysis of young men hospitalized for a first AMI. Clinical and demographic data were collected from Beijing Anzhen Hospital by trained abstractors, using physician notes, laboratory reports, patient histories, and discharge summaries from January 2007 through December 2017. Young men aged 18–44 years hospitalized for a first AMI were identified from 2007 to 2017; 136 patients were sampled at the end of 2007 and a total of 2739 patients were recruited at the end of 2017 (Figure S1). The reference population for this analysis has previously been reported by Yandrapalli et al. Briefly, hospitalizations for a first AMI in young adults aged 18–44 years were identified from the US Healthcare Cost and Utilization Project (HCUP) National Inpatient Sample (NIS) from January 2005 through September 2015.\\n10\\n Hospitalizations for AMI were determined according to the fourth universal definition of MI.\\n13\\n AMI cases were first identified by excluding cases with secondary diagnoses of prior MI, prior percutaneous coronary intervention, prior coronary artery bypass grafting, post‐AMI syndrome, chronic ischemic heart disease, heart transplant recipient, and coronary arterial disease of bypass grafts or in transplanted hearts. Cases with a history of heart failure (HF), arteritis, congenital heart disease, and cancer were excluded. Those without coronary angiography and those with missing values for laboratory reports were excluded.\",\n \"Primary outcomes of interest were the overall prevalence of the RFs and their respective temporal trends and subtypes of AMI. AMI was defined based on established criteria, which included\\n13\\n: detection of a rise and/or fall of cardiac biomarker values (preferably cardiac troponin [cTn]) with at least one value above the 99th percentile upper reference limit (URL) and with at least one of the following: (1) ischemia; (2) new or presumed new significant ST‐segment–T wave (ST–T) changes or new left bundle branch block; (3) development of pathological Q waves in the ECG; (4) imaging evidence of new loss of viable myocardium or new regional wall motion abnormality; and (5) detection of an intracoronary thrombus by angiography or autopsy. AMI was classified as ST‐segment elevation myocardial infarction (STEMI) and non‐STEMI (NSTEMI). Prevalent hypertension and diabetes were defined based on a documented history of hypertension and diabetes, respectively, in medical records. Hypercholesterolemia was defined as total cholesterol (TC) ≥ 5.2 mmol/L (200 mg/dl) or low‐density lipoprotein cholesterol (LDL‐C) ≥ 3.4 mmol/L (130 mg/dl). Obesity was defined as a body mass index (BMI) ≥ 28 (kg/m2). Smokers were defined as those who reported smoking cigarettes for >6 months. When the conventional RFs were compared with that of the US population, dyslipidemia and obesity were defined according to criteria in the report by Yandrapalli' et al.\\n10\\n\\n\",\n \"Categorical variables were expressed as total numbers (proportions), differences in RF prevalence across age groups were compared using χ\\n2 tests for categorical variables and trends in the prevalence of RFs were analyzed using linear‐by‐linear association. Normally distributed continuous variables were presented as means ± standard deviations. A student's t test was used to compare two independent samples for normally distributed continuous variables and a Mann–Whitney U test for continuous variables with skewed distributions. Odds ratios (ORs) with 95% confidence intervals (CIs) for associations were derived from multivariable logistic regression. All reported p values were two sided. Statistical analysis was performed using IBM SPSS Statistics version 25.0 (IBM).\",\n \"In this study, 2739 cases of first AMI hospitalizations in young men were enrolled from January 1, 2007, to December 31, 2017. Of the total, 462 (16.9%) patients were <35 years, 761 (27.8%) were 35–39 years and 1516 (55.3%) were 40–44 years. The most frequent presentation of AMI was STEMI (77.5%) and over 80% underwent revascularization. Baseline characteristics of the overall sample and by age subgroups are presented in Table 1. The most prevalent RFs were smoking (75.5%), followed by hypertension (40.6%) and then obesity (38.3%). The proportion of patients without any of the five conventional CVD RFs was only 5.8% and 71.7% of patients had at least 2 RFs. Significant differences were noted for the prevalence of obesity, hypertension, diabetes, and hypercholesterolemia across age groups. Patients aged <35 years had a higher prevalence of hypercholesterolemia and obesity, with lower values for hypertension; the proportion of patients with 2 or 3 RFs in this age group was also higher than that for the other age groups.\\nBaseline characteristics of the study population\\n\\nNote: Data are presented as mean ± standard deviation or n (%).\\nAbbreviations: AMI, acute myocardial infarction; NSTEMI, non‐ST‐segment elevation myocardial infarction; STEMI, ST‐segment elevation myocardial infarction.\",\n \"We compared the prevalence of classic modifiable cardiovascular RFs in the study population with that of the reference population. Compared with the reference population, young Chinese men had a higher prevalence of smoking (75.5% vs. 58.1%; rate difference 17.4%) and dyslipidemia (65.0% vs. 54.6%; rate difference 10.4%), with lower prevalence of obesity (13.1% vs. 18.6%; rate difference 19.7%), hypertension (40.6% vs. 49.3%; rate difference 8.7%), and diabetes (14.9% vs. 19.9%; rate difference 5.0%). The three leading conventional RFs in the reference population were smoking, dyslipidemia, and hypertension, and this was similar for the target population (Table 2).\\nPrevalence of conventional cardiovascular risk factors in the study population compared with the reference population\\n\\nNote: Data are presented as mean ± standard deviation or n (%).\\nAbbreviation: BMI, body mass index.\",\n \"Compared to STEMI patients, NSTEMI patients had a high prevalence of all individual conventional RFs except for smoking, which was similar in prevalence. The proportion of patients who had at least 3 RFs was 39.6% in NSTEMI patients, which was higher than that in STEMI patients (27.5%). The coronary artery involved most frequently was left anterior descending coronary artery (LAD) in both groups, but this was higher in STEMI patients. Patients with multivessel coronary disease and those without significant coronary stenosis were higher in the NSTEMI group (Table 3).\\nConventional risk factors and clinic characteristics according to AMI subtype\\n\\nNote: Data are presented as n (%).\\nAbbreviations: EF, ejection fraction; LAD, left anterior descending coronary artery; LCX, left circumflex coronary artery; NSTEMI, non‐ST elevation acute coronary syndrome; RCA, right coronary artery; STEMI, ST‐segment elevation myocardial infarction.\\nWe also conducted logistic regression analyses to evaluate the relationships between AMI subtype and conventional RFs. Multiple conventional RFs significantly increased the risk of NSTEMI; the OR was 1.69 (95% confident interval [CI]: 1.11–2.57) for patients with three RFs and 2.50 (95% CI: 1.55–4.03) for patients with at least 4 RFs. Patients with multivessel coronary disease (OR = 1.32, 95% CI: 1.08–1.60) and patients without significant coronary stenosis or normal patients (OR = 2.01, 95% CI: 1.47–2.76) had an increased risk of NSTEMI compared with those with the single vessel disease after adjusting for the other covariates (Table 4).\\nRelationship between AMI subtype and conventional RFs and coronary artery disease\",\n \"The trends in the prevalence of conventional RFs are shown in Figure 1A. Compared with 2007, the prevalence of hypertension increased in 2017 (p trend = 0.004) that for smoking decreased gradually (p trend <0.05) and those for diabetes, hypercholesterolemia, and obesity remained unchanged (p trend >0.05). Between 2007 and 2017, the prevalence of hypertension increased from 26.5% to 44.2% (rate difference 17.7%) and that for smoking decreased from 77.9% to 72.4% (rate difference 5.5%). The prevalence of hypertension increased through 2007 and 2011, and then maintained a little shift. The greatest relative increase in prevalence between 2007 and 2009 was observed for hypercholesterolemia (from 28.7% to 35.5%), decreased in 2010 (from 35.5% to 20.5%), and then maintained a little shift. There were little shifts for diabetes and obesity (diabetes, 9.6% in 2007 and 14.8% in 2017, p = .283, rate difference 5.2%; obesity, 36.8% in 2007, and 40.7% in 2017, p = .105, rate difference 3.9%).\\nTrends in the percentage of five conventional risk factors and acute myocardial infarction (AMI) subtype during a first acute myocardial infarction in young men 18–44 years old between 2007 and 2017. (A) Trends in the percentage of conventional risk factors, the increasing prevalence was noted for hypertension (p trend = 0.004). Decline in the rates of current smoking and drinking are appreciated. There was a similar prevalence for diabetes and hypercholesterolemia and obesity over time. (B) Trends in the percentage of AMI subtybe, the Increasing percentage was noted for NSTEMI(p trend <0.001)\\nThe most frequent presentation of AMI was STEMI, but the proportion decreased gradually from 90.4% to 63.2% (p trend <0.001), then stabilized at around 65% during the current years (Figure 1B).\\nThe mean SBP and BMI increased over the three periods; the mean SBP increased from 119.4 mmHg to 123.3 mmHg (mean difference 3.9 mmHg), with BMI increasing from 26.5 kg/m2 to 27.6 kg/m2 (mean difference 1.2 kg/m2). The mean TC decreased from 4.62 mmol/L to 4.49 mmol/L and LDL‐C decreased from 2.97 mmol/L to 2.80 mmol/L. No differences were seen in mean DBP and FPG over the three periods (Table S1).\",\n \"The control rate of hypertension and diabetes was 62.8% (699/1113) and 17.7% (72/407), respectively; smoking cessation was 4.8% (105/2173).\",\n \"There are some limitations that deserve consideration. This was a single‐center study and the data were collected from Beijing Anzhen hospital, which is well known for the management of CHD. Furthermore, some patients may have more severe symptoms. Hence, the results may not be extrapolated to the entire nation. Data on conventional RFs were collected from medical records; therefore, health behaviors such as physical activity, sleep duration, emotion, and stress could not be included. The sample for the annual number of AMI hospitalizations was not large enough, which may have influenced the comparisons of conventional RFs and trends in RFs over time with the reference US population.\"\n]"},"other_sections_sec_types":{"kind":"list like","value":[null,null,null,null,null,null,null,null,null,null],"string":"[\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null\n]"},"all_sections_titles":{"kind":"list like","value":["INTRODUCTION","METHODS","Participants","Measurements and diagnostic criteria","Statistical analysis","RESULTS","Prevalence of conventional cardiovascular RFs and MI subtypes across different groups","Prevalence of conventional CVD RFs compared with a reference population","Conventional cardiovascular RFs and other clinical characteristics across AMI subtypes","Trends of conventional RFs and AMI subtypes","CONTROL OF CONVENTIONAL RFS","DISCUSSION","LIMITATIONS","CONCLUSIONS","CONFLICT OF INTERESTS","","Supporting information"],"string":"[\n \"INTRODUCTION\",\n \"METHODS\",\n \"Participants\",\n \"Measurements and diagnostic criteria\",\n \"Statistical analysis\",\n \"RESULTS\",\n \"Prevalence of conventional cardiovascular RFs and MI subtypes across different groups\",\n \"Prevalence of conventional CVD RFs compared with a reference population\",\n \"Conventional cardiovascular RFs and other clinical characteristics across AMI subtypes\",\n \"Trends of conventional RFs and AMI subtypes\",\n \"CONTROL OF CONVENTIONAL RFS\",\n \"DISCUSSION\",\n \"LIMITATIONS\",\n \"CONCLUSIONS\",\n \"CONFLICT OF INTERESTS\",\n \"\",\n \"Supporting information\"\n]"},"all_sections_texts":{"kind":"list like","value":["The primary and secondary prevention of coronary heart disease (CHD) in young adults has garnered tremendous attention given the rapid increase in the incidence of acute coronary events and hospitalization rates, especially in young men. Evidence from observational epidemiological studies showed the incidence of acute coronary events increased by 37.4% in the year 2009 compared to 2007 in young adults aged 35–39 years, making it the largest increase for this age group.\n1\n Hospitalization rates for acute myocardial infarction (AMI) per 100 000 population experienced the most significant increase in young men (<55 years), by 45.8% from 2007 to 2012 in Beijing;\n2\n the proportion of young adults hospitalized for CHD was nearly 90% in men from 2013 to 2014 in Beijing.\n3\n This trend parallels an increase in cardiovascular risk factors (RFs) including smoking, hypertension, diabetes, obesity, and dyslipidemia in the general Chinese population as well as an increase in hospitalizations for AMI.\n2\n, \n4\n, \n5\n, \n6\n\n\nThe Prospective Urban Rural Epidemiology (PURE) study showed that approximately 70% of cardiovascular disease (CVD) cases were attributed to modifiable RFs.\n7\n Though several studies have evaluated the prevalence of these RFs during a first or any episode of AMI and have found a high prevalence of at least 1 RF (approximately 90%),\n8\n, \n9\n, \n10\n most patients were classified as being at low or intermediate risk by traditional CHD risk prediction scores.\n11\n, \n12\n Unfortunately, this does not aid in the development of appropriate primary preventive strategies to decrease the risk of CHD. The prevalence of conventional RFs, other clinical characteristics, and their trends need to be clarified, which can be used in formulating preventive strategies.\nFew studies have evaluated recent long‐term trends and prevalence of modifiable RFs during a first AMI in young adults in China. In a retrospective analysis of patients with coronary artery disease aged ≤45 years conducted from 2010 to 2014,\n6\n the prevalence figures varied for the United States and German populations.\n8\n, \n10\n Contemporary data about trends in and prevalence of modifiable RFs, and subtypes of AMI are lacking in young patients. Using a retrospective analysis of hospital data from 2007 to 2017, we aimed to evaluate the trends in and prevalence of modifiable RFs and subtypes of MI during the first AMI in young Chinese men. This evidence will provide a reference point for the development of preventive strategies in this population.","Participants This study was based on a retrospective, single‐center analysis of young men hospitalized for a first AMI. Clinical and demographic data were collected from Beijing Anzhen Hospital by trained abstractors, using physician notes, laboratory reports, patient histories, and discharge summaries from January 2007 through December 2017. Young men aged 18–44 years hospitalized for a first AMI were identified from 2007 to 2017; 136 patients were sampled at the end of 2007 and a total of 2739 patients were recruited at the end of 2017 (Figure S1). The reference population for this analysis has previously been reported by Yandrapalli et al. Briefly, hospitalizations for a first AMI in young adults aged 18–44 years were identified from the US Healthcare Cost and Utilization Project (HCUP) National Inpatient Sample (NIS) from January 2005 through September 2015.\n10\n Hospitalizations for AMI were determined according to the fourth universal definition of MI.\n13\n AMI cases were first identified by excluding cases with secondary diagnoses of prior MI, prior percutaneous coronary intervention, prior coronary artery bypass grafting, post‐AMI syndrome, chronic ischemic heart disease, heart transplant recipient, and coronary arterial disease of bypass grafts or in transplanted hearts. Cases with a history of heart failure (HF), arteritis, congenital heart disease, and cancer were excluded. Those without coronary angiography and those with missing values for laboratory reports were excluded.\nThis study was based on a retrospective, single‐center analysis of young men hospitalized for a first AMI. Clinical and demographic data were collected from Beijing Anzhen Hospital by trained abstractors, using physician notes, laboratory reports, patient histories, and discharge summaries from January 2007 through December 2017. Young men aged 18–44 years hospitalized for a first AMI were identified from 2007 to 2017; 136 patients were sampled at the end of 2007 and a total of 2739 patients were recruited at the end of 2017 (Figure S1). The reference population for this analysis has previously been reported by Yandrapalli et al. Briefly, hospitalizations for a first AMI in young adults aged 18–44 years were identified from the US Healthcare Cost and Utilization Project (HCUP) National Inpatient Sample (NIS) from January 2005 through September 2015.\n10\n Hospitalizations for AMI were determined according to the fourth universal definition of MI.\n13\n AMI cases were first identified by excluding cases with secondary diagnoses of prior MI, prior percutaneous coronary intervention, prior coronary artery bypass grafting, post‐AMI syndrome, chronic ischemic heart disease, heart transplant recipient, and coronary arterial disease of bypass grafts or in transplanted hearts. Cases with a history of heart failure (HF), arteritis, congenital heart disease, and cancer were excluded. Those without coronary angiography and those with missing values for laboratory reports were excluded.\nMeasurements and diagnostic criteria Primary outcomes of interest were the overall prevalence of the RFs and their respective temporal trends and subtypes of AMI. AMI was defined based on established criteria, which included\n13\n: detection of a rise and/or fall of cardiac biomarker values (preferably cardiac troponin [cTn]) with at least one value above the 99th percentile upper reference limit (URL) and with at least one of the following: (1) ischemia; (2) new or presumed new significant ST‐segment–T wave (ST–T) changes or new left bundle branch block; (3) development of pathological Q waves in the ECG; (4) imaging evidence of new loss of viable myocardium or new regional wall motion abnormality; and (5) detection of an intracoronary thrombus by angiography or autopsy. AMI was classified as ST‐segment elevation myocardial infarction (STEMI) and non‐STEMI (NSTEMI). Prevalent hypertension and diabetes were defined based on a documented history of hypertension and diabetes, respectively, in medical records. Hypercholesterolemia was defined as total cholesterol (TC) ≥ 5.2 mmol/L (200 mg/dl) or low‐density lipoprotein cholesterol (LDL‐C) ≥ 3.4 mmol/L (130 mg/dl). Obesity was defined as a body mass index (BMI) ≥ 28 (kg/m2). Smokers were defined as those who reported smoking cigarettes for >6 months. When the conventional RFs were compared with that of the US population, dyslipidemia and obesity were defined according to criteria in the report by Yandrapalli' et al.\n10\n\n\nPrimary outcomes of interest were the overall prevalence of the RFs and their respective temporal trends and subtypes of AMI. AMI was defined based on established criteria, which included\n13\n: detection of a rise and/or fall of cardiac biomarker values (preferably cardiac troponin [cTn]) with at least one value above the 99th percentile upper reference limit (URL) and with at least one of the following: (1) ischemia; (2) new or presumed new significant ST‐segment–T wave (ST–T) changes or new left bundle branch block; (3) development of pathological Q waves in the ECG; (4) imaging evidence of new loss of viable myocardium or new regional wall motion abnormality; and (5) detection of an intracoronary thrombus by angiography or autopsy. AMI was classified as ST‐segment elevation myocardial infarction (STEMI) and non‐STEMI (NSTEMI). Prevalent hypertension and diabetes were defined based on a documented history of hypertension and diabetes, respectively, in medical records. Hypercholesterolemia was defined as total cholesterol (TC) ≥ 5.2 mmol/L (200 mg/dl) or low‐density lipoprotein cholesterol (LDL‐C) ≥ 3.4 mmol/L (130 mg/dl). Obesity was defined as a body mass index (BMI) ≥ 28 (kg/m2). Smokers were defined as those who reported smoking cigarettes for >6 months. When the conventional RFs were compared with that of the US population, dyslipidemia and obesity were defined according to criteria in the report by Yandrapalli' et al.\n10\n\n\nStatistical analysis Categorical variables were expressed as total numbers (proportions), differences in RF prevalence across age groups were compared using χ\n2 tests for categorical variables and trends in the prevalence of RFs were analyzed using linear‐by‐linear association. Normally distributed continuous variables were presented as means ± standard deviations. A student's t test was used to compare two independent samples for normally distributed continuous variables and a Mann–Whitney U test for continuous variables with skewed distributions. Odds ratios (ORs) with 95% confidence intervals (CIs) for associations were derived from multivariable logistic regression. All reported p values were two sided. Statistical analysis was performed using IBM SPSS Statistics version 25.0 (IBM).\nCategorical variables were expressed as total numbers (proportions), differences in RF prevalence across age groups were compared using χ\n2 tests for categorical variables and trends in the prevalence of RFs were analyzed using linear‐by‐linear association. Normally distributed continuous variables were presented as means ± standard deviations. A student's t test was used to compare two independent samples for normally distributed continuous variables and a Mann–Whitney U test for continuous variables with skewed distributions. Odds ratios (ORs) with 95% confidence intervals (CIs) for associations were derived from multivariable logistic regression. All reported p values were two sided. Statistical analysis was performed using IBM SPSS Statistics version 25.0 (IBM).","This study was based on a retrospective, single‐center analysis of young men hospitalized for a first AMI. Clinical and demographic data were collected from Beijing Anzhen Hospital by trained abstractors, using physician notes, laboratory reports, patient histories, and discharge summaries from January 2007 through December 2017. Young men aged 18–44 years hospitalized for a first AMI were identified from 2007 to 2017; 136 patients were sampled at the end of 2007 and a total of 2739 patients were recruited at the end of 2017 (Figure S1). The reference population for this analysis has previously been reported by Yandrapalli et al. Briefly, hospitalizations for a first AMI in young adults aged 18–44 years were identified from the US Healthcare Cost and Utilization Project (HCUP) National Inpatient Sample (NIS) from January 2005 through September 2015.\n10\n Hospitalizations for AMI were determined according to the fourth universal definition of MI.\n13\n AMI cases were first identified by excluding cases with secondary diagnoses of prior MI, prior percutaneous coronary intervention, prior coronary artery bypass grafting, post‐AMI syndrome, chronic ischemic heart disease, heart transplant recipient, and coronary arterial disease of bypass grafts or in transplanted hearts. Cases with a history of heart failure (HF), arteritis, congenital heart disease, and cancer were excluded. Those without coronary angiography and those with missing values for laboratory reports were excluded.","Primary outcomes of interest were the overall prevalence of the RFs and their respective temporal trends and subtypes of AMI. AMI was defined based on established criteria, which included\n13\n: detection of a rise and/or fall of cardiac biomarker values (preferably cardiac troponin [cTn]) with at least one value above the 99th percentile upper reference limit (URL) and with at least one of the following: (1) ischemia; (2) new or presumed new significant ST‐segment–T wave (ST–T) changes or new left bundle branch block; (3) development of pathological Q waves in the ECG; (4) imaging evidence of new loss of viable myocardium or new regional wall motion abnormality; and (5) detection of an intracoronary thrombus by angiography or autopsy. AMI was classified as ST‐segment elevation myocardial infarction (STEMI) and non‐STEMI (NSTEMI). Prevalent hypertension and diabetes were defined based on a documented history of hypertension and diabetes, respectively, in medical records. Hypercholesterolemia was defined as total cholesterol (TC) ≥ 5.2 mmol/L (200 mg/dl) or low‐density lipoprotein cholesterol (LDL‐C) ≥ 3.4 mmol/L (130 mg/dl). Obesity was defined as a body mass index (BMI) ≥ 28 (kg/m2). Smokers were defined as those who reported smoking cigarettes for >6 months. When the conventional RFs were compared with that of the US population, dyslipidemia and obesity were defined according to criteria in the report by Yandrapalli' et al.\n10\n\n","Categorical variables were expressed as total numbers (proportions), differences in RF prevalence across age groups were compared using χ\n2 tests for categorical variables and trends in the prevalence of RFs were analyzed using linear‐by‐linear association. Normally distributed continuous variables were presented as means ± standard deviations. A student's t test was used to compare two independent samples for normally distributed continuous variables and a Mann–Whitney U test for continuous variables with skewed distributions. Odds ratios (ORs) with 95% confidence intervals (CIs) for associations were derived from multivariable logistic regression. All reported p values were two sided. Statistical analysis was performed using IBM SPSS Statistics version 25.0 (IBM).","Prevalence of conventional cardiovascular RFs and MI subtypes across different groups In this study, 2739 cases of first AMI hospitalizations in young men were enrolled from January 1, 2007, to December 31, 2017. Of the total, 462 (16.9%) patients were <35 years, 761 (27.8%) were 35–39 years and 1516 (55.3%) were 40–44 years. The most frequent presentation of AMI was STEMI (77.5%) and over 80% underwent revascularization. Baseline characteristics of the overall sample and by age subgroups are presented in Table 1. The most prevalent RFs were smoking (75.5%), followed by hypertension (40.6%) and then obesity (38.3%). The proportion of patients without any of the five conventional CVD RFs was only 5.8% and 71.7% of patients had at least 2 RFs. Significant differences were noted for the prevalence of obesity, hypertension, diabetes, and hypercholesterolemia across age groups. Patients aged <35 years had a higher prevalence of hypercholesterolemia and obesity, with lower values for hypertension; the proportion of patients with 2 or 3 RFs in this age group was also higher than that for the other age groups.\nBaseline characteristics of the study population\n\nNote: Data are presented as mean ± standard deviation or n (%).\nAbbreviations: AMI, acute myocardial infarction; NSTEMI, non‐ST‐segment elevation myocardial infarction; STEMI, ST‐segment elevation myocardial infarction.\nIn this study, 2739 cases of first AMI hospitalizations in young men were enrolled from January 1, 2007, to December 31, 2017. Of the total, 462 (16.9%) patients were <35 years, 761 (27.8%) were 35–39 years and 1516 (55.3%) were 40–44 years. The most frequent presentation of AMI was STEMI (77.5%) and over 80% underwent revascularization. Baseline characteristics of the overall sample and by age subgroups are presented in Table 1. The most prevalent RFs were smoking (75.5%), followed by hypertension (40.6%) and then obesity (38.3%). The proportion of patients without any of the five conventional CVD RFs was only 5.8% and 71.7% of patients had at least 2 RFs. Significant differences were noted for the prevalence of obesity, hypertension, diabetes, and hypercholesterolemia across age groups. Patients aged <35 years had a higher prevalence of hypercholesterolemia and obesity, with lower values for hypertension; the proportion of patients with 2 or 3 RFs in this age group was also higher than that for the other age groups.\nBaseline characteristics of the study population\n\nNote: Data are presented as mean ± standard deviation or n (%).\nAbbreviations: AMI, acute myocardial infarction; NSTEMI, non‐ST‐segment elevation myocardial infarction; STEMI, ST‐segment elevation myocardial infarction.\nPrevalence of conventional CVD RFs compared with a reference population We compared the prevalence of classic modifiable cardiovascular RFs in the study population with that of the reference population. Compared with the reference population, young Chinese men had a higher prevalence of smoking (75.5% vs. 58.1%; rate difference 17.4%) and dyslipidemia (65.0% vs. 54.6%; rate difference 10.4%), with lower prevalence of obesity (13.1% vs. 18.6%; rate difference 19.7%), hypertension (40.6% vs. 49.3%; rate difference 8.7%), and diabetes (14.9% vs. 19.9%; rate difference 5.0%). The three leading conventional RFs in the reference population were smoking, dyslipidemia, and hypertension, and this was similar for the target population (Table 2).\nPrevalence of conventional cardiovascular risk factors in the study population compared with the reference population\n\nNote: Data are presented as mean ± standard deviation or n (%).\nAbbreviation: BMI, body mass index.\nWe compared the prevalence of classic modifiable cardiovascular RFs in the study population with that of the reference population. Compared with the reference population, young Chinese men had a higher prevalence of smoking (75.5% vs. 58.1%; rate difference 17.4%) and dyslipidemia (65.0% vs. 54.6%; rate difference 10.4%), with lower prevalence of obesity (13.1% vs. 18.6%; rate difference 19.7%), hypertension (40.6% vs. 49.3%; rate difference 8.7%), and diabetes (14.9% vs. 19.9%; rate difference 5.0%). The three leading conventional RFs in the reference population were smoking, dyslipidemia, and hypertension, and this was similar for the target population (Table 2).\nPrevalence of conventional cardiovascular risk factors in the study population compared with the reference population\n\nNote: Data are presented as mean ± standard deviation or n (%).\nAbbreviation: BMI, body mass index.\nConventional cardiovascular RFs and other clinical characteristics across AMI subtypes Compared to STEMI patients, NSTEMI patients had a high prevalence of all individual conventional RFs except for smoking, which was similar in prevalence. The proportion of patients who had at least 3 RFs was 39.6% in NSTEMI patients, which was higher than that in STEMI patients (27.5%). The coronary artery involved most frequently was left anterior descending coronary artery (LAD) in both groups, but this was higher in STEMI patients. Patients with multivessel coronary disease and those without significant coronary stenosis were higher in the NSTEMI group (Table 3).\nConventional risk factors and clinic characteristics according to AMI subtype\n\nNote: Data are presented as n (%).\nAbbreviations: EF, ejection fraction; LAD, left anterior descending coronary artery; LCX, left circumflex coronary artery; NSTEMI, non‐ST elevation acute coronary syndrome; RCA, right coronary artery; STEMI, ST‐segment elevation myocardial infarction.\nWe also conducted logistic regression analyses to evaluate the relationships between AMI subtype and conventional RFs. Multiple conventional RFs significantly increased the risk of NSTEMI; the OR was 1.69 (95% confident interval [CI]: 1.11–2.57) for patients with three RFs and 2.50 (95% CI: 1.55–4.03) for patients with at least 4 RFs. Patients with multivessel coronary disease (OR = 1.32, 95% CI: 1.08–1.60) and patients without significant coronary stenosis or normal patients (OR = 2.01, 95% CI: 1.47–2.76) had an increased risk of NSTEMI compared with those with the single vessel disease after adjusting for the other covariates (Table 4).\nRelationship between AMI subtype and conventional RFs and coronary artery disease\nCompared to STEMI patients, NSTEMI patients had a high prevalence of all individual conventional RFs except for smoking, which was similar in prevalence. The proportion of patients who had at least 3 RFs was 39.6% in NSTEMI patients, which was higher than that in STEMI patients (27.5%). The coronary artery involved most frequently was left anterior descending coronary artery (LAD) in both groups, but this was higher in STEMI patients. Patients with multivessel coronary disease and those without significant coronary stenosis were higher in the NSTEMI group (Table 3).\nConventional risk factors and clinic characteristics according to AMI subtype\n\nNote: Data are presented as n (%).\nAbbreviations: EF, ejection fraction; LAD, left anterior descending coronary artery; LCX, left circumflex coronary artery; NSTEMI, non‐ST elevation acute coronary syndrome; RCA, right coronary artery; STEMI, ST‐segment elevation myocardial infarction.\nWe also conducted logistic regression analyses to evaluate the relationships between AMI subtype and conventional RFs. Multiple conventional RFs significantly increased the risk of NSTEMI; the OR was 1.69 (95% confident interval [CI]: 1.11–2.57) for patients with three RFs and 2.50 (95% CI: 1.55–4.03) for patients with at least 4 RFs. Patients with multivessel coronary disease (OR = 1.32, 95% CI: 1.08–1.60) and patients without significant coronary stenosis or normal patients (OR = 2.01, 95% CI: 1.47–2.76) had an increased risk of NSTEMI compared with those with the single vessel disease after adjusting for the other covariates (Table 4).\nRelationship between AMI subtype and conventional RFs and coronary artery disease\nTrends of conventional RFs and AMI subtypes The trends in the prevalence of conventional RFs are shown in Figure 1A. Compared with 2007, the prevalence of hypertension increased in 2017 (p trend = 0.004) that for smoking decreased gradually (p trend <0.05) and those for diabetes, hypercholesterolemia, and obesity remained unchanged (p trend >0.05). Between 2007 and 2017, the prevalence of hypertension increased from 26.5% to 44.2% (rate difference 17.7%) and that for smoking decreased from 77.9% to 72.4% (rate difference 5.5%). The prevalence of hypertension increased through 2007 and 2011, and then maintained a little shift. The greatest relative increase in prevalence between 2007 and 2009 was observed for hypercholesterolemia (from 28.7% to 35.5%), decreased in 2010 (from 35.5% to 20.5%), and then maintained a little shift. There were little shifts for diabetes and obesity (diabetes, 9.6% in 2007 and 14.8% in 2017, p = .283, rate difference 5.2%; obesity, 36.8% in 2007, and 40.7% in 2017, p = .105, rate difference 3.9%).\nTrends in the percentage of five conventional risk factors and acute myocardial infarction (AMI) subtype during a first acute myocardial infarction in young men 18–44 years old between 2007 and 2017. (A) Trends in the percentage of conventional risk factors, the increasing prevalence was noted for hypertension (p trend = 0.004). Decline in the rates of current smoking and drinking are appreciated. There was a similar prevalence for diabetes and hypercholesterolemia and obesity over time. (B) Trends in the percentage of AMI subtybe, the Increasing percentage was noted for NSTEMI(p trend <0.001)\nThe most frequent presentation of AMI was STEMI, but the proportion decreased gradually from 90.4% to 63.2% (p trend <0.001), then stabilized at around 65% during the current years (Figure 1B).\nThe mean SBP and BMI increased over the three periods; the mean SBP increased from 119.4 mmHg to 123.3 mmHg (mean difference 3.9 mmHg), with BMI increasing from 26.5 kg/m2 to 27.6 kg/m2 (mean difference 1.2 kg/m2). The mean TC decreased from 4.62 mmol/L to 4.49 mmol/L and LDL‐C decreased from 2.97 mmol/L to 2.80 mmol/L. No differences were seen in mean DBP and FPG over the three periods (Table S1).\nThe trends in the prevalence of conventional RFs are shown in Figure 1A. Compared with 2007, the prevalence of hypertension increased in 2017 (p trend = 0.004) that for smoking decreased gradually (p trend <0.05) and those for diabetes, hypercholesterolemia, and obesity remained unchanged (p trend >0.05). Between 2007 and 2017, the prevalence of hypertension increased from 26.5% to 44.2% (rate difference 17.7%) and that for smoking decreased from 77.9% to 72.4% (rate difference 5.5%). The prevalence of hypertension increased through 2007 and 2011, and then maintained a little shift. The greatest relative increase in prevalence between 2007 and 2009 was observed for hypercholesterolemia (from 28.7% to 35.5%), decreased in 2010 (from 35.5% to 20.5%), and then maintained a little shift. There were little shifts for diabetes and obesity (diabetes, 9.6% in 2007 and 14.8% in 2017, p = .283, rate difference 5.2%; obesity, 36.8% in 2007, and 40.7% in 2017, p = .105, rate difference 3.9%).\nTrends in the percentage of five conventional risk factors and acute myocardial infarction (AMI) subtype during a first acute myocardial infarction in young men 18–44 years old between 2007 and 2017. (A) Trends in the percentage of conventional risk factors, the increasing prevalence was noted for hypertension (p trend = 0.004). Decline in the rates of current smoking and drinking are appreciated. There was a similar prevalence for diabetes and hypercholesterolemia and obesity over time. (B) Trends in the percentage of AMI subtybe, the Increasing percentage was noted for NSTEMI(p trend <0.001)\nThe most frequent presentation of AMI was STEMI, but the proportion decreased gradually from 90.4% to 63.2% (p trend <0.001), then stabilized at around 65% during the current years (Figure 1B).\nThe mean SBP and BMI increased over the three periods; the mean SBP increased from 119.4 mmHg to 123.3 mmHg (mean difference 3.9 mmHg), with BMI increasing from 26.5 kg/m2 to 27.6 kg/m2 (mean difference 1.2 kg/m2). The mean TC decreased from 4.62 mmol/L to 4.49 mmol/L and LDL‐C decreased from 2.97 mmol/L to 2.80 mmol/L. No differences were seen in mean DBP and FPG over the three periods (Table S1).","In this study, 2739 cases of first AMI hospitalizations in young men were enrolled from January 1, 2007, to December 31, 2017. Of the total, 462 (16.9%) patients were <35 years, 761 (27.8%) were 35–39 years and 1516 (55.3%) were 40–44 years. The most frequent presentation of AMI was STEMI (77.5%) and over 80% underwent revascularization. Baseline characteristics of the overall sample and by age subgroups are presented in Table 1. The most prevalent RFs were smoking (75.5%), followed by hypertension (40.6%) and then obesity (38.3%). The proportion of patients without any of the five conventional CVD RFs was only 5.8% and 71.7% of patients had at least 2 RFs. Significant differences were noted for the prevalence of obesity, hypertension, diabetes, and hypercholesterolemia across age groups. Patients aged <35 years had a higher prevalence of hypercholesterolemia and obesity, with lower values for hypertension; the proportion of patients with 2 or 3 RFs in this age group was also higher than that for the other age groups.\nBaseline characteristics of the study population\n\nNote: Data are presented as mean ± standard deviation or n (%).\nAbbreviations: AMI, acute myocardial infarction; NSTEMI, non‐ST‐segment elevation myocardial infarction; STEMI, ST‐segment elevation myocardial infarction.","We compared the prevalence of classic modifiable cardiovascular RFs in the study population with that of the reference population. Compared with the reference population, young Chinese men had a higher prevalence of smoking (75.5% vs. 58.1%; rate difference 17.4%) and dyslipidemia (65.0% vs. 54.6%; rate difference 10.4%), with lower prevalence of obesity (13.1% vs. 18.6%; rate difference 19.7%), hypertension (40.6% vs. 49.3%; rate difference 8.7%), and diabetes (14.9% vs. 19.9%; rate difference 5.0%). The three leading conventional RFs in the reference population were smoking, dyslipidemia, and hypertension, and this was similar for the target population (Table 2).\nPrevalence of conventional cardiovascular risk factors in the study population compared with the reference population\n\nNote: Data are presented as mean ± standard deviation or n (%).\nAbbreviation: BMI, body mass index.","Compared to STEMI patients, NSTEMI patients had a high prevalence of all individual conventional RFs except for smoking, which was similar in prevalence. The proportion of patients who had at least 3 RFs was 39.6% in NSTEMI patients, which was higher than that in STEMI patients (27.5%). The coronary artery involved most frequently was left anterior descending coronary artery (LAD) in both groups, but this was higher in STEMI patients. Patients with multivessel coronary disease and those without significant coronary stenosis were higher in the NSTEMI group (Table 3).\nConventional risk factors and clinic characteristics according to AMI subtype\n\nNote: Data are presented as n (%).\nAbbreviations: EF, ejection fraction; LAD, left anterior descending coronary artery; LCX, left circumflex coronary artery; NSTEMI, non‐ST elevation acute coronary syndrome; RCA, right coronary artery; STEMI, ST‐segment elevation myocardial infarction.\nWe also conducted logistic regression analyses to evaluate the relationships between AMI subtype and conventional RFs. Multiple conventional RFs significantly increased the risk of NSTEMI; the OR was 1.69 (95% confident interval [CI]: 1.11–2.57) for patients with three RFs and 2.50 (95% CI: 1.55–4.03) for patients with at least 4 RFs. Patients with multivessel coronary disease (OR = 1.32, 95% CI: 1.08–1.60) and patients without significant coronary stenosis or normal patients (OR = 2.01, 95% CI: 1.47–2.76) had an increased risk of NSTEMI compared with those with the single vessel disease after adjusting for the other covariates (Table 4).\nRelationship between AMI subtype and conventional RFs and coronary artery disease","The trends in the prevalence of conventional RFs are shown in Figure 1A. Compared with 2007, the prevalence of hypertension increased in 2017 (p trend = 0.004) that for smoking decreased gradually (p trend <0.05) and those for diabetes, hypercholesterolemia, and obesity remained unchanged (p trend >0.05). Between 2007 and 2017, the prevalence of hypertension increased from 26.5% to 44.2% (rate difference 17.7%) and that for smoking decreased from 77.9% to 72.4% (rate difference 5.5%). The prevalence of hypertension increased through 2007 and 2011, and then maintained a little shift. The greatest relative increase in prevalence between 2007 and 2009 was observed for hypercholesterolemia (from 28.7% to 35.5%), decreased in 2010 (from 35.5% to 20.5%), and then maintained a little shift. There were little shifts for diabetes and obesity (diabetes, 9.6% in 2007 and 14.8% in 2017, p = .283, rate difference 5.2%; obesity, 36.8% in 2007, and 40.7% in 2017, p = .105, rate difference 3.9%).\nTrends in the percentage of five conventional risk factors and acute myocardial infarction (AMI) subtype during a first acute myocardial infarction in young men 18–44 years old between 2007 and 2017. (A) Trends in the percentage of conventional risk factors, the increasing prevalence was noted for hypertension (p trend = 0.004). Decline in the rates of current smoking and drinking are appreciated. There was a similar prevalence for diabetes and hypercholesterolemia and obesity over time. (B) Trends in the percentage of AMI subtybe, the Increasing percentage was noted for NSTEMI(p trend <0.001)\nThe most frequent presentation of AMI was STEMI, but the proportion decreased gradually from 90.4% to 63.2% (p trend <0.001), then stabilized at around 65% during the current years (Figure 1B).\nThe mean SBP and BMI increased over the three periods; the mean SBP increased from 119.4 mmHg to 123.3 mmHg (mean difference 3.9 mmHg), with BMI increasing from 26.5 kg/m2 to 27.6 kg/m2 (mean difference 1.2 kg/m2). The mean TC decreased from 4.62 mmol/L to 4.49 mmol/L and LDL‐C decreased from 2.97 mmol/L to 2.80 mmol/L. No differences were seen in mean DBP and FPG over the three periods (Table S1).","The control rate of hypertension and diabetes was 62.8% (699/1113) and 17.7% (72/407), respectively; smoking cessation was 4.8% (105/2173).","In this study comprising of young men with a first hospitalization for AMI in China, the most prevalent CVD RFs were smoking (75.5%), hypertension (40.6%), and obesity (38.3%). Additionally, over 70% of them had at least two conventional RFs. Compared with a similar reference population, these young Chinese men had a higher prevalence of smoking and dyslipidemia and a lower prevalence of obesity, hypertension, and diabetes. Between 2007 and 2017, the prevalence of hypertension increased, whereas that of smoking decreased. Patients presenting with NSTEMI had a high prevalence of all individual conventional RFs except for smoking, which was more prevalent in STEMI patients. LAD was the most frequently involved coronary artery in the subtypes of AMI, but it was more common in STEMI patients. Multivessel coronary disease was more common in NSTEMI.\nConventional RFs were highly prevalent in young men aged 18–44 years who were hospitalized for first AMI events. The prevalence values were higher compared to national figures smoking (53.9% for young adults aged 25–44 years), hypertension (11.3% for urban adults and 10.0% for rural adults aged 18–44 years), obesity (11.0% for young adults aged 18–44 years), and diabetes (5.9% for young adults aged 18–44 years); high TC ( ≥ 5.2 mmol/L) and high LDL‐C( ≥ 3.4 mmol/L) were 28.5% and 26.3%, respectively.\n14\n, \n15\n, \n16\n, \n17\n Our results are consistent with previous studies conducted in young patients diagnosed with AMI and CHD; the prevalence for hypertension, diabetes, and smoking has been reported as 34.3%–41.7%, 11.1%–22.4%, and 57.4%–74.0%, respectively.\n3\n, \n6\n However, the prevalence figures were different from that of the US report. Compared with the data from the US NIS for patients hospitalized for a first AMI from 2005 to 2015,\n10\n young Chinese men had a higher prevalence of smoking and dyslipidemia, with a lower prevalence of hypertension, obesity, and diabetes. There were clear age differences in the prevalence of some of the RFs; patients aged <35 years had a higher prevalence of smoking, obesity, and hypercholesterolemia, whereas patients aged 35–44 years had a higher prevalence of smoking, hypertension, and diabetes.\nOur study also showed differences in the temporal trends of the conventional RFs between China and US populations. The prevalence of all the evaluated conventional RFs progressively increased between 2005 and 2015 in the United States,\n10\n but hypertension prevalence increased in China, whereas smoking decreased gradually; which was also consistent with a previous study conducted in China. In a retrospective analysis of coronary artery disease patients aged ≤45 years conducted from 2010 to 2014, the prevalence of hypertension increased from 40.7% to 47.5%, 20.3% to 26.1% for diabetes, and 27.3% to 35.7% for hyperlipidemia. However, the prevalence of smoking exhibited a downward trend.\n6\n\n\nApproximately 77% of young Chinese men hospitalized with their first MI presented with STEMI, which was consistent with other studies conducted in young Chinese adults.\n2\n, \n18\n, \n19\n Previous population‐based studies have, however, reported NSTEMI to be the most common presentation\n2\n, \n19\n; the proportion of patients presenting with NSTEMI increased from 11.6% to 36.2% in males and from 15.8% to 45.5% in females from 2007 through 2012,\n2\n which was quite different from the presentation in young adults in the US population.\n10\n, \n20\n, \n21\n Latest studies conducted in the United States have shown that the presentation of AMI was more frequently NSTEMI and increased from 54.6% to 67.9% in males and from 58.8% to 80.2% in females from 2000 through 2014.\n21\n A similar trend has been observed in Germany.\n8\n According to our findings, multivessel disease and cluster of conventional RFs are closely related to NSTEMI, which are consistent with the results of previous studies.\n10\n, \n22\n\n\nIn our study, we focused on young adults at high risk of CVD in routine clinical practice; but most of these young adults are classified as low or intermediate risk based on traditional CVD risk prediction scores that use lifetime risk estimates.\n11\n, \n12\n Using this approach leads to decreased ability to identify RFs in young adults and suboptimal utilization of preventive strategies. Furthermore, the majority of CHD events seem to occur in these “low” and “intermediate” risk groups. The knowledge of the prevalence and trends of conventional RFs among young Chinese men hospitalized with first MI have important healthcare implications with regard to planning appropriate primary preventative strategies. Considerable attention should be paid to young men with at least two out of five conventional RFs (hypercholesterolemia, hypertension, diabetes, obesity, smoking). Healthcare providers should also focus more attention on the control of metabolic factors and encourage smoking cessation.","There are some limitations that deserve consideration. This was a single‐center study and the data were collected from Beijing Anzhen hospital, which is well known for the management of CHD. Furthermore, some patients may have more severe symptoms. Hence, the results may not be extrapolated to the entire nation. Data on conventional RFs were collected from medical records; therefore, health behaviors such as physical activity, sleep duration, emotion, and stress could not be included. The sample for the annual number of AMI hospitalizations was not large enough, which may have influenced the comparisons of conventional RFs and trends in RFs over time with the reference US population.","In conclusion, the characteristics of conventional RFs among young Chinese men hospitalized with a first MI have important healthcare implications with regard to the planning of appropriate primary preventative strategies. The three leading cardiovascular RFs (smoking, hypertension, and obesity) should be the target of intervention and treatment strategies aimed at reducing the incidence of MI in young adults. Furthermore, preventing hypercholesterolemia in patients aged 34 years or younger. should also be a primary focus. Young adults with two or more conventional RFs should be given considerable attention even if they were classified as being at low or intermediate risk by traditional CHD risk prediction scores.","The authors declare that there are no conflict of interests.","","Supplementary information.\nClick here for additional data file.\nSupplementary information.\nClick here for additional data file."],"string":"[\n \"The primary and secondary prevention of coronary heart disease (CHD) in young adults has garnered tremendous attention given the rapid increase in the incidence of acute coronary events and hospitalization rates, especially in young men. Evidence from observational epidemiological studies showed the incidence of acute coronary events increased by 37.4% in the year 2009 compared to 2007 in young adults aged 35–39 years, making it the largest increase for this age group.\\n1\\n Hospitalization rates for acute myocardial infarction (AMI) per 100 000 population experienced the most significant increase in young men (<55 years), by 45.8% from 2007 to 2012 in Beijing;\\n2\\n the proportion of young adults hospitalized for CHD was nearly 90% in men from 2013 to 2014 in Beijing.\\n3\\n This trend parallels an increase in cardiovascular risk factors (RFs) including smoking, hypertension, diabetes, obesity, and dyslipidemia in the general Chinese population as well as an increase in hospitalizations for AMI.\\n2\\n, \\n4\\n, \\n5\\n, \\n6\\n\\n\\nThe Prospective Urban Rural Epidemiology (PURE) study showed that approximately 70% of cardiovascular disease (CVD) cases were attributed to modifiable RFs.\\n7\\n Though several studies have evaluated the prevalence of these RFs during a first or any episode of AMI and have found a high prevalence of at least 1 RF (approximately 90%),\\n8\\n, \\n9\\n, \\n10\\n most patients were classified as being at low or intermediate risk by traditional CHD risk prediction scores.\\n11\\n, \\n12\\n Unfortunately, this does not aid in the development of appropriate primary preventive strategies to decrease the risk of CHD. The prevalence of conventional RFs, other clinical characteristics, and their trends need to be clarified, which can be used in formulating preventive strategies.\\nFew studies have evaluated recent long‐term trends and prevalence of modifiable RFs during a first AMI in young adults in China. In a retrospective analysis of patients with coronary artery disease aged ≤45 years conducted from 2010 to 2014,\\n6\\n the prevalence figures varied for the United States and German populations.\\n8\\n, \\n10\\n Contemporary data about trends in and prevalence of modifiable RFs, and subtypes of AMI are lacking in young patients. Using a retrospective analysis of hospital data from 2007 to 2017, we aimed to evaluate the trends in and prevalence of modifiable RFs and subtypes of MI during the first AMI in young Chinese men. This evidence will provide a reference point for the development of preventive strategies in this population.\",\n \"Participants This study was based on a retrospective, single‐center analysis of young men hospitalized for a first AMI. Clinical and demographic data were collected from Beijing Anzhen Hospital by trained abstractors, using physician notes, laboratory reports, patient histories, and discharge summaries from January 2007 through December 2017. Young men aged 18–44 years hospitalized for a first AMI were identified from 2007 to 2017; 136 patients were sampled at the end of 2007 and a total of 2739 patients were recruited at the end of 2017 (Figure S1). The reference population for this analysis has previously been reported by Yandrapalli et al. Briefly, hospitalizations for a first AMI in young adults aged 18–44 years were identified from the US Healthcare Cost and Utilization Project (HCUP) National Inpatient Sample (NIS) from January 2005 through September 2015.\\n10\\n Hospitalizations for AMI were determined according to the fourth universal definition of MI.\\n13\\n AMI cases were first identified by excluding cases with secondary diagnoses of prior MI, prior percutaneous coronary intervention, prior coronary artery bypass grafting, post‐AMI syndrome, chronic ischemic heart disease, heart transplant recipient, and coronary arterial disease of bypass grafts or in transplanted hearts. Cases with a history of heart failure (HF), arteritis, congenital heart disease, and cancer were excluded. Those without coronary angiography and those with missing values for laboratory reports were excluded.\\nThis study was based on a retrospective, single‐center analysis of young men hospitalized for a first AMI. Clinical and demographic data were collected from Beijing Anzhen Hospital by trained abstractors, using physician notes, laboratory reports, patient histories, and discharge summaries from January 2007 through December 2017. Young men aged 18–44 years hospitalized for a first AMI were identified from 2007 to 2017; 136 patients were sampled at the end of 2007 and a total of 2739 patients were recruited at the end of 2017 (Figure S1). The reference population for this analysis has previously been reported by Yandrapalli et al. Briefly, hospitalizations for a first AMI in young adults aged 18–44 years were identified from the US Healthcare Cost and Utilization Project (HCUP) National Inpatient Sample (NIS) from January 2005 through September 2015.\\n10\\n Hospitalizations for AMI were determined according to the fourth universal definition of MI.\\n13\\n AMI cases were first identified by excluding cases with secondary diagnoses of prior MI, prior percutaneous coronary intervention, prior coronary artery bypass grafting, post‐AMI syndrome, chronic ischemic heart disease, heart transplant recipient, and coronary arterial disease of bypass grafts or in transplanted hearts. Cases with a history of heart failure (HF), arteritis, congenital heart disease, and cancer were excluded. Those without coronary angiography and those with missing values for laboratory reports were excluded.\\nMeasurements and diagnostic criteria Primary outcomes of interest were the overall prevalence of the RFs and their respective temporal trends and subtypes of AMI. AMI was defined based on established criteria, which included\\n13\\n: detection of a rise and/or fall of cardiac biomarker values (preferably cardiac troponin [cTn]) with at least one value above the 99th percentile upper reference limit (URL) and with at least one of the following: (1) ischemia; (2) new or presumed new significant ST‐segment–T wave (ST–T) changes or new left bundle branch block; (3) development of pathological Q waves in the ECG; (4) imaging evidence of new loss of viable myocardium or new regional wall motion abnormality; and (5) detection of an intracoronary thrombus by angiography or autopsy. AMI was classified as ST‐segment elevation myocardial infarction (STEMI) and non‐STEMI (NSTEMI). Prevalent hypertension and diabetes were defined based on a documented history of hypertension and diabetes, respectively, in medical records. Hypercholesterolemia was defined as total cholesterol (TC) ≥ 5.2 mmol/L (200 mg/dl) or low‐density lipoprotein cholesterol (LDL‐C) ≥ 3.4 mmol/L (130 mg/dl). Obesity was defined as a body mass index (BMI) ≥ 28 (kg/m2). Smokers were defined as those who reported smoking cigarettes for >6 months. When the conventional RFs were compared with that of the US population, dyslipidemia and obesity were defined according to criteria in the report by Yandrapalli' et al.\\n10\\n\\n\\nPrimary outcomes of interest were the overall prevalence of the RFs and their respective temporal trends and subtypes of AMI. AMI was defined based on established criteria, which included\\n13\\n: detection of a rise and/or fall of cardiac biomarker values (preferably cardiac troponin [cTn]) with at least one value above the 99th percentile upper reference limit (URL) and with at least one of the following: (1) ischemia; (2) new or presumed new significant ST‐segment–T wave (ST–T) changes or new left bundle branch block; (3) development of pathological Q waves in the ECG; (4) imaging evidence of new loss of viable myocardium or new regional wall motion abnormality; and (5) detection of an intracoronary thrombus by angiography or autopsy. AMI was classified as ST‐segment elevation myocardial infarction (STEMI) and non‐STEMI (NSTEMI). Prevalent hypertension and diabetes were defined based on a documented history of hypertension and diabetes, respectively, in medical records. Hypercholesterolemia was defined as total cholesterol (TC) ≥ 5.2 mmol/L (200 mg/dl) or low‐density lipoprotein cholesterol (LDL‐C) ≥ 3.4 mmol/L (130 mg/dl). Obesity was defined as a body mass index (BMI) ≥ 28 (kg/m2). Smokers were defined as those who reported smoking cigarettes for >6 months. When the conventional RFs were compared with that of the US population, dyslipidemia and obesity were defined according to criteria in the report by Yandrapalli' et al.\\n10\\n\\n\\nStatistical analysis Categorical variables were expressed as total numbers (proportions), differences in RF prevalence across age groups were compared using χ\\n2 tests for categorical variables and trends in the prevalence of RFs were analyzed using linear‐by‐linear association. Normally distributed continuous variables were presented as means ± standard deviations. A student's t test was used to compare two independent samples for normally distributed continuous variables and a Mann–Whitney U test for continuous variables with skewed distributions. Odds ratios (ORs) with 95% confidence intervals (CIs) for associations were derived from multivariable logistic regression. All reported p values were two sided. Statistical analysis was performed using IBM SPSS Statistics version 25.0 (IBM).\\nCategorical variables were expressed as total numbers (proportions), differences in RF prevalence across age groups were compared using χ\\n2 tests for categorical variables and trends in the prevalence of RFs were analyzed using linear‐by‐linear association. Normally distributed continuous variables were presented as means ± standard deviations. A student's t test was used to compare two independent samples for normally distributed continuous variables and a Mann–Whitney U test for continuous variables with skewed distributions. Odds ratios (ORs) with 95% confidence intervals (CIs) for associations were derived from multivariable logistic regression. All reported p values were two sided. Statistical analysis was performed using IBM SPSS Statistics version 25.0 (IBM).\",\n \"This study was based on a retrospective, single‐center analysis of young men hospitalized for a first AMI. Clinical and demographic data were collected from Beijing Anzhen Hospital by trained abstractors, using physician notes, laboratory reports, patient histories, and discharge summaries from January 2007 through December 2017. Young men aged 18–44 years hospitalized for a first AMI were identified from 2007 to 2017; 136 patients were sampled at the end of 2007 and a total of 2739 patients were recruited at the end of 2017 (Figure S1). The reference population for this analysis has previously been reported by Yandrapalli et al. Briefly, hospitalizations for a first AMI in young adults aged 18–44 years were identified from the US Healthcare Cost and Utilization Project (HCUP) National Inpatient Sample (NIS) from January 2005 through September 2015.\\n10\\n Hospitalizations for AMI were determined according to the fourth universal definition of MI.\\n13\\n AMI cases were first identified by excluding cases with secondary diagnoses of prior MI, prior percutaneous coronary intervention, prior coronary artery bypass grafting, post‐AMI syndrome, chronic ischemic heart disease, heart transplant recipient, and coronary arterial disease of bypass grafts or in transplanted hearts. Cases with a history of heart failure (HF), arteritis, congenital heart disease, and cancer were excluded. Those without coronary angiography and those with missing values for laboratory reports were excluded.\",\n \"Primary outcomes of interest were the overall prevalence of the RFs and their respective temporal trends and subtypes of AMI. AMI was defined based on established criteria, which included\\n13\\n: detection of a rise and/or fall of cardiac biomarker values (preferably cardiac troponin [cTn]) with at least one value above the 99th percentile upper reference limit (URL) and with at least one of the following: (1) ischemia; (2) new or presumed new significant ST‐segment–T wave (ST–T) changes or new left bundle branch block; (3) development of pathological Q waves in the ECG; (4) imaging evidence of new loss of viable myocardium or new regional wall motion abnormality; and (5) detection of an intracoronary thrombus by angiography or autopsy. AMI was classified as ST‐segment elevation myocardial infarction (STEMI) and non‐STEMI (NSTEMI). Prevalent hypertension and diabetes were defined based on a documented history of hypertension and diabetes, respectively, in medical records. Hypercholesterolemia was defined as total cholesterol (TC) ≥ 5.2 mmol/L (200 mg/dl) or low‐density lipoprotein cholesterol (LDL‐C) ≥ 3.4 mmol/L (130 mg/dl). Obesity was defined as a body mass index (BMI) ≥ 28 (kg/m2). Smokers were defined as those who reported smoking cigarettes for >6 months. When the conventional RFs were compared with that of the US population, dyslipidemia and obesity were defined according to criteria in the report by Yandrapalli' et al.\\n10\\n\\n\",\n \"Categorical variables were expressed as total numbers (proportions), differences in RF prevalence across age groups were compared using χ\\n2 tests for categorical variables and trends in the prevalence of RFs were analyzed using linear‐by‐linear association. Normally distributed continuous variables were presented as means ± standard deviations. A student's t test was used to compare two independent samples for normally distributed continuous variables and a Mann–Whitney U test for continuous variables with skewed distributions. Odds ratios (ORs) with 95% confidence intervals (CIs) for associations were derived from multivariable logistic regression. All reported p values were two sided. Statistical analysis was performed using IBM SPSS Statistics version 25.0 (IBM).\",\n \"Prevalence of conventional cardiovascular RFs and MI subtypes across different groups In this study, 2739 cases of first AMI hospitalizations in young men were enrolled from January 1, 2007, to December 31, 2017. Of the total, 462 (16.9%) patients were <35 years, 761 (27.8%) were 35–39 years and 1516 (55.3%) were 40–44 years. The most frequent presentation of AMI was STEMI (77.5%) and over 80% underwent revascularization. Baseline characteristics of the overall sample and by age subgroups are presented in Table 1. The most prevalent RFs were smoking (75.5%), followed by hypertension (40.6%) and then obesity (38.3%). The proportion of patients without any of the five conventional CVD RFs was only 5.8% and 71.7% of patients had at least 2 RFs. Significant differences were noted for the prevalence of obesity, hypertension, diabetes, and hypercholesterolemia across age groups. Patients aged <35 years had a higher prevalence of hypercholesterolemia and obesity, with lower values for hypertension; the proportion of patients with 2 or 3 RFs in this age group was also higher than that for the other age groups.\\nBaseline characteristics of the study population\\n\\nNote: Data are presented as mean ± standard deviation or n (%).\\nAbbreviations: AMI, acute myocardial infarction; NSTEMI, non‐ST‐segment elevation myocardial infarction; STEMI, ST‐segment elevation myocardial infarction.\\nIn this study, 2739 cases of first AMI hospitalizations in young men were enrolled from January 1, 2007, to December 31, 2017. Of the total, 462 (16.9%) patients were <35 years, 761 (27.8%) were 35–39 years and 1516 (55.3%) were 40–44 years. The most frequent presentation of AMI was STEMI (77.5%) and over 80% underwent revascularization. Baseline characteristics of the overall sample and by age subgroups are presented in Table 1. The most prevalent RFs were smoking (75.5%), followed by hypertension (40.6%) and then obesity (38.3%). The proportion of patients without any of the five conventional CVD RFs was only 5.8% and 71.7% of patients had at least 2 RFs. Significant differences were noted for the prevalence of obesity, hypertension, diabetes, and hypercholesterolemia across age groups. Patients aged <35 years had a higher prevalence of hypercholesterolemia and obesity, with lower values for hypertension; the proportion of patients with 2 or 3 RFs in this age group was also higher than that for the other age groups.\\nBaseline characteristics of the study population\\n\\nNote: Data are presented as mean ± standard deviation or n (%).\\nAbbreviations: AMI, acute myocardial infarction; NSTEMI, non‐ST‐segment elevation myocardial infarction; STEMI, ST‐segment elevation myocardial infarction.\\nPrevalence of conventional CVD RFs compared with a reference population We compared the prevalence of classic modifiable cardiovascular RFs in the study population with that of the reference population. Compared with the reference population, young Chinese men had a higher prevalence of smoking (75.5% vs. 58.1%; rate difference 17.4%) and dyslipidemia (65.0% vs. 54.6%; rate difference 10.4%), with lower prevalence of obesity (13.1% vs. 18.6%; rate difference 19.7%), hypertension (40.6% vs. 49.3%; rate difference 8.7%), and diabetes (14.9% vs. 19.9%; rate difference 5.0%). The three leading conventional RFs in the reference population were smoking, dyslipidemia, and hypertension, and this was similar for the target population (Table 2).\\nPrevalence of conventional cardiovascular risk factors in the study population compared with the reference population\\n\\nNote: Data are presented as mean ± standard deviation or n (%).\\nAbbreviation: BMI, body mass index.\\nWe compared the prevalence of classic modifiable cardiovascular RFs in the study population with that of the reference population. Compared with the reference population, young Chinese men had a higher prevalence of smoking (75.5% vs. 58.1%; rate difference 17.4%) and dyslipidemia (65.0% vs. 54.6%; rate difference 10.4%), with lower prevalence of obesity (13.1% vs. 18.6%; rate difference 19.7%), hypertension (40.6% vs. 49.3%; rate difference 8.7%), and diabetes (14.9% vs. 19.9%; rate difference 5.0%). The three leading conventional RFs in the reference population were smoking, dyslipidemia, and hypertension, and this was similar for the target population (Table 2).\\nPrevalence of conventional cardiovascular risk factors in the study population compared with the reference population\\n\\nNote: Data are presented as mean ± standard deviation or n (%).\\nAbbreviation: BMI, body mass index.\\nConventional cardiovascular RFs and other clinical characteristics across AMI subtypes Compared to STEMI patients, NSTEMI patients had a high prevalence of all individual conventional RFs except for smoking, which was similar in prevalence. The proportion of patients who had at least 3 RFs was 39.6% in NSTEMI patients, which was higher than that in STEMI patients (27.5%). The coronary artery involved most frequently was left anterior descending coronary artery (LAD) in both groups, but this was higher in STEMI patients. Patients with multivessel coronary disease and those without significant coronary stenosis were higher in the NSTEMI group (Table 3).\\nConventional risk factors and clinic characteristics according to AMI subtype\\n\\nNote: Data are presented as n (%).\\nAbbreviations: EF, ejection fraction; LAD, left anterior descending coronary artery; LCX, left circumflex coronary artery; NSTEMI, non‐ST elevation acute coronary syndrome; RCA, right coronary artery; STEMI, ST‐segment elevation myocardial infarction.\\nWe also conducted logistic regression analyses to evaluate the relationships between AMI subtype and conventional RFs. Multiple conventional RFs significantly increased the risk of NSTEMI; the OR was 1.69 (95% confident interval [CI]: 1.11–2.57) for patients with three RFs and 2.50 (95% CI: 1.55–4.03) for patients with at least 4 RFs. Patients with multivessel coronary disease (OR = 1.32, 95% CI: 1.08–1.60) and patients without significant coronary stenosis or normal patients (OR = 2.01, 95% CI: 1.47–2.76) had an increased risk of NSTEMI compared with those with the single vessel disease after adjusting for the other covariates (Table 4).\\nRelationship between AMI subtype and conventional RFs and coronary artery disease\\nCompared to STEMI patients, NSTEMI patients had a high prevalence of all individual conventional RFs except for smoking, which was similar in prevalence. The proportion of patients who had at least 3 RFs was 39.6% in NSTEMI patients, which was higher than that in STEMI patients (27.5%). The coronary artery involved most frequently was left anterior descending coronary artery (LAD) in both groups, but this was higher in STEMI patients. Patients with multivessel coronary disease and those without significant coronary stenosis were higher in the NSTEMI group (Table 3).\\nConventional risk factors and clinic characteristics according to AMI subtype\\n\\nNote: Data are presented as n (%).\\nAbbreviations: EF, ejection fraction; LAD, left anterior descending coronary artery; LCX, left circumflex coronary artery; NSTEMI, non‐ST elevation acute coronary syndrome; RCA, right coronary artery; STEMI, ST‐segment elevation myocardial infarction.\\nWe also conducted logistic regression analyses to evaluate the relationships between AMI subtype and conventional RFs. Multiple conventional RFs significantly increased the risk of NSTEMI; the OR was 1.69 (95% confident interval [CI]: 1.11–2.57) for patients with three RFs and 2.50 (95% CI: 1.55–4.03) for patients with at least 4 RFs. Patients with multivessel coronary disease (OR = 1.32, 95% CI: 1.08–1.60) and patients without significant coronary stenosis or normal patients (OR = 2.01, 95% CI: 1.47–2.76) had an increased risk of NSTEMI compared with those with the single vessel disease after adjusting for the other covariates (Table 4).\\nRelationship between AMI subtype and conventional RFs and coronary artery disease\\nTrends of conventional RFs and AMI subtypes The trends in the prevalence of conventional RFs are shown in Figure 1A. Compared with 2007, the prevalence of hypertension increased in 2017 (p trend = 0.004) that for smoking decreased gradually (p trend <0.05) and those for diabetes, hypercholesterolemia, and obesity remained unchanged (p trend >0.05). Between 2007 and 2017, the prevalence of hypertension increased from 26.5% to 44.2% (rate difference 17.7%) and that for smoking decreased from 77.9% to 72.4% (rate difference 5.5%). The prevalence of hypertension increased through 2007 and 2011, and then maintained a little shift. The greatest relative increase in prevalence between 2007 and 2009 was observed for hypercholesterolemia (from 28.7% to 35.5%), decreased in 2010 (from 35.5% to 20.5%), and then maintained a little shift. There were little shifts for diabetes and obesity (diabetes, 9.6% in 2007 and 14.8% in 2017, p = .283, rate difference 5.2%; obesity, 36.8% in 2007, and 40.7% in 2017, p = .105, rate difference 3.9%).\\nTrends in the percentage of five conventional risk factors and acute myocardial infarction (AMI) subtype during a first acute myocardial infarction in young men 18–44 years old between 2007 and 2017. (A) Trends in the percentage of conventional risk factors, the increasing prevalence was noted for hypertension (p trend = 0.004). Decline in the rates of current smoking and drinking are appreciated. There was a similar prevalence for diabetes and hypercholesterolemia and obesity over time. (B) Trends in the percentage of AMI subtybe, the Increasing percentage was noted for NSTEMI(p trend <0.001)\\nThe most frequent presentation of AMI was STEMI, but the proportion decreased gradually from 90.4% to 63.2% (p trend <0.001), then stabilized at around 65% during the current years (Figure 1B).\\nThe mean SBP and BMI increased over the three periods; the mean SBP increased from 119.4 mmHg to 123.3 mmHg (mean difference 3.9 mmHg), with BMI increasing from 26.5 kg/m2 to 27.6 kg/m2 (mean difference 1.2 kg/m2). The mean TC decreased from 4.62 mmol/L to 4.49 mmol/L and LDL‐C decreased from 2.97 mmol/L to 2.80 mmol/L. No differences were seen in mean DBP and FPG over the three periods (Table S1).\\nThe trends in the prevalence of conventional RFs are shown in Figure 1A. Compared with 2007, the prevalence of hypertension increased in 2017 (p trend = 0.004) that for smoking decreased gradually (p trend <0.05) and those for diabetes, hypercholesterolemia, and obesity remained unchanged (p trend >0.05). Between 2007 and 2017, the prevalence of hypertension increased from 26.5% to 44.2% (rate difference 17.7%) and that for smoking decreased from 77.9% to 72.4% (rate difference 5.5%). The prevalence of hypertension increased through 2007 and 2011, and then maintained a little shift. The greatest relative increase in prevalence between 2007 and 2009 was observed for hypercholesterolemia (from 28.7% to 35.5%), decreased in 2010 (from 35.5% to 20.5%), and then maintained a little shift. There were little shifts for diabetes and obesity (diabetes, 9.6% in 2007 and 14.8% in 2017, p = .283, rate difference 5.2%; obesity, 36.8% in 2007, and 40.7% in 2017, p = .105, rate difference 3.9%).\\nTrends in the percentage of five conventional risk factors and acute myocardial infarction (AMI) subtype during a first acute myocardial infarction in young men 18–44 years old between 2007 and 2017. (A) Trends in the percentage of conventional risk factors, the increasing prevalence was noted for hypertension (p trend = 0.004). Decline in the rates of current smoking and drinking are appreciated. There was a similar prevalence for diabetes and hypercholesterolemia and obesity over time. (B) Trends in the percentage of AMI subtybe, the Increasing percentage was noted for NSTEMI(p trend <0.001)\\nThe most frequent presentation of AMI was STEMI, but the proportion decreased gradually from 90.4% to 63.2% (p trend <0.001), then stabilized at around 65% during the current years (Figure 1B).\\nThe mean SBP and BMI increased over the three periods; the mean SBP increased from 119.4 mmHg to 123.3 mmHg (mean difference 3.9 mmHg), with BMI increasing from 26.5 kg/m2 to 27.6 kg/m2 (mean difference 1.2 kg/m2). The mean TC decreased from 4.62 mmol/L to 4.49 mmol/L and LDL‐C decreased from 2.97 mmol/L to 2.80 mmol/L. No differences were seen in mean DBP and FPG over the three periods (Table S1).\",\n \"In this study, 2739 cases of first AMI hospitalizations in young men were enrolled from January 1, 2007, to December 31, 2017. Of the total, 462 (16.9%) patients were <35 years, 761 (27.8%) were 35–39 years and 1516 (55.3%) were 40–44 years. The most frequent presentation of AMI was STEMI (77.5%) and over 80% underwent revascularization. Baseline characteristics of the overall sample and by age subgroups are presented in Table 1. The most prevalent RFs were smoking (75.5%), followed by hypertension (40.6%) and then obesity (38.3%). The proportion of patients without any of the five conventional CVD RFs was only 5.8% and 71.7% of patients had at least 2 RFs. Significant differences were noted for the prevalence of obesity, hypertension, diabetes, and hypercholesterolemia across age groups. Patients aged <35 years had a higher prevalence of hypercholesterolemia and obesity, with lower values for hypertension; the proportion of patients with 2 or 3 RFs in this age group was also higher than that for the other age groups.\\nBaseline characteristics of the study population\\n\\nNote: Data are presented as mean ± standard deviation or n (%).\\nAbbreviations: AMI, acute myocardial infarction; NSTEMI, non‐ST‐segment elevation myocardial infarction; STEMI, ST‐segment elevation myocardial infarction.\",\n \"We compared the prevalence of classic modifiable cardiovascular RFs in the study population with that of the reference population. Compared with the reference population, young Chinese men had a higher prevalence of smoking (75.5% vs. 58.1%; rate difference 17.4%) and dyslipidemia (65.0% vs. 54.6%; rate difference 10.4%), with lower prevalence of obesity (13.1% vs. 18.6%; rate difference 19.7%), hypertension (40.6% vs. 49.3%; rate difference 8.7%), and diabetes (14.9% vs. 19.9%; rate difference 5.0%). The three leading conventional RFs in the reference population were smoking, dyslipidemia, and hypertension, and this was similar for the target population (Table 2).\\nPrevalence of conventional cardiovascular risk factors in the study population compared with the reference population\\n\\nNote: Data are presented as mean ± standard deviation or n (%).\\nAbbreviation: BMI, body mass index.\",\n \"Compared to STEMI patients, NSTEMI patients had a high prevalence of all individual conventional RFs except for smoking, which was similar in prevalence. The proportion of patients who had at least 3 RFs was 39.6% in NSTEMI patients, which was higher than that in STEMI patients (27.5%). The coronary artery involved most frequently was left anterior descending coronary artery (LAD) in both groups, but this was higher in STEMI patients. Patients with multivessel coronary disease and those without significant coronary stenosis were higher in the NSTEMI group (Table 3).\\nConventional risk factors and clinic characteristics according to AMI subtype\\n\\nNote: Data are presented as n (%).\\nAbbreviations: EF, ejection fraction; LAD, left anterior descending coronary artery; LCX, left circumflex coronary artery; NSTEMI, non‐ST elevation acute coronary syndrome; RCA, right coronary artery; STEMI, ST‐segment elevation myocardial infarction.\\nWe also conducted logistic regression analyses to evaluate the relationships between AMI subtype and conventional RFs. Multiple conventional RFs significantly increased the risk of NSTEMI; the OR was 1.69 (95% confident interval [CI]: 1.11–2.57) for patients with three RFs and 2.50 (95% CI: 1.55–4.03) for patients with at least 4 RFs. Patients with multivessel coronary disease (OR = 1.32, 95% CI: 1.08–1.60) and patients without significant coronary stenosis or normal patients (OR = 2.01, 95% CI: 1.47–2.76) had an increased risk of NSTEMI compared with those with the single vessel disease after adjusting for the other covariates (Table 4).\\nRelationship between AMI subtype and conventional RFs and coronary artery disease\",\n \"The trends in the prevalence of conventional RFs are shown in Figure 1A. Compared with 2007, the prevalence of hypertension increased in 2017 (p trend = 0.004) that for smoking decreased gradually (p trend <0.05) and those for diabetes, hypercholesterolemia, and obesity remained unchanged (p trend >0.05). Between 2007 and 2017, the prevalence of hypertension increased from 26.5% to 44.2% (rate difference 17.7%) and that for smoking decreased from 77.9% to 72.4% (rate difference 5.5%). The prevalence of hypertension increased through 2007 and 2011, and then maintained a little shift. The greatest relative increase in prevalence between 2007 and 2009 was observed for hypercholesterolemia (from 28.7% to 35.5%), decreased in 2010 (from 35.5% to 20.5%), and then maintained a little shift. There were little shifts for diabetes and obesity (diabetes, 9.6% in 2007 and 14.8% in 2017, p = .283, rate difference 5.2%; obesity, 36.8% in 2007, and 40.7% in 2017, p = .105, rate difference 3.9%).\\nTrends in the percentage of five conventional risk factors and acute myocardial infarction (AMI) subtype during a first acute myocardial infarction in young men 18–44 years old between 2007 and 2017. (A) Trends in the percentage of conventional risk factors, the increasing prevalence was noted for hypertension (p trend = 0.004). Decline in the rates of current smoking and drinking are appreciated. There was a similar prevalence for diabetes and hypercholesterolemia and obesity over time. (B) Trends in the percentage of AMI subtybe, the Increasing percentage was noted for NSTEMI(p trend <0.001)\\nThe most frequent presentation of AMI was STEMI, but the proportion decreased gradually from 90.4% to 63.2% (p trend <0.001), then stabilized at around 65% during the current years (Figure 1B).\\nThe mean SBP and BMI increased over the three periods; the mean SBP increased from 119.4 mmHg to 123.3 mmHg (mean difference 3.9 mmHg), with BMI increasing from 26.5 kg/m2 to 27.6 kg/m2 (mean difference 1.2 kg/m2). The mean TC decreased from 4.62 mmol/L to 4.49 mmol/L and LDL‐C decreased from 2.97 mmol/L to 2.80 mmol/L. No differences were seen in mean DBP and FPG over the three periods (Table S1).\",\n \"The control rate of hypertension and diabetes was 62.8% (699/1113) and 17.7% (72/407), respectively; smoking cessation was 4.8% (105/2173).\",\n \"In this study comprising of young men with a first hospitalization for AMI in China, the most prevalent CVD RFs were smoking (75.5%), hypertension (40.6%), and obesity (38.3%). Additionally, over 70% of them had at least two conventional RFs. Compared with a similar reference population, these young Chinese men had a higher prevalence of smoking and dyslipidemia and a lower prevalence of obesity, hypertension, and diabetes. Between 2007 and 2017, the prevalence of hypertension increased, whereas that of smoking decreased. Patients presenting with NSTEMI had a high prevalence of all individual conventional RFs except for smoking, which was more prevalent in STEMI patients. LAD was the most frequently involved coronary artery in the subtypes of AMI, but it was more common in STEMI patients. Multivessel coronary disease was more common in NSTEMI.\\nConventional RFs were highly prevalent in young men aged 18–44 years who were hospitalized for first AMI events. The prevalence values were higher compared to national figures smoking (53.9% for young adults aged 25–44 years), hypertension (11.3% for urban adults and 10.0% for rural adults aged 18–44 years), obesity (11.0% for young adults aged 18–44 years), and diabetes (5.9% for young adults aged 18–44 years); high TC ( ≥ 5.2 mmol/L) and high LDL‐C( ≥ 3.4 mmol/L) were 28.5% and 26.3%, respectively.\\n14\\n, \\n15\\n, \\n16\\n, \\n17\\n Our results are consistent with previous studies conducted in young patients diagnosed with AMI and CHD; the prevalence for hypertension, diabetes, and smoking has been reported as 34.3%–41.7%, 11.1%–22.4%, and 57.4%–74.0%, respectively.\\n3\\n, \\n6\\n However, the prevalence figures were different from that of the US report. Compared with the data from the US NIS for patients hospitalized for a first AMI from 2005 to 2015,\\n10\\n young Chinese men had a higher prevalence of smoking and dyslipidemia, with a lower prevalence of hypertension, obesity, and diabetes. There were clear age differences in the prevalence of some of the RFs; patients aged <35 years had a higher prevalence of smoking, obesity, and hypercholesterolemia, whereas patients aged 35–44 years had a higher prevalence of smoking, hypertension, and diabetes.\\nOur study also showed differences in the temporal trends of the conventional RFs between China and US populations. The prevalence of all the evaluated conventional RFs progressively increased between 2005 and 2015 in the United States,\\n10\\n but hypertension prevalence increased in China, whereas smoking decreased gradually; which was also consistent with a previous study conducted in China. In a retrospective analysis of coronary artery disease patients aged ≤45 years conducted from 2010 to 2014, the prevalence of hypertension increased from 40.7% to 47.5%, 20.3% to 26.1% for diabetes, and 27.3% to 35.7% for hyperlipidemia. However, the prevalence of smoking exhibited a downward trend.\\n6\\n\\n\\nApproximately 77% of young Chinese men hospitalized with their first MI presented with STEMI, which was consistent with other studies conducted in young Chinese adults.\\n2\\n, \\n18\\n, \\n19\\n Previous population‐based studies have, however, reported NSTEMI to be the most common presentation\\n2\\n, \\n19\\n; the proportion of patients presenting with NSTEMI increased from 11.6% to 36.2% in males and from 15.8% to 45.5% in females from 2007 through 2012,\\n2\\n which was quite different from the presentation in young adults in the US population.\\n10\\n, \\n20\\n, \\n21\\n Latest studies conducted in the United States have shown that the presentation of AMI was more frequently NSTEMI and increased from 54.6% to 67.9% in males and from 58.8% to 80.2% in females from 2000 through 2014.\\n21\\n A similar trend has been observed in Germany.\\n8\\n According to our findings, multivessel disease and cluster of conventional RFs are closely related to NSTEMI, which are consistent with the results of previous studies.\\n10\\n, \\n22\\n\\n\\nIn our study, we focused on young adults at high risk of CVD in routine clinical practice; but most of these young adults are classified as low or intermediate risk based on traditional CVD risk prediction scores that use lifetime risk estimates.\\n11\\n, \\n12\\n Using this approach leads to decreased ability to identify RFs in young adults and suboptimal utilization of preventive strategies. Furthermore, the majority of CHD events seem to occur in these “low” and “intermediate” risk groups. The knowledge of the prevalence and trends of conventional RFs among young Chinese men hospitalized with first MI have important healthcare implications with regard to planning appropriate primary preventative strategies. Considerable attention should be paid to young men with at least two out of five conventional RFs (hypercholesterolemia, hypertension, diabetes, obesity, smoking). Healthcare providers should also focus more attention on the control of metabolic factors and encourage smoking cessation.\",\n \"There are some limitations that deserve consideration. This was a single‐center study and the data were collected from Beijing Anzhen hospital, which is well known for the management of CHD. Furthermore, some patients may have more severe symptoms. Hence, the results may not be extrapolated to the entire nation. Data on conventional RFs were collected from medical records; therefore, health behaviors such as physical activity, sleep duration, emotion, and stress could not be included. The sample for the annual number of AMI hospitalizations was not large enough, which may have influenced the comparisons of conventional RFs and trends in RFs over time with the reference US population.\",\n \"In conclusion, the characteristics of conventional RFs among young Chinese men hospitalized with a first MI have important healthcare implications with regard to the planning of appropriate primary preventative strategies. The three leading cardiovascular RFs (smoking, hypertension, and obesity) should be the target of intervention and treatment strategies aimed at reducing the incidence of MI in young adults. Furthermore, preventing hypercholesterolemia in patients aged 34 years or younger. should also be a primary focus. Young adults with two or more conventional RFs should be given considerable attention even if they were classified as being at low or intermediate risk by traditional CHD risk prediction scores.\",\n \"The authors declare that there are no conflict of interests.\",\n \"\",\n \"Supplementary information.\\nClick here for additional data file.\\nSupplementary information.\\nClick here for additional data file.\"\n]"},"all_sections_sec_types":{"kind":"list like","value":[null,"methods",null,null,null,"results",null,null,null,null,null,"discussion",null,"conclusions","COI-statement",null,"supplementary-material"],"string":"[\n null,\n \"methods\",\n null,\n null,\n null,\n \"results\",\n null,\n null,\n null,\n null,\n null,\n \"discussion\",\n null,\n \"conclusions\",\n \"COI-statement\",\n null,\n \"supplementary-material\"\n]"},"keywords":{"kind":"list like","value":["acute myocardial infarction","risk factor","trends","youth"],"string":"[\n \"acute myocardial infarction\",\n \"risk factor\",\n \"trends\",\n \"youth\"\n]"},"whole_article_text":{"kind":"string","value":"INTRODUCTION:\nThe primary and secondary prevention of coronary heart disease (CHD) in young adults has garnered tremendous attention given the rapid increase in the incidence of acute coronary events and hospitalization rates, especially in young men. Evidence from observational epidemiological studies showed the incidence of acute coronary events increased by 37.4% in the year 2009 compared to 2007 in young adults aged 35–39 years, making it the largest increase for this age group.\n1\n Hospitalization rates for acute myocardial infarction (AMI) per 100 000 population experienced the most significant increase in young men (<55 years), by 45.8% from 2007 to 2012 in Beijing;\n2\n the proportion of young adults hospitalized for CHD was nearly 90% in men from 2013 to 2014 in Beijing.\n3\n This trend parallels an increase in cardiovascular risk factors (RFs) including smoking, hypertension, diabetes, obesity, and dyslipidemia in the general Chinese population as well as an increase in hospitalizations for AMI.\n2\n, \n4\n, \n5\n, \n6\n\n\nThe Prospective Urban Rural Epidemiology (PURE) study showed that approximately 70% of cardiovascular disease (CVD) cases were attributed to modifiable RFs.\n7\n Though several studies have evaluated the prevalence of these RFs during a first or any episode of AMI and have found a high prevalence of at least 1 RF (approximately 90%),\n8\n, \n9\n, \n10\n most patients were classified as being at low or intermediate risk by traditional CHD risk prediction scores.\n11\n, \n12\n Unfortunately, this does not aid in the development of appropriate primary preventive strategies to decrease the risk of CHD. The prevalence of conventional RFs, other clinical characteristics, and their trends need to be clarified, which can be used in formulating preventive strategies.\nFew studies have evaluated recent long‐term trends and prevalence of modifiable RFs during a first AMI in young adults in China. In a retrospective analysis of patients with coronary artery disease aged ≤45 years conducted from 2010 to 2014,\n6\n the prevalence figures varied for the United States and German populations.\n8\n, \n10\n Contemporary data about trends in and prevalence of modifiable RFs, and subtypes of AMI are lacking in young patients. Using a retrospective analysis of hospital data from 2007 to 2017, we aimed to evaluate the trends in and prevalence of modifiable RFs and subtypes of MI during the first AMI in young Chinese men. This evidence will provide a reference point for the development of preventive strategies in this population.\n\nMETHODS:\nParticipants This study was based on a retrospective, single‐center analysis of young men hospitalized for a first AMI. Clinical and demographic data were collected from Beijing Anzhen Hospital by trained abstractors, using physician notes, laboratory reports, patient histories, and discharge summaries from January 2007 through December 2017. Young men aged 18–44 years hospitalized for a first AMI were identified from 2007 to 2017; 136 patients were sampled at the end of 2007 and a total of 2739 patients were recruited at the end of 2017 (Figure S1). The reference population for this analysis has previously been reported by Yandrapalli et al. Briefly, hospitalizations for a first AMI in young adults aged 18–44 years were identified from the US Healthcare Cost and Utilization Project (HCUP) National Inpatient Sample (NIS) from January 2005 through September 2015.\n10\n Hospitalizations for AMI were determined according to the fourth universal definition of MI.\n13\n AMI cases were first identified by excluding cases with secondary diagnoses of prior MI, prior percutaneous coronary intervention, prior coronary artery bypass grafting, post‐AMI syndrome, chronic ischemic heart disease, heart transplant recipient, and coronary arterial disease of bypass grafts or in transplanted hearts. Cases with a history of heart failure (HF), arteritis, congenital heart disease, and cancer were excluded. Those without coronary angiography and those with missing values for laboratory reports were excluded.\nThis study was based on a retrospective, single‐center analysis of young men hospitalized for a first AMI. Clinical and demographic data were collected from Beijing Anzhen Hospital by trained abstractors, using physician notes, laboratory reports, patient histories, and discharge summaries from January 2007 through December 2017. Young men aged 18–44 years hospitalized for a first AMI were identified from 2007 to 2017; 136 patients were sampled at the end of 2007 and a total of 2739 patients were recruited at the end of 2017 (Figure S1). The reference population for this analysis has previously been reported by Yandrapalli et al. Briefly, hospitalizations for a first AMI in young adults aged 18–44 years were identified from the US Healthcare Cost and Utilization Project (HCUP) National Inpatient Sample (NIS) from January 2005 through September 2015.\n10\n Hospitalizations for AMI were determined according to the fourth universal definition of MI.\n13\n AMI cases were first identified by excluding cases with secondary diagnoses of prior MI, prior percutaneous coronary intervention, prior coronary artery bypass grafting, post‐AMI syndrome, chronic ischemic heart disease, heart transplant recipient, and coronary arterial disease of bypass grafts or in transplanted hearts. Cases with a history of heart failure (HF), arteritis, congenital heart disease, and cancer were excluded. Those without coronary angiography and those with missing values for laboratory reports were excluded.\nMeasurements and diagnostic criteria Primary outcomes of interest were the overall prevalence of the RFs and their respective temporal trends and subtypes of AMI. AMI was defined based on established criteria, which included\n13\n: detection of a rise and/or fall of cardiac biomarker values (preferably cardiac troponin [cTn]) with at least one value above the 99th percentile upper reference limit (URL) and with at least one of the following: (1) ischemia; (2) new or presumed new significant ST‐segment–T wave (ST–T) changes or new left bundle branch block; (3) development of pathological Q waves in the ECG; (4) imaging evidence of new loss of viable myocardium or new regional wall motion abnormality; and (5) detection of an intracoronary thrombus by angiography or autopsy. AMI was classified as ST‐segment elevation myocardial infarction (STEMI) and non‐STEMI (NSTEMI). Prevalent hypertension and diabetes were defined based on a documented history of hypertension and diabetes, respectively, in medical records. Hypercholesterolemia was defined as total cholesterol (TC) ≥ 5.2 mmol/L (200 mg/dl) or low‐density lipoprotein cholesterol (LDL‐C) ≥ 3.4 mmol/L (130 mg/dl). Obesity was defined as a body mass index (BMI) ≥ 28 (kg/m2). Smokers were defined as those who reported smoking cigarettes for >6 months. When the conventional RFs were compared with that of the US population, dyslipidemia and obesity were defined according to criteria in the report by Yandrapalli' et al.\n10\n\n\nPrimary outcomes of interest were the overall prevalence of the RFs and their respective temporal trends and subtypes of AMI. AMI was defined based on established criteria, which included\n13\n: detection of a rise and/or fall of cardiac biomarker values (preferably cardiac troponin [cTn]) with at least one value above the 99th percentile upper reference limit (URL) and with at least one of the following: (1) ischemia; (2) new or presumed new significant ST‐segment–T wave (ST–T) changes or new left bundle branch block; (3) development of pathological Q waves in the ECG; (4) imaging evidence of new loss of viable myocardium or new regional wall motion abnormality; and (5) detection of an intracoronary thrombus by angiography or autopsy. AMI was classified as ST‐segment elevation myocardial infarction (STEMI) and non‐STEMI (NSTEMI). Prevalent hypertension and diabetes were defined based on a documented history of hypertension and diabetes, respectively, in medical records. Hypercholesterolemia was defined as total cholesterol (TC) ≥ 5.2 mmol/L (200 mg/dl) or low‐density lipoprotein cholesterol (LDL‐C) ≥ 3.4 mmol/L (130 mg/dl). Obesity was defined as a body mass index (BMI) ≥ 28 (kg/m2). Smokers were defined as those who reported smoking cigarettes for >6 months. When the conventional RFs were compared with that of the US population, dyslipidemia and obesity were defined according to criteria in the report by Yandrapalli' et al.\n10\n\n\nStatistical analysis Categorical variables were expressed as total numbers (proportions), differences in RF prevalence across age groups were compared using χ\n2 tests for categorical variables and trends in the prevalence of RFs were analyzed using linear‐by‐linear association. Normally distributed continuous variables were presented as means ± standard deviations. A student's t test was used to compare two independent samples for normally distributed continuous variables and a Mann–Whitney U test for continuous variables with skewed distributions. Odds ratios (ORs) with 95% confidence intervals (CIs) for associations were derived from multivariable logistic regression. All reported p values were two sided. Statistical analysis was performed using IBM SPSS Statistics version 25.0 (IBM).\nCategorical variables were expressed as total numbers (proportions), differences in RF prevalence across age groups were compared using χ\n2 tests for categorical variables and trends in the prevalence of RFs were analyzed using linear‐by‐linear association. Normally distributed continuous variables were presented as means ± standard deviations. A student's t test was used to compare two independent samples for normally distributed continuous variables and a Mann–Whitney U test for continuous variables with skewed distributions. Odds ratios (ORs) with 95% confidence intervals (CIs) for associations were derived from multivariable logistic regression. All reported p values were two sided. Statistical analysis was performed using IBM SPSS Statistics version 25.0 (IBM).\n\nParticipants:\nThis study was based on a retrospective, single‐center analysis of young men hospitalized for a first AMI. Clinical and demographic data were collected from Beijing Anzhen Hospital by trained abstractors, using physician notes, laboratory reports, patient histories, and discharge summaries from January 2007 through December 2017. Young men aged 18–44 years hospitalized for a first AMI were identified from 2007 to 2017; 136 patients were sampled at the end of 2007 and a total of 2739 patients were recruited at the end of 2017 (Figure S1). The reference population for this analysis has previously been reported by Yandrapalli et al. Briefly, hospitalizations for a first AMI in young adults aged 18–44 years were identified from the US Healthcare Cost and Utilization Project (HCUP) National Inpatient Sample (NIS) from January 2005 through September 2015.\n10\n Hospitalizations for AMI were determined according to the fourth universal definition of MI.\n13\n AMI cases were first identified by excluding cases with secondary diagnoses of prior MI, prior percutaneous coronary intervention, prior coronary artery bypass grafting, post‐AMI syndrome, chronic ischemic heart disease, heart transplant recipient, and coronary arterial disease of bypass grafts or in transplanted hearts. Cases with a history of heart failure (HF), arteritis, congenital heart disease, and cancer were excluded. Those without coronary angiography and those with missing values for laboratory reports were excluded.\n\nMeasurements and diagnostic criteria:\nPrimary outcomes of interest were the overall prevalence of the RFs and their respective temporal trends and subtypes of AMI. AMI was defined based on established criteria, which included\n13\n: detection of a rise and/or fall of cardiac biomarker values (preferably cardiac troponin [cTn]) with at least one value above the 99th percentile upper reference limit (URL) and with at least one of the following: (1) ischemia; (2) new or presumed new significant ST‐segment–T wave (ST–T) changes or new left bundle branch block; (3) development of pathological Q waves in the ECG; (4) imaging evidence of new loss of viable myocardium or new regional wall motion abnormality; and (5) detection of an intracoronary thrombus by angiography or autopsy. AMI was classified as ST‐segment elevation myocardial infarction (STEMI) and non‐STEMI (NSTEMI). Prevalent hypertension and diabetes were defined based on a documented history of hypertension and diabetes, respectively, in medical records. Hypercholesterolemia was defined as total cholesterol (TC) ≥ 5.2 mmol/L (200 mg/dl) or low‐density lipoprotein cholesterol (LDL‐C) ≥ 3.4 mmol/L (130 mg/dl). Obesity was defined as a body mass index (BMI) ≥ 28 (kg/m2). Smokers were defined as those who reported smoking cigarettes for >6 months. When the conventional RFs were compared with that of the US population, dyslipidemia and obesity were defined according to criteria in the report by Yandrapalli' et al.\n10\n\n\n\nStatistical analysis:\nCategorical variables were expressed as total numbers (proportions), differences in RF prevalence across age groups were compared using χ\n2 tests for categorical variables and trends in the prevalence of RFs were analyzed using linear‐by‐linear association. Normally distributed continuous variables were presented as means ± standard deviations. A student's t test was used to compare two independent samples for normally distributed continuous variables and a Mann–Whitney U test for continuous variables with skewed distributions. Odds ratios (ORs) with 95% confidence intervals (CIs) for associations were derived from multivariable logistic regression. All reported p values were two sided. Statistical analysis was performed using IBM SPSS Statistics version 25.0 (IBM).\n\nRESULTS:\nPrevalence of conventional cardiovascular RFs and MI subtypes across different groups In this study, 2739 cases of first AMI hospitalizations in young men were enrolled from January 1, 2007, to December 31, 2017. Of the total, 462 (16.9%) patients were <35 years, 761 (27.8%) were 35–39 years and 1516 (55.3%) were 40–44 years. The most frequent presentation of AMI was STEMI (77.5%) and over 80% underwent revascularization. Baseline characteristics of the overall sample and by age subgroups are presented in Table 1. The most prevalent RFs were smoking (75.5%), followed by hypertension (40.6%) and then obesity (38.3%). The proportion of patients without any of the five conventional CVD RFs was only 5.8% and 71.7% of patients had at least 2 RFs. Significant differences were noted for the prevalence of obesity, hypertension, diabetes, and hypercholesterolemia across age groups. Patients aged <35 years had a higher prevalence of hypercholesterolemia and obesity, with lower values for hypertension; the proportion of patients with 2 or 3 RFs in this age group was also higher than that for the other age groups.\nBaseline characteristics of the study population\n\nNote: Data are presented as mean ± standard deviation or n (%).\nAbbreviations: AMI, acute myocardial infarction; NSTEMI, non‐ST‐segment elevation myocardial infarction; STEMI, ST‐segment elevation myocardial infarction.\nIn this study, 2739 cases of first AMI hospitalizations in young men were enrolled from January 1, 2007, to December 31, 2017. Of the total, 462 (16.9%) patients were <35 years, 761 (27.8%) were 35–39 years and 1516 (55.3%) were 40–44 years. The most frequent presentation of AMI was STEMI (77.5%) and over 80% underwent revascularization. Baseline characteristics of the overall sample and by age subgroups are presented in Table 1. The most prevalent RFs were smoking (75.5%), followed by hypertension (40.6%) and then obesity (38.3%). The proportion of patients without any of the five conventional CVD RFs was only 5.8% and 71.7% of patients had at least 2 RFs. Significant differences were noted for the prevalence of obesity, hypertension, diabetes, and hypercholesterolemia across age groups. Patients aged <35 years had a higher prevalence of hypercholesterolemia and obesity, with lower values for hypertension; the proportion of patients with 2 or 3 RFs in this age group was also higher than that for the other age groups.\nBaseline characteristics of the study population\n\nNote: Data are presented as mean ± standard deviation or n (%).\nAbbreviations: AMI, acute myocardial infarction; NSTEMI, non‐ST‐segment elevation myocardial infarction; STEMI, ST‐segment elevation myocardial infarction.\nPrevalence of conventional CVD RFs compared with a reference population We compared the prevalence of classic modifiable cardiovascular RFs in the study population with that of the reference population. Compared with the reference population, young Chinese men had a higher prevalence of smoking (75.5% vs. 58.1%; rate difference 17.4%) and dyslipidemia (65.0% vs. 54.6%; rate difference 10.4%), with lower prevalence of obesity (13.1% vs. 18.6%; rate difference 19.7%), hypertension (40.6% vs. 49.3%; rate difference 8.7%), and diabetes (14.9% vs. 19.9%; rate difference 5.0%). The three leading conventional RFs in the reference population were smoking, dyslipidemia, and hypertension, and this was similar for the target population (Table 2).\nPrevalence of conventional cardiovascular risk factors in the study population compared with the reference population\n\nNote: Data are presented as mean ± standard deviation or n (%).\nAbbreviation: BMI, body mass index.\nWe compared the prevalence of classic modifiable cardiovascular RFs in the study population with that of the reference population. Compared with the reference population, young Chinese men had a higher prevalence of smoking (75.5% vs. 58.1%; rate difference 17.4%) and dyslipidemia (65.0% vs. 54.6%; rate difference 10.4%), with lower prevalence of obesity (13.1% vs. 18.6%; rate difference 19.7%), hypertension (40.6% vs. 49.3%; rate difference 8.7%), and diabetes (14.9% vs. 19.9%; rate difference 5.0%). The three leading conventional RFs in the reference population were smoking, dyslipidemia, and hypertension, and this was similar for the target population (Table 2).\nPrevalence of conventional cardiovascular risk factors in the study population compared with the reference population\n\nNote: Data are presented as mean ± standard deviation or n (%).\nAbbreviation: BMI, body mass index.\nConventional cardiovascular RFs and other clinical characteristics across AMI subtypes Compared to STEMI patients, NSTEMI patients had a high prevalence of all individual conventional RFs except for smoking, which was similar in prevalence. The proportion of patients who had at least 3 RFs was 39.6% in NSTEMI patients, which was higher than that in STEMI patients (27.5%). The coronary artery involved most frequently was left anterior descending coronary artery (LAD) in both groups, but this was higher in STEMI patients. Patients with multivessel coronary disease and those without significant coronary stenosis were higher in the NSTEMI group (Table 3).\nConventional risk factors and clinic characteristics according to AMI subtype\n\nNote: Data are presented as n (%).\nAbbreviations: EF, ejection fraction; LAD, left anterior descending coronary artery; LCX, left circumflex coronary artery; NSTEMI, non‐ST elevation acute coronary syndrome; RCA, right coronary artery; STEMI, ST‐segment elevation myocardial infarction.\nWe also conducted logistic regression analyses to evaluate the relationships between AMI subtype and conventional RFs. Multiple conventional RFs significantly increased the risk of NSTEMI; the OR was 1.69 (95% confident interval [CI]: 1.11–2.57) for patients with three RFs and 2.50 (95% CI: 1.55–4.03) for patients with at least 4 RFs. Patients with multivessel coronary disease (OR = 1.32, 95% CI: 1.08–1.60) and patients without significant coronary stenosis or normal patients (OR = 2.01, 95% CI: 1.47–2.76) had an increased risk of NSTEMI compared with those with the single vessel disease after adjusting for the other covariates (Table 4).\nRelationship between AMI subtype and conventional RFs and coronary artery disease\nCompared to STEMI patients, NSTEMI patients had a high prevalence of all individual conventional RFs except for smoking, which was similar in prevalence. The proportion of patients who had at least 3 RFs was 39.6% in NSTEMI patients, which was higher than that in STEMI patients (27.5%). The coronary artery involved most frequently was left anterior descending coronary artery (LAD) in both groups, but this was higher in STEMI patients. Patients with multivessel coronary disease and those without significant coronary stenosis were higher in the NSTEMI group (Table 3).\nConventional risk factors and clinic characteristics according to AMI subtype\n\nNote: Data are presented as n (%).\nAbbreviations: EF, ejection fraction; LAD, left anterior descending coronary artery; LCX, left circumflex coronary artery; NSTEMI, non‐ST elevation acute coronary syndrome; RCA, right coronary artery; STEMI, ST‐segment elevation myocardial infarction.\nWe also conducted logistic regression analyses to evaluate the relationships between AMI subtype and conventional RFs. Multiple conventional RFs significantly increased the risk of NSTEMI; the OR was 1.69 (95% confident interval [CI]: 1.11–2.57) for patients with three RFs and 2.50 (95% CI: 1.55–4.03) for patients with at least 4 RFs. Patients with multivessel coronary disease (OR = 1.32, 95% CI: 1.08–1.60) and patients without significant coronary stenosis or normal patients (OR = 2.01, 95% CI: 1.47–2.76) had an increased risk of NSTEMI compared with those with the single vessel disease after adjusting for the other covariates (Table 4).\nRelationship between AMI subtype and conventional RFs and coronary artery disease\nTrends of conventional RFs and AMI subtypes The trends in the prevalence of conventional RFs are shown in Figure 1A. Compared with 2007, the prevalence of hypertension increased in 2017 (p trend = 0.004) that for smoking decreased gradually (p trend <0.05) and those for diabetes, hypercholesterolemia, and obesity remained unchanged (p trend >0.05). Between 2007 and 2017, the prevalence of hypertension increased from 26.5% to 44.2% (rate difference 17.7%) and that for smoking decreased from 77.9% to 72.4% (rate difference 5.5%). The prevalence of hypertension increased through 2007 and 2011, and then maintained a little shift. The greatest relative increase in prevalence between 2007 and 2009 was observed for hypercholesterolemia (from 28.7% to 35.5%), decreased in 2010 (from 35.5% to 20.5%), and then maintained a little shift. There were little shifts for diabetes and obesity (diabetes, 9.6% in 2007 and 14.8% in 2017, p = .283, rate difference 5.2%; obesity, 36.8% in 2007, and 40.7% in 2017, p = .105, rate difference 3.9%).\nTrends in the percentage of five conventional risk factors and acute myocardial infarction (AMI) subtype during a first acute myocardial infarction in young men 18–44 years old between 2007 and 2017. (A) Trends in the percentage of conventional risk factors, the increasing prevalence was noted for hypertension (p trend = 0.004). Decline in the rates of current smoking and drinking are appreciated. There was a similar prevalence for diabetes and hypercholesterolemia and obesity over time. (B) Trends in the percentage of AMI subtybe, the Increasing percentage was noted for NSTEMI(p trend <0.001)\nThe most frequent presentation of AMI was STEMI, but the proportion decreased gradually from 90.4% to 63.2% (p trend <0.001), then stabilized at around 65% during the current years (Figure 1B).\nThe mean SBP and BMI increased over the three periods; the mean SBP increased from 119.4 mmHg to 123.3 mmHg (mean difference 3.9 mmHg), with BMI increasing from 26.5 kg/m2 to 27.6 kg/m2 (mean difference 1.2 kg/m2). The mean TC decreased from 4.62 mmol/L to 4.49 mmol/L and LDL‐C decreased from 2.97 mmol/L to 2.80 mmol/L. No differences were seen in mean DBP and FPG over the three periods (Table S1).\nThe trends in the prevalence of conventional RFs are shown in Figure 1A. Compared with 2007, the prevalence of hypertension increased in 2017 (p trend = 0.004) that for smoking decreased gradually (p trend <0.05) and those for diabetes, hypercholesterolemia, and obesity remained unchanged (p trend >0.05). Between 2007 and 2017, the prevalence of hypertension increased from 26.5% to 44.2% (rate difference 17.7%) and that for smoking decreased from 77.9% to 72.4% (rate difference 5.5%). The prevalence of hypertension increased through 2007 and 2011, and then maintained a little shift. The greatest relative increase in prevalence between 2007 and 2009 was observed for hypercholesterolemia (from 28.7% to 35.5%), decreased in 2010 (from 35.5% to 20.5%), and then maintained a little shift. There were little shifts for diabetes and obesity (diabetes, 9.6% in 2007 and 14.8% in 2017, p = .283, rate difference 5.2%; obesity, 36.8% in 2007, and 40.7% in 2017, p = .105, rate difference 3.9%).\nTrends in the percentage of five conventional risk factors and acute myocardial infarction (AMI) subtype during a first acute myocardial infarction in young men 18–44 years old between 2007 and 2017. (A) Trends in the percentage of conventional risk factors, the increasing prevalence was noted for hypertension (p trend = 0.004). Decline in the rates of current smoking and drinking are appreciated. There was a similar prevalence for diabetes and hypercholesterolemia and obesity over time. (B) Trends in the percentage of AMI subtybe, the Increasing percentage was noted for NSTEMI(p trend <0.001)\nThe most frequent presentation of AMI was STEMI, but the proportion decreased gradually from 90.4% to 63.2% (p trend <0.001), then stabilized at around 65% during the current years (Figure 1B).\nThe mean SBP and BMI increased over the three periods; the mean SBP increased from 119.4 mmHg to 123.3 mmHg (mean difference 3.9 mmHg), with BMI increasing from 26.5 kg/m2 to 27.6 kg/m2 (mean difference 1.2 kg/m2). The mean TC decreased from 4.62 mmol/L to 4.49 mmol/L and LDL‐C decreased from 2.97 mmol/L to 2.80 mmol/L. No differences were seen in mean DBP and FPG over the three periods (Table S1).\n\nPrevalence of conventional cardiovascular RFs and MI subtypes across different groups:\nIn this study, 2739 cases of first AMI hospitalizations in young men were enrolled from January 1, 2007, to December 31, 2017. Of the total, 462 (16.9%) patients were <35 years, 761 (27.8%) were 35–39 years and 1516 (55.3%) were 40–44 years. The most frequent presentation of AMI was STEMI (77.5%) and over 80% underwent revascularization. Baseline characteristics of the overall sample and by age subgroups are presented in Table 1. The most prevalent RFs were smoking (75.5%), followed by hypertension (40.6%) and then obesity (38.3%). The proportion of patients without any of the five conventional CVD RFs was only 5.8% and 71.7% of patients had at least 2 RFs. Significant differences were noted for the prevalence of obesity, hypertension, diabetes, and hypercholesterolemia across age groups. Patients aged <35 years had a higher prevalence of hypercholesterolemia and obesity, with lower values for hypertension; the proportion of patients with 2 or 3 RFs in this age group was also higher than that for the other age groups.\nBaseline characteristics of the study population\n\nNote: Data are presented as mean ± standard deviation or n (%).\nAbbreviations: AMI, acute myocardial infarction; NSTEMI, non‐ST‐segment elevation myocardial infarction; STEMI, ST‐segment elevation myocardial infarction.\n\nPrevalence of conventional CVD RFs compared with a reference population:\nWe compared the prevalence of classic modifiable cardiovascular RFs in the study population with that of the reference population. Compared with the reference population, young Chinese men had a higher prevalence of smoking (75.5% vs. 58.1%; rate difference 17.4%) and dyslipidemia (65.0% vs. 54.6%; rate difference 10.4%), with lower prevalence of obesity (13.1% vs. 18.6%; rate difference 19.7%), hypertension (40.6% vs. 49.3%; rate difference 8.7%), and diabetes (14.9% vs. 19.9%; rate difference 5.0%). The three leading conventional RFs in the reference population were smoking, dyslipidemia, and hypertension, and this was similar for the target population (Table 2).\nPrevalence of conventional cardiovascular risk factors in the study population compared with the reference population\n\nNote: Data are presented as mean ± standard deviation or n (%).\nAbbreviation: BMI, body mass index.\n\nConventional cardiovascular RFs and other clinical characteristics across AMI subtypes:\nCompared to STEMI patients, NSTEMI patients had a high prevalence of all individual conventional RFs except for smoking, which was similar in prevalence. The proportion of patients who had at least 3 RFs was 39.6% in NSTEMI patients, which was higher than that in STEMI patients (27.5%). The coronary artery involved most frequently was left anterior descending coronary artery (LAD) in both groups, but this was higher in STEMI patients. Patients with multivessel coronary disease and those without significant coronary stenosis were higher in the NSTEMI group (Table 3).\nConventional risk factors and clinic characteristics according to AMI subtype\n\nNote: Data are presented as n (%).\nAbbreviations: EF, ejection fraction; LAD, left anterior descending coronary artery; LCX, left circumflex coronary artery; NSTEMI, non‐ST elevation acute coronary syndrome; RCA, right coronary artery; STEMI, ST‐segment elevation myocardial infarction.\nWe also conducted logistic regression analyses to evaluate the relationships between AMI subtype and conventional RFs. Multiple conventional RFs significantly increased the risk of NSTEMI; the OR was 1.69 (95% confident interval [CI]: 1.11–2.57) for patients with three RFs and 2.50 (95% CI: 1.55–4.03) for patients with at least 4 RFs. Patients with multivessel coronary disease (OR = 1.32, 95% CI: 1.08–1.60) and patients without significant coronary stenosis or normal patients (OR = 2.01, 95% CI: 1.47–2.76) had an increased risk of NSTEMI compared with those with the single vessel disease after adjusting for the other covariates (Table 4).\nRelationship between AMI subtype and conventional RFs and coronary artery disease\n\nTrends of conventional RFs and AMI subtypes:\nThe trends in the prevalence of conventional RFs are shown in Figure 1A. Compared with 2007, the prevalence of hypertension increased in 2017 (p trend = 0.004) that for smoking decreased gradually (p trend <0.05) and those for diabetes, hypercholesterolemia, and obesity remained unchanged (p trend >0.05). Between 2007 and 2017, the prevalence of hypertension increased from 26.5% to 44.2% (rate difference 17.7%) and that for smoking decreased from 77.9% to 72.4% (rate difference 5.5%). The prevalence of hypertension increased through 2007 and 2011, and then maintained a little shift. The greatest relative increase in prevalence between 2007 and 2009 was observed for hypercholesterolemia (from 28.7% to 35.5%), decreased in 2010 (from 35.5% to 20.5%), and then maintained a little shift. There were little shifts for diabetes and obesity (diabetes, 9.6% in 2007 and 14.8% in 2017, p = .283, rate difference 5.2%; obesity, 36.8% in 2007, and 40.7% in 2017, p = .105, rate difference 3.9%).\nTrends in the percentage of five conventional risk factors and acute myocardial infarction (AMI) subtype during a first acute myocardial infarction in young men 18–44 years old between 2007 and 2017. (A) Trends in the percentage of conventional risk factors, the increasing prevalence was noted for hypertension (p trend = 0.004). Decline in the rates of current smoking and drinking are appreciated. There was a similar prevalence for diabetes and hypercholesterolemia and obesity over time. (B) Trends in the percentage of AMI subtybe, the Increasing percentage was noted for NSTEMI(p trend <0.001)\nThe most frequent presentation of AMI was STEMI, but the proportion decreased gradually from 90.4% to 63.2% (p trend <0.001), then stabilized at around 65% during the current years (Figure 1B).\nThe mean SBP and BMI increased over the three periods; the mean SBP increased from 119.4 mmHg to 123.3 mmHg (mean difference 3.9 mmHg), with BMI increasing from 26.5 kg/m2 to 27.6 kg/m2 (mean difference 1.2 kg/m2). The mean TC decreased from 4.62 mmol/L to 4.49 mmol/L and LDL‐C decreased from 2.97 mmol/L to 2.80 mmol/L. No differences were seen in mean DBP and FPG over the three periods (Table S1).\n\nCONTROL OF CONVENTIONAL RFS:\nThe control rate of hypertension and diabetes was 62.8% (699/1113) and 17.7% (72/407), respectively; smoking cessation was 4.8% (105/2173).\n\nDISCUSSION:\nIn this study comprising of young men with a first hospitalization for AMI in China, the most prevalent CVD RFs were smoking (75.5%), hypertension (40.6%), and obesity (38.3%). Additionally, over 70% of them had at least two conventional RFs. Compared with a similar reference population, these young Chinese men had a higher prevalence of smoking and dyslipidemia and a lower prevalence of obesity, hypertension, and diabetes. Between 2007 and 2017, the prevalence of hypertension increased, whereas that of smoking decreased. Patients presenting with NSTEMI had a high prevalence of all individual conventional RFs except for smoking, which was more prevalent in STEMI patients. LAD was the most frequently involved coronary artery in the subtypes of AMI, but it was more common in STEMI patients. Multivessel coronary disease was more common in NSTEMI.\nConventional RFs were highly prevalent in young men aged 18–44 years who were hospitalized for first AMI events. The prevalence values were higher compared to national figures smoking (53.9% for young adults aged 25–44 years), hypertension (11.3% for urban adults and 10.0% for rural adults aged 18–44 years), obesity (11.0% for young adults aged 18–44 years), and diabetes (5.9% for young adults aged 18–44 years); high TC ( ≥ 5.2 mmol/L) and high LDL‐C( ≥ 3.4 mmol/L) were 28.5% and 26.3%, respectively.\n14\n, \n15\n, \n16\n, \n17\n Our results are consistent with previous studies conducted in young patients diagnosed with AMI and CHD; the prevalence for hypertension, diabetes, and smoking has been reported as 34.3%–41.7%, 11.1%–22.4%, and 57.4%–74.0%, respectively.\n3\n, \n6\n However, the prevalence figures were different from that of the US report. Compared with the data from the US NIS for patients hospitalized for a first AMI from 2005 to 2015,\n10\n young Chinese men had a higher prevalence of smoking and dyslipidemia, with a lower prevalence of hypertension, obesity, and diabetes. There were clear age differences in the prevalence of some of the RFs; patients aged <35 years had a higher prevalence of smoking, obesity, and hypercholesterolemia, whereas patients aged 35–44 years had a higher prevalence of smoking, hypertension, and diabetes.\nOur study also showed differences in the temporal trends of the conventional RFs between China and US populations. The prevalence of all the evaluated conventional RFs progressively increased between 2005 and 2015 in the United States,\n10\n but hypertension prevalence increased in China, whereas smoking decreased gradually; which was also consistent with a previous study conducted in China. In a retrospective analysis of coronary artery disease patients aged ≤45 years conducted from 2010 to 2014, the prevalence of hypertension increased from 40.7% to 47.5%, 20.3% to 26.1% for diabetes, and 27.3% to 35.7% for hyperlipidemia. However, the prevalence of smoking exhibited a downward trend.\n6\n\n\nApproximately 77% of young Chinese men hospitalized with their first MI presented with STEMI, which was consistent with other studies conducted in young Chinese adults.\n2\n, \n18\n, \n19\n Previous population‐based studies have, however, reported NSTEMI to be the most common presentation\n2\n, \n19\n; the proportion of patients presenting with NSTEMI increased from 11.6% to 36.2% in males and from 15.8% to 45.5% in females from 2007 through 2012,\n2\n which was quite different from the presentation in young adults in the US population.\n10\n, \n20\n, \n21\n Latest studies conducted in the United States have shown that the presentation of AMI was more frequently NSTEMI and increased from 54.6% to 67.9% in males and from 58.8% to 80.2% in females from 2000 through 2014.\n21\n A similar trend has been observed in Germany.\n8\n According to our findings, multivessel disease and cluster of conventional RFs are closely related to NSTEMI, which are consistent with the results of previous studies.\n10\n, \n22\n\n\nIn our study, we focused on young adults at high risk of CVD in routine clinical practice; but most of these young adults are classified as low or intermediate risk based on traditional CVD risk prediction scores that use lifetime risk estimates.\n11\n, \n12\n Using this approach leads to decreased ability to identify RFs in young adults and suboptimal utilization of preventive strategies. Furthermore, the majority of CHD events seem to occur in these “low” and “intermediate” risk groups. The knowledge of the prevalence and trends of conventional RFs among young Chinese men hospitalized with first MI have important healthcare implications with regard to planning appropriate primary preventative strategies. Considerable attention should be paid to young men with at least two out of five conventional RFs (hypercholesterolemia, hypertension, diabetes, obesity, smoking). Healthcare providers should also focus more attention on the control of metabolic factors and encourage smoking cessation.\n\nLIMITATIONS:\nThere are some limitations that deserve consideration. This was a single‐center study and the data were collected from Beijing Anzhen hospital, which is well known for the management of CHD. Furthermore, some patients may have more severe symptoms. Hence, the results may not be extrapolated to the entire nation. Data on conventional RFs were collected from medical records; therefore, health behaviors such as physical activity, sleep duration, emotion, and stress could not be included. The sample for the annual number of AMI hospitalizations was not large enough, which may have influenced the comparisons of conventional RFs and trends in RFs over time with the reference US population.\n\nCONCLUSIONS:\nIn conclusion, the characteristics of conventional RFs among young Chinese men hospitalized with a first MI have important healthcare implications with regard to the planning of appropriate primary preventative strategies. The three leading cardiovascular RFs (smoking, hypertension, and obesity) should be the target of intervention and treatment strategies aimed at reducing the incidence of MI in young adults. Furthermore, preventing hypercholesterolemia in patients aged 34 years or younger. should also be a primary focus. Young adults with two or more conventional RFs should be given considerable attention even if they were classified as being at low or intermediate risk by traditional CHD risk prediction scores.\n\nCONFLICT OF INTERESTS:\nThe authors declare that there are no conflict of interests.\n\n:\n\n\nSupporting information:\nSupplementary information.\nClick here for additional data file.\nSupplementary information.\nClick here for additional data file.\n\n"},"whole_article_abstract":{"kind":"string","value":"Background:\nThere is limited data on the characteristics of conventional risk factors (RFs) in young Chinese men hospitalized with a first acute myocardial infarction (AMI).\n\nMethods:\nA total of 2739 men aged 18-44 years hospitalized for a first AMI were identified from 2007 to 2017. The overall prevalence of RFs and their respective temporal trends and subtypes of AMI were evaluated.\n\nResults:\nThe most prevalent conditions were smoking, followed by hypertension and then obesity. Patients aged <35 years had a much higher prevalence of hypercholesterolemia and obesity. Compared with a similar reference population in the United States, young Chinese men had a higher prevalence of smoking and dyslipidemia, but a lower prevalence of obesity, hypertension, and diabetes. The prevalence of hypertension increased from 2007 through 2017 (p trend <.001), whereas smoking decreased gradually. AMI frequently presented as ST-segment elevation MI (STEMI) (77.5%). Cluster of conventional RFs (3 RFs, odds ratio [OR]: 1.69, 95% confidence interval [CI]: 1.11-2.57; ≥4 RFs, OR: 2.50, 95% CI: 1.55-4.03] and multivessel disease (OR = 1.32, 95% CI: 1.08-1.60) increased the risk of non-STEMI (NSTEMI).\n\nConclusions:\nConventional RFs were highly prevalent in young Chinese men who were hospitalized for first AMI events, and the temporal trends varied different between China and US populations. Multivessel disease and cluster of conventional RFs are closely related to NSTEMI. Optimized preventive strategies among young adults are warranted."},"background_conclusion_text":{"kind":"string","value":"INTRODUCTION:\nThe primary and secondary prevention of coronary heart disease (CHD) in young adults has garnered tremendous attention given the rapid increase in the incidence of acute coronary events and hospitalization rates, especially in young men. Evidence from observational epidemiological studies showed the incidence of acute coronary events increased by 37.4% in the year 2009 compared to 2007 in young adults aged 35–39 years, making it the largest increase for this age group.\n1\n Hospitalization rates for acute myocardial infarction (AMI) per 100 000 population experienced the most significant increase in young men (<55 years), by 45.8% from 2007 to 2012 in Beijing;\n2\n the proportion of young adults hospitalized for CHD was nearly 90% in men from 2013 to 2014 in Beijing.\n3\n This trend parallels an increase in cardiovascular risk factors (RFs) including smoking, hypertension, diabetes, obesity, and dyslipidemia in the general Chinese population as well as an increase in hospitalizations for AMI.\n2\n, \n4\n, \n5\n, \n6\n\n\nThe Prospective Urban Rural Epidemiology (PURE) study showed that approximately 70% of cardiovascular disease (CVD) cases were attributed to modifiable RFs.\n7\n Though several studies have evaluated the prevalence of these RFs during a first or any episode of AMI and have found a high prevalence of at least 1 RF (approximately 90%),\n8\n, \n9\n, \n10\n most patients were classified as being at low or intermediate risk by traditional CHD risk prediction scores.\n11\n, \n12\n Unfortunately, this does not aid in the development of appropriate primary preventive strategies to decrease the risk of CHD. The prevalence of conventional RFs, other clinical characteristics, and their trends need to be clarified, which can be used in formulating preventive strategies.\nFew studies have evaluated recent long‐term trends and prevalence of modifiable RFs during a first AMI in young adults in China. In a retrospective analysis of patients with coronary artery disease aged ≤45 years conducted from 2010 to 2014,\n6\n the prevalence figures varied for the United States and German populations.\n8\n, \n10\n Contemporary data about trends in and prevalence of modifiable RFs, and subtypes of AMI are lacking in young patients. Using a retrospective analysis of hospital data from 2007 to 2017, we aimed to evaluate the trends in and prevalence of modifiable RFs and subtypes of MI during the first AMI in young Chinese men. This evidence will provide a reference point for the development of preventive strategies in this population.\n\nCONCLUSIONS:\nIn conclusion, the characteristics of conventional RFs among young Chinese men hospitalized with a first MI have important healthcare implications with regard to the planning of appropriate primary preventative strategies. The three leading cardiovascular RFs (smoking, hypertension, and obesity) should be the target of intervention and treatment strategies aimed at reducing the incidence of MI in young adults. Furthermore, preventing hypercholesterolemia in patients aged 34 years or younger. should also be a primary focus. Young adults with two or more conventional RFs should be given considerable attention even if they were classified as being at low or intermediate risk by traditional CHD risk prediction scores."},"background_conclusion_abstract":{"kind":"string","value":"Background:\nThere is limited data on the characteristics of conventional risk factors (RFs) in young Chinese men hospitalized with a first acute myocardial infarction (AMI).\n\nMethods:\nA total of 2739 men aged 18-44 years hospitalized for a first AMI were identified from 2007 to 2017. The overall prevalence of RFs and their respective temporal trends and subtypes of AMI were evaluated.\n\nResults:\nThe most prevalent conditions were smoking, followed by hypertension and then obesity. Patients aged <35 years had a much higher prevalence of hypercholesterolemia and obesity. Compared with a similar reference population in the United States, young Chinese men had a higher prevalence of smoking and dyslipidemia, but a lower prevalence of obesity, hypertension, and diabetes. The prevalence of hypertension increased from 2007 through 2017 (p trend <.001), whereas smoking decreased gradually. AMI frequently presented as ST-segment elevation MI (STEMI) (77.5%). Cluster of conventional RFs (3 RFs, odds ratio [OR]: 1.69, 95% confidence interval [CI]: 1.11-2.57; ≥4 RFs, OR: 2.50, 95% CI: 1.55-4.03] and multivessel disease (OR = 1.32, 95% CI: 1.08-1.60) increased the risk of non-STEMI (NSTEMI).\n\nConclusions:\nConventional RFs were highly prevalent in young Chinese men who were hospitalized for first AMI events, and the temporal trends varied different between China and US populations. Multivessel disease and cluster of conventional RFs are closely related to NSTEMI. Optimized preventive strategies among young adults are warranted."},"whole_article_text_length":{"kind":"number","value":7764,"string":"7,764"},"whole_article_abstract_length":{"kind":"number","value":309,"string":"309"},"other_sections_lengths":{"kind":"list like","value":[489,259,305,130,264,184,319,487,31,122],"string":"[\n 489,\n 259,\n 305,\n 130,\n 264,\n 184,\n 319,\n 487,\n 31,\n 122\n]"},"num_sections":{"kind":"number","value":17,"string":"17"},"most_frequent_words":{"kind":"list like","value":["rfs","prevalence","ami","patients","conventional","coronary","hypertension","young","smoking","population"],"string":"[\n \"rfs\",\n \"prevalence\",\n \"ami\",\n \"patients\",\n \"conventional\",\n \"coronary\",\n \"hypertension\",\n \"young\",\n \"smoking\",\n \"population\"\n]"},"keybert_topics":{"kind":"list like","value":["coronary events increased","incidence acute coronary","myocardial infarction prevalence","prevention coronary heart","cardiovascular risk factors"],"string":"[\n \"coronary events increased\",\n \"incidence acute coronary\",\n \"myocardial infarction prevalence\",\n \"prevention coronary heart\",\n \"cardiovascular risk factors\"\n]"},"annotated_base_background_abstract_prompt":{"kind":"string","value":""},"annotated_base_methods_abstract_prompt":{"kind":"string","value":""},"annotated_base_results_abstract_prompt":{"kind":"string","value":""},"annotated_base_conclusions_abstract_prompt":{"kind":"string","value":""},"annotated_base_whole_article_abstract_prompt":{"kind":"string","value":""},"annotated_base_background_conclusion_abstract_prompt":{"kind":"string","value":""},"annotated_keywords_background_abstract_prompt":{"kind":"string","value":"[CONTENT] acute myocardial infarction | risk factor | trends | youth [SUMMARY]"},"annotated_keywords_methods_abstract_prompt":{"kind":"string","value":"[CONTENT] acute myocardial infarction | risk factor | trends | youth [SUMMARY]"},"annotated_keywords_results_abstract_prompt":{"kind":"string","value":"[CONTENT] acute myocardial infarction | risk factor | trends | youth [SUMMARY]"},"annotated_keywords_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] acute myocardial infarction | risk factor | trends | youth [SUMMARY]"},"annotated_keywords_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] acute myocardial infarction | risk factor | trends | youth [SUMMARY]"},"annotated_keywords_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] acute myocardial infarction | risk factor | trends | youth [SUMMARY]"},"annotated_mesh_background_abstract_prompt":{"kind":"string","value":"[CONTENT] Cardiovascular Diseases | China | Heart Disease Risk Factors | Humans | Male | Myocardial Infarction | Non-ST Elevated Myocardial Infarction | Risk Factors | ST Elevation Myocardial Infarction | United States | Young Adult 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increased | incidence acute coronary | myocardial infarction prevalence | prevention coronary heart | cardiovascular risk factors [SUMMARY]"},"annotated_keybert_results_abstract_prompt":{"kind":"string","value":"[CONTENT] coronary events increased | incidence acute coronary | myocardial infarction prevalence | prevention coronary heart | cardiovascular risk factors [SUMMARY]"},"annotated_keybert_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] coronary events increased | incidence acute coronary | myocardial infarction prevalence | prevention coronary heart | cardiovascular risk factors [SUMMARY]"},"annotated_keybert_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] coronary events increased | incidence acute coronary | myocardial infarction prevalence | prevention coronary heart | cardiovascular risk factors [SUMMARY]"},"annotated_keybert_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] coronary events increased | incidence acute coronary | myocardial infarction prevalence | prevention coronary heart | cardiovascular risk factors [SUMMARY]"},"annotated_most_frequent_background_abstract_prompt":{"kind":"string","value":"[CONTENT] rfs | prevalence | ami | patients | conventional | coronary | hypertension | young | smoking | population [SUMMARY]"},"annotated_most_frequent_methods_abstract_prompt":{"kind":"string","value":"[CONTENT] rfs | prevalence | ami | patients | conventional | coronary | hypertension | young | smoking | population [SUMMARY]"},"annotated_most_frequent_results_abstract_prompt":{"kind":"string","value":"[CONTENT] rfs | prevalence | ami | patients | conventional | coronary | hypertension | young | smoking | population [SUMMARY]"},"annotated_most_frequent_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] rfs | prevalence | ami | patients | conventional | coronary | hypertension | young | smoking | population [SUMMARY]"},"annotated_most_frequent_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] rfs | prevalence | ami | patients | conventional | coronary | hypertension | young | smoking | population [SUMMARY]"},"annotated_most_frequent_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] rfs | prevalence | ami | patients | conventional | coronary | hypertension | young | smoking | population [SUMMARY]"},"annotated_tf_idf_background_abstract_prompt":{"kind":"string","value":"[CONTENT] modifiable rfs | young | increase | prevalence | modifiable | trends prevalence modifiable | prevalence modifiable | trends prevalence modifiable rfs | prevalence modifiable rfs | rfs [SUMMARY]"},"annotated_tf_idf_methods_abstract_prompt":{"kind":"string","value":"[CONTENT] defined | new | variables | ami | heart | continuous | continuous variables | identified | prior | coronary [SUMMARY]"},"annotated_tf_idf_results_abstract_prompt":{"kind":"string","value":"[CONTENT] difference | patients | prevalence | rate difference | coronary | rfs | rate | conventional | mean | decreased [SUMMARY]"},"annotated_tf_idf_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] strategies | young | primary | young adults | adults | rfs | mi | risk | attention classified | reducing [SUMMARY]"},"annotated_tf_idf_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] rfs | prevalence | patients | ami | coronary | young | conventional | hypertension | difference | rate [SUMMARY]"},"annotated_tf_idf_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] rfs | prevalence | patients | ami | coronary | young | conventional | hypertension | difference | rate [SUMMARY]"},"annotated_entity_plan_background_abstract_prompt":{"kind":"string","value":"[CONTENT] Chinese | first | AMI [SUMMARY]"},"annotated_entity_plan_methods_abstract_prompt":{"kind":"string","value":"[CONTENT] 2739 | 18-44 years | first | AMI | 2007 to 2017 ||| AMI [SUMMARY]"},"annotated_entity_plan_results_abstract_prompt":{"kind":"string","value":"[CONTENT] ||| 35 years ||| the United States | Chinese ||| 2007 | 2017 ||| AMI | 77.5% ||| 3 ||| 1.69 | 95% ||| CI | 1.11 | ≥4 | 2.50 | 95% | CI | 1.55-4.03 | 1.32 | 95% | CI | 1.08-1.60 | NSTEMI [SUMMARY]"},"annotated_entity_plan_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] Chinese | first | AMI | China | US ||| Multivessel | NSTEMI ||| [SUMMARY]"},"annotated_entity_plan_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] Chinese | first | AMI ||| 2739 | 18-44 years | first | AMI | 2007 to 2017 ||| AMI ||| ||| ||| 35 years ||| the United States | Chinese ||| 2007 | 2017 ||| AMI | 77.5% ||| 3 ||| 1.69 | 95% ||| CI | 1.11 | ≥4 | 2.50 | 95% | CI | 1.55-4.03 | 1.32 | 95% | CI | 1.08-1.60 | NSTEMI ||| Chinese | first | AMI | China | US ||| Multivessel | NSTEMI ||| [SUMMARY]"},"annotated_entity_plan_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] Chinese | first | AMI ||| 2739 | 18-44 years | first | AMI | 2007 to 2017 ||| AMI ||| ||| ||| 35 years ||| the United States | Chinese ||| 2007 | 2017 ||| AMI | 77.5% ||| 3 ||| 1.69 | 95% ||| CI | 1.11 | ≥4 | 2.50 | 95% | CI | 1.55-4.03 | 1.32 | 95% | CI | 1.08-1.60 | NSTEMI ||| Chinese | first | AMI | China | US ||| Multivessel | NSTEMI ||| [SUMMARY]"}}},{"rowIdx":295,"cells":{"title":{"kind":"string","value":"Economic and health impacts of introducing Helicobacter pylori eradication strategy into national gastric cancer policy in Japan: A cost-effectiveness analysis."},"pmid":{"kind":"string","value":"34278663"},"background_abstract":{"kind":"string","value":"Helicobacter pylori (H. pylori) eradication reduces gastric cancer risk. Since 2013, a population-wide H. pylori eradication strategy for patients with chronic gastritis has begun to prevent gastric cancer in Japan. The aim of this study was to evaluate the economic and health effects of H. pylori eradication strategy in national gastric cancer prevention program."},"background_abstract_label":{"kind":"string","value":"BACKGROUND"},"methods_abstract":{"kind":"string","value":"We developed a cohort state-transition model for H. pylori eradication and no eradication over a lifetime horizon from a healthcare payer perspective, and performed one-way and probabilistic sensitivity analyses. We targeted a hypothetical cohort of H. pylori-positive patients aged 20, 30, 40, 50, 60, 70, and 80. The main outcomes were costs, quality-adjusted life-years (QALYs), life expectancy life-years (LYs), incremental cost-effectiveness ratios, gastric cancer cases, and deaths from gastric cancer."},"methods_abstract_label":{"kind":"string","value":"MATERIALS AND METHODS"},"results_abstract":{"kind":"string","value":"H. pylori eradication was more effective and cost-saving for all age groups than no eradication. Sensitivity analyses showed strong robustness of the results. From 2013-2019 for 8.50 million patients, H. pylori eradication saved US$3.75 billion, increased 11.11 million QALYs and 0.45 million LYs, and prevented 284,188 cases and 65,060 deaths. For 35.59 million patients without eradication, H. pylori eradication has the potential to save US$14.82 billion, increase 43.10 million QALYs and 1.66 million LYs, and prevent 1,084,532 cases and 250,256 deaths."},"results_abstract_label":{"kind":"string","value":"RESULTS"},"conclusions_abstract":{"kind":"string","value":"National policy using population-wide H. pylori eradication to prevent gastric cancer has significant cost savings and health impacts for young-, middle-, and old-aged individuals in Japan. The findings strongly support the promotion of H. pylori eradication strategy for all age groups in high-incidence countries."},"conclusions_abstract_label":{"kind":"string","value":"CONCLUSIONS"},"mesh_descriptor_names":{"kind":"list like","value":["Aged","Cost-Benefit Analysis","Helicobacter Infections","Helicobacter pylori","Humans","Japan","Middle Aged","Policy","Stomach Neoplasms"],"string":"[\n \"Aged\",\n \"Cost-Benefit Analysis\",\n \"Helicobacter Infections\",\n \"Helicobacter pylori\",\n \"Humans\",\n \"Japan\",\n \"Middle Aged\",\n \"Policy\",\n \"Stomach Neoplasms\"\n]"},"pmcid":{"kind":"string","value":"9286640"},"background_title":{"kind":"string","value":"INTRODUCTION"},"background_text":{"kind":"string","value":"More than half of the world's population is infected with Helicobacter pylori (H. pylori).\n1\n\nH. pylori infection causes chronic atrophic gastritis, a common stage of progression to gastric cancer, and is responsible for 98% of the causes of gastric cancer in Japan.\n2\n, \n3\n, \n4\n, \n5\n Japan has the third highest age‐standardized rate for gastric cancer in the world.\n6\n The incidence of gastric cancer in Japan is almost 10 times higher than that observed in the United States. The Taipei global consensus guidelines for screening and eradication of H. pylori for gastric cancer prevention recommend that mass screening and eradication of H. pylori should be considered in populations at higher risk of gastric cancer and that eradication therapy should be offered to all individuals infected with H. pylori.\n7\n In the guidelines for the management of H. pylori infection by the Japanese Society for Helicobacter Research, H. pylori eradication treatment is recommended to prevent gastric cancer for patients with H. pylori infection.\n8\n The Ministry of Health, Labour and Welfare approved expansion of National Health Insurance coverage for H. pylori eradication treatment in patients with chronic gastritis from February 2013.\n9\n During 2013‐2019, 8.50 million H. pylori‐positive patients received eradication treatment.\n10\n, \n11\n The number of deaths from gastric cancer is gradually declining, with 42,931 deaths in 2019 and 42,318 deaths in 2020 (Figure 1).\n12\n, \n13\n\n\nChanges in gastric cancer deaths in Japan from 2000 to 2020\nIn this study, we aimed to evaluate the economic and health effects of H. pylori eradication strategy in national gastric cancer prevention program in Japan."},"methods_title":{"kind":"null"},"methods_text":{"kind":"null"},"results_title":{"kind":"string","value":"RESULTS"},"results_text":{"kind":"string","value":"Base‐case analysis \nH. pylori eradication strategy was less costly and yielded greater benefits than no eradication strategy for all age groups (Table 2). No eradication strategy was dominated for all age groups. The patients aged 40 had the highest per capita cost‐savings. Per capita gains of QALYs in younger patients were higher than in older patients (Table 2). From 2013 to 2019, the patients aged 60 had the highest cost savings and health outcomes (Table S1).\n\nH. pylori eradication strategy was less costly and yielded greater benefits than no eradication strategy for all age groups (Table 2). No eradication strategy was dominated for all age groups. The patients aged 40 had the highest per capita cost‐savings. Per capita gains of QALYs in younger patients were higher than in older patients (Table 2). From 2013 to 2019, the patients aged 60 had the highest cost savings and health outcomes (Table S1).\nOne‐way sensitivity analysis and probabilistic sensitivity analysis Incremental cost‐effectiveness ratio tornado diagram of H. pylori eradication strategy versus no eradication strategy showed that cost‐effectiveness was not sensitive to any variables in all age groups (Figure 3A, Figure S2).\nIn probabilistic sensitivity analysis using Monte‐Carlo simulation for 10,000 trials, the acceptability curve showed that H. pylori eradication strategy was cost‐effective 100% of the time at two willingness‐to‐pay thresholds of US$50,000 per QALY gained and US$100,000 per QALY gained in all age groups (Figure 3B, Figure S3). Incremental cost‐effectiveness scatterplots showed that H. pylori eradication strategy dominated no‐eradication strategy in more than 9800 trials in all age groups (Figure 3C, Figure S4). The results showed strong robustness of H. pylori eradication strategy in all age groups.\nResults of the base‐case analysis\nICER\n(US$/\nQALY gained)\nLife expectancy\nlife‐years (LYs)\nICER\n(US$/LY\ngained)\n\nAbbrevations H. pylori = Helicobacter pylori; QALY = quality‐adjusted life‐year; LY = life expectancy life‐years; ICER = incremental cost‐effectiveness ratio; dominated = less effective and more costly than others;\n\nOne‐way sensitivity analysis and probabilistic sensitivity analysis in 60‐year‐old H. pylori‐positive patients. A, The incremental cost‐effectiveness ratio (ICER) tornado diagram for H. pylori eradication strategy versus no eradication strategy. The cost‐effectiveness of H. pylori eradication strategy was not sensitive to changes in any variables. B, Cost‐effectiveness acceptability curve for H. pylori eradication strategy versus no eradication strategy. The probabilistic sensitivity analysis analyzed 10,000 simulations of the model in which input parameters were randomly varied across pre‐specified statistical distributions. The x‐axis represents the willingness‐to‐pay threshold. The acceptability curve showed that H. pylori eradication strategy was cost‐effective 100% of the time at two willingness‐to‐pay thresholds of US$50,000 per QALY gained and US$100,000 per QALY gained. C, Incremental cost‐effectiveness scatterplots with 95% confidence ellipses for H. pylori eradication strategy versus no eradication strategy. Each dot represents a single simulation for a total of 10,000 simulations. Incremental cost‐effectiveness scatterplots showed that H. pylori eradication strategy dominated no‐eradication strategy in 9811 trials, and that H. pylori eradication strategy was more cost‐effective than no‐eradication strategy in 189 trials. EV = expected value; H. pylori = Helicobacter pylori; ICER = incremental cost‐effectivenessratio; QALY = quality‐adjusted life‐year; WTP = willingness‐to‐pay threshold\nIncremental cost‐effectiveness ratio tornado diagram of H. pylori eradication strategy versus no eradication strategy showed that cost‐effectiveness was not sensitive to any variables in all age groups (Figure 3A, Figure S2).\nIn probabilistic sensitivity analysis using Monte‐Carlo simulation for 10,000 trials, the acceptability curve showed that H. pylori eradication strategy was cost‐effective 100% of the time at two willingness‐to‐pay thresholds of US$50,000 per QALY gained and US$100,000 per QALY gained in all age groups (Figure 3B, Figure S3). Incremental cost‐effectiveness scatterplots showed that H. pylori eradication strategy dominated no‐eradication strategy in more than 9800 trials in all age groups (Figure 3C, Figure S4). The results showed strong robustness of H. pylori eradication strategy in all age groups.\nResults of the base‐case analysis\nICER\n(US$/\nQALY gained)\nLife expectancy\nlife‐years (LYs)\nICER\n(US$/LY\ngained)\n\nAbbrevations H. pylori = Helicobacter pylori; QALY = quality‐adjusted life‐year; LY = life expectancy life‐years; ICER = incremental cost‐effectiveness ratio; dominated = less effective and more costly than others;\n\nOne‐way sensitivity analysis and probabilistic sensitivity analysis in 60‐year‐old H. pylori‐positive patients. A, The incremental cost‐effectiveness ratio (ICER) tornado diagram for H. pylori eradication strategy versus no eradication strategy. The cost‐effectiveness of H. pylori eradication strategy was not sensitive to changes in any variables. B, Cost‐effectiveness acceptability curve for H. pylori eradication strategy versus no eradication strategy. The probabilistic sensitivity analysis analyzed 10,000 simulations of the model in which input parameters were randomly varied across pre‐specified statistical distributions. The x‐axis represents the willingness‐to‐pay threshold. The acceptability curve showed that H. pylori eradication strategy was cost‐effective 100% of the time at two willingness‐to‐pay thresholds of US$50,000 per QALY gained and US$100,000 per QALY gained. C, Incremental cost‐effectiveness scatterplots with 95% confidence ellipses for H. pylori eradication strategy versus no eradication strategy. Each dot represents a single simulation for a total of 10,000 simulations. Incremental cost‐effectiveness scatterplots showed that H. pylori eradication strategy dominated no‐eradication strategy in 9811 trials, and that H. pylori eradication strategy was more cost‐effective than no‐eradication strategy in 189 trials. EV = expected value; H. pylori = Helicobacter pylori; ICER = incremental cost‐effectivenessratio; QALY = quality‐adjusted life‐year; WTP = willingness‐to‐pay threshold\nCumulative lifetime economic and health outcomes \nH. pylori‐positive patients aged 60 had the highest cumulative lifetime economic and health outcomes (Table S1). From 2013 to 2019 for 8.50 million patients, H. pylori eradication saved US$3.75 billion, increased 11.11 million QALYs and 0.45 million LYs, and prevented 284,188 cases and 65,060 deaths. For 35.59 million patients without eradication, H. pylori eradication has the potential to save US$14.82 billion, increase 43.10 million QALYs and 1.66 million LYs, and prevent 1,084,532 cases and 250,256 deaths (Table S1).\nIn the Markov cohort analysis, the cumulative lifetime potential of gastric cancer cases and deaths from gastric cancer in H. pylori eradication strategy compared with no eradication strategy decreased by 30 to 33% in patients under 50 and by 25 to 28% in patients aged 50 and over (Figure S5). H. pylori eradication reduced the incidence and mortality of gastric cancer in the younger age groups greater than in the older age groups (Table 2, Figure S5).\n\nH. pylori‐positive patients aged 60 had the highest cumulative lifetime economic and health outcomes (Table S1). From 2013 to 2019 for 8.50 million patients, H. pylori eradication saved US$3.75 billion, increased 11.11 million QALYs and 0.45 million LYs, and prevented 284,188 cases and 65,060 deaths. For 35.59 million patients without eradication, H. pylori eradication has the potential to save US$14.82 billion, increase 43.10 million QALYs and 1.66 million LYs, and prevent 1,084,532 cases and 250,256 deaths (Table S1).\nIn the Markov cohort analysis, the cumulative lifetime potential of gastric cancer cases and deaths from gastric cancer in H. pylori eradication strategy compared with no eradication strategy decreased by 30 to 33% in patients under 50 and by 25 to 28% in patients aged 50 and over (Figure S5). H. pylori eradication reduced the incidence and mortality of gastric cancer in the younger age groups greater than in the older age groups (Table 2, Figure S5)."},"conclusions_title":{"kind":"null"},"conclusions_text":{"kind":"null"},"other_sections_titles":{"kind":"list like","value":["INTRODUCTION","Study design and model structure","H. pylori eradication strategy","No eradication strategy","Target population","Epidemiologic parameters and clinical probabilities","Costs","Health utilities, effectiveness, and health outcomes","Sensitivity analyses","Base‐case analysis","One‐way sensitivity analysis and probabilistic sensitivity analysis","Cumulative lifetime economic and health outcomes","AUTHOR CONTRIBUTIONS"],"string":"[\n \"INTRODUCTION\",\n \"Study design and model structure\",\n \"H. pylori eradication strategy\",\n \"No eradication strategy\",\n \"Target population\",\n \"Epidemiologic parameters and clinical probabilities\",\n \"Costs\",\n \"Health utilities, effectiveness, and health outcomes\",\n \"Sensitivity analyses\",\n \"Base‐case analysis\",\n \"One‐way sensitivity analysis and probabilistic sensitivity analysis\",\n \"Cumulative lifetime economic and health outcomes\",\n \"AUTHOR CONTRIBUTIONS\"\n]"},"other_sections_texts":{"kind":"list like","value":["More than half of the world's population is infected with Helicobacter pylori (H. pylori).\n1\n\nH. pylori infection causes chronic atrophic gastritis, a common stage of progression to gastric cancer, and is responsible for 98% of the causes of gastric cancer in Japan.\n2\n, \n3\n, \n4\n, \n5\n Japan has the third highest age‐standardized rate for gastric cancer in the world.\n6\n The incidence of gastric cancer in Japan is almost 10 times higher than that observed in the United States. The Taipei global consensus guidelines for screening and eradication of H. pylori for gastric cancer prevention recommend that mass screening and eradication of H. pylori should be considered in populations at higher risk of gastric cancer and that eradication therapy should be offered to all individuals infected with H. pylori.\n7\n In the guidelines for the management of H. pylori infection by the Japanese Society for Helicobacter Research, H. pylori eradication treatment is recommended to prevent gastric cancer for patients with H. pylori infection.\n8\n The Ministry of Health, Labour and Welfare approved expansion of National Health Insurance coverage for H. pylori eradication treatment in patients with chronic gastritis from February 2013.\n9\n During 2013‐2019, 8.50 million H. pylori‐positive patients received eradication treatment.\n10\n, \n11\n The number of deaths from gastric cancer is gradually declining, with 42,931 deaths in 2019 and 42,318 deaths in 2020 (Figure 1).\n12\n, \n13\n\n\nChanges in gastric cancer deaths in Japan from 2000 to 2020\nIn this study, we aimed to evaluate the economic and health effects of H. pylori eradication strategy in national gastric cancer prevention program in Japan.","We constructed a cohort state‐transition model with a Markov cycle tree for two strategies: H. pylori eradication strategy and no eradication strategy, using a healthcare payer perspective and a lifetime horizon. A cycle length of one year was chosen. The half‐cycle correction was applied. In the model, decision branches leaded directly to one Markov node per intervention strategy and the first events were modeled within the Markov cycle tree (Figure 2). We used TreeAge Pro (TreeAge Software Inc., Williamstown, Massachusetts) for the Decision‐analytical calculations. As this was a modeling study with all inputs and parameters derived from the published literature and Japanese statistics, ethics approval was not required.\nSchematic depiction of the Markov cycle tree in the cohort state‐transition model. We show that health states in the model as ovals. In a yearly model cycle, transitions can occur between the health states and other health states, represented by the arrows. H. pylori = Helicobacter pylori; GC = gastric cancer\nH. pylori eradication strategy The H. pylori‐positive patient receives first‐line eradication treatment (proton‐pump inhibitor, clarithromycin, and amoxicillin). The patient who fails first‐line eradication treatment receives second‐line eradication treatment (proton‐pump inhibitor, metronidazole, and amoxicillin). We consider the eradication and compliance rates of first‐line and second‐line eradication treatments in the model. After successful H. pylori eradication, H. pylori‐positive changes to H. pylori‐negative. When the patient fails both treatments, H. pylori‐positive remains until death. We calculate the costs of H. pylori test, endoscopy, and two urea breath tests when the patient receives H. pylori eradication treatment. When the patient has gastric cancer, the patient receives the standard treatment of gastric cancer followed by the Japanese gastric cancer treatment guidelines: endoscopic mucosal resection (EMR), endoscopic submucosal dissection treatment (ESD), surgery, chemotherapy, and radiotherapy with palliative care according to cancer stages, stage I‐IV.\n14\n The model includes the relative risk of developing gastric cancer after successful eradication, stage‐specific 5‐year survival rates, and mortality due to other causes (Table 1).\n12\n, \n15\n The patient aged 50 and over receives endoscopic screening every year from the year after eradication.\nBaseline estimates for selected variables\n20y\n30y\n40y\n50y\n60y\n70y\n80y\n0.000771\n0.001167\n0.001881\n0.002803\n0.005122\n0.007949\n0.009117\n20y\n30y\n40y\n50y\n60y\n70y\n80y\n0.000509\n0.00077\n0.00124\n0.00185\n0.00338\n0.00524\n0.00602\n20y\n30y\n40y\n50y\n60y\n70y\n80y\n6.1\n14.7\n23.7\n33.7\n47.7\n58.6\n63.6\nStage I\nStage II\nStage III\nStage IV\n96.0\n69.2\n41.9\n6.3\n90‐99\n50‐80\n30‐50\n0‐20\n20‐29y\n30‐39y\n40‐49y\n50‐59y\n60‐69y\n70‐79y\n80‐89y\n123,986\n422,965\n1,083,631\n1,664,732\n2,860,031\n1,903,756\n440,503\n20‐29y\n30‐39y\n40‐49y\n50‐59y\n60‐69y\n70‐79y\n80‐89y\n773,480\n2,034,480\n4,256,520\n5,634,640\n7,331,490\n9,622,120\n5,933,880\nStage I\nStage II\nStage III\nStage IV\n62.5\n11.0\n7.5\n19.0\n30‐80\n5‐20\n2‐15\n10‐50\nStage I\nStage II\nStage III\nStage IV\n3675\n15,898\n24,841\n29,809\n1838‐7350\n7949‐31,796\n12,421‐49,682\n14,905‐59,618\n25,26\n\nAbbrevations H. pylori = Helicobacter pylori; N/A = not applicable\n\nThe H. pylori‐positive patient receives first‐line eradication treatment (proton‐pump inhibitor, clarithromycin, and amoxicillin). The patient who fails first‐line eradication treatment receives second‐line eradication treatment (proton‐pump inhibitor, metronidazole, and amoxicillin). We consider the eradication and compliance rates of first‐line and second‐line eradication treatments in the model. After successful H. pylori eradication, H. pylori‐positive changes to H. pylori‐negative. When the patient fails both treatments, H. pylori‐positive remains until death. We calculate the costs of H. pylori test, endoscopy, and two urea breath tests when the patient receives H. pylori eradication treatment. When the patient has gastric cancer, the patient receives the standard treatment of gastric cancer followed by the Japanese gastric cancer treatment guidelines: endoscopic mucosal resection (EMR), endoscopic submucosal dissection treatment (ESD), surgery, chemotherapy, and radiotherapy with palliative care according to cancer stages, stage I‐IV.\n14\n The model includes the relative risk of developing gastric cancer after successful eradication, stage‐specific 5‐year survival rates, and mortality due to other causes (Table 1).\n12\n, \n15\n The patient aged 50 and over receives endoscopic screening every year from the year after eradication.\nBaseline estimates for selected variables\n20y\n30y\n40y\n50y\n60y\n70y\n80y\n0.000771\n0.001167\n0.001881\n0.002803\n0.005122\n0.007949\n0.009117\n20y\n30y\n40y\n50y\n60y\n70y\n80y\n0.000509\n0.00077\n0.00124\n0.00185\n0.00338\n0.00524\n0.00602\n20y\n30y\n40y\n50y\n60y\n70y\n80y\n6.1\n14.7\n23.7\n33.7\n47.7\n58.6\n63.6\nStage I\nStage II\nStage III\nStage IV\n96.0\n69.2\n41.9\n6.3\n90‐99\n50‐80\n30‐50\n0‐20\n20‐29y\n30‐39y\n40‐49y\n50‐59y\n60‐69y\n70‐79y\n80‐89y\n123,986\n422,965\n1,083,631\n1,664,732\n2,860,031\n1,903,756\n440,503\n20‐29y\n30‐39y\n40‐49y\n50‐59y\n60‐69y\n70‐79y\n80‐89y\n773,480\n2,034,480\n4,256,520\n5,634,640\n7,331,490\n9,622,120\n5,933,880\nStage I\nStage II\nStage III\nStage IV\n62.5\n11.0\n7.5\n19.0\n30‐80\n5‐20\n2‐15\n10‐50\nStage I\nStage II\nStage III\nStage IV\n3675\n15,898\n24,841\n29,809\n1838‐7350\n7949‐31,796\n12,421‐49,682\n14,905‐59,618\n25,26\n\nAbbrevations H. pylori = Helicobacter pylori; N/A = not applicable\n\nNo eradication strategy The latest version of Japanese guidelines for effective secondary prevention of gastric cancer recommends upper gastrointestinal series and endoscopy in adults 50 years of age and older. In the model, the H. pylori‐positive patient does not receive H. pylori eradication treatment, and the patient aged 50 and over receives annual endoscopic screening annually. When the patient has gastric cancer, the patient receives the standard treatment of gastric cancer.\nThe latest version of Japanese guidelines for effective secondary prevention of gastric cancer recommends upper gastrointestinal series and endoscopy in adults 50 years of age and older. In the model, the H. pylori‐positive patient does not receive H. pylori eradication treatment, and the patient aged 50 and over receives annual endoscopic screening annually. When the patient has gastric cancer, the patient receives the standard treatment of gastric cancer.","The H. pylori‐positive patient receives first‐line eradication treatment (proton‐pump inhibitor, clarithromycin, and amoxicillin). The patient who fails first‐line eradication treatment receives second‐line eradication treatment (proton‐pump inhibitor, metronidazole, and amoxicillin). We consider the eradication and compliance rates of first‐line and second‐line eradication treatments in the model. After successful H. pylori eradication, H. pylori‐positive changes to H. pylori‐negative. When the patient fails both treatments, H. pylori‐positive remains until death. We calculate the costs of H. pylori test, endoscopy, and two urea breath tests when the patient receives H. pylori eradication treatment. When the patient has gastric cancer, the patient receives the standard treatment of gastric cancer followed by the Japanese gastric cancer treatment guidelines: endoscopic mucosal resection (EMR), endoscopic submucosal dissection treatment (ESD), surgery, chemotherapy, and radiotherapy with palliative care according to cancer stages, stage I‐IV.\n14\n The model includes the relative risk of developing gastric cancer after successful eradication, stage‐specific 5‐year survival rates, and mortality due to other causes (Table 1).\n12\n, \n15\n The patient aged 50 and over receives endoscopic screening every year from the year after eradication.\nBaseline estimates for selected variables\n20y\n30y\n40y\n50y\n60y\n70y\n80y\n0.000771\n0.001167\n0.001881\n0.002803\n0.005122\n0.007949\n0.009117\n20y\n30y\n40y\n50y\n60y\n70y\n80y\n0.000509\n0.00077\n0.00124\n0.00185\n0.00338\n0.00524\n0.00602\n20y\n30y\n40y\n50y\n60y\n70y\n80y\n6.1\n14.7\n23.7\n33.7\n47.7\n58.6\n63.6\nStage I\nStage II\nStage III\nStage IV\n96.0\n69.2\n41.9\n6.3\n90‐99\n50‐80\n30‐50\n0‐20\n20‐29y\n30‐39y\n40‐49y\n50‐59y\n60‐69y\n70‐79y\n80‐89y\n123,986\n422,965\n1,083,631\n1,664,732\n2,860,031\n1,903,756\n440,503\n20‐29y\n30‐39y\n40‐49y\n50‐59y\n60‐69y\n70‐79y\n80‐89y\n773,480\n2,034,480\n4,256,520\n5,634,640\n7,331,490\n9,622,120\n5,933,880\nStage I\nStage II\nStage III\nStage IV\n62.5\n11.0\n7.5\n19.0\n30‐80\n5‐20\n2‐15\n10‐50\nStage I\nStage II\nStage III\nStage IV\n3675\n15,898\n24,841\n29,809\n1838‐7350\n7949‐31,796\n12,421‐49,682\n14,905‐59,618\n25,26\n\nAbbrevations H. pylori = Helicobacter pylori; N/A = not applicable\n","The latest version of Japanese guidelines for effective secondary prevention of gastric cancer recommends upper gastrointestinal series and endoscopy in adults 50 years of age and older. In the model, the H. pylori‐positive patient does not receive H. pylori eradication treatment, and the patient aged 50 and over receives annual endoscopic screening annually. When the patient has gastric cancer, the patient receives the standard treatment of gastric cancer.","We targeted a hypothetical cohort of Japanese H. pylori‐positive chronic gastritis patients who had the initial endoscopic diagnosis needing H. pylori eradication at the age of 20, 30, 40, 50, 60, 70, and 80. Children and adolescents (age <20 y) were not included in the model.","Epidemiologic parameters and clinical probabilities were collected using MEDLINE from 2000 to June 2, 2021, national census, and Japanese cancer statistics (Table 1).\n2\n, \n3\n, \n10\n, \n11\n, \n12\n, \n15\n, \n16\n, \n17\n, \n18\n, \n19\n, \n20\n We estimated annual age‐specific numbers of H. pylori‐positive patients with eradication treatment from the literature\n10\n, \n11\n and expert opinion (Table1, Figure S1A). The numbers of H. pylori‐positive patients with and without eradication were estimated from the literature\n16\n and national census (Table1, Figure S1B). Relative risk of gastric cancer development after successful eradication\n15\n, and eradication and compliance rates of first‐ and second‐line eradication treatments\n19\n were obtained from the literature. Age‐specific gastric cancer incidence and stage‐specific 5‐year survival rate were obtained from Japanese cancer statistics.\n10\n The responsibility rate of H. pylori infection for gastric cancer development was assumed to be 98%.\n2\n, \n3\n, \n4\n, \n5\n The incidence of gastric cancer in H. pylori‐positive patients was estimated using the responsibility rate of H. pylori infection for gastric cancer development and the prevalence of H. pylori infection.\n2\n, \n3\n, \n4\n, \n5\n, \n12\n, \n16\n The incidence of gastric cancer in H. pylori‐positive patients after successful eradication treatment was estimated using the relative risk of gastric cancer development after successful eradication treatment.\n2\n, \n3\n, \n4\n, \n5\n, \n12\n, \n15\n, \n16\n The sensitivity and specificity of endoscopy were obtained from the literature.\n20\n\n","Costs were calculated based on the costs from the Japanese national fee schedule\n17\n and were adjusted to 2019 Japanese yen, using the medical care component of the Japanese consumer price index and were converted to US dollars, using the Organisation for Economic Co‐operation and Development (OECD) purchasing power parity rate in 2019 (US$1 = ¥100.64) (Table 1).\n21\n All costs were discounted by 3%.\n22\n, \n23\n Incremental cost‐effectiveness ratios (ICERs) were calculated and compared to two willingness‐to‐pay levels of US$50,000 per quality‐adjusted life‐year (QALY) gained and US$100,000 per QALY gained.\n24\n Age‐specific and total cumulative lifetime cost savings of H. pylori eradication strategy compared with no eradication strategy were calculated.","Health status was included to represent possible eight clinical states: (i) No H. pylori infection, (ii) H. pylori infection, (iii) gastric cancer on stage I; (iv) gastric cancer on stage II; (v) gastric cancer on stage III; (vi) gastric cancer on stage IV; (vii) cured gastric cancer; and (viii) death (Figure 2). Health state utilities were obtained from the literature and were calculated using utility weights (Table 1).\n25\n, \n26\n The annual discounting of the utilities in this analysis was set at a rate of 3%.\n22\n, \n23\n\n\nThe health outcomes were QALYs, life expectancy life‐years (LYs), gastric cancer cases, and deaths from gastric cancer. Age‐specific and total cumulative lifetime health outcomes of H. pylori eradication strategy compared with no eradication strategy were calculated and evaluated.","We performed a one‐way sensitivity analysis to determine which strategy was more cost‐effective when we tested a single variable over a wide range of possible values while holding all other variables constant, and performed a probabilistic sensitivity analysis using a second‐order Monte‐Carlo simulation for 10,000 trials to assess the impact of the uncertainty in the model on the base‐case estimates. The uncertainty had a beta distribution in clinical probabilities and accuracies, and a log‐normal distribution in costs.","\nH. pylori eradication strategy was less costly and yielded greater benefits than no eradication strategy for all age groups (Table 2). No eradication strategy was dominated for all age groups. The patients aged 40 had the highest per capita cost‐savings. Per capita gains of QALYs in younger patients were higher than in older patients (Table 2). From 2013 to 2019, the patients aged 60 had the highest cost savings and health outcomes (Table S1).","Incremental cost‐effectiveness ratio tornado diagram of H. pylori eradication strategy versus no eradication strategy showed that cost‐effectiveness was not sensitive to any variables in all age groups (Figure 3A, Figure S2).\nIn probabilistic sensitivity analysis using Monte‐Carlo simulation for 10,000 trials, the acceptability curve showed that H. pylori eradication strategy was cost‐effective 100% of the time at two willingness‐to‐pay thresholds of US$50,000 per QALY gained and US$100,000 per QALY gained in all age groups (Figure 3B, Figure S3). Incremental cost‐effectiveness scatterplots showed that H. pylori eradication strategy dominated no‐eradication strategy in more than 9800 trials in all age groups (Figure 3C, Figure S4). The results showed strong robustness of H. pylori eradication strategy in all age groups.\nResults of the base‐case analysis\nICER\n(US$/\nQALY gained)\nLife expectancy\nlife‐years (LYs)\nICER\n(US$/LY\ngained)\n\nAbbrevations H. pylori = Helicobacter pylori; QALY = quality‐adjusted life‐year; LY = life expectancy life‐years; ICER = incremental cost‐effectiveness ratio; dominated = less effective and more costly than others;\n\nOne‐way sensitivity analysis and probabilistic sensitivity analysis in 60‐year‐old H. pylori‐positive patients. A, The incremental cost‐effectiveness ratio (ICER) tornado diagram for H. pylori eradication strategy versus no eradication strategy. The cost‐effectiveness of H. pylori eradication strategy was not sensitive to changes in any variables. B, Cost‐effectiveness acceptability curve for H. pylori eradication strategy versus no eradication strategy. The probabilistic sensitivity analysis analyzed 10,000 simulations of the model in which input parameters were randomly varied across pre‐specified statistical distributions. The x‐axis represents the willingness‐to‐pay threshold. The acceptability curve showed that H. pylori eradication strategy was cost‐effective 100% of the time at two willingness‐to‐pay thresholds of US$50,000 per QALY gained and US$100,000 per QALY gained. C, Incremental cost‐effectiveness scatterplots with 95% confidence ellipses for H. pylori eradication strategy versus no eradication strategy. Each dot represents a single simulation for a total of 10,000 simulations. Incremental cost‐effectiveness scatterplots showed that H. pylori eradication strategy dominated no‐eradication strategy in 9811 trials, and that H. pylori eradication strategy was more cost‐effective than no‐eradication strategy in 189 trials. EV = expected value; H. pylori = Helicobacter pylori; ICER = incremental cost‐effectivenessratio; QALY = quality‐adjusted life‐year; WTP = willingness‐to‐pay threshold","\nH. pylori‐positive patients aged 60 had the highest cumulative lifetime economic and health outcomes (Table S1). From 2013 to 2019 for 8.50 million patients, H. pylori eradication saved US$3.75 billion, increased 11.11 million QALYs and 0.45 million LYs, and prevented 284,188 cases and 65,060 deaths. For 35.59 million patients without eradication, H. pylori eradication has the potential to save US$14.82 billion, increase 43.10 million QALYs and 1.66 million LYs, and prevent 1,084,532 cases and 250,256 deaths (Table S1).\nIn the Markov cohort analysis, the cumulative lifetime potential of gastric cancer cases and deaths from gastric cancer in H. pylori eradication strategy compared with no eradication strategy decreased by 30 to 33% in patients under 50 and by 25 to 28% in patients aged 50 and over (Figure S5). H. pylori eradication reduced the incidence and mortality of gastric cancer in the younger age groups greater than in the older age groups (Table 2, Figure S5).","AK had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. AK and MA approved the final version of the manuscript, and involved in concept, design, and critical revision of the manuscript for important intellectual content. AK involved in acquisition, analysis, interpretation of data, drafting of the manuscript, and administrative, technical, or material support. MA involved in supervision."],"string":"[\n \"More than half of the world's population is infected with Helicobacter pylori (H. pylori).\\n1\\n\\nH. pylori infection causes chronic atrophic gastritis, a common stage of progression to gastric cancer, and is responsible for 98% of the causes of gastric cancer in Japan.\\n2\\n, \\n3\\n, \\n4\\n, \\n5\\n Japan has the third highest age‐standardized rate for gastric cancer in the world.\\n6\\n The incidence of gastric cancer in Japan is almost 10 times higher than that observed in the United States. The Taipei global consensus guidelines for screening and eradication of H. pylori for gastric cancer prevention recommend that mass screening and eradication of H. pylori should be considered in populations at higher risk of gastric cancer and that eradication therapy should be offered to all individuals infected with H. pylori.\\n7\\n In the guidelines for the management of H. pylori infection by the Japanese Society for Helicobacter Research, H. pylori eradication treatment is recommended to prevent gastric cancer for patients with H. pylori infection.\\n8\\n The Ministry of Health, Labour and Welfare approved expansion of National Health Insurance coverage for H. pylori eradication treatment in patients with chronic gastritis from February 2013.\\n9\\n During 2013‐2019, 8.50 million H. pylori‐positive patients received eradication treatment.\\n10\\n, \\n11\\n The number of deaths from gastric cancer is gradually declining, with 42,931 deaths in 2019 and 42,318 deaths in 2020 (Figure 1).\\n12\\n, \\n13\\n\\n\\nChanges in gastric cancer deaths in Japan from 2000 to 2020\\nIn this study, we aimed to evaluate the economic and health effects of H. pylori eradication strategy in national gastric cancer prevention program in Japan.\",\n \"We constructed a cohort state‐transition model with a Markov cycle tree for two strategies: H. pylori eradication strategy and no eradication strategy, using a healthcare payer perspective and a lifetime horizon. A cycle length of one year was chosen. The half‐cycle correction was applied. In the model, decision branches leaded directly to one Markov node per intervention strategy and the first events were modeled within the Markov cycle tree (Figure 2). We used TreeAge Pro (TreeAge Software Inc., Williamstown, Massachusetts) for the Decision‐analytical calculations. As this was a modeling study with all inputs and parameters derived from the published literature and Japanese statistics, ethics approval was not required.\\nSchematic depiction of the Markov cycle tree in the cohort state‐transition model. We show that health states in the model as ovals. In a yearly model cycle, transitions can occur between the health states and other health states, represented by the arrows. H. pylori = Helicobacter pylori; GC = gastric cancer\\nH. pylori eradication strategy The H. pylori‐positive patient receives first‐line eradication treatment (proton‐pump inhibitor, clarithromycin, and amoxicillin). The patient who fails first‐line eradication treatment receives second‐line eradication treatment (proton‐pump inhibitor, metronidazole, and amoxicillin). We consider the eradication and compliance rates of first‐line and second‐line eradication treatments in the model. After successful H. pylori eradication, H. pylori‐positive changes to H. pylori‐negative. When the patient fails both treatments, H. pylori‐positive remains until death. We calculate the costs of H. pylori test, endoscopy, and two urea breath tests when the patient receives H. pylori eradication treatment. When the patient has gastric cancer, the patient receives the standard treatment of gastric cancer followed by the Japanese gastric cancer treatment guidelines: endoscopic mucosal resection (EMR), endoscopic submucosal dissection treatment (ESD), surgery, chemotherapy, and radiotherapy with palliative care according to cancer stages, stage I‐IV.\\n14\\n The model includes the relative risk of developing gastric cancer after successful eradication, stage‐specific 5‐year survival rates, and mortality due to other causes (Table 1).\\n12\\n, \\n15\\n The patient aged 50 and over receives endoscopic screening every year from the year after eradication.\\nBaseline estimates for selected variables\\n20y\\n30y\\n40y\\n50y\\n60y\\n70y\\n80y\\n0.000771\\n0.001167\\n0.001881\\n0.002803\\n0.005122\\n0.007949\\n0.009117\\n20y\\n30y\\n40y\\n50y\\n60y\\n70y\\n80y\\n0.000509\\n0.00077\\n0.00124\\n0.00185\\n0.00338\\n0.00524\\n0.00602\\n20y\\n30y\\n40y\\n50y\\n60y\\n70y\\n80y\\n6.1\\n14.7\\n23.7\\n33.7\\n47.7\\n58.6\\n63.6\\nStage I\\nStage II\\nStage III\\nStage IV\\n96.0\\n69.2\\n41.9\\n6.3\\n90‐99\\n50‐80\\n30‐50\\n0‐20\\n20‐29y\\n30‐39y\\n40‐49y\\n50‐59y\\n60‐69y\\n70‐79y\\n80‐89y\\n123,986\\n422,965\\n1,083,631\\n1,664,732\\n2,860,031\\n1,903,756\\n440,503\\n20‐29y\\n30‐39y\\n40‐49y\\n50‐59y\\n60‐69y\\n70‐79y\\n80‐89y\\n773,480\\n2,034,480\\n4,256,520\\n5,634,640\\n7,331,490\\n9,622,120\\n5,933,880\\nStage I\\nStage II\\nStage III\\nStage IV\\n62.5\\n11.0\\n7.5\\n19.0\\n30‐80\\n5‐20\\n2‐15\\n10‐50\\nStage I\\nStage II\\nStage III\\nStage IV\\n3675\\n15,898\\n24,841\\n29,809\\n1838‐7350\\n7949‐31,796\\n12,421‐49,682\\n14,905‐59,618\\n25,26\\n\\nAbbrevations H. pylori = Helicobacter pylori; N/A = not applicable\\n\\nThe H. pylori‐positive patient receives first‐line eradication treatment (proton‐pump inhibitor, clarithromycin, and amoxicillin). The patient who fails first‐line eradication treatment receives second‐line eradication treatment (proton‐pump inhibitor, metronidazole, and amoxicillin). We consider the eradication and compliance rates of first‐line and second‐line eradication treatments in the model. After successful H. pylori eradication, H. pylori‐positive changes to H. pylori‐negative. When the patient fails both treatments, H. pylori‐positive remains until death. We calculate the costs of H. pylori test, endoscopy, and two urea breath tests when the patient receives H. pylori eradication treatment. When the patient has gastric cancer, the patient receives the standard treatment of gastric cancer followed by the Japanese gastric cancer treatment guidelines: endoscopic mucosal resection (EMR), endoscopic submucosal dissection treatment (ESD), surgery, chemotherapy, and radiotherapy with palliative care according to cancer stages, stage I‐IV.\\n14\\n The model includes the relative risk of developing gastric cancer after successful eradication, stage‐specific 5‐year survival rates, and mortality due to other causes (Table 1).\\n12\\n, \\n15\\n The patient aged 50 and over receives endoscopic screening every year from the year after eradication.\\nBaseline estimates for selected variables\\n20y\\n30y\\n40y\\n50y\\n60y\\n70y\\n80y\\n0.000771\\n0.001167\\n0.001881\\n0.002803\\n0.005122\\n0.007949\\n0.009117\\n20y\\n30y\\n40y\\n50y\\n60y\\n70y\\n80y\\n0.000509\\n0.00077\\n0.00124\\n0.00185\\n0.00338\\n0.00524\\n0.00602\\n20y\\n30y\\n40y\\n50y\\n60y\\n70y\\n80y\\n6.1\\n14.7\\n23.7\\n33.7\\n47.7\\n58.6\\n63.6\\nStage I\\nStage II\\nStage III\\nStage IV\\n96.0\\n69.2\\n41.9\\n6.3\\n90‐99\\n50‐80\\n30‐50\\n0‐20\\n20‐29y\\n30‐39y\\n40‐49y\\n50‐59y\\n60‐69y\\n70‐79y\\n80‐89y\\n123,986\\n422,965\\n1,083,631\\n1,664,732\\n2,860,031\\n1,903,756\\n440,503\\n20‐29y\\n30‐39y\\n40‐49y\\n50‐59y\\n60‐69y\\n70‐79y\\n80‐89y\\n773,480\\n2,034,480\\n4,256,520\\n5,634,640\\n7,331,490\\n9,622,120\\n5,933,880\\nStage I\\nStage II\\nStage III\\nStage IV\\n62.5\\n11.0\\n7.5\\n19.0\\n30‐80\\n5‐20\\n2‐15\\n10‐50\\nStage I\\nStage II\\nStage III\\nStage IV\\n3675\\n15,898\\n24,841\\n29,809\\n1838‐7350\\n7949‐31,796\\n12,421‐49,682\\n14,905‐59,618\\n25,26\\n\\nAbbrevations H. pylori = Helicobacter pylori; N/A = not applicable\\n\\nNo eradication strategy The latest version of Japanese guidelines for effective secondary prevention of gastric cancer recommends upper gastrointestinal series and endoscopy in adults 50 years of age and older. In the model, the H. pylori‐positive patient does not receive H. pylori eradication treatment, and the patient aged 50 and over receives annual endoscopic screening annually. When the patient has gastric cancer, the patient receives the standard treatment of gastric cancer.\\nThe latest version of Japanese guidelines for effective secondary prevention of gastric cancer recommends upper gastrointestinal series and endoscopy in adults 50 years of age and older. In the model, the H. pylori‐positive patient does not receive H. pylori eradication treatment, and the patient aged 50 and over receives annual endoscopic screening annually. When the patient has gastric cancer, the patient receives the standard treatment of gastric cancer.\",\n \"The H. pylori‐positive patient receives first‐line eradication treatment (proton‐pump inhibitor, clarithromycin, and amoxicillin). The patient who fails first‐line eradication treatment receives second‐line eradication treatment (proton‐pump inhibitor, metronidazole, and amoxicillin). We consider the eradication and compliance rates of first‐line and second‐line eradication treatments in the model. After successful H. pylori eradication, H. pylori‐positive changes to H. pylori‐negative. When the patient fails both treatments, H. pylori‐positive remains until death. We calculate the costs of H. pylori test, endoscopy, and two urea breath tests when the patient receives H. pylori eradication treatment. When the patient has gastric cancer, the patient receives the standard treatment of gastric cancer followed by the Japanese gastric cancer treatment guidelines: endoscopic mucosal resection (EMR), endoscopic submucosal dissection treatment (ESD), surgery, chemotherapy, and radiotherapy with palliative care according to cancer stages, stage I‐IV.\\n14\\n The model includes the relative risk of developing gastric cancer after successful eradication, stage‐specific 5‐year survival rates, and mortality due to other causes (Table 1).\\n12\\n, \\n15\\n The patient aged 50 and over receives endoscopic screening every year from the year after eradication.\\nBaseline estimates for selected variables\\n20y\\n30y\\n40y\\n50y\\n60y\\n70y\\n80y\\n0.000771\\n0.001167\\n0.001881\\n0.002803\\n0.005122\\n0.007949\\n0.009117\\n20y\\n30y\\n40y\\n50y\\n60y\\n70y\\n80y\\n0.000509\\n0.00077\\n0.00124\\n0.00185\\n0.00338\\n0.00524\\n0.00602\\n20y\\n30y\\n40y\\n50y\\n60y\\n70y\\n80y\\n6.1\\n14.7\\n23.7\\n33.7\\n47.7\\n58.6\\n63.6\\nStage I\\nStage II\\nStage III\\nStage IV\\n96.0\\n69.2\\n41.9\\n6.3\\n90‐99\\n50‐80\\n30‐50\\n0‐20\\n20‐29y\\n30‐39y\\n40‐49y\\n50‐59y\\n60‐69y\\n70‐79y\\n80‐89y\\n123,986\\n422,965\\n1,083,631\\n1,664,732\\n2,860,031\\n1,903,756\\n440,503\\n20‐29y\\n30‐39y\\n40‐49y\\n50‐59y\\n60‐69y\\n70‐79y\\n80‐89y\\n773,480\\n2,034,480\\n4,256,520\\n5,634,640\\n7,331,490\\n9,622,120\\n5,933,880\\nStage I\\nStage II\\nStage III\\nStage IV\\n62.5\\n11.0\\n7.5\\n19.0\\n30‐80\\n5‐20\\n2‐15\\n10‐50\\nStage I\\nStage II\\nStage III\\nStage IV\\n3675\\n15,898\\n24,841\\n29,809\\n1838‐7350\\n7949‐31,796\\n12,421‐49,682\\n14,905‐59,618\\n25,26\\n\\nAbbrevations H. pylori = Helicobacter pylori; N/A = not applicable\\n\",\n \"The latest version of Japanese guidelines for effective secondary prevention of gastric cancer recommends upper gastrointestinal series and endoscopy in adults 50 years of age and older. In the model, the H. pylori‐positive patient does not receive H. pylori eradication treatment, and the patient aged 50 and over receives annual endoscopic screening annually. When the patient has gastric cancer, the patient receives the standard treatment of gastric cancer.\",\n \"We targeted a hypothetical cohort of Japanese H. pylori‐positive chronic gastritis patients who had the initial endoscopic diagnosis needing H. pylori eradication at the age of 20, 30, 40, 50, 60, 70, and 80. Children and adolescents (age <20 y) were not included in the model.\",\n \"Epidemiologic parameters and clinical probabilities were collected using MEDLINE from 2000 to June 2, 2021, national census, and Japanese cancer statistics (Table 1).\\n2\\n, \\n3\\n, \\n10\\n, \\n11\\n, \\n12\\n, \\n15\\n, \\n16\\n, \\n17\\n, \\n18\\n, \\n19\\n, \\n20\\n We estimated annual age‐specific numbers of H. pylori‐positive patients with eradication treatment from the literature\\n10\\n, \\n11\\n and expert opinion (Table1, Figure S1A). The numbers of H. pylori‐positive patients with and without eradication were estimated from the literature\\n16\\n and national census (Table1, Figure S1B). Relative risk of gastric cancer development after successful eradication\\n15\\n, and eradication and compliance rates of first‐ and second‐line eradication treatments\\n19\\n were obtained from the literature. Age‐specific gastric cancer incidence and stage‐specific 5‐year survival rate were obtained from Japanese cancer statistics.\\n10\\n The responsibility rate of H. pylori infection for gastric cancer development was assumed to be 98%.\\n2\\n, \\n3\\n, \\n4\\n, \\n5\\n The incidence of gastric cancer in H. pylori‐positive patients was estimated using the responsibility rate of H. pylori infection for gastric cancer development and the prevalence of H. pylori infection.\\n2\\n, \\n3\\n, \\n4\\n, \\n5\\n, \\n12\\n, \\n16\\n The incidence of gastric cancer in H. pylori‐positive patients after successful eradication treatment was estimated using the relative risk of gastric cancer development after successful eradication treatment.\\n2\\n, \\n3\\n, \\n4\\n, \\n5\\n, \\n12\\n, \\n15\\n, \\n16\\n The sensitivity and specificity of endoscopy were obtained from the literature.\\n20\\n\\n\",\n \"Costs were calculated based on the costs from the Japanese national fee schedule\\n17\\n and were adjusted to 2019 Japanese yen, using the medical care component of the Japanese consumer price index and were converted to US dollars, using the Organisation for Economic Co‐operation and Development (OECD) purchasing power parity rate in 2019 (US$1 = ¥100.64) (Table 1).\\n21\\n All costs were discounted by 3%.\\n22\\n, \\n23\\n Incremental cost‐effectiveness ratios (ICERs) were calculated and compared to two willingness‐to‐pay levels of US$50,000 per quality‐adjusted life‐year (QALY) gained and US$100,000 per QALY gained.\\n24\\n Age‐specific and total cumulative lifetime cost savings of H. pylori eradication strategy compared with no eradication strategy were calculated.\",\n \"Health status was included to represent possible eight clinical states: (i) No H. pylori infection, (ii) H. pylori infection, (iii) gastric cancer on stage I; (iv) gastric cancer on stage II; (v) gastric cancer on stage III; (vi) gastric cancer on stage IV; (vii) cured gastric cancer; and (viii) death (Figure 2). Health state utilities were obtained from the literature and were calculated using utility weights (Table 1).\\n25\\n, \\n26\\n The annual discounting of the utilities in this analysis was set at a rate of 3%.\\n22\\n, \\n23\\n\\n\\nThe health outcomes were QALYs, life expectancy life‐years (LYs), gastric cancer cases, and deaths from gastric cancer. Age‐specific and total cumulative lifetime health outcomes of H. pylori eradication strategy compared with no eradication strategy were calculated and evaluated.\",\n \"We performed a one‐way sensitivity analysis to determine which strategy was more cost‐effective when we tested a single variable over a wide range of possible values while holding all other variables constant, and performed a probabilistic sensitivity analysis using a second‐order Monte‐Carlo simulation for 10,000 trials to assess the impact of the uncertainty in the model on the base‐case estimates. The uncertainty had a beta distribution in clinical probabilities and accuracies, and a log‐normal distribution in costs.\",\n \"\\nH. pylori eradication strategy was less costly and yielded greater benefits than no eradication strategy for all age groups (Table 2). No eradication strategy was dominated for all age groups. The patients aged 40 had the highest per capita cost‐savings. Per capita gains of QALYs in younger patients were higher than in older patients (Table 2). From 2013 to 2019, the patients aged 60 had the highest cost savings and health outcomes (Table S1).\",\n \"Incremental cost‐effectiveness ratio tornado diagram of H. pylori eradication strategy versus no eradication strategy showed that cost‐effectiveness was not sensitive to any variables in all age groups (Figure 3A, Figure S2).\\nIn probabilistic sensitivity analysis using Monte‐Carlo simulation for 10,000 trials, the acceptability curve showed that H. pylori eradication strategy was cost‐effective 100% of the time at two willingness‐to‐pay thresholds of US$50,000 per QALY gained and US$100,000 per QALY gained in all age groups (Figure 3B, Figure S3). Incremental cost‐effectiveness scatterplots showed that H. pylori eradication strategy dominated no‐eradication strategy in more than 9800 trials in all age groups (Figure 3C, Figure S4). The results showed strong robustness of H. pylori eradication strategy in all age groups.\\nResults of the base‐case analysis\\nICER\\n(US$/\\nQALY gained)\\nLife expectancy\\nlife‐years (LYs)\\nICER\\n(US$/LY\\ngained)\\n\\nAbbrevations H. pylori = Helicobacter pylori; QALY = quality‐adjusted life‐year; LY = life expectancy life‐years; ICER = incremental cost‐effectiveness ratio; dominated = less effective and more costly than others;\\n\\nOne‐way sensitivity analysis and probabilistic sensitivity analysis in 60‐year‐old H. pylori‐positive patients. A, The incremental cost‐effectiveness ratio (ICER) tornado diagram for H. pylori eradication strategy versus no eradication strategy. The cost‐effectiveness of H. pylori eradication strategy was not sensitive to changes in any variables. B, Cost‐effectiveness acceptability curve for H. pylori eradication strategy versus no eradication strategy. The probabilistic sensitivity analysis analyzed 10,000 simulations of the model in which input parameters were randomly varied across pre‐specified statistical distributions. The x‐axis represents the willingness‐to‐pay threshold. The acceptability curve showed that H. pylori eradication strategy was cost‐effective 100% of the time at two willingness‐to‐pay thresholds of US$50,000 per QALY gained and US$100,000 per QALY gained. C, Incremental cost‐effectiveness scatterplots with 95% confidence ellipses for H. pylori eradication strategy versus no eradication strategy. Each dot represents a single simulation for a total of 10,000 simulations. Incremental cost‐effectiveness scatterplots showed that H. pylori eradication strategy dominated no‐eradication strategy in 9811 trials, and that H. pylori eradication strategy was more cost‐effective than no‐eradication strategy in 189 trials. EV = expected value; H. pylori = Helicobacter pylori; ICER = incremental cost‐effectivenessratio; QALY = quality‐adjusted life‐year; WTP = willingness‐to‐pay threshold\",\n \"\\nH. pylori‐positive patients aged 60 had the highest cumulative lifetime economic and health outcomes (Table S1). From 2013 to 2019 for 8.50 million patients, H. pylori eradication saved US$3.75 billion, increased 11.11 million QALYs and 0.45 million LYs, and prevented 284,188 cases and 65,060 deaths. For 35.59 million patients without eradication, H. pylori eradication has the potential to save US$14.82 billion, increase 43.10 million QALYs and 1.66 million LYs, and prevent 1,084,532 cases and 250,256 deaths (Table S1).\\nIn the Markov cohort analysis, the cumulative lifetime potential of gastric cancer cases and deaths from gastric cancer in H. pylori eradication strategy compared with no eradication strategy decreased by 30 to 33% in patients under 50 and by 25 to 28% in patients aged 50 and over (Figure S5). H. pylori eradication reduced the incidence and mortality of gastric cancer in the younger age groups greater than in the older age groups (Table 2, Figure S5).\",\n \"AK had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. AK and MA approved the final version of the manuscript, and involved in concept, design, and critical revision of the manuscript for important intellectual content. AK involved in acquisition, analysis, interpretation of data, drafting of the manuscript, and administrative, technical, or material support. MA involved in supervision.\"\n]"},"other_sections_sec_types":{"kind":"list like","value":[null,null,null,null,null,null,null,null,null,null,null,null,null],"string":"[\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null\n]"},"all_sections_titles":{"kind":"list like","value":["INTRODUCTION","MATERIALS AND METHODS","Study design and model structure","H. pylori eradication strategy","No eradication strategy","Target population","Epidemiologic parameters and clinical probabilities","Costs","Health utilities, effectiveness, and health outcomes","Sensitivity analyses","RESULTS","Base‐case analysis","One‐way sensitivity analysis and probabilistic sensitivity analysis","Cumulative lifetime economic and health outcomes","DISCUSSION","CONFLICTS OF INTEREST","AUTHOR CONTRIBUTIONS","Supporting information"],"string":"[\n \"INTRODUCTION\",\n \"MATERIALS AND METHODS\",\n \"Study design and model structure\",\n \"H. pylori eradication strategy\",\n \"No eradication strategy\",\n \"Target population\",\n \"Epidemiologic parameters and clinical probabilities\",\n \"Costs\",\n \"Health utilities, effectiveness, and health outcomes\",\n \"Sensitivity analyses\",\n \"RESULTS\",\n \"Base‐case analysis\",\n \"One‐way sensitivity analysis and probabilistic sensitivity analysis\",\n \"Cumulative lifetime economic and health outcomes\",\n \"DISCUSSION\",\n \"CONFLICTS OF INTEREST\",\n \"AUTHOR CONTRIBUTIONS\",\n \"Supporting information\"\n]"},"all_sections_texts":{"kind":"list like","value":["More than half of the world's population is infected with Helicobacter pylori (H. pylori).\n1\n\nH. pylori infection causes chronic atrophic gastritis, a common stage of progression to gastric cancer, and is responsible for 98% of the causes of gastric cancer in Japan.\n2\n, \n3\n, \n4\n, \n5\n Japan has the third highest age‐standardized rate for gastric cancer in the world.\n6\n The incidence of gastric cancer in Japan is almost 10 times higher than that observed in the United States. The Taipei global consensus guidelines for screening and eradication of H. pylori for gastric cancer prevention recommend that mass screening and eradication of H. pylori should be considered in populations at higher risk of gastric cancer and that eradication therapy should be offered to all individuals infected with H. pylori.\n7\n In the guidelines for the management of H. pylori infection by the Japanese Society for Helicobacter Research, H. pylori eradication treatment is recommended to prevent gastric cancer for patients with H. pylori infection.\n8\n The Ministry of Health, Labour and Welfare approved expansion of National Health Insurance coverage for H. pylori eradication treatment in patients with chronic gastritis from February 2013.\n9\n During 2013‐2019, 8.50 million H. pylori‐positive patients received eradication treatment.\n10\n, \n11\n The number of deaths from gastric cancer is gradually declining, with 42,931 deaths in 2019 and 42,318 deaths in 2020 (Figure 1).\n12\n, \n13\n\n\nChanges in gastric cancer deaths in Japan from 2000 to 2020\nIn this study, we aimed to evaluate the economic and health effects of H. pylori eradication strategy in national gastric cancer prevention program in Japan.","Study design and model structure We constructed a cohort state‐transition model with a Markov cycle tree for two strategies: H. pylori eradication strategy and no eradication strategy, using a healthcare payer perspective and a lifetime horizon. A cycle length of one year was chosen. The half‐cycle correction was applied. In the model, decision branches leaded directly to one Markov node per intervention strategy and the first events were modeled within the Markov cycle tree (Figure 2). We used TreeAge Pro (TreeAge Software Inc., Williamstown, Massachusetts) for the Decision‐analytical calculations. As this was a modeling study with all inputs and parameters derived from the published literature and Japanese statistics, ethics approval was not required.\nSchematic depiction of the Markov cycle tree in the cohort state‐transition model. We show that health states in the model as ovals. In a yearly model cycle, transitions can occur between the health states and other health states, represented by the arrows. H. pylori = Helicobacter pylori; GC = gastric cancer\nH. pylori eradication strategy The H. pylori‐positive patient receives first‐line eradication treatment (proton‐pump inhibitor, clarithromycin, and amoxicillin). The patient who fails first‐line eradication treatment receives second‐line eradication treatment (proton‐pump inhibitor, metronidazole, and amoxicillin). We consider the eradication and compliance rates of first‐line and second‐line eradication treatments in the model. After successful H. pylori eradication, H. pylori‐positive changes to H. pylori‐negative. When the patient fails both treatments, H. pylori‐positive remains until death. We calculate the costs of H. pylori test, endoscopy, and two urea breath tests when the patient receives H. pylori eradication treatment. When the patient has gastric cancer, the patient receives the standard treatment of gastric cancer followed by the Japanese gastric cancer treatment guidelines: endoscopic mucosal resection (EMR), endoscopic submucosal dissection treatment (ESD), surgery, chemotherapy, and radiotherapy with palliative care according to cancer stages, stage I‐IV.\n14\n The model includes the relative risk of developing gastric cancer after successful eradication, stage‐specific 5‐year survival rates, and mortality due to other causes (Table 1).\n12\n, \n15\n The patient aged 50 and over receives endoscopic screening every year from the year after eradication.\nBaseline estimates for selected variables\n20y\n30y\n40y\n50y\n60y\n70y\n80y\n0.000771\n0.001167\n0.001881\n0.002803\n0.005122\n0.007949\n0.009117\n20y\n30y\n40y\n50y\n60y\n70y\n80y\n0.000509\n0.00077\n0.00124\n0.00185\n0.00338\n0.00524\n0.00602\n20y\n30y\n40y\n50y\n60y\n70y\n80y\n6.1\n14.7\n23.7\n33.7\n47.7\n58.6\n63.6\nStage I\nStage II\nStage III\nStage IV\n96.0\n69.2\n41.9\n6.3\n90‐99\n50‐80\n30‐50\n0‐20\n20‐29y\n30‐39y\n40‐49y\n50‐59y\n60‐69y\n70‐79y\n80‐89y\n123,986\n422,965\n1,083,631\n1,664,732\n2,860,031\n1,903,756\n440,503\n20‐29y\n30‐39y\n40‐49y\n50‐59y\n60‐69y\n70‐79y\n80‐89y\n773,480\n2,034,480\n4,256,520\n5,634,640\n7,331,490\n9,622,120\n5,933,880\nStage I\nStage II\nStage III\nStage IV\n62.5\n11.0\n7.5\n19.0\n30‐80\n5‐20\n2‐15\n10‐50\nStage I\nStage II\nStage III\nStage IV\n3675\n15,898\n24,841\n29,809\n1838‐7350\n7949‐31,796\n12,421‐49,682\n14,905‐59,618\n25,26\n\nAbbrevations H. pylori = Helicobacter pylori; N/A = not applicable\n\nThe H. pylori‐positive patient receives first‐line eradication treatment (proton‐pump inhibitor, clarithromycin, and amoxicillin). The patient who fails first‐line eradication treatment receives second‐line eradication treatment (proton‐pump inhibitor, metronidazole, and amoxicillin). We consider the eradication and compliance rates of first‐line and second‐line eradication treatments in the model. After successful H. pylori eradication, H. pylori‐positive changes to H. pylori‐negative. When the patient fails both treatments, H. pylori‐positive remains until death. We calculate the costs of H. pylori test, endoscopy, and two urea breath tests when the patient receives H. pylori eradication treatment. When the patient has gastric cancer, the patient receives the standard treatment of gastric cancer followed by the Japanese gastric cancer treatment guidelines: endoscopic mucosal resection (EMR), endoscopic submucosal dissection treatment (ESD), surgery, chemotherapy, and radiotherapy with palliative care according to cancer stages, stage I‐IV.\n14\n The model includes the relative risk of developing gastric cancer after successful eradication, stage‐specific 5‐year survival rates, and mortality due to other causes (Table 1).\n12\n, \n15\n The patient aged 50 and over receives endoscopic screening every year from the year after eradication.\nBaseline estimates for selected variables\n20y\n30y\n40y\n50y\n60y\n70y\n80y\n0.000771\n0.001167\n0.001881\n0.002803\n0.005122\n0.007949\n0.009117\n20y\n30y\n40y\n50y\n60y\n70y\n80y\n0.000509\n0.00077\n0.00124\n0.00185\n0.00338\n0.00524\n0.00602\n20y\n30y\n40y\n50y\n60y\n70y\n80y\n6.1\n14.7\n23.7\n33.7\n47.7\n58.6\n63.6\nStage I\nStage II\nStage III\nStage IV\n96.0\n69.2\n41.9\n6.3\n90‐99\n50‐80\n30‐50\n0‐20\n20‐29y\n30‐39y\n40‐49y\n50‐59y\n60‐69y\n70‐79y\n80‐89y\n123,986\n422,965\n1,083,631\n1,664,732\n2,860,031\n1,903,756\n440,503\n20‐29y\n30‐39y\n40‐49y\n50‐59y\n60‐69y\n70‐79y\n80‐89y\n773,480\n2,034,480\n4,256,520\n5,634,640\n7,331,490\n9,622,120\n5,933,880\nStage I\nStage II\nStage III\nStage IV\n62.5\n11.0\n7.5\n19.0\n30‐80\n5‐20\n2‐15\n10‐50\nStage I\nStage II\nStage III\nStage IV\n3675\n15,898\n24,841\n29,809\n1838‐7350\n7949‐31,796\n12,421‐49,682\n14,905‐59,618\n25,26\n\nAbbrevations H. pylori = Helicobacter pylori; N/A = not applicable\n\nNo eradication strategy The latest version of Japanese guidelines for effective secondary prevention of gastric cancer recommends upper gastrointestinal series and endoscopy in adults 50 years of age and older. In the model, the H. pylori‐positive patient does not receive H. pylori eradication treatment, and the patient aged 50 and over receives annual endoscopic screening annually. When the patient has gastric cancer, the patient receives the standard treatment of gastric cancer.\nThe latest version of Japanese guidelines for effective secondary prevention of gastric cancer recommends upper gastrointestinal series and endoscopy in adults 50 years of age and older. In the model, the H. pylori‐positive patient does not receive H. pylori eradication treatment, and the patient aged 50 and over receives annual endoscopic screening annually. When the patient has gastric cancer, the patient receives the standard treatment of gastric cancer.\nWe constructed a cohort state‐transition model with a Markov cycle tree for two strategies: H. pylori eradication strategy and no eradication strategy, using a healthcare payer perspective and a lifetime horizon. A cycle length of one year was chosen. The half‐cycle correction was applied. In the model, decision branches leaded directly to one Markov node per intervention strategy and the first events were modeled within the Markov cycle tree (Figure 2). We used TreeAge Pro (TreeAge Software Inc., Williamstown, Massachusetts) for the Decision‐analytical calculations. As this was a modeling study with all inputs and parameters derived from the published literature and Japanese statistics, ethics approval was not required.\nSchematic depiction of the Markov cycle tree in the cohort state‐transition model. We show that health states in the model as ovals. In a yearly model cycle, transitions can occur between the health states and other health states, represented by the arrows. H. pylori = Helicobacter pylori; GC = gastric cancer\nH. pylori eradication strategy The H. pylori‐positive patient receives first‐line eradication treatment (proton‐pump inhibitor, clarithromycin, and amoxicillin). The patient who fails first‐line eradication treatment receives second‐line eradication treatment (proton‐pump inhibitor, metronidazole, and amoxicillin). We consider the eradication and compliance rates of first‐line and second‐line eradication treatments in the model. After successful H. pylori eradication, H. pylori‐positive changes to H. pylori‐negative. When the patient fails both treatments, H. pylori‐positive remains until death. We calculate the costs of H. pylori test, endoscopy, and two urea breath tests when the patient receives H. pylori eradication treatment. When the patient has gastric cancer, the patient receives the standard treatment of gastric cancer followed by the Japanese gastric cancer treatment guidelines: endoscopic mucosal resection (EMR), endoscopic submucosal dissection treatment (ESD), surgery, chemotherapy, and radiotherapy with palliative care according to cancer stages, stage I‐IV.\n14\n The model includes the relative risk of developing gastric cancer after successful eradication, stage‐specific 5‐year survival rates, and mortality due to other causes (Table 1).\n12\n, \n15\n The patient aged 50 and over receives endoscopic screening every year from the year after eradication.\nBaseline estimates for selected variables\n20y\n30y\n40y\n50y\n60y\n70y\n80y\n0.000771\n0.001167\n0.001881\n0.002803\n0.005122\n0.007949\n0.009117\n20y\n30y\n40y\n50y\n60y\n70y\n80y\n0.000509\n0.00077\n0.00124\n0.00185\n0.00338\n0.00524\n0.00602\n20y\n30y\n40y\n50y\n60y\n70y\n80y\n6.1\n14.7\n23.7\n33.7\n47.7\n58.6\n63.6\nStage I\nStage II\nStage III\nStage IV\n96.0\n69.2\n41.9\n6.3\n90‐99\n50‐80\n30‐50\n0‐20\n20‐29y\n30‐39y\n40‐49y\n50‐59y\n60‐69y\n70‐79y\n80‐89y\n123,986\n422,965\n1,083,631\n1,664,732\n2,860,031\n1,903,756\n440,503\n20‐29y\n30‐39y\n40‐49y\n50‐59y\n60‐69y\n70‐79y\n80‐89y\n773,480\n2,034,480\n4,256,520\n5,634,640\n7,331,490\n9,622,120\n5,933,880\nStage I\nStage II\nStage III\nStage IV\n62.5\n11.0\n7.5\n19.0\n30‐80\n5‐20\n2‐15\n10‐50\nStage I\nStage II\nStage III\nStage IV\n3675\n15,898\n24,841\n29,809\n1838‐7350\n7949‐31,796\n12,421‐49,682\n14,905‐59,618\n25,26\n\nAbbrevations H. pylori = Helicobacter pylori; N/A = not applicable\n\nThe H. pylori‐positive patient receives first‐line eradication treatment (proton‐pump inhibitor, clarithromycin, and amoxicillin). The patient who fails first‐line eradication treatment receives second‐line eradication treatment (proton‐pump inhibitor, metronidazole, and amoxicillin). We consider the eradication and compliance rates of first‐line and second‐line eradication treatments in the model. After successful H. pylori eradication, H. pylori‐positive changes to H. pylori‐negative. When the patient fails both treatments, H. pylori‐positive remains until death. We calculate the costs of H. pylori test, endoscopy, and two urea breath tests when the patient receives H. pylori eradication treatment. When the patient has gastric cancer, the patient receives the standard treatment of gastric cancer followed by the Japanese gastric cancer treatment guidelines: endoscopic mucosal resection (EMR), endoscopic submucosal dissection treatment (ESD), surgery, chemotherapy, and radiotherapy with palliative care according to cancer stages, stage I‐IV.\n14\n The model includes the relative risk of developing gastric cancer after successful eradication, stage‐specific 5‐year survival rates, and mortality due to other causes (Table 1).\n12\n, \n15\n The patient aged 50 and over receives endoscopic screening every year from the year after eradication.\nBaseline estimates for selected variables\n20y\n30y\n40y\n50y\n60y\n70y\n80y\n0.000771\n0.001167\n0.001881\n0.002803\n0.005122\n0.007949\n0.009117\n20y\n30y\n40y\n50y\n60y\n70y\n80y\n0.000509\n0.00077\n0.00124\n0.00185\n0.00338\n0.00524\n0.00602\n20y\n30y\n40y\n50y\n60y\n70y\n80y\n6.1\n14.7\n23.7\n33.7\n47.7\n58.6\n63.6\nStage I\nStage II\nStage III\nStage IV\n96.0\n69.2\n41.9\n6.3\n90‐99\n50‐80\n30‐50\n0‐20\n20‐29y\n30‐39y\n40‐49y\n50‐59y\n60‐69y\n70‐79y\n80‐89y\n123,986\n422,965\n1,083,631\n1,664,732\n2,860,031\n1,903,756\n440,503\n20‐29y\n30‐39y\n40‐49y\n50‐59y\n60‐69y\n70‐79y\n80‐89y\n773,480\n2,034,480\n4,256,520\n5,634,640\n7,331,490\n9,622,120\n5,933,880\nStage I\nStage II\nStage III\nStage IV\n62.5\n11.0\n7.5\n19.0\n30‐80\n5‐20\n2‐15\n10‐50\nStage I\nStage II\nStage III\nStage IV\n3675\n15,898\n24,841\n29,809\n1838‐7350\n7949‐31,796\n12,421‐49,682\n14,905‐59,618\n25,26\n\nAbbrevations H. pylori = Helicobacter pylori; N/A = not applicable\n\nNo eradication strategy The latest version of Japanese guidelines for effective secondary prevention of gastric cancer recommends upper gastrointestinal series and endoscopy in adults 50 years of age and older. In the model, the H. pylori‐positive patient does not receive H. pylori eradication treatment, and the patient aged 50 and over receives annual endoscopic screening annually. When the patient has gastric cancer, the patient receives the standard treatment of gastric cancer.\nThe latest version of Japanese guidelines for effective secondary prevention of gastric cancer recommends upper gastrointestinal series and endoscopy in adults 50 years of age and older. In the model, the H. pylori‐positive patient does not receive H. pylori eradication treatment, and the patient aged 50 and over receives annual endoscopic screening annually. When the patient has gastric cancer, the patient receives the standard treatment of gastric cancer.\nTarget population We targeted a hypothetical cohort of Japanese H. pylori‐positive chronic gastritis patients who had the initial endoscopic diagnosis needing H. pylori eradication at the age of 20, 30, 40, 50, 60, 70, and 80. Children and adolescents (age <20 y) were not included in the model.\nWe targeted a hypothetical cohort of Japanese H. pylori‐positive chronic gastritis patients who had the initial endoscopic diagnosis needing H. pylori eradication at the age of 20, 30, 40, 50, 60, 70, and 80. Children and adolescents (age <20 y) were not included in the model.\nEpidemiologic parameters and clinical probabilities Epidemiologic parameters and clinical probabilities were collected using MEDLINE from 2000 to June 2, 2021, national census, and Japanese cancer statistics (Table 1).\n2\n, \n3\n, \n10\n, \n11\n, \n12\n, \n15\n, \n16\n, \n17\n, \n18\n, \n19\n, \n20\n We estimated annual age‐specific numbers of H. pylori‐positive patients with eradication treatment from the literature\n10\n, \n11\n and expert opinion (Table1, Figure S1A). The numbers of H. pylori‐positive patients with and without eradication were estimated from the literature\n16\n and national census (Table1, Figure S1B). Relative risk of gastric cancer development after successful eradication\n15\n, and eradication and compliance rates of first‐ and second‐line eradication treatments\n19\n were obtained from the literature. Age‐specific gastric cancer incidence and stage‐specific 5‐year survival rate were obtained from Japanese cancer statistics.\n10\n The responsibility rate of H. pylori infection for gastric cancer development was assumed to be 98%.\n2\n, \n3\n, \n4\n, \n5\n The incidence of gastric cancer in H. pylori‐positive patients was estimated using the responsibility rate of H. pylori infection for gastric cancer development and the prevalence of H. pylori infection.\n2\n, \n3\n, \n4\n, \n5\n, \n12\n, \n16\n The incidence of gastric cancer in H. pylori‐positive patients after successful eradication treatment was estimated using the relative risk of gastric cancer development after successful eradication treatment.\n2\n, \n3\n, \n4\n, \n5\n, \n12\n, \n15\n, \n16\n The sensitivity and specificity of endoscopy were obtained from the literature.\n20\n\n\nEpidemiologic parameters and clinical probabilities were collected using MEDLINE from 2000 to June 2, 2021, national census, and Japanese cancer statistics (Table 1).\n2\n, \n3\n, \n10\n, \n11\n, \n12\n, \n15\n, \n16\n, \n17\n, \n18\n, \n19\n, \n20\n We estimated annual age‐specific numbers of H. pylori‐positive patients with eradication treatment from the literature\n10\n, \n11\n and expert opinion (Table1, Figure S1A). The numbers of H. pylori‐positive patients with and without eradication were estimated from the literature\n16\n and national census (Table1, Figure S1B). Relative risk of gastric cancer development after successful eradication\n15\n, and eradication and compliance rates of first‐ and second‐line eradication treatments\n19\n were obtained from the literature. Age‐specific gastric cancer incidence and stage‐specific 5‐year survival rate were obtained from Japanese cancer statistics.\n10\n The responsibility rate of H. pylori infection for gastric cancer development was assumed to be 98%.\n2\n, \n3\n, \n4\n, \n5\n The incidence of gastric cancer in H. pylori‐positive patients was estimated using the responsibility rate of H. pylori infection for gastric cancer development and the prevalence of H. pylori infection.\n2\n, \n3\n, \n4\n, \n5\n, \n12\n, \n16\n The incidence of gastric cancer in H. pylori‐positive patients after successful eradication treatment was estimated using the relative risk of gastric cancer development after successful eradication treatment.\n2\n, \n3\n, \n4\n, \n5\n, \n12\n, \n15\n, \n16\n The sensitivity and specificity of endoscopy were obtained from the literature.\n20\n\n\nCosts Costs were calculated based on the costs from the Japanese national fee schedule\n17\n and were adjusted to 2019 Japanese yen, using the medical care component of the Japanese consumer price index and were converted to US dollars, using the Organisation for Economic Co‐operation and Development (OECD) purchasing power parity rate in 2019 (US$1 = ¥100.64) (Table 1).\n21\n All costs were discounted by 3%.\n22\n, \n23\n Incremental cost‐effectiveness ratios (ICERs) were calculated and compared to two willingness‐to‐pay levels of US$50,000 per quality‐adjusted life‐year (QALY) gained and US$100,000 per QALY gained.\n24\n Age‐specific and total cumulative lifetime cost savings of H. pylori eradication strategy compared with no eradication strategy were calculated.\nCosts were calculated based on the costs from the Japanese national fee schedule\n17\n and were adjusted to 2019 Japanese yen, using the medical care component of the Japanese consumer price index and were converted to US dollars, using the Organisation for Economic Co‐operation and Development (OECD) purchasing power parity rate in 2019 (US$1 = ¥100.64) (Table 1).\n21\n All costs were discounted by 3%.\n22\n, \n23\n Incremental cost‐effectiveness ratios (ICERs) were calculated and compared to two willingness‐to‐pay levels of US$50,000 per quality‐adjusted life‐year (QALY) gained and US$100,000 per QALY gained.\n24\n Age‐specific and total cumulative lifetime cost savings of H. pylori eradication strategy compared with no eradication strategy were calculated.\nHealth utilities, effectiveness, and health outcomes Health status was included to represent possible eight clinical states: (i) No H. pylori infection, (ii) H. pylori infection, (iii) gastric cancer on stage I; (iv) gastric cancer on stage II; (v) gastric cancer on stage III; (vi) gastric cancer on stage IV; (vii) cured gastric cancer; and (viii) death (Figure 2). Health state utilities were obtained from the literature and were calculated using utility weights (Table 1).\n25\n, \n26\n The annual discounting of the utilities in this analysis was set at a rate of 3%.\n22\n, \n23\n\n\nThe health outcomes were QALYs, life expectancy life‐years (LYs), gastric cancer cases, and deaths from gastric cancer. Age‐specific and total cumulative lifetime health outcomes of H. pylori eradication strategy compared with no eradication strategy were calculated and evaluated.\nHealth status was included to represent possible eight clinical states: (i) No H. pylori infection, (ii) H. pylori infection, (iii) gastric cancer on stage I; (iv) gastric cancer on stage II; (v) gastric cancer on stage III; (vi) gastric cancer on stage IV; (vii) cured gastric cancer; and (viii) death (Figure 2). Health state utilities were obtained from the literature and were calculated using utility weights (Table 1).\n25\n, \n26\n The annual discounting of the utilities in this analysis was set at a rate of 3%.\n22\n, \n23\n\n\nThe health outcomes were QALYs, life expectancy life‐years (LYs), gastric cancer cases, and deaths from gastric cancer. Age‐specific and total cumulative lifetime health outcomes of H. pylori eradication strategy compared with no eradication strategy were calculated and evaluated.\nSensitivity analyses We performed a one‐way sensitivity analysis to determine which strategy was more cost‐effective when we tested a single variable over a wide range of possible values while holding all other variables constant, and performed a probabilistic sensitivity analysis using a second‐order Monte‐Carlo simulation for 10,000 trials to assess the impact of the uncertainty in the model on the base‐case estimates. The uncertainty had a beta distribution in clinical probabilities and accuracies, and a log‐normal distribution in costs.\nWe performed a one‐way sensitivity analysis to determine which strategy was more cost‐effective when we tested a single variable over a wide range of possible values while holding all other variables constant, and performed a probabilistic sensitivity analysis using a second‐order Monte‐Carlo simulation for 10,000 trials to assess the impact of the uncertainty in the model on the base‐case estimates. The uncertainty had a beta distribution in clinical probabilities and accuracies, and a log‐normal distribution in costs.","We constructed a cohort state‐transition model with a Markov cycle tree for two strategies: H. pylori eradication strategy and no eradication strategy, using a healthcare payer perspective and a lifetime horizon. A cycle length of one year was chosen. The half‐cycle correction was applied. In the model, decision branches leaded directly to one Markov node per intervention strategy and the first events were modeled within the Markov cycle tree (Figure 2). We used TreeAge Pro (TreeAge Software Inc., Williamstown, Massachusetts) for the Decision‐analytical calculations. As this was a modeling study with all inputs and parameters derived from the published literature and Japanese statistics, ethics approval was not required.\nSchematic depiction of the Markov cycle tree in the cohort state‐transition model. We show that health states in the model as ovals. In a yearly model cycle, transitions can occur between the health states and other health states, represented by the arrows. H. pylori = Helicobacter pylori; GC = gastric cancer\nH. pylori eradication strategy The H. pylori‐positive patient receives first‐line eradication treatment (proton‐pump inhibitor, clarithromycin, and amoxicillin). The patient who fails first‐line eradication treatment receives second‐line eradication treatment (proton‐pump inhibitor, metronidazole, and amoxicillin). We consider the eradication and compliance rates of first‐line and second‐line eradication treatments in the model. After successful H. pylori eradication, H. pylori‐positive changes to H. pylori‐negative. When the patient fails both treatments, H. pylori‐positive remains until death. We calculate the costs of H. pylori test, endoscopy, and two urea breath tests when the patient receives H. pylori eradication treatment. When the patient has gastric cancer, the patient receives the standard treatment of gastric cancer followed by the Japanese gastric cancer treatment guidelines: endoscopic mucosal resection (EMR), endoscopic submucosal dissection treatment (ESD), surgery, chemotherapy, and radiotherapy with palliative care according to cancer stages, stage I‐IV.\n14\n The model includes the relative risk of developing gastric cancer after successful eradication, stage‐specific 5‐year survival rates, and mortality due to other causes (Table 1).\n12\n, \n15\n The patient aged 50 and over receives endoscopic screening every year from the year after eradication.\nBaseline estimates for selected variables\n20y\n30y\n40y\n50y\n60y\n70y\n80y\n0.000771\n0.001167\n0.001881\n0.002803\n0.005122\n0.007949\n0.009117\n20y\n30y\n40y\n50y\n60y\n70y\n80y\n0.000509\n0.00077\n0.00124\n0.00185\n0.00338\n0.00524\n0.00602\n20y\n30y\n40y\n50y\n60y\n70y\n80y\n6.1\n14.7\n23.7\n33.7\n47.7\n58.6\n63.6\nStage I\nStage II\nStage III\nStage IV\n96.0\n69.2\n41.9\n6.3\n90‐99\n50‐80\n30‐50\n0‐20\n20‐29y\n30‐39y\n40‐49y\n50‐59y\n60‐69y\n70‐79y\n80‐89y\n123,986\n422,965\n1,083,631\n1,664,732\n2,860,031\n1,903,756\n440,503\n20‐29y\n30‐39y\n40‐49y\n50‐59y\n60‐69y\n70‐79y\n80‐89y\n773,480\n2,034,480\n4,256,520\n5,634,640\n7,331,490\n9,622,120\n5,933,880\nStage I\nStage II\nStage III\nStage IV\n62.5\n11.0\n7.5\n19.0\n30‐80\n5‐20\n2‐15\n10‐50\nStage I\nStage II\nStage III\nStage IV\n3675\n15,898\n24,841\n29,809\n1838‐7350\n7949‐31,796\n12,421‐49,682\n14,905‐59,618\n25,26\n\nAbbrevations H. pylori = Helicobacter pylori; N/A = not applicable\n\nThe H. pylori‐positive patient receives first‐line eradication treatment (proton‐pump inhibitor, clarithromycin, and amoxicillin). The patient who fails first‐line eradication treatment receives second‐line eradication treatment (proton‐pump inhibitor, metronidazole, and amoxicillin). We consider the eradication and compliance rates of first‐line and second‐line eradication treatments in the model. After successful H. pylori eradication, H. pylori‐positive changes to H. pylori‐negative. When the patient fails both treatments, H. pylori‐positive remains until death. We calculate the costs of H. pylori test, endoscopy, and two urea breath tests when the patient receives H. pylori eradication treatment. When the patient has gastric cancer, the patient receives the standard treatment of gastric cancer followed by the Japanese gastric cancer treatment guidelines: endoscopic mucosal resection (EMR), endoscopic submucosal dissection treatment (ESD), surgery, chemotherapy, and radiotherapy with palliative care according to cancer stages, stage I‐IV.\n14\n The model includes the relative risk of developing gastric cancer after successful eradication, stage‐specific 5‐year survival rates, and mortality due to other causes (Table 1).\n12\n, \n15\n The patient aged 50 and over receives endoscopic screening every year from the year after eradication.\nBaseline estimates for selected variables\n20y\n30y\n40y\n50y\n60y\n70y\n80y\n0.000771\n0.001167\n0.001881\n0.002803\n0.005122\n0.007949\n0.009117\n20y\n30y\n40y\n50y\n60y\n70y\n80y\n0.000509\n0.00077\n0.00124\n0.00185\n0.00338\n0.00524\n0.00602\n20y\n30y\n40y\n50y\n60y\n70y\n80y\n6.1\n14.7\n23.7\n33.7\n47.7\n58.6\n63.6\nStage I\nStage II\nStage III\nStage IV\n96.0\n69.2\n41.9\n6.3\n90‐99\n50‐80\n30‐50\n0‐20\n20‐29y\n30‐39y\n40‐49y\n50‐59y\n60‐69y\n70‐79y\n80‐89y\n123,986\n422,965\n1,083,631\n1,664,732\n2,860,031\n1,903,756\n440,503\n20‐29y\n30‐39y\n40‐49y\n50‐59y\n60‐69y\n70‐79y\n80‐89y\n773,480\n2,034,480\n4,256,520\n5,634,640\n7,331,490\n9,622,120\n5,933,880\nStage I\nStage II\nStage III\nStage IV\n62.5\n11.0\n7.5\n19.0\n30‐80\n5‐20\n2‐15\n10‐50\nStage I\nStage II\nStage III\nStage IV\n3675\n15,898\n24,841\n29,809\n1838‐7350\n7949‐31,796\n12,421‐49,682\n14,905‐59,618\n25,26\n\nAbbrevations H. pylori = Helicobacter pylori; N/A = not applicable\n\nNo eradication strategy The latest version of Japanese guidelines for effective secondary prevention of gastric cancer recommends upper gastrointestinal series and endoscopy in adults 50 years of age and older. In the model, the H. pylori‐positive patient does not receive H. pylori eradication treatment, and the patient aged 50 and over receives annual endoscopic screening annually. When the patient has gastric cancer, the patient receives the standard treatment of gastric cancer.\nThe latest version of Japanese guidelines for effective secondary prevention of gastric cancer recommends upper gastrointestinal series and endoscopy in adults 50 years of age and older. In the model, the H. pylori‐positive patient does not receive H. pylori eradication treatment, and the patient aged 50 and over receives annual endoscopic screening annually. When the patient has gastric cancer, the patient receives the standard treatment of gastric cancer.","The H. pylori‐positive patient receives first‐line eradication treatment (proton‐pump inhibitor, clarithromycin, and amoxicillin). The patient who fails first‐line eradication treatment receives second‐line eradication treatment (proton‐pump inhibitor, metronidazole, and amoxicillin). We consider the eradication and compliance rates of first‐line and second‐line eradication treatments in the model. After successful H. pylori eradication, H. pylori‐positive changes to H. pylori‐negative. When the patient fails both treatments, H. pylori‐positive remains until death. We calculate the costs of H. pylori test, endoscopy, and two urea breath tests when the patient receives H. pylori eradication treatment. When the patient has gastric cancer, the patient receives the standard treatment of gastric cancer followed by the Japanese gastric cancer treatment guidelines: endoscopic mucosal resection (EMR), endoscopic submucosal dissection treatment (ESD), surgery, chemotherapy, and radiotherapy with palliative care according to cancer stages, stage I‐IV.\n14\n The model includes the relative risk of developing gastric cancer after successful eradication, stage‐specific 5‐year survival rates, and mortality due to other causes (Table 1).\n12\n, \n15\n The patient aged 50 and over receives endoscopic screening every year from the year after eradication.\nBaseline estimates for selected variables\n20y\n30y\n40y\n50y\n60y\n70y\n80y\n0.000771\n0.001167\n0.001881\n0.002803\n0.005122\n0.007949\n0.009117\n20y\n30y\n40y\n50y\n60y\n70y\n80y\n0.000509\n0.00077\n0.00124\n0.00185\n0.00338\n0.00524\n0.00602\n20y\n30y\n40y\n50y\n60y\n70y\n80y\n6.1\n14.7\n23.7\n33.7\n47.7\n58.6\n63.6\nStage I\nStage II\nStage III\nStage IV\n96.0\n69.2\n41.9\n6.3\n90‐99\n50‐80\n30‐50\n0‐20\n20‐29y\n30‐39y\n40‐49y\n50‐59y\n60‐69y\n70‐79y\n80‐89y\n123,986\n422,965\n1,083,631\n1,664,732\n2,860,031\n1,903,756\n440,503\n20‐29y\n30‐39y\n40‐49y\n50‐59y\n60‐69y\n70‐79y\n80‐89y\n773,480\n2,034,480\n4,256,520\n5,634,640\n7,331,490\n9,622,120\n5,933,880\nStage I\nStage II\nStage III\nStage IV\n62.5\n11.0\n7.5\n19.0\n30‐80\n5‐20\n2‐15\n10‐50\nStage I\nStage II\nStage III\nStage IV\n3675\n15,898\n24,841\n29,809\n1838‐7350\n7949‐31,796\n12,421‐49,682\n14,905‐59,618\n25,26\n\nAbbrevations H. pylori = Helicobacter pylori; N/A = not applicable\n","The latest version of Japanese guidelines for effective secondary prevention of gastric cancer recommends upper gastrointestinal series and endoscopy in adults 50 years of age and older. In the model, the H. pylori‐positive patient does not receive H. pylori eradication treatment, and the patient aged 50 and over receives annual endoscopic screening annually. When the patient has gastric cancer, the patient receives the standard treatment of gastric cancer.","We targeted a hypothetical cohort of Japanese H. pylori‐positive chronic gastritis patients who had the initial endoscopic diagnosis needing H. pylori eradication at the age of 20, 30, 40, 50, 60, 70, and 80. Children and adolescents (age <20 y) were not included in the model.","Epidemiologic parameters and clinical probabilities were collected using MEDLINE from 2000 to June 2, 2021, national census, and Japanese cancer statistics (Table 1).\n2\n, \n3\n, \n10\n, \n11\n, \n12\n, \n15\n, \n16\n, \n17\n, \n18\n, \n19\n, \n20\n We estimated annual age‐specific numbers of H. pylori‐positive patients with eradication treatment from the literature\n10\n, \n11\n and expert opinion (Table1, Figure S1A). The numbers of H. pylori‐positive patients with and without eradication were estimated from the literature\n16\n and national census (Table1, Figure S1B). Relative risk of gastric cancer development after successful eradication\n15\n, and eradication and compliance rates of first‐ and second‐line eradication treatments\n19\n were obtained from the literature. Age‐specific gastric cancer incidence and stage‐specific 5‐year survival rate were obtained from Japanese cancer statistics.\n10\n The responsibility rate of H. pylori infection for gastric cancer development was assumed to be 98%.\n2\n, \n3\n, \n4\n, \n5\n The incidence of gastric cancer in H. pylori‐positive patients was estimated using the responsibility rate of H. pylori infection for gastric cancer development and the prevalence of H. pylori infection.\n2\n, \n3\n, \n4\n, \n5\n, \n12\n, \n16\n The incidence of gastric cancer in H. pylori‐positive patients after successful eradication treatment was estimated using the relative risk of gastric cancer development after successful eradication treatment.\n2\n, \n3\n, \n4\n, \n5\n, \n12\n, \n15\n, \n16\n The sensitivity and specificity of endoscopy were obtained from the literature.\n20\n\n","Costs were calculated based on the costs from the Japanese national fee schedule\n17\n and were adjusted to 2019 Japanese yen, using the medical care component of the Japanese consumer price index and were converted to US dollars, using the Organisation for Economic Co‐operation and Development (OECD) purchasing power parity rate in 2019 (US$1 = ¥100.64) (Table 1).\n21\n All costs were discounted by 3%.\n22\n, \n23\n Incremental cost‐effectiveness ratios (ICERs) were calculated and compared to two willingness‐to‐pay levels of US$50,000 per quality‐adjusted life‐year (QALY) gained and US$100,000 per QALY gained.\n24\n Age‐specific and total cumulative lifetime cost savings of H. pylori eradication strategy compared with no eradication strategy were calculated.","Health status was included to represent possible eight clinical states: (i) No H. pylori infection, (ii) H. pylori infection, (iii) gastric cancer on stage I; (iv) gastric cancer on stage II; (v) gastric cancer on stage III; (vi) gastric cancer on stage IV; (vii) cured gastric cancer; and (viii) death (Figure 2). Health state utilities were obtained from the literature and were calculated using utility weights (Table 1).\n25\n, \n26\n The annual discounting of the utilities in this analysis was set at a rate of 3%.\n22\n, \n23\n\n\nThe health outcomes were QALYs, life expectancy life‐years (LYs), gastric cancer cases, and deaths from gastric cancer. Age‐specific and total cumulative lifetime health outcomes of H. pylori eradication strategy compared with no eradication strategy were calculated and evaluated.","We performed a one‐way sensitivity analysis to determine which strategy was more cost‐effective when we tested a single variable over a wide range of possible values while holding all other variables constant, and performed a probabilistic sensitivity analysis using a second‐order Monte‐Carlo simulation for 10,000 trials to assess the impact of the uncertainty in the model on the base‐case estimates. The uncertainty had a beta distribution in clinical probabilities and accuracies, and a log‐normal distribution in costs.","Base‐case analysis \nH. pylori eradication strategy was less costly and yielded greater benefits than no eradication strategy for all age groups (Table 2). No eradication strategy was dominated for all age groups. The patients aged 40 had the highest per capita cost‐savings. Per capita gains of QALYs in younger patients were higher than in older patients (Table 2). From 2013 to 2019, the patients aged 60 had the highest cost savings and health outcomes (Table S1).\n\nH. pylori eradication strategy was less costly and yielded greater benefits than no eradication strategy for all age groups (Table 2). No eradication strategy was dominated for all age groups. The patients aged 40 had the highest per capita cost‐savings. Per capita gains of QALYs in younger patients were higher than in older patients (Table 2). From 2013 to 2019, the patients aged 60 had the highest cost savings and health outcomes (Table S1).\nOne‐way sensitivity analysis and probabilistic sensitivity analysis Incremental cost‐effectiveness ratio tornado diagram of H. pylori eradication strategy versus no eradication strategy showed that cost‐effectiveness was not sensitive to any variables in all age groups (Figure 3A, Figure S2).\nIn probabilistic sensitivity analysis using Monte‐Carlo simulation for 10,000 trials, the acceptability curve showed that H. pylori eradication strategy was cost‐effective 100% of the time at two willingness‐to‐pay thresholds of US$50,000 per QALY gained and US$100,000 per QALY gained in all age groups (Figure 3B, Figure S3). Incremental cost‐effectiveness scatterplots showed that H. pylori eradication strategy dominated no‐eradication strategy in more than 9800 trials in all age groups (Figure 3C, Figure S4). The results showed strong robustness of H. pylori eradication strategy in all age groups.\nResults of the base‐case analysis\nICER\n(US$/\nQALY gained)\nLife expectancy\nlife‐years (LYs)\nICER\n(US$/LY\ngained)\n\nAbbrevations H. pylori = Helicobacter pylori; QALY = quality‐adjusted life‐year; LY = life expectancy life‐years; ICER = incremental cost‐effectiveness ratio; dominated = less effective and more costly than others;\n\nOne‐way sensitivity analysis and probabilistic sensitivity analysis in 60‐year‐old H. pylori‐positive patients. A, The incremental cost‐effectiveness ratio (ICER) tornado diagram for H. pylori eradication strategy versus no eradication strategy. The cost‐effectiveness of H. pylori eradication strategy was not sensitive to changes in any variables. B, Cost‐effectiveness acceptability curve for H. pylori eradication strategy versus no eradication strategy. The probabilistic sensitivity analysis analyzed 10,000 simulations of the model in which input parameters were randomly varied across pre‐specified statistical distributions. The x‐axis represents the willingness‐to‐pay threshold. The acceptability curve showed that H. pylori eradication strategy was cost‐effective 100% of the time at two willingness‐to‐pay thresholds of US$50,000 per QALY gained and US$100,000 per QALY gained. C, Incremental cost‐effectiveness scatterplots with 95% confidence ellipses for H. pylori eradication strategy versus no eradication strategy. Each dot represents a single simulation for a total of 10,000 simulations. Incremental cost‐effectiveness scatterplots showed that H. pylori eradication strategy dominated no‐eradication strategy in 9811 trials, and that H. pylori eradication strategy was more cost‐effective than no‐eradication strategy in 189 trials. EV = expected value; H. pylori = Helicobacter pylori; ICER = incremental cost‐effectivenessratio; QALY = quality‐adjusted life‐year; WTP = willingness‐to‐pay threshold\nIncremental cost‐effectiveness ratio tornado diagram of H. pylori eradication strategy versus no eradication strategy showed that cost‐effectiveness was not sensitive to any variables in all age groups (Figure 3A, Figure S2).\nIn probabilistic sensitivity analysis using Monte‐Carlo simulation for 10,000 trials, the acceptability curve showed that H. pylori eradication strategy was cost‐effective 100% of the time at two willingness‐to‐pay thresholds of US$50,000 per QALY gained and US$100,000 per QALY gained in all age groups (Figure 3B, Figure S3). Incremental cost‐effectiveness scatterplots showed that H. pylori eradication strategy dominated no‐eradication strategy in more than 9800 trials in all age groups (Figure 3C, Figure S4). The results showed strong robustness of H. pylori eradication strategy in all age groups.\nResults of the base‐case analysis\nICER\n(US$/\nQALY gained)\nLife expectancy\nlife‐years (LYs)\nICER\n(US$/LY\ngained)\n\nAbbrevations H. pylori = Helicobacter pylori; QALY = quality‐adjusted life‐year; LY = life expectancy life‐years; ICER = incremental cost‐effectiveness ratio; dominated = less effective and more costly than others;\n\nOne‐way sensitivity analysis and probabilistic sensitivity analysis in 60‐year‐old H. pylori‐positive patients. A, The incremental cost‐effectiveness ratio (ICER) tornado diagram for H. pylori eradication strategy versus no eradication strategy. The cost‐effectiveness of H. pylori eradication strategy was not sensitive to changes in any variables. B, Cost‐effectiveness acceptability curve for H. pylori eradication strategy versus no eradication strategy. The probabilistic sensitivity analysis analyzed 10,000 simulations of the model in which input parameters were randomly varied across pre‐specified statistical distributions. The x‐axis represents the willingness‐to‐pay threshold. The acceptability curve showed that H. pylori eradication strategy was cost‐effective 100% of the time at two willingness‐to‐pay thresholds of US$50,000 per QALY gained and US$100,000 per QALY gained. C, Incremental cost‐effectiveness scatterplots with 95% confidence ellipses for H. pylori eradication strategy versus no eradication strategy. Each dot represents a single simulation for a total of 10,000 simulations. Incremental cost‐effectiveness scatterplots showed that H. pylori eradication strategy dominated no‐eradication strategy in 9811 trials, and that H. pylori eradication strategy was more cost‐effective than no‐eradication strategy in 189 trials. EV = expected value; H. pylori = Helicobacter pylori; ICER = incremental cost‐effectivenessratio; QALY = quality‐adjusted life‐year; WTP = willingness‐to‐pay threshold\nCumulative lifetime economic and health outcomes \nH. pylori‐positive patients aged 60 had the highest cumulative lifetime economic and health outcomes (Table S1). From 2013 to 2019 for 8.50 million patients, H. pylori eradication saved US$3.75 billion, increased 11.11 million QALYs and 0.45 million LYs, and prevented 284,188 cases and 65,060 deaths. For 35.59 million patients without eradication, H. pylori eradication has the potential to save US$14.82 billion, increase 43.10 million QALYs and 1.66 million LYs, and prevent 1,084,532 cases and 250,256 deaths (Table S1).\nIn the Markov cohort analysis, the cumulative lifetime potential of gastric cancer cases and deaths from gastric cancer in H. pylori eradication strategy compared with no eradication strategy decreased by 30 to 33% in patients under 50 and by 25 to 28% in patients aged 50 and over (Figure S5). H. pylori eradication reduced the incidence and mortality of gastric cancer in the younger age groups greater than in the older age groups (Table 2, Figure S5).\n\nH. pylori‐positive patients aged 60 had the highest cumulative lifetime economic and health outcomes (Table S1). From 2013 to 2019 for 8.50 million patients, H. pylori eradication saved US$3.75 billion, increased 11.11 million QALYs and 0.45 million LYs, and prevented 284,188 cases and 65,060 deaths. For 35.59 million patients without eradication, H. pylori eradication has the potential to save US$14.82 billion, increase 43.10 million QALYs and 1.66 million LYs, and prevent 1,084,532 cases and 250,256 deaths (Table S1).\nIn the Markov cohort analysis, the cumulative lifetime potential of gastric cancer cases and deaths from gastric cancer in H. pylori eradication strategy compared with no eradication strategy decreased by 30 to 33% in patients under 50 and by 25 to 28% in patients aged 50 and over (Figure S5). H. pylori eradication reduced the incidence and mortality of gastric cancer in the younger age groups greater than in the older age groups (Table 2, Figure S5).","\nH. pylori eradication strategy was less costly and yielded greater benefits than no eradication strategy for all age groups (Table 2). No eradication strategy was dominated for all age groups. The patients aged 40 had the highest per capita cost‐savings. Per capita gains of QALYs in younger patients were higher than in older patients (Table 2). From 2013 to 2019, the patients aged 60 had the highest cost savings and health outcomes (Table S1).","Incremental cost‐effectiveness ratio tornado diagram of H. pylori eradication strategy versus no eradication strategy showed that cost‐effectiveness was not sensitive to any variables in all age groups (Figure 3A, Figure S2).\nIn probabilistic sensitivity analysis using Monte‐Carlo simulation for 10,000 trials, the acceptability curve showed that H. pylori eradication strategy was cost‐effective 100% of the time at two willingness‐to‐pay thresholds of US$50,000 per QALY gained and US$100,000 per QALY gained in all age groups (Figure 3B, Figure S3). Incremental cost‐effectiveness scatterplots showed that H. pylori eradication strategy dominated no‐eradication strategy in more than 9800 trials in all age groups (Figure 3C, Figure S4). The results showed strong robustness of H. pylori eradication strategy in all age groups.\nResults of the base‐case analysis\nICER\n(US$/\nQALY gained)\nLife expectancy\nlife‐years (LYs)\nICER\n(US$/LY\ngained)\n\nAbbrevations H. pylori = Helicobacter pylori; QALY = quality‐adjusted life‐year; LY = life expectancy life‐years; ICER = incremental cost‐effectiveness ratio; dominated = less effective and more costly than others;\n\nOne‐way sensitivity analysis and probabilistic sensitivity analysis in 60‐year‐old H. pylori‐positive patients. A, The incremental cost‐effectiveness ratio (ICER) tornado diagram for H. pylori eradication strategy versus no eradication strategy. The cost‐effectiveness of H. pylori eradication strategy was not sensitive to changes in any variables. B, Cost‐effectiveness acceptability curve for H. pylori eradication strategy versus no eradication strategy. The probabilistic sensitivity analysis analyzed 10,000 simulations of the model in which input parameters were randomly varied across pre‐specified statistical distributions. The x‐axis represents the willingness‐to‐pay threshold. The acceptability curve showed that H. pylori eradication strategy was cost‐effective 100% of the time at two willingness‐to‐pay thresholds of US$50,000 per QALY gained and US$100,000 per QALY gained. C, Incremental cost‐effectiveness scatterplots with 95% confidence ellipses for H. pylori eradication strategy versus no eradication strategy. Each dot represents a single simulation for a total of 10,000 simulations. Incremental cost‐effectiveness scatterplots showed that H. pylori eradication strategy dominated no‐eradication strategy in 9811 trials, and that H. pylori eradication strategy was more cost‐effective than no‐eradication strategy in 189 trials. EV = expected value; H. pylori = Helicobacter pylori; ICER = incremental cost‐effectivenessratio; QALY = quality‐adjusted life‐year; WTP = willingness‐to‐pay threshold","\nH. pylori‐positive patients aged 60 had the highest cumulative lifetime economic and health outcomes (Table S1). From 2013 to 2019 for 8.50 million patients, H. pylori eradication saved US$3.75 billion, increased 11.11 million QALYs and 0.45 million LYs, and prevented 284,188 cases and 65,060 deaths. For 35.59 million patients without eradication, H. pylori eradication has the potential to save US$14.82 billion, increase 43.10 million QALYs and 1.66 million LYs, and prevent 1,084,532 cases and 250,256 deaths (Table S1).\nIn the Markov cohort analysis, the cumulative lifetime potential of gastric cancer cases and deaths from gastric cancer in H. pylori eradication strategy compared with no eradication strategy decreased by 30 to 33% in patients under 50 and by 25 to 28% in patients aged 50 and over (Figure S5). H. pylori eradication reduced the incidence and mortality of gastric cancer in the younger age groups greater than in the older age groups (Table 2, Figure S5).","To the best of our knowledge, this is the first study to assess the economic and health impacts of population‐wide H. pylori eradication strategy in national gastric cancer prevention program covered by National Health Insurance in the world.\nWe demonstrated that population‐wide H. pylori eradication strategy reduced costs, prevented gastric cancer, and reduced deaths from gastric cancer for all age groups in the modeling study with real‐life settings in Japan, even though most older adults with gastric mucosal atrophy require more than 10 years of follow‐up endoscopic screening after successful H. pylori eradication.\n27\n, \n28\n The cost savings of H. pylori eradication strategy from 2013 to 2019 were US$3.75 billion, 10.46 times the annual budget for cancer control in Japan. This means that the promotion of H. pylori eradication strategy focused on primary prevention of gastric cancer not only saves many lives from gastric cancer, but also leads to significant cost savings in the national budget.\nIt is well known that the benefits of H. pylori eradication on the reduction of gastric cancer risk in the younger age groups are greater than those in the older age groups. Young individuals would benefit most from H. pylori eradication because it cures H. pylori related gastritis, reduces the risk of gastric cancer, and reduces transmission to their children.\n7\n This modeling study using the constant risk of gastric cancer development after successful eradication treatment demonstrated that H. pylori eradication reduced the incidence and mortality of gastric cancer in the younger age groups greater than in the older age groups. If we could modify to reduce the risk of gastric cancer development after successful eradication treatment in the younger age groups, more significant effects on reducing the incidence and mortality of gastric cancer could be shown in the younger age groups.\nSurveillance of the local antibiotic resistance of H. pylori is recommended to identify the optimal empirical therapy for H. pylori eradication in the country.\n7\n Chiang et al demonstrated no change of the antibiotic resistance rate of H. pylori through the selection of effective eradication regimens and retesting those who had completed H. pylori treatments in mass H. pylori eradication program.\n29\n Guo et al found that successful H. pylori eradication potentially restored gastric microbiota to a similar status as found in uninfected individuals, and showed beneficial effects on gut microbiota.\n30\n Liou et al showed that H pylori eradication had no effect on antibiotic resistance of E coli and no significant change in the prevalence of metabolic syndrome.\n31\n These recent studies suggested that H. pylori eradication strategy with effective regimens and high compliance rates could provide significant benefits with minimal adverse effects in high‐risk countries.\nSeveral economic analyses suggested that H. pylori screening followed by eradication treatment is cost‐effective to prevent gastric cancer, particularly in high‐risk populations.\n32\n, \n33\n, \n34\n, \n35\n, \n36\n, \n37\n, \n38\n, \n39\n, \n40\n Han et al demonstrated that H. pylori screening and eradication treatment effectively reduced the morbidity of gastric cancer and cancer‐related costs in asymptomatic infected individuals in China.\n33\n Chen et al showed that population‐based screen‐and‐treat strategy for H pylori infection proved cheaper and more effective for preventing gastric cancer, peptic ulcer disease, and nonulcer dyspepsia in asymptomatic general population compared with no‐screen strategy in China.\n34\n Zheng et al found that H. pylori eradication treatment was an economical strategy with lower costs and greater efficacy in first‐degree relatives of patients with gastric cancer in China.\n35\n Cheng et al demonstrated that H. pylori test‐and‐treat program was cost‐effective to prevent gastric cancer in an endemic area where H. pylori prevalence was >73.5% in Taiwan.\n36\n Teng et al found that H. pylori screening was likely to be cost‐effective particularly for Māori in New Zealand.\n37\n Beresniak et al showed that H. pylori test and eradication strategy including the use of urea breath test was the most cost‐effective compared to symptomatic treatment and upper gastrointestinal endoscopy in Spain.\n38\n Our previous studies demonstrated the superior cost‐effectiveness of H. pylori screening with eradication, compared to no screening, upper gastrointestinal series, and endoscopic screening for asymptomatic general populations in Japan.\n39\n, \n40\n\n\nThis study has several limitations. First, age‐specific numbers of patients with eradication were estimated based on database for Hokkaido (the north island of Japan), the expert opinion, and the literature.\n10\n, \n11\n Second, we did not consider reinfection and recurrence of H. pylori infection in the model. The reinfection rate after H. pylori eradication is very low. H. pylori infection is mainly transmitted in childhood, and recurrence of H. pylori infection after successful eradication is rare in adults.\n41\n Third, nonmedical indirect costs, such as lost productivity, were not included in this study. Forth, we did not consider other risk factors of gastric cancer such as smoking, high salt intake, a diet low in fruit and vegetables, and genetic factors in this study. Fifth, the difference in the stage distribution of gastric cancer between different age groups was not included in the model. Sixth, we did not consider the histological changes after eradication in chronic gastritis patients in the model. H. pylori infection is well known to initiate sequential histological changes such as non‐atrophic gastritis, atrophic gastritis, intestinal metaplasia, dysplasia, and intestinal‐type gastric cancer. Diffuse‐type gastric cancer is also associated with H. pylori infection. Persistent inflammation results in the development of gastric atrophy. Earlier H. pylori eradication should be considered for preventing gastric cancer development prior to the appearance of precancerous lesions.\n42\n\nH. pylori eradication strongly correlates with improvement in intestinal metaplasia in the antrum and gastric atrophy in the corpus and antrum of the stomach.\n43\n More research is needed to incorporate the histological changes of gastric mucosa and future development of gastric cancer in chronic gastritis patients into the model. Finally, there are different costs, different epidemiological parameters, and medical systems in each country. Further cost‐effectiveness studies based on the variance of each country are required.\nIn conclusion, we demonstrated in the modeling study with real‐life settings that national policy using population‐wide H. pylori eradication to prevent gastric cancer has significant cost savings and health impacts for young‐, middle‐, and old‐aged individuals in Japan. The findings strongly support the promotion of H. pylori eradication strategy for all age groups in high‐incidence countries. Based on cost‐effectiveness, introducing H. pylori eradication strategy into national gastric cancer policy should be considered in high‐risk countries around the world.","The author has no conflicts of interest to declare.","AK had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. AK and MA approved the final version of the manuscript, and involved in concept, design, and critical revision of the manuscript for important intellectual content. AK involved in acquisition, analysis, interpretation of data, drafting of the manuscript, and administrative, technical, or material support. MA involved in supervision.","Supplementary Material\nClick here for additional data file."],"string":"[\n \"More than half of the world's population is infected with Helicobacter pylori (H. pylori).\\n1\\n\\nH. pylori infection causes chronic atrophic gastritis, a common stage of progression to gastric cancer, and is responsible for 98% of the causes of gastric cancer in Japan.\\n2\\n, \\n3\\n, \\n4\\n, \\n5\\n Japan has the third highest age‐standardized rate for gastric cancer in the world.\\n6\\n The incidence of gastric cancer in Japan is almost 10 times higher than that observed in the United States. The Taipei global consensus guidelines for screening and eradication of H. pylori for gastric cancer prevention recommend that mass screening and eradication of H. pylori should be considered in populations at higher risk of gastric cancer and that eradication therapy should be offered to all individuals infected with H. pylori.\\n7\\n In the guidelines for the management of H. pylori infection by the Japanese Society for Helicobacter Research, H. pylori eradication treatment is recommended to prevent gastric cancer for patients with H. pylori infection.\\n8\\n The Ministry of Health, Labour and Welfare approved expansion of National Health Insurance coverage for H. pylori eradication treatment in patients with chronic gastritis from February 2013.\\n9\\n During 2013‐2019, 8.50 million H. pylori‐positive patients received eradication treatment.\\n10\\n, \\n11\\n The number of deaths from gastric cancer is gradually declining, with 42,931 deaths in 2019 and 42,318 deaths in 2020 (Figure 1).\\n12\\n, \\n13\\n\\n\\nChanges in gastric cancer deaths in Japan from 2000 to 2020\\nIn this study, we aimed to evaluate the economic and health effects of H. pylori eradication strategy in national gastric cancer prevention program in Japan.\",\n \"Study design and model structure We constructed a cohort state‐transition model with a Markov cycle tree for two strategies: H. pylori eradication strategy and no eradication strategy, using a healthcare payer perspective and a lifetime horizon. A cycle length of one year was chosen. The half‐cycle correction was applied. In the model, decision branches leaded directly to one Markov node per intervention strategy and the first events were modeled within the Markov cycle tree (Figure 2). We used TreeAge Pro (TreeAge Software Inc., Williamstown, Massachusetts) for the Decision‐analytical calculations. As this was a modeling study with all inputs and parameters derived from the published literature and Japanese statistics, ethics approval was not required.\\nSchematic depiction of the Markov cycle tree in the cohort state‐transition model. We show that health states in the model as ovals. In a yearly model cycle, transitions can occur between the health states and other health states, represented by the arrows. H. pylori = Helicobacter pylori; GC = gastric cancer\\nH. pylori eradication strategy The H. pylori‐positive patient receives first‐line eradication treatment (proton‐pump inhibitor, clarithromycin, and amoxicillin). The patient who fails first‐line eradication treatment receives second‐line eradication treatment (proton‐pump inhibitor, metronidazole, and amoxicillin). We consider the eradication and compliance rates of first‐line and second‐line eradication treatments in the model. After successful H. pylori eradication, H. pylori‐positive changes to H. pylori‐negative. When the patient fails both treatments, H. pylori‐positive remains until death. We calculate the costs of H. pylori test, endoscopy, and two urea breath tests when the patient receives H. pylori eradication treatment. When the patient has gastric cancer, the patient receives the standard treatment of gastric cancer followed by the Japanese gastric cancer treatment guidelines: endoscopic mucosal resection (EMR), endoscopic submucosal dissection treatment (ESD), surgery, chemotherapy, and radiotherapy with palliative care according to cancer stages, stage I‐IV.\\n14\\n The model includes the relative risk of developing gastric cancer after successful eradication, stage‐specific 5‐year survival rates, and mortality due to other causes (Table 1).\\n12\\n, \\n15\\n The patient aged 50 and over receives endoscopic screening every year from the year after eradication.\\nBaseline estimates for selected variables\\n20y\\n30y\\n40y\\n50y\\n60y\\n70y\\n80y\\n0.000771\\n0.001167\\n0.001881\\n0.002803\\n0.005122\\n0.007949\\n0.009117\\n20y\\n30y\\n40y\\n50y\\n60y\\n70y\\n80y\\n0.000509\\n0.00077\\n0.00124\\n0.00185\\n0.00338\\n0.00524\\n0.00602\\n20y\\n30y\\n40y\\n50y\\n60y\\n70y\\n80y\\n6.1\\n14.7\\n23.7\\n33.7\\n47.7\\n58.6\\n63.6\\nStage I\\nStage II\\nStage III\\nStage IV\\n96.0\\n69.2\\n41.9\\n6.3\\n90‐99\\n50‐80\\n30‐50\\n0‐20\\n20‐29y\\n30‐39y\\n40‐49y\\n50‐59y\\n60‐69y\\n70‐79y\\n80‐89y\\n123,986\\n422,965\\n1,083,631\\n1,664,732\\n2,860,031\\n1,903,756\\n440,503\\n20‐29y\\n30‐39y\\n40‐49y\\n50‐59y\\n60‐69y\\n70‐79y\\n80‐89y\\n773,480\\n2,034,480\\n4,256,520\\n5,634,640\\n7,331,490\\n9,622,120\\n5,933,880\\nStage I\\nStage II\\nStage III\\nStage IV\\n62.5\\n11.0\\n7.5\\n19.0\\n30‐80\\n5‐20\\n2‐15\\n10‐50\\nStage I\\nStage II\\nStage III\\nStage IV\\n3675\\n15,898\\n24,841\\n29,809\\n1838‐7350\\n7949‐31,796\\n12,421‐49,682\\n14,905‐59,618\\n25,26\\n\\nAbbrevations H. pylori = Helicobacter pylori; N/A = not applicable\\n\\nThe H. pylori‐positive patient receives first‐line eradication treatment (proton‐pump inhibitor, clarithromycin, and amoxicillin). The patient who fails first‐line eradication treatment receives second‐line eradication treatment (proton‐pump inhibitor, metronidazole, and amoxicillin). We consider the eradication and compliance rates of first‐line and second‐line eradication treatments in the model. After successful H. pylori eradication, H. pylori‐positive changes to H. pylori‐negative. When the patient fails both treatments, H. pylori‐positive remains until death. We calculate the costs of H. pylori test, endoscopy, and two urea breath tests when the patient receives H. pylori eradication treatment. When the patient has gastric cancer, the patient receives the standard treatment of gastric cancer followed by the Japanese gastric cancer treatment guidelines: endoscopic mucosal resection (EMR), endoscopic submucosal dissection treatment (ESD), surgery, chemotherapy, and radiotherapy with palliative care according to cancer stages, stage I‐IV.\\n14\\n The model includes the relative risk of developing gastric cancer after successful eradication, stage‐specific 5‐year survival rates, and mortality due to other causes (Table 1).\\n12\\n, \\n15\\n The patient aged 50 and over receives endoscopic screening every year from the year after eradication.\\nBaseline estimates for selected variables\\n20y\\n30y\\n40y\\n50y\\n60y\\n70y\\n80y\\n0.000771\\n0.001167\\n0.001881\\n0.002803\\n0.005122\\n0.007949\\n0.009117\\n20y\\n30y\\n40y\\n50y\\n60y\\n70y\\n80y\\n0.000509\\n0.00077\\n0.00124\\n0.00185\\n0.00338\\n0.00524\\n0.00602\\n20y\\n30y\\n40y\\n50y\\n60y\\n70y\\n80y\\n6.1\\n14.7\\n23.7\\n33.7\\n47.7\\n58.6\\n63.6\\nStage I\\nStage II\\nStage III\\nStage IV\\n96.0\\n69.2\\n41.9\\n6.3\\n90‐99\\n50‐80\\n30‐50\\n0‐20\\n20‐29y\\n30‐39y\\n40‐49y\\n50‐59y\\n60‐69y\\n70‐79y\\n80‐89y\\n123,986\\n422,965\\n1,083,631\\n1,664,732\\n2,860,031\\n1,903,756\\n440,503\\n20‐29y\\n30‐39y\\n40‐49y\\n50‐59y\\n60‐69y\\n70‐79y\\n80‐89y\\n773,480\\n2,034,480\\n4,256,520\\n5,634,640\\n7,331,490\\n9,622,120\\n5,933,880\\nStage I\\nStage II\\nStage III\\nStage IV\\n62.5\\n11.0\\n7.5\\n19.0\\n30‐80\\n5‐20\\n2‐15\\n10‐50\\nStage I\\nStage II\\nStage III\\nStage IV\\n3675\\n15,898\\n24,841\\n29,809\\n1838‐7350\\n7949‐31,796\\n12,421‐49,682\\n14,905‐59,618\\n25,26\\n\\nAbbrevations H. pylori = Helicobacter pylori; N/A = not applicable\\n\\nNo eradication strategy The latest version of Japanese guidelines for effective secondary prevention of gastric cancer recommends upper gastrointestinal series and endoscopy in adults 50 years of age and older. In the model, the H. pylori‐positive patient does not receive H. pylori eradication treatment, and the patient aged 50 and over receives annual endoscopic screening annually. When the patient has gastric cancer, the patient receives the standard treatment of gastric cancer.\\nThe latest version of Japanese guidelines for effective secondary prevention of gastric cancer recommends upper gastrointestinal series and endoscopy in adults 50 years of age and older. In the model, the H. pylori‐positive patient does not receive H. pylori eradication treatment, and the patient aged 50 and over receives annual endoscopic screening annually. When the patient has gastric cancer, the patient receives the standard treatment of gastric cancer.\\nWe constructed a cohort state‐transition model with a Markov cycle tree for two strategies: H. pylori eradication strategy and no eradication strategy, using a healthcare payer perspective and a lifetime horizon. A cycle length of one year was chosen. The half‐cycle correction was applied. In the model, decision branches leaded directly to one Markov node per intervention strategy and the first events were modeled within the Markov cycle tree (Figure 2). We used TreeAge Pro (TreeAge Software Inc., Williamstown, Massachusetts) for the Decision‐analytical calculations. As this was a modeling study with all inputs and parameters derived from the published literature and Japanese statistics, ethics approval was not required.\\nSchematic depiction of the Markov cycle tree in the cohort state‐transition model. We show that health states in the model as ovals. In a yearly model cycle, transitions can occur between the health states and other health states, represented by the arrows. H. pylori = Helicobacter pylori; GC = gastric cancer\\nH. pylori eradication strategy The H. pylori‐positive patient receives first‐line eradication treatment (proton‐pump inhibitor, clarithromycin, and amoxicillin). The patient who fails first‐line eradication treatment receives second‐line eradication treatment (proton‐pump inhibitor, metronidazole, and amoxicillin). We consider the eradication and compliance rates of first‐line and second‐line eradication treatments in the model. After successful H. pylori eradication, H. pylori‐positive changes to H. pylori‐negative. When the patient fails both treatments, H. pylori‐positive remains until death. We calculate the costs of H. pylori test, endoscopy, and two urea breath tests when the patient receives H. pylori eradication treatment. When the patient has gastric cancer, the patient receives the standard treatment of gastric cancer followed by the Japanese gastric cancer treatment guidelines: endoscopic mucosal resection (EMR), endoscopic submucosal dissection treatment (ESD), surgery, chemotherapy, and radiotherapy with palliative care according to cancer stages, stage I‐IV.\\n14\\n The model includes the relative risk of developing gastric cancer after successful eradication, stage‐specific 5‐year survival rates, and mortality due to other causes (Table 1).\\n12\\n, \\n15\\n The patient aged 50 and over receives endoscopic screening every year from the year after eradication.\\nBaseline estimates for selected variables\\n20y\\n30y\\n40y\\n50y\\n60y\\n70y\\n80y\\n0.000771\\n0.001167\\n0.001881\\n0.002803\\n0.005122\\n0.007949\\n0.009117\\n20y\\n30y\\n40y\\n50y\\n60y\\n70y\\n80y\\n0.000509\\n0.00077\\n0.00124\\n0.00185\\n0.00338\\n0.00524\\n0.00602\\n20y\\n30y\\n40y\\n50y\\n60y\\n70y\\n80y\\n6.1\\n14.7\\n23.7\\n33.7\\n47.7\\n58.6\\n63.6\\nStage I\\nStage II\\nStage III\\nStage IV\\n96.0\\n69.2\\n41.9\\n6.3\\n90‐99\\n50‐80\\n30‐50\\n0‐20\\n20‐29y\\n30‐39y\\n40‐49y\\n50‐59y\\n60‐69y\\n70‐79y\\n80‐89y\\n123,986\\n422,965\\n1,083,631\\n1,664,732\\n2,860,031\\n1,903,756\\n440,503\\n20‐29y\\n30‐39y\\n40‐49y\\n50‐59y\\n60‐69y\\n70‐79y\\n80‐89y\\n773,480\\n2,034,480\\n4,256,520\\n5,634,640\\n7,331,490\\n9,622,120\\n5,933,880\\nStage I\\nStage II\\nStage III\\nStage IV\\n62.5\\n11.0\\n7.5\\n19.0\\n30‐80\\n5‐20\\n2‐15\\n10‐50\\nStage I\\nStage II\\nStage III\\nStage IV\\n3675\\n15,898\\n24,841\\n29,809\\n1838‐7350\\n7949‐31,796\\n12,421‐49,682\\n14,905‐59,618\\n25,26\\n\\nAbbrevations H. pylori = Helicobacter pylori; N/A = not applicable\\n\\nThe H. pylori‐positive patient receives first‐line eradication treatment (proton‐pump inhibitor, clarithromycin, and amoxicillin). The patient who fails first‐line eradication treatment receives second‐line eradication treatment (proton‐pump inhibitor, metronidazole, and amoxicillin). We consider the eradication and compliance rates of first‐line and second‐line eradication treatments in the model. After successful H. pylori eradication, H. pylori‐positive changes to H. pylori‐negative. When the patient fails both treatments, H. pylori‐positive remains until death. We calculate the costs of H. pylori test, endoscopy, and two urea breath tests when the patient receives H. pylori eradication treatment. When the patient has gastric cancer, the patient receives the standard treatment of gastric cancer followed by the Japanese gastric cancer treatment guidelines: endoscopic mucosal resection (EMR), endoscopic submucosal dissection treatment (ESD), surgery, chemotherapy, and radiotherapy with palliative care according to cancer stages, stage I‐IV.\\n14\\n The model includes the relative risk of developing gastric cancer after successful eradication, stage‐specific 5‐year survival rates, and mortality due to other causes (Table 1).\\n12\\n, \\n15\\n The patient aged 50 and over receives endoscopic screening every year from the year after eradication.\\nBaseline estimates for selected variables\\n20y\\n30y\\n40y\\n50y\\n60y\\n70y\\n80y\\n0.000771\\n0.001167\\n0.001881\\n0.002803\\n0.005122\\n0.007949\\n0.009117\\n20y\\n30y\\n40y\\n50y\\n60y\\n70y\\n80y\\n0.000509\\n0.00077\\n0.00124\\n0.00185\\n0.00338\\n0.00524\\n0.00602\\n20y\\n30y\\n40y\\n50y\\n60y\\n70y\\n80y\\n6.1\\n14.7\\n23.7\\n33.7\\n47.7\\n58.6\\n63.6\\nStage I\\nStage II\\nStage III\\nStage IV\\n96.0\\n69.2\\n41.9\\n6.3\\n90‐99\\n50‐80\\n30‐50\\n0‐20\\n20‐29y\\n30‐39y\\n40‐49y\\n50‐59y\\n60‐69y\\n70‐79y\\n80‐89y\\n123,986\\n422,965\\n1,083,631\\n1,664,732\\n2,860,031\\n1,903,756\\n440,503\\n20‐29y\\n30‐39y\\n40‐49y\\n50‐59y\\n60‐69y\\n70‐79y\\n80‐89y\\n773,480\\n2,034,480\\n4,256,520\\n5,634,640\\n7,331,490\\n9,622,120\\n5,933,880\\nStage I\\nStage II\\nStage III\\nStage IV\\n62.5\\n11.0\\n7.5\\n19.0\\n30‐80\\n5‐20\\n2‐15\\n10‐50\\nStage I\\nStage II\\nStage III\\nStage IV\\n3675\\n15,898\\n24,841\\n29,809\\n1838‐7350\\n7949‐31,796\\n12,421‐49,682\\n14,905‐59,618\\n25,26\\n\\nAbbrevations H. pylori = Helicobacter pylori; N/A = not applicable\\n\\nNo eradication strategy The latest version of Japanese guidelines for effective secondary prevention of gastric cancer recommends upper gastrointestinal series and endoscopy in adults 50 years of age and older. In the model, the H. pylori‐positive patient does not receive H. pylori eradication treatment, and the patient aged 50 and over receives annual endoscopic screening annually. When the patient has gastric cancer, the patient receives the standard treatment of gastric cancer.\\nThe latest version of Japanese guidelines for effective secondary prevention of gastric cancer recommends upper gastrointestinal series and endoscopy in adults 50 years of age and older. In the model, the H. pylori‐positive patient does not receive H. pylori eradication treatment, and the patient aged 50 and over receives annual endoscopic screening annually. When the patient has gastric cancer, the patient receives the standard treatment of gastric cancer.\\nTarget population We targeted a hypothetical cohort of Japanese H. pylori‐positive chronic gastritis patients who had the initial endoscopic diagnosis needing H. pylori eradication at the age of 20, 30, 40, 50, 60, 70, and 80. Children and adolescents (age <20 y) were not included in the model.\\nWe targeted a hypothetical cohort of Japanese H. pylori‐positive chronic gastritis patients who had the initial endoscopic diagnosis needing H. pylori eradication at the age of 20, 30, 40, 50, 60, 70, and 80. Children and adolescents (age <20 y) were not included in the model.\\nEpidemiologic parameters and clinical probabilities Epidemiologic parameters and clinical probabilities were collected using MEDLINE from 2000 to June 2, 2021, national census, and Japanese cancer statistics (Table 1).\\n2\\n, \\n3\\n, \\n10\\n, \\n11\\n, \\n12\\n, \\n15\\n, \\n16\\n, \\n17\\n, \\n18\\n, \\n19\\n, \\n20\\n We estimated annual age‐specific numbers of H. pylori‐positive patients with eradication treatment from the literature\\n10\\n, \\n11\\n and expert opinion (Table1, Figure S1A). The numbers of H. pylori‐positive patients with and without eradication were estimated from the literature\\n16\\n and national census (Table1, Figure S1B). Relative risk of gastric cancer development after successful eradication\\n15\\n, and eradication and compliance rates of first‐ and second‐line eradication treatments\\n19\\n were obtained from the literature. Age‐specific gastric cancer incidence and stage‐specific 5‐year survival rate were obtained from Japanese cancer statistics.\\n10\\n The responsibility rate of H. pylori infection for gastric cancer development was assumed to be 98%.\\n2\\n, \\n3\\n, \\n4\\n, \\n5\\n The incidence of gastric cancer in H. pylori‐positive patients was estimated using the responsibility rate of H. pylori infection for gastric cancer development and the prevalence of H. pylori infection.\\n2\\n, \\n3\\n, \\n4\\n, \\n5\\n, \\n12\\n, \\n16\\n The incidence of gastric cancer in H. pylori‐positive patients after successful eradication treatment was estimated using the relative risk of gastric cancer development after successful eradication treatment.\\n2\\n, \\n3\\n, \\n4\\n, \\n5\\n, \\n12\\n, \\n15\\n, \\n16\\n The sensitivity and specificity of endoscopy were obtained from the literature.\\n20\\n\\n\\nEpidemiologic parameters and clinical probabilities were collected using MEDLINE from 2000 to June 2, 2021, national census, and Japanese cancer statistics (Table 1).\\n2\\n, \\n3\\n, \\n10\\n, \\n11\\n, \\n12\\n, \\n15\\n, \\n16\\n, \\n17\\n, \\n18\\n, \\n19\\n, \\n20\\n We estimated annual age‐specific numbers of H. pylori‐positive patients with eradication treatment from the literature\\n10\\n, \\n11\\n and expert opinion (Table1, Figure S1A). The numbers of H. pylori‐positive patients with and without eradication were estimated from the literature\\n16\\n and national census (Table1, Figure S1B). Relative risk of gastric cancer development after successful eradication\\n15\\n, and eradication and compliance rates of first‐ and second‐line eradication treatments\\n19\\n were obtained from the literature. Age‐specific gastric cancer incidence and stage‐specific 5‐year survival rate were obtained from Japanese cancer statistics.\\n10\\n The responsibility rate of H. pylori infection for gastric cancer development was assumed to be 98%.\\n2\\n, \\n3\\n, \\n4\\n, \\n5\\n The incidence of gastric cancer in H. pylori‐positive patients was estimated using the responsibility rate of H. pylori infection for gastric cancer development and the prevalence of H. pylori infection.\\n2\\n, \\n3\\n, \\n4\\n, \\n5\\n, \\n12\\n, \\n16\\n The incidence of gastric cancer in H. pylori‐positive patients after successful eradication treatment was estimated using the relative risk of gastric cancer development after successful eradication treatment.\\n2\\n, \\n3\\n, \\n4\\n, \\n5\\n, \\n12\\n, \\n15\\n, \\n16\\n The sensitivity and specificity of endoscopy were obtained from the literature.\\n20\\n\\n\\nCosts Costs were calculated based on the costs from the Japanese national fee schedule\\n17\\n and were adjusted to 2019 Japanese yen, using the medical care component of the Japanese consumer price index and were converted to US dollars, using the Organisation for Economic Co‐operation and Development (OECD) purchasing power parity rate in 2019 (US$1 = ¥100.64) (Table 1).\\n21\\n All costs were discounted by 3%.\\n22\\n, \\n23\\n Incremental cost‐effectiveness ratios (ICERs) were calculated and compared to two willingness‐to‐pay levels of US$50,000 per quality‐adjusted life‐year (QALY) gained and US$100,000 per QALY gained.\\n24\\n Age‐specific and total cumulative lifetime cost savings of H. pylori eradication strategy compared with no eradication strategy were calculated.\\nCosts were calculated based on the costs from the Japanese national fee schedule\\n17\\n and were adjusted to 2019 Japanese yen, using the medical care component of the Japanese consumer price index and were converted to US dollars, using the Organisation for Economic Co‐operation and Development (OECD) purchasing power parity rate in 2019 (US$1 = ¥100.64) (Table 1).\\n21\\n All costs were discounted by 3%.\\n22\\n, \\n23\\n Incremental cost‐effectiveness ratios (ICERs) were calculated and compared to two willingness‐to‐pay levels of US$50,000 per quality‐adjusted life‐year (QALY) gained and US$100,000 per QALY gained.\\n24\\n Age‐specific and total cumulative lifetime cost savings of H. pylori eradication strategy compared with no eradication strategy were calculated.\\nHealth utilities, effectiveness, and health outcomes Health status was included to represent possible eight clinical states: (i) No H. pylori infection, (ii) H. pylori infection, (iii) gastric cancer on stage I; (iv) gastric cancer on stage II; (v) gastric cancer on stage III; (vi) gastric cancer on stage IV; (vii) cured gastric cancer; and (viii) death (Figure 2). Health state utilities were obtained from the literature and were calculated using utility weights (Table 1).\\n25\\n, \\n26\\n The annual discounting of the utilities in this analysis was set at a rate of 3%.\\n22\\n, \\n23\\n\\n\\nThe health outcomes were QALYs, life expectancy life‐years (LYs), gastric cancer cases, and deaths from gastric cancer. Age‐specific and total cumulative lifetime health outcomes of H. pylori eradication strategy compared with no eradication strategy were calculated and evaluated.\\nHealth status was included to represent possible eight clinical states: (i) No H. pylori infection, (ii) H. pylori infection, (iii) gastric cancer on stage I; (iv) gastric cancer on stage II; (v) gastric cancer on stage III; (vi) gastric cancer on stage IV; (vii) cured gastric cancer; and (viii) death (Figure 2). Health state utilities were obtained from the literature and were calculated using utility weights (Table 1).\\n25\\n, \\n26\\n The annual discounting of the utilities in this analysis was set at a rate of 3%.\\n22\\n, \\n23\\n\\n\\nThe health outcomes were QALYs, life expectancy life‐years (LYs), gastric cancer cases, and deaths from gastric cancer. Age‐specific and total cumulative lifetime health outcomes of H. pylori eradication strategy compared with no eradication strategy were calculated and evaluated.\\nSensitivity analyses We performed a one‐way sensitivity analysis to determine which strategy was more cost‐effective when we tested a single variable over a wide range of possible values while holding all other variables constant, and performed a probabilistic sensitivity analysis using a second‐order Monte‐Carlo simulation for 10,000 trials to assess the impact of the uncertainty in the model on the base‐case estimates. The uncertainty had a beta distribution in clinical probabilities and accuracies, and a log‐normal distribution in costs.\\nWe performed a one‐way sensitivity analysis to determine which strategy was more cost‐effective when we tested a single variable over a wide range of possible values while holding all other variables constant, and performed a probabilistic sensitivity analysis using a second‐order Monte‐Carlo simulation for 10,000 trials to assess the impact of the uncertainty in the model on the base‐case estimates. The uncertainty had a beta distribution in clinical probabilities and accuracies, and a log‐normal distribution in costs.\",\n \"We constructed a cohort state‐transition model with a Markov cycle tree for two strategies: H. pylori eradication strategy and no eradication strategy, using a healthcare payer perspective and a lifetime horizon. A cycle length of one year was chosen. The half‐cycle correction was applied. In the model, decision branches leaded directly to one Markov node per intervention strategy and the first events were modeled within the Markov cycle tree (Figure 2). We used TreeAge Pro (TreeAge Software Inc., Williamstown, Massachusetts) for the Decision‐analytical calculations. As this was a modeling study with all inputs and parameters derived from the published literature and Japanese statistics, ethics approval was not required.\\nSchematic depiction of the Markov cycle tree in the cohort state‐transition model. We show that health states in the model as ovals. In a yearly model cycle, transitions can occur between the health states and other health states, represented by the arrows. H. pylori = Helicobacter pylori; GC = gastric cancer\\nH. pylori eradication strategy The H. pylori‐positive patient receives first‐line eradication treatment (proton‐pump inhibitor, clarithromycin, and amoxicillin). The patient who fails first‐line eradication treatment receives second‐line eradication treatment (proton‐pump inhibitor, metronidazole, and amoxicillin). We consider the eradication and compliance rates of first‐line and second‐line eradication treatments in the model. After successful H. pylori eradication, H. pylori‐positive changes to H. pylori‐negative. When the patient fails both treatments, H. pylori‐positive remains until death. We calculate the costs of H. pylori test, endoscopy, and two urea breath tests when the patient receives H. pylori eradication treatment. When the patient has gastric cancer, the patient receives the standard treatment of gastric cancer followed by the Japanese gastric cancer treatment guidelines: endoscopic mucosal resection (EMR), endoscopic submucosal dissection treatment (ESD), surgery, chemotherapy, and radiotherapy with palliative care according to cancer stages, stage I‐IV.\\n14\\n The model includes the relative risk of developing gastric cancer after successful eradication, stage‐specific 5‐year survival rates, and mortality due to other causes (Table 1).\\n12\\n, \\n15\\n The patient aged 50 and over receives endoscopic screening every year from the year after eradication.\\nBaseline estimates for selected variables\\n20y\\n30y\\n40y\\n50y\\n60y\\n70y\\n80y\\n0.000771\\n0.001167\\n0.001881\\n0.002803\\n0.005122\\n0.007949\\n0.009117\\n20y\\n30y\\n40y\\n50y\\n60y\\n70y\\n80y\\n0.000509\\n0.00077\\n0.00124\\n0.00185\\n0.00338\\n0.00524\\n0.00602\\n20y\\n30y\\n40y\\n50y\\n60y\\n70y\\n80y\\n6.1\\n14.7\\n23.7\\n33.7\\n47.7\\n58.6\\n63.6\\nStage I\\nStage II\\nStage III\\nStage IV\\n96.0\\n69.2\\n41.9\\n6.3\\n90‐99\\n50‐80\\n30‐50\\n0‐20\\n20‐29y\\n30‐39y\\n40‐49y\\n50‐59y\\n60‐69y\\n70‐79y\\n80‐89y\\n123,986\\n422,965\\n1,083,631\\n1,664,732\\n2,860,031\\n1,903,756\\n440,503\\n20‐29y\\n30‐39y\\n40‐49y\\n50‐59y\\n60‐69y\\n70‐79y\\n80‐89y\\n773,480\\n2,034,480\\n4,256,520\\n5,634,640\\n7,331,490\\n9,622,120\\n5,933,880\\nStage I\\nStage II\\nStage III\\nStage IV\\n62.5\\n11.0\\n7.5\\n19.0\\n30‐80\\n5‐20\\n2‐15\\n10‐50\\nStage I\\nStage II\\nStage III\\nStage IV\\n3675\\n15,898\\n24,841\\n29,809\\n1838‐7350\\n7949‐31,796\\n12,421‐49,682\\n14,905‐59,618\\n25,26\\n\\nAbbrevations H. pylori = Helicobacter pylori; N/A = not applicable\\n\\nThe H. pylori‐positive patient receives first‐line eradication treatment (proton‐pump inhibitor, clarithromycin, and amoxicillin). The patient who fails first‐line eradication treatment receives second‐line eradication treatment (proton‐pump inhibitor, metronidazole, and amoxicillin). We consider the eradication and compliance rates of first‐line and second‐line eradication treatments in the model. After successful H. pylori eradication, H. pylori‐positive changes to H. pylori‐negative. When the patient fails both treatments, H. pylori‐positive remains until death. We calculate the costs of H. pylori test, endoscopy, and two urea breath tests when the patient receives H. pylori eradication treatment. When the patient has gastric cancer, the patient receives the standard treatment of gastric cancer followed by the Japanese gastric cancer treatment guidelines: endoscopic mucosal resection (EMR), endoscopic submucosal dissection treatment (ESD), surgery, chemotherapy, and radiotherapy with palliative care according to cancer stages, stage I‐IV.\\n14\\n The model includes the relative risk of developing gastric cancer after successful eradication, stage‐specific 5‐year survival rates, and mortality due to other causes (Table 1).\\n12\\n, \\n15\\n The patient aged 50 and over receives endoscopic screening every year from the year after eradication.\\nBaseline estimates for selected variables\\n20y\\n30y\\n40y\\n50y\\n60y\\n70y\\n80y\\n0.000771\\n0.001167\\n0.001881\\n0.002803\\n0.005122\\n0.007949\\n0.009117\\n20y\\n30y\\n40y\\n50y\\n60y\\n70y\\n80y\\n0.000509\\n0.00077\\n0.00124\\n0.00185\\n0.00338\\n0.00524\\n0.00602\\n20y\\n30y\\n40y\\n50y\\n60y\\n70y\\n80y\\n6.1\\n14.7\\n23.7\\n33.7\\n47.7\\n58.6\\n63.6\\nStage I\\nStage II\\nStage III\\nStage IV\\n96.0\\n69.2\\n41.9\\n6.3\\n90‐99\\n50‐80\\n30‐50\\n0‐20\\n20‐29y\\n30‐39y\\n40‐49y\\n50‐59y\\n60‐69y\\n70‐79y\\n80‐89y\\n123,986\\n422,965\\n1,083,631\\n1,664,732\\n2,860,031\\n1,903,756\\n440,503\\n20‐29y\\n30‐39y\\n40‐49y\\n50‐59y\\n60‐69y\\n70‐79y\\n80‐89y\\n773,480\\n2,034,480\\n4,256,520\\n5,634,640\\n7,331,490\\n9,622,120\\n5,933,880\\nStage I\\nStage II\\nStage III\\nStage IV\\n62.5\\n11.0\\n7.5\\n19.0\\n30‐80\\n5‐20\\n2‐15\\n10‐50\\nStage I\\nStage II\\nStage III\\nStage IV\\n3675\\n15,898\\n24,841\\n29,809\\n1838‐7350\\n7949‐31,796\\n12,421‐49,682\\n14,905‐59,618\\n25,26\\n\\nAbbrevations H. pylori = Helicobacter pylori; N/A = not applicable\\n\\nNo eradication strategy The latest version of Japanese guidelines for effective secondary prevention of gastric cancer recommends upper gastrointestinal series and endoscopy in adults 50 years of age and older. In the model, the H. pylori‐positive patient does not receive H. pylori eradication treatment, and the patient aged 50 and over receives annual endoscopic screening annually. When the patient has gastric cancer, the patient receives the standard treatment of gastric cancer.\\nThe latest version of Japanese guidelines for effective secondary prevention of gastric cancer recommends upper gastrointestinal series and endoscopy in adults 50 years of age and older. In the model, the H. pylori‐positive patient does not receive H. pylori eradication treatment, and the patient aged 50 and over receives annual endoscopic screening annually. When the patient has gastric cancer, the patient receives the standard treatment of gastric cancer.\",\n \"The H. pylori‐positive patient receives first‐line eradication treatment (proton‐pump inhibitor, clarithromycin, and amoxicillin). The patient who fails first‐line eradication treatment receives second‐line eradication treatment (proton‐pump inhibitor, metronidazole, and amoxicillin). We consider the eradication and compliance rates of first‐line and second‐line eradication treatments in the model. After successful H. pylori eradication, H. pylori‐positive changes to H. pylori‐negative. When the patient fails both treatments, H. pylori‐positive remains until death. We calculate the costs of H. pylori test, endoscopy, and two urea breath tests when the patient receives H. pylori eradication treatment. When the patient has gastric cancer, the patient receives the standard treatment of gastric cancer followed by the Japanese gastric cancer treatment guidelines: endoscopic mucosal resection (EMR), endoscopic submucosal dissection treatment (ESD), surgery, chemotherapy, and radiotherapy with palliative care according to cancer stages, stage I‐IV.\\n14\\n The model includes the relative risk of developing gastric cancer after successful eradication, stage‐specific 5‐year survival rates, and mortality due to other causes (Table 1).\\n12\\n, \\n15\\n The patient aged 50 and over receives endoscopic screening every year from the year after eradication.\\nBaseline estimates for selected variables\\n20y\\n30y\\n40y\\n50y\\n60y\\n70y\\n80y\\n0.000771\\n0.001167\\n0.001881\\n0.002803\\n0.005122\\n0.007949\\n0.009117\\n20y\\n30y\\n40y\\n50y\\n60y\\n70y\\n80y\\n0.000509\\n0.00077\\n0.00124\\n0.00185\\n0.00338\\n0.00524\\n0.00602\\n20y\\n30y\\n40y\\n50y\\n60y\\n70y\\n80y\\n6.1\\n14.7\\n23.7\\n33.7\\n47.7\\n58.6\\n63.6\\nStage I\\nStage II\\nStage III\\nStage IV\\n96.0\\n69.2\\n41.9\\n6.3\\n90‐99\\n50‐80\\n30‐50\\n0‐20\\n20‐29y\\n30‐39y\\n40‐49y\\n50‐59y\\n60‐69y\\n70‐79y\\n80‐89y\\n123,986\\n422,965\\n1,083,631\\n1,664,732\\n2,860,031\\n1,903,756\\n440,503\\n20‐29y\\n30‐39y\\n40‐49y\\n50‐59y\\n60‐69y\\n70‐79y\\n80‐89y\\n773,480\\n2,034,480\\n4,256,520\\n5,634,640\\n7,331,490\\n9,622,120\\n5,933,880\\nStage I\\nStage II\\nStage III\\nStage IV\\n62.5\\n11.0\\n7.5\\n19.0\\n30‐80\\n5‐20\\n2‐15\\n10‐50\\nStage I\\nStage II\\nStage III\\nStage IV\\n3675\\n15,898\\n24,841\\n29,809\\n1838‐7350\\n7949‐31,796\\n12,421‐49,682\\n14,905‐59,618\\n25,26\\n\\nAbbrevations H. pylori = Helicobacter pylori; N/A = not applicable\\n\",\n \"The latest version of Japanese guidelines for effective secondary prevention of gastric cancer recommends upper gastrointestinal series and endoscopy in adults 50 years of age and older. In the model, the H. pylori‐positive patient does not receive H. pylori eradication treatment, and the patient aged 50 and over receives annual endoscopic screening annually. When the patient has gastric cancer, the patient receives the standard treatment of gastric cancer.\",\n \"We targeted a hypothetical cohort of Japanese H. pylori‐positive chronic gastritis patients who had the initial endoscopic diagnosis needing H. pylori eradication at the age of 20, 30, 40, 50, 60, 70, and 80. Children and adolescents (age <20 y) were not included in the model.\",\n \"Epidemiologic parameters and clinical probabilities were collected using MEDLINE from 2000 to June 2, 2021, national census, and Japanese cancer statistics (Table 1).\\n2\\n, \\n3\\n, \\n10\\n, \\n11\\n, \\n12\\n, \\n15\\n, \\n16\\n, \\n17\\n, \\n18\\n, \\n19\\n, \\n20\\n We estimated annual age‐specific numbers of H. pylori‐positive patients with eradication treatment from the literature\\n10\\n, \\n11\\n and expert opinion (Table1, Figure S1A). The numbers of H. pylori‐positive patients with and without eradication were estimated from the literature\\n16\\n and national census (Table1, Figure S1B). Relative risk of gastric cancer development after successful eradication\\n15\\n, and eradication and compliance rates of first‐ and second‐line eradication treatments\\n19\\n were obtained from the literature. Age‐specific gastric cancer incidence and stage‐specific 5‐year survival rate were obtained from Japanese cancer statistics.\\n10\\n The responsibility rate of H. pylori infection for gastric cancer development was assumed to be 98%.\\n2\\n, \\n3\\n, \\n4\\n, \\n5\\n The incidence of gastric cancer in H. pylori‐positive patients was estimated using the responsibility rate of H. pylori infection for gastric cancer development and the prevalence of H. pylori infection.\\n2\\n, \\n3\\n, \\n4\\n, \\n5\\n, \\n12\\n, \\n16\\n The incidence of gastric cancer in H. pylori‐positive patients after successful eradication treatment was estimated using the relative risk of gastric cancer development after successful eradication treatment.\\n2\\n, \\n3\\n, \\n4\\n, \\n5\\n, \\n12\\n, \\n15\\n, \\n16\\n The sensitivity and specificity of endoscopy were obtained from the literature.\\n20\\n\\n\",\n \"Costs were calculated based on the costs from the Japanese national fee schedule\\n17\\n and were adjusted to 2019 Japanese yen, using the medical care component of the Japanese consumer price index and were converted to US dollars, using the Organisation for Economic Co‐operation and Development (OECD) purchasing power parity rate in 2019 (US$1 = ¥100.64) (Table 1).\\n21\\n All costs were discounted by 3%.\\n22\\n, \\n23\\n Incremental cost‐effectiveness ratios (ICERs) were calculated and compared to two willingness‐to‐pay levels of US$50,000 per quality‐adjusted life‐year (QALY) gained and US$100,000 per QALY gained.\\n24\\n Age‐specific and total cumulative lifetime cost savings of H. pylori eradication strategy compared with no eradication strategy were calculated.\",\n \"Health status was included to represent possible eight clinical states: (i) No H. pylori infection, (ii) H. pylori infection, (iii) gastric cancer on stage I; (iv) gastric cancer on stage II; (v) gastric cancer on stage III; (vi) gastric cancer on stage IV; (vii) cured gastric cancer; and (viii) death (Figure 2). Health state utilities were obtained from the literature and were calculated using utility weights (Table 1).\\n25\\n, \\n26\\n The annual discounting of the utilities in this analysis was set at a rate of 3%.\\n22\\n, \\n23\\n\\n\\nThe health outcomes were QALYs, life expectancy life‐years (LYs), gastric cancer cases, and deaths from gastric cancer. Age‐specific and total cumulative lifetime health outcomes of H. pylori eradication strategy compared with no eradication strategy were calculated and evaluated.\",\n \"We performed a one‐way sensitivity analysis to determine which strategy was more cost‐effective when we tested a single variable over a wide range of possible values while holding all other variables constant, and performed a probabilistic sensitivity analysis using a second‐order Monte‐Carlo simulation for 10,000 trials to assess the impact of the uncertainty in the model on the base‐case estimates. The uncertainty had a beta distribution in clinical probabilities and accuracies, and a log‐normal distribution in costs.\",\n \"Base‐case analysis \\nH. pylori eradication strategy was less costly and yielded greater benefits than no eradication strategy for all age groups (Table 2). No eradication strategy was dominated for all age groups. The patients aged 40 had the highest per capita cost‐savings. Per capita gains of QALYs in younger patients were higher than in older patients (Table 2). From 2013 to 2019, the patients aged 60 had the highest cost savings and health outcomes (Table S1).\\n\\nH. pylori eradication strategy was less costly and yielded greater benefits than no eradication strategy for all age groups (Table 2). No eradication strategy was dominated for all age groups. The patients aged 40 had the highest per capita cost‐savings. Per capita gains of QALYs in younger patients were higher than in older patients (Table 2). From 2013 to 2019, the patients aged 60 had the highest cost savings and health outcomes (Table S1).\\nOne‐way sensitivity analysis and probabilistic sensitivity analysis Incremental cost‐effectiveness ratio tornado diagram of H. pylori eradication strategy versus no eradication strategy showed that cost‐effectiveness was not sensitive to any variables in all age groups (Figure 3A, Figure S2).\\nIn probabilistic sensitivity analysis using Monte‐Carlo simulation for 10,000 trials, the acceptability curve showed that H. pylori eradication strategy was cost‐effective 100% of the time at two willingness‐to‐pay thresholds of US$50,000 per QALY gained and US$100,000 per QALY gained in all age groups (Figure 3B, Figure S3). Incremental cost‐effectiveness scatterplots showed that H. pylori eradication strategy dominated no‐eradication strategy in more than 9800 trials in all age groups (Figure 3C, Figure S4). The results showed strong robustness of H. pylori eradication strategy in all age groups.\\nResults of the base‐case analysis\\nICER\\n(US$/\\nQALY gained)\\nLife expectancy\\nlife‐years (LYs)\\nICER\\n(US$/LY\\ngained)\\n\\nAbbrevations H. pylori = Helicobacter pylori; QALY = quality‐adjusted life‐year; LY = life expectancy life‐years; ICER = incremental cost‐effectiveness ratio; dominated = less effective and more costly than others;\\n\\nOne‐way sensitivity analysis and probabilistic sensitivity analysis in 60‐year‐old H. pylori‐positive patients. A, The incremental cost‐effectiveness ratio (ICER) tornado diagram for H. pylori eradication strategy versus no eradication strategy. The cost‐effectiveness of H. pylori eradication strategy was not sensitive to changes in any variables. B, Cost‐effectiveness acceptability curve for H. pylori eradication strategy versus no eradication strategy. The probabilistic sensitivity analysis analyzed 10,000 simulations of the model in which input parameters were randomly varied across pre‐specified statistical distributions. The x‐axis represents the willingness‐to‐pay threshold. The acceptability curve showed that H. pylori eradication strategy was cost‐effective 100% of the time at two willingness‐to‐pay thresholds of US$50,000 per QALY gained and US$100,000 per QALY gained. C, Incremental cost‐effectiveness scatterplots with 95% confidence ellipses for H. pylori eradication strategy versus no eradication strategy. Each dot represents a single simulation for a total of 10,000 simulations. Incremental cost‐effectiveness scatterplots showed that H. pylori eradication strategy dominated no‐eradication strategy in 9811 trials, and that H. pylori eradication strategy was more cost‐effective than no‐eradication strategy in 189 trials. EV = expected value; H. pylori = Helicobacter pylori; ICER = incremental cost‐effectivenessratio; QALY = quality‐adjusted life‐year; WTP = willingness‐to‐pay threshold\\nIncremental cost‐effectiveness ratio tornado diagram of H. pylori eradication strategy versus no eradication strategy showed that cost‐effectiveness was not sensitive to any variables in all age groups (Figure 3A, Figure S2).\\nIn probabilistic sensitivity analysis using Monte‐Carlo simulation for 10,000 trials, the acceptability curve showed that H. pylori eradication strategy was cost‐effective 100% of the time at two willingness‐to‐pay thresholds of US$50,000 per QALY gained and US$100,000 per QALY gained in all age groups (Figure 3B, Figure S3). Incremental cost‐effectiveness scatterplots showed that H. pylori eradication strategy dominated no‐eradication strategy in more than 9800 trials in all age groups (Figure 3C, Figure S4). The results showed strong robustness of H. pylori eradication strategy in all age groups.\\nResults of the base‐case analysis\\nICER\\n(US$/\\nQALY gained)\\nLife expectancy\\nlife‐years (LYs)\\nICER\\n(US$/LY\\ngained)\\n\\nAbbrevations H. pylori = Helicobacter pylori; QALY = quality‐adjusted life‐year; LY = life expectancy life‐years; ICER = incremental cost‐effectiveness ratio; dominated = less effective and more costly than others;\\n\\nOne‐way sensitivity analysis and probabilistic sensitivity analysis in 60‐year‐old H. pylori‐positive patients. A, The incremental cost‐effectiveness ratio (ICER) tornado diagram for H. pylori eradication strategy versus no eradication strategy. The cost‐effectiveness of H. pylori eradication strategy was not sensitive to changes in any variables. B, Cost‐effectiveness acceptability curve for H. pylori eradication strategy versus no eradication strategy. The probabilistic sensitivity analysis analyzed 10,000 simulations of the model in which input parameters were randomly varied across pre‐specified statistical distributions. The x‐axis represents the willingness‐to‐pay threshold. The acceptability curve showed that H. pylori eradication strategy was cost‐effective 100% of the time at two willingness‐to‐pay thresholds of US$50,000 per QALY gained and US$100,000 per QALY gained. C, Incremental cost‐effectiveness scatterplots with 95% confidence ellipses for H. pylori eradication strategy versus no eradication strategy. Each dot represents a single simulation for a total of 10,000 simulations. Incremental cost‐effectiveness scatterplots showed that H. pylori eradication strategy dominated no‐eradication strategy in 9811 trials, and that H. pylori eradication strategy was more cost‐effective than no‐eradication strategy in 189 trials. EV = expected value; H. pylori = Helicobacter pylori; ICER = incremental cost‐effectivenessratio; QALY = quality‐adjusted life‐year; WTP = willingness‐to‐pay threshold\\nCumulative lifetime economic and health outcomes \\nH. pylori‐positive patients aged 60 had the highest cumulative lifetime economic and health outcomes (Table S1). From 2013 to 2019 for 8.50 million patients, H. pylori eradication saved US$3.75 billion, increased 11.11 million QALYs and 0.45 million LYs, and prevented 284,188 cases and 65,060 deaths. For 35.59 million patients without eradication, H. pylori eradication has the potential to save US$14.82 billion, increase 43.10 million QALYs and 1.66 million LYs, and prevent 1,084,532 cases and 250,256 deaths (Table S1).\\nIn the Markov cohort analysis, the cumulative lifetime potential of gastric cancer cases and deaths from gastric cancer in H. pylori eradication strategy compared with no eradication strategy decreased by 30 to 33% in patients under 50 and by 25 to 28% in patients aged 50 and over (Figure S5). H. pylori eradication reduced the incidence and mortality of gastric cancer in the younger age groups greater than in the older age groups (Table 2, Figure S5).\\n\\nH. pylori‐positive patients aged 60 had the highest cumulative lifetime economic and health outcomes (Table S1). From 2013 to 2019 for 8.50 million patients, H. pylori eradication saved US$3.75 billion, increased 11.11 million QALYs and 0.45 million LYs, and prevented 284,188 cases and 65,060 deaths. For 35.59 million patients without eradication, H. pylori eradication has the potential to save US$14.82 billion, increase 43.10 million QALYs and 1.66 million LYs, and prevent 1,084,532 cases and 250,256 deaths (Table S1).\\nIn the Markov cohort analysis, the cumulative lifetime potential of gastric cancer cases and deaths from gastric cancer in H. pylori eradication strategy compared with no eradication strategy decreased by 30 to 33% in patients under 50 and by 25 to 28% in patients aged 50 and over (Figure S5). H. pylori eradication reduced the incidence and mortality of gastric cancer in the younger age groups greater than in the older age groups (Table 2, Figure S5).\",\n \"\\nH. pylori eradication strategy was less costly and yielded greater benefits than no eradication strategy for all age groups (Table 2). No eradication strategy was dominated for all age groups. The patients aged 40 had the highest per capita cost‐savings. Per capita gains of QALYs in younger patients were higher than in older patients (Table 2). From 2013 to 2019, the patients aged 60 had the highest cost savings and health outcomes (Table S1).\",\n \"Incremental cost‐effectiveness ratio tornado diagram of H. pylori eradication strategy versus no eradication strategy showed that cost‐effectiveness was not sensitive to any variables in all age groups (Figure 3A, Figure S2).\\nIn probabilistic sensitivity analysis using Monte‐Carlo simulation for 10,000 trials, the acceptability curve showed that H. pylori eradication strategy was cost‐effective 100% of the time at two willingness‐to‐pay thresholds of US$50,000 per QALY gained and US$100,000 per QALY gained in all age groups (Figure 3B, Figure S3). Incremental cost‐effectiveness scatterplots showed that H. pylori eradication strategy dominated no‐eradication strategy in more than 9800 trials in all age groups (Figure 3C, Figure S4). The results showed strong robustness of H. pylori eradication strategy in all age groups.\\nResults of the base‐case analysis\\nICER\\n(US$/\\nQALY gained)\\nLife expectancy\\nlife‐years (LYs)\\nICER\\n(US$/LY\\ngained)\\n\\nAbbrevations H. pylori = Helicobacter pylori; QALY = quality‐adjusted life‐year; LY = life expectancy life‐years; ICER = incremental cost‐effectiveness ratio; dominated = less effective and more costly than others;\\n\\nOne‐way sensitivity analysis and probabilistic sensitivity analysis in 60‐year‐old H. pylori‐positive patients. A, The incremental cost‐effectiveness ratio (ICER) tornado diagram for H. pylori eradication strategy versus no eradication strategy. The cost‐effectiveness of H. pylori eradication strategy was not sensitive to changes in any variables. B, Cost‐effectiveness acceptability curve for H. pylori eradication strategy versus no eradication strategy. The probabilistic sensitivity analysis analyzed 10,000 simulations of the model in which input parameters were randomly varied across pre‐specified statistical distributions. The x‐axis represents the willingness‐to‐pay threshold. The acceptability curve showed that H. pylori eradication strategy was cost‐effective 100% of the time at two willingness‐to‐pay thresholds of US$50,000 per QALY gained and US$100,000 per QALY gained. C, Incremental cost‐effectiveness scatterplots with 95% confidence ellipses for H. pylori eradication strategy versus no eradication strategy. Each dot represents a single simulation for a total of 10,000 simulations. Incremental cost‐effectiveness scatterplots showed that H. pylori eradication strategy dominated no‐eradication strategy in 9811 trials, and that H. pylori eradication strategy was more cost‐effective than no‐eradication strategy in 189 trials. EV = expected value; H. pylori = Helicobacter pylori; ICER = incremental cost‐effectivenessratio; QALY = quality‐adjusted life‐year; WTP = willingness‐to‐pay threshold\",\n \"\\nH. pylori‐positive patients aged 60 had the highest cumulative lifetime economic and health outcomes (Table S1). From 2013 to 2019 for 8.50 million patients, H. pylori eradication saved US$3.75 billion, increased 11.11 million QALYs and 0.45 million LYs, and prevented 284,188 cases and 65,060 deaths. For 35.59 million patients without eradication, H. pylori eradication has the potential to save US$14.82 billion, increase 43.10 million QALYs and 1.66 million LYs, and prevent 1,084,532 cases and 250,256 deaths (Table S1).\\nIn the Markov cohort analysis, the cumulative lifetime potential of gastric cancer cases and deaths from gastric cancer in H. pylori eradication strategy compared with no eradication strategy decreased by 30 to 33% in patients under 50 and by 25 to 28% in patients aged 50 and over (Figure S5). H. pylori eradication reduced the incidence and mortality of gastric cancer in the younger age groups greater than in the older age groups (Table 2, Figure S5).\",\n \"To the best of our knowledge, this is the first study to assess the economic and health impacts of population‐wide H. pylori eradication strategy in national gastric cancer prevention program covered by National Health Insurance in the world.\\nWe demonstrated that population‐wide H. pylori eradication strategy reduced costs, prevented gastric cancer, and reduced deaths from gastric cancer for all age groups in the modeling study with real‐life settings in Japan, even though most older adults with gastric mucosal atrophy require more than 10 years of follow‐up endoscopic screening after successful H. pylori eradication.\\n27\\n, \\n28\\n The cost savings of H. pylori eradication strategy from 2013 to 2019 were US$3.75 billion, 10.46 times the annual budget for cancer control in Japan. This means that the promotion of H. pylori eradication strategy focused on primary prevention of gastric cancer not only saves many lives from gastric cancer, but also leads to significant cost savings in the national budget.\\nIt is well known that the benefits of H. pylori eradication on the reduction of gastric cancer risk in the younger age groups are greater than those in the older age groups. Young individuals would benefit most from H. pylori eradication because it cures H. pylori related gastritis, reduces the risk of gastric cancer, and reduces transmission to their children.\\n7\\n This modeling study using the constant risk of gastric cancer development after successful eradication treatment demonstrated that H. pylori eradication reduced the incidence and mortality of gastric cancer in the younger age groups greater than in the older age groups. If we could modify to reduce the risk of gastric cancer development after successful eradication treatment in the younger age groups, more significant effects on reducing the incidence and mortality of gastric cancer could be shown in the younger age groups.\\nSurveillance of the local antibiotic resistance of H. pylori is recommended to identify the optimal empirical therapy for H. pylori eradication in the country.\\n7\\n Chiang et al demonstrated no change of the antibiotic resistance rate of H. pylori through the selection of effective eradication regimens and retesting those who had completed H. pylori treatments in mass H. pylori eradication program.\\n29\\n Guo et al found that successful H. pylori eradication potentially restored gastric microbiota to a similar status as found in uninfected individuals, and showed beneficial effects on gut microbiota.\\n30\\n Liou et al showed that H pylori eradication had no effect on antibiotic resistance of E coli and no significant change in the prevalence of metabolic syndrome.\\n31\\n These recent studies suggested that H. pylori eradication strategy with effective regimens and high compliance rates could provide significant benefits with minimal adverse effects in high‐risk countries.\\nSeveral economic analyses suggested that H. pylori screening followed by eradication treatment is cost‐effective to prevent gastric cancer, particularly in high‐risk populations.\\n32\\n, \\n33\\n, \\n34\\n, \\n35\\n, \\n36\\n, \\n37\\n, \\n38\\n, \\n39\\n, \\n40\\n Han et al demonstrated that H. pylori screening and eradication treatment effectively reduced the morbidity of gastric cancer and cancer‐related costs in asymptomatic infected individuals in China.\\n33\\n Chen et al showed that population‐based screen‐and‐treat strategy for H pylori infection proved cheaper and more effective for preventing gastric cancer, peptic ulcer disease, and nonulcer dyspepsia in asymptomatic general population compared with no‐screen strategy in China.\\n34\\n Zheng et al found that H. pylori eradication treatment was an economical strategy with lower costs and greater efficacy in first‐degree relatives of patients with gastric cancer in China.\\n35\\n Cheng et al demonstrated that H. pylori test‐and‐treat program was cost‐effective to prevent gastric cancer in an endemic area where H. pylori prevalence was >73.5% in Taiwan.\\n36\\n Teng et al found that H. pylori screening was likely to be cost‐effective particularly for Māori in New Zealand.\\n37\\n Beresniak et al showed that H. pylori test and eradication strategy including the use of urea breath test was the most cost‐effective compared to symptomatic treatment and upper gastrointestinal endoscopy in Spain.\\n38\\n Our previous studies demonstrated the superior cost‐effectiveness of H. pylori screening with eradication, compared to no screening, upper gastrointestinal series, and endoscopic screening for asymptomatic general populations in Japan.\\n39\\n, \\n40\\n\\n\\nThis study has several limitations. First, age‐specific numbers of patients with eradication were estimated based on database for Hokkaido (the north island of Japan), the expert opinion, and the literature.\\n10\\n, \\n11\\n Second, we did not consider reinfection and recurrence of H. pylori infection in the model. The reinfection rate after H. pylori eradication is very low. H. pylori infection is mainly transmitted in childhood, and recurrence of H. pylori infection after successful eradication is rare in adults.\\n41\\n Third, nonmedical indirect costs, such as lost productivity, were not included in this study. Forth, we did not consider other risk factors of gastric cancer such as smoking, high salt intake, a diet low in fruit and vegetables, and genetic factors in this study. Fifth, the difference in the stage distribution of gastric cancer between different age groups was not included in the model. Sixth, we did not consider the histological changes after eradication in chronic gastritis patients in the model. H. pylori infection is well known to initiate sequential histological changes such as non‐atrophic gastritis, atrophic gastritis, intestinal metaplasia, dysplasia, and intestinal‐type gastric cancer. Diffuse‐type gastric cancer is also associated with H. pylori infection. Persistent inflammation results in the development of gastric atrophy. Earlier H. pylori eradication should be considered for preventing gastric cancer development prior to the appearance of precancerous lesions.\\n42\\n\\nH. pylori eradication strongly correlates with improvement in intestinal metaplasia in the antrum and gastric atrophy in the corpus and antrum of the stomach.\\n43\\n More research is needed to incorporate the histological changes of gastric mucosa and future development of gastric cancer in chronic gastritis patients into the model. Finally, there are different costs, different epidemiological parameters, and medical systems in each country. Further cost‐effectiveness studies based on the variance of each country are required.\\nIn conclusion, we demonstrated in the modeling study with real‐life settings that national policy using population‐wide H. pylori eradication to prevent gastric cancer has significant cost savings and health impacts for young‐, middle‐, and old‐aged individuals in Japan. The findings strongly support the promotion of H. pylori eradication strategy for all age groups in high‐incidence countries. Based on cost‐effectiveness, introducing H. pylori eradication strategy into national gastric cancer policy should be considered in high‐risk countries around the world.\",\n \"The author has no conflicts of interest to declare.\",\n \"AK had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. AK and MA approved the final version of the manuscript, and involved in concept, design, and critical revision of the manuscript for important intellectual content. AK involved in acquisition, analysis, interpretation of data, drafting of the manuscript, and administrative, technical, or material support. MA involved in supervision.\",\n \"Supplementary Material\\nClick here for additional data file.\"\n]"},"all_sections_sec_types":{"kind":"list like","value":[null,"materials-and-methods",null,null,null,null,null,null,null,null,"results",null,null,null,"discussion","COI-statement",null,"supplementary-material"],"string":"[\n null,\n \"materials-and-methods\",\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n \"results\",\n null,\n null,\n null,\n \"discussion\",\n \"COI-statement\",\n null,\n \"supplementary-material\"\n]"},"keywords":{"kind":"list like","value":["cancer prevention","cost‐effectiveness","gastric cancer","health economics","\nHelicobacter pylori eradication"],"string":"[\n \"cancer prevention\",\n \"cost‐effectiveness\",\n \"gastric cancer\",\n \"health economics\",\n \"\\nHelicobacter pylori eradication\"\n]"},"whole_article_text":{"kind":"string","value":"INTRODUCTION:\nMore than half of the world's population is infected with Helicobacter pylori (H. pylori).\n1\n\nH. pylori infection causes chronic atrophic gastritis, a common stage of progression to gastric cancer, and is responsible for 98% of the causes of gastric cancer in Japan.\n2\n, \n3\n, \n4\n, \n5\n Japan has the third highest age‐standardized rate for gastric cancer in the world.\n6\n The incidence of gastric cancer in Japan is almost 10 times higher than that observed in the United States. The Taipei global consensus guidelines for screening and eradication of H. pylori for gastric cancer prevention recommend that mass screening and eradication of H. pylori should be considered in populations at higher risk of gastric cancer and that eradication therapy should be offered to all individuals infected with H. pylori.\n7\n In the guidelines for the management of H. pylori infection by the Japanese Society for Helicobacter Research, H. pylori eradication treatment is recommended to prevent gastric cancer for patients with H. pylori infection.\n8\n The Ministry of Health, Labour and Welfare approved expansion of National Health Insurance coverage for H. pylori eradication treatment in patients with chronic gastritis from February 2013.\n9\n During 2013‐2019, 8.50 million H. pylori‐positive patients received eradication treatment.\n10\n, \n11\n The number of deaths from gastric cancer is gradually declining, with 42,931 deaths in 2019 and 42,318 deaths in 2020 (Figure 1).\n12\n, \n13\n\n\nChanges in gastric cancer deaths in Japan from 2000 to 2020\nIn this study, we aimed to evaluate the economic and health effects of H. pylori eradication strategy in national gastric cancer prevention program in Japan.\n\nMATERIALS AND METHODS:\nStudy design and model structure We constructed a cohort state‐transition model with a Markov cycle tree for two strategies: H. pylori eradication strategy and no eradication strategy, using a healthcare payer perspective and a lifetime horizon. A cycle length of one year was chosen. The half‐cycle correction was applied. In the model, decision branches leaded directly to one Markov node per intervention strategy and the first events were modeled within the Markov cycle tree (Figure 2). We used TreeAge Pro (TreeAge Software Inc., Williamstown, Massachusetts) for the Decision‐analytical calculations. As this was a modeling study with all inputs and parameters derived from the published literature and Japanese statistics, ethics approval was not required.\nSchematic depiction of the Markov cycle tree in the cohort state‐transition model. We show that health states in the model as ovals. In a yearly model cycle, transitions can occur between the health states and other health states, represented by the arrows. H. pylori = Helicobacter pylori; GC = gastric cancer\nH. pylori eradication strategy The H. pylori‐positive patient receives first‐line eradication treatment (proton‐pump inhibitor, clarithromycin, and amoxicillin). The patient who fails first‐line eradication treatment receives second‐line eradication treatment (proton‐pump inhibitor, metronidazole, and amoxicillin). We consider the eradication and compliance rates of first‐line and second‐line eradication treatments in the model. After successful H. pylori eradication, H. pylori‐positive changes to H. pylori‐negative. When the patient fails both treatments, H. pylori‐positive remains until death. We calculate the costs of H. pylori test, endoscopy, and two urea breath tests when the patient receives H. pylori eradication treatment. When the patient has gastric cancer, the patient receives the standard treatment of gastric cancer followed by the Japanese gastric cancer treatment guidelines: endoscopic mucosal resection (EMR), endoscopic submucosal dissection treatment (ESD), surgery, chemotherapy, and radiotherapy with palliative care according to cancer stages, stage I‐IV.\n14\n The model includes the relative risk of developing gastric cancer after successful eradication, stage‐specific 5‐year survival rates, and mortality due to other causes (Table 1).\n12\n, \n15\n The patient aged 50 and over receives endoscopic screening every year from the year after eradication.\nBaseline estimates for selected variables\n20y\n30y\n40y\n50y\n60y\n70y\n80y\n0.000771\n0.001167\n0.001881\n0.002803\n0.005122\n0.007949\n0.009117\n20y\n30y\n40y\n50y\n60y\n70y\n80y\n0.000509\n0.00077\n0.00124\n0.00185\n0.00338\n0.00524\n0.00602\n20y\n30y\n40y\n50y\n60y\n70y\n80y\n6.1\n14.7\n23.7\n33.7\n47.7\n58.6\n63.6\nStage I\nStage II\nStage III\nStage IV\n96.0\n69.2\n41.9\n6.3\n90‐99\n50‐80\n30‐50\n0‐20\n20‐29y\n30‐39y\n40‐49y\n50‐59y\n60‐69y\n70‐79y\n80‐89y\n123,986\n422,965\n1,083,631\n1,664,732\n2,860,031\n1,903,756\n440,503\n20‐29y\n30‐39y\n40‐49y\n50‐59y\n60‐69y\n70‐79y\n80‐89y\n773,480\n2,034,480\n4,256,520\n5,634,640\n7,331,490\n9,622,120\n5,933,880\nStage I\nStage II\nStage III\nStage IV\n62.5\n11.0\n7.5\n19.0\n30‐80\n5‐20\n2‐15\n10‐50\nStage I\nStage II\nStage III\nStage IV\n3675\n15,898\n24,841\n29,809\n1838‐7350\n7949‐31,796\n12,421‐49,682\n14,905‐59,618\n25,26\n\nAbbrevations H. pylori = Helicobacter pylori; N/A = not applicable\n\nThe H. pylori‐positive patient receives first‐line eradication treatment (proton‐pump inhibitor, clarithromycin, and amoxicillin). The patient who fails first‐line eradication treatment receives second‐line eradication treatment (proton‐pump inhibitor, metronidazole, and amoxicillin). We consider the eradication and compliance rates of first‐line and second‐line eradication treatments in the model. After successful H. pylori eradication, H. pylori‐positive changes to H. pylori‐negative. When the patient fails both treatments, H. pylori‐positive remains until death. We calculate the costs of H. pylori test, endoscopy, and two urea breath tests when the patient receives H. pylori eradication treatment. When the patient has gastric cancer, the patient receives the standard treatment of gastric cancer followed by the Japanese gastric cancer treatment guidelines: endoscopic mucosal resection (EMR), endoscopic submucosal dissection treatment (ESD), surgery, chemotherapy, and radiotherapy with palliative care according to cancer stages, stage I‐IV.\n14\n The model includes the relative risk of developing gastric cancer after successful eradication, stage‐specific 5‐year survival rates, and mortality due to other causes (Table 1).\n12\n, \n15\n The patient aged 50 and over receives endoscopic screening every year from the year after eradication.\nBaseline estimates for selected variables\n20y\n30y\n40y\n50y\n60y\n70y\n80y\n0.000771\n0.001167\n0.001881\n0.002803\n0.005122\n0.007949\n0.009117\n20y\n30y\n40y\n50y\n60y\n70y\n80y\n0.000509\n0.00077\n0.00124\n0.00185\n0.00338\n0.00524\n0.00602\n20y\n30y\n40y\n50y\n60y\n70y\n80y\n6.1\n14.7\n23.7\n33.7\n47.7\n58.6\n63.6\nStage I\nStage II\nStage III\nStage IV\n96.0\n69.2\n41.9\n6.3\n90‐99\n50‐80\n30‐50\n0‐20\n20‐29y\n30‐39y\n40‐49y\n50‐59y\n60‐69y\n70‐79y\n80‐89y\n123,986\n422,965\n1,083,631\n1,664,732\n2,860,031\n1,903,756\n440,503\n20‐29y\n30‐39y\n40‐49y\n50‐59y\n60‐69y\n70‐79y\n80‐89y\n773,480\n2,034,480\n4,256,520\n5,634,640\n7,331,490\n9,622,120\n5,933,880\nStage I\nStage II\nStage III\nStage IV\n62.5\n11.0\n7.5\n19.0\n30‐80\n5‐20\n2‐15\n10‐50\nStage I\nStage II\nStage III\nStage IV\n3675\n15,898\n24,841\n29,809\n1838‐7350\n7949‐31,796\n12,421‐49,682\n14,905‐59,618\n25,26\n\nAbbrevations H. pylori = Helicobacter pylori; N/A = not applicable\n\nNo eradication strategy The latest version of Japanese guidelines for effective secondary prevention of gastric cancer recommends upper gastrointestinal series and endoscopy in adults 50 years of age and older. In the model, the H. pylori‐positive patient does not receive H. pylori eradication treatment, and the patient aged 50 and over receives annual endoscopic screening annually. When the patient has gastric cancer, the patient receives the standard treatment of gastric cancer.\nThe latest version of Japanese guidelines for effective secondary prevention of gastric cancer recommends upper gastrointestinal series and endoscopy in adults 50 years of age and older. In the model, the H. pylori‐positive patient does not receive H. pylori eradication treatment, and the patient aged 50 and over receives annual endoscopic screening annually. When the patient has gastric cancer, the patient receives the standard treatment of gastric cancer.\nWe constructed a cohort state‐transition model with a Markov cycle tree for two strategies: H. pylori eradication strategy and no eradication strategy, using a healthcare payer perspective and a lifetime horizon. A cycle length of one year was chosen. The half‐cycle correction was applied. In the model, decision branches leaded directly to one Markov node per intervention strategy and the first events were modeled within the Markov cycle tree (Figure 2). We used TreeAge Pro (TreeAge Software Inc., Williamstown, Massachusetts) for the Decision‐analytical calculations. As this was a modeling study with all inputs and parameters derived from the published literature and Japanese statistics, ethics approval was not required.\nSchematic depiction of the Markov cycle tree in the cohort state‐transition model. We show that health states in the model as ovals. In a yearly model cycle, transitions can occur between the health states and other health states, represented by the arrows. H. pylori = Helicobacter pylori; GC = gastric cancer\nH. pylori eradication strategy The H. pylori‐positive patient receives first‐line eradication treatment (proton‐pump inhibitor, clarithromycin, and amoxicillin). The patient who fails first‐line eradication treatment receives second‐line eradication treatment (proton‐pump inhibitor, metronidazole, and amoxicillin). We consider the eradication and compliance rates of first‐line and second‐line eradication treatments in the model. After successful H. pylori eradication, H. pylori‐positive changes to H. pylori‐negative. When the patient fails both treatments, H. pylori‐positive remains until death. We calculate the costs of H. pylori test, endoscopy, and two urea breath tests when the patient receives H. pylori eradication treatment. When the patient has gastric cancer, the patient receives the standard treatment of gastric cancer followed by the Japanese gastric cancer treatment guidelines: endoscopic mucosal resection (EMR), endoscopic submucosal dissection treatment (ESD), surgery, chemotherapy, and radiotherapy with palliative care according to cancer stages, stage I‐IV.\n14\n The model includes the relative risk of developing gastric cancer after successful eradication, stage‐specific 5‐year survival rates, and mortality due to other causes (Table 1).\n12\n, \n15\n The patient aged 50 and over receives endoscopic screening every year from the year after eradication.\nBaseline estimates for selected variables\n20y\n30y\n40y\n50y\n60y\n70y\n80y\n0.000771\n0.001167\n0.001881\n0.002803\n0.005122\n0.007949\n0.009117\n20y\n30y\n40y\n50y\n60y\n70y\n80y\n0.000509\n0.00077\n0.00124\n0.00185\n0.00338\n0.00524\n0.00602\n20y\n30y\n40y\n50y\n60y\n70y\n80y\n6.1\n14.7\n23.7\n33.7\n47.7\n58.6\n63.6\nStage I\nStage II\nStage III\nStage IV\n96.0\n69.2\n41.9\n6.3\n90‐99\n50‐80\n30‐50\n0‐20\n20‐29y\n30‐39y\n40‐49y\n50‐59y\n60‐69y\n70‐79y\n80‐89y\n123,986\n422,965\n1,083,631\n1,664,732\n2,860,031\n1,903,756\n440,503\n20‐29y\n30‐39y\n40‐49y\n50‐59y\n60‐69y\n70‐79y\n80‐89y\n773,480\n2,034,480\n4,256,520\n5,634,640\n7,331,490\n9,622,120\n5,933,880\nStage I\nStage II\nStage III\nStage IV\n62.5\n11.0\n7.5\n19.0\n30‐80\n5‐20\n2‐15\n10‐50\nStage I\nStage II\nStage III\nStage IV\n3675\n15,898\n24,841\n29,809\n1838‐7350\n7949‐31,796\n12,421‐49,682\n14,905‐59,618\n25,26\n\nAbbrevations H. pylori = Helicobacter pylori; N/A = not applicable\n\nThe H. pylori‐positive patient receives first‐line eradication treatment (proton‐pump inhibitor, clarithromycin, and amoxicillin). The patient who fails first‐line eradication treatment receives second‐line eradication treatment (proton‐pump inhibitor, metronidazole, and amoxicillin). We consider the eradication and compliance rates of first‐line and second‐line eradication treatments in the model. After successful H. pylori eradication, H. pylori‐positive changes to H. pylori‐negative. When the patient fails both treatments, H. pylori‐positive remains until death. We calculate the costs of H. pylori test, endoscopy, and two urea breath tests when the patient receives H. pylori eradication treatment. When the patient has gastric cancer, the patient receives the standard treatment of gastric cancer followed by the Japanese gastric cancer treatment guidelines: endoscopic mucosal resection (EMR), endoscopic submucosal dissection treatment (ESD), surgery, chemotherapy, and radiotherapy with palliative care according to cancer stages, stage I‐IV.\n14\n The model includes the relative risk of developing gastric cancer after successful eradication, stage‐specific 5‐year survival rates, and mortality due to other causes (Table 1).\n12\n, \n15\n The patient aged 50 and over receives endoscopic screening every year from the year after eradication.\nBaseline estimates for selected variables\n20y\n30y\n40y\n50y\n60y\n70y\n80y\n0.000771\n0.001167\n0.001881\n0.002803\n0.005122\n0.007949\n0.009117\n20y\n30y\n40y\n50y\n60y\n70y\n80y\n0.000509\n0.00077\n0.00124\n0.00185\n0.00338\n0.00524\n0.00602\n20y\n30y\n40y\n50y\n60y\n70y\n80y\n6.1\n14.7\n23.7\n33.7\n47.7\n58.6\n63.6\nStage I\nStage II\nStage III\nStage IV\n96.0\n69.2\n41.9\n6.3\n90‐99\n50‐80\n30‐50\n0‐20\n20‐29y\n30‐39y\n40‐49y\n50‐59y\n60‐69y\n70‐79y\n80‐89y\n123,986\n422,965\n1,083,631\n1,664,732\n2,860,031\n1,903,756\n440,503\n20‐29y\n30‐39y\n40‐49y\n50‐59y\n60‐69y\n70‐79y\n80‐89y\n773,480\n2,034,480\n4,256,520\n5,634,640\n7,331,490\n9,622,120\n5,933,880\nStage I\nStage II\nStage III\nStage IV\n62.5\n11.0\n7.5\n19.0\n30‐80\n5‐20\n2‐15\n10‐50\nStage I\nStage II\nStage III\nStage IV\n3675\n15,898\n24,841\n29,809\n1838‐7350\n7949‐31,796\n12,421‐49,682\n14,905‐59,618\n25,26\n\nAbbrevations H. pylori = Helicobacter pylori; N/A = not applicable\n\nNo eradication strategy The latest version of Japanese guidelines for effective secondary prevention of gastric cancer recommends upper gastrointestinal series and endoscopy in adults 50 years of age and older. In the model, the H. pylori‐positive patient does not receive H. pylori eradication treatment, and the patient aged 50 and over receives annual endoscopic screening annually. When the patient has gastric cancer, the patient receives the standard treatment of gastric cancer.\nThe latest version of Japanese guidelines for effective secondary prevention of gastric cancer recommends upper gastrointestinal series and endoscopy in adults 50 years of age and older. In the model, the H. pylori‐positive patient does not receive H. pylori eradication treatment, and the patient aged 50 and over receives annual endoscopic screening annually. When the patient has gastric cancer, the patient receives the standard treatment of gastric cancer.\nTarget population We targeted a hypothetical cohort of Japanese H. pylori‐positive chronic gastritis patients who had the initial endoscopic diagnosis needing H. pylori eradication at the age of 20, 30, 40, 50, 60, 70, and 80. Children and adolescents (age <20 y) were not included in the model.\nWe targeted a hypothetical cohort of Japanese H. pylori‐positive chronic gastritis patients who had the initial endoscopic diagnosis needing H. pylori eradication at the age of 20, 30, 40, 50, 60, 70, and 80. Children and adolescents (age <20 y) were not included in the model.\nEpidemiologic parameters and clinical probabilities Epidemiologic parameters and clinical probabilities were collected using MEDLINE from 2000 to June 2, 2021, national census, and Japanese cancer statistics (Table 1).\n2\n, \n3\n, \n10\n, \n11\n, \n12\n, \n15\n, \n16\n, \n17\n, \n18\n, \n19\n, \n20\n We estimated annual age‐specific numbers of H. pylori‐positive patients with eradication treatment from the literature\n10\n, \n11\n and expert opinion (Table1, Figure S1A). The numbers of H. pylori‐positive patients with and without eradication were estimated from the literature\n16\n and national census (Table1, Figure S1B). Relative risk of gastric cancer development after successful eradication\n15\n, and eradication and compliance rates of first‐ and second‐line eradication treatments\n19\n were obtained from the literature. Age‐specific gastric cancer incidence and stage‐specific 5‐year survival rate were obtained from Japanese cancer statistics.\n10\n The responsibility rate of H. pylori infection for gastric cancer development was assumed to be 98%.\n2\n, \n3\n, \n4\n, \n5\n The incidence of gastric cancer in H. pylori‐positive patients was estimated using the responsibility rate of H. pylori infection for gastric cancer development and the prevalence of H. pylori infection.\n2\n, \n3\n, \n4\n, \n5\n, \n12\n, \n16\n The incidence of gastric cancer in H. pylori‐positive patients after successful eradication treatment was estimated using the relative risk of gastric cancer development after successful eradication treatment.\n2\n, \n3\n, \n4\n, \n5\n, \n12\n, \n15\n, \n16\n The sensitivity and specificity of endoscopy were obtained from the literature.\n20\n\n\nEpidemiologic parameters and clinical probabilities were collected using MEDLINE from 2000 to June 2, 2021, national census, and Japanese cancer statistics (Table 1).\n2\n, \n3\n, \n10\n, \n11\n, \n12\n, \n15\n, \n16\n, \n17\n, \n18\n, \n19\n, \n20\n We estimated annual age‐specific numbers of H. pylori‐positive patients with eradication treatment from the literature\n10\n, \n11\n and expert opinion (Table1, Figure S1A). The numbers of H. pylori‐positive patients with and without eradication were estimated from the literature\n16\n and national census (Table1, Figure S1B). Relative risk of gastric cancer development after successful eradication\n15\n, and eradication and compliance rates of first‐ and second‐line eradication treatments\n19\n were obtained from the literature. Age‐specific gastric cancer incidence and stage‐specific 5‐year survival rate were obtained from Japanese cancer statistics.\n10\n The responsibility rate of H. pylori infection for gastric cancer development was assumed to be 98%.\n2\n, \n3\n, \n4\n, \n5\n The incidence of gastric cancer in H. pylori‐positive patients was estimated using the responsibility rate of H. pylori infection for gastric cancer development and the prevalence of H. pylori infection.\n2\n, \n3\n, \n4\n, \n5\n, \n12\n, \n16\n The incidence of gastric cancer in H. pylori‐positive patients after successful eradication treatment was estimated using the relative risk of gastric cancer development after successful eradication treatment.\n2\n, \n3\n, \n4\n, \n5\n, \n12\n, \n15\n, \n16\n The sensitivity and specificity of endoscopy were obtained from the literature.\n20\n\n\nCosts Costs were calculated based on the costs from the Japanese national fee schedule\n17\n and were adjusted to 2019 Japanese yen, using the medical care component of the Japanese consumer price index and were converted to US dollars, using the Organisation for Economic Co‐operation and Development (OECD) purchasing power parity rate in 2019 (US$1 = ¥100.64) (Table 1).\n21\n All costs were discounted by 3%.\n22\n, \n23\n Incremental cost‐effectiveness ratios (ICERs) were calculated and compared to two willingness‐to‐pay levels of US$50,000 per quality‐adjusted life‐year (QALY) gained and US$100,000 per QALY gained.\n24\n Age‐specific and total cumulative lifetime cost savings of H. pylori eradication strategy compared with no eradication strategy were calculated.\nCosts were calculated based on the costs from the Japanese national fee schedule\n17\n and were adjusted to 2019 Japanese yen, using the medical care component of the Japanese consumer price index and were converted to US dollars, using the Organisation for Economic Co‐operation and Development (OECD) purchasing power parity rate in 2019 (US$1 = ¥100.64) (Table 1).\n21\n All costs were discounted by 3%.\n22\n, \n23\n Incremental cost‐effectiveness ratios (ICERs) were calculated and compared to two willingness‐to‐pay levels of US$50,000 per quality‐adjusted life‐year (QALY) gained and US$100,000 per QALY gained.\n24\n Age‐specific and total cumulative lifetime cost savings of H. pylori eradication strategy compared with no eradication strategy were calculated.\nHealth utilities, effectiveness, and health outcomes Health status was included to represent possible eight clinical states: (i) No H. pylori infection, (ii) H. pylori infection, (iii) gastric cancer on stage I; (iv) gastric cancer on stage II; (v) gastric cancer on stage III; (vi) gastric cancer on stage IV; (vii) cured gastric cancer; and (viii) death (Figure 2). Health state utilities were obtained from the literature and were calculated using utility weights (Table 1).\n25\n, \n26\n The annual discounting of the utilities in this analysis was set at a rate of 3%.\n22\n, \n23\n\n\nThe health outcomes were QALYs, life expectancy life‐years (LYs), gastric cancer cases, and deaths from gastric cancer. Age‐specific and total cumulative lifetime health outcomes of H. pylori eradication strategy compared with no eradication strategy were calculated and evaluated.\nHealth status was included to represent possible eight clinical states: (i) No H. pylori infection, (ii) H. pylori infection, (iii) gastric cancer on stage I; (iv) gastric cancer on stage II; (v) gastric cancer on stage III; (vi) gastric cancer on stage IV; (vii) cured gastric cancer; and (viii) death (Figure 2). Health state utilities were obtained from the literature and were calculated using utility weights (Table 1).\n25\n, \n26\n The annual discounting of the utilities in this analysis was set at a rate of 3%.\n22\n, \n23\n\n\nThe health outcomes were QALYs, life expectancy life‐years (LYs), gastric cancer cases, and deaths from gastric cancer. Age‐specific and total cumulative lifetime health outcomes of H. pylori eradication strategy compared with no eradication strategy were calculated and evaluated.\nSensitivity analyses We performed a one‐way sensitivity analysis to determine which strategy was more cost‐effective when we tested a single variable over a wide range of possible values while holding all other variables constant, and performed a probabilistic sensitivity analysis using a second‐order Monte‐Carlo simulation for 10,000 trials to assess the impact of the uncertainty in the model on the base‐case estimates. The uncertainty had a beta distribution in clinical probabilities and accuracies, and a log‐normal distribution in costs.\nWe performed a one‐way sensitivity analysis to determine which strategy was more cost‐effective when we tested a single variable over a wide range of possible values while holding all other variables constant, and performed a probabilistic sensitivity analysis using a second‐order Monte‐Carlo simulation for 10,000 trials to assess the impact of the uncertainty in the model on the base‐case estimates. The uncertainty had a beta distribution in clinical probabilities and accuracies, and a log‐normal distribution in costs.\n\nStudy design and model structure:\nWe constructed a cohort state‐transition model with a Markov cycle tree for two strategies: H. pylori eradication strategy and no eradication strategy, using a healthcare payer perspective and a lifetime horizon. A cycle length of one year was chosen. The half‐cycle correction was applied. In the model, decision branches leaded directly to one Markov node per intervention strategy and the first events were modeled within the Markov cycle tree (Figure 2). We used TreeAge Pro (TreeAge Software Inc., Williamstown, Massachusetts) for the Decision‐analytical calculations. As this was a modeling study with all inputs and parameters derived from the published literature and Japanese statistics, ethics approval was not required.\nSchematic depiction of the Markov cycle tree in the cohort state‐transition model. We show that health states in the model as ovals. In a yearly model cycle, transitions can occur between the health states and other health states, represented by the arrows. H. pylori = Helicobacter pylori; GC = gastric cancer\nH. pylori eradication strategy The H. pylori‐positive patient receives first‐line eradication treatment (proton‐pump inhibitor, clarithromycin, and amoxicillin). The patient who fails first‐line eradication treatment receives second‐line eradication treatment (proton‐pump inhibitor, metronidazole, and amoxicillin). We consider the eradication and compliance rates of first‐line and second‐line eradication treatments in the model. After successful H. pylori eradication, H. pylori‐positive changes to H. pylori‐negative. When the patient fails both treatments, H. pylori‐positive remains until death. We calculate the costs of H. pylori test, endoscopy, and two urea breath tests when the patient receives H. pylori eradication treatment. When the patient has gastric cancer, the patient receives the standard treatment of gastric cancer followed by the Japanese gastric cancer treatment guidelines: endoscopic mucosal resection (EMR), endoscopic submucosal dissection treatment (ESD), surgery, chemotherapy, and radiotherapy with palliative care according to cancer stages, stage I‐IV.\n14\n The model includes the relative risk of developing gastric cancer after successful eradication, stage‐specific 5‐year survival rates, and mortality due to other causes (Table 1).\n12\n, \n15\n The patient aged 50 and over receives endoscopic screening every year from the year after eradication.\nBaseline estimates for selected variables\n20y\n30y\n40y\n50y\n60y\n70y\n80y\n0.000771\n0.001167\n0.001881\n0.002803\n0.005122\n0.007949\n0.009117\n20y\n30y\n40y\n50y\n60y\n70y\n80y\n0.000509\n0.00077\n0.00124\n0.00185\n0.00338\n0.00524\n0.00602\n20y\n30y\n40y\n50y\n60y\n70y\n80y\n6.1\n14.7\n23.7\n33.7\n47.7\n58.6\n63.6\nStage I\nStage II\nStage III\nStage IV\n96.0\n69.2\n41.9\n6.3\n90‐99\n50‐80\n30‐50\n0‐20\n20‐29y\n30‐39y\n40‐49y\n50‐59y\n60‐69y\n70‐79y\n80‐89y\n123,986\n422,965\n1,083,631\n1,664,732\n2,860,031\n1,903,756\n440,503\n20‐29y\n30‐39y\n40‐49y\n50‐59y\n60‐69y\n70‐79y\n80‐89y\n773,480\n2,034,480\n4,256,520\n5,634,640\n7,331,490\n9,622,120\n5,933,880\nStage I\nStage II\nStage III\nStage IV\n62.5\n11.0\n7.5\n19.0\n30‐80\n5‐20\n2‐15\n10‐50\nStage I\nStage II\nStage III\nStage IV\n3675\n15,898\n24,841\n29,809\n1838‐7350\n7949‐31,796\n12,421‐49,682\n14,905‐59,618\n25,26\n\nAbbrevations H. pylori = Helicobacter pylori; N/A = not applicable\n\nThe H. pylori‐positive patient receives first‐line eradication treatment (proton‐pump inhibitor, clarithromycin, and amoxicillin). The patient who fails first‐line eradication treatment receives second‐line eradication treatment (proton‐pump inhibitor, metronidazole, and amoxicillin). We consider the eradication and compliance rates of first‐line and second‐line eradication treatments in the model. After successful H. pylori eradication, H. pylori‐positive changes to H. pylori‐negative. When the patient fails both treatments, H. pylori‐positive remains until death. We calculate the costs of H. pylori test, endoscopy, and two urea breath tests when the patient receives H. pylori eradication treatment. When the patient has gastric cancer, the patient receives the standard treatment of gastric cancer followed by the Japanese gastric cancer treatment guidelines: endoscopic mucosal resection (EMR), endoscopic submucosal dissection treatment (ESD), surgery, chemotherapy, and radiotherapy with palliative care according to cancer stages, stage I‐IV.\n14\n The model includes the relative risk of developing gastric cancer after successful eradication, stage‐specific 5‐year survival rates, and mortality due to other causes (Table 1).\n12\n, \n15\n The patient aged 50 and over receives endoscopic screening every year from the year after eradication.\nBaseline estimates for selected variables\n20y\n30y\n40y\n50y\n60y\n70y\n80y\n0.000771\n0.001167\n0.001881\n0.002803\n0.005122\n0.007949\n0.009117\n20y\n30y\n40y\n50y\n60y\n70y\n80y\n0.000509\n0.00077\n0.00124\n0.00185\n0.00338\n0.00524\n0.00602\n20y\n30y\n40y\n50y\n60y\n70y\n80y\n6.1\n14.7\n23.7\n33.7\n47.7\n58.6\n63.6\nStage I\nStage II\nStage III\nStage IV\n96.0\n69.2\n41.9\n6.3\n90‐99\n50‐80\n30‐50\n0‐20\n20‐29y\n30‐39y\n40‐49y\n50‐59y\n60‐69y\n70‐79y\n80‐89y\n123,986\n422,965\n1,083,631\n1,664,732\n2,860,031\n1,903,756\n440,503\n20‐29y\n30‐39y\n40‐49y\n50‐59y\n60‐69y\n70‐79y\n80‐89y\n773,480\n2,034,480\n4,256,520\n5,634,640\n7,331,490\n9,622,120\n5,933,880\nStage I\nStage II\nStage III\nStage IV\n62.5\n11.0\n7.5\n19.0\n30‐80\n5‐20\n2‐15\n10‐50\nStage I\nStage II\nStage III\nStage IV\n3675\n15,898\n24,841\n29,809\n1838‐7350\n7949‐31,796\n12,421‐49,682\n14,905‐59,618\n25,26\n\nAbbrevations H. pylori = Helicobacter pylori; N/A = not applicable\n\nNo eradication strategy The latest version of Japanese guidelines for effective secondary prevention of gastric cancer recommends upper gastrointestinal series and endoscopy in adults 50 years of age and older. In the model, the H. pylori‐positive patient does not receive H. pylori eradication treatment, and the patient aged 50 and over receives annual endoscopic screening annually. When the patient has gastric cancer, the patient receives the standard treatment of gastric cancer.\nThe latest version of Japanese guidelines for effective secondary prevention of gastric cancer recommends upper gastrointestinal series and endoscopy in adults 50 years of age and older. In the model, the H. pylori‐positive patient does not receive H. pylori eradication treatment, and the patient aged 50 and over receives annual endoscopic screening annually. When the patient has gastric cancer, the patient receives the standard treatment of gastric cancer.\n\nH. pylori eradication strategy:\nThe H. pylori‐positive patient receives first‐line eradication treatment (proton‐pump inhibitor, clarithromycin, and amoxicillin). The patient who fails first‐line eradication treatment receives second‐line eradication treatment (proton‐pump inhibitor, metronidazole, and amoxicillin). We consider the eradication and compliance rates of first‐line and second‐line eradication treatments in the model. After successful H. pylori eradication, H. pylori‐positive changes to H. pylori‐negative. When the patient fails both treatments, H. pylori‐positive remains until death. We calculate the costs of H. pylori test, endoscopy, and two urea breath tests when the patient receives H. pylori eradication treatment. When the patient has gastric cancer, the patient receives the standard treatment of gastric cancer followed by the Japanese gastric cancer treatment guidelines: endoscopic mucosal resection (EMR), endoscopic submucosal dissection treatment (ESD), surgery, chemotherapy, and radiotherapy with palliative care according to cancer stages, stage I‐IV.\n14\n The model includes the relative risk of developing gastric cancer after successful eradication, stage‐specific 5‐year survival rates, and mortality due to other causes (Table 1).\n12\n, \n15\n The patient aged 50 and over receives endoscopic screening every year from the year after eradication.\nBaseline estimates for selected variables\n20y\n30y\n40y\n50y\n60y\n70y\n80y\n0.000771\n0.001167\n0.001881\n0.002803\n0.005122\n0.007949\n0.009117\n20y\n30y\n40y\n50y\n60y\n70y\n80y\n0.000509\n0.00077\n0.00124\n0.00185\n0.00338\n0.00524\n0.00602\n20y\n30y\n40y\n50y\n60y\n70y\n80y\n6.1\n14.7\n23.7\n33.7\n47.7\n58.6\n63.6\nStage I\nStage II\nStage III\nStage IV\n96.0\n69.2\n41.9\n6.3\n90‐99\n50‐80\n30‐50\n0‐20\n20‐29y\n30‐39y\n40‐49y\n50‐59y\n60‐69y\n70‐79y\n80‐89y\n123,986\n422,965\n1,083,631\n1,664,732\n2,860,031\n1,903,756\n440,503\n20‐29y\n30‐39y\n40‐49y\n50‐59y\n60‐69y\n70‐79y\n80‐89y\n773,480\n2,034,480\n4,256,520\n5,634,640\n7,331,490\n9,622,120\n5,933,880\nStage I\nStage II\nStage III\nStage IV\n62.5\n11.0\n7.5\n19.0\n30‐80\n5‐20\n2‐15\n10‐50\nStage I\nStage II\nStage III\nStage IV\n3675\n15,898\n24,841\n29,809\n1838‐7350\n7949‐31,796\n12,421‐49,682\n14,905‐59,618\n25,26\n\nAbbrevations H. pylori = Helicobacter pylori; N/A = not applicable\n\n\nNo eradication strategy:\nThe latest version of Japanese guidelines for effective secondary prevention of gastric cancer recommends upper gastrointestinal series and endoscopy in adults 50 years of age and older. In the model, the H. pylori‐positive patient does not receive H. pylori eradication treatment, and the patient aged 50 and over receives annual endoscopic screening annually. When the patient has gastric cancer, the patient receives the standard treatment of gastric cancer.\n\nTarget population:\nWe targeted a hypothetical cohort of Japanese H. pylori‐positive chronic gastritis patients who had the initial endoscopic diagnosis needing H. pylori eradication at the age of 20, 30, 40, 50, 60, 70, and 80. Children and adolescents (age <20 y) were not included in the model.\n\nEpidemiologic parameters and clinical probabilities:\nEpidemiologic parameters and clinical probabilities were collected using MEDLINE from 2000 to June 2, 2021, national census, and Japanese cancer statistics (Table 1).\n2\n, \n3\n, \n10\n, \n11\n, \n12\n, \n15\n, \n16\n, \n17\n, \n18\n, \n19\n, \n20\n We estimated annual age‐specific numbers of H. pylori‐positive patients with eradication treatment from the literature\n10\n, \n11\n and expert opinion (Table1, Figure S1A). The numbers of H. pylori‐positive patients with and without eradication were estimated from the literature\n16\n and national census (Table1, Figure S1B). Relative risk of gastric cancer development after successful eradication\n15\n, and eradication and compliance rates of first‐ and second‐line eradication treatments\n19\n were obtained from the literature. Age‐specific gastric cancer incidence and stage‐specific 5‐year survival rate were obtained from Japanese cancer statistics.\n10\n The responsibility rate of H. pylori infection for gastric cancer development was assumed to be 98%.\n2\n, \n3\n, \n4\n, \n5\n The incidence of gastric cancer in H. pylori‐positive patients was estimated using the responsibility rate of H. pylori infection for gastric cancer development and the prevalence of H. pylori infection.\n2\n, \n3\n, \n4\n, \n5\n, \n12\n, \n16\n The incidence of gastric cancer in H. pylori‐positive patients after successful eradication treatment was estimated using the relative risk of gastric cancer development after successful eradication treatment.\n2\n, \n3\n, \n4\n, \n5\n, \n12\n, \n15\n, \n16\n The sensitivity and specificity of endoscopy were obtained from the literature.\n20\n\n\n\nCosts:\nCosts were calculated based on the costs from the Japanese national fee schedule\n17\n and were adjusted to 2019 Japanese yen, using the medical care component of the Japanese consumer price index and were converted to US dollars, using the Organisation for Economic Co‐operation and Development (OECD) purchasing power parity rate in 2019 (US$1 = ¥100.64) (Table 1).\n21\n All costs were discounted by 3%.\n22\n, \n23\n Incremental cost‐effectiveness ratios (ICERs) were calculated and compared to two willingness‐to‐pay levels of US$50,000 per quality‐adjusted life‐year (QALY) gained and US$100,000 per QALY gained.\n24\n Age‐specific and total cumulative lifetime cost savings of H. pylori eradication strategy compared with no eradication strategy were calculated.\n\nHealth utilities, effectiveness, and health outcomes:\nHealth status was included to represent possible eight clinical states: (i) No H. pylori infection, (ii) H. pylori infection, (iii) gastric cancer on stage I; (iv) gastric cancer on stage II; (v) gastric cancer on stage III; (vi) gastric cancer on stage IV; (vii) cured gastric cancer; and (viii) death (Figure 2). Health state utilities were obtained from the literature and were calculated using utility weights (Table 1).\n25\n, \n26\n The annual discounting of the utilities in this analysis was set at a rate of 3%.\n22\n, \n23\n\n\nThe health outcomes were QALYs, life expectancy life‐years (LYs), gastric cancer cases, and deaths from gastric cancer. Age‐specific and total cumulative lifetime health outcomes of H. pylori eradication strategy compared with no eradication strategy were calculated and evaluated.\n\nSensitivity analyses:\nWe performed a one‐way sensitivity analysis to determine which strategy was more cost‐effective when we tested a single variable over a wide range of possible values while holding all other variables constant, and performed a probabilistic sensitivity analysis using a second‐order Monte‐Carlo simulation for 10,000 trials to assess the impact of the uncertainty in the model on the base‐case estimates. The uncertainty had a beta distribution in clinical probabilities and accuracies, and a log‐normal distribution in costs.\n\nRESULTS:\nBase‐case analysis \nH. pylori eradication strategy was less costly and yielded greater benefits than no eradication strategy for all age groups (Table 2). No eradication strategy was dominated for all age groups. The patients aged 40 had the highest per capita cost‐savings. Per capita gains of QALYs in younger patients were higher than in older patients (Table 2). From 2013 to 2019, the patients aged 60 had the highest cost savings and health outcomes (Table S1).\n\nH. pylori eradication strategy was less costly and yielded greater benefits than no eradication strategy for all age groups (Table 2). No eradication strategy was dominated for all age groups. The patients aged 40 had the highest per capita cost‐savings. Per capita gains of QALYs in younger patients were higher than in older patients (Table 2). From 2013 to 2019, the patients aged 60 had the highest cost savings and health outcomes (Table S1).\nOne‐way sensitivity analysis and probabilistic sensitivity analysis Incremental cost‐effectiveness ratio tornado diagram of H. pylori eradication strategy versus no eradication strategy showed that cost‐effectiveness was not sensitive to any variables in all age groups (Figure 3A, Figure S2).\nIn probabilistic sensitivity analysis using Monte‐Carlo simulation for 10,000 trials, the acceptability curve showed that H. pylori eradication strategy was cost‐effective 100% of the time at two willingness‐to‐pay thresholds of US$50,000 per QALY gained and US$100,000 per QALY gained in all age groups (Figure 3B, Figure S3). Incremental cost‐effectiveness scatterplots showed that H. pylori eradication strategy dominated no‐eradication strategy in more than 9800 trials in all age groups (Figure 3C, Figure S4). The results showed strong robustness of H. pylori eradication strategy in all age groups.\nResults of the base‐case analysis\nICER\n(US$/\nQALY gained)\nLife expectancy\nlife‐years (LYs)\nICER\n(US$/LY\ngained)\n\nAbbrevations H. pylori = Helicobacter pylori; QALY = quality‐adjusted life‐year; LY = life expectancy life‐years; ICER = incremental cost‐effectiveness ratio; dominated = less effective and more costly than others;\n\nOne‐way sensitivity analysis and probabilistic sensitivity analysis in 60‐year‐old H. pylori‐positive patients. A, The incremental cost‐effectiveness ratio (ICER) tornado diagram for H. pylori eradication strategy versus no eradication strategy. The cost‐effectiveness of H. pylori eradication strategy was not sensitive to changes in any variables. B, Cost‐effectiveness acceptability curve for H. pylori eradication strategy versus no eradication strategy. The probabilistic sensitivity analysis analyzed 10,000 simulations of the model in which input parameters were randomly varied across pre‐specified statistical distributions. The x‐axis represents the willingness‐to‐pay threshold. The acceptability curve showed that H. pylori eradication strategy was cost‐effective 100% of the time at two willingness‐to‐pay thresholds of US$50,000 per QALY gained and US$100,000 per QALY gained. C, Incremental cost‐effectiveness scatterplots with 95% confidence ellipses for H. pylori eradication strategy versus no eradication strategy. Each dot represents a single simulation for a total of 10,000 simulations. Incremental cost‐effectiveness scatterplots showed that H. pylori eradication strategy dominated no‐eradication strategy in 9811 trials, and that H. pylori eradication strategy was more cost‐effective than no‐eradication strategy in 189 trials. EV = expected value; H. pylori = Helicobacter pylori; ICER = incremental cost‐effectivenessratio; QALY = quality‐adjusted life‐year; WTP = willingness‐to‐pay threshold\nIncremental cost‐effectiveness ratio tornado diagram of H. pylori eradication strategy versus no eradication strategy showed that cost‐effectiveness was not sensitive to any variables in all age groups (Figure 3A, Figure S2).\nIn probabilistic sensitivity analysis using Monte‐Carlo simulation for 10,000 trials, the acceptability curve showed that H. pylori eradication strategy was cost‐effective 100% of the time at two willingness‐to‐pay thresholds of US$50,000 per QALY gained and US$100,000 per QALY gained in all age groups (Figure 3B, Figure S3). Incremental cost‐effectiveness scatterplots showed that H. pylori eradication strategy dominated no‐eradication strategy in more than 9800 trials in all age groups (Figure 3C, Figure S4). The results showed strong robustness of H. pylori eradication strategy in all age groups.\nResults of the base‐case analysis\nICER\n(US$/\nQALY gained)\nLife expectancy\nlife‐years (LYs)\nICER\n(US$/LY\ngained)\n\nAbbrevations H. pylori = Helicobacter pylori; QALY = quality‐adjusted life‐year; LY = life expectancy life‐years; ICER = incremental cost‐effectiveness ratio; dominated = less effective and more costly than others;\n\nOne‐way sensitivity analysis and probabilistic sensitivity analysis in 60‐year‐old H. pylori‐positive patients. A, The incremental cost‐effectiveness ratio (ICER) tornado diagram for H. pylori eradication strategy versus no eradication strategy. The cost‐effectiveness of H. pylori eradication strategy was not sensitive to changes in any variables. B, Cost‐effectiveness acceptability curve for H. pylori eradication strategy versus no eradication strategy. The probabilistic sensitivity analysis analyzed 10,000 simulations of the model in which input parameters were randomly varied across pre‐specified statistical distributions. The x‐axis represents the willingness‐to‐pay threshold. The acceptability curve showed that H. pylori eradication strategy was cost‐effective 100% of the time at two willingness‐to‐pay thresholds of US$50,000 per QALY gained and US$100,000 per QALY gained. C, Incremental cost‐effectiveness scatterplots with 95% confidence ellipses for H. pylori eradication strategy versus no eradication strategy. Each dot represents a single simulation for a total of 10,000 simulations. Incremental cost‐effectiveness scatterplots showed that H. pylori eradication strategy dominated no‐eradication strategy in 9811 trials, and that H. pylori eradication strategy was more cost‐effective than no‐eradication strategy in 189 trials. EV = expected value; H. pylori = Helicobacter pylori; ICER = incremental cost‐effectivenessratio; QALY = quality‐adjusted life‐year; WTP = willingness‐to‐pay threshold\nCumulative lifetime economic and health outcomes \nH. pylori‐positive patients aged 60 had the highest cumulative lifetime economic and health outcomes (Table S1). From 2013 to 2019 for 8.50 million patients, H. pylori eradication saved US$3.75 billion, increased 11.11 million QALYs and 0.45 million LYs, and prevented 284,188 cases and 65,060 deaths. For 35.59 million patients without eradication, H. pylori eradication has the potential to save US$14.82 billion, increase 43.10 million QALYs and 1.66 million LYs, and prevent 1,084,532 cases and 250,256 deaths (Table S1).\nIn the Markov cohort analysis, the cumulative lifetime potential of gastric cancer cases and deaths from gastric cancer in H. pylori eradication strategy compared with no eradication strategy decreased by 30 to 33% in patients under 50 and by 25 to 28% in patients aged 50 and over (Figure S5). H. pylori eradication reduced the incidence and mortality of gastric cancer in the younger age groups greater than in the older age groups (Table 2, Figure S5).\n\nH. pylori‐positive patients aged 60 had the highest cumulative lifetime economic and health outcomes (Table S1). From 2013 to 2019 for 8.50 million patients, H. pylori eradication saved US$3.75 billion, increased 11.11 million QALYs and 0.45 million LYs, and prevented 284,188 cases and 65,060 deaths. For 35.59 million patients without eradication, H. pylori eradication has the potential to save US$14.82 billion, increase 43.10 million QALYs and 1.66 million LYs, and prevent 1,084,532 cases and 250,256 deaths (Table S1).\nIn the Markov cohort analysis, the cumulative lifetime potential of gastric cancer cases and deaths from gastric cancer in H. pylori eradication strategy compared with no eradication strategy decreased by 30 to 33% in patients under 50 and by 25 to 28% in patients aged 50 and over (Figure S5). H. pylori eradication reduced the incidence and mortality of gastric cancer in the younger age groups greater than in the older age groups (Table 2, Figure S5).\n\nBase‐case analysis:\n\nH. pylori eradication strategy was less costly and yielded greater benefits than no eradication strategy for all age groups (Table 2). No eradication strategy was dominated for all age groups. The patients aged 40 had the highest per capita cost‐savings. Per capita gains of QALYs in younger patients were higher than in older patients (Table 2). From 2013 to 2019, the patients aged 60 had the highest cost savings and health outcomes (Table S1).\n\nOne‐way sensitivity analysis and probabilistic sensitivity analysis:\nIncremental cost‐effectiveness ratio tornado diagram of H. pylori eradication strategy versus no eradication strategy showed that cost‐effectiveness was not sensitive to any variables in all age groups (Figure 3A, Figure S2).\nIn probabilistic sensitivity analysis using Monte‐Carlo simulation for 10,000 trials, the acceptability curve showed that H. pylori eradication strategy was cost‐effective 100% of the time at two willingness‐to‐pay thresholds of US$50,000 per QALY gained and US$100,000 per QALY gained in all age groups (Figure 3B, Figure S3). Incremental cost‐effectiveness scatterplots showed that H. pylori eradication strategy dominated no‐eradication strategy in more than 9800 trials in all age groups (Figure 3C, Figure S4). The results showed strong robustness of H. pylori eradication strategy in all age groups.\nResults of the base‐case analysis\nICER\n(US$/\nQALY gained)\nLife expectancy\nlife‐years (LYs)\nICER\n(US$/LY\ngained)\n\nAbbrevations H. pylori = Helicobacter pylori; QALY = quality‐adjusted life‐year; LY = life expectancy life‐years; ICER = incremental cost‐effectiveness ratio; dominated = less effective and more costly than others;\n\nOne‐way sensitivity analysis and probabilistic sensitivity analysis in 60‐year‐old H. pylori‐positive patients. A, The incremental cost‐effectiveness ratio (ICER) tornado diagram for H. pylori eradication strategy versus no eradication strategy. The cost‐effectiveness of H. pylori eradication strategy was not sensitive to changes in any variables. B, Cost‐effectiveness acceptability curve for H. pylori eradication strategy versus no eradication strategy. The probabilistic sensitivity analysis analyzed 10,000 simulations of the model in which input parameters were randomly varied across pre‐specified statistical distributions. The x‐axis represents the willingness‐to‐pay threshold. The acceptability curve showed that H. pylori eradication strategy was cost‐effective 100% of the time at two willingness‐to‐pay thresholds of US$50,000 per QALY gained and US$100,000 per QALY gained. C, Incremental cost‐effectiveness scatterplots with 95% confidence ellipses for H. pylori eradication strategy versus no eradication strategy. Each dot represents a single simulation for a total of 10,000 simulations. Incremental cost‐effectiveness scatterplots showed that H. pylori eradication strategy dominated no‐eradication strategy in 9811 trials, and that H. pylori eradication strategy was more cost‐effective than no‐eradication strategy in 189 trials. EV = expected value; H. pylori = Helicobacter pylori; ICER = incremental cost‐effectivenessratio; QALY = quality‐adjusted life‐year; WTP = willingness‐to‐pay threshold\n\nCumulative lifetime economic and health outcomes:\n\nH. pylori‐positive patients aged 60 had the highest cumulative lifetime economic and health outcomes (Table S1). From 2013 to 2019 for 8.50 million patients, H. pylori eradication saved US$3.75 billion, increased 11.11 million QALYs and 0.45 million LYs, and prevented 284,188 cases and 65,060 deaths. For 35.59 million patients without eradication, H. pylori eradication has the potential to save US$14.82 billion, increase 43.10 million QALYs and 1.66 million LYs, and prevent 1,084,532 cases and 250,256 deaths (Table S1).\nIn the Markov cohort analysis, the cumulative lifetime potential of gastric cancer cases and deaths from gastric cancer in H. pylori eradication strategy compared with no eradication strategy decreased by 30 to 33% in patients under 50 and by 25 to 28% in patients aged 50 and over (Figure S5). H. pylori eradication reduced the incidence and mortality of gastric cancer in the younger age groups greater than in the older age groups (Table 2, Figure S5).\n\nDISCUSSION:\nTo the best of our knowledge, this is the first study to assess the economic and health impacts of population‐wide H. pylori eradication strategy in national gastric cancer prevention program covered by National Health Insurance in the world.\nWe demonstrated that population‐wide H. pylori eradication strategy reduced costs, prevented gastric cancer, and reduced deaths from gastric cancer for all age groups in the modeling study with real‐life settings in Japan, even though most older adults with gastric mucosal atrophy require more than 10 years of follow‐up endoscopic screening after successful H. pylori eradication.\n27\n, \n28\n The cost savings of H. pylori eradication strategy from 2013 to 2019 were US$3.75 billion, 10.46 times the annual budget for cancer control in Japan. This means that the promotion of H. pylori eradication strategy focused on primary prevention of gastric cancer not only saves many lives from gastric cancer, but also leads to significant cost savings in the national budget.\nIt is well known that the benefits of H. pylori eradication on the reduction of gastric cancer risk in the younger age groups are greater than those in the older age groups. Young individuals would benefit most from H. pylori eradication because it cures H. pylori related gastritis, reduces the risk of gastric cancer, and reduces transmission to their children.\n7\n This modeling study using the constant risk of gastric cancer development after successful eradication treatment demonstrated that H. pylori eradication reduced the incidence and mortality of gastric cancer in the younger age groups greater than in the older age groups. If we could modify to reduce the risk of gastric cancer development after successful eradication treatment in the younger age groups, more significant effects on reducing the incidence and mortality of gastric cancer could be shown in the younger age groups.\nSurveillance of the local antibiotic resistance of H. pylori is recommended to identify the optimal empirical therapy for H. pylori eradication in the country.\n7\n Chiang et al demonstrated no change of the antibiotic resistance rate of H. pylori through the selection of effective eradication regimens and retesting those who had completed H. pylori treatments in mass H. pylori eradication program.\n29\n Guo et al found that successful H. pylori eradication potentially restored gastric microbiota to a similar status as found in uninfected individuals, and showed beneficial effects on gut microbiota.\n30\n Liou et al showed that H pylori eradication had no effect on antibiotic resistance of E coli and no significant change in the prevalence of metabolic syndrome.\n31\n These recent studies suggested that H. pylori eradication strategy with effective regimens and high compliance rates could provide significant benefits with minimal adverse effects in high‐risk countries.\nSeveral economic analyses suggested that H. pylori screening followed by eradication treatment is cost‐effective to prevent gastric cancer, particularly in high‐risk populations.\n32\n, \n33\n, \n34\n, \n35\n, \n36\n, \n37\n, \n38\n, \n39\n, \n40\n Han et al demonstrated that H. pylori screening and eradication treatment effectively reduced the morbidity of gastric cancer and cancer‐related costs in asymptomatic infected individuals in China.\n33\n Chen et al showed that population‐based screen‐and‐treat strategy for H pylori infection proved cheaper and more effective for preventing gastric cancer, peptic ulcer disease, and nonulcer dyspepsia in asymptomatic general population compared with no‐screen strategy in China.\n34\n Zheng et al found that H. pylori eradication treatment was an economical strategy with lower costs and greater efficacy in first‐degree relatives of patients with gastric cancer in China.\n35\n Cheng et al demonstrated that H. pylori test‐and‐treat program was cost‐effective to prevent gastric cancer in an endemic area where H. pylori prevalence was >73.5% in Taiwan.\n36\n Teng et al found that H. pylori screening was likely to be cost‐effective particularly for Māori in New Zealand.\n37\n Beresniak et al showed that H. pylori test and eradication strategy including the use of urea breath test was the most cost‐effective compared to symptomatic treatment and upper gastrointestinal endoscopy in Spain.\n38\n Our previous studies demonstrated the superior cost‐effectiveness of H. pylori screening with eradication, compared to no screening, upper gastrointestinal series, and endoscopic screening for asymptomatic general populations in Japan.\n39\n, \n40\n\n\nThis study has several limitations. First, age‐specific numbers of patients with eradication were estimated based on database for Hokkaido (the north island of Japan), the expert opinion, and the literature.\n10\n, \n11\n Second, we did not consider reinfection and recurrence of H. pylori infection in the model. The reinfection rate after H. pylori eradication is very low. H. pylori infection is mainly transmitted in childhood, and recurrence of H. pylori infection after successful eradication is rare in adults.\n41\n Third, nonmedical indirect costs, such as lost productivity, were not included in this study. Forth, we did not consider other risk factors of gastric cancer such as smoking, high salt intake, a diet low in fruit and vegetables, and genetic factors in this study. Fifth, the difference in the stage distribution of gastric cancer between different age groups was not included in the model. Sixth, we did not consider the histological changes after eradication in chronic gastritis patients in the model. H. pylori infection is well known to initiate sequential histological changes such as non‐atrophic gastritis, atrophic gastritis, intestinal metaplasia, dysplasia, and intestinal‐type gastric cancer. Diffuse‐type gastric cancer is also associated with H. pylori infection. Persistent inflammation results in the development of gastric atrophy. Earlier H. pylori eradication should be considered for preventing gastric cancer development prior to the appearance of precancerous lesions.\n42\n\nH. pylori eradication strongly correlates with improvement in intestinal metaplasia in the antrum and gastric atrophy in the corpus and antrum of the stomach.\n43\n More research is needed to incorporate the histological changes of gastric mucosa and future development of gastric cancer in chronic gastritis patients into the model. Finally, there are different costs, different epidemiological parameters, and medical systems in each country. Further cost‐effectiveness studies based on the variance of each country are required.\nIn conclusion, we demonstrated in the modeling study with real‐life settings that national policy using population‐wide H. pylori eradication to prevent gastric cancer has significant cost savings and health impacts for young‐, middle‐, and old‐aged individuals in Japan. The findings strongly support the promotion of H. pylori eradication strategy for all age groups in high‐incidence countries. Based on cost‐effectiveness, introducing H. pylori eradication strategy into national gastric cancer policy should be considered in high‐risk countries around the world.\n\nCONFLICTS OF INTEREST:\nThe author has no conflicts of interest to declare.\n\nAUTHOR CONTRIBUTIONS:\nAK had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. AK and MA approved the final version of the manuscript, and involved in concept, design, and critical revision of the manuscript for important intellectual content. AK involved in acquisition, analysis, interpretation of data, drafting of the manuscript, and administrative, technical, or material support. MA involved in supervision.\n\nSupporting information:\nSupplementary Material\nClick here for additional data file.\n\n"},"whole_article_abstract":{"kind":"string","value":"Background:\nHelicobacter pylori (H. pylori) eradication reduces gastric cancer risk. Since 2013, a population-wide H. pylori eradication strategy for patients with chronic gastritis has begun to prevent gastric cancer in Japan. The aim of this study was to evaluate the economic and health effects of H. pylori eradication strategy in national gastric cancer prevention program.\n\nMethods:\nWe developed a cohort state-transition model for H. pylori eradication and no eradication over a lifetime horizon from a healthcare payer perspective, and performed one-way and probabilistic sensitivity analyses. We targeted a hypothetical cohort of H. pylori-positive patients aged 20, 30, 40, 50, 60, 70, and 80. The main outcomes were costs, quality-adjusted life-years (QALYs), life expectancy life-years (LYs), incremental cost-effectiveness ratios, gastric cancer cases, and deaths from gastric cancer.\n\nResults:\nH. pylori eradication was more effective and cost-saving for all age groups than no eradication. Sensitivity analyses showed strong robustness of the results. From 2013-2019 for 8.50 million patients, H. pylori eradication saved US$3.75 billion, increased 11.11 million QALYs and 0.45 million LYs, and prevented 284,188 cases and 65,060 deaths. For 35.59 million patients without eradication, H. pylori eradication has the potential to save US$14.82 billion, increase 43.10 million QALYs and 1.66 million LYs, and prevent 1,084,532 cases and 250,256 deaths.\n\nConclusions:\nNational policy using population-wide H. pylori eradication to prevent gastric cancer has significant cost savings and health impacts for young-, middle-, and old-aged individuals in Japan. The findings strongly support the promotion of H. pylori eradication strategy for all age groups in high-incidence countries."},"background_conclusion_text":{"kind":"null"},"background_conclusion_abstract":{"kind":"null"},"whole_article_text_length":{"kind":"number","value":10643,"string":"10,643"},"whole_article_abstract_length":{"kind":"number","value":331,"string":"331"},"other_sections_lengths":{"kind":"list like","value":[322,1275,468,74,57,341,145,175,81,87,421,182,87],"string":"[\n 322,\n 1275,\n 468,\n 74,\n 57,\n 341,\n 145,\n 175,\n 81,\n 87,\n 421,\n 182,\n 87\n]"},"num_sections":{"kind":"number","value":18,"string":"18"},"most_frequent_words":{"kind":"list like","value":["pylori","eradication","cancer","gastric","gastric cancer","stage","strategy","pylori eradication","eradication strategy","treatment"],"string":"[\n \"pylori\",\n \"eradication\",\n \"cancer\",\n \"gastric\",\n \"gastric cancer\",\n \"stage\",\n \"strategy\",\n \"pylori eradication\",\n \"eradication strategy\",\n \"treatment\"\n]"},"keybert_topics":{"kind":"list like","value":["cancer pylori eradication","pylori test eradication","effectiveness pylori eradication","eradication pylori gastric","pylori screening eradication"],"string":"[\n \"cancer pylori eradication\",\n \"pylori test eradication\",\n \"effectiveness pylori eradication\",\n \"eradication pylori gastric\",\n \"pylori screening eradication\"\n]"},"annotated_base_background_abstract_prompt":{"kind":"string","value":""},"annotated_base_methods_abstract_prompt":{"kind":"null"},"annotated_base_results_abstract_prompt":{"kind":"string","value":""},"annotated_base_conclusions_abstract_prompt":{"kind":"null"},"annotated_base_whole_article_abstract_prompt":{"kind":"string","value":""},"annotated_base_background_conclusion_abstract_prompt":{"kind":"null"},"annotated_keywords_background_abstract_prompt":{"kind":"string","value":"[CONTENT] cancer prevention | cost‐effectiveness | gastric cancer | health economics | \nHelicobacter pylori eradication [SUMMARY]"},"annotated_keywords_methods_abstract_prompt":{"kind":"null"},"annotated_keywords_results_abstract_prompt":{"kind":"string","value":"[CONTENT] cancer prevention | cost‐effectiveness | gastric cancer | health economics | \nHelicobacter pylori eradication [SUMMARY]"},"annotated_keywords_conclusions_abstract_prompt":{"kind":"null"},"annotated_keywords_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] cancer prevention | cost‐effectiveness | gastric cancer | health economics | \nHelicobacter pylori eradication [SUMMARY]"},"annotated_keywords_background_conclusion_abstract_prompt":{"kind":"null"},"annotated_mesh_background_abstract_prompt":{"kind":"string","value":"[CONTENT] Aged | Cost-Benefit Analysis | Helicobacter Infections | Helicobacter pylori | Humans | Japan | Middle Aged | Policy | Stomach Neoplasms [SUMMARY]"},"annotated_mesh_methods_abstract_prompt":{"kind":"null"},"annotated_mesh_results_abstract_prompt":{"kind":"string","value":"[CONTENT] Aged | Cost-Benefit Analysis | Helicobacter Infections | Helicobacter pylori | Humans | Japan | Middle Aged | Policy | Stomach Neoplasms [SUMMARY]"},"annotated_mesh_conclusions_abstract_prompt":{"kind":"null"},"annotated_mesh_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] Aged | Cost-Benefit Analysis | Helicobacter Infections | Helicobacter pylori | Humans | Japan | Middle Aged | Policy | Stomach Neoplasms [SUMMARY]"},"annotated_mesh_background_conclusion_abstract_prompt":{"kind":"null"},"annotated_keybert_background_abstract_prompt":{"kind":"string","value":"[CONTENT] cancer pylori eradication | pylori test eradication | effectiveness pylori eradication | eradication pylori gastric | pylori screening eradication [SUMMARY]"},"annotated_keybert_methods_abstract_prompt":{"kind":"null"},"annotated_keybert_results_abstract_prompt":{"kind":"string","value":"[CONTENT] cancer pylori eradication | pylori test eradication | effectiveness pylori eradication | eradication pylori gastric | pylori screening eradication [SUMMARY]"},"annotated_keybert_conclusions_abstract_prompt":{"kind":"null"},"annotated_keybert_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] cancer pylori eradication | pylori test eradication | effectiveness pylori eradication | eradication pylori gastric | pylori screening eradication [SUMMARY]"},"annotated_keybert_background_conclusion_abstract_prompt":{"kind":"null"},"annotated_most_frequent_background_abstract_prompt":{"kind":"string","value":"[CONTENT] pylori | eradication | cancer | gastric | gastric cancer | stage | strategy | pylori eradication | eradication strategy | treatment [SUMMARY]"},"annotated_most_frequent_methods_abstract_prompt":{"kind":"null"},"annotated_most_frequent_results_abstract_prompt":{"kind":"string","value":"[CONTENT] pylori | eradication | cancer | gastric | gastric cancer | stage | strategy | pylori eradication | eradication strategy | treatment [SUMMARY]"},"annotated_most_frequent_conclusions_abstract_prompt":{"kind":"null"},"annotated_most_frequent_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] pylori | eradication | cancer | gastric | gastric cancer | stage | strategy | pylori eradication | eradication strategy | treatment [SUMMARY]"},"annotated_most_frequent_background_conclusion_abstract_prompt":{"kind":"null"},"annotated_tf_idf_background_abstract_prompt":{"kind":"string","value":"[CONTENT] gastric cancer | cancer | gastric | pylori | japan | eradication | deaths | screening eradication pylori | pylori pylori | cancer japan [SUMMARY]"},"annotated_tf_idf_methods_abstract_prompt":{"kind":"null"},"annotated_tf_idf_results_abstract_prompt":{"kind":"string","value":"[CONTENT] eradication strategy | strategy | eradication | cost | pylori | pylori eradication | pylori eradication strategy | cost effectiveness | effectiveness | groups [SUMMARY]"},"annotated_tf_idf_conclusions_abstract_prompt":{"kind":"null"},"annotated_tf_idf_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] pylori | eradication | cancer | gastric | gastric cancer | strategy | eradication strategy | stage | patient | pylori eradication [SUMMARY]"},"annotated_tf_idf_background_conclusion_abstract_prompt":{"kind":"null"},"annotated_entity_plan_background_abstract_prompt":{"kind":"string","value":"[CONTENT] ||| 2013 | Japan ||| [SUMMARY]"},"annotated_entity_plan_methods_abstract_prompt":{"kind":"null"},"annotated_entity_plan_results_abstract_prompt":{"kind":"string","value":"[CONTENT] ||| ||| 2013-2019 | 8.50 million | US$3.75 billion | 11.11 million | 0.45 million | 284,188 | 65,060 ||| 35.59 million | US$14.82 billion | 43.10 million | 1.66 million | 1,084,532 | 250,256 [SUMMARY]"},"annotated_entity_plan_conclusions_abstract_prompt":{"kind":"null"},"annotated_entity_plan_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] ||| 2013 | Japan ||| ||| one ||| 20, 30 | 40 | 50 | 60 | 70 | 80 ||| ||| ||| ||| 2013-2019 | 8.50 million | US$3.75 billion | 11.11 million | 0.45 million | 284,188 | 65,060 ||| 35.59 million | US$14.82 billion | 43.10 million | 1.66 million | 1,084,532 | 250,256 ||| Japan ||| [SUMMARY]"},"annotated_entity_plan_background_conclusion_abstract_prompt":{"kind":"null"}}},{"rowIdx":296,"cells":{"title":{"kind":"string","value":"Immune checkpoint inhibitor-mediated colitis is associated with cancer overall survival."},"pmid":{"kind":"string","value":"36338892"},"background_abstract":{"kind":"string","value":"Immune checkpoint inhibitor-mediated colitis (IMC) is a common adverse event following immune checkpoint inhibitor (ICI) therapy for cancer. IMC has been associated with improved overall survival (OS) and progression-free survival (PFS), but data are limited to a single site and predominantly for melanoma patients."},"background_abstract_label":{"kind":"string","value":"BACKGROUND"},"methods_abstract":{"kind":"string","value":"We performed a retrospective case-control study including 64 ICI users who developed IMC matched according to age, sex, ICI class, and malignancy to a cohort of ICI users without IMC, from May 2011 to May 2020. Using univariate and multivariate logistic regression, we determined association of presence of IMC on OS, PFS, and clinical predictors of IMC. Kaplan-Meier curves were generated to compare OS and PFS between ICI users with and without IMC."},"methods_abstract_label":{"kind":"string","value":"METHODS"},"results_abstract":{"kind":"string","value":"IMC was significantly associated with a higher OS (mean 24.3 mo vs 17.7 mo, P = 0.05) but not PFS (mean 13.7 mo vs 11.9 mo, P = 0.524). IMC was significantly associated with OS greater than 12 mo [Odds ratio (OR) 2.81, 95% confidence interval (CI) 1.17-6.77]. Vitamin D supplementation was significantly associated with increased risk of IMC (OR 2.48, 95%CI 1.01-6.07)."},"results_abstract_label":{"kind":"string","value":"RESULTS"},"conclusions_abstract":{"kind":"string","value":"IMC was significantly associated with OS greater than 12 mo. In contrast to prior work, we found that vitamin D use may be a risk factor for IMC."},"conclusions_abstract_label":{"kind":"string","value":"CONCLUSION"},"mesh_descriptor_names":{"kind":"list like","value":["Humans","Immune Checkpoint Inhibitors","Antineoplastic Agents, Immunological","Retrospective Studies","Case-Control Studies","Melanoma","Colitis","Vitamin D"],"string":"[\n \"Humans\",\n \"Immune Checkpoint Inhibitors\",\n \"Antineoplastic Agents, Immunological\",\n \"Retrospective Studies\",\n \"Case-Control Studies\",\n \"Melanoma\",\n \"Colitis\",\n \"Vitamin D\"\n]"},"pmcid":{"kind":"string","value":"9627421"},"background_title":{"kind":"string","value":"INTRODUCTION"},"background_text":{"kind":"string","value":"Immune checkpoint inhibitors (ICI) have dramatically changed the landscape of cancer therapy. Early studies showed significantly prolonged survival in patients with metastatic melanoma compared to standard chemotherapy[1], and evidence now exists for improved outcomes in a variety of tumors ranging from lung cancers to urothelial carcinoma to breast cancer[2-5]. Although these are powerful treatments in our armamentarium against malignancy, ICI can cause immune-related adverse events (irAE) characterized by autoimmune-like inflammation in a variety of non-tumor organs, leading to increased morbidity for patients[6].\nOne of the most common irAE is immune checkpoint inhibitor-mediated colitis (IMC). IMC may occur in up to 40% of patients treated with ipilimumab, an antibody targeting CTLA-4, 11%-17% of patients treated with antibodies against anti-PD-1 or anti-PD-L1, such as nivolumab, pembrolizumab, or atezolizumab, and around 32% of patients treated with a combination of anti-CTLA-4 and anti-PD-1[7]. Prior retrospective analyses of patients with IMC have attempted to identify characteristics associated with development of IMC, including type of malignancy, ICI class, dose of ICI, cancer stage, and vitamin D use[8-11]. Intriguingly, two prior studies have suggested that development of IMC may positively correlate with improved progression-free survival (PFS) and overall survival (OS)[9,10]. One of these studies controlled for confounding effects of ICI class via frequency matching, but was limited to patients with melanoma, hindering wider applicability of their findings[10]. These findings also conflict with data suggesting that use of steroids and the anti-TNF antibody infliximab in patients treated with ICI are associated with worse cancer outcomes[12,13]. These discrepancies represent a significant knowledge gap that impedes our ability to evaluate and manage IMC and ICI use.\nHere we present data from a retrospective study of patients treated with ICI at our institution who developed IMC across malignancy types. We compare this cohort to a matched control cohort to determine whether IMC was associated with improved progression-free survival and overall survival. We also evaluate which clinical characteristics increase the risk of developing IMC, including severe IMC."},"methods_title":{"kind":"string","value":"MATERIALS AND METHODS"},"methods_text":{"kind":"string","value":"Study design and population We conducted a retrospective case-control single-center study after obtaining approval from the Institutional Review Board at Stanford University (IRB 57125, approved 6/30/2020). Our primary aim was to determine the association of presence and severity of IMC on OS and PFS in ICI users. Our secondary aim was to identify clinical variables which predicted development of IMC in ICI users. We evaluated all patients over the age of 18 who had been treated with immune checkpoint inhibitors (ICI) for malignancy at Stanford Health Care from May 2011 to May 2020, including anti-CTLA-4 (ipilimumab), anti-PD-1 (nivolumab, pembrolizumab), and anti-PD-L1 (atezolizumab, avelumab, durvalumab), with follow up through October 2020. Using the Stanford Research Repository tool, we screened patients treated with ICI who were assigned International Classification of Diseases (ICD) 9 and ICD 10 codes associated with non-infectious colitis and diarrhea (Supplementary Table 1). Each chart which passed the initial screen was further screened by review of clinic notes to confirm diagnosis of immune checkpoint inhibitor-related colitis by oncology providers. Any patient found to have other explanations for their clinical presentation was excluded from the study.\nControl patients were matched one to one with each IMC patient for sex, age, malignancy, type of ICI used, prior ICI exposure, and duration of ICI exposure (matched to number of doses from initiation of ICI to development of colitis in study cohort). Control patients were initially screened by those lacking the above ICD codes and were confirmed via direct evaluation of each chart to lack diarrhea and/or colitis ascribable to ICI per their treating oncologist.\nWe extracted clinical data on IMC and control patient charts including demographics (age at time of ICI initiation, sex, body mass index, race per patient report), medical history (presence of prior non-liver and non-upper gastrointestinal disease, personal history of autoimmune disease, family history of autoimmune disease), and cancer history (type of malignancy, tumor stage at ICI initiation, prior chemotherapy, prior radiation therapy, type of ICI used, duration of ICI use, OS and PFS) (Supplementary Table 2). OS was determined as time from initiation of ICI to death, while PFS was determined as time from initiation of ICI to death or progression of disease as determined by oncology providers, based on radiographic evidence of progression. IMC severity was graded using commonly accepted determinants of IMC and irAE grading[14]. We specifically noted prior use of therapies designed to increase immune responses [interleukin (IL)-2, interferon (IFN)-γ, toll-like receptor (TLR)-9 agonist, tebentafusp, or anti-CD47 antibody]. Vitamin D and non-steroidal anti-inflammatory (NSAID) use were defined as vitamin D supplement or NSAID medication, respectively, noted in the history of present illness or on the patient’s medication list at the clinic visit closest to their date of ICI initiation.\nWe collected data on IMC diagnosis including number of patients who received endoscopy (flexible sigmoidoscopy or colonoscopy), findings on endoscopy, and fecal calprotectin (Supplementary Table 3). Data on management of IMC included treatment with anti-diarrheal medications, mesalamine, steroids (prednisone, budesonide, dexamethasone), infliximab, and vedolizumab.\nWe conducted a retrospective case-control single-center study after obtaining approval from the Institutional Review Board at Stanford University (IRB 57125, approved 6/30/2020). Our primary aim was to determine the association of presence and severity of IMC on OS and PFS in ICI users. Our secondary aim was to identify clinical variables which predicted development of IMC in ICI users. We evaluated all patients over the age of 18 who had been treated with immune checkpoint inhibitors (ICI) for malignancy at Stanford Health Care from May 2011 to May 2020, including anti-CTLA-4 (ipilimumab), anti-PD-1 (nivolumab, pembrolizumab), and anti-PD-L1 (atezolizumab, avelumab, durvalumab), with follow up through October 2020. Using the Stanford Research Repository tool, we screened patients treated with ICI who were assigned International Classification of Diseases (ICD) 9 and ICD 10 codes associated with non-infectious colitis and diarrhea (Supplementary Table 1). Each chart which passed the initial screen was further screened by review of clinic notes to confirm diagnosis of immune checkpoint inhibitor-related colitis by oncology providers. Any patient found to have other explanations for their clinical presentation was excluded from the study.\nControl patients were matched one to one with each IMC patient for sex, age, malignancy, type of ICI used, prior ICI exposure, and duration of ICI exposure (matched to number of doses from initiation of ICI to development of colitis in study cohort). Control patients were initially screened by those lacking the above ICD codes and were confirmed via direct evaluation of each chart to lack diarrhea and/or colitis ascribable to ICI per their treating oncologist.\nWe extracted clinical data on IMC and control patient charts including demographics (age at time of ICI initiation, sex, body mass index, race per patient report), medical history (presence of prior non-liver and non-upper gastrointestinal disease, personal history of autoimmune disease, family history of autoimmune disease), and cancer history (type of malignancy, tumor stage at ICI initiation, prior chemotherapy, prior radiation therapy, type of ICI used, duration of ICI use, OS and PFS) (Supplementary Table 2). OS was determined as time from initiation of ICI to death, while PFS was determined as time from initiation of ICI to death or progression of disease as determined by oncology providers, based on radiographic evidence of progression. IMC severity was graded using commonly accepted determinants of IMC and irAE grading[14]. We specifically noted prior use of therapies designed to increase immune responses [interleukin (IL)-2, interferon (IFN)-γ, toll-like receptor (TLR)-9 agonist, tebentafusp, or anti-CD47 antibody]. Vitamin D and non-steroidal anti-inflammatory (NSAID) use were defined as vitamin D supplement or NSAID medication, respectively, noted in the history of present illness or on the patient’s medication list at the clinic visit closest to their date of ICI initiation.\nWe collected data on IMC diagnosis including number of patients who received endoscopy (flexible sigmoidoscopy or colonoscopy), findings on endoscopy, and fecal calprotectin (Supplementary Table 3). Data on management of IMC included treatment with anti-diarrheal medications, mesalamine, steroids (prednisone, budesonide, dexamethasone), infliximab, and vedolizumab.\nStatistical analysis The rate of the primary outcomes (OS > 12 mo and PFS > 6 mo among all ICI users, OS > 12 mo and PFS > 6 mo in patients with IMC) and secondary outcomes (risks of IMC among patients with malignancy using ICI, IMC severity), predictive value of clinical variables on primary and secondary outcomes, odds ratio (OR) with its 95% confidence interval (CI), and P values were calculated using Statistics/Data Analysis (Stata/IC 15.1 for Windows, College Station, TX, United States). Dichotomous variables were analyzed for outcomes using the chi-squared test or the Fisher’s exact test where appropriate, and continuous variables were analyzed using Student’s t-tests if normally distributed, or the Wilcoxon signed-rank test for non-normal data. For our multivariate analyses, model building was based on forward stepwise logistic regression, with a P value of 0.05 required for entry, and known predictors were also included. We constructed Kaplan Meier curves for the outcomes of OS and PFS between patients with and without IMC and patients with mild vs severe IMC using GraphPad Prism (version 8.3; GraphPad Software, Inc., La Jolla, CA, United States). All authors had access to the study data and reviewed and approved the final manuscript.\nThe rate of the primary outcomes (OS > 12 mo and PFS > 6 mo among all ICI users, OS > 12 mo and PFS > 6 mo in patients with IMC) and secondary outcomes (risks of IMC among patients with malignancy using ICI, IMC severity), predictive value of clinical variables on primary and secondary outcomes, odds ratio (OR) with its 95% confidence interval (CI), and P values were calculated using Statistics/Data Analysis (Stata/IC 15.1 for Windows, College Station, TX, United States). Dichotomous variables were analyzed for outcomes using the chi-squared test or the Fisher’s exact test where appropriate, and continuous variables were analyzed using Student’s t-tests if normally distributed, or the Wilcoxon signed-rank test for non-normal data. For our multivariate analyses, model building was based on forward stepwise logistic regression, with a P value of 0.05 required for entry, and known predictors were also included. We constructed Kaplan Meier curves for the outcomes of OS and PFS between patients with and without IMC and patients with mild vs severe IMC using GraphPad Prism (version 8.3; GraphPad Software, Inc., La Jolla, CA, United States). All authors had access to the study data and reviewed and approved the final manuscript."},"results_title":{"kind":"null"},"results_text":{"kind":"null"},"conclusions_title":{"kind":"string","value":"CONCLUSION"},"conclusions_text":{"kind":"string","value":"Future research in this area should seek to expand current knowledge of the relationship between IMC and cancer survival. In particular, future work should focus on broadening the type and number of patients treated with immune checkpoint inhibitors and on tracking patients prior to initiating checkpoint inhibitors to determine if this relationship remains significant prospectively."},"other_sections_titles":{"kind":"list like","value":["INTRODUCTION","Study design and population","Statistical analysis","RESULTS","Clinical characteristics associated with IMC","IMC significantly increases overall survival","Significant risk factors for developing IMC and severe IMC","DISCUSSION","CONCLUSION"],"string":"[\n \"INTRODUCTION\",\n \"Study design and population\",\n \"Statistical analysis\",\n \"RESULTS\",\n \"Clinical characteristics associated with IMC\",\n \"IMC significantly increases overall survival\",\n \"Significant risk factors for developing IMC and severe IMC\",\n \"DISCUSSION\",\n \"CONCLUSION\"\n]"},"other_sections_texts":{"kind":"list like","value":["Immune checkpoint inhibitors (ICI) have dramatically changed the landscape of cancer therapy. Early studies showed significantly prolonged survival in patients with metastatic melanoma compared to standard chemotherapy[1], and evidence now exists for improved outcomes in a variety of tumors ranging from lung cancers to urothelial carcinoma to breast cancer[2-5]. Although these are powerful treatments in our armamentarium against malignancy, ICI can cause immune-related adverse events (irAE) characterized by autoimmune-like inflammation in a variety of non-tumor organs, leading to increased morbidity for patients[6].\nOne of the most common irAE is immune checkpoint inhibitor-mediated colitis (IMC). IMC may occur in up to 40% of patients treated with ipilimumab, an antibody targeting CTLA-4, 11%-17% of patients treated with antibodies against anti-PD-1 or anti-PD-L1, such as nivolumab, pembrolizumab, or atezolizumab, and around 32% of patients treated with a combination of anti-CTLA-4 and anti-PD-1[7]. Prior retrospective analyses of patients with IMC have attempted to identify characteristics associated with development of IMC, including type of malignancy, ICI class, dose of ICI, cancer stage, and vitamin D use[8-11]. Intriguingly, two prior studies have suggested that development of IMC may positively correlate with improved progression-free survival (PFS) and overall survival (OS)[9,10]. One of these studies controlled for confounding effects of ICI class via frequency matching, but was limited to patients with melanoma, hindering wider applicability of their findings[10]. These findings also conflict with data suggesting that use of steroids and the anti-TNF antibody infliximab in patients treated with ICI are associated with worse cancer outcomes[12,13]. These discrepancies represent a significant knowledge gap that impedes our ability to evaluate and manage IMC and ICI use.\nHere we present data from a retrospective study of patients treated with ICI at our institution who developed IMC across malignancy types. We compare this cohort to a matched control cohort to determine whether IMC was associated with improved progression-free survival and overall survival. We also evaluate which clinical characteristics increase the risk of developing IMC, including severe IMC.","We conducted a retrospective case-control single-center study after obtaining approval from the Institutional Review Board at Stanford University (IRB 57125, approved 6/30/2020). Our primary aim was to determine the association of presence and severity of IMC on OS and PFS in ICI users. Our secondary aim was to identify clinical variables which predicted development of IMC in ICI users. We evaluated all patients over the age of 18 who had been treated with immune checkpoint inhibitors (ICI) for malignancy at Stanford Health Care from May 2011 to May 2020, including anti-CTLA-4 (ipilimumab), anti-PD-1 (nivolumab, pembrolizumab), and anti-PD-L1 (atezolizumab, avelumab, durvalumab), with follow up through October 2020. Using the Stanford Research Repository tool, we screened patients treated with ICI who were assigned International Classification of Diseases (ICD) 9 and ICD 10 codes associated with non-infectious colitis and diarrhea (Supplementary Table 1). Each chart which passed the initial screen was further screened by review of clinic notes to confirm diagnosis of immune checkpoint inhibitor-related colitis by oncology providers. Any patient found to have other explanations for their clinical presentation was excluded from the study.\nControl patients were matched one to one with each IMC patient for sex, age, malignancy, type of ICI used, prior ICI exposure, and duration of ICI exposure (matched to number of doses from initiation of ICI to development of colitis in study cohort). Control patients were initially screened by those lacking the above ICD codes and were confirmed via direct evaluation of each chart to lack diarrhea and/or colitis ascribable to ICI per their treating oncologist.\nWe extracted clinical data on IMC and control patient charts including demographics (age at time of ICI initiation, sex, body mass index, race per patient report), medical history (presence of prior non-liver and non-upper gastrointestinal disease, personal history of autoimmune disease, family history of autoimmune disease), and cancer history (type of malignancy, tumor stage at ICI initiation, prior chemotherapy, prior radiation therapy, type of ICI used, duration of ICI use, OS and PFS) (Supplementary Table 2). OS was determined as time from initiation of ICI to death, while PFS was determined as time from initiation of ICI to death or progression of disease as determined by oncology providers, based on radiographic evidence of progression. IMC severity was graded using commonly accepted determinants of IMC and irAE grading[14]. We specifically noted prior use of therapies designed to increase immune responses [interleukin (IL)-2, interferon (IFN)-γ, toll-like receptor (TLR)-9 agonist, tebentafusp, or anti-CD47 antibody]. Vitamin D and non-steroidal anti-inflammatory (NSAID) use were defined as vitamin D supplement or NSAID medication, respectively, noted in the history of present illness or on the patient’s medication list at the clinic visit closest to their date of ICI initiation.\nWe collected data on IMC diagnosis including number of patients who received endoscopy (flexible sigmoidoscopy or colonoscopy), findings on endoscopy, and fecal calprotectin (Supplementary Table 3). Data on management of IMC included treatment with anti-diarrheal medications, mesalamine, steroids (prednisone, budesonide, dexamethasone), infliximab, and vedolizumab.","The rate of the primary outcomes (OS > 12 mo and PFS > 6 mo among all ICI users, OS > 12 mo and PFS > 6 mo in patients with IMC) and secondary outcomes (risks of IMC among patients with malignancy using ICI, IMC severity), predictive value of clinical variables on primary and secondary outcomes, odds ratio (OR) with its 95% confidence interval (CI), and P values were calculated using Statistics/Data Analysis (Stata/IC 15.1 for Windows, College Station, TX, United States). Dichotomous variables were analyzed for outcomes using the chi-squared test or the Fisher’s exact test where appropriate, and continuous variables were analyzed using Student’s t-tests if normally distributed, or the Wilcoxon signed-rank test for non-normal data. For our multivariate analyses, model building was based on forward stepwise logistic regression, with a P value of 0.05 required for entry, and known predictors were also included. We constructed Kaplan Meier curves for the outcomes of OS and PFS between patients with and without IMC and patients with mild vs severe IMC using GraphPad Prism (version 8.3; GraphPad Software, Inc., La Jolla, CA, United States). All authors had access to the study data and reviewed and approved the final manuscript.","Clinical characteristics associated with IMC We identified a total of 314 patients treated with ICI at Stanford Health Care from May 2011 to May 2020 who had ICD codes matching our query (Supplementary Table 1). Of these, 64 had a diagnosis of IMC per review of Oncology providers’ notes, after excluding patients with alternative diagnoses for their symptoms. 24 (37.5%) of these IMC patients underwent an endoscopy (colonoscopy or flexible sigmoidoscopy) during workup, of which seven (29.2%) had a normal endoscopic appearance, consistent with prior reports demonstrating that approximately one third of patients with IMC related to anti-PD-1 therapy have microscopic colitis[15] (Supplementary Table 3). An additional 14 patients (21.9%) had imaging findings suggestive of IMC while 3 patients (4.69%) without imaging or endoscopy had an elevated calprotectin or fecal lactoferrin.\nThese 64 patients were manually matched 1:1 with control patients based on age, sex, malignancy, type of ICI, whether or not the patient had prior ICI exposure, and duration of ICI use. We compared clinical characteristics of patients from the IMC cohort and the control cohort (Table 1). None of the matched characteristics were significantly different between the two cohorts. The mean age across the combined cohorts was 66.6 years, with an average age of 67.4 in the cohort with IMC compared with 65.8 in the control cohort (P = 0.42). 57.81% of patients in each group were male (P = 1.00). Patients were predominantly white in both groups, with 52 (81.25%) white individuals in the IMC cohort compared to 50 (78.13%) in the control group (P = 0.66). The most common malignancy in each group was melanoma [33 (51.56%) in both cohorts], followed by renal cell carcinoma [8 (12.5%) in the IMC cohort and 7 (10.94%) in the control cohort] and non-small cell lung cancer [6 (9.38%) in both cohorts]. Both groups had similar numbers of patients with stage IV malignancy [56 (87.5%) in the IMC cohort and 58 (90.63%) in the control cohort, P = 0.778]. Combination ipilimumab and nivolumab was the most commonly used checkpoint therapy [24 (37.5%) of patients in each cohort], followed by nivolumab monotherapy [19 (29.69%) of each cohort] and ipilimumab monotherapy [11 (17.19%) of each cohort].\nBaseline characteristics of patients with immune checkpoint inhibitor use\nVariable matched between cases and controls.\nNumber of infusions of immune checkpoint inhibitor prior to immune checkpoint inhibitor-mediated colitis diagnosis (cases) or total (controls).\nSee Supplementary Table 2.\nIMC: Immune checkpoint inhibitor-mediated colitis; ICI: Immune checkpoint inhibitor; SD: Standard deviation; RCC: Renal cell carcinoma; NSCLC: Non-small cell lung cancer; SCC: Squamous cell carcinoma; PFS: Progression-Free Survival; irAE: Immune related adverse event; OS: Overall survival.\nAmong the remainder of the clinical characteristics evaluated, personal history of autoimmune disease (including prior irAE) and family history of autoimmune disease were significantly more common in patients with IMC (P = 0.037 and 0.048, respectively). Intriguingly, prior use of a therapy designed to increase immune responses was more common in the control cohort without IMC (P = 0.027). In contrast to prior data[11], use of vitamin D supplementation at the time of first dose of ICI was significantly more prevalent in patients with IMC (P = 0.020). Neither smoking status, NSAID use at time of ICI initiation, steroid use at the time of ICI initiation, nor recent vaccination were significantly more common in IMC patients compared to controls.\nWe identified a total of 314 patients treated with ICI at Stanford Health Care from May 2011 to May 2020 who had ICD codes matching our query (Supplementary Table 1). Of these, 64 had a diagnosis of IMC per review of Oncology providers’ notes, after excluding patients with alternative diagnoses for their symptoms. 24 (37.5%) of these IMC patients underwent an endoscopy (colonoscopy or flexible sigmoidoscopy) during workup, of which seven (29.2%) had a normal endoscopic appearance, consistent with prior reports demonstrating that approximately one third of patients with IMC related to anti-PD-1 therapy have microscopic colitis[15] (Supplementary Table 3). An additional 14 patients (21.9%) had imaging findings suggestive of IMC while 3 patients (4.69%) without imaging or endoscopy had an elevated calprotectin or fecal lactoferrin.\nThese 64 patients were manually matched 1:1 with control patients based on age, sex, malignancy, type of ICI, whether or not the patient had prior ICI exposure, and duration of ICI use. We compared clinical characteristics of patients from the IMC cohort and the control cohort (Table 1). None of the matched characteristics were significantly different between the two cohorts. The mean age across the combined cohorts was 66.6 years, with an average age of 67.4 in the cohort with IMC compared with 65.8 in the control cohort (P = 0.42). 57.81% of patients in each group were male (P = 1.00). Patients were predominantly white in both groups, with 52 (81.25%) white individuals in the IMC cohort compared to 50 (78.13%) in the control group (P = 0.66). The most common malignancy in each group was melanoma [33 (51.56%) in both cohorts], followed by renal cell carcinoma [8 (12.5%) in the IMC cohort and 7 (10.94%) in the control cohort] and non-small cell lung cancer [6 (9.38%) in both cohorts]. Both groups had similar numbers of patients with stage IV malignancy [56 (87.5%) in the IMC cohort and 58 (90.63%) in the control cohort, P = 0.778]. Combination ipilimumab and nivolumab was the most commonly used checkpoint therapy [24 (37.5%) of patients in each cohort], followed by nivolumab monotherapy [19 (29.69%) of each cohort] and ipilimumab monotherapy [11 (17.19%) of each cohort].\nBaseline characteristics of patients with immune checkpoint inhibitor use\nVariable matched between cases and controls.\nNumber of infusions of immune checkpoint inhibitor prior to immune checkpoint inhibitor-mediated colitis diagnosis (cases) or total (controls).\nSee Supplementary Table 2.\nIMC: Immune checkpoint inhibitor-mediated colitis; ICI: Immune checkpoint inhibitor; SD: Standard deviation; RCC: Renal cell carcinoma; NSCLC: Non-small cell lung cancer; SCC: Squamous cell carcinoma; PFS: Progression-Free Survival; irAE: Immune related adverse event; OS: Overall survival.\nAmong the remainder of the clinical characteristics evaluated, personal history of autoimmune disease (including prior irAE) and family history of autoimmune disease were significantly more common in patients with IMC (P = 0.037 and 0.048, respectively). Intriguingly, prior use of a therapy designed to increase immune responses was more common in the control cohort without IMC (P = 0.027). In contrast to prior data[11], use of vitamin D supplementation at the time of first dose of ICI was significantly more prevalent in patients with IMC (P = 0.020). Neither smoking status, NSAID use at time of ICI initiation, steroid use at the time of ICI initiation, nor recent vaccination were significantly more common in IMC patients compared to controls.\nIMC significantly increases overall survival As IMC has previously been associated with increased OS and PFS in cancer patients[9,10], we evaluated whether this association was seen in our study. We found that OS was significantly longer in patients who developed IMC compared to those who did not, with a mean OS of 24.3 mo in patients with IMC and 17.7 mo in control (P = 0.05, Table 1). OS at 12 mo following ICI initiation was significantly higher in patients who developed IMC compared to those who did not (P = 0.02, Figure 1). However, in contrast to prior findings, our study did not find a significant difference in PFS between IMC patients and controls, with a mean PFS 13.7 mo in IMC patients and 11.9 mo in controls (P = 0.524) (Table 1). PFS also did not differ between patients who developed mild vs severe IMC (P = 0.690, Supplementary Table 5).\n\nOverall survival at 12 mo in patients with and without immune checkpoint inhibitor-mediated colitis. Kaplan-Meier curve of overall survival at 12 mo in patients with immune checkpoint inhibitor-mediated colitis (IMC, red) and without IMC (black). IMC: Immune checkpoint inhibitor-mediated colitis; HR: Hazard ratio.\nAcross both cohorts, we identified clinical characteristics significantly associated with OS greater than 12 mo and PFS greater than 6 mo, which are correlated with cancer outcomes in patients treated with ICI[16] (Tables 2 and 3) (Supplementary Tables 4 and 5). IMC was significantly and independently associated with OS > 12 mo in the multivariate model (OR 2.81, 95%CI 1.17-6.77, P = 0.021) (Table 2). Number of ICI infusions was also positively associated with OS > 12 mo (OR 1.23, 95%CI 1.09-1.40), while sarcoma as underlying malignancy was significantly associated with OS < 12 mo (OR 0.17, 95%CI 0.029-0.947). Within the IMC cohort, nivolumab use was associated with OS < 12 mo in the univariate analysis (OR 0.09, 95%CI 0.01-0.83), while only age was associated with OS < 12 mo in multivariate analysis (OR 0.93, 95%CI 0.88-0.99) (Table 3). No individual malignancy was significantly associated with OS > 12 mo within the IMC cohort (Table 3).\nUnivariate and multivariate predictors of overall survival > 12 mo among patients with malignancy using immune checkpoint inhibitor (n = 128)\nNumber of infusions of immune checkpoint inhibitor prior to immune checkpoint inhibitor-mediated colitis diagnosis (cases) or total (controls).\nSee Supplementary Table 2.\nICI: Immune checkpoint inhibitor; IMC: Immune checkpoint inhibitor-mediated colitis; OR: Odds ratios; CI: Confidence interval; SD: Standard deviation; RCC: Renal cell carcinoma; NSCLC: Non-small cell lung cancer; SCC: Squamous cell carcinoma; irAE: Immune related adverse event.\nUnivariate and multivariate predictors of overall survival > 12 mo among patients with immune checkpoint inhibitor colitis (n = 64)\nNumber of infusions of immune checkpoint inhibitor prior to immune checkpoint inhibitor-mediated colitis diagnosis (cases) or total (controls).\nSee Supplementary Table 2.\nICI: Immune checkpoint inhibitor; IMC: Immune checkpoint inhibitor-mediated colitis; SD: Standard deviation; RCC: Renal cell carcinoma; NSCLC: Non-small cell lung cancer; SCC: Squamous cell carcinoma; irAE: Immune related adverse event.\nAs IMC has previously been associated with increased OS and PFS in cancer patients[9,10], we evaluated whether this association was seen in our study. We found that OS was significantly longer in patients who developed IMC compared to those who did not, with a mean OS of 24.3 mo in patients with IMC and 17.7 mo in control (P = 0.05, Table 1). OS at 12 mo following ICI initiation was significantly higher in patients who developed IMC compared to those who did not (P = 0.02, Figure 1). However, in contrast to prior findings, our study did not find a significant difference in PFS between IMC patients and controls, with a mean PFS 13.7 mo in IMC patients and 11.9 mo in controls (P = 0.524) (Table 1). PFS also did not differ between patients who developed mild vs severe IMC (P = 0.690, Supplementary Table 5).\n\nOverall survival at 12 mo in patients with and without immune checkpoint inhibitor-mediated colitis. Kaplan-Meier curve of overall survival at 12 mo in patients with immune checkpoint inhibitor-mediated colitis (IMC, red) and without IMC (black). IMC: Immune checkpoint inhibitor-mediated colitis; HR: Hazard ratio.\nAcross both cohorts, we identified clinical characteristics significantly associated with OS greater than 12 mo and PFS greater than 6 mo, which are correlated with cancer outcomes in patients treated with ICI[16] (Tables 2 and 3) (Supplementary Tables 4 and 5). IMC was significantly and independently associated with OS > 12 mo in the multivariate model (OR 2.81, 95%CI 1.17-6.77, P = 0.021) (Table 2). Number of ICI infusions was also positively associated with OS > 12 mo (OR 1.23, 95%CI 1.09-1.40), while sarcoma as underlying malignancy was significantly associated with OS < 12 mo (OR 0.17, 95%CI 0.029-0.947). Within the IMC cohort, nivolumab use was associated with OS < 12 mo in the univariate analysis (OR 0.09, 95%CI 0.01-0.83), while only age was associated with OS < 12 mo in multivariate analysis (OR 0.93, 95%CI 0.88-0.99) (Table 3). No individual malignancy was significantly associated with OS > 12 mo within the IMC cohort (Table 3).\nUnivariate and multivariate predictors of overall survival > 12 mo among patients with malignancy using immune checkpoint inhibitor (n = 128)\nNumber of infusions of immune checkpoint inhibitor prior to immune checkpoint inhibitor-mediated colitis diagnosis (cases) or total (controls).\nSee Supplementary Table 2.\nICI: Immune checkpoint inhibitor; IMC: Immune checkpoint inhibitor-mediated colitis; OR: Odds ratios; CI: Confidence interval; SD: Standard deviation; RCC: Renal cell carcinoma; NSCLC: Non-small cell lung cancer; SCC: Squamous cell carcinoma; irAE: Immune related adverse event.\nUnivariate and multivariate predictors of overall survival > 12 mo among patients with immune checkpoint inhibitor colitis (n = 64)\nNumber of infusions of immune checkpoint inhibitor prior to immune checkpoint inhibitor-mediated colitis diagnosis (cases) or total (controls).\nSee Supplementary Table 2.\nICI: Immune checkpoint inhibitor; IMC: Immune checkpoint inhibitor-mediated colitis; SD: Standard deviation; RCC: Renal cell carcinoma; NSCLC: Non-small cell lung cancer; SCC: Squamous cell carcinoma; irAE: Immune related adverse event.\nSignificant risk factors for developing IMC and severe IMC As certain clinical characteristics were significantly more common in patients with IMC compared to controls, we evaluated whether any of these clinical characteristics were associated with risk of developing IMC (Table 4). In univariate analysis, history of autoimmune disease and vitamin D use were both significantly associated with increased risk of IMC (OR 2.45, 95%CI 1.04-5.78, P = 0.040 for autoimmune disease; OR 2.51, 95%CI 1.14-5.54, P = 0.022 for vitamin D use). Interestingly, the use of vitamin D supplementation has previously been associated with a decreased risk of IMC, in contrast to our findings here[11]. Prior use of an immune-enhancing therapy (Supplementary Table 2) was associated with a significantly decreased risk of IMC (OR 0.20, 95%CI 0.04-0.95, P = 0.043). In the multivariate model which incorporated these characteristics, only the use of immune-enhancing therapy remained significantly associated with decreased risk of IMC, with an OR of 0.20 (95%CI 0.04-1.00, P = 0.050). \nUnivariate and multivariate predictors of immune checkpoint inhibitor-mediated colitis among patients using immune checkpoint inhibitor (n = 128)\nNumber of infusions of immune checkpoint inhibitor prior to immune checkpoint inhibitor-mediated colitis diagnosis (cases) or total (controls).\nSee Supplementary Table 2.\nICI: Immune checkpoint inhibitor; IMC: Immune checkpoint inhibitor-mediated colitis; RCC: Renal cell carcinoma; NSCLC: Non-small cell lung cancer; SCC: Squamous cell carcinoma; irAE: Immune related adverse event.\nWe next determined if any variables were associated with an increased risk of severe IMC. Consistent with prior studies of irAE in ICI[17-19], we defined grade 1-2 IMC as mild and grade 3 or higher IMC as severe. In our study, 38 of the 64 patients (59.4%) had severe IMC (Supplementary Table 3). In the univariate model, ipilimumab and vitamin D supplementation were significantly associated with development of severe IMC (OR 8.93, 95%CI 1.07-74.8, P = 0.043 for ipilimumab; OR 3.33, 95%CI 1.10-10.14, P = 0.034 for vitamin D) (Supplementary Table 6). Combination therapy (ipilimumab plus nivolumab) trended towards an increased risk of severe IMC but did not reach significance (P = 0.053). In contrast, pembrolizumab was significantly associated with a decreased risk of severe IMC (OR 0.26, 95%CI 0.09-0.81, P = 0.020). In the multivariate model no characteristic reached significance for association with severe IMC, although both combination therapy and ipilimumab monotherapy approached significance for increased risk of severe IMC (P = 0.058 and 0.060, respectively).\nAs certain clinical characteristics were significantly more common in patients with IMC compared to controls, we evaluated whether any of these clinical characteristics were associated with risk of developing IMC (Table 4). In univariate analysis, history of autoimmune disease and vitamin D use were both significantly associated with increased risk of IMC (OR 2.45, 95%CI 1.04-5.78, P = 0.040 for autoimmune disease; OR 2.51, 95%CI 1.14-5.54, P = 0.022 for vitamin D use). Interestingly, the use of vitamin D supplementation has previously been associated with a decreased risk of IMC, in contrast to our findings here[11]. Prior use of an immune-enhancing therapy (Supplementary Table 2) was associated with a significantly decreased risk of IMC (OR 0.20, 95%CI 0.04-0.95, P = 0.043). In the multivariate model which incorporated these characteristics, only the use of immune-enhancing therapy remained significantly associated with decreased risk of IMC, with an OR of 0.20 (95%CI 0.04-1.00, P = 0.050). \nUnivariate and multivariate predictors of immune checkpoint inhibitor-mediated colitis among patients using immune checkpoint inhibitor (n = 128)\nNumber of infusions of immune checkpoint inhibitor prior to immune checkpoint inhibitor-mediated colitis diagnosis (cases) or total (controls).\nSee Supplementary Table 2.\nICI: Immune checkpoint inhibitor; IMC: Immune checkpoint inhibitor-mediated colitis; RCC: Renal cell carcinoma; NSCLC: Non-small cell lung cancer; SCC: Squamous cell carcinoma; irAE: Immune related adverse event.\nWe next determined if any variables were associated with an increased risk of severe IMC. Consistent with prior studies of irAE in ICI[17-19], we defined grade 1-2 IMC as mild and grade 3 or higher IMC as severe. In our study, 38 of the 64 patients (59.4%) had severe IMC (Supplementary Table 3). In the univariate model, ipilimumab and vitamin D supplementation were significantly associated with development of severe IMC (OR 8.93, 95%CI 1.07-74.8, P = 0.043 for ipilimumab; OR 3.33, 95%CI 1.10-10.14, P = 0.034 for vitamin D) (Supplementary Table 6). Combination therapy (ipilimumab plus nivolumab) trended towards an increased risk of severe IMC but did not reach significance (P = 0.053). In contrast, pembrolizumab was significantly associated with a decreased risk of severe IMC (OR 0.26, 95%CI 0.09-0.81, P = 0.020). In the multivariate model no characteristic reached significance for association with severe IMC, although both combination therapy and ipilimumab monotherapy approached significance for increased risk of severe IMC (P = 0.058 and 0.060, respectively).","We identified a total of 314 patients treated with ICI at Stanford Health Care from May 2011 to May 2020 who had ICD codes matching our query (Supplementary Table 1). Of these, 64 had a diagnosis of IMC per review of Oncology providers’ notes, after excluding patients with alternative diagnoses for their symptoms. 24 (37.5%) of these IMC patients underwent an endoscopy (colonoscopy or flexible sigmoidoscopy) during workup, of which seven (29.2%) had a normal endoscopic appearance, consistent with prior reports demonstrating that approximately one third of patients with IMC related to anti-PD-1 therapy have microscopic colitis[15] (Supplementary Table 3). An additional 14 patients (21.9%) had imaging findings suggestive of IMC while 3 patients (4.69%) without imaging or endoscopy had an elevated calprotectin or fecal lactoferrin.\nThese 64 patients were manually matched 1:1 with control patients based on age, sex, malignancy, type of ICI, whether or not the patient had prior ICI exposure, and duration of ICI use. We compared clinical characteristics of patients from the IMC cohort and the control cohort (Table 1). None of the matched characteristics were significantly different between the two cohorts. The mean age across the combined cohorts was 66.6 years, with an average age of 67.4 in the cohort with IMC compared with 65.8 in the control cohort (P = 0.42). 57.81% of patients in each group were male (P = 1.00). Patients were predominantly white in both groups, with 52 (81.25%) white individuals in the IMC cohort compared to 50 (78.13%) in the control group (P = 0.66). The most common malignancy in each group was melanoma [33 (51.56%) in both cohorts], followed by renal cell carcinoma [8 (12.5%) in the IMC cohort and 7 (10.94%) in the control cohort] and non-small cell lung cancer [6 (9.38%) in both cohorts]. Both groups had similar numbers of patients with stage IV malignancy [56 (87.5%) in the IMC cohort and 58 (90.63%) in the control cohort, P = 0.778]. Combination ipilimumab and nivolumab was the most commonly used checkpoint therapy [24 (37.5%) of patients in each cohort], followed by nivolumab monotherapy [19 (29.69%) of each cohort] and ipilimumab monotherapy [11 (17.19%) of each cohort].\nBaseline characteristics of patients with immune checkpoint inhibitor use\nVariable matched between cases and controls.\nNumber of infusions of immune checkpoint inhibitor prior to immune checkpoint inhibitor-mediated colitis diagnosis (cases) or total (controls).\nSee Supplementary Table 2.\nIMC: Immune checkpoint inhibitor-mediated colitis; ICI: Immune checkpoint inhibitor; SD: Standard deviation; RCC: Renal cell carcinoma; NSCLC: Non-small cell lung cancer; SCC: Squamous cell carcinoma; PFS: Progression-Free Survival; irAE: Immune related adverse event; OS: Overall survival.\nAmong the remainder of the clinical characteristics evaluated, personal history of autoimmune disease (including prior irAE) and family history of autoimmune disease were significantly more common in patients with IMC (P = 0.037 and 0.048, respectively). Intriguingly, prior use of a therapy designed to increase immune responses was more common in the control cohort without IMC (P = 0.027). In contrast to prior data[11], use of vitamin D supplementation at the time of first dose of ICI was significantly more prevalent in patients with IMC (P = 0.020). Neither smoking status, NSAID use at time of ICI initiation, steroid use at the time of ICI initiation, nor recent vaccination were significantly more common in IMC patients compared to controls.","As IMC has previously been associated with increased OS and PFS in cancer patients[9,10], we evaluated whether this association was seen in our study. We found that OS was significantly longer in patients who developed IMC compared to those who did not, with a mean OS of 24.3 mo in patients with IMC and 17.7 mo in control (P = 0.05, Table 1). OS at 12 mo following ICI initiation was significantly higher in patients who developed IMC compared to those who did not (P = 0.02, Figure 1). However, in contrast to prior findings, our study did not find a significant difference in PFS between IMC patients and controls, with a mean PFS 13.7 mo in IMC patients and 11.9 mo in controls (P = 0.524) (Table 1). PFS also did not differ between patients who developed mild vs severe IMC (P = 0.690, Supplementary Table 5).\n\nOverall survival at 12 mo in patients with and without immune checkpoint inhibitor-mediated colitis. Kaplan-Meier curve of overall survival at 12 mo in patients with immune checkpoint inhibitor-mediated colitis (IMC, red) and without IMC (black). IMC: Immune checkpoint inhibitor-mediated colitis; HR: Hazard ratio.\nAcross both cohorts, we identified clinical characteristics significantly associated with OS greater than 12 mo and PFS greater than 6 mo, which are correlated with cancer outcomes in patients treated with ICI[16] (Tables 2 and 3) (Supplementary Tables 4 and 5). IMC was significantly and independently associated with OS > 12 mo in the multivariate model (OR 2.81, 95%CI 1.17-6.77, P = 0.021) (Table 2). Number of ICI infusions was also positively associated with OS > 12 mo (OR 1.23, 95%CI 1.09-1.40), while sarcoma as underlying malignancy was significantly associated with OS < 12 mo (OR 0.17, 95%CI 0.029-0.947). Within the IMC cohort, nivolumab use was associated with OS < 12 mo in the univariate analysis (OR 0.09, 95%CI 0.01-0.83), while only age was associated with OS < 12 mo in multivariate analysis (OR 0.93, 95%CI 0.88-0.99) (Table 3). No individual malignancy was significantly associated with OS > 12 mo within the IMC cohort (Table 3).\nUnivariate and multivariate predictors of overall survival > 12 mo among patients with malignancy using immune checkpoint inhibitor (n = 128)\nNumber of infusions of immune checkpoint inhibitor prior to immune checkpoint inhibitor-mediated colitis diagnosis (cases) or total (controls).\nSee Supplementary Table 2.\nICI: Immune checkpoint inhibitor; IMC: Immune checkpoint inhibitor-mediated colitis; OR: Odds ratios; CI: Confidence interval; SD: Standard deviation; RCC: Renal cell carcinoma; NSCLC: Non-small cell lung cancer; SCC: Squamous cell carcinoma; irAE: Immune related adverse event.\nUnivariate and multivariate predictors of overall survival > 12 mo among patients with immune checkpoint inhibitor colitis (n = 64)\nNumber of infusions of immune checkpoint inhibitor prior to immune checkpoint inhibitor-mediated colitis diagnosis (cases) or total (controls).\nSee Supplementary Table 2.\nICI: Immune checkpoint inhibitor; IMC: Immune checkpoint inhibitor-mediated colitis; SD: Standard deviation; RCC: Renal cell carcinoma; NSCLC: Non-small cell lung cancer; SCC: Squamous cell carcinoma; irAE: Immune related adverse event.","As certain clinical characteristics were significantly more common in patients with IMC compared to controls, we evaluated whether any of these clinical characteristics were associated with risk of developing IMC (Table 4). In univariate analysis, history of autoimmune disease and vitamin D use were both significantly associated with increased risk of IMC (OR 2.45, 95%CI 1.04-5.78, P = 0.040 for autoimmune disease; OR 2.51, 95%CI 1.14-5.54, P = 0.022 for vitamin D use). Interestingly, the use of vitamin D supplementation has previously been associated with a decreased risk of IMC, in contrast to our findings here[11]. Prior use of an immune-enhancing therapy (Supplementary Table 2) was associated with a significantly decreased risk of IMC (OR 0.20, 95%CI 0.04-0.95, P = 0.043). In the multivariate model which incorporated these characteristics, only the use of immune-enhancing therapy remained significantly associated with decreased risk of IMC, with an OR of 0.20 (95%CI 0.04-1.00, P = 0.050). \nUnivariate and multivariate predictors of immune checkpoint inhibitor-mediated colitis among patients using immune checkpoint inhibitor (n = 128)\nNumber of infusions of immune checkpoint inhibitor prior to immune checkpoint inhibitor-mediated colitis diagnosis (cases) or total (controls).\nSee Supplementary Table 2.\nICI: Immune checkpoint inhibitor; IMC: Immune checkpoint inhibitor-mediated colitis; RCC: Renal cell carcinoma; NSCLC: Non-small cell lung cancer; SCC: Squamous cell carcinoma; irAE: Immune related adverse event.\nWe next determined if any variables were associated with an increased risk of severe IMC. Consistent with prior studies of irAE in ICI[17-19], we defined grade 1-2 IMC as mild and grade 3 or higher IMC as severe. In our study, 38 of the 64 patients (59.4%) had severe IMC (Supplementary Table 3). In the univariate model, ipilimumab and vitamin D supplementation were significantly associated with development of severe IMC (OR 8.93, 95%CI 1.07-74.8, P = 0.043 for ipilimumab; OR 3.33, 95%CI 1.10-10.14, P = 0.034 for vitamin D) (Supplementary Table 6). Combination therapy (ipilimumab plus nivolumab) trended towards an increased risk of severe IMC but did not reach significance (P = 0.053). In contrast, pembrolizumab was significantly associated with a decreased risk of severe IMC (OR 0.26, 95%CI 0.09-0.81, P = 0.020). In the multivariate model no characteristic reached significance for association with severe IMC, although both combination therapy and ipilimumab monotherapy approached significance for increased risk of severe IMC (P = 0.058 and 0.060, respectively).","In our study, development of IMC following ICI use was associated with improved overall survival, although not improved progression-free survival, compared to ICI users without IMC. This is similar to findings at another center demonstrating both improved OS and PFS in patients with IMC[9,10]. We also found that vitamin D supplementation at the start of ICI treatment is a risk factor for developing IMC, in contrast to other research suggesting vitamin D use is associated with lower risk of IMC[11]. Our results, therefore, provide critical additional information on these previous associations and present a need for prospective studies.\nBoth publications showing improved survival in patients with IMC were retrospective analyses performed at the same center[9,10]. One study noted that ICI class was significantly associated with development of IMC[9], a finding that has been demonstrated several times in retrospective work[8,17,18,20-23]. However, unlike our work, this study did not match control patients to account for this likely confounder, as ICI class has been associated with differences in PFS in some malignancies[24,25]. The second study at this center examined survival in melanoma patients with IMC, compared to our work across multiple malignancies, although frequency matching was performed to account for use of different ICI classes[10]. Since our study is the first to examine survival in patients with IMC at a different center, our work here reinforces that IMC may be associated with increased overall survival and prompts a need for prospective studies.\nThe only other independent factor in our study positively associated with OS > 12 mo was number of ICI doses. This finding may be due to trivial length-time bias, as patients who survive longer are more likely to receive more doses of ICI. It is also possible that patients who required cessation of ICI due to IMC had worse outcomes, although prior work has suggested that patients still derive equivalent long-term benefit from ICI even if stopped due to irAE[26]. Type of underlying malignancy (sarcoma) was independently associated with OS < 12 mo in our study. These findings are not unexpected, as most advanced soft tissue sarcomas have a median OS of less than one year[27].\nIn contrast to prior work, we found a positive association between vitamin D supplementation and development of IMC[11]. It is unclear if this is related to low serum vitamin D levels or negative impact of the supplementation itself, as vitamin D levels near the time of ICI initiation were not recorded in most patients. Additionally, the prior report on vitamin D in IMC was in melanoma patients only, which may partially account for discrepancies with our study. As this association did not remain significant in our multivariate analysis, it is possible that another confounding factor may explain the association between vitamin D supplementation and IMC in our study.\nIn addition to challenging existing findings, we report here on additional novel risk factors for IMC. We are the first to report that prior use of immune-enhancing medications prior to ICI, such as IL-2 or interferon-γ, is significantly and independently associated with decreased risk of IMC. Much more work should be done to evaluate the relationship between these medications and future risk of IMC.\nFinally, our study is the first to examine risk factors for severe IMC. In addition to increasing risk for IMC overall, we find that vitamin D supplementation may also be a risk factor for severe IMC. Similarly, our results suggest that the use of ipilimumab may be associated with increased risk of severe IMC, while pembrolizumab may be associated with decreased risk of severe IMC in patients who develop this syndrome. As ipilimumab has previously been associated with increased risk of IMC overall, while anti-PD-1, including pembrolizumab, are associated with lower risk of IMC overall[8,9], these findings emphasize that ICI class may affect severity of IMC.\nOur findings may significantly impact clinical practice by identifying novel risks for IMC and severe IMC that clinicians, including oncologists and gastroenterologists, should be aware of, while also potentially providing reassurance to physicians and patients that development of IMC may be a positive prognosticator for cancer survival. Neither prior work nor ours found that treatment of IMC, including steroids or infliximab, negatively impacts OS[9,10], and therefore appropriate treatment of IMC should be pursued early on to minimize morbidity and mortality. Both steroid and infliximab use have been suggested to worsen survival in ICI users[12,13], but all current evidence suggests that use of these medications for IMC specifically does not impair cancer outcomes. Our work also cautions against supplementation with vitamin D in ICI users, as this may increase risk of IMC and severe IMC, although carefully designed studies with vitamin D measurements should be performed.\nOur work has several strengths. We performed robust cohort matching to minimize confounding effects of ICI class and malignancy. This is also the first study to explore risk factors associated with severe IMC. However, there are limitations to our work. As a retrospective, observational study, it is subject to recall bias and cannot evaluate causation, and may also be subject to immortal time bias (ITB). Patients may have longer exposure to checkpoint inhibitors before developing IMC, compared to patients who do not manifest this irAE, leading to a period where they must survive for long enough to develop IMC and are therefore “immortal”[28]. We found that OS > 12 mo was significantly associated with greater numbers of ICI infusions (Table 2), which is likely due to ITB. However, greater numbers of infusions were not associated with IMC (Table 4). This suggests that the association between OS > 12 mo and IMC is likely independent of the number of ICI infusions, limiting this as a source of ITB in our study.\nOther weaknesses of our work include selection of patients based on clinical criteria for IMC, including those who did not undergo endoscopy or other objective testing for intestinal inflammation, and therefore may not have had a true colitis. Like prior work, this is also a single-center study, and our results may not be widely generalizable, particularly since we identified fewer patients compared to prior work and our patient population is highly variable, including individuals with several different underlying malignancies. We did not exclude patients with prior non-GI irAEs in either group, although the presence of these was not independently associated with increased OS in our study. We also have not accounted for other factors which may be potential predictors of ICI response, including tumor PD-L1 expression burden, tumor mutational burden, gut microbial composition, proton pump inhibitor use, and combination treatment with tyrosine kinase inhibitors[29-34].","In conclusion, our findings suggest presence of IMC is associated with improved OS in cancer patients when cases were matched closely to controls. We also found that vitamin D supplementation was significantly associated with development of both IMC and severe IMC, while immune-enhancing medications were significantly associated with decreased risk of IMC. Future work should focus on broader populations to resolve the discrepancies raised in our work, and to confirm the association between IMC and increased cancer survival. Closely involving gastroenterologists with the workup and management of IMC will be crucial to ensuring the best care possible for these patients."],"string":"[\n \"Immune checkpoint inhibitors (ICI) have dramatically changed the landscape of cancer therapy. Early studies showed significantly prolonged survival in patients with metastatic melanoma compared to standard chemotherapy[1], and evidence now exists for improved outcomes in a variety of tumors ranging from lung cancers to urothelial carcinoma to breast cancer[2-5]. Although these are powerful treatments in our armamentarium against malignancy, ICI can cause immune-related adverse events (irAE) characterized by autoimmune-like inflammation in a variety of non-tumor organs, leading to increased morbidity for patients[6].\\nOne of the most common irAE is immune checkpoint inhibitor-mediated colitis (IMC). IMC may occur in up to 40% of patients treated with ipilimumab, an antibody targeting CTLA-4, 11%-17% of patients treated with antibodies against anti-PD-1 or anti-PD-L1, such as nivolumab, pembrolizumab, or atezolizumab, and around 32% of patients treated with a combination of anti-CTLA-4 and anti-PD-1[7]. Prior retrospective analyses of patients with IMC have attempted to identify characteristics associated with development of IMC, including type of malignancy, ICI class, dose of ICI, cancer stage, and vitamin D use[8-11]. Intriguingly, two prior studies have suggested that development of IMC may positively correlate with improved progression-free survival (PFS) and overall survival (OS)[9,10]. One of these studies controlled for confounding effects of ICI class via frequency matching, but was limited to patients with melanoma, hindering wider applicability of their findings[10]. These findings also conflict with data suggesting that use of steroids and the anti-TNF antibody infliximab in patients treated with ICI are associated with worse cancer outcomes[12,13]. These discrepancies represent a significant knowledge gap that impedes our ability to evaluate and manage IMC and ICI use.\\nHere we present data from a retrospective study of patients treated with ICI at our institution who developed IMC across malignancy types. We compare this cohort to a matched control cohort to determine whether IMC was associated with improved progression-free survival and overall survival. We also evaluate which clinical characteristics increase the risk of developing IMC, including severe IMC.\",\n \"We conducted a retrospective case-control single-center study after obtaining approval from the Institutional Review Board at Stanford University (IRB 57125, approved 6/30/2020). Our primary aim was to determine the association of presence and severity of IMC on OS and PFS in ICI users. Our secondary aim was to identify clinical variables which predicted development of IMC in ICI users. We evaluated all patients over the age of 18 who had been treated with immune checkpoint inhibitors (ICI) for malignancy at Stanford Health Care from May 2011 to May 2020, including anti-CTLA-4 (ipilimumab), anti-PD-1 (nivolumab, pembrolizumab), and anti-PD-L1 (atezolizumab, avelumab, durvalumab), with follow up through October 2020. Using the Stanford Research Repository tool, we screened patients treated with ICI who were assigned International Classification of Diseases (ICD) 9 and ICD 10 codes associated with non-infectious colitis and diarrhea (Supplementary Table 1). Each chart which passed the initial screen was further screened by review of clinic notes to confirm diagnosis of immune checkpoint inhibitor-related colitis by oncology providers. Any patient found to have other explanations for their clinical presentation was excluded from the study.\\nControl patients were matched one to one with each IMC patient for sex, age, malignancy, type of ICI used, prior ICI exposure, and duration of ICI exposure (matched to number of doses from initiation of ICI to development of colitis in study cohort). Control patients were initially screened by those lacking the above ICD codes and were confirmed via direct evaluation of each chart to lack diarrhea and/or colitis ascribable to ICI per their treating oncologist.\\nWe extracted clinical data on IMC and control patient charts including demographics (age at time of ICI initiation, sex, body mass index, race per patient report), medical history (presence of prior non-liver and non-upper gastrointestinal disease, personal history of autoimmune disease, family history of autoimmune disease), and cancer history (type of malignancy, tumor stage at ICI initiation, prior chemotherapy, prior radiation therapy, type of ICI used, duration of ICI use, OS and PFS) (Supplementary Table 2). OS was determined as time from initiation of ICI to death, while PFS was determined as time from initiation of ICI to death or progression of disease as determined by oncology providers, based on radiographic evidence of progression. IMC severity was graded using commonly accepted determinants of IMC and irAE grading[14]. We specifically noted prior use of therapies designed to increase immune responses [interleukin (IL)-2, interferon (IFN)-γ, toll-like receptor (TLR)-9 agonist, tebentafusp, or anti-CD47 antibody]. Vitamin D and non-steroidal anti-inflammatory (NSAID) use were defined as vitamin D supplement or NSAID medication, respectively, noted in the history of present illness or on the patient’s medication list at the clinic visit closest to their date of ICI initiation.\\nWe collected data on IMC diagnosis including number of patients who received endoscopy (flexible sigmoidoscopy or colonoscopy), findings on endoscopy, and fecal calprotectin (Supplementary Table 3). Data on management of IMC included treatment with anti-diarrheal medications, mesalamine, steroids (prednisone, budesonide, dexamethasone), infliximab, and vedolizumab.\",\n \"The rate of the primary outcomes (OS > 12 mo and PFS > 6 mo among all ICI users, OS > 12 mo and PFS > 6 mo in patients with IMC) and secondary outcomes (risks of IMC among patients with malignancy using ICI, IMC severity), predictive value of clinical variables on primary and secondary outcomes, odds ratio (OR) with its 95% confidence interval (CI), and P values were calculated using Statistics/Data Analysis (Stata/IC 15.1 for Windows, College Station, TX, United States). Dichotomous variables were analyzed for outcomes using the chi-squared test or the Fisher’s exact test where appropriate, and continuous variables were analyzed using Student’s t-tests if normally distributed, or the Wilcoxon signed-rank test for non-normal data. For our multivariate analyses, model building was based on forward stepwise logistic regression, with a P value of 0.05 required for entry, and known predictors were also included. We constructed Kaplan Meier curves for the outcomes of OS and PFS between patients with and without IMC and patients with mild vs severe IMC using GraphPad Prism (version 8.3; GraphPad Software, Inc., La Jolla, CA, United States). All authors had access to the study data and reviewed and approved the final manuscript.\",\n \"Clinical characteristics associated with IMC We identified a total of 314 patients treated with ICI at Stanford Health Care from May 2011 to May 2020 who had ICD codes matching our query (Supplementary Table 1). Of these, 64 had a diagnosis of IMC per review of Oncology providers’ notes, after excluding patients with alternative diagnoses for their symptoms. 24 (37.5%) of these IMC patients underwent an endoscopy (colonoscopy or flexible sigmoidoscopy) during workup, of which seven (29.2%) had a normal endoscopic appearance, consistent with prior reports demonstrating that approximately one third of patients with IMC related to anti-PD-1 therapy have microscopic colitis[15] (Supplementary Table 3). An additional 14 patients (21.9%) had imaging findings suggestive of IMC while 3 patients (4.69%) without imaging or endoscopy had an elevated calprotectin or fecal lactoferrin.\\nThese 64 patients were manually matched 1:1 with control patients based on age, sex, malignancy, type of ICI, whether or not the patient had prior ICI exposure, and duration of ICI use. We compared clinical characteristics of patients from the IMC cohort and the control cohort (Table 1). None of the matched characteristics were significantly different between the two cohorts. The mean age across the combined cohorts was 66.6 years, with an average age of 67.4 in the cohort with IMC compared with 65.8 in the control cohort (P = 0.42). 57.81% of patients in each group were male (P = 1.00). Patients were predominantly white in both groups, with 52 (81.25%) white individuals in the IMC cohort compared to 50 (78.13%) in the control group (P = 0.66). The most common malignancy in each group was melanoma [33 (51.56%) in both cohorts], followed by renal cell carcinoma [8 (12.5%) in the IMC cohort and 7 (10.94%) in the control cohort] and non-small cell lung cancer [6 (9.38%) in both cohorts]. Both groups had similar numbers of patients with stage IV malignancy [56 (87.5%) in the IMC cohort and 58 (90.63%) in the control cohort, P = 0.778]. Combination ipilimumab and nivolumab was the most commonly used checkpoint therapy [24 (37.5%) of patients in each cohort], followed by nivolumab monotherapy [19 (29.69%) of each cohort] and ipilimumab monotherapy [11 (17.19%) of each cohort].\\nBaseline characteristics of patients with immune checkpoint inhibitor use\\nVariable matched between cases and controls.\\nNumber of infusions of immune checkpoint inhibitor prior to immune checkpoint inhibitor-mediated colitis diagnosis (cases) or total (controls).\\nSee Supplementary Table 2.\\nIMC: Immune checkpoint inhibitor-mediated colitis; ICI: Immune checkpoint inhibitor; SD: Standard deviation; RCC: Renal cell carcinoma; NSCLC: Non-small cell lung cancer; SCC: Squamous cell carcinoma; PFS: Progression-Free Survival; irAE: Immune related adverse event; OS: Overall survival.\\nAmong the remainder of the clinical characteristics evaluated, personal history of autoimmune disease (including prior irAE) and family history of autoimmune disease were significantly more common in patients with IMC (P = 0.037 and 0.048, respectively). Intriguingly, prior use of a therapy designed to increase immune responses was more common in the control cohort without IMC (P = 0.027). In contrast to prior data[11], use of vitamin D supplementation at the time of first dose of ICI was significantly more prevalent in patients with IMC (P = 0.020). Neither smoking status, NSAID use at time of ICI initiation, steroid use at the time of ICI initiation, nor recent vaccination were significantly more common in IMC patients compared to controls.\\nWe identified a total of 314 patients treated with ICI at Stanford Health Care from May 2011 to May 2020 who had ICD codes matching our query (Supplementary Table 1). Of these, 64 had a diagnosis of IMC per review of Oncology providers’ notes, after excluding patients with alternative diagnoses for their symptoms. 24 (37.5%) of these IMC patients underwent an endoscopy (colonoscopy or flexible sigmoidoscopy) during workup, of which seven (29.2%) had a normal endoscopic appearance, consistent with prior reports demonstrating that approximately one third of patients with IMC related to anti-PD-1 therapy have microscopic colitis[15] (Supplementary Table 3). An additional 14 patients (21.9%) had imaging findings suggestive of IMC while 3 patients (4.69%) without imaging or endoscopy had an elevated calprotectin or fecal lactoferrin.\\nThese 64 patients were manually matched 1:1 with control patients based on age, sex, malignancy, type of ICI, whether or not the patient had prior ICI exposure, and duration of ICI use. We compared clinical characteristics of patients from the IMC cohort and the control cohort (Table 1). None of the matched characteristics were significantly different between the two cohorts. The mean age across the combined cohorts was 66.6 years, with an average age of 67.4 in the cohort with IMC compared with 65.8 in the control cohort (P = 0.42). 57.81% of patients in each group were male (P = 1.00). Patients were predominantly white in both groups, with 52 (81.25%) white individuals in the IMC cohort compared to 50 (78.13%) in the control group (P = 0.66). The most common malignancy in each group was melanoma [33 (51.56%) in both cohorts], followed by renal cell carcinoma [8 (12.5%) in the IMC cohort and 7 (10.94%) in the control cohort] and non-small cell lung cancer [6 (9.38%) in both cohorts]. Both groups had similar numbers of patients with stage IV malignancy [56 (87.5%) in the IMC cohort and 58 (90.63%) in the control cohort, P = 0.778]. Combination ipilimumab and nivolumab was the most commonly used checkpoint therapy [24 (37.5%) of patients in each cohort], followed by nivolumab monotherapy [19 (29.69%) of each cohort] and ipilimumab monotherapy [11 (17.19%) of each cohort].\\nBaseline characteristics of patients with immune checkpoint inhibitor use\\nVariable matched between cases and controls.\\nNumber of infusions of immune checkpoint inhibitor prior to immune checkpoint inhibitor-mediated colitis diagnosis (cases) or total (controls).\\nSee Supplementary Table 2.\\nIMC: Immune checkpoint inhibitor-mediated colitis; ICI: Immune checkpoint inhibitor; SD: Standard deviation; RCC: Renal cell carcinoma; NSCLC: Non-small cell lung cancer; SCC: Squamous cell carcinoma; PFS: Progression-Free Survival; irAE: Immune related adverse event; OS: Overall survival.\\nAmong the remainder of the clinical characteristics evaluated, personal history of autoimmune disease (including prior irAE) and family history of autoimmune disease were significantly more common in patients with IMC (P = 0.037 and 0.048, respectively). Intriguingly, prior use of a therapy designed to increase immune responses was more common in the control cohort without IMC (P = 0.027). In contrast to prior data[11], use of vitamin D supplementation at the time of first dose of ICI was significantly more prevalent in patients with IMC (P = 0.020). Neither smoking status, NSAID use at time of ICI initiation, steroid use at the time of ICI initiation, nor recent vaccination were significantly more common in IMC patients compared to controls.\\nIMC significantly increases overall survival As IMC has previously been associated with increased OS and PFS in cancer patients[9,10], we evaluated whether this association was seen in our study. We found that OS was significantly longer in patients who developed IMC compared to those who did not, with a mean OS of 24.3 mo in patients with IMC and 17.7 mo in control (P = 0.05, Table 1). OS at 12 mo following ICI initiation was significantly higher in patients who developed IMC compared to those who did not (P = 0.02, Figure 1). However, in contrast to prior findings, our study did not find a significant difference in PFS between IMC patients and controls, with a mean PFS 13.7 mo in IMC patients and 11.9 mo in controls (P = 0.524) (Table 1). PFS also did not differ between patients who developed mild vs severe IMC (P = 0.690, Supplementary Table 5).\\n\\nOverall survival at 12 mo in patients with and without immune checkpoint inhibitor-mediated colitis. Kaplan-Meier curve of overall survival at 12 mo in patients with immune checkpoint inhibitor-mediated colitis (IMC, red) and without IMC (black). IMC: Immune checkpoint inhibitor-mediated colitis; HR: Hazard ratio.\\nAcross both cohorts, we identified clinical characteristics significantly associated with OS greater than 12 mo and PFS greater than 6 mo, which are correlated with cancer outcomes in patients treated with ICI[16] (Tables 2 and 3) (Supplementary Tables 4 and 5). IMC was significantly and independently associated with OS > 12 mo in the multivariate model (OR 2.81, 95%CI 1.17-6.77, P = 0.021) (Table 2). Number of ICI infusions was also positively associated with OS > 12 mo (OR 1.23, 95%CI 1.09-1.40), while sarcoma as underlying malignancy was significantly associated with OS < 12 mo (OR 0.17, 95%CI 0.029-0.947). Within the IMC cohort, nivolumab use was associated with OS < 12 mo in the univariate analysis (OR 0.09, 95%CI 0.01-0.83), while only age was associated with OS < 12 mo in multivariate analysis (OR 0.93, 95%CI 0.88-0.99) (Table 3). No individual malignancy was significantly associated with OS > 12 mo within the IMC cohort (Table 3).\\nUnivariate and multivariate predictors of overall survival > 12 mo among patients with malignancy using immune checkpoint inhibitor (n = 128)\\nNumber of infusions of immune checkpoint inhibitor prior to immune checkpoint inhibitor-mediated colitis diagnosis (cases) or total (controls).\\nSee Supplementary Table 2.\\nICI: Immune checkpoint inhibitor; IMC: Immune checkpoint inhibitor-mediated colitis; OR: Odds ratios; CI: Confidence interval; SD: Standard deviation; RCC: Renal cell carcinoma; NSCLC: Non-small cell lung cancer; SCC: Squamous cell carcinoma; irAE: Immune related adverse event.\\nUnivariate and multivariate predictors of overall survival > 12 mo among patients with immune checkpoint inhibitor colitis (n = 64)\\nNumber of infusions of immune checkpoint inhibitor prior to immune checkpoint inhibitor-mediated colitis diagnosis (cases) or total (controls).\\nSee Supplementary Table 2.\\nICI: Immune checkpoint inhibitor; IMC: Immune checkpoint inhibitor-mediated colitis; SD: Standard deviation; RCC: Renal cell carcinoma; NSCLC: Non-small cell lung cancer; SCC: Squamous cell carcinoma; irAE: Immune related adverse event.\\nAs IMC has previously been associated with increased OS and PFS in cancer patients[9,10], we evaluated whether this association was seen in our study. We found that OS was significantly longer in patients who developed IMC compared to those who did not, with a mean OS of 24.3 mo in patients with IMC and 17.7 mo in control (P = 0.05, Table 1). OS at 12 mo following ICI initiation was significantly higher in patients who developed IMC compared to those who did not (P = 0.02, Figure 1). However, in contrast to prior findings, our study did not find a significant difference in PFS between IMC patients and controls, with a mean PFS 13.7 mo in IMC patients and 11.9 mo in controls (P = 0.524) (Table 1). PFS also did not differ between patients who developed mild vs severe IMC (P = 0.690, Supplementary Table 5).\\n\\nOverall survival at 12 mo in patients with and without immune checkpoint inhibitor-mediated colitis. Kaplan-Meier curve of overall survival at 12 mo in patients with immune checkpoint inhibitor-mediated colitis (IMC, red) and without IMC (black). IMC: Immune checkpoint inhibitor-mediated colitis; HR: Hazard ratio.\\nAcross both cohorts, we identified clinical characteristics significantly associated with OS greater than 12 mo and PFS greater than 6 mo, which are correlated with cancer outcomes in patients treated with ICI[16] (Tables 2 and 3) (Supplementary Tables 4 and 5). IMC was significantly and independently associated with OS > 12 mo in the multivariate model (OR 2.81, 95%CI 1.17-6.77, P = 0.021) (Table 2). Number of ICI infusions was also positively associated with OS > 12 mo (OR 1.23, 95%CI 1.09-1.40), while sarcoma as underlying malignancy was significantly associated with OS < 12 mo (OR 0.17, 95%CI 0.029-0.947). Within the IMC cohort, nivolumab use was associated with OS < 12 mo in the univariate analysis (OR 0.09, 95%CI 0.01-0.83), while only age was associated with OS < 12 mo in multivariate analysis (OR 0.93, 95%CI 0.88-0.99) (Table 3). No individual malignancy was significantly associated with OS > 12 mo within the IMC cohort (Table 3).\\nUnivariate and multivariate predictors of overall survival > 12 mo among patients with malignancy using immune checkpoint inhibitor (n = 128)\\nNumber of infusions of immune checkpoint inhibitor prior to immune checkpoint inhibitor-mediated colitis diagnosis (cases) or total (controls).\\nSee Supplementary Table 2.\\nICI: Immune checkpoint inhibitor; IMC: Immune checkpoint inhibitor-mediated colitis; OR: Odds ratios; CI: Confidence interval; SD: Standard deviation; RCC: Renal cell carcinoma; NSCLC: Non-small cell lung cancer; SCC: Squamous cell carcinoma; irAE: Immune related adverse event.\\nUnivariate and multivariate predictors of overall survival > 12 mo among patients with immune checkpoint inhibitor colitis (n = 64)\\nNumber of infusions of immune checkpoint inhibitor prior to immune checkpoint inhibitor-mediated colitis diagnosis (cases) or total (controls).\\nSee Supplementary Table 2.\\nICI: Immune checkpoint inhibitor; IMC: Immune checkpoint inhibitor-mediated colitis; SD: Standard deviation; RCC: Renal cell carcinoma; NSCLC: Non-small cell lung cancer; SCC: Squamous cell carcinoma; irAE: Immune related adverse event.\\nSignificant risk factors for developing IMC and severe IMC As certain clinical characteristics were significantly more common in patients with IMC compared to controls, we evaluated whether any of these clinical characteristics were associated with risk of developing IMC (Table 4). In univariate analysis, history of autoimmune disease and vitamin D use were both significantly associated with increased risk of IMC (OR 2.45, 95%CI 1.04-5.78, P = 0.040 for autoimmune disease; OR 2.51, 95%CI 1.14-5.54, P = 0.022 for vitamin D use). Interestingly, the use of vitamin D supplementation has previously been associated with a decreased risk of IMC, in contrast to our findings here[11]. Prior use of an immune-enhancing therapy (Supplementary Table 2) was associated with a significantly decreased risk of IMC (OR 0.20, 95%CI 0.04-0.95, P = 0.043). In the multivariate model which incorporated these characteristics, only the use of immune-enhancing therapy remained significantly associated with decreased risk of IMC, with an OR of 0.20 (95%CI 0.04-1.00, P = 0.050). \\nUnivariate and multivariate predictors of immune checkpoint inhibitor-mediated colitis among patients using immune checkpoint inhibitor (n = 128)\\nNumber of infusions of immune checkpoint inhibitor prior to immune checkpoint inhibitor-mediated colitis diagnosis (cases) or total (controls).\\nSee Supplementary Table 2.\\nICI: Immune checkpoint inhibitor; IMC: Immune checkpoint inhibitor-mediated colitis; RCC: Renal cell carcinoma; NSCLC: Non-small cell lung cancer; SCC: Squamous cell carcinoma; irAE: Immune related adverse event.\\nWe next determined if any variables were associated with an increased risk of severe IMC. Consistent with prior studies of irAE in ICI[17-19], we defined grade 1-2 IMC as mild and grade 3 or higher IMC as severe. In our study, 38 of the 64 patients (59.4%) had severe IMC (Supplementary Table 3). In the univariate model, ipilimumab and vitamin D supplementation were significantly associated with development of severe IMC (OR 8.93, 95%CI 1.07-74.8, P = 0.043 for ipilimumab; OR 3.33, 95%CI 1.10-10.14, P = 0.034 for vitamin D) (Supplementary Table 6). Combination therapy (ipilimumab plus nivolumab) trended towards an increased risk of severe IMC but did not reach significance (P = 0.053). In contrast, pembrolizumab was significantly associated with a decreased risk of severe IMC (OR 0.26, 95%CI 0.09-0.81, P = 0.020). In the multivariate model no characteristic reached significance for association with severe IMC, although both combination therapy and ipilimumab monotherapy approached significance for increased risk of severe IMC (P = 0.058 and 0.060, respectively).\\nAs certain clinical characteristics were significantly more common in patients with IMC compared to controls, we evaluated whether any of these clinical characteristics were associated with risk of developing IMC (Table 4). In univariate analysis, history of autoimmune disease and vitamin D use were both significantly associated with increased risk of IMC (OR 2.45, 95%CI 1.04-5.78, P = 0.040 for autoimmune disease; OR 2.51, 95%CI 1.14-5.54, P = 0.022 for vitamin D use). Interestingly, the use of vitamin D supplementation has previously been associated with a decreased risk of IMC, in contrast to our findings here[11]. Prior use of an immune-enhancing therapy (Supplementary Table 2) was associated with a significantly decreased risk of IMC (OR 0.20, 95%CI 0.04-0.95, P = 0.043). In the multivariate model which incorporated these characteristics, only the use of immune-enhancing therapy remained significantly associated with decreased risk of IMC, with an OR of 0.20 (95%CI 0.04-1.00, P = 0.050). \\nUnivariate and multivariate predictors of immune checkpoint inhibitor-mediated colitis among patients using immune checkpoint inhibitor (n = 128)\\nNumber of infusions of immune checkpoint inhibitor prior to immune checkpoint inhibitor-mediated colitis diagnosis (cases) or total (controls).\\nSee Supplementary Table 2.\\nICI: Immune checkpoint inhibitor; IMC: Immune checkpoint inhibitor-mediated colitis; RCC: Renal cell carcinoma; NSCLC: Non-small cell lung cancer; SCC: Squamous cell carcinoma; irAE: Immune related adverse event.\\nWe next determined if any variables were associated with an increased risk of severe IMC. Consistent with prior studies of irAE in ICI[17-19], we defined grade 1-2 IMC as mild and grade 3 or higher IMC as severe. In our study, 38 of the 64 patients (59.4%) had severe IMC (Supplementary Table 3). In the univariate model, ipilimumab and vitamin D supplementation were significantly associated with development of severe IMC (OR 8.93, 95%CI 1.07-74.8, P = 0.043 for ipilimumab; OR 3.33, 95%CI 1.10-10.14, P = 0.034 for vitamin D) (Supplementary Table 6). Combination therapy (ipilimumab plus nivolumab) trended towards an increased risk of severe IMC but did not reach significance (P = 0.053). In contrast, pembrolizumab was significantly associated with a decreased risk of severe IMC (OR 0.26, 95%CI 0.09-0.81, P = 0.020). In the multivariate model no characteristic reached significance for association with severe IMC, although both combination therapy and ipilimumab monotherapy approached significance for increased risk of severe IMC (P = 0.058 and 0.060, respectively).\",\n \"We identified a total of 314 patients treated with ICI at Stanford Health Care from May 2011 to May 2020 who had ICD codes matching our query (Supplementary Table 1). Of these, 64 had a diagnosis of IMC per review of Oncology providers’ notes, after excluding patients with alternative diagnoses for their symptoms. 24 (37.5%) of these IMC patients underwent an endoscopy (colonoscopy or flexible sigmoidoscopy) during workup, of which seven (29.2%) had a normal endoscopic appearance, consistent with prior reports demonstrating that approximately one third of patients with IMC related to anti-PD-1 therapy have microscopic colitis[15] (Supplementary Table 3). An additional 14 patients (21.9%) had imaging findings suggestive of IMC while 3 patients (4.69%) without imaging or endoscopy had an elevated calprotectin or fecal lactoferrin.\\nThese 64 patients were manually matched 1:1 with control patients based on age, sex, malignancy, type of ICI, whether or not the patient had prior ICI exposure, and duration of ICI use. We compared clinical characteristics of patients from the IMC cohort and the control cohort (Table 1). None of the matched characteristics were significantly different between the two cohorts. The mean age across the combined cohorts was 66.6 years, with an average age of 67.4 in the cohort with IMC compared with 65.8 in the control cohort (P = 0.42). 57.81% of patients in each group were male (P = 1.00). Patients were predominantly white in both groups, with 52 (81.25%) white individuals in the IMC cohort compared to 50 (78.13%) in the control group (P = 0.66). The most common malignancy in each group was melanoma [33 (51.56%) in both cohorts], followed by renal cell carcinoma [8 (12.5%) in the IMC cohort and 7 (10.94%) in the control cohort] and non-small cell lung cancer [6 (9.38%) in both cohorts]. Both groups had similar numbers of patients with stage IV malignancy [56 (87.5%) in the IMC cohort and 58 (90.63%) in the control cohort, P = 0.778]. Combination ipilimumab and nivolumab was the most commonly used checkpoint therapy [24 (37.5%) of patients in each cohort], followed by nivolumab monotherapy [19 (29.69%) of each cohort] and ipilimumab monotherapy [11 (17.19%) of each cohort].\\nBaseline characteristics of patients with immune checkpoint inhibitor use\\nVariable matched between cases and controls.\\nNumber of infusions of immune checkpoint inhibitor prior to immune checkpoint inhibitor-mediated colitis diagnosis (cases) or total (controls).\\nSee Supplementary Table 2.\\nIMC: Immune checkpoint inhibitor-mediated colitis; ICI: Immune checkpoint inhibitor; SD: Standard deviation; RCC: Renal cell carcinoma; NSCLC: Non-small cell lung cancer; SCC: Squamous cell carcinoma; PFS: Progression-Free Survival; irAE: Immune related adverse event; OS: Overall survival.\\nAmong the remainder of the clinical characteristics evaluated, personal history of autoimmune disease (including prior irAE) and family history of autoimmune disease were significantly more common in patients with IMC (P = 0.037 and 0.048, respectively). Intriguingly, prior use of a therapy designed to increase immune responses was more common in the control cohort without IMC (P = 0.027). In contrast to prior data[11], use of vitamin D supplementation at the time of first dose of ICI was significantly more prevalent in patients with IMC (P = 0.020). Neither smoking status, NSAID use at time of ICI initiation, steroid use at the time of ICI initiation, nor recent vaccination were significantly more common in IMC patients compared to controls.\",\n \"As IMC has previously been associated with increased OS and PFS in cancer patients[9,10], we evaluated whether this association was seen in our study. We found that OS was significantly longer in patients who developed IMC compared to those who did not, with a mean OS of 24.3 mo in patients with IMC and 17.7 mo in control (P = 0.05, Table 1). OS at 12 mo following ICI initiation was significantly higher in patients who developed IMC compared to those who did not (P = 0.02, Figure 1). However, in contrast to prior findings, our study did not find a significant difference in PFS between IMC patients and controls, with a mean PFS 13.7 mo in IMC patients and 11.9 mo in controls (P = 0.524) (Table 1). PFS also did not differ between patients who developed mild vs severe IMC (P = 0.690, Supplementary Table 5).\\n\\nOverall survival at 12 mo in patients with and without immune checkpoint inhibitor-mediated colitis. Kaplan-Meier curve of overall survival at 12 mo in patients with immune checkpoint inhibitor-mediated colitis (IMC, red) and without IMC (black). IMC: Immune checkpoint inhibitor-mediated colitis; HR: Hazard ratio.\\nAcross both cohorts, we identified clinical characteristics significantly associated with OS greater than 12 mo and PFS greater than 6 mo, which are correlated with cancer outcomes in patients treated with ICI[16] (Tables 2 and 3) (Supplementary Tables 4 and 5). IMC was significantly and independently associated with OS > 12 mo in the multivariate model (OR 2.81, 95%CI 1.17-6.77, P = 0.021) (Table 2). Number of ICI infusions was also positively associated with OS > 12 mo (OR 1.23, 95%CI 1.09-1.40), while sarcoma as underlying malignancy was significantly associated with OS < 12 mo (OR 0.17, 95%CI 0.029-0.947). Within the IMC cohort, nivolumab use was associated with OS < 12 mo in the univariate analysis (OR 0.09, 95%CI 0.01-0.83), while only age was associated with OS < 12 mo in multivariate analysis (OR 0.93, 95%CI 0.88-0.99) (Table 3). No individual malignancy was significantly associated with OS > 12 mo within the IMC cohort (Table 3).\\nUnivariate and multivariate predictors of overall survival > 12 mo among patients with malignancy using immune checkpoint inhibitor (n = 128)\\nNumber of infusions of immune checkpoint inhibitor prior to immune checkpoint inhibitor-mediated colitis diagnosis (cases) or total (controls).\\nSee Supplementary Table 2.\\nICI: Immune checkpoint inhibitor; IMC: Immune checkpoint inhibitor-mediated colitis; OR: Odds ratios; CI: Confidence interval; SD: Standard deviation; RCC: Renal cell carcinoma; NSCLC: Non-small cell lung cancer; SCC: Squamous cell carcinoma; irAE: Immune related adverse event.\\nUnivariate and multivariate predictors of overall survival > 12 mo among patients with immune checkpoint inhibitor colitis (n = 64)\\nNumber of infusions of immune checkpoint inhibitor prior to immune checkpoint inhibitor-mediated colitis diagnosis (cases) or total (controls).\\nSee Supplementary Table 2.\\nICI: Immune checkpoint inhibitor; IMC: Immune checkpoint inhibitor-mediated colitis; SD: Standard deviation; RCC: Renal cell carcinoma; NSCLC: Non-small cell lung cancer; SCC: Squamous cell carcinoma; irAE: Immune related adverse event.\",\n \"As certain clinical characteristics were significantly more common in patients with IMC compared to controls, we evaluated whether any of these clinical characteristics were associated with risk of developing IMC (Table 4). In univariate analysis, history of autoimmune disease and vitamin D use were both significantly associated with increased risk of IMC (OR 2.45, 95%CI 1.04-5.78, P = 0.040 for autoimmune disease; OR 2.51, 95%CI 1.14-5.54, P = 0.022 for vitamin D use). Interestingly, the use of vitamin D supplementation has previously been associated with a decreased risk of IMC, in contrast to our findings here[11]. Prior use of an immune-enhancing therapy (Supplementary Table 2) was associated with a significantly decreased risk of IMC (OR 0.20, 95%CI 0.04-0.95, P = 0.043). In the multivariate model which incorporated these characteristics, only the use of immune-enhancing therapy remained significantly associated with decreased risk of IMC, with an OR of 0.20 (95%CI 0.04-1.00, P = 0.050). \\nUnivariate and multivariate predictors of immune checkpoint inhibitor-mediated colitis among patients using immune checkpoint inhibitor (n = 128)\\nNumber of infusions of immune checkpoint inhibitor prior to immune checkpoint inhibitor-mediated colitis diagnosis (cases) or total (controls).\\nSee Supplementary Table 2.\\nICI: Immune checkpoint inhibitor; IMC: Immune checkpoint inhibitor-mediated colitis; RCC: Renal cell carcinoma; NSCLC: Non-small cell lung cancer; SCC: Squamous cell carcinoma; irAE: Immune related adverse event.\\nWe next determined if any variables were associated with an increased risk of severe IMC. Consistent with prior studies of irAE in ICI[17-19], we defined grade 1-2 IMC as mild and grade 3 or higher IMC as severe. In our study, 38 of the 64 patients (59.4%) had severe IMC (Supplementary Table 3). In the univariate model, ipilimumab and vitamin D supplementation were significantly associated with development of severe IMC (OR 8.93, 95%CI 1.07-74.8, P = 0.043 for ipilimumab; OR 3.33, 95%CI 1.10-10.14, P = 0.034 for vitamin D) (Supplementary Table 6). Combination therapy (ipilimumab plus nivolumab) trended towards an increased risk of severe IMC but did not reach significance (P = 0.053). In contrast, pembrolizumab was significantly associated with a decreased risk of severe IMC (OR 0.26, 95%CI 0.09-0.81, P = 0.020). In the multivariate model no characteristic reached significance for association with severe IMC, although both combination therapy and ipilimumab monotherapy approached significance for increased risk of severe IMC (P = 0.058 and 0.060, respectively).\",\n \"In our study, development of IMC following ICI use was associated with improved overall survival, although not improved progression-free survival, compared to ICI users without IMC. This is similar to findings at another center demonstrating both improved OS and PFS in patients with IMC[9,10]. We also found that vitamin D supplementation at the start of ICI treatment is a risk factor for developing IMC, in contrast to other research suggesting vitamin D use is associated with lower risk of IMC[11]. Our results, therefore, provide critical additional information on these previous associations and present a need for prospective studies.\\nBoth publications showing improved survival in patients with IMC were retrospective analyses performed at the same center[9,10]. One study noted that ICI class was significantly associated with development of IMC[9], a finding that has been demonstrated several times in retrospective work[8,17,18,20-23]. However, unlike our work, this study did not match control patients to account for this likely confounder, as ICI class has been associated with differences in PFS in some malignancies[24,25]. The second study at this center examined survival in melanoma patients with IMC, compared to our work across multiple malignancies, although frequency matching was performed to account for use of different ICI classes[10]. Since our study is the first to examine survival in patients with IMC at a different center, our work here reinforces that IMC may be associated with increased overall survival and prompts a need for prospective studies.\\nThe only other independent factor in our study positively associated with OS > 12 mo was number of ICI doses. This finding may be due to trivial length-time bias, as patients who survive longer are more likely to receive more doses of ICI. It is also possible that patients who required cessation of ICI due to IMC had worse outcomes, although prior work has suggested that patients still derive equivalent long-term benefit from ICI even if stopped due to irAE[26]. Type of underlying malignancy (sarcoma) was independently associated with OS < 12 mo in our study. These findings are not unexpected, as most advanced soft tissue sarcomas have a median OS of less than one year[27].\\nIn contrast to prior work, we found a positive association between vitamin D supplementation and development of IMC[11]. It is unclear if this is related to low serum vitamin D levels or negative impact of the supplementation itself, as vitamin D levels near the time of ICI initiation were not recorded in most patients. Additionally, the prior report on vitamin D in IMC was in melanoma patients only, which may partially account for discrepancies with our study. As this association did not remain significant in our multivariate analysis, it is possible that another confounding factor may explain the association between vitamin D supplementation and IMC in our study.\\nIn addition to challenging existing findings, we report here on additional novel risk factors for IMC. We are the first to report that prior use of immune-enhancing medications prior to ICI, such as IL-2 or interferon-γ, is significantly and independently associated with decreased risk of IMC. Much more work should be done to evaluate the relationship between these medications and future risk of IMC.\\nFinally, our study is the first to examine risk factors for severe IMC. In addition to increasing risk for IMC overall, we find that vitamin D supplementation may also be a risk factor for severe IMC. Similarly, our results suggest that the use of ipilimumab may be associated with increased risk of severe IMC, while pembrolizumab may be associated with decreased risk of severe IMC in patients who develop this syndrome. As ipilimumab has previously been associated with increased risk of IMC overall, while anti-PD-1, including pembrolizumab, are associated with lower risk of IMC overall[8,9], these findings emphasize that ICI class may affect severity of IMC.\\nOur findings may significantly impact clinical practice by identifying novel risks for IMC and severe IMC that clinicians, including oncologists and gastroenterologists, should be aware of, while also potentially providing reassurance to physicians and patients that development of IMC may be a positive prognosticator for cancer survival. Neither prior work nor ours found that treatment of IMC, including steroids or infliximab, negatively impacts OS[9,10], and therefore appropriate treatment of IMC should be pursued early on to minimize morbidity and mortality. Both steroid and infliximab use have been suggested to worsen survival in ICI users[12,13], but all current evidence suggests that use of these medications for IMC specifically does not impair cancer outcomes. Our work also cautions against supplementation with vitamin D in ICI users, as this may increase risk of IMC and severe IMC, although carefully designed studies with vitamin D measurements should be performed.\\nOur work has several strengths. We performed robust cohort matching to minimize confounding effects of ICI class and malignancy. This is also the first study to explore risk factors associated with severe IMC. However, there are limitations to our work. As a retrospective, observational study, it is subject to recall bias and cannot evaluate causation, and may also be subject to immortal time bias (ITB). Patients may have longer exposure to checkpoint inhibitors before developing IMC, compared to patients who do not manifest this irAE, leading to a period where they must survive for long enough to develop IMC and are therefore “immortal”[28]. We found that OS > 12 mo was significantly associated with greater numbers of ICI infusions (Table 2), which is likely due to ITB. However, greater numbers of infusions were not associated with IMC (Table 4). This suggests that the association between OS > 12 mo and IMC is likely independent of the number of ICI infusions, limiting this as a source of ITB in our study.\\nOther weaknesses of our work include selection of patients based on clinical criteria for IMC, including those who did not undergo endoscopy or other objective testing for intestinal inflammation, and therefore may not have had a true colitis. Like prior work, this is also a single-center study, and our results may not be widely generalizable, particularly since we identified fewer patients compared to prior work and our patient population is highly variable, including individuals with several different underlying malignancies. We did not exclude patients with prior non-GI irAEs in either group, although the presence of these was not independently associated with increased OS in our study. We also have not accounted for other factors which may be potential predictors of ICI response, including tumor PD-L1 expression burden, tumor mutational burden, gut microbial composition, proton pump inhibitor use, and combination treatment with tyrosine kinase inhibitors[29-34].\",\n \"In conclusion, our findings suggest presence of IMC is associated with improved OS in cancer patients when cases were matched closely to controls. We also found that vitamin D supplementation was significantly associated with development of both IMC and severe IMC, while immune-enhancing medications were significantly associated with decreased risk of IMC. Future work should focus on broader populations to resolve the discrepancies raised in our work, and to confirm the association between IMC and increased cancer survival. Closely involving gastroenterologists with the workup and management of IMC will be crucial to ensuring the best care possible for these patients.\"\n]"},"other_sections_sec_types":{"kind":"list like","value":[null,null,null,null,null,null,null,null,null],"string":"[\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null\n]"},"all_sections_titles":{"kind":"list like","value":["INTRODUCTION","MATERIALS AND METHODS","Study design and population","Statistical analysis","RESULTS","Clinical characteristics associated with IMC","IMC significantly increases overall survival","Significant risk factors for developing IMC and severe IMC","DISCUSSION","CONCLUSION"],"string":"[\n \"INTRODUCTION\",\n \"MATERIALS AND METHODS\",\n \"Study design and population\",\n \"Statistical analysis\",\n \"RESULTS\",\n \"Clinical characteristics associated with IMC\",\n \"IMC significantly increases overall survival\",\n \"Significant risk factors for developing IMC and severe IMC\",\n \"DISCUSSION\",\n \"CONCLUSION\"\n]"},"all_sections_texts":{"kind":"list like","value":["Immune checkpoint inhibitors (ICI) have dramatically changed the landscape of cancer therapy. Early studies showed significantly prolonged survival in patients with metastatic melanoma compared to standard chemotherapy[1], and evidence now exists for improved outcomes in a variety of tumors ranging from lung cancers to urothelial carcinoma to breast cancer[2-5]. Although these are powerful treatments in our armamentarium against malignancy, ICI can cause immune-related adverse events (irAE) characterized by autoimmune-like inflammation in a variety of non-tumor organs, leading to increased morbidity for patients[6].\nOne of the most common irAE is immune checkpoint inhibitor-mediated colitis (IMC). IMC may occur in up to 40% of patients treated with ipilimumab, an antibody targeting CTLA-4, 11%-17% of patients treated with antibodies against anti-PD-1 or anti-PD-L1, such as nivolumab, pembrolizumab, or atezolizumab, and around 32% of patients treated with a combination of anti-CTLA-4 and anti-PD-1[7]. Prior retrospective analyses of patients with IMC have attempted to identify characteristics associated with development of IMC, including type of malignancy, ICI class, dose of ICI, cancer stage, and vitamin D use[8-11]. Intriguingly, two prior studies have suggested that development of IMC may positively correlate with improved progression-free survival (PFS) and overall survival (OS)[9,10]. One of these studies controlled for confounding effects of ICI class via frequency matching, but was limited to patients with melanoma, hindering wider applicability of their findings[10]. These findings also conflict with data suggesting that use of steroids and the anti-TNF antibody infliximab in patients treated with ICI are associated with worse cancer outcomes[12,13]. These discrepancies represent a significant knowledge gap that impedes our ability to evaluate and manage IMC and ICI use.\nHere we present data from a retrospective study of patients treated with ICI at our institution who developed IMC across malignancy types. We compare this cohort to a matched control cohort to determine whether IMC was associated with improved progression-free survival and overall survival. We also evaluate which clinical characteristics increase the risk of developing IMC, including severe IMC.","Study design and population We conducted a retrospective case-control single-center study after obtaining approval from the Institutional Review Board at Stanford University (IRB 57125, approved 6/30/2020). Our primary aim was to determine the association of presence and severity of IMC on OS and PFS in ICI users. Our secondary aim was to identify clinical variables which predicted development of IMC in ICI users. We evaluated all patients over the age of 18 who had been treated with immune checkpoint inhibitors (ICI) for malignancy at Stanford Health Care from May 2011 to May 2020, including anti-CTLA-4 (ipilimumab), anti-PD-1 (nivolumab, pembrolizumab), and anti-PD-L1 (atezolizumab, avelumab, durvalumab), with follow up through October 2020. Using the Stanford Research Repository tool, we screened patients treated with ICI who were assigned International Classification of Diseases (ICD) 9 and ICD 10 codes associated with non-infectious colitis and diarrhea (Supplementary Table 1). Each chart which passed the initial screen was further screened by review of clinic notes to confirm diagnosis of immune checkpoint inhibitor-related colitis by oncology providers. Any patient found to have other explanations for their clinical presentation was excluded from the study.\nControl patients were matched one to one with each IMC patient for sex, age, malignancy, type of ICI used, prior ICI exposure, and duration of ICI exposure (matched to number of doses from initiation of ICI to development of colitis in study cohort). Control patients were initially screened by those lacking the above ICD codes and were confirmed via direct evaluation of each chart to lack diarrhea and/or colitis ascribable to ICI per their treating oncologist.\nWe extracted clinical data on IMC and control patient charts including demographics (age at time of ICI initiation, sex, body mass index, race per patient report), medical history (presence of prior non-liver and non-upper gastrointestinal disease, personal history of autoimmune disease, family history of autoimmune disease), and cancer history (type of malignancy, tumor stage at ICI initiation, prior chemotherapy, prior radiation therapy, type of ICI used, duration of ICI use, OS and PFS) (Supplementary Table 2). OS was determined as time from initiation of ICI to death, while PFS was determined as time from initiation of ICI to death or progression of disease as determined by oncology providers, based on radiographic evidence of progression. IMC severity was graded using commonly accepted determinants of IMC and irAE grading[14]. We specifically noted prior use of therapies designed to increase immune responses [interleukin (IL)-2, interferon (IFN)-γ, toll-like receptor (TLR)-9 agonist, tebentafusp, or anti-CD47 antibody]. Vitamin D and non-steroidal anti-inflammatory (NSAID) use were defined as vitamin D supplement or NSAID medication, respectively, noted in the history of present illness or on the patient’s medication list at the clinic visit closest to their date of ICI initiation.\nWe collected data on IMC diagnosis including number of patients who received endoscopy (flexible sigmoidoscopy or colonoscopy), findings on endoscopy, and fecal calprotectin (Supplementary Table 3). Data on management of IMC included treatment with anti-diarrheal medications, mesalamine, steroids (prednisone, budesonide, dexamethasone), infliximab, and vedolizumab.\nWe conducted a retrospective case-control single-center study after obtaining approval from the Institutional Review Board at Stanford University (IRB 57125, approved 6/30/2020). Our primary aim was to determine the association of presence and severity of IMC on OS and PFS in ICI users. Our secondary aim was to identify clinical variables which predicted development of IMC in ICI users. We evaluated all patients over the age of 18 who had been treated with immune checkpoint inhibitors (ICI) for malignancy at Stanford Health Care from May 2011 to May 2020, including anti-CTLA-4 (ipilimumab), anti-PD-1 (nivolumab, pembrolizumab), and anti-PD-L1 (atezolizumab, avelumab, durvalumab), with follow up through October 2020. Using the Stanford Research Repository tool, we screened patients treated with ICI who were assigned International Classification of Diseases (ICD) 9 and ICD 10 codes associated with non-infectious colitis and diarrhea (Supplementary Table 1). Each chart which passed the initial screen was further screened by review of clinic notes to confirm diagnosis of immune checkpoint inhibitor-related colitis by oncology providers. Any patient found to have other explanations for their clinical presentation was excluded from the study.\nControl patients were matched one to one with each IMC patient for sex, age, malignancy, type of ICI used, prior ICI exposure, and duration of ICI exposure (matched to number of doses from initiation of ICI to development of colitis in study cohort). Control patients were initially screened by those lacking the above ICD codes and were confirmed via direct evaluation of each chart to lack diarrhea and/or colitis ascribable to ICI per their treating oncologist.\nWe extracted clinical data on IMC and control patient charts including demographics (age at time of ICI initiation, sex, body mass index, race per patient report), medical history (presence of prior non-liver and non-upper gastrointestinal disease, personal history of autoimmune disease, family history of autoimmune disease), and cancer history (type of malignancy, tumor stage at ICI initiation, prior chemotherapy, prior radiation therapy, type of ICI used, duration of ICI use, OS and PFS) (Supplementary Table 2). OS was determined as time from initiation of ICI to death, while PFS was determined as time from initiation of ICI to death or progression of disease as determined by oncology providers, based on radiographic evidence of progression. IMC severity was graded using commonly accepted determinants of IMC and irAE grading[14]. We specifically noted prior use of therapies designed to increase immune responses [interleukin (IL)-2, interferon (IFN)-γ, toll-like receptor (TLR)-9 agonist, tebentafusp, or anti-CD47 antibody]. Vitamin D and non-steroidal anti-inflammatory (NSAID) use were defined as vitamin D supplement or NSAID medication, respectively, noted in the history of present illness or on the patient’s medication list at the clinic visit closest to their date of ICI initiation.\nWe collected data on IMC diagnosis including number of patients who received endoscopy (flexible sigmoidoscopy or colonoscopy), findings on endoscopy, and fecal calprotectin (Supplementary Table 3). Data on management of IMC included treatment with anti-diarrheal medications, mesalamine, steroids (prednisone, budesonide, dexamethasone), infliximab, and vedolizumab.\nStatistical analysis The rate of the primary outcomes (OS > 12 mo and PFS > 6 mo among all ICI users, OS > 12 mo and PFS > 6 mo in patients with IMC) and secondary outcomes (risks of IMC among patients with malignancy using ICI, IMC severity), predictive value of clinical variables on primary and secondary outcomes, odds ratio (OR) with its 95% confidence interval (CI), and P values were calculated using Statistics/Data Analysis (Stata/IC 15.1 for Windows, College Station, TX, United States). Dichotomous variables were analyzed for outcomes using the chi-squared test or the Fisher’s exact test where appropriate, and continuous variables were analyzed using Student’s t-tests if normally distributed, or the Wilcoxon signed-rank test for non-normal data. For our multivariate analyses, model building was based on forward stepwise logistic regression, with a P value of 0.05 required for entry, and known predictors were also included. We constructed Kaplan Meier curves for the outcomes of OS and PFS between patients with and without IMC and patients with mild vs severe IMC using GraphPad Prism (version 8.3; GraphPad Software, Inc., La Jolla, CA, United States). All authors had access to the study data and reviewed and approved the final manuscript.\nThe rate of the primary outcomes (OS > 12 mo and PFS > 6 mo among all ICI users, OS > 12 mo and PFS > 6 mo in patients with IMC) and secondary outcomes (risks of IMC among patients with malignancy using ICI, IMC severity), predictive value of clinical variables on primary and secondary outcomes, odds ratio (OR) with its 95% confidence interval (CI), and P values were calculated using Statistics/Data Analysis (Stata/IC 15.1 for Windows, College Station, TX, United States). Dichotomous variables were analyzed for outcomes using the chi-squared test or the Fisher’s exact test where appropriate, and continuous variables were analyzed using Student’s t-tests if normally distributed, or the Wilcoxon signed-rank test for non-normal data. For our multivariate analyses, model building was based on forward stepwise logistic regression, with a P value of 0.05 required for entry, and known predictors were also included. We constructed Kaplan Meier curves for the outcomes of OS and PFS between patients with and without IMC and patients with mild vs severe IMC using GraphPad Prism (version 8.3; GraphPad Software, Inc., La Jolla, CA, United States). All authors had access to the study data and reviewed and approved the final manuscript.","We conducted a retrospective case-control single-center study after obtaining approval from the Institutional Review Board at Stanford University (IRB 57125, approved 6/30/2020). Our primary aim was to determine the association of presence and severity of IMC on OS and PFS in ICI users. Our secondary aim was to identify clinical variables which predicted development of IMC in ICI users. We evaluated all patients over the age of 18 who had been treated with immune checkpoint inhibitors (ICI) for malignancy at Stanford Health Care from May 2011 to May 2020, including anti-CTLA-4 (ipilimumab), anti-PD-1 (nivolumab, pembrolizumab), and anti-PD-L1 (atezolizumab, avelumab, durvalumab), with follow up through October 2020. Using the Stanford Research Repository tool, we screened patients treated with ICI who were assigned International Classification of Diseases (ICD) 9 and ICD 10 codes associated with non-infectious colitis and diarrhea (Supplementary Table 1). Each chart which passed the initial screen was further screened by review of clinic notes to confirm diagnosis of immune checkpoint inhibitor-related colitis by oncology providers. Any patient found to have other explanations for their clinical presentation was excluded from the study.\nControl patients were matched one to one with each IMC patient for sex, age, malignancy, type of ICI used, prior ICI exposure, and duration of ICI exposure (matched to number of doses from initiation of ICI to development of colitis in study cohort). Control patients were initially screened by those lacking the above ICD codes and were confirmed via direct evaluation of each chart to lack diarrhea and/or colitis ascribable to ICI per their treating oncologist.\nWe extracted clinical data on IMC and control patient charts including demographics (age at time of ICI initiation, sex, body mass index, race per patient report), medical history (presence of prior non-liver and non-upper gastrointestinal disease, personal history of autoimmune disease, family history of autoimmune disease), and cancer history (type of malignancy, tumor stage at ICI initiation, prior chemotherapy, prior radiation therapy, type of ICI used, duration of ICI use, OS and PFS) (Supplementary Table 2). OS was determined as time from initiation of ICI to death, while PFS was determined as time from initiation of ICI to death or progression of disease as determined by oncology providers, based on radiographic evidence of progression. IMC severity was graded using commonly accepted determinants of IMC and irAE grading[14]. We specifically noted prior use of therapies designed to increase immune responses [interleukin (IL)-2, interferon (IFN)-γ, toll-like receptor (TLR)-9 agonist, tebentafusp, or anti-CD47 antibody]. Vitamin D and non-steroidal anti-inflammatory (NSAID) use were defined as vitamin D supplement or NSAID medication, respectively, noted in the history of present illness or on the patient’s medication list at the clinic visit closest to their date of ICI initiation.\nWe collected data on IMC diagnosis including number of patients who received endoscopy (flexible sigmoidoscopy or colonoscopy), findings on endoscopy, and fecal calprotectin (Supplementary Table 3). Data on management of IMC included treatment with anti-diarrheal medications, mesalamine, steroids (prednisone, budesonide, dexamethasone), infliximab, and vedolizumab.","The rate of the primary outcomes (OS > 12 mo and PFS > 6 mo among all ICI users, OS > 12 mo and PFS > 6 mo in patients with IMC) and secondary outcomes (risks of IMC among patients with malignancy using ICI, IMC severity), predictive value of clinical variables on primary and secondary outcomes, odds ratio (OR) with its 95% confidence interval (CI), and P values were calculated using Statistics/Data Analysis (Stata/IC 15.1 for Windows, College Station, TX, United States). Dichotomous variables were analyzed for outcomes using the chi-squared test or the Fisher’s exact test where appropriate, and continuous variables were analyzed using Student’s t-tests if normally distributed, or the Wilcoxon signed-rank test for non-normal data. For our multivariate analyses, model building was based on forward stepwise logistic regression, with a P value of 0.05 required for entry, and known predictors were also included. We constructed Kaplan Meier curves for the outcomes of OS and PFS between patients with and without IMC and patients with mild vs severe IMC using GraphPad Prism (version 8.3; GraphPad Software, Inc., La Jolla, CA, United States). All authors had access to the study data and reviewed and approved the final manuscript.","Clinical characteristics associated with IMC We identified a total of 314 patients treated with ICI at Stanford Health Care from May 2011 to May 2020 who had ICD codes matching our query (Supplementary Table 1). Of these, 64 had a diagnosis of IMC per review of Oncology providers’ notes, after excluding patients with alternative diagnoses for their symptoms. 24 (37.5%) of these IMC patients underwent an endoscopy (colonoscopy or flexible sigmoidoscopy) during workup, of which seven (29.2%) had a normal endoscopic appearance, consistent with prior reports demonstrating that approximately one third of patients with IMC related to anti-PD-1 therapy have microscopic colitis[15] (Supplementary Table 3). An additional 14 patients (21.9%) had imaging findings suggestive of IMC while 3 patients (4.69%) without imaging or endoscopy had an elevated calprotectin or fecal lactoferrin.\nThese 64 patients were manually matched 1:1 with control patients based on age, sex, malignancy, type of ICI, whether or not the patient had prior ICI exposure, and duration of ICI use. We compared clinical characteristics of patients from the IMC cohort and the control cohort (Table 1). None of the matched characteristics were significantly different between the two cohorts. The mean age across the combined cohorts was 66.6 years, with an average age of 67.4 in the cohort with IMC compared with 65.8 in the control cohort (P = 0.42). 57.81% of patients in each group were male (P = 1.00). Patients were predominantly white in both groups, with 52 (81.25%) white individuals in the IMC cohort compared to 50 (78.13%) in the control group (P = 0.66). The most common malignancy in each group was melanoma [33 (51.56%) in both cohorts], followed by renal cell carcinoma [8 (12.5%) in the IMC cohort and 7 (10.94%) in the control cohort] and non-small cell lung cancer [6 (9.38%) in both cohorts]. Both groups had similar numbers of patients with stage IV malignancy [56 (87.5%) in the IMC cohort and 58 (90.63%) in the control cohort, P = 0.778]. Combination ipilimumab and nivolumab was the most commonly used checkpoint therapy [24 (37.5%) of patients in each cohort], followed by nivolumab monotherapy [19 (29.69%) of each cohort] and ipilimumab monotherapy [11 (17.19%) of each cohort].\nBaseline characteristics of patients with immune checkpoint inhibitor use\nVariable matched between cases and controls.\nNumber of infusions of immune checkpoint inhibitor prior to immune checkpoint inhibitor-mediated colitis diagnosis (cases) or total (controls).\nSee Supplementary Table 2.\nIMC: Immune checkpoint inhibitor-mediated colitis; ICI: Immune checkpoint inhibitor; SD: Standard deviation; RCC: Renal cell carcinoma; NSCLC: Non-small cell lung cancer; SCC: Squamous cell carcinoma; PFS: Progression-Free Survival; irAE: Immune related adverse event; OS: Overall survival.\nAmong the remainder of the clinical characteristics evaluated, personal history of autoimmune disease (including prior irAE) and family history of autoimmune disease were significantly more common in patients with IMC (P = 0.037 and 0.048, respectively). Intriguingly, prior use of a therapy designed to increase immune responses was more common in the control cohort without IMC (P = 0.027). In contrast to prior data[11], use of vitamin D supplementation at the time of first dose of ICI was significantly more prevalent in patients with IMC (P = 0.020). Neither smoking status, NSAID use at time of ICI initiation, steroid use at the time of ICI initiation, nor recent vaccination were significantly more common in IMC patients compared to controls.\nWe identified a total of 314 patients treated with ICI at Stanford Health Care from May 2011 to May 2020 who had ICD codes matching our query (Supplementary Table 1). Of these, 64 had a diagnosis of IMC per review of Oncology providers’ notes, after excluding patients with alternative diagnoses for their symptoms. 24 (37.5%) of these IMC patients underwent an endoscopy (colonoscopy or flexible sigmoidoscopy) during workup, of which seven (29.2%) had a normal endoscopic appearance, consistent with prior reports demonstrating that approximately one third of patients with IMC related to anti-PD-1 therapy have microscopic colitis[15] (Supplementary Table 3). An additional 14 patients (21.9%) had imaging findings suggestive of IMC while 3 patients (4.69%) without imaging or endoscopy had an elevated calprotectin or fecal lactoferrin.\nThese 64 patients were manually matched 1:1 with control patients based on age, sex, malignancy, type of ICI, whether or not the patient had prior ICI exposure, and duration of ICI use. We compared clinical characteristics of patients from the IMC cohort and the control cohort (Table 1). None of the matched characteristics were significantly different between the two cohorts. The mean age across the combined cohorts was 66.6 years, with an average age of 67.4 in the cohort with IMC compared with 65.8 in the control cohort (P = 0.42). 57.81% of patients in each group were male (P = 1.00). Patients were predominantly white in both groups, with 52 (81.25%) white individuals in the IMC cohort compared to 50 (78.13%) in the control group (P = 0.66). The most common malignancy in each group was melanoma [33 (51.56%) in both cohorts], followed by renal cell carcinoma [8 (12.5%) in the IMC cohort and 7 (10.94%) in the control cohort] and non-small cell lung cancer [6 (9.38%) in both cohorts]. Both groups had similar numbers of patients with stage IV malignancy [56 (87.5%) in the IMC cohort and 58 (90.63%) in the control cohort, P = 0.778]. Combination ipilimumab and nivolumab was the most commonly used checkpoint therapy [24 (37.5%) of patients in each cohort], followed by nivolumab monotherapy [19 (29.69%) of each cohort] and ipilimumab monotherapy [11 (17.19%) of each cohort].\nBaseline characteristics of patients with immune checkpoint inhibitor use\nVariable matched between cases and controls.\nNumber of infusions of immune checkpoint inhibitor prior to immune checkpoint inhibitor-mediated colitis diagnosis (cases) or total (controls).\nSee Supplementary Table 2.\nIMC: Immune checkpoint inhibitor-mediated colitis; ICI: Immune checkpoint inhibitor; SD: Standard deviation; RCC: Renal cell carcinoma; NSCLC: Non-small cell lung cancer; SCC: Squamous cell carcinoma; PFS: Progression-Free Survival; irAE: Immune related adverse event; OS: Overall survival.\nAmong the remainder of the clinical characteristics evaluated, personal history of autoimmune disease (including prior irAE) and family history of autoimmune disease were significantly more common in patients with IMC (P = 0.037 and 0.048, respectively). Intriguingly, prior use of a therapy designed to increase immune responses was more common in the control cohort without IMC (P = 0.027). In contrast to prior data[11], use of vitamin D supplementation at the time of first dose of ICI was significantly more prevalent in patients with IMC (P = 0.020). Neither smoking status, NSAID use at time of ICI initiation, steroid use at the time of ICI initiation, nor recent vaccination were significantly more common in IMC patients compared to controls.\nIMC significantly increases overall survival As IMC has previously been associated with increased OS and PFS in cancer patients[9,10], we evaluated whether this association was seen in our study. We found that OS was significantly longer in patients who developed IMC compared to those who did not, with a mean OS of 24.3 mo in patients with IMC and 17.7 mo in control (P = 0.05, Table 1). OS at 12 mo following ICI initiation was significantly higher in patients who developed IMC compared to those who did not (P = 0.02, Figure 1). However, in contrast to prior findings, our study did not find a significant difference in PFS between IMC patients and controls, with a mean PFS 13.7 mo in IMC patients and 11.9 mo in controls (P = 0.524) (Table 1). PFS also did not differ between patients who developed mild vs severe IMC (P = 0.690, Supplementary Table 5).\n\nOverall survival at 12 mo in patients with and without immune checkpoint inhibitor-mediated colitis. Kaplan-Meier curve of overall survival at 12 mo in patients with immune checkpoint inhibitor-mediated colitis (IMC, red) and without IMC (black). IMC: Immune checkpoint inhibitor-mediated colitis; HR: Hazard ratio.\nAcross both cohorts, we identified clinical characteristics significantly associated with OS greater than 12 mo and PFS greater than 6 mo, which are correlated with cancer outcomes in patients treated with ICI[16] (Tables 2 and 3) (Supplementary Tables 4 and 5). IMC was significantly and independently associated with OS > 12 mo in the multivariate model (OR 2.81, 95%CI 1.17-6.77, P = 0.021) (Table 2). Number of ICI infusions was also positively associated with OS > 12 mo (OR 1.23, 95%CI 1.09-1.40), while sarcoma as underlying malignancy was significantly associated with OS < 12 mo (OR 0.17, 95%CI 0.029-0.947). Within the IMC cohort, nivolumab use was associated with OS < 12 mo in the univariate analysis (OR 0.09, 95%CI 0.01-0.83), while only age was associated with OS < 12 mo in multivariate analysis (OR 0.93, 95%CI 0.88-0.99) (Table 3). No individual malignancy was significantly associated with OS > 12 mo within the IMC cohort (Table 3).\nUnivariate and multivariate predictors of overall survival > 12 mo among patients with malignancy using immune checkpoint inhibitor (n = 128)\nNumber of infusions of immune checkpoint inhibitor prior to immune checkpoint inhibitor-mediated colitis diagnosis (cases) or total (controls).\nSee Supplementary Table 2.\nICI: Immune checkpoint inhibitor; IMC: Immune checkpoint inhibitor-mediated colitis; OR: Odds ratios; CI: Confidence interval; SD: Standard deviation; RCC: Renal cell carcinoma; NSCLC: Non-small cell lung cancer; SCC: Squamous cell carcinoma; irAE: Immune related adverse event.\nUnivariate and multivariate predictors of overall survival > 12 mo among patients with immune checkpoint inhibitor colitis (n = 64)\nNumber of infusions of immune checkpoint inhibitor prior to immune checkpoint inhibitor-mediated colitis diagnosis (cases) or total (controls).\nSee Supplementary Table 2.\nICI: Immune checkpoint inhibitor; IMC: Immune checkpoint inhibitor-mediated colitis; SD: Standard deviation; RCC: Renal cell carcinoma; NSCLC: Non-small cell lung cancer; SCC: Squamous cell carcinoma; irAE: Immune related adverse event.\nAs IMC has previously been associated with increased OS and PFS in cancer patients[9,10], we evaluated whether this association was seen in our study. We found that OS was significantly longer in patients who developed IMC compared to those who did not, with a mean OS of 24.3 mo in patients with IMC and 17.7 mo in control (P = 0.05, Table 1). OS at 12 mo following ICI initiation was significantly higher in patients who developed IMC compared to those who did not (P = 0.02, Figure 1). However, in contrast to prior findings, our study did not find a significant difference in PFS between IMC patients and controls, with a mean PFS 13.7 mo in IMC patients and 11.9 mo in controls (P = 0.524) (Table 1). PFS also did not differ between patients who developed mild vs severe IMC (P = 0.690, Supplementary Table 5).\n\nOverall survival at 12 mo in patients with and without immune checkpoint inhibitor-mediated colitis. Kaplan-Meier curve of overall survival at 12 mo in patients with immune checkpoint inhibitor-mediated colitis (IMC, red) and without IMC (black). IMC: Immune checkpoint inhibitor-mediated colitis; HR: Hazard ratio.\nAcross both cohorts, we identified clinical characteristics significantly associated with OS greater than 12 mo and PFS greater than 6 mo, which are correlated with cancer outcomes in patients treated with ICI[16] (Tables 2 and 3) (Supplementary Tables 4 and 5). IMC was significantly and independently associated with OS > 12 mo in the multivariate model (OR 2.81, 95%CI 1.17-6.77, P = 0.021) (Table 2). Number of ICI infusions was also positively associated with OS > 12 mo (OR 1.23, 95%CI 1.09-1.40), while sarcoma as underlying malignancy was significantly associated with OS < 12 mo (OR 0.17, 95%CI 0.029-0.947). Within the IMC cohort, nivolumab use was associated with OS < 12 mo in the univariate analysis (OR 0.09, 95%CI 0.01-0.83), while only age was associated with OS < 12 mo in multivariate analysis (OR 0.93, 95%CI 0.88-0.99) (Table 3). No individual malignancy was significantly associated with OS > 12 mo within the IMC cohort (Table 3).\nUnivariate and multivariate predictors of overall survival > 12 mo among patients with malignancy using immune checkpoint inhibitor (n = 128)\nNumber of infusions of immune checkpoint inhibitor prior to immune checkpoint inhibitor-mediated colitis diagnosis (cases) or total (controls).\nSee Supplementary Table 2.\nICI: Immune checkpoint inhibitor; IMC: Immune checkpoint inhibitor-mediated colitis; OR: Odds ratios; CI: Confidence interval; SD: Standard deviation; RCC: Renal cell carcinoma; NSCLC: Non-small cell lung cancer; SCC: Squamous cell carcinoma; irAE: Immune related adverse event.\nUnivariate and multivariate predictors of overall survival > 12 mo among patients with immune checkpoint inhibitor colitis (n = 64)\nNumber of infusions of immune checkpoint inhibitor prior to immune checkpoint inhibitor-mediated colitis diagnosis (cases) or total (controls).\nSee Supplementary Table 2.\nICI: Immune checkpoint inhibitor; IMC: Immune checkpoint inhibitor-mediated colitis; SD: Standard deviation; RCC: Renal cell carcinoma; NSCLC: Non-small cell lung cancer; SCC: Squamous cell carcinoma; irAE: Immune related adverse event.\nSignificant risk factors for developing IMC and severe IMC As certain clinical characteristics were significantly more common in patients with IMC compared to controls, we evaluated whether any of these clinical characteristics were associated with risk of developing IMC (Table 4). In univariate analysis, history of autoimmune disease and vitamin D use were both significantly associated with increased risk of IMC (OR 2.45, 95%CI 1.04-5.78, P = 0.040 for autoimmune disease; OR 2.51, 95%CI 1.14-5.54, P = 0.022 for vitamin D use). Interestingly, the use of vitamin D supplementation has previously been associated with a decreased risk of IMC, in contrast to our findings here[11]. Prior use of an immune-enhancing therapy (Supplementary Table 2) was associated with a significantly decreased risk of IMC (OR 0.20, 95%CI 0.04-0.95, P = 0.043). In the multivariate model which incorporated these characteristics, only the use of immune-enhancing therapy remained significantly associated with decreased risk of IMC, with an OR of 0.20 (95%CI 0.04-1.00, P = 0.050). \nUnivariate and multivariate predictors of immune checkpoint inhibitor-mediated colitis among patients using immune checkpoint inhibitor (n = 128)\nNumber of infusions of immune checkpoint inhibitor prior to immune checkpoint inhibitor-mediated colitis diagnosis (cases) or total (controls).\nSee Supplementary Table 2.\nICI: Immune checkpoint inhibitor; IMC: Immune checkpoint inhibitor-mediated colitis; RCC: Renal cell carcinoma; NSCLC: Non-small cell lung cancer; SCC: Squamous cell carcinoma; irAE: Immune related adverse event.\nWe next determined if any variables were associated with an increased risk of severe IMC. Consistent with prior studies of irAE in ICI[17-19], we defined grade 1-2 IMC as mild and grade 3 or higher IMC as severe. In our study, 38 of the 64 patients (59.4%) had severe IMC (Supplementary Table 3). In the univariate model, ipilimumab and vitamin D supplementation were significantly associated with development of severe IMC (OR 8.93, 95%CI 1.07-74.8, P = 0.043 for ipilimumab; OR 3.33, 95%CI 1.10-10.14, P = 0.034 for vitamin D) (Supplementary Table 6). Combination therapy (ipilimumab plus nivolumab) trended towards an increased risk of severe IMC but did not reach significance (P = 0.053). In contrast, pembrolizumab was significantly associated with a decreased risk of severe IMC (OR 0.26, 95%CI 0.09-0.81, P = 0.020). In the multivariate model no characteristic reached significance for association with severe IMC, although both combination therapy and ipilimumab monotherapy approached significance for increased risk of severe IMC (P = 0.058 and 0.060, respectively).\nAs certain clinical characteristics were significantly more common in patients with IMC compared to controls, we evaluated whether any of these clinical characteristics were associated with risk of developing IMC (Table 4). In univariate analysis, history of autoimmune disease and vitamin D use were both significantly associated with increased risk of IMC (OR 2.45, 95%CI 1.04-5.78, P = 0.040 for autoimmune disease; OR 2.51, 95%CI 1.14-5.54, P = 0.022 for vitamin D use). Interestingly, the use of vitamin D supplementation has previously been associated with a decreased risk of IMC, in contrast to our findings here[11]. Prior use of an immune-enhancing therapy (Supplementary Table 2) was associated with a significantly decreased risk of IMC (OR 0.20, 95%CI 0.04-0.95, P = 0.043). In the multivariate model which incorporated these characteristics, only the use of immune-enhancing therapy remained significantly associated with decreased risk of IMC, with an OR of 0.20 (95%CI 0.04-1.00, P = 0.050). \nUnivariate and multivariate predictors of immune checkpoint inhibitor-mediated colitis among patients using immune checkpoint inhibitor (n = 128)\nNumber of infusions of immune checkpoint inhibitor prior to immune checkpoint inhibitor-mediated colitis diagnosis (cases) or total (controls).\nSee Supplementary Table 2.\nICI: Immune checkpoint inhibitor; IMC: Immune checkpoint inhibitor-mediated colitis; RCC: Renal cell carcinoma; NSCLC: Non-small cell lung cancer; SCC: Squamous cell carcinoma; irAE: Immune related adverse event.\nWe next determined if any variables were associated with an increased risk of severe IMC. Consistent with prior studies of irAE in ICI[17-19], we defined grade 1-2 IMC as mild and grade 3 or higher IMC as severe. In our study, 38 of the 64 patients (59.4%) had severe IMC (Supplementary Table 3). In the univariate model, ipilimumab and vitamin D supplementation were significantly associated with development of severe IMC (OR 8.93, 95%CI 1.07-74.8, P = 0.043 for ipilimumab; OR 3.33, 95%CI 1.10-10.14, P = 0.034 for vitamin D) (Supplementary Table 6). Combination therapy (ipilimumab plus nivolumab) trended towards an increased risk of severe IMC but did not reach significance (P = 0.053). In contrast, pembrolizumab was significantly associated with a decreased risk of severe IMC (OR 0.26, 95%CI 0.09-0.81, P = 0.020). In the multivariate model no characteristic reached significance for association with severe IMC, although both combination therapy and ipilimumab monotherapy approached significance for increased risk of severe IMC (P = 0.058 and 0.060, respectively).","We identified a total of 314 patients treated with ICI at Stanford Health Care from May 2011 to May 2020 who had ICD codes matching our query (Supplementary Table 1). Of these, 64 had a diagnosis of IMC per review of Oncology providers’ notes, after excluding patients with alternative diagnoses for their symptoms. 24 (37.5%) of these IMC patients underwent an endoscopy (colonoscopy or flexible sigmoidoscopy) during workup, of which seven (29.2%) had a normal endoscopic appearance, consistent with prior reports demonstrating that approximately one third of patients with IMC related to anti-PD-1 therapy have microscopic colitis[15] (Supplementary Table 3). An additional 14 patients (21.9%) had imaging findings suggestive of IMC while 3 patients (4.69%) without imaging or endoscopy had an elevated calprotectin or fecal lactoferrin.\nThese 64 patients were manually matched 1:1 with control patients based on age, sex, malignancy, type of ICI, whether or not the patient had prior ICI exposure, and duration of ICI use. We compared clinical characteristics of patients from the IMC cohort and the control cohort (Table 1). None of the matched characteristics were significantly different between the two cohorts. The mean age across the combined cohorts was 66.6 years, with an average age of 67.4 in the cohort with IMC compared with 65.8 in the control cohort (P = 0.42). 57.81% of patients in each group were male (P = 1.00). Patients were predominantly white in both groups, with 52 (81.25%) white individuals in the IMC cohort compared to 50 (78.13%) in the control group (P = 0.66). The most common malignancy in each group was melanoma [33 (51.56%) in both cohorts], followed by renal cell carcinoma [8 (12.5%) in the IMC cohort and 7 (10.94%) in the control cohort] and non-small cell lung cancer [6 (9.38%) in both cohorts]. Both groups had similar numbers of patients with stage IV malignancy [56 (87.5%) in the IMC cohort and 58 (90.63%) in the control cohort, P = 0.778]. Combination ipilimumab and nivolumab was the most commonly used checkpoint therapy [24 (37.5%) of patients in each cohort], followed by nivolumab monotherapy [19 (29.69%) of each cohort] and ipilimumab monotherapy [11 (17.19%) of each cohort].\nBaseline characteristics of patients with immune checkpoint inhibitor use\nVariable matched between cases and controls.\nNumber of infusions of immune checkpoint inhibitor prior to immune checkpoint inhibitor-mediated colitis diagnosis (cases) or total (controls).\nSee Supplementary Table 2.\nIMC: Immune checkpoint inhibitor-mediated colitis; ICI: Immune checkpoint inhibitor; SD: Standard deviation; RCC: Renal cell carcinoma; NSCLC: Non-small cell lung cancer; SCC: Squamous cell carcinoma; PFS: Progression-Free Survival; irAE: Immune related adverse event; OS: Overall survival.\nAmong the remainder of the clinical characteristics evaluated, personal history of autoimmune disease (including prior irAE) and family history of autoimmune disease were significantly more common in patients with IMC (P = 0.037 and 0.048, respectively). Intriguingly, prior use of a therapy designed to increase immune responses was more common in the control cohort without IMC (P = 0.027). In contrast to prior data[11], use of vitamin D supplementation at the time of first dose of ICI was significantly more prevalent in patients with IMC (P = 0.020). Neither smoking status, NSAID use at time of ICI initiation, steroid use at the time of ICI initiation, nor recent vaccination were significantly more common in IMC patients compared to controls.","As IMC has previously been associated with increased OS and PFS in cancer patients[9,10], we evaluated whether this association was seen in our study. We found that OS was significantly longer in patients who developed IMC compared to those who did not, with a mean OS of 24.3 mo in patients with IMC and 17.7 mo in control (P = 0.05, Table 1). OS at 12 mo following ICI initiation was significantly higher in patients who developed IMC compared to those who did not (P = 0.02, Figure 1). However, in contrast to prior findings, our study did not find a significant difference in PFS between IMC patients and controls, with a mean PFS 13.7 mo in IMC patients and 11.9 mo in controls (P = 0.524) (Table 1). PFS also did not differ between patients who developed mild vs severe IMC (P = 0.690, Supplementary Table 5).\n\nOverall survival at 12 mo in patients with and without immune checkpoint inhibitor-mediated colitis. Kaplan-Meier curve of overall survival at 12 mo in patients with immune checkpoint inhibitor-mediated colitis (IMC, red) and without IMC (black). IMC: Immune checkpoint inhibitor-mediated colitis; HR: Hazard ratio.\nAcross both cohorts, we identified clinical characteristics significantly associated with OS greater than 12 mo and PFS greater than 6 mo, which are correlated with cancer outcomes in patients treated with ICI[16] (Tables 2 and 3) (Supplementary Tables 4 and 5). IMC was significantly and independently associated with OS > 12 mo in the multivariate model (OR 2.81, 95%CI 1.17-6.77, P = 0.021) (Table 2). Number of ICI infusions was also positively associated with OS > 12 mo (OR 1.23, 95%CI 1.09-1.40), while sarcoma as underlying malignancy was significantly associated with OS < 12 mo (OR 0.17, 95%CI 0.029-0.947). Within the IMC cohort, nivolumab use was associated with OS < 12 mo in the univariate analysis (OR 0.09, 95%CI 0.01-0.83), while only age was associated with OS < 12 mo in multivariate analysis (OR 0.93, 95%CI 0.88-0.99) (Table 3). No individual malignancy was significantly associated with OS > 12 mo within the IMC cohort (Table 3).\nUnivariate and multivariate predictors of overall survival > 12 mo among patients with malignancy using immune checkpoint inhibitor (n = 128)\nNumber of infusions of immune checkpoint inhibitor prior to immune checkpoint inhibitor-mediated colitis diagnosis (cases) or total (controls).\nSee Supplementary Table 2.\nICI: Immune checkpoint inhibitor; IMC: Immune checkpoint inhibitor-mediated colitis; OR: Odds ratios; CI: Confidence interval; SD: Standard deviation; RCC: Renal cell carcinoma; NSCLC: Non-small cell lung cancer; SCC: Squamous cell carcinoma; irAE: Immune related adverse event.\nUnivariate and multivariate predictors of overall survival > 12 mo among patients with immune checkpoint inhibitor colitis (n = 64)\nNumber of infusions of immune checkpoint inhibitor prior to immune checkpoint inhibitor-mediated colitis diagnosis (cases) or total (controls).\nSee Supplementary Table 2.\nICI: Immune checkpoint inhibitor; IMC: Immune checkpoint inhibitor-mediated colitis; SD: Standard deviation; RCC: Renal cell carcinoma; NSCLC: Non-small cell lung cancer; SCC: Squamous cell carcinoma; irAE: Immune related adverse event.","As certain clinical characteristics were significantly more common in patients with IMC compared to controls, we evaluated whether any of these clinical characteristics were associated with risk of developing IMC (Table 4). In univariate analysis, history of autoimmune disease and vitamin D use were both significantly associated with increased risk of IMC (OR 2.45, 95%CI 1.04-5.78, P = 0.040 for autoimmune disease; OR 2.51, 95%CI 1.14-5.54, P = 0.022 for vitamin D use). Interestingly, the use of vitamin D supplementation has previously been associated with a decreased risk of IMC, in contrast to our findings here[11]. Prior use of an immune-enhancing therapy (Supplementary Table 2) was associated with a significantly decreased risk of IMC (OR 0.20, 95%CI 0.04-0.95, P = 0.043). In the multivariate model which incorporated these characteristics, only the use of immune-enhancing therapy remained significantly associated with decreased risk of IMC, with an OR of 0.20 (95%CI 0.04-1.00, P = 0.050). \nUnivariate and multivariate predictors of immune checkpoint inhibitor-mediated colitis among patients using immune checkpoint inhibitor (n = 128)\nNumber of infusions of immune checkpoint inhibitor prior to immune checkpoint inhibitor-mediated colitis diagnosis (cases) or total (controls).\nSee Supplementary Table 2.\nICI: Immune checkpoint inhibitor; IMC: Immune checkpoint inhibitor-mediated colitis; RCC: Renal cell carcinoma; NSCLC: Non-small cell lung cancer; SCC: Squamous cell carcinoma; irAE: Immune related adverse event.\nWe next determined if any variables were associated with an increased risk of severe IMC. Consistent with prior studies of irAE in ICI[17-19], we defined grade 1-2 IMC as mild and grade 3 or higher IMC as severe. In our study, 38 of the 64 patients (59.4%) had severe IMC (Supplementary Table 3). In the univariate model, ipilimumab and vitamin D supplementation were significantly associated with development of severe IMC (OR 8.93, 95%CI 1.07-74.8, P = 0.043 for ipilimumab; OR 3.33, 95%CI 1.10-10.14, P = 0.034 for vitamin D) (Supplementary Table 6). Combination therapy (ipilimumab plus nivolumab) trended towards an increased risk of severe IMC but did not reach significance (P = 0.053). In contrast, pembrolizumab was significantly associated with a decreased risk of severe IMC (OR 0.26, 95%CI 0.09-0.81, P = 0.020). In the multivariate model no characteristic reached significance for association with severe IMC, although both combination therapy and ipilimumab monotherapy approached significance for increased risk of severe IMC (P = 0.058 and 0.060, respectively).","In our study, development of IMC following ICI use was associated with improved overall survival, although not improved progression-free survival, compared to ICI users without IMC. This is similar to findings at another center demonstrating both improved OS and PFS in patients with IMC[9,10]. We also found that vitamin D supplementation at the start of ICI treatment is a risk factor for developing IMC, in contrast to other research suggesting vitamin D use is associated with lower risk of IMC[11]. Our results, therefore, provide critical additional information on these previous associations and present a need for prospective studies.\nBoth publications showing improved survival in patients with IMC were retrospective analyses performed at the same center[9,10]. One study noted that ICI class was significantly associated with development of IMC[9], a finding that has been demonstrated several times in retrospective work[8,17,18,20-23]. However, unlike our work, this study did not match control patients to account for this likely confounder, as ICI class has been associated with differences in PFS in some malignancies[24,25]. The second study at this center examined survival in melanoma patients with IMC, compared to our work across multiple malignancies, although frequency matching was performed to account for use of different ICI classes[10]. Since our study is the first to examine survival in patients with IMC at a different center, our work here reinforces that IMC may be associated with increased overall survival and prompts a need for prospective studies.\nThe only other independent factor in our study positively associated with OS > 12 mo was number of ICI doses. This finding may be due to trivial length-time bias, as patients who survive longer are more likely to receive more doses of ICI. It is also possible that patients who required cessation of ICI due to IMC had worse outcomes, although prior work has suggested that patients still derive equivalent long-term benefit from ICI even if stopped due to irAE[26]. Type of underlying malignancy (sarcoma) was independently associated with OS < 12 mo in our study. These findings are not unexpected, as most advanced soft tissue sarcomas have a median OS of less than one year[27].\nIn contrast to prior work, we found a positive association between vitamin D supplementation and development of IMC[11]. It is unclear if this is related to low serum vitamin D levels or negative impact of the supplementation itself, as vitamin D levels near the time of ICI initiation were not recorded in most patients. Additionally, the prior report on vitamin D in IMC was in melanoma patients only, which may partially account for discrepancies with our study. As this association did not remain significant in our multivariate analysis, it is possible that another confounding factor may explain the association between vitamin D supplementation and IMC in our study.\nIn addition to challenging existing findings, we report here on additional novel risk factors for IMC. We are the first to report that prior use of immune-enhancing medications prior to ICI, such as IL-2 or interferon-γ, is significantly and independently associated with decreased risk of IMC. Much more work should be done to evaluate the relationship between these medications and future risk of IMC.\nFinally, our study is the first to examine risk factors for severe IMC. In addition to increasing risk for IMC overall, we find that vitamin D supplementation may also be a risk factor for severe IMC. Similarly, our results suggest that the use of ipilimumab may be associated with increased risk of severe IMC, while pembrolizumab may be associated with decreased risk of severe IMC in patients who develop this syndrome. As ipilimumab has previously been associated with increased risk of IMC overall, while anti-PD-1, including pembrolizumab, are associated with lower risk of IMC overall[8,9], these findings emphasize that ICI class may affect severity of IMC.\nOur findings may significantly impact clinical practice by identifying novel risks for IMC and severe IMC that clinicians, including oncologists and gastroenterologists, should be aware of, while also potentially providing reassurance to physicians and patients that development of IMC may be a positive prognosticator for cancer survival. Neither prior work nor ours found that treatment of IMC, including steroids or infliximab, negatively impacts OS[9,10], and therefore appropriate treatment of IMC should be pursued early on to minimize morbidity and mortality. Both steroid and infliximab use have been suggested to worsen survival in ICI users[12,13], but all current evidence suggests that use of these medications for IMC specifically does not impair cancer outcomes. Our work also cautions against supplementation with vitamin D in ICI users, as this may increase risk of IMC and severe IMC, although carefully designed studies with vitamin D measurements should be performed.\nOur work has several strengths. We performed robust cohort matching to minimize confounding effects of ICI class and malignancy. This is also the first study to explore risk factors associated with severe IMC. However, there are limitations to our work. As a retrospective, observational study, it is subject to recall bias and cannot evaluate causation, and may also be subject to immortal time bias (ITB). Patients may have longer exposure to checkpoint inhibitors before developing IMC, compared to patients who do not manifest this irAE, leading to a period where they must survive for long enough to develop IMC and are therefore “immortal”[28]. We found that OS > 12 mo was significantly associated with greater numbers of ICI infusions (Table 2), which is likely due to ITB. However, greater numbers of infusions were not associated with IMC (Table 4). This suggests that the association between OS > 12 mo and IMC is likely independent of the number of ICI infusions, limiting this as a source of ITB in our study.\nOther weaknesses of our work include selection of patients based on clinical criteria for IMC, including those who did not undergo endoscopy or other objective testing for intestinal inflammation, and therefore may not have had a true colitis. Like prior work, this is also a single-center study, and our results may not be widely generalizable, particularly since we identified fewer patients compared to prior work and our patient population is highly variable, including individuals with several different underlying malignancies. We did not exclude patients with prior non-GI irAEs in either group, although the presence of these was not independently associated with increased OS in our study. We also have not accounted for other factors which may be potential predictors of ICI response, including tumor PD-L1 expression burden, tumor mutational burden, gut microbial composition, proton pump inhibitor use, and combination treatment with tyrosine kinase inhibitors[29-34].","In conclusion, our findings suggest presence of IMC is associated with improved OS in cancer patients when cases were matched closely to controls. We also found that vitamin D supplementation was significantly associated with development of both IMC and severe IMC, while immune-enhancing medications were significantly associated with decreased risk of IMC. Future work should focus on broader populations to resolve the discrepancies raised in our work, and to confirm the association between IMC and increased cancer survival. Closely involving gastroenterologists with the workup and management of IMC will be crucial to ensuring the best care possible for these patients."],"string":"[\n \"Immune checkpoint inhibitors (ICI) have dramatically changed the landscape of cancer therapy. Early studies showed significantly prolonged survival in patients with metastatic melanoma compared to standard chemotherapy[1], and evidence now exists for improved outcomes in a variety of tumors ranging from lung cancers to urothelial carcinoma to breast cancer[2-5]. Although these are powerful treatments in our armamentarium against malignancy, ICI can cause immune-related adverse events (irAE) characterized by autoimmune-like inflammation in a variety of non-tumor organs, leading to increased morbidity for patients[6].\\nOne of the most common irAE is immune checkpoint inhibitor-mediated colitis (IMC). IMC may occur in up to 40% of patients treated with ipilimumab, an antibody targeting CTLA-4, 11%-17% of patients treated with antibodies against anti-PD-1 or anti-PD-L1, such as nivolumab, pembrolizumab, or atezolizumab, and around 32% of patients treated with a combination of anti-CTLA-4 and anti-PD-1[7]. Prior retrospective analyses of patients with IMC have attempted to identify characteristics associated with development of IMC, including type of malignancy, ICI class, dose of ICI, cancer stage, and vitamin D use[8-11]. Intriguingly, two prior studies have suggested that development of IMC may positively correlate with improved progression-free survival (PFS) and overall survival (OS)[9,10]. One of these studies controlled for confounding effects of ICI class via frequency matching, but was limited to patients with melanoma, hindering wider applicability of their findings[10]. These findings also conflict with data suggesting that use of steroids and the anti-TNF antibody infliximab in patients treated with ICI are associated with worse cancer outcomes[12,13]. These discrepancies represent a significant knowledge gap that impedes our ability to evaluate and manage IMC and ICI use.\\nHere we present data from a retrospective study of patients treated with ICI at our institution who developed IMC across malignancy types. We compare this cohort to a matched control cohort to determine whether IMC was associated with improved progression-free survival and overall survival. We also evaluate which clinical characteristics increase the risk of developing IMC, including severe IMC.\",\n \"Study design and population We conducted a retrospective case-control single-center study after obtaining approval from the Institutional Review Board at Stanford University (IRB 57125, approved 6/30/2020). Our primary aim was to determine the association of presence and severity of IMC on OS and PFS in ICI users. Our secondary aim was to identify clinical variables which predicted development of IMC in ICI users. We evaluated all patients over the age of 18 who had been treated with immune checkpoint inhibitors (ICI) for malignancy at Stanford Health Care from May 2011 to May 2020, including anti-CTLA-4 (ipilimumab), anti-PD-1 (nivolumab, pembrolizumab), and anti-PD-L1 (atezolizumab, avelumab, durvalumab), with follow up through October 2020. Using the Stanford Research Repository tool, we screened patients treated with ICI who were assigned International Classification of Diseases (ICD) 9 and ICD 10 codes associated with non-infectious colitis and diarrhea (Supplementary Table 1). Each chart which passed the initial screen was further screened by review of clinic notes to confirm diagnosis of immune checkpoint inhibitor-related colitis by oncology providers. Any patient found to have other explanations for their clinical presentation was excluded from the study.\\nControl patients were matched one to one with each IMC patient for sex, age, malignancy, type of ICI used, prior ICI exposure, and duration of ICI exposure (matched to number of doses from initiation of ICI to development of colitis in study cohort). Control patients were initially screened by those lacking the above ICD codes and were confirmed via direct evaluation of each chart to lack diarrhea and/or colitis ascribable to ICI per their treating oncologist.\\nWe extracted clinical data on IMC and control patient charts including demographics (age at time of ICI initiation, sex, body mass index, race per patient report), medical history (presence of prior non-liver and non-upper gastrointestinal disease, personal history of autoimmune disease, family history of autoimmune disease), and cancer history (type of malignancy, tumor stage at ICI initiation, prior chemotherapy, prior radiation therapy, type of ICI used, duration of ICI use, OS and PFS) (Supplementary Table 2). OS was determined as time from initiation of ICI to death, while PFS was determined as time from initiation of ICI to death or progression of disease as determined by oncology providers, based on radiographic evidence of progression. IMC severity was graded using commonly accepted determinants of IMC and irAE grading[14]. We specifically noted prior use of therapies designed to increase immune responses [interleukin (IL)-2, interferon (IFN)-γ, toll-like receptor (TLR)-9 agonist, tebentafusp, or anti-CD47 antibody]. Vitamin D and non-steroidal anti-inflammatory (NSAID) use were defined as vitamin D supplement or NSAID medication, respectively, noted in the history of present illness or on the patient’s medication list at the clinic visit closest to their date of ICI initiation.\\nWe collected data on IMC diagnosis including number of patients who received endoscopy (flexible sigmoidoscopy or colonoscopy), findings on endoscopy, and fecal calprotectin (Supplementary Table 3). Data on management of IMC included treatment with anti-diarrheal medications, mesalamine, steroids (prednisone, budesonide, dexamethasone), infliximab, and vedolizumab.\\nWe conducted a retrospective case-control single-center study after obtaining approval from the Institutional Review Board at Stanford University (IRB 57125, approved 6/30/2020). Our primary aim was to determine the association of presence and severity of IMC on OS and PFS in ICI users. Our secondary aim was to identify clinical variables which predicted development of IMC in ICI users. We evaluated all patients over the age of 18 who had been treated with immune checkpoint inhibitors (ICI) for malignancy at Stanford Health Care from May 2011 to May 2020, including anti-CTLA-4 (ipilimumab), anti-PD-1 (nivolumab, pembrolizumab), and anti-PD-L1 (atezolizumab, avelumab, durvalumab), with follow up through October 2020. Using the Stanford Research Repository tool, we screened patients treated with ICI who were assigned International Classification of Diseases (ICD) 9 and ICD 10 codes associated with non-infectious colitis and diarrhea (Supplementary Table 1). Each chart which passed the initial screen was further screened by review of clinic notes to confirm diagnosis of immune checkpoint inhibitor-related colitis by oncology providers. Any patient found to have other explanations for their clinical presentation was excluded from the study.\\nControl patients were matched one to one with each IMC patient for sex, age, malignancy, type of ICI used, prior ICI exposure, and duration of ICI exposure (matched to number of doses from initiation of ICI to development of colitis in study cohort). Control patients were initially screened by those lacking the above ICD codes and were confirmed via direct evaluation of each chart to lack diarrhea and/or colitis ascribable to ICI per their treating oncologist.\\nWe extracted clinical data on IMC and control patient charts including demographics (age at time of ICI initiation, sex, body mass index, race per patient report), medical history (presence of prior non-liver and non-upper gastrointestinal disease, personal history of autoimmune disease, family history of autoimmune disease), and cancer history (type of malignancy, tumor stage at ICI initiation, prior chemotherapy, prior radiation therapy, type of ICI used, duration of ICI use, OS and PFS) (Supplementary Table 2). OS was determined as time from initiation of ICI to death, while PFS was determined as time from initiation of ICI to death or progression of disease as determined by oncology providers, based on radiographic evidence of progression. IMC severity was graded using commonly accepted determinants of IMC and irAE grading[14]. We specifically noted prior use of therapies designed to increase immune responses [interleukin (IL)-2, interferon (IFN)-γ, toll-like receptor (TLR)-9 agonist, tebentafusp, or anti-CD47 antibody]. Vitamin D and non-steroidal anti-inflammatory (NSAID) use were defined as vitamin D supplement or NSAID medication, respectively, noted in the history of present illness or on the patient’s medication list at the clinic visit closest to their date of ICI initiation.\\nWe collected data on IMC diagnosis including number of patients who received endoscopy (flexible sigmoidoscopy or colonoscopy), findings on endoscopy, and fecal calprotectin (Supplementary Table 3). Data on management of IMC included treatment with anti-diarrheal medications, mesalamine, steroids (prednisone, budesonide, dexamethasone), infliximab, and vedolizumab.\\nStatistical analysis The rate of the primary outcomes (OS > 12 mo and PFS > 6 mo among all ICI users, OS > 12 mo and PFS > 6 mo in patients with IMC) and secondary outcomes (risks of IMC among patients with malignancy using ICI, IMC severity), predictive value of clinical variables on primary and secondary outcomes, odds ratio (OR) with its 95% confidence interval (CI), and P values were calculated using Statistics/Data Analysis (Stata/IC 15.1 for Windows, College Station, TX, United States). Dichotomous variables were analyzed for outcomes using the chi-squared test or the Fisher’s exact test where appropriate, and continuous variables were analyzed using Student’s t-tests if normally distributed, or the Wilcoxon signed-rank test for non-normal data. For our multivariate analyses, model building was based on forward stepwise logistic regression, with a P value of 0.05 required for entry, and known predictors were also included. We constructed Kaplan Meier curves for the outcomes of OS and PFS between patients with and without IMC and patients with mild vs severe IMC using GraphPad Prism (version 8.3; GraphPad Software, Inc., La Jolla, CA, United States). All authors had access to the study data and reviewed and approved the final manuscript.\\nThe rate of the primary outcomes (OS > 12 mo and PFS > 6 mo among all ICI users, OS > 12 mo and PFS > 6 mo in patients with IMC) and secondary outcomes (risks of IMC among patients with malignancy using ICI, IMC severity), predictive value of clinical variables on primary and secondary outcomes, odds ratio (OR) with its 95% confidence interval (CI), and P values were calculated using Statistics/Data Analysis (Stata/IC 15.1 for Windows, College Station, TX, United States). Dichotomous variables were analyzed for outcomes using the chi-squared test or the Fisher’s exact test where appropriate, and continuous variables were analyzed using Student’s t-tests if normally distributed, or the Wilcoxon signed-rank test for non-normal data. For our multivariate analyses, model building was based on forward stepwise logistic regression, with a P value of 0.05 required for entry, and known predictors were also included. We constructed Kaplan Meier curves for the outcomes of OS and PFS between patients with and without IMC and patients with mild vs severe IMC using GraphPad Prism (version 8.3; GraphPad Software, Inc., La Jolla, CA, United States). All authors had access to the study data and reviewed and approved the final manuscript.\",\n \"We conducted a retrospective case-control single-center study after obtaining approval from the Institutional Review Board at Stanford University (IRB 57125, approved 6/30/2020). Our primary aim was to determine the association of presence and severity of IMC on OS and PFS in ICI users. Our secondary aim was to identify clinical variables which predicted development of IMC in ICI users. We evaluated all patients over the age of 18 who had been treated with immune checkpoint inhibitors (ICI) for malignancy at Stanford Health Care from May 2011 to May 2020, including anti-CTLA-4 (ipilimumab), anti-PD-1 (nivolumab, pembrolizumab), and anti-PD-L1 (atezolizumab, avelumab, durvalumab), with follow up through October 2020. Using the Stanford Research Repository tool, we screened patients treated with ICI who were assigned International Classification of Diseases (ICD) 9 and ICD 10 codes associated with non-infectious colitis and diarrhea (Supplementary Table 1). Each chart which passed the initial screen was further screened by review of clinic notes to confirm diagnosis of immune checkpoint inhibitor-related colitis by oncology providers. Any patient found to have other explanations for their clinical presentation was excluded from the study.\\nControl patients were matched one to one with each IMC patient for sex, age, malignancy, type of ICI used, prior ICI exposure, and duration of ICI exposure (matched to number of doses from initiation of ICI to development of colitis in study cohort). Control patients were initially screened by those lacking the above ICD codes and were confirmed via direct evaluation of each chart to lack diarrhea and/or colitis ascribable to ICI per their treating oncologist.\\nWe extracted clinical data on IMC and control patient charts including demographics (age at time of ICI initiation, sex, body mass index, race per patient report), medical history (presence of prior non-liver and non-upper gastrointestinal disease, personal history of autoimmune disease, family history of autoimmune disease), and cancer history (type of malignancy, tumor stage at ICI initiation, prior chemotherapy, prior radiation therapy, type of ICI used, duration of ICI use, OS and PFS) (Supplementary Table 2). OS was determined as time from initiation of ICI to death, while PFS was determined as time from initiation of ICI to death or progression of disease as determined by oncology providers, based on radiographic evidence of progression. IMC severity was graded using commonly accepted determinants of IMC and irAE grading[14]. We specifically noted prior use of therapies designed to increase immune responses [interleukin (IL)-2, interferon (IFN)-γ, toll-like receptor (TLR)-9 agonist, tebentafusp, or anti-CD47 antibody]. Vitamin D and non-steroidal anti-inflammatory (NSAID) use were defined as vitamin D supplement or NSAID medication, respectively, noted in the history of present illness or on the patient’s medication list at the clinic visit closest to their date of ICI initiation.\\nWe collected data on IMC diagnosis including number of patients who received endoscopy (flexible sigmoidoscopy or colonoscopy), findings on endoscopy, and fecal calprotectin (Supplementary Table 3). Data on management of IMC included treatment with anti-diarrheal medications, mesalamine, steroids (prednisone, budesonide, dexamethasone), infliximab, and vedolizumab.\",\n \"The rate of the primary outcomes (OS > 12 mo and PFS > 6 mo among all ICI users, OS > 12 mo and PFS > 6 mo in patients with IMC) and secondary outcomes (risks of IMC among patients with malignancy using ICI, IMC severity), predictive value of clinical variables on primary and secondary outcomes, odds ratio (OR) with its 95% confidence interval (CI), and P values were calculated using Statistics/Data Analysis (Stata/IC 15.1 for Windows, College Station, TX, United States). Dichotomous variables were analyzed for outcomes using the chi-squared test or the Fisher’s exact test where appropriate, and continuous variables were analyzed using Student’s t-tests if normally distributed, or the Wilcoxon signed-rank test for non-normal data. For our multivariate analyses, model building was based on forward stepwise logistic regression, with a P value of 0.05 required for entry, and known predictors were also included. We constructed Kaplan Meier curves for the outcomes of OS and PFS between patients with and without IMC and patients with mild vs severe IMC using GraphPad Prism (version 8.3; GraphPad Software, Inc., La Jolla, CA, United States). All authors had access to the study data and reviewed and approved the final manuscript.\",\n \"Clinical characteristics associated with IMC We identified a total of 314 patients treated with ICI at Stanford Health Care from May 2011 to May 2020 who had ICD codes matching our query (Supplementary Table 1). Of these, 64 had a diagnosis of IMC per review of Oncology providers’ notes, after excluding patients with alternative diagnoses for their symptoms. 24 (37.5%) of these IMC patients underwent an endoscopy (colonoscopy or flexible sigmoidoscopy) during workup, of which seven (29.2%) had a normal endoscopic appearance, consistent with prior reports demonstrating that approximately one third of patients with IMC related to anti-PD-1 therapy have microscopic colitis[15] (Supplementary Table 3). An additional 14 patients (21.9%) had imaging findings suggestive of IMC while 3 patients (4.69%) without imaging or endoscopy had an elevated calprotectin or fecal lactoferrin.\\nThese 64 patients were manually matched 1:1 with control patients based on age, sex, malignancy, type of ICI, whether or not the patient had prior ICI exposure, and duration of ICI use. We compared clinical characteristics of patients from the IMC cohort and the control cohort (Table 1). None of the matched characteristics were significantly different between the two cohorts. The mean age across the combined cohorts was 66.6 years, with an average age of 67.4 in the cohort with IMC compared with 65.8 in the control cohort (P = 0.42). 57.81% of patients in each group were male (P = 1.00). Patients were predominantly white in both groups, with 52 (81.25%) white individuals in the IMC cohort compared to 50 (78.13%) in the control group (P = 0.66). The most common malignancy in each group was melanoma [33 (51.56%) in both cohorts], followed by renal cell carcinoma [8 (12.5%) in the IMC cohort and 7 (10.94%) in the control cohort] and non-small cell lung cancer [6 (9.38%) in both cohorts]. Both groups had similar numbers of patients with stage IV malignancy [56 (87.5%) in the IMC cohort and 58 (90.63%) in the control cohort, P = 0.778]. Combination ipilimumab and nivolumab was the most commonly used checkpoint therapy [24 (37.5%) of patients in each cohort], followed by nivolumab monotherapy [19 (29.69%) of each cohort] and ipilimumab monotherapy [11 (17.19%) of each cohort].\\nBaseline characteristics of patients with immune checkpoint inhibitor use\\nVariable matched between cases and controls.\\nNumber of infusions of immune checkpoint inhibitor prior to immune checkpoint inhibitor-mediated colitis diagnosis (cases) or total (controls).\\nSee Supplementary Table 2.\\nIMC: Immune checkpoint inhibitor-mediated colitis; ICI: Immune checkpoint inhibitor; SD: Standard deviation; RCC: Renal cell carcinoma; NSCLC: Non-small cell lung cancer; SCC: Squamous cell carcinoma; PFS: Progression-Free Survival; irAE: Immune related adverse event; OS: Overall survival.\\nAmong the remainder of the clinical characteristics evaluated, personal history of autoimmune disease (including prior irAE) and family history of autoimmune disease were significantly more common in patients with IMC (P = 0.037 and 0.048, respectively). Intriguingly, prior use of a therapy designed to increase immune responses was more common in the control cohort without IMC (P = 0.027). In contrast to prior data[11], use of vitamin D supplementation at the time of first dose of ICI was significantly more prevalent in patients with IMC (P = 0.020). Neither smoking status, NSAID use at time of ICI initiation, steroid use at the time of ICI initiation, nor recent vaccination were significantly more common in IMC patients compared to controls.\\nWe identified a total of 314 patients treated with ICI at Stanford Health Care from May 2011 to May 2020 who had ICD codes matching our query (Supplementary Table 1). Of these, 64 had a diagnosis of IMC per review of Oncology providers’ notes, after excluding patients with alternative diagnoses for their symptoms. 24 (37.5%) of these IMC patients underwent an endoscopy (colonoscopy or flexible sigmoidoscopy) during workup, of which seven (29.2%) had a normal endoscopic appearance, consistent with prior reports demonstrating that approximately one third of patients with IMC related to anti-PD-1 therapy have microscopic colitis[15] (Supplementary Table 3). An additional 14 patients (21.9%) had imaging findings suggestive of IMC while 3 patients (4.69%) without imaging or endoscopy had an elevated calprotectin or fecal lactoferrin.\\nThese 64 patients were manually matched 1:1 with control patients based on age, sex, malignancy, type of ICI, whether or not the patient had prior ICI exposure, and duration of ICI use. We compared clinical characteristics of patients from the IMC cohort and the control cohort (Table 1). None of the matched characteristics were significantly different between the two cohorts. The mean age across the combined cohorts was 66.6 years, with an average age of 67.4 in the cohort with IMC compared with 65.8 in the control cohort (P = 0.42). 57.81% of patients in each group were male (P = 1.00). Patients were predominantly white in both groups, with 52 (81.25%) white individuals in the IMC cohort compared to 50 (78.13%) in the control group (P = 0.66). The most common malignancy in each group was melanoma [33 (51.56%) in both cohorts], followed by renal cell carcinoma [8 (12.5%) in the IMC cohort and 7 (10.94%) in the control cohort] and non-small cell lung cancer [6 (9.38%) in both cohorts]. Both groups had similar numbers of patients with stage IV malignancy [56 (87.5%) in the IMC cohort and 58 (90.63%) in the control cohort, P = 0.778]. Combination ipilimumab and nivolumab was the most commonly used checkpoint therapy [24 (37.5%) of patients in each cohort], followed by nivolumab monotherapy [19 (29.69%) of each cohort] and ipilimumab monotherapy [11 (17.19%) of each cohort].\\nBaseline characteristics of patients with immune checkpoint inhibitor use\\nVariable matched between cases and controls.\\nNumber of infusions of immune checkpoint inhibitor prior to immune checkpoint inhibitor-mediated colitis diagnosis (cases) or total (controls).\\nSee Supplementary Table 2.\\nIMC: Immune checkpoint inhibitor-mediated colitis; ICI: Immune checkpoint inhibitor; SD: Standard deviation; RCC: Renal cell carcinoma; NSCLC: Non-small cell lung cancer; SCC: Squamous cell carcinoma; PFS: Progression-Free Survival; irAE: Immune related adverse event; OS: Overall survival.\\nAmong the remainder of the clinical characteristics evaluated, personal history of autoimmune disease (including prior irAE) and family history of autoimmune disease were significantly more common in patients with IMC (P = 0.037 and 0.048, respectively). Intriguingly, prior use of a therapy designed to increase immune responses was more common in the control cohort without IMC (P = 0.027). In contrast to prior data[11], use of vitamin D supplementation at the time of first dose of ICI was significantly more prevalent in patients with IMC (P = 0.020). Neither smoking status, NSAID use at time of ICI initiation, steroid use at the time of ICI initiation, nor recent vaccination were significantly more common in IMC patients compared to controls.\\nIMC significantly increases overall survival As IMC has previously been associated with increased OS and PFS in cancer patients[9,10], we evaluated whether this association was seen in our study. We found that OS was significantly longer in patients who developed IMC compared to those who did not, with a mean OS of 24.3 mo in patients with IMC and 17.7 mo in control (P = 0.05, Table 1). OS at 12 mo following ICI initiation was significantly higher in patients who developed IMC compared to those who did not (P = 0.02, Figure 1). However, in contrast to prior findings, our study did not find a significant difference in PFS between IMC patients and controls, with a mean PFS 13.7 mo in IMC patients and 11.9 mo in controls (P = 0.524) (Table 1). PFS also did not differ between patients who developed mild vs severe IMC (P = 0.690, Supplementary Table 5).\\n\\nOverall survival at 12 mo in patients with and without immune checkpoint inhibitor-mediated colitis. Kaplan-Meier curve of overall survival at 12 mo in patients with immune checkpoint inhibitor-mediated colitis (IMC, red) and without IMC (black). IMC: Immune checkpoint inhibitor-mediated colitis; HR: Hazard ratio.\\nAcross both cohorts, we identified clinical characteristics significantly associated with OS greater than 12 mo and PFS greater than 6 mo, which are correlated with cancer outcomes in patients treated with ICI[16] (Tables 2 and 3) (Supplementary Tables 4 and 5). IMC was significantly and independently associated with OS > 12 mo in the multivariate model (OR 2.81, 95%CI 1.17-6.77, P = 0.021) (Table 2). Number of ICI infusions was also positively associated with OS > 12 mo (OR 1.23, 95%CI 1.09-1.40), while sarcoma as underlying malignancy was significantly associated with OS < 12 mo (OR 0.17, 95%CI 0.029-0.947). Within the IMC cohort, nivolumab use was associated with OS < 12 mo in the univariate analysis (OR 0.09, 95%CI 0.01-0.83), while only age was associated with OS < 12 mo in multivariate analysis (OR 0.93, 95%CI 0.88-0.99) (Table 3). No individual malignancy was significantly associated with OS > 12 mo within the IMC cohort (Table 3).\\nUnivariate and multivariate predictors of overall survival > 12 mo among patients with malignancy using immune checkpoint inhibitor (n = 128)\\nNumber of infusions of immune checkpoint inhibitor prior to immune checkpoint inhibitor-mediated colitis diagnosis (cases) or total (controls).\\nSee Supplementary Table 2.\\nICI: Immune checkpoint inhibitor; IMC: Immune checkpoint inhibitor-mediated colitis; OR: Odds ratios; CI: Confidence interval; SD: Standard deviation; RCC: Renal cell carcinoma; NSCLC: Non-small cell lung cancer; SCC: Squamous cell carcinoma; irAE: Immune related adverse event.\\nUnivariate and multivariate predictors of overall survival > 12 mo among patients with immune checkpoint inhibitor colitis (n = 64)\\nNumber of infusions of immune checkpoint inhibitor prior to immune checkpoint inhibitor-mediated colitis diagnosis (cases) or total (controls).\\nSee Supplementary Table 2.\\nICI: Immune checkpoint inhibitor; IMC: Immune checkpoint inhibitor-mediated colitis; SD: Standard deviation; RCC: Renal cell carcinoma; NSCLC: Non-small cell lung cancer; SCC: Squamous cell carcinoma; irAE: Immune related adverse event.\\nAs IMC has previously been associated with increased OS and PFS in cancer patients[9,10], we evaluated whether this association was seen in our study. We found that OS was significantly longer in patients who developed IMC compared to those who did not, with a mean OS of 24.3 mo in patients with IMC and 17.7 mo in control (P = 0.05, Table 1). OS at 12 mo following ICI initiation was significantly higher in patients who developed IMC compared to those who did not (P = 0.02, Figure 1). However, in contrast to prior findings, our study did not find a significant difference in PFS between IMC patients and controls, with a mean PFS 13.7 mo in IMC patients and 11.9 mo in controls (P = 0.524) (Table 1). PFS also did not differ between patients who developed mild vs severe IMC (P = 0.690, Supplementary Table 5).\\n\\nOverall survival at 12 mo in patients with and without immune checkpoint inhibitor-mediated colitis. Kaplan-Meier curve of overall survival at 12 mo in patients with immune checkpoint inhibitor-mediated colitis (IMC, red) and without IMC (black). IMC: Immune checkpoint inhibitor-mediated colitis; HR: Hazard ratio.\\nAcross both cohorts, we identified clinical characteristics significantly associated with OS greater than 12 mo and PFS greater than 6 mo, which are correlated with cancer outcomes in patients treated with ICI[16] (Tables 2 and 3) (Supplementary Tables 4 and 5). IMC was significantly and independently associated with OS > 12 mo in the multivariate model (OR 2.81, 95%CI 1.17-6.77, P = 0.021) (Table 2). Number of ICI infusions was also positively associated with OS > 12 mo (OR 1.23, 95%CI 1.09-1.40), while sarcoma as underlying malignancy was significantly associated with OS < 12 mo (OR 0.17, 95%CI 0.029-0.947). Within the IMC cohort, nivolumab use was associated with OS < 12 mo in the univariate analysis (OR 0.09, 95%CI 0.01-0.83), while only age was associated with OS < 12 mo in multivariate analysis (OR 0.93, 95%CI 0.88-0.99) (Table 3). No individual malignancy was significantly associated with OS > 12 mo within the IMC cohort (Table 3).\\nUnivariate and multivariate predictors of overall survival > 12 mo among patients with malignancy using immune checkpoint inhibitor (n = 128)\\nNumber of infusions of immune checkpoint inhibitor prior to immune checkpoint inhibitor-mediated colitis diagnosis (cases) or total (controls).\\nSee Supplementary Table 2.\\nICI: Immune checkpoint inhibitor; IMC: Immune checkpoint inhibitor-mediated colitis; OR: Odds ratios; CI: Confidence interval; SD: Standard deviation; RCC: Renal cell carcinoma; NSCLC: Non-small cell lung cancer; SCC: Squamous cell carcinoma; irAE: Immune related adverse event.\\nUnivariate and multivariate predictors of overall survival > 12 mo among patients with immune checkpoint inhibitor colitis (n = 64)\\nNumber of infusions of immune checkpoint inhibitor prior to immune checkpoint inhibitor-mediated colitis diagnosis (cases) or total (controls).\\nSee Supplementary Table 2.\\nICI: Immune checkpoint inhibitor; IMC: Immune checkpoint inhibitor-mediated colitis; SD: Standard deviation; RCC: Renal cell carcinoma; NSCLC: Non-small cell lung cancer; SCC: Squamous cell carcinoma; irAE: Immune related adverse event.\\nSignificant risk factors for developing IMC and severe IMC As certain clinical characteristics were significantly more common in patients with IMC compared to controls, we evaluated whether any of these clinical characteristics were associated with risk of developing IMC (Table 4). In univariate analysis, history of autoimmune disease and vitamin D use were both significantly associated with increased risk of IMC (OR 2.45, 95%CI 1.04-5.78, P = 0.040 for autoimmune disease; OR 2.51, 95%CI 1.14-5.54, P = 0.022 for vitamin D use). Interestingly, the use of vitamin D supplementation has previously been associated with a decreased risk of IMC, in contrast to our findings here[11]. Prior use of an immune-enhancing therapy (Supplementary Table 2) was associated with a significantly decreased risk of IMC (OR 0.20, 95%CI 0.04-0.95, P = 0.043). In the multivariate model which incorporated these characteristics, only the use of immune-enhancing therapy remained significantly associated with decreased risk of IMC, with an OR of 0.20 (95%CI 0.04-1.00, P = 0.050). \\nUnivariate and multivariate predictors of immune checkpoint inhibitor-mediated colitis among patients using immune checkpoint inhibitor (n = 128)\\nNumber of infusions of immune checkpoint inhibitor prior to immune checkpoint inhibitor-mediated colitis diagnosis (cases) or total (controls).\\nSee Supplementary Table 2.\\nICI: Immune checkpoint inhibitor; IMC: Immune checkpoint inhibitor-mediated colitis; RCC: Renal cell carcinoma; NSCLC: Non-small cell lung cancer; SCC: Squamous cell carcinoma; irAE: Immune related adverse event.\\nWe next determined if any variables were associated with an increased risk of severe IMC. Consistent with prior studies of irAE in ICI[17-19], we defined grade 1-2 IMC as mild and grade 3 or higher IMC as severe. In our study, 38 of the 64 patients (59.4%) had severe IMC (Supplementary Table 3). In the univariate model, ipilimumab and vitamin D supplementation were significantly associated with development of severe IMC (OR 8.93, 95%CI 1.07-74.8, P = 0.043 for ipilimumab; OR 3.33, 95%CI 1.10-10.14, P = 0.034 for vitamin D) (Supplementary Table 6). Combination therapy (ipilimumab plus nivolumab) trended towards an increased risk of severe IMC but did not reach significance (P = 0.053). In contrast, pembrolizumab was significantly associated with a decreased risk of severe IMC (OR 0.26, 95%CI 0.09-0.81, P = 0.020). In the multivariate model no characteristic reached significance for association with severe IMC, although both combination therapy and ipilimumab monotherapy approached significance for increased risk of severe IMC (P = 0.058 and 0.060, respectively).\\nAs certain clinical characteristics were significantly more common in patients with IMC compared to controls, we evaluated whether any of these clinical characteristics were associated with risk of developing IMC (Table 4). In univariate analysis, history of autoimmune disease and vitamin D use were both significantly associated with increased risk of IMC (OR 2.45, 95%CI 1.04-5.78, P = 0.040 for autoimmune disease; OR 2.51, 95%CI 1.14-5.54, P = 0.022 for vitamin D use). Interestingly, the use of vitamin D supplementation has previously been associated with a decreased risk of IMC, in contrast to our findings here[11]. Prior use of an immune-enhancing therapy (Supplementary Table 2) was associated with a significantly decreased risk of IMC (OR 0.20, 95%CI 0.04-0.95, P = 0.043). In the multivariate model which incorporated these characteristics, only the use of immune-enhancing therapy remained significantly associated with decreased risk of IMC, with an OR of 0.20 (95%CI 0.04-1.00, P = 0.050). \\nUnivariate and multivariate predictors of immune checkpoint inhibitor-mediated colitis among patients using immune checkpoint inhibitor (n = 128)\\nNumber of infusions of immune checkpoint inhibitor prior to immune checkpoint inhibitor-mediated colitis diagnosis (cases) or total (controls).\\nSee Supplementary Table 2.\\nICI: Immune checkpoint inhibitor; IMC: Immune checkpoint inhibitor-mediated colitis; RCC: Renal cell carcinoma; NSCLC: Non-small cell lung cancer; SCC: Squamous cell carcinoma; irAE: Immune related adverse event.\\nWe next determined if any variables were associated with an increased risk of severe IMC. Consistent with prior studies of irAE in ICI[17-19], we defined grade 1-2 IMC as mild and grade 3 or higher IMC as severe. In our study, 38 of the 64 patients (59.4%) had severe IMC (Supplementary Table 3). In the univariate model, ipilimumab and vitamin D supplementation were significantly associated with development of severe IMC (OR 8.93, 95%CI 1.07-74.8, P = 0.043 for ipilimumab; OR 3.33, 95%CI 1.10-10.14, P = 0.034 for vitamin D) (Supplementary Table 6). Combination therapy (ipilimumab plus nivolumab) trended towards an increased risk of severe IMC but did not reach significance (P = 0.053). In contrast, pembrolizumab was significantly associated with a decreased risk of severe IMC (OR 0.26, 95%CI 0.09-0.81, P = 0.020). In the multivariate model no characteristic reached significance for association with severe IMC, although both combination therapy and ipilimumab monotherapy approached significance for increased risk of severe IMC (P = 0.058 and 0.060, respectively).\",\n \"We identified a total of 314 patients treated with ICI at Stanford Health Care from May 2011 to May 2020 who had ICD codes matching our query (Supplementary Table 1). Of these, 64 had a diagnosis of IMC per review of Oncology providers’ notes, after excluding patients with alternative diagnoses for their symptoms. 24 (37.5%) of these IMC patients underwent an endoscopy (colonoscopy or flexible sigmoidoscopy) during workup, of which seven (29.2%) had a normal endoscopic appearance, consistent with prior reports demonstrating that approximately one third of patients with IMC related to anti-PD-1 therapy have microscopic colitis[15] (Supplementary Table 3). An additional 14 patients (21.9%) had imaging findings suggestive of IMC while 3 patients (4.69%) without imaging or endoscopy had an elevated calprotectin or fecal lactoferrin.\\nThese 64 patients were manually matched 1:1 with control patients based on age, sex, malignancy, type of ICI, whether or not the patient had prior ICI exposure, and duration of ICI use. We compared clinical characteristics of patients from the IMC cohort and the control cohort (Table 1). None of the matched characteristics were significantly different between the two cohorts. The mean age across the combined cohorts was 66.6 years, with an average age of 67.4 in the cohort with IMC compared with 65.8 in the control cohort (P = 0.42). 57.81% of patients in each group were male (P = 1.00). Patients were predominantly white in both groups, with 52 (81.25%) white individuals in the IMC cohort compared to 50 (78.13%) in the control group (P = 0.66). The most common malignancy in each group was melanoma [33 (51.56%) in both cohorts], followed by renal cell carcinoma [8 (12.5%) in the IMC cohort and 7 (10.94%) in the control cohort] and non-small cell lung cancer [6 (9.38%) in both cohorts]. Both groups had similar numbers of patients with stage IV malignancy [56 (87.5%) in the IMC cohort and 58 (90.63%) in the control cohort, P = 0.778]. Combination ipilimumab and nivolumab was the most commonly used checkpoint therapy [24 (37.5%) of patients in each cohort], followed by nivolumab monotherapy [19 (29.69%) of each cohort] and ipilimumab monotherapy [11 (17.19%) of each cohort].\\nBaseline characteristics of patients with immune checkpoint inhibitor use\\nVariable matched between cases and controls.\\nNumber of infusions of immune checkpoint inhibitor prior to immune checkpoint inhibitor-mediated colitis diagnosis (cases) or total (controls).\\nSee Supplementary Table 2.\\nIMC: Immune checkpoint inhibitor-mediated colitis; ICI: Immune checkpoint inhibitor; SD: Standard deviation; RCC: Renal cell carcinoma; NSCLC: Non-small cell lung cancer; SCC: Squamous cell carcinoma; PFS: Progression-Free Survival; irAE: Immune related adverse event; OS: Overall survival.\\nAmong the remainder of the clinical characteristics evaluated, personal history of autoimmune disease (including prior irAE) and family history of autoimmune disease were significantly more common in patients with IMC (P = 0.037 and 0.048, respectively). Intriguingly, prior use of a therapy designed to increase immune responses was more common in the control cohort without IMC (P = 0.027). In contrast to prior data[11], use of vitamin D supplementation at the time of first dose of ICI was significantly more prevalent in patients with IMC (P = 0.020). Neither smoking status, NSAID use at time of ICI initiation, steroid use at the time of ICI initiation, nor recent vaccination were significantly more common in IMC patients compared to controls.\",\n \"As IMC has previously been associated with increased OS and PFS in cancer patients[9,10], we evaluated whether this association was seen in our study. We found that OS was significantly longer in patients who developed IMC compared to those who did not, with a mean OS of 24.3 mo in patients with IMC and 17.7 mo in control (P = 0.05, Table 1). OS at 12 mo following ICI initiation was significantly higher in patients who developed IMC compared to those who did not (P = 0.02, Figure 1). However, in contrast to prior findings, our study did not find a significant difference in PFS between IMC patients and controls, with a mean PFS 13.7 mo in IMC patients and 11.9 mo in controls (P = 0.524) (Table 1). PFS also did not differ between patients who developed mild vs severe IMC (P = 0.690, Supplementary Table 5).\\n\\nOverall survival at 12 mo in patients with and without immune checkpoint inhibitor-mediated colitis. Kaplan-Meier curve of overall survival at 12 mo in patients with immune checkpoint inhibitor-mediated colitis (IMC, red) and without IMC (black). IMC: Immune checkpoint inhibitor-mediated colitis; HR: Hazard ratio.\\nAcross both cohorts, we identified clinical characteristics significantly associated with OS greater than 12 mo and PFS greater than 6 mo, which are correlated with cancer outcomes in patients treated with ICI[16] (Tables 2 and 3) (Supplementary Tables 4 and 5). IMC was significantly and independently associated with OS > 12 mo in the multivariate model (OR 2.81, 95%CI 1.17-6.77, P = 0.021) (Table 2). Number of ICI infusions was also positively associated with OS > 12 mo (OR 1.23, 95%CI 1.09-1.40), while sarcoma as underlying malignancy was significantly associated with OS < 12 mo (OR 0.17, 95%CI 0.029-0.947). Within the IMC cohort, nivolumab use was associated with OS < 12 mo in the univariate analysis (OR 0.09, 95%CI 0.01-0.83), while only age was associated with OS < 12 mo in multivariate analysis (OR 0.93, 95%CI 0.88-0.99) (Table 3). No individual malignancy was significantly associated with OS > 12 mo within the IMC cohort (Table 3).\\nUnivariate and multivariate predictors of overall survival > 12 mo among patients with malignancy using immune checkpoint inhibitor (n = 128)\\nNumber of infusions of immune checkpoint inhibitor prior to immune checkpoint inhibitor-mediated colitis diagnosis (cases) or total (controls).\\nSee Supplementary Table 2.\\nICI: Immune checkpoint inhibitor; IMC: Immune checkpoint inhibitor-mediated colitis; OR: Odds ratios; CI: Confidence interval; SD: Standard deviation; RCC: Renal cell carcinoma; NSCLC: Non-small cell lung cancer; SCC: Squamous cell carcinoma; irAE: Immune related adverse event.\\nUnivariate and multivariate predictors of overall survival > 12 mo among patients with immune checkpoint inhibitor colitis (n = 64)\\nNumber of infusions of immune checkpoint inhibitor prior to immune checkpoint inhibitor-mediated colitis diagnosis (cases) or total (controls).\\nSee Supplementary Table 2.\\nICI: Immune checkpoint inhibitor; IMC: Immune checkpoint inhibitor-mediated colitis; SD: Standard deviation; RCC: Renal cell carcinoma; NSCLC: Non-small cell lung cancer; SCC: Squamous cell carcinoma; irAE: Immune related adverse event.\",\n \"As certain clinical characteristics were significantly more common in patients with IMC compared to controls, we evaluated whether any of these clinical characteristics were associated with risk of developing IMC (Table 4). In univariate analysis, history of autoimmune disease and vitamin D use were both significantly associated with increased risk of IMC (OR 2.45, 95%CI 1.04-5.78, P = 0.040 for autoimmune disease; OR 2.51, 95%CI 1.14-5.54, P = 0.022 for vitamin D use). Interestingly, the use of vitamin D supplementation has previously been associated with a decreased risk of IMC, in contrast to our findings here[11]. Prior use of an immune-enhancing therapy (Supplementary Table 2) was associated with a significantly decreased risk of IMC (OR 0.20, 95%CI 0.04-0.95, P = 0.043). In the multivariate model which incorporated these characteristics, only the use of immune-enhancing therapy remained significantly associated with decreased risk of IMC, with an OR of 0.20 (95%CI 0.04-1.00, P = 0.050). \\nUnivariate and multivariate predictors of immune checkpoint inhibitor-mediated colitis among patients using immune checkpoint inhibitor (n = 128)\\nNumber of infusions of immune checkpoint inhibitor prior to immune checkpoint inhibitor-mediated colitis diagnosis (cases) or total (controls).\\nSee Supplementary Table 2.\\nICI: Immune checkpoint inhibitor; IMC: Immune checkpoint inhibitor-mediated colitis; RCC: Renal cell carcinoma; NSCLC: Non-small cell lung cancer; SCC: Squamous cell carcinoma; irAE: Immune related adverse event.\\nWe next determined if any variables were associated with an increased risk of severe IMC. Consistent with prior studies of irAE in ICI[17-19], we defined grade 1-2 IMC as mild and grade 3 or higher IMC as severe. In our study, 38 of the 64 patients (59.4%) had severe IMC (Supplementary Table 3). In the univariate model, ipilimumab and vitamin D supplementation were significantly associated with development of severe IMC (OR 8.93, 95%CI 1.07-74.8, P = 0.043 for ipilimumab; OR 3.33, 95%CI 1.10-10.14, P = 0.034 for vitamin D) (Supplementary Table 6). Combination therapy (ipilimumab plus nivolumab) trended towards an increased risk of severe IMC but did not reach significance (P = 0.053). In contrast, pembrolizumab was significantly associated with a decreased risk of severe IMC (OR 0.26, 95%CI 0.09-0.81, P = 0.020). In the multivariate model no characteristic reached significance for association with severe IMC, although both combination therapy and ipilimumab monotherapy approached significance for increased risk of severe IMC (P = 0.058 and 0.060, respectively).\",\n \"In our study, development of IMC following ICI use was associated with improved overall survival, although not improved progression-free survival, compared to ICI users without IMC. This is similar to findings at another center demonstrating both improved OS and PFS in patients with IMC[9,10]. We also found that vitamin D supplementation at the start of ICI treatment is a risk factor for developing IMC, in contrast to other research suggesting vitamin D use is associated with lower risk of IMC[11]. Our results, therefore, provide critical additional information on these previous associations and present a need for prospective studies.\\nBoth publications showing improved survival in patients with IMC were retrospective analyses performed at the same center[9,10]. One study noted that ICI class was significantly associated with development of IMC[9], a finding that has been demonstrated several times in retrospective work[8,17,18,20-23]. However, unlike our work, this study did not match control patients to account for this likely confounder, as ICI class has been associated with differences in PFS in some malignancies[24,25]. The second study at this center examined survival in melanoma patients with IMC, compared to our work across multiple malignancies, although frequency matching was performed to account for use of different ICI classes[10]. Since our study is the first to examine survival in patients with IMC at a different center, our work here reinforces that IMC may be associated with increased overall survival and prompts a need for prospective studies.\\nThe only other independent factor in our study positively associated with OS > 12 mo was number of ICI doses. This finding may be due to trivial length-time bias, as patients who survive longer are more likely to receive more doses of ICI. It is also possible that patients who required cessation of ICI due to IMC had worse outcomes, although prior work has suggested that patients still derive equivalent long-term benefit from ICI even if stopped due to irAE[26]. Type of underlying malignancy (sarcoma) was independently associated with OS < 12 mo in our study. These findings are not unexpected, as most advanced soft tissue sarcomas have a median OS of less than one year[27].\\nIn contrast to prior work, we found a positive association between vitamin D supplementation and development of IMC[11]. It is unclear if this is related to low serum vitamin D levels or negative impact of the supplementation itself, as vitamin D levels near the time of ICI initiation were not recorded in most patients. Additionally, the prior report on vitamin D in IMC was in melanoma patients only, which may partially account for discrepancies with our study. As this association did not remain significant in our multivariate analysis, it is possible that another confounding factor may explain the association between vitamin D supplementation and IMC in our study.\\nIn addition to challenging existing findings, we report here on additional novel risk factors for IMC. We are the first to report that prior use of immune-enhancing medications prior to ICI, such as IL-2 or interferon-γ, is significantly and independently associated with decreased risk of IMC. Much more work should be done to evaluate the relationship between these medications and future risk of IMC.\\nFinally, our study is the first to examine risk factors for severe IMC. In addition to increasing risk for IMC overall, we find that vitamin D supplementation may also be a risk factor for severe IMC. Similarly, our results suggest that the use of ipilimumab may be associated with increased risk of severe IMC, while pembrolizumab may be associated with decreased risk of severe IMC in patients who develop this syndrome. As ipilimumab has previously been associated with increased risk of IMC overall, while anti-PD-1, including pembrolizumab, are associated with lower risk of IMC overall[8,9], these findings emphasize that ICI class may affect severity of IMC.\\nOur findings may significantly impact clinical practice by identifying novel risks for IMC and severe IMC that clinicians, including oncologists and gastroenterologists, should be aware of, while also potentially providing reassurance to physicians and patients that development of IMC may be a positive prognosticator for cancer survival. Neither prior work nor ours found that treatment of IMC, including steroids or infliximab, negatively impacts OS[9,10], and therefore appropriate treatment of IMC should be pursued early on to minimize morbidity and mortality. Both steroid and infliximab use have been suggested to worsen survival in ICI users[12,13], but all current evidence suggests that use of these medications for IMC specifically does not impair cancer outcomes. Our work also cautions against supplementation with vitamin D in ICI users, as this may increase risk of IMC and severe IMC, although carefully designed studies with vitamin D measurements should be performed.\\nOur work has several strengths. We performed robust cohort matching to minimize confounding effects of ICI class and malignancy. This is also the first study to explore risk factors associated with severe IMC. However, there are limitations to our work. As a retrospective, observational study, it is subject to recall bias and cannot evaluate causation, and may also be subject to immortal time bias (ITB). Patients may have longer exposure to checkpoint inhibitors before developing IMC, compared to patients who do not manifest this irAE, leading to a period where they must survive for long enough to develop IMC and are therefore “immortal”[28]. We found that OS > 12 mo was significantly associated with greater numbers of ICI infusions (Table 2), which is likely due to ITB. However, greater numbers of infusions were not associated with IMC (Table 4). This suggests that the association between OS > 12 mo and IMC is likely independent of the number of ICI infusions, limiting this as a source of ITB in our study.\\nOther weaknesses of our work include selection of patients based on clinical criteria for IMC, including those who did not undergo endoscopy or other objective testing for intestinal inflammation, and therefore may not have had a true colitis. Like prior work, this is also a single-center study, and our results may not be widely generalizable, particularly since we identified fewer patients compared to prior work and our patient population is highly variable, including individuals with several different underlying malignancies. We did not exclude patients with prior non-GI irAEs in either group, although the presence of these was not independently associated with increased OS in our study. We also have not accounted for other factors which may be potential predictors of ICI response, including tumor PD-L1 expression burden, tumor mutational burden, gut microbial composition, proton pump inhibitor use, and combination treatment with tyrosine kinase inhibitors[29-34].\",\n \"In conclusion, our findings suggest presence of IMC is associated with improved OS in cancer patients when cases were matched closely to controls. We also found that vitamin D supplementation was significantly associated with development of both IMC and severe IMC, while immune-enhancing medications were significantly associated with decreased risk of IMC. Future work should focus on broader populations to resolve the discrepancies raised in our work, and to confirm the association between IMC and increased cancer survival. Closely involving gastroenterologists with the workup and management of IMC will be crucial to ensuring the best care possible for these patients.\"\n]"},"all_sections_sec_types":{"kind":"list like","value":[null,"methods",null,null,null,null,null,null,null,null],"string":"[\n null,\n \"methods\",\n null,\n null,\n null,\n null,\n null,\n null,\n null,\n null\n]"},"keywords":{"kind":"list like","value":["Immune checkpoint inhibitors","Immune checkpoint inhibitor-mediated colitis","Immune-related adverse events"],"string":"[\n \"Immune checkpoint inhibitors\",\n \"Immune checkpoint inhibitor-mediated colitis\",\n \"Immune-related adverse events\"\n]"},"whole_article_text":{"kind":"string","value":"INTRODUCTION:\nImmune checkpoint inhibitors (ICI) have dramatically changed the landscape of cancer therapy. Early studies showed significantly prolonged survival in patients with metastatic melanoma compared to standard chemotherapy[1], and evidence now exists for improved outcomes in a variety of tumors ranging from lung cancers to urothelial carcinoma to breast cancer[2-5]. Although these are powerful treatments in our armamentarium against malignancy, ICI can cause immune-related adverse events (irAE) characterized by autoimmune-like inflammation in a variety of non-tumor organs, leading to increased morbidity for patients[6].\nOne of the most common irAE is immune checkpoint inhibitor-mediated colitis (IMC). IMC may occur in up to 40% of patients treated with ipilimumab, an antibody targeting CTLA-4, 11%-17% of patients treated with antibodies against anti-PD-1 or anti-PD-L1, such as nivolumab, pembrolizumab, or atezolizumab, and around 32% of patients treated with a combination of anti-CTLA-4 and anti-PD-1[7]. Prior retrospective analyses of patients with IMC have attempted to identify characteristics associated with development of IMC, including type of malignancy, ICI class, dose of ICI, cancer stage, and vitamin D use[8-11]. Intriguingly, two prior studies have suggested that development of IMC may positively correlate with improved progression-free survival (PFS) and overall survival (OS)[9,10]. One of these studies controlled for confounding effects of ICI class via frequency matching, but was limited to patients with melanoma, hindering wider applicability of their findings[10]. These findings also conflict with data suggesting that use of steroids and the anti-TNF antibody infliximab in patients treated with ICI are associated with worse cancer outcomes[12,13]. These discrepancies represent a significant knowledge gap that impedes our ability to evaluate and manage IMC and ICI use.\nHere we present data from a retrospective study of patients treated with ICI at our institution who developed IMC across malignancy types. We compare this cohort to a matched control cohort to determine whether IMC was associated with improved progression-free survival and overall survival. We also evaluate which clinical characteristics increase the risk of developing IMC, including severe IMC.\n\nMATERIALS AND METHODS:\nStudy design and population We conducted a retrospective case-control single-center study after obtaining approval from the Institutional Review Board at Stanford University (IRB 57125, approved 6/30/2020). Our primary aim was to determine the association of presence and severity of IMC on OS and PFS in ICI users. Our secondary aim was to identify clinical variables which predicted development of IMC in ICI users. We evaluated all patients over the age of 18 who had been treated with immune checkpoint inhibitors (ICI) for malignancy at Stanford Health Care from May 2011 to May 2020, including anti-CTLA-4 (ipilimumab), anti-PD-1 (nivolumab, pembrolizumab), and anti-PD-L1 (atezolizumab, avelumab, durvalumab), with follow up through October 2020. Using the Stanford Research Repository tool, we screened patients treated with ICI who were assigned International Classification of Diseases (ICD) 9 and ICD 10 codes associated with non-infectious colitis and diarrhea (Supplementary Table 1). Each chart which passed the initial screen was further screened by review of clinic notes to confirm diagnosis of immune checkpoint inhibitor-related colitis by oncology providers. Any patient found to have other explanations for their clinical presentation was excluded from the study.\nControl patients were matched one to one with each IMC patient for sex, age, malignancy, type of ICI used, prior ICI exposure, and duration of ICI exposure (matched to number of doses from initiation of ICI to development of colitis in study cohort). Control patients were initially screened by those lacking the above ICD codes and were confirmed via direct evaluation of each chart to lack diarrhea and/or colitis ascribable to ICI per their treating oncologist.\nWe extracted clinical data on IMC and control patient charts including demographics (age at time of ICI initiation, sex, body mass index, race per patient report), medical history (presence of prior non-liver and non-upper gastrointestinal disease, personal history of autoimmune disease, family history of autoimmune disease), and cancer history (type of malignancy, tumor stage at ICI initiation, prior chemotherapy, prior radiation therapy, type of ICI used, duration of ICI use, OS and PFS) (Supplementary Table 2). OS was determined as time from initiation of ICI to death, while PFS was determined as time from initiation of ICI to death or progression of disease as determined by oncology providers, based on radiographic evidence of progression. IMC severity was graded using commonly accepted determinants of IMC and irAE grading[14]. We specifically noted prior use of therapies designed to increase immune responses [interleukin (IL)-2, interferon (IFN)-γ, toll-like receptor (TLR)-9 agonist, tebentafusp, or anti-CD47 antibody]. Vitamin D and non-steroidal anti-inflammatory (NSAID) use were defined as vitamin D supplement or NSAID medication, respectively, noted in the history of present illness or on the patient’s medication list at the clinic visit closest to their date of ICI initiation.\nWe collected data on IMC diagnosis including number of patients who received endoscopy (flexible sigmoidoscopy or colonoscopy), findings on endoscopy, and fecal calprotectin (Supplementary Table 3). Data on management of IMC included treatment with anti-diarrheal medications, mesalamine, steroids (prednisone, budesonide, dexamethasone), infliximab, and vedolizumab.\nWe conducted a retrospective case-control single-center study after obtaining approval from the Institutional Review Board at Stanford University (IRB 57125, approved 6/30/2020). Our primary aim was to determine the association of presence and severity of IMC on OS and PFS in ICI users. Our secondary aim was to identify clinical variables which predicted development of IMC in ICI users. We evaluated all patients over the age of 18 who had been treated with immune checkpoint inhibitors (ICI) for malignancy at Stanford Health Care from May 2011 to May 2020, including anti-CTLA-4 (ipilimumab), anti-PD-1 (nivolumab, pembrolizumab), and anti-PD-L1 (atezolizumab, avelumab, durvalumab), with follow up through October 2020. Using the Stanford Research Repository tool, we screened patients treated with ICI who were assigned International Classification of Diseases (ICD) 9 and ICD 10 codes associated with non-infectious colitis and diarrhea (Supplementary Table 1). Each chart which passed the initial screen was further screened by review of clinic notes to confirm diagnosis of immune checkpoint inhibitor-related colitis by oncology providers. Any patient found to have other explanations for their clinical presentation was excluded from the study.\nControl patients were matched one to one with each IMC patient for sex, age, malignancy, type of ICI used, prior ICI exposure, and duration of ICI exposure (matched to number of doses from initiation of ICI to development of colitis in study cohort). Control patients were initially screened by those lacking the above ICD codes and were confirmed via direct evaluation of each chart to lack diarrhea and/or colitis ascribable to ICI per their treating oncologist.\nWe extracted clinical data on IMC and control patient charts including demographics (age at time of ICI initiation, sex, body mass index, race per patient report), medical history (presence of prior non-liver and non-upper gastrointestinal disease, personal history of autoimmune disease, family history of autoimmune disease), and cancer history (type of malignancy, tumor stage at ICI initiation, prior chemotherapy, prior radiation therapy, type of ICI used, duration of ICI use, OS and PFS) (Supplementary Table 2). OS was determined as time from initiation of ICI to death, while PFS was determined as time from initiation of ICI to death or progression of disease as determined by oncology providers, based on radiographic evidence of progression. IMC severity was graded using commonly accepted determinants of IMC and irAE grading[14]. We specifically noted prior use of therapies designed to increase immune responses [interleukin (IL)-2, interferon (IFN)-γ, toll-like receptor (TLR)-9 agonist, tebentafusp, or anti-CD47 antibody]. Vitamin D and non-steroidal anti-inflammatory (NSAID) use were defined as vitamin D supplement or NSAID medication, respectively, noted in the history of present illness or on the patient’s medication list at the clinic visit closest to their date of ICI initiation.\nWe collected data on IMC diagnosis including number of patients who received endoscopy (flexible sigmoidoscopy or colonoscopy), findings on endoscopy, and fecal calprotectin (Supplementary Table 3). Data on management of IMC included treatment with anti-diarrheal medications, mesalamine, steroids (prednisone, budesonide, dexamethasone), infliximab, and vedolizumab.\nStatistical analysis The rate of the primary outcomes (OS > 12 mo and PFS > 6 mo among all ICI users, OS > 12 mo and PFS > 6 mo in patients with IMC) and secondary outcomes (risks of IMC among patients with malignancy using ICI, IMC severity), predictive value of clinical variables on primary and secondary outcomes, odds ratio (OR) with its 95% confidence interval (CI), and P values were calculated using Statistics/Data Analysis (Stata/IC 15.1 for Windows, College Station, TX, United States). Dichotomous variables were analyzed for outcomes using the chi-squared test or the Fisher’s exact test where appropriate, and continuous variables were analyzed using Student’s t-tests if normally distributed, or the Wilcoxon signed-rank test for non-normal data. For our multivariate analyses, model building was based on forward stepwise logistic regression, with a P value of 0.05 required for entry, and known predictors were also included. We constructed Kaplan Meier curves for the outcomes of OS and PFS between patients with and without IMC and patients with mild vs severe IMC using GraphPad Prism (version 8.3; GraphPad Software, Inc., La Jolla, CA, United States). All authors had access to the study data and reviewed and approved the final manuscript.\nThe rate of the primary outcomes (OS > 12 mo and PFS > 6 mo among all ICI users, OS > 12 mo and PFS > 6 mo in patients with IMC) and secondary outcomes (risks of IMC among patients with malignancy using ICI, IMC severity), predictive value of clinical variables on primary and secondary outcomes, odds ratio (OR) with its 95% confidence interval (CI), and P values were calculated using Statistics/Data Analysis (Stata/IC 15.1 for Windows, College Station, TX, United States). Dichotomous variables were analyzed for outcomes using the chi-squared test or the Fisher’s exact test where appropriate, and continuous variables were analyzed using Student’s t-tests if normally distributed, or the Wilcoxon signed-rank test for non-normal data. For our multivariate analyses, model building was based on forward stepwise logistic regression, with a P value of 0.05 required for entry, and known predictors were also included. We constructed Kaplan Meier curves for the outcomes of OS and PFS between patients with and without IMC and patients with mild vs severe IMC using GraphPad Prism (version 8.3; GraphPad Software, Inc., La Jolla, CA, United States). All authors had access to the study data and reviewed and approved the final manuscript.\n\nStudy design and population:\nWe conducted a retrospective case-control single-center study after obtaining approval from the Institutional Review Board at Stanford University (IRB 57125, approved 6/30/2020). Our primary aim was to determine the association of presence and severity of IMC on OS and PFS in ICI users. Our secondary aim was to identify clinical variables which predicted development of IMC in ICI users. We evaluated all patients over the age of 18 who had been treated with immune checkpoint inhibitors (ICI) for malignancy at Stanford Health Care from May 2011 to May 2020, including anti-CTLA-4 (ipilimumab), anti-PD-1 (nivolumab, pembrolizumab), and anti-PD-L1 (atezolizumab, avelumab, durvalumab), with follow up through October 2020. Using the Stanford Research Repository tool, we screened patients treated with ICI who were assigned International Classification of Diseases (ICD) 9 and ICD 10 codes associated with non-infectious colitis and diarrhea (Supplementary Table 1). Each chart which passed the initial screen was further screened by review of clinic notes to confirm diagnosis of immune checkpoint inhibitor-related colitis by oncology providers. Any patient found to have other explanations for their clinical presentation was excluded from the study.\nControl patients were matched one to one with each IMC patient for sex, age, malignancy, type of ICI used, prior ICI exposure, and duration of ICI exposure (matched to number of doses from initiation of ICI to development of colitis in study cohort). Control patients were initially screened by those lacking the above ICD codes and were confirmed via direct evaluation of each chart to lack diarrhea and/or colitis ascribable to ICI per their treating oncologist.\nWe extracted clinical data on IMC and control patient charts including demographics (age at time of ICI initiation, sex, body mass index, race per patient report), medical history (presence of prior non-liver and non-upper gastrointestinal disease, personal history of autoimmune disease, family history of autoimmune disease), and cancer history (type of malignancy, tumor stage at ICI initiation, prior chemotherapy, prior radiation therapy, type of ICI used, duration of ICI use, OS and PFS) (Supplementary Table 2). OS was determined as time from initiation of ICI to death, while PFS was determined as time from initiation of ICI to death or progression of disease as determined by oncology providers, based on radiographic evidence of progression. IMC severity was graded using commonly accepted determinants of IMC and irAE grading[14]. We specifically noted prior use of therapies designed to increase immune responses [interleukin (IL)-2, interferon (IFN)-γ, toll-like receptor (TLR)-9 agonist, tebentafusp, or anti-CD47 antibody]. Vitamin D and non-steroidal anti-inflammatory (NSAID) use were defined as vitamin D supplement or NSAID medication, respectively, noted in the history of present illness or on the patient’s medication list at the clinic visit closest to their date of ICI initiation.\nWe collected data on IMC diagnosis including number of patients who received endoscopy (flexible sigmoidoscopy or colonoscopy), findings on endoscopy, and fecal calprotectin (Supplementary Table 3). Data on management of IMC included treatment with anti-diarrheal medications, mesalamine, steroids (prednisone, budesonide, dexamethasone), infliximab, and vedolizumab.\n\nStatistical analysis:\nThe rate of the primary outcomes (OS > 12 mo and PFS > 6 mo among all ICI users, OS > 12 mo and PFS > 6 mo in patients with IMC) and secondary outcomes (risks of IMC among patients with malignancy using ICI, IMC severity), predictive value of clinical variables on primary and secondary outcomes, odds ratio (OR) with its 95% confidence interval (CI), and P values were calculated using Statistics/Data Analysis (Stata/IC 15.1 for Windows, College Station, TX, United States). Dichotomous variables were analyzed for outcomes using the chi-squared test or the Fisher’s exact test where appropriate, and continuous variables were analyzed using Student’s t-tests if normally distributed, or the Wilcoxon signed-rank test for non-normal data. For our multivariate analyses, model building was based on forward stepwise logistic regression, with a P value of 0.05 required for entry, and known predictors were also included. We constructed Kaplan Meier curves for the outcomes of OS and PFS between patients with and without IMC and patients with mild vs severe IMC using GraphPad Prism (version 8.3; GraphPad Software, Inc., La Jolla, CA, United States). All authors had access to the study data and reviewed and approved the final manuscript.\n\nRESULTS:\nClinical characteristics associated with IMC We identified a total of 314 patients treated with ICI at Stanford Health Care from May 2011 to May 2020 who had ICD codes matching our query (Supplementary Table 1). Of these, 64 had a diagnosis of IMC per review of Oncology providers’ notes, after excluding patients with alternative diagnoses for their symptoms. 24 (37.5%) of these IMC patients underwent an endoscopy (colonoscopy or flexible sigmoidoscopy) during workup, of which seven (29.2%) had a normal endoscopic appearance, consistent with prior reports demonstrating that approximately one third of patients with IMC related to anti-PD-1 therapy have microscopic colitis[15] (Supplementary Table 3). An additional 14 patients (21.9%) had imaging findings suggestive of IMC while 3 patients (4.69%) without imaging or endoscopy had an elevated calprotectin or fecal lactoferrin.\nThese 64 patients were manually matched 1:1 with control patients based on age, sex, malignancy, type of ICI, whether or not the patient had prior ICI exposure, and duration of ICI use. We compared clinical characteristics of patients from the IMC cohort and the control cohort (Table 1). None of the matched characteristics were significantly different between the two cohorts. The mean age across the combined cohorts was 66.6 years, with an average age of 67.4 in the cohort with IMC compared with 65.8 in the control cohort (P = 0.42). 57.81% of patients in each group were male (P = 1.00). Patients were predominantly white in both groups, with 52 (81.25%) white individuals in the IMC cohort compared to 50 (78.13%) in the control group (P = 0.66). The most common malignancy in each group was melanoma [33 (51.56%) in both cohorts], followed by renal cell carcinoma [8 (12.5%) in the IMC cohort and 7 (10.94%) in the control cohort] and non-small cell lung cancer [6 (9.38%) in both cohorts]. Both groups had similar numbers of patients with stage IV malignancy [56 (87.5%) in the IMC cohort and 58 (90.63%) in the control cohort, P = 0.778]. Combination ipilimumab and nivolumab was the most commonly used checkpoint therapy [24 (37.5%) of patients in each cohort], followed by nivolumab monotherapy [19 (29.69%) of each cohort] and ipilimumab monotherapy [11 (17.19%) of each cohort].\nBaseline characteristics of patients with immune checkpoint inhibitor use\nVariable matched between cases and controls.\nNumber of infusions of immune checkpoint inhibitor prior to immune checkpoint inhibitor-mediated colitis diagnosis (cases) or total (controls).\nSee Supplementary Table 2.\nIMC: Immune checkpoint inhibitor-mediated colitis; ICI: Immune checkpoint inhibitor; SD: Standard deviation; RCC: Renal cell carcinoma; NSCLC: Non-small cell lung cancer; SCC: Squamous cell carcinoma; PFS: Progression-Free Survival; irAE: Immune related adverse event; OS: Overall survival.\nAmong the remainder of the clinical characteristics evaluated, personal history of autoimmune disease (including prior irAE) and family history of autoimmune disease were significantly more common in patients with IMC (P = 0.037 and 0.048, respectively). Intriguingly, prior use of a therapy designed to increase immune responses was more common in the control cohort without IMC (P = 0.027). In contrast to prior data[11], use of vitamin D supplementation at the time of first dose of ICI was significantly more prevalent in patients with IMC (P = 0.020). Neither smoking status, NSAID use at time of ICI initiation, steroid use at the time of ICI initiation, nor recent vaccination were significantly more common in IMC patients compared to controls.\nWe identified a total of 314 patients treated with ICI at Stanford Health Care from May 2011 to May 2020 who had ICD codes matching our query (Supplementary Table 1). Of these, 64 had a diagnosis of IMC per review of Oncology providers’ notes, after excluding patients with alternative diagnoses for their symptoms. 24 (37.5%) of these IMC patients underwent an endoscopy (colonoscopy or flexible sigmoidoscopy) during workup, of which seven (29.2%) had a normal endoscopic appearance, consistent with prior reports demonstrating that approximately one third of patients with IMC related to anti-PD-1 therapy have microscopic colitis[15] (Supplementary Table 3). An additional 14 patients (21.9%) had imaging findings suggestive of IMC while 3 patients (4.69%) without imaging or endoscopy had an elevated calprotectin or fecal lactoferrin.\nThese 64 patients were manually matched 1:1 with control patients based on age, sex, malignancy, type of ICI, whether or not the patient had prior ICI exposure, and duration of ICI use. We compared clinical characteristics of patients from the IMC cohort and the control cohort (Table 1). None of the matched characteristics were significantly different between the two cohorts. The mean age across the combined cohorts was 66.6 years, with an average age of 67.4 in the cohort with IMC compared with 65.8 in the control cohort (P = 0.42). 57.81% of patients in each group were male (P = 1.00). Patients were predominantly white in both groups, with 52 (81.25%) white individuals in the IMC cohort compared to 50 (78.13%) in the control group (P = 0.66). The most common malignancy in each group was melanoma [33 (51.56%) in both cohorts], followed by renal cell carcinoma [8 (12.5%) in the IMC cohort and 7 (10.94%) in the control cohort] and non-small cell lung cancer [6 (9.38%) in both cohorts]. Both groups had similar numbers of patients with stage IV malignancy [56 (87.5%) in the IMC cohort and 58 (90.63%) in the control cohort, P = 0.778]. Combination ipilimumab and nivolumab was the most commonly used checkpoint therapy [24 (37.5%) of patients in each cohort], followed by nivolumab monotherapy [19 (29.69%) of each cohort] and ipilimumab monotherapy [11 (17.19%) of each cohort].\nBaseline characteristics of patients with immune checkpoint inhibitor use\nVariable matched between cases and controls.\nNumber of infusions of immune checkpoint inhibitor prior to immune checkpoint inhibitor-mediated colitis diagnosis (cases) or total (controls).\nSee Supplementary Table 2.\nIMC: Immune checkpoint inhibitor-mediated colitis; ICI: Immune checkpoint inhibitor; SD: Standard deviation; RCC: Renal cell carcinoma; NSCLC: Non-small cell lung cancer; SCC: Squamous cell carcinoma; PFS: Progression-Free Survival; irAE: Immune related adverse event; OS: Overall survival.\nAmong the remainder of the clinical characteristics evaluated, personal history of autoimmune disease (including prior irAE) and family history of autoimmune disease were significantly more common in patients with IMC (P = 0.037 and 0.048, respectively). Intriguingly, prior use of a therapy designed to increase immune responses was more common in the control cohort without IMC (P = 0.027). In contrast to prior data[11], use of vitamin D supplementation at the time of first dose of ICI was significantly more prevalent in patients with IMC (P = 0.020). Neither smoking status, NSAID use at time of ICI initiation, steroid use at the time of ICI initiation, nor recent vaccination were significantly more common in IMC patients compared to controls.\nIMC significantly increases overall survival As IMC has previously been associated with increased OS and PFS in cancer patients[9,10], we evaluated whether this association was seen in our study. We found that OS was significantly longer in patients who developed IMC compared to those who did not, with a mean OS of 24.3 mo in patients with IMC and 17.7 mo in control (P = 0.05, Table 1). OS at 12 mo following ICI initiation was significantly higher in patients who developed IMC compared to those who did not (P = 0.02, Figure 1). However, in contrast to prior findings, our study did not find a significant difference in PFS between IMC patients and controls, with a mean PFS 13.7 mo in IMC patients and 11.9 mo in controls (P = 0.524) (Table 1). PFS also did not differ between patients who developed mild vs severe IMC (P = 0.690, Supplementary Table 5).\n\nOverall survival at 12 mo in patients with and without immune checkpoint inhibitor-mediated colitis. Kaplan-Meier curve of overall survival at 12 mo in patients with immune checkpoint inhibitor-mediated colitis (IMC, red) and without IMC (black). IMC: Immune checkpoint inhibitor-mediated colitis; HR: Hazard ratio.\nAcross both cohorts, we identified clinical characteristics significantly associated with OS greater than 12 mo and PFS greater than 6 mo, which are correlated with cancer outcomes in patients treated with ICI[16] (Tables 2 and 3) (Supplementary Tables 4 and 5). IMC was significantly and independently associated with OS > 12 mo in the multivariate model (OR 2.81, 95%CI 1.17-6.77, P = 0.021) (Table 2). Number of ICI infusions was also positively associated with OS > 12 mo (OR 1.23, 95%CI 1.09-1.40), while sarcoma as underlying malignancy was significantly associated with OS < 12 mo (OR 0.17, 95%CI 0.029-0.947). Within the IMC cohort, nivolumab use was associated with OS < 12 mo in the univariate analysis (OR 0.09, 95%CI 0.01-0.83), while only age was associated with OS < 12 mo in multivariate analysis (OR 0.93, 95%CI 0.88-0.99) (Table 3). No individual malignancy was significantly associated with OS > 12 mo within the IMC cohort (Table 3).\nUnivariate and multivariate predictors of overall survival > 12 mo among patients with malignancy using immune checkpoint inhibitor (n = 128)\nNumber of infusions of immune checkpoint inhibitor prior to immune checkpoint inhibitor-mediated colitis diagnosis (cases) or total (controls).\nSee Supplementary Table 2.\nICI: Immune checkpoint inhibitor; IMC: Immune checkpoint inhibitor-mediated colitis; OR: Odds ratios; CI: Confidence interval; SD: Standard deviation; RCC: Renal cell carcinoma; NSCLC: Non-small cell lung cancer; SCC: Squamous cell carcinoma; irAE: Immune related adverse event.\nUnivariate and multivariate predictors of overall survival > 12 mo among patients with immune checkpoint inhibitor colitis (n = 64)\nNumber of infusions of immune checkpoint inhibitor prior to immune checkpoint inhibitor-mediated colitis diagnosis (cases) or total (controls).\nSee Supplementary Table 2.\nICI: Immune checkpoint inhibitor; IMC: Immune checkpoint inhibitor-mediated colitis; SD: Standard deviation; RCC: Renal cell carcinoma; NSCLC: Non-small cell lung cancer; SCC: Squamous cell carcinoma; irAE: Immune related adverse event.\nAs IMC has previously been associated with increased OS and PFS in cancer patients[9,10], we evaluated whether this association was seen in our study. We found that OS was significantly longer in patients who developed IMC compared to those who did not, with a mean OS of 24.3 mo in patients with IMC and 17.7 mo in control (P = 0.05, Table 1). OS at 12 mo following ICI initiation was significantly higher in patients who developed IMC compared to those who did not (P = 0.02, Figure 1). However, in contrast to prior findings, our study did not find a significant difference in PFS between IMC patients and controls, with a mean PFS 13.7 mo in IMC patients and 11.9 mo in controls (P = 0.524) (Table 1). PFS also did not differ between patients who developed mild vs severe IMC (P = 0.690, Supplementary Table 5).\n\nOverall survival at 12 mo in patients with and without immune checkpoint inhibitor-mediated colitis. Kaplan-Meier curve of overall survival at 12 mo in patients with immune checkpoint inhibitor-mediated colitis (IMC, red) and without IMC (black). IMC: Immune checkpoint inhibitor-mediated colitis; HR: Hazard ratio.\nAcross both cohorts, we identified clinical characteristics significantly associated with OS greater than 12 mo and PFS greater than 6 mo, which are correlated with cancer outcomes in patients treated with ICI[16] (Tables 2 and 3) (Supplementary Tables 4 and 5). IMC was significantly and independently associated with OS > 12 mo in the multivariate model (OR 2.81, 95%CI 1.17-6.77, P = 0.021) (Table 2). Number of ICI infusions was also positively associated with OS > 12 mo (OR 1.23, 95%CI 1.09-1.40), while sarcoma as underlying malignancy was significantly associated with OS < 12 mo (OR 0.17, 95%CI 0.029-0.947). Within the IMC cohort, nivolumab use was associated with OS < 12 mo in the univariate analysis (OR 0.09, 95%CI 0.01-0.83), while only age was associated with OS < 12 mo in multivariate analysis (OR 0.93, 95%CI 0.88-0.99) (Table 3). No individual malignancy was significantly associated with OS > 12 mo within the IMC cohort (Table 3).\nUnivariate and multivariate predictors of overall survival > 12 mo among patients with malignancy using immune checkpoint inhibitor (n = 128)\nNumber of infusions of immune checkpoint inhibitor prior to immune checkpoint inhibitor-mediated colitis diagnosis (cases) or total (controls).\nSee Supplementary Table 2.\nICI: Immune checkpoint inhibitor; IMC: Immune checkpoint inhibitor-mediated colitis; OR: Odds ratios; CI: Confidence interval; SD: Standard deviation; RCC: Renal cell carcinoma; NSCLC: Non-small cell lung cancer; SCC: Squamous cell carcinoma; irAE: Immune related adverse event.\nUnivariate and multivariate predictors of overall survival > 12 mo among patients with immune checkpoint inhibitor colitis (n = 64)\nNumber of infusions of immune checkpoint inhibitor prior to immune checkpoint inhibitor-mediated colitis diagnosis (cases) or total (controls).\nSee Supplementary Table 2.\nICI: Immune checkpoint inhibitor; IMC: Immune checkpoint inhibitor-mediated colitis; SD: Standard deviation; RCC: Renal cell carcinoma; NSCLC: Non-small cell lung cancer; SCC: Squamous cell carcinoma; irAE: Immune related adverse event.\nSignificant risk factors for developing IMC and severe IMC As certain clinical characteristics were significantly more common in patients with IMC compared to controls, we evaluated whether any of these clinical characteristics were associated with risk of developing IMC (Table 4). In univariate analysis, history of autoimmune disease and vitamin D use were both significantly associated with increased risk of IMC (OR 2.45, 95%CI 1.04-5.78, P = 0.040 for autoimmune disease; OR 2.51, 95%CI 1.14-5.54, P = 0.022 for vitamin D use). Interestingly, the use of vitamin D supplementation has previously been associated with a decreased risk of IMC, in contrast to our findings here[11]. Prior use of an immune-enhancing therapy (Supplementary Table 2) was associated with a significantly decreased risk of IMC (OR 0.20, 95%CI 0.04-0.95, P = 0.043). In the multivariate model which incorporated these characteristics, only the use of immune-enhancing therapy remained significantly associated with decreased risk of IMC, with an OR of 0.20 (95%CI 0.04-1.00, P = 0.050). \nUnivariate and multivariate predictors of immune checkpoint inhibitor-mediated colitis among patients using immune checkpoint inhibitor (n = 128)\nNumber of infusions of immune checkpoint inhibitor prior to immune checkpoint inhibitor-mediated colitis diagnosis (cases) or total (controls).\nSee Supplementary Table 2.\nICI: Immune checkpoint inhibitor; IMC: Immune checkpoint inhibitor-mediated colitis; RCC: Renal cell carcinoma; NSCLC: Non-small cell lung cancer; SCC: Squamous cell carcinoma; irAE: Immune related adverse event.\nWe next determined if any variables were associated with an increased risk of severe IMC. Consistent with prior studies of irAE in ICI[17-19], we defined grade 1-2 IMC as mild and grade 3 or higher IMC as severe. In our study, 38 of the 64 patients (59.4%) had severe IMC (Supplementary Table 3). In the univariate model, ipilimumab and vitamin D supplementation were significantly associated with development of severe IMC (OR 8.93, 95%CI 1.07-74.8, P = 0.043 for ipilimumab; OR 3.33, 95%CI 1.10-10.14, P = 0.034 for vitamin D) (Supplementary Table 6). Combination therapy (ipilimumab plus nivolumab) trended towards an increased risk of severe IMC but did not reach significance (P = 0.053). In contrast, pembrolizumab was significantly associated with a decreased risk of severe IMC (OR 0.26, 95%CI 0.09-0.81, P = 0.020). In the multivariate model no characteristic reached significance for association with severe IMC, although both combination therapy and ipilimumab monotherapy approached significance for increased risk of severe IMC (P = 0.058 and 0.060, respectively).\nAs certain clinical characteristics were significantly more common in patients with IMC compared to controls, we evaluated whether any of these clinical characteristics were associated with risk of developing IMC (Table 4). In univariate analysis, history of autoimmune disease and vitamin D use were both significantly associated with increased risk of IMC (OR 2.45, 95%CI 1.04-5.78, P = 0.040 for autoimmune disease; OR 2.51, 95%CI 1.14-5.54, P = 0.022 for vitamin D use). Interestingly, the use of vitamin D supplementation has previously been associated with a decreased risk of IMC, in contrast to our findings here[11]. Prior use of an immune-enhancing therapy (Supplementary Table 2) was associated with a significantly decreased risk of IMC (OR 0.20, 95%CI 0.04-0.95, P = 0.043). In the multivariate model which incorporated these characteristics, only the use of immune-enhancing therapy remained significantly associated with decreased risk of IMC, with an OR of 0.20 (95%CI 0.04-1.00, P = 0.050). \nUnivariate and multivariate predictors of immune checkpoint inhibitor-mediated colitis among patients using immune checkpoint inhibitor (n = 128)\nNumber of infusions of immune checkpoint inhibitor prior to immune checkpoint inhibitor-mediated colitis diagnosis (cases) or total (controls).\nSee Supplementary Table 2.\nICI: Immune checkpoint inhibitor; IMC: Immune checkpoint inhibitor-mediated colitis; RCC: Renal cell carcinoma; NSCLC: Non-small cell lung cancer; SCC: Squamous cell carcinoma; irAE: Immune related adverse event.\nWe next determined if any variables were associated with an increased risk of severe IMC. Consistent with prior studies of irAE in ICI[17-19], we defined grade 1-2 IMC as mild and grade 3 or higher IMC as severe. In our study, 38 of the 64 patients (59.4%) had severe IMC (Supplementary Table 3). In the univariate model, ipilimumab and vitamin D supplementation were significantly associated with development of severe IMC (OR 8.93, 95%CI 1.07-74.8, P = 0.043 for ipilimumab; OR 3.33, 95%CI 1.10-10.14, P = 0.034 for vitamin D) (Supplementary Table 6). Combination therapy (ipilimumab plus nivolumab) trended towards an increased risk of severe IMC but did not reach significance (P = 0.053). In contrast, pembrolizumab was significantly associated with a decreased risk of severe IMC (OR 0.26, 95%CI 0.09-0.81, P = 0.020). In the multivariate model no characteristic reached significance for association with severe IMC, although both combination therapy and ipilimumab monotherapy approached significance for increased risk of severe IMC (P = 0.058 and 0.060, respectively).\n\nClinical characteristics associated with IMC:\nWe identified a total of 314 patients treated with ICI at Stanford Health Care from May 2011 to May 2020 who had ICD codes matching our query (Supplementary Table 1). Of these, 64 had a diagnosis of IMC per review of Oncology providers’ notes, after excluding patients with alternative diagnoses for their symptoms. 24 (37.5%) of these IMC patients underwent an endoscopy (colonoscopy or flexible sigmoidoscopy) during workup, of which seven (29.2%) had a normal endoscopic appearance, consistent with prior reports demonstrating that approximately one third of patients with IMC related to anti-PD-1 therapy have microscopic colitis[15] (Supplementary Table 3). An additional 14 patients (21.9%) had imaging findings suggestive of IMC while 3 patients (4.69%) without imaging or endoscopy had an elevated calprotectin or fecal lactoferrin.\nThese 64 patients were manually matched 1:1 with control patients based on age, sex, malignancy, type of ICI, whether or not the patient had prior ICI exposure, and duration of ICI use. We compared clinical characteristics of patients from the IMC cohort and the control cohort (Table 1). None of the matched characteristics were significantly different between the two cohorts. The mean age across the combined cohorts was 66.6 years, with an average age of 67.4 in the cohort with IMC compared with 65.8 in the control cohort (P = 0.42). 57.81% of patients in each group were male (P = 1.00). Patients were predominantly white in both groups, with 52 (81.25%) white individuals in the IMC cohort compared to 50 (78.13%) in the control group (P = 0.66). The most common malignancy in each group was melanoma [33 (51.56%) in both cohorts], followed by renal cell carcinoma [8 (12.5%) in the IMC cohort and 7 (10.94%) in the control cohort] and non-small cell lung cancer [6 (9.38%) in both cohorts]. Both groups had similar numbers of patients with stage IV malignancy [56 (87.5%) in the IMC cohort and 58 (90.63%) in the control cohort, P = 0.778]. Combination ipilimumab and nivolumab was the most commonly used checkpoint therapy [24 (37.5%) of patients in each cohort], followed by nivolumab monotherapy [19 (29.69%) of each cohort] and ipilimumab monotherapy [11 (17.19%) of each cohort].\nBaseline characteristics of patients with immune checkpoint inhibitor use\nVariable matched between cases and controls.\nNumber of infusions of immune checkpoint inhibitor prior to immune checkpoint inhibitor-mediated colitis diagnosis (cases) or total (controls).\nSee Supplementary Table 2.\nIMC: Immune checkpoint inhibitor-mediated colitis; ICI: Immune checkpoint inhibitor; SD: Standard deviation; RCC: Renal cell carcinoma; NSCLC: Non-small cell lung cancer; SCC: Squamous cell carcinoma; PFS: Progression-Free Survival; irAE: Immune related adverse event; OS: Overall survival.\nAmong the remainder of the clinical characteristics evaluated, personal history of autoimmune disease (including prior irAE) and family history of autoimmune disease were significantly more common in patients with IMC (P = 0.037 and 0.048, respectively). Intriguingly, prior use of a therapy designed to increase immune responses was more common in the control cohort without IMC (P = 0.027). In contrast to prior data[11], use of vitamin D supplementation at the time of first dose of ICI was significantly more prevalent in patients with IMC (P = 0.020). Neither smoking status, NSAID use at time of ICI initiation, steroid use at the time of ICI initiation, nor recent vaccination were significantly more common in IMC patients compared to controls.\n\nIMC significantly increases overall survival:\nAs IMC has previously been associated with increased OS and PFS in cancer patients[9,10], we evaluated whether this association was seen in our study. We found that OS was significantly longer in patients who developed IMC compared to those who did not, with a mean OS of 24.3 mo in patients with IMC and 17.7 mo in control (P = 0.05, Table 1). OS at 12 mo following ICI initiation was significantly higher in patients who developed IMC compared to those who did not (P = 0.02, Figure 1). However, in contrast to prior findings, our study did not find a significant difference in PFS between IMC patients and controls, with a mean PFS 13.7 mo in IMC patients and 11.9 mo in controls (P = 0.524) (Table 1). PFS also did not differ between patients who developed mild vs severe IMC (P = 0.690, Supplementary Table 5).\n\nOverall survival at 12 mo in patients with and without immune checkpoint inhibitor-mediated colitis. Kaplan-Meier curve of overall survival at 12 mo in patients with immune checkpoint inhibitor-mediated colitis (IMC, red) and without IMC (black). IMC: Immune checkpoint inhibitor-mediated colitis; HR: Hazard ratio.\nAcross both cohorts, we identified clinical characteristics significantly associated with OS greater than 12 mo and PFS greater than 6 mo, which are correlated with cancer outcomes in patients treated with ICI[16] (Tables 2 and 3) (Supplementary Tables 4 and 5). IMC was significantly and independently associated with OS > 12 mo in the multivariate model (OR 2.81, 95%CI 1.17-6.77, P = 0.021) (Table 2). Number of ICI infusions was also positively associated with OS > 12 mo (OR 1.23, 95%CI 1.09-1.40), while sarcoma as underlying malignancy was significantly associated with OS < 12 mo (OR 0.17, 95%CI 0.029-0.947). Within the IMC cohort, nivolumab use was associated with OS < 12 mo in the univariate analysis (OR 0.09, 95%CI 0.01-0.83), while only age was associated with OS < 12 mo in multivariate analysis (OR 0.93, 95%CI 0.88-0.99) (Table 3). No individual malignancy was significantly associated with OS > 12 mo within the IMC cohort (Table 3).\nUnivariate and multivariate predictors of overall survival > 12 mo among patients with malignancy using immune checkpoint inhibitor (n = 128)\nNumber of infusions of immune checkpoint inhibitor prior to immune checkpoint inhibitor-mediated colitis diagnosis (cases) or total (controls).\nSee Supplementary Table 2.\nICI: Immune checkpoint inhibitor; IMC: Immune checkpoint inhibitor-mediated colitis; OR: Odds ratios; CI: Confidence interval; SD: Standard deviation; RCC: Renal cell carcinoma; NSCLC: Non-small cell lung cancer; SCC: Squamous cell carcinoma; irAE: Immune related adverse event.\nUnivariate and multivariate predictors of overall survival > 12 mo among patients with immune checkpoint inhibitor colitis (n = 64)\nNumber of infusions of immune checkpoint inhibitor prior to immune checkpoint inhibitor-mediated colitis diagnosis (cases) or total (controls).\nSee Supplementary Table 2.\nICI: Immune checkpoint inhibitor; IMC: Immune checkpoint inhibitor-mediated colitis; SD: Standard deviation; RCC: Renal cell carcinoma; NSCLC: Non-small cell lung cancer; SCC: Squamous cell carcinoma; irAE: Immune related adverse event.\n\nSignificant risk factors for developing IMC and severe IMC:\nAs certain clinical characteristics were significantly more common in patients with IMC compared to controls, we evaluated whether any of these clinical characteristics were associated with risk of developing IMC (Table 4). In univariate analysis, history of autoimmune disease and vitamin D use were both significantly associated with increased risk of IMC (OR 2.45, 95%CI 1.04-5.78, P = 0.040 for autoimmune disease; OR 2.51, 95%CI 1.14-5.54, P = 0.022 for vitamin D use). Interestingly, the use of vitamin D supplementation has previously been associated with a decreased risk of IMC, in contrast to our findings here[11]. Prior use of an immune-enhancing therapy (Supplementary Table 2) was associated with a significantly decreased risk of IMC (OR 0.20, 95%CI 0.04-0.95, P = 0.043). In the multivariate model which incorporated these characteristics, only the use of immune-enhancing therapy remained significantly associated with decreased risk of IMC, with an OR of 0.20 (95%CI 0.04-1.00, P = 0.050). \nUnivariate and multivariate predictors of immune checkpoint inhibitor-mediated colitis among patients using immune checkpoint inhibitor (n = 128)\nNumber of infusions of immune checkpoint inhibitor prior to immune checkpoint inhibitor-mediated colitis diagnosis (cases) or total (controls).\nSee Supplementary Table 2.\nICI: Immune checkpoint inhibitor; IMC: Immune checkpoint inhibitor-mediated colitis; RCC: Renal cell carcinoma; NSCLC: Non-small cell lung cancer; SCC: Squamous cell carcinoma; irAE: Immune related adverse event.\nWe next determined if any variables were associated with an increased risk of severe IMC. Consistent with prior studies of irAE in ICI[17-19], we defined grade 1-2 IMC as mild and grade 3 or higher IMC as severe. In our study, 38 of the 64 patients (59.4%) had severe IMC (Supplementary Table 3). In the univariate model, ipilimumab and vitamin D supplementation were significantly associated with development of severe IMC (OR 8.93, 95%CI 1.07-74.8, P = 0.043 for ipilimumab; OR 3.33, 95%CI 1.10-10.14, P = 0.034 for vitamin D) (Supplementary Table 6). Combination therapy (ipilimumab plus nivolumab) trended towards an increased risk of severe IMC but did not reach significance (P = 0.053). In contrast, pembrolizumab was significantly associated with a decreased risk of severe IMC (OR 0.26, 95%CI 0.09-0.81, P = 0.020). In the multivariate model no characteristic reached significance for association with severe IMC, although both combination therapy and ipilimumab monotherapy approached significance for increased risk of severe IMC (P = 0.058 and 0.060, respectively).\n\nDISCUSSION:\nIn our study, development of IMC following ICI use was associated with improved overall survival, although not improved progression-free survival, compared to ICI users without IMC. This is similar to findings at another center demonstrating both improved OS and PFS in patients with IMC[9,10]. We also found that vitamin D supplementation at the start of ICI treatment is a risk factor for developing IMC, in contrast to other research suggesting vitamin D use is associated with lower risk of IMC[11]. Our results, therefore, provide critical additional information on these previous associations and present a need for prospective studies.\nBoth publications showing improved survival in patients with IMC were retrospective analyses performed at the same center[9,10]. One study noted that ICI class was significantly associated with development of IMC[9], a finding that has been demonstrated several times in retrospective work[8,17,18,20-23]. However, unlike our work, this study did not match control patients to account for this likely confounder, as ICI class has been associated with differences in PFS in some malignancies[24,25]. The second study at this center examined survival in melanoma patients with IMC, compared to our work across multiple malignancies, although frequency matching was performed to account for use of different ICI classes[10]. Since our study is the first to examine survival in patients with IMC at a different center, our work here reinforces that IMC may be associated with increased overall survival and prompts a need for prospective studies.\nThe only other independent factor in our study positively associated with OS > 12 mo was number of ICI doses. This finding may be due to trivial length-time bias, as patients who survive longer are more likely to receive more doses of ICI. It is also possible that patients who required cessation of ICI due to IMC had worse outcomes, although prior work has suggested that patients still derive equivalent long-term benefit from ICI even if stopped due to irAE[26]. Type of underlying malignancy (sarcoma) was independently associated with OS < 12 mo in our study. These findings are not unexpected, as most advanced soft tissue sarcomas have a median OS of less than one year[27].\nIn contrast to prior work, we found a positive association between vitamin D supplementation and development of IMC[11]. It is unclear if this is related to low serum vitamin D levels or negative impact of the supplementation itself, as vitamin D levels near the time of ICI initiation were not recorded in most patients. Additionally, the prior report on vitamin D in IMC was in melanoma patients only, which may partially account for discrepancies with our study. As this association did not remain significant in our multivariate analysis, it is possible that another confounding factor may explain the association between vitamin D supplementation and IMC in our study.\nIn addition to challenging existing findings, we report here on additional novel risk factors for IMC. We are the first to report that prior use of immune-enhancing medications prior to ICI, such as IL-2 or interferon-γ, is significantly and independently associated with decreased risk of IMC. Much more work should be done to evaluate the relationship between these medications and future risk of IMC.\nFinally, our study is the first to examine risk factors for severe IMC. In addition to increasing risk for IMC overall, we find that vitamin D supplementation may also be a risk factor for severe IMC. Similarly, our results suggest that the use of ipilimumab may be associated with increased risk of severe IMC, while pembrolizumab may be associated with decreased risk of severe IMC in patients who develop this syndrome. As ipilimumab has previously been associated with increased risk of IMC overall, while anti-PD-1, including pembrolizumab, are associated with lower risk of IMC overall[8,9], these findings emphasize that ICI class may affect severity of IMC.\nOur findings may significantly impact clinical practice by identifying novel risks for IMC and severe IMC that clinicians, including oncologists and gastroenterologists, should be aware of, while also potentially providing reassurance to physicians and patients that development of IMC may be a positive prognosticator for cancer survival. Neither prior work nor ours found that treatment of IMC, including steroids or infliximab, negatively impacts OS[9,10], and therefore appropriate treatment of IMC should be pursued early on to minimize morbidity and mortality. Both steroid and infliximab use have been suggested to worsen survival in ICI users[12,13], but all current evidence suggests that use of these medications for IMC specifically does not impair cancer outcomes. Our work also cautions against supplementation with vitamin D in ICI users, as this may increase risk of IMC and severe IMC, although carefully designed studies with vitamin D measurements should be performed.\nOur work has several strengths. We performed robust cohort matching to minimize confounding effects of ICI class and malignancy. This is also the first study to explore risk factors associated with severe IMC. However, there are limitations to our work. As a retrospective, observational study, it is subject to recall bias and cannot evaluate causation, and may also be subject to immortal time bias (ITB). Patients may have longer exposure to checkpoint inhibitors before developing IMC, compared to patients who do not manifest this irAE, leading to a period where they must survive for long enough to develop IMC and are therefore “immortal”[28]. We found that OS > 12 mo was significantly associated with greater numbers of ICI infusions (Table 2), which is likely due to ITB. However, greater numbers of infusions were not associated with IMC (Table 4). This suggests that the association between OS > 12 mo and IMC is likely independent of the number of ICI infusions, limiting this as a source of ITB in our study.\nOther weaknesses of our work include selection of patients based on clinical criteria for IMC, including those who did not undergo endoscopy or other objective testing for intestinal inflammation, and therefore may not have had a true colitis. Like prior work, this is also a single-center study, and our results may not be widely generalizable, particularly since we identified fewer patients compared to prior work and our patient population is highly variable, including individuals with several different underlying malignancies. We did not exclude patients with prior non-GI irAEs in either group, although the presence of these was not independently associated with increased OS in our study. We also have not accounted for other factors which may be potential predictors of ICI response, including tumor PD-L1 expression burden, tumor mutational burden, gut microbial composition, proton pump inhibitor use, and combination treatment with tyrosine kinase inhibitors[29-34].\n\nCONCLUSION:\nIn conclusion, our findings suggest presence of IMC is associated with improved OS in cancer patients when cases were matched closely to controls. We also found that vitamin D supplementation was significantly associated with development of both IMC and severe IMC, while immune-enhancing medications were significantly associated with decreased risk of IMC. Future work should focus on broader populations to resolve the discrepancies raised in our work, and to confirm the association between IMC and increased cancer survival. Closely involving gastroenterologists with the workup and management of IMC will be crucial to ensuring the best care possible for these patients.\n\n"},"whole_article_abstract":{"kind":"string","value":"Background:\nImmune checkpoint inhibitor-mediated colitis (IMC) is a common adverse event following immune checkpoint inhibitor (ICI) therapy for cancer. IMC has been associated with improved overall survival (OS) and progression-free survival (PFS), but data are limited to a single site and predominantly for melanoma patients.\n\nMethods:\nWe performed a retrospective case-control study including 64 ICI users who developed IMC matched according to age, sex, ICI class, and malignancy to a cohort of ICI users without IMC, from May 2011 to May 2020. Using univariate and multivariate logistic regression, we determined association of presence of IMC on OS, PFS, and clinical predictors of IMC. Kaplan-Meier curves were generated to compare OS and PFS between ICI users with and without IMC.\n\nResults:\nIMC was significantly associated with a higher OS (mean 24.3 mo vs 17.7 mo, P = 0.05) but not PFS (mean 13.7 mo vs 11.9 mo, P = 0.524). IMC was significantly associated with OS greater than 12 mo [Odds ratio (OR) 2.81, 95% confidence interval (CI) 1.17-6.77]. Vitamin D supplementation was significantly associated with increased risk of IMC (OR 2.48, 95%CI 1.01-6.07).\n\nConclusions:\nIMC was significantly associated with OS greater than 12 mo. In contrast to prior work, we found that vitamin D use may be a risk factor for IMC."},"background_conclusion_text":{"kind":"string","value":"INTRODUCTION:\nImmune checkpoint inhibitors (ICI) have dramatically changed the landscape of cancer therapy. Early studies showed significantly prolonged survival in patients with metastatic melanoma compared to standard chemotherapy[1], and evidence now exists for improved outcomes in a variety of tumors ranging from lung cancers to urothelial carcinoma to breast cancer[2-5]. Although these are powerful treatments in our armamentarium against malignancy, ICI can cause immune-related adverse events (irAE) characterized by autoimmune-like inflammation in a variety of non-tumor organs, leading to increased morbidity for patients[6].\nOne of the most common irAE is immune checkpoint inhibitor-mediated colitis (IMC). IMC may occur in up to 40% of patients treated with ipilimumab, an antibody targeting CTLA-4, 11%-17% of patients treated with antibodies against anti-PD-1 or anti-PD-L1, such as nivolumab, pembrolizumab, or atezolizumab, and around 32% of patients treated with a combination of anti-CTLA-4 and anti-PD-1[7]. Prior retrospective analyses of patients with IMC have attempted to identify characteristics associated with development of IMC, including type of malignancy, ICI class, dose of ICI, cancer stage, and vitamin D use[8-11]. Intriguingly, two prior studies have suggested that development of IMC may positively correlate with improved progression-free survival (PFS) and overall survival (OS)[9,10]. One of these studies controlled for confounding effects of ICI class via frequency matching, but was limited to patients with melanoma, hindering wider applicability of their findings[10]. These findings also conflict with data suggesting that use of steroids and the anti-TNF antibody infliximab in patients treated with ICI are associated with worse cancer outcomes[12,13]. These discrepancies represent a significant knowledge gap that impedes our ability to evaluate and manage IMC and ICI use.\nHere we present data from a retrospective study of patients treated with ICI at our institution who developed IMC across malignancy types. We compare this cohort to a matched control cohort to determine whether IMC was associated with improved progression-free survival and overall survival. We also evaluate which clinical characteristics increase the risk of developing IMC, including severe IMC.\n\nCONCLUSION:\nFuture research in this area should seek to expand current knowledge of the relationship between IMC and cancer survival. In particular, future work should focus on broadening the type and number of patients treated with immune checkpoint inhibitors and on tracking patients prior to initiating checkpoint inhibitors to determine if this relationship remains significant prospectively."},"background_conclusion_abstract":{"kind":"string","value":"Background:\nImmune checkpoint inhibitor-mediated colitis (IMC) is a common adverse event following immune checkpoint inhibitor (ICI) therapy for cancer. IMC has been associated with improved overall survival (OS) and progression-free survival (PFS), but data are limited to a single site and predominantly for melanoma patients.\n\nMethods:\nWe performed a retrospective case-control study including 64 ICI users who developed IMC matched according to age, sex, ICI class, and malignancy to a cohort of ICI users without IMC, from May 2011 to May 2020. Using univariate and multivariate logistic regression, we determined association of presence of IMC on OS, PFS, and clinical predictors of IMC. Kaplan-Meier curves were generated to compare OS and PFS between ICI users with and without IMC.\n\nResults:\nIMC was significantly associated with a higher OS (mean 24.3 mo vs 17.7 mo, P = 0.05) but not PFS (mean 13.7 mo vs 11.9 mo, P = 0.524). IMC was significantly associated with OS greater than 12 mo [Odds ratio (OR) 2.81, 95% confidence interval (CI) 1.17-6.77]. Vitamin D supplementation was significantly associated with increased risk of IMC (OR 2.48, 95%CI 1.01-6.07).\n\nConclusions:\nIMC was significantly associated with OS greater than 12 mo. In contrast to prior work, we found that vitamin D use may be a risk factor for IMC."},"whole_article_text_length":{"kind":"number","value":10141,"string":"10,141"},"whole_article_abstract_length":{"kind":"number","value":279,"string":"279"},"other_sections_lengths":{"kind":"list like","value":[406,625,249,3796,718,658,510,1251,109],"string":"[\n 406,\n 625,\n 249,\n 3796,\n 718,\n 658,\n 510,\n 1251,\n 109\n]"},"num_sections":{"kind":"number","value":10,"string":"10"},"most_frequent_words":{"kind":"list like","value":["imc","patients","ici","immune","checkpoint","immune checkpoint","inhibitor","checkpoint inhibitor","immune checkpoint inhibitor","associated"],"string":"[\n \"imc\",\n \"patients\",\n \"ici\",\n \"immune\",\n \"checkpoint\",\n \"immune checkpoint\",\n \"inhibitor\",\n \"checkpoint inhibitor\",\n \"immune checkpoint inhibitor\",\n \"associated\"\n]"},"keybert_topics":{"kind":"list like","value":["imc immune checkpoint","predictors immune checkpoint","malignancy immune checkpoint","ici immune checkpoint","checkpoint inhibitor colitis"],"string":"[\n \"imc immune checkpoint\",\n \"predictors immune checkpoint\",\n \"malignancy immune checkpoint\",\n \"ici immune checkpoint\",\n \"checkpoint inhibitor colitis\"\n]"},"annotated_base_background_abstract_prompt":{"kind":"string","value":""},"annotated_base_methods_abstract_prompt":{"kind":"string","value":""},"annotated_base_results_abstract_prompt":{"kind":"null"},"annotated_base_conclusions_abstract_prompt":{"kind":"string","value":""},"annotated_base_whole_article_abstract_prompt":{"kind":"string","value":""},"annotated_base_background_conclusion_abstract_prompt":{"kind":"string","value":""},"annotated_keywords_background_abstract_prompt":{"kind":"string","value":"[CONTENT] Immune checkpoint inhibitors | Immune checkpoint inhibitor-mediated colitis | Immune-related adverse events [SUMMARY]"},"annotated_keywords_methods_abstract_prompt":{"kind":"string","value":"[CONTENT] Immune checkpoint inhibitors | Immune checkpoint inhibitor-mediated colitis | Immune-related adverse events [SUMMARY]"},"annotated_keywords_results_abstract_prompt":{"kind":"null"},"annotated_keywords_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] Immune checkpoint inhibitors | Immune checkpoint inhibitor-mediated colitis | Immune-related adverse events [SUMMARY]"},"annotated_keywords_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] Immune checkpoint inhibitors | Immune checkpoint inhibitor-mediated colitis | Immune-related adverse events [SUMMARY]"},"annotated_keywords_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] Immune checkpoint inhibitors | Immune checkpoint inhibitor-mediated colitis | Immune-related adverse events [SUMMARY]"},"annotated_mesh_background_abstract_prompt":{"kind":"string","value":"[CONTENT] Humans | Immune Checkpoint Inhibitors | Antineoplastic Agents, Immunological | Retrospective Studies | Case-Control Studies | Melanoma | Colitis | Vitamin D [SUMMARY]"},"annotated_mesh_methods_abstract_prompt":{"kind":"string","value":"[CONTENT] Humans | Immune Checkpoint Inhibitors | Antineoplastic Agents, Immunological | Retrospective Studies | Case-Control Studies | Melanoma | Colitis | Vitamin D [SUMMARY]"},"annotated_mesh_results_abstract_prompt":{"kind":"null"},"annotated_mesh_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] Humans | Immune Checkpoint Inhibitors | Antineoplastic Agents, Immunological | Retrospective Studies | Case-Control Studies | Melanoma | Colitis | Vitamin D [SUMMARY]"},"annotated_mesh_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] Humans | Immune Checkpoint Inhibitors | Antineoplastic Agents, Immunological | Retrospective Studies | Case-Control Studies | Melanoma | Colitis | Vitamin D [SUMMARY]"},"annotated_mesh_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] Humans | Immune Checkpoint Inhibitors | Antineoplastic Agents, Immunological | Retrospective Studies | Case-Control Studies | Melanoma | Colitis | Vitamin D [SUMMARY]"},"annotated_keybert_background_abstract_prompt":{"kind":"string","value":"[CONTENT] imc immune checkpoint | predictors immune checkpoint | malignancy immune checkpoint | ici immune checkpoint | checkpoint inhibitor colitis [SUMMARY]"},"annotated_keybert_methods_abstract_prompt":{"kind":"string","value":"[CONTENT] imc immune checkpoint | predictors immune checkpoint | malignancy immune checkpoint | ici immune checkpoint | checkpoint inhibitor colitis [SUMMARY]"},"annotated_keybert_results_abstract_prompt":{"kind":"null"},"annotated_keybert_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] imc immune checkpoint | predictors immune checkpoint | malignancy immune checkpoint | ici immune checkpoint | checkpoint inhibitor colitis [SUMMARY]"},"annotated_keybert_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] imc immune checkpoint | predictors immune checkpoint | malignancy immune checkpoint | ici immune checkpoint | checkpoint inhibitor colitis [SUMMARY]"},"annotated_keybert_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] imc immune checkpoint | predictors immune checkpoint | malignancy immune checkpoint | ici immune checkpoint | checkpoint inhibitor colitis [SUMMARY]"},"annotated_most_frequent_background_abstract_prompt":{"kind":"string","value":"[CONTENT] imc | patients | ici | immune | checkpoint | immune checkpoint | inhibitor | checkpoint inhibitor | immune checkpoint inhibitor | associated [SUMMARY]"},"annotated_most_frequent_methods_abstract_prompt":{"kind":"string","value":"[CONTENT] imc | patients | ici | immune | checkpoint | immune checkpoint | inhibitor | checkpoint inhibitor | immune checkpoint inhibitor | associated [SUMMARY]"},"annotated_most_frequent_results_abstract_prompt":{"kind":"null"},"annotated_most_frequent_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] imc | patients | ici | immune | checkpoint | immune checkpoint | inhibitor | checkpoint inhibitor | immune checkpoint inhibitor | associated [SUMMARY]"},"annotated_most_frequent_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] imc | patients | ici | immune | checkpoint | immune checkpoint | inhibitor | checkpoint inhibitor | immune checkpoint inhibitor | associated [SUMMARY]"},"annotated_most_frequent_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] imc | patients | ici | immune | checkpoint | immune checkpoint | inhibitor | checkpoint inhibitor | immune checkpoint inhibitor | associated [SUMMARY]"},"annotated_tf_idf_background_abstract_prompt":{"kind":"string","value":"[CONTENT] imc | patients | ici | anti | treated | patients treated | survival | improved | variety | studies [SUMMARY]"},"annotated_tf_idf_methods_abstract_prompt":{"kind":"string","value":"[CONTENT] ici | imc | patients | data | initiation | anti | patient | history | outcomes | pfs [SUMMARY]"},"annotated_tf_idf_results_abstract_prompt":{"kind":"null"},"annotated_tf_idf_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] imc | closely | work | associated | significantly associated | focus | care possible patients | os cancer | os cancer patients | os cancer patients cases [SUMMARY]"},"annotated_tf_idf_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] imc | patients | ici | immune | mo | associated | immune checkpoint | checkpoint | checkpoint inhibitor | immune checkpoint inhibitor [SUMMARY]"},"annotated_tf_idf_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] imc | patients | ici | immune | mo | associated | immune checkpoint | checkpoint | checkpoint inhibitor | immune checkpoint inhibitor [SUMMARY]"},"annotated_entity_plan_background_abstract_prompt":{"kind":"string","value":"[CONTENT] IMC | ICI ||| IMC [SUMMARY]"},"annotated_entity_plan_methods_abstract_prompt":{"kind":"string","value":"[CONTENT] 64 | ICI | IMC | ICI | IMC | May 2011 to May 2020 ||| IMC | IMC ||| Kaplan-Meier | ICI | IMC [SUMMARY]"},"annotated_entity_plan_results_abstract_prompt":{"kind":"null"},"annotated_entity_plan_conclusions_abstract_prompt":{"kind":"string","value":"[CONTENT] IMC ||| IMC [SUMMARY]"},"annotated_entity_plan_whole_article_abstract_prompt":{"kind":"string","value":"[CONTENT] IMC | ICI ||| IMC ||| 64 | ICI | IMC | ICI | IMC | May 2011 to May 2020 ||| IMC | IMC ||| Kaplan-Meier | ICI | IMC ||| IMC | 24.3 mo | 17.7 mo | 0.05 | 13.7 mo | 11.9 | 0.524 ||| IMC ||| 2.81 | 95% | CI | 1.17-6.77 ||| Vitamin D | IMC | 2.48 | 1.01-6.07 ||| IMC ||| IMC [SUMMARY]"},"annotated_entity_plan_background_conclusion_abstract_prompt":{"kind":"string","value":"[CONTENT] IMC | ICI ||| IMC ||| 64 | ICI | IMC | ICI | IMC | May 2011 to May 2020 ||| IMC | IMC ||| Kaplan-Meier | ICI | IMC ||| IMC | 24.3 mo | 17.7 mo | 0.05 | 13.7 mo | 11.9 | 0.524 ||| IMC ||| 2.81 | 95% | CI | 1.17-6.77 ||| Vitamin D | IMC | 2.48 | 1.01-6.07 ||| IMC ||| IMC [SUMMARY]"}}},{"rowIdx":297,"cells":{"title":{"kind":"string","value":"Magnesium Isoglycyrrhizinate Induces an Inhibitory Effect on Progression and Epithelial-Mesenchymal Transition of Laryngeal Cancer via the NF-κB/Twist Signaling."},"pmid":{"kind":"string","value":"33376307"},"background_abstract":{"kind":"string","value":"Magnesium isoglycyrrhizinate (MI) was extracted from roots of the plant Glycyrrhiza glabra, which displays multiple pharmacological activities such as anti-inflammation, anti-apoptosis, and anti-tumor. Here, we aimed to investigate the effect of MI on the progression and epithelial-mesenchymal transition (EMT) of laryngeal cancer."},"background_abstract_label":{"kind":"string","value":"BACKGROUND"},"methods_abstract":{"kind":"string","value":"Forty laryngeal cancer clinical samples were used. The role of MI in the proliferation of laryngeal cancer cells was assessed by MTT assay, Edu assay and colony formation assay. The function of MI in the migration and invasion of laryngeal cancer cells was tested by transwell assays. The effect of MI on apoptosis of laryngeal cancer cells was determined by cell apoptosis assay. The impact of MI on tumor growth in vivo was analyzed by tumorigenicity analysis using Balb/c nude mice. qPCR and Western blot analysis were performed to measure the expression levels of gene and protein, respectively."},"methods_abstract_label":{"kind":"string","value":"METHODS"},"results_abstract":{"kind":"string","value":"We identified that EMT-related transcription factor Twist was significantly elevated in the laryngeal cancer tissues. The expression of Twist was also enhanced in the human laryngeal carcinoma HEP-2 cells compared with that in the primary laryngeal epithelial cells. The high expression of Twist was remarkably correlated with poor overall survival of patients with laryngeal cancer. Meanwhile, our data revealed that MI reduced cell proliferation, migration and invasion and enhanced apoptosis of laryngeal cancer cells in vitro. Moreover, MI decreased transcriptional activation and the expression levels of NF-κB and Twist, and alleviated EMT in vitro and in vivo. MI remarkably inhibited tumor growth and EMT of laryngeal cancer cells in vivo."},"results_abstract_label":{"kind":"string","value":"RESULTS"},"conclusions_abstract":{"kind":"string","value":"MI restrains the progression of laryngeal cancer and induces an inhibitory effect on EMT in laryngeal cancer by modulating the NF-κB/Twist signaling. Our finding provides new insights into the mechanism by which MI inhibits laryngeal carcinoma development, enriching the understanding of the anti-tumor function of MI."},"conclusions_abstract_label":{"kind":"string","value":"CONCLUSION"},"mesh_descriptor_names":{"kind":"list like","value":["Animals","Apoptosis","Cell Proliferation","Epithelial-Mesenchymal Transition","Humans","Laryngeal Neoplasms","Mice","Mice, Inbred BALB C","Mice, Nude","NF-kappa B","Neoplasms, Experimental","Nuclear Proteins","Saponins","Signal Transduction","Triterpenes","Tumor Cells, Cultured","Twist-Related Protein 1"],"string":"[\n \"Animals\",\n \"Apoptosis\",\n \"Cell Proliferation\",\n \"Epithelial-Mesenchymal Transition\",\n \"Humans\",\n \"Laryngeal Neoplasms\",\n \"Mice\",\n \"Mice, Inbred BALB C\",\n \"Mice, Nude\",\n \"NF-kappa B\",\n \"Neoplasms, Experimental\",\n \"Nuclear Proteins\",\n \"Saponins\",\n \"Signal Transduction\",\n \"Triterpenes\",\n \"Tumor Cells, Cultured\",\n \"Twist-Related Protein 1\"\n]"},"pmcid":{"kind":"string","value":"7765753"},"background_title":{"kind":"string","value":"Introduction"},"background_text":{"kind":"string","value":"Laryngeal cancer is one of the most prevalent malignancies of the respiratory system, accounting for 26% to 30% of head and neck cancers.1,2 Almost 95% of the histologic pathogeny of laryngeal cancer is laryngeal squamous carcinoma, and the survival incidence of patients is low.3 Despite the development of new strategies in surgery, chemotherapy, and radiation, the targeted treatment of laryngeal cancer is still a challenge.4 Thus, identification of safe and effective treatment candidates for laryngeal cancer is urgently needed. Epithelial–mesenchymal transition (EMT) serves as a cellular program, in which cells drop their epithelial features and gain mesenchymal characteristics.5,6 EMT is correlated with multiple tumor progressions, such as resistance to therapy, blood intravasation, tumor initiation, tumor cell migration, tumor stemness, malignant progression, and metastasis.7–9 As a critical process of cancer development, EMT contributes to the development of laryngeal cancer. It has been reported that the combination of photodynamic therapy and carboplatin suppresses the expression of MMP-2/MMP-9 and EMT of laryngeal cancer by ROS-inhibited MEK/ERK signaling.10 EZH2 increases metastasis and aggression of laryngeal squamous cell carcinoma through the EMT program by modulating H3K27me3.11 However, investigation about the inhibitory candidates of EMT in laryngeal cancer remains limited.\nNutraceutical agents display a unique therapeutic activity to treat diseases such as inflammation and cancer.12–17 Glycyrrhizic acid (GA) is extracted from the roots of licorice and serves as a major component of licorice, presenting multiple biomedical activities, such as anti-oxidant and anti-inflammatory.18 Magnesium isoglycyrrhizinate (MI), refined from GA, is a18-α-GA stereoisomer magnesium salt and demonstrates a better activity than 18-β-GA.19 As a natural and safe compound, MI shows many biomedical activities such as anti-inflammation capacities,20 anti-apoptosis21 and anti-tumor.22 It has been reported that MI inhibits fructose-modulated lipid metabolism disorder and activation of the NF-κB/NLRP3 inflammasome.23 MI reduces paclitaxel in patients with epithelial ovarian cancer managed with cisplatin and paclitaxel.24 Moreover, MI attenuates high fructose-induced liver fibrosis and EMT by upregulating miR-375-3p to overcome the TGF-β1/Smad pathway and JAK2/STAT3 signaling.25 However, the role of MI in cancer development is unknown. The effect of MI on the progression and EMT of laryngeal cancer remains unreported.\nMany transcription factors, such as Twist, serve as molecule switches in EMT progression.26 Twist is a crucial transcription factor for the modulating of EMT, which advances cell invasion, migration, and cancer metastasis, conferring tumor cells with stem cell-like properties and providing therapy resistance.27,28 The function of Twist in EMT of cancer development has been well reported. Disrupting the diacetylated Twist represses the progression of Basal-like breast cancer.29 The activation of Twist promotes progression and EMT of breast cancer.30 The inhibition of Twist limits the stem cell features and EMT of prostate cancer.31 Twist serves as a critical factor in promoting metastasis of pancreatic cancer.32 Besides, tumor necrosis factor α provokes EMT of hypopharyngeal cancer and induces metastasis through NF-κB signaling-regulated expression of Twist.33 NF-κB/Twist signaling is involved in the mechanism of Chysin inhibiting stem cell characteristics of ovarian cancer.34 Repression of the NF-κB/Twist axis decreases the stemness features of lung cancer stem cell.35 Down-regulation of TWIST reduces invasion and migration of laryngeal carcinoma cells by controlling the expression of N-cadherin and E-cadherin.36 The expression of Twist displays the clinical significance of laryngeal cancer.37 However, whether NF-κB and Twist are involved in the biomedical activities of MI is unclear.\nIn this study, we aimed to explore the function of MI in the development and EMT process of laryngeal cancer. We identified a novel inhibitory effect of MI in the progression and EMT of laryngeal cancer by regulating the NF-κB/Twist signaling."},"methods_title":{"kind":"null"},"methods_text":{"kind":"null"},"results_title":{"kind":"null"},"results_text":{"kind":"null"},"conclusions_title":{"kind":"string","value":"Conclusion"},"conclusions_text":{"kind":"string","value":"In conclusion, we discovered that magnesium isoglycyrrhizinate attenuated the progression of laryngeal cancer in vitro and in vivo. Magnesium isoglycyrrhizinate induced an inhibitory effect on epithelial–mesenchymal transition in laryngeal cancer by modulating the NF-κB/Twist signaling. Our findings provide new insights into the mechanism by which magnesium isoglycyrrhizinate inhibits laryngeal carcinoma development. Magnesium isoglycyrrhizinate may be applied as a potential anti-tumor candidate for laryngeal cancer in clinical treatment strategy."},"other_sections_titles":{"kind":"list like","value":["Methods","Laryngeal Cancer Clinical Samples","Cell Culture and Treatment","Quantitative Reverse Transcription-PCR (qRT-PCR)","MTT Assays","EdU Assays","Colony Formation Assay","Transwell Assays","Analysis of Cell Apoptosis","Luciferase Reporter Gene Assay","Western Blot Analysis","Analysis of Tumorigenicity in Nude Mice","Statistical Analysis","Results","Twist is Potentially Correlated with the Progression and Poor Prognosis of Laryngeal Cancer","Magnesium Isoglycyrrhizinate (MI) Attenuates Cell Proliferation of Laryngeal Cancer in vitro","MI Inhibits Laryngeal Cancer Cell Migration and Invasion and Enhances Cell Apoptosis in vitro","MI Inhibits Transcriptional Activation and the Expression of NF-κB and Twist and Alleviates EMT in Laryngeal Cancer Cells","MI Inhibits Tumor Growth and EMT of Laryngeal Cancer via the Twist/NF-κB Signaling in vivo","Discussion","Conclusion"],"string":"[\n \"Methods\",\n \"Laryngeal Cancer Clinical Samples\",\n \"Cell Culture and Treatment\",\n \"Quantitative Reverse Transcription-PCR (qRT-PCR)\",\n \"MTT Assays\",\n \"EdU Assays\",\n \"Colony Formation Assay\",\n \"Transwell Assays\",\n \"Analysis of Cell Apoptosis\",\n \"Luciferase Reporter Gene Assay\",\n \"Western Blot Analysis\",\n \"Analysis of Tumorigenicity in Nude Mice\",\n \"Statistical Analysis\",\n \"Results\",\n \"Twist is Potentially Correlated with the Progression and Poor Prognosis of Laryngeal Cancer\",\n \"Magnesium Isoglycyrrhizinate (MI) Attenuates Cell Proliferation of Laryngeal Cancer in vitro\",\n \"MI Inhibits Laryngeal Cancer Cell Migration and Invasion and Enhances Cell Apoptosis in vitro\",\n \"MI Inhibits Transcriptional Activation and the Expression of NF-κB and Twist and Alleviates EMT in Laryngeal Cancer Cells\",\n \"MI Inhibits Tumor Growth and EMT of Laryngeal Cancer via the Twist/NF-κB Signaling in vivo\",\n \"Discussion\",\n \"Conclusion\"\n]"},"other_sections_texts":{"kind":"list like","value":[" Laryngeal Cancer Clinical Samples A total of 40 laryngeal cancer clinical samples used in this study were obtained from the Second Affiliated Hospital, Harbin Medical University between June 2016 and August 2018. All the patients were diagnosed by clinical, radiographic, and histopathological analysis. Before surgery, no systemic or local therapy was performed on the subjects. The laryngeal cancer tissues (n = 40) and adjacent normal tissues (n = 40) obtained from the patients were immediately frozen into liquid nitrogen and stored at −80 °C before use. The clinical laryngeal cancer samples were separated into two groups according to the mean expression of Twist. The overall survival was analyzed by Kaplan–Meier survival analysis. The patients and healthy controls provided written informed consent, and that the Ethics Committee of The Second Affiliated Hospital, Harbin Medical University approved this study.\nA total of 40 laryngeal cancer clinical samples used in this study were obtained from the Second Affiliated Hospital, Harbin Medical University between June 2016 and August 2018. All the patients were diagnosed by clinical, radiographic, and histopathological analysis. Before surgery, no systemic or local therapy was performed on the subjects. The laryngeal cancer tissues (n = 40) and adjacent normal tissues (n = 40) obtained from the patients were immediately frozen into liquid nitrogen and stored at −80 °C before use. The clinical laryngeal cancer samples were separated into two groups according to the mean expression of Twist. The overall survival was analyzed by Kaplan–Meier survival analysis. The patients and healthy controls provided written informed consent, and that the Ethics Committee of The Second Affiliated Hospital, Harbin Medical University approved this study.\n Cell Culture and Treatment The human laryngeal carcinoma cells HEP-2 and primary laryngeal epithelial cells LEC-P were purchased from American Type Tissue Culture Collection. Cells were cultured in DMEM (Solarbio, China) containing 10% fetal bovine serum (Gibco, USA), 0.1 mg/mL streptomycin (Solarbio, China) and 100 units/mL penicillin (Solarbio, China) at 37 °C with 5% CO2. The Magnesium isoglycyrrhizinate (MI) (purity > 98%) was obtained from Zhengda Tianqing Pharmaceutical Co., Ltd (Jiangsu, China). Cells were treated with MI of indicated dose before further analysis.\nThe human laryngeal carcinoma cells HEP-2 and primary laryngeal epithelial cells LEC-P were purchased from American Type Tissue Culture Collection. Cells were cultured in DMEM (Solarbio, China) containing 10% fetal bovine serum (Gibco, USA), 0.1 mg/mL streptomycin (Solarbio, China) and 100 units/mL penicillin (Solarbio, China) at 37 °C with 5% CO2. The Magnesium isoglycyrrhizinate (MI) (purity > 98%) was obtained from Zhengda Tianqing Pharmaceutical Co., Ltd (Jiangsu, China). Cells were treated with MI of indicated dose before further analysis.\n Quantitative Reverse Transcription-PCR (qRT-PCR) Total RNAs were extracted using TRIZOL (Invitrogen, USA), followed by reverse transcription into cDNA. The qRT-PCR reactions were prepared using the SYBR Real-time PCR I kit (Takara, Japan). GAPDH was used as the internal control. The qRT-PCR experiment was conducted in triplicate. The primer sequences were as follows:\nTwist forward: 5′-CGCTGAACGAGGCATTTGC-3′\nTwist reverse: 5′-CCAGTTTGAGGGTCTGAATC-3′\nSlug forward: 5′-GTGTTTGCAAGATCTGCGGC-3′\nSlug reverse: 5′-GCAGATGAGCCCTCAGATTTGA-3′\nZEB1 forward: 5′-CCCCAGGTGTAAGCGCAGAA-3′\nZEB1 reverse: 5′-TGGCAGGTCATCCTCTGGTACAC-3′\nSnail forward: 5′-CCACACTGGTGAGAAGCCTTTC-3′\nSnail reverse: 5′-GTCTGGAGGTGGGCACGTA-3′\nGAPDH forward: 5′-AAGAAGGTGGTGAAGCAGGC-3′.\nGAPDH reverse: 5′-TCCACCACCCAGTTGCTGTA-3′\nTotal RNAs were extracted using TRIZOL (Invitrogen, USA), followed by reverse transcription into cDNA. The qRT-PCR reactions were prepared using the SYBR Real-time PCR I kit (Takara, Japan). GAPDH was used as the internal control. The qRT-PCR experiment was conducted in triplicate. The primer sequences were as follows:\nTwist forward: 5′-CGCTGAACGAGGCATTTGC-3′\nTwist reverse: 5′-CCAGTTTGAGGGTCTGAATC-3′\nSlug forward: 5′-GTGTTTGCAAGATCTGCGGC-3′\nSlug reverse: 5′-GCAGATGAGCCCTCAGATTTGA-3′\nZEB1 forward: 5′-CCCCAGGTGTAAGCGCAGAA-3′\nZEB1 reverse: 5′-TGGCAGGTCATCCTCTGGTACAC-3′\nSnail forward: 5′-CCACACTGGTGAGAAGCCTTTC-3′\nSnail reverse: 5′-GTCTGGAGGTGGGCACGTA-3′\nGAPDH forward: 5′-AAGAAGGTGGTGAAGCAGGC-3′.\nGAPDH reverse: 5′-TCCACCACCCAGTTGCTGTA-3′\n MTT Assays MTT assays were conducted to measure cell viability of HEP-2 cells. Briefly, about 1 × 104 HEP-2 cells were put into 96 wells and cultured for 12 h. Cells were then added with 10 μL MTT solution (5 mg/mL) (Sigma, USA) and cultured for another 4 h. The culture medium was discarded, and 150 μL/well DMSO (Thermo, USA) was added to the wells. An ELISA browser was used to analyze the absorbance at 570 nm (Bio-Tek EL 800, USA).\nMTT assays were conducted to measure cell viability of HEP-2 cells. Briefly, about 1 × 104 HEP-2 cells were put into 96 wells and cultured for 12 h. Cells were then added with 10 μL MTT solution (5 mg/mL) (Sigma, USA) and cultured for another 4 h. The culture medium was discarded, and 150 μL/well DMSO (Thermo, USA) was added to the wells. An ELISA browser was used to analyze the absorbance at 570 nm (Bio-Tek EL 800, USA).\n EdU Assays The cell proliferation was analyzed by EdU assays using EdU detecting kit (RiboBio, China). Briefly, HEP-2 cells were cultured with EdU for 2 h, followed by fixation with 4% paraformaldehyde at room temperature for 30 min. Then, cells were permeabilized with 0.4% Triton X-100 for 10 min and stained with staining cocktail of EdU at room temperature for 30 min in the dark. Next, nuclear of the cells was stained with Hoechst at room temperature for 30 min. Images were analyzed using a fluorescence microscope.\nThe cell proliferation was analyzed by EdU assays using EdU detecting kit (RiboBio, China). Briefly, HEP-2 cells were cultured with EdU for 2 h, followed by fixation with 4% paraformaldehyde at room temperature for 30 min. Then, cells were permeabilized with 0.4% Triton X-100 for 10 min and stained with staining cocktail of EdU at room temperature for 30 min in the dark. Next, nuclear of the cells was stained with Hoechst at room temperature for 30 min. Images were analyzed using a fluorescence microscope.\n Colony Formation Assay About 1 × 103 HEP-2 cells were layered in 6 wells and incubated in DMEM at 37 °C. After two weeks, cells were cleaned with PBS Buffer, made in methanol for 30 min, and dyed with 1% crystal violet. The number of colonies was then calculated.\nAbout 1 × 103 HEP-2 cells were layered in 6 wells and incubated in DMEM at 37 °C. After two weeks, cells were cleaned with PBS Buffer, made in methanol for 30 min, and dyed with 1% crystal violet. The number of colonies was then calculated.\n Transwell Assays Transwell assays were conducted to evaluate the impacts of MI on cell invasion and migration of HEP-2 cells using a Transwell plate (Corning, USA). Briefly, the upper chambers were plated with about 1 × 105 cells. Cells were then solidified using 4% paraformaldehyde and dyed with crystal violet. The invaded and migrated cells were recorded and calculated.\nTranswell assays were conducted to evaluate the impacts of MI on cell invasion and migration of HEP-2 cells using a Transwell plate (Corning, USA). Briefly, the upper chambers were plated with about 1 × 105 cells. Cells were then solidified using 4% paraformaldehyde and dyed with crystal violet. The invaded and migrated cells were recorded and calculated.\n Analysis of Cell Apoptosis Approximately 2 × 105 HEP-2 cells were plated on 6-well dishes. Cell apoptosis was detected using the Annexin V-FITC Apoptosis Detection Kit (CST, USA) following the manufacturer’s instructions. Briefly, cells were collected and washed with binding buffer (BD Biosciences, USA), and then dyed at 25 °C, followed by flow cytometry analysis.\nApproximately 2 × 105 HEP-2 cells were plated on 6-well dishes. Cell apoptosis was detected using the Annexin V-FITC Apoptosis Detection Kit (CST, USA) following the manufacturer’s instructions. Briefly, cells were collected and washed with binding buffer (BD Biosciences, USA), and then dyed at 25 °C, followed by flow cytometry analysis.\n Luciferase Reporter Gene Assay Luciferase reporter gene assay was performed using the Dual-luciferase Reporter Assay System (Promega, USA). Briefly, cells were treated MI as indicated dose, followed by transfection with pGL3-NF-κB and pGL3-Twist using Lipofectamine 3000 (Invitrogen, USA). Luciferase activities were detected and Renilla was used as a normalized control.\nLuciferase reporter gene assay was performed using the Dual-luciferase Reporter Assay System (Promega, USA). Briefly, cells were treated MI as indicated dose, followed by transfection with pGL3-NF-κB and pGL3-Twist using Lipofectamine 3000 (Invitrogen, USA). Luciferase activities were detected and Renilla was used as a normalized control.\n Western Blot Analysis Total proteins were extracted from cells or tumor tissues using RIPA buffer (CST, USA). Protein concentrations were measured using the BCA Protein Quantification Kit (Abbkine, USA). Same amount of protein samples was separated by SDS-PAGE (12% polyacrylamide gels), transferred to PVDF membranes (Millipore, USA) in the subsequent step. The membranes were blocked with 5% milk and incubated with the primary antibodies for Twist (1:1000) (Abcam, UK), E-Cadherin (1:1000) (Abcam, UK), occluding (1:1000) (Abcam, UK), vimentin (1:1000) (Abcam, UK), N-cadherin (1:1000) (Abcam, UK), Ki-67 (1:1000) (Abcam, UK), IKK (1:1000) (Abcam, UK), NF-κB p65 (1:1000) (Abcam, UK), NF-κB (1:1000) (Abcam, UK), and β-actin (1:1000) (Abcam, UK) at 4 °C overnight, in which β-actin served as the control. Then, the corresponding second antibodies (1:1000) (Abcam, UK) were used to incubate the membranes at room temperature for 1 h, followed by visualization using an Odyssey CLx Infrared Imaging System. ImageJ software was used to quantify the results.\nTotal proteins were extracted from cells or tumor tissues using RIPA buffer (CST, USA). Protein concentrations were measured using the BCA Protein Quantification Kit (Abbkine, USA). Same amount of protein samples was separated by SDS-PAGE (12% polyacrylamide gels), transferred to PVDF membranes (Millipore, USA) in the subsequent step. The membranes were blocked with 5% milk and incubated with the primary antibodies for Twist (1:1000) (Abcam, UK), E-Cadherin (1:1000) (Abcam, UK), occluding (1:1000) (Abcam, UK), vimentin (1:1000) (Abcam, UK), N-cadherin (1:1000) (Abcam, UK), Ki-67 (1:1000) (Abcam, UK), IKK (1:1000) (Abcam, UK), NF-κB p65 (1:1000) (Abcam, UK), NF-κB (1:1000) (Abcam, UK), and β-actin (1:1000) (Abcam, UK) at 4 °C overnight, in which β-actin served as the control. Then, the corresponding second antibodies (1:1000) (Abcam, UK) were used to incubate the membranes at room temperature for 1 h, followed by visualization using an Odyssey CLx Infrared Imaging System. ImageJ software was used to quantify the results.\n Analysis of Tumorigenicity in Nude Mice The effect of MI on tumor growth in vivo was analyzed in nude mice of Balb/c. Mice were randomly separated into two groups (n = 3). To establish the in vivo tumor model, HEP-2 cells were treated with MI (300 mg/kg) or equal volume of saline. And about 2 × 106 cells were subcutaneously injected into mice. After 7 days of injection, tumor growth was measured every 7 days. The mice were sacrificed after 35 days of injection and tumors were scaled. Tumor volume (V) was observed by estimating the length (L) and width (W) with calipers and measured with the formula (L ×W2) × 0.5. The expression levels of Ki-67 of tumor tissues were measured by immunohistochemical staining with the Ki67 antibody (1:1000) (Abcam, UK). Protein expression levels in tumor tissues were determined by Western blot analysis using Twist (1:1000) (Abcam, UK), E-Cadherin (1:1000) (Abcam, UK), occluding (1:1000) (Abcam, UK), vimentin (1:1000) (Abcam, UK), N-cadherin (1:1000) (Abcam, UK), Ki-67 (1:1000) (Abcam, UK), IKK (1:1000) (Abcam, UK), NF-κB p65 (1:1000) (Abcam, UK), NF-κB (1:1000) (Abcam, UK), and β-actin (1:1000) (Abcam, UK). Animal care and experimental procedures in this study were approved by the Animal Ethics Committee of the Second Affiliated Hospital, Harbin Medical University.\nThe effect of MI on tumor growth in vivo was analyzed in nude mice of Balb/c. Mice were randomly separated into two groups (n = 3). To establish the in vivo tumor model, HEP-2 cells were treated with MI (300 mg/kg) or equal volume of saline. And about 2 × 106 cells were subcutaneously injected into mice. After 7 days of injection, tumor growth was measured every 7 days. The mice were sacrificed after 35 days of injection and tumors were scaled. Tumor volume (V) was observed by estimating the length (L) and width (W) with calipers and measured with the formula (L ×W2) × 0.5. The expression levels of Ki-67 of tumor tissues were measured by immunohistochemical staining with the Ki67 antibody (1:1000) (Abcam, UK). Protein expression levels in tumor tissues were determined by Western blot analysis using Twist (1:1000) (Abcam, UK), E-Cadherin (1:1000) (Abcam, UK), occluding (1:1000) (Abcam, UK), vimentin (1:1000) (Abcam, UK), N-cadherin (1:1000) (Abcam, UK), Ki-67 (1:1000) (Abcam, UK), IKK (1:1000) (Abcam, UK), NF-κB p65 (1:1000) (Abcam, UK), NF-κB (1:1000) (Abcam, UK), and β-actin (1:1000) (Abcam, UK). Animal care and experimental procedures in this study were approved by the Animal Ethics Committee of the Second Affiliated Hospital, Harbin Medical University.\n Statistical Analysis Data were presented as mean ± SD, and statistical analysis was performed using SPSS software (version 18.0). The unpaired Student’s t-test was applied for comparing two groups, and one-way ANOVA was applied for comparing multiple groups. P < 0.05 was considered as statistically significant.\nData were presented as mean ± SD, and statistical analysis was performed using SPSS software (version 18.0). The unpaired Student’s t-test was applied for comparing two groups, and one-way ANOVA was applied for comparing multiple groups. P < 0.05 was considered as statistically significant.","A total of 40 laryngeal cancer clinical samples used in this study were obtained from the Second Affiliated Hospital, Harbin Medical University between June 2016 and August 2018. All the patients were diagnosed by clinical, radiographic, and histopathological analysis. Before surgery, no systemic or local therapy was performed on the subjects. The laryngeal cancer tissues (n = 40) and adjacent normal tissues (n = 40) obtained from the patients were immediately frozen into liquid nitrogen and stored at −80 °C before use. The clinical laryngeal cancer samples were separated into two groups according to the mean expression of Twist. The overall survival was analyzed by Kaplan–Meier survival analysis. The patients and healthy controls provided written informed consent, and that the Ethics Committee of The Second Affiliated Hospital, Harbin Medical University approved this study.","The human laryngeal carcinoma cells HEP-2 and primary laryngeal epithelial cells LEC-P were purchased from American Type Tissue Culture Collection. Cells were cultured in DMEM (Solarbio, China) containing 10% fetal bovine serum (Gibco, USA), 0.1 mg/mL streptomycin (Solarbio, China) and 100 units/mL penicillin (Solarbio, China) at 37 °C with 5% CO2. The Magnesium isoglycyrrhizinate (MI) (purity > 98%) was obtained from Zhengda Tianqing Pharmaceutical Co., Ltd (Jiangsu, China). Cells were treated with MI of indicated dose before further analysis.","Total RNAs were extracted using TRIZOL (Invitrogen, USA), followed by reverse transcription into cDNA. The qRT-PCR reactions were prepared using the SYBR Real-time PCR I kit (Takara, Japan). GAPDH was used as the internal control. The qRT-PCR experiment was conducted in triplicate. The primer sequences were as follows:\nTwist forward: 5′-CGCTGAACGAGGCATTTGC-3′\nTwist reverse: 5′-CCAGTTTGAGGGTCTGAATC-3′\nSlug forward: 5′-GTGTTTGCAAGATCTGCGGC-3′\nSlug reverse: 5′-GCAGATGAGCCCTCAGATTTGA-3′\nZEB1 forward: 5′-CCCCAGGTGTAAGCGCAGAA-3′\nZEB1 reverse: 5′-TGGCAGGTCATCCTCTGGTACAC-3′\nSnail forward: 5′-CCACACTGGTGAGAAGCCTTTC-3′\nSnail reverse: 5′-GTCTGGAGGTGGGCACGTA-3′\nGAPDH forward: 5′-AAGAAGGTGGTGAAGCAGGC-3′.\nGAPDH reverse: 5′-TCCACCACCCAGTTGCTGTA-3′","MTT assays were conducted to measure cell viability of HEP-2 cells. Briefly, about 1 × 104 HEP-2 cells were put into 96 wells and cultured for 12 h. Cells were then added with 10 μL MTT solution (5 mg/mL) (Sigma, USA) and cultured for another 4 h. The culture medium was discarded, and 150 μL/well DMSO (Thermo, USA) was added to the wells. An ELISA browser was used to analyze the absorbance at 570 nm (Bio-Tek EL 800, USA).","The cell proliferation was analyzed by EdU assays using EdU detecting kit (RiboBio, China). Briefly, HEP-2 cells were cultured with EdU for 2 h, followed by fixation with 4% paraformaldehyde at room temperature for 30 min. Then, cells were permeabilized with 0.4% Triton X-100 for 10 min and stained with staining cocktail of EdU at room temperature for 30 min in the dark. Next, nuclear of the cells was stained with Hoechst at room temperature for 30 min. Images were analyzed using a fluorescence microscope.","About 1 × 103 HEP-2 cells were layered in 6 wells and incubated in DMEM at 37 °C. After two weeks, cells were cleaned with PBS Buffer, made in methanol for 30 min, and dyed with 1% crystal violet. The number of colonies was then calculated.","Transwell assays were conducted to evaluate the impacts of MI on cell invasion and migration of HEP-2 cells using a Transwell plate (Corning, USA). Briefly, the upper chambers were plated with about 1 × 105 cells. Cells were then solidified using 4% paraformaldehyde and dyed with crystal violet. The invaded and migrated cells were recorded and calculated.","Approximately 2 × 105 HEP-2 cells were plated on 6-well dishes. Cell apoptosis was detected using the Annexin V-FITC Apoptosis Detection Kit (CST, USA) following the manufacturer’s instructions. Briefly, cells were collected and washed with binding buffer (BD Biosciences, USA), and then dyed at 25 °C, followed by flow cytometry analysis.","Luciferase reporter gene assay was performed using the Dual-luciferase Reporter Assay System (Promega, USA). Briefly, cells were treated MI as indicated dose, followed by transfection with pGL3-NF-κB and pGL3-Twist using Lipofectamine 3000 (Invitrogen, USA). Luciferase activities were detected and Renilla was used as a normalized control.","Total proteins were extracted from cells or tumor tissues using RIPA buffer (CST, USA). Protein concentrations were measured using the BCA Protein Quantification Kit (Abbkine, USA). Same amount of protein samples was separated by SDS-PAGE (12% polyacrylamide gels), transferred to PVDF membranes (Millipore, USA) in the subsequent step. The membranes were blocked with 5% milk and incubated with the primary antibodies for Twist (1:1000) (Abcam, UK), E-Cadherin (1:1000) (Abcam, UK), occluding (1:1000) (Abcam, UK), vimentin (1:1000) (Abcam, UK), N-cadherin (1:1000) (Abcam, UK), Ki-67 (1:1000) (Abcam, UK), IKK (1:1000) (Abcam, UK), NF-κB p65 (1:1000) (Abcam, UK), NF-κB (1:1000) (Abcam, UK), and β-actin (1:1000) (Abcam, UK) at 4 °C overnight, in which β-actin served as the control. Then, the corresponding second antibodies (1:1000) (Abcam, UK) were used to incubate the membranes at room temperature for 1 h, followed by visualization using an Odyssey CLx Infrared Imaging System. ImageJ software was used to quantify the results.","The effect of MI on tumor growth in vivo was analyzed in nude mice of Balb/c. Mice were randomly separated into two groups (n = 3). To establish the in vivo tumor model, HEP-2 cells were treated with MI (300 mg/kg) or equal volume of saline. And about 2 × 106 cells were subcutaneously injected into mice. After 7 days of injection, tumor growth was measured every 7 days. The mice were sacrificed after 35 days of injection and tumors were scaled. Tumor volume (V) was observed by estimating the length (L) and width (W) with calipers and measured with the formula (L ×W2) × 0.5. The expression levels of Ki-67 of tumor tissues were measured by immunohistochemical staining with the Ki67 antibody (1:1000) (Abcam, UK). Protein expression levels in tumor tissues were determined by Western blot analysis using Twist (1:1000) (Abcam, UK), E-Cadherin (1:1000) (Abcam, UK), occluding (1:1000) (Abcam, UK), vimentin (1:1000) (Abcam, UK), N-cadherin (1:1000) (Abcam, UK), Ki-67 (1:1000) (Abcam, UK), IKK (1:1000) (Abcam, UK), NF-κB p65 (1:1000) (Abcam, UK), NF-κB (1:1000) (Abcam, UK), and β-actin (1:1000) (Abcam, UK). Animal care and experimental procedures in this study were approved by the Animal Ethics Committee of the Second Affiliated Hospital, Harbin Medical University.","Data were presented as mean ± SD, and statistical analysis was performed using SPSS software (version 18.0). The unpaired Student’s t-test was applied for comparing two groups, and one-way ANOVA was applied for comparing multiple groups. P < 0.05 was considered as statistically significant."," Twist is Potentially Correlated with the Progression and Poor Prognosis of Laryngeal Cancer To assess the potential correlation of EMT-related transcription factors with laryngeal cancer, the expression of Twist, Slug, ZEB1, and Snail in the clinical laryngeal samples and laryngeal cells were measured by qPCR assays. The data showed that the expression levels of Twist, Slug, ZEB1, and Snail were significantly elevated in laryngeal cancer tissues (n = 40) compared to that in normal tissues (n = 40), among which Twist displayed the highest expression levels (P < 0.01) (Figure 1A). Meanwhile, the expression levels of Twist, Slug, ZEB1, and Snail were also enhanced in human laryngeal carcinoma HEP-2 cells compared with that in primary laryngeal epithelial cells (LEC-P), and Twist exerted the highest expression levels among these transcription factors (P < 0.001) (Figure 1B), implying that Twist is closely associated with the development of laryngeal cancer. To determine whether Twist was able to serve as the potential biomarker for patients with laryngeal cancer, we separated the clinical laryngeal cancer samples into two groups according to the mean expression of Twist. We observed that high expression levels of Twist was remarkably correlated with the poor overall survival (P < 0.01) (Figure 1C), suggesting that Twist may play a crucial role in the progression of laryngeal cancer.Figure 1Twist is potentially correlated with the progression and poor prognosis of laryngeal cancer. (A) The expression levels of Twist, Slug, ZEB1, and Snail were measured by qPCR in the laryngeal cancer tissues (n = 40) and adjacent normal tissues (n = 40). (B) The expression levels of Twist, Slug, ZEB1, and Snail were assessed by qPCR in HEP-2 cells and primary laryngeal epithelial cells. (C) The clinical laryngeal cancer samples were separated into two groups according to the mean expression of Twist. The overall survival was analyzed by Kaplan-Meier survival analysis. Data are presented as mean ± SD. Statistic significant differences were indicated: ** P < 0.01, *** P < 0.001.\nTwist is potentially correlated with the progression and poor prognosis of laryngeal cancer. (A) The expression levels of Twist, Slug, ZEB1, and Snail were measured by qPCR in the laryngeal cancer tissues (n = 40) and adjacent normal tissues (n = 40). (B) The expression levels of Twist, Slug, ZEB1, and Snail were assessed by qPCR in HEP-2 cells and primary laryngeal epithelial cells. (C) The clinical laryngeal cancer samples were separated into two groups according to the mean expression of Twist. The overall survival was analyzed by Kaplan-Meier survival analysis. Data are presented as mean ± SD. Statistic significant differences were indicated: ** P < 0.01, *** P < 0.001.\nTo assess the potential correlation of EMT-related transcription factors with laryngeal cancer, the expression of Twist, Slug, ZEB1, and Snail in the clinical laryngeal samples and laryngeal cells were measured by qPCR assays. The data showed that the expression levels of Twist, Slug, ZEB1, and Snail were significantly elevated in laryngeal cancer tissues (n = 40) compared to that in normal tissues (n = 40), among which Twist displayed the highest expression levels (P < 0.01) (Figure 1A). Meanwhile, the expression levels of Twist, Slug, ZEB1, and Snail were also enhanced in human laryngeal carcinoma HEP-2 cells compared with that in primary laryngeal epithelial cells (LEC-P), and Twist exerted the highest expression levels among these transcription factors (P < 0.001) (Figure 1B), implying that Twist is closely associated with the development of laryngeal cancer. To determine whether Twist was able to serve as the potential biomarker for patients with laryngeal cancer, we separated the clinical laryngeal cancer samples into two groups according to the mean expression of Twist. We observed that high expression levels of Twist was remarkably correlated with the poor overall survival (P < 0.01) (Figure 1C), suggesting that Twist may play a crucial role in the progression of laryngeal cancer.Figure 1Twist is potentially correlated with the progression and poor prognosis of laryngeal cancer. (A) The expression levels of Twist, Slug, ZEB1, and Snail were measured by qPCR in the laryngeal cancer tissues (n = 40) and adjacent normal tissues (n = 40). (B) The expression levels of Twist, Slug, ZEB1, and Snail were assessed by qPCR in HEP-2 cells and primary laryngeal epithelial cells. (C) The clinical laryngeal cancer samples were separated into two groups according to the mean expression of Twist. The overall survival was analyzed by Kaplan-Meier survival analysis. Data are presented as mean ± SD. Statistic significant differences were indicated: ** P < 0.01, *** P < 0.001.\nTwist is potentially correlated with the progression and poor prognosis of laryngeal cancer. (A) The expression levels of Twist, Slug, ZEB1, and Snail were measured by qPCR in the laryngeal cancer tissues (n = 40) and adjacent normal tissues (n = 40). (B) The expression levels of Twist, Slug, ZEB1, and Snail were assessed by qPCR in HEP-2 cells and primary laryngeal epithelial cells. (C) The clinical laryngeal cancer samples were separated into two groups according to the mean expression of Twist. The overall survival was analyzed by Kaplan-Meier survival analysis. Data are presented as mean ± SD. Statistic significant differences were indicated: ** P < 0.01, *** P < 0.001.\n Magnesium Isoglycyrrhizinate (MI) Attenuates Cell Proliferation of Laryngeal Cancer in vitro Then, the role of MI in the modulation of laryngeal cancer proliferation was investigated, and the structure formula of MI is shown in Figure 2A. To evaluate the effect of MI on the progression of laryngeal cancer, MTT assay, colony formation assay, and EdU assay were performed in HEP-2 cells treated with MI. MTT assay demonstrated that MI significantly inhibited cell viability in a dose-dependent manner, and the half-maximal inhibitory concentrations (IC50) of MI in HEP-2 cells was 3.22 mg/mL (P < 0.01) (Figure 2B). Hence, we selected the MI dose of 3.22 mg/mL in the following experiments. Similarly, colony formation assay showed that the colony numbers of HEP-2 cells were remarkably reduced by MI treatment (P < 0.01) (Figure 2C). Besides, EdU assay showed that MI notably decreased number of EdU-positive cells (P < 0.01) (Figure 2D). These data suggested that MI was able to inhibit cell proliferation of laryngeal cancer.Figure 2Magnesium isoglycyrrhizinate (MI) attenuates cell proliferation of laryngeal cancer in vitro. (A) The structure formula of MI was shown. (B) The cell viability was analyzed by MTT assays in the HEP-2 cells treated with MI at indicated dosage. (C) The cell proliferation was measured by colony formation assays in the HEP-2 cells treated with MI at indicated dosage. (D) The cell proliferation was examined by EdU assays in the HEP-2 cells treated with MI at indicated dosage. Data are presented as mean ± SD. Statistic significant differences were indicated: ** P < 0.01.\nMagnesium isoglycyrrhizinate (MI) attenuates cell proliferation of laryngeal cancer in vitro. (A) The structure formula of MI was shown. (B) The cell viability was analyzed by MTT assays in the HEP-2 cells treated with MI at indicated dosage. (C) The cell proliferation was measured by colony formation assays in the HEP-2 cells treated with MI at indicated dosage. (D) The cell proliferation was examined by EdU assays in the HEP-2 cells treated with MI at indicated dosage. Data are presented as mean ± SD. Statistic significant differences were indicated: ** P < 0.01.\nThen, the role of MI in the modulation of laryngeal cancer proliferation was investigated, and the structure formula of MI is shown in Figure 2A. To evaluate the effect of MI on the progression of laryngeal cancer, MTT assay, colony formation assay, and EdU assay were performed in HEP-2 cells treated with MI. MTT assay demonstrated that MI significantly inhibited cell viability in a dose-dependent manner, and the half-maximal inhibitory concentrations (IC50) of MI in HEP-2 cells was 3.22 mg/mL (P < 0.01) (Figure 2B). Hence, we selected the MI dose of 3.22 mg/mL in the following experiments. Similarly, colony formation assay showed that the colony numbers of HEP-2 cells were remarkably reduced by MI treatment (P < 0.01) (Figure 2C). Besides, EdU assay showed that MI notably decreased number of EdU-positive cells (P < 0.01) (Figure 2D). These data suggested that MI was able to inhibit cell proliferation of laryngeal cancer.Figure 2Magnesium isoglycyrrhizinate (MI) attenuates cell proliferation of laryngeal cancer in vitro. (A) The structure formula of MI was shown. (B) The cell viability was analyzed by MTT assays in the HEP-2 cells treated with MI at indicated dosage. (C) The cell proliferation was measured by colony formation assays in the HEP-2 cells treated with MI at indicated dosage. (D) The cell proliferation was examined by EdU assays in the HEP-2 cells treated with MI at indicated dosage. Data are presented as mean ± SD. Statistic significant differences were indicated: ** P < 0.01.\nMagnesium isoglycyrrhizinate (MI) attenuates cell proliferation of laryngeal cancer in vitro. (A) The structure formula of MI was shown. (B) The cell viability was analyzed by MTT assays in the HEP-2 cells treated with MI at indicated dosage. (C) The cell proliferation was measured by colony formation assays in the HEP-2 cells treated with MI at indicated dosage. (D) The cell proliferation was examined by EdU assays in the HEP-2 cells treated with MI at indicated dosage. Data are presented as mean ± SD. Statistic significant differences were indicated: ** P < 0.01.\n MI Inhibits Laryngeal Cancer Cell Migration and Invasion and Enhances Cell Apoptosis in vitro The role of MI in HEP-2 cell migration, invasion, and apoptosis was then evaluated. Transwell assays revealed that MI treatment remarkably reduced cell migration (P < 0.01) (Figure 3A). Similarly, cell invasion was significantly decreased by MI in HEP-2 cells (P < 0.01) (Figure 3B). Moreover, flow cytometry analysis showed that cell apoptosis was notably increased by MI treatment in the system (P < 0.01) (Figure 3C). These results indicated that MI inhibited laryngeal cancer cell migration and invasion and enhanced cell apoptosis in vitro.Figure 3MI inhibits laryngeal cancer cell migration and invasion and enhances cell apoptosis in vitro. (A) The cell migration was examined by transwell assays in the HEP-2 cells treated with MI at indicated dosage. (B) The cell invasion was examined by transwell assays in the HEP-2 cells treated with MI at indicated dosage. (C) The cell apoptosis was measure by flow cytometry analysis in the HEP-2 cells treated with MI at indicated dosage. Data are presented as mean ± SD. Statistic significant differences were indicated: ** P < 0.01.\nMI inhibits laryngeal cancer cell migration and invasion and enhances cell apoptosis in vitro. (A) The cell migration was examined by transwell assays in the HEP-2 cells treated with MI at indicated dosage. (B) The cell invasion was examined by transwell assays in the HEP-2 cells treated with MI at indicated dosage. (C) The cell apoptosis was measure by flow cytometry analysis in the HEP-2 cells treated with MI at indicated dosage. Data are presented as mean ± SD. Statistic significant differences were indicated: ** P < 0.01.\nThe role of MI in HEP-2 cell migration, invasion, and apoptosis was then evaluated. Transwell assays revealed that MI treatment remarkably reduced cell migration (P < 0.01) (Figure 3A). Similarly, cell invasion was significantly decreased by MI in HEP-2 cells (P < 0.01) (Figure 3B). Moreover, flow cytometry analysis showed that cell apoptosis was notably increased by MI treatment in the system (P < 0.01) (Figure 3C). These results indicated that MI inhibited laryngeal cancer cell migration and invasion and enhanced cell apoptosis in vitro.Figure 3MI inhibits laryngeal cancer cell migration and invasion and enhances cell apoptosis in vitro. (A) The cell migration was examined by transwell assays in the HEP-2 cells treated with MI at indicated dosage. (B) The cell invasion was examined by transwell assays in the HEP-2 cells treated with MI at indicated dosage. (C) The cell apoptosis was measure by flow cytometry analysis in the HEP-2 cells treated with MI at indicated dosage. Data are presented as mean ± SD. Statistic significant differences were indicated: ** P < 0.01.\nMI inhibits laryngeal cancer cell migration and invasion and enhances cell apoptosis in vitro. (A) The cell migration was examined by transwell assays in the HEP-2 cells treated with MI at indicated dosage. (B) The cell invasion was examined by transwell assays in the HEP-2 cells treated with MI at indicated dosage. (C) The cell apoptosis was measure by flow cytometry analysis in the HEP-2 cells treated with MI at indicated dosage. Data are presented as mean ± SD. Statistic significant differences were indicated: ** P < 0.01.\n MI Inhibits Transcriptional Activation and the Expression of NF-κB and Twist and Alleviates EMT in Laryngeal Cancer Cells Next, the underlying mechanism of the effect of MI on the development of laryngeal cancer in HEP-2 cells was further explored. It showed that MI treatment significantly reduced the luciferase activities of NF-κB in the cells (P < 0.01) (Figure 4A), suggesting that MI may inhibit NF-κB at transcriptional level. Meanwhile, dual-luciferase reporter gene assays further revealed that MI treatment remarkably decreased the transcriptional activities of Twist in the cells (P < 0.01) (Figure 4B). Furthermore, Western blot analysis demonstrated that the total expression (Figure 4C) and nucleus expression (Figure 4D) of NF-κB and Twist were significantly down-regulated by MI in HEP-2 cells (P < 0.01), suggesting that MI may inhibit Twist by modulating NF-κB. Moreover, to assess the effect of MI on the EMT of laryngeal cancer, the expression of EMT markers, including E-Cadherin, occluding, vimentin, and N-cadherin, in HEP-2 cells treated with MI were measured. Our data showed that the expression levels of E-Cadherin and occluding were enhanced while the expression levels of vimentin and N-cadherin were reduced by MI treatment in HEP-2 cells (P < 0.01) (Figure 4E), suggesting that MI can inhibit EMT of laryngeal cancer.Figure 4MI inhibits transcriptional activation and expression of NF-κB and Twist and alleviates EMT in laryngeal cancer cells. (A) The luciferase activities of NF-κB were determined by luciferase reporter gene assays in the HEP-2 cells treated with MI at indicated dosage. (B) The luciferase activities of Twist were determined by luciferase reporter gene assays in the HEP-2 cells treated with MI at indicated dosage. (C) The total expression of NF-κB, Twist, and β-actin was measured by Western blot analysis in the HEP-2 cells treated with MI at indicated dosage. The results of Western blot analysis were quantified by ImageJ software. (D) The nucleus expression of NF-κB, Twist, and histone H3 was tested by Western blot analysis in the HEP-2 cells treated with MI at indicated dosage. The results of Western blot analysis were quantified by ImageJ software. (E) The expression levels of E-Cadherin, occluding, vimentin, N-cadherin, and β-actin were analyzed by Western blot analysis in the HEP-2 cells treated with MI at indicated dosage. The results of Western blot analysis were quantified by ImageJ software. Data are presented as mean ± SD. Statistic significant differences were indicated: ** P < 0.01.\nMI inhibits transcriptional activation and expression of NF-κB and Twist and alleviates EMT in laryngeal cancer cells. (A) The luciferase activities of NF-κB were determined by luciferase reporter gene assays in the HEP-2 cells treated with MI at indicated dosage. (B) The luciferase activities of Twist were determined by luciferase reporter gene assays in the HEP-2 cells treated with MI at indicated dosage. (C) The total expression of NF-κB, Twist, and β-actin was measured by Western blot analysis in the HEP-2 cells treated with MI at indicated dosage. The results of Western blot analysis were quantified by ImageJ software. (D) The nucleus expression of NF-κB, Twist, and histone H3 was tested by Western blot analysis in the HEP-2 cells treated with MI at indicated dosage. The results of Western blot analysis were quantified by ImageJ software. (E) The expression levels of E-Cadherin, occluding, vimentin, N-cadherin, and β-actin were analyzed by Western blot analysis in the HEP-2 cells treated with MI at indicated dosage. The results of Western blot analysis were quantified by ImageJ software. Data are presented as mean ± SD. Statistic significant differences were indicated: ** P < 0.01.\nNext, the underlying mechanism of the effect of MI on the development of laryngeal cancer in HEP-2 cells was further explored. It showed that MI treatment significantly reduced the luciferase activities of NF-κB in the cells (P < 0.01) (Figure 4A), suggesting that MI may inhibit NF-κB at transcriptional level. Meanwhile, dual-luciferase reporter gene assays further revealed that MI treatment remarkably decreased the transcriptional activities of Twist in the cells (P < 0.01) (Figure 4B). Furthermore, Western blot analysis demonstrated that the total expression (Figure 4C) and nucleus expression (Figure 4D) of NF-κB and Twist were significantly down-regulated by MI in HEP-2 cells (P < 0.01), suggesting that MI may inhibit Twist by modulating NF-κB. Moreover, to assess the effect of MI on the EMT of laryngeal cancer, the expression of EMT markers, including E-Cadherin, occluding, vimentin, and N-cadherin, in HEP-2 cells treated with MI were measured. Our data showed that the expression levels of E-Cadherin and occluding were enhanced while the expression levels of vimentin and N-cadherin were reduced by MI treatment in HEP-2 cells (P < 0.01) (Figure 4E), suggesting that MI can inhibit EMT of laryngeal cancer.Figure 4MI inhibits transcriptional activation and expression of NF-κB and Twist and alleviates EMT in laryngeal cancer cells. (A) The luciferase activities of NF-κB were determined by luciferase reporter gene assays in the HEP-2 cells treated with MI at indicated dosage. (B) The luciferase activities of Twist were determined by luciferase reporter gene assays in the HEP-2 cells treated with MI at indicated dosage. (C) The total expression of NF-κB, Twist, and β-actin was measured by Western blot analysis in the HEP-2 cells treated with MI at indicated dosage. The results of Western blot analysis were quantified by ImageJ software. (D) The nucleus expression of NF-κB, Twist, and histone H3 was tested by Western blot analysis in the HEP-2 cells treated with MI at indicated dosage. The results of Western blot analysis were quantified by ImageJ software. (E) The expression levels of E-Cadherin, occluding, vimentin, N-cadherin, and β-actin were analyzed by Western blot analysis in the HEP-2 cells treated with MI at indicated dosage. The results of Western blot analysis were quantified by ImageJ software. Data are presented as mean ± SD. Statistic significant differences were indicated: ** P < 0.01.\nMI inhibits transcriptional activation and expression of NF-κB and Twist and alleviates EMT in laryngeal cancer cells. (A) The luciferase activities of NF-κB were determined by luciferase reporter gene assays in the HEP-2 cells treated with MI at indicated dosage. (B) The luciferase activities of Twist were determined by luciferase reporter gene assays in the HEP-2 cells treated with MI at indicated dosage. (C) The total expression of NF-κB, Twist, and β-actin was measured by Western blot analysis in the HEP-2 cells treated with MI at indicated dosage. The results of Western blot analysis were quantified by ImageJ software. (D) The nucleus expression of NF-κB, Twist, and histone H3 was tested by Western blot analysis in the HEP-2 cells treated with MI at indicated dosage. The results of Western blot analysis were quantified by ImageJ software. (E) The expression levels of E-Cadherin, occluding, vimentin, N-cadherin, and β-actin were analyzed by Western blot analysis in the HEP-2 cells treated with MI at indicated dosage. The results of Western blot analysis were quantified by ImageJ software. Data are presented as mean ± SD. Statistic significant differences were indicated: ** P < 0.01.\n MI Inhibits Tumor Growth and EMT of Laryngeal Cancer via the Twist/NF-κB Signaling in vivo The effect of MI on laryngeal cancer development was further investigated in vivo. Tumorigenicity analysis was conducted in nude mice injected with HEP-2 cells, which were treated with MI or corresponding control. The MI treatment significantly reduced tumor size (Figure 5A), tumor weight (P < 0.01) (Figure 5B), tumor volume (P < 0.01) (Figure 5C), and the expression levels of Ki-67 in tumor tissues of the mice (Figure 5D), suggesting that MI inhibited tumor growth of laryngeal cancer in vivo.Figure 5MI inhibits tumor growth of laryngeal cancer in vivo. (A–D) The effect of MI on tumor growth of laryngeal cancer in vivo was analyzed by tumorigenicity assay in nude mice. The HEP-2 cells were treated with MI (300 mg/kg) or equal volume saline and injected into the nude mice (n = 3). (A) Representative images of dissected tumors from nude mice were presented. (B) The average tumor weight was calculated and shown. (C) The average tumor volume was calculated and shown. (D) The expression levels of Ki-67 of the tumor tissues were measured by immunohistochemical staining. Data are presented as mean ± SD. Statistic significant differences were indicated: ** P < 0.01.\nMI inhibits tumor growth of laryngeal cancer in vivo. (A–D) The effect of MI on tumor growth of laryngeal cancer in vivo was analyzed by tumorigenicity assay in nude mice. The HEP-2 cells were treated with MI (300 mg/kg) or equal volume saline and injected into the nude mice (n = 3). (A) Representative images of dissected tumors from nude mice were presented. (B) The average tumor weight was calculated and shown. (C) The average tumor volume was calculated and shown. (D) The expression levels of Ki-67 of the tumor tissues were measured by immunohistochemical staining. Data are presented as mean ± SD. Statistic significant differences were indicated: ** P < 0.01.\nMoreover, our data showed that MI treatment significantly enhanced the expression levels of E-Cadherin and occluding, whereas reduced the expression levels of vimentin and N-cadherin in tumor tissues of the mice (P < 0.01) (Figure 6A and B), indicating that MI attenuated EMT of laryngeal cancer in vivo. Besides, the expression levels of Twist, Ki-67, and NF-κB signaling proteins containing p65 and IKK were significantly decreased by MI in tumor tissues of the mice (P < 0.01) (Figure 6C and D), implying that MI may inhibit EMT of laryngeal cancer via the Twist/NF-κB signaling.Figure 6MI inhibits EMT of laryngeal cancer via Twist/NF-κB signaling in vivo. (A–D) The effect of MI on tumor growth of laryngeal cancer in vivo was analyzed by nude mice tumorigenicity assay. The HEP-2 cells were treated with MI (300 mg/kg) or equal volume saline and injected into the nude mice (n = 3). (A) The expression levels of E-Cadherin, occluding, vimentin, N-cadherin, and β-actin were analyzed by Western blot analysis in the tumor tissues of the mice. (B) The results of Western blot analysis in (A) were quantified by ImageJ software. (C) The expression levels of NF-κB, NF-κB p65, Twist, Ki-67, IKK, and β-actin were measured by Western blot analysis in the tumor tissues of the mice. (D) The results of Western blot analysis in (C) were quantified by ImageJ software. Data are presented as mean ± SD. Statistic significant differences were indicated: ** P < 0.01.\nMI inhibits EMT of laryngeal cancer via Twist/NF-κB signaling in vivo. (A–D) The effect of MI on tumor growth of laryngeal cancer in vivo was analyzed by nude mice tumorigenicity assay. The HEP-2 cells were treated with MI (300 mg/kg) or equal volume saline and injected into the nude mice (n = 3). (A) The expression levels of E-Cadherin, occluding, vimentin, N-cadherin, and β-actin were analyzed by Western blot analysis in the tumor tissues of the mice. (B) The results of Western blot analysis in (A) were quantified by ImageJ software. (C) The expression levels of NF-κB, NF-κB p65, Twist, Ki-67, IKK, and β-actin were measured by Western blot analysis in the tumor tissues of the mice. (D) The results of Western blot analysis in (C) were quantified by ImageJ software. Data are presented as mean ± SD. Statistic significant differences were indicated: ** P < 0.01.\nThe effect of MI on laryngeal cancer development was further investigated in vivo. Tumorigenicity analysis was conducted in nude mice injected with HEP-2 cells, which were treated with MI or corresponding control. The MI treatment significantly reduced tumor size (Figure 5A), tumor weight (P < 0.01) (Figure 5B), tumor volume (P < 0.01) (Figure 5C), and the expression levels of Ki-67 in tumor tissues of the mice (Figure 5D), suggesting that MI inhibited tumor growth of laryngeal cancer in vivo.Figure 5MI inhibits tumor growth of laryngeal cancer in vivo. (A–D) The effect of MI on tumor growth of laryngeal cancer in vivo was analyzed by tumorigenicity assay in nude mice. The HEP-2 cells were treated with MI (300 mg/kg) or equal volume saline and injected into the nude mice (n = 3). (A) Representative images of dissected tumors from nude mice were presented. (B) The average tumor weight was calculated and shown. (C) The average tumor volume was calculated and shown. (D) The expression levels of Ki-67 of the tumor tissues were measured by immunohistochemical staining. Data are presented as mean ± SD. Statistic significant differences were indicated: ** P < 0.01.\nMI inhibits tumor growth of laryngeal cancer in vivo. (A–D) The effect of MI on tumor growth of laryngeal cancer in vivo was analyzed by tumorigenicity assay in nude mice. The HEP-2 cells were treated with MI (300 mg/kg) or equal volume saline and injected into the nude mice (n = 3). (A) Representative images of dissected tumors from nude mice were presented. (B) The average tumor weight was calculated and shown. (C) The average tumor volume was calculated and shown. (D) The expression levels of Ki-67 of the tumor tissues were measured by immunohistochemical staining. Data are presented as mean ± SD. Statistic significant differences were indicated: ** P < 0.01.\nMoreover, our data showed that MI treatment significantly enhanced the expression levels of E-Cadherin and occluding, whereas reduced the expression levels of vimentin and N-cadherin in tumor tissues of the mice (P < 0.01) (Figure 6A and B), indicating that MI attenuated EMT of laryngeal cancer in vivo. Besides, the expression levels of Twist, Ki-67, and NF-κB signaling proteins containing p65 and IKK were significantly decreased by MI in tumor tissues of the mice (P < 0.01) (Figure 6C and D), implying that MI may inhibit EMT of laryngeal cancer via the Twist/NF-κB signaling.Figure 6MI inhibits EMT of laryngeal cancer via Twist/NF-κB signaling in vivo. (A–D) The effect of MI on tumor growth of laryngeal cancer in vivo was analyzed by nude mice tumorigenicity assay. The HEP-2 cells were treated with MI (300 mg/kg) or equal volume saline and injected into the nude mice (n = 3). (A) The expression levels of E-Cadherin, occluding, vimentin, N-cadherin, and β-actin were analyzed by Western blot analysis in the tumor tissues of the mice. (B) The results of Western blot analysis in (A) were quantified by ImageJ software. (C) The expression levels of NF-κB, NF-κB p65, Twist, Ki-67, IKK, and β-actin were measured by Western blot analysis in the tumor tissues of the mice. (D) The results of Western blot analysis in (C) were quantified by ImageJ software. Data are presented as mean ± SD. Statistic significant differences were indicated: ** P < 0.01.\nMI inhibits EMT of laryngeal cancer via Twist/NF-κB signaling in vivo. (A–D) The effect of MI on tumor growth of laryngeal cancer in vivo was analyzed by nude mice tumorigenicity assay. The HEP-2 cells were treated with MI (300 mg/kg) or equal volume saline and injected into the nude mice (n = 3). (A) The expression levels of E-Cadherin, occluding, vimentin, N-cadherin, and β-actin were analyzed by Western blot analysis in the tumor tissues of the mice. (B) The results of Western blot analysis in (A) were quantified by ImageJ software. (C) The expression levels of NF-κB, NF-κB p65, Twist, Ki-67, IKK, and β-actin were measured by Western blot analysis in the tumor tissues of the mice. (D) The results of Western blot analysis in (C) were quantified by ImageJ software. Data are presented as mean ± SD. Statistic significant differences were indicated: ** P < 0.01.","To assess the potential correlation of EMT-related transcription factors with laryngeal cancer, the expression of Twist, Slug, ZEB1, and Snail in the clinical laryngeal samples and laryngeal cells were measured by qPCR assays. The data showed that the expression levels of Twist, Slug, ZEB1, and Snail were significantly elevated in laryngeal cancer tissues (n = 40) compared to that in normal tissues (n = 40), among which Twist displayed the highest expression levels (P < 0.01) (Figure 1A). Meanwhile, the expression levels of Twist, Slug, ZEB1, and Snail were also enhanced in human laryngeal carcinoma HEP-2 cells compared with that in primary laryngeal epithelial cells (LEC-P), and Twist exerted the highest expression levels among these transcription factors (P < 0.001) (Figure 1B), implying that Twist is closely associated with the development of laryngeal cancer. To determine whether Twist was able to serve as the potential biomarker for patients with laryngeal cancer, we separated the clinical laryngeal cancer samples into two groups according to the mean expression of Twist. We observed that high expression levels of Twist was remarkably correlated with the poor overall survival (P < 0.01) (Figure 1C), suggesting that Twist may play a crucial role in the progression of laryngeal cancer.Figure 1Twist is potentially correlated with the progression and poor prognosis of laryngeal cancer. (A) The expression levels of Twist, Slug, ZEB1, and Snail were measured by qPCR in the laryngeal cancer tissues (n = 40) and adjacent normal tissues (n = 40). (B) The expression levels of Twist, Slug, ZEB1, and Snail were assessed by qPCR in HEP-2 cells and primary laryngeal epithelial cells. (C) The clinical laryngeal cancer samples were separated into two groups according to the mean expression of Twist. The overall survival was analyzed by Kaplan-Meier survival analysis. Data are presented as mean ± SD. Statistic significant differences were indicated: ** P < 0.01, *** P < 0.001.\nTwist is potentially correlated with the progression and poor prognosis of laryngeal cancer. (A) The expression levels of Twist, Slug, ZEB1, and Snail were measured by qPCR in the laryngeal cancer tissues (n = 40) and adjacent normal tissues (n = 40). (B) The expression levels of Twist, Slug, ZEB1, and Snail were assessed by qPCR in HEP-2 cells and primary laryngeal epithelial cells. (C) The clinical laryngeal cancer samples were separated into two groups according to the mean expression of Twist. The overall survival was analyzed by Kaplan-Meier survival analysis. Data are presented as mean ± SD. Statistic significant differences were indicated: ** P < 0.01, *** P < 0.001.","Then, the role of MI in the modulation of laryngeal cancer proliferation was investigated, and the structure formula of MI is shown in Figure 2A. To evaluate the effect of MI on the progression of laryngeal cancer, MTT assay, colony formation assay, and EdU assay were performed in HEP-2 cells treated with MI. MTT assay demonstrated that MI significantly inhibited cell viability in a dose-dependent manner, and the half-maximal inhibitory concentrations (IC50) of MI in HEP-2 cells was 3.22 mg/mL (P < 0.01) (Figure 2B). Hence, we selected the MI dose of 3.22 mg/mL in the following experiments. Similarly, colony formation assay showed that the colony numbers of HEP-2 cells were remarkably reduced by MI treatment (P < 0.01) (Figure 2C). Besides, EdU assay showed that MI notably decreased number of EdU-positive cells (P < 0.01) (Figure 2D). These data suggested that MI was able to inhibit cell proliferation of laryngeal cancer.Figure 2Magnesium isoglycyrrhizinate (MI) attenuates cell proliferation of laryngeal cancer in vitro. (A) The structure formula of MI was shown. (B) The cell viability was analyzed by MTT assays in the HEP-2 cells treated with MI at indicated dosage. (C) The cell proliferation was measured by colony formation assays in the HEP-2 cells treated with MI at indicated dosage. (D) The cell proliferation was examined by EdU assays in the HEP-2 cells treated with MI at indicated dosage. Data are presented as mean ± SD. Statistic significant differences were indicated: ** P < 0.01.\nMagnesium isoglycyrrhizinate (MI) attenuates cell proliferation of laryngeal cancer in vitro. (A) The structure formula of MI was shown. (B) The cell viability was analyzed by MTT assays in the HEP-2 cells treated with MI at indicated dosage. (C) The cell proliferation was measured by colony formation assays in the HEP-2 cells treated with MI at indicated dosage. (D) The cell proliferation was examined by EdU assays in the HEP-2 cells treated with MI at indicated dosage. Data are presented as mean ± SD. Statistic significant differences were indicated: ** P < 0.01.","The role of MI in HEP-2 cell migration, invasion, and apoptosis was then evaluated. Transwell assays revealed that MI treatment remarkably reduced cell migration (P < 0.01) (Figure 3A). Similarly, cell invasion was significantly decreased by MI in HEP-2 cells (P < 0.01) (Figure 3B). Moreover, flow cytometry analysis showed that cell apoptosis was notably increased by MI treatment in the system (P < 0.01) (Figure 3C). These results indicated that MI inhibited laryngeal cancer cell migration and invasion and enhanced cell apoptosis in vitro.Figure 3MI inhibits laryngeal cancer cell migration and invasion and enhances cell apoptosis in vitro. (A) The cell migration was examined by transwell assays in the HEP-2 cells treated with MI at indicated dosage. (B) The cell invasion was examined by transwell assays in the HEP-2 cells treated with MI at indicated dosage. (C) The cell apoptosis was measure by flow cytometry analysis in the HEP-2 cells treated with MI at indicated dosage. Data are presented as mean ± SD. Statistic significant differences were indicated: ** P < 0.01.\nMI inhibits laryngeal cancer cell migration and invasion and enhances cell apoptosis in vitro. (A) The cell migration was examined by transwell assays in the HEP-2 cells treated with MI at indicated dosage. (B) The cell invasion was examined by transwell assays in the HEP-2 cells treated with MI at indicated dosage. (C) The cell apoptosis was measure by flow cytometry analysis in the HEP-2 cells treated with MI at indicated dosage. Data are presented as mean ± SD. Statistic significant differences were indicated: ** P < 0.01.","Next, the underlying mechanism of the effect of MI on the development of laryngeal cancer in HEP-2 cells was further explored. It showed that MI treatment significantly reduced the luciferase activities of NF-κB in the cells (P < 0.01) (Figure 4A), suggesting that MI may inhibit NF-κB at transcriptional level. Meanwhile, dual-luciferase reporter gene assays further revealed that MI treatment remarkably decreased the transcriptional activities of Twist in the cells (P < 0.01) (Figure 4B). Furthermore, Western blot analysis demonstrated that the total expression (Figure 4C) and nucleus expression (Figure 4D) of NF-κB and Twist were significantly down-regulated by MI in HEP-2 cells (P < 0.01), suggesting that MI may inhibit Twist by modulating NF-κB. Moreover, to assess the effect of MI on the EMT of laryngeal cancer, the expression of EMT markers, including E-Cadherin, occluding, vimentin, and N-cadherin, in HEP-2 cells treated with MI were measured. Our data showed that the expression levels of E-Cadherin and occluding were enhanced while the expression levels of vimentin and N-cadherin were reduced by MI treatment in HEP-2 cells (P < 0.01) (Figure 4E), suggesting that MI can inhibit EMT of laryngeal cancer.Figure 4MI inhibits transcriptional activation and expression of NF-κB and Twist and alleviates EMT in laryngeal cancer cells. (A) The luciferase activities of NF-κB were determined by luciferase reporter gene assays in the HEP-2 cells treated with MI at indicated dosage. (B) The luciferase activities of Twist were determined by luciferase reporter gene assays in the HEP-2 cells treated with MI at indicated dosage. (C) The total expression of NF-κB, Twist, and β-actin was measured by Western blot analysis in the HEP-2 cells treated with MI at indicated dosage. The results of Western blot analysis were quantified by ImageJ software. (D) The nucleus expression of NF-κB, Twist, and histone H3 was tested by Western blot analysis in the HEP-2 cells treated with MI at indicated dosage. The results of Western blot analysis were quantified by ImageJ software. (E) The expression levels of E-Cadherin, occluding, vimentin, N-cadherin, and β-actin were analyzed by Western blot analysis in the HEP-2 cells treated with MI at indicated dosage. The results of Western blot analysis were quantified by ImageJ software. Data are presented as mean ± SD. Statistic significant differences were indicated: ** P < 0.01.\nMI inhibits transcriptional activation and expression of NF-κB and Twist and alleviates EMT in laryngeal cancer cells. (A) The luciferase activities of NF-κB were determined by luciferase reporter gene assays in the HEP-2 cells treated with MI at indicated dosage. (B) The luciferase activities of Twist were determined by luciferase reporter gene assays in the HEP-2 cells treated with MI at indicated dosage. (C) The total expression of NF-κB, Twist, and β-actin was measured by Western blot analysis in the HEP-2 cells treated with MI at indicated dosage. The results of Western blot analysis were quantified by ImageJ software. (D) The nucleus expression of NF-κB, Twist, and histone H3 was tested by Western blot analysis in the HEP-2 cells treated with MI at indicated dosage. The results of Western blot analysis were quantified by ImageJ software. (E) The expression levels of E-Cadherin, occluding, vimentin, N-cadherin, and β-actin were analyzed by Western blot analysis in the HEP-2 cells treated with MI at indicated dosage. The results of Western blot analysis were quantified by ImageJ software. Data are presented as mean ± SD. Statistic significant differences were indicated: ** P < 0.01.","The effect of MI on laryngeal cancer development was further investigated in vivo. Tumorigenicity analysis was conducted in nude mice injected with HEP-2 cells, which were treated with MI or corresponding control. The MI treatment significantly reduced tumor size (Figure 5A), tumor weight (P < 0.01) (Figure 5B), tumor volume (P < 0.01) (Figure 5C), and the expression levels of Ki-67 in tumor tissues of the mice (Figure 5D), suggesting that MI inhibited tumor growth of laryngeal cancer in vivo.Figure 5MI inhibits tumor growth of laryngeal cancer in vivo. (A–D) The effect of MI on tumor growth of laryngeal cancer in vivo was analyzed by tumorigenicity assay in nude mice. The HEP-2 cells were treated with MI (300 mg/kg) or equal volume saline and injected into the nude mice (n = 3). (A) Representative images of dissected tumors from nude mice were presented. (B) The average tumor weight was calculated and shown. (C) The average tumor volume was calculated and shown. (D) The expression levels of Ki-67 of the tumor tissues were measured by immunohistochemical staining. Data are presented as mean ± SD. Statistic significant differences were indicated: ** P < 0.01.\nMI inhibits tumor growth of laryngeal cancer in vivo. (A–D) The effect of MI on tumor growth of laryngeal cancer in vivo was analyzed by tumorigenicity assay in nude mice. The HEP-2 cells were treated with MI (300 mg/kg) or equal volume saline and injected into the nude mice (n = 3). (A) Representative images of dissected tumors from nude mice were presented. (B) The average tumor weight was calculated and shown. (C) The average tumor volume was calculated and shown. (D) The expression levels of Ki-67 of the tumor tissues were measured by immunohistochemical staining. Data are presented as mean ± SD. Statistic significant differences were indicated: ** P < 0.01.\nMoreover, our data showed that MI treatment significantly enhanced the expression levels of E-Cadherin and occluding, whereas reduced the expression levels of vimentin and N-cadherin in tumor tissues of the mice (P < 0.01) (Figure 6A and B), indicating that MI attenuated EMT of laryngeal cancer in vivo. Besides, the expression levels of Twist, Ki-67, and NF-κB signaling proteins containing p65 and IKK were significantly decreased by MI in tumor tissues of the mice (P < 0.01) (Figure 6C and D), implying that MI may inhibit EMT of laryngeal cancer via the Twist/NF-κB signaling.Figure 6MI inhibits EMT of laryngeal cancer via Twist/NF-κB signaling in vivo. (A–D) The effect of MI on tumor growth of laryngeal cancer in vivo was analyzed by nude mice tumorigenicity assay. The HEP-2 cells were treated with MI (300 mg/kg) or equal volume saline and injected into the nude mice (n = 3). (A) The expression levels of E-Cadherin, occluding, vimentin, N-cadherin, and β-actin were analyzed by Western blot analysis in the tumor tissues of the mice. (B) The results of Western blot analysis in (A) were quantified by ImageJ software. (C) The expression levels of NF-κB, NF-κB p65, Twist, Ki-67, IKK, and β-actin were measured by Western blot analysis in the tumor tissues of the mice. (D) The results of Western blot analysis in (C) were quantified by ImageJ software. Data are presented as mean ± SD. Statistic significant differences were indicated: ** P < 0.01.\nMI inhibits EMT of laryngeal cancer via Twist/NF-κB signaling in vivo. (A–D) The effect of MI on tumor growth of laryngeal cancer in vivo was analyzed by nude mice tumorigenicity assay. The HEP-2 cells were treated with MI (300 mg/kg) or equal volume saline and injected into the nude mice (n = 3). (A) The expression levels of E-Cadherin, occluding, vimentin, N-cadherin, and β-actin were analyzed by Western blot analysis in the tumor tissues of the mice. (B) The results of Western blot analysis in (A) were quantified by ImageJ software. (C) The expression levels of NF-κB, NF-κB p65, Twist, Ki-67, IKK, and β-actin were measured by Western blot analysis in the tumor tissues of the mice. (D) The results of Western blot analysis in (C) were quantified by ImageJ software. Data are presented as mean ± SD. Statistic significant differences were indicated: ** P < 0.01.","Laryngeal cancer is the second most prevalent head and neck malignancy.38 Laryngeal cancer patients hold poor survival incidences and low prognosis, and many cases still suffer from recurrence.39 Although improvements have been made in chemotherapy, radiotherapy, and surgery, invasion and metastasis are still the principal reasons for laryngeal cancer-related mortality for patients with advanced grade.40 Searching for more safe and practical treatment candidates for the effective therapy of laryngeal cancer is of great importance.41 As a natural compound, MI has exerted practical potential in the medical application. It has been reported that MI represses cardiac hypertrophy by modulating the TLR4/NF-κB signaling in mice.42 MI preserves against triptolide-produced hepatotoxicity through activating the Nrf2 signaling.43 MI relieves fructose-mediated apoptosis of podocyte by down-regulation of miR-193a to enhance WT1.44 However, the investigation of MI in cancer progression is limited. Previous study identified that the exposure-impact-toxicity was correlated with MI and docetaxel in non-small cell lung cancer mice.22 MI inhibits chemotherapy-caused damage to liver throughout the initial therapy of gastrointestinal tumor patients.45 MI decreases paclitaxel in patients with epithelial ovarian cancer treated with cisplatin and paclitaxel.24 The function of MI is associated with its activity to restrain several crucial pathways such as phospholipase A2/arachidonic signaling,46 STAT3 signaling,47 and NF-κB signaling.23,48 In this study, we firstly identified that MI inhibited cell proliferation, migration, invasion, and enhanced cell apoptosis in laryngeal cancer. MI reduced tumor growth of laryngeal cancer in vivo. Our data present a novel inhibitory function of MI in laryngeal cancer and provide valuable insights into the role of MI in cancer development.\nEMT plays a critical role in the development of laryngeal cancer, especially in cancer cell metastasis progression by promoting resistance to apoptotic stimulation, invasion, and mobility.49 It was reported that TGF β-induced lncRNA MIR155HG promoted EMT of laryngeal squamous cell carcinoma by regulating the miR-155/SOX10 signaling.50 YAP modulates the Wnt/β-catenin signaling and EMT program of laryngeal cancer.51 Abnormal methylation and down-regulation of ZNF667 and ZNF667-AS1 enhance EMT of laryngeal carcinoma.52 Meanwhile, it was reported that MI alleviated high fructose-caused liver fibrosis and EMT by enhancing miR-375-3p to repress the TGF-β1/Smad signaling and JAK2/STAT3 signaling in rat.25 Our data demonstrated that MI attenuated EMT of laryngeal cancer via modulating the Twist/NF-κB signaling. It provides valuable information that MI exerts an inhibitory function of EMT in cancer progression.\nAs a critical EMT transcription factor, Twist regulates EMT program during cancer development.53 The correlation of Twist with NF-κB signaling in the modulation of EMT in cancer progression is well identified. It has been reported that presentation to TGF β combined with TNF α provokes tumorigenesis by modulating NF-κB/Twist signaling in vitro.54 Antrodia salmonea represses aggression and metastasis through modifying EMT via the NF-κB/Twist signaling in triple-negative breast cancer cells.55 Twist is involved in the mechanism that ursolic acid restrains EMT of gastric cancer through the Axl/NF-κB signaling.56 NF-κB activation by the RANKL/RANK pathway enhances the expression of snail and twist and promotes EMT of mammary cancer cells.57 Twist plays a crucial role in regulating the NF-κB and HIF-1α signaling on hypoxia-induced chemoresistance and EMT in pancreatic cancer.58 MiR-153 depletion down-regulates the expression of metastasis-associated family member 3 and Twist family BHLH transcription factor 1 in laryngeal squamous carcinoma cells.59 The expression of TWIST is remarkably down-regulated in response to paclitaxel, and TWIST may present a crucial function in paclitaxel-related laryngeal cancer cell apoptosis.60 TWIST is involved in the mechanism that TrkB elevates the metastasis of laryngeal cancer by activating the PI3K/AKT signaling.61 In the present study, we revealed that Twist was significantly elevated in laryngeal cancer tissues and human laryngeal carcinoma HEP-2 cells. It suggests that Twist may play a critical role in the development of laryngeal cancer. NF-κB/Twist signaling was involved in the mechanism of MI inhibiting EMT of laryngeal cancer. It provides new evidence of the role of Twist in cancer progression.","In conclusion, we discovered that magnesium isoglycyrrhizinate attenuated the progression of laryngeal cancer in vitro and in vivo. Magnesium isoglycyrrhizinate induced an inhibitory effect on epithelial–mesenchymal transition in laryngeal cancer by modulating the NF-κB/Twist signaling. Our findings provide new insights into the mechanism by which magnesium isoglycyrrhizinate inhibits laryngeal carcinoma development. Magnesium isoglycyrrhizinate may be applied as a potential anti-tumor candidate for laryngeal cancer in clinical treatment strategy."],"string":"[\n \"